[
  {
    "path": "2020年/2020.07.13Network_igraph/edge.csv",
    "content": "V1,V2\r\n24,25\r\n1,23\r\n13,18\r\n23,24\r\n14,25\r\n9,14\r\n14,26\r\n16,22\r\n1,16\r\n2,23\r\n18,32\r\n9,17\r\n24,31\r\n7,8\r\n2,21\r\n23,32\r\n7,35\r\n19,27\r\n7,23\r\n1,19\r\n3,17\r\n10,11\r\n15,30\r\n10,20\r\n15,18\r\n11,34\r\n16,20\r\n10,33\r\n6,34\r\n12,21\r\n4,29\r\n29,34\r\n5,30\r\n13,28\r\n5,36\r\n"
  },
  {
    "path": "2020年/2020.07.13Network_igraph/graph.r",
    "content": "\r\n\r\nlibrary(igraph)\r\n\r\n#1)导入边数据和节点数据=======\r\nsetwd(\"\")\r\nedges <- read.table('edge.csv', header=T, sep=',') #导入边数据，里面可以包含每个边的频次数据或权重\r\nvertices <- read.table('vertices.csv', header=T, sep=',') #导入节点数据，可以包含属性数据，如分类\r\nedges ;vertices\r\n#2)导入数据后，要转化成图数据才能用R作图，不同数据格式用不同方式=======\r\ngraph <- graph_from_data_frame(edges, directed = F, vertices=vertices) #directed = TRUE表示有方向,如果不需要点数据，可以设置vertices=NULL\r\n\r\n#生成方式1（没有颜色分类）：======\r\nigraph.options(vertex.size=3, vertex.label=NA, edge.arrow.size=0.5)\r\n\r\nV(graph)$color <- colrs[V(graph)$color]\r\nplot(graph,  \r\n     layout=layout.reingold.tilford(graph,circular=T),  #layout.fruchterman.reingold表示弹簧式发散的布局，\r\n     #其他还有环形布局layout.circle，分层布局layout.reingold.tilford，中心向外发散layout.reingold.tilford(graph,circular=T) ，核心布局layout_as_star，大型网络可视化layout_with_drl\r\n     vertex.size=5,     #节点大小  \r\n     vertex.shape='circle',    #节点不带边框none,,圆形边框circle,方块形rectangle  \r\n     vertex.color=\"lightgreen\",#设置颜色，其他如red,blue,cyan,yellow等\r\n     vertex.label=vertices$name, #NULL表示不设置，为默认状态，即默认显示数据中点的名称，可以是中文。如果是NA则表示不显示任何点信息\t \r\n     vertex.label.cex=0.8,    #节点字体大小  \r\n     vertex.label.color='black',  #节点字体颜色,red  \r\n     vertex.label.dist=0.4,   #标签和节点位置错开\r\n     edge.arrow.size=0,#连线的箭头的大小,若为0即为无向图，当然有些数据格式不支持有向图  \r\n     edge.width = 0.5, #连接线宽度\r\n     edge.label=NA, #不显示连接线标签，默认为频次\r\n     edge.color=\"gray\")  #连线颜色 \r\n\r\n#生成方式2（有颜色分类）：==========\r\n#set.seed() #生成随机数，这样图的布局就会可重复，而不是每次生成的时候都变\r\n#l<-layout.fruchterman.reingold(graph) #设置图的布局方式为弹簧式发散的布局\r\nl = layout.reingold.tilford(graph,circular=T)\r\n\r\n#具体修改过程\r\nV(graph)$size <- 8  #节点大小与点中心度成正比，中心度即与该点相连的点的总数\r\ncolrs <- c('#0096ff', \"lightblue\", \"azure3\",\"firebrick1\")\r\nV(graph)$color <- colrs[vertices$color] #根据类型设置颜色,按照类型分组\r\nV(graph)$label.color <- 'black' #设置节点标记的颜色\r\nV(graph)$label <- V(graph)$name \r\n#E(graph)$width <- E(graph)$fre #根据频次列设置边宽度\r\n#E(graph)$label <- E(graph)$fre #根据频次列设置边标签\r\nE(graph)$arrow.size=0.3 #设置箭头大小\r\n#生成图\r\nplot(graph, layout=l)\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.13Network_igraph/vertices.csv",
    "content": "id,name,color\r\n1,,1\r\n2,,1\r\n3,,2\r\n4,ҵ,3\r\n5,ͳƵʵ,1\r\n6,ְҵķչ,3\r\n7,,4\r\n8,һ,4\r\n9,,2\r\n10,ʵ,3\r\n11,¡,3\r\n12,ݷ,1\r\n13,ԪͳƷ,1\r\n14,,2\r\n15,˼ͷ,1\r\n16,ߵȴ(),1\r\n17,ߵȴ(һ),2\r\n18,ѧ,1\r\n19,ͳƷ뽨ģ,1\r\n20,˼,3\r\n21,ʱз,1\r\n22,ʵ,1\r\n23,ͳ,1\r\n24,ѧ(),2\r\n25,ѧ(),2\r\n26,ѧ(һ),2\r\n27,ͳƷ׫д,1\r\n28,ͳƽģ,1\r\n29,,3\r\n30,ͳƶӦ,1\r\n31,ͳѧ,2\r\n32,Ӧ,1\r\n33,й˼,3\r\n34,йִʷҪ,3\r\n35,רҵ,4\r\n36,רҵʵϰ,1\r\n"
  },
  {
    "path": "2020年/2020.07.14China_map/china_map.r",
    "content": "#9.1中国疫情图============\r\n#（https://my.oschina.net/u/2306127/blog/473842）\r\nlibrary(mapdata)\r\nlibrary(maptools)\r\nlibrary(ggplot2)\r\nlibrary(plyr)\r\nchina_map = readShapePoly(\"bou2_4p.shp\")#导入shp格式的中国地图\r\nx<-china_map@data\r\nxs<-data.frame(x,id=seq(0:924)-1)#地图中共计有925个地域信息\r\nchina_map1<-fortify(china_map)  #转化为数据框\r\nchina_map_data<-join(china_map1,xs,type=\"full\")#基于id进行连接\r\na = data.frame(unique(china_map@data$NAME))\r\n#准备数据\r\nmydata<-read.csv(\"data_dt.csv\",header=T,as.is=T)\r\nchina_data <- join(china_map_data, mydata, type=\"full\")#基于NAME字段进行连接，NAME字段来自于地图文件中\r\n\r\n#3、绘制地图\r\n\r\n## 版本一(无省份名称的当日各省确认人数)\r\nggplot(china_data, aes(x = long, y = lat, group = group, fill = people)) +    \r\n    geom_polygon(colour=\"grey40\") +\r\n    scale_fill_gradient(low=\"white\",high=\"steelblue\") +#指定渐变填充色，可使用RGB\r\n    theme( #清除不需要的元素\r\n        panel.grid = element_blank(),\r\n        panel.background = element_blank(),\r\n        axis.text = element_blank(),\r\n        axis.ticks = element_blank(),\r\n        axis.title = element_blank(),\r\n        legend.position = c(0.2,0.3)\r\n      )    \r\n\r\n## 版本二（有省份名称的当日各省确认人数）\r\nmidpos <- function(x) mean(range(x,na.rm=TRUE)) #取形状内的平均坐标\r\ncentres <- ddply(china_data,.(NAME),colwise(midpos,.(long,lat)))\r\n\r\nggplot(china_data,aes(long,lat))+      #此处语法与前面不同，参考ggplot2一书P85\r\n    geom_polygon(aes(group=group,fill=people),colour=\"black\")+\r\n    scale_fill_gradient(low=\"white\",high=\"steelblue\") +\r\n    geom_text(aes(label=NAME),data=centres) +\r\n    theme(\r\n         panel.grid = element_blank(),\r\n        panel.background = element_blank(),\r\n         axis.text = element_blank(),\r\n         axis.ticks = element_blank(),\r\n         axis.title = element_blank()\r\n        )\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.14China_map/data_dt.csv",
    "content": "NAME,ratio,people,province\r\nʡ,68135,1405,\r\n㶫ʡ,1637,732,㶫\r\nʡ,1276,667,\r\n㽭ʡ,1269,572,㽭\r\nʡ,1019,532,\r\nʡ,991,510,\r\nʡ,947,452,\r\nʡ,932,340,\r\n,905,322,\r\nɽʡ,792,297,ɽ\r\nϺ,707,270,Ϻ\r\nʡ,654,242,\r\nĴʡ,589,205,Ĵ\r\n,582,201,\r\nʡ,363,176,\r\nӱʡ,349,166,ӱ\r\nʡ,320,154,\r\n׳,254,103,\r\nɹ,238,93,ɹ\r\nʡ,198,84,\r\nɽʡ,198,77,ɽ\r\nʡ,185,54,\r\nʡ,171,49,\r\nʡ,163,45,\r\nʡ,155,45,\r\nʡ,154,42,\r\nʡ,147,40,\r\n½ά,76,27,½\r\nĻ,75,9,\r\nຣʡ,18,7,ຣ\r\n,1,5,\r\nر,1197,1,\r\n̨ʡ,447,100,̨\r\n"
  },
  {
    "path": "2020年/2020.07.15World_map/Country_Data.csv",
    "content": "\"Country\t\",Scale\r\nAruba,\r\nAfghanistan,14337184953\r\nAngola,122686093\r\nAnguilla,\r\nAlbania,52348966\r\nFinland,\r\nAndorra,\r\nUnited Arab Emirates,1420302\r\nArgentina,4793871\r\nArmenia,118083985\r\nAmerican Samoa,1706789\r\nAntarctica,\r\nAustralia,\r\nFrench Southern and Antarctic Lands,\r\nAntigua,1024000\r\nBarbuda,\r\nAustria,\r\nAzerbaijan,99645874\r\nBurundi,71626580\r\nBelgium,4143675\r\nBenin,72834739\r\nBurkina Faso,291773051\r\nBangladesh,657712016\r\nBulgaria,40346336\r\nBahrain,22984534\r\nBahamas,5530627\r\nBosnia and Herzegovina,113820206\r\nSaint Barthelemy,\r\nBelarus,33366555\r\nBelize,19013177\r\nBermuda,\r\nBolivia,40456906\r\nBrazil,47003710\r\nBarbados,1195377\r\nBrunei,336196\r\nBhutan,827607\r\nBotswana,108084913\r\nCentral African Republic,163301353\r\nCanada,25171012\r\nSwitzerland,1182463\r\nChile,9166231\r\nChina,109468893\r\nIvory Coast,230099050\r\nCameroon,107714146\r\nDemocratic Republic of the Congo,774104905\r\nRepublic of Congo,9313958\r\nCook Islands,\r\nColombia,1008396303\r\nComoros,1162798\r\nCape Verde,\r\nCosta Rica,23807023\r\nCuba,19674492\r\nCuracao,\r\nCayman Islands,\r\nCyprus,5878004\r\nCzech Republic,12429517\r\nGermany,627585\r\nDjibouti,43437611\r\nDominica,958000\r\nDenmark,\r\nDominican Republic,105256853\r\nAlgeria,21999637\r\nEcuador,66630571\r\nEgypt,285844459\r\nEritrea,1113076\r\nCanary Islands,\r\nSpain,5227911\r\nEstonia,12470092\r\nEthiopia,1421649545\r\nFiji,4602905\r\nFalkland Islands,\r\nReunion,\r\nMayotte,\r\nFrench Guiana,\r\nMartinique,\r\nGuadeloupe,\r\nFrance,1464862\r\nFaroe Islands,\r\nMicronesia,302553791\r\nGabon,13844417\r\nUK,244658\r\nGeorgia,495691015\r\nGuernsey,\r\nGhana,307723748\r\nGuinea,62034795\r\nGambia,4398110\r\nGuinea-Bissau,3299809\r\nEquatorial Guinea,335973\r\nGreece,1347158\r\nGrenada,991550\r\nGreenland,\r\nGuatemala,304130054\r\nGuam,\r\nGuyana,18365479\r\nHeard Island,\r\nHonduras,197701659\r\nCroatia,14439801\r\nHaiti,702753017\r\nHungary,32842750\r\nIndonesia,425241264\r\nIsle of Man,\r\nIndia,261495537\r\nCocos Islands,\r\nChristmas Island,\r\nChagos Archipelago,\r\nIreland,7389871\r\nIran,2064968\r\nIraq,817392816\r\nIceland,\r\nIsrael,6249503135\r\nItaly,2758463\r\nSan Marino,\r\nJamaica,63406681\r\nJersey,\r\nJordan,2229268225\r\nJapan,5583196\r\nSiachen Glacier,\r\nKazakhstan,284994227\r\nKenya,1757291419\r\nKyrgyzstan,142055012\r\nCambodia,215972560\r\nKiribati,\r\nNevis,\r\nSaint Kitts,\r\nSouth Korea,2457582\r\nKosovo,133600340\r\nKuwait,92372\r\nLaos,50370856\r\nLebanon,639875848\r\nLiberia,456462263\r\nLibya,70061408\r\nSaint Lucia,\r\nLiechtenstein,\r\nSri Lanka,81459320\r\nLesotho,108025026\r\nLithuania,18158581\r\nLuxembourg,\r\nLatvia,9766796\r\nSaint Martin,\r\nMorocco,170856475\r\nMonaco,\r\nMoldova,186466454\r\nMadagascar,125564678\r\nMaldives,4842109\r\nMexico,557646557\r\nMarshall Islands,128170833\r\nMacedonia,56725720\r\nMali,361809062\r\nMalta,1602646\r\nMyanmar,225938871\r\nMontenegro,9798308\r\nMongolia,30992965\r\nNorthern Mariana Islands,\r\nMozambique,777992540\r\nMauritania,41288351\r\nMontserrat,\r\nMauritius,2569586\r\nMalawi,440212898\r\nMalaysia,25398180\r\nNamibia,231306565\r\nNew Caledonia,\r\nNiger,217698383\r\nNorfolk Island,\r\nNigeria,1165516940\r\nNicaragua,94025520\r\nNiue,\r\nBonaire,\r\nSint Eustatius,\r\nSaba,\r\nNetherlands,80365\r\nNorway,\r\nNepal,169303579\r\nNauru,\r\nNew Zealand,7154\r\nOman,26659268\r\nPakistan,1972822305\r\nPanama,24522344\r\nPitcairn Islands,\r\nPeru,304607830\r\nPhilippines,728003613\r\nPalau,28769020\r\nPapua New Guinea,14424130\r\nPoland,93171533\r\nPuerto Rico,\r\nNorth Korea,3997348\r\nMadeira Islands,\r\nAzores,\r\nPortugal,916654\r\nParaguay,36856601\r\nPalestine,884730488\r\nFrench Polynesia,\r\nQatar,3098\r\nRomania,76362825\r\nRussia,297186633\r\nRwanda,343709226\r\nWestern Sahara,\r\nSaudi Arabia,713501\r\nSudan,412033512\r\nSouth Sudan,1480315209\r\nSenegal,389694501\r\nSingapore,1040834\r\nSouth Sandwich Islands,\r\nSouth Georgia,\r\nSaint Helena,\r\nAscension Island,\r\nSolomon Islands,2817980\r\nSierra Leone,53212837\r\nEl Salvador,153467672\r\nSomalia,699889670\r\nSaint Pierre and Miquelon,\r\nSerbia,75712324\r\nSao Tome and Principe,933472\r\nSuriname,1453709\r\nSlovakia,4589332\r\nSlovenia,7086955\r\nSweden,45798\r\nSwaziland,91046517\r\nSint Maarten,\r\nSeychelles,552879\r\nSyria,1581881739\r\nTurks and Caicos Islands,\r\nChad,245554164\r\nTogo,7650651\r\nThailand,132772482\r\nTajikistan,132816030\r\nTurkmenistan,17402234\r\nTimor-Leste,72044958\r\nTonga,3221962\r\nTrinidad,\r\nTobago,\r\nTunisia,137888152\r\nTurkey,190530825\r\nTaiwan,2722516\r\nTanzania,1187669230\r\nUganda,1058084960\r\nUkraine,500475016\r\nUruguay,2884931\r\nUSA,\r\nUzbekistan,104348366\r\nVatican,\r\nGrenadines,\r\nSaint Vincent,\r\nVenezuela,14047441\r\nVirgin Islands,\r\nVietnam,240210318\r\nVanuatu,5726365\r\nWallis and Futuna,\r\nSamoa,\r\nYemen,485596030\r\nSouth Africa,1072163281\r\nZambia,1011659748\r\nZimbabwe,357528936\r\n"
  },
  {
    "path": "2020年/2020.07.15World_map/cores.r",
    "content": "#========================================================================\r\n#============================= Task =====================================\r\n#========================================================================\r\n\r\nsetwd(\"C:/Users/DELL/Desktop/wechat/2020.07.15World_map\")\r\n\r\n# 世界疫情图==========\r\nlibrary(maps)\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)\r\nlibrary(plyr)\r\ncolormap<-c(rev(brewer.pal(9,\"Greens\")[c(4,6)]), brewer.pal(9,\"YlOrRd\")[c(3,4,5,6,7,8,9)])\r\nmydata1<-read.csv(\"Country_Data.csv\",stringsAsFactors=FALSE)#这个是全球数据\r\nnames(mydata1)=c(\"Country\",\"Scale\") #重新命名\r\nmydata2 =  read.csv(\"world_data.csv\",header=TRUE)  #我们的数据（疫情）\r\nhead(mydata2)\r\n#将两个表格匹配\r\nmydata <- join(mydata1, mydata2, type=\"full\") \r\nhead(mydata)\r\n#把ratio参数设置成分类型，以便于好绘制\r\nmydata$fan<-cut(mydata$ratio,\r\nbreaks=c(min(mydata$million,na.rm=TRUE),\r\n0,1000,5000,10000,50000,200000,500000,2000000,\r\nmax(mydata$ratio,na.rm=TRUE)),\r\nlabels=c(\" <=0\",\"0~1000\",\"1000~5000\",\"5000~10000\",\"10000~50000\",\"50000~200000\",\r\n\"200000~500000\",\"500000~2000000\",\" >=2000000\"),\r\norder=TRUE)\r\n#定义地图用全球的\r\nworld_map <- map_data(\"world\")\r\n#绘图\r\nggplot()+\r\ngeom_map(data=mydata,aes(map_id=Country,fill=fan),map=world_map)+\r\ngeom_path(data=world_map,aes(x=long,y=lat,group=group),colour=\"black\",size=.2)+\r\nscale_y_continuous(breaks=(-3:3)*30) +\r\nscale_x_continuous(breaks=(-6:6)*30) +\r\nscale_fill_manual(name=\"Ratio\",values= colormap,na.value=\"grey75\")+\r\nguides(fill=guide_legend(reverse=TRUE)) +\r\ntheme_minimal()\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.15World_map/world_data.csv",
    "content": "Country,long,lat,ratio\r\nAndorra,1.601554,42.546245,855\r\nUnited Arab Emirates,53.847818,23.424076,NA\r\nAfghanistan,67.709953,33.93911,30175\r\nAntigua and Barbuda,-61.796428,17.060816,26\r\nAnguilla,-63.068615,18.220554,3\r\nAlbania,20.168331,41.153332,2192\r\nArmenia,45.038189,40.069099,22488\r\nNetherlands Antilles,-69.060087,12.226079,NA\r\nAngola,17.873887,-11.202692,189\r\nAntarctica,-0.071389,-75.250973,NA\r\nArgentina,-63.616672,-38.416097,49851\r\nAmerican Samoa,-170.132217,-14.270972,NA\r\nAustria,14.550072,47.516231,17477\r\nAustralia,133.775136,-25.274398,7558\r\nAruba,-69.968338,12.52111,101\r\nAzerbaijan,47.576927,40.143105,14852\r\nBosnia and Herzegovina,17.679076,43.915886,NA\r\nBarbados,-59.543198,13.193887,97\r\nBangladesh,90.356331,23.684994,126606\r\nBelgium,4.469936,50.503887,61007\r\nBurkina Faso,-1.561593,12.238333,NA\r\nBulgaria,25.48583,42.733883,4242\r\nBahrain,50.637772,25.930414,23570\r\nBurundi,29.918886,-3.373056,144\r\nBenin,2.315834,9.30769,1017\r\nBermuda,-64.75737,32.321384,141\r\nBrunei,114.727669,4.535277,141\r\nBolivia,-63.588653,-16.290154,28631\r\nBrazil,-51.92528,-14.235004,1193609\r\nBahamas,-77.39628,25.03428,104\r\nBhutan,90.433601,27.514162,70\r\nBouvet Island,3.413194,-54.423199,NA\r\nBotswana,24.684866,-22.328474,89\r\nBelarus,27.953389,53.709807,60382\r\nBelize,-88.49765,17.189877,23\r\nCanada,-106.346771,56.130366,102242\r\nCocos [Keeling] Islands,96.870956,-12.164165,NA\r\nCongo [DRC],21.758664,-4.038333,NA\r\nCentral African Republic,20.939444,6.611111,NA\r\nCongo [Republic],15.827659,-0.228021,NA\r\nSwitzerland,8.227512,46.818188,31428\r\nC?te d'Ivoire,-5.54708,7.539989,NA\r\nCook Islands,-159.777671,-21.236736,NA\r\nChile,-71.542969,-35.675147,254416\r\nCameroon,12.354722,7.369722,12592\r\nChina,104.195397,35.86166,85260\r\nColombia,-74.297333,4.570868,77113\r\nCosta Rica,-83.753428,9.748917,2515\r\nCuba,-77.781167,21.521757,2319\r\nCape Verde,-24.013197,16.002082,NA\r\nChristmas Island,105.690449,-10.447525,NA\r\nCyprus,33.429859,35.126413,991\r\nCzech Republic,15.472962,49.817492,NA\r\nGermany,10.451526,51.165691,193987\r\nDjibouti,42.590275,11.825138,4630\r\nDenmark,9.501785,56.26392,12636\r\nDominica,-61.370976,15.414999,NA\r\nDominican Republic,-70.162651,18.735693,NA\r\nAlgeria,1.659626,28.033886,12248\r\nEcuador,-78.183406,-1.831239,51643\r\nEstonia,25.013607,58.595272,1984\r\nEgypt,30.802498,26.820553,59561\r\nWestern Sahara,-12.885834,24.215527,9\r\nEritrea,39.782334,15.179384,143\r\nSpain,-3.74922,40.463667,247086\r\nEthiopia,40.489673,9.145,5034\r\nFinland,25.748151,61.92411,7172\r\nFiji,179.414413,-16.578193,18\r\nFalkland Islands [Islas Malvinas],-59.523613,-51.796253,NA\r\nMicronesia,150.550812,7.425554,NA\r\nFaroe Islands,-6.911806,61.892635,187\r\nFrance,2.213749,46.227638,161348\r\nGabon,11.609444,-0.803689,4956\r\nUnited Kingdom,-3.435973,55.378051,306862\r\nGrenada,-61.604171,12.262776,23\r\nGeorgia,43.356892,42.315407,917\r\nFrench Guiana,-53.125782,3.933889,2827\r\nGuernsey,-2.585278,49.465691,252\r\nGhana,-1.023194,7.946527,15013\r\nGibraltar,-5.345374,36.137741,176\r\nGreenland,-42.604303,71.706936,13\r\nGambia,-15.310139,13.443182,42\r\nGuinea,-9.696645,9.945587,5174\r\nGuadeloupe,-62.067641,16.995971,174\r\nEquatorial Guinea,10.267895,1.650801,NA\r\nGreece,21.824312,39.074208,3310\r\nSouth Georgia and the South Sandwich Islands,-36.587909,-54.429579,NA\r\nGuatemala,-90.230759,15.783471,14819\r\nGuam,144.793731,13.444304,226\r\nGuinea-Bissau,-15.180413,11.803749,1556\r\nGuyana,-58.93018,4.860416,206\r\nGaza Strip,34.308825,31.354676,NA\r\nHong Kong,114.109497,22.396428,NA\r\nHeard Island and McDonald Islands,73.504158,-53.08181,NA\r\nHonduras,-86.241905,15.199999,14571\r\nCroatia,15.2,45.1,2483\r\nHaiti,-72.285215,18.971187,5429\r\nHungary,19.503304,47.162494,4123\r\nIndonesia,113.921327,-0.789275,51087\r\nIreland,-8.24389,53.41291,25396\r\nIsrael,34.851612,31.046051,22139\r\nIsle of Man,-4.548056,54.236107,NA\r\nIndia,78.96288,20.593684,473634\r\nBritish Indian Ocean Territory,71.876519,-6.343194,NA\r\nIraq,43.679291,33.223191,39139\r\nIran,53.688046,32.427908,215096\r\nIceland,-19.020835,64.963051,1824\r\nItaly,12.56738,41.87194,239410\r\nJersey,-2.13125,49.214439,308\r\nJamaica,-77.297508,18.109581,678\r\nJordan,36.238414,30.585164,1071\r\nJapan,138.252924,36.204824,18212\r\nKenya,37.906193,-0.023559,5384\r\nKyrgyzstan,74.766098,41.20438,3954\r\nCambodia,104.990963,12.565679,130\r\nKiribati,-168.734039,-3.370417,NA\r\nComoros,43.872219,-11.875001,265\r\nSaint Kitts and Nevis,-62.782998,17.357822,15\r\nNorth Korea,127.510093,40.339852,NA\r\nSouth Korea,127.766922,35.907757,NA\r\nKuwait,47.481766,29.31166,42788\r\nCayman Islands,-80.566956,19.513469,NA\r\nKazakhstan,66.923684,48.019573,19285\r\nLaos,102.495496,19.85627,19\r\nLebanon,35.862285,33.854721,1644\r\nSaint Lucia,-60.978893,13.909444,NA\r\nLiechtenstein,9.555373,47.166,86\r\nSri Lanka,80.771797,7.873054,NA\r\nLiberia,-9.429499,6.428055,662\r\nLesotho,28.233608,-29.609988,17\r\nLithuania,23.881275,55.169438,1806\r\nLuxembourg,6.129583,49.815273,4140\r\nLatvia,24.603189,56.879635,1111\r\nLibya,17.228331,26.3351,670\r\nMorocco,-7.09262,31.791702,11279\r\nMonaco,7.412841,43.750298,101\r\nMoldova,28.369885,47.411631,15078\r\nMontenegro,19.37439,42.708678,389\r\nMadagascar,46.869107,-18.766947,1829\r\nMarshall Islands,171.184478,7.131474,NA\r\nMacedonia [FYROM],21.745275,41.608635,NA\r\nMali,-3.996166,17.570692,2005\r\nMyanmar [Burma],95.956223,21.913965,NA\r\nMongolia,103.846656,46.862496,213\r\nMacau,113.543873,22.198745,NA\r\nNorthern Mariana Islands,145.38469,17.33083,31\r\nMartinique,-61.024174,14.641528,236\r\nMauritania,-10.940835,21.00789,3519\r\nMontserrat,-62.187366,16.742498,11\r\nMalta,14.375416,35.937496,668\r\nMauritius,57.552152,-20.348404,341\r\nMaldives,73.22068,3.202778,2261\r\nMalawi,34.301525,-13.254308,941\r\nMexico,-102.552784,23.634501,196847\r\nMalaysia,101.975766,4.210484,8600\r\nMozambique,35.529562,-18.665695,762\r\nNamibia,18.49041,-22.95764,76\r\nNew Caledonia,165.618042,-20.904305,21\r\nNiger,8.081666,17.607789,1051\r\nNorfolk Island,167.954712,-29.040835,NA\r\nNigeria,8.675277,9.081999,22020\r\nNicaragua,-85.207229,12.865416,2170\r\nNetherlands,5.291266,52.132633,49914\r\nNorway,8.468946,60.472024,8788\r\nNepal,84.124008,28.394857,11162\r\nNauru,166.931503,-0.522778,NA\r\nNiue,-169.867233,-19.054445,NA\r\nNew Zealand,174.885971,-40.900557,1519\r\nOman,55.923255,21.512583,34902\r\nPanama,-80.782127,8.537981,28030\r\nPeru,-75.015152,-9.189967,264689\r\nFrench Polynesia,-149.406843,-17.679742,60\r\nPapua New Guinea,143.95555,-6.314993,NA\r\nPhilippines,121.774017,12.879721,33069\r\nPakistan,69.345116,30.375321,192970\r\nPoland,19.145136,51.919438,33119\r\nSaint Pierre and Miquelon,-56.27111,46.941936,NA\r\nPitcairn Islands,-127.439308,-24.703615,NA\r\nPuerto Rico,-66.590149,18.220833,6877\r\nPalestinian Territories,35.233154,31.952162,NA\r\nPortugal,-8.224454,39.399872,40415\r\nPalau,134.58252,7.51498,NA\r\nParaguay,-58.443832,-23.442503,1528\r\nQatar,51.183884,25.354826,91838\r\nRunion,55.536384,-21.115141,NA\r\nRomania,24.96676,45.943161,NA\r\nSerbia,21.005859,44.016521,13372\r\nRussia,105.318756,61.52401,613994\r\nRwanda,29.873888,-1.940278,830\r\nSaudi Arabia,45.079162,23.885942,170639\r\nSolomon Islands,160.156194,-9.64571,NA\r\nSeychelles,55.491977,-4.679574,11\r\nSudan,30.217636,12.862807,8889\r\nSweden,18.643501,60.128161,62324\r\nSingapore,103.819836,1.352083,42736\r\nSaint Helena,-10.030696,-24.143474,NA\r\nSlovenia,14.995463,46.151241,1547\r\nSvalbard and Jan Mayen,23.670272,77.553604,NA\r\nSlovakia,19.699024,48.669026,1630\r\nSierra Leone,-11.779889,8.460555,NA\r\nSan Marino,12.457777,43.94236,NA\r\nSenegal,-14.452362,14.497401,6233\r\nSomalia,46.199616,5.152149,2835\r\nSuriname,-56.027783,3.919305,357\r\nS?o Tom and Prncipe,6.613081,0.18636,NA\r\nEl Salvador,-88.89653,13.794185,5336\r\nSyria,38.996815,34.802075,231\r\nSwaziland,31.465866,-26.522503,690\r\nTurks and Caicos Islands,-71.797928,21.694025,NA\r\nChad,18.732207,15.454166,860\r\nFrench Southern Territories,69.348557,-49.280366,NA\r\nTogo,0.824782,8.619543,583\r\nThailand,100.992541,15.870032,3158\r\nTajikistan,71.276093,38.861034,5630\r\nTokelau,-171.855881,-8.967363,NA\r\nTimor-Leste,125.727539,-8.874217,NA\r\nTurkmenistan,59.556278,38.969719,NA\r\nTunisia,9.537499,33.886917,1160\r\nTonga,-175.198242,-21.178986,NA\r\nTurkey,35.243322,38.963745,191657\r\nTrinidad and Tobago,-61.222503,10.691803,123\r\nTuvalu,177.64933,-7.109535,NA\r\nTaiwan,120.960515,23.69781,NA\r\nTanzania,34.888822,-6.369028,509\r\nUkraine,31.16558,48.379433,40008\r\nUganda,32.290275,1.373333,805\r\nU.S. Minor Outlying Islands,NA,NA,NA\r\nUnited States,-95.712891,37.09024,NA\r\nUruguay,-55.765835,-32.522779,902\r\nUzbekistan,64.585262,41.377491,6990\r\nVatican City,12.453389,41.902916,NA\r\nSaint Vincent and the Grenadines,-61.287228,12.984305,29\r\nVenezuela,-66.58973,6.42375,4366\r\nBritish Virgin Islands,-64.639968,18.420695,NA\r\nU.S. Virgin Islands,-64.896335,18.335765,NA\r\nVietnam,108.277199,14.058324,NA\r\nVanuatu,166.959158,-15.376706,NA\r\nWallis and Futuna,-177.156097,-13.768752,NA\r\nSamoa,-172.104629,-13.759029,NA\r\nKosovo,20.902977,42.602636,2216\r\nYemen,48.516388,15.552727,1015\r\nMayotte,45.166244,-12.8275,2467\r\nSouth Africa,22.937506,-30.559482,111796\r\nZambia,27.849332,-13.133897,1497\r\nZimbabwe,29.154857,-19.015438,530\r\nUSA,NA,NA,2462554\r\nBosnia,NA,NA,3676\r\nBurkina faso,NA,NA,934\r\nBurma,NA,NA,293\r\nBvi,NA,NA,8\r\nCape verde,NA,NA,999\r\nCayman islands,NA,NA,195\r\nCongo (Bu),NA,NA,1087\r\nCongo (DRC),NA,NA,6213\r\nCuracao,NA,NA,20\r\nCzech,NA,NA,10780\r\nDiamond Princess,NA,NA,712\r\nDominic,NA,NA,18\r\nDominican,NA,NA,28631\r\nEast timor,NA,NA,24\r\nEquatorial guinea,NA,NA,1664\r\nFalkland islands,NA,NA,13\r\nIsle,NA,NA,336\r\nIvory coast,NA,NA,8164\r\nKorea,NA,NA,12563\r\nLucia,NA,NA,19\r\nNetherlands Caribbean,NA,NA,7\r\nNon -,NA,NA,3099\r\nNorth Macedonia,NA,NA,5445\r\nPalestinian,NA,NA,1328\r\nPapua new guinea,NA,NA,9\r\nReunion,NA,NA,426\r\nRomanian,NA,NA,25286\r\nSaint Barthelemi,NA,NA,6\r\nSaint-Pierre and Miquelon Islands,NA,NA,1\r\nSan marino,NA,NA,698\r\nSao Tome and Principe,NA,NA,710\r\nSierra leone,NA,NA,1354\r\nSouth Sudan,NA,NA,1942\r\nSri lanka,NA,NA,2007\r\n\"St. Martin, France\",NA,NA,117\r\n\"St. Martin, Netherlands\",NA,NA,77\r\nTci,NA,NA,14\r\nU.A.E,NA,NA,46563\r\nUsvi,NA,NA,76\r\nVatican,NA,NA,12\r\nViet Nam,NA,NA,352\r\n"
  },
  {
    "path": "2020年/2020.07.22Movielense/ch11-task.Rmd",
    "content": "---\r\ntitle: \"第11章作业MovieLense数据集分析\"\r\nauthor:\r\ndocumentclass: ctexart\r\noutput:\r\n  word_document: default\r\n  html_document: default\r\n  pdf_document: default\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# 前言\r\n\r\nR的recommenderlab包可以实现协同过滤算法。这个包中有许多关于推荐算法建立、处理及可视化的函数。选用recommenderlab包中内置的MovieLense数据集进行分析，该数据集收集了网站MovieLens（movielens.umn.edu）从1997年9月19日到1998年4月22日的数据，包括943名用户对1664部电影的评分。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(recommenderlab)\r\nlibrary(ggplot2)\r\n```\r\n\r\n\r\n\r\n# 数据处理与数据探索性分析\r\n```{r  message=FALSE, warning=FALSE}\r\ndata(MovieLense)\r\nimage(MovieLense)\r\n# 获取评分\r\nratings.movie <- data.frame(ratings = getRatings(MovieLense))\r\nsummary(ratings.movie$ratings)\r\nggplot(ratings.movie, aes(x = ratings)) + \r\n  geom_histogram(fill = \"beige\", color = \"black\",\r\n    binwidth = 1, alpha = 0.7) + xlab(\"rating\") + ylab(\"count\")\r\n\r\n```\r\n利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为3.53。并将其柱状图进行绘制，如下所示。\r\n\r\n\r\n## 数据标准化\r\n\r\n在进行数据分析前，利用`normalize()`我们将数据进行标准化，并进行绘制。\r\n```{r message=FALSE, warning=FALSE}\r\nratings.movie1 <- data.frame(ratings = \r\n    getRatings(normalize(MovieLense, method = \"Z-score\")))\r\nsummary(ratings.movie1$ratings)\r\n\r\nggplot(ratings.movie1, aes(x = ratings)) + \r\n  geom_histogram(fill = \"beige\", color = \"black\",\r\n    alpha = 0.7) + xlab(\"rating\") + ylab(\"count\")\r\n```\r\n\r\n## 用户的电影点评数\r\n\r\n我们还对用户的电影点评数进行描述性分析，具体结果如下所示。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nmovie.count <- data.frame(count = rowCounts(MovieLense))\r\nggplot(movie.count, aes(x = count)) + \r\n  geom_histogram(fill = \"beige\", color = \"black\",\r\n    alpha = 0.7) + xlab(\"counts of users\") + ylab(\"counts of movies rated\")\r\n\r\nrating.mean <- data.frame(rating = colMeans(MovieLense))\r\nggplot(rating.mean, aes(x = rating)) + \r\n  geom_histogram(fill = \"beige\", color = \"black\",\r\n    alpha = 0.7) + xlab(\"rating\") + ylab(\"counts of movies \")\r\n```\r\n\r\n# 建立推荐模型与模型评估\r\n\r\n对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。\r\n\r\n模型评估主要使用：recommenderlab包中自带的评估方案，对应的函数是evaluationScheme，能够设置采用n-fold交叉验证还是简单的training/train分开验证，本文采用后一种方法，即将数据集简单分为training和test，在training训练模型，然后在test上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。\r\n```{r  message=FALSE, warning=FALSE}\r\nlibrary(recommenderlab)\r\ndata(MovieLense)\r\nscheme <- evaluationScheme(MovieLense, method = \"split\", \r\n  train = 0.9, k = 1,  given = 10, goodRating = 4)\r\nalgorithms <- list(popular = list(name = \"POPULAR\", \r\n  param = list(normalize = \"Z-score\")),\r\n    ubcf = list(name = \"UBCF\", param = list(normalize = \"Z-score\", \r\n      method = \"Cosine\",nn = 25, minRating = 3)), \r\n  ibcf = list(name = \"IBCF\", param = list(normalize = \"Z-score\")))\r\nresults <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))\r\nplot(results, annotate = 1:3, legend = \"topleft\") #ROC\r\nplot(results, \"prec/rec\", annotate = 3)#precision-recall\r\n```\r\n\r\n```{r}\r\n# 按照评价方案建立推荐模型\r\nmodel.popular <- Recommender(getData(scheme, \"train\"), method = \"POPULAR\")\r\nmodel.ibcf <- Recommender(getData(scheme, \"train\"), method = \"IBCF\")\r\nmodel.ubcf <- Recommender(getData(scheme, \"train\"), method = \"UBCF\")\r\n# 对推荐模型进行预测\r\npredict.popular <- predict(model.popular, getData(scheme, \"known\"), type = \"ratings\")\r\npredict.ibcf <- predict(model.ibcf, getData(scheme, \"known\"), type = \"ratings\")\r\npredict.ubcf <- predict(model.ubcf, getData(scheme, \"known\"), type = \"ratings\")\r\n# 做误差的计算\r\npredict.err <- rbind(calcPredictionAccuracy(predict.popular, \r\n  getData(scheme, \"unknown\")),calcPredictionAccuracy(predict.ubcf, getData(scheme,\r\n    \"unknown\")), calcPredictionAccuracy(predict.ibcf,getData(scheme, \"unknown\")))\r\nrownames(predict.err) <- c(\"POPULAR\", \"UBCF\", \"IBCF\")\r\npredict.err\r\n```\r\n\r\n\r\n通过结果我们可以看到：基于流行度推荐系统对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于用户的推荐，最后是基于项目协同过滤。\r\n\r\n\r\n# 参考资料{-}\r\n\r\n1. [Recommenderlab包实现电影评分预测(R语言)\r\n](https://blog.csdn.net/seuponder/article/details/21040917)\r\n\r\n2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)\r\n\r\n3. [recommender system handbook](https://www.amazon.com/Recommender-Systems-Handbook-Francesco-Ricci/dp/1489976361)\r\n\r\n4. [Item-Based Collaborative Filtering Recommendation Algorithms](http://www.ra.ethz.ch/CDstore/www10/papers/pdf/p519.pdf)\r\n\r\n5. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)\r\n\r\n"
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  {
    "path": "2020年/2020.07.22坐标轴截断画图/code_truncation .R",
    "content": "\r\n##  [ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)\r\nlibrary(ggplot2)\r\nset.seed(2019-01-19)\r\nd <- data.frame(\r\n    x = 1:20, \r\n    y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)\r\n)\r\n\r\nggplot(d, aes(x, y)) + geom_col()\r\n\r\nlibrary(dplyr)\r\n\r\nbreaks = c(7, 17)\r\nd$.type <- NA\r\nd$.type[d$y < breaks[1]] = \"small\"\r\nd$.type[d$y > breaks[2]] = \"big\"\r\n\r\nd <- filter(d, .type == 'big') %>% \r\n    mutate(.type = \"small\", y = breaks[1]) %>% \r\n    bind_rows(d)\r\n\r\nmymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])               \r\np <- ggplot(d, aes(x, y)) + \r\n    geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +\r\n    facet_grid(.type ~ ., scales = \"free\") + \r\n    theme(strip.text=element_blank())\r\np\r\n\r\n## [R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)\r\n\r\nlibrary(plotrix)\r\n\r\nw <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)\r\nx <- c(1:15)\r\ngap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab =\"mumbers\", ylab = \"height\", main=\"test image\")\r\n\r\naxis.break(2,90,breakcol=\"snow\",style=\"gap\")##去掉中间的那两道横线；\r\naxis.break(2,90*(1+0.02),breakcol=\"black\",style=\"slash\")##在左侧Y轴把gap位置换成slash；\r\naxis.break(4,90*(1+0.02),breakcol=\"black\",style=\"slash\")##在右侧Y轴把gap位置换成slash；\r\n\r\n### 案例\r\ntwogrp<-c(rnorm(10)+4,rnorm(10)+20)\r\ngap.barplot(twogrp,gap=c(8,16),xlab=\"Index\",ytics=c(3,6,17,20),\r\n  ylab=\"Group values\",main=\"Barplot with gap\")\r\ngap.barplot(twogrp,gap=c(8,16),xlab=\"Index\",ytics=c(3,6,17,20),\r\n  ylab=\"Group values\",horiz=TRUE,main=\"Horizontal barplot with gap\")\r\n\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.22坐标轴截断画图/code_truncation .Rmd",
    "content": "---\r\ntitle: \"R中坐标轴截断的不同实现方式\"\r\nauthor:\r\n  - 庄闪闪\r\ndocumentclass: ctexart\r\noutput:\r\n  word_document: default\r\n  html_document: default\r\n  pdf_document: default\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# plotrix包\r\n\r\n[R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)\r\n\r\n利用gap.barplot()进进行绘制，将gap参数设置为90，420进行y轴截断，可加入参数axis.break()对截断形状进行修改\r\n```{r}\r\nlibrary(plotrix)\r\n\r\nw <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)\r\nx <- c(1:15)\r\ngap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab =\"mumbers\", ylab = \"height\", main=\"test image\")\r\n\r\naxis.break(2,90,breakcol=\"snow\",style=\"gap\")##去掉中间的那两道横线；\r\naxis.break(2,90*(1+0.02),breakcol=\"black\",style=\"slash\")##在左侧Y轴把gap位置换成slash；\r\naxis.break(4,90*(1+0.02),breakcol=\"black\",style=\"slash\")##在右侧Y轴把gap位置换成slash；\r\n\r\n```\r\n\r\n- 其他案例\r\n\r\n```{r}\r\ntwogrp<-c(rnorm(10)+4,rnorm(10)+20)\r\ngap.barplot(twogrp,gap=c(8,16),xlab=\"Index\",ytics=c(3,6,17,20),\r\n  ylab=\"Group values\",main=\"Barplot with gap\")\r\ngap.barplot(twogrp,gap=c(8,16),xlab=\"Index\",ytics=c(3,6,17,20),\r\n  ylab=\"Group values\",horiz=TRUE,main=\"Horizontal barplot with gap\")\r\n\r\n```\r\n\r\n\r\n# ggplot包\r\n\r\n[ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(ggplot2)\r\nset.seed(123)\r\nd <- data.frame(\r\n    x = 1:20, \r\n    y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)\r\n)\r\n\r\nggplot(d, aes(x, y)) + geom_col()\r\n\r\nlibrary(dplyr)\r\n\r\nbreaks = c(7, 17)\r\nd$type <- NA\r\nd$type[d$y < breaks[1]] = \"small\"\r\nd$type[d$y > breaks[2]] = \"big\"\r\n\r\nd <- filter(d, type == 'big') %>% \r\n    mutate(type = \"small\", y = breaks[1]) %>% \r\n    bind_rows(d)\r\n\r\nmymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])               \r\np <- ggplot(d, aes(x, y)) + \r\n    geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +\r\n    facet_grid(type ~ ., scales = \"free\") + \r\n    theme(strip.text=element_blank())#去除text\r\np\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.24饼图与圆环图/pie.rmd",
    "content": "---\r\ntitle: \"饼状图\"\r\ndate: 2020-08-05\r\nauthor:\r\n  - 庄闪闪\r\ndocumentclass: ctexart\r\noutput:\r\n  word_document: default\r\n  pdf_document: default\r\n  html_document: default\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# 饼图\r\n\r\n饼图（pie chart）被广泛地应用于各个领域，用于表示不同分类的占比情况，通过弧度大小来对比各种分类。饼图通过将一个圆饼按照分类的占比划分成多个切片，整个圆饼代表数据的总量，每个切片（圆弧）表示该分类占总体的比例，所有切片（圆弧）的加和等于100%。\r\n\r\n## graphics绘制饼图\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(RColorBrewer)  \r\nlibrary(dplyr)\r\nlibrary(graphics)\r\nlibrary(ggplot2)\r\n```\r\n\r\n```{r}\r\nsessionInfo(\r\n)\r\n``` \r\n\r\ninit.angle可设定饼图的初始角度，labels可添加标签。颜色用了brewer.pal函数，第一个参数为个数，第二个参数为名字，这里用的是BrBG，具体可help一下。\r\n```{r message=FALSE, warning=FALSE}\r\ndf <- data.frame(value = c(24.20,30.90,12.50,12.30,8.10,12.10), \r\n                 group = c('LVS','SJM','MCE','Galaxy','MGM','Wynn'))\r\ndf <-arrange(df,value)\r\n\r\nlabs <- paste0(df$group,\" \\n(\", round(df$value/sum(df$value)*100,2), \"%)\") #标签\r\nlab <- paste0(round(df$value/sum(df$value)*100,2), \"%\") #标签\r\npie(df$value,labels=labs, init.angle=90,col =  brewer.pal(nrow(df), \"BrBG\"),\r\n    border=\"black\")\r\npie(df$value,labels=lab, init.angle=90,col =  brewer.pal(nrow(df), \"Blues\"),\r\n    border=\"black\")\r\n```\r\n\r\n## ggplot2包绘制\r\n\r\n使用R中ggplot2包的geom_bar()函数绘制堆积柱形图，然后将直角坐标系转换成极坐标系，\r\n就可以显示为饼图，但还是需要使用geom_text()函数添加数据标签。注意的是：ymax，ymin也需要自己计算得到。\r\n```{r message=FALSE, warning=FALSE}\r\ndf$fraction = df$value / sum(df$value)\r\ndf$ymax = cumsum(df$fraction)\r\ndf$ymin = c(0, head(df$ymax, n = -1))\r\nggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +\r\n  geom_rect(show.legend = F,alpha=0.8) +\r\n  scale_fill_brewer(palette = 'Set3')+\r\n  coord_polar(theta = \"y\") +\r\n  labs(x = \"\", y = \"\", title = \"\",fill='地区') + \r\n  theme_light() +\r\n  theme(panel.grid=element_blank()) + ## 去掉白色外框\r\n  theme(axis.text=element_blank()) + ## 把图旁边的标签去掉\r\n  theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线\r\n  theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框\r\n geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)\r\n```\r\n\r\n但是可以看到：由于缺乏饼图与数据标签之间的引导线，总感觉美观度不够，所以推荐使用graphics 包的pie()函数绘制饼图。\r\n\r\n# 圆环图\r\n\r\n## ggplot绘制圆环图\r\n\r\n在刚才的gglpot绘制饼图的基础上，我们只要再加一条代码即可完成：xlim(c(0, 5))，即将x轴范围控制在0-5。\r\n\r\n```{r}\r\ndf$fraction = df$value / sum(df$value)\r\ndf$ymax = cumsum(df$fraction)\r\ndf$ymin = c(0, head(df$ymax, n = -1))\r\nggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +\r\n  geom_rect(show.legend = F,alpha=0.8) +\r\n  scale_fill_brewer(palette = 'Set3')+\r\n  coord_polar(theta = \"y\") +\r\n  labs(x = \"\", y = \"\", title = \"\",fill='地区') + \r\n  xlim(c(0, 5)) +\r\n  theme_light() +\r\n  theme(panel.grid=element_blank()) + ## 去掉白色外框\r\n  theme(axis.text=element_blank()) + ## 把图旁边的标签去掉\r\n  theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线\r\n  theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框\r\n geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)\r\n```\r\n\r\n\r\n# 复合饼图系列\r\n\r\n散点复合饼图（compound scatter and pie chart）可以展示三个数据变量的信息：(x, y, P)，其中x\r\n和y 决定气泡在直角坐标系中的位置，P 表示饼图的数据信息，决定饼图中各个类别的占比情况，\r\n如图(a)所示。\r\n气泡复合饼图（compound bubble and pie chart）可以展示四个数据变量的信息：(x, y, z, P)，其中\r\nx 和y 决定气泡在直角坐标系中的位置，z 决定气泡的大小，P 表示饼图的数据信息，决定饼图中各\r\n个类别的占比情况，如图(b)所示。\r\n\r\n\r\n```{r}\r\nlibrary(ggplot2)\r\nlibrary(scatterpie)\r\nlibrary(RColorBrewer)\r\n\r\ncrime <- read.csv(\"C:/Users/DELL/Desktop/我的书籍/R语言数据可视化之美/第7章 局部整体型图表/crimeRatesByState2005.tsv\",header = TRUE, sep = \"\\t\", stringsAsFactors = F)\r\nradius <- sqrt(crime$population / pi)\r\nMax_radius<-max(radius)\r\nBubble_Scale<-0.1\r\ncrime$radius <- Bubble_Scale * radius/Max_radius\r\n\r\nmydata<-crime[,c(2,4,3,5:8)]  #数据集构造\r\nCol_Mean<-apply(mydata,2,mean)\r\nCol_Sort<-sort(Col_Mean,index.return=TRUE,decreasing = TRUE)\r\nmydata<-mydata[,Col_Sort$ix]\r\nx<-(mydata$murder-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001\r\ny<-(mydata$Robbery-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001\r\n\r\nxlabel<-seq(0,10,2)\r\nxbreak<-(xlabel-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001\r\nylabel<-seq(0,260,50)\r\nybreak<-(ylabel-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001\r\n\r\nmydata2<-data.frame(x,y,radius=crime$radius)\r\nmydata2<-cbind(mydata2,mydata)\r\n\r\nLegnd_label<-colnames(mydata2)[4:10]\r\ncolnames(mydata2)[4:10]<-LETTERS[1:7]\r\n```\r\n\r\n\r\n\r\n## 散点复合饼图系列(a)\r\n```{r}\r\nggplot() + \r\n  geom_scatterpie(aes(x=x, y=y,r=0.05), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.1) +\r\n  scale_fill_manual(values=colorRampPalette(brewer.pal(7, \"Set2\"))(7),labels=Legnd_label)+\r\n  #geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+\r\n  #geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+\r\n  scale_x_continuous(breaks=xbreak, labels=xlabel)+\r\n  scale_y_continuous(breaks=ybreak, labels=ylabel)+\r\n  xlab(\"murder\")+\r\n  ylab(\"Robbery\")+\r\n  coord_fixed()+\r\n  theme(\r\n    axis.title=element_text(size=15,face=\"plain\",color=\"black\"),\r\n    axis.text = element_text(size=13,face=\"plain\",color=\"black\"),\r\n    legend.title=element_text(size=15,face=\"plain\",color=\"black\"),\r\n    legend.text = element_text(size=14,face=\"plain\",color=\"black\")\r\n  )\r\n```\r\n\r\n## 散点复合饼图系列(b)\r\n```{r}\r\nggplot() + \r\n  geom_scatterpie(aes(x=x, y=y,r=radius), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.25) +\r\n  scale_fill_manual(values=colorRampPalette(brewer.pal(7, \"Set2\"))(7),labels=Legnd_label)+\r\n  geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,\r\n                         labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+\r\n  #geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+\r\n  scale_x_continuous(breaks=xbreak, labels=xlabel)+\r\n  scale_y_continuous(breaks=ybreak, labels=ylabel)+\r\n  xlab(\"murder\")+\r\n  ylab(\"Robbery\")+\r\n  coord_fixed()+\r\n  theme(\r\n    axis.title=element_text(size=15,face=\"plain\",color=\"black\"),\r\n    axis.text = element_text(size=13,face=\"plain\",color=\"black\"),\r\n    legend.title=element_text(size=15,face=\"plain\",color=\"black\"),\r\n    legend.text = element_text(size=14,face=\"plain\",color=\"black\")\r\n  )\r\n\r\n```\r\n\r\n\r\n\r\n参考资料\r\n\r\nhttps://zhuanlan.zhihu.com/p/69617844\r\n\r\n\r\n\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.07.29温大招生/code_ggimage.r",
    "content": "require(magick)\r\nrequire(ggplot2)\r\nrequire(ggplotify)\r\nrequire(shadowtext)\r\nrequire(ggimage)\r\nwindows()\r\nx = image_read(\"C:/Users/DELL/Desktop/bing/3.jpg\")\r\np = as.ggplot(x)\r\n#图上嵌图\r\nsmu = \"http://www.wzu.edu.cn/dfiles/9987/template/default/newzhuzhan/wenda/images/logo.jpg\"\r\n#smu = \"C:/Users/DELL/Desktop/bing/2.jpg\"\r\n\r\np <- p + geom_rect(xmin=.2, xmax=.8, ymin=.4,\r\n              ymax=.6, fill='steelblue', alpha=.5) +\r\n\tgeom_image(x=.5, y=.5, image=smu, size=.4)\r\n#寄语\r\nmsg = \"填志愿一定要遵从本心\\n第一眼看到哪个，就报哪个！\"\r\n\r\np  + geom_shadowtext(\r\n          x=.5, y=.8, label=msg,\r\n          size=10, color='firebrick')\r\n\r\n#参考https://mp.weixin.qq.com/s?__biz=MzI5NjUyNzkxMg==&mid=2247489599&idx=1&sn=e689331bc5500222ec7d0c1c61942362&chksm=ec43a978db34206ed932e7e086db00f707e5cb9f9aa4e38254e40c3ef642dee5693a03532994&mpshare=1&scene=1&srcid=07293Jcp2dA2gLL6If2GGh8s&sharer_sharetime=1596004011941&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=6614a0a10b7b6719e9a2b6aaf9b5a70639efbf3c7c8e8e92b20a32c4badbf01c713092a60b32600156388a8c91a282772e89bcfe2a7eb0236746b8e10cee021cae0a3722f18222f708988e462583107f&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A%2FUKJTXVcz2E27Ve0FcZuIk%3D&pass_ticket=GHX0j6fsfiEATjqcMrcVQQYSihtF3L6yDim2tm78a1XP0v2qucpofrFRF8%2Bz4zjt\r\n\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.08.14amazon/RECOM.Rmd",
    "content": "---\r\ntitle: \"亚马逊产品的推荐算法\"\r\nauthor:\r\ndocumentclass: ctexart\r\nalways_allow_html: true\r\noutput:\r\n  word_document: default\r\n  pdf_document: default\r\n  html_document: default\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# 前言\r\n\r\nR的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括\r\n\r\n1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。\r\n\r\n2. 给出每个用户Top3的产品推荐。\r\n\r\n3. 对于某个产品，预测出用户的评分情况。\r\n\r\n\r\n\r\n# 数据处理与数据探索性分析\r\n```{r  message=FALSE, warning=FALSE}\r\nlibrary(recommenderlab)\r\nlibrary(reshape)\r\n```\r\n\r\n## 数据处理\r\n\r\n选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。\r\n```{r  message=FALSE, warning=FALSE}\r\ndata = read.csv(\"C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv\",header = T)\r\ndata = data[,c(21,3,15)] #userid,product,rating\r\n#删除na的行\r\ndata = na.omit(data)\r\ndim(data)\r\nnames(data) = c('V1','V2','V3')\r\nunique(data$V2)\r\n```\r\n\r\n## 数据探索性分析\r\n\r\n利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nsummary(data[, 3])\r\n\r\nbarplot(prop.table(table(data[, 3])),col=\"skyblue\",\r\n  main=\"各评分分数占比情况\",xlab=\"rating\",ylab=\"proportion\")\r\n\r\nlength(unique(data[, 2]))\r\nfactor(unique(data[, 2]))\r\nbarplot(table(data[, 2]),col=\"skyblue\",\r\n  main=\"\",xlab=\"Product\",ylab=\"Frequent\")\r\nnames(table(data[, 2]))\r\n\r\nfre = as.numeric(table(data[, 2]))\r\ntype = unique(data[, 2])\r\n\r\nlevels(type)[28]<-'B01E6AO69U'\r\nfre = fre[fre!=0]\r\nlibrary(tidyverse)\r\nproduct <- tibble(\r\n  type = factor(unique(data[, 2])),\r\n  freq = fre\r\n)\r\np = ggplot(data = product, mapping = aes(\r\n  x = fct_reorder(type, desc(freq)),\r\n  y = freq ))\r\np + geom_col() +\r\n  coord_flip()\r\nplot(as.numeric(table(data[, 2])),)\r\n```\r\n\r\n## 数据格式构造\r\n\r\n构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。\r\n```{r message=FALSE, warning=FALSE}\r\nmydata <- cast(data,V1~V2,value=\"V3\",fun.aggregate=mean) \r\n#生成一个以v1为行，v2为列的矩阵，使用v3进行填充\r\nmydata <- mydata[,-1] #第一列数字为序列，可以删除\r\nclass(mydata)\r\nclass(mydata)<-\"data.frame\"  #只选取data.frame\r\nmydata<-as.matrix(mydata)\r\nmydata<-as(mydata,\"realRatingMatrix\") \r\nmydata\r\n```\r\n\r\n\r\n# 模型评估与构建最优模型\r\n\r\n- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。\r\n\r\n## 模型评估\r\n\r\n主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。\r\n\r\n- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下\r\n```{r  message=FALSE, warning=FALSE}\r\nscheme <- evaluationScheme(mydata, method = \"split\",\r\n  train = 0.9, k = 1, given = 1, goodRating = 4)\r\n  algorithms <- list(popular = list(name = \"POPULAR\",\r\n  param = list(normalize = \"Z-score\")),random =\r\n  list(name = \"RANDOM\",param = list(normalize = \"Z-score\", method = \"Cosine\",nn = 25, minRating = 3)),\r\n  ibcf = list(name = \"IBCF\", param = list(normalize = \"Z-score\")))\r\nresults <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))\r\n\r\nplot(results, annotate = 1:3, legend = \"topleft\") #ROC\r\nplot(results, \"prec/rec\", annotate = 3)#precision-recall\r\n```\r\n\r\n- 按照评价方案建立推荐模型\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n# 按照评价方案建立推荐模型\r\nmodel.popular <- Recommender(getData(scheme, \"train\"), method = \"POPULAR\")\r\nmodel.ibcf <- Recommender(getData(scheme, \"train\"), method = \"IBCF\")\r\nmodel.random <- Recommender(getData(scheme, \"train\"), method = \"RANDOM\")\r\n```\r\n\r\n- 对推荐模型进行预测\r\n\r\n```{r message=FALSE, warning=FALSE}\r\npredict.popular <- predict(model.popular, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\npredict.ibcf <- predict(model.ibcf, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\npredict.random <- predict(model.random, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\n```\r\n\r\n- 做误差的计算\r\n\r\n```{r message=FALSE, warning=FALSE}\r\npredict.err <- rbind(calcPredictionAccuracy(predict.popular,\r\n  getData(scheme, \"unknown\")),calcPredictionAccuracy(predict.random,\r\n    getData(scheme,\"unknown\")),\r\n  calcPredictionAccuracy(predict.ibcf,getData(scheme, \"unknown\")))\r\nrownames(predict.err) <- c(\"POPULAR\", \"RANDOM\", \"IBCF\")\r\npredict.err\r\n```\r\n\r\n\r\n通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。\r\n\r\n## 构建最优模型\r\n\r\n利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。\r\n```{r message=FALSE, warning=FALSE}\r\n\tcolnames(mydata)<-paste0(\"asins\",1:dim(mydata)[2],sep=\"\")\r\nmydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = \"RANDOM\")\r\n```\r\n数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.\r\n\r\n### TopN推荐\r\n\r\n给出users201，202，203每人前三个产品的推荐。\r\n```{r message=FALSE, warning=FALSE}\r\n##TopN推荐\r\nmydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)\r\n#n指数量\r\nas(mydata.predict1,\"list\")\r\n```\r\n\r\n### 用户对产品的评分预测\r\n\r\n给出前三个users对前6个产品的评分预测。\r\n```{r message=FALSE, warning=FALSE}\r\nmydata.predict2 <- predict(mydata.model, mydata[201:403], type = \"ratings\")\r\nmydata.predict2\r\na = as(mydata.predict2, \"matrix\")[1:3, 1:6] \r\nknitr::kable(a)\r\n```\r\n\r\n\r\n# 参考资料{-}\r\n\r\n1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)\r\n\r\n2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)\r\n\r\n3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)\r\n\r\n"
  },
  {
    "path": "2020年/2020.08.14amazon/code.Rmd",
    "content": "---\r\ntitle: \"亚马逊产品的推荐算法\"\r\nauthor:\r\ndocumentclass: ctexart\r\nalways_allow_html: true\r\noutput:\r\n  word_document: default\r\n  pdf_document: default\r\n  html_document: default\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# 前言\r\n\r\nR的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括\r\n\r\n1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。\r\n\r\n2. 给出每个用户Top3的产品推荐。\r\n\r\n3. 对于某个产品，预测出用户的评分情况。\r\n\r\n\r\n\r\n# 数据处理与数据探索性分析\r\n```{r  message=FALSE, warning=FALSE}\r\nlibrary(recommenderlab)\r\nlibrary(reshape)\r\n```\r\n\r\n## 数据处理\r\n\r\n选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。\r\n```{r  message=FALSE, warning=FALSE}\r\ndata = read.csv(\"C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv\",header = T)\r\ndata = data[,c(21,3,15)] #userid,product,rating\r\n#删除na的行\r\ndata = na.omit(data)\r\ndim(data)\r\nnames(data) = c('V1','V2','V3')\r\nunique(data$V2)\r\n```\r\n\r\n## 数据探索性分析\r\n\r\n利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nsummary(data[, 3])\r\n\r\nbarplot(prop.table(table(data[, 3])),col=\"skyblue\",\r\n  main=\"各评分分数占比情况\",xlab=\"rating\",ylab=\"proportion\")\r\n\r\nlength(unique(data[, 2]))\r\nunique(data[, 2])\r\nbarplot(table(data[, 2]),col=\"skyblue\",\r\n  main=\"\",xlab=\"Product\",ylab=\"Frequent\")\r\nnames(table(data[, 2]))\r\nplot(as.numeric(table(data[, 2])),)\r\n```\r\n\r\n## 数据格式构造\r\n\r\n构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。\r\n```{r message=FALSE, warning=FALSE}\r\nmydata <- cast(data,V1~V2,value=\"V3\",fun.aggregate=mean) \r\n#生成一个以v1为行，v2为列的矩阵，使用v3进行填充\r\nmydata <- mydata[,-1] #第一列数字为序列，可以删除\r\nclass(mydata)\r\nclass(mydata)<-\"data.frame\"  #只选取data.frame\r\nmydata<-as.matrix(mydata)\r\nmydata<-as(mydata,\"realRatingMatrix\") \r\nmydata\r\n```\r\n\r\n\r\n# 模型评估与构建最优模型\r\n\r\n- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。\r\n\r\n## 模型评估\r\n\r\n主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。\r\n\r\n- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下\r\n```{r  message=FALSE, warning=FALSE}\r\nscheme <- evaluationScheme(mydata, method = \"split\",\r\n  train = 0.9, k = 1, given = 1, goodRating = 4)\r\n  algorithms <- list(popular = list(name = \"POPULAR\",\r\n  param = list(normalize = \"Z-score\")),random =\r\n  list(name = \"RANDOM\",param = list(normalize = \"Z-score\", method = \"Cosine\",nn = 25, minRating = 3)),\r\n  ibcf = list(name = \"IBCF\", param = list(normalize = \"Z-score\")))\r\nresults <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))\r\n\r\nplot(results, annotate = 1:3, legend = \"topleft\") #ROC\r\nplot(results, \"prec/rec\", annotate = 3)#precision-recall\r\n```\r\n\r\n- 按照评价方案建立推荐模型\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n# 按照评价方案建立推荐模型\r\nmodel.popular <- Recommender(getData(scheme, \"train\"), method = \"POPULAR\")\r\nmodel.ibcf <- Recommender(getData(scheme, \"train\"), method = \"IBCF\")\r\nmodel.random <- Recommender(getData(scheme, \"train\"), method = \"RANDOM\")\r\n```\r\n\r\n- 对推荐模型进行预测\r\n\r\n```{r message=FALSE, warning=FALSE}\r\npredict.popular <- predict(model.popular, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\npredict.ibcf <- predict(model.ibcf, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\npredict.random <- predict(model.random, getData(scheme, \"known\"), \r\n  type = \"ratings\")\r\n```\r\n\r\n- 做误差的计算\r\n\r\n```{r message=FALSE, warning=FALSE}\r\npredict.err <- rbind(calcPredictionAccuracy(predict.popular,\r\n  getData(scheme, \"unknown\")),calcPredictionAccuracy(predict.random,\r\n    getData(scheme,\"unknown\")),\r\n  calcPredictionAccuracy(predict.ibcf,getData(scheme, \"unknown\")))\r\nrownames(predict.err) <- c(\"POPULAR\", \"RANDOM\", \"IBCF\")\r\npredict.err\r\n```\r\n\r\n\r\n通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。\r\n\r\n## 构建最优模型\r\n\r\n利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。\r\n```{r message=FALSE, warning=FALSE}\r\n\tcolnames(mydata)<-paste0(\"asins\",1:dim(mydata)[2],sep=\"\")\r\nmydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = \"RANDOM\")\r\n```\r\n数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.\r\n\r\n### TopN推荐\r\n\r\n给出users201，202，203每人前三个产品的推荐。\r\n```{r message=FALSE, warning=FALSE}\r\n##TopN推荐\r\nmydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)\r\n#n指数量\r\nas(mydata.predict1,\"list\")\r\n```\r\n\r\n### 用户对产品的评分预测\r\n\r\n给出前三个users对前6个产品的评分预测。\r\n```{r message=FALSE, warning=FALSE}\r\nmydata.predict2 <- predict(mydata.model, mydata[201:403], type = \"ratings\")\r\nmydata.predict2\r\na = as(mydata.predict2, \"matrix\")[1:3, 1:6] \r\nknitr::kable(a)\r\n```\r\n\r\n\r\n# 参考资料{-}\r\n\r\n1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)\r\n\r\n2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)\r\n\r\n3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)\r\n\r\n"
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`BeforeClearDocument' on input line 360.\n[10]\nPackage atveryend Info: Empty hook `AfterLastShipout' on input line 360.\n(./facet.aux)\nPackage atveryend Info: Empty hook `AtVeryEndDocument' on input line 360.\nPackage atveryend Info: Executing hook `AtEndAfterFileList' on input line 360.\nPackage rerunfilecheck Info: File `facet.out' has not changed.\n(rerunfilecheck)             Checksum: E7EEAF92712EBD086E349EC0BCF313EC;359.\n\nLaTeX Font Warning: Some font shapes were not available, defaults substituted.\n\nPackage atveryend Info: Empty hook `AtVeryVeryEnd' on input line 360.\n ) \nHere is how much of TeX's memory you used:\n 21542 strings out of 481765\n 433426 string characters out of 5934263\n 672163 words of memory out of 5000000\n 38189 multiletter control sequences out of 15000+600000\n 557439 words of font info for 100 fonts, out of 8000000 for 9000\n 14 hyphenation exceptions out of 8191\n 55i,6n,116p,384b,600s stack positions out of 5000i,500n,10000p,200000b,80000s\n\nOutput written on facet.pdf (10 pages).\n"
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    "path": "2020年/2020.08.15分面/facet.rmd",
    "content": "---\r\ntitle: \"分面|一页多图\"\r\ndate: 2020-08-15\r\nauthor:\r\n  - 庄闪闪\r\ndocumentclass: ctexart\r\noutput:\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n# 前言\r\n\r\n双变量数据可视化可能对于我们比较简单， 但是如果变量是三个或者更多，怎么在一幅图一起显示呢？今天我们就来讨论这个问题，解决方案有两种。\r\n\r\n\r\n# 使用图形属性\r\n\r\n**使用图形属性**，比如说：散点图点的形状/透明度/颜色用第三个属性表示。\r\n\r\n```{r}\r\nlibrary(ggplot2)\r\nhead(mpg)\r\n#散点图的点的形状表示第三个属性（离散）\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=cty,shape=as.factor(cyl)),size=2,color='skyblue')\r\n#散点图点的透明度表示第三个属性\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=cty,alpha=cyl),size=5,color='purple')\r\n```\r\n\r\n`geom_point()`中可以改变的参数alpha，colour，fill，group，shape，size，stroke（边缘的厚度）。所以我们还可以通过其他参数来引进更多的属性，但是越多图就显得越复杂。看下面这个图，但是可读性不是很高。\r\n\r\n```{r}\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=cty,shape=drv,color=fl,alpha=cyl), size=2)\r\n```\r\n\r\n# 分面\r\n\r\n我们可以将图片按照第三个属性进行**分面**处理。ggplot2的分面有两种方式，分别使用 facet_wrap 或 facet_grid 函数。\r\n\r\n## facet_wrap()\r\n\r\n当想通过单个变量进行分面，则可以使用函数`facet_wrap()`其第一个参数是一个公式，创建公式的方式是在~符号后面加一个变量名，并且该变量应该是离散的。facet_wrap的参数如下\r\n\r\n```{r eval=FALSE, include=TRUE}\r\nfacet_wrap(facets, nrow = NULL, ncol = NULL, scales = \"fixed\",\r\n           shrink = TRUE, as.table = TRUE, drop = TRUE)\r\n```\r\n\r\n\r\nfacets：分面参数如 ~cut，表示用 cut 变量进行数据分类\r\n\r\nnrow：绘制图形的行数\r\n\r\nncol：绘制图形的列数，一般nrow/ncol只设定一个即可\r\n\r\nscales：坐标刻度的范围，可以设定四种类型。fixed\r\n表示所有小图均使用统一坐标范围；free表示每个小图按照各自数据范围自由调整坐标刻度范围；free_x为自由调整x轴刻度范围；free_y为自由调整y轴刻度范围。\r\n\r\nshrinks：也和坐标轴刻度有关，如果为TRUE（默认值）则按统计后的数据调整刻度范围，否则按统计前的数据设定坐标。\r\n\r\nas.table：和小图排列顺序有关的选项。如果为TRUE（默认）则按表格方式排列，即最大值（指分组level值）排在表格最后即右下角，否则排在左上角。\r\n\r\ndrop：是否丢弃没有数据的分组，如果为TRUE（默认），则空数据组不绘图。\r\n\r\nstrip.position： 条子位置，默认为\"top\"，可改为bottom\", \"left\", \"right\"\r\n\r\n具体例子如下：\r\n\r\nx轴是displ，y轴是hwy，用class（离散，7个分类）进行分面。\r\n\r\n```{r}\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=hwy))+\r\n\tfacet_wrap(~class,nrow = 2)\r\n```\r\n\r\n\r\n## facet_grid()\r\n\r\n如果想通过两个变量对图进行分面，则使用`facet_grid()`。这个函数第一个参数也是公式，但该公式包含由~隔开的两个变量。\r\n\r\n```{r eval=FALSE, include=TRUE}\r\nfacet_grid(facets, margins = FALSE, scales = \"fixed\", space = \"fixed\", shrink = TRUE,\r\n           labeller = \"label_value\", as.table = TRUE, drop = TRUE)\r\n```\r\n\r\n和facet_wrap比较，除不用设置ncol和nrow外（facets公式已经包含）外还有几个参数不同：\r\n\r\nmargins：这不是设定图形边界的参数。它是指用于分面的包含每个变量元素所有数据的数据组。很好用的参数！\r\n\r\n具体例子如下：用drv与cyl变量进行分面，x轴方向是cyl，y轴方向是drv的值。注意的是俩都是分类型变量。\r\n```{r}\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=hwy))+\r\n\tfacet_grid(drv~cyl)\r\n```\r\n\r\n\r\n# 思考及拓展\r\n\r\n1. 如果使用连续变量进行分面，得到的图会非常的多，每个数值分一次面，可读性很差，不建议使用该方法。\r\n\r\n2. 使用`facet_grid(drv~cyl)`生成的图中，空白单元的意义说明drv与cyl在该单元没有关系。以下代码可以看出两者之间的关系。\r\n\r\n```{r}\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(drv,cyl))\r\n```\r\n\r\n3. facet_grid()可以转换为facet_wrap图，只需改为facet_grid(drv~.)或facet_grid(.~cyl)。\r\n```{r}\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=hwy))+\r\n\tfacet_grid(drv~.)\r\n\r\nggplot(data=mpg)+\r\n\tgeom_point(mapping = aes(x=displ,y=hwy))+\r\n\tfacet_grid(.~cyl)\r\n```\r\n\r\n4.要在每个面板中重复相同的数据，只需构造一个不包含faceting变量的数据框架。\r\n\r\n```{r}\r\nggplot(mpg, aes(displ, hwy)) +\r\n  geom_point(data = transform(mpg, class = NULL), colour = \"grey85\") +\r\n  geom_point(color='purple') +\r\n  facet_wrap(~class)\r\n```\r\n\r\n5. 去除条子框以及改变条子位置\r\n\r\n加入参数：strip.position = \"top\"（默认），可改为其他（见上面参数详解）并加入theme将strip.placement=\"outside\"就可以去除条子的框了\r\n```{r}\r\nggplot(economics_long, aes(date, value)) +\r\n  geom_line() +\r\n  facet_wrap(vars(variable), scales = \"free_y\", nrow = 2, strip.position = \"top\") +\r\n  theme(strip.background = element_blank(), strip.placement = \"outside\")\r\n```\r\n\r\n\r\n\r\n# 参考资料{-}\r\n\r\n[ggplot2作图详解4：分面（faceting）](https://blog.csdn.net/u014801157/article/details/24372507)\r\n\r\n[R数据科学](R数据科学.pdf)\r\n"
  },
  {
    "path": "2020年/2020.08.23好玩的图/fun.r",
    "content": "# 有趣的图=====\r\n\r\n# 1.画爱心蛋糕======\r\nrequire(grid)\r\nrequire(gridExtra)\r\nrequire(yyplot)\r\nlibrary(devtools)\r\n#install_github(\"GuangchuangYu/yyplot\")\r\nrequire(ggplot2)\r\n\r\nt <- seq(0,2*pi, by=0.2)\r\nx <- 16*sin(t)^3\r\ny <- 13*cos(t) - 5*cos(2*t) - 2*cos(3*t) - cos(4*t)\r\nd <- data.frame(x=x, y=y)\r\n\r\nggplot(d, aes(x, y)) + geom_cake(size=0.1)\r\n\r\n\r\n# 2.构建自己的二维码，只要输入链接即可==========\r\nrequire(ggplot2)\r\nrequire(ggimage)\r\nrequire(yyplot)\r\n\r\nggqrcode(\"https://mp.weixin.qq.com/mp/profile_ext?action=home&__biz=MzI1NjUwMjQxMQ==&scene=124#wechat_redirect\") #自己公众号\r\n\r\n\r\n#表白用=======\r\nggqrcode('I miss you!')\r\n\r\nd <- data.frame(x=1, y=1,\r\n    img=\"C:/Users/DELL/Desktop/avatar.jpg\")\r\np <- ggplot(d, aes(x,y)) + geom_image(aes(image=img), size=Inf) + theme_void()\r\n\r\n\r\np2 <- ggqrcode(\"http://mp.weixin.qq.com/s/oLgpTGdQgcka-OD757_3lA\", \"blue\", alpha=.8)\r\np + geom_subview(p2, width=Inf, height=Inf, x=1, y=1)\r\n\r\n\r\n\r\npg <- ggqrcode(\"https://mp.weixin.qq.com/s/tposUJYrPuRnptXyNX03mA\")\r\nd <- data.frame(x=15, y=15,\r\n    img=\"C:/Users/DELL/Desktop/avatar.jpg\")\r\npg + geom_image(aes(x,y, image=img), data=d, size=.2)\r\n\r\n\r\ngeom_emoji(\"heart_eyes\" )\r\nggplot() + geom_emoji(\"duck\") + theme_void()\r\n\r\nrequire(ggplot2)\r\nrequire(magick)\r\nrequire(purrr)\r\n\r\nx <- search_emoji(\"heart\")\r\n\r\nplot_heart=function(x) {\r\n p = ggplot() + geom_emoji(x)\r\n o = paste0(x, \".png\")\r\n ggsave(o, p, width=5, height=5)\r\n o\r\n }\r\n\r\nx %>% map(plot_heart) %>% \r\nmap(image_read) %>% \r\nimage_join() %>% \r\nimage_animate(fps=1) %>% \r\nimage_write(\"heart.gif\")\r\n\r\n"
  },
  {
    "path": "2020年/2020.08.23好玩的图/readme.txt",
    "content": ""
  },
  {
    "path": "2020年/2020.08.25马赛克/马赛克.html",
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \r\ncode span.dt { color: #795da3; } \r\ncode span.dv { color: #0086b3; } \r\ncode span.bn { color: #0086b3; } \r\ncode span.fl { color: #0086b3; } \r\ncode span.ch { color: #4070a0; } \r\ncode span.st { color: #183691; } \r\ncode span.co { color: #969896; font-style: italic; } \r\ncode span.ot { color: #007020; } \r\n</style>\r\n\r\n\r\n\r\n\r\n\r\n</head>\r\n\r\n<body>\r\n\r\n\r\n\r\n\r\n<section class=\"page-header\">\r\n<h1 class=\"title toc-ignore project-name\">马赛克图</h1>\r\n<h4 class=\"author project-author\">庄亮亮</h4>\r\n<h4 class=\"date project-date\">2020/11/10</h4>\r\n</section>\r\n\r\n\r\n\r\n<section class=\"main-content\">\r\n<p>记得安装prettydoc包，html模板在该包渲染而成。</p>\r\n<div id=\"前言\" class=\"section level2\">\r\n<h2>1.前言</h2>\r\n<p><strong>马赛克图</strong>（mosaic plot），显示分类数据中一对变量之间的关系，原理类似双向的100%堆叠式条形图，但其中所有条形在数值/标尺轴上具有相等长度，并会被划分成段。可以通过这两个变量来检测类别与其子类别之间的关系。</p>\r\n<div id=\"主要优点\" class=\"section level3\">\r\n<h3>主要优点</h3>\r\n<p>马赛克图能按行或按列展示多个类别的比较关系。</p>\r\n</div>\r\n<div id=\"主要缺点\" class=\"section level3\">\r\n<h3>主要缺点</h3>\r\n<p>难以阅读，特别是当含有大量分段的时候。此外，我们也很难准确地对每个分段进行比较，因为它们并非沿着共同基线排列在一起。</p>\r\n</div>\r\n<div id=\"适用\" class=\"section level3\">\r\n<h3>适用</h3>\r\n<p>马赛克图比较适合提供数据概览。</p>\r\n</div>\r\n<div id=\"注意\" class=\"section level3\">\r\n<h3>注意</h3>\r\n<p><strong>非坐标轴非均匀的马赛克图</strong>也是统计学领域标准的马赛克图，一个非均匀的马赛克图包含以下构成元素：①非均匀的分类坐标轴；②面积、颜色均有含义的矩形块；③图例。对于非均匀的马赛克图，关注的数据维度非常多，一般的用户很难直观理解，在多数情况下可以被拆解成多个不同的图表，以下我们会对其进行绘制。</p>\r\n</div>\r\n</div>\r\n<div id=\"数据介绍\" class=\"section level2\">\r\n<h2>2.数据介绍</h2>\r\n<p>数据构建代码来源《R数据可视化之美》，任意拟定一个数据框。并用melt()函数将数据转化成以下结果：</p>\r\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb1-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\r\n<a class=\"sourceLine\" id=\"cb1-2\" title=\"2\"><span class=\"kw\">library</span>(RColorBrewer)</a>\r\n<a class=\"sourceLine\" id=\"cb1-3\" title=\"3\"><span class=\"kw\">library</span>(reshape2)  <span class=\"co\">#提供melt()函数</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-4\" title=\"4\"><span class=\"kw\">library</span>(plyr)      <span class=\"co\">#提供ddply()函数,join()函数</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-5\" title=\"5\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-6\" title=\"6\">df &lt;-<span class=\"st\"> </span><span class=\"kw\">data.frame</span>(<span class=\"dt\">segment =</span> <span class=\"kw\">c</span>(<span class=\"st\">&quot;A&quot;</span>, <span class=\"st\">&quot;B&quot;</span>, <span class=\"st\">&quot;C&quot;</span>,<span class=\"st\">&quot;D&quot;</span>),<span class=\"dt\">Alpha =</span> <span class=\"kw\">c</span>(<span class=\"dv\">2400</span>  ,<span class=\"dv\">1200</span>,  <span class=\"dv\">600</span> ,<span class=\"dv\">250</span>), </a>\r\n<a class=\"sourceLine\" id=\"cb1-7\" title=\"7\">         <span class=\"dt\">Beta =</span> <span class=\"kw\">c</span>(<span class=\"dv\">1000</span>  ,<span class=\"dv\">900</span>,   <span class=\"dv\">600</span>,    <span class=\"dv\">250</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb1-8\" title=\"8\">         <span class=\"dt\">Gamma =</span> <span class=\"kw\">c</span>(<span class=\"dv\">400</span>, <span class=\"dv\">600</span> ,<span class=\"dv\">400</span>,   <span class=\"dv\">250</span>), </a>\r\n<a class=\"sourceLine\" id=\"cb1-9\" title=\"9\">         <span class=\"dt\">Delta =</span> <span class=\"kw\">c</span>(<span class=\"dv\">200</span>, <span class=\"dv\">300</span> ,<span class=\"dv\">400</span>,   <span class=\"dv\">250</span>))</a>\r\n<a class=\"sourceLine\" id=\"cb1-10\" title=\"10\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-11\" title=\"11\">melt_df&lt;-<span class=\"kw\">melt</span>(df,<span class=\"dt\">id=</span><span class=\"st\">&quot;segment&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb1-12\" title=\"12\"><span class=\"kw\">str</span>(melt_df)</a></code></pre></div>\r\n<pre><code>## &#39;data.frame&#39;:    16 obs. of  3 variables:\r\n##  $ segment : chr  &quot;A&quot; &quot;B&quot; &quot;C&quot; &quot;D&quot; ...\r\n##  $ variable: Factor w/ 4 levels &quot;Alpha&quot;,&quot;Beta&quot;,..: 1 1 1 1 2 2 2 2 3 3 ...\r\n##  $ value   : num  2400 1200 600 250 1000 900 600 250 400 600 ...</code></pre>\r\n<p>计算出每行的最大，最小值，并计算每行各数的百分比。ddply()对data.frame分组计算，并利用join()函数进行两个表格连接。</p>\r\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb3-1\" title=\"1\">segpct&lt;-<span class=\"kw\">rowSums</span>(df[,<span class=\"dv\">2</span><span class=\"op\">:</span><span class=\"kw\">ncol</span>(df)])</a>\r\n<a class=\"sourceLine\" id=\"cb3-2\" title=\"2\"><span class=\"cf\">for</span> (i <span class=\"cf\">in</span> <span class=\"dv\">1</span><span class=\"op\">:</span><span class=\"kw\">nrow</span>(df)){</a>\r\n<a class=\"sourceLine\" id=\"cb3-3\" title=\"3\">  <span class=\"cf\">for</span> (j <span class=\"cf\">in</span> <span class=\"dv\">2</span><span class=\"op\">:</span><span class=\"kw\">ncol</span>(df)){</a>\r\n<a class=\"sourceLine\" id=\"cb3-4\" title=\"4\">    df[i,j]&lt;-df[i,j]<span class=\"op\">/</span>segpct[i]<span class=\"op\">*</span><span class=\"dv\">100</span>  <span class=\"co\">#将数字转换成百分比</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-5\" title=\"5\">  }</a>\r\n<a class=\"sourceLine\" id=\"cb3-6\" title=\"6\">}</a>\r\n<a class=\"sourceLine\" id=\"cb3-7\" title=\"7\"></a>\r\n<a class=\"sourceLine\" id=\"cb3-8\" title=\"8\">segpct&lt;-segpct<span class=\"op\">/</span><span class=\"kw\">sum</span>(segpct)<span class=\"op\">*</span><span class=\"dv\">100</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-9\" title=\"9\">df<span class=\"op\">$</span>xmax &lt;-<span class=\"st\"> </span><span class=\"kw\">cumsum</span>(segpct)</a>\r\n<a class=\"sourceLine\" id=\"cb3-10\" title=\"10\">df<span class=\"op\">$</span>xmin &lt;-<span class=\"st\"> </span>(df<span class=\"op\">$</span>xmax <span class=\"op\">-</span><span class=\"st\"> </span>segpct)</a>\r\n<a class=\"sourceLine\" id=\"cb3-11\" title=\"11\"></a>\r\n<a class=\"sourceLine\" id=\"cb3-12\" title=\"12\">dfm &lt;-<span class=\"st\"> </span><span class=\"kw\">melt</span>(df, <span class=\"dt\">id =</span> <span class=\"kw\">c</span>(<span class=\"st\">&quot;segment&quot;</span>, <span class=\"st\">&quot;xmin&quot;</span>, <span class=\"st\">&quot;xmax&quot;</span>),<span class=\"dt\">value.name=</span><span class=\"st\">&quot;percentage&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb3-13\" title=\"13\"><span class=\"kw\">colnames</span>(dfm)[<span class=\"kw\">ncol</span>(dfm)]&lt;-<span class=\"st\">&quot;percentage&quot;</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-14\" title=\"14\"></a>\r\n<a class=\"sourceLine\" id=\"cb3-15\" title=\"15\"><span class=\"co\">#ddply()函数使用自定义统计函数，对data.frame分组计算</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-16\" title=\"16\">dfm1 &lt;-<span class=\"st\"> </span><span class=\"kw\">ddply</span>(dfm, .(segment), transform, <span class=\"dt\">ymax =</span> <span class=\"kw\">cumsum</span>(percentage))</a>\r\n<a class=\"sourceLine\" id=\"cb3-17\" title=\"17\">dfm1 &lt;-<span class=\"st\"> </span><span class=\"kw\">ddply</span>(dfm1, .(segment), transform,<span class=\"dt\">ymin =</span> ymax <span class=\"op\">-</span><span class=\"st\"> </span>percentage)</a>\r\n<a class=\"sourceLine\" id=\"cb3-18\" title=\"18\">dfm1<span class=\"op\">$</span>xtext &lt;-<span class=\"st\"> </span><span class=\"kw\">with</span>(dfm1, xmin <span class=\"op\">+</span><span class=\"st\"> </span>(xmax <span class=\"op\">-</span><span class=\"st\"> </span>xmin)<span class=\"op\">/</span><span class=\"dv\">2</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb3-19\" title=\"19\">dfm1<span class=\"op\">$</span>ytext &lt;-<span class=\"st\"> </span><span class=\"kw\">with</span>(dfm1, ymin <span class=\"op\">+</span><span class=\"st\"> </span>(ymax <span class=\"op\">-</span><span class=\"st\"> </span>ymin)<span class=\"op\">/</span><span class=\"dv\">2</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb3-20\" title=\"20\"></a>\r\n<a class=\"sourceLine\" id=\"cb3-21\" title=\"21\"><span class=\"co\">#join()函数，连接两个表格data.frame</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-22\" title=\"22\">dfm2&lt;-<span class=\"kw\">join</span>(melt_df, dfm1, <span class=\"dt\">by =</span> <span class=\"kw\">c</span>(<span class=\"st\">&quot;segment&quot;</span>, <span class=\"st\">&quot;variable&quot;</span>), <span class=\"dt\">type =</span> <span class=\"st\">&quot;left&quot;</span>, <span class=\"dt\">match =</span> <span class=\"st\">&quot;all&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb3-23\" title=\"23\"><span class=\"kw\">str</span>(dfm2)</a></code></pre></div>\r\n<pre><code>## &#39;data.frame&#39;:    16 obs. of  10 variables:\r\n##  $ segment   : chr  &quot;A&quot; &quot;B&quot; &quot;C&quot; &quot;D&quot; ...\r\n##  $ variable  : Factor w/ 4 levels &quot;Alpha&quot;,&quot;Beta&quot;,..: 1 1 1 1 2 2 2 2 3 3 ...\r\n##  $ value     : num  2400 1200 600 250 1000 900 600 250 400 600 ...\r\n##  $ xmin      : num  0 40 70 90 0 40 70 90 0 40 ...\r\n##  $ xmax      : num  40 70 90 100 40 70 90 100 40 70 ...\r\n##  $ percentage: num  60 40 30 25 25 30 30 25 10 20 ...\r\n##  $ ymax      : num  60 40 30 25 85 70 60 50 95 90 ...\r\n##  $ ymin      : num  0 0 0 0 60 40 30 25 85 70 ...\r\n##  $ xtext     : num  20 55 80 95 20 55 80 95 20 55 ...\r\n##  $ ytext     : num  30 20 15 12.5 72.5 55 45 37.5 90 80 ...</code></pre>\r\n</div>\r\n<div id=\"方法\" class=\"section level2\">\r\n<h2>2.方法</h2>\r\n<p>绘制马赛克图可以使用ggplot2包的geom_rect()函数、ggmosaic包的geom_mosai()函数、graphics包的mosaicplot()函数，或者vcd包的mosaic()函数绘制马赛克图。接下来对他们进行一一实现。</p>\r\n<div id=\"ggplot2包的geom_rect函数\" class=\"section level3\">\r\n<h3>2.1 ggplot2包的geom_rect()函数</h3>\r\n<p>这个方法比较复杂，图层一层一层叠加得到的，不过灵活性比较强，可根据自己喜好进行修改。</p>\r\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb5-1\" title=\"1\"><span class=\"kw\">ggplot</span>()<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_rect</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">ymin =</span> ymin, <span class=\"dt\">ymax =</span> ymax, <span class=\"dt\">xmin =</span> xmin, <span class=\"dt\">xmax =</span> xmax, <span class=\"dt\">fill =</span> variable),dfm2,<span class=\"dt\">colour =</span> <span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span><span class=\"st\"> </span></a>\r\n<a class=\"sourceLine\" id=\"cb5-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">geom_text</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x =</span> xtext, <span class=\"dt\">y =</span> ytext,  <span class=\"dt\">label =</span> value),dfm2 ,<span class=\"dt\">size =</span> <span class=\"dv\">4</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">geom_text</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x =</span> xtext, <span class=\"dt\">y =</span> <span class=\"dv\">103</span>, <span class=\"dt\">label =</span> <span class=\"kw\">paste</span>(<span class=\"st\">&quot;Seg &quot;</span>, segment)),dfm2 ,<span class=\"dt\">size =</span> <span class=\"dv\">4</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">geom_text</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x =</span> <span class=\"dv\">102</span>, <span class=\"dt\">y =</span> <span class=\"kw\">seq</span>(<span class=\"fl\">12.5</span>,<span class=\"dv\">100</span>,<span class=\"dv\">25</span>), <span class=\"dt\">label =</span> <span class=\"kw\">c</span>(<span class=\"st\">&quot;Alpha&quot;</span>,<span class=\"st\">&quot;Beta&quot;</span>,<span class=\"st\">&quot;Gamma&quot;</span>,<span class=\"st\">&quot;Delta&quot;</span>)), <span class=\"dt\">size =</span> <span class=\"dv\">4</span>,<span class=\"dt\">hjust =</span> <span class=\"dv\">0</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-6\" title=\"6\"><span class=\"st\">  </span><span class=\"kw\">scale_x_continuous</span>(<span class=\"dt\">breaks=</span><span class=\"kw\">seq</span>(<span class=\"dv\">0</span>,<span class=\"dv\">100</span>,<span class=\"dv\">25</span>),<span class=\"dt\">limits=</span><span class=\"kw\">c</span>(<span class=\"dv\">0</span>,<span class=\"dv\">110</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-7\" title=\"7\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(<span class=\"dt\">panel.background=</span><span class=\"kw\">element_rect</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;white&quot;</span>,<span class=\"dt\">colour=</span><span class=\"ot\">NA</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb5-8\" title=\"8\">        <span class=\"dt\">panel.grid.major =</span> <span class=\"kw\">element_line</span>(<span class=\"dt\">colour =</span> <span class=\"st\">&quot;grey60&quot;</span>,<span class=\"dt\">size=</span>.<span class=\"dv\">25</span>,<span class=\"dt\">linetype =</span><span class=\"st\">&quot;dotted&quot;</span> ),</a>\r\n<a class=\"sourceLine\" id=\"cb5-9\" title=\"9\">        <span class=\"dt\">panel.grid.minor =</span> <span class=\"kw\">element_line</span>(<span class=\"dt\">colour =</span> <span class=\"st\">&quot;grey60&quot;</span>,<span class=\"dt\">size=</span>.<span class=\"dv\">25</span>,<span class=\"dt\">linetype =</span><span class=\"st\">&quot;dotted&quot;</span> ),</a>\r\n<a class=\"sourceLine\" id=\"cb5-10\" title=\"10\">        <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb5-11\" title=\"11\">        <span class=\"dt\">legend.position=</span><span class=\"st\">&quot;none&quot;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p><strong>图形解释：</strong>这个马赛克图，从A这列纵向看可以看出各个指标(Delta,Gamma等)的占比情况，从横向来看，不同变量（A,B等）的宽度代表该变量占所有数据的占比情况，越宽说明该变量数据总和越大。</p>\r\n</div>\r\n<div id=\"vcd包的mosaic函数\" class=\"section level3\">\r\n<h3>2.2 vcd包的mosaic()函数</h3>\r\n<p>用该函数，我们就不用前面那么复杂的数据集进行绘制了，只要使用xtabs转换成以下数据格式即可，方便简单。</p>\r\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb6-1\" title=\"1\"><span class=\"kw\">library</span>(vcd)</a></code></pre></div>\r\n<pre><code>## Warning: package &#39;vcd&#39; was built under R version 4.0.3</code></pre>\r\n<pre><code>## Loading required package: grid</code></pre>\r\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb9-1\" title=\"1\">table&lt;-<span class=\"kw\">xtabs</span>(value <span class=\"op\">~</span>variable<span class=\"op\">+</span>segment, melt_df)</a>\r\n<a class=\"sourceLine\" id=\"cb9-2\" title=\"2\"><span class=\"kw\">mosaic</span>( <span class=\"op\">~</span>segment<span class=\"op\">+</span>variable,table,<span class=\"dt\">shade=</span><span class=\"ot\">TRUE</span>,<span class=\"dt\">legend=</span><span class=\"ot\">TRUE</span>,<span class=\"dt\">color=</span><span class=\"ot\">TRUE</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"graphics包的mosaicplot函数\" class=\"section level3\">\r\n<h3>2.3 graphics包的mosaicplot()函数</h3>\r\n<p>该方法和上面类似，掉包就行，数据类型与上面相同。</p>\r\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb10-1\" title=\"1\"><span class=\"kw\">library</span>(graphics)</a>\r\n<a class=\"sourceLine\" id=\"cb10-2\" title=\"2\"><span class=\"kw\">library</span>(wesanderson) <span class=\"co\">#颜色提取</span></a></code></pre></div>\r\n<pre><code>## Warning: package &#39;wesanderson&#39; was built under R version 4.0.3</code></pre>\r\n<div class=\"sourceCode\" id=\"cb12\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb12-1\" title=\"1\"><span class=\"kw\">mosaicplot</span>( <span class=\"op\">~</span>segment<span class=\"op\">+</span>variable,table, <span class=\"dt\">color =</span> <span class=\"kw\">wes_palette</span>(<span class=\"st\">&quot;GrandBudapest1&quot;</span>),<span class=\"dt\">main =</span> <span class=\"st\">&#39;&#39;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n</div>\r\n<div id=\"参考\" class=\"section level2\">\r\n<h2>参考</h2>\r\n<ol style=\"list-style-type: decimal\">\r\n<li><p>《R数据可视化之美》</p></li>\r\n<li><p><a href=\"https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E\" class=\"uri\">https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E</a></p></li>\r\n</ol>\r\n<p><img 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\" 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    "path": "2020年/2020.08.25马赛克/马赛克.rmd",
    "content": "---\r\ntitle: \"马赛克图\"\r\nauthor:\r\n  - 庄亮亮\r\ndate: \"2020/11/10\"\r\noutput:\r\n  prettydoc::html_pretty:\r\n    theme: cayman\r\n    highlight: github\r\n---\r\n\r\n记得安装prettydoc包，html模板在该包渲染而成。\r\n\r\n## 1.前言\r\n\r\n**马赛克图**（mosaic plot），显示分类数据中一对变量之间的关系，原理类似双向的100%堆叠式条形图，但其中所有条形在数值/标尺轴上具有相等长度，并会被划分成段。可以通过这两个变量来检测类别与其子类别之间的关系。\r\n\r\n### 主要优点\r\n\r\n马赛克图能按行或按列展示多个类别的比较关系。\r\n\r\n### 主要缺点 \r\n\r\n难以阅读，特别是当含有大量分段的时候。此外，我们也很难准确地对每个分段进行比较，因为它们并非沿着共同基线排列在一起。\r\n\r\n\r\n### 适用\r\n\r\n马赛克图比较适合提供数据概览。\r\n\r\n### 注意\r\n\r\n**非坐标轴非均匀的马赛克图**也是统计学领域标准的马赛克图，一个非均匀的马赛克图包含以下构成元素：①非均匀的分类坐标轴；②面积、颜色均有含义的矩形块；③图例。对于非均匀的马赛克图，关注的数据维度非常多，一般的用户很难直观理解，在多数情况下可以被拆解成多个不同的图表，以下我们会对其进行绘制。\r\n\r\n## 2.数据介绍\r\n\r\n数据构建代码来源《R数据可视化之美》，任意拟定一个数据框。并用melt()函数将数据转化成以下结果：\r\n\r\n```{r}\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)\r\nlibrary(reshape2)  #提供melt()函数\r\nlibrary(plyr)      #提供ddply()函数,join()函数\r\n\r\ndf <- data.frame(segment = c(\"A\", \"B\", \"C\",\"D\"),Alpha = c(2400\t,1200,\t600\t,250), \r\n         Beta = c(1000\t,900,\t600,\t250),\r\n         Gamma = c(400,\t600\t,400,\t250), \r\n         Delta = c(200,\t300\t,400,\t250))\r\n\r\nmelt_df<-melt(df,id=\"segment\")\r\nstr(melt_df)\r\n```\r\n\r\n\r\n计算出每行的最大，最小值，并计算每行各数的百分比。ddply()对data.frame分组计算，并利用join()函数进行两个表格连接。\r\n\r\n```{r}\r\nsegpct<-rowSums(df[,2:ncol(df)])\r\nfor (i in 1:nrow(df)){\r\n  for (j in 2:ncol(df)){\r\n    df[i,j]<-df[i,j]/segpct[i]*100  #将数字转换成百分比\r\n  }\r\n}\r\n\r\nsegpct<-segpct/sum(segpct)*100\r\ndf$xmax <- cumsum(segpct)\r\ndf$xmin <- (df$xmax - segpct)\r\n\r\ndfm <- melt(df, id = c(\"segment\", \"xmin\", \"xmax\"),value.name=\"percentage\")\r\ncolnames(dfm)[ncol(dfm)]<-\"percentage\"\r\n\r\n#ddply()函数使用自定义统计函数，对data.frame分组计算\r\ndfm1 <- ddply(dfm, .(segment), transform, ymax = cumsum(percentage))\r\ndfm1 <- ddply(dfm1, .(segment), transform,ymin = ymax - percentage)\r\ndfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)\r\ndfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)\r\n\r\n#join()函数，连接两个表格data.frame\r\ndfm2<-join(melt_df, dfm1, by = c(\"segment\", \"variable\"), type = \"left\", match = \"all\")\r\nstr(dfm2)\r\n```\r\n\r\n## 2.方法\r\n\r\n绘制马赛克图可以使用ggplot2包的geom_rect()函数、ggmosaic包的geom_mosai()函数、graphics包的mosaicplot()函数，或者vcd包的mosaic()函数绘制马赛克图。接下来对他们进行一一实现。\r\n\r\n### 2.1 ggplot2包的geom_rect()函数\r\n\r\n这个方法比较复杂，图层一层一层叠加得到的，不过灵活性比较强，可根据自己喜好进行修改。\r\n\r\n```{r}\r\nggplot()+\r\n  geom_rect(aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable),dfm2,colour = \"black\") + \r\n  geom_text(aes(x = xtext, y = ytext,  label = value),dfm2 ,size = 4)+\r\n  geom_text(aes(x = xtext, y = 103, label = paste(\"Seg \", segment)),dfm2 ,size = 4)+\r\n  geom_text(aes(x = 102, y = seq(12.5,100,25), label = c(\"Alpha\",\"Beta\",\"Gamma\",\"Delta\")), size = 4,hjust = 0)+\r\n  scale_x_continuous(breaks=seq(0,100,25),limits=c(0,110))+\r\n  theme(panel.background=element_rect(fill=\"white\",colour=NA),\r\n        panel.grid.major = element_line(colour = \"grey60\",size=.25,linetype =\"dotted\" ),\r\n        panel.grid.minor = element_line(colour = \"grey60\",size=.25,linetype =\"dotted\" ),\r\n        text=element_text(size=15),\r\n        legend.position=\"none\")\r\n```\r\n\r\n**图形解释：**这个马赛克图，从A这列纵向看可以看出各个指标(Delta,Gamma等)的占比情况，从横向来看，不同变量（A,B等）的宽度代表该变量占所有数据的占比情况，越宽说明该变量数据总和越大。\r\n\r\n\r\n\r\n\r\n### 2.2 vcd包的mosaic()函数\r\n\r\n用该函数，我们就不用前面那么复杂的数据集进行绘制了，只要使用xtabs转换成以下数据格式即可，方便简单。\r\n\r\n```{r}\r\nlibrary(vcd)\r\ntable<-xtabs(value ~variable+segment, melt_df)\r\nmosaic( ~segment+variable,table,shade=TRUE,legend=TRUE,color=TRUE)\r\n```\r\n\r\n\r\n### 2.3 graphics包的mosaicplot()函数\r\n\r\n该方法和上面类似，掉包就行，数据类型与上面相同。\r\n\r\n```{r}\r\nlibrary(graphics)\r\nlibrary(wesanderson) #颜色提取\r\nmosaicplot( ~segment+variable,table, color = wes_palette(\"GrandBudapest1\"),main = '')\r\n```\r\n\r\n\r\n\r\n## 参考\r\n\r\n1.  《R数据可视化之美》 \r\n\r\n2.  https://blog.csdn.net/tobeyourlover/article/details/52704333?%3E\r\n\r\n![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  \r\n欢迎**关注**我的**公众号**，**点赞，在看，收藏\\~\\~\\~**"
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4rem;margin:0 auto;font-size:1.1rem}.toc{padding-bottom:0}.toc .toc-box{padding:1.5rem;background-color:#f3f6fa;border:solid 1px #dce6f0;border-radius:.3rem}.toc .toc-box .toc-title{margin:0 0 .5rem;text-align:center}.toc .toc-box>ul{margin:0;padding-left:1.5rem}@media screen and (min-width:42em) and (max-width:64em){.toc{padding:2rem 2rem 0}}@media screen and (max-width:42em){.toc{padding:2rem 1rem 0;font-size:1rem}}.main-content :first-child{margin-top:0}@media screen and (min-width:42em) and (max-width:64em){.main-content{padding:2rem}}@media screen and (max-width:42em){.main-content{padding:2rem 1rem;font-size:1rem}}.main-content img{max-width:100%}.main-content h1,.main-content h2,.main-content h3,.main-content h4,.main-content h5,.main-content h6{margin-top:2rem;margin-bottom:1rem;font-weight:400;color:#159957}.main-content p{margin-bottom:1em}.main-content code{padding:2px 4px;font-family:Consolas,\"Liberation 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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \r\ncode span.dt { color: #795da3; } \r\ncode span.dv { color: #0086b3; } \r\ncode span.bn { color: #0086b3; } \r\ncode span.fl { color: #0086b3; } \r\ncode span.ch { color: #4070a0; } \r\ncode span.st { color: #183691; } \r\ncode span.co { color: #969896; font-style: italic; } \r\ncode span.ot { color: #007020; } \r\n</style>\r\n\r\n\r\n\r\n\r\n\r\n</head>\r\n\r\n<body>\r\n\r\n\r\n\r\n\r\n<section class=\"page-header\">\r\n<h1 class=\"title toc-ignore project-name\">混合图</h1>\r\n<h4 class=\"author project-author\">庄亮亮</h4>\r\n<h4 class=\"date project-date\">2020/11/10</h4>\r\n</section>\r\n\r\n\r\n\r\n<section class=\"main-content\">\r\n<p>记得安装prettydoc包，html模板在该包渲染而成。</p>\r\n<p><img 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\" /><br />\r\n欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>\r\n<p>创作不易！感谢大家支持。</p>\r\n<hr />\r\n<div id=\"前言\" class=\"section level2\">\r\n<h2>前言</h2>\r\n<p>在同一页面上混合多个图形是一种常见的做法。它可以在同一数字上 总结大量信息，例如，它被广泛用于科学出版物。</p>\r\n</div>\r\n<div id=\"基础包par\" class=\"section level2\">\r\n<h2>基础包——par()</h2>\r\n<p>一页多图用mfrow参数或mfcol参数规定，这也是我几年前经常用的一种方法。</p>\r\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb1-1\" title=\"1\">x &lt;-<span class=\"st\"> </span><span class=\"kw\">rnorm</span>(<span class=\"dv\">50</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb1-2\" title=\"2\">y &lt;-<span class=\"st\"> </span><span class=\"kw\">rnorm</span>(<span class=\"dv\">50</span>,<span class=\"dv\">2</span>,<span class=\"dv\">2</span>)</a></code></pre></div>\r\n<p>随便模拟产生数据，并对数据绘制一些简单的图，用该函数将一页中对他们进行全部展示。</p>\r\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb2-1\" title=\"1\"><span class=\"kw\">par</span>(<span class=\"dt\">mfrow=</span><span class=\"kw\">c</span>(<span class=\"dv\">2</span>,<span class=\"dv\">2</span>))</a>\r\n<a class=\"sourceLine\" id=\"cb2-2\" title=\"2\"><span class=\"kw\">plot</span>(x, y, <span class=\"dt\">xlab =</span> <span class=\"st\">&quot;&quot;</span>, <span class=\"dt\">ylab =</span> <span class=\"st\">&quot;&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb2-3\" title=\"3\"><span class=\"kw\">hist</span>(x,<span class=\"dt\">main=</span><span class=\"st\">&#39;&#39;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb2-4\" title=\"4\"><span class=\"kw\">qqnorm</span>(x,<span class=\"dt\">main =</span> <span class=\"st\">&#39;&#39;</span>);<span class=\"kw\">qqline</span>(x)</a>\r\n<a class=\"sourceLine\" id=\"cb2-5\" title=\"5\"><span class=\"kw\">barplot</span>(x, <span class=\"dt\">axes =</span> <span class=\"ot\">FALSE</span>, <span class=\"dt\">space =</span> <span class=\"dv\">0</span>,<span class=\"dt\">col=</span><span class=\"st\">&#39;white&#39;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"基础包layout\" class=\"section level2\">\r\n<h2>基础包——layout()</h2>\r\n<pre><code>layout(mat, widths = rep.int(1, ncol(mat)),\r\n       heights = rep.int(1, nrow(mat)), respect = FALSE)</code></pre>\r\n<ul>\r\n<li><p>mat 参数为一个矩阵，提供了作图的顺序以及图形版面的安排。0代表空缺，不绘制图形，大于0 的数代表绘图顺序，相同数字代表占位符。</p></li>\r\n<li><p>widths 和 heights 参数提供了各个矩形作图区域的长和宽的比例。</p></li>\r\n<li><p>respect 参数控制着各图形内的横纵轴刻度长度的比例尺是否一样。</p></li>\r\n<li><p>n 参数为欲显示的区域的序号。</p></li>\r\n</ul>\r\n<p>生成2行2列的版面，并设置宽度和高度。par()中oma参数指四个外边空的行数</p>\r\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb4-1\" title=\"1\"><span class=\"kw\">par</span>(<span class=\"dt\">oma =</span> <span class=\"kw\">c</span>(<span class=\"dv\">2</span>,<span class=\"dv\">2</span>,<span class=\"dv\">2</span>,<span class=\"dv\">2</span>))</a>\r\n<a class=\"sourceLine\" id=\"cb4-2\" title=\"2\">nf &lt;-<span class=\"st\"> </span><span class=\"kw\">layout</span>(<span class=\"kw\">matrix</span>(<span class=\"kw\">c</span>(<span class=\"dv\">1</span>,<span class=\"dv\">2</span>,<span class=\"dv\">1</span>,<span class=\"dv\">3</span>),<span class=\"dv\">2</span>,<span class=\"dv\">2</span>),<span class=\"dt\">widths =</span> <span class=\"kw\">c</span>(<span class=\"dv\">1</span>, <span class=\"dv\">3</span>), <span class=\"dt\">heights =</span> <span class=\"kw\">c</span>(<span class=\"dv\">1</span>, <span class=\"dv\">2</span>))</a>\r\n<a class=\"sourceLine\" id=\"cb4-3\" title=\"3\"><span class=\"kw\">layout.show</span>(nf)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>再将各个图进行填充</p>\r\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb5-1\" title=\"1\"><span class=\"kw\">plot</span>(x, y, <span class=\"dt\">xlab =</span> <span class=\"st\">&quot;&quot;</span>, <span class=\"dt\">ylab =</span> <span class=\"st\">&quot;&quot;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb6-1\" title=\"1\"><span class=\"kw\">hist</span>(x,<span class=\"dt\">main=</span><span class=\"st\">&#39;&#39;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb7-1\" title=\"1\"><span class=\"kw\">qqnorm</span>(x,<span class=\"dt\">main =</span> <span class=\"st\">&#39;&#39;</span>);<span class=\"kw\">qqline</span>(x)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>前面两种方法，说实话可以实现，但是比较费劲。那么下面看看gridExtra包。</p>\r\n</div>\r\n<div id=\"gridextra包混合多个图形\" class=\"section level2\">\r\n<h2>gridExtra包——混合多个图形</h2>\r\n<p>gridExtra包让混合多个图片变得轻而易举。它提供了grid.arrange() 函数来完成这个任务。它的nrow参数允许指定如何安排布局。</p>\r\n<p>对于更复杂的布局，arrangeGrob() 函数允许做一些嵌套。这里有 4 个 例子来说明 gridExtra 是如何工作的:</p>\r\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb8-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\r\n<a class=\"sourceLine\" id=\"cb8-2\" title=\"2\"><span class=\"kw\">library</span>(gridExtra)</a></code></pre></div>\r\n<p>这里我们用ggplot绘图，并存在变量名称（g1,g2,g3）中，然后用grid.arrange()将各个变量名称展现出来。</p>\r\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb9-1\" title=\"1\"><span class=\"co\"># Make 3 simple graphics:</span></a>\r\n<a class=\"sourceLine\" id=\"cb9-2\" title=\"2\">g1 &lt;-<span class=\"st\"> </span><span class=\"kw\">ggplot</span>(mtcars, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>qsec)) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">geom_density</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;slateblue&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb9-3\" title=\"3\">g2 &lt;-<span class=\"st\"> </span><span class=\"kw\">ggplot</span>(mtcars, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>drat, <span class=\"dt\">y=</span>qsec, <span class=\"dt\">color=</span>cyl)) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">size=</span><span class=\"dv\">5</span>) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">theme</span>(<span class=\"dt\">legend.position=</span><span class=\"st\">&quot;none&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb9-4\" title=\"4\">g3 &lt;-<span class=\"st\"> </span><span class=\"kw\">ggplot</span>(mtcars, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span><span class=\"kw\">factor</span>(cyl), <span class=\"dt\">y=</span>qsec, <span class=\"dt\">fill=</span>cyl)) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">geom_boxplot</span>() <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">theme</span>(<span class=\"dt\">legend.position=</span><span class=\"st\">&quot;none&quot;</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb9-5\" title=\"5\">g4 &lt;-<span class=\"st\"> </span><span class=\"kw\">ggplot</span>(mtcars , <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span><span class=\"kw\">factor</span>(cyl), <span class=\"dt\">fill=</span><span class=\"kw\">factor</span>(cyl))) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">geom_bar</span>()</a>\r\n<a class=\"sourceLine\" id=\"cb9-6\" title=\"6\"><span class=\"co\"># Plots</span></a>\r\n<a class=\"sourceLine\" id=\"cb9-7\" title=\"7\"><span class=\"kw\">grid.arrange</span>(g1, g2, g3, <span class=\"dt\">nrow =</span> <span class=\"dv\">3</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb10-1\" title=\"1\"><span class=\"kw\">grid.arrange</span>(g2, <span class=\"kw\">arrangeGrob</span>(g3, g4, <span class=\"dt\">ncol=</span><span class=\"dv\">2</span>), <span class=\"dt\">nrow =</span> <span class=\"dv\">2</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb11-1\" title=\"1\"><span class=\"kw\">grid.arrange</span>(g2, <span class=\"kw\">arrangeGrob</span>(g3, g4,g1, <span class=\"dt\">ncol=</span><span class=\"dv\">3</span>), <span class=\"dt\">nrow =</span> <span class=\"dv\">2</span>)</a></code></pre></div>\r\n<p><img 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\" /><!-- --></p>\r\n</div>\r\n</section>\r\n\r\n\r\n\r\n<!-- code folding -->\r\n\r\n\r\n<!-- dynamically load mathjax for compatibility with self-contained -->\r\n<script>\r\n  (function () {\r\n    var script = document.createElement(\"script\");\r\n    script.type = \"text/javascript\";\r\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\r\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\r\n  })();\r\n</script>\r\n\r\n</body>\r\n</html>\r\n"
  },
  {
    "path": "2020年/2020.08.26混合多个图形/混合图.rmd",
    "content": "---\r\ntitle: \"混合图\"\r\nauthor:\r\n  - 庄亮亮\r\ndate: \"2020/11/10\"\r\noutput:\r\n  prettydoc::html_pretty:\r\n    theme: cayman\r\n    highlight: github\r\n---\r\n\r\n记得安装prettydoc包，html模板在该包渲染而成。\r\n\r\n![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  \r\n欢迎**关注**我的**公众号**，**点赞，在看，收藏\\~\\~\\~**\r\n\r\n\r\n创作不易！感谢大家支持。\r\n\r\n-----------------------\r\n\r\n\r\n## 前言\r\n\r\n在同一页面上混合多个图形是一种常见的做法。它可以在同一数字上\r\n总结大量信息，例如，它被广泛用于科学出版物。\r\n\r\n## 基础包——par()\r\n\r\n一页多图用mfrow参数或mfcol参数规定，这也是我几年前经常用的一种方法。\r\n```{r}\r\nx <- rnorm(50)\r\ny <- rnorm(50,2,2)\r\n```\r\n随便模拟产生数据，并对数据绘制一些简单的图，用该函数将一页中对他们进行全部展示。\r\n```{r}\r\npar(mfrow=c(2,2))\r\nplot(x, y, xlab = \"\", ylab = \"\")\r\nhist(x,main='')\r\nqqnorm(x,main = '');qqline(x)\r\nbarplot(x, axes = FALSE, space = 0,col='white')\r\n```\r\n\r\n\r\n\r\n## 基础包——layout()\r\n\r\n```\r\nlayout(mat, widths = rep.int(1, ncol(mat)),\r\n       heights = rep.int(1, nrow(mat)), respect = FALSE)\r\n```      \r\n       \r\n- mat 参数为一个矩阵，提供了作图的顺序以及图形版面的安排。0代表空缺，不绘制图形，大于0 的数代表绘图顺序，相同数字代表占位符。\r\n\r\n- widths 和 heights 参数提供了各个矩形作图区域的长和宽的比例。\r\n\r\n- respect 参数控制着各图形内的横纵轴刻度长度的比例尺是否一样。\r\n\r\n- n 参数为欲显示的区域的序号。\r\n\r\n生成2行2列的版面，并设置宽度和高度。par()中oma参数指四个外边空的行数\r\n```{r}\r\npar(oma = c(2,2,2,2))\r\nnf <- layout(matrix(c(1,2,1,3),2,2),widths = c(1, 3), heights = c(1, 2))\r\nlayout.show(nf)\r\n```\r\n\r\n\r\n再将各个图进行填充\r\n\r\n```{r}\r\nplot(x, y, xlab = \"\", ylab = \"\")\r\nhist(x,main='')\r\nqqnorm(x,main = '');qqline(x)\r\n```\r\n\r\n\r\n前面两种方法，说实话可以实现，但是比较费劲。那么下面看看gridExtra包。\r\n\r\n## gridExtra包——混合多个图形\r\n\r\ngridExtra包让混合多个图片变得轻而易举。它提供了grid.arrange() 函数来完成这个任务。它的nrow参数允许指定如何安排布局。\r\n\r\n对于更复杂的布局，arrangeGrob() 函数允许做一些嵌套。这里有 4 个\r\n例子来说明 gridExtra 是如何工作的:\r\n\r\n```{r}\r\nlibrary(ggplot2)\r\nlibrary(gridExtra)\r\n```\r\n\r\n这里我们用ggplot绘图，并存在变量名称（g1,g2,g3）中，然后用grid.arrange()将各个变量名称展现出来。\r\n\r\n\r\n\r\n```{r}\r\n# Make 3 simple graphics:\r\ng1 <- ggplot(mtcars, aes(x=qsec)) + geom_density(fill=\"slateblue\")\r\ng2 <- ggplot(mtcars, aes(x=drat, y=qsec, color=cyl)) + geom_point(size=5) + theme(legend.position=\"none\")\r\ng3 <- ggplot(mtcars, aes(x=factor(cyl), y=qsec, fill=cyl)) + geom_boxplot() + theme(legend.position=\"none\")\r\ng4 <- ggplot(mtcars , aes(x=factor(cyl), fill=factor(cyl))) + geom_bar()\r\n# Plots\r\ngrid.arrange(g1, g2, g3, nrow = 3)\r\n\r\n```\r\n\r\n```{r}\r\ngrid.arrange(g2, arrangeGrob(g3, g4, ncol=2), nrow = 2)\r\n```\r\n\r\n```{r}\r\ngrid.arrange(g2, arrangeGrob(g3, g4,g1, ncol=3), nrow = 2)\r\n\r\n```\r\n\r\n"
  },
  {
    "path": "2020年/2020.09.15reticulate/reticulate.rmd",
    "content": "---\r\ntitle: \"reticulate包\"\r\nauthor:\r\n  - 庄亮亮\r\ndocumentclass: ctexart\r\noutput:\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n```{r}\r\nlibrary(\"reticulate\")\r\n```\r\n\r\n- 下载python模块\r\n```{r}\r\npy_install(\"math\")\r\npy_install(\"matplotlib.pyplot\")\r\npy_install(\"math\")\r\n\r\n#py_install(\"scipy\") #安装\r\npy_module_available(\"math\") #查看是否安装成功\r\n\r\nrepl_python()# 查看路径 \r\n#use_python(\"D:/anaconda/python.exe\") #更换路径\r\n\r\n```\r\n\r\n\r\n```{python}\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom math import pi\r\nfrom matplotlib.pyplot import figure, show, rc \r\nplt.rcParams[\"patch.force_edgecolor\"] = True\r\n\r\n\r\ndf = pd.DataFrame(dict(categories=['var1', 'var2', 'var3', 'var4', 'var5'],\r\n                       group_A=[38.0, 29, 8, 7, 28],\r\n                       group_B=[1.5, 10, 39, 31, 15]))\r\nN = df.shape[0]\r\n \r\nangles = [n / float(N) * 2 * pi for n in range(N)]\r\nangles += angles[:1]\r\n \r\nfig = figure(figsize=(4,4),dpi =90)  \r\nax = fig.add_axes([0.1, 0.1, 0.6, 0.6], polar=True)\r\n \r\nax.set_theta_offset(pi / 2)\r\nax.set_theta_direction(-1)\r\nax.set_rlabel_position(0)\r\n\r\nplt.xticks(angles[:-1], df['categories'], color=\"black\", size=12)\r\nplt.ylim(0,45)\r\nplt.yticks(np.arange(10,50,10),color=\"black\", size=12,verticalalignment='center',horizontalalignment='right')\r\n\r\nplt.grid(which='major',axis =\"x\", linestyle='-', linewidth='0.5', color='gray',alpha=0.5)\r\nplt.grid(which='major',axis =\"y\", linestyle='-', linewidth='0.5', color='gray',alpha=0.5)\r\n\r\nvalues=df['group_A'].values.flatten().tolist()\r\nvalues += values[:1]\r\nax.fill(angles, values, '#7FBC41', alpha=0.3)\r\nax.plot(angles, values, marker='o', markerfacecolor='#7FBC41', markersize=8, color='k', linewidth=0.25,label=\"group A\")\r\n\r\nvalues=df['group_B'].values.flatten().tolist()\r\nvalues += values[:1]\r\nax.fill(angles, values, '#C51B7D', alpha=0.3)\r\nax.plot(angles, values, marker='o', markerfacecolor='#C51B7D', markersize=8, color='k', linewidth=0.25,label=\"group B\")\r\n\r\nplt.legend(loc=\"center\",bbox_to_anchor=(1.25, 0, 0, 1))\r\n```\r\n\r\n\r\n\r\n```{python}\r\nimport numpy as np \r\nimport matplotlib.pyplot as plt \r\n# 计算正弦曲线上点的 x 和 y 坐标\r\nx = np.arange(0,  3  * np.pi,  0.1) \r\ny = np.sin(x)\r\nplt.title(\"sine wave form\")  # 使用 matplotlib 来绘制点\r\nplt.plot(x, y) \r\nplt.show()\r\n```\r\n\r\n\r\n\r\n```{python}\r\nimport pandas as pd\r\n#df = pd.read_csv(\"C:/Users/DELL/Desktop/test.csv\")\r\n#df.head\r\n```\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.09.27tidyverse数据清洗/Tidy_data.rmd",
    "content": "---\r\ntitle: \"[R数据科学]Tidy data案例\"\r\nauthor:\r\n  - 庄亮亮\r\ndocumentclass: ctexart\r\nalways_allow_html: true\r\noutput:\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\n    toc_depth: 3\r\nclassoption: \"hyperref,\"\r\neditor_options: \r\n  chunk_output_type: console\r\n---\r\n\r\n# Tidy data介绍\r\n\r\n在本章中，你将学习在R中组织数据的一种一致的方法，这种组织被称为tidy data。将数据转换为这种格式需要一些前期工作，但这些工作从长远来看是值得的。一旦你有了整洁的数据和一些包提供的整洁工具，您将花费很少时间将数据从一种表示转换到另一种，从而可以将更多的时间花在分析问题上。\r\n\r\n本文将为您提供整理数据的实用介绍以及tidyr包中附带的工具。如果你想了解更多的基本理论，你可能会喜欢发表在《统计软件杂志》上的整洁数据论文<http://www.jstatsoft.org/v59/i10/paper>\r\n\r\n\r\n# 数据清洗案例\r\n\r\n我们主要通过一个案例,来了解如何整洁数据,并将案例中的各个有用函数进行详细解读.该案例数据来自`tidyr::who`,其包含按年份，国家，年龄，性别和诊断方法细分的结核病（TB）病例。数据来自2014年世界卫生组织《全球结核病报告》，网址为<http://www.who.int/tb/country/data/download/en/>。\r\n\r\n```{r}\r\nlibrary(tidyverse) \r\nwho\r\n```\r\n\r\n\r\n这是一个非常典型的现实示例数据集。它包含**冗余列，奇数变量代码和许多缺失值**。我们需要采取多个步骤来对其进行整理。\r\n\r\n## 不是变量的列汇集在一起\r\n\r\n首先将不是变量的列聚集在一起。所包含的列包括：\r\n\r\n- country，iso2和iso3是三个指定国家/地区的变量。\r\n\r\n- year是一个变量。\r\n\r\n- 变量名中给出的结构（例如new_sp_m014，new_ep_m014，new_ep_f014）可能是值，而不是变量。\r\n\r\n因此，我们需要将从`new_sp_m014`到`newrel_f65`的所有列汇总在一起。我们用通用名称\"key\"来表示他们。我们知道单元格代表案件数，因此我们将变量数存储在`cases`中,并用`na.rm`去除含有缺失值的行。\r\n\r\n这里使用`pivot_longer()`将数据变长,具体见后面函数详情.\r\n\r\n\r\n```{r}\r\nwho1 <- who %>% \r\n\tpivot_longer(\r\n\t\tcols = new_sp_m014:newrel_f65,\r\n\t\tnames_to = 'key',\r\n\t\tvalues_to = 'cases',\r\n\t\tvalues_drop_na = T\r\n)\r\n\r\nwho1\r\n```\r\n\r\n对key进行计数，我们可以得到一些有关值结构的提示：\r\n\r\n```{r}\r\nwho1 %>% count(key)\r\n```\r\n\r\n其中key的具体含义，查阅可得：\r\n\r\n每列的前三个字母：新、旧病例。\r\n\r\n之后两个字母：结核的类型。\r\n\r\n- rel 代表复发病例\r\n\r\n- ep 代表肺外结核病例\r\n\r\n- sn 代表无法通过肺部涂片诊断（涂片阴性）的肺结核病例\r\n\r\n- sp 代表可被诊断为肺部涂片（涂片阳性）的肺结核病例\r\n\r\n第六字母：结核病患者的性别。男性（m）和女性（f）\r\n\r\n其余数字给出了年龄段。数据集将案例分为七个年龄组：\r\n\r\n- 014 = 0 – 14岁\r\n\r\n- 1524 = 15 – 24岁\r\n\r\n- 2534 = 25 – 34岁\r\n\r\n- 3544 = 35 – 44岁\r\n\r\n- 4554 = 45 – 54岁\r\n\r\n- 5564 = 55 – 64岁\r\n\r\n- 65 = 65岁或以上\r\n\r\n## 替换数据\r\n\r\n我们需要对列名称的格式进行较小的修正：将new_rel替换为newrel（很难在这里找到它，但是如果您不修正它，我们将在后续步骤中出错）。这里使用了stringr包中的str_replace(),将`newrel`替换`new_rel`.\r\n\r\n```{r}\r\nwho2 <- who1 %>% \r\n\tmutate( names_from = stringr::str_replace(key,'newrel','new_rel')\r\n\t)\r\nwho2\r\n```\r\n\r\n## 字符分割\r\n\r\n接下来就是将`key`中的字符进行分割,我们使用`separate()`对字符进行两次分割.\r\n\r\n1.将在每个下划线处拆分代码。\r\n\r\n```{r}\r\nwho3 <- who2 %>% \r\n\tseparate(key,c('new','type','sexage'),sep = '_')\r\nwho3\r\n```\r\n\r\n利用select()删除没用的列:new,iso2，iso3.\r\n\r\n```{r}\r\nwho3 %>% count(new)\r\nwho4 <- who3 %>% select(-new,-iso2,-iso3)\r\nwho4\r\n```\r\n\r\n2. 将分离sexage到sex和age通过的第一个字符后拆分：\r\n\r\n```{r}\r\nwho5 <- who4 %>% \r\n\tseparate(sexage,c('sex','age'),sep=1)\r\nwho5\r\n```\r\n\r\n\r\n这时,who数据集是目前整洁！\r\n\r\n## 可视化\r\n\r\n数据清洗完毕,就可以做一些初步的可视化,探索性分析.这里简单绘制了\r\n前几个国家不同年份,不同性别的结核病病例总数。\r\n\r\n```{r}\r\nwho5 %>% group_by(country,year,sex)  %>% filter(year<2003) %>% \r\n\tcount() %>% \r\n\thead(100) %>%\r\n\tggplot(aes(x=as.factor(year),y=n,fill=country))+geom_col() +facet_wrap(~sex,nrow = 1)+\r\n\t  scale_fill_brewer(palette = \"Paired\")\r\n\r\n```\r\n\r\n## 复杂的管道函数\r\n\r\n事实上你可以直接只用管道函数构建一个复杂的函数：\r\n\r\n```{r eval=FALSE, include=T}\r\nwho %>%\r\n  pivot_longer(\r\n    cols = new_sp_m014:newrel_f65, \r\n    names_to = \"key\", \r\n    values_to = \"cases\", \r\n    values_drop_na = TRUE\r\n  ) %>% \r\n  mutate(\r\n    key = stringr::str_replace(key, \"newrel\", \"new_rel\")\r\n  ) %>%\r\n  separate(key, c(\"new\", \"var\", \"sexage\")) %>% \r\n  select(-new, -iso2, -iso3) %>% \r\n  separate(sexage, c(\"sex\", \"age\"), sep = 1)\r\n```\r\n\r\n# 所用函数详细解释\r\n\r\n## pivot_longer()、poivot_wider()\r\n\r\n`pivot_longer()` 将在列中列名（数值）转换到一列上。具体可见下图,将列变量转化为数据存在year列名中,相当于把数据变长(longer).\r\n\r\n\r\n![pivot_longer，从左到右](36.jpg)\r\n\r\n函数主要参数:\r\n\r\n> cols选取的列\r\n> names_to 字符串，指定要从数据的列名中存储的数据创建的列的名称。\r\n> values_to 字符串，指定要从存储在单元格值中的数据创建的列的名称。\r\n> values_drop_na 如果为真，将删除value_to列中只包含NAs的行。这有效地将显式缺失值转换为隐式缺失值，通常只在由其结构创建的数据中缺失值时使用.\r\n\r\n例子如上面例子:将new_sp_m014到newrel_f65之间的列选取,汇总到`key`列名中,值存在`cases`列名中,并将含有缺失值的行进行删除.\r\n\r\n```{r}\r\nwho1 <- who %>% \r\n\tpivot_longer(\r\n\t\tcols = new_sp_m014:newrel_f65,\r\n\t\tnames_to = 'key',\r\n\t\tvalues_to = 'cases',\r\n\t\tvalues_drop_na = T\r\n)\r\n```\r\n\r\n\r\n当然还有一个和他相反功能的函数`poivot_wider()`。具体见下图,相当于把key中的值变为列名,对应的values数据转化到population中.下面是简单的例子.\r\n\r\n\r\n![pivot_wider，从右到左](37.jpg)\r\n\r\n```{r}\r\nlibrary(tidyverse)\r\nstocks <- tibble(\r\n  year   = c(2015, 2015, 2016, 2016),\r\n  half  = c(   1,    2,     1,    2),\r\n  return = c(1.88, 0.59, 0.92, 0.17)\r\n)\r\nstocks\r\n```\r\n\r\n我们将数据变宽,将year变为列名,对应在return中的数据进行填充.\r\n\r\n```{r}\r\nstocks %>%\r\n\tpivot_wider(names_from = year,values_from = return) \r\n```\r\n\r\n\r\n## separate()\r\n\r\n该函数可将字符进行分割,具体案例如上.\r\n\r\n默认情况下，当separate()看到非字母数字字符(即不是数字或字母的字符)时，它将分割值。可以用里面的参数`sep`。比如：`sep='_'`。他还有一个功能,当`sep=2`时,可通过第二个位置进行分割,使用在省份市级,等数据上.例如以下函数,其中into = c(\"century\", \"year\")将原始分割后的数据导入两个新列上,分别叫century和year.\r\n\r\n\r\n```\r\ntable3 %>% \r\n  separate(year, into = c(\"century\", \"year\"), sep = 2)\r\n```\r\n\r\n**注意**:默认情况下，会转化成字符形式，你可以用参数`convert=T`，将数据转化最佳结构.\r\n\r\n![](38.jpg)\r\n\r\n## unite\r\n\r\n是`separate()`的反函数,这里做个补充\r\n\r\n![unite](39.jpg)\r\n\r\n默认情况下，`sep='_'`如果我们不需要任何分隔符可以使用`sep=''`.\r\n\r\n\r\n# 缺失值处理\r\n\r\n两种情况会出现缺失值: \r\n\r\n\r\n1. **显式**: 标记为`NA`.\r\n\r\n2. **隐式**: 没出在书中的.\r\n\r\n以下为例子\r\n\r\n```{r}\r\nstocks <- tibble(\r\n  year   = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),\r\n  qtr    = c(   1,    2,    3,    4,    2,    3,    4),\r\n  return = c(1.88, 0.59, 0.35,   NA, 0.92, 0.17, 2.66)\r\n)\r\n```\r\n\r\n在这个数据集中有两个缺失的值:\r\n\r\n1. 2015年第四季度出现显式丢失，因为它的值应该在的单元格中包含NA。\r\n\r\n2. 2016年第一季度的收益是隐式缺失的，因为它根本没有出现在数据集中。\r\n\r\n对于隐式缺失值,我们可以将数据进行变化,显示隐性缺失.\r\n\r\n```{r}\r\nstocks %>% \r\n\tpivot_wider(names_from = year,values_from = return)\r\n```\r\n这样就可以看到原来隐型的缺失值了.\r\n\r\n这些显式缺失的值在数据的其他表示中可能并不重要，你可以在pivot_longer()中设置values_drop_na = TRUE来将显式缺失的值变成隐式缺失值:\r\n\r\n\r\n```{r}\r\nstocks %>% \r\n  pivot_wider(names_from = year, values_from = return) %>% \r\n  pivot_longer(\r\n    cols = c(`2015`, `2016`), \r\n    names_to = \"year\", \r\n    values_to = \"return\", \r\n    values_drop_na = TRUE\r\n  )\r\n```\r\n\r\n#### complete()\r\n\r\n在整洁数据中显式显示缺失值的另一个重要工具是complete().\r\n\r\ncomplete()获取一组列，并查找所有唯一的组合。然后，它确保原始数据集包含所有这些值，并在必要时显式填充NAs。\r\n\r\n```{r}\r\nstocks %>% \r\n\tcomplete(year,qtr)\r\n```\r\n\r\n有时，当一个数据源主要用于数据输入时，丢失的值表明以前的值应该结转:\r\n\r\n```{r}\r\ntreatment <- tribble(\r\n\t~person,~treatment,~response,\r\n\t'A',1,7,\r\n\tNA,2,10,\r\n\tNA,3,9,\r\n\t'B',1,4\r\n)\r\n```\r\n\r\n\r\n#### fill()\r\n\r\n您可以使用fill()填充这些缺失的值。它采用一组列，在这些列中，您希望用最近的非缺失值(有时称为最后一次观察结转)替换缺失值。\r\n\r\n```{r}\r\ntreatment %>% fill(person)\r\n```\r\n直接将缺失值填写为前面的字符('A').\r\n"
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    "path": "2020年/2020.10.09散点图系列一/scater_plot1.html",
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \r\ncode span.dt { color: #795da3; } \r\ncode span.dv { color: #0086b3; } \r\ncode span.bn { color: #0086b3; } \r\ncode span.fl { color: #0086b3; } \r\ncode span.ch { color: #4070a0; } \r\ncode span.st { color: #183691; } \r\ncode span.co { color: #969896; font-style: italic; } \r\ncode span.ot { color: #007020; } \r\n</style>\r\n\r\n\r\n\r\n\r\n\r\n</head>\r\n\r\n<body>\r\n\r\n\r\n\r\n\r\n<section class=\"page-header\">\r\n<h1 class=\"title toc-ignore project-name\">散点图系列(1)</h1>\r\n<h4 class=\"author project-author\">庄亮亮</h4>\r\n<h4 class=\"date project-date\">2020/10/09</h4>\r\n</section>\r\n\r\n\r\n\r\n<section class=\"main-content\">\r\n<p>记得安装prettydoc包，html模板在该包渲染而成。</p>\r\n<p><img 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\" /><br />\r\n欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>\r\n<hr />\r\n<div id=\"简介\" class=\"section level2\">\r\n<h2>简介</h2>\r\n<p><strong>散点图</strong>（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。</p>\r\n<p>散点图可以提供三类关键信息：</p>\r\n<p>（1）变量之间是否存在数量关联趋势；</p>\r\n<p>（2）如果存在关联趋势，那么其是线性还是非线性的；</p>\r\n<p>（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。</p>\r\n<hr />\r\n<p>本文可以看作是<a href=\"https://github.com/EasyChart/Beautiful-Visualization-with-R\" title=\"《R语言数据可视化之美》\">《R语言数据可视化之美》</a>的学习笔记。该书第四章——<strong>数据关系型图表</strong>中展示的散点图系列包括以下四个方面：</p>\r\n<ol style=\"list-style-type: decimal\">\r\n<li><p>趋势显示的二维散点图</p></li>\r\n<li><p>分布显示的二维散点图</p></li>\r\n<li><p>气泡图</p></li>\r\n<li><p>三维散点图</p></li>\r\n</ol>\r\n<p>本文主要对第一部分进行介绍，并加上小编自己的理解。下面几个部分也会在这星期陆续推出，敬请关注。</p>\r\n</div>\r\n<div id=\"本文框架\" class=\"section level2\">\r\n<h2>2.本文框架</h2>\r\n<p><img src=\"data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=\" /></p>\r\n</div>\r\n<div id=\"数据介绍\" class=\"section level2\">\r\n<h2>2.数据介绍</h2>\r\n<p>随机产生2列20行的数据，列名分别为x，y。x为序号，y由标准正态分布中产生。</p>\r\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb1-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\r\n<a class=\"sourceLine\" id=\"cb1-2\" title=\"2\">mydata =<span class=\"st\"> </span><span class=\"kw\">data.frame</span>(<span class=\"st\">&#39;x&#39;</span>=<span class=\"st\"> </span><span class=\"dv\">1</span><span class=\"op\">:</span><span class=\"dv\">20</span>,<span class=\"st\">&#39;y&#39;</span>=<span class=\"kw\">sort</span>(<span class=\"kw\">rnorm</span>(<span class=\"dv\">20</span>)))</a>\r\n<a class=\"sourceLine\" id=\"cb1-3\" title=\"3\"><span class=\"kw\">head</span>(mydata)</a></code></pre></div>\r\n<pre><code>##   x       y\r\n## 1 1 -1.8720\r\n## 2 2 -1.3863\r\n## 3 3 -1.3316\r\n## 4 4 -0.6965\r\n## 5 5 -0.6241\r\n## 6 6 -0.5514</code></pre>\r\n</div>\r\n<div id=\"不同类型拟合曲线的绘制\" class=\"section level2\">\r\n<h2>3. 不同类型拟合曲线的绘制</h2>\r\n<div id=\"loess数据平滑曲线\" class=\"section level3\">\r\n<h3>3.1 loess数据平滑曲线</h3>\r\n<p><strong>局部加权回归</strong>（Locally Weighted Scatterplot Smoothing，LOESS）主要思想是取一定比例的局部数据，在这部分子集中拟合多项式回归曲线，这样就可以观察到数据在局部展现出来的规律和趋势。曲线的光滑程度与选取数据比例有关：比例越少，拟合越不光滑，反之越光滑。</p>\r\n<p>ggplot2绘制时，使用<code>geom_point</code>绘制散点图，<code>geom_smooth</code>加入拟合曲线，<code>method</code>选择为loess,<code>se=TRUE</code>表示加入置信带，<code>span</code>控制loess平滑的平滑量，较小的数字产生波动线，较大的数字产生平滑线。其他参数对颜色，填充色以及透明度进行了修改。</p>\r\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb3-1\" title=\"1\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-2\" title=\"2\"><span class=\"kw\">geom_point</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">size=</span><span class=\"dv\">3</span>,<span class=\"dt\">shape=</span><span class=\"dv\">21</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-3\" title=\"3\"><span class=\"kw\">geom_smooth</span>(<span class=\"dt\">method =</span> <span class=\"st\">&#39;loess&#39;</span>,<span class=\"dt\">span=</span><span class=\"fl\">0.4</span>,<span class=\"dt\">se=</span><span class=\"ot\">TRUE</span>,</a>\r\n<a class=\"sourceLine\" id=\"cb3-4\" title=\"4\"><span class=\"dt\">colour=</span><span class=\"st\">&quot;#00A5FF&quot;</span>,<span class=\"dt\">fill=</span><span class=\"st\">&quot;#00A5FF&quot;</span>,<span class=\"dt\">alpha=</span><span class=\"fl\">0.2</span>)</a></code></pre></div>\r\n<pre><code>## `geom_smooth()` using formula &#39;y ~ x&#39;</code></pre>\r\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAqFBMVEUAAAAAADoAAGYAITMAOpAAZrYApf8zMzM6ADo6AGY6kNtNTU1NTW5NTY5NbqtNjshmAABmtv9uTU1uTW5uTY5ubqtuq+SOTU2OTW6OTY6OyP+QOgCQtpCQ2/+rbk2rbm6rbo6ryKur5P+2ZgC2/7a2//+83e/Ijk3I///M7f/bkDrb///kq27k///r6+v/tmb/yI7/25D/5Kv//7b//8j//9v//+T///9Zk8YMAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAaU0lEQVR4nO2dDZfdxJGGb5hkvMkAJoQAcTbexWxki4BxBsf3//+zvd9XLbWkbqmq663WW4fDMTPDM291P259jEa927NYwLWzDsBiTRUFZUEXBWVBFwVlQRcFZUEXBWVBV46g76WqbhJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKHftUVA1EmQod+1RUDUSZCh37VFQNRJkKMT2GgpqQoIMhdgeBbUhQYYCbK+hoDYkyFCA7VFQIxJkKLz2GnlBWSy5ats25cu4goqTIEPBtdcorKAiwU7fvGoSZCi49g5+Pux2E9+FgmqRIEOhtXfQ8+Eg6LiiFFSNBBkKrT0KakeCDAXW3snPk6BjhlJQNRJkKKz2GgpqSIIMhdUeBbUkQYaCau94i4nnoGYkyFBQ7VFQUxJkKKT2mkvxPqgNCTIUUntXQfmTJBsSZCig9hoKakuCDIXTXkNBjUmQoXDao6DWJMhQMO01FNSaBBkKpj0Kak6CDIXSXkNBzUmQoUDaC/x8fJz6LhRUiwQZCqS90E+uoCYkyFAY7YV+8hBvQ4IMBdFez08KakOCDAXRXiAoL5KsSJChENrrL6AU1IYEGQqgvYGfFNSGBBkKoL2BnxTUhgQZyr69/gkoBbUiQYYyb294gKegRiTIUNbtxfykoDYkyFDW7cX8pKA2JMhQxu1F/aSgNiTIULbtRS6QKKgZCTKUaXvRE1AKakWCDGXZ3pifFNSGBBnKsL1RPymoDQkylF17TSBoQ0HNSZChzNoL/aSgACTIUFbtTflJQW1IkKGM2pv0U0LQ56+fnl5RUCsUIikHNe2ngKAfv3+zf/7LGwpqhEIkZaBCHQd+Cgj64eXhX2+vS+j63q7fvGoSZCiL9np+agh6XUX3+xeHmv1SFutebVCPj22/kiizgn56/e31jwJ/+y5/O6omQYYq397s+jlNShX043c3PxFHC5EEGap4ewl+Sgj6/PWr+3+ItPd+Jph/EmSo0u31/RwIOkdKEzTwE3G0EEmQoQq3l+SngKA/Px2LV/FWKERSCqqvZ9/PBFLiIT4oie5mg/knQYYq2l6inxTUhgQZqmR7SYf3ORIFVSNBhirXXtrp5yyJgqqRIEMVay/DTwpqQ4IMVaq9HD8pqA0JMlSh9ub8TCdRUDUSZKgy7eX5SUFtSJChirSX6ScFtSFBhirQXpPrJwW1IUGG0m8v308KakOCDKXe3gI/KagNCTKUcnvNnJ/ZoSioGgkylG57y/ykoDYkyFCa7fX1TPWTgtqQIEMptrfYTwpqQ4IMpdbeQM90PymoDQkylFJ7Qz0z/KSgNiTIUCrtRfTM8ZOC2pAgQym0N6LnY6qeFNSIBBlKur2Ynbl+UlAbEmQo0fbidkZ++2hFKAqqRoIMJdheK+UnBbUhQYYSIzXNiKCxn77vdruloSioGgkylAzp5F1c0Iifu3MtC0VB1UiQoQRIV/FCQR8eHsYO7xQUkwQZaiWpq15X0IdzTfk5YSgFNSFBhlpFCt0bChp/+JOCgpIgQ60g9c82O4JO+UlBUUmQoZaS+nYOBT0e3h8ifvIcFJUEGWoRKWJnX9DHk5+hoNf/nYJikiBDZZPicvYEbc56xv18z/ugmCTIUOmkcTMHgl70DAUVCkVB1UiQoRJIs2b2BD0d3ZuJ9XNVKAqqRoIMNUNKlfMu6GPkxbSZflJQGxJkqAlSjpxXQUf0zPNTXFBWhTXYXmtQh4N47yMnPWNfqpCPK6g4CTLUCGl+vbz8MPP+gcex1TN3/eQh3ogEGSpGSjqgB4I+TtiZrScFNSJBhhqQkuy8+Xl7ZOlg58jzoBKhup+joFokyFABKVHOQND72hkXdHWo/ucoqBYJMtSdlGFnEzxPdz20xwRdGyryOQqqRYIMdSblyXk1tHfiGRFUoT0KqkaCDLVfZOftsqj706KBoCrtUVA1EmKosSubNDvDH2b2UTrtUVA1El6o2LKXKGfkMz2UUnsUVI2EFiq67AUVuRE/cb8zQKm1R0HVSFChRpa9np63W/FzcvZReu1RUDUSUKiRZS8u6KyZA5RmexRUjQQTamzZi/g5v24OUbrtUVA1Ekao0WVvIGiOnHeUdnsUVI1kH2pi2evX7U5S717SjKD67VFQNZJtqMllb1TODD8PqBLtUVA1kmGo6WUvZmcTeeJzpsq0R0HVSEah5pa9qJznytGzVHsUVI1kE2rGq/B3hTMuiXp2FmuPgqqRLELNqtX5VcyFct7OPCmoc1L5UAl23X4Vc5md5dujoGqk0qGSDGsl1s6S7VFQNVLZUGmKyelJQd2TSobKsDPjSn3CzmLtUVA1UrFQ6XIOX/G1xMyi7VFQNVKhUOl6dn91eK2dxdqjoGqkIqGGUg0E7P+oKFFQiPYoqCJJP1REq6k31CQKujLUgqKgJiTtUFG7Jt9Qk+LnylCLioKakHRDxfW6L5KxpztnBV0bamFRUBOSYqhRw4YvqBl8XsbOSKjlRUFNSHqhxpfASTuPJfe+GgrqnaQValzP5rYhzIiekq8DoaDeSTqhJvW8XBdNHMflfpmdgnonaYRKsHP6Z5kdQcVCrS4KakKSD5Wi5/TX3AUVCyVQFNSEJBxqxrzEx5TkflcYTdDnb95RUCvUPsXOBD1PgoqFkgKJCPrh6QsKaoaafiddxkOemO0JCPr28x+4glqh+jeHwrvtGY8gC2YSRUke4l8cau5LWZIV2U/r4b6n1knP+U24lHbXKlg8BxUniaBuZ47d9TN8HCR18RTLJI7iRZIJSQB11asjaH/Dohw7RTIpoCioCWk16m5YVNC0k0/hTCooCmpCWocKLBsKmnjlLptJC0VBTUhrUD3P+uegaXoKZ9JD8SdJJqTlqIFqoaCL7VyTSRNFQU1IC1Ex2zqCLl88V2RSRlFQE9IiVNy3m6Ar9TRvL59EQdVIC1BjxnXfSbfCzkWZCqAoqAkpFzUhXfo76YQzlUFRUBNSJmpKu9R30klnKoSioCakLNSkdzKLZ3amYigKakLKQM3rKbB4ZmYqiKKgJqRk1LR5p8eVhPSkoHLB/JOSUHPmnVfPaUGlMxVHUVAT0ixqTs770X1KUNlMJigKakKaQaXZ+Xj647igspmMUBTUhDSJytFzVFDhTGYoCmpCGkfN2tm/co8LKprJEkVBTUgjqEQ7H7sfkXvlFwWVCuafFEHNyxm/Kz8UVC7T4qKgzkldVIqZdz0HH+0LKpJpbVFQ56QbKtnOm57998yGgkpkEigK6px0Qo2pGH3V8V3P3pu6u4KuzyRUFNQ1abDu9fQcviz+dnCfEHR1LAoqFcw1KXZgnhH0se9n99OtkJ4UVC6YW1L8wBzz865gcOU+IqhEcxRULpg70tDDVEF7N5Zigtq3p4mioKqkMQ3TBI3c9hz4CTlQFNQFadTOKUHvCkafle8JatleGRQF1SFNyZki6Phvctz1NGyvHIqCqpDm/Jx8ivNiZ1TPThm2VxBFQRVIs3pOCJomZ+fCHXGgKCgyKUHPMUFT7dR5qycFlQqGTErSMyZospy9256IA0VBYUmJfvYFzbBT77WzFFQqGCwpVc/IO+nS5FR97SwFlQoGSkrXs/tGpVV2lmzPBkVBpUg5et52xs6Qc+wn7ogDRUHxSDl65po5oSfmQAELutFK2y6r7RzU7/tt1b/RlnZxBZ0hpS6ZHTkjDyXnL56F2jNF8RC/mpRh5vmDkWfmFuuJOVAUFIiUomf44RxBzduzRlHQdaQ5O6OfSvbTvD17FAVdQ1qkZ7Kg5u0hoCjoctKMnlMCiumJOVAUFIE0Ktn9Sn1cv7n3Itu3B4KioAtJu9HDdOdG0kJBAdqDQVHQZaTR88jTqef8dfqooBjt4aAo6CLSmIKXS6Mlgq4PtbYoqFQwc1JcwfuVe7agEqFWFwWVCmZMiisY3FjKOQeVCSVQFFQqmC0pqmDvtmeyoFKhJIqCSgUzJcUUjP88c1xPzDcqUVCpYJakmIL5D3dCvlGJgkoFMyRFXFv07DFoe3goCppFWuinbig8EgU1Ii3SUzsUIImC2pDib1vIllM2FCLJUNDfvvrdP7Yq6ODHP4v1hGyvDkH3+x93u9//a4uCRl8HskxPxPaqEfS4iu52f9qcoE1P0Gk9C4UCJZmfgx4V/eyfmxK06Qk66WepUKgkY0F/2u3+cDjURw/0RYIZkJpQ0JV6wrUnTLIU9D9/3+3+fPzDr9EltEiw4qSLeTdBJ/wsFwqYZHoVHz+0Vy3oVb5WSk+s9uRJ5ueg2xL0pl8742fJUNAkClqS1ISCjulZNhQ2iYIWJDWhoDJ+4rSnQqKg5UhNKKiQnzDt6ZAoaClSaGE75mfZUPgkClqI1POwFfMToz01EgUtQ+qLGPezcCgPJApahKToJ0J7iiQKWoKU5GfpUD5IFLQAKeLn4IHl4qGckCioOmmwVB7XT7nXgVi3p0zCEvTjd09f/lKZoFE/e4IWD+WHBCXop9ev9j+/rEvQuJ+hoMVDOSJBCfrxb+/2z9+8q0nQm4TXdzNcro/kXqgEqVWlgj7/9Zf9x+/fHP704lCTX+qkrrtnXd5uc9p/ixtrAde0oB++vAp6rCJ/c3RJ3fXz9Pql2/2lVmr9xFz3ql9B6xC072fn8c9WSk9MrSoVtLJz0KYnaPfx5FbMT0itKhX00+tvK7qKb3qCBtvJtGJ+QmpVqaBV3QcN7i31/RR8qSekVrUKGlSRYGqkpifoaUOZQFCDUA5JFFSH1PSqv11cK+YnpFYUVCqYEmmo52P4EXczaEWioAqkyPLZ89PfDFqRKKg8KeJn7yMOZ9CKREHFSSl++ptBKxIFFSb19Yz76W8GrUgUVJaU6Ke/GbQiUVBRUkTPqJ/+ZtCKREEFSbHlM+6nvxm0IlFQOVLC4f12e97dDFqRKKgYKcdPfzNoRaKgQqSBnpN++ptBKxIFlSFl+ulvBq1IFFSCFNVzyk9/M2hFoqACpHw//c2gFYmCriclHd6bFJJgqFpIFHQ1aYmf/mbQikRB15IW+elvBq1IFHQlKf/0UzgTpFbu2qtV0OjymeCnvxm0IlHQNaSkw7tyJkit3LVXp6BLl0/RTJBauWuvRkEXH95lM0Fq5a69CgVNOryP/nKxuxm0IlHQZaShnll++ptBKxIFXURKO7xPvJzB3QxakSjoEtKq00/hTJBauWuvKkHXHt5lM0Fq5a69mgSV8NPfDFqRKGgmKaLnAj/9zaAViYLmkeJ6ZvvpbwatSBQ0iyTlp78ZtCJR0Izqb2A4pmfCuz/dzaAViYKmV38DwxV++ptBKxKwoGDVRmq4LRe35vJdblfQ89LYTi2f180Oi2USRyGSgFfQIsHS6upgO+7ndS+5YpkUUIgkCjpfdwvbMT1vgpbKpIJCJFHQuepa2M75udsVyaSEQiRR0JlqooIOLt4velJQaRIFnazQwqugkZtLVz8pqDCJgk5UM7RwxM/j53L89DeDViQKOloRPft7Fndqt6OgGiQKOlaRNXLCz8P/kKGnwxm0IlHQeA0VvPkZ17NAJnUUIomCxiriYHNdPh+GnyqSqQAKkURBhxXT87rle7Cn9nI//c2gFYmCBnU8j4z7eb54fxj6qZ6pHAqRREE7dboOjyyRXT/7n9XOVBKFSKKgnTrpGRf0dPEupqfDGbQiUdB7XfyMGXq5udR/YFk/U1EUIomC3uui51DQ273PVspPfzNoRaKg93oYEfR+b74V0tPhDFqRKOit7g8dj+gZClokU1kUIomCXquJCvoY/GizFdLT4QxakSjopc7eTerZEbRMptIoRBIFPVcTq8GDIVdBy2QqjkIkUdBjpel5E7RIJgMUIomCvh99IdjwwaVWyk9/M2hFoqBj71OMPVfXSvnpbwatSBQ08eh+FbRIJiMUImnzgmboeRS0SCYrFCJp44LmLJ9HQUtkskMhkrYtaJ6ezZZn0Iq0aUFz/dzyDFqRtixorp6bnkEr0oYFzfJzvkWRTLYoRNJ2BY36OannpmfQirRVQXOWz7QW12cyRyGSNipohp+pLa7OZI9CJG1T0EXL50yLazMBoBBJmxR02fI50+LKTAgoRBKaoM/fvFMXNOZnip6bnkErEpigH56+UBc01c/MFldlwkAhkrAEffv5D+oraOrhPbfFNZlAUIgkLEFvh/gXh5r70kUV24srthkXd+PaZlmfg644vM/9HVycCQeFSIJZQd8+Pb1UF3Sln1ueQSsSjKAlVtBEP5e1uDATEgqRtCVBI3rm+bnlGbQibUjQ9X5ueQatSGiCdko2mICem55BK9JWBBXxc8szaEXaiKARP/P13PQMWpG2IajI8jnTYm4mQBQiaROCSvm55Rm0Im1B0Hbg5zI9Nz2DVqQNCBq+uHv58vl+0zNoRapf0PDF3XE/U1kbnkErUvWCNoGgK/eC3fAMWpFqF7QJBF3p55Zn0IpUuaBNIOiqw/upDYlMwiTIUO7aMxK0CQRd7eeWZ9CKVLWgTSBozM9M4IZn0IpUs6BNIKiAn1ueQStSxYI2gaASfm55Bq1I9QraBIKK+LnlGbQiVStoEwgq4+eWZ9CKVKugXRPb6I+PllA3PINWpEoFDVQM/Txvx7kMu+EZtCLVKWjfz0DPYy3kbngGrUg1CtqM+3kWdLdbSN7wDFqRKhR04Gc78HOpoRueQStSfYKGm74f189Q0N2OgjoiVSfo9Syzc3zvCrrbUVBXpNoEbQJBz+efHUHfr/JzyzNoRapM0JufJ0Mv10d3Qd9TUG+kugRtAkGv1+/t3c/3J0UXtXdqY/H/qUeCDOWuvTKCNoGgt/tLbdfPVbXhGbQi1SRocCu+c/+zlfJzyzNoRapI0CYQtHN/vhXSc9MzaEWqRtDOvflw/TwLuqqzexsyGFESZCh37akL2oQV/HyzlfJzyzNoRapD0GbKz4Oga1u7tSEF8jeDVqQqBJ32s2lXt3ZrA5AEGcpde5qC9vUcPD6POO7uZtCK5F/QOT8xx93dDFqRgAVNquEWcb2d46S/IavqEl5BB6tnZP3EXBjcLTFWJOAVdP47pvkJOe7uZtCK5FjQiJ5RPyHH3d0MWpHcChrTM+4n5Li7m0ErklNBo3qO+Ak57u5m0IpUk6AjfkKOu7sZtCJVJOiYn5Dj7m4GrUj1CDrqJ+S4u5tBK1I1go77CTnu7mbQilSLoBN+Qo67uxm0IlUi6JSfkOPubgatSHUIOukn5Li7m0ErUhWCTvsJOe7uZtCKVIOg/ec/c4LlFSIJMpS79jQFnfMTctzdzaAVyb+gs35Cjru7GbQiuRd03k/IcXc3g1Yk74IOtkfIDJZXiCTIUO7a0xI0xU/IcXc3g1Yk34Im+Qk57u5m0IrkWtA0PyHH3d0MWpE8C5roJ+S4u5tBK5JjQYfbxy0IlleIJMhQ7tpTEDTZT8hxdzeDViTPgib6CTnu7mbQiuRT0N3xBbWpfkKOu7sZtCJ5FHS32z2kH+Axx93dDFqRnAp68PMh1U/IcXc3g1Ykh4LuToJ29zuc9hNy3N3NoBXJqaCd3Q5n/YQcd3czaEWqQ9DlwfIKkQQZyl17koK+vwiauoBCjru7GbQiVSHoimB5hUiCDOWuPVFBT/dBk9dPzHF3N4NWJJ+CBr+TtCpYXiGSIEO5a09R0HXB8gqRBBnKXXsUVI0EGcpde4mCPn/99PQqT9CVwfIKkQQZyl17aYJ+/P7N/vkvb3IEXRssrxBJkKHctZcm6IeXh3+9vS6hU98rx0/IcXc3g1YkKEGvq+h+/+JQU1/FXbpYKjUr6KfX317/OPWXIWcBhVwY3C0xViSYFfTt09PhAP/xu5ufCYIKBMsrRBJkKHftJa6gz1+/uv/H1PeioBooRBKUoIGf84JKBMsrRBJkKHftpQn689Oxkq/iJYLlFSIJMpS79hIP8UFNfa8cPyHH3d0MWpH8CioTLK8QSZCh3LVHQdVIkKHctScvqFCwvEIkQYZy1x4FVSNBhnLXnrigUsHyCpEEGcpde9KCigXzT4IM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a49CqpGggzlrj0KqkaCDOWuPQqqRoIM5a69JYIC1uQrIa0KMRRipqRQFFS+EEMhZqKgRoUYCjETBTUqxFCImbYgKKv2oqAs6KKgLOiioCzooqAs6PIs6PEF5V+8s04R1vM3745bozx9+Yt1knudMmEN1mWLzYSB8izo21fzX1O4Phwl+PT61f7nl9ZRbnXKhDVYly02UwbKsaCf/ufN/BeVrbef/3BYrT7+7d151YKocyaswbpssZkyUI4FPRwfOlsxg9RxvJ//+stlA0mMOmbCG6zDCKUMlGNBj9swYy0M+7MMH77EExRusI5bbKYMlGNBTwV1arXHXUFPBTRYpy02K19BTwU05qd6hjsHhRT0vIVh5eegxwPEp//F8eBUx/E+Hr2AruJvpx04g3XZYjNloBwLery19znOgfRcyPdBcQbrusVm5fdBWRsoCsqCLgrKgi4KyoIuCsqCLgrKgi4KyoIuCsqCLgrKgi4Kqlo//e4f+9+++pN1DMdFQXXrxz8c/2EtLgqqW//+r//+4z+tQ3guCqpcP+3+bB3BdVFQ5fpxxyP8mqKguvXrZ//3FZfQFUVBVeu3g52/fsaT0OVFQVXrx9//a/+fv/Mgv7woKAu6KCgLuigoC7ooKAu6KCgLuigoC7ooKAu6KCgLuigoC7ooKAu6/h8YzdP0cX2ifAAAAABJRU5ErkJggg==\" /><!-- --></p>\r\n</div>\r\n<div id=\"样条数据平滑曲线\" class=\"section level3\">\r\n<h3>3.2 样条数据平滑曲线</h3>\r\n<p>这里使用了splines包中的样条函数，df=5，样条具有五个基函数，其他参数变化不大。具体非线性模型相关资料可参考：<a href=\"http://tecdat.cn/r%e8%af%ad%e8%a8%80%e9%87%8c%e7%9a%84%e9%9d%9e%e7%ba%bf%e6%80%a7%e6%a8%a1%e5%9e%8b%ef%bc%9a%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92%e3%80%81%e5%b1%80%e9%83%a8%e6%a0%b7%e6%9d%a1%e3%80%81%e5%b9%b3/\" title=\"R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析\">R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析</a></p>\r\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb5-1\" title=\"1\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-2\" title=\"2\"><span class=\"kw\">geom_point</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">size=</span><span class=\"dv\">3</span>,<span class=\"dt\">shape=</span><span class=\"dv\">21</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-3\" title=\"3\"><span class=\"kw\">geom_smooth</span>(<span class=\"dt\">method=</span><span class=\"st\">&quot;lm&quot;</span>,<span class=\"dt\">se=</span><span class=\"ot\">TRUE</span>,</a>\r\n<a class=\"sourceLine\" id=\"cb5-4\" title=\"4\"><span class=\"dt\">formula=</span>y <span class=\"op\">~</span><span class=\"st\"> </span>splines<span class=\"op\">::</span><span class=\"kw\">bs</span>(x, <span class=\"dv\">5</span>),<span class=\"dt\">colour=</span><span class=\"st\">&quot;red&quot;</span>)</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"gam-数据平滑曲线\" class=\"section level3\">\r\n<h3>3.3 GAM 数据平滑曲线</h3>\r\n<p>GAM 模型的拟合是通过一个迭代过程（向后拟合算法）对每个预测变量进行样条平滑的。其算法要在拟合误差和自由度之间进行权衡最终达到最优。</p>\r\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb6-1\" title=\"1\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb6-2\" title=\"2\"><span class=\"kw\">geom_point</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">size=</span><span class=\"dv\">3</span>,<span class=\"dt\">shape=</span><span class=\"dv\">21</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb6-3\" title=\"3\"><span class=\"kw\">geom_smooth</span>(<span class=\"dt\">method =</span> <span class=\"st\">&#39;gam&#39;</span>,<span class=\"dt\">formula=</span>y <span class=\"op\">~</span><span class=\"kw\">s</span>(x))</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n</div>\r\n<div id=\"残差分析图\" class=\"section level2\">\r\n<h2>4. 残差分析图</h2>\r\n<p><strong>残差分析</strong>（residual analysis）就是通过残差所提供 的信息，分析出数据的可靠性、周期性或其他干扰。用于分析模型的假定正确与否的方法。所谓残 差是指观测值与预测值（拟合值）之间的差，即实际观察值与回归估计值的差。以下给出两种拟合方法的残差分析图。<strong>注意：</strong> 这里还是使用前面随机模拟产生的数据。</p>\r\n<div id=\"线性拟合\" class=\"section level3\">\r\n<h3>4.1 线性拟合</h3>\r\n<p>通过lm函数进行回归分析，公式为 <span class=\"math inline\">\\(y = ax+b\\)</span>。并将预测值 <span class=\"math inline\">\\(\\hat{y}\\)</span>，残差<span class=\"math inline\">\\(\\varepsilon\\)</span>，残差的绝对值<span class=\"math inline\">\\(|\\varepsilon|\\)</span>进行存储，结果如下所示。</p>\r\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb7-1\" title=\"1\">fit &lt;-<span class=\"st\"> </span><span class=\"kw\">lm</span>(y <span class=\"op\">~</span><span class=\"st\"> </span>x, <span class=\"dt\">data =</span> mydata)</a>\r\n<a class=\"sourceLine\" id=\"cb7-2\" title=\"2\">mydata<span class=\"op\">$</span>predicted &lt;-<span class=\"st\"> </span><span class=\"kw\">predict</span>(fit) <span class=\"co\"># Save the predicted values</span></a>\r\n<a class=\"sourceLine\" id=\"cb7-3\" title=\"3\">mydata<span class=\"op\">$</span>residuals &lt;-<span class=\"st\"> </span><span class=\"kw\">residuals</span>(fit) <span class=\"co\"># Save the residual values</span></a>\r\n<a class=\"sourceLine\" id=\"cb7-4\" title=\"4\">mydata<span class=\"op\">$</span>Abs_Residuals &lt;-<span class=\"st\"> </span><span class=\"kw\">abs</span>(mydata<span class=\"op\">$</span>residuals)</a>\r\n<a class=\"sourceLine\" id=\"cb7-5\" title=\"5\"><span class=\"kw\">head</span>(mydata)</a></code></pre></div>\r\n<pre><code>##   x       y predicted residuals Abs_Residuals\r\n## 1 1 -1.8720   -1.4250  -0.44700       0.44700\r\n## 2 2 -1.3863   -1.2344  -0.15189       0.15189\r\n## 3 3 -1.3316   -1.0437  -0.28792       0.28792\r\n## 4 4 -0.6965   -0.8531   0.15655       0.15655\r\n## 5 5 -0.6241   -0.6624   0.03835       0.03835\r\n## 6 6 -0.5514   -0.4718  -0.07963       0.07963</code></pre>\r\n<p>完整代码如下所示：</p>\r\n<div id=\"代码详解\" class=\"section level4\">\r\n<h4>代码详解</h4>\r\n<p>绘制的方式比较简单，根据ggplot的思想不断叠加图层。我们对以下代码进行详细分析：</p>\r\n<ol style=\"list-style-type: decimal\">\r\n<li>以x为横坐标，y为纵坐标，<code>geom_point()</code>绘制散点图，以Abs_Residuals的大小来填充点和尺寸，颜色为黑色。<code>scale_fill_continuous()</code>将填充色从“black”到“red”渐变。<code>geom_smooth()</code>给数据加入拟合曲线，这里使用<code>lm()</code>方法，置信带不展示，颜色为“lightgrey”。这时候的图形如下：</li>\r\n</ol>\r\n<p><img src=\"data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=\" /></p>\r\n<ol start=\"2\" style=\"list-style-type: decimal\">\r\n<li>将预测值的点进行绘制，<code>geom_segment()</code>可加入线段，其中<code>xend = x, yend = predicted</code>表示从x到x，y到predicted，所以就会产生下图中的竖直线了。</li>\r\n</ol>\r\n<p><img src=\"data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=\" /></p>\r\n<ol start=\"3\" style=\"list-style-type: decimal\">\r\n<li>这时残差图基本完成，但是可以看到横纵坐标的标题有问题，右边的legend太累赘了以及字体颜色和大小还可以再做修改。最后图形如下所示：</li>\r\n</ol>\r\n<p><img src=\"data:application/json;base64,eyJSZXRDb2RlIjotMTQ4NjYwLCAiRXJyTXNnIjoicmVxdWVzdCBleHBpcmVkIn0=\" /></p>\r\n</div>\r\n</div>\r\n<div id=\"非线性拟合\" class=\"section level3\">\r\n<h3>4.2 非线性拟合</h3>\r\n<p>非线性拟合绘制残差图与线性拟合类似，唯一不同的点在：利用lm函数拟合不同的回归模型，以下使用了公式：<span class=\"math inline\">\\(y = ax+bx^2+c\\)</span>，后面的绘制与上面相同。</p>\r\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb9-1\" title=\"1\">d&lt;-mydata</a>\r\n<a class=\"sourceLine\" id=\"cb9-2\" title=\"2\">fit &lt;-<span class=\"st\"> </span><span class=\"kw\">lm</span>(y <span class=\"op\">~</span><span class=\"st\"> </span>x<span class=\"op\">+</span><span class=\"kw\">I</span>(x<span class=\"op\">^</span><span class=\"dv\">2</span>), <span class=\"dt\">data =</span> d)</a>\r\n<a class=\"sourceLine\" id=\"cb9-3\" title=\"3\">d<span class=\"op\">$</span>predicted &lt;-<span class=\"st\"> </span><span class=\"kw\">predict</span>(fit) </a>\r\n<a class=\"sourceLine\" id=\"cb9-4\" title=\"4\">d<span class=\"op\">$</span>residuals0 &lt;-<span class=\"st\"> </span><span class=\"kw\">residuals</span>(fit)</a>\r\n<a class=\"sourceLine\" id=\"cb9-5\" title=\"5\">d<span class=\"op\">$</span>Residuals&lt;-<span class=\"kw\">abs</span>(d<span class=\"op\">$</span>residuals0 )</a></code></pre></div>\r\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb10-1\" title=\"1\"><span class=\"kw\">ggplot</span>(d, <span class=\"kw\">aes</span>(<span class=\"dt\">x =</span> x, <span class=\"dt\">y =</span> y)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-2\" title=\"2\"><span class=\"kw\">geom_smooth</span>(<span class=\"dt\">method =</span> <span class=\"st\">&quot;lm&quot;</span>,<span class=\"dt\">formula =</span> y <span class=\"op\">~</span><span class=\"st\"> </span>x<span class=\"op\">+</span><span class=\"kw\">I</span>(x<span class=\"op\">^</span><span class=\"dv\">2</span>), <span class=\"dt\">se =</span> <span class=\"ot\">FALSE</span>, <span class=\"dt\">color =</span> <span class=\"st\">&quot;lightgrey&quot;</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-3\" title=\"3\"><span class=\"kw\">geom_segment</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">xend =</span> x, <span class=\"dt\">yend =</span> predicted), <span class=\"dt\">alpha =</span> <span class=\"fl\">.2</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-4\" title=\"4\"><span class=\"kw\">geom_point</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">fill =</span>Residuals, <span class=\"dt\">size =</span> Residuals),<span class=\"dt\">shape=</span><span class=\"dv\">21</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"co\"># size also mapped</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-5\" title=\"5\"><span class=\"kw\">scale_fill_continuous</span>(<span class=\"dt\">low =</span> <span class=\"st\">&quot;black&quot;</span>, <span class=\"dt\">high =</span> <span class=\"st\">&quot;red&quot;</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-6\" title=\"6\"><span class=\"kw\">geom_point</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">y =</span> predicted), <span class=\"dt\">shape =</span> <span class=\"dv\">1</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-7\" title=\"7\"><span class=\"kw\">xlab</span>(<span class=\"st\">&quot;X-Axis&quot;</span>)<span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">ylab</span>(<span class=\"st\">&quot;Y-Axis&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-8\" title=\"8\"><span class=\"kw\">geom_point</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">y =</span> predicted), <span class=\"dt\">shape =</span> <span class=\"dv\">1</span>) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-9\" title=\"9\"><span class=\"kw\">guides</span>(<span class=\"dt\">fill =</span> <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Rresidual&quot;</span>)),</a>\r\n<a class=\"sourceLine\" id=\"cb10-10\" title=\"10\"><span class=\"dt\">size =</span> <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Rresidual&quot;</span>)))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb10-11\" title=\"11\"><span class=\"kw\">theme</span>(<span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb10-12\" title=\"12\"><span class=\"dt\">axis.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb10-13\" title=\"13\"><span class=\"dt\">axis.text =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb10-14\" title=\"14\"><span class=\"dt\">legend.position =</span> <span class=\"st\">&quot;right&quot;</span>,</a>\r\n<a class=\"sourceLine\" id=\"cb10-15\" title=\"15\"><span class=\"dt\">legend.title =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">13</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb10-16\" title=\"16\"><span class=\"dt\">legend.text =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb10-17\" title=\"17\"><span class=\"dt\">legend.background =</span> <span class=\"kw\">element_rect</span>(<span class=\"dt\">fill=</span><span class=\"kw\">alpha</span>(<span class=\"st\">&quot;white&quot;</span>,<span class=\"dv\">0</span>)))</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>这两个图采用黑色到红色渐变颜色和气泡面积大小两个视觉暗示对应残差的绝对值大小，用于实际数据点的表示；而拟合数据点则用小空心圆圈表示，并放置在灰色的拟合曲线上。用直线连接实际数据点和拟合数据点。残差的绝对值越大，颜色越红、气泡也越大，连接直线越长，这样可以很清晰地观察数据的拟合效果。</p>\r\n</div>\r\n<div id=\"有趣的拓展\" class=\"section level3\">\r\n<h3>4.3 有趣的拓展</h3>\r\n<p>R 中的<a href=\"https://cran.r-project.org/web/packages/ggimage/vignettes/ggimage.html\" title=\"ggimage\">ggimage</a>包提供了<code>geom_image()</code>函数可以将对应的圆形数据点使用图片替代展示。我们将其运用到上面的数据集中，就可以得到有趣的图了。</p>\r\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb11-1\" title=\"1\"><span class=\"kw\">library</span>(ggimage)</a></code></pre></div>\r\n<pre><code>## Warning: package &#39;ggimage&#39; was built under R version 4.0.3</code></pre>\r\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb13-1\" title=\"1\">mydata<span class=\"op\">$</span>image =<span class=\"st\"> &quot;https://www.r-project.org/logo/Rlogo.png&quot;</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-2\" title=\"2\"><span class=\"kw\">ggplot</span>(mydata, <span class=\"kw\">aes</span>(x, y)) <span class=\"op\">+</span><span class=\"st\"> </span><span class=\"kw\">geom_image</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">image=</span>image))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">geom_smooth</span>(<span class=\"dt\">method =</span> <span class=\"st\">&#39;lm&#39;</span>)</a></code></pre></div>\r\n<pre><code>## `geom_smooth()` using formula &#39;y ~ x&#39;</code></pre>\r\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3df3RU9b3v/3dQbm9LhIrTKiQBUlEaEAKFGI1oSS34o35TBdqraD3ndvW2i3vvKfot9XsqpX9IoQrtUtZd93Da23vW/dpDv1dO/F7MsSKoKwg0Nhh+yY9IxA6QhIJEQjVVvtfO5/39Y8/s2bNnksxkZs/M3vN8LGP3zN6zfySNr7w/+7M/nzJVFQAAECyjCn0CAAAg9wh4AAACiIAHACCACHgAAAKIgAcAIIAIeAAAAujyQp+A9PX1FfoUvBIKhbg6nwr21UnQL5Cr87VgX6AXVxcKhVK+TwUPAEAAEfAAAAQQAQ8AQAAR8AAABBABDwBAABHwAAAEEAEPAEAAEfAAAAQQAQ8AQAAR8AAABBABDwBAABV+LHoAAIKnu+fMxQ8+VFU10S9rObakzlXTpk0NXTU+tydQpqq53SMAAKXs9d1txpXrqsakyHUTfxn1jaVfz9VpFD7gmTXIp7g6Xwv2BXJ1vubrCzzS2eUK84RcV1VjJk+qnDDhamv7U6d7Tp7sjq5wbLlo4YL0DzrYbHI00QMAkK0jnV2uMLdf1c2ttTdz/fkyeVLl5EmVzv1s395qVF/a9pqq+drdC7M5JQIeAICsHD76dsqS3Rnt85/oVBURVVURdSxL7B0Vkb0/bRSRF1/coaotLS83Nd054rMi4AEAyIoxxlW9j7589OzaGc5tVDWdgJ/3+D4R7Vi3aOsL29RkdQ+dx+QAAMhKJGKMMcZETCRiIpFIxLjSXRJSfPh/N6zuMJGIMZFszooKHgCArBgTSXj+LXXl7a7g29fEG/DrVu1zVvOXPjGLF9+zZcsL2ZwVAQ8AQFYikfhj7tZd+ORtUjXRu9bGA37fz+qfe26rqsnmrAh4AACyUl83p+2NN9XxXPvO19sWfLnBuY2q1l71wcG+K+zb7XWr9ifdhrcWtaxMTCTywLIl2ZwV9+ABAMhWw811kYgx1j/GREzk1dd2OTeoverPsUJ9KPufrD/w1E2bNzdnme5CBQ8AQE7cdutNrTv3OLvTb9/RqkbHlP/byooJyffgU/ao37y5WVUfeugb2Z8PAQ8AQG40LpgvIjte2Rnrc2f++tf//b8vfHzh/X7VK8TRl/6yUbKytldVn9w/wfG+bDg08eD6hmEOkx6a6AEAyKVFCxfccUfjF6dV//WT/0+NGuOeYkZVjbEeqTMrZ/W4Vs1auScnp0EFDwBAjoXDYRGZOXO6/c5L4dMiYjfLi5Tdf/991qply2T2Y23O5vqZP9h1+Be3ZXkOVPAAAORMOBy20j1JwlA2rufkDq5vcA13s3BNe5ZnQgUPAEAODJLrUY/fO1lE6urqBpsr79CG+bk9HwIeAICsDB3thUITPQAAI1ec6S4EPAAAI1a06S400QMAMALFHO0WKngAADJT/OkuVPAAAKTPF9FuIeABABiej6LdQsADADAU30W7hYAHACA1n0a7hYAHAMDN19FuoRc9AAAJApDuQsADAODkXbov/3WdR3tOiSZ6AABE8hLt874nL6/16CBuVPAAAOSpcO/4pUcHSYEKHgBQ0vIT7Zu+86aIiOSvlZ4KHgBQuvKb7nlFBQ8AKEUBjnYLAQ8AKC156ydfwHQXAh4AUDo8fcC9SAp3GwEPACgJgW+TdyHgAQDB51G6F1WbvAsBDwAIshKMdgsBDwAIrDykexFGu4WABwAEUClHu4WABwAEjRfpXvxt8i4EPAAgOIh2m5cB39u8Yvmz1ne6cVXLo/UeHgoAUOKIdhfvAr796eW7b93UsrHCSvoVzZs2Lq3w7GgAgFJFtKfkWcC3t7U2LmuxEr1i6bLGZ9t6RAh4AEBO5TzdAxDtljJV9f4o7U83/bbKUcHPmzfPXtfR0eH9CQAAAqizszOHe/vWMzXOl795JJc7t9TU1Ay/UY7koZNdb/OKtScf3vSoo3x3hnpfX5/351AYoVCIq/OpYF+dBP0CuTpfS/MCPS3crap9YCC3R4jK+Y8vFAqlfN/rgG9/umntyYe5/Q4AyJncpruPHm3PiOe96KesatlI/3kAQC54F+0SrHQXDwM+lu48HQcAyJ7fo726utrrQ7h4FfC9b+wOi4TXNrXG3uFReADACOThdrun8h/tFq8CvmLpxpalHu0bAFASfB3thcp1G0PVAgCKjt+fbi94ugsBDwAoNr6+3V4M0W4h4AEARSS3Y9eUVJu8CwEPACgKVuFeXl6ek72VcrRbRhX6BAAA8PHYNcWZ7kIFDwAoLKLdIwQ8AKBgfJruRR7tFgIeAFAARLvXCHgAQF75dPgaH0W7hYAHAOQJ0Z5PBDwAIB/8OHyNT6PdQsADADznuzvuvo52CwEPAPAQ0V4oBDwAwCs5TPc8tMkHJtotBDwAIPd8FO0By3UbAQ8AyLFcpTtVezYIeABAzhDtxYOABwDkQK6i/VvP1DhfEu0jRsADALJC1V6cCHgAwMh5ke48/5YTBDwAYIRyku6uaC8vLx8YyH6vCUot2i0EPAAgYzmPdilQm3x3z5mLH3yoqmqiX9ZybEmdq6ZNmxq6anzOT9IjBDwAIDPZp3sxRPvRzi7jynVVY1Lkuom9PHr0uLXYuOCWnJ9wzhHwAIB0edEmn85H+vv//NGlS6KqKipGVBKWVMR6Q42oXH3N52becMMQezvS2eUK84RcV1VjJk+qnDDhamv7U6d7Tp7sjq4wqqqvvPq6qi5auGDk3wXvEfAAgLTktnBPM9rPnD2nKmLlrmg00ONLsWSPxfynPvWZCxc+3Pl6W8o6+0hnlyvM7Vd1c2sHO4fJkyonT6p0vrN9e6tRfWnba6rma3cvzOBbkEcEPABgGAVpk08n2seP/+y4cWOt7UddNvrkyW5jzGB19uGjb6cs2Z3RPv+Jzlh7QPRvB3E2EcSW9/60UURefHGHqra0vNzUdGeW3x8vEPAAgEEVLNqNHa0J0T4lsZK22Lfbh66zjTGu6n305aNn185wfkRV0wn4eY/vE9GOdYu2vrDNOtUiRMADAFIo1O32410nJBbtaiTaBJ8Y7Rt+d9aO3rcujFM9kk6dXTmp0sp1q9ecUa2bO8N19OSdJCV9fLlhdcfKORHrdREi4AEAbgW53W6JldcSi3Zz2ajLqiorkraRty6My6jONiaS8Pxb6srbneXta+IN+HWr9jmPcukTs3jxPVu2vJD+1eUTAQ8AiCtU4W4zxr7xHm2er5o00bXNoffHSeZ1diQSf8zduguffPRUTfSutfHD7ftZ/XPPbbXaGYoQAQ8AiCpg4W6LPXYudv9459rY7fYjI6iz6+vmtL3xpjqea9/5etuCLzckHt3dDFC3av9gf0aUlYmJRB5YtmQEl5kHBDwAoPCFuy1Wwce/rPedA9eMuM5uuLlu1+4/OBrpzauv7frq7bc5PyuJAZ+yneDAU/Uisnlz84MPLh3ZZeYBAQ8AJa14ot0y84aaQ28difWdl0996tOnTv8ph3X2bbfe1Lpzj7M7/fYdrWp0wsRramdNH6yd33Wnf/PmZlV96KFvZHOlXiPgAaB0FVu6W679wpTunnPRItuYnNfZjQvmi8iOV3Y6OtRrd3fP6VPdqp937u2yUbKytldVn9w/wbnnDYcmHlyf8DdHESLgAaAUFWe0W6qrq6urq72us+0BcKzn6NR181+sPy+iXfNWzurZcKjCeYhZK/e89fP5ubpkL5S5713kXV9fX2FPwDuhUIir86lgX50E/QK5uqEV80Rw1dXVrgt01dnW/65pT6vOFtFM6+y6Vfucfy6MKit7c138Smc/1ub6M+LwL24bdF+pePF/zlAolPJ9KngAKBU5iXYZsnDPdFaY8jFjrA8ONvlbnuts155dFfDB9Q21P9zj3GbhmvZXVtenv/98ooL3EGWEfwX76iToF8jVpeR1m3yms8LY/eT/zac+7ZwVJp0L9LrO9g4VPAAglzwdUj7TWWEuXOjve7/fivfRo/9tJBLJdPbVINXZ3il8BQ8A8E5nZ2eWe/jWMzXOl795JL7DrhPvDjYrzA3Tvzj0bmtq4rvdvPlf7Bvsf/PwA1meMCyFD3ha0nyKq/O1YF8gV2fzdGS63jNnJRbt6cwKE3tPD/aNlcQH2/b+dI447rIX5+yrOUETPQAgK3kYdDZacts31AefFUaio9Hpwb5xKZ9c98Xsq75DwANA0GSZ7mk+ApfOrDBJhXu8u90Qs8Jkc/KwEfAAEBz5iXbL0LPCOB16f6yd5f6dfdV3CHgACIJ8RrtlsFlhnA69P3Zks8IgewQ8APhb/qPdUj2l6t0/nrRnhVHVrq53r7/+2uja6moRUX1LMpwV5u++/70A95HMJwIeAHwsm3TPfrjZa78w5Z0TYbuBXlU7336n5ovX2cPSJfenS7Xsj9lXfYeABwBfymG0P37nC6p6+vTwA8omu25q9fGuE3ZD/eWjP9X1zsnjx8PBm33Vdwh4APCZbKL9zNlza168x35pRbsxQw0oe+bMOSuOp8Wa312mXT9VRN45cUqjY8IHc/ZV3yHgAcA3wuHw+fPnR/bZdKJ9sAFlra/Ozi4VmV5zvWvPVoO83Swf1NlXfYeABwB/GHHh7op2EfnRHVutaHeOOucyfvyV48df6Xzn2LHjRuXIkbdVdOYN0YFmk2eBu+eeRfby2r0Js8KIlN1//33WqmXL3LPCzPzBruKZFSYACHgA8IERp7vrdvuP7tiqKkaHH1A2thh/vO1HX58mIocPH1OVQ4eO3ntvwh8NKbkq+HRmhTmw8Wsju1K4EPAAUNRy1ZkuFu3DDyjriHZnQsu6radF9PF7px966+00x5vrWDdv6A0ObaBZ3isEPAAUr5wU7j+6Y6t1/zudAWWTC3fn2v3vjXnwn95nQFlfIOABoBjlqk3+7xf9L2PSGlDWLtdF5O+b4g34VuF+4Hw5A8r6CwEPAEVnZOmecuCa8MnhB5RNWbjbDpwf4xidhgFlfYOAB4AikpPC3Tkm3dADylqS033d1tMH+8qHGFD2gWVLRnaeyBsCHgCKQs6j3TbYgLKOTRL60x3su4IBZQOAgAeAAstVP/khBpN3DSirIseOHVeVcZ8dW1kxwS7cD5wvdwwly4Cy/kbAA0AheVe4u1gDyh7r7BJVoyJqVKT/Qv+F9/tVr3BW7QwoGwyjCn0CAFCiwuFw3tLdNr3m+unTp90wY5o1lqwxqjLK6iFvM8ZEIiYSMStn9WiiWSv3jOyEkX9U8ABQAOlEe3//nz+6dMlqINfYTG8/236vvcFjX31eVd794/BzviWbOXO6xAaaZUDZQKKCB4C8SqdwP3P2XO+fzn308cdqjFGjaqxq25nuP7y92RhjjLGmdTlz5lxX17vHu95N8zSqq6sTZ22P33p3PSd3cH2D68b8wjXtmVwxCoMKHgDyJJ2q/czZc/GctWd6U3lyRzzan/zGa+PGjRWZLJnM+WZLnh6GAWUDiYAHgHxIp2pXYzeUp452EfnNI50DA2Ptl2nO+WZJjnYEGAEPANnq7jlz8YMPrW5rsWnQrWZ1VdWBDz+IjSU36M3y3jNnJRbtaiS6vehTO+6zt/nChH9VlZ/8T2cTeooR6FxzvtXWzrC2IN1LDQEPACN3tLPLuHJd1cQWBz78INZwHh8z5syZc1YWT3MMJxf7qMSi3Ty1Y7G91uonv/5FzWjOt4OHjljbEu2liYAHgJE40tmVEOYJi9FoHz/+s+PGRZvTh75Zbky8g9tTryx2Hsh+Cm7owj1xrT7VcmpRlaqWke4li4AHgMwc6exKCHNHtNfNrRWRcDj8uavGuT419M3ysWOvGCLaE8Xr+OQ53+zVe//06X9csYA530oZAQ8AGTh89O2UJbsd7SKy4XdnnSO8isiwN8uN0fWvxtP9B41bpl6bXHmn2Eni2uhb+9/7DHO+gYAHgAzEnjyPp/zoy0fPrp3h7CRv302PvYq+LYPcLHcOS/eDxi1W3qc555trz/vfG8Ocb7AQ8ACQgUjEivdoF3mjGhr/GdcjcOnfLO/uW7r81/EP/p8LthiT7pxvjkMkRDtzvsFCwANABoyJ2M+/qepfBj68+nNXDrLtUDfLu/sS0vc7c349d26tSFpzvrnS/cD5Mcz5hmQEPABkIFbBqxr96C8fqrjvhIvI0DfLu/sSms03fefNjo6DxkQ3GnrON8eudVSZLKy8aIzsf+8zsfeVOd9gI+ABIAP1dXPa3njzo78M2GV2+jfLXdFeedUWVRGpVmPqbvySc5U90KzVBU819i/7CKr7z49pvKZfVVfO6tlwqMIOeBGdtXLPWz9ncNlSR8ADQGYmXBN658SHdit6OjfLU0b7qvuqRWTv3v03Jqa7kzXnm+WlcLRLnXW7fVQZc75hKAQ8AKTL7kx33dR0b5YnRfu/2Gv37t2vqvX1c9M+vloTt8f76TscXN9Q+8M9jr8qdOGa9ldW12dzvfA1Ah4A0uLqKp/OzXJnun9nzn97+fQ4iTbgq6ps7x63eklC2/7Q/tujC0QkFAr19fWl3IA53+BEwAPAMIaYCG6wm+XOaK8K/cvCiovG6MKK/h09V1rltbXqieYTP1k6ddgTYLhZjAABDwCDSmcGd4t9s9w5ao1E2+Slrm629fLGG2XN8++KiN1Q/0TzOz9Z6rx/n4Box4gR8ABKxdCTutovra+xYz+TPKnr0FJGu4iKljnfX73k2jXPn3D2tH/md+FHvuYOcqIdWfI+4HubVyzvXtbyKD09ABTK0JO6OnPdeu/jj/4yMPBh8qSuQ3Cme2ySmCmDbbx6yVDN8kQ7csLjgG9/umltq0ijt0cBgEEMPamr9XrypMoJE662tt+3b/8Qk7qmlCraR4hoRw55GPDtTzetba1+eNXDu9d2e3cUAEhp2EldXazb7UNP6jrzhhrnKleb/IjTnVyHF8qSnqXMtVRN9PPmzbOXOzo6vD0BAKWn9fU9KUv2u+643d7mi9/fHR0T5sr+2HvqvDVuLa99sEZE3nhjr1XQ33JLvYh865mEpP/NI50jO8+amprhNwJGpDCd7JyhPtgDnQEwxOOqAcDV+VqwLzAUCg02qavzqq3BYmaN74+9ir4tiYPQPf7Px6xJXQ8eOiIqAwMDyW3yAwMZn6RVtY/gpxDsn50E/QK9uLpQKJTyfXrRAwig5Eld6+bOcG2jqrPGX4wtRt8bbMa2p1pOLarSX+37tsTKExrkUeQIeAAB5JrUVY37XmQ4HE5M99STutqr/xoxv9r3bXvtyNKdaEc+EfAAAsg5qat1F965NjZ8TYrC3UHtt3rej8+tTuEOvyDgAQSQNalr/LF3oztfb1vw5QbnyHSDTerqLNwlF+lOtKMgvA/4iqUbWzw/CAC4NNxct2v3H+xG+kuXPnpp246hJ3W13kwZ7f9+1i+HmNR1MEQ7CogKHkBg3XbrTa0791y69HFs3JpBJ3WNfSJ1uv/tzH+88cb0J3UVIdpRBAh4AIEVDoenTK6Q4SZ1FRERHVUmCysviuivD/wHew8Tr3xOJKMp20VIdxQHAh5AALlmgRtsUtfYelVVI/LrA99xfmrilf/TWkhzUlch2lFMCHgAQdPZOei4cvakriLyUjjapc5K+u6+hM50GU3qKkQ7ig8BDyA4rMK9vLw8vc2j3etS9pNPc1JXIdpRrAh4AEHgapNPx+P3Th56tpihJ3W1kO4oWgQ8AH8bQbRLLiaCI9pR5Ah4AD6WfboT7QgqAh6AX40g3Yl2lA4CHoD/ZBntQrqjBBDwAHwm03Qn2lGaCHgAvkG0A+kj4AH4Q0bpTrQDBDyAYpfnaBfSHYFAwAMoXkQ7MGIEPIBilM3t9t880jkwMJDpEYl2BAwBD6C4dPecOXX6dHTGNzFizeRuL0XHjxdVIypXX/O5H/4/C+zPxqr2NMeijyLaEUgEPICR6O45c/GDD1VVTfTLWo4tqXPVtGlTQ1eNH3afRzu7+vsviPVZ0Wigx5diyR6L+V+0ftP5cdrkAScCHkBmjnZ2GU3MdVXjDHbXaqNHjx63FhsX3JJyn0c6uy4OF+3jx3923Lix1vau2+3fn/9bUT3WGZ/3PR1EO4KNgAeQriOdXa4wT8h1VTVm8qTKCROutrY/dbrn5Mnu6AqjqvrKq6+r6qKFC5z7vHjhfRFJCHQVFZ0yqTLlaaQabva6Y8eOG5UjR95W0Ztvqkv5QRvRjlJAwAMY3pHOLleY26/q5tYO9qnJkyonJ4b09u2tRvWlba+pmq/dvfDw0bf/fLFfY7fUo03widG+4XdnY/fd9eS5r8d3/vmtIvpki9VcLz/6+jQROXz4mKr8/vfttbUzUp4S0Y7SQcADGMbho2+nLNmd0T7/iU47hq2cFmePuNjy3p82isiLL+5Q1a1bX7wqNF4dW6mYy0ZdVlVZ4Ty6tcHJc03ONyd//n+J4zAism7raRF9/N7pBw8dEU1xFUQ7Sg0BD2AYxhhX9T768tGzE0tkR04PFfDzHt8nov/yH66zYtiY6Cq7eb5q0kTX0cNnXdG+VcQ+ROx/YqfwVMupRVXqeFOEaEepIuABDCMSseI92kXeqNbNdTeAJ2d5UtKrqsz53Ieq+lTLgBXDsX54YvePd+3Webs9Fu3y903xBnyrcLdP4K8RM2fOzH37DllriXaUMgIewDCMiSQ8/2ZStYAnZXn7mngDft2qfbNDH0pSDMcq+PiX/ZGkaBdn4Z543HgRv+q+6o6Og6pKtAMEPIBhxCr46LNvJiljJXUTfVQ4HJ4d+jB1DE+pevePJ2N950VVu7revf76a4dO93VbT8fecTXRi4ioMf/xP32vr68vh98BwI8IeADDqK+b0/bGm+p4rn3n620Lvtzg3Ca5ib5u1f7ZoQ+HiOG6G78kItd+Yco7J8J2A/0zu+6XXdFNrUfgnmzRxP3b+0nY548XV4tIW9u+Bx9c6tH3AfCXUYU+AQA+0HBzXSRijPWPMRETefW1Xc4NNFHtVR9Y6a5qJ7Gq6qr7qn+8uHrv3v1Wuluum1ptNQw8s+t++83/fPM/9/T+yf6gDJnuItLWtu/3v+8g3QEbFTyAtNx2602tO/c4u9Nv39GqRidMvKZ21nTnPXjrdnvKGN67d7+q1tfPde38mV0P2Mv/6eZ/FjVGpf9C/4X3+1WviH9edFSZLKy8KKIvnx4nIqqy/71Pq8q+cxMPrm8QADFU8ADS1bhg/u1fuTVawkdMJBKJmEh3d8+//ut2u3HeKtxHlekdVRfvnHTRce9cVXV797jkdHeNTHfDjGnWjQBj4n37RKJ7MKrGqDG6sKJfVfad+7T9B8eslXvy970Aih4VPIDM2APNWuPV2PfmnYW7kegz7gsr+nf0XGnFv/WpJ5pP/GTpVGs51aCzIiIzZ063l18Kn7Z3a71TVzdbRKqrq/+dyOzH2hzd+nTmD3Yd/sVtXlw14DsEPIARuueeRfby7/74uogkx7CI3HijrHn+3dhaEZEnmt/508Vlzl0NORGc8wa8ipY5H4E7uL6h9od7nF38Fq5pP7Dxa1ldGBAIBDyArITDYRFJjmHnNquXXLvm+ROx6Fdnug87x+vj9062l1M+3X5ow/wRnDYQeAQ8EHBeTNxui6V7QgyntHrJVBm8TT4djF0DZISABwLLi4nbbXa0p8k1g3tG6U60AyNAwAMB5MXE7TaiHfAFAh4IlDQnbg+FQs7BXIeduN16k2gHfISAB4LDo4nbW1pebmq6M6N0zybahXQHcqFM40NNAfC311p3uar3T/2bT32l8VbnNtP+blc6AW8tn/zVnb/+779R89f5829K8xy+9UyN8+VvHunM6BJqamqG3whAGgpfwQd40idXK2jAcHVFKHni9lkza1wXMliWp0z9paubF1V9oqoDAwPDHj1l1Z7G56Ksqj0n33af/vjSFOyrk6BfoBdXFwqFUr5f+IAHkCs5mbjdWm0NS/fXiFoTtw97aJ5/A4oNAQ8UhZw8rZ7lxO2xtTrnc1bdHZ+4fYgzzybahXQHPEPAAwWWw6fVRzxxu70cG0/e3jA+cXsyetIBxYyABzKQ21HhvHhaveHmul27/+A4F/Pqa7u+ent8/pXkgLeWXdH+48XVIrJ37/4bU6U70Q4UPwIeSEtuR4VL82l1lzSfVk9/4nZrec7nPtS0J24XbrcDPkHAA8PIeZ3t6dPq1scbF8wXkR2v7HR0qNfu7p7Tp7pVP+/cydzPDyysvCiiL58eJyL2EbZ3j1u95FrXt4JoB3yEgAcG5VGdbYxx7XX05aNn185wfkTjaT5UwM97fJ+IdqxbtPWFbcl95gebuN3aiVW4G8144nZhTDrADwh4IDXv6uzKSZXRtI0V13VzZ7iOnryTpEPElxtWd6ycE9HB+7o7J25fu3ff7NCANxO3uxHtQAER8EBq3tXZOXxa3Vp76ROzePE9W7a8MOxFhcNhqzNdLN1zOXG7E9EOFBwBD6SWPCpcrursXD2tbh9u38/qn3tuq6oZ+oqsweStidvLy8sHG5yOiduBYCDggdS8q7Ozf1rd9adDWZmYSOSBZUsGu5a8zRNDtAPFg4AHUvO0zh7x0+qugD/wVL2IbN7c/OCDS1NeBdEOlCwCHkjN6zo706fVU97p37y5WVUfeugbyeefzdztRDsQAAQ8MCiv6+z0n1a/bJSsrO1V1Sf3T3DuecOhiQfXJ/zNIXmMdiHdgSJGwAND8brOluGeVrd2Yky0a97KWT0bDlU4DzFr5Z63fj7f2kOm0S6kOxBcZe7bhils+17Z3b+Shmfe+f2Kqbk/A+b99alSuzpXnW3975r2tOpsEU2us4dWt2qf88+FUWVlb66LJ/Hsx9pcf0a0/OeqjPZfXl7+rWdq7JcBi/ZS+z9nwAT7AvM5H/yoND571y9VX/pu2yPXlZWVlZXdsvFEbk8N8INFCxfccUfjXXd+xUSM9U/EPWKtGmMiEROJmJWzelyrZq3ck9HhHE390T041x5c32CvnXv1xzwGe6QAAB3TSURBVPOu+fiZ32VWuwc43QFY0qngnaxqXkRyVtHzl5pPcXWSeZ19+Be3DbqvEaFNPiX+z+lrwb7AYqvgne76parqS98ViVX039uW/dkB/pR+nW39e+Ga9lwdOhwOk+4AhpBZJ7sTG2+57pE2EZHvvqS/vEusiv6W6d7cnQeKXce6eUNvcGjD/JwfdAS5LknRXl5ePshAdikQ7YAfpRnwyckedde935VfHXtHhIAHvJZ9tAvPuAMlI52At+67u5I95q5fZnYTH8CI5KRwT/+DRDvgd+kE/F2/VP2l52cCILU8R7uQ7kAgMNANULxokwcwYgQ8UIyIdgBZIuCBopPl829CtAMg4IGikudoF9IdCC4CHigKRDuA3CLggQIrSLQHezRQAELAA4XFcLMAPELAA4WR52gX0h0oMQQ8kG/cbgeQBwQ8kD9EO4C8IeCBPOF2O4B8IuABz3G7HUD+EfCAh2iTB1AoBDzgifxHu5DuABwIeCD3aJMHUHAEPJBLRDuAIkHAI5i6e85c/OBDVVUT/bKWY0vqXDVt2tTQVeOzPGL2E7yS7gByyNOAb3+6aW2riFQ/vGnj0govjwTYjnZ2GVeuqxqTItdN7OXRo8etxcYFt4zgiEQ7gCLkXcD3Nq9YK6taWuqlt3nF8qerWh6t9+xYgIjIkc4uV5gn5LqqGjN5UuWECVdb25863XPyZHd0hVFVfeXV11V10cIFaR5xZNEuPOAOwHveBXxPd7ixoV5EpOLmW6ufbWt/tJ6Eh0eOdHa5wtx+VTe3drBPTZ5UOXlSpfOd7dtbjepL215TNV+7e+EQRyTaARQ5zwK+t/tkddU3reWKqimyu7tX6mPN9PPmzbM37Ojo8OocikAoFCr0KXioSK6u9fU9KUv2u+643d7mi9/frSoiqqoi6liW2DsqIn/8x2+IyP/4H79V1ZaWl7/97YeSD9fZ2Ski5eXlIzjVbz1TYy//5pFOkQx2UlNTM/xGmSiSH59HuDpfC/YF5u3qPAv4nu6wVA220hnqAZ6UOthTbhfP1RljXNX76MtHz66d4Tw9VU0n4Kd8d7uIdqxbtPWFbWrUdYEjrtolVeE+MJDuZ63CPbff7eL58XmBq/O1YF+gF1c32F8MngV8ZRWNiciPSMREO8uZaBe6urkzXNskZ3lS0seXG1Z3rJwTsV7bctImLxk2y9MmD2DEPAv4iqop4bYekQoR6e0+KVMa6EYPbxgTSXj+zWiqrdxZ3r4mfm++btU+Z+pf+sR897t/+w//8N+ttbkt3NNEtAPIkned7Cqrqlt/2/zN+qUVvW/sDjcuo4cdPBKr4KPPvhlNEfCpmuhda+MBv+9n9f/1v/5a1RQk2oV0B5AL3gV8xdKNq7qbljc9az0HT77DK/V1c9reeFMdz7XvfL1twZcbnNskN9HXrdo/WHN9WZmUSeTG+i+N7HxokwdQDDwd6Kb+0ZaWR708AGBpuLlu1+4/OBrpzauv7frq7bfZGwx+Dz4h4A88VS8izz33/Fe+cttA+r3gYoh2AMWDoWoRELfdelPrzj3O7vTbd7Sq0QkTr6mdNX2w/nSuHvXPPfe8qtbXzx3BCXC7HUBRIeARHI0L5ovIjld2OjrUa3d3z+lT3aqfF0fVftkoWVnbq6pP7p9gvf+lz/9FVbZ3j1u95NpMj8vtdgBFiIBH0NgDzb744o5437vEZnlj1Oqat3JWz2t/utLuZCciTzSf+MnSqWkeK8sZ3El3AN4h4BFY99yzyF5eu3efs1lepOz+++8TkXA4fLPImuffFRG7d/0Tze+s/9s5Q++caAdQ5Ah4lARXBa+JT7evXnLtmudPxGp4FdG1W46suHtKyl1lGe1CugPICwIeJaFjXXz6g5RPt69ektAsX15entyLnmgH4CMEPEpIQYabtZHuAPKJgEepGFm6E+0AfIqAR/AVZO52C9EOoFAIeATZiKPdOXE70Q7Ajwh4BBO32wGUOAIeAcTtdgAg4BEoubrdXl5enulcM0Q7gKJCwCMg6EkHAE4EPHyvgLfbiXYARYuAh78V6nY70Q6gyBHw8KtCtckT7QB8gYCH/xDtADAsAh5+MuJoF9IdQIkh4OEPBYx2Id0B+BABj2KXTbQLhTuAUkXAo3gVNtqFdAfgZwQ8ilGW0S5Zp3tNTU1fX1+W5wAABUTAo+hQuANA9gh4FJEcRrtwxx1AaSPgURQK3iZPtAMIGAIeBUa0A4AXCHgUEm3yAOARAh6FUfBoF9IdQKAR8Mi3grfJC9EOoAQQ8MifYoh2Id0BlAYCHvmQ22gXCncAGA4BD28VSbQL6Q6gxBDw8ET2uS5EOwBkgYBH7lG1A0DBEfDIJaIdAIoEAY/cGFm09/f/+aNLl0RVVdZu+z+cq/6vhf+vqoRPqqqoGlG5+prPlY8Zk85uSXcAIOCRrZFF+5mz51RFVFVVRddt+7q9yop2Naqx1SKqKmfOnBNVFZl2/bWD7ZZoBwBLmfWfT2BkOjs7M/1I14l3h452UZ0w4epQ6CrrzbNnz535kxXtzi+5se5Lzt3W1NRkeS0AECSFD/i+vr7CnoB3QqFQgK/u/PnzAwMDGX3kzNlzaqL1uCvaJZPb7ceOHTfWnwiiM2+o8aJqD/bPToJ+gVydrwX7Ar24ulAolPJ9muiRMatNvry8PKNP9Z45K8NF+4bfnVX7D4Aoe1Ht5R99fZqIHD58TFUOHz5OszwAJCPgkYFsOslrlPxsezzdXVW7fcc99ir6tv3C+ve6radF9PF7px96620TMSM+JQAIMAIew+vuOXPq9Olo9S1GojfCY0ux5B26r7sx6oz2x776fPWUSa5thi7cnWv3vzfmwX96f+WcSMHvMQFAcSLgMZSjnV39/RfsDnHRQI8vxZJ9uL7urqfbf3j788YMEczxOv7vmyrtd63CXUT2vzdGRC99YhYvvmfLlhdye8kAEAwEPFI70tl1cbhoHz/+s+PGjbW2v3Chv+/9fmc/987OLhX5L79/0LnbH97+vKpaj8ClOmyKwt25dv97Y2Ib6b6f1T/33FZVmugBIAUCHm5HOrsuXnhfYh3inNE+ZVK8ni4vL3f2oh8//srx46907ufYseP/pe0h++Wm77z57h9Pqt3Ar9rV9e71iU+0J6f7uq2nrXcOnC9XHeO4F69lZWIikQeWLcnt5QNAMBDwSHD46Nt/vtivsVvq0Sb4xGhPp6/76fOLRaLN8stv/L9VVWTGtV+Y8s6JsP0gu6p2vv1OzRevcxzf3Z9ORA6cHxPbPLrzA0/Vi8jmzc0PPrjUw+8FAPgZAY+4cDh8sf+C84a6irls1GVVlRXOzYbt6376/GJ7403fefPgIWO3x183tfp41wnneDXHjh1XlXGfHVtZMcHVn+7A+XJnrtt/Umze3KyqDz30DS+/GQDgbwQ8RBzPvxljDzIXbZ6vmjTRtfFwhXtUVahZVZ9qKVtU5az1Zdr1U0XkWGeXqBoVUaMi/Rf6L7zfr3qFtdODfeWqetkoXVnbq6pP7p8QO5iqyoZDEw+ub/DmOwEAAUHAB0d3z5mLH3wY7cKmJprSxl5S56pp06aGrhovSY+2xz4oEq+xB+Pu6+7sKm+lu4j8NWLmzJm5b98h14en11xvLVjj1WisN744CndjNBIxqrpyVs+GQxXOUn7Wyj1v/Xx+tt8yAAguAj4IjnZ2GU3MdVXjDHbXaqNHjx5X1UuXPnJN3GLsMWBjX6kO6C7ck6Pd3mDVfdUdHQeH+ENh5szp9vJL4dPOZnmRsvvvv89atWyZzH6szZnxM3+w6/AvbhvRNwwAgm9UoU8AWTnS2XX42PFIJGIiEWMixhhrMRIxUZHIpMqJN9V/6eab5jXcXFdVOdFEjDHm448HPv74L8aYzs6uY51d9g6rp1QZY9QYjX11db3rOuiwzfLxDa0b6sbU1c0Z9lqqq6sPnB/juOmurj8LDq5vcK4V0YVr2jP/ngFASaCC96sjnV2JRXr8Vd3c2sE+NXlSpYl84nzHmrjlyJG3rYlbRCT9vu7J0W5nv6r8eHG1iOzdu//GGxOmfUvJGk++Y928oTc7tIFmeQBICwHvS4ePvp2yKd4Z7fOf6HT0PFdVqb3q4tATtxw6dLS2doak0df91HuLneeTsnDfu3e/qtbXzx36WpgqBgC8QMD7kjHGVb2Pvnz07NoZzm0cfeXUina7T5y9QhwTtxw8dMR5w32Ivu6n3vsbe7OqUPOoMllYeVFEXz49TqIN+Koq27vHrV6ScIM/GekOAB4h4H3J6ltud5E3qnVzZ7i2sfJ71viLw07coqpPtZxyPcxmcfV137TXGe3PR/u6x7rmLazo39FzZax/nIjIE80nfrJ0aspLINoBwFMEvC8ZE0l4/i1p4pZwODxr/EURGXbiFh3yYTbbP7T/jfOlne7Wy7q62dbCjTfKmuffdRxXnmh+5ydLnffviXYAyAd60ftSJGKi3eQjxkQiERNxro092m73Q1dnGMc4u6lHH2YzJsXELct/Xed8Cm7Td96sCjU7duh+km71kmtdbQbP/C76qH11dTXpDgD5QQXvS/V1c9reeFMdz7XvfL1twZcbnKPWDDFxi6QajU6NqUvs7u6a43XTd960Fh6/d7IkTTbjtHpJimZ5oh0A8omA96uGm+t27f6D3Uh/6dJHL23bMezELckzxKR8mG2waB8Zoh0A8o+A97Hbbr2pdeeeS5c+HuxhtpT96RzLIkkPsxHtABAMBLy/TZlcIcNN3CIiIjrsw2xEOwAECQHvV87b7UNM3CIi6TzM9qeLD9h7yzLahXQHgCJAwPuPa/43J9fELSIy7MNsZ/pzGe1CugNAcSDg/WSIaE/F2b1ORcuc61YvudbVJj9twhaRrLKZaAeA4kHA+0aG6R59mC2lQW63k+4AEBwEvA9kGu1Dc41ak/0OiXYAKEIEfFEr8mgX0h0AihUBX6S8i3bJUbrX1NT09fVlvx8AgBcI+KJT/NFO1Q4AxY+ALyK5jXbhdjsAlDACvigUf7QL6Q4AvkLAF5in0S4U7gBQqgj4Qir+2+1CugOAPxHwhUGbPADAUwR8vhHtAIA8IODzJ+fRLqQ7AGAQBHw+EO0AgDwj4L3lRbQLD7gDAIZDwHvCyvXz58/nfM9EOwAgHaMKfQIB5FHVLqQ7ACBt3lfwvc0rlncva3m03vMjFZ6Pol1IdwAINI8Dvv3pprWtIo3eHqUI5CfahcIdAJAeDwO+/emmta3VD696ePfabu+OUnDeRbtQuAMARqpMVb09Qqom+nnz5tnLHR0d3p6Alzo7Oz3a87eeqbGXf/NIbo5SU1Mz/EYAgEAoTC96Z6j39fUV5ByylE7hXl5ePjAwkOmek9vkM99HCtXV1bn9VodCIZ/+7NIR7KuToF8gV+drwb5AL64uFAqlfD+XAR+94S4ijasC3KnOX7fbhTZ5AChJuQz4+kdbWh7N4f6KDtEOAPALBrpJVx7GpBOiHQCQIwT88PwV7UK6AwDyEfAVSze2eH4Qj/gu2oV0BwCICBX8YPw1Jp2FaAcA2Ah4Nz9Gu5DuAIBEBHyc7zrJW4h2AEAyAj5/Y80KhTsAIF9KOuA9jXZJHG6WaAcA5FOJBrzX0c7tdgBAYZVcwOcz2oXb7QCAAimhgM9/tJeXl+dknhgL6Q4ASF+pBLyv2+SFdAcAZCj4AU+0AwBKUJAD3te32y2kOwBgZIIZ8EQ7AKDEBTPgPUWbPACg+BHwGSDaAQB+QcCni7FrAAA+QsAPL+fR3t//548uXRJVVbli7FhV3X/wsBo16mCiX9OmTQ1dNT77gwIASgoBP4zcpvuZs+dURawIF73iinHGGGNS5LqJvTx69Li12LjgliyPDgAoHQT8oDyNdlEZUz42EnFkuzGTJ1VOmHC1tf2p0z0nT3ZHVxhV1VdefV1VFy1ckOWZAABKAQGfWg7T/czZc2pUrHCPRbuV23Vzawf71ORJlZMnVTrf2b691ai+tO01VfO1uxdmc0oAgMAj4N1yW7j3njkrsWhXIyI6pnysK9rnP9Gp9h8Aoo5lib2jIrL3p40i8uKLO1S1peXlpqY7szw3AECAEfBxruFr1i15tfdPl2J1txGVhKVY/qoaUbn6ms+VjxmTvM9Y+7tY0a5qLr/sstm1M5K2kWEDft7j+0S0Y92irS9ss5oEAAAYDAEf5Uz31fe8qCoffRy/X564FEt2RwqfOXPOSuNp11/r3K0x0YQeM+YKE4kY1bq5M1yHTs7ypKSPLzes7lg5J2K9BgBgMAR8imiPdmEfJNrHj//suHFjre0vXOjve78/lveiqp2dXSoyveZ6awNVM2bMFUY1YvWUS115u7O8fU28Ab9u1T5n6l/6xCxefM+WLS949e0AAARCSQe8q03+x3f/qxpJjvYpiZ3dnMaPv3L8+Cud7xw7dtyoHDnytop++98//MeTPXa0qxqTqvJO1UTvWhsP+H0/q3/uua3W/XwAAAZTogGfHO2S2BUuOdo3/O6s8+64iDjCOB7MP/r6NBE5evQdUf2nf/rnpqY72954Ux3Pte98vW3BlxucR09uoq9btX+w5vqyMjGRyAPLlnjwXQEABEfJBXzKieB6ehPqZBVz2ajLqiornFvaG8ReRd+WhISWdVtPHzg/xtkVruHmul27/xDrbadGzauv7frq7bc59yyJAZ/yfvyBp+pFZPPm5gcfXOrJtwYAECAlFPBDzPFqd4Wzm+erJk10fXzowt1+68D5Mcld4W679abWnXucI9Zt39GqRidMvKZ21vTB+tO5etRv3tysqg899I1cflMAAAFVEgE/7PTtsRZ0sfvHD76zeB3/903xBnyrcI+tTtEVrnHBfBHZ8crO2JjzRlW7u3tOn+pW/bw4qvbLRsnK2l5VfXL/BMf7suHQxIPrGwQAgDQEPOCHjXZLrIKPf6XaKkXhbrMK99hGg3aFsweatcarUddfFqJWTzxrCNuVs3o2HKpwlvKzVu556+fzM/weAABKUZADPv0x6aqnVL37x5OxvvOiql1d716f+ER7crqv23paRJyFu6sr3N99/3t9fX0pj3jPPYvs5bV79zmb5UXK7r//PmvVsmUy+7E2585n/mDX4V/clnKfAADYghnwIxhu9tovTHnnRNhuoFfVzrffqfnidY5N3P3pJKlwH1lXOFcF72o9OLi+ofaHe5zbLFzT/srq+jR3DgAoTcEMeFtGg8lfN7X6eNcJR0O9HDt2XFXGfXZsZcUEV3+6A+fLc9UVrmPdvKE3OLSBZnkAQGaCGfAjniRm2vVTReRYZ5dYg86pUZH+C/0X3u9XvUJEVPVgX7mqXjZK6QoHAChaowp9AsVoes3106dPu2HGtOhz6yba8/3A+fID58ujneOMiURMJGJWzurRRLNW7in0FQAASl0wK/hcmTlzur287s0L9m1yusIBAIocFXxaqqurnemuSZ3hDq5vcK61usIV6mwBAKCCH0Z1dbW1QFc4AICPUMEPxU53AAD8pWzIYVn9qrOzM8s91NTU5ORMAAAoiMI30Q821ls2BgYGsvl4dXV1Ts4qFAp5cXVFgqvztWBfIFfna8G+QC+uLhQKpXy/8AFfVGiTBwAEAwEfRbQDAIKEgCfaAQABVOq96El3AEAglW4FT7QDAAKsRCt40h0AEGwlV8ET7QCAUlBCAU+0AwBKR6k00ZPuAICSEvwKnmgHAJSgIAc80Q4AKFnBDHiiHQBQ4krlHjwAACWFgAcAIIAIeAAAAoiABwAggAh4AAACiIAHACCACHgAAAKIgAcAIIAIeAAAAoiABwAggAh4AAACiIAHACCAfD/ZTHfPmYsffKiqaqJf1nJsSZ2rpk2bGrpqfKFPGQAAz/k44I92dhlXrqsakyLXTezl0aPHrcXGBbcU+vQBAPCQLwP+SGeXK8wTcl1VjZk8qXLChKut7U+d7jl5sju6wqiqvvLq66q6aOGCgl4HAABe8VnAH+nscoW5/apubu1gn5o8qXLypErnO9u3txrVl7a9pmq+dvdC708cAIC88lPAHz76dsqS3Rnt85/oVBURVVURdSxL7B0Vkb0/bRSRF1/coaotLS83Nd1ZmEsCAMAbfgp4Y4yreh99+ejZtTOc26hqOgE/7/F9ItqxbtHWF7ap0UJcDQAAHvLTY3KRiDHGGBMxkYiJRCIR40p3SUjx4f/dsLrDRCLGRApyOQAAeMdfFXwk4fm31JW3u4JvXxNvwK9btc9ZzV/6xCxefM+WLS/k6QIAAMgXPwV8JBJ/zN26C5+8TaometfaeMDv+1n9c89tVTV5OX0AAPLHTwFfXzen7Y031fFc+87X2xZ8ucG5TfLt9rpV+5Nuw1uLWlYmJhJ5YNmSQlwNAAAe8tM9eBFpuLkuEjHG+seYiIm8+tou5waanv1P1h946qbNm5tJdwBAIPmpgrfcdutNrTv3OLvTb9/RqkYnTLymdtb05HvwKXvUb97crKoPPfSNwl4LAAAe8V/Ai0jjgvkisuOVnbE+d0ZVu7t7Tp/qVv28OJroLxslK2t7VfXJ/RMc78uGQxMPrm8Y5jAAAPiWz5ronRYtXHDHHY133fkVE2u0j7hHrFVjrEfqzMpZPa5Vs1buKfQVAADgFV9W8C733LPIXl67d5+zWV6k7P7777NWLVsmsx9rczbXz/zBrsO/uK0g5wwAgKd8XMGnpIlD2biekzu4vsE13M3CNe2FOlUAALzjZQXf27xi+bNhERFpXNXyaL2Hh7J1rJs39AaHNszPx3kAAFBQ3lXw7U8v333rppaWlpaWTQ+fXLuiudezQwEAgESeBXx7W2vjsqUVIiJSsXRZY7i7x6tDAQAAlzL3aK6eaH+66bdVmzZG815k3rx4Q3pHR4f3JwAAQGnJQ8D3Nq9YvvtWR7wn6uvr8/gECiYUCnF1PhXsq5OgXyBX52vBvkAvri4UCqV8P5ed7NqfblrbKiLOPnXtTzetPfnwoOkO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/><!-- --></p>\r\n</div>\r\n</div>\r\n</section>\r\n\r\n\r\n\r\n<!-- code folding -->\r\n\r\n\r\n<!-- dynamically load mathjax for compatibility with self-contained -->\r\n<script>\r\n  (function () {\r\n    var script = document.createElement(\"script\");\r\n    script.type = \"text/javascript\";\r\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\r\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\r\n  })();\r\n</script>\r\n\r\n</body>\r\n</html>\r\n"
  },
  {
    "path": "2020年/2020.10.09散点图系列一/scater_plot1.rmd",
    "content": "---\r\ntitle: \"散点图系列(1)\"\r\nauthor:\r\n  - 庄亮亮\r\ndate: \"2020/10/09\"\r\noutput:\r\n  prettydoc::html_pretty:\r\n    theme: cayman\r\n    highlight: github\r\n---\r\n\r\n\r\n记得安装prettydoc包，html模板在该包渲染而成。\r\n\r\n![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  \r\n欢迎**关注**我的**公众号**，**点赞，在看，收藏\\~\\~\\~**\r\n\r\n---------\r\n\r\n## 简介\r\n\r\n**散点图**（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。\r\n\r\n散点图可以提供三类关键信息：\r\n\r\n（1）变量之间是否存在数量关联趋势；\r\n\r\n（2）如果存在关联趋势，那么其是线性还是非线性的；\r\n\r\n（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。\r\n\r\n-----\r\n\r\n本文可以看作是[《R语言数据可视化之美》](https://github.com/EasyChart/Beautiful-Visualization-with-R \"《R语言数据可视化之美》\")的学习笔记。该书第四章——**数据关系型图表**中展示的散点图系列包括以下四个方面：\r\n\r\n1. 趋势显示的二维散点图 \r\n\r\n2. 分布显示的二维散点图\r\n\r\n3. 气泡图 \r\n\r\n4. 三维散点图\r\n\r\n本文主要对第一部分进行介绍，并加上小编自己的理解。下面几个部分也会在这星期陆续推出，敬请关注。\r\n\r\n## 2.本文框架 \r\n\r\n\r\n![](https://imgkr2.cn-bj.ufileos.com/0dce4c7a-cf88-4cd7-a5fb-a387a81ca46a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=yw7%252BqqJH9pFaw0RcxoVcXlJTpc4%253D&Expires=1602321049)\r\n\r\n\r\n\r\n## 2.数据介绍\r\n\r\n随机产生2列20行的数据，列名分别为x，y。x为序号，y由标准正态分布中产生。\r\n\r\n```{r}\r\nlibrary(ggplot2)\r\nmydata = data.frame('x'= 1:20,'y'=sort(rnorm(20)))\r\nhead(mydata)\r\n```\r\n\r\n\r\n## 3. 不同类型拟合曲线的绘制\r\n\r\n### 3.1 loess数据平滑曲线\r\n\r\n**局部加权回归**（Locally Weighted Scatterplot Smoothing，LOESS）主要思想是取一定比例的局部数据，在这部分子集中拟合多项式回归曲线，这样就可以观察到数据在局部展现出来的规律和趋势。曲线的光滑程度与选取数据比例有关：比例越少，拟合越不光滑，反之越光滑。\r\n\r\nggplot2绘制时，使用`geom_point`绘制散点图，`geom_smooth`加入拟合曲线，`method`选择为loess,`se=TRUE`表示加入置信带，`span`控制loess平滑的平滑量，较小的数字产生波动线，较大的数字产生平滑线。其他参数对颜色，填充色以及透明度进行了修改。\r\n\r\n```{r}\r\nggplot(data = mydata, aes(x,y)) +\r\ngeom_point(fill=\"black\",colour=\"black\",size=3,shape=21) +\r\ngeom_smooth(method = 'loess',span=0.4,se=TRUE,\r\ncolour=\"#00A5FF\",fill=\"#00A5FF\",alpha=0.2)\r\n```\r\n\r\n\r\n\r\n### 3.2 样条数据平滑曲线\r\n\r\n这里使用了splines包中的样条函数，df=5，样条具有五个基函数，其他参数变化不大。具体非线性模型相关资料可参考：[R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析](http://tecdat.cn/r%e8%af%ad%e8%a8%80%e9%87%8c%e7%9a%84%e9%9d%9e%e7%ba%bf%e6%80%a7%e6%a8%a1%e5%9e%8b%ef%bc%9a%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92%e3%80%81%e5%b1%80%e9%83%a8%e6%a0%b7%e6%9d%a1%e3%80%81%e5%b9%b3/ \"R语言里的非线性模型：多项式回归、局部样条、平滑样条、广义加性模型分析\")\r\n\r\n```{r}\r\nggplot(data = mydata, aes(x,y)) +\r\ngeom_point(fill=\"black\",colour=\"black\",size=3,shape=21) +\r\ngeom_smooth(method=\"lm\",se=TRUE,\r\nformula=y ~ splines::bs(x, 5),colour=\"red\")\r\n```\r\n\r\n\r\n\r\n### 3.3 GAM 数据平滑曲线\r\n\r\nGAM 模型的拟合是通过一个迭代过程（向后拟合算法）对每个预测变量进行样条平滑的。其算法要在拟合误差和自由度之间进行权衡最终达到最优。\r\n\r\n```{r}\r\nggplot(data = mydata, aes(x,y)) +\r\ngeom_point(fill=\"black\",colour=\"black\",size=3,shape=21) +\r\ngeom_smooth(method = 'gam',formula=y ~s(x))\r\n```\r\n\r\n\r\n\r\n## 4. 残差分析图\r\n\r\n**残差分析**（residual analysis）就是通过残差所提供\r\n的信息，分析出数据的可靠性、周期性或其他干扰。用于分析模型的假定正确与否的方法。所谓残\r\n差是指观测值与预测值（拟合值）之间的差，即实际观察值与回归估计值的差。以下给出两种拟合方法的残差分析图。**注意：** 这里还是使用前面随机模拟产生的数据。\r\n\r\n\r\n### 4.1 线性拟合\r\n\r\n通过lm函数进行回归分析，公式为 $y = ax+b$。并将预测值 $\\hat{y}$，残差$\\varepsilon$，残差的绝对值$|\\varepsilon|$进行存储，结果如下所示。\r\n\r\n```{r}\r\nfit <- lm(y ~ x, data = mydata)\r\nmydata$predicted <- predict(fit) # Save the predicted values\r\nmydata$residuals <- residuals(fit) # Save the residual values\r\nmydata$Abs_Residuals <- abs(mydata$residuals)\r\nhead(mydata)\r\n```\r\n\r\n\r\n完整代码如下所示：\r\n\r\n```{r include=FALSE}\r\nggplot(mydata, aes(x = x, y = y)) +\r\n  geom_point(aes(fill =Abs_Residuals, size = Abs_Residuals),shape=21,colour=\"black\") + # size also mapped\r\n  scale_fill_continuous(low = \"black\", high = \"red\") +\r\n  geom_smooth(method = \"lm\", se = FALSE, color = \"lightgrey\") +\r\n  geom_point(aes(y = predicted), shape = 1) +\r\n  geom_segment(aes(xend = x, yend = predicted), alpha = .2) +\r\n  guides(fill = guide_legend((title=\"Rresidual\")),\r\n         size = guide_legend((title=\"Rresidual\")))+\r\n  xlab(\"X-Axis\")+\r\n  ylab(\"Y-Axis\")+\r\n  theme(text=element_text(size=15,face=\"plain\",color=\"black\"),\r\n        axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\r\n        axis.text = element_text(size=10,face=\"plain\",color=\"black\"),\r\n        legend.position = \"right\",\r\n        legend.title  = element_text(size=13,face=\"plain\",color=\"black\"),\r\n        legend.text = element_text(size=10,face=\"plain\",color=\"black\"),\r\n        legend.background = element_rect(fill=alpha(\"white\",0)))\r\n```\r\n\r\n#### 代码详解\r\n\r\n绘制的方式比较简单，根据ggplot的思想不断叠加图层。我们对以下代码进行详细分析：\r\n\r\n1. 以x为横坐标，y为纵坐标，`geom_point()`绘制散点图，以Abs_Residuals的大小来填充点和尺寸，颜色为黑色。`scale_fill_continuous()`将填充色从\"black\"到\"red\"渐变。`geom_smooth()`给数据加入拟合曲线，这里使用`lm()`方法，置信带不展示，颜色为\"lightgrey\"。这时候的图形如下：\r\n\r\n![](https://imgkr2.cn-bj.ufileos.com/07b50a94-50de-4f7d-a995-b825789986d7.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=0Hkgl4JPfi%252FokI6OtholM0UxFBg%253D&Expires=1602318448)\r\n\r\n\r\n2. 将预测值的点进行绘制，`geom_segment()`可加入线段，其中`xend = x, yend = predicted`表示从x到x，y到predicted，所以就会产生下图中的竖直线了。\r\n\r\n![](https://imgkr2.cn-bj.ufileos.com/10d02b70-707a-4361-ac15-dfb1096e5757.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=AGI6HCOJsJf6kxDDBHUpjpeh%252B6A%253D&Expires=1602318537)\r\n\r\n3. 这时残差图基本完成，但是可以看到横纵坐标的标题有问题，右边的legend太累赘了以及字体颜色和大小还可以再做修改。最后图形如下所示：\r\n\r\n![](https://imgkr2.cn-bj.ufileos.com/4c9a07c1-b7d0-41fa-ac8b-c3c92439c30f.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=xoAbaDqw7uzKX3nZP%252FHpEus%252BjxI%253D&Expires=1602315306)\r\n\r\n\r\n\r\n\r\n\r\n### 4.2 非线性拟合\r\n\r\n非线性拟合绘制残差图与线性拟合类似，唯一不同的点在：利用lm函数拟合不同的回归模型，以下使用了公式：$y = ax+bx^2+c$，后面的绘制与上面相同。\r\n\r\n```{r}\r\nd<-mydata\r\nfit <- lm(y ~ x+I(x^2), data = d)\r\nd$predicted <- predict(fit) \r\nd$residuals0 <- residuals(fit)\r\nd$Residuals<-abs(d$residuals0 )\r\n```\r\n\r\n```{r}\r\nggplot(d, aes(x = x, y = y)) +\r\ngeom_smooth(method = \"lm\",formula = y ~ x+I(x^2), se = FALSE, color = \"lightgrey\") +\r\ngeom_segment(aes(xend = x, yend = predicted), alpha = .2) +\r\ngeom_point(aes(fill =Residuals, size = Residuals),shape=21,colour=\"black\") + # size also mapped\r\nscale_fill_continuous(low = \"black\", high = \"red\") +\r\ngeom_point(aes(y = predicted), shape = 1) +\r\nxlab(\"X-Axis\")+ ylab(\"Y-Axis\")+\r\ngeom_point(aes(y = predicted), shape = 1) +\r\nguides(fill = guide_legend((title=\"Rresidual\")),\r\nsize = guide_legend((title=\"Rresidual\")))+\r\ntheme(text=element_text(size=15,face=\"plain\",color=\"black\"),\r\naxis.title=element_text(size=10,face=\"plain\",color=\"black\"),\r\naxis.text = element_text(size=10,face=\"plain\",color=\"black\"),\r\nlegend.position = \"right\",\r\nlegend.title = element_text(size=13,face=\"plain\",color=\"black\"),\r\nlegend.text = element_text(size=10,face=\"plain\",color=\"black\"),\r\nlegend.background = element_rect(fill=alpha(\"white\",0)))\r\n```\r\n\r\n\r\n\r\n这两个图采用黑色到红色渐变颜色和气泡面积大小两个视觉暗示对应残差的绝对值大小，用于实际数据点的表示；而拟合数据点则用小空心圆圈表示，并放置在灰色的拟合曲线上。用直线连接实际数据点和拟合数据点。残差的绝对值越大，颜色越红、气泡也越大，连接直线越长，这样可以很清晰地观察数据的拟合效果。\r\n\r\n### 4.3 有趣的拓展\r\n\r\nR 中的[ggimage](https://cran.r-project.org/web/packages/ggimage/vignettes/ggimage.html \"ggimage\")包提供了`geom_image()`函数可以将对应的圆形数据点使用图片替代展示。我们将其运用到上面的数据集中，就可以得到有趣的图了。\r\n\r\n```{r}\r\nlibrary(ggimage)\r\nmydata$image = \"https://www.r-project.org/logo/Rlogo.png\"\r\nggplot(mydata, aes(x, y)) + geom_image(aes(image=image))+\r\n  geom_smooth(method = 'lm')\r\n```\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.10.27散点图系列二/HighDensity_Scatter_Data.csv",
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \r\ncode span.dt { color: #795da3; } \r\ncode span.dv { color: #0086b3; } \r\ncode span.bn { color: #0086b3; } \r\ncode span.fl { color: #0086b3; } \r\ncode span.ch { color: #4070a0; } \r\ncode span.st { color: #183691; } \r\ncode span.co { color: #969896; font-style: italic; } \r\ncode span.ot { color: #007020; } \r\n</style>\r\n\r\n\r\n\r\n\r\n\r\n</head>\r\n\r\n<body>\r\n\r\n\r\n\r\n\r\n<section class=\"page-header\">\r\n<h1 class=\"title toc-ignore project-name\">散点图系列(2)</h1>\r\n<h4 class=\"author project-author\">庄亮亮</h4>\r\n<h4 class=\"date project-date\">2020/10/27</h4>\r\n</section>\r\n\r\n\r\n\r\n<section class=\"main-content\">\r\n<p>记得安装prettydoc包，html模板在该包渲染而成。</p>\r\n<p><img 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\" /><br />\r\n欢迎<strong>关注</strong>我的<strong>公众号</strong>，<strong>点赞，在看，收藏~~~</strong></p>\r\n<hr />\r\n<div id=\"前言\" class=\"section level2\">\r\n<h2>1.前言</h2>\r\n<p><strong>散点图</strong>（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。</p>\r\n<p>散点图可以提供三类关键信息：</p>\r\n<p>（1）变量之间是否存在数量关联趋势；</p>\r\n<p>（2）如果存在关联趋势，那么其是线性还是非线性的；</p>\r\n<p>（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。</p>\r\n<hr />\r\n<p>本文可以看作是<a href=\"https://github.com/EasyChart/Beautiful-Visualization-with-R\" title=\"《R语言数据可视化之美》\">《R语言数据可视化之美》</a>的学习笔记。该书第四章——<strong>数据关系型图表</strong>中展示的散点图系列包括以下四个方面：</p>\r\n<ol style=\"list-style-type: decimal\">\r\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN#rd\">趋势显示的二维散点图</a></p></li>\r\n<li><p>分布显示的二维散点图</p></li>\r\n<li><p>气泡图</p></li>\r\n<li><p>三维散点图</p></li>\r\n</ol>\r\n<p>本文主要对<strong>第二部分</strong>进行介绍，并加上小编自己的理解。下面几个部分也会在最近陆续推出，敬请关注。</p>\r\n</div>\r\n<div id=\"单数据系列\" class=\"section level2\">\r\n<h2>3.单数据系列</h2>\r\n<div id=\"数据格式\" class=\"section level3\">\r\n<h3>3.1数据格式</h3>\r\n<p>这里我们使用正态分布随机产生250个数据（这个就是实际我们采集的一维数据）。<code>step</code>是指按照多少的区间进行划分类别。我们通过<code>hist()</code>将直方图内部数据进行存储(我也是第一次见这种操作，以后学起来)。输出<code>hg</code>.</p>\r\n<p>其中breaks表示边界点，counts表示每个区间内的个数，density表示密度函数值。mids表示区间的中间点，并利用这些参数来构建后续绘图所需要的数据。通过循环语句，计算出x，y坐标数据。 完整代码如下：</p>\r\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb1-1\" title=\"1\"><span class=\"co\">#加载包</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-2\" title=\"2\"><span class=\"kw\">library</span>(ggplot2)</a>\r\n<a class=\"sourceLine\" id=\"cb1-3\" title=\"3\"><span class=\"kw\">library</span>(RColorBrewer) <span class=\"co\">#颜色</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-4\" title=\"4\"><span class=\"kw\">library</span>(scales)</a>\r\n<a class=\"sourceLine\" id=\"cb1-5\" title=\"5\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-6\" title=\"6\">x &lt;-<span class=\"st\"> </span><span class=\"kw\">rnorm</span>(<span class=\"dv\">250</span> , <span class=\"dt\">mean=</span><span class=\"dv\">10</span> , <span class=\"dt\">sd=</span><span class=\"dv\">1</span>) </a>\r\n<a class=\"sourceLine\" id=\"cb1-7\" title=\"7\">step&lt;-<span class=\"fl\">0.2</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-8\" title=\"8\">breaks&lt;-<span class=\"st\"> </span><span class=\"kw\">seq</span>(<span class=\"kw\">min</span>(x)<span class=\"op\">-</span>step,<span class=\"kw\">max</span>(x)<span class=\"op\">+</span>step,step)</a>\r\n<a class=\"sourceLine\" id=\"cb1-9\" title=\"9\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-10\" title=\"10\">hg &lt;-<span class=\"st\"> </span><span class=\"kw\">hist</span>(x, <span class=\"dt\">breaks =</span> breaks , <span class=\"dt\">plot =</span> <span class=\"ot\">FALSE</span>)<span class=\"co\">#使用直方图数据，但不绘图</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-11\" title=\"11\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-12\" title=\"12\">bins &lt;-<span class=\"st\"> </span><span class=\"kw\">length</span>(hg<span class=\"op\">$</span>counts) <span class=\"co\"># bin类别数</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-13\" title=\"13\">yvals &lt;-<span class=\"st\"> </span><span class=\"kw\">numeric</span>(<span class=\"dv\">0</span>)       </a>\r\n<a class=\"sourceLine\" id=\"cb1-14\" title=\"14\">xvals &lt;-<span class=\"st\"> </span><span class=\"kw\">numeric</span>(<span class=\"dv\">0</span>) </a>\r\n<a class=\"sourceLine\" id=\"cb1-15\" title=\"15\"><span class=\"cf\">for</span>(i <span class=\"cf\">in</span> <span class=\"dv\">1</span><span class=\"op\">:</span>bins) {       </a>\r\n<a class=\"sourceLine\" id=\"cb1-16\" title=\"16\">  yvals &lt;-<span class=\"st\"> </span><span class=\"kw\">c</span>(yvals, hg<span class=\"op\">$</span>counts[i]<span class=\"op\">:</span><span class=\"dv\">0</span>)  </a>\r\n<a class=\"sourceLine\" id=\"cb1-17\" title=\"17\">  xvals &lt;-<span class=\"st\"> </span><span class=\"kw\">c</span>(xvals, <span class=\"kw\">rep</span>(hg<span class=\"op\">$</span>mids[i], hg<span class=\"op\">$</span>counts[i]<span class=\"op\">+</span><span class=\"dv\">1</span>))  </a>\r\n<a class=\"sourceLine\" id=\"cb1-18\" title=\"18\">}    </a>\r\n<a class=\"sourceLine\" id=\"cb1-19\" title=\"19\">                                                   </a>\r\n<a class=\"sourceLine\" id=\"cb1-20\" title=\"20\">dat &lt;-<span class=\"st\"> </span><span class=\"kw\">data.frame</span>(xvals, yvals)  <span class=\"co\"># 变成dataframe格式</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-21\" title=\"21\">dat &lt;-<span class=\"st\"> </span>dat[yvals <span class=\"op\">&gt;</span><span class=\"st\"> </span><span class=\"dv\">0</span>, ]          <span class=\"co\"># 去除小于0的数</span></a>\r\n<a class=\"sourceLine\" id=\"cb1-22\" title=\"22\"></a>\r\n<a class=\"sourceLine\" id=\"cb1-23\" title=\"23\">colormap &lt;-<span class=\"st\"> </span><span class=\"kw\">colorRampPalette</span>(<span class=\"kw\">rev</span>(<span class=\"kw\">brewer.pal</span>(<span class=\"dv\">11</span>,<span class=\"st\">&#39;Spectral&#39;</span>)))(<span class=\"dv\">32</span>) <span class=\"co\">#颜色选择</span></a></code></pre></div>\r\n</div>\r\n<div id=\"原始数据绘制\" class=\"section level3\">\r\n<h3>3.2原始数据绘制</h3>\r\n<p>接下来我们使用该数据（单数据）进行绘制：</p>\r\n<ul>\r\n<li><strong>柱状图（正态分布）</strong></li>\r\n</ul>\r\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb2-1\" title=\"1\"><span class=\"kw\">ggplot</span>(dat, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>xvals,<span class=\"dt\">y=</span>yvals,<span class=\"dt\">fill=</span>yvals))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb2-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_tile</span>(<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb2-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">scale_fill_gradientn</span>(<span class=\"dt\">colours=</span>colormap)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb2-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">ylim</span> (<span class=\"dv\">0</span>, <span class=\"kw\">max</span>(yvals)<span class=\"op\">*</span><span class=\"fl\">1.3</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb2-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb2-6\" title=\"6\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb2-7\" title=\"7\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,</a>\r\n<a class=\"sourceLine\" id=\"cb2-8\" title=\"8\">      <span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">hjust=</span>.<span class=\"dv\">5</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb2-9\" title=\"9\">    <span class=\"dt\">legend.background =</span> <span class=\"kw\">element_blank</span>(),</a>\r\n<a class=\"sourceLine\" id=\"cb2-10\" title=\"10\">    <span class=\"dt\">legend.position=</span><span class=\"kw\">c</span>(<span class=\"fl\">0.9</span>,<span class=\"fl\">0.75</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb2-11\" title=\"11\">  )</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3df2hj6X7f8a/mx73NH6GkldIWj8nIyAOzLKWEUcXKTKnrLjfW3ni2FzNxthdNk5tYmL1BNmL3j9hQWuxAdzG2yb248k3Srkg3zmAuO8pdOTdb1aXDaFE1vS1lWYMtJBePaIkcLiSUZnd2Rv3jSEdHtmTr6MfROY/fLwRr6ZxHz6NnZX/mOec5z3FVKhUBAABquTLoBgAAgN4j4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAVdG3QD2nJyctJqk9vtPmfrpUJX6OgKI3pDR1fo6Aojp/eG2+1u+jojeAAAFETAAwCgIAIeAAAFEfAAACjIxVr0AACoh1n06qArdHSFEb2hoyt0dIWR03uDWfQAAFwiBDwAAAoi4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoCACHgAABRHwAAAoiIAHAEBBBDwAAAoi4AEAUBABDwCAggh4AAAURMADANBUIR7yeDyx9KDb0RkCHgAABRHwAAAoiIAHAEBBBDwAwMnSMY/HE4oXGl4sxEMeTyheaLax8bVCOjI25tGFYvF041sZ3jQdC4Xa2dEeCHgAgJNNhMIiuUefGNO28MmjnPjvvT7iveUXyR0UDdvSqYSI/97rIyKSjnkCM1uZTH1rLrE0E2g6rS4dC8wkcrmLd7QLAh4A4GhnE17Pdxl5/Z5fJJGqB7Eh3wvx1YSIfz11WK7KbodFGvdvLBfezhr3bLajbRDwAABnm3h72W9M+Hq+SzXh9/O1bcbx+0gkVS6XU9FJX+2dRiZC4eZ1FPL7IqKP32VkYrVcLpdXJ/rxgXqDgAcAONzI6/cMCW/M99PbjPmuy+cL6XQ6Ho/FQqGZxDlViCRmAvoJeHufgSfgAQDOZ0zxxnxv3HY63wvpWMjjGh0NzMzMLC0lDKfYz1YRSWWXw37tSS6xNBMIeEKxuI1TnoAHADhePcVP53tt20HxTL4X4t+dSeREgrPh8PL29vZ2NlvebnGIXkRkJLKaKpez28vLYb9fRCSXWPqufSP+2qAbAABA10Zev+dfWnr0SVoe5cS/3HAMfsR3WySRSoekMd8/eZQTCW9XPviVk5OT2mv7F9Y0EYlMRCKrko6FZhK5g6LIyEVlBoIRPABAASORWFhyj1ZPj99FqvPs91dXm5x/l0RqN6/9VEjHQoGlFgfp0zGPx+MJ1a99L6Tz+zkR/y1vjz9IzxDwAAAlTITCkss1yff6poZttXlzoVGXx+PxeAIziVq612fd6+/w9rJfJLekTbLzeDyBmaWcSDgWsefwXQh4AIAqvLf80mSMLlIdw5/e1jBvTsTvDy9va9e3Ny6No++8XT33ru0eXt7O2vkyOVelUhl0Gy6mnx05y+12n7P1UqErdHSFEb2hoyt0inZFOuaZSfiXsylzo2qn94bb7W76OiN4AIACCunYasLex8wtxix6AICjFeL63Dj/8ts2PmZuMUbwAABHG/HdFhERf3j7ewzf6xjBAwCcbWK1XF4ddCPshxE8AAAK6vsIvrQTnUtolxt4w5sb00OGbdm1qZW95psAAEAX+juCL+1E5xI3F5PJZDKZXLyZmIvulGrbsmtTK0fhzWabAABAd/oa8NmHiaI3fD+gPQvcD3uLiYdZEREp7Xy45w2/Wx21BxYWx/VNAACgW30N+MBCMtn8yHvp08dFuTlc33Rj2Ct7GRIeAICesG6SXWnnPeN4XsQ7fKO+dWj4pmUtAQBAeZZcJlebTOcNb1bH88+OiyJ3T+93dFySQG1Uf+fOHX3D06dPz3n7Vqv0XUJ0hY6uMKI3dHSFjq4waqM38htjo/OZ6pPg+uGTqK+2aTfiCm0Z951NVeKTPW6ieZYEfGAhmVzQptxNPW53vrwx1FmLvh10hY6uMKI3dHSFTqWu+JOf/5Xu3+SX//yPz92uLZZXXea+EA8F5kddP9kua7eaKfzkv8vpFfCt7F47rEU/NP3WuBQff1rSTrmfZTwpDwDAxa5dc3X/uKCOwiePchLerkb4SCSVXfZLIpXWthYPcnLbZ78l9Kxdye7GsFf2jp+J3BCR4vEzkVqil46PRG5a2hgAgPNdu35RPHdvJJIqRxpe8N3Wbho/MSKF/L6EYzZcAr+fI/jSTnRqas04M/7ZcVHGgwGRodfueuXouHR2CwAA7bv+NVf3D7OVplMJqY3aiwc5//6qpyYUL/T+Q3aknyP4oem3xhMrH+7cD2gn3bVr3zcD9W3v7by2MT0kkl1b0bcAANC26Z/+mf7zo5//Rpul7v35j41PzZ0yT8dmEvqN69KphOT897Ll1Eh1oycUN3tL+r5wVSqVvlZQX45WZHwxuWAI8fNWsW3EJLt20BU6usKI3tDRFTqVuiL7D77d/ZsE/scftrtrOuaZSUi4NsOu+Q7SenPvtZpk1/dz8NUZ9M0MTW8kp/tdPwBAZdctOAevS8c8M4nTM+ZP8d7yy1IqvTox6NPy3C4WAOBgXzN/Br0zDZfKXcB/q9mlYhbjdrEAAAe7dt3V/ePCWrR0D2+XT6d7IR7yeGJpwyu2uWqOgAcAONj161e6f1xQRyH+3aWcfznb5Lz6SCQWrl8SLyLpVKI2/27AOEQPAHAwC87BFz55lBORpYBnyfhy9Wj9xGp5O+bxeKqvhrfLKTvEOwEPAHA0Cxa6ObPOzWkTq+Xyar9bYRoBDwBwsGvX+3uxt3MR8AAAB7tKjrVAxwAAHOzqNUbwzRHwAAAHI+BbIeABAA5GwLdCwAMAHOwqk+xaIOABAA525SoB3xwBDwBwMA7Rt0LAAwAcrCcBr+S/EQh4AICDXenFOfgX3b+F/XCzGQCAg1299rL7Rxv1FOIhjy4ULzRsTMdabRkgAh4A4GBXrl/p/nFRJfq94Mvlcjm77M8tBer3iE3HPDP72qby9u2lgF0ynoAHADiY67qr+8cFdRQ+eZST8Hb1XvAjkVR22V+7R2whvprwL3+vepv4idXtcG7p++lz3swyBDwAwMFc1690/7igjpFIqlw23gx+xHdbZD9fqGb/bd+Ivsl7y99wf/jBYZIdAMDBXF8bwEg1nUqIhH0jIgUR8d/y1jeN+G5b356mCHgAgIP9zL/+WP/5r5en2iz1N5aSxqf/9+TERJXp2ExC/MtvT4hI8SAncu/0Hvv5gkyMNClqJQIeAOBgX/ybf6b/7Lp+tYNSIiLf+UG79aVjnplE/YS8jRHwAAAnu3gOfO+kY56ZhH85W0937y2/7J/Zz3hSfmAIeACAg7m+3u6ovUv6pXJnxu65g6JI7bVCfl/EFqfhmUUPAHAwK2bR19I9vF0+ne4jr9/za/Ppq4oHOQmHJk6/wQAQ8AAAB3N97Wr3jwvqKMS/u5TzL2dXm+T2SCQWzi19t7q4TTo2k6hOvxs4DtEDABys/Yl1HSt88ignIksBz5Lx5drR+onV7HIoUN3Y7BD+gBDwAAAn6/8ku5FIqhzpaoeBIOABAA7m+hpB1hz9AgBwsmsWzaJ3HAIeAOBk/T8H71AEPIBe8ng8HZQql8s9bwkui+sEWXP0C4Ae+wPXPzG1/69X/lOfWoJL4RpB1hz9AgBwMkbwLdAvAAAnY5JdCwQ8AMDJOETfAv0CAHAyAr4F+gUA4GRcJtcCN5sBADjZtWs9eLSvEA95YumGl9IxT6NT2weEETwAwMFcV60MsnQssJSTcMNrhfy+ne4xoyPgAQBOZlnAp2OemUST14sHObkds1m6CwEPAHC2a9etqCUd88wk/MvZlO/7npmGLYX8voRjtrgDfCMCHkCPsTIdLGXNCH5itVxeFRE5c3q9eJDz7696arFvn4P1BDyAHvuTv/cNU/v/8v/+cZ9agsvA9Xfqd2KvnPxBu6Xcv97w/OSk0/rTqYTk/Pey5ZSW6umYJxS3RcYT8AAAB6v81HBevO3RfEMpEZFQp/XrQ/va81B4Zub76cjqwA/ac5kcAMDJrlzrwaOHvLf8kkjZ4EI5RvAAACe72otJds978B4G/lve3r5hJxjBAwCc7Mr1Hjw6VoiHTi1sUzzIyW3f4E/Bi6tSqQy6DQDU4XK5Ophkxx8idKzyxY+6fxPX17/Z7q67EVdIUpX4ZIsXdiOu5VcOn0R93beqS844RH/Sen6j2+0+Z+ulQlfo6AojR/SGNS10RFdYQ6Wu+Ft/swdr0f9F+73xl38tIn9p6D7/Snk75nG5qk/D2+VHExZ9pTVut7vp684IeAAAmtOj1RoTq+Vys9dWm+08UAQ8AMDBXlqb7w5CwAMAnMziEbxzEPAAAAd7WXk56CbYFAEPoMesWXrW4/F0VrB89gwqnOyFfDXoJtgUAQ+gx/7rL7Z9xZGIiPzDn3R4mZPZirqpC7bFCL4VAh4A4GAvK4zgmyPgAQAO9rLyYtBNsCkCHgDgYIzgWyHgAQAO9oIRfAsEPADAwSq9CHglL6XnbnIAAAf76uXL7h8m6ivEQ413jxMRScc8VaF4oZefrhsEPADAwV5UKt0/2q4tHQss5c685pnZX86Wy+Vyefv2UsAuGU/AAwAc7EXlZfePtmpKxzyemcTpVwvx1YR/+XsR7QbwE6vb4dzS90+P8AeCgAcAONhXL3vwuFg65plJ+Jez5e1ww+uFTx7l5LZvRH/Be8sviZQdEp5JdgB6zLLV4liWDiLypTW3k9PvCNskuv23vPUnI77bVrSnDQQ8gB47/Ia5FWRHf9xhTputqJu6YFu3f+439J//51/8fpul/v7f/o7x6cnJSYfVFw9yIvdOv7qfL8jESLP9LUTAAwAc7L+d/IHhWbuj+cZS8gsy1bsW2QUBDwBwsOcvenGI/mqnBb23/LJ/5lXjSfmBIeABAA72vCfn4DsOeBGR3EFRpJbohfy+iC1OwzOLHgDgYF++dHX/6Lz6kdfv+WU/X7/yvXiQk3BoogefrFsEPADAwZ6/dHX/6KL+kUgsnFv6bnVxm3RsJuFfftsO+c4hegCAk/XmHHw3Jlazy6FAwLMkIuJfzqYiNjgBLwQ8AMDRenMOvn0Tq+Xy6ddGIqlyxNJWtIOABwA4mNUB7xwEPADAwb4wcyu4S4WAB9Bjlq0Wx7J0EJEve3A7eDUR8AB67P/86jdM7f93/+jH1lTUTV2wreeM4Fsg4AEADvYlAd8CAQ8AcLAvOETfAgEPAHAwRvCtEPAAAAdjkl0rBDwAwMEI+FYIeACAg7HQTSsEPADAwayaZJeOeWYSxhfC2+VVW9xUphUCHgDgYBaN4Av5fTvdSKYdBDwAwMGef2VJwBcPcnI75px0FwIeAOBoz59fsaCWQn5fwjFbH5E/g4AH0GOWLQer5LqzHo+ns4LlszcxvRyef2lFwBcPcv79VY9nRnvqiIP1BDyAHvur3/qnpvb/2d/9j9ZU1E1dVlL1c/XJ9tS0/vP0D3/YZqmdb33L+PTk5OTc3dOphOT897LllJbq6ZgnFLd7xhPwAAAHe/OPHxmeXe2olPzexNi5u0+slsurxueh8MzM99MRW0+jt+LIBgAAffLV8yvdP0zX6r3ll0Qq3YfP0zuM4AEADmbNJLtm/Le8A6q5PYzgAQAO9vzLK90/LqijEA95PDHjeL14kJPbPlufgifgAQBO9uL5le4fF9QxEomFG47Ip1MJ//Lbdj4BLxyiBwA4Widn0M2bWC1vx+rXMIa3yymbxzsBDwBwtBfWrGR3dia97RHwAAAHe/l80C2wKwIeAOBg1758Oegm2BQBD6DHOlhVrbP1WVVdvk3Vz9Un155bdL9Yx+l3wJd2onOJYvWJN7y5MT1U35hdm1rZa7oFgHP9v9+ZMrX/z/x20myRbkqZLWI9VT9Xn1x9zgi+ub5OPtTS3RveTCaTyc2wt5iYm1rLVjdm16ZWjrRNycWbibnoTqmfbQEAqOjqVy+7fwz6Q/RFPwO+9OnjoowvVofmQ9Mbm2Gv7GWyIiKlnQ/3vOF3q6P2wMLieDHxMHvOmwEAcNb1L190/xj0h+iLfh6iH5reSE43vDB8U+TouCQB+fRxUW6+VT8of2PYK4lMdiEQ6GODAACq4RB9K5ZOsstm9kTGh4dESiLiHb5R3zQ0fPPUznfu3NF/fvr06Tlv63a7e9hIR6MrdHSFEb1hpGpvmP1cyvTDta96MP5WpjeMLAz47NrKnnjD9wMi8uy4KHL39B5HxyUJ1Eb1xlA/5069brf7ovv4XhZ0hY6uMKI3TlG1N0x9LpW+Fdd6MYJ3dG+0+teJVQGvTZjXT8gDANAL1xSdItc9SwI+uza1stdwJdyNYa8cndnv5jDpDwAwg4VuWul7wOuXyp0ZuxePn4nUXisdH4nc7HdjAACKWf+33+jyHVQ6YWHU35vwaOk+vpg8ne5Dr931ytFx/cr3Z8dFGQ8yhR4AgJ7o5wi+tPNeougNby40ye2h6bfGEyvv7by2MT2kzb/zhjfJd0AFHayq1tlCbKou36bq54LF+hjwpU8fF0UkMTeVML5cO1ofWNgMR+eqG1mqFlDH83//z03tf/1f/AezRbopZbaI9VT9XLBYHwP+zDo3pncAAACd6e85eAAAMBAEPAAACiLgAQBQEAEPAICCCHgAABRkLuDzu/l89aeNyJjL5XK5xsY2dvN9aBgAAOhc+wG/G3G5RkPvH4qI5DcezG9lREQkk5kPjUZ2+9M6AADQkXYDfjcS2hKR2TdHRWT3/fmMiMymKpVKalZEtpY3GMUDAGAfbS50s/vRlojMpuKTvtoTmX1zUkQm35yVra3M54civv41E4BzdLCqWmcLsam6fJuqnwsW62Alu2q+B18Z7XVjAKjgxZ/Omdr/6i9tmi3STSmzRayn6ueCxdo8RD/6SlBEPjvIi+QPPhMRCd5/wydSe0raAwBgJ20GvO+N+0GRzPyDsbEH2vn3pahPtNl2mXraAwAAW2h3kp0v+kFqNiiZTCYjEpxNxSdFROTw84xIcP2DKPkOAICNtH8O3jcZf1KJS75hMt3oO4eVOOEOAIDNnBvw+XzTi9/yTXby+Yh5AABs45yA342MhrbafZ/ZVKV61B4AAAzcOQE/+srs7Gy778MsegAAbOScgPdF43HrGgIAAHqHu8kBAKAgEyvZ5Xc33l9++NnZDZlMhnPwAADYSdsBf/6Uu+ArPWkNAAV0sGxqZyutWrY+q8fj6axguVzuoBTrzqIn2gz4/MaydoOZ1OE78v6D0FYmuH74wRsfvz86v6WtdMPwHYDmxdPfNrX/1Tu/Y7ZIN6XMFtGoWhcU1uY5+MPPMyISXH9n0uebfPNVEcl8fujzReOH60GRzPz73BAeAAAbMTXJ7tVbPpGGO89UF6mvPgEAAPZg6m5yVb5br55+kvn8sKfNAgAA3Wj3bnK3XhWRrY+0I/GjrwRFMg8/zot+91gAAGAj7R6in3xnPSiyFYrsivHmsZHa3WPfZJIdAAD20fZlcr7ok0OJPPi89iT1+VhoK7MlYrx7LAAAsAUTC92ILxp/oj+ZjD+pxPP5PLeRAwDAfto8RL+7EdnYPXvzWNIdAABbavcc/Odb86HRUZdrbCyyscslcQAA2JurUqm0tWM+v/vx+8vzW5nq82Bw9v7SO9FJS4bwJycnrTa53e5ztl4qdIWOrjByu90ul6uzsh2stNrxqq6WsfJD2bYufkeMnN4bbre76evtT7LzTUbjk9F4Pem3MqGteZHg7PrSO29McrAesLMXhffNFrk68o41dV0deaez5tn5QzmlLijM/O1ifb7JaPxJpVI5TK3PBoOS2ZoPjY5GWKsWAAD76Px+8PnDAxHJXLgfAACwnJnL5ESkybl4jtADAGA7bQd8fnfj/eWHW5lqsFs6xw4AAJjTZsDvRkZD1VXrGLADAGB77Y7gX5ldTzFgBwDAITqfZAcAAGzL1Ep2LlayAwDAEdoM+Mn44WFqfTYomUwt6ZsuTg8AAOyg7aVqdYO4To6lattBV+joCqNulqpFl1iq1hGc3hutlqo1H/A6Len1K+eCwdmlD+L9mYZHwLeDrtDRFUZawL88+XdmC15x/5o1pSyryPpSnQW8BXXxO2Lk9N5oFfCdTrJrTPdgUCST2QqNusYinKEHAGDgTAZ8Pr+7ERlzuVyjo6H5rYzMrqcODyuVJ9ra9LNByWyFHmwQ8QAADFabAX8q14Oz66nDSqXyJB6tn3z3TcaXZkUk8/lhv1oLAADa0uZCN4cfzW9l2lmfNhgMvvrKaG/aBgAAOtRmwI++c3gYv3ii/GT8yWS3LWqm1QyCdrZeKnSFjq6AHVj5PTRbF78jRkr2RpsB7xvs4vPMom8HXaGjK4yU/MvlFFZ+D03Vxe+IkdN7o9ez6AEAgI0R8AAAKIiABwBAQe3eLhaAo11x/5qdS9m8eR2X6oyVdUFhBDxwKbz84kdmi1z5+jetKWVZRdaXMltEY2VdUBiH6AEAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIJYqha4FDpbytSyUjZvXselOsO6s+gJAh64FL54sWu2yNevTlpTyrKKrC9ltojGyrqgMA7RAwCgIAIeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoCBWsgMuhc5WOrOslM2b13GpzrAsHXqCgAcuhZ9+8Udmi/zc13/VmlKWVWR9KbNFNFbWBYVxiB4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFuSqVyqDbcLGTk5NWm9xu9zlbLxW6QkdXGLndbpfLNehWoO/K5XL7O/M7YuT03nC73U1fZ6la4FI4/qsfmC0y/LO/aU0pyypSu5TZIlAeh+gBAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCWIteHXSFzuKu8Hg8ltUlJpccF9aiR6+Z/Qban9P/eA56LfrSTnTu+K3kQsD4YnZtamVPRES84c2N6SGL2gL03E/Kv2e2yC96fqOzUmaLaKxsodlSllVEqZ5UZLYIBsWaQ/TZtblE8cxrUytH4c1kMplMLt5MzEV3Spa0BQCAS6D/AZ9dm6qN0w1KOx/uecPvVkftgYXF8WLiYbbvjQEA4HLoc8Bn16ZW9rzhzeTieMPrpU8fF+XmcP2g/I1hr+xlSHgAAHqiz+fgAwvJ5IKISJPo9g7fqD8ZGr55avOdO3f0n58+fXpOJa3mF1xCdIVO4a5Q+KPBEZT8Bir5oayaZHfKs+OiyN3Trx4dlyRQG9UbQ51Z9O2gK3Rqd4XZj6bkXy4MkHq/XE7/i9Hqd5zr4AEAUNCAAv7GsLfJq8aT8gAAoHMDHMEXj5/Vn5SOjwbWEAAAlDOggB967a5Xjo7rV74/Oy7KeDBwThEAANA2q5aqza5NrciicSW76kI3G9NDjT83wyS7dtAVOpaqNWKpWvRWB0vVdvw7Ys2yuE7/4znopWrPCixshqNzc1MJEZaqhdOl/tcHZouEfuFBZ6XMFtFY2UKzpSyriFI9qchsEY2VdUFjVcAHFpLJ068NTW8kpy2qHwCAS4XL5AAAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQQQ8AAAKsmqp2u6wVG076AodS9UasVQteoulau3GfkvVAgr5/f0/NFvkO7e/3Vkps0U0VrbQbCnLKqJUTyoyW0RjZV3QcIgeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoCACHgAABbFUrTroCl3HXWHxorOdYalaDBZL1doNS9UCbflXT00vqPkv73zbylJmi2js/Lks7kBKdVmR2SIaK+uChkP0AAAoiIAHAEBBBDwAAAoi4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQaxFrw66Qsda9EasRY/eYi16u2EteqAts//5Q7NFtv7xW1aWMltEY+fPZXEHUqrLiswW0VhZFzQcogcAQEEEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoCACHgAABRHwAAAoiIAHAEBBLFWrDrpCx1K1RixVi96ycqnaznTwO2LxX4zeLsHLUrVAW958+EOzRT66/y0rS5ktorHz57K4AynVZUVmi2js/zvSGTu3kEP0AAAoiIAHAEBBBDwAAAoi4AEAUJAzJtm1miLYztZLha7QKdwVCn80OIL9v4EdtNDiD2VNdc4IeC6TawddoVO7K8x+NPv/OYaz2P+Xq4PfEYs/VG+ra/U7ziF6AAAURMADAKAgAh4AAAWxVK066Aqd2kvVAjgfS9VqnDHJDrDMP/rdPzVb5L/81i9RqptSNm+eqqVs3rxuSpkt0g07t5BD9AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIpWrVoXBXdLYeZAeLQbJULXBpWfkXg6VqgbqxpV1T+z9ZnrSmIq0uSnVTyubNU7WUzZtnfSmzRTRW1mUWh+gBAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCWIteHQp3BUvEA+g31qIHBuObv7ljav8f/WDabBFKDaqUzZunaimbN8/6UmaLaKysyywO0QMAoCACHgAABRHwAAAoiIAHAEBBBDwAAAoi4AEAUBABDwCAggh4AAAURMADAKCgy7tUrU2WGOwhi5eqZflYACphqVqlhO8lzBZJPAr3oyUO1VkHWlPKsooo1X0pmzdP1VI2b571pcwW0dg5RzhEDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiAB+IoGL8AAAfkSURBVABAQQQ8AAAKIuABAFAQAQ8AgIJYqtY0lqrVsFQtAJWwVG1PZdemVvZERMQb3tyYHrK0ctvmtFPYuQMt/reOzdEbOrpCR1cYddwbdv4zKIM8RJ9dm1o5Cm8mk8lkcvFmYi66UxpYWwAAUMygAr608+GeN/xuddQeWFgcLyYeZgfUGAAAVDOggC99+rgoN4frB+VvDHtlL0PCAwDQEwOcRe8dvlF/MjR8c2ANAQBAOQOaZPfsuChy9/SrR8clCdRG9Xfu3NE3PH369Jw3azWB8BKiK3R0hRG9oaMrdHSFkZK9MdBZ9Ocyhno/LpNTD12hoyuM6A0dXaGjK4yc3hut/nUyoEP0N4a9TV41npQHAACdG+A5+OLxs/qT0vHRwBoCAIByBhTwQ6/d9crRcf3K92fHRRkPBgbTGgAAVDOoEfzQ9FvjxcR71cVtsmsre97wffIdAIDeGNwku8DCZjg6NzeVEBnIUrUAAChskLPoh6Y3ktMDrB8AAGVxu1gAABREwAMAoCACHgAABRHwAAAoiIAHAEBBBDwAAAoi4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAW5KpXKoNvQlTt37jx9+nTQrYC98K1AU3wx0JSqXwxG8AAAKIiABwBAQQQ8AAAKcvw5eAAAcBYjeAAAFETAAwCgIAIeAAAFEfAAACjo2qAb0KnSTnQuUdR+Hl9MLgQG2xzYAV8KnFbaic4dv3Xqu5Bdm1rZExERb3hzY3poIC3DQDX5Yhj/fijyzXDmCD67NvWevJvULI7vrUytZQfdJgxYaSc6l5DwJl8K6LJr9b/Y9demVo5q35ObibnoTmkgbcMAnf1iaOnu1b4Ym2FvMTGnwF8QJwZ8aefDPe/d12r/uArcD3tlL+P4/xXoSunTx0UZf6v6b+7AwuI4X4rLLbs2VRunG5R2Ptzzht81fE+KiYd8Ty6Tpl8M7e/HYnXQPjS9salErDgw4EufPi4a8l1kaHojyeFYALrs2tTKnje8mVwcb3i99Onjotwcrv/1uDGswt9xtKvVF+NMigwN3xQ5Onb40R0HBvyz46LcHB7Krk1VcYgNIkOv3fXK3ofV70J2bWVPxoP8q++yCiwkk8kW51C9wzfqT4aGb1rUJNjBeV+MBtnMnjT8U9CRnDfJrnR8JFJcmZLFZHJBRDt3MnXMlKrLbmh6Izm8NjU3lRAR8YY3k86fIoNee3ZcFLl7+tWj45IE+LZAl11b2RNv+L7TQ8WBI3gREW94U8/zoem3ON0K7cyaLGozLzfvPp7j0A4A87SLLPQT8k7m0IBvPHLCaTSUdj7ck/HF2j/7hqbfDXuZPYXTbgx7m7zq+COx6Bn9JL0Sh4SdF/Atzpk1nFfDpfPsuNjk3Krj58igD4rHz+pPSsdHA2sIbKa0E9XSXYGxu8Z5AS+B4Okj8tVpdwNrEQbvxrC38Q+3iDAyw2lDr931Nvy779lxkdmYkNqF8OOLbU3BcwoHBrwE7oe9+nTp2oWtjp8Nga5oMzFWaitTlHaiK3wrcNbQ9FvjxcR7hqst+JpApLTzXqJonNulBufNohd9vvTUlPZUpSMq6FhgIbkoUytT1RUs+FagucDCZjg6V7/agq8JtPURRBLVr0WN478erkqlMug2AACAHnPiIXoAAHABAh4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ+gV/IbYy6XK7I76HYAEAIeAAAlEfAAACiIgAcAQEEEPHDpaKfKjefKdyMul8s1tpHfjWj/Ne7e+Fp+NzI25tKNRTZ2G/Y21mPc9bwdAfQBAQ9cOr7oB+tBka3lamjvRkJbIsH1D6K+0VeCIpnPDw177360JRK8/4ZPRHYjrtHQViZT35rZmg+NNp1Wtxtp2LX1jgD6goAHLiFfdGlWJDP/YCMv+Y3lWryL+N64HxTZ+sgwuK/ne23P1GGl6jA1K9K4f2O52dq+2p7NdgTQHwQ8cClNxlNaxI89mM+IzC5FfSJSS/jPDmpH043jd1/0SaVSeRKd9NXexTf55mzz988ffCYi9aG+bzJeqVQq8ck+fR4ApxHwwCVVjfhMRmQ2VQ9e3xv3g5J5+HH16L0h33X5fH53d3djIxIZGwttNX937R8KshUa1U/AcwYesBQBD1xateH37JvGcbUx4U/ne343MuZyjY6OhkKh+fmthrPxp/iiTw7XZ4Pak8zWfGh01DUW2SDlAasQ8MBlpc2tE5GtUMPkNy3hPz88k+/5jQehrYxIcHZ2dj2VSqUODyupFofoRUR80fiTSuUwtb4+GwyKiGS25h8Q8YBFCHjgctJmzM2mDhsm1IuIiO/WqyJbH+2ezvePH2ZEZlOVJ/F4PDo5OTnp82mn2s/lm4xG40+eVCqp2TMz9AH0DwEPXEb5jQfzGQmuvzNpnFBfM/nmrMhny8tNzr/L1ke7hiviR+dbHKSvXVmvX/ue3z34LCMSfGW0Dx8HQBMVAJfN4XpQRGQ2VX2uHWfXn+qviATXD88Ua6K6m7aD9j7NdzbWAaCvGMEDl87u+/ONU+cn31kPNp6Kr86/axy/N8ybEwkGZ9dT2vXtTQ68+6JPDlPVc+/a7rPrqUMukwMs46pUKoNuAwAA6DFG8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIgAcAQEH/H1EfzZCueI1MAAAAAElFTkSuQmCC\" /><!-- --></p>\r\n<p><code>geom_tile(colour=&quot;black&quot;)</code>改为<code>geom_point(colour=&quot;black&quot;,shape=21,size=4)</code>会得到以下图片</p>\r\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb3-1\" title=\"1\"><span class=\"kw\">ggplot</span>(dat, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>xvals,<span class=\"dt\">y=</span>yvals,<span class=\"dt\">fill=</span>yvals))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">shape=</span><span class=\"dv\">21</span>,<span class=\"dt\">size=</span><span class=\"dv\">4</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">scale_fill_gradientn</span>(<span class=\"dt\">colours=</span>colormap)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">ylim</span> (<span class=\"dv\">0</span>, <span class=\"kw\">max</span>(yvals)<span class=\"op\">*</span><span class=\"fl\">1.3</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb3-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb3-6\" title=\"6\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb3-7\" title=\"7\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">hjust=</span>.<span class=\"dv\">5</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb3-8\" title=\"8\">    <span class=\"dt\">legend.background =</span> <span class=\"kw\">element_blank</span>(),</a>\r\n<a class=\"sourceLine\" id=\"cb3-9\" title=\"9\">    <span class=\"dt\">legend.position=</span><span class=\"kw\">c</span>(<span class=\"fl\">0.9</span>,<span class=\"fl\">0.75</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb3-10\" title=\"10\">  )</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"q-q图的绘制\" class=\"section level3\">\r\n<h3>3.3 Q-Q图的绘制</h3>\r\n<p>在R中可以使用<code>CircStats</code>包的<code>pp.plot()</code>函数绘制P-P图；<code>ggplot2</code> 包的<code>geom_qq()</code>函数和<code>geom_qq_line()</code>函数结合可以绘制Q-Q 图；另外，<code>ggplot2</code>包结合<code>ggpubr</code>包也可以绘制，<a href=\"https://rpkgs.datanovia.com/ggpubr/index.html\" title=\"ggpubr简介\">当然改包还有其他好用的功能</a>。</p>\r\n<p>下面对第三种方式进行实现： <code>ggpubr</code>包中的<code>ggqqplot</code>相应参数如下，包括了非常多的参数，前两个参数分别表示：数据，要绘制的变量。当然其他数据包括设置主题（<code>ggtheme</code>）;添加qqline（<code>add = c(&quot;qqline&quot;)）</code>等。</p>\r\n<pre><code>ggqqplot(\r\n  data,  x,  combine = FALSE,  merge = FALSE,  color = &quot;black&quot;,  palette = NULL,  size = NULL,  shape = NULL,  add = c(&quot;qqline&quot;, &quot;none&quot;),  add.params = list(linetype = &quot;solid&quot;),  conf.int = TRUE,  conf.int.level = 0.95,\r\n  title = NULL,  xlab = NULL,  ylab = NULL,  facet.by = NULL,  panel.labs = NULL,  short.panel.labs = TRUE,  ggtheme = theme_pubr(),  ...\r\n)</code></pre>\r\n<p>为了更好解释这个函数，我们重新模拟一个数据集。</p>\r\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb5-1\" title=\"1\"><span class=\"kw\">library</span>(ggpubr)</a>\r\n<a class=\"sourceLine\" id=\"cb5-2\" title=\"2\"><span class=\"co\"># 创建一个数据集</span></a>\r\n<a class=\"sourceLine\" id=\"cb5-3\" title=\"3\"><span class=\"kw\">set.seed</span>(<span class=\"dv\">1234</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb5-4\" title=\"4\">wdata =<span class=\"st\"> </span><span class=\"kw\">data.frame</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb5-5\" title=\"5\">   <span class=\"dt\">sex =</span> <span class=\"kw\">factor</span>(<span class=\"kw\">rep</span>(<span class=\"kw\">c</span>(<span class=\"st\">&quot;F&quot;</span>, <span class=\"st\">&quot;M&quot;</span>), <span class=\"dt\">each=</span><span class=\"dv\">200</span>)),</a>\r\n<a class=\"sourceLine\" id=\"cb5-6\" title=\"6\">   <span class=\"dt\">weight =</span> <span class=\"kw\">c</span>(<span class=\"kw\">rnorm</span>(<span class=\"dv\">200</span>, <span class=\"dv\">55</span>), <span class=\"kw\">rnorm</span>(<span class=\"dv\">200</span>, <span class=\"dv\">58</span>)))</a>\r\n<a class=\"sourceLine\" id=\"cb5-7\" title=\"7\"><span class=\"kw\">head</span>(wdata, <span class=\"dv\">4</span>)</a></code></pre></div>\r\n<pre><code>##   sex weight\r\n## 1   F  53.79\r\n## 2   F  55.28\r\n## 3   F  56.08\r\n## 4   F  52.65</code></pre>\r\n<pre><code># 基本的Q-Q图\r\nggqqplot(wdata, x = &quot;weight&quot;)</code></pre>\r\n<pre><code># 按性别改变颜色和形状\r\nggqqplot(wdata, x = &quot;weight&quot;,\r\n   color = &quot;sex&quot;,\r\n  ggtheme = ggplot2::theme_grey())#更改主题（灰色）当然可以用其他主题</code></pre>\r\n</div>\r\n<div id=\"带透明度设置的散点图\" class=\"section level3\">\r\n<h3>3.4 带透明度设置的散点图</h3>\r\n<ul>\r\n<li>数据设定</li>\r\n</ul>\r\n<p>这个数据是张杰老师书中的数据，是经过一定处理得到的，结果图可以看下面。</p>\r\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb9-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\r\n<a class=\"sourceLine\" id=\"cb9-2\" title=\"2\"><span class=\"kw\">library</span>(RColorBrewer)  </a>\r\n<a class=\"sourceLine\" id=\"cb9-3\" title=\"3\"></a>\r\n<a class=\"sourceLine\" id=\"cb9-4\" title=\"4\">mydata&lt;-<span class=\"kw\">read.csv</span>(<span class=\"st\">&quot;HighDensity_Scatter_Data.csv&quot;</span>,<span class=\"dt\">stringsAsFactors=</span><span class=\"ot\">FALSE</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb9-5\" title=\"5\"><span class=\"kw\">head</span>(mydata)</a></code></pre></div>\r\n<pre><code>##         x       y\r\n## 1 -0.6035 -0.4126\r\n## 2  0.1387  0.1736\r\n## 3  0.5422 -0.4600\r\n## 4 -1.1728 -0.1437\r\n## 5  0.2146 -0.2756\r\n## 6  0.2530  0.4243</code></pre>\r\n<p>我们利用<code>ggplot()</code>简单绘制二维数据的散点图，之后在对该数据进行聚类。</p>\r\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb11-1\" title=\"1\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb11-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span>( <span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">alpha=</span><span class=\"fl\">0.1</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb11-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">labs</span>(<span class=\"dt\">x =</span> <span class=\"st\">&quot;Axis X&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Axis Y&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb11-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb11-5\" title=\"5\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb11-6\" title=\"6\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">hjust=</span>.<span class=\"dv\">5</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb11-7\" title=\"7\">    <span class=\"dt\">legend.position=</span><span class=\"st\">&quot;none&quot;</span></a>\r\n<a class=\"sourceLine\" id=\"cb11-8\" title=\"8\">  )</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"kmeans聚类的散点图\" class=\"section level3\">\r\n<h3>3.5 kmeans聚类的散点图</h3>\r\n<p>对上面数据进行kmeans聚类，这里使用base中的 <code>kmeans()</code>进行分类（两类），然后将分类情况(<code>cluster</code>)进行存储，并转化成因子类型。绘图时，cluster来设置颜色，这样就可以很明显看出分类结果。</p>\r\n<div class=\"sourceCode\" id=\"cb12\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb12-1\" title=\"1\">kmeansResult&lt;-<span class=\"st\"> </span><span class=\"kw\">kmeans</span>(mydata, <span class=\"dv\">2</span>, <span class=\"dt\">nstart =</span> <span class=\"dv\">20</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb12-2\" title=\"2\">mydata<span class=\"op\">$</span>cluster &lt;-<span class=\"st\"> </span><span class=\"kw\">as.factor</span>(kmeansResult<span class=\"op\">$</span>cluster)</a>\r\n<a class=\"sourceLine\" id=\"cb12-3\" title=\"3\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y,<span class=\"dt\">color=</span>cluster)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb12-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span>( <span class=\"dt\">alpha=</span><span class=\"fl\">0.2</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb12-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">scale_color_manual</span>(<span class=\"dt\">values=</span><span class=\"kw\">c</span>(<span class=\"st\">&quot;#00AFBB&quot;</span>,  <span class=\"st\">&quot;#FC4E07&quot;</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb12-6\" title=\"6\"><span class=\"st\">  </span><span class=\"kw\">labs</span>(<span class=\"dt\">x =</span> <span class=\"st\">&quot;Axis X&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Axis Y&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb12-7\" title=\"7\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb12-8\" title=\"8\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb12-9\" title=\"9\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb12-10\" title=\"10\">    <span class=\"dt\">legend.background=</span><span class=\"kw\">element_blank</span>(),</a>\r\n<a class=\"sourceLine\" id=\"cb12-11\" title=\"11\">    <span class=\"dt\">legend.position=</span><span class=\"kw\">c</span>(<span class=\"fl\">0.85</span>,<span class=\"fl\">0.15</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb12-12\" title=\"12\">  )</a></code></pre></div>\r\n<p><img 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\" /><!-- --></p>\r\n</div>\r\n<div id=\"带椭圆标定的聚类散点图\" class=\"section level3\">\r\n<h3>3.6 带椭圆标定的聚类散点图</h3>\r\n<p>有时候我们想突出聚类结果会在结果上画上椭圆，那么可以使用<code>stat_ellipse()</code>。</p>\r\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb13-1\" title=\"1\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y,<span class=\"dt\">color=</span>cluster)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span> (<span class=\"dt\">alpha=</span><span class=\"fl\">0.2</span>)<span class=\"op\">+</span><span class=\"st\">  </span><span class=\"co\"># 绘制透明度为0.2 的散点图</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">stat_ellipse</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>x,<span class=\"dt\">y=</span>y,<span class=\"dt\">fill=</span> cluster), <span class=\"dt\">geom=</span><span class=\"st\">&quot;polygon&quot;</span>, <span class=\"dt\">level=</span><span class=\"fl\">0.95</span>, <span class=\"dt\">alpha=</span><span class=\"fl\">0.2</span>)<span class=\"op\">+</span><span class=\"co\">#绘制椭圆标定不同类别</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">scale_color_manual</span>(<span class=\"dt\">values=</span><span class=\"kw\">c</span>(<span class=\"st\">&quot;#00AFBB&quot;</span>,<span class=\"st\">&quot;#FC4E07&quot;</span>))<span class=\"op\">+</span><span class=\"co\">#使用不同颜色标定不同数据类别</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">scale_fill_manual</span>(<span class=\"dt\">values=</span><span class=\"kw\">c</span>(<span class=\"st\">&quot;#00AFBB&quot;</span>,<span class=\"st\">&quot;#FC4E07&quot;</span>))<span class=\"op\">+</span><span class=\"co\">#使用不同颜色标定不同椭类别</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-6\" title=\"6\"><span class=\"st\">  </span><span class=\"kw\">labs</span>(<span class=\"dt\">x =</span> <span class=\"st\">&quot;Axis X&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Axis Y&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb13-7\" title=\"7\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb13-8\" title=\"8\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb13-9\" title=\"9\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb13-10\" title=\"10\">    <span class=\"dt\">legend.background=</span><span class=\"kw\">element_blank</span>(),</a>\r\n<a class=\"sourceLine\" id=\"cb13-11\" title=\"11\">    <span class=\"dt\">legend.position=</span><span class=\"kw\">c</span>(<span class=\"fl\">0.85</span>,<span class=\"fl\">0.15</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb13-12\" title=\"12\">  )</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOy9a2xc55km+Na51Kkbq+giRdGSxbY0kqYdrR3POC2rle4eB60V2j3bbmPWnvEGRpDFGAskPzobzCab2QZm+8dmO5v+ETgLJFjACwSBt1c7CgZq93Z7oGGv3ZNEKyv2tFta2YlEUA4pyiTFKrOup8619sejevPpnKpikeKd7wNBKBbP+c53LjzP996eN9Fut0kgEAgEAsHugrbVExAIBAKBQLD+EIIXCAQCgWAXQgheIBAIBIJdCCF4gUAgEAh2IYTgBQKBQCDYhRCCFwgEAoFgF8LY6gkMhKWlpXUfc3R0dCOG3bmQCxKBXBAVcjUikAsSgVyQCDbzgoyOjnb9Xix4gUAgEAh2IYTgBQKBQCDYhRCCFwgEAoFgF0IIXiAQCASCXQgheIFAIBAIdiGE4AUCgUAg2IUQghcIBAKBYBdCCF4gEAgEgl0IIXiBQCAQCHYhhOAFAoFAINiFEIIXCAQCgWAXQgheIBAIBIJdCCF4gUAgEAh2IYTgBQKBQCDYhRCCFwgEAoFgF0IIXiAQCASCXQhjqycgEAg2FecXy/jw4lhxa2ciEAg2FGLBCwR7COcXy8czKfxjphcIBLsSQvACgUAgEOxCCMELBAKBQLALITF4gWAP4cWxosTgBYI9AiF4gWBvQXhdINgjEBe9QCAQCAS7EGLBCwQDQTzbAoFgZ2GzLPi5H33lue+8E/nyne88dw9f+dHcJk1EIFgDpLpMIBDsOGwOwb/znS/98Fbsu+e++dEXvv/GG2+88cYfP/rDLwnHCwQCgUCwbth4gn/nO8899823ot/O/ejP3zr8ha+/cJCIiJ7+6h9/7tYP/23UwhcIBAKBQLA2bDDBv/Od57751uEvfP+NP/7cfd/P/b8/vkWPHjrIXzxy6DC9dUkYXrA98eJY8UazhX8SgxcIBDsCG5xk9/RX33jjq0REXaj78KFHfvXDwUOPbuxMBIIHg/C6QCDYWdiiLPrbs7eIfjv67Uezc/R0x6r/zGc+w7949913N2IWo6OjGzHszoVckAjkgqiQqxGBXJAI5IJEsOUXZPuWyamkvrS0tO7jj46ObsSwOxdyQSKQC6JCrkYEckEikAsSwWZekF4riS0Sunnk0OEu36pBeYFAIBAIBGvHFirZ3Zq9/asf5mY/2rKJCAQCgUCw67BFBH/wN3/7MH00+6vK99uzt+hzp5/emtkIBAKBQLDbsFUW/MEXPv+5Wz/89j1xm3e+8823Dn/hnwu/CwQCgUCwPti6JLunv/r9L3zlS1967odERIe/8P1XX5AAvEAgEAgE64TNIvinv/rGG9HvDr7w6hsvbNLxBQKBQCDYU5B2sQKBQCAQ7EIIwQsEAoFAsAuxfYVuBALBgJBe9QKBIA6x4AWCnQ3pVS8QCLpCCF4gEAgEgl0IIXiBQCAQCHYhJAYvEOxsvDhWlBi8QCCIQwheINjxEF4XCARxiIteIBAIBIJdCCF4gUAgEAh2IYTgBQKBQCDYhRCCFwgEAoFgF0IIXiAQCASCXQgheIFAIBAIdiGE4AUCgUAg2IWQOniBQCC4D6sVDhKhIcH2hBC8QLC3IGzUH2jew59XvEqr3V4g2DSIi14g2EOQ1nMCwd6BWPACgaAfsA4Ysr3fS5tbPRdxPwgEq4AQvEAg6An2Pw8PZc/fmd9aWt0cZ/hqm/dIsx/BtoUQvECwh7AX2OjBT3C1O+7WKynY6RCCFwi2GJvMuNuQjdbxCkjKm0DAEIIXCLYS25yQ2OIf0hu95vaA9Dz4FdgL7geBYB0hBC8QCPoBVDo6Orq0tBT/7SYvUITXBYLBIQQvEAgGwo6wnjfayufBvzQ6uu6DCwTrCyF4gWArsVPczq/PfrxBlvq6X4GNu4yqu+L12Y+3Q92gQNAHQvACwRZjO/P6ilgXet7RV0AQx45Ys+4FCMELBIIHwh58iU+Wq8u6XjP1PXjuK2Kb543uKYhUrUAgWBkvH3r4RrOFf3v2lf3iWPFGs/X6fImIXvm1gyL3K9jmEAteINgN2ASn6BpG3nJX7bpPAOOwhSoQbGcIwQsEOx7b0ym65bPa8gnsTeyUvNG9ACF4gUCw5/AgDAQCG9IbtT0cregPuSzbBELwAoFg+yLOxA9uHaqW/demZk/ms6sd7cWx4ujo6NKSlMmtBWLfbxqE4AWCHY/t6RR98FnFfeyr8rqvOIHJcnU8aWJA8eFvDiRuspkQghcIdgO254tyy2e15RMQCLYQUiYnEAj2FlDtdqPZmne9M8X8Vk9HINgoiAUvEAi2KeI+9vUKRqzvaILBIdd8MyEELxDsKuyyt2f8LNb3vHbHVdpZkGu+aRCCF/TDLmOLXY/tk8G07k+OPIoCwWohBC/oie3DFluC12c/rtVqtNsZZSOIs38d2hqOuMcfRYFgbZAkO4GgC84vlh8byh7PpHa33jiIc+NOk+vQePyNPqJAIGCIBS/YE9gjDl7JYBIIBAwheEFP7Bq22FMO3u1wdvzkzLvey+Mj6zggDXCCu+OhFQgeHELwgn7Y5q/IjXuVvzhW/Pe1Rq3Zig++m/hj49ZwverQ1nzEATfeU4s5gaA/hOAFOxUb/Sp/+dDDcbHxHc0fXZl1o09ho+vc9gJ205pSsJkQghfsfuyaWMODYEcvTTYf2+eBkRsnWDOE4AV7AvJa3Hx8bWoWH/7s6KFNOygWc1eqjWnbOZK2aE23XjhVsDsgBC/YqdgSu1ycAQOCy9/xeXCOX5fLO540cfSZlrujGRqLlZmWK5r5gjVACF6wBVgvjtySF/fg2V6r2n6jsSOWJl37w+LHbTvnjQNrBkyWq6/Pl07ms1t+Efby7diJEIIXbDb2gv9ze57jNpnG4Di/WJ5pufx5z/ppzhTzN5qtLZ/J9nyqBX0gBC8QrBHbigO6Ym2isKvdpesgJ/PZcwtlRMHXHIO/Um2wn/9KtdF/SurM4dkmojVbvdjr/GIZw27cLd7+T5Fg50IIXiBYNcAf40kTkdHtac2saG/FqWVdTDQe5N8cPrBau/PFsaKamgeS7j/nXjN/8Dsy4NWITGlVhN3/ENvNkSDYcRCCF2w2dvprCy/lmZY7kUpOlqtds5+2/zluT3fr+cUyK9/BDcAu+pP57Dacc2RKRLS+M9wO58jY/k+1IIKdQfCjo6M7aNidi027IF8aHX199mMi+ve29/KhhzfnoGtA1wsyZHvDQ9mM4w9l0hndHh4eHtIb8S2/1Pdi4vSJaONOH/O89zk2w66/7b/LgI9H/0FWte/Lhx5WL9Trsx/3GvlBDjrgZOJjjo6ORrYholVNYw3T3oQnpw/6P9XyRo1gyy9Iot1ub+0MBsHS0tK6jzk6OroRw+5cbOYFUe2e7ZA91BW9LghPfrJcnXe9QaK8cS/u5px+n1B0rzl0NdHOL5aHhoZqtdogfv5eXw444T5XZsXfru2g/efTa0w8HpEpkWLBD3hn1+zS325/OPJGjWAzL0ivlcTOsOAFew3b2RPIjsqJVPJfTYzHN+hD55vvWGaPd6Qve8Td2ueCY/7DQ9nlwIvMv9eprfkc+89kxd+u4Yj9n7QVx4xPabWP7jZ8wgW7BkLwgm2HbRhqjaDPlDZz8oNzCfdlV2c1eG7dZLmacfxmozmRSq7vxOIb999lbbJ0vfZdl5sVz4xbwyACwUZACF6wBdjj2Trrcvogp8lylWJScTx4n33jRz/z/g18+PKNmT778m/v/taTfSbGn1esbdvQ9dCmVdJvToBgj//hCFYLIXjB1mDvvJ66vpTX5fQny1U2qZm9vjY1O540qaOOgsDwvOv9YL70rZn5yAgql3/v+AQRgQ55WPw473qpdKplt7gykIj2/eT9PqMx1iU2v2bEK+k5LwHoqgK72nluxDJl3cMfgj0IIXjB1iPupH2QLC1SXuub/zbcODofBOcXyz+YL+FzhM6/oaQLqPwNepssV8HlZ4p5eAVUnMxnkWTH1jARTT55/N6HzvY8lDp+fB1ARF++MdPLAbChOL9YfrNUGTENtKLBSiiiArv9I0QCwYAQghdsMbq+Tx9EYmWyXEX99Jlifkte0Bt9RF5AzLseKUQOA/qL4yPjSfN6w674QcHQv3Jof2T3OH9Th5u7ApSf0e1mN2NXpXOoAqhLBPwY37IX8a/q0g2S3RappL9SbYyYxnjSLHn+tO3AIdEnHZ1tffGNC3YihOAFgu2CQSgEixiOl1PHNFet53ML5YKhE9GzI4W4FE+EgPl79cvIBmeK+eHh4eXl5QFPZJDWZ+wAUKGyfn8Tf0CFmQgxx6Xx+s1QyUz82tSsqsCzCZJzsqQQPDiE4AUCor4l4+o2+NB1gwd8HQ/iGWb+43h5nKHfLFUKhj5iGs+OFHodaxBDfA2n0B+8brjesE9k03yU+xYWCuv3IfvzAzdRjdwUGPTXGza+nEgl4+Y7M+u86zGpr4iNeCTWt2RAsAchBC/YYqyjpYKhJlJJxOAHVwJBrnX/DuL9CXhtgdtBTlzlucknj6tMrIK/n0gl3yxVOLOsD7ra8QOij7nfB9iYVx7YsdfCAmSPbdSL8L3jE8czqTdLFSJ6dXYhHoNgxG+K+oRgGmsrfx8Emx/Ll+wBQQRC8IKtxzq+iR4khL/JiDuZVTClRVzZgxDqiWz6vmy4cjW+ccRkX9XMN8jcZ8M6Hrb/3vEJttfV8AQRjZgGSgS7rpb41HjC4Pg+/naGuvT8s6OHxDgW7DgIwQv2KNb8voZC7Rp27A/QCVeafWNivBdxDkKo8YT2/ky8KkM8jgH3nSxXrzds6PtGfoX0N8y2/1T/YKRARBU/IKJFz//BfIkLB+7+1pPnlQavq+o22xXqLisW9K/jZgLBukAIXrAXEXdmrhiD523Gkybsvz7JXKuaDExSUPs3JsbXZhx3ZWiwKU6KVqLhNRviAy4jXp1dGDGNEdMgojdLlRPZtBqDL3l+n6QBFSey6QtLy2OmQUTH0taxtMVe+khmfiSFfg2nNiB6VYJssmix5OUJIhCCFwiIiF4coIM4NuB3dNffDgK8hbsa66v1lquIDHK9YaMkjIiuVBsqg4KGV8yl73OgrjH4PpgsV3kyWHNcb9jItluDX+FUPlvyfCKq+MHpQo6/V5dHqurO5JPHIfgDbBoRbr41v7b8UMFuhRC8QLBuGLDODdyjxpUHxCDsy/Y0eBT/lzz/RDYd2bLrqqK/LQ4fOxGBmCfLVXzTR6NeXW1g35u2M2YaIOZLlTrTfNezi8/hTDEPZwARPTtSULMNVGDZdC//QPGRICF/RZ7baEa8Um0MkgfQH/1l9vlXkny3ZyEEL9iLWLMN12fHwevcUOTWFX1i4WtLbePSgF4bqMO+War0p+p518NCYd71Xp1dgEtgIpV87c7SQcskokhOu7ramLYdVOczrjdsVPRNpJKrikd85dB+XKXICqlrBT/dfyVxC1ZVZD+Ia2eQx0nd7MFDBn0m2T9/U7B3IAQv2KNYsx2zth3BK6hz67/leiWln8xn3yxVWICWBnAAzDkeJyKsOD5vTERP5NLQiXt1dqGr1/1kPnu1bh9JW0QEpdjBTyQ+7T4nsuLVQ1XCIDS/Kgz4VKiCuOt16D7ghNBecSXBINi5AQ4heIGgH742NYsParu2VYGpHT8+YL76ilDHj7vl++BKtZHTNVBvyfP71Jdfb9g3bYeIEFO/VKkfSVtIPySiXub4QcvkdUPJ86837DnHw8qgz6zifot1KdLbIJpX0Z8VNiEPgC/UmWIeevsbd6xdjB0d4BCCFwh64mtTs0w/kZascXR9Ze/7yftxTda1cdLgK4NIjRwMayKKJNlFhr1at88q6feRMf96YYmISp5/tW7ndA1BdFB7PQhP5rPnFsolz6/4gUrY6pzZr06KJx9NbiLaduuI/ldMpXlSmD5SVbGG467ICg/O7n2WCPgVbHeceB99RsEuhhC8YJdgO7jR1ENHDHdak3Jc14jygHsh940Na7q/d6oKDBsndXXAE/tGaoEPmbxnRwogvyNp62Q+O540oStHRKcLOfWINEDGXETbLr4NBwLUYQdBZInTK2Of75GaIbEuSXBxcLfDdRm8f7yfxDO/5yEEL9h6PDg3bys3WpzaaU0pcmvzRat5bavqrXIim4bNRwP49hFxL3k+p7lhMTHveiXPH1zCfQ1YbYxjxdgBAxkSLEiw7h4FfkpnWu4GCf6rkLL4dcGOvoxC8IItxrbi5gj+7Oih1cbgu/rktwRIslO/uVq3QXW9hG4GIU7WwWXBH4AXE6sK/A8C9jpwLcCqqHHaduKxA6Dr+cKCB81/a2Z+bXdzm7DCOh56O5zOVmHnnrIQvEDQE+cXy6tKTdr3k/dXtPxg+NLAme0DomtyPpzSXDX+yoHR/pX3fX514eO7t6r1g5bZX5omMpkBVXVXtX1k4/4Z9Ug+IKKCoX/39mJG0yiWo0DdHCTfOz5xT27v/RvULfv9S6Oj/efZ55lB1hu0d7YDeaxI3tt5FS7oAyF4wW7ARhhMr89+PPhLLVLg3qsLO/ReEHLu30uNVsN8TOEQZ41YuieyaT5KvKHLigARLnvBsWJ+3vUijm6O93McHUF69aT61LmpJ9v16HHVvEiPnP5RDJz7d28vtsKQiJ4fHZ53vT4rsEhWIE+M7y8/Eq/Pfvx76ftyAlZ8AiPda1a177qg61GEvHcxhOAFW4z14uYtfDHBLT9ggXsfJZleu/SHqgJ7bqFcMPReRvbgDV0GRCTer/rA4bFHORwvPiJ1bmqaen/S7T8NPlbXXa5UG8fSFprTXG/YFT9QtxzwcHDUw5pnp/0a1OK6ptMjSwDH3TiKFSLfgxCCF2w9tsO7Zs2LjEjQncmmq8D7hqKXMBxbpV0burATu0/h+yC4WrchiHsim+a8NlTVIyofya7vitUGLK437EuVOgTybtpOxQ9e2l8kZbkAL33FD+pB+EQufbVuH7RMXKK4q2NFQOue+9WqTH+l2lA1hQYESHem5a5Wy2+9wDPvf+jBV+F7OVS/DSEELxB0N25ePvTw9z+49yrvozurKtioGun8vla9ynFSWRd+RU47jFSVwiPS8dcbdqS2G8Vj/FktT+dJYn1wIJv52WIJGXYRMrjH3Ll7AQI18n0kbQ0uWjdI1QBHOvDjsyOFcwvlOccjomNpi7qVAsJLf6XamLadsu+r0jqrJVQc/RsT40SUyWbYoMcVYNfIqpw0Wwg89sczqclyFUo4K9bdDTIgfxaO33IIwQu2I7aJHdDn6F2z5dVody9ELHs1XN11mxUB1hlPmhEDvat0PAj+zVKlz3oiTrRnivnh4eHTlhHZDEPhR7AmUy9+vFJtHElb3PDm5fGRyF5ExEI3/ZvWUCzef6XawPhwuYPmb3aU+CIOjPGkOZ40T3cLkUSWQX0mQMqtueT4SLlgmsf05l3vX02MY5sBH2PMcN71bjRba3jgBzxKHyv8TDG/tkMLtjmE4AXbBao69xbaAVzu1SdNmrPlqRsZd/XSq+jqsVfz1OJZbF0PxGB3dCQfLb7lir1nKNZFvtcpgCkhfQMlu0XPJ6IR06j4wbdn5h/PprG8YPqcvL/THcbnpLx4IL/rESOb8aGPpa05x8to2rTtvLS/GAlSqOOoX8aXQfjcPRKv7Pv7+0d/NH2vYv56w2aa5760g5izTLoTqSQvC1aFVRnNQuF7DULwgm0B9T31+nxpMxW48IaFi1KVGIunSQNg967O5K5e+j554P27t9FatW5UgFwrfhDp5IYpncimz3XWVa/9+qN0f8reIFFzrGZA5/UgzOna28u1Z4aHkA2AbfABGjh9zjfeHac/ePuCodeDsOIHOV07XcixXE9X9Bl52naOpK1eSYiRe/HC8DAvEZ4dKeAcIzV1g2Brq+TX11XGA2IFJl76LYcQvGC3YVXSNLywOJ5J3Wi2VhQeXzFhPu6l72OCs2gMKZlo1Nf674P4agD5ZUR0JG2NJ03VcMeW5xbKbGdzDB4LHeo41bseC1b+tO2grfuElSSig5aJw0W2hPudiK4pXoquYCd53BXBDvyT+exrd5bgloe+PUIP40nzUqW+uku2HlAdHkiGX7Ep8PbBuhMwOH4dVX63SbRuh0IIXrDtcDKfZT/nav+qV9UeZrXor1IX4SH+sr8Jrn7TNUfvQaCuNtTkuMlyFRS+6PnxHu1gbrR27QrmVCK6sLR8Kp/FOCey6Yvlqhu2kU7PljRXyk1YSajHgJ7xmZvQcMsZUiLlatd5bH+9Yb9yYJRngg9YlBQMHYPgZAeMbnAMvmDo6gpvkN27OjzUmjrQfFwnZ73o6sH74mxnSNbeA0IIXrAtsH2kPX8lVfapiaWlJf6Vyu5x8ojEpDmO21XRpRdzxK3/1aq8DQiwIBFlNA25aUQ053hgbhTTxyPZKnh9cCxtjSfN50eH4eo/aJlni/nrDRuHwL4XlpZxlCdyaa6M5wA8j6lK5XChPLrOY2NO+O8aqkcOP/84eHQDYRTekr+PZxqq6Xjqcbs6PJjm0aRuQ+vdN6IvzgOCUw63yXz2JoTgBdsFm/kiiEiUqBPoVREXsd37EMaKydirTZJXf+yvzBr5bZyTeAOY10/k0m8v13K6RkSvHBgFs8Kf3yeSzeuDY2nrpu3AOn9prNg1qU0FOwbGkyZIkYV34ukIoMNp3eGNr9btJ3JpWMzM8epZq6I6K+Y3dEUfrT2AVyF/euOjY4YGh03X1EWM8I2JcS6nVOvdOZdz4x77rVoxvzhW/NrULJwZL4+PbJ81xx6EELxgByP+ChukPcxq/X4P2D/mzVJlzvGg4s5fduXpFe31VTn8GcxJ7O5Wk9i7qt/E8dcLS81GE4dg93vFD8bM6DskcgrXG/bzo8Mcg39pf3HFHnfwscONfyRtXarUZxz3meGhJ3JprDyQr8dBB3aiqN5ydcCIQO+DO0WuVBv70qkJU484bNSLwE6IPxgp0P0ee5Tt4SHcIP7bQuc2/1U+uM9pmzj2di6E4AXbGn3+vHu9wgaMuw/oQhyQ3btyM97yqMJSVdzjPD1guBcB8jWEWkF47I1X89XVZQczKxTfmEEnO/3gqXPdeA7QsUHHNnwT0Yq/1rBx3IOWyUfnjju8tlDzDfkizLse6tfp/uw5VtTpf9bs/4+IDfRaJEWs9q7MjYs5bTtG0H7ioRz1ddioZQhfHB85mc+iSd0Xx0fW1lE38ufAP645Z2Ud8frsx7VaDXUo66jNJ7z+IBCCF6wdG7243jgrRKXYPiOvynaPUwX8t5F2pa/OLsw5XjMMT3UMaDUm3asCntcKqnd6QPxKOFZ31Hh2rwONmMZIzC6PnClr38IiLxh6xQ8ihXA4r6JhsLf/meEhsCa3mu0fcfj61O1p3UF5/aLnn8pnI7Z7BBwOR6kedeuZ2xWT5erFcjWna5xaeDKfhZxt5Cq9dmfpiVyaiJ4ZHb7ySWXAG8Erqm9MjJ8p5s+8f2MNBI8/B0wDyaT817HlMjXnF8snD4wvBx63un8Q6R4eEx+E49cMIXjBGrFTElzjrwnECPEZGl5dd0xcmFybZ16VZEHCGvjySrVxtW6fLebnHO9Y2oKVXPEDxLwjtD0Za5tGHQJj1ffIQfmM1O9V4diX9hffLFVAeCey6bhKLniOownXGza72Zf15Zb9qzJC9ShqZP1q3VaPPm07T+TS82UPV+OV/Ar9aiNnhKQ8Ilr0/FYYXq42jqWtPhp8zOXPDA+pvnr22GM01QsCv0jFD6BRj6VYvIKAbwfY/aX9xastp+tSg28ERxm6rKiePM4e+1V5odWn4kq10UcuYsvV4x9QHW+nvF62OYTgBTsVg7zCer0mVOvnwRGPOnOl2YzjPpMewueS58NNDV5vhiGqxmHi9zeaVYBjILsGtusfm0eAYNp2pm2nHoSvHBi9Um1cWFrOaNrZYp6Ivj51G6nvz44UcrpW8QPQPA/47EjhqheQonHLLorrDfvDZqsehLw9LyBOZNOXKvVp22E3dUQ5Z8WUAqjUzThuStOeGR7q0wUO9iKuYa/NcBewSGL3O/wiRDTneJFbwHEE6txTdQX22eLw9SDoI4Yz03JRAdhVukctpdsgJtsS9fgz26zV/UZgB7kWhOAF2xcrUvia/8BWHHnfT95/95mTy8vLKw4VN7VhCILRoQCDFmcQXceWR9LWoucvej5Y9nrDvlq3uYYqDtUrzqlkNLC83bTtcL37uYXy6UIuo2k5XcN8croGnrtSbcAf3gp/RdjAZ4vDtVoNXAU2vViueu22324fTt2zd1ETzynlZ4r56w37pu1kNK3s+ylNu1q3Xz460kcmKI4jaQsCef0348R+Irpat7lMH66OSDo9h/DVmRy0TJjvBUMfT5pvliojpsHjqKn7cLZndPvBA8xsyg/iK4p7nrZD6J0n8O9rjVqzRQMnwexQ7CzXghC8YI3YuARXddh1HDlSmMTzj/+Vrjlt/nrDnnO8nK6BJtG9VJVfZYc5gsTg3UuV+pG0FQ+K90ryiqBX8p3KXogCEFFE6w3pb80wxI/TtoPKt0YQvldrPjWU4S1/Wl5u2S1I7F0sV8u+77Xbh6zkrONi9xHTmHOic/jKof1fn7rdDMOiYZwt5rnnDX7bv5KNr1XZ93N6Etl8BUPvKiKLE8HlQnrgeNKEH+JiucrCOBGwCN2Iaby9XCsaxkHL5BCAOjdODJxpuZHgSB+wctHJfHZS6S7IJ0hKC9pBHrk/O3oID+2WB93jePnQw0tLKwgbDw7Jn18XJNrt9lbPYWWoeiPrhdHR0Y0Ydudim1wQdYH8gG8xVRZbFZnHsFyqqwYLscuXb8xMPnl8eHh4VRY8kqsRcee+473yvbEXXNbgZjVlPS4/p37JeV6wJrm1GqzV+PZvliqsRY+5YffLnRXP86PDmMNrd5aaYbjgekia2580EfN+dXbhYD6HGDxGW/T8qh8ktUQjCEdMY8w0sJRhL6I04PwAACAASURBVDe8EdDKnbKd8aSZ07X4NnxBuirUqqeAyavugciV5PA5H5qZ+GK5ejaWmsA7EtHV+q/U8di459tK9zfpwfdDQ0PX75a63ln+jKRI/sCpZ+ol4l2+NTNPRFDF2UysF49ukxfIRmPwF9RmXpDRHp2xxIIXbAg2Z/U9SBFdV5H584vleANv7HLm/RtfHB95dXbhNxw/0iC1K1TnOdjlesPO6dq1hv14Nt2/MatqFw7u74UzmYhUa5J13Lo6wNW+7N8++gi2OZa21MXBa3eWiGi25WZ0reIHaU1rheGIaUyWqyey6RPF4VqtxqPNOC6z+4LrZTQN/gZVHmeyXFUT1hY9/0q1ca1hv9S5Wdcbtprfp1YToNYAae0n81mOl8dPTbXC5xyv5PnxBjkHrZ6xDxYDiHzPtxWjsQ9mxfhCxA3DzX4wjhpK4BxG9X+o4jwIzavKtYP89Yl9vCrsLNeCELxg/bE5YapeR2HDfQ1pdGB3vIsPZ9KT95tofTLV8eFSpc5U8czwULzyTd2F+WNVFVNXqg3VkFV/pTacjXi/Tyqh8fj8YbsjFeBWy3HCMKfreUNvdVz3EWCoy9VGIwhbYfiHo8ORxq/q3LCw+LDZguH+zPAQQhKklOBHigCJSE1r59S8SDI8wBkJlyp1Nf2QFH2bAS8sPownTaw2IE/E84xctJ+Wl+cadvx7Uqi9V+IkBi/7fpdww5PHiWjfT95fG8efXyzPtFy+15G/vl5l9OvyF4o6+PUabTtjB52gELxgfZC8/O/wwT31zx5wqNfnSzSw/REBsz7X4+L7yLr7/GKZKXbe9dRW3L1y2gfRNmdDecZxj6QtrnzrpVvX9RTOKP3TqGPzqX4CVXguYlOy6XYim1brv7s2I1H35ZS6/Umz6gd2GNpumNY0jh1catrNlkudBm7Qn4840qEbHz8QEWV1bcw0uE49Aq47UI1vtK5phuGc440XTRSz4QpzU1csKTBmPQhJcfvDY49kiGNpK34X1NNn38O07Vyt2zldKxj6E7k0Qi2IX0QU6Q/mc7gRcbUc7krAwZRnRwosK3SpUq8HIfISkBIRFySYfPL4g5vyEfCfxmS5imS99e35hjr4dRlNsF4QghesA5KX/13w8FH+TEeeWds4eAfhNdSrPL3PvqQY7vFyHfWlA47Hi5jZ/cs3Zr44PsKu7DUAhvK5hXJK0xARh/dbVVIbxBXP0Wu0aJt3vT47dhVq5YOqhBqPF/CYbOyeymf/Ymn5cMqad72zxTzv8vv7RzkjQSUkzu+7Um2AzNQDgZWJaMJKwmqftu8J17DrnmeOfq/IFVj0/HOL5ZSmZTStGYach8g0TMoyopeb/UjaQjdbNbnvSrWB8PyZ+3vM4BROF3KYQ7xqbg04aJnxRdX1hr3o+WOmgToLdtRHVpAPaMr3wiDr1J2OHeRC32gIwQvWH5sTplKPQp1iYtVw7+8DiGfOI5+Z4+K3mmupgzq3UK4HYUbTiAhU0T/pOmJWRry7I6YR8fSq9j2aq7JNWfEDTjWPHLSrS18FMumI6Grd/q1CbsQ0XsmPEtGbpQoO98LwcGTCRDSRSiJPDd6CuH9eLVFjjZ3nR4cvlquXq42MpvGdwkKkYOiYybG0da1hV/2g1PYPWcl6EMK+53o/OPPhqEAKPUIMfL5xATs1C3LNQqocTTiQss7dWQRJ9xpH9aP0ScXocyz0qunD8fFWSauNwe8y7Kwyto2GELxgQ7A5f1fxNtsr6mwMsvKAX5ptVlWbjO5nVnzPG/DbHBJsXJwWB5N0V5n0S5U6yse7GpEsKHthaZmIzi2Wnxkeumk7RNQKw6Jh1IMQzgNSZObUQ3N/OZWZsCZQ09kQ74fR/Kc3PvrS2HAksx2fI31aI1NV9fK4gB5SPwVD51x3juKj5g1J7zgpLoKfd716EOJ0oFk7njRHTAMjQPOf5wYPP+UIg397Zv7xbBq7T9tO12gFfAAQ351zPPjniWg8aXICI+f0nZtbGE0kiOjZkYL6MESCJhHnDQrnbtpOPQgvVxun8tleTwiX2H3v+ERXjo/3nyUlFtM19H6j2cLkscs6KtKodfB7nFO3FYTgBesA99Q/W5cYfFfTf7XOgD6GS6/VPRe+x4PlkaI1ir2y1Q2uN2xOpIdrOqK4Th2SwCCXKvVzC2UkivMGkHaHlxhiMnEaQJYWQsuXq42rdRtiMm7YBh2y3Q8Tkwn41dmFm7ZzrMORanE214zBb0xEc46HBm5ElNL1/gnkvVwLkRXMyXz2crVR8YMPm61DVrLs+yB+nNETufREKgkFnhPZ9LWGTUQw2eccbzxpjhdNdYUBIR2cDs6XD3ql2sjp2tvLNdQyTFhJ5LVxlaB6R/CBhefGk+bLR0ci2/Dai4hOZNNZx5vqOAxwibo+PJFBEIyA2EBG01Rpo8jKQA24xO14PMmR/rPjSZNdNRCrx8ZqWRcq6W9sgCLN+tbBC9YFQvCC9cEaeL0rc0e4eUCH23oFBfr7bEF7/fu5wdsMsgHPkeJ+5y6r7JGmjrc5kl92YWn5WNpqhiGK1BFF7t9pvmgYzTBUK+LYY88LkUuV+phpIFsN0u48vV/1pOkY/RU/uFxtPD863PX04/3fujI6dczKknePyzOaNuO4p/LZOcdrBOErP//o8Wz62ZHCd28vXq3bl6uNP3pkDDV7RcNgMlZL/K837GsNe8JKIpjNyXeqJi48KKoswZHOeohXPOpii/rGpNlwx4EuVeolPziatrB+Yon7Fceh++MvkV/1WhlQJ+0O3WZxgn0CLlge4Q/n9flSpJxkd1vYO6uMbaMhBC/YGgweKuOXXR/VM7r/j3nwMOT5xfKXb8x8Q8mi7wWm7UhjGH71I3cd9h82i2QzcQ10yfNBosiphreZbX2wBax2FqtHshibayey6Zu2g98espLzrpc39JLnH7KS2P1awy4aBqz5m7YTJ4zpTgiAi8oQ759zvHnXm7Kdp4YyBUNf9HwkuKXS91rmIM/8rJKhxgBfchUcG6ARYd0zxfwrP//oWsN2wzZovuIH3729OGYaENg5t1Dm8bsWIIyYxuPZdMUPWmEIpXrqrGN63b4T2TQWKBlNw2dUG0Kgt8+KjfMbcO/47PZp+q1qHbn6pwu5XgXu6vzhdX+v1hxPms0w5NT6ruC8BDwbk+Xq945P8LOKp2um5c60XDRt6xMo2WsQXmcIwQvWE+tYLAeoL98r1caA/TN6lQJH8vJYzK5PyhWnsMGBHJGmYW8wxXLU+zi0ER7miC8pWWlqIRYpqebUMceR0oWpQjKWiI52NsOPT+TSR9LW28u1ZqiNmcaxtMX0cyRtXa3fW5EcTVtIOoOk7rdn5rEmyBt61Q9gFrM2+03HGyIqeT5r2tP97MseAiKCxQyTnWPwKgM9nk0T0U3bgds8pyerfjBmGjdtB8X3p2PXkD+DblEU54btvKFDR0+9hqhBoE79G0rdUDXXCkO0peGa+z7glDosxSCMX/L8OccbTqeQKKBq+8ShGuXPjhTOLZTxyD0/Ohxx20T2Qi0iEYHdsY4ExxPR5JPH4RA6mc9yGQjWtZEykJP57PaRrBdsMoTgBeuGSLHcunC8ygqraoJOHT/tvOvFQwAcwvzWzPwX++rMsMKa2m4kIjzXS3aGLXvO7ka1NxFB83yyR6sxpnnqsPjUfOmpoQzHepnjWaWViE4XcnAGYBUCtoYPmTqccbVuN8OQFwRqK1vYxAVDrwdhqe3POK7a5eXThdzknUV8rgchzije0DaOeK8XIvrKof2v/PwjIqr6wdG0NaVwEtrzzLve16duwz+PK4bcN+p4tmcct2jc6x3XCsPIHeG4PlY2L4+PYJ2BkETJ8xG5jzcL6HM6WGxhkUdEf1NvRXIVmen71E0gRQNum6t1e7xo9llZ8mRUFSA8rtCuj0il4jO7gl6fL8U9WKtyX+8gX/cOmupmQghesDUYPFTGb7pVVcazeTSeNNdcLaO+WxEI7yUryyzLjmW27F+7swSdc1Y+mbadEdNQm72Cfpgq2Oo9kU0jze1iuQpXNpuqakZ3HCfzWa44p06PFiLipDkQDB+FA8wlzy/7/ohpoNJv0fPBrDW/zmp0Y6Yx53i9aAyDwDT/+tRttHvhHvOclPDSWPHC0nIj8D9stvx2+/FsGvr2oPzxpDmtO7ikMNaRjUidSvoF957rQs05UHHm/iACzhH5j8hPRDAC2fXxm47PqBRgtw0pKZafLuROW8Pq4djv0ssup46MEj4jo5AGq0fnOeAyfmNi/Mz7N/qUz53p1o59wLgYJ+SzEg6n7G1P+pTSuF4QghdsGQYUyh6k6bu6AXaBaiwos9fKAOb74FLwXF3NKeKk9EGJ65rFBVhQdoWAOnXYjuu7kNauRnMvlquwYrEjp2VFVh4oJ2P6xwoAR1EF3tVGLwDYEQsXuNMrfgB5GdjuT+TS5xbKBUNvaG0zkVhwvbSmEVErDCPhdlwQVH+BpImMnK6h5m3e9SLpbNcbdkbTjuWzIFqIB5TaflbX2ENARFCkJyIe+WwxfyRtZTTtVsupG2E8K74XmFybYYgk9rPF/NvLtWsN+6btnFIavjE4kRDP0orat7i8kfIE/AqBA1biU9vbRNA17YCULrf8WzXtbr1YLa4FiWABvhT63FnYQoJ/5zvPffMt9YvP/fEbX316q2YjWBW6xtrXq1gughWz5OKLd/zfR4ue1w2DSMFDnK4ehOpLmZXRJlLJN0uV1YYP+oOr1Mq+j+RwxIBJ6XVGHavuat0+1Tn6IHkA98ZxXPSrhcGNveBO5x53RDRtOzCgb/uu124XDL1oGMjp45y7Ix0h2OsNG/6Jqh/MOC58/n1Oc9710Gr2aNq61rCNRMJvt1Oahha0p/JZMPpTQxlI2YyZBnUC/AirH7TMOcdTzWLqXQoBckUYfsJKFgx92nbcsI2Ut6t1+yp1kTZSSwNwr7H8euHI+PLyskrGkZWZmreB6wMvAi4XawNHEFeai2TqqRt/Y2IcnQ+p8/BLDrlAxdYR/NzsR3T4C99/9YWDWzYFwdrQJ9a+jry+Ivo3lVnxTffiWPHLN2b6G+7sd0Wp+rmFcrxsvc+O+Ix4MH/++tTtaf2eSxlO8ogBClaYtp2DlomyN7i4Txdy6mxZx4YUllXd5qqVjxx1kDp4bsJKQh1W1YcBVG8wu/FTurYUhmYikdO1pJYYMw0k6C16Pmb4yoFReALqQei12xQSKtl42GsdbYCX9hfhV3hqKFPxgxnHnXHcwynrVstBC7vHMqn3681p2xgzDVTB5XQNGQbU8djPOd7RtIWeNBU/QDlAn+4+XJyGssNp24ERnzd0ZCbyRetDqHS/OlCEjNVjNcOQKyY4UVRdAajiBCt6j/ps8L3jE5Fe8v15ffBGD6wZNe96q+qHtPmQZU0vbB3B3569RY9+XthdsCZ0dSSCANSceWyJP/61/eWruWxq2bq6TbwinO5/KauU8MqB0SvVBoRaz3aS4UuWTwp5XKrUFz2/HoSn8tmT+awqh8KYtp0p20lqCTRuh24MbFAWnEH8+Lu3F1thCF6EelqfuPXk/T3uwEkXy1XSEpam5Q39/XozaJMbtkHPY6ZR8vycruGazDkejO/3ak03bMOOhy370lgRngkOe4+YRsUPkCvXDMO0pqGF3YfN1gErCamfZhhmNO2gZXI4oBmGFT+Yd72jnTXNpUr9snJH1GQIvuxYW8D5P9LpXg/HQCMID1nJZhii3OBMtwrAQcCpDLgsfTagmIxgHJGsjj4Ax7OvvhcGbPSgkiWUcLY/fW7z6W0htozg52Y/os99XjzyggcE7IzX50vMSZHWsb2yb1i9bnCoZeuqXb5aPkAGHEeakVFPRK/OLkDC5ZnhIepkxZPSCYYPdL1hFwz9aNqqB2HFD0qen9W1ZhiOmQbHDtQK744lbZR9v+T579WaeUMHZXIJPt2vYsunDEf6kJlcTiRKnp/T9aNpa8Zxb9pO1Q/Gk+ai5yNGcDKfhfQeEZ0t5rl8n4igykdEZd+fsJKgWzVncN713l6uLbheK2yntATq9GCdYwSuGMSx0BWGiKAIhF/Bex+Xmr/esFGqTkQ3bWfENLAc+fbRRybL1XOLZSI6lrawlFG1iRgsQswifX+9sMSNdvjuTHY6/rHSUSSwwhv3Efflwg3cR1bz7RU+oI6vfr3oLT7OdiZOybDrg0S73d6SA7/znef+/KPDt27dwo9xZ/1nPvMZ/vzuu+9u6uQEK6H9t3+OD4l/8vktmcDrsx8/NnTvXflhrfHT8vKBlEVEv79/9MNa4+VDD3fdjL8nosSFyXefOTng4f56YenvK/V9lvnZ4jAR3Wrav7+/e4ZUfEd8+P39o+ogf3rzIyIatywiqvn+9z/9639646NPF3J/Ob80ZBi/qDf+YHz0veXaH4yP/n2l/vzD+zAIH/dPb3xERJ94fs33P6g1srqeM/RHM2kiesg0ePsLH9/9dCH3g5mPz44Vb9Tth0zjraVPhiGjljSHDKPm+xPpFBF9upAjojst50DK+vtK/RPPf8g0/vXxR3Es/EhEf1eppXSNiJYcz9QSpx4q7LPMu46H/3lKGPmp4aG7jofJfOODqQMpayKd+rtKbcFxf29sBBvzUb709z/nCfyy2fq1TKrkeo9m0jzOPsu8Ubd/UW8QUcE0HstlcSW/Oz07ZBiY3of1xrhl/dcTD/O1wvX/y/mlPxi/d79wNYjos8Vh3ubtpWUi+kW98Q9z2eO5NH6F7fn6/7S8fKNuH8+liYjP66fl5buO9+lCLvI84F7jwqq/Up+HPg+MelBMdZCn7jNvX2k/f0b95vXZj/Hh5UMP9/lbWC3UYdc8yHphHc9r92GrLPh3Lr1Ftw7/9vffeBWs/s53nvvKj+7jeJXUl5aW1n0Go6OjGzHszsXqLsiJs/c+9N1lI3LugN9Lm+fvzPOPw0EwGvhE9KPpmYlUkjWxa7UaelQTUa3Zimhlc//TrhgeHuYNTlvG6bHhyXL1+t0SEZ0p5vvvC6gC7//N7PwrB0Z/N5e6Um38249up8I2ES3bLSI6aJnLy8uu4/xs0UmHYTYM9ut6y2797lDmtGU0Da1Wq2HAZsvFcV3nXgh51nELmmYHwbBpTNfqzwwPlRznb+buXZljSfO0ZTRHChcWSlU/SGqJMUMfM3QiOmKZcGu7WoKIpj8JYHf+rFofMQ03DIbCxI+mZ84U88cMbSKXu+oFH5Qrnzju4ZSV07WUadxqOXcbzbsNOpK25qoOkswnTIN0c9p2giAcDoK5zmT+QdKcsVt37ZYbtvcZ+lAYlDx/wjTGDQ2pauNa4qbt7Nf1dBg+nUvftJ0kUTYMTg5l5u3WrbrtWqbvB/t1fd71mm3vVrU+lEv/zdx84Pl+GB5LJ4nobiIxoSdwuX5WqvxssYQKghOWOXlnET6A4USiZbfwbOB6NhvNCT1xudqoud615eqy3fqgXKkHIfLjhsLgb+bmT+azby+UMpo2FwYnsukr1To9vK9Wqz1h6jNB0Gw0fzQ9Q4qRjRtHRM1Gkx8V1UES6WOkdgQgIvWgRPRBufLS/iLf/Z7P25PHExcmuXZONW2//8GNF8eK/Cfz4lhxzW+/+LD4vC5v1DU42/v/jW8hNpNiRke7r/y2iuCf/uobb3xV/fn05775zX/7zguSRr97sBG6NypUPzz7Kudd718purOR8OEDxurWEJr9lcC77sBhztI9qrJpZC8kz0O0ThXMofuFd6ZtJ6trcL8ven7RMBC1BXPDf44B4daeth3I1yCtDxtwMrwqURfHZ4vDT5h6/c7SvOt57XZW1/YnTc43RL04l+2x4507plyuNjjuDhW/ih9Aq5Ul8NAv54ncUMnzT+WzOBckAYwXzUuVej0IodID9b23l2spTUO0m/MQiWim5eISIc8Alx2t55Dnj5G/e3vxmCIU2ArDA1YSF7/sE5IQOdniSrWB0kEI+qpXhsv/0GmeOrK4XBMIHzumpNZD0v1KQfx04aBX6zZ6CKFK8Lu3F7kpQB9Asr5Xffw2d1+vzdm+/VMEthDbpg7+kUOH6YeX3vnq08Lw2x4bZ5c/CPB+jGcP9YrHr8tB1xyGj+RbUSfBPqdr79WayF97dqSAgDpL4aKMjXkUyqkqy0IMdcw0Fj0f1WXIGADZoI6fmS+pJdRiLUzjat2+aTtjpqEy2UzLvVm/6zr3EvvBqQVDv1q31f4u1Oniqoac8T2o9FrDTnVSC0mJTF+pNhAjb4bhX5Uqh6wkKQsgbIN0AbXuzg3bE9a9maBDLv8KOjzYHiIBEb25OceDFD/Uh3K61gjCCSs547j3DqG02EUpQU7XCoaOMc8W87eadlNZqGElh7QDSOFiYYEWBrhrajucXviVfm3uXkpBpNRwxUeuP8fvSgiv98K2IXgiosOHHtnqKQhWQtwu33K+H3AJzw24+pMxv0BfGF7BYIqXLEc2OKN0Q39pf/G1O0uwHZGgh89qk7Szxfz1hv3UUIYTrLoCxj3EVnO6hh9PZNN9FFLZEsU2oJDTinSu2qmFK+CPpC2c4Gt3lk7ue6gVBkT0Xq0JufgJSp7tVGmzpY6iu7iOL5LUUHl/udpoheFLY8W4qkzFD1A4cKXa4Pw1WMCqqh0YfTxp4scPm63HMimOhpzMZ9FwFkn4C67H7hD1IvCVQVEiEV1r2GlNS2vaeNIsGPqFpeVWGD4zPITCh8id5QhOH5neVQHjXK3b3MMeCwu1H4H6yPVJvmOO3wjTVizmHYQtIvi5H33lSz98VBW2kaq5nYn+NfHrzv293iwrvmjOL5a5vdtkufqtmfmuKfTqC/SvF5ZOW0Yvg4mVYvvXxHOy/ZulSkQkBwdi3Vn8GJfNgZAcdbq9zbsejN2qH0Akjjrty16dXeBKcdAh5GapI+9KRMfSlpr/z2Xu/+bwAT5ipDn9tO00w/BAymoGwYWl5fGkCafxoqItA5EAHJo7qasFe/BDlDq1f+gRwBTFMjXx5jqXq40x04BNXPJ87jL36uwCitwi4PI/5CgQ0ePZNCT3sQjDkgst4FSh30NW8lbLscMwrWmQ3yGiCSs5bTsIrLx2Zwm6OmeL+clyldd/OE2cAs8fzpKZlqu6T67WbZQS8MII6x5cBDwAiJ7gFpwt5s8tltEr748eGVMT79kxQD0Wlxtqx8f/3M4vlodsr1arPQjly9Jh3aH/yZ/8yRYcNv+p4vz/+cPZI//Vb96z2d85/6e3f+e/f+VT3U2rZrO57lPIZDIbMezOxYAXRL/9YXvo3t+eVi8Tkfpj8Mhj6sbBI4/h37rMED52hJ9/vFzrY7DG8UHD/o189u9qzYofXKrUf+m4B5KmSieAGopuatovqvWJVLJg6L9ott76pAZ/OHX4+L1ac9bxflKpJ4hyuh4fDUDR+V3P/8QPDlpJIpqynWOZFBFdqTZ+2XJhhv6i2ZpzvDnHm7Kd/1Rr/qdac9Q0xpLmrOOmNA2t01OaZodhSJTRtQTRvqQxljSJ6K7n14Mw7NDbvOcdTVuNMHzI0MFUKU07nkk9YiXnXe/9evMz+WzB0N8sVfYljd8s5Fph+NrHSz9Zrn/QsO96PuYGhbjHsulqELTCtkM0oiV+3myZiURSSxBRIwx/PZPC2f2vtxfrYVgJgqfz2UXXP5K2JsvVadu53rDHkibGnHM8Owx/6bjzrn/H9eZd/3gm9alsumDolqbldP2XLXfKdvYnzYNW8sfL9YKh22FoJBL1IDQSiSnbaRPNOu779WY9CMeS5oLrue02EZlaAlWCF8vVehhW/XBf0rjjevtMo03069mUHYYZXcM9OlXIvfVJLaVp/89ybUjXccSQ6HDacsP2Q4bxDzOpEdOYc7xHU8mUpqU07RM/+PFyPaklnLD9zEND8673qWz6hx8vVRx32nZwzceSZk7Xc7oOWaSKH3x+fASH46fuX+wv/uOhzD8eylT8YNp2JlLJY5nUsUyq4gfXG/Z7teb/17AfMo2S50/ZzpTt3PX8Lz48miB6xEperdufHx/h5xOLGzxOyE5gsSPGgaT5h9em7roe97rdIOAP81Ahnw381f5hRgBH1IbOdtOwmRSTyWS6fr9lLvqnv/rGH3/nueeeu/fj5/74jVcl+r4TELHL+fO2hSp4B7PpdCH3Hyv1Pi1iI+CwqLoL694QUf/YKrqA4yWrdr+FaV7xA4SBWfSGiNguP5FNI/aM1DBYsTzC5WoDAVruR45pzCyWj6QtxOaRcYZGNTwlWNtTtjOeNOHHJiI+BIL940nzat2G5M7zo8NXbHc+TCDnCxI0Y6YBz8G5jtVVNIzL1cbzo8NcPoBUA7R4P5K2btpOIwix5azjwjiO9HG5Um28WarA5oZawEHLRPB+1nEfy6TeqzXRLhYl9USU07VFz1/0/HoQ6GHCDds4xEv7i+cWymigh1xCTlEkopfGivOu9+xIATF46sjHoqts1Q/QIOdSpQ49fFzki+VqMwwvVxsHspmJXI6DAtRJkogr2Jzp3E328+N82VeEBrhHOp3u5hwPzp4r1UY8NMCl9ny5upry0H36xsT4t2bm19Bv6QEtaTHEtwm2MgYfzaQX7BBsgv58f+x/9y9Cx0sWcr2OqObQIbHueCaF7pnQ3fzWzHzXHVWZ0heOjKP2iTqvYIo1CgP7jsRky9g1TUSgdjQJLRj6TMtVu6SfLuSuVBtQVici6KmpQ8H7DRpGGja3PznVLTpwvWGnNA0Ucm6hzFIzwKLnv19vHrCSY6ZhhyHUYZNaoqz423kcbj13qVLfl82MG9rLR0fQkAbkh6aubthOaokJKwlXRATIO4PeS0bTqomEG7bnXS+ra6TmlHXS3bnKgP3n79WaXrvthu1DVnLG+ZWbumgYHCZ4fnT43GLZ0rQR04Aob9Ew3ixVZhwXLm4E/p8ZHkK6wKVKnY970DIRQYAtfiRtnS7kLiwt48ShIMzbyQAAIABJREFUeYdbjKTFvKETUc3342rBvXzmnKaAR4Jb9PJajbv64tpGrh4PwtL0J7JprCeY6VUgftEMw+dHh8HxK+rcqXhA6Zhe2axC9puPbZVkJ9iR2DReR4ju6N/93x+OHTlTzAc9qu/U98vr8yX+zC/T/llREYlZ9FDnEqyLnX05o5vz4dVh2X69sLSc0bSL5Sp6oLHoLGucsUF5slPJRkqzGcTLYcWi1Opq3T6rNIMnoivVBraP6KRyl7aDlok8MtDh0XSKiKZsJ61p9eCeolxO16GeC1NyIpW8sLT8Xq1JRChL+9fHH0VOGVYn0H4nIkjTI//cDdteu61OhgHztOz7Xrt9qCNQz7kCnGyvAp1m6kGQ03WsBpph6IZt8Ous43JP21st52K52gjCrK5BlPeue08v7/FsWu1Hh7t2tW7jynC3+5mWO540MZm3l2sTVhIa+PUgnGm53IOnGYZeu40ihZLrXXZclBpiXzgkej1XOEc8M6Bk7kOPR4gr6Dgj4WQ++/ZyjSp1IlLbBHO6BuYWORBnNlT8AA/eF8dHvnzj3lJ1zRY536B431j8YQ7pjVqzpcbR6f6esyIzB2yme0MIXrA1WJvd/+JYMVnIBWuSCse75ngmBSbmzpvxNDp8k3H805bBtVVwVo913KHA9YbNHUTUJuLITkcd8xO5NHzdBUMfT5qwrsq+z23Ir9ZtaKYS0elCDk5gIjqVz+JlDflVjIAm4jxn9vPD0ER7mJyuXa42zESCiLgXC9q6c3c4wNK0o2nrw2Yrqd1rBeu120fSFnz440kTaWUIRrBOy5zjqU6Lx7Ppy9VGMwiTWoKL3XGgq2Szc2I8aSKkDbcBEX1hfAQDwpZlwjtTzH996ja6yWF6Z4fzF8vVx7PWe7VmyfPvOK6eSGQ0rRmGVT/Y38n7g6p82feRZl/2/UYz5PUEmspAxH48aZZ9vxlqR9IWyvGJCHY81gRcLo/7ixUA+tvi3H/ecjMJSmqJsu/j2aBO0/rxpBnpEotFxkv7u7/Q1V7yJ7Lpl8dH2AP0zPAQPA2qqyO+b+QBxjgXy9VZx4VCAHVcWREhZ3xYsWHjTMvlRRj+cCId4l8cK46Ojg4iMrPHvfebLKy7RUl2q4Qk2W0CNvOCIPe+PVRsDxXNn/90VVl4kSy/+L4fdGxiIoKzGv8OWkl8fyRt/XC+9CeHD1DHHioYesHQX58v1YPwL5eWkYO2P5d9p7SMJLJ6ED6WTQftNlKuDlrJih+cKeZPFXKnCjlswyH5v12ufzqXqQZBm8htt41Eok1UMHQYanYYHk5bVT+sBWGb6KCVRB4ZEZ0p5o+krf9Qrj6aSo6Yhh2GFT/47HDuvVrzEz8goqeGsvUg5KO/Pl9ywnZK054ayv6i2YKVnNW1/1ipa4lESPQPM6mbtlMw9FnHW3A9OwxxuIVOUl5W15DLVjSMhwzjdCHXCtsLrj/juJamee22kUgsB8HvDA995HqzjeaRtHXX82tBeKvlumF71vGQwpagRC0IDqcst91uE/2s1vwvRochhmOHIf6FRG7YzupaLQjzhu6EbSORwFLgEz+4XG38ZiFHHZb6D+Xq/qR52/GyumaH4ZCul3zfb5Pbbo8nzUYYhkQlz9+fNN12O6/r+5LGkK6nNe2gZfptqgeh227Dpp93vayuz3veM8NDC65fD8Kkpp0t5hNERiLxkGnMOd4vW+5Y0kRuwZG0hXq5m7bznxfzIdGnsmlc8ATRlO38wnY+dj1d1353eChsE1rqHUtbrbC9L2nMu141CPAIFQz9f/nl/G8Wcv8gbVWDAA9GTtcrfnB+8ZPDaasehJ/Kpv+u1sSzhMdg2nbwJOC3v2i24Hv4/PgI3Z8HimQ9fGZ2f6faGEua1xv24bRlJBJfeHhkynb+20P7/8X16S+Mj6Ci8vxi+QcfLwVt+o18Np6yeiKb/vFyDX81L44VP2jYFT/AxSGinK5P205O1yP78gtE3Z3/6Iio5PkfNOw158luMs4vlj9o2B8oDQBXi65vVPXthHvxQLNUjtX1e7HgBRuCjQvMRzL78DlylK49MfuL26iCJGpwlJRy9nhtVWQEfIDNOmIarBjDNqIK2OUYVnXyQy6NiKDUhjg6bMQLS8tq3B0rBtWwg7lZMPRGEDaDkKvGOeGOiK7W7THTOJXPMjdMWElQVGSGY6aBRcP1hp30w7u2M568l++W0TQ4G5DCdqzT5w0ua6/dPrdYRqt11H1drdsjpnEkbb29XMsb+phpHEtbaG3HXePQ/IY9zPB/LHp+StMqfjBhJY+kLYj0jZhGKwzLng9nAGfD4QMRlX0f3oIF9577oRGEf7G0vL9zrWAuL3o+R7jRfI8UYcGuHeGQvThtO0vtNi4g0gIQO8eJdM1FUGvWJ8tVzm+AaT5Zrl4sV3O6hgvOFyEiYMAll0wMcSUGxFCQVqn6DLjpHIzImZbbJ8+0v2UZ734U353/3Fh7KuK9387YNQ1stK2egGAXAgY6/q17mr06YPwo+Mt8eXwk3sH6xbHijWYL/wY50E/Ly9cbNl6pJ7Lpk/nsS/uLqlJbn32RLPZHj4zBLd91+5u28/Zy7e3lGrbhDCzkW6FC/VjaKhj6xXJ10fMznWSx1+4sqQead+/J1XG2dkrTHsukCoY+67hlz6/4gdrx7Gwxr773X9pffGl/EdH3t5drZd8v+/7ZYh6palfrdsHQnx0pfOL59SBEWTZy6RFKyOkaJnbQMkuenzf0ZicKbodh2ff/YmmZab7k+WXPb4UhSNFMJFBf0AxDiOdAR2/e9V45MAqve0bTnh8drgfh6UIOTvIpu9UKQzds64kEWupV/ODcYhnOdhCeG7YzmpbRNIQMnsilD1nJ3yrkiOho2kJi/5zjHUtbuHon81n44RGkP5XPIgAf1znAnX1pf7EVhPUgrAch2t0uej44fsw0mmEYTynoA8gbHLTMI2nrat1G2uZEKhnRxIW3aSKVRAi/zxP4lUP71buMU3uzVPmDkQIH4wfHi2NFFhGiTnYFLnWvdBZuTQuafHGsuHM5cn2hvoUkBi/YnRgw9z6+jaqrY/34HH/mjY9W6vTZF3oNyH9R/JqL1x0hGPxmqXIwn8M33G+elEbpcaidQNleV11wrIiCNC5UXl1YWoaCDW/J2Vg5PTliGpc7VWQtXaOOGBzbXpPlKgxBzAqGbEbTPmy2/HZ7f9I8lc9CU52l7InotTtLT+TSMBZhDn776CNfn7pdNAzUnk3bDrK6WEr2mdHhny2W5l3PDYNMUisY+pTtPDWUQaz3pu2cymc56y2lJVphm4jcsG0kEpxIeK1hF00Dae3v1ZpH0xakb1ph2AhCSM1nNA3ZaljooOqMqe6gZf6i2ULeft4wcbhFz28EoZrjdraYxwoJpYDTtoNcBKwt0EgekWlcbZTqoWkvlgjs8OilfviPCkNDYQCHOXIYcSzwNFaBRPTto49wxQTGV+8ydfy0qGWPqNJGHip84L36LzEjsfnJchUZBki4m3zyOL6cd71BmEZlaJA3Br9UqU+kin1sXByCOub7lWoDaQoR79qewmaeuBC8YGuwouu+j0aeee0tItIWpvkbY+pd57dfIqJlq/rYu3+x8Jk/HHwmeFGqL0Tkri93XqZgUN6gaxGUqmqnsjX787nGCeNMpJLnFsrgLViNnEcNdTPYzUR0udoYMY2qH3DxW3zyKj5strK6dshKgtK40JxbsND9cmnsXeDqLJjXIH7VRIOULKupJLUEJny2mL9Yro4nzWNpCysSInrI0MeT5qzj+u02svzGk6Ybtp8ayiByUfJ85P29V2vaYRjc37c6oj3ARQfQ2MdSoOT5LV0r+1Q0jGoisej53729iBDGeNK8SnbZ94+mLdDt5WrDb7cbYXvU0NFuBxp5/+OtO6isgzhPn8sbcY9/upBDA0PuC0BEcPgjHZL36loZr67MXp1dQBhl2nbmXS+nayDjOee+aNH1hn2pUkcFYDMM1XVn1ych8g1f0i+Oj8BXP5FKqp2ZVgWWlODcvS/FGppxwv/xTOprU7PjSVMtg9zO2DWaekLwOw9brv2+IvoY6IMb7sbUu6qBzjCvvRWOHiIin8j68Tn/6GeICP8T0Zli/t1Fb6rZothfZte/WPX9qL4Qrzfs3xgbaQYBdYRgeynPs+MUCi3UKYIiIpavIaW+jjpvami5EBEymJjPWMIW70FUb6OMuxmESJLvmlMNa+lUPnvTdsq+P+96Z+8/I1aMR7Sbu5ZhA8SPM5pWNO5Jw/JMrtbtmaDtuy4U9bEUgFAM4tAHLRMX8NXZhVYYFgzdDdvIWv/D0WHkW/FRiAgZZBj5qaHMtYbthu0pu3U0nUIFAZfP8fx5YQEiJCIyDSJacD0iSmqJ2ZZLnbyB6w0bAvKoXpuyHScMLU0r6Ak7DOddj+qU0bQjaWvGccdMA11k3l6uPZ5Nq+szPB7q3YeWMBEl/fBWtY5gP/TzS54PTSH0mUXNQiSmzo9NJOOdryScIqi8xynwkuLZkcL/9NHH40mzGYan8tmLnSUjrQlr5i0mP0hK9NkGq9s1z3BrsaN5nSEEv8Ow0T1Y1wtdJzbI5HkbbWnWvPaW9/jnIsNmXv8f8Nl7/HP6x1MYRA3MP13I/aNuWtnsV/wvr01R593dq1vMiWz6TstpuR7d72bvA5aNQ43ZkbRV9n2uK2uCV4iICD3fbtoObFkEmOMDgleKhgGX/qLnJ7UEqrx4zrwl74U+aURUNDS0HOXULVbcQ6w3p2tTtpPUEsjdK/t+IwiriUTe0FFrjhUAjj7vOKOJxGt3llhRHxFrjHwyn8Vknh0pQG4PPdwaQYgFxEHLnLadp4YyUK25WK5+YXwEvvc5x0tpWtHQZp02EY2ZBkr+4sFv6gRQOG3wSrXxF0vLSAu46/o+0Yzjcr4382tSS1BAqJfDAgIJcepFY/15TnJWnw3e8kq1gTY/M3aLiFBTd7naQK0jdRZ8V6oN6AogtQJ6hRR7zLgcbqSTz8hKCWqkgC31vKFH+ssN2M9Q7Xt0Mp8dT5oPolQ/CPlhG05VE2wJhOD3HLa/AwDwHv+c9eNz+sdTdP9U/aOfiVv2K/oMjDA3+cgTePMeSVuQqkUX814T+GxxuFarUSf8GenL3hUXy1UonY2ZxtW6jWAzV71DQI0t0WNpC9o1i56Pka/WbdV5Dqp45cDod28voswaaWIw+NSO48wT8SCuqkZ3JG3xgYioHoSY6qLnX63bbthGah60WqGhxsHv3xkZnqvWI/nhUIAhoivVxtW6jfg9lNjR3P1fPjwK9oIDww7DwymLKWqyXEVO/rG0Ned4frvdDMMncmloBPFlxzVkURoUHDL//VWpcsdxgzZ5RFktQUQVP5h1XI5VY6mR03Wk8qFWHgOWPB8MjbsGbRzwd69V3bTtoM9sI2hP2y1SJH7BoKxri7RBLkNXofbgoU5Pv169DCK4aTvwkRy0zBX7GargJkNYMZwp5lWOX4NHekU/trrBnx09xGo5ezkAv8kQgt9b2CkOAMA/+pn4DHtxOZvyauEcn2/YIP32B3R0UJPlTDF/qdPwW81RgnyN6vpWe6bldO1o2qoHIcTMsVnFD5qhFiEM+JkvLC2jjgt2XjMM4adFwhcbZ1waF4/BU4dcn8ilMUP0UwdFcVwANImZw30NN8O863ExWN7QuWINZW/INkeWeByQoIeD+u3lWiMIIa0D4T+cII57udqAS4B8mnVcEB7o7ZnhobeXa9cadtEw9ifNY2kLy5HrDRtXAPELIkLsmWsLmf7/6Ujh7eVaxQ+8sE1EjSBE91go9y16PtIM/7c7d+tOELTpqaHM6UJO1ZZRCRgKOUh7jCz+4JmHMmD8auDGnS7kIF94YWm5aBg5XasHITIQeUtmZbUgk9kd8aB4cB0mOMR/JqzknOOxONLgiOaOKF1l11YVNkh2nvp5RUWdAYfduBF2GYTgdxi2Q//1NWPA2PyKJ9jr+z7Ll3+epf9u2Qg67Tt/MF+iTvkQNog7TjPZjPoly8DldG3e9VSDiT8gEYwtVDbLxkwDv4JiCaiCiPC+hpYtEcEyi58X/NLU8R6jKI7rpMeT5rTucKo8a+NzRAB9zd2wDd8+doSX3mu34aXHJCt+kNO1g5YJcp1IJbnUe1+nXc1rd5ZmHdcJwwNWEsX0/9fiJ7+WSoLPPmy2RkwDGfLXGvbhlEVEVT+A7b4QemlNQ8dVnPK07TyeTb9Xa+Z0rez7rICLSV5v2PAoNMOwEYSPZ032XmAD/Ph4Nv23yzWdKGi3C4Z+1/VR1g/9gKt1e1p3fquQQ+wArdtwZU4oEXekB+L+wjGOaz7neFhVsBw9xHmMZJKIyr6Pan64PdheHzMN9n8ctEyUStL9uZwqumrOR7bByXIWYXyQteH8Yhl6iPFkwK4b48O6M+iDl57vmuL1dYQQ/M7DjuN1FWuOzUe27zNaL3xHW/o/Uv8ZV/T2qmXnwp6U7kw27glRQdMbtumc48Hgju+OhG3OVlOdrrBokX5FRK/dWZqyW8h4h5O86gdH0xZYBC70iIKpyg1QM32zVJlzPMrd10adqRHZXtO2Aw1a7laHDyfz2WnbweGqftAIfOq4Cq7WbXibIb0CBfgfl5bbYfgvHx69WK6eymf/drk2Zhrgtsu1ZtUPSm3/sUwqq2tVP8gb+pTtoM3uoVSy5Acjhl72fUjhIh3hoGVeqTZOF3LnFstJLTFlO0fTFsrzSKkKQ6S8FYZ+u32tYRMRDGIYwWgE16mqp5yuo4IO+fzMr9cadsrTQNXIbeTVD1gTPeVQ348edNylJqNpNzuKQCeyacpRyfNPF3I3/fDrE+NY9iFNIaNpWNsdS1vXGvYR10PRQa+SNrVnfD24p/PTJ8lczf7rZej3/zGOb0yMf/nGzBfHR1ALoPr84xAG3XEQgt9b2FoHwLocus9qwJh6V1uaJSJOzYuc74tERPTiWPHPjh7a95P341Y7Ka/RTMq6MDuPoHhO1y5V6s0whJgJ6JONeN4XTUdYT019V6qR+3ML5bLv64mEmUi4YRuF4EktUe+kpKlgczzeDBQqMdQpqmYFHg4oYDQjkUAtOPL+qFPrVQ/CMdMAs6JyjLXtqJN3BlN7LG3YmnbXbv3vHy9l9fvUsS5XG6kEJbVE3Qsu15qpBFmaVvWDpJZIaVpG1xZcL69raOQatNtP5jJoXg4DF0dMaVpKIwjLfHtmHq540DNy31Ka1kiEbtjmVjpEdKXaeCKXRoVexQ/2J82S5x+yks0wvGk73E7tUqUOuZs5x3uv1swbOpIc+ZbxULiM356ZfzybLgb3LnsmqZU8vxWG3DsAi62M41/5pAKFIqrUsYDDQRc9v2gYf1WqjJgGYgSc4BZZqMFTgpsFilWvbWR5x22N8E3XHgr8yOGII6bB+Rzx2k7qxG7Upr1rLpwTbEMIwe85bJUDYEPD/8nL/86YejccvickYv34XPPl/xmfBzmQ+ma8WK6ynVT2fRAAC5whaetY2lKbvvC+aAXLY8YPBD6bsh0iejybnrKdehDMOu1/OlKYth01zIw3sqplphqCvApBcRc89pGapWnbQWIaDFb0d5+wkpDjZR06Ippx3EYQoqs6EUFelwV0iWjR8zVDr/iBkUi4YTuna0XTwF71INyfNO66/r6kUfb8fzI89F6tiVYuROS3227YJo0sTWsGIY71eDbNfvJFz0ef2VnHXWi39yfvaddQx7ZGeJ6IMlYS5QDTtqNq2iDugNDGiGlwAsG1hl32/AtLy1gQYFaExrILZQ6FXFe0wYnoZD57tW6fyKbhObDDEMrBrTC8aTvgy5mW++rswnzYrjnu86PDvC/azPxwvoQGOWYigTyMt5drEADAGXXNhlOXaKT0rcddvlSp43JxWuWbpQp/5ho8BnSXiSiucYQnR83v+51C7ss3Zr53fALitfGHNgKMM+9662vBP3jp+a4pXl9HCMHvEuzcwDx18yus6nSwdNCWZsPRQ9rSLMrn1jwZ7n6W0h3OL0PCGgz0Y2kLyi29GnwB3EYMY6pFZYyjaQvuZRiC9XIVTd/ZTws3OzZ+e7nGiwYu8RoxjZSmwR0doQ1E2YnoWNp6r9YE4z6RGzq3UIZQTCsMweiNIBzp1JRDgQ7zzBv6vOtBUuZRK1kw9I9dP6MlEFxHtALDHkolS55vadqc4/FJlTzfSCRcaiOOrifI0rRnhodgKeI0b95eBLtTR1+27Pu47FwUgKj2nONBOh7FCCw5AM8ECBurgZu2k9G0CStJRKhCJKKklngilz7omdCM4xsBOldLEvgxuGk7RiIxZTteu20mEkVDQygdHPkb2cyF2XlUGHIrv4vlKsITRPSJH6CtX0rTiO4l7o1007dXEUmtR8QHdwQufU4+wPbcQ4GI4lUhfA2ReoLyBPh+QPxI2qBOQ7/+vPjiWBF6NUT08vjIunvpH3w04fUIhOB3A3ZWbjwQoXB1zv1PJ95sppckTvyIxtS71CM5H1DVQzMp62wxz1bs2U4fdw5j0/1h0QhenV1gfoLHmDr+9ryhz7bc9+tNS9NY3pWU7DwYc2eKeQi8E9Gc48H4xgxRmoVhOfNc9RnMtFz0qqn6QTMM84b+R4+Mfff2IorZHs+mUZ9GREfT1s9qzWbgLrjeH44Oo3nMxXL1aNo6lrawoPlly73TcpZc/3AqWfLuFb8RUTMMJ6zklO3cdd2kltifNJthiC9nHDeraylNGzHvrRs0IjORgC+E+fVUJxWg2smWbwQhkY+KO/SYR8oenA24HaqMLnreYLWBoDh060ZMAw4GlPtTp4gAKYrsAEDduVoKcdAyzy2Wi4aR0bQSkdduj5gG0iEjt5sl/FTPzYWl5aSWSFKCiJwwLHl+Vv//2Xv74LjKM1/wOe/56tOfUkuWZAsJW5EgtmMDgXiMyUycG0JiM7nDpGYZ11Q27J1MmZ2PGrhbG+pWbnYnW5WavZX8E3I3dxZXZmuTZe71ZGYppnZiB4cMZgDFIXYAC9sDamSQLFstq9v9eT7f857949f9ctz6QCbGGEZPUVTTOv2e06eb/r3P8/ye3491TCJ0yCvJfB0YLHPu+YBj5pCIULSQPLgl60OSNCrhfPExEvLlR7A1ZT001H/3y69/e3RoyW9yPHZkU2uj7R+gWAP4D3NcV2l9BzD/OjsSmeWj467US5DE0SeeYZWi9fd/KfpHtMJxVOmlLp7o6ofgHVuYMY49QTSC1fSJZ/YQHYmsk92b8CNLRNOuv7e/t1KpxH9JkR5tT1tynL2jpRrvhvboGlhdk7HG9tFKnYh2d2XcdmsZZX/gFl6CI/f05KByij9JUESRf8QycXBabQ3gYX4vTuLb15dH+7zgeF8e6DlYLGPcvBmK56uNnKa6QkCevVtTs5oK0TciSjLmspb2DrYUm5OJYhhmVXbe803GkASnVQbaP1J2LEVEvoj6dA1Y++NStRYKU6G0ytp4r9Hlyv+gmI1lWnV1XVGwaQAh3xchtWffj5RrHSIEcoSM2pi3I5v65psXdEVJMpY0jTLnadXABqjM+ZMLlSRj8QGz+CgEPrUBo8XVLwV8e9qCIhBE7pArx5s4kPCLX9LObOrHpWpKZTyK8rrWDMXurgw+oEYoOsgZccJ8R/KNZoGUuJcyAPLK41i++IbA9UB+n1HwkGMdW1PWwfky9o7YmsiROaxzdWvda5Xz9yvWAP5DG9dhWh/3jLmiFy65U9EKxwHYonfIeOFHoneIVYqsOCX6R/B88vGvSVUctjCjzpyGxm089Ilnwt4hIvoMEV08O9s3Ftefp8sxGw8kE00W0leQqSeiGmqkEcfENhGdbDjQV8Hgu9SpJaKS+fakO/JFlBNQbJfNYxCyMNQ+5wfHas0xy5wPeCngMPOAiB7gzRECcjqyUQ1o1xQFDvcJRkTkCXHGdu/tycXfI1jiRBSIKKWylMqAvphr392V+XGpqitKEEXbUi33GlwqMumd2RRkaA2mIIttsdKI4ImHcoKcbodOzo5sCjx2bDUgPtOja4OmDqBacqALAsBElNVUVwj0Dqg9uQ69vBoPd+ZTcRX3JVNhpLkYiMe1SYWfF2tNpPjrLDsMuFSvk9+WR2eKm5OJRihANZDDb1KiDuY98smOU0tqPRGh3gA2YpWHqM/LklJH7YEup8p3bBqQ3Oc0FRg/Xm3s7sosx79bgS3/Lvrca9z79zHWAP7DENeSG7+aQvevH+ZzB+UpVrNTEf0jROT95r7Eoe+Jrn6J/ZJXvzgufvLWdc+//Pz500qliIMnyNwS8wtf0pMOk10AEqiyd7Cf5M8l5tAmmg5qvEQ05bSa+tI4HMLpx2Kl1CoPkeQBpyFNf7hUhSw5lFv6dG3OD042nIG8jv4xeGSwfAUVv8pDVGLP2C5Gz7E4EsHzng/V9yRjp2x3vaER0QbTeMv1MZ4+apmS34dknftRQlVLAYdPHURjXrbdtEI9pvHnN/T9xdnzQMG4lioKBrqi5DUNBW3Zh8ZNQEu+R9cwgtgIhbRZu+i3+AGSECc54VJIQEImIFB24rHbAG+RiECaQwFD2vJKDT5qc9zQ1EgyJnkPOOmU45U5x5OYR9jXl6+o6nZdXSx4jE+kEQrUb2RVP06QlItL4mQHtV4eIMnwGOLvaA/FCxiLY0nsj9cJOozpOpL45eJ9ROi1SsCVxhrAf0ji2iToxrEn9JeeijI91C50v7vzrqx4A4t3IlqZK8dH75DgLc1m4sGKU/zmO5G1s4WZKNMT35qgSi8yPVF7Q7CNvNnYGBIt1StFVRM2aLI8Pl5txF2/KOa11SjXdndZUFXDQBrFKHI5TT3ZcDBpDe4edg/wYsGC0lUMIqlEhGNA4yeiU01npN2Gn2iDx6aEOdF0+g29HPDN6eR8wP/8hj6Ji6NWAr2DGc9H5bfoc0bE2qR0akui4nHB4cSYK8SQacz5QTUUCYWIyCL3p0EMAAAgAElEQVQKIkoydrBYBmyXOce+4WilDoReZ2gXfV7lYcFxv7ium9rysZJ5QETgplV5+EcbeqWVbcHxqjy0GPNFRCRkLVpq8EkBfMwFyA8OmjPzAU8yhvK4HHGktkKAlAnC7gp1FAzCnajbdk3szKb29OSQNA8Y+v8a0yRYQVMWFLm4ezpqLfJSqa2cE3/VCtPqEOWVdE5MH3TQPlZvKCchH5sqWT94R5nb6yHWKgHvIlYA+MOPPkj3PrbnnclLa3FdxnuR1muF41GmB9NoS1a8r+jyVn/Sjul2ucJi/Tt3758mH/+a4tmsOOXfdX+4frTlLVspysG5ePDRO8wXfqS4DVYvhTv/+x2xgbdDxYXleqWA2/lFwrGoS3ckbcMJo8NbDD+sGF2TKus9uia79UgZZbNgSvWk0jsUUZArSxTZkU1999x80Q9sEf1GJhm/JJMxIurTNRzcKg/Ml7FajYcJplz0ebemNsIwraq/rNsYDBuzzOerDUjRDZvGiaaT17Szrtdv6CUeuhEJIkakEs14Po4HE3A+4GmVEWm+iBwhij7HgknGAO07sym862nPn3I8TJShmHGwWB40dexsjpRrFmNQy0cijrcgmxQrTDGgpIEjZZt/ounkNBVv/OB8OcGYK0SCsT5dO2O7RJRS2b6+vCw/gHMOUIw7tSxJXot/ByjW7ZYh6fH4uKUe0cp68vFtJW4LLGXl8XLZF2tNzMu9o+sMXe49Q+2iwt35bFy8Fn+6Ksz21a+2lqNf3Vgpgz99YO/Dr+7/zte/+tAayn8w43rou/+aYRx7wvvNfQDp+HQ7okOLHu2D4JbPYitgPncwXD+Kx8sVA7TCcX7znajS75k9cXjwdjx/dz477rXGh+jyUuojhXMYGR+zTGA51Mpg8YnEKJ60ybwZGm3UFtF7ulw7Uq7NeD6826mtaIuV9+XyLSBPvz3whqwUi0jN9lkv+Nb0HBH1G3rRD6T0rC8iItqcTAAdRyxT8ge//9GNjxTOzQc8iKJNCfM128VMl8GUtMp0RQFaW4wBSxphqDKWVpmmKEU/iIh0IpcooVAYka4o59sT4fExsKymZkl9y/UbobBDf52hDZo6nOABcs1QFByPRxEEYeYDDmV+OcONs7/csDeYBpoO054/bBpobGOKjNpbKLTtkR8jiW+0x/+ICAV2LAgBQWSxNR6e93xVUSzGtqUsAD/F0vRHZ4ooZkCLF5/myYazKZueDkN8Kx6dKcrayb6+PL4wy/nHSN93WmRUKKv3y3XTsVlBl0FO08VZI9BmWKyBuPgyOmoDwwkjPk+/Gqvl1cdat/79CvUb3/jGMn9StAsn/ubJJ3/yN//5pxd+41NfGHsfb7Zt21d9zWQy+V4s+8GN1dwQxa6SCNWZ06x2Mcr0uHv/7KpfRnjDZv1fXmCNMmuU/Z1fVM+diTJ50b9J9G8iIxHesNk49oR67ox67kx4w+bFr1XsKv9IC6TJSGAdLLX44K+fH79Z3/KHtUKLXW/XNm786Ihl4nd5JhSvVusDhp5WVUE07/MRy3y6XEupbHPK+mgyUXC8UhC6QoxY5h8M9DRCsSVlzXpBWlUboRg0jfFq445saiyZGEsmCo431v7xAiV+yvE+3Z1JqUxTlCoPL/Ewp6kJxm5KJl6sN39Ra875QbemfaG367lKw48iR4gNhu5HkcEUP4qKfnCiYffo2ibLnPO5pihjllkNw4UgTKssr2m9uqYrylwQ+CJSFeX+9s8lzq4QdWlqj64VA17hISNKqiyMqBYKX0QNIdbpGicyGfNEZDAmFAWS8hHRR5OJCwEXRD5RTmWDpkEK1XjoiUgQeSKCP15SZec9PyDSiHKamlLVN12/IUSCsX39eVsITVF+u7erHIRBFNV4GBExUm5NJ082HdTnRy2zKURE1AjFOl2DfGxKZTpTsOP53XXdY8nE4VK14HieiG7LJPsNveB4u7szH00m3nC8M7YLkuOcH7zheoLIEUJTFEcIk7EKD4dMw48i/Hs+4L6Ihkxjg6nP+UG3rr3l+lUeNtt33o+ieih+vz//8Uzylp7ui7aDr0TB8W7LJDcmzBoXE02nS1OTKhNEcKbHqFufoaOiMGgaM54/YplpVSWiU03nEg8HTeO75+bLPCwH/K6u9Et1u0Pn+KW6XeXheLUBDJaryXbPW64P0YK0qmIDMWKZfYY+5wf46nZ8/6ccb7zaKDheSmUoSyRVtiVl3duT2/3Sa5sSxul2IQq4i83rc5V6x6bk7+bLp5vO6abziXU9i39A5F9X47l8OqYTIN0WZGxNWc9V6qWAlwL+gcD+awkxyWRyyedXyOBH9zz2QvTVww8+sPfAgb1jB3btP/SDtYL9v/JAlozK/HukGE+LqPLx+vyVjgasfIC/84v0/Mvx/j1KBTjdntnj/8eCH0V+8cbb9sweX/C5nk5SO8WntnLZKpuXyxnbyJKvrAb/cK6U1dS8pkGzHXLrSPEhUAN9+Hsu17mr8fBYrQk/N7wWiujbUhYS0O+em//zG/rk8ShFQO7m9/u6f1yqBlHkCXF3dwbHS8k5IppyfZ0pnogMptgiOmW7lkLIeokorbK0akgdfuTKScbm/CCMKKe2RuNmXH8oYeyMseSAOvfksz+cK+GqIPiPKkUz5KicExF47KAoIjUvc767KwPqA3htSOtPNZ2C41KZiAjiu2DJTXu+J4QXkdamDRpMcQQVHFdVlLTKBk2LiPB+MYJYCvhE04FgzpKB+49/Y9YA9X/U0qUrqzy+g0aH4jwEbeABiFt3sFiW3Pv45wVRW/yn1LQB6x51C3kH3hFNceo4V0AGnkEmHU+jOwTskNO/WGtK/cTHZy583rqsY3LVM/IPBK5fV7FCBk9ERPmxL3zlG1/6jQsn/ubJJ//mP//0QveGzTuvfS6/lsFfg1jlDVHPncGDxQl0PIDEUSYfZfL6v7yw8sFLvlw9d8Y8+v94v7lPnX+TiPTXf+Hu/TMk9K1jXj6i2NWOVD68YbP1j49q069q06/GCwzL5f2PDA987MyFP3TPMbtGRGHvUJTMRcmc+dx/07bsmo3MjbmuW175R2d4W8lIbehe9+bcW5vXD79mu/CF+4OYOizSrLSqxpO212x31guQ1t+dz8ryALWJXUQ0CK2YgP+i1uwz9DO2izTFjyJVUW5OJn53XfeJul0Jw1LAP5ayJh2PR9Gv6nZTiDIPv7iu+6zjFQPOo4iIDKZsSSVuSiaSKjvVdC/xcIOh35ZJnvOCcsBNxgA5aDRMuV4YtexbHBFlNPW8H2wwdFVRfiObcoT43XXdv6g1LabYIvIj8iPq1VQRRV5EvboWRuQIoSjKVzb0DpqGydhHLHNLyrKFuCWdbIZCYwpT6JO59Ommy6NIZ8rObBpCchcDnlTZ05fq5YA3hPhMdzajqiebzluu50dRRGQwxRFCEAVR5EeRzpQgiuphmFVVW4jNyYQjxIhlvma7aVW9GPA+QweX3o+idbo2FwQ1Lj6aShDRJR5Ww9ARwmQKbpEtIkNRPCFGrcSnujKXeFgKwnooPppKXOLheK0ZUsQjWqdrRBQRVcKwHobrdM2PIh5Fs15wgXNDiC0p6xIPf1m3N1lmgrEEY9OeP2QaVR4u6UXU8QU4XKoKogRjjojm/KDMw3Ne0BQijKK43F7HF+bFWtMRouB4JmNVHv7BQI9CJIgaoZCvaoQCFQI8WG6pQdM42XBMxt5y/d3dGSKa9YJb08n/qXDuywM9SKNPN52X6vZwwshpahjRrOefbjrI6ef9tzsRDaaO6pd5IS7OyFdO6D9wOfrKcZ1n8G9HLJd/eO+BH+3/ztfvu6njkLGxPaMfoOzeOPZElEkb9caHoEt9LeO9m62PT8ZDlCbY9mm2MAMFG1rURNcnnhFd/bgYeSVSkV6m+x2u8NQejqfLNHFHiAg6OXJ9Vp6lMz//vOv9ZPAOitSKH+zIponoHsU57PpSrZMul/im5WeWEItp+dSWQ8ea49UGYBjsthnPB4Uew9DPVxvQaEsyhhwd49E5TcXsdVpl0IUloq0pa6idfcq5dtkzllKstVAkmYLsH5UD+ZKcpn7//EIp4JdCkdc1NeJEdImHCaYkFUJPGmYqi98pnF535dJPLlT+dv4SrN62pawj5dr2dEsaFto7VR4+MjxwuFSVbWyDKeWAj1qJKg83JcwztmsSzbj+OkMjIhjLgqYHORpwFSfbKj0gwycCNucHaYdhsDCvaXlNK3OeYOys62dVltVUTO6VKxxTfEQ05wfzAe/RNdD0oFObUlkzFEOm0QjFH23oBY3/XN3+nwfXUVuRBmfv07V9sZR35S8DbhEg8J8u1W9OJoiozPlFn+/MpqBaLw/GUqBq4lWQYZCnkEMckuEhe/Dyi7dcMz7+bYz7KSOgUCsv4/V2WeVKYzUJ/YcA16+rWP2Y3Oiex16Y3PLg2MMHDjy890DnX/cfih77oAB86+e+qyusVK4TBZh/5SEBGBAruvpF71AcbuOBEXkikuQ7VinKcXko0sttQUd0bAuIKFw/WibKzww+O/GMOnNaqbSoxVEiTT3ro6bz+dnjTPH97Nsl046iK+qcUCDZkU09OlNc7AjyYq15qunEBc87PL9BucIv/q3p5BnbLTguhGwHTR2O5jlN3ZQwMT0/H3CI1UC0ldqcfKnQAtI4OO1Qet+ZTUkPGyKa9QJXCAiuhVFERHB6LYqgndtZsM6DYowrhBMRI4qIcppa9Dlc4T/VlTlaqaMjLlVfBgwdDqrj1YaEf1eI56sNPEbHAT0ODBkSkZxSa4bCjWjOD/oNnYggtDdkMskex/uyRStblZo/k45ni5Za3KTjwaAvyRia95j6O+v6GpEnRJ+uHa3UBwwd7n/YK+ADOlqpg9tPRLqiAN076ucvOr6cjRw0dfnJYsyPiCTLcjmy29aUBUGFWS/IaSrols1Q3JxMYLd3pFyD/gEt5ShDl5PyOobl5LdrSZb+kiPy8T/9h+GBu19+Xc7EL6dQe3c++/hcCZD/x1uGFxYW4n/t4M/Lx2txzWLVAF84/Oi3v/nwgXGiXUtn8Ff5wtbi+hKalYKvq1F9p3c7pKfOnGblWSIKb9yORNx87iBydwnhuADzuYPJx78WJdJExG++s0MaT505TW3gX/2pRaZHLU4RkeI23M/8oXFuQnE9Vi+5e/9Un3gGa4ZDW+KvgpDclOMtOa8l59dlS1W2PDus4ao8LMWG7mTmDRk1cMJzmgprc4DWWNs8nto/ytCoQZLap2tp1ehYTdLOKeap0xQREalR1M8YkRgwWi4yqNuXAp7XtKLvhu0fi4Doos9vTBilgN+aTk46Hob3Bk39kcI5gOWpprO7KyNrEuDG+yLSFMUTIq2yE3VbJtzUHsvGKCAR8SjSiS61FQCRT+c0FdaxaMBLgxxcKrzYccGYLCgFfHMbk2Y8sbsrA92hHk2FlS3I+XJDID+gY7WmlMEv8RDy+9AcjBe6b0pbs7UGdldwfcVHuT1tSQtavC/cEDjCxUVsiAje8/gs7u3JyU8HyF3mXGoX9ugaih/U9tl7sdaUjn8rVwsWC+et7BOPJ//T9Jx8ZvGom/zPlRXs1zLy9zdWA/BvYzutMe2uVVxXQrPyYsL1o+ZzB+Ml7hXiSq9Zn3hGsasiPxgObdFe+zkrToneoSWH14mIFaeiRDpK5ujyifxg26cTh76HXQK1C/Jyt8EqRe839y1erTzE8vTZZ9VZ3tVPRPorPyUi2nwnvXSU2nUF/7bPyetEeUBKlwNU4hNiwHWgwnzAx6sNaONIH/QqDx+dKcbtwDERLjFpzg/kz/ftmSTsUqo83N2Vgegb6NNQYkE9FtkeqGE5TYX3edEPUO0nIpR2kc8dnC8T0SUeqkScSFfIFaIZirzWKuqizo9K+CcyyV/UbUZkMCVBlNPUMcus8RAnymnqfMBxtYOmLoX6cfHTnv/JXPpE3TaYcskPP5FJnrHdIdM4Y7tplZ0NxT3dmVkvQLXgjO12a6rF2LaU+XLDviefPVZrukKgJuEKcdHnSZUREeAQN7nGQ5cJzOiPWebWlLWnJ/fdc/NSWU9XFMAzbiMKACZj2AQATTGDh9l9AHM58BnRec8PI+JRgKL9tOfD/f0zgz2nwrAj/ZXvOh74buDzwocuX/J0uYYP5esb18Plj9qD8kSUYEz21I/Vmn26BqHf8Wojp6mzXrBY1W5xxDl3OGzlyXsZHcJ2HVB9pciNDcHjcyXUPNYk7q9NvBPAFx598IEPG7a3fu4baXWtB3/l8V4o1OITYZVilMxFmR48CG773AonYtV5kesjonBoC3JrieKKXfU+84doxqszp8P1o3KHJGfl5RtJPv611oob75eLi/ygujBDpwtkZIDrxktPQbJeKxxn9RLOaFjrd/X0jfdtRvYJnvzWlAXeO4Tk4lx0NGvhdyJN33FG/OrJn/IkYwZT1hkamtCTjnd7Jgkp1gFD/9JojyRUv1hrojUg56ER0GSd8wNNUajtaNfXth451XRgBvMPCxUi0hSFR1HR592aisa2pJGXObcFK/pBSlM3GjoRnXU9X0STjod5M19EgK75gBtMQYPgWK0pK/N5TavyMIgiEqQrNB9wXVHOul6CKUTUo6kTTQe7FiLSlVaWKbXfsc7RSh2qdjcnE3N+EEQRrOHGq40aDwcMHXz7GdffmU0dKdfSaku+JqUyIspqKm6dwRRfRCiBzIRiKGHMB3yi6UBRh2I57pwfmIwF7ctwRXTGdkcsUxrWnbWXxlQ5FgFaGfB+yvHSKsObeqRw7p7Lq+j47ORQxiOFc/K9H6s1kdZDCAir5TT1oaH+uFJNR3QI7D+5UJHKP9gLrsb3fTURh9jHZy7U63VaCm5l9/2mZOJ1271a6L42NP+OsQLAFw4/+MDeFrZ/2NRu/J1fVHp7/cs7Rh+CuK6q+lcUuGB00PFgyWOAx6w4Fa4fA7Rrr/1cvhz/Bn6vEDKJTz7+Nfm4+dzB1Mb7/7n+KyIKh7YE2z5NUxa3evFXVp7VCsfV4hS/+U6lOIXKQdizQWnM76IzfPQO/DTDIhYIZIuoxsMgauGfnJFLMgZlt8WF/XgRGGX2Ce7M+YHBFEDF4qk8sOemYoZ1KIafdT0i6jf0+3q7xqsNALZU2kHsyKaO1Zo1HgL2NCXKaiqmA4gI9QBos38yl34j4DOON2QawMIaD7elLPS2X7NdlJENpiAJRg8eMrHb09axWnNzMgFdnWJ7z4FdBY8iR7RM9u7J5+cDDj/4MucbTEOyCohoU8KEDq4jxKaEiY0IIHPG8+FWV+XhyYaDVB7CurYQKGtj7HvYNF5u2LZQazy8OZlAzd9iDJ34Y7VmXmtthuAg0KMT7gZcdlb+XtHlwnNy7hEOvJC4xz4GRY4ls+enyzVZ5I8zGGa9QNLoDpeqcmiN2nRIudrT5Zp8HtMcO9um71UeYjCvQ1l5uVhZnT4OsV8tzPzZRz9SCQNag9vrKVYA+Mkn0XH/sCTuH6x4Fz3s966qf23MbBan1x0HQNWO2jVzNOwVz+7Q24FGvVIvsUUtc6zPFmZQY2fFKSwVbPs0H72DOEVd/RTTxMXFqMUpfuP21jOv/TzsH9HeOqk0yveWZ3+ybkuY7gvbvGU0yw2mLPhhQqEgipCtPrhhHQ6QP/GwdqUY3QmGpPBfR0G7EYp+QwelDhSwLw30yF4vyH0o1Up7+D5dQ7o/6XiAdiKCPxtsy+Oj80TUp2uozOuKoivKfb1dqAd8//wCur+QtJt0vK1d2V5FGbHMwUCHcyvAFT7u9+SzPy5VG0FIRHldA0jLs5QCnmQsydhYNoUpgLdcv1tjRKQritMWiTtSrvXpGtr5RLQ9bT25UIGabIIx6Q5nMWYLMWjqA4Y+kNefXKgU/cAXkR0KIio47gbTSDJW4+FrtptUGToaUq6fiBqhSBpszg9uzyRnvcBlosw5TjrnB9if4eY0QkG6RkRjbV92Kf22KWk9fbG0ZBK/mEqJnR9QFpMO2LtIXyIcL3eB+ODiNoMo21C74I8qCxbE5ytPJ2vyUk2vFPC4B3zcsO46kaBfK7a/R7FSiX7L/kOTa9j+/sV1lYX/Ohez+s2Bv/OLqz9ROLQluO1zbGFGFuElE5CI4IgjeofUC4X4sJyk2RNRlMyhYKBPPIMHS3LvEVIPRy1OkW9H6bzID37+4mm6eDpQHJolGrx9PuA3LbyRYMo/GusczQxC0W9oQaTEf0alQA3s4KSwKHzViKjKwwFDR+JLRC837FErUQo4GvYorsJiDgfbQsAGDVVcIBkwoBGKI+XaoKnL0ak4w39ryspp6m4rgxSwpWnjeEjrAS2ocicYuxTwM7YLUJc9XV9EwELAdo2HsGrNa1rBeXuYyg4FcuUqDyXJwBViQNPn/OB3eruO1ZpoPIOg8NBQ/yOFcycbTingYAgOmvqJuj1g6GXOs5o6ZpnoZD86U9yZTf3d/KWkyjwh8rpW9HmNh6WIXwqFTqQrCpAb9xYcCCS4dk3g7kGbtspD7DZcEekK5XVNiv4mGIP8nyTBxdNfWSrvINDFAxV12A1gN7bckYBnqUO8WLP2VNN521w44FCcXW41RNxkVj6zGmjHluLXz8hXlqNfodi+wqvehXHtv8JYAeD3PPTYtbuOtfiwxntRVwC7Xj6OnwjIrdRL4dAW0TsUd6CXubs+8QyrFIkI9HgiYpWi+9sPXSRa9/zLT9/69ohIh0kdH73DeOmpKJlTavOEWXnf5mM70Z6/9ehfn+zb5gpRyK5XBQkekKajiU5Ej84Upa+M1FSXFOiON4geLbLGMcvES9A9xUvi8m1HyjXk2XD7JqJSwMGwIyKo1yFdk0LrsvALeEDbeNYLwN4iIjSwdUUBc9AV4heXqkOGXnDcW9NJ9HQxtt5v6EnGypz7IgK6o0EwaiXSKis43iUe3pgwcFVFP/id3i4iwtuX73RnNoU69ni1sSuXfrpcw43SFQXFgK0pC5uAtGrIyUCUTIjo5mQCU4VEhNZ+IxQmkSDKairk/OS9Rc1/xDJdIaRdEEbpvjzQA3eARiig5D9sGnJUndo7GwmoR8q1g/NlOUMfJ9AtDmB8kjEQFOb8AOxIam+25JGSpbika5y0wlsuJJxLSf84z271iTt2k//lpuE/eX2a3glivz069JN6s267Sx4Zf/KKjGdW7rKv4fo7xppd7IcnrqUr/DWL+DuKv0H7S38Z3zrIUGdOh0NbGJHoHdIKx2XuLov24MBL2RypotNeeSRx6HuE2n77NxG4ri7MUHtPoNhVtOEVu2UkoxWOb1i3/gbhnbfydd+zjGSP16DkunLAKcarmvWCH5eq9/bkiAgZIXAav7/o1s/5QU5Tt6YszEkDw5CLx0ewBozW9HZaZdg6oNG+PW0dKddGLRMg+s03L4xaZiMUQHdsBdAgH04Y066PX/xSwDGDlwhahuiYRgN4N0NhMlbjoaooyOkbochpKprlRAR0RzeBSASRAseXAUN3hADtAJk98GbYNIBhuAMoM0iDNYlGaPYD+O+N1R6Ao9jfnGo6M54fRLRB1/p0bZ5xiM9zomybjj7rBRCoQVK+ry9/qumg5gEZnH39+e+emwe68yjSFcpqaingmE5crCFDRD+7OAOdnEFTj9u0LzliTkTI4AG6Umhd1mPwKaBBLjUVZMWeYuPs29NWfAew5AS8fAZaTO9dHT4OsV8aWr+wsLS5n4w1Ztw1jjWA/1DFhwbXEYuz/8X2cYsfExErTmlErDiF/4TdOyBfe+3nMrOPl/ThgdusHE9tvP9ZdVapFCm1UW1z/dy9f9pxbWxukojISMaz/C8p9afMnlqlUomCnY0Lx3vWJxhDMgp47tE1u22yHg9wpzH8dk8+O+V4KMLDWQ7HnLFd+eMu+/HAHoxO4bAd2RQ6r2ds94zt8iiCB50vom0pE9PnvogkaAEPpJQeVM0x+Z1gbNDUkYh7URS0mXHlgOc1TfbFMZF/tFLHEBr2E4BtOMqAQJ5gDFAN8Tsp2E5tkj+2MsBsLJXVVIMpScaAsnJMQOay+LeuKN2ailtERD26hp3BJR42Qj+tsm0pHQI1oCxIyz7srqo8fLHWxNYnpbKLPlcVqvEQJrYQM8CNjQvLTPIlPkdaZsQ8focB4XEYjgfeZingcZW6jgMg3hc/3WKT2SWv7fqM5YrtL9aa1xVR4IMYawC/Fr9urFw2eE/rCovxPsr06K/8NLxxOytORclc8vGvxSfp+egdWuxVeAkU8UTvkP7SU+jcxwMTcRTrBagzp/nNd8rH2ASwegkDdXtf+JGaGX5TdJ1eN5a4+CalcqOuVyCdkpetDAfYOT+YdLyxpUxFiWhrypr1gommUw54EBEjAl/sWK05Zplo/SKDlJS97WlL+pHzKPJFJIjio/B9+rL/ywO9bCGA8bB/PdKult9gmW81nKTKfBFBMhZ1fnAGSwFPMDZstmaxcpp6pFyD5A7480jfpWXOlOMB4KHuB7o4OtMH58vDpjFg6LYQpYBDTm7WC6TkC7YC+/rzKF2UOXeEsBjra+vLUltitt/QfBHFh8WprR205N2u8tAWzGAKToQxdEmGOFqpPzI8gIMlNrfEbjkHdSA+Yi61565IgmblkMcvN86+soLNFYXcgvyH4YE/eX16uWxbYvMf9/a+63Mt2ZiXlNLH50ory+msxXLxTmYz10esmc1cg3h3N2Q1pjLhDZvxz5UuHreWYY3ykivEXWTCGzYH2z+jTb+q1EtRfpBv/S0SISufF/kNbGFGsWuKXZNXghdqhePhjR9T59+Mkjl17g0yk/+xcmosd+e/8y/opRkeBKJ/U5Rbp1SKrHZR9G8iIm3yl1FuXeuqahf9T94v+jfxsR3am68wu6Y49aFbdy+kerkIjcrcSM/6HenEtoTx03pTMP0SD7+yofdkwyk4HubfsqoaEb3csDdZpiNEPRRnbBeqbWlVRYulCsUAACAASURBVJ/YUpmhKDyKLgb8DdcPo8gREVDqzlxaEGmKsrs7M2ga8A65PZM6cqkWRJHOlB5ds0PRFGJPT+5k0ylxrirKvT052KsDBqYc72TDGTSNpMosxuqhuDOXJqK781mF6K6u9G91ZV5sOFnGeBRtTJgRURBF5SDUFOV/2bRhZy79i1rTFVElDOF/06Nrv9+f1xXldNMt83DINB4cXPerup3V1FYbIghqPHy20njL82dcn4gE0Ym6HRFFRBtMvR6Kbk2NiGDkOmjqM15w3g/mA55VVT+KII9aCcOsqsLTthhwRwgvippCeCL6d+t7P5aymqHo1rUd2dSJuu2JCO+xFPBSEBYD/i+2qylKTlPhatOta3dkUr6IcpqKu+QKgXH/lMouBjzB2KBpEFGVhw+O3vh35+YuBjyrqX/5kRuwa4H366wXPFdpDJrGlpSV09SX6jYRNUJxsuHMeL4ggkfRlOOBtDjleLUw7DP08Wrj2UqjGYrfXdeNF8qxyRHLfHyuBJGf3d0ZrImNCLUtgB+dKcJyZkvK6jCcfXch3XF+OFeSBrJxw5i4k+x43e4wm1kcq7eTgVENzp5gbDVus9dbfGDMZtZiLd6XWC77j1fjFzP4MCYnte1oUQEfhLvWxN1LTyUOfS8c2sIWZhS72uqpb6Soq59SG2n2zcVXFQ5tkZL11J7ZC7Z9Os7Av9uiz8++qp0/eShpTPeNENEPlPnDuaFTTQfs9+1pCx4nmPia9QLo2U06HuhdiK0pC0X1ZshVRVEVshgzmOK28+zFl4fyL9LKZiiqPAwj4lFUCjiSclrkKIrjj7STv1HLRA9YMvyJaHM65XveRLMlogebmT09Ofx1oulgYp7aw9moM49aJhHlNPVgsVzmfMbzsS+BVE6SMYjKlQLuCUFEtlDlW4NaPqbmZr0AFP0+XUP1HpPrBccby5gTTcdgCgmitiDM0Ur9h3Ol0cst1Y9W6lhta8pCTwFX/slcGg0FlBwkLREjZxNNByN/BlOOxRLuQ8UFmaDDmo+I5gOOiwfhUdbbqU156wgcAGI/Jt9QyV9yTh2MyBdrTay5mIInuwBylu+qBD5i9M6/WpiR/rDvTlt+re9+LWMN4Nfiuo4lp+ElqJvPHZTMeak8H9enk3y6Dls5+NQRUXDb58yf/V9EpF6YbH7lO3hV9UIhx0d+Scv+frF6y2srrl8r/6ouzEDZnt+4/fPlSeXccSKKkrlbJ1++7cYtRHR48I5jtaZszyOpRYy1yVNzfiA7vn26lmSs4LhBRGEU5lnrf1u8EEX+OBP70ZliWmVlTrVQMKJuTb09k6T2CLUcFZMQ/uhMcc4Psu10sBEKYAxAAgcMZtOuCHd3ZSC0R0RJxqCuuj1tJRir8bBGYVZT5TqzXgDCwbTnExF626UYcR27BHS77YiSKnOFGDaNguOhuz9sGrtyadADMbF2xnaBlGXO5egXiv89unas1oRQz7aUdaJug+f/csMGRR8+b7ixk463LWXNekFe01DzHzR1LHKwWJaKQLNe0AwF0WVj/biTg6rq+gERgTyBG1sKuKu2djmjlhl3dl+sUoyZCDRckIIv932TE/AQO5LPx1l4WPmKcH319fz/ctPw3S+/Hh8wea9jbQruqgS7oqMLhwst8nHh0QfvUhRFUe6669HDhZVftRYf4vB3flG9UMA/7wvFD+1z0TskuvrjHHs8ueRVwTkGzXWtcDy8cXtw2+fC9WMSpKUWXtTVjyOjtgWtPvFM2Dvk3/Y5Ce0IdeZ02DsU9g4plaK0zFGLU6w8q84VFLfB5iYrZtqaesnN33BT4QVgNoTb0iq7J589XKpK19FTTedIuVblYY+u7cympj1/xvNVRenWVJio+iIqOK4txIm6DY5bj67hVXN+cKJuT3t+OeBJpqRVhsL4RNvSZtrzD86Xj9WaB4vlg/PlRwrnTtRtao+xAYBPNR2pjofUcEPCLAUcKSwqB6DOzfnB0Uo9yZgjRCMM3TaF8GilDpCu8bAc8ARjqHsTUUp9+2cHjHciMpgCKToQ5R4ZHnhkeAC9fNQG8ppWCjhKEa4QMLOBgm9aZVUe7simZA9+ouk0wrDf0C3GiOis6yUZkxsm1APi+yrE1pRVCngjFJItvz1tDZkG6IQJxsAN3JFNzXrBhoQJZXipwAMV3mHTSKtswNDnAz4f8CcXKjIXxwYLc3dwHZxyvCRjs16wK5eWN3y82jjZcCTJDr324YSxpycH3gY2Z/KyoXqElZ9cqIxXG3Ero+UCu7o5PxhOGEvS/VYT/11f/nXbxT9fGlr/7hZZYXH8c3WX/VcVq8/gDz+o7D3QsoUtPNoSqCcaH39479jpQ9Fje96rS1yL6zxWj+tXnW3HR++QYCyT9ZXPIjcE+ktPaa/9HKL3hKH2dij1Ur3ybIY+9aw6u5g/v0JoheNRV79SnBL5QVYvsYXpyEhEmhEl0qxRFlZGRL46c+YjqUyjXrqVvALpI303HK3UG+VaWmWQQAHwgOoFPfl9fXmk0ZOOd9b1cpqaYAxJOZDbFxGgbtDUYYFa5jyva3I2HcXwRijmg5YQLJAVtmkgqQ2ZBoxlgVgdbP9Xqo0qD5OMwQUHzHwiCqIoCKMq98IoUhUFc/ZoNARRBHqgIOrTtRoP+w0dqnM8ijDOV+VhUmW6ouQ0tRxwkzFPiJ1dGdSoMSuItPVIudajazLFB9RJ21kgInrSz1cbtohCIleIRhgSURhFthATbVOZ7WnLromzrueKqFtTRy1zPuDTno/q+qB5mR4waHRE5AoRh9VXqg3fa0EyKvzYG4GoP+cHXx7owZYC7wVsRLpcdgYDEZOOBwUCmNxAmS6enUtu3aCpx7cLkC2K2xCgQ/GOefwV1fNlm+Z12/326FBHYr1KXZq1eF9itRn84Qf3HiCi/feNEdHhbz88TkT7D0VRdGg/ER345qNrWfxarBzS7BXCc+96nY6aAR+9I97/lnJ1bGHmbSOZ2GtZpYi/unv/1L/r/ijTwxZm9Jee8j7zh60VXvyH4LbPoYUvKfQIpP5ySxEObVEXZvBPOLQFYvXqzGnI4GhTJ3CY6B1Wp36lVOY//vI/1GqXitXypbnp71RPbbDLv2UXT86f39eXh5bqlONByv6s6zVDsTjFvK+365O5NDTeiQhAgscAXTweNHVfRL6IDKaMWSZ6ASOWOWjqrhBplTVDgUY+ysLo6wPXPSG2paxBUx+zzCPlGn7Zd2RTZxotCPnyQE8jFGOWmVYZjNp0RdEUxWRMUxRk8MB+ItpgGgmmaG1a+1uuH0aRwZR+Qz9aqWc19d90Z8DGr/LQZKxH10zGZr0grq4PKx1bCFeIbSmrEYogigZN/fZMcqI9UPdHG1oU7oLjbUqYW5MJIir6kIZVg4j6dM0XESSGcKTFWFplQRShZtAMRZnzE3V71gsenSliSGFHNjVebQyaep+uPTI8AIXXadePsx+2payD82VbCF1RdmZTqCiMtlstraqJ639r9Abk7jL/hjUttIx25dKzXtDxxhHYOsjRuGnXP1yqgsnREVtTlpStvbpxdz779K03ScWbJRPrx2cu3JRM4J816/frJFaZwR9+8gAR7T/02J7R9n/Q/vv2ENGe+/bTgQPjpyeJ1kRt1+LaxMrT8FJ3lpbSzuOjd8TlcSCYA9RHsg73GiIS3fOMPvtsrLmOIrxWOG689FTLkKYdsrzPyrNkJEWmR3S1RqrUsy+TEKRqZFg3XXztIwZT3EYw/NnPEhHR+Ytn6YYb5TrzAc9q6gDT0yo7Y7tI7AYMXZqLVGO1ZaA7xsfRnN6ask7U7SoPDabkNQ0Z+Zhlxhl8OU2VyfqU4+GwIIxSKkOKjMUh44rsedr1U6qaY0pOU081nTLnMq/FiXAlqBxAKVZXFJTTLcYsRo0wVBWFEW0wDVeIMcsEdWDS8cBUhyIeXOCmPd8W7PvnF741egMRPV2uDRg6yvLoEXhCgKNHRKhkULsAbjAlrbJpzzeJPCKdyBGiW1MhmI8jgZdEBHldRwhXCGAz4B9sBrzBMufDZKArL+fXZ73ADTjE+UsBR7+AiKQUIKbtRyxzX38eYPx0uSZt4+E6OOcHLbl7ovFqAxr1UJhHVg3tQukeC5m/uDExagPQlsczq0f3uOud5M3JuIrjdmvxPsa7INm18H3XlrGrfTFr8eEMqSezWHjuilag5UftV7/UYmY+/i2vTfSP6C89RURkGE0zk9p4/z/1M4o5xkLYjmLG8DLC/hFt8liU7VOLU1Ei3b56MzLzRKR4TQo5vGoWX9jJhiM9WuA2S0TTnr8vlwd3GoNt0jsEHqwobicYKwW8T9eOlGtfHugBK80W4t6eXNzmDpPiYLxD4x3KbgCMGg+HTQ3rn6jbo5aZZOxkwznK6/v68l8Y6H36/HxOU8Eqf3KhsjObOlqpo8g/YOhnXQ8ycP2GTkRFP3BFxChKqgzKM6WAEyNUodEFn2g6zVDwKLIYq/EwaTAYzBNRkjFbCJDRJMY8dv4ieup5XQMkTznerBdIwNuRTR0p1wDSNycTM54P6ftGKHp0DZA2H3C4y3drqitEVlMbXphgTAoJY6gdDMExyyxzPu35YN6dajrgsg2aepOpFceFjL/8EHH3pJNvKeCHS9UO7xn5GA/kXP6U40nhHSIaMPThhDFebeBq78lnQQ+MC+ZgfYrtPN5RGUaed9r1sc6S6B6fsF9uqbW4/mOVAD+2ZRfR+KuvF2gPvf4qEdGu++8dJSIqvP4qraH9Wiwbkrgerh81nzsI3bcrguQrUrNfkkK/+JjFz0gjO5IedEmLzr+5+OXmCz8iIkzKJR//msgPwrgWrxIDY4pd9e66n4iwD6CQR+k8K82ITI/iu1FXP71FctDugXX9/75cS6vMFmJ3VwaecifqNtrVeU07OF9Ggohm7bTn7+7KADO+NT2HhjSR0BUFIu1xP7FTTQetX5n0D5p6PtSk5O2cH0A8B6Zq8wHf158/WCyjcN2ja2C3jVcb67hArgnnN1uIkw0HfX2Mum1KmDBFxYk0Rek31GYoiKjoB2EUeRElmcKjqMbDGe5DQH6DaRT94PZMEkQ/i7FmKCRgTzqeBMVTTWdTwsSlYidRCjjAFTVwvOWvb1z/zTcvEBH2N3h3oLJPu/7JhoM6QbemOkIEkeKLaNRKEFGZc1swX0SjlonqxYzr9+natpQ16XjzAf/++YXtaQvD641QdFlv4/qObOpgsUxEQGL5ZLyPTm2lHSKSnxGEe6XfHbU79LI7ntNUMAxQG4j376ccD3a9fbo25wdLms92RBy5l1PK6wjZ6X/H+NLQ+r86/Toer/Xgr5NYJcCP3nv/rofHxx9+4K4f0fg4Ee3/+kOjRFR49IGHx99G+7VYi5ViSRPYqx6ouhPRlRL74160wbZPU1cXnf8/y0MsPyP+qZ+1Ou4zp4mI33wnqPJAd6A1/i0yPYxICtcnDn3P++x+44W/JSK2cM6/8/eUSjG4BeV5UmdOh139X7n4yi837QQIAeMdIRxfQO7GFxHmrwB70rQUK6CAn47x0lFAxuMeXUNTH9aoRDTpeKWAg+YNKdnzno9uepnzUsC/e27+vt4utPORVZc5HzStdaaeSVtTjgdZWcBkTlMbocCeADiETjkRpVSW17R9fVnYuaqKokVRGEWY9Mtp6qCpFxwXNXwicmDc3qCC4065/gZDAxnwSBu3IAmAU2PnIUfL5G4GCHp7Jilz6O1pS2riAlZxc7KaaggF/HxbiD5dI9Jg9IdTuEKsMzRo0YOcj+lB1PanPR+SAjgA2E9tJ9Yl7d1erDV7dA1/erpck1CNeoB8C3S5wjxyetxqVPhRh5hyPFRfsKNC0+HXKacvWZOHBdEqzePpnXB9ZQreGkHvvYjVluhHH/rBodMP7D0wPk5Eu/YfapHmJ0+PE+36zg8eWsP3tbiOYmVcXz2Zn4/eoV4oVDXKFUeeVVsO9G/X3hcdjAdSwV6feAbwH2zdrU0eiwb7Wmu2Te0wa1c3Gunz/0Ibt+FV21IWGHNo0LpMyOFvoIIUpt3dlZlyPKSVOU1FvijtZ0CDBwRmNRVr1niIpf76wgLG1UzGXm7YG0wjr2lEnIggHHuy4bisZfM66wWDRKCCj1jmibrdZ2mNUIAaRkTYEJQ5HzaNE3W7JbjWZQGxNiXMOT/wRehFLQMYzMrfmk4CpU7U7Zymnmw4aZWNWonXbBfT53Cck7npzmzqSLnmMpFgrYkDwC2QMu7RIh10QD7AYywCFM9p6rFaE0JDuP4kYzgAXXAY1PoiBLW+R9dONlobiEYoygFfxzkuCR8Bxfrf8bI5tbFTlu6pDeT4IKBJAL8ZmOg83eY2SqLAnp6crL2DkXCy4dyeSU46HrQE4m2CdxHI7HGj0BZB+79HbxV7lsP4d0TlONtuBZuZNROa9yhW34Mf3fPYC9FjVLiMTDf21cnosTVwX4vl49fXon/HFa5o/Xcs+MOLVr1QoEZa/rV67IkcH3m2LY7XIuL5dquY32bUy0VCyfJ76Sn/ts9phePBJ34HrzJeeqpj9G5HNn2mWmu0fWAPl6r7+vP/cWq2xsNGGH5xXTeY1cjF4TUif21BrpZjTrA1Q+F3ounIxnOfruU0ddrzYaa+NWWdsV14oc56QcFxXSH6dG3QtJAWI1Of84O8xmo8lKRxoGMjDAHJc36ACX7sRfKaNu35NycT29PWyYYDSRxZtDeYokYRPNeJCP7uRIQ6/z35LPrNaZWtMzTU9uFSL1PzyTbDvE/XcKOA30crdajopFUVex1p2xqfAQMxHty3KceDPR12FeidA9sgXENEcI/F4wFDL5mciKB+bzKW0bTzno+Xy48SaXrcR4cW5cT4NPf151+sNU82HEpfxoyLV9FlSHtcirnCY2sFwkGVh1JRZ4VYzumu40bJHRWuRJrfdKzWgcqLtejjBzw+V5KP1+KaxYoAXygUaHR0VD5uP73oIJKHrcVaLIol1eiW+9OVxuo79Ctz/Tq8aIlI6e2lhYX4MajPs7lJMTAGaMczyzHq8SfjpafU6YnISPCxnUjfqT1xp1SK2uQxIvofffcfRr9FREjj/unZJx8IRYnz54Z3wNqViE42HEmqQkaFCipOhOwwrb4t5wJvVnCpAPlEJC3UPCGQ+QHIm6GYD3jB8UYtE4a2U46HWa+vb1z/Yq150Qumw5CIJh3vU12ZWS8463pPXLz0qa6MlFCdcjxbMCCizHoxfWcwpcojVSGDKQOGSUQo/t+eSUrZvmnP90U0YOj7+vKwnAduAfjPup7FWuNnBcf7/vkFnOhIuQbTOYuxRhhCXkay8wDqRARYHbFMJOgd9nTxAJ2NiDAgR+0WANx7qb0jqXOebBcSiAgfzUTTQdVhcb6Li5FZO5oaSx5J7RZMKeAyp1/sKyPVfLHgKuvz76KMj6LI3fns3fnsuudfvvjJW690hafLtakYnWItrlmsAPCHHxxrKdvsaT1eIfavSd2sxSrjikhz7+L4JSP5+NdgCCu6+jvY7yvsNuLz+vbEXyW3/fE/13/FyrPq9AQRhUNb4rl4HNdlQLOWhreJTA+rl7TCcVYv4Un/ts8l/9+/FMlslO3j/SP/9tB/wsZCn3gmO7Bx/dSJDWG0/aWD/+0TX0JWCr02DDofLJaPVuoJxiAvP15tAPJHLBPSp0S0Pf220zx+nZ8u1+b84MmFSpIxjKuBEj9qmXghnF5RdkaKOeV4AJtbculdpiZ9Wmc832IsiCIiOjhffmR4APNa3z+/ANFWaL/Deb3Kw4mmc3smCZqeFIGBmZ7kFuDgScc7Uq7d19uFJBXWrrIMUHA8wLktxKTjTZ6bJyJXCE1RHCHCiGo8HDD0SceTiTi1bV0GTR3CQVLTd0c2hbk7yTSUz0MiHpNpKJwcLlWR6GOL8EbAe1WGd4qXV3m4LWXF59w6vgzYCRERegQgQ3z//MI9bR93HAbfPLD6sVNZ/L3CrqjKw7Rq5DT11wTO+E5IcgNXSPevKNq7kzQRPT5X2pFNLZahXROmfY9iBYAf27J//34CPR6PV4j3lkXf+2sYEV77ZT+4cW1uSJRJU1fL9YQaaeWdTrry8dG5CbIXiIhu//xyq0XP/lfasJH6NxIRFd+k8gVqzBGR8tv7o2f/K22+g4joxE/o7/832nyn8qk/kK/KyD+VL9D8WfH8N9gnv/FL9TSN3a5t2kalCzR1TPn454ko+tVPaFPb7O4X/x+9+gwRUd8QffR2Kl2gwdvp7AQFNp0r0hf+hM78nHrW07kJYqrKNNJ1SpikarmpY0RE84UhJXk+P5jXNevN2p4zP/lF/iPG2G1396VfqTamA54Iwrs39B1dqHTr2iQXt+TSmuMnrEQmk/lMJvPP9akmU+c8r8lUYmpFVV9vOD97a+4LA72/NzL8v7/+JtNUVVW3WolLAa9zPmwlph03YSWY7W7J5y56ARFpjGcymUQQLtjugu0SUZ2pe2/amPR4/eKlDVaiGIaBiGwhmkxNGvrPGu4tuTQRHbjjY4eKCyPdhJckU0mDiy1ZfSGK1qWSWsCJqMtKTHLRZCpXFF9RpsPoprSlhREOrtuuH0U/q9t/PjL0Qrly0QtOzJdMxkJFqQohoqgrYSRJJaIB06xzPuM3SVBKVZNM8QOe0LViGBJRRtc+0ddzdKHyhu0SkRtFm0wzmUomVC+ZMPf29x4qLrxSbWRM4y3b7TX1JlP//c03jNvO740MHyounPKC6TBaiKJJLs7Y7nQY1UU05wUZTTOY+om+XHOhQkQ3pa3XG86Odd1EZHgBEdWJmiwymPpnI8Md38Okx0+6XsJKvGFXiGhzOnV3X/q827oeeYzm+HOcd1mJOucaU/9qvnJLLp1JtjYlSdXp6upKejyhend354jovOuNe1yeRS51RfEXXV2HigtEtED0eyMtCYffk//fxUL+Svxxb+/jMxdaj7cM06IfEBxQUdWkqmHNM/Xml4bWPz5zYceG1il+Um9C4/bXcZu9buN9hxgliqJ3Pur9joXLK6VXJXp7e9+LZT+4cc1uSDwjX47oHh9aIyLRloLvOB5LSbOZuPV7x2rSXw4P5CJyBdE7xBZmMFmHv/acOlJJDyy2ik9tvP/58z8Fe65Do56IoGcHw3ilUmT1ksj0RF39RKS99nPFbfAbtxNR1NWvvfZztjAdpfNERL7N6iX7/r/Ay5Xa/D9+ZHe2UcoE7rqunidE8r7KG6X1N/3khjuQxA8YutRUIaIqD9HGBjEKWabURkUZGfPTcXoX7E+ICHNZmJ5PMjZo6sjsMZcPBtl0GO2wDDinnXU9W0Qa0bo2131bypKJpkz74trvZc53d2VQjj5cqh6rNT0hwohgIbMpYeIiJT9fDoDlNPXIpXpWZTCGX+DhSMJwhUi0DW+ICKI9digEUbemErWWRakf9QxY0YA8KDP7jnsia9EodeB6oOk7aplShab1zTHNjAhxG1Fsx5/ic24UM+6Ty045HjiJqL7g4Ph8PHiUYP/BaCBeDJBHyoI/CieS6LfK+bflYuWs/e6XX1+uRL/cD0i8E/+67SJZ73jmXV/t9RzXEmKW20mskmRXKBRW6rIXDh+mPXvWuvBr8Y6BovfKA/HAY6kzj9I6XrLk8RL7VzgvWOtExCpF97cfWuHItzcWmctYS3z0jpYATntQXi1ORW1bOTTUJZFehmzVs/KshHalUlQqRcVtBFt3G68ciTRD4b7/8XvNF37Eb76Td/Xrv/yHey+efkPocyN3qDMTvznwkYtedjrTf+dbv/j7gY+fbDhTaquEjkJ9n96CYRSBMZMNnRZZdobpO8AYUNQIBVBnRzb13XPzeU0763r9hj7peJi8f3Khkm5Xoak9h5ZW2aaEWeZ8xucXfV7lYb+hH6s1t7aBQTKzwOTv07W0yu7J56lNL6c2U11XlCCKeBQREZjhRT+w2tX4vMbaojcKEUHtzmBKn66dsd0qDy3GkgYjop3ZFBrkLzfett8uB/xIuVb0A9yEiaZTDvjJBkurLXPx8Wqjw34N9xMCdn+0ofe75+b7dO2856dV9YztfmV9L9gA1LbbwX5lyvFwi7CpAuteEvglt18CJzZVcIXBjT1SrsWlC6ptA554xOG2Q3zmYLGMuUd6t0ax8T2Z3DRcrZb5Wvn9fYxVAvzkt8ceoO/84LHF43CFw48+sPfh8f2HojWAX4t3iLjozRUNqccT63cRINAhg4+ju9xtyLPoE8+Irv5WgaExp14osEqR2jgNMl3zuYOpjfePv/p/8xu3y10IqPJQtgn7R5a8DLU4FfaPwOEmHNoSdH0WST+rlxS7qhan5JF8bCerl0aINjpF6u2L0smputqja7VQQHT9xVoT/rDb2u3k+MyYnN5uJaANGjR1NKGRnWNPMGjqT5drR8q1tMrAF0MjGSRzuQnA4nMB71UUPAPDG41IEGmKct7zTcbGqw3IssICFfPlHUaocjx90vHsUBhMCaPIZKzMOYbUMWg3H3BXCMz7yRkwX0Tg5M8HHBKzQRRBqw60wUYo8rqGyTdcEhFtSpjPVxvbUtacH6VV9azrbUqYEk2pTUef8wODKQnGJJ/xYLGM8fcNpoH3cnC+TETNUGxOJma9oIup2EiB6o+7dE8+K/c3YDCgDIC/bk1Z8sbuzKbwkVV5iB488H5PTw6ALe+enJFDSMIdigErmMyuMuSCUEvEnbm6jvIduL4G+dcsVj8mN37g4bEDP9p/6AePSSQvHH7wgb0Hxol27V9TsvuAxVU3dntfYvUzeCv4ymO3QUSAc8jdyFfJU2iF4xiLt7/0lxeJ1tH/AAlbGXLAPXHoe+ytk0QEQRulUgyHtsCtjtVLUTInOXrqwoz21kmRH0RJ33jhb5X+EewDWHk2uOWzYOkLohu3ffJGojPl1m+urMQeKdeQuxMR+PbHak1XiBN1O6upfbq2M5s6VmseqzV1RQGPzuwYvQAAIABJREFUDFJ0RFTlIYjiJ+q2wRRw4sarDXD6ICgrhWsGrMQOyzjVdM7Ybk5Th00DTnFElFZVIspp6ou1Jmr7RDRo6sfa43myDC5V2JAZa4vcYxOMnbFdTwiTsc3JxL7+/Lem57alrImmA6H7fX35H86VBgw9aTD41cJSFjsDjPmVOVcVZcg0AJx/fWGhEbbk7h1f2ELMegF8WU42HGwmBgw97p4nmxQQCIKwLrUr/2dsV1cUlynY8RDRYv831OHRQehrN01kzwIBKMU9OdV0ULeXpPqd7QdLltzluJ08HussKSx/HcYarl+bWCXA73ksmrzvwQf2Hjiwd+zV/d/5+lfvpW8D22nXdw794KG17P0DFVeFl361rgQPFvvHyFL5krbuxrEnjBd+RESifwSF8Xf9RvSJZ1iluILKXlyyXkZVm8oVRzowHqtJq/j4ZLzID7LyrFKbJyMZDm97u4V/y2dRsSci/67fV2dOh/0janEKz4N7z+ol1AaK1vobCsfV4tmwf5NKGuV3ohoszzJg6EnGxiwTMF9wvBHLvK+3S06dARSRoyMzhjMNJOU7OtYy/z5aqbued0qERGSHgkdRQxNBRD5RLRTrDQ0d8WO1prROn3S8obZBLUxcMD+GWjoKA/i3Fooh04AZritEj65hqh7qsHlNk9k2EZ1qOjKznPb8vKbJqgB06QuOS0SqoqRVhin2lMrKnMPpLqeprhCuEC2l2Lwu6+Twn8XKs14wbBpQ6Jt0vD5dw3jeGdvFdiTB2Jzr78hncc8lSFMbaHOaWnC8pMEKjgcn3z09OSgcxC1eUBtHYRwf2TffvPDlgR7M4yHpp8tF7KmdXuNzx/QEyjm0lLD8FcWIZX6wNgorxFqRgK5U6Gbyvke//c2HDzy898DDRES7Lsvn12It3iE6suHk41+T6XIHPCN1Xm4d49gT+ktPIevVXvu5Xi9hXG2VGB8fiAe6K/USW5jBy+WeQ/nt/bSII3PZjuT5l9WFGRDogOWy0y9DnTmt2FW06kV+ULGrUbJT2TsugRcObQl7h2A7y+Ymg0/8TtyV/TNd3T87/yYZKbrw5ueSWjThbCtX5zxORIWxXUidiehYe7w7q6nHas37ersAmRisymlq2mHUFqif84NLPKzyUFOUYdMATqBmCypfKeB5TdvR0/WTCxfzmpbT1CCKXrFdgygPd5aAbzANlKmxJyCiRihsIWo8hKkrnslpKrYaqDqgMO6LCG6wKDac9/yiH/QbOrzm+nQNKTv2DRgew84AuwrpqHa0UieitKriYND0MLp2om7ritIMBZh6t2eScr5c3lu01eXg/vZ0hto6gNgPzQfcDoUnhKooCYNpiiJdX+OmMgeL5TO2O2Qat2eSPbo2V+70/I3L4IADcbLhnKjbaH43Q/7kQuXPb+gjorjqrcR4OdKG4yFB0zFKd0XjbXIcLl6T/0Cg+woQviaNh7gyN7nRsZu2fGwX1OiJiLZc/Qtaiw93AEGB62xhZrEh29WK5Ur3HeY3rDgl+keC2z5HRFrh+Mobi+TjX1ParDqtcLw6ekeOjzx9603LHa/OnEZvXi1OvU2Yb0vZA/jdvX8q9wTYH2iF41EyJzI9rDzbGqPHasWpqF7aO3XCv+Ue5lTDri3Fl54JPv5vNR4S0cfPHntj7C4gJY+inKZKu1ikg9vTVrnC/013ZqIt1Lozm5r1gioPdYXg1lpop/XoTE+pHnzcUyo7UalXeXjR5+sMLcvURuinVaYryqhlnrFdjK2D3i/l3JOMkUYTTccXUYmHF32OljlQc9DUZzx/QNNhUYOO8phlornuCiE3Co4QqMCPWOaRcm3UMtFisIWA541sRaP7kNNUcAl72oJ39+SzJxvOjOcHUdSjaz26JufLpz0fjvKjlglNeIj2QJ8Vd14Kv68zWj+YY5ZpmCZAesDQZS7+3XPzScZ4FEEteKItwNena4dLVWkXi1wfBYatKeuefFbWThA4tSRIIiRsy3n6JYV6FqvivCPefxD1Z9YgfDXRWWBcIQqHH7xrbO/DB8Z37T80eeg7+3fR+MN7x+568PBK5OW1uB7D3/lF9UIB/3yge/BLhnHsieTjX2MLM+H60XD9aFyspiP46B2ywq8VjrPi1AoHG8eeUOolqU2r1Evh+tGqNnX3/8/euwbHdV3ngt/Z59XvBrshNEQQoAgDkkmalGQxupAoWeJYoiIpd/wYh1FlHCdlJ05Nckt2TcWpxJXr+KZ8PS771lTkspOy/Jg7vpqMrHhkuxyLIfW2RBKWqVAEQtIkYIgECBINopv9PKfPa+/5sbq3DhsgCL4kMelVKFWjcc4+5zQofHut9a3ve+NY+DBv0zZ1YUZdmKGiOtUqgtygt/Fef+1mf+1mpVHTDz7L5iYA8GSWtjj0RafTZB0ZzipW2R/aouantIlRtjCtWGXYNW1ilDr6FMOVU8OVU732WXIno74v2cWuj0UIUGsBn7IdlwsifBFyUMX+OkNTFaXg+UethsEUglWyUyN8oqL0r+s2ABKZb3DOWjNmAMh5dtbxpmyHqOl0DwmVEU7P+0GMKXTuGzVLto3jKuszdeKypTS1FvAJ20lpalxl9YC/UbOIahcIUfR8AKOVeq+hU6/6iNUYjpojqXhCZZO2M2U7A6ZBwD9hO2U/oCeVYn99pt5vGnGVDUfNXkMnjJ9zvUd6MlLrN6trT84XiTpHY3JjNZuqBfSRbopHZZnkL2+8gYiBh+o2pdR0IsVkS1W319A/3N0ldWSl3D0AWcagj5QsgImdQP31zYmohPBDdXsgYtAXgOmGSyMJD2bTAxEjXIo4VLeJ4kffEt7T19Xwfv3H+SJ9XfGVr2BcEzd5NWKFAD/52FZl+KHH9+LOv31mYs+3Hhx68DPf2jPxzKfvxN7HHxpWtj7WAflrLChVfWfR3du0jZXyl7DPcEc+6t36gHZ0n3Z0H+XfchFK0HlXjnf3Lykt1xb+0Ba2MEPzb/5Ndyy/IVgc2uT+Noyni1LuThPwRCZQ81OiKye6ciKSUGpF3jvs33QHqxb0g8+23WfQv8Ef2uJs3eFs3SFiaePALqLl8+4BdfYIz65m1QLP9GlH9/HEKlY8JWJdItYljLh68jCAzYnoH6++LsZYxQ/I/vX2VJzcSA2mNDgnmfe4yuQYPVHqVAV51x+v2zRVDyAQotfQI4wVPZ+M3uMqK/uBy8Vtydgpx32z4RyxGiQjj1A2TNB1xGrQIirQ4MIXwhcio2vTDbfg+YS7E63jZf4qSQCegMuFJ0RG1wLxlp/NcNS8M50gDmD4cxuMmvOe/8uqVfGDTfFoj64RTP7h6u6xmp3VtYTKNoXa+QCmbIcIevQtNSNIrm6qhdBtsT2TkkP/j83kN8aj9EVSu3TMuohZCwJyApQz98vHIz0ZAORVv9o00po6ZTvkOyCxPJyv35dJbYxHF8vcPleslP2A+PlL5veXE4uH4CmNpi8pevNuiN/uyRyzGvQFQN7kvzeMX+mY3OG9izvuQw9+a4/43GN//PufffyzX9v5mY5U7TUZb+HZby2rVXjlYrHq+6UtcsE9QVM6vpSnI+VF5cS8fJ+V8lJLZ5kI+jewUl6xygD8m+6QM3VFIPPGseduuVHK3SilPOXupE0LIMgNqjOHiT+v+C5PZvWDz/prNytWOejuD7cqwo18KuBTVz7IDZJOjjYxypNZrTh7/c13lI/8crprAIAY2vJAfWZnS2aVrFflTB2Al0rVjKZN2g1VUWYcdyQVp0I6ccXrga8qylDUnHO9WsBnHLffNAIB6qNndM0SAED5q8vF61WLVG4SKqP5sU3xKJHP0SIBUNs7EOBAjCkkbXvG9b97euHhbJrmwaYdl2xYqen72Ey+7Ae0bMHzifoOQNbGI6w5xd6ja/OeT14vvYZeC3jB84ejJgn1EJWPyO3y10etdAA0mP6dUwtotd4pje41dCItUlGd7pA4iYNR8ycLpQbns4731aE1cs02YjzdVd710NqpSC3ejefuLdpCWsiQeyydQmP0AGikfhlXt/D93JlO0F2N1eyPD2WvRtb+NseSvfYLztrJN/+9gXo4Vgjww597ZmJoSTrd0Ge+tefhDz/2syt6V514myJMpxcv/wM2bn97rtumRrf4zYuKxSvQrBq9du5+hA44n3wevW5zoDFGnxbJhFGtySNpX8JKed7Mp/u1yf2SJFjWptJv4OVFpp1ydi72xOcBCCPCe4fV6XHtxBip1y05NC/BXnrSBLlBVi3QKSLVQwX8oLt//fX59YDcSfTXLNz8QcIAcpohhvaD2fSE7VicJ1TVE8LhnCCEBGdIRY78ZGm43BdizvXSmkpY5QnlhkTsjN0oeH5cZb2GTuQ4GognshsJvNQDTu3qTfHoq+WapihpTR2Kmr+oWhYPYkz5n1YlJ2yHvOZ6dC2jaX2mfqhuE8KRmRtNrI2k4uN1m6r9JOh2eyr+1ek5qhbQzD0l8WSY+3rVAmAwpS1pprFy6tOnNZUgc2ehTMb2pLJHQ/B0PBnPo7Uxsjin/cqmeJQ6/ZS4xxx/vG7T3DxaM4pkCBQIkdE1ALSFIjK87HNTU5zq//ROr6FLGJY8BuqVyO4+7dsooZdsOzJ1xaLdQ/jbi5WUvyL681cwlum1d/ruFwz1i1/84goOy2SGz/NRTk7u/Mvf/9J/VT7wxf949UbhLcu68EEXGbFY7Gose22FevKISDZ/sxG3Zve8522+AdphiGRGJDP6r/YEa9Zf+JxlV3BHPqr/ag+rFVm95Nz3SZ5bB4DVigDkkxpv7FassnryiLxcsGZ986xaUdb5Iz1rbD0WvqtgzXpv8wfpSz15RDvyqnZ8TGnUeW4dqxW/wlf94ewvlHqJZ1bzzGrtxDh0k1mVJlorjJXnFSEgBJgK11LcRrBmAwBWPKWemfaHb297NMrdg+uHWPEUlQH89Xfr//qCiKWVyry/+T711AQA7cSYPv68IjjP9GUHN585+suZrjVlP7gvk6Luey3gs453uN5wuDCY4glhcVEP+I2xyO2p+KTt2JyP121HiBhjd3clTrmew4XD+YZ4NGfoOUOPMnZDItbNlDWmkTP0asAF0G8aJx2vEvCaH1xv6DOOe9Lx4ioLBFRFebPhro2YAURcVeMq69LUwYiRo4K/H+iKUvD8dVFTAH2m8Vq1/qbt/KJSf/pMKaGy1aZeDXje830hhqImmccPRs295dofXN9tcX7WD0Yr9TvSifdEza1diT7TeKFU7TcNH0ip6v6qFQhRC/jNidjecs3h4s50oj9iUC5+XyY1ZTtbUvGzfhBlrBrwKGNRxm5LxnKGPmk7v7adVZp6ZzpxouEWvKDg+SZjBc+/Ttc4YHM+YTtFz/9grvvXldrPy7XphnvG89fHIveuSh6zGlHGoODeruSk7dwQMdZFzXtXJcNqP/QsI+mEAmxblRyORY5ajVrAN8SjZ/3gpVK14gd9pvFANm0yNmU7h+r2WT/oM40+0zBbpvUU9CDDsUhaUw9ULfp1Uw1mrGZHGJPlB/q64P9N1LBPa6pcMPzT788V/nyg9x/ni4fr9uG6Tb2JV0pV6gh8auiGq/EX9XBLXA+hucGLivBNvp17grcTYmKx2JLvXxyLPhyTOx/72pc++3iTUP++S16nE1cwrqwN65Va6qqGNrmftUbUVnI8ZfYkOx8eq7uox6Q5PQDq6QlWnKUx+mI/y+D+n1f/xTiwa7HRHPULvJvv1w8+C0Ckehpbd0Se/x7p17q3PkBVetnCD/o3qDOHqeAvywD6+IvexntFV06dOUxzdIrboNE7EUtTv/99XbmbQhgga8gGU/KunzM0AKmWtgzxwh7JZUhmdcZxSRqdtGVI/I5+dHM6YdUZAGpaRxh7o2YZTAEXN8UipHGLljg8+cHPuV5KU0dScaKyUx6cbWnveEJI1TyXC2pjA0hr6njdppG27ZmU/JtOwEbz7mlNHYyaAxFjuuESlT3GWC3gPbo2aTvXGdq854+k4jTsLqfbKSjrpSr910/OU4liXaTp3ErD5VldI6e7BufbM6kp22lwTryEec8veH6D8/9j4niEC0rrJaWAXveZOj3+nOttz6SWVIUL3wa9Q4dRSUD6rxM5TgoXtmXVtAJCcjcyrZftCTlfd0VS87eBu3415tf/3eb6Fw/wk5M7v/b7X3pcjsrd+em//avPPdyZhn/n4xLka8439v3uUcJZPozRp4lPB4BcYdqm4MKK97EnPs/yU0qj5nzwk/L4S7uuNrlfsco8N8hzgywkMVvsZ5mZ97/cNSsdaNAi3AXd/drkfkJ3AnUi3701BD9zODwUp//yJwDMPWWq4bNqIbyDCfo36L/8CcsfRywJVQOgTo9Lhv/5QleAFoW74geEuMNR8+sn54ejpsV5v2kQxlMPPqEyquFvz6RONZyG603ZDtXDj1gNSyCpIKYyi3NNUR5d00M1ZAAFz7c4H2pljWSFQiX3vOuRZB4AUs2b9/yUptJgXtH3i77vckFXpxY78cZJVZc0d2lbcHsLwpsfvu9bnKU0le6BziWlP8kBJO8c4v8/mS/GGNvUkrSj6rrsdtMpMkgBl5oXNIJYCnhEUaj3T3KzRB4kt9w+U59zvaGWk2zB88OlcgADEYP2UuFhPFrqkVxGet7Qj5a0kHmuWJHmN0/mi21EfTkpJw9ePFK/wgiP9r1WqUuAx7l4/8TM6d+MXq667eINREfX9nLiIgD+3JQdwJ1/O7FnsTh9J66teGfBe+Vas20hxWqcux8Ju8iEOfBhcTraCqgnxhTHMvY8xXODSrUg+XdL31UtYR54yR/aIq/VtiYAlp9i5XkAaOF0sZ9lZvqeC/nLScKdP7SFVHFIww6AyCckF4+4ewygCToAItWjzk0qlXmR6glyg/SYdLA2Mcrmp6FqPNuv1IqKU4eqkz1dMLAJ59YzJARSlxqUZ2tNFljZD0g/brRSryiKFfD+iFFpzaBL+ttex7eCgODkznSiFvCy1QiE0FpyrQDIqB4AOa+glVlSxx3AgGnUNC575NQRJ9jO6tqE7UQYI2Vcyn1llxrAobpNXIGi75PFjpxnm7KdNxtOIISqKA0uIqx5S0esBsHtpO2U/eCmWAQAYTDVsS3OifAvb4nglkzo6R26B1Lpp8evB7zsOy7QYOwTvVm0OHr3ZVLktlf0/YjHhs6tb4chlhRsZFtdVi/os72yOvAy5E5imcUXN+zptonu99NCmegCi/cHzxUrJVWt6urVwOAOrl9yrADg21L2O+/89F/twEOffRzvu7GD7p247LiEHYZM08NSOcu7yTWN6TJ9IpnVD+4GIGJp4t8tVsKRr4lDRwV2EsMhwTt/aIteLWjHRoUZ4+keEUtrk/ultm55aMuShDsK4tILI+IPjzhbdxgHdpEiPSnP82RW+9WrvGcdAKVRg1VBVy9p9qkzh4lUr50Y473DittQfJfNv8m7ckq1KJJxAO7W3wGglPLyY/nNk/vLQQCg3LeFqs0EbHQzG+PRJ+eLZT94qVQlb7eYyhqcG0xJqOzOdGKZVG8kGXujZumKkne9/zrYR6NctYAXfZ+q2X+4upsq6uT+klDVguevj0XG6zaJvs253qG6Tegux9IijAG8HvCxmv1mw9kUj0rVF5LhGzANGsCb9/zhqFnw/GnHpay66PuB5wdCACj6flbXqLSe0lSSkScheuLrEdITZtPKcrKfZHoJd0mUnqoL/aYhC/63dqVO1S0pVkMf1GMz+e2ZFPn3jNftD3V3teF3W4RTc8JRmr+nTdWS/Lg2VnxY0D58cNuJbf46y/xaz/ejr0zP/cVA732Z1BNzBZo9k7k1bQJiqnajqXU0Z95VsRzAn5uyt0rxQwB2/vHbcW+duLi45Gz4qi61wljJ5RZPqHubtpmvPEnQLhXjl1mKGtvQDBFLE1iSWVybPXzz6F/+CL/xkfPdjHp6QinlFVXj3f08N0iettQ7Zwsz5W6k/cEXAAD+0BZCcTKN5Zm+YGATSdkAIMkapVZsDvVVC1A1pVYURgRGDGYUrqUffJZn+uQjN69SnBUAOz2puA2RzLhbf4cE7Zu3N3MYgH7w2VsiiTOlYiWV+19P/Wr6vR8owkQyR2V2MpvJaBp13AFYAY+prOwH1JOWSED5WcP1mjbzCVD9mcxmZNBs+oBp1AK+ORH96vQcWcPFGFOVt7LquMoIs2UqGR46/3B31+5ipR645PyGkPJun6m/cLbaHzEAkK1O2y8lo2n1gEsYLnpuRteIMUAH0BiblHclNjsAKQkgReDJrH1jPDrreDRt+GS+SIfNuZ7DedX3e3RNFsYJUGcdb9bx6OONMkaTAnvLNbk+xcZ4lAYccC7jnXJ6nCtMGz4xXAagjoA8UWrlYqkifJiLcEGe2vLd+ttTcQnhhPG08scGe0ul0vIrXzCWLMh3SvSXHMsA/M6vPfTZx5vA3nGTuTbiCoLx21m6X0nLP3yM+cqT8rXUoaNa9/nm48nNnQEimfUHNgX9G6QKvX5glzx3JXerTe5XqoXg+mHNawg9ojgW78oRoIpklvr62uT+Mx//6HWvvvHqqWfRcpt1b33A3POU9I9hC9NomdAAIOxXrLI/PMKTWW1iFABUXaR6lEZt8W00+XqGKRIZsq2T0RyaL+UBKJX57tXDPXMTMGLvO/aCsmbL4MQLrw7fA+jTRpp60mjlzbclY/OeP9NwqXYtTcEP1e3f6MlaQQCgYPpjNTulqb+sWqs0FcC6SLOLP2U7JBRvcU6asjSTPV63c4be4LzsBh7gC0E1dhp+k0KwNJG/t1yzOH84m36pVCWVOvJz25yIvlSqqgoqfkBnkX0O3XyPrpHOTFbXLM5pFPCWRIzebHBObjoWZ8NRUyq3U1FdVqELni/n6EgzH6Hq/WDrAQ2m3BKNbehKzlZqu4uVsNgASenRfiWlqQ3epAu0WcnRRdvwmMhxNNP4WqVOw3jLN8ul9u10w70gbN8esqdb5rAllW5pKzYQMY5ZjcXerwDCjfnLjLb1O5K0lxPLAPzwhjuBvdj7+GcfevypOz/9V//35yh970Qn3uEg21b5LVuYIZiXhjGLT6E3qYpOxXYyowMgYmnSnW2vEKy/g67Cu/tp6p2E6wGw4mywdnPQPcDK89TLb9YGzo2yNpVefT85zkWe/57uWix/XJ36F5hRf3iEpOl5MqtYZRFJKJV5nsyylrgN7x1mcxM8lmIL07x7gLYF1IZvPPSncsrf2vHX9EJdmGHVAtnU8kyfNKpvfkRWBdUCz/Yzu3Zw1WCscCrS857+2pkzqZwUcne5qAV8OGr26FqbREw4CGt7Df27pxdoGj6hsqLvk7bMpO0QsBlMkcr2LhcAL/sBB/RWupx3veGWqR2hu8U5FdJJhC7CGP0orFi32mzyA16vWjfFIlQeB7C7WJEqdWU/GI6as45XCzhZ2E07LjHs5PFtQfBM+jZzric9ZgBI0zYAtYBvz6RqxQqAYzV7oDW+9VqlTuUEovHbnK+LmGSWQ2Y/8kJLGr/SJcKFegmoFyTEna90v/iwi2LRS8s72gMt1rALB6XdSbVeXQT/VyM6Cf3KYxmAH/rMHvGZycmdP/valz77+N7HHxp+HLjzzk/v+KvPfebtu79OdOJ88ZafbFfufMeEy/VtyX3kmW8E1w+z4izPDaonxuRh4bEC91w3uaDVktcPFQO6dLpHMWO8K8dKeZHM8q4cW5hRZw6LZJbWKfazzAwHMFqaE5ohVvUq1SIcW5t63R+8jYh1SmWe1veHtkSe/15zkK84CyPGCjM8mVUq88gN8mSWWHuRZ75JJYEwbZ64+s2nPrCr+clk+tTpcal7z0pzDxV+9IP+kbNGMluY+Y3y8Se6fovYZMNR8/WqVfR9qr3j3D/xG+PRlxZKvusilNHGVUaSczR+Teb0BlNmGq4P+EC/oQGYdTyDKfWApzXVdf2bYhGi6EcYC8vNUu5LifimeHTCdgikI4zRjwqe73IxYDb/ZF1naFRjoB52GOeInU7flv3gznRisFWdXnJybHexQs7xZIUnqYIyaAoAwB+u7p5uuNszqYGIMeYFDbsx63iE0FT8R8s9ds71+kydHiHc/26LNixf8phwLN+Vl6n5knNxK2TOH6rb1FNY0qn2fPHbPZnu7u6FhXPoe1cDiTsJ/UWFIoS48FGABPrQe59+RrxN8rQLi1w7Lz+6u7uvxrLXbtAH8k6Nv19sD54tzDSn4w7sQisppzeXbKi3qdcZo0+bz38PAKsVeSLDM32Nh/607ZjwvxDyp5dyttEffllp1Eg/J+ge4Oeq0cnDzFeelDS9ZNc9o69+pXlALCVSPeQpx+YmYMQAiEhCcuBZcZaV5ngsxaoFODY0nfesa3zwkzh3uI7G6Kkgr50YowMAUL8frbK/Oj3OuwfU6XERSytWeS7Rw11nvm8D86xygJ+OfILmxGYcV1cU8oanJjpaLDAAJVVt2A0AcpYMwM8KZbKiobG3Q3Wb1OsA+EK4XKQ1lVxTw+Q+coPt0TVSo3swm/7S8dM254EQQ9FmUv7kfNHlIqWpZHTbZ+rS3JYq9kTNk7dHEa63A6ARNTqM3pFWrfJItGbrw/n661WLPGdpBwNAljTo3Mdm8n2pRMNukOQtWdjNOt6M49K+BwA9CJX66fTwqBsV5MfrNrnkyZ/KQn3YVVY+3eL/Kdq68uH1l3wfy7bY6SxiG0zYzkgqXvD8nxbKy6TvMtr+ooaReHFh/6IivFG4gste7Xg7Iaa7u3vJ91c6Jjc09OBnvvXgZ74VBvrHH1Ieb6b0nRb9v4m4suPvF7VXWPkxhLVKtUAAT/rwS86whUOy3OkYbXJ/sHazQq4t0+PB2s3Sq0aeIpKJGI3J7XnKv+kOpVrgQ1soeyZE91qVgzDHnpXyckRNthJYKW/N/H3srr/Y+y9/J9dvqtlMj/NUDwBn647oT/4bzcErVhm5qvg0AAAgAElEQVRWhQUeT2YVs6H4rlJr19PWJvcTuotYmpRrzT1PBblBf2hLWzZPnD6lMg8jJoxIr1VZcPmvtbiXXpOpLfzm7P5/7tticR5XGXHiAPSZeluJfmumq1qtIuRjBuBT13eHMRJAWlPJUrbsB9cZGinYUNGbGthUt48xdmc6QZ1j0oQnyCfRvayubYpHy35wxGpQ+5w8XkmahnRkJWvsUIuTj5aILGnCAOjN6LLzLXl8bc8l71wCKiX0U7Yz54oZx/3U9d2H6vbouTNmG+PRja0PhPYQvYZOd05wTkT6Odd7dE3PzkKZZIPb0HfO9TKaRpP04dE4Yt2TEK/0yZV8iCX/ea88VjIQT3I9ZLt3mZe7IvFuRvF3eVyEXSyAJtDvEWKCDGOxd+/jn31oWFH+eOdVub1OXKtBe4ULGrauZB36Ci/Lu3IimZUlen9oi/XxL1sf//L50F0ff1GpFmgSXT+wixRv/KEtIpkVyWzQPUCeN9Jkli3MsIUZ5I8DaCb6pbx36wP0Gq3xOenxukxQX8D6+JdFMuvs/t/vfP+fsPlpVjjFFqZ5Mqsd3ScSGaVR82+6I9wyB4BYCq7DrIri1HksBcA4sMs4sEs7MQaAPOYpdwegVOaVRk2dm9QmRiPPf0+W7rXJ/TyZJbBn1YKIJPzhkaB3qNtgN3T3DkfN/oiRVtW0plb8INISg1v+iQhQ6SsMn2lNJUo5bzmrkhccibXdnorXAk4J/Xfee8NXh9bIlvPmRPS2ZCzGWFpTEyoLK8lndW3ANMi9fsp2yG294gc9ura7WOk19LDN2muVelbXBiKGxHv5+qvTc3JNOuWxmfyT88Wvn5wveD7d9s5CmTC46PuSJRdXL/wXklx65UeR1lSSBkprKuH6Z/pz4SaCDFINotdjNVsWFQYiRlbXyHWGOARXxBruuWJFatdTkNHtYzP5tu7A7ak4edeGhfauSIRtWy/NwjVsE9cB/gvGpUrVtjL61izdFb2pTnQCwPkrCiTaqh3dpy2btcuMnEhwALTJ/dQp51058/nv0eyZu3UHXYgq/G+Zt745znNDLJ9QGjXt2OgK7zlMAJQ3QII8vCtn/+Ir0d/8yp7JHyqNGkG1v3azdmJMO7pPxNLu1t/Rju6jc3lXL2JvCVkLI0IN+8YHP2m26IH+TXeYL/+PoHeIFU6R0r4/PKLmp6TGbfiuWLWgWGWazeNdvZv8arO2/+s9AO4Ign1r/8Os49FwuaRno1XI/fvTZ1ynmTqjhalthq0AHunJfPf0QsUPqA4fY4xE4oCmtR0Ack+noGwyvA7Nu9cC/mbDcbmo+AGZ3Etf9qGomdbUWsAPtVTKC54vBXYQoq3JVJV2CWhtRCj1v7cr+VKpOut4shbdhFizeVaD8whjc65HNu1oic1RT/3Hp88MawyhDne4O1DwfOLqP1es0POejxIvNzT0gdPpbWa19JnQHbadfj72XNv7MnGfcz2ypHutUieCAr1J2bw8K62ptDf6k2PTf3fjwOLbvmD8dk/mc5Mz9PprQ/04t2j/uckZmvXHxbfSO7i+8lhpD/6djU4P/m2IK9uDX6b/fTmLXNqyxujTBN5NuZvQzHr0R1+7hBtbMpxtn5C7DfoYqbYvD5B98cg9X9o3+n8Si95fu5lVC+QcHzaKRUsSB4AwIt5vfIhOl1q2/tAWc89TSmWeFG8QeDzbLyIJAPKiYe1bEaIikrudiCTU6XHesw6u9fOtf5A6+NzJ3FCfod90x8PySClTuuOGNdVq9bVKfaxmW5xL13aC0lnHS7SSXYLSsh/Q8BsAIs2NpOIARiv1ih/clowR655OmXZclwsyhx1JxadsZ7ylS+9yAYD8ZkiaZiQVJxlaMl778UIpxpjFm7UHGrunn6I1WT5aqY+0NPWo6E2yNlKljgr+ku9G2wXizAN4cr64KR6l8YHdxcptyVjZD/5oaO2hM4XFc+r0QtLc5LL0oq0HT2N+dDPUzt/e0qUhmmFW18jnN6EyKWG7on+O50abjp78ldEcI+n8yOnB8LN8ZXpuJQ14rKAHf46u7VxBAvy/1Vz8WurBd+Lfdix2R73MuIJSOVKJlsrdl7AsnUVzbv5Nd7wF6vt/Vv+jr0veO72nVAuU7sfzk07pjDo3Cc1AvQwAvhe85/0ApE4OnUKF+tTfPGS++P34tx+V13W2fYJ8aPQDu2hNVpxltaL9i69ER/4CwOgLf22U5tybt7NqodEq+0P6yHX349YHjAO7JEiHgzYrzKrwrl7hWsyqwLWUltC97B2E1W/k4pFnvkmcPhFLK7Ui7x74wJ7/LlI972MN+A0x/qK3aVuY7TXnenuKpYbd6DX0KdVByxyWlqVUT+bQBFR9pj4UNWkMvewHMcZGK3UqsJMhG4BJ2wFgc74pHq0FPKGyaccteP6851NLft7z64HvcH5bMgagR9eOWA3ycZFAGGMsbHdLYA+AaH1kA0MJOtUJCN2pX06OsbIWLRltj+QyAL5zaoEU8u/tStI+hkiFBId7iqWulTHVqSpe9gPZ0qazqBYSptSRgj3p85P3/KzjkVbBJZTKlyHTEU2y4Pmk1Q8gq2ttLXmajvu7Gwc6ZPVrNzoZfCeaiXJXV1epVLrkbPsqReyJz1PC7W3aFp59DxPucJ5CfdtWYNWn1tC39kc+J2lxAPQDu7xbH6B1lGpBusB1dXXVX/mRuecH8N2gd0idm4RdQzLDaX490ydXIDs7VsrTrWpH9xELz3zx++H78TbcRS/UuUnUSnzNeyP3fGnvv/53AEQCkBF55psE6uQjJxNxNT8lRW9odN4fHtEPvcS7B9S5yaB3SGnUeKaPNhNUD5C1evKjo5VpHTb/JowoXBu+h8BHLOkPj9DBO9fcthb+KCJnVvVTlrkhkyaAl7pslPUSqR6t2jJlzC+VqgAijJF/vOyjx1VGNnEkRKMqSpQxtDRhKFknCfo3G85d6USYOU/4DYAyddpJfP3kPNXwt2dSVA8gyn3ZD6YdlwRwBqMmmbVP2c6d6QTV52Xujpb7DloEOsLjWccLJ9Okr0dbBxp+u/26VbOVmuz3L2kGQ+dSTYJaDF8dWvPYTF4K9UvLuObHXihTUYSqC0TO3xiPno8Pv0wsZtG3Wd3Qr+ylUrWNxh9e4SvTc8/dciNWlmSvhEXfxoeXr1fyRNdcvBsy+A7Ad+JdDfBtAnbEbkNLeV4Oy7UNyIVPTP3NQ/RO5QvPoFUP0A8+yxZm4DnQTd7dH6zdLKVyyPANgPGf/q5UKkWe+aZ2bBSROKpF6CYAkcwqdkVEUzzd07yZhWl/YJN6eiK4fhgAGc0B4F05Anv1xJh38/2ycuAP3qI4ln/jCID4DTsA0J9RCn38RelGo5TyrFqQKjoE86IrRz17GrGjsryIpZvD8S1Ep2l4xWr60ckFtRNjSq2oVItQVQBo1KEZCHwkMwCC3iG2MP3T7X82oOHpqiMcu5Dq7TP1OS56mULIJM3WCE3DAE8//fFCU7J0puFygAG3JZuickTUn2m4DSCjMpKLL3p+RtcI4A2mkOUMfdtr6FRJnjt3lp3qzNRFpiIzjfbR5qDBha7AZM317+1KEqJToVs26YmvHrYbl4AdrpaTre1opZ53PVLPTajsunhsWGPyrsLld4Tw9cl8cdJ2eg2dyPYE3m1F+3Cq/dhMnp6FNAFlFZ0OaKuiLxPLj8mFtxTn26Pc98Yx+c/yogB+MW/uWnGVvbLxbgD4Tom+E9deSA35ZSRuZKmcivCyOs27+409TymOhUhcJLNKtUD5N5pd7R94G+9tOsB+9eP49DeC/g3a1OtCjyhuAwAcW4nE4diIptQ33xCZ1QCEHtGmXg96hwjaFausnhjjmT69ZSFDBrXOtk/QVejeCODtX3yF5wbj2PGyOku30bScOboPAFuY9jbeS+V6Amw1PyWqBaUyH+QGWXGWJuWcrTukYTyVIppKtwCMmCTbN983YiKRUSoLCABVhR4BAPO8Qqfx0ukMt2/1Kh/Y+luyJS/bxgBoLI1q4NThppL4C2eraU2tBcEtidi859/bldxdrFAWHlMZD3hW10iWDgBJydJ/SXDmznRib7lG0L63XJMGNghR/MgKltrtX1i3+rGZ/FGrEVNZQlUczh3OafSOJt+oukCqNYSyc65HM/o0xVcLOBHQ5LMTspKZTY+uUe5ucd5n6n954w3/ZfyoLLPTBmJxek33RiBNl8gumlNYUo2OyiRpTZVquFhWdmblQnXhLcViNuVlxtszp96Ru1lhdAC+E2+5o6pXrgd/pSLcdKckO+zjrs4cbvN0D9fh236kj79o7vmB0CNKKQ/fg+CK4DCiMKNsYYad/JW+5ykA+sHd/uBtAFArUrrv3bxd3/tDqBpl/CieFrl1wowpgU+pfHD9sDb1uoil1dMTAKTlPAC2MCOH1pp6+Asz/uAtIpKgAj7P9PLcYFmbSvuDL+QY+caK/g3KiTEAwoiIrhxJ5bPibNC/QVQLPJllcxM09tacg2/tYJqz9S3RXJHqeevxDz4L6tkbMYDG8Gzes46V5nhXL5t/E4EHQMTSIpG5L4rnbFSEsvns9B1BBcDZQOjjL963aRvOZWWjlQfTWBcAAlSDKapCsm6Y93wCcoMpPS03+rURgxRty35wT1ey7AdEZIswRp7or1XqaU0liB1sWa9Sd5/G4mcdj6RsEDJQIU/You9bATeYQuo6qZbXC5nISd0bAMQMoHr4nOv1efqT+SJd7sn5IhX5/3ygd2ehTNPhVP+ny1GTgpj5mxPR8ynLTjvu/EJpOGrSlgUt8zfpGkchZ+Req9R/Viijxf5bPKqApdh8i6fbqcixJHu/rde+eH3Spn33Z8mdWD46AN8JAHBHPqp0d7vvyp5FW+GdcndqJ6unJyh15rnB6I++Fv/2o5UvPCO5bzI1p9TWeO0nQo8oXgOOBc6hKBACjgXHAlPBA2SuR/E06hXt0MtIZmDV9IPPktQdGEOyG9UiogmUzggzpjgWVA2AYldYcRaOHfRvEMksoXjzbvc8RS1zifcE9s4HP6lN7g/K84rXYPPT5ovf146NVm//UBL3IPn+nwNvDcv1DssGPM/0KaV804bOiPFk1r/1AbSa63J9pZRXrLLiNnj3AIngMqsiYmnqzZvPPg7XEqke9+btxr/8zF+7WY0k1Olx+B5UzR8eUayyd/P9xoFdDwFa9PobSlOnczcC6InF1JnDS879yyp32Q+IKEfatAlVnXFcAAXP94UYq9k0at9n6lSyJujKaBopvpHVrHR+C4u7vaU+mwAh9JP5IhXkAYzV7N6M/lyxQgv2mXqfqb/sVTVFIWm5esAlqNMxU7ZDZD1atuj7FmekkUeNdmrqh1sDCHmzHqrbsfyC1NihYkD4Mwlr0v35QO+T+SJR+Ui8j9ZpQ/dDdVtqBlAln64VFgaQBy8vViP3CriQtczyITvlnSz5Gg31i1/84jt9DxcOy7IufNBFRiwWuxrLXrtxTXwgwZr1+tjzIpKIPPtt/8YRdfZoMPA+4+Dz2vGD7paH3f/wYXX+OO/uF7G0iKUVq+Jt2sZz6/TxF/WDz7JGNViznuXfhOeAMQgBCAgBhSEIoGoIfADwGuABnDqDEI4Fu6aU5uA5cBtgLOjfwMpnRCrLagV+3YBSOyviq1hpLhjYqM7+Krhhs2JVRPo6EUuTmY3iuzzTpx/+uUh10/2o88dFLM0zq7WJ13hmtf++e0UsqU0dcO9+5C/Z2S8cfHKo/3/+A73BynkAIppSrLIiOHRTpK8L1qznmdXBwEaR6lbPTKtzv1bnfi3S16nzx9X54zy3Tp0/HqxZr80cAlPV/K/5dTewakFE4kqt6K+/m+5HceqKU2fFk8Ham3lunWJX4TvB4G204xGxtEhf57/n/cH1Qzd05/xfvRZ09/flJ9cLS5yZ9jZ/kH4Rg1HzibnCpO1M2s7GeLQWcJtzhwsARJ0j3Zt/rdtRxioBz+rarOMmNZVod3OeF2HMYMp745GbE7FDdfsDXclf244AKn6Q0bUZxzVZ05ntUN2OqSyhqgDGanbBC45ZjXnPd7m4ztA2xqO/tp1tq5JpTfWFqAd8xvGKfqAzJaWpTAGAoag5WqnnDB1AztD/n3zREUJXlISqFn1/KGr6Aqs0NcJYSlM1RRmMmrQ76Y8YJK5nc/5atV7xgxnHXaVrMZW9P7tqQFV2Fso257zFDKBuvfyIflGpx1Q263irdG1Vq6NhMnZfJiUPQwuPz/pBr6EftRp9pmEyllBV+vrd3uyBqkXKM4TlP10onfWDWcfrMw264lTL14fuASEDXHlLzxUrU7YjRXmXCWktQ5Vwqpe8Uqoub1hHf0A2xqOvlKoFzy94/tXbE7w9V7nMeDv/osZisSXf72Tw13C8U7rx72AYo087dz9CObrx2k/U2WPq7DHeM+APbFKsMivl1RNjMl2mSjUR5kUkIRxLOzYKzwEPEE3CtcEMBB4UBRAQHI4FAJrxFthbFQgBxqBH4DnwPfXEONLdrFaEZohIQiSzwozxdA/PDSrVgrxP/cAudWE66B4I1m4GgOKsJAGoM4dpdi78XEHfjdSY9zbcVT/+VPyGHXjf+8lqlnTu5LPIgj/p02mT+5XWavr4i1TYCHKDan6K5Gl59wBafja0iOI2vI33okXRB8B7h3kySx61IpYO6+Ss7s71zR5UGjU4Z0UkEXvi86QO9E8jvycV6alJTGwymmVvcJ7RiAeuGkxJqCwSsoQHkNGa1izTjvtI6A90wfMdzqcdd5MWRYvsPed6RPYGkNbU8bqtKYovRIyxec+fsJ0Gb/bOKfeVHu2yPU/j7/Rmr6FL3RsAVHK3OJ9zParkSy2/wag553pjNZt69jTN36NrYzVbNsWpAP5csSITZSqe05thPl3B82XNfMl/21RjIK/6tqS8jSInl5UCt4tlbdoWD4vwPDaTX0zWu4Kd+LcHcd+1uP6uig7AX6txZXXj36lYvEdpe2fJTYy3aRsx0r0NdymNGluY0abH/YFNvCunH5yLPPMNyW8nkzdtcj/PDaoL0wh8cA6mwrWbEB5LIfBhV8E0eI3mBQjgfR9MgRDNIxUFqgbH8tffxYqzbGFGffMNJDM83cPK8+Tjoh3dx8pNazg06gCafD0ALfYA6e3oB3YF1w8rVpnlp1h53r9xhKd7RCxt7P8ZDr9a3wbelUuuvn/P5A+VWlH/5U94b5Ofr84cDo/gq6315Y4h6N8QdPeTXayanyLRG+3oPmPPD3j3QJAbJMIdHcCqBVacJSYg2dK7tz5Auv1yQbYwLRIZ9PQrp9+Uz/L+f/pvz2z/jCybS7+1acedabiqArKK8YTo1XTAb3AeCNFotcPJx4Xm16m4vbdcO2Q1UipbbRo9ujZaqUvJvF5Dv7crCeClUhUO1kXMhMrmWyeSzp0Uc6UKfBhESQPf4ozo/cNRc97z5aTfjxdKJHffo2tpTaX0d6xmj8EmFn1vRifpG9orbIxHdy+Cz7CsDb1DeEmYTdw6OW6ARXZ2h+o2bWUWz6otGbLdUPD8XkOn8j49clhWDy2SIP03bLLXVtgP1/zl7Pvy99CJayI6AN+JdywW71Ha3gGw5CYm9TcPOds+oY8+rZ4Yh6ohEkejzoqzrDhLs+BKo8ZKc849v6fOHFZPT/B0j7t1h3ZsVIkD1SLMFDwHCoPCoOmwq2AqNB3pbvgu7Bo8B6rW3AdQBD6YClWD4Nr4i0h3o1GH78JtqAuz0HStWuDd/ez0JJhKOwBl4aR68ggiTUo2tQkoofeHtrAW6Z1+Kmlx9kc+x0p588XvV77wTHX8xeTQxzCEPZM/JMtX8p2jswi/qcfPqoXIzm/QJkCGP7RFP/QS/R/ubN1hHNglk376r+jKcYAY+0FuULHKwdrNzZ+eGBNdOTKtMa1ykBvU3SoApVbkazer+SnDtT6w5/86tu3TdC0ypxmt1DOaVg94VtdOOS6p0wBwueg1dBp+I9FZ8ltDS4SVlOw4YAU8rnIizJNfC1rj6ben4tRxD0evodPGYrRSJ2k26ljTsH4t4GQSMxw1B6OmFM77cHcX4fp43aatA2F/WlPnPf/D3V1S92bJbBjAzkLZqDWGNRaGybAtbPi1bORLjVga0kOrRDEQMWRjfoW2b3J+gfJyovuFDWnaSgh7y7Ww0s754k+OTf9BbzZMgz9mNXe979qMucMEXCY6AN+JS4nL7w40peVCBLFwyEE4CfAUzQGzwVu0Y6P+xntIGA4AInFWK5IiDR0poin94LMikhBmDED0B/8FjToERxDAcyCE6B1UiqcAgGnQdPgefBeagWgCgY9IHFa1eVWi4AHNVN53UTgFwaFqzQK+7yIw2elJeA4A7dgoNB2Cw3MI4Hkiox3dxzN9JJNHzrMI6c2hpZOjzhxWrLK75WHaxFgzfy+S2fjQjtEX/lrE0gIQmb5wob55bnGWVQsAFKvs33QHFd7VmcPexntp/L3NyaYpg4/W9H+1ILpyolqg1wBotv4t05oTY/BdcF8kMsTb72nU7MKpjf/01bN64tYbbz1Ys1/T7674QUH4uqLEGFMVhZRtAPQaOqnbhgnhCZVR9XusZtcD3m8anhAAyn4QYUxXlO2ZFAnF7C3XSFqOhOdI4S7GWEH4koAm828ae5t23IjHHl3TQ9q6cqKPsvC95RqZ1UouPSniZXVtvG7TxkIOyxFS0l6hR9f2lmukK5dMJg+dKeDc2bZw0ImE5TLnDhvAEzzTwYu57mE3W3LJa8vOESpUSNXb80E4OQLTPqPNY1fGnxybfu6WG5+YK4TfvKo8u8vH5s683PLRIdldqxGsWa//ag+rFVmteEXq8yv/QCjPFsmMSGb0X+0J1qy/2Gs1zV0qZ3h3v3b8oDp/XD01QWYqIplpDsLxQEQSxB0DwGrF1F/fX/nCMzyzGpGEMKIimWXlvOLaQfdAsO5Wxa4oZ+cUxkRilbowrRRmlVKeFWYVu8IWphFNwHPhe4CAACCU2tlmjp5IQ9PhOQg8nu1TCqegG2CMqQb3GmAqdBM8AGMQHKksGnVAAIDgEAKiaYIC7oOp4BxuA5rR7OUDwowrgcdKp73N9xlv/LM6f1w9fjAYvFU+GjXmYcbUE+PQI4rneLd/KFj7vsjOv1dPTTj3f+qv931r3d3/+btr7vhk/QSNrYv0dUqjzupnicTHzpwQXb0ivirIDRqv/ywYeB8dE6xZbxzYxQon1cKM/b/8pXb8oH50VJgxd8vDtGXhmdUAoJvepm3+8O3mK/8goilWnBWp67Tjb/De96inJvia96r5KZUHXI8AUKwSSQKkk13J0xNp4aWmXs8Ezk1Hnn/Pwpuv5DbEVWZzbgVcU5ShqCmAou+vNvQIY7+Ty7w/GXt/Mtata5UgOOG4c66/SlNtzqOMbYhHjjfcQAidKR9cldpbrlcDXvB8g7G865mM/W5v1lCUI1ZDVxQAG+KRsh+c9YOzfkCKbzOOe9YPagEv+4EAdEW5PRXv1rUTDZf4gPWAn/WDSdvJ6Bqx5QHcnoq/XrUMphy1nZSqArh3VfKo1UioKm0Ipmxn26qkAqzStRnH+4/dXQBM0zxj2T9dKHGgFvAN8eik7dDrWsAHoyZx3854fkxlfabRZxpjNTvMyJu0neEWOIXZeRTEm6MNAQc2xKMHqhYdQ4tLbt0rpVqfaXBgMGoeqtsjLQoCtQaInFgL+M2JGLH2JFlPxmDUPFC1Xi3XRlLxPtMIK/8crtsr5Nld7F/Ui2LwnS8On6tTdGmLXKXokOw6cVlxjfbdZUhTOP+mO0iiTj09qZ6elINwAKI//DJ9S1Xr8OnN9na9rGKaJFpZrSjMmHryCOrlJu4qrEmdKy+AMUAACqIJ2FVAgDEwHZUCdBOMwXPYyV8BgBlFpUAg3gTsSBx2DYqC0vxbCX04eADO0d2LahGOhXoZ6W7UK7AqimPz3DrFa5h7ftAsMKR7tKP73K07qNguG/NKo0Y6Ocaep9SFaZLUjX/7UW/DXdZ4M5XfM/lD4twF/Rv8rpx+8Flt6nW4NlnXqPkpYUSIImcc2KWU8tSDV0r5sI0NAH9oi3FgF+X6bSY3IpJQrDJcCwCrFtjEKIwY+m8SZ8+IWJrUdbSJUcW1jFhC11VWruHMm/a6W2fXbLn/xOiza0dyhk7iNqTF1mdGScRNZsykGUcF5L3lWkbTLM7LfrAuYtLwNzXLZx2Psv8Hs2lKOqXjGSWj5Ra0N5lujnvG9dOamtLUHl2bsp3bU3E5zPb1k/NEtUtpai3gNNtGpDYyTyM03Vuu0fvhMTa0UFk+xfTZWsluJFQmuQJhdJFSATQoP1azt2dSpCoTztrDmbRk553v/xepZyePoaukNZW48XOut/ge6KzzednJCJvKhF3gOs34azo6AN+JdzK8TdtYKR+uVNOuJbh+iPJaEUs7dz+S+puH3C0PSxDyh7YQBMJ3pQQbK86iUUcig8AHYwh4OxJTgR0Cdqj2TjPxRK0HwAMoDLUShAAUQIGqIvDhWGAMugnHAg8ApZnEy6DTS/MIfOgRCA67hsCDEIin2ewxxFOwa2JgEyvOBms30+6ExGdEJOFu3eEPbVFPjGnHRokhGHQP0CaALO9ovr9aejk59DEEeLm/6SIjIomgd0ipzJPTDELiNkH/Bv3gsyKfQKsHz5NZxSozIMyhQ8vbBoCydrPxy5+QQL2IpfWDz/JMH3Hs9eKM0qgFuUH31geMA7sUt6E4dQAikQHnXGCqdLZvjTesenPxKFHYHsllnswXqXL+YDb9nVMLEn7GavaT+SIJ4MiZeGKiEQpSgxzAgGkkFpmyyw43GbFLyH+kJ0PqNOQuP163ibgO4LVKPcaYZMgTR49wFy1Bt4LXdIKnzPh8tXc64Lp4zHcZSfHI52pryRMVIDxMv6S765Jz7Ysr/OR2j3MpcvQirEobvs8lfegXh5yLA/CP88WwkesFzxx0dhcAACAASURBVH1n49+Dpv3lRAfgO3HRcflOcW36dBK5SXvOHfnoWx4ztz5A3WgAlMrTNJq6MM0TGZ7pozY8vRDJLKsVwYMmlofRXRbS3wrlHFwPH+mH/krSUlSKf+v98zg40AFeA5oBx24eVjwNpsL3EE0A4Jk+Ys5HnvkGadeLZNbY8xTPDQZrN5PxvLow7W28Vzs2qk3uV0+M2R/5XOpvHqr/0dcBnBm5BcB1rwLJvhdyTeTzt+4wpajO1h2SKOev3Sx78CSE53fl1JnD2okxVi24LbMc48AuAng1PwXHRhCQTD0rzQGAa7HiLFSNBuQo7A/9Wez//c8A2PS/Qo8kPKsaz9QW5kTAydUUwHdOLaBlujrdcLdnUhLnir6fUA3ynJ2wnY3xqBzcIqgmuZtGq4oeJqYBmHW8xcVYMmsn+l5CZRO2M9CqNhOVndRsJMYTu15CJr057/l0V9JiDsDOQlnmwfJyN6cTz9UtMr1dnPETwQ1AVtfCcvcIYby8qGTRt4UsV0w33LBO/uLDLmfILYzui+PqIeiVWrmD68tEB+A7cSlx+d2BxXNx4SCeOQAqUBMiurd/CACjcfDuATJ0YdPjIJbZwgw0HY6NZAYOFTOXN1IS5wD58kH7A8rOgbcG6hYHlQ0Cv6mUB6reB7A579+gHRvl3f1N4KwWtWrBv3FEqRaURk2pFhSrrNgVBvBEhuWnhBlTqgWe6dOO7nO2fSL+7UfPfvckXeTMXbcYo0+n84OvtnCacvSgf0Pk+e+RQD0J2dLOhVULEp6bnjTn0vRAI/WNGpIZuDZUTXHqwozDtZpW9PNvKlbZb+0JtMn9zv2fNl/4HiJxGFEeS3VX873wGg/+KVradpQcE6iTjYp0URur2USVR2junIIS4uGoOWE7LhfEpwPwYDYtHWnDEvRSDZ5KBQmVkblLj67RQDxl6gmVkRTuvOcXPP/hbJrM5YjZTmX8veVajDHykZOUe0qdJdJTW6Hg+QfLNaqKpzW119Db6vn0mHTDE7ZDMN8mK0vbjsGoWfaDNg38cKwoBb8SMvLni6uHoB1svtrRAfhOXGJcMIlfeZYvS/Tq6Uk6y9jzlLt1R/RHX/MHb2HleZ7uCa4fluxxVpxVHEs9eQQAHBuMMd8F6b5phtAjioR2hS2Vu19GLAPtzSsqwFIFg8Bn+Tcpn+ZdvQCYY8OxaeiOJzKsOEv1dvXEuMIDAMG6W8ihjuDf23DXqk+tkRjvjnz0DKXyef6yOksjbQCUtZtp/F2O3klBm7A3vDpzWCrkBP0b1IUZVi3AtYLeIXX2CFQdgEhkRCRB+rgwDb9niNYhQVwA/vCINjEqNANG7L1rNz3nG8HkGwA2rtnQJt36XLEiEZoE5JeUWA/HSCo+WqmPpOJjNZtq1FT5p945wSpa2EaZNG0ahqImeeHIpWgG/bVKnajmwy0oDefWcn2Eut2LQ1bLD9mNDyZjsj6/ZA5NTQrawTyYTVNpnSRp0WoE9Bp6r6HvLlao3bAxHl1SffYy0/TzxeL0vVP0/rcUHYDvxKXEBWV2Vq7DQ8NyALxN27TJ/cS282+6o5m7l+cB8NwgNadZKc/yU80M2HMAQHBEUwAQiSvVAgJfcefA1GZR/cqi+0oi8KEZ8N0l+vSOpRRPKQAZsyKextk5KAyleUWPKNWCSGaVUp649/76uwAEsbQ2PR50D4hkloRowh+mMfp0WQOAtD/4cj8gh+Mr8yRcQ0YyoisndfQA6Aef9Ulfj4rz/RskyU6kepp+83aNZ1eLSCLIDTa79faCNzgiHyXyzDc5wOYm3Pc/TEN3xujTD6y7JdBcf2jL8ycPo+ecoXwAr1XqMqUueD6J1QAYjpphxKKR7rIfzDrecNTsNfQp1QmL2ADI6loba4wgmQh6tH5YDUaW1gnCJ2wHLYe3sZrdazTPItecCduhtgKA89XGp2wHjEn6Hl2IUnCCZ4nHcqtBDL7wbPpoa0sh5WgWK+GE44qn6ecrzv/7dHf9NxkdgO/EOxkkPRt55psAiFNG70d/9DVvw13+TXeYe34A39UPveQPbGLFWfXEmOJY0Aw06mAqzCjqFTg2fA/xFOwqkhnY1SufuF9UKCRtuyjX5wF8lxC9qbFjRJu+dgsn4TUUQHTlhBljpychfeUbdaVR0w+9FHQPeBvuin/7UUmRk1uo8unJtD+IPH+5K+d35cyX/4cyPc67B9ybt6v5KZnKE5BTY54G7kUsTW+SI6255ym4VtC3HoCIpQGwaoE2AcrdH0GphNYugUopvHeYegFKo8avHwpyg6xa0Cb3b+/K7TR02bd+cPb13SKas2sYvj3s3U419vDoNoEiyc3uLlayukaleJK4kVPvYzX740NZhJTgZHmAhuanG+50w6WVqXj+XLFClQPCdSqby43CYzP58bodYWwkFZeMfYqwoepn+nPk157W1Pt6ss+dmr9gaf1Q3ZYP+OR8MaNpkjNIk/dExCO5nmVWC38+uBJgv3zr/cpGZ1r9nYoOwHeiGeLlfzCqNbzt03f6+IverQ8AYAsz2uR+CVpKo6Yd3YdGHZE46mVin9HMvVLKw65BVdGoQ1HgNZqoyVSUzgBLUurermBqUzCnmcefGwoDD6Ao8F2yZ4WqQzdhRlHxUC8rdlWJJqGbAHhukKj1sn3OyvP2Rz4X/dHXeFeOxgtld+PMXbfEnvh8/IYdAPb0DimVeRFLk2oNT2aJQyfJjCQBRKz48IBckBtEK2WXyncA1JYvn+Tb05skiyslb8MPel8m9eDs6wDU8cM8mX0AeLHmvnr8WOS6/jvTiR8vlCRpTurTyRR2IGJIUl5TWD4BabBGIb3aAFAPWwrXLKn2SrVxmcrLxv+huk3vU/r+UqlKNyZT87DIzGKFmQsG9Q4ATNoOSfuRFM8juQwBPz1X2MG2LcJk/uVN5FYei9H9EjLsTlL+7o8OwHcCoH75+i1BqYSVKdtfkEi/cqY9ma8A4F05f2iL+cqT5ovfd7c8HPRv0I7uE8ksAMV0oRkA1IVp1Muwa0BLIl6QdHwAzVhiPP3tD7oHmb63jeqR0o4Zg2Y0h/QEh++BB1B1cL9JvxdcG3seXT1KteDdfD8r5YVVFrG03LawUl5p1Hh3PxnMyOXL2hSA9NDHALx66ln6DFdy10S8V/NTQW6QXrcB9pJB2T+rFshklqoCDGjc/YjcCmhH9zEg6N9wbxcW3hyva5g/i5GedaOVOvHVyYj99lR8b7n2peOnAaQ0dThqUrosrzXreBIIaWCMgHNjPDpWs3cXK0RYayvdt6W8dMpgSFPvwWz6q9Nz8vgB06BuPTHgiLhHRu+0n6D0fXexss7nAGiMnqBaTuqHP6JDdZv2KLWAj9ftTfGmxW14FyKF6HcWym2LhCfowsp3lxNLovvFZtjLnNIB/ndPdAC+E5cYK9kELPNTgv92CdXJ/eaL33e2fYLlp6QXOyvNwffg2Iin0aiDBzCj8N3WXHtIQfZdGEvuOTyHcnQoChQGxsBUMBW2Aw3gPowoAG/jvSw/RW54GsDyU+rCNAB3y8P0KekHdolkVj09SR81jR6oM4fryax6Yixyz5cQ4NVYOjzyTnZzIpIQsbR/0x1h2h2AIDdIw3UA/JYuPZr6tR9Z8vm8TdsI5qkREC4qtIU6c3hT9WxXMQrgdbvU07OB8mAC3dcqdTKSHzCNWsAnbOe5YmVjPEpa672GjgQA7CyUSXN+ynbIM+ZQ3U6ozGBKLeC7ixWDKbI1QJBMo+1keSe78tszKUlljzBGlQAi29PdErrL9jnl2USI+/FCCcBZzx+Mmr2Gfj76G+0JSGyf+PZDUZP6AmM1u02shl4vNnm7snHfG8cArLwyf2lp/ZLATwq4t6fii5fqbAiuXnQAvhNXOMKJ+/mSeEnBYwsz6Gr+USMTVfPF7yvVArmvkiJ6c+bNjHob79X3/hCaDtcG094C+MV0tndvyME5Ds+BqjdVch0LegRmVGRWK2emaUBfP7g76B0K+jeYz3+PPgr7Q39mPv89WojgnJQD5OdM4MpK+WDt5rI2xRZmkqvvJ5q9/Clx4NX8lHZ0n4ilKduWmwBizNH6NHEHwL31AfNf/hmDI2HNOwnksiu/JLQHuUHj4G42N8FK+Q3vvWtvIgdgXX7y8OpNRCaXEq0ZTZtzvVrALc6phN4m9gJgZ6GcUBmdQvuAHl0j95qEykjAbtbx+kxdMvWkG835auAjqThtAoq+/4ere3GusY3k50uzlh5dG4yaH+zrrVar0w13SZkamuYPV/K3Z1KjrRvoM/Wwbt0Ki+1h5bsrPu++OK5U45zWoaWkdc0Vv0onlowOwHcCIAA+tFu97B58mDwfe+LzRInHhcr+BAzq6cn4tx91tn1CPTEmrLLSqAVrN6snxoK1myhz1Q/uRrob1eI51q7XErqHIvDAVNLHDYa20AOivKAsnITvQdNhxgCoc5Milg7WbuYtvRqe6fNufYDnBuPffjTou5FMZhsP/SlCQr9KteC12udlbcod+eh1rwLAy608W7HKZBorunKUfNPInDa5X0QSghRwJ0YVt0FvapP74VYxOIJzDXIA6OMvyjJAuF8gtwLaiTF/8DaSylGnx+8EALCzs0Mh8hqAQ3Xb4pwm3GiYjaJNqQ3AYNQkyJfecQDazqKY9/wG51S6r/gBNQUk6E43XMqwNyeiZGZD1Dz6KaX4r1XqVGDYnIhS7t6ja9OOS8evJGQPHsCja3rQotwv1qRbMtrkZld40SXWudJqNp1pumsiOgDfiWYo9/yuu7Bwta8Se+LzAFh+yv7Y55sAcHSfFLCLf/tR9cQYAJp9146Nkmm6t/FeAEq1wIqzrF4GQr1tVQWAwIcZa8rOXyvhu0THU08eaRrSx1PwPXC/+SO7isSqoH8DCxXSg/4NbGGG8L7x0H8iY7rIM98kjHdHPko6gHLyUD09CaB+/CnelUt23YPVfaRmT4hOTDoiyhkHdgFwtu6I7PyGSPWwWlEkMrH/78vOPb8HAA2zrd+Plgcuz/QRqV6dORw+oLlvo9L92s1qJKHOHqHWvogkIs98876H/lQeTEi2u1iZdtyMpm0Paay2+afJeTZ6h1juu4sVi/MYY5JyP2U7w1FzvG7Pe/4Rq9FvGuQuQzR7krDN6hp13MmtdbGfG2naEOGOJtoHoyad/huqZoX8ZGXxfzFg045kYzxKV1lGk+58JPnzDeMtucjiWEnufgkgveQpHeB/V0UH4Dvx9kXsic83S8FDW+Lf/Swp09kf+zyB0KpPrXG3PAxAscqsVlQXpoUeIfkX/dBL1IwniRiRWa14jaatiyzUX1voTqL3JH9rc6zqhe82ixOcQ9WIVAjPMZ//nnfbw9rRfdS28O5+BK2quLHnKQC8q71xa338y1S0p/a8Mfo078rx7v765FMA4kM7EODnOKf7LsVwQNPwlXlhRKgvQG7xuPVevHkkfArR6LRIQrHKBPPhH9GLMN4HuUF1bhKuxXuHqU3QtmMII1YberX1tsnRdTBqSgd0mem2qehQR79C6kPArONZnH/95DxBda+h02RamPJ2PpSlvgDh68Z49KFc9w+npung75xaoOb9I7lM2/3QueHpAJwL1eHnulIk+fAjXPI43G/3ZMKuMxd1Ytu3y0B+Z0NwVaMD8J24khHuu0uYwVJlf57ukX/cpT86sb5ZeZ5SdvXEmIgkWHk+6B5QTx4J1qxX5yahasrcFFQNvtsUtNGMCwvMvXtCivAAzf5C4KNRR+AjmoSmw641LXAicQCIxLVjo6RoS2m6Pv4iK+V5z4B++FXeM6DOTYpoynzlSevjX6YrUJkEgHwnHNb43wOIbfrfkOevtniOtPEiCh4p2fmDtwGA29J0O7JPPXMKLcyOPPNNVpzVIonF64dH6SLPfDPo32Ds+YHy/7f39sFR3Gme5zez3lR6rRYlJCwERiPjBRbc7uYde20dfhmre+m2j2YIzqu5mImG9bivaR/hiwmb7Y3txb0T6yVs+uzwmbmdu1Y4HFrbi9vsWIxfaOzGvAhkYzdjPAadwBQySCrVVKmkes/M++PJ+ilVbypJJdWLnk84uktV+fKrlMhvPu/OJQCoLj+5Sy6RjcKRdFFnuoSPxGAVaiYjcuAfqq3+ndu7ptL+kdc/ElOimtZks9IENoqpi0b3YgSccQ3CRk+wvLsG3CJNr9IkU6q8GPeesB6xF5UCksAfG/blKqvOqOjiK/zV5evGGXFTJWHqzOy1i2ddnz1Y4JkcY9TydHF3c2+P7BsUdp480Gc70RF89GkAan2zyX1ditdVy77B2PKNwm+P4CgiIWgqlBgkWVfKIlJ3SON59ZqqfwXZBAA2O0a9ena9pQxmC2x2xKKapUyzlcsDfdR5hpAH+hTnEnnwOvweyCbN0aA66stfeybw+K/H3SRA5cGdkS076PnJcuE9AKabVyLrf2RyXfJ7PzZ/fca+4a8B/MH/md7HvutltWpB5Hs/sHz5kfnKWQDqwmWgITQWi1rbJHkHqCuR3uimaoH5ylm14Q5pZJDa5tB5RdEdAMk7EF11v+wfpl00/7CI2ZP3Pl3WfTLUZIZei54wxiGqpHPve0Yeqq2mzq/Xw5HmSHRjPGmOsvCooy1ltv9xNLim0k7KbXS2Y2IHHiTluJ0Ox5AEDbzBZElwtPKEyW/pEN8ruRjP2OeHNhZK/1eXr3/43eXJSW3MvIIFnpk7SH5Iq8b+8kUhFbE7N1kufUK3e9PNK2plrbnvU9XRII96YktW63PfzRbJP0yd5yGbARiy6IuIifmAZMerCsJBBP2w2lFRAwDeQQRHEYtClqXgiBQciTV/X6takBwFp2iFWtsoewdUR72x+JCus+x2Sf5hyzd/jN71IADqK0ApeJrrkt/7cXR1a63re6jC752y5B3QHPXmr88oS1aLOnhKx4PHpTfSB8zf/FFpWqk0rTS5LkmRkBTwRdf9CPGp86Jxnqn/K81WoZXXKE0r4R+mlYuaOrVqATXSSf5SSDX7/EPPCNWbARiOxqjFrFHbDrkG6HWlSRa9a/rD0eFojCrrGm12ej5otFnoxaoKe0OtxZjBZ2yGIzrIimEz1+NB9w89I1di6tZKPf2bnhUoFV88K6TLIUj3l5Gh23w6Zwat/FYkamyB9zfXbwH48LvL051oJrA7vbhggWfmFPLbU6Y9mXR0c7e//bwU8Mm+Qcqqo1g7KT1iUYTGUOlAaAzQoCowWxGbZFRJkWG2ABbY7KAUwrKK6Pd/YPnifc1eLQVHVEeDue/T4P9sSJ1zXTLdvAJAs5VrtnLjkeSBvtidmwBI/mHz9YvRux+WvQMK6frqVtvJzuSTe5pk28nOCuxA1feg4A/1zdSAFoCxVl5k4IsdhbNdnz0TD+TLnn5p1AMlplnLKEJvPJ1eredMDOsKhTP6sQ+5BsiP/eVYkHrXk8Y32iwJstcfjlK3WsSHvdJAWFF6TnPbaMuHkvznwlFvHJOzpMx6KxIVoXoYZHVVVdUb126sqrALQ5+cAZnbzWaeGTPVuDtdIioToHfIJ//moIds9xn61RPknEvaio68Cnz3C9ueIyftsvZXDm1vnGRzplSp/lVbZO0Ponc/TM5k89dn9PnuZZXxwa+Azw2zVQ9aF2ZPm5lAE2ZNZgCao17yD5svn8XYiDTqhcksh68CoPnxpI6htictF0/Y334egOx2SeGA5ukP3/tr083eyJYd5I2XPf2xJasTzkNlb/Q61Pak7WQnZTjGWtZ6FskALBdPVDnuQ9X3aNg8taFVmlbixiXUlAOgOXXCTKeVGNVa9vTTvFopNCrf7FWqFwKILV2T0lJPQCjcuZGxxsErAGSYP6ywAxBdaakdjd7C1iByAVVdU2nvC4ZHFdUTi22uqfzjaPCheEN4xFvcI0lHqXkOOeqN6W+UaZ8BY+L9h54RKqXDxEg84jXx4nWuZsYIZ4MYRici7rmSXpbwYid/At/9wrbnrrW/cnR7I9D9wrYn9oI1fn4gEvGoOo4EJiHxSmlaabp5RXa7oCrqohZ54KpeP1aMJe/ZQI1uQmOw2CTPt7BX6nazyQxVRSwCq12tWSgFfEImo6tb7W8/r9Y2KkvXkDee8uysZ4+E4uVn9rd+LWrqZO8AJdWL62+62WvMwhO/lKGN3wVQ98nnAD521IeomUGFXe2/BkDyDoieNmVdL1Ocxaj3Jtcl2T8shUYRCcBkkkKjWlmlMO5F4oVI2k+p+vLgVa3SCQAKTDcuKYtXUpNaX0zxxGKNthSO7oUWs2hFd7+jCvHhNORjF0PZyR9gdBVQvTsdxNgvluSTzkstaY32dwJ0FhqNMxyNGcfDJ7j9cyXwVDEIoC8Y/mw08NloIDmfjj3q85x8CXz/W6+fWNb+iq7oG556tnXbc290b39qQ57Ww8wpCcl3FX/7cwrNqo56k+uSVl5j+eIDzVYueQdgsck3e6GqgAalAFrNzxImM2x2mC26x4Lm45ktNGsOkqw56tX6Zj0dwYDStFJ1NiHe1Y5E2nayk/LsRvd1ikKG0A/3Zl6CEH7qSkRqUfcJ8PllAOerIXL3SJtJ2gGQryW8ZQcohb5ppeQdkAI+CVAd9Wpto1qlq13CrJp0Bv0DtdW/uWGCAgDrbXDFnThkWJM7+rRv9H3PiDGdvi8YbrRZFljMYoxN8uRZxKWdRN0XUzoHPAldaxLscgANVosQbKHx5aYgDB5+0cg2oYFdStK1tk1+MzPrqyv+6vJ1AH+9pOGB2urknvC59ahPu6SNnzPyRZ4Evv/Myau4fde4wb64aRk6Tnc/tYEVft4ibvd6+9VTbyAWRdAPSJCkYu1Ylw2SDCWGoB/faUBQ73qGGifGfHrrGwCA+fJZKTiiz2tPhbm3h1oHKotaRIN6o2zTZtZTb1CQHvHOBLSlsQuh6DwojMK6Tz7HiArgY1O/3hDX00+NbKWATxoZFMugZzXNP0yz5MnJT88EomXepJdkn+R9z1QF4HoMD0vBqKFWjSScDGWaI7e+umJ9dQUl01EinihtF51uAdCkWqMXnT6iI/zf37rpR2PpWkqt1efWhGPGMTDCvT8cjdHcd7GlMLXpdbrWtuJNml+X7uwCSqb7XxsWUD/8SS9pTpiGQnPkPo/kMQa/rGnx+A+NTbcnfLx27fhdoKenB7OA0+mcjcMWL3N5QbSPX6cX0n27FMDhcOgflNtRVYmvziAaQv3tuHUVAFQFJhNikTnW+Dn656GpgAQlBvcNQMbi5RjzIRxCJATJhNoGAHDfQLUT/2ITAOv/14MH/g0AFahYtgJfnQGAFZuAFXZxGUcrJacTdJ1XrAWAT/9B/2jpClvIq7++7XasWIsv35fu26VVVSJp9/E1/vgBeiH97kPdpg+MWDwu3NYCiwWy2VJVCQBBu+Rw4N5Htf/eizIbblzE3fdj8BoALLwdIS8WLMKNi1ixSd+SDv6Zvjbpe3+qn+/eRx8Vb977KIDtDgeA8gH3t6GwVY6W2SxWObqy2jIUjrpNZgD/fvWdAP7T5WtfBkNfDnn/dYPT4XBsdzi2A0988U91NhuABxZWfuEbBfDPiuaPqesWLvjI7bXKpv9nyLu+7jsAttQ6TgeCbfVZ/EMYcFdVVdFL62ioqqpqa1UVgN/dHFpVp5v7dKjtDkfXgN4jsq3e+Z8uX/NGFTpXuSlIf/nl4VhVuR3AKY+3sbpyVa1D7C5OSAf55Vd9xt/Ia66bK6r0nL4q05jxn3BVMOpI81E2vOa6SS8eb1qU5S4pTzHDZRQ1ef+yeRL4G66rwL2J715z9WND3Ko3irp7FlqoOp3O2Ths8TKXF8RoLJr+/nAF4PXqkmMJBFX/qDkUNikxSYlJZeUAEA0DgCJBmzuBNwNzVIdHk+NNZmgaJAn9l1FWgYoamGSYrarZSpNYASASIX94xD8KoAIYXvUQVj1Eh7GePaLEL6PJP4q/Pwwy6ysbAFgCuqdbDoXFmdVAMOr1mvyjEbfb6h+VT76tv+9sSuhbLP48hu75Lv36avFz/bOlqz6xfKBc/QrkhvF6LRdPKCtb9Wq9P/x3PRHPP4rFq/WsvbKvaEsk+O3jC4AxNh//UgA228yHBod9MSUSNq2qsN8KhiLRWGBMBuD1eqlQvsFsWlVh7/tn31tjATJtt1aV6+Hqf/ZForFHFtR0DngiqvLhtwHqO7vKZnEoCoDj/bcarBav4YxI7zz3+/304g6z/OXQsHhN71PkPjAWeKC2erNNv9O+1Xe9SlUcipxwrsBYwK/EAISCIXHkQCgiVvKhZ0TUv10OhH6ysJZ+HX9qt7z5rT7uVrxJZPhoUoxm9yuXLmdjdqe7gfj9fq+iOzD8gZDbbUnepiSZyztquicJLpNjCgjKwLId/zt51EN92vXObrJJRKPjPeBKA0NRgBIDJJgt0BSoCjw3leUbpNCoWttIZWbK0jUAtIBP9o37w429Ao1tBAGIeX0J6euxlrWUZg8gGp8GNCnax69bJ84i8jTJ1FMv1rK25rYHKWT+oWEXcsWLZDrxZobou8l1KRIfk5Mu656az385FqTceGMdPGXaL7CYqZetsTO8KF07Nuw7NzLWbLdRq7vroYjRhU6kbAyHiTnw1KpWbJawr0ikp1nyxg3WV1dQOd+ooj7eskDskqFTPY15TVfankF98xgpNx6NY/D5Ik8Cv7hpGa4lvXt7E2fRzxtENrUat94sF0+oziYamCaPemC2YswHk1mrvQ2A5PkWACQJKCWN1xJ/jEUgybBXwmIzfXNRWbpaq1qgBXxaeY3IsFMW3WG58J61591wa7uyqIWuJLWxE+prHCBLtXBiYo3sHdDiWW9UwiD2EoJK74vjmHt78K//LbkHjM8QNMwmsvExX/zNms/p/xsxoP7B/5kx3G698J7s6ReTacQfwDRI2QUWANXI0QxZmuae0G6WoA45NWZTfzhKjwuUKo94BN2o6OnWILzuyU1pdCVM+gAAIABJREFUEkbcJnBuZIyq9W5FoiLjD/FO9eLxBcD/GPaR1U6l7VO+TFMhIVJ+bmTM2DJvhkebM13nJ4kE8mjBX3XdAOKK3u+6Btyev8UwBYT58lmUVaiVtZpzienGVwAk9w3UOBEOGLq4lyQSoKGiBmYrxkagqZQ5L/sGtdBoeOtf0EaWC+9F737Y2vOu5B8uf+0ZZeka0lHRpDahSIHeDMen1FjPvaMsuoNK3UQuXjLWs0eoLUF0davsduHTf8CfbKSPyFUgmtgbAy4+Knpc1KLX0w+oQCM+v/z7+iZqbyAFfIqzqazrZWGpm9wuYeWna1OfcnpNAtS+5sux4MWxYG8w/FBtNaXa0adGoTrtG60xmwCsqbTTEwANjgPweMOCDz0j6brVCj70jKyqW+BXYudGxignLnlSjrFBntElcMilP2kZlze+7+eXx/eKe+Mxt3bwuZExug4AXrs1PKVJM3mEs/mSyZPAN266d1nHSUPI/YbrKlp3cQr9/EHcqW0nO8Ot7dTrRnU2mS+fhRJDLCKFA/LAVUSCktkCNUbWfHG2p50MSxmiIZjM+E4D/B5Ew1rVAvVPvmf65qLli/dVR0Nk/Y9k74DlwnvUHRZAWddLoLa+NQtVR725t0fyDytNK0loKQfe6L0vf+0Zy4X3KH6v1iykEbTkMhGrSPbwU+ldSlN7fD7QohbbyU4h8ILo6laPYZxdzUAzWrbTR3/oTcyZNcq2aHYrxF4E6c29PTS9RgybfwQ41vh92qzRZjntGx2MxspkeYnNnKGdXHO8ZE545o3ynFC2vtcwtz7BnM3ghzf6DKhEHgDNvltVYU9ojnsrEv1/b+khfDLWhVAZybliZX5ioK9TdN3s6bLfikRZ4JE/C75x+67Wjuf+81ubDlGjm+dOLGt/hfV93mG5eELyD0fvfth2okOrWmA7/ndqzUKTfxixqOQfBgDZrNmrJQDhIDRNb/wiGUa2FC/ieUVPIYwhFoG9ErGoWrMQQHT9jyxffqSVVcqGZrHRux8u63rJ1H851vxdrazSdPVzAHq/dwNGdbeePSLFZ73Inn7Ze0srrzHdvKIsukP2DqQcDmRUep2LJ23fXqOaOjF/Fll72gMXX6FpNFrVgorbd6AKGFABUL+8BJLD8ObeHtNAn1Zeo1YtSHAAPNL/aXR1K02QQ3z4ui+mpBzxjixs6ISqNqRxU5/yePuCYdFaJ9nWN462M9bmCe0n9zvx10sabkWiwlYWDwfGR4HckmzvCr3P8GyUJfmKuxsTJtiIRz5d9BueeqV97xNPbOsAuFXtPCFBdQCQdNFcc3mgT61t1KoWaOU15stn9X0qqgEoDS2mby4CgKrCWgZVLQVfvfBGyDJs5QgHYLZqljJSd7W+WXXUK84lsTs3mb8+E7tzEymhubdHrVlo6r8s919GRbVWe5vuxvcOhOMZc6IgHvFrTia76eYVyTug1i+TPf2arVz29AfanhQl78mQoUxnR6VD8g9bT72RvHGsZa2I2YuSeuOPhGjUM3btDTFKuGagmXz4tM3Hpv7kZZhcl6i5PXW2ByB7+s29PaLIHgAav0/JazTxfWN1hVFKMVGk9zbVk8anG9gqFC7zwLfBaIyC/RTUzyZc/TfXbwldTzDW824rG+VwepPg0x1tbjAOsH+gtjrv17MQyGcWfeP2Q0e35/H8zJxiPXtEjodahaiYe3ukgE+tbw7XN9tOdMSav0tjUWgUuj4p1e0y+Ydhs1OrdgT9UFVoKixl0NTi6Uuf1KiHZsXS+ByTWatfJkVDmq2czGspNAqsoWGvUmiUktUp7G259El05T2yb1CzlcveW5J/OLJlh3hm0rdJlYIeWf8j67l3pHBAioaiq+4nH3tKjO2EI84my4X3cOf3lUBQ9g5QpF84yWXvQHKPvOSHgFDbk2VdL2vlNVLAN/bEq2KbIQCGsslaVyOgG/cAgMaPL54wXb+oOpcgEoC13HSrN3bnJm2gUnPUlx17SW24g4L6TV+fxF1bAayptFOiHOWppUyA/9AzkjDMxvgckHkejGBLrSMUDJG3IEP3OqOZDkPjoAREv3rSRXHey4HQ3JvCOZwEP8esr65IGd2Yt3CZHDNHkMklXtP9Pday1vbNH4W9TgXfUsBHU1KkgM/kvo6yCsQimr1aioYQi2gLb0d8fBkiQdjKEYsUQWxeliFJ4+ukej9JhgbY7FBikn8YSkxZdT8AhfrEtawFEGtZS9EKirtL0RAAk/u6ZikDEL3rIaHTpMrhe3dScF1ovJ4T9/UZyT+sLLojevfDlgvvxVrWyhML2JBUdJfwpkD30jubkEUHXMJy8QTNqJXdrgw+A0+TLJwB5IeodTVi4/9u3OYP3s/CW3aUHXtJDozA0x+960Fzb8+/dNQfC0Uc/gG3O7RKHbu19O4ExU2YP2ucByPmzCL+HJB96jj58xNc/cZEOWMCfLJSkiubFkOaSjYoteFDPN+NNs6t1pZk9VpJfqmZwALPzAXWs0fkgT4zMKFu6uwREi1K6ja5Ltnffj746NOyd4AkTfYNRlfdT35d042vqCZeoj7tcjx2q8SKQN0hQVUgyUA89E7qvuA2+NwANEc9+Sroi+vxckNuufXcO+S6J/M9ducmKimU/MMyILrQCywX3pMCPtXZJBrWIl4Zb7l4QqtaQM9bxiz6lK1qaV/rqTfguSmHwsKLkE6hU0JZfvSaUvcn3V7/FhdP+JxN1lNvmNzX1cpamjFYdt8BDKj43l/pWytAVePH6H+k/1O5smmJowqo+5PBr/oWrxEHPDcytsBipv7z5JyngL1xA3qRLm0+gQdqq08HgjTADQClyBl978aNMysNfUp2JyXzkwvhtVvD1II3m/VMj5KUwJL8UtOGBZ6ZdUg5YnduEqngVFslu12x5Rsl/7Dp5hXZ00+NXABYvvwotmQ1xYnJuKeR55q9WhoZhq0cNrvqbJJv/BM0DWrhqzt057ymARIUBZYyyDLCAQx/C1mG2a7ZyrWyytjyjVShLhsmtoE6BNQsVOubbSc6wq3tAEjdAZBZLITZdrKTUt9VZxM9HyTnPajOpizNbsHovk7bW/8BgLm3xziATpAy6G4k1rI2OdNekJDzn/jplh32//Yf5NCYPOoJb/mzYPffAJBCo+Gtf0GPOHriHkDd8gEAC3H9FoC/XtIAgLrHCKudOs+jEuL1pNPejEa5kXQu9xlCIWSjPTqX8FCZkoEFnpkjKGpL5iaZdJJ/WAr4Yndu0qoWqI562TsQfPRp+9vPR1feo+d4e/qVRXeYbl6RB65CVaSBqwAQGtMamqVwACYzVKWoUu3iMXglCgUwmSGbASAWlb23onc9JPmHKd3d5LoER32GAepCBfV2sPHecMZx79TixphtNyWzOwHp3/6fgTR9N9OZ/jD0yYm1rBUz7lIuI4Wux+1+k+uSsngFVQqYvz5DL1RnEz0sykDg8V8PGZYh+utFNj5GQ28B/A/DcHcyuD8bDSS8Tt5SILrIicL0HDYiFYJ6KxI1zplFPnzO0z7da66bXIZeaLDAM3OH6KxCdVYyIG7Z5HCnsK5WXgPfoNK0kj6VoiGoCmQTbOU0FV4Kjmj2aqgk7UU0ZS6+VFWBYyF8biCG7zSolbXywFXz5bOx5RsBWL74QFm6hiSKNJ7Emx59AFAKOjWTob6zanxLTGYrZyDZjJ7ULs+McZGy2xW+d2eGvjoC4cwPPP5r+qZUaqFVLZC9A1JoNHbnJn1gXdUCozdCtN9RHfWqo152u8pfe2Yo7m94c9DTOeChQXMAzo2MifzwhFT2ZE3N0oymzWg2HdJrc8o3M3SzmUul5F4xJQYLPDPrpHTA0j2aWppE736YzDVzb0+4td12okNduIRu65EtOywX3jPdvCK5b8Bmh82OsRGMeqXgKGIRSEWi7nrJu6a/1jSMeiHLsFcpziU0AVZZdIfqqDe5LsnkqAAAiAQ6+9vPj/30NymPrTStlN0u2Tsg5FNc7QyDWVOKd4LxbbTL8cPdGXbMFaIZHyY236W4AwC9O4LhYdG4e8LGMHgUSKuuhyKUDWeMbSfI6vREjvairreUIvd0r4vM8Q89I0/3utZXV2RzcNZUJoewwDNzQUoxINs0+Tata/yWesQTzdSahSbPt1rVAikaQkW1Pn5GlqFphT1+RhqPvou2NpIMqNBUmK0AZN+gFA1pcSE3X78odjZfPhtZ/yMA1b9qi668hzzSmKiCRMJlNF5Po0Es3szgVE+GPN7ax5VY9VD5a8/QM0d0davYMXMEXVDW9bIU8FEUf0pPCcbOuJRzYGyhn/lQImBBnxrLz4ybTZoHZzTi6fUT2U0CFa1XlpeX5SWgbmRWvf2PNy165dLl2Ts+Mw1Y4Jk8QLds6m+j1jeLkjlj4RY5nKm8ytzbI/sGJe8AbHaEg7DYVGeT7L0F71C+vkK2yCY9vmCxITQGWYYkw2JDLIqqWsW5BIDJfV2Khkw3r0gBH8Z8av0y2lXyD0dXt5Z1vQxArW+W/MPmr89EtuygT003eynVLiEZ3ghVzYnX2dvcJKj069CLGgaulV94xtjALqHNbfJBRDWdubdHHuhT65tJnisP7gxufyZhVXRGyqlMdjwYy/YSFknd8sWhjBn7AFRHvWjf+78A3b5RAL13/3CqCiR89cL+fs1180/t4yn3NJ3ly7Hg7PWemyHpnAdG1Z9hyJ91vdBggWfyg7m3R2gVOWNh6EmuOpuqf9U28ssu+jHWsjbWstb+zn/RLGUS9Kw0mK2w2ADAbEE0rM9ho9Yx+YImu1vKoET1FAGLTTfcVUW5c6PsGyQnc2zFPea+T03u6wCiq+6X/MPm6xdN31xENCyFA3ozO2cTAGvPu2M//Y05qX+7sf4tt5B9T+Fz67l3Yss3Ru9+uKyqUr7w+wy9cVIiFll5cCcm9kJIOKPsdtFv2Xb876gwMrJlR4aHElqk6JZvzEYUHgJjRz96vXYRAGzo+yiyMO11m4bIUWcY6jPfOeDZWV97ORB6vqXpzUEPta8RnoNCK9ROVv1CWBWTK1jgmUIhwdMb2fjYd/5ycWTtDygYLw/0IRZRnUvU5Rstn76LsRHY7Grjcnngqt7kLoTxOPfcI5ugaYhFAcSb62mQTYDelQ/RsOlWr2avVp1Nam2j+fpFzV5N5X+Wc+9ojnq1shaVtVQKbwqOKA0t5r7Pq3/VRnVxsZa11lNvSKFRSpiftJQ8M5M61ceH+dYsNEpycgO7bJzt1rNHtPIaejhIflJJIHrXg8a2u9NL/p/hc0/2IicS65aXl4khcsb8OFJ0igiITLoMh5rS2RkmMyzwTAGRcF8e++lvKv725+HWdhqZqjoaqE29sniF6VZvrPn7UsAX3PUfLRfeM1+/iDGfXmierPHkJ59VNA01Tvg90DSYLFBj0CTIJlTVwu/R12C2StGQFByR3S4oMXXpEgCybxCqIkVDkn8YZRVa1QKqibdc+uSf/+sNOjb5nNX6ZtLabHLRiQxCnuEIE+zsr8/ocZOAPbJlh5grTxtkH8unVrUApIBvdF9nTjL16FHDcuE9rWpBymtidNdnyDdMYNLGc/T6iZVLXrl0mczf66FIwjS5hL2yOelsp68XmvOAmQNY4Jn8EHj81ynzvzCxN+rIL7uqf9UGILryHupkLvmHTdHEMRLRVfdbvvwIfg+UGGJRmC1QFV3UHQsRDiLoT1wBudMTmHbKnqZibASqihonwkHEADWm1S2RgiOIhFBWodYvk8IByfOtuqhFK6ukzHkdi03vthuLqM4lWnmNtefdcGu7kMyUvWWMZOoSk9GwTrmNMRlChFFQVRlZ9VBCUH9KhxWzbpMfBRIGzBuj+5NCNfGTpiCYbvYmTMRJSZZZ7m8Oel5z3STDHcADtdWv3Ro2munJhzXuni8yP7Kw6pceLPBM3kjXE83Yl01Z1DLyyy7LxRP2t59XGpdL3gHEoqqzSVm6xvTNH7WySsotN399RrOUofY2KTgCn1uPxxPewdSnTzmlZiYJ+bEINBWjXjgWivMqDS2mWFRZvIKarUqySSurlH2DNPcFgBQcCW/9C5PrkunmFck/bLn0CYCxn/5GdruokpsatmQ47ZRS4rPcS4S0pxoLSHlY0ddIq1oQvfvhlO3oE9Lokp8SUlZaTmmRucpXeHPQcz0UKTcFh6MxYbhTFVy67adtnc+G+iYck3W9hGGBZwoXMYdUdTZFV95jufRJrPm7ytI1li/eV2sbtbLKyJYdeh1dfbMUGqWGd5BlqICmpfLMS3poXFVTePInNd8dCzEynHpfQjYhFoHnJsqr4VhIRW5aeY2571OMjch+D2QT2e7mr89otnIariN7B7SqBaaedwFQPp3+pZxNAJRFLTNsQjdVf7jRsQ/REj9gt/pTT51JeXAqrhPvk1dgSqZ5wleYxkPMLEGtbKrK7c4FNZ0DHkqbn6FMprSkZ8Nvz61sckKxuD1Y4JmCxtiTdWzLjoq//bm573OlcTkNOKePqJZMWbpG9t7S7NWSfxhBPzQNGsmwBJMJiOu3Eou/n4Sm6k8AyUpP75AzwFKGaGi8rn18dw3WMgBa7W0AIut/NN5wzdEgm60IjcWWb6Qe+wDCW/9CdrtCbU+Sf2Lsp78RX5Yy2I3HzmDRpkR8SoVkmFi5LqrgJm2FO15zX1VpvvBRcgl7uhy9hKcTfS6OdyCbWHg2cp7u2SIhT3OqDzcZ2tilvJU32225usUXuFQwgiJ6SGKBZwoO28lOACld02Tg2k50WL74QK1ttFx4L9T2JACSSdXRoNY2mqMhKDFEgrDYEA3DVg5V0Z32FhuCfn2uPI10E5itkGTY7Bj16k8Akgla3FiXZXznNrj7YbFBlrVFLdLgNUDSPwLGzXp7JU10FdopD/QpS9cogOXLj0w3r6g1C2nuDn1KGQaUTyfUSOSy0UGM5V4keEYJTI4uG7uyS/5htWUtDKJLzWpI9ak//FRHw2X+VDSXFVdADLE1uS5ZT71B0+uzP2My6VrqwjB3R5TFT/XgCW1lk2/l1Kuu3BQMhCLZTHubUs9ahskhLPBMASEqsJEUWDUm5VF2Pfo+D7e2l3W9TBqvdzs5947iXGKK9cJmh9+D6gUY9ZKia87FUnAENjvMVvg94zF4itZXOuD3IDACswWSDFlGODTeXFZV4e6HJKHGiTGfNHQdAIk9ZBMqajDmQ3AUSkxpaFHrm+WBvuRRMWplrVZWqdY3A5AH+ipOdCAu7Zhotppu9pKtTBdhUqt3Sh1sRLMaKj3PvO94wl3AjriUTqrxMAitwNi4cIZRgwk9dCcO05teH/4pQYJdVW73K7EsBTv5HQrki9cpNX42MuA4q25ewQLPFA0iKc969kjw0adVZ1PF3/4cgJisGl3davniA628RnU0AJBjUaWhBYDpVq/qaIje9aDliw/oCPKYDwAsZQCgqVAVhIOwV6nOJnnUgzGfnoofC0OSx13xFQ695k2SUekI3/dvrOfekfzD8NyErRzORsQism8QgEgOiK5uNbkuUYM2tWYhlYPb334eQPDRp41u8wSSx7xOD+pUj3jluvXsEYr6w9DoPgPjZ79xMfva9AQJL3/tGXE1kN4Dn/IrJ/ffNV6NhGF6WTLzIr2fLKx1Op1u93gnu5S2fgYpFTNp6HU6rZ2lermcH3NeUUQPSSzwTBFD2eb2t5+3v/08JdvLox551KM4l6j1zVSNpixdQ6JrPfcOdYiTfYOao16KRSHLuh1vscFmVxpaZN+g4lxi8n2Gimr4hvWJrtQfV40hMAKTRW+GD5i/PqMsukO2lUvhgGYrV2sbpYBvvFrddYkWqTStpCnvthMdANDz7thPf5PZ0Ezpk6cfjXnp9E5yMULEMPpdvEl9acy9PdT6l9Y/msVgeF0F37pY1vWy7OlXaxu1qgWTSqNRj8k/D8B2spM8E+ONdAxTYaaURicmFemjYy+eiK5uFVmZSC/eKdvpp2Mmt/IiitQyU6VYfpss8EyeySYrKoO8RVe3Uhn0d/5yMYDgo0/bTv03k/u67BsMtf3McuE9ANQ6hrZX65tJ71FRDbMV3kGoCuyVmr1a9g1qtnIpNIqKatXRAGs5Aj6ExhD0Q5K1hbdLg9cAIDiq1d4mRUOxOzeZXJdI12lIObVtl90uk+sS9V0HQG4GACO/7EpwUBsxvp+yGTt9KvQ+sz2dTrf0bHZHfcJZMhu11rNH4HMDUJauoaH1U01lJwvbdrKTZhCI8npzb0/ycRImxCR/tXGRvvthqic0DtPLgDFCkdBOPx2zdCunQL54PRunYBgWeCafJNttIm1KhHtT2nYinZvSxKxnj4Rb2yX/MHm/jVCOmzzQJ496MOqRQqNqzUIpNKrZymXvLZRVwGwhT77sG5RHdXNNCgcQDSMaRqUDAMZGpGgIZivsldSPVrOUiTnl1MKFBttTQgAMuh5ubRdiLMiQSJjNhcq8Jb1Id+Tk8vGsrOdlq6ULvwcgBXyxOzdls+YELBdP0OiX2J2b6MELAPk2Um6WYTEJM+9TziScY6Zq6+fKzVssvmImL7DAMwVH9mJGNWBkMcuefmXpmuCjT5MZrVUtEBKLS58AiK68R/YNKkvXgDrbAzBbo3c9pDrqLV98QG3mYqvukz39Wlml6ZuLuO0OaEqs+fumm1eU5u8DUJpWWs+9I0VDas1CPVfO0x+++2Fzb49WtcDa8y4A+l8AYlJOQpZ7ukTCzInxWTKp/3kaCW4Ctb5ZddSL/P8sEWc0dphHfPCraHqTbrMMUBVilrZ7wl4AzF+fiTibpl1V/5rrpt/vh0FZEyR2UgmfuSRzFIDJDAs8U5SIYWLmr8/QO+Q9NvmHpYCPCrEoOG09e8Ry4T0p4KMmcab+y9GV9xhtUNk7QPti0R1SwEfOfLV+GVruji5eqTrqyco0ffNHxKvby7pe1gPqgLlPf4wwFrJjxplcU8piE2fMxv88jfVENj6GL9/Ht9fMX59R65vTtYbNtLuB6OpWc7zFvXFwTvbHNCYZhLJII0jYkSbYJo+szZ43Bz3rb2vwKlEAT/e6yMeeTfI8w8wlLPBMPkkISE8anE64EZtcl7TyGnotD/TF7tyU7K2lLisAwq3tFIG2v/08iT2gG/cJkLcfKzbhwkf0ji7nfZ+j512RA6+fN968Jbn3alaXIDtS5tkZo/JTPWC61AfxUcIxpft2BVY9lM3RsvoKW3akTDM0hl0mPVq6jzIz7R1TfscPPSMNVguZ0WxDM4UGCzyTZxJ6syCVgzrdTZm60APQHeZpvLUUsrVcPEHZWGM//Q11sAdAu1POl+gfRxKu9rxrix8huvIeOgUJj7m3R9jHsncg+YwZBC/d88qkzvOEN1MGzpPHuaYkQ+qDeF/0sMsmcy3LBPjMHg4RXwjfuzPvMfUEEmIfaL4/zwsCUFT1WkxeYIFnCpFsqrCoY5pwtssTC+WNxxGOWcSrxayn3jA+GQhxJYEkW7/6h7vdbrf17BFqom4suRbt4pEq431SwUv37SYtiM/ymqRc1ZTIJtNthiQ/r0j+YVmcfepd66cUE0l+nMqwe3Ls4ycLa//BP+YPhG5Foo83JOYJJjN7Msy6zmSABZ4pVhJ8y8JgTamvKcz6+JOBbsTHJ5aqjnqq29b+r//Nunh1urNPe97alJhqdTiA7NvRFBSUqEhWssl1aUoCL35xopFOlhpvPMJUr/PjTYvcbks2NnSWqXBFbYu/OeipCkb9fn8xLr6EYYFnCoJpZ3dPM4Vtyw5RqSVqsiPxHqu6BRmwK4tabCc7U3Z8m2qdWwam8cWzj1WnZNIwQfap7BmOlj3Ws0fkgT61vpncD1LAN6VLQemWot3NNBYwKRliH6U65C37Bw5avKOqwqtEC2TxDMECzxQKc2xuioL1zCZ4rGWtubdH+PNVZ5PtZKdozWY99QZtNqVoupGUtiOZpPRmcjKa2EVZ1DLtWHWGMAEt3v7Wr6kr31St4alCX4cG8Ej+YaVp5UziCwljanNCcoin5CnABw5mGrDAM6VGNvqa2YTVS73vfRRer7FrrFBTMvHNvT1qfTNF5RP8ujMUPBoDA0B2u4xmOq3Z3NtjVP1sWgFO5+xizEzSBjM5fjr0onZgqjV4xt3NX5+J3bmJfl9TilBMNcNxSnAqHJMvWOCZEiSbevQM2q/f7m/8k8k/OsNstZkjbGhhuMtul/BFJ3etn1VHyDRi1dkzvYZ0xpr4iLNp2tPkZvW6TarrRf0QoI/XM435A6GiW3xpwwLP5IxZsu1mwrQFKbLxMcnpjLjdyaX5kXgnfMk/TL3QZ7LCadjc0dWttpOdxkmyIjxcOFc+e2budRB7Ze57WOAUlDROo/Nuwng9phBggWdyw6zadjlnqrVnCRtTJ3zy0tMEs5xHfCkGny4EbjTrjUVlM7/yGc5O7091NmuWJ83VcQrwKbNIKagHDmZ6sMAz846pPotkCMrmJIU+ZVFflh1yIhMH6+WkZi/l2bWPX6cW+paLJygtYIZ5BuJc019oKljXGUbAAs+UMiVm0qWz5qcdeJ4GMx/dNm1nTyn9KhlmDmCBZ3JDwUppQS1mUqZxDQv2yueWXMWA5sO1YhiCBZ7JGcVyxywoRTQuBoaq9wQNm70iriyR7ttl+vvDc3a6WaK4MkUYZoawwDPzkYK6s0+aBJ4sS3P8gGI9e0SrqszV6QrqAYthShgWeIYpMubYDNVP53AoXm+uTjeNg/BjAcNMFRZ4hikUWMOSSejTN8Oj8RVm5hUs8AxTQKQLsRtlqajbuUyJnPgqkmcH52p5DFPgyPleAMMwk0P2q5Ao081e+m8O5IpOhxv/NDenyy30iED/5fbBSPv4devZI/PnYWt26Hu1ra6ubt/xHB3teF9fbo5UKrAFzzDFR3Je3qxKr2jcO3unKC6sZ49gxVrF68UU6x2Y2aLv+L6f7exY0Tl0sDnfSykgWOCZOYJvfDkbyD6+AAARO0lEQVSnoIq+ZuP3W3Qh84L6jcwzer86D6zI9yoKDBZ4Zi7gG19pM3u/35y01s/JoRim6OAYPMMUARTunT8R3xx+X2P6Qq6IbHyMkhKKMS8hD/Qd39fWVldXV1dX17bv1TRx8uR4/PF9dXV1bWJ7w1Hq6tr2vXq8z7Dhhv3nAXTsnHiIvuP7Uu8RP9++4xCbpF1Z8cIWPDPvKDqTLp19nBfzdA7OWBT+Hum+XclJCewwSEHfq22kvgCA8x37N3yF7q49Uw2VH9+3YWfH+I/nO/bv7LjcOXRwa4Y96pL2eOdAwqm79u3soMWtW/5gyYXv2YJn5oI5TvzOwOylVeeF2TBPM5Du6k3191vCDok5/o0UPsdf3n8eWNfe2T00NNTdeWAdcH7/y2nz5sMnBz1iFL3xMF0d4ihDQ0Pdne0AOrroOFsPDnUfWAegvXNoSNf84/t2TthjqLuzfR3O7//ZBDu9o6Nj3QHaYurPHIUPW/DMHMG3vNJmSkPhZslAnxvvgv7qh7tn6RSlBQnzgZcObm0G0Lx1z772/Ts7vurtw9ZEPf0iCsDUWF62HHhz0POThYbP+nq/AiD8AGjeenBo6OBk523v7Bq38Ju3HnzpwFcb9r/zQd+ecS1fd+ClEhT2OCzwDFPoFLXjN93KLRdPAJC9A8nfaHrfdw4c+8ZTaB+/jlUP5fwUpQYJ84qWcQ2dTJlT0/zgj9btP3++Y+eGDgDr2g/se/LBrc3plZnO27GzriP5s8tXAbGncWmlBws8M78oKLHMfiV5Xyox1auXTnQtF0+ozqbk940nys2KmRKheU9XN/b9bH/Heejh9P1Y137gpYOp7e+rl8+nenu+wQLPzDsKRDyKIpUsmZw0hC9/7Rl6HV3darrZO+NFFTSF80BZ4NxlwZtQ+gOhy8BPFtYmfd6852DXnoN9x1/9oOuddzrOn8f5jv0/W/5gytj5suXrgPPtGZPw5gHFIfBOp7OIDlu88AVJYFYviFZVCYdD/2G0Uir4iz+Nq5HuO2p334/F/yL5/Rnxw93ax6/TS2mWAuTGU9y3K5tFax+/jhVr9R++fF+6b9esLKwwSPEX4vze3cD53ltep1N/mMWxPVLb4c0vXjm1twXeSguAsmqn06m/rvnByuWP0Ia9t3oBWCprJxzX+WfPrv+zZ5/9LY7t2dJ2+PT1YadzPTDhSOK8Hb8//9s/eyTNahP2mA3yfkctDoF3z0KPTKfTORuHLV74giQw2xfE6h+lXqcATP7RAm8EO82rseqhCfarOEK692eICIrn9GJOWGr8FM7s7kvF9VueCWn+Qtb9T+043PGLXX/e8NLBrc3oO76v7TCwrm2Tw+12wzMaBRAacbvdcDS0AKcP//vn/nLdnmb0HX/1Z784DSA66nG73VTytu5A50t7KDmv7/hnF04D65YsoLPSkS58ds69rnn8vIfb1i8x7LLvZzs7zq/TK+WM557DCzJb50r5fnEIPMOUHgWVDTB7pPtqM//Kc3P1CjySUvh/QlufPLCuY388PY5o35fKrb61rR0dHef3b6jbT1sdOID9+yccZf/ODfuNu4wfqLllBXD+/P4NdfvbO7sPbm3eerCzvWNnR+IupZ01nwjXwTNM3uCa6Wkz2/0MclWpP6sdIIqjqUPznq7uzvZ16+inde0HutMFxrce7D7Qvk5sdvDBtEehLTq7DQfa+mR8369644cb6j5g2GVd+4HOaXTYKWYkTdPyvYbJYRf9HMAXJIFivCCzZ88V2tUwGtY5107jwW0nO8P37kw+USFckFm9CFOlEC5IQcEueoZhckaBO5OLlFjLWpHnz5eUKS5Y4BmGKT5EBoO5tyfWsnZWH2gKVtfnSRoHM21Y4BmGKUpI3oT/PIcaX0TCWeDLY/ILCzzDzDWzJB5FJEuFD19ApgRggWeYOWVWI+UsSwzDCFjgGYYpVthpwTAZYIFnGKaIYV1nmHSwwDPMnMJGJ8MwcwMLPMPMNZPqevZPAPyswDBMOrhVLcMUFtn3Hy2OTqUMw+QJtuAZhmGYWcFms838IOFweOYHmZ+wwDMMM1twBKHYeXPQQy9+srB2ekeoPt49wzUM3fPdGR5h3sICzzCFRfZZeAWer8e98YudNwc9y8vLxOtpa/wf1v/Laa/hX537x2nvy7DAM0zBkb0WsmoyDJMOTrJjGIZhmBKELXiGYWaFAo8gMJPyk4W1M4/BM3mEBZ5hmNmCdb3YYV0vathFzzAMw5QEfa+21e07nu9VFA4s8AzDMEwJcHzfhv3n872IgoJd9AzDMEyRc3xf3c6OfC+i4GALnmEYhskD77u99N9MD3R8X93OjnUHuoc623OxrtKBLXiGYRhmrnnf7V1qt4nXDzkd0z/W1oNDQwcBgMPvE2ELnmEYhmFKEBZ4hmEYhilB2EXPMAzDzDUPOR0i+j4j/zyTHhZ4hmEYJg+wrs827KJnGIZhmBKEBZ5hGIZhShAWeIZhGIYpQTgGzzAMw5QEWw8ODeV7DYUEW/AMwzAMU4KwwDMMwzBMCcIueoZhihLr2SP0gqfOFzj/6tw/5nsJ8xQWeIZhig/r2SPKohbxmjW+YBnZumGGRwiHwzlZyTyEBZ5hGIaZLcofr5vhEcL/9UZOVjIPYYFnGIZhZpHQf/xw2vuW/bsHcriS+QYLPMMwxUdk42Mcg2eYzLDAMwxTlLCuM0xmuEyOYRiGYUoQFniGYRiGKUFY4BmGYZiipu/VtjpB26t9+V5PocACzzAMwxQvfa+2bdh/ft2B7qGhoaHuA+vO799Qt+94vldVELDAMwzDMEVL3wfvnEd7Z9eeZgBo3tPVfWAdOrpY4cFZ9AzDMExeMH2h18crd82g2L15T9fQnglvtKwAvurtw9bmmayuFGALnmEYhplrTF98qNUtof+E0ueE410dwIqWea/uYIFnGIZhSofj+3Z2YN2BJ7fmeyGFAAs8wzAMUxIc31e3s2M8ID/v4Rg8wzAMM9codz2Qmxi84Pi+up0d6w50s7oLWOAZhmGYPJAbXQdgKJVjdTfCAs8wDMMUMaTu7Z1DBznwPhGOwTMMwzBFS9+rP9t/ft2Bblb3ZPJowXe/sO25E8Y3Wp89+tSGfK2GYRiGKTr6PnjnPID9G+r2G99mbz2QT4Hvd13DsvZXDm1vzNsSGIZhmKImqc8NM07+XPQ3XFdxexOrO8MwDMPMAnkT+H7XNbRuZo88wzAMw8wGeXPR33BdXXbt9W3bnqMf2VnPMAxTkpT9u5yVwzFTQtI0LR/n7X5h23MnDKre/cK215smaPzatWvF656enjlfIcMwDMMUMfkS+CS6X9j2HNKl0bvd7pyf0Ol0zsZhixe+IAnwBTHCVyMBviAJ8AVJYC4viNPpTPl+wdTBL25ahhOnu/O9DIZhGIYpCeYoBt//1t4nOq7S6/Th9mVNi+dmOQzDMAxT4syRwDduP3R0u+Hn/rf2PtFxu9Ejf8N1Fbfv4jQ7ppixnj1CLyIbH8vvShiGYfLkom/cvqt1gke++/SJZe07uGqOKV6sZ48oi1roP6H0DMMw+SJvZXIbnjr67Avbtm3Tf2x99ughlneGYRiGyRH5nCa34amjR5/K4/kZhmEYpmThcbEMkxsiGx/jGDzDMIUDCzzD5AzWdYZhCoeCqYNnGIZhGCZ3sMAzDMMwTAnCAs8wDMMwJQgLPMMwDMOUICzwDMMwDFOCsMAzDMMwTAnCAs8wDMMwJQgLPMMwDMOUICzwDMMwDFOCsMAzDMMwTAnCAs8wDMMwJQgLPMMwDMOUICzwDMMwDFOCsMAzDMMwTAnCAs8wDMMwJYikaVq+15Af1q5d29PTk+9VMIUL/4UwGeA/DyYzhfAXwhY8wzAMw5QgLPAMwzAMU4KwwDMMwzBMCTJ/Y/AMwzAMU8KwBc8wDMMwJQgLPMMwDMOUICzwDMMwDFOCsMAzDMMwTAlizvcC8kX3C9ueO2F8o/XZo09tyNdqmAJi/E9jWfsrh7Y35nk5TCHB9w0mPf1v7X3CtSvhDyKv95P5KvD9rmt8+2aS6X5h23PX2l85ur0R6H5h2xN7wX8kjIDvG0xaul94ouMqWhPey+/9ZL666G+4ruL2Jv5Xykyg/63XTyxr/z/0f4Ebnnq29WrHG915XhRTOPB9g0lJ9wvbElw7QCHcT+apwPe7rqF1M3vWmAn0nzk58f69uGkZTpxmhWcIvm8wKeh+YdtzJ5a1v3L02YnmewHcT+api/6G6+qya69v2/Yc/chONybOsqbF4z80Nt2et4UwhQffN5gUbHjq6NGnACCFdOf5fjI/Lfju0ydwFfe+clRnl+uJvW/153tVTL654bqa4t1rLv7TYAC+bzBTowDuJ/NT4Dc8dfToUcOz94bNHGtlGCYzfN9gioz5KfBJcKyVAf0ZJMNJVUwa+L7BZKAA7ifzIgbf/9beJzp0Z0n6sNmEYAkzX7nqugHE/0D6XdeA2/O4Gqbg4fsGk4E830/mhQXfuP1QPGp29ND2RvS/tXfbtheMz91c/cIAaNx077IJIbIbrqucNc3o8H2DmRIFcD+ZFwKfSOP2Xa0TPGvdp08sa9/B9/F5T+P2Xa1XO/6znjjV/cJz/HfBCPi+wUyN/N9P5u88eGPPSW43yQgMAR0ug2IS4fsGk5buF7Y9h4Q/ivzeT+avwDMMwzBMCTMvXfQMwzAMU+qwwDMMwzBMCcICzzAMwzAlCAs8wzAMw5QgLPAMwzAMU4KwwDMMwzBMCcICzzDzimN7JEmSthzqndJevYe2SJK051iOz0gfTe+wDMNMAgs8w8wnjv3uMACcfuPdqSn8DHjk1a7dwOlf/HmixB/b03YY2N316iNztRSGmVewwDPMPOLY7w4Du3fvxulfPD8Vu7ll7ylN06arxCklnuWdYWYZFniGmT8c+91hYPPKp3+8Gzj8uzn0jCdKfO+hLSzvDDPLsMAzzLyB9H3HD1oeSaHwFGY3BsQNwfOEGHzvsT1btkg6W/YcOja5v98o8b2H/vwXp1neGWa20RiGmRdceXEzgN1dmqZpWtduAJtfvJK8QfzNrt0Y/yl53wT0j7JYADZv3pztHgzDzAC24BlmftD77hungd0/JqP5kR/vTkq1a9m732BkHzgMbH7xt3tbEg9EfoDdXfrDwZWu3cjO4d+y97cvbgZOn2brnWHmAhZ4hpkXTNT31Ao/7kffQj70/cnyjt7L/wjgtPi55ZFXNS3LBLyWH+zYDADYvPKO6X4RhmGyhAWeYeYDifoeV/jEZHpd4jMY2bpIH267QwTgs624O7bnjl/Qk0GKojmGYXIMCzzDzANI33G4TRqn7TCQwrf+yI8pxG58GJhAy95TV17cTZY4Th/+Rdsdd0hb9kyu11QXt/nFK1de3Jzi0YJhmBzDAs8wpY+u7ylJUHiSYQCH29J3mGvZ++opTbvS9eKLuylj7vThyUzyuLz/dm+LHovPdAKGYWYOCzzDlDzHnqeqtKQk2ysvbgYOHxiXZsqt292V9EEqWh7Zu/fVU6c0rWv3ZuD0pSsZVjAu74BIt2OJZ5jZhAWeYUodak+byuVO8XSRakf16ZtffPoRY0J94tH06nhR+9577PI/ns6YN5cg7wBLPMPMBXNalMcwzJxDZetpys4Nte96mbrY0LifsQ5e3y6BtGXt8er3iTX3hk+4IJ5hZge24BmmtElvvwPChv/F88fifnyROv/I06lt7Ja9p6506bF3AMDm3S92XUlTJqc3rUtZUE9uArbiGWaWkDRNy/caGIZhGIbJMWzBMwzDMEwJwgLPMAzDMCUICzzDMAzDlCAs8AzDMAxTgrDAMwzDMEwJwgLPMAzDMCUICzzDMAzDlCAs8AzDMAxTgrDAMwzDMEwJwgLPMAzDMCXI/w/Qpj7su+P3aQAAAABJRU5ErkJggg==\" /><!-- --></p>\r\n</div>\r\n</div>\r\n<div id=\"多数据系列\" class=\"section level2\">\r\n<h2>3.7 多数据系列</h2>\r\n<p>多数据系列，其实在聚类散点图就已经涉及到。这里对较少数据进行绘制，这样看起来结果更加美观。</p>\r\n<div class=\"sourceCode\" id=\"cb14\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb14-1\" title=\"1\">mydata&lt;-mydata[<span class=\"kw\">round</span>(<span class=\"kw\">runif</span>(<span class=\"dv\">300</span>,<span class=\"dv\">0</span>,<span class=\"dv\">10000</span>)),]</a>\r\n<a class=\"sourceLine\" id=\"cb14-2\" title=\"2\">kmeansResult&lt;-<span class=\"st\"> </span><span class=\"kw\">kmeans</span>(mydata, <span class=\"dv\">2</span>, <span class=\"dt\">nstart =</span> <span class=\"dv\">20</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb14-3\" title=\"3\">mydata<span class=\"op\">$</span>cluster &lt;-<span class=\"st\"> </span><span class=\"kw\">as.factor</span>(kmeansResult<span class=\"op\">$</span>cluster)</a>\r\n<a class=\"sourceLine\" id=\"cb14-4\" title=\"4\"><span class=\"kw\">ggplot</span>(<span class=\"dt\">data =</span> mydata, <span class=\"kw\">aes</span>(x,y,<span class=\"dt\">fill=</span>cluster,<span class=\"dt\">shape=</span>cluster)) <span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">size=</span><span class=\"dv\">4</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">alpha=</span><span class=\"fl\">0.7</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-6\" title=\"6\"><span class=\"st\">  </span><span class=\"kw\">scale_shape_manual</span>(<span class=\"dt\">values=</span><span class=\"kw\">c</span>(<span class=\"dv\">21</span>,<span class=\"dv\">23</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-7\" title=\"7\"><span class=\"st\">  </span><span class=\"kw\">scale_fill_manual</span>(<span class=\"dt\">values=</span><span class=\"kw\">c</span>(<span class=\"st\">&quot;#00AFBB&quot;</span>,  <span class=\"st\">&quot;#FC4E07&quot;</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-8\" title=\"8\"><span class=\"st\">  </span><span class=\"kw\">labs</span>(<span class=\"dt\">x =</span> <span class=\"st\">&quot;Axis X&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Axis Y&quot;</span>)<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-9\" title=\"9\"><span class=\"st\">  </span><span class=\"kw\">scale_y_continuous</span>(<span class=\"dt\">limits =</span> <span class=\"kw\">c</span>(<span class=\"op\">-</span><span class=\"dv\">5</span>, <span class=\"dv\">10</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-10\" title=\"10\"><span class=\"st\">  </span><span class=\"kw\">scale_x_continuous</span>(<span class=\"dt\">limits =</span> <span class=\"kw\">c</span>(<span class=\"op\">-</span><span class=\"dv\">5</span>, <span class=\"dv\">10</span>))<span class=\"op\">+</span></a>\r\n<a class=\"sourceLine\" id=\"cb14-11\" title=\"11\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\r\n<a class=\"sourceLine\" id=\"cb14-12\" title=\"12\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb14-13\" title=\"13\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\r\n<a class=\"sourceLine\" id=\"cb14-14\" title=\"14\">    <span class=\"dt\">legend.background=</span><span class=\"kw\">element_blank</span>(),</a>\r\n<a class=\"sourceLine\" id=\"cb14-15\" title=\"15\">    <span class=\"dt\">legend.position=</span><span class=\"kw\">c</span>(<span class=\"fl\">0.85</span>,<span class=\"fl\">0.15</span>)</a>\r\n<a class=\"sourceLine\" id=\"cb14-16\" title=\"16\">  )</a></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n</section>\r\n\r\n\r\n\r\n<!-- code folding -->\r\n\r\n\r\n<!-- dynamically load mathjax for compatibility with self-contained -->\r\n<script>\r\n  (function () {\r\n    var script = document.createElement(\"script\");\r\n    script.type = \"text/javascript\";\r\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\r\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\r\n  })();\r\n</script>\r\n\r\n</body>\r\n</html>\r\n"
  },
  {
    "path": "2020年/2020.10.27散点图系列二/scater_plot2.rmd",
    "content": "---\r\ntitle: \"散点图系列(2)\"\r\nauthor:\r\n  - 庄亮亮\r\ndate: \"2020/10/27\"\r\noutput:\r\n  prettydoc::html_pretty:\r\n    theme: cayman\r\n    highlight: github\r\n---\r\n\r\n\r\n记得安装prettydoc包，html模板在该包渲染而成。\r\n\r\n![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  \r\n欢迎**关注**我的**公众号**，**点赞，在看，收藏\\~\\~\\~**\r\n\r\n---------\r\n\r\n\r\n## 1.前言\r\n\r\n**散点图**（scatter graph、point graph、X-Y plot、scatter chart ）是科研绘图中最常见的图表类型之一，通常用于显示和比较数值。散点图是使用一系列的散点在直角坐标系中展示变量的数值分布。在二维散点图中，可以通过观察两个变量的数据变化，发现两者的关系与相关性。\r\n\r\n散点图可以提供三类关键信息：\r\n\r\n（1）变量之间是否存在数量关联趋势；\r\n\r\n（2）如果存在关联趋势，那么其是线性还是非线性的；\r\n\r\n（3）观察是否有存在离群值，从而分析这些离群值对建模分析的影响。\r\n\r\n-----\r\n\r\n本文可以看作是[《R语言数据可视化之美》](https://github.com/EasyChart/Beautiful-Visualization-with-R \"《R语言数据可视化之美》\")的学习笔记。该书第四章——**数据关系型图表**中展示的散点图系列包括以下四个方面：\r\n\r\n1. [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN#rd)\r\n\r\n2. 分布显示的二维散点图\r\n\r\n3. 气泡图 \r\n\r\n4. 三维散点图\r\n\r\n本文主要对**第二部分**进行介绍，并加上小编自己的理解。下面几个部分也会在最近陆续推出，敬请关注。\r\n\r\n\r\n## 3.单数据系列\r\n\r\n### 3.1数据格式\r\n\r\n这里我们使用正态分布随机产生250个数据（这个就是实际我们采集的一维数据）。`step`是指按照多少的区间进行划分类别。我们通过`hist()`将直方图内部数据进行存储(我也是第一次见这种操作，以后学起来)。输出`hg`.\r\n\r\n其中breaks表示边界点，counts表示每个区间内的个数，density表示密度函数值。mids表示区间的中间点，并利用这些参数来构建后续绘图所需要的数据。通过循环语句，计算出x，y坐标数据。\r\n完整代码如下：\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n#加载包\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer) #颜色\r\nlibrary(scales)\r\n\r\nx <- rnorm(250 , mean=10 , sd=1) \r\nstep<-0.2\r\nbreaks<- seq(min(x)-step,max(x)+step,step)\r\n\r\nhg <- hist(x, breaks = breaks , plot = FALSE)#使用直方图数据，但不绘图\r\n\r\nbins <- length(hg$counts) # bin类别数\r\nyvals <- numeric(0)       \r\nxvals <- numeric(0) \r\nfor(i in 1:bins) {       \r\n  yvals <- c(yvals, hg$counts[i]:0)  \r\n  xvals <- c(xvals, rep(hg$mids[i], hg$counts[i]+1))  \r\n}    \r\n                                                   \r\ndat <- data.frame(xvals, yvals)  # 变成dataframe格式\r\ndat <- dat[yvals > 0, ]          # 去除小于0的数\r\n\r\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32) #颜色选择\r\n```\r\n\r\n\r\n\r\n\r\n### 3.2原始数据绘制\r\n\r\n接下来我们使用该数据（单数据）进行绘制：\r\n\r\n- **柱状图（正态分布）**\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nggplot(dat, aes(x=xvals,y=yvals,fill=yvals))+\r\n  geom_tile(colour=\"black\")+\r\n  scale_fill_gradientn(colours=colormap)+\r\n  ylim (0, max(yvals)*1.3)+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",\r\n      face=\"bold.italic\",hjust=.5,color=\"black\"),\r\n    legend.background = element_blank(),\r\n    legend.position=c(0.9,0.75)\r\n  )\r\n```\r\n\r\n\r\n`geom_tile(colour=\"black\")`改为`geom_point(colour=\"black\",shape=21,size=4)`会得到以下图片\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nggplot(dat, aes(x=xvals,y=yvals,fill=yvals))+\r\n  geom_point(colour=\"black\",shape=21,size=4)+\r\n  scale_fill_gradientn(colours=colormap)+\r\n  ylim (0, max(yvals)*1.3)+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",hjust=.5,color=\"black\"),\r\n    legend.background = element_blank(),\r\n    legend.position=c(0.9,0.75)\r\n  )\r\n```\r\n\r\n### 3.3 Q-Q图的绘制\r\n\r\n在R中可以使用`CircStats`包的`pp.plot()`函数绘制P-P图；`ggplot2` 包的`geom_qq()`函数和`geom_qq_line()`函数结合可以绘制Q-Q 图；另外，`ggplot2`包结合`ggpubr`包也可以绘制，[当然改包还有其他好用的功能](https://rpkgs.datanovia.com/ggpubr/index.html \"ggpubr简介\")。\r\n\r\n下面对第三种方式进行实现：\r\n`ggpubr`包中的`ggqqplot`相应参数如下，包括了非常多的参数，前两个参数分别表示：数据，要绘制的变量。当然其他数据包括设置主题（`ggtheme`）;添加qqline（`add = c(\"qqline\")）`等。\r\n\r\n```\r\nggqqplot(\r\n  data,  x,  combine = FALSE,  merge = FALSE,  color = \"black\",  palette = NULL,  size = NULL,  shape = NULL,  add = c(\"qqline\", \"none\"),  add.params = list(linetype = \"solid\"),  conf.int = TRUE,  conf.int.level = 0.95,\r\n  title = NULL,  xlab = NULL,  ylab = NULL,  facet.by = NULL,  panel.labs = NULL,  short.panel.labs = TRUE,  ggtheme = theme_pubr(),  ...\r\n)\r\n```\r\n\r\n为了更好解释这个函数，我们重新模拟一个数据集。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(ggpubr)\r\n# 创建一个数据集\r\nset.seed(1234)\r\nwdata = data.frame(\r\n   sex = factor(rep(c(\"F\", \"M\"), each=200)),\r\n   weight = c(rnorm(200, 55), rnorm(200, 58)))\r\nhead(wdata, 4)\r\n```\r\n\r\n\r\n```\r\n# 基本的Q-Q图\r\nggqqplot(wdata, x = \"weight\")\r\n```\r\n\r\n\r\n\r\n```\r\n# 按性别改变颜色和形状\r\nggqqplot(wdata, x = \"weight\",\r\n   color = \"sex\",\r\n  ggtheme = ggplot2::theme_grey())#更改主题（灰色）当然可以用其他主题\r\n```\r\n\r\n\r\n### 3.4 带透明度设置的散点图\r\n\r\n- 数据设定\r\n\r\n这个数据是张杰老师书中的数据，是经过一定处理得到的，结果图可以看下面。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)  \r\n\r\nmydata<-read.csv(\"HighDensity_Scatter_Data.csv\",stringsAsFactors=FALSE)\r\nhead(mydata)\r\n```\r\n\r\n\r\n我们利用`ggplot()`简单绘制二维数据的散点图，之后在对该数据进行聚类。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nggplot(data = mydata, aes(x,y)) +\r\n  geom_point( colour=\"black\",alpha=0.1)+\r\n  labs(x = \"Axis X\",y=\"Axis Y\")+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",hjust=.5,color=\"black\"),\r\n    legend.position=\"none\"\r\n  )\r\n```\r\n\r\n\r\n\r\n### 3.5 kmeans聚类的散点图\r\n\r\n对上面数据进行kmeans聚类，这里使用base中的 `kmeans()`进行分类（两类），然后将分类情况(`cluster`)进行存储，并转化成因子类型。绘图时，cluster来设置颜色，这样就可以很明显看出分类结果。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nkmeansResult<- kmeans(mydata, 2, nstart = 20)\r\nmydata$cluster <- as.factor(kmeansResult$cluster)\r\nggplot(data = mydata, aes(x,y,color=cluster)) +\r\n  geom_point( alpha=0.2)+\r\n  scale_color_manual(values=c(\"#00AFBB\",  \"#FC4E07\"))+\r\n  labs(x = \"Axis X\",y=\"Axis Y\")+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",color=\"black\"),\r\n    legend.background=element_blank(),\r\n    legend.position=c(0.85,0.15)\r\n  )\r\n```\r\n\r\n\r\n\r\n### 3.6 带椭圆标定的聚类散点图\r\n\r\n有时候我们想突出聚类结果会在结果上画上椭圆，那么可以使用`stat_ellipse()`。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nggplot(data = mydata, aes(x,y,color=cluster)) +\r\n  geom_point (alpha=0.2)+  # 绘制透明度为0.2 的散点图\r\n  stat_ellipse(aes(x=x,y=y,fill= cluster), geom=\"polygon\", level=0.95, alpha=0.2)+#绘制椭圆标定不同类别\r\n  scale_color_manual(values=c(\"#00AFBB\",\"#FC4E07\"))+#使用不同颜色标定不同数据类别\r\n  scale_fill_manual(values=c(\"#00AFBB\",\"#FC4E07\"))+#使用不同颜色标定不同椭类别\r\n  labs(x = \"Axis X\",y=\"Axis Y\")+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",color=\"black\"),\r\n    legend.background=element_blank(),\r\n    legend.position=c(0.85,0.15)\r\n  )\r\n```\r\n\r\n\r\n\r\n## 3.7 多数据系列\r\n\r\n多数据系列，其实在聚类散点图就已经涉及到。这里对较少数据进行绘制，这样看起来结果更加美观。\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nmydata<-mydata[round(runif(300,0,10000)),]\r\nkmeansResult<- kmeans(mydata, 2, nstart = 20)\r\nmydata$cluster <- as.factor(kmeansResult$cluster)\r\nggplot(data = mydata, aes(x,y,fill=cluster,shape=cluster)) +\r\n  geom_point(size=4,colour=\"black\",alpha=0.7)+\r\n  scale_shape_manual(values=c(21,23))+\r\n  scale_fill_manual(values=c(\"#00AFBB\",  \"#FC4E07\"))+\r\n  labs(x = \"Axis X\",y=\"Axis Y\")+\r\n  scale_y_continuous(limits = c(-5, 10))+\r\n  scale_x_continuous(limits = c(-5, 10))+\r\n  theme(\r\n    text=element_text(size=15,color=\"black\"),\r\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",color=\"black\"),\r\n    legend.background=element_blank(),\r\n    legend.position=c(0.85,0.15)\r\n  )\r\n```\r\n\r\n"
  },
  {
    "path": "2020年/2020.10.30ggpubr/ggpubr.rmd",
    "content": "---\r\ntitle: \"ggpubr\"\r\nauthor: \"庄闪闪\"\r\ndate: \"2020/10/1\"\r\ndocumentclass: ctexart\r\nalways_allow_html: true\r\noutput:\r\n  rticles::ctex:\r\n    fig_caption: yes\r\n    number_sections: yes\r\n    toc: yes\r\nclassoption: \"hyperref,\"\r\n---\r\n\r\n\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE,warning = F, message= F)\r\n```\r\n\r\n# 简介\r\n\r\nHadley Wickham撰写的[ggplot2](https://ggplot2.tidyverse.org/)是出色且灵活的软件包，可用于R中的优雅数据可视化。但是，默认生成的绘图需要先进行一些格式化，然后才能发送它们进行发布。此外，要自定义ggplot，语法是不透明的，这对没有高级R编程技能的研究人员增加了难度。\r\n\r\n“ ggpubr”软件包提供了一些易于使用的功能，用于创建和自定义基于“ ggplot2”的可发布出版物的图表。\r\n\r\n该文章来自[ggpubr: Publication Ready Plots](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/)\r\n\r\n# 安装\r\n\r\n- 从CRAN安装如下：\r\n```{r}\r\n#install.packages(\"ggpubr\")\r\n```\r\n\r\n- 从GitHub安装最新版本，如下所示：\r\n```{r}\r\n#安装\r\n#if(!require(devtools))install.packages(\"devtools\")\r\n# devtools :: install_github(\"kassambara / ggpubr\")\r\n```\r\n\r\n# 分布\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nlibrary(ggpubr)\r\n#> Loading required package: ggplot2\r\n#> Loading required package: magrittr\r\n# Create some data format\r\n# :::::::::::::::::::::::::::::::::::::::::::::::::::\r\nset.seed(1234)\r\nwdata = data.frame(\r\n   sex = factor(rep(c(\"F\", \"M\"), each=200)),\r\n   weight = c(rnorm(200, 55), rnorm(200, 58)))\r\nhead(wdata, 4)\r\n#>   sex   weight\r\n#> 1   F 53.79293\r\n#> 2   F 55.27743\r\n#> 3   F 56.08444\r\n#> 4   F 52.65430\r\n\r\n# Density plot with mean lines and marginal rug\r\n# :::::::::::::::::::::::::::::::::::::::::::::::::::\r\n# Change outline and fill colors by groups (\"sex\")\r\n# Use custom palette\r\nggdensity(wdata, x = \"weight\",\r\n   add = \"mean\", rug = TRUE,\r\n   color = \"sex\", fill = \"sex\",\r\n   palette = c(\"#00AFBB\", \"#E7B800\"))\r\n```\r\n\r\n\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n# Histogram plot with mean lines and marginal rug\r\n# :::::::::::::::::::::::::::::::::::::::::::::::::::\r\n# Change outline and fill colors by groups (\"sex\")\r\n# Use custom color palette\r\ngghistogram(wdata, x = \"weight\",\r\n   add = \"mean\", rug = TRUE,\r\n   color = \"sex\", fill = \"sex\",\r\n   palette = c(\"#00AFBB\", \"#E7B800\"))\r\n```\r\n\r\n\r\n# 箱型图\r\n```{r fig.align='center', fig.pos='h', message=FALSE, out.width='100%'}\r\n# Load data\r\ndata(\"ToothGrowth\")\r\ndf <- ToothGrowth\r\nhead(df, 4)\r\n#>    len supp dose\r\n#> 1  4.2   VC  0.5\r\n#> 2 11.5   VC  0.5\r\n#> 3  7.3   VC  0.5\r\n#> 4  5.8   VC  0.5\r\n\r\n# Box plots with jittered points\r\n# :::::::::::::::::::::::::::::::::::::::::::::::::::\r\n# Change outline colors by groups: dose\r\n# Use custom color palette\r\n# Add jitter points and change the shape by groups\r\n p <- ggboxplot(df, x = \"dose\", y = \"len\",\r\n                color = \"dose\", palette =c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n                add = \"jitter\", shape = \"dose\")\r\n p\r\n```\r\n\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n # Add p-values comparing groups\r\n # Specify the comparisons you want\r\nmy_comparisons <- list( c(\"0.5\", \"1\"), c(\"1\", \"2\"), c(\"0.5\", \"2\") )\r\np + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value\r\n  stat_compare_means(label.y = 50)                   # Add global p-value\r\n```\r\n\r\n# 小提琴图\r\n\r\n\r\n```{r message=FALSE, warning=FALSE}\r\n# Violin plots with box plots inside\r\n# :::::::::::::::::::::::::::::::::::::::::::::::::::\r\n# Change fill color by groups: dose\r\n# add boxplot with white fill color\r\nggviolin(df, x = \"dose\", y = \"len\", fill = \"dose\",\r\n         palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\r\n         add = \"boxplot\", add.params = list(fill = \"white\"))+\r\n  stat_compare_means(comparisons = my_comparisons, label = \"p.signif\")+ # Add significance levels\r\n  stat_compare_means(label.y = 50)                                      # Add global the p-value \r\n```\r\n\r\n# 条形图\r\n\r\n## 数据集\r\n```{r message=FALSE, warning=FALSE}\r\n# Load data\r\ndata(\"mtcars\")\r\ndfm <- mtcars\r\n# Convert the cyl variable to a factor\r\ndfm$cyl <- as.factor(dfm$cyl)\r\n# Add the name colums\r\ndfm$name <- rownames(dfm)\r\n# Inspect the data\r\nhead(dfm[, c(\"name\", \"wt\", \"mpg\", \"cyl\")])\r\n#>                                name    wt  mpg cyl\r\n#> Mazda RX4                 Mazda RX4 2.620 21.0   6\r\n#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0   6\r\n#> Datsun 710               Datsun 710 2.320 22.8   4\r\n#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4   6\r\n#> Hornet Sportabout Hornet Sportabout 3.440 18.7   8\r\n#> Valiant                     Valiant 3.460 18.1   6\r\n```\r\n\r\n通过分组变量“cyl”改变填充颜色。排序将全局执行，而不是按组执行。\r\n\r\n## 有序的条形图\r\n```{r message=FALSE, warning=FALSE}\r\nggbarplot(dfm, x = \"name\", y = \"mpg\",\r\n          fill = \"cyl\",               # change fill color by cyl\r\n          color = \"white\",            # Set bar border colors to white\r\n          palette = \"jco\",            # jco journal color palett. see ?ggpar\r\n          sort.val = \"desc\",          # Sort the value in dscending order\r\n          sort.by.groups = FALSE,     # Don't sort inside each group\r\n          x.text.angle = 90           # Rotate vertically x axis texts\r\n          )\r\n```\r\n\r\nSort bars inside each group. Use the argument sort.by.groups = TRUE.\r\n```{r message=FALSE, warning=FALSE}\r\nggbarplot(dfm, x = \"name\", y = \"mpg\",\r\n          fill = \"cyl\",               # change fill color by cyl\r\n          color = \"white\",            # Set bar border colors to white\r\n          palette = \"jco\",            # jco journal color palett. see ?ggpar\r\n          sort.val = \"asc\",           # Sort the value in dscending order\r\n          sort.by.groups = TRUE,      # Sort inside each group\r\n          x.text.angle = 90           # Rotate vertically x axis texts\r\n          )\r\n```\r\n\r\n\r\n# Deviation graphs\r\n\r\nThe deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.\r\n\r\nCalculate the z-score of the mpg data:\r\n```{r message=FALSE, warning=FALSE}\r\n# Calculate the z-score of the mpg data\r\ndfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)\r\ndfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, \"low\", \"high\"),\r\n                     levels = c(\"low\", \"high\"))\r\n# Inspect the data\r\nhead(dfm[, c(\"name\", \"wt\", \"mpg\", \"mpg_z\", \"mpg_grp\", \"cyl\")])\r\n#>                                name    wt  mpg      mpg_z mpg_grp cyl\r\n#> Mazda RX4                 Mazda RX4 2.620 21.0  0.1508848    high   6\r\n#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0  0.1508848    high   6\r\n#> Datsun 710               Datsun 710 2.320 22.8  0.4495434    high   4\r\n#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4  0.2172534    high   6\r\n#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345     low   8\r\n#> Valiant                     Valiant 3.460 18.1 -0.3302874     low   6\r\n```\r\n\r\n\r\n创建一个有序的barplot，根据mpg级别着色:\r\n```{r message=FALSE, warning=FALSE}\r\nggbarplot(dfm, x = \"name\", y = \"mpg_z\",\r\n          fill = \"mpg_grp\",           # change fill color by mpg_level\r\n          color = \"white\",            # Set bar border colors to white\r\n          palette = \"jco\",            # jco journal color palett. see ?ggpar\r\n          sort.val = \"asc\",           # Sort the value in ascending order\r\n          sort.by.groups = FALSE,     # Don't sort inside each group\r\n          x.text.angle = 90,          # Rotate vertically x axis texts\r\n          ylab = \"MPG z-score\",\r\n          xlab = FALSE,\r\n          legend.title = \"MPG Group\"\r\n          )\r\n```\r\nRotate the plot: use rotate = TRUE and sort.val = “desc”\r\n```{r message=FALSE, warning=FALSE}\r\nggbarplot(dfm, x = \"name\", y = \"mpg_z\",\r\n          fill = \"mpg_grp\",           # change fill color by mpg_level\r\n          color = \"white\",            # Set bar border colors to white\r\n          palette = \"jco\",            # jco journal color palett. see ?ggpar\r\n          sort.val = \"desc\",          # Sort the value in descending order\r\n          sort.by.groups = FALSE,     # Don't sort inside each group\r\n          x.text.angle = 90,          # Rotate vertically x axis texts\r\n          ylab = \"MPG z-score\",\r\n          legend.title = \"MPG Group\",\r\n          rotate = TRUE,\r\n          ggtheme = theme_minimal()\r\n          )\r\n```\r\n\r\n\r\n# Dot charts\r\n\r\n## Lollipop chart\r\n\r\nLollipop chart is an alternative to bar plots, when you have a large set of values to visualize.\r\n\r\nLollipop chart colored by the grouping variable “cyl”:\r\n\r\n```{r message=FALSE, warning=FALSE}\r\nggdotchart(dfm, x = \"name\", y = \"mpg\",\r\n           color = \"cyl\",                                # Color by groups\r\n           palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), # Custom color palette\r\n           sorting = \"ascending\",                        # Sort value in descending order\r\n           add = \"segments\",                             # Add segments from y = 0 to dots\r\n           ggtheme = theme_pubr()                        # ggplot2 theme\r\n           )\r\n```\r\n\r\n- Sort in decending order. sorting = “descending”.\r\n\r\n- Rotate the plot vertically, using rotate = TRUE.\r\n\r\n- Sort the mpg value inside each group by using group = “cyl”.\r\n- Set dot.size to 6.\r\n\r\n- Add mpg values as label. label = “mpg” or label = round(dfm$mpg).\r\n```{r fig.height=8, fig.width=6, message=FALSE, warning=FALSE}\r\nggdotchart(dfm, x = \"name\", y = \"mpg\",\r\n           color = \"cyl\",                                # Color by groups\r\n           palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), # Custom color palette\r\n           sorting = \"descending\",                       # Sort value in descending order\r\n           add = \"segments\",                             # Add segments from y = 0 to dots\r\n           rotate = TRUE,                                # Rotate vertically\r\n           group = \"cyl\",                                # Order by groups\r\n           dot.size = 6,                                 # Large dot size\r\n           label = round(dfm$mpg),                        # Add mpg values as dot labels\r\n           font.label = list(color = \"white\", size = 9,\r\n                             vjust = 0.5),               # Adjust label parameters\r\n           ggtheme = theme_pubr()                        # ggplot2 theme\r\n           )\r\n```\r\n\r\nDeviation graph:\r\n\r\n- Use y = “mpg_z”\r\n\r\n- Change segment color and size: add.params = list(color = “lightgray”, size = 2)\r\n\r\n\r\n```{r fig.align='center', fig.height=6, fig.pos='h', fig.width=8, message=FALSE, warning=FALSE, out.width='100%'}\r\nggdotchart(dfm, x = \"name\", y = \"mpg_z\",\r\n           color = \"cyl\",                                # Color by groups\r\n           palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), # Custom color palette\r\n           sorting = \"descending\",                       # Sort value in descending order\r\n           add = \"segments\",                             # Add segments from y = 0 to dots\r\n           add.params = list(color = \"lightgray\", size = 2), # Change segment color and size\r\n           group = \"cyl\",                                # Order by groups\r\n           dot.size = 6,                                 # Large dot size\r\n           label = round(dfm$mpg_z,1),                        # Add mpg values as dot labels\r\n           font.label = list(color = \"white\", size = 9,\r\n                             vjust = 0.5),               # Adjust label parameters\r\n           ggtheme = theme_pubr()                        # ggplot2 theme\r\n           )+\r\n  geom_hline(yintercept = 0, linetype = 2, color = \"lightgray\")\r\n```\r\n\r\n# Cleveland’s dot plot\r\n\r\nColor y text by groups. Use y.text.col = TRUE.\r\n\r\n```{r fig.align='center', fig.height=8, fig.pos='h', fig.width=6, message=FALSE, warning=FALSE, fig_height=8, out.width='80%'}\r\nggdotchart(dfm, x = \"name\", y = \"mpg\",\r\n           color = \"cyl\",                                # Color by groups\r\n           palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), # Custom color palette\r\n           sorting = \"descending\",                       # Sort value in descending order\r\n           rotate = TRUE,                                # Rotate vertically\r\n           dot.size = 2,                                 # Large dot size\r\n           y.text.col = TRUE,                            # Color y text by groups\r\n           ggtheme = theme_pubr()                        # ggplot2 theme\r\n           )+\r\n  theme_cleveland()                                      # Add dashed grids\r\n```\r\n\r\n\r\n\r\n\r\n"
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">气泡图</h1>\n<h4 class=\"author project-author\">庄亮亮</h4>\n<h4 class=\"date project-date\">2020/11/10</h4>\n</section>\n\n\n\n<section class=\"main-content\">\n<div id=\"气泡图\" class=\"section level1\">\n<h1>气泡图</h1>\n<p>气泡图是一种多变量图表，是散点图的变体，也可以认为是 <a href=\"R语言数据可视化之美\">散点图和百分比区域图的组合</a>。气泡图最基本的用法是使用三个值来确定每个数据序列，和散点图一样。气泡图通过气泡的位置及面积大小，可分析数据之间的相关性。</p>\n<p>本文可以看作是《R语言数据可视化之美》的学习笔记。前两部分可见（跳转）：</p>\n<ul>\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN&amp;scene=21#wechat_redirect\">趋势显示的二维散点图</a></p></li>\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485276&amp;idx=1&amp;sn=f98a2aede13555fa1c372f08c3cdec44&amp;chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&amp;token=682523778&amp;lang=zh_CN#rd\">分布显示的二维散点图</a></p></li>\n</ul>\n<p>该书对气泡图的绘制并不是非常详细，小编将内容进行了大量拓展。下面的例子将一步步带你完成气泡图的绘制。本文内容丰富，希望大家都能学到自己想要的内容。</p>\n<div id=\"本文框架\" class=\"section level2\">\n<h2>本文框架</h2>\n<p><img src=\"data:image/png;base64,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\" /></p>\n</div>\n<div id=\"数据介绍\" class=\"section level2\">\n<h2>数据介绍</h2>\n<p>数据集来源gapminder包中，包含了1704行和6个变量。其中6个变量含义分别为：</p>\n<ul>\n<li><p>country 国家142个水平</p></li>\n<li><p>continent 大陆5个水平</p></li>\n<li><p>year 1952年-2007年（间隔为5年）</p></li>\n<li><p>lifeExp 出生预期寿命，以年计数</p></li>\n<li><p>pop 人口数</p></li>\n<li><p>gdpPercap 人均国内生产总值(扣除通货膨胀因素后的美元)</p></li>\n</ul>\n<p>由于数据过多，我们感兴趣的是年份为2007年的数据，所以使用dplyr包进行数据处理，具体数据处理案例可见我写的另一篇推送：<a href=\"https://mp.weixin.qq.com/s/ozH-TsltnLwp51i3aDNpSQ\">[R数据科学]tidyverse数据清洗案例详解</a>。数据缩略版如下：</p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb1-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\n<a class=\"sourceLine\" id=\"cb1-2\" title=\"2\"><span class=\"kw\">library</span>(dplyr)</a>\n<a class=\"sourceLine\" id=\"cb1-3\" title=\"3\"><span class=\"kw\">library</span>(gapminder)</a>\n<a class=\"sourceLine\" id=\"cb1-4\" title=\"4\">data &lt;-<span class=\"st\"> </span>gapminder <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">filter</span>(year<span class=\"op\">==</span><span class=\"st\">&quot;2007&quot;</span>) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span>dplyr<span class=\"op\">::</span><span class=\"kw\">select</span>(<span class=\"op\">-</span>year)</a></code></pre></div>\n</div>\n<div id=\"手把手绘制\" class=\"section level2\">\n<h2>手把手绘制</h2>\n<div id=\"geom_point函数构建\" class=\"section level3\">\n<h3>geom_point()函数构建</h3>\n<p>气泡图是添加了第三维度的散点图：附加数值变量的值通过点的大小表示。(来源:<a href=\"https://www.data-to-viz.com/graph/bubble.html\" title=\"data-to-viz\">data-to-viz</a>)。 使用ggplot2，可以通过<code>geom_point()</code>函数构建气泡图。<code>aes()</code>设定至少三个变量:x、y和size。其实就是散点图绘制的升级版吧，<code>aes()</code>中多了一个参数。</p>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb2-1\" title=\"1\"><span class=\"kw\">ggplot</span>(data, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size =</span> pop)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb2-2\" title=\"2\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.7</span>)</a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>上图展示了世界各国的预期寿命(y)与人均国内生产总值(x)之间的关系。每个国家的人口用圆的大小表示。但是这个图不是非常美观，而且圆的大小并不是很直观，大家都差不多大。接下来对圆的大小进行设定。</p>\n</div>\n<div id=\"scale_size控制圆的大小\" class=\"section level3\">\n<h3>scale_size()控制圆的大小</h3>\n<p>scale_size()允许使用range参数设置最小和最大的圆的大小，用<code>name</code>改变图例名称(<code>scale_size(range = c(0.1, 24), name=&quot;Population (M)&quot;)</code>)。</p>\n<ul>\n<li><p>图中可以看到，有些圆圈重叠了。k可将点的透明度进行调整（<code>geom_point(alpha=0.5)</code>）</p></li>\n<li><p>为了避免在图表顶部出现大的圆圈，可以将数据集进行排序(<code>arrange(desc(pop))</code>)，代码如下。</p></li>\n</ul>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb3-1\" title=\"1\">data <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb3-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">arrange</span>(<span class=\"kw\">desc</span>(pop)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb3-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">mutate</span>(<span class=\"dt\">country =</span> <span class=\"kw\">factor</span>(country)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb3-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">ggplot</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size =</span> pop)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb3-5\" title=\"5\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.5</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb3-6\" title=\"6\"><span class=\"st\">    </span><span class=\"kw\">scale_size</span>(<span class=\"dt\">range =</span> <span class=\"kw\">c</span>(.<span class=\"dv\">1</span>, <span class=\"dv\">24</span>), <span class=\"dt\">name=</span><span class=\"st\">&quot;Population (M)&quot;</span>)</a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>现在图可读性有所提高，但如果数据集中还有一个变量想加入图中该怎么办呢？</p>\n</div>\n<div id=\"添加第四个维度颜色\" class=\"section level3\">\n<h3>添加第四个维度：颜色</h3>\n<p>这里可以用每个国家的洲来控制圆圈的颜色（<code>aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)</code>）:</p>\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb4-1\" title=\"1\">data <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb4-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">arrange</span>(<span class=\"kw\">desc</span>(pop)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb4-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">mutate</span>(<span class=\"dt\">country =</span> <span class=\"kw\">factor</span>(country, country)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb4-4\" title=\"4\"><span class=\"st\">  </span><span class=\"kw\">ggplot</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size=</span>pop, <span class=\"dt\">color=</span>continent)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb4-5\" title=\"5\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.5</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb4-6\" title=\"6\"><span class=\"st\">    </span><span class=\"kw\">scale_size</span>(<span class=\"dt\">range =</span> <span class=\"kw\">c</span>(.<span class=\"dv\">1</span>, <span class=\"dv\">24</span>), <span class=\"dt\">name=</span><span class=\"st\">&quot;Population (M)&quot;</span>)</a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>该图基本可以满足我们日常生活的气泡图的可视化了。相信大家通过前面的详细的介绍，应该可以自行绘制，只要换个数据，懂得各个代码的含义即可。后面是图表美化的过程，参考<a href=\"https://www.r-graph-gallery.com/320-the-basis-of-bubble-plot.html\" title=\"thr R Graph Gallery: Bubble plot with ggplot2\">thr R Graph Gallery: Bubble plot with ggplot2</a>。</p>\n</div>\n</div>\n<div id=\"美化气泡图\" class=\"section level2\">\n<h2>美化气泡图</h2>\n<div id=\"一些经典的改进\" class=\"section level3\">\n<h3>一些经典的改进</h3>\n<ul>\n<li><p>使用viridis包的调色板：（<code>scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;)</code>）</p></li>\n<li><p>使用<code>hrbrthemes</code>包的<code>theme_ipsum()</code></p></li>\n<li><p>使用xlab和ylab自定义轴标题</p></li>\n<li><p>为圆添加描边：将形状改为21，并指定颜色(描边)和填充</p></li>\n</ul>\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb5-1\" title=\"1\"><span class=\"co\"># Libraries</span></a>\n<a class=\"sourceLine\" id=\"cb5-2\" title=\"2\"><span class=\"kw\">library</span>(hrbrthemes)</a>\n<a class=\"sourceLine\" id=\"cb5-3\" title=\"3\"><span class=\"kw\">library</span>(viridis)</a>\n<a class=\"sourceLine\" id=\"cb5-4\" title=\"4\"></a>\n<a class=\"sourceLine\" id=\"cb5-5\" title=\"5\"><span class=\"co\"># Most basic bubble plot</span></a>\n<a class=\"sourceLine\" id=\"cb5-6\" title=\"6\">data <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb5-7\" title=\"7\"><span class=\"st\">  </span><span class=\"kw\">arrange</span>(<span class=\"kw\">desc</span>(pop)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb5-8\" title=\"8\"><span class=\"st\">  </span><span class=\"kw\">mutate</span>(<span class=\"dt\">country =</span> <span class=\"kw\">factor</span>(country, country)) <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb5-9\" title=\"9\"><span class=\"st\">  </span><span class=\"kw\">ggplot</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size=</span>pop, <span class=\"dt\">fill=</span>continent)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb5-10\" title=\"10\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.5</span>, <span class=\"dt\">shape=</span><span class=\"dv\">21</span>, <span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb5-11\" title=\"11\"><span class=\"st\">    </span><span class=\"kw\">scale_size</span>(<span class=\"dt\">range =</span> <span class=\"kw\">c</span>(.<span class=\"dv\">1</span>, <span class=\"dv\">24</span>), <span class=\"dt\">name=</span><span class=\"st\">&quot;Population (M)&quot;</span>) <span class=\"co\">#+</span></a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb6-1\" title=\"1\">    <span class=\"co\">#scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb6-2\" title=\"2\">    <span class=\"co\">#theme_ipsum() +</span></a>\n<a class=\"sourceLine\" id=\"cb6-3\" title=\"3\">    <span class=\"co\">#theme(legend.position=&quot;bottom&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb6-4\" title=\"4\">    <span class=\"co\">#ylab(&quot;Life Expectancy&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb6-5\" title=\"5\">    <span class=\"co\">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb6-6\" title=\"6\">    <span class=\"co\">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>\n</div>\n<div id=\"带数据标签\" class=\"section level3\">\n<h3>带数据标签</h3>\n<p>这里使用ggrepel包中的(<code>geom_text_repel()</code>)，可以给每个点自动加入标签，我这里是加入了各个国家名字，其他可以根据你实际需求进行设置。</p>\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb7-1\" title=\"1\"><span class=\"kw\">library</span>(ggrepel)</a>\n<a class=\"sourceLine\" id=\"cb7-2\" title=\"2\"><span class=\"kw\">library</span>(viridis)</a>\n<a class=\"sourceLine\" id=\"cb7-3\" title=\"3\"><span class=\"kw\">library</span>(hrbrthemes)</a>\n<a class=\"sourceLine\" id=\"cb7-4\" title=\"4\">data1 =<span class=\"st\"> </span>data <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb7-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">mutate</span>(<span class=\"dt\">country =</span> <span class=\"kw\">factor</span>(country)) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">head</span>(<span class=\"dv\">20</span>)</a>\n<a class=\"sourceLine\" id=\"cb7-6\" title=\"6\"><span class=\"kw\">attach</span>(data1)</a>\n<a class=\"sourceLine\" id=\"cb7-7\" title=\"7\">  <span class=\"kw\">ggplot</span>(data1,<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size=</span>pop, <span class=\"dt\">fill=</span>continent)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb7-8\" title=\"8\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.5</span>, <span class=\"dt\">shape=</span><span class=\"dv\">21</span>, <span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb7-9\" title=\"9\"><span class=\"st\">    </span><span class=\"kw\">scale_size</span>(<span class=\"dt\">range =</span> <span class=\"kw\">c</span>(.<span class=\"dv\">1</span>, <span class=\"dv\">24</span>), <span class=\"dt\">name=</span><span class=\"st\">&quot;Population (M)&quot;</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb7-10\" title=\"10\"><span class=\"st\">    </span><span class=\"kw\">geom_text_repel</span>(<span class=\"dt\">label =</span> country,<span class=\"dt\">size=</span><span class=\"dv\">5</span>)<span class=\"co\">#+</span></a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb8-1\" title=\"1\">    <span class=\"co\">#scale_fill_viridis(discrete=TRUE, guide=FALSE, option=&quot;A&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb8-2\" title=\"2\">    <span class=\"co\">#theme_ipsum() +</span></a>\n<a class=\"sourceLine\" id=\"cb8-3\" title=\"3\">    <span class=\"co\">#theme(legend.position=&quot;bottom&quot;) #+</span></a>\n<a class=\"sourceLine\" id=\"cb8-4\" title=\"4\">    <span class=\"co\">#ylab(&quot;Life Expectancy&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb8-5\" title=\"5\">    <span class=\"co\">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb8-6\" title=\"6\">    <span class=\"co\">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>\n<p>如果不喜欢圆形的气泡图，可以将代码中的<code>shape=21</code>进行更改，正方形是<code>shape=22</code>，得到的图如下：</p>\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb9-1\" title=\"1\"><span class=\"kw\">library</span>(ggrepel)</a>\n<a class=\"sourceLine\" id=\"cb9-2\" title=\"2\">data1 =<span class=\"st\"> </span>data <span class=\"op\">%&gt;%</span></a>\n<a class=\"sourceLine\" id=\"cb9-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">mutate</span>(<span class=\"dt\">country =</span> <span class=\"kw\">factor</span>(country)) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">head</span>(<span class=\"dv\">20</span>)</a>\n<a class=\"sourceLine\" id=\"cb9-4\" title=\"4\"><span class=\"kw\">attach</span>(data1)</a>\n<a class=\"sourceLine\" id=\"cb9-5\" title=\"5\">  <span class=\"kw\">ggplot</span>(data1,<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>gdpPercap, <span class=\"dt\">y=</span>lifeExp, <span class=\"dt\">size=</span>pop, <span class=\"dt\">fill=</span>continent)) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb9-6\" title=\"6\"><span class=\"st\">    </span><span class=\"kw\">geom_point</span>(<span class=\"dt\">alpha=</span><span class=\"fl\">0.5</span>, <span class=\"dt\">shape=</span><span class=\"dv\">22</span>, <span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb9-7\" title=\"7\"><span class=\"st\">    </span><span class=\"kw\">scale_size</span>(<span class=\"dt\">range =</span> <span class=\"kw\">c</span>(.<span class=\"dv\">1</span>, <span class=\"dv\">24</span>), <span class=\"dt\">name=</span><span class=\"st\">&quot;Population (M)&quot;</span>) <span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb9-8\" title=\"8\"><span class=\"st\">    </span><span class=\"kw\">geom_text_repel</span>(<span class=\"dt\">label =</span> country,<span class=\"dt\">size=</span><span class=\"dv\">5</span>)<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb9-9\" title=\"9\"><span class=\"st\">    </span><span class=\"kw\">scale_fill_viridis</span>(<span class=\"dt\">discrete=</span><span class=\"ot\">TRUE</span>, <span class=\"dt\">guide=</span><span class=\"ot\">FALSE</span>, <span class=\"dt\">option=</span><span class=\"st\">&quot;A&quot;</span>) <span class=\"co\">#+</span></a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb10-1\" title=\"1\">    <span class=\"co\">#theme_ipsum() +</span></a>\n<a class=\"sourceLine\" id=\"cb10-2\" title=\"2\">    <span class=\"co\">#theme(legend.position=&quot;bottom&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb10-3\" title=\"3\">    <span class=\"co\">#ylab(&quot;Life Expectancy&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb10-4\" title=\"4\">    <span class=\"co\">#xlab(&quot;欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita&quot;) +</span></a>\n<a class=\"sourceLine\" id=\"cb10-5\" title=\"5\">    <span class=\"co\">#theme(legend.position = &quot;none&quot;)</span></a></code></pre></div>\n</div>\n</div>\n<div id=\"拓展知识\" class=\"section level2\">\n<h2>拓展知识</h2>\n<p>其他扩展可自行学（小编做推送的时候已经学过啦，但是篇幅有限，就没继续整理下去了）</p>\n<ul>\n<li><a href=\"https://wencke.github.io/\" title=\"GOplot包\">GOplot包</a>提供了直接做气泡图的方法，函数是：GOBubble。</li>\n</ul>\n<p><img src=\"data:image/png;base64,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\" /></p>\n<ul>\n<li><a href=\"https://www.data-to-viz.com/graph/bubble.html\" title=\"BUBBLE PLOT理论定义\">BUBBLE PLOT理论定义</a></li>\n</ul>\n<p><img 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OYEjlWD4/L2TydMLz5h/vXYu/cjjJH8lzzGnx70TBnW13uWPQIIh/YWPY+71j56mcMcdff6uezF67cX+KUkSDOMwch0CEIAABCAAAQhAAAIQgEBO4MgkGJ7ePueeixaWx46dLP71e+pFbE45O+6dMKjr9S5/BBIM7uG7yU9BPvPdW25IimFv+5Xop0RLv/RBgiELWQ4hAAEIQAACEIAABCAAAQiMJXAkEgyP//X1LLnwjDv53udlOPlPGfZ+RL4sMr7SO2FQV+pd/igkGPJfeOhIFMXMq/3f33NX/zj6SOQfX3V2FJBgaLHjBAQgAAEIQAACEIAABCAAgTEEVjrBsPfLD92lE89Gf7H2i8tn3As//HTMX72fulvffSaqNyYhkUF+/Nk948OBTaHeCYO6Su/yi5Rg+OVVtx49MfLiT+ylfEPFOddT//xDnce+es7t7ieSzIN7//BC4tOND9qfdxxVJMFgAuQkBCAAAQhAAAIQgAAEIACBDgLLmWD4xlX36f/uub3438N7bvdnt9yt7avujb97xT33B9FfqsNC91n3SteTCzGoh1vuZPxzlj4x8eYt9/j3caF0PyQ0xjzx0DthUIvvXb7nAj3V2rl+8gcuup9+6F4P3I85MwmQs+yt/567cTpOAB1zz/zRd9y7nxUSBr9/7G69+UL6/Y1vbbnHOYhwPNDWUK/PByRDYXYgAAEIQAACEIAABCAAAQisFIHlTDDEC9ee+9UC9BdD3tZ3Lv8o5DHf1pe+7F749jn3xttb7tbPbrmtt9+oEhrrfxgteEkwOOf23NZfZEmeP3jBbZwd/eTn69/6smt9YLF3gsE5t7/r3vijiHkdB89+9RX3evSToq+/vO6+nCSKfDLijTFPPJBgWKlRDmMgAAEIQAACEIAABCAAgbkQWPkEw7N/vOHe+td7bi//a3lPvJ9/8J3s+w3ZotlIcDz7Nx/yioTn+8ur7gWDT5WoOXZsugSDl79/z139i/wVmG7/PPsXV929sa9TkGDo2T0oBgEIQAACEIAABCAAAQhAIBBYsQTDs+65r627F799zr313i137/GwJxYClXzn4S331rdfcM92LJaPHXvWPffyOffu7fKD9xLb75UEle77CkP/bxg0kkd7/fSZbNH99D/eci+ar6s8417ZzlgNeYIhMuLxz95yG3/cnWioEk0/y9qLZKS7k9nqHK9IpBw5ggAEIAABCEAAAhCAAASOEoElSTAsiEt+/9Q9/uVu9WqEfz1C/z79rz33dMInJBbEstmq8funbu+/Pg28bv3sU/f52KcIJlBp/7G7d7vxS+Wf//jc7c2irQnUowoEIAABCEAAAhCAAAQgAIFVJkCCYZW9i20QgAAEIAABCEAAAhCAAAQgAIE5ESDBMCfQNAMBCEAAAhCAAAQgAAEIQAACEFhlAiQYVtm72AYBCEAAAhCAAAQgAAEIQAACEJgTARIMcwJNMxCAAAQgAAEIQAACEIAABCAAgVUmQIJhlb2LbRCAAAQgAAEIQAACEIAABCAAgTkRIMEwJ9A0AwEIQAACEIAABCAAAQhAAAIQWGUCJBhW2bvYBgEIQAACEIAABCAAAQhAAAIQmBMBEgxzAk0zEIAABCAAAQhAAAIQgAAEIACBVSZAgmGVvYttEIAABCAAAQhAAAIQgAAEIACBOREgwTAn0DQDAQhAAAIQgAAEIAABCEAAAhBYZQIkGFbZu9gGAQhAAAIQgAAEIAABCEAAAhCYEwESDHMCTTMQgAAEIAABCEAAAhCAAAQgAIFVJkCCYZW9i20QgAAEIAABCEAAAhCAAAQgAIE5ESDBMCfQNAMBCEAAAhCAAAQgAAEIQAACEFhlAiQYVtm72AYBCEAAAhCAAAQgAAEIQAACEJgTARIMcwJNMxCAAAQgAAEIQAACEIAABCAAgVUmQIJhlb2LbRCAAAQgAAEIQAACEIAABCAAgTkRIMEwJ9A0AwEIQAACEIAABCAAAQhAAAIQWGUCJBhW2bvYBgEIQAACEIAABCAAAQhAAAIQmBMBEgxzAk0zEIAABCAAAQhAAAIQgAAEIACBVSZAgmGVvYttEIAABCAAAQhAAAIQgAAEIACBOREgwTAn0DQDAQhAAAIQgAAEIAABCEAAAhBYZQIkGFbZu9gGAQhAAAIQgAAEIAABCEAAAhCYEwESDHMCTTMQgAAEIAABCEAAAhCAAAQgAIFVJkCCYZW9i20QgAAEIAABCEAAAhCAAAQgAIE5ESDBMCfQNAMBCEAAAhCAAAQgAAEIQAACEFhlAiQYVtm72AYBCEAAAhCAAAQgAAEIQAACEJgTgYVNMPy///f/HP9gQAwQA8QAMUAMEAPEADFADBADxAAxQAz0j4E55RLMZhY2wWBqy0kIQAACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElosACYbl8hfaQgACEIAABCAAAQhAAAIQgAAEFpIACYaFdAtKQQACEIAABCAAAQhAAAIQgAAElovAkUww/O53v3O/+c1v3P7+Pv8WkIH3jfcR/0EAAqtBgDGXe83Q+y33gdXo+1gBAQhAAAJHj8CRSzD89re/JamwgEkFa/LpfcV/EIDAchNgzCW5YI3vfc9xH1ju/o/2EIAABCBw9AgcqQSD/yta30kN5RZjUjz2SYbdTbe2tubWLt45er13ZS3+2P3g/cvum+9vudtLaeNDt316za2tbbjtL5bSgANTmjF3McbRZb+fjbsPPLy+Ud0HNq4/PLDYPThBk40Hs7fpjtv09861TTebu+dkdh8c9xlIWqX5xhfbbqPy/1G4V00Wi3YfnKTfTFJnBvHbQ6Rtc4+KR7HIKo0HM/DfkiUYNEhMtqDktYjlm+x6n3X+RwfvxLOcF0kwLKff2loz5i7fmLuIyYhx94HFnhRr3jIs4Th7m2a96JnM7vYoskBnVmW+ITtIMHQGl90HJ+k3k9TpVG1mF22bZ9bccgtWP+IPnKYflyvBkGRch2fdF3HihE7jJ+Bm5HJyDIF6kf7P/+Z+ZZT81e233Tffv+x+cNe4eOinDjLB0M1hNqau4MR6QlCMb+PHNxj1YzRhCC5AtcnGg9lP9A9m0SM9N3dz1JPZnUtZqOMVWVDcueifWlhzi/nEzyw8PlksKrb7chpxHZZInIW108gcavM0bVF3tQksVYJBnXfz4uhxyPYNrdtZ+UTuyb0dd3P7kjt//f6Ur048cJ98dMO9890rbqfj+wYP/u9Nd+PHr7krO2MmVP9z3+18dNPdeO+SO3PyUqfM3KaZHT/4xN386KbbunrWnTpzzd3vsPOgdej2KldNAl/8m/tr/5pBIcFw+0P/CsIRSDCM4WCym/rkZJOZqZtdQAETjQXV+LflLm/emOs40+jqx/ObbuutM+PH6jHjYHWPuXHNnf/+1iHZMuZeM0b/hsnhy1nA8O6p0mTjwewn+geTYNBitT0fm8zunlAPp9hKJBhW0C9jo2Eym4f1wcnaGKv6nAsMs3nOytHcUhFYogRDfTM8ve0e6kkGvz8Adz5ZerTzjnvtz9bdqfemTTDcdTe+/6o7vnahMxlw94Pz7tVvrLkLt8ZM1n694975wQV3oYfM3KaZHX92w13wOn33Zbf+lyQYBoTd4RQds7AmwTBLt6zGROMgCE00Hvnxb9A488j99Lvrbv2bV9wnB7Jgvutu/OA19/J6j7F6THv+HnP+1eNuzRwzD1rvMfeVMbr299Xh6H0Q8Xg4MiYbD2Y/0SfBMDgeSDAMRrYYFebRBydrYzH4NFrMftxp2mJvtQksT4KhHthHjypN1pGtCdT9904dQILBT+x23IUxCQbf/s6bQyat/WRads3s3H9ec6fMyfLQye1dd+Ufd3o9OdLZBTtu+PrLSvURyMEfgtTka/Qo4UjG0NdyfuW2/nn0pIB/WsD/++vb1gsLzjklBOpy9tMFenUglpl/CNEq0zytoFcjpE/YWk863N2qdA5l3n/bbbU+Wqj2RnoocRGenJCMDz+u3Ji3b/NIZeb+z2V4/dpyJCNm1XDIZYbjHvHU+kudEp7hPbxsfNJ1ve/alRjNy66tuVZ7zrlkEhDVycu2+kBX2wFCutOSUdAprTU66hyHPrvirpSSrQPHmQc3r7jL/zJtojgdw27+/ZCxOq2b2H3vHfdyYcychd5J2weWVEjtOwy9rfjSuaQ/6GS1VV9sxvG+jztX1aO+Fe4jXX1I44f6evUBRelQenS6fa/x/bhsk9dMMhu7uj50LFmpDWo3va+pv4/j1JIpmwMf6VjbnbMM5RKH1Qeq28++RkJTLx8LR2V0PfWFZYtpv/wbxnp/764/lmjaYzMO+qqu2A0YW4OM6H4Q/Ft4/UG+jctV+7E9seB439B1zbI5Y5S2mXKPxRf3+7YrAWo/MPXxbftdVZyTn5p4K/dBlW36TWpjI6Ppk+06TdtRDAWd15zJNvJ1uhZSm41OifyoXux7K8bVF6xrucyGW91uxt6SIVZW/7TbznyXtJHGk2SPbEyvtXRP5Hh+VvnUb0G+4l4yCv1H9jTMy/5p6bcCJ5YkwZAFWNRZrAAu+cWaeJFgSCeOFqPk3MCJf1I3nuz++oY78+asEgyKFw26zdYa1NrxokGlqTd8gCgtcNuLdGvBPFrUx8mDkjy/aO5Xzn9vodhWlmAIiYIo4aFEQ/rdBum15bbq7zpU5SQvSjAUZdbJh8YPjcz0VyR0vp00GMIr1b9pdbQn3+c3Ap03PjBb32SasUjxt+G2d+MvdUfxZNyQ2jejpnwje6Slym5c366/BD8q28R3pG88aan2c9tyBs1xuKG2ZNiJj6bmaK/Y//f33aMPzpSf5jqocSYecwbuD0sGd4yjC2BLlx+W5VoeW/Fx0x/iZxpLfcCaSMbS6n1rYaN+oAlmVE06NPcK9d8Nt1H6VZmONjZOF34Zo6OOtSgp9uHTG/WvCKTjgcrnY05karVbtDewmWwcDAt2sY63QXauTXMc9DLGWGcsCEL5uJ16v8XAqB/0NXVTDKaMvbaD2m3My/YkX7GWb9N25dtWjFqs4paGxFzEyG6vZ+aAOY8AACAASURBVP/z7Q9pt5PpQfZBMW/Y2nbGc4V2HeEt1q1jsLmnj2oobjau36l/rarb576W6rT8biSiVLYV+1I42UZ2ye9ZP8rlyN7crljPtE40jlyvfzUuaWMUT5Kb2mjHml12xDHVq7Fvu/6lokq++rpsbvUf6Zz7Jo6JBORKHixHgkGDjJxauaJxfN+fWLImUiQYOibG1oT8ICbLew/czTePu7VZJRjU6ZN4cc5PLtLBo9SnfWw1N49RqWbASAc/W0ZYyGeL51/d3kqfAtAC3Pgr/O0P88RBfOzbbZ6QaP0FX09EaKGfqanFvrnYlk55XZ0vJjTayRMX6uSJEOfia6keSiTE9ja2pgkVb5jKX3bfzHiHJ0NyWzIe8aFuPkmsVDFVT1KyuBqVj29kTaz4m1EsRzfvVrZcY1wed+F8LD+bMLRubs7JhjxWdb75y0pseXvfl4/19yWCDRmHdm1XfkLpwU13oet1saHjzP/cd/f/+0m5PWssG3NuLgmGDr2fPBk4No+xx7r/TXyuQ++JZY7R34ovnVNMxvGuc3msP7y+2e/nY33fa8W45h1Zv9A9J+vvzV/4/EQz7cPJtawPh37aWgA0Y0vaL5vzJoO87TCueL3ye52o9ttK11QfX7fRqfc4GNVJ5TWyYvtMDYNtbbssXX2ctGQGf2YydD72l9prxYrXTvGSyVGdnH04n8eKZWnDpO1DtWstaFSvTxt1u16vln1NG4mvxMgvApM6Tfm8T1rWVecmbDfRJ/jgoPqg7Mh8GmLX4mrXCWNUa9xo7uG5b+M6CccQO+2Pdw6Jcclv9QnTSbKrzVZ9Lddf51MfjYTbbSte8zaa86NkbMy9uZYw8s0oPpPYjM4nfbJsX6WxZMXjge/19UdUzTE/K2tiXZGTS5FgUNDlASkn5udLvrEmPkowPLh5yZ36+rpbW1t3L5294e7v1ZM7P9H1A2X8iOutC9UXeNMFcv06w6NP3LWzL7n1tTW3/vVT7vLtR8mkdzRpfeQ+ee+se+lra25t/Xl36q1d98icXI15RWLvrts6+5J7fr2W8/0b7q70NuXlE1b/rvEFd/7MFbfzaNdd3nh+pPfJs+7az1O9A7sxE/9HO5fdq8c9xzW3fvxVd+nmg8R+z7vKACYZyO5HkUv+rM5bHdw61ymk58UBcpVgaC38k6aaRXO6wE4KdR9oAT9wYV1OMEineHHfqDCqFycSmsW9aYP0S5ISjTxxShMDkhnpoIRJQU5IJOTXVW9AgkE3oPgGOxqDNt2dKgaMG1lys2pubrGMkdXNtXjcGo1lsdyGkTX+6Vw6gavraKJh3sjqG2aib9NWvz3ddPMJVrt2GDfCeHTfXftLP1HI/2Xfr9E48+udZFy6cS8dw/x4KlnWt3T8NxAu/OC8O/Pnx92r/ls79ZhZjc9/lX548e52Mya/tLnjbgx6nS3SK9b5+Bl37eY7rdfKuvX24/6aW3vzhtv5wQm3vrbuXr1+391//9XR+PzaDfcg8Nx3+7/Ycme/VY/dXz/lzt+4G425fow/786c3XL3szE+Z7nv9X71eNWGvy+99Lfn3dn3diNZo1f8unjL3xXL+n76/Ley+4n/ns/3z7iz1++7R7cv2/fd2L56vx1dzRn1h7i/WeeaGpPvWXI1F4nbb1pQf8n6t+4nhb5oyqzrmO20+r091gS9VD6ZUIervXekZzyejSo37bf1ba4l9QbZV1bR1kl+GD9uNcmRgs/isVUcTT/abY70y2TX5ii+Ei6WqWq35L/idbG327eaKp2Trol/FdeWXrpmsiq10j5vtSufJ7qEqvJDZvMYfWyZkpXHURdXq47KZ8nKoLN9XbZb9/5wLY7PIC/fkfyUiWTYHHMZssuyQddS+WJqxbfdtvRsJ07iJ1xa8kzfSlbuu5Fd7X4pGyz7oqREzLvY73J2q3+8BAkGOdgICAVQ7NwOn2nyE2/9gvflM+fd+e277olfnO/dde9srLuXfhxN0qx3aG+ez/4C7yeFr7rXNi+7nQejyeajmxfc8fWz7qf/00w+/cTy1dcuucu36oX3r/1f8tbd2Z9af33rSjDcd9c2TrizN+7Xej9xd98/446fySafxkStsf+B++TGJXdq7ZR7dfMdt1vr/eAjr/cZd6M+bsrvu31N/A25T3YuuOPfeM1tfTay5cm9G+7sN467C7fayYoq0TCrJxhCJrlefPSMDzN0FGPxgqiXPC2SR4/z24mGusyQxa9XMizao1cFDirBoAW58WqEXpHw2yaZIDujZEAMUrrm+qmM2ksYtGUqEWFz9MKUGImTH9G3LRL5aryw1Q0iTILqm5L3e30t3Hzz40qkbmLpjVWttW+wGuOaxbIWcfE2tBk9RRCfk3zdpOO67X1jPJUAc2vpOF5GMnYkY8Yo0VD84K0fZ/7sjDv//WthXLr741NuvTBm3P/Jy+a3dKpfcfjopnvn7PPu1Hs7buvseXfjP/fd/s+vuJf/vPko5IMPzrj1b1xwN+sx7/5Hl/t9kDexyd8/dt2lb6y7Uz+p7ye//sRd+/uX3fNxgjqqU9J73yex/Vj6i3237+8/6yfchZuP3P7eTXd+LbqneE7fPOtu3KvvH0/uumtnjrszHyix+8B9cv28O/Fn592lHzZjfMXyB/Erao/cjddedpd0X9rfdw/+75Y7+/c3zOR3Ue/9fXf//VNu/c8uhfvgg4+vuFNfO+WuKTn04BO39f0T7uW/v+Qu/WTXPdB99y/X3YWOX1gyw7I+qZhP+0Mas+m1Lmnta5If96NGnvp7qT/oejoeSGZrYtxhk8aOWI/Wfhi3avvDcW6X+JT0zsvbx9KpbYdtt6RY9XSuZVN8/y3aI8mFiX99L2/8FpWvdqVvPA6nPlPyOfmraOt+Ecu1GOtc3E57v6znSL7ip1xO9mQ2hPlRfj7Wu7yvdmMfJTpozmTNkzpZldv0V7rbla2lWNb11GbJbMfuSBddT+wLT0TkbdltjCTJ51GdHiys9q1zgVwX+6qQdIzjzWaS2hxayHYMu0IJtZXKVx+3mNu22XJGzXS0b/HVuXg8MfYb3TrkewUs3mPHmQBo5XcWP8EgBxpB0AxwUaftcJk12fUL3fVzP3VPoknfvk8e/Nk7zc+KWYtqPwFMJrs+GfCSe8dPCIOs++6dP1tz528253yCIUle+AnZT152a39/M6qn8h0JhtuX3PNnM733d92lr59xN36t+n22vo3X3I1Hcdkn7qdn19yp9zVJja5ZLCp777p3vtVOlDz56Vm3/q133N3AZCRrtgmGURBosApxYt3wzHjRoBIPwtF+bznO6UkBLc6TBbK5uDYVSl8DsBb/+QJ+jGzp1SQK6nZVz2ojOtfUaycDEgvmlmAQ6wNIMIQJWD2u1Del0U2nvtnVk1zFWHND8tZ33RCbiVJTZ0y81WNffMNXu/E5cde1EPfm2NlvzNRkwJY1XkYzFkZjSDUW9Egw/Mll90n8RJaV6K3HFT+eWE8wqH1//fk/v+x2knFOOt11V/6kPXb5sbqYAMnGM7Xz5F/OurV8vCuOmfuuqHd8f0nq+/H6lLvmkyT7+25383l39l+y5PTHl9zzZ6LEgK8/luUDd23jVbdlJZUNW4t6/89P3dn1/D647+7++KXkPuvrn3jrk+Se15W08LZ2/aeYt/pDHsNWGVP2mMloI0f9t9Qf7PFAejXjQKqFZZPq2P2xvkdpAS79dZyKLz++3yrXfUI6te2w7ZY02RfXk6xe9kmQuW37ZCQ7Xez4qtLDbjMrr/loPAfo5NzWo3ltIppTGGN0E1+mgUHvrnK2zd1+MVuTjYaenluig8UoCK15FGMyFBzt9G7X4hzLsm1WvMUxmNSq37tP7JtjgkEL2Lh9xWt8LugsXnF8DozxTvmhIe10c7fir4u53bbtu5EGHe2LRRxrOleIY40BTTx0yPcKGLFu2yBeR2u78AkGBaMcX9qanS3zpSaB8dacKPkJWfyLEMkEr56UxhPAagJmJwP8JDWe+JqTVi/L/AuXLdPr7/W2WTzvLn2siXOfrd3G3X884daTv3DVsiwW3n7/0cY1I7lROF/pnyRoyrpmbkwPjQ6eFvBHGiSs9xHz0hrMjEeierWVy6uPo4V7SDLo3Ni/ruuv8/GTA7Xc0gJ+jOyxCYaxOsnOKRMMpv5tmXN9giG6IfsbzeiG0Swe4uPR+NRcG1FRDGWT0xqZbkC9b2JCHW0lwxr3uq5FIsbuSk7y17qqlvpTbndbZDzWpvs9Egz5mFgae+rxMB5n07Y6FvJh7GoW7aprjtXGYlvl/Xb3h89niefup77M+49vI76/JHbHCYbS6yZrbu3rl9yudE3qd4zj/omzk8fd8VfPusvb9ZMFkpFti3r75Ebctupl56361rmYbTu6mjOKVas/hFLR5LKzXFVBMd7uw+22VLbUH+zxQHObZhwImlY77Xaad3tLdRIJsjeeYCcFxumdFC4elO2w7ZYg2RfbUpalWv23iawSC93TW4wKuqt8vIArya5UtRhb5/rbpZLiV47lgg1jEuCS32ylb5++YC+6WrJavJsSzd6QdlV29n2wmU/mbZV4e4sM/TrjZkTB8rF1LjCTzDg+FbMt5ra+nfJDQ9ox7NKl8C2CNG6SfhmV9bt227aeo6od7YtFbLd1LtMhPeyQ7wuKbcTbtiGVelSOFjzBMMa5sYPjICp4L56saN+c1PgJ2TIkGHou0GWrvbUTDH6iPDzBED26q4ml/4uWkXjw3NMnQGaZYPABUcfS2Dgpl9PA0V5wFQIuP61Ff3jaQAtpI3GQ1K3LGYt+LbzTbxiMfzWgmGAIH0zMngRI9IkPZMOYVyQM3b0U6RGSLpVoQ6bY5d9YkCql6zpfaF/VW1vdiC7eGX2wJ46b+qayuVvHSnRzGcnpuiE2N9FmYq3yRlKrpVjdgvmXlbqwbnqxzgU5XadHE4F0clCVFxvrHdtMoD3m+L5OgsGzMe8/fuwckGAY+5RF3wRDPWY/ubfrtq6edaeOv2S+3tapt08k/MmV1hNr+59dcSeixINlt3Uujp8stJJDjc3lxVZdXLHb6rOJuPAqlDXWa4LctDWm/6rN7EOL0tlqo3kKKv3rcHedzAbd87J2QymNEz36cahj7IhHM56pkLgYY0i0mIjrDbNP7RS24n7xTli4xG35WmovPx/mCzk7MYvjR+1YHFU+uSYu/cd708Kudn2F4nW1b/ul1ZbkxDbXheT7pi/Yi65GZnl+1ZSp9wa1K5sKTCUr86f8P6QPNrExZYIhJHoKOheuS+eEueDJzshXKt83xlXelK92wrb2ZxLf4WL9scM0zsyYqao0Pkzb1vlUzqiVjvbFIpkHqbwlq9G72VP53Nd1CfXviHdIOhSYNLJXf2+hEwwKdLvzyzkKgPEBE09WtO8nNS//JP399Oox141rzYe0rAma+Q2G8+5m/Ejv/ujR2/i9Uv9XsfiVCa9H8WmBfXvxX+l+64JbtyZy9+43r3Zokd+59W3kej9wW3+15s580P52QvkbDP4ViefdpdtZosC/ypE/Mlz/RF2SYPjvBw3vTF952twWOng6QEU3vWSwsSQqntJBP8Sif7QqHkwsEf6LALffjr5TMCpkJQPCueS7BqPyza9IaMGdJiLiusUEQ2FBbi/sMz3fbycZqjZDgsSXl25jEgz+9Ypskd/on7djyWye4uj6FYk0URElWgocRhZb/69vavVPuaXxNIqRjYub1c+8tW/cXTfEwqRWcWx8Tbq6YWVxq3hM9ZIdaj//ire/PrrW1ll1m60mAkm866ZdPWJYuOk2IpJH4DXmjraP3I0z6SsID/47eiXLGnOtc/VYMW5x2n3dv1p2wl35LB67RmPg2MV7Nlb5n95cy19d82Ng/jTGOL17JRj8RxfX3Yl/jL4XJLn3onuaxS0/t/dk9C2f2J7qexLZxzclv/RKSvWKRPtViwfbr7ZekcifOOn20fBXJHwfyeNc/SaJ6Shew26I83ReEfpE9lh4kJv33yDHPw6fymoWgPk9Jeq/WTvN4iav4zX341LaRqNver6ZBHu9xvfjwMXYURvtsUh2ZG3XMsQs9VFz/237qG2foU50Su1vus3qq+5tO6VD+sG8SIeMZ+CWzAHUTuaTxPdZ2wPH+8ioaDdqt+XDxoahfokaGO0GO1I/yu/+SdqkDdmWMJLUWq/sfqaryXZgu8GXc+uDmU8LCYGRTfJHWqeoc/jrf/serjoJc4ETs4i9yveNcZU35audsLXt0uVRjKRxE/pQ1reK8RS4ZnKqRjraF4ss1mRfazxWwjFi14y3qd9kX7AlqTOmXyZlg6SV3FngBEPjpPQG1PaDAnNch0gnuKPJpJ/UHP/zS6MPf/nJ04Mdd+nkevSRrH23X/0V/lT4voL/eOHlv3rJrZ284LZ27tffb9hxF75+wr3240/co+qjVaOPLj7/jUtuN0o67Lz5vDvx3XfcJ/V3Ep58ds2d+fpx+7WG6oNe+eK/ngTv3XVXvrXuXv7BTXe//jmzJ59tufP/uJN+TyKeMJr7PsFw3L28Wf9yxt4j98k/nqo+eLYTfZwysPPvQcffp4hkPrrlP/IYfXDsP8sfedzfueDW//Kd0a9e+A9r/rD8ccq2x6Mz1s1M54z3rMbFkpeseEpfQdlw2/oN3h4DRLN4jj7EWH3DIF9MxwvnvGyzaFdCQN9yGG3fdlu3t1y1nyz6vRVapDcym+8mjBIgiaxk8d+lU/5TkGqn0TXyTvNByg//zW39c6NL3Has16huSabO23JaSZZKWLtOu71E43DQxEF+Y2vGJusmpUW8fa2QYCjGXfZeda2dbpLFMU83V6MP+Lju0w/CzTOTsXF9221OnWDYdzs/WHen9DHdX7zjLoUPE9YfNsy/G+D/Cl4Ye/z7+3miOIxZ9Xduuq5X3wjYeMfd9WPp3hN3d/uye+0v19ypH95wn/T8LkHVXrXAPu4ufFQnSx7suCt/f8qd+PpZ9064VzSJjKLePoGt7/L4ZECw24/X0escn11xL62/7C58FH3sd/u8u7ITfZfBj9njWP76hjv/mv+gZlPvyWfvVO3m38/xdhb1rp/KWN+4Ej7O+ejn2Uce6/r5Nxh8or3LR6FjGjtWf9C5dBw3FvqGvKYP1/0v9IFNp99DT/tePCbkdeqft80m1L7Zso5rbvTTa9nizVfquL+1xxxNwHOd/MJlo0qQ5gkGjXupfSak6mTLhjChF5N8/BzJUr3WWDTIvrJe/oraqGLAum+XxsmL2277tBEr0i2TlbQTYsX7rjxWinM7PtuLyrKVHf71emR61uRt24qNyI95DBX6QoHRSHytb4iRYqMhGd7mU2g3LERzPUd+3LD8mcdI5Dvfrt0Hxby96Gz5NPAfUCfRod2GYs3sn4rn0G78JEvGpRDjnfJb7irb5YuOeOT9X3UyfdbG+TWXU7VQnouIRSvWSvFc6xOzC0+Btf1QoSjFutpOfGnJbwFdqROLm2CQ41qZWYN/cGYhCOoq8YRT+/fff8f99NF9dyP8tORL7uy28RehD15zJ/RzkJs3nf9ZS/+u6oUf79Rf2d5x7/z4bvKzW8dfvex2sg8u7vz4HXc3/rmw46+6yzvZkwL+r1dGYLb+mlb/JGb1M5X5z2tGC3/Zam9HT0ncrN6/Hf1M5/Mbl9zN/24mwb6eT8S0dYomunV7nkvXz1QGHfbuuxvf9T/BtubWT/qkRNpeKDfm415hspUMChrY4gGsOzbyqEpvFPXAppjM2srrjo6NRXqyiM9q6VsE5ocUR2XTJEOdqFC9VoIh/uv9aEGeL6wTeYZuVpKk9YRASGSMSzB8HP3SgxIEebJFTJQUsGUmete8ctskqdrqNYk+ZeOK8nfrBhVNXI1rzeLEuiE2dVsTa9+22oz7vxFv/SYB1o3U1ik2O9nP9BlNajRBGN+n4n7c2r93w732zY6fBvYM9Jd/v8gWE53bT382MYxP0atj/omxcF71o+tBp71HbuetU6Of/P3ay9WYfHPzJXfm+xfcjeTJhvI4FWT5n408ObLrxHe33N1fXHNnNuJ7xTi9/Zg80vvCreg7C2/uuGBPxODRz6+Fn6lc+9pL7uwH2dMLslt1TJY77pLX8WzNYG3d+Z+XrH7FIrqXhPYl029bPOufYS78TGV8L9FTDNa5wLNuP4nL7MDuD0b8m/01ExYO8/qjeLfbGlVK7xta4ElOoe9lfUwL/q52mr+sxfFd6o9qvynb1Y9lg7mACWzSHdWp+lpgrHZtu2WfOQ6GiX2js7ikLY85CnNDW4eqdihTt1WNtwXd5auOMVnjTRfjoLXkxf3JkB3KF3YS/rUsm6sXULCtIHt0WnXkj46+IJtMO+p7R4iRzkYjXXu0W4tqsejyp5qXzsEPHfaF2LT6W8YpMBhzz2y1r7FDCjZb9RuzfyqWQ7t1PZ2XfR1MOuU3atR73XaNfGH1PdXr41cx7ZJj+EI2F2JNdqq/+m2bqfQ05HsC8lvOW3SqJ6dko98W5LS4rsaJxU0wzIBvPmHh2P+1zv/sWf4rEj0m0dGkc9YcZxAKiJwXga4EyLx0oJ1DIzDrsQH5izVWz9IfhxbENAwBCEAAAhCAwCACRyrB8Jvf/KbjneCjM1FrJoHNX8p89q71hMQckwiNTqkfvM/4b4kJkGBYYudNrzpjbjqelcY5zndz4j4wfV9EAgQgAAEIQGBeBI5UguF3v/sdCYYFSBoMmUx7n/HfEhMgwbDEzptedcbc7oXzkLHwKJflPjB9X0QCBCAAAQhAYF4EjlSCwUP97W9/S5JhSZIM3lf8t+QESDAsuQOnV58xlyTDNMkR7gPT90EkQAACEIAABOZJ4MglGDxc/9cQHt1d3Emv9w1/sZrnMDDDtkgwzBDu8ohmzF3c8Xaaxf8s63IfWJ7+jaYQgAAEIACBmMCRTDDEANiHAAQgAAEIQAACEIAABCAAAQhAYHoCJBimZ4gECEAAAhCAAAQgAAEIQAACEIDAkSdAguHIhwAAIAABCEAAAhCAAAQgAAEIQAAC0xMgwTA9QyRAAAIQgAAEIAABCEAAAhCAAASOPAESDEc+BAAAAQhAAAIQgAAEIAABCEAAAhCYngAJhukZIgECEIAABCAAAQhAAAIQgAAEIHDkCZBgOPIhAAAIQAACEIAABCAAAQhAAAIQgMD0BEgwTM8QCRCAAAQgAAEIQAACEIAABCAAgSNPgATDkQ8BAEAAAhCAAAQgAAEIQAACEIAABKYnQIJheoZIgAAEIAABCEAAAhCAAAQgAAEIHHkCJBiOfAgAAAIQgAAEIAABCEAAAhCAAAQgMD0BEgzTM0QCBCAAgZUj8Lvf/c795je/cfv7+wv5z+vmdeQ/CEAAAhCAAAQgAIHFIUCCYXF8gSYQgAAEFoLAb3/724VMKljJDq8r/0EAAhCAAAQgAAEILAYBEgyWH3Y33dramlu7eMe62uvcw+sblYyN6w97lfeFJqnTW/gB2NS7LQouCYE7btPH+dqmmzzSZepDt33ay9pw21/o3OpuZ9pXY2yH0G/9UwHWQn6Rz836SYY7F31s1/+muC+4Q/BnHE6l/bnFc0kBzkMAAhCAAAQgsDIESDBYrjyASeAkE7ZJ6ljqm+cOwCZTLieXmAAJhkmdN9O+Git1CP12kV+LKCU5vM6z+U+JMxIMs+GLVAhAAAIQgAAEVo0ACYYZeXSSBcgkdWakPmIPlUC98D+97fo//zKJwsMTDIrRzd28PS3EeIIhJzP+uMPfh5BgKC3iF/38eM4TlPhi220MfcpHdawnHQ7Bn32sVr8e8sRdUW6X/cVKXIAABCAAAQhAYFUIkGCYkScnmbBNUmdG6iP2MAlogr6ACQY9Kk6CYfgrUMWQ6vL3ISxIFz2RUNKvyHeaC5Pw76rTdW0aPaese6D3ngW1cUpEVIcABCAAAQhAoCcBEgw9QQ0tNsmEbZI6Q/Wi/BIQ6FpwHqj6w59gIMEwcsCB9tUufx/CYq20gB90/udX3In1dffavzya2/ccDrRrSNgk/LvqdF1Tm4ewPdB4XlAbDwErTUIAAhCAAASOJIHFTTBoklL6K66ux4+haqKuj3H5rVk/XVhp0RTKWrLr8NBELHzwa23NWY+VqpyuhTZq3dp/AR73kUc9gj7hu8AdNuW69f64ZSJTTA0eKhf8Yj1Gr/r1BwdzX5p+FJORPDFvf7RQ5Xqwa+la+gBiT5mSV8dpyjrnIAaRnsV46dl+KW4rnmqvZGMzJjZsM92CX6RPbVMv/6XtBzZBptpXubhtW2fpqX4nCdU280VyLfrIaujblc/Udtpe2o5sl365X/OWdCzZqtdsw/iQ6RwYFeNCsp3Ly4bxrSli7g1KJJR+wvLRJ27rR9fc7qP5/cSlaYxxssVlbc0F3iqfx2/n2FVX6lPnEPwpk0bbdsx529N4Tmv4owNl1uLUt7+09eIMBCAAAQhAAAKLR2BxEwxOEyFr8qEJfXStNWlpJuvtibVkb7rt+tceqkWFFjbZJFBu0yQsLEDCpLO9qFbZjet36q/rR/rU9fJFUFMne/N+kG3SNtuaNoljW7fWhDsTVx0GmduJjbFd1sRU/NI2Gp/ckdyI76hOushzTvpvuO3r9S9/VHWicgPYib/0C9s4ieUNHyAz/mq8zSKK4RDzY/wxpP3C4qCy7fRG7/fLi2zUZ2Jf7Oq99cyOnGOwt9APrUV/FhNxrHnXSM/8fBqv+W9mKI4yfX1bFzfNX9po2in17ygGq8at/ynm2+2GvqG+cPGOucizf7WjLLedfGvrdSAJhlLiYYbn25ZkZ7r6jfd1iOWuPh73177yozqH4M+gZYf9G6cLr/x01BmNIdF3YoplDfuzfpywDwqzAwEIQAACEIDAMhJY4ARD81eT1mJBE5l8QhgfV95oJtphwp6dNyfo0SQwdqpfVLR0Udnsp/60AGlNwqJFUN626qRtNIufKmYcagAAIABJREFU1IbmfFo+1jjal57xIk/ncm67m+2/6EWiwq7q55NzFdB1Q/5o8R4vwhpf+WuprdG1WP+wqB0t0NI6XomGUXqtOd+wUxvRZLiy447bLLQ5XqZzIcHQYqT2jJ9DteJbTAfZ1Cy481hLkySxH0JD5o6SJKntvmjDNPef4rqlQ0gweP/l3FN2jZ9GapVk6nxevqqleEz8ORkjtVPFcSxPvis82WRCVZ28n/jC0nlA/MhHOQOdH/eE0oEkGPaeuEe/uO8ezDChkOtpsg0no/6WcxZ/y2fiH/s4yCzsdNXRtTn6c6RlZH9mS4iLlv1RnQNh1owR6fjx0G1fjBIVBaychgAEIAABCEBgOQgsdIIhLIKyyY0m9/kE2kJul20mTulEp5agSWA2EbPkNwurdIGkdkt/mdGkLrZBdeJzWmAk56SIJsZ99LRsss5Jdp+t6mfJlVFVTSbtxevI/pjZGJ/I1qQttdF+gqTSodavHzu1b+sbcAySGS8QDbnil8V3Ke6H29TwMePcZBosNXcUt215TVtt3s21tJ6Y5wmlUdNqqy2vfN3sQ7JEvJP+UtKtrlRgpHas/h2uJe1ICWOrNvI48EWlcxL3tQxdi+tJltl2zTsub6iTL9zj47vbZ91LX193PrGyfvxVd/b7l9xP/zt6DeI/r7lTfvFc/bvgdqwEQ1JGZdfc2ps76fca9h65nbdedce/5susu5f+9orb+XXUVibbMKU5ZbFqrjacczaqZ/KMBUT7XXV0bY7+rDRTu7l9tdpmXxtTJ8RmLlP1WszU1+JxP+LGLgQgAAEIQAACK0FgsRMM4a+i8YREkxRjwVa7JEzww0Q3X4BqYVOQUZwgyefSIZocZ3+BlQ6lxVGYnEWTMKuOJn6jCXvcXrSfT/CkZrw1bcrsiHSJqxb3TZl1aS10Ih9YNjQLzjE+6YyFOD4abYeyE/+RnnZsDJVp+TloKEa5/0rnu153iDkHeeMWlOOYB03Djuxv/KZLiqVuX6T1utrvula3acSffGj2O6O801MUgZns0dbWY3g7kmdsO/w9NH6kl9XXmnN2bEuzOKGQ7P/8sjvxt1vu/pN6kf/kvtv50Wvu8sfGon/vpju/Vkgw7FxwJ9686R7t1fUe7bgL3zzlrt2L5TxxOz844U794657UJd7cOuSe+kbl9yu6g1IMJTjVlbbfu7kr6r51oyzulDXNSMODsKfvmXJSftfo7iux/1mJsxkfzVe2WNFoxV7EIAABCAAAQgsI4EFTzA0E6Mw8dEkLF8M63y80Ir2Q/3KS4XJpDyoSVDWhiZhzUQ9WuQvZYJhZHDLrsxuYWltC5yqcmP8IYbNhHeMT+aQYLD1ThdjmnRLf3MbL1a7GJUWt2IXy6nhD2q/Q85I3DjmdaPRRu03ftPF7gSDYiyt19V+17W6TdkXxavaSft7Xd7yhWQYrEe1bD0625HMSC9RMrcqb+lg6RyE1LpF9aSXGZdhPExjOoird5KkQryI37ngjm/upk8ZxNeT/R13oZRguHXNXfvPOpmwd99d2zjhLtzKfm3iwZZ79U+uuLuJzEfuxpnn3SUrobG/n5uRHJfjVsUUvxmbTv6qm2276nRdM8aDg/Cn126c/Won7jfj6jRP7w1lpj6l+yeJhiyCOIQABCAAAQgsNYGFTzDkj4vbkx5NWNoTFWviFP5qaT2m6t1pTQJ1LprMjzyviWnatt1uEyvWdeucbW8jp/ee9O9c9Iij8V0Aq6EumV2LJkuWJtcln5jXbfYSPy071Y8/jKdz6UJZLRrbLkayKY+pDnaD2u+QM9JU/s4WCIYZOlVuv9sXiu2UW1f7XddqbQy2aideKEl3s19PyKizHcns7GtBq+ajoXkc+CKGjU3NmlFUr1OvpmLnXjHBsP/I7b51yh0//pI78/0r7ua9Jx3Jho4EQ0gaPHI7bx53x9/ccY/CuTrxcOtC/ZqFFqHN9swHWTKirttlVDluVasQb538VTfbdtXpumaMBwfhT6/dOPutdsbVKd5HO21MWald8xssaVGOIAABCEAAAhBYEgKLn2AIkyO/gG9PqCvOHRN6TZLSBUdhMimnGRMkTYTSBZKvIFl2gsH+oJoWY+m752oj1lXnbDlSuMfWsMmuVWBsFe6UaXOxxIzOjSmvtqLFVPMXtJS92jgIdqP4aeQPlim9zcVmgbXiObF1ZNWw9nsyLSZ1RLLZqj+1+4FiumHV1GqeRErrST87waG24v4Qy7Sud/FR+bQvSQdb77DAzxipHVM3+c/0eWxBva/yhr9D+6YsI34Ub5Yso2nrVDnBoKcOHrhPPrrmLv3tS+74X11z981XFsYnGO6/d8qtb0T1HzxoPgrpEwx/ec3dzxMPHceWLeHcOC6l6zpv8g/S052uOl3XdC+Jfafy8bm0tV5Hitc09lVVfTd7lXBc26XrOt+TmXRLxwbpxhYCEIAABCAAgWUjsBQJBk2yNy5uVj+r15rUa4KevaYQFhTFr2PbCxu1F0/GNAlKP+qmxYn/61q6QAnl/WPJ2USr0SttX3VS+6I2Mjmj5EbabjEArUnf7mb5VzH6TGgtmZECsidn44tU1xJ7IjszluEplpYfNTEuMYhkJm15Dfy1uN4dt9myWfWzcnrUfKzM4X+BrvCFeE7jY4RWOrXjqm1T85fLlg/ku8oWq51Ra/n/FbtpjPpS3b5QLKSLCNlSaD/SMW9P8uKnSypdAzs7eVe9OpD5TTYNYaT2c70SHbJ2cpbhOOhscBADU1bNL4lb+SH72cWqsdG11AdBi7BTTDA8aT+x8GD7VXfq/QfGkwzdCYZHty6449+44HYeNd9d2P3h5eajkNWHIM+6n/5Pc73S68F9dz8/1+MJhiYRbHAJ/NOYqYB08g/I0h3VSfxSF9G1OfqzajmyMb0fRfHSGl/VPydk1rLft5XHeNP+uLhMIXMEAQhAAAIQgMCiEliOBIP+slMthuLFnrA2k5T03eNNt33d+n1vTZzyyU4tz5oExhM0LTD99uK22z5dTjBsXN92m3H5sN+2o7hokT6hbvO4cGtRJCT5VjLiia3OGXJ7TfZUP5aZtFvyS61/Uq/xiXyW+tJaUEt+m2VQQzoaNqbs1H7M1tIzShqMlRmVTWyVdnWbrYl4W5fEH71t8u20ZQWupzeqhF1rkS71jK1itJGhn5fr9oXqJXYE3Qr9UIkok7P3jeV36ZH7ccNtX98cPXbf8kWZ0eaurqU6yp4DSTAEDo3OgZN83dI58m0eP6WxquYYZBv+9adKCYbdt151Fz66657oiYW9B+7mmyfc2Z+2Ew/71Ucez7ubKhs/efDopjv/jfSjjo9+fsWd+qtrzRMM+4/cT8+uu+OvXXO7D+okw4Mdd/n7W8WnGgrmNKfHcDF92cm/EZ3stdqJ4rRTXh1rB+xP6aaYDX036lcbp637pGte34nKxvVNZkX7S30zS2CE+mmfkx1sIQABCEAAAhBYbAJLkmCI/hJrTrQ95HzyMpqcaFKVToTsRUNwVWkSGCY+8cJT7UaTyGhhNGpX7WkBYU+cbF2lVS7Dy7LlqEayLdjU/PW2W7dElg4KMnVZW9nVPTGVfanfVCf1X5BsJnd0tdlKtmwssJM90WS6vBgbKNOM21pGvqDwimex1tajZ/sVBMVoY38alwPiKLyyVMsKuquNtB/IB4qB1A7ZMKb9jEUVE6FdtRBvpYvsrXWSf01f5HX012xbR9ljxqX0NduJ9Yz2VSdPAnTq3BE/rfHQs7B9E2lR7ZYSDPevn3Vnvn/enTk5+pnKta8dd6++tdP8GoRPIpg/QXmq+ajj/r7zr0aoXyfb1s9UPnA3N5ufqXx+47LbjZ54yPXM7bCP237u5NLJ326hOqt6lT8j7jpvxsZs/JloqfbDGJeOt2Y8TxJLSTuR/Vmce/+32gxlxowLiWEcQAACEIAABCCwKASWJsGwKMDQY1YE7IXcrFpDLgR6EdBipzOh0UvS0hTKF+7Lcrw0gFEUAhCAAAQgAAEIrDABEgwr7NzlMo0Ew3L562hoG57wMf/ivJoMfvOb3xRfk1jUZIPXmf8gAAEIQAACEIAABA6fAAmGw/cBGlQESDAQCIdEoHqcu/04dkgu9Hy14JC0P/Bmf/e73y1dgsHrzH8QgAAEIAABCEAAAodPgATD4fsADSoCJBgIhEMikLwvru82NNvWO+KHpOY8m/3tb3+7NEkGryv/QQACEIAABCAAAQgsBgESDIvhB7QIX9Jv/yUZOBCYOQF9ayF8/M4nGI52LPqnAhb5dQmvG08uzLxn0AAEIAABCEAAAhAYRIAEwyBcFIYABCAAAQhAAAIQgAAEIAABCEDAIkCCwaLCOQhAAAIQgAAEIAABCEAAAhCAAAQGESDBMAgXhSEAAQhAAAIQgAAEIAABCEAAAhCwCJBgsKhwDgIQgAAEIAABCEAAAhCAAAQgAIFBBEgwDMJFYQhAAAIQgAAEIAABCEAAAhCAAAQsAiQYLCqcgwAEIAABCEAAAhCAAAQgAAEIQGAQARIMg3BRGAIQgAAEIAABCEAAAhCAAAQgAAGLAAkGiwrnIAABCEAAAhCAAAQgAAEIQAACEBhEgATDIFwUhgAEIAABCEAAAhCAAAQgAAEIQMAiQILBosI5CEAAAhCAAAQgAAEIQAACEIAABAYRIMEwCBeFIQABCEAAAhCAAAQgAAEIQAACELAIkGCwqHAOAhCAAAQgAAEIQAACEIAABCAAgUEESDAMwkVhCEAAAhCAAAQgAAEIQAACEIAABCwCJBgsKpyDAAQgAAEIQAACEIAABCAAAQhAYBABEgyDcFEYAhCAAAQgAAEIQAACEIAABCAAAYsACQaLCucgAAEIQAACEIAABCAAAQhAAAIQGESABMMgXBSGAAQgAAEIQAACEIAABCAAAQhAwCJAgsGiwjkIQAACEIAABCAAAQhAAAIQgAAEBhEgwTAIF4UhAAEIQAACEIAABCAAAQhAAAIQsAiQYLCocA4CEIAABCAAAQhAAAIQgAAEIACBQQRIMAzCRWEIQAACEIAABCAAAQhAAAIQgAAELAIkGCwqnIMABCAAAQhAAAIQgAAEIAABCEBgEAESDINwURgCEIAABCAAAQhAAAIQgAAEIAABiwAJBosK5yAAAQhAAAIQgAAEIAABCEAAAhAYRIAEwyBcq1T4ods+vebW1jbc9herZNeC27K76dbW1tzaxTuzV3SebU1szUHE4R236Zmubbr+VCepM7GRh17x4fWNKu42rj88dF1QoETgIPpCSTbnl5XAsL67oDG0FPeiw4qQo3Uv6keZOO7HaVFKLai/DhjPsLH4gBtfQnGLm2DQDalz0aCBmUXy8NibbEBQB6sWydWibs1t7g5v/cjWUFyTYKhDYLI4TONH4wAJhpRLc6R+S4KhYbJ4ewfRFxbPKjSajsCwvrugMTTP+17ALRbdCf07F31yurtMEDmTnUnuXzNRZIGEyncLNrc/jDhWmwu9FlpQfx1wRA8biw+48SUUt7gJBuecBv7SpHjc9SX0xxxVHj4giHecXNB+yUdzNIimcgK6Mc0jmZG3PdfjSSZok9SZq1FHvLHaP6e3Hc9cHPFQWFDzNdmcdYJd7fS7xw6/rx8Y3i+23UZpoX5o9yKN84U/hEivzsXbgRFCUG8ChxjHruPeo3iZ85xKc+/SGDDuem/sFOwkMGws7hR1JC4udILB6YZlDf5d146E66Y1cvgA7gexfIBThxv2ePq0ulO/F4FDuhn20u1AC2kSyRMMB4r1MIVpfCfBcJheoO0OAprUk2CoIXXdb7qudTA+iEthjtIaSzQHKiQfDqJxZExIQL45hCcYuu49hxXH0om10ITxdDDVNJbk66CDkb56UhY7wdDxFMO8bu6r53JZdFADuOQMWdxJB7YzJXBYN8OZGmUJJ8FgUVnqc5pQtRYFS20Vyq8QgXnNQYZNanU/PoSFWdf9puvazGNCTNJEgrjO5XtIM7dx1RqQzw4hjrvuPYcYxxpv8sWtzs860blqETaJPRozch9MIuso1Fn4BEPzFEM00HQNALXXFAh6hN9v7aDoWpwMHeQyWRqM6m8V2O3XCmdlx318UYNKsK9rIt6S7ZMBQ20rdYfM5lKxcD4rn+lmMZKt1gAqP6f1MtuSNqI4ihJYI47ptaCydhI5pQ9kpvZJ9zX5RzIKj9jJnuDXzox1/e6ojy/Jl65+O6atUFT9yWrLF9L1rI1gWx3fXuexPpKsUDbzVVDK74hlZKepo8rVSa6ojYpjpveoiaxO0m7ELthWig3p3+iYxmIuOD+WHk398tNAk7eluEp1U9s1N8VLx3iVy8ljwPK/yljXcnm2z0dsVF/yUltyrvmxbO3BWRzqPqr2Dm6MyHXTsfxrxJp0CvHYN6GbyhTvdoypXMTHGKNUX+xTNnb/T8ehxg+SIetDuWCjwaEq3Na1JUtCW9wsmZJXX8vHj4yDGDRjdM3MGmd6tS9lGzaS7WNe7RVtVPWEjezMZWZxE2zNzgeZdX3LNpUJMqLYqXwoHaLx1OxThbip5ecxZt7rpEtpG3SUneKi41LF9nn5Qz7y29w3oYzBTfY0dTp0acXPcH3bFjSvH8c2aHxNy2e6Zfo0NqS1qqOs7Lg5bSoh65OtuUDGoeXfVFq4rxj+aErK1jyOo/iUTRPEcf/xrdEo2Qs2Rv1K5zrtSqSMDlQvjLXGHFK2GnOuEN9hbMz9FbUZ5Ihr5ruoaN9d9aHu+M10ym0OuluttmNh+FhsyT1a5xY/wRAvArNO3T0gKpjzbR7cCqT8vA+ELEDHxkYkq9WpRnpYA7LVWdRx2jaqjdwuf9y2IQwE8UBS3/w3pv4VCfGJBuAZMBKfNovS5Et6bbjt6/WvNiT2jwZoyRXr0TYavCNb7LIjH6R6yT+bbrv+cn8lVzcAxUVrcJPOhl/jsvkgGdulNqR3sS0V0LZpO7VldF22x9d0LmVn8Yh9tF3/2kNcTm3n3MXR4NGKc5XddHdkc8yl2s/7RlRHGOptf9skI9cxtyVrIByW6nt5JX0na0vjQDr+qP0yt7R87Ms79a/Q5Pq0J9viGcePELT1kk5tuaoveblukplv1YYVq/5cS45i6OKd8B2gtK7tX+mVlh3ZId1z3dJjuy8U9Y/HhVRQdBTJTMbCKL4GjCnSZeN6f/+HifXF7SRmYu792ZXio+2T/jIjRrv1dwTy8SNiLQYtP2fjb//2oyRu3q6Pz9NDfgGmhy3JL0epvH0Pl62xr6LgGu0W4yfyyUR9quRr36ei+G0pZJ+QP7wt2u/XLxt54tHyfWscKXCNODRSZWdqU7GtKBYbGT33ir6qx9sshsPC3POW7lmMWrEhvhanfszFb8Ntl/rkQcdxK4nR3IOCzmIw93vDyL+Bax0DOg769QmDrhjI/C/5iY9VPykb+Sv6VbpZxLB0Gh9bkU6lGLL6kuzL4ty3N2ws7uOM1S6zFAmGZpCLFoxJcMtJCijrBqRBPP9asM6ng/tIouRFN0o1ZW4ly7ef1mk6RdaOBqzcHp3PbqSSk3R4IwlTqRdk5JOHsp6mWdFJtd907tTOqGhht9x2IztlpPPWIKoBLOUhv+V+aM6PBopY9+Za65FJcezlo7J9FRDJygY22ZjHTRX7cVk/+OV6RDfGhFGhLcsx4tiyPchu+yRpy6fjlFDJ9Avn/YAd21IpIu6xL/wFzzFts0n45YvCmHlHrCdtq07WhphlNoRFUqRTsCuR6zls9vzp1/42TtuW6qf9RAzyfhL/hSvlIzlV/88YNddSXyq283jxXladVK9owZW1YcVu5zn5s7UAaNpu9bmoTvrX0ohX5vMQH7m+QVbK0dbZ6gtqM2XaGhdsgVGS3Ps47xu+ktrMrzXnY9/IX0P8H9j4/p/z8SqIUX5N5yfpc6rbQ2bMIGfU2Jvzb/qIFddDbGrmN+3xUX3H6xX7oejuyJ/tOo1PEz+IVR7TQVaf2I382JITXWvFgOK7bHtud2BitVMG034iLo+Nzrqjiz4ecn2Cr6M4rUproRLaEf88lsQg5qxzRtnBdsswyTT6oXRtxVlUZ0ZzWmmXbsVqNG6lzKNrgW0UYy0+Kh/zTVtLjsQilq0C6isD4jjERy4vyOqpV6WD/DFuLSSFje2QOaQx/1P/S8c9MY7jNdI1Sjr0v3cZutdrnbTt6F6eMJZO7XtfeVyXzuXxqD2u2npy1rklSTBEAeQ7tjlRiial+UAvT2vgSK4roKxOrgCNO42EWVvJyidrvqyuxbIk32pbE5iovPRvDaCR/KiDaSBIB2fpbemja+WtZHofNP9s/W0parcvI3GwyjdxkdoorsakTAytGNKAHzFsJp+2jSMekY+Cn219w80m9mHQyW7D5pie1YCZcJA9cVtptehIfsl0OAAZ0i2Z1IaW5auYYbjY3jH1ke4F5iZf1YntlS7xuUaF3NeyK2HeFJ98z7Bx2rbs+mJgcdO11C+SY/uy6asxE40Z+aTAA5K8uHwFTj5L+uJwpGq7Jb8WZV4X/+Q+UVfQtUSvYXFTtkJyYubygx2TZVm6IpnGWOiL1PaYfOSDaPyQv4b4X23Yf3WWfrZ9k/W5YTKbMd5iJFntPqLYace16vSzKfBJYkr+s/tUczXfU9uWLb6s4qlHjBn+z1tLjtU3ongJ13Wtb5/qbLu2ocArtGntBD1i+62CQ86JeVum+kvVv4p9TT6J48U6N0Qno6xsL3Ezr0uPdvzbsSQWsS2NLnl/bq7ke5JzCHGs2LM4iVHfOB6TpOvPo+GjmNL8uz3+NGWH7Elu614gm32/FptWH5e/4j6g2LFjYYhu48tabUknK4aaawk/2Wr5vk5ueO4tRuMVPJIllibB0Axm7cySPFfsICoQOnvfTqAgjMsHYcaOFeQqZshSZ00W6/HCPU2myD4NLPZWnVnt6Vh6aKvrfW1TvXSrSZY9eUzLjo4GMoo6dTIQ1KLFJO3wXbZ1tC9/xIOLzvX0UROnBe4awOIBuj6X2mCxS8/J9jgOEhlWW6mI5Ei+jDmPvwGKZxy3qe3SM9EttNzlq7qQ7Ih9EPMLk+a03dBE334/sa9Httv2NVp07vW2cbK2bB/IdxY32y+2nMgy2RH5x4or1SjKky/ivqhKvbdd9tVCDH3Dgi+yITRp6aVzcXwa+3G/CvKSnW7mo35u+SoRkh3YMlVIvonHkNZ+5IOivyRwUp4Gr1iPhp182tEPBvujH6NGh5GxYpefD5PwnjaJaUtOzVTX+40v3bZ4kZbeaiPWwTonN5tby/cq2HVN/jLiLI6B9v7QvpDGTilJJpW7t+Ic3/esuVSfNlUmtUf8R3an17p1s69afk9LWnpY51RLDCK75cuesS9J7a0hOytk2SNmU8WxbIjiMTQ9MI6HjgWhnc4d+aS8FuqsHl0Ur7hvtccZ+UKxbsWiykSxEP0B4aBiOFI9SpZKL7+NdbN1koy+8aPyfitebUZxKfZFYIkSDPaNUYb4bR/nj4Iq7gTqrHFgSmp3gKpUs+2SJf2jtjWQ9RyMZV88GLT3ZUe3Ls1fbSJ9GkMG7Umvfp2uW6+2f8TNyqCXfN7lt4725Y/4xqJzPX00SYJhEL8x+iQ+6LoZWh5ula9ZxTzqehqc2/GXD/IlH0mBkq/kp/jmEe0niz+VVexLtrZWG0adMWxlazx58S3kLBIfSAVzKx0iu+I4S2wcCZi0LTvG1L7NzeqLtpzIuFYMNXxybr5WUZ58YcRe1NqY3W77qspqJ2Zt2NA0VMuM9ZKM2HfGvmV/I9fvWXFal2i1YfsslTdGphG7ivFkG9la9JcatthZ51S+ZZfdF3J2nf1gsMwO7lGMlnTIzw9dVMiWlpya0VjmYlltu23xRcz2xCz0AyPOk3aMgy4/d11TgtiIsyQOW32qbx8Y6Rrbrf3+Y/VIhnxh61WYS8n27BWDhmDHOCW/BNuH2dy0UfB7XCCMP3EbHbqFWIrsbunbrz8nalQHhxjHsiGKx6CffBn6SbjiaYy+MRXXk6zgv0l5xO308WVaPjkao5PZJ6I69jjV4a+o7qjfxPGVaNbrQH3X7oOx7A6dCuO6ZNs2dsxXeml+9AodsQSDFXBdA6hVvitIumRpUDAG43hA6hCvm5s5ALTqdevSOZltyRpzoh50D0KvUQePGIWb2CEnGHr6KNxkkkxqxM+4QfX3q3ya8vHSTRlGW5Emxq7kjwZpU2bUVvt7Cml9NVCSM7pu9TGdM3xu2mS3q/Ztnxh1dCPs7eumhWpP9Xs9QjfUxmnaKsSHJkSFWLX6Yrcv7Xa6btpFeWI5qS8qXIaPM4zm0wpmjKliLTPW60B09fIVE+3+rdb9VjzTv9bEJeL9bpmSVZpQxZL8ftFfdUHzehfPadmpftzndC72UW5IctzNSDbljIrsBrZflNPFNNE/Pui2pRxjWb3aZ/3u6XX7XX7uuqZxKPKXmA9qP8aQ7+fty0fFRX8uoHmdqP3kQ8Yuqapr9cKya2FaGIclTnHSr9+rVrNV/TyOoxL1R5jjRVr3GDqSGY1X4hr5spE/ZE/cItlJ9dL17PwkcdxlQx5HiU6zvDckDYV7QNmXafnmSP5sc+3qc4qdalFv+jbj3jSY7DVy4hhLinQeSMd+c89unSQrZij94nOxQqpzYONSLHwF91cqwdD85aAQvBo4koG83OEaee3OaMeCZNnttwZj3Vj73uQ0uJkdPNdInctYpPmigUVf23L5zfGwTjeUUTOhbnfqxsb0ms5btnW0LyYJX5W3ZDUMmj2Vt2Ogc0GTxGUjMexJP2OSooEx4aB4McoHmdmOfLm5K4ZtO9pxXAuRfpkdkpnoFtpVOzHfmmHih1EFyUpvMGIeywgNhPfM04mh6sT26VxBTiSyuCsGY5kPtdFosXdbpYWh7I0ZNO1Yfrb5q458mY45ZmxWVZryrdiQbUYMqLU+23Lbo9rm9c5+Y/lNHKeIm4THeDndV3usAAAgAElEQVSWb2weYmzL7PZnW2J3ebWV+t8c84LoA2CnWAl9bqhM6d3NKJ90Knby801C05YXTK93+jJt9ZFcUHUsWzIfqKxYZWN0dbmOe99O//iS4GgBHvzQ85rmQXFfVx+Mz0Xihu3a8dDNvd2Cyg/xd1xnfLzY43CsyUR+kYBxTM3rYmfr1tZH5fvFvlRrbw8xjtVHrNgTIyvGrTjWub5z/DYI80w5lszizUnZZugvma1xprbZn1c8t8r0TI57Rdox06g3bq9YV3Yl45piyI5F2RL3Z51L55fSSvL4BoOIjNuuVoIhBLnPFucDoga+dnCoY9kLEC/LDtA2XLWRtz0qaXWOENBGG9W1ZCBoAjzV1csfXTM7S/5Bw9AZB9hW1Wnb1aV/m48/M5xRmKBmjILf4r9eVY2Kk+W3jvbFJbuxdNnY9lGHfK+beYOSvoW4VQxIv74czLZsr4SzauPi5uivGWo7FNANInv/T/WqRwHTOBG/9k3JC5Xtsa/EMJ0kS06VRU/0asq3+mqkV9q+6ti6tuTor7dRu16fuL9V1uiXNKJyEbpoV+33s3G6tmaQYPB+zmxs+mPKNMR837j1lILfMlkRwV676gOtMaJh0rpXqE5m36i92m9TjRElzQt9IWurGUPjPjNEZly2icPcn6N20jbKfTAaF/J7byfP2A9pW17Lqr3ID337QaPneJn2GNQwkqy8ryve03FlVE91+owjTaznfUrx4O8L7XlLo2G8l9ZJde7ytZeh2N5wG0b/jlsx9+XnVryW7nuSona33UOdCvcF4xcPjLlOqGbslP3UsEo5GUIUi55LYl/ENBvfgl9DeZXNxzTr/B23GepJH5Vrx7RKdG9VP7chHm/T+1GIibxP1w2N2Kb6DIr9osKNb3zsp/6J7IjGhkZUff30hHHcde9RjHe2G8fxsPGtsaF7TzGdcumuU10NtqU+k7z2OCPWiln5Ja1vj6EHH8NBz5h/sCmfP5d0HXFSnCYMY1lxG/F41Hss7uGPFS+yYgkG7y11iNFNuVqMVIue+jgJmtq7GjTictUNVr/bnXemUlSobXXGtJw1GDcds6e+cQfI9W0NxOpgluwNt3Han+9p26B2U7vTo0kYqU5ux6bbrhd06SRPdlu2SZbhI9nYurFLXt5+fZzEVId8D0KxltRJb/DlmC3pUeBQait1SHaUtpEMviopuVn8bVzfNh6xbG6wqY8kTO2lvgo3kqSN6KeZEn4Nc8VDmaHaberc0alqK33G+1o3qFZbPfvUEBunbUv1Ux+UGIyAWONVI0e+zjmlfqwl1XGRly3EbVVJujV1FIviltoyasn6v3Ru+8nLNvRVfCcxJsm1XlONEZKVbxV7sU5tDsEOU78+MrMysjfpa+Ie6xL35QH+l/yivrJbbWbbqF7Zl6mew+6raj+XMeKkNhV/oqfzwR9JTEhmZosYRzZ5eS1ZKucns6c3JkgwRGNlJKvSNdFT1qS2+nK5vWlJ40j3z9BexLMzBgp9qiUvZdlLP7VbslnXCwvoxMqSPheteaL8HzGI/Zz4X308npPoXGpz5b+4rnSKzyVKZwcqH3yUym+PqdIj1q2Rad0jhvW9Rla6F/G7vhn9Ylmkb8mnMedJ4thYQ4RYU7yYvAtxnC1Ow3ghH5iyUhr5ke6BQa+8QPFYXCOOlR72vdhsRwwS/pIbx7tiJ28rS6QqJvtwUNtiV2/tuaelUwNGY27OUOdbfho8FjdtHdW9FUwwjFypjhEHSR5IidPzwK2CvTtAk/rVgTrUkMF4JMUK6vZgrxalV9xx446tcqNti8VEthUWwckgk7ZrH03KSPVk84ixuKWsxMdiIjmGjzTQFWxSW3FMpe16izvk+8uKs8Jg2vJVa+Ij23pwGNOW7Z9oslvgUNWT7DDA+7892baLW5uVlyR72r5KWdTX1W7CL21X7clPdrtpnZxFLsPLasuR7vKF8ZehXHB23N/G6dqSPakN3QxGuqV+SeWovuw3+lSw1y6byguFRzvqj3WMafwWs9SWrG5+mMmqYqMU32aMSWBtR6Gu7FHs2XEjWflWPk6ZhzEjmlSJRS6hfVyQ2SqY+8f7tO1P2Tdin9dpl6+a6eTZKCLZ3exkj2Kuu88Nk5lxr1WTDIu5YrEUT6rbbVPdkDgFP494Ska/eBefbLyUzGTcbNiHvdBPCr4MBQs7iQ0RT5032+/qU7In8reVFLTUUZtjFpnBh4U+nYgOfGp9Knuko+zVcfc9o/Gn+lHGPNJf8dOKwbpM63yidH7Q6Ce5ZqK1qlbQrRZp3SPUmuK2acPiodL5VjoeUhxnfg585ZPBcRzNq9QXzTlFzsE+VswGvexihbNiqz5ljzNqo/1kW/S0WuAgmeoDddPiFdnc0nloDGcy03tR3IcKOtWqKT5b+vjrWRu6F6pO03cLiDldEViqBAM+gwAEIACBwyPADfbw2C9Cy/h/EbwwOx3w7+zYzkLyaBEYL6pm0cryySSOl8dnxPDy+GqopiQYhhKjPAQgAIEjSoCJ2xF1fG02/l9l/+uv1dlfIVfZ5KW2rf4Lbfgr8lIbc4DKE8cHCHPGoojhGQM+VPEkGA4VP41DAAIQWB4CLDCXx1ez0BT/z4LqYsjseiR6MTREi4RA/Ri/+Yh3UvBoHRDHS+RvYniJnDVcVRIMw5lRAwIQgMCRJMAC80i6PRiN/wOKFdnRX3vT97FXxDjMODIEiOMj42oMXRoCJBiWxlUoCgEIQOBwCbDAPFz+h906/j9sDxx0+/HCjHf5D5ou8uZFgDieF2nagUBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimi4AAEIQAACEIAABCAAAQhAAAIQgEBfAiQY+pKiHAQgAAEIQAACEIAABCAAAQhAAAJFAiQYimiGXnjotk+vubW1Dbf9xdC6Myy/u+nW1tbc2sU7M2xkuOiH1zcqvTauPxxeeW41FtSnc7OfhiAAAQhAAAIQgAAEIAABCPQnQIKhP6sxJRd0MUqCYYzfui4vqE+7VOYaBCAAAQhAAAIQgAAEIACBQyJAguHAwB/mYvSO2/RPKZzedq3nAY5EgqHD/gPzL4IgAAEIQAACEIAABCAAAQhAoIsACYYuOoOuHWKC4Yttt3GUEwxd9g/yIYUhAAEIQAACEIAABCAAAQhAYFICJBgmJdeqR4KhhaTjxIF+g4EEQwdpLkEAAhCAAAQgAAEIQAACEJgPgaVJMNy56D+gmP7b3LUg1Y/Lr2266rOGekWgrtv5UcGs7LAPNuYJBukhnWt9pLIWxdJT58O2z2P/eRtqa80FNrKp/shjzjGUC+02O3lZ8xWMpnhhr62jb3NcgkHXY5+3fdeWrfJddgVFgw/ELf9AZ+5TX7PcZrHtVjuRf4Iy7EAAAhCAAAQgAAEIQAACEFhuAoufYDAWZ1rIVdvWdwe0ANx0d7S4zhIT7YWqc63FdFSn12LVRYvR3fqVhUjGSOd4Aavy9mJTC2xL1ybkZKsWyM026CwGF+8UbIx1kuSy3LViQkR1o22H7zZOl35Foqttb1+cqCmXDfZH6iS74pL7KIkn+ShmVG5z5ONYvyaRksRs3Wa3bxNtOYAABCAAAQhAAAIQgAAEILDwBBY8wRAt5pKFn3MuWrymC7WoTvaTkU0SIV0EOi028zZ0PlnUlnyqxehokZ/qFF2L25D81k9IqnymZ6lpsYhlq6za8Iva5HrEKWtfnFIboiRMVl5Npdvx8v2iO21DdueJBC+5LC/EQmJfqk161LSTJiIeuu2L8YcyVS5OMKSSRkeFcvJLHj/h/Di5VlucgwAEIAABCEAAAhCAAAQgsJgEFjvBoMVxaeFoXm8Wouni0TtA1+KFnRaH9mJ+tNiOy5ccKTn5olnlrbZ1LmtbC9BeC/ko2WJxEqN8kevV0rW4Xmfbtb5xeZmXby3ZURkziaG2LV193dJ1ne+jV6WDfDXOr/3K6WmTPN66YqdUJ0LELgQgAAEIQAACEIAABCAAgaUisNAJBi1C84VbQ9haoFvnVMNYMGpxmj8qnx2XdeiQrUv11rLHWmha5zJR6aFssBbYWuhbyQqjntq2HulvzmUJkVSb6khyStx0PX6CwTqXijb85wsYdqT1jCNxqfxcSjQU2ovFSU6Lr+KweW2l4deci+2PxbIPAQhAAAIQgAAEIAABCEBg2QgseYJBC8B4wauFXXyucUvrr8panGYJhXwxWFooN5KlS2mx2rxikMhS+2GBWutvJQuaxtI9ybDqFBfAXkS7LS3yc/vTY5ttrJSVTImvq514gW2di+tUGlcf+8wYd9mfC0iOFSta8Gdy4+9qfJFUHB10tpvLVhvpNrbfaIFTEIAABCAAAQhAAAIQgAAElobAkicYtIiLF7zWucYfxQSDtThvqvXYG5dgKF3PztcJgUELz66F7oQJhkHtG3Rmk2DIWKndLvtVZsxWyY30l0MK7VWyuuMsJG9Kr3uM0YfLEIAABCAAAQhAAAIQgAAElo3AQicYzG8ExIS1eE6SA90Lv1aCQX/Fzz4IGTfTb1+LUftXIcJj/NaCM0oqtPXr0XrXAluMwhMSsbyaVcxP5eNzcZWe+2HBbrbbsEoSGbLDYuTbLV3X+QPSuXnCRHrmTzYUnkZJ2KhuIR6SshxAAAIQgAAEIAABCEAAAhBYfgKLnWAIi//8FxDixWa+gBuaYIh/SrC9kKwWyuYiOXd+s6D0rxM0i1RfTjqtuTVTlhb6G27Dv6phlsnbi461wLYW5koYmDLVrvXLCQbz+pWB1LZIj3g36JTbk3JKEgzhlQT/GkH8VIoX3DBM68SxkNeJFYr3vQ552Uavxj6dS+NCyZOWHnETfl/sW/FQX5syIZI3xzEEIAABCEAAAhCAAAQgAIHDJLDgCYZ48Zi+u65vArQXeVqI5gvIEWb7CQEtJO02+i34JWPDbV/fdNIv2XYsKLVobScn+oSHbG70D4tkLXJ7JxjGMw+yx6gW25Rw8D9PeXqjYlT2X2NLUrfLjug7Gt06yldGG4mPVC5KMIhn1FaiX5ZM0KsieZnq2GwrT06NgcxlCEAAAhCAAAQgAAEIQAACC0Jg8RMMFSgt9OIFYbToS2BqsT0kwTASYC2I2wvgpLHoQDrWeuULUXNhHFUPf/G39Y5K2ruh/ohRWGBLD7P9mlWy0JV42dOHueoYW7UfFuQj+8S6xNdamAebjGaa1ycy+62y/lzGyy/427qIQRNrll558qClZ4tB/lSHV0htkWAouYzzEIAABCAAAQhAAAIQgMBiE1iSBMNiQzwI7cYtuA+iDWRAAAIQgAAEIAABCEAAAhCAAARmRYAEw6zIDpKrpy6av5QPqk5hCEAAAhCAAAQgAAEIQAACEIDAIRMgwXDIDvDNh8fuzdcYFkBBVIAABCAAAQhAAAIQgAAEIAABCIwhQIJhDKDZXdZTC/rGwYTfXpidgkiGAAQgAAEIQAACEIAABCAAAQj0JkCCoTeqgy4YJxhILhw0XeRBAAIQgAAEIAABCEAAAhCAwHwJkGCYL29agwAEIAABCEAAAhCAAAQgAAEIrCQBEgwr6VaMggAEIAABCEAAAhCAAAQgAAEIzJcACYb58qY1CEAAAhCAAAQgAAEIQAACEIDAShIgwbCSbsUoCEAAAhCAAAQgAAEIQAACEIDAfAmQYJgvb1qDAAQgAAEIQAACEIAABCAAAQisJAESDCvpVoyCAAQgAAEIQAACEIAABCAAAQjMlwAJhvnypjUIQAACEIAABCAAAQhAAAIQgMBKEiDBsJJuxSgIQAACEIAABCAAAQhAAAIQgMB8CZBgmC9vWoMABCAAAQhAAAIQgAAEIAABCKwkARIMK+lWjIIABCAAAQhAAAIQgAAEIAABCMyXAAmG+fKmNQhAAAIQgAAEIAABCEAAAhCAwEoSIMGwkm7FKAhAAAIQgAAEIAABCEAAAhCAwHwJkGCYL29agwAEIAABCEAAAhCAAAQgAAEIrCQBEgwr6VaMggAEIAABCEAAAhCAAAQgAAEIzJcACYb58qY1CEAAAhCAAAQgAAEIQAACEIDAShIgwbCSbsUoCEAAAhCAAAQgAAEIQAACEIDAfAmQYJgvb1qDAAQgAAEIQAACEIAABCAAAQisJAESDCvpVoyCAAQgAAEIQAACEIAABCAAAQjMlwAJhvnypjUIQAACEIAABCAAAQhAAAIQgMBKEiDBsJJuxSgIQAACEIAABCAAAQhAAAIQgMB8CZBgmC9vWoMABCAAAQhAAAIQgAAEIAABCKwkARIMK+lWjIIABCAAAQhAAAIQgAAEIAABCMyXAAmG+fKmNQhAAAIQgAAEIAABCEAAAhCAwEoSIMGwkm7FKAhAAAIQgAAEIAABCEAAAhCAwHwJkGCYL2/ndjfd2tqaW7t4Z94t095MCDx026fX3Nrahtv+YiYNzETow+sbVRxuXH84E/mrK/SO2/T9d23T0YNn42XFZjVOnt52k0covpqNh2Kpv3Jb/3zZffP9t93WLMe/u1vum+9fdt/88OO48QXYn5P9C2Dp6qgwn3Hh4Max1SE/jSXieSTmLHNbJ8ynL0zjd+ouL4GFTzDcudhj8fbFttvwk/6pJqNzcuLcBo452XPkmyHBcLRCgBvyLP09Gu/9mF//m2pMXyBfaYHsF8nRv7++/atZ4pyD7DktsMWPBMMcfLrqTcx+XDjYcWzV/dHPPhIM/TgNKzX7vlDS51e3367uhT+4Wyrhz3/sflDdL7fc7a5iXFtIAiQYFtItKLU8BJYzwbA8fBdN09nekHslVOeMZH46ie2wp4HK+kneIT5t8sW/ub+OEgpxciHs//O/uWVPM/z/7L3rsx1Hdff/16Qqlaq8TvIuVbxIAbknqvAL1O8hIQbEzQYMJFIgKGAwGNvYMhDjwwM4XIJlW5aFJWEcsJHBNjdZlmzZlu8XZEmWJcuSZV37qZ6Zb/da3atnz+yzz5x95nxVdbTn0r0un17Tl7VnZi95SI4+wdBMpOc2Fubdvj4RuNT9AuT368f6eDDasviy0LjDlwmGaVu9iUczWY9YHX6MZIJh2vZcOfWYYFg5bUVL55IAEwxz2SxLZtRSDsjzGEsD2oTJpTkRKjVom31L2VYle8RxLIpbvoHZde+Nbu3cLiqFL8u9CZZjvYMBiah5jYV5t69XfC5xvzBVP9bLgfEWbrnDlwmGKZu9NR6X+FpoMZkJhhY4IznFBMNIGpJuLBeBtgXOctlEvUtHYCkH5HmMpQFtap0IlVq0zb6lbKuSPTiOWztvdO23gKI8P1sJMMHQimfJTzLB0B3xVP1Yd/GjLskEQ928LRx6t39rPC7fGMkEQ++WXHEVVkeCARcYnutds8Yt7E7bSk9UkS21Xua2qOfrWjqOTK5xm1hqddxHRyGeXzZfRIdyzS1RsKdhM9ULdDrwDTyNbyfht9YNO/v5E/RU/sjbvtC+kCfPgSLK4NbG1IYudSCr+ezAJqmhdsEmj1fn4Kvm5qvDD/i6xqVl7Lrwt39sQF54dr6K3USe8qy0gzrRdusadA7l+ttqa4a8nnrT9jXiG/rQlppRfTY7h74q9AFo0zo2I28dk5YcK3ZgU5QDv6M8S1Zle7AJUsqfufw8FmNbwob42Wb7ZPvQpnaMtL5oN21Xc8wo+43J09TvWMCCTj5eYX67jURG84xqWq9DHdhaP7Ihn3XF+xXw3gh5Dr6jjH7JI2TW/qNMm5xUHsre6CoZExIM1Z0gPVmFOiYj2CM+YUPQ43nAN+0/agUdoU6acEL7RX/x6IxOTFnlrPaoNWd6S3d+pPGydVr74LH81Nef7gtiP5OPV/KclOecdc239RFaJ97Tpe3SGlx8ETf64M4vcIbc2H+hr5c2ZjatsfrEdHzXstV43msOp+XU9tm8YafSBVjQaY0FOBf42fIhqvo0+tvaNszFUh4YD8E6lotyUaZ9zPTlO8cV7DTHebC1/QVPxEQ9/hTqgGHDN7VPxlP0N92CbDCKn7E+yjQ2Qy/azmpfqEnLdr5OagEYI3Q/B+H4RL9n93VZP5f1X1pXv/EIfXvsm6cez+HOKvscfYIhu6hx4WSduuiMtjW/9FCVlZ0FLsZ4oYbOwlzMG9GEi1JduNCdy40dgSErHOpjF8ouuD2wRTDx/pgDStClN/rzTZI7sEHxgI05j3yxibILbkfzywixTXx9334lvrJtvV8ot97t2N28ODRhk/9ahKiTvEW9OxvNVO5hYLHiAPJ1e4FHyk4PwO11+8QG/E/1+V9KWej5iwsl29GOikyUjRhK2kpzkXXT7dnqzWO0zAjtinbWsSt/bQYy1rsdhf6pKMNcHEOe1W7172MU5alrNWWJ/TamaXuWy4IPpMrPyfZBbp94jhPZrC06942YmNiLTumDuW0s+rDozB+nEBOwbAGMiVE6OYt1tjcv2gryq4WwXDhDBj5TWbavmDxu2PVQ8ysTqI/PVI4nAbtQJn5uuLN+IVj+KxLlOmu3pjpQdrtTfndIMMAfzcnbt8ltKPyKhjX5Rf04qYZN0dc+ZXIf0R5t8uqoK/vUJHWqYl3sM6PYL9tCH734sdleBOIatfqJYv+wbn39UnBjzlasY/ahqd/wN+9TYR/GXdgtP9PxCmXXb9vRcNRyq/I9xj7Ikzrldlm/8Ts+0JuMBUUdSbmUnMPCPRnD5XwLstdv29P8cpfmUR53y2NmWW8jO00kwM70eOUQ2r80r0ztbZkjCb52TOr5XMZT2ZPrRTzKa7TPesC2qdYTZdtW4Sj6n9gX4oz8RP+T9uXOdetfa1nQ1X08gt60H51yTJcuraLtFZJgyC8Q2TGG7fSiR2eQDiThuLxI9YTbukhwUaUdMY63fiOGoBIdBw6Fn65M7d+9YNxpEWqJDd+xlTs1bS86Qc9U+i8H8FSWUCU3A8ekfDiu5YfOPPgJ5km5amKSyAyL/zQBIv2RdeLx9ev8TzIWzqmfaYQ9dbxpbuJcsN/DwPHEh8BA6nUuMEjYS6xyG7FlxWMccOMEAMfSWDy4bUH9jCbKaR8js66xATlp+einZ5kwkA6qba8/LQu+be2u2YPZ0utNkmVhQi0TA+IbMRU39XHdrvBV+1Mjwrk6NnW9uoT3Oz0e2ifRHRmluva4BTUZhN60nGq4ZAd1rLYXMab0iGsjsTURnuxCl2Wf0JVcb9H/JN5mcd0iQdBh4Zo4U+/6+lndOOHRE7J43C9Mi+fUN9iyjpy4xeP1gr5wTv3yBRa0euKFCV21WJa6wWarXMR6tyEnfydFUZaLk8z0m6Uw+ZS6VQJD22u2Aw6KxE2Rr/Eznd4GXd654EvavuCSHq9s8O0i20LzUr7D1lTO/u3aFuhL5YbjCR8cT+WCkfkprz95ncXj3cfmen7StX8L/V5y3beOS5ibpf0Pjmdjk+l0HONTOX62sG19/gVOQX70QY994XiTKJFjeDiX2hp0aFneg1hH96E4LuUHjyFP9eFoVy2nWsSqckFKvmHKrYvBnmq+L+WFPjv1DWNDacyEvbizRZhTkonjRtuqxboQFe1OuEBWlVSR14eYM/hzSpewWTIQ+rJN6FFyUErIS66V4hiJNkrl4Xgae1CVfIa+UNzdheRq/pn2f3Xf37V/VbrkmIB+LRmPQnlZ1o9Su7Yv7c8xJ4xW+u6oEwz1BZJc1E2L4aKPA1bsjMwOFRepeVE3F2l6wVnRgYtQyrGOWXX7HjPlxg4l+g7BOGczQyl89uNb1wL3inFjn8kbSuRnX39QPuk4vUjYoRfiE2IgLCAlH9SRx5Cs0cfgCnTn/FEifqKTt8pCjuRnHYvS4pZdDu2fL1Tj4Cl9gu9WebFg7DjgROuSLbSjvGZCW1i64Ye0NZHZZbe33oLPphzLAPC07Ma5dBJlyZHHwEJMYNCXdWoX6LVsknrE9iT5pfM43qUfDera7IPv3WNkmj4tmIINTFrMxVhcxMdJVLKYg5zkE5MetaAUi+Z0slVVhy1qIRltyOpggWosmKFf30WAxID2IZQ1GIRzcvJm2hkBtNaRckKVxkelv8XvUC/fQLJCc0c5yNT+42z+ifLJhBn+K3vz2uoI2kr6bx1Tleqd2ifbZrBWsTGNfW19NPrEzmOz4UR1CNe46N9C0t+67gt9dKgj5USdbf1CLNVs9e7H7D4MY7ReYHodKJ8ksiv14KH7a8wj5FxB2m2dh36zDtrPHJNthlJfcduUW5eGPTmPKedz0FUab6zzrW0L9tL/2FbWHC4mvGQdmWBIjnsUll1FoCLeTT9hs3Wt4JyMJfhj2BUeNZHly4ahn4njYHq3gNxP+suiWLt/DbqM/jWcE/0ojtl9flE5TyQEVkiCYULAmhc9Lo46exnuckhuwYqdJy4cW1fo3JL6Wq590Snm6BxUxwzdja3qnKo9eQfypZ1KHrhYtsIOm4FWDjld+aJ2Us/s9FBWdKbT+GPGRSMbnBSbyf5jII6DhVUn8VHaLrZj7Al/k81cXyyAmNRytG59rmvdrrHR6Cq2IWyx5EVbzC20j+Clk0Ftsq02MbXkBxel14uzdOPYpOsb5azrr+1c6gbYyGtTtEHjYyk2tLQ+euuadlxKqQWZbderrK62CyMWtWAAACAASURBVLKqMuAgfA91rXooL7nl2xO5tS7GMAGSkyd7oedNxURHTsL0pAfyShMwKwHQUqfNdnPxasmPdmtbG/iWnOaYWd5XM+pYbCSneltyafE7xEW6Af+kHFkG58ttaD/6kchr4y7VgYP81k9MjNWdIL6MOgdB4CBjMN9WbdHVPqioPnE9Gddf27WOPliNzVIw5MprU+pozvcZl2CPHG+M7TjuS3uSbcgq6vfl0f9IH3S/39aPlucFkCtlgZdklNhsMG/THxa5SRuhTj0vbtGXqA+7hh04B9lm/2vWs1hAGr4EshbWKGNwa21bozySbMVYsOqIeW/Ct7Ks1QbYLj5byxf0V9UNfpBlXBtyLdTlOkH/rRKZwux6E31V0l+qcigj+zBdHrpUnwYZ6FNVX6llmvVQn59FAkwwhFvkjYtJYEPnJi+ifLtDh2p2hLWiTIfVuQib4iY6CTlYiW0lB2VtW+uBSw5OUYveghyhx+h0WgcD49uLWscE2V39QWdode5mO7THgLctH9itOhPsbziZbDRkQ18sgHix5MBOxGhaxq4LuzvGRhvfysx2edETbKF8Iaa6tntopy5x7HXPTm+cOOa6wRxtohMm3g4rlsCm7VxdJm3zoKeKt9imsCONCWjSn5P16vLx26Q2+WY/MzGeUk1+v80+tGv0XUrIbUD5Qvx1vm4xOdGTHKkb2+Y3yVjMyUWk2NaTnUm6rAVwSx3oNr7psRb5cUGrF9itEzroEBO61vIeljEJRJ08qVCaaLb4jQbJPifVsfjWQnDng21fEhtgYnEXd6mYsgRHmJ+xUWXgk+SUb6s4a7UPWtNPXE/G9dd2rZtjcxx7db+Ga1XoaJNdmWjYhTrGHEbq67JwCt9KG/MO9L1SZtzWYwbKWv0o+vrcHqs/NPxNmwr+izG2TX8pwVCJhazAUrRNqjfdL7S9L9ZqD3QK+9vHhhhPOUMYBZbCfugx2jbOI7qW93oKbdPCIdQxbYDt4rOvzaJqNkZCVmhbXH/6s8w0CkcfNW2CoU//Cl2qT4Mp6NtUH1mfTHWY9SGHnxmBVZBgEBd75r48gM5Ed/Io0dq5oVCXz9aOAwLQ6Vi3wKEMPmG3kYk1dUG2zSXrUKAm+2yXkxUPB6K91cCqBgRfKJ7POqm+/qAztDpiUxZ02zEQbZPnrTrTsgmQwkZ5IjFhwIUEMEheUGfHc7vdWWxAtsW30t8uDybWn+A4dBzPVm8Y/FsfPwCX9PqGLTK+QKntXIyFPGkBXfF6t9seetLPdr1pab8/WX5B5sR4MrU1L/yymOW+SwlZPJcme7JSx21MTCZNSOpycnGOxZ88Viu1J0konyxYg53WeetYUwGTLWuhayzy5yHBMIlxQBEW6iVWsWTcamFVFbITDGir/A6Cgrwid8jP3+dgJV2i3diCPnk3A4714FC0D3qsz5brr+1aN8Zm9Cld+re2BX5tpWFXmz2Wa23HSrLgVzZe2v0hfB4kwQDbxHysTX9rgkGwwfyl8/uQDDsgrtUeMBf22/M1SOuSYOgbJ33Le1uMOv5wC4dQJ4uj6JvaAhuzfEF/IyAbI1tlKa0Td9BHTpNgQN2u/SvKm2MF+jYjwRCcQJnkXQ3hPDdMAiNOMKDTNhYsJgqUtyaq4oI3L1JToH2wteOQVZoLf6K+cjl0yHpQ7tmhSJPUNnh15VtXhk0+eYDBRycSZuhPW2dotsMEnyBPLR5RR8YNjvVjo/A2O2CUTzKijvxcIgl2i8EX7aDr9o0NlJe+C91grHiJ82pzhu3e6w6GafVO8Hmq6xZtasluO4dryaiHtpdt0Ktd2vWqJsSOpRPn/GfpPI5PZCeFtdmH+IzJFVkzmzy1JTdlxS7bWIwb7zKQ1bMEAyYyxmTHTlpgsZgnJCo9sEMlDFDHWGBCvyrfWAxZyjYsgLX+3hM6yLb0ipc5qgnlhDqSc73d4ndeuDkC/4wFvi8BXkk7Z+0K+aF8wh7HM/8bm7Pj8TEUxQR61GcqY4JPqm6zU7TPKoxjLddf27WO/kmMV/m1Crvwa0/yGodeoz/01SBf9olhoVeoA5e6fBZ8w5ir5zteoG0vyusxujYA84Jclt0forwlq7LghvobaHke+vX8Ueu3ztUl4v/FtotF4hbaRrQ9TsIeaSPOhTFF1bNZhDrQVRpvrPNoWxU7jUSUV+fsts1sUHVEjCp/UKuRWbIbxfAJm83ysE9eP6hozStQfvHXCcaIaRIMfftX6Jo6weCRoA9U419kxa2cwIgTDOIitX5iyHcG6oKb0BmJyaeu56HWdfPOPgceBjfZcexeKL9ZWNloyAuDk17MojPO7xJAB9G1Q7F0NsdCh6p1V2czvmJhEXyybMExLXMqf9o6Vtgu20G2cRYz0S49qBbiBvIzOU1cBgYtfP2pIEd36Jgw+PaVA67nlMZhYCd8xTFZN050usdGtEPbF+32ExdbnvY88pX2w86li+Np9Xq/Ep8Rb0mbeBaas2hXFQeIJR37NSecS3Q2EEM7iDYOk67qdkbZBpBltU3pVyQsm3QLxr0J8pvbKzMm4KeYRKn2VtQl46Yui7aVvkcp5sQ3XG+Gv/5cD9uQEKhuazcnJVjoicU5JjHmgrW+jV1PkrBo9ueEHO9mkJX+YgPqJItcWcdY0NrfmBs+iHdHaFsb9rBLMYFN+a9IlDlCd14Hd1boySt0GH7HsMi2MDn17ajkwY/q8RXNPtgsfVTlExvCueR4uOtC65Y2qQTD/u0uY24lYnAs9cl778+l7V+0L8MlDrRcf23XOq5B0Zf169+wMDL6aMjO+sR451XWr+OuLGGPcDLfLPgWxjLVh4BRbivKZ/1kSKAbfVSYwyTjhPA7lQc92TgNP5I5TCyf3oG3xy0o3zwa+JfYk1Orj8DOTE5sn9T+qiJsVW2EsaGkG7alv9Qg5qqJ75jrZ/MR6DfiKsRuOl+Ar0adMH9S/gBaY7fBCCXUZ7DNGgfBwDqH60jzi+2vj3ud1TnTZmVRtYM+TPWpWTG7z+7bv0JX1jd6fejbRF/ty6d2QYbqbzN7eUASGHeCQXTEVYfQTGzDtrpAJ3VGutMJMoTMfIIrUTfb6FTkRYhjQhbkd5EZOzA/SOFP/Pav1BU6/O4diuFFOGTrbmzowDd0VsJGW+YU/qBjVXa0tIMcnLctCJZgagxEss6hgKXasP2w2Oh6eg8DgLChauMFh98WlwNu4BniAPX0YIBysm6cDPSJjZJ9fvKDc7Y87ScGM9iLzynaPVz32udUH/btdpqsF/zjNdfYLGK50tHj+s5sCbIm9E8FHfE31JM2wLWRxUk6aTTaJdgEgtYn2h7tmHxaMmCTdb1aKppjZWawIfFd1ctjJJMnGfW0LUyExDsU8ufo5eJULJpVne1u+65NztfVk6Q4AcP5TL6YONWuxzq7Uq6YbKULTF8Oi1IlD/ZKH+K369rWRhl0KDlCvvK7SarcWfueTe4gy6iTJQTCYj1dxKcQ0n34mL+nwCd1NtxpJHfAKrFrw66d7vrqWGoD2iTqwATXjqFNbvuu7VU8KCYFvTmL+BOfWbx4+7L2L9uX0or7Lddf27WOvkz2ETgmr8UqkbvDLVTH0mscupN+x5dtft4xW1CHsdyo4+tJe6KT+VbJNxxPfFhzww7zMS97jK7VoY/K54flcQLysvGqsifvB9ViWtk8aWw0+PVlF/RFu2C/nrM0+MFW6SmzCI2GekGftt3SBTtSjsWxNsy5tWxfvzhHQrwrf2B1E9udx6L8Wohxg3Pp9SPjLLZBfRRcc38qJqbNsD1+YsGOfi6ekVvod5L+stDPlfpX6Oo6HqF83jfqcU5ayu2cwOgTDJXLuFhlJ5JdBLho0osphYZy8uKaVEfIgC2JfgwYsdOyL3ghSW3q+o09pq5pOhSlKt+BniLfyCzvsO1zM/EHg4fVEcNm1Q6wJeEHv1RZYEjq4DA+oQcy/KcpBxWsT7QZYq6ODQx0minsQVkrKVL6NgB67Nir28SK9VxnPYC1yzM9bW7VrK+DpB0Ut3bZZVstrekiurtetAGuW90WUZeOZ982NuNsUhd8BmOLf6MnibXali6cRKyYdkF3Uy7YFP0rbeV+W9+6NbXbrteSgup4yb4uvhd4Jiz7TJxyU8sLVHPC49Ly9eQKkx5dR0/AUAYTI10Wluk6OFp9YsGeLTBFAkAlBmCrnnjBDlM/dCg5jRU4FxbmjVxMKK06GS9jwV+Jb/FbQbB3soV+ZYvtfyUBNje+1CxabEh8lxNvrbudiS7rWSSTc+leYmMVNyZj8U1f44+0T4qM2y3XX9u1jmsv7WdwvBlLJ/dvSb8Q7ixrsQvfwsrxOtSLnrVutfmGc5Bf+Qg7dV+EscUaU9CvxoUiLLJl4ay+q63pz635UagAeRgj2sZGcWce/MvuAAiCyxuqnSOTNh7BLxUzsD3KsJWiHHz0n+11YIse99viKtfROkcCA+UPrG/0tLYbyjafSdzFuGmzGfMim0XKwLOwYjWxJOxijGjvR1r6y6Tvautfoav7eIR+PSZ886RrcIUbBQJzn2Ao2M3DJLAEBDAI2B3qEigct0gMan0GwnEToXckMGMCLROwGWuiOBIgARIggRkS4BxphjApat4IMMEwby1Ce5aRABMMs4SPb1n637ExSysoiwTGTIAJhjG3Ln0jARIYLwHOkcbbtvTMOSYYGAUkEAgwwRBQdN2obuXLb/cPA+eEWw27qmE5EiABiwATDBYVHiMBEiCBuSDAOdJcNAONGJ4AEwzDM6fGuSXABEPvpsGzguKZSzyX2PeZvN66WYEEVj0BJhhWfQgQAAmQwPwS4BxpftuGli0pASYYlhQvha8sAkwwTNVeeI5QJRnyuxqmks1KJEACLQSYYGiBw1MkQAIksPwEOEda/jagBYMTYIJhcORUSAIkQAIkQAIkQAIkQAIkQAIkQALjI8AEw/jalB6RAAmQAAmQAAmQAAmQAAmQAAmQwOAEmGAYHDkVkgAJkAAJkAAJkAAJkAAJkAAJkMD4CDDBML42pUckQAIkQAIkQAIkQAIkQAIkQAIkMDgBJhgGR06FJEACJEACJEACJEACJEACJEACJDA+AkwwjK9N6REJkAAJkAAJkAAJkAAJkAAJkAAJDE6ACYbBkVMhCZAACZAACZAACZAACZAACZAACYyPABMM42tTekQCJEACJEACJEACJEACJEACJEACgxNggmFw5FRIAiRAAiRAAiRAAiRAAiRAAiRAAuMjwATD+NqUHpEACZAACZAACZAACZAACZAACZDA4ASYYBgcORWSAAmQAAmQAAmQAAmQAAmQAAmQwPgIMMEwvjalRyRAAiRAAiRAAiRAAiRAAiRAAiQwOAEmGAZHToUkQAIkQAIkQAIkQAIkQAIkQAIkMD4CTDD0adP9293arTe6tfc+JGodcNvvvNGt3brJbT8kDq/azXni8ZC73rfX1u1ul2iPA7s2Ve24YdcBcZSbiyawe8GtWbPGrblhT3dR09TpLn0GJQ+6HevWuDVr1rsdvL5nwHO5ROxxCz421yw4GZ0Ht62vYnb9toOmYXtu8HWavz5xbUrjQRJYBIG57ysX4dtKqrpa22G1+l3Fpj1+zDJsJ41Fs9Q1W1lLz6aPveBYjdvrdjh7ZO8jkWWnJTD3CYZd9/oFov13/f5p3Z6y3gpJMLQx0yz1wntKKkm1HgmGQzvdhkLbajtvdNMlAwZOMBza4db3XWAn9Fb07jQTkGnqDAqJCYZBcS+ZMnsShMlInmBAuzPBsGRNQsH9CMx9X9nPnRVberW2w2r1uwpUe/yYZQyXx6JZalkKWUvPpqvV6gsBPxdngqEruiUpt6ITDPndBEvCKAo1Ewzx9LxsMcGAlpifBEPd8fFbcLRM+JyTSUu5fbDQZNuFNluRGz0nQUgWJnc8rEjXl9FoTJoXdi+jEWNRPSd95Vhwzt6Ppo8Z66JmVcdfz/GjEFzsDwtgZnIYbcS52kxwzkDICkkwGI8fYLG/9UY32J0M0KkekZhBKyy1iEHt7nEHQ+b3Yupmwpxz85Jg4CLVap3q2FxMWtrap+1c0SuemDsCmHzoRySKZs5FXBatWzEn8I0SEwwzaDLG5AwgLqEIJCWZYFhCyMsluuf4UTCT/WEBzCwOj/36mwWjgWWs3ASDc27wZ+kHXajPMBIGtXsxSYLF1LV4McFgUZmrY3MxaW5LIrSdmyuSNKaVQM8J4lzEZatDK+IkJ9QzbCbG5AxhLoGosS9wVnX89Rw/CuHF/rAAZhaHx379zYLRwDJGnWBAAkI+z9/2LH/2aEF6p4K5ULcXxdBd60MZvEui7d0HadnmDg1Td8do6Vw3161faJnog9zwHgXvF2QYd50k1fPdjnWtdzfcudPlr2zsl2BAm039wk50cOJlcBhQwoviOr8wLl3YYoDDM+HWN7G6Dm7HS19s57lbdmXfMmJCUfpGBufhb7qvGhi2wf41rnruvbWOcw7nwa34ssWCfGVDvmNx0C+qhNzmtju0MewpsfGq0rJr1riMcW6SPmLISJ8rhA/5ewRqUTif6e7EVvtfiinokHGe6QueQWaMhaos7EE8hfLaj6CjjX1S1zlcP/q6gT+BncW7auv0tsvch14vN4V9hj7NLeoJNqJu8EnYljCEf2CWywjCesdrKlv2M/m5pq2TNrPKWTaiXH0uMqn90m0qPKo2URcMop1RjmYeJdRxLfjGUx23og7onxzreZ3K76RtYQD8A7f0Wiz5hvryc1GyJsay1wTfaqbQF9tEWmNvxzroP4z2N2xJ+81KesI0lQ2mypKkTuxbYE/81OzRB8XzffyGDamNPq4sO1GuPgfu0G0wg4LQRijbcawO9f0G9OHaSX1v02+PnWb7eU3Ji3q7xD/K6PapHUjl1Udhv2F3h1iDTPQB4VP0hyhjtaW3ATaHusX5RGIr4rWZs5Tk134a/yf185hN9HkRFhPMmczxtM9cL7UR+mO8glHavmCM8/6zxANlq/PCn1Rmag33I4EVnWBAQiB/RAILSyzo0890gY+FbVouefwCC2qVeEBdvaDGYnXDroeaX5lIZac2+Iuy/NLD6++1fsEiNmTrlml3UqNF91pj8Q7/ZPKm3t7kNkz9qxo2S2VpLzsRB5o1bFfJJjBKfnFC6Z60g05ILJCsQaHq3EQZW6wYoHc3L49UHbTvTDF4Q4Kos635RYeqjhgUYWMmq+mcxYAXJ06pHq9P6MKvK2AgynwrDwDr19Vv8bcWZ0V22cBakm/ZDVb1Z1FH8EH4WWqHUDbKxuAkBzJslwa0WLvZamsr2U7gLo9BGGQk54p+Z2yF/4WY6i6rfeKxcEPpV0hK7etjVsQ2fDY/IUOXRzuFNgGv7PoQsVQs0++lUtCNuJCfwR7vS9CnbQd3NeFBLNywx5yQVjoWHa+IiXxCh+u46JuIw2IZY9KHsuu37Wl+1SXVrdnUIdDDToNJ4C5sNkOrdDC0W2rrGjdNrJf6yslsypPo1PRpZaGejGFsq1iW40ahP0ltivuT27Mq28I9W6ROc72IOrVt6FuMdg7vHimX6d6HxcU02MpPzTmW7XfNlO0sxV9sH7mFtlrvdpTGzWz+0tCUv9yT9cN5kh6xV/Yzj3+z72zMj/Lk7w+AS9LPdIw1yJTtVW2LvgVl0nYM/ZDBIpXREIy/mIRYTepmOmTTiW3YlNmt+kuDTRsXb4vw26tDe2R6svmIMC5sQv+015+vl7SrSlztaHjW8tV4G2zghkVgxSYYsEjMF79YpPoFvV5Yxmfyk5+axOIyXUjv367f74ByPRIM2YsoxQJZLXDDN/83utSn4Ku/U0DptprUOGbaLctFZjpZE48rWyEve/8FFvSevU64SG3lbehrqev5pe0U3rWQJITCcR0H4Bl8Cv7ocmU7C2fQqarO15cVgy0W4wUR8TDq1J2aHhDEOdVRi+Nmxyw6YlVPLmL0YIyOX+sX5aUcDGbKf2GTLCs68GpQUXVENjupE+9oiANCGAQTGQe3LXT8aUnYKBaRoSFwLh9cgt50ooQ4SAetcNzSExTGDV8+9T98ay0nWmjXXC5sVO2Hdkpl47iyu+w/DPUxkg660KsnEkJWojuU95OPpB1LMYjjaXnYpT/BKMaNPw+9io8/ARaJLfFaTn2OvmWytCH1XogFbU+cTOq2zOxE/YRjsLuaUCay4VPaN0CWandxjSfxHbgnx6uEZMILZdP4APuMVbBR2w7/s74i2K77LS8fuvNE7B63EOy04wL2eX2W7XUjtv0f40FfAzHmMl/COJFPwIv+ixiu5CXxEOvpeCpZHsu32ZDICm2g28yOZcGlJ9tu7dnEbcIhJsuT9gzxZiwyxDkVAzgeYqihCQ6Zbn/ex1nCR7R3dh0UGsi3T1YW9iTyVVtKW2FnlsgTbZP4UJRVsFP2kz4utc3d9Cjm6nrWHJVtRbt1zCKWUh3eHcjTNhf6Cc8y0VmMNeFDf7359Rivr5QvbPUxbfttLajzpoQcLSPv51FOt0tZXlIO8ZtyxPEkrnO5zRHEdSpHXGe537A9n3cgDvJ+umgBTyQEVkiCIf32v9nPFpp+cMFdAIXFonUeC8xJi3eznL0oxiI2TRZ4/uGc1GfZJRrLrCPOt26adosazfmw4BanAk9hK+4cMcuHRX1LkkDKV9s2S1WksAM+2iYkPHQsqLKB+zT2Jsagg5ODeVUEA2raUSf11S7qpIMHCqFjlDIn1EGHnXXAjUzrPHxK6qDzVYMw6kv/Ub8wSECOXiTCj2QgasysJwfRb8hQtgBTp0/oizJjNZyz2iGekxOG1L4oK05eZHl5vsu25a91LE7ypF+wuRvbKMPyv81axKfQM00soI6MqaC20ZHEZjitNgx7ipPJlgRDE+NmrLXaqoxpFr+yXeJ5tKWOEbRbXacYY7gGe1xvRVmCT7AFPhbkRy/qrVp2sqhLC6l97SdOgUm6WPfnwzkZIz3stG2EHSJ+YUyXzwn6p7HZrCP9L1wH8M+M2cSXoKOHrF7xIyb7XewJ5k3gGcq1bMA3pXeK6yUk8WS8eb2wscDONA36U1lm4dJBxKruT+DvrK6ZIK+TrbCpNG6gP5Y2xzqhv1Eu2+eDXQXuVvzjmKUH8lSchMR+t/7AlhGTnp31Ij4KvoVYVOfB1up3cU5yV5DFDspO8rlbObufQJvaOuw6wkS5Wbr+cLw0ZhXOow2t60eq5XaZwMpNMIgFr3RPLR7libBtLWJxrElcFGQ7c6GOunqB2mqHJadtke9tt+oEnyZsTKiLhEH+uINI7IRkDvzVi/ZoAc5rHvF821a/umAs7e6XYNjprq/eHzGNrYYf6KiyARidaJdOHXIn18kHyfY6eXnowqc1UFgycSwZFDAYSv+bY3qwhr7CQg4ck9v6qkyyOBYHadht3e0hdBU34Y/VPm3nrAmDtiW1GftFHgUbw2An/FcywExONHBMtgeOCTmwSX5Gtu3+a3Mt30WMTBELlt/Sznpb6NAGiT3YpstCvmLpa1mxLL6Bym2oY686LttAWBA3YYuoY7RH0aambHa+xe6gG+0fbOxpy6Q2DIrqjcl9ji+HGJM89LVYbCdf3WqrPnaCiXWdyGOJb627k/RPY7NVRyQYzHgo8SkY38rZlNUzfkJb6/YtmBMPT+IZS6ot+COvV8WpwDQIQWyE66UQb76CVTYIEhvQKa/53nG2DNcM7O5kK+wrt3PWN3Tgh/aUbWgdE7TN/iHTLSrY8hDnevwQ1WKiU7SrtNOX7au3rXyt27LLOgZLJ7cLSvpPsGgfZ9v01dIgJ+URrhnBTF6r2I7zEWldsl2In6LuUN1mMrleEMCNAoEVkmCQC0AsQu3HBbDo1AtN7X29oJYy6/OoGxasaaLBXKjDHi0Pskw78K25kN9a3ptn6tZ+Ffcm1O2XYLDvCoi6bR7xfNtWh7pgF14sKZIgW290mrdtK1gX27nNxLZz6OCyAdjuwNpExUl3jwF6wuRt8mAFO/UgmnW0JT+NCUhWN3XaqDPtoAP/MChlg1mqO+zDb4t127k4AMcBEIOtXCzl251sA+fC4Ktl5HaCfbRNTIILMsEu1snlBmzNRsodMurPGEuwR9stpBmxgDpaZsoz6hDSkk20iy4L+ZlNhi1eYLuvjV1yMZJYUe/CltQPvZ/ZJPWXdBTsDmYgpkL9frYUeQUFegO8YjzF85Blt62+FlHWYhL6C9HvtpaPJjRbiPEYG7Xd2oasWsuBifqNdpqmjjdh2nqW+f1l9YufLuPaVHbJSojxQh+nYshoBykqxFa4XqZNMEzgJGJX6U920D7Lcs1MYqVsxTVVvoayvgHtJlkrmZG9bEMwkcdUNcPuTLeoYMtD+8U+oqoCm7vEmui/2/pD6UebnbXJ4CztKtja+Ni7b8t8lLq80HZ9ZgK4sSVcXwV+iHOLF0SET9iZxI/dnqFWtWEx6VJPS+FeSmAFJhi8C1iIps/cx8cP9EJTuo26OiEgS0x8V4NIDERbtDwsYk07sEgWclrLe+MmJAm0/cnehLpIMOj3LyQywq69aA+nQ9toHvF829aktoHuXLbND+X13RaxbHwBp9lObaZa59DBZZMFDALlwTYXN6mOdd46FiVPHqwKAwX8ajruohxjIJ/YSRt1wqCTDBTRkwlbsDd79rNUr41b27k4uY8DYIFhSXXxOOTkMVNiqo+jfjIZAJvObLv5rx9x8U7l+rV9huNGLEysY4ixD+X2+HJF+YYtvnwx9m2lhaO2LYXC8TDarpmMxZiLRVonc75Y5lc/W4q8hAlys8gLdmRxaMdbq15wEf1ua3lpYLOtyzdMMtuMioVDWp5RCP73sdmo4yVP0jXpvLRuUtn8fL/4WfoEA+zp2G8WmAYm1nnrmK+AOMziBjFt3LJekhUMEBsoW5Svfc7bSsiCrTOIPyFVbMJnbVMsYJyHTZl/olbyixH+TKufhfPFfqlQ3hrP4rHcHmVWfAAAIABJREFUx5JNffW2la+pIN7lOG8diwxrmbnNsUR5C/bodxm06JvUppPOl03JzxRkldoiCjBisRgHsRa3JhNYoQkGseBOX+SIxXt6HCwmnUc5vEsgPBogdIrEwKwSDCGBIPUFW5xDEmApXvKIBXc32UgC5MmdytzAN08CCHcKm5BdqAvZin8tCnx0omBSgsH/sCXKpHc/FExsO4wOTgzadXG7A2sTFSdixqTEV4Qu9VzZBD3FCUpjScv5OCg1g4k1CUB96T+OWeXFgk0vUDFgTTcIVt6Aj7SlCLyNW9u5OLmJiz2UL7Rb0YbkRIv9GOTlNx11bdE2DfdimeTlT4l2sQt/7LaIcSGq+E3YL+NzmliYUCfR2rKLmJITsdh+GSfoTeIHkxUdry1qzVNg2idGUMfbb/tSqYLdE6636C/kdrQF8mW7mj7WBxGr8fqoj4Njerw0aUf5aLdQiliTbdXTzqDXc2vqmrqE2tZN6J/QDiqOpqkjJsJKVjCuX/uCc3dZ/eTHcc3uT4LZ6QbYTIo7KxYaWYhF1a6QO6GdzDoy3rwO6M5kiX458audty6Mskt+zWT213aAnx0b2tbYzoV+BaxUe06KJfs8uNh22XXgi2rXyoVYXp8z+lz4kMaBmNdoGe0Javih6kyIz5AwVm1m2Cqap/a95/XXWr+kDyzbdKFuWxmhvG0T7aFYiOtSxZoQhHrJebM9RDVuTiawchMMrrToxiLV3zqvv7kuLib3b09urRfJBLngN+8EgD69KMaiXS94mwYxF8pxoZu+GBKL5+qWfmNxPbGZTbtlLaE7k+/P2b55e9RdD/Br6vca2CyDpUG+tkfy0bzhl46DrG2C3MSfoLjjBjqqbMBBR1sYbE3xsY6/TUxPKtApp2++RZ1SZy3qFTvhVFdjXDPQrb9hwa0v3RmAwVD5X9aJQb66DU7ViQu/9C3I3pqq4xfl/b7mI+qLcibm6iC4Wb7jnM0Ug5DSDw5ZuzXfIKfsLcMQS0kiQDJTE5FGBiYPCzf4n/9st9k6n7KNE0VbVrBHcg62+1v+5YJ+mlgAf+Mt2s0jQYq9xbI6Bt3SnhgnGUu0ofRLyUmvPX/S67A5ZWZBfscYQZwFO1E/tQ/H/V0OSZxBRtbuok7G0p9TckR7qLZt/E/sQXwEuxsQwRYlG23k40ZzRPlUTiUO8aZ097Ozsr76ebz1bv26NG4bo3t9CF+Uj3GRkfd709SJMZzLk7p03JdcAedesqaKH92+JXvi8Y7tiVhI4gdx6P1SMSRs13EuuSa2oo6Kt7aFTGxXeX21sY5+x61QXsVTlL34a0bIUjpkHFn9XrQxbsn2SsdVoSdhGHw0+sXQholtsk6aZAh10r4Kbdg1Tqykbt9Ya0k8eG7wQ8Vn0Jv35yGhlbECX/uar5kkMR0bTmztcQsJ65CIVdxsfWAvY14ID5vwO41fX6A6l8RIqJhuoD0ym2Uspkxge9IvQHfaX6Q6ud9KYEUnGGTCQC10xbfS4Tl7+cx+uojGAlyWabaVXJRT9e1FcbaIlc2ABa2SI5IamR3b3S5TtxTast2lLspkun2iRi/o410b+v0HNetNbsOdVp0W+8Ipm2U4HR6/SPVud9t3bXJe/1QJBq8AbaKSJrFjmtRJVjaigzM6RHS21cTNT/6NMtFPvwXd690O9Xvh4jntYkfaMnjAxsIzb3pwkxbFjtgaCKqSGLRT33Dc0Fn+bW34L/yV9YWOOEClZVs4SNfEwJ+3D+ywZUF3Gh9Ze0vbs3ZLjKl2oTf1acHtMG4TDRIk66KekuxGl2Cr4tD6iVWpT/i4fht+OzoZ0Avlq0QEzin9cuKesqj3U/aBhdpA/Gp70H5Z3Jds8TJxTvgb4kZNvJQB2U7nGME1q9oztqHyH7bdsMPtqBbKOTNVvrGqsy2+POyx/E/aDnwDH/hQkhHs1tcb5GTtJO1JdPexs8IgbUplVQWaGIIP1bGW/9AWGacFtwfnUj04ntVZ40p9ZWSDay5tc82yxWKxyOknq3v8IG672xTsle2T8gkcIT9lUOg3wTvEXVovXRyL6z/ohIXoY6IMXGs2HzG2Z7IgU3yW/A+2a6YxLg4KIc0mZKV6wSPl6xda63zSusvcxetAOwgfU5mF68hmBaa6/640hfGwT8zmbVX3UYU4CQt9qR8+wjZ8lmTEJELWH7YtaNFWKb9mP+8T4Zu0NYZAzVfHSjwrtyAHfolPFTcoF/Uh9oKfme1Sf4ljo0/pkvYl2+BkxhVsFD5Imwwd8CHnm+jlbpHACk8wyEV5uggWdziIRbNKGAgs8lvweqGsv/WuimIRrhID9qJ4qgSDVyIWusoOU7dwoG2zc1184y8X8AaHRlfGrOJi82gzL57rUhdlYGNtn80b/mgf7LIylpCoiB0fJgrRVmMLHZzRWcUBt2unCd1NR5wO/K06ZOdt2BkGf9nZTqojvsUwdbdNvKxFSeJXQSY6eTlQ5R0+WAl/zEHGYoFjiYxgD47bfGCfGR9pm/kBLciF3rZP6IZf9QAOnTkHLysOpKZNQh3kdGNr+1+JS/ys7YIdcdIRVOM6CQN8UwZyTEYpC8+kxaagDBu2PWCQsWy1xcuEPLSN/zR8hfrSJ/QEFkmMBFaGr9Y5yKsYpswMGdIu1C3ZIstaSbmC/2rBIK/LYH/DUNmsbS22k7cJcsy4Ef1W8KvUTpGXee00erJYSbioXdiW6gZry+asTsOiUEezSeOy5KuyMuwsShbsC74msVxpAWPdvsGADhsqnipdqY/QgWuzPq99axTBZhV7qFewUdVJDE7aTsaRtru9TROpcTeRX48n8Ffba/oLSZAzg/iDSP2Z2ARmiA1LrxSQlvf1CnW0n33i3y6r5cEolF1ErDWiVByI/tDWC/3gidj0n7q9UTKOTamtdYlaf6lulFJtGe0gY7qR6BbUdWjZKu22bYf/7fORxD65i5gWTOVpv63YN7GY+1PXgj29+vtU4Srfn/sEwypvn+B+cVEcSnBjXATQSXccCMblPL1ZhQQ4oM+g0TEhLEzGZ6BhvCImTFDr+JxNfzzLWJ9XWSsiUHi9LFEzDTd/mWX8LxEMiiWBVUmACYYV0ez4Jn6R7whYEb7SyJrAcAM0iZPA8hPAN0TGLcnLb9zKsYALpqnbCt9ulb6xqs63fDvWXfFsY32WC6xZyurOYxlL8npZIvjDzV9WXcwuUYtRLAnMmgATDLMmugh51SMH6vELLywmF9KXPy5CFavOPYHhBui5R0EDR0OgWqRl367HBVf6orXROD6UI1wwTUca3AqPeeC241LywVI6VKzPcoE1S1kWk7k7hnbP+qS5s3SFGTTc/GXVxewKiwSau3oJMMEwR22fvdNAvDsif9HiHBlOU5aAwHAD9BIYT5EkYBLAt8TyOcu4PZvbz03Fq+UgF0y9WjqNx9LzuL2ENoVT2THO7WeQp9Hh68xygTVLWdP6M2g9Xi9LhHu4+cuqi9klajGKJYFZE2CCYdZEFykP71qoX/DYvMgwu6thkUpYfQUQGG6AXgEwaOKICGBCqBZc/AZxNi3MBVMvjjIJMMvkAowYItaho8/dFbAv/ZylrFT2XO7zelmiZhlu/rLqYnaJWoxiSWDWBJhgmDVRyiMBEiABEiABEiABEiABEiABEiCBVUiACYZV2Oh0mQRIgARIgARIgARIgARIgARIgARmTYAJhlkTpTwSIAESIAESIAESIAESIAESIAESWIUEmGBYhY1Ol0mABEiABEiABEiABEiABEiABEhg1gSYYJg1UcojARIgARIgARIgARIgARIgARIggVVIgAmGVdjodJkESIAESIAESIAESIAESIAESIAEZk2ACYZZE6U8EiABEiABEiABEiABEiABEiABEliFBJhgWIWNTpdJgARIgARIgARIgARIgARIgARIYNYEmGCYNVHKIwESIAESIAESIAESIAESIAESIIFVSIAJhlXY6HSZBEiABEiABEiABEiABEiABEiABGZNgAmGWROlPBIgARIgARIgARIgARIgARIgARJYhQSYYFiFjU6XSYAESIAESIAESIAESIAESIAESGDWBJhgmDVRyiMBEiABEiABEiABEiABEiABEiCBVUiACYa5b/SDbse6NW7NmvVux6G5N5YGrgQCuxfcmjVr3Job9qwEaws27nEL3oc1C66zF4P7zWu30HjODd4WRUtmfMKOy4Pb1lfX3PptB6fUN10sLV7vlOa66eydVtvy1hvO1+Vrz+UlPIT2Ydja/cMQ/o1XxwG3/c4b3dqtm9x2zpHH28z0bMURYIJh7ptsuMnL7FHUg+nESfWhHW59tVj0C8YOyZS+5Wfv2MqWOHeLO0y60P4tnyEpgjpMMKzIYJy7GJwVRTsuF794mW4cWLzeablMZ++02pa33nC+Ll97Li/hIbQPw9buH4bwz+s4sGuTW7vVL8bzvw27Dgxlxoz1MMEwY6AURwIzIcAEw0wwLl7InhtKC+vhJi+L96KRgMVDkzRoSzBgUK8TC3phubA7t6hv+VzCWI40E5V1O9y034nODwlMunT7WzER77pAnR4JhsEdntG1i4RaSK4M7ki7wnm3r936Qc6i32rrC9sNmS6WFq+33apRjVvtrracna5tigJbrqelbs+iTTwxIwLLO261JRiqpMOdO93KSzMwwTCj4KQYEpgpASYYZopzWmFtE5S2c9PqW6p6GDzrZMnCDRNuC8ZEas0aJ5MJmERlt7/3Lb9Ubs6DXLAYRYIhB1ovXHRc6FKItVWQYEDCbl4TDPNunw6cZdlDnzauBEPb2NR2blmaYAmVztjXlutp8XG0hBgougOB5R23kGC4fn9i6qGdbkNzV8PKu5OBCYakNblLAnNBgAmGuWiGtglK27m5MF4YUQ+emERPmgyVz8NnvcDsW14YNr5NJhj6v4Nh8ChAHC/y/SktC47BXbIUzrt9ls0DHyv3XV0NmS6WFq+3zb42m9rOtclciedm7GvL9bS07bkS2a80m+c0weAx7t9ePzpx70MrDCoTDCuswWjuKiEw5wkG3RljcK1vnZbfXGKAxy3W8lzekvh2VN6CLb9BjzW0/lAv+9Y41d/9BXpBpnoHgawP2c0iBQtLlM9sgfWwHUz8p8UF5ZpzmNx0eLwBmkqfaC8kHHS5xC990ngJXN/yqUBrH75HRratzlntZMYM2sdsF+hL2gHMm2+ota50cQoZ0WbEcbQHZWo9QR5sSvSlZEL5STEGX1Fuhi8ihQ3Rp8xKnWCAT7DF+rYfZbJziK3ItBQHqRVhH7Khv7rWIDdtQ18LbRR1ZtdnxhdlU3kdZAVDYRNkrXFFXzOfEr2d7QvK4wZkZ21hXGtGmSgo3erAArpxPaQicF7p7SC3koNy+hpv6wsR67iO/Wce92g3tAH0oB21PrjUptcZ7ZfrhST9adlc2R+YJfamuiz2inv0L4vPVJa/5ix5/iqrHj20eDrXyia8pBJ8GxnKRjCZwldUlZ+WX1V/gjZPbYZe2BjLSbHYBosQZwVmKG9+wn/0c5UM2GHrz/TOLL4Tvco2bYu2QZ+Dn3Y8IA6b60vpaOk7XWwrzTuRB+X4NGIgvya137A7Gz8gU3wW72DwZXomGCALdzzsule/1yG7SyLY8ZC7PnsHxHa3K5zPN1LZa9WjHKUEA47f6HR5+10U8CPXLtjA7ko/5BdeLinuCsE7L8pMTK08SAIrmsCKSTDsaN7CHTrraoDznT46Wwyy+DQmXEbnreRlA24cDJR+Wa5NpixXCBM98MH2QoJhd/oyxKZ8mNRBCewW8jAhyJIMKLvg9iSDJ9hkEzyomfCJgc+uL/RacsA1MOxb3hIqjhV8zX6tA3YEfgnTYF8jG+XT49Xpgg+w5YY9YUIM9vWnnBBBRmKHmrShzIIz41boE0QKi17oSa4nyEi5mH5rLV32cF3kkyvUjj52jlvYrK4XyIGf+JTModP+RJzrNvNy1rv15i/AlHT6OoIzYillrBI5HWVVppfK5r6Cf+6TWKx1ss9mZv+KRKkvFzoL4urDJf8SriG5k/vtwngiz3WV661AWdGOYrGR9oWdOFfOgc16t6M0Dqi4qIkgNlO9OG61b1q2lqT/L9odrq0O9oayjexwfe5ofjmpvhalPUW9zTWS9hconx73GsFAyq8sKcb1Grdwg/ULPFP4qnHWe0W9MRajzXsUo9iOOu5qwYjJmmcsm14XllHxGFjq+j65s+AWzH4uJniyOmq88jo6MMziW9TZ1rSL6itrbrbdkSk8jGzlW43Art/8yNbpWa1vXmidtxP0W6x0jJb8zmXCN3wiKWAtdHGudaENQeKFkRt2PdT8ioNOMPhFdS7LSi6gnpVkKJePsu2FfkhKJHdkwE8s+uVnlBkdDXKQXMDnndvd9YVfr+irI2rjFgmMh8AKSTCkAyE6/TVu/Tr/nL/sWMU59ZNg8Xj2bYcY2HVHLupkg5sPAnT06QQ4HtfySoGD8vmgJ3X4gUdOlOKAlNbzdksm2lZtU9nHOEimskp+6OOwT+tryoB5aUGank/3tSoXvo0ryZPlIctPRpJJ7sFtC+LnQAWbVK6QofzD8bR8pR/yEp6YWHt7VD2Uz+1s91fUs+IW+hLf0d7KH/EtYGSFeNXxWMXqDbN56SRskfEumzAu5HzfoOMfdXW/4Iy7YuIiI/pWa9FxoDWrPbBMrs02++pzSQyIviTlby/GYYVv626ycD1O9BU+qVgU/FJ9KJ/EEyw0P606OGboLceBlN6dBWIkY21ev93lxnbXbQL2qT5vR+obyuq+IF5zfhzQcsS5hB1kqfLwMW3HcFxfT5Kw3oZeqzzO1Yta6SNsSq/bEOdZP1hrjfVyZmjP9JrHcakfPkCeYiOuQ81f9BXZuDGFrzDC+sR1YFxPsLlahMrzoe3S2IiLfO1nPJ72B5ZJsm00y/axZmnjW3KXMRiP1/ND+1zqN9hqTmX/EFtpzEFOFt+ijdI6YTzvdE1G/9I5odl24iAWvlmCAXcvbLUW+UKA2ISsaoGu7iiQdwik3+77hEGqAwmCNCERj69N6xza6a4Pv3qBclFXSAokyQVvvrc7SySU/A/Hb3SamUx8RL0VnnDnQuJnOJ6UF0y5SQJjIrBiEgx6UJOTXTl41E0TOng5AGPQTiZgoTHN83FwyfT7ik0dPSA1EjGYSBuCsnQDA0bui0ww5HpQL59wpRqqffiobGrzEecsu0wN6iDaIbfbdUgINLrRXuCJfaXJ7yTls/PxACYGpl2xWGjfdKIZioCntKnVTvDUiw/EUTbp8IosHf54Jz2FuIBMGQeQJ48FR1O2iLvp4iKIbdlAG5nXXVUPLC0fcS6xz/C7NUZb7MMp2GnHUsEOVE4/DfuqIqXjaX25b9Tp5ivaNonRRnbt72Su0hRz27AvxLoZg6aUbgctXYh3ee22fattabLkoh9KFgrd2EMJ4ka2AdolX0DWtVBHt42l12zDRjXKl6872Og/YZPWWZfAOcveeE7pAc+EXSpP1Qnm2DJxfVp14Ku6dhEXpg0iyaBiNOpWsirb4jnLhmC+3AAHpaMuAJutMSmck/XgjzwWdDUxk1wD4bTYAMfcP1/Ijj1RPdlE+cXGd2Sb2QW/s8RveUwFPy0LtnYdZ6JNZnsHu6TvSPZY11GMuSgv6tC2JpiNXZUUwDfx4TNZEBv15aEgK0kuoAwW+dliHgXkJxbyMiGAYwX5sbpOMEyyK9aTW1oGzrT7gCSDThjUdfQxyINtOlmBs/wkgXERWCEJBt0ZV02AjtoaHI0BGgNk7KTThsRAInVZx2I9yKy+TfDfalh/ln1RRLOFAcMaYNrOYWCyBr9GNFhI29Rko83Hdt2ZG8kBe8BuCrW1ny+Snk/3E11Z+fR82IdPsp3DSbWB9u0VM612FlijjVS7NKaU5JWOV9UKeuCdoQ9tZcZwiB3BDDKqc1bcQtl0n1OxD6rQxoldsFlxBqv6+u03YYMewSXY4DdwPrFDloFNgbFxtwrKKLulkGYb5YqyOviKuJIyjG11TUDvJPukyWYd8Gr60j7ypGy/DfnSdiUPumTb4FipPbvIBWMtA9dXOb5QT44jUgZsk/Zqp61rJtdr6ZE6+1wHbTa1nSuMW2gz1U6Nj4jLlvE097WgpxFplUfcFNvJtHEKX3XT6T1TR13EtBm1jXoo37lfhyz1Cf9kPMoCOF+OzZiEkLEm5U2Wkcd3Wx3EudTR2FyIJbDSbd8ix+zfm/LFOLXk4Zhkk29Hu9r8lu2Sb2OBKx8LsO5AyGvmRyCrmEBAgkAmDaQYnA8JjhvdWlEW8icvxkVyYFfzosqJSQlvCOrhEQ3/KRMDOF9KvOC8rIOkg5SZbxeZST7cJoEVToAJhtCA6LTlgISOXx4LFQrPyycDQ3GgiXLaFyKwyx68MSiqCX/4RiGxBZNtNYHr4qOtW3pgbcO2ODDKUu1684RB3/JSl9yeIEcUzSc04mS1ibYR8VGYvNQ1C7qNiWHU1NRJ42gaPRBq6ENb9ZuIwh/E2XRxArPk52T20C3YCwF1/cQew29UgT74b8csSuOz3YbydY164JZ8qutTLGrT45UZPWXJR16a/kD5irhCX1H4VP1NC1eQyj5b6mSxaPqdSRTfpCY84UMiB3qC//A9KWcvjoQOVR7toeMy09WYn8Yd4q/+lDLQ1yQxLTBAlmybXC/sE/aDj/gMTIT8fLPNprZz1reyE+IcbZP2g9KoJqak7RYTVMnZRLukDJSvPs24ncJXJTTZMXXUZSybQ20wEvGI8jqu0raXcRakiQ3ETKlc2X/wt/VLeWUZMASyYny31WmxGZySWAIr3fYtckJfKq7Jgmz4EPsS6Tt0pO2i96NdbX5HTdaWtWjHt/TpixCt+vIYZBUXy0ggiKSBcxMW4KIs7OqeYIgL+bY6sDtLslSJDitZwASDbHduk0BXAkwwBFLo5K2OXx4LFUKCIQ548Vy/rbYBo+1cnBBFG1DeuKvBnLhYfkfr60FdDKDx1MQte8BGNdhZkJ1NFvuWh570s91fWTqf0MizftuQ1TrBMMp7MWa7QFdTJ5kM5QkYlC/YJU8b+trbSla2t1E/e+7ULj7x6FTshVQzbg2/RZV6E+2XPeeelbTbXxWzYhbHul6fbfExhSxpn+UrjqXxJuul2124TlUH14txV0cqL3yb2INr4qsdc30Zw2Y9ZuD6iIuE2Henz4Kb/Urwr9BfFs7nem37MpydDoCNZVPbueh7HLfa4ty4o82wL/e1/x0Mlgylyoz1KXxVQpMdU0ddptU+xLORYJBxl2jrsDspZmz/YevSxbett3aoxWZwSvo42KtZtciZeYJB9xnlhmnzu1zLn8HiOl2AYzFfTBYYYiGrVCc/j2/80/cZiF9pWFSCYZPbvn+n25AlCoTxSHpkdzjAtlklGEpJCWELN0lg5ARWTYIhLOKSQSW0LwZ1db59cMGAlA+gQWrHjbYBo+2cNVFrbFZ+1GbY9rb7aC7UunrV/PKHHrBjZUzorfM4JyegONa1fNQkt8DTejZYlhMTXoNlVdKKGUxerGd4UT49h+NiYhgtKbQn9Ji2tbdpuBakPthgyovWtG0hvmSbtZVvO4e2Lstq97Gunyx84KP02zICbCeVC4s6Y1Hr5UKOeglloT3F8/9Zf1K0ewpZqb+wMfgKrgm7tJ7cL9onCyXbneuUfdQSy+UQlxlXtUAo1S8dj32vltuUT65x2CD7LjNGvVNoEyUj9lvmNWHWiTZGvRPkaKgT9iDLipW2c9Eu5UtrTEBe4VorXIvoR6L/cCnKU+dgQ6EfhDzd5pBlcSj4CjOsT9gQrslYyIqjcBYxIOtBVsGfULd1A/4V2EOv6ueQ3DGYhPJyQd1Vh1XH0GEl/+Ej9CdMbLb29QxR+TWM8pZNYk7R59qGsvAJVgUdoVy+gUV/mmBwU7yAELLkYw1RIxbsMpnQ3L2QLe5j4kPKCvKN8lGP34KuJjmAJIJ63KGuAZmZ/+HOCplggFzpg9BsMptQR1TnJgmMncDqSTCEASd9U7+c0KUDKAYLOajJkMB56xs2f67rAIABI9XvdeGcLQuDYpyoRZvisTjJqW5VlBOQwMX2MR9Apf/t27BNTeBkFQz0yYuYUC97kVXf8lKX3Maky/iWWv96QGTZ1ZbYXklMCNuzlznCHtUuMLixIZkM2YuQpI6axOCcmOQofYgz4/poYjDGky+bxkusH8sJnT03MZEvy0LbpHbUisy4NTj7WEt1hPhTfGwHQtkkhmP7+Ntc5bULu/W1LuXoxYtorzQGwrXbTVZXX6Mt0u7a/+pcygVcM/tsZtVR1JGydi8kv47Q5nsqewquXkRjx/obFqqfj8v7qr5yUV7HJZhK+Yhx1d7FfiJeX74P1zELnUmfI5JWUi98zuU0PDq3Y7RJ2+PB4lweQ9XZJvms6lkxIZoZDC27A8vUdshU1yAWv/Ut6IqNuKbSPj/o8I+SyLidxlfhV7YJm1NfSu0JAYgd07Yu/ToE5Z9F9tBZPV6j2zrwkvao8vIaQbzUbaLiQraJlNXKHdeE1NH4BRsSvvDRjgdDjkpQRmbB7yTm5HVXnANk17Z1TYKV5h0tKG+VF9hikT9xQV/Lh6zqUQNx54E/izsi9K8/xMcj5AK/LCeWzx7fmPArElGmvpMgHFc+Cj1JUiKU35okGUJyIX1vg7gbI63jwfjkh9JdbiueIYGVTmAVJRh0IqFaaItnTv2+Hlh807YMUmh5TAYSWbX87gNAHJSa5+7CQNo+mGBQlANyJguDP34rOsie7GMtq7sfwOI/YVvONZaybU0XZdOXjzX1Vme9mIyY7WvFTPQ7jbH123a4hUpOMllBDKl2gb1NDCaToRibTbyoicmEuC3pm+BrjDHEZNQdfJV2BnmJv3Ct5RPtE3Wmhdt9NOPW8BsxGuwP7dw15ltYrFnv1hu/Dw/ftM71bod5fVr9VrStj6xapD2UAAAgAElEQVTuvrb5lC6u2u1LWy3sG22hJ+A6tspxECSGR9Y6c62qIo769jml9oI8HfNgr/pCMAgxV/ts9xNoE6E3qZcuiL17pt6wMNKMAzd5DUe85lYWf6EPE/YeyqvCLtWu4BFk5PUyfYqBZl7XRnukvi64HaU77GCHku3rL7g9OKdsnMLX3LV4JPSbsDle7+Cm4gg1UU/ZZl2fkFt/qjaArOwTPuq6PmbWb9vjdhj9XOl6nl18w6bIJ5qNdjdiApySOLfZtsgJ11GqH3VyVmvWra8SmVmCIcgy6vg4VLa2+R0JWFtYMMsFfiwXv30vPfYQy8aExIZdO9318kWNYVveDVDXjImH+L6E6sWKeDljkqioFuRBnqwjf9ISdmt98FUlOVRiQMi7d6fbfqeRLAh3R4iyjT0bdj1UqCMTLHk9nWBAW6ZJY0ma2ySwMgmsrgRD1Ubxgg4TqjTTHNoSA4UxSIUyfgPl5OAwqY4SIL7xaWSESQLsTQexuj4GxXSSoCdiTV1zcgTbbXtrObbu1IN0H7aZkyFZGHaFCZ1tS6jSt3yomGxgohH02gmD+G2cbN92JvAdMVYzKLCGP6HNpZ1NHTXBaM4n9scYKOiB2FZ9iLcJvia664nmQWioP0OZCe2pa1V7iN/oU1qo3Uczbk2/DX8t1qn6ZB/2or3rbzghO48VXb7t+mwUwfYqVrW87rJgj2jbFl/TGDbbGBxa7EMR9YnyScxrX7yd/WJH1+/AVU7sE1ukvd3l2nEJlllfCA5NH1TuJ9B2iU/ouwq2F/V65xLdVewW5EgWeht2NTEV6uO4jlXUhV3q+oY9QQZKJ58oB9/9Z2sdtAnivo4p2JC1iVcX+i5dJzBT+qbwNXEp21U+RoadbFa2QTJshD/+M8pFqUmf+jrAeAnZhjzlB8qjPeS1nchI6tntm9RRxls6mgJo26Tvs9m2yAl9h+F3uLsi8i5f28Lw1G8zttv8FrKMTSy67QSDj3u8wyD5xr5FVp2MkHcB+EW1vnNAVtdJhuSxhjTBUFVEAkEs1tVdADivEwy+atQlzgkf490XZRlaTm1D7XN7HTM5kvmHtmSCQcYIt8dBYM4TDOOATC9IgARIgARIgARWNgF7EbqyfVq89e2L8G7ysdCyFuvdJLDUsASQrOhyt8Owlg2lDUmVcjJlKEuohwTmkQATDPPYKrSJBEiABEiABEhgjghgIc1vG2WjIOmib+OXJbpsM8HQhdI8lVntCQb4rx95mKcWoi0ksLwEmGBYXv7UTgIkQAIkQAIkMCcEqscAsscMYnJhcQvpOXGyrxnVYwX53QUhuaDeAdRXuC/PBMM01JazDhbYo76DoXqcQjxe0QCH7/4Ri+LjJsvZONRNAnNAgAmGOWgEmkACJEACJEACJLD8BNL3DIT3qlTvfMgX2ctv8QAW4L0F8r0XcjtLyPS1iQmGvsSWuzwW2eNPMIh3P6QvnMzeqbDcrUL9JDA/BJhgmJ+2oCUkQAIkQAIkQALLTEB+Mx8SDIteRC+zU4tWL+7iCMmFWSVcmGBYdPMMLGBVJBgqpnjXgkw05Hc1DIyf6khg7gkwwTD3TUQDSYAESIAESIAESIAESIAESIAESGD+CTDBMP9tRAtJgARIgARIgARIgARIgARIgARIYO4JMMEw901EA0mABEiABEiABEiABEiABEiABEhg/gkwwTD/bUQLSYAESIAESIAESIAESIAESIAESGDuCTDBMPdNRANJgARIgARIgARIgARIgARIgARIYP4JMMEw/21EC0mABEiABEiABEiABEiABEiABEhg7gkwwTD3TUQDSYAESIAESIAESIAESIAESIAESGD+CTDBMP9tRAtJgARIgARIgARIgARIgARIgARIYO4JMMEw901EA0mABEiABEiABEiABEiABEiABEhg/gkwwTD/bUQLSYAESIAESIAESIAESIAESIAESGDuCTDBMPdNRANJgARIgARIgARIgARIgARIgARIYP4JMMEw/21EC0mABEiABEiABEiABEiABEiABEhg7gkwwTD3TUQDSYAESIAESIAESIAESIAESIAESGD+CTDBMP9tRAtJgARIgARIgARIgARIgARIgARIYO4JMMEw901EA0mABEiABEiABEiABEiABEiABEhg/gkwwTD/bUQLSYAESIAESIAESIAESIAESIAESGDuCTDBMPdNRANJgARIgARIgARIgARIgARIgARIYP4JMMEw/21EC0mABEiABEiABEiABEiABEiABEhg7gkwwTD3TUQDSYAESIAESIAESIAESIAESIAESGD+CTDBMP9tRAtJgARIgARIgARIgARIgARIgARIYO4JMMEw901EA0mABEiABEiABEiABEiABEiABEhg/gkwwTD/bUQLSYAESIAESIAESIAESIAESIAESGDuCTDBMPdNRANJgARIgARIgARIgARIgARIgARIYP4JMMEw/21EC0mABEiABEiABEiABEiABEiABJaZwIFdm9zarTfGvzt3ugOZTQfc9jtFGV/+3od0qf3bo4wWeZk+WTaVqTUs2x4TDMuGnopJgARIgARIgARIgARIgARIgATmn0CTNEgSCrvu9YmE7W4XHDi0020wEgpZOZ9gSGR5EVU5cbxKMMxpIgEup59MMKREuE8CJEACJEACJEACJEACJEACJEACDYFqoS8W/hFMnXjYsMvfxyC3YwlsVckDJAsKCYZUBhMMoMdPEiABEiABEiABEiABEiABEiABEljxBNoTB8G96u6FTW77oXBEb1Tnm7sdigkG52RSQW5rYfO7xzsY5rdtaBkJkAAJkAAJkAAJkAAJkAAJkMCyEnjIXb/1Rnf9/glGVO9VEI9LZMW9nCYB0ZJgcOJclWCQ710Q2xPtyfQPc4AJhmE4UwsJkAAJkAAJkAAJkAAJkAAJkMCKI7DMCQY8VrFCuDHBsEIaimaSAAmQAAmQAAmQAAmQAAmQAAkMTYCPSPQhzgRDH1osSwIkQAIkQAIkQAIkQAIkQAIksKoIVI8qmC95bH75obrLoD0RwZc8rqqQobMkQAIkQAIkQAIkQAIkQAIkQAIkYBGokwfpT0vWPz8pXuzIn6l0vIPBih8eIwESIAESIAESIAESIAESIAESIAFBoE4o3OjW4mWL5l0NTTICZfxn+h6F6oWQQg7KJuXaXvK4dmvbCyWF0QNvMsEwMHCqIwESIAESIAESIAESIAESIAESIIExEmCCYYytSp9IgARIgARIgARIgARIgARIgARIYGACTDAMDJzqSIAESIAESIAESIAESIAESIAESGCMBJhgGGOr0icSIAESIAESIAESIAESIAESIAESGJgAEwwDA6c6EiABEiABEiABEiABEiABEiABEhgjASYYxtiq9IkESIAESIAESIAESIAESIAESIAEBibABMPAwKmOBEiABEiABEiABEiABEiABEiABMZIgAmGMbYqfSIBEiABEiABEiABEiABEiABEiCBgQkwwTAwcKojARIgARIgARIgARIgARIgARIggTESYIJhjK1Kn0iABEiABEiABEiABEiABEiABEhgYAJMMAwMnOpIgARIgARIgARIgARIgARIgARIYIwEmGAYY6vSJxIgARIgARIgARIgARIgARIgARIYmAATDAMDpzoSIAESIAESIAESIAESIAESIAESGCMBJhjG2Kr0iQRIgARIgARIgARIgARIgARIgAQGJsAEw8DAqY4ESIAESIAESIAESIAESIAESIAExkiACYYxtip9IgESIAESIAESIAESIAESIAESIIGBCTDBMDBwqiMBEiABEiABEiABEiABEiABEiCBMRJggmGMrUqfSIAESIAESIAESIAESIAESIAESGBgAkwwDAyc6kiABEiABEiABEiABEiABEiABEhgjASYYBhjq9InEiABEiABEiABEiABEiABEiABEhiYABMMAwOnOhIgARIgARIgARIgARIgARIgARIYIwEmGMbYqvSJBEiABEiABEiABEiABEiABEiABAYmwATDwMCpjgRIgARIgARIgARIgARIgARIgATGSIAJhjG2Kn0iARIgARIgARIgARIgARIgARIggYEJMMEwMHCqIwESIAESIAESIAESIAESIAESIIExEmCCYYytSp9IgARIgARIgARIgARIgARIgARIYGACTDAMDJzqSIAESIAESIAESIAESIAESIAESGCMBJhgGGOr0icSIAESIAESIAESIAESIAESIAESGJgAEwwDA6c6EiABEiABEiABEiABEiABEiABEhgjASYYxtiq9IkESIAESIAESIAESIAESIAESIAEBibABMPAwKmOBEiABEiABEiABEiABEiABEiABMZIgAmGMbYqfSIBEiABEiABEiABEiABEiABEiCBgQkwwTAwcKojARIgARIgARIgARIgARIgARIggTESYIJhjK1Kn0iABEiABEiABEiABEiABEiABEhgYAJMMAwMnOpIgARIgARIgARIgARIgARIgARIYIwEmGAYY6vSJxIgARIgARIgARIgARIgARIgARIYmAATDAMDpzoSIAESIAESIAESIAESIAESIAESGCMBJhjG2Kr0iQRIgARIgARIgARIgARIgARIgAQGJsAEw8DAqY4ESIAESIAESIAESIAESIAESIAExkiACYYxtip9IgESIAESIAESIAESIAESIAESIIGBCTDBMDBwqiMBEiABEiABEiABEiABEiABEiCBMRJggmGMrUqfSIAESIAESIAESIAESIAESIAESGBgAkwwDAyc6kiABEiABEiABEiABEiABEiABEhgjASYYBhjq9InEiABEiABEiABEiABEiABEiABEhiYABMMAwOnOhIgARIgARIgARIgARIgARIgARIYIwEmGMbYqvSJBEiABEiABEiABEiABEiABEiABAYmwATDwMCpjgRIgARIgARIgARIgARIgARIgATGSIAJhjG2Kn0iARIgARIgARIgARIgARIgARIggYEJMMEwMHCqIwESIAESIAESIAESIAESIAESIIExEmCCYYytSp9IgARIgARIgARIgARIgARIgARIYGACTDAMDJzqSIAESIAESIAESIAESIAESIAESGCMBJhgGGOr0icSIAESIAESIAESIAESIAESIAESGJgAEwwDA6c6EiABEiABEiABEiABEiABEiABEhgjASYYxtiq9IkESIAESIAESIAESIAESIAESIAEBibABMPAwKmOBEiABEiABEiABEiABEiABEiABMZIgAmGMbYqfSIBEiABEiABEiABEiABEiABEiCBgQkwwTAw8LGpO3/+vDt79qw7c+ZM9Xfu3LmxuVj058KFC87/8V8k4OOB/2oCPjY8D8ZIjAjyiCz8lo+N1dRnau/zPc8Df/nZ1XmE8aHbnfGhebBP1TwYH5qH32OM5Ex4ZOkJMMGw9IxHreHpp592mzdvdldddZXbuHGju+eee6qEw6idbpw7ceKEe+WVV7hAaHj4gf3w4cPk0fDwibdjx445Hyf8VxM4evRolYgkj5rAG2+84Q4ePEgcDYHTp0+71157bdWMIZMa3vchL7zwApOUApSPD/apNRA/5vo5iGfiF5H859ypU6cqJmRRE/B9yEsvveT8WMN/JDAkASYYhqQ9Ml1btmxx733ve9369evdDTfc4K655hq3du1a9853vtP5hYQf/Mb8zw/qTDDEFvbtfejQISYYGiR+YPfXgY8T/qsJMMGgI8FP+vzkj/9qAp7H8ePHmYRqAsL3Ic8///zox9I+8e/jgwmGmhgTDHnk+ATDkSNH8hOr9AgSDJ4L/5HAkASYYBiS9oh0+Uci3v/+97vvfOc77rnnnqu+ufYT5d/+9rfun/7pn9zdd99dZZJH5HLmChMMGgkTDJoHEwyah99jgkEzYYIh58EEQ2TCBENkgS0mGECifsSKdzBEHn6LCQbNgwkGzYN7wxFggmE41qPS5J8LveSSS9z3v//9kC32Hdn+/fvdO97xDvfAAw84f7vrmP8xwaBblwkGzYMJBs3D7zHBoJkwwZDzYIIhMmGCIbLAFhMMIMEEQyQRt5hgiCz8FhMMmgf3hiPABMNwrEen6bOf/ax729ve5jZs2OCuv/56d91117mPfvSj7k/+5E/cZz7zGfflL3/Z3XTTTaN9xpgJBh3STDBoHkwwaB5+jwkGzYQJhpwHEwyRCRMMkQW2mGAACSYYIom4xQRDZOG3mGDQPLg3HAEmGIZjPTpN/u4Fn0jwCYbLLrus+vuP//gP98lPftKtW7fOfepTn3Lf+MY3qpdUjc5556pn6/kOhtiyTDBEFn6LCQbNw+8xwaCZMMGQ82CCITJhgiGywBYTDCDBBEMkEbeYYIgs/BYTDJoH94YjwATDcKxHp2nPnj3umWeeca+//nrlm38vw4EDB9x9991XJRXG/vNavINBhzQTDJoHEwyah99jgkEzYYIh58EEQ2TCBENkgS0mGECCCYZIIm4xwRBZ+C0mGDQP7g1HgAmG4ViPTtPHP/5x94lPfMLdf//97sUXX3QPP/ywu/zyy92f/dmfub17947+TeBMMOiQZoJB82CCQfPwe0wwaCZMMOQ8mGCITJhgiCywxQQDSDDBEEnELSYYIgu/xQSD5sG94QgwwTAc69Fp8o8HbNy40f35n/+5e8tb3uLe9KY3VQmH1fK73Uww6JBmgkHzYIJB8/B7TDBoJkww5DyYYIhMmGCILLDFBANIMMEQScQtJhgiC7/FBIPmwb3hCDDBMBzr0WnyC+zvfve77q/+6q/cH/3RH7k//dM/dVdccUW1iPCLzbH/Y4JBtzATDJoHEwyah99jgkEzYYIh58EEQ2TCBENkgS0mGECCCYZIIm4xwRBZ+C0mGDQP7g1HgAmG4ViPTtOll17q3v3ud7vt27dX72L47W9/W73c8a1vfat77LHHqo5tdE4Lh5hgEDBcPdk5dOiQG/u7N7TX5T0mGHI2TDBoJkww5DyYYIhMljLBcO7CBXfu/Hm30r4MYIIhxodvO38nqZ+LnD9/Pp5YxVtMMOjGZ4JB8+DecASYYBiO9eg0/fKXv6wSCX7A9wOdnyw/++yz7q677qoSDr5jG/M/Jhh06/IOBs2DCQbNw+8xwaCZMMGQ82CCITKZVYLh/IUL7ujrr7tdL7zgbtr9oLt658/cZ//3LveZ/73TffHun7hv/uoBd/eTT7jnjx11Z+d8ocoEQ4wPJhgiC2wxwQAS9ScTDJoH94YjwATDcKxXvKbTp087/8sRmzZtqjLmvuN68skn3b59+5wf9P0/n033P1n54IMPOl9+zP+YYNCtywSD5sEEg+bh95hg0EyYYMh5MMEQmSw2weDvUDh84oT78eOPuS/dc7f7+LYfuotvv829b/Mt7r233lz9rd18s/vgls3uo3fc7v7zrjvdd3b9xj358svujTn9goAJhhgfTDBEFthiggEk6k8mGDQP7g1HgAmG4ViveE0nT550t912m7voooucvxXe/9uyZYu76qqr3HPPPVft+1+TePOb3+zuvvtu5zv6Mf9jgkG3LhMMmgcTDJqH32OCQTNhgiHnwQRDZLKYBINPEDx66KC74YH73Ye3bnH/cvNNbu2tN7sPbdlc7X9k6xaHv4tv3+zef9st7t23bHLv3Xyzu+wnd7mfPfVkddfDvL1NiQmGGB9MMEQW2GKCASTqTyYYNA/uDUeACYbhWK94TSdOnKjuXnj729/uXnrppcqfb37zm9V7F/bv31/tM8Gw4pt5ageYYNDomGDQPPweEwyaCRMMOQ8mGCKTaRMMPrnw0O9+Vz3+8K6bf+D8XQpIJkz69AkIX+fSO7a6HY896l45eTIaNAdbTDDERmCCIbLAFhMMIFF/MsGgeXBvOAJMMAzHesVrYoJBNyHvYNA8mGDQPJhg0Dz8HhMMmgkTDDkPJhgik2kSDP59C48fPuQu/+n/un+++QfVHQuTkgrZ+dtvq+5m8Hc++McrTs7R445MMMT4YIIhssAWEwwgUX8ywaB5cG84AkwwDMd6xWtCguHv/u7vqncxHDhwwF1zzTXugx/8oLvvvvuc3/fvXnjTm97ERyRWfGv3d4AJBs2MCQbNw+8xwaCZMMGQ82CCITKZJsHwyusn3XW/uNdddMum6ZILeHTi9tuqxyr+/Ufb3a4XX3A+cTEP/5hgiK3ABENkgS0mGECi/mSCQfPg3nAEmGAYjvWK1+TfweBf8PgHf/AH7i//8i/dmjVr3B//8R+7P/zDP3Rvectbqv2/+Iu/cL/3e79XJRj85HnM/3gHg25dJhg0DyYYNA+/xwSDZsIEQ86DCYbIpG+CwacAfvTYo+7irbf1eiwiu4OhSTJc0iQZrv35Tnd8TsZzJhhifDDBEFlgiwkGkKg/mWDQPLg3HAEmGIZjveI1nTt3zh08eLC6W8HfsXD//fe7X/ziF9Uf9v2n//O/JjH232VmgkGHNBMMmgcTDJqH32OCQTNhgiHnwQRDZNI3wXD81Cm34a47q1+H+PDtt3V+70IpweCP+1+b8O9jeOC5Z6Nhy7jFBEOEzwRDZIEtJhhAov5kgkHz4N5wBJhgGI71KDT5pIH/+Uk/MfZ/zzzzjNu+fXv1axL+Jyx37drlDh8+7HwyYuz/mGDQLcwEg+bBBIPm4feYYNBMmGDIeTDBEJn0TTD4Rxk+tLX+Gcq2pEGfc/5nLd9/263ua/f9wvk+frn/McEQW4AJhsgCW0wwgET9yQSD5sG94QgwwTAc69Fpuueee9xHP/pR9853vtN94AMfcNu2bXOf/exn3bve9a5qIcE7GEbX5K0OMcGg8TDBoHn4PSYYNBMmGHIeTDBEJn0TDD/Yvct9YMut7oNbNs/k7gUkIvyvUPzHj3/kjr3+ejRumbaYYIjgmWCILLDFBANI1J9MMGge3BuOABMMw7EelSbfaV188cXuK1/5SvVeho997GPuxz/+sXvggQfc3//93zuffPCT5zH/4x0MunWZYNA8mGDQPPweEwyaCRMMOQ8mGCKTvgmGL91zd5Vc8O9OQHJgFp8+wfCxbVvdM6+8Eo1bpq3VnGA4e/589Yse/lGYU2fPVneU+MdR/Vxk7F/odA03Jhg0KSYYNA/uDUeACYbhWI9K05kzZ9zb3va2Krng38Xw6U9/2u3cudO98MIL7u1vf7vbsWOHe30Ovu1YSuhMMGi6TDBoHkwwaB5+jwkGzYQJhpwHEwyRSd8Ew/ofbZv53Qs+QfG+zbe4i7ducY8cfCkat0xbqzHBcPrcWffkkZfd9kf3uW/9+ldu4Zf3u+8/uMvd+/RT7qkXX3THXn2VCYYmHplg0BcmEwyaB/eGI8AEw3CsR6XJLyavvfZat27dOnfZZZdVyYarrrrKff3rX3f/+I//6J566innO7Yx/2OCQbcuEwyaBxMMmoffY4JBM2GCIefBBENk0jfB8K/b73AfvO3Wmd69gASDf7fD3pcOROOWaWu1JRjOnDtX/UzolT+7u0r0/POmH7h33Vz/fXjrFnfDzp+5pw++5M6MfL7VNdyYYNCkmGDQPLg3HAEmGIZjveI1+Vvw/OB+7Nix6iWOTz/9dPWIxN/8zd+4N7/5ze6v//qv3Xve8x73k5/8ZPTJBd+YTDDokGaCQfNggkHz8HtMMGgmTDDkPJhgiEz6Jhj8L0j4BMOsfkECj1f4Oxg+8sMt7vHDh6Jxy7S12hIMz7xypHr/xUW33KSSR76N/aMrb/vOt93//ObX7ujI7xjtGm5MMGhSTDBoHtwbjgATDMOxXvGa/CMP/hcj/vu//9sdOXKk+nnKZ599tko6+J+vPHToUPVYhJ80r4bnAZlg0CHNBIPmwQSD5uH3mGDQTJhgyHkwwRCZ9E0wXH//L9wHt9zqLl6Clzz+24473EvHX43GLdPWakow+N/s8I9E+ASPdWeKv4PhXTf9j1t7683u8cOH3fk5+JWPZQqLoJYJhoCi2mCCQfPg3nAEmGAYjvWK1+QHdv8IxFvf+la3efPm6q6FK6+80v385z9Xf/fdd1+1kBh7koEJBh3STDBoHkwwaB5+jwkGzYQJhpwHEwyRSd8Ew137H3cf2LK5+llJ3H2w2E//Tfl7b73Zfe6nd7nTc/Dz0/OQYLjgLrjzF85Xf357qf553pfesdV9oOWxl/ffssm97Xv/7X76xH536syZpTJlxchlgkE3FRMMmgf3hiPABMNwrFe8Jp8w8G8svvzyy6ufpfz93//9KsngH4uQf/4nK/ft2+f8iyDH/I8JBt26TDBoHkwwaB5+jwkGzYQJhpwHEwyRSd8Ew3NHX3Ef2/bDKiGw2MQC6vvF7cW33+a2PLw3GraMW8udYPCJhcOnXnZPHH/CPXH8SffyGy+7cxfOzZyIT1v4nwX17CclGP6/793ofvjIw+61kf9yVxfITDBoSkwwaB7cG44AEwzDsR6FJp9k8O9gOHz4sPv+97/vfvOb31SPRvjHI/Dnz50+fbr6CaVROF1wggkGDYYJBs2DCQbNw+8xwaCZMMGQ82CCITLpm2A4c/68u+GB+937brvFfWhGj0lcdMsm9+m77nQ+eTEP/xaTYDh9/rQ78sYRd/T0Uee3+/zzSYRDpw67W57d7DY+9lV35b6r3ZWPXO02PvoVt+X5290LJ1/sI65T2TfOnnWX3nH7xASDv4PhJ7yDoWLKBIMOLSYYNA/uDUeACYbhWK94TX4y7B+HuPTSS6t3MHziE59wf/u3f+v+4R/+Qf294x3vcHv27OEdDCu+xfs5wASD5sUEg+bh95hg0EyYYMh5MMEQmfRNMPia/ln8T925w737lk3uw1tvW9QvSvhn/z/U3L0wD49HeP+mSTD4uw6efu0Z94NnbnbX719wX9+/4La+cId74eQLrssjDj658NLrB903n/h2lVj48r5rHf6u3neNu2rfNWrU/IMAACAASURBVO6/n/que/7kC7HxZrDlx1T/k5TVOxiMhJF/B8NFN33fvffWTe6xQ4f4DgbnHBMMOvCYYNA8uDccASYYhmO94jX5jurFF190d999tzt58qS7//773datW93tt9+u/u64447qDodzc/C85lJC5x0Mmi4TDJoHEwyah99jgkEzYYIh58EEQ2QyTYLh1Nmz7kePPeY+8sPb3Xtuvdn5RSged+jz6W/Lf/etm9zXH7jf/W6wlzued+7CaecuvOGcOx9BiK2+CQafXDjyxivuW09+2131yJfdldXf1VVS4Lbnb3evnJ58Z8arZ4677S/+yF2+94vumn0b3XWPflX9+WSDTzLc+txt7sz52T4a6n+545NNwsi/wBNt6N+N8b5bb3Zv/+6N7tu/fMC9cvKkoLR6N5lg0G3PBIPmwb3hCDDBMBzrFa/Jv1Phsccec1/96lfdxo0bi39f+9rX3PPPP1/9lOWKd7rFASYYNBwmGDQPJhg0D7/HBINmwgRDzoMJhshkmgSDr33k5El369497pKtW9x7bt3kLrm9350M/htzn1y45t6fVT9Nee68vdiPli5y6/wxd+HMHnfh9W3uwonvuwsnvucuvL7FXTj9K+fOH3HOxXcc9E0w+EchHnzlQXfZ3stVcsAnG772+PXu4WMPtxrv73B48fUX3TWPXlclKNLkAvav3netu+bRje6l11/qdFdEq1Jx0t858vNnnnaf/+n/Vo9K+J+rfHf1V7frNT/9X/fEgQPu9Nmzotbq3WSCQbc9EwyaB/eGI8AEw3CsV7wm/16F3bt3u//8z/90n/rUp4p//vyTTz7pfMc25n9MMOjWZYJB82CCQfPwe0wwaCZMMOQ8xpBg8LfUnzh7wh05/Yo7/Mbh6ltyv3/2Qr8xcdoEg6d6+MQJd/vDe8NLH33SwL8wEN+Ap58+CeHvWvAJiQ9u2ey+et/P3f7Dh92ZJb0T8YJz5150F05uduePfdqdP7LWXXj5XfXfkYvc+aP/6i689m3nzjzR3NXQ/xGJU+dOuZ8d3Ok+t/dyd+2+68KdB/6ug+se+5q77/D9OgiTPd+Wj7/6uPvsns+HukgqyE8v74pHrnKPHNtX/bpEImZRu6fPnXWPHTrobnroQXfNz3e6q3be4/7r/l+4Hz/+qHvyxRfdsVdfXRU/Dd4FIhMMmhITDJoH94YjwATDcKypaWQEmGDQDcoEg+bBBIPm4feYYNBMmGDIeazkBIO/Pf6V00fd/uNPuHte+pm77fktbtOzN1ef9xzc6R4/vr9KNpw93y3RsJgEgyfrf1XgV88/5z7nv/3esrlKIPgkQunv/bfd6j6+/Ydu896H3KETry3xM/0XqrsTLhz/L3fhyLvdBZ9ceOUSd+GVS5u/j7gLR97vLrz8/7sLx65w7uxTzl040/sdDKfPn3G7X3nIfX7vF6v3JviXMvo//6jE1x7/epUQ0FGo93yC4dFXH3OX7e2WYHj42CMzTzDAIn8nyetnzrjjb7zhfNLBj7n+l738XGTsPwsOBpM+mWDQhJhg0Dy4NxwBJhiGY01NIyPABINuUCYYNA8mGDQPv8cEg2bCBEPOY6UmGPyt+M+eeNbd+txmd/nDX6wWpH5R+4WHr6gWt5ftudxd8fBV7uZnb3XPnHi207P6aYLBL3ZPnXujujvi+Jnjzv/5OyP8t/T+nPXv/IUL7tVTp9wvnn7aXfWze9xHf3i784mE926+ufrzvzjh72z49I9/5Lbs3eNeOHbUnV3qRyK8oRdOuwsnbnLnj/yLu3DkQyKxgAQDPj/izr/8f9yF16537tyB3gkGPy55Tt944lvVrz74dyj4P/8LEP4dDP5c2z//iMTzJ5+vXu5ovX8BdzH4RyT8XQz+1yS8ziH+McGQU2aCQTNhgkHz4N5wBJhgGI41NY2MABMMukGZYNA8mGDQPPweEwyaCRMMOY+VmGDwi/u9xx6uvhH/7J7PqW/KsQD1n34B6pMOGx/7itv36r6Jz+ojweC/nfbfxD934nm38+DP3aZnb3H/94lvuW888U130zObqkcAnj/xgnvj/OmJMv2338++8op75KUDbu+BA+6Jw4erxynOLfGi2C/U/QsXPatz50+7c2dfdOd8cuEVn1z4aEuC4VJ34cjF7ry/y+GNe93x40fdiRMndOBM2PO6X37jZbf1+R9W71LwbXHX737iDp96uVMy4OjpY1Uywj9m4e9+kG3qt/2jFz5h4X9JouvdKRNM7nSaCYYcExMMmgkTDJoH94YjwATDcKypaWQEmGDQDcoEg+bBBIPm4feYYNBMmGDIeazEBIN/9MG/MPDfHvx398ndn3af3L3B/eeey9wX9l7hrhHP/cvF6LWPXle9PNAvfkv/fB/y3HPPuQMnD7ibnrnZXfHwle5ze79QPet/9SPXOP/n74rwC19/3Cce/DfopbsZvB6vzffV/s4G/Pn9pf732tnXqkcNfvLST932F3/o7vvd/3UHDvwfd/bIh9uTC80jE+df/qfq5Y/Hjz3fO8FQ+32hegfGsRMn3FPPv+Qefvx599iTB9zBw6+6c+faX2LpefoXPV732FcrzvJOhi/v2+guf/iKKrn03Mnnlxqjks8Eg8JR7TDBoJkwwaB5cG84AkwwDMeamkZGgAkG3aBMMGgeTDBoHn6PCQbNhAmGnMdKSjC8cf4Nt+uVB92X9l3tPvjrD7v3/epD7gO/vqT++9Ul7gO/usR9+Dcfcxv2fNZdve+a8M23X6B+6ZGr3P88fVN1Z4KmEPdOnTnl7n3y5+7aR79Slff1/LfleI8APv0xf84nG/x7BfYcfbi6myFKWr4tf9fC48cfd995+nvuS49cXX3Tf9UjV7or9nzCXfXQP7u7nnq/O3roI+58ePcCHo3Qn9WjFMe/4o4ffbx3gsGPTT6RcNuPfus+d+0P3b99/hb38cs2uU987mb371/c7L68cKe7/zdPuFOnzhTvaDhz/qx78eSL7pZnb3VffORKd9mez1cvfvQ++cdi/N0lfV/iudhWYYIhJ8gEg2bCBIPmwb3hCDDBMBxrahoZASYYdIMywaB5MMGgefg9Jhg0EyYYch4yweC/YT/6+uvuZ0896b7yi5+7z//0J9WvG9zz5BPu8InXiotBLXX2e/6ug2NnjrmtL9zhLn/kS+6S31zq3v+ri6skg080WH+f2LXeffHhL4XkgE8IfPHhK90LJ18w7zjwL4x84tUn3Rd3X+m+9PDVVQLBuj1f3q7vZfpb9b++/xvOv2yw7U6G2VOxJT712lPuW0/eWL1U0T8i4m28Zt+X3dV7P+6u2v3P7ordF7n/feoD7tihj7TfyXDkInfh+EZ3/OhjvRIMp8+cdY88/qL74te2u3/93M1u3edvqf8uv8Wta/7+7fM3u3WX3+pu3PQLd/jI8eILEz3Po6ePVu/a2HN0r9t7dG/1Pg1/bMhHI0CaCQaQiJ9MMEQWfosJBs2De8MRYIJhONbUNDICTDDoBmWCQfNggkHz8HtMMGgmTDDkPJBg8C8afO7oUfe1+3/hPlK9mPAWt3bzLdUvIPj96x+4zz1++NDgSQbfz7125jW35fnb3VX7rqkei/B3Lfikwod+/RHzr7qr4VeXuI/vWlclFfCohH+04YHDv3T+Tgj5zycwDp867L7z1Pfcp3/7GfXzijKhYG37RzL8nQw/eGaTO/D6S1Ls4Nv+5ZO3PLfZXbXvy1ViIdq70W18+JNu40P/5L60+yJ37Z73uH0vXOzOHdF3LcRflLjUXXj5n92FE992x4892znB4JML+596yX3+ujvcpZ+5qUoofOqKze4/vnSb+vvkFze7T1y2yX3sMze57956nzt8pD155WPAJ4B8UsHfobFc/5hgyMkzwaCZMMGgeXBvOAJMMAzHmppGRoAJBt2gTDBoHkwwaB5+jwkGzYQJhpwHEgwvvXbcfevXv3T/cstNbu3mm90lt9/mPrJ1S/X5vs23VL+A4JMM/k6GIf/5RfO9h35e3Y3gv5H/xK51rckFJB384xL+b92Dn6pe9ugfa/DP7m9/8Ufu9XOvKxe8jt8e2eU+v+cL7vO7vmC+WDAu1r8aHr3AsepOgUc3up0H713Wuxh+9/rv3Fcf/6/q7gXYVn9+xV237wtu4553uWv3XOQ+9+BF7idPfcCdeLn0ssdLqp+ydKfucsdfPdw5wXDo5eNu4Xs/cxd/6vvOJxHSxEK67+9w8EmGnQ887k6c1Ekf1UBzssMEQ94QTDBoJkwwaB7cG44AEwzDsaamkRFggkE3KBMMmgcTDJqH32OCQTNhgiHn4RMMp06fdg8896z7wJZbnU8m+MRC+lcd/+Ht7mdPPaGFLOGev0X+d68fqN6JcOUjX64W/v6uBH+HAhIJbZ8+wXDxrz9aPb/vX/LoH5nwv2xw8uxJZfXhNw5Xz/r7BMMXdl3RO8HgF/H+hZDff/oH7tUJP8OoFM9459FXH3XXPLrRXfVIfP9ETDRsdBv3fvj/sfceXm0se77v33TfmztzZs6sNendCWeffGfW2eecnfe2vZ1zzsbknIwTzuSMbcDgnA0mSyLnHAQiSyJ83/qW3EgtEAgsNRhXr9Wru6RWV/W3WlLXp34B0dU/IaDiJ2TXb8PQom4S+wRcmBsJBKab3E5TSeuFCl0b9p5NxvHAzGXhggIb9p9NQVT8Q7R1Gj2shudPJwHDQk0lYFBrIgGDWg9Z0k4BCRi001rWtMEUkIBB3aESMKj1kIBBrQdLEjCoNZGAYaEeBAwjExPI1FXjm9SkBWBBAQ20aNiRnYm4N6/UJ/FiiZYGr/vfwKfab37Qz8wRSsyFpeAC3+NxhAyEEkr2gYc9jxZYMDSPtYisFKH68FUDBlsshqtoG2/3oiJLn7p2pE4ABma7sIMFxeIiFtE1wYjW7RCAIaf+Zwz17/6QspKWDIzJsBtzxm2YHdoPWMqAuSm3AcPwyAQy80qx62Si23CBkOFEYKZwpyitatXc/WZpNRe+KwHDQk0kYFBrIgGDWg9Z0k4BCRi001rWtMEUkIBB3aESMKj1kIBBrQdLEjCoNZGAYaEeo6MjGBwdQkrFW2zOuIOD97JcQgbGZAh48lh9Ei+WhixDSGlNFa4NSsDFM1U+wnphqRgMjuCBgIFBIWlh4KsLQOVQ1YJMEgZTjYjVwICIq7VgoNXAhfpLYDDCtVp6Jntwsf4SwgwRiwAGG2iIqvGHf+UuPGnejPGB7Zgz7sHcEF0idmLOuAWzw4cxZykF5mxuJARQ4+Pjy15Sd98wLt15KiwYFOsEd7c7TyTg0UsDaAWxnhcJGBb2zucCGBinhVlLLLNWEQ/EVSwQCRgW3iPyFW0UkIBBG51lLS4U4B/kzMyMWGdnFwZLcnyf++tpkYBB3Rvsn/7+ftGX6nc+z5IEDAv7XQIGtSYSMKj1sJinMDrSh9FRHV7XJSKsyA+BRddw6F4m9t+1xWBwtGDYnp2JC29eq0/ixVLvVK+wLHCckSco2Fd6UGSRcAQJrvYJGPier84fwYYwkZXAeXBAKOCnCxTBHVcLGNjG2LqLIpaDFyVZ8tQMgpjelikyWxCWLLRiuCDgQ2RNOOoGEjE7EiOAAlNWzo4EY24iH5ilq4L92cBdwNDeZUT09YfCGsFdsKAct/tUIgqeVmNyyrLk9a31m/zPHRoaAp9FFnt+Wuv2rUX9nwNg4O/F5PQkmsaaBaCkK9KgeVAEHSV4cFwkYHBUQ+5rqYAEDFqqLetaoABnIt6+fYvCwkLU1dXBarXOH8M/T+X9p0+foqOjY10NXiVgmO8qsSMBg1oPCRjUerAkAYNaEwkY1HpMT9XBPBiE6YEfMdr1J7Q2/Cca6n6DorJdOJ2fpIIMDPzIbBIvmpvUJ/FiqWuyG+E1kaqMCIylcKLytIjD4I4VAwHDtpJdOFV1FnSP4Cyk82IYqUGwIVTUs1rAoFgw6Ib1zqfXtNw12YXrTbfgrwsSwS0dIUOIPgznq/3xqv81JqZHgTkrMGcB5sy2LaiNesDkLmDo7jXh0u2n2HcmeUUuEoQMtGB4/KoGFuuMplqttDIJGBYqttEBA6Fdw2gj4uouIUAfJKygaAkVURuNx71PF8RzkYBh4T0iX9FGAQkYtNFZ1uKkAP8YOUD39/fHiRMncObMGRw9ehTZ2dkiby/f54NEQEAATp8+jePHj+PUqVN49+7duiH1EjCoO1UCBrUeEjCo9WBJAga1JhIwOOgx043ZYR9Y+zdhZnAnLAM70d36DXSG36Gh/gs8KduOk/m2mAwM8MjsEpfevoFpasrhJN7dpcl/TN2FBQNlxjs4XH5cgAPFQsGVBcP2kt3YVboP8Y3XMWodhfOMI6+gZbwVl+qvIOQjYjCEGMJxpTEeHROd3hVlmbMToLAN2R05CDGEiQCXvtUBwoLjQt1FlAy+x9j00mkhHatwFzCYRiaQlV+28hgMQbYYDGXVbTIGg6Pwn8i+1oBhZnYWY2Yz+sfGxHbWi5a21tlptIy1ILwmSmRmoVUQ09JyJfjk6wVdhcJlQukuCRgUJeRWawUkYNBacVmfUIBuEY8fP8bJkycFNGhtbUVSUhKCgoLQ0tIC/kk8efJEwIeqqio0NTXhwoULuHLlCnp6etaFihIwqLtBAga1HhIwqPVgSQIGtSYSMNj1mJvIwMzgdlj7t2JmcC9mjfsx1b8LXS3foqb292is+y+EFAZjU9pt7MjJws33JegwDcObD/T21tn2aIZ8uzlBxE9wnIlnPAZaHBypOI5t73cJd4mdJXtVmSVo3UC4sLVkJ85WnwcDIC4GF1iT0WxEdnsO/D4yi0RKaxrGp5ePV8A6mSFj2GJC+3gH6kbrUTNSi5bxFvSbB8C0mR+zcNZ12DIMBq8sNZbh3UAx9CY9eiZ7xbmdXUSWqstdwGCdnkGloQN7zzCLRAZOhqfjeEwijsUm4kRkKk6FqjNLsHwsJhG7oy7j7O3bKG7RYcQ6sqZpPpfSge/xP1e6SKhV0gowTM/OomXIiNtl73GqsABHC+6L7Z2yUvG7ZJ3xvPULv5+Z7dnCcoFpbp1/gxjrJK7+ElrHW+dFkYBhXgq5o7ECEjBoLLiszqYAf/Ru376N8PDweWBQXFwsAENpaSlMJhMiIiKQlpYmLB34R/rmzRtxPN9fD4sEDOpekIBBrYcEDGo9WJKAQa2JBAyKHnOYM50VgMHSvxMzg8wgcEBABvPAbgx3b8Fw5x/xtiYE9/VvUdzehr6xMfAhX8tlbHocj3oei1l4Jcij8pDPMi0Zzlb54GDZEQEX6Aoh1ve7RHl/2WEcrzyN6003hQ+1q7ZbZi0iOGOALgh+5YHzGSuUupbbMv4C2/N24B2WG7wzMwZ9uR90FyGhJQmXG+Jxsf6yGKhcariCm023kdORK8AAg1w6L0yxSQuF8qFKvOh/iad9z/Bm4C0MploMmAdVs6lsi3nGLKCCddbuDul8zqXK7gIGTiT3G0dxNf0xtl2IxM40f2zPPodtOWexI9MXe+9E4VjsHZwKyxDbPYlh2Jp5FptzjuPUyyDEGi6BgIYwhAO79bhIwLCwV7QADIx3oevtQcjzp2AcmJ8z00GXLW5ZDn3+FHWMR+Xh3yemyGXa14iaKBVcUH4P+DqtGl70vZwXRgKGeSnkjsYKSMCgseCyOpsCtGDIz8/H+fPnhXXC5OQkHj16hODgYBGLYWBgAAcOHABhgsViC7TU2NiImJgY8bn1oKMEDOpekIBBrYcEDGo9WJKAQa2JBAyKHrOYGz6I6cHtsPTvmgcMhAwCNAztw8zA15gcvoYpi+cf3JVWLLflTDxn4RnYkQ/zzpCBD/p8wKc1w/lqP5yuOoeTVWfElmU/XYAwY+YA3JX1AtvA94zmIREg8XTZOWECrQwiltuK+vVhYqaTA3xXCwf7/VP9eN73UrhrhBoixHXRtYIzoVwJTOjWwNdi6+KQ3ZEr/L/p9kCrhobRBgEmbjbfEUAisjbGpktdLC43XBUDdKb17J3qA827PbG4DRgwh8GJIaRU38XWvBP4Ie8ANhUcxOaCQ9j84BC25B3FznQ/7L8Rix0Z5/FT3kH8mH8Qh16eRWB1qLh2BuGkS0xhdxGMi8AVT1zPx5xDAoaF6mkBGAbGx3G1+C02ZaSJVLlK4FlumTr3x/QU3CwtwcD42MIGfsQrBHn++qBFf3eU3x66SzzoLpyvRQKGeSnkjsYKSMCgseCyOpsC/GNk0MZz587h2rVryMnJQWhoKG7duoWRkRH09fVh06ZNaGhoEDEZ+Kmuri7ExcUhNTV1gYwEFgQRfGDXaqVpIts5MTGhWZ1aXdtq6uEfe3d3t9Tjwz3I+4JZNXifrEbPjfgZ6kEwtxGvbTXXxMFSZ2en1MM8Bcugr3CPMPdug7V/t3CToKuEbd2Dmf6/wmzKhHlq0Ot6TZgnMDI1IlbuT5mn5us0jhuR3pQJ/+pAhOsjEWmIXnKNMESJ92lVEFgdgoSGJHSNdM+fz9V9Mz45jgZjA8IrohBQHYwwN+oK00eIOq7WXoNuQI+JqcX/m3g9HWOdyG2/Z4vzUB285LVE6KMQoguHf3UQLtdfFZHrX/W9xuW6q6JtbB/fpx7h+ijR1mBdqNCI27TWDNSbGjA29fHf/cHBQQEqXemmvD4yOYLSgTIE6kJwqsQXOx6cwKZ7R+bXn/IO4ccH+/Fj0V788GAvNucdxv4nZxFUGY4Iva1Ped3BujAwGGVR1yOMT40v229K/Vps+Z/LZxCj0QhO0mhR53qvg8+Pvb29XtXibWszjuTdw+b0VOzOzlywbk5PEe8Xt7Z4tB2tpjaE6yIRqg9f9DcnVB8B/gY87Xw2Xy+fQ/isTV282Xd8ppeLVMBRAQkYHNWQ+5opwB8jPigwrsLevXuxY8cOEW+B2SL4g8g4C3/5y1/A2AyEB1z4GgFDYmLignbyj5ZWD/yz1Wpta2sT7WO7tKpzPdfDP3XGypB62O5B6sB7hOt67jct28b4KoRQWta5nusiNKVl1npuo1ZtG+7NwFTvFkx2fY/J7p8x1bP9w7oVUz0/wdL3EwZ736Cvr8srevX29aKjpwO6dh1etbzGg+ZCsXKfr7V1277HPb09MHTU4IL+IvwqAxFQEYygylAEV4a5WEPFMTw2Vn8Rr1vegHW5o2tHdweeGp6JugKqguFfESTOFVgRgiCulaHgPl8X71UF46LhMt62vkNnT6fLOho6G5BUlyLaTxcM121XXxProkXFmcrzOFF2Gj7lfqJ+158Pxflyf3FslC4Wz5qfo6ajZl5LdzRwPqa9vR1cnV93Lus7DYivuYGz5ecRXBGG8yWBOPjkLLYVHMeWvGPYdP8Ifijag+9e/Iwtjw7g2AsfBJSFiGMdr4ca+1cEIqgqFNUdOrf7zrk93ijzP5e/qdRD/u/a/ncJbKmJN/RWzplc/BZb01OxKSUJW9NSFqybU5PwU2oSMt6XeLQdjV2NSKxNxtmK8wu+d/w98K0IQKQuGuXtFfP18v+Wz2XURWm/p7ecOJBpUhcMSz77FyRg+OxvgbURgGZbCQkJIjsEM0M0NzcjJSUFvr6+YFBH/ll++eWX4o/CETBcunRJHOfcav64McWllivNvfnDSrihZb3rtS5akLDfpB62+5AzSsps23rtM63bRQhIgKh1veu1PlpzEDKs1/Zp2i6LEdbhaFj6NmOagR4HdmFmgPEYtmFmYDNmxtJhtZi8ohV/u0yTIyjvr8Cl2ivwqfLF+So/nK/yF+uVuniU9L0HZ8WpidliQdtoO+IbbiBIFyJmDTl7zxnvSM5+66PFPl8TVgW6YFxpiEeVsRqTlkm3r4G/IYTsvWO9uN+Rh+iaCwjShSJEFybOy3Nzdj1QHyKiyN/vzEf3WDemLK7/k/je466nwsqA54kyxLi98vqoyebi7dhXeki0hRYci52Dr/P8pyvOiWMZ4PJI+Qlcr7+J1z1vMTg+CLPF7LYWyr1IizDGaFLKrraGoRrQaoR94Ni+0OooBJaH4cx7f+x6dxA/F+/EsfJTqmMcj+c+rUeo8eOuJ0tq66ot3nqd962jlZy36vmUzsvfVGrizTbnGfTYk5uF7Vnp2JOTtWDdkZWB7VkZKKyr9Wg7+NvRMtKKqNpY8btjsxiy3ePMOMMsEg+7H6vuUf6GEC5QF29qIi0YnEclsiwBg7wH1kQBmmpt3boVRUVFGB+3RbnmjyABwuXLl4WJ2/fffy/iMRBGcKGZF9/Pzc1dkzY7V8ofbD7sKADE+f3Prcw/GP6xSz1sPS9jMCz8BsgYDGpN+DvIWUi52BSwmvsxOZSM6cH9mB38AbODmzA7fBRzU08ArC4ooDvammfNIkghB5FcHWMrcD9IHyJiCzCegGPQRAb/o79zRE20iK8QqA8WvvuMXcDPMD99gD4YuR130T3ZrfqsO+3ibwj/9/jbKn5fpwZQZizH/c48JLWkiPVeZ54IwMjME45tc3X+vql+EcSRsRWWi+fg/D4H7HtLD4psGD8X78C5al8Rc4LHUSdGtldWarC/9LAIckm4wHVbyW4w0CV1udOcKDJLuNNmx2txNwaD3mQQ2jv2peP10HWCkMQGPo4vqUVULdMARomYEozDsV4W3hN8BuGzyHqYQaaV/OzsHKZnOOFDt9VpTJmtYjVbpmGxToPZPWZmZr2W/YUTHHQZ8eZS1dONk4X52JKZBsf4C8r+low0kVGiuqfb482YnpsRmV6uN94Uv1V+ukBwvVB/UQRXZRBVx4W/IfyPoS5ykQpoqYAEDFqqLeuaV4A/dl999ZUqiCP/KOn+wFgMnOncv3+/yBzBh3AudXV1Isgj01uuh0UCBnUvSMCg1kMCBrUeLEnAoNZEAga1HhazGaOjw7BahjE304u5mT5gbpJJFNUHerjEtG6XGq6KIIeLDUijay8IcMAUcAy0piwMxsiAhwMfBv55nfli0M9sDDntZ8Px/wAAIABJREFUd0UWh97JPjArxFJBHZXzOW8dAQPf4zk4IGedHOiKdW5avObu+ZnpIbwm0mUkesdBuOM+wYFvtT+2Fu+cz5BxsPwoGAiRsOJk5WnsLT0ApuPcW3YQO0v3CrjA8q73+8S6o2QP9pQeQJA+FP66IGS0ZbmdRlPRxl3A0DDaCIIBVxH3A3TB87DkROXpJQED7wmeJ7ElWWiutGWtt+sJMLAtYxNTqDS0Izu/DFHXHuJkcBb2n0sRqUIP+abhfORdxCc9x+OXBrS2DwgA4WkNtQAM42Yz7pS9x/dpyQuCPO7MzsQ3KYlIr6rE6IdnV09fI1PzWmYs6JroRv1oA9rG2zBqHV00paoEDJ5WX57PXQUkYHBXKXmcRxXgg/XBgweRlZUlzB15cvrNxcbGivSVNIGMjIzE1atXwQcKLg8fPkR0dDTq6+s92pbVnkwCBrVyEjCo9ZCAQa0HSxIwqDWRgGGhHvy9pykvwBSU3k9DycE/0zlywLsYXFAG2bRS4CDzUS+tKdQLB/08z8T0JMamx8TK9I2cTVzpDL3jmZ0Bg+N7q9nnb/TN5tviOpa6VuWaHbeEEscqTmJriS3lJsEB16MVJ7CvzGYJsLPE9hrdDr5/txk/vvtZWAjMA4b3e8Rnzlf7C8sHat402rSiQbu7gIEZMlLb0kXUfcfrUPYJRQ6UHcb297vBdKDK64ttmZmDFhn5XQ8WHcStpi888Zn1ABholdDWOYiMvPc4HZqDg+fTcOh8Go4FZOBEUKZtDVS2GTjqn44DPqk44p+B8CuFeFlcj6FhmxWrJzTRAjBwgN9lMuFa8Tt8n5aCb1KS8G1qstgSOtx8X4Ke0RGvWWkoOs3MzYjvDreuAKMEDIpacqu1AhIwaK24rE8oQDP6goICkYoyMzMTL1++FNYJjMFASwX6Fr5//14EfySEoCsFM04kJSUJc8D1IKMEDOpekIBBrYcEDGo9WJKAQa2JBAwL9bADBvV73ipx5o8Dx+UGmbZZ7GjcbknwVlMWnNeTgIEDEEIPpl10Nau/2OBaeU24PHBAXrJn3iKBbhJbS3YKiKBYKnC7uXibAAzfv92MLcXbBVQgZFCgxKnKMwLm+FT74VnfC9GuBRfv4gV3AQOBj8FUg7Aapt9c6A5Cy4tD5UcFHFlOD77PVJ21I3UfBYxcXNKqX15LwMC6ewdGcP9hJc5H3sMRv3Qc9c8QYOF4YKawXjgdkg3H9VRwFk4GZeF4QAaO+RM28PhMxN54hPdVLZiYsqUkX7UggHAF8LaLBNs3PTsLpqssbm9HUnkZLr97g+SKcpR0tGNwYly8/zHX4anPSsDgKSXleVaqgAQMK1VMHu8RBZQ/xuzsbERERCAsLExklHjz5o1It8T3GZvh7t274n26TTAoJK0c1oOvIUWQgEF9K7DPZAwGuyYSMNi1UPYkYFCUsG0/JcBgnZmBcWICLUYj9L09qOnrRfvwMMbMZszMecbSgHpoDRhMFpOIacBYCcpgerEtAUNkbTSuN91Ud6IXS54GDEbzkLiG5QbUi10/g0nSvYFuDopFwqZ3WwVMcHyNEGFT8VYbYHCyYlAAw7HykwIw+FYHIK8rX5h3uyuju4CBQGVsehzvBkoQV38ZdIlgXAy6Z7Cvlb6MqYsTry12zXyNliuEKymtacJCxdVMsbvt9+RxynMUn0W0fC6anp5BTUO3cHcgTDjsa7NYcIQJ7uwTNhAyEE7QfSK3sBzGobGPkkgLCwalgdTfMjODoclJ9I+NYXhyEvydXE8JGyVgUHpLbrVWQAIGrRWX9akUYG5eujxUVlaKVEuMeKssCmTg+3q9XsRl4I/lelkkYFD3hAQMaj0kYFDrwZIEDGpNPgXAYJ6eRtPgIO7XGHDhzSucf1QkApidLnqAgKePEV/yDs+am9A7NvrRs3ZrARgmpyfxpPeZCJTmapDJ1xmDgAPznA7tggx7HjAYVw8YDOEiZoEjTPjp3c8qCwXFSmHeguHdZgEanN0q6GpBYENXiYc9jwQIUH8zXJfcBQw8A2HA5Mwkaky1IhhnWluGAAV3O+/jvbFUBMsrN1aAsTUIH8IMEbYglYRJH8BCqCECd5oT0DLW6rpRa/TOcoCBpvMj1hERN6R1vA10G5mamXJpTu/OZRAuMM5CZHwRGFeBLg+0THAHKLg6hq4UhBSEFSm5xRgYHBVBTd1pj/MxWgIG57rXY1kChvXYK59HmyRg+Dz6WV6lFxSQgEEt6ucIGBg1m9GyB4fG0N5lRGfPEEwjk+BDmAQM6vuDpc8FMDBKOqOnT05ZMTPrenZ/vQMGWie8aWtF8LMn2JaVgR/SUrApI01ET2cE9Z8yUoUP8t67OYgvfofa/j4QSKx2WQvAwEEYMw4wS0BYTaRLK4YwQ6RID6cb1q328lb8OU8DBs7oR6/SRYKD74PlR1QxFRhjYUvxjnkXCAUw0HXiu3eb5t0knAHD6apzYiB/XueP6mGdiF/hrjgrAQyO5+TAesgyhEGzUQSWZL9zYdyMyqEqkdUiti5OBIYkXGAqwIsNV0QGkJaxlnXlGqFclyvAoIAVBgC835WPW80JuNF0C6mt6SLeSL+5f1WxJBh7gJYLIRcLRKwFxllwBQ1W+johBa0ZGJ8hNbcYppEJkZFCuVZ3txIwqJWSgEGthyxpp4AEDNppLWvaYApIwKDu0M8NMDAd18jYJKoMHUjMeovo649w4eZjZOWXor65F6aRcTGg5n2yXhf22fiEGf2DowKOEJBwn6/x+jy9bHTAwBRshE18CH/xrg7P39VBX9+FAeMYpqcXgob1DBgs09N43tyEffdysCk9FYyOrqRhc94q8CHg6SMY+npXbcmwFoCB9zgHnkz9SPN5Wik4BkBUUhQSPmS0rzzrwcd8hzwJGNgOBpy81nRzwTUuZbmhvEddmG2BMRcUkLCpeBsIExjcUXGb4JZWDj+82yIAA7fbPgSGVKwfmKaSQRavNMQL7VfidrBawLBUPxA2DJmHUD5UgQfdRbjXeV9YtXCAzhgd63VxBRgYXJTxIpi60E8X8CHTR7hIaxisDwVTmxK0rGRhXX2DIyIwI4M4Hg/I9BhcUGAEIQOhxe5TSSh8phP/Q4T4K1kkYFCrJQGDWg9Z0k4BCRi001rWtMEUkIBB3aGfG2AgXCh8rsOBc6nYeTJRzLzsO5uCnScS4BNxFy+L69DXP7hugpI69haf2WhlQZjw6KUBcbeewDfqLnyj7on9hy8M6BsYFcc4fu5j9zcyYKDVAqOp30h9KVKz8T7guutUEuKTXqCze2iBnusVMHCmknEWduZmYWtmOvbkZruECwps2JWTJSwaaO3ACOqrWdYKMIjfLmYdaE0XqSqZMSGyJkasBAshNeFIa89Az1TvkpdF3aasVpimpkSgNwaBo1/2hNWypCWLq5N6GjCwnie9T4W1BmMLKPDAnS1BS4A+aD715PaS3TbQULIHO0p2q6wYCBmYSYJwgfEYeCzjL3C7t/SgGOgyBkL9SAOss8wY4v7iDcDgfu3r60hXgKFzogs3m+4sGriU9zPv76d9z922YuAgnxZZydnvwP84BmlUoICnt4QMjMnA9JaG+m5hIbgS1SVgUKslAYNaD1nSTgEJGLTTWta0wRSQgEHdoZ8TYKDZe2lVq3gQ4myO40MWH5D2nklCQMw9lJTXrUvAQLjQ1NYvAmvtOJGAfWeSccQvTVwP93eeSIRv1H00tvStamCkvjPspY0MGGi5EHvzCfacThazcMo9cSIwE9uO3RGR0nv71QPv9QoYJq1WnCl6IFwhdudkLQsXFMiwIzsT27MzkVhetiof6rUCDLxDFbPyUmMZbjbdErO+wYZQ3Gq+g9LB0mVnshnosm9sDEX1dQh99hT77uZiV24WfB8VIVtXLQJirjQAnDcAQ9dkt4g5wMCF7oAFx2NoxcDMC3R5IEA4U+Uj1t2l+23g4YMlA2HC1mK7pQMtGGjpwJSQJ6vO4GbTbbSOta7K7UACBvvv6WKAgVYqtMTw0wWqLHEc+5FWDAktyTBZ1L9H9jOr92iZRausXScTvQoXlN/MU8HZoi6C774B99qotFgCBkUJ21YCBrUesqSdAhIwaKe1rGmDKSABg7pDPyfAMGSaEMGoaMqpPBQ5bk8EZeHg+VTcSX++7gAD4UhHtxGnQrKx/2yKSBvGBzql/dxn0K2DPqki6FZXzzA4O++JZSMDBpr0KlHRFS2VLV8nyHn9vlHMBCparkfAwEFwVXcXvklNAq0SFHjg7vbnzHQcK8hD7+jKTcvXEjCwT2jZwwGadXYaNDM3z5rFDDtfW8qMnynritvbcLIwH1+lJGFzRhp25mQK/agHtdxzNxsP6msxYXV/xt4bgIHXUtjzUMSbCDe4jjnhOCBV9uk6QteGbe93Y3fpATCzBGMVBOqDcbTiBHZ9sFIgcDhZeVakiGQ8A5rpM+4CYwEw2CB1ZTtWs0jAYFdtMcDA9Jyv+l8vGbSUKTfpKtM7ubRFjlLTyOiksGzbdzZZ/MYpv2ve3PI3kxZgJZUtsFjcj+siAYPSa7atBAxqPWRJOwUkYNBOa1nTBlNAAgZ1h240wECT0KbWPjx9XYu8x1VicMgYBXzY6ekz4fKdZ8Jc1NVDFh/G4m4WwWRa+UBLraxnS8MjE0jOfSdmiPgQ56r9nHnfeyYZt9NfY2x8yiON2KiAgfc+Z9sO+zGiuh3WOGrL2b/UuyUgnFKW9QgYxi0WpFaW47u0ZOx2wzXCGTwwHsOOnCyRH165Tne3aw0Y3G2n43Hs+zetrTianyfAAqEMXUoY+JIr92kFsjUrXUCGuwa9SG3neA5X+94ADKyLMSfS2zLnY04oAGG5LWECsy0wOCNnyP31QcLdgu4TdLkgsGAwTJuLSbSYQSeEoGtFdkeuyGIwPef+YHExXQgY+N/L7B/t4+2oGalF81izsDBZLbRYrJ5P4bXFAAP1LR4sAeNcuOpPQqKbzbcxaB5c9jJp7dbQ3IvdpxJBcO74m+bt/T2nkxCf/By9/aZl26kcIAGDooRtKwGDWg9Z0k4BCRi001rWtMEUkIBB3aEbBTDwgaq5rR/XU17idGi2MHc/FpgJrj6Rd5FdUCbSdF1Neg5CBFcPWfvPJuPSrUcYHV1fQR5bOweFhQLTi7lqO1/nQJlpwxjVu7t32CNBHzcyYIi8+lCkbXOlKR/QE7Lewjg8Pv/FWY+AgfEDwp4/xebMNLdiLzgDBgaD3JmThRz9yrMtfIqAocNkQuDTJ8u6kxA00Jrh+IN8VPV0z98DS+14CzBwIE5XiYz2TBEPgTPajoEtFxuY0j2CcIFZKKqGq9Ew2iiCXjKNo78uUMSuYKYJAga6XwTqQ+BbHYCL9Zfwou+lGMwqmRuWuubl3hsYHkBpdxkSWpIQU3dBpN2MqotBfOMN0L1lbHp9/d4udz0f8/5igIGWNg2jDaKf2DfOfcl0qwQMOR13hZXOcvWPjk3hXlEFth+/89HpKF39Nrp6/Zh/hvivqq7phLvBHiVgUPeoBAxqPWRJOwUkYNBOa1nTBlNAAgZ1h24EwMAgbcwAEXfzicjLzdzcfMhhZGsOyEU5IAPxSc/FSheJxawAeCwDYeXkF68rFwm6OlTXdgrTU8aKcPVgp7zOYxg/QFfbueJgW+q7w1baqICBV5ec807cJ7T8UPRz3NJFouBJNcbGzfPSOAOGWczCaDaKtIkclD3rfY4yYzl6JnvdDsg2f/JV7gxNTuL8oyIx4753FRYMnMEnYEgqL1txCz5FwHCvxiAsExh7whm2OJepDY+7/PaNW5k2vAUY2DGc6e6Z7MHjnie4WH8ZAfrgD5AgUlgjKBYJhA+EBQwOmNqWLrIT0H2Enx8wD6ByuAr5XQUizePVhmu43BCPa003xLFPe5+jcbRJDPo9YV0wPTuN972luKC/KCCGAjS4FZCk7oKYvZ+YtlsJrfgm/IQ+sBhgYPNHrCN41PPkAzwKEylBCZBoWcK+vNZ4Q0CIpdx+FBl6B0YQfe2hCLroyjrL8XfOk/v8/+F/7ONXNTC76SYhAYPSc7atBAxqPWRJOwUkYNBOa1nTBlNAAgZ1h24EwED3gZTcYhF/wFWOb0a45nt86PKLuifiGLDMh6GTHyJgHziXgst3nsBQ17quAAOtM8p1bWI2yp0HQQUwlFe3rsgPVn1n2EsbGTDo67oQEJMnYm84QidqyEwjtIapa+pVpat0BAwcPBlGapDZno1L9VcQ+cHs/ELdRSS2JKNk8D0mZyaXjAVgV3r1e6apSYQ8s83Iu5M9wnkQzdgDBAxZ1VUrbsSnBhgYeyH0+VPQLcRdrZiVg7EahiaXHwR7EzCwczjoZxpGg8kgIMHt5gRcariK2Lo4xNTFIa7ushiM8p58O/BOZNFwBgW8bzmg7ZroElYNdSP1aB5rQd9Uv7hfnY9f8U3h8IGBqQEkN6fCvzpIDJodZ+dFfAh9mGhv41iTw6c27q4rwMCAowPmQRR2P8TlhqsiLWlYTYSw+EhqTYHOpMfUjHtub60dgzhCYO4CnLrzP/Ixx9BNIvVuMfjf7M4iAYNaJQkY1HrIknYKSMCgndaypg2mgAQM6g7dCICBM/UBsXlwzgzh+IDEWRwOGANj81D0TIewS4Xzwf0EaAjJxpWEZ6iuacfgoHFdAYbZ2TkRDXyPC8sLx+vkPoHJjuN3UNfYsyDForr33SttZMAwMWlB4TO9yMxBCEVrF662eykHD1/oMeoUy0IBDByEcWDGwQAjvHNGlsHxaM7MWccgfagYJNC32t2BgXs9svCoMYsZd8pK8X1ayqpiMGzPzhDpLV+3tiw8+TKvfGqAYcJiwemiB8L1wRm0uCozFsPBvLtoHx5aRg2mkp1GR0fHqjJyLHtyhwN4/zGmQfdEt7CeIcx6N1iMiqFKtIy1YtgyLCwWHD6yJrt0zYitiUOwLmyB6T9hA78v/P686H+lmcXPmgjxoVJXgIFvs0/HpyeExcnTvmco6nkkIGXvVC8sbqYG5f+FoaFbBKh1/m/Qqsy0mPw/ZdwjdxYJGNQqScCg1kOWtFNAAgbttJY1bTAFJGBQd+hGAAxKJoDlZmsO+aaJgWRVTQe6eoaQ/7ga8ckvcCv9NZ6/rUPf4CjMZgs4oOZ9sp4WZoXwj74vrDSWekjkzDsHyqdCsjBgHHPbB3apa93IgIHXzWCYxeXNIuCjf8x9BMTcR/S1R3hX1oSJKYvIUuCoDwfU3T3dwoQ8vvG6MPPmIMlxZpb7tuj94QI2cKbYE77sju1w3LfMTON9Rzu+TUtecRYJBjXkAPpIQR46TMOOp3Vr3x3AQMAyaDaie7JbmPhz8Gt1c8DkViNWcNDHAIa2ofUDGFZwyWt66Mv+VwjTRYjV+TuilAN0wXjQXQTLjGVN26pF5UsBBk/Ub7XOiHTM248nLOr2tdT/h6feI8yPin8IWlK4s0jAoFZJAga1HrKknQISMGintaxpgykgAYO6QzcCYMgpLMdR/4xF4yo4PjAxW8C58FzhbqBWwV7iH/t6BAxjE2YUPdfZcpoHZi6a9UAEeAzIxJ4zySh4UoXJKc88rG90wKD0Pv2FGczRODQGi3VGeXnBlgPqju4O1JhqRdT3yEXggjJwImTwqfYTcRk4M+mthd/jUfMUDufdW5HpP2fslRgDV4rfuhVjwPkalgMMnGVnTIqE5iRh4cGAgxntWcIkn6b6Wi9M+Rr07InQiXDFldWC4+sEMHSRME7Yg326ardWFgyu6l9vr9NNI0IfhVBd+AIIp4C4QF0wHvY8XjPopKVm3gYMzJhEOMp0kY7/f1ruHzyfhvArhWhs7XNLWgkY1DJJwKDWQ5a0U0ACBu20ljVtMAUkYFB36EYADA9fGIRbAAM0LvUQRbN3xl+ob+5Ri+BQWq+AgdG4mSrxws3H2HUqSQSjpLWC40pXj31nUhAZXyRm5dm3nlg+F8DgrlYcULd0tYhAe4zEv5j1giNg4OAppTVNpBl0t47VHMeBM1MvMusB4wu4E+yRx/yUnoqzDwvRbHRvttG5bUsBBgakY9BLRsBnoDq6kITXRIEz1nQjYapCT/r7O7fNVTnXoBeuJO4EeWTaz61ZGYh5/QoMKLvcIgGDWqGmsWZcro9HoC5kUcDATBe8J94bVx5gVF3Tp1HyOmCwTqO4onlNXSQO+qQi4mqhyOzkTq9IwKBWSQIGtR6ypJ0CEjBop7WsaYMpIAGDukM3AmBgBomQiwUiNeNSgIEpKDlAXyrw1HoFDOw1+taaRidF5oN9Z5JFpgjOUjHTAbNGMEhlUvZbmEYmPOr/LQGD+jvDAXVzZwsKuh4gUB+8JGAgaAjWh4lo/e7kr1fXtPISAximV1ViU0Y6NmekLQkZGODwu9RkHM2/j3dtbW4NnhdrkSvAQLjAoIGRtdEimwGtORTwQijDdHzXGq+vyax198gIfB8/xI/pKVjOimFTRhoO599HVffapqlcTPtP4TW6x+R3PMD5Sn8BlZR7gNuomlj46vyR1Z4jUmJ+CtfzsW30NmCYnplFpb7d7aDAS/1nrva9/edSxH9tR/fyLkXUUwIG9V0lAYNaD1nSTgEJGLTTWta0wRSQgEHdoRsBMNAVILewXKTkYqrJxR6KOKPC11+8q8Ps7KxaBIfSegYMbCb7a3zCLExPi57rkZL7Dik578D9hpY+kU6Rx3hykYBBrSYH1G1dbXjd/wZ+uoAlAQMH1X66QJG/3mR1L+CZuraVl8bMZhTW14qghN+kJuGnjFTszM4UsIFQYXtWBn5IS8HXKUkIfPoYFd1dmJpevauCK8BAy4SqoSoEG8JEsEvHgSX3ac0QpA8Rlh1aWzEQxLxrb8PRgvtCi905WQtcJWjdsSkjFbtys3DPoHdbI/6GaBHkceV3xtp8gqCpzdiGzMYsYcXiqwuAvy5IfHd8q/2R1paBTi/HKFmbK1+8Vm8DBv78N7b2i1SRtHBb7P/Q26/tPZMsYhsNGEcXF8Hp1bUADLT46hkdxYO6Wtx8X4L0qgroenowbvGMa6HTJa6oKAHDiuSSB3tQAQkYPCimPNXnpYAEDOr+3giAgdfQ029CYvZbMHo1V4KG4wGZOOybLmISHA/MQv6TamEBoFZAXVrvgEFpLWepCBporcCV+0xn6Y1FAga1qhxQd/V0iUFRgD5YDJ4dZ+cdB9JRtTHwqfJD+VAFzDNm9Ym8WCJkqB/oR0Z1FfyePBTuAAQNnI0/cP8uIl48w6OGenSYTG4PnF01dynAUG6sWAIwxMBfHyRmrrUGDLwWBntkYEy/xw/xXVqyAA3Uh+sP6SkgnDlekIeHDfUYnpx0dfkLXpeAYYEkGB4ZRtdwl8iO8LDnEbI7clHQ/QCVQ1Wi/9cq4OfClnr/FW8DBl5BZ88wfKPu4Yhv2poAhl0nE3HvYSUmJtz7zdMaMExZrSjv6sSxB3nYkZMp4tDsyMnC3nu5SK4oR98aB3mWgMH730NZw+IKSMCwuC7yVanAsgpIwKCWaCMABl4RB9w9/SN4/KpGmGb6RN4VD1bMChCf9ALvyppFAD9e71LLpwIYlroGT78nAYNaUQ6oe3p6YJ41I68rH8GGUGH67QwZOEMfoA9CQksyjJYhzWMNcIbONDWFZqNRWClwxr64vR3VPT3oNJkwYbWs2i3CURFXgIHftfbxDoTVRCwwjadWjMFwoe6iSLXIWe61WCatVjQODiC/tgZxb17D/8kjARyiXr1Alq4aut4eETxzJa2TgGFhT46OjmJsfAwM6jlqHRVWKyPWEVhmmaVlJeouPPen9ooWgGFoeBy3M15j16nERQMCe9OC4URgpkgJzcw8MzOurQUd+01LwMA4Ki1DRpwqLBDWXTuybYCBVl6MX7PnbjZy9DpYZrwD7B2v29W+BAyulJGve1sBCRi8rbA8/4ZVQAIGddduFMDAq+K1TE5Z0dFtFJkiGEm7uqYDvQMjYIYAdxYJGBaqJAGDWhMOqHt7e8XAaMA8gNyOe6ClAl0BGMwwxBAu4i5wYJ3YkoyW8VYxsFKfZeOUXAEGXiH97+lfTy0YcyGiJlpYfIQawsX2df9br6bvdEdl/m5MTVvROzqKJuOgAA6M0UBTaXeCOjrXIQGDsyIAAcP4+PIZOBZ+cuO9ogVgmDJb8a68ScRhOBmkrZsE3RFDLz1Ac/uA252nJWCg5VJhfZ2wUKLLmGO2GLpFMXYNQWPfmHvuHW5f5AoOlIBhBWLJQz2qgAQMHpVTnuxzUkACBnVvbyTAoL6y1ZUkYFiomwQMak0UwKC8ajQb8bL/NVLb0nC96SauNd4QYKGo5xG6Jro1t1xQ2qXVdinAwDZ0TXaLGBQX6y/bgjzWXcCVxniRlnB8enzDzWBLwLDwzpOAwa6JFoCBAYH7BkZwNjxXuAlqFYuB9dBq4v7DSoyMuu9WpCVgME5M4E7Ze3ybmqSCCwpooBXD0YI84WJm7zVt9yRg0FZvWZtdAQkY7FrIPanAihSQgEEtlwQMaj0kYFDrwZIEDGpNnAED32UMgVHrGHqn+tAz2YNhy/Caz8yrW+290nKAgTXTHL7GVIcXfa/weuAtWsZahYuJ91q1dmeWgGGh9hIw2DXRAjCwNgY/fvBUJ4I90m3Bm24RyrkPM+ZDaLawXiDkcHfREjAwnkpSRRm+SVkcMGzJTMexB/loHFxd2l53r3mp4yRgWEod+Z43FZCAwZvqynNvaAUkYFB3rwQMaj0kYFDrwZIEDGpNFgMM6iM+r5I7gOFzUkQChoW9LQGDXROtAAMH+MOmCZyPvIcjfmnwtqvEiaBM7DqZgAdPqzE2PmW/YDf2tAQMdId62dIsArs6Z4+hywRdJMJePAMtHdZqkYBhrZSX9UrAIO8BqcAqFZCAQS2cBAxqPbQCDNSdD4Dz69wclok/qW6ohiUJGNRiS8CwUA8OIK1Wq/qNz7QkAcPCjpeAwa6JVoBHol6pAAAgAElEQVSBNfL/pULXBloWHPFLh7dcJQgvmJoy/HLhh2DK9ut1Z09LwMD29I6NIvTFU3yVkigySDD2AuHCT+mp2H8vF8+am9Y09KgEDO7cNfIYbyggAYM3VJXn/CwUkIBB3c0SMKj10AIwMOBkd58JhoZuVOjaUVXTgea2foyOTa5LyCABg/oekYBhoR4SMNg1kYDBroWyJwGDooQtGPHQ0BD4LDI7616WBfunV75HyHD/USUOnk8VoEFxZ/DU9mQQs0akCAsJBlh2N3OE45VoDRgYvJWpKOOL3wlLhr8mJ+Cr5EScLXqAt22tME+7FxTa8Ro8uS8BgyfVlOdaiQISMKxELXmsVMBBAQkYHMT4kHmhv78fM2uYkkndorUteQsw8IGmvcuI7IIyMHXmsYAMHPJNwwGfVPHgZ5tdysblhGciA8b4hFlkxVhbNWy1S8Cg7gUJGBbqIQGDXRMJGOxaKHsSMChKaA8YWPPYxBSy8kuFFQOzPJwKzvZITIbjgZnYfy4FPuG5qGnsxvT06lI7ag0YqMnM3BxGzWZ0moah7+1F0+AgBicmBFxwP3qEvV89uScBgyfVlOdaiQISMKxELXmsVMBBAQkYHMSQgEEtBgBPAwbOHtEP9t7DSvhE3MVR/3QBF475Z9i2AR+2LPtniPePB2XiauIz1Df3wuJmes0FF+LBFyRgUIu5HgHDHOZgmbWIYIrMzDAzt7oHffWVuleiHhIw2LWSgMGuhbInAYOixNoABg6Yh0wTIujjmdAc7DubjOOBGauGDHS1OOyXLuBCxNVC1DR0w2pd/W/OWgAGpUdoxTk9OyuAw1qDBaVNEjAoSsit1gpIwKC14rK+DaOABAzqrpQuEmo9PAkYaCra1TuM2+mvcdQ/A4fOpwmosJQfLM1Nac1An1nmEi+uaBbRwNWt1LYkAYNa7/UEGAgShixDeDdYgsz2bCS1pCClNRV5XQWoHakDYYO3FwkY1ApLwKDWgyUJGOyaaBmDwV6rbc80Min+U2JvPMKB86nCteF4gPsZJgRY8E0XgILZIlLvFqOprf+j4AJbtpaAwVmj9VCWgGE99MLn2QYJGD7PfpdX7QEFJGBQi7gSwKAMZphirmG0EV0TXR4fwLA9FrMFVsv0mrgILAcYOEszNm7G6NgUGEvB1cLrIFy4k/FGuEEcYfquEPfNUvnQR1PW4Lh8vK9sgXWVpqeu2reS1yVgUKu1XgADv4+dE13I6biL6NpYBOtDEawPE9tQQziuNMTjVf9rmCwm9QV4uCQBg1pQCRjUerAkAYNdk7UEDGzFlNmKlvYBYVUXfqUQdHNgDAX+3xz2TRcwnK9xpSsfre4Ix4U7n08qzoXnIj75BV6/b8SgkXEkPn7eXwIG+/3BPQkY1HrIknYKSMCgndaypg2mgAQM6g51FzDQ/Jozormd93C96SauNlxDQnMSnvU+R89kD2ii/THLzPQMTIMjqHplwJOMV3iW9RqG4jrxmhaBsJS2uwIM9C3t6DLiVUkDch6UIyu/DE9f14rgjIuZhppGJ0VgLT6U0SJhJXBBOZa5y/nQFxlfhJaOAaWJmm8lYFBLvl4Aw4B5EHc77iFQH4wwQwRiai/Mr1G1MQgxhCGm7gLeDLwV7hPqq/BcSQIGtZYSMKj1YEkCBrsmaw0Y2BJmLJqasoGGgifVuJrwHCEXC4QbH10o+B906gMQPxuWC9+oe4iKf4iEzLd4W9aE/sHRVcdbsCth35OAwa4F9yRgUOshS9opIAGDdlrLmjaYAhIwqDvUHcAwOzcr4EJs3UUE6kMQYghHqCFCzJQG6UOQ0poGk9W0asgwNzuHof5h3LtRiJN/CsCBL05j/69O4fx3YXiU9hymgRF1o71YWgwwUCOagV64+RgHzqVi96kk7D6dhH1nkoUbg76uSxU5mzM65bo2nI+6KwI4KsBgNVvbDFIGErLeLmkx4UVJIAGDWt31ABhovVAy+B4RNVEIN0TOgwVHyKBYNRAI9k71qi/CgyUJGNRiSsCg1oMlCRjsmqwHwGBvjQ02TEyYwQwQzGj07E0dCB2YeaLouR5vSptQ19SDAeOosKT7uKkEx5rt+xIw2LXgngQMaj1kSTsFJGDQTmtZ0wZTQAIGdYcuBxhomTAxPYFL9VdBs+uomhjVYIYDHEKG530vVh1YbmrCjLcF7wVYOPXnAJz/Pkysx//bD2e/Csab/BLMzng/nReVcQYMnOlhRgeaktIawdFflRYGB3xScDYsR2UqOjY+haTsd9hzOtkjeccZj4GzSkxluRaLBAxq1dcDYGBshbud9xGgC1Z9Hx0BA/fDayKFFUPxQIn6IjxYkoBBLaYEDGo9WJKAwa7JegMM9pat3Z4EDGrtJWBQ6yFL2ikgAYN2WsuaNpgCEjCoO3Q5wDA9N43WsVb46QIQWRO9YDDDWdKImmhcqL8E66xVfXI3S/2dA7gVkIKDvzk7DxcUyHDod2eREpmNMZP3g9Wxuc6Age4ZpZWtAi4wy4OzFQIhw44TCXjyqkb4tvIc9U29wtyUQML5+NWUFSuG3MJyNxX17GESMKj1XA+AYdA8KCyHgvShC76TjpCBrhKRtdEo6CpUX4QHSxIwqMWUgEGtB0sSMNg1kYDBroWyJwGDooRtKwGDWg9Z0k4BCRi001rWtMEUkIBB3aHLAQZCA92w3iVg4GCGVg20YjDPWtQnd7PU1dyDyydv4egffBYAhiN/8MHtwDQM93s3UJ3SVGfAMD0zi+wHZTjkmyaCXjkDgpNBWdh/NgW30l6BlgtcHr+qEf6rxwMWAgnnz7tTZs5ywo3guII1CXwpAYNyd9i26wEwGM1GpLVliO+dI1Bw3ud3k5ChqPuR+iI8WJKAQS2mBAxqPViSgMGuiQQMdi2UPQkYFCVsWwkY1HrIknYKSMCgndaypg2mgAQM6g5dDjDQgqFlrMUlYKAFAy0bYusvrtqCgfEX0mNybXEXPrhH0ILB57tQ0IIh+3IezJNmdcO9VFoMMNwtqhBRtBlV2xkIEDAwFkNC5muRXYLNSrtXIo5fKh2l83mWKyvWE3TX8IYP7FJyOgKG6dlpkZWA2QvaxtvRM9mLqZmpVcffWKre9freegAM1LyguxAB+iCRQcIZLChlBn+8UHcR1cPVXpNTAga1tBIwqPVgSQIGuyYSMNi1UPYkYFCUsG0lYFDrIUvaKSABg3Zay5o2mAISMKg7dDnAwBgM49ZxXKy/LCLVL4zBEI0gQyie9D5ddQwGq8WK2rIGnPsmBAd/ewaMw3DyT/44+NuzCN9zCTWlDR5JhaW+8sVLzoCBARsrDe0iWKMyyHeEAYqLxIviepjNVmFhcDvjNfafS1kAIxw/t9J91n0iKBPGoTGvWDEwkOf49AQGzUYMW4ZVsIiAwTRpgsFUg6z2bJH+kAE/maEgrv6SyCryuPcp+qb6QACx0Zf1ABjYX3qTQegvskU4ZJBQ4AK/q0xdmdyaKvrUW/0iAYNaWQkY1HqwJAGDXRMJGOxaKHsSMChK2LYSMKj1kCXtFJCAQTutZU0bTAEJGNQduhxg4NEzc7OoGq5GbF2cGLAw2CNnRjmwCTaEIbElGfQJ/5hUleOjEyh7WoWrZ+4gaGsMQrbH4qZfMnRvazAxNqlutBdLzoCBQR5Hx6Zw6fZTkTKSOcFpmcCVsREIEui6wAjbhBHU0xuAgfUSMvT0mTwOW2huz1SGNLm/1XwHd1qScK/zPhpGGwU0ah1sxf32fAGZGDRQ6X/eA1xZpp//7eYE4U4zOa1df3nxVnB56rUGDLNztvts1DqKF30vEVYTKb6XSowUWhUxswTdluIbrwswxKwT3lokYFArKwGDWg+WJGCwayIBg10LZU8CBkUJ21YCBrUesqSdAhIwaKe1rGmDKSABg7pD3QEM/IR51oyKoUqkt2WKQcvlhqu40XQLD7oL0TbR/lFwQbRoDrCarWA8hurXBujf1aKvYwCWqdXFdVBf5fIlBQ5YrVaRlpH3iePS2NKH6ykvcDo0R1gzHPRJBd0j4m49QXVtJ2Zm7Vku6CLBzA8f6yLBjBWM77D16B18t/cavtt3HYlZb0XqMF1dF0wjE6r0mI7tdXef1gr5XQXCzD5YH4YQvQ0aER5dbbyGMmM5UprSEKJnatKFWUSU2fJwkU0kVFg3EEZZVhmPw912r+VxawEYpmdn0WQcRFFDPdKrK5Gj16G4ow0Nw5142fdKWJEQMBD4sZ9oYZLWli7gwuSMd4HP5w4Y6LJknp7GyNQUxsxmmC0WdHR0eMXSaC3v+4+pWwIGu3oSMNi1UPYkYFCUsG0lYFDrIUvaKSABg3Zay5o2mAISMKg71F3AwE9xFpSz3Y2jjWLg0j7egbFp9UBcffb1XeK1j4xOoraxG2/KmvD6fQMqdK1oaO6EaWRU1Xge29NvwvO3dUjJLRZpKB++MKC9y7hgIMEgj3SBWG2QR4KJQ+fTsOXwLfxp22X87odYfPFtFH7/0wXsOZ2EA+dSEBCbh4y892ho6cPk1Oqyd9Di5FX/K5HKkJYInPlWgAHN6wN0QfDTBeFU+VmE6sNV7yvHOW75Gc6a326+g/bxdpV+G6mgNWDg4LW8qxNBT59gW3YGfkhLwY/pqTicfw8pleVoHupD63ibAA15Xfl40F0kwFD/1IDK1cVbffA5AwbrzAxahox42FCPpIoypFVV4EVTE3SNdOuyQ0dvaf+pnFcCBntPScBg10LZk4BBUcK2lYBBrYcsaaeABAzaaS1r2mAKfG6AgbNrlpkZMbM2ODGO7pERtA0PodE4iPqBAdT29+FNXR1q+nrFDCnf6x4dgXFiAmMWC/gAvREXWiwMj0zg/qNK+ITnioH7zhMJOBaQjvjEJ6hr6nI5QKCmSwVarGvqEW4Th86vLk3lwfOp+GrXVfz2+xj85rtosf3dj7H4Yf8NAS5oOcFz7zqViPArhaiq6RDxH1baT5YZCy41XAGtDxzhAqEBy5wJ31K8A8dLTyFcHzkPHxyhgvM+Z9HDaiKEZctq05au9Dq0Pl5LwEDLmPbhYRzJv4/v05KxKycL++7mYE9uNn7OTMfWrAwklJWKGXStdVDq+5wBQ+vQECJePsdPGan4LjUZ36UlY0tGGq48e4IJi2UBfFQ0+9y2EjDYe1wCBrsWyp4EDIoStq0EDGo9ZEk7BSRg0E5rWdMGU+BzAAz00Z6anoZpago9o6Oo6OrEPYMeV969gd/jhziUd1cMTPgw/FVKIv505ya+TU0Ws6NH8u4h4MkjxBe/Q35tDaq6u9A7NibMfzmTynNvhIXZGHILy7HjeAIO+9riKtDqgK4Nu04lICq+CEPD46u6VNPoJBKy3mLf2eQVu0nQ6uEvO67g19/F4Hc/xOAPP8Xitz/E4I+b4sT5HINDEjTQoiHk0gPUNvWsKDYDrRcYN4NAIKImagE8oDWCT7UvNr3bhsPvjyPMTcBA4EBXi5tNtzEwNbAq/db7h7QEDDS5v2vQ489JCdibmy3gAgGDshIyHLh/F61DxjWT7XMFDIQ/l9+9wdasdOzIzhR9wj7alpmOL+/chKG3d8MC2pXebBIw2BWTgMGuhbInAYOihG0rAYNaD1nSTgEJGLTTWta0wRTYyICBg39aK/SPj+FZUyMiP8ys/TU5AX9JTsC3qUnYlJGG7dkZ2J2TNT9g2ZqWgj05WeI1vsdjeCw/w89uyUxHzOuXeNXSjMHxcfHQvFLQQC6xXtgErRfo2rD7VBKYevJUSPZ8xoeTQYx7kCQCOj55VbPqu7+ksgVnwnIEsHCEAkvt0zWCbhG//i56Hi78/kcbYPh619VFYYVIk3k2BVeTnmNsfMrt9hIw0N0lsiZmUcAQURONo+UnHABDxAII4Wy9oJRDDRGIq7+M+pF6t9vzKR2oJWDoHxtD1MsXwi1irwNYUADD9qwM7MjJwsuW5jWT8HMFDMaJcRx/kIetmenzwIf9QiuTLxNuIqOqUlgxrFnHrKOKJWCwd4YEDHYtlD0JGBQlbFsJGNR6yJJ2CkjAoJ3WsqYNpsBGBQwEC3RzuFr8Fj9npePrlET8lJ46DxI4s8YBymKDFAIGx9lR5Tjxmdxs8cD8Y3oKvklJxPbsTNx8X4KGgQFMz7rnPjEzMysyMZhGPz4ooSduxymzFW/LmrD92J0Fg3YChsPnU8TKzBGrXUbHp4SFxM6TCWAqy6XAgvIegzr+4ccL83CB1guEDf/z80URd0E5znlLq4uz4bkoq25dUXOn52ZEwE5mhnB2kaBVw4GyI9hSvB1nyn0QYVho5aAABectz3eh7qKIA7CiBn0iB2sJGPrGxoQJ/g/pqYt+dwkECRieNzetmXqfK2Ag/DlakCcsGBTgYwcMt5BcUYZxizZBates892sWAIGu1ASMNi1UPYkYFCUsG0lYFDrIUvaKSABg3Zay5o2mAIbDTAwury+twcxr16KgcbmzDTszMkUUIB+2o4Pvq72nQGD83EEDjwXrR52Zmdic2Y6duVm49LbN6jr71dlUHC8XQYGR/HwhR4hcQU4GZwlVt/Ie7hbWCECJhI8rMXCoIivShqwzQVgOOJLwJCKmOsPV908Wkl095lwK+0VdpxYHjJQn71nkvHFt9Gg1cLvf4wRcOH/bo4Tbhx83xksKOUTQbaUmQlZb1bcXmYGIUwIMaiDOIYZIrGn9AD2lx1CQGUwIg0xblswMEUiAQPPvREXLQEDMxOkV1UIayJHCKh8R2mev+9ergB+a6X15woYGJ8m9PlTEQtjd64tNgb7he4S/3P7Jkra24RF2Vr1y3qqVwIGe29IwGDXQtmTgEFRwraVgEGthyxpp4AEDNppLWvaYApsFMDAhxQGbLxV+h4H79/FNppKZ9vAwt677oGF+UGKkwWD8vpiWw5yaALMurZlZ+Jw/n0kV5QLtwy2iQs39c29uHznGY76Z+CYw8ryEb90hF8uBFMtWqzTmt9h09MzqG3swfbjCaDFgjJQ55blA+eScdAnBVn5pR/VNtbT1TuMO5lvsPtUonC7oEuDY33K/omgTGw9dge/+jYKv/k+WoAGWi4QTtCNQznO1ZZWDJFXi8A6V7IwheHr/jciraG/LggB+mBwe17nL9YzVT4IqgpF1AoAA10kLtZfQdPY2pntr0SDlR6rJWAgQGw2DoIDWGaOcBzI8jtP9yXGAZi0ri6TyEqvfbHjP1fAwF+76p5unCosEMEdaeX1Q3oKvk1Jgl/+fRG3RvlNXEy3z+k1CRjsvS0Bg10LZU8CBkUJ21YCBrUesqSdAhIwaKe1rGmDKfCpAwY+1DI6+du2Vvg9eSRcFhhJnoP+xdwfFoMEzq8tZ8HgfDzLrIt1sm7ChuBnT1Da2YEpqxU9fSbEJz0XwRMJFBhbQBkYnwrOFsDhoE8qoq8/RGVDC7omulBjqkGtqQ69U72YnpsGYwR4axEPeKZxXLj5WARJdIzDcCwgA3tOJ8InIhdNbf0f3QQO+HsHRlD4TIfA2DzsP5siVgIBghfWTdcIpqX8bu91/MdXEWDGiG92x8+3TdFuqe0h3zSEXCyA2bLygSZTjdaPNuB53wvkdRWINIdvB4vxqv81fKv9EVwVtiLAEKwPxZ2WRAxZhj9av/V4Ai0BA6+f8OB1awtOFxaIbAVMU8l1d242rpW8AzMZrOXyuQIGpW+qerqRUF6KsOdPRbyMrKpKFNcYXFp2rWVfrVXdEjDYlZeAwa6FsicBg6KEbSsBg1oPWdJOAQkYtNNa1rTBFFgrwEBzWqaJZOwCDsQJCN53tIs0kb1jo2JgvpzUDKw4MD6OezV6kbaOMRZ25GRipRYLzsBgNYBBOQctGggY2JbjBXl4UFeL5LxiEXeAcMHVoPh4YAb2R9xA6ONbuNF4C5cbruJKQzwSWpLEwNZkNWF2znsuFNbpGWFlEX3tIY74p+OAT6qIc8D0j76RuXj8Ug+LxTPWFXygnJyyQF/fhZwH5Yi79QQBsXk4F54j9DkTmiP2D55PE/EWfj56W8CZpdwinHUlsIi4snILBuWeo9ZTM1MYsY6AwMEyaxH77JeAyiC3YzAw/gIDRD7rfY7pWc/op7RxvWy1BgxEbZbpaeh6epCtr8at0hIklpfhaVOjsGJaacBVT+v4OQMGakkrk+HJSXSaTKI/TBMT6OjokCkqHW40CRjsYkjAYNdC2ZOAQVHCtpWAQa2HLGmngAQM2mkta9pgCmgNGJguknnsnzU3idlG38cPcSw/T7g1HM2/D5+HRYh781oMzAkfXJk6MyVa18gIUisrhNUCMz0s5pOtDPxXsv0YwKDUwxgNNBHekZGJTefvYO+5ZBFzwXkgrJSPRydiW5IfthUdw7nyAIQZIsQapA9BsCEMT3qfYtQ66tW7b3Z2Fq0dA2LQT4uLKwnPkJzzFm9KajBo9M7sOwNM0sJDV9uJl8X1ePTSgKdvakXQyccvDSIuhDsuEYqO3NKtg9YQtzNee1QvQocyYzlCdOEI0YeDqSudAzo6lhnLgX2X2paOnslej7ZltSfjNRCYtI+3w2Cqgc6kR9NYE4Ytw5iZW5k7idIGrQGDUi+3/B3gbwSBw1qDBaVdnztgUHRQthwcSMCgqGHbSsBg10MCBrsWyp4EDIoStq0EDGo9ZEk7BSRg0E5rWZMXFeAAz2q1aroODw+jv78f/EPzZt0WiwXGsTER6Iumsz+mpeCb5ESx/TkjTeRK/zkzDT998Nn9PjUZZ4oe4GljI/pHR8DPK+2bMpvRZhzE9ZJ3YhC/OT1VpJVkasmPXRm48efUZOzOzvzoc7Et399MxK/2xeLrQ9dw0C8VjC3AAbBqDc7A3tsR+On+YWwtPAqfkmBhgk8/fwYTDNaFIVgfhtrhOkyaJ+d1UPTw9NZstmBkdBymkXGMjo5jcHAQvE88Xc9S52MbBo0m0LLjiF8aTgRmqDVz1tChTKuL0yFZeFVS59E28x4cnxpHbvNdhOoiEKoPR7g+EhGGaIf+ihbWDWH6CATrQhFffx0GowFTZu9+v5bSUnnPbDGjd7xPWFNcabiGAF0wfKsDEF0Ti3sd99E+1oEJ8wQsVvt3TfnsUltCyq6uLo9qvVR96/298fFxDA0NYXLS+9/V9a4F20cdWltbQfDyKbRXizby/jCZTFIPwkGLRTyDUBNvP4do0beeqIMAis9lnjjXRjgHf0M6OzvB/xpvXg9hl1ykAo4KSMDgqIbc/2QV4APYyMiIGMxxQKfF2tPTg/b2dhiNRq/W19nXh/SSYmzPSMVXSXfwc3oKtmekuVy3pqfiu5QkceyVF8/Q1tMD49CQWOs62hH55BG+TrqDTWnJLs+x1PmXem9zciK2pad65LybbiXhV3tj8X+2ReLPh65g37kkHPFLVa2HAxPxc+Yp/Hj/AH7OO4bTb/0RWh2hWs9WnMe9pvvoHOj86H4aMA6ge6BbnIvbfuOAGBAtdr8RLvCPnffJYu9787WBASOSc99g26nb2OWTgL1+Sdjvl4yDfik47KShoumh8ynYfTIB0fGFaG33TptbOlrwqO0xYmvi4F8ZiMCqEBGXgbEZgqtC4V8ZBP+qICQ0JsHQX4PBIRug8aZW7py7vb8dSbUpOF/pL9oXUh0u7rGg6lCcqfBBXPUl1PTUrri9fBBuaWnR/P5w55rX4hjq0d3dPQ/m1qIN66lO/oY0Nja6/I1ZT23Vqi0Ecmvxm6rV9a2kHoIFWrjwO+Pt55CVtGstj+3r6xPPZWvZhvVUN39DmpubQV282S5O8slFKuCogAQMjmrIfanAChTQwkWCpstF9XUiCNumjFS3UkXS1YCBE3dkZ4iUdBlVlRgzmzFqNuPG+2JsyUzD9qwMt87F8yir4sKw1NYTLhLK+XelZ+D3hy/hP7ZH4792x+Db49dx3ClTw/HoZGzNJWA4iJ2FJxFYHr7A9N5fF4jcznvClH0F3bvgUJrIt42342HPI6S3ZYgAhk2jzZiZXdw8nqaJ/EPnfaLlwnuGpu+vGprwbfQd/FfQJXwRehn/HXoNP4Um4FBIOk6F2INl0jWCwTP3nU2BX/R9VOjbvdZc6iFm3ab68br/NW43JyCsJhJBhlDE1MUhuz0HtSN1IoaD1xqxihO/HXiHqNoY4Xrj6MrB/ejaWJyv9kNRz6MVu+KspYvEKmTw+keki4RaYukiodaDJekiYddEukjYtVD2pIuEooRtK10k1HrIknYKSMCgndaypg2mgBaAoX6gHwfu38WWVcRJIBjYmZOJr1OS8Ka1FTffF2NbdoZIQ6kM4l1tGexxe3aGcKP4NjVZBF7c6UZ2CU8CBrbtS//r+NWuGPznzmh8sScWP568qcokcTIyFdtyT+P7+wdw8JkPIvTRCwCDny4QD7oLRaDBj7kFq4arEVMXiwB9kIgPwC1jBXDwudiyFoCBfvVtQ0MIpJVKahK+vHMb/xZ9Eb8IjsIvQqLxi7Bo/GtYHH4KvTMPGZh5YueJBJwLz0VxefOK01Mudu2uXlMAAzN7MG4BgzdaZ62wzFrFllk/CHLW08I23WlOQqghXMAEZ8DAcoghHNcab6BjomNFTZeAQS2XBAxqPSRgUOvBkgQMdk0kYLBroexJwKAoYdtKwKDWQ5a0U0ACBu20ljVtMAW8DRhm5mYR9eq5SN/INI6uYMBSrxMUMOf9psw0EBQwFeSSAR1zswVM+PWt6/jllTj84lIs/u5SLP7+ciz+8Uoc/njnJrZmpbvMNuFpwLA1IQ1/OHoJ/7ktGv+5Kwa/2RuHHecS5zNKnArJxNYEf2wtPAaf0mBE11xQAQYGE6SvPOEAB7KrWTgYnpiewJXGeITVRCCyJkYMNHluzr5fqL8Eo3loQTpMrQEDI9ATSJ14kC/6kPeMSP+ZkY7/ib+JfwmPwy8Co/GLoCj8Q3A0/uR/HdtPJGDfuRTcSH2JhpZeMHCkNxcCBvqBfkoL+z6+8QZCDRGqe8sRNBA0xQEkVlYAACAASURBVNRdQONo04ouTQIGtVwSMKj1kIBBrQdLEjDYNZGAwa6FsicBg6KEbSsBg1oPWdJOAQkYtNNa1rTBFPAmYKCZe4vRiF25WSJ1I60RlgIJrt7j537KSMXfX47DnxLvgFYIro7dl5uNb1KS8K/xlwVY+HuCBYeVsIHrv1+/Kgawi4EKTwOGPdnZ2HQ1SUCG/9gajX/fFo3f772IfT4pOOiTil0nE3H2WhJ8X0fBvzoITG2omK1z37faHxltWTCajQsAgLu3IwFDx0SngAnhHEjW2iEGB5YhNWGoG6kXM++c7Z6YnsTUjBlmq1lTFwmmtot+9UJYnTgCKfbTzqxMbE5NxVe3EvB/r97Aby7F44u4q7hy/yV0DV0YHGIAqMVdPdzVyZ3jPkXAwDSb15tuCQsGx7533Gfmkov1V9Ay1uqODPPHSMAwL4XYkYBBrYcEDGo9WJKAwa6JBAx2LZQ9CRgUJWxbCRjUesiSdgpIwKCd1rKmDaaANwEDTd0L6mqwOTNNuDm4hALLgIfduVn44tY1/K8L0fjtrevC7cHVuRjjwREu/PLyBTivikXDFzev4efM9AWwwtOAgW3dnZmFLTdS8JegG/j90Uv4Yt8F/OXoVZz0T0Nk9D0UPSzBi9q3SG5MFX7ydF0I1AeLGeW7nffQPdktTPFXe/sRMHR+AAxiptoJMATpQ1FqLMN7YylYX2prOtLaMlDQWYjyrgoMjhpXW7Xqc3yYdBVIyTIzg2cNjdiclIxtKanYl7sQSO3NzQEzfezIysTWjHR8nZyI8KdPMTw5qarHm4VPETAQ9hV0FQp3GOf+VyBDgC5IgKxB8+CK5JOAQS2XBAxqPSRgUOvBkgQMdk0kYLBroexJwKAoYdtKwKDWQ5a0U0ACBu20ljVtMAW8CRisMzOIe/NKxEvYnZu9YCDvChI4vr4nNxvfpSULF4e/iYvBv1+/IgI8Oh6j7NOV4je3rs9bLjiDBccyrRr+4fIF/CnpDliHcg5uvQEYeF7Owu9Kz8SW68n4OvAqfveDH45siULAzjjEHrmGm0HJSE/JQX55IZ50P8OL/peoHq4WgR0/1qefgGFsegzXmmxm8pE1tjgPdJFgCkx/XZAIVkgT+RAD02KGipWpGOMMl/Gs+wXGp8dXfPfz4XFkdBJ1TT149qYW9x5W4l5RBR69rIG+vku8RxA1NjyO8jd6+AXcwh9/OIevtwXgh2OR2H75NvZmLW6xosTn+DkrHd2jI2BdWiyfImCgLu0THbjdfEf0LyEDAzsSLtjugVBE1cbCYKqBedayIhklYFDLJQGDWg8JGNR6sCQBg10TCRjsWih7EjAoSti2EjCo9ZAl7RSQgEE7rWVNG0wBbwIGzkj7PCoS1gvOg3jHAf1S+8wUQXeGv70Yg7+7GIN/ib8kXBsW+wwzTjDGAuGBI0xwtf+/L8bgVzfjsT07UxPAICBDdhZ2XE/EV1v98etf7sSX/7gHB353Fkf/6IPDvzuHM38NQmJoBlrrOlzO9H/MLVg+VIErDdeEqwQD/tEsPtgQCgaR5MqyMvDk4DPCECVSGoYbooR1w0pAx8zMLNo6jbhbVIGQiwU44peOPaeTxErXkMDYPGTml0Kva8XDtBfw/TkKf/2n/fjib7bid3+7Hb//5S58+eUpbI2Mx56MxTOGENr8JekOavv7wPgNWiyfKmCg64tuWI+EliRbNomaSNHfhA2XGq7gzcDbVUEkCRjUd50EDGo9JGBQ68GSBAx2TSRgsGuh7EnAoChh20rAoNZDlrRTQAIG7bSWNW0wBbwNGE4XPRBB+haLdbAYJHB8jVYP36Qm4W8uxuAfLjN2Qgz++eolEfDR8TgxcL+bI16n+4O7gIHQ4t+uXcbmjDTNAMOuxBR8uycYv/2b7fjdP+7Eb/5pNw78yRfnvgvF+e/DcOrPATj+37646ZeM0WHPp4a0zk6LFIpZ7Tm42XQbya2pSG/LxPlqf2E+r5jLK1vObodUhyOoOgRx9Zdgsoy4FQeCD41tnYOIu/UE+8/ZYk2cCMycD2x5MihLxJ/YefwOju25gn2/88HOL07hz//nIH77z7vxx3/biz/882789v/dij/94Ri2X7mNvTlqSxPR77nZ+GtyAqp7ekCLGS2WTxUwUBtChp7JHjzreyH6nrAhr6sATWPNIgvGavSTgEGtmgQMaj0kYFDrwZIEDHZNJGCwa6HsScCgKGHbSsCg1kOWtFNAAgbttJY1bTAFvA0YQp49FRYCq7Fg2JaVgf+6EY//HRcjLBJowUAgsMkJCCiAga4UKwEMNouIyyKApCOw8JaLBANQbgm9ij/+6z78/pc7xSD61/+0C9//+hhOfh0kAAMhw8kvA3D6L0F4/6TCa3cbLRFmZmeE+0VB1wP46QJUgR8dAUNYdQSCq8NEWkvOgC9nxUBPhbHxKVy4+RgHfFJwLCBjHiycDslW7R8+nYj/+dsD+Prv9mPPH87hm18dwW8+AAZChj/+6x789n9txfeHI7A7ZWG8DN5XX6ckosk4CLpaaLF8yoDBWR8+3H/sIgGDWkEJGNR6SMCg1oMlCRjsmkjAYNdC2ZOAQVHCtpWAQa2HLGmngAQM2mkta9pgCngTMNBkPbmiDFsy04UVg+Mgfrl9+td/n54iXCP+4YPLg82l4ZpIU+n8eR7PAI9KxghXbhGOr9OC4f+7xpgO6oGrtwADzfy/PxKO3/4/WwVc4AD69/+yR6yH//z/s/feX1FlWf//f/Fdn9+fmXmmp2emp6cNrWDANhAV7WQb20TOZjEHBCVXFUFEJIeCIphD2+asbcLUaptbDBgBQez3d+1TXk4dCjHBtZpn11p33X2qoE7V616g7ot99o7CkgnxQjIsHhcrlkrkLC/u9LOtpuEuiq+aEVMd265goCyG2LMJ4j/fzX+0nylASyOo3kLEomLMWf56uUCyITw0A99+Ho5hnwZh8sA5mOw1Fx79Qlr4EKOhPQLxw+iFCFqTr2SaUM2NwIoyRKyvxP26d68P8b5wu5JgeF8Gtt/HgsGWBsCCQeXBgkHlQSMWDJIJCwbJQotYMGgkrHsWDCoPHulHgAWDfqx5pi5GoDMFA1Wu/+XWTXEBH9SqzkFrQdB6TF8/Ii8H3dIMLfUUvkg1iDaVtu0Lbb+P7u+TkSayGGxFwuvi7qkGeOasQXCr19ZZgiG0yIxxwSswtFuAcgHt0TcEAcMXYOG42JYsBqrFEBeU0uln293n91ByrRQxZ1a+hWCIF4Un3yQYGpteINq4EbOXl2H+ynIlY6F1BkOIXwq+/TwM3p8GYazzDPh7zceIAeEY5hLWwmhYryB8/00kAlflKIKBjveY4gJUVp9GXdO7FSb8ELAsGFR6LBjsedAFZFNTk/rA/9ERCwb7A8+CQTJhwSBZaBELBo2Edc+CQeXBI/0IsGDQjzXP1MUIdKZgoOTrZ42NiNy8CQEVZXbdGmzlQOuYlkFQu8n/piTj85Rk/MuYiH+bkvBDUf5rsyEoi+Gb/Bx8mZ7yRslAyyOcMlIxurgA9H2283eaYCgpxfjwWDvBQEsCxrjOxJzR0UIwLPkxTmQwGKev7vSz7UnTU/z0+w4sO/3mJRKUwXDx8cV2l0iQVKp99ExkL9jWXGgtFrRxRPhqfPdFOEb8Mwg/9JyKkKGL4O89H579QkVmh8hg6B6IUeMWieKY2nEiuTCptASLtm3F/bo60Lx63RxVMNDSlQfPH+DUw9PimG7/fQdO1J5CbePDdo/Zh3JjwaAS5AwGlQcLBpUHjVgwSCYsGCQLLWLBoJGw7lkwqDx4pB8BFgz6seaZuhiBzhQMhIo+PPx86RICKiyiXaV2gdjeni74xxQX4j8pBvxvcoLY/poUh3+ZktB7VSpG5GW/9rmo64Tr2kzR1pLqMbSVvUBygZZHjMjPRluZFZ0lGMIryjF5cQqG9QwSBQxFjQFaJjEgDMMHhGPq98uEYFgwegXm/xCDDWu3dvrZRkX/Lj+9jLhziYg7m2iXxWAt8hgvijxmXcpG3Yu6dos8Uh2E2zUPRaeItxEMcxYXYXTf2RjxaRBGdotAmNcizBuzAlOGzhXFHr36hMDzcz9MjExCaEkpgivLRVFOkgvLf/4Jv967h2Yd5QIdEEcUDA3NDTj64DjWXs5B0nkDEmk7Z91T15BfHpxAfXN9p5xPLBhUrCwYVB4sGFQeNGLBIJmwYJAstIgFg0bCumfBoPLgkX4EWDDox5pn6mIEOlswEK6H9fXIOHRQ1E4gAdCeXKDH6KLfK3ct/pYcj78lJ+CvyfH41JhobVWZTh0lUvBtQW6bmQxaLQbPnCzxdVS3gWSCtnVPM4hWll/n58C/vNQue4Hm7yzBQM8dkJ6D70ZEwutzf3j1DIZnjyB49AiGW48g+HrOw7xRMYj8Lgor/Y24ev7GB51tL5pf4tHjOly6WoMDRy9h08+nUbH5OCq3HMfmnadx8Nhl8diN+zXY8ftOrKiOw8rqOCSdMwjRQO0q46sTEXUqBinn03Du0bl25QK92Jcv/8C9B08QtqAIbyMY5q8oR7CvCd91n4rvPgtBmOdC0VFj7ugY+HpE4tsvQzBu9CJMTFuLyeYSUGeRpT9tQ+mpk7h8X7/CjrYHwtEEAy1ZOXL/KNIurgK1Hk04lySOIR1HimOr42G8kIKTD091imRgwWB7dnANBpUGwIKhNREWDLZEWDDY0rDGLBhUJiwYVB480o8ACwb9WPNMXYyAHoKBkF2trUXqgX2iowRd2LfXVYKWRzhnpOEvSfEie4HkAokCEguUkSC7SZS0KSuo+J+fpRQjC/Pgkb1GFHKk5RYDszLglbNGrNsnidF6aYQmPjpLMISUWTDJlIvhU1ZiyGdBGPyXKRj0Vx8M+rsfBv7dF8P/E4yJzjMQ+UMMtpXsRuPz91vD/bzxBW7cfiAKLeaY9yM2bQsWJ6zD3BUWzI4qxezlpSJekliFuPQtyC7di8pdh5F/ohKrLmQi4VyyEA2xZ+NBGQzZF3JxtOYYnr983ubZT6n5jS8b8bz5uejkUFffiAVxlaJ7xJtqMNBSicilJfAZl4BJQxZgxtfLRJvOud9FYenEBGQsKcCWnUex8Uw1Npw/i52XL+H83Ro8amho87XocaejCYY79TXIvJQl5AIdL60DiLYnUbSiOhZrL2fjRt2HSau2+LJgUKlwBoPKgwWDyoNGnMEgmbBgkCy0iAWDRsK6Z8Gg8uCRfgRYMOjHmmfqYgT0Egz0IYIkQ86xI5i6vkpkD/iXl4lshdDKciEcQqvKxfj7wjx8nmIQmQv/MiYJuWC71IE6RfSh+glFalcBTRBoe5IYJBKoSwRJC9oHV75eLGjf1xmCIdhswfjkPAybkQZXn0S4fr0EQ3qEYdCnAVbB8L9+cPtnIL51nomZwauwa081GhreTTCID2oPn+HQL1eQnrcTkTEWhC8swvQlJaLgIgkGuuCnjbILSDbQYxGLijArqhSG7K0o3vMTqn7djMrr67Hp1mYcqDmIizUX8ejpI7szn6TC7/V3xJr/fXf3Y2/NPpysPY1rT26iaN0B0aKSXoNWb+F1+7kryhExrwDG5HXYmLcDpcYqVKRtxMHNR/HgTi2am9vvWmH3wjr5DkcTDAfvHULi+WQknku2kwuaZEg+b0R09UocfXAML16+6FBCHSkYqJJGU3Mz7j57ikv37+NszR2cv3sXNx49wpPnz3WttfG+kFgwqORYMKg8aMSCQTJhwSBZaBELBo2Edc+CQeXBI/0IsGDQjzXP1MUI6CUYNGy0XGLPlcuI2blDZBAEVVrEuvoQkgvlFkwsLMKQ9Az8MzEB/0xKRI8UI3qnWzMXNMlAgsE5I1VkKGhSoCP3HS0YQi3lGJeYC8+wFLgFGuAZZoJnqBEevglw+3YpXF0jMXjwHAz9bhn8IlYjYkEhFidU4WT1dbx8+VJD1+6ePqTV3H8ilkCQPCBpMCe6/RaR2gU/CYfI6DKELyjEzGWlWLftBGruPcaLF80ivZkuqOk8sb2RXLj45FcUXi3GyrPxiDoTjagzMYiujkX2pTxsP3cA8xPKMDPKLLIltLla7+etKMeMpWZEGTbgRPV1UbPDdh5HjB1NMJTfqBB1F9rKXtAEA+2Xn1khij9SHY2OvHWUYKC2ttRu9PD1a8g8fAhLftqG2Zs3YN6WTUjYswvrz1bjyoMHqHfw7gwsGNSziwWDyoNGLBgkExYMkoUWsWDQSFj3LBhUHjzSjwALBv1Y80xdjIDegoHw0QcK6i5xrqYGm86fQ9aRQzDu3YPoTdsRkV6Kr5esxueLE/DP5fH4fEUiuicb0DtNFmyk4o39VqdjfElhm0skPlQ2dLRgmLy6EF5TU+EWZIBnuKnNzT3MiKERqQhaVCAuyMVFd/J6Uc+AeLV3Ex/QHj7DmuK9onvDzGVmzF9Z8cbMgdYX+zQmIRA8rwCFFQdx5+4jPG9sFEUNWwuGy0+vIOVimki912o20EUsXeRSy8v4s0nI37kNc2PLQK+HpAfJBC2DgmK6jx4jEbL70EXUN+jXarI9nm96zNEEg/lamVUwnLdfHtFaMGy9vR1Pm5696S2+0+MdIRhILtx+/Bhrjx7BuJIijC8pgo/FLIq5+ltKMclcLH7e52zegEPXr+H5i47NwninN/yGL2bBoAJiwaDyoBELBsmEBYNkoUUsGDQS1j0LBpUHj/QjwIJBP9Y8Uxcj8DEEQ1sI6T/mSau3IWx+IUbPXoMv5ybhH9Fx+OfSePxzaRy6JSQJyUB1GKgeg3v2GgRWvLlg5PvIho4UDGHl5fh6Xgbcgo3woMyF1wgGjzCjyGzwX5gvxABlFExdXIx1234BFWt83Y3cAy2lWFuyV3RueNushbbkgnZfZEwZgufmw7zuCO7ee2QnGKjzRPaVXLs1/4ZzJiRXm5B00oCF+5Yi70whNu8/gijDeoTMKxDyQ6sBMW1xCYLm5mPeynIcOXHlTyMX6Dg4mmDYcedn0THCVvTYigUtjjodjf13D4h6Ga87n97n/g8VDKTPbj1+JGq0jCp8fRtaWvLkU2aGj6UUR65fF/U+3uf1dvb3sGBQCbNgUHnQiAWDZMKCQbLQIhYMGgnrngWDyoNH+hFgwaAfa56pixFwBMFAHzDoApn+a08X1pPm5cBlajK6L0rEJzFx+MfyeHy6LA5fGJLx3xQDvlqTgYmlxZ2SvUBCoiMFg192CbwiUuAebHytXCDpQPLBLdQInwW5QjDQf/pJFlDdhPrnr//PPsmFHXvPiayDt6l3oEmEN+1nLy/DjGVmbPzpBO7evd+yROIP/IHbdbeVNf9URDDhbBKWHYnG9Ky5GPedP77rPxE/DJyMVVG5+OXQeew9fBHZ5n2ITd2MFSmbkFGwG7sOXMD92qdCoLSfo+FYP3SOJhiuPbsmukRQBwlNJrTea49denKpw2F+qGB4+vw5LGdOY1xxIageS3tSkCSDqN1SaUHN0ycOWZOBBYN6irFgUHnQiAWDZMKCQbLQIhYMGgnrngWDyoNH+hFgwaAfa56pixH42IKBPlzcvf8E82MrRCFCSu2fOC8bg0INGDLNiD5zk/HFkgT8e3E8esUaMDx7rSjW+LouFOK/nBYzxpUUYlRRvrho8bWYW7pWhFdVgMYTzMWYaC4WxSZbP9d7CYaqCoSWmBGQlgPflaswZXka/JIyMTZqNTyCDfAIaV8wUF0G1xADJsxZa+3yEGMRSwjCFhSKjhDNbWQx0H3Xbj7ArOVlokgjSYk3iYO3fZyeiwpARhs34NCx8y2CgTpGXHh8EbFnE0RRQdrPPbkAwXvC8aPBD8MHjIX3wDHwdrVuU9zCkTwjA+eOX8LTZw14+LhObE+eNqDheZNoa/ln+5FyNMHQ9LIJtPQh7myiyCppLRfizyWK+hh7avbi2YuOXR5Bx+5DBcPZO3ewaNsWkZ3QnlzQHgupLBfLJSqrT6Ou6fXy7WOdVywYVPIsGFQeNGLBIJmwYJAstIgFg0bCumfBoPLgkX4EWDDox5pn6mIEPrZgePnyD/x65Y4oLqil95NgGBxigEe4Ce5TjRgy3YivwpPhNT8dvoVmkCTQLja0PbWcnFxaArfsTPRdnSZaWvZalSL2NHZfuwbjigvE0grr46lwWpUqajkMy7VKC+253lUwhOQVYdK8ZHz/dSS+/moqhvcLx/B+YRjhEgHPXmEY4jIL7uNWwDPkdTUYjHANMGDAjwlwn2jAN75p+CEwAz4zc8QygnO/3kbTC/tOCo+f1KN80zGRvdCRckGTEFQjYfrSEqwp2okHtdYuEiQYrjz9DXHnErDsTDSm/zIbQUfD4VMWhG/Gjsew/qMx3G0shnuMxTD30fjOfSL8PKZh3dqteF7fdpvLP9uPlKMJBuL3sPEhdt7ZJTIZRLHNMytF1wiKqe3ozju78bDxEej4dfTtQwQDZa5suXBetJV9U/aC9vNJP//UhnbR9i2ora/v6Lfzwc/HgkFFyIJB5UEjFgySCQsGyUKLWDBoJKx7FgwqDx7pR4AFg36seaYuRsARBMOlqzWYGVXa0vWAlkgMaXUx7hpowDfzVyOoyL7uQmhVhfiPpktmOqgAZOutZ5oJPdKM6JZqxBepBvRMM8qvSTOJLhUkJvzLS4W4eBfBEJiRizE+URgxIALeziHWrU8IvGlzDoH7v/0x6H99MfiLULh9uwQeQcl2SyVc/ZPhMi4e/UbFYcjYRAydYMTQiUYMm2TC6ODVqL5w004w0Icyyl5YFF8lCiVqUqCj99OWFGNZchV+vXJbnPm0ROJx02NQ28Opx2ci6GgYgo+GY3JWALwHjcHwIWMwwoMEwxgM9xyLH4ZNxsgBU2Bakon7dx90iZ8eRxQM2nG58PiCEA2VN9ah8kYVdtzZifOPL+Bp09NOkQt0QD9EMFBxx5JTJ0RGkSYQ3mavZSpRO0tHu7FgUI8ICwaVB41YMEgmLBgkCy1iwaCRsO5ZMKg8eKQfARYM+rHmmboYgY8tGMSHi0d1oi3j7OWlosvAlPm5cA21WVIQZsJgv2T8sDILgSWlYp22tqxBXGiUmUFygSRC73TZbcK2reVnKcn4S1IcPjEkgISD9hjtqe1lr/QUfJ2fjfB1FW9dgyE4twijpkTB02U6XF1mwG3IHLi7zobX4BnwHjgVI74Kh2fPYAz6my8G/s8UDP48GG7fLxUtKrVijx6hRgwYn4C+o+KEYHD7MQnDJ5vgPckErwkGeE0w4vAvV9DYqFbNp/HhE1dE8UTKXpgfZ8G8RDPmJZWI/fz4MiyI+/AlE7OiShEZXYrte860nPn0X/DMS1lCLAQeCUXI0QhMzgqE9+Ax8B4yRmQveHuMwXdeEzHG2xffuUzCwsgYXLp5ueU5/syBIwoGjScdm/oX9ahtfIjaxlpQS0r6GevM24cIhqbmZhSeOI4J5iK7rKT2RANlMfxoLkJNq/apnfk+3/a5WTCopFgwqDxoxIJBMmHBIFloEQsGjYR1z4JB5cEj/QiwYNCPNc/UxQh8bMFAOF/+8QcqNh8XGQx0QeszP1cUPXQPM8E91IghgcnoH5iIoaZMfJ2bjW8LcjCyME9kLVAtBVriQJkJ1GHCVhxocc90E/5hTMTfkuPx16R4fJ6SLKSC9jh9H0mGfqvTEFJpeSvBEF5ZgfFzjXBznYuvhiyAi9cS9B+6VOy/8lwIV9dIDBs0DUOdQzH4H/5CMgz6yxQM+TICHlPiWrIY3AINQiyQYOg/Oh7uPyYLwUCSgbIYPH80Yt22E6ivV9eaP3pcj4qtRxG4dA1mpmdh2tpUhOcnIqwwHuEFCZiaY8SM1asxx5SHeQml712bQSyTWFKMzMLdLReqtOY//dcMzPwlUkiGoCNh8LEE4tvx4zG07yh4u4/BN54/YswwX4zx8MUPg6dgVuwC7Lt5oEv89DiyYPgYgD9EMIif/eozYnlTW0uf2pIMtBwquMKC8PWVuF/X8TUlPpQhCwaVIAsGlQeNWDBIJiwYJAstYsGgkbDuWTCoPHikHwEWDPqx5pm6GAFHEAyE9N6DJ8gq2QvqhOA3Lw8eoSYMCkqGS0ASnAPi0WNBIroZDeiWJjeqsUD1FKhtZbd2BANlNvzNkID/NSQIyfAPQyJIOmiCgfYkGEg00DKJNy6RqKpEUH4J3L+PQn8vkgpLMcBrScvmMnSpkA1D3OZh2ICpcP0sAIP+7odBf/XFoE/84TZsQYtgoOURJBdocxkTD48JBiEYhk0ywmO8ASMDVqGg/CCe1cn6BZQOf+P+XRgrqzBl7TL4bYjElG1TMWXbNEzZ+mrbNg0+m2chyLIU07PSEJlc9F4ZDfNWWBCxsBCxaZvQ2PQCNHft81oknjcgpjoWC04txtTjMxCyJwJTUgLxresEfDP4R4xy98Fod1+MGuILv4BwzKtajPU3N+IlOve/6Xr8eLJgUCl/iGCgZ9p/9TdMXV+FwArLW2UxUNbSpNJipB3Yj8fPG9QX4wAjFgzqQWDBoPKgEQsGyYQFg2ShRSwYNBLWPQsGlQeP9CPAgkE/1jxTFyPgKIKBsD58VIfte6oxK96CwaEGfOkfh14RCegdnYzerZY1aFKA2lbS0gfKULAukbDPYhCCITleCIb/TSbJkCC+trVgoHFAedkbBQNlL4xZthoDvl4uMhZs5YJt3G/oMrgPmQ3P3qEY8inVYiDJ4IMhPcNblknYZjAMGJMAr4kGeE+iGgy0N8FnRg7Wbz+JulcZDHSB//TFU+y8uQ8zdi3BpC0R8N08E34b59htvptmw2fLdEzZMh3B5mhEGgow/x2XTVgFQwGWG9aDikrSh8Ebz24j5lQyok4kYcVJk4hjTscj+shKRK5dhEljgjFmmD/GDvNH8MwZWL5pJWJOxaL8RiVe/GFfrPLP9iPFgkE9Yh8qGG49fgTT/r2i9ay29KmtzAW6T8teoMylozduoLFZXTqkV4yU1QAAIABJREFUvrKPM2LBoHJnwaDyoBELBsmEBYNkoUUsGDQS1j0LBpUHj/QjwIJBP9Y8Uxcj4EiCgdA+rK9H5qFDGJSRgW4Gg5JlYCsEtLh7qkEse6DlDyQPqJBj73RVMtgukfi7IQF/TY5vUzD0z0wX7Sxfm8FQVQGSCyGlFrj7J6H/sGXo77XktZKh/9BlGOi+AMO+mgqPbkEii2Hg//hg8GfB8AhIFFkMVIOh/9h49PkhFgPHWgUD1V6gAo++M3NAbSov/VaDF6+6SDQ0N2B3zR4s+SUG/ttmYfL6WXZiobVsINFAGQ6BFYsQaSh8p0wGLYNhWfI61D58Blozf+DGBYxfb4BbYRw8ihIwZn0i5hwwILE6BdQiMbnahLgDiUg8ngzDWZO4b2V1HNbd3NCyzOLP/GPEgkE9eh8qGJpfvsTRG9cRtq5CtKqkOihtCQZNLlD2gnH/Xjx/4XhygciwYFDPDxYMKg8asWCQTFgwSBZaxIJBI2Hds2BQefBIPwIsGPRjzTN1MQKOJBjogoEKvtEFhPvaTHRLM75RMKjZCVbJ0J0kg009Blr+8N9UA/4iJES8WC5B36dJCuo6QUUefyjMf20XibDSMgSszse46Ex4BBvQ55uVcB4WDWfvaPQZtlwsibDNXqCY6jIM8FqMoYOmY7hLGDy6B4kMhkH/DIT7lPiWZRJDaJnE6DgMGBWPIWOTMHSiCePCMoVcqNr6S0sHCfogdvT+MRgupGDJ8RiEbpv/VoLBKhxmY/K2CIQUr8Dc5OK3rslgm8FQU/sYP1+6JIrrDchJwMDcOAzKi0f/7HgMyk+A//ZkJJ+zSgajtj+fgsRzyaLrxP57XIOhi/36EG/nQwUDPUl9UxP2/XZFZBCNLS4AdYaxlQyU2eBbZhbdJuL37BLtKennwRFvLBjUo8KCQeVBIxYMkgkLBslCi1gwaCSsexYMKg8e6UeABYN+rHmmLkbAkQTDgWtXMX3jOvhazPihME/UVrAVBZoQsN2L7ASDtYAjZSdQJsOnxkT0TDXCKd4Ip7lJcApNwJfB8fhsThz+GhuLv4tMB4PIYqDaDSQXvHPXIqSyXBUMlRWgNpTjw2PxjddseLhFYsDXUUImCLngFQVn2oYth7P3ctCSCFvJQIKBNhIM1FGCJINX90C49QiFR0AShvgmi22QTxIGTk7CuGlrEL6wCPNWliMtdycOHr8slkZo11F3G+4h70oBVp6Nx8pTiYjYsQiT1rW9PKJ1FgONfTfNEksmqPij6DIRW/FG0aBlMESnbMT5OzXwLy8TF4EjzZkYnB8nNtd8kg0JcCtKQORBg8hYoEwGbVtRHYvcK/m4VW9tdfln/xHiDAb1CHaEYKCLjLqmRlTf+R1Je3eLrhIkGsaVFMC6L8TMTRtQWX1GFHak4pCOemPBoB4ZFgwqDxqxYJBMWDBIFlrEgkEjYd2zYFB58Eg/AiwY9GPNM3UxAo4iGJ48f46Vu34WRRZDK8sx0VyMvhlpoOwCW6HQOqbsBOoKQcseqIij2BLj8cWiRPQOSYCTb7zYevvFi5oO/w1dKR7rY0rFV5mr4JGzBmOLC5UCc7REIqSoBBNnJ+Fbz1nw7hMqWlEOcFsohIGL5xKRtSDkgiYZhi5Hn6FRSsFHKvY40GOh6CahCQZv5xB8P3IB/HNKMGV1ISak5GNk8lpMy6vAur2nsG33GRRVHkJx1SHs2HcOV67dRX1Do1haQBkAhvMmkRGQcMaA2XuWYWLl2wsGkgxTtkxDsHm5qMew4C0Ew9yYMkxfWgxDznasqz6D0UUFYhlJYKUZw4pMGJQfhyEF8RiSnyCyGX7cmNQiFkgwxFbHi+yFw/ePovGl2gnjz/qjxIJBPXIdIRjoGUkZNDY3i9aT1TW/Y8elX7HubDU2nT+Hw9ev4WptrSjq6KiZCxoVFgwaCeueBYPKg0YsGCQTFgyShRaxYNBIWPcsGFQePNKPAAsG/VjzTF2MgKMIhtO/30bYukrx33FKjab/lHtkrxFZDLZSgTIaSDpQ5wha9vCflGT8y5QkCj3+T1KcEA3/WBqH7kGx6D0lzioY/OLR2y8OvX3j0C8gAS5BSfCOWY0f8wvFfKFV1swFUUSOUrFz8jA2KAYjBkSAhMCwfmFwGzJHWQbRd2gUnIcut2YwaJLBKwp9qaOE52KRyUA1GNxcI+E9cKrIYKDnGvHVVExekiIyJaieQ1h5OUZn52F6VjlWZm5GlGE9FsZXYmFcBRbFVyHauBFZxXtx8tw1ZJ3PQ/zZRCEZks+asPjwyncWDL6bZ4muE9Ta8m0EA7UNjYwpQ9n2o8g6cgjjigtFoT1aJ+9TXgjv4hSRxTAoPxYD8+IwvDQBSecM4nXGVK+E8UIK9t7dh0dNj7rMTw4LBvVQdpRgsH1WylCgZRNPnz/Hs8ZGUfvDcXMWbF8512BQaQAsGFoTYcFgS4QFgy0Na8yCQWXCgkHlwSP9CLBg0I81z9TFCDiKYDCfPIGgynIEv1qmQFkMo4vy0T3NIOopUKYCSQUSCv80JoFaTdKSiL9rdRUSYvG3ZdH46/IY/CdsBXpMWokvp8RaxYJfHJz84tE/MBGuoQa4BRkwdFoqfLOKlXXeJBjCyiwYHRYjshaEEHAJE4JgsPs8RTBQa0rKWLCTDJ7L0N91kchkoOyFluUR/ULh7RSCUeMXIyS3qGXe4NIyDIvKwOQFOQhfXIQZy8yYvbwUc6LLxH760hJELCrGEkMlZlcmI+ZYYkuGwIoTCQjYHIkp62e/sdCj7ZKJKVunY/qa9Lcq9jhtcTGiDOtw7NwV5B8/hjFFBQh/tT6e9r4VRRhpXg3PwmQMzk/Ad+WJImMh/WIGSq6W4pfaE3jc9Fi0t6QfHfowSbU2qJjn/bpnqK2vExeSzQ6c8t76R54Fg0qkMwSDOsOfa8QZDOrxYsGg8qARZzBIJiwYJAstYsGgkbDuWTCoPHikHwEWDPqx5pm6GAFHEQwrdv0s5IJtmzofixl9V6fhi1QDPjMl4xNaApEcL+os0J62v8fH4Z8LovC5/1x0+3YaPh89Ez1+WIqeo5ej57hofDk+Gk6TVsIlMBHuYUZrYcUwE1z9DWJ5gnaxLLIXysvhn7QGQ3sHi8yFES5hIvNg2MBpaC0YqNZCf5IMNtkLQja4L0U/l3n4auBceA2egeEDwuHdJ0TIhe+/mQu/xExEvLpAD6uowKj4bHiHpyFsURHmrShvM6uAajIELcjF6MXxiNyQiKTT1voGYpnE3mWYWDUTvm20qbSVCrYxdZSYmm3C/HhLm/NpmQ2RMRbMXFaCHPNu3Lp7D/uu/obxJYXiOFFFf2JGW0hVGSaU5mOUOQvJRzZi3939qH50Fg+eP8CLl9ZK/yQVbj9+jGM3b2DDubNCVqw5cgi5x46govo09l+7it9qH4j/Vjv6jxgLBvUIsWCw50EXkE1NTeoD/0dHLBjsDzwLBsmEBYNkoUUsGDQS1j0LBpUHj/QjwIJBP9Y8Uxcj4CiCYfbmjQiusLRctNIFrJ+lFIOyMoRQEDUWSChQ1sKr7R+xsfj39MXo5RaCvp9ORN9/TESfz33hNGQeeg9djN5eC+HktQh9vlmKgVPi4R76SjCEmzDELxnjDXmi7WTLhXJhCUaNXQTPL/xFQUaqm0Cb98AIuLlFgmoq2BZx1DpF9PVYij6ey+DsvhR9+0ViwKeBcP3ExyoW+obC2zkUI7+fD7+ETISXW5dj0PII/zwzBgcaMDkyB3Ni2r/Ynx5diG+mJ2BiQjyW7zOKLAbDORNWnEyE70bKYnhzu0pNMlgFg7HdQo/zV5Zj+pISrEzdiGMnLor/uN179gzLf/5JtBIMrLAI0UAZJ0EVFrGkJfrnn3DrsboUgj48PmyoF2LBtH+f+B4q2vdjSZEo5DfBXCSkha+lFPT9tO6+5ulTUOtCR72xYFCPDAsGex4sGCQTFgyShRaxYNBIWLPaamtrQZ9FXjrw7335ijs/YsGgMmbBoPLgkX4EWDDox5pn6mIEHEUwUIX4oEopGOii9buCXLEsgjpDUPFGTSzQ/pP4OPwnbCGcevmh7ycT0OeLKejT3QfOPf3gNGQunDwWwsl9IZzdFsB5yAL08VqEAZPihGTwCDPBLcCAiamU7m/9Lzxd8Aek5WDoFwEY2idEZB5ogoH2noNnipaTtDTCTjJ4LkF/j8Xo7xKJQX/zxZD/NxqD/7/RGNYzSBSJnDgnEcE5hS2ZCyQ0Qi3l+GHFWngEmDAjyoz5byi4OCumGCMjE/D17JWYWZYMw6s2kElnjZh/MBqT182Cz4a3WyoxZds0TFuTivlxbWdMzF9ZgdlRZYiMtmDbrtO4e/e++PD34uVL3Hj4EMt2bIdfeRlICvhYSuFbXioKdJ6tuaP8dNCa+dr6epSdPongqnLR3jKwoqxFImliR2RBVJZjcmkJppSZYdq/FzcfPYKjdgpgwaAcZrBgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYAUcRDEt/2obgSguoeCC1iyS5QC0oe7yqu/C3JDV74d+zlqC3SxD6/mOCEAskF7TNecBMq1zwWAhn2kg0uC6Ak9diuPglYEigAd4z0+CXXdJysRtmLsWk+QZ4/scPw/qGYsQAa/aCJhmoUKObq7XQY2vJ0H/oUtFdggpBevcLw7AvA+H5qQ8mRiaBntf2QlqLg0rK4D7dhElzchC5QmYvUOYALYmw3ei+uSvLMGZBEoZNXYmgzCQknjK11GJIPmvEtJ2LMektJAO1qqRtZkbma5dHREaXIXR+ASq2HMf9B49BF9R0nmi3hhcv8Mutm6g6ewbrzp4R8eOGBu3hln1DUxNyjh0RIoKyUbT33t6exNKk0mIhMR7U1YmaDS1P6CABCwb1QLBgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYAUcRDHQhqhV5HFVUAKdVqUIwUAcJKvD4SbK1/oLIXoiLRfeRM8SyiD7drJkLmlwQWQy9AuE0eC6cPBdbBYMmGQYvQO/vl8NlcgLGG/NEFoF2sRtSUIKxAdHw+ty/TcGgLZXwGDILX3kuRr+hy0BdImijYo4eg2dh+FcRYknF8P5hGPZFAH4Mj21TMIRWVeDHvEK4BhkwK7pUZC+QRJix3Az/hXkYO3sNvpuRIbbRs9fAZ0EupkeVYPKiNAyfFgvflATEHpOCgdpBJlYbWiRDe0UffbZOR1D5Uswx5bUpGGYuNSMwMh+WTcdwv/apWEfeWjDQjwBlF1BGA20Ut1Xhf8uF86L9p395qeg8obF+055EE2UzpB7cj8YX1hoOjvRjx4JBPRosGOx5sGCQTFgwSBZaxIJBI8GCQZKQEQsGyYIiFgwqDx7pR4AFg36seaYuRsBRBAP1uac0elqfP3BNhshcoJaUWovKbikG/C9JBkMC/jUvCr2+CkLff01qyVpQBEMPXzg7BaPP4Eg4k2TwWiz2Th6L4TxsMXrNS8SPBYWwLSgZnFuE0ROWYGi3AAzrZ5/BoGUykESgbIahg2bAa/BM0SWCxppcoK8b7hKGYT0CMdZvOUKL1f/cU20Jql8wKjsPEyOzMTfGIuTB2Dlr4BWeCtdQI9zCjHAPM7VsdB8t6/h6Rgo8w2PhY4pH7DFrHQaSC9oWf8aAOfuiRMHHSVX2SyZ8N87G5G1TMW1tCuYllLYIBloSYc1aKMTMZWZs33MWtY/qRB0E+sPelmBo78eA6i7cffoUMzeuR2B5mcL5TXKBHidGVNthUlkJTt6+hcbm5vam0/0xFgwqchYM9jxYMEgmLBgkCy1iwaCRYMEgSciIBYNkQRELBpUHj/QjwIJBP9Y8Uxcj4CiCgdoVLty+Ba5rM/FlOmUtSLlAkqF3egr+bUoSkuE/wfPh1NsPff8zuW3B0N0Hfb/0R3/nYPTvF45+A6aj31cz0M9lGvp/EYAeUXHwWJsJ+s+6dsEbkleE0ROXvlEwaKJBiIRXGQu29ymCISDaTjCQXKCaBav27Mdy00b4zcvF8Klpr2QCiQWSCUZ4hJtaNrqPtiGhBnzlH4exphVYfjypRSxogoH21Fli2dFYTN2xSNRkoA4TWn2GyVunIahsKeYY8jF3hbUN5rTFJQidXyjaY2aV7MW5i7fx9NlzvHxpzUl4H8FABRq3Xjwv6inYFu7UWL/NnuQPLZVIPbAPj5/bL7/4mD+GLBhU+iwY7HmwYJBMWDBIFlrEgkEjwYJBkpARCwbJgiIWDCoPHulHgAWDfqx5pi5GwFEEA6XZl5w6AdfsNaKwo5a5YLunmgyfGhPxX59IOPf0RZ//trE8gmox9PBB/y/9MKCXPwb0DoBL70C4OAViQK8ADPhkEnovixXCYmxJIbQ2lSGFJRgXsgJDP2+7BkNridDemJZIULHICTMSEGaWRQ21bguJe3fj1zt3kWnZC68gE1xDDLKFZrjJ2kqzjb1biBGDg5LhlRSPwJ0rEFud2KZkSD5rQuypJCw+vBIzdy9F8PZ5mLJ1OgLWz0dI4ipMXVaIOdFlWJq0Doasn1C28RiOnrqKO/ceo7lZ7d7wPoKBMg6oI0TAe2QvaPLBmsVggX9FGX5/8sShajGwYFB/CbJgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYAUcRDIS1+OQJDM/PEbUXvkw3tSyPsJUM3VON6O4/H85f+qLP52oGgzN1kejhg36aXCDBYLt96Y8B/5gI5+Vx6JZqhHdetmixSBe1YaVlmLzEBK/POk4w+MZlILzc2hmDUv6p0GH0zztQXXMHvz9+jPnlG+EaYoRbsHUJhGcbUqHlPup8EWjAiHmr8H3mGoyqMGLO4VisrI5DwtlkGM6rNRkomyHxbDKiT8Vi8fFomM6sxuZze7Ft/2mxBGLPoYv45cw1XL1xH4+e1LdkLLQ+vd9HMNQ1Noq2lVSsUxMG77Mn+TOqKB/n79aIWg+tX9vHGrNgUMmzYLDnwYJBMmHBIFloEQsGjQQLBklCRiwYJAuKWDCoPHikHwEWDPqx5pm6GAFHEQz0X++Vu37GuJLCV8skTCDJYFuHQSyVoOUSs5ahT98A9P2slWAgudDTDy62UkGLSS5094NLDz84xyaIGg+D1mSINH66+KU2lYEZufB2CsFQp2AMHxAmCja2l6nQ5mMDwjCculD0Dwctu4iosnbFoJaOS37ahuO3buLJ8+fYcelXjCkswOi4tfAINcE95PWSwYPqMgQZMHRqKialFyCgrBQTyvIwY8daZFzMEnIh/lwi4s7KLf5sIpLOGZB+MQPrbqzH1WfX0PzHu9cyeFfBQPUXqDXlBHMRQjtAMIwuysexm9fR2Ow4xR5ZMKi/BFkw2PNgwSCZsGCQLLSIBYNGggWDJCEjFgySBUUsGFQePNKPAAsG/VjzTF2MgKMIhnvPnmLO5o3iv/w+ZWZ4Zq9Br1cdJKiLBG29V70SDjHx6OsVgb7/nATnbj6gzIU+PXyFXFAyFjS5QPuefhjw2RT0+2Y6nJOSxfP1XZ0mhIb23/XQYjPGTFkGzy/8QcschlPBxtfUWWhTLtDX9wuFd69gjAuKQXhFuWi5SXJh3tbNOHT9mihYSCn/MTt/xkRzMULKLBgVsxae4SlWyUCigYo6aluIEe7BRnhFpGJCSr74enq9lA0xbWMlTt69jP33DqD4agmyLmVj9aU1yLyUhbzfCrDl9jZcenIZ9c3vX8PgvQRDXR0mlBZ3mGA4euO6Q3WTYMGg/hJkwWDPgwWDZMKCQbLQIhYMGgkWDJKEjFgwSBYUsWBQefBIPwIsGPRjzTN1MQKOIhh+vXcPEeurRGo9XUBTvYLRRQVwyUwXmQw906wZDUI2pBrRx28+XL7wsdZh6OGL/iQQbIWCbSyyF3zh8p8p6BMZDacUI3qn0/OlYGRhfksaPwmBgPQcePUKgVf/CHgNmgHPwTPa7BTRlmCg7hHeziH4xnU6gtYWIKSiXLRbXLBti+iG0NTcLNo5nr93F5NKS0DtKum9hpWXY2JKPobPWQWPEBPcg4xiE0snQlPw9bwM+GQVI6xCLjmg+gbU1vPgtavijKTMgfrmejxpeoJnL56h6Y+mDjlT31Uw0KT1TU3wKy/rMMFwvoaXSHTIweykJ2HBoIIlHiwYJBMWDJKFFrFg0EiwYJAkZMSCQbKgiAWDyoNH+hFgwaAfa56pixFwFMFw8vZtBFdViMKAWkYB7SnNfqK5CF/n58BtbaZoYUlLGzwTUzB09DwM/rcvXHoFoJ8QCgFtS4buvtbshR9mwSnV2FLboUeaEd8U5ErBUFkB/5xieEyJRf+hS9Hfayn6DV0m4oEeC+A5eOZrl02I1pS9gzHCJQJTotNE5sKYogIs27Edlx/cFy0f6dQhyUBSgFL/bd+neK+WcvjnlmBSWgEmmPIweVUBAvLNYvlG668NrrSIVo6lp0526hn5PoJBK/JIHTNsW4G2fg9vGodWWsQSljtPn3bqe3zXJ+cMBpUYCwZ7HiwYJBMWDJKFFrFg0EiwYJAkZMSCQbKgiAWDyoNH+hFgwaAfa56pixFwFMHwy82b4j/y9J/51hee1FGACv7Rxaq2+eeXYOTCVRj4TRT6ui2Gs/siOA+eg/59wkTnCJHN8GpZhFga8d1MOCUbWuQC1XPonmYU4oLmC6uogE9mEdyDTRjkm4TB42Lh4rkYLu4LxZ5kg4vXYri7zraTDFRzYVi3AHw9cBomLjFhckkxSC7kHT+K+8+egTpkaLeGpibsvHQJY4oL7N6neN9V1vdK71d0uKiqbPPrSLxQEcW1Rw5rT90p+/cRDNSmctvFC5hSVtJSRLP1MX3TmLI7JpiLkXZwP7ep7JQj23FPyoJBZckZDCoPFgwqDxqxYJBMRN2e2lrQZ5GXL9UuRvKr/m9FLBjU482CQeXBI/0IsGDQjzXPZEOA/hj++uuvOHv2LKqrq1u2c+fO4ebNmy1fWV9fj1OnTuHo0aO4c+cOmpvfvdhey5N1cOAoguH0721nMLR1IUrLBUZErhJdFdz8k60yYNhS9HNfhD6uC9G3Vzhc/uWDAf+ejP6DQtA3ZBGcE5PglJ6iCIYeaSZ8SxkMVZUIyDNj2PQ0UUzRNdgAD+ruMCVBCAwX90Xo77YI/d0Xw8VjMbwGTIN3nxAM+zIIQ7/wFzUXRo5bhPHxqzA+Lw/ztm4S9RaomKOtXKBD9/zFC+y+cvn1gmFd20KhNQeSC7SRxOjM2/sIBvrA+KCuDjM3rn+vVpVai8ofS4pQfed3kfXRme/xXZ+bMxhUYiwY7HlwBoNkwoJBstAiFgwaCc5gkCRkxIJBsqCIBYPKg0f6EWDBoB9rnsmGQGNjIxYtWoSwsDCEhISILTg4GOHh4cjKygJdaD179gwpKSlYunQp5s+fj5iYGJw8eVI8ZvNUHy10FMFw5cF9TN2wDv7lpW3+x167wA4qLsO3C1eLwoeiEGKYCR4hBrj7J8F1cgIGjY9F/2+j4DxhIb6csQy9omLhlJysiIWWbhSrUvBDYT5Cy8oxLikXrv4GeIQZ4RpigAe1jAw1wj0wGa6T4jFoVAy++joKLt7LMNh1Hr52nYGR387F6MBojFmegjGrsjC9shJlp0/i6sNaNLxou+sB/Xf/xK1bQjCEr7PWYNDe27vsqUYF1WDYfOFcp5477yMY6AWRWNl5+RICKy3imL7Lew2usGBiaQmyjx5GXVOTw/ysaKBZMGgkrHsWDPY8WDBIJiwYJAstYsGgkWDBIEnIiAWDZEERCwaVB4/0I8CCQT/WPJMNAcpEOHjwIHbt2oWdO3fi559/hslkEsJhy5YtoA/eBw4cwJw5c7B3714cO3YMCQkJQj7cu3fP5pk+XugogqG2rg5zt2wS3RHau9CmTgpeEdaOC54kAWy3MKPIPBjskwT3yBR4pGWg3+p0UIFIqrdg2/aS4n6r0zC+pAiBhaX4dtFquAYYxPO1CAbtuamjQ9AriTElAYN/jMO4lekYY8jAhLW5mLdxI4pO/IJTt2/jYX19uxfEtFjit9oHmLqhyq7eRHvvu/Vj/uVlCFtfibM1dzr15HlfwUAv6nFDA0pOnhC1Inwt5jfWY6AlIbREhrqIJO7djduPH7fLslPfeDtPzoJBhcOCwZ4HCwbJhAWDZKFFLBg0EiwYJAkZsWCQLChiwaDy4JF+BFgw6MeaZ2qHwN27d4U8oIwFih8/fiyEQ15enlhzSRkNu3fvFpKBshgc4eYoguHFy5dI3rsHgRVl7V6IjoxaA49QEzzCWskFTQaEm8Qyh+Fz0jExqxA/moswIi8Hg7NWo29GaktHii9SDRiQuUqs8/fJLcbwyHSx5IKEhRAMlBkRbp3HPcwIt1AjXEONGBJswBB/A6ZXVCF+9y6UnzkNWt5BYqH1cojXHV9aPpB19DDGFRfiXf6zr0kGqkNBgiFm5w5xEf+6eTri/g8RDDT/3adPBaNZmzbAx2IW2Qy0tIOWQWjvh+pJaGIhbF0FVh8+KApjdsTr74zncHTBQL9nml40o7Gp7SyajmbCgkElSjxYMEgmLBgkCy1iwaCRYMEgSciIBYNkQRELBpUHj/QjwIJBP9Y802sI0Id6ymSgJRD79+8XX3X//n3MmDEDR44cAS2noBvVbDAajaAMB0e4OYpgIBZUGJAuOkkyaBefrfeUaeAeYnyDYDDCe3Y6fNcWi+ehgoF+llKxLGFY7loMyloN54xUjCzKRwg9lm/G1/MzMMQ/GW5hRgwOToY7LY94JRm8wlMwNCIVw6em47tpqzB+9lrs/fWy+A87dUx41xt9D9UWCKgoExfWopijzQV36/dsO6YLc5ILU9dX4edLv77r1O/89R8qGGjCRw0N2H/1NyTv24MZG9eL9pyB5WXwKy8V75+Ew7QNVYjeuQMbzp2BNLBpAAAgAElEQVTFnadP3vl16vkNjioYXr78Aw8ePsOpczfw8/5z2LHvHE6evYFHj+tBj3XWjQWDSpYFg8qDBYPKg0YsGCQT+uxUy0UeJRAALBgUHCwYVBw80pEACwYdYfNUbROgWgvJyclIT0+HtvyhpqYGvr6+QirQhyy6UfHH1NRUmM1muyeiJRckIugDql4b/WGn10mFKPWa83XzXK6pwaKtmzHZXIyQCguoTWHrbWT0GuvFv5AMJBrsN1rqMHxuOnxziuy+P5TW95uLxBy5Rw6j6tRJrN61F9NMpfAKNGHE1DR4h6fgm2npGDkjA2NnZWLS3LUIXJiHsMWFCF9UiIRVm/H0Wd0H8Xrw5Anyjx7GhJJC+FtKX/t+bd8/MQmwlMKntBjp+/ai5uHDD3oNrzsOtvfX1dWJbBw6T2zvf9e4vqEBvz98iENXriD38CHE/LQNC7dsRNS2LcjYvxc7LpzH1Xv38Kz+w7i+6+t6n6+n7CQSc+/zvZ35PXfuPkTF5uOIjClD4JxcBEXmYeYyMyq3HMe9+4/Fh9bOmJ8uluj3Wmc895/xOencePDggai/82d8/R39mul3yNWrVzvt/Ovo16vH89H5QaJSj7kcfQ66mKbPIMTEET6HOAIvyn4lJo7wWhzhNdDvkBs3bois4M58PSS7+MYEbAmwYLClwfFHIXDhwgUsX74c27Zta1k3/vvvv2P06NHiw5XWOYLuI8FQUFBg9zrpl+jt27fFL1L6ZarHdvnyZVy6dAnXr1/XZb723tO169eRuWc3JhUXYGJRPnxLCu22sSlr4R5uwuCAJLgGJ9tvQckY7JuEb5dnYHKe/XNMKS7A97nZMP78E07/+qt4z9euXcfu/ScRtiAfIXNzETY/B1MX5WPa4oKWjcah8+mxfGzafgT0Wtt7L2/z2IXfriBm62aML8oX73dycQF8igvs3rNPcSEmv/qacYV5iNm6BUfOnfvg+d/mNdJ5QecIbW/z9f8XvoaykK5du+ZQPOgcLrDsRviCfATPzRHn7dRFBQidl4vJ09agavNBXLr8W6e8Zrp4PH/+fKc895/xfCIeV65ccbhz5GOxpJ8V6qz0seZ3xHnp/KDNEV/bx3hN9DuVeDjC55CP8f5bz0m/Q+hzWev7/6+O6by4ePEifvutc/6GEddbt245VIc3uwsEvuOjEGDB8FGw86S2BKqqqkQGA/2h1G4kE77//ntFMFCbyrS0NBQVFWlf9lH3jrREgkBQYT8q8DfBXNRmfYKw8nL8EL0W7kFGsVRCLfJoEoUavWemwy+7xG6ZBdU7mFJWgmnrq3Dp/j2lZsLTZw3YuusMAufkIXxBAeausGD+yvKWbVZUKaYtKUFu2X6xvr2jDhp1myg7dVJ0W6B6EVSLgJZM2G7UMYIeo9dOxSSproFet45YIqHXa9VrHkdcIvHwUR1Ma3dg2uJiLIitaNnmr6xA+MIipGbvwN37nbP0hP6jRL/X+GYlQDy4BoM8G3iJhGShRbxEQiPBNRgkCRnxEgnJgiL6HUJ/Y4gL35iAngRYMOhJm+eyI0DZCUuWLEF+fr5Ie9S+gH4hjh8/XphX2yUStIyisrJS+7KPunc0wUApasdv3kTk5o2YaLbWULCtQUBxsLkMo1esFa0qh/gmY4hfMmg/2CcZVNzRN7sY4ZVqC0i6YKd1/2OKC3Dg6m9oaGpSuFNmXF19I3YdOC8yGQJn5yEwMh+BkXkImJOLOdFlWLf1BJ7VPVe+ryMGVOCS2nQWnTiOGRvXidc4qigfo4ryMJrqRFSWi5aNv967K9pf6pnGx4LB/gg7omD4veYR4ldtxfQlJS1yQRMNtEwiJmUjbt15aP9mOuAeFgwqRBYMKg8WDCoPGrFgkEy4BoNkoUUsGDQS1j0LBpUHj/QjwIJBP9Y8UxsEaP3xrFmzsGPHDiXFimoxhIWF4fDhw2ItHX0rpXlRK8vt27e38Uz63+VogoEI1Dc1Yd9vvyF8fVWbkoFkQWiZBX7ZxRiXmIMflmcJ4TB5VQGCS8rs5AJ1XaDMgPElhdh47hyePH/esozFljh90KlvaMTps5ex5+B5VGw5Lta07zl0Addu3hcCorMu7kkyPGtsxP26Z7j2sBbVd+7gzJ3fceXBA9x79gxPnz8HfY3eKwRZMNieIdbYEQXD4yf1SMnegaltZDCELihERv4u3HvQOZkvLBjUc4QFg8qDBYPKg0YsGCQTFgyShRaxYNBIWPcsGFQePNKPAAsG/VjzTG0QoC4R8+fPx/Hjx5VH6UIkLi4OOTk54gMFPUidJqgYJK1JdYSbIwoGuoimC+qD169i9uYNop1j69aGlMlAWQqhlnIEmy0IKbUgrKICEVWy/SF9DX0fLS0IqrRg0/lzoq1je+0k6cPOnZoaPH1WjydPG/D4aYOQDs3NL3U7XPT6SCY0NTcryzh0ewE2E7FgsIHxKnREwUDnJ3WNmLuiXGQxzFtRDtpmLC1B2IJCHD5xBQ3P1awd+3f2fvewYFC5sWBQebBgUHnQiAWDZMKCQbLQIhYMGgnrngWDyoNH+hFgwaAfa56pDQKbN29GVFSUnTSgD5p79uxBREQEtm7dKtpXUhvLvLw8PHr0qI1n0v8uRxQMRIEkA/1H/8TtW0jauwc+FjMml5aA6hFQBkPrZROtx8EVFviUmeFrKcWyn7Zjz29X8OR5wxszAOjDDnUJ0Ipy6n9EHGtGFgz2x8MRBQO9SmpRSXVEok0bMWOpWYiG5YYN2LLzNCjDoT2xZv8u3/4eFgwqKxYMKg8WDCoPGrFgkExYMEgWWsSCQSNh3bNgUHnwSD8CLBj0Y80ztUGAlkZkZ2eLDhC2D798+VK01SGhEB8fj9jYWGRkZAgRQY85ws1RBYPGhv6Tf+PRQ1RWn0H0zh0IW1cBX4tZyAM/Syn8y8vE8gdaAkFjeowyFoIry7F4+1aUnDyBC3fv4vmrNqHa875uz4JBJcOCQeVBI0cVDHTuPnpSj+oLt0Q2A2U0nD5/E3V1bS8Jsn9n73cPCwaVGwsGlQcLBpUHjVgwSCYsGCQLLWLBoJGw7lkwqDx4pB8BFgz6seaZ2iBA3SJoox7OrW/0x5M+TJw8eRK0lILaUDa1KjDY+nv0HDu6YNBY0HKB32ofiGUOqQf3Y9mO7ZizeSNmbFyP6RvXiW3mpg1CKiTt3Y2KM6dx/m4Nnja+W1FGFgwaceueBYPKg0aOKhjsX6k+97BgUDmzYFB5sGBQedCIBYNkwoJBstAiFgwaCeueBYPKg0f6EWDBoB9rnqmLEfizCAZb7FQE8s6TJyIz4djNGzh47SqO3LguCiPefPRQLK1433RwFgy2pK3toeiCms4TvlkJsGBQzwQWDPY86ALSkUSy+gr1HbFgsOfNgkEyYcEgWWgRCwaNhHXPgkHlwSP9CLBg0I81z9TFCPwZBUNnHgIWDCpd+sPOgkFlwoJB5cGCwZ4HCwbJhAWDZKFFLBg0EhAdnWpra4XEdpSlo/LVfZyIBYPKnQWDyoNH+hFgwaAfa56pixFgwaAeUBYMKg8WDCoPGrFgUJmwYLDnwYJBMmHBIFloEQsGjQQLBklCRiwYJAuKWDCoPHikHwEWDPqx5pm6GAEWDOoBZcGg8mDBoPKgEQsGlQkLBnseLBgkExYMkoUWsWDQSLBgkCRkxIJBsqCIBYPKg0f6EWDBoB9rnqmLEWDBoB5QFgwqDxYMKg8asWBQmbBgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYARYM6gFlwaDyYMGg8qARCwaVCQsGex4sGCQTFgyShRaxYNBIsGCQJGTEgkGyoIgFg8qDR/oRYMGgH2ueqYsRYMGgHlAWDCoPFgwqDxqxYFCZsGCw58GCQTJhwSBZaBELBo0ECwZJQkYsGCQLilgwqDx4pB8BFgz6seaZuhgBFgzqAWXBoPJgwaDyoBELBpUJCwZ7HiwYJBMWDJKFFrFg0EiwYJAkZMSCQbKgiAWDyoNH+hFgwaAfa56pixFgwaAeUBYMKg8WDCoPGrFgUJmwYLDnwYJBMmHBIFloEQsGjQQLBklCRiwYJAuKWDCoPHikHwEWDPqx5pm6GAEWDOoBZcGg8mDBoPKgEQsGlQkLBnseLBgkExYMkoUWsWDQSLBgkCRkxIJBsqCIBYPKg0f6EWDBoB9rnqmLEWDBoB5QFgwqDxYMKg8asWBQmbBgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYARYM6gFlwaDyYMGg8qARCwaVCQsGex4sGCQTFgyShRaxYNBIsGCQJGTEgkGyoIgFg8qDR/oRYMGgH2ueqYsRYMGgHlAWDCoPFgwqDxqxYFCZsGCw58GCQTJhwSBZaBELBo0ECwZJQkYsGCQLilgwqDx4pB8BFgz6seaZuhgBFgzqAWXBoPJgwaDyoBELBpUJCwZ7HiwYJBMWDJKFFrFg0EiwYJAkZMSCQbKgiAWDyoNH+hFgwaAfa56pixHoaMHQ+OIF7j17ht8ePMCVB/dx5+kTNDa/wB9/Em4sGNQDxYJB5UEjFgwqExYM9jxYMEgmLBgkCy1iwaCRYMEgSciIBYNkQRELBpUHj/QjwIJBP9Y8Uxcj0FGCoa6xEadu30b2sSNY/vNPmLd1M+Zt3YSon7cj8/Ah/HLrJp4+f+7w9FgwqIeIBYPKg0YsGFQmLBjsebBgkExYMEgWWsSCQSPBgkGSkBELBsmCIhYMKg8e6UeABYN+rHmmLkagIwTD44YGbLt4AQu3bYF/eSl8yszwtVg3EZeZEbllIzadP4f7dc8cmiALBvXwsGBQedCIBYPKhAWDPQ8WDJIJCwbJQotYMGgkWDBIEjJiwSBZUMSCQeXBI/0IsGDQjzXP1MUIfKhgoOUPOy79imkb1gmxEFJZjoh1lcoWWlUBn7IShK+rxOYL5/C00XEzGVgwqCc4CwaVB41YMKhMWDDY82DBIJmwYJAstIgFg0aCBYMkISMWDJIFRSwYVB480o8ACwb9WPNMXYzAhwoGqrUwe/MG+FlKEVZVoYiF1qJhSpkZc7dswsnbtxyWIgsG9dCwYFB50IgFg8qEBYM9DxYMkgkLBslCi1gwaCRYMEgSMmLBIFlQxIJB5cEj/QiwYNCPNc/UxQh8qGAoOvELAsotCKqwtCsXSDaEVpaLpRNZRw+jqbnZIUmyYFAPCwsGlQeNWDCoTFgw2PNgwSCZsGCQLLSIBYNGggWDJCEjFgySBUUsGFQePNKPAAsG/VjzTF2MwIcIBroYj9yyScgFWv7QOmOhrfGUshJE/fwTfn/yWEiGxuZmPH/xQm7NL0D3kYB48fIlmv94CZpHrxsLBpU0CwaVB41YMKhMWDDY82DBIJmwYJAstIgFg0aCBYMkISMWDJIFRSwYVB480o8ACwb9WPNMXYzAhwiGusYmUVfhbbIXwqsqEFRehlFF+RhnLkLCnt2I27UT87ZsQsT6KoRUVYBqNczcuAFLf9qG1AP7YDl9CgevXcP1h7VCOOihGVgwqCc4CwaVB41YMKhMWDDY82DBIJmwYJAstIgFg0aCBYMkISMWDJIFRSwYVB480o8ACwb9WPNMXYzAhwgGyjygzIXXCQaSCr5lZnxfkIfBa1bDOSMV3dOM6LM6DeNKChFYUYaQSotYOkHLJ2ijIpFBlRYElJfB11IKqtsQUFEmJITpwH7svHwZNU+f4mUnZTWwYFBPcBYMKg8asWBQmbBgsOfBgkEyYcEgWWgRCwaNBAsGSUJGLBgkC4pYMKg8eKQfARYM+rHmmboYgQ8RDHSRv+SnbQiusIBkgrYkIrjSgvElhXBbm4m+GWnolZ7yajOhe5oBX61ZBf9XRSHD18nvo++nMRWLDK0qFwKCRIRLZjp6r0pBv8xVcMvJwkRLKXKOH8NvtbV40cG1HFgwqCc4CwaVB41YMKhMWDDY82DBIJmwYJAstIgFg0aCBYMkISMWDJIFRSwYVB480o8ACwb9WPNMXYzAhwgGQlFZfQaBFdYij5TJMK64EO7Za9BHiAUTvkw3oVe6SQgC2tPmnZf9xo4TJCwoe4FkRM906/P0TDOhR5pRjN1zsjBj0waxjIKWUJAY6IgbCwaVIgsGlQeNWDCoTFgw2PNgwSCZsGCQLLSIBYNGggWDJCEjFgySBUUsGFQePNKPAAsG/VjzTF2MwIcKhhuPHmHR9q0YW1KEYblr0W91GkgEfJlmlQpOq1JAG2UgkCj4ak0GJpWWtGQ7aFkPrfckK77OzxFLKnq9eg7tuUhSdE81YuCaDPiXlyJu907sunIZjxsaPnjpBAsG9QRnwaDyoBELBpUJCwZ7HiwYJBMWDJKFFrFg0EiwYJAkZMSCQbKgiAWDyoNH+hFgwaAfa56pixH4UMHwqKEBaQcPiKwFqq9AW+90k5AKmlggIUBygeTDqKICUWuhtVBoPab6C4OzVouMBU0s2O57phnhkrkKk0qLxUYFIs2nTuL6w4cf1AKTBYN6grNgUHnQiAWDyoQFgz0PFgySCQsGyUKLWDBoJFgwSBIyYsEgWVDEgkHlwSP9CLBg0I81z9TFCLyvYKAFCY8a6rHt4gWEr6/CN/k5olaCtd6CdSmEtiSC7qM6CmOKC0QRx9YygWouiOKONkstfjQXoX9mOkgk2IoFLbYKhnT4lJnFcgv/8jJRONK4bw8u3ruLhhdN73WkWDCo2FgwqDxoxIJBZcKCwZ4HCwbJhAWDZKFFLBg0EiwYJAkZsWCQLChiwaDy4JF+BFgw6MeaZ+piBN5HMFBxR8pcMJ86IZY7+FlKRVYC7b8tyBVLF6hjBG0DMlcJ+UAZCSQSNLlANRZIKlC3CMpCGFmYh+F52Riau1YstfDMWSPEwn9TDS01HDS5QPue6UYMysoQ3689J3WhGFtcgMXbt+LErVtoePHinY8WCwYVGQsGlQeNWDCoTFgw2PNgwSCZsGCQLLSIBYNGggWDJCEjFgySBUUsGFQePNKPAAsG/VjzTF2MwLsKBroAf9hQj+yjhzGqMB9BFWWiVaV2ka/tSSbYCgXtftrT/SQjvs7LgfOqVHyRasAXKQbrnuJUA0gs/MuUiL8kxeMfhkT8NyW5RTRQZgQVjyQhYdu9gp6bxuOLCzF70wYcun4NL16+fKcjxoJBxcWCQeVBIxYMKhMWDPY8WDBIJiwYJAstYsGgkWDBIEnIiAWDZEERCwaVB4/0I8CCQT/WPFMXI/CugqG2vh4Fx48JuUAZCOHrKluyEmwlwuti6jhBWQ5U9LFbqkGIAoptsxO0mCTCZylJ+EtSHP4nMQ6fGBLwWUoyuqUZ4ZWThcDysjbnptdEWRGRWzbiyI3r79RhggWDeoKzYFB50IgFg8qEBYM9DxYMkgkLBslCi1gwaCRYMEgSMmLBIFlQxIJB5cEj/QiwYNCPNc/UxQi8i2B4WF8v2lJONBeDujy8q1zwsZhFMUit7eTrxIImGGhP2QokFP5lSsKnxkSxDcpajSllJXbZC7ZSg5ZLUH0G6nBx9s6dtz5qLBhUVCwYVB40YsGgMmHBYM+DBYNkwoJBstAiFgwaCRYMkoSMWDBIFhSxYFB58Eg/AiwY9GPNM3UxAm8rGBqamrD7ymUEVVpEa0jbi/m3iX0tZriuzRQZCyQN3kYuaKKBvlZbFtEjzdiyPIKyIdqbmzIsqPjjip0/487Tp2915FgwqJhYMKg8aMSCQWXCgsGeBwsGyYQFg2ShRSwYNBIsGCQJGbFgkCwoYsGg8uCRfgRYMOjHmmfqYgTeRjBQUcdTt2+L4ok+b8gcaOuCn0SAR/YaaB0mNHHwvntaOkEFJL8ryEVoVXm7koEyLUgy5Bw7iudvUfSRBYN6grNgUHnQiAWDyoQFgz0PFgySCQsGyUKLWDBoJFgwSBIyYsEgWVDEgkHlwSP9CLBg0I81z9TFCLyNYKh5+gSrDh0QSw4oK6AtifC6+2gZBYmA3ukpIvPgfaWC7fdRRgMts6A2lhPMRfL1VFUitKwcIaUWhFrKxRIKKvpIgiOoslwUfWz+o/2ijywY1BOcBYPKg0YsGFQmLBjsebBgkExYMEgWWsSCQSPBgkGSkBELBsmCIhYMKg8e6UeABYN+rHmmLkbgTYKhqbkZWy6cF/UWKBPgdSLhdffT9/TLSGvpAGErCj4kpiUTtLlnZyK43ILAwlJMSivAmNhsjIpZizHxOZi0qhBBRaUIqSgHLdGg9pUP6upAGRmvu7FgUMmwYFB50IgFg8qEBYM9DxYMkgkLBslCi1gwaCRYMEgSMmLBIFlQxIJB5cEj/QiwYNCPNc/UxQi8STDcevwIy3b8ZC2quK7inQQDZQ9Q9kLPNKOQAR8iFNr6Xloq4ZSWgjGr8/DdotVwCzTA1d+6DfEzwCPUhJHL1iAgzywkxNjiAmy7eAH1TU2vPYosGFQ0LBhUHjRiwaAyYcFgz4MFg2TCgkGy0CIWDBoJFgyShIxYMEgWFLFgUHnwSD8CLBj0Y80zdTEC7QkG+k9/xZnToGURr2sJ2TpzIbyiAqElpQjOLUJgdgFckk3olWpE71WmNltRtiUO3vY+ymDomZCM/hHJGOKbDI9QIzzDTS2be4hRSIev52UgsKgUvpZSzNy4XhR8JJHQ1o0Fg0qFBYPKg0YsGFQmLBjsebBgkExYMEgWWsSCQSPBgkGSkBELBsmCIhYMKg8e6UeABYN+rHmmLkagPcFQW1eHeVs3w89ifrvMhapKBK8twLjgFRjeJwxePQLhPDQCTlGxcErteMHgZDLBaX4S+kyMg2eYFAutJQOJhjFx2aImww+Fedh1+fJrsxhYMKgnOAsGlQeNWDCoTFgw2PNgwSCZsGCQLLSIBYNGggWDJCEjFgySBUUsGFQePNKPAAsG/VjzTF2MQHuCYc+VK6ItZcBb1l4IyS/G2IAYDP0iAMP7hsKzTzD6fzoR/YeEwTkmvsMzGJzijXAKTYDT5Di4tcpesJUMHiFGDJuehrCKCkwoKULcrp248+RJm0eSBYOKhQWDyoNGLBhUJiwY7HmwYJBMWDBIFlrEgkEjwYJBkpARCwbJgiIWDCoPHulHgAWDfqx5pi5G4HWCgS6043bvFB0YQqvervaCf/IafDt0Nob1DsbwAWHw6BuC/t194fKvyegzZzmc0jo4i2GlEU4B8XDyiYNrqKFlaYStXKCYBINnWAqCzRax3GNSaQmqa+60WeyRBYN6grNgUHnQiAWDyoQFgz0PFgySCQsGyUKLWDBoJFgwSBIyYsEgWVDEgkHlwSP9CLBg0I81z9TFCLQlGOgim1pThq2rRFCFpWV5RHhlBULyijF5sQnjAqLxY0QcAkxrEVZq7S7hl7Aa33rOhLeTVTC4UwZDTz8M+GQS+s6K6hTB0DswHr194jDkLQQDta+MqKoEFXtcd7YaT54/tzuaLBhUJCwYVB40YsGgMmHBYM+DBYNkwoJBstAiFgwaCRYMkoSMWDBIFhSxYFB58Eg/AiwY9GPNM3UxAm0JhuY/XuLQ9avwsZS2CAbqCBG4KhejJyzBiK8iMKJ/GEa4hOMb9xmYvMiE0GIzgrIKMGrcYgztFgjvviFwdwpC/39NhotzIPosjYVTekrHLpNINKJ3RAJ6T359BoNHmDWDYUTkKpAgoaKUE83FSNq7GzcfPbI7miwYVCQsGFQeNGLBoDJhwWDPgwWDZMKCQbLQIhYMGgkWDJKEjFgwSBYUsWBQefBIPwIsGPRjzTN1MQJtCYYXL18i7/hR+JaXiiUFdFEeWlSCHyNiMezLIHg7hwjBMLxfqBh/4zYDPkmZCCoxY+KKdHwzfA68XcLh4RyMvv2C0Dd4EZyTkztWLqxKgVOKCb0XJ6H3lDi4Bre9RIIKPJJk+NGU15KJQTUlZmxYhzN3frc7miwYVCQsGFQeNGLBoDJhwWDPgwWDZMKCQbLQIhYMGgkWDJKEjFgwSBYUsWBQefBIPwIsGPRjzTN1MQJtCYbGFy+wcNsWkb0Q9qr+QmB6DkZ+O1cIhRFfhYM2b5cwePULgdvnfvCOiMaI7CwMW5MJz5UGuIYswxD/xeg2cyl6GZI7PnuBBEN6CnonGeE0IxFuQQa4BxtFvQVqV0liQYxDTRi5bA2CzdZlHCRL6D1NLC3Gvqu/2dVhYMGgnuAsGFQeNGLBoDJhwWDPgwWDZMKCQbLQIhYMGgkWDJKEjFgwSBYUsWBQefBIPwIsGPRjzTN1IgG6uH358qWuG33QuX//PpqamlrmfdLQgCllZpG9QEsjaPM3ZOE77zkY1ivolVgIxRDnIHzVKwAun05G74mR6J6ciG5pBnRLtW490oz4xJCI/6Qko2eaEb3TO7bIIz1frzQTBqWk4/slmfCMSIFHiAnutIUa4TUtFaNishBYWCreg/ZeaE91GNafrUZ9U2PL+yb2zc3NqKmpUXjofUwcab7GxkbU1taCzhNHel0f87U8ePAAdFH9MV+DI81dX1+P33//nXm8+t1NFwePHz8G/ew40nH6WK+FOFy/fl38bv1Yr8HR5qXzg3+nWj/r0N9c+gxCTOhC0tGO1cd4PXV1df9/e+fhVtWV7/0/4n3e+7y3zL1z7/TMzJ3JmMREjS2TmUyasfdesGM3CkoRpQiCggr2Sm+SMdEYazRGjb0k9oaKKIgKSPX7Pr+Fi8XiIMXAFk6++3n2c37rcM5ee3/2ZpfP+a21FJOXUXdzrFPuT2/fvg251jTl+jXh7T0X3UIJUDC00B3H1bYJyMVVTqBycXFqvnfvnno4kEwGqfNxfj6u3M3CoPhYjEtNxsT0FDWPWbkBvfv74IPXxuDvbcah45uj0aH1aHR63QOd/3ck3vb0R5tlFRJBHvxlfjMmCn+IisCvFofhlchw/GVZJN58Jhr0Z37Mq0iLt2KWoW/sRkxITcaItbEYELEefUPWYuCSDcMHZ6QAACAASURBVBi1Pg4TUsw26G2R14Fxm7Hh8CFk5eZarPPz85GZmQnNw6n90FzrkZtgES5ynDTXdXR6vbKystTNsNP1Ntf68vLy1ANkc10/p9dLeMj/i/zvOF13c6xPOFy5cgVybm2O6/cy1kmOD3mofhl1N8c6RVAKE153K+79JEtOmDTHffUy1knOITdv3lTZg01Vv9x7y498nEigKgEKhqo0GLdYAmJp5SZMTqZOzfLweOvWLchNsdSZm5eH41evYkDcJoxJSVIP7vLwPjYxAf28w/FOKw90/N8RanQIkQudXhmGjh9PQdv5C/GWPPBXm19fHonfLFmEXy1ehF8tWYQ/RkWg9fJIl89V/159ym8sj0SHldEYnZxYuZ6yrvWZB8ZvxtKv9+JqVpbFWn5FkV/bNA+n9kNzrUff6GRnZ1ucmuv6OrFe8kuKzupwor7mXodkdFy/fp3Hx7Pzthwbcl6V/53mvu+cWD/hcPnyZSXlnKivJdQhkpLnVHOfI/cg8j+jMztawj5synUU+SRMmrKOlrRsuR+Ta4xca5pqvUVuUTC02MenJltxCoYmQ8sFO01ATnBOznKylpO2ZE9IvdL/wonMTAxKjKvs4FGaFAxOjEPHpUvx5lR/dPzrRHT+/XB0bjUKHft9inbBi2odgvKPSxcryfDrJYsgs5Tf+pHNJd6U769cjj5xmzH5sy2VHThKHwv1mYcnJSDywNe4/fChxVvS7+RGR/Nwcl80x7pEeskDgr74Nsd1dHqd5AFS0r6drre51idNAuTXtua6fk6vlzSfkfOq/O84XXdzrE84yK+Pcm5tjuv3MtZJjg+eU829jn5wlOYSL2N/NLc6dR8MzW29Xtb6yP2YXGOES1Oug9P3+6yv+ROgYGj++4hr2EwJyE2OPDDJhV2m4rJSHLlxA4MS4+G5JU09rI9MScS7a1dB+lRoszQS7RYtRruF4WgXGl4xOkR07X0riAz4fWSEkgySzfCbyHD8edniF+6TQeRE65il+HDD2sphNOsjFap+ZnhyAiK+dh2qUi5e8suS5tFMd5tjqyUXdi0YHKu0mVckPOShiVMFAXbyaB8JVQWD/ZefZknOISIY5NzKqYKACAbJVuTETh5rOga0YKjpbz/F9+QcIlk/woUTCThJgILBSdqsy60I1CQYDt+4rjIYRDBM3JKGjzeug0iCt1bULhLaysgOz5lfVx0+hqsMBpEMv40MV51BPu/zz3tf5IKIjvfWrYZHatILZS+IaJBRJMK/3ovMvDxrf1IwWDhUJgcFg82EgsHmQcHgykNnMNh/+WmWKBhc9zsFg2Ei11z5kUPuRSTLhRPUg7RkdXCqIEDBwCPhZRGgYHhZ5FlviyfgKhjKcCzzZqVgGJIYj/arolWHjc976K/x/eVL0S4oDO3H+KBD30/Rvv8stJ7giz8EBOHXEWFKNPw+KqJBWQyStSDz++tXY0RKohpusmpWQkNiEQyLnzWRqLoTKRiq0qgYHoqCwWZCwWDzoGBw5UHBYJhQMBgWOqJg0CSYwWBImIgZDIaFRBQMNg+WnCNAweAca9bkZgSqC4aSsjKczbqj+lyYkJ6KD9avUf0lyGgPIhLarFimshkki6DV8kg1y7CUrWOi0EZnOCyNwttzgtDxwyno9OYYdP7LqIr+GtqMRZse0/Anr/n4zeKK/hjku3rZNYkK+ZtkT0h9b6+Mxicb12FUStKPkgsqgyEpAdHffoO7jx9be5SCwcLBDAYbhypRMNhQKBhceVAwGCYUDIaFjigYNAkKBkPCRBQMhoVEFAw2D5acI0DB4Bxr1uRmBKoLhrLyclzLzYFkLsiDfIdVMeoBXx7+pXmCCIH/XboYr0RGqGYOv12yCL+LDFfDUb66bAlaL49Cu8AwdPzHZHT+4wh0lmEsRTLI3MoDnV4dhXbdp+EvIaF4JSoCf1q6RC1fJEJNswiNt1dF4+/rVqF/fKzqc+FFOnWsnt0wNCkeG44eQW5hgbVHKRgsHBQMNg5VomCwoVAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qzJzQhUFwyqPWRhAaQTxIGJcSprQT/4/3nZEtVBoww3qTprrPJaOUJE+CK0meBbMcrEm6PR+a0x9txqFDq1HYt20+errATpm+Ef61aj85oV6LAqWjXHkCYZIjbeWbNS9f8gI1iMT0994f4WqssFKcsy086cQkFxsbVHKRgsHBQMNg5VomCwoVAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qzJzQhUFwyyeUWlpZj8WQZ6xG7AWzHL0Do6CkouPBtmUjporGmW0SF+FxiM1/tMR+c/DLfFghYNb4xGp9Zj0GGwV2VzC92Z5Ni0FIxOTcaY1GSMT0+BZ0bFKBY1CYIf855kQIg82XXpIiRjo+pEwVCVRsWFXR6o5TjhVEGAgsE+EigYXHlQMBgmFAyGhY4oGDQJCgZDwkQUDIaFRBQMNg+WnCNAweAca9bkZgRqEgzSD0Pk/q/x0YaK0SOk6YMIBclSqEksVH3vlcAQvN57Gjq+MqxmwdB6NDq3Ho0OA2crwSAZDJKd8GOEQUO/KyNjjE1PwdHMmy57k4LBRiIXdgoGmwkFg82DgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoCbBIL/qbz//Pd7fsEb1uSCjPYhckAyFqjKhpvh3YaF41cMb7X8zFB1rbCLhgU5txqL9lHkvTTCMTElCwM4duJxz32VvUjDYSCgYbB5SomCwmVAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qzJzQjUJBjKnz7Fpfv30St+E15dHqmkwm+W1C0XlHBYEo4/+CxAmw7j0OmV4ehcVTJIh49/GomOXaah3aIIJRjeiF4KyShoaBbCj/n84IQ4bDz6HXIK7A4eZddSMNgHOAWDzUNKFAw2EwoGVx4UDIYJBYNhoSMKBk2CgsGQMBEFg2EhEQWDzYMl5whQMDjHmjW5GYGaBINs4pPSUkzamoFW0VH1zl7QGQ2/WxSGP87wR9t3JlT0xSCjSfxhBDq/Ogodu07D2/4L0TZ6qerfof2qGHhmpDsmGCZnpGNAQhwOXruG4tJSl71JwWAjoWCweUiJgsFmQsHgyoOCwTChYDAsdETBoElQMBgSJqJgMCwkomCwebDkHAEKBudYsyY3I/A8wSCbuen4UbRfvQJq1Ih6NI/QguG3ku2wKBSvzg1E+3G+qkPHDkO90X7yPDWEZdtlUWjzbFjKjzasxSSHBIN07uiRmoRZX2zFtdxcla1QfXdSMNhEKBhsHlKiYLCZUDC48qBgMEwoGAwLHVEwaBIUDIaEiSgYDAuJKBhsHiw5R4CCwTnWrMnNCNQmGM5n30WP+M2q74X6dPBoBMMi/DYsFH9ZsBDt/EPwtm8I2gUvQtulUWrYy7YrluGtZ4JhUGIcJn/mTAZDRfZCLBJOHkfek8Ia9yQFg42FgsHmISUKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmtyMQG2CQYarDNi9E6/HLFVZDJUCoZZsBunk8Y9eAfjL0Flo13UqOr4/GR27TFVZDG/PCkS78MUqe0EEQ6fVMY6NICHZCxPSU1UGw/nsbMhIGTVNFAw2FQoGm4eUKBhsJhQMrjwoGAwTCgbDQkcUDJoEBYMhYSIKBsNCIgoGmwdLzhGgYHCONWtyMwK1CQbZ1MM3b6CnZDEsCa9zFAmRC3+a6oe33hmPDjJMpfS98KeRFfMfRqBT27Fo7zEXbcLCVQePfeI2Odb3gmdGGoYmxWPloYN4VFT03L1IwWCjoWCweUiJgsFmQsHgyoOCwTChYDAsdETBoElQMBgSJqJgMCwkomCwebDkHAEKBudYsyY3I1CXYCgsKUH4/q/ROmYZfrW4lqEqlyzCn6b7q9Ej2v9+GDq+MRqd3xpjz6+OQqfXR6PdyDn464oYlU0wLj1VZTHISBJN1ReDNI0Yn56KUanJuJJzH6Xl5c/dixQMNhoKBpuHlCgYbCYUDK48KBgMEwoGw0JHFAyaBAWDIWEiCgbDQiIKBpsHS84RoGBwjjVrcjMCdQkG2dxzd7Mw4bMteCUyAr98jmR4JTgEb3SdApELHVrXIBfeGoNOb45BR8lo6DwRfwuMwDtrVkBGkfjrmhXotmk9RqYkNcmIEiIXhicnIu74sVrlgmwrBYN9gFMw2DykRMFgM6FgcOVBwWCYUDAYFjqiYNAkKBgMCRNRMBgWElEw2DxYco4ABYNzrFmTmxGoj2CQ/go+/+F7dI/diN9HiWQIU7OMLqHmxWH4www/tO0wFp3+PNLOWniWxdDpzdFKPLRvNQod3vBA2+FeeDMmCm9KZ4/RUXgtOgqtY5ai2+b1GJee0mhNJzy3pMEjNRlzvtyGB4WFeFrH/qNgsAFRMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqI9gkE3OKSjA+qNHIMNKtlu5HH+MWow/LF2MPy1bgr8sj0SbiX7o2HYsOr/mYQkGlbWg5UJrDyUXOr3mgQ7dp6t+GGREiTbPZun4USRDl43rIE0npny25UfNMjqFyIUp/8zA6Tt3UP60Lr3ADIbqhzcFQ3UiFAzViVAw2ESEBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoL6Coay8HFdzcxC6bw+6b96gmjR0WBWDVssj1dxmoq8SDJ1aeUCyFTo+mzu/OQZ/azMO77w1Vr3XSZpPvO6Bjt2nVw5ZKZJBz5LRIAKjd9wmyMgPP0YyjE5NxsSMdHxx/nvIiBj1mZjBYFOiYLB5SIkZDDYTCgZXHhQMhgkFg2GhIwoGTYKCwZAwEQWDYSERBYPNgyXnCFAwOMeaNbkZgfoKBtnsJyUlOHn7FmZt24pesRsxKDEOfeM3o+um9fhgXjj+/u4kvNvKA++2HYe/txuP99+egA87TFSzdPgo2QwiF0QytB/hXSkVtFzQ2Qyto6Pwt7WrMC7txZtKjEkTuZCGxJMnkFtYUO+9RsFgo6JgsHlIiYLBZkLB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQH8EgI0lcycnBrksXsObIIUzf+hn+vnYV/rFutcpmGJAQi2FrN6L3UH989MZYfNx+Ij7pNAldO09Gt3cm44MOE9FJCYbR6CwjSXSagLd9Q2oUDCIZJIuh/apoDEtKaHAGg2Q9SObChC1p2Hz8KLIePWrQHqNgsHFRMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqE0wSI8F9/LzsfvSJSzcu1s9tA9NSsDQpHj0jt2E9iujVeeMMhJE1w3r0DcgCt3en4EubSagawdPJRdEMPzj7Qkqa6FTq1Ho1HoMOgz3RtuoyFoFgzST6B+/uUGCQYa5FLkwfksaYo8fw+2HD+vs1LH67qRgsIlQMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqE0w3C8oUA/q47akYkRyImS4R90ngmQKyNCPndeswOvLo/Da8ii8s3w5es0JR/f3pqNrO090bT8RXdt74r03x0E6duwocmGwF9qFhj9XLugMBhEM/eopGGRdJm5JwxjJXMhIQ9qZ07ifn/9Ce4qCwcZGwWDzkBIFg82EgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5G4HmCobisDPEnjmNUSpJ6cNdiofrr6LRkfLB+jWrWIB0+do6JxuDIVRgwOhDd352Kbh2n4MP2nujw4RS0mz4f7RYvqVUuaMEgTSQkW6J6fdXLkrUgzSFGpCRh2j8/w8Hr11BQXPzCe4mCwUZHwWDzkBIFg82EgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoCbBIMM5nrx9G6NSk+rV0aLnljT0iduEtiuWqyEr31+/BvLepLRUTIxLxMi4eLy9YrkaglIEQl1z65govLt2JcalP7+TR8lakDpEgAxJSkD4vr24lZcHGe3ix0wUDDY9Cgabh5QoGGwmFAyuPCgYDBMKBsNCRxQMmgQFgyFhIgoGw0IiCgabB0vOEaBgcI41a3IzAjUJBsleCNy1E5Kd4JmRVmcWgWQVTM5Ih0dqEj7asBatY5apkSXGpqWo9ydnbEGXjetUloN04FibYBC50GbFMtXHQ/VsBV0WsTA8OUGNYjH3y204eO2qGoZS5MCPnSgYbIIUDDYPKVEw2EwoGFx5UDAYJhQMhoWOKBg0CQoGQ8JEFAyGhUQUDDYPlpwjQMHgHGvW5GYEqguGsqdPVeeII1MSVb8G+qG+Pq8iGcakpaB/QizGpKdgYkY6BsTHqqYO0l/De+tW4Y3oKCUaapIMMjxl65il+GTTOqu/B6lbmkJItsLgxDgMS05A0O5d2HP5ErLzHyu50Fi7hYLBJknBYPOQEgWDzYSCwZUHBYNhQsFgWOiIgkGToGAwJExEwWBYSETBYPNgyTkCFAzOsWZNbkagumCQ7IXjmZkqO0AyBeojFqp+RkTAuLQU+O3Yjks59/HlhfNYvH8fPDPS0St2IzqtjoH01fDqsiV4bXkkXo+OwuvRkeo9aWLRddN61aGkjAYxMiVJjVihpYLPl9ux6eh3OJp5U0mQgpJiNEbWQtVdSsFQlUbFhV0eqOU44VRBgILBPhIoGFx5UDAYJhQMhoWOKBg0CQoGQ8JEFAyGhUQUDDYPlpwjQMHgHGvW5GYEqguGotJSfH3lMgYmxKo+DqrKg/rGMqLDnC+3qSEipcPFWw/zcOL2LSUb1nx3GH47d2D0llT0SYhFl83r8eHGdeiyaT36xcdidFoKpm3NwJztXyBo907EHDqoRoU4cO0qLtzLRk5BAUSCNNVEwWCTlQs7BYPNhILB5kHB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQk2A4cPXKCwsGPWSk747tLqREDDx4UoibeQ9w+s4diDT46tJFbLvwA7af/wE7Ll5QzR5kJIiTt2/hcs59ZD9+rEaFaOxMBZeVe/YGBYNNhoLB5iElCgabCQWDKw8KBsOEgsGw0BEFgyZBwWBImIiCwbCQiILB5sGScwQoGJxjzZrcjEB1wVBSVoZzWXdUXweSiVDfrAX9OWkKIcNGLjt4oEWSomCwdxsFg81DShQMNhMKBlceFAyGCQWDYaEjCgZNgoLBkDARBYNhIREFg82DJecIUDA4x5o1uRmB6oJBhqiUZghj01OVKJCMBC0P6vM6Pj0V47ek4auLF1okKQoGe7dRMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqC4YZPMki2H14UMYlZrcoJEkKkaRSIb39i9UvwstERUFg73XKBhsHlKiYLCZUDC48qBgMEwoGAwLHVEwaBIUDIaEiSgYDAuJKBhsHiw5R4CCwTnWrMnNCNQkGOQhW/pJ8PxsC8akJashIuuTvTA2LUVlL6SdOdXoozs4hZ2CwSZNwWDzkBIFg82EgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoCbBIJsoD9oymsS4LakYmZJYp2QQETEiJRGrDn+Lh0+etFhKFAz2rqNgsHlIiYLBZkLB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyPwPMEgm/mktAT7r17BrG1bVaePHqlJmJRhOn6UJhGj45IwcM0mDNsUjw2Hj6hRH6Qfh5Y6UTDYe46CweYhJQoGmwkFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IxAbYJBNrWguBgX7t1D/InjmPPlFxienIABcZvRe8V6fDQ3Bh/PiEbPmSsxwncDAmM+x/7DF/AonxkM7nKYUDC47kkKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmtyMQF2CQTa3tLwcuYUF+CE7G7svXETUF3vRf84aDJu9HuN8NmOKfwKmByTh0wXJ8A/PwJbtx/G4hUoGZjDYBzgFg81DShQMNhMKBlceFAyGCQWDYaEjCgZNgoLBkDARBYNhIREFg82DJecIUDA4x5o1uRmB+ggGvcnS9CHzTi7CV34JT584eAenwic0vXKeE5KGmfOTMHdhOg4cuai/1qJeKRjs3UXBYPOQEgWDzYSCwZUHBYNhQsFgWOiIgkGToGAwJExEwWBYSETBYPNgyTkCFAzOsWZNbkagIYLhSVEJvjlyERPnxEJkQlW5oGN5f1ZgMsJitqOoqKTF0aJgsHcZBYPNQ0oUDDYTCgZXHhQMhgkFg2GhIwoGTYKCwZAwEQWDYSERBYPNgyXnCFAwOMeaNbkZgYYIhty8fMRnHIbn3Nga5YKWDLMDUzA7KBW37z5occNVUjDYBzgFg81DShQMNhMKBlceFAyGCQWDYaEjCgZNgoLBkDARBYNhIREFg82DJecIUDA4x5o1uRmBhgiG7PuPsDZhPzznxtUuGIJS8OmCFFy5no2WNqIEBYN9gFMw2DykRMFgM6FgcOVBwWCYUDAYFjqiYNAkKBgMCRNRMBgWElEw2DxYco4ABYNzrFmTmxFoiGDIzStA0j+PYMKcujMYvIJTkZWdxwyGFn68UDC47kAKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmtyMQEMEQ3FJKb47eRXjvTfX2geDjCYRsXIHysrKWxwtZjDYu4yCweYhJQoGmwkFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IxAQwSDevi+/wir4/ZhnNdmeAWlYLLXJgwbG42+gyLQq98i9BwUgVGeq7Bt6xGUl1MwtPTDhYLBdQ9SMNhMKBhceVAwGCYUDIaFjigYNAkKBkPCRBQMhoVEFAw2D5acI0DB4Bxr1lQDgZs3byI+Ph4BAQFYtWoVLly4UNk0QB6yT506hWXLliE4OBg7d+5ETk5ODUt5OW81RDDIGpaUluF65n0sjtmOvsOj0KV3KD7qEYIPn809eoRgdI8g+A8Nx+cbvkLBowI8LX/6cjbuBWplBoMNjYLB5iElCgabCQWDKw8KBsOEgsGw0BEFgyZBwWBImIiCwbCQiILB5sGScwQoGJxjzZqqEJCH0fv37yMiIgJLlixBXFwcQkJClEy4ffs2ysrKIPLBx8dHiYcNGzZg3rx52LZtG+QC0hymhgoGESbZt3MQMXs9RnSdj0G9QjCgdwgG9QmFR/9QTBsQBu/+C+HdKxBz+wbjszXb8fhBfqVwaQ7bXNs6UDDYdCgYbB5SomCwmVAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qypCgERCNu3b4evry+OHz+O3NxcfPPNN1i8eDGOHTuGgoICpKenY/78+bh27Rru3r2LjRs3IjIyEtevX6+ypJcXNlQw5D8swO6U/ZjZxR9z+gbDe1AYvAaHwXtwGHyHLMK8oYsQMHQR/IaEYUa/QEwbGIQD+07gUX5hi5AMFAz2sUjBYPOQEgWDzYSCwZUHBYNhQsFgWOiIgkGToGAwJExEwWBYSETBYPNgyTkCFAzOsWZNVQgUFxdjwYIFSEpKUg8d8uu+NH84ceIELl++rISDyAWREIWFheqbJ0+eVALiwIEDVZb08sKGCAZ5+M68fBuh45ZidvcAzBtSIRNEKOjZb/gizBy9CKM9w9B/Wig+Gh8Az8j1SD16HN9n38WjoiK8jAYTsu5PSktx9/FjXM3NweX793Ez7wEeFBaipKyscgdQMFSiUAEFg81DShQMNhMKBlceFAyGCQWDYaEjCgZNgoLBkDARBYNhIREFg82DJecIUDA4x5o1VSEgN9YeHh7Yv3+/6ndBxIL0vyAPIPpBdcKECTh9+jRKSkrUNyWTQfpjyMjIqLKklxc2RDCUlpTi5IGzmPKPuZg3JEzNWizIq+/wRZg8LgxdvULxdkAo2s4PRduAhXjTPxg9163DnO1fYNelC+qh3sktLiotRebDPOy9fBkrvj2IwN07EbBzB8L27UHCyeM4efsWcgoKUP70aeV+k+wUThUXdjme5TjhVEGAgsE+EigYXHlQMBgmFAyGhY4oGDQJCgZDwkQUDIaFRBQMNg+WnCNAweAca9ZUhYA0gejVq5fKYJBmEiIbpkyZgrS0NNU8IisrC0OHDsWVK1fUCVK+Kn0zLF++XHUKWWVRKpQMCHmwlZOpU3NeXh7u3bsHeUioq86HuY/wVeJeTHxnNrx6LcCsbgHw6rkAc/sGwWdgCDzHhOKDOaFoM38hOvkvxDt+FXM7rwUYtHY9hibGYcKWVHx29gwK6lFfXetTn7/nP3mCM3duI2j3TvSN3YSB8bEYnpSAkcmJGJoUjwHxmzEmLRkbvjuCrIcPUVxSgjt37tSLR33qb+mfkRsd6WdEjpOWvi2Ntf7CQzKSGmt5LX05ch68desWeTw7bwsPkVDyv9PS921jrL9wkCaBItkbY3nusAw5Ph4+fEgepaXquJB7EGEiWaHusH9/7Dbk5+er+7Ifuxx3+b7cn8o1Rrg05TZVvydnmQQoGHgMOE5AMhTkBqFnz54YO3Ys9u3bp/pZ2Lx5M7y9vXH06FH1gqj6VwAAIABJREFUoNq7d2/1vv5FXKSDCAb5XPVJTp7ycCsdQzo1i/y4ePEibty4UWud8vfjh05i8fQYdPvZEHT510H4+P8NVK+9fzkSQzpOxQfj56lshQ4+Qegw18xtZs1Hn5hVGBUfiwGbN2JiSjL2nDpVa32Nsf3Xb9zA9uPHMDYpAb02roNHQhxGJ8ocXzl7JMZjSNxm9Nu0Ab7//AznL19WWSh18WiM9WsJyxAOly5dUpKsJayvE+soPOSByYm6WkIdkpX1ww8/kMez87bwkCZyPEYqrmNyDjl37hyPjyrXdTk+5NrbEv6/nVhHOacKE153K/5n5Bwi92VOsG8JdchxIdcY4dJU6ysCQ37k40QCVQlQMFSlwdgRAiIYJG1cBMP69etV3wtycrp69SqWLl2qOnMUWdCvXz/1nhYM8l5MTIzKeqi+orJMp2dJ1ZR+I8QK11b3jYu3sDYgDiPemKSkQp9fjIKee/58OP7edjxaT/BBO78Q/NU/tHJ+xz8Ub3sFYsSmzZickY6JW9IwLj0Va48cqrW+2talvn+7dO8eQvbswuDEOFX35M+2QOYpVWb1XkY6xqenYnhyIuKPH8WtO3fq5FHfdWjpn5NfHaXzUjlOWvq2NNb6Cw/5RaWxltfSlyO/UMt5raVvR2Otv/AQ+Sy/xjbWMlvycuQcIg8Icg1sydvRmOsux4fcPzTmMlvqsnTfVXKNqes+pKVuY0PXWzLk5L6sod9z18/LOUSuMcKlKbex+j05yyRAwcBj4KUQkBvJgQMH4quvvlKpW7ISkuonI0XIsJUyasSoUaNw5swZqw8GyWDYunXrS1nn6pXW1QdDUWERDn91HP4Dw9D/t2PQ639G4uN/GYiePx+BPr8cpebevxiFdz6ehlcn++CNmfPQySe4UjB0mBuM9xaEY0xConqwlwf60WnJCNj1FYqbsJ+D0vIyfPb9WXikJimpUVUq1BSL/BDJIM0lvr92DSWlpdVR/STLcsMnqavsg8HsfuEhNzycKgiwDwb7SBAe8rDEY6SCi5xD5FdHeTDgVEFAjg/JWOTEPhhqOgbk3lIEA6cKAnIOkexf4cKJBJwkQMHgJG3WVUlAfqGaOXMmtmzZon6xkj9IHwtr1qzBypUr1QXCy8tLCQg9ioR0+CjDVB48eLByOS8zqE0wFBeVYNumnZj+gR/6/MoDkqnQ47+G46N/GYAP/29/JRu0ZOjcbQZeneqLVlN90ebT+XjHL1T1wSDZC/1i1mBiWmpl5sDo1GT47NiOwiZ8SJNOG5cdPIDhyQmV9dYkFqq+J9kVAxJi8cXJEygsLn6Zu6XZ1E3B4LorKBhsJhQMrjwoGAwTCgbDQkcUDJoEBYMhYSIKBsNCIgoGmwdLzhGgYHCONWuqQkBOerGxsQgICFDt5eSi8O233yIwMBC7d+9Wv/pK8wkpS3qXPMzHx8erUSQkZbQ5TLUJhgsnLiFgyCL0/90YdP/PYej9i5Fqlvj9/9MPXf51MHr9d0Umw98+nIpWk33xl6m+eH2aHzrODUb7OUHosmgpxiYlqSYK8jAvmQJj0lIQtGcXSpuwvdv1B7lqtIiRKUn1FgyeW9IwKCEO6w8ewOMimnI5PikYXP9LKRhsJhQMrjwoGAwTCgbDQkcUDJoEBYMhYSIKBsNCIgoGmwdLzhGgYHCONWuqQkDaDkqnMyEhIVi8eLHqiyEoKAjR0dFKKEiKrAxb6ePjo96TzAaREdu3b282qV61CYat677EjC5+6PPriuwFna0gouGTfxuCT/5tsMpokMyG998cjzc8vPGn6b5oNcVXCYZPIpZhdHxCpVwQwSDNECZmpCPl9KkqJBs/vJKTg3k7d6BBgiEjHUMS47Fy/z48Yiqe2ikUDK7HJgWDzYSCwZUHBYNhQsFgWOiIgkGToGAwJExEwWBYSETBYPNgyTkCFAzOsWZNNRCQPhakSURwcDDWrl2rervV7U2lY6tDhw4hKioKYWFh+Pzzz5GdnV3DUl7OW7UJhvjwVMz4yA99lWCw+1yQphIDXhkLj7emYuirE9G31QS82282Wn06D2/MCkC3JdEYl2xnD0gTBI/UZMzftRNXc5u2feGth3lYuHc3RqRU9P1QtSnE82LJYBiYEIfYw9/icVHRy9khzaxWCgbXHULBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZ03MIyAlQ+lmQfhlqmuR9yWiQrIfmNNUmGHbE78HMLv6V/S9UzWDo/rNhGNN2Onz6h2Bu32DM7jEf4wcH4oPpQWg/LxRDNm/GhC2pqoPFCVvSnnWgmIJZ2z7H3suXK5tHiIgpeFSI7Mz7uHEhE1fPXsfVczdw6/Id5GY/QPGTmnnWxfDhkydY+91hNTKENMt4nlSo+r4IEBlxYs+5s3jShP1D1LXuzenvFAyue4OCwWZCweDKg4LBMKFgMCx0RMGgSVAwGBImomAwLCSiYLB5sOQcAQoG51izJjcjUJtguH4+E8GjIlWmQtU+GKTfBZlnfuIP/8FhCBi6SM3+Q8IweWgw+nmFwyMlCSNSktSIEaNSk+AhI0fs3IHDN66j6NmQmNKJZPate9iTdgAx3uvh2z8E0z/yw8xP5iFo1BLELkrBqW/O4vGDxw0WMyIu9ly+BM+MdIxLT6lTMExSfUMkY8bn/8SVzJvqguZmu/qFNoeCwRUbBYPNhILBlQcFg2FCwWBY6IiCQZOgYDAkTETBYFhIRMFg82DJOQIUDM6xZk1uRqA2wSAP6Ue+OgavHgtUXwtd/30Iuv3HUPT+nxGY+M5szBti5IJIBslk8OkXgi3rtuPK/fvYfv4HxB0/hvSzZ3D8ViYkq0BPMvzlmUM/IHBEBCa964VPu86D74AQtcx5g8Mwp08Qpn3oq+YNgQnIycpFeVnDsj/u5edj3XeHVbMHEQ1VsxWqxpLhMDYtRUmQb65dxZ2sLDVmu17Xn/IrBYPr3qdgsJlQMLjyoGAwTCgYDAsdUTBoEhQMhoSJKBgMC4koGGweLDlHgILBOdasyc0I1CYYZFPLyspx/UImln+6DsNaTcKYdtPh1XO+i1yQphLSnGJtQBwe3MtD+dOnKCsvV00hZLQIKetR0IueFOPYvlPw7rVAiQXJgqguK0RYyHt+Axdi6gc+iJi8Ancz7zUok0EEyY0HDxB1YD/6xm1Wo1dUFQsSS78LMpSl9A2RcfYMSsvKVB8Z0ncGp4oLuzxQy3HCqYIABYN9JFAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qzJzQjUJRhkc0tLynDn2l0kRWWojIKp7/vAq+cClbHg3XMBpn/kq+TC5tAU3L6aVWumgTz0nz9+CQvHRmF29wD4Dw6tbGKhm1pYr0MWVUqGhMXpyM3Oa9AeELmR9fgRkk+fhPQFMTAhVg1FKX0tDEqIxdCkBATs+goHr19DQXExZP2kE04KhgrMcmGnYLAPOQoGmwcFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IxAfQSDbPLD3Ef4btcJRM1YDc93vTCuw0zM6OKP0HFLsTE4EYe/OoZ7t3NQWlJaK6G8ew+RvuILNfyl36A65MKzvh1EOPj0C8bs7vNx9tAPKCkuqbWO6n+UTAppnnHp/n3VL0PKqZOIP3EM/zx3FkczbyLzYR4Kn3XqSMFg06NgsHlIiYLBZkLB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQH8Fw+1oWMlZvw8IxS1VHjDO6+CnJMPk9b2wISsDlM9fwpKBI/fpfF54Lxy8jYnKMyoCwMhWqyITnvT/tAx+kxWxVTTDqqqemv4s8kNEh8p48wYPCQjUUZUm1phAUDDY5Cgabh5QoGGwmFAyuPCgYDBMKBsNCRxQMmgQFgyFhIgoGw0IiCgabB0vOEaBgcI41a3IzAnUJhrz7D5EWvVVlEEjfC5JJIP0izO0XXNl/wuGvjqPgcWG9yBzYerhyGc8TCc97f1a3ACybtQY3L96qV10v8iEKBpsaBYPNQ0oUDDYTCgZXHhQMhgkFg2GhIwoGTYKCwZAwEQWDYSERBYPNgyXnCFAwOMeaNbkZgboEw7G9p7BgeAS8ewXW2FeCDFW5dOZq3LiQWS8yX8buwYyP/Wtc1vPEgn5/Tt8ghIyJxMWTV+pV14t8iILBpkbBYPOQEgWDzYSCwZUHBYNhQsFgWOiIgkGToGAwJExEwWBYSETBYPNgyTkCFAzOsWZNbkagNsGQ/7AAq/1jMf0jPyUFpGNHGS2i6ogPfoMWqkyGY3tO1d1E4imwffNuzPjY74UEw9y+QQj2WIILJy432V6gYLDRUjDYPKREwWAzoWBw5UHBYJhQMBgWOqJg0CQoGAwJE1EwGBYSUTDYPFhyjgAFg3OsWZObEahJMMhD9qPcx9iTuh/jO3yKbv8xFN3/cxj6/26M6txxTp8gBAxZVCkJpvxjLqTpgwxpWde0N/0bNTylfwM6eNQZDCI4lkxdiWvf36irmhf+OwWDjY6CweYhJQoGmwkFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IyAi2B4ChQVFiEuIhXTPvRRUqHnz4ej9y9Gosd/DUf3nw3DkD9PhO+AECUYJJtBPndk5/G6MxgAnPn2BywcuxTevWtucqFlQk2v0z/0RXx4Gu7fzmmyvUDBYKOlYLB5SImCwWZCweDKg4LBMKFgMCx0RMGgSVAwGBImomAwLCSiYLB5sOQcAQoG51izJjcjUF0wFBeV4MS+05j6vo8aMWLiO7PR55ej0Ou/R1S8/s9I9Pj5cIzvNAvzBodhVvcAhI1fhsunr9WLzP07uYhbVCEvapIIz3vPf3AoJr83B9/tPoHiouJ61fUiH6JgsKlRMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqC4YHuflY1NIUsUIEYNCIf0eDPuLJ3r857AKyfCLCtnQ/7djMLt7ACSrYE/aAeQ/KqgXmbLSMpw6cBb+g0ROzK9sZvE8sSDvS5bElPfmYM28zcjOvFevTIl6rUwNH6JgsKFQMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqC4YZFjKiMkxkH4W5MFe5tnd52PEG5OVYOj+H0PR7WdD0fXfh8CnXwi+jNuDB9l5KC+vu/8Fje7Rg8fYnbofs7sFQIaelDqeJxhkSEyRGCGjI3Hl7DWUFJfoxTTJKwWDjZWCweYhJQoGmwkFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IxAdcHwMPcRor3XQTpUlCYQ8uDvPzhMDVMpnTmOfXsGRr05FZ5/9cLJ/WchsqC8Hp07VsUmMuLBvTzsTt6PwBERalQJr57z4dMvGCIUpH8HERwiH2SO9lqHc0fOo/hJ0zWN0OtHwaBJVLxSMNg8pETBYDOhYHDlQcFgmFAwGBY6omDQJCgYDAkTUTAYFhJRMNg8WHKOAAWDc6xZk5sRqC4YCvOfYEfCHkz70Fc97FfNLJBMA3nwn/GxPzaHJqO0uPSFaTwtfwoZBvPE16dVx40Rk2KwYEQEpK8FqUcyFpbPXovP13+Faz/cgDStcGKiYLApUzDYPKREwWAzoWBw5UHBYJhQMBgWOqJg0CQoGAwJE1EwGBYSUTDYPFhyjgAFg3OsWZObEaguGORB/s61LISOXwavXgswV7IKBi2EDCspTSK8ewUiZEwULp2+2qBmEc/DJg/0kjVx8eQVfPPFEexM3IfdKftxdM9JZF65jSeFRc/7apO8T8FgY6VgsHlIiYLBZkLB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQXTDI5kmTh9Pffo/FU1aokSR8B4ZUNF3oH4IIzxgc2nGsUeRCc0RJwWDvFQoGm4eUKBhsJhQMrjwoGAwTCgbDQkcUDJoEBYMhYSIKBsNCIgoGmwdLzhGgYHCONWtyMwI1CQa9iXdv3sO+LQcRG5aimkTsSvkamZdvN+koDrrul/VKwWCTp2CweUiJgsFmQsHgyoOCwTChYDAsdETBoElQMBgSJqJgMCwkomCwebDkHAEKBudYsyY3I1CbYJB+EsrKylX/B9J0QjpnlAdwd54oGOy9S8Fg85ASBYPNhILBlQcFg2FCwWBY6IiCQZOgYDAkTETBYFhIRMFg82DJOQIUDM6xZk1uRuB5gkEetG9fy8KhL49he+xu1S/C999dQP6jAjcjYG8OBYPNg4LB5iElCgabCQWDKw8KBsOEgsGw0BEFgyZBwWBImIiCwbCQiILB5sGScwQoGJxjzZrcjEB1wVBe/hSPch8jLXorAoaEYXa3+ZjdYwFmd5+vOniMnLYKp74567aZDBQM9gFOwWDzkBIFg82EgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoKpgkIfrB/ceYlNIErx7LoBPv2D49A+B74CFqrNHKc/tG4SgUYtxbO9J1Rmk4JDv5WY/wNlDP2BP6n7sST2Ac0fO42Hu4xYnIigY7AOcgsHmISUKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmtyMQFXBUPC4EPv/eQiffjIPc/sGY96QMAQMXWTNfgMXqkyG0HFLkZOVi6InxTi5/yzWzY9H8KgllZ8NGrUEa/xj8f3RiyguKmkx1CgY7F3VkgTDw5xHuHL2Os4c+gFXz13HoweP7Y1ppBIFgw2SgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoKpguHsjG1EzVmNW13k1ygUtG0QyTP3AB99u/w5Hd59EuGc0vHougHfvQPj0C1GZD969AvFp13kInxSDy2euoaSFSAYKBvsAbymC4er3N5AavRVLpq5E2PhliJy6EukrvsCNC5n2BjVCiYLBhkjB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyOgBYOcwOXXX8le8B8UWpmJoKVC1Vf5+8wuflg3Pw6h46KUWJBmFFU/I7F8bur7Pti0MBk5WQ9aBDkKBns3NXfBIPsrL+ch1syLVf2E6L5C5HV2j/lYvyBe9Skin2usiYLBJknB4MqDgsEwoWAwLHREwaBJUDAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQVTCcP3YJk//m7SIKXMTB4DDM6hYA3wHBmPQ3L5WxUP0zuuw7IEQ96Im8kGEvm/tEwWDvoeYuGGQY1SO7jqvjUfoI0cedvEpZOik98fXpyv5C7K17sRIFg82NgsGVBwWDYULBYFjoiIJBk6BgMCRMRMFgWEhEwWDzYMk5AhQMzrFmTW5GoKpguHLmGqZ/7FevDIbpH/liRhc/eL7rpTqCrPpgVz2e/N4cHN97CiXFpc2eHgWDvYuau2AoLSlFclQG5vQOhDTdqXrs+Q2o6C9ky6ovUFpSZm/YjyhRMNjwKBhceVAwGCYUDIaFjigYNAkKBkPCRBQMhoVEFAw2D5acI0DB4Bxr1uRmBLRgKCsrQ9b1u1gydQU+7VZ3HwySubBw3FJMfX+uGmGi6oNd9XjKP+bg2B4RDM2/s0cKBvsAbxGCYWmGaqbjIhhUh6QLkLFqGwWDvVsbtUTBYOMUHhQMhgkFg2GhIwoGTYKCwZAwEQWDYSERBYPNgyXnCFAwOMeaNbkZgaqCoTD/Cb75/DAm/nUW/AbZvwZraSD9KkiHjvJ6aMcxzO4eUGsTCVnOzE/8cfn01RYxZCUFg32AN3fBUF5WjuP7TtfYRGJuv2D1/plvv2cTCXu3NmqJgsHGScFg86BgsHlIiYLBMJFrbm5uLuRepLy83PzhJxxRMNg7n4LB5sGScwQoGJxjzZrcjEBVwSB9JOTdf4jEyC2QZg1KJAyu6PDRb2AoZnzsj+GvTUbfX3lg0t+9sXz2Wkx5bw5mdvF3SU8XISHDXE79x1xsCEpAzp3cFkGOgsHeTc1dMOAp8CT/CTYEJaoOSiX7Ro9gIqOYbA5NwZPCIsjnGmtiEwmbJAWDKw9mMBgmFAyGhY4oGDQJZjAYEiaiYDAsJKJgsHmw5BwBCgbnWLMmNyNQVTDIpkmneTLiw5fxexA8OhLS14L0szCslSf6/toDQ/48UUmDOX2CIL8Qj2k3HYP/d4L6zNy+QUoqiFiY2zcY0z/0Vcu4eu56i+h/QbafgsE+wJu9YFA7DcjOvIevEvYi2msdFk1cjhjv9diV/DXu3c6xN6gRShQMNkQKBlceFAyGCQWDYaEjCgZNgoLBkDARBYNhIREFg82DJecIUDA4x5o1uRmB6oJBNk8esh89yMeFk1ewb8tBhIyOwui20zHpXW81IoQ0j5AMBd1cQiTDuA4zVVOIWV3nqbT0BcMjsDE4Ed9/dxHFT4pbDDUKBntXtQjBUHnMPkbmpdu4fOYaMi/fxuO8/CZplkPBYB8jFAyuPCgYDBMKBsNCRxQMmgQFgyFhIgoGw0IiCgabB0vOEaBgcI41a3IzAjUJhqqbeOPiLSyevAKSbq7Fgu6PQb969w7E1A98sMpnE3bE78WupK9Vp45ZN7Mbte171fVqqpiCwSbbUgSDvdZNW6JgsPlSMLjyoGAwTCgYDAsdUTBoEhQMhoSJKBgMC4koGGweLDlHgILBOdasyc0I1CUYvvniCOYPC4c0f9BCofrrvCGLMKOLP5KXfoaCx4UtmhAFg737KBhsHlKiYLCZUDC48qBgMEwoGAwLHVEwaBIUDIaEiSgYDAuJKBhsHiw5R4CCwTnWrMnNCNQlGDJWfaEyF6oPAVhdMkiGw/oF8cjJahmdOT5vN1Iw2GQoGGweUqJgsJlQMLjyoGAwTCgYDAsdUTBoEhQMhoSJKBgMC4koGGweLDlHgILBOdasyc0I1CUYtq77UgkG3wE1D1upRYMIBunJP/fugxZNiILB3n0UDDYPKVEw2EwoGFx5UDAYJhQMhoWOKBg0CQoGQ8JEFAyGhUQUDDYPlpwjQMHgHGvW5GYE6hIMR/echHTYKKNGaJlQ/VU1kfjYD1tWfoEiGRLQ4am8/Kka/aK8rPxHd+pHwWDvPAoGm4eUKBhsJhQMrjwoGAwTCgbDQkcUDJoEBYMhYSIKBsNCIgoGmwdLzhGgYHCONWtyMwJ1CYbc7DzEeK3Hp5/MU0NQVpcLUp7dYz4CRy7Gyf1nHadT9KQYMgzm4R3HIDIk68ZdlJaUvvB6UDDY6CgYbB5SomCwmVAwuPKgYDBMKBgMCx1RMGgSFAyGhIkoGAwLiSgYbB4sOUeAgsE51qzJzQjUJRjKyspx6ptzCB2/DNM/9oPvgJDKTIZ5Q8Kgh6XcmbgPjx/mO0anrKwMV85exxr/zZjTO1ANjTmrWwB8+4WoTIrsW/dfaF0oGGxsFAw2DylRMNhMKBhceVAwGCYUDIaFjigYNAkKBkPCRBQMhoVEFAw2D5acI0DB4Bxr1uRmBOoSDLK5hflPcO7weaz2j4VXjwWY+r4Ppn1QMYd7RuPA1kN4cC8P0lTBqen6+ZtY7bsJXj3mw6dfMHz6h6h5bt9glVGRsXr7C/UHQcFg70EKBpuHlCgYbCYUDK48KBgMEwoGw0JHFAyaBAWDIWEiCgbDQiIKBpsHS84RoGBwjjVrcjMC9REMsslFhcW4fS0Lx/eexq6kr7Ejfg8OfvEdrpy5hoJHBY7KheKiEuxI2AvvXoHw6WcyKqS5hvQH4d07UDXZOL7vdIP3FgWDjYyCweYhJQoGmwkFgysPCgbDhILBsNARBYMmQcFgSJiIgsGwkIiCwebBknMEKBicY82a3IxAfQWD3mzJUih+UownBU8gnSq+jCnv/kNsXpiMmZ/4VzbXqNo3xLzBYZBRLbZt2oWy0rIGrSIFg42LgsHmISUKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmtyMQEMFQ3PY/Pt3crFufhw+7TavRsEgsmHGx374bPX2Bnf4SMFg72EKBpuHlCgYbCYUDK48KBgMEwoGw0JHFAyaBAWDIWEiCgbDQiIKBpsHS84RoGBwjjVrcjMCTgoGeXgvLipGwaNC1a+DdCD5ItPjvMdIXLJFZSlIR5NVsxck9hu4UPXDsCt5f4ObblAw2HuEgsHmISUKBpsJBYMrDwoGw4SCwbDQEQWDJkHBYEiYiILBsJCIgsHmwZJzBCgYnGPNmpqQgDzclpeXQ0ZIcGp++PAh7t+/j5KSkgbVWVJcoppKyGt91lU+l3v3AU59cxY7k/dh/9ZDuHnpFooKi+r1/ap1yDCU+//5rerccU6fIPgPClVDaIps8BsUilndAxA+KQY/HLvY4GVLPVlZWQ3mUXX93CkuLi5GTk4O5Dhxp+36MdsiPOQG8Mcsw52+W1hYiNu3b5PHs/O28MjLy4OIF3fazy+6LXIOuXHjhnpIeNFluNv35PgQyeBu2/Wi2yP3IHKNaeh9yIvW19y/V1BQoO7Lmvt6OrV+cg6Ra4xwaao65d6bEwlUJ0DBUJ0Iyy2SgNyQyq+j8gDj1Hzr1i1183fv3r0665SbgOy72ci8lonTR87hyN7jOH3kLG5cvYm7d7PVBfF5633x7CVEe6+D599mY2zH6RjXaSa8ei3AN9sO4V523XVXX+6VC1exOSIZk/7uhRkf+8K79wJ49Z6Pqe/PxYyuftiWsBO3b92pc5uqL1e28erVq6gPj+rfdcdydnY2bt68CTlO3HH7XmSb5GHp7t275PFPtH7AAAAENElEQVTsPCVC7sqVK+TxjIccG/L/Iv87L3J8udt3hMPFixdrvT642zbXtT1yfMgDU12f+yn8Xa65ck4VJrzuVtz7yTlVmPwU9n99tlHOIXKNES71+fyLfCY3N1f9wNciHx640k1GgIKhydBywU4SEIMqqWBi8Z2a5ZcUuaiL3Kirzvt3c7Er5WuVMSAP8lPen6se6P0HLcT2+N3IvlORCeGynOISxIalwKvnAsztGwS/QQvhOyAE3r0WYE6fQPU9MdQu36uFQ3FxCR7ce4Bvt32HyGkrMaOLH7x6zsfagDicO3Ie+Y/yG7Q8Xbesx507d+rFQ3/HnV91qqYcJ+68nQ3ZNrkhll9SGvIdd/5sfn6+ejhw521syLYJDxHFksnQkO+562flHHLt2jU09Bzvrjxku+T44DnV3OfIPYgwqc99iDsfF3rbpOmqMNHln/qrnENEQAmXpmIh996cSKA6AQqG6kRYbtEEpKmEU7OcsMX2ysm1tjrvZt5D8tLP8GnXAMztG6z6OZC+DmSe2y8YM7r4I3r2OuxN/waHvjyK0wfPqWEtZbl5OQ8R7hmthpWs7DNhyCL4DliIaR/64My330OGnqyt/pr+JkJGhs/Mu/8I0vFjTlYuHj14DGmOUdPn6/OeLFNseV086rMsd/iMXMzlxk+OE3fYnsbYBvmlQx6WGmNZ7rAMufkTKecO29IY2yAPSboPhsZYXktfhpxDJAtKzq0tfVsaa/11HwyNtbyWvhy5B9FNRlr6tjTG+mux3xjLcodlyP2YXGOES1NuT4t+cODKNwkBCoYmwcqF/hQIyIOjPDBJu7bnTTIk5Y74vZjTO0jJBRkGUnesKBkJ4zrORL/fjka/347B+A6fYm6fIPj2X4hgjyVYtyAex/acxILh4Uow6O/pzhinfjBX9ctQUlTyvOodfV8uXiIYauPh6Aq95Mrkwq4Fw0telWZTvfCQhyZOFQTkgVpSVzkZHlowkElFB20iGOTcyqmCgBYM5MFOHms6BrRgqOlvP8X35D5ErjHChRMJOEmAgsFJ2qzLrQjURzDcOJ+JpTPXYFa3ANWZosiBeUMWwavHAgx/bRL6/HIUev33CHT/2TD0/bUHpn3gA+l80bt3oJoDRyzGzE/8Me1DX/j2D1Fywn9wqPqMCIqsG9kof8ERJRp7Z1Aw2EQpGGweUqJgsJlQMLjyoGAwTOQcQsFgeEhEwWB4yDVXfuSQexHJcuEE9SAtWR2cKghQMPBIeFkEKBheFnnW2+IJ1EcwfLv9OwSOiFD9J1TIhTAVD2s1CT1+PlzJhT6/qJAMfX41Cp7velVmOPgNrBjVYXznWZj0Ny/VV4KM8jC7x3zMHxaOL+P2oLlkL8jOpGCwD2kKBpuHlCgYbCYUDK48KBgMEwoGw0JHFAyaBDMYDAkTMYPBsJCIgsHmwZJzBP4/DT4Yn7d53GcAAAAASUVORK5CYII=\" /></p>\n<ul>\n<li><a href=\"https://plotly.com/r/bubble-charts/\" title=\"plotly包\">plotly包</a>绘制可以互动的气泡图</li>\n</ul>\n<p><img src=\"data:image/png;base64,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\" /></p>\n</div>\n<div id=\"r语言数可视化书内代码\" class=\"section level2\">\n<h2>R语言数可视化书内代码</h2>\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb11-1\" title=\"1\"><span class=\"kw\">library</span>(ggplot2)</a>\n<a class=\"sourceLine\" id=\"cb11-2\" title=\"2\"><span class=\"kw\">library</span>(RColorBrewer)</a>\n<a class=\"sourceLine\" id=\"cb11-3\" title=\"3\"><span class=\"kw\">library</span>(ggrepel)</a>\n<a class=\"sourceLine\" id=\"cb11-4\" title=\"4\"><span class=\"kw\">attach</span>(mtcars)</a>\n<a class=\"sourceLine\" id=\"cb11-5\" title=\"5\"><span class=\"kw\">head</span>(mtcars)</a></code></pre></div>\n<pre><code>##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb\n## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4\n## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4\n## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1\n## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1\n## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2\n## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1</code></pre>\n<div id=\"带数据标签的气泡图\" class=\"section level3\">\n<h3>带数据标签的气泡图</h3>\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb13-1\" title=\"1\"> <span class=\"kw\">ggplot</span>(<span class=\"dt\">data=</span>mtcars, <span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>wt,<span class=\"dt\">y=</span>mpg))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-2\" title=\"2\"><span class=\"st\">   </span><span class=\"kw\">geom_point</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">size=</span>disp,<span class=\"dt\">fill=</span>disp),<span class=\"dt\">shape=</span><span class=\"dv\">21</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">alpha=</span><span class=\"fl\">0.8</span>)<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-3\" title=\"3\"><span class=\"st\">   </span><span class=\"kw\">scale_fill_gradient2</span>(<span class=\"dt\">low=</span><span class=\"st\">&quot;#377EB8&quot;</span>,<span class=\"dt\">high=</span><span class=\"st\">&quot;#E41A1C&quot;</span>,<span class=\"dt\">midpoint =</span> <span class=\"kw\">mean</span>(mtcars<span class=\"op\">$</span>disp))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-4\" title=\"4\"><span class=\"st\">   </span><span class=\"kw\">geom_text_repel</span>(<span class=\"dt\">label =</span> disp )<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-5\" title=\"5\"><span class=\"st\">   </span><span class=\"kw\">scale_size_area</span>(<span class=\"dt\">max_size=</span><span class=\"dv\">12</span>)<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-6\" title=\"6\"><span class=\"st\">   </span><span class=\"kw\">guides</span>(<span class=\"dt\">size =</span> <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Value&quot;</span>)),</a>\n<a class=\"sourceLine\" id=\"cb13-7\" title=\"7\">          <span class=\"dt\">fill =</span> <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Value&quot;</span>)))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb13-8\" title=\"8\"><span class=\"st\">   </span><span class=\"kw\">theme</span>(</a>\n<a class=\"sourceLine\" id=\"cb13-9\" title=\"9\">     <span class=\"dt\">legend.text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\n<a class=\"sourceLine\" id=\"cb13-10\" title=\"10\">     <span class=\"dt\">axis.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\n<a class=\"sourceLine\" id=\"cb13-11\" title=\"11\">     <span class=\"dt\">axis.text =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">10</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\n<a class=\"sourceLine\" id=\"cb13-12\" title=\"12\">     <span class=\"dt\">legend.position =</span> <span class=\"st\">&quot;right&quot;</span></a>\n<a class=\"sourceLine\" id=\"cb13-13\" title=\"13\">   )</a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"方块状的气泡图\" class=\"section level3\">\n<h3>方块状的气泡图</h3>\n<div class=\"sourceCode\" id=\"cb14\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><a class=\"sourceLine\" id=\"cb14-1\" title=\"1\"><span class=\"kw\">ggplot</span>(mtcars, <span class=\"kw\">aes</span>(wt,mpg))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb14-2\" title=\"2\"><span class=\"st\">  </span><span class=\"kw\">geom_point</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">size=</span>disp,<span class=\"dt\">fill=</span>disp),<span class=\"dt\">shape=</span><span class=\"dv\">22</span>,<span class=\"dt\">colour=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"dt\">alpha=</span><span class=\"fl\">0.8</span>)<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb14-3\" title=\"3\"><span class=\"st\">  </span><span class=\"kw\">scale_fill_gradient2</span>(<span class=\"dt\">low=</span><span class=\"kw\">brewer.pal</span>(<span class=\"dv\">7</span>,<span class=\"st\">&quot;Set1&quot;</span>)[<span class=\"dv\">2</span>],<span class=\"dt\">high=</span><span class=\"kw\">brewer.pal</span>(<span class=\"dv\">7</span>,<span class=\"st\">&quot;Set1&quot;</span>)[<span class=\"dv\">1</span>],</a>\n<a class=\"sourceLine\" id=\"cb14-4\" title=\"4\">                       <span class=\"dt\">midpoint =</span> <span class=\"kw\">mean</span>(mtcars<span class=\"op\">$</span>disp))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb14-5\" title=\"5\"><span class=\"st\">  </span><span class=\"kw\">scale_size_area</span>(<span class=\"dt\">max_size=</span><span class=\"dv\">12</span>)<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb14-6\" title=\"6\"><span class=\"st\">  </span><span class=\"kw\">guides</span>(<span class=\"dt\">fill =</span> <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Value&quot;</span>)),</a>\n<a class=\"sourceLine\" id=\"cb14-7\" title=\"7\">         <span class=\"dt\">size =</span>  <span class=\"kw\">guide_legend</span>((<span class=\"dt\">title=</span><span class=\"st\">&quot;Value&quot;</span>)))<span class=\"op\">+</span></a>\n<a class=\"sourceLine\" id=\"cb14-8\" title=\"8\"><span class=\"st\">  </span><span class=\"kw\">theme</span>(</a>\n<a class=\"sourceLine\" id=\"cb14-9\" title=\"9\">    <span class=\"dt\">text=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</a>\n<a class=\"sourceLine\" id=\"cb14-10\" title=\"10\">    <span class=\"dt\">plot.title=</span><span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">family=</span><span class=\"st\">&quot;myfont&quot;</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;bold.italic&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>)<span class=\"co\">#,</span></a>\n<a class=\"sourceLine\" id=\"cb14-11\" title=\"11\">    <span class=\"co\">#legend.position=c(0.9,0.05)</span></a>\n<a class=\"sourceLine\" id=\"cb14-12\" title=\"12\">  )</a></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" 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    "path": "2020年/2020.11.05气泡图/Bubble_plot.rmd",
    "content": "---\ntitle: \"气泡图\"\nauthor:\n  - 庄亮亮\ndate: \"2020/11/10\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\n---\n\n\n# 气泡图\n\n气泡图是一种多变量图表，是散点图的变体，也可以认为是\n[散点图和百分比区域图的组合](R语言数据可视化之美 )。气泡图最基本的用法是使用三个值来确定每个数据序列，和散点图一样。气泡图通过气泡的位置及面积大小，可分析数据之间的相关性。\n\n本文可以看作是《R语言数据可视化之美》的学习笔记。前两部分可见（跳转）：\n\n- [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN&scene=21#wechat_redirect)\n\n- [分布显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)\n\n该书对气泡图的绘制并不是非常详细，小编将内容进行了大量拓展。下面的例子将一步步带你完成气泡图的绘制。本文内容丰富，希望大家都能学到自己想要的内容。\n\n\n## 本文框架\n\n![](https://imgkr2.cn-bj.ufileos.com/b4382ada-392f-4868-be6b-53e619921374.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=qppb6xaOmm72QdYDTIA9aO34REA%253D&Expires=1605239247)\n\n\n\n## 数据介绍\n\n数据集来源gapminder包中，包含了1704行和6个变量。其中6个变量含义分别为：\n\n- country 国家142个水平\n\n- continent 大陆5个水平\n\n- year 1952年-2007年（间隔为5年）\n\n- lifeExp 出生预期寿命，以年计数\n\n- pop 人口数\n\n- gdpPercap 人均国内生产总值(扣除通货膨胀因素后的美元)\n\n由于数据过多，我们感兴趣的是年份为2007年的数据，所以使用dplyr包进行数据处理，具体数据处理案例可见我写的另一篇推送：[[R数据科学]tidyverse数据清洗案例详解](https://mp.weixin.qq.com/s/ozH-TsltnLwp51i3aDNpSQ)。数据缩略版如下：\n\n\n```{r message=FALSE, warning=FALSE}\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(gapminder)\ndata <- gapminder %>% filter(year==\"2007\") %>% dplyr::select(-year)\n```\n\n## 手把手绘制\n\n### geom_point()函数构建\n\n气泡图是添加了第三维度的散点图：附加数值变量的值通过点的大小表示。(来源:[data-to-viz](https://www.data-to-viz.com/graph/bubble.html \"data-to-viz\"))。\n使用ggplot2，可以通过`geom_point()`函数构建气泡图。`aes()`设定至少三个变量:x、y和size。其实就是散点图绘制的升级版吧，`aes()`中多了一个参数。\n\n```{r}\nggplot(data, aes(x=gdpPercap, y=lifeExp, size = pop)) +\n    geom_point(alpha=0.7)\n```\n\n上图展示了世界各国的预期寿命(y)与人均国内生产总值(x)之间的关系。每个国家的人口用圆的大小表示。但是这个图不是非常美观，而且圆的大小并不是很直观，大家都差不多大。接下来对圆的大小进行设定。\n\n### scale_size()控制圆的大小\n\nscale_size()允许使用range参数设置最小和最大的圆的大小，用`name`改变图例名称(`scale_size(range = c(0.1, 24), name=\"Population (M)\")`)。\n\n- 图中可以看到，有些圆圈重叠了。k可将点的透明度进行调整（`geom_point(alpha=0.5)`）\n\n- 为了避免在图表顶部出现大的圆圈，可以将数据集进行排序(`arrange(desc(pop))`)，代码如下。\n\n```{r}\ndata %>%\n  arrange(desc(pop)) %>%\n  mutate(country = factor(country)) %>%\n  ggplot(aes(x=gdpPercap, y=lifeExp, size = pop)) +\n    geom_point(alpha=0.5) +\n    scale_size(range = c(.1, 24), name=\"Population (M)\")\n```\n\n\n现在图可读性有所提高，但如果数据集中还有一个变量想加入图中该怎么办呢？\n\n### 添加第四个维度：颜色\n\n这里可以用每个国家的洲来控制圆圈的颜色（`aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)`）:\n\n```{r}\ndata %>%\n  arrange(desc(pop)) %>%\n  mutate(country = factor(country, country)) %>%\n  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, color=continent)) +\n    geom_point(alpha=0.5) +\n    scale_size(range = c(.1, 24), name=\"Population (M)\")\n```\n\n该图基本可以满足我们日常生活的气泡图的可视化了。相信大家通过前面的详细的介绍，应该可以自行绘制，只要换个数据，懂得各个代码的含义即可。后面是图表美化的过程，参考[thr R Graph Gallery: Bubble plot with ggplot2](https://www.r-graph-gallery.com/320-the-basis-of-bubble-plot.html \"thr R Graph Gallery: Bubble plot with ggplot2\")。\n\n\n## 美化气泡图\n\n### 一些经典的改进\n\n- 使用viridis包的调色板：（`scale_fill_viridis(discrete=TRUE, guide=FALSE, option=\"A\")`）\n\n- 使用`hrbrthemes`包的`theme_ipsum()`\n\n- 使用xlab和ylab自定义轴标题\n\n- 为圆添加描边：将形状改为21，并指定颜色(描边)和填充\n\n\n```{r message=FALSE, warning=FALSE}\n# Libraries\nlibrary(hrbrthemes)\nlibrary(viridis)\n\n# Most basic bubble plot\ndata %>%\n  arrange(desc(pop)) %>%\n  mutate(country = factor(country, country)) %>%\n  ggplot(aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +\n    geom_point(alpha=0.5, shape=21, color=\"black\") +\n    scale_size(range = c(.1, 24), name=\"Population (M)\") #+\n    #scale_fill_viridis(discrete=TRUE, guide=FALSE, option=\"A\") +\n    #theme_ipsum() +\n    #theme(legend.position=\"bottom\") +\n    #ylab(\"Life Expectancy\") +\n    #xlab(\"欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita\") +\n    #theme(legend.position = \"none\")\n```\n\n### 带数据标签\n\n这里使用ggrepel包中的(`geom_text_repel()`)，可以给每个点自动加入标签，我这里是加入了各个国家名字，其他可以根据你实际需求进行设置。\n\n```{r message=FALSE, warning=FALSE}\nlibrary(ggrepel)\nlibrary(viridis)\nlibrary(hrbrthemes)\ndata1 = data %>%\n  mutate(country = factor(country)) %>% head(20)\nattach(data1)\n  ggplot(data1,aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +\n    geom_point(alpha=0.5, shape=21, color=\"black\") +\n    scale_size(range = c(.1, 24), name=\"Population (M)\") +\n    geom_text_repel(label = country,size=5)#+\n    #scale_fill_viridis(discrete=TRUE, guide=FALSE, option=\"A\") +\n    #theme_ipsum() +\n    #theme(legend.position=\"bottom\") #+\n    #ylab(\"Life Expectancy\") +\n    #xlab(\"欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita\") +\n    #theme(legend.position = \"none\")\n```\n如果不喜欢圆形的气泡图，可以将代码中的`shape=21`进行更改，正方形是`shape=22`，得到的图如下：\n```{r message=FALSE, warning=FALSE}\nlibrary(ggrepel)\ndata1 = data %>%\n  mutate(country = factor(country)) %>% head(20)\nattach(data1)\n  ggplot(data1,aes(x=gdpPercap, y=lifeExp, size=pop, fill=continent)) +\n    geom_point(alpha=0.5, shape=22, color=\"black\") +\n    scale_size(range = c(.1, 24), name=\"Population (M)\") +\n    geom_text_repel(label = country,size=5)+\n    scale_fill_viridis(discrete=TRUE, guide=FALSE, option=\"A\") #+\n    #theme_ipsum() +\n    #theme(legend.position=\"bottom\") +\n    #ylab(\"Life Expectancy\") +\n    #xlab(\"欢迎关注：庄闪闪的成长手册 \\nq  Gdp per Capita\") +\n    #theme(legend.position = \"none\")\n```\n\n## 拓展知识\n\n其他扩展可自行学（小编做推送的时候已经学过啦，但是篇幅有限，就没继续整理下去了）\n\n- [GOplot包](https://wencke.github.io/ \"GOplot包\")提供了直接做气泡图的方法，函数是：GOBubble。\n\n\n![](https://imgkr2.cn-bj.ufileos.com/bdf4074a-8cb1-440d-9194-6e8d487e8ae5.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=190m6qR3V1exwcuPayCDULY0Ios%253D&Expires=1605238660)\n\n\n- [BUBBLE PLOT理论定义](https://www.data-to-viz.com/graph/bubble.html \"BUBBLE PLOT理论定义\")\n\n![](https://imgkr2.cn-bj.ufileos.com/40810411-d8fc-413f-b665-ece61d55531c.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9tj23TfNB23j468EBKqr46wN1Ec%253D&Expires=1605238689)\n\n- [plotly包](https://plotly.com/r/bubble-charts/ \"plotly包\")绘制可以互动的气泡图\n\n\n![](https://imgkr2.cn-bj.ufileos.com/47d4f17c-9b83-4340-9a09-ad122467565b.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=2uehzuNnkzKRhgkrmPmOmnEpGWg%253D&Expires=1605238713)\n\n\n## R语言数可视化书内代码\n\n```{r message=FALSE, warning=FALSE}\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(ggrepel)\nattach(mtcars)\nhead(mtcars)\n```\n\n### 带数据标签的气泡图\n```{r message=FALSE, warning=FALSE}\n ggplot(data=mtcars, aes(x=wt,y=mpg))+\n   geom_point(aes(size=disp,fill=disp),shape=21,colour=\"black\",alpha=0.8)+\n   scale_fill_gradient2(low=\"#377EB8\",high=\"#E41A1C\",midpoint = mean(mtcars$disp))+\n   geom_text_repel(label = disp )+\n   scale_size_area(max_size=12)+\n   guides(size = guide_legend((title=\"Value\")),\n          fill = guide_legend((title=\"Value\")))+\n   theme(\n     legend.text=element_text(size=10,face=\"plain\",color=\"black\"),\n     axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\n     axis.text = element_text(size=10,face=\"plain\",color=\"black\"),\n     legend.position = \"right\"\n   )\n```\n\n\n\n### 方块状的气泡图\n\n```{r message=FALSE, warning=FALSE}\nggplot(mtcars, aes(wt,mpg))+\n  geom_point(aes(size=disp,fill=disp),shape=22,colour=\"black\",alpha=0.8)+\n  scale_fill_gradient2(low=brewer.pal(7,\"Set1\")[2],high=brewer.pal(7,\"Set1\")[1],\n                       midpoint = mean(mtcars$disp))+\n  scale_size_area(max_size=12)+\n  guides(fill = guide_legend((title=\"Value\")),\n         size =  guide_legend((title=\"Value\")))+\n  theme(\n    text=element_text(size=15,color=\"black\"),\n    plot.title=element_text(size=15,family=\"myfont\",face=\"bold.italic\",color=\"black\")#,\n    #legend.position=c(0.9,0.05)\n  )\n```\n"
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replace div.sourceCode by a pre.sourceCode rule\r\n      sheets[i].deleteRule(j);\r\n      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);\r\n    }\r\n  }\r\n})();\r\n</script>\r\n\r\n\r\n\r\n<style type=\"text/css\">@font-face{font-family:\"Open Sans\";font-style:normal;font-weight:400;src:local(\"Open Sans\"),local(\"OpenSans\"),url(data:application/font-woff;base64,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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \r\ncode span.dt { color: #795da3; } \r\ncode span.dv { color: #0086b3; } \r\ncode span.bn { color: #0086b3; } \r\ncode span.fl { color: #0086b3; } \r\ncode span.ch { color: #4070a0; } \r\ncode span.st { color: #183691; } \r\ncode span.co { color: #969896; font-style: italic; } \r\ncode span.ot { color: #007020; } \r\n</style>\r\n\r\n\r\n\r\n\r\n\r\n</head>\r\n\r\n<body>\r\n\r\n\r\n\r\n\r\n<section class=\"page-header\">\r\n<h1 class=\"title toc-ignore project-name\">Vignette Title</h1>\r\n<h4 class=\"author project-author\">Vignette Author</h4>\r\n<h4 class=\"date project-date\">2020-11-14</h4>\r\n</section>\r\n\r\n\r\n\r\n<section class=\"main-content\">\r\n<div id=\"gghalves包\" class=\"section level1\">\r\n<h1>gghalves包</h1>\r\n<div id=\"介绍\" class=\"section level2\">\r\n<h2>介绍</h2>\r\n<p><code>gghalves</code>可以通过<code>ggplot2</code>轻松地编写自己想要的一半一半（half-half plots）的图片。比如：在散点旁边显示箱线图、在小提琴图旁边显示点图。</p>\r\n<p>gghalves将<code>_half_</code>扩展添加到选定的<code>geom</code>。比如：<code>geom_half_violin()</code>函数，相当于<code>geom_violin()</code>函数的变体，该函数主要作用就是展示一半的小提琴图，然后与其他图形组合。还包含以下函数：</p>\r\n<ul>\r\n<li>geom_half_boxplot</li>\r\n<li>geom_half_violin</li>\r\n<li>geom_half_point</li>\r\n</ul>\r\n</div>\r\n<div id=\"安装\" class=\"section level2\">\r\n<h2>安装</h2>\r\n<p>gghalves通过GitHub安装:</p>\r\n<pre><code>if (!require(devtools)) {\r\n    install.packages(&#39;devtools&#39;)\r\n}\r\ndevtools::install_github(&#39;erocoar/gghalves&#39;)</code></pre>\r\n</div>\r\n<div id=\"函数介绍\" class=\"section level2\">\r\n<h2>函数介绍</h2>\r\n<pre><code>geom_half_violin(mapping = NULL, data = NULL, stat = &quot;half_ydensity&quot;,\r\n  position = &quot;dodge&quot;, ..., side = &quot;l&quot;, nudge = 0,\r\n  draw_quantiles = NULL, trim = TRUE, scale = &quot;area&quot;,\r\n  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)</code></pre>\r\n<p>其参数包括：翻译来源<a href=\"https://blog.csdn.net/weixin_43700050/article/details/107512448\">生信玩家</a></p>\r\n<table>\r\n<colgroup>\r\n<col width=\"50%\" />\r\n<col width=\"50%\" />\r\n</colgroup>\r\n<thead>\r\n<tr class=\"header\">\r\n<th>参数</th>\r\n<th>解释</th>\r\n</tr>\r\n</thead>\r\n<tbody>\r\n<tr class=\"odd\">\r\n<td><code>mapping</code></td>\r\n<td>通过<code>aes()</code>指定图形属性映射。默认为<code>NULL</code>，使用<code>ggplot()</code>中<code>aes()</code>指定的映射。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>data</code></td>\r\n<td>指定数据框。默认为<code>NULL</code>，使用<code>ggplot()</code>中的数据。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>stat</code></td>\r\n<td>覆盖geom_density()和stat_density()之间的默认连接。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>position</code></td>\r\n<td>位置调整，可以是字符串，默认为<code>&quot;dodge&quot;</code>，也可以是位置调整函数的调用结果。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>side</code></td>\r\n<td>画半小提琴图的一侧。 “ l”代表左，“ r”代表右，默认为“ l”。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>nudge</code></td>\r\n<td>在小提琴图和分配给x轴上给定因子的空间中间之间添加空间。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>draw_quantiles</code></td>\r\n<td>如果不是<code>MULL</code>（默认为<code>NULL</code>），在给定的密度估计分位数处绘制水平线。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>trim</code></td>\r\n<td>若为<code>TRUE</code>（默认），将小提琴的尾部修整到数据范围。 若为<code>FALSE</code>，不修剪尾巴。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>scale</code></td>\r\n<td>如果为<code>&quot;area&quot;</code>（默认），则所有小提琴都具有相同的面积（修剪尾部之前）。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>na.rm</code></td>\r\n<td>如果为<code>FALSE</code>（默认），则会使用警告删除缺失值。如果为<code>TRUE</code>，则会自动删除缺少的值。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>show.legend</code></td>\r\n<td>逻辑值，默认为<code>NA</code>，若为<code>FALSE</code>，不显示该图层的图例； 若为<code>TRUE</code>，则显示该图层的图例。 它也可以是带有名称（图形属性）的逻辑向量，用来选择要显示的图形属性。 如<code>show.legend = c(size = TRUE,color = FALSE)</code>表示显示<code>size</code>对应的图例，而不显示<code>color</code>对应的图例。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>inherit.aes</code></td>\r\n<td>默认为<code>TRUE</code>，若为<code>FALSE</code>，覆盖<code>ggplot()</code>中<code>aes()</code>默认属性，而不是与他们组合。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>geom</code></td>\r\n<td>覆盖<code>geom_density()</code>和<code>stat_density()</code>之间的默认连接。</td>\r\n</tr>\r\n<tr class=\"even\">\r\n<td><code>bw</code></td>\r\n<td>要使用的平滑带宽度。如果是数字，则为平滑内核的标准差。</td>\r\n</tr>\r\n<tr class=\"odd\">\r\n<td><code>adjust</code></td>\r\n<td>多次带宽调整。这使得可以在仍使用带宽估计器的情况下调整带宽。例如，<code>adjust = 1/2</code>表示使用默认带宽的一半。</td>\r\n</tr>\r\n</tbody>\r\n</table>\r\n</div>\r\n<div id=\"示例\" class=\"section level2\">\r\n<h2>示例</h2>\r\n<div id=\"单个函数\" class=\"section level3\">\r\n<h3>单个函数</h3>\r\n<p>我们以iris数据集作为本例数据，先使用单个函数进行绘制。</p>\r\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"cf\">if</span> (<span class=\"sc\">!</span><span class=\"fu\">require</span>(devtools)) {</span>\r\n<span id=\"cb3-2\"><a href=\"#cb3-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">install.packages</span>(<span class=\"st\">&#39;devtools&#39;</span>)</span>\r\n<span id=\"cb3-3\"><a href=\"#cb3-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>}</span>\r\n<span id=\"cb3-4\"><a href=\"#cb3-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>devtools<span class=\"sc\">::</span><span class=\"fu\">install_github</span>(<span class=\"st\">&#39;erocoar/gghalves&#39;</span>)</span></code></pre></div>\r\n</div>\r\n<div id=\"geom_half_boxplot\" class=\"section level3\">\r\n<h3>geom_half_boxplot</h3>\r\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb4-1\"><a href=\"#cb4-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(gghalves)</span></code></pre></div>\r\n<pre><code>## Loading required package: ggplot2</code></pre>\r\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb6-1\"><a href=\"#cb6-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Petal.Width, <span class=\"at\">fill =</span> Species)) <span class=\"sc\">+</span> </span>\r\n<span id=\"cb6-2\"><a href=\"#cb6-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_boxplot</span>() </span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>默认为箱子在右，使用<code>center = TRUE</code>将箱子居中。下面函数参数调整类似，就不再绘制结果了，就把最原始的进行展示。</p>\r\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb7-1\"><a href=\"#cb7-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Petal.Width, <span class=\"at\">fill =</span> Species)) <span class=\"sc\">+</span> </span>\r\n<span id=\"cb7-2\"><a href=\"#cb7-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_boxplot</span>(<span class=\"at\">center =</span> <span class=\"cn\">TRUE</span>) </span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"geom_half_violin\" class=\"section level3\">\r\n<h3>geom_half_violin</h3>\r\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb8-1\"><a href=\"#cb8-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Petal.Width, <span class=\"at\">fill =</span> Species)) <span class=\"sc\">+</span> </span>\r\n<span id=\"cb8-2\"><a href=\"#cb8-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>()</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"geom_half_point\" class=\"section level3\">\r\n<h3>geom_half_point</h3>\r\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb9-1\"><a href=\"#cb9-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Petal.Width, <span class=\"at\">fill =</span> Species)) <span class=\"sc\">+</span> </span>\r\n<span id=\"cb9-2\"><a href=\"#cb9-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_point</span>()</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n<div id=\"综合案例\" class=\"section level3\">\r\n<h3>综合案例</h3>\r\n<div id=\"云雨图\" class=\"section level4\">\r\n<h4>云雨图</h4>\r\n<p>该案例来自<strong>生信玩家</strong>公众号<a href=\"https://blog.csdn.net/weixin_43700050/article/details/107512448\">ggplot扩展包–gghalves</a>但并没有对代码进行详细解释。</p>\r\n<p>这里小编对代码进行详细解释，喜欢的伙伴，可以按照解释自己理解，并用到自己实际所需的复合图中。</p>\r\n<p>先将数据的统计摘要进行计算存到了<code>summ_iris</code>中，包含了均值，标准差，数量标准误差。<code>iris_plot</code>为所需数据，这里将<code>Species</code>变量设置为因子，因为要用它作为分类变量。</p>\r\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb10-1\"><a href=\"#cb10-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(tidyverse)</span></code></pre></div>\r\n<pre><code>## Warning: package &#39;tidyverse&#39; was built under R version 4.0.3</code></pre>\r\n<pre><code>## -- Attaching packages --------------------------------- tidyverse 1.3.0 --</code></pre>\r\n<pre><code>## √ tibble  3.0.3     √ dplyr   1.0.2\r\n## √ tidyr   1.1.2     √ stringr 1.4.0\r\n## √ readr   1.3.1     √ forcats 0.5.0\r\n## √ purrr   0.3.4</code></pre>\r\n<pre><code>## Warning: package &#39;tidyr&#39; was built under R version 4.0.3</code></pre>\r\n<pre><code>## -- Conflicts ------------------------------------ tidyverse_conflicts() --\r\n## x dplyr::filter() masks stats::filter()\r\n## x dplyr::lag()    masks stats::lag()</code></pre>\r\n<div class=\"sourceCode\" id=\"cb16\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb16-1\"><a href=\"#cb16-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 统计摘要</span></span>\r\n<span id=\"cb16-2\"><a href=\"#cb16-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>summ_iris <span class=\"ot\">&lt;-</span> iris <span class=\"sc\">%&gt;%</span> </span>\r\n<span id=\"cb16-3\"><a href=\"#cb16-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">group_by</span>(Species) <span class=\"sc\">%&gt;%</span> </span>\r\n<span id=\"cb16-4\"><a href=\"#cb16-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">summarise</span>(</span>\r\n<span id=\"cb16-5\"><a href=\"#cb16-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">mean =</span> <span class=\"fu\">mean</span>(Sepal.Length),</span>\r\n<span id=\"cb16-6\"><a href=\"#cb16-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">sd =</span> <span class=\"fu\">sd</span>(Sepal.Length),</span>\r\n<span id=\"cb16-7\"><a href=\"#cb16-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">n =</span> <span class=\"fu\">n</span>()</span>\r\n<span id=\"cb16-8\"><a href=\"#cb16-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ) <span class=\"sc\">%&gt;%</span> </span>\r\n<span id=\"cb16-9\"><a href=\"#cb16-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">mutate</span>(<span class=\"at\">se =</span> sd<span class=\"sc\">/</span><span class=\"fu\">sqrt</span>(n),</span>\r\n<span id=\"cb16-10\"><a href=\"#cb16-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">Species =</span> <span class=\"fu\">factor</span>(Species, <span class=\"at\">levels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&#39;versicolor&#39;</span>, <span class=\"st\">&#39;setosa&#39;</span>, <span class=\"st\">&#39;virginica&#39;</span>)))</span></code></pre></div>\r\n<pre><code>## `summarise()` ungrouping output (override with `.groups` argument)</code></pre>\r\n<div class=\"sourceCode\" id=\"cb18\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb18-1\"><a href=\"#cb18-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>summ_iris</span></code></pre></div>\r\n<pre><code>## # A tibble: 3 x 5\r\n##   Species     mean    sd     n     se\r\n##   &lt;fct&gt;      &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt;  &lt;dbl&gt;\r\n## 1 setosa      5.01 0.352    50 0.0498\r\n## 2 versicolor  5.94 0.516    50 0.0730\r\n## 3 virginica   6.59 0.636    50 0.0899</code></pre>\r\n<div class=\"sourceCode\" id=\"cb20\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb20-1\"><a href=\"#cb20-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 数据转换  </span></span>\r\n<span id=\"cb20-2\"><a href=\"#cb20-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>iris_plot <span class=\"ot\">&lt;-</span> iris <span class=\"sc\">%&gt;%</span> </span>\r\n<span id=\"cb20-3\"><a href=\"#cb20-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">mutate</span>(<span class=\"at\">Species =</span> <span class=\"fu\">factor</span>(Species, <span class=\"at\">levels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&#39;versicolor&#39;</span>, <span class=\"st\">&#39;setosa&#39;</span>, <span class=\"st\">&#39;virginica&#39;</span>)))</span>\r\n<span id=\"cb20-4\"><a href=\"#cb20-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(iris_plot) </span></code></pre></div>\r\n<pre><code>##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species\r\n## 1          5.1         3.5          1.4         0.2  setosa\r\n## 2          4.9         3.0          1.4         0.2  setosa\r\n## 3          4.7         3.2          1.3         0.2  setosa\r\n## 4          4.6         3.1          1.5         0.2  setosa\r\n## 5          5.0         3.6          1.4         0.2  setosa\r\n## 6          5.4         3.9          1.7         0.4  setosa</code></pre>\r\n<p>接下来进行绘图，我们想要得到<code>Species</code>与<code>Sepal.Length</code>的关系，其中<code>Species</code>为离散变量，<code>Sepal.Length</code>为连续变量。并绘制了半边的小提琴图，并将该图往右移了0.15，上下位置不变（<code>position_nudge(x = .15, y = 0)</code>），为了后面绘制其他图形留位置。</p>\r\n<div class=\"sourceCode\" id=\"cb22\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb22-1\"><a href=\"#cb22-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(gghalves)</span>\r\n<span id=\"cb22-2\"><a href=\"#cb22-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggsignif)</span></code></pre></div>\r\n<pre><code>## Warning: package &#39;ggsignif&#39; was built under R version 4.0.3</code></pre>\r\n<div class=\"sourceCode\" id=\"cb24\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb24-1\"><a href=\"#cb24-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggsci)</span></code></pre></div>\r\n<pre><code>## Warning: package &#39;ggsci&#39; was built under R version 4.0.3</code></pre>\r\n<div class=\"sourceCode\" id=\"cb26\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb26-1\"><a href=\"#cb26-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggpubr)</span></code></pre></div>\r\n<pre><code>## Warning: package &#39;ggpubr&#39; was built under R version 4.0.3</code></pre>\r\n<div class=\"sourceCode\" id=\"cb28\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb28-1\"><a href=\"#cb28-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb28-2\"><a href=\"#cb28-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb28-3\"><a href=\"#cb28-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb28-4\"><a href=\"#cb28-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                      <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>)</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --> 接下来加入散点图，并使x坐标往左移动0.1（<code>x = as.numeric(Species)-0.1</code>），使用<code>position_jitter</code>使得重复的点分散开。</p>\r\n<div class=\"sourceCode\" id=\"cb29\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb29-1\"><a href=\"#cb29-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb29-2\"><a href=\"#cb29-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb29-3\"><a href=\"#cb29-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb29-4\"><a href=\"#cb29-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb29-5\"><a href=\"#cb29-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"fu\">as.numeric</span>(Species)<span class=\"sc\">-</span><span class=\"fl\">0.1</span>,</span>\r\n<span id=\"cb29-6\"><a href=\"#cb29-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                 <span class=\"at\">y =</span> Sepal.Length,<span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb29-7\"><a href=\"#cb29-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">05</span>),<span class=\"at\">size =</span> .<span class=\"dv\">25</span>, <span class=\"at\">shape =</span> <span class=\"dv\">20</span>)</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAA9lBMVEUAAAAAADoAAGYAOmYAOpAAZpAAZrYAujgzMzM6AAA6ADo6AGY6OmY6OpA6kNtNTU1NTW5NTY5Nbo5NbqtNjqtNjshhnP9mAABmADpmAGZmOgBmOjpmtv9uTU1uTW5uTY5ubo5ubqtuq+SOTU2OTW6OTY6Obk2Obm6ObquOyP+QOgCQOjqQZgCQkGaQtpCQ2/+rbk2rbm6rbo6rjk2ryKur5P+2ZgC225C2///Ijk3I5KvI///bkDrb25Db/7bb/9vb///kq27k5Kvk/8jk/+Tk///r6+vy8vL4dm3/tmb/yI7/25D/5Kv//7b//8j//9v//+T///+Irn7ZAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAcxklEQVR4nO2dDXvcuHWFx47rj3Vt2dlGTlN7t96PpFaaeLepXblWU7utt5YlS+b//zMhSM6QHAIYEBcHuJw575OsRpSewbnwK4DkEOSqIkQxq9IBCPFBQYlqKChRDQUlqqGgRDUUlKgmVNBfwMAbEKI9Hzwg1EIPFDQM7fkoKLp+dANCtOejoOj60Q0I0Z6PgqLrRzcgRHu+QxT08tnR47frb8DlqxdAe74DFPTqh1fV+68/dN+By1cvgPZ8Byjo5fMP1dWP6yEUXL56AbTnO0BB+xH0bk2+RIQM8OyDXn1/tJnhOYKWDrCLwxtBL795VX16zCm+RXu+AxT0Uz18mmm+BVy+egG05ztAQTmCDtGe7wAFrT4dHT1aD6AUtHSAXRygoCPA5asXQHs+CoquH92AEO35KCi6fnQDQrTno6Do+tENCNGej4Ki60c3IER7PgqKrh/dgBDt+ZwB73aI378QFDQM7fncgh43UFBp/egGhGjPR0HBaBdAV77j48mmHYKKDYVa6IGChqErHwWdICxvd/3oBoRoz0dBwWgXQHs+CgpGuwDa8+0UVGoo1EIPFDQM7fkoKBjtAmjPR0HBaBdAez4KCka7ANrzUVAw2gXQnm+3oEJDoRZ6oKBhaM9HQcFoF0B7PgoKRrsA2vNRUDDaBdCej4KC0S6AznyDi0Z2CyozFGqhBwoahs58FHSDpLag+tENCNGej4KC0S6A9nwUFIx2AbTkM5O65XJlCiqpLah+dANCtOSjoA4ktQXVj25AiPZ8FBSMdgG056OgYLQLoCefdYKnoJLagupHNyBETz4KakVSW1D96AaEaM9HQcFoF0B7PgoKRrsA2vMFCSoxFGqhBwoahvZ8IYKKhlCohR4oaBja81FQMNoF0J6PgoLRLoCufBE3D6OgMnQJMEVXPgo6QVBaWP3oBoRoz0dBwWgXQHs+ngcFo10A7fkoKBjtAmjPR0HBaBdAez4KCka7ANrzUVAw2gXQno+CgtEugPZ8FBSMdgG05zt4QclCGQpaOksMHEHD0J7v4EdQSW1B9aMbEKI9HwUFo10A7fkoKBjtAmjPR0HBaBdAez4KCka7ANrzUVAw2gXQk483brAiqS2ofnQDQvTko6BWJLUF1Y9uQIj2fBQUjHYBtOfjkg8w2gXQno+CgtEugPZ8FBSMdgG056OgYLQLoD0fBQWjXQDt+SgoGO0CaM9HQcFoF0B7PgoKRrsA2vNRUDDaBdCej4KC0S6A9nwUFIx2AbTno6BgtAugPR8FBaNdAO35KCgY7QJoyee4GjRMUImhUAs9UNAwtOSjoA7iKwusH92AEO35KCgY7QLoybcZQ8eDKQXFokcAO3rytV7W/6WgQ+IrC6wf3YAQbfkm+6IUFIs2AbbRno+CgtEugPZ8FBSMdgG056OgYLQLoD1fmKACQ6EWeqCgYWjPR0HBaBdAez4KCka7ANrzUVAw2gXQno+CgtEugPZ8FBSMdgG05Yv7JImCxtePbkCItnwUdIvowkLrRzcgRHu+QEHjDYVa6IGChqE9HwUFo10A7fkoKBjtAmjPR0HBaBdAez4KCka7ANrzUVAw2gXQni9U0GhDoRZ6oKBhaM9HQcFoF0B7PgoKRrsA2vMdoKDvjwwvuu9i+y24fnQDQrTnO0BBDZ++/tC9iu234PrRDQjRni9Y0FhD8Sra8Qp69cOr9cvojgutH92AEO35DlPQ979pvtytyRLGwt01pQIsnamgpRPNwyfoYABFDSAfP67/QB2/cPdji+hJvQngCFoIn6D9HigFpaCF8An65rv+dXTHhdbv2L4WtLSh+yNopKFwEx14BP3ycz/DU9Cyze/mAAUd7oJS0LLN7+YABR0R33MTNvudo/odv9wLWtbQfRI0qiehFnpYjKCFh9A9EjRuCIVa6EH7Z/EUNBAKioWCCqGgWHYLWtZQVYIeT+5QP0/QmJ6EWuiBgoaxT4JGDaFQCz1Q0DBUCWqDgmIJEbSkofslaERPQi30sCBBiw6heyVozBAKtdADBQ2DghaCgoZBQQuxJEFLGrpfgkYYCrXQAwUNg4IWgoKGoVPQwclQCoolTNByhioSdGBltKDzDYVa6GFRghYcQnUKOoCCYqGgQigoFgoqhIImxHLJcqCgxQzdN0FnGwq10MOyBC03hFLQQixriqegTigoFgo6B8uB/FxB5xoKtdADBQ1DpaBDTykolkBBixmqS9AOCtoT3Yle+qMlCipktqAzDYVa6IGChkFBC8EpPgw9gto/6aSgYChoKBtBx6bOF3SeoVALPZQTdHy2PlTQUobqEbRlsvCYgqbFuDaq3/F7FNTOZKKnoGnZ/rSTggqJEXROX0It9LC0fVAK6iBC0FlDKNRCD2UEnV4tQkGFUNCUUNDkRAk6ozOhFnpY3BRfyNC9FHTOEAq10AMFDYOCFoKChkFBC7G4fVAKaidK0BmGQi30QEHDoKCF4BQfBgUtxPIELWPovgoa3JlQCz1Q0DD2VNDwIRRqoQcKGgYFLQQFDYOCFoKChqFM0Oll9ZGCBhsKtdADBQ1Dl6DHFHSb+M4MrN+x3SZoCUNVCWrxk4KCmSFokSFUmaDTbYcuaCmsgpYOpYPj45Df2iXosfLe5AgahqoRtEW8aG7WEOoS42y1Wt146Rbn4ivPDwMYCXpxb2W4eTr9PWFnjom//SIFdVFI0DMjy/nqqUhCH0NBr09uO38vWUcaKGh6ygh6fdKo+frWu5RSDhkK+vmJ+w8hWUe66ndstwpawFAKauX65GH36uKrP61WxtPrk3YONl9vt1P8elMzQ88cbMcj6DIELTGE7q+ggYY6vDhfrVpFL+7dPDVTcDMNn916Z76aEa8WdL2p2R29uDfP0NE+6Llt75OCGiioC3OUdLsTr1awcag2c31wNNx036lXgKCfn6zWwA+SbPU7tlNQC0ar7W3FBG3c6YbH2sKz1qGH68Gu3r7eVL1uVI4UdAeS/hxhOUBq6nf8OgW1oE1QY+FG0O54aShofwhVD4PuWXqnoN1BknWil/TniCSC5jdUlaANW46WEbSbyI2gzRR///T8xsvhT5opfnie1HckrkJQV/2O7VZBCwyhFNTOayOfOQrqD5Lq8bJWcn3A1BwktZsaseaeuO8FPdvsg9r2E4S9ubt+x3YK6iTRJ0nCKf6s3b1sTzMZc8w5pdba0Wkms+l8x6dOfkGXcx6UgjboEHSN9CNNF4s8SKKga3pJD0HQ/kzTdJJP0ZsNaQTNbigF3UGWEfSsPaP60PKhfIre9Nbv2G4XNP8QqlTQntKCorAfxU8P5NP1pKN+x3YKGggFTcR4it98R0GFHIKgmym+OptcPpWsI42SvaTRguY2VJ2gxzrOg8IZH8WbE1Wrp9XZ9GSVsDvHCK8HLTGE7q+gosvt/t8OTFA3st4MqN+xnYI60CHoXTvJ9KSgoSgStDVz+3KRQoLa/3GS6VloTZKtfsd2p6CZDVUn6DaHIGi2NUm2+h3bXYLmHkIVCWrnEARdzmfxFHRCmRs35B5BKagLCqpA0MWsSWo6Ia+hFFSBoOuLRZZwkJR7CNUmaHekJL5YJPjvXIOgPoT9ubt+x3YKaieZoKEN2rWIEnR7dad3tWchQSefJVFQIQsSdJsZgp6tVk+nn8MrFTSroXsqqPQxNF5BLx68q67/+LJdyXnx97+7eXre7D8aIeuNN152izy779e/5BH09a2/PnlqPxuaphc99Tu2uwXNO4RS0PmC1nIaSV8/rM5uNzd2+PyPp83L+6fmhNH5LfOj+r9G0O7V9LYjW+dBzanQbKs6h8MoBRWiUNDqrFbz4effvjRmNsPkb9tFyvdP21ndCFtvq7/pX1HQOPZTUPHDZP2CXjz472aGN2s5G/cu7pmJ3Qj64F1rqhlmO2HbVz5BqzMzxZvrQfMIOqrfsd0naE5DKWiEoNd//PODd2ZwrPojoXZKjxtBu+tBbX5qFDTrELqXgs7pwBhB64PuWqZ+R/Pc3MCheWn2Qesvk33QXYK6SdGH3vod2yloICoF7W7Y1M3rlbl7mP8oPkjQBdz6hoJOiBB01j5SlKAJoKCyfGqIEXTW+x+IoI77NkQJmtNQCkpBrVDQQOYLOq/3DkVQZ/2O7RQ0EAqKhYIKKSRovlWdGe9Rb5vmKaiQ2YLO7Lzigu5A3IEDLDcWiRM0o6F7J+jcz+HsWty9Y2Phgg4GUAo6H2OXZfNsQWc2a9eiiKDofVDrgTwFDYWCWplZzmwoqJCZgs7uObsWFJSCBkJBscQJms9QVYLa5vh5gs6/VNGuRUZBl/QoRAo63TZT0NltFhd0ypefjh69oqD+fGrQL2jUs2R9gr55UX36+gMF9eZTwyxBI7pNhaCj2y9e/fh28CNR522R7JMkCtqjUtB2mXF7NXL/n81249uuJ3eObx728Prk6foed5fP/9JN8Yk/vKo+fgz/3V2CJg22j9gFTfbuXkG7ZcZVt/B4s7Kj224Wee4cVrdvv9i+g/nu8tmLWlLwFB/7EIXsQ6jSETTu1jcxyw2jBG2XGfcLj7u1cd32tcOzBK3F7j5JMnJe/bA+SorswAnD6b1+TUFlRAoa0VKMoN0y437hcbe6uNu+XqMULGj1urGzu/fN1b9kELSv3/H7FDQQlYJW1XpGr5qDpG4EXW9/8nTeFG92Qmup18+geZNhiu/rd2ynoIHMEDTqhgIxgnbLjKvt1cX98uPdj/j0nGa6+v7o8eZAXtJ3QfU7tlPQQOYIGvX+EYKup/B2jfH1yeYovtt+tlr96nc7DuMX/lFnNkMpaIygCZjcftFxZkrQdWH1O7ZT0EAOQtDNszopaGg+NYQLGtdjGgTtn3acQ9Dx50kUVAgFTQ0FFTC9nukQBK3Ob7zkFD8vXyEEgkbetdIhaNZVnb5LQqOK2ol00Vw+Q5UJOiVc0Mj3t7Lvj+OmoMkoI+h+rou31u/YTkEDKSTo9CTWccKLpapij6GZ1u/YTkEDCRU0trtUCLqsx9BQ0CGHIGjup3yM6ndsp6CBUFAs8YLmMXRfBI1+NIoGQbM8hsZxB1sKKiRU0Oj31yBojsfQbAT9OFJVmaB37mxtUCbo8fH2uXq1go4vSe6/C1zkWe40034Iujn1BwjhYUGCCln+edCyU/zmw+jMhv6y/XFnmKDxf0h2LbyCtk87/vP90+Yhxp+frH71T83CpAf/aq7p7J+W5F19PBD0rFvftJgnzVHQAYGCRrcWIWj7tOP/qpU0DzE2qz3alXP3Hg6eN3d+66/e1ce9oOfNVfm3l3axyOEKOkahoO3TjrvFnGa9XP/g2PZLr6V79fFGULNgrnmod2X9KCm6sND6HdsXI2hpQzUK2jzteK1kM99vCfqgNc23+nhwd7un3fV2PA86J9+iBBVkjBG0edrxzhHUv/p4LGgzeC5N0NSGTo7gvfkoqFvQ5mnHaxH7fdCNoO0zj//Du/p4MMU/rdqP4V8vbIrXImhZQ1UKarxbC1off//d1gjaHcV7Vx/3B0n16Nnsgp5bD/njKwus37G9gKDz8t1RMoSGCCpJGCXoFjE3YBycZnptzjBdn9ywDraC0lykOlGfXFDbEEpBhYJen6xWdrWCBfUiKM0FBU2JdkFj2YNPkg52ih+dqQ8QVLSXbNdi3wVNsSZJjaDZDZ0tqKQxuxb7KGgnZfMliaBZDp81CjqikKB7uGiuM3PrutDln6hXL6jsDzmhc7MoNMVPLlumoEICBJW9fyH24CBJj6AlDaWgWPZB0KJDKAVNiGVdEgUVslNQYTiohR4KCqp5yceEMEELGkpB0zCwMpGgZdfFj+8/SEGTU0jQhEfxFLRhl6DSaFALPSz/NJMqQcsZSkGxRAuayYkwQfFDqO1R8Q0UFEu8oOhkO/LdyWtorKDiXFALPZQUNMWjEA9NUCcUND1JntWpTNBihlJQLNGCooPtyjcRtJChFBTL3ghaagj1CyoPBbXQw8IFzWbDDEHLGEpBseyPoIUMpaAIpFfU53NhhqBlJnmvoAkSQS30kFnQ2sjhsbtY0ESxNlhv2uDNZxG0iKGHLmgiPn7s/heKV9CUa19a7tyxv3bnswqaPlg0d1OvYsvMoq8HxY5Uo9F01ghaYgg99BFUXuE28qcd59RgpqD5DfUJmiIN1EIPCxY0qwXzBC0whFJQLDGCojMF5bMLmt9QCoplvqB5p9HZguY2lIJiiRAUGSf8MTQOQbMPoR5Bk/yxQC30sFhBMx7Be/M5Bc1sqE/QJO9fCAoaxmxBcw+hFBTLXEHVzKBuQfNGpKBYZgqq5xjEKWjmIZSCpmX7w6S5gqbOs4sFC5rmjxlqoYdlCppjAB0fJ0UImtdQj6Bp3r8Qy5zi4f/0tZ0UdPz+hVikoPgBdG3nxlIKWohlCopOk0bQrIZSUCxzBFV1PTAFxUJBw4gTNGNSCoplhqC6lvz4BM05hDoDJsoAtdDD8gRVtmiSgmJZoKDoKFYiBc0XloJi2U9BMw6hFBRLsKDZP0NqiRW0/MJ9Cpqmfsf2hQuaz1AKiiVUUHV3N9whaLZJnoJi2VtBcxlKQbEEClrsKQXxgmYylIJiCRUUncOFQNA8hlJQLHstaI7UFBRLmKC5Z/j+eF4iaJYxlIJiCRQUHWOLVIJmGEQpaEqmt7fTKWiPUFC8oRQ0JZGCanzSYKCgcEMpKJYwQdEp3IgFRStKQbHoEnT6YWcCQbGGUlAsigSdrOg0pBAUaigFxRIiaKYB1PoghSSCIhWloFgUCWolkaA4QykoFn2Cym/ckFVRCpoMyzM+1Ama5M4iWQ2loMlYiKBjEgqKMZSCYgkQtKSfSQWFfDZPQbEclqAAQykoFl2C/jJxNLGg6ad5CopFjaCT24a1pBY0uaEUFIsiQTdqDh1NLmjqWZ6CYtktaKZLmbIJmthQCoolQFB0hBbHE+MpaCEoaBgAQdMaSkGxKBI01xRPQUOgoBOGgspuAU5B5XgEfX90dPT4bfdNmio99Tu2F57iKejm/QvhEfTNi8E3aaocMfpMXqmgGyhoIdyCfvn51eC7NFWOmCdowQVzBgpaCLegV9/XU3wziN6tSdrox4/Bv9oLmjSBkzt35v2+SFBMCVsBM3UcCregl9+8Goyiaf4MOywX3O0eQZMmcGJ2OtsdT9j1oBs/OYIGsOMofrMfmqZKT/2O7bkFNQwERR4k8UR9CBTUSponzWUcQA9Q0E9ff6i+/Jua00xFL7bj1UwKBTXnQR9tDuTTVOmp37H9bqkZfov0giZOfYCCjkhTpad+x/Z9FTR5PRQUiyJBraRe8iGr5/g4PCAFTVO/Y/t+Ciq1hoJOSFOlp37H9r0UFPGpGAXFoknQlA/yyuUnBQXj7t8WdPtDwILyziJz0H5nES0s9t5Myd6/EBTUzvYomkxQ1GRAQcH1oxsIxLEsfrm3X0z2/oWgoGMcizqXewPbZO9fCAoaRhpBgQd7FBRcP7oBIUkELXGP+mTvXwgK6gBwb6YiT/lI9v6FoKAO0gta5jlJyd6/EBQ0DLmg4A8bKCi4fnQDQsSClnoUYrL3LwQFDUMuaKGAyd6/EBR0AuSC5WKP4072/oWgoFs0Ok03CwXFX+xCQcH1oxsIBSJohouxKCi4fnQDwdg/7BQJmuNqQQoKrh/dgBCJoFmuZqWg4PrRDUTRj6YiQXNEpaDg+tENRJFE0DzLASgouH50A+EkPs2UabkKBQXXj24gnNSCIrPuDpjs/QtBQcOIFjTXcj8KCq4f3YCQWEGzrUeloOD60Q0IiRa0dMBk718IChpGpKD5FvRTUHD96AaExApaPGCy9y8EBQ0jTtCMN0ShoOD60Q3MJsVDFCioGArqwgjWfxclaM47SlFQcP3oBuYxfsTHLxSUgqIbmEeShyhkvScfBQXXj25ASJSgGgIme/9CUFA3wik+702hKSi4fnQDEVDQ4fsXgoKGMV/QzI9npqDg+tENCIkQNGs+CoquH92AkNmC5n6+PQUF149uQMh8QbPGo6Dg8jUKOjoXSkELQUGdiATN/uAxCgquH92AEApaCAoaxkxBcx8iUVBw+XsnaNZwhkMXlNhxCVo6197AETSMeSNogWczH/oICi6fgkqhoOD60Q0ImSVo/kMkCgouf88EzRqthYKC60c3IGSOoCUGUAqKrh/dgJBZgmZN1kFBwfWjGxBCQQtBQW0YzcZbZghaZIanoOj60Q3MQiYoPJ4NCgquH92AkHBBywygFBRdP7oBITMEzZprAwUF149uQEiwoIUGUAqKrh/dgJBwQbPG6qGg4PrRDUQQsy6+1ABKQdH1oxuIIErQLMksUFBw/egGhAQKWsxPCoquH92AkDBBi03wFBRc/r4ImjXTCAoKrh/dQAyDndAgQQsOoBQUXT+6gRhmC5onlhUKCq4f3YCQEEFL+klB0fWjGxDiFHRI1kRbUFBw/egGhGjPR0HR9aMbEKI9HwVF149uQIj2fBQUXT+6ASHa81FQdP3oBoRoz0dB0fWjGxCiPR8FRdePbkCI9nwUFF0/ugEh2vNRUHT96AaEaM9HQdH1oxsQoj0fBUXXj25AiPZ8FBRdP7oBIdrzUVB0/egGhGjPR0HR9aMbEKI9HwVF149uQIj2fBQUXT+6ASHa81FQdP3oBoRoz0dB0fWjGxCiPd/BC4pG+5OvtOfTHzASChqG9nz6A0ZCQcPQnk9/wEgoaBja8+kPGIkWQQmxQkGJaigoUQ0FJaqhoEQ1GgS9/Pat9/vCKIvTMM7Uf6cxqwwNgm6jrJeVxTkwcgp6+fxD9eXnV1ffHz1+W13+8+8fv/101LysDag3PnpVdT9qv1//UsaEa9pcbYj+P5vtl8+Ojl4UiGVoO/Hfv33b9E2d69d/eFV32OXzv5hMfVeWzJiSnILWcpr+ffNd9f43dQe+qK5+fNu8/Pbtl59eVJ++Nj+q/2t6uXv1rEgfd7mqLukmTrf96odX5YbVthP/p1bS9I3J9agR9Nm669qu/N+SGVOSdYp/X/+Df2f+eet/6eZvvX5ZNXNo25dGgHpb/U3/Kme+NV2uPmkXp9tedVHL0HRi12UmRW1s9137pe+zchkTklXQy+f/18zwR0ePWvfqeehR85duJq52b6/v7/ZVznx90GfdDkeXtIvTba8HrnaqL5PNdOJayXanaSxo05VlMyYkq6D1vtPzD93f9dq9dl7SNYJ2ufqkXZz19u9fFJw+m07cOYKWzZiQvEfx74++q6p+R7P+Mthxqr9M9kHLdHGXq9qO0+etLr95tft9MJhOXIvY74NuBG278j/LZkxHXkGbLuuOMpv+3XUUX2gMeLM5iq9DfflpcxTfbX9/dPTr3xc7RDaduBa0zvUPWyNol7psxnRoPA9K5rAXE7kbCrpkvvx01B617S8UlKiGghLVUFCiGgpKVENBR5ytVqsbL90/v/jK80MCgIIOObt5WlXnq6elc5ANFHTA9Umj5utb70onIWso6IDrk4fdq4uv/rRaGU+vT1YrM6yar7fbKX696eJevT/AwRYMBR1yvlq1il7cu3l6fXK7Mv+vzm69M18/P3lqBF1vanZHL+7RUCwUdIw5SrrdiVcreG6GytrM9cHRcNP908JZDwIKOuHzk254rC00vtY8bKysGkHXm6rXjcoECwWdUlu4EbQ7XhoK2h9CfX6yuslhFAsFHdBN5EbQZoq/f3p+4+XwJ80UPzxPavZLCRIKOuS1kc8cBfUHSfV4WSu5PmBqDpLaTc2gyhP3aCjoiLN297I9zWT2MM05pdba0Wkms+l8x6dOJAUU1ApHRi1QUCsUVAsU1AoF1QIFJaqhoEQ1FJSohoIS1VBQohoKSlRDQYlq/gY3KxTnVsKKewAAAABJRU5ErkJggg==\" /><!-- --></p>\r\n<p>在原来基础上加入箱子图，位置放在正中间</p>\r\n<div class=\"sourceCode\" id=\"cb30\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb30-1\"><a href=\"#cb30-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb30-2\"><a href=\"#cb30-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb30-3\"><a href=\"#cb30-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb30-4\"><a href=\"#cb30-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">adjust=</span><span class=\"fl\">1.5</span>, <span class=\"at\">trim=</span><span class=\"cn\">FALSE</span>, <span class=\"at\">colour=</span><span class=\"cn\">NA</span>, <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb30-5\"><a href=\"#cb30-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"fu\">as.numeric</span>(Species)<span class=\"sc\">-</span><span class=\"fl\">0.1</span>,</span>\r\n<span id=\"cb30-6\"><a href=\"#cb30-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                 <span class=\"at\">y =</span> Sepal.Length,<span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb30-7\"><a href=\"#cb30-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">05</span>),<span class=\"at\">size =</span> .<span class=\"dv\">25</span>, <span class=\"at\">shape =</span> <span class=\"dv\">20</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb30-8\"><a href=\"#cb30-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_boxplot</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species,<span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb30-9\"><a href=\"#cb30-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">outlier.shape =</span> <span class=\"cn\">NA</span>,</span>\r\n<span id=\"cb30-10\"><a href=\"#cb30-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">width =</span> .<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb30-11\"><a href=\"#cb30-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>)</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>这里比较有趣的是，作者还通过<code>geom_point</code>和<code>geom_errorbar</code>加入和汇总信息以及对应的误差项。</p>\r\n<div class=\"sourceCode\" id=\"cb31\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb31-1\"><a href=\"#cb31-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb31-2\"><a href=\"#cb31-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb31-3\"><a href=\"#cb31-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb31-4\"><a href=\"#cb31-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">adjust=</span><span class=\"fl\">1.5</span>, <span class=\"at\">trim=</span><span class=\"cn\">FALSE</span>, <span class=\"at\">colour=</span><span class=\"cn\">NA</span>, <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb31-5\"><a href=\"#cb31-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"fu\">as.numeric</span>(Species)<span class=\"sc\">-</span><span class=\"fl\">0.1</span>,</span>\r\n<span id=\"cb31-6\"><a href=\"#cb31-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                 <span class=\"at\">y =</span> Sepal.Length,<span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb31-7\"><a href=\"#cb31-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">05</span>),<span class=\"at\">size =</span> .<span class=\"dv\">25</span>, <span class=\"at\">shape =</span> <span class=\"dv\">20</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb31-8\"><a href=\"#cb31-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_boxplot</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species,<span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb31-9\"><a href=\"#cb31-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">outlier.shape =</span> <span class=\"cn\">NA</span>,</span>\r\n<span id=\"cb31-10\"><a href=\"#cb31-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">width =</span> .<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb31-11\"><a href=\"#cb31-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>)<span class=\"sc\">+</span></span>\r\n<span id=\"cb31-12\"><a href=\"#cb31-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"at\">data=</span>summ_iris,</span>\r\n<span id=\"cb31-13\"><a href=\"#cb31-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>Species,<span class=\"at\">y =</span> mean,<span class=\"at\">group =</span> Species, <span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb31-14\"><a href=\"#cb31-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">shape=</span><span class=\"dv\">18</span>,</span>\r\n<span id=\"cb31-15\"><a href=\"#cb31-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">size =</span> <span class=\"fl\">1.5</span>,</span>\r\n<span id=\"cb31-16\"><a href=\"#cb31-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>,<span class=\"at\">y =</span> <span class=\"dv\">0</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb31-17\"><a href=\"#cb31-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_errorbar</span>(<span class=\"at\">data =</span> summ_iris,</span>\r\n<span id=\"cb31-18\"><a href=\"#cb31-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> mean, <span class=\"at\">group =</span> Species, <span class=\"at\">colour =</span> Species,</span>\r\n<span id=\"cb31-19\"><a href=\"#cb31-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                        <span class=\"at\">ymin =</span> mean<span class=\"sc\">-</span>se, <span class=\"at\">ymax =</span> mean<span class=\"sc\">+</span>se),</span>\r\n<span id=\"cb31-20\"><a href=\"#cb31-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">width=</span>.<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb31-21\"><a href=\"#cb31-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">position=</span><span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>)</span>\r\n<span id=\"cb31-22\"><a href=\"#cb31-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>    )</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>这里使用<code>ggsci</code>包的<code>scale_color_aaas()</code>,<code>scale_fill_aaas()</code>将尺度的颜色进行改变（非常好用！)</p>\r\n<div class=\"sourceCode\" id=\"cb32\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb32-1\"><a href=\"#cb32-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb32-2\"><a href=\"#cb32-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb32-3\"><a href=\"#cb32-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb32-4\"><a href=\"#cb32-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">adjust=</span><span class=\"fl\">1.5</span>, <span class=\"at\">trim=</span><span class=\"cn\">FALSE</span>, <span class=\"at\">colour=</span><span class=\"cn\">NA</span>, <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb32-5\"><a href=\"#cb32-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"fu\">as.numeric</span>(Species)<span class=\"sc\">-</span><span class=\"fl\">0.1</span>,</span>\r\n<span id=\"cb32-6\"><a href=\"#cb32-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                 <span class=\"at\">y =</span> Sepal.Length,<span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb32-7\"><a href=\"#cb32-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">05</span>),<span class=\"at\">size =</span> .<span class=\"dv\">25</span>, <span class=\"at\">shape =</span> <span class=\"dv\">20</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb32-8\"><a href=\"#cb32-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_boxplot</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species,<span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb32-9\"><a href=\"#cb32-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">outlier.shape =</span> <span class=\"cn\">NA</span>,</span>\r\n<span id=\"cb32-10\"><a href=\"#cb32-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">width =</span> .<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb32-11\"><a href=\"#cb32-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>)<span class=\"sc\">+</span></span>\r\n<span id=\"cb32-12\"><a href=\"#cb32-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"at\">data=</span>summ_iris,</span>\r\n<span id=\"cb32-13\"><a href=\"#cb32-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>Species,<span class=\"at\">y =</span> mean,<span class=\"at\">group =</span> Species, <span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb32-14\"><a href=\"#cb32-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">shape=</span><span class=\"dv\">18</span>,</span>\r\n<span id=\"cb32-15\"><a href=\"#cb32-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">size =</span> <span class=\"fl\">1.5</span>,</span>\r\n<span id=\"cb32-16\"><a href=\"#cb32-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>,<span class=\"at\">y =</span> <span class=\"dv\">0</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb32-17\"><a href=\"#cb32-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_errorbar</span>(<span class=\"at\">data =</span> summ_iris,</span>\r\n<span id=\"cb32-18\"><a href=\"#cb32-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> mean, <span class=\"at\">group =</span> Species, <span class=\"at\">colour =</span> Species,</span>\r\n<span id=\"cb32-19\"><a href=\"#cb32-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                        <span class=\"at\">ymin =</span> mean<span class=\"sc\">-</span>se, <span class=\"at\">ymax =</span> mean<span class=\"sc\">+</span>se),</span>\r\n<span id=\"cb32-20\"><a href=\"#cb32-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">width=</span>.<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb32-21\"><a href=\"#cb32-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">position=</span><span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>)</span>\r\n<span id=\"cb32-22\"><a href=\"#cb32-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ) <span class=\"sc\">+</span></span>\r\n<span id=\"cb32-23\"><a href=\"#cb32-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_color_aaas</span>() <span class=\"sc\">+</span></span>\r\n<span id=\"cb32-24\"><a href=\"#cb32-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_fill_aaas</span>()</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n<p>最后使用<code>ggpubr</code>包的<code>geom_signif</code>加入显著性结果，<code>ggsave</code>保存图片。</p>\r\n<div class=\"sourceCode\" id=\"cb33\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb33-1\"><a href=\"#cb33-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 绘图</span></span>\r\n<span id=\"cb33-2\"><a href=\"#cb33-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(iris_plot , <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species))<span class=\"sc\">+</span></span>\r\n<span id=\"cb33-3\"><a href=\"#cb33-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb33-4\"><a href=\"#cb33-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">15</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>),</span>\r\n<span id=\"cb33-5\"><a href=\"#cb33-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">adjust=</span><span class=\"fl\">1.5</span>, <span class=\"at\">trim=</span><span class=\"cn\">FALSE</span>, <span class=\"at\">colour=</span><span class=\"cn\">NA</span>, <span class=\"at\">side =</span> <span class=\"st\">&#39;r&#39;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-6\"><a href=\"#cb33-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"fu\">as.numeric</span>(Species)<span class=\"sc\">-</span><span class=\"fl\">0.1</span>,</span>\r\n<span id=\"cb33-7\"><a href=\"#cb33-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                 <span class=\"at\">y =</span> Sepal.Length,<span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb33-8\"><a href=\"#cb33-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">05</span>),<span class=\"at\">size =</span> .<span class=\"dv\">25</span>, <span class=\"at\">shape =</span> <span class=\"dv\">20</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-9\"><a href=\"#cb33-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_boxplot</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species,<span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species),</span>\r\n<span id=\"cb33-10\"><a href=\"#cb33-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">outlier.shape =</span> <span class=\"cn\">NA</span>,</span>\r\n<span id=\"cb33-11\"><a href=\"#cb33-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">width =</span> .<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb33-12\"><a href=\"#cb33-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>                             <span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>)<span class=\"sc\">+</span></span>\r\n<span id=\"cb33-13\"><a href=\"#cb33-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_point</span>(<span class=\"at\">data=</span>summ_iris,</span>\r\n<span id=\"cb33-14\"><a href=\"#cb33-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>Species,<span class=\"at\">y =</span> mean,<span class=\"at\">group =</span> Species, <span class=\"at\">color =</span> Species),</span>\r\n<span id=\"cb33-15\"><a href=\"#cb33-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">shape=</span><span class=\"dv\">18</span>,</span>\r\n<span id=\"cb33-16\"><a href=\"#cb33-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">size =</span> <span class=\"fl\">1.5</span>,</span>\r\n<span id=\"cb33-17\"><a href=\"#cb33-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>                         <span class=\"at\">position =</span> <span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>,<span class=\"at\">y =</span> <span class=\"dv\">0</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-18\"><a href=\"#cb33-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_errorbar</span>(<span class=\"at\">data =</span> summ_iris,</span>\r\n<span id=\"cb33-19\"><a href=\"#cb33-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> mean, <span class=\"at\">group =</span> Species, <span class=\"at\">colour =</span> Species,</span>\r\n<span id=\"cb33-20\"><a href=\"#cb33-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                        <span class=\"at\">ymin =</span> mean<span class=\"sc\">-</span>se, <span class=\"at\">ymax =</span> mean<span class=\"sc\">+</span>se),</span>\r\n<span id=\"cb33-21\"><a href=\"#cb33-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">width=</span>.<span class=\"dv\">05</span>,</span>\r\n<span id=\"cb33-22\"><a href=\"#cb33-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                <span class=\"at\">position=</span><span class=\"fu\">position_nudge</span>(<span class=\"at\">x =</span> .<span class=\"dv\">1</span>, <span class=\"at\">y =</span> <span class=\"dv\">0</span>)</span>\r\n<span id=\"cb33-23\"><a href=\"#cb33-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ) <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-24\"><a href=\"#cb33-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_color_jco</span>() <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-25\"><a href=\"#cb33-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_fill_jco</span>() <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-26\"><a href=\"#cb33-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_signif</span>(<span class=\"at\">comparisons =</span> <span class=\"fu\">list</span>(<span class=\"fu\">c</span>(<span class=\"st\">&quot;versicolor&quot;</span>, <span class=\"st\">&quot;setosa&quot;</span>),</span>\r\n<span id=\"cb33-27\"><a href=\"#cb33-27\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                                 <span class=\"fu\">c</span>(<span class=\"st\">&quot;versicolor&quot;</span>, <span class=\"st\">&quot;virginica&quot;</span>),</span>\r\n<span id=\"cb33-28\"><a href=\"#cb33-28\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                                 <span class=\"fu\">c</span>(<span class=\"st\">&quot;setosa&quot;</span>, <span class=\"st\">&quot;virginica&quot;</span>)),</span>\r\n<span id=\"cb33-29\"><a href=\"#cb33-29\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">y_position =</span> <span class=\"fu\">c</span>(<span class=\"fl\">8.2</span>, <span class=\"fl\">8.6</span>, <span class=\"fl\">8.4</span>),</span>\r\n<span id=\"cb33-30\"><a href=\"#cb33-30\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">map_signif_level =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;***&quot;</span> <span class=\"ot\">=</span> <span class=\"fl\">0.001</span>, <span class=\"st\">&quot;**&quot;</span> <span class=\"ot\">=</span> <span class=\"fl\">0.01</span>, <span class=\"st\">&quot;*&quot;</span> <span class=\"ot\">=</span> <span class=\"fl\">0.05</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb33-31\"><a href=\"#cb33-31\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">ggsave</span>(<span class=\"st\">&#39;云雨图.pdf&#39;</span>, <span class=\"at\">width =</span> <span class=\"dv\">6</span>, <span class=\"at\">height =</span> <span class=\"dv\">8</span>)</span></code></pre></div>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n</div>\r\n<div id=\"混合图\" class=\"section level3\">\r\n<h3>混合图</h3>\r\n<p>最后是混合图，根据自己想要的图，可以自行添加。相信这个代码简单的图给大家学术作图上省了不少时间。</p>\r\n<div class=\"sourceCode\" id=\"cb34\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb34-1\"><a href=\"#cb34-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(tidyverse)</span>\r\n<span id=\"cb34-2\"><a href=\"#cb34-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>() <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-3\"><a href=\"#cb34-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_boxplot</span>(</span>\r\n<span id=\"cb34-4\"><a href=\"#cb34-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;setosa&quot;</span>), </span>\r\n<span id=\"cb34-5\"><a href=\"#cb34-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species), <span class=\"at\">outlier.color =</span> <span class=\"cn\">NA</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-6\"><a href=\"#cb34-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ggbeeswarm<span class=\"sc\">::</span><span class=\"fu\">geom_beeswarm</span>(</span>\r\n<span id=\"cb34-7\"><a href=\"#cb34-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;setosa&quot;</span>),</span>\r\n<span id=\"cb34-8\"><a href=\"#cb34-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species, <span class=\"at\">color =</span> Species), <span class=\"at\">beeswarmArgs=</span><span class=\"fu\">list</span>(<span class=\"at\">side=</span><span class=\"sc\">+</span><span class=\"dv\">1</span>)</span>\r\n<span id=\"cb34-9\"><a href=\"#cb34-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-10\"><a href=\"#cb34-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_violin</span>(</span>\r\n<span id=\"cb34-11\"><a href=\"#cb34-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;versicolor&quot;</span>), </span>\r\n<span id=\"cb34-12\"><a href=\"#cb34-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species), <span class=\"at\">side=</span><span class=\"st\">&quot;r&quot;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-13\"><a href=\"#cb34-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_dotplot</span>(</span>\r\n<span id=\"cb34-14\"><a href=\"#cb34-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;versicolor&quot;</span>), </span>\r\n<span id=\"cb34-15\"><a href=\"#cb34-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species), <span class=\"at\">method=</span><span class=\"st\">&quot;histodot&quot;</span>, <span class=\"at\">stackdir=</span><span class=\"st\">&quot;down&quot;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-16\"><a href=\"#cb34-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_boxplot</span>(</span>\r\n<span id=\"cb34-17\"><a href=\"#cb34-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;virginica&quot;</span>), </span>\r\n<span id=\"cb34-18\"><a href=\"#cb34-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species), <span class=\"at\">side =</span> <span class=\"st\">&quot;r&quot;</span>, <span class=\"at\">errorbar.draw =</span> <span class=\"cn\">TRUE</span>,</span>\r\n<span id=\"cb34-19\"><a href=\"#cb34-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">outlier.color =</span> <span class=\"cn\">NA</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-20\"><a href=\"#cb34-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">geom_half_point</span>(</span>\r\n<span id=\"cb34-21\"><a href=\"#cb34-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">data =</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">filter</span>(Species<span class=\"sc\">==</span><span class=\"st\">&quot;virginica&quot;</span>), </span>\r\n<span id=\"cb34-22\"><a href=\"#cb34-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Species, <span class=\"at\">y =</span> Sepal.Length, <span class=\"at\">fill =</span> Species, <span class=\"at\">color =</span> Species), <span class=\"at\">side =</span> <span class=\"st\">&quot;l&quot;</span>) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-23\"><a href=\"#cb34-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_fill_manual</span>(<span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;setosa&quot;</span> <span class=\"ot\">=</span> <span class=\"st\">&quot;#cba1d2&quot;</span>, <span class=\"st\">&quot;versicolor&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#7067CF&quot;</span>,<span class=\"st\">&quot;virginica&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#B7C0EE&quot;</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-24\"><a href=\"#cb34-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;setosa&quot;</span> <span class=\"ot\">=</span> <span class=\"st\">&quot;#cba1d2&quot;</span>, <span class=\"st\">&quot;versicolor&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#7067CF&quot;</span>,<span class=\"st\">&quot;virginica&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#B7C0EE&quot;</span>)) <span class=\"sc\">+</span></span>\r\n<span id=\"cb34-25\"><a href=\"#cb34-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">theme</span>(<span class=\"at\">legend.position =</span> <span class=\"st\">&quot;none&quot;</span>) <span class=\"sc\">+</span>   </span>\r\n<span id=\"cb34-26\"><a href=\"#cb34-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">ggsave</span>(<span class=\"st\">&#39;综合图.pdf&#39;</span>, <span class=\"at\">width =</span> <span class=\"dv\">6</span>, <span class=\"at\">height =</span> <span class=\"dv\">8</span>)</span></code></pre></div>\r\n<pre><code>## Warning: Ignoring unknown parameters: beeswarmArgs</code></pre>\r\n<pre><code>## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.\r\n## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>\r\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\r\n</div>\r\n</div>\r\n</div>\r\n</section>\r\n\r\n\r\n\r\n<!-- code folding -->\r\n\r\n\r\n<!-- dynamically load mathjax for compatibility with self-contained -->\r\n<script>\r\n  (function () {\r\n    var script = document.createElement(\"script\");\r\n    script.type = \"text/javascript\";\r\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\r\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\r\n  })();\r\n</script>\r\n\r\n</body>\r\n</html>\r\n"
  },
  {
    "path": "2020年/2020.11.14gghalves/gghalves.rmd",
    "content": "---\r\ntitle: \"Vignette Title\"\r\nauthor: \"Vignette Author\"\r\ndate: \"`r Sys.Date()`\"\r\noutput:\r\n  prettydoc::html_pretty:\r\n    theme: cayman\r\n    highlight: github\r\nvignette: >\r\n  %\\VignetteIndexEntry{Vignette Title}\r\n  %\\VignetteEngine{knitr::rmarkdown}\r\n  %\\VignetteEncoding{UTF-8}\r\n---\r\n# gghalves包\r\n\r\n## 介绍\r\n\r\n`gghalves`可以通过`ggplot2`轻松地编写自己想要的一半一半（half-half plots）的图片。比如：在散点旁边显示箱线图、在小提琴图旁边显示点图。\r\n\r\n\r\n[gghalves](https://github.com/erocoar/gghalves)将`_half_`扩展添加到选定的`geom`。比如：`geom_half_violin()`函数，相当于`geom_violin()`函数的变体，该函数主要作用就是展示一半的小提琴图，然后与其他图形组合。还包含以下函数：\r\n\r\n- geom_half_boxplot\r\n- geom_half_violin\r\n- geom_half_point\r\n\r\n## 安装\r\n\r\ngghalves通过GitHub安装:\r\n```\r\nif (!require(devtools)) {\r\n    install.packages('devtools')\r\n}\r\ndevtools::install_github('erocoar/gghalves')\r\n```\r\n\r\n\r\n## 函数介绍\r\n\r\n```\r\ngeom_half_violin(mapping = NULL, data = NULL, stat = \"half_ydensity\",\r\n  position = \"dodge\", ..., side = \"l\", nudge = 0,\r\n  draw_quantiles = NULL, trim = TRUE, scale = \"area\",\r\n  na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)\r\n```\r\n其参数包括：翻译来源[生信玩家](https://blog.csdn.net/weixin_43700050/article/details/107512448)\r\n\r\n| 参数 | 解释 |\r\n| --- | --- |\r\n| `mapping` | 通过`aes()`指定图形属性映射。默认为`NULL`，使用`ggplot()`中`aes()`指定的映射。 |\r\n| `data` | 指定数据框。默认为`NULL`，使用`ggplot()`中的数据。 |\r\n| `stat` | 覆盖geom\\_density\\(\\)和stat\\_density\\(\\)之间的默认连接。 |\r\n| `position` | 位置调整，可以是字符串，默认为`\"dodge\"`，也可以是位置调整函数的调用结果。 |\r\n| `side` | 画半小提琴图的一侧。 “ l”代表左，“ r”代表右，默认为“ l”。 |\r\n| `nudge` | 在小提琴图和分配给x轴上给定因子的空间中间之间添加空间。 |\r\n| `draw_quantiles` | 如果不是`MULL`（默认为`NULL`），在给定的密度估计分位数处绘制水平线。 |\r\n| `trim` | 若为`TRUE`（默认），将小提琴的尾部修整到数据范围。 若为`FALSE`，不修剪尾巴。 |\r\n| `scale` | 如果为`\"area\"`（默认），则所有小提琴都具有相同的面积（修剪尾部之前）。  \r\n| `na.rm` | 如果为`FALSE`（默认），则会使用警告删除缺失值。如果为`TRUE`，则会自动删除缺少的值。 |\r\n| `show.legend` | 逻辑值，默认为`NA`，若为`FALSE`，不显示该图层的图例；  若为`TRUE`，则显示该图层的图例。  它也可以是带有名称（图形属性）的逻辑向量，用来选择要显示的图形属性。 如`show.legend = c(size = TRUE,color = FALSE)`表示显示`size`对应的图例，而不显示`color`对应的图例。 |\r\n| `inherit.aes` | 默认为`TRUE`，若为`FALSE`，覆盖`ggplot()`中`aes()`默认属性，而不是与他们组合。 |\r\n| `geom` | 覆盖`geom_density()`和`stat_density()`之间的默认连接。 |\r\n| `bw` | 要使用的平滑带宽度。如果是数字，则为平滑内核的标准差。 |\r\n| `adjust` | 多次带宽调整。这使得可以在仍使用带宽估计器的情况下调整带宽。例如，`adjust = 1/2`表示使用默认带宽的一半。 |\r\n\r\n## 示例\r\n\r\n### 单个函数\r\n\r\n我们以iris数据集作为本例数据，先使用单个函数进行绘制。\r\n\r\n```{r eval=FALSE, message=FALSE, warning=FALSE, include=TRUE}\r\nif (!require(devtools)) {\r\n\tinstall.packages('devtools')\r\n}\r\ndevtools::install_github('erocoar/gghalves')\r\n```\r\n\r\n\r\n### geom_half_boxplot\r\n\r\n\r\n\r\n\r\n```{r}\r\nlibrary(gghalves) # Compose Half-Half Plots Using Your Favourite Geoms # Compose Half-Half Plots Using Your Favourite Geoms\r\n\r\nggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + \r\n\tgeom_half_boxplot() \r\n```\r\n\r\n默认为箱子在右，使用`center = TRUE`将箱子居中。下面函数参数调整类似，就不再绘制结果了，就把最原始的进行展示。\r\n```{r}\r\nggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + \r\n\tgeom_half_boxplot(center = TRUE) \r\n```\r\n\r\n### geom_half_violin\r\n\r\n\r\n```{r}\r\nggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + \r\n\tgeom_half_violin()\r\n```\r\n\r\n\r\n### geom_half_point\r\n\r\n```{r}\r\nggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) + \r\n\tgeom_half_point()\r\n```\r\n\r\n### 综合案例\r\n\r\n#### 云雨图\r\n\r\n该案例来自**生信玩家**公众号[ggplot扩展包--gghalves](https://blog.csdn.net/weixin_43700050/article/details/107512448)但并没有对代码进行详细解释。\r\n\r\n这里小编对代码进行详细解释，喜欢的伙伴，可以按照解释自己理解，并用到自己实际所需的复合图中。\r\n\r\n先将数据的统计摘要进行计算存到了`summ_iris`中，包含了均值，标准差，数量标准误差。`iris_plot`为所需数据，这里将`Species`变量设置为因子，因为要用它作为分类变量。\r\n```{r}\r\nlibrary(tidyverse) # Easily Install and Load the 'Tidyverse' # Easily Install and Load the 'Tidyverse'\r\n# 统计摘要\r\nsumm_iris <- iris %>% \r\n\tgroup_by(Species) %>% \r\n\tsummarise(\r\n\t\tmean = mean(Sepal.Length),\r\n\t\tsd = sd(Sepal.Length),\r\n\t\tn = n()\r\n\t) %>% \r\n\tmutate(se = sd/sqrt(n),\r\n\t\t\t\t Species = factor(Species, levels = c('versicolor', 'setosa', 'virginica')))\r\nsumm_iris\r\n# 数据转换  \r\niris_plot <- iris %>% \r\n\tmutate(Species = factor(Species, levels = c('versicolor', 'setosa', 'virginica')))\r\nhead(iris_plot) \r\n```\r\n接下来进行绘图，我们想要得到`Species`与`Sepal.Length`的关系，其中`Species`为离散变量，`Sepal.Length`为连续变量。并绘制了半边的小提琴图，并将该图往右移了0.15，上下位置不变（`position_nudge(x = .15, y = 0)`），为了后面绘制其他图形留位置。\r\n\r\n```{r}\r\nlibrary(gghalves) # Compose Half-Half Plots Using Your Favourite Geoms # Compose Half-Half Plots Using Your Favourite Geoms\r\nlibrary(ggsignif) # Significance Brackets for 'ggplot2'\r\nlibrary(ggsci) # Scientific Journal and Sci-Fi Themed Color Palettes for\r\n'ggplot2'\r\nlibrary(ggpubr) # 'ggplot2' Based Publication Ready Plots\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t  side = 'r')\r\n```\r\n\r\n\r\n接下来加入散点图，并使x坐标往左移动0.1（`x = as.numeric(Species)-0.1`），使用`position_jitter`使得重复的点分散开。\r\n\r\n\r\n```{r}\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t side = 'r') +\r\n\tgeom_point(aes(x = as.numeric(Species)-0.1,\r\n\t\t\t\t\t\t\t\t y = Sepal.Length,color = Species),\r\n\t\t\t\t\t\t position = position_jitter(width = .05),size = .25, shape = 20)\r\n```\r\n\r\n在原来基础上加入箱子图，位置放在正中间\r\n\r\n\r\n```{r}\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t adjust=1.5, trim=FALSE, colour=NA, side = 'r') +\r\n\tgeom_point(aes(x = as.numeric(Species)-0.1,\r\n\t\t\t\t\t\t\t\t y = Sepal.Length,color = Species),\r\n\t\t\t\t\t\t position = position_jitter(width = .05),size = .25, shape = 20) +\r\n\tgeom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),\r\n\t\t\t\t\t\t\t outlier.shape = NA,\r\n\t\t\t\t\t\t\t width = .05,\r\n\t\t\t\t\t\t\t color = \"black\")\r\n```\r\n\r\n这里比较有趣的是，作者还通过`geom_point`和`geom_errorbar`加入和汇总信息以及对应的误差项。\r\n\r\n```{r}\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t adjust=1.5, trim=FALSE, colour=NA, side = 'r') +\r\n\tgeom_point(aes(x = as.numeric(Species)-0.1,\r\n\t\t\t\t\t\t\t\t y = Sepal.Length,color = Species),\r\n\t\t\t\t\t\t position = position_jitter(width = .05),size = .25, shape = 20) +\r\n\tgeom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),\r\n\t\t\t\t\t\t\t outlier.shape = NA,\r\n\t\t\t\t\t\t\t width = .05,\r\n\t\t\t\t\t\t\t color = \"black\")+\r\n\tgeom_point(data=summ_iris,\r\n\t\t\t\t\t\t aes(x=Species,y = mean,group = Species, color = Species),\r\n\t\t\t\t\t\t shape=18,\r\n\t\t\t\t\t\t size = 1.5,\r\n\t\t\t\t\t\t position = position_nudge(x = .1,y = 0)) +\r\n\tgeom_errorbar(data = summ_iris,\r\n\t\t\t\t\t\t\t\taes(x = Species, y = mean, group = Species, colour = Species,\r\n\t\t\t\t\t\t\t\t\t\tymin = mean-se, ymax = mean+se),\r\n\t\t\t\t\t\t\t\twidth=.05,\r\n\t\t\t\t\t\t\t\tposition=position_nudge(x = .1, y = 0)\r\n\t)\r\n```\r\n\r\n这里使用`ggsci`包的`scale_color_aaas()`,`scale_fill_aaas()`将尺度的颜色进行改变（非常好用！)在下面展示另外一种配色（`scale_color_jco`）\r\n\r\n\r\n```{r}\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t adjust=1.5, trim=FALSE, colour=NA, side = 'r') +\r\n\tgeom_point(aes(x = as.numeric(Species)-0.1,\r\n\t\t\t\t\t\t\t\t y = Sepal.Length,color = Species),\r\n\t\t\t\t\t\t position = position_jitter(width = .05),size = .25, shape = 20) +\r\n\tgeom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),\r\n\t\t\t\t\t\t\t outlier.shape = NA,\r\n\t\t\t\t\t\t\t width = .05,\r\n\t\t\t\t\t\t\t color = \"black\")+\r\n\tgeom_point(data=summ_iris,\r\n\t\t\t\t\t\t aes(x=Species,y = mean,group = Species, color = Species),\r\n\t\t\t\t\t\t shape=18,\r\n\t\t\t\t\t\t size = 1.5,\r\n\t\t\t\t\t\t position = position_nudge(x = .1,y = 0)) +\r\n\tgeom_errorbar(data = summ_iris,\r\n\t\t\t\t\t\t\t\taes(x = Species, y = mean, group = Species, colour = Species,\r\n\t\t\t\t\t\t\t\t\t\tymin = mean-se, ymax = mean+se),\r\n\t\t\t\t\t\t\t\twidth=.05,\r\n\t\t\t\t\t\t\t\tposition=position_nudge(x = .1, y = 0)\r\n\t) +\r\n\tscale_color_aaas() +\r\n\tscale_fill_aaas()\r\n```\r\n\r\n\r\n最后使用`ggpubr`包的`geom_signif`加入显著性结果，`ggsave`保存图片。\r\n\r\n```{r}\r\n# 绘图\r\nggplot(iris_plot , aes(x = Species, y = Sepal.Length, fill = Species))+\r\n\tgeom_half_violin(aes(fill = Species),\r\n\t\t\t\t\t\t\t\t\t position = position_nudge(x = .15, y = 0),\r\n\t\t\t\t\t\t\t\t\t adjust=1.5, trim=FALSE, colour=NA, side = 'r') +\r\n\tgeom_point(aes(x = as.numeric(Species)-0.1,\r\n\t\t\t\t\t\t\t\t y = Sepal.Length,color = Species),\r\n\t\t\t\t\t\t position = position_jitter(width = .05),size = .25, shape = 20) +\r\n\tgeom_boxplot(aes(x = Species,y = Sepal.Length, fill = Species),\r\n\t\t\t\t\t\t\t outlier.shape = NA,\r\n\t\t\t\t\t\t\t width = .05,\r\n\t\t\t\t\t\t\t color = \"black\")+\r\n\tgeom_point(data=summ_iris,\r\n\t\t\t\t\t\t aes(x=Species,y = mean,group = Species, color = Species),\r\n\t\t\t\t\t\t shape=18,\r\n\t\t\t\t\t\t size = 1.5,\r\n\t\t\t\t\t\t position = position_nudge(x = .1,y = 0)) +\r\n\tgeom_errorbar(data = summ_iris,\r\n\t\t\t\t\t\t\t\taes(x = Species, y = mean, group = Species, colour = Species,\r\n\t\t\t\t\t\t\t\t\t\tymin = mean-se, ymax = mean+se),\r\n\t\t\t\t\t\t\t\twidth=.05,\r\n\t\t\t\t\t\t\t\tposition=position_nudge(x = .1, y = 0)\r\n\t) +\r\n\tscale_color_jco() +\r\n\tscale_fill_jco() +\r\n\tgeom_signif(comparisons = list(c(\"versicolor\", \"setosa\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t c(\"versicolor\", \"virginica\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t c(\"setosa\", \"virginica\")),\r\n\t\t\t\t\t\t\ty_position = c(8.2, 8.6, 8.4),\r\n\t\t\t\t\t\t\tmap_signif_level = c(\"***\" = 0.001, \"**\" = 0.01, \"*\" = 0.05)) +\r\n\tggsave('云雨图.pdf', width = 6, height = 8)\r\n```\r\n\r\n### 混合图\r\n\r\n最后是混合图，根据自己想要的图，可以自行添加。相信这个代码简单的图给大家学术作图上省了不少时间。\r\n\r\n```{r}\r\nlibrary(tidyverse) # Easily Install and Load the 'Tidyverse' # Easily Install and Load the 'Tidyverse'\r\nggplot() +\r\n\tgeom_half_boxplot(\r\n\t\tdata = iris %>% filter(Species==\"setosa\"), \r\n\t\taes(x = Species, y = Sepal.Length, fill = Species), outlier.color = NA) +\r\n\tggbeeswarm::geom_beeswarm(\r\n\t\tdata = iris %>% filter(Species==\"setosa\"),\r\n\t\taes(x = Species, y = Sepal.Length, fill = Species, color = Species), beeswarmArgs=list(side=+1)\r\n\t) +\r\n\tgeom_half_violin(\r\n\t\tdata = iris %>% filter(Species==\"versicolor\"), \r\n\t\taes(x = Species, y = Sepal.Length, fill = Species), side=\"r\") +\r\n\tgeom_half_dotplot(\r\n\t\tdata = iris %>% filter(Species==\"versicolor\"), \r\n\t\taes(x = Species, y = Sepal.Length, fill = Species), method=\"histodot\", stackdir=\"down\") +\r\n\tgeom_half_boxplot(\r\n\t\tdata = iris %>% filter(Species==\"virginica\"), \r\n\t\taes(x = Species, y = Sepal.Length, fill = Species), side = \"r\", errorbar.draw = TRUE,\r\n\t\toutlier.color = NA) +\r\n\tgeom_half_point(\r\n\t\tdata = iris %>% filter(Species==\"virginica\"), \r\n\t\taes(x = Species, y = Sepal.Length, fill = Species, color = Species), side = \"l\") +\r\n\tscale_fill_manual(values = c(\"setosa\" = \"#cba1d2\", \"versicolor\"=\"#7067CF\",\"virginica\"=\"#B7C0EE\")) +\r\n\tscale_color_manual(values = c(\"setosa\" = \"#cba1d2\", \"versicolor\"=\"#7067CF\",\"virginica\"=\"#B7C0EE\")) +\r\n\ttheme(legend.position = \"none\") +\t\r\n\tggsave('综合图.pdf', width = 8, height = 6)\r\n```\r\n### 其他参考资料\r\n\r\n[Using gghalves--Frederik Tiedemann](https://erocoar.github.io/gghalves/)\r\n\r\n[gghalves: Compose Half-Half Plots Using Your Favourite Geoms](https://cran.r-project.org/web/packages/gghalves/index.html)\r\n\r\n[CRAN](https://rdrr.io/cran/gghalves/f/vignettes/gghalves.Rmd)\r\n\r\n\r\n"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/flexdashboard.md",
    "content": "## 简介\n\n- 使用R Markdown可以将一组相关的数据可视化发布为仪表板。\n\n- 支持多种组件，包括htmlwidgets; 基本，晶格和网格图形；表格数据 量表和值箱；和文字注释。\n\n- 灵活且易于指定基于行和列的布局。可以智能地调整组件的大小以填充浏览器并适合在移动设备上显示。\n\n- 演示图板布局，用于呈现可视化效果序列和相关评论。\n\n- 使用Shiny动态驱动可视化。\n\n去年师兄用这个包做了一个不错的应用（企业可靠性统计方向的项目）。今天正好需要学习下数据可视化仪表盘的制作。尝试了下，还不错，比Tableau还要优秀。最近出一期入门，有机会可以把自己的例子介绍一下。\n\n下载方式如下：\n```\ninstall.packages(\"flexdashboard\")\n```\n\n## 官网案例分享\n\n今天分享下官网的一些[小案例](https://rmarkdown.rstudio.com/flexdashboard/examples.html \"小案例\")。主要是截图呈现，当然你可以把他的[github](https://github.com/rstudio/flexdashboard \"github\")克隆到本地，有个文件夹专门放例子的代码，尝试修改代码，应用到自己实际项目中。\n\n### 2008年NBA运动员得分情况\n\n\n\n![](https://imgkr2.cn-bj.ufileos.com/20176c63-2213-4ead-b45c-fa378b217eef.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=w%252FdtpUnaHBk1BklxKe6%252BPq1ry70%253D&Expires=1605537297)\n\n\n### 各种散点图\n\n![](https://imgkr2.cn-bj.ufileos.com/bde1939f-1930-49c4-bb44-81d716a09ce1.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=jLDVKbiSgrpJXlKfSDVTeK7sClo%253D&Expires=1605537248)\n\n\n![](https://imgkr2.cn-bj.ufileos.com/42b53872-fcfe-4603-ba3c-9ee6b775ca96.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Z7jj50JIv3HzDzImGtaiUVT2szk%253D&Expires=1605537256)\n\n### 其他例子\n\n\n![](https://imgkr2.cn-bj.ufileos.com/6f819551-f375-4cb5-8d5b-b4a5a03a551a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=zQoPXU9PUkadIF6hYSYn2KXFb5U%253D&Expires=1605537340)\n\n\n![](https://imgkr2.cn-bj.ufileos.com/ff9d45a7-a1ab-4d93-9a09-427b4eaf29e6.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=ig0Bp6ORQ8cXVxHXOUpE2ZY7rbw%253D&Expires=1605537186)\n\n\n![](https://imgkr2.cn-bj.ufileos.com/2e94d2a6-85f7-4bab-8402-929a47d70de0.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=ICP%252FysUWGCUXKEhwXpUmYtLwZW0%253D&Expires=1605537355)\n\n当然这些都是可以交互的。大家可以去上面的网站访问下。\n\n"
  },
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    "path": "2020年/2020.11.16flexdashboard/test.html",
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) 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-webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: horizontal;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: row;\n      -ms-flex-direction: row;\n          flex-direction: row;\n}\n\n.dashboard-column {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.chart-wrapper-flex {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.chart-stage-flex {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  position: relative;\n}\n\n.chart-shim {\n  position: absolute;\n  left: 8px; top: 8px; right: 8px; bottom: 8px;\n}\n\n.no-padding .chart-shim {\n  left: 0; top: 0; right: 0; bottom: 0;\n}\n\n.flowing-content-shim {\n  overflow: auto;\n  left: 0; top: 0; right: 0; bottom: 0;\n  padding-left: 8px;\n  padding-right: 8px;\n}\n\n.flowing-content-container {\n  padding-top: 8px;\n  padding-bottom: 8px;\n}\n\n.chart-stage .table-bordered {\n  border: none;\n}\n\n.chart-stage .table-bordered > tbody > tr > th,\n.chart-stage .table-bordered > tfoot > tr > th,\n.chart-stage .table-bordered > tbody > tr > td,\n.chart-stage .table-bordered > tfoot > tr > td {\n  border: none;\n}\n\n.chart-stage .table-bordered > tbody > tr > td {\n  border-top: 1px solid #dddddd;\n}\n\n.chart-stage .table-bordered > thead > tr > th {\n  border: none;\n  border-bottom: 2px solid #dddddd;\n}\n\n.bootstrap-table table>thead {\n  background-color: #fff;\n}\n\n.bootstrap-table table.data,\n.bootstrap-table table.shiny-table {\n  width: inherit !important;\n}\n\n.bootstrap-table table.data>tbody>tr>th,\n.bootstrap-table table.shiny-table>tbody>tr>th {\n  border-top: none;\n  border-bottom: 2px solid #dddddd;\n  padding: 5px;\n}\n\n.bootstrap-table .data td[align=right] {\n  font-family: inherit;\n}\n\n.chart-wrapper form {\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n.shiny-input-container label {\n  font-weight: normal;\n}\n\n.chart-stage .html-widget {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n.chart-stage .html-widget-static-bound {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n.chart-stage .shiny-bound-output {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n/* Omit display of empty paragraphs */\n\n.chart-shim>p:empty {\n  display: none;\n}\n\n.chart-stage>p:empty {\n  display: none;\n}\n\n/* Omit display of special knitr options div*/\n\n.chart-stage .knitr-options {\n  display: none;\n}\n\n/* Automatically resizing images */\n\n.image-container {\n  position: absolute;\n  top: 0;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  margin: 0;\n}\n\n.image-container img {\n  opacity: 0;\n  overflow: hidden;\n}\n\n/* Value box */\n\n.value-box {\n  border-radius: 2px;\n  position: relative;\n  display: block;\n  margin-right: 8px;\n  margin-bottom: 8px;\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.1);\n}\n\n.value-box > .inner {\n  padding: 10px;\n  padding-left: 20px;\n  padding-right: 20px;\n}\n\n.value-box .value {\n  font-size: 38px;\n  font-weight: bold;\n  margin: 0 0 3px 0;\n  white-space: nowrap;\n  padding: 0;\n}\n\n.value-box .caption {\n  font-size: 15px;\n}\n.value-box .caption > small {\n  display: block;\n  font-size: 13px;\n  margin-top: 5px;\n}\n\n.value-box .icon i {\n  position: absolute;\n  top: 15px;\n  right: 15px;\n  font-size: 80px;\n  color: rgba(0, 0, 0, 0.15);\n}\n\n.linked-value:hover {\n  cursor: pointer;\n}\n\n.value-box.linked-value:hover {\n  box-shadow: 0 2px 2px rgba(0, 0, 0, 0.3);\n}\n\n/* STORYBOARD */\n\n.storyboard-nav button {\n  background: transparent;\n  border: 0;\n  opacity: .3;\n  outline: none;\n  padding: 0;\n}\n\n.storyboard-nav button:hover,\n.storyboard-nav button:hover {\n  opacity: .5;\n}\n\n.storyboard-nav button:disabled,\n.storyboard-nav button:disabled {\n  opacity: .1;\n}\n\n.storyboard-nav .sbnext,\n.storyboard-nav .sbprev {\n  float: left;\n  width: 2%;\n  height: 120px;\n  font-size: 50px;\n}\n\n.storyboard-nav .sbprev {\n  text-align: left;\n  width: 2%;\n}\n\n.storyboard-nav .sbnext {\n  float: right;\n  text-align: right;\n  margin-right: 8px;\n}\n\n.storyboard-nav .sbframelist {\n  margin: 0 auto;\n  width: 94%;\n  height: 120px;\n  overflow: hidden;\n  text-shadow: none;\n  margin-bottom: 8px;\n}\n\n.storyboard-nav .sbframelist ul {\n  list-style: none;\n  margin: 0;\n  padding: 0;\n  height: 100%;\n}\n\n.storyboard-nav .sbframelist ul li {\n  float: left;\n  width: 270px;\n  height: 100%;\n  padding: 10px 10px 10px 10px;\n  margin-right: 8px;\n  background: #fff;\n  border: 1px solid #e2e2e2;\n  border-radius: 3px;\n  color: #3a3c47;\n  text-align: left;\n  font-size: 14px;\n  cursor: pointer;\n}\n\n.storyboard-nav .sbframelist ul li:last-child {\n  margin-right: 0px;\n}\n\n.storyboard-nav .sbframelist ul li.active {\n  color: #fff;\n  background: #B8B8B8;\n}\n\n.sbframe-commentary {\n  width: 300px;\n  background: #fbfbfb;\n  font-size: 14px;\n}\n\n.sbframe-commentary ul {\n  padding-left: 22px;\n}\n\n.sbframe.active {\n  display: flex;\n}\n\n.sbframe:not(.active) {\n  display: none;\n}\n\n/* NAV TABS */\n\n.nav-tabs-custom > .nav-tabs {\n  margin: 0;\n  border-bottom: 1px solid #d7d7d7;\n  color: #666;\n  font-size: 14px;\n  font-weight: 300;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.nav-tabs-custom > .nav-tabs > li {\n  border-top: 3px solid transparent;\n  margin-bottom: -1px;\n  margin-right: 5px;\n}\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:active {\n  color: #666;\n  font-weight: 300;\n  font-size: 14px;\n  border-radius: 0;\n  padding: 3px 10px 5px;\n  text-transform: none;\n}\n\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a {\n  border-bottom-color: transparent;\n}\n\n.nav-tabs-custom > .nav-tabs > li > a.text-muted {\n  color: #999;\n}\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:hover {\n  background: transparent;\n  margin: 0;\n}\n.nav-tabs-custom > .nav-tabs > li > a:hover {\n  color: #999;\n}\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:hover,\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:focus,\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:active {\n  border-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs > li.active {\n  border-top-color: #3c8dbc;\n}\n.nav-tabs-custom > .nav-tabs > li.active > a,\n.nav-tabs-custom > .nav-tabs > li.active:hover > a {\n  background-color: #fff;\n  color: #666;\n}\n.nav-tabs-custom > .nav-tabs > li.active > a {\n  border-top-color: transparent;\n  border-left-color: #d7d7d7;\n  border-right-color: #d7d7d7;\n}\n.nav-tabs-custom > .nav-tabs > li:first-of-type {\n  margin-left: 0;\n}\n.nav-tabs-custom > .nav-tabs > li:first-of-type.active > a {\n  border-left-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs.pull-right {\n  float: none !important;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li {\n  float: right;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type {\n  margin-right: 0;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type > a {\n  border-left-width: 1px;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type.active > a {\n  border-left-color: #d7d7d7;\n  border-right-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs > li.header {\n  line-height: 35px;\n  padding: 0 10px;\n  font-size: 20px;\n  color: #666;\n}\n.nav-tabs-custom > .nav-tabs > li.header > .fa,\n.nav-tabs-custom > .nav-tabs > li.header > .glyphicon,\n.nav-tabs-custom > .nav-tabs > li.header > .ion {\n  margin-right: 5px;\n}\n.nav-tabs-custom > .tab-content {\n  background: #fff;\n  padding: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n}\n\n.nav-tabs-custom > .tab-content > .chart-wrapper {\n  background: #fff;\n  border: none;\n  margin: 0;\n}\n\n\n.nav-tabs-custom > .tab-content > .active {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n}\n\n.nav-tabs-custom .dropdown.open > a:active,\n.nav-tabs-custom .dropdown.open > a:focus {\n  background: transparent;\n  color: #999;\n}\n\n\n@media (max-width: 767px) {\n  .value-box {\n    text-align: center;\n    margin-right: 8px;\n  }\n  .value-box .icon {\n    display: none;\n  }\n}\n\n/* Fixed position sidebar */\n\n.section.sidebar {\n  position: fixed;\n  top: 51px; /* overridden by theme */\n  left: 0;\n  bottom: 0;\n  border-right: 1px solid #e2e2e2;\n  background-color: white; /* overridden by theme */\n  padding-left: 10px;\n  padding-right: 10px;\n  visibility: hidden;\n  overflow: auto;\n}\n\n.section.sidebar form p:first-child {\n  margin-top: 10px;\n}\n\n/* Embedded source code */\n\n#flexdashboard-source-code {\n  display: none;\n}\n\n.featherlight-content #flexdashboard-source-code {\n  display: inline-block;\n}\n\n.featherlight-content {\n  width: 80%;\n  max-width: 800px;\n  padding: 0 !important;\n  border-bottom: none !important;\n}\n\n.featherlight:last-of-type {\n  background: rgba(0, 0, 0, 0.7);\n}\n\n.featherlight-inner {\n  width: 100%;\n}\n\n.featherlight-content pre {\n  margin: 0;\n  border: 0;\n  background: #fff;\n  font-size: 12px;\n}\n\n.unselectable {\n  -ms-user-select: none;\n  -webkit-user-select: none;\n  -khtml-user-select: none;\n  -moz-user-select: -moz-none;\n  -o-user-select: none;\n  user-select: none;\n}\n\n#flexdashboard-source-code code {\n  -ms-user-select: text;\n  -webkit-user-select: text;\n  -khtml-user-select: text;\n  -moz-user-select: text;\n  -o-user-select: text;\n  user-select: text;\n}\n\n/* DataTables Tweaks */\n\n.dataTables_filter input[type=\"search\"] {\n  -webkit-appearance: searchfield;\n  outline: none;\n}\n\n.dataTables_wrapper.no-footer\n.dataTables_info {\n  padding-top: 0;\n}\n\n\ntable.dataTable thead th {\n  border-bottom: 1px solid #d7d7d7;\n}\n\n.dataTables_wrapper.no-footer .dataTables_scrollBody {\n  border-bottom: 1px solid #d7d7d7;\n}\n\n/* Mobile phone only CSS */\n\n.desktop-layout div.section.mobile {\n  display: none;\n}\n\n.mobile-layout div.section.no-mobile {\n  display: none;\n}\n\n.mobile-figure {\n  display: none;\n}\n\n\n\nbody {\n  padding-top: 60px;\n}\n\n.section.sidebar {\n  top: 51px;\n  background-color: rgba(39, 128, 227, 0.1);\n}\n\n.value-box {\n  color: #f9f9f9;\n}\n\n.bg-primary {\n  background-color: rgba(39, 128, 227, 0.7);\n}\n\n.storyboard-nav .sbframelist ul li.active {\n  background-color: rgba(39, 128, 227, 0.7);\n}\n\n.nav-tabs-custom > .nav-tabs > li.active {\n  border-top-color: rgba(39, 128, 227, 0.7);\n}\n\n.bg-info {\n  background-color: rgba(153, 84, 187, 0.7);\n}\n\n.bg-warning {\n  background-color: rgba(255, 117, 24, 0.7);\n}\n\n.bg-danger {\n  background-color: rgba(255, 0, 57, 0.7);\n}\n\n.bg-success {\n  background-color: rgba(63, 182, 24, 0.7);\n}\n\n.chart-title {\n  font-weight: 500;\n}\n\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:active {\n  font-weight: 500;\n}\n\n@media only screen and (min-width: 768px) {\nhtml, body {\n  height: 100%;\n}\n\n#dashboard-container {\n  height: 100%;\n}\n}\n</style>\n\n\n\n</head>\n\n<body>\n\n<div class=\"navbar navbar-inverse navbar-fixed-top\" role=\"navigation\">\n<div class=\"container-fluid\">\n<div class=\"navbar-header\">\n\n<button id=\"navbar-button\" type=\"button\" class=\"navbar-toggle collapsed\" data-toggle=\"collapse\" data-target=\"#navbar\">\n<span class=\"icon-bar\"></span>\n<span class=\"icon-bar\"></span>\n<span class=\"icon-bar\"></span>\n</button>\n\n<span class=\"navbar-logo pull-left\">\n  \n</span>\n<span class=\"navbar-brand\">\n  庄闪闪的成长手册\n  <span class=\"navbar-author\">\n        </span>\n</span>\n\n</div>\n<div id=\"navbar\" class=\"navbar-collapse collapse\">\n<ul class=\"nav navbar-nav navbar-left\">\n</ul>\n<ul class=\"nav navbar-nav navbar-right\">\n</ul>\n</div><!--/.nav-collapse-->\n</div><!--/.container-->\n</div><!--/.navbar-->\n\n<script type=\"text/javascript\">\n\n\nvar FlexDashboard = (function () {\n\n  // initialize options\n  var _options = {};\n\n  var FlexDashboard = function() {\n\n    // default options\n    _options = $.extend(_options, {\n      theme: \"cosmo\",\n      fillPage: false,\n      orientation: 'columns',\n      storyboard: false,\n      defaultFigWidth: 576,\n      defaultFigHeight: 461,\n      defaultFigWidthMobile: 360,\n      defaultFigHeightMobile: 461,\n      isMobile: false,\n      isPortrait: false,\n      resize_reload: true\n    });\n  };\n\n  function init(options) {\n\n    // extend default options\n    $.extend(true, _options, options);\n\n    // add ids to sections that don't have them (pandoc won't assign ids\n    // to e.g. sections with titles consisting of only chinese characters)\n    var nextId = 1;\n    $('.level1:not([id]),.level2:not([id]),.level3:not([id])').each(function() {\n      $(this).attr('id', 'dashboard-' + nextId++);\n    });\n\n    // find navbar items\n    var navbarItems = $('#flexdashboard-navbar');\n    if (navbarItems.length)\n      navbarItems = JSON.parse(navbarItems.html());\n    addNavbarItems(navbarItems);\n\n    // find the main dashboard container\n    var dashboardContainer = $('#dashboard-container');\n\n    // resolve mobile classes\n    resolveMobileClasses(dashboardContainer);\n\n    // one time global initialization for components\n    componentsInit(dashboardContainer);\n\n    // look for a global sidebar\n    var globalSidebar = dashboardContainer.find(\".section.level1.sidebar\");\n    if (globalSidebar.length > 0) {\n\n      // global layout for fullscreen displays\n      if (!isMobilePhone()) {\n\n         // hoist it up to the top level\n         globalSidebar.insertBefore(dashboardContainer);\n\n         // lay it out (set width/positions)\n         layoutSidebar(globalSidebar, dashboardContainer);\n\n      // tuck sidebar into first page for mobile phones\n      } else {\n\n        // convert it into a level3 section\n        globalSidebar.removeClass('sidebar');\n        globalSidebar.removeClass('level1');\n        globalSidebar.addClass('level3');\n        var h1 = globalSidebar.children('h1');\n        var h3 = $('<h3></h3>');\n        h3.append(h1.contents());\n        h3.insertBefore(h1);\n        h1.detach();\n\n        // move it into the first page\n        var page = dashboardContainer.find('.section.level1').first();\n        if (page.length > 0)\n          page.prepend(globalSidebar);\n      }\n    }\n\n    // look for pages to layout\n    var pages = $('div.section.level1');\n    if (pages.length > 0) {\n\n        // find the navbar and collapse on clicked\n        var navbar = $('#navbar');\n        navbar.on(\"click\", \"a[data-toggle!=dropdown]\", null, function () {\n           navbar.collapse('hide');\n        });\n\n        // envelop the dashboard container in a tab content div\n        dashboardContainer.wrapInner('<div class=\"tab-content\"></div>');\n\n        pages.each(function(index) {\n\n          // lay it out\n          layoutDashboardPage($(this));\n\n          // add it to the navbar\n          addToNavbar($(this), index === 0);\n\n        });\n\n    } else {\n\n      // remove the navbar and navbar button if we don't\n      // have any navbuttons\n      if (navbarItems.length === 0) {\n        $('#navbar').remove();\n        $('#navbar-button').remove();\n      }\n\n      // add the storyboard class if requested\n      if (_options.storyboard)\n        dashboardContainer.addClass('storyboard');\n\n      // layout the entire page\n      layoutDashboardPage(dashboardContainer);\n    }\n\n    // if we are in shiny we need to trigger a window resize event to\n    // force correct layout of shiny-bound-output elements\n    if (isShinyDoc())\n      $(window).trigger('resize');\n\n    // make main components visible\n    $('.section.sidebar').css('visibility', 'visible');\n    dashboardContainer.css('visibility', 'visible');\n\n    // handle location hash\n    handleLocationHash();\n\n    // intialize prism highlighting\n    initPrismHighlighting();\n\n    // record mobile and orientation state then register a handler\n    // to refresh if resize_reload is set to true and it changes\n    _options.isMobile = isMobilePhone();\n    _options.isPortrait = isPortrait();\n    if (_options.resize_reload) {\n      $(window).on('resize', function() {\n        if (_options.isMobile !== isMobilePhone() ||\n            _options.isPortrait !== isPortrait()) {\n          window.location.reload();\n        }\n      });\n    } else {\n      // if in desktop mode and resizing to mobile, make sure the heights are 100%\n      // This enforces what `fillpage.css` does for \"wider\" pages.\n      // Since we are not reloading once the page becomes small, we need to force the height to 100%\n      // This is a new situation introduced when `_options.resize_reload` is `false`\n      if (! _options.isMobile) {\n        // only add if `fillpage.css` was added in the first place\n        if (_options.fillPage) {\n          // fillpage.css\n          $(\"html,body,#dashboard\").css(\"height\", \"100%\");\n        }\n      }\n    }\n    // trigger layoutcomplete event\n    dashboardContainer.trigger('flexdashboard:layoutcomplete');\n  }\n\n  function resolveMobileClasses(dashboardContainer) {\n     // add top level layout class\n    dashboardContainer.addClass(isMobilePhone() ? 'mobile-layout' :\n                                                  'desktop-layout');\n\n    // look for .mobile sections and add .no-mobile to their peers\n    var mobileSections = $('.section.mobile');\n    mobileSections.each(function() {\n       var id = $(this).attr('id');\n       var nomobileId = id.replace(/-\\d+$/, '');\n       $('#' + nomobileId).addClass('no-mobile');\n    });\n  }\n\n  function addNavbarItems(navbarItems) {\n\n    var navbarLeft = $('ul.navbar-left');\n    var navbarRight = $('ul.navbar-right');\n\n    for (var i = 0; i<navbarItems.length; i++) {\n\n      // get the item\n      var item = navbarItems[i];\n\n      // determine the container\n      var container = null;\n      if (item.align === \"left\")\n        container = navbarLeft;\n      else\n        container = navbarRight;\n\n      // navbar menu if we have multiple items\n      if (item.items) {\n        var menu = navbarMenu(null, item.icon, item.title, container);\n        for (var j = 0; j<item.items.length; j++) {\n          var subItem = item.items[j];\n          var li = $('<li></li>');\n          li.append(navbarLink(subItem.icon, subItem.title, subItem.href, subItem.target));\n          menu.append(li);\n        }\n      } else {\n        var li = $('<li></li>');\n        li.append(navbarLink(item.icon, item.title, item.href, item.target));\n        container.append(li);\n      }\n    }\n  }\n\n  // create or get a reference to an existing dropdown menu\n  function navbarMenu(id, icon, title, container) {\n    var existingMenu = [];\n    if (id)\n      existingMenu = container.children('#' + id);\n    if (existingMenu.length > 0) {\n      return existingMenu.children('ul');\n    } else {\n      var li = $('<li></li>');\n      if (id)\n        li.attr('id', id);\n      li.addClass('dropdown');\n      // auto add \"Share\" title on mobile if necessary\n      if (!title && icon && (icon === \"fa-share-alt\") && isMobilePhone())\n        title = \"Share\";\n      if (title) {\n        title = title + ' <span class=\"caret\"></span>';\n      }\n      var a = navbarLink(icon, title, \"#\");\n      a.addClass('dropdown-toggle');\n      a.attr('data-toggle', 'dropdown');\n      a.attr('role', 'button');\n      a.attr('aria-expanded', 'false');\n      li.append(a);\n      var ul = $('<ul class=\"dropdown-menu\"></ul>');\n      ul.attr('role', 'menu');\n      li.append(ul);\n      container.append(li);\n      return ul;\n    }\n  }\n\n  function addToNavbar(page, active) {\n\n    // capture the id and data-icon attribute (if any)\n    var id = page.attr('id');\n    var icon = page.attr('data-icon');\n    var navmenu = page.attr('data-navmenu');\n\n    // get hidden state (transfer this to navbar)\n    var hidden = page.hasClass('hidden');\n    page.removeClass('hidden');\n\n    // sanitize the id for use with bootstrap tabs\n    id = id.replace(/[.\\/?&!#<>]/g, '').replace(/\\s/g, '_');\n    page.attr('id', id);\n\n    // get the wrapper\n    var wrapper = page.closest('.dashboard-page-wrapper');\n\n    // move the id to the wrapper\n    page.removeAttr('id');\n    wrapper.attr('id', id);\n\n    // add the tab-pane class to the wrapper\n    wrapper.addClass('tab-pane');\n    if (active)\n      wrapper.addClass('active');\n\n    // get a reference to the h1, discover its inner contens, then detach it\n    var h1 = wrapper.find('h1').first();\n    var title = h1.contents();\n    h1.detach();\n\n    // create a navbar item\n    var li = $('<li></li>');\n    var a = navbarLink(icon, title, '#' + id);\n    a.attr('data-toggle', 'tab');\n    li.append(a);\n\n    // add it to the navbar (or navbar menu if specified)\n    var container = $('ul.navbar-left');\n    if (navmenu) {\n      var menuId = navmenu.replace(/\\s+/g, '');\n      var menu = navbarMenu(menuId, null, navmenu, container);\n      menu.append(li);\n    } else {\n      container.append(li);\n    }\n\n    // hide it if requested\n    if (hidden)\n      li.addClass('hidden');\n  }\n\n  function navbarLink(icon, title, href, target) {\n\n    var a = $('<a></a>');\n    if (icon) {\n\n      // get the name of the icon set and icon\n      var dashPos = icon.indexOf(\"-\");\n      var iconSet = null;\n      var iconSplit = icon.split(\" \");\n      if (iconSplit.length > 1) {\n        iconSet = iconSplit[0];\n        icon = iconSplit.slice(1).join(\" \");\n      } else {\n        iconSet = icon.substring(0, dashPos);\n      }\n      var iconName = icon.substring(dashPos + 1);\n\n      // create the icon\n      var iconElement = $('<span class=\"' + iconSet + ' ' + icon + '\"></span>');\n      if (title)\n        iconElement.css('margin-right', '7px');\n      a.append(iconElement);\n      // if href is null see if we can auto-generate based on icon (e.g. social)\n      if (!href)\n        maybeGenerateLinkFromIcon(iconName, a);\n    }\n    if (title)\n      a.append(title);\n\n    // add the href.\n    if (href) {\n      if (href === \"source_embed\") {\n        a.attr('href', '#');\n        a.attr('data-featherlight', \"#flexdashboard-source-code\");\n        a.featherlight({\n            beforeOpen: function(event){\n              $('body').addClass('unselectable');\n            },\n            afterClose: function(event){\n              $('body').removeClass('unselectable');\n            }\n        });\n      } else {\n        a.attr('href', href);\n      }\n    }\n\n    // add the arget\n    if (target)\n      a.attr('target', target);\n\n    return a;\n  }\n\n  // auto generate a link from an icon name (e.g. twitter) when possible\n  function maybeGenerateLinkFromIcon(iconName, a) {\n\n     var serviceLinks = {\n      \"twitter\": \"https://twitter.com/share?text=\" + encodeURIComponent(document.title) + \"&url=\"+encodeURIComponent(location.href),\n      \"facebook\": \"https://www.facebook.com/sharer/sharer.php?s=100&p[url]=\"+encodeURIComponent(location.href),\n      \"google-plus\": \"https://plus.google.com/share?url=\"+encodeURIComponent(location.href),\n      \"linkedin\": \"https://www.linkedin.com/shareArticle?mini=true&url=\"+encodeURIComponent(location.href) + \"&title=\" + encodeURIComponent(document.title),\n      \"pinterest\": \"https://pinterest.com/pin/create/link/?url=\"+encodeURIComponent(location.href) + \"&description=\" + encodeURIComponent(document.title)\n    };\n\n    var makeSocialLink = function(a, href) {\n      a.attr('href', '#');\n      a.on('click', function(e) {\n        e.preventDefault();\n        window.open(href);\n      });\n    };\n\n    $.each(serviceLinks, function(key, value) {\n      if (iconName.indexOf(key) !== -1)\n        makeSocialLink(a, value);\n    });\n  }\n\n  // layout a dashboard page\n  function layoutDashboardPage(page) {\n\n    // use a page wrapper so that free form content above the\n    // dashboard appears at the top rather than the side (as it\n    // would without the wrapper in a column orientation)\n    var wrapper = $('<div class=\"dashboard-page-wrapper\"></div>');\n    page.wrap(wrapper);\n\n    // if there are no level2 or level3 headers synthesize a level3\n    // header to contain the (e.g. frame it, scroll container, etc.)\n    var headers = page.find('h2,h3');\n    if (headers.length === 0)\n      page.wrapInner('<div class=\"section level3\"></div>');\n\n    // hoist up any content before level 2 or level 3 headers\n    var children = page.children();\n    children.each(function(index) {\n      if ($(this).hasClass('level2') || $(this).hasClass('level3'))\n        return false;\n      $(this).insertBefore(page);\n    });\n\n    // determine orientation and fillPage behavior for distinct media\n    var orientation, fillPage, storyboard;\n\n    // media: mobile phone\n    if (isMobilePhone()) {\n\n      // if there is a sidebar we need to ensure it's content\n      // is properly framed as an h3\n      var sidebar = page.find('.section.sidebar');\n      sidebar.removeClass('sidebar');\n      sidebar.wrapInner('<div class=\"section level3\"></div>');\n      var h2 = sidebar.find('h2');\n      var h3 = $('<h3></h3>');\n      h3.append(h2.contents());\n      h3.insertBefore(h2);\n      h2.detach();\n\n      // wipeout h2 elements then enclose them in a single h2\n      var level2 = page.find('div.section.level2');\n      level2.each(function() {\n        level2.children('h2').remove();\n        level2.children().unwrap();\n      });\n      page.wrapInner('<div class=\"section level2\"></div>');\n\n      // substitute mobile images\n      if (isPortrait()) {\n        var mobileFigures = $('img.mobile-figure');\n        mobileFigures.each(function() {\n          // get the src (might be base64 encoded)\n          var src = $(this).attr('src');\n\n          // find it's peer\n          var id = $(this).attr('data-mobile-figure-id');\n          var img = $('img[data-figure-id=' + id + \"]\");\n          img.attr('src', src)\n             .attr('width', _options.defaultFigWidthMobile)\n             .attr('height', _options.defaultFigHeightMobile);\n        });\n      }\n\n      // hoist storyboard commentary into it's own section\n      if (page.hasClass('storyboard')) {\n        var commentaryHR = page.find('div.section.level3 hr');\n        commentaryHR.each(function() {\n          var commentary = $(this).nextAll().detach();\n          var commentarySection = $('<div class=\"section level3\"></div>');\n          commentarySection.append(commentary);\n          commentarySection.insertAfter($(this).closest('div.section.level3'));\n          $(this).remove();\n        });\n      }\n\n      // force a non full screen layout by columns\n      orientation = _options.orientation = 'columns';\n      fillPage = _options.fillPage = false;\n      storyboard = _options.storyboard = false;\n\n    // media: desktop\n    } else {\n\n      // determine orientation\n      orientation = page.attr('data-orientation');\n      if (orientation !== 'rows' && orientation != 'columns')\n        orientation = _options.orientation;\n\n      // determine storyboard mode\n      storyboard = page.hasClass('storyboard');\n\n      // fillPage based on options (force for storyboard)\n      fillPage = _options.fillPage || storyboard;\n\n      // handle sidebar\n      var sidebar = page.find('.section.level2.sidebar');\n      if (sidebar.length > 0)\n        layoutSidebar(sidebar, page);\n    }\n\n    // give it and it's parent divs height: 100% if we are in fillPage mode\n    if (fillPage) {\n      page.addClass('vertical-layout-fill');\n      page.css('height', '100%');\n      page.parents('div').css('height', '100%');\n    } else {\n      page.addClass('vertical-layout-scroll');\n    }\n\n    // perform the layout\n    if (storyboard)\n      layoutPageAsStoryboard(page);\n    else if (orientation === 'rows')\n      layoutPageByRows(page, fillPage);\n    else if (orientation === 'columns')\n      layoutPageByColumns(page, fillPage);\n  }\n\n  function layoutSidebar(sidebar, content) {\n\n    // get it out of the header hierarchy\n    sidebar = sidebar.first();\n    if (sidebar.hasClass('level1')) {\n      sidebar.removeClass('level1');\n      sidebar.children('h1').remove();\n    } else if (sidebar.hasClass('level2')) {\n      sidebar.removeClass('level2');\n      sidebar.children('h2').remove();\n    }\n\n    // determine width\n    var sidebarWidth = isTablet() ? 220 : 250;\n    var dataWidth = parseInt(sidebar.attr('data-width'));\n    if (dataWidth)\n      sidebarWidth = dataWidth;\n\n    // set the width and shift the page right to accomodate the sidebar\n    sidebar.css('width', sidebarWidth + 'px');\n    content.css('padding-left', sidebarWidth + 'px');\n\n    // wrap it's contents in a form\n    sidebar.wrapInner($('<form></form>'));\n  }\n\n  function layoutPageAsStoryboard(page) {\n\n    // create storyboard navigation\n    var nav = $('<div class=\"storyboard-nav\"></div>');\n\n    // add navigation buttons\n    var prev = $('<button class=\"sbprev\"><i class=\"fa fa-angle-left\"></i></button>');\n    nav.append(prev);\n    var next= $('<button class=\"sbnext\"><i class=\"fa fa-angle-right\"></i></button>');\n    nav.append(next);\n\n    // add navigation frame\n    var frameList = $('<div class=\"sbframelist\"></div>');\n    nav.append(frameList);\n    var ul = $('<ul></ul>');\n    frameList.append(ul);\n\n     // find all the level3 sections (those are the storyboard frames)\n    var frames = page.find('div.section.level3');\n    frames.each(function() {\n\n      // mark it\n      $(this).addClass('sbframe');\n\n      // divide it into chart content and (optional) commentary\n      $(this).addClass('dashboard-column-orientation');\n\n      // stuff the chart into it's own div w/ flex\n      $(this).wrapInner('<div class=\"sbframe-component\"></div>');\n      setFlex($(this), 1);\n      var frame = $(this).children('.sbframe-component');\n\n      // extract the title from the h3\n      var li = $('<li></li>');\n      var h3 = frame.children('h3');\n      li.append(h3.contents());\n      h3.detach();\n      ul.append(li);\n\n      // extract commentary\n      var hr = frame.children('hr');\n      if (hr.length) {\n        var commentary = hr.nextAll().detach();\n        hr.remove();\n        var commentaryFrame = $('<div class=\"sbframe-commentary\"></div>');\n        commentaryFrame.addClass('flowing-content-shim');\n        commentaryFrame.addClass('flowing-content-container');\n        commentaryFrame.append(commentary);\n        $(this).append(commentaryFrame);\n\n        // look for a data-commentary-width attribute\n        var commentaryWidth = $(this).attr('data-commentary-width');\n        if (commentaryWidth)\n          commentaryFrame.css('width', commentaryWidth + 'px');\n      }\n\n      // layout the chart (force flex)\n      var result = layoutChart(frame, true);\n\n      // ice the notes if there are none\n      if (!result.notes)\n        frame.find('.chart-notes').remove();\n\n      // set flex on chart\n      setFlex(frame, 1);\n    });\n\n    // create a div to hold all the frames\n    var frameContent = $('<div class=\"sbframe-content\"></div>');\n    frameContent.addClass('dashboard-row-orientation');\n    frameContent.append(frames.detach());\n\n    // row orientation to stack nav and frame content\n    page.addClass('dashboard-row-orientation');\n    page.append(nav);\n    page.append(frameContent);\n    setFlex(frameContent, 1);\n\n    // initialize sly\n    var sly = new Sly(frameList, {\n    \t\thorizontal: true,\n    \t\titemNav: 'basic',\n    \t\tsmart: true,\n    \t\tactivateOn: 'click',\n    \t\tstartAt: 0,\n    \t\tscrollBy: 1,\n    \t\tactivatePageOn: 'click',\n    \t\tspeed: 200,\n    \t\tmoveBy: 600,\n    \t\tdragHandle: true,\n    \t\tdynamicHandle: true,\n    \t\tclickBar: true,\n    \t\tkeyboardNavBy: 'items',\n    \t\tnext: next,\n    \t\tprev: prev\n    \t}).init();\n\n    // make first frame active\n    frames.removeClass('active');\n    frames.first().addClass('active');\n\n    // subscribe to frame changed events\n    sly.on('active', function (eventName, itemIndex) {\n      frames.removeClass('active');\n      frames.eq(itemIndex).addClass('active')\n                          .trigger('shown');\n    });\n  }\n\n  function layoutPageByRows(page, fillPage) {\n\n    // row orientation\n    page.addClass('dashboard-row-orientation');\n\n    // find all the level2 sections (those are the rows)\n    var rows = page.find('div.section.level2');\n\n    // if there are no level2 sections then treat the\n    // entire page as if it's a level 2 section\n    if (rows.length === 0) {\n      page.wrapInner('<div class=\"section level2\"></div>');\n      rows = page.find('div.section.level2');\n    }\n\n    rows.each(function () {\n\n      // flags\n      var haveNotes = false;\n      var haveFlexHeight = true;\n\n      // remove the h2\n      $(this).children('h2').remove();\n\n      // check for a tabset\n      var isTabset = $(this).hasClass('tabset');\n      if (isTabset)\n        layoutTabset($(this));\n\n      // give it row layout semantics if it's not a tabset\n      if (!isTabset)\n        $(this).addClass('dashboard-row');\n\n      // find all of the level 3 subheads\n      var columns = $(this).find('div.section.level3');\n\n      // determine figureSizes sizes\n      var figureSizes = chartFigureSizes(columns);\n\n      // fixup the columns\n      columns.each(function(index) {\n\n        // layout the chart (force flex if we are in a tabset)\n        var result = layoutChart($(this), isTabset);\n\n        // update flexHeight state\n        if (!result.flex)\n          haveFlexHeight = false;\n\n        // update state\n        if (result.notes)\n          haveNotes = true;\n\n        // set the column flex based on the figure width\n        // (value boxes will just get the default figure width)\n        var chartWidth = figureSizes[index].width;\n        setFlex($(this), chartWidth + ' ' + chartWidth + ' 0px');\n\n      });\n\n      // remove empty chart note divs\n      if (isTabset)\n        $(this).find('.chart-notes').filter(function() {\n            return $(this).html() === \"&nbsp;\";\n        }).remove();\n      if (!haveNotes)\n        $(this).find('.chart-notes').remove();\n\n       // make it a flexbox row\n      if (haveFlexHeight)\n        $(this).addClass('dashboard-row-flex');\n\n      // now we can set the height on all the wrappers (based on maximum\n      // figure height + room for title and notes, or data-height on the\n      // container if specified). However, don't do this if there is\n      // no flex on any of the constituent columns\n      var flexHeight = null;\n      var dataHeight = parseInt($(this).attr('data-height'));\n      if (dataHeight)\n        flexHeight = adjustedHeight(dataHeight, columns.first());\n      else if (haveFlexHeight)\n        flexHeight = maxChartHeight(figureSizes, columns);\n      if (flexHeight) {\n        if (fillPage)\n          setFlex($(this), flexHeight + ' ' + flexHeight + ' 0px');\n        else {\n          $(this).css('height', flexHeight + 'px');\n          setFlex($(this), '0 0 ' + flexHeight + 'px');\n        }\n      }\n\n    });\n  }\n\n  function layoutPageByColumns(page, fillPage) {\n\n    // column orientation\n    page.addClass('dashboard-column-orientation');\n\n    // find all the level2 sections (those are the columns)\n    var columns = page.find('div.section.level2');\n\n    // if there are no level2 sections then treat the\n    // entire page as if it's a level 2 section\n    if (columns.length === 0) {\n      page.wrapInner('<div class=\"section level2\"></div>');\n      columns = page.find('div.section.level2');\n    }\n\n    // layout each column\n    columns.each(function (index) {\n\n      // remove the h2\n      $(this).children('h2').remove();\n\n      // make it a flexbox column\n      $(this).addClass('dashboard-column');\n\n      // check for a tabset\n      var isTabset = $(this).hasClass('tabset');\n      if (isTabset)\n        layoutTabset($(this));\n\n      // find all the h3 elements\n      var rows = $(this).find('div.section.level3');\n\n      // get the figure sizes for the rows\n      var figureSizes = chartFigureSizes(rows);\n\n      // column flex is the max row width (or data-width if specified)\n      var flexWidth;\n      var dataWidth = parseInt($(this).attr('data-width'));\n      if (dataWidth)\n        flexWidth = dataWidth;\n      else\n        flexWidth = maxChartWidth(figureSizes);\n      setFlex($(this), flexWidth + ' ' + flexWidth + ' 0px');\n\n      // layout each chart\n      rows.each(function(index) {\n\n        // perform the layout\n        var result = layoutChart($(this), false);\n\n        // ice the notes if there are none\n        if (!result.notes)\n          $(this).find('.chart-notes').remove();\n\n        // set flex height based on figHeight, then adjust\n        if (result.flex) {\n          var chartHeight = figureSizes[index].height;\n          chartHeight = adjustedHeight(chartHeight, $(this));\n          if (fillPage)\n            setFlex($(this), chartHeight + ' ' + chartHeight + ' 0px');\n          else {\n            $(this).css('height', chartHeight + 'px');\n            setFlex($(this), chartHeight + ' ' + chartHeight + ' ' + chartHeight + 'px');\n          }\n        }\n      });\n    });\n  }\n\n  function chartFigureSizes(charts) {\n\n    // sizes\n    var figureSizes = new Array(charts.length);\n\n    // check each chart\n    charts.each(function(index) {\n\n      // start with default\n      figureSizes[index] = {\n        width: _options.defaultFigWidth,\n        height: _options.defaultFigHeight\n      };\n\n      // look for data-height or data-width then knit options\n      var dataWidth = parseInt($(this).attr('data-width'));\n      var dataHeight = parseInt($(this).attr('data-height'));\n      var knitrOptions = $(this).find('.knitr-options:first');\n      var knitrWidth, knitrHeight;\n      if (knitrOptions) {\n        knitrWidth = parseInt(knitrOptions.attr('data-fig-width'));\n        knitrHeight =  parseInt(knitrOptions.attr('data-fig-height'));\n      }\n\n      // width\n      if (dataWidth)\n        figureSizes[index].width = dataWidth;\n      else if (knitrWidth)\n        figureSizes[index].width = knitrWidth;\n\n      // height\n      if (dataHeight)\n        figureSizes[index].height = dataHeight;\n      else if (knitrHeight)\n        figureSizes[index].height = knitrHeight;\n    });\n\n    // return sizes\n    return figureSizes;\n  }\n\n  function maxChartHeight(figureSizes, charts) {\n\n    // first compute the maximum height\n    var maxHeight = _options.defaultFigHeight;\n    for (var i = 0; i<figureSizes.length; i++)\n      if (figureSizes[i].height > maxHeight)\n        maxHeight = figureSizes[i].height;\n\n    // now add offests for chart title and chart notes\n    if (charts.length)\n      maxHeight = adjustedHeight(maxHeight, charts.first());\n\n    return maxHeight;\n  }\n\n  function adjustedHeight(height, chart) {\n    if (chart.length > 0) {\n      var chartTitle = chart.find('.chart-title');\n      if (chartTitle.length)\n        height += chartTitle.first().outerHeight();\n      var chartNotes = chart.find('.chart-notes');\n      if (chartNotes.length)\n        height += chartNotes.first().outerHeight();\n    }\n    return height;\n  }\n\n  function maxChartWidth(figureSizes) {\n    var maxWidth = _options.defaultFigWidth;\n    for (var i = 0; i<figureSizes.length; i++)\n      if (figureSizes[i].width > maxWidth)\n        maxWidth = figureSizes[i].width;\n    return maxWidth;\n  }\n\n  // layout a chart\n  function layoutChart(chart, forceFlex) {\n\n    // state to return\n    var result = {\n      notes: false,\n      flex: false\n    };\n\n    // extract the title\n    var title = extractTitle(chart);\n\n    // find components that apply to this container\n    var components = componentsFind(chart);\n\n    // if it's a custom component then call it and return\n    var customComponents = componentsCustom(components);\n    if (customComponents.length) {\n      componentsLayout(customComponents, title, chart);\n      result.notes = false;\n      result.flex = forceFlex || componentsFlex(customComponents);\n      return result;\n    }\n\n    // put all the content in a chart wrapper div\n    chart.addClass('chart-wrapper');\n    chart.wrapInner('<div class=\"chart-stage\"></div>');\n    var chartContent = chart.children('.chart-stage');\n\n    // flex the content if appropriate\n    result.flex = forceFlex || componentsFlex(components);\n    if (result.flex) {\n      // add flex classes\n      chart.addClass('chart-wrapper-flex');\n      chartContent.addClass('chart-stage-flex');\n\n      // additional shim to break out of flexbox sizing\n      chartContent.wrapInner('<div class=\"chart-shim\"></div>');\n      chartContent = chartContent.children('.chart-shim');\n    }\n\n    // set custom data-padding attribute\n    var pad = chart.attr('data-padding');\n    if (pad) {\n      if (pad === \"0\")\n        chart.addClass('no-padding');\n      else {\n        pad = pad + 'px';\n        chartContent.css('left', pad)\n                    .css('top', pad)\n                    .css('right', pad)\n                    .css('bottom', pad)\n      }\n    }\n\n    // call compoents\n    componentsLayout(components, title, chartContent);\n\n    // also activate components on shiny output\n    findShinyOutput(chartContent).on('shiny:value',\n      function(event) {\n        var element = $(event.target);\n        setTimeout(function() {\n\n          // see if we opted out of flex based on our output (for shiny\n          // we can't tell what type of output we have until after the\n          // value is bound)\n          var components = componentsFind(element);\n          var flex = forceFlex || componentsFlex(components);\n          if (!flex) {\n            chart.css('height', \"\");\n            setFlex(chart, \"\");\n            chart.removeClass('chart-wrapper-flex');\n            chartContent.removeClass('chart-stage-flex');\n            chartContent.children().unwrap();\n          }\n\n          // perform layout\n          componentsLayout(components, title, element.parent());\n        }, 10);\n      });\n\n    // add the title\n    var chartTitle = $('<div class=\"chart-title\"></div>');\n    chartTitle.append(title);\n    chart.prepend(chartTitle);\n\n    // add the notes section\n    var chartNotes = $('<div class=\"chart-notes\"></div>');\n    chartNotes.html('&nbsp;');\n    chart.append(chartNotes);\n\n    // attempt to extract notes if we have a component\n    if (components.length)\n      result.notes = extractChartNotes(chartContent, chartNotes);\n\n    // return result\n    return result;\n  }\n\n  // build a tabset from a section div with the .tabset class\n  function layoutTabset(tabset) {\n\n    // check for fade option\n    var fade = tabset.hasClass(\"tabset-fade\");\n    var navClass = \"nav-tabs\";\n\n    // determine the heading level of the tabset and tabs\n    var match = tabset.attr('class').match(/level(\\d) /);\n    if (match === null)\n      return;\n    var tabsetLevel = Number(match[1]);\n    var tabLevel = tabsetLevel + 1;\n\n    // find all subheadings immediately below\n    var tabs = tabset.find(\"div.section.level\" + tabLevel);\n    if (!tabs.length)\n      return;\n\n    // create tablist and tab-content elements\n    var tabList = $('<ul class=\"nav ' + navClass + '\" role=\"tablist\"></ul>');\n    $(tabs[0]).before(tabList);\n    var tabContent = $('<div class=\"tab-content\"></div>');\n    $(tabs[0]).before(tabContent);\n\n    // build the tabset\n    tabs.each(function(i) {\n\n      // get the tab div\n      var tab = $(tabs[i]);\n\n      // get the id then sanitize it for use with bootstrap tabs\n      var id = tab.attr('id');\n\n      // sanitize the id for use with bootstrap tabs\n      id = id.replace(/[.\\/?&!#<>]/g, '').replace(/\\s/g, '_');\n      tab.attr('id', id);\n\n      // get the heading element within it and grab it's text\n      var heading = tab.find('h' + tabLevel + ':first');\n      var headingDom = heading.contents();\n\n      // build and append the tab list item\n      var a = $('<a role=\"tab\" data-toggle=\"tab\"></a>');\n      a.append(headingDom);\n      a.attr('href', '#' + id);\n      a.attr('aria-controls', id);\n      var li = $('<li role=\"presentation\"></li>');\n      li.append(a);\n      if (i === 0)\n        li.attr('class', 'active');\n      tabList.append(li);\n\n      // set it's attributes\n      tab.attr('role', 'tabpanel');\n      tab.addClass('tab-pane');\n      tab.addClass('tabbed-pane');\n      tab.addClass('no-title');\n      if (fade)\n        tab.addClass('fade');\n      if (i === 0) {\n        tab.addClass('active');\n        if (fade)\n          tab.addClass('in');\n      }\n\n      // move it into the tab content div\n      tab.detach().appendTo(tabContent);\n    });\n\n    // add nav-tabs-custom\n    tabset.addClass('nav-tabs-custom');\n\n    // internal layout is dashboard-column with tab-content flexing\n    tabset.addClass('dashboard-column');\n    setFlex(tabContent, 1);\n  }\n\n  // one time global initialization for components\n  function componentsInit(dashboardContainer) {\n    for (var i=0; i<window.FlexDashboardComponents.length; i++) {\n      var component = window.FlexDashboardComponents[i];\n      if (component.init)\n        component.init(dashboardContainer);\n    }\n  }\n\n  // find components that apply within a container\n  function componentsFind(container) {\n\n    // look for components\n    var components = [];\n    for (var i=0; i<window.FlexDashboardComponents.length; i++) {\n      var component = window.FlexDashboardComponents[i];\n      if (component.find(container).length)\n        components.push(component);\n    }\n\n    // if there were none then use a special flowing content component\n    // that just adds a scrollbar in fillPage mode\n    if (components.length == 0) {\n      components.push({\n        find: function(container) {\n          return container;\n        },\n\n        flex: function(fillPage) {\n          return fillPage;\n        },\n\n        layout: function(title, container, element, fillPage) {\n          if (fillPage) {\n            container.addClass('flowing-content-shim');\n            container.addClass('flowing-content-container');\n          }\n        }\n      });\n    }\n\n    return components;\n  }\n\n  // if there is a custom component then pick it out\n  function componentsCustom(components) {\n    var customComponent = [];\n    for (var i=0; i<components.length; i++)\n      if (components[i].type === \"custom\") {\n        customComponent.push(components[i]);\n        break;\n      }\n    return customComponent;\n  }\n\n  // query all components for flex\n  function componentsFlex(components) {\n\n    // no components at all means no flex\n    if (components.length === 0)\n      return false;\n\n    // otherwise query components (assume true unless we see false)\n    var isMobile = isMobilePhone();\n    for (var i=0; i<components.length; i++)\n      if (components[i].flex && !components[i].flex(_options.fillPage))\n        return false;\n    return true;\n  }\n\n  // layout all components\n  function componentsLayout(components, title, container) {\n    var isMobile = isMobilePhone();\n    for (var i=0; i<components.length; i++) {\n      var element = components[i].find(container);\n      if (components[i].layout) {\n        // call layout (don't call other components if it returns false)\n        var result = components[i].layout(title, container, element, _options.fillPage);\n        if (result === false)\n          return;\n      }\n    }\n  }\n\n  // get a reference to the h3, discover it's inner html, and remove it\n  function extractTitle(container) {\n    var h3 = container.children('h3').first();\n    var title = '';\n    if (!container.hasClass('no-title'))\n      title = h3.contents();\n    h3.detach();\n    return title;\n  }\n\n  // extract chart notes\n  function extractChartNotes(chartContent, chartNotes) {\n    // look for a terminating blockquote or image caption\n    var blockquote = chartContent.children('blockquote:last-child');\n    var caption = chartContent.children('div.image-container')\n                              .children('p.caption');\n    if (blockquote.length) {\n      chartNotes.empty().append(blockquote.children('p:first-child').contents());\n      blockquote.remove();\n      return true;\n    } else if (caption.length) {\n      chartNotes.empty().append(caption.contents());\n      caption.remove();\n      return true;\n    } else {\n      return false;\n    }\n  }\n\n  function findShinyOutput(chartContent) {\n    return chartContent.find('.shiny-text-output, .shiny-html-output');\n  }\n\n  // safely detect rendering on a mobile phone\n  function isMobilePhone() {\n    try\n    {\n      return ! window.matchMedia(\"only screen and (min-width: 768px)\").matches;\n    }\n    catch(e) {\n      return false;\n    }\n  }\n\n  function isFillPage() {\n    return _options.fillPage;\n  }\n\n  // detect portrait mode\n  function isPortrait() {\n    return ($(window).width() < $(window).height());\n  }\n\n  // safely detect rendering on a tablet\n  function isTablet() {\n    try\n    {\n      return window.matchMedia(\"only screen and (min-width: 769px) and (max-width: 992px)\").matches;\n    }\n    catch(e) {\n      return false;\n    }\n  }\n\n  // test whether this is a shiny doc\n  function isShinyDoc() {\n    return (typeof(window.Shiny) !== \"undefined\" && !!window.Shiny.outputBindings);\n  }\n\n  // set flex using vendor specific prefixes\n  function setFlex(el, flex) {\n    el.css('-webkit-box-flex', flex)\n      .css('-webkit-flex', flex)\n      .css('-ms-flex', flex)\n      .css('flex', flex);\n  }\n\n  // support bookmarking of pages\n  function handleLocationHash() {\n\n    // restore tab/page from bookmark\n    var hash = window.decodeURIComponent(window.location.hash);\n    if (hash.length > 0)\n      $('ul.nav a[href=\"' + hash + '\"]').tab('show');\n    FlexDashboardUtils.manageActiveNavbarMenu();\n\n    // navigate to a tab when the history changes\n    window.addEventListener(\"popstate\", function(e) {\n      var hash = window.decodeURIComponent(window.location.hash);\n      var activeTab = $('ul.nav a[href=\"' + hash + '\"]');\n      if (activeTab.length) {\n        activeTab.tab('show');\n      } else {\n        $('ul.nav a:first').tab('show');\n      }\n      FlexDashboardUtils.manageActiveNavbarMenu();\n    });\n\n    // add a hash to the URL when the user clicks on a tab/page\n    $('.navbar-nav a[data-toggle=\"tab\"]').on('click', function(e) {\n      var baseUrl = FlexDashboardUtils.urlWithoutHash(window.location.href);\n      var hash = FlexDashboardUtils.urlHash($(this).attr('href'));\n      var href = baseUrl + hash;\n      FlexDashboardUtils.setLocation(href);\n    });\n\n    // handle clicks of other links that should activate pages\n    var navPages = $('ul.navbar-nav li a[data-toggle=tab]');\n    navPages.each(function() {\n      var href =  $(this).attr('href');\n      var links = $('a[href=\"' + href + '\"][data-toggle!=tab]');\n      links.each(function() {\n        $(this).on('click', function(e) {\n          window.FlexDashboardUtils.showPage(href);\n        });\n      });\n    });\n  }\n\n  // tweak Prism highlighting\n  function initPrismHighlighting() {\n\n    if (window.Prism) {\n      Prism.languages.insertBefore('r', 'comment', {\n        'heading': [\n          {\n            // title 1\n        \t  // =======\n\n        \t  // title 2\n        \t  // -------\n        \t  pattern: /\\w+.*(?:\\r?\\n|\\r)(?:====+|----+)/,\n            alias: 'operator'\n          },\n          {\n            // ### title 3\n            pattern: /(^\\s*)###[^#].+/m,\n            lookbehind: true,\n            alias: 'operator'\n          }\n        ]\n      });\n\n      // prism highlight\n      Prism.highlightAll();\n    }\n  }\n\n\n  // get theme color\n  var themeColors = {\n    bootstrap: {\n      primary: \"rgba(51, 122, 183, 0.4)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    cerulean: {\n      primary: \"rgb(47, 164, 231)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    journal: {\n      primary: \"rgba(235, 104, 100, 0.70)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    flatly: {\n      primary: \"rgba(44, 62, 80, 0.70)\",\n      info: \"rgba(52, 152, 219, 0.70)\",\n      success: \"rgba(24, 188, 156, 0.70)\",\n      warning: \"rgba(243, 156, 18, 0.70)\",\n      danger: \"rgba(231, 76, 60, 0.70)\"\n    },\n    readable: {\n      primary: \"rgba(69, 130, 236, 0.4)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    spacelab: {\n      primary: \"rgba(68, 110, 155, 0.25)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    united: {\n      primary: \"rgba(221, 72, 20, 0.30)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    cosmo: {\n      primary: \"rgba(39, 128, 227, 0.7)\",\n      info: \"rgba(153, 84, 187, 0.7)\",\n      success: \"rgba(63, 182, 24, 0.7)\",\n      warning: \"rgba(255, 117, 24, 0.7)\",\n      danger: \"rgba(255, 0, 57, 0.7)\"\n    },\n    lumen: {\n      primary: \"rgba(21, 140, 186, 0.70)\",\n      info: \"rgba(117, 202, 235, 0.90)\",\n      success: \"rgba(40, 182, 44, 0.70)\",\n      warning: \"rgba(255, 133, 27, 0.70)\",\n      danger: \"rgba(255, 65, 54, 0.70)\"\n    },\n    paper: {\n      primary: \"rgba(33, 150, 243, 0.35)\",\n      info: \"rgb(225, 190, 231)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(255, 224, 178)\",\n      danger: \"rgb(249, 189, 187)\"\n    },\n    sandstone: {\n      primary: \"rgba(50, 93, 136, 0.3)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    simplex: {\n      primary: \"rgba(217, 35, 15, 0.25)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    yeti: {\n      primary: \"rgba(0, 140, 186, 0.298039)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    }\n  }\n  function themeColor(color) {\n    return themeColors[_options.theme][color];\n  }\n\n  FlexDashboard.prototype = {\n    constructor: FlexDashboard,\n    init: init,\n    isMobilePhone: isMobilePhone,\n    isFillPage: isFillPage,\n    themeColor: themeColor\n  };\n\n  return FlexDashboard;\n\n})();\n\n// utils\nwindow.FlexDashboardUtils = {\n  resizableImage: function(img) {\n    var src = img.attr('src');\n    var url = 'url(\"' + src + '\")';\n    img.parent().css('background', url)\n                .css('background-size', 'contain')\n                .css('background-repeat', 'no-repeat')\n                .css('background-position', 'center')\n                .addClass('image-container');\n  },\n  setLocation: function(href) {\n    if (history && history.pushState) {\n      history.pushState(null, null, href);\n    } else {\n      window.location.replace(href);\n    }\n    setTimeout(function() {\n        window.scrollTo(0, 0);\n    }, 10);\n    this.manageActiveNavbarMenu();\n  },\n  showPage: function(href) {\n    $('ul.navbar-nav li a[href=\"' + href + '\"]').tab('show');\n    var baseUrl = this.urlWithoutHash(window.location.href);\n    var loc = baseUrl + href;\n    this.setLocation(loc);\n  },\n  showLinkedValue: function(href) {\n    // check for a page link\n    if ($('ul.navbar-nav li a[data-toggle=tab][href=\"' + href + '\"]').length > 0)\n      this.showPage(href);\n    else\n      window.open(href);\n  },\n  urlWithoutHash: function(url) {\n    var hashLoc = url.indexOf('#');\n    if (hashLoc != -1)\n      return url.substring(0, hashLoc);\n    else\n      return url;\n  },\n  urlHash: function(url) {\n    var hashLoc = url.indexOf('#');\n    if (hashLoc != -1)\n      return url.substring(hashLoc);\n    else\n      return \"\";\n  },\n  manageActiveNavbarMenu: function () {\n    // remove active from anyone currently active\n    $('.navbar ul.nav').find('li').removeClass('active');\n    // find the active tab\n    var activeTab = $('.dashboard-page-wrapper.tab-pane.active');\n    if (activeTab.length > 0) {\n      var tabId = activeTab.attr('id');\n      if (tabId)\n        $(\".navbar ul.nav a[href='#\" + tabId + \"']\").parents('li').addClass('active');\n    }\n  }\n};\n\nwindow.FlexDashboard = new FlexDashboard();\n\n// empty content\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    if (container.find('p').length == 0)\n      return container;\n    else\n      return $();\n  }\n})\n\n// plot image\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('p')\n                    .children('img:only-child');\n  },\n\n  layout: function(title, container, element, fillPage) {\n    FlexDashboardUtils.resizableImage(element);\n  }\n});\n\n// plot image (figure style)\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('div.figure').children('img');\n  },\n\n  layout: function(title, container, element, fillPage) {\n    FlexDashboardUtils.resizableImage(element);\n  }\n});\n\n// htmlwidget\nwindow.FlexDashboardComponents.push({\n\n  init: function(dashboardContainer) {\n    // trigger \"shown\" after initial layout to force static htmlwidgets\n    // in runtime: shiny to be resized after the dom has been transformed\n    dashboardContainer.on('flexdashboard:layoutcomplete', function(event) {\n      setTimeout(function() {\n        dashboardContainer.trigger('shown');\n      }, 200);\n    });\n  },\n\n  find: function(container) {\n    return container.children('div[id^=\"htmlwidget-\"],div.html-widget');\n  }\n});\n\n// gauge\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('div.html-widget.gauge');\n  },\n\n  flex: function(fillPage) {\n    return false;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n\n  }\n\n});\n\n// shiny output\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    return container.children('div[class^=\"shiny-\"]');\n  }\n});\n\n// datatables\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    return container.find('.datatables');\n  },\n  flex: function(fillPage) {\n    return fillPage;\n  }\n});\n\n// bootstrap table\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    var bsTable = container.find('table.table');\n    if (bsTable.length !== 0)\n      return bsTable;\n    else\n      return container.find('tr.header').parent('thead').parent('table');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n    // alias variables\n    var bsTable = element;\n\n    // fixup xtable generated tables with a proper thead\n    var headerRow = bsTable.find('tbody > tr:first-child > th').parent();\n    if (headerRow.length > 0) {\n      var thead = $('<thead></thead>');\n      bsTable.prepend(thead);\n      headerRow.detach().appendTo(thead);\n    }\n\n    // improve appearance\n    container.addClass('bootstrap-table');\n\n    // for fill page provide scrolling w/ sticky headers\n    if (fillPage) {\n      // force scrollbar on overflow\n      container.addClass('flowing-content-shim');\n\n      // stable table headers when scrolling\n      bsTable.stickyTableHeaders({\n        scrollableArea: container\n      });\n    }\n  }\n});\n\n// embedded shiny app\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.find('iframe.shiny-frame');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n    if (fillPage) {\n      element.attr('height', '100%');\n    } else {\n      // provide default height if necessary\n      var height = element.get(0).style.height;\n      if (!height)\n        height = element.attr('height');\n      if (!height)\n        element.attr('height', 500);\n    }\n  }\n});\n\n// shiny fillRow or fillCol\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.find('.flexfill-container');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n    if (fillPage)\n      element.css('height', '100%');\n    else {\n      // provide default height if necessary\n      var height = element.get(0).style.height;\n      if (height === \"100%\" || height === \"auto\" || height === \"initial\" ||\n          height === \"inherit\" || !height) {\n        element.css('height', 500);\n      }\n    }\n  }\n});\n\n// valueBox\nwindow.FlexDashboardComponents.push({\n\n  type: \"custom\",\n\n  find: function(container) {\n    if (container.find('span.value-output, .shiny-valuebox-output').length)\n      return container;\n    else\n      return $();\n  },\n\n  flex: function(fillPage) {\n    return false;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n    // alias variables\n    var chartTitle = title;\n    var valueBox = element;\n\n    // add value-box class to container\n    container.addClass('value-box');\n\n    // value paragraph\n    var value = $('<p class=\"value\"></p>');\n\n    // if we have shiny-text-output then just move it in\n    var valueOutputSpan = [];\n    var shinyOutput = valueBox.find('.shiny-valuebox-output').detach();\n    if (shinyOutput.length) {\n      valueBox.children().remove();\n      shinyOutput.html(\"&mdash;\");\n      value.append(shinyOutput);\n    } else {\n      // extract the value (remove leading vector index)\n      var chartValue = valueBox.text().trim();\n      chartValue = chartValue.replace(\"[1] \", \"\");\n      valueOutputSpan = valueBox.find('span.value-output').detach();\n      valueBox.children().remove();\n      value.text(chartValue);\n    }\n\n    // caption\n    var caption = $('<p class=\"caption\"></p>');\n    caption.append(chartTitle);\n\n    // build inner div for value box and add it\n    var inner = $('<div class=\"inner\"></div>');\n    inner.append(value);\n    inner.append(caption);\n    valueBox.append(inner);\n\n    // add icon if specified\n    var icon = $('<div class=\"icon\"><i></i></div>');\n    valueBox.append(icon);\n    function setIcon(chartIcon) {\n      var iconLib = \"\";\n      var iconSplit = chartIcon.split(\" \");\n      if (iconSplit.length > 1) {\n        iconLib = iconSplit[0];\n        chartIcon = iconSplit.slice(1).join(\" \");\n      } else {\n        var components = chartIcon.split(\"-\");\n        if (components.length > 1)\n          iconLib = components[0];\n      }\n      icon.children('i').attr('class', iconLib + ' ' + chartIcon);\n    }\n    var chartIcon = valueBox.attr('data-icon');\n    if (chartIcon)\n      setIcon(chartIcon);\n\n    // set color based on data-background if necessary\n    var dataBackground = valueBox.attr('data-background');\n    if (dataBackground)\n      valueBox.css('background-color', bgColor);\n    else {\n      // default to bg-primary if no other background is specified\n      if (!valueBox.hasClass('bg-primary') &&\n          !valueBox.hasClass('bg-info') &&\n          !valueBox.hasClass('bg-warning') &&\n          !valueBox.hasClass('bg-success') &&\n          !valueBox.hasClass('bg-danger')) {\n        valueBox.addClass('bg-primary');\n      }\n    }\n\n    // handle data attributes in valueOutputSpan\n    function handleValueOutput(valueOutput) {\n\n      // caption\n      var dataCaption = valueOutput.attr('data-caption');\n      if (dataCaption)\n        caption.html(dataCaption);\n\n      // icon\n      var dataIcon = valueOutput.attr('data-icon');\n      if (dataIcon)\n        setIcon(dataIcon);\n\n      // color\n      var dataColor = valueOutput.attr('data-color');\n      if (dataColor) {\n        if (dataColor.indexOf('bg-') === 0) {\n          valueBox.css('background-color', '');\n          if (!valueBox.hasClass(dataColor)) {\n             valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');\n             valueBox.addClass(dataColor);\n          }\n        } else {\n          valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');\n          valueBox.css('background-color', dataColor);\n        }\n      }\n\n      // url\n      var dataHref = valueOutput.attr('data-href');\n      if (dataHref) {\n        valueBox.addClass('linked-value');\n        valueBox.off('click.value-box');\n        valueBox.on('click.value-box', function(e) {\n          window.FlexDashboardUtils.showLinkedValue(dataHref);\n        });\n      }\n    }\n\n    // check for a valueOutputSpan\n    if (valueOutputSpan.length > 0) {\n      handleValueOutput(valueOutputSpan);\n    }\n\n    // if we have a shinyOutput then bind a listener to handle\n    // new valueOutputSpan values\n    shinyOutput.on('shiny:value',\n      function(event) {\n        var element = $(event.target);\n        setTimeout(function() {\n          var valueOutputSpan = element.find('span.value-output');\n          if (valueOutputSpan.length > 0)\n            handleValueOutput(valueOutputSpan);\n        }, 10);\n      }\n    );\n  }\n});\n</script>\n\n<div id=\"dashboard-container\">\n\n<div id=\"row\" class=\"section level2\" data-width=\"450\">\n<h2 data-width=\"450\">Row</h2>\n<div id=\"chart-a\" class=\"section level3\">\n<h3>Chart A</h3>\n<div class=\"knitr-options\" data-fig-width=\"576\" data-fig-height=\"460\">\n\n</div>\n</div>\n<div id=\"chart-e\" class=\"section level3\">\n<h3>Chart E</h3>\n</div>\n</div>\n<div id=\"column\" class=\"section level2\" data-width=\"350\">\n<h2 data-width=\"350\">Column</h2>\n<div id=\"chart-b\" class=\"section level3\">\n<h3>Chart B</h3>\n<div class=\"knitr-options\" data-fig-width=\"576\" data-fig-height=\"460\">\n\n</div>\n</div>\n<div id=\"chart-c\" class=\"section level3\">\n<h3>Chart C</h3>\n<div class=\"knitr-options\" data-fig-width=\"576\" data-fig-height=\"460\">\n\n</div>\n</div>\n<div id=\"chart-d\" class=\"section level3\">\n<h3>Chart D</h3>\n</div>\n</div>\n<div id=\"column-1\" class=\"section level2\" data-width=\"350\">\n<h2 data-width=\"350\">Column</h2>\n<div id=\"chart-f\" class=\"section level3\">\n<h3>Chart F</h3>\n</div>\n</div>\n\n</div>\n\n<script>\n\n$(document).ready(function () {\n\n  // add bootstrap table styles to pandoc tables\n  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');\n\n  // initialize mathjax\n  var script = document.createElement(\"script\");\n  script.type = \"text/javascript\";\n  script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\n  document.getElementsByTagName(\"head\")[0].appendChild(script);\n\n});\n</script>\n\n<script type=\"text/javascript\">\n$(document).ready(function () {\n  FlexDashboard.init({\n    theme: \"cosmo\",\n    fillPage: true,\n    orientation: \"rows\",\n    storyboard: false,\n    defaultFigWidth: 576,\n    defaultFigHeight: 460,\n    defaultFigWidthMobile: 360,\n    defaultFigHeightMobile: 460,\n    resize_reload: true\n  });\n});\n</script>\n\n</body>\n</html>\n"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/test.rmd",
    "content": "---\ntitle: \"庄闪闪的成长手册\"\noutput: \n  flexdashboard::flex_dashboard:\n    orientation: rows\n    vertical_layout: fill\n---\n\n\nRow {data-width=450}\n-----------------------------------------------------------------------\n\n### Chart A\n\n```{r}\n\n```\n\n### Chart E\n\n\nColumn {data-width=350}\n-----------------------------------------------------------------------\n\n### Chart B\n\n```{r}\n\n```\n\n### Chart C\n\n```{r}\n\n```\n\n### Chart D \n\nColumn {data-width=350}\n-----------------------------------------------------------------------\n\n### Chart F\n"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/例子/09_rbokeh-iris-dataset/dashboard-pandoc2.0.3.html",
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) 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fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.fa-rotate-90{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1);-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2);-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3);-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);-webkit-transform:scale(-1, 1);-ms-transform:scale(-1, 1);transform:scale(-1, 1)}.fa-flip-vertical{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);-webkit-transform:scale(1, -1);-ms-transform:scale(1, -1);transform:scale(1, -1)}:root .fa-rotate-90,:root .fa-rotate-180,:root .fa-rotate-270,:root .fa-flip-horizontal,:root .fa-flip-vertical{filter:none}.fa-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:middle}.fa-stack-1x,.fa-stack-2x{position:absolute;left:0;width:100%;text-align:center}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:#fff}.fa-glass:before{content:\"\\f000\"}.fa-music:before{content:\"\\f001\"}.fa-search:before{content:\"\\f002\"}.fa-envelope-o:before{content:\"\\f003\"}.fa-heart:before{content:\"\\f004\"}.fa-star:before{content:\"\\f005\"}.fa-star-o:before{content:\"\\f006\"}.fa-user:before{content:\"\\f007\"}.fa-film:before{content:\"\\f008\"}.fa-th-large:before{content:\"\\f009\"}.fa-th:before{content:\"\\f00a\"}.fa-th-list:before{content:\"\\f00b\"}.fa-check:before{content:\"\\f00c\"}.fa-remove:before,.fa-close:before,.fa-times:before{content:\"\\f00d\"}.fa-search-plus:before{content:\"\\f00e\"}.fa-search-minus:before{content:\"\\f010\"}.fa-power-off:before{content:\"\\f011\"}.fa-signal:before{content:\"\\f012\"}.fa-gear:before,.fa-cog:before{content:\"\\f013\"}.fa-trash-o:before{content:\"\\f014\"}.fa-home:before{content:\"\\f015\"}.fa-file-o:before{content:\"\\f016\"}.fa-clock-o:before{content:\"\\f017\"}.fa-road:before{content:\"\\f018\"}.fa-download:before{content:\"\\f019\"}.fa-arrow-circle-o-down:before{content:\"\\f01a\"}.fa-arrow-circle-o-up:before{content:\"\\f01b\"}.fa-inbox:before{content:\"\\f01c\"}.fa-play-circle-o:before{content:\"\\f01d\"}.fa-rotate-right:before,.fa-repeat:before{content:\"\\f01e\"}.fa-refresh:before{content:\"\\f021\"}.fa-list-alt:before{content:\"\\f022\"}.fa-lock:before{content:\"\\f023\"}.fa-flag:before{content:\"\\f024\"}.fa-headphones:before{content:\"\\f025\"}.fa-volume-off:before{content:\"\\f026\"}.fa-volume-down:before{content:\"\\f027\"}.fa-volume-up:before{content:\"\\f028\"}.fa-qrcode:before{content:\"\\f029\"}.fa-barcode:before{content:\"\\f02a\"}.fa-tag:before{content:\"\\f02b\"}.fa-tags:before{content:\"\\f02c\"}.fa-book:before{content:\"\\f02d\"}.fa-bookmark:before{content:\"\\f02e\"}.fa-print:before{content:\"\\f02f\"}.fa-camera:before{content:\"\\f030\"}.fa-font:before{content:\"\\f031\"}.fa-bold:before{content:\"\\f032\"}.fa-italic:before{content:\"\\f033\"}.fa-text-height:before{content:\"\\f034\"}.fa-text-width:before{content:\"\\f035\"}.fa-align-left:before{content:\"\\f036\"}.fa-align-center:before{content:\"\\f037\"}.fa-align-right:before{content:\"\\f038\"}.fa-align-justify:before{content:\"\\f039\"}.fa-list:before{content:\"\\f03a\"}.fa-dedent:before,.fa-outdent:before{content:\"\\f03b\"}.fa-indent:before{content:\"\\f03c\"}.fa-video-camera:before{content:\"\\f03d\"}.fa-photo:before,.fa-image:before,.fa-picture-o:before{content:\"\\f03e\"}.fa-pencil:before{content:\"\\f040\"}.fa-map-marker:before{content:\"\\f041\"}.fa-adjust:before{content:\"\\f042\"}.fa-tint:before{content:\"\\f043\"}.fa-edit:before,.fa-pencil-square-o:before{content:\"\\f044\"}.fa-share-square-o:before{content:\"\\f045\"}.fa-check-square-o:before{content:\"\\f046\"}.fa-arrows:before{content:\"\\f047\"}.fa-step-backward:before{content:\"\\f048\"}.fa-fast-backward:before{content:\"\\f049\"}.fa-backward:before{content:\"\\f04a\"}.fa-play:before{content:\"\\f04b\"}.fa-pause:before{content:\"\\f04c\"}.fa-stop:before{content:\"\\f04d\"}.fa-forward:before{content:\"\\f04e\"}.fa-fast-forward:before{content:\"\\f050\"}.fa-step-forward:before{content:\"\\f051\"}.fa-eject:before{content:\"\\f052\"}.fa-chevron-left:before{content:\"\\f053\"}.fa-chevron-right:before{content:\"\\f054\"}.fa-plus-circle:before{content:\"\\f055\"}.fa-minus-circle:before{content:\"\\f056\"}.fa-times-circle:before{content:\"\\f057\"}.fa-check-circle:before{content:\"\\f058\"}.fa-question-circle:before{content:\"\\f059\"}.fa-info-circle:before{content:\"\\f05a\"}.fa-crosshairs:before{content:\"\\f05b\"}.fa-times-circle-o:before{content:\"\\f05c\"}.fa-check-circle-o:before{content:\"\\f05d\"}.fa-ban:before{content:\"\\f05e\"}.fa-arrow-left:before{content:\"\\f060\"}.fa-arrow-right:before{content:\"\\f061\"}.fa-arrow-up:before{content:\"\\f062\"}.fa-arrow-down:before{content:\"\\f063\"}.fa-mail-forward:before,.fa-share:before{content:\"\\f064\"}.fa-expand:before{content:\"\\f065\"}.fa-compress:before{content:\"\\f066\"}.fa-plus:before{content:\"\\f067\"}.fa-minus:before{content:\"\\f068\"}.fa-asterisk:before{content:\"\\f069\"}.fa-exclamation-circle:before{content:\"\\f06a\"}.fa-gift:before{content:\"\\f06b\"}.fa-leaf:before{content:\"\\f06c\"}.fa-fire:before{content:\"\\f06d\"}.fa-eye:before{content:\"\\f06e\"}.fa-eye-slash:before{content:\"\\f070\"}.fa-warning:before,.fa-exclamation-triangle:before{content:\"\\f071\"}.fa-plane:before{content:\"\\f072\"}.fa-calendar:before{content:\"\\f073\"}.fa-random:before{content:\"\\f074\"}.fa-comment:before{content:\"\\f075\"}.fa-magnet:before{content:\"\\f076\"}.fa-chevron-up:before{content:\"\\f077\"}.fa-chevron-down:before{content:\"\\f078\"}.fa-retweet:before{content:\"\\f079\"}.fa-shopping-ca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If not, it's a static document.\n  // Note that static widgets can appear in both Shiny and static modes, but\n  // obviously, Shiny widgets can only appear in Shiny apps/documents.\n  var shinyMode = window.HTMLWidgets.shinyMode =\n      typeof(window.Shiny) !== \"undefined\" && !!window.Shiny.outputBindings;\n\n  // We can't count on jQuery being available, so we implement our own\n  // version if necessary.\n  function querySelectorAll(scope, selector) {\n    if (typeof(jQuery) !== \"undefined\" && scope instanceof jQuery) {\n      return scope.find(selector);\n    }\n    if (scope.querySelectorAll) {\n      return scope.querySelectorAll(selector);\n    }\n  }\n\n  function asArray(value) {\n    if (value === null)\n      return [];\n    if ($.isArray(value))\n      return value;\n    return [value];\n  }\n\n  // Implement jQuery's extend\n  function extend(target /*, ... */) {\n    if (arguments.length == 1) {\n      return target;\n    }\n    for (var i = 1; i < arguments.length; i++) {\n      var source = arguments[i];\n      for (var prop in source) {\n        if (source.hasOwnProperty(prop)) {\n          target[prop] = source[prop];\n        }\n      }\n    }\n    return target;\n  }\n\n  // IE8 doesn't support Array.forEach.\n  function forEach(values, callback, thisArg) {\n    if (values.forEach) {\n      values.forEach(callback, thisArg);\n    } else {\n      for (var i = 0; i < values.length; i++) {\n        callback.call(thisArg, values[i], i, values);\n      }\n    }\n  }\n\n  // Replaces the specified method with the return value of funcSource.\n  //\n  // Note that funcSource should not BE the new method, it should be a function\n  // that RETURNS the new method. funcSource receives a single argument that is\n  // the overridden method, it can be called from the new method. 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Or, can be an array of objects with \"code\" and \"data\"\n  //   properties; in this case, the \"code\" property should be a string\n  //   of JS that's an expr that yields a function, and \"data\" should be\n  //   an object that will be added as an additional argument when that\n  //   function is called.\n  // @param target The object that will be \"this\" for each function\n  //   execution.\n  // @param args Array of arguments to be passed to the functions. (The\n  //   same arguments will be passed to all functions.)\n  function evalAndRun(tasks, target, args) {\n    if (tasks) {\n      forEach(tasks, function(task) {\n        var theseArgs = args;\n        if (typeof(task) === \"object\") {\n          theseArgs = theseArgs.concat([task.data]);\n          task = task.code;\n        }\n        var taskFunc = eval(\"(\" + task + \")\");\n        if (typeof(taskFunc) !== \"function\") {\n          throw new Error(\"Task must be a function! Source:\\n\" + task);\n        }\n        taskFunc.apply(target, theseArgs);\n      });\n    }\n  }\n\n  function initSizing(el) {\n    var sizing = sizingPolicy(el);\n    if (!sizing)\n      return;\n\n    var cel = document.getElementById(\"htmlwidget_container\");\n    if (!cel)\n      return;\n\n    if (typeof(sizing.padding) !== \"undefined\") {\n      document.body.style.margin = \"0\";\n      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));\n    }\n\n    if (sizing.fill) {\n      document.body.style.overflow = \"hidden\";\n      document.body.style.width = \"100%\";\n      document.body.style.height = \"100%\";\n      document.documentElement.style.width = \"100%\";\n      document.documentElement.style.height = \"100%\";\n      if (cel) {\n        cel.style.position = \"absolute\";\n        var pad = unpackPadding(sizing.padding);\n        cel.style.top = pad.top + \"px\";\n        cel.style.right = pad.right + \"px\";\n        cel.style.bottom = pad.bottom + \"px\";\n        cel.style.left = pad.left + \"px\";\n        el.style.width = \"100%\";\n        el.style.height = \"100%\";\n      }\n\n      return {\n        getWidth: function() { return cel.offsetWidth; },\n        getHeight: function() { return cel.offsetHeight; }\n      };\n\n    } else {\n      el.style.width = px(sizing.width);\n      el.style.height = px(sizing.height);\n\n      return {\n        getWidth: function() { return el.offsetWidth; },\n        getHeight: function() { return el.offsetHeight; }\n      };\n    }\n  }\n\n  // Default implementations for methods\n  var defaults = {\n    find: function(scope) {\n      return querySelectorAll(scope, \".\" + this.name);\n    },\n    renderError: function(el, err) {\n      var $el = $(el);\n\n      this.clearError(el);\n\n      // Add all these error classes, as Shiny does\n      var errClass = \"shiny-output-error\";\n      if (err.type !== null) {\n        // use the classes of the error condition as CSS class names\n        errClass = errClass + \" \" + $.map(asArray(err.type), function(type) {\n          return errClass + \"-\" + type;\n        }).join(\" \");\n      }\n      errClass = errClass + \" htmlwidgets-error\";\n\n      // Is el inline or block? If inline or inline-block, just display:none it\n      // and add an inline error.\n      var display = $el.css(\"display\");\n      $el.data(\"restore-display-mode\", display);\n\n      if (display === \"inline\" || display === \"inline-block\") {\n        $el.hide();\n        if (err.message !== \"\") {\n          var errorSpan = $(\"<span>\").addClass(errClass);\n          errorSpan.text(err.message);\n          $el.after(errorSpan);\n        }\n      } else if (display === \"block\") {\n        // If block, add an error just after the el, set visibility:none on the\n        // el, and position the error to be on top of the el.\n        // Mark it with a unique ID and CSS class so we can remove it later.\n        $el.css(\"visibility\", \"hidden\");\n        if (err.message !== \"\") {\n          var errorDiv = $(\"<div>\").addClass(errClass).css(\"position\", \"absolute\")\n            .css(\"top\", el.offsetTop)\n            .css(\"left\", el.offsetLeft)\n            // setting width can push out the page size, forcing otherwise\n            // unnecessary scrollbars to appear and making it impossible for\n            // the element to shrink; so use max-width instead\n            .css(\"maxWidth\", el.offsetWidth)\n            .css(\"height\", el.offsetHeight);\n          errorDiv.text(err.message);\n          $el.after(errorDiv);\n\n          // Really dumb way to keep the size/position of the error in sync with\n          // the parent element as the window is resized or whatever.\n          var intId = setInterval(function() {\n            if (!errorDiv[0].parentElement) {\n              clearInterval(intId);\n              return;\n            }\n            errorDiv\n              .css(\"top\", el.offsetTop)\n              .css(\"left\", el.offsetLeft)\n              .css(\"maxWidth\", el.offsetWidth)\n              .css(\"height\", el.offsetHeight);\n          }, 500);\n        }\n      }\n    },\n    clearError: function(el) {\n      var $el = $(el);\n      var display = $el.data(\"restore-display-mode\");\n      $el.data(\"restore-display-mode\", null);\n\n      if (display === \"inline\" || display === \"inline-block\") {\n        if (display)\n          $el.css(\"display\", display);\n        $(el.nextSibling).filter(\".htmlwidgets-error\").remove();\n      } else if (display === \"block\"){\n        $el.css(\"visibility\", \"inherit\");\n        $(el.nextSibling).filter(\".htmlwidgets-error\").remove();\n      }\n    },\n    sizing: {}\n  };\n\n  // Called by widget bindings to register a new type of widget. The definition\n  // object can contain the following properties:\n  // - name (required) - A string indicating the binding name, which will be\n  //   used by default as the CSS classname to look for.\n  // - initialize (optional) - A function(el) that will be called once per\n  //   widget element; if a value is returned, it will be passed as the third\n  //   value to renderValue.\n  // - renderValue (required) - A function(el, data, initValue) that will be\n  //   called with data. Static contexts will cause this to be called once per\n  //   element; Shiny apps will cause this to be called multiple times per\n  //   element, as the data changes.\n  window.HTMLWidgets.widget = function(definition) {\n    if (!definition.name) {\n      throw new Error(\"Widget must have a name\");\n    }\n    if (!definition.type) {\n      throw new Error(\"Widget must have a type\");\n    }\n    // Currently we only support output widgets\n    if (definition.type !== \"output\") {\n      throw new Error(\"Unrecognized widget type '\" + definition.type + \"'\");\n    }\n    // TODO: Verify that .name is a valid CSS classname\n\n    // Support new-style instance-bound definitions. Old-style class-bound\n    // definitions have one widget \"object\" per widget per type/class of\n    // widget; the renderValue and resize methods on such widget objects\n    // take el and instance arguments, because the widget object can't\n    // store them. New-style instance-bound definitions have one widget\n    // object per widget instance; the definition that's passed in doesn't\n    // provide renderValue or resize methods at all, just the single method\n    //   factory(el, width, height)\n    // which returns an object that has renderValue(x) and resize(w, h).\n    // This enables a far more natural programming style for the widget\n    // author, who can store per-instance state using either OO-style\n    // instance fields or functional-style closure variables (I guess this\n    // is in contrast to what can only be called C-style pseudo-OO which is\n    // what we required before).\n    if (definition.factory) {\n      definition = createLegacyDefinitionAdapter(definition);\n    }\n\n    if (!definition.renderValue) {\n      throw new Error(\"Widget must have a renderValue function\");\n    }\n\n    // For static rendering (non-Shiny), use a simple widget registration\n    // scheme. We also use this scheme for Shiny apps/documents that also\n    // contain static widgets.\n    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];\n    // Merge defaults into the definition; don't mutate the original definition.\n    var staticBinding = extend({}, defaults, definition);\n    overrideMethod(staticBinding, \"find\", function(superfunc) {\n      return function(scope) {\n        var results = superfunc(scope);\n        // Filter out Shiny outputs, we only want the static kind\n        return filterByClass(results, \"html-widget-output\", false);\n      };\n    });\n    window.HTMLWidgets.widgets.push(staticBinding);\n\n    if (shinyMode) {\n      // Shiny is running. Register the definition with an output binding.\n      // The definition itself will not be the output binding, instead\n      // we will make an output binding object that delegates to the\n      // definition. This is because we foolishly used the same method\n      // name (renderValue) for htmlwidgets definition and Shiny bindings\n      // but they actually have quite different semantics (the Shiny\n      // bindings receive data that includes lots of metadata that it\n      // strips off before calling htmlwidgets renderValue). We can't\n      // just ignore the difference because in some widgets it's helpful\n      // to call this.renderValue() from inside of resize(), and if\n      // we're not delegating, then that call will go to the Shiny\n      // version instead of the htmlwidgets version.\n\n      // Merge defaults with definition, without mutating either.\n      var bindingDef = extend({}, defaults, definition);\n\n      // This object will be our actual Shiny binding.\n      var shinyBinding = new Shiny.OutputBinding();\n\n      // With a few exceptions, we'll want to simply use the bindingDef's\n      // version of methods if they are available, otherwise fall back to\n      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional\n      // methods in the future, and we want them to be overrideable by\n      // HTMLWidget binding definitions, then we'll need to add them to this\n      // list.\n      delegateMethod(shinyBinding, bindingDef, \"getId\");\n      delegateMethod(shinyBinding, bindingDef, \"onValueChange\");\n      delegateMethod(shinyBinding, bindingDef, \"onValueError\");\n      delegateMethod(shinyBinding, bindingDef, \"renderError\");\n      delegateMethod(shinyBinding, bindingDef, \"clearError\");\n      delegateMethod(shinyBinding, bindingDef, \"showProgress\");\n\n      // The find, renderValue, and resize are handled differently, because we\n      // want to actually decorate the behavior of the bindingDef methods.\n\n      shinyBinding.find = function(scope) {\n        var results = bindingDef.find(scope);\n\n        // Only return elements that are Shiny outputs, not static ones\n        var dynamicResults = results.filter(\".html-widget-output\");\n\n        // It's possible that whatever caused Shiny to think there might be\n        // new dynamic outputs, also caused there to be new static outputs.\n        // Since there might be lots of different htmlwidgets bindings, we\n        // schedule execution for later--no need to staticRender multiple\n        // times.\n        if (results.length !== dynamicResults.length)\n          scheduleStaticRender();\n\n        return dynamicResults;\n      };\n\n      // Wrap renderValue to handle initialization, which unfortunately isn't\n      // supported natively by Shiny at the time of this writing.\n\n      shinyBinding.renderValue = function(el, data) {\n        Shiny.renderDependencies(data.deps);\n        // Resolve strings marked as javascript literals to objects\n        if (!(data.evals instanceof Array)) data.evals = [data.evals];\n        for (var i = 0; data.evals && i < data.evals.length; i++) {\n          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);\n        }\n        if (!bindingDef.renderOnNullValue) {\n          if (data.x === null) {\n            el.style.visibility = \"hidden\";\n            return;\n          } else {\n            el.style.visibility = \"inherit\";\n          }\n        }\n        if (!elementData(el, \"initialized\")) {\n          initSizing(el);\n\n          elementData(el, \"initialized\", true);\n          if (bindingDef.initialize) {\n            var result = bindingDef.initialize(el, el.offsetWidth,\n              el.offsetHeight);\n            elementData(el, \"init_result\", result);\n          }\n        }\n        bindingDef.renderValue(el, data.x, elementData(el, \"init_result\"));\n        evalAndRun(data.jsHooks.render, elementData(el, \"init_result\"), [el, data.x]);\n      };\n\n      // Only override resize if bindingDef implements it\n      if (bindingDef.resize) {\n        shinyBinding.resize = function(el, width, height) {\n          // Shiny can call resize before initialize/renderValue have been\n          // called, which doesn't make sense for widgets.\n          if (elementData(el, \"initialized\")) {\n            bindingDef.resize(el, width, height, elementData(el, \"init_result\"));\n          }\n        };\n      }\n\n      Shiny.outputBindings.register(shinyBinding, bindingDef.name);\n    }\n  };\n\n  var scheduleStaticRenderTimerId = null;\n  function scheduleStaticRender() {\n    if (!scheduleStaticRenderTimerId) {\n      scheduleStaticRenderTimerId = setTimeout(function() {\n        scheduleStaticRenderTimerId = null;\n        window.HTMLWidgets.staticRender();\n      }, 1);\n    }\n  }\n\n  // Render static widgets after the document finishes loading\n  // Statically render all elements that are of this widget's class\n  window.HTMLWidgets.staticRender = function() {\n    var bindings = window.HTMLWidgets.widgets || [];\n    forEach(bindings, function(binding) {\n      var matches = binding.find(document.documentElement);\n      forEach(matches, function(el) {\n        var sizeObj = initSizing(el, binding);\n\n        if (hasClass(el, \"html-widget-static-bound\"))\n          return;\n        el.className = el.className + \" html-widget-static-bound\";\n\n        var initResult;\n        if (binding.initialize) {\n          initResult = binding.initialize(el,\n            sizeObj ? sizeObj.getWidth() : el.offsetWidth,\n            sizeObj ? sizeObj.getHeight() : el.offsetHeight\n          );\n          elementData(el, \"init_result\", initResult);\n        }\n\n        if (binding.resize) {\n          var lastSize = {};\n          var resizeHandler = function(e) {\n            var size = {\n              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,\n              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight\n            };\n            if (size.w === 0 && size.h === 0)\n              return;\n            if (size.w === lastSize.w && size.h === lastSize.h)\n              return;\n            lastSize = size;\n            binding.resize(el, size.w, size.h, initResult);\n          };\n\n          on(window, \"resize\", resizeHandler);\n\n          // This is needed for cases where we're running in a Shiny\n          // app, but the widget itself is not a Shiny output, but\n          // rather a simple static widget. One example of this is\n          // an rmarkdown document that has runtime:shiny and widget\n          // that isn't in a render function. Shiny only knows to\n          // call resize handlers for Shiny outputs, not for static\n          // widgets, so we do it ourselves.\n          if (window.jQuery) {\n            window.jQuery(document).on(\n              \"shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets\",\n              resizeHandler\n            );\n            window.jQuery(document).on(\n              \"hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets\",\n              resizeHandler\n            );\n          }\n\n          // This is needed for the specific case of ioslides, which\n          // flips slides between display:none and display:block.\n          // Ideally we would not have to have ioslide-specific code\n          // here, but rather have ioslides raise a generic event,\n          // but the rmarkdown package just went to CRAN so the\n          // window to getting that fixed may be long.\n          if (window.addEventListener) {\n            // It's OK to limit this to window.addEventListener\n            // browsers because ioslides itself only supports\n            // such browsers.\n            on(document, \"slideenter\", resizeHandler);\n            on(document, \"slideleave\", resizeHandler);\n          }\n        }\n\n        var scriptData = document.querySelector(\"script[data-for='\" + el.id + \"'][type='application/json']\");\n        if (scriptData) {\n          var data = JSON.parse(scriptData.textContent || scriptData.text);\n          // Resolve strings marked as javascript literals to objects\n          if (!(data.evals instanceof Array)) data.evals = [data.evals];\n          for (var k = 0; data.evals && k < data.evals.length; k++) {\n            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);\n          }\n          binding.renderValue(el, data.x, initResult);\n          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);\n        }\n      });\n    });\n\n    invokePostRenderHandlers();\n  }\n\n  // Wait until after the document has loaded to render the widgets.\n  if (document.addEventListener) {\n    document.addEventListener(\"DOMContentLoaded\", function() {\n      document.removeEventListener(\"DOMContentLoaded\", arguments.callee, false);\n      window.HTMLWidgets.staticRender();\n    }, false);\n  } else if (document.attachEvent) {\n    document.attachEvent(\"onreadystatechange\", function() {\n      if (document.readyState === \"complete\") {\n        document.detachEvent(\"onreadystatechange\", arguments.callee);\n        window.HTMLWidgets.staticRender();\n      }\n    });\n  }\n\n\n  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {\n    // If no key, default to the first item\n    if (typeof(key) === \"undefined\")\n      key = 1;\n\n    var link = document.getElementById(depname + \"-\" + key + \"-attachment\");\n    if (!link) {\n      throw new Error(\"Attachment \" + depname + \"/\" + key + \" not found in document\");\n    }\n    return link.getAttribute(\"href\");\n  };\n\n  window.HTMLWidgets.dataframeToD3 = function(df) {\n    var names = [];\n    var length;\n    for (var name in df) {\n        if (df.hasOwnProperty(name))\n            names.push(name);\n        if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n            throw new Error(\"All fields must be arrays\");\n        } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n            throw new Error(\"All fields must be arrays of the same length\");\n        }\n        length = df[name].length;\n    }\n    var results = [];\n    var item;\n    for (var row = 0; row < length; row++) {\n        item = {};\n        for (var col = 0; col < names.length; col++) {\n            item[names[col]] = df[names[col]][row];\n        }\n        results.push(item);\n    }\n    return results;\n  };\n\n  window.HTMLWidgets.transposeArray2D = function(array) {\n      if (array.length === 0) return array;\n      var newArray = array[0].map(function(col, i) {\n          return array.map(function(row) {\n              return row[i]\n          })\n      });\n      return newArray;\n  };\n  // Split value at splitChar, but allow splitChar to be escaped\n  // using escapeChar. 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binary\"):(this._fragments.push(t.data),3===this._fragments.length&&(this._partial=u.assemble(this._fragments[0],this._fragments[1],this._fragments[2]),this._fragments=[],r=this._partial.problem(),null!==r&&this._close_bad_protocol(r))),null!=this._partial&&this._partial.complete())return e=this._partial,this._partial=null,this._current_handler(e)},t.prototype._on_close=function(t){var e,r;for(f.info(\"Lost websocket \"+this._number+\" connection, \"+t.code+\" (\"+t.reason+\")\"),this.socket=null,null!=this._pending_ack&&(this._pending_ack[1](new Error(\"Lost websocket connection, \"+t.code+\" (\"+t.reason+\")\")),this._pending_ack=null),e=function(){var t,e,r;e=this._pending_replies;for(r in e)return t=e[r],delete this._pending_replies[r],t;return null},r=e();null!==r;)r[1](\"Disconnected\"),r=e();if(!this.closed_permanently)return this._schedule_reconnect(2e3)},t.prototype._on_error=function(t){return f.debug(\"Websocket error on socket  \"+this._number),t(new Error(\"Could not open websocket\"))},t.prototype._close_bad_protocol=function(t){if(f.error(\"Closing connection: \"+t),null!=this.socket)return this.socket.close(1002,t)},t.prototype._awaiting_ack_handler=function(t){return\"ACK\"!==t.msgtype()?this._close_bad_protocol(\"First message was not an ACK\"):(this._current_handler=function(t){return function(e){return t._steady_state_handler(e)}}(this),this._repull_session_doc(),null!=this._pending_ack?(this._pending_ack[0](this),this._pending_ack=null):void 0)},t.prototype._steady_state_handler=function(t){var e;return t.reqid()in this._pending_replies?(e=this._pending_replies[t.reqid()],delete this._pending_replies[t.reqid()],e[0](t)):t.msgtype()in m?m[t.msgtype()](this,t):f.debug(\"Doing nothing with message \"+t.msgtype())},t}(),o=function(){function t(t,e,r){this._connection=t,this.document=e,this.id=r,this._current_patch=null,this.document_listener=function(t){return function(e){return t._document_changed(e)}}(this),this.document.on_change(this.document_listener)}return t.prototype.close=function(){return this._connection.close()},t.prototype._connection_closed=function(){return this.document.remove_on_change(this.document_listener)},t.prototype.request_server_info=function(){var t,e;return t=u.create(\"SERVER-INFO-REQ\",{}),e=this._connection.send_with_reply(t),e.then(function(t){return t.content})},t.prototype.force_roundtrip=function(){return this.request_server_info().then(function(t){})},t.prototype._should_suppress_on_change=function(t,e){var r,n,o,i,s,a,u,c,f,m,g,y,v,b;if(e instanceof h){for(g=t.content.events,n=0,a=g.length;n<a;n++)if(r=g[n],\"ModelChanged\"===r.kind&&r.model.id===e.model.id&&r.attr===e.attr)if(m=r[\"new\"],e.new_ instanceof l){if(\"object\"==typeof m&&\"id\"in m&&m.id===e.new_.id)return!0}else if(d.isEqual(m,e.new_))return!0}else if(e instanceof p){for(y=t.content.events,o=0,u=y.length;o<u;o++)if(r=y[o],\"RootAdded\"===r.kind&&r.model.id===e.model.id)return!0}else if(e instanceof _){for(v=t.content.events,i=0,c=v.length;i<c;i++)if(r=v[i],\"RootRemoved\"===r.kind&&r.model.id===e.model.id)return!0}else if(e instanceof TitleChangedEvent)for(b=t.content.events,s=0,f=b.length;s<f;s++)if(r=b[s],\"TitleChanged\"===r.kind&&r.title===e.title)return!0;return!1},t.prototype._document_changed=function(t){var e;if(!(null!=this._current_patch&&this._should_suppress_on_change(this._current_patch,t)||t instanceof h&&!(t.attr in t.model.serializable_attributes())))return e=u.create(\"PATCH-DOC\",{},this.document.create_json_patch([t])),this._connection.send(e)},t.prototype._handle_patch=function(t){this._current_patch=t;try{return this.document.apply_json_patch(t.content)}finally{this._current_patch=null}},t}(),g=function(t,e){var r,o,i;return i=null,r=null,o=new c(function(o,i){return r=new n(t,e,function(t){var e,r;try{return o(t)}catch(r){throw e=r,f.error(\"Promise handler threw an error, closing session \"+error),t.close(),e}},function(){return i(new Error(\"Connection was closed before we successfully pulled a session\"))}),r.connect().then(function(t){},function(t){throw f.error(\"Failed to connect to Bokeh server \"+t),t})}),o.close=function(){return r.close()},o},e.exports={pull_session:g,DEFAULT_SERVER_WEBSOCKET_URL:i,DEFAULT_SESSION_ID:s}},{\"./core/has_props\":\"core/has_props\",\"./core/logging\":\"core/logging\",\"./document\":\"document\",\"es6-promise\":\"es6-promise\",underscore:\"underscore\"}],\"common/build_views\":[function(t,e,r){var n,o,i;n=t(\"underscore\"),o=function(t,e,r,o){var s,a,l,u,h,c,p,_,d,f,m;for(null==o&&(o=[]),s=[],d=n.filter(e,function(e){return!n.has(t,e.id)}),l=a=0,c=d.length;a<c;l=++a)_=d[l],m=n.extend({},r,{model:_}),l<o.length?t[_.id]=new o[l](m):t[_.id]=new _.default_view(m),t[_.id].$el.find(\"*[class*='ui-']\").each(function(t,e){return e.className=i(e)}),s.push(t[_.id]);for(f=n.difference(n.keys(t),n.pluck(e,\"id\")),u=0,p=f.length;u<p;u++)h=f[u],t[h].remove(),delete t[h];return s},i=function(t){var e,r;if(null!=t.className)return e=t.className.split(\" \"),r=n.map(e,function(t){return t=t.trim(),0===t.indexOf(\"ui-\")?\"bk-\"+t:t}),r.join(\" \")},o.jQueryUIPrefixer=i,e.exports=o=o},{underscore:\"underscore\"}],\"common/hittest\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p;h=function(t,e,r,n){var o,i,s,a,l,u,h,c;for(i=!1,l=r[r.length-1],h=n[n.length-1],o=s=0,a=r.length;0<=a?s<a:s>a;o=0<=a?++s:--s)u=r[o],c=n[o],h<e!=c<e&&l+(e-h)/(c-h)*(u-l)<t&&(i=!i),l=u,h=c;return i},u=function(){return null},n=function(){function t(){this[\"0d\"]={glyph:null,get_view:u,indices:[]},this[\"1d\"]={indices:[]},this[\"2d\"]={}}return Object.defineProperty(t.prototype,\"_0d\",{get:function(){return this[\"0d\"]}}),Object.defineProperty(t.prototype,\"_1d\",{get:function(){return this[\"1d\"]}}),Object.defineProperty(t.prototype,\"_2d\",{get:function(){return this[\"2d\"]}}),t.prototype.is_empty=function(){return 0===this._0d.indices.length&&0===this._1d.indices.length},t}(),i=function(){return new n},p=function(t,e){var r,n,o,i,s,a;return o=t[0],i=t[1],s=e[0],a=e[1],o>i&&(r=[i,o],o=r[0],i=r[1]),s>a&&(n=[a,s],s=n[0],a=n[1]),{minX:o,minY:s,maxX:i,maxY:a}},c=function(t){return t*t},s=function(t,e,r,n){return c(t-r)+c(e-n)},l=function(t,e,r){var n,o;return n=s(e.x,e.y,r.x,r.y),0===n?s(t.x,t.y,e.x,e.y):(o=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n,o<0?s(t.x,t.y,e.x,e.y):o>1?s(t.x,t.y,r.x,r.y):s(t.x,t.y,e.x+o*(r.x-e.x),e.y+o*(r.y-e.y)))},a=function(t,e,r){return Math.sqrt(l(t,e,r))},o=function(t,e,r,n,o,i,s,a){var l,u,h,c,p,_,d;return h=(a-i)*(r-t)-(s-o)*(n-e),0===h?{hit:!1,x:null,y:null}:(l=e-i,u=t-o,c=(s-o)*l-(a-i)*u,p=(r-t)*l-(n-e)*u,l=c/h,u=p/h,_=t+l*(r-t),d=e+l*(n-e),{hit:l>0&&l<1&&u>0&&u<1,x:_,y:d})},e.exports={point_in_poly:h,HitTestResult:n,create_hit_test_result:i,dist_2_pts:s,dist_to_segment:a,check_2_segments_intersect:o,validate_bbox_coords:p}},{}],\"common/models\":[function(t,e,r){e.exports={SelectionManager:t(\"./selection_manager\"),Selector:t(\"./selector\"),ToolEvents:t(\"./tool_events\"),Arrow:t(\"../models/annotations/arrow\"),BoxAnnotation:t(\"../models/annotations/box_annotation\"),ColorBar:t(\"../models/annotations/color_bar\"),Label:t(\"../models/annotations/label\"),LabelSet:t(\"../models/annotations/label_set\"),Legend:t(\"../models/annotations/legend\"),PolyAnnotation:t(\"../models/annotations/poly_annotation\"),Span:t(\"../models/annotations/span\"),Title:t(\"../models/annotations/title\"),Tooltip:t(\"../models/annotations/tooltip\"),OpenHead:t(\"../models/annotations/arrow_head\").OpenHead,NormalHead:t(\"../models/annotations/arrow_head\").NormalHead,VeeHead:t(\"../models/annotations/arrow_head\").VeeHead,CategoricalAxis:t(\"../models/axes/categorical_axis\"),DatetimeAxis:t(\"../models/axes/datetime_axis\"),LinearAxis:t(\"../models/axes/linear_axis\"),LogAxis:t(\"../models/axes/log_axis\"),CustomJS:t(\"../models/callbacks/customjs\"),OpenURL:t(\"../models/callbacks/open_url\"),Canvas:t(\"../models/canvas/canvas\"),CartesianFrame:t(\"../models/canvas/cartesian_frame\"),BasicTickFormatter:t(\"../models/formatters/basic_tick_formatter\"),CategoricalTickFormatter:t(\"../models/formatters/categorical_tick_formatter\"),DatetimeTickFormatter:t(\"../models/formatters/datetime_tick_formatter\"),LogTickFormatter:t(\"../models/formatters/log_tick_formatter\"),FuncTickFormatter:t(\"../models/formatters/func_tick_formatter\"),NumeralTickFormatter:t(\"../models/formatters/numeral_tick_formatter\"),PrintfTickFormatter:t(\"../models/formatters/printf_tick_formatter\"),AnnularWedge:t(\"../models/glyphs/annular_wedge\"),Annulus:t(\"../models/glyphs/annulus\"),Arc:t(\"../models/glyphs/arc\"),Bezier:t(\"../models/glyphs/bezier\"),Circle:t(\"../models/glyphs/circle\"),Ellipse:t(\"../models/glyphs/ellipse\"),Gear:t(\"../models/glyphs/gear\"),HBar:t(\"../models/glyphs/hbar\"),Image:t(\"../models/glyphs/image\"),ImageRGBA:t(\"../models/glyphs/image_rgba\"),ImageURL:t(\"../models/glyphs/image_url\"),Line:t(\"../models/glyphs/line\"),MultiLine:t(\"../models/glyphs/multi_line\"),Oval:t(\"../models/glyphs/oval\"),Patch:t(\"../models/glyphs/patch\"),Patches:t(\"../models/glyphs/patches\"),Quad:t(\"../models/glyphs/quad\"),Quadratic:t(\"../models/glyphs/quadratic\"),Ray:t(\"../models/glyphs/ray\"),Rect:t(\"../models/glyphs/rect\"),Segment:t(\"../models/glyphs/segment\"),Text:t(\"../models/glyphs/text\"),VBar:t(\"../models/glyphs/vbar\"),Wedge:t(\"../models/glyphs/wedge\"),Grid:t(\"../models/grids/grid\"),Column:t(\"../models/layouts/column\"),Row:t(\"../models/layouts/row\"),Spacer:t(\"../models/layouts/spacer\"),WidgetBox:t(\"../models/layouts/widget_box\"),CategoricalMapper:t(\"../models/mappers/categorical_mapper\"),GridMapper:t(\"../models/mappers/grid_mapper\"),LinearColorMapper:t(\"../models/mappers/linear_color_mapper\"),LinearMapper:t(\"../models/mappers/linear_mapper\"),LogColorMapper:t(\"../models/mappers/log_color_mapper\"),LogMapper:t(\"../models/mappers/log_mapper\"),Transform:t(\"../models/transforms/transform\"),Jitter:t(\"../models/transforms/jitter\"),Interpolator:t(\"../models/transforms/interpolator\"),LinearInterpolator:t(\"../models/transforms/linear_interpolator\"),StepInterpolator:t(\"../models/transforms/step_interpolator\"),Asterisk:t(\"../models/markers/index\").Asterisk,CircleCross:t(\"../models/markers/index\").CircleCross,CircleX:t(\"../models/markers/index\").CircleX,Cross:t(\"../models/markers/index\").Cross,Diamond:t(\"../models/markers/index\").Diamond,DiamondCross:t(\"../models/markers/index\").DiamondCross,InvertedTriangle:t(\"../models/markers/index\").InvertedTriangle,Square:t(\"../models/markers/index\").Square,SquareCross:t(\"../models/markers/index\").SquareCross,SquareX:t(\"../models/markers/index\").SquareX,Triangle:t(\"../models/markers/index\").Triangle,X:t(\"../models/markers/index\").X,Plot:t(\"../models/plots/plot\"),GMapPlot:t(\"../models/plots/gmap_plot\"),DataRange1d:t(\"../models/ranges/data_range1d\"),FactorRange:t(\"../models/ranges/factor_range\"),Range1d:t(\"../models/ranges/range1d\"),GlyphRenderer:t(\"../models/renderers/glyph_renderer\"),AjaxDataSource:t(\"../models/sources/ajax_data_source\"),ColumnDataSource:t(\"../models/sources/column_data_source\"),GeoJSONDataSource:t(\"../models/sources/geojson_data_source\"),AdaptiveTicker:t(\"../models/tickers/adaptive_ticker\"),BasicTicker:t(\"../models/tickers/basic_ticker\"),CategoricalTicker:t(\"../models/tickers/categorical_ticker\"),CompositeTicker:t(\"../models/tickers/composite_ticker\"),ContinuousTicker:t(\"../models/tickers/continuous_ticker\"),DatetimeTicker:t(\"../models/tickers/datetime_ticker\"),DaysTicker:t(\"../models/tickers/days_ticker\"),FixedTicker:t(\"../models/tickers/fixed_ticker\"),LogTicker:t(\"../models/tickers/log_ticker\"),MonthsTicker:t(\"../models/tickers/months_ticker\"),SingleIntervalTicker:t(\"../models/tickers/single_interval_ticker\"),YearsTicker:t(\"../models/tickers/years_ticker\"),TileRenderer:t(\"../models/tiles/tile_renderer\"),TileSource:t(\"../models/tiles/tile_source\"),TMSTileSource:t(\"../models/tiles/tms_tile_source\"),WMTSTileSource:t(\"../models/tiles/wmts_tile_source\"),QUADKEYTileSource:t(\"../models/tiles/quadkey_tile_source\"),BBoxTileSource:t(\"../models/tiles/bbox_tile_source\"),DynamicImageRenderer:t(\"../models/tiles/dynamic_image_renderer\"),ImageSource:t(\"../models/tiles/image_source\"),Toolbar:t(\"../models/tools/toolbar\"),ToolbarBox:t(\"../models/tools/toolbar_box\"),ButtonTool:t(\"../models/tools/button_tool\"),ActionTool:t(\"../models/tools/actions/action_tool\"),SaveTool:t(\"../models/tools/actions/save_tool\"),UndoTool:t(\"../models/tools/actions/undo_tool\"),RedoTool:t(\"../models/tools/actions/redo_tool\"),ResetTool:t(\"../models/tools/actions/reset_tool\"),HelpTool:t(\"../models/tools/actions/help_tool\"),BoxSelectTool:t(\"../models/tools/gestures/box_select_tool\"),BoxZoomTool:t(\"../models/tools/gestures/box_zoom_tool\"),GestureTool:t(\"../models/tools/gestures/gesture_tool\"),LassoSelectTool:t(\"../models/tools/gestures/lasso_select_tool\"),PanTool:t(\"../models/tools/gestures/pan_tool\"),PolySelectTool:t(\"../models/tools/gestures/poly_select_tool\"),SelectTool:t(\"../models/tools/gestures/select_tool\"),ResizeTool:t(\"../models/tools/gestures/resize_tool\"),TapTool:t(\"../models/tools/gestures/tap_tool\"),WheelZoomTool:t(\"../models/tools/gestures/wheel_zoom_tool\"),CrosshairTool:t(\"../models/tools/inspectors/crosshair_tool\"),HoverTool:t(\"../models/tools/inspectors/hover_tool\"),InspectTool:t(\"../models/tools/inspectors/inspect_tool\")}},{\"../models/annotations/arrow\":\"models/annotations/arrow\",\"../models/annotations/arrow_head\":\"models/annotations/arrow_head\",\"../models/annotations/box_annotation\":\"models/annotations/box_annotation\",\"../models/annotations/color_bar\":\"models/annotations/color_bar\",\"../models/annotations/label\":\"models/annotations/label\",\"../models/annotations/label_set\":\"models/annotations/label_set\",\"../models/annotations/legend\":\"models/annotations/legend\",\"../models/annotations/poly_annotation\":\"models/annotations/poly_annotation\",\"../models/annotations/span\":\"models/annotations/span\",\"../models/annotations/title\":\"models/annotations/title\",\"../models/annotations/tooltip\":\"models/annotations/tooltip\",\"../models/axes/categorical_axis\":\"models/axes/categorical_axis\",\"../models/axes/datetime_axis\":\"models/axes/datetime_axis\",\"../models/axes/linear_axis\":\"models/axes/linear_axis\",\"../models/axes/log_axis\":\"models/axes/log_axis\",\"../models/callbacks/customjs\":\"models/callbacks/customjs\",\"../models/callbacks/open_url\":\"models/callbacks/open_url\",\"../models/canvas/canvas\":\"models/canvas/canvas\",\"../models/canvas/cartesian_frame\":\"models/canvas/cartesian_frame\",\"../models/formatters/basic_tick_formatter\":\"models/formatters/basic_tick_formatter\",\"../models/formatters/categorical_tick_formatter\":\"models/formatters/categorical_tick_formatter\",\"../models/formatters/datetime_tick_formatter\":\"models/formatters/datetime_tick_formatter\",\"../models/formatters/func_tick_formatter\":\"models/formatters/func_tick_formatter\",\"../models/formatters/log_tick_formatter\":\"models/formatters/log_tick_formatter\",\"../models/formatters/numeral_tick_formatter\":\"models/formatters/numeral_tick_formatter\",\"../models/formatters/printf_tick_formatter\":\"models/formatters/printf_tick_formatter\",\"../models/glyphs/annular_wedge\":\"models/glyphs/annular_wedge\",\"../models/glyphs/annulus\":\"models/glyphs/annulus\",\"../models/glyphs/arc\":\"models/glyphs/arc\",\"../models/glyphs/bezier\":\"models/glyphs/bezier\",\"../models/glyphs/circle\":\"models/glyphs/circle\",\"../models/glyphs/ellipse\":\"models/glyphs/ellipse\",\"../models/glyphs/gear\":\"models/glyphs/gear\",\"../models/glyphs/hbar\":\"models/glyphs/hbar\",\"../models/glyphs/image\":\"models/glyphs/image\",\"../models/glyphs/image_rgba\":\"models/glyphs/image_rgba\",\"../models/glyphs/image_url\":\"models/glyphs/image_url\",\"../models/glyphs/line\":\"models/glyphs/line\",\"../models/glyphs/multi_line\":\"models/glyphs/multi_line\",\"../models/glyphs/oval\":\"models/glyphs/oval\",\"../models/glyphs/patch\":\"models/glyphs/patch\",\"../models/glyphs/patches\":\"models/glyphs/patches\",\"../models/glyphs/quad\":\"models/glyphs/quad\",\"../models/glyphs/quadratic\":\"models/glyphs/quadratic\",\"../models/glyphs/ray\":\"models/glyphs/ray\",\"../models/glyphs/rect\":\"models/glyphs/rect\",\"../models/glyphs/segment\":\"models/glyph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\",\"./selection_manager\":\"common/selection_manager\",\"./selector\":\"common/selector\",\"./tool_events\":\"common/tool_events\"}],\"common/selection_manager\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"../core/has_props\"),l=t(\"../core/logging\").logger,i=t(\"./selector\"),a=t(\"./hittest\"),u=t(\"../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type=\"SelectionManager\",e.internal({source:[u.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.selectors={},this.inspectors={},this.last_inspection_was_empty={}},e.prototype.select=function(t,e,r,n,o){var i,s,a;return null==o&&(o=!1),a=this.get(\"source\"),a!==e.mget(\"data_source\")&&l.warn(\"select called with mis-matched data 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more than one object matching given selector\")}},e}(n),e.exports=o},{\"./core/has_props\":\"core/has_props\",\"./core/properties\":\"core/properties\",underscore:\"underscore\"}],\"models/annotations/annotation\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;a=t(\"underscore\"),s=t(\"../../core/layout/side_panel\"),l=t(\"../../core/properties\"),i=t(\"../renderers/renderer\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._get_panel_offset=function(){var t,e;return t=this.model.panel._left._value,e=this.model.panel._bottom._value,{x:t,y:-e}},e.prototype._get_size=function(){return-1},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return 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e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),null==this.mget(\"source\")&&this.mset(\"source\",new s.Model),this.canvas=this.plot_model.get(\"canvas\"),this.xmapper=this.plot_view.frame.get(\"x_mappers\")[this.mget(\"x_range_name\")],this.ymapper=this.plot_view.frame.get(\"y_mappers\")[this.mget(\"y_range_name\")],this.set_data()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.plot_view.request_render),this.listenTo(this.mget(\"source\"),\"change\",function(){return this.set_data(),this.plot_view.request_render()})},e.prototype.set_data=function(){return e.__super__.set_data.call(this,this.mget(\"source\")),this.set_visuals(this.mget(\"source\"))},e.prototype._map_data=function(){var t,e,r,n;return e=\"data\"===this.mget(\"start_units\")?this.plot_view.map_to_screen(this._x_start,this._y_start,r=this.mget(\"x_range_name\"),n=this.mget(\"y_range_name\")):[this.canvas.v_vx_to_sx(this._x_start),this.canvas.v_vy_to_sy(this._y_start)],t=\"data\"===this.mget(\"end_units\")?this.plot_view.map_to_screen(this._x_end,this._y_end,r=this.mget(\"x_range_name\"),n=this.mget(\"y_range_name\")):[this.canvas.v_vx_to_sx(this._x_end),this.canvas.v_vy_to_sy(this._y_end)],[e,t]},e.prototype.render=function(){var t;if(t=this._map_data(),this.start=t[0],this.end=t[1],this._draw_arrow_body(),null!=this.mget(\"end\")&&this._draw_arrow_head(this.mget(\"end\"),this.start,this.end),null!=this.mget(\"start\"))return this._draw_arrow_head(this.mget(\"start\"),this.end,this.start)},e.prototype._draw_arrow_body=function(){var t,e,r,n;for(t=this.plot_view.canvas_view.ctx,t.save(),e=r=0,n=this._x_start.length;0<=n?r<n:r>n;e=0<=n?++r:--r)this.visuals.line.set_vectorize(t,e),t.beginPath(),t.moveTo(this.start[0][e],this.start[1][e]),t.lineTo(this.end[0][e],this.end[1][e]),this.visuals.line.doit&&t.stroke();return t.restore()},e.prototype._draw_arrow_head=function(t,e,r){var n,o,i,s,a,l;for(o=this.plot_view.canvas_view.ctx,l=[],i=s=0,a=this._x_start.length;0<=a?s<a:s>a;i=0<=a?++s:--s)n=Math.PI/2+u([e[0][i],e[1][i]],[r[0][i],r[1][i]]),o.save(),o.translate(r[0][i],r[1][i]),o.rotate(n),t.render(o,i),l.push(o.restore());return l},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.default_view=i,e.prototype.type=\"Arrow\",e.mixins([\"line\"]),e.define({x_start:[h.NumberSpec],y_start:[h.NumberSpec],start_units:[h.String,\"data\"],start:[h.Instance,null],x_end:[h.NumberSpec],y_end:[h.NumberSpec],end_units:[h.String,\"data\"],end:[h.Instance,new a.Model({})],source:[h.Instance],x_range_name:[h.String,\"default\"],y_range_name:[h.String,\"default\"]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"../../core/util/math\":\"core/util/math\",\"../sources/column_data_source\":\"models/sources/column_data_source\",\"./annotation\":\"models/annotations/annotation\",\"./arrow_head\":\"models/annotations/arrow_head\",underscore:\"underscore\"}],\"models/annotations/arrow_head\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;n=t(\"./annotation\"),a=t(\"../renderers/renderer\"),u=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type=\"ArrowHead\",e.prototype.initialize=function(t){var r,n,o,i,s,l,u,h;for(e.__super__.initialize.call(this,t),this.visuals={},s=this.mixins,u=[],r=0,n=s.length;r<n;r++)h=s[r],l=h.split(\":\"),o=l[0],i=l[1],null==i&&(i=\"\"),u.push(this.visuals[i+o]=new a.Visuals[o]({obj:this,prefix:i}));return u},e.prototype.render=function(t,e){return null},e}(n.Model),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type=\"OpenHead\",e.prototype.render=function(t,e){if(this.visuals.line.doit)return this.visuals.line.set_vectorize(t,e),t.beginPath(),t.moveTo(.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,0),t.lineTo(-.5*this.get(\"size\"),this.get(\"size\")),t.stroke()},e.mixins([\"line\"]),e.define({size:[u.Number,25]}),e}(o),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type=\"NormalHead\",e.prototype.render=function(t,e){if(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,e),t.beginPath(),t.moveTo(.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,0),t.lineTo(-.5*this.get(\"size\"),this.get(\"size\")),t.closePath(),t.fill()),this.visuals.line.doit)return this.visuals.line.set_vectorize(t,e),t.beginPath(),t.moveTo(.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,0),t.lineTo(-.5*this.get(\"size\"),this.get(\"size\")),t.closePath(),t.stroke()},e.mixins([\"line\",\"fill\"]),e.define({size:[u.Number,25]}),e.override({fill_color:\"black\"}),e}(o),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.type=\"VeeHead\",e.prototype.render=function(t,e){if(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,e),t.beginPath(),t.moveTo(.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,0),t.lineTo(-.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,.5*this.get(\"size\")),t.closePath(),t.fill()),this.visuals.line.doit)return this.visuals.line.set_vectorize(t,e),t.beginPath(),t.moveTo(.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,0),t.lineTo(-.5*this.get(\"size\"),this.get(\"size\")),t.lineTo(0,.5*this.get(\"size\")),t.closePath(),t.stroke()},e.mixins([\"line\",\"fill\"]),e.define({size:[u.Number,25]}),e.override({fill_color:\"black\"}),e}(o),e.exports={OpenHead:{Model:s},NormalHead:{Model:i},VeeHead:{Model:l}}},{\"../../core/properties\":\"core/properties\",\"../renderers/renderer\":\"models/renderers/renderer\",\"./annotation\":\"models/annotations/annotation\"}],\"models/annotations/box_annotation\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./annotation\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find(\"div.bk-canvas-overlays\")),this.$el.addClass(\"bk-shading\"),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return\"css\"===this.mget(\"render_mode\")?(this.listenTo(this.model,\"change\",this.render),this.listenTo(this.model,\"data_update\",this.render)):(this.listenTo(this.model,\"change\",this.plot_view.request_render),this.listenTo(this.model,\"data_update\",this.plot_view.request_render))},e.prototype.render=function(){var t,e,r,n;return null==this.mget(\"left\")&&null==this.mget(\"right\")&&null==this.mget(\"top\")&&null==this.mget(\"bottom\")?(this.$el.hide(),null):(this.frame=this.plot_model.get(\"frame\"),this.canvas=this.plot_model.get(\"canvas\"),this.xmapper=this.plot_view.frame.get(\"x_mappers\")[this.mget(\"x_range_name\")],this.ymapper=this.plot_view.frame.get(\"y_mappers\")[this.mget(\"y_range_name\")],e=this.canvas.vx_to_sx(this._calc_dim(\"left\",this.xmapper,this.frame.get(\"h_range\").get(\"start\"))),r=this.canvas.vx_to_sx(this._calc_dim(\"right\",this.xmapper,this.frame.get(\"h_range\").get(\"end\"))),t=this.canvas.vy_to_sy(this._calc_dim(\"bottom\",this.ymapper,this.frame.get(\"v_range\").get(\"start\"))),n=this.canvas.vy_to_sy(this._calc_dim(\"top\",this.ymapper,this.frame.get(\"v_range\").get(\"end\"))),\"css\"===this.mget(\"render_mode\")?this._css_box(e,r,t,n):this._canvas_box(e,r,t,n))},e.prototype._css_box=function(t,e,r,n){var o,i,a,l,u,h,c,p;return p=Math.abs(e-t),h=Math.abs(r-n),u=this.mget(\"line_width\").value,a=this.mget(\"line_color\").value,i=this.mget(\"fill_color\").value,o=this.mget(\"fill_alpha\").value,c=\"left:\"+t+\"px; width:\"+p+\"px; top:\"+n+\"px; height:\"+h+\"px; border-width:\"+u+\"px; border-color:\"+a+\"; background-color:\"+i+\"; opacity:\"+o+\";\",l=this.mget(\"line_dash\"),s.isArray(l)&&(l=l.length<2?\"solid\":\"dashed\"),s.isString(l)&&(c+=\" border-style:\"+l+\";\"),this.$el.attr(\"style\",c),this.$el.show()},e.prototype._canvas_box=function(t,e,r,n){var o;return o=this.plot_view.canvas_view.ctx,o.save(),o.beginPath(),o.rect(t,n,e-t,r-n),this.visuals.fill.set_value(o),o.fill(),this.visuals.line.set_value(o),o.stroke(),o.restore()},e.prototype._calc_dim=function(t,e,r){var n;return n=null!=this.mget(t)?\"data\"===this.mget(t+\"_units\")?e.map_to_target(this.mget(t)):this.mget(t):r},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"BoxAnnotation\",e.mixins([\"line\",\"fill\"]),e.define({render_mode:[a.RenderMode,\"canvas\"],x_range_name:[a.String,\"default\"],y_range_name:[a.String,\"default\"],top:[a.Number,null],top_units:[a.SpatialUnits,\"data\"],bottom:[a.Number,null],bottom_units:[a.SpatialUnits,\"data\"],left:[a.Number,null],left_units:[a.SpatialUnits,\"data\"],right:[a.Number,null],right_units:[a.SpatialUnits,\"data\"]}),e.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3}),e.prototype.update=function(t){var e,r,n,o;return r=t.left,n=t.right,o=t.top,e=t.bottom,this.set({left:r,right:n,top:o,bottom:e},{silent:!0}),this.trigger(\"data_update\")},e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/color_bar\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y=function(t,e){function r(){this.constructor=t}for(var n in e)v.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},v={}.hasOwnProperty;f=t(\"underscore\"),n=t(\"./annotation\"),i=t(\"../tickers/basic_ticker\"),o=t(\"../formatters/basic_tick_formatter\"),h=t(\"../mappers/linear_color_mapper\"),c=t(\"../mappers/linear_mapper\"),p=t(\"../mappers/log_mapper\"),_=t(\"../ranges/range1d\"),m=t(\"../../core/properties\"),g=t(\"../../core/util/text\"),d=25,u=.3,l=.8,a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return y(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this._set_canvas_image()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change:visible\",this.plot_view.request_render),this.listenTo(this.model.ticker,\"change\",this.plot_view.request_render),this.listenTo(this.model.formatter,\"change\",this.plot_view.request_render),this.listenTo(this.model.color_mapper,\"change\",function(){return this._set_canvas_image(),this.plot_view.request_render()})},e.prototype._get_panel_offset=function(){var t,e;return t=this.model.panel._left._value,e=this.model.panel._top._value,{x:t,y:-e}},e.prototype._get_size=function(){var t,e;return t=this.compute_legend_dimensions(),e=this.model.panel.side,\"above\"===e||\"below\"===e?t.height:\"left\"===e||\"right\"===e?t.width:void 0},e.prototype._set_canvas_image=function(){var t,e,r,n,o,i,s,a,l,u,c,p,_;switch(a=this.model.color_mapper.palette,\"vertical\"===this.model.orientation&&(a=a.slice(0).reverse()),this.model.orientation){case\"vertical\":l=[1,a.length],_=l[0],o=l[1];break;case\"horizontal\":u=[a.length,1],_=u[0],o=u[1]}return r=document.createElement(\"canvas\"),c=[_,o],r.width=c[0],r.height=c[1],i=r.getContext(\"2d\"),s=i.getImageData(0,0,_,o),n=new h.Model({palette:a}),t=n.v_map_screen(function(){p=[];for(var t=0,e=a.length;0<=e?t<e:t>e;0<=e?t++:t--)p.push(t);return p}.apply(this)),e=new Uint8ClampedArray(t),s.data.set(e),i.putImageData(s,0,0),this.image=r},e.prototype.compute_legend_dimensions=function(){var t,e,r,n,o,i,s,a,l,u;switch(t=this.model._computed_image_dimensions(),a=[t.height,t.width],e=a[0],r=a[1],n=this._get_label_extent(),u=this.model._title_extent(),l=this.model._tick_extent(),s=this.model.padding,this.model.orientation){case\"vertical\":o=e+u+2*s,i=r+l+n+2*s;break;case\"horizontal\":o=e+u+l+n+2*s,i=r+2*s}return{height:o,width:i}},e.prototype.compute_legend_location=function(){var t,e,r,n,o,i,s,a,l,u,h,c;if(e=this.compute_legend_dimensions(),s=[e.height,e.width],r=s[0],o=s[1],n=this.model.margin,i=this.model.location,t=this.plot_view.frame.get(\"h_range\"),u=this.plot_view.frame.get(\"v_range\"),f.isString(i))switch(i){case\"top_left\":h=t.get(\"start\")+n,c=u.get(\"end\")-n;break;case\"top_center\":h=(t.get(\"end\")+t.get(\"start\"))/2-o/2,c=u.get(\"end\")-n;break;case\"top_right\":h=t.get(\"end\")-n-o,c=u.get(\"end\")-n;break;case\"right_center\":h=t.get(\"end\")-n-o,c=(u.get(\"end\")+u.get(\"start\"))/2+r/2;break;case\"bottom_right\":h=t.get(\"end\")-n-o,c=u.get(\"start\")+n+r;break;case\"bottom_center\":h=(t.get(\"end\")+t.get(\"start\"))/2-o/2,c=u.get(\"start\")+n+r;break;case\"bottom_left\":h=t.get(\"start\")+n,c=u.get(\"start\")+n+r;break;case\"left_center\":h=t.get(\"start\")+n,c=(u.get(\"end\")+u.get(\"start\"))/2+r/2;break;case\"center\":h=(t.get(\"end\")+t.get(\"start\"))/2-o/2,c=(u.get(\"end\")+u.get(\"start\"))/2+r/2}else f.isArray(i)&&2===i.length&&(h=i[0],c=i[1]);return a=this.plot_view.canvas.vx_to_sx(h),l=this.plot_view.canvas.vy_to_sy(c),{sx:a,sy:l}},e.prototype.render=function(){var t,e,r,n,o;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),null!=this.model.panel&&(o=this._get_panel_offset(),t.translate(o.x,o.y),e=this._get_frame_offset(),t.translate(e.x,e.y)),n=this.compute_legend_location(),t.translate(n.sx,n.sy),this._draw_bbox(t),r=this._get_image_offset(),t.translate(r.x,r.y),this._draw_image(t),null!=this.model.color_mapper.low&&null!=this.model.color_mapper.high&&(this._draw_major_ticks(t),this._draw_minor_ticks(t),this._draw_major_labels(t)),this.model.title&&this._draw_title(t),t.restore()},e.prototype._draw_bbox=function(t){var e;return e=this.compute_legend_dimensions(),t.save(),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fillRect(0,0,e.width,e.height)),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.strokeRect(0,0,e.width,e.height)),t.restore()},e.prototype._draw_image=function(t){var e;return e=this.model._computed_image_dimensions(),t.save(),t.setImageSmoothingEnabled(!1),t.globalAlpha=this.model.scale_alpha,t.drawImage(this.image,0,0,e.width,e.height),this.visuals.bar_line.doit&&(this.visuals.bar_line.set_value(t),t.strokeRect(0,0,e.width,e.height)),t.restore()},e.prototype._draw_major_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f;if(this.visuals.major_tick_line.doit){for(s=this.model._normals(),o=s[0],i=s[1],r=this.model._computed_image_dimensions(),a=[r.width*o,r.height*i],d=a[0],f=a[1],l=this.model._tick_coordinates().major,h=l[0],c=l[1],p=this.model.major_tick_in,_=this.model.major_tick_out,t.save(),t.translate(d,f),this.visuals.major_tick_line.set_value(t),e=n=0,u=h.length;0<=u?n<u:n>u;e=0<=u?++n:--n)t.beginPath(),t.moveTo(Math.round(h[e]+o*_),Math.round(c[e]+i*_)),t.lineTo(Math.round(h[e]-o*p),Math.round(c[e]-i*p)),t.stroke();return t.restore()}},e.prototype._draw_minor_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f;if(this.visuals.minor_tick_line.doit){for(s=this.model._normals(),o=s[0],i=s[1],r=this.model._computed_image_dimensions(),a=[r.width*o,r.height*i],d=a[0],f=a[1],l=this.model._tick_coordinates().minor,h=l[0],c=l[1],p=this.model.minor_tick_in,_=this.model.minor_tick_out,t.save(),t.translate(d,f),this.visuals.minor_tick_line.set_value(t),e=n=0,u=h.length;0<=u?n<u:n>u;e=0<=u?++n:--n)t.beginPath(),t.moveTo(Math.round(h[e]+o*_),Math.round(c[e]+i*_)),t.lineTo(Math.round(h[e]-o*p),Math.round(c[e]-i*p)),t.stroke();return t.restore()}},e.prototype._draw_major_labels=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.major_label_text.doit){for(l=this.model._normals(),s=l[0],a=l[1],n=this.model._computed_image_dimensions(),u=[n.width*s,n.height*a],m=u[0],y=u[1],_=this.model.label_standoff+this.model._tick_extent(),h=[_*s,_*a],g=h[0],v=h[1],c=this.model._tick_coordinates().major,d=c[0],f=c[1],i=this.model._tick_coordinates().major_labels,e=this.mget(\"formatter\").doFormat(i),this.visuals.major_label_text.set_value(t),t.save(),t.translate(m+g,y+v),r=o=0,p=d.length;0<=p?o<p:o>p;r=0<=p?++o:--o)t.fillText(e[r],Math.round(d[r]+s*this.model.label_standoff),Math.round(f[r]+a*this.model.label_standoff));return t.restore()}},e.prototype._draw_title=function(t){if(this.visuals.title_text.doit)return t.save(),this.visuals.title_text.set_value(t),t.fillText(this.model.title,0,-this.model.title_standoff),t.restore()},e.prototype._get_label_extent=function(){var t,e,r,n;if(null!=this.model.color_mapper.low&&null!=this.model.color_mapper.high){switch(t=this.plot_view.canvas_view.ctx,t.save(),this.visuals.major_label_text.set_value(t),this.model.orientation){case\"vertical\":e=this.model.formatter.doFormat(this.model._tick_coordinates().major_labels),n=f.max(function(){var n,o,i;for(i=[],n=0,o=e.length;n<o;n++)r=e[n],i.push(t.measureText(r.toString()).width);return i}());break;case\"horizontal\":n=g.get_text_height(this.visuals.major_label_text.font_value()).height}n+=this.model.label_standoff,t.restore()}else n=0;return n},e.prototype._get_frame_offset=function(){var t,e,r,n,o;switch(r=[0,0],n=r[0],o=r[1],e=this.model.panel,t=this.plot_view.frame,e.side){case\"left\":case\"right\":o=Math.abs(e.get(\"top\")-t.get(\"top\"));break;case\"above\":case\"below\":n=Math.abs(t.get(\"left\"))}return{x:n,y:o}},e.prototype._get_image_offset=function(){var t,e;return t=this.model.padding,e=this.model.padding+this.model._title_extent(),{x:t,y:e}},e}(n.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return y(e,t),e.prototype.default_view=a,e.prototype.type=\"ColorBar\",e.mixins([\"text:major_label_\",\"text:title_\",\"line:major_tick_\",\"line:minor_tick_\",\"line:border_\",\"line:bar_\",\"fill:background_\"]),e.define({location:[m.Any,\"top_right\"],orientation:[m.Orientation,\"vertical\"],title:[m.String],title_standoff:[m.Number,2],height:[m.Any,\"auto\"],width:[m.Any,\"auto\"],scale_alpha:[m.Number,1],ticker:[m.Instance,function(){return new i.Model}],formatter:[m.Instance,function(){return new o.Model}],color_mapper:[m.Instance],label_standoff:[m.Number,5],margin:[m.Number,30],padding:[m.Number,10],major_tick_in:[m.Number,5],major_tick_out:[m.Number,0],minor_tick_in:[m.Number,0],minor_tick_out:[m.Number,0]}),e.override({background_fill_color:\"#ffffff\",background_fill_alpha:.95,bar_line_color:null,border_line_color:null,major_label_text_align:\"center\",major_label_text_baseline:\"middle\",major_label_text_font_size:\"8pt\",major_tick_line_color:\"#ffffff\",minor_tick_line_color:null,title_text_font_size:\"10pt\",title_text_font_style:\"italic\"}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r)},e.prototype._normals=function(){var t,e,r,n;return\"vertical\"===this.orientation?(r=[1,0],t=r[0],e=r[1]):(n=[0,1],t=n[0],e=n[1]),[t,e]},e.prototype._title_extent=function(){var t,e;return t=this.title_text_font+\" \"+this.title_text_font_size+\" \"+this.title_text_font_style,e=this.title?g.get_text_height(t).height+this.title_standoff:0},e.prototype._tick_extent=function(){var t;return t=null!=this.color_mapper.low&&null!=this.color_mapper.high?f.max([this.major_tick_out,this.minor_tick_out]):0},e.prototype._computed_image_dimensions=function(){var t,e,r,n,o;switch(t=this.plot.plot_canvas.frame.get(\"height\"),e=this.plot.plot_canvas.frame.get(\"width\"),n=this._title_extent(),this.orientation){case\"vertical\":\"auto\"===this.height?null!=this.panel?r=t-2*this.padding-n:(r=f.max([this.color_mapper.palette.length*d,t*u]),r=f.min([r,t*l-2*this.padding-n])):r=this.height,o=\"auto\"===this.width?d:this.width;break;case\"horizontal\":r=\"auto\"===this.height?d:this.height,\"auto\"===this.width?null!=this.panel?o=e-2*this.padding:(o=f.max([this.color_mapper.palette.length*d,e*u]),o=f.min([o,e*l-2*this.padding])):o=this.width}return{height:r,width:o}},e.prototype._tick_coordinate_mapper=function(t){var e,r;switch(r={source_range:new _.Model({start:this.color_mapper.low,end:this.color_mapper.high}),target_range:new _.Model({start:0,end:t})},this.color_mapper.type){case\"LinearColorMapper\":e=new c.Model(r);break;case\"LogColorMapper\":e=new p.Model(r)}return e},e.prototype._tick_coordinates=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b;switch(o=this._computed_image_dimensions(),this.orientation){case\"vertical\":y=o.height;break;case\"horizontal\":y=o.width}for(c=this._tick_coordinate_mapper(y),d=this._normals(),r=d[0],i=d[1],f=[this.color_mapper.low,this.color_mapper.high],v=f[0],e=f[1],b=this.ticker.get_ticks(v,e,null,this.ticker.desired_num_ticks),h=b.major,_=b.minor,l=[[],[]],p=[[],[]],n=s=0,m=h.length;0<=m?s<m:s>m;n=0<=m?++s:--s)h[n]<v||h[n]>e||(l[r].push(h[n]),l[i].push(0));for(n=a=0,g=_.length;0<=g?a<g:a>g;n=0<=g?++a:--a)_[n]<v||_[n]>e||(p[r].push(_[n]),p[i].push(0));return u=l[r].slice(0),l[r]=c.v_map_to_target(l[r]),p[r]=c.v_map_to_target(p[r]),\"vertical\"===this.orientation&&(l[r]=new Float64Array(function(){var e,n,o,i;for(o=l[r],i=[],n=0,e=o.length;n<e;n++)t=o[n],i.push(y-t);return i}()),p[r]=new Float64Array(function(){var e,n,o,i;for(o=p[r],i=[],n=0,e=o.length;n<e;n++)t=o[n],i.push(y-t);return i}())),{major:l,minor:p,major_labels:u}},e}(n.Model),e.exports={Model:s,View:a}},{\"../../core/properties\":\"core/properties\",\"../../core/util/text\":\"core/util/text\",\"../formatters/basic_tick_formatter\":\"models/formatters/basic_tick_formatter\",\"../mappers/linear_color_mapper\":\"models/mappers/linear_color_mapper\",\"../mappers/linear_mapper\":\"models/mappers/linear_mapper\",\"../mappers/log_mapper\":\"models/mappers/log_mapper\",\"../ranges/range1d\":\"models/ranges/range1d\",\"../tickers/basic_ticker\":\"models/tickers/basic_ticker\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/label\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"./text_annotation\"),s=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.initialize=function(t){var r,n,o,i;e.__super__.initialize.call(this,t),this.canvas=this.plot_model.get(\"canvas\"),this.xmapper=this.plot_view.frame.get(\"x_mappers\")[this.mget(\"x_range_name\")],this.ymapper=this.plot_view.frame.get(\"y_mappers\")[this.mget(\"y_range_name\")],o=this.visuals,i=[];for(r in o)n=o[r],i.push(n.warm_cache(null));return i},e.prototype._get_size=function(){var t,e,r,n;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),r=this.model.panel.side,\"above\"===r||\"below\"===r?e=t.measureText(this.mget(\"text\")).ascent:\"left\"===r||\"right\"===r?n=t.measureText(this.mget(\"text\")).width:void 0},e.prototype.render=function(){var t,e,r,n,o,i,s;switch(e=this.plot_view.canvas_view.ctx,this.mget(\"angle_units\")){case\"rad\":t=-1*this.mget(\"angle\");break;case\"deg\":t=-1*this.mget(\"angle\")*Math.PI/180}return i=\"data\"===this.mget(\"x_units\")?this.xmapper.map_to_target(this.mget(\"x\")):this.mget(\"x\"),n=this.canvas.vx_to_sx(i),s=\"data\"===this.mget(\"y_units\")?this.ymapper.map_to_target(this.mget(\"y\")):this.mget(\"y\"),o=this.canvas.vy_to_sy(s),null!=this.model.panel&&(r=this._get_panel_offset(),n+=r.x,o+=r.y),\"canvas\"===this.mget(\"render_mode\")?this._canvas_text(e,this.mget(\"text\"),n+this.mget(\"x_offset\"),o-this.mget(\"y_offset\"),t):this._css_text(e,this.mget(\"text\"),n+this.mget(\"x_offset\"),o-this.mget(\"y_offset\"),t)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=o,e.prototype.type=\"Label\",e.mixins([\"text\",\"line:border_\",\"fill:background_\"]),e.define({x:[s.Number],x_units:[s.SpatialUnits,\"data\"],y:[s.Number],y_units:[s.SpatialUnits,\"data\"],text:[s.String],angle:[s.Angle,0],angle_units:[s.AngleUnits,\"rad\"],x_offset:[s.Number,0],y_offset:[s.Number,0],x_range_name:[s.String,\"default\"],y_range_name:[s.String,\"default\"],render_mode:[s.RenderMode,\"canvas\"]}),e.override({background_fill_color:null,border_line_color:null}),e}(i.Model),e.exports={Model:n,View:o}},{\"../../core/properties\":\"core/properties\",\"./text_annotation\":\"models/annotations/text_annotation\"}],\"models/annotations/label_set\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),n=t(\"jquery\"),a=t(\"./text_annotation\"),o=t(\"../sources/column_data_source\"),u=t(\"../../core/properties\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){var r,o,i,s;if(e.__super__.initialize.call(this,t),this.xmapper=this.plot_view.frame.get(\"x_mappers\")[this.model.x_range_name],this.ymapper=this.plot_view.frame.get(\"y_mappers\")[this.model.y_range_name],this.set_data(),\"css\"===this.model.render_mode){for(s=[],r=o=0,i=this._text.length;0<=i?o<i:o>i;r=0<=i?++o:--o)this.title_div=n(\"<div>\").addClass(\"bk-annotation-child\").hide(),\ns.push(this.title_div.appendTo(this.$el));return s}},e.prototype.bind_bokeh_events=function(){return\"css\"===this.model.render_mode?(this.listenTo(this.model,\"change\",function(){return this.set_data(),this.render()}),this.listenTo(this.model.source,\"change\",function(){return this.set_data(),this.render()})):(this.listenTo(this.model,\"change\",function(){return this.set_data(),this.plot_view.request_render()}),this.listenTo(this.model.source,\"change\",function(){return this.set_data(),this.plot_view.request_render()}))},e.prototype.set_data=function(){return e.__super__.set_data.call(this,this.model.source),this.set_visuals(this.model.source)},e.prototype._map_data=function(){var t,e,r,n;return r=\"data\"===this.model.x_units?this.xmapper.v_map_to_target(this._x):this._x.slice(0),t=this.canvas.v_vx_to_sx(r),n=\"data\"===this.model.y_units?this.ymapper.v_map_to_target(this._y):this._y.slice(0),e=this.canvas.v_vy_to_sy(n),[t,e]},e.prototype.render=function(){var t,e,r,n,o,i,s,a,l,u,h;if(t=this.plot_view.canvas_view.ctx,o=this._map_data(),u=o[0],h=o[1],\"canvas\"===this.model.render_mode){for(a=[],e=r=0,i=this._text.length;0<=i?r<i:r>i;e=0<=i?++r:--r)a.push(this._v_canvas_text(t,e,this._text[e],u[e]+this._x_offset[e],h[e]-this._y_offset[e],this._angle[e]));return a}for(l=[],e=n=0,s=this._text.length;0<=s?n<s:n>s;e=0<=s?++n:--n)l.push(this._v_css_text(t,e,this._text[e],u[e]+this._x_offset[e],h[e]-this._y_offset[e],this._angle[e]));return l},e.prototype._get_size=function(){var t,e,r,n;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),r=this.model.panel.side,\"above\"===r||\"below\"===r?e=t.measureText(this._text[0]).ascent:\"left\"===r||\"right\"===r?n=t.measureText(this._text[0]).width:void 0},e.prototype._v_canvas_text=function(t,e,r,n,o,i){var s;return this.visuals.text.set_vectorize(t,e),s=this._calculate_bounding_box_dimensions(t,r),t.save(),t.beginPath(),t.translate(n,o),t.rotate(i),t.rect(s[0],s[1],s[2],s[3]),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_vectorize(t,e),t.fill()),this.visuals.border_line.doit&&(this.visuals.border_line.set_vectorize(t,e),t.stroke()),this.visuals.text.doit&&(this.visuals.text.set_vectorize(t,e),t.fillText(r,0,0)),t.restore()},e.prototype._v_css_text=function(t,e,r,n,o,i){var s,a,u,h;return this.visuals.text.set_vectorize(t,e),s=this._calculate_bounding_box_dimensions(t,r),u=this.visuals.border_line.line_dash.value(),l.isArray(u)&&(h=u.length<2?\"solid\":\"dashed\"),l.isString(u)&&(h=u),this.visuals.border_line.set_vectorize(t,e),this.visuals.background_fill.set_vectorize(t,e),a={position:\"absolute\",left:n+s[0]+\"px\",top:o+s[1]+\"px\",color:\"\"+this.visuals.text.text_color.value(),opacity:\"\"+this.visuals.text.text_alpha.value(),font:\"\"+this.visuals.text.font_value(),\"line-height\":\"normal\"},i&&l.extend(a,{transform:\"rotate(\"+i+\"rad)\"}),this.visuals.background_fill.doit&&l.extend(a,{\"background-color\":\"\"+this.visuals.background_fill.color_value()}),this.visuals.border_line.doit&&l.extend(a,{\"border-style\":\"\"+h,\"border-width\":\"\"+this.visuals.border_line.line_width.value(),\"border-color\":\"\"+this.visuals.border_line.color_value()}),this.$el.children().eq(e).html(r).css(a).show()},e}(a.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"Label\",e.mixins([\"text\",\"line:border_\",\"fill:background_\"]),e.coords([[\"x\",\"y\"]]),e.define({x_units:[u.SpatialUnits,\"data\"],y_units:[u.SpatialUnits,\"data\"],text:[u.StringSpec,{field:\"text\"}],angle:[u.AngleSpec,0],x_offset:[u.NumberSpec,{value:0}],y_offset:[u.NumberSpec,{value:0}],source:[u.Instance,function(){return new o.Model}],x_range_name:[u.String,\"default\"],y_range_name:[u.String,\"default\"],render_mode:[u.RenderMode,\"canvas\"]}),e.override({background_fill_color:null,border_line_color:null}),e}(a.Model),e.exports={Model:i,View:s}},{\"../../core/properties\":\"core/properties\",\"../sources/column_data_source\":\"models/sources/column_data_source\",\"./text_annotation\":\"models/annotations/text_annotation\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/annotations/legend\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./annotation\"),l=t(\"../../core/properties\"),a=t(\"../../core/util/text\").get_text_height,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t)},e.prototype.compute_legend_bbox=function(){var t,e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;for(d=function(){var t,e,r,o,i;for(r=this.mget(\"legends\"),i=[],t=0,e=r.length;t<e;t++)o=r[t],_=o[0],n=o[1],i.push(_);return i}.call(this),e=this.mget(\"glyph_height\"),r=this.mget(\"glyph_width\"),l=this.mget(\"label_height\"),h=this.mget(\"label_width\"),this.max_label_height=s.max([a(this.visuals.label_text.font_value()).height,l,e]),t=this.plot_view.canvas_view.ctx,t.save(),this.visuals.label_text.set_value(t),this.text_widths={},i=0,y=d.length;i<y;i++)x=d[i],this.text_widths[x]=s.max([t.measureText(x).width,h]);if(t.restore(),b=s.max(s.values(this.text_widths)),p=this.mget(\"legend_margin\"),f=this.mget(\"legend_padding\"),m=this.mget(\"legend_spacing\"),u=this.mget(\"label_standoff\"),\"vertical\"===this.mget(\"orientation\"))c=d.length*this.max_label_height+(d.length-1)*m+2*f,g=b+r+u+2*f;else{g=2*f+(d.length-1)*m,w=this.text_widths;for(x in w)k=w[x],g+=s.max([k,h])+r+u;c=this.max_label_height+2*f}if(v=this.mget(\"location\"),o=this.plot_view.frame.get(\"h_range\"),M=this.plot_view.frame.get(\"v_range\"),s.isString(v))switch(v){case\"top_left\":j=o.get(\"start\")+p,T=M.get(\"end\")-p;break;case\"top_center\":j=(o.get(\"end\")+o.get(\"start\"))/2-g/2,T=M.get(\"end\")-p;break;case\"top_right\":j=o.get(\"end\")-p-g,T=M.get(\"end\")-p;break;case\"right_center\":j=o.get(\"end\")-p-g,T=(M.get(\"end\")+M.get(\"start\"))/2+c/2;break;case\"bottom_right\":j=o.get(\"end\")-p-g,T=M.get(\"start\")+p+c;break;case\"bottom_center\":j=(o.get(\"end\")+o.get(\"start\"))/2-g/2,T=M.get(\"start\")+p+c;break;case\"bottom_left\":j=o.get(\"start\")+p,T=M.get(\"start\")+p+c;break;case\"left_center\":j=o.get(\"start\")+p,T=(M.get(\"end\")+M.get(\"start\"))/2+c/2;break;case\"center\":j=(o.get(\"end\")+o.get(\"start\"))/2-g/2,T=(M.get(\"end\")+M.get(\"start\"))/2+c/2}else s.isArray(v)&&2===v.length&&(j=v[0],T=v[1]);return j=this.plot_view.canvas.vx_to_sx(j),T=this.plot_view.canvas.vy_to_sy(T),{x:j,y:T,width:g,height:c}},e.prototype.render=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j;if(0!==this.model.legends.length){for(e=this.compute_legend_bbox(),n=this.mget(\"glyph_height\"),o=this.mget(\"glyph_width\"),d=this.mget(\"orientation\"),r=this.plot_view.canvas_view.ctx,r.save(),null!=this.model.panel&&(f=this._get_panel_offset(),r.translate(f.x,f.y)),r.beginPath(),r.rect(e.x,e.y,e.width,e.height),this.visuals.background_fill.set_value(r),r.fill(),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(r),r.stroke()),t=this.mget(\"legends\").length,c=this.mget(\"legend_spacing\"),u=this.mget(\"label_standoff\"),w=j=this.mget(\"legend_padding\"),m=this.mget(\"legends\"),a=s=0,p=m.length;s<p;a=++s)for(g=m[a],h=g[0],i=g[1],b=e.x+w,M=e.y+j,x=b+o,k=M+n,\"vertical\"===d?j+=this.max_label_height+c:w+=this.text_widths[h]+o+u+c,this.visuals.label_text.set_value(r),r.fillText(h,x+u,M+this.max_label_height/2),l=0,_=i.length;l<_;l++)y=i[l],v=this.plot_view.renderer_views[y.id],v.draw_legend(r,b,x,M,k);return r.restore()}},e.prototype._get_size=function(){var t,e;return t=this.compute_legend_bbox(),e=this.model.panel.side,\"above\"===e||\"below\"===e?t.height:\"left\"===e||\"right\"===e?t.width:void 0},e.prototype._get_panel_offset=function(){var t,e;return t=this.model.panel._left._value,e=this.model.panel._top._value,{x:t,y:-e}},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"Legend\",e.mixins([\"text:label_\",\"line:border_\",\"fill:background_\"]),e.define({legends:[l.Array,[]],orientation:[l.Orientation,\"vertical\"],location:[l.Any,\"top_right\"],label_standoff:[l.Number,5],glyph_height:[l.Number,20],glyph_width:[l.Number,20],label_height:[l.Number,20],label_width:[l.Number,20],legend_margin:[l.Number,10],legend_padding:[l.Number,10],legend_spacing:[l.Number,3]}),e.override({border_line_color:\"#e5e5e5\",border_line_alpha:.5,border_line_width:1,background_fill_color:\"#ffffff\",background_fill_alpha:.95,label_text_font_size:\"10pt\",label_text_baseline:\"middle\"}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"../../core/util/text\":\"core/util/text\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/poly_annotation\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./annotation\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.plot_view.request_render),this.listenTo(this.model,\"data_update\",this.plot_view.request_render)},e.prototype.render=function(t){var e,r,n,o,i,s,a,l,u,h;if(u=this.mget(\"xs\"),h=this.mget(\"ys\"),u.length!==h.length)return null;if(u.length<3||h.length<3)return null;for(e=this.plot_view.canvas,t=this.plot_view.canvas_view.ctx,r=n=0,o=u.length;0<=o?n<o:n>o;r=0<=o?++n:--n)\"screen\"===this.mget(\"xs_units\")&&(a=u[r]),\"screen\"===this.mget(\"ys_units\")&&(l=h[r]),i=e.vx_to_sx(a),s=e.vy_to_sy(l),0===r?(t.beginPath(),t.moveTo(i,s)):t.lineTo(i,s);return t.closePath(),this.visuals.line.doit&&(this.visuals.line.set_value(t),t.stroke()),this.visuals.fill.doit?(this.visuals.fill.set_value(t),t.fill()):void 0},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"PolyAnnotation\",e.mixins([\"line\",\"fill\"]),e.define({xs:[a.Array,[]],xs_units:[a.SpatialUnits,\"data\"],ys:[a.Array,[]],ys_units:[a.SpatialUnits,\"data\"],x_range_name:[a.String,\"default\"],y_range_name:[a.String,\"default\"]}),e.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3}),e.prototype.update=function(t){var e,r;return e=t.xs,r=t.ys,this.set({xs:e,ys:r},{silent:!0}),this.trigger(\"data_update\")},e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/span\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./annotation\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find(\"div.bk-canvas-overlays\")),this.$el.css({position:\"absolute\"}),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return this.mget(\"for_hover\")?this.listenTo(this.model,\"change:computed_location\",this._draw_span):\"canvas\"===this.mget(\"render_mode\")?this.listenTo(this.model,\"change:location\",this.plot_view.request_render):this.listenTo(this.model,\"change:location\",this._draw_span)},e.prototype.render=function(){return this._draw_span()},e.prototype._draw_span=function(){var t,e,r,n,o,i,s,a,l,u;return o=this.mget(\"for_hover\")?this.mget(\"computed_location\"):this.mget(\"location\"),null==o?void this.$el.hide():(r=this.plot_model.get(\"frame\"),t=this.plot_model.get(\"canvas\"),l=this.plot_view.frame.get(\"x_mappers\")[this.mget(\"x_range_name\")],u=this.plot_view.frame.get(\"y_mappers\")[this.mget(\"y_range_name\")],\"width\"===this.mget(\"dimension\")?(s=t.vy_to_sy(this._calc_dim(o,u)),i=t.vx_to_sx(r.get(\"left\")),a=r.get(\"width\"),n=this.model.properties.line_width.value()):(s=t.vy_to_sy(r.get(\"top\")),i=t.vx_to_sx(this._calc_dim(o,l)),a=this.model.properties.line_width.value(),n=r.get(\"height\")),\"css\"===this.mget(\"render_mode\")?(this.$el.css({top:s,left:i,width:a+\"px\",height:n+\"px\",\"z-index\":1e3,\"background-color\":this.model.properties.line_color.value(),opacity:this.model.properties.line_alpha.value()}),this.$el.show()):\"canvas\"===this.mget(\"render_mode\")?(e=this.plot_view.canvas_view.ctx,e.save(),e.beginPath(),this.visuals.line.set_value(e),e.moveTo(i,s),\"width\"===this.mget(\"dimension\")?e.lineTo(i+a,s):e.lineTo(i,s+n),e.stroke(),e.restore()):void 0)},e.prototype._calc_dim=function(t,e){var r;return r=\"data\"===this.mget(\"location_units\")?e.map_to_target(t):t},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Span\",e.mixins([\"line\"]),e.define({render_mode:[a.RenderMode,\"canvas\"],x_range_name:[a.String,\"default\"],y_range_name:[a.String,\"default\"],location:[a.Number,null],location_units:[a.SpatialUnits,\"data\"],dimension:[a.Dimension,\"width\"]}),e.override({line_color:\"black\"}),e.internal({for_hover:[a.Boolean,!1],computed_location:[a.Number,null]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/text_annotation\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./annotation\"),l=t(\"../../core/properties\"),a=t(\"../../core/util/text\").get_text_height,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t){if(e.__super__.initialize.call(this,t),this.canvas=this.plot_model.get(\"canvas\"),this.frame=this.plot_model.get(\"frame\"),\"css\"===this.mget(\"render_mode\"))return this.$el.addClass(\"bk-annotation\"),this.$el.appendTo(this.plot_view.$el.find(\"div.bk-canvas-overlays\"))},e.prototype.bind_bokeh_events=function(){return\"css\"===this.mget(\"render_mode\")?this.listenTo(this.model,\"change\",this.render):this.listenTo(this.model,\"change\",this.plot_view.request_render)},e.prototype._calculate_text_dimensions=function(t,e){var r,n;return n=t.measureText(e).width,r=a(this.visuals.text.font_value()).height,[n,r]},e.prototype._calculate_bounding_box_dimensions=function(t,e){var r,n,o,i,s;switch(n=this._calculate_text_dimensions(t,e),o=n[0],r=n[1],t.textAlign){case\"left\":i=0;break;case\"center\":i=-o/2;break;case\"right\":i=-o}switch(t.textBaseline){case\"top\":s=0;break;case\"middle\":s=-.5*r;break;case\"bottom\":s=-1*r;break;case\"alphabetic\":s=-.8*r;break;case\"hanging\":s=-.17*r;break;case\"ideographic\":s=-.83*r}return[i,s,o,r]},e.prototype._get_size=function(){var t;return t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),t.measureText(this.mget(\"text\")).ascent},e.prototype.render=function(){return null},e.prototype._canvas_text=function(t,e,r,n,o){var i;return this.visuals.text.set_value(t),i=this._calculate_bounding_box_dimensions(t,e),t.save(),t.beginPath(),t.translate(r,n),o&&t.rotate(o),t.rect(i[0],i[1],i[2],i[3]),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fill()),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.stroke()),this.visuals.text.doit&&(this.visuals.text.set_value(t),t.fillText(e,0,0)),t.restore()},e.prototype._css_text=function(t,e,r,n,o){var i,a,l,u;return this.$el.hide(),this.visuals.text.set_value(t),i=this._calculate_bounding_box_dimensions(t,e),l=this.visuals.border_line.line_dash.value(),s.isArray(l)&&(u=l.length<2?\"solid\":\"dashed\"),s.isString(l)&&(u=l),this.visuals.border_line.set_value(t),this.visuals.background_fill.set_value(t),a={position:\"absolute\",left:r+i[0]+\"px\",top:n+i[1]+\"px\",color:\"\"+this.visuals.text.text_color.value(),opacity:\"\"+this.visuals.text.text_alpha.value(),font:\"\"+this.visuals.text.font_value(),\"line-height\":\"normal\"},o&&s.extend(a,{transform:\"rotate(\"+o+\"rad)\"}),this.visuals.background_fill.doit&&s.extend(a,{\"background-color\":\"\"+this.visuals.background_fill.color_value()}),this.visuals.border_line.doit&&s.extend(a,{\"border-style\":\"\"+u,\"border-width\":\"\"+this.visuals.border_line.line_width.value(),\"border-color\":\"\"+this.visuals.border_line.color_value()}),this.$el.html(e).css(a).show()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.type=\"TextAnnotation\",e.prototype.default_view=i,e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"../../core/util/text\":\"core/util/text\",\"./annotation\":\"models/annotations/annotation\",underscore:\"underscore\"}],\"models/annotations/title\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;n=t(\"./text_annotation\"),a=t(\"../../core/properties\"),s=t(\"../renderers/renderer\").Visuals,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),this.visuals.text=new s.text({obj:this.model,prefix:\"\"}),r=this.plot_view.canvas_view.ctx,r.save(),this.model.panel.apply_label_text_heuristics(r,\"justified\"),this.model.text_baseline=r.textBaseline,this.model.text_align=this.model.align,r.restore()},e.prototype._get_computed_location=function(){var t,e,r,n,o,i,s;switch(e=this._calculate_text_dimensions(this.plot_view.canvas_view.ctx,this.text),s=e[0],t=e[1],this.model.panel.side){case\"left\":o=0,i=this._get_text_location(this.mget(\"align\"),this.frame.get(\"v_range\"))+this.mget(\"offset\");break;case\"right\":o=this.canvas.get(\"right\")-1,i=this.canvas.get(\"height\")-this._get_text_location(this.mget(\"align\"),this.frame.get(\"v_range\"))-this.mget(\"offset\");break;case\"above\":o=this._get_text_location(this.mget(\"align\"),this.frame.get(\"h_range\"))+this.mget(\"offset\"),i=this.canvas.get(\"top\")-10;break;case\"below\":o=this._get_text_location(this.mget(\"align\"),this.frame.get(\"h_range\"))+this.mget(\"offset\"),i=0}return r=this.canvas.vx_to_sx(o),n=this.canvas.vy_to_sy(i),[r,n]},e.prototype._get_text_location=function(t,e){var r;switch(t){case\"left\":r=e.get(\"start\");break;case\"center\":r=(e.get(\"end\")+e.get(\"start\"))/2;break;case\"right\":r=e.get(\"end\")}return r},e.prototype.render=function(){var t,e,r,n,o;if(t=this.model.panel.get_label_angle_heuristic(\"parallel\"),r=this._get_computed_location(),n=r[0],o=r[1],e=this.plot_view.canvas_view.ctx,\"\"!==this.model.text&&null!==this.model.text)return\"canvas\"===this.model.render_mode?this._canvas_text(e,this.model.text,n,o,t):this._css_text(e,this.model.text,n,o,t)},e.prototype._get_size=function(){var t,e;return e=this.model.text,\"\"===e||null===e?0:(t=this.plot_view.canvas_view.ctx,this.visuals.text.set_value(t),t.measureText(e).ascent+10)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Title\",e.mixins([\"line:border_\",\"fill:background_\"]),e.define({text:[a.String],text_font:[a.Font,\"helvetica\"],text_font_size:[a.FontSizeSpec,\"10pt\"],text_font_style:[a.FontStyle,\"bold\"],text_color:[a.ColorSpec,\"#444444\"],text_alpha:[a.NumberSpec,1],align:[a.TextAlign,\"left\"],offset:[a.Number,0],render_mode:[a.RenderMode,\"canvas\"]}),e.override({background_fill_color:null,border_line_color:null}),e.internal({text_align:[a.TextAlign,\"left\"],text_baseline:[a.TextBaseline,\"bottom\"]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"../renderers/renderer\":\"models/renderers/renderer\",\"./text_annotation\":\"models/annotations/text_annotation\"}],\"models/annotations/tooltip\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;n=t(\"jquery\"),a=t(\"underscore\"),o=t(\"./annotation\"),l=t(\"../../core/logging\").logger,u=t(\"../../core/properties\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.className=\"bk-tooltip\",e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.appendTo(this.plot_view.$el.find(\"div.bk-canvas-overlays\")),this.$el.css({\"z-index\":1010}),this.$el.hide()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change:data\",this._draw_tips)},e.prototype.render=function(){return this._draw_tips()},e.prototype._draw_tips=function(){var t,e,r,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v;if(i=this.model.data,this.$el.empty(),this.$el.hide(),this.$el.toggleClass(\"bk-tooltip-custom\",this.mget(\"custom\")),!a.isEmpty(i)){for(l=0,h=i.length;l<h;l++)m=i[l],g=m[0],y=m[1],o=m[2],this.mget(\"inner_only\")&&!this.plot_view.frame.contains(g,y)||(d=n(\"<div />\").appendTo(this.$el),d.append(o));switch(p=this.plot_view.mget(\"canvas\").vx_to_sx(g),_=this.plot_view.mget(\"canvas\").vy_to_sy(y),e=this.model.attachment){case\"horizontal\":v=this.plot_view.frame.get(\"width\"),u=this.plot_view.frame.get(\"left\"),c=g-u<v/2?\"right\":\"left\";break;case\"vertical\":s=this.plot_view.frame.get(\"height\"),r=this.plot_view.frame.get(\"bottom\"),c=y-r<s/2?\"below\":\"above\";break;default:c=e}switch(this.$el.removeClass(\"bk-right bk-left bk-above bk-below\"),t=10,c){case\"right\":this.$el.addClass(\"bk-left\"),u=p+(this.$el.outerWidth()-this.$el.innerWidth())+t,f=_-this.$el.outerHeight()/2;break;case\"left\":this.$el.addClass(\"bk-right\"),u=p-this.$el.outerWidth()-t,f=_-this.$el.outerHeight()/2;break;case\"above\":this.$el.addClass(\"bk-above\"),f=_+(this.$el.outerHeight()-this.$el.innerHeight())+t,u=Math.round(p-this.$el.outerWidth()/2);break;case\"below\":this.$el.addClass(\"bk-below\"),f=_-this.$el.outerHeight()-t,u=Math.round(p-this.$el.outerWidth()/2)}return this.model.show_arrow&&this.$el.addClass(\"bk-tooltip-arrow\"),this.$el.children().length>0?(this.$el.css({top:f,left:u}),this.$el.show()):void 0}},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"Tooltip\",e.define({attachment:[u.String,\"horizontal\"],inner_only:[u.Bool,!0],show_arrow:[u.Bool,!0]}),e.override({level:\"overlay\"}),e.internal({data:[u.Any,[]],custom:[u.Any]}),e.prototype.clear=function(){return this.data=[]},e.prototype.add=function(t,e,r){var n;return n=this.data,n.push([t,e,r]),this.data=n,this.trigger(\"change:data\")},e}(o.Model),e.exports={Model:i,View:s}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"./annotation\":\"models/annotations/annotation\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/axes/axis\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;u=t(\"underscore\"),l=t(\"../../core/layout/side_panel\"),s=t(\"../renderers/guide_renderer\"),a=t(\"../renderers/renderer\"),i=t(\"../../core/layout/solver\").GE,h=t(\"../../core/logging\").logger,c=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this._x_range_name=this.mget(\"x_range_name\"),this._y_range_name=this.mget(\"y_range_name\")},e.prototype.render=function(){var t;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),this._draw_rule(t),this._draw_major_ticks(t),this._draw_minor_ticks(t),this._draw_major_labels(t),this._draw_axis_label(t),t.restore()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.plot_view.request_render)},e.prototype._get_size=function(){return this._tick_extent()+this._tick_label_extent()+this._axis_label_extent()},e.prototype._draw_rule=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m;if(this.visuals.axis_line.doit){for(s=e=this.mget(\"rule_coords\"),_=s[0],f=s[1],a=this.plot_view.map_to_screen(_,f,this._x_range_name,this._y_range_name),c=a[0],p=a[1],l=this.mget(\"normals\"),o=l[0],i=l[1],u=this.mget(\"offsets\"),d=u[0],m=u[1],this.visuals.axis_line.set_value(t),t.beginPath(),t.moveTo(Math.round(c[0]+o*d),Math.round(p[0]+i*m)),r=n=1,h=c.length;1<=h?n<h:n>h;r=1<=h?++n:--n)t.lineTo(Math.round(c[r]+o*d),Math.round(p[r]+i*m));return t.stroke()}},e.prototype._draw_major_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.major_tick_line.doit){for(e=this.mget(\"tick_coords\"),s=e.major,m=s[0],y=s[1],a=this.plot_view.map_to_screen(m,y,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget(\"normals\"),o=l[0],i=l[1],u=this.mget(\"offsets\"),g=u[0],v=u[1],d=this.mget(\"major_tick_in\"),f=this.mget(\"major_tick_out\"),this.visuals.major_tick_line.set_value(t),c=[],r=n=0,h=p.length;0<=h?n<h:n>h;r=0<=h?++n:--n)t.beginPath(),t.moveTo(Math.round(p[r]+o*f+o*g),Math.round(_[r]+i*f+i*v)),t.lineTo(Math.round(p[r]-o*d+o*g),Math.round(_[r]-i*d+i*v)),c.push(t.stroke());return c}},e.prototype._draw_minor_ticks=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;if(this.visuals.minor_tick_line.doit){for(e=this.mget(\"tick_coords\"),s=e.minor,m=s[0],y=s[1],a=this.plot_view.map_to_screen(m,y,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget(\"normals\"),o=l[0],i=l[1],u=this.mget(\"offsets\"),g=u[0],v=u[1],d=this.mget(\"minor_tick_in\"),f=this.mget(\"minor_tick_out\"),this.visuals.minor_tick_line.set_value(t),c=[],r=n=0,h=p.length;0<=h?n<h:n>h;r=0<=h?++n:--n)t.beginPath(),t.moveTo(Math.round(p[r]+o*f+o*g),Math.round(_[r]+i*f+i*v)),t.lineTo(Math.round(p[r]-o*d+o*g),Math.round(_[r]-i*d+i*v)),c.push(t.stroke());return c}},e.prototype._draw_major_labels=function(t){var e,r,n,o,i,s,a,l,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k;for(r=this.mget(\"tick_coords\"),c=r.major,x=c[0],M=c[1],p=this.plot_view.map_to_screen(x,M,this._x_range_name,this._y_range_name),v=p[0],b=p[1],_=this.mget(\"normals\"),a=_[0],l=_[1],d=this.mget(\"offsets\"),w=d[0],k=d[1],n=this.mget(\"dimension\"),g=this.mget(\"panel_side\"),h=this.mget(\"major_label_orientation\"),e=u.isString(h)?this.model.panel.get_label_angle_heuristic(h):-h,y=this._tick_extent()+this.mget(\"major_label_standoff\"),s=this.mget(\"formatter\").doFormat(r.major[n]),this.visuals.major_label_text.set_value(t),this.model.panel.apply_label_text_heuristics(t,h),m=[],o=i=0,f=v.length;0<=f?i<f:i>f;o=0<=f?++i:--i)e?(t.translate(v[o]+a*y+a*w,b[o]+l*y+l*k),t.rotate(e),t.fillText(s[o],0,0),t.rotate(-e),m.push(t.translate(-v[o]-a*y+a*w,-b[o]-l*y+l*k))):m.push(t.fillText(s[o],Math.round(v[o]+a*y+a*w),Math.round(b[o]+l*y+l*k)));return m},e.prototype._draw_axis_label=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g;if(r=this.mget(\"axis_label\"),null!=r&&(s=this.mget(\"rule_coords\"),d=s[0],m=s[1],a=this.plot_view.map_to_screen(d,m,this._x_range_name,this._y_range_name),p=a[0],_=a[1],l=this.mget(\"normals\"),n=l[0],o=l[1],u=this.mget(\"offsets\"),f=u[0],g=u[1],h=this.mget(\"panel_side\"),i=\"parallel\",e=this.model.panel.get_label_angle_heuristic(i),c=this._tick_extent()+this._tick_label_extent()+this.mget(\"axis_label_standoff\"),p=(p[0]+p[p.length-1])/2,_=(_[0]+_[_.length-1])/2,this.visuals.axis_label_text.set_value(t),this.model.panel.apply_label_text_heuristics(t,i),d=p+n*c+n*f,m=_+o*c+o*g,!isNaN(d)&&!isNaN(m)))return e?(t.translate(d,m),t.rotate(e),t.fillText(r,0,0),t.rotate(-e),t.translate(-d,-m)):t.fillText(r,d,m)},e.prototype._tick_extent=function(){return this.mget(\"major_tick_out\")},e.prototype._tick_label_extent=function(){var t,e,r,n,o,i,s,a,l,h,c,p,_,d,f,m,g,y,v;for(i=0,n=this.plot_view.canvas_view.ctx,o=this.mget(\"dimension\"),r=this.mget(\"tick_coords\").major,m=this.mget(\"panel_side\"),_=this.mget(\"major_label_orientation\"),p=this.mget(\"formatter\").doFormat(r[o]),this.visuals.major_label_text.set_value(n),u.isString(_)?(l=1,t=this.model.panel.get_label_angle_heuristic(_)):(l=2,t=-_),t=Math.abs(t),e=Math.cos(t),f=Math.sin(t),\"above\"===m||\"below\"===m?(v=f,a=e):(v=e,a=f),h=c=0,d=p.length;0<=d?c<d:c>d;h=0<=d?++c:--c)null!=p[h]&&(y=1.1*n.measureText(p[h]).width,s=.9*n.measureText(p[h]).ascent,g=y*v+s/l*a,g>i&&(i=g));return i>0&&(i+=this.mget(\"major_label_standoff\")),i},e.prototype._axis_label_extent=function(){var t,e,r,n,o,i,s,a,l,u;return o=0,l=this.mget(\"panel_side\"),e=this.mget(\"axis_label\"),s=\"parallel\",n=this.plot_view.canvas_view.ctx,this.visuals.axis_label_text.set_value(n),t=Math.abs(this.model.panel.get_label_angle_heuristic(s)),r=Math.cos(t),a=Math.sin(t),e&&(o+=this.mget(\"axis_label_standoff\"),this.visuals.axis_label_text.set_value(n),u=1.1*n.measureText(e).width,i=.9*n.measureText(e).ascent,o+=\"above\"===l||\"below\"===l?u*a+i*r:u*r+i*a),o},e}(a.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=o,e.prototype.type=\"Axis\",e.mixins([\"line:axis_\",\"line:major_tick_\",\"line:minor_tick_\",\"text:major_label_\",\"text:axis_label_\"]),e.define({bounds:[c.Any,\"auto\"],ticker:[c.Instance,null],formatter:[c.Instance,null],x_range_name:[c.String,\"default\"],y_range_name:[c.String,\"default\"],axis_label:[c.String,\"\"],axis_label_standoff:[c.Int,5],major_label_standoff:[c.Int,5],major_label_orientation:[c.Any,\"horizontal\"],major_tick_in:[c.Number,2],major_tick_out:[c.Number,6],minor_tick_in:[c.Number,0],minor_tick_out:[c.Number,4]}),e.override({axis_line_color:\"black\",major_tick_line_color:\"black\",minor_tick_line_color:\"black\",major_label_text_font_size:\"8pt\",major_label_text_align:\"center\",major_label_text_baseline:\"alphabetic\",axis_label_text_font_size:\"10pt\",axis_label_text_font_style:\"italic\"}),e.internal({panel_side:[c.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"computed_bounds\",this._computed_bounds,!1),this.add_dependencies(\"computed_bounds\",this,[\"bounds\"]),this.add_dependencies(\"computed_bounds\",this.get(\"plot\"),[\"x_range\",\"y_range\"]),this.define_computed_property(\"rule_coords\",this._rule_coords,!1),this.add_dependencies(\"rule_coords\",this,[\"computed_bounds\",\"side\"]),this.define_computed_property(\"tick_coords\",this._tick_coords,!1),this.add_dependencies(\"tick_coords\",this,[\"computed_bounds\",\"panel_side\"]),this.define_computed_property(\"ranges\",this._ranges,!0),this.define_computed_property(\"normals\",function(){return this.panel._normals},!0),this.define_computed_property(\"dimension\",function(){return this.panel._dim},!0),this.define_computed_property(\"offsets\",this._offsets,!0)},e.prototype.add_panel=function(t){return this.panel=new l.Model({side:t}),this.panel.attach_document(this.document),this.set(\"panel_side\",t)},e.prototype._offsets=function(){var t,e,r,n,o;return r=this.get(\"panel_side\"),e=[0,0],n=e[0],o=e[1],t=this.plot.plot_canvas.get(\"frame\"),\"below\"===r?o=Math.abs(this.panel.get(\"top\")-t.get(\"bottom\")):\"above\"===r?o=Math.abs(this.panel.get(\"bottom\")-t.get(\"top\")):\"right\"===r?n=Math.abs(this.panel.get(\"left\")-t.get(\"right\")):\"left\"===r&&(n=Math.abs(this.panel.get(\"right\")-t.get(\"left\"))),[n,o]},e.prototype._ranges=function(){var t,e,r,n;return e=this.get(\"dimension\"),r=(e+1)%2,t=this.plot.plot_canvas.get(\"frame\"),n=[t.get(\"x_ranges\")[this.get(\"x_range_name\")],t.get(\"y_ranges\")[this.get(\"y_range_name\")]],[n[e],n[r]]},e.prototype._computed_bounds=function(){var t,e,r,n,o,i,s,a;return o=this.get(\"ranges\"),r=o[0],t=o[1],a=null!=(i=this.get(\"bounds\"))?i:\"auto\",n=[r.get(\"min\"),r.get(\"max\")],\"auto\"===a?n:u.isArray(a)?(Math.abs(a[0]-a[1])>Math.abs(n[0]-n[1])?(s=Math.max(Math.min(a[0],a[1]),n[0]),e=Math.min(Math.max(a[0],a[1]),n[1])):(s=Math.min(a[0],a[1]),\ne=Math.max(a[0],a[1])),[s,e]):(h.error(\"user bounds '\"+a+\"' not understood\"),null)},e.prototype._rule_coords=function(){var t,e,r,n,o,i,s,a,l,u,h,c;return n=this.get(\"dimension\"),o=(n+1)%2,a=this.get(\"ranges\"),s=a[0],e=a[1],l=this.get(\"computed_bounds\"),u=l[0],r=l[1],h=new Array(2),c=new Array(2),t=[h,c],i=this._get_loc(e),t[n][0]=Math.max(u,s.get(\"min\")),t[n][1]=Math.min(r,s.get(\"max\")),t[n][0]>t[n][1]&&(t[n][0]=t[n][1]=NaN),t[o][0]=i,t[o][1]=i,t},e.prototype._tick_coords=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S;if(n=this.get(\"dimension\"),i=(n+1)%2,y=this.get(\"ranges\"),f=y[0],e=y[1],v=this.get(\"computed_bounds\"),k=v[0],r=v[1],j=this.get(\"ticker\").get_ticks(k,r,f,{}),h=j.major,d=j.minor,l=this._get_loc(e),T=[],S=[],t=[T,S],p=[],_=[],c=[p,_],\"FactorRange\"===f.type)for(o=s=0,b=h.length;0<=b?s<b:s>b;o=0<=b?++s:--s)t[n].push(h[o]),t[i].push(l);else{for(x=[f.get(\"min\"),f.get(\"max\")],g=x[0],m=x[1],o=a=0,w=h.length;0<=w?a<w:a>w;o=0<=w?++a:--a)h[o]<g||h[o]>m||(t[n].push(h[o]),t[i].push(l));for(o=u=0,M=d.length;0<=M?u<M:u>M;o=0<=M?++u:--u)d[o]<g||d[o]>m||(c[n].push(d[o]),c[i].push(l))}return{major:t,minor:c}},e.prototype._get_loc=function(t){var e,r,n,o;return r=t.get(\"start\"),e=t.get(\"end\"),o=this.get(\"panel_side\"),\"left\"===o||\"below\"===o?n=\"start\":\"right\"!==o&&\"above\"!==o||(n=\"end\"),t.get(n)},e}(s.Model),e.exports={Model:n,View:o}},{\"../../core/layout/side_panel\":\"core/layout/side_panel\",\"../../core/layout/solver\":\"core/layout/solver\",\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../renderers/guide_renderer\":\"models/renderers/guide_renderer\",\"../renderers/renderer\":\"models/renderers/renderer\",underscore:\"underscore\"}],\"models/axes/categorical_axis\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),n=t(\"./axis\"),s=t(\"../formatters/categorical_tick_formatter\"),a=t(\"../tickers/categorical_ticker\"),u=t(\"../../core/logging\").logger,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type=\"CategoricalAxis\",e.override({ticker:function(){return new a.Model},formatter:function(){return new s.Model}}),e.prototype._computed_bounds=function(){var t,e,r,n,o,i;return n=this.get(\"ranges\"),e=n[0],t=n[1],i=null!=(o=this.get(\"bounds\"))?o:\"auto\",r=[e.get(\"min\"),e.get(\"max\")],\"auto\"!==i&&u.warn(\"Categorical Axes only support user_bounds='auto', ignoring\"),r},e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/logging\":\"core/logging\",\"../formatters/categorical_tick_formatter\":\"models/formatters/categorical_tick_formatter\",\"../tickers/categorical_ticker\":\"models/tickers/categorical_ticker\",\"./axis\":\"models/axes/axis\",underscore:\"underscore\"}],\"models/axes/continuous_axis\":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t(\"./axis\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return i(e,t),e.prototype.type=\"ContinuousAxis\",e}(n.Model),e.exports={Model:o}},{\"./axis\":\"models/axes/axis\"}],\"models/axes/datetime_axis\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./axis\"),i=t(\"../formatters/datetime_tick_formatter\"),s=t(\"../tickers/datetime_ticker\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e}(a.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type=\"DatetimeAxis\",e.override({ticker:function(){return new s.Model},formatter:function(){return new i.Model}}),e}(a.Model),e.exports={Model:n,View:o}},{\"../formatters/datetime_tick_formatter\":\"models/formatters/datetime_tick_formatter\",\"../tickers/datetime_ticker\":\"models/tickers/datetime_ticker\",\"./axis\":\"models/axes/axis\",underscore:\"underscore\"}],\"models/axes/linear_axis\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;u=t(\"underscore\"),n=t(\"./axis\"),s=t(\"./continuous_axis\"),o=t(\"../formatters/basic_tick_formatter\"),i=t(\"../tickers/basic_ticker\"),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e}(n.View),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=l,e.prototype.type=\"LinearAxis\",e.override({ticker:function(){return new i.Model},formatter:function(){return new o.Model}}),e}(s.Model),e.exports={Model:a,View:l}},{\"../formatters/basic_tick_formatter\":\"models/formatters/basic_tick_formatter\",\"../tickers/basic_ticker\":\"models/tickers/basic_ticker\",\"./axis\":\"models/axes/axis\",\"./continuous_axis\":\"models/axes/continuous_axis\",underscore:\"underscore\"}],\"models/axes/log_axis\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;u=t(\"underscore\"),n=t(\"./axis\"),o=t(\"./continuous_axis\"),a=t(\"../formatters/log_tick_formatter\"),l=t(\"../tickers/log_ticker\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e}(n.View),i=function(t){function e(){return 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c.Model({start:this.get(\"left\"),end:this.get(\"left\")+this.get(\"width\")}),this.define_computed_property(\"h_range\",function(t){return function(){return t._h_range.set(\"start\",t.get(\"left\")),t._h_range.set(\"end\",t.get(\"left\")+t.get(\"width\")),t._h_range}}(this),!1),this.add_dependencies(\"h_range\",this,[\"left\",\"width\"]),this._v_range=new c.Model({start:this.get(\"bottom\"),end:this.get(\"bottom\")+this.get(\"height\")}),this.define_computed_property(\"v_range\",function(t){return function(){return t._v_range.set(\"start\",t.get(\"bottom\")),t._v_range.set(\"end\",t.get(\"bottom\")+t.get(\"height\")),t._v_range}}(this),!1),this.add_dependencies(\"v_range\",this,[\"bottom\",\"height\"]),null},e.prototype._doc_attached=function(){return this.listenTo(this.document.solver(),\"layout_update\",this._update_mappers),null},e.prototype.contains=function(t,e){return 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u(e,t),e.prototype.default_view=o,e.prototype.type=\"Bezier\",e.coords([[\"x0\",\"y0\"],[\"x1\",\"y1\"],[\"cx0\",\"cy0\"],[\"cx1\",\"cy1\"]]),e.mixins([\"line\"]),e}(i.Model),e.exports={Model:n,View:o}},{\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/circle\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),i=t(\"./glyph\"),a=t(\"../../common/hittest\"),l=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){var t,e;return null!=this._radius?\"data\"===this.model.properties.radius.spec.units?(t=this.model.properties.radius_dimension.spec.value,this.sradius=this.sdist(this.renderer[t+\"mapper\"],this[\"_\"+t],this._radius)):(this.sradius=this._radius,this.max_size=2*this.max_radius):this.sradius=function(){var t,r,n,o;for(n=this._size,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(e/2);return o}.call(this)},e.prototype._mask_data=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g;return r=this.renderer.plot_view.frame.get(\"h_range\"),p=this.renderer.plot_view.frame.get(\"v_range\"),null!=this._radius&&\"data\"===this.model.properties.radius.units?(l=r.get(\"start\"),u=r.get(\"end\"),n=this.renderer.xmapper.v_map_from_target([l,u],!0),d=n[0],f=n[1],d-=this.max_radius,f+=this.max_radius,h=p.get(\"start\"),c=p.get(\"end\"),o=this.renderer.ymapper.v_map_from_target([h,c],!0),m=o[0],g=o[1],m-=this.max_radius,g+=this.max_radius):(l=r.get(\"start\")-this.max_size,u=r.get(\"end\")+this.max_size,i=this.renderer.xmapper.v_map_from_target([l,u],!0),d=i[0],f=i[1],h=p.get(\"start\")-this.max_size,c=p.get(\"end\")+this.max_size,s=this.renderer.ymapper.v_map_from_target([h,c],!0),m=s[0],g=s[1]),e=a.validate_bbox_coords([d,f],[m,g]),function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)_=n[t],o.push(_.i);return o}.call(this)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u;for(l=r.sx,u=r.sy,a=r.sradius,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+u[n]+a[n])||(t.beginPath(),t.arc(l[n],u[n],a[n],0,2*Math.PI,!1),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I;if(d=[t.vx,t.vy],T=d[0],P=d[1],C=this.renderer.xmapper.map_from_target(T,!0),q=this.renderer.ymapper.map_from_target(P,!0),null!=this._radius&&\"data\"===this.model.properties.radius.units?(N=C-this.max_radius,O=C+this.max_radius,D=q-this.max_radius,I=q+this.max_radius):(S=T-this.max_size,z=T+this.max_size,f=this.renderer.xmapper.v_map_from_target([S,z],!0),N=f[0],O=f[1],m=[Math.min(N,O),Math.max(N,O)],N=m[0],O=m[1],E=P-this.max_size,A=P+this.max_size,g=this.renderer.ymapper.v_map_from_target([E,A],!0),D=g[0],I=g[1],y=[Math.min(D,I),Math.max(D,I)],D=y[0],I=y[1]),e=a.validate_bbox_coords([N,O],[D,I]),r=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)p=n[t],o.push(p.i);return o}.call(this),o=[],null!=this._radius&&\"data\"===this.model.properties.radius.units)for(l=0,h=r.length;l<h;l++)i=r[l],_=Math.pow(this.sradius[i],2),x=this.renderer.xmapper.map_to_target(C,!0),w=this.renderer.xmapper.map_to_target(this._x[i],!0),k=this.renderer.ymapper.map_to_target(q,!0),j=this.renderer.ymapper.map_to_target(this._y[i],!0),n=Math.pow(x-w,2)+Math.pow(k-j,2),n<=_&&o.push([i,n]);else for(b=this.renderer.plot_view.canvas.vx_to_sx(T),M=this.renderer.plot_view.canvas.vy_to_sy(P),u=0,c=r.length;u<c;u++)i=r[u],_=Math.pow(this.sradius[i],2),n=Math.pow(this.sx[i]-b,2)+Math.pow(this.sy[i]-M,2),n<=_&&o.push([i,n]);return o=s.chain(o).sortBy(function(t){return t[1]}).map(function(t){return t[0]}).value(),v=a.create_hit_test_result(),v[\"1d\"].indices=o,v},e.prototype._hit_span=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;return u=[t.vx,t.vy],m=u[0],v=u[1],h=this.bounds(),i=h.minX,s=h.minY,n=h.maxX,o=h.maxY,f=a.create_hit_test_result(),\"h\"===t.direction?(j=s,T=o,null!=this._radius&&\"data\"===this.model.properties.radius.units?(g=m-this.max_radius,y=m+this.max_radius,c=this.renderer.xmapper.v_map_from_target([g,y]),w=c[0],M=c[1]):(l=this.max_size/2,g=m-l,y=m+l,p=this.renderer.xmapper.v_map_from_target([g,y],!0),w=p[0],M=p[1])):(w=i,M=n,null!=this._radius&&\"data\"===this.model.properties.radius.units?(b=v-this.max_radius,x=v+this.max_radius,_=this.renderer.ymapper.v_map_from_target([b,x]),j=_[0],T=_[1]):(l=this.max_size/2,b=v-l,x=v+l,d=this.renderer.ymapper.v_map_from_target([b,x],!0),j=d[0],T=d[1])),e=a.validate_bbox_coords([w,M],[j,T]),r=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)k=n[t],o.push(k.i);return o}.call(this),f[\"1d\"].indices=r,f},e.prototype._hit_rect=function(t){var e,r,n,o,i,s,l,u,h;return r=this.renderer.xmapper.v_map_from_target([t.vx0,t.vx1],!0),s=r[0],l=r[1],n=this.renderer.ymapper.v_map_from_target([t.vy0,t.vy1],!0),u=n[0],h=n[1],e=a.validate_bbox_coords([s,l],[u,h]),o=a.create_hit_test_result(),o[\"1d\"].indices=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)i=n[t],o.push(i.i);return o}.call(this),o},e.prototype._hit_poly=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d;for(s=[t.vx,t.vy],_=s[0],d=s[1],c=this.renderer.plot_view.canvas.v_vx_to_sx(_),p=this.renderer.plot_view.canvas.v_vy_to_sy(d),e=function(){h=[];for(var t=0,e=this.sx.length;0<=e?t<e:t>e;0<=e?t++:t--)h.push(t);return h}.apply(this),r=[],n=i=0,l=e.length;0<=l?i<l:i>l;n=0<=l?++i:--i)o=e[n],a.point_in_poly(this.sx[n],this.sy[n],c,p)&&r.push(o);return u=a.create_hit_test_result(),u[\"1d\"].indices=r,u},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c;return l=null!=(a=this.get_reference_point())?a:0,s=[l],h={},h[l]=(e+r)/2,c={},c[l]=(n+o)/2,u={},u[l]=.2*Math.min(Math.abs(r-e),Math.abs(o-n)),i={sx:h,sy:c,sradius:u},this._render(t,s,i)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type=\"Circle\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({angle:[l.AngleSpec,0],size:[l.DistanceSpec,{units:\"screen\",value:4}],radius:[l.DistanceSpec,null],radius_dimension:[l.String,\"x\"]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.properties.radius.optional=!0},e}(i.Model),e.exports={Model:n,View:o}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/ellipse\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),i=t(\"./glyph\"),a=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._set_data=function(){if(this.max_w2=0,\"data\"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,\"data\"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return\"data\"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,\"center\"):this.sw=this._width,\"data\"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,\"center\"):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=r.sx,h=r.sy,l=r.sw,a=r.sh,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+h[n]+l[n]+a[n]+this._angle[n])||(t.beginPath(),t.ellipse(u[n],h[n],l[n]/2,a[n]/2,this._angle[n],0,2*Math.PI),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d;return u=null!=(l=this.get_reference_point())?l:0,a=[u],_={},_[u]=(e+r)/2,d={},d[u]=(n+o)/2,h=this.sw[u]/this.sh[u],i=.8*Math.min(Math.abs(r-e),Math.abs(o-n)),p={},c={},h>1?(p[u]=i,c[u]=i/h):(p[u]=i*h,c[u]=i),s={sx:_,sy:d,sw:p,sh:c},this._render(t,a,s)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=o,e.prototype.type=\"Ellipse\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({angle:[a.AngleSpec,0],width:[a.DistanceSpec],height:[a.DistanceSpec]}),e}(i.Model),e.exports={Model:n,View:o}},{\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/gear\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./glyph\"),i=t(\"gear_utils\"),u=t(\"../../core/properties\"),n=t(\"../../util/bezier\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return this.smodule=this.sdist(this.renderer.xmapper,this._x,this._module,\"edge\")},e.prototype._render=function(t,e,r){var n,o,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z;for(j=r.sx,T=r.sy,k=r.smodule,o=r._angle,u=r._teeth,a=r._pressure_angle,l=r._shaft_size,s=r._internal,_=0,f=e.length;_<f;_++)if(c=e[_],!isNaN(j[c]+T[c]+o[c]+k[c]+u[c]+a[c]+l[c]+s[c])){for(m=k[c]*u[c]/2,h=s[c]?i.create_internal_gear_tooth:i.create_gear_tooth,w=h(k[c],u[c],a[c]),g=w.slice(0,3),n=g[0],S=g[1],z=g[2],x=w.slice(3),t.save(),t.translate(j[c],T[c]),t.rotate(o[c]),t.beginPath(),b=2*Math.PI/u[c],t.moveTo(S,z),p=d=0,y=u[c];0<=y?d<y:d>y;p=0<=y?++d:--d)this._render_seq(t,x),t.rotate(b);t.closePath(),s[c]?(v=m+2.75*k[c],t.moveTo(v,0),t.arc(0,0,v,0,2*Math.PI,!0)):l[c]>0&&(M=m*l[c],t.moveTo(M,0),t.arc(0,0,M,0,2*Math.PI,!0)),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,c),t.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,c),t.stroke()),t.restore()}},e.prototype._render_seq=function(t,e){var r,o,i,s,a,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C;for(u=0;u<e.length;)switch(l.isString(e[u])&&(r=e[u],u+=1),r){case\"M\":f=e.slice(u,u+2),E=f[0],C=f[1],t.moveTo(E,C),m=[E,C],_=m[0],d=m[1],u+=2;break;case\"L\":y=e.slice(u,u+2),E=y[0],C=y[1],t.lineTo(E,C),v=[E,C],_=v[0],d=v[1],u+=2;break;case\"C\":b=e.slice(u,u+6),o=b[0],s=b[1],i=b[2],a=b[3],E=b[4],C=b[5],t.bezierCurveTo(o,s,i,a,E,C),x=[E,C],_=x[0],d=x[1],u+=6;break;case\"Q\":w=e.slice(u,u+4),o=w[0],s=w[1],E=w[2],C=w[3],t.quadraticCurveTo(o,s,E,C),M=[E,C],_=M[0],d=M[1],u+=4;break;case\"A\":for(k=e.slice(u,u+7),T=k[0],S=k[1],A=k[2],c=k[3],P=k[4],E=k[5],C=k[6],z=n.arc_to_bezier(_,d,T,S,-A,c,1-P,E,C),h=0,p=z.length;h<p;h++)j=z[h],o=j[0],s=j[1],i=j[2],a=j[3],E=j[4],C=j[5],t.bezierCurveTo(o,s,i,a,E,C);g=[E,C],_=g[0],d=g[1],u+=7;break;default:throw new Error(\"unexpected command: \"+r)}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"Gear\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({angle:[u.AngleSpec,0],module:[u.NumberSpec,null],pressure_angle:[u.NumberSpec,20],shaft_size:[u.NumberSpec,.3],teeth:[u.NumberSpec,null],internal:[u.NumberSpec,!1]}),e}(a.Model),e.exports={Model:o,View:s}},{\"../../core/properties\":\"core/properties\",\"../../util/bezier\":\"util/bezier\",\"./glyph\":\"models/glyphs/glyph\",gear_utils:\"gear_utils\",underscore:\"underscore\"}],\"models/glyphs/glyph\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty;l=t(\"underscore\"),p=t(\"rbush\"),n=t(\"../mappers/categorical_mapper\"),a=t(\"../renderers/renderer\"),c=t(\"../../core/properties\"),u=t(\"../../core/util/bbox\"),s=t(\"../../model\"),h=t(\"./webgl/main\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.initialize=function(t){var r,n,o;if(e.__super__.initialize.call(this,t),this.renderer=t.renderer,null!=(null!=(o=this.renderer)?o.plot_view:void 0)&&(n=this.renderer.plot_view.canvas_view.ctx,null!=n.glcanvas&&(r=h[this.model.type+\"GLGlyph\"])))return this.glglyph=new r(n.glcanvas.gl,this)},e.prototype.render=function(t,e,r){if(this.mget(\"visible\")){if(t.beginPath(),null!=this.glglyph&&this._render_gl(t,e,r))return;this._render(t,e,r)}},e.prototype._render_gl=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;return c=p=1,i=this.renderer.map_to_screen([0*c,1*c,2*c],[0*p,1*p,2*p]),n=i[0],o=i[1],c=100/Math.min(Math.max(Math.abs(n[1]-n[0]),1e-12),1e12),p=100/Math.min(Math.max(Math.abs(o[1]-o[0]),1e-12),1e12),s=this.renderer.map_to_screen([0*c,1*c,2*c],[0*p,1*p,2*p]),n=s[0],o=s[1],!(Math.abs(n[1]-n[0]-(n[2]-n[1]))>1e-6||Math.abs(o[1]-o[0]-(o[2]-o[1]))>1e-6)&&(a=[(n[1]-n[0])/c,(o[1]-o[0])/p],l=a[0],u=a[1],h={pixel_ratio:t.pixel_ratio,width:t.glcanvas.width,height:t.glcanvas.height,dx:n[0]/l,dy:o[0]/u,sx:l,sy:u},this.glglyph.draw(e,r,h),!0)},e.prototype.bounds=function(){var t,e;return null==this.index?u.empty():(e=this.index.data,t={minX:e.minX,minY:e.minY,maxX:e.maxX,maxY:e.maxY},this._bounds(t))},e.prototype.max_wh2_bounds=function(t){return{minX:t.minX-this.max_w2,maxX:t.maxX+this.max_w2,minY:t.minY-this.max_h2,maxY:t.maxY+this.max_h2}},e.prototype.get_anchor_point=function(t,e,r){var n,o;switch(n=r[0],o=r[1],t){case\"center\":return{x:this.scx(e,n,o),y:this.scy(e,n,o)};default:return null}},e.prototype.scx=function(t){return this.sx[t]},e.prototype.scy=function(t){return this.sy[t]},e.prototype._xy_index=function(){var t,e,r,o,i,s,a,l,u;for(e=p(),o=[],a=this.renderer.xmapper instanceof n.Model?this.renderer.xmapper.v_map_to_target(this._x,!0):this._x,u=this.renderer.ymapper instanceof n.Model?this.renderer.ymapper.v_map_to_target(this._y,!0):this._y,t=r=0,i=a.length;0<=i?r<i:r>i;t=0<=i?++r:--r)s=a[t],!isNaN(s)&&isFinite(s)&&(l=u[t],!isNaN(l)&&isFinite(l)&&o.push({minX:s,minY:l,maxX:s,maxY:l,i:t}));return e.load(o),e},e.prototype.sdist=function(t,e,r,n,o){var i,s,a,u,h,c,p;return null==n&&(n=\"edge\"),null==o&&(o=!1),l.isString(e[0])&&(e=t.v_map_to_target(e)),\"center\"===n?(s=function(){var t,e,n;for(n=[],t=0,e=r.length;t<e;t++)i=r[t],n.push(i/2);return n}(),u=function(){var t,r,n;for(n=[],a=t=0,r=e.length;0<=r?t<r:t>r;a=0<=r?++t:--t)n.push(e[a]-s[a]);return n}(),h=function(){var t,r,n;for(n=[],a=t=0,r=e.length;0<=r?t<r:t>r;a=0<=r?++t:--t)n.push(e[a]+s[a]);return n}()):(u=e,h=function(){var t,e,n;for(n=[],a=t=0,e=u.length;0<=e?t<e:t>e;a=0<=e?++t:--t)n.push(u[a]+r[a]);return n}()),c=t.v_map_to_target(u),p=t.v_map_to_target(h),o?function(){var t,e,r;for(r=[],a=t=0,e=c.length;0<=e?t<e:t>e;a=0<=e?++t:--t)r.push(Math.ceil(Math.abs(p[a]-c[a])));return r}():function(){var t,e,r;for(r=[],a=t=0,e=c.length;0<=e?t<e:t>e;a=0<=e?++t:--t)r.push(Math.abs(p[a]-c[a]));return r}()},e.prototype.get_reference_point=function(){},e.prototype.draw_legend=function(t,e,r,n,o){return null},e.prototype._generic_line_legend=function(t,e,r,n,o){var i,s;return s=null!=(i=this.get_reference_point())?i:0,t.save(),t.beginPath(),t.moveTo(e,(n+o)/2),t.lineTo(r,(n+o)/2),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,s),t.stroke()),t.restore()},e.prototype._generic_area_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d,f;if(h=null!=(u=this.get_reference_point())?u:0,l=[h],f=Math.abs(r-e),s=.1*f,a=Math.abs(o-n),i=.1*a,c=e+s,p=r-s,_=n+i,d=o-i,this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,h),t.fillRect(c,_,p-c,d-_)),this.visuals.line.doit)return t.beginPath(),t.rect(c,_,p-c,d-_),this.visuals.line.set_vectorize(t,h),t.stroke()},e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.define({visible:[c.Bool,!0]}),e.internal({x_range_name:[c.String,\"default\"],y_range_name:[c.String,\"default\"]}),e}(s),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"../../core/util/bbox\":\"core/util/bbox\",\"../../model\":\"model\",\"../mappers/categorical_mapper\":\"models/mappers/categorical_mapper\",\"../renderers/renderer\":\"models/renderers/renderer\",\"./webgl/main\":\"models/glyphs/webgl/main\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/hbar\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;l=t(\"underscore\"),c=t(\"rbush\"),a=t(\"./quad\"),o=t(\"./glyph\"),n=t(\"../mappers/categorical_mapper\"),u=t(\"../../common/hittest\"),h=t(\"../../core/properties\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype._map_data=function(){var t,e,r,n,o,i;for(i=this.renderer.ymapper.v_map_to_target(this._y),this.sy=this.plot_view.canvas.v_vy_to_sy(i),o=this.renderer.xmapper.v_map_to_target(this._right),n=this.renderer.xmapper.v_map_to_target(this._left),this.sright=this.plot_view.canvas.v_vx_to_sx(o),this.sleft=this.plot_view.canvas.v_vx_to_sx(n),this.stop=[],this.sbottom=[],this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,\"center\"),t=e=0,r=this.sy.length;0<=r?e<r:e>r;t=0<=r?++e:--e)this.stop.push(this.sy[t]-this.sh[t]/2),this.sbottom.push(this.sy[t]+this.sh[t]/2);return null},e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,u,h,p,_,d,f;for(l=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},a=l(this.renderer.xmapper,this._left),_=l(this.renderer.xmapper,this._right),f=l(this.renderer.ymapper,this._y),e=l(this.renderer.ymapper,this._height),o=c(),u=[],r=i=0,p=f.length;0<=p?i<p:i>p;r=0<=p?++i:--i)s=a[r],h=_[r],d=f[r]+.5*e[r],t=f[r]-.5*e[r],!isNaN(s+h+d+t)&&isFinite(s+h+d+t)&&u.push({minX:s,minY:t,maxX:h,maxY:d,i:r});return o.load(u),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=u.create_hit_test_result(),n[\"1d\"].indices=e,n},e.prototype.scx=function(t){return(this.sleft[t]+this.sright[t])/2},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=s,e.prototype.type=\"HBar\",e.mixins([\"line\",\"fill\"]),e.define({y:[h.NumberSpec],height:[h.DistanceSpec],left:[h.NumberSpec,0],right:[h.NumberSpec]}),e}(o.Model),e.exports={Model:i,View:s}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"../mappers/categorical_mapper\":\"models/mappers/categorical_mapper\",\"./glyph\":\"models/glyphs/glyph\",\"./quad\":\"models/glyphs/quad\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/image\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),n=t(\"../glyphs/glyph\"),a=t(\"../mappers/linear_color_mapper\"),u=t(\"../../core/properties\"),o=t(\"../../palettes/palettes\").Greys,s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model.color_mapper,\"change\",this._update_image)},e.prototype._update_image=function(){if(null!=this.image_data)return this._set_data(),this.plot_view.request_render()},e.prototype._index_data=function(){return this._xy_index()},e.prototype._set_data=function(){var t,e,r,n,o,i,s,a,u,h,c;for(null!=this.image_data&&this.image_data.length===this._image.length||(this.image_data=new Array(this._image.length)),null!=this._width&&this._width.length===this._image.length||(this._width=new Array(this._image.length)),null!=this._height&&this._height.length===this._image.length||(this._height=new Array(this._image.length)),c=[],i=u=0,h=this._image.length;0<=h?u<h:u>h;i=0<=h?++u:--u)null!=this._rows?(this._height[i]=this._rows[i],this._width[i]=this._cols[i]):(this._height[i]=this._image[i].length,this._width[i]=this._image[i][0].length),r=document.createElement(\"canvas\"),r.width=this._width[i],r.height=this._height[i],o=r.getContext(\"2d\"),s=o.getImageData(0,0,this._width[i],this._height[i]),n=this.mget(\"color_mapper\"),a=null!=this._rows?this._image[i]:l.flatten(this._image[i]),t=n.v_map_screen(a),e=new Uint8ClampedArray(t),s.data.set(e),o.putImageData(s,0,0),this.image_data[i]=r,this.max_dw=0,\"data\"===this._dw.units&&(this.max_dw=l.max(this._dw)),this.max_dh=0,\"data\"===this._dh.units&&(this.max_dh=l.max(this._dh)),c.push(this._xy_index());return c},e.prototype._map_data=function(){switch(this.model.properties.dw.units){case\"data\":this.sw=this.sdist(this.renderer.xmapper,this._x,this._dw,\"edge\",this.mget(\"dilate\"));break;case\"screen\":this.sw=this._dw}switch(this.model.properties.dh.units){case\"data\":return this.sh=this.sdist(this.renderer.ymapper,this._y,this._dh,\"edge\",this.mget(\"dilate\"));case\"screen\":return this.sh=this._dh}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(o=r.image_data,h=r.sx,c=r.sy,u=r.sw,l=r.sh,a=t.getImageSmoothingEnabled(),t.setImageSmoothingEnabled(!1),i=0,s=e.length;i<s;i++)n=e[i],null!=o[n]&&(isNaN(h[n]+c[n]+u[n]+l[n])||(p=c[n],t.translate(0,p),t.scale(1,-1),t.translate(0,-p),t.drawImage(o[n],0|h[n],0|c[n],u[n],l[n]),t.translate(0,p),t.scale(1,-1),t.translate(0,-p)));return t.setImageSmoothingEnabled(a)},e.prototype.bounds=function(){var t;return t=this.index.data,{minX:t.minX,minY:t.minY,maxX:t.maxX+this.max_dw,maxY:t.maxY+this.max_dh}},e}(n.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"Image\",e.coords([[\"x\",\"y\"]]),e.mixins([]),e.define({image:[u.NumberSpec],dw:[u.DistanceSpec],dh:[u.DistanceSpec],dilate:[u.Bool,!1],color_mapper:[u.Instance,function(){return new a.Model({palette:o.Greys9})}]}),e}(n.Model),e.exports={Model:i,View:s}},{\"../../core/properties\":\"core/properties\",\"../../palettes/palettes\":\"palettes/palettes\",\"../glyphs/glyph\":\"models/glyphs/glyph\",\"../mappers/linear_color_mapper\":\"models/mappers/linear_color_mapper\",\nunderscore:\"underscore\"}],\"models/glyphs/image_rgba\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._set_data=function(t,e){var r,n,o,i,a,l,u,h,c,p,_,d,f,m;for(null!=this.image_data&&this.image_data.length===this._image.length||(this.image_data=new Array(this._image.length)),null!=this._width&&this._width.length===this._image.length||(this._width=new Array(this._image.length)),null!=this._height&&this._height.length===this._image.length||(this._height=new Array(this._image.length)),m=[],u=p=0,d=this._image.length;0<=d?p<d:p>d;u=0<=d?++p:--p)if(null==e||u===e){if(null!=this._rows?(this._height[u]=this._rows[u],this._width[u]=this._cols[u]):(this._height[u]=this._image[u].length,this._width[u]=this._image[u][0].length),o=document.createElement(\"canvas\"),o.width=this._width[u],o.height=this._height[u],a=o.getContext(\"2d\"),h=a.getImageData(0,0,this._width[u],this._height[u]),null!=this._rows)h.data.set(new Uint8ClampedArray(this._image[u]));else{for(l=s.flatten(this._image[u]),r=new ArrayBuffer(4*l.length),i=new Uint32Array(r),c=_=0,f=l.length;0<=f?_<f:_>f;c=0<=f?++_:--_)i[c]=l[c];n=new Uint8ClampedArray(r),h.data.set(n)}a.putImageData(h,0,0),this.image_data[u]=o,this.max_dw=0,\"data\"===this._dw.units&&(this.max_dw=s.max(this._dw)),this.max_dh=0,\"data\"===this._dh.units?m.push(this.max_dh=s.max(this._dh)):m.push(void 0)}return m},e.prototype._map_data=function(){switch(this.model.properties.dw.units){case\"data\":this.sw=this.sdist(this.renderer.xmapper,this._x,this._dw,\"edge\",this.mget(\"dilate\"));break;case\"screen\":this.sw=this._dw}switch(this.model.properties.dh.units){case\"data\":return this.sh=this.sdist(this.renderer.ymapper,this._y,this._dh,\"edge\",this.mget(\"dilate\"));case\"screen\":return this.sh=this._dh}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(o=r.image_data,h=r.sx,c=r.sy,u=r.sw,l=r.sh,a=t.getImageSmoothingEnabled(),t.setImageSmoothingEnabled(!1),i=0,s=e.length;i<s;i++)n=e[i],isNaN(h[n]+c[n]+u[n]+l[n])||(p=c[n],t.translate(0,p),t.scale(1,-1),t.translate(0,-p),t.drawImage(o[n],0|h[n],0|c[n],u[n],l[n]),t.translate(0,p),t.scale(1,-1),t.translate(0,-p));return t.setImageSmoothingEnabled(a)},e.prototype.bounds=function(){var t;return t=this.index.data,{minX:t.minX,minY:t.minY,maxX:t.maxX+this.max_dw,maxY:t.maxY+this.max_dh}},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"ImageRGBA\",e.coords([[\"x\",\"y\"]]),e.mixins([]),e.define({image:[a.NumberSpec],rows:[a.NumberSpec],cols:[a.NumberSpec],dw:[a.DistanceSpec],dh:[a.DistanceSpec],dilate:[a.Bool,!1]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.properties.rows.optional=!0,this.properties.cols.optional=!0},e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/image_url\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../core/logging\").logger,l=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,\"change:global_alpha\",this.renderer.request_render)},e.prototype._index_data=function(){},e.prototype._set_data=function(){var t,e,r,n,o,i,s;for(null!=this.image&&this.image.length===this._url.length||(this.image=function(){var t,r,n,o;for(n=this._url,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(null);return o}.call(this)),i=this.model.retry_attempts,s=this.model.retry_timeout,this.retries=function(){var t,r,n,o;for(n=this._url,o=[],t=0,r=n.length;t<r;t++)e=n[t],o.push(i);return o}.call(this),o=[],t=r=0,n=this._url.length;0<=n?r<n:r>n;t=0<=n?++r:--r)null!=this._url[t]&&(e=new Image,e.onerror=function(t){return function(e,r){return function(){return t.retries[e]>0?(a.trace(\"ImageURL failed to load \"+t._url[e]+\" image, retrying in \"+s+\" ms\"),setTimeout(function(){return r.src=t._url[e]},s)):a.warn(\"ImageURL unable to load \"+t._url[e]+\" image after \"+i+\" retries\"),t.retries[e]-=1}}}(this)(t,e),e.onload=function(t){return function(e,r){return function(){return t.image[r]=e,t.renderer.request_render()}}}(this)(e,t),o.push(e.src=this._url[t]));return o},e.prototype._map_data=function(){var t,e,r;switch(e=function(){var t,e,n,o;if(null!=this._w)return this._w;for(n=this._x,o=[],t=0,e=n.length;t<e;t++)r=n[t],o.push(NaN);return o}.call(this),t=function(){var t,e,n,o;if(null!=this._h)return this._h;for(n=this._x,o=[],t=0,e=n.length;t<e;t++)r=n[t],o.push(NaN);return o}.call(this),this.model.properties.w.units){case\"data\":this.sw=this.sdist(this.renderer.xmapper,this._x,e,\"edge\",this.model.dilate);break;case\"screen\":this.sw=e}switch(this.model.properties.h.units){case\"data\":return this.sh=this.sdist(this.renderer.ymapper,this._y,t,\"edge\",this.model.dilate);case\"screen\":return this.sh=t}},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d;for(o=r._url,a=r.image,_=r.sx,d=r.sy,p=r.sw,c=r.sh,n=r._angle,i=this.renderer.plot_view.frame,t.rect(i.get(\"left\")+1,i.get(\"bottom\")+1,i.get(\"width\")-2,i.get(\"height\")-2),t.clip(),h=[],l=0,u=e.length;l<u;l++)s=e[l],isNaN(_[s]+d[s]+n[s])||this.retries[s]!==-1&&null!=a[s]&&h.push(this._render_image(t,s,a[s],_,d,p,c,n));return h},e.prototype._final_sx_sy=function(t,e,r,n,o){switch(t){case\"top_left\":return[e,r];case\"top_center\":return[e-n/2,r];case\"top_right\":return[e-n,r];case\"right_center\":return[e-n,r-o/2];case\"bottom_right\":return[e-n,r-o];case\"bottom_center\":return[e-n/2,r-o];case\"bottom_left\":return[e,r-o];case\"left_center\":return[e,r-o/2];case\"center\":return[e-n/2,r-o/2]}},e.prototype._render_image=function(t,e,r,n,o,i,s,a){var l,u;return isNaN(i[e])&&(i[e]=r.width),isNaN(s[e])&&(s[e]=r.height),l=this.model.anchor,u=this._final_sx_sy(l,n[e],o[e],i[e],s[e]),n=u[0],o=u[1],t.save(),t.globalAlpha=this.model.global_alpha,a[e]?(t.translate(n,o),t.rotate(a[e]),t.drawImage(r,0,0,i[e],s[e]),t.rotate(-a[e]),t.translate(-n,-o)):t.drawImage(r,n,o,i[e],s[e]),t.restore()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"ImageURL\",e.coords([[\"x\",\"y\"]]),e.mixins([]),e.define({url:[l.StringSpec],anchor:[l.Anchor,\"top_left\"],global_alpha:[l.Number,1],angle:[l.AngleSpec,0],w:[l.DistanceSpec],h:[l.DistanceSpec],dilate:[l.Bool,!1],retry_attempts:[l.Number,0],retry_timeout:[l.Number,0]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/line\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../common/hittest\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l;for(a=r.sx,l=r.sy,n=!1,this.visuals.line.set_value(t),i=0,s=e.length;i<s;i++)o=e[i],isFinite(a[o]+l[o])||!n?n?t.lineTo(a[o],l[o]):(t.beginPath(),t.moveTo(a[o],l[o]),n=!0):(t.stroke(),t.beginPath(),n=!1);if(n)return t.stroke()},e.prototype._hit_point=function(t){var e,r,n,o,i,s,l,u,h,c,p;for(h=a.create_hit_test_result(),s={x:this.renderer.plot_view.canvas.vx_to_sx(t.vx),y:this.renderer.plot_view.canvas.vy_to_sy(t.vy)},c=9999,p=Math.max(2,this.visuals.line.line_width.value()/2),r=n=0,l=this.sx.length-1;0<=l?n<l:n>l;r=0<=l?++n:--n)u=[{x:this.sx[r],y:this.sy[r]},{x:this.sx[r+1],y:this.sy[r+1]}],o=u[0],i=u[1],e=a.dist_to_segment(s,o,i),e<p&&e<c&&(c=e,h[\"0d\"].glyph=this.model,h[\"0d\"].get_view=function(){return this}.bind(this),h[\"0d\"].flag=!0,h[\"0d\"].indices=[r]);return h},e.prototype._hit_span=function(t){var e,r,n,o,i,s,l,u,h;for(n=[t.vx,t.vy],u=n[0],h=n[1],i=a.create_hit_test_result(),\"v\"===t.direction?(s=this.renderer.ymapper.map_from_target(h),l=this._y):(s=this.renderer.xmapper.map_from_target(u),l=this._x),e=r=0,o=l.length-1;0<=o?r<o:r>o;e=0<=o?++r:--r)l[e]<=s&&s<=l[e+1]&&(i[\"0d\"].glyph=this.model,i[\"0d\"].get_view=function(){return this}.bind(this),i[\"0d\"].flag=!0,i[\"0d\"].indices.push(e));return i},e.prototype.get_interpolation_hit=function(t,e){var r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x;return r=[e.vx,e.vy],p=r[0],_=r[1],n=[this._x[t],this._y[t],this._x[t+1],this._y[t+1]],m=n[0],b=n[1],g=n[2],x=n[3],\"point\"===e.type?(o=this.renderer.ymapper.v_map_from_target([_-1,_+1]),y=o[0],v=o[1],i=this.renderer.xmapper.v_map_from_target([p-1,p+1]),d=i[0],f=i[1]):\"v\"===e.direction?(s=this.renderer.ymapper.v_map_from_target([_,_]),y=s[0],v=s[1],l=[m,g],d=l[0],f=l[1]):(u=this.renderer.xmapper.v_map_from_target([p,p]),d=u[0],f=u[1],h=[b,x],y=h[0],v=h[1]),c=a.check_2_segments_intersect(d,y,f,v,m,b,g,x),[c.x,c.y]},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Line\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/multi_line\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),l=t(\"rbush\"),a=t(\"../../common/hittest\"),n=t(\"./glyph\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){var t,e,r,n,o,i,a,u,h;for(e=l(),n=[],t=r=0,o=this._xs.length;0<=o?r<o:r>o;t=0<=o?++r:--r)a=function(){var e,r,n,o;for(n=this._xs[t],o=[],e=0,r=n.length;e<r;e++)i=n[e],s.isNaN(i)||o.push(i);return o}.call(this),h=function(){var e,r,n,o;for(n=this._ys[t],o=[],e=0,r=n.length;e<r;e++)u=n[e],s.isNaN(u)||o.push(u);return o}.call(this),0!==a.length&&n.push({minX:s.min(a),minY:s.min(h),maxX:s.max(a),maxY:s.max(h),i:t});return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d;for(p=r.sxs,d=r.sys,h=[],i=0,a=e.length;i<a;i++){for(n=e[i],l=[p[n],d[n]],c=l[0],_=l[1],this.visuals.line.set_vectorize(t,n),o=s=0,u=c.length;0<=u?s<u:s>u;o=0<=u?++s:--s)0!==o?isNaN(c[o])||isNaN(_[o])?(t.stroke(),t.beginPath()):t.lineTo(c[o],_[o]):(t.beginPath(),t.moveTo(c[o],_[o]));h.push(t.stroke())}return h},e.prototype._hit_point=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m,g,y;for(m=a.create_hit_test_result(),c={x:this.renderer.plot_view.canvas.vx_to_sx(t.vx),y:this.renderer.plot_view.canvas.vy_to_sy(t.vy)},g=9999,y=Math.max(2,this.visuals.line.line_width.value()/2),r={},n=i=0,_=this.sxs.length;0<=_?i<_:i>_;n=0<=_?++i:--i){for(p=null,o=l=0,d=this.sxs[n].length-1;0<=d?l<d:l>d;o=0<=d?++l:--l)f=[{x:this.sxs[n][o],y:this.sys[n][o]},{x:this.sxs[n][o+1],y:this.sys[n][o+1]}],u=f[0],h=f[1],e=a.dist_to_segment(c,u,h),e<y&&e<g&&(g=e,p=[o]);p&&(r[n]=p)}return m[\"1d\"].indices=s.keys(r),m[\"2d\"]=r,m},e.prototype._hit_span=function(t){var e,r,n,o,i,l,u,h,c,p,_,d,f,m;for(u=[t.vx,t.vy],f=u[0],m=u[1],p=a.create_hit_test_result(),\"v\"===t.direction?(_=this.renderer.ymapper.map_from_target(m),d=this._ys):(_=this.renderer.xmapper.map_from_target(f),d=this._xs),e={},r=o=0,h=d.length;0<=h?o<h:o>h;r=0<=h?++o:--o){for(l=[],n=i=0,c=d[r].length-1;0<=c?i<c:i>c;n=0<=c?++i:--i)d[r][n]<=_&&_<=d[r][n+1]&&l.push(n);l.length>0&&(e[r]=l)}return p[\"1d\"].indices=s.keys(e),p[\"2d\"]=e,p},e.prototype.get_interpolation_hit=function(t,e,r){var n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;return n=[r.vx,r.vy],_=n[0],d=n[1],o=[this._xs[t][e],this._ys[t][e],this._xs[t][e+1],this._ys[t][e+1]],g=o[0],x=o[1],y=o[2],w=o[3],\"point\"===r.type?(i=this.renderer.ymapper.v_map_from_target([d-1,d+1]),v=i[0],b=i[1],s=this.renderer.xmapper.v_map_from_target([_-1,_+1]),f=s[0],m=s[1]):\"v\"===r.direction?(l=this.renderer.ymapper.v_map_from_target([d,d]),v=l[0],b=l[1],u=[g,y],f=u[0],m=u[1]):(h=this.renderer.xmapper.v_map_from_target([_,_]),f=h[0],m=h[1],c=[x,w],v=c[0],b=c[1]),p=a.check_2_segments_intersect(f,v,m,b,g,x,y,w),[p.x,p.y]},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"MultiLine\",e.coords([[\"xs\",\"ys\"]]),e.mixins([\"line\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/oval\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._set_data=function(){if(this.max_w2=0,\"data\"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,\"data\"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return\"data\"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,\"center\"):this.sw=this._width,\"data\"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,\"center\"):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=r.sx,h=r.sy,l=r.sw,a=r.sh,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+h[n]+l[n]+a[n]+this._angle[n])||(t.translate(u[n],h[n]),t.rotate(this._angle[n]),t.beginPath(),t.moveTo(0,-a[n]/2),t.bezierCurveTo(l[n]/2,-a[n]/2,l[n]/2,a[n]/2,0,a[n]/2),t.bezierCurveTo(-l[n]/2,a[n]/2,-l[n]/2,-a[n]/2,0,-a[n]/2),t.closePath(),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(t,n),t.stroke()),t.rotate(-this._angle[n]),s.push(t.translate(-u[n],-h[n])));return s},e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p,_,d;return u=null!=(l=this.get_reference_point())?l:0,a=[u],_={},_[u]=(e+r)/2,d={},d[u]=(n+o)/2,h=this.sw[u]/this.sh[u],i=.8*Math.min(Math.abs(r-e),Math.abs(o-n)),p={},c={},h>1?(p[u]=i,c[u]=i/h):(p[u]=i*h,c[u]=i),s={sx:_,sy:d,sw:p,sh:c},this._render(t,a,s)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Oval\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({angle:[a.AngleSpec,0],width:[a.DistanceSpec],height:[a.DistanceSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/patch\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u;if(l=r.sx,u=r.sy,this.visuals.fill.doit){for(this.visuals.fill.set_value(t),o=0,s=e.length;o<s;o++)n=e[o],0!==n?isNaN(l[n]+u[n])?(t.closePath(),t.fill(),t.beginPath()):t.lineTo(l[n],u[n]):(t.beginPath(),t.moveTo(l[n],u[n]));t.closePath(),t.fill()}if(this.visuals.line.doit){for(this.visuals.line.set_value(t),i=0,a=e.length;i<a;i++)n=e[i],0!==n?isNaN(l[n]+u[n])?(t.closePath(),t.stroke(),t.beginPath()):t.lineTo(l[n],u[n]):(t.beginPath(),t.moveTo(l[n],u[n]));return t.closePath(),t.stroke()}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=i,e.prototype.type=\"Patch\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/patches\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),l=t(\"rbush\"),n=t(\"./glyph\"),a=t(\"../../common/hittest\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._build_discontinuous_object=function(t){var e,r,n,o,i,a,l,u,h;for(r={},n=o=0,h=t.length;0<=h?o<h:o>h;n=0<=h?++o:--o)for(r[n]=[],l=s.toArray(t[n]);l.length>0;)i=s.findLastIndex(l,function(t){return s.isNaN(t)}),i>=0?u=l.splice(i):(u=l,l=[]),e=function(){var t,e,r;for(r=[],t=0,e=u.length;t<e;t++)a=u[t],s.isNaN(a)||r.push(a);return r}(),r[n].push(e);return r},e.prototype._index_data=function(){var t,e,r,n,o,i,a,u,h,c,p,_;for(e=l(),i=[],c=this._build_discontinuous_object(this._xs),_=this._build_discontinuous_object(this._ys),t=n=0,a=this._xs.length;0<=a?n<a:n>a;t=0<=a?++n:--n)for(r=o=0,u=c[t].length;0<=u?o<u:o>u;r=0<=u?++o:--o)h=c[t][r],p=_[t][r],0!==h.length&&i.push({minX:s.min(h),minY:s.min(p),maxX:s.max(h),maxY:s.max(p),i:t});return e.load(i),e},e.prototype._mask_data=function(t){var e,r,n,o,i,s,l,u,h,c;return l=this.renderer.plot_view.x_range,r=[l.get(\"min\"),l.get(\"max\")],i=r[0],s=r[1],c=this.renderer.plot_view.y_range,n=[c.get(\"min\"),c.get(\"max\")],u=n[0],h=n[1],e=a.validate_bbox_coords([i,s],[u,h]),function(){var t,r,n,i;for(n=this.index.search(e),i=[],t=0,r=n.length;t<r;t++)o=n[t],i.push(o.i);return i}.call(this)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m;for(d=r.sxs,m=r.sys,this.renderer.sxss=this._build_discontinuous_object(d),this.renderer.syss=this._build_discontinuous_object(m),p=[],i=0,a=e.length;i<a;i++){if(n=e[i],u=[d[n],m[n]],_=u[0],f=u[1],this.visuals.fill.doit){for(this.visuals.fill.set_vectorize(t,n),o=s=0,h=_.length;0<=h?s<h:s>h;o=0<=h?++s:--s)0!==o?isNaN(_[o]+f[o])?(t.closePath(),t.fill(),t.beginPath()):t.lineTo(_[o],f[o]):(t.beginPath(),t.moveTo(_[o],f[o]));t.closePath(),t.fill()}if(this.visuals.line.doit){for(this.visuals.line.set_vectorize(t,n),o=l=0,c=_.length;0<=c?l<c:l>c;o=0<=c?++l:--l)0!==o?isNaN(_[o]+f[o])?(t.closePath(),t.stroke(),t.beginPath()):t.lineTo(_[o],f[o]):(t.beginPath(),t.moveTo(_[o],f[o]));t.closePath(),p.push(t.stroke())}else p.push(void 0)}return p},e.prototype._hit_point=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b;for(u=[t.vx,t.vy],g=u[0],y=u[1],_=this.renderer.plot_view.canvas.vx_to_sx(g),f=this.renderer.plot_view.canvas.vy_to_sy(y),v=this.renderer.xmapper.map_from_target(g,!0),b=this.renderer.ymapper.map_from_target(y,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:v,minY:b,maxX:v,maxY:b}),n=[],t=0,e=r.length;t<e;t++)v=r[t],n.push(v.i);return n}.call(this),r=[],n=s=0,h=e.length;0<=h?s<h:s>h;n=0<=h?++s:--s)for(o=e[n],d=this.renderer.sxss[o],m=this.renderer.syss[o],i=l=0,c=d.length;0<=c?l<c:l>c;i=0<=c?++l:--l)a.point_in_poly(_,f,d[i],m[i])&&r.push(o);return p=a.create_hit_test_result(),p[\"1d\"].indices=r,p},e.prototype._get_snap_coord=function(t){var e,r,n,o;for(o=0,e=0,r=t.length;e<r;e++)n=t[e],o+=n;return o/t.length},e.prototype.scx=function(t,e,r){var n,o,i,s,l;if(1===this.renderer.sxss[t].length)return this._get_snap_coord(this.sxs[t]);for(s=this.renderer.sxss[t],l=this.renderer.syss[t],n=o=0,i=s.length;0<=i?o<i:o>i;n=0<=i?++o:--o)if(a.point_in_poly(e,r,s[n],l[n]))return this._get_snap_coord(s[n]);return null},e.prototype.scy=function(t,e,r){var n,o,i,s,l;if(1===this.renderer.syss[t].length)return this._get_snap_coord(this.sys[t]);for(s=this.renderer.sxss[t],l=this.renderer.syss[t],n=o=0,i=s.length;0<=i?o<i:o>i;n=0<=i?++o:--o)if(a.point_in_poly(e,r,s[n],l[n]))return this._get_snap_coord(l[n])},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"Patches\",e.coords([[\"xs\",\"ys\"]]),e.mixins([\"line\",\"fill\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/quad\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t(\"underscore\"),u=t(\"rbush\"),o=t(\"./glyph\"),n=t(\"../mappers/categorical_mapper\"),l=t(\"../../common/hittest\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,h,c,p,_,d,f;for(l=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},a=l(this.renderer.xmapper,this._left),_=l(this.renderer.xmapper,this._right),f=l(this.renderer.ymapper,this._top),e=l(this.renderer.ymapper,this._bottom),o=u(),h=[],r=i=0,p=a.length;0<=p?i<p:i>p;r=0<=p?++i:--i)s=a[r],c=_[r],d=f[r],t=e[r],!isNaN(s+c+d+t)&&isFinite(s+c+d+t)&&h.push({minX:s,minY:t,maxX:c,maxY:d,i:r});return o.load(h),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=l.create_hit_test_result(),n[\"1d\"].indices=e,n},e.prototype.get_anchor_point=function(t,e,r){var n,o,i,s;switch(o=Math.min(this.sleft[e],this.sright[e]),i=Math.max(this.sright[e],this.sleft[e]),s=Math.min(this.stop[e],this.sbottom[e]),n=Math.max(this.sbottom[e],this.stop[e]),t){case\"top_left\":return{x:o,y:s};case\"top_center\":return{x:(o+i)/2,y:s};case\"top_right\":return{x:i,y:s};case\"right_center\":return{x:i,y:(s+n)/2};case\"bottom_right\":return{x:i,y:n};case\"bottom_center\":return{x:(o+i)/2,y:n};case\"bottom_left\":return{x:o,y:n};case\"left_center\":return{x:o,y:(s+n)/2};case\"center\":return{x:(o+i)/2,y:(s+n)/2}}},e.prototype.scx=function(t){return(this.sleft[t]+this.sright[t])/2},e.prototype.scy=function(t){return(this.stop[t]+this.sbottom[t])/2},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"Quad\",e.coords([[\"right\",\"bottom\"],[\"left\",\"top\"]]),e.mixins([\"line\",\"fill\"]),e}(o.Model),e.exports={Model:i,View:s}},{\"../../common/hittest\":\"common/hittest\",\"../mappers/categorical_mapper\":\"models/mappers/categorical_mapper\",\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/quadratic\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),l=t(\"rbush\"),n=t(\"./glyph\"),a=function(t,e,r){var n,o;return e===(t+r)/2?[t,r]:(o=(t-e)/(t-2*e+r),n=t*Math.pow(1-o,2)+2*e*(1-o)*o+r*Math.pow(o,2),[Math.min(t,r,n),Math.max(t,r,n)])},i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._index_data=function(){var t,e,r,n,o,i,s,u,h,c,p;for(e=l(),n=[],t=r=0,o=this._x0.length;0<=o?r<o:r>o;t=0<=o?++r:--r)isNaN(this._x0[t]+this._x1[t]+this._y0[t]+this._y1[t]+this._cx[t]+this._cy[t])||(i=a(this._x0[t],this._cx[t],this._x1[t]),u=i[0],h=i[1],s=a(this._y0[t],this._cy[t],this._y1[t]),c=s[0],p=s[1],n.push({minX:u,minY:c,maxX:h,maxY:p,i:t}));return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;if(u=r.sx0,c=r.sy0,h=r.sx1,p=r.sy1,a=r.scx,l=r.scy,this.visuals.line.doit){for(s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(u[n]+c[n]+h[n]+p[n]+a[n]+l[n])||(t.beginPath(),t.moveTo(u[n],c[n]),t.quadraticCurveTo(a[n],l[n],h[n],p[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke()));return s}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"Quadratic\",e.coords([[\"x0\",\"y0\"],[\"x1\",\"y1\"],[\"cx\",\"cy\"]]),e.mixins([\"line\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/ray\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return this.slength=this.sdist(this.renderer.xmapper,this._x,this._length)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f;if(_=r.sx,d=r.sy,p=r.slength,n=r._angle,this.visuals.line.doit){for(f=this.renderer.plot_view.frame.get(\"width\"),o=this.renderer.plot_view.frame.get(\"height\"),s=2*(f+o),i=a=0,h=p.length;0<=h?a<h:a>h;i=0<=h?++a:--a)0===p[i]&&(p[i]=s);for(c=[],l=0,u=e.length;l<u;l++)i=e[l],isNaN(_[i]+d[i]+n[i]+p[i])||(t.translate(_[i],d[i]),t.rotate(n[i]),t.beginPath(),t.moveTo(0,0),t.lineTo(p[i],0),this.visuals.line.set_vectorize(t,i),t.stroke(),t.rotate(-n[i]),c.push(t.translate(-_[i],-d[i])));return c}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Ray\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\"]),e.define({length:[a.DistanceSpec],angle:[a.AngleSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/rect\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../common/hittest\"),l=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._set_data=function(){if(this.max_w2=0,\"data\"===this.model.properties.width.units&&(this.max_w2=this.max_width/2),this.max_h2=0,\"data\"===this.model.properties.height.units)return this.max_h2=this.max_height/2},e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return\"data\"===this.model.properties.width.units?this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,\"center\",this.mget(\"dilate\")):this.sw=this._width,\"data\"===this.model.properties.height.units?this.sh=this.sdist(this.renderer.ymapper,this._y,this._height,\"center\",this.mget(\"dilate\")):this.sh=this._height},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;if(c=r.sx,p=r.sy,h=r.sw,u=r.sh,n=r._angle,this.visuals.fill.doit)for(i=0,a=e.length;i<a;i++)o=e[i],isNaN(c[o]+p[o]+h[o]+u[o]+n[o])||(this.visuals.fill.set_vectorize(t,o),n[o]?(t.translate(c[o],p[o]),t.rotate(n[o]),t.fillRect(-h[o]/2,-u[o]/2,h[o],u[o]),t.rotate(-n[o]),t.translate(-c[o],-p[o])):t.fillRect(c[o]-h[o]/2,p[o]-u[o]/2,h[o],u[o]));if(this.visuals.line.doit){for(t.beginPath(),s=0,l=e.length;s<l;s++)o=e[s],isNaN(c[o]+p[o]+h[o]+u[o]+n[o])||0!==h[o]&&0!==u[o]&&(n[o]?(t.translate(c[o],p[o]),t.rotate(n[o]),t.rect(-h[o]/2,-u[o]/2,h[o],u[o]),t.rotate(-n[o]),t.translate(-c[o],-p[o])):t.rect(c[o]-h[o]/2,p[o]-u[o]/2,h[o],u[o]),this.visuals.line.set_vectorize(t,o),t.stroke(),t.beginPath());return t.stroke()}},e.prototype._hit_rect=function(t){var e,r,n,o,i,s,l,u,h;return r=this.renderer.xmapper.v_map_from_target([t.vx0,t.vx1],!0),s=r[0],l=r[1],n=this.renderer.ymapper.v_map_from_target([t.vy0,t.vy1],!0),u=n[0],h=n[1],e=a.validate_bbox_coords([s,l],[u,h]),o=a.create_hit_test_result(),o[\"1d\"].indices=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)i=n[t],o.push(i.i);return o}.call(this),o},e.prototype._hit_point=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N;for(_=[t.vx,t.vy],x=_[0],k=_[1],z=this.renderer.xmapper.map_from_target(x,!0),A=this.renderer.ymapper.map_from_target(k,!0),\"screen\"===this.model.properties.width.units?(w=x-2*this.max_width,M=x+2*this.max_width,d=this.renderer.xmapper.v_map_from_target([w,M],!0),P=d[0],E=d[1]):(P=z-2*this.max_width,E=z+2*this.max_width),\"screen\"===this.model.properties.height.units?(j=k-2*this.max_height,T=k+2*this.max_height,f=this.renderer.ymapper.v_map_from_target([j,T],!0),C=f[0],N=f[1]):(C=A-2*this.max_height,N=A+2*this.max_height),i=[],e=a.validate_bbox_coords([P,E],[C,N]),m=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)h=n[t],o.push(h.i);return o}.call(this),l=0,u=m.length;l<u;l++)s=m[l],v=this.renderer.plot_view.canvas.vx_to_sx(x),b=this.renderer.plot_view.canvas.vy_to_sy(k),this._angle[s]&&(n=Math.sqrt(Math.pow(v-this.sx[s],2)+Math.pow(b-this.sy[s],2)),y=Math.sin(-this._angle[s]),r=Math.cos(-this._angle[s]),c=r*(v-this.sx[s])-y*(b-this.sy[s])+this.sx[s],p=y*(v-this.sx[s])+r*(b-this.sy[s])+this.sy[s],v=c,b=p),S=Math.abs(this.sx[s]-v)<=this.sw[s]/2,o=Math.abs(this.sy[s]-b)<=this.sh[s]/2,o&&S&&i.push(s);return g=a.create_hit_test_result(),g[\"1d\"].indices=i,g},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e.prototype._bounds=function(t){return this.max_wh2_bounds(t)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"Rect\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({angle:[l.AngleSpec,0],width:[l.DistanceSpec],height:[l.DistanceSpec],dilate:[l.Bool,!1]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/segment\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,\nt.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),a=t(\"rbush\"),n=t(\"./glyph\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){var t,e,r,n,o;for(e=a(),n=[],t=r=0,o=this._x0.length;0<=o?r<o:r>o;t=0<=o?++r:--r)isNaN(this._x0[t]+this._x1[t]+this._y0[t]+this._y1[t])||n.push({minX:Math.min(this._x0[t],this._x1[t]),minY:Math.min(this._y0[t],this._y1[t]),maxX:Math.max(this._x0[t],this._x1[t]),maxY:Math.max(this._y0[t],this._y1[t]),i:t});return e.load(n),e},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;if(a=r.sx0,u=r.sy0,l=r.sx1,h=r.sy1,this.visuals.line.doit){for(s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(a[n]+u[n]+l[n]+h[n])||(t.beginPath(),t.moveTo(a[n],u[n]),t.lineTo(l[n],h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke()));return s}},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_line_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Segment\",e.coords([[\"x0\",\"y0\"],[\"x1\",\"y1\"]]),e.mixins([\"line\"]),e}(n.Model),e.exports={Model:o,View:i}},{\"./glyph\":\"models/glyphs/glyph\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/text\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),a=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(c=r.sx,p=r.sy,i=r._x_offset,s=r._y_offset,n=r._angle,o=r._text,h=[],l=0,u=e.length;l<u;l++)a=e[l],isNaN(c[a]+p[a]+i[a]+s[a]+n[a])||null==o[a]||(this.visuals.text.doit?(t.save(),t.translate(c[a]+i[a],p[a]+s[a]),t.rotate(n[a]),this.visuals.text.set_vectorize(t,a),t.fillText(o[a],0,0),h.push(t.restore())):h.push(void 0));return h},e.prototype.draw_legend=function(t,e,r,n,o){return t.save(),this.text_props.set_value(t),t.font=this.text_props.font_value(),t.font=t.font.replace(/\\b[\\d\\.]+[\\w]+\\b/,\"10pt\"),t.textAlign=\"right\",t.textBaseline=\"middle\",t.fillText(\"text\",r,(n+o)/2),t.restore()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"Text\",e.coords([[\"x\",\"y\"]]),e.mixins([\"text\"]),e.define({text:[a.StringSpec,{field:\"text\"}],angle:[a.AngleSpec,0],x_offset:[a.NumberSpec,0],y_offset:[a.NumberSpec,0]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../core/properties\":\"core/properties\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/glyphs/vbar\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;l=t(\"underscore\"),c=t(\"rbush\"),i=t(\"./quad\"),o=t(\"./glyph\"),n=t(\"../mappers/categorical_mapper\"),u=t(\"../../common/hittest\"),h=t(\"../../core/properties\"),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype._map_data=function(){var t,e,r,n,o;for(this.sx=this.renderer.xmapper.v_map_to_target(this._x),o=this.renderer.ymapper.v_map_to_target(this._top),n=this.renderer.ymapper.v_map_to_target(this._bottom),this.stop=this.plot_view.canvas.v_vy_to_sy(o),this.sbottom=this.plot_view.canvas.v_vy_to_sy(n),this.sleft=[],this.sright=[],this.sw=this.sdist(this.renderer.xmapper,this._x,this._width,\"center\"),t=e=0,r=this.sx.length;0<=r?e<r:e>r;t=0<=r?++e:--e)this.sleft.push(this.sx[t]-this.sw[t]/2),this.sright.push(this.sx[t]+this.sw[t]/2);return null},e.prototype._index_data=function(){var t,e,r,o,i,s,a,l,u,h,p,_,d,f;for(a=function(t,e){return t instanceof n.Model?t.v_map_to_target(e,!0):e},f=a(this.renderer.xmapper,this._x),d=a(this.renderer.xmapper,this._width),_=a(this.renderer.ymapper,this._top),e=a(this.renderer.ymapper,this._bottom),o=c(),l=[],r=i=0,h=f.length;0<=h?i<h:i>h;r=0<=h?++i:--i)s=f[r]-d[r]/2,u=f[r]+d[r]/2,p=_[r],t=e[r],!isNaN(s+u+p+t)&&isFinite(s+u+p+t)&&l.push({minX:s,minY:t,maxX:u,maxY:p,i:r});return o.load(l),o},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h;for(l=r.sleft,u=r.sright,h=r.stop,a=r.sbottom,s=[],o=0,i=e.length;o<i;o++)n=e[o],isNaN(l[n]+h[n]+u[n]+a[n])||(this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,n),t.fillRect(l[n],h[n],u[n]-l[n],a[n]-h[n])),this.visuals.line.doit?(t.beginPath(),t.rect(l[n],h[n],u[n]-l[n],a[n]-h[n]),this.visuals.line.set_vectorize(t,n),s.push(t.stroke())):s.push(void 0));return s},e.prototype._hit_point=function(t){var e,r,n,o,i,s,a;return r=[t.vx,t.vy],o=r[0],i=r[1],s=this.renderer.xmapper.map_from_target(o,!0),a=this.renderer.ymapper.map_from_target(i,!0),e=function(){var t,e,r,n;for(r=this.index.search({minX:s,minY:a,maxX:s,maxY:a}),n=[],t=0,e=r.length;t<e;t++)s=r[t],n.push(s.i);return n}.call(this),n=u.create_hit_test_result(),n[\"1d\"].indices=e,n},e.prototype.scy=function(t){return(this.stop[t]+this.sbottom[t])/2},e}(o.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e.prototype.default_view=a,e.prototype.type=\"VBar\",e.mixins([\"line\",\"fill\"]),e.define({x:[h.NumberSpec],width:[h.DistanceSpec],top:[h.NumberSpec],bottom:[h.NumberSpec,0]}),e}(o.Model),e.exports={Model:s,View:a}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"../mappers/categorical_mapper\":\"models/mappers/categorical_mapper\",\"./glyph\":\"models/glyphs/glyph\",\"./quad\":\"models/glyphs/quad\",rbush:\"rbush\",underscore:\"underscore\"}],\"models/glyphs/webgl/base\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p;o=t(\"underscore\"),a=t(\"../../../core/util/color\"),l=a.color2rgba,n=function(){function t(t,e){this.gl=t,this.glyph=e,this.nvertices=0,this.size_changed=!1,this.data_changed=!1,this.visuals_changed=!1,this.init()}return t.prototype.GLYPH=\"\",t.prototype.VERT=\"\",t.prototype.FRAG=\"\",t.prototype.set_data_changed=function(t){return t!==this.nvertices&&(this.nvertices=t,this.size_changed=!0),this.data_changed=!0},t.prototype.set_visuals_changed=function(){return this.visuals_changed=!0},t}(),c=function(t){return t<2&&(t=Math.sqrt(2*t)),t},u=function(t,e){var r,n,o,i;for(r=new Float32Array(t),n=o=0,i=t;0<=i?o<i:o>i;n=0<=i?++o:--o)r[n]=e;return r},h=function(t,e,r){var n,o,i,s,a,l,u;for(n=new Float32Array(t*e),o=s=0,l=t;0<=l?s<l:s>l;o=0<=l?++s:--s)for(i=a=0,u=e;0<=u?a<u:a>u;i=0<=u?++a:--a)n[o*e+i]=r[i];return n},p=function(t,e){return!o.isUndefined(t[e].spec.value)},s=function(t,e,r,n,o,i){var s;return o.doit?p(o,i)?(e.used=!1,t.set_attribute(r,\"float\",o[i].value())):(e.used=!0,s=new Float32Array(o.cache[i+\"_array\"]),e.set_size(4*n),e.set_data(0,s),t.set_attribute(r,\"float\",e)):(e.used=!1,t.set_attribute(r,\"float\",[0]))},i=function(t,e,r,n,o,i){var s,a,h,c,_,d,f,m,g,y,v,b,x;if(y=4,c=i+\"_color\",a=i+\"_alpha\",o.doit){if(p(o,c)&&p(o,a))return e.used=!1,x=l(o[c].value(),o[a].value()),t.set_attribute(r,\"vec4\",x);for(e.used=!0,_=p(o,c)?function(){var t,e,r;for(r=[],d=t=0,e=n;0<=e?t<e:t>e;d=0<=e?++t:--t)r.push(o[c].value());return r}():o.cache[c+\"_array\"],h=p(o,a)?u(n,o[a].value()):o.cache[a+\"_array\"],s=new Float32Array(n*y),d=m=0,v=n;0<=v?m<v:m>v;d=0<=v?++m:--m)for(x=l(_[d],h[d]),f=g=0,b=y;0<=b?g<b:g>b;f=0<=b?++g:--g)s[d*y+f]=x[f];return e.set_size(n*y*4),e.set_data(0,s),t.set_attribute(r,\"vec4\",e)}return e.used=!1,t.set_attribute(r,\"vec4\",[0,0,0,0])},e.exports={BaseGLGlyph:n,line_width:c,attach_float:s,attach_color:i,color2rgba:l}},{\"../../../core/util/color\":\"core/util/color\",underscore:\"underscore\"}],\"models/glyphs/webgl/line\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty;u=t(\"gloo2\"),c=t(\"../../../core/logging\").logger,p=t(\"./base\"),n=p.BaseGLGlyph,h=p.line_width,a=p.attach_float,s=p.attach_color,l=p.color2rgba,o=function(){function t(t){this._atlas={},this._index=0,this._width=256,this._height=256,this.tex=new u.Texture2D(t),this.tex.set_wrapping(t.REPEAT,t.REPEAT),this.tex.set_interpolation(t.NEAREST,t.NEAREST),this.tex.set_size([this._height,this._width],t.RGBA),this.tex.set_data([0,0],[this._height,this._width],new Uint8Array(this._height*this._width*4)),this.get_atlas_data([1])}return t.prototype.get_atlas_data=function(t){var e,r,n,o,i,s;return n=t.join(\"-\"),r=this._atlas[n],void 0===r&&(i=this.make_pattern(t),e=i[0],o=i[1],this.tex.set_data([this._index,0],[1,this._width],new Uint8Array(function(){var t,r,n;for(n=[],t=0,r=e.length;t<r;t++)s=e[t],n.push(s+10);return n}())),this._atlas[n]=[this._index/this._height,o],this._index+=1),this._atlas[n]},t.prototype.make_pattern=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j;for(t.length>1&&t.length%2&&(t=t.concat(t)),m=0,p=0,_=t.length;p<_;p++)w=t[p],m+=w;for(e=[],i=0,u=f=0,v=t.length+2;f<v;u=f+=2)n=Math.max(1e-4,t[u%t.length]),o=Math.max(1e-4,t[(u+1)%t.length]),e.push.apply(e,[i,i+n]),i+=n+o;for(d=this._width,r=new Float32Array(4*d),u=g=0,b=d;0<=b?g<b:g>b;u=0<=b?++g:--g){for(j=m*u/(d-1),h=0,k=1e16,c=y=0,x=e.length;0<=x?y<x:y>x;c=0<=x?++y:--y)M=Math.abs(e[c]-j),M<k&&(h=c,k=M);h%2===0?(l=j<=e[h]?1:0,a=e[h],s=e[h+1]):(l=j>e[h]?-1:0,a=e[h-1],s=e[h]),r[4*u+0]=e[h],r[4*u+1]=l,r[4*u+2]=a,r[4*u+3]=s}return[r,m]},t}(),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.GLYPH=\"line\",e.prototype.JOINS={miter:0,round:1,bevel:2},e.prototype.CAPS={\"\":0,none:0,\".\":0,round:1,\")\":1,\"(\":1,o:1,\"triangle in\":2,\"<\":2,\"triangle out\":3,\">\":3,square:4,\"[\":4,\"]\":4,\"=\":4,butt:5,\"|\":5},e.prototype.VERT=\"precision mediump float;\\n\\nconst float PI = 3.14159265358979323846264;\\nconst float THETA = 15.0 * 3.14159265358979323846264/180.0;\\n\\nuniform float u_pixel_ratio;\\nuniform vec2 u_canvas_size, u_offset;\\nuniform vec2 u_scale_aspect;\\nuniform float u_scale_length;\\n\\nuniform vec4 u_color;\\nuniform float u_antialias;\\nuniform float u_length;\\nuniform float u_linewidth;\\nuniform float u_dash_index;\\nuniform float u_closed;\\n\\nattribute vec2 a_position;\\nattribute vec4 a_tangents;\\nattribute vec2 a_segment;\\nattribute vec2 a_angles;\\nattribute vec2 a_texcoord;\\n\\nvarying vec4  v_color;\\nvarying vec2  v_segment;\\nvarying vec2  v_angles;\\nvarying vec2  v_texcoord;\\nvarying vec2  v_miter;\\nvarying float v_length;\\nvarying float v_linewidth;\\n\\nfloat cross(in vec2 v1, in vec2 v2)\\n{\\n    return v1.x*v2.y - v1.y*v2.x;\\n}\\n\\nfloat signed_distance(in vec2 v1, in vec2 v2, in vec2 v3)\\n{\\n    return cross(v2-v1,v1-v3) / length(v2-v1);\\n}\\n\\nvoid rotate( in vec2 v, in float alpha, out vec2 result )\\n{\\n    float c = cos(alpha);\\n    float s = sin(alpha);\\n    result = vec2( c*v.x - s*v.y,\\n                   s*v.x + c*v.y );\\n}\\n\\nvoid main()\\n{\\n    bool closed = (u_closed > 0.0);\\n\\n    // Attributes and uniforms to varyings\\n    v_color = u_color;\\n    v_linewidth = u_linewidth;\\n    v_segment = a_segment * u_scale_length;\\n    v_length = u_length * u_scale_length;\\n\\n    // Scale to map to pixel coordinates. The original algorithm from the paper\\n    // assumed isotropic scale. We obviously do not have this.\\n    vec2 abs_scale_aspect = abs(u_scale_aspect);\\n    vec2 abs_scale = u_scale_length * abs_scale_aspect;\\n\\n    // Correct angles for aspect ratio\\n    vec2 av;\\n    av = vec2(1.0, tan(a_angles.x)) / abs_scale_aspect;\\n    v_angles.x = atan(av.y, av.x);\\n    av = vec2(1.0, tan(a_angles.y)) / abs_scale_aspect;\\n    v_angles.y = atan(av.y, av.x);\\n\\n    // Thickness below 1 pixel are represented using a 1 pixel thickness\\n    // and a modified alpha\\n    v_color.a = min(v_linewidth, v_color.a);\\n    v_linewidth = max(v_linewidth, 1.0);\\n\\n    // If color is fully transparent we just will discard the fragment anyway\\n    if( v_color.a <= 0.0 ) {\\n        gl_Position = vec4(0.0,0.0,0.0,1.0);\\n        return;\\n    }\\n\\n    // This is the actual half width of the line\\n    float w = ceil(u_antialias+v_linewidth)/2.0;\\n\\n    vec2 position = (a_position + u_offset) * abs_scale;\\n\\n    vec2 t1 = normalize(a_tangents.xy * abs_scale_aspect);  // note the scaling for aspect ratio here\\n    vec2 t2 = normalize(a_tangents.zw * abs_scale_aspect);\\n    float u = a_texcoord.x;\\n    float v = a_texcoord.y;\\n    vec2 o1 = vec2( +t1.y, -t1.x);\\n    vec2 o2 = vec2( +t2.y, -t2.x);\\n\\n    // This is a join\\n    // ----------------------------------------------------------------\\n    if( t1 != t2 ) {\\n        float angle = atan (t1.x*t2.y-t1.y*t2.x, t1.x*t2.x+t1.y*t2.y);  // Angle needs recalculation for some reason\\n        vec2 t  = normalize(t1+t2);\\n        vec2 o  = vec2( + t.y, - t.x);\\n\\n        if ( u_dash_index > 0.0 )\\n        {\\n            // Broken angle\\n            // ----------------------------------------------------------------\\n            if( (abs(angle) > THETA) ) {\\n                position += v * w * o / cos(angle/2.0);\\n                float s = sign(angle);\\n                if( angle < 0.0 ) {\\n                    if( u == +1.0 ) {\\n                        u = v_segment.y + v * w * tan(angle/2.0);\\n                        if( v == 1.0 ) {\\n                            position -= 2.0 * w * t1 / sin(angle);\\n                            u -= 2.0 * w / sin(angle);\\n                        }\\n                    } else {\\n                        u = v_segment.x - v * w * tan(angle/2.0);\\n                        if( v == 1.0 ) {\\n                            position += 2.0 * w * t2 / sin(angle);\\n                            u += 2.0*w / sin(angle);\\n                        }\\n                    }\\n                } else {\\n                    if( u == +1.0 ) {\\n                        u = v_segment.y + v * w * tan(angle/2.0);\\n                        if( v == -1.0 ) {\\n                            position += 2.0 * w * t1 / sin(angle);\\n                            u += 2.0 * w / sin(angle);\\n                        }\\n                    } else {\\n                        u = v_segment.x - v * w * tan(angle/2.0);\\n                        if( v == -1.0 ) {\\n                            position -= 2.0 * w * t2 / sin(angle);\\n                            u -= 2.0*w / sin(angle);\\n                        }\\n                    }\\n                }\\n                // Continuous angle\\n                // ------------------------------------------------------------\\n            } else {\\n                position += v * w * o / cos(angle/2.0);\\n                if( u == +1.0 ) u = v_segment.y;\\n                else            u = v_segment.x;\\n            }\\n        }\\n\\n        // Solid line\\n        // --------------------------------------------------------------------\\n        else\\n        {\\n            position.xy += v * w * o / cos(angle/2.0);\\n            if( angle < 0.0 ) {\\n                if( u == +1.0 ) {\\n                    u = v_segment.y + v * w * tan(angle/2.0);\\n                } else {\\n                    u = v_segment.x - v * w * tan(angle/2.0);\\n                }\\n            } else {\\n                if( u == +1.0 ) {\\n                    u = v_segment.y + v * w * tan(angle/2.0);\\n                } else {\\n                    u = v_segment.x - v * w * tan(angle/2.0);\\n                }\\n            }\\n        }\\n\\n    // This is a line start or end (t1 == t2)\\n    // ------------------------------------------------------------------------\\n    } else {\\n        position += v * w * o1;\\n        if( u == -1.0 ) {\\n            u = v_segment.x - w;\\n            position -= w * t1;\\n        } else {\\n            u = v_segment.y + w;\\n            position += w * t2;\\n        }\\n    }\\n\\n    // Miter distance\\n    // ------------------------------------------------------------------------\\n    vec2 t;\\n    vec2 curr = a_position * abs_scale;\\n    if( a_texcoord.x < 0.0 ) {\\n        vec2 next = curr + t2*(v_segment.y-v_segment.x);\\n\\n        rotate( t1, +v_angles.x/2.0, t);\\n        v_miter.x = signed_distance(curr, curr+t, position);\\n\\n        rotate( t2, +v_angles.y/2.0, t);\\n        v_miter.y = signed_distance(next, next+t, position);\\n    } else {\\n        vec2 prev = curr - t1*(v_segment.y-v_segment.x);\\n\\n        rotate( t1, -v_angles.x/2.0,t);\\n        v_miter.x = signed_distance(prev, prev+t, position);\\n\\n        rotate( t2, -v_angles.y/2.0,t);\\n        v_miter.y = signed_distance(curr, curr+t, position);\\n    }\\n\\n    if (!closed && v_segment.x <= 0.0) {\\n        v_miter.x = 1e10;\\n    }\\n    if (!closed && v_segment.y >= v_length)\\n    {\\n        v_miter.y = 1e10;\\n    }\\n\\n    v_texcoord = vec2( u, v*w );\\n\\n    // Calculate position in device coordinates. Note that we\\n    // already scaled with abs scale above.\\n    vec2 normpos = position * sign(u_scale_aspect);\\n    normpos += 0.5;  // make up for Bokeh's offset\\n    normpos /= u_canvas_size / u_pixel_ratio;  // in 0..1\\n    gl_Position = vec4(normpos*2.0-1.0, 0.0, 1.0);\\n    gl_Position.y *= -1.0;\\n}\\n\",e.prototype.FRAG_=\"// Fragment shader that can be convenient during debugging to show the line skeleton.\\nprecision mediump float;\\nuniform vec4  u_color;\\nvoid main () {\\n  gl_FragColor = u_color;\\n}\",e.prototype.FRAG=\"precision mediump float;\\n\\nconst float PI = 3.14159265358979323846264;\\nconst float THETA = 15.0 * 3.14159265358979323846264/180.0;\\n\\nuniform sampler2D u_dash_atlas;\\n\\nuniform vec2 u_linecaps;\\nuniform float u_miter_limit;\\nuniform float u_linejoin;\\nuniform float u_antialias;\\nuniform float u_dash_phase;\\nuniform float u_dash_period;\\nuniform float u_dash_index;\\nuniform vec2 u_dash_caps;\\nuniform float u_closed;\\n\\nvarying vec4  v_color;\\nvarying vec2  v_segment;\\nvarying vec2  v_angles;\\nvarying vec2  v_texcoord;\\nvarying vec2  v_miter;\\nvarying float v_length;\\nvarying float v_linewidth;\\n\\n// Compute distance to cap ----------------------------------------------------\\nfloat cap( int type, float dx, float dy, float t, float linewidth )\\n{\\n    float d = 0.0;\\n    dx = abs(dx);\\n    dy = abs(dy);\\n    if      (type == 0)  discard;  // None\\n    else if (type == 1)  d = sqrt(dx*dx+dy*dy);  // Round\\n    else if (type == 3)  d = (dx+abs(dy));  // Triangle in\\n    else if (type == 2)  d = max(abs(dy),(t+dx-abs(dy)));  // Triangle out\\n    else if (type == 4)  d = max(dx,dy);  // Square\\n    else if (type == 5)  d = max(dx+t,dy);  // Butt\\n    return d;\\n}\\n\\n// Compute distance to join -------------------------------------------------\\nfloat join( in int type, in float d, in vec2 segment, in vec2 texcoord, in vec2 miter,\\n           in float linewidth )\\n{\\n    // texcoord.x is distance from start\\n    // texcoord.y is distance from centerline\\n    // segment.x and y indicate the limits (as for texcoord.x) for this segment\\n\\n    float dx = texcoord.x;\\n\\n    // Round join\\n    if( type == 1 ) {\\n        if (dx < segment.x) {\\n            d = max(d,length( texcoord - vec2(segment.x,0.0)));\\n            //d = length( texcoord - vec2(segment.x,0.0));\\n        } else if (dx > segment.y) {\\n            d = max(d,length( texcoord - vec2(segment.y,0.0)));\\n            //d = length( texcoord - vec2(segment.y,0.0));\\n        }\\n    }\\n    // Bevel join\\n    else if ( type == 2 ) {\\n        if (dx < segment.x) {\\n            vec2 x = texcoord - vec2(segment.x,0.0);\\n            d = max(d, max(abs(x.x), abs(x.y)));\\n\\n        } else if (dx > segment.y) {\\n            vec2 x = texcoord - vec2(segment.y,0.0);\\n            d = max(d, max(abs(x.x), abs(x.y)));\\n        }\\n        /*  Original code for bevel which does not work for us\\n        if( (dx < segment.x) ||  (dx > segment.y) )\\n            d = max(d, min(abs(x.x),abs(x.y)));\\n        */\\n    }\\n\\n    return d;\\n}\\n\\nvoid main()\\n{\\n    // If color is fully transparent we just discard the fragment\\n    if( v_color.a <= 0.0 ) {\\n        discard;\\n    }\\n\\n    // Test if dash pattern is the solid one (0)\\n    bool solid =  (u_dash_index == 0.0);\\n\\n    // Test if path is closed\\n    bool closed = (u_closed > 0.0);\\n\\n    vec4 color = v_color;\\n    float dx = v_texcoord.x;\\n    float dy = v_texcoord.y;\\n    float t = v_linewidth/2.0-u_antialias;\\n    float width = 1.0;  //v_linewidth; original code had dashes scale with line width, we do not\\n    float d = 0.0;\\n\\n    vec2 linecaps = u_linecaps;\\n    vec2 dash_caps = u_dash_caps;\\n    float line_start = 0.0;\\n    float line_stop = v_length;\\n\\n    // Apply miter limit; fragments too far into the miter are simply discarded\\n    if( (dx < v_segment.x) || (dx > v_segment.y) ) {\\n        float into_miter = max(v_segment.x - dx, dx - v_segment.y);\\n        if (into_miter > u_miter_limit*v_linewidth/2.0)\\n          discard;\\n    }\\n\\n    // Solid line --------------------------------------------------------------\\n    if( solid ) {\\n        d = abs(dy);\\n        if( (!closed) && (dx < line_start) ) {\\n            d = cap( int(u_linecaps.x), abs(dx), abs(dy), t, v_linewidth );\\n        }\\n        else if( (!closed) &&  (dx > line_stop) ) {\\n            d = cap( int(u_linecaps.y), abs(dx)-line_stop, abs(dy), t, v_linewidth );\\n        }\\n        else {\\n            d = join( int(u_linejoin), abs(dy), v_segment, v_texcoord, v_miter, v_linewidth );\\n        }\\n\\n    // Dash line --------------------------------------------------------------\\n    } else {\\n        float segment_start = v_segment.x;\\n        float segment_stop  = v_segment.y;\\n        float segment_center= (segment_start+segment_stop)/2.0;\\n        float freq          = u_dash_period*width;\\n        float u = mod( dx + u_dash_phase*width, freq);\\n        vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\\n        float dash_center= tex.x * width;\\n        float dash_type  = tex.y;\\n        float _start = tex.z * width;\\n        float _stop  = tex.a * width;\\n        float dash_start = dx - u + _start;\\n        float dash_stop  = dx - u + _stop;\\n\\n        // Compute extents of the first dash (the one relative to v_segment.x)\\n        // Note: this could be computed in the vertex shader\\n        if( (dash_stop < segment_start) && (dash_caps.x != 5.0) ) {\\n            float u = mod(segment_start + u_dash_phase*width, freq);\\n            vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\\n            dash_center= tex.x * width;\\n            //dash_type  = tex.y;\\n            float _start = tex.z * width;\\n            float _stop  = tex.a * width;\\n            dash_start = segment_start - u + _start;\\n            dash_stop = segment_start - u + _stop;\\n        }\\n\\n        // Compute extents of the last dash (the one relatives to v_segment.y)\\n        // Note: This could be computed in the vertex shader\\n        else if( (dash_start > segment_stop)  && (dash_caps.y != 5.0) ) {\\n            float u = mod(segment_stop + u_dash_phase*width, freq);\\n            vec4 tex = texture2D(u_dash_atlas, vec2(u/freq, u_dash_index)) * 255.0 -10.0;  // conversion to int-like\\n            dash_center= tex.x * width;\\n            //dash_type  = tex.y;\\n            float _start = tex.z * width;\\n            float _stop  = tex.a * width;\\n            dash_start = segment_stop - u + _start;\\n            dash_stop  = segment_stop - u + _stop;\\n        }\\n\\n        // This test if the we are dealing with a discontinuous angle\\n        bool discontinuous = ((dx <  segment_center) && abs(v_angles.x) > THETA) ||\\n                             ((dx >= segment_center) && abs(v_angles.y) > THETA);\\n        //if( dx < line_start) discontinuous = false;\\n        //if( dx > line_stop)  discontinuous = false;\\n\\n        float d_join = join( int(u_linejoin), abs(dy),\\n                            v_segment, v_texcoord, v_miter, v_linewidth );\\n\\n        // When path is closed, we do not have room for linecaps, so we make room\\n        // by shortening the total length\\n        if (closed) {\\n             line_start += v_linewidth/2.0;\\n             line_stop  -= v_linewidth/2.0;\\n        }\\n\\n        // We also need to take antialias area into account\\n        //line_start += u_antialias;\\n        //line_stop  -= u_antialias;\\n\\n        // Check is dash stop is before line start\\n        if( dash_stop <= line_start ) {\\n            discard;\\n        }\\n        // Check is dash start is beyond line stop\\n        if( dash_start >= line_stop ) {\\n            discard;\\n        }\\n\\n        // Check if current dash start is beyond segment stop\\n        if( discontinuous ) {\\n            // Dash start is beyond segment, we discard\\n            if( (dash_start > segment_stop) ) {\\n                discard;\\n                //gl_FragColor = vec4(1.0,0.0,0.0,.25); return;\\n            }\\n\\n            // Dash stop is before segment, we discard\\n            if( (dash_stop < segment_start) ) {\\n                discard;  //gl_FragColor = vec4(0.0,1.0,0.0,.25); return;\\n            }\\n\\n            // Special case for round caps (nicer with this)\\n            if( dash_caps.x == 1.0 ) {\\n                if( (u > _stop) && (dash_stop > segment_stop )  && (abs(v_angles.y) < PI/2.0)) {\\n                    discard;\\n                }\\n            }\\n\\n            // Special case for round caps  (nicer with this)\\n            if( dash_caps.y == 1.0 ) {\\n                if( (u < _start) && (dash_start < segment_start )  && (abs(v_angles.x) < PI/2.0)) {\\n                    discard;\\n                }\\n            }\\n\\n            // Special case for triangle caps (in & out) and square\\n            // We make sure the cap stop at crossing frontier\\n            if( (dash_caps.x != 1.0) && (dash_caps.x != 5.0) ) {\\n                if( (dash_start < segment_start )  && (abs(v_angles.x) < PI/2.0) ) {\\n                    float a = v_angles.x/2.0;\\n                    float x = (segment_start-dx)*cos(a) - dy*sin(a);\\n                    float y = (segment_start-dx)*sin(a) + dy*cos(a);\\n                    if( x > 0.0 ) discard;\\n                    // We transform the cap into square to avoid holes\\n                    dash_caps.x = 4.0;\\n                }\\n            }\\n\\n            // Special case for triangle caps (in & out) and square\\n            // We make sure the cap stop at crossing frontier\\n            if( (dash_caps.y != 1.0) && (dash_caps.y != 5.0) ) {\\n                if( (dash_stop > segment_stop )  && (abs(v_angles.y) < PI/2.0) ) {\\n                    float a = v_angles.y/2.0;\\n                    float x = (dx-segment_stop)*cos(a) - dy*sin(a);\\n                    float y = (dx-segment_stop)*sin(a) + dy*cos(a);\\n                    if( x > 0.0 ) discard;\\n                    // We transform the caps into square to avoid holes\\n                    dash_caps.y = 4.0;\\n                }\\n            }\\n        }\\n\\n        // Line cap at start\\n        if( (dx < line_start) && (dash_start < line_start) && (dash_stop > line_start) ) {\\n            d = cap( int(linecaps.x), dx-line_start, dy, t, v_linewidth);\\n        }\\n        // Line cap at stop\\n        else if( (dx > line_stop) && (dash_stop > line_stop) && (dash_start < line_stop) ) {\\n            d = cap( int(linecaps.y), dx-line_stop, dy, t, v_linewidth);\\n        }\\n        // Dash cap left - dash_type = -1, 0 or 1, but there may be roundoff errors\\n        else if( dash_type < -0.5 ) {\\n            d = cap( int(dash_caps.y), abs(u-dash_center), dy, t, v_linewidth);\\n            if( (dx > line_start) && (dx < line_stop) )\\n                d = max(d,d_join);\\n        }\\n        // Dash cap right\\n        else if( dash_type > 0.5 ) {\\n            d = cap( int(dash_caps.x), abs(dash_center-u), dy, t, v_linewidth);\\n            if( (dx > line_start) && (dx < line_stop) )\\n                d = max(d,d_join);\\n        }\\n        // Dash body (plain)\\n        else {// if( dash_type > -0.5 &&  dash_type < 0.5) {\\n            d = abs(dy);\\n        }\\n\\n        // Line join\\n        if( (dx > line_start) && (dx < line_stop)) {\\n            if( (dx <= segment_start) && (dash_start <= segment_start)\\n                && (dash_stop >= segment_start) ) {\\n                d = d_join;\\n                // Antialias at outer border\\n                float angle = PI/2.+v_angles.x;\\n                float f = abs( (segment_start - dx)*cos(angle) - dy*sin(angle));\\n                d = max(f,d);\\n            }\\n            else if( (dx > segment_stop) && (dash_start <= segment_stop)\\n                     && (dash_stop >= segment_stop) ) {\\n                d = d_join;\\n                // Antialias at outer border\\n                float angle = PI/2.+v_angles.y;\\n                float f = abs((dx - segment_stop)*cos(angle) - dy*sin(angle));\\n                d = max(f,d);\\n            }\\n            else if( dx < (segment_start - v_linewidth/2.)) {\\n                discard;\\n            }\\n            else if( dx > (segment_stop + v_linewidth/2.)) {\\n                discard;\\n            }\\n        }\\n        else if( dx < (segment_start - v_linewidth/2.)) {\\n            discard;\\n        }\\n        else if( dx > (segment_stop + v_linewidth/2.)) {\\n            discard;\\n        }\\n    }\\n\\n    // Distance to border ------------------------------------------------------\\n    d = d - t;\\n    if( d < 0.0 ) {\\n        gl_FragColor = color;\\n    } else {\\n        d /= u_antialias;\\n        gl_FragColor = vec4(color.rgb, exp(-d*d)*color.a);\\n    }\\n}\",e.prototype.init=function(){var t;return t=this.gl,this._scale_aspect=0,this.prog=new u.Program(t),this.prog.set_shaders(this.VERT,this.FRAG),this.index_buffer=new u.IndexBuffer(t),this.vbo_position=new u.VertexBuffer(t),this.vbo_tangents=new u.VertexBuffer(t),this.vbo_segment=new u.VertexBuffer(t),this.vbo_angles=new u.VertexBuffer(t),this.vbo_texcoord=new u.VertexBuffer(t),this.dash_atlas=new o(t)},e.prototype.draw=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;if(u=e.glglyph,u.data_changed){if(!isFinite(r.dx)||!isFinite(r.dy))return;u._baked_offset=[r.dx,r.dy],u._set_data(),u.data_changed=!1}if(this.visuals_changed&&(this._set_visuals(),this.visuals_changed=!1),v=r.sx,b=r.sy,y=Math.sqrt(v*v+b*b),v/=y,b/=y,Math.abs(this._scale_aspect-b/v)>Math.abs(.001*this._scale_aspect)&&(u._update_scale(v,b),this._scale_aspect=b/v),this.prog.set_attribute(\"a_position\",\"vec2\",u.vbo_position),this.prog.set_attribute(\"a_tangents\",\"vec4\",u.vbo_tangents),this.prog.set_attribute(\"a_segment\",\"vec2\",u.vbo_segment),this.prog.set_attribute(\"a_angles\",\"vec2\",u.vbo_angles),this.prog.set_attribute(\"a_texcoord\",\"vec2\",u.vbo_texcoord),this.prog.set_uniform(\"u_length\",\"float\",[u.cumsum]),this.prog.set_texture(\"u_dash_atlas\",this.dash_atlas.tex),n=u._baked_offset,this.prog.set_uniform(\"u_pixel_ratio\",\"float\",[r.pixel_ratio]),this.prog.set_uniform(\"u_canvas_size\",\"vec2\",[r.width,r.height]),this.prog.set_uniform(\"u_offset\",\"vec2\",[r.dx-n[0],r.dy-n[1]]),this.prog.set_uniform(\"u_scale_aspect\",\"vec2\",[v,b]),this.prog.set_uniform(\"u_scale_length\",\"float\",[y]),this.I_triangles=u.I_triangles,this.I_triangles.length<65535)return this.index_buffer.set_size(2*this.I_triangles.length),this.index_buffer.set_data(0,new Uint16Array(this.I_triangles)),this.prog.draw(this.gl.TRIANGLES,this.index_buffer);for(t=this.I_triangles,h=this.I_triangles.length,s=64008,i=[],a=l=0,d=Math.ceil(h/s);0<=d?l<d:l>d;a=0<=d?++l:--l)i.push([]);for(a=p=0,f=t.length;0<=f?p<f:p>f;a=0<=f?++p:--p)w=t[a]%s,o=Math.floor(t[a]/s),i[o].push(w);for(g=[],o=_=0,m=i.length;0<=m?_<m:_>m;o=0<=m?++_:--_)x=new Uint16Array(i[o]),c=o*s*4,0!==x.length&&(this.prog.set_attribute(\"a_position\",\"vec2\",u.vbo_position,0,2*c),this.prog.set_attribute(\"a_tangents\",\"vec4\",u.vbo_tangents,0,4*c),this.prog.set_attribute(\"a_segment\",\"vec2\",u.vbo_segment,0,2*c),\nthis.prog.set_attribute(\"a_angles\",\"vec2\",u.vbo_angles,0,2*c),this.prog.set_attribute(\"a_texcoord\",\"vec2\",u.vbo_texcoord,0,2*c),this.index_buffer.set_size(2*x.length),this.index_buffer.set_data(0,x),g.push(this.prog.draw(this.gl.TRIANGLES,this.index_buffer)));return g},e.prototype._set_data=function(){return this._bake(),this.vbo_position.set_size(4*this.V_position.length),this.vbo_position.set_data(0,this.V_position),this.vbo_tangents.set_size(4*this.V_tangents.length),this.vbo_tangents.set_data(0,this.V_tangents),this.vbo_angles.set_size(4*this.V_angles.length),this.vbo_angles.set_data(0,this.V_angles),this.vbo_texcoord.set_size(4*this.V_texcoord.length),this.vbo_texcoord.set_data(0,this.V_texcoord)},e.prototype._set_visuals=function(){var t,e,r,n,o,i,s;return e=l(this.glyph.visuals.line.line_color.value(),this.glyph.visuals.line.line_alpha.value()),t=this.CAPS[this.glyph.visuals.line.line_cap.value()],i=this.JOINS[this.glyph.visuals.line.line_join.value()],this.prog.set_uniform(\"u_color\",\"vec4\",e),this.prog.set_uniform(\"u_linewidth\",\"float\",[this.glyph.visuals.line.line_width.value()]),this.prog.set_uniform(\"u_antialias\",\"float\",[.9]),this.prog.set_uniform(\"u_linecaps\",\"vec2\",[t,t]),this.prog.set_uniform(\"u_linejoin\",\"float\",[i]),this.prog.set_uniform(\"u_miter_limit\",\"float\",[10]),n=this.glyph.visuals.line.line_dash.value(),r=0,o=1,n.length&&(s=this.dash_atlas.get_atlas_data(n),r=s[0],o=s[1]),this.prog.set_uniform(\"u_dash_index\",\"float\",[r]),this.prog.set_uniform(\"u_dash_phase\",\"float\",[this.glyph.visuals.line.line_dash_offset.value()]),this.prog.set_uniform(\"u_dash_period\",\"float\",[o]),this.prog.set_uniform(\"u_dash_caps\",\"vec2\",[t,t]),this.prog.set_uniform(\"u_closed\",\"float\",[0])},e.prototype._bake=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B,L;for(x=this.nvertices,_=new Float64Array(this.glyph._x),d=new Float64Array(this.glyph._y),i=c=new Float32Array(2*x),n=new Float32Array(2*x),a=p=new Float32Array(4*x),u=new Float32Array(2*x),m=v=0,S=x;0<=S?v<S:v>S;m=0<=S?++v:--v)i[2*m+0]=_[m]+this._baked_offset[0],i[2*m+1]=d[m]+this._baked_offset[1];for(this.tangents=r=new Float32Array(2*x-2),m=k=0,z=x-1;0<=z?k<z:k>z;m=0<=z?++k:--k)r[2*m+0]=c[2*(m+1)+0]-c[2*m+0],r[2*m+1]=c[2*(m+1)+1]-c[2*m+1];for(m=j=0,P=x-1;0<=P?j<P:j>P;m=0<=P?++j:--j)a[4*(m+1)+0]=r[2*m+0],a[4*(m+1)+1]=r[2*m+1],a[4*m+2]=r[2*m+0],a[4*m+3]=r[2*m+1];for(a[0]=r[0],a[1]=r[1],a[4*(x-1)+2]=r[2*(x-2)+0],a[4*(x-1)+3]=r[2*(x-2)+1],t=new Float32Array(x),m=T=0,E=x;0<=E?T<E:T>E;m=0<=E?++T:--T)t[m]=Math.atan2(p[4*m+0]*p[4*m+3]-p[4*m+1]*p[4*m+2],p[4*m+0]*p[4*m+2]+p[4*m+1]*p[4*m+3]);for(m=D=0,A=x-1;0<=A?D<A:D>A;m=0<=A?++D:--D)n[2*m+0]=t[m],n[2*m+1]=t[m+1];for(b=4*x-4,this.V_position=s=new Float32Array(2*b),this.V_angles=o=new Float32Array(2*b),this.V_tangents=l=new Float32Array(4*b),this.V_texcoord=h=new Float32Array(2*b),M=2,m=I=0,C=x;0<=C?I<C:I>C;m=0<=C?++I:--I)for(g=R=0;R<4;g=++R){for(y=F=0;F<2;y=++F)s[2*(4*m+g-M)+y]=i[2*m+y],o[2*(4*m+g)+y]=n[2*m+y];for(y=B=0;B<4;y=++B)l[4*(4*m+g-M)+y]=a[4*m+y]}for(m=L=0,N=x;0<=N?L<=N:L>=N;m=0<=N?++L:--L)h[2*(4*m+0)+0]=-1,h[2*(4*m+1)+0]=-1,h[2*(4*m+2)+0]=1,h[2*(4*m+3)+0]=1,h[2*(4*m+0)+1]=-1,h[2*(4*m+1)+1]=1,h[2*(4*m+2)+1]=-1,h[2*(4*m+3)+1]=1;for(w=6*(x-1),this.I_triangles=e=new Uint32Array(w),q=[],m=f=0,O=x;0<=O?f<O:f>O;m=0<=O?++f:--f)e[6*m+0]=0+4*m,e[6*m+1]=1+4*m,e[6*m+2]=3+4*m,e[6*m+3]=2+4*m,e[6*m+4]=0+4*m,q.push(e[6*m+5]=3+4*m);return q},e.prototype._update_scale=function(t,e){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;for(p=this.nvertices,c=4*p-4,n=this.tangents,r=new Float32Array(p-1),o=new Float32Array(2*p),this.V_segment=i=new Float32Array(2*c),a=h=0,m=p-1;0<=m?h<m:h>m;a=0<=m?++h:--h)r[a]=Math.sqrt(Math.pow(n[2*a+0]*t,2)+Math.pow(n[2*a+1]*e,2));for(s=0,a=_=0,g=p-1;0<=g?_<g:_>g;a=0<=g?++_:--_)s+=r[a],o[2*(a+1)+0]=s,o[2*a+1]=s;for(a=d=0,y=p;0<=y?d<y:d>y;a=0<=y?++d:--d)for(l=f=0;f<4;l=++f)for(u=v=0;v<2;u=++v)i[2*(4*a+l)+u]=o[2*a+u];return this.cumsum=s,this.vbo_segment.set_size(4*this.V_segment.length),this.vbo_segment.set_data(0,this.V_segment)},e}(n),e.exports={LineGLGlyph:i}},{\"../../../core/logging\":\"core/logging\",\"./base\":\"models/glyphs/webgl/base\",gloo2:\"gloo2\"}],\"models/glyphs/webgl/main\":[function(t,e,r){var n,o;n=t(\"./line\"),o=t(\"./markers\"),e.exports={LineGLGlyph:n.LineGLGlyph,CircleGLGlyph:o.CircleGLGlyph,SquareGLGlyph:o.SquareGLGlyph,AnnulusGLGlyph:o.AnnulusGLGlyph,DiamondGLGlyph:o.DiamondGLGlyph,TriangleGLGlyph:o.TriangleGLGlyph,InvertedTriangleGLGlyph:o.InvertedTriangleGLGlyph,CrossGLGlyph:o.CrossGLGlyph,CircleCrossGLGlyph:o.CircleCrossGLGlyph,SquareCrossGLGlyph:o.SquareCrossGLGlyph,DiamondCrossGLGlyph:o.DiamondCrossGLGlyph,XGLGlyph:o.XGLGlyph,CircleXGLGlyph:o.CircleXGLGlyph,SquareXGLGlyph:o.SquareXGLGlyph,AsteriskGLGlyph:o.AsteriskGLGlyph}},{\"./line\":\"models/glyphs/webgl/line\",\"./markers\":\"models/glyphs/webgl/markers\"}],\"models/glyphs/webgl/markers\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j=function(t,e){function r(){this.constructor=t}for(var n in e)T.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},T={}.hasOwnProperty;x=t(\"gloo2\"),M=t(\"../../../core/logging\").logger,k=t(\"./base\"),i=k.BaseGLGlyph,w=k.line_width,b=k.attach_float,v=k.attach_color,_=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.VERT=\"precision mediump float;\\nconst float SQRT_2 = 1.4142135623730951;\\n//\\nuniform float u_pixel_ratio;\\nuniform vec2 u_canvas_size;\\nuniform vec2 u_offset;\\nuniform vec2 u_scale;\\nuniform float u_antialias;\\n//\\nattribute float a_x;\\nattribute float a_y;\\nattribute float a_size;\\nattribute float a_angle;  // in radians\\nattribute float a_linewidth;\\nattribute vec4  a_fg_color;\\nattribute vec4  a_bg_color;\\n//\\nvarying float v_linewidth;\\nvarying float v_size;\\nvarying vec4  v_fg_color;\\nvarying vec4  v_bg_color;\\nvarying vec2  v_rotation;\\n\\nvoid main (void)\\n{\\n    v_size = a_size * u_pixel_ratio;\\n    v_linewidth = a_linewidth * u_pixel_ratio;\\n    v_fg_color = a_fg_color;\\n    v_bg_color = a_bg_color;\\n    v_rotation = vec2(cos(-a_angle), sin(-a_angle));\\n    // Calculate position - the -0.5 is to correct for canvas origin\\n    vec2 pos = (vec2(a_x, a_y) + u_offset) * u_scale; // in pixels\\n    pos += 0.5;  // make up for Bokeh's offset\\n    pos /= u_canvas_size / u_pixel_ratio;  // in 0..1\\n    gl_Position = vec4(pos*2.0-1.0, 0.0, 1.0);\\n    gl_Position.y *= -1.0;\\n    gl_PointSize = SQRT_2 * v_size + 2.0 * (v_linewidth + 1.5*u_antialias);\\n}\",e.prototype.FRAG=\"precision mediump float;\\nconst float SQRT_2 = 1.4142135623730951;\\nconst float PI = 3.14159265358979323846264;\\n//\\nuniform float u_antialias;\\n//\\nvarying vec4  v_fg_color;\\nvarying vec4  v_bg_color;\\nvarying float v_linewidth;\\nvarying float v_size;\\nvarying vec2  v_rotation;\\n\\nMARKERCODE\\n\\nvec4 outline(float distance, float linewidth, float antialias, vec4 fg_color, vec4 bg_color)\\n{\\n    vec4 frag_color;\\n    float t = linewidth/2.0 - antialias;\\n    float signed_distance = distance;\\n    float border_distance = abs(signed_distance) - t;\\n    float alpha = border_distance/antialias;\\n    alpha = exp(-alpha*alpha);\\n\\n    // If fg alpha is zero, it probably means no outline. To avoid a dark outline\\n    // shining through due to aa, we set the fg color to the bg color. Avoid if (i.e. branching).\\n    float select = float(bool(fg_color.a));\\n    fg_color.rgb = select * fg_color.rgb + (1.0  - select) * bg_color.rgb;\\n    // Similarly, if we want a transparent bg\\n    select = float(bool(bg_color.a));\\n    bg_color.rgb = select * bg_color.rgb + (1.0  - select) * fg_color.rgb;\\n\\n    if( border_distance < 0.0)\\n        frag_color = fg_color;\\n    else if( signed_distance < 0.0 ) {\\n        frag_color = mix(bg_color, fg_color, sqrt(alpha));\\n    } else {\\n        if( abs(signed_distance) < (linewidth/2.0 + antialias) ) {\\n            frag_color = vec4(fg_color.rgb, fg_color.a * alpha);\\n        } else {\\n            discard;\\n        }\\n    }\\n    return frag_color;\\n}\\n\\nvoid main()\\n{\\n    vec2 P = gl_PointCoord.xy - vec2(0.5, 0.5);\\n    P = vec2(v_rotation.x*P.x - v_rotation.y*P.y,\\n             v_rotation.y*P.x + v_rotation.x*P.y);\\n    float point_size = SQRT_2*v_size  + 2.0 * (v_linewidth + 1.5*u_antialias);\\n    float distance = marker(P*point_size, v_size);\\n    gl_FragColor = outline(distance, v_linewidth, u_antialias, v_fg_color, v_bg_color);\\n    //gl_FragColor.rgb *= gl_FragColor.a;  // pre-multiply alpha\\n}\",e.prototype.MARKERCODE=\"<defined in subclasses>\",e.prototype.init=function(){var t,e;return e=this.gl,t=this.FRAG.replace(/MARKERCODE/,this.MARKERCODE),this.last_trans={},this.prog=new x.Program(e),this.prog.set_shaders(this.VERT,t),this.vbo_x=new x.VertexBuffer(e),this.prog.set_attribute(\"a_x\",\"float\",this.vbo_x),this.vbo_y=new x.VertexBuffer(e),this.prog.set_attribute(\"a_y\",\"float\",this.vbo_y),this.vbo_s=new x.VertexBuffer(e),this.prog.set_attribute(\"a_size\",\"float\",this.vbo_s),this.vbo_a=new x.VertexBuffer(e),this.prog.set_attribute(\"a_angle\",\"float\",this.vbo_a),this.vbo_linewidth=new x.VertexBuffer(e),this.vbo_fg_color=new x.VertexBuffer(e),this.vbo_bg_color=new x.VertexBuffer(e),this.index_buffer=new x.IndexBuffer(e)},e.prototype.draw=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x;if(c=e.glglyph,p=c.nvertices,c.data_changed){if(!isFinite(r.dx)||!isFinite(r.dy))return;c._baked_offset=[r.dx,r.dy],c._set_data(p),c.data_changed=!1}else null==this.glyph._radius||r.sx===this.last_trans.sx&&r.sy===this.last_trans.sy||(this.last_trans=r,this.vbo_s.set_data(0,new Float32Array(function(){var t,e,r,n;for(r=this.glyph.sradius,n=[],t=0,e=r.length;t<e;t++)y=r[t],n.push(2*y);return n}.call(this))));if(this.visuals_changed&&(this._set_visuals(p),this.visuals_changed=!1),n=c._baked_offset,this.prog.set_uniform(\"u_pixel_ratio\",\"float\",[r.pixel_ratio]),this.prog.set_uniform(\"u_canvas_size\",\"vec2\",[r.width,r.height]),this.prog.set_uniform(\"u_offset\",\"vec2\",[r.dx-n[0],r.dy-n[1]]),this.prog.set_uniform(\"u_scale\",\"vec2\",[r.sx,r.sy]),this.prog.set_attribute(\"a_x\",\"float\",c.vbo_x),this.prog.set_attribute(\"a_y\",\"float\",c.vbo_y),this.prog.set_attribute(\"a_size\",\"float\",c.vbo_s),this.prog.set_attribute(\"a_angle\",\"float\",c.vbo_a),0!==t.length){if(t.length===p)return this.prog.draw(this.gl.POINTS,[0,p]);if(p<65535)return b=window.navigator.userAgent,b.indexOf(\"MSIE \")+b.indexOf(\"Trident/\")+b.indexOf(\"Edge/\")>0&&M.warn(\"WebGL warning: IE is known to produce 1px sprites whith selections.\"),this.index_buffer.set_size(2*t.length),this.index_buffer.set_data(0,new Uint16Array(t)),this.prog.draw(this.gl.POINTS,this.index_buffer);for(s=64e3,i=[],a=l=0,d=Math.ceil(p/s);0<=d?l<d:l>d;a=0<=d?++l:--l)i.push([]);for(a=u=0,f=t.length;0<=f?u<f:u>f;a=0<=f?++u:--u)x=t[a]%s,o=Math.floor(t[a]/s),i[o].push(x);for(g=[],o=h=0,m=i.length;0<=m?h<m:h>m;o=0<=m?++h:--h)v=new Uint16Array(i[o]),_=o*s*4,0!==v.length&&(this.prog.set_attribute(\"a_x\",\"float\",c.vbo_x,0,_),this.prog.set_attribute(\"a_y\",\"float\",c.vbo_y,0,_),this.prog.set_attribute(\"a_size\",\"float\",c.vbo_s,0,_),this.prog.set_attribute(\"a_angle\",\"float\",c.vbo_a,0,_),this.vbo_linewidth.used&&this.prog.set_attribute(\"a_linewidth\",\"float\",this.vbo_linewidth,0,_),this.vbo_fg_color.used&&this.prog.set_attribute(\"a_fg_color\",\"vec4\",this.vbo_fg_color,0,4*_),this.vbo_bg_color.used&&this.prog.set_attribute(\"a_bg_color\",\"vec4\",this.vbo_bg_color,0,4*_),this.index_buffer.set_size(2*v.length),this.index_buffer.set_data(0,v),g.push(this.prog.draw(this.gl.POINTS,this.index_buffer)));return g}},e.prototype._set_data=function(t){var e,r,n,o,i,s,a;for(n=4*t,this.vbo_x.set_size(n),this.vbo_y.set_size(n),this.vbo_a.set_size(n),this.vbo_s.set_size(n),s=new Float64Array(this.glyph._x),a=new Float64Array(this.glyph._y),e=r=0,o=t;0<=o?r<o:r>o;e=0<=o?++r:--r)s[e]+=this._baked_offset[0],a[e]+=this._baked_offset[1];return this.vbo_x.set_data(0,new Float32Array(s)),this.vbo_y.set_data(0,new Float32Array(a)),null!=this.glyph._angle&&this.vbo_a.set_data(0,new Float32Array(this.glyph._angle)),null!=this.glyph._radius?this.vbo_s.set_data(0,new Float32Array(function(){var t,e,r,n;for(r=this.glyph.sradius,n=[],t=0,e=r.length;t<e;t++)i=r[t],n.push(2*i);return n}.call(this))):this.vbo_s.set_data(0,new Float32Array(this.glyph._size))},e.prototype._set_visuals=function(t){return b(this.prog,this.vbo_linewidth,\"a_linewidth\",t,this.glyph.visuals.line,\"line_width\"),v(this.prog,this.vbo_fg_color,\"a_fg_color\",t,this.glyph.visuals.line,\"line\"),v(this.prog,this.vbo_bg_color,\"a_bg_color\",t,this.glyph.visuals.fill,\"fill\"),this.prog.set_uniform(\"u_antialias\",\"float\",[.8])},e}(i),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"circle\",e.prototype.MARKERCODE=\"// --- disc\\nfloat marker(vec2 P, float size)\\n{\\n    return length(P) - size/2.0;\\n}\",e}(_),f=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"square\",e.prototype.MARKERCODE=\"// --- square\\nfloat marker(vec2 P, float size)\\n{\\n    return max(abs(P.x), abs(P.y)) - size/2.0;\\n}\",e}(_),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"annulus\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    float r1 = length(P) - size/2.0;\\n    float r2 = length(P) - size/4.0;  // half width\\n    return max(r1, -r2);\\n}\",e}(_),c=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"diamond\",e.prototype.MARKERCODE=\"// --- diamond\\nfloat marker(vec2 P, float size)\\n{\\n    float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\\n    float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\\n    float r1 = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\\n    return r1 / SQRT_2;\\n}\",e}(_),g=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"triangle\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    P.y -= size * 0.3;\\n    float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\\n    float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\\n    float r1 = max(abs(x), abs(y)) - size / 1.6;\\n    float r2 = P.y;\\n    return max(r1 / SQRT_2, r2);  // Instersect diamond with rectangle\\n}\",e}(_),p=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"invertedtriangle\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    P.y += size * 0.3;\\n    float x = SQRT_2 / 2.0 * (P.x * 1.7 - P.y);\\n    float y = SQRT_2 / 2.0 * (P.x * 1.7 + P.y);\\n    float r1 = max(abs(x), abs(y)) - size / 1.6;\\n    float r2 = - P.y;\\n    return max(r1 / SQRT_2, r2);  // Instersect diamond with rectangle\\n}\",e}(_),u=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"cross\",e.prototype.MARKERCODE='float marker(vec2 P, float size)\\n{\\n    float square = max(abs(P.x), abs(P.y)) - size / 2.5;  // 2.5 is a tweak\\n    float cross = min(abs(P.x), abs(P.y)) - size / 100.0;  // bit of \"width\" for aa\\n    return max(square, cross);\\n}',e}(_),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"circlecross\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    // Define quadrants\\n    float qs = size / 2.0;  // quadrant size\\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n    // Intersect main shape with quadrants (to form cross)\\n    float circle = length(P) - size/2.0;\\n    float c1 = max(circle, s1);\\n    float c2 = max(circle, s2);\\n    float c3 = max(circle, s3);\\n    float c4 = max(circle, s4);\\n    // Union\\n    return min(min(min(c1, c2), c3), c4);\\n}\",e}(_),d=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"squarecross\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    // Define quadrants\\n    float qs = size / 2.0;  // quadrant size\\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n    // Intersect main shape with quadrants (to form cross)\\n    float square = max(abs(P.x), abs(P.y)) - size/2.0;\\n    float c1 = max(square, s1);\\n    float c2 = max(square, s2);\\n    float c3 = max(square, s3);\\n    float c4 = max(square, s4);\\n    // Union\\n    return min(min(min(c1, c2), c3), c4);\\n}\",e}(_),h=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"diamondcross\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    // Define quadrants\\n    float qs = size / 2.0;  // quadrant size\\n    float s1 = max(abs(P.x - qs), abs(P.y - qs)) - qs;\\n    float s2 = max(abs(P.x + qs), abs(P.y - qs)) - qs;\\n    float s3 = max(abs(P.x - qs), abs(P.y + qs)) - qs;\\n    float s4 = max(abs(P.x + qs), abs(P.y + qs)) - qs;\\n    // Intersect main shape with quadrants (to form cross)\\n    float x = SQRT_2 / 2.0 * (P.x * 1.5 - P.y);\\n    float y = SQRT_2 / 2.0 * (P.x * 1.5 + P.y);\\n    float diamond = max(abs(x), abs(y)) - size / (2.0 * SQRT_2);\\n    diamond /= SQRT_2;\\n    float c1 = max(diamond, s1);\\n    float c2 = max(diamond, s2);\\n    float c3 = max(diamond, s3);\\n    float c4 = max(diamond, s4);\\n    // Union\\n    return min(min(min(c1, c2), c3), c4);\\n}\",e}(_),y=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"x\",e.prototype.MARKERCODE='float marker(vec2 P, float size)\\n{\\n    float circle = length(P) - size / 1.6;\\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of \"width\" for aa\\n    return max(circle, X);\\n}',e}(_),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"circlex\",e.prototype.MARKERCODE='float marker(vec2 P, float size)\\n{\\n    float x = P.x - P.y;\\n    float y = P.x + P.y;\\n    // Define quadrants\\n    float qs = size / 2.0;  // quadrant size\\n    float s1 = max(abs(x - qs), abs(y - qs)) - qs;\\n    float s2 = max(abs(x + qs), abs(y - qs)) - qs;\\n    float s3 = max(abs(x - qs), abs(y + qs)) - qs;\\n    float s4 = max(abs(x + qs), abs(y + qs)) - qs;\\n    // Intersect main shape with quadrants (to form cross)\\n    float circle = length(P) - size/2.0;\\n    float c1 = max(circle, s1);\\n    float c2 = max(circle, s2);\\n    float c3 = max(circle, s3);\\n    float c4 = max(circle, s4);\\n    // Union\\n    float almost = min(min(min(c1, c2), c3), c4);\\n    // In this case, the X is also outside of the main shape\\n    float Xmask = length(P) - size / 1.6;  // a circle\\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of \"width\" for aa\\n    return min(max(X, Xmask), almost);\\n}',e}(_),m=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"squarex\",e.prototype.MARKERCODE=\"float marker(vec2 P, float size)\\n{\\n    float x = P.x - P.y;\\n    float y = P.x + P.y;\\n    // Define quadrants\\n    float qs = size / 2.0;  // quadrant size\\n    float s1 = max(abs(x - qs), abs(y - qs)) - qs;\\n    float s2 = max(abs(x + qs), abs(y - qs)) - qs;\\n    float s3 = max(abs(x - qs), abs(y + qs)) - qs;\\n    float s4 = max(abs(x + qs), abs(y + qs)) - qs;\\n    // Intersect main shape with quadrants (to form cross)\\n    float square = max(abs(P.x), abs(P.y)) - size/2.0;\\n    float c1 = max(square, s1);\\n    float c2 = max(square, s2);\\n    float c3 = max(square, s3);\\n    float c4 = max(square, s4);\\n    // Union\\n    return min(min(min(c1, c2), c3), c4);\\n}\",e}(_),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return j(e,t),e.prototype.GLYPH=\"asterisk\",e.prototype.MARKERCODE='float marker(vec2 P, float size)\\n{\\n    // Masks\\n    float diamond = max(abs(SQRT_2 / 2.0 * (P.x - P.y)), abs(SQRT_2 / 2.0 * (P.x + P.y))) - size / (2.0 * SQRT_2);\\n    float square = max(abs(P.x), abs(P.y)) - size / (2.0 * SQRT_2);\\n    // Shapes\\n    float X = min(abs(P.x - P.y), abs(P.x + P.y)) - size / 100.0;  // bit of \"width\" for aa\\n    float cross = min(abs(P.x), abs(P.y)) - size / 100.0;  // bit of \"width\" for aa\\n    // Result is union of masked shapes\\n    return min(max(X, diamond), max(cross, square));\\n}',e}(_),e.exports={CircleGLGlyph:a,SquareGLGlyph:f,AnnulusGLGlyph:n,DiamondGLGlyph:c,TriangleGLGlyph:g,InvertedTriangleGLGlyph:p,CrossGLGlyph:u,CircleCrossGLGlyph:s,SquareCrossGLGlyph:d,DiamondCrossGLGlyph:h,XGLGlyph:y,CircleXGLGlyph:l,SquareXGLGlyph:m,AsteriskGLGlyph:o}},{\"../../../core/logging\":\"core/logging\",\"./base\":\"models/glyphs/webgl/base\",gloo2:\"gloo2\"}],\"models/glyphs/wedge\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./glyph\"),l=t(\"../../common/hittest\"),u=t(\"../../core/properties\"),a=t(\"../../core/util/math\").angle_between,i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._index_data=function(){return this._xy_index()},e.prototype._map_data=function(){return\"data\"===this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xmapper,this._x,this._radius):this.sradius=this._radius},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c,p;for(c=r.sx,p=r.sy,h=r.sradius,o=r._start_angle,n=r._end_angle,i=this.model.properties.direction.value(),u=[],a=0,l=e.length;a<l;a++)s=e[a],isNaN(c[s]+p[s]+h[s]+o[s]+n[s])||(t.beginPath(),t.arc(c[s],p[s],h[s],o[s],n[s],i),t.lineTo(c[s],p[s]),t.closePath(),this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(t,s),t.fill()),this.visuals.line.doit?(this.visuals.line.set_vectorize(t,s),u.push(t.stroke())):u.push(void 0));return u},e.prototype._hit_point=function(t){var e,r,n,o,i,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B;for(g=[t.vx,t.vy],P=g[0],C=g[1],q=this.renderer.xmapper.map_from_target(P,!0),R=this.renderer.ymapper.map_from_target(C,!0),\"data\"===this.model.properties.radius.units?(D=q-this.max_radius,I=q+this.max_radius,F=R-this.max_radius,B=R+this.max_radius):(E=P-this.max_radius,A=P+this.max_radius,y=this.renderer.xmapper.v_map_from_target([E,A],!0),D=y[0],I=y[1],N=C-this.max_radius,O=C+this.max_radius,v=this.renderer.ymapper.v_map_from_target([N,O],!0),F=v[0],B=v[1]),n=[],r=l.validate_bbox_coords([D,I],[F,B]),b=function(){var t,e,n,o;for(n=this.index.search(r),o=[],t=0,e=n.length;t<e;t++)f=n[t],o.push(f.i);return o}.call(this),c=0,_=b.length;c<_;c++)h=b[c],m=Math.pow(this.sradius[h],2),k=this.renderer.xmapper.map_to_target(q,!0),j=this.renderer.xmapper.map_to_target(this._x[h],!0),S=this.renderer.ymapper.map_to_target(R,!0),z=this.renderer.ymapper.map_to_target(this._y[h],!0),i=Math.pow(k-j,2)+Math.pow(S-z,2),i<=m&&n.push([h,i]);for(o=this.model.properties.direction.value(),u=[],p=0,d=n.length;p<d;p++)x=n[p],h=x[0],i=x[1],M=this.renderer.plot_view.canvas.vx_to_sx(P),T=this.renderer.plot_view.canvas.vy_to_sy(C),e=Math.atan2(T-this.sy[h],M-this.sx[h]),a(-e,-this._start_angle[h],-this._end_angle[h],o)&&u.push([h,i]);return w=l.create_hit_test_result(),w[\"1d\"].indices=s.chain(u).sortBy(function(t){return t[1]}).map(function(t){return t[0]}).value(),w},e.prototype.draw_legend=function(t,e,r,n,o){return this._generic_area_legend(t,e,r,n,o)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type=\"Wedge\",e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({direction:[u.Direction,\"anticlock\"],radius:[u.DistanceSpec],start_angle:[u.AngleSpec],end_angle:[u.AngleSpec]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"../../core/util/math\":\"core/util/math\",\"./glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/grids/grid\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;a=t(\"underscore\"),i=t(\"../renderers/guide_renderer\"),s=t(\"../renderers/renderer\"),l=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this._x_range_name=this.mget(\"x_range_name\"),this._y_range_name=this.mget(\"y_range_name\")},e.prototype.render=function(){var t;if(this.model.visible!==!1)return t=this.plot_view.canvas_view.ctx,t.save(),this._draw_regions(t),this._draw_minor_grids(t),this._draw_grids(t),t.restore()},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.request_render)},e.prototype._draw_regions=function(t){var e,r,n,o,i,s,a,l,u,h,c,p;if(this.visuals.band_fill.doit)for(n=this.model.grid_coords(\"major\",!1),c=n[0],p=n[1],this.visuals.band_fill.set_value(t),e=r=0,o=c.length-1;0<=o?r<o:r>o;e=0<=o?++r:--r)e%2===1&&(i=this.plot_view.map_to_screen(c[e],p[e],this._x_range_name,this._y_range_name),a=i[0],u=i[1],s=this.plot_view.map_to_screen(c[e+1],p[e+1],this._x_range_name,this._y_range_name),l=s[0],h=s[1],t.fillRect(a[0],u[0],l[1]-a[0],h[1]-u[0]),t.fill())},e.prototype._draw_grids=function(t){var e,r,n;if(this.visuals.grid_line.doit)return e=this.model.grid_coords(\"major\"),r=e[0],n=e[1],this._draw_grid_helper(t,this.visuals.grid_line,r,n)},e.prototype._draw_minor_grids=function(t){var e,r,n;if(this.visuals.minor_grid_line.doit)return e=this.model.grid_coords(\"minor\"),r=e[0],n=e[1],this._draw_grid_helper(t,this.visuals.minor_grid_line,r,n)},e.prototype._draw_grid_helper=function(t,e,r,n){var o,i,s,a,l,u,h,c;for(e.set_value(t),o=i=0,a=r.length;0<=a?i<a:i>a;o=0<=a?++i:--i){for(l=this.plot_view.map_to_screen(r[o],n[o],this._x_range_name,this._y_range_name),h=l[0],c=l[1],t.beginPath(),t.moveTo(Math.round(h[0]),Math.round(c[0])),o=s=1,u=h.length;1<=u?s<u:s>u;o=1<=u?++s:--s)t.lineTo(Math.round(h[o]),Math.round(c[o]));t.stroke()}},e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type=\"Grid\",e.mixins([\"line:grid_\",\"line:minor_grid_\",\"fill:band_\"]),e.define({bounds:[l.Any,\"auto\"],dimension:[l.Number,0],ticker:[l.Instance],x_range_name:[l.String,\"default\"],y_range_name:[l.String,\"default\"]}),e.override({level:\"underlay\",band_fill_color:null,band_fill_alpha:0,grid_line_color:\"#e5e5e5\",minor_grid_line_color:null}),e.prototype.ranges=function(){var t,e,r,n;return e=this.get(\"dimension\"),r=(e+1)%2,t=this.plot.plot_canvas.get(\"frame\"),n=[t.get(\"x_ranges\")[this.get(\"x_range_name\")],t.get(\"y_ranges\")[this.get(\"y_range_name\")]],[n[e],n[r]]},e.prototype.computed_bounds=function(){var t,e,r,n,o,i,s;return o=this.ranges(),r=o[0],t=o[1],s=this.get(\"bounds\"),n=[r.get(\"min\"),r.get(\"max\")],a.isArray(s)?(i=Math.min(s[0],s[1]),e=Math.max(s[0],s[1]),i<n[0]?i=n[0]:i>n[1]&&(i=null),e>n[1]?e=n[1]:e<n[0]&&(e=null)):(i=n[0],e=n[1]),[i,e]},e.prototype.grid_coords=function(t,e){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T;for(null==e&&(e=!0),h=this.get(\"dimension\"),p=(h+1)%2,b=this.ranges(),v=b[0],s=b[1],x=this.computed_bounds(),k=x[0],u=x[1],T=Math.min(k,u),u=Math.max(k,u),k=T,j=this.get(\"ticker\").get_ticks(k,u,v,{})[t],g=v.get(\"min\"),m=v.get(\"max\"),o=s.get(\"min\"),n=s.get(\"max\"),i=[[],[]],c=_=0,w=j.length;0<=w?_<w:_>w;c=0<=w?++_:--_)if(j[c]!==g&&j[c]!==m||!e){for(a=[],l=[],r=2,y=d=0,M=r;0<=M?d<M:d>M;y=0<=M?++d:--d)f=o+(n-o)/(r-1)*y,a.push(j[c]),l.push(f);i[h].push(a),i[p].push(l)}return i},e}(i.Model),e.exports={Model:n,View:o}},{\"../../core/properties\":\"core/properties\",\"../renderers/guide_renderer\":\"models/renderers/guide_renderer\",\"../renderers/renderer\":\"models/renderers/renderer\",underscore:\"underscore\"}],\"models/layouts/box\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d=function(t,e){function r(){this.constructor=t}for(var n in e)f.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},f={}.hasOwnProperty,m=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};c=t(\"underscore\"),_=t(\"../../core/layout/solver\"),i=_.EQ,s=_.GE,l=_.Strength,u=_.Variable,h=_.WEAK_EQ,p=t(\"../../core/properties\"),a=t(\"./layout_dom\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return d(e,t),e.prototype.className=\"bk-grid\",e.prototype.bind_bokeh_events=function(){return e.__super__.bind_bokeh_events.call(this),this.listenTo(this.model,\"change:children\",this.build_child_views)},e.prototype.get_height=function(){var t,e,r;return e=this.model.get_layoutable_children(),t=c.map(e,function(t){return t._height._value}),r=this.model._horizontal?c.reduce(t,function(t,e){return Math.max(t,e)}):c.reduce(t,function(t,e){return t+e})},e.prototype.get_width=function(){var t,e,r;return e=this.model.get_layoutable_children(),t=c.map(e,function(t){return t._width._value}),r=this.model._horizontal?c.reduce(t,function(t,e){return t+e}):c.reduce(t,function(t,e){return Math.max(t,e)})},e}(a.View),n=function(t){function e(t,r){e.__super__.constructor.call(this,t,r),this._child_equal_size_width=new u,this._child_equal_size_height=new u,this._box_equal_size_top=new u,this._box_equal_size_bottom=new u,this._box_equal_size_left=new u,this._box_equal_size_right=new u,this._box_cell_align_top=new u,this._box_cell_align_bottom=new u,this._box_cell_align_left=new u,this._box_cell_align_right=new u}return d(e,t),e.prototype.default_view=o,e.define({children:[p.Array,[]]}),e.internal({spacing:[p.Number,6]}),e.prototype.get_layoutable_children=function(){return this.get(\"children\")},e.prototype.variables_updated=function(){var t,e,r,n;for(n=this.get_layoutable_children(),e=0,r=n.length;e<r;e++)t=n[e],t.trigger(\"change\");return this.trigger(\"change\")},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=c.extend(t,{\"box-equal-size-top\":this._box_equal_size_top,\"box-equal-size-bottom\":this._box_equal_size_bottom,\"box-equal-size-left\":this._box_equal_size_left,\"box-equal-size-right\":this._box_equal_size_right,\"box-cell-align-top\":this._box_cell_align_top,\"box-cell-align-bottom\":this._box_cell_align_bottom,\"box-cell-align-left\":this._box_cell_align_left,\"box-cell-align-right\":this._box_cell_align_right})},e.prototype.get_constraints=function(){var t,e,r,n,o,a,l,u,p,_,d,f,m;if(r=[],e=this.get_layoutable_children(),0===e.length)return r;for(o=0,u=e.length;o<u;o++)t=e[o],this._test_layoutable(t),m=t.get_constrained_variables(),f=c.keys(m),_=this._child_rect(m),this._horizontal?this._has_var(\"height\",f)&&r.push(i(_.height,[-1,this._height])):this._has_var(\"width\",f)&&r.push(i(_.width,[-1,this._width])),this._horizontal?this._has_var([\"box-equal-size-left\",\"box-equal-size-right\",\"width\"],f)&&r.push(i([-1,m[\"box-equal-size-left\"]],[-1,m[\"box-equal-size-right\"]],m.width,this._child_equal_size_width)):this._has_var([\"box-equal-size-top\",\"box-equal-size-bottom\",\"height\"],f)&&r.push(i([-1,m[\"box-equal-size-top\"]],[-1,m[\"box-equal-size-bottom\"]],m.height,this._child_equal_size_height)),r=r.concat(t.get_constraints());for(l=this._info(e[0].get_constrained_variables()),r.push(i(l.span.start,0)),n=a=1,d=e.length;1<=d?a<d:a>d;n=1<=d?++a:--a)p=this._info(e[n].get_constrained_variables()),l.span.size&&r.push(i(l.span.start,l.span.size,[-1,p.span.start])),\nr.push(h(l.whitespace.after,p.whitespace.before,0-this.spacing)),r.push(s(l.whitespace.after,p.whitespace.before,0-this.spacing)),l=p;return this._horizontal?this._has_var(\"width\",f)&&r.push(i(l.span.start,l.span.size,[-1,this._width])):this._has_var(\"height\",f)&&r.push(i(l.span.start,l.span.size,[-1,this._height])),r=r.concat(this._align_outer_edges_constraints(!0)),r=r.concat(this._align_outer_edges_constraints(!1)),r=r.concat(this._align_inner_cell_edges_constraints()),r=r.concat(this._box_equal_size_bounds(!0)),r=r.concat(this._box_equal_size_bounds(!1)),r=r.concat(this._box_cell_align_bounds(!0)),r=r.concat(this._box_cell_align_bounds(!1)),r=r.concat(this._box_whitespace(!0)),r=r.concat(this._box_whitespace(!1))},e.prototype._has_var=function(t,e){var r;return r=\"string\"==typeof t?[t]:t,c.every(r,function(t){return m.call(e,t)>=0})},e.prototype._test_layoutable=function(t){var e,r,n,o,i;if(o=[\"origin-x\",\"origin-y\",\"whitespace-top\",\"whitespace-right\",\"whitespace-bottom\",\"whitespace-left\"],null==t.get_constrained_variables)throw new Error(t+\" is missing get_constrained_variables method\");for(i=t.get_constrained_variables(),e=0,n=o.length;e<n;e++){if(r=o[e],m.call(c.keys(i),r)<0)throw new Error(t+\" is missing constrained_variable \"+r);if(!i[r]instanceof u)throw new Error(t+\" \"+r+\" is not a solver Variable\")}return!0},e.prototype._child_rect=function(t){var e,r,n,o,i;return n=t.width,e=t.height,r=[t[\"origin-x\"],t[\"origin-y\"]],o=r[0],i=r[1],{x:o,y:i,width:n,height:e}},e.prototype._span=function(t){return this._horizontal?{start:t.x,size:t.width}:{start:t.y,size:t.height}},e.prototype._info=function(t){var e,r;return r=this._horizontal?{before:t[\"whitespace-left\"],after:t[\"whitespace-right\"]}:{before:t[\"whitespace-top\"],after:t[\"whitespace-bottom\"]},e=this._span(this._child_rect(t)),{span:e,whitespace:r}},e.prototype._flatten_cell_edge_variables=function(t){var r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w;for(x=t?e._top_bottom_inner_cell_edge_variables:e._left_right_inner_cell_edge_variables,r=t!==this._horizontal,l=this.get_layoutable_children(),o=l.length,h={},i=0,c=0,f=l.length;c<f;c++){for(a=l[c],s=a instanceof e?a._flatten_cell_edge_variables(t):{},n=a.get_constrained_variables(),p=0,m=x.length;p<m;p++)g=x[p],g in n&&(s[g]=[n[g]]);for(_ in s)w=s[_],r?(v=_.split(\" \"),d=v[0],b=v.length>1?v[1]:\"\",u=this._horizontal?\"row\":\"col\",y=d+\" \"+u+\"-\"+o+\"-\"+i+\"-\"+b):y=_,y in h?h[y]=h[y].concat(w):h[y]=w;i+=1}return h},e.prototype._align_inner_cell_edges_constraints=function(){var t,e,r,n,o,s,a,l;if(t=[],this._is_root){e=this._flatten_cell_edge_variables(this._horizontal);for(o in e)if(l=e[o],l.length>1)for(s=l[0],r=n=1,a=l.length;1<=a?n<a:n>a;r=1<=a?++n:--n)t.push(i(l[r],[-1,s]))}return t},e.prototype._find_edge_leaves=function(t){var r,n,o,i,s,a,l,u;if(o=this.get_layoutable_children(),a=[[],[]],o.length>0)if(this._horizontal===t)u=o[0],i=o[o.length-1],u instanceof e?a[0]=a[0].concat(u._find_edge_leaves(t)[0]):a[0].push(u),i instanceof e?a[1]=a[1].concat(i._find_edge_leaves(t)[1]):a[1].push(i);else for(s=0,l=o.length;s<l;s++)r=o[s],r instanceof e?(n=r._find_edge_leaves(t),a[0]=a[0].concat(n[0]),a[1]=a[1].concat(n[1])):(a[0].push(r),a[1].push(r));return a},e.prototype._align_outer_edges_constraints=function(t){var e,r,n,o,s,a,l,u,h,c;return a=this._find_edge_leaves(t),h=a[0],o=a[1],t?(c=\"on-edge-align-left\",s=\"on-edge-align-right\"):(c=\"on-edge-align-top\",s=\"on-edge-align-bottom\"),r=function(t,e){var r,n,o,i,s;for(r=[],n=0,i=t.length;n<i;n++)o=t[n],s=o.get_constrained_variables(),e in s&&r.push(s[e]);return r},u=r(h,c),n=r(o,s),l=[],e=function(t){var e,r,n,o,s;if(t.length>1){for(r=t[0],n=o=1,s=t.length;1<=s?o<s:o>s;n=1<=s?++o:--o)e=t[n],l.push(i([-1,r],e));return null}},e(u),e(n),l},e.prototype._box_insets_from_child_insets=function(t,e,r,n){var o,a,l,u,h,c,p,_,d;return c=this._find_edge_leaves(t),_=c[0],a=c[1],t?(d=e+\"-left\",l=e+\"-right\",h=this[r+\"_left\"],u=this[r+\"_right\"]):(d=e+\"-top\",l=e+\"-bottom\",h=this[r+\"_top\"],u=this[r+\"_bottom\"]),p=[],o=function(t,e,r){var o,a,l,u,h;for(o=[],a=0,u=e.length;a<u;a++)l=e[a],h=l.get_constrained_variables(),r in h&&(n?p.push(s([-1,t],h[r])):p.push(i([-1,t],h[r])));return null},o(h,_,d),o(u,a,l),p},e.prototype._box_equal_size_bounds=function(t){return this._box_insets_from_child_insets(t,\"box-equal-size\",\"_box_equal_size\",!1)},e.prototype._box_cell_align_bounds=function(t){return this._box_insets_from_child_insets(t,\"box-cell-align\",\"_box_cell_align\",!1)},e.prototype._box_whitespace=function(t){return this._box_insets_from_child_insets(t,\"whitespace\",\"_whitespace\",!0)},e._left_right_inner_cell_edge_variables=[\"box-cell-align-left\",\"box-cell-align-right\"],e._top_bottom_inner_cell_edge_variables=[\"box-cell-align-top\",\"box-cell-align-bottom\"],e}(a.Model),e.exports={Model:n,View:o}},{\"../../core/layout/solver\":\"core/layout/solver\",\"../../core/properties\":\"core/properties\",\"./layout_dom\":\"models/layouts/layout_dom\",underscore:\"underscore\"}],\"models/layouts/column\":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;n=t(\"./box\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.className=\"bk-grid-column\",e}(n.View),o=function(t){function e(t,r){e.__super__.constructor.call(this,t,r),this._horizontal=!1}return s(e,t),e.prototype.type=\"Column\",e.prototype.default_view=i,e}(n.Model),e.exports={View:i,Model:o}},{\"./box\":\"models/layouts/box\"}],\"models/layouts/layout_dom\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g=function(t,e){function r(){this.constructor=t}for(var n in e)y.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},y={}.hasOwnProperty;p=t(\"underscore\"),n=t(\"jquery\"),u=t(\"../../model\"),f=t(\"../../core/properties\"),m=t(\"../../core/layout/solver\"),s=m.GE,i=m.EQ,h=m.Strength,c=m.Variable,_=t(\"../../common/build_views\"),o=t(\"../../core/bokeh_view\"),d=t(\"../../core/logging\").logger,l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return g(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.attr(\"id\",\"modelid_\"+this.model.id),this.$el.addClass(\"bk-layout-\"+this.model.sizing_mode),this.child_views={},this.build_child_views(!1)},e.prototype.build_child_views=function(t){var e,r,n,o,i;for(null==t&&(t=!0),this.unbind_bokeh_events(),t&&(this.model.document._invalidate_all_models(),this.model.document._init_solver()),n=this.model.get_layoutable_children(),this.child_views={},_(this.child_views,n),this.$el.empty(),o=0,i=n.length;o<i;o++)e=n[o],r=this.child_views[e.id],this.$el.append(r.$el);return this.bind_bokeh_events()},e.prototype.unbind_bokeh_events=function(){var t,e,r,n;this.stopListening(),e=this.child_views,r=[];for(t in e)n=e[t],n.stopListening(),r.push(\"function\"==typeof n.unbind_bokeh_events?n.unbind_bokeh_events():void 0);return r},e.prototype.bind_bokeh_events=function(){var t;return this.listenTo(this.model,\"change\",this.render),\"fixed\"===this.model.sizing_mode?this.listenToOnce(this.model.document.solver(),\"resize\",function(t){return function(){return t.render()}}(this)):this.listenTo(this.model.document.solver(),\"resize\",function(t){return function(){return t.render()}}(this)),t=\"Changing sizing_mode after initialization is not currently supported.\",this.listenTo(this.model,\"change:sizing_mode\",function(){return d.warn(t)})},e.prototype.render=function(){var t,e,r;if(e=this.model.document.solver(),\"fixed\"===this.model.sizing_mode&&(null!=this.model.width?r=this.model.width:(r=this.get_width(),this.model.width=r),null!=this.model.height?t=this.model.height:(t=this.get_height(),this.model.height=t),e.suggest_value(this.model._width,r),e.suggest_value(this.model._height,t),e.update_variables(),this.$el.css({width:r,height:t})),\"scale_width\"===this.model.sizing_mode&&(t=this.get_height(),e.suggest_value(this.model._height,t),e.update_variables(),this.$el.css({width:this.model._width._value,height:this.model._height._value})),\"scale_height\"===this.model.sizing_mode&&(r=this.get_width(),e.suggest_value(this.model._width,r),e.update_variables(),this.$el.css({width:this.model._width._value,height:this.model._height._value})),\"stretch_both\"===this.model.sizing_mode)return this.$el.css({position:\"absolute\",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value})},e.prototype.get_height=function(){return null},e.prototype.get_width=function(){return null},e}(o),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return g(e,t),e.prototype.type=\"LayoutDOM\",e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this._width=new c(\"_width \"+this.id),this._height=new c(\"_height \"+this.id),this._left=new c(\"_left \"+this.id),this._right=new c(\"_right \"+this.id),this._top=new c(\"_top \"+this.id),this._bottom=new c(\"_bottom \"+this.id),this._dom_top=new c(\"_dom_top \"+this.id),this._dom_left=new c(\"_dom_left \"+this.id),this._width_minus_right=new c(\"_width_minus_right \"+this.id),this._height_minus_bottom=new c(\"_height_minus_bottom \"+this.id),this._whitespace_top=new c,this._whitespace_bottom=new 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t={\"origin-x\":this._dom_left,\"origin-y\":this._dom_top,\"whitespace-top\":this._whitespace_top,\"whitespace-bottom\":this._whitespace_bottom,\"whitespace-left\":this._whitespace_left,\"whitespace-right\":this._whitespace_right},\"stretch_both\"===this.sizing_mode&&(t=p.extend(t,{width:this._width,height:this._height})),\"scale_width\"===this.sizing_mode&&(t=p.extend(t,{width:this._width})),\"scale_height\"===this.sizing_mode&&(t=p.extend(t,{height:this._height})),t},e.define({height:[f.Number],width:[f.Number],disabled:[f.Bool,!1],sizing_mode:[f.SizingMode,\"fixed\"]}),e.internal({layoutable:[f.Bool,!0]}),e}(u),e.exports={Model:a,View:l}},{\"../../common/build_views\":\"common/build_views\",\"../../core/bokeh_view\":\"core/bokeh_view\",\"../../core/layout/solver\":\"core/layout/solver\",\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../model\":\"model\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/layouts/row\":[function(t,e,r){var 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t=e.__super__.get_constrained_variables.call(this),t=s.extend(t,{\"on-edge-align-top\":this._top,\"on-edge-align-bottom\":this._height_minus_bottom,\"on-edge-align-left\":this._left,\"on-edge-align-right\":this._width_minus_right,\"box-cell-align-top\":this._top,\"box-cell-align-bottom\":this._height_minus_bottom,\"box-cell-align-left\":this._left,\"box-cell-align-right\":this._width_minus_right,\"box-equal-size-top\":this._top,\"box-equal-size-bottom\":this._height_minus_bottom,\"box-equal-size-left\":this._left,\"box-equal-size-right\":this._width_minus_right})},e}(n.Model),e.exports={Model:o}},{\"./layout_dom\":\"models/layouts/layout_dom\",underscore:\"underscore\"}],\"models/layouts/widget_box\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y=function(t,e){function r(){this.constructor=t}for(var n in e)v.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new 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r=this.model.document.solver(),\"fixed\"!==this.model.sizing_mode&&\"scale_height\"!==this.model.sizing_mode||(n=this.get_width(),this.model._width._value!==n&&(r.suggest_value(this.model._width,n),r.update_variables())),\"fixed\"!==this.model.sizing_mode&&\"scale_width\"!==this.model.sizing_mode||(e=this.get_height(),this.model._height._value!==e&&(r.suggest_value(this.model._height,e),r.update_variables())),t=this.model._width._value-20>0?this.model._width._value-20:\"100%\",\"stretch_both\"===this.model.sizing_mode?this.$el.css({position:\"absolute\",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value}):this.$el.css({width:t})},e.prototype.get_height=function(){var t,e,r,n;e=0,n=this.child_views;for(r in n)v.call(n,r)&&(t=n[r],e+=t.el.scrollHeight);return e+20},e.prototype.get_width=function(){var t,e,r,n,o;if(null!=this.model.width)return this.model.width;o=this.el.scrollWidth+20,n=this.child_views;for(r in 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Using default of 300.\")),\"scale_height\"===this.sizing_mode)return f.warn(\"sizing_mode `scale_height` is not experimental for WidgetBox. Please report your results to the bokeh dev team so we can improve.\")},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constraints=function(){var t,r,n,o,i;for(r=e.__super__.get_constraints.call(this),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_constraints());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=_.extend(t,{\"on-edge-align-top\":this._top,\"on-edge-align-bottom\":this._height_minus_bottom,\"on-edge-align-left\":this._left,\"on-edge-align-right\":this._width_minus_right,\"box-cell-align-top\":this._top,\"box-cell-align-bottom\":this._height_minus_bottom,\"box-cell-align-left\":this._left,\"box-cell-align-right\":this._width_minus_right,\"box-equal-size-top\":this._top,\"box-equal-size-bottom\":this._height_minus_bottom}),\"fixed\"!==this.sizing_mode&&(t=_.extend(t,{\"box-equal-size-left\":this._left,\"box-equal-size-right\":this._width_minus_right})),t},e.prototype.get_layoutable_children=function(){return 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null==r&&(r=!1),t=e.__super__.map_from_target.call(this,t),r?t:(o=this.get(\"source_range\"),n=o.get(\"factors\"),n[Math.floor(t-.5-o.get(\"offset\"))])},e.prototype.v_map_from_target=function(t,r){var n,o,i,s,a,l,u,h,c;for(null==r&&(r=!1),c=e.__super__.v_map_from_target.call(this,t),o=i=0,l=c.length;0<=l?i<l:i>l;o=0<=l?++i:--i)c[o]=c[o];if(r)return c;for(h=Array(c),a=this.get(\"source_range\"),n=a.get(\"factors\"),o=s=0,u=t.length;0<=u?s<u:s>u;o=0<=u?++s:--s)h[o]=n[Math.floor(c[o]-.5-a.get(\"offset\"))];return h},e}(o.Model),e.exports={Model:n}},{\"./linear_mapper\":\"models/mappers/linear_mapper\",underscore:\"underscore\"}],\"models/mappers/color_mapper\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),s=t(\"../../core/properties\"),o=t(\"../../model\"),n=function(t){function e(){return 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e.__super__.initialize.call(this,t,r),this.define_computed_property(\"mapper_state\",this._mapper_state,!0),this.add_dependencies(\"mapper_state\",this,[\"source_range\",\"target_range\"]),this.add_dependencies(\"mapper_state\",this.get(\"source_range\"),[\"start\",\"end\"]),this.add_dependencies(\"mapper_state\",this.get(\"target_range\"),[\"start\",\"end\"])},e.prototype.map_to_target=function(t){var e,r,n,o,i,s,a;return i=this.get(\"mapper_state\"),a=i[0],o=i[1],r=i[2],e=i[3],s=0,0===r?n=0:(n=(Math.log(t)-e)/r,!isNaN(n)&&isFinite(n)||(n=0)),s=n*a+o},e.prototype.v_map_to_target=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_;if(h=this.get(\"mapper_state\"),p=h[0],u=h[1],n=h[2],r=h[3],c=new Float64Array(t.length),0===n)o=t.map(function(t){return 0});else for(o=t.map(function(t){return(Math.log(t)-r)/n}),e=i=0,a=o.length;i<a;e=++i)_=o[e],!isNaN(o[e])&&isFinite(o[e])||(o[e]=0);for(e=s=0,l=t.length;s<l;e=++s)_=t[e],c[e]=o[e]*p+u;return c},e.prototype.map_from_target=function(t){var e,r,n,o,i,s;return i=this.get(\"mapper_state\"),s=i[0],o=i[1],r=i[2],e=i[3],n=(t-o)/s,n=Math.exp(r*n+e)},e.prototype.v_map_from_target=function(t){var e,r,n,o,i,s,a,l,u,h,c;for(u=new Float64Array(t.length),l=this.get(\"mapper_state\"),h=l[0],a=l[1],n=l[2],r=l[3],o=t.map(function(t){return(t-a)/h}),e=i=0,s=t.length;i<s;e=++i)c=t[e],u[e]=Math.exp(n*o[e]+r);return u},e.prototype._get_safe_scale=function(t,e){var r,n,o,i;return i=t<0?0:t,r=e<0?0:e,i===r&&(0===i?(o=[1,10],i=o[0],r=o[1]):(n=Math.log(i)/Math.log(10),i=Math.pow(10,Math.floor(n)),r=Math.ceil(n)!==Math.floor(n)?Math.pow(10,Math.ceil(n)):Math.pow(10,Math.ceil(n)+1))),[i,r]},e.prototype._mapper_state=function(){var t,e,r,n,o,i,s,a,l,u,h,c;return l=this.get(\"source_range\").get(\"start\"),a=this.get(\"source_range\").get(\"end\"),c=this.get(\"target_range\").get(\"start\"),h=this.get(\"target_range\").get(\"end\"),s=h-c,o=this._get_safe_scale(l,a),u=o[0],t=o[1],0===u?(r=Math.log(t),e=0):(r=Math.log(t)-Math.log(u),e=Math.log(u)),i=s,n=c,[i,n,r,e]},e.internal({source_range:[i.Any],target_range:[i.Any]}),e}(o),e.exports={Model:n}},{\"../../core/properties\":\"core/properties\",\"../../model\":\"model\"}],\"models/markers/index\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w=function(t,e){function r(){this.constructor=t}for(var n in e)M.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},M={}.hasOwnProperty;n=t(\"./marker\"),o=Math.sqrt(3),f=function(t,e){var r,o;return o=function(t){function r(){return r.__super__.constructor.apply(this,arguments)}return w(r,t),r.prototype._render_one=e,r}(n.View),r=function(e){function r(){return 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l;l=2*o,t.rect(-o,-o,l,l),a.doit&&(a.set_vectorize(t,e),t.fill()),s.doit&&(s.set_vectorize(t,e),i(t,o),t.stroke())},v=function(t,e,r,n,o,i,s){var a;a=2*o,t.rect(-o,-o,a,a),s.doit&&(s.set_vectorize(t,e),t.fill()),i.doit&&(i.set_vectorize(t,e),l(t,o),t.stroke())},b=function(t,e,r,n,o,i,s){a(t,o),s.doit&&(s.set_vectorize(t,e),t.fill()),i.doit&&(i.set_vectorize(t,e),t.stroke())},x=function(t,e,r,n,o,i,s){l(t,o),i.doit&&(i.set_vectorize(t,e),t.stroke())},e.exports={Asterisk:f(\"Asterisk\",u),CircleCross:f(\"CircleCross\",h),CircleX:f(\"CircleX\",c),Cross:f(\"Cross\",p),Diamond:f(\"Diamond\",_),DiamondCross:f(\"DiamondCross\",d),InvertedTriangle:f(\"InvertedTriangle\",m),Square:f(\"Square\",g),SquareCross:f(\"SquareCross\",y),SquareX:f(\"SquareX\",v),Triangle:f(\"Triangle\",b),X:f(\"X\",x)}},{\"./marker\":\"models/markers/marker\"}],\"models/markers/marker\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"../glyphs/glyph\"),a=t(\"../../common/hittest\"),l=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.draw_legend=function(t,e,r,n,o){var i,s,a,l,u,h,c,p;return u=null!=(l=this.get_reference_point())?l:0,a=[u],c={},c[u]=(e+r)/2,p={},p[u]=(n+o)/2,h={},h[u]=.4*Math.min(Math.abs(r-e),Math.abs(o-n)),i={},i[u]=0,s={sx:c,sy:p,_size:h,_angle:i},this._render(t,a,s)},e.prototype._render=function(t,e,r){var n,o,i,s,a,l,u,h,c;for(h=r.sx,c=r.sy,o=r._size,n=r._angle,u=[],s=0,a=e.length;s<a;s++)i=e[s],isNaN(h[i]+c[i]+o[i]+n[i])||(l=o[i]/2,t.beginPath(),t.translate(h[i],c[i]),n[i]&&t.rotate(n[i]),\nthis._render_one(t,i,h[i],c[i],l,this.visuals.line,this.visuals.fill),n[i]&&t.rotate(-n[i]),u.push(t.translate(-h[i],-c[i])));return u},e.prototype._index_data=function(){return this._xy_index()},e.prototype._mask_data=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d,f;return r=this.renderer.plot_view.frame.get(\"h_range\"),s=r.get(\"start\")-this.max_size,l=r.get(\"end\")+this.max_size,n=this.renderer.xmapper.v_map_from_target([s,l],!0),p=n[0],_=n[1],i=this.renderer.plot_view.frame.get(\"v_range\"),u=i.get(\"start\")-this.max_size,h=i.get(\"end\")+this.max_size,o=this.renderer.ymapper.v_map_from_target([u,h],!0),d=o[0],f=o[1],e=a.validate_bbox_coords([p,_],[d,f]),function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)c=n[t],o.push(c.i);return o}.call(this)},e.prototype._hit_point=function(t){var 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r=this.renderer.xmapper.v_map_from_target([t.vx0,t.vx1],!0),s=r[0],l=r[1],n=this.renderer.ymapper.v_map_from_target([t.vy0,t.vy1],!0),u=n[0],h=n[1],e=a.validate_bbox_coords([s,l],[u,h]),o=a.create_hit_test_result(),o[\"1d\"].indices=function(){var t,r,n,o;for(n=this.index.search(e),o=[],t=0,r=n.length;t<r;t++)i=n[t],o.push(i.i);return o}.call(this),o},e.prototype._hit_poly=function(t){var e,r,n,o,i,s,l,u,h,c,p,_,d;for(s=[t.vx,t.vy],_=s[0],d=s[1],c=this.renderer.plot_view.canvas.v_vx_to_sx(_),p=this.renderer.plot_view.canvas.v_vy_to_sy(d),e=function(){h=[];for(var t=0,e=this.sx.length;0<=e?t<e:t>e;0<=e?t++:t--)h.push(t);return h}.apply(this),r=[],n=i=0,l=e.length;0<=l?i<l:i>l;n=0<=l?++i:--i)o=e[n],a.point_in_poly(this.sx[n],this.sy[n],c,p)&&r.push(o);return u=a.create_hit_test_result(),u[\"1d\"].indices=r,u},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.coords([[\"x\",\"y\"]]),e.mixins([\"line\",\"fill\"]),e.define({size:[l.DistanceSpec,{units:\"screen\",value:4}],angle:[l.AngleSpec,0]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../common/hittest\":\"common/hittest\",\"../../core/properties\":\"core/properties\",\"../glyphs/glyph\":\"models/glyphs/glyph\",underscore:\"underscore\"}],\"models/plots/gmap_plot\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){function r(){this.constructor=t}for(var n in e)_.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},_={}.hasOwnProperty;a=t(\"underscore\"),h=t(\"proj4\"),c=h.defs(\"GOOGLE\"),l=t(\"../../core/logging\").logger,o=t(\"./gmap_plot_canvas\"),s=t(\"./plot\"),u=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return p(e,t),e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return 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See https://developers.google.com/maps/documentation/javascript/get-api-key for more information on how to obtain your own.\"),this._plot_canvas=new o.Model({plot:this}),this.plot_canvas.toolbar=this.toolbar},e.define({map_options:[u.Any],api_key:[u.String]}),e}(s.Model),e.exports={Model:n,View:i}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"./gmap_plot_canvas\":\"models/plots/gmap_plot_canvas\",\"./plot\":\"models/plots/plot\",proj4:\"proj4\",underscore:\"underscore\"}],\"models/plots/gmap_plot_canvas\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){return function(){return t.apply(e,arguments)}},c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},p={}.hasOwnProperty;s=t(\"underscore\"),l=t(\"proj4\"),u=l.defs(\"GOOGLE\"),i=t(\"./plot_canvas\"),a=t(\"../../core/properties\"),o=function(t){function e(){return this.setRanges=h(this.setRanges,this),this.getProjectedBounds=h(this.getProjectedBounds,this),this.getLatLngBounds=h(this.getLatLngBounds,this),e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,s.defaults(t,this.default_options)),this.zoom_count=0},e.prototype.getLatLngBounds=function(){var t,e,r,n,o,i,s;return e=this.map.getBounds(),r=e.getNorthEast(),t=e.getSouthWest(),o=t.lng(),n=r.lng(),s=t.lat(),i=r.lat(),[o,n,s,i]},e.prototype.getProjectedBounds=function(){var t,e,r,n,o,i,s,a,h,c,p;return o=this.getLatLngBounds(),h=o[0],a=o[1],p=o[2],c=o[3],i=l(u,[h,p]),e=i[0],n=i[1],s=l(u,[a,c]),t=s[0],r=s[1],[e,t,n,r]},e.prototype.setRanges=function(){var t,e,r,n,o;return o=this.getProjectedBounds(),e=o[0],t=o[1],n=o[2],r=o[3],this.x_range.set({start:e,end:t}),this.y_range.set({start:n,end:r})},e.prototype.update_range=function(t){var r,n,o,i,s,a,l,u;if(this.pause(),null==t.sdx&&null==t.sdy||(this.map.panBy(t.sdx,t.sdy),e.__super__.update_range.call(this,t)),null!=t.factor){if(10!==this.zoom_count)return void(this.zoom_count+=1);this.zoom_count=0,e.__super__.update_range.call(this,t),u=t.factor<0?-1:1,n=this.map.getZoom(),r=n+u,r>=2&&(this.map.setZoom(r),l=this.getProjectedBounds(),i=l[0],o=l[1],a=l[2],s=l[3],o-i<0&&this.map.setZoom(n)),this.setRanges()}return this.unpause()},e.prototype.bind_bokeh_events=function(){var t,r,n,o,i,a;return e.__super__.bind_bokeh_events.call(this),a=this.frame.get(\"width\"),r=this.frame.get(\"height\"),n=this.canvas.vx_to_sx(this.frame.get(\"left\")),i=this.canvas.vy_to_sy(this.frame.get(\"top\")),this.canvas_view.map_div.attr(\"style\",\"top: \"+i+\"px; left: \"+n+\"px; position: absolute\"),this.canvas_view.map_div.attr(\"style\",\"width:\"+a+\"px;\"),this.canvas_view.map_div.attr(\"style\",\"height:\"+r+\"px;\"),this.canvas_view.map_div.width(a+\"px\").height(r+\"px\"),this.initial_zoom=this.model.plot.map_options.zoom,t=function(t){return function(){var e,r,n,o;return n=window.google.maps,r={satellite:n.MapTypeId.SATELLITE,terrain:n.MapTypeId.TERRAIN,roadmap:n.MapTypeId.ROADMAP,hybrid:n.MapTypeId.HYBRID},o=t.model.plot.map_options,e={center:new n.LatLng(o.lat,o.lng),zoom:o.zoom,disableDefaultUI:!0,mapTypeId:r[o.map_type]},null!=o.styles&&(e.styles=JSON.parse(o.styles)),t.map=new n.Map(t.canvas_view.map_div[0],e),n.event.addListenerOnce(t.map,\"idle\",t.setRanges)}}(this),null==window._bokeh_gmap_loads&&(window._bokeh_gmap_loads=[]),null!=window.google&&null!=window.google.maps?s.defer(t):null!=window._bokeh_gmap_callback?window._bokeh_gmap_loads.push(t):(window._bokeh_gmap_loads.push(t),window._bokeh_gmap_callback=function(){return s.each(window._bokeh_gmap_loads,s.defer)},o=document.createElement(\"script\"),o.type=\"text/javascript\",o.src=\"https://maps.googleapis.com/maps/api/js?key=\"+this.model.plot.api_key+\"&callback=_bokeh_gmap_callback\",document.body.appendChild(o))},e.prototype._map_hook=function(t,e){var r,n,o,i;return n=e[0],o=e[1],i=e[2],r=e[3],this.canvas_view.map_div.attr(\"style\",\"top: \"+o+\"px; left: \"+n+\"px;\"),this.canvas_view.map_div.width(i+\"px\").height(r+\"px\")},e.prototype._paint_empty=function(t,e){var r,n,o,i,s,a;return s=this.canvas.get(\"width\"),i=this.canvas.get(\"height\"),o=e[0],a=e[1],n=e[2],r=e[3],t.clearRect(0,0,s,i),t.beginPath(),t.moveTo(0,0),t.lineTo(0,i),t.lineTo(s,i),t.lineTo(s,0),t.lineTo(0,0),t.moveTo(o,a),t.lineTo(o+n,a),t.lineTo(o+n,a+r),t.lineTo(o,a+r),t.lineTo(o,a),t.closePath(),t.fillStyle=this.model.plot.border_fill_color,t.fill()},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.type=\"GMapPlotCanvas\",e.prototype.default_view=o,e.prototype.initialize=function(t,r){return this.use_map=!0,e.__super__.initialize.call(this,t,r)},e}(i.Model),e.exports={Model:n,View:o}},{\"../../core/properties\":\"core/properties\",\"./plot_canvas\":\"models/plots/plot_canvas\",proj4:\"proj4\",underscore:\"underscore\"}],\"models/plots/plot\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x=function(t,e){function r(){this.constructor=t}for(var n in e)w.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},w={}.hasOwnProperty,M=[].slice;g=t(\"underscore\"),b=t(\"../../core/layout/solver\"),m=b.WEAK_EQ,i=b.GE,o=b.EQ,c=b.Strength,f=b.Variable,y=t(\"../../core/logging\").logger,v=t(\"../../core/properties\"),a=t(\"../layouts/layout_dom\"),p=t(\"../annotations/title\"),d=t(\"../tools/toolbar\"),_=t(\"../../common/tool_events\"),u=t(\"./plot_canvas\").Model,n=t(\"../sources/column_data_source\"),s=t(\"../renderers/glyph_renderer\"),p=t(\"../annotations/title\"),h=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return x(e,t),e.prototype.className=\"bk-plot-layout\",e.prototype.bind_bokeh_events=function(){var t;return e.__super__.bind_bokeh_events.call(this),t=\"Title object cannot be replaced. Try changing properties on title to update it after initialization.\",this.listenTo(this.model,\"change:title\",function(e){return function(){return y.warn(t)}}(this))},e.prototype.render=function(){var t,r,n,o;if(e.__super__.render.call(this),\"scale_both\"===this.model.sizing_mode)return r=this.get_width_height(),o=r[0],t=r[1],n=this.model.document.solver(),n.suggest_value(this.model._width,o),n.suggest_value(this.model._height,t),this.$el.css({position:\"absolute\",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width.value(),height:this.model._height.value()})},e.prototype.get_width_height=function(){var t,e,r,n,o,i,s,a,l;return s=this.el.parentNode.clientHeight,a=this.el.parentNode.clientWidth,t=this.model.get_aspect_ratio(),o=a,r=a/t,i=s*t,n=s,o<i?(l=o,e=r):(l=i,e=n),[l,e]},e.prototype.get_height=function(){return this.model._width._value/this.model.get_aspect_ratio()},e.prototype.get_width=function(){return this.model._height._value*this.model.get_aspect_ratio()},e}(a.View),l=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return x(e,t),e.prototype.type=\"Plot\",e.prototype.default_view=h,e.prototype.initialize=function(t){var r,n,o,i,s,a,l,h,c,_,d;for(e.__super__.initialize.call(this,t),a=g.values(this.extra_x_ranges).concat(this.x_range),r=0,o=a.length;r<o;r++)_=a[r],s=_.get(\"plots\"),g.isArray(s)&&(s=s.concat(this),_.set(\"plots\",s));for(l=g.values(this.extra_y_ranges).concat(this.y_range),n=0,i=l.length;n<i;n++)d=l[n],s=d.get(\"plots\"),g.isArray(s)&&(s=s.concat(this),d.set(\"plots\",s));if(this._horizontal=!1,\"left\"!==(h=this.toolbar_location)&&\"right\"!==h||(this._horizontal=!0),null!=this.min_border&&(null==this.min_border_top&&(this.min_border_top=this.min_border),null==this.min_border_bottom&&(this.min_border_bottom=this.min_border),null==this.min_border_left&&(this.min_border_left=this.min_border),null==this.min_border_right&&(this.min_border_right=this.min_border)),null!=this.title&&(c=g.isString(this.title)?new p.Model({text:this.title}):this.title,this.add_layout(c,this.title_location)),this._plot_canvas=new u({plot:this}),this.toolbar.toolbar_location=this.toolbar_location,this.toolbar.toolbar_sticky=this.toolbar_sticky,this.plot_canvas.toolbar=this.toolbar,null==this.width&&(this.width=this.plot_width),null==this.height)return this.height=this.plot_height},Object.defineProperty(e.prototype,\"plot_canvas\",{get:function(){return this._plot_canvas}}),e.prototype._doc_attached=function(){var t,e,r,n,o,i,s,a;for(s=[\"above\",\"below\",\"left\",\"right\"],t=0,n=s.length;t<n;t++)for(a=s[t],r=this.get(a),e=0,o=r.length;e<o;e++)i=r[e],this.plot_canvas.add_renderer_to_canvas_side(i,a);return this.plot_canvas.attach_document(this.document),this._set_orientation_variables(this),this._set_orientation_variables(this.toolbar),this._set_orientation_variables(this.plot_canvas)},e.prototype.add_renderers=function(){var t,e;return t=1<=arguments.length?M.call(arguments,0):[],e=this.get(\"renderers\"),e=e.concat(t),this.set(\"renderers\",e)},e.prototype.add_layout=function(t,e){var r;if(null==e&&(e=\"center\"),null!=t.props.plot&&(t.plot=this),this.add_renderers(t),\"center\"!==e)return r=this.get(e),r.push(t)},e.prototype.add_glyph=function(t,e,r){var o;return null==r&&(r={}),null==e&&(e=new n.Model),r=g.extend({},r,{data_source:e,glyph:t}),o=new s.Model(r),this.add_renderers(o),o},e.prototype.add_tools=function(){var t,e,r,n;return n=1<=arguments.length?M.call(arguments,0):[],e=function(){var e,o,i;for(i=[],e=0,o=n.length;e<o;e++)r=n[e],null!=r.overlay&&this.add_renderers(r.overlay),null!=r.plot?i.push(r):(t=g.clone(r.attributes),t.plot=this,i.push(new r.constructor(t)));return i}.call(this),this.toolbar.tools=this.toolbar.tools.concat(e)},e.prototype.get_aspect_ratio=function(){return this.width/this.height},e.prototype.get_layoutable_children=function(){var t;return t=[this.plot_canvas],null!=this.toolbar_location&&(t=[this.toolbar,this.plot_canvas]),t},e.prototype.get_edit_variables=function(){var t,r,n,o,i;for(r=e.__super__.get_edit_variables.call(this),\"scale_both\"===this.sizing_mode&&(r.push({edit_variable:this._width,strength:c.strong}),r.push({edit_variable:this._height,strength:c.strong})),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_edit_variables());return r},e.prototype.get_constraints=function(){var t,r,n,s,a,l,u,h;for(r=e.__super__.get_constraints.call(this),null!=this.toolbar_location&&(this.toolbar_sticky===!0?r.push(o(this._sizeable,[-1,this.plot_canvas._sizeable])):r.push(o(this._sizeable,[-1,this.plot_canvas._sizeable],[-1,this.toolbar._sizeable])),r.push(o(this._full,[-1,this.plot_canvas._full])),\"above\"===this.toolbar_location&&(h=this.toolbar_sticky===!0?this.plot_canvas._top:this.plot_canvas._dom_top,r.push(o(h,[-1,this.toolbar._dom_top],[-1,this.toolbar._height]))),\"below\"===this.toolbar_location&&(this.toolbar_sticky===!1&&r.push(o(this.toolbar._dom_top,[-1,this.plot_canvas._height],this.toolbar._bottom,[-1,this.toolbar._height])),this.toolbar_sticky===!0&&(r.push(i(this.plot_canvas.below_panel._height,[-1,this.toolbar._height])),r.push(m(this.toolbar._dom_top,[-1,this.plot_canvas._height],this.plot_canvas.below_panel._height)))),\"left\"===this.toolbar_location&&(h=this.toolbar_sticky===!0?this.plot_canvas._left:this.plot_canvas._dom_left,r.push(o(h,[-1,this.toolbar._dom_left],[-1,this.toolbar._width]))),\"right\"===this.toolbar_location&&(this.toolbar_sticky===!1&&r.push(o(this.toolbar._dom_left,[-1,this.plot_canvas._width],this.toolbar._right,[-1,this.toolbar._width])),this.toolbar_sticky===!0&&(r.push(i(this.plot_canvas.right_panel._width,[-1,this.toolbar._width])),r.push(m(this.toolbar._dom_left,[-1,this.plot_canvas._width],this.plot_canvas.right_panel._width)))),\"above\"!==(a=this.toolbar_location)&&\"below\"!==a||r.push(o(this._width,[-1,this.toolbar._width],[-1,this.plot_canvas._width_minus_right])),\"left\"!==(l=this.toolbar_location)&&\"right\"!==l||(r.push(o(this._height,[-1,this.toolbar._height],[-1,this.plot_canvas.above_panel._height])),r.push(o(this.toolbar._dom_top,[-1,this.plot_canvas.above_panel._height])))),null==this.toolbar_location&&(r.push(o(this._width,[-1,this.plot_canvas._width])),r.push(o(this._height,[-1,this.plot_canvas._height]))),u=this.get_layoutable_children(),n=0,s=u.length;n<s;n++)t=u[n],r=r.concat(t.get_constraints());return r},e.prototype.get_constrained_variables=function(){var t;return t=e.__super__.get_constrained_variables.call(this),t=g.extend(t,{\"on-edge-align-top\":this.plot_canvas._top,\"on-edge-align-bottom\":this.plot_canvas._height_minus_bottom,\"on-edge-align-left\":this.plot_canvas._left,\"on-edge-align-right\":this.plot_canvas._width_minus_right,\"box-cell-align-top\":this.plot_canvas._top,\"box-cell-align-bottom\":this.plot_canvas._height_minus_bottom,\"box-cell-align-left\":this.plot_canvas._left,\"box-cell-align-right\":this.plot_canvas._width_minus_right,\"box-equal-size-top\":this.plot_canvas._top,\"box-equal-size-bottom\":this.plot_canvas._height_minus_bottom}),\"fixed\"!==this.sizing_mode&&(t=g.extend(t,{\"box-equal-size-left\":this.plot_canvas._left,\"box-equal-size-right\":this.plot_canvas._width_minus_right})),t},e.prototype._set_orientation_variables=function(t){if(this._horizontal===!1&&(t._sizeable=t._height,t._full=t._width),this._horizontal===!0)return t._sizeable=t._width,t._full=t._height},e.mixins([\"line:outline_\",\"fill:background_\",\"fill:border_\"]),e.define({toolbar:[v.Instance,function(){return new d.Model}],toolbar_location:[v.Location,\"right\"],toolbar_sticky:[v.Bool,!0],plot_width:[v.Number,600],plot_height:[v.Number,600],title:[v.Any,function(){return new p.Model({text:\"\"})}],title_location:[v.Location,\"above\"],h_symmetry:[v.Bool,!0],v_symmetry:[v.Bool,!1],above:[v.Array,[]],below:[v.Array,[]],left:[v.Array,[]],right:[v.Array,[]],renderers:[v.Array,[]],x_range:[v.Instance],extra_x_ranges:[v.Any,{}],y_range:[v.Instance],extra_y_ranges:[v.Any,{}],x_mapper_type:[v.String,\"auto\"],y_mapper_type:[v.String,\"auto\"],tool_events:[v.Instance,function(){return new _.Model}],lod_factor:[v.Number,10],lod_interval:[v.Number,300],lod_threshold:[v.Number,2e3],lod_timeout:[v.Number,500],webgl:[v.Bool,!1],hidpi:[v.Bool,!0],min_border:[v.Number,5],min_border_top:[v.Number,null],min_border_left:[v.Number,null],min_border_bottom:[v.Number,null],min_border_right:[v.Number,null]}),e.override({outline_line_color:\"#e5e5e5\",border_fill_color:\"#ffffff\",background_fill_color:\"#ffffff\"}),e}(a.Model),e.exports={View:h,Model:l}},{\"../../common/tool_events\":\"common/tool_events\",\"../../core/layout/solver\":\"core/layout/solver\",\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../annotations/title\":\"models/annotations/title\",\"../layouts/layout_dom\":\"models/layouts/layout_dom\",\"../renderers/glyph_renderer\":\"models/renderers/glyph_renderer\",\"../sources/column_data_source\":\"models/sources/column_data_source\",\"../tools/toolbar\":\"models/tools/toolbar\",\"./plot_canvas\":\"models/plots/plot_canvas\",underscore:\"underscore\"}],\"models/plots/plot_canvas\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j=function(t,e){return function(){return t.apply(e,arguments)}},T=function(t,e){function r(){this.constructor=t}for(var n in e)S.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},S={}.hasOwnProperty,z=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};m=t(\"underscore\"),n=t(\"jquery\"),o=t(\"../canvas/canvas\"),i=t(\"../canvas/cartesian_frame\"),s=t(\"../ranges/data_range1d\"),u=t(\"../renderers/glyph_renderer\"),c=t(\"../layouts/layout_dom\"),d=t(\"../renderers/renderer\"),g=t(\"../../common/build_views\"),f=t(\"../../common/ui_events\"),y=t(\"../../core/enums\"),h=t(\"../../core/layout/layout_canvas\"),w=t(\"../../core/layout/solver\"),a=w.EQ,l=w.GE,b=t(\"../../core/logging\").logger,x=t(\"../../core/properties\"),M=t(\"../../core/util/throttle\").throttle,k=t(\"../../core/layout/side_panel\").update_constraints,v=null,_=function(t){function e(){return this.remove=j(this.remove,this),this.request_render=j(this.request_render,this),e.__super__.constructor.apply(this,arguments)}return T(e,t),e.prototype.className=\"bk-plot-wrapper\",e.prototype.state={history:[],index:-1},e.prototype.view_options=function(){return m.extend({plot_model:this.model,plot_view:this},this.options)},e.prototype.pause=function(){return this.is_paused=!0},e.prototype.unpause=function(){return this.is_paused=!1,this.request_render()},e.prototype.request_render=function(){this.is_paused||this.throttled_render()},e.prototype.remove=function(){var t,r,n,o;e.__super__.remove.call(this),r=this.tool_views,n=[];for(t in r)o=r[t],n.push(o.remove());return n},e.prototype.initialize=function(t){var r,n,o,i,s,a,l,u,h,c,p;for(e.__super__.initialize.call(this,t),this.pause(),this.visuals={},u=this.model.plot.mixins,r=0,o=u.length;r<o;r++)p=u[r],h=p.split(\":\"),a=h[0],l=h[1],null==l&&(l=\"\"),this.visuals[l+a]=new d.Visuals[a]({obj:this.model.plot,prefix:l});for(this._initial_state_info={range:null,selection:{},dimensions:{width:this.mget(\"canvas\").get(\"width\"),height:this.mget(\"canvas\").get(\"height\")}},this.frame=this.mget(\"frame\"),this.x_range=this.frame.get(\"x_ranges\")[\"default\"],this.y_range=this.frame.get(\"y_ranges\")[\"default\"],this.xmapper=this.frame.get(\"x_mappers\")[\"default\"],this.ymapper=this.frame.get(\"y_mappers\")[\"default\"],this.canvas=this.mget(\"canvas\"),this.canvas_view=new this.canvas.default_view({model:this.canvas}),this.$el.append(this.canvas_view.el),this.canvas_view.render(!0),(this.model.plot.webgl||window.location.search.indexOf(\"webgl=1\")>0)&&window.location.search.indexOf(\"webgl=0\")===-1&&this.init_webgl(),this.throttled_render=M(this.render,15),null==this.model.document._unrendered_plots&&(this.model.document._unrendered_plots={}),this.model.document._unrendered_plots[this.id]=!0,this.ui_event_bus=new f({toolbar:this.mget(\"toolbar\"),hit_area:this.canvas_view.$el}),this.renderer_views={},this.tool_views={},this.levels={},c=y.RenderLevel,n=0,i=c.length;n<i;n++)s=c[n],this.levels[s]={};return this.build_levels(),this.bind_bokeh_events(),this.update_dataranges(),this.unpause(),b.debug(\"PlotView initialized\"),this},e.prototype.get_canvas_element=function(){return this.canvas_view.ctx.canvas},e.prototype.init_webgl=function(){var t,e,r;return t=this.canvas_view.ctx,e=v,null==e&&(v=e=document.createElement(\"canvas\"),r={premultipliedAlpha:!0},e.gl=e.getContext(\"webgl\",r)||e.getContext(\"experimental-webgl\",r)),null!=e.gl?t.glcanvas=e:b.warn(\"WebGL is not supported, falling back to 2D canvas.\")},e.prototype.prepare_webgl=function(t,e){var r,n,o,i;if(n=this.canvas_view.ctx,r=this.canvas_view.get_canvas_element(),n.glcanvas)return n.glcanvas.width=r.width,n.glcanvas.height=r.height,i=n.glcanvas.gl,i.viewport(0,0,n.glcanvas.width,n.glcanvas.height),i.clearColor(0,0,0,0),i.clear(i.COLOR_BUFFER_BIT||i.DEPTH_BUFFER_BIT),i.enable(i.SCISSOR_TEST),o=n.glcanvas.height-t*(e[1]+e[3]),i.scissor(t*e[0],o,t*e[2],t*e[3]),i.enable(i.BLEND),i.blendFuncSeparate(i.SRC_ALPHA,i.ONE_MINUS_SRC_ALPHA,i.ONE_MINUS_DST_ALPHA,i.ONE)},e.prototype.blit_webgl=function(t){var e;if(e=this.canvas_view.ctx,e.glcanvas)return b.debug(\"drawing with WebGL\"),e.restore(),e.drawImage(e.glcanvas,0,0),e.save(),e.scale(t,t),e.translate(.5,.5)},e.prototype.update_dataranges=function(){var t,e,r,n,o,i,a,l,u,h,c,p,_,d,f,g,y,v,x,w,M,k,j;n=this.model.frame,e={},f=this.renderer_views;for(a in f)M=f[a],t=null!=(g=M.glyph)&&\"function\"==typeof g.bounds?g.bounds():void 0,null!=t&&(e[a]=t);for(r=!1,o=!1,y=m.values(n.get(\"x_ranges\")),i=0,u=y.length;i<u;i++)k=y[i],k instanceof s.Model&&(k.update(e,0,this.model.id),k.get(\"follow\")&&(r=!0)),null!=k.get(\"bounds\")&&(o=!0);for(v=m.values(n.get(\"y_ranges\")),l=0,h=v.length;l<h;l++)j=v[l],j instanceof s.Model&&(j.update(e,1,this.model.id),j.get(\"follow\")&&(r=!0)),null!=j.get(\"bounds\")&&(o=!0);if(r&&o){for(b.warn(\"Follow enabled so bounds are unset.\"),x=m.values(n.get(\"x_ranges\")),_=0,c=x.length;_<c;_++)k=x[_],k.set(\"bounds\",null);for(w=m.values(n.get(\"y_ranges\")),d=0,p=w.length;d<p;d++)j=w[d],j.set(\"bounds\",null)}return this.range_update_timestamp=Date.now()},e.prototype.map_to_screen=function(t,e,r,n){return null==r&&(r=\"default\"),null==n&&(n=\"default\"),this.frame.map_to_screen(t,e,this.canvas,r,n)},e.prototype.push_state=function(t,e){var r,n;return r=(null!=(n=this.state.history[this.state.index])?n.info:void 0)||{},e=m.extend({},this._initial_state_info,r,e),this.state.history.slice(0,this.state.index+1),this.state.history.push({type:t,info:e}),this.state.index=this.state.history.length-1,this.trigger(\"state_changed\")},e.prototype.clear_state=function(){return this.state={history:[],index:-1},this.trigger(\"state_changed\")},e.prototype.can_undo=function(){return this.state.index>=0},e.prototype.can_redo=function(){return this.state.index<this.state.history.length-1},e.prototype.undo=function(){if(this.can_undo())return this.state.index-=1,this._do_state_change(this.state.index),this.trigger(\"state_changed\")},e.prototype.redo=function(){if(this.can_redo())return this.state.index+=1,this._do_state_change(this.state.index),this.trigger(\"state_changed\")},e.prototype._do_state_change=function(t){var e,r;if(e=(null!=(r=this.state.history[t])?r.info:void 0)||this._initial_state_info,null!=e.range&&this.update_range(e.range),null!=e.selection&&this.update_selection(e.selection),null!=e.dimensions)return this.canvas_view.set_dims([e.dimensions.width,e.dimensions.height])},e.prototype.reset_dimensions=function(){return this.update_dimensions(this.canvas.initial_width,this.canvas.initial_height)},e.prototype.update_dimensions=function(t,e){return this.pause(),this.model.plot.width=t,this.model.plot.height=e,this.model.document.resize(),this.unpause()},e.prototype.get_selection=function(){var t,e,r,n,o,i;for(i=[],r=this.model.plot.renderers,t=0,e=r.length;t<e;t++)n=r[t],n instanceof u.Model&&(o=n.get(\"data_source\").get(\"selected\"),i[n.id]=o);return i},e.prototype.update_selection=function(t){var e,r,n,o,i,s,a;for(o=this.model.plot.renderers,a=[],r=0,n=o.length;r<n;r++)s=o[r],s instanceof u.Model&&(e=s.get(\"data_source\"),null!=t?(i=s.id,z.call(t,i)>=0?a.push(e.set(\"selected\",t[s.id])):a.push(void 0)):a.push(e.get(\"selection_manager\").clear()));return a},e.prototype.reset_selection=function(){return this.update_selection(null)},e.prototype._update_ranges_together=function(t){var e,r,n,o,i,s,a,l,u,h;for(h=1,e=0,n=t.length;e<n;e++)s=t[e],u=s[0],i=s[1],h=Math.min(h,this._get_weight_to_constrain_interval(u,i));if(h<1){for(l=[],r=0,o=t.length;r<o;r++)a=t[r],u=a[0],i=a[1],i.start=h*i.start+(1-h)*u.get(\"start\"),l.push(i.end=h*i.end+(1-h)*u.get(\"end\"));return l}},e.prototype._update_ranges_individually=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y;for(n=!1,o=0,s=t.length;o<s;o++)p=t[o],g=p[0],c=p[1],m=g.get(\"start\")>g.get(\"end\"),r||(y=this._get_weight_to_constrain_interval(g,c),y<1&&(c.start=y*c.start+(1-y)*g.get(\"start\"),c.end=y*c.end+(1-y)*g.get(\"end\"))),null!=g.get(\"bounds\")&&(u=g.get(\"bounds\")[0],l=g.get(\"bounds\")[1],h=Math.abs(c.end-c.start),m?(null!=u&&u>=c.end&&(n=!0,c.end=u,null==e&&null==r||(c.start=u+h)),null!=l&&l<=c.start&&(n=!0,c.start=l,null==e&&null==r||(c.end=l-h))):(null!=u&&u>=c.start&&(n=!0,c.start=u,null==e&&null==r||(c.end=u+h)),null!=l&&l<=c.end&&(n=!0,c.end=l,null==e&&null==r||(c.start=l-h))));if(!r||!n){for(f=[],i=0,a=t.length;i<a;i++)_=t[i],g=_[0],c=_[1],g.have_updated_interactively=!0,g.get(\"start\")!==c.start||g.get(\"end\")!==c.end?(g.set(c),f.push(null!=(d=g.get(\"callback\"))?d.execute(g):void 0)):f.push(void 0);return f}},e.prototype._get_weight_to_constrain_interval=function(t,e){var r,n,o,i,s,a,l,u,h;return s=t.get(\"min_interval\"),n=t.get(\"max_interval\"),h=1,null!=t.get(\"bounds\")&&(u=t.get(\"bounds\"),i=u[0],r=u[1],null!=i&&null!=r&&(o=Math.abs(r-i),n=null!=n?Math.min(n,o):o)),null==s&&null==n||(l=Math.abs(t.get(\"end\")-t.get(\"start\")),a=Math.abs(e.end-e.start),s>0&&a<s&&(h=(l-s)/(l-a)),n>0&&a>n&&(h=(n-l)/(a-l)),h=Math.max(0,Math.min(1,h))),h},e.prototype.update_range=function(t,e,r){var n,o,i,s,a,l,u,h,c;if(this.pause,null==t){i=this.frame.get(\"x_ranges\");for(n in i)c=i[n],c.reset(),null!=(s=c.get(\"callback\"))&&s.execute(c);a=this.frame.get(\"y_ranges\");for(n in a)c=a[n],c.reset(),null!=(l=c.get(\"callback\"))&&l.execute(c);this.update_dataranges()}else{o=[],u=this.frame.get(\"x_ranges\");for(n in u)c=u[n],o.push([c,t.xrs[n]]);h=this.frame.get(\"y_ranges\");for(n in h)c=h[n],o.push([c,t.yrs[n]]);r&&this._update_ranges_together(o),this._update_ranges_individually(o,e,r)}return this.unpause()},e.prototype.reset_range=function(){return this.update_range(null)},e.prototype.build_levels=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,y,v,b,x;for(p=this.model.plot.renderers,c=this.model.plot.toolbar.tools,e=0,n=c.length;e<n;e++)f=c[e],d=f.get(\"synthetic_renderers\"),p=p.concat(d);for(h=m.keys(this.renderer_views),x=g(this.renderer_views,p,this.view_options()),_=m.difference(h,m.pluck(p,\"id\")),r=0,o=_.length;r<o;r++)t=_[r],delete this.levels.glyph[t];for(v=g(this.tool_views,this.model.plot.toolbar.tools,this.view_options()),l=0,i=x.length;l<i;l++)b=x[l],a=b.mget(\"level\"),this.levels[a][b.model.id]=b,b.bind_bokeh_events();for(u=0,s=v.length;u<s;u++)y=v[u],a=y.mget(\"level\"),this.levels[a][y.model.id]=y,y.bind_bokeh_events(),this.ui_event_bus.register_tool(y);return this},e.prototype.bind_bokeh_events=function(){var t,e,r,n;e=this.model.frame.get(\"x_ranges\");for(t in e)n=e[t],this.listenTo(n,\"change\",this.request_render);r=this.model.frame.get(\"y_ranges\");for(t in r)n=r[t],this.listenTo(n,\"change\",this.request_render);return this.listenTo(this.model.plot,\"change:renderers\",this.build_levels),this.listenTo(this.model.plot.toolbar,\"change:tools\",this.build_levels),this.listenTo(this.model.plot,\"change\",this.request_render),this.listenTo(this.model.plot,\"destroy\",function(t){return function(){return t.remove()}}(this)),this.listenTo(this.model.plot.document.solver(),\"layout_update\",function(t){return function(){return t.request_render()}}(this)),this.listenTo(this.model.plot.document.solver(),\"resize\",function(t){return function(){return t.resize()}}(this)),this.listenTo(this.canvas,\"change:pixel_ratio\",function(t){return function(){return t.request_render()}}(this))},e.prototype.set_initial_range=function(){var t,e,r,n,o,i,s;t=!0,i={},r=this.frame.get(\"x_ranges\");for(e in r){if(o=r[e],null==o.get(\"start\")||null==o.get(\"end\")||m.isNaN(o.get(\"start\")+o.get(\"end\"))){t=!1;break}i[e]={start:o.get(\"start\"),end:o.get(\"end\")}}if(t){s={},n=this.frame.get(\"y_ranges\");for(e in n){if(o=n[e],null==o.get(\"start\")||null==o.get(\"end\")||m.isNaN(o.get(\"start\")+o.get(\"end\"))){t=!1;break}s[e]={start:o.get(\"start\"),end:o.get(\"end\")}}}return t?(this._initial_state_info.range=this.initial_range_info={xrs:i,yrs:s},b.debug(\"initial ranges set\")):b.warn(\"could not set initial ranges\")},e.prototype.render=function(t){var e,r,o,i,s,a,l;if(null==t&&(t=!1),b.trace(\"PlotCanvas.render(force_canvas=\"+t+\") for \"+this.model.id),null!=this.model.document){Date.now()-this.interactive_timestamp<this.model.plot.lod_interval?(this.interactive=!0,i=this.model.plot.lod_timeout,setTimeout(function(t){return function(){return t.interactive&&Date.now()-t.interactive_timestamp>i&&(t.interactive=!1),t.request_render()}}(this),i)):this.interactive=!1,a=this.renderer_views;for(o in a)if(l=a[o],null==this.range_update_timestamp||l.set_data_timestamp>this.range_update_timestamp){this.update_dataranges();break}return this.update_constraints(),this.model.get(\"frame\")._update_mappers(),e=this.canvas_view.ctx,e.pixel_ratio=s=this.canvas_view.pixel_ratio,e.save(),e.scale(s,s),e.translate(.5,.5),r=[this.canvas.vx_to_sx(this.frame.get(\"left\")),this.canvas.vy_to_sy(this.frame.get(\"top\")),this.frame.get(\"width\"),this.frame.get(\"height\")],this._map_hook(e,r),this._paint_empty(e,r),this.prepare_webgl(s,r),e.save(),this.visuals.outline_line.doit&&(this.visuals.outline_line.set_value(e),e.strokeRect.apply(e,r)),e.restore(),this._render_levels(e,[\"image\",\"underlay\",\"glyph\"],r),this.blit_webgl(s),this._render_levels(e,[\"annotation\"],r),this._render_levels(e,[\"overlay\",\"tool\"]),null==this.initial_range_info&&this.set_initial_range(),e.restore(),null!=this.model.document._unrendered_plots&&(delete this.model.document._unrendered_plots[this.id],m.isEmpty(this.model.document._unrendered_plots))?(this.model.document._unrendered_plots=null,m.delay(n.proxy(this.model.document.resize,this.model.document),1)):void 0;\n}},e.prototype.resize=function(){var t,e,r,n;n=this.model._width._value,e=this.model._height._value,this.canvas_view.set_dims([n,e],!0),this.canvas_view.prepare_canvas();try{this.update_constraints()}catch(t){r=t}return this.$el.css({position:\"absolute\",left:this.model._dom_left._value,top:this.model._dom_top._value,width:this.model._width._value,height:this.model._height._value})},e.prototype.update_constraints=function(){var t,e,r,n;r=this.model.document.solver(),r.suggest_value(this.frame._width,this.canvas.get(\"width\")-1),r.suggest_value(this.frame._height,this.canvas.get(\"height\")-1),e=this.renderer_views;for(t in e)n=e[t],null!=n.model.panel&&k(n);return r.update_variables(!1)},e.prototype._render_levels=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,g;for(t.save(),null!=r&&(t.beginPath(),t.rect.apply(t,r),t.clip(),t.beginPath()),o={},p=this.model.plot.renderers,n=i=0,a=p.length;i<a;n=++i)_=p[n],o[_.id]=n;for(g=function(t){return o[t.model.id]},s=0,l=e.length;s<l;s++)for(h=e[s],f=m.sortBy(m.values(this.levels[h]),g),c=0,u=f.length;c<u;c++)d=f[c],d.render();return t.restore()},e.prototype._map_hook=function(t,e){},e.prototype._paint_empty=function(t,e){if(t.clearRect(0,0,this.canvas_view.mget(\"width\"),this.canvas_view.mget(\"height\")),this.visuals.border_fill.doit&&(this.visuals.border_fill.set_value(t),t.fillRect(0,0,this.canvas_view.mget(\"width\"),this.canvas_view.mget(\"height\")),t.clearRect.apply(t,e)),this.visuals.background_fill.doit)return this.visuals.background_fill.set_value(t),t.fillRect.apply(t,e)},e}(d.View),p=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return T(e,t),e.prototype.type=\"PlotCanvas\",e.prototype.default_view=_,e.prototype.initialize=function(t,r){var n;return e.__super__.initialize.call(this,t,r),this.canvas=new o.Model({map:null!=(n=this.use_map)&&n,initial_width:this.plot.plot_width,initial_height:this.plot.plot_height,use_hidpi:this.plot.hidpi}),this.frame=new i.Model({x_range:this.plot.x_range,extra_x_ranges:this.plot.extra_x_ranges,x_mapper_type:this.plot.x_mapper_type,y_range:this.plot.y_range,extra_y_ranges:this.plot.extra_y_ranges,y_mapper_type:this.plot.y_mapper_type}),this.above_panel=new h.Model,this.below_panel=new h.Model,this.left_panel=new h.Model,this.right_panel=new h.Model,b.debug(\"PlotCanvas initialized\")},e.prototype.add_renderer_to_canvas_side=function(t,e){if(\"center\"!==e)return t.add_panel(e)},e.prototype._doc_attached=function(){return this.canvas.attach_document(this.document),this.frame.attach_document(this.document),this.above_panel.attach_document(this.document),this.below_panel.attach_document(this.document),this.left_panel.attach_document(this.document),this.right_panel.attach_document(this.document),b.debug(\"PlotCanvas attached to document\")},e.override({sizing_mode:\"stretch_both\"}),e.internal({plot:[x.Instance],toolbar:[x.Instance],canvas:[x.Instance],frame:[x.Instance]}),e.prototype.get_layoutable_children=function(){var t,e,r,n,o,i,s,a,l;for(t=[this.above_panel,this.below_panel,this.left_panel,this.right_panel,this.canvas,this.frame],a=[\"above\",\"below\",\"left\",\"right\"],e=0,o=a.length;e<o;e++)for(l=a[e],n=this.plot.get(l),r=0,i=n.length;r<i;r++)s=n[r],null!=s.panel&&t.push(s.panel);return t},e.prototype.get_edit_variables=function(){var t,e,r,n,o;for(e=[],o=this.get_layoutable_children(),r=0,n=o.length;r<n;r++)t=o[r],e=e.concat(t.get_edit_variables());return e},e.prototype.get_constraints=function(){var t,r,n,o,i;for(r=e.__super__.get_constraints.call(this),r=r.concat(this._get_constant_constraints()),r=r.concat(this._get_side_constraints()),i=this.get_layoutable_children(),n=0,o=i.length;n<o;n++)t=i[n],r=r.concat(t.get_constraints());return r},e.prototype._get_constant_constraints=function(){var t,e,r,n,o;return o=this.plot.min_border_top,e=this.plot.min_border_bottom,r=this.plot.min_border_left,n=this.plot.min_border_right,t=[],t.push(l(this.above_panel._height,-o)),t.push(l(this.below_panel._height,-e)),t.push(l(this.left_panel._width,-r)),t.push(l(this.right_panel._width,-n)),t.push(a(this.above_panel._top,[-1,this.canvas._top])),t.push(a(this.above_panel._bottom,[-1,this.frame._top])),t.push(a(this.below_panel._bottom,[-1,this.canvas._bottom])),t.push(a(this.below_panel._top,[-1,this.frame._bottom])),t.push(a(this.left_panel._left,[-1,this.canvas._left])),t.push(a(this.left_panel._right,[-1,this.frame._left])),t.push(a(this.right_panel._right,[-1,this.canvas._right])),t.push(a(this.right_panel._left,[-1,this.frame._right])),t.push(a(this.above_panel._height,[-1,this._top])),t.push(a(this.above_panel._height,[-1,this.canvas._top],this.frame._top)),t.push(a(this.below_panel._height,[-1,this._height],this._bottom)),t.push(a(this.below_panel._height,[-1,this.frame._bottom])),t.push(a(this.left_panel._width,[-1,this._left])),t.push(a(this.left_panel._width,[-1,this.frame._left])),t.push(a(this.right_panel._width,[-1,this._width],this._right)),t.push(a(this.right_panel._width,[-1,this.canvas._right],this.frame._right)),t},e.prototype._get_side_constraints=function(){var t,e,r,n,o,i,s,l,u,h;for(t=[],u=[\"above\",\"below\",\"left\",\"right\"],e=0,i=u.length;e<i;e++){for(h=u[e],o=this.plot.get(h),n=this.frame,r=0,s=o.length;r<s;r++)l=o[r],\"above\"===h&&t.push(a(n.panel._top,[-1,l.panel._bottom])),\"below\"===h&&t.push(a(n.panel._bottom,[-1,l.panel._top])),\"left\"===h&&t.push(a(n.panel._left,[-1,l.panel._right])),\"right\"===h&&t.push(a(n.panel._right,[-1,l.panel._left])),n=l;0!==o.length&&(\"above\"===h&&t.push(a(n.panel._top,[-1,this.above_panel._top])),\"below\"===h&&t.push(a(n.panel._bottom,[-1,this.below_panel._bottom])),\"left\"===h&&t.push(a(n.panel._left,[-1,this.left_panel._left])),\"right\"===h&&t.push(a(n.panel._right,[-1,this.right_panel._right])))}return t},e.prototype.plot_canvas=function(){return this},e}(c.Model),e.exports={Model:p,View:_}},{\"../../common/build_views\":\"common/build_views\",\"../../common/ui_events\":\"common/ui_events\",\"../../core/enums\":\"core/enums\",\"../../core/layout/layout_canvas\":\"core/layout/layout_canvas\",\"../../core/layout/side_panel\":\"core/layout/side_panel\",\"../../core/layout/solver\":\"core/layout/solver\",\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../core/util/throttle\":\"core/util/throttle\",\"../canvas/canvas\":\"models/canvas/canvas\",\"../canvas/cartesian_frame\":\"models/canvas/cartesian_frame\",\"../layouts/layout_dom\":\"models/layouts/layout_dom\",\"../ranges/data_range1d\":\"models/ranges/data_range1d\",\"../renderers/glyph_renderer\":\"models/renderers/glyph_renderer\",\"../renderers/renderer\":\"models/renderers/renderer\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/ranges/data_range\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),o=t(\"./range\"),s=t(\"../../core/properties\"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type=\"DataRange\",e.define({names:[s.Array,[]],renderers:[s.Array,[]]}),e}(o.Model),e.exports={Model:n}},{\"../../core/properties\":\"core/properties\",\"./range\":\"models/ranges/range\",underscore:\"underscore\"}],\"models/ranges/data_range1d\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;i=t(\"underscore\"),n=t(\"./data_range\"),a=t(\"../../core/logging\").logger,l=t(\"../../core/properties\"),s=t(\"../../core/util/bbox\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.type=\"DataRange1d\",e.define({start:[l.Number],end:[l.Number],range_padding:[l.Number,.1],flipped:[l.Bool,!1],follow:[l.String],follow_interval:[l.Number],default_span:[l.Number,2],bounds:[l.Any],min_interval:[l.Any],max_interval:[l.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"min\",function(){return Math.min(this.get(\"start\"),this.get(\"end\"))},!0),this.add_dependencies(\"min\",this,[\"start\",\"end\"]),this.define_computed_property(\"max\",function(){return Math.max(this.get(\"start\"),this.get(\"end\"))},!0),this.add_dependencies(\"max\",this,[\"start\",\"end\"]),this.plot_bounds={},this.have_updated_interactively=!1,this._initial_start=this.get(\"start\"),this._initial_end=this.get(\"end\"),this._initial_range_padding=this.get(\"range_padding\"),this._initial_follow=this.get(\"follow\"),this._initial_follow_interval=this.get(\"follow_interval\"),this._initial_default_span=this.get(\"default_span\")},e.prototype.computed_renderers=function(){var t,e,r,n,o,i,s,l,u,h,c;if(i=this.get(\"names\"),h=this.get(\"renderers\"),0===h.length)for(u=this.get(\"plots\"),e=0,n=u.length;e<n;e++)s=u[e],t=s.get(\"renderers\"),c=function(){var e,r,n;for(n=[],e=0,r=t.length;e<r;e++)l=t[e],\"GlyphRenderer\"===l.type&&n.push(l);return n}(),h=h.concat(c);for(i.length>0&&(h=function(){var t,e,r;for(r=[],t=0,e=h.length;t<e;t++)l=h[t],i.indexOf(l.get(\"name\"))>=0&&r.push(l);return r}()),a.debug(\"computed \"+h.length+\" renderers for DataRange1d \"+this.id),r=0,o=h.length;r<o;r++)l=h[r],a.trace(\" - \"+l.type+\" \"+l.id);return h},e.prototype._compute_plot_bounds=function(t,e){var r,n,o,i;for(i=s.empty(),r=0,n=t.length;r<n;r++)o=t[r],null!=e[o.id]&&(i=s.union(i,e[o.id]));return i},e.prototype._compute_min_max=function(t,e){var r,n,o,i,a,l,u;i=s.empty();for(r in t)u=t[r],i=s.union(i,u);return 0===e?(a=[i.minX,i.maxX],o=a[0],n=a[1]):(l=[i.minY,i.maxY],o=l[0],n=l[1]),[o,n]},e.prototype._compute_range=function(t,e){var r,n,o,i,s,a,l,u,h,c;return s=this.get(\"range_padding\"),null!=s&&s>0?(h=e===t?this.get(\"default_span\"):(e-t)*(1+s),r=(e+t)/2,a=[r-h/2,r+h/2],c=a[0],n=a[1]):(l=[t,e],c=l[0],n=l[1]),i=1,this.get(\"flipped\")&&(u=[n,c],c=u[0],n=u[1],i=-1),o=this.get(\"follow_interval\"),null!=o&&Math.abs(c-n)>o&&(\"start\"===this.get(\"follow\")?n=c+i*o:\"end\"===this.get(\"follow\")&&(c=n-i*o)),[c,n]},e.prototype.update=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_;if(!this.have_updated_interactively)return p=this.computed_renderers(),this.plot_bounds[r]=this._compute_plot_bounds(p,t),u=this._compute_min_max(this.plot_bounds,e),a=u[0],s=u[1],h=this._compute_range(a,s),_=h[0],i=h[1],null!=this._initial_start&&(_=this._initial_start),null!=this._initial_end&&(i=this._initial_end),c=[this.get(\"start\"),this.get(\"end\")],o=c[0],n=c[1],_===o&&i===n||(l={},_!==o&&(l.start=_),i!==n&&(l.end=i),this.set(l)),\"auto\"===this.get(\"bounds\")?this.set(\"bounds\",[_,i]):void 0},e.prototype.reset=function(){return this.have_updated_interactively=!1,this.set({range_padding:this._initial_range_padding,follow:this._initial_follow,follow_interval:this._initial_follow_interval,default_span:this._initial_default_span})},e}(n.Model),e.exports={Model:o}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../core/util/bbox\":\"core/util/bbox\",\"./data_range\":\"models/ranges/data_range\",underscore:\"underscore\"}],\"models/ranges/factor_range\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),o=t(\"./range\"),s=t(\"../../core/properties\"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type=\"FactorRange\",e.define({offset:[s.Number,0],factors:[s.Array,[]],bounds:[s.Any],min_interval:[s.Any],max_interval:[s.Any]}),e.internal({_bounds_as_factors:[s.Any],start:[s.Number],end:[s.Number]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),null!=this.get(\"bounds\")&&\"auto\"!==this.get(\"bounds\")?this.set(\"_bounds_as_factors\",this.get(\"bounds\")):this.set(\"_bounds_as_factors\",this.get(\"factors\")),this._init(),this.define_computed_property(\"min\",function(){return this.get(\"start\")},!1),this.add_dependencies(\"min\",this,[\"factors\",\"offset\"]),this.define_computed_property(\"max\",function(){return this.get(\"end\")},!1),this.add_dependencies(\"max\",this,[\"factors\",\"offset\"]),this.listenTo(this,\"change:factors\",this._update_factors),this.listenTo(this,\"change:offset\",this._init)},e.prototype.reset=function(){return this._init()},e.prototype._update_factors=function(){return this.set(\"_bounds_as_factors\",this.get(\"factors\")),this._init()},e.prototype._init=function(){var t,e,r;if(e=this.get(\"factors\"),null!=this.get(\"bounds\")&&\"auto\"!==this.get(\"bounds\")&&(e=this.get(\"_bounds_as_factors\"),this.set(\"factors\",e)),r=.5+this.get(\"offset\"),t=e.length+r,this.set(\"start\",r),this.set(\"end\",t),null!=this.get(\"bounds\"))return this.set(\"bounds\",[r,t])},e}(o.Model),e.exports={Model:n}},{\"../../core/properties\":\"core/properties\",\"./range\":\"models/ranges/range\",underscore:\"underscore\"}],\"models/ranges/range\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),n=t(\"../../model\"),s=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type=\"Range\",e.define({callback:[s.Instance]}),e.internal({plots:[s.Array,[]]}),e.prototype.reset=function(){},e}(n),e.exports={Model:o}},{\"../../core/properties\":\"core/properties\",\"../../model\":\"model\",underscore:\"underscore\"}],\"models/ranges/range1d\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),n=t(\"./range\"),s=t(\"../../core/properties\"),o=function(t){function e(){var t,r;return this instanceof e?e.__super__.constructor.apply(this,arguments):(r=arguments[0],t=arguments[1],new e({start:r,end:t}))}return a(e,t),e.prototype.type=\"Range1d\",e.define({start:[s.Number,0],end:[s.Number,1],bounds:[s.Any],min_interval:[s.Any],max_interval:[s.Any]}),e.prototype._set_auto_bounds=function(){var t,e;if(\"auto\"===this.get(\"bounds\"))return e=Math.min(this._initial_start,this._initial_end),t=Math.max(this._initial_start,this._initial_end),this.set(\"bounds\",[e,t])},e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"min\",function(){return Math.min(this.get(\"start\"),this.get(\"end\"))},!0),this.add_dependencies(\"min\",this,[\"start\",\"end\"]),this.define_computed_property(\"max\",function(){return Math.max(this.get(\"start\"),this.get(\"end\"))},!0),this.add_dependencies(\"max\",this,[\"start\",\"end\"]),this._initial_start=this.get(\"start\"),this._initial_end=this.get(\"end\"),this._set_auto_bounds()},e.prototype.reset=function(){return this.set({start:this._initial_start,end:this._initial_end}),this._set_auto_bounds()},e}(n.Model),e.exports={Model:o}},{\"../../core/properties\":\"core/properties\",\"./range\":\"models/ranges/range\",underscore:\"underscore\"}],\"models/renderers/glyph_renderer\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t(\"underscore\"),s=t(\"./renderer\"),i=t(\"../sources/remote_data_source\"),l=t(\"../../core/logging\").logger,u=t(\"../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){var r,n,o,s,l,u,h,c,p;if(e.__super__.initialize.call(this,t),r=this.mget(\"glyph\"),s=a.contains(r.mixins,\"fill\"),l=a.contains(r.mixins,\"line\"),o=a.omit(a.clone(r.attributes),\"id\"),h=function(t){var e;return e=a.clone(o),s&&a.extend(e,t.fill),l&&a.extend(e,t.line),new r.constructor(e)},this.glyph=this.build_glyph_view(r),p=this.mget(\"selection_glyph\"),null==p&&(p=h(this.model.selection_defaults)),this.selection_glyph=this.build_glyph_view(p),c=this.mget(\"nonselection_glyph\"),null==c&&(c=h(this.model.nonselection_defaults)),this.nonselection_glyph=this.build_glyph_view(c),u=this.mget(\"hover_glyph\"),null!=u&&(this.hover_glyph=this.build_glyph_view(u)),n=h(this.model.decimated_defaults),this.decimated_glyph=this.build_glyph_view(n),this.xmapper=this.plot_view.frame.get(\"x_mappers\")[this.mget(\"x_range_name\")],this.ymapper=this.plot_view.frame.get(\"y_mappers\")[this.mget(\"y_range_name\")],this.set_data(!1),this.mget(\"data_source\")instanceof i.Model)return this.mget(\"data_source\").setup(this.plot_view,this.glyph)},e.prototype.build_glyph_view=function(t){return new t.default_view({model:t,renderer:this,plot_view:this.plot_view,plot_model:this.plot_model})},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.request_render),this.listenTo(this.mget(\"data_source\"),\"change\",this.set_data),this.listenTo(this.mget(\"data_source\"),\"patch\",this.set_data),this.listenTo(this.mget(\"data_source\"),\"stream\",this.set_data),this.listenTo(this.mget(\"data_source\"),\"select\",this.request_render),null!=this.hover_glyph&&this.listenTo(this.mget(\"data_source\"),\"inspect\",this.request_render),this.listenTo(this.mget(\"glyph\"),\"propchange\",function(){return this.glyph.set_visuals(this.mget(\"data_source\")),this.request_render()})},e.prototype.have_selection_glyphs=function(){return null!=this.selection_glyph&&null!=this.nonselection_glyph},e.prototype.set_data=function(t,e){var r,n,o,i,s,a,u,h,c;for(null==t&&(t=!0),c=Date.now(),h=this.mget(\"data_source\"),this.glyph.model.set({x_range_name:this.mget(\"x_range_name\"),y_range_name:this.mget(\"y_range_name\")},{silent:!0}),this.glyph.set_data(h,e),this.glyph.set_visuals(h),this.decimated_glyph.set_visuals(h),this.have_selection_glyphs()&&(this.selection_glyph.set_visuals(h),this.nonselection_glyph.set_visuals(h)),null!=this.hover_glyph&&this.hover_glyph.set_visuals(h),i=h.get_length(),null==i&&(i=1),this.all_indices=function(){u=[];for(var t=0;0<=i?t<i:t>i;0<=i?t++:t--)u.push(t);return u}.apply(this),s=this.plot_model.plot.lod_factor,this.decimated=[],n=o=0,a=Math.floor(this.all_indices.length/s);0<=a?o<a:o>a;n=0<=a?++o:--o)this.decimated.push(this.all_indices[n*s]);if(r=Date.now()-c,l.debug(this.glyph.model.type+\" GlyphRenderer (\"+this.model.id+\"): set_data finished in \"+r+\"ms\"),this.set_data_timestamp=Date.now(),t)return this.request_render()},e.prototype.render=function(){var t,e,r,n,o,i,s,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S;if(this.model.visible!==!1){if(M=Date.now(),s=this.glyph.glglyph,k=Date.now(),this.glyph.map_data(),e=Date.now()-M,j=Date.now(),c=s?this.all_indices:this.glyph._mask_data(this.all_indices),r=Date.now()-j,t=this.plot_view.canvas_view.ctx,t.save(),b=this.mget(\"data_source\").get(\"selected\"),b=b&&0!==b.length?b[\"0d\"].glyph?c:b[\"1d\"].indices.length>0?b[\"1d\"].indices:[]:[],p=this.mget(\"data_source\").get(\"inspected\"),p=p&&0!==p.length?p[\"0d\"].glyph?c:p[\"1d\"].indices.length>0?p[\"1d\"].indices:[]:[],g=this.plot_model.plot.lod_threshold,this.plot_view.interactive&&!s&&null!=g&&this.all_indices.length>g?(c=this.decimated,u=this.decimated_glyph,v=this.decimated_glyph,w=this.selection_glyph):(u=this.glyph,v=this.nonselection_glyph,w=this.selection_glyph),null!=this.hover_glyph&&p.length&&(c=a.without.bind(null,c).apply(null,p)),b.length&&this.have_selection_glyphs()){for(S=Date.now(),x={},_=0,f=b.length;_<f;_++)h=b[_],x[h]=!0;for(b=new 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h(e,t),e.prototype.default_view=o,e.prototype.type=\"GlyphRenderer\",e.define({x_range_name:[u.String,\"default\"],y_range_name:[u.String,\"default\"],data_source:[u.Instance],glyph:[u.Instance],hover_glyph:[u.Instance],nonselection_glyph:[u.Instance],selection_glyph:[u.Instance]}),e.override({level:\"glyph\"}),e.prototype.selection_defaults={fill:{},line:{}},e.prototype.decimated_defaults={fill:{fill_alpha:.3,fill_color:\"grey\"},line:{line_alpha:.3,line_color:\"grey\"}},e.prototype.nonselection_defaults={fill:{fill_alpha:.2,line_alpha:.2},line:{}},e}(s.Model),e.exports={Model:n,View:o}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../sources/remote_data_source\":\"models/sources/remote_data_source\",\"./renderer\":\"models/renderers/renderer\",underscore:\"underscore\"}],\"models/renderers/guide_renderer\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return 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l},e.prototype.cache_select=function(t,e){var r,n,o;return r=this.get(\"obj\"),n=this.get(\"prefix\"),o=r.properties[n+t],u.isUndefined(o.spec.value)?this.cache[t]=this.cache[t+\"_array\"][e]:this.cache[t]=o.spec.value},e}(n.Model),p=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return x(e,t),e.prototype.attrs=u.keys(g.line()),e.prototype.do_attr=\"line_color\",e.prototype.set_value=function(t){return t.strokeStyle=this.line_color.value(),t.globalAlpha=this.line_alpha.value(),t.lineWidth=this.line_width.value(),t.lineJoin=this.line_join.value(),t.lineCap=this.line_cap.value(),t.setLineDash(this.line_dash.value()),t.setLineDashOffset(this.line_dash_offset.value())},e.prototype.set_vectorize=function(t,e){if(this.cache_select(\"line_color\",e),t.strokeStyle!==this.cache.line_color&&(t.strokeStyle=this.cache.line_color),this.cache_select(\"line_alpha\",e),t.globalAlpha!==this.cache.line_alpha&&(t.globalAlpha=this.cache.line_alpha),this.cache_select(\"line_width\",e),t.lineWidth!==this.cache.line_width&&(t.lineWidth=this.cache.line_width),this.cache_select(\"line_join\",e),t.lineJoin!==this.cache.line_join&&(t.lineJoin=this.cache.line_join),this.cache_select(\"line_cap\",e),t.lineCap!==this.cache.line_cap&&(t.lineCap=this.cache.line_cap),this.cache_select(\"line_dash\",e),t.getLineDash()!==this.cache.line_dash&&t.setLineDash(this.cache.line_dash),this.cache_select(\"line_dash_offset\",e),t.getLineDashOffset()!==this.cache.line_dash_offset)return 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r,t.__super__=e.prototype,t},p={}.hasOwnProperty;a=t(\"underscore\"),i=t(\"./image_pool\"),s=t(\"../renderers/renderer\"),l=t(\"../../core/logging\").logger,u=t(\"../../core/properties\"),o=function(t){function e(){return this._on_image_error=h(this._on_image_error,this),this._on_image_load=h(this._on_image_load,this),e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.request_render)},e.prototype.get_extent=function(){return[this.x_range.get(\"start\"),this.y_range.get(\"start\"),this.x_range.get(\"end\"),this.y_range.get(\"end\")]},e.prototype._set_data=function(){return this.map_plot=this.plot_view.model.plot,this.map_canvas=this.plot_view.canvas_view.ctx,this.map_frame=this.plot_view.frame,this.x_range=this.map_plot.get(\"x_range\"),this.x_mapper=this.map_frame.get(\"x_mappers\")[\"default\"],this.y_range=this.map_plot.get(\"y_range\"),this.y_mapper=this.map_frame.get(\"y_mappers\")[\"default\"],this.lastImage=void 0,this.extent=this.get_extent()},e.prototype._map_data=function(){return this.initial_extent=this.get_extent()},e.prototype._on_image_load=function(t){var e;if(e=t.target.image_data,e.img=t.target,e.loaded=!0,this.lastImage=e,this.get_extent().join(\":\")===e.cache_key)return this.request_render()},e.prototype._on_image_error=function(t){var e;return l.error(\"Error loading image: #{e.target.src}\"),e=t.target.image_data,this.mget(\"image_source\").remove_image(e)},e.prototype._create_image=function(t){var e;return e=new Image,e.onload=this._on_image_load,e.onerror=this._on_image_error,e.alt=\"\",e.image_data={bounds:t,loaded:!1,cache_key:t.join(\":\")},this.mget(\"image_source\").add_image(e.image_data),e.src=this.mget(\"image_source\").get_image_url(t[0],t[1],t[2],t[3],Math.ceil(this.map_frame.get(\"height\")),Math.ceil(this.map_frame.get(\"width\"))),e},e.prototype.render=function(t,e,r){var n,o;return null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),n=this.get_extent(),this.render_timer&&clearTimeout(this.render_timer),o=this.mget(\"image_source\").images[n.join(\":\")],null!=o&&o.loaded?void this._draw_image(n.join(\":\")):(null!=this.lastImage&&this._draw_image(this.lastImage.cache_key),null==o?this.render_timer=setTimeout(function(t){return function(){return t._create_image(n)}}(this),125):void 0)},e.prototype._draw_image=function(t){var e,r,n,o,i,s,a,l,u,h,c;if(e=this.mget(\"image_source\").images[t],null!=e)return this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.mget(\"alpha\"),r=this.plot_view.frame.map_to_screen([e.bounds[0]],[e.bounds[3]],this.plot_view.canvas),l=r[0],c=r[1],n=this.plot_view.frame.map_to_screen([e.bounds[2]],[e.bounds[1]],this.plot_view.canvas),a=n[0],h=n[1],l=l[0],c=c[0],a=a[0],h=h[0],i=a-l,o=h-c,s=l,u=c,this.map_canvas.drawImage(e.img,s,u,i,o),this.map_canvas.restore()},e.prototype._set_rect=function(){var t,e,r,n,o;return r=this.plot_model.plot.properties.outline_line_width.value(),e=this.plot_view.canvas.vx_to_sx(this.map_frame.get(\"left\"))+r/2,n=this.plot_view.canvas.vy_to_sy(this.map_frame.get(\"top\"))+r/2,o=this.map_frame.get(\"width\")-r,t=this.map_frame.get(\"height\")-r,this.map_canvas.rect(e,n,o,t),this.map_canvas.clip()},e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.default_view=o,e.prototype.type=\"DynamicImageRenderer\",e.define({alpha:[u.Number,1],image_source:[u.Instance],render_parents:[u.Bool,!0]}),e.override({level:\"underlay\"}),e}(s.Model),e.exports={Model:n,View:o}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../renderers/renderer\":\"models/renderers/renderer\",\"./image_pool\":\"models/tiles/image_pool\",underscore:\"underscore\"}],\"models/tiles/image_pool\":[function(t,e,r){var n;n=function(){function t(){this.images=[]}return t.prototype.pop=function(){var t;return t=this.images.pop(),null!=t?t:new Image},t.prototype.push=function(t){if(!(this.images.length>50))return t.constructor===Array?Array.prototype.push.apply(this.images,t):this.images.push(t)},t}(),e.exports=n},{}],\"models/tiles/image_source\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;i=t(\"underscore\"),s=t(\"../../core/logging\").logger,a=t(\"../../core/properties\"),o=t(\"../../model\"),n=function(t){function e(t){null==t&&(t={}),e.__super__.constructor.apply(this,arguments),this.images={},this.normalize_case()}return l(e,t),e.prototype.type=\"ImageSource\",e.define({url:[a.String,\"\"],extra_url_vars:[a.Any,{}]}),e.prototype.normalize_case=function(){\"Note: should probably be refactored into subclasses.\";var t;return t=this.get(\"url\"),t=t.replace(\"{xmin}\",\"{XMIN}\"),t=t.replace(\"{ymin}\",\"{YMIN}\"),t=t.replace(\"{xmax}\",\"{XMAX}\"),t=t.replace(\"{ymax}\",\"{YMAX}\"),t=t.replace(\"{height}\",\"{HEIGHT}\"),t=t.replace(\"{width}\",\"{WIDTH}\"),this.set(\"url\",t)},e.prototype.string_lookup_replace=function(t,e){var r,n,o;n=t;for(r in e)o=e[r],n=n.replace(\"{\"+r+\"}\",o.toString());return n},e.prototype.add_image=function(t){return this.images[t.cache_key]=t},e.prototype.remove_image=function(t){return delete this.images[t.cache_key]},e.prototype.get_image_url=function(t,e,r,n,o,i){var s;return s=this.string_lookup_replace(this.get(\"url\"),this.get(\"extra_url_vars\")),\ns.replace(\"{XMIN}\",t).replace(\"{YMIN}\",e).replace(\"{XMAX}\",r).replace(\"{YMAX}\",n).replace(\"{WIDTH}\",i).replace(\"{HEIGHT}\",o)},e}(o),e.exports={Model:n}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../model\":\"model\",underscore:\"underscore\"}],\"models/tiles/mercator_tile_source\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;i=t(\"underscore\"),o=t(\"./tile_source\"),s=t(\"../../core/properties\"),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.type=\"MercatorTileSource\",e.define({wrap_around:[s.Bool,!0]}),e.override({x_origin_offset:20037508.34,y_origin_offset:20037508.34,initial_resolution:156543.03392804097}),e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),this._resolutions=function(){var t,e;for(e=[],r=t=0;t<=30;r=++t)e.push(this.get_resolution(r));return e}.call(this)},e.prototype._computed_initial_resolution=function(){return null!=this.get(\"initial_resolution\")?this.get(\"initial_resolution\"):2*Math.PI*6378137/this.get(\"tile_size\")},e.prototype.is_valid_tile=function(t,e,r){return!(!this.get(\"wrap_around\")&&(t<0||t>=Math.pow(2,r)))&&!(e<0||e>=Math.pow(2,r))},e.prototype.retain_children=function(t){var e,r,n,o,i,s,a;o=t.quadkey,n=o.length,r=n+3,i=this.tiles,s=[];for(e in i)a=i[e],0===a.quadkey.indexOf(o)&&a.quadkey.length>n&&a.quadkey.length<=r?s.push(a.retain=!0):s.push(void 0);return s},e.prototype.retain_neighbors=function(t){var e,r,n,o,s,a,l,u,h,c,p,_,d;r=4,s=t.tile_coords,h=s[0],c=s[1],p=s[2],n=function(){var t,e,n,o;for(o=[],_=t=e=h-r,n=h+r;e<=n?t<=n:t>=n;_=e<=n?++t:--t)o.push(_);return o}(),o=function(){var t,e,n,o;for(o=[],d=t=e=c-r,n=c+r;e<=n?t<=n:t>=n;d=e<=n?++t:--t)o.push(d);return o}(),a=this.tiles,l=[];for(e in a)u=a[e],u.tile_coords[2]===p&&i.contains(n,u.tile_coords[0])&&i.contains(o,u.tile_coords[1])?l.push(u.retain=!0):l.push(void 0);return l},e.prototype.retain_parents=function(t){var e,r,n,o,i;r=t.quadkey,n=this.tiles,o=[];for(e in n)i=n[e],o.push(i.retain=0===r.indexOf(i.quadkey));return o},e.prototype.children_by_tile_xyz=function(t,e,r){var n,o,i,s,a,l,u,h,c;for(c=this.calculate_world_x_by_tile_xyz(t,e,r),0!==c&&(l=this.normalize_xyz(t,e,r),t=l[0],e=l[1],r=l[2]),a=this.tile_xyz_to_quadkey(t,e,r),o=[],i=s=0;s<=3;i=s+=1)u=this.quadkey_to_tile_xyz(a+i.toString()),t=u[0],e=u[1],r=u[2],0!==c&&(h=this.denormalize_xyz(t,e,r,c),t=h[0],e=h[1],r=h[2]),n=this.get_tile_meter_bounds(t,e,r),null!=n&&o.push([t,e,r,n]);return o},e.prototype.parent_by_tile_xyz=function(t,e,r){var n,o;return o=this.tile_xyz_to_quadkey(t,e,r),n=o.substring(0,o.length-1),this.quadkey_to_tile_xyz(n)},e.prototype.get_resolution=function(t){return this._computed_initial_resolution()/Math.pow(2,t)},e.prototype.get_resolution_by_extent=function(t,e,r){var n,o;return n=(t[2]-t[0])/r,o=(t[3]-t[1])/e,[n,o]},e.prototype.get_level_by_extent=function(t,e,r){var n,o,i,s,a,l,u,h;for(u=(t[2]-t[0])/r,h=(t[3]-t[1])/e,l=Math.max(u,h),n=0,a=this._resolutions,o=0,i=a.length;o<i;o++){if(s=a[o],l>s){if(0===n)return 0;if(n>0)return n-1}n+=1}},e.prototype.get_closest_level_by_extent=function(t,e,r){var n,o,i,s,a;return s=(t[2]-t[0])/r,a=(t[3]-t[1])/e,o=Math.max(s,a),i=this._resolutions,n=this._resolutions.reduce(function(t,e){return Math.abs(e-o)<Math.abs(t-o)?e:t}),this._resolutions.indexOf(n)},e.prototype.snap_to_zoom=function(t,e,r,n){var o,i,s,a,l,u,h,c,p;return o=this._resolutions[n],i=r*o,s=e*o,u=t[0],p=t[1],l=t[2],c=t[3],a=(i-(l-u))/2,h=(s-(c-p))/2,[u-a,p-h,l+a,c+h]},e.prototype.tms_to_wmts=function(t,e,r){\"Note this works both ways\";return[t,Math.pow(2,r)-1-e,r]},e.prototype.wmts_to_tms=function(t,e,r){\"Note this works both ways\";return[t,Math.pow(2,r)-1-e,r]},e.prototype.pixels_to_meters=function(t,e,r){var n,o,i;return i=this.get_resolution(r),n=t*i-this.get(\"x_origin_offset\"),o=e*i-this.get(\"y_origin_offset\"),[n,o]},e.prototype.meters_to_pixels=function(t,e,r){var n,o,i;return i=this.get_resolution(r),n=(t+this.get(\"x_origin_offset\"))/i,o=(e+this.get(\"y_origin_offset\"))/i,[n,o]},e.prototype.pixels_to_tile=function(t,e){var r,n;return r=Math.ceil(t/parseFloat(this.get(\"tile_size\"))),r=0===r?r:r-1,n=Math.max(Math.ceil(e/parseFloat(this.get(\"tile_size\")))-1,0),[r,n]},e.prototype.pixels_to_raster=function(t,e,r){var n;return n=this.get(\"tile_size\")<<r,[t,n-e]},e.prototype.meters_to_tile=function(t,e,r){var n,o,i;return i=this.meters_to_pixels(t,e,r),n=i[0],o=i[1],this.pixels_to_tile(n,o)},e.prototype.get_tile_meter_bounds=function(t,e,r){var n,o,i,s,a,l;return n=this.pixels_to_meters(t*this.get(\"tile_size\"),e*this.get(\"tile_size\"),r),s=n[0],l=n[1],o=this.pixels_to_meters((t+1)*this.get(\"tile_size\"),(e+1)*this.get(\"tile_size\"),r),i=o[0],a=o[1],null!=s&&null!=l&&null!=i&&null!=a?[s,l,i,a]:void 0},e.prototype.get_tile_geographic_bounds=function(t,e,r){var n,o,i,s,a,l;return n=this.get_tile_meter_bounds(t,e,r),l=this.utils.meters_extent_to_geographic(n),a=l[0],s=l[1],i=l[2],o=l[3],[a,s,i,o]},e.prototype.get_tiles_by_extent=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x;for(null==r&&(r=1),v=t[0],x=t[1],y=t[2],b=t[3],i=this.meters_to_tile(v,x,e),d=i[0],g=i[1],s=this.meters_to_tile(y,b,e),_=s[0],m=s[1],d-=r,g-=r,_+=r,m+=r,c=[],f=n=a=m,l=g;n>=l;f=n+=-1)for(p=o=u=d,h=_;o<=h;p=o+=1)this.is_valid_tile(p,f,e)&&c.push([p,f,e,this.get_tile_meter_bounds(p,f,e)]);return c=this.sort_tiles_from_center(c,[d,g,_,m])},e.prototype.quadkey_to_tile_xyz=function(t){\"Computes tile x, y and z values based on quadKey.\";var e,r,n,o,i,s,a,l;for(i=0,s=0,a=t.length,e=r=o=a;r>0;e=r+=-1)if(l=t.charAt(a-e),n=1<<e-1,\"0\"!==l)if(\"1\"===l)i|=n;else if(\"2\"===l)s|=n;else{if(\"3\"!==l)throw new TypeError(\"Invalid Quadkey: \"+t);i|=n,s|=n}return[i,s,a]},e.prototype.tile_xyz_to_quadkey=function(t,e,r){\"Computes quadkey value based on tile x, y and z values.\";var n,o,i,s,a,l;for(a=\"\",o=i=l=r;i>0;o=i+=-1)n=0,s=1<<o-1,0!==(t&s)&&(n+=1),0!==(e&s)&&(n+=2),a+=n.toString();return a},e.prototype.children_by_tile_xyz=function(t,e,r){var n,o,i,s,a,l;for(a=this.tile_xyz_to_quadkey(t,e,r),o=[],i=s=0;s<=3;i=s+=1)l=this.quadkey_to_tile_xyz(a+i.toString()),t=l[0],e=l[1],r=l[2],n=this.get_tile_meter_bounds(t,e,r),null!=n&&o.push([t,e,r,n]);return o},e.prototype.parent_by_tile_xyz=function(t,e,r){var n,o;return o=this.tile_xyz_to_quadkey(t,e,r),n=o.substring(0,o.length-1),this.quadkey_to_tile_xyz(n)},e.prototype.get_closest_parent_by_tile_xyz=function(t,e,r){var n,o,i,s,a;for(a=this.calculate_world_x_by_tile_xyz(t,e,r),o=this.normalize_xyz(t,e,r),t=o[0],e=o[1],r=o[2],n=this.tile_xyz_to_quadkey(t,e,r);n.length>0;)if(n=n.substring(0,n.length-1),i=this.quadkey_to_tile_xyz(n),t=i[0],e=i[1],r=i[2],s=this.denormalize_xyz(t,e,r,a),t=s[0],e=s[1],r=s[2],this.tile_xyz_to_key(t,e,r)in this.tiles)return[t,e,r];return[0,0,0]},e.prototype.normalize_xyz=function(t,e,r){var n;return this.get(\"wrap_around\")?(n=Math.pow(2,r),[(t%n+n)%n,e,r]):[t,e,r]},e.prototype.denormalize_xyz=function(t,e,r,n){return[t+n*Math.pow(2,r),e,r]},e.prototype.denormalize_meters=function(t,e,r,n){return[t+2*n*Math.PI*6378137,e]},e.prototype.calculate_world_x_by_tile_xyz=function(t,e,r){return Math.floor(t/Math.pow(2,r))},e}(o),e.exports=n},{\"../../core/properties\":\"core/properties\",\"./tile_source\":\"models/tiles/tile_source\",underscore:\"underscore\"}],\"models/tiles/quadkey_tile_source\":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t(\"./mercator_tile_source\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return i(e,t),e.prototype.type=\"QUADKEYTileSource\",e.prototype.get_image_url=function(t,e,r){var n,o,i;return n=this.string_lookup_replace(this.get(\"url\"),this.get(\"extra_url_vars\")),i=this.tms_to_wmts(t,e,r),t=i[0],e=i[1],r=i[2],o=this.tile_xyz_to_quadkey(t,e,r),n.replace(\"{Q}\",o)},e}(n),e.exports={Model:o}},{\"./mercator_tile_source\":\"models/tiles/mercator_tile_source\"}],\"models/tiles/tile_renderer\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p=function(t,e){return function(){return t.apply(e,arguments)}},_=function(t,e){function r(){this.constructor=t}for(var n in e)d.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},d={}.hasOwnProperty,f=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};l=t(\"underscore\"),n=t(\"jquery\"),o=t(\"./image_pool\"),c=t(\"./wmts_tile_source\"),i=t(\"../renderers/renderer\"),u=t(\"../../core/logging\").logger,h=t(\"../../core/properties\"),a=function(t){function e(){return this._update=p(this._update,this),this._prefetch_tiles=p(this._prefetch_tiles,this),this._on_tile_error=p(this._on_tile_error,this),this._on_tile_cache_load=p(this._on_tile_cache_load,this),this._on_tile_load=p(this._on_tile_load,this),this._add_attribution=p(this._add_attribution,this),e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.initialize=function(t){return this.attributionEl=null,e.__super__.initialize.apply(this,arguments)},e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change\",this.request_render)},e.prototype.get_extent=function(){return[this.x_range.get(\"start\"),this.y_range.get(\"start\"),this.x_range.get(\"end\"),this.y_range.get(\"end\")]},e.prototype._set_data=function(){return this.pool=new o,this.map_plot=this.plot_model.plot,this.map_canvas=this.plot_view.canvas_view.ctx,this.map_frame=this.plot_model.frame,this.x_range=this.map_plot.get(\"x_range\"),this.x_mapper=this.map_frame.get(\"x_mappers\")[\"default\"],this.y_range=this.map_plot.get(\"y_range\"),this.y_mapper=this.map_frame.get(\"y_mappers\")[\"default\"],this.extent=this.get_extent(),this._last_height=void 0,this._last_width=void 0},e.prototype._add_attribution=function(){var t,e,r,o,i,s;if(t=this.mget(\"tile_source\").get(\"attribution\"),l.isString(t)&&t.length>0)return null!=this.attributionEl?this.attributionEl.html(t):(e=this.map_plot.get(\"outline_line_width\"),r=this.map_plot.get(\"min_border_bottom\")+e,s=this.map_frame.get(\"right\")-this.map_frame.get(\"width\"),o=this.map_frame.get(\"width\")-e,this.attributionEl=n(\"<div>\").html(t).addClass(\"bk-tile-attribution\").css({position:\"absolute\",bottom:r+\"px\",right:s+\"px\",\"max-width\":o+\"px\",\"background-color\":\"rgba(255,255,255,0.8)\",\"font-size\":\"9pt\",\"font-family\":\"sans-serif\"}),i=this.plot_view.$el.find(\"div.bk-canvas-events\"),this.attributionEl.appendTo(i))},e.prototype._map_data=function(){var t,e;return this.initial_extent=this.get_extent(),e=this.mget(\"tile_source\").get_level_by_extent(this.initial_extent,this.map_frame.get(\"height\"),this.map_frame.get(\"width\")),t=this.mget(\"tile_source\").snap_to_zoom(this.initial_extent,this.map_frame.get(\"height\"),this.map_frame.get(\"width\"),e),this.x_range.set(\"start\",t[0]),this.y_range.set(\"start\",t[1]),this.x_range.set(\"end\",t[2]),this.y_range.set(\"end\",t[3]),this._add_attribution()},e.prototype._on_tile_load=function(t){var e;return e=t.target.tile_data,e.img=t.target,e.current=!0,e.loaded=!0,this.request_render()},e.prototype._on_tile_cache_load=function(t){var e;return e=t.target.tile_data,e.img=t.target,e.loaded=!0},e.prototype._on_tile_error=function(t){return\"\"},e.prototype._create_tile=function(t,e,r,n,o){var i,s,a;return null==o&&(o=!1),i=this.mget(\"tile_source\").normalize_xyz(t,e,r),a=this.pool.pop(),o?a.onload=this._on_tile_cache_load:a.onload=this._on_tile_load,a.onerror=this._on_tile_error,a.alt=\"\",a.tile_data={tile_coords:[t,e,r],normalized_coords:i,quadkey:this.mget(\"tile_source\").tile_xyz_to_quadkey(t,e,r),cache_key:this.mget(\"tile_source\").tile_xyz_to_key(t,e,r),bounds:n,loaded:!1,x_coord:n[0],y_coord:n[3]},this.mget(\"tile_source\").tiles[a.tile_data.cache_key]=a.tile_data,a.src=(s=this.mget(\"tile_source\")).get_image_url.apply(s,i),a},e.prototype._enforce_aspect_ratio=function(){var t,e,r;return(this._last_height!==this.map_frame.get(\"height\")||this._last_width!==this.map_frame.get(\"width\"))&&(t=this.get_extent(),r=this.mget(\"tile_source\").get_level_by_extent(t,this.map_frame.get(\"height\"),this.map_frame.get(\"width\")),e=this.mget(\"tile_source\").snap_to_zoom(t,this.map_frame.get(\"height\"),this.map_frame.get(\"width\"),r),this.x_range.set({start:e[0],end:e[2]}),this.y_range.set({start:e[1],end:e[3]}),this.extent=e,this._last_height=this.map_frame.get(\"height\"),this._last_width=this.map_frame.get(\"width\"),!0)},e.prototype.render=function(t,e,r){if(null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),!this._enforce_aspect_ratio())return this._update(),null!=this.prefetch_timer&&clearTimeout(this.prefetch_timer),this.prefetch_timer=setTimeout(this._prefetch_tiles,500)},e.prototype._draw_tile=function(t){var e,r,n,o,i,s,a,l,u,h,c;if(c=this.mget(\"tile_source\").tiles[t],null!=c)return e=this.plot_view.frame.map_to_screen([c.bounds[0]],[c.bounds[3]],this.plot_view.canvas),a=e[0],h=e[1],r=this.plot_view.frame.map_to_screen([c.bounds[2]],[c.bounds[1]],this.plot_view.canvas),s=r[0],u=r[1],a=a[0],h=h[0],s=s[0],u=u[0],o=s-a,n=u-h,i=a,l=h,this.map_canvas.drawImage(c.img,i,l,o,n)},e.prototype._set_rect=function(){var t,e,r,n,o;return r=this.plot_model.plot.properties.outline_line_width.value(),e=this.plot_view.canvas.vx_to_sx(this.map_frame.get(\"left\"))+r/2,n=this.plot_view.canvas.vy_to_sy(this.map_frame.get(\"top\"))+r/2,o=this.map_frame.get(\"width\")-r,t=this.map_frame.get(\"height\")-r,this.map_canvas.rect(e,n,o,t),this.map_canvas.clip()},e.prototype._render_tiles=function(t){var e,r,n;for(this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.mget(\"alpha\"),e=0,r=t.length;e<r;e++)n=t[e],this._draw_tile(n);return this.map_canvas.restore()},e.prototype._prefetch_tiles=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v;for(_=this.mget(\"tile_source\"),a=this.get_extent(),l=this.map_frame.get(\"height\"),f=this.map_frame.get(\"width\"),v=this.mget(\"tile_source\").get_level_by_extent(a,l,f),d=this.mget(\"tile_source\").get_tiles_by_extent(a,v),c=[],p=u=0,h=Math.min(10,d.length);u<=h;p=u+=1)m=p[0],g=p[1],y=p[2],t=p[3],n=this.mget(\"tile_source\").children_by_tile_xyz(m,g,y),c.push(function(){var t,a,l;for(l=[],t=0,a=n.length;t<a;t++)e=n[t],o=e[0],i=e[1],s=e[2],r=e[3],_.tile_xyz_to_key(o,i,s)in _.tiles||l.push(this._create_tile(o,i,s,r,!0));return l}.call(this));return c},e.prototype._fetch_tiles=function(t){var e,r,n,o,i,s,a,l;for(o=[],r=0,n=t.length;r<n;r++)i=t[r],s=i[0],a=i[1],l=i[2],e=i[3],o.push(this._create_tile(s,a,l,e));return o},e.prototype._update=function(){var t,e,r,n,o,i,s,a,l,u,h,c,p,_,d,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F;for(C=this.mget(\"tile_source\"),b=C.get(\"min_zoom\"),v=C.get(\"max_zoom\"),C.update(),u=this.get_extent(),F=this.extent[2]-this.extent[0]<u[2]-u[0],h=this.map_frame.get(\"height\"),O=this.map_frame.get(\"width\"),R=C.get_level_by_extent(u,h,O),P=!1,R<b?(u=this.extent,R=b,P=!0):R>v&&(u=this.extent,R=v,P=!0),P&&(this.x_range.set({x_range:{start:u[0],end:u[2]}}),this.y_range.set({start:u[1],end:u[3]}),this.extent=u),this.extent=u,N=C.get_tiles_by_extent(u,R),k=[],x=[],r=[],i=[],c=0,m=N.length;c<m;c++){if(E=N[c],q=E[0],D=E[1],I=E[2],t=E[3],d=C.tile_xyz_to_key(q,D,I),A=C.tiles[d],null!=A&&A.loaded===!0)r.push(d);else if(this.mget(\"render_parents\")&&(z=C.get_closest_parent_by_tile_xyz(q,D,I),j=z[0],T=z[1],S=z[2],w=C.tile_xyz_to_key(j,T,S),M=C.tiles[w],null!=M&&M.loaded&&f.call(k,w)<0&&k.push(w),F))for(i=C.children_by_tile_xyz(q,D,I),p=0,g=i.length;p<g;p++)e=i[p],s=e[0],a=e[1],l=e[2],n=e[3],o=C.tile_xyz_to_key(s,a,l),o in C.tiles&&i.push(o);null==A&&x.push(E)}for(this._render_tiles(k),this._render_tiles(i),this._render_tiles(r),_=0,y=r.length;_<y;_++)E=r[_],C.tiles[E].current=!0;return null!=this.render_timer&&clearTimeout(this.render_timer),this.render_timer=setTimeout(function(t){return function(){return t._fetch_tiles(x)}}(this),65)},e}(i.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return _(e,t),e.prototype.default_view=a,e.prototype.type=\"TileRenderer\",e.define({alpha:[h.Number,1],x_range_name:[h.String,\"default\"],y_range_name:[h.String,\"default\"],tile_source:[h.Instance,function(){return new c.Model}],render_parents:[h.Bool,!0]}),e.override({level:\"underlay\"}),e}(i.Model),e.exports={Model:s,View:a}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../renderers/renderer\":\"models/renderers/renderer\",\"./image_pool\":\"models/tiles/image_pool\",\"./wmts_tile_source\":\"models/tiles/wmts_tile_source\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/tiles/tile_source\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./image_pool\"),u=t(\"./tile_utils\"),a=t(\"../../core/logging\").logger,l=t(\"../../core/properties\"),o=t(\"../../model\"),i=function(t){function e(t){null==t&&(t={}),e.__super__.constructor.apply(this,arguments),this.utils=new u.ProjectionUtils,this.pool=new n,this.tiles={},this.normalize_case()}return h(e,t),e.prototype.type=\"TileSource\",e.define({url:[l.String,\"\"],tile_size:[l.Number,256],max_zoom:[l.Number,30],min_zoom:[l.Number,0],extra_url_vars:[l.Any,{}],attribution:[l.String,\"\"],x_origin_offset:[l.Number],y_origin_offset:[l.Number],initial_resolution:[l.Number]}),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.normalize_case()},e.prototype.string_lookup_replace=function(t,e){var r,n,o;n=t;for(r in e)o=e[r],n=n.replace(\"{\"+r+\"}\",o.toString());return n},e.prototype.normalize_case=function(){\"Note: should probably be refactored into subclasses.\";var t;return t=this.get(\"url\"),t=t.replace(\"{x}\",\"{X}\"),t=t.replace(\"{y}\",\"{Y}\"),t=t.replace(\"{z}\",\"{Z}\"),t=t.replace(\"{q}\",\"{Q}\"),t=t.replace(\"{xmin}\",\"{XMIN}\"),t=t.replace(\"{ymin}\",\"{YMIN}\"),t=t.replace(\"{xmax}\",\"{XMAX}\"),t=t.replace(\"{ymax}\",\"{YMAX}\"),this.set(\"url\",t)},e.prototype.update=function(){var t,e,r,n;a.debug(\"TileSource: tile cache count: \"+Object.keys(this.tiles).length),e=this.tiles,r=[];for(t in e)n=e[t],n.current=!1,r.push(n.retain=!1);return r},e.prototype.tile_xyz_to_key=function(t,e,r){var n;return n=t+\":\"+e+\":\"+r},e.prototype.key_to_tile_xyz=function(t){var e;return function(){var r,n,o,i;for(o=t.split(\":\"),i=[],r=0,n=o.length;r<n;r++)e=o[r],i.push(parseInt(e));return i}()},e.prototype.sort_tiles_from_center=function(t,e){var r,n,o,i,s,a;return i=e[0],a=e[1],o=e[2],s=e[3],r=(o-i)/2+i,n=(s-a)/2+a,t.sort(function(t,e){var o,i;return o=Math.sqrt(Math.pow(r-t[0],2)+Math.pow(n-t[1],2)),i=Math.sqrt(Math.pow(r-e[0],2)+Math.pow(n-e[1],2)),o-i}),t},e.prototype.prune_tiles=function(){var t,e,r,n,o;e=this.tiles;for(t in e)o=e[t],o.retain=o.current||o.tile_coords[2]<3,o.current&&(this.retain_neighbors(o),this.retain_children(o),this.retain_parents(o));r=this.tiles,n=[];for(t in r)o=r[t],o.retain?n.push(void 0):n.push(this.remove_tile(t));return n},e.prototype.remove_tile=function(t){var e;if(e=this.tiles[t],null!=e)return this.pool.push(e.img),delete this.tiles[t]},e.prototype.get_image_url=function(t,e,r){var n;return n=this.string_lookup_replace(this.get(\"url\"),this.get(\"extra_url_vars\")),n.replace(\"{X}\",t).replace(\"{Y}\",e).replace(\"{Z}\",r)},e.prototype.retain_neighbors=function(t){throw Error(\"Not Implemented\")},e.prototype.retain_parents=function(t){throw Error(\"Not Implemented\")},e.prototype.retain_children=function(t){throw Error(\"Not Implemented\")},e.prototype.tile_xyz_to_quadkey=function(t,e,r){throw Error(\"Not Implemented\")},e.prototype.quadkey_to_tile_xyz=function(t){throw Error(\"Not Implemented\")},e}(o),e.exports=i},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../model\":\"model\",\"./image_pool\":\"models/tiles/image_pool\",\"./tile_utils\":\"models/tiles/tile_utils\",underscore:\"underscore\"}],\"models/tiles/tile_utils\":[function(t,e,r){var n,o,i,s;i=t(\"proj4\"),o=i.defs(\"GOOGLE\"),s=i.defs(\"WGS84\"),n=function(){function t(){this.origin_shift=2*Math.PI*6378137/2}return t.prototype.geographic_to_meters=function(t,e){return i(s,o,[t,e])},t.prototype.meters_to_geographic=function(t,e){return i(o,s,[t,e])},t.prototype.geographic_extent_to_meters=function(t){var e,r,n,o,i,s;return o=t[0],s=t[1],n=t[2],i=t[3],e=this.geographic_to_meters(o,s),o=e[0],s=e[1],r=this.geographic_to_meters(n,i),n=r[0],i=r[1],[o,s,n,i]},t.prototype.meters_extent_to_geographic=function(t){var e,r,n,o,i,s;return o=t[0],s=t[1],n=t[2],i=t[3],e=this.meters_to_geographic(o,s),o=e[0],s=e[1],r=this.meters_to_geographic(n,i),n=r[0],i=r[1],[o,s,n,i]},t}(),e.exports={ProjectionUtils:n}},{proj4:\"proj4\"}],\"models/tiles/tms_tile_source\":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t(\"./mercator_tile_source\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return i(e,t),e.prototype.type=\"TMSTileSource\",e.prototype.get_image_url=function(t,e,r){var n;return n=this.string_lookup_replace(this.get(\"url\"),this.get(\"extra_url_vars\")),n.replace(\"{X}\",t).replace(\"{Y}\",e).replace(\"{Z}\",r)},e}(n),e.exports={Model:o}},{\"./mercator_tile_source\":\"models/tiles/mercator_tile_source\"}],\"models/tiles/wmts_tile_source\":[function(t,e,r){var n,o,i=function(t,e){function r(){this.constructor=t}for(var n in e)s.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},s={}.hasOwnProperty;n=t(\"./mercator_tile_source\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return i(e,t),e.prototype.type=\"WMTSTileSource\",e.prototype.get_image_url=function(t,e,r){var n,o;return n=this.string_lookup_replace(this.get(\"url\"),this.get(\"extra_url_vars\")),o=this.tms_to_wmts(t,e,r),t=o[0],e=o[1],r=o[2],n.replace(\"{X}\",t).replace(\"{Y}\",e).replace(\"{Z}\",r)},e}(n),e.exports={Model:o}},{\"./mercator_tile_source\":\"models/tiles/mercator_tile_source\"}],\"models/tools/actions/action_tool\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;s=t(\"../button_tool\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype._clicked=function(){return this.model.trigger(\"do\")},e}(s.ButtonView),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,\"do\",this[\"do\"])},e}(s.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e}(s.Model),e.exports={Model:n,View:i,ButtonView:o}},{\"../button_tool\":\"models/tools/button_tool\"}],\"models/tools/actions/help_tool\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./action_tool\"),a=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype[\"do\"]=function(){return window.open(this.mget(\"redirect\"))},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"HelpTool\",e.prototype.tool_name=\"Help\",e.prototype.icon=\"bk-tool-icon-help\",e.define({help_tooltip:[a.String,\"Click the question mark to learn more about Bokeh plot tools.\"],redirect:[a.String,\"http://bokeh.pydata.org/en/latest/docs/user_guide/tools.html\"]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this.get(\"help_tooltip\")})},e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./action_tool\":\"models/tools/actions/action_tool\",underscore:\"underscore\"}],\"models/tools/actions/redo_tool\":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;n=t(\"./action_tool\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.plot_view,\"state_changed\",function(t){return function(){return t.model.set(\"disabled\",!t.plot_view.can_redo())}}(this))},e.prototype[\"do\"]=function(){return this.plot_view.redo()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.default_view=i,e.prototype.type=\"RedoTool\",e.prototype.tool_name=\"Redo\",e.prototype.icon=\"bk-tool-icon-redo\",e.override({disabled:!0}),e}(n.Model),e.exports={Model:o,View:i}},{\"./action_tool\":\"models/tools/actions/action_tool\"}],\"models/tools/actions/reset_tool\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;n=t(\"./action_tool\"),s=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype[\"do\"]=function(){if(this.plot_view.clear_state(),this.plot_view.reset_range(),this.plot_view.reset_selection(),this.model.reset_size)return this.plot_view.reset_dimensions()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=i,e.prototype.type=\"ResetTool\",e.prototype.tool_name=\"Reset\",e.prototype.icon=\"bk-tool-icon-reset\",e.define({reset_size:[s.Bool,!0]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./action_tool\":\"models/tools/actions/action_tool\"}],\"models/tools/actions/save_tool\":[function(t,e,r){var n,o,i,s,a=function(t,e){function r(){this.constructor=t}for(var n in e)l.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},l={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./action_tool\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype[\"do\"]=function(){var t,e,r,n;return e=this.plot_view.get_canvas_element(),n=\"bokeh_plot.png\",null!=e.msToBlob?(t=e.msToBlob(),window.navigator.msSaveBlob(t,n)):(r=document.createElement(\"a\"),r.href=e.toDataURL(\"image/png\"),r.download=n,r.target=\"_blank\",r.dispatchEvent(new MouseEvent(\"click\")))},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return a(e,t),e.prototype.default_view=i,e.prototype.type=\"SaveTool\",e.prototype.tool_name=\"Save\",e.prototype.icon=\"bk-tool-icon-save\",e}(n.Model),e.exports={Model:o,View:i}},{\"./action_tool\":\"models/tools/actions/action_tool\",underscore:\"underscore\"}],\"models/tools/actions/undo_tool\":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;n=t(\"./action_tool\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.plot_view,\"state_changed\",function(t){return function(){return t.model.set(\"disabled\",!t.plot_view.can_undo())}}(this))},e.prototype[\"do\"]=function(){return this.plot_view.undo()},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.default_view=i,e.prototype.type=\"UndoTool\",e.prototype.tool_name=\"Undo\",e.prototype.icon=\"bk-tool-icon-undo\",e.override({disabled:!0}),e}(n.Model),e.exports={Model:o,View:i}},{\"./action_tool\":\"models/tools/actions/action_tool\"}],\"models/tools/button_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h,c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},p={}.hasOwnProperty;l=t(\"underscore\"),n=t(\"backbone\"),a=t(\"./tool\"),u=t(\"./button_tool_template\"),h=t(\"../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.tagName=\"li\",e.prototype.template=u,e.prototype.events=function(){return{\"click .bk-toolbar-button\":\"_clicked\"}},e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.$el.html(this.template({model:this.model})),this.listenTo(this.model,\"change:active\",function(t){return function(){return t.render()}}(this)),this.listenTo(this.model,\"change:disabled\",function(t){return function(){return t.render()}}(this)),this.render()},e.prototype.render=function(){return this.$el.children(\"button\").prop(\"disabled\",this.model.get(\"disabled\")).toggleClass(\"active\",this.model.get(\"active\")),this},e.prototype._clicked=function(t){},e}(n.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.icon=null,e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"tooltip\",function(){return this.tool_name})},e.internal({disabled:[h.Boolean,!1]}),e}(a.Model),e.exports={Model:o,View:s,ButtonView:i}},{\"../../core/properties\":\"core/properties\",\"./button_tool_template\":\"models/tools/button_tool_template\",\"./tool\":\"models/tools/tool\",backbone:\"backbone\",underscore:\"underscore\"}],\"models/tools/button_tool_template\":[function(t,e,r){e.exports=function(t){t||(t={});var e=[],r=function(t){return t&&t.ecoSafe?t:\"undefined\"!=typeof t&&null!=t?n(t):\"\"},n=function(t){return(\"\"+t).replace(/&/g,\"&amp;\").replace(/</g,\"&lt;\").replace(/>/g,\"&gt;\").replace(/\"/g,\"&quot;\")};return function(){(function(){e.push('<button type=\"button\" class=\"bk-toolbar-button hover\">\\n  <div class=\\'bk-btn-icon '),e.push(r(this.model.icon)),e.push(\"' />\\n  <span class='tip'>\"),e.push(r(this.model.get(\"tooltip\"))),e.push(\"</span>\\n</button>\\n\")}).call(this)}.call(t),e.join(\"\")}},{}],\"models/tools/gestures/box_select_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./select_tool\"),n=t(\"../../annotations/box_annotation\"),u=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._pan_start=function(t){var e;return e=this.plot_view.canvas,this._baseboint=[e.sx_to_vx(t.bokeh.sx),e.sy_to_vy(t.bokeh.sy)],null},e.prototype._pan=function(t){var e,r,n,o,i,s,a,l,u;return r=this.plot_view.canvas,n=[r.sx_to_vx(t.bokeh.sx),r.sy_to_vy(t.bokeh.sy)],i=this.plot_model.get(\"frame\"),o=this.mget(\"dimensions\"),s=this.model._get_dim_limits(this._baseboint,n,i,o),l=s[0],u=s[1],this.mget(\"overlay\").update({left:l[0],right:l[1],top:u[1],bottom:u[0]}),this.mget(\"select_every_mousemove\")&&(e=null!=(a=t.srcEvent.shiftKey)&&a,this._select(l,u,!1,e)),null},e.prototype._pan_end=function(t){var e,r,n,o,i,s,a,l,u;return r=this.plot_view.canvas,n=[r.sx_to_vx(t.bokeh.sx),r.sy_to_vy(t.bokeh.sy)],i=this.plot_model.get(\"frame\"),o=this.mget(\"dimensions\"),s=this.model._get_dim_limits(this._baseboint,n,i,o),l=s[0],u=s[1],e=null!=(a=t.srcEvent.shiftKey)&&a,this._select(l,u,!0,e),\nthis.mget(\"overlay\").update({left:null,right:null,top:null,bottom:null}),this._baseboint=null,this.plot_view.push_state(\"box_select\",{selection:this.plot_view.get_selection()}),null},e.prototype._select=function(t,e,r,n){var o,i,s,a,l,u,h,c,p,_,d;for(c=t[0],p=t[1],_=e[0],d=e[1],null==n&&(n=!1),i={type:\"rect\",vx0:c,vx1:p,vy0:_,vy1:d},u=this.mget(\"computed_renderers\"),s=0,a=u.length;s<a;s++)l=u[s],o=l.get(\"data_source\"),h=o.get(\"selection_manager\"),h.select(this,this.plot_view.renderer_views[l.id],i,r,n);return null!=this.mget(\"callback\")&&this._emit_callback(i),this._save_geometry(i,r,n),null},e.prototype._emit_callback=function(t){var e,r,n,o,i;n=this.mget(\"computed_renderers\")[0],e=this.plot_model.get(\"canvas\"),r=this.plot_model.get(\"frame\"),t.sx0=e.vx_to_sx(t.vx0),t.sx1=e.vx_to_sx(t.vx1),t.sy0=e.vy_to_sy(t.vy0),t.sy1=e.vy_to_sy(t.vy1),o=r.get(\"x_mappers\")[n.get(\"x_range_name\")],i=r.get(\"y_mappers\")[n.get(\"y_range_name\")],t.x0=o.map_from_target(t.vx0),t.x1=o.map_from_target(t.vx1),t.y0=i.map_from_target(t.vy0),t.y1=i.map_from_target(t.vy1),this.mget(\"callback\").execute(this.model,{geometry:t})},e}(a.View),s=function(){return new n.Model({level:\"overlay\",render_mode:\"css\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]})},o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type=\"BoxSelectTool\",e.prototype.tool_name=\"Box Select\",e.prototype.icon=\"bk-tool-icon-box-select\",e.prototype.event_type=\"pan\",e.prototype.default_order=30,e.define({dimensions:[u.Array,[\"width\",\"height\"]],select_every_mousemove:[u.Bool,!1],callback:[u.Instance],overlay:[u.Instance,s]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this._get_dim_tooltip(this.tool_name,this._check_dims(this.get(\"dimensions\"),\"box select tool\"))},!1),this.add_dependencies(\"tooltip\",this,[\"dimensions\"])},e}(a.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"../../annotations/box_annotation\":\"models/annotations/box_annotation\",\"./select_tool\":\"models/tools/gestures/select_tool\",underscore:\"underscore\"}],\"models/tools/gestures/box_zoom_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./gesture_tool\"),n=t(\"../../annotations/box_annotation\"),u=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._match_aspect=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b;return s=r.get(\"h_range\").get(\"end\"),a=r.get(\"h_range\").get(\"start\"),d=r.get(\"v_range\").get(\"end\"),m=r.get(\"v_range\").get(\"start\"),y=s-a,i=d-m,n=y/i,g=Math.abs(t[0]-e[0]),f=Math.abs(t[1]-e[1]),_=0===f?0:g/f,_>=n?(u=[1,_/n],v=u[0],b=u[1]):(h=[n/_,1],v=h[0],b=h[1]),t[0]<=e[0]?(l=t[0],c=t[0]+g*v,c>s&&(c=s)):(c=t[0],l=t[0]-g*v,l<a&&(l=a)),g=Math.abs(c-l),t[1]<=e[1]?(o=t[1],p=t[1]+g/n,p>d&&(p=d)):(p=t[1],o=t[1]-g/n,o<m&&(o=m)),f=Math.abs(p-o),t[0]<=e[0]?c=t[0]+n*f:l=t[0]-n*f,[[l,c],[o,p]]},e.prototype._pan_start=function(t){var e;return e=this.plot_view.canvas,this._baseboint=[e.sx_to_vx(t.bokeh.sx),e.sy_to_vy(t.bokeh.sy)],null},e.prototype._pan=function(t){var e,r,n,o,i,s,a,l;return e=this.plot_view.canvas,r=[e.sx_to_vx(t.bokeh.sx),e.sy_to_vy(t.bokeh.sy)],o=this.plot_model.get(\"frame\"),n=this.mget(\"dimensions\"),this.mget(\"match_aspect\")&&2===n.length?(i=this._match_aspect(this._baseboint,r,o),a=i[0],l=i[1]):(s=this.model._get_dim_limits(this._baseboint,r,o,n),a=s[0],l=s[1]),this.mget(\"overlay\").update({left:a[0],right:a[1],top:l[1],bottom:l[0]}),null},e.prototype._pan_end=function(t){var e,r,n,o,i,s,a,l;return e=this.plot_view.canvas,r=[e.sx_to_vx(t.bokeh.sx),e.sy_to_vy(t.bokeh.sy)],o=this.plot_model.get(\"frame\"),n=this.mget(\"dimensions\"),this.mget(\"match_aspect\")&&2===n.length?(i=this._match_aspect(this._baseboint,r,o),a=i[0],l=i[1]):(s=this.model._get_dim_limits(this._baseboint,r,o,n),a=s[0],l=s[1]),this._update(a,l),this.mget(\"overlay\").update({left:null,right:null,top:null,bottom:null}),this._baseboint=null,null},e.prototype._update=function(t,e){var r,n,o,i,s,a,l,u,h,c,p;if(!(Math.abs(t[1]-t[0])<=5||Math.abs(e[1]-e[0])<=5)){h={},i=this.plot_view.frame.get(\"x_mappers\");for(o in i)n=i[o],s=n.v_map_from_target(t,!0),u=s[0],r=s[1],h[o]={start:u,end:r};c={},a=this.plot_view.frame.get(\"y_mappers\");for(o in a)n=a[o],l=n.v_map_from_target(e,!0),u=l[0],r=l[1],c[o]={start:u,end:r};return p={xrs:h,yrs:c},this.plot_view.push_state(\"box_zoom\",{range:p}),this.plot_view.update_range(p)}},e}(a.View),s=function(){return new n.Model({level:\"overlay\",render_mode:\"css\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]})},o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type=\"BoxZoomTool\",e.prototype.tool_name=\"Box Zoom\",e.prototype.icon=\"bk-tool-icon-box-zoom\",e.prototype.event_type=\"pan\",e.prototype.default_order=20,e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this._get_dim_tooltip(this.tool_name,this._check_dims(this.get(\"dimensions\"),\"box zoom tool\"))},!1),this.add_dependencies(\"tooltip\",this,[\"dimensions\"])},e.define({dimensions:[u.Array,[\"width\",\"height\"]],overlay:[u.Instance,s],match_aspect:[u.Bool,!1]}),e}(a.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"../../annotations/box_annotation\":\"models/annotations/box_annotation\",\"./gesture_tool\":\"models/tools/gestures/gesture_tool\",underscore:\"underscore\"}],\"models/tools/gestures/gesture_tool\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;a=t(\"underscore\"),n=t(\"../button_tool\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._clicked=function(){var t;return t=this.model.get(\"active\"),this.model.set(\"active\",!t)},e}(n.ButtonView),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.event_type=null,e.prototype.default_order=null,e}(n.Model),e.exports={Model:o,View:s,ButtonView:i}},{\"../button_tool\":\"models/tools/button_tool\",underscore:\"underscore\"}],\"models/tools/gestures/lasso_select_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./select_tool\"),s=t(\"../../annotations/poly_annotation\"),u=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,\"change:active\",this._active_change),this.data=null},e.prototype._active_change=function(){if(!this.mget(\"active\"))return this._clear_overlay()},e.prototype._keyup=function(t){if(13===t.keyCode)return this._clear_overlay()},e.prototype._pan_start=function(t){var e,r,n;return e=this.plot_view.canvas,r=e.sx_to_vx(t.bokeh.sx),n=e.sy_to_vy(t.bokeh.sy),this.data={vx:[r],vy:[n]},null},e.prototype._pan=function(t){var e,r,n,o,i,s,a,l;if(r=this.plot_view.canvas,a=r.sx_to_vx(t.bokeh.sx),l=r.sy_to_vy(t.bokeh.sy),n=this.plot_model.get(\"frame\").get(\"h_range\"),s=this.plot_model.get(\"frame\").get(\"v_range\"),a>n.get(\"end\")&&(a=n.get(\"end\")),a<n.get(\"start\")&&(a=n.get(\"start\")),l>s.get(\"end\")&&(l=s.get(\"end\")),l<s.get(\"start\")&&(l=s.get(\"start\")),this.data.vx.push(a),this.data.vy.push(l),o=this.mget(\"overlay\"),o.update({xs:this.data.vx,ys:this.data.vy}),this.mget(\"select_every_mousemove\"))return e=null!=(i=t.srcEvent.shiftKey)&&i,this._select(this.data.vx,this.data.vy,!1,e)},e.prototype._pan_end=function(t){var e,r;return this._clear_overlay(),e=null!=(r=t.srcEvent.shiftKey)&&r,this._select(this.data.vx,this.data.vy,!0,e),this.plot_view.push_state(\"lasso_select\",{selection:this.plot_view.get_selection()})},e.prototype._clear_overlay=function(){return this.mget(\"overlay\").update({xs:[],ys:[]})},e.prototype._select=function(t,e,r,n){var o,i,s,a,l,u,h;for(i={type:\"poly\",vx:t,vy:e},u=this.mget(\"computed_renderers\"),s=0,a=u.length;s<a;s++)l=u[s],o=l.get(\"data_source\"),h=o.get(\"selection_manager\"),h.select(this,this.plot_view.renderer_views[l.id],i,r,n);return null!=this.mget(\"callback\")&&this._emit_callback(i),this._save_geometry(i,r,n),null},e.prototype._emit_callback=function(t){var e,r,n,o,i;n=this.mget(\"computed_renderers\")[0],e=this.plot_model.get(\"canvas\"),r=this.plot_model.get(\"frame\"),t.sx=e.v_vx_to_sx(t.vx),t.sy=e.v_vy_to_sy(t.vy),o=r.get(\"x_mappers\")[n.get(\"x_range_name\")],i=r.get(\"y_mappers\")[n.get(\"y_range_name\")],t.x=o.v_map_from_target(t.vx),t.y=i.v_map_from_target(t.vy),this.mget(\"callback\").execute(this.model,{geometry:t})},e}(a.View),n=function(){return new s.Model({level:\"overlay\",xs_units:\"screen\",ys_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]})},o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=i,e.prototype.type=\"LassoSelectTool\",e.prototype.tool_name=\"Lasso Select\",e.prototype.icon=\"bk-tool-icon-lasso-select\",e.prototype.event_type=\"pan\",e.prototype.default_order=12,e.define({select_every_mousemove:[u.Bool,!0],callback:[u.Instance],overlay:[u.Instance,n]}),e}(a.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"../../annotations/poly_annotation\":\"models/annotations/poly_annotation\",\"./select_tool\":\"models/tools/gestures/select_tool\",underscore:\"underscore\"}],\"models/tools/gestures/pan_tool\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./gesture_tool\"),a=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._pan_start=function(t){var e,r,n,o,i,s;return this.last_dx=0,this.last_dy=0,e=this.plot_view.canvas,r=this.plot_view.frame,i=e.sx_to_vx(t.bokeh.sx),s=e.sy_to_vy(t.bokeh.sy),r.contains(i,s)||(n=r.get(\"h_range\"),o=r.get(\"v_range\"),(i<n.get(\"start\")||i>n.get(\"end\"))&&(this.v_axis_only=!0),(s<o.get(\"start\")||s>o.get(\"end\"))&&(this.h_axis_only=!0)),this.plot_view.interactive_timestamp=Date.now()},e.prototype._pan=function(t){return this._update(t.deltaX,-t.deltaY),this.plot_view.interactive_timestamp=Date.now()},e.prototype._pan_end=function(t){if(this.h_axis_only=!1,this.v_axis_only=!1,null!=this.pan_info)return this.plot_view.push_state(\"pan\",{range:this.pan_info})},e.prototype._update=function(t,e){var r,n,o,i,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P;o=this.plot_view.frame,h=t-this.last_dx,c=e-this.last_dy,i=s.clone(o.get(\"h_range\")),w=i.get(\"start\")-h,x=i.get(\"end\")-h,S=s.clone(o.get(\"v_range\")),T=S.get(\"start\")-c,j=S.get(\"end\")-c,r=this.mget(\"dimensions\"),r.indexOf(\"width\")>-1&&!this.v_axis_only?(v=w,b=x,m=-h):(v=i.get(\"start\"),b=i.get(\"end\"),m=0),r.indexOf(\"height\")>-1&&!this.h_axis_only?(M=T,k=j,g=c):(M=S.get(\"start\"),k=S.get(\"end\"),g=0),this.last_dx=t,this.last_dy=e,z={},p=o.get(\"x_mappers\");for(u in p)l=p[u],_=l.v_map_from_target([v,b],!0),y=_[0],n=_[1],z[u]={start:y,end:n};P={},d=o.get(\"y_mappers\");for(u in d)l=d[u],f=l.v_map_from_target([M,k],!0),y=f[0],n=f[1],P[u]={start:y,end:n};return this.pan_info={xrs:z,yrs:P,sdx:m,sdy:g},this.plot_view.update_range(this.pan_info,a=!0),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"PanTool\",e.prototype.tool_name=\"Pan\",e.prototype.icon=\"bk-tool-icon-pan\",e.prototype.event_type=\"pan\",e.prototype.default_order=10,e.define({dimensions:[a.Array,[\"width\",\"height\"]]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this._get_dim_tooltip(\"Pan\",this._check_dims(this.get(\"dimensions\"),\"pan tool\"))},!1),this.add_dependencies(\"tooltip\",this,[\"dimensions\"])},e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./gesture_tool\":\"models/tools/gestures/gesture_tool\",underscore:\"underscore\"}],\"models/tools/gestures/poly_select_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),a=t(\"./select_tool\"),o=t(\"../../annotations/poly_annotation\"),u=t(\"../../../core/properties\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this.model,\"change:active\",this._active_change),this.data=null},e.prototype._active_change=function(){if(!this.mget(\"active\"))return this._clear_data()},e.prototype._keyup=function(t){if(13===t.keyCode)return this._clear_data()},e.prototype._doubletap=function(t){var e,r;return e=null!=(r=t.srcEvent.shiftKey)&&r,this._select(this.data.vx,this.data.vy,!0,e),this._clear_data()},e.prototype._clear_data=function(){return this.data=null,this.mget(\"overlay\").update({xs:[],ys:[]})},e.prototype._tap=function(t){var e,r,n,o,i;return e=this.plot_view.canvas,o=e.sx_to_vx(t.bokeh.sx),i=e.sy_to_vy(t.bokeh.sy),null==this.data?(this.data={vx:[o],vy:[i]},null):(this.data.vx.push(o),this.data.vy.push(i),n=this.mget(\"overlay\"),r={},r.vx=l.clone(this.data.vx),r.vy=l.clone(this.data.vy),n.update({xs:this.data.vx,ys:this.data.vy}))},e.prototype._select=function(t,e,r,n){var o,i,s,a,l,u,h;for(i={type:\"poly\",vx:t,vy:e},u=this.mget(\"computed_renderers\"),s=0,a=u.length;s<a;s++)l=u[s],o=l.get(\"data_source\"),h=o.get(\"selection_manager\"),h.select(this,this.plot_view.renderer_views[l.id],i,r,n);return this._save_geometry(i,r,n),this.plot_view.push_state(\"poly_select\",{selection:this.plot_view.get_selection()}),null},e}(a.View),n=function(){return new o.Model({level:\"overlay\",xs_units:\"screen\",ys_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]})},i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.default_view=s,e.prototype.type=\"PolySelectTool\",e.prototype.tool_name=\"Poly Select\",e.prototype.icon=\"bk-tool-icon-polygon-select\",e.prototype.event_type=\"tap\",e.prototype.default_order=11,e.define({overlay:[u.Instance,n]}),e}(a.Model),e.exports={Model:i,View:s}},{\"../../../core/properties\":\"core/properties\",\"../../annotations/poly_annotation\":\"models/annotations/poly_annotation\",\"./select_tool\":\"models/tools/gestures/select_tool\",underscore:\"underscore\"}],\"models/tools/gestures/resize_tool\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./gesture_tool\"),a=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.className=\"bk-resize-popup\",e.prototype.initialize=function(t){var r;return e.__super__.initialize.call(this,t),r=this.plot_view.$el.find(\"div.bk-canvas-wrapper\"),this.$el.appendTo(r),this.$el.hide(),this.active=!1,null},e.prototype.activate=function(){return this.active=!0,this.render(),null},e.prototype.deactivate=function(){return this.active=!1,this.render(),null},e.prototype.render=function(t){var e,r,n,o;return this.active?(e=this.plot_view.canvas,r=this.plot_view.frame,n=e.vx_to_sx(r.get(\"h_range\").get(\"end\")-40),o=e.vy_to_sy(r.get(\"v_range\").get(\"start\")+40),this.$el.attr(\"style\",\"position:absolute; top:\"+o+\"px; left:\"+n+\"px;\"),this.$el.show()):this.$el.hide(),this},e.prototype._pan_start=function(t){var e;return e=this.plot_view.canvas,this.ch=e.get(\"height\"),this.cw=e.get(\"width\"),this.plot_view.interactive_timestamp=Date.now(),null},e.prototype._pan=function(t){return this._update(t.deltaX,t.deltaY),this.plot_view.interactive_timestamp=Date.now(),null},e.prototype._pan_end=function(t){return this.plot_view.push_state(\"resize\",{dimensions:{width:this.plot_view.canvas.get(\"width\"),height:this.plot_view.canvas.get(\"height\")}})},e.prototype._update=function(t,e){var r,n;n=this.cw+t,r=this.cw+e,n<100||r<100||this.plot_view.update_dimensions(n,r)},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"ResizeTool\",e.prototype.tool_name=\"Resize\",e.prototype.icon=\"bk-tool-icon-resize\",e.prototype.event_type=\"pan\",e.prototype.default_order=40,e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./gesture_tool\":\"models/tools/gestures/gesture_tool\",underscore:\"underscore\"}],\"models/tools/gestures/select_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t(\"underscore\"),n=t(\"./gesture_tool\"),o=t(\"../../renderers/glyph_renderer\"),l=t(\"../../../core/logging\").logger,u=t(\"../../../core/properties\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype._keyup=function(t){var e,r,n,o,i,s,a;if(27===t.keyCode){for(i=this.mget(\"computed_renderers\"),s=[],r=0,n=i.length;r<n;r++)o=i[r],e=o.get(\"data_source\"),a=e.get(\"selection_manager\"),s.push(a.clear());return s}},e.prototype._save_geometry=function(t,e,r){var n,o,i,s,u,h,c,p;if(n=a.clone(t),c=this.plot_view.frame.get(\"x_mappers\")[\"default\"],p=this.plot_view.frame.get(\"y_mappers\")[\"default\"],\"point\"===n.type)n.x=c.map_from_target(n.vx),n.y=p.map_from_target(n.vy);else if(\"rect\"===n.type)n.x0=c.map_from_target(n.vx0),n.y0=p.map_from_target(n.vy0),n.x1=c.map_from_target(n.vx1),n.y1=p.map_from_target(n.vy1);else if(\"poly\"===n.type)for(n.x=new Array(n.vx.length),n.y=new Array(n.vy.length),i=s=0,u=n.vx.length;0<=u?s<u:s>u;i=0<=u?++s:--s)n.x[i]=c.map_from_target(n.vx[i]),n.y[i]=p.map_from_target(n.vy[i]);else l.debug(\"Unrecognized selection geometry type: '\"+n.type+\"'\");return e&&(h=this.plot_model.plot.tool_events,r?(o=h.get(\"geometries\"),o.push(n)):o=[n],h.set(\"geometries\",o)),null},e}(n.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.define({renderers:[u.Array,[]],names:[u.Array,[]]}),e.internal({multi_select_modifier:[u.String,\"shift\"]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"computed_renderers\",function(){var t,e,r,n;return n=this.get(\"renderers\"),e=this.get(\"names\"),0===n.length&&(t=this.get(\"plot\").get(\"renderers\"),n=function(){var e,n,i;for(i=[],e=0,n=t.length;e<n;e++)r=t[e],r instanceof o.Model&&i.push(r);return i}()),e.length>0&&(n=function(){var t,o,i;for(i=[],t=0,o=n.length;t<o;t++)r=n[t],e.indexOf(r.get(\"name\"))>=0&&i.push(r);return i}()),n},!0),this.add_dependencies(\"computed_renderers\",this,[\"renderers\",\"names\",\"plot\"]),this.add_dependencies(\"computed_renderers\",this.get(\"plot\"),[\"renderers\"]),null},e}(n.Model),e.exports={Model:i,View:s}},{\"../../../core/logging\":\"core/logging\",\"../../../core/properties\":\"core/properties\",\"../../renderers/glyph_renderer\":\"models/renderers/glyph_renderer\",\"./gesture_tool\":\"models/tools/gestures/gesture_tool\",underscore:\"underscore\"}],\"models/tools/gestures/tap_tool\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./select_tool\"),a=t(\"../../../core/properties\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype._tap=function(t){var e,r,n,o,i;return r=this.plot_view.canvas,o=r.sx_to_vx(t.bokeh.sx),i=r.sy_to_vy(t.bokeh.sy),e=null!=(n=t.srcEvent.shiftKey)&&n,this._select(o,i,!0,e)},e.prototype._select=function(t,e,r,n){var o,i,a,l,u,h,c,p,_,d,f;for(u={type:\"point\",vx:t,vy:e},o=this.mget(\"callback\"),this._save_geometry(u,r,n),i={geometries:this.plot_model.plot.tool_events.get(\"geometries\")},_=this.mget(\"computed_renderers\"),h=0,c=_.length;h<c;h++)p=_[h],l=p.get(\"data_source\"),d=l.get(\"selection_manager\"),f=this.plot_view.renderer_views[p.id],a=\"select\"===this.model.behavior?d.select(this,f,u,r,n):d.inspect(this,f,u,{geometry:u}),a&&null!=o&&(s.isFunction(o)?o(l,i):o.execute(l,i));return\"select\"===this.model.behavior&&this.plot_view.push_state(\"tap\",{selection:this.plot_view.get_selection()}),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.default_view=i,e.prototype.type=\"TapTool\",e.prototype.tool_name=\"Tap\",e.prototype.icon=\"bk-tool-icon-tap-select\",e.prototype.event_type=\"tap\",e.prototype.default_order=10,e.define({behavior:[a.String,\"select\"],callback:[a.Any]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./select_tool\":\"models/tools/gestures/select_tool\",underscore:\"underscore\"}],\"models/tools/gestures/wheel_zoom_tool\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;s=t(\"underscore\"),n=t(\"./gesture_tool\"),l=t(\"../../../core/properties\"),\"undefined\"!=typeof a&&null!==a||(a={}),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._pinch=function(t){var e;return e=t.scale>=1?20*(t.scale-1):-20/t.scale,t.bokeh.delta=e,this._scroll(t)},e.prototype._scroll=function(t){var e,r,n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A;i=this.plot_model.get(\"frame\"),a=i.get(\"h_range\"),w=i.get(\"v_range\"),M=this.plot_view.canvas.sx_to_vx(t.bokeh.sx),T=this.plot_view.canvas.sy_to_vy(t.bokeh.sy),(M<a.get(\"start\")||M>a.get(\"end\"))&&(x=!0),(T<w.get(\"start\")||T>w.get(\"end\"))&&(s=!0),u=navigator.userAgent.toLowerCase().indexOf(\"firefox\")>-1?20:1,e=null!=(null!=(c=t.originalEvent)?c.deltaY:void 0)?-t.originalEvent.deltaY*u:t.bokeh.delta,o=this.mget(\"speed\")*e,o>.9?o=.9:o<-.9&&(o=-.9),j=a.get(\"start\"),k=a.get(\"end\"),z=w.get(\"start\"),S=w.get(\"end\"),r=this.mget(\"dimensions\"),r.indexOf(\"width\")>-1&&!x?(g=j-(j-M)*o,y=k-(k-M)*o):(g=j,y=k),r.indexOf(\"height\")>-1&&!s?(v=z-(z-T)*o,b=S-(S-T)*o):(v=z,b=S),P={},p=i.get(\"x_mappers\");for(h in p)l=p[h],_=l.v_map_from_target([g,y],!0),m=_[0],n=_[1],P[h]={start:m,end:n};E={},d=i.get(\"y_mappers\");for(h in d)l=d[h],f=l.v_map_from_target([v,b],!0),m=f[0],n=f[1],E[h]={start:m,end:n};return A={xrs:P,yrs:E,factor:o},this.plot_view.push_state(\"wheel_zoom\",{range:A}),this.plot_view.update_range(A,!1,!0),this.plot_view.interactive_timestamp=Date.now(),null},e}(n.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=i,e.prototype.type=\"WheelZoomTool\",e.prototype.tool_name=\"Wheel Zoom\",e.prototype.icon=\"bk-tool-icon-wheel-zoom\",e.prototype.event_type=\"ontouchstart\"in window||navigator.maxTouchPoints>0?\"pinch\":\"scroll\",e.prototype.default_order=10,e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this._get_dim_tooltip(this.tool_name,this._check_dims(this.get(\"dimensions\"),\"wheel zoom tool\"))},!1),this.add_dependencies(\"tooltip\",this,[\"dimensions\"])},e.define({dimensions:[l.Array,[\"width\",\"height\"]]}),e.internal({speed:[l.Number,1/600]}),e}(n.Model),e.exports={Model:o,View:i}},{\"../../../core/properties\":\"core/properties\",\"./gesture_tool\":\"models/tools/gestures/gesture_tool\",underscore:\"underscore\"}],\"models/tools/inspectors/crosshair_tool\":[function(t,e,r){var n,o,i,s,a,l,u=function(t,e){function r(){this.constructor=t}for(var n in e)h.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},h={}.hasOwnProperty;a=t(\"underscore\"),i=t(\"./inspect_tool\"),s=t(\"../../annotations/span\"),l=t(\"../../../core/properties\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype._move=function(t){var e,r,n,o,i,s,a,l,u,h;if(this.mget(\"active\")){for(n=this.plot_model.get(\"frame\"),e=this.plot_model.get(\"canvas\"),u=e.sx_to_vx(t.bokeh.sx),h=e.sy_to_vy(t.bokeh.sy),s=this.mget(\"dimensions\"),a=[],o=0,i=s.length;o<i;o++)r=s[o],l=this.mget(\"spans\")[r],n.contains(u,h)?\"width\"===r?a.push(l.set(\"computed_location\",h)):a.push(l.set(\"computed_location\",u)):a.push(l.unset(\"computed_location\"));return a}},e.prototype._move_exit=function(t){var e,r,n,o,i,s;for(o=this.mget(\"dimensions\"),i=[],r=0,n=o.length;r<n;r++)e=o[r],s=this.mget(\"spans\")[e],i.push(s.unset(\"computed_location\"));return i},e}(i.View),n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return u(e,t),e.prototype.default_view=o,e.prototype.type=\"CrosshairTool\",e.prototype.tool_name=\"Crosshair\",e.define({dimensions:[l.Array,[\"width\",\"height\"]],line_color:[l.Color,\"black\"],line_width:[l.Number,1],line_alpha:[l.Number,1]}),e.internal({location_units:[l.SpatialUnits,\"screen\"],render_mode:[l.RenderMode,\"css\"],spans:[l.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.override_computed_property(\"tooltip\",function(){return this._get_dim_tooltip(\"Crosshair\",this._check_dims(this.get(\"dimensions\"),\"crosshair tool\"))},!1),this.add_dependencies(\"tooltip\",this,[\"dimensions\"]),this.spans={width:new s.Model({for_hover:!0,dimension:\"width\",render_mode:this.get(\"render_mode\"),location_units:this.get(\"location_units\"),line_color:this.get(\"line_color\"),line_width:this.get(\"line_width\"),line_alpha:this.get(\"line_alpha\")}),height:new s.Model({for_hover:!0,dimension:\"height\",render_mode:this.get(\"render_mode\"),location_units:this.get(\"location_units\"),line_color:this.get(\"line_color\"),line_width:this.get(\"line_width\"),line_alpha:this.get(\"line_alpha\")})},this.override_computed_property(\"synthetic_renderers\",function(t){return function(){return a.values(t.get(\"spans\"))}}(this),!0)},e}(i.Model),e.exports={Model:n,View:o}},{\"../../../core/properties\":\"core/properties\",\"../../annotations/span\":\"models/annotations/span\",\"./inspect_tool\":\"models/tools/inspectors/inspect_tool\",underscore:\"underscore\"}],\"models/tools/inspectors/hover_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f=function(t,e){function r(){this.constructor=t}for(var n in e)m.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},m={}.hasOwnProperty;h=t(\"underscore\"),n=t(\"jquery\"),a=t(\"./inspect_tool\"),l=t(\"../../annotations/tooltip\"),o=t(\"../../renderers/glyph_renderer\"),p=t(\"../../../common/hittest\"),_=t(\"../../../core/logging\").logger,d=t(\"../../../core/properties\"),u=t(\"../../../util/util\"),c=function(t){var e,r,n,o,i;return\"#\"===t.substr(0,1)?t:(r=/(.*?)rgb\\((\\d+), (\\d+), (\\d+)\\)/.exec(t),o=parseInt(r[2]),n=parseInt(r[3]),e=parseInt(r[4]),i=e|n<<8|o<<16,r[1]+\"#\"+i.toString(16))},s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return f(e,t),e.prototype.bind_bokeh_events=function(){var t,e,r,n;for(n=this.mget(\"computed_renderers\"),t=0,e=n.length;t<e;t++)r=n[t],this.listenTo(r.data_source,\"inspect\",this._update);return this.plot_view.canvas_view.$el.css(\"cursor\",\"crosshair\")},e.prototype._clear=function(){var t,e,r,n;this._inspect(1/0,1/0),t=this.mget(\"ttmodels\"),e=[];for(r in t)n=t[r],e.push(n.clear());return e},e.prototype._move=function(t){var e,r,n;if(this.mget(\"active\"))return e=this.plot_view.canvas,r=e.sx_to_vx(t.bokeh.sx),n=e.sy_to_vy(t.bokeh.sy),this.plot_view.frame.contains(r,n)?this._inspect(r,n):this._clear()},e.prototype._move_exit=function(){return this._clear()},e.prototype._inspect=function(t,e,r){var n,o,i,s,a,l,u,h;for(n={type:\"point\",vx:t,vy:e},\"mouse\"===this.model.mode?n.type=\"point\":(n.type=\"span\",\"vline\"===this.model.mode?n.direction=\"h\":n.direction=\"v\"),o=[],i=[],u=this.mget(\"computed_renderers\"),s=0,a=u.length;s<a;s++)l=u[s],h=l.data_source.get(\"selection_manager\"),h.inspect(this,this.plot_view.renderer_views[l.id],n,{geometry:n});null!=this.mget(\"callback\")&&this._emit_callback(n)},e.prototype._update=function(t,e,r,n,o){var i,s,a,l,u,c,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O,q,D,I,R,F,B,L,G,V,U,Y,H,X,$,W,J,Q,K,Z,tt,et,rt,nt;if(g=o.geometry,J=null!=(z=this.mget(\"ttmodels\")[r.model.id])?z:null,null!=J&&(J.clear(),null!==t[\"0d\"].glyph||0!==t[\"1d\"].indices.length)){for(K=g.vx,Z=g.vy,i=this.plot_model.get(\"canvas\"),m=this.plot_model.get(\"frame\"),$=i.vx_to_sx(K),W=i.vy_to_sy(Z),et=m.get(\"x_mappers\")[r.mget(\"x_range_name\")],nt=m.get(\"y_mappers\")[r.mget(\"y_range_name\")],tt=et.map_from_target(K),rt=nt.map_from_target(Z),P=t[\"0d\"].indices,b=0,w=P.length;b<w;b++)y=P[b],c=r.glyph._x[y+1],_=r.glyph._y[y+1],\"interp\"===this.model.line_policy?(D=r.glyph.get_interpolation_hit(y,g),c=D[0],_=D[1],U=et.map_to_target(c),Y=nt.map_to_target(_)):\"prev\"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sx[y]),Y=i.sy_to_vy(r.glyph.sy[y])):\"next\"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sx[y+1]),Y=i.sy_to_vy(r.glyph.sy[y+1])):\"nearest\"===this.model.line_policy?(s=r.glyph.sx[y],a=r.glyph.sy[y],d=p.dist_2_pts(s,a,$,W),l=r.glyph.sx[y+1],u=r.glyph.sy[y+1],f=p.dist_2_pts(l,u,$,W),d<f?(I=[s,a],H=I[0],X=I[1]):(R=[l,u],H=R[0],X=R[1],y+=1),c=r.glyph._x[y],_=r.glyph._y[y],U=i.sx_to_vx(H),Y=i.sy_to_vy(X)):(F=[K,Z],U=F[0],Y=F[1]),Q={index:y,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_,rx:U,ry:Y},J.add(U,Y,this._render_tooltips(n,y,Q));for(B=t[\"1d\"].indices,x=0,M=B.length;x<M;x++)if(y=B[x],h.isEmpty(t[\"2d\"]))c=null!=(C=r.glyph._x)?C[y]:void 0,_=null!=(N=r.glyph._y)?N[y]:void 0,\"snap_to_data\"===this.model.point_policy?(S=r.glyph.get_anchor_point(this.model.anchor,y,[$,W]),null!=S?(tt=S.x,rt=S.y):(O=r.glyph.get_anchor_point(\"center\",y,[$,W]),tt=O.x,rt=O.y),U=i.sx_to_vx(tt),Y=i.sy_to_vy(rt)):(q=[K,Z],U=q[0],Y=q[1]),Q={index:y,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_},J.add(U,Y,this._render_tooltips(n,y,Q));else for(L=h.pairs(t[\"2d\"]),j=0,k=L.length;j<k;j++)T=L[j],G=[T[0],T[1][0]],y=G[0],v=G[1],c=r.glyph._xs[y][v],_=r.glyph._ys[y][v],\"interp\"===this.model.line_policy?(V=r.glyph.get_interpolation_hit(y,v,g),c=V[0],_=V[1],U=et.map_to_target(c),Y=nt.map_to_target(_)):\"prev\"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sxs[y][v]),Y=i.sy_to_vy(r.glyph.sys[y][v])):\"next\"===this.model.line_policy?(U=i.sx_to_vx(r.glyph.sxs[y][v+1]),Y=i.sy_to_vy(r.glyph.sys[y][v+1])):\"nearest\"===this.model.line_policy&&(s=r.glyph.sx[y][v],a=r.glyph.sy[y][v],d=p.dist_2_pts(s,a,$,W),l=r.glyph.sx[y][v+1],u=r.glyph.sy[y][v+1],f=p.dist_2_pts(l,u,$,W),d<f?(E=[s,a],H=E[0],X=E[1]):(A=[l,u],H=A[0],X=A[1],v+=1),c=r.glyph._x[y][v],_=r.glyph._y[y][v],U=i.sx_to_vx(H),Y=i.sy_to_vy(X)),Q={index:y,segment_index:v,x:tt,y:rt,vx:K,vy:Z,sx:$,sy:W,data_x:c,data_y:_\n},J.add(U,Y,this._render_tooltips(n,y,Q));return null}},e.prototype._emit_callback=function(t){var e,r,n,o,i,s,a,l,u,c;a=this.mget(\"computed_renderers\")[0],i=this.plot_view.renderer_views[a.id].hit_test(t),r=this.plot_model.canvas,o=this.plot_model.frame,t.sx=r.vx_to_sx(t.vx),t.sy=r.vy_to_sy(t.vy),u=o.get(\"x_mappers\")[a.get(\"x_range_name\")],c=o.get(\"y_mappers\")[a.get(\"y_range_name\")],t.x=u.map_from_target(t.vx),t.y=c.map_from_target(t.vy),e=this.model.callback,l=[e,{index:i,geometry:t}],s=l[0],n=l[1],h.isFunction(e)?e(s,n):e.execute(s,n)},e.prototype._render_tooltips=function(t,e,r){var o,i,s,a,l,p,_,d,f,m,g,y,v,b,x,w,M,k;if(M=this.mget(\"tooltips\"),h.isString(M))return n(\"<div>\").html(u.replace_placeholders(M,t,e,r));if(h.isFunction(M))return M(t,r);for(x=n(\"<table></table>\"),l=0,_=M.length;l<_;l++){if(m=M[l],p=m[0],k=m[1],y=n(\"<tr></tr>\"),y.append(n(\"<td class='bk-tooltip-row-label'>\").text(p+\": \")),w=n(\"<td class='bk-tooltip-row-value'></td>\"),k.indexOf(\"$color\")>=0){if(g=k.match(/\\$color(\\[.*\\])?:(\\w*)/),d=g[0],f=g[1],o=g[2],s=t.get_column(o),null==s){v=n(\"<span>\").text(o+\" unknown\"),w.append(v);continue}if(a=(null!=f?f.indexOf(\"hex\"):void 0)>=0,b=(null!=f?f.indexOf(\"swatch\"):void 0)>=0,i=s[e],null==i){v=n(\"<span>(null)</span>\"),w.append(v);continue}a&&(i=c(i)),v=n(\"<span>\").text(i),w.append(v),b&&(v=n(\"<span class='bk-tooltip-color-block'> </span>\"),v.css({backgroundColor:i})),w.append(v)}else k=k.replace(\"$~\",\"$data_\"),k=u.replace_placeholders(k,t,e,r),w.append(n(\"<span>\").html(k));y.append(w),x.append(y)}return x},e}(a.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return f(e,t),e.prototype.default_view=s,e.prototype.type=\"HoverTool\",e.prototype.tool_name=\"Hover Tool\",e.prototype.icon=\"bk-tool-icon-hover\",e.define({tooltips:[d.Any,[[\"index\",\"$index\"],[\"data (x, y)\",\"($x, $y)\"],[\"canvas (x, y)\",\"($sx, $sy)\"]]],renderers:[d.Array,[]],names:[d.Array,[]],mode:[d.String,\"mouse\"],point_policy:[d.String,\"snap_to_data\"],line_policy:[d.String,\"prev\"],show_arrow:[d.Boolean,!0],anchor:[d.String,\"center\"],attachment:[d.String,\"horizontal\"],callback:[d.Any]}),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"computed_renderers\",function(){var t,e,r,n;return n=this.get(\"renderers\"),e=this.get(\"names\"),0===n.length&&(t=this.get(\"plot\").get(\"renderers\"),n=function(){var e,n,i;for(i=[],e=0,n=t.length;e<n;e++)r=t[e],r instanceof o.Model&&i.push(r);return i}()),e.length>0&&(n=function(){var t,o,i;for(i=[],t=0,o=n.length;t<o;t++)r=n[t],e.indexOf(r.get(\"name\"))>=0&&i.push(r);return i}()),n},!0),this.add_dependencies(\"computed_renderers\",this,[\"renderers\",\"names\",\"plot\"]),this.add_dependencies(\"computed_renderers\",this.get(\"plot\"),[\"renderers\"]),this.define_computed_property(\"ttmodels\",function(){var t,e,r,n,o,i,s;if(s={},i=this.get(\"tooltips\"),null!=i)for(n=this.get(\"computed_renderers\"),t=0,e=n.length;t<e;t++)r=n[t],o=new l.Model({custom:h.isString(i)||h.isFunction(i),attachment:this.attachment,show_arrow:this.show_arrow}),s[r.id]=o;return s},!0),this.add_dependencies(\"ttmodels\",this,[\"computed_renderers\",\"tooltips\"]),this.override_computed_property(\"synthetic_renderers\",function(){return h.values(this.get(\"ttmodels\"))},!0),this.add_dependencies(\"synthetic_renderers\",this,[\"ttmodels\"])},e}(a.Model),e.exports={Model:i,View:s}},{\"../../../common/hittest\":\"common/hittest\",\"../../../core/logging\":\"core/logging\",\"../../../core/properties\":\"core/properties\",\"../../../util/util\":\"util/util\",\"../../annotations/tooltip\":\"models/annotations/tooltip\",\"../../renderers/glyph_renderer\":\"models/renderers/glyph_renderer\",\"./inspect_tool\":\"models/tools/inspectors/inspect_tool\",jquery:\"jquery\",underscore:\"underscore\"}],\"models/tools/inspectors/inspect_tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;l=t(\"underscore\"),n=t(\"backbone\"),a=t(\"../tool\"),u=t(\"./inspect_tool_list_item_template\"),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.className=\"bk-toolbar-inspector\",e.prototype.template=u,e.prototype.events={'click [type=\"checkbox\"]':\"_clicked\"},e.prototype.initialize=function(t){return this.listenTo(this.model,\"change:active\",this.render),this.render()},e.prototype.render=function(){return this.$el.html(this.template({model:this.model})),this},e.prototype._clicked=function(t){var e;return e=this.model.get(\"active\"),this.model.set(\"active\",!e)},e}(n.View),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e}(a.View),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.event_type=\"move\",e.override({active:!0}),e.prototype.bind_bokeh_events=function(){return e.__super__.bind_bokeh_events.call(this),this.listenTo(events,\"move\",this._inspect)},e.prototype._inspect=function(t,e,r){},e.prototype._exit_inner=function(){},e.prototype._exit_outer=function(){},e}(a.Model),e.exports={Model:o,View:s,ListItemView:i}},{\"../tool\":\"models/tools/tool\",\"./inspect_tool_list_item_template\":\"models/tools/inspectors/inspect_tool_list_item_template\",backbone:\"backbone\",underscore:\"underscore\"}],\"models/tools/inspectors/inspect_tool_list_item_template\":[function(t,e,r){e.exports=function(t){t||(t={});var e=[],r=function(t){return t&&t.ecoSafe?t:\"undefined\"!=typeof t&&null!=t?n(t):\"\"},n=function(t){return(\"\"+t).replace(/&/g,\"&amp;\").replace(/</g,\"&lt;\").replace(/>/g,\"&gt;\").replace(/\"/g,\"&quot;\")};return function(){(function(){e.push('<input type=\"checkbox\" '),this.model.active&&e.push(\"checked\"),e.push(\">\"),e.push(r(this.model.tool_name)),e.push(\"</input>\\n\")}).call(this)}.call(t),e.join(\"\")}},{}],\"models/tools/tool\":[function(t,e,r){var n,o,i,s,a,l,u,h=function(t,e){function r(){this.constructor=t}for(var n in e)c.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},c={}.hasOwnProperty;a=t(\"underscore\"),o=t(\"../renderers/renderer\"),l=t(\"../../core/logging\").logger,u=t(\"../../core/properties\"),n=t(\"../../model\"),s=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.bind_bokeh_events=function(){return this.listenTo(this.model,\"change:active\",function(t){return function(){return t.mget(\"active\")?t.activate():t.deactivate()}}(this))},e.prototype.activate=function(){},e.prototype.deactivate=function(){},e}(o.View),i=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return h(e,t),e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.define_computed_property(\"synthetic_renderers\",function(){return[]},!0)},e.define({plot:[u.Instance]}),e.internal({level:[u.RenderLevel,\"overlay\"],active:[u.Boolean,!1]}),e.prototype._check_dims=function(t,e){var r,n,o;return n=[!1,!1],o=n[0],r=n[1],0===t.length?l.warn(e+\" given empty dimensions\"):1===t.length?\"width\"!==t[0]&&\"height\"!==t[0]&&l.warn(e+\" given unrecognized dimensions: \"+t):2===t.length?(t.indexOf(\"width\")<0||t.indexOf(\"height\")<0)&&l.warn(e+\" given unrecognized dimensions: \"+t):l.warn(e+\" given more than two dimensions: \"+t),t.indexOf(\"width\")>=0&&(o=!0),t.indexOf(\"height\")>=0&&(r=!0),[o,r]},e.prototype._get_dim_tooltip=function(t,e){var r,n;return n=e[0],r=e[1],n&&!r?t+\" (x-axis)\":r&&!n?t+\" (y-axis)\":t},e.prototype._get_dim_limits=function(t,e,r,n){var o,i,s,l,u,h,c,p;return s=t[0],h=t[1],l=e[0],c=e[1],o=r.get(\"h_range\"),n.indexOf(\"width\")>=0?(u=[a.min([s,l]),a.max([s,l])],u=[a.max([u[0],o.get(\"min\")]),a.min([u[1],o.get(\"max\")])]):u=[o.get(\"min\"),o.get(\"max\")],i=r.get(\"v_range\"),n.indexOf(\"height\")>=0?(p=[a.min([h,c]),a.max([h,c])],p=[a.max([p[0],i.get(\"min\")]),a.min([p[1],i.get(\"max\")])]):p=[i.get(\"min\"),i.get(\"max\")],[u,p]},e}(n),e.exports={Model:i,View:s}},{\"../../core/logging\":\"core/logging\",\"../../core/properties\":\"core/properties\",\"../../model\":\"model\",\"../renderers/renderer\":\"models/renderers/renderer\",underscore:\"underscore\"}],\"models/tools/tool_proxy\":[function(t,e,r){var n,o,i,s=function(t,e){function r(){this.constructor=t}for(var n in e)a.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},a={}.hasOwnProperty;i=t(\"../../core/properties\"),n=t(\"../../model\"),o=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return s(e,t),e.prototype.initialize=function(t){return e.__super__.initialize.call(this,t),this.listenTo(this,\"do\",this[\"do\"]),this.listenTo(this,\"change:active\",this.set_active)},e.prototype[\"do\"]=function(){var t,e,r,n;for(r=this.tools,t=0,e=r.length;t<e;t++)n=r[t],n.trigger(\"do\");return null},e.prototype.set_active=function(){var t,e,r,n;for(r=this.tools,t=0,e=r.length;t<e;t++)n=r[t],n.active=this.active;return null},e.define({tools:[i.Array,[]],active:[i.Bool,!1],tooltip:[i.String],tool_name:[i.String],disabled:[i.Bool,!1],event_type:[i.String],icon:[i.String]}),e.prototype._clicked=function(){var t;return t=this.model.get(\"active\"),this.model.set(\"active\",!t)},e}(n),e.exports={ToolProxy:o}},{\"../../core/properties\":\"core/properties\",\"../../model\":\"model\"}],\"models/tools/toolbar\":[function(t,e,r){var n,o,i,s,a,l,u,h,c=function(t,e){function r(){this.constructor=t}for(var n in e)p.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},p={}.hasOwnProperty;u=t(\"underscore\"),h=t(\"../../core/properties\"),n=t(\"./actions/action_tool\"),i=t(\"./actions/help_tool\"),o=t(\"./gestures/gesture_tool\"),s=t(\"./inspectors/inspect_tool\"),l=t(\"./toolbar_base\"),a=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return c(e,t),e.prototype.type=\"Toolbar\",e.prototype.default_view=l.View,e.prototype.initialize=function(t,r){return e.__super__.initialize.call(this,t,r),this.listenTo(this,\"change:tools\",this._init_tools),this._init_tools()},e.prototype._init_tools=function(){var t,e,r,a,l,h,c;for(a=this.get(\"tools\"),e=0,r=a.length;e<r;e++)if(h=a[e],h instanceof s.Model)u.some(this.inspectors,function(t){return function(t){return t.id===h.id}}(this))||(this.inspectors=this.inspectors.concat([h]));else if(h instanceof i.Model)u.some(this.help,function(t){return function(t){return t.id===h.id}}(this))||(this.help=this.help.concat([h]));else if(h instanceof n.Model)u.some(this.actions,function(t){return function(t){return t.id===h.id}}(this))||(this.actions=this.actions.concat([h]));else if(h instanceof o.Model){if(t=h.event_type,!(t in this.gestures)){logger.warn(\"Toolbar: unknown event type '\"+t+\"' for tool: \"+h.type+\" (\"+h.id+\")\");continue}u.some(this.gestures[t].tools,function(t){return function(t){return t.id===h.id}}(this))||(this.gestures[t].tools=this.gestures[t].tools.concat([h])),this.listenTo(h,\"change:active\",u.bind(this._active_change,h))}l=[];for(t in this.gestures)if(c=this.gestures[t].tools,0!==c.length){if(this.gestures[t].tools=u.sortBy(c,function(t){return t.default_order}),\"tap\"===t){if(null===this.active_tap)continue;\"auto\"===this.active_tap?this.gestures[t].tools[0].active=!0:this.active_tap.active=!0}if(\"pan\"===t){if(null===this.active_drag)continue;\"auto\"===this.active_drag?this.gestures[t].tools[0].active=!0:this.active_drag.active=!0}if(\"pinch\"===t||\"scroll\"===t){if(null===this.active_scroll||\"auto\"===this.active_scroll)continue;l.push(this.active_scroll.active=!0)}else l.push(void 0)}return l},e.define({active_drag:[h.Any,\"auto\"],active_scroll:[h.Any,\"auto\"],active_tap:[h.Any,\"auto\"]}),e}(l.Model),e.exports={Model:a}},{\"../../core/properties\":\"core/properties\",\"./actions/action_tool\":\"models/tools/actions/action_tool\",\"./actions/help_tool\":\"models/tools/actions/help_tool\",\"./gestures/gesture_tool\":\"models/tools/gestures/gesture_tool\",\"./inspectors/inspect_tool\":\"models/tools/inspectors/inspect_tool\",\"./toolbar_base\":\"models/tools/toolbar_base\",underscore:\"underscore\"}],\"models/tools/toolbar_base\":[function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_,d,f,m,g,y,v=function(t,e){function r(){this.constructor=t}for(var n in e)b.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},b={}.hasOwnProperty,x=function(t,e){return function(){return 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class='bk-button-bar-list' type=\\\"rotate\\\" />\\n    <ul class='bk-button-bar-list' type=\\\"actions\\\" />\\n    <div class='bk-button-bar-list bk-bs-dropdown' type=\\\"inspectors\\\" />\\n    <ul class='bk-button-bar-list' type=\\\"help\\\" />\\n  </div>\\n</div>\\n\")}).call(this)}.call(t),e.join(\"\")}},{}],\"models/transforms/interpolator\":[function(t,e,r){var n,o,i,s,a,l=function(t,e){function r(){this.constructor=t}for(var n in e)u.call(e,n)&&(t[n]=e[n]);return r.prototype=e.prototype,t.prototype=new r,t.__super__=e.prototype,t},u={}.hasOwnProperty,h=[].indexOf||function(t){for(var e=0,r=this.length;e<r;e++)if(e in this&&this[e]===t)return e;return-1};i=t(\"underscore\"),o=t(\"./transform\"),a=t(\"../../core/properties\"),s=t(\"../../core/logging\").logger,n=function(t){function e(){return e.__super__.constructor.apply(this,arguments)}return l(e,t),e.prototype.initialize=function(t,r){return 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r.clone(this._previousAttributes)},fetch:function(t){t=r.extend({parse:!0},t);var e=this,n=t.success;return t.success=function(r){var o=t.parse?e.parse(r,t):r;return!!e.set(o,t)&&(n&&n.call(t.context,e,r,t),void e.trigger(\"sync\",e,r,t))},L(this,t),this.sync(\"read\",this,t)},save:function(t,e,n){var o;null==t||\"object\"==typeof t?(o=t,n=e):(o={})[t]=e,n=r.extend({validate:!0,parse:!0},n);var i=n.wait;if(o&&!i){if(!this.set(o,n))return!1}else if(!this._validate(o,n))return!1;var s=this,a=n.success,l=this.attributes;n.success=function(t){s.attributes=l;var e=n.parse?s.parse(t,n):t;return i&&(e=r.extend({},o,e)),!(e&&!s.set(e,n))&&(a&&a.call(n.context,s,t,n),void s.trigger(\"sync\",s,t,n))},L(this,n),o&&i&&(this.attributes=r.extend({},l,o));var u=this.isNew()?\"create\":n.patch?\"patch\":\"update\";\"patch\"!==u||n.attrs||(n.attrs=o);var h=this.sync(u,this,n);return this.attributes=l,h},destroy:function(t){t=t?r.clone(t):{};var e=this,n=t.success,o=t.wait,i=function(){e.stopListening(),e.trigger(\"destroy\",e,e.collection,t)};t.success=function(r){o&&i(),n&&n.call(t.context,e,r,t),e.isNew()||e.trigger(\"sync\",e,r,t)};var s=!1;return this.isNew()?r.defer(t.success):(L(this,t),s=this.sync(\"delete\",this,t)),o||i(),s},url:function(){var t=r.result(this,\"urlRoot\")||r.result(this.collection,\"url\")||B();if(this.isNew())return t;var e=this.get(this.idAttribute);return t.replace(/[^\\/]$/,\"$&/\")+encodeURIComponent(e)},parse:function(t,e){return t},clone:function(){return new this.constructor(this.attributes)},isNew:function(){return!this.has(this.idAttribute)},isValid:function(t){return this._validate({},r.extend({},t,{validate:!0}))},_validate:function(t,e){if(!e.validate||!this.validate)return!0;t=r.extend({},this.attributes,t);var n=this.validationError=this.validate(t,e)||null;return!n||(this.trigger(\"invalid\",this,n,r.extend(e,{validationError:n})),!1)}});var b={keys:1,values:1,pairs:1,invert:1,pick:0,omit:0,chain:1,isEmpty:1};a(v,b,\"attributes\");var x=e.Collection=function(t,e){e||(e={}),e.model&&(this.model=e.model),void 0!==e.comparator&&(this.comparator=e.comparator),this._reset(),this.initialize.apply(this,arguments),t&&this.reset(t,r.extend({silent:!0},e))},w={add:!0,remove:!0,merge:!0},M={add:!0,remove:!1},k=function(t,e,r){r=Math.min(Math.max(r,0),t.length);var n,o=Array(t.length-r),i=e.length;for(n=0;n<o.length;n++)o[n]=t[n+r];for(n=0;n<i;n++)t[n+r]=e[n];for(n=0;n<o.length;n++)t[n+i+r]=o[n]};r.extend(x.prototype,h,{model:v,initialize:function(){},toJSON:function(t){return this.map(function(e){return e.toJSON(t)})},sync:function(){return e.sync.apply(this,arguments)},add:function(t,e){return this.set(t,r.extend({merge:!1},e,M))},remove:function(t,e){e=r.extend({},e);var n=!r.isArray(t);t=n?[t]:t.slice();var o=this._removeModels(t,e);return!e.silent&&o.length&&(e.changes={added:[],merged:[],removed:o},this.trigger(\"update\",this,e)),n?o[0]:o},set:function(t,e){if(null!=t){e=r.extend({},w,e),e.parse&&!this._isModel(t)&&(t=this.parse(t,e)||[]);var n=!r.isArray(t);t=n?[t]:t.slice();var o=e.at;null!=o&&(o=+o),o>this.length&&(o=this.length),o<0&&(o+=this.length+1);var i,s,a=[],l=[],u=[],h=[],c={},p=e.add,_=e.merge,d=e.remove,f=!1,m=this.comparator&&null==o&&e.sort!==!1,g=r.isString(this.comparator)?this.comparator:null;for(s=0;s<t.length;s++){i=t[s];var y=this.get(i);if(y){if(_&&i!==y){var v=this._isModel(i)?i.attributes:i;e.parse&&(v=y.parse(v,e)),y.set(v,e),u.push(y),m&&!f&&(f=y.hasChanged(g))}c[y.cid]||(c[y.cid]=!0,a.push(y)),t[s]=y}else p&&(i=t[s]=this._prepareModel(i,e),i&&(l.push(i),this._addReference(i,e),c[i.cid]=!0,a.push(i)))}if(d){for(s=0;s<this.length;s++)i=this.models[s],c[i.cid]||h.push(i);h.length&&this._removeModels(h,e)}var b=!1,x=!m&&p&&d;if(a.length&&x?(b=this.length!==a.length||r.some(this.models,function(t,e){return t!==a[e]}),this.models.length=0,k(this.models,a,0),this.length=this.models.length):l.length&&(m&&(f=!0),k(this.models,l,null==o?this.length:o),this.length=this.models.length),f&&this.sort({silent:!0}),!e.silent){for(s=0;s<l.length;s++)null!=o&&(e.index=o+s),i=l[s],i.trigger(\"add\",i,this,e);(f||b)&&this.trigger(\"sort\",this,e),(l.length||h.length||u.length)&&(e.changes={added:l,removed:h,merged:u},this.trigger(\"update\",this,e))}return n?t[0]:t}},reset:function(t,e){e=e?r.clone(e):{};for(var n=0;n<this.models.length;n++)this._removeReference(this.models[n],e);return e.previousModels=this.models,this._reset(),t=this.add(t,r.extend({silent:!0},e)),e.silent||this.trigger(\"reset\",this,e),t},push:function(t,e){return this.add(t,r.extend({at:this.length},e))},pop:function(t){var e=this.at(this.length-1);return this.remove(e,t)},unshift:function(t,e){return this.add(t,r.extend({at:0},e))},shift:function(t){var e=this.at(0);return this.remove(e,t)},slice:function(){return i.apply(this.models,arguments)},get:function(t){if(null!=t)return this._byId[t]||this._byId[this.modelId(t.attributes||t)]||t.cid&&this._byId[t.cid]},has:function(t){return null!=this.get(t)},at:function(t){return t<0&&(t+=this.length),this.models[t]},where:function(t,e){return this[e?\"find\":\"filter\"](t)},findWhere:function(t){return this.where(t,!0)},sort:function(t){var e=this.comparator;if(!e)throw new Error(\"Cannot sort a set without a comparator\");t||(t={});var n=e.length;return r.isFunction(e)&&(e=r.bind(e,this)),1===n||r.isString(e)?this.models=this.sortBy(e):this.models.sort(e),t.silent||this.trigger(\"sort\",this,t),this},pluck:function(t){return this.map(t+\"\")},fetch:function(t){t=r.extend({parse:!0},t);var e=t.success,n=this;return t.success=function(r){var o=t.reset?\"reset\":\"set\";n[o](r,t),e&&e.call(t.context,n,r,t),n.trigger(\"sync\",n,r,t)},L(this,t),this.sync(\"read\",this,t)},create:function(t,e){e=e?r.clone(e):{};var n=e.wait;if(t=this._prepareModel(t,e),!t)return!1;n||this.add(t,e);var o=this,i=e.success;return e.success=function(t,e,r){n&&o.add(t,r),i&&i.call(r.context,t,e,r)},t.save(null,e),t},parse:function(t,e){return t},clone:function(){return new this.constructor(this.models,{model:this.model,comparator:this.comparator})},modelId:function(t){return t[this.model.prototype.idAttribute||\"id\"]},_reset:function(){this.length=0,this.models=[],this._byId={}},_prepareModel:function(t,e){if(this._isModel(t))return t.collection||(t.collection=this),t;e=e?r.clone(e):{},e.collection=this;var n=new this.model(t,e);return n.validationError?(this.trigger(\"invalid\",this,n.validationError,e),!1):n},_removeModels:function(t,e){for(var r=[],n=0;n<t.length;n++){var o=this.get(t[n]);if(o){var i=this.indexOf(o);this.models.splice(i,1),this.length--,delete this._byId[o.cid];var s=this.modelId(o.attributes);null!=s&&delete this._byId[s],e.silent||(e.index=i,o.trigger(\"remove\",o,this,e)),r.push(o),this._removeReference(o,e)}}return r},_isModel:function(t){return t instanceof v},_addReference:function(t,e){this._byId[t.cid]=t;var r=this.modelId(t.attributes);null!=r&&(this._byId[r]=t),t.on(\"all\",this._onModelEvent,this)},_removeReference:function(t,e){delete this._byId[t.cid];var r=this.modelId(t.attributes);null!=r&&delete this._byId[r],this===t.collection&&delete t.collection,t.off(\"all\",this._onModelEvent,this)},_onModelEvent:function(t,e,r,n){if(e){if((\"add\"===t||\"remove\"===t)&&r!==this)return;if(\"destroy\"===t&&this.remove(e,n),\"change\"===t){var o=this.modelId(e.previousAttributes()),i=this.modelId(e.attributes);o!==i&&(null!=o&&delete this._byId[o],null!=i&&(this._byId[i]=e))}}this.trigger.apply(this,arguments)}});var j={forEach:3,each:3,map:3,collect:3,reduce:0,foldl:0,inject:0,reduceRight:0,foldr:0,find:3,detect:3,filter:3,select:3,reject:3,every:3,all:3,some:3,any:3,include:3,includes:3,contains:3,invoke:0,max:3,min:3,toArray:1,size:1,first:3,head:3,take:3,initial:3,rest:3,tail:3,drop:3,last:3,without:0,difference:0,indexOf:3,shuffle:1,lastIndexOf:3,isEmpty:1,chain:1,sample:3,partition:3,groupBy:3,countBy:3,sortBy:3,indexBy:3,findIndex:3,findLastIndex:3};a(x,j,\"models\");var T=e.View=function(t){this.cid=r.uniqueId(\"view\"),r.extend(this,r.pick(t,z)),this._ensureElement(),this.initialize.apply(this,arguments)},S=/^(\\S+)\\s*(.*)$/,z=[\"model\",\"collection\",\"el\",\"id\",\"attributes\",\"className\",\"tagName\",\"events\"];r.extend(T.prototype,h,{tagName:\"div\",$:function(t){return this.$el.find(t)},initialize:function(){},render:function(){return this},remove:function(){return this._removeElement(),this.stopListening(),this},_removeElement:function(){this.$el.remove()},setElement:function(t){return this.undelegateEvents(),this._setElement(t),this.delegateEvents(),this},_setElement:function(t){this.$el=t instanceof e.$?t:e.$(t),this.el=this.$el[0]},delegateEvents:function(t){if(t||(t=r.result(this,\"events\")),!t)return this;this.undelegateEvents();for(var e in t){var n=t[e];if(r.isFunction(n)||(n=this[n]),n){var o=e.match(S);this.delegate(o[1],o[2],r.bind(n,this))}}return this},delegate:function(t,e,r){return this.$el.on(t+\".delegateEvents\"+this.cid,e,r),this},undelegateEvents:function(){return this.$el&&this.$el.off(\".delegateEvents\"+this.cid),this},undelegate:function(t,e,r){return this.$el.off(t+\".delegateEvents\"+this.cid,e,r),this},_createElement:function(t){return document.createElement(t)},_ensureElement:function(){if(this.el)this.setElement(r.result(this,\"el\"));else{var t=r.extend({},r.result(this,\"attributes\"));this.id&&(t.id=r.result(this,\"id\")),this.className&&(t[\"class\"]=r.result(this,\"className\")),this.setElement(this._createElement(r.result(this,\"tagName\"))),this._setAttributes(t)}},_setAttributes:function(t){this.$el.attr(t)}}),e.sync=function(t,n,o){var i=P[t];r.defaults(o||(o={}),{emulateHTTP:e.emulateHTTP,emulateJSON:e.emulateJSON});var s={type:i,dataType:\"json\"};if(o.url||(s.url=r.result(n,\"url\")||B()),null!=o.data||!n||\"create\"!==t&&\"update\"!==t&&\"patch\"!==t||(s.contentType=\"application/json\",s.data=JSON.stringify(o.attrs||n.toJSON(o))),o.emulateJSON&&(s.contentType=\"application/x-www-form-urlencoded\",s.data=s.data?{model:s.data}:{}),o.emulateHTTP&&(\"PUT\"===i||\"DELETE\"===i||\"PATCH\"===i)){s.type=\"POST\",o.emulateJSON&&(s.data._method=i);var a=o.beforeSend;o.beforeSend=function(t){if(t.setRequestHeader(\"X-HTTP-Method-Override\",i),a)return a.apply(this,arguments)}}\"GET\"===s.type||o.emulateJSON||(s.processData=!1);var l=o.error;o.error=function(t,e,r){o.textStatus=e,o.errorThrown=r,l&&l.call(o.context,t,e,r)};var u=o.xhr=e.ajax(r.extend(s,o));return n.trigger(\"request\",n,u,o),u};var P={create:\"POST\",update:\"PUT\",patch:\"PATCH\",\"delete\":\"DELETE\",read:\"GET\"};e.ajax=function(){return e.$.ajax.apply(e.$,arguments)};var E=e.Router=function(t){t||(t={}),t.routes&&(this.routes=t.routes),this._bindRoutes(),this.initialize.apply(this,arguments)},A=/\\((.*?)\\)/g,C=/(\\(\\?)?:\\w+/g,N=/\\*\\w+/g,O=/[\\-{}\\[\\]+?.,\\\\\\^$|#\\s]/g;r.extend(E.prototype,h,{initialize:function(){},route:function(t,n,o){r.isRegExp(t)||(t=this._routeToRegExp(t)),r.isFunction(n)&&(o=n,n=\"\"),o||(o=this[n]);var i=this;return e.history.route(t,function(r){var s=i._extractParameters(t,r);i.execute(o,s,n)!==!1&&(i.trigger.apply(i,[\"route:\"+n].concat(s)),i.trigger(\"route\",n,s),e.history.trigger(\"route\",i,n,s))}),this},execute:function(t,e,r){t&&t.apply(this,e)},navigate:function(t,r){return e.history.navigate(t,r),this},_bindRoutes:function(){if(this.routes){this.routes=r.result(this,\"routes\");for(var t,e=r.keys(this.routes);null!=(t=e.pop());)this.route(t,this.routes[t])}},_routeToRegExp:function(t){return t=t.replace(O,\"\\\\$&\").replace(A,\"(?:$1)?\").replace(C,function(t,e){return e?t:\"([^/?]+)\"}).replace(N,\"([^?]*?)\"),new RegExp(\"^\"+t+\"(?:\\\\?([\\\\s\\\\S]*))?$\")},_extractParameters:function(t,e){var n=t.exec(e).slice(1);return r.map(n,function(t,e){return e===n.length-1?t||null:t?decodeURIComponent(t):null})}});var q=e.History=function(){this.handlers=[],this.checkUrl=r.bind(this.checkUrl,this),\"undefined\"!=typeof window&&(this.location=window.location,this.history=window.history)},D=/^[#\\/]|\\s+$/g,I=/^\\/+|\\/+$/g,R=/#.*$/;q.started=!1,r.extend(q.prototype,h,{interval:50,atRoot:function(){var t=this.location.pathname.replace(/[^\\/]$/,\"$&/\");return t===this.root&&!this.getSearch()},matchRoot:function(){var t=this.decodeFragment(this.location.pathname),e=t.slice(0,this.root.length-1)+\"/\";return e===this.root},decodeFragment:function(t){return decodeURI(t.replace(/%25/g,\"%2525\"))},getSearch:function(){var t=this.location.href.replace(/#.*/,\"\").match(/\\?.+/);return t?t[0]:\"\"},getHash:function(t){var e=(t||this).location.href.match(/#(.*)$/);return e?e[1]:\"\"},getPath:function(){var t=this.decodeFragment(this.location.pathname+this.getSearch()).slice(this.root.length-1);return\"/\"===t.charAt(0)?t.slice(1):t},getFragment:function(t){return null==t&&(t=this._usePushState||!this._wantsHashChange?this.getPath():this.getHash()),t.replace(D,\"\")},start:function(t){if(q.started)throw new Error(\"Backbone.history has already been started\");if(q.started=!0,this.options=r.extend({root:\"/\"},this.options,t),this.root=this.options.root,this._wantsHashChange=this.options.hashChange!==!1,this._hasHashChange=\"onhashchange\"in window&&(void 0===document.documentMode||document.documentMode>7),this._useHashChange=this._wantsHashChange&&this._hasHashChange,this._wantsPushState=!!this.options.pushState,this._hasPushState=!(!this.history||!this.history.pushState),this._usePushState=this._wantsPushState&&this._hasPushState,this.fragment=this.getFragment(),this.root=(\"/\"+this.root+\"/\").replace(I,\"/\"),this._wantsHashChange&&this._wantsPushState){if(!this._hasPushState&&!this.atRoot()){var e=this.root.slice(0,-1)||\"/\";return this.location.replace(e+\"#\"+this.getPath()),!0}this._hasPushState&&this.atRoot()&&this.navigate(this.getHash(),{replace:!0})}if(!this._hasHashChange&&this._wantsHashChange&&!this._usePushState){this.iframe=document.createElement(\"iframe\"),this.iframe.src=\"javascript:0\",this.iframe.style.display=\"none\",this.iframe.tabIndex=-1;var n=document.body,o=n.insertBefore(this.iframe,n.firstChild).contentWindow;o.document.open(),o.document.close(),o.location.hash=\"#\"+this.fragment}var i=window.addEventListener||function(t,e){return attachEvent(\"on\"+t,e)};if(this._usePushState?i(\"popstate\",this.checkUrl,!1):this._useHashChange&&!this.iframe?i(\"hashchange\",this.checkUrl,!1):this._wantsHashChange&&(this._checkUrlInterval=setInterval(this.checkUrl,this.interval)),!this.options.silent)return this.loadUrl()},stop:function(){var t=window.removeEventListener||function(t,e){return detachEvent(\"on\"+t,e)};this._usePushState?t(\"popstate\",this.checkUrl,!1):this._useHashChange&&!this.iframe&&t(\"hashchange\",this.checkUrl,!1),this.iframe&&(document.body.removeChild(this.iframe),this.iframe=null),this._checkUrlInterval&&clearInterval(this._checkUrlInterval),q.started=!1},route:function(t,e){this.handlers.unshift({route:t,callback:e})},checkUrl:function(t){var e=this.getFragment();return e===this.fragment&&this.iframe&&(e=this.getHash(this.iframe.contentWindow)),e!==this.fragment&&(this.iframe&&this.navigate(e),void this.loadUrl())},loadUrl:function(t){return!!this.matchRoot()&&(t=this.fragment=this.getFragment(t),r.some(this.handlers,function(e){if(e.route.test(t))return e.callback(t),!0}))},navigate:function(t,e){if(!q.started)return!1;e&&e!==!0||(e={trigger:!!e}),t=this.getFragment(t||\"\");var r=this.root;\"\"!==t&&\"?\"!==t.charAt(0)||(r=r.slice(0,-1)||\"/\");var 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fe=1,me=2,ge=4,ye=8,ve=ye,be=16,xe=32;K.prototype={defaults:{},set:function(t){return _t(this.options,t),this.manager&&this.manager.touchAction.update(),this},recognizeWith:function(t){if(a(t,\"recognizeWith\",this))return this;var e=this.simultaneous;return t=et(t,this),e[t.id]||(e[t.id]=t,t.recognizeWith(this)),this},dropRecognizeWith:function(t){return a(t,\"dropRecognizeWith\",this)?this:(t=et(t,this),delete this.simultaneous[t.id],this)},requireFailure:function(t){if(a(t,\"requireFailure\",this))return this;var e=this.requireFail;return t=et(t,this),v(e,t)===-1&&(e.push(t),t.requireFailure(this)),this},dropRequireFailure:function(t){if(a(t,\"dropRequireFailure\",this))return this;t=et(t,this);var e=v(this.requireFail,t);return e>-1&&this.requireFail.splice(e,1),this},hasRequireFailures:function(){return this.requireFail.length>0},canRecognizeWith:function(t){return!!this.simultaneous[t.id]},emit:function(t){function e(e){r.manager.emit(e,t)}var 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e=t.scale<1?\"in\":\"out\";t.additionalEvent=this.options.event+e}this._super.emit.call(this,t)}}),h(it,K,{defaults:{event:\"press\",pointers:1,time:251,threshold:9},getTouchAction:function(){return[ue]},process:function(t){var e=this.options,r=t.pointers.length===e.pointers,n=t.distance<e.threshold,o=t.deltaTime>e.time;if(this._input=t,!n||!r||t.eventType&(Ot|qt)&&!o)this.reset();else if(t.eventType&Ct)this.reset(),this._timer=s(function(){this.state=ve,this.tryEmit()},e.time,this);else if(t.eventType&Ot)return ve;return xe},reset:function(){clearTimeout(this._timer)},emit:function(t){this.state===ve&&(t&&t.eventType&Ot?this.manager.emit(this.options.event+\"up\",t):(this._input.timeStamp=vt(),this.manager.emit(this.options.event,this._input)))}}),h(st,rt,{defaults:{event:\"rotate\",threshold:0,pointers:2},getTouchAction:function(){return[ce]},attrTest:function(t){return 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e=this.options,r=t.pointers.length===e.pointers,n=t.distance<e.threshold,o=t.deltaTime<e.time;if(this.reset(),t.eventType&Ct&&0===this.count)return this.failTimeout();if(n&&o&&r){if(t.eventType!=Ot)return this.failTimeout();var i=!this.pTime||t.timeStamp-this.pTime<e.interval,a=!this.pCenter||q(this.pCenter,t.center)<e.posThreshold;this.pTime=t.timeStamp,this.pCenter=t.center,a&&i?this.count+=1:this.count=1,this._input=t;var l=this.count%e.taps;if(0===l)return this.hasRequireFailures()?(this._timer=s(function(){this.state=ve,this.tryEmit()},e.interval,this),me):ve}return xe},failTimeout:function(){return 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l(y(t),function(t){e?r[t]&&r[t].splice(v(r[t],e),1):delete r[t]}),this}},emit:function(t,e){this.options.domEvents&&pt(t,e);var r=this.handlers[t]&&this.handlers[t].slice();if(r&&r.length){e.type=t,e.preventDefault=function(){e.srcEvent.preventDefault()};for(var 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r&&r.exports?r.exports=ut:e[o]=ut}(window,document,\"Hammer\")},{}],\"jquery-mousewheel\":[function(e,r,n){/*!\n * jQuery Mousewheel 3.1.13\n *\n * Copyright jQuery Foundation and other contributors\n * Released under the MIT license\n * http://jquery.org/license\n */\n!function(e){\"function\"==typeof t&&t.amd?t([\"jquery\"],e):\"object\"==typeof n?r.exports=e:e(jQuery)}(function(t){function e(e){var s=e||window.event,a=l.call(arguments,1),u=0,c=0,p=0,_=0,d=0,f=0;if(e=t.event.fix(s),e.type=\"mousewheel\",\"detail\"in s&&(p=s.detail*-1),\"wheelDelta\"in s&&(p=s.wheelDelta),\"wheelDeltaY\"in s&&(p=s.wheelDeltaY),\"wheelDeltaX\"in s&&(c=s.wheelDeltaX*-1),\"axis\"in s&&s.axis===s.HORIZONTAL_AXIS&&(c=p*-1,p=0),u=0===p?c:p,\"deltaY\"in s&&(p=s.deltaY*-1,u=p),\"deltaX\"in s&&(c=s.deltaX,0===p&&(u=c*-1)),0!==p||0!==c){if(1===s.deltaMode){var m=t.data(this,\"mousewheel-line-height\");u*=m,p*=m,c*=m}else if(2===s.deltaMode){var 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te=/^(none|table(?!-c[ea]).+)/,ee={position:\"absolute\",visibility:\"hidden\",display:\"block\"},re={letterSpacing:\"0\",fontWeight:\"400\"},ne=[\"Webkit\",\"O\",\"Moz\",\"ms\"],oe=J.createElement(\"div\").style;st.extend({cssHooks:{opacity:{get:function(t,e){if(e){var r=S(t,\"opacity\");return\"\"===r?\"1\":r}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{\"float\":\"cssFloat\"},style:function(t,e,r,n){if(t&&3!==t.nodeType&&8!==t.nodeType&&t.style){var o,i,s,a=st.camelCase(e),l=t.style;return e=st.cssProps[a]||(st.cssProps[a]=P(a)||a),s=st.cssHooks[e]||st.cssHooks[a],void 0===r?s&&\"get\"in s&&void 0!==(o=s.get(t,!1,n))?o:l[e]:(i=typeof 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s&&(o=At.exec(r))&&\"px\"!==(o[3]||\"px\")&&(t.style[e]=r,r=st.css(t,e)),E(t,r,s)}}}),st.cssHooks.marginLeft=z(ot.reliableMarginLeft,function(t,e){if(e)return(parseFloat(S(t,\"marginLeft\"))||t.getBoundingClientRect().left-Kt(t,{marginLeft:0},function(){return t.getBoundingClientRect().left}))+\"px\"}),st.cssHooks.marginRight=z(ot.reliableMarginRight,function(t,e){if(e)return Kt(t,{display:\"inline-block\"},S,[t,\"marginRight\"])}),st.each({margin:\"\",padding:\"\",border:\"Width\"},function(t,e){st.cssHooks[t+e]={expand:function(r){for(var n=0,o={},i=\"string\"==typeof r?r.split(\" \"):[r];n<4;n++)o[t+Ct[n]+e]=i[n]||i[n-2]||i[0];return o}},Wt.test(t)||(st.cssHooks[t+e].set=E)}),st.fn.extend({css:function(t,e){return kt(this,function(t,e,r){var n,o,i={},s=0;if(st.isArray(e)){for(n=Qt(t),o=e.length;s<o;s++)i[e[s]]=st.css(t,e[s],!1,n);return i}return void 0!==r?st.style(t,e,r):st.css(t,e)},t,e,arguments.length>1)},show:function(){return N(this,!0)},hide:function(){return N(this)},toggle:function(t){return\"boolean\"==typeof t?t?this.show():this.hide():this.each(function(){Nt(this)?st(this).show():st(this).hide()})}}),st.Tween=O,O.prototype={constructor:O,init:function(t,e,r,n,o,i){this.elem=t,this.prop=r,this.easing=o||st.easing._default,this.options=e,this.start=this.now=this.cur(),this.end=n,this.unit=i||(st.cssNumber[r]?\"\":\"px\")},cur:function(){var t=O.propHooks[this.prop];return t&&t.get?t.get(this):O.propHooks._default.get(this)},run:function(t){var e,r=O.propHooks[this.prop];return this.options.duration?this.pos=e=st.easing[this.easing](t,this.options.duration*t,0,1,this.options.duration):this.pos=e=t,this.now=(this.end-this.start)*e+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),r&&r.set?r.set(this):O.propHooks._default.set(this),this}},O.prototype.init.prototype=O.prototype,O.propHooks={_default:{get:function(t){var e;return 1!==t.elem.nodeType||null!=t.elem[t.prop]&&null==t.elem.style[t.prop]?t.elem[t.prop]:(e=st.css(t.elem,t.prop,\"\"),e&&\"auto\"!==e?e:0)},set:function(t){st.fx.step[t.prop]?st.fx.step[t.prop](t):1!==t.elem.nodeType||null==t.elem.style[st.cssProps[t.prop]]&&!st.cssHooks[t.prop]?t.elem[t.prop]=t.now:st.style(t.elem,t.prop,t.now+t.unit)}}},O.propHooks.scrollTop=O.propHooks.scrollLeft={set:function(t){t.elem.nodeType&&t.elem.parentNode&&(t.elem[t.prop]=t.now)}},st.easing={linear:function(t){return t},swing:function(t){return.5-Math.cos(t*Math.PI)/2},_default:\"swing\"},st.fx=O.prototype.init,st.fx.step={};var ie,se,ae=/^(?:toggle|show|hide)$/,le=/queueHooks$/;st.Animation=st.extend(B,{tweeners:{\"*\":[function(t,e){var r=this.createTween(t,e);return h(r.elem,t,At.exec(e),r),r}]},tweener:function(t,e){st.isFunction(t)?(e=t,t=[\"*\"]):t=t.match(wt);for(var r,n=0,o=t.length;n<o;n++)r=t[n],B.tweeners[r]=B.tweeners[r]||[],B.tweeners[r].unshift(e)},prefilters:[R],prefilter:function(t,e){e?B.prefilters.unshift(t):B.prefilters.push(t)}}),st.speed=function(t,e,r){var n=t&&\"object\"==typeof t?st.extend({},t):{complete:r||!r&&e||st.isFunction(t)&&t,duration:t,easing:r&&e||e&&!st.isFunction(e)&&e};return n.duration=st.fx.off?0:\"number\"==typeof n.duration?n.duration:n.duration in st.fx.speeds?st.fx.speeds[n.duration]:st.fx.speeds._default,null!=n.queue&&n.queue!==!0||(n.queue=\"fx\"),n.old=n.complete,n.complete=function(){st.isFunction(n.old)&&n.old.call(this),n.queue&&st.dequeue(this,n.queue)},n},st.fn.extend({fadeTo:function(t,e,r,n){return this.filter(Nt).css(\"opacity\",0).show().end().animate({opacity:e},t,r,n)},animate:function(t,e,r,n){var o=st.isEmptyObject(t),i=st.speed(e,r,n),s=function(){var e=B(this,st.extend({},t),i);(o||Tt.get(this,\"finish\"))&&e.stop(!0)};return s.finish=s,o||i.queue===!1?this.each(s):this.queue(i.queue,s)},stop:function(t,e,r){var n=function(t){var e=t.stop;delete t.stop,e(r)};return\"string\"!=typeof t&&(r=e,e=t,t=void 0),e&&t!==!1&&this.queue(t||\"fx\",[]),this.each(function(){var e=!0,o=null!=t&&t+\"queueHooks\",i=st.timers,s=Tt.get(this);if(o)s[o]&&s[o].stop&&n(s[o]);else for(o in s)s[o]&&s[o].stop&&le.test(o)&&n(s[o]);for(o=i.length;o--;)i[o].elem!==this||null!=t&&i[o].queue!==t||(i[o].anim.stop(r),e=!1,i.splice(o,1));!e&&r||st.dequeue(this,t)})},finish:function(t){return t!==!1&&(t=t||\"fx\"),this.each(function(){var e,r=Tt.get(this),n=r[t+\"queue\"],o=r[t+\"queueHooks\"],i=st.timers,s=n?n.length:0;for(r.finish=!0,st.queue(this,t,[]),o&&o.stop&&o.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===t&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<s;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete r.finish})}}),st.each([\"toggle\",\"show\",\"hide\"],function(t,e){var r=st.fn[e];st.fn[e]=function(t,n,o){return null==t||\"boolean\"==typeof t?r.apply(this,arguments):this.animate(D(e,!0),t,n,o)}}),st.each({slideDown:D(\"show\"),slideUp:D(\"hide\"),slideToggle:D(\"toggle\"),fadeIn:{opacity:\"show\"},fadeOut:{opacity:\"hide\"},fadeToggle:{opacity:\"toggle\"}},function(t,e){st.fn[t]=function(t,r,n){return this.animate(e,t,r,n)}}),st.timers=[],st.fx.tick=function(){var t,e=0,r=st.timers;for(ie=st.now();e<r.length;e++)t=r[e],t()||r[e]!==t||r.splice(e--,1);r.length||st.fx.stop(),ie=void 0},st.fx.timer=function(t){st.timers.push(t),t()?st.fx.start():st.timers.pop()},st.fx.interval=13,st.fx.start=function(){se||(se=e.setInterval(st.fx.tick,st.fx.interval))},st.fx.stop=function(){e.clearInterval(se),se=null},st.fx.speeds={slow:600,fast:200,_default:400},st.fn.delay=function(t,r){return t=st.fx?st.fx.speeds[t]||t:t,r=r||\"fx\",this.queue(r,function(r,n){var o=e.setTimeout(r,t);n.stop=function(){e.clearTimeout(o)}})},function(){var t=J.createElement(\"input\"),e=J.createElement(\"select\"),r=e.appendChild(J.createElement(\"option\"));t.type=\"checkbox\",ot.checkOn=\"\"!==t.value,ot.optSelected=r.selected,e.disabled=!0,ot.optDisabled=!r.disabled,t=J.createElement(\"input\"),t.value=\"t\",t.type=\"radio\",ot.radioValue=\"t\"===t.value}();var ue,he=st.expr.attrHandle;st.fn.extend({attr:function(t,e){return kt(this,st.attr,t,e,arguments.length>1)},removeAttr:function(t){return this.each(function(){st.removeAttr(this,t)})}}),st.extend({attr:function(t,e,r){var n,o,i=t.nodeType;if(3!==i&&8!==i&&2!==i)return\"undefined\"==typeof t.getAttribute?st.prop(t,e,r):(1===i&&st.isXMLDoc(t)||(e=e.toLowerCase(),o=st.attrHooks[e]||(st.expr.match.bool.test(e)?ue:void 0)),void 0!==r?null===r?void st.removeAttr(t,e):o&&\"set\"in o&&void 0!==(n=o.set(t,r,e))?n:(t.setAttribute(e,r+\"\"),r):o&&\"get\"in o&&null!==(n=o.get(t,e))?n:(n=st.find.attr(t,e),null==n?void 0:n))},attrHooks:{type:{set:function(t,e){if(!ot.radioValue&&\"radio\"===e&&st.nodeName(t,\"input\")){var r=t.value;return t.setAttribute(\"type\",e),r&&(t.value=r),e}}}},removeAttr:function(t,e){var r,n,o=0,i=e&&e.match(wt);if(i&&1===t.nodeType)for(;r=i[o++];)n=st.propFix[r]||r,st.expr.match.bool.test(r)&&(t[n]=!1),t.removeAttribute(r)}}),ue={set:function(t,e,r){return e===!1?st.removeAttr(t,r):t.setAttribute(r,r),r}},st.each(st.expr.match.bool.source.match(/\\w+/g),function(t,e){var r=he[e]||st.find.attr;he[e]=function(t,e,n){var o,i;return n||(i=he[e],he[e]=o,o=null!=r(t,e,n)?e.toLowerCase():null,he[e]=i),o}});var ce=/^(?:input|select|textarea|button)$/i,pe=/^(?:a|area)$/i;st.fn.extend({prop:function(t,e){return kt(this,st.prop,t,e,arguments.length>1)},removeProp:function(t){return this.each(function(){delete this[st.propFix[t]||t]})}}),st.extend({prop:function(t,e,r){var n,o,i=t.nodeType;if(3!==i&&8!==i&&2!==i)return 1===i&&st.isXMLDoc(t)||(e=st.propFix[e]||e,o=st.propHooks[e]),void 0!==r?o&&\"set\"in o&&void 0!==(n=o.set(t,r,e))?n:t[e]=r:o&&\"get\"in o&&null!==(n=o.get(t,e))?n:t[e]},propHooks:{tabIndex:{get:function(t){var e=st.find.attr(t,\"tabindex\");return e?parseInt(e,10):ce.test(t.nodeName)||pe.test(t.nodeName)&&t.href?0:-1}}},propFix:{\"for\":\"htmlFor\",\"class\":\"className\"}}),ot.optSelected||(st.propHooks.selected={get:function(t){var e=t.parentNode;return e&&e.parentNode&&e.parentNode.selectedIndex,null},set:function(t){var e=t.parentNode;e&&(e.selectedIndex,e.parentNode&&e.parentNode.selectedIndex)}}),st.each([\"tabIndex\",\"readOnly\",\"maxLength\",\"cellSpacing\",\"cellPadding\",\"rowSpan\",\"colSpan\",\"useMap\",\"frameBorder\",\"contentEditable\"],function(){st.propFix[this.toLowerCase()]=this});var _e=/[\\t\\r\\n\\f]/g;st.fn.extend({addClass:function(t){var e,r,n,o,i,s,a,l=0;if(st.isFunction(t))return this.each(function(e){st(this).addClass(t.call(this,e,L(this)))});if(\"string\"==typeof t&&t)for(e=t.match(wt)||[];r=this[l++];)if(o=L(r),n=1===r.nodeType&&(\" \"+o+\" \").replace(_e,\" \")){for(s=0;i=e[s++];)n.indexOf(\" \"+i+\" \")<0&&(n+=i+\" \");a=st.trim(n),o!==a&&r.setAttribute(\"class\",a)}return this},removeClass:function(t){var e,r,n,o,i,s,a,l=0;if(st.isFunction(t))return this.each(function(e){st(this).removeClass(t.call(this,e,L(this)))});if(!arguments.length)return this.attr(\"class\",\"\");if(\"string\"==typeof t&&t)for(e=t.match(wt)||[];r=this[l++];)if(o=L(r),n=1===r.nodeType&&(\" \"+o+\" \").replace(_e,\" \")){for(s=0;i=e[s++];)for(;n.indexOf(\" \"+i+\" \")>-1;)n=n.replace(\" \"+i+\" \",\" \");a=st.trim(n),o!==a&&r.setAttribute(\"class\",a)}return this},toggleClass:function(t,e){var r=typeof t;return\"boolean\"==typeof e&&\"string\"===r?e?this.addClass(t):this.removeClass(t):st.isFunction(t)?this.each(function(r){st(this).toggleClass(t.call(this,r,L(this),e),e)}):this.each(function(){var e,n,o,i;if(\"string\"===r)for(n=0,o=st(this),i=t.match(wt)||[];e=i[n++];)o.hasClass(e)?o.removeClass(e):o.addClass(e);else void 0!==t&&\"boolean\"!==r||(e=L(this),e&&Tt.set(this,\"__className__\",e),this.setAttribute&&this.setAttribute(\"class\",e||t===!1?\"\":Tt.get(this,\"__className__\")||\"\"))})},hasClass:function(t){var e,r,n=0;for(e=\" \"+t+\" \";r=this[n++];)if(1===r.nodeType&&(\" \"+L(r)+\" \").replace(_e,\" \").indexOf(e)>-1)return!0;return!1}});var de=/\\r/g,fe=/[\\x20\\t\\r\\n\\f]+/g;st.fn.extend({val:function(t){var e,r,n,o=this[0];{if(arguments.length)return n=st.isFunction(t),this.each(function(r){var o;1===this.nodeType&&(o=n?t.call(this,r,st(this).val()):t,null==o?o=\"\":\"number\"==typeof o?o+=\"\":st.isArray(o)&&(o=st.map(o,function(t){return null==t?\"\":t+\"\"})),e=st.valHooks[this.type]||st.valHooks[this.nodeName.toLowerCase()],e&&\"set\"in e&&void 0!==e.set(this,o,\"value\")||(this.value=o))});if(o)return e=st.valHooks[o.type]||st.valHooks[o.nodeName.toLowerCase()],e&&\"get\"in e&&void 0!==(r=e.get(o,\"value\"))?r:(r=o.value,\"string\"==typeof r?r.replace(de,\"\"):null==r?\"\":r)}}}),st.extend({valHooks:{option:{get:function(t){var e=st.find.attr(t,\"value\");return null!=e?e:st.trim(st.text(t)).replace(fe,\" \")}},select:{get:function(t){for(var e,r,n=t.options,o=t.selectedIndex,i=\"select-one\"===t.type||o<0,s=i?null:[],a=i?o+1:n.length,l=o<0?a:i?o:0;l<a;l++)if(r=n[l],(r.selected||l===o)&&(ot.optDisabled?!r.disabled:null===r.getAttribute(\"disabled\"))&&(!r.parentNode.disabled||!st.nodeName(r.parentNode,\"optgroup\"))){if(e=st(r).val(),i)return e;s.push(e)}return s},set:function(t,e){for(var r,n,o=t.options,i=st.makeArray(e),s=o.length;s--;)n=o[s],(n.selected=st.inArray(st.valHooks.option.get(n),i)>-1)&&(r=!0);return r||(t.selectedIndex=-1),i}}}}),st.each([\"radio\",\"checkbox\"],function(){st.valHooks[this]={set:function(t,e){if(st.isArray(e))return t.checked=st.inArray(st(t).val(),e)>-1}},ot.checkOn||(st.valHooks[this].get=function(t){return null===t.getAttribute(\"value\")?\"on\":t.value})});var me=/^(?:focusinfocus|focusoutblur)$/;st.extend(st.event,{trigger:function(t,r,n,o){var i,s,a,l,u,h,c,p=[n||J],_=nt.call(t,\"type\")?t.type:t,d=nt.call(t,\"namespace\")?t.namespace.split(\".\"):[];if(s=a=n=n||J,3!==n.nodeType&&8!==n.nodeType&&!me.test(_+st.event.triggered)&&(_.indexOf(\".\")>-1&&(d=_.split(\".\"),_=d.shift(),d.sort()),u=_.indexOf(\":\")<0&&\"on\"+_,t=t[st.expando]?t:new st.Event(_,\"object\"==typeof t&&t),t.isTrigger=o?2:3,t.namespace=d.join(\".\"),t.rnamespace=t.namespace?new RegExp(\"(^|\\\\.)\"+d.join(\"\\\\.(?:.*\\\\.|)\")+\"(\\\\.|$)\"):null,t.result=void 0,t.target||(t.target=n),r=null==r?[t]:st.makeArray(r,[t]),c=st.event.special[_]||{},o||!c.trigger||c.trigger.apply(n,r)!==!1)){if(!o&&!c.noBubble&&!st.isWindow(n)){for(l=c.delegateType||_,me.test(l+_)||(s=s.parentNode);s;s=s.parentNode)p.push(s),a=s;a===(n.ownerDocument||J)&&p.push(a.defaultView||a.parentWindow||e)}for(i=0;(s=p[i++])&&!t.isPropagationStopped();)t.type=i>1?l:c.bindType||_,h=(Tt.get(s,\"events\")||{})[t.type]&&Tt.get(s,\"handle\"),h&&h.apply(s,r),h=u&&s[u],h&&h.apply&&jt(s)&&(t.result=h.apply(s,r),t.result===!1&&t.preventDefault());return t.type=_,o||t.isDefaultPrevented()||c._default&&c._default.apply(p.pop(),r)!==!1||!jt(n)||u&&st.isFunction(n[_])&&!st.isWindow(n)&&(a=n[u],a&&(n[u]=null),st.event.triggered=_,n[_](),st.event.triggered=void 0,a&&(n[u]=a)),t.result}},simulate:function(t,e,r){var n=st.extend(new st.Event,r,{type:t,isSimulated:!0});st.event.trigger(n,null,e)}}),st.fn.extend({trigger:function(t,e){return this.each(function(){st.event.trigger(t,e,this)})},triggerHandler:function(t,e){var r=this[0];if(r)return st.event.trigger(t,e,r,!0)}}),st.each(\"blur focus focusin focusout load resize scroll unload click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup error contextmenu\".split(\" \"),function(t,e){st.fn[e]=function(t,r){return arguments.length>0?this.on(e,null,t,r):this.trigger(e)}}),st.fn.extend({hover:function(t,e){return this.mouseenter(t).mouseleave(e||t)}}),ot.focusin=\"onfocusin\"in e,ot.focusin||st.each({focus:\"focusin\",blur:\"focusout\"},function(t,e){var r=function(t){st.event.simulate(e,t.target,st.event.fix(t))};st.event.special[e]={setup:function(){var n=this.ownerDocument||this,o=Tt.access(n,e);o||n.addEventListener(t,r,!0),Tt.access(n,e,(o||0)+1)},teardown:function(){var n=this.ownerDocument||this,o=Tt.access(n,e)-1;o?Tt.access(n,e,o):(n.removeEventListener(t,r,!0),Tt.remove(n,e))}}});var 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e=this.datum_params[0],r=this.datum_params[1],n=this.datum_params[2],s=this.datum_params[3],a=this.datum_params[4],l=this.datum_params[5],u=this.datum_params[6],h=(t.x-e)/u,c=(t.y-r)/u,p=(t.z-n)/u;t.x=h+l*c-a*p,t.y=-l*h+c+s*p,t.z=a*h-s*c+p}}},e.exports=p},{}],\"proj4/lib/datum_transform\":[function(t,e,r){var n=1,o=2,i=3,s=5,a=6378137,l=.006694379990141316;e.exports=function(t,e,r){function u(t){return t===n||t===o}var h,c,p;if(t.compare_datums(e))return r;if(t.datum_type===s||e.datum_type===s)return r;var _=t.a,d=t.es,f=e.a,m=e.es,g=t.datum_type;if(g===i)if(0===this.apply_gridshift(t,0,r))t.a=a,t.es=l;else{if(!t.datum_params)return t.a=_,t.es=t.es,r;for(h=1,c=0,p=t.datum_params.length;c<p;c++)h*=t.datum_params[c];if(0===h)return t.a=_,t.es=t.es,r;g=t.datum_params.length>3?o:n}return e.datum_type===i&&(e.a=a,e.es=l),(t.es!==e.es||t.a!==e.a||u(g)||u(e.datum_type))&&(t.geodetic_to_geocentric(r),u(t.datum_type)&&t.geocentric_to_wgs84(r),u(e.datum_type)&&e.geocentric_from_wgs84(r),e.geocentric_to_geodetic(r)),e.datum_type===i&&this.apply_gridshift(e,1,r),t.a=_,t.es=d,e.a=f,e.es=m,r}},{}],\"proj4/lib/defs\":[function(t,e,r){function n(t){var e=this;if(2===arguments.length){var r=arguments[1];\"string\"==typeof r?\"+\"===r.charAt(0)?n[t]=i(arguments[1]):n[t]=s(arguments[1]):n[t]=r}else if(1===arguments.length){if(Array.isArray(t))return t.map(function(t){Array.isArray(t)?n.apply(e,t):n(t)});if(\"string\"==typeof t){if(t in n)return n[t]}else\"EPSG\"in t?n[\"EPSG:\"+t.EPSG]=t:\"ESRI\"in t?n[\"ESRI:\"+t.ESRI]=t:\"IAU2000\"in t?n[\"IAU2000:\"+t.IAU2000]=t:console.log(t);return}}var o=t(\"./global\"),i=t(\"./projString\"),s=t(\"./wkt\");o(n),e.exports=n},{\"./global\":\"proj4/lib/global\",\"./projString\":\"proj4/lib/projString\",\"./wkt\":\"proj4/lib/wkt\"}],\"proj4/lib/deriveConstants\":[function(t,e,r){var n=t(\"./constants/Datum\"),o=t(\"./constants/Ellipsoid\"),i=t(\"./extend\"),s=t(\"./datum\"),a=1e-10,l=.16666666666666666,u=.04722222222222222,h=.022156084656084655;e.exports=function(t){if(t.datumCode&&\"none\"!==t.datumCode){var e=n[t.datumCode];e&&(t.datum_params=e.towgs84?e.towgs84.split(\",\"):null,t.ellps=e.ellipse,t.datumName=e.datumName?e.datumName:t.datumCode)}if(!t.a){var r=o[t.ellps]?o[t.ellps]:o.WGS84;i(t,r)}return t.rf&&!t.b&&(t.b=(1-1/t.rf)*t.a),(0===t.rf||Math.abs(t.a-t.b)<a)&&(t.sphere=!0,t.b=t.a),t.a2=t.a*t.a,t.b2=t.b*t.b,t.es=(t.a2-t.b2)/t.a2,t.e=Math.sqrt(t.es),t.R_A&&(t.a*=1-t.es*(l+t.es*(u+t.es*h)),t.a2=t.a*t.a,t.b2=t.b*t.b,t.es=0),t.ep2=(t.a2-t.b2)/t.b2,t.k0||(t.k0=1),t.axis||(t.axis=\"enu\"),t.datum||(t.datum=s(t)),t}},{\"./constants/Datum\":\"proj4/lib/constants/Datum\",\"./constants/Ellipsoid\":\"proj4/lib/constants/Ellipsoid\",\"./datum\":\"proj4/lib/datum\",\"./extend\":\"proj4/lib/extend\"}],\"proj4/lib/extend\":[function(t,e,r){e.exports=function(t,e){t=t||{};var r,n;if(!e)return t;for(n in e)r=e[n],void 0!==r&&(t[n]=r);return t}},{}],\"proj4/lib/global\":[function(t,e,r){e.exports=function(t){t(\"EPSG:4326\",\"+title=WGS 84 (long/lat) +proj=longlat +ellps=WGS84 +datum=WGS84 +units=degrees\"),t(\"EPSG:4269\",\"+title=NAD83 (long/lat) +proj=longlat +a=6378137.0 +b=6356752.31414036 +ellps=GRS80 +datum=NAD83 +units=degrees\"),t(\"EPSG:3857\",\"+title=WGS 84 / Pseudo-Mercator +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs\"),t.WGS84=t[\"EPSG:4326\"],t[\"EPSG:3785\"]=t[\"EPSG:3857\"],t.GOOGLE=t[\"EPSG:3857\"],t[\"EPSG:900913\"]=t[\"EPSG:3857\"],t[\"EPSG:102113\"]=t[\"EPSG:3857\"]}},{}],\"proj4/lib/includedProjections\":[function(t,e,r){var n=[t(\"./projections/tmerc\"),t(\"./projections/utm\"),t(\"./projections/sterea\"),t(\"./projections/stere\"),t(\"./projections/somerc\"),t(\"./projections/omerc\"),t(\"./projections/lcc\"),t(\"./projections/krovak\"),t(\"./projections/cass\"),t(\"./projections/laea\"),t(\"./projections/aea\"),t(\"./projections/gnom\"),t(\"./projections/cea\"),t(\"./projections/eqc\"),t(\"./projections/poly\"),t(\"./projections/nzmg\"),t(\"./projections/mill\"),t(\"./projections/sinu\"),t(\"./projections/moll\"),t(\"./projections/eqdc\"),t(\"./projections/vandg\"),t(\"./projections/aeqd\")];e.exports=function(t){n.forEach(function(e){t.Proj.projections.add(e)})}},{\"./projections/aea\":\"proj4/lib/projections/aea\",\"./projections/aeqd\":\"proj4/lib/projections/aeqd\",\"./projections/cass\":\"proj4/lib/projections/cass\",\"./projections/cea\":\"proj4/lib/projections/cea\",\"./projections/eqc\":\"proj4/lib/projections/eqc\",\"./projections/eqdc\":\"proj4/lib/projections/eqdc\",\"./projections/gnom\":\"proj4/lib/projections/gnom\",\"./projections/krovak\":\"proj4/lib/projections/krovak\",\"./projections/laea\":\"proj4/lib/projections/laea\",\"./projections/lcc\":\"proj4/lib/projections/lcc\",\"./projections/mill\":\"proj4/lib/projections/mill\",\"./projections/moll\":\"proj4/lib/projections/moll\",\"./projections/nzmg\":\"proj4/lib/projections/nzmg\",\"./projections/omerc\":\"proj4/lib/projections/omerc\",\"./projections/poly\":\"proj4/lib/projections/poly\",\"./projections/sinu\":\"proj4/lib/projections/sinu\",\"./projections/somerc\":\"proj4/lib/projections/somerc\",\"./projections/stere\":\"proj4/lib/projections/stere\",\"./projections/sterea\":\"proj4/lib/projections/sterea\",\"./projections/tmerc\":\"proj4/lib/projections/tmerc\",\"./projections/utm\":\"proj4/lib/projections/utm\",\"./projections/vandg\":\"proj4/lib/projections/vandg\"}],proj4:[function(t,e,r){var n=t(\"./core\");n.defaultDatum=\"WGS84\",n.Proj=t(\"./Proj\"),n.WGS84=new n.Proj(\"WGS84\"),n.Point=t(\"./Point\"),n.toPoint=t(\"./common/toPoint\"),n.defs=t(\"./defs\"),n.transform=t(\"./transform\"),n.mgrs=t(\"mgrs\"),n.version=t(\"../package.json\").version,t(\"./includedProjections\")(n),e.exports=n},{\"../package.json\":\"proj4/package.json\",\"./Point\":\"proj4/lib/Point\",\"./Proj\":\"proj4/lib/Proj\",\"./common/toPoint\":\"proj4/lib/common/toPoint\",\"./core\":\"proj4/lib/core\",\"./defs\":\"proj4/lib/defs\",\"./includedProjections\":\"proj4/lib/includedProjections\",\"./transform\":\"proj4/lib/transform\",mgrs:\"proj4/node_modules/mgrs/mgrs\"}],\"proj4/lib/parseCode\":[function(t,e,r){function n(t){return\"string\"==typeof t}function o(t){return t in l}function i(t){var e=[\"GEOGCS\",\"GEOCCS\",\"PROJCS\",\"LOCAL_CS\"];return e.reduce(function(e,r){return e+1+t.indexOf(r)},0)}function s(t){return\"+\"===t[0]}function a(t){return n(t)?o(t)?l[t]:i(t)?u(t):s(t)?h(t):void 0:t}var l=t(\"./defs\"),u=t(\"./wkt\"),h=t(\"./projString\");e.exports=a},{\"./defs\":\"proj4/lib/defs\",\"./projString\":\"proj4/lib/projString\",\"./wkt\":\"proj4/lib/wkt\"}],\"proj4/lib/projString\":[function(t,e,r){var n=.017453292519943295,o=t(\"./constants/PrimeMeridian\"),i=t(\"./constants/units\");e.exports=function(t){var e={},r={};t.split(\"+\").map(function(t){return t.trim()}).filter(function(t){return t}).forEach(function(t){var e=t.split(\"=\");e.push(!0),r[e[0].toLowerCase()]=e[1]});var s,a,l,u={proj:\"projName\",datum:\"datumCode\",rf:function(t){e.rf=parseFloat(t)},lat_0:function(t){e.lat0=t*n},lat_1:function(t){e.lat1=t*n},lat_2:function(t){e.lat2=t*n},lat_ts:function(t){e.lat_ts=t*n},lon_0:function(t){e.long0=t*n},lon_1:function(t){e.long1=t*n},lon_2:function(t){e.long2=t*n},alpha:function(t){e.alpha=parseFloat(t)*n},lonc:function(t){e.longc=t*n},x_0:function(t){e.x0=parseFloat(t)},y_0:function(t){e.y0=parseFloat(t)},k_0:function(t){e.k0=parseFloat(t)},k:function(t){e.k0=parseFloat(t)},a:function(t){e.a=parseFloat(t)},b:function(t){e.b=parseFloat(t)},r_a:function(){e.R_A=!0},zone:function(t){e.zone=parseInt(t,10)},south:function(){e.utmSouth=!0},towgs84:function(t){e.datum_params=t.split(\",\").map(function(t){return parseFloat(t)})},to_meter:function(t){e.to_meter=parseFloat(t)},units:function(t){e.units=t,i[t]&&(e.to_meter=i[t].to_meter)},from_greenwich:function(t){e.from_greenwich=t*n},pm:function(t){e.from_greenwich=(o[t]?o[t]:parseFloat(t))*n},nadgrids:function(t){\"@null\"===t?e.datumCode=\"none\":e.nadgrids=t},axis:function(t){var r=\"ewnsud\";3===t.length&&r.indexOf(t.substr(0,1))!==-1&&r.indexOf(t.substr(1,1))!==-1&&r.indexOf(t.substr(2,1))!==-1&&(e.axis=t)}};for(s in r)a=r[s],s in u?(l=u[s],\"function\"==typeof l?l(a):e[l]=a):e[s]=a;return\"string\"==typeof e.datumCode&&\"WGS84\"!==e.datumCode&&(e.datumCode=e.datumCode.toLowerCase()),e}},{\"./constants/PrimeMeridian\":\"proj4/lib/constants/PrimeMeridian\",\"./constants/units\":\"proj4/lib/constants/units\"}],\"proj4/lib/projections\":[function(t,e,r){function n(t,e){var r=s.length;return t.names?(s[r]=t,t.names.forEach(function(t){i[t.toLowerCase()]=r}),this):(console.log(e),!0)}var o=[t(\"./projections/merc\"),t(\"./projections/longlat\")],i={},s=[];r.add=n,r.get=function(t){if(!t)return!1;var e=t.toLowerCase();return\"undefined\"!=typeof i[e]&&s[i[e]]?s[i[e]]:void 0},r.start=function(){o.forEach(n)}},{\"./projections/longlat\":\"proj4/lib/projections/longlat\",\"./projections/merc\":\"proj4/lib/projections/merc\"}],\"proj4/lib/projections/aea\":[function(t,e,r){var n=1e-10,o=t(\"../common/msfnz\"),i=t(\"../common/qsfnz\"),s=t(\"../common/adjust_lon\"),a=t(\"../common/asinz\");r.init=function(){Math.abs(this.lat1+this.lat2)<n||(this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e3=Math.sqrt(this.es),this.sin_po=Math.sin(this.lat1),this.cos_po=Math.cos(this.lat1),this.t1=this.sin_po,this.con=this.sin_po,this.ms1=o(this.e3,this.sin_po,this.cos_po),this.qs1=i(this.e3,this.sin_po,this.cos_po),this.sin_po=Math.sin(this.lat2),this.cos_po=Math.cos(this.lat2),this.t2=this.sin_po,this.ms2=o(this.e3,this.sin_po,this.cos_po),this.qs2=i(this.e3,this.sin_po,this.cos_po),this.sin_po=Math.sin(this.lat0),this.cos_po=Math.cos(this.lat0),this.t3=this.sin_po,this.qs0=i(this.e3,this.sin_po,this.cos_po),Math.abs(this.lat1-this.lat2)>n?this.ns0=(this.ms1*this.ms1-this.ms2*this.ms2)/(this.qs2-this.qs1):this.ns0=this.con,this.c=this.ms1*this.ms1+this.ns0*this.qs1,this.rh=this.a*Math.sqrt(this.c-this.ns0*this.qs0)/this.ns0)},r.forward=function(t){var e=t.x,r=t.y;this.sin_phi=Math.sin(r),this.cos_phi=Math.cos(r);var n=i(this.e3,this.sin_phi,this.cos_phi),o=this.a*Math.sqrt(this.c-this.ns0*n)/this.ns0,a=this.ns0*s(e-this.long0),l=o*Math.sin(a)+this.x0,u=this.rh-o*Math.cos(a)+this.y0;return t.x=l,t.y=u,t},r.inverse=function(t){var e,r,n,o,i,a;return t.x-=this.x0,t.y=this.rh-t.y+this.y0,this.ns0>=0?(e=Math.sqrt(t.x*t.x+t.y*t.y),n=1):(e=-Math.sqrt(t.x*t.x+t.y*t.y),n=-1),o=0,0!==e&&(o=Math.atan2(n*t.x,n*t.y)),n=e*this.ns0/this.a,this.sphere?a=Math.asin((this.c-n*n)/(2*this.ns0)):(r=(this.c-n*n)/this.ns0,a=this.phi1z(this.e3,r)),i=s(o/this.ns0+this.long0),t.x=i,t.y=a,t},r.phi1z=function(t,e){var r,o,i,s,l,u=a(.5*e);if(t<n)return u;for(var h=t*t,c=1;c<=25;c++)if(r=Math.sin(u),o=Math.cos(u),i=t*r,s=1-i*i,l=.5*s*s/o*(e/(1-h)-r/s+.5/t*Math.log((1-i)/(1+i))),u+=l,Math.abs(l)<=1e-7)return u;return null},r.names=[\"Albers_Conic_Equal_Area\",\"Albers\",\"aea\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/asinz\":\"proj4/lib/common/asinz\",\"../common/msfnz\":\"proj4/lib/common/msfnz\",\"../common/qsfnz\":\"proj4/lib/common/qsfnz\"}],\"proj4/lib/projections/aeqd\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=Math.PI/2,i=1e-10,s=t(\"../common/mlfn\"),a=t(\"../common/e0fn\"),l=t(\"../common/e1fn\"),u=t(\"../common/e2fn\"),h=t(\"../common/e3fn\"),c=t(\"../common/gN\"),p=t(\"../common/asinz\"),_=t(\"../common/imlfn\");r.init=function(){this.sin_p12=Math.sin(this.lat0),this.cos_p12=Math.cos(this.lat0)},r.forward=function(t){var e,r,p,_,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N=t.x,O=t.y,q=Math.sin(t.y),D=Math.cos(t.y),I=n(N-this.long0);return this.sphere?Math.abs(this.sin_p12-1)<=i?(t.x=this.x0+this.a*(o-O)*Math.sin(I),t.y=this.y0-this.a*(o-O)*Math.cos(I),t):Math.abs(this.sin_p12+1)<=i?(t.x=this.x0+this.a*(o+O)*Math.sin(I),t.y=this.y0+this.a*(o+O)*Math.cos(I),t):(S=this.sin_p12*q+this.cos_p12*D*Math.cos(I),j=Math.acos(S),T=j/Math.sin(j),t.x=this.x0+this.a*T*D*Math.sin(I),t.y=this.y0+this.a*T*(this.cos_p12*q-this.sin_p12*D*Math.cos(I)),t):(e=a(this.es),r=l(this.es),p=u(this.es),_=h(this.es),Math.abs(this.sin_p12-1)<=i?(d=this.a*s(e,r,p,_,o),f=this.a*s(e,r,p,_,O),t.x=this.x0+(d-f)*Math.sin(I),t.y=this.y0-(d-f)*Math.cos(I),t):Math.abs(this.sin_p12+1)<=i?(d=this.a*s(e,r,p,_,o),f=this.a*s(e,r,p,_,O),t.x=this.x0+(d+f)*Math.sin(I),t.y=this.y0+(d+f)*Math.cos(I),t):(m=q/D,g=c(this.a,this.e,this.sin_p12),y=c(this.a,this.e,q),v=Math.atan((1-this.es)*m+this.es*g*this.sin_p12/(y*D)),b=Math.atan2(Math.sin(I),this.cos_p12*Math.tan(v)-this.sin_p12*Math.cos(I)),z=0===b?Math.asin(this.cos_p12*Math.sin(v)-this.sin_p12*Math.cos(v)):Math.abs(Math.abs(b)-Math.PI)<=i?-Math.asin(this.cos_p12*Math.sin(v)-this.sin_p12*Math.cos(v)):Math.asin(Math.sin(I)*Math.cos(v)/Math.sin(b)),x=this.e*this.sin_p12/Math.sqrt(1-this.es),w=this.e*this.cos_p12*Math.cos(b)/Math.sqrt(1-this.es),M=x*w,k=w*w,P=z*z,E=P*z,A=E*z,C=A*z,j=g*z*(1-P*k*(1-k)/6+E/8*M*(1-2*k)+A/120*(k*(4-7*k)-3*x*x*(1-7*k))-C/48*M),t.x=this.x0+j*Math.sin(b),t.y=this.y0+j*Math.cos(b),t))},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,d,f,m,g,y,v,b,x,w,M,k,j,T,S,z,P,E,A,C,N,O;if(this.sphere){if(e=Math.sqrt(t.x*t.x+t.y*t.y),e>2*o*this.a)return;return r=e/this.a,d=Math.sin(r),f=Math.cos(r),m=this.long0,Math.abs(e)<=i?g=this.lat0:(g=p(f*this.sin_p12+t.y*d*this.cos_p12/e),y=Math.abs(this.lat0)-o,m=n(Math.abs(y)<=i?this.lat0>=0?this.long0+Math.atan2(t.x,-t.y):this.long0-Math.atan2(-t.x,t.y):this.long0+Math.atan2(t.x*d,e*this.cos_p12*f-t.y*this.sin_p12*d))),t.x=m,t.y=g,t}return v=a(this.es),b=l(this.es),x=u(this.es),w=h(this.es),Math.abs(this.sin_p12-1)<=i?(M=this.a*s(v,b,x,w,o),e=Math.sqrt(t.x*t.x+t.y*t.y),k=M-e,g=_(k/this.a,v,b,x,w),m=n(this.long0+Math.atan2(t.x,-1*t.y)),t.x=m,t.y=g,t):Math.abs(this.sin_p12+1)<=i?(M=this.a*s(v,b,x,w,o),e=Math.sqrt(t.x*t.x+t.y*t.y),k=e-M,g=_(k/this.a,v,b,x,w),m=n(this.long0+Math.atan2(t.x,t.y)),t.x=m,t.y=g,t):(e=Math.sqrt(t.x*t.x+t.y*t.y),S=Math.atan2(t.x,t.y),j=c(this.a,this.e,this.sin_p12),z=Math.cos(S),P=this.e*this.cos_p12*z,E=-P*P/(1-this.es),A=3*this.es*(1-E)*this.sin_p12*this.cos_p12*z/(1-this.es),C=e/j,N=C-E*(1+E)*Math.pow(C,3)/6-A*(1+3*E)*Math.pow(C,4)/24,O=1-E*N*N/2-C*N*N*N/6,T=Math.asin(this.sin_p12*Math.cos(N)+this.cos_p12*Math.sin(N)*z),m=n(this.long0+Math.asin(Math.sin(S)*Math.sin(N)/Math.cos(T))),g=Math.atan((1-this.es*O*this.sin_p12/Math.sin(T))*Math.tan(T)/(1-this.es)),t.x=m,t.y=g,t)},r.names=[\"Azimuthal_Equidistant\",\"aeqd\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/asinz\":\"proj4/lib/common/asinz\",\"../common/e0fn\":\"proj4/lib/common/e0fn\",\"../common/e1fn\":\"proj4/lib/common/e1fn\",\"../common/e2fn\":\"proj4/lib/common/e2fn\",\"../common/e3fn\":\"proj4/lib/common/e3fn\",\"../common/gN\":\"proj4/lib/common/gN\",\"../common/imlfn\":\"proj4/lib/common/imlfn\",\"../common/mlfn\":\"proj4/lib/common/mlfn\"}],\"proj4/lib/projections/cass\":[function(t,e,r){var n=t(\"../common/mlfn\"),o=t(\"../common/e0fn\"),i=t(\"../common/e1fn\"),s=t(\"../common/e2fn\"),a=t(\"../common/e3fn\"),l=t(\"../common/gN\"),u=t(\"../common/adjust_lon\"),h=t(\"../common/adjust_lat\"),c=t(\"../common/imlfn\"),p=Math.PI/2,_=1e-10;r.init=function(){\nthis.sphere||(this.e0=o(this.es),this.e1=i(this.es),this.e2=s(this.es),this.e3=a(this.es),this.ml0=this.a*n(this.e0,this.e1,this.e2,this.e3,this.lat0))},r.forward=function(t){var e,r,o=t.x,i=t.y;if(o=u(o-this.long0),this.sphere)e=this.a*Math.asin(Math.cos(i)*Math.sin(o)),r=this.a*(Math.atan2(Math.tan(i),Math.cos(o))-this.lat0);else{var s=Math.sin(i),a=Math.cos(i),h=l(this.a,this.e,s),c=Math.tan(i)*Math.tan(i),p=o*Math.cos(i),_=p*p,d=this.es*a*a/(1-this.es),f=this.a*n(this.e0,this.e1,this.e2,this.e3,i);e=h*p*(1-_*c*(1/6-(8-c+8*d)*_/120)),r=f-this.ml0+h*s/a*_*(.5+(5-c+6*d)*_/24)}return t.x=e+this.x0,t.y=r+this.y0,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,n=t.x/this.a,o=t.y/this.a;if(this.sphere){var i=o+this.lat0;e=Math.asin(Math.sin(i)*Math.cos(n)),r=Math.atan2(Math.tan(n),Math.cos(i))}else{var s=this.ml0/this.a+o,a=c(s,this.e0,this.e1,this.e2,this.e3);if(Math.abs(Math.abs(a)-p)<=_)return t.x=this.long0,t.y=p,o<0&&(t.y*=-1),t;var d=l(this.a,this.e,Math.sin(a)),f=d*d*d/this.a/this.a*(1-this.es),m=Math.pow(Math.tan(a),2),g=n*this.a/d,y=g*g;e=a-d*Math.tan(a)/f*g*g*(.5-(1+3*m)*g*g/24),r=g*(1-y*(m/3+(1+3*m)*m*y/15))/Math.cos(a)}return t.x=u(r+this.long0),t.y=h(e),t},r.names=[\"Cassini\",\"Cassini_Soldner\",\"cass\"]},{\"../common/adjust_lat\":\"proj4/lib/common/adjust_lat\",\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/e0fn\":\"proj4/lib/common/e0fn\",\"../common/e1fn\":\"proj4/lib/common/e1fn\",\"../common/e2fn\":\"proj4/lib/common/e2fn\",\"../common/e3fn\":\"proj4/lib/common/e3fn\",\"../common/gN\":\"proj4/lib/common/gN\",\"../common/imlfn\":\"proj4/lib/common/imlfn\",\"../common/mlfn\":\"proj4/lib/common/mlfn\"}],\"proj4/lib/projections/cea\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=t(\"../common/qsfnz\"),i=t(\"../common/msfnz\"),s=t(\"../common/iqsfnz\");r.init=function(){this.sphere||(this.k0=i(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)))},r.forward=function(t){var e,r,i=t.x,s=t.y,a=n(i-this.long0);if(this.sphere)e=this.x0+this.a*a*Math.cos(this.lat_ts),r=this.y0+this.a*Math.sin(s)/Math.cos(this.lat_ts);else{var l=o(this.e,Math.sin(s));e=this.x0+this.a*this.k0*a,r=this.y0+this.a*l*.5/this.k0}return t.x=e,t.y=r,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r;return this.sphere?(e=n(this.long0+t.x/this.a/Math.cos(this.lat_ts)),r=Math.asin(t.y/this.a*Math.cos(this.lat_ts))):(r=s(this.e,2*t.y*this.k0/this.a),e=n(this.long0+t.x/(this.a*this.k0))),t.x=e,t.y=r,t},r.names=[\"cea\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/iqsfnz\":\"proj4/lib/common/iqsfnz\",\"../common/msfnz\":\"proj4/lib/common/msfnz\",\"../common/qsfnz\":\"proj4/lib/common/qsfnz\"}],\"proj4/lib/projections/eqc\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=t(\"../common/adjust_lat\");r.init=function(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||\"Equidistant Cylindrical (Plate Carre)\",this.rc=Math.cos(this.lat_ts)},r.forward=function(t){var e=t.x,r=t.y,i=n(e-this.long0),s=o(r-this.lat0);return t.x=this.x0+this.a*i*this.rc,t.y=this.y0+this.a*s,t},r.inverse=function(t){var e=t.x,r=t.y;return t.x=n(this.long0+(e-this.x0)/(this.a*this.rc)),t.y=o(this.lat0+(r-this.y0)/this.a),t},r.names=[\"Equirectangular\",\"Equidistant_Cylindrical\",\"eqc\"]},{\"../common/adjust_lat\":\"proj4/lib/common/adjust_lat\",\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\"}],\"proj4/lib/projections/eqdc\":[function(t,e,r){var n=t(\"../common/e0fn\"),o=t(\"../common/e1fn\"),i=t(\"../common/e2fn\"),s=t(\"../common/e3fn\"),a=t(\"../common/msfnz\"),l=t(\"../common/mlfn\"),u=t(\"../common/adjust_lon\"),h=t(\"../common/adjust_lat\"),c=t(\"../common/imlfn\"),p=1e-10;r.init=function(){Math.abs(this.lat1+this.lat2)<p||(this.lat2=this.lat2||this.lat1,this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.sinphi=Math.sin(this.lat1),this.cosphi=Math.cos(this.lat1),this.ms1=a(this.e,this.sinphi,this.cosphi),this.ml1=l(this.e0,this.e1,this.e2,this.e3,this.lat1),Math.abs(this.lat1-this.lat2)<p?this.ns=this.sinphi:(this.sinphi=Math.sin(this.lat2),this.cosphi=Math.cos(this.lat2),this.ms2=a(this.e,this.sinphi,this.cosphi),this.ml2=l(this.e0,this.e1,this.e2,this.e3,this.lat2),this.ns=(this.ms1-this.ms2)/(this.ml2-this.ml1)),this.g=this.ml1+this.ms1/this.ns,this.ml0=l(this.e0,this.e1,this.e2,this.e3,this.lat0),this.rh=this.a*(this.g-this.ml0))},r.forward=function(t){var e,r=t.x,n=t.y;if(this.sphere)e=this.a*(this.g-n);else{var o=l(this.e0,this.e1,this.e2,this.e3,n);e=this.a*(this.g-o)}var i=this.ns*u(r-this.long0),s=this.x0+e*Math.sin(i),a=this.y0+this.rh-e*Math.cos(i);return t.x=s,t.y=a,t},r.inverse=function(t){t.x-=this.x0,t.y=this.rh-t.y+this.y0;var e,r,n,o;this.ns>=0?(r=Math.sqrt(t.x*t.x+t.y*t.y),e=1):(r=-Math.sqrt(t.x*t.x+t.y*t.y),e=-1);var i=0;if(0!==r&&(i=Math.atan2(e*t.x,e*t.y)),this.sphere)return o=u(this.long0+i/this.ns),n=h(this.g-r/this.a),t.x=o,t.y=n,t;var s=this.g-r/this.a;return n=c(s,this.e0,this.e1,this.e2,this.e3),o=u(this.long0+i/this.ns),t.x=o,t.y=n,t},r.names=[\"Equidistant_Conic\",\"eqdc\"]},{\"../common/adjust_lat\":\"proj4/lib/common/adjust_lat\",\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/e0fn\":\"proj4/lib/common/e0fn\",\"../common/e1fn\":\"proj4/lib/common/e1fn\",\"../common/e2fn\":\"proj4/lib/common/e2fn\",\"../common/e3fn\":\"proj4/lib/common/e3fn\",\"../common/imlfn\":\"proj4/lib/common/imlfn\",\"../common/mlfn\":\"proj4/lib/common/mlfn\",\"../common/msfnz\":\"proj4/lib/common/msfnz\"}],\"proj4/lib/projections/gauss\":[function(t,e,r){var n=Math.PI/4,o=t(\"../common/srat\"),i=Math.PI/2,s=20;r.init=function(){var t=Math.sin(this.lat0),e=Math.cos(this.lat0);e*=e,this.rc=Math.sqrt(1-this.es)/(1-this.es*t*t),this.C=Math.sqrt(1+this.es*e*e/(1-this.es)),this.phic0=Math.asin(t/this.C),this.ratexp=.5*this.C*this.e,this.K=Math.tan(.5*this.phic0+n)/(Math.pow(Math.tan(.5*this.lat0+n),this.C)*o(this.e*t,this.ratexp))},r.forward=function(t){var e=t.x,r=t.y;return t.y=2*Math.atan(this.K*Math.pow(Math.tan(.5*r+n),this.C)*o(this.e*Math.sin(r),this.ratexp))-i,t.x=this.C*e,t},r.inverse=function(t){for(var e=1e-14,r=t.x/this.C,a=t.y,l=Math.pow(Math.tan(.5*a+n)/this.K,1/this.C),u=s;u>0&&(a=2*Math.atan(l*o(this.e*Math.sin(t.y),-.5*this.e))-i,!(Math.abs(a-t.y)<e));--u)t.y=a;return u?(t.x=r,t.y=a,t):null},r.names=[\"gauss\"]},{\"../common/srat\":\"proj4/lib/common/srat\"}],\"proj4/lib/projections/gnom\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=1e-10,i=t(\"../common/asinz\");r.init=function(){this.sin_p14=Math.sin(this.lat0),this.cos_p14=Math.cos(this.lat0),this.infinity_dist=1e3*this.a,this.rc=1},r.forward=function(t){var e,r,i,s,a,l,u,h,c=t.x,p=t.y;return i=n(c-this.long0),e=Math.sin(p),r=Math.cos(p),s=Math.cos(i),l=this.sin_p14*e+this.cos_p14*r*s,a=1,l>0||Math.abs(l)<=o?(u=this.x0+this.a*a*r*Math.sin(i)/l,h=this.y0+this.a*a*(this.cos_p14*e-this.sin_p14*r*s)/l):(u=this.x0+this.infinity_dist*r*Math.sin(i),h=this.y0+this.infinity_dist*(this.cos_p14*e-this.sin_p14*r*s)),t.x=u,t.y=h,t},r.inverse=function(t){var e,r,o,s,a,l;return t.x=(t.x-this.x0)/this.a,t.y=(t.y-this.y0)/this.a,t.x/=this.k0,t.y/=this.k0,(e=Math.sqrt(t.x*t.x+t.y*t.y))?(s=Math.atan2(e,this.rc),r=Math.sin(s),o=Math.cos(s),l=i(o*this.sin_p14+t.y*r*this.cos_p14/e),a=Math.atan2(t.x*r,e*this.cos_p14*o-t.y*this.sin_p14*r),a=n(this.long0+a)):(l=this.phic0,a=0),t.x=a,t.y=l,t},r.names=[\"gnom\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/asinz\":\"proj4/lib/common/asinz\"}],\"proj4/lib/projections/krovak\":[function(t,e,r){var n=t(\"../common/adjust_lon\");r.init=function(){this.a=6377397.155,this.es=.006674372230614,this.e=Math.sqrt(this.es),this.lat0||(this.lat0=.863937979737193),this.long0||(this.long0=.4334234309119251),this.k0||(this.k0=.9999),this.s45=.785398163397448,this.s90=2*this.s45,this.fi0=this.lat0,this.e2=this.es,this.e=Math.sqrt(this.e2),this.alfa=Math.sqrt(1+this.e2*Math.pow(Math.cos(this.fi0),4)/(1-this.e2)),this.uq=1.04216856380474,this.u0=Math.asin(Math.sin(this.fi0)/this.alfa),this.g=Math.pow((1+this.e*Math.sin(this.fi0))/(1-this.e*Math.sin(this.fi0)),this.alfa*this.e/2),this.k=Math.tan(this.u0/2+this.s45)/Math.pow(Math.tan(this.fi0/2+this.s45),this.alfa)*this.g,this.k1=this.k0,this.n0=this.a*Math.sqrt(1-this.e2)/(1-this.e2*Math.pow(Math.sin(this.fi0),2)),this.s0=1.37008346281555,this.n=Math.sin(this.s0),this.ro0=this.k1*this.n0/Math.tan(this.s0),this.ad=this.s90-this.uq},r.forward=function(t){var e,r,o,i,s,a,l,u=t.x,h=t.y,c=n(u-this.long0);return e=Math.pow((1+this.e*Math.sin(h))/(1-this.e*Math.sin(h)),this.alfa*this.e/2),r=2*(Math.atan(this.k*Math.pow(Math.tan(h/2+this.s45),this.alfa)/e)-this.s45),o=-c*this.alfa,i=Math.asin(Math.cos(this.ad)*Math.sin(r)+Math.sin(this.ad)*Math.cos(r)*Math.cos(o)),s=Math.asin(Math.cos(r)*Math.sin(o)/Math.cos(i)),a=this.n*s,l=this.ro0*Math.pow(Math.tan(this.s0/2+this.s45),this.n)/Math.pow(Math.tan(i/2+this.s45),this.n),t.y=l*Math.cos(a)/1,t.x=l*Math.sin(a)/1,this.czech||(t.y*=-1,t.x*=-1),t},r.inverse=function(t){var e,r,n,o,i,s,a,l,u=t.x;t.x=t.y,t.y=u,this.czech||(t.y*=-1,t.x*=-1),s=Math.sqrt(t.x*t.x+t.y*t.y),i=Math.atan2(t.y,t.x),o=i/Math.sin(this.s0),n=2*(Math.atan(Math.pow(this.ro0/s,1/this.n)*Math.tan(this.s0/2+this.s45))-this.s45),e=Math.asin(Math.cos(this.ad)*Math.sin(n)-Math.sin(this.ad)*Math.cos(n)*Math.cos(o)),r=Math.asin(Math.cos(n)*Math.sin(o)/Math.cos(e)),t.x=this.long0-r/this.alfa,a=e,l=0;var h=0;do t.y=2*(Math.atan(Math.pow(this.k,-1/this.alfa)*Math.pow(Math.tan(e/2+this.s45),1/this.alfa)*Math.pow((1+this.e*Math.sin(a))/(1-this.e*Math.sin(a)),this.e/2))-this.s45),Math.abs(a-t.y)<1e-10&&(l=1),a=t.y,h+=1;while(0===l&&h<15);return h>=15?null:t},r.names=[\"Krovak\",\"krovak\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\"}],\"proj4/lib/projections/laea\":[function(t,e,r){var n=Math.PI/2,o=Math.PI/4,i=1e-10,s=t(\"../common/qsfnz\"),a=t(\"../common/adjust_lon\");r.S_POLE=1,r.N_POLE=2,r.EQUIT=3,r.OBLIQ=4,r.init=function(){var t=Math.abs(this.lat0);if(Math.abs(t-n)<i?this.mode=this.lat0<0?this.S_POLE:this.N_POLE:Math.abs(t)<i?this.mode=this.EQUIT:this.mode=this.OBLIQ,this.es>0){var e;switch(this.qp=s(this.e,1),this.mmf=.5/(1-this.es),this.apa=this.authset(this.es),this.mode){case this.N_POLE:this.dd=1;break;case this.S_POLE:this.dd=1;break;case this.EQUIT:this.rq=Math.sqrt(.5*this.qp),this.dd=1/this.rq,this.xmf=1,this.ymf=.5*this.qp;break;case this.OBLIQ:this.rq=Math.sqrt(.5*this.qp),e=Math.sin(this.lat0),this.sinb1=s(this.e,e)/this.qp,this.cosb1=Math.sqrt(1-this.sinb1*this.sinb1),this.dd=Math.cos(this.lat0)/(Math.sqrt(1-this.es*e*e)*this.rq*this.cosb1),this.ymf=(this.xmf=this.rq)/this.dd,this.xmf*=this.dd}}else this.mode===this.OBLIQ&&(this.sinph0=Math.sin(this.lat0),this.cosph0=Math.cos(this.lat0))},r.forward=function(t){var e,r,l,u,h,c,p,_,d,f,m=t.x,g=t.y;if(m=a(m-this.long0),this.sphere){if(h=Math.sin(g),f=Math.cos(g),l=Math.cos(m),this.mode===this.OBLIQ||this.mode===this.EQUIT){if(r=this.mode===this.EQUIT?1+f*l:1+this.sinph0*h+this.cosph0*f*l,r<=i)return null;r=Math.sqrt(2/r),e=r*f*Math.sin(m),r*=this.mode===this.EQUIT?h:this.cosph0*h-this.sinph0*f*l}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(l=-l),Math.abs(g+this.phi0)<i)return null;r=o-.5*g,r=2*(this.mode===this.S_POLE?Math.cos(r):Math.sin(r)),e=r*Math.sin(m),r*=l}}else{switch(p=0,_=0,d=0,l=Math.cos(m),u=Math.sin(m),h=Math.sin(g),c=s(this.e,h),this.mode!==this.OBLIQ&&this.mode!==this.EQUIT||(p=c/this.qp,_=Math.sqrt(1-p*p)),this.mode){case this.OBLIQ:d=1+this.sinb1*p+this.cosb1*_*l;break;case this.EQUIT:d=1+_*l;break;case this.N_POLE:d=n+g,c=this.qp-c;break;case this.S_POLE:d=g-n,c=this.qp+c}if(Math.abs(d)<i)return null;switch(this.mode){case this.OBLIQ:case this.EQUIT:d=Math.sqrt(2/d),r=this.mode===this.OBLIQ?this.ymf*d*(this.cosb1*p-this.sinb1*_*l):(d=Math.sqrt(2/(1+_*l)))*p*this.ymf,e=this.xmf*d*_*u;break;case this.N_POLE:case this.S_POLE:c>=0?(e=(d=Math.sqrt(c))*u,r=l*(this.mode===this.S_POLE?d:-d)):e=r=0}}return t.x=this.a*e+this.x0,t.y=this.a*r+this.y0,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,o,s,l,u,h,c=t.x/this.a,p=t.y/this.a;if(this.sphere){var _,d=0,f=0;if(_=Math.sqrt(c*c+p*p),r=.5*_,r>1)return null;switch(r=2*Math.asin(r),this.mode!==this.OBLIQ&&this.mode!==this.EQUIT||(f=Math.sin(r),d=Math.cos(r)),this.mode){case this.EQUIT:r=Math.abs(_)<=i?0:Math.asin(p*f/_),c*=f,p=d*_;break;case this.OBLIQ:r=Math.abs(_)<=i?this.phi0:Math.asin(d*this.sinph0+p*f*this.cosph0/_),c*=f*this.cosph0,p=(d-Math.sin(r)*this.sinph0)*_;break;case this.N_POLE:p=-p,r=n-r;break;case this.S_POLE:r-=n}e=0!==p||this.mode!==this.EQUIT&&this.mode!==this.OBLIQ?Math.atan2(c,p):0}else{if(h=0,this.mode===this.OBLIQ||this.mode===this.EQUIT){if(c/=this.dd,p*=this.dd,u=Math.sqrt(c*c+p*p),u<i)return t.x=0,t.y=this.phi0,t;s=2*Math.asin(.5*u/this.rq),o=Math.cos(s),c*=s=Math.sin(s),this.mode===this.OBLIQ?(h=o*this.sinb1+p*s*this.cosb1/u,l=this.qp*h,p=u*this.cosb1*o-p*this.sinb1*s):(h=p*s/u,l=this.qp*h,p=u*o)}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(p=-p),l=c*c+p*p,!l)return t.x=0,t.y=this.phi0,t;h=1-l/this.qp,this.mode===this.S_POLE&&(h=-h)}e=Math.atan2(c,p),r=this.authlat(Math.asin(h),this.apa)}return t.x=a(this.long0+e),t.y=r,t},r.P00=.3333333333333333,r.P01=.17222222222222222,r.P02=.10257936507936508,r.P10=.06388888888888888,r.P11=.0664021164021164,r.P20=.016415012942191543,r.authset=function(t){var e,r=[];return r[0]=t*this.P00,e=t*t,r[0]+=e*this.P01,r[1]=e*this.P10,e*=t,r[0]+=e*this.P02,r[1]+=e*this.P11,r[2]=e*this.P20,r},r.authlat=function(t,e){var r=t+t;return t+e[0]*Math.sin(r)+e[1]*Math.sin(r+r)+e[2]*Math.sin(r+r+r)},r.names=[\"Lambert Azimuthal Equal Area\",\"Lambert_Azimuthal_Equal_Area\",\"laea\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/qsfnz\":\"proj4/lib/common/qsfnz\"}],\"proj4/lib/projections/lcc\":[function(t,e,r){var n=1e-10,o=t(\"../common/msfnz\"),i=t(\"../common/tsfnz\"),s=Math.PI/2,a=t(\"../common/sign\"),l=t(\"../common/adjust_lon\"),u=t(\"../common/phi2z\");r.init=function(){if(this.lat2||(this.lat2=this.lat1),this.k0||(this.k0=1),this.x0=this.x0||0,this.y0=this.y0||0,!(Math.abs(this.lat1+this.lat2)<n)){var t=this.b/this.a;this.e=Math.sqrt(1-t*t);var e=Math.sin(this.lat1),r=Math.cos(this.lat1),s=o(this.e,e,r),a=i(this.e,this.lat1,e),l=Math.sin(this.lat2),u=Math.cos(this.lat2),h=o(this.e,l,u),c=i(this.e,this.lat2,l),p=i(this.e,this.lat0,Math.sin(this.lat0));Math.abs(this.lat1-this.lat2)>n?this.ns=Math.log(s/h)/Math.log(a/c):this.ns=e,isNaN(this.ns)&&(this.ns=e),this.f0=s/(this.ns*Math.pow(a,this.ns)),this.rh=this.a*this.f0*Math.pow(p,this.ns),this.title||(this.title=\"Lambert Conformal Conic\")}},r.forward=function(t){var e=t.x,r=t.y;Math.abs(2*Math.abs(r)-Math.PI)<=n&&(r=a(r)*(s-2*n));var o,u,h=Math.abs(Math.abs(r)-s);if(h>n)o=i(this.e,r,Math.sin(r)),u=this.a*this.f0*Math.pow(o,this.ns);else{if(h=r*this.ns,h<=0)return null;u=0}var c=this.ns*l(e-this.long0);return t.x=this.k0*(u*Math.sin(c))+this.x0,t.y=this.k0*(this.rh-u*Math.cos(c))+this.y0,t},r.inverse=function(t){var e,r,n,o,i,a=(t.x-this.x0)/this.k0,h=this.rh-(t.y-this.y0)/this.k0;this.ns>0?(e=Math.sqrt(a*a+h*h),r=1):(e=-Math.sqrt(a*a+h*h),r=-1);var c=0;if(0!==e&&(c=Math.atan2(r*a,r*h)),0!==e||this.ns>0){if(r=1/this.ns,n=Math.pow(e/(this.a*this.f0),r),o=u(this.e,n),o===-9999)return null}else o=-s;return i=l(c/this.ns+this.long0),t.x=i,t.y=o,t},r.names=[\"Lambert Tangential Conformal Conic Projection\",\"Lambert_Conformal_Conic\",\"Lambert_Conformal_Conic_2SP\",\"lcc\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/msfnz\":\"proj4/lib/common/msfnz\",\"../common/phi2z\":\"proj4/lib/common/phi2z\",\"../common/sign\":\"proj4/lib/common/sign\",\"../common/tsfnz\":\"proj4/lib/common/tsfnz\"}],\"proj4/lib/projections/longlat\":[function(t,e,r){function n(t){return t}r.init=function(){},r.forward=n,r.inverse=n,r.names=[\"longlat\",\"identity\"]},{}],\"proj4/lib/projections/merc\":[function(t,e,r){var n=t(\"../common/msfnz\"),o=Math.PI/2,i=1e-10,s=57.29577951308232,a=t(\"../common/adjust_lon\"),l=Math.PI/4,u=t(\"../common/tsfnz\"),h=t(\"../common/phi2z\");r.init=function(){var t=this.b/this.a;this.es=1-t*t,\"x0\"in this||(this.x0=0),\"y0\"in this||(this.y0=0),this.e=Math.sqrt(this.es),this.lat_ts?this.sphere?this.k0=Math.cos(this.lat_ts):this.k0=n(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)):this.k0||(this.k?this.k0=this.k:this.k0=1)},r.forward=function(t){var e=t.x,r=t.y;if(r*s>90&&r*s<-90&&e*s>180&&e*s<-180)return null;var n,h;if(Math.abs(Math.abs(r)-o)<=i)return null;if(this.sphere)n=this.x0+this.a*this.k0*a(e-this.long0),h=this.y0+this.a*this.k0*Math.log(Math.tan(l+.5*r));else{var c=Math.sin(r),p=u(this.e,r,c);n=this.x0+this.a*this.k0*a(e-this.long0),h=this.y0-this.a*this.k0*Math.log(p)}return t.x=n,t.y=h,t},r.inverse=function(t){var e,r,n=t.x-this.x0,i=t.y-this.y0;if(this.sphere)r=o-2*Math.atan(Math.exp(-i/(this.a*this.k0)));else{var s=Math.exp(-i/(this.a*this.k0));if(r=h(this.e,s),r===-9999)return null}return e=a(this.long0+n/(this.a*this.k0)),t.x=e,t.y=r,t},r.names=[\"Mercator\",\"Popular Visualisation Pseudo Mercator\",\"Mercator_1SP\",\"Mercator_Auxiliary_Sphere\",\"merc\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/msfnz\":\"proj4/lib/common/msfnz\",\"../common/phi2z\":\"proj4/lib/common/phi2z\",\"../common/tsfnz\":\"proj4/lib/common/tsfnz\"}],\"proj4/lib/projections/mill\":[function(t,e,r){var n=t(\"../common/adjust_lon\");r.init=function(){},r.forward=function(t){var e=t.x,r=t.y,o=n(e-this.long0),i=this.x0+this.a*o,s=this.y0+this.a*Math.log(Math.tan(Math.PI/4+r/2.5))*1.25;return t.x=i,t.y=s,t},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e=n(this.long0+t.x/this.a),r=2.5*(Math.atan(Math.exp(.8*t.y/this.a))-Math.PI/4);return t.x=e,t.y=r,t},r.names=[\"Miller_Cylindrical\",\"mill\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\"}],\"proj4/lib/projections/moll\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=1e-10;r.init=function(){},r.forward=function(t){for(var e=t.x,r=t.y,i=n(e-this.long0),s=r,a=Math.PI*Math.sin(r),l=0;!0;l++){var u=-(s+Math.sin(s)-a)/(1+Math.cos(s));if(s+=u,Math.abs(u)<o)break}s/=2,Math.PI/2-Math.abs(r)<o&&(i=0);var h=.900316316158*this.a*i*Math.cos(s)+this.x0,c=1.4142135623731*this.a*Math.sin(s)+this.y0;return t.x=h,t.y=c,t},r.inverse=function(t){var e,r;t.x-=this.x0,t.y-=this.y0,r=t.y/(1.4142135623731*this.a),Math.abs(r)>.999999999999&&(r=.999999999999),e=Math.asin(r);var o=n(this.long0+t.x/(.900316316158*this.a*Math.cos(e)));o<-Math.PI&&(o=-Math.PI),o>Math.PI&&(o=Math.PI),r=(2*e+Math.sin(2*e))/Math.PI,Math.abs(r)>1&&(r=1);var i=Math.asin(r);return t.x=o,t.y=i,t},r.names=[\"Mollweide\",\"moll\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\"}],\"proj4/lib/projections/nzmg\":[function(t,e,r){var n=484813681109536e-20;r.iterations=1,r.init=function(){this.A=[],this.A[1]=.6399175073,this.A[2]=-.1358797613,this.A[3]=.063294409,this.A[4]=-.02526853,this.A[5]=.0117879,this.A[6]=-.0055161,this.A[7]=.0026906,this.A[8]=-.001333,this.A[9]=67e-5,this.A[10]=-34e-5,this.B_re=[],this.B_im=[],this.B_re[1]=.7557853228,this.B_im[1]=0,this.B_re[2]=.249204646,this.B_im[2]=.003371507,this.B_re[3]=-.001541739,this.B_im[3]=.04105856,this.B_re[4]=-.10162907,this.B_im[4]=.01727609,this.B_re[5]=-.26623489,this.B_im[5]=-.36249218,this.B_re[6]=-.6870983,this.B_im[6]=-1.1651967,this.C_re=[],this.C_im=[],this.C_re[1]=1.3231270439,this.C_im[1]=0,this.C_re[2]=-.577245789,this.C_im[2]=-.007809598,this.C_re[3]=.508307513,this.C_im[3]=-.112208952,this.C_re[4]=-.15094762,this.C_im[4]=.18200602,this.C_re[5]=1.01418179,this.C_im[5]=1.64497696,this.C_re[6]=1.9660549,this.C_im[6]=2.5127645,this.D=[],this.D[1]=1.5627014243,this.D[2]=.5185406398,this.D[3]=-.03333098,this.D[4]=-.1052906,this.D[5]=-.0368594,this.D[6]=.007317,this.D[7]=.0122,this.D[8]=.00394,this.D[9]=-.0013},r.forward=function(t){var e,r=t.x,o=t.y,i=o-this.lat0,s=r-this.long0,a=i/n*1e-5,l=s,u=1,h=0;for(e=1;e<=10;e++)u*=a,h+=this.A[e]*u;var c,p,_=h,d=l,f=1,m=0,g=0,y=0;for(e=1;e<=6;e++)c=f*_-m*d,p=m*_+f*d,f=c,m=p,g=g+this.B_re[e]*f-this.B_im[e]*m,y=y+this.B_im[e]*f+this.B_re[e]*m;return t.x=y*this.a+this.x0,t.y=g*this.a+this.y0,t},r.inverse=function(t){var e,r,o,i=t.x,s=t.y,a=i-this.x0,l=s-this.y0,u=l/this.a,h=a/this.a,c=1,p=0,_=0,d=0;for(e=1;e<=6;e++)r=c*u-p*h,o=p*u+c*h,c=r,p=o,_=_+this.C_re[e]*c-this.C_im[e]*p,d=d+this.C_im[e]*c+this.C_re[e]*p;for(var f=0;f<this.iterations;f++){var m,g,y=_,v=d,b=u,x=h;for(e=2;e<=6;e++)m=y*_-v*d,g=v*_+y*d,y=m,v=g,b+=(e-1)*(this.B_re[e]*y-this.B_im[e]*v),x+=(e-1)*(this.B_im[e]*y+this.B_re[e]*v);y=1,v=0;var w=this.B_re[1],M=this.B_im[1];for(e=2;e<=6;e++)m=y*_-v*d,g=v*_+y*d,y=m,v=g,w+=e*(this.B_re[e]*y-this.B_im[e]*v),M+=e*(this.B_im[e]*y+this.B_re[e]*v);var k=w*w+M*M;_=(b*w+x*M)/k,d=(x*w-b*M)/k}var j=_,T=d,S=1,z=0;for(e=1;e<=9;e++)S*=j,z+=this.D[e]*S;var P=this.lat0+z*n*1e5,E=this.long0+T;return t.x=E,t.y=P,t},r.names=[\"New_Zealand_Map_Grid\",\"nzmg\"]},{}],\"proj4/lib/projections/omerc\":[function(t,e,r){var n=t(\"../common/tsfnz\"),o=t(\"../common/adjust_lon\"),i=t(\"../common/phi2z\"),s=Math.PI/2,a=Math.PI/4,l=1e-10;r.init=function(){this.no_off=this.no_off||!1,this.no_rot=this.no_rot||!1,isNaN(this.k0)&&(this.k0=1);var t=Math.sin(this.lat0),e=Math.cos(this.lat0),r=this.e*t;this.bl=Math.sqrt(1+this.es/(1-this.es)*Math.pow(e,4)),this.al=this.a*this.bl*this.k0*Math.sqrt(1-this.es)/(1-r*r);var i=n(this.e,this.lat0,t),s=this.bl/e*Math.sqrt((1-this.es)/(1-r*r));s*s<1&&(s=1);var a,l;if(isNaN(this.longc)){var u=n(this.e,this.lat1,Math.sin(this.lat1)),h=n(this.e,this.lat2,Math.sin(this.lat2));this.lat0>=0?this.el=(s+Math.sqrt(s*s-1))*Math.pow(i,this.bl):this.el=(s-Math.sqrt(s*s-1))*Math.pow(i,this.bl);var c=Math.pow(u,this.bl),p=Math.pow(h,this.bl);a=this.el/c,l=.5*(a-1/a);var _=(this.el*this.el-p*c)/(this.el*this.el+p*c),d=(p-c)/(p+c),f=o(this.long1-this.long2);this.long0=.5*(this.long1+this.long2)-Math.atan(_*Math.tan(.5*this.bl*f)/d)/this.bl,this.long0=o(this.long0);var m=o(this.long1-this.long0);this.gamma0=Math.atan(Math.sin(this.bl*m)/l),this.alpha=Math.asin(s*Math.sin(this.gamma0))}else a=this.lat0>=0?s+Math.sqrt(s*s-1):s-Math.sqrt(s*s-1),this.el=a*Math.pow(i,this.bl),l=.5*(a-1/a),this.gamma0=Math.asin(Math.sin(this.alpha)/s),this.long0=this.longc-Math.asin(l*Math.tan(this.gamma0))/this.bl;this.no_off?this.uc=0:this.lat0>=0?this.uc=this.al/this.bl*Math.atan2(Math.sqrt(s*s-1),Math.cos(this.alpha)):this.uc=-1*this.al/this.bl*Math.atan2(Math.sqrt(s*s-1),Math.cos(this.alpha))},r.forward=function(t){var e,r,i,u=t.x,h=t.y,c=o(u-this.long0);if(Math.abs(Math.abs(h)-s)<=l)i=h>0?-1:1,r=this.al/this.bl*Math.log(Math.tan(a+i*this.gamma0*.5)),e=-1*i*s*this.al/this.bl;else{var p=n(this.e,h,Math.sin(h)),_=this.el/Math.pow(p,this.bl),d=.5*(_-1/_),f=.5*(_+1/_),m=Math.sin(this.bl*c),g=(d*Math.sin(this.gamma0)-m*Math.cos(this.gamma0))/f;r=Math.abs(Math.abs(g)-1)<=l?Number.POSITIVE_INFINITY:.5*this.al*Math.log((1-g)/(1+g))/this.bl,e=Math.abs(Math.cos(this.bl*c))<=l?this.al*this.bl*c:this.al*Math.atan2(d*Math.cos(this.gamma0)+m*Math.sin(this.gamma0),Math.cos(this.bl*c))/this.bl}return this.no_rot?(t.x=this.x0+e,t.y=this.y0+r):(e-=this.uc,t.x=this.x0+r*Math.cos(this.alpha)+e*Math.sin(this.alpha),t.y=this.y0+e*Math.cos(this.alpha)-r*Math.sin(this.alpha)),t},r.inverse=function(t){var e,r;this.no_rot?(r=t.y-this.y0,e=t.x-this.x0):(r=(t.x-this.x0)*Math.cos(this.alpha)-(t.y-this.y0)*Math.sin(this.alpha),e=(t.y-this.y0)*Math.cos(this.alpha)+(t.x-this.x0)*Math.sin(this.alpha),e+=this.uc);var n=Math.exp(-1*this.bl*r/this.al),a=.5*(n-1/n),u=.5*(n+1/n),h=Math.sin(this.bl*e/this.al),c=(h*Math.cos(this.gamma0)+a*Math.sin(this.gamma0))/u,p=Math.pow(this.el/Math.sqrt((1+c)/(1-c)),1/this.bl);return Math.abs(c-1)<l?(t.x=this.long0,t.y=s):Math.abs(c+1)<l?(t.x=this.long0,t.y=-1*s):(t.y=i(this.e,p),t.x=o(this.long0-Math.atan2(a*Math.cos(this.gamma0)-h*Math.sin(this.gamma0),Math.cos(this.bl*e/this.al))/this.bl)),t},r.names=[\"Hotine_Oblique_Mercator\",\"Hotine Oblique Mercator\",\"Hotine_Oblique_Mercator_Azimuth_Natural_Origin\",\"Hotine_Oblique_Mercator_Azimuth_Center\",\"omerc\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/phi2z\":\"proj4/lib/common/phi2z\",\"../common/tsfnz\":\"proj4/lib/common/tsfnz\"}],\"proj4/lib/projections/poly\":[function(t,e,r){var n=t(\"../common/e0fn\"),o=t(\"../common/e1fn\"),i=t(\"../common/e2fn\"),s=t(\"../common/e3fn\"),a=t(\"../common/adjust_lon\"),l=t(\"../common/adjust_lat\"),u=t(\"../common/mlfn\"),h=1e-10,c=t(\"../common/gN\"),p=20;r.init=function(){this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.ml0=this.a*u(this.e0,this.e1,this.e2,this.e3,this.lat0)},r.forward=function(t){var e,r,n,o=t.x,i=t.y,s=a(o-this.long0);if(n=s*Math.sin(i),this.sphere)Math.abs(i)<=h?(e=this.a*s,r=-1*this.a*this.lat0):(e=this.a*Math.sin(n)/Math.tan(i),r=this.a*(l(i-this.lat0)+(1-Math.cos(n))/Math.tan(i)));else if(Math.abs(i)<=h)e=this.a*s,r=-1*this.ml0;else{var p=c(this.a,this.e,Math.sin(i))/Math.tan(i);e=p*Math.sin(n),r=this.a*u(this.e0,this.e1,this.e2,this.e3,i)-this.ml0+p*(1-Math.cos(n))}return t.x=e+this.x0,t.y=r+this.y0,t},r.inverse=function(t){var e,r,n,o,i,s,l,c,_;if(n=t.x-this.x0,o=t.y-this.y0,this.sphere)if(Math.abs(o+this.a*this.lat0)<=h)e=a(n/this.a+this.long0),r=0;else{s=this.lat0+o/this.a,l=n*n/this.a/this.a+s*s,c=s;var d;for(i=p;i;--i)if(d=Math.tan(c),_=-1*(s*(c*d+1)-c-.5*(c*c+l)*d)/((c-s)/d-1),c+=_,Math.abs(_)<=h){r=c;break}e=a(this.long0+Math.asin(n*Math.tan(c)/this.a)/Math.sin(r))}else if(Math.abs(o+this.ml0)<=h)r=0,e=a(this.long0+n/this.a);else{s=(this.ml0+o)/this.a,l=n*n/this.a/this.a+s*s,c=s;var f,m,g,y,v;for(i=p;i;--i)if(v=this.e*Math.sin(c),f=Math.sqrt(1-v*v)*Math.tan(c),m=this.a*u(this.e0,this.e1,this.e2,this.e3,c),g=this.e0-2*this.e1*Math.cos(2*c)+4*this.e2*Math.cos(4*c)-6*this.e3*Math.cos(6*c),y=m/this.a,_=(s*(f*y+1)-y-.5*f*(y*y+l))/(this.es*Math.sin(2*c)*(y*y+l-2*s*y)/(4*f)+(s-y)*(f*g-2/Math.sin(2*c))-g),c-=_,Math.abs(_)<=h){r=c;break}f=Math.sqrt(1-this.es*Math.pow(Math.sin(r),2))*Math.tan(r),e=a(this.long0+Math.asin(n*f/this.a)/Math.sin(r))}return t.x=e,t.y=r,t},r.names=[\"Polyconic\",\"poly\"]},{\"../common/adjust_lat\":\"proj4/lib/common/adjust_lat\",\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/e0fn\":\"proj4/lib/common/e0fn\",\"../common/e1fn\":\"proj4/lib/common/e1fn\",\"../common/e2fn\":\"proj4/lib/common/e2fn\",\"../common/e3fn\":\"proj4/lib/common/e3fn\",\"../common/gN\":\"proj4/lib/common/gN\",\"../common/mlfn\":\"proj4/lib/common/mlfn\"}],\"proj4/lib/projections/sinu\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=t(\"../common/adjust_lat\"),i=t(\"../common/pj_enfn\"),s=20,a=t(\"../common/pj_mlfn\"),l=t(\"../common/pj_inv_mlfn\"),u=Math.PI/2,h=1e-10,c=t(\"../common/asinz\");r.init=function(){this.sphere?(this.n=1,this.m=0,this.es=0,this.C_y=Math.sqrt((this.m+1)/this.n),this.C_x=this.C_y/(this.m+1)):this.en=i(this.es)},r.forward=function(t){var e,r,o=t.x,i=t.y;if(o=n(o-this.long0),this.sphere){if(this.m)for(var l=this.n*Math.sin(i),u=s;u;--u){var c=(this.m*i+Math.sin(i)-l)/(this.m+Math.cos(i));if(i-=c,Math.abs(c)<h)break}else i=1!==this.n?Math.asin(this.n*Math.sin(i)):i;e=this.a*this.C_x*o*(this.m+Math.cos(i)),r=this.a*this.C_y*i}else{var p=Math.sin(i),_=Math.cos(i);r=this.a*a(i,p,_,this.en),e=this.a*o*_/Math.sqrt(1-this.es*p*p)}return t.x=e,t.y=r,t},r.inverse=function(t){var e,r,i,s;return t.x-=this.x0,i=t.x/this.a,t.y-=this.y0,e=t.y/this.a,this.sphere?(e/=this.C_y,i/=this.C_x*(this.m+Math.cos(e)),this.m?e=c((this.m*e+Math.sin(e))/this.n):1!==this.n&&(e=c(Math.sin(e)/this.n)),i=n(i+this.long0),e=o(e)):(e=l(t.y/this.a,this.es,this.en),s=Math.abs(e),s<u?(s=Math.sin(e),r=this.long0+t.x*Math.sqrt(1-this.es*s*s)/(this.a*Math.cos(e)),i=n(r)):s-h<u&&(i=this.long0)),t.x=i,t.y=e,t},r.names=[\"Sinusoidal\",\"sinu\"]},{\"../common/adjust_lat\":\"proj4/lib/common/adjust_lat\",\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/asinz\":\"proj4/lib/common/asinz\",\"../common/pj_enfn\":\"proj4/lib/common/pj_enfn\",\"../common/pj_inv_mlfn\":\"proj4/lib/common/pj_inv_mlfn\",\"../common/pj_mlfn\":\"proj4/lib/common/pj_mlfn\"}],\"proj4/lib/projections/somerc\":[function(t,e,r){r.init=function(){var t=this.lat0;this.lambda0=this.long0;var e=Math.sin(t),r=this.a,n=this.rf,o=1/n,i=2*o-Math.pow(o,2),s=this.e=Math.sqrt(i);this.R=this.k0*r*Math.sqrt(1-i)/(1-i*Math.pow(e,2)),this.alpha=Math.sqrt(1+i/(1-i)*Math.pow(Math.cos(t),4)),this.b0=Math.asin(e/this.alpha);var a=Math.log(Math.tan(Math.PI/4+this.b0/2)),l=Math.log(Math.tan(Math.PI/4+t/2)),u=Math.log((1+s*e)/(1-s*e));this.K=a-this.alpha*l+this.alpha*s/2*u},r.forward=function(t){var e=Math.log(Math.tan(Math.PI/4-t.y/2)),r=this.e/2*Math.log((1+this.e*Math.sin(t.y))/(1-this.e*Math.sin(t.y))),n=-this.alpha*(e+r)+this.K,o=2*(Math.atan(Math.exp(n))-Math.PI/4),i=this.alpha*(t.x-this.lambda0),s=Math.atan(Math.sin(i)/(Math.sin(this.b0)*Math.tan(o)+Math.cos(this.b0)*Math.cos(i))),a=Math.asin(Math.cos(this.b0)*Math.sin(o)-Math.sin(this.b0)*Math.cos(o)*Math.cos(i));return t.y=this.R/2*Math.log((1+Math.sin(a))/(1-Math.sin(a)))+this.y0,t.x=this.R*s+this.x0,t},r.inverse=function(t){for(var e=t.x-this.x0,r=t.y-this.y0,n=e/this.R,o=2*(Math.atan(Math.exp(r/this.R))-Math.PI/4),i=Math.asin(Math.cos(this.b0)*Math.sin(o)+Math.sin(this.b0)*Math.cos(o)*Math.cos(n)),s=Math.atan(Math.sin(n)/(Math.cos(this.b0)*Math.cos(n)-Math.sin(this.b0)*Math.tan(o))),a=this.lambda0+s/this.alpha,l=0,u=i,h=-1e3,c=0;Math.abs(u-h)>1e-7;){if(++c>20)return;l=1/this.alpha*(Math.log(Math.tan(Math.PI/4+i/2))-this.K)+this.e*Math.log(Math.tan(Math.PI/4+Math.asin(this.e*Math.sin(u))/2)),h=u,u=2*Math.atan(Math.exp(l))-Math.PI/2}return t.x=a,t.y=u,t},r.names=[\"somerc\"]},{}],\"proj4/lib/projections/stere\":[function(t,e,r){var n=Math.PI/2,o=1e-10,i=t(\"../common/sign\"),s=t(\"../common/msfnz\"),a=t(\"../common/tsfnz\"),l=t(\"../common/phi2z\"),u=t(\"../common/adjust_lon\");r.ssfn_=function(t,e,r){return e*=r,Math.tan(.5*(n+t))*Math.pow((1-e)/(1+e),.5*r)},r.init=function(){this.coslat0=Math.cos(this.lat0),this.sinlat0=Math.sin(this.lat0),this.sphere?1===this.k0&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=o&&(this.k0=.5*(1+i(this.lat0)*Math.sin(this.lat_ts))):(Math.abs(this.coslat0)<=o&&(this.lat0>0?this.con=1:this.con=-1),this.cons=Math.sqrt(Math.pow(1+this.e,1+this.e)*Math.pow(1-this.e,1-this.e)),1===this.k0&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=o&&(this.k0=.5*this.cons*s(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts))/a(this.e,this.con*this.lat_ts,this.con*Math.sin(this.lat_ts))),this.ms1=s(this.e,this.sinlat0,this.coslat0),this.X0=2*Math.atan(this.ssfn_(this.lat0,this.sinlat0,this.e))-n,this.cosX0=Math.cos(this.X0),this.sinX0=Math.sin(this.X0))},r.forward=function(t){var e,r,i,s,l,h,c=t.x,p=t.y,_=Math.sin(p),d=Math.cos(p),f=u(c-this.long0);return Math.abs(Math.abs(c-this.long0)-Math.PI)<=o&&Math.abs(p+this.lat0)<=o?(t.x=NaN,t.y=NaN,t):this.sphere?(e=2*this.k0/(1+this.sinlat0*_+this.coslat0*d*Math.cos(f)),t.x=this.a*e*d*Math.sin(f)+this.x0,t.y=this.a*e*(this.coslat0*_-this.sinlat0*d*Math.cos(f))+this.y0,t):(r=2*Math.atan(this.ssfn_(p,_,this.e))-n,s=Math.cos(r),i=Math.sin(r),Math.abs(this.coslat0)<=o?(l=a(this.e,p*this.con,this.con*_),h=2*this.a*this.k0*l/this.cons,t.x=this.x0+h*Math.sin(c-this.long0),t.y=this.y0-this.con*h*Math.cos(c-this.long0),t):(Math.abs(this.sinlat0)<o?(e=2*this.a*this.k0/(1+s*Math.cos(f)),t.y=e*i):(e=2*this.a*this.k0*this.ms1/(this.cosX0*(1+this.sinX0*i+this.cosX0*s*Math.cos(f))),t.y=e*(this.cosX0*i-this.sinX0*s*Math.cos(f))+this.y0),t.x=e*s*Math.sin(f)+this.x0,t))},r.inverse=function(t){t.x-=this.x0,t.y-=this.y0;var e,r,i,s,a,h=Math.sqrt(t.x*t.x+t.y*t.y);if(this.sphere){var c=2*Math.atan(h/(.5*this.a*this.k0));return e=this.long0,r=this.lat0,h<=o?(t.x=e,t.y=r,t):(r=Math.asin(Math.cos(c)*this.sinlat0+t.y*Math.sin(c)*this.coslat0/h),e=u(Math.abs(this.coslat0)<o?this.lat0>0?this.long0+Math.atan2(t.x,-1*t.y):this.long0+Math.atan2(t.x,t.y):this.long0+Math.atan2(t.x*Math.sin(c),h*this.coslat0*Math.cos(c)-t.y*this.sinlat0*Math.sin(c))),t.x=e,t.y=r,t)}if(Math.abs(this.coslat0)<=o){if(h<=o)return r=this.lat0,e=this.long0,\nt.x=e,t.y=r,t;t.x*=this.con,t.y*=this.con,i=h*this.cons/(2*this.a*this.k0),r=this.con*l(this.e,i),e=this.con*u(this.con*this.long0+Math.atan2(t.x,-1*t.y))}else s=2*Math.atan(h*this.cosX0/(2*this.a*this.k0*this.ms1)),e=this.long0,h<=o?a=this.X0:(a=Math.asin(Math.cos(s)*this.sinX0+t.y*Math.sin(s)*this.cosX0/h),e=u(this.long0+Math.atan2(t.x*Math.sin(s),h*this.cosX0*Math.cos(s)-t.y*this.sinX0*Math.sin(s)))),r=-1*l(this.e,Math.tan(.5*(n+a)));return t.x=e,t.y=r,t},r.names=[\"stere\",\"Stereographic_South_Pole\",\"Polar Stereographic (variant B)\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/msfnz\":\"proj4/lib/common/msfnz\",\"../common/phi2z\":\"proj4/lib/common/phi2z\",\"../common/sign\":\"proj4/lib/common/sign\",\"../common/tsfnz\":\"proj4/lib/common/tsfnz\"}],\"proj4/lib/projections/sterea\":[function(t,e,r){var n=t(\"./gauss\"),o=t(\"../common/adjust_lon\");r.init=function(){n.init.apply(this),this.rc&&(this.sinc0=Math.sin(this.phic0),this.cosc0=Math.cos(this.phic0),this.R2=2*this.rc,this.title||(this.title=\"Oblique Stereographic Alternative\"))},r.forward=function(t){var e,r,i,s;return t.x=o(t.x-this.long0),n.forward.apply(this,[t]),e=Math.sin(t.y),r=Math.cos(t.y),i=Math.cos(t.x),s=this.k0*this.R2/(1+this.sinc0*e+this.cosc0*r*i),t.x=s*r*Math.sin(t.x),t.y=s*(this.cosc0*e-this.sinc0*r*i),t.x=this.a*t.x+this.x0,t.y=this.a*t.y+this.y0,t},r.inverse=function(t){var e,r,i,s,a;if(t.x=(t.x-this.x0)/this.a,t.y=(t.y-this.y0)/this.a,t.x/=this.k0,t.y/=this.k0,a=Math.sqrt(t.x*t.x+t.y*t.y)){var l=2*Math.atan2(a,this.R2);e=Math.sin(l),r=Math.cos(l),s=Math.asin(r*this.sinc0+t.y*e*this.cosc0/a),i=Math.atan2(t.x*e,a*this.cosc0*r-t.y*this.sinc0*e)}else s=this.phic0,i=0;return t.x=i,t.y=s,n.inverse.apply(this,[t]),t.x=o(t.x+this.long0),t},r.names=[\"Stereographic_North_Pole\",\"Oblique_Stereographic\",\"Polar_Stereographic\",\"sterea\",\"Oblique Stereographic Alternative\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"./gauss\":\"proj4/lib/projections/gauss\"}],\"proj4/lib/projections/tmerc\":[function(t,e,r){var n=t(\"../common/e0fn\"),o=t(\"../common/e1fn\"),i=t(\"../common/e2fn\"),s=t(\"../common/e3fn\"),a=t(\"../common/mlfn\"),l=t(\"../common/adjust_lon\"),u=Math.PI/2,h=1e-10,c=t(\"../common/sign\"),p=t(\"../common/asinz\");r.init=function(){this.e0=n(this.es),this.e1=o(this.es),this.e2=i(this.es),this.e3=s(this.es),this.ml0=this.a*a(this.e0,this.e1,this.e2,this.e3,this.lat0)},r.forward=function(t){var e,r,n,o=t.x,i=t.y,s=l(o-this.long0),u=Math.sin(i),h=Math.cos(i);if(this.sphere){var c=h*Math.sin(s);if(Math.abs(Math.abs(c)-1)<1e-10)return 93;r=.5*this.a*this.k0*Math.log((1+c)/(1-c)),e=Math.acos(h*Math.cos(s)/Math.sqrt(1-c*c)),i<0&&(e=-e),n=this.a*this.k0*(e-this.lat0)}else{var p=h*s,_=Math.pow(p,2),d=this.ep2*Math.pow(h,2),f=Math.tan(i),m=Math.pow(f,2);e=1-this.es*Math.pow(u,2);var g=this.a/Math.sqrt(e),y=this.a*a(this.e0,this.e1,this.e2,this.e3,i);r=this.k0*g*p*(1+_/6*(1-m+d+_/20*(5-18*m+Math.pow(m,2)+72*d-58*this.ep2)))+this.x0,n=this.k0*(y-this.ml0+g*f*(_*(.5+_/24*(5-m+9*d+4*Math.pow(d,2)+_/30*(61-58*m+Math.pow(m,2)+600*d-330*this.ep2)))))+this.y0}return t.x=r,t.y=n,t},r.inverse=function(t){var e,r,n,o,i,s,a=6;if(this.sphere){var _=Math.exp(t.x/(this.a*this.k0)),d=.5*(_-1/_),f=this.lat0+t.y/(this.a*this.k0),m=Math.cos(f);e=Math.sqrt((1-m*m)/(1+d*d)),i=p(e),f<0&&(i=-i),s=0===d&&0===m?this.long0:l(Math.atan2(d,m)+this.long0)}else{var g=t.x-this.x0,y=t.y-this.y0;for(e=(this.ml0+y/this.k0)/this.a,r=e,o=0;!0&&(n=(e+this.e1*Math.sin(2*r)-this.e2*Math.sin(4*r)+this.e3*Math.sin(6*r))/this.e0-r,r+=n,!(Math.abs(n)<=h));o++)if(o>=a)return 95;if(Math.abs(r)<u){var v=Math.sin(r),b=Math.cos(r),x=Math.tan(r),w=this.ep2*Math.pow(b,2),M=Math.pow(w,2),k=Math.pow(x,2),j=Math.pow(k,2);e=1-this.es*Math.pow(v,2);var T=this.a/Math.sqrt(e),S=T*(1-this.es)/e,z=g/(T*this.k0),P=Math.pow(z,2);i=r-T*x*P/S*(.5-P/24*(5+3*k+10*w-4*M-9*this.ep2-P/30*(61+90*k+298*w+45*j-252*this.ep2-3*M))),s=l(this.long0+z*(1-P/6*(1+2*k+w-P/20*(5-2*w+28*k-3*M+8*this.ep2+24*j)))/b)}else i=u*c(y),s=this.long0}return t.x=s,t.y=i,t},r.names=[\"Transverse_Mercator\",\"Transverse Mercator\",\"tmerc\"]},{\"../common/adjust_lon\":\"proj4/lib/common/adjust_lon\",\"../common/asinz\":\"proj4/lib/common/asinz\",\"../common/e0fn\":\"proj4/lib/common/e0fn\",\"../common/e1fn\":\"proj4/lib/common/e1fn\",\"../common/e2fn\":\"proj4/lib/common/e2fn\",\"../common/e3fn\":\"proj4/lib/common/e3fn\",\"../common/mlfn\":\"proj4/lib/common/mlfn\",\"../common/sign\":\"proj4/lib/common/sign\"}],\"proj4/lib/projections/utm\":[function(t,e,r){var n=.017453292519943295,o=t(\"./tmerc\");r.dependsOn=\"tmerc\",r.init=function(){this.zone&&(this.lat0=0,this.long0=(6*Math.abs(this.zone)-183)*n,this.x0=5e5,this.y0=this.utmSouth?1e7:0,this.k0=.9996,o.init.apply(this),this.forward=o.forward,this.inverse=o.inverse)},r.names=[\"Universal Transverse Mercator System\",\"utm\"]},{\"./tmerc\":\"proj4/lib/projections/tmerc\"}],\"proj4/lib/projections/vandg\":[function(t,e,r){var n=t(\"../common/adjust_lon\"),o=Math.PI/2,i=1e-10,s=t(\"../common/asinz\");r.init=function(){this.R=this.a},r.forward=function(t){var e,r,a=t.x,l=t.y,u=n(a-this.long0);Math.abs(l)<=i&&(e=this.x0+this.R*u,r=this.y0);var h=s(2*Math.abs(l/Math.PI));(Math.abs(u)<=i||Math.abs(Math.abs(l)-o)<=i)&&(e=this.x0,r=l>=0?this.y0+Math.PI*this.R*Math.tan(.5*h):this.y0+Math.PI*this.R*-Math.tan(.5*h));var c=.5*Math.abs(Math.PI/u-u/Math.PI),p=c*c,_=Math.sin(h),d=Math.cos(h),f=d/(_+d-1),m=f*f,g=f*(2/_-1),y=g*g,v=Math.PI*this.R*(c*(f-y)+Math.sqrt(p*(f-y)*(f-y)-(y+p)*(m-y)))/(y+p);u<0&&(v=-v),e=this.x0+v;var b=p+f;return v=Math.PI*this.R*(g*b-c*Math.sqrt((y+p)*(p+1)-b*b))/(y+p),r=l>=0?this.y0+v:this.y0-v,t.x=e,t.y=r,t},r.inverse=function(t){var e,r,o,s,a,l,u,h,c,p,_,d,f;return 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t.PROJECTION?t.projName=Object.keys(t.PROJECTION)[0]:t.projName=t.PROJECTION,t.UNIT&&(t.units=t.UNIT.name.toLowerCase(),\"metre\"===t.units&&(t.units=\"meter\"),t.UNIT.convert&&(\"GEOGCS\"===t.type?t.DATUM&&t.DATUM.SPHEROID&&(t.to_meter=parseFloat(t.UNIT.convert,10)*t.DATUM.SPHEROID.a):t.to_meter=parseFloat(t.UNIT.convert,10))),t.GEOGCS&&(t.GEOGCS.DATUM?t.datumCode=t.GEOGCS.DATUM.name.toLowerCase():t.datumCode=t.GEOGCS.name.toLowerCase(),\"d_\"===t.datumCode.slice(0,2)&&(t.datumCode=t.datumCode.slice(2)),\"new_zealand_geodetic_datum_1949\"!==t.datumCode&&\"new_zealand_1949\"!==t.datumCode||(t.datumCode=\"nzgd49\"),\"wgs_1984\"===t.datumCode&&(\"Mercator_Auxiliary_Sphere\"===t.PROJECTION&&(t.sphere=!0),t.datumCode=\"wgs84\"),\"_ferro\"===t.datumCode.slice(-6)&&(t.datumCode=t.datumCode.slice(0,-6)),\"_jakarta\"===t.datumCode.slice(-8)&&(t.datumCode=t.datumCode.slice(0,-8)),~t.datumCode.indexOf(\"belge\")&&(t.datumCode=\"rnb72\"),t.GEOGCS.DATUM&&t.GEOGCS.DATUM.SPHEROID&&(t.ellps=t.GEOGCS.DATUM.SPHEROID.name.replace(\"_19\",\"\").replace(/[Cc]larke\\_18/,\"clrk\"),\"international\"===t.ellps.toLowerCase().slice(0,13)&&(t.ellps=\"intl\"),t.a=t.GEOGCS.DATUM.SPHEROID.a,t.rf=parseFloat(t.GEOGCS.DATUM.SPHEROID.rf,10)),~t.datumCode.indexOf(\"osgb_1936\")&&(t.datumCode=\"osgb36\")),t.b&&!isFinite(t.b)&&(t.b=t.a);var r=function(e){return i(t,e)},n=[[\"standard_parallel_1\",\"Standard_Parallel_1\"],[\"standard_parallel_2\",\"Standard_Parallel_2\"],[\"false_easting\",\"False_Easting\"],[\"false_northing\",\"False_Northing\"],[\"central_meridian\",\"Central_Meridian\"],[\"latitude_of_origin\",\"Latitude_Of_Origin\"],[\"latitude_of_origin\",\"Central_Parallel\"],[\"scale_factor\",\"Scale_Factor\"],[\"k0\",\"scale_factor\"],[\"latitude_of_center\",\"Latitude_of_center\"],[\"lat0\",\"latitude_of_center\",s],[\"longitude_of_center\",\"Longitude_Of_Center\"],[\"longc\",\"longitude_of_center\",s],[\"x0\",\"false_easting\",e],[\"y0\",\"false_northing\",e],[\"long0\",\"central_meridian\",s],[\"lat0\",\"latitude_of_origin\",s],[\"lat0\",\"standard_parallel_1\",s],[\"lat1\",\"standard_parallel_1\",s],[\"lat2\",\"standard_parallel_2\",s],[\"alpha\",\"azimuth\",s],[\"srsCode\",\"name\"]];n.forEach(r),t.long0||!t.longc||\"Albers_Conic_Equal_Area\"!==t.projName&&\"Lambert_Azimuthal_Equal_Area\"!==t.projName||(t.long0=t.longc),t.lat_ts||!t.lat1||\"Stereographic_South_Pole\"!==t.projName&&\"Polar 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e,r,o,i,s,l,u,h,c,p=t.lat,_=t.lon,d=6378137,f=.00669438,m=.9996,g=n(p),y=n(_);c=Math.floor((_+180)/6)+1,180===_&&(c=60),p>=56&&p<64&&_>=3&&_<12&&(c=32),p>=72&&p<84&&(_>=0&&_<9?c=31:_>=9&&_<21?c=33:_>=21&&_<33?c=35:_>=33&&_<42&&(c=37)),e=6*(c-1)-180+3,h=n(e),r=f/(1-f),o=d/Math.sqrt(1-f*Math.sin(g)*Math.sin(g)),i=Math.tan(g)*Math.tan(g),s=r*Math.cos(g)*Math.cos(g),l=Math.cos(g)*(y-h),u=d*((1-f/4-3*f*f/64-5*f*f*f/256)*g-(3*f/8+3*f*f/32+45*f*f*f/1024)*Math.sin(2*g)+(15*f*f/256+45*f*f*f/1024)*Math.sin(4*g)-35*f*f*f/3072*Math.sin(6*g));var v=m*o*(l+(1-i+s)*l*l*l/6+(5-18*i+i*i+72*s-58*r)*l*l*l*l*l/120)+5e5,b=m*(u+o*Math.tan(g)*(l*l/2+(5-i+9*s+4*s*s)*l*l*l*l/24+(61-58*i+i*i+600*s-330*r)*l*l*l*l*l*l/720));return p<0&&(b+=1e7),{northing:Math.round(b),easting:Math.round(v),zoneNumber:c,zoneLetter:a(p)}}function s(t){var e=t.northing,r=t.easting,n=t.zoneLetter,i=t.zoneNumber;if(i<0||i>60)return null;var a,l,u,h,c,p,_,d,f,m,g=.9996,y=6378137,v=.00669438,b=(1-Math.sqrt(1-v))/(1+Math.sqrt(1-v)),x=r-5e5,w=e;n<\"N\"&&(w-=1e7),d=6*(i-1)-180+3,a=v/(1-v),_=w/g,f=_/(y*(1-v/4-3*v*v/64-5*v*v*v/256)),m=f+(3*b/2-27*b*b*b/32)*Math.sin(2*f)+(21*b*b/16-55*b*b*b*b/32)*Math.sin(4*f)+151*b*b*b/96*Math.sin(6*f),l=y/Math.sqrt(1-v*Math.sin(m)*Math.sin(m)),u=Math.tan(m)*Math.tan(m),h=a*Math.cos(m)*Math.cos(m),c=y*(1-v)/Math.pow(1-v*Math.sin(m)*Math.sin(m),1.5),p=x/(l*g);var M=m-l*Math.tan(m)/c*(p*p/2-(5+3*u+10*h-4*h*h-9*a)*p*p*p*p/24+(61+90*u+298*h+45*u*u-252*a-3*h*h)*p*p*p*p*p*p/720);M=o(M);var k=(p-(1+2*u+h)*p*p*p/6+(5-2*h+28*u-3*h*h+8*a+24*u*u)*p*p*p*p*p/120)/Math.cos(m);k=d+o(k);var j;if(t.accuracy){var T=s({northing:t.northing+t.accuracy,easting:t.easting+t.accuracy,zoneLetter:t.zoneLetter,zoneNumber:t.zoneNumber});j={top:T.lat,right:T.lon,bottom:M,left:k}}else j={lat:M,lon:k};return j}function a(t){var e=\"Z\";return 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Marasteanu <alexaholic [at) gmail (dot] com>\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n    * Redistributions of source code must retain the above copyright\n      notice, this list of conditions and the following disclaimer.\n    * Redistributions in binary form must reproduce the above copyright\n      notice, this list of conditions and the following disclaimer in the\n      documentation and/or other materials provided with the distribution.\n    * Neither the name of sprintf() for JavaScript nor the\n      names of its contributors may be used to endorse or promote products\n      derived from this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL Alexandru Marasteanu BE LIABLE FOR ANY\nDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nChangelog:\n2010.11.07 - 0.7-beta1-node\n  - converted it to a node.js compatible module\n\n2010.09.06 - 0.7-beta1\n  - features: vsprintf, support for named placeholders\n  - enhancements: format cache, reduced global namespace pollution\n\n2010.05.22 - 0.6:\n - reverted to 0.4 and fixed the bug regarding the sign of the number 0\n Note:\n Thanks to Raphael Pigulla <raph (at] n3rd [dot) org> (http://www.n3rd.org/)\n who warned me about a bug in 0.5, I discovered that the last update was\n a regress. 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null},r=function(){f(this,arguments)},r.prototype._base_class=Object,r.prototype._class_name=\"GlooObject\",r.prototype.__init__=function(t){if(this._gl=t,this.handle=null,this._create(),null===this.handle)throw\"AssertionError: this.handle !== null\";return null},r.prototype._create=function(){var t;throw t=new Error(\"NotImplementedError:\"),t.name=\"NotImplementedError\",t},o=function(){f(this,arguments)},o.prototype=Object.create(r.prototype),o.prototype._base_class=r.prototype,o.prototype._class_name=\"Program\",o.prototype.UTYPEMAP={\"float\":\"uniform1fv\",vec2:\"uniform2fv\",vec3:\"uniform3fv\",vec4:\"uniform4fv\",\"int\":\"uniform1iv\",ivec2:\"uniform2iv\",ivec3:\"uniform3iv\",ivec4:\"uniform4iv\",bool:\"uniform1iv\",bvec2:\"uniform2iv\",bvec3:\"uniform3iv\",bvec4:\"uniform4iv\",mat2:\"uniformMatrix2fv\",mat3:\"uniformMatrix3fv\",mat4:\"uniformMatrix4fv\",sampler1D:\"uniform1i\",sampler2D:\"uniform1i\",sampler3D:\"uniform1i\"},o.prototype.ATYPEMAP={\"float\":\"vertexAttrib1f\",vec2:\"vertexAttrib2f\",vec3:\"vertexAttrib3f\",vec4:\"vertexAttrib4f\"},o.prototype.ATYPEINFO={\"float\":[1,5126],vec2:[2,5126],vec3:[3,5126],vec4:[4,5126]},o.prototype._create=function(){return this.handle=this._gl.createProgram(),this.locations={},this._unset_variables=[],this._validated=!1,this._samplers={},this._attributes={},this._known_invalid=[],null},o.prototype[\"delete\"]=function(){return this._gl.deleteProgram(this.handle),null},o.prototype.activate=function(){return this._gl.useProgram(this.handle),null},o.prototype.deactivate=function(){return this._gl.useProgram(0),null},o.prototype.set_shaders=function(t,e){var r,n,o,i,s,a,l,u,h,p,_,d,f;for(a=this._gl,this._linked=!1,f=a.createShader(a.VERTEX_SHADER),s=a.createShader(a.FRAGMENT_SHADER),_=[[t,f,\"vertex\"],[e,s,\"fragment\"]],u=0;u<2;u+=1)if(p=_[u],r=p[0],l=p[1],d=p[2],a.shaderSource(l,r),a.compileShader(l),h=a.getShaderParameter(l,a.COMPILE_STATUS),!g(h))throw i=a.getShaderInfoLog(l),o=new Error(\"RuntimeError:\"+c(\"errors in \"+d+\" shader:\\n\",i)),o.name=\"RuntimeError\",o;if(a.attachShader(this.handle,f),a.attachShader(this.handle,s),a.linkProgram(this.handle),!g(a.getProgramParameter(this.handle,a.LINK_STATUS)))throw n=new Error(\"RuntimeError:Program link error:\\n\"+a.getProgramInfoLog(this.handle)),n.name=\"RuntimeError\",n;return this._unset_variables=this._get_active_attributes_and_uniforms(),a.detachShader(this.handle,f),a.detachShader(this.handle,s),a.deleteShader(f),a.deleteShader(s),this._known_invalid=[],this._linked=!0,null},o.prototype._get_active_attributes_and_uniforms=function(){var t,e,r,n,o,i,s,a,l,u,h,p,_,d,f,m,v,b,x;for(a=this._gl,this.locations={},d=new window.RegExp(\"(\\\\w+)\\\\s*(\\\\[(\\\\d+)\\\\])\\\\s*\"),o=a.getProgramParameter(this.handle,a.ACTIVE_UNIFORMS),e=a.getProgramParameter(this.handle,a.ACTIVE_ATTRIBUTES),t=[],b=[],m=[[t,e,a.getActiveAttrib,a.getAttribLocation],[b,o,a.getActiveUniform,a.getUniformLocation]],\"object\"!=typeof m||Array.isArray(m)||(m=Object.keys(m)),v=0;v<m.length;v+=1)for(x=m[v],f=x,r=f[0],n=f[1],i=f[2],s=f[3],l=0;l<n;l+=1){if(u=i.call(a,this.handle,l),_=u.name,p=_.match(d),g(p))for(_=p[1],h=0;h<u.size;h+=1)y.call(r,[\"\"+_+\"[\"+h+\"]\",u.type]);else y.call(r,[_,u.type]);this.locations[_]=s.call(a,this.handle,_)}return c(function(){var e,r,n,o=[];for(r=t,\"object\"!=typeof r||Array.isArray(r)||(r=Object.keys(r)),n=0;n<r.length;n++)e=r[n],o.push(e[0]);return o}.apply(this),function(){var t,e,r,n=[];for(e=b,\"object\"!=typeof e||Array.isArray(e)||(e=Object.keys(e)),r=0;r<e.length;r++)t=e[r],n.push(t[0]);return n}.apply(this))},o.prototype.set_texture=function(t,e){var r,n,o;if(!g(this._linked))throw r=new Error(\"RuntimeError:Cannot set uniform when program has no code\"),r.name=\"RuntimeError\",r;return n=v.call(this.locations,t,-1),g(n<0)?(_(t,this._known_invalid)||(y.call(this._known_invalid,t),h.log(\"Variable \"+t+\" is not an active texture\")),null):(_(t,this._unset_variables)&&w.call(this._unset_variables,t),this.activate(),o=b.call(this._samplers).length,_(t,this._samplers)&&(o=this._samplers[t][this._samplers[t].length-1]),this._samplers[t]=[e._target,e.handle,o],this._gl.uniform1i(n,o),null)},o.prototype.set_uniform=function(t,e,r){var n,o,i,s,a,l,u;if(!g(this._linked))throw i=new Error(\"RuntimeError:Cannot set uniform when program has no code\"),i.name=\"RuntimeError\",i;if(a=v.call(this.locations,t,-1),g(a<0))return _(t,this._known_invalid)||(y.call(this._known_invalid,t),h.log(\"Variable \"+t+\" is not an active uniform\")),null;if(_(t,this._unset_variables)&&w.call(this._unset_variables,t),o=1,k.call(e,\"mat\")||(n=v.call({\"int\":\"float\",bool:\"float\"},e,x.call(e,\"ib\")),o=Math.floor(r.length/this.ATYPEINFO[n][0])),g(o>1))for(l=0;l<o;l+=1)_(\"\"+t+\"[\"+l+\"]\",this._unset_variables)&&(u=\"\"+t+\"[\"+l+\"]\",_(u,this._unset_variables)&&w.call(this._unset_variables,u));return s=this.UTYPEMAP[e],this.activate(),k.call(e,\"mat\")?this._gl[s](a,!1,r):this._gl[s](a,r),null},o.prototype.set_attribute=function(t,e,r,n,o){var i,s,l,u,c,p,d,f;if(n=void 0===n?0:n,o=void 0===o?0:o,!g(this._linked))throw s=new Error(\"RuntimeError:Cannot set attribute when program has no code\"),s.name=\"RuntimeError\",s;return p=r instanceof a,c=v.call(this.locations,t,-1),g(c<0)?(_(t,this._known_invalid)||(y.call(this._known_invalid,t),g(p)&&g(o>0)||h.log(\"Variable \"+t+\" is not an active attribute\")),null):(_(t,this._unset_variables)&&w.call(this._unset_variables,t),this.activate(),g(p)?(f=this.ATYPEINFO[e],d=f[0],u=f[1],l=\"vertexAttribPointer\",i=[d,u,this._gl.FALSE,n,o],this._attributes[t]=[r.handle,c,l,i]):(l=this.ATYPEMAP[e],this._attributes[t]=[0,c,l,r]),null)},o.prototype._pre_draw=function(){var t,e,r,n,o,i,s,a,l,u,h,p;this.activate(),s=this._samplers;for(p in s)s.hasOwnProperty(p)&&(p=s[p],i=p,l=i[0],a=i[1],u=i[2],this._gl.activeTexture(c(this._gl.TEXTURE0,u)),this._gl.bindTexture(l,a));o=this._attributes;for(p in o)o.hasOwnProperty(p)&&(p=o[p],n=p,h=n[0],e=n[1],r=n[2],t=n[3],g(h)?(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,h),this._gl.enableVertexAttribArray(e),this._gl[r].apply(this._gl,[].concat([e],t))):(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,null),this._gl.disableVertexAttribArray(e),this._gl[r].apply(this._gl,[].concat([e],t))));return g(this._validated)||(this._validated=!0,this._validate()),null},o.prototype._validate=function(){var t;if(this._unset_variables.length&&h.log(\"Program has unset variables: \"+this._unset_variables),this._gl.validateProgram(this.handle),!g(this._gl.getProgramParameter(this.handle,this._gl.VALIDATE_STATUS)))throw h.log(this._gl.getProgramInfoLog(this.handle)),t=new Error(\"RuntimeError:Program validation error\"),t.name=\"RuntimeError\",t;return null},o.prototype.draw=function(t,e){var r,o,i,s,a;if(!g(this._linked))throw o=new Error(\"RuntimeError:Cannot draw program if code has not been set\"),o.name=\"RuntimeError\",o;return u(this._gl,\"before draw\"),g(e instanceof n)?(this._pre_draw(),e.activate(),r=e._buffer_size/2,s=this._gl.UNSIGNED_SHORT,this._gl.drawElements(t,r,s,0),e.deactivate()):(a=e,i=a[0],r=a[1],g(r)&&(this._pre_draw(),this._gl.drawArrays(t,i,r))),u(this._gl,\"after draw\"),null},t=function(){f(this,arguments)},t.prototype=Object.create(r.prototype),t.prototype._base_class=r.prototype,t.prototype._class_name=\"Buffer\",\nt.prototype._target=null,t.prototype._usage=35048,t.prototype._create=function(){return this.handle=this._gl.createBuffer(),this._buffer_size=0,null},t.prototype[\"delete\"]=function(){return this._gl.deleteBuffer(this.handle),null},t.prototype.activate=function(){return this._gl.bindBuffer(this._target,this.handle),null},t.prototype.deactivate=function(){return this._gl.bindBuffer(this._target,null),null},t.prototype.set_size=function(t){return d(t,this._buffer_size)||(this.activate(),this._gl.bufferData(this._target,t,this._usage),this._buffer_size=t),null},t.prototype.set_data=function(t,e){return this.activate(),this._gl.bufferSubData(this._target,t,e),null},a=function(){f(this,arguments)},a.prototype=Object.create(t.prototype),a.prototype._base_class=t.prototype,a.prototype._class_name=\"VertexBuffer\",a.prototype._target=34962,n=function(){f(this,arguments)},n.prototype=Object.create(t.prototype),n.prototype._base_class=t.prototype,n.prototype._class_name=\"IndexBuffer\",n.prototype._target=34963,i=function(){f(this,arguments)},i.prototype=Object.create(r.prototype),i.prototype._base_class=r.prototype,i.prototype._class_name=\"Texture2D\",i.prototype._target=3553,i.prototype._types={Int8Array:5120,Uint8Array:5121,Int16Array:5122,Uint16Array:5123,Int32Array:5124,Uint32Array:5125,Float32Array:5126},i.prototype._create=function(){return this.handle=this._gl.createTexture(),this._shape_format=null,null},i.prototype[\"delete\"]=function(){return this._gl.deleteTexture(this.handle),null},i.prototype.activate=function(){return this._gl.bindTexture(this._target,this.handle),null},i.prototype.deactivate=function(){return this._gl.bindTexture(this._target,0),null},i.prototype._get_alignment=function(t){var e,r,n,o;for(r=[4,8,2,1],n=r,\"object\"!=typeof n||Array.isArray(n)||(n=Object.keys(n)),o=0;o<n.length;o+=1)if(e=n[o],d(t%e,0))return e;return null},i.prototype.set_wrapping=function(t,e){return this.activate(),this._gl.texParameterf(this._target,this._gl.TEXTURE_WRAP_S,t),this._gl.texParameterf(this._target,this._gl.TEXTURE_WRAP_T,e),null},i.prototype.set_interpolation=function(t,e){return this.activate(),this._gl.texParameterf(this._target,this._gl.TEXTURE_MIN_FILTER,t),this._gl.texParameterf(this._target,this._gl.TEXTURE_MAG_FILTER,e),null},i.prototype.set_size=function(t,e){var r,n,o;return n=t,r=n[0],o=n[1],d([r,o,e],this._shape_format)||(this._shape_format=[r,o,e],this.activate(),this._gl.texImage2D(this._target,0,e,o,r,0,e,this._gl.UNSIGNED_BYTE,null)),this.u_shape=[r,o],null},i.prototype.set_data=function(t,e,r){var n,o,i,s,a,l,u,h,c,p,_;if(d(e.length,2)&&(e=[e[0],e[1],1]),this.activate(),s=this._shape_format[2],u=e,l=u[0],c=u[1],n=u[2],h=t,_=h[0],p=h[1],a=v.call(this._types,r.constructor.name,null),null===a)throw i=new Error(\"ValueError:\"+(\"Type \"+r.constructor.name+\" not allowed for texture\")),i.name=\"ValueError\",i;return o=this._get_alignment(m(e[e.length-2],e[e.length-1])),d(o,4)||this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,o),this._gl.texSubImage2D(this._target,0,p,_,c,l,s,a,r),d(o,4)||this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,4),null},s=function(){f(this,arguments)},s.prototype=Object.create(i.prototype),s.prototype._base_class=i.prototype,s.prototype._class_name=\"Texture3DLike\",s.prototype.GLSL_SAMPLE_NEAREST=\"\\n        vec4 sample3D(sampler2D tex, vec3 texcoord, vec3 shape, vec2 tiles) {\\n            shape.xyz = shape.zyx;  // silly row-major convention\\n            float nrows = tiles.y, ncols = tiles.x;\\n            // Don't let adjacent frames be interpolated into this one\\n            texcoord.x = min(texcoord.x * shape.x, shape.x - 0.5);\\n            texcoord.x = max(0.5, texcoord.x) / shape.x;\\n            texcoord.y = min(texcoord.y * shape.y, shape.y - 0.5);\\n            texcoord.y = max(0.5, texcoord.y) / shape.y;\\n\\n            float zindex = floor(texcoord.z * shape.z);\\n\\n            // Do a lookup in the 2D texture\\n            float u = (mod(zindex, ncols) + texcoord.x) / ncols;\\n            float v = (floor(zindex / ncols) + texcoord.y) / nrows;\\n\\n            return texture2D(tex, vec2(u,v));\\n        }\\n    \",s.prototype.GLSL_SAMPLE_LINEAR=\"\\n        vec4 sample3D(sampler2D tex, vec3 texcoord, vec3 shape, vec2 tiles) {\\n            shape.xyz = shape.zyx;  // silly row-major convention\\n            float nrows = tiles.y, ncols = tiles.x;\\n            // Don't let adjacent frames be interpolated into this one\\n            texcoord.x = min(texcoord.x * shape.x, shape.x - 0.5);\\n            texcoord.x = max(0.5, texcoord.x) / shape.x;\\n            texcoord.y = min(texcoord.y * shape.y, shape.y - 0.5);\\n            texcoord.y = max(0.5, texcoord.y) / shape.y;\\n\\n            float z = texcoord.z * shape.z;\\n            float zindex1 = floor(z);\\n            float u1 = (mod(zindex1, ncols) + texcoord.x) / ncols;\\n            float v1 = (floor(zindex1 / ncols) + texcoord.y) / nrows;\\n\\n            float zindex2 = zindex1 + 1.0;\\n            float u2 = (mod(zindex2, ncols) + texcoord.x) / ncols;\\n            float v2 = (floor(zindex2 / ncols) + texcoord.y) / nrows;\\n\\n            vec4 s1 = texture2D(tex, vec2(u1, v1));\\n            vec4 s2 = texture2D(tex, vec2(u2, v2));\\n\\n            return s1 * (zindex2 - z) + s2 * (z - zindex1);\\n        }\\n    \",s.prototype._get_tile_info=function(t){var e,r,n,o;if(r=this._gl.getParameter(this._gl.MAX_TEXTURE_SIZE),o=Math.floor(r/t[1]),o=Math.min(o,t[0]),n=window.Math.ceil(t[0]/o),g(m(n,t[2])>r))throw e=new Error(\"RuntimeError:\"+(\"Cannot fit 3D data with shape \"+t+\" onto simulated 2D texture.\")),e.name=\"RuntimeError\",e;return[o,n]},s.prototype.set_size=function(t,e){var r,n,o,i;return i=this._get_tile_info(t),n=i[0],r=i[1],o=[m(t[1],n),m(t[2],r)],s.prototype._base_class.set_size.call(this,o,e),this.u_shape=[t[0],t[1],t[2]],this.u_tiles=[r,n],null},s.prototype.set_data=function(t,e,r){var n,o,i,a,l,u,h,c,_,f,g,y,v;if(d(e.length,3)&&(e=[e[0],e[1],e[2],1]),!p(function(){var e,r,n,o=[];for(r=t,\"object\"!=typeof r||Array.isArray(r)||(r=Object.keys(r)),n=0;n<r.length;n++)e=r[n],o.push(d(e,0));return o}.apply(this)))throw a=new Error(\"ValueError:Texture3DLike does not support nonzero offset (for now)\"),a.name=\"ValueError\",a;if(_=this._get_tile_info(e),u=_[0],l=_[1],c=[m(e[1],u),m(e[2],l),e[3]],d(l,1))s.prototype._base_class.set_data.call(this,[0,0],c,r);else for(n=r.constructor,v=new n(m(m(c[0],c[1]),c[2])),s.prototype._base_class.set_data.call(this,[0,0],c,v),y=0;y<e[0];y+=1)f=[Math.floor(y/l),y%l],h=f[0],o=f[1],i=Math.floor(r.length/e[0]),g=r.slice(m(y,i),m(y+1,i)),s.prototype._base_class.set_data.call(this,[m(h,e[1]),m(o,e[2])],e.slice(1),g);return null},{Buffer:t,GlooObject:r,IndexBuffer:n,Program:o,Texture2D:i,Texture3DLike:s,VertexBuffer:a,check_error:u,console:h}})}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{}],kiwi:[function(t,e,r){/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar n;!function(t){function e(t){return t instanceof Array?new l(t):t.__iter__()}function r(t){return t instanceof Array?new u(t):t.__reversed__()}function n(t){return t.__next__()}function o(t){if(t instanceof Array)return t.slice();for(var e,r=[],n=t.__iter__();void 0!==(e=n.__next__());)r.push(e);return r}function i(t,e){if(t instanceof Array){for(var r=0,n=t.length;r<n;++r)if(e(t[r])===!1)return}else for(var o,i=t.__iter__();void 0!==(o=i.__next__());)if(e(o)===!1)return}function s(t,e){var r=[];if(t instanceof Array)for(var n=0,o=t.length;n<o;++n)r.push(e(t[n]));else for(var i,s=t.__iter__();void 0!==(i=s.__next__());)r.push(e(i));return r}function a(t,e){var r,n=[];if(t instanceof Array)for(var o=0,i=t.length;o<i;++o)r=t[o],e(r)&&n.push(r);else for(var s=t.__iter__();void 0!==(r=s.__next__());)e(r)&&n.push(r);return n}var l=function(){function t(t,e){\"undefined\"==typeof 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t(t,e){this.first=t,this.second=e}return t.prototype.copy=function(){return new t(this.first,this.second)},t}();t.Pair=e}(n||(n={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar n;!function(t){function e(t,e,r){for(var n,o,i=0,s=t.length;s>0;)n=s>>1,o=i+n,r(t[o],e)<0?(i=o+1,s-=n+1):s=n;return i}function r(t,r,n){var o=e(t,r,n);if(o===t.length)return-1;var i=t[o];return 0!==n(i,r)?-1:o}function n(t,r,n){var o=e(t,r,n);if(o!==t.length){var i=t[o];if(0===n(i,r))return i}}function o(e,r){var n=t.asArray(e),o=n.length;if(o<=1)return n;n.sort(r);for(var i=[n[0]],s=1,a=0;s<o;++s){var l=n[s];0!==r(i[a],l)&&(i.push(l),++a)}return i}function i(t,e,r){for(var 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l=t[n],u=e[o],h=r(l,u);h<0?(a.push(l),++n):h>0?(a.push(u),++o):(++n,++o)}for(;n<i;)a.push(t[n]),++n;for(;o<s;)a.push(e[o]),++o;return a}t.lowerBound=e,t.binarySearch=r,t.binaryFind=n,t.asSet=o,t.setIsDisjoint=i,t.setIsSubset=s,t.setUnion=a,t.setIntersection=l,t.setDifference=u,t.setSymmetricDifference=h}(n||(n={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar n;!function(t){var e=function(){function e(){this._array=[]}return e.prototype.size=function(){return this._array.length},e.prototype.empty=function(){return 0===this._array.length},e.prototype.itemAt=function(t){return this._array[t]},e.prototype.takeAt=function(t){return this._array.splice(t,1)[0]},e.prototype.clear=function(){this._array=[]},e.prototype.swap=function(t){var e=this._array;this._array=t._array,t._array=e},e.prototype.__iter__=function(){return t.iter(this._array)},e.prototype.__reversed__=function(){return t.reversed(this._array)},e}();t.ArrayBase=e}(n||(n={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar n,o=this.__extends||function(t,e){function r(){this.constructor=t}for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);r.prototype=e.prototype,t.prototype=new r};!function(t){function e(t){return function(e,r){return t(e.first,r)}}function r(t,e,r){for(var n=0,o=0,i=t.length,s=e.length,a=[];n<i&&o<s;){var 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s=n[o];if(0!==this._compare(s.first,e)){var i=new t.Pair(e,r);return n.splice(o,0,i),i}return s.second=r,s},i.prototype.update=function(e){var n=this;if(e instanceof i){var o=e;this._array=r(this._array,o._array,this._compare)}else t.forEach(e,function(t){n.insert(t.first,t.second)})},i.prototype.erase=function(e){var r=this._array,n=t.binarySearch(r,e,this._wrapped);if(!(n<0))return r.splice(n,1)[0]},i.prototype.copy=function(){for(var t=new i(this._compare),e=t._array,r=this._array,n=0,o=r.length;n<o;++n)e.push(r[n].copy());return t},i}(t.ArrayBase);t.AssociativeArray=n}(n||(n={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar n;!function(t){function e(e,n){return e instanceof r?e._array:t.asSet(e,n)}var r=function(r){function n(t){r.call(this),this._compare=t}return o(n,r),n.prototype.comparitor=function(){return this._compare},n.prototype.indexOf=function(e){return t.binarySearch(this._array,e,this._compare)},n.prototype.contains=function(e){return t.binarySearch(this._array,e,this._compare)>=0},n.prototype.insert=function(e){var r=this._array,n=t.lowerBound(r,e,this._compare);return n===r.length?(r.push(e),!0):0!==this._compare(r[n],e)&&(r.splice(n,0,e),!0)},n.prototype.erase=function(e){var r=this._array,n=t.binarySearch(r,e,this._compare);return!(n<0)&&(r.splice(n,1),!0)},n.prototype.copy=function(){var t=new n(this._compare);return t._array=this._array.slice(),t},n.prototype.isDisjoint=function(r){var n=this._compare,o=e(r,n);return t.setIsDisjoint(this._array,o,n)},n.prototype.isSubset=function(r){var n=this._compare,o=e(r,n);return t.setIsSubset(this._array,o,n)},n.prototype.isSuperset=function(r){var n=this._compare,o=e(r,n);return t.setIsSubset(o,this._array,n)},n.prototype.union=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setUnion(this._array,s,o),i},n.prototype.intersection=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setIntersection(this._array,s,o),i},n.prototype.difference=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setDifference(this._array,s,o),i},n.prototype.symmetricDifference=function(r){var o=this._compare,i=new n(o),s=e(r,o);return i._array=t.setSymmetricDifference(this._array,s,o),i},n.prototype.unionUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setUnion(this._array,o,n)},n.prototype.intersectionUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setIntersection(this._array,o,n)},n.prototype.differenceUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setDifference(this._array,o,n)},n.prototype.symmetricDifferenceUpdate=function(r){var n=this._compare,o=e(r,n);this._array=t.setSymmetricDifference(this._array,o,n)},n}(t.ArrayBase);t.UniqueArray=r}(n||(n={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\n/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){!function(t){t[t.Le=0]=\"Le\",t[t.Ge=1]=\"Ge\",t[t.Eq=2]=\"Eq\"}(t.Operator||(t.Operator={}));var e=(t.Operator,function(){function e(e,n,o){\"undefined\"==typeof o&&(o=t.Strength.required),this._id=r++,this._operator=n,this._expression=e,this._strength=t.Strength.clip(o)}return e.Compare=function(t,e){return t.id()-e.id()},e.prototype.id=function(){return this._id},e.prototype.expression=function(){return this._expression},e.prototype.op=function(){return this._operator},e.prototype.strength=function(){return this._strength},e}());t.Constraint=e;var r=0}(i||(i={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){function e(t){return new n.AssociativeArray(t)}t.createMap=e}(i||(i={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){var e=function(){function t(t){\"undefined\"==typeof t&&(t=\"\"),this._value=0,this._context=null,this._id=r++,this._name=t}return t.Compare=function(t,e){return t.id()-e.id()},t.prototype.id=function(){return this._id},t.prototype.name=function(){return this._name},t.prototype.setName=function(t){this._name=t},t.prototype.context=function(){return this._context},t.prototype.setContext=function(t){this._context=t},t.prototype.value=function(){return this._value},t.prototype.setValue=function(t){this._value=t},t}();t.Variable=e;var r=0}(i||(i={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){function e(e){for(var r=0,n=function(){return 0},o=t.createMap(t.Variable.Compare),i=0,s=e.length;i<s;++i){var a=e[i];if(\"number\"==typeof a)r+=a;else if(a instanceof t.Variable)o.setDefault(a,n).second+=1;else{if(!(a instanceof Array))throw new Error(\"invalid Expression argument: \"+a);if(2!==a.length)throw new Error(\"array must have length 2\");var l=a[0],u=a[1];if(\"number\"!=typeof l)throw new Error(\"array item 0 must be a number\");if(!(u instanceof t.Variable))throw new Error(\"array item 1 must be a variable\");o.setDefault(u,n).second+=l}}return{terms:o,constant:r}}var r=function(){function t(){var t=e(arguments);this._terms=t.terms,this._constant=t.constant}return t.prototype.terms=function(){return this._terms},t.prototype.constant=function(){return this._constant},t.prototype.value=function(){var t=this._constant;return n.forEach(this._terms,function(e){t+=e.first.value()*e.second}),t},t}();t.Expression=r}(i||(i={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){!function(t){function e(t,e,r,n){\"undefined\"==typeof n&&(n=1);var o=0;return o+=1e6*Math.max(0,Math.min(1e3,t*n)),o+=1e3*Math.max(0,Math.min(1e3,e*n)),o+=Math.max(0,Math.min(1e3,r*n))}function r(e){return Math.max(0,Math.min(t.required,e))}t.create=e,t.required=e(1e3,1e3,1e3),t.strong=e(1,0,0),t.medium=e(0,1,0),t.weak=e(0,0,1),t.clip=r}(t.Strength||(t.Strength={}));t.Strength}(i||(i={}));/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\nvar i;!function(t){function e(t){var e=1e-8;return t<0?-t<e:t<e}function r(){return t.createMap(t.Constraint.Compare)}function n(){return t.createMap(l.Compare)}function o(){return t.createMap(t.Variable.Compare)}function i(){return t.createMap(t.Variable.Compare)}var s=function(){function s(){this._cnMap=r(),this._rowMap=n(),this._varMap=o(),this._editMap=i(),this._infeasibleRows=[],this._objective=new h,this._artificial=null,this._idTick=0}return s.prototype.addConstraint=function(t){var r=this._cnMap.find(t);if(void 0!==r)throw new Error(\"duplicate constraint\");var n=this._createRow(t),o=n.row,i=n.tag,s=this._chooseSubject(o,i);if(0===s.type()&&o.allDummies()){if(!e(o.constant()))throw new Error(\"unsatisfiable constraint\");s=i.marker}if(0===s.type()){if(!this._addWithArtificialVariable(o)){for(var a,l=\"\",u=0;a=t._expression._terms._array[u];u++)l+=a.first._name,l+=\", \";var h=[\"LE\",\"GE\",\"EQ\"];throw new Error(\"Unsatisfiable constraint [\"+l.slice(0,-2)+\"] operator: \"+h[t._operator])}}else o.solveFor(s),this._substitute(s,o),this._rowMap.insert(s,o);this._cnMap.insert(t,i),this._optimize(this._objective)},s.prototype.removeConstraint=function(t){var e=this._cnMap.erase(t);if(void 0===e)throw new Error(\"unknown constraint\");this._removeConstraintEffects(t,e.second);var r=e.second.marker,n=this._rowMap.erase(r);if(void 0===n){var o=this._getMarkerLeavingSymbol(r);if(0===o.type())throw new Error(\"failed to find leaving row\");n=this._rowMap.erase(o),n.second.solveForEx(o,r),this._substitute(r,n.second)}this._optimize(this._objective)},s.prototype.hasConstraint=function(t){return this._cnMap.contains(t)},s.prototype.addEditVariable=function(e,r){var n=this._editMap.find(e);if(void 0!==n)throw new Error(\"duplicate edit variable\");if(r=t.Strength.clip(r),r===t.Strength.required)throw new Error(\"bad required strength\");var o=new t.Expression(e),i=new t.Constraint(o,2,r);this.addConstraint(i);var s=this._cnMap.find(i).second,a={tag:s,constraint:i,constant:0};this._editMap.insert(e,a)},s.prototype.removeEditVariable=function(t){var e=this._editMap.erase(t);if(void 0===e)throw new Error(\"unknown edit variable\");this.removeConstraint(e.second.constraint)},s.prototype.hasEditVariable=function(t){return this._editMap.contains(t)},s.prototype.suggestValue=function(t,e){var r=this._editMap.find(t);if(void 0===r)throw new Error(\"unknown edit variable\");var n=this._rowMap,o=r.second,i=e-o.constant;o.constant=e;var s=o.tag.marker,a=n.find(s);if(void 0!==a)return a.second.add(-i)<0&&this._infeasibleRows.push(s),void this._dualOptimize();var l=o.tag.other,a=n.find(l);if(void 0!==a)return a.second.add(i)<0&&this._infeasibleRows.push(l),void this._dualOptimize();for(var u=0,h=n.size();u<h;++u){var a=n.itemAt(u),c=a.second,p=c.coefficientFor(s);0!==p&&c.add(i*p)<0&&1!==a.first.type()&&this._infeasibleRows.push(a.first)}this._dualOptimize()},s.prototype.updateVariables=function(){for(var t=this._varMap,e=this._rowMap,r=0,n=t.size();r<n;++r){var o=t.itemAt(r),i=e.find(o.second);void 0!==i?o.first.setValue(i.second.constant()):o.first.setValue(0)}},s.prototype._getVarSymbol=function(t){var e=this,r=function(){return e._makeSymbol(1)};return this._varMap.setDefault(t,r).second},s.prototype._createRow=function(r){for(var n=r.expression(),o=new h(n.constant()),i=n.terms(),s=0,a=i.size();s<a;++s){var l=i.itemAt(s);if(!e(l.second)){var c=this._getVarSymbol(l.first),p=this._rowMap.find(c);void 0!==p?o.insertRow(p.second,l.second):o.insertSymbol(c,l.second)}}var _=this._objective,d=r.strength(),f={marker:u,other:u};switch(r.op()){case 0:case 1:var m=0===r.op()?1:-1,g=this._makeSymbol(2);if(f.marker=g,o.insertSymbol(g,m),d<t.Strength.required){var y=this._makeSymbol(3);f.other=y,o.insertSymbol(y,-m),_.insertSymbol(y,d)}break;case 2:if(d<t.Strength.required){var v=this._makeSymbol(3),b=this._makeSymbol(3);f.marker=v,f.other=b,o.insertSymbol(v,-1),o.insertSymbol(b,1),_.insertSymbol(v,d),_.insertSymbol(b,d)}else{var x=this._makeSymbol(4);f.marker=x,o.insertSymbol(x)}}return o.constant()<0&&o.reverseSign(),{row:o,tag:f}},s.prototype._chooseSubject=function(t,e){for(var r=t.cells(),n=0,o=r.size();n<o;++n){var i=r.itemAt(n);if(1===i.first.type())return i.first}var s=e.marker.type();return(2===s||3===s)&&t.coefficientFor(e.marker)<0?e.marker:(s=e.other.type(),(2===s||3===s)&&t.coefficientFor(e.other)<0?e.other:u)},s.prototype._addWithArtificialVariable=function(t){var r=this._makeSymbol(2);this._rowMap.insert(r,t.copy()),this._artificial=t.copy(),this._optimize(this._artificial);var n=e(this._artificial.constant());this._artificial=null;var o=this._rowMap.erase(r);if(void 0!==o){var i=o.second;if(i.isConstant())return n;var s=this._anyPivotableSymbol(i);if(0===s.type())return!1;i.solveForEx(r,s),this._substitute(s,i),this._rowMap.insert(s,i)}for(var a=this._rowMap,l=0,u=a.size();l<u;++l)a.itemAt(l).second.removeSymbol(r);return this._objective.removeSymbol(r),n},s.prototype._substitute=function(t,e){for(var r=this._rowMap,n=0,o=r.size();n<o;++n){var i=r.itemAt(n);i.second.substitute(t,e),i.second.constant()<0&&1!==i.first.type()&&this._infeasibleRows.push(i.first)}this._objective.substitute(t,e),this._artificial&&this._artificial.substitute(t,e)},s.prototype._optimize=function(t){for(;;){var e=this._getEnteringSymbol(t);if(0===e.type())return;var r=this._getLeavingSymbol(e);if(0===r.type())throw new Error(\"the objective is unbounded\");var n=this._rowMap.erase(r).second;n.solveForEx(r,e),this._substitute(e,n),this._rowMap.insert(e,n)}},s.prototype._dualOptimize=function(){for(var t=this._rowMap,e=this._infeasibleRows;0!==e.length;){var r=e.pop(),n=t.find(r);if(void 0!==n&&n.second.constant()<0){var o=this._getDualEnteringSymbol(n.second);if(0===o.type())throw new Error(\"dual optimize failed\");var i=n.second;t.erase(r),i.solveForEx(r,o),this._substitute(o,i),t.insert(o,i)}}},s.prototype._getEnteringSymbol=function(t){for(var e=t.cells(),r=0,n=e.size();r<n;++r){var o=e.itemAt(r),i=o.first;if(o.second<0&&4!==i.type())return i}return u},s.prototype._getDualEnteringSymbol=function(t){for(var e=Number.MAX_VALUE,r=u,n=t.cells(),o=0,i=n.size();o<i;++o){var s=n.itemAt(o),a=s.first,l=s.second;if(l>0&&4!==a.type()){var h=this._objective.coefficientFor(a),c=h/l;c<e&&(e=c,r=a)}}return r},s.prototype._getLeavingSymbol=function(t){for(var e=Number.MAX_VALUE,r=u,n=this._rowMap,o=0,i=n.size();o<i;++o){var s=n.itemAt(o),a=s.first;if(1!==a.type()){var l=s.second,h=l.coefficientFor(t);if(h<0){var c=-l.constant()/h;c<e&&(e=c,r=a)}}}return r},s.prototype._getMarkerLeavingSymbol=function(t){for(var e=Number.MAX_VALUE,r=e,n=e,o=u,i=o,s=o,a=o,l=this._rowMap,h=0,c=l.size();h<c;++h){var p=l.itemAt(h),_=p.second,d=_.coefficientFor(t);if(0!==d){var f=p.first;if(1===f.type())a=f;else if(d<0){var m=-_.constant()/d;m<r&&(r=m,i=f)}else{var m=_.constant()/d;m<n&&(n=m,s=f)}}}return i!==o?i:s!==o?s:a},s.prototype._removeConstraintEffects=function(t,e){3===e.marker.type()&&this._removeMarkerEffects(e.marker,t.strength()),3===e.other.type()&&this._removeMarkerEffects(e.other,t.strength())},s.prototype._removeMarkerEffects=function(t,e){var r=this._rowMap.find(t);void 0!==r?this._objective.insertRow(r.second,-e):this._objective.insertSymbol(t,-e)},s.prototype._anyPivotableSymbol=function(t){for(var e=t.cells(),r=0,n=e.size();r<n;++r){var o=e.itemAt(r),i=o.first.type();if(2===i||3===i)return o.first}return u},s.prototype._makeSymbol=function(t){return new l(t,(this._idTick++))},s}();t.Solver=s;var a;!function(t){t[t.Invalid=0]=\"Invalid\",t[t.External=1]=\"External\",t[t.Slack=2]=\"Slack\",t[t.Error=3]=\"Error\",t[t.Dummy=4]=\"Dummy\"}(a||(a={}));var l=function(){function t(t,e){this._id=e,this._type=t}return t.Compare=function(t,e){return t.id()-e.id()},t.prototype.id=function(){return this._id},t.prototype.type=function(){return this._type},t}(),u=new l(0,(-1)),h=function(){function r(e){\"undefined\"==typeof e&&(e=0),this._cellMap=t.createMap(l.Compare),this._constant=e}return r.prototype.cells=function(){return this._cellMap},r.prototype.constant=function(){return this._constant},r.prototype.isConstant=function(){return this._cellMap.empty()},r.prototype.allDummies=function(){for(var t=this._cellMap,e=0,r=t.size();e<r;++e){var n=t.itemAt(e);if(4!==n.first.type())return!1}return!0},r.prototype.copy=function(){var t=new r(this._constant);return t._cellMap=this._cellMap.copy(),t},r.prototype.add=function(t){return this._constant+=t},r.prototype.insertSymbol=function(t,r){\"undefined\"==typeof r&&(r=1);var n=this._cellMap.setDefault(t,function(){return 0});e(n.second+=r)&&this._cellMap.erase(t)},r.prototype.insertRow=function(t,e){\"undefined\"==typeof e&&(e=1),this._constant+=t._constant*e;for(var r=t._cellMap,n=0,o=r.size();n<o;++n){var i=r.itemAt(n);this.insertSymbol(i.first,i.second*e)}},r.prototype.removeSymbol=function(t){this._cellMap.erase(t)},r.prototype.reverseSign=function(){this._constant=-this._constant;for(var t=this._cellMap,e=0,r=t.size();e<r;++e){var n=t.itemAt(e);n.second=-n.second}},r.prototype.solveFor=function(t){var e=this._cellMap,r=e.erase(t),n=-1/r.second;this._constant*=n;for(var o=0,i=e.size();o<i;++o)e.itemAt(o).second*=n},r.prototype.solveForEx=function(t,e){this.insertSymbol(t,-1),this.solveFor(e)},r.prototype.coefficientFor=function(t){var e=this._cellMap.find(t);return void 0!==e?e.second:0},r.prototype.substitute=function(t,e){var r=this._cellMap.erase(t);void 0!==r&&this.insertRow(e,r.second)},r}()}(i||(i={})),/*-----------------------------------------------------------------------------\n| Copyright (c) 2014, Nucleic Development Team.\n|\n| Distributed under the terms of the Modified BSD License.\n|\n| The full license is in the file COPYING.txt, distributed with this software.\n|----------------------------------------------------------------------------*/\ne.exports=i},{}]},{},[\"main\"])}();/*\nCopyright (c) 2012, Continuum Analytics, Inc.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification,\nare permitted provided that the following conditions are met:\n\nRedistributions of source code must retain the above copyright notice,\nthis list of conditions and the following disclaimer.\n\nRedistributions in binary form must reproduce the above copyright notice,\nthis list of conditions and the following disclaimer in the documentation\nand/or other materials provided with the distribution.\n\nNeither the name of Continuum Analytics nor the names of any contributors\nmay be used to endorse or promote products derived from this software \nwithout specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE \nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE \nARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE \nLIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR \nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF \nSUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS \nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN \nCONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) \nARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF \nTHE POSSIBILITY OF SUCH DAMAGE.\n*/\n</script>\n<script>HTMLWidgets.widget({\n  name: 'rbokeh',\n\n  type: 'output',\n\n  initialize: function(el, width, height) {\n    return {\n      modelid: '',\n      elementid: '',\n      width: width,\n      height: height\n    };\n  },\n\n  renderValue: function(el, x, instance) {\n\n    //clear el for Shiny/dynamic contexts\n    el.innerHTML = '';\n\n    if(x.isJSON === true) {\n      x.docs_json = JSON.parse(x.docs_json);\n    }\n\n    var refkey = 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type=\"text/css\">\n\n/*\n Dashboard CSS from Keen IO Dashboards\n (https://github.com/keen/dashboards)\n*/\n\nbody {\n  background: #f2f2f2;\n  padding: 60px 0 0 8px; /* padding-top overridden by theme */\n}\n\nbody hr {\n  border-color: #d7d7d7;\n  margin: 10px 0;\n}\n\n.navbar-inverse .navbar-nav > li > a,\n.navbar .navbar-brand {\n  text-decoration: none;\n}\n\n.navbar-logo {\n  margin-top: 1px;\n}\n\n.navbar-logo img {\n  margin-right: 12px;\n}\n\n.navbar-author {\n  margin-left: 10px;\n  font-size: 15px;\n}\n\n.navbar .dropdown-menu .fa {\n  min-width: 20px;\n}\n\n.navbar .dropdown-menu {\n  min-width: 150px;\n  max-height: 500px;\n  overflow: auto;\n}\n\n.chart-wrapper,\n.nav-tabs-custom,\n.sbframe-commentary\n{\n  background: #fff;\n  border: 1px solid #e2e2e2;\n  border-radius: 3px;\n  margin-bottom: 8px;\n  margin-right: 8px;\n}\n\n.chart-title {\n  border-bottom: 1px solid #d7d7d7;\n  color: #666;\n  font-size: 14px;\n  font-weight: 300;\n  padding: 7px 10px 4px;\n}\n\n.chart-wrapper .chart-title:empty {\n  display: none;\n}\n\n.chart-wrapper .chart-stage {\n  overflow: hidden;\n  padding: 5px 10px;\n  position: relative;\n}\n\n.chart-wrapper .chart-notes {\n  background: #fbfbfb;\n  border-top: 1px solid #e2e2e2;\n  color: #808080;\n  font-size: 12px;\n  padding: 8px 10px 5px;\n}\n\n/*\n CSS for handling flexbox layout\n*/\n\n#dashboard-container {\n  visibility: hidden;\n}\n\n.tab-content>.dashboard-page-wrapper.active {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n}\n\n.dashboard-page-wrapper {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.dashboard-row-orientation {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.dashboard-row {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: horizontal;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: row;\n      -ms-flex-direction: row;\n          flex-direction: row;\n}\n\n.dashboard-row-flex {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n}\n\n.dashboard-column-orientation {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: horizontal;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: row;\n      -ms-flex-direction: row;\n          flex-direction: row;\n}\n\n.dashboard-column {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.chart-wrapper-flex {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n  -webkit-box-orient: vertical;\n  -webkit-box-direction: normal;\n  -webkit-flex-direction: column;\n      -ms-flex-direction: column;\n          flex-direction: column;\n}\n\n.chart-stage-flex {\n  -webkit-box-flex: 1;\n  -webkit-flex: 1;\n      -ms-flex: 1;\n          flex: 1;\n  position: relative;\n}\n\n.chart-shim {\n  position: absolute;\n  left: 8px; top: 8px; right: 8px; bottom: 8px;\n}\n\n.no-padding .chart-shim {\n  left: 0; top: 0; right: 0; bottom: 0;\n}\n\n.flowing-content-shim {\n  overflow: auto;\n  left: 0; top: 0; right: 0; bottom: 0;\n  padding-left: 8px;\n  padding-right: 8px;\n}\n\n.flowing-content-container {\n  padding-top: 8px;\n  padding-bottom: 8px;\n}\n\n.chart-stage .table-bordered {\n  border: none;\n}\n\n.chart-stage .table-bordered > tbody > tr > th,\n.chart-stage .table-bordered > tfoot > tr > th,\n.chart-stage .table-bordered > tbody > tr > td,\n.chart-stage .table-bordered > tfoot > tr > td {\n  border: none;\n}\n\n.chart-stage .table-bordered > tbody > tr > td {\n  border-top: 1px solid #dddddd;\n}\n\n.chart-stage .table-bordered > thead > tr > th {\n  border: none;\n  border-bottom: 2px solid #dddddd;\n}\n\n.bootstrap-table table>thead {\n  background-color: #fff;\n}\n\n.bootstrap-table table.data,\n.bootstrap-table table.shiny-table {\n  width: inherit !important;\n}\n\n.bootstrap-table table.data>tbody>tr>th,\n.bootstrap-table table.shiny-table>tbody>tr>th {\n  border-top: none;\n  border-bottom: 2px solid #dddddd;\n  padding: 5px;\n}\n\n.bootstrap-table .data td[align=right] {\n  font-family: inherit;\n}\n\n.chart-wrapper form {\n  padding-left: 5px;\n  padding-right: 5px;\n}\n\n.shiny-input-container label {\n  font-weight: normal;\n}\n\n.chart-stage .html-widget {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n.chart-stage .html-widget-static-bound {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n.chart-stage .shiny-bound-output {\n  width: 100% !important;\n  height: 100% !important;\n}\n\n/* Omit display of empty paragraphs */\n\n.chart-shim>p:empty {\n  display: none;\n}\n\n.chart-stage>p:empty {\n  display: none;\n}\n\n/* Omit display of special knitr options div*/\n\n.chart-stage .knitr-options {\n  display: none;\n}\n\n/* Automatically resizing images */\n\n.image-container {\n  position: absolute;\n  top: 0;\n  left: 0;\n  right: 0;\n  bottom: 0;\n  margin: 0;\n}\n\n.image-container img {\n  opacity: 0;\n  overflow: hidden;\n}\n\n/* Value box */\n\n.value-box {\n  border-radius: 2px;\n  position: relative;\n  display: block;\n  margin-right: 8px;\n  margin-bottom: 8px;\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.1);\n}\n\n.value-box > .inner {\n  padding: 10px;\n  padding-left: 20px;\n  padding-right: 20px;\n}\n\n.value-box .value {\n  font-size: 38px;\n  font-weight: bold;\n  margin: 0 0 3px 0;\n  white-space: nowrap;\n  padding: 0;\n}\n\n.value-box .caption {\n  font-size: 15px;\n}\n.value-box .caption > small {\n  display: block;\n  font-size: 13px;\n  margin-top: 5px;\n}\n\n.value-box .icon i {\n  position: absolute;\n  top: 15px;\n  right: 15px;\n  font-size: 80px;\n  color: rgba(0, 0, 0, 0.15);\n}\n\n.linked-value:hover {\n  cursor: pointer;\n}\n\n.value-box.linked-value:hover {\n  box-shadow: 0 2px 2px rgba(0, 0, 0, 0.3);\n}\n\n/* STORYBOARD */\n\n.storyboard-nav button {\n  background: transparent;\n  border: 0;\n  opacity: .3;\n  outline: none;\n  padding: 0;\n}\n\n.storyboard-nav button:hover,\n.storyboard-nav button:hover {\n  opacity: .5;\n}\n\n.storyboard-nav button:disabled,\n.storyboard-nav button:disabled {\n  opacity: .1;\n}\n\n.storyboard-nav .sbnext,\n.storyboard-nav .sbprev {\n  float: left;\n  width: 2%;\n  height: 120px;\n  font-size: 50px;\n}\n\n.storyboard-nav .sbprev {\n  text-align: left;\n  width: 2%;\n}\n\n.storyboard-nav .sbnext {\n  float: right;\n  text-align: right;\n  margin-right: 8px;\n}\n\n.storyboard-nav .sbframelist {\n  margin: 0 auto;\n  width: 94%;\n  height: 120px;\n  overflow: hidden;\n  text-shadow: none;\n  margin-bottom: 8px;\n}\n\n.storyboard-nav .sbframelist ul {\n  list-style: none;\n  margin: 0;\n  padding: 0;\n  height: 100%;\n}\n\n.storyboard-nav .sbframelist ul li {\n  float: left;\n  width: 270px;\n  height: 100%;\n  padding: 10px 10px 10px 10px;\n  margin-right: 8px;\n  background: #fff;\n  border: 1px solid #e2e2e2;\n  border-radius: 3px;\n  color: #3a3c47;\n  text-align: left;\n  font-size: 14px;\n  cursor: pointer;\n}\n\n.storyboard-nav .sbframelist ul li:last-child {\n  margin-right: 0px;\n}\n\n.storyboard-nav .sbframelist ul li.active {\n  color: #fff;\n  background: #B8B8B8;\n}\n\n.sbframe-commentary {\n  width: 300px;\n  background: #fbfbfb;\n  font-size: 14px;\n}\n\n.sbframe-commentary ul {\n  padding-left: 22px;\n}\n\n.sbframe.active {\n  display: flex;\n}\n\n.sbframe:not(.active) {\n  display: none;\n}\n\n/* NAV TABS */\n\n.nav-tabs-custom > .nav-tabs {\n  margin: 0;\n  border-bottom: 1px solid #d7d7d7;\n  color: #666;\n  font-size: 14px;\n  font-weight: 300;\n  border-top-right-radius: 3px;\n  border-top-left-radius: 3px;\n}\n.nav-tabs-custom > .nav-tabs > li {\n  border-top: 3px solid transparent;\n  margin-bottom: -1px;\n  margin-right: 5px;\n}\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:active {\n  color: #666;\n  font-weight: 300;\n  font-size: 14px;\n  border-radius: 0;\n  padding: 3px 10px 5px;\n  text-transform: none;\n}\n\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a {\n  border-bottom-color: transparent;\n}\n\n.nav-tabs-custom > .nav-tabs > li > a.text-muted {\n  color: #999;\n}\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:hover {\n  background: transparent;\n  margin: 0;\n}\n.nav-tabs-custom > .nav-tabs > li > a:hover {\n  color: #999;\n}\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:hover,\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:focus,\n.nav-tabs-custom > .nav-tabs > li:not(.active) > a:active {\n  border-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs > li.active {\n  border-top-color: #3c8dbc;\n}\n.nav-tabs-custom > .nav-tabs > li.active > a,\n.nav-tabs-custom > .nav-tabs > li.active:hover > a {\n  background-color: #fff;\n  color: #666;\n}\n.nav-tabs-custom > .nav-tabs > li.active > a {\n  border-top-color: transparent;\n  border-left-color: #d7d7d7;\n  border-right-color: #d7d7d7;\n}\n.nav-tabs-custom > .nav-tabs > li:first-of-type {\n  margin-left: 0;\n}\n.nav-tabs-custom > .nav-tabs > li:first-of-type.active > a {\n  border-left-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs.pull-right {\n  float: none !important;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li {\n  float: right;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type {\n  margin-right: 0;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type > a {\n  border-left-width: 1px;\n}\n.nav-tabs-custom > .nav-tabs.pull-right > li:first-of-type.active > a {\n  border-left-color: #d7d7d7;\n  border-right-color: transparent;\n}\n.nav-tabs-custom > .nav-tabs > li.header {\n  line-height: 35px;\n  padding: 0 10px;\n  font-size: 20px;\n  color: #666;\n}\n.nav-tabs-custom > .nav-tabs > li.header > .fa,\n.nav-tabs-custom > .nav-tabs > li.header > .glyphicon,\n.nav-tabs-custom > .nav-tabs > li.header > .ion {\n  margin-right: 5px;\n}\n.nav-tabs-custom > .tab-content {\n  background: #fff;\n  padding: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n}\n\n.nav-tabs-custom > .tab-content > .chart-wrapper {\n  background: #fff;\n  border: none;\n  margin: 0;\n}\n\n\n.nav-tabs-custom > .tab-content > .active {\n  display: -webkit-box;\n  display: -webkit-flex;\n  display: -ms-flexbox;\n  display: flex;\n}\n\n.nav-tabs-custom .dropdown.open > a:active,\n.nav-tabs-custom .dropdown.open > a:focus {\n  background: transparent;\n  color: #999;\n}\n\n\n@media (max-width: 767px) {\n  .value-box {\n    text-align: center;\n    margin-right: 8px;\n  }\n  .value-box .icon {\n    display: none;\n  }\n}\n\n/* Fixed position sidebar */\n\n.section.sidebar {\n  position: fixed;\n  top: 51px; /* overridden by theme */\n  left: 0;\n  bottom: 0;\n  border-right: 1px solid #e2e2e2;\n  background-color: white; /* overridden by theme */\n  padding-left: 10px;\n  padding-right: 10px;\n  visibility: hidden;\n  overflow: auto;\n}\n\n.section.sidebar form p:first-child {\n  margin-top: 10px;\n}\n\n/* Embedded source code */\n\n#flexdashboard-source-code {\n  display: none;\n}\n\n.featherlight-content #flexdashboard-source-code {\n  display: inline-block;\n}\n\n.featherlight-content {\n  width: 80%;\n  max-width: 800px;\n  padding: 0 !important;\n  border-bottom: none !important;\n}\n\n.featherlight:last-of-type {\n  background: rgba(0, 0, 0, 0.7);\n}\n\n.featherlight-inner {\n  width: 100%;\n}\n\n.featherlight-content pre {\n  margin: 0;\n  border: 0;\n  background: #fff;\n  font-size: 12px;\n}\n\n.unselectable {\n  -ms-user-select: none;\n  -webkit-user-select: none;\n  -khtml-user-select: none;\n  -moz-user-select: -moz-none;\n  -o-user-select: none;\n  user-select: none;\n}\n\n#flexdashboard-source-code code {\n  -ms-user-select: text;\n  -webkit-user-select: text;\n  -khtml-user-select: text;\n  -moz-user-select: text;\n  -o-user-select: text;\n  user-select: text;\n}\n\n/* DataTables Tweaks */\n\n.dataTables_filter input[type=\"search\"] {\n  -webkit-appearance: searchfield;\n  outline: none;\n}\n\n.dataTables_wrapper.no-footer\n.dataTables_info {\n  padding-top: 0;\n}\n\n\ntable.dataTable thead th {\n  border-bottom: 1px solid #d7d7d7;\n}\n\n.dataTables_wrapper.no-footer .dataTables_scrollBody {\n  border-bottom: 1px solid #d7d7d7;\n}\n\n/* Mobile phone only CSS */\n\n.desktop-layout div.section.mobile {\n  display: none;\n}\n\n.mobile-layout div.section.no-mobile {\n  display: none;\n}\n\n.mobile-figure {\n  display: none;\n}\n\n\n\nbody {\n  padding-top: 60px;\n}\n\n.section.sidebar {\n  top: 51px;\n  background-color: rgba(39, 128, 227, 0.1);\n}\n\n.value-box {\n  color: #f9f9f9;\n}\n\n.bg-primary {\n  background-color: rgba(39, 128, 227, 0.7);\n}\n\n.storyboard-nav .sbframelist ul li.active {\n  background-color: rgba(39, 128, 227, 0.7);\n}\n\n.nav-tabs-custom > .nav-tabs > li.active {\n  border-top-color: rgba(39, 128, 227, 0.7);\n}\n\n.bg-info {\n  background-color: rgba(153, 84, 187, 0.7);\n}\n\n.bg-warning {\n  background-color: rgba(255, 117, 24, 0.7);\n}\n\n.bg-danger {\n  background-color: rgba(255, 0, 57, 0.7);\n}\n\n.bg-success {\n  background-color: rgba(63, 182, 24, 0.7);\n}\n\n.chart-title {\n  font-weight: 500;\n}\n\n.nav-tabs-custom > .nav-tabs > li > a,\n.nav-tabs-custom > .nav-tabs > li > a:active {\n  font-weight: 500;\n}\n\n@media only screen and (min-width: 768px) {\nhtml, body {\n  height: 100%;\n}\n\n#dashboard-container {\n  height: 100%;\n}\n}\n</style>\n\n\n\n</head>\n\n<body>\n\n<div class=\"navbar navbar-inverse navbar-fixed-top\" role=\"navigation\">\n<div class=\"container-fluid\">\n<div class=\"navbar-header\">\n\n<button id=\"navbar-button\" type=\"button\" class=\"navbar-toggle collapsed\" data-toggle=\"collapse\" data-target=\"#navbar\">\n<span class=\"icon-bar\"></span>\n<span class=\"icon-bar\"></span>\n<span class=\"icon-bar\"></span>\n</button>\n\n<span class=\"navbar-logo pull-left\">\n  \n</span>\n<span class=\"navbar-brand\">\n  rbokeh iris dataset\n  <span class=\"navbar-author\">\n    Ryan Hafen\n    </span>\n</span>\n\n</div>\n<div id=\"navbar\" class=\"navbar-collapse collapse\">\n<ul class=\"nav navbar-nav navbar-left\">\n</ul>\n<ul class=\"nav navbar-nav navbar-right\">\n</ul>\n</div><!--/.nav-collapse-->\n</div><!--/.container-->\n</div><!--/.navbar-->\n\n<script type=\"text/javascript\">\n\n\nvar FlexDashboard = (function () {\n\n  // initialize options\n  var _options = {};\n\n  var FlexDashboard = function() {\n\n    // default options\n    _options = $.extend(_options, {\n      theme: \"cosmo\",\n      fillPage: false,\n      orientation: 'columns',\n      storyboard: false,\n      defaultFigWidth: 576,\n      defaultFigHeight: 461,\n      defaultFigWidthMobile: 360,\n      defaultFigHeightMobile: 461,\n      isMobile: false,\n      isPortrait: false\n    });\n  };\n\n  function init(options) {\n\n    // extend default options\n    $.extend(true, _options, options);\n\n    // add ids to sections that don't have them (pandoc won't assign ids\n    // to e.g. sections with titles consisting of only chinese characters)\n    var nextId = 1;\n    $('.level1:not([id]),.level2:not([id]),.level3:not([id])').each(function() {\n      $(this).attr('id', 'dashboard-' + nextId++);\n    });\n\n    // find navbar items\n    var navbarItems = $('#flexdashboard-navbar');\n    if (navbarItems.length)\n      navbarItems = JSON.parse(navbarItems.html());\n    addNavbarItems(navbarItems);\n\n    // find the main dashboard container\n    var dashboardContainer = $('#dashboard-container');\n\n    // resolve mobile classes\n    resolveMobileClasses(dashboardContainer);\n\n    // one time global initialization for components\n    componentsInit(dashboardContainer);\n\n    // look for a global sidebar\n    var globalSidebar = dashboardContainer.find(\".section.level1.sidebar\");\n    if (globalSidebar.length > 0) {\n\n      // global layout for fullscreen displays\n      if (!isMobilePhone()) {\n\n         // hoist it up to the top level\n         globalSidebar.insertBefore(dashboardContainer);\n\n         // lay it out (set width/positions)\n         layoutSidebar(globalSidebar, dashboardContainer);\n\n      // tuck sidebar into first page for mobile phones\n      } else {\n\n        // convert it into a level3 section\n        globalSidebar.removeClass('sidebar');\n        globalSidebar.removeClass('level1');\n        globalSidebar.addClass('level3');\n        var h1 = globalSidebar.children('h1');\n        var h3 = $('<h3></h3>');\n        h3.html(h1.html());\n        h3.insertBefore(h1);\n        h1.remove();\n\n        // move it into the first page\n        var page = dashboardContainer.find('.section.level1').first();\n        if (page.length > 0)\n          page.prepend(globalSidebar);\n      }\n    }\n\n    // look for pages to layout\n    var pages = $('div.section.level1');\n    if (pages.length > 0) {\n\n        // find the navbar and collapse on clicked\n        var navbar = $('#navbar');\n        navbar.on(\"click\", \"a[data-toggle!=dropdown]\", null, function () {\n           navbar.collapse('hide');\n        });\n\n        // envelop the dashboard container in a tab content div\n        dashboardContainer.wrapInner('<div class=\"tab-content\"></div>');\n\n        pages.each(function(index) {\n\n          // lay it out\n          layoutDashboardPage($(this));\n\n          // add it to the navbar\n          addToNavbar($(this), index === 0);\n\n        });\n\n    } else {\n\n      // remove the navbar and navbar button if we don't\n      // have any navbuttons\n      if (navbarItems.length === 0) {\n        $('#navbar').remove();\n        $('#navbar-button').remove();\n      }\n\n      // add the storyboard class if requested\n      if (_options.storyboard)\n        dashboardContainer.addClass('storyboard');\n\n      // layout the entire page\n      layoutDashboardPage(dashboardContainer);\n    }\n\n    // if we are in shiny we need to trigger a window resize event to\n    // force correct layout of shiny-bound-output elements\n    if (isShinyDoc())\n      $(window).trigger('resize');\n\n    // make main components visible\n    $('.section.sidebar').css('visibility', 'visible');\n    dashboardContainer.css('visibility', 'visible');\n\n    // handle location hash\n    handleLocationHash();\n\n    // intialize prism highlighting\n    initPrismHighlighting();\n\n    // record mobile and orientation state then register a handler\n    // to refresh if it changes\n    _options.isMobile = isMobilePhone();\n    _options.isPortrait = isPortrait();\n    $(window).on('resize', function() {\n      if (_options.isMobile !== isMobilePhone() ||\n          _options.isPortrait !== isPortrait()) {\n        window.location.reload();\n      }\n    });\n\n    // trigger layoutcomplete event\n    dashboardContainer.trigger('flexdashboard:layoutcomplete');\n  }\n\n  function resolveMobileClasses(dashboardContainer) {\n     // add top level layout class\n    dashboardContainer.addClass(isMobilePhone() ? 'mobile-layout' :\n                                                  'desktop-layout');\n\n    // look for .mobile sections and add .no-mobile to their peers\n    var mobileSections = $('.section.mobile');\n    mobileSections.each(function() {\n       var id = $(this).attr('id');\n       var nomobileId = id.replace(/-\\d+$/, '');\n       $('#' + nomobileId).addClass('no-mobile');\n    });\n  }\n\n  function addNavbarItems(navbarItems) {\n\n    var navbarLeft = $('ul.navbar-left');\n    var navbarRight = $('ul.navbar-right');\n\n    for (var i = 0; i<navbarItems.length; i++) {\n\n      // get the item\n      var item = navbarItems[i];\n\n      // determine the container\n      var container = null;\n      if (item.align === \"left\")\n        container = navbarLeft;\n      else\n        container = navbarRight;\n\n      // navbar menu if we have multiple items\n      if (item.items) {\n        var menu = navbarMenu(null, item.icon, item.title, container);\n        for (var j = 0; j<item.items.length; j++) {\n          var subItem = item.items[j];\n          var li = $('<li></li>');\n          li.append(navbarLink(subItem.icon, subItem.title, subItem.href, subItem.target));\n          menu.append(li);\n        }\n      } else {\n        var li = $('<li></li>');\n        li.append(navbarLink(item.icon, item.title, item.href, item.target));\n        container.append(li);\n      }\n    }\n  }\n\n  // create or get a reference to an existing dropdown menu\n  function navbarMenu(id, icon, title, container) {\n    var existingMenu = [];\n    if (id)\n      existingMenu = container.children('#' + id);\n    if (existingMenu.length > 0) {\n      return existingMenu.children('ul');\n    } else {\n      var li = $('<li></li>');\n      if (id)\n        li.attr('id', id);\n      li.addClass('dropdown');\n      // auto add \"Share\" title on mobile if necessary\n      if (!title && icon && (icon === \"fa-share-alt\") && isMobilePhone())\n        title = \"Share\";\n      if (title) {\n        title = title + ' <span class=\"caret\"></span>';\n      }\n      var a = navbarLink(icon, title, \"#\");\n      a.addClass('dropdown-toggle');\n      a.attr('data-toggle', 'dropdown');\n      a.attr('role', 'button');\n      a.attr('aria-expanded', 'false');\n      li.append(a);\n      var ul = $('<ul class=\"dropdown-menu\"></ul>');\n      ul.attr('role', 'menu');\n      li.append(ul);\n      container.append(li);\n      return ul;\n    }\n  }\n\n  function addToNavbar(page, active) {\n\n    // capture the id and data-icon attribute (if any)\n    var id = page.attr('id');\n    var icon = page.attr('data-icon');\n    var navmenu = page.attr('data-navmenu');\n\n    // get hidden state (transfer this to navbar)\n    var hidden = page.hasClass('hidden');\n    page.removeClass('hidden');\n\n    // sanitize the id for use with bootstrap tabs\n    id = id.replace(/[.\\/?&!#<>]/g, '').replace(/\\s/g, '_');\n    page.attr('id', id);\n\n    // get the wrapper\n    var wrapper = page.closest('.dashboard-page-wrapper');\n\n    // move the id to the wrapper\n    page.removeAttr('id');\n    wrapper.attr('id', id);\n\n    // add the tab-pane class to the wrapper\n    wrapper.addClass('tab-pane');\n    if (active)\n      wrapper.addClass('active');\n\n    // get a reference to the h1, discover it's id and title, then remove it\n    var h1 = wrapper.find('h1').first();\n    var title = h1.html();\n    h1.remove();\n\n    // create a navbar item\n    var li = $('<li></li>');\n    var a = navbarLink(icon, title, '#' + id);\n    a.attr('data-toggle', 'tab');\n    li.append(a);\n\n    // add it to the navbar (or navbar menu if specified)\n    var container = $('ul.navbar-left');\n    if (navmenu) {\n      var menuId = navmenu.replace(/\\s+/g, '');\n      var menu = navbarMenu(menuId, null, navmenu, container);\n      menu.append(li);\n    } else {\n      container.append(li);\n    }\n\n    // hide it if requested\n    if (hidden)\n      li.addClass('hidden');\n  }\n\n  function navbarLink(icon, title, href, target) {\n\n    var a = $('<a></a>');\n    if (icon) {\n\n      // get the name of the icon set and icon\n      var dashPos = icon.indexOf(\"-\");\n      var iconSet = icon.substring(0, dashPos);\n      var iconName = icon.substring(dashPos + 1);\n\n      // create the icon\n      var iconElement = $('<span class=\"' + iconSet + ' ' + icon + '\"></span>');\n      if (title)\n        iconElement.css('margin-right', '7px');\n      a.append(iconElement);\n      // if href is null see if we can auto-generate based on icon (e.g. social)\n      if (!href)\n        maybeGenerateLinkFromIcon(iconName, a);\n    }\n    if (title)\n      a.append(title);\n\n    // add the href.\n    if (href) {\n      if (href === \"source_embed\") {\n        a.attr('href', '#');\n        a.attr('data-featherlight', \"#flexdashboard-source-code\");\n        a.featherlight({\n            beforeOpen: function(event){\n              $('body').addClass('unselectable');\n            },\n            afterClose: function(event){\n              $('body').removeClass('unselectable');\n            }\n        });\n      } else {\n        a.attr('href', href);\n      }\n    }\n\n    // add the arget\n    if (target)\n      a.attr('target', target);\n\n    return a;\n  }\n\n  // auto generate a link from an icon name (e.g. twitter) when possible\n  function maybeGenerateLinkFromIcon(iconName, a) {\n\n     var serviceLinks = {\n      \"twitter\": \"https://twitter.com/share?text=\" + encodeURIComponent(document.title) + \"&url=\"+encodeURIComponent(location.href),\n      \"facebook\": \"https://www.facebook.com/sharer/sharer.php?s=100&p[url]=\"+encodeURIComponent(location.href),\n      \"google-plus\": \"https://plus.google.com/share?url=\"+encodeURIComponent(location.href),\n      \"linkedin\": \"https://www.linkedin.com/shareArticle?mini=true&url=\"+encodeURIComponent(location.href) + \"&title=\" + encodeURIComponent(document.title),\n      \"pinterest\": \"https://pinterest.com/pin/create/link/?url=\"+encodeURIComponent(location.href) + \"&description=\" + encodeURIComponent(document.title)\n    };\n\n    var makeSocialLink = function(a, href) {\n      a.attr('href', '#');\n      a.on('click', function(e) {\n        e.preventDefault();\n        window.open(href);\n      });\n    };\n\n    $.each(serviceLinks, function(key, value) {\n      if (iconName.indexOf(key) !== -1)\n        makeSocialLink(a, value);\n    });\n  }\n\n  // layout a dashboard page\n  function layoutDashboardPage(page) {\n\n    // use a page wrapper so that free form content above the\n    // dashboard appears at the top rather than the side (as it\n    // would without the wrapper in a column orientation)\n    var wrapper = $('<div class=\"dashboard-page-wrapper\"></div>');\n    page.wrap(wrapper);\n\n    // if there are no level2 or level3 headers synthesize a level3\n    // header to contain the (e.g. frame it, scroll container, etc.)\n    var headers = page.find('h2,h3');\n    if (headers.length === 0)\n      page.wrapInner('<div class=\"section level3\"></div>');\n\n    // hoist up any content before level 2 or level 3 headers\n    var children = page.children();\n    children.each(function(index) {\n      if ($(this).hasClass('level2') || $(this).hasClass('level3'))\n        return false;\n      $(this).insertBefore(page);\n    });\n\n    // determine orientation and fillPage behavior for distinct media\n    var orientation, fillPage, storyboard;\n\n    // media: mobile phone\n    if (isMobilePhone()) {\n\n      // if there is a sidebar we need to ensure it's content\n      // is properly framed as an h3\n      var sidebar = page.find('.section.sidebar');\n      sidebar.removeClass('sidebar');\n      sidebar.wrapInner('<div class=\"section level3\"></div>');\n      var h2 = sidebar.find('h2');\n      var h3 = $('<h3></h3>');\n      h3.html(h2.html());\n      h3.insertBefore(h2);\n      h2.remove();\n\n      // wipeout h2 elements then enclose them in a single h2\n      var level2 = page.find('div.section.level2');\n      level2.each(function() {\n        level2.children('h2').remove();\n        level2.children().unwrap();\n      });\n      page.wrapInner('<div class=\"section level2\"></div>');\n\n      // substitute mobile images\n      if (isPortrait()) {\n        var mobileFigures = $('img.mobile-figure');\n        mobileFigures.each(function() {\n          // get the src (might be base64 encoded)\n          var src = $(this).attr('src');\n\n          // find it's peer\n          var id = $(this).attr('data-mobile-figure-id');\n          var img = $('img[data-figure-id=' + id + \"]\");\n          img.attr('src', src)\n             .attr('width', _options.defaultFigWidthMobile)\n             .attr('height', _options.defaultFigHeightMobile);\n        });\n      }\n\n      // hoist storyboard commentary into it's own section\n      if (page.hasClass('storyboard')) {\n        var commentaryHR = page.find('div.section.level3 hr');\n        commentaryHR.each(function() {\n          var commentary = $(this).nextAll().detach();\n          var commentarySection = $('<div class=\"section level3\"></div>');\n          commentarySection.append(commentary);\n          commentarySection.insertAfter($(this).closest('div.section.level3'));\n          $(this).remove();\n        });\n      }\n\n      // force a non full screen layout by columns\n      orientation = _options.orientation = 'columns';\n      fillPage = _options.fillPage = false;\n      storyboard = _options.storyboard = false;\n\n    // media: desktop\n    } else {\n\n      // determine orientation\n      orientation = page.attr('data-orientation');\n      if (orientation !== 'rows' && orientation != 'columns')\n        orientation = _options.orientation;\n\n      // determine storyboard mode\n      storyboard = page.hasClass('storyboard');\n\n      // fillPage based on options (force for storyboard)\n      fillPage = _options.fillPage || storyboard;\n\n      // handle sidebar\n      var sidebar = page.find('.section.level2.sidebar');\n      if (sidebar.length > 0)\n        layoutSidebar(sidebar, page);\n    }\n\n    // give it and it's parent divs height: 100% if we are in fillPage mode\n    if (fillPage) {\n      page.addClass('vertical-layout-fill');\n      page.css('height', '100%');\n      page.parents('div').css('height', '100%');\n    } else {\n      page.addClass('vertical-layout-scroll');\n    }\n\n    // perform the layout\n    if (storyboard)\n      layoutPageAsStoryboard(page);\n    else if (orientation === 'rows')\n      layoutPageByRows(page, fillPage);\n    else if (orientation === 'columns')\n      layoutPageByColumns(page, fillPage);\n  }\n\n  function layoutSidebar(sidebar, content) {\n\n    // get it out of the header hierarchy\n    sidebar = sidebar.first();\n    if (sidebar.hasClass('level1')) {\n      sidebar.removeClass('level1');\n      sidebar.children('h1').remove();\n    } else if (sidebar.hasClass('level2')) {\n      sidebar.removeClass('level2');\n      sidebar.children('h2').remove();\n    }\n\n    // determine width\n    var sidebarWidth = isTablet() ? 220 : 250;\n    var dataWidth = parseInt(sidebar.attr('data-width'));\n    if (dataWidth)\n      sidebarWidth = dataWidth;\n\n    // set the width and shift the page right to accomodate the sidebar\n    sidebar.css('width', sidebarWidth + 'px');\n    content.css('padding-left', sidebarWidth + 'px');\n\n    // wrap it's contents in a form\n    sidebar.wrapInner($('<form></form>'));\n  }\n\n  function layoutPageAsStoryboard(page) {\n\n    // create storyboard navigation\n    var nav = $('<div class=\"storyboard-nav\"></div>');\n\n    // add navigation buttons\n    var prev = $('<button class=\"sbprev\"><i class=\"fa fa-angle-left\"></i></button>');\n    nav.append(prev);\n    var next= $('<button class=\"sbnext\"><i class=\"fa fa-angle-right\"></i></button>');\n    nav.append(next);\n\n    // add navigation frame\n    var frameList = $('<div class=\"sbframelist\"></div>');\n    nav.append(frameList);\n    var ul = $('<ul></ul>');\n    frameList.append(ul);\n\n     // find all the level3 sections (those are the storyboard frames)\n    var frames = page.find('div.section.level3');\n    frames.each(function() {\n\n      // mark it\n      $(this).addClass('sbframe');\n\n      // divide it into chart content and (optional) commentary\n      $(this).addClass('dashboard-column-orientation');\n\n      // stuff the chart into it's own div w/ flex\n      $(this).wrapInner('<div class=\"sbframe-component\"></div>');\n      setFlex($(this), 1);\n      var frame = $(this).children('.sbframe-component');\n\n      // extract the title from the h3\n      var li = $('<li></li>');\n      var h3 = frame.children('h3');\n      li.html(h3.html());\n      h3.remove();\n      ul.append(li);\n\n      // extract commentary\n      var hr = frame.children('hr');\n      if (hr.length) {\n        var commentary = hr.nextAll().detach();\n        hr.remove();\n        var commentaryFrame = $('<div class=\"sbframe-commentary\"></div>');\n        commentaryFrame.addClass('flowing-content-shim');\n        commentaryFrame.addClass('flowing-content-container');\n        commentaryFrame.append(commentary);\n        $(this).append(commentaryFrame);\n\n        // look for a data-commentary-width attribute\n        var commentaryWidth = $(this).attr('data-commentary-width');\n        if (commentaryWidth)\n          commentaryFrame.css('width', commentaryWidth + 'px');\n      }\n\n      // layout the chart (force flex)\n      var result = layoutChart(frame, true);\n\n      // ice the notes if there are none\n      if (!result.notes)\n        frame.find('.chart-notes').remove();\n\n      // set flex on chart\n      setFlex(frame, 1);\n    });\n\n    // create a div to hold all the frames\n    var frameContent = $('<div class=\"sbframe-content\"></div>');\n    frameContent.addClass('dashboard-row-orientation');\n    frameContent.append(frames.detach());\n\n    // row orientation to stack nav and frame content\n    page.addClass('dashboard-row-orientation');\n    page.append(nav);\n    page.append(frameContent);\n    setFlex(frameContent, 1);\n\n    // initialize sly\n    var sly = new Sly(frameList, {\n    \t\thorizontal: true,\n    \t\titemNav: 'basic',\n    \t\tsmart: true,\n    \t\tactivateOn: 'click',\n    \t\tstartAt: 0,\n    \t\tscrollBy: 1,\n    \t\tactivatePageOn: 'click',\n    \t\tspeed: 200,\n    \t\tmoveBy: 600,\n    \t\tdragHandle: true,\n    \t\tdynamicHandle: true,\n    \t\tclickBar: true,\n    \t\tkeyboardNavBy: 'items',\n    \t\tnext: next,\n    \t\tprev: prev\n    \t}).init();\n\n    // make first frame active\n    frames.removeClass('active');\n    frames.first().addClass('active');\n\n    // subscribe to frame changed events\n    sly.on('active', function (eventName, itemIndex) {\n      frames.removeClass('active');\n      frames.eq(itemIndex).addClass('active')\n                          .trigger('shown');\n    });\n  }\n\n  function layoutPageByRows(page, fillPage) {\n\n    // row orientation\n    page.addClass('dashboard-row-orientation');\n\n    // find all the level2 sections (those are the rows)\n    var rows = page.find('div.section.level2');\n\n    // if there are no level2 sections then treat the\n    // entire page as if it's a level 2 section\n    if (rows.length === 0) {\n      page.wrapInner('<div class=\"section level2\"></div>');\n      rows = page.find('div.section.level2');\n    }\n\n    rows.each(function () {\n\n      // flags\n      var haveNotes = false;\n      var haveFlexHeight = true;\n\n      // remove the h2\n      $(this).children('h2').remove();\n\n      // check for a tabset\n      var isTabset = $(this).hasClass('tabset');\n      if (isTabset)\n        layoutTabset($(this));\n\n      // give it row layout semantics if it's not a tabset\n      if (!isTabset)\n        $(this).addClass('dashboard-row');\n\n      // find all of the level 3 subheads\n      var columns = $(this).find('div.section.level3');\n\n      // determine figureSizes sizes\n      var figureSizes = chartFigureSizes(columns);\n\n      // fixup the columns\n      columns.each(function(index) {\n\n        // layout the chart (force flex if we are in a tabset)\n        var result = layoutChart($(this), isTabset);\n\n        // update flexHeight state\n        if (!result.flex)\n          haveFlexHeight = false;\n\n        // update state\n        if (result.notes)\n          haveNotes = true;\n\n        // set the column flex based on the figure width\n        // (value boxes will just get the default figure width)\n        var chartWidth = figureSizes[index].width;\n        setFlex($(this), chartWidth + ' ' + chartWidth + ' 0px');\n\n      });\n\n      // remove empty chart note divs\n      if (isTabset)\n        $(this).find('.chart-notes').filter(function() {\n            return $(this).html() === \"&nbsp;\";\n        }).remove();\n      if (!haveNotes)\n        $(this).find('.chart-notes').remove();\n\n       // make it a flexbox row\n      if (haveFlexHeight)\n        $(this).addClass('dashboard-row-flex');\n\n      // now we can set the height on all the wrappers (based on maximum\n      // figure height + room for title and notes, or data-height on the\n      // container if specified). However, don't do this if there is\n      // no flex on any of the constituent columns\n      var flexHeight = null;\n      var dataHeight = parseInt($(this).attr('data-height'));\n      if (dataHeight)\n        flexHeight = adjustedHeight(dataHeight, columns.first());\n      else if (haveFlexHeight)\n        flexHeight = maxChartHeight(figureSizes, columns);\n      if (flexHeight) {\n        if (fillPage)\n          setFlex($(this), flexHeight + ' ' + flexHeight + ' 0px');\n        else {\n          $(this).css('height', flexHeight + 'px');\n          setFlex($(this), '0 0 ' + flexHeight + 'px');\n        }\n      }\n\n    });\n  }\n\n  function layoutPageByColumns(page, fillPage) {\n\n    // column orientation\n    page.addClass('dashboard-column-orientation');\n\n    // find all the level2 sections (those are the columns)\n    var columns = page.find('div.section.level2');\n\n    // if there are no level2 sections then treat the\n    // entire page as if it's a level 2 section\n    if (columns.length === 0) {\n      page.wrapInner('<div class=\"section level2\"></div>');\n      columns = page.find('div.section.level2');\n    }\n\n    // layout each column\n    columns.each(function (index) {\n\n      // remove the h2\n      $(this).children('h2').remove();\n\n      // make it a flexbox column\n      $(this).addClass('dashboard-column');\n\n      // check for a tabset\n      var isTabset = $(this).hasClass('tabset');\n      if (isTabset)\n        layoutTabset($(this));\n\n      // find all the h3 elements\n      var rows = $(this).find('div.section.level3');\n\n      // get the figure sizes for the rows\n      var figureSizes = chartFigureSizes(rows);\n\n      // column flex is the max row width (or data-width if specified)\n      var flexWidth;\n      var dataWidth = parseInt($(this).attr('data-width'));\n      if (dataWidth)\n        flexWidth = dataWidth;\n      else\n        flexWidth = maxChartWidth(figureSizes);\n      setFlex($(this), flexWidth + ' ' + flexWidth + ' 0px');\n\n      // layout each chart\n      rows.each(function(index) {\n\n        // perform the layout\n        var result = layoutChart($(this), false);\n\n        // ice the notes if there are none\n        if (!result.notes)\n          $(this).find('.chart-notes').remove();\n\n        // set flex height based on figHeight, then adjust\n        if (result.flex) {\n          var chartHeight = figureSizes[index].height;\n          chartHeight = adjustedHeight(chartHeight, $(this));\n          if (fillPage)\n            setFlex($(this), chartHeight + ' ' + chartHeight + ' 0px');\n          else {\n            $(this).css('height', chartHeight + 'px');\n            setFlex($(this), chartHeight + ' ' + chartHeight + ' ' + chartHeight + 'px');\n          }\n        }\n      });\n    });\n  }\n\n  function chartFigureSizes(charts) {\n\n    // sizes\n    var figureSizes = new Array(charts.length);\n\n    // check each chart\n    charts.each(function(index) {\n\n      // start with default\n      figureSizes[index] = {\n        width: _options.defaultFigWidth,\n        height: _options.defaultFigHeight\n      };\n\n      // look for data-height or data-width then knit options\n      var dataWidth = parseInt($(this).attr('data-width'));\n      var dataHeight = parseInt($(this).attr('data-height'));\n      var knitrOptions = $(this).find('.knitr-options:first');\n      var knitrWidth, knitrHeight;\n      if (knitrOptions) {\n        knitrWidth = parseInt(knitrOptions.attr('data-fig-width'));\n        knitrHeight =  parseInt(knitrOptions.attr('data-fig-height'));\n      }\n\n      // width\n      if (dataWidth)\n        figureSizes[index].width = dataWidth;\n      else if (knitrWidth)\n        figureSizes[index].width = knitrWidth;\n\n      // height\n      if (dataHeight)\n        figureSizes[index].height = dataHeight;\n      else if (knitrHeight)\n        figureSizes[index].height = knitrHeight;\n    });\n\n    // return sizes\n    return figureSizes;\n  }\n\n  function maxChartHeight(figureSizes, charts) {\n\n    // first compute the maximum height\n    var maxHeight = _options.defaultFigHeight;\n    for (var i = 0; i<figureSizes.length; i++)\n      if (figureSizes[i].height > maxHeight)\n        maxHeight = figureSizes[i].height;\n\n    // now add offests for chart title and chart notes\n    if (charts.length)\n      maxHeight = adjustedHeight(maxHeight, charts.first());\n\n    return maxHeight;\n  }\n\n  function adjustedHeight(height, chart) {\n    if (chart.length > 0) {\n      var chartTitle = chart.find('.chart-title');\n      if (chartTitle.length)\n        height += chartTitle.first().outerHeight();\n      var chartNotes = chart.find('.chart-notes');\n      if (chartNotes.length)\n        height += chartNotes.first().outerHeight();\n    }\n    return height;\n  }\n\n  function maxChartWidth(figureSizes) {\n    var maxWidth = _options.defaultFigWidth;\n    for (var i = 0; i<figureSizes.length; i++)\n      if (figureSizes[i].width > maxWidth)\n        maxWidth = figureSizes[i].width;\n    return maxWidth;\n  }\n\n  // layout a chart\n  function layoutChart(chart, forceFlex) {\n\n    // state to return\n    var result = {\n      notes: false,\n      flex: false\n    };\n\n    // extract the title\n    var title = extractTitle(chart);\n\n    // find components that apply to this container\n    var components = componentsFind(chart);\n\n    // if it's a custom component then call it and return\n    var customComponents = componentsCustom(components);\n    if (customComponents.length) {\n      componentsLayout(customComponents, title, chart);\n      result.notes = false;\n      result.flex = forceFlex || componentsFlex(customComponents);\n      return result;\n    }\n\n    // put all the content in a chart wrapper div\n    chart.addClass('chart-wrapper');\n    chart.wrapInner('<div class=\"chart-stage\"></div>');\n    var chartContent = chart.children('.chart-stage');\n\n    // flex the content if appropriate\n    result.flex = forceFlex || componentsFlex(components);\n    if (result.flex) {\n      // add flex classes\n      chart.addClass('chart-wrapper-flex');\n      chartContent.addClass('chart-stage-flex');\n\n      // additional shim to break out of flexbox sizing\n      chartContent.wrapInner('<div class=\"chart-shim\"></div>');\n      chartContent = chartContent.children('.chart-shim');\n    }\n\n    // set custom data-padding attribute\n    var pad = chart.attr('data-padding');\n    if (pad) {\n      if (pad === \"0\")\n        chart.addClass('no-padding');\n      else {\n        pad = pad + 'px';\n        chartContent.css('left', pad)\n                    .css('top', pad)\n                    .css('right', pad)\n                    .css('bottom', pad)\n      }\n    }\n\n    // call compoents\n    componentsLayout(components, title, chartContent);\n\n    // also activate components on shiny output\n    findShinyOutput(chartContent).on('shiny:value',\n      function(event) {\n        var element = $(event.target);\n        setTimeout(function() {\n\n          // see if we opted out of flex based on our output (for shiny\n          // we can't tell what type of output we have until after the\n          // value is bound)\n          var components = componentsFind(element);\n          var flex = forceFlex || componentsFlex(components);\n          if (!flex) {\n            chart.css('height', \"\");\n            setFlex(chart, \"\");\n            chart.removeClass('chart-wrapper-flex');\n            chartContent.removeClass('chart-stage-flex');\n            chartContent.children().unwrap();\n          }\n\n          // perform layout\n          componentsLayout(components, title, element.parent());\n        }, 10);\n      });\n\n    // add the title\n    var chartTitle = $('<div class=\"chart-title\"></div>');\n    chartTitle.html(title);\n    chart.prepend(chartTitle);\n\n    // add the notes section\n    var chartNotes = $('<div class=\"chart-notes\"></div>');\n    chartNotes.html('&nbsp;');\n    chart.append(chartNotes);\n\n    // attempt to extract notes if we have a component\n    if (components.length)\n      result.notes = extractChartNotes(chartContent, chartNotes);\n\n    // return result\n    return result;\n  }\n\n  // build a tabset from a section div with the .tabset class\n  function layoutTabset(tabset) {\n\n    // check for fade option\n    var fade = tabset.hasClass(\"tabset-fade\");\n    var navClass = \"nav-tabs\";\n\n    // determine the heading level of the tabset and tabs\n    var match = tabset.attr('class').match(/level(\\d) /);\n    if (match === null)\n      return;\n    var tabsetLevel = Number(match[1]);\n    var tabLevel = tabsetLevel + 1;\n\n    // find all subheadings immediately below\n    var tabs = tabset.find(\"div.section.level\" + tabLevel);\n    if (!tabs.length)\n      return;\n\n    // create tablist and tab-content elements\n    var tabList = $('<ul class=\"nav ' + navClass + '\" role=\"tablist\"></ul>');\n    $(tabs[0]).before(tabList);\n    var tabContent = $('<div class=\"tab-content\"></div>');\n    $(tabs[0]).before(tabContent);\n\n    // build the tabset\n    tabs.each(function(i) {\n\n      // get the tab div\n      var tab = $(tabs[i]);\n\n      // get the id then sanitize it for use with bootstrap tabs\n      var id = tab.attr('id');\n\n      // sanitize the id for use with bootstrap tabs\n      id = id.replace(/[.\\/?&!#<>]/g, '').replace(/\\s/g, '_');\n      tab.attr('id', id);\n\n      // get the heading element within it and grab it's text\n      var heading = tab.find('h' + tabLevel + ':first');\n      var headingText = heading.html();\n\n      // build and append the tab list item\n      var a = $('<a role=\"tab\" data-toggle=\"tab\">' + headingText + '</a>');\n      a.attr('href', '#' + id);\n      a.attr('aria-controls', id);\n      var li = $('<li role=\"presentation\"></li>');\n      li.append(a);\n      if (i === 0)\n        li.attr('class', 'active');\n      tabList.append(li);\n\n      // set it's attributes\n      tab.attr('role', 'tabpanel');\n      tab.addClass('tab-pane');\n      tab.addClass('tabbed-pane');\n      tab.addClass('no-title');\n      if (fade)\n        tab.addClass('fade');\n      if (i === 0) {\n        tab.addClass('active');\n        if (fade)\n          tab.addClass('in');\n      }\n\n      // move it into the tab content div\n      tab.detach().appendTo(tabContent);\n    });\n\n    // add nav-tabs-custom\n    tabset.addClass('nav-tabs-custom');\n\n    // internal layout is dashboard-column with tab-content flexing\n    tabset.addClass('dashboard-column');\n    setFlex(tabContent, 1);\n  }\n\n  // one time global initialization for components\n  function componentsInit(dashboardContainer) {\n    for (var i=0; i<window.FlexDashboardComponents.length; i++) {\n      var component = window.FlexDashboardComponents[i];\n      if (component.init)\n        component.init(dashboardContainer);\n    }\n  }\n\n  // find components that apply within a container\n  function componentsFind(container) {\n\n    // look for components\n    var components = [];\n    for (var i=0; i<window.FlexDashboardComponents.length; i++) {\n      var component = window.FlexDashboardComponents[i];\n      if (component.find(container).length)\n        components.push(component);\n    }\n\n    // if there were none then use a special flowing content component\n    // that just adds a scrollbar in fillPage mode\n    if (components.length == 0) {\n      components.push({\n        find: function(container) {\n          return container;\n        },\n\n        flex: function(fillPage) {\n          return fillPage;\n        },\n\n        layout: function(title, container, element, fillPage) {\n          if (fillPage) {\n            container.addClass('flowing-content-shim');\n            container.addClass('flowing-content-container');\n          }\n        }\n      });\n    }\n\n    return components;\n  }\n\n  // if there is a custom component then pick it out\n  function componentsCustom(components) {\n    var customComponent = [];\n    for (var i=0; i<components.length; i++)\n      if (components[i].type === \"custom\") {\n        customComponent.push(components[i]);\n        break;\n      }\n    return customComponent;\n  }\n\n  // query all components for flex\n  function componentsFlex(components) {\n\n    // no components at all means no flex\n    if (components.length === 0)\n      return false;\n\n    // otherwise query components (assume true unless we see false)\n    var isMobile = isMobilePhone();\n    for (var i=0; i<components.length; i++)\n      if (components[i].flex && !components[i].flex(_options.fillPage))\n        return false;\n    return true;\n  }\n\n  // layout all components\n  function componentsLayout(components, title, container) {\n    var isMobile = isMobilePhone();\n    for (var i=0; i<components.length; i++) {\n      var element = components[i].find(container);\n      if (components[i].layout) {\n        // call layout (don't call other components if it returns false)\n        var result = components[i].layout(title, container, element, _options.fillPage);\n        if (result === false)\n          return;\n      }\n    }\n  }\n\n  // get a reference to the h3, discover it's inner html, and remove it\n  function extractTitle(container) {\n    var h3 = container.children('h3').first();\n    var title = '';\n    if (!container.hasClass('no-title'))\n      title = h3.html();\n    h3.remove();\n    return title;\n  }\n\n  // extract chart notes\n  function extractChartNotes(chartContent, chartNotes) {\n    // look for a terminating blockquote or image caption\n    var blockquote = chartContent.children('blockquote:last-child');\n    var caption = chartContent.children('div.image-container')\n                              .children('p.caption');\n    if (blockquote.length) {\n      chartNotes.html(blockquote.children('p:first-child').html());\n      blockquote.remove();\n      return true;\n    } else if (caption.length) {\n      chartNotes.html(caption.html());\n      caption.remove();\n      return true;\n    } else {\n      return false;\n    }\n  }\n\n  function findShinyOutput(chartContent) {\n    return chartContent.find('.shiny-text-output, .shiny-html-output');\n  }\n\n  // safely detect rendering on a mobile phone\n  function isMobilePhone() {\n    try\n    {\n      return ! window.matchMedia(\"only screen and (min-width: 768px)\").matches;\n    }\n    catch(e) {\n      return false;\n    }\n  }\n\n  function isFillPage() {\n    return _options.fillPage;\n  }\n\n  // detect portrait mode\n  function isPortrait() {\n    return ($(window).width() < $(window).height());\n  }\n\n  // safely detect rendering on a tablet\n  function isTablet() {\n    try\n    {\n      return window.matchMedia(\"only screen and (min-width: 769px) and (max-width: 992px)\").matches;\n    }\n    catch(e) {\n      return false;\n    }\n  }\n\n  // test whether this is a shiny doc\n  function isShinyDoc() {\n    return (typeof(window.Shiny) !== \"undefined\" && !!window.Shiny.outputBindings);\n  }\n\n  // set flex using vendor specific prefixes\n  function setFlex(el, flex) {\n    el.css('-webkit-box-flex', flex)\n      .css('-webkit-flex', flex)\n      .css('-ms-flex', flex)\n      .css('flex', flex);\n  }\n\n  // support bookmarking of pages\n  function handleLocationHash() {\n\n    // restore tab/page from bookmark\n    var hash = window.location.hash;\n    if (hash.length > 0)\n      $('ul.nav a[href=\"' + hash + '\"]').tab('show');\n    FlexDashboardUtils.manageActiveNavbarMenu();\n\n    // navigate to a tab when the history changes\n    window.addEventListener(\"popstate\", function(e) {\n      var hash = window.location.hash;\n      var activeTab = $('ul.nav a[href=\"' + hash + '\"]');\n      if (activeTab.length) {\n        activeTab.tab('show');\n      } else {\n        $('ul.nav a:first').tab('show');\n      }\n      FlexDashboardUtils.manageActiveNavbarMenu();\n    });\n\n    // add a hash to the URL when the user clicks on a tab/page\n    $('.navbar-nav a[data-toggle=\"tab\"]').on('click', function(e) {\n      var baseUrl = FlexDashboardUtils.urlWithoutHash(window.location.href);\n      var hash = FlexDashboardUtils.urlHash($(this).attr('href'));\n      var href = baseUrl + hash;\n      FlexDashboardUtils.setLocation(href);\n    });\n\n    // handle clicks of other links that should activate pages\n    var navPages = $('ul.navbar-nav li a[data-toggle=tab]');\n    navPages.each(function() {\n      var href =  $(this).attr('href');\n      var links = $('a[href=\"' + href + '\"][data-toggle!=tab]');\n      links.each(function() {\n        $(this).on('click', function(e) {\n          window.FlexDashboardUtils.showPage(href);\n        });\n      });\n    });\n  }\n\n  // tweak Prism highlighting\n  function initPrismHighlighting() {\n\n    if (window.Prism) {\n      Prism.languages.insertBefore('r', 'comment', {\n        'heading': [\n          {\n            // title 1\n        \t  // =======\n\n        \t  // title 2\n        \t  // -------\n        \t  pattern: /\\w+.*(?:\\r?\\n|\\r)(?:====+|----+)/,\n            alias: 'operator'\n          },\n          {\n            // ### title 3\n            pattern: /(^\\s*)###[^#].+/m,\n            lookbehind: true,\n            alias: 'operator'\n          }\n        ]\n      });\n\n      // prism highlight\n      Prism.highlightAll();\n    }\n  }\n\n\n  // get theme color\n  var themeColors = {\n    bootstrap: {\n      primary: \"rgba(51, 122, 183, 0.4)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    cerulean: {\n      primary: \"rgb(47, 164, 231)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    journal: {\n      primary: \"rgba(235, 104, 100, 0.70)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    flatly: {\n      primary: \"rgba(44, 62, 80, 0.70)\",\n      info: \"rgba(52, 152, 219, 0.70)\",\n      success: \"rgba(24, 188, 156, 0.70)\",\n      warning: \"rgba(243, 156, 18, 0.70)\",\n      danger: \"rgba(231, 76, 60, 0.70)\"\n    },\n    readable: {\n      primary: \"rgba(69, 130, 236, 0.4)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    spacelab: {\n      primary: \"rgba(68, 110, 155, 0.25)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    united: {\n      primary: \"rgba(221, 72, 20, 0.30)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    cosmo: {\n      primary: \"rgba(39, 128, 227, 0.7)\",\n      info: \"rgba(153, 84, 187, 0.7)\",\n      success: \"rgba(63, 182, 24, 0.7)\",\n      warning: \"rgba(255, 117, 24, 0.7)\",\n      danger: \"rgba(255, 0, 57, 0.7)\"\n    },\n    lumen: {\n      primary: \"rgba(21, 140, 186, 0.70)\",\n      info: \"rgba(117, 202, 235, 0.90)\",\n      success: \"rgba(40, 182, 44, 0.70)\",\n      warning: \"rgba(255, 133, 27, 0.70)\",\n      danger: \"rgba(255, 65, 54, 0.70)\"\n    },\n    paper: {\n      primary: \"rgba(33, 150, 243, 0.35)\",\n      info: \"rgb(225, 190, 231)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(255, 224, 178)\",\n      danger: \"rgb(249, 189, 187)\"\n    },\n    sandstone: {\n      primary: \"rgba(50, 93, 136, 0.3)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    simplex: {\n      primary: \"rgba(217, 35, 15, 0.25)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    },\n    yeti: {\n      primary: \"rgba(0, 140, 186, 0.298039)\",\n      info: \"rgb(217, 237, 247)\",\n      success: \"rgb(223, 240, 216)\",\n      warning: \"rgb(252, 248, 227)\",\n      danger: \"rgb(242, 222, 222)\"\n    }\n  }\n  function themeColor(color) {\n    return themeColors[_options.theme][color];\n  }\n\n  FlexDashboard.prototype = {\n    constructor: FlexDashboard,\n    init: init,\n    isMobilePhone: isMobilePhone,\n    isFillPage: isFillPage,\n    themeColor: themeColor\n  };\n\n  return FlexDashboard;\n\n})();\n\n// utils\nwindow.FlexDashboardUtils = {\n  resizableImage: function(img) {\n    var src = img.attr('src');\n    var url = 'url(\"' + src + '\")';\n    img.parent().css('background', url)\n                .css('background-size', 'contain')\n                .css('background-repeat', 'no-repeat')\n                .css('background-position', 'center')\n                .addClass('image-container');\n  },\n  setLocation: function(href) {\n    if (history && history.pushState) {\n      history.pushState(null, null, href);\n    } else {\n      window.location.replace(href);\n    }\n    setTimeout(function() {\n        window.scrollTo(0, 0);\n    }, 10);\n    this.manageActiveNavbarMenu();\n  },\n  showPage: function(href) {\n    $('ul.navbar-nav li a[href=\"' + href + '\"]').tab('show');\n    var baseUrl = this.urlWithoutHash(window.location.href);\n    var loc = baseUrl + href;\n    this.setLocation(loc);\n  },\n  showLinkedValue: function(href) {\n    // check for a page link\n    if ($('ul.navbar-nav li a[data-toggle=tab][href=\"' + href + '\"]').length > 0)\n      this.showPage(href);\n    else\n      window.open(href);\n  },\n  urlWithoutHash: function(url) {\n    var hashLoc = url.indexOf('#');\n    if (hashLoc != -1)\n      return url.substring(0, hashLoc);\n    else\n      return url;\n  },\n  urlHash: function(url) {\n    var hashLoc = url.indexOf('#');\n    if (hashLoc != -1)\n      return url.substring(hashLoc);\n    else\n      return \"\";\n  },\n  manageActiveNavbarMenu: function () {\n    // remove active from anyone currently active\n    $('.navbar ul.nav').find('li').removeClass('active');\n    // find the active tab\n    var activeTab = $('.dashboard-page-wrapper.tab-pane.active');\n    if (activeTab.length > 0) {\n      var tabId = activeTab.attr('id');\n      if (tabId)\n        $(\".navbar ul.nav a[href='#\" + tabId + \"']\").parents('li').addClass('active');\n    }\n  }\n};\n\nwindow.FlexDashboard = new FlexDashboard();\n\n// empty content\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    if (container.find('p').length == 0)\n      return container;\n    else\n      return $();\n  }\n})\n\n// plot image\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('p')\n                    .children('img:only-child');\n  },\n\n  layout: function(title, container, element, fillPage) {\n    FlexDashboardUtils.resizableImage(element);\n  }\n});\n\n// plot image (figure style)\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('div.figure').children('img');\n  },\n\n  layout: function(title, container, element, fillPage) {\n    FlexDashboardUtils.resizableImage(element);\n  }\n});\n\n// htmlwidget\nwindow.FlexDashboardComponents.push({\n\n  init: function(dashboardContainer) {\n    // trigger \"shown\" after initial layout to force static htmlwidgets\n    // in runtime: shiny to be resized after the dom has been transformed\n    dashboardContainer.on('flexdashboard:layoutcomplete', function(event) {\n      setTimeout(function() {\n        dashboardContainer.trigger('shown');\n      }, 200);\n    });\n  },\n\n  find: function(container) {\n    return container.children('div[id^=\"htmlwidget-\"],div.html-widget');\n  }\n});\n\n// gauge\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.children('div.html-widget.gauge');\n  },\n\n  flex: function(fillPage) {\n    return false;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n\n  }\n\n});\n\n// shiny output\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    return container.children('div[class^=\"shiny-\"]');\n  }\n});\n\n// datatables\nwindow.FlexDashboardComponents.push({\n  find: function(container) {\n    return container.find('.datatables');\n  },\n  flex: function(fillPage) {\n    return fillPage;\n  }\n});\n\n// bootstrap table\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    var bsTable = container.find('table.table');\n    if (bsTable.length !== 0)\n      return bsTable;\n    else\n      return container.find('tr.header').parent('thead').parent('table');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n    // alias variables\n    var bsTable = element;\n\n    // fixup xtable generated tables with a proper thead\n    var headerRow = bsTable.find('tbody > tr:first-child > th').parent();\n    if (headerRow.length > 0) {\n      var thead = $('<thead></thead>');\n      bsTable.prepend(thead);\n      headerRow.detach().appendTo(thead);\n    }\n\n    // improve appearance\n    container.addClass('bootstrap-table');\n\n    // for fill page provide scrolling w/ sticky headers\n    if (fillPage) {\n      // force scrollbar on overflow\n      container.addClass('flowing-content-shim');\n\n      // stable table headers when scrolling\n      bsTable.stickyTableHeaders({\n        scrollableArea: container\n      });\n    }\n  }\n});\n\n// embedded shiny app\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.find('iframe.shiny-frame');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n    if (fillPage) {\n      element.attr('height', '100%');\n    } else {\n      // provide default height if necessary\n      var height = element.get(0).style.height;\n      if (!height)\n        height = element.attr('height');\n      if (!height)\n        element.attr('height', 500);\n    }\n  }\n});\n\n// shiny fillRow or fillCol\nwindow.FlexDashboardComponents.push({\n\n  find: function(container) {\n    return container.find('.flexfill-container');\n  },\n\n  flex: function(fillPage) {\n    return fillPage;\n  },\n\n  layout: function(title, container, element, fillPage) {\n    if (fillPage)\n      element.css('height', '100%');\n    else {\n      // provide default height if necessary\n      var height = element.get(0).style.height;\n      if (height === \"100%\" || height === \"auto\" || height === \"initial\" ||\n          height === \"inherit\" || !height) {\n        element.css('height', 500);\n      }\n    }\n  }\n});\n\n// valueBox\nwindow.FlexDashboardComponents.push({\n\n  type: \"custom\",\n\n  find: function(container) {\n    if (container.find('span.value-output, .shiny-valuebox-output').length)\n      return container;\n    else\n      return $();\n  },\n\n  flex: function(fillPage) {\n    return false;\n  },\n\n  layout: function(title, container, element, fillPage) {\n\n    // alias variables\n    var chartTitle = title;\n    var valueBox = element;\n\n    // add value-box class to container\n    container.addClass('value-box');\n\n    // value paragraph\n    var value = $('<p class=\"value\"></p>');\n\n    // if we have shiny-text-output then just move it in\n    var valueOutputSpan = [];\n    var shinyOutput = valueBox.find('.shiny-valuebox-output').detach();\n    if (shinyOutput.length) {\n      valueBox.children().remove();\n      shinyOutput.html(\"&mdash;\");\n      value.append(shinyOutput);\n    } else {\n      // extract the value (remove leading vector index)\n      var chartValue = valueBox.text().trim();\n      chartValue = chartValue.replace(\"[1] \", \"\");\n      valueOutputSpan = valueBox.find('span.value-output').detach();\n      valueBox.children().remove();\n      value.text(chartValue);\n    }\n\n    // caption\n    var caption = $('<p class=\"caption\"></p>');\n    caption.html(chartTitle);\n\n    // build inner div for value box and add it\n    var inner = $('<div class=\"inner\"></div>');\n    inner.append(value);\n    inner.append(caption);\n    valueBox.append(inner);\n\n    // add icon if specified\n    var icon = $('<div class=\"icon\"><i></i></div>');\n    valueBox.append(icon);\n    function setIcon(chartIcon) {\n      var iconLib = \"\";\n      var components = chartIcon.split(\"-\");\n      if (components.length > 1)\n        iconLib = components[0];\n      icon.children('i').attr('class', iconLib + ' ' + chartIcon);\n    }\n    var chartIcon = valueBox.attr('data-icon');\n    if (chartIcon)\n      setIcon(chartIcon);\n\n    // set color based on data-background if necessary\n    var dataBackground = valueBox.attr('data-background');\n    if (dataBackground)\n      valueBox.css('background-color', bgColor);\n    else {\n      // default to bg-primary if no other background is specified\n      if (!valueBox.hasClass('bg-primary') &&\n          !valueBox.hasClass('bg-info') &&\n          !valueBox.hasClass('bg-warning') &&\n          !valueBox.hasClass('bg-success') &&\n          !valueBox.hasClass('bg-danger')) {\n        valueBox.addClass('bg-primary');\n      }\n    }\n\n    // handle data attributes in valueOutputSpan\n    function handleValueOutput(valueOutput) {\n\n      // caption\n      var dataCaption = valueOutput.attr('data-caption');\n      if (dataCaption)\n        caption.html(dataCaption);\n\n      // icon\n      var dataIcon = valueOutput.attr('data-icon');\n      if (dataIcon)\n        setIcon(dataIcon);\n\n      // color\n      var dataColor = valueOutput.attr('data-color');\n      if (dataColor) {\n        if (dataColor.indexOf('bg-') === 0) {\n          valueBox.css('background-color', '');\n          if (!valueBox.hasClass(dataColor)) {\n             valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');\n             valueBox.addClass(dataColor);\n          }\n        } else {\n          valueBox.removeClass('bg-primary bg-info bg-warning bg-danger bg-success');\n          valueBox.css('background-color', dataColor);\n        }\n      }\n\n      // url\n      var dataHref = valueOutput.attr('data-href');\n      if (dataHref) {\n        valueBox.addClass('linked-value');\n        valueBox.off('click.value-box');\n        valueBox.on('click.value-box', function(e) {\n          window.FlexDashboardUtils.showLinkedValue(dataHref);\n        });\n      }\n    }\n\n    // check for a valueOutputSpan\n    if (valueOutputSpan.length > 0) {\n      handleValueOutput(valueOutputSpan);\n    }\n\n    // if we have a shinyOutput then bind a listener to handle\n    // new valueOutputSpan values\n    shinyOutput.on('shiny:value',\n      function(event) {\n        var element = $(event.target);\n        setTimeout(function() {\n          var valueOutputSpan = element.find('span.value-output');\n          if (valueOutputSpan.length > 0)\n            handleValueOutput(valueOutputSpan);\n        }, 10);\n      }\n    );\n  }\n});\n\n\n</script>\n\n<div id=\"dashboard-container\">\n\n<div id=\"column\" class=\"section level2\" data-width=\"600\">\n<h2>Column</h2>\n<div id=\"species\" class=\"section level3\">\n<h3>Species</h3>\n<div class=\"knitr-options\" data-fig-width=\"576\" data-fig-height=\"460\">\n\n</div>\n<div 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() {\n\n  // add bootstrap table styles to pandoc tables\n  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');\n\n  // initialize mathjax\n  var script = document.createElement(\"script\");\n  script.type = \"text/javascript\";\n  script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\n  document.getElementsByTagName(\"head\")[0].appendChild(script);\n\n});\n</script>\n\n<div id=\"flexdashboard-source-code\">\n<pre class=\"line-numbers\"><code class=\"language-r\">---\ntitle: &quot;rbokeh iris dataset&quot;\nauthor: &quot;Ryan Hafen&quot;\noutput: \n  flexdashboard::flex_dashboard:\n    orientation: columns\n    social: menu\n    source_code: embed\n---\n\n```{r setup, include=FALSE}\nlibrary(rbokeh)\nlibrary(flexdashboard)\n```\n\nColumn {data-width=600}\n-----------------------------------------------------------------------\n\n### Species\n\n```{r}\nfigure(width = NULL, height = NULL) %&gt;%\n  ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species)\n# figure() %&gt;%\n#   ly_points(Sepal.Length, Sepal.Width, data = iris,\n#     color = Species, glyph = Species)\n```\n\n\nColumn {data-width=400}\n-----------------------------------------------------------------------\n\n### Species (Quantile)\n\n```{r}\nfigure(width = NULL, height = NULL, legend_location = &quot;top_left&quot;) %&gt;%\n  ly_quantile(Sepal.Length, group = Species, data = iris)\n```\n\n### Petal Width\n\n```{r}\nfigure(width = NULL, height = NULL) %&gt;%\n  ly_points(Sepal.Length, Sepal.Width, data = iris,\n    color = Petal.Width)\n```\n</code></pre>\n</div>\n<script type=\"text/javascript\">\n$(document).ready(function () {\n  FlexDashboard.init({\n    theme: \"cosmo\",\n    fillPage: true,\n    orientation: \"columns\",\n    storyboard: false,\n    defaultFigWidth: 576,\n    defaultFigHeight: 460,\n    defaultFigWidthMobile: 360,\n    defaultFigHeightMobile: 460\n  });\n});\n</script>\n\n</body>\n</html>\n"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/例子/09_rbokeh-iris-dataset/dashboard.Rmd",
    "content": "---\ntitle: \"rbokeh iris dataset\"\nauthor: \"Ryan Hafen\"\noutput: \n  flexdashboard::flex_dashboard:\n    orientation: columns\n    social: menu\n    source_code: embed\n---\n\n```{r setup, include=FALSE}\nlibrary(rbokeh)\nlibrary(flexdashboard)\n```\n\nColumn {data-width=600}\n-----------------------------------------------------------------------\n\n### Species\n\n```{r}\nfigure(width = NULL, height = NULL) %>%\n  ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species)\n# figure() %>%\n#   ly_points(Sepal.Length, Sepal.Width, data = iris,\n#     color = Species, glyph = Species)\n```\n\n\nColumn {data-width=400}\n-----------------------------------------------------------------------\n\n### Species (Quantile)\n\n```{r}\nfigure(width = NULL, height = NULL, legend_location = \"top_left\") %>%\n  ly_quantile(Sepal.Length, group = Species, data = iris)\n```\n\n### Petal Width\n\n```{r}\nfigure(width = NULL, height = NULL) %>%\n  ly_points(Sepal.Length, Sepal.Width, data = iris,\n    color = Petal.Width)\n```"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/教程.rmd",
    "content": "---\ntitle: \"Vignette Title\"\nauthor: \"Vignette Author\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEngine{knitr::rmarkdown}\n  %\\VignetteEncoding{UTF-8}\n---\n\n# 介绍\n\n从CRAN安装flexdashboard软件包\n```{r}\n# install.packages(\"flexdashboard\")\n```\n\n要编写flexdashboard，您可以使用输出格式创建R Markdown文档flexdashboard::flex_dashboard。您可以使用新的R Markdown对话框在RStudio中执行此操作：\n\n![](1.jpg)\n\n如果不使用RStudio，则可以flexdashboard从R控制台创建一个新的R Markdown文件：\n\n```{r eval=FALSE, include=TRUE}\nrmarkdown::draft(\"dashboard.Rmd\", template = \"flex_dashboard\", package = \"flexdashboard\")\n```\n\n\n## 仪表板基础\n\n### 组件\n\n您可以使用flexdashboard将相关数据可视化组发布为仪表板。Flexdashboard可以是静态的（标准网页），也可以是动态的（Shiny交互式文档）。flexdashboard布局中可以包含各种各样的组件，包括：\n\n基于htmlwidgets的交互式JavaScript数据可视化。\n\nR图形输出，包括基础，晶格和网格图形。\n\n表格数据（具有可选的排序，过滤和分页）。\n\n用于突出显示重要摘要数据的值框。\n\n用于在指定范围内的仪表上显示值的仪表。\n\n各种文本注释。\n\n有关每种组件类型的使用的更多详细信息，请参见仪表板组件文档。\n\n\n## 布局\n\n### 方向\n\n- 按列布局\n\n仪表板分为列和行，并使用3级降价标题（###）描绘了输出组件。默认情况下，仪表板布置在单列中，图表垂直堆叠在列中，其大小可填充可用的浏览器高度。\n\n默认情况下，仪表板中的2级降价标题（）定义列，各个图表垂直堆叠在每一列中。这是两列仪表板的定义，左侧有一个图表，右侧有两个图表：\n\n\n\n\n两列，第二列包含两行\n\n![](3.jpg)\n\n- 按行布局\n\n通过指定`orientation:rows`选项，您还可以选择按行而不是按列定向仪表板。例如，此布局定义了两行，其中第一行有一个图表，第二行有两个图表：\n\n![](4.jpg)\n\n- vertical_layout: scroll 将列的图片进行滚动\n\n- 通过data-width属性为该列提供了更大的尺寸\n\n- orientation: rows 以行填充为默认仪表盘形式\n\n### 滚动布局\n\n默认情况下，Flexdashboard图表的布局可以自动填充浏览器的高度。这对于少数垂直堆叠的图表效果很好，但是，如果您有很多图表，则可能需要滚动而不是全部放置在页面上。您可以使用vertical_layout选项控制此行为。指定fill垂直调整图表大小，以便它们完全填满页面，并scroll以自然高度布置图表，并在必要时滚动页面。\n\n\n### 标签集\n\n如果您要在行或列中显示多个组件，则可以尝试将它们作为选项卡布局，而不是尝试同时将它们全部显示在屏幕上。当一个组件是主要组件（即，所有读者都应该看到）而其他组件提供仅某些读者可能感兴趣的次要信息时，这尤其合适。\n\n在许多情况下，选项卡集比vertical_layout: scroll显示大量组件更好，因为它们很容易导航。\n\n要将行或列布置为选项卡集，只需将{.tabset}属性添加到节标题即可。例如，以下代码列出了tabset中的第二列：\n\n![](5.jpg)\n行也可以作为选项卡布局，如下所示：\n\n请注意，在此我们还应用了{.tabset-fade}在切换标签时导致淡入/淡出效果的属性。\n\n![](6.jpg)\n\n注意，这里我们还应用了{.tabset-fade}选项卡时产生淡入/淡出效果。\n\n## 组件\n\n### HTML小部件\n\n所述htmlwidgets框架提供为JavaScript数据可视化库高级的R绑定。基于htmlwidgets的图表非常适合与flexdashboard一起使用，因为它们可以动态调整自身大小，因此几乎总是完美地适合其flexdashboard容器的范围。\n\n可用的htmlwidgets包括：\n\nLeaflet，一个用于创建支持平移和缩放的动态地图的库，带有各种注释，例如标记，多边形和弹出窗口。\n\ndygraphs，它提供了用于绘制时间序列数据的丰富功能，并支持许多交互式功能，包括序列/点突出显示，缩放和平移。\n\nPlotly，通过ggplotly界面，可以轻松地将ggplot2图形转换为基于Web的交互式版本。\n\nrbokeh是Bokeh的接口，Bokeh是用于创建基于Web的绘图的强大的声明性Bokeh框架。\n\nHighcharter，是流行的Highcharts JavaScript图形库的丰富R接口。\n\nvisNetwork，是vis.js库的网络可视化功能的接口。\n\nCRAN上有30多个提供htmlwidgets的软件包。您可以在htmlwidgets展示柜中找到一些较流行的htmlwidget的示例用法，并浏览图库中所有可用的widget 。\n\n您在R Markdown文档中包含htmlwidget，就像您包含R图一样。例如，以下是一个简单的仪表板定义，其中包含3个笔形时间序列图：\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.11.16flexdashboard/链接.txt",
    "content": "https://rmarkdown.rstudio.com/flexdashboard/"
  },
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">三维散点图</h1>\n<h4 class=\"author project-author\">庄闪闪</h4>\n<h4 class=\"date project-date\">2020-11-22</h4>\n</section>\n\n\n\n<section class=\"main-content\">\n<p>上期我们说了<a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247486060&amp;idx=1&amp;sn=b613c8d0239c93185641c1bb6a062f7b&amp;chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&amp;token=1457590009&amp;lang=zh_CN#rd\">气泡图</a>。如果我们将<a href=\"R语言数据可视化之美\" title=\"R语言数据可视化之美\">气泡图的三维数据绘制到三维坐标系</a>中，通常称其为<strong>三维散点图</strong>，即用在三维X-Y-Z图上针对一个或多个数据序列绘出三个度量的一种图表。</p>\n<p>有关散点图前几部分系列可见（可跳转）：</p>\n<ul>\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485142&amp;idx=1&amp;sn=564bffc9e7765ebae9b9b81a17a188d9&amp;chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&amp;token=1544929502&amp;lang=zh_CN&amp;scene=21#wechat_redirect\">趋势显示的二维散点图</a></p></li>\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247485276&amp;idx=1&amp;sn=f98a2aede13555fa1c372f08c3cdec44&amp;chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&amp;token=682523778&amp;lang=zh_CN#rd\">分布显示的二维散点图</a></p></li>\n<li><p><a href=\"https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&amp;mid=2247486060&amp;idx=1&amp;sn=b613c8d0239c93185641c1bb6a062f7b&amp;chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&amp;token=1457590009&amp;lang=zh_CN#rd\">气泡图</a></p></li>\n</ul>\n<p>R 中<code>scatterplot3d</code>包的<code>scatterplot3d()</code>函数、<code>rgl</code>包的<a href=\"http://www.rforscience.com/rpackages/visualisation/oceanview/\">plot3d()</a>函数、<code>plot3D</code>包的<code>scatter3D()</code>函数等都可以绘制三维散点图。</p>\n<p>下面将从两个包（<code>scatter3D()</code>,<code>plot3d()</code>）入手，一步步带你完成三维散点图的绘制。本文内容丰富，希望大家都能学到自己想要的内容，学习不易，欢迎反馈建议。</p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(plot3D)</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(scales)</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(RColorBrewer)</span></code></pre></div>\n<div id=\"数据介绍\" class=\"section level2\">\n<h2>数据介绍</h2>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"ot\">&lt;-</span><span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;ThreeD_Scatter_Data.csv&quot;</span>,<span class=\"at\">header=</span>T)</span>\n<span id=\"cb2-2\"><a href=\"#cb2-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(df)</span></code></pre></div>\n<pre><code>##   mph Gas_Mileage Power Weight Engine_Displacement\n## 1  23          19    69    821              3687.7\n## 2  13          17    80   1287              4261.4\n## 3  13          22    55   1535              1983.2\n## 4  22          34    55   1037              1770.1\n## 5  14          29    55   1082              1589.8\n## 6  19          26    49   1285              1294.8</code></pre>\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb4-1\"><a href=\"#cb4-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>knitr<span class=\"sc\">::</span><span class=\"fu\">kable</span>(<span class=\"fu\">head</span>(iris))</span></code></pre></div>\n<table>\n<thead>\n<tr class=\"header\">\n<th align=\"right\">Sepal.Length</th>\n<th align=\"right\">Sepal.Width</th>\n<th align=\"right\">Petal.Length</th>\n<th align=\"right\">Petal.Width</th>\n<th align=\"left\">Species</th>\n</tr>\n</thead>\n<tbody>\n<tr class=\"odd\">\n<td align=\"right\">5.1</td>\n<td align=\"right\">3.5</td>\n<td align=\"right\">1.4</td>\n<td align=\"right\">0.2</td>\n<td align=\"left\">setosa</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">4.9</td>\n<td align=\"right\">3.0</td>\n<td align=\"right\">1.4</td>\n<td align=\"right\">0.2</td>\n<td align=\"left\">setosa</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">4.7</td>\n<td align=\"right\">3.2</td>\n<td align=\"right\">1.3</td>\n<td align=\"right\">0.2</td>\n<td align=\"left\">setosa</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">4.6</td>\n<td align=\"right\">3.1</td>\n<td align=\"right\">1.5</td>\n<td align=\"right\">0.2</td>\n<td align=\"left\">setosa</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">5.0</td>\n<td align=\"right\">3.6</td>\n<td align=\"right\">1.4</td>\n<td align=\"right\">0.2</td>\n<td align=\"left\">setosa</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">5.4</td>\n<td align=\"right\">3.9</td>\n<td align=\"right\">1.7</td>\n<td align=\"right\">0.4</td>\n<td align=\"left\">setosa</td>\n</tr>\n</tbody>\n</table>\n</div>\n<div id=\"plot3d包scatter3d\" class=\"section level2\">\n<h2>plot3D包scatter3D()</h2>\n<pre><code>scatter3D (x, y, z, ..., colvar = z, phi = 40, theta = 40,\n           col = NULL, NAcol = &quot;white&quot;, breaks = NULL,\n           colkey = NULL, panel.first = NULL, \n           clim = NULL, clab = NULL, \n           bty = &quot;b&quot;, CI = NULL, surf = NULL, \n           add = FALSE, plot = TRUE)</code></pre>\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb6-1\"><a href=\"#cb6-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># pmar &lt;- par(mar = c(5.1, 4.1, 4.1, 6.1))</span></span>\n<span id=\"cb6-2\"><a href=\"#cb6-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">with</span>(iris, <span class=\"fu\">scatter3D</span>(<span class=\"at\">x =</span> Sepal.Length, <span class=\"at\">y =</span> Sepal.Width, <span class=\"at\">z =</span> Petal.Length,</span>\n<span id=\"cb6-3\"><a href=\"#cb6-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">pch =</span> <span class=\"dv\">21</span>, <span class=\"at\">cex =</span> <span class=\"fl\">1.5</span>,<span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"at\">bg=</span><span class=\"st\">&quot;#F57446&quot;</span>,</span>\n<span id=\"cb6-4\"><a href=\"#cb6-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">xlab =</span> <span class=\"st\">&quot;Sepal.Length&quot;</span>,</span>\n<span id=\"cb6-5\"><a href=\"#cb6-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">ylab =</span> <span class=\"st\">&quot;Sepal.Width&quot;</span>,</span>\n<span id=\"cb6-6\"><a href=\"#cb6-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">zlab =</span> <span class=\"st\">&quot;Petal.Length&quot;</span>, </span>\n<span id=\"cb6-7\"><a href=\"#cb6-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"co\"># zlim=c(40,180),</span></span>\n<span id=\"cb6-8\"><a href=\"#cb6-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">ticktype =</span> <span class=\"st\">&quot;detailed&quot;</span>,<span class=\"at\">bty =</span> <span class=\"st\">&quot;f&quot;</span>,<span class=\"at\">box =</span> <span class=\"cn\">TRUE</span>,</span>\n<span id=\"cb6-9\"><a href=\"#cb6-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"co\">#panel.first = panelfirst,</span></span>\n<span id=\"cb6-10\"><a href=\"#cb6-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">theta =</span> <span class=\"dv\">60</span>, <span class=\"at\">phi =</span> <span class=\"dv\">20</span>, <span class=\"at\">d=</span><span class=\"dv\">3</span>,</span>\n<span id=\"cb6-11\"><a href=\"#cb6-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">colkey =</span> <span class=\"cn\">FALSE</span>)<span class=\"co\">#list(length = 0.5, width = 0.5, cex.clab = 0.75))</span></span>\n<span id=\"cb6-12\"><a href=\"#cb6-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n<p>将Z轴变量数据“Power(KW)”映射到数据点颜色，这样可以更加清晰地观察Z 轴变量与X、Y 轴变量数据的变化关系。</p>\n<p>我们先自己构造一个颜色映射的颜色条RdYlGn，再绘制三维散点图，然后根据映射的数值添加图例颜色条。</p>\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb7-1\"><a href=\"#cb7-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(tidyverse)</span>\n<span id=\"cb7-2\"><a href=\"#cb7-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>iris <span class=\"ot\">=</span> iris <span class=\"sc\">%&gt;%</span> <span class=\"fu\">mutate</span>(<span class=\"at\">quan =</span> <span class=\"fu\">ntile</span>(Petal.Width,<span class=\"dv\">6</span>))</span>\n<span id=\"cb7-3\"><a href=\"#cb7-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>colormap <span class=\"ot\">&lt;-</span> <span class=\"fu\">colorRampPalette</span>(<span class=\"fu\">rev</span>(<span class=\"fu\">brewer.pal</span>(<span class=\"dv\">11</span>,<span class=\"st\">&#39;RdYlGn&#39;</span>)))(<span class=\"dv\">6</span>)<span class=\"co\">#legend颜色配置</span></span>\n<span id=\"cb7-4\"><a href=\"#cb7-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>pmar <span class=\"ot\">&lt;-</span> <span class=\"fu\">par</span>(<span class=\"at\">mar =</span> <span class=\"fu\">c</span>(<span class=\"fl\">5.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">6.1</span>))</span>\n<span id=\"cb7-5\"><a href=\"#cb7-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 绘图</span></span>\n<span id=\"cb7-6\"><a href=\"#cb7-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">with</span>(iris, <span class=\"fu\">scatter3D</span>(<span class=\"at\">x =</span> Sepal.Length, <span class=\"at\">y =</span> Sepal.Width, <span class=\"at\">z =</span> Petal.Length,<span class=\"at\">pch =</span> <span class=\"dv\">21</span>, <span class=\"at\">cex =</span> <span class=\"fl\">1.5</span>,<span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"at\">bg=</span>colormap[iris<span class=\"sc\">$</span>quan],</span>\n<span id=\"cb7-7\"><a href=\"#cb7-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">xlab =</span> <span class=\"st\">&quot;Sepal.Length&quot;</span>,</span>\n<span id=\"cb7-8\"><a href=\"#cb7-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">ylab =</span> <span class=\"st\">&quot;Sepal.Width&quot;</span>,</span>\n<span id=\"cb7-9\"><a href=\"#cb7-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">zlab =</span> <span class=\"st\">&quot;Petal.Length&quot;</span>, </span>\n<span id=\"cb7-10\"><a href=\"#cb7-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">ticktype =</span> <span class=\"st\">&quot;detailed&quot;</span>,<span class=\"at\">bty =</span> <span class=\"st\">&quot;f&quot;</span>,<span class=\"at\">box =</span> <span class=\"cn\">TRUE</span>,</span>\n<span id=\"cb7-11\"><a href=\"#cb7-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">theta =</span> <span class=\"dv\">60</span>, <span class=\"at\">phi =</span> <span class=\"dv\">20</span>, <span class=\"at\">d=</span><span class=\"dv\">3</span>,</span>\n<span id=\"cb7-12\"><a href=\"#cb7-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>     <span class=\"at\">colkey =</span> <span class=\"cn\">FALSE</span>)</span>\n<span id=\"cb7-13\"><a href=\"#cb7-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span>\n<span id=\"cb7-14\"><a href=\"#cb7-14\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">colkey</span> (<span class=\"at\">col=</span>colormap,<span class=\"at\">clim=</span><span class=\"fu\">range</span>(iris<span class=\"sc\">$</span>quan),<span class=\"at\">clab =</span> <span class=\"st\">&quot;Petal.Width&quot;</span>, <span class=\"at\">add=</span><span class=\"cn\">TRUE</span>, <span class=\"at\">length=</span><span class=\"fl\">0.4</span>,<span class=\"at\">side =</span> <span class=\"dv\">4</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>三维散点图可以展示三维数据，如果添加一维数据，则使图表展示四维数据。第1种方法就是将图4-1-14(c)的填充颜色渐变映射到第四维数据，而不是原来的第三维数据</p>\n<p>第2种方法就是将第四维数据映射到数据点的大小上，即三维气泡图</p>\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb8-1\"><a href=\"#cb8-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>pmar <span class=\"ot\">&lt;-</span> <span class=\"fu\">par</span>(<span class=\"at\">mar =</span> <span class=\"fu\">c</span>(<span class=\"fl\">5.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">6.1</span>))</span>\n<span id=\"cb8-2\"><a href=\"#cb8-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 绘图</span></span>\n<span id=\"cb8-3\"><a href=\"#cb8-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">with</span>(iris, <span class=\"fu\">scatter3D</span>(<span class=\"at\">x =</span> Sepal.Length, <span class=\"at\">y =</span> Sepal.Width, <span class=\"at\">z =</span> Petal.Length,<span class=\"at\">pch =</span> <span class=\"dv\">21</span>, </span>\n<span id=\"cb8-4\"><a href=\"#cb8-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">cex =</span> <span class=\"fu\">rescale</span>(iris<span class=\"sc\">$</span>quan, <span class=\"fu\">c</span>(.<span class=\"dv\">5</span>, <span class=\"dv\">4</span>)),<span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"at\">bg=</span>colormap[iris<span class=\"sc\">$</span>quan],</span>\n<span id=\"cb8-5\"><a href=\"#cb8-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">xlab =</span> <span class=\"st\">&quot;Sepal.Length&quot;</span>,</span>\n<span id=\"cb8-6\"><a href=\"#cb8-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">ylab =</span> <span class=\"st\">&quot;Sepal.Width&quot;</span>,</span>\n<span id=\"cb8-7\"><a href=\"#cb8-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">zlab =</span> <span class=\"st\">&quot;Petal.Length&quot;</span>, </span>\n<span id=\"cb8-8\"><a href=\"#cb8-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">ticktype =</span> <span class=\"st\">&quot;detailed&quot;</span>,<span class=\"at\">bty =</span> <span class=\"st\">&quot;f&quot;</span>,<span class=\"at\">box =</span> <span class=\"cn\">TRUE</span>,</span>\n<span id=\"cb8-9\"><a href=\"#cb8-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">theta =</span> <span class=\"dv\">30</span>, <span class=\"at\">phi =</span> <span class=\"dv\">15</span>, <span class=\"at\">d=</span><span class=\"dv\">2</span>,</span>\n<span id=\"cb8-10\"><a href=\"#cb8-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">colkey =</span> <span class=\"cn\">FALSE</span>)</span>\n<span id=\"cb8-11\"><a href=\"#cb8-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span>\n<span id=\"cb8-12\"><a href=\"#cb8-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>breaks <span class=\"ot\">=</span><span class=\"dv\">1</span><span class=\"sc\">:</span><span class=\"dv\">6</span></span>\n<span id=\"cb8-13\"><a href=\"#cb8-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">legend</span>(<span class=\"st\">&quot;right&quot;</span>,<span class=\"at\">title =</span>  <span class=\"st\">&quot;Weight&quot;</span>,<span class=\"at\">legend=</span>breaks,<span class=\"at\">pch=</span><span class=\"dv\">21</span>,</span>\n<span id=\"cb8-14\"><a href=\"#cb8-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">pt.cex=</span><span class=\"fu\">rescale</span>(breaks, <span class=\"fu\">c</span>(.<span class=\"dv\">5</span>, <span class=\"dv\">4</span>)),<span class=\"at\">y.intersp=</span><span class=\"fl\">1.6</span>,</span>\n<span id=\"cb8-15\"><a href=\"#cb8-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">pt.bg =</span> colormap[<span class=\"dv\">1</span><span class=\"sc\">:</span><span class=\"dv\">6</span>],<span class=\"at\">bg=</span><span class=\"st\">&quot;white&quot;</span>,<span class=\"at\">bty=</span><span class=\"st\">&quot;n&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb9-1\"><a href=\"#cb9-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(wesanderson)</span></code></pre></div>\n<pre><code>## Warning: package &#39;wesanderson&#39; was built under R version 4.0.3</code></pre>\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb11-1\"><a href=\"#cb11-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>pmar <span class=\"ot\">&lt;-</span> <span class=\"fu\">par</span>(<span class=\"at\">mar =</span> <span class=\"fu\">c</span>(<span class=\"fl\">5.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">4.1</span>, <span class=\"fl\">7.1</span>))</span>\n<span id=\"cb11-2\"><a href=\"#cb11-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>colors0 <span class=\"ot\">&lt;-</span>  <span class=\"fu\">wes_palette</span>(<span class=\"at\">n=</span><span class=\"dv\">3</span>, <span class=\"at\">name=</span><span class=\"st\">&quot;Darjeeling1&quot;</span>)</span>\n<span id=\"cb11-3\"><a href=\"#cb11-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>colors <span class=\"ot\">&lt;-</span> colors0[<span class=\"fu\">as.numeric</span>(iris<span class=\"sc\">$</span>Species)]</span>\n<span id=\"cb11-4\"><a href=\"#cb11-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">with</span>(iris, <span class=\"fu\">scatter3D</span>(<span class=\"at\">x =</span> Sepal.Length, <span class=\"at\">y =</span> Sepal.Width, <span class=\"at\">z =</span> Petal.Length, <span class=\"co\">#bgvar = mag,</span></span>\n<span id=\"cb11-5\"><a href=\"#cb11-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">pch =</span> <span class=\"dv\">21</span>, <span class=\"at\">cex =</span> <span class=\"fl\">1.5</span>,<span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>,<span class=\"at\">bg=</span>colors,</span>\n<span id=\"cb11-6\"><a href=\"#cb11-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">xlab =</span> <span class=\"st\">&quot;longitude&quot;</span>, <span class=\"at\">ylab =</span> <span class=\"st\">&quot;latitude&quot;</span>,</span>\n<span id=\"cb11-7\"><a href=\"#cb11-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">zlab =</span> <span class=\"st\">&quot;depth, km&quot;</span>, </span>\n<span id=\"cb11-8\"><a href=\"#cb11-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">ticktype =</span> <span class=\"st\">&quot;detailed&quot;</span>,<span class=\"at\">bty =</span> <span class=\"st\">&quot;f&quot;</span>,<span class=\"at\">box =</span> <span class=\"cn\">TRUE</span>,</span>\n<span id=\"cb11-9\"><a href=\"#cb11-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"co\">#panel.first = panelfirst,</span></span>\n<span id=\"cb11-10\"><a href=\"#cb11-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">theta =</span> <span class=\"dv\">140</span>, <span class=\"at\">phi =</span> <span class=\"dv\">20</span>, <span class=\"at\">d=</span><span class=\"dv\">3</span>,</span>\n<span id=\"cb11-11\"><a href=\"#cb11-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">colkey =</span> <span class=\"cn\">FALSE</span>)<span class=\"co\">#list(length = 0.5, width = 0.5, cex.clab = 0.75))</span></span>\n<span id=\"cb11-12\"><a href=\"#cb11-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span>\n<span id=\"cb11-13\"><a href=\"#cb11-13\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb11-14\"><a href=\"#cb11-14\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">legend</span>(<span class=\"st\">&quot;right&quot;</span>,<span class=\"at\">title =</span>  <span class=\"st\">&quot;Species&quot;</span>,<span class=\"at\">legend=</span><span class=\"fu\">c</span>(<span class=\"st\">&quot;setosa&quot;</span>, <span class=\"st\">&quot;versicolor&quot;</span>, <span class=\"st\">&quot;virginica&quot;</span>),<span class=\"at\">pch=</span><span class=\"dv\">21</span>,</span>\n<span id=\"cb11-15\"><a href=\"#cb11-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">cex=</span><span class=\"dv\">1</span>,<span class=\"at\">y.intersp=</span><span class=\"dv\">1</span>,<span class=\"at\">pt.bg =</span> colors0,<span class=\"at\">bg=</span><span class=\"st\">&quot;white&quot;</span>,<span class=\"at\">bty=</span><span class=\"st\">&quot;n&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"rgl包-plot3d\" class=\"section level2\">\n<h2>rgl包 plot3d()</h2>\n<p><a href=\"https://www.rdocumentation.org/packages/rgl/versions/0.100.54\">rgl包</a>是在r中构建3d图表的最佳选择，请参阅这篇介绍3d散点图使用它的文章。</p>\n<pre><code>plot3d(x, y, z,  \n    xlab, ylab, zlab, type = &quot;p&quot;, col,  \n    size, lwd, radius,\n    add = FALSE, aspect = !add, \n    xlim = NULL, ylim = NULL, zlim = NULL, \n    forceClipregion = FALSE, ...)</code></pre>\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb13-1\"><a href=\"#cb13-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># library</span></span>\n<span id=\"cb13-2\"><a href=\"#cb13-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(rgl)</span>\n<span id=\"cb13-3\"><a href=\"#cb13-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb13-4\"><a href=\"#cb13-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Add a new column with color</span></span>\n<span id=\"cb13-5\"><a href=\"#cb13-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>mycolors <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&#39;royalblue1&#39;</span>, <span class=\"st\">&#39;darkcyan&#39;</span>, <span class=\"st\">&#39;oldlace&#39;</span>)</span>\n<span id=\"cb13-6\"><a href=\"#cb13-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>iris<span class=\"sc\">$</span>color <span class=\"ot\">&lt;-</span> mycolors[ <span class=\"fu\">as.numeric</span>(iris<span class=\"sc\">$</span>Species) ]</span>\n<span id=\"cb13-7\"><a href=\"#cb13-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb13-8\"><a href=\"#cb13-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb13-9\"><a href=\"#cb13-9\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">plot3d</span>( </span>\n<span id=\"cb13-10\"><a href=\"#cb13-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">x=</span>iris<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">Sepal.Length</span><span class=\"st\">`</span>, <span class=\"at\">y=</span>iris<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">Sepal.Width</span><span class=\"st\">`</span>, <span class=\"at\">z=</span>iris<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">Petal.Length</span><span class=\"st\">`</span>, </span>\n<span id=\"cb13-11\"><a href=\"#cb13-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">col =</span> iris<span class=\"sc\">$</span>color, </span>\n<span id=\"cb13-12\"><a href=\"#cb13-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">type =</span> <span class=\"st\">&#39;s&#39;</span>, </span>\n<span id=\"cb13-13\"><a href=\"#cb13-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">radius =</span> .<span class=\"dv\">1</span>,</span>\n<span id=\"cb13-14\"><a href=\"#cb13-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">xlab=</span><span class=\"st\">&quot;Sepal Length&quot;</span>, <span class=\"at\">ylab=</span><span class=\"st\">&quot;Sepal Width&quot;</span>, <span class=\"at\">zlab=</span><span class=\"st\">&quot;Petal Length&quot;</span>)</span>\n<span id=\"cb13-15\"><a href=\"#cb13-15\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb13-16\"><a href=\"#cb13-16\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># writeWebGL( filename=&quot;HtmlWidget/3dscatter.html&quot; ,  width=600, height=600)</span></span></code></pre></div>\n<p>它还提供了plot3d()和play3d()函数，允许将3d图表动画化，并最终以.gif格式导出结果。下面是著名iris数据集的一个应用程序，它具有一个很好的动画3d散点图。</p>\n</div>\n<div id=\"动态3d图\" class=\"section level2\">\n<h2>动态3D图</h2>\n<p><a href=\"https://www.r-graph-gallery.com/3-r-animated-cube.html\">Animated 3d chart with R.</a></p>\n<div class=\"sourceCode\" id=\"cb14\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb14-1\"><a href=\"#cb14-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(rgl)</span>\n<span id=\"cb14-2\"><a href=\"#cb14-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(magick)</span></code></pre></div>\n<pre><code>## Warning: package &#39;magick&#39; was built under R version 4.0.3</code></pre>\n<pre><code>## Linking to ImageMagick 6.9.11.34\n## Enabled features: cairo, freetype, fftw, ghostscript, lcms, pango, rsvg, webp\n## Disabled features: fontconfig, x11</code></pre>\n<div class=\"sourceCode\" id=\"cb17\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb17-1\"><a href=\"#cb17-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># This is ugly</span></span>\n<span id=\"cb17-2\"><a href=\"#cb17-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>colors <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;royalblue1&quot;</span>, <span class=\"st\">&quot;darkcyan&quot;</span>, <span class=\"st\">&quot;oldlace&quot;</span>)</span>\n<span id=\"cb17-3\"><a href=\"#cb17-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>iris<span class=\"sc\">$</span>color <span class=\"ot\">&lt;-</span> colors[ <span class=\"fu\">as.numeric</span>( <span class=\"fu\">as.factor</span>(iris<span class=\"sc\">$</span>Species) ) ]</span>\n<span id=\"cb17-4\"><a href=\"#cb17-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb17-5\"><a href=\"#cb17-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Static chart</span></span>\n<span id=\"cb17-6\"><a href=\"#cb17-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot3d( iris[,1], iris[,2], iris[,3], col = iris$color, type = &quot;s&quot;, radius = .2 )</span></span>\n<span id=\"cb17-7\"><a href=\"#cb17-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb17-8\"><a href=\"#cb17-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># We can indicate the axis and the rotation velocity</span></span>\n<span id=\"cb17-9\"><a href=\"#cb17-9\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># play3d( spin3d( axis = c(0, 0, 1), rpm = 20), duration = 50 )</span></span>\n<span id=\"cb17-10\"><a href=\"#cb17-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb17-11\"><a href=\"#cb17-11\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Save like gif</span></span>\n<span id=\"cb17-12\"><a href=\"#cb17-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># movie3d(</span></span>\n<span id=\"cb17-13\"><a href=\"#cb17-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   movie=&quot;3dAnimatedScatterplot&quot;, </span></span>\n<span id=\"cb17-14\"><a href=\"#cb17-14\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   spin3d( axis = c(0, 0, 1), rpm = 7),</span></span>\n<span id=\"cb17-15\"><a href=\"#cb17-15\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   duration = 10, </span></span>\n<span id=\"cb17-16\"><a href=\"#cb17-16\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   dir = &quot;~/Desktop&quot;,</span></span>\n<span id=\"cb17-17\"><a href=\"#cb17-17\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   type = &quot;gif&quot;, </span></span>\n<span id=\"cb17-18\"><a href=\"#cb17-18\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#   clean = TRUE</span></span>\n<span id=\"cb17-19\"><a href=\"#cb17-19\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># )</span></span></code></pre></div>\n</div>\n</section>\n\n\n\n<!-- code folding -->\n\n\n<!-- dynamically load mathjax for compatibility with self-contained -->\n<script>\n  (function () {\n    var script = document.createElement(\"script\");\n    script.type = \"text/javascript\";\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\n  })();\n</script>\n\n</body>\n</html>\n"
  },
  {
    "path": "2020年/2020.11.22三维散点图/3d_scatter.rmd",
    "content": "---\ntitle: \"三维散点图\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEngine{knitr::rmarkdown}\n  %\\VignetteEncoding{UTF-8}\n---\n\n\n上期我们说了[气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=1457590009&lang=zh_CN#rd)。如果我们将[气泡图的三维数据绘制到三维坐标系](R语言数据可视化之美 \"R语言数据可视化之美\")中，通常称其为**三维散点图**，即用在三维X-Y-Z图上针对一个或多个数据序列绘出三个度量的一种图表。\n\n\n有关散点图前几部分系列可见（可跳转）：\n\n- [趋势显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=1544929502&lang=zh_CN&scene=21#wechat_redirect)\n\n- [分布显示的二维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)\n\n- [气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=1457590009&lang=zh_CN#rd)\n\nR 中`scatterplot3d`包的`scatterplot3d()`函数、`rgl`包的[plot3d()](http://www.rforscience.com/rpackages/visualisation/oceanview/)函数、`plot3D`包的`scatter3D()`函数等都可以绘制三维散点图。\n\n下面将从两个包（`scatter3D()`,`plot3d()`）入手，一步步带你完成三维散点图的绘制。本文内容丰富，希望大家都能学到自己想要的内容，学习不易，欢迎反馈建议。\n\n\n```{r message=FALSE, warning=FALSE}\nlibrary(plot3D)\nlibrary(scales)\nlibrary(RColorBrewer)\n```\n\n## 数据介绍\n```{r}\ndf<-read.csv(\"ThreeD_Scatter_Data.csv\",header=T)\nhead(df)\n```\n```{r}\nknitr::kable(head(iris))\n```\n\n\n## plot3D包scatter3D()\n\n```\nscatter3D (x, y, z, ..., colvar = z, phi = 40, theta = 40,\n           col = NULL, NAcol = \"white\", breaks = NULL,\n           colkey = NULL, panel.first = NULL, \n           clim = NULL, clab = NULL, \n           bty = \"b\", CI = NULL, surf = NULL, \n           add = FALSE, plot = TRUE)\n```\n\n\n\n```{r}\n# pmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))\nwith(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,\n  pch = 21, cex = 1.5,col=\"black\",bg=\"#F57446\",\n                   xlab = \"Sepal.Length\",\n                   ylab = \"Sepal.Width\",\n                   zlab = \"Petal.Length\", \n                   # zlim=c(40,180),\n                   ticktype = \"detailed\",bty = \"f\",box = TRUE,\n                   #panel.first = panelfirst,\n                   theta = 60, phi = 20, d=3,\n                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))\n)\n```\n\n将Z轴变量数据“Power(KW)”映射到数据点颜色，这样可以更加清晰地观察Z 轴变量与X、Y 轴变量数据的变化关系。\n\n我们先自己构造一个颜色映射的颜色条RdYlGn，再绘制三维散点图，然后根据映射的数值添加图例颜色条。\n\n\n```{r message=FALSE, warning=FALSE}\nlibrary(tidyverse)\niris = iris %>% mutate(quan = ntile(Petal.Width,6))\ncolormap <- colorRampPalette(rev(brewer.pal(11,'RdYlGn')))(6)#legend颜色配置\npmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))\n# 绘图\nwith(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,pch = 21, cex = 1.5,col=\"black\",bg=colormap[iris$quan],\n     xlab = \"Sepal.Length\",\n     ylab = \"Sepal.Width\",\n     zlab = \"Petal.Length\", \n     ticktype = \"detailed\",bty = \"f\",box = TRUE,\n     theta = 60, phi = 20, d=3,\n     colkey = FALSE)\n)\ncolkey (col=colormap,clim=range(iris$quan),clab = \"Petal.Width\", add=TRUE, length=0.4,side = 4)\n```\n\n三维散点图可以展示三维数据，如果添加一维数据，则使图表展示四维数据。第1种方法就是将图4-1-14(c)的填充颜色渐变映射到第四维数据，而不是原来的第三维数据\n\n第2种方法就是将第四维数据映射到数据点的大小上，即三维气泡图\n```{r message=FALSE, warning=FALSE}\npmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))\n# 绘图\nwith(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length,pch = 21, \n                     cex = rescale(iris$quan, c(.5, 4)),col=\"black\",bg=colormap[iris$quan],\n                     xlab = \"Sepal.Length\",\n                     ylab = \"Sepal.Width\",\n                     zlab = \"Petal.Length\", \n                     ticktype = \"detailed\",bty = \"f\",box = TRUE,\n                     theta = 30, phi = 15, d=2,\n                     colkey = FALSE)\n)\nbreaks =1:6\nlegend(\"right\",title =  \"Weight\",legend=breaks,pch=21,\n       pt.cex=rescale(breaks, c(.5, 4)),y.intersp=1.6,\n       pt.bg = colormap[1:6],bg=\"white\",bty=\"n\")\n```\n\n```{r}\nlibrary(wesanderson)\npmar <- par(mar = c(5.1, 4.1, 4.1, 7.1))\ncolors0 <-  wes_palette(n=3, name=\"Darjeeling1\")\ncolors <- colors0[as.numeric(iris$Species)]\nwith(iris, scatter3D(x = Sepal.Length, y = Sepal.Width, z = Petal.Length, #bgvar = mag,\n                   pch = 21, cex = 1.5,col=\"black\",bg=colors,\n                   xlab = \"longitude\", ylab = \"latitude\",\n                   zlab = \"depth, km\", \n                   ticktype = \"detailed\",bty = \"f\",box = TRUE,\n                   #panel.first = panelfirst,\n                   theta = 140, phi = 20, d=3,\n                   colkey = FALSE)#list(length = 0.5, width = 0.5, cex.clab = 0.75))\n)\n\nlegend(\"right\",title =  \"Species\",legend=c(\"setosa\", \"versicolor\", \"virginica\"),pch=21,\n       cex=1,y.intersp=1,pt.bg = colors0,bg=\"white\",bty=\"n\")\n```\n\n\n\n## rgl包 plot3d()\n\n[rgl包](https://www.rdocumentation.org/packages/rgl/versions/0.100.54)是在r中构建3d图表的最佳选择，请参阅这篇介绍3d散点图使用它的文章。\n\n\n```\nplot3d(x, y, z,  \n\txlab, ylab, zlab, type = \"p\", col,  \n\tsize, lwd, radius,\n\tadd = FALSE, aspect = !add, \n\txlim = NULL, ylim = NULL, zlim = NULL, \n\tforceClipregion = FALSE, ...)\n```\n\n```{r webgl=TRUE, results='hide'}\n# library\nlibrary(rgl)\n\n# Add a new column with color\nmycolors <- c('royalblue1', 'darkcyan', 'oldlace')\niris$color <- mycolors[ as.numeric(iris$Species) ]\n\n# Plot\nplot3d( \n  x=iris$`Sepal.Length`, y=iris$`Sepal.Width`, z=iris$`Petal.Length`, \n  col = iris$color, \n  type = 's', \n  radius = .1,\n  xlab=\"Sepal Length\", ylab=\"Sepal Width\", zlab=\"Petal Length\")\n\n# writeWebGL( filename=\"HtmlWidget/3dscatter.html\" ,  width=600, height=600)\n```\n\n它还提供了plot3d()和play3d()函数，允许将3d图表动画化，并最终以.gif格式导出结果。下面是著名iris数据集的一个应用程序，它具有一个很好的动画3d散点图。\n\n## 动态3D图\n\n[Animated 3d chart with R.](https://www.r-graph-gallery.com/3-r-animated-cube.html)\n\n```{r }\nlibrary(rgl)\nlibrary(magick)\n\n# This is ugly\ncolors <- c(\"royalblue1\", \"darkcyan\", \"oldlace\")\niris$color <- colors[ as.numeric( as.factor(iris$Species) ) ]\n\n# Static chart\n# plot3d( iris[,1], iris[,2], iris[,3], col = iris$color, type = \"s\", radius = .2 )\n\n# We can indicate the axis and the rotation velocity\n# play3d( spin3d( axis = c(0, 0, 1), rpm = 20), duration = 50 )\n\n# Save like gif\n# movie3d(\n#   movie=\"3dAnimatedScatterplot\", \n#   spin3d( axis = c(0, 0, 1), rpm = 7),\n#   duration = 10, \n#   dir = \"~/Desktop\",\n#   type = \"gif\", \n#   clean = TRUE\n# )\n```\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.11.22三维散点图/ThreeD_Scatter_Data.csv",
    "content": "mph,Gas_Mileage,Power,Weight,Engine_Displacement\n23,19,69,821,3687.7\n13,17,80,1287,4261.4\n13,22,55,1535,1983.2\n22,34,55,1037,1770.1\n14,29,55,1082,1589.8\n19,26,49,1285,1294.8\n11,27,52,1246,2311\n16,15,128,1655,6556\n18,15,110,2121,5212\n15,13,139,1680,6556\n17,27,66,1124,2474.9\n17,29,58,1187,1819.3\n18,21,73,739,3687.7\n10,14,161,1597,7441\n20,20,71,883,1147.3\n23,17,139,1675,6556\n12,29,57,1036,1589.8\n19,21,77,1457,3786.1\n12,33,51,1005,1393.1\n12,29,61,923,2212.6\n13,17,124,1251,5736.5\n18,21,73,1389,3802.5\n17,20,79,1138,2556.8\n15,22,80,1099,1983.2\n19,33,52,1228,1753.7\n12,19,102,1867,5212\n12,19,121,1587,3786.1\n16,24,69,970,1704.6\n16,21,80,935,3687.7\n16,30,49,902,1606.2\n14,14,113,2152,5736.5\n10,14,106,1417,5736.5\n18,28,66,843,1901.2\n14,35,38,1173,1491.5\n13,19,106,1435,4998.9\n16,27,61,1498,2474.9\n15,24,69,1339,1852.1\n13,20,74,1347,2130.7\n17,26,61,987,2212.6\n15,16,102,1352,4949.8\n16,20,64,1979,1589.8\n13,10,158,1531,5900.4\n19,19,99,1505,5212\n14,21,66,1432,3802.5\n16,31,57,1509,1589.8\n11,19,69,1387,4228.6\n16,27,44,1613,1589.8\n17,16,110,1487,6556\n17,12,145,1426,7031.3\n16,19,106,1052,4998.9\n"
  },
  {
    "path": "2020年/2020.11.22数据处理ntile()/数据处理数据按从小到大分成n类.md",
    "content": "## 数据处理|数据按从小到大分成n类\n\n最近做项目遇到了一个实际数据清洗的问题，如何将连续数据按从大到小分成n类？刚开始我是打算用tidyverse包的，但是找不到合适的函数。只能通过较为笨拙的方法进行了。\n![](https://imgkr2.cn-bj.ufileos.com/dde70a2c-d9fa-4076-8b52-ae17d0aa96b3.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=xb9feNgbDRAYJbQM8t67howdNmo%253D&Expires=1606120382)\n\n之后通过[stackoverflow网站](https://stackoverflow.com/questions/4126326/how-to-quickly-form-groups-quartiles-deciles-etc-by-ordering-columns-in-a?noredirect=1 \"How to quickly form groups \")进行查询才发现原来有这么好用的**窗口函数**。\n\n![](https://imgkr2.cn-bj.ufileos.com/c02dd36a-b258-4dc6-9350-3569d9980b3f.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=SAb7a4hlRpv3l43MWN6b%252Bi6ciaY%253D&Expires=1606121218)\n\n\n## 较为笨拙的方法\n\n使用Rbase包中的数据框操作进行，首先随机产生一个数据框作为模拟数据。\n```{r}\ntemp <- data.frame(name=letters[1:12], value=rnorm(12), q=rep(NA, 12))\nhead(temp)\n#    name       value quartile\n# 1     a  2.55118169       NA\n# 2     b  0.79755259       NA\n# 3     c  0.16918905       NA\n# 4     d  1.73359245       NA\n# 5     e  0.41027113       NA\n# 6     f  0.73012966       NA\n```\n```{r}\ntemp.sorted <- temp[order(temp$value), ]\ntemp.sorted$q <- rep(1:4, each=12/4)\ntemp <- temp.sorted[order(as.numeric(rownames(temp.sorted))), ]\nhead(temp)\n#    name       value        q\n# 1     a  2.55118169        4\n# 2     b  0.79755259        3\n# 3     c  0.16918905        2\n# 4     d  1.73359245        4\n# 5     e  0.41027113        2\n# 6     f  0.73012966        3\n```\n\n## 使用dplyr包中的ntile()\n\n\n首先构建一个数据框，包含a，b变量。以该数据框进行演示：\n\n```{r}\nfoo <- data.frame(a = 1:100,\n                  b = runif(100, 50, 200),\n                  stringsAsFactors = FALSE)\n```\n\n载入[tidyverse包](https://www.tidyverse.org/ \"tidyverse包\")，内部包含了[dplyr包](https://dplyr.tidyverse.org/ \"dplyr包\")。然后使用管道函数，利用函数`ntile()`构建新的列，列名为q。或者不用通道函数，直接加载`dplyr`包也可以。\n```{r}\nlibrary(tidyverse)\nfoo %>%\n    mutate(q = ntile(b, 10))\n\n#  a         b        q\n#1 1  93.94754        2\n#2 2 172.51323        8\n#3 3  99.79261        3\n#4 4  81.55288        2\n#5 5 116.59942        5\n#6 6 128.75947        6\n```\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.11.28等高线/.Rhistory",
    "content": "install.packages(\"prettydoc\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\ninstall.packages(\"reshape2\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\ninstall.packages(\"Rcpp\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\ninstall.packages(\"reshape2\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\ninstall.packages(\"RCPP\")\ninstall.packages(\"Rcpp\")\ninstall.packages(\"Rcpp\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\nlibrary(reshape)\nlibrary(reshape2)\nrf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)\n# 数据\nz <- as.matrix(read.table(\"等高线.txt\",header=TRUE))\ncolnames(z) <- seq(1,ncol(z),by=1) #列名设置\nmax_z <- max(z)\nmin_z <- min(z)\nbreaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)\nmap <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数\ncolnames(map)<-c(\"Var1\",\"Var2\",\"value\")\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\n"
  },
  {
    "path": "2020年/2020.11.28等高线/等高线.html",
    "content": "<!DOCTYPE html>\n\n<html>\n\n<head>\n\n<meta charset=\"utf-8\" />\n<meta name=\"generator\" content=\"pandoc\" />\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=EDGE\" />\n\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n\n<meta name=\"author\" content=\"庄闪闪\" />\n\n<meta name=\"date\" content=\"2020-12-01\" />\n\n<title>等高线图</title>\n\n<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->\n// v0.0.1\n// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.\n\ndocument.addEventListener('DOMContentLoaded', function() {\n  const codeList = document.getElementsByClassName(\"sourceCode\");\n  for (var i = 0; i < codeList.length; i++) {\n    var linkList = codeList[i].getElementsByTagName('a');\n    for (var j = 0; j < linkList.length; j++) {\n      if (linkList[j].innerHTML === \"\") {\n        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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">等高线图</h1>\n<h4 class=\"author project-author\">庄闪闪</h4>\n<h4 class=\"date project-date\">2020-12-01</h4>\n</section>\n\n\n\n<section class=\"main-content\">\n<div id=\"等高线图\" class=\"section level1\">\n<h1>等高线图</h1>\n<p><strong>等高线图（contour map）</strong> 是<a href=\"https://github.com/EasyChart/Beautiful-Visualization-with-R\" title=\"R语言数据可视化之美\">可视化二维空间标量场的基本方法</a>，可以将三维数据使用二维的方法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温线等。假设 <span class=\"math inline\">\\(f(x, y)\\)</span> 是在点 <span class=\"math inline\">\\((x, y)\\)</span> 处的数值，等值线是在二维数据场中满足 <span class=\"math inline\">\\(f(x, y)=c\\)</span> 的空间点集按一定的顺序连接而成的线。数值为c的等值线可以将二维空间标量场分为两部分：如果 函数大于c，则该点在等值线内；反之，则该点在等值线外。</p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\"></a><span class=\"kw\">library</span>(directlabels)</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\"></a><span class=\"kw\">library</span>(reshape2)</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\"></a><span class=\"kw\">library</span>(ggplot2)</span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\"></a><span class=\"kw\">library</span>(grDevices)</span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\"></a><span class=\"kw\">library</span>(RColorBrewer)</span></code></pre></div>\n<div id=\"数据介绍\" class=\"section level2\">\n<h2>数据介绍</h2>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\"></a>rf &lt;-<span class=\"st\"> </span><span class=\"kw\">colorRampPalette</span>(<span class=\"kw\">rev</span>(<span class=\"kw\">brewer.pal</span>(<span class=\"dv\">11</span>,<span class=\"st\">&#39;Spectral&#39;</span>)))</span>\n<span id=\"cb2-2\"><a href=\"#cb2-2\"></a>colormap &lt;-<span class=\"st\"> </span><span class=\"kw\">colorRampPalette</span>(<span class=\"kw\">rev</span>(<span class=\"kw\">brewer.pal</span>(<span class=\"dv\">11</span>,<span class=\"st\">&#39;Spectral&#39;</span>)))(<span class=\"dv\">32</span>)</span>\n<span id=\"cb2-3\"><a href=\"#cb2-3\"></a><span class=\"co\"># 数据</span></span>\n<span id=\"cb2-4\"><a href=\"#cb2-4\"></a>z &lt;-<span class=\"st\"> </span><span class=\"kw\">as.matrix</span>(<span class=\"kw\">read.table</span>(<span class=\"st\">&quot;等高线.txt&quot;</span>,<span class=\"dt\">header=</span><span class=\"ot\">TRUE</span>))</span>\n<span id=\"cb2-5\"><a href=\"#cb2-5\"></a><span class=\"kw\">colnames</span>(z) &lt;-<span class=\"st\"> </span><span class=\"kw\">seq</span>(<span class=\"dv\">1</span>,<span class=\"kw\">ncol</span>(z),<span class=\"dt\">by=</span><span class=\"dv\">1</span>) <span class=\"co\">#列名设置</span></span>\n<span id=\"cb2-6\"><a href=\"#cb2-6\"></a>max_z &lt;-<span class=\"st\"> </span><span class=\"kw\">max</span>(z)</span>\n<span id=\"cb2-7\"><a href=\"#cb2-7\"></a>min_z &lt;-<span class=\"st\"> </span><span class=\"kw\">min</span>(z)</span>\n<span id=\"cb2-8\"><a href=\"#cb2-8\"></a>breaks_lines &lt;-<span class=\"st\"> </span><span class=\"kw\">seq</span>(<span class=\"kw\">min</span>(z),<span class=\"kw\">max</span>(z),<span class=\"dt\">by=</span>(max_z<span class=\"op\">-</span>min_z)<span class=\"op\">/</span><span class=\"dv\">10</span>)</span>\n<span id=\"cb2-9\"><a href=\"#cb2-9\"></a>map &lt;-<span class=\"st\"> </span><span class=\"kw\">melt</span>(z) <span class=\"co\">#这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数</span></span>\n<span id=\"cb2-10\"><a href=\"#cb2-10\"></a><span class=\"kw\">colnames</span>(map)&lt;-<span class=\"kw\">c</span>(<span class=\"st\">&quot;Var1&quot;</span>,<span class=\"st\">&quot;Var2&quot;</span>,<span class=\"st\">&quot;value&quot;</span>)</span></code></pre></div>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\"></a>knitr<span class=\"op\">::</span><span class=\"kw\">kable</span>(<span class=\"kw\">head</span>(z[,<span class=\"dv\">1</span><span class=\"op\">:</span><span class=\"dv\">10</span>]),<span class=\"dt\">caption =</span> <span class=\"st\">&quot;转换前数据&quot;</span>)</span></code></pre></div>\n<table>\n<caption>转换前数据</caption>\n<thead>\n<tr class=\"header\">\n<th align=\"right\">1</th>\n<th align=\"right\">2</th>\n<th align=\"right\">3</th>\n<th align=\"right\">4</th>\n<th align=\"right\">5</th>\n<th align=\"right\">6</th>\n<th align=\"right\">7</th>\n<th align=\"right\">8</th>\n<th align=\"right\">9</th>\n<th align=\"right\">10</th>\n</tr>\n</thead>\n<tbody>\n<tr class=\"odd\">\n<td align=\"right\">1076</td>\n<td align=\"right\">1067</td>\n<td align=\"right\">1060</td>\n<td align=\"right\">1063</td>\n<td align=\"right\">1076</td>\n<td align=\"right\">1078</td>\n<td align=\"right\">1073</td>\n<td align=\"right\">1071</td>\n<td align=\"right\">1073</td>\n<td align=\"right\">1086</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">1083</td>\n<td align=\"right\">1074</td>\n<td align=\"right\">1065</td>\n<td align=\"right\">1075</td>\n<td align=\"right\">1091</td>\n<td align=\"right\">1090</td>\n<td align=\"right\">1082</td>\n<td align=\"right\">1083</td>\n<td align=\"right\">1087</td>\n<td align=\"right\">1097</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">1087</td>\n<td align=\"right\">1085</td>\n<td align=\"right\">1079</td>\n<td align=\"right\">1083</td>\n<td align=\"right\">1101</td>\n<td align=\"right\">1102</td>\n<td align=\"right\">1094</td>\n<td align=\"right\">1098</td>\n<td align=\"right\">1105</td>\n<td align=\"right\">1108</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">1087</td>\n<td align=\"right\">1089</td>\n<td align=\"right\">1089</td>\n<td align=\"right\">1092</td>\n<td align=\"right\">1106</td>\n<td align=\"right\">1111</td>\n<td align=\"right\">1109</td>\n<td align=\"right\">1116</td>\n<td align=\"right\">1118</td>\n<td align=\"right\">1122</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">1089</td>\n<td align=\"right\">1093</td>\n<td align=\"right\">1095</td>\n<td align=\"right\">1100</td>\n<td align=\"right\">1112</td>\n<td align=\"right\">1124</td>\n<td align=\"right\">1129</td>\n<td align=\"right\">1135</td>\n<td align=\"right\">1134</td>\n<td align=\"right\">1137</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">1094</td>\n<td align=\"right\">1104</td>\n<td align=\"right\">1103</td>\n<td align=\"right\">1103</td>\n<td align=\"right\">1117</td>\n<td align=\"right\">1138</td>\n<td align=\"right\">1149</td>\n<td align=\"right\">1154</td>\n<td align=\"right\">1155</td>\n<td align=\"right\">1150</td>\n</tr>\n</tbody>\n</table>\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb4-1\"><a href=\"#cb4-1\"></a>knitr<span class=\"op\">::</span><span class=\"kw\">kable</span>(<span class=\"kw\">head</span>(map),<span class=\"dt\">caption =</span> <span class=\"st\">&quot;转换后数据&quot;</span>)</span></code></pre></div>\n<table>\n<caption>转换后数据</caption>\n<thead>\n<tr class=\"header\">\n<th align=\"right\">Var1</th>\n<th align=\"right\">Var2</th>\n<th align=\"right\">value</th>\n</tr>\n</thead>\n<tbody>\n<tr class=\"odd\">\n<td align=\"right\">1</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1076</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">2</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1083</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">3</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1087</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">4</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1087</td>\n</tr>\n<tr class=\"odd\">\n<td align=\"right\">5</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1089</td>\n</tr>\n<tr class=\"even\">\n<td align=\"right\">6</td>\n<td align=\"right\">1</td>\n<td align=\"right\">1094</td>\n</tr>\n</tbody>\n</table>\n</div>\n<div id=\"热力分布图\" class=\"section level2\">\n<h2>热力分布图</h2>\n<p>使用ggplot2包中的<code>geom_tile()</code>或者<code>geom_raster()</code>绘制热 力分布图。其主要区别在于<code>geom_raster()</code>函数中存在<code>interpolate=TRUE/FALSE</code>这个参数，决定是否对热力图进行平滑处理。</p>\n<p>这里使用<code>geom_tile()</code>进行演示，将三维数据<code>(x,y,z)</code>中<code>(x,y)</code>表示位置信息，<code>z</code>映射到颜色。这里的<code>scale_fill_gradientn()</code>将颜色填充呈n个梯度。</p>\n<ul>\n<li><strong>拓展：</strong><code>scale_*_gradient</code>创建一个双色梯度(低-高)，<code>scale_*_gradient2</code>创建一个渐变的颜色梯度(低-中-高)，<code>scale_*_gradientn</code>创建一个n色梯度。</li>\n</ul>\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb5-1\"><a href=\"#cb5-1\"></a><span class=\"kw\">ggplot</span>(map,<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>Var1,<span class=\"dt\">y=</span>Var2,<span class=\"dt\">z=</span>value))<span class=\"op\">+</span></span>\n<span id=\"cb5-2\"><a href=\"#cb5-2\"></a><span class=\"st\">    </span><span class=\"kw\">geom_tile</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">fill=</span>value))<span class=\"op\">+</span></span>\n<span id=\"cb5-3\"><a href=\"#cb5-3\"></a><span class=\"st\">    </span><span class=\"kw\">scale_fill_gradientn</span>(<span class=\"dt\">colours=</span>colormap)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOy9Pa8lyZEleMw97kv2YsXSqgsgsX+hgcI00AKl1SgRI4zAkSkRJaxErEiZoESdwkiU6g9QIMABgf4LAxIoFrAClQVmuqvyRbitYHbMzP3eJGfQmcne99yAfHmvXw8Pdw+PCD/2cUxUFVu2bNmyZcuWlyXtb92BLVu2bNmyZcv7l/2C37Jly5YtW16g7Bf8li1btmzZ8gJlv+C3bNmyZcuWFyj7Bb9ly5YtW7a8QNkv+C1btmzZsuUFyvG37sBfkT//+c//9kY++eST99LO/y/kVQ0Wr2y8e7AvWF7VeN/jYD/55JP30s6LlI3gt2zZsmXLlhco+wW/ZcuWLVu2vEDZL/gtW7Zs2bLlBcp+wW/ZsmXLli0vUPYLfsuWLVu2bHmBsl/wW7Zs2bJlywuU/YLfsmXLli1bXqDsF/yWLVu2bNnyAmW/4Lds2bJly5YXKPsFv2XLli1btrxA2S/4LVu2bNmy5QXKfsFv2bJly5YtL1D2C37Lli1btmx5gbJf8Fu2bNmyZcsLlP2C37Jly5YtW16g7Bf8li1btmzZ8gJlv+C3bNmyZcuWFyj7Bb9ly5YtW7a8QDnec3tf//onP/7VHwAA3//pl198bqW///kPfvYbAN/70S9/8cNP8RcKP5T85//7twIFoBAAgMI+iFdQ+DcVAaCNRSyBV1CvIH6witXxapJHTR+iERVBtpei9STAONp1WPsiqrXl0WQ90LrEo61C7XZtv3dVBYDW0LsCkKbwNnIqytxArFp8bd62tSNNWwMAYRft19byq/80D1zE24kPUR49iAreN5t3WQZXjm1TH0IaO2C98p6Lz4ACOrJ9AK0rgNttPD1dAG5djwZwR9wEvfmcyLSOyuxxoV2aLQ+2cOs6vDwO8PrKU8j9QP4buuQppoEv543+aNbX8p/y1K3hTfMzWjvWsaPBhnwIesuuCvB2AMA5ODR2WPnVutDF23nTvQN2isZODjY45rH4if6ftSQGaHM4eCK5G3VOnZbvlBZnnCc/OnY073w8LE520fpsf09e3PK08A8xFXHgVYZwDnxzAcDbgf/32wbg+cLbtx3AN//aATw/9/NtAzAuua5WZ2AMsb/f/PcDwPXfvZ921/dryFAAT/9ytqHgjT+OdtnlFFy9gfWP5+t4HlbtvHUALc6kANBPb/D5TX/7nQPA8R0FcHszjqcLwJs34+k7J3jLvHlzvfnOBaA1PboCeOrT4+JoeHMo/hv+r//j/8SWDyzvF8H//uc//u0//fLLL7/88stf/uiPP/vJr78G8PWvf/Iz/PTLL7/88pf/9Nsf//z3wLsKt2zZsmXLli3vR94rgv/9737z/f/0pYHxT3/4n77/q9/9Cfj0T1/94fv/+DkAfPof/ul7v/rd77/4/HM8LPyAIrEtFYJRrYB+RQDQ+wOjnKBAKrAWrMhKCiiHqG+lBapa0EFUCOWB/TiGaAdwHWJV+zkchVrPhe1L6UItD8A2j20MsSNUE5N5M3O/fN8dZ1BJNULtOQH6u1B1THNRUigPzBNNc/HOlisKRRNdp/FxH9gB9r/5zlYDystSwiMDZXKhrKdbcTZXR5vr28VrhLehhdEZuGteDm/hThUxr7NVFbXqEkRyynQ5Iw+sDcaIhvoPrRExE4fJXBMxRVwwBn9vDQD6ncYqJy2+rrfO+k2XH20O49aUuwrvbqp+iDs0bu4Yez1OuQxQZhXATSADAC7WH6UmypxEoeH4Z8VpOoAB6/vQNi4BoAbQL7GnxHm265I4oyrsq15yvBkAFA2ADp/Dt2e/vb3s3NetARhdAFy92VcVGYerwQC87cformQ4bw2A4XXhk00F/RwARFkyzSikaai7ALSurhFsmpPGuwxAk20Y/njyXl/wn3/xZb6lf/+733zvs/8IfP3VH7/32X+0sk8/+y5++9XX+ByPCqmm/4d/+Ido5Z//+Z/fZw+3bNmyZcu/A/nkk0/+1l14+fK+bfAuX//6Jz/7449++cWnwO+/+gM+W3//06NCSn2p//nPf/639+aTTz55hDxNZiucBiSvlu8J62ftsp+V3P0nQnJo5RthjVO4N8BiI+deO3C8GcnC4i5NaFS7UwCw75j215MKAY/QraEBs+odt9EPg8vTWCvsTjv9PJ+p5piBcjRRdQ1a2gzb/DqaBWpxEr395jg7jPQJxKmcWI2yMn0QWtCzG+y5QagyNIK9Muuhe7iXqNZiQQEIuElVgBQQD/PhMONuGXd7pN4IU3fWWwYYbXr/15rQx/XXgdz5E8QQbIY6LXw6j3S6fKUdYX2pv73j1HUImOcqP98ffjc0LRXz6/3YYznZN8X16AwB5cNfIR0XrL7yurPrixpGWBq2/KduLSveXADOswHAt2h9AJAm5sBgtyrg060q/TYAHLcB4DrlfHb/kFM7gPNpaDPs7ggeNwBoDU8G2UXt17ete8+PnK2h/lhgRUBcd8jSdLUxR5Z+DADHMcw95dY11CHmw2EanaeupuB5L8927I3CX5QPoSz5/c9/8OPf/hM95/7+s+/dV3lYuGXLli1btmx5T/JBvOi/+9MvfxG6+k8/++4ffvcn4FMAX3/1R3z3Hz8F8LDwg8o9kJm+LJBUNI8ID24ssLvABZ3LYJi+esbfqxDGXETjqu1vRxM1p1el335zfwCa3h+Ps4hjqgJeaax/BMR1OMKhNTphcZrQ0txftBPZG31sjFcskI5wOUpmI72sXV0m+UH/U5tiQ9bpQK0NJgpJXYXmiQzcpxLDkZZgNs4vaFVqKdL4uoLRwIgF/HkFsRPR6i9rBZMmBItz+doTTo4W+/TQ6ciwYTsqZUPh696Xy8QP/RGG1jtFjsmlOP7iWr3/MdQkQ+cK8qDafdGke5hrvhP8P5rqKKk3ar1r05Gi9Hl9ksxdsmunw2f7qeFsZoPH1QHg9nQBGAPuPD/EML3GAjoMf8vTmwvwddP49DifvU/X0cymbs+T1vQ4FNVG7vgbTYa1b6Z0H9GQYbES2oRjdkd9v8e1sR374Mu1qeH1p5ZKrKNlywLcOrZ8HHmvCJ5v9y8mf7m//+x7v/kv5k//X39L17qHhVu2bNmyZcuW9yPvE8F//V9/+wfgDz/7wW9Y8v2ffvnF55/+8Bc//eoHP/7Bryzk3V7lDws/oBQAPW3u9d5M57V0RafzN6VtvkTEu3VOCSfrcWY4s56497J6B9wMfant79s1AGj3YPPRxaxoo7dRbZj6AF7MvZ11FQUTrwZp25K31SM9DZN3GK6g5cnXXdMBwTzViRXmOPggBVgA8b1F+eEQwCD7xc1eNQPZ8xSA3DUXIMZhLgG6SIA5XXpo3x3dZgTD1OEKEBeInyb4ecgB0wPhXTydASeDywGa2925orUJn+cAMiZ7JlBYNQFC4B6G6oDytcNCZcN96PnzyJHWAVYn6qUpzB/qd6nlZci1w6FV0fnwxyqDZbGVLi3KqkDkflEiAIGanqU8Au6lzHYdwuKWH3N7aawohuAfA8B4kvGNAGhdzfOmPU26MR0Y7mYvAN5+25+/dcRvFZ6fWjejex8wS/nB2xxWboBexGgzhlRkPy53p1dt1xD4g+vhI8fDglzHoNJ4S5ox/k2fHGVuDU/bjf5jyft8wX/6w198+cOHv3z+xZdffvE/Vbhly5YtW7ZseQ/ygbzo//1JmLCImzPMkzXsj2SQuu/Cw7BoX7hnJ1APPB17dQIlnba6hZ9NyxkJP0TRz9z8j67mBCuXmKOtipewN+lJ/j8pd+HmEwwNFKKVIWyuvwITDqyR6YxIyHb03PE/UoHYGSVjqL2p5YzWcjeP4mLgjPIJFza1YOLraoTm/jdN7BULLuqUu87ee8svmDIuyAroS4mUyyRYvaxX3MySSycnj4gd73cx3+9aBWHUD9DpSgj412VEy4cYQpOpzyJuUx/zEOLwc1A3ENiUPa+OEai31qNJXqYadZLvbPNtxs3LiP6yiX2qMGPU0IIsDjMVpluXTO/SkNAcs/NJdWhQrrshieCrfkXEXdOfQ1lldu5CIvnNv9xA7/q337Tntw1A72rAHcDtyQPT7XCH7IHgg/OxOfS3Cj600ewubk0v+zBclxBDjqtmfbjdAGCMMNn7iG6NC6D7PLT7K7Hlw8jWlWzZsmXLli0vUF4Lgl8o4meTvNxX1TQ3BTafdABI7Kds1tuRGq3OYpl86rO9iscqsm8Dl6MKsQbb0LNPTaTyQbJgos8zcNAm9DH1i9gOgCqG++eKH0KskAArjp295QPbLedaAVHp5tKlCbsTPLbm2N3Mh61Ra6JOxtf6FNZQ8I3GWACMkakAlo5IQS3T5MQAZ6wWrunLlCygpP4qj6rlh5iBuWW9WzARFl935bF82zw2LRclu3RnC78fQlWTRIO27gartdAN2K/sUIDRsxjjQycRPV/Pf4/IWf4Q6cW6rR9GwejyaIxTC3mVV6f3/yUZ5dlw2ZDZcuedFVqHOvbR6E4PWFD60TzZgXhaB8ffY1zmRW+2+dbU4PL57Ax3prJq4gHxvdwR/ZYIPqLVhYQcjQR2zM7g95R577+lLb812GNn3D/BfAgyrjL7sfBY4dLJjyRSG2z5CLJnesuWLVu2bHmB8loQfKFRn7E8MNlIEShHE+4Ub2ON+roCjIXXOstpyk/zf6mmJLaDOrIgCPXzjCZkuPMSc6oXdWI7eQcG0Xf5Eld4WmqoPiY/fwD4ksRqbXbxaa+zZpPgXSOkuzPz+x9rp3cn1zN33NY1YaFdnTmvHWitb32MqwG4TgMrdwg7GPR49cNa36ov8XC3/KPTR5jdfAgW42MwxkuhtKv1U/vC/8KdntApA+JRcHObzxjXvs0lAa+lXo6Y9tqx2WKacfaFZj8cBVBM6WFMDb2SfWAkduZPY3zJOy+3lq/1x2WWFq1JGuMXG/xdgoDFNx7l8lUdU55/PrDWWlIMxKroMpU/Gmge0Mpk2hwezYne3tgjWYcx0715cxl2t558+83x9lunvntmujkA/bCHBFrL6Bq7dxz6dy3ZHR3rW8+shfNsdrNQ9RV3q9ccYXQXRbk1ME9RWdHe50vdbd5cNzpHuuUjyJ7pLVu2bNmy5QXKq0HwrcnlOZGiDDAr3KP69988E3kNvX0I/QNH6IQQFIFkNYzbFY2Jg3sD6P1auzUYfO0ILLxbr3vrJZskMTsKDoMkuPPj7gC91ZBH3G1sOcc8AcpK3td0NcctQCyh/JrfPTQEBbtDhCRckZGPY/SE9E1vzEx/WRLrQwCMS4hX1JC98Wyr8rK2tQPREev0YtLOi3afGI1fFxApele/YMeQ8Dlfos9baXClwMODr8sYap+WgcYVi1T3oe3oTAdXYauUuPZlSYhOLSye55jnqg55XRfR37vZu69mJTVBYxyYfgmc6mjhYU+qf35V2OTY9XH97Nts1A+dQeW8Q5mik/bpcwTLvQK4HRg6bAjnvxyAZ4v/9l+Pb77pAEQ8YMRt8N3D3IONTsSp64wivh8jbO1Wx3RdY4hdeTnhbPaGsw+30I904/eb0VZDUN0pn4POXe8uQ6kxOpaxVybELR9YNoLfsmXLli1bXqC8GgS/whas7tEmiRhksdE/8CDV8jM8+1xFrDV9cgJEJWLmr8FYN47M36znqCfxMyTyh4jbxjSp4yV+hYMJqUevPUehI7diw7UxdBpp79FtMmPPLVcbvNy1X7DkZAJf4Ts9flsfNXg3gb5opF3zTFaerspdkQFchR/76skgMNoAI3cjQ5e0tFBqcch+OkZk/TqHgN4AkAdQHjNwjw8LXcFagRQCi2m86bRypADrBRRGB7yrI0uq9Ihfh5/Rx8sK7W4IbjS91xnwa4W5Cbg1lQEoyo/a1UVS26FztcDTMpfHUTE584HL5Me6WyZ/7UFpfIpBKHM13zB51eJEUrQX4TDxLgQvgK3YoXhuCvgyAxf2dcm//ssNcHgdF/c6W6WOb10dqdOLXpoexO5egfVb0Yqdz562vfXRj0J63/3pIfRKgaBy4cUtM4YMN94LAGliFHuDRB2n4roAAvcmaJEzccsHlo3gt2zZsmXLlhcorwXBt6EVSck9fpnxeFjQ7ys8tB8VSjTWVHfEF5naCfb7bEcVJfDdduDXrXs8fSsG6iaglS660g+lrZ0I3pGcjPGws+nnKqWdaWCLiTJ9zqcG1kmQd1DXzdCzAiJHCXS/T0BG/B3x7lhUBQbcBbeuYKrpmrSqOTWBAngeK6SzmnqI0aePIaYDaIJRUsX35sb+txe5yQKozQNdAhDana09erbgXYfj3GlLBJ3PmLIVFFgPrGC0um30krbOim/0Xjbd0ImsUAG3SCKtBcGHaKlfS0Pb4cZ7+OkC8T/MjzdNhUzl7a5mbQfIdHP1JrtfyMvn+7viQZdQGpRsenIsKFd50fDFiGIyq7ajcZJvDUqvdSux9PDnwNtvOoCL68At7kNSo9Pz1uhdnTGCyeIC07slvo9wm59CXYbrsYSR9NbFU9p1+kjsXhOeiwH3Q8JkbyvWEPzltvlr4BS/u/+upI/7txAPbPlflY3gt2zZsmXLlhcorwbBn2H2mTeQGR3sX51HHavpvRrUvWRB5nOYO9Y8ZKkiIM6b9vyi6M9pm7pufQSnvSP7yS7YGhNDqdtxg5pKHxJLpT1SmD5umgnppNWPIRm2uEveHA75a4J5meYys61jAg33AEpnLUJrhJ3ziQqOcoz41NVCbHsgfjZ4qQKecavyx81B5G6bf86Q7sr3j0NgWQWHSu1hIvg55LpixHuEjXn9VNA/8ZDDejJ1NbBgAKakwGPLrTRVYbctq2iBXtDp1WzFN5/JYiuNSStdjRRqjT2xBTOoQhA25Q4QzXUGC3d9TEe43z8A1gssno5LrUb64cdSZ32dr8uD9st9LVieDhOOR7ms913KrzL1JCjowyoPuxDNW7ZJO8SN8bFun587gOdnOr0fpqWDRbFrnyanEcFHvHs/3AZPS7xXWCZRh5ynI/6bWCp6PgC5plW9r47grdnbiOxzdLkv9zhwqdhY/i5uLlez4di48mPJnuktW7Zs2bLlBcqrQfCqBidXU1kxmd//ei8lzHXG5wJL1q7Ng29VpM2BwAsayCMJ0H3XT3660ZuVG6hcfNlFtDM+FSUWHAGVLo497LtEw80Rv9YM0K07fztUHroQx5e0qRcAh3ujbKlWsXswD8hf3V7ScSK57gnIzIh+a26DD9AZvRSzJ5qtHW4XbHeh2K0DTiruQ6uDiK9PffKFb8WLvjYY9ds7HLbTEq8Tdg936yvwMQH0ID62HvS2wv04sIYXVwR/EURazYM9j4xndqLDIxGynXvzP2aY+zDH2lN35jJzFrnUEo7j22tqJP6TMquzgXtt/wHEjwOLvm1Sn5T1U0P8/3rLBfqPkheyuhFMvY27oA6NvOuq2VWUY7u4tkMAyyX5jTmcN33znRPAdd1aS9O4SDNjfCi9TILzMaB8IPjwrg/cHARz9uvtJlZoFBH6LNaBiCvxMw5PURFO+GGMD50BgOMYCwvCUB9aXJSdTe6jyWt5wQ9ZM5G63GvlUh9nL1pfjGtaWHKtSP0VAPSy9ItNrhrHMrC4l8QRYQuoiXBk+Nt39Db8Bc9bi2kr/E1QbkUUy0Djiz8fpvaY65k4svVy52kq5ZjxQmF+PZcfKHznT9rjokzmuWbVfbzR5/LKbzPRg2g4DPokW+iaHOr5Kpq+6QDwxMdWvJPy6k27Bz0heBBapubMFn6LedC8Y+gNsUVAcWHLsfDwe63yw8fZqgqmLrdqg+uD8hyP3zEhoTAPX7nYedxc9+uT7Kr47qr7yG8cbl+Hb3td1V2tAPXryOXg7R9kJLX5ZD89zjJ09TG0NR1O8klnyf18LSYDFMV46PwXX8iYmSBJfeZLc7GDrOQ8PGO9wWJoWtZtLIN6deJlpuUQ2FlIgE39N97YZXKqWow3RkRzXmfLIyOMdmAJk7sx2Uww2xwlgrRLiSC1Bw4ZcuxAVU8wE+llPV5UdQynxAnGG6tgG4jbbXR+AHA0v/pBSavVugREFOWWjyB7qrds2bJly5YXKK8FwYOIV2fkoyIPk7VI7M0zbs5h9ALxA4R6bhiq3K+DqnCLFRlKCkdibwMZPLsGjw1xvGeJvaICT0t4FCB1qeGKd0V1j5OK+Blg0wsIuC4CskxKwdNFhlzzABroPSvkHAh1BrlhJwR8RGgTlWS+LKquUR9DemEqaeKa+aeOp0MBvGmTXrrCXy1wNqa7VkBBfpG/csF80UITrUNe2onydqfM6LOKPo5dMq+EFiQumSw6WNYPTVBLXlG2UHoSzbc0/UzwtItH4rWkCbK/AfW4DOaeBzXpIWpr85mXOhPOArjzqruROTbNFveot0xvXLCFy7baQaKZ0C7YiO6DDOHuXWpDOJcLial+/LRcaEVd21OF8Hk8ynVJxYysDfq6as760mgi+d/8dD63+p3r2Ti2pcPsaMbRNKSkf8VxUGF+DDNaHU0j8hN+lX1Gu+nD4rGhA0AfcjjjjbvaDdJAPQHw50OeUfihH+PN0wDwxlT0Dd3YdpuH/AXDkskmufmYshH8li1btmzZ8gLlFSH4CSQWIH7nWFfRwgOT/MM0s8qIq+Cl0SaXU8/S7UYdWNONDn5knCFoO5DJXsNTTwWXNBC6qaqHw73DppUmOuKqNIGbUwwRfImjUwDXxc27k8xwFlTIZYvq0dAIEqmDKA5rC8SPkSag9yFLmc2M09PovCktlI5gHh13a6sLWyAnj91iU0tq1OiP2QsPlZITOD9EgwGgwWZDN9BL++HGpTX967zuTHrzqxaebuXie1NTRB/hb6M3ln1dZiBaaHf8spF/NlQIB/UYB2MOYTCXKqJlhQZKvjz4MMy0WEQ1J0vEG+zkTn6g/+Dfeu9N7qxlLA8dGJdsNJH2pmo7enhaFI+H2vfQGTwA7vw65h8WQB+zWkzyfouNojMIpUWnsmE03ErDAxg3n71zKID/AQDopwbjTeSDAXAcwwhtnrrnW+qCY767Y2WeRZc5VIelnz3cy8PC5M7n5kNog3erL7Wgqn2gkAMkQ1jdq+BGt1A60DzS5G35MLIR/JYtW7Zs2fIC5RUheJf/yc0j9+b34CSwq9YoMcIKBePlRAy7n5bCgWYoVaaCVTdlhTHewLr9leGgUnueTuYddDSokfwUlgRiGXLYzPxrjwwuhc1HRCOJ6juc5EdaqAtMAUiJ05j0I+MBiNQLEAceXIXqb98agnez/toKY8ZRMEGIIoF79ayoPvb1gjbSj1TCkwp8W+okJgbZQVrWo6WNs/awTo6Ur4E+D3FYeShQnOQDxbY7m7rODcaQlssd/V8QfGg1MhCALiI382wg3e9B/coIzA4fNRvUWrIwKWlG+gmAxhulC+yX5zHFYS46mChRWQ2297qKQKXLSoj1SQWM6yQkD1QAA5MHThrvidnvVT4POXkSkUeC4/moWJngagneG0f81MRYrRtvfIEy2GEAeO5umxeZwNnR9IlxJfZBNUMGageCL2twQbrLxc3nOjIwKQG9nyrcRIxRp2VWm/q4GMs9ZroEYnq4IgdbPo5sBL9ly5YtW7a8QHktCP5xEPxfqunUFA8CtRe7XPHIBiBhPY8DzZc1NryiesvfxxDbMrdL1d3yJ3t2nnaoB8STSzJMpDqjiov25OaUtIGnYyyG1LVOyxjiEbeYQmxRkHdxo51wl/vzx9xxRrSAsalB/08XpBWZM8wluAbK21EE1is+vr+8Turi43HNwqWTJ3loNaT4VgR2R/HTPmgyv497rl/DuIt3BcoTeAUjLHjguaDVOJ6wOwF0PbUU/QRBIYrNNbfwOkUW9GBRpXbJYO5RAqanHvCMF1dNxIWY1HiBejmk0ABzQvViXAaWKPNyoui96qp3aXO9pQWNfLWss/IgkWhhOhdSfyK1xIp5gonzJ+pzMg/a4Nt8UYagw3QGQKEQCOIEWcZYPP8vR/AK4JmXptFtxQZyNDe9B8tQdNMZcAfeWg4YjQS1CqA3ybzEdtM1AdC76/PClz5aDP1foyuPxSbYwI+mi/dJrOvQun27EfzHko3gt2zZsmXLlhcorwXBF6sagGnfXiHgDPSLOZm7UUXCE6HtHGaddiwoHr9+jqM0cMIh/HH4FjjO2Bib636tPF1tHxadfikAJ2Vr3EpnVLG3I9FhwlOYkY/EtI0ftEDrMRDW+kqNN4ZYtdizh799zuEc5r4SkD2yj/oYSbLbHbs7y+bt6UKxnZdDtLanMz6+h1lWcJcup3TG1SCJqFqxoabVP3QMPDDIuQK7268LWERBzFbfUO99DlY4rlotlDXaG0XbEdoSZX+qCqDfe9EX6z6AJom0GLjsf+/PaDl7nqkFOQ3bccWGzV7cMTsO1Nq+MbABkCbmGX65kkzDC6QXrcOAG4pVpky4bb7ocAWMDdDaqfMQp4bw5tBsSsfs0u1z+w4oD6zaslDANMZ33Jj3yKaC2jQP01BeffBEWnqDu/XTyTT8v98A4IynQQQCqAI4WmRMzpUWCwPA1YATAN4Ot747w51zOeJornq0BdUOdc3B5e70rU3Mg70Py26lQ65CCz3IuV2nNXPRwKIqsOXjyEbwW7Zs2bJlywuU14LgBbOdT+8+vFMm6F/dYuufluhp2LapX9doAwhCum7wRN/i7ZsJUgqDds2TuvF03KxHNhqGiscPvrMOxOQ9E1LW17SwUvB3uP/TGG9fMyPqxJWWlvKctYnpXYWJZXVJC5tk5guTnaxmvE5yPVhQLwOXFyj8kIY9roLc/bbQw2lxXFgkoF5EtGO2rZbzWH2vIMj6Ndeq8HJUnjhh4Ps9QX0oOy5+iD6gQH9gQqhD3UZ7m/OHSnhgIP+sofbec8fNkc0zepTB4o7dGzwNKGCmbknjq5DYDvP0KjxunrTrTicHpHlWOfnVmjtoSj+1ujSk7iFs80I92Z01P9UVMYUCUKUAACAASURBVF3wWIlQjJk6bUL2sZDuUktNlzsUcaGlOJobpBPBq2k1/DHCDisriAFoUaZHCs0QZ+hyhA0AzyM7MOljcqRrEH8sSI93X7A1OzZ4NSM7jqcw6IyDbz7drs/rHnnfuj8fPCmwoF0AcElJPQCAK/82rbEtH1b2TG/ZsmXLli0vUF4Lgr+XgMMRhm7lgRlLvHuxJHPDK0s0p6pFt4sm0D1vDUA/B4A21NNAERHbVn10MYDZmhvEL4seHlj9fBuqBTrDjpE2daveArgX3Nx76dkyFdQELMjeEFZrK+JHAKBA9kuovXckob9rEYh54oydqavYN//aAk8Xl+BOmvQ17jbPuDLWxQ+dFexQQ8kn6dlrgH4vplDhhx6IfEztt6Rw979BZOZe95ItRCftvwqkomMq6LOuKKQTFFar/yhxAVW90UVj8+4RAcC5EMktygbvSeqHSCogz0PA6IwG7eK3gCN4qlsuLoorVwcGHMVKntFbCFd6O+XRlC7oBMHehAwtKz+0GqKDI1oueKFvSzydOVuLPXjUAyCLvmSZK0SQQpkvFIt4zw8K4CqPmaJQwYBrQeIyXYgVAwBSb7oWxXXF+uUw3/hTc4w2ovAaOeer/KZPmiGNriquUCYA5xDC7lgPvtDMrz5ceZ7aFHYRKoTBPl8asQP+97Zx5ceSPdNbtmzZsmXLC5RXg+A1MftabqW2YZfcy99ZenX+MKfEYjR6uxCmxdYFRPAQsQ3yONpxDgDXIbDsc9aKJC4BoKRIo59sbplZKsyVDhQDtmTAvXc1TOkksY8WOPbA2dNQSn9o5yvzlVOZ0ep0y18Y6ySM8cQo0cPUPTimB4qtPSKeV6/pAuYqZE+baFFvYMJ8XAUWE8yvrZjS3U7Mk61ezYabA8/NiyRMoenEfpf6LIbxGMEXKyxYUk+tM2v9URjmPcKeeD3sxDFX6vEagpq3W1ZwKe4LEghMrhJtIeSWP2gyp9+4Krv+fKWq4FK3xbaWV6eaxpXqM+HKiasvBO6F+zEnUyQVOKNOJyP+e4kU4IEKZ2R0pYIrQuDt51zVq1No48ayFLl+FvoBumhYqjagce2rzYk7r9er6SvNvFJUTMt0IvQo/tdN3VSoPDO6PTQ61vPQGK0LEhPjwiGWbALnmDj4vqUHxpVz7vqSc842qVwwN6afT0ULe7LwDC5ft3w42Qh+y5YtW7ZseYHyahB8bLoDqVth5DovJVYvtqnVck1j4p23biFdMzJ5Bdo1QI/zIe5pr9dohm8sNZzoaDTXGqLqUz9AvrAmpdB6yN6oc04BFiVMqql3ZmEvvb6vIOv4HM8IO6AqdRo5Jbi38S8lVTGwVKMewxEbdQNptcXsIXzXwnyKGVvL3fUK33gDVqMkhate9MD6NQ+fgTiRmV+i5KhP/vO/fC3q8swG73uSVTlwR97Ui3gWcNYL1kHhGA3jJQadQxWGOoc87nIALpEFT21YAjpD8F0QuqtvpAFu031mUHnY1LuApI12jEYqv6rtCPzdvBHevGXsZEl3U3Hle0e5Cj40OKEhpNQs6pCIyC8l/nlZAHnRM9zD4C/dRwwNRwV1KGyh/x0e6nLxUoi4os6m4iTx3DcX3l5+XVAC6/XOsD3msQdMtyFcRZP07ciKb5kEIUISQgFw6wBwC+/6y9kLIn4kbmc7hRPOt3WdhP9KZJPrRRO55YPKRvBbtmzZsmXLC5RXhOArWV0hHvc9+z1bekm0NjcjpVL9SotU7KLd03yGJwK0y+CLbYc9APbUZti9kc2NFnFtQ4BMH5c9GeU0xZc+bOfMM5aYZxqiTB+mtFrzxm8eYskfRUSVe/l37M1ndOEFqp58OjpvDrpXd65yybziRKMEyp1ajUwHVwdaYCsKzI007W5Kb46YenFBX+z9gVemqWgJdzy6l832mMwEf4mYa/DFw4iDiFMYuGemUwBDZc2hFwCd1neUOO/KWFCpEoG0TydX/LwqmH2OBHMiVs1qfqfrmz7AuPajeQuR2+3tEAB9yGle95yFFvkabKSeN7GqTxzxu3H3UFd6QQBcJfmCeb3HZeUwA61SOeHgOBVKMcw6yVJ0Tms4RkzL7I4QfgyBVutkQsTcCK4RE6A8qbCrBuXFYhwMuP/LiW9OAHge+B+nt+wHhmoPs8zrVpHQHAXQd/FTmDrnHJO3Sm3MbPxHiyxw+vYqs3zHPxhBGQd1SK32pvbt/qm65cPIRvBbtmzZsmXLC5TXhOABoJjYY2v/YFdahNnR16j0sBuzINFjNd/BPYGbuvkN6tx25l0vqsJA1wsN4XuvGqb0B2oE79rkL2D1q2v6u2zeHMHjxMzhNh/VAtYEEHdXfw54VX/MkH9pMA7Q4ajlOEZj5wGMS54jGrtsQfsdvMZSUnjaFyeCFcob2lDS/MWpZ3jUGRaf4IMHXrR9xoc68DhRScQHFNP4naN29DwR5KRTCcUMpmtRP9R1V2n84wByPsg0J+UUADoZIRqjHRonAU577nTrT91t8E9OP6ehq/A+GIK/5C2vIpnYJ074oRrsfrVLgtAcqOHNQZVY8ZmPdTitsAjQr7nPUSkfU9lQ7u6id6kHLlwL+TlIAEXNrtybHoUbAKCpnBEEjqfVVU8CMUqGtzS6/+sJAN9e7h4/gO90gN7y78qkPu78/1uY5wEAF23tIng2oz6jYngVpptcBDpMY+RjedMnN/uA/XG5rQP9jvghdQnwIVzvGMWW9y4bwW/ZsmXLli0vUF4Rgl9xrE6fZEHJYShd8DOBj8YH7vkDqS1Weaarku7BvPSDFQcSbaSNFgEdhhhLtohb5e+BuNe8aKp2Y6wO3+wzQ9o7d3G6zEqEsz8a8QQ4qxUtKPAWjwUhq5+Qg49R8uvpg9KuND7rDOaxt2JTf1hB7tyeY4CBMwBcUuDOUpPVltxcqQAYXmEUG+c3lwfQK3UPTecwDHHzvdxFBDCXV7pEDKm/sqvzuJLGrk3W98JPl3oWdytx9nVqKWg7jx5FJj1bYTe6Vtjl66Rbj8xpNyJ4TrIc6tfdzj+4kiMepFsfuGzj4o95QQb6ZaYAot7UmlATULK8Ff9tpc+52NdwHhF63kx5ESWvcqhqMBvvF2s1uQeqDb52NQLttQ7woHZE00Yu3xC7m9xmhO1Xn97yF1f+CMWAZjWbhKpb0sxtQWJBS+inZZXUQVLjOKj4iZsueOsaJ63eUyKZ9/2g9ssajv6MaQq3fEDZCH7Lli1btmx5gfJaEHxa/ADYzt0B9LqZLPHnceRjIWCZGkSapN2o1QhWLuOin8OZR5PoiTHcjSYAhuBy+7Bbz6SFcZ0QMLo6b70XyL2Eq77LjDeB4GJUq3MU3HmuG+gRbKsAWtOVyc6z1WkNZ8fk509sVxx02zGW+OPlIgxNq2rFMQFb22yQDiVERNKT6Sxt540AqAb13kp2uEpO3gqjWRDMwdz7BQCeL8Y9K25NQFfzXnp4x1iXoNZnqeT6Q6Eqr1eXMNvo0A1J29A06sVsT/7S6ugtE8Pzb3PEpoSgSiBrJ3Izc5dFZxDTjEPoNQ5cDU88Z8xh0Ndb+cmRVlcGBXsc+Lj54haf20yQJ+UW78Wv22eJzXaqT0ypMB74t/AuLhqbU5NN0rK/RTB6RL2HB8NBNkYU3BxOKE6HwMfM88A3l9iHpLuwmrzep9qUAoXYDphwsM4Wdy+qJZpgvRV9RLm1wV+XnuPZSff8eOclJEA/gnphvmezwpL4TrYN/uPJv/cX/CeffPK37sKWLVu2bHnPsp/tH0H+vb/g//znP//bG/nkk09AwuvA2Wk0t43qwrmWBsxphz9vPateQJb/wxgfQN9t6tc4jySFF3WP4nb5lntIB3BJZGd3hCVN1fPOxV/HzZNDgcoC2d8tj38umBJAIV4PJ+fm2eGO27Cv5JB3GGA4voLy2nQw6veu/XD3AocLFvHP7GGcJIJdtqcIXjCaHhWwZG5MNxdRuTBAP4+qEaYHE/hVhxw2+Lbikpjbh3zarVCRB5v988gmj/DQaJmlfpGwucqC6R/X9qvSpficE1TZaYNEjy27lqVUSbVPg0ZieLsKbbbmhrf80eJyR0cqYAZyKtRGFFHp1S9EwVToRKvLiGu4OQBtmoHkJNzvJayjsX7mP+QySNO4TcJ8RwuHoAuyH561vcEzPpA/TiL3YCeUZ1w7AFyMEzmHA2gyHso5BMDbS7417geG8t+4zGLV2Dpxk/y4iwiYJ+uQNNJ7EorQUXFodkSs/ATxpTwUReGf/3z5qZieTg3K94Zb0TH04sphkfS98yea5C1e4L0827E3Cn9R/r2/4D+cSKzjRV9U38oAZA57yxyotbrpV+dbLQqsweKH1q8BoNuNPfS6AcDVXVffhwI4z3YCAMZo/VAAtzc1i0yE75WOy/JB61f69AT3xjt19eGwEw0PT5cDex+3Q4/D2Hgsn6xGPhu2LGDe22zz7mGUKnq+8u0Fr+qbgzOe1ACMeYNbo6Xv9hJ90/GkAPDU/IFYc9XANOrUK8JTZPqH0MgO/gTgHPSVm+e2FeKRdEkrYXVVt18lqEm7Til0ZXnVIF+5uWcqSy3K0dxfLN4xVBHfUzdFelZve3mZxToJj7bYo/DF7O/7ks8mdwYa7mrCPQRsTtTmKPKggAYRmdu/1Gc/rlow7VQCnE6DiijpmUty1dpsbCkO5whKIh3fUizb8rJHCQpYAKPwvPaciUlJHnuXZ+XW26Ioh7+/n4vP5KXuJ3iq2Aoc85KOW7Dk/AVK4N/JF3nkQfb9AbekiyNbtK/KALx5LxW3mbNnlTnUuSZf/L7pOVWDEsf+XvPkiPC2pW5/O9l9NNlOdlu2bNmyZcsLlNeL4BOyIT8ow+XCFe6dClLlZj/aaYGR409pGmrecyri2Wis2kBzDgsZhv6uAeCAXqMBOA+P4TpuUOqfywBWuc/44rXvEH/9CQUwAVKB+H3LvQ9Tzh9UsHuFRr4Qg3pU2Y8h19WiXNPtKxXpDuUPtQkwlUD1XcIctGZyDudDdQclaPjvvDG1IbWCDneI7MMzzqKSnoMYlk1b/bd0fXrTM2wJha9GNDAiR8QKARbrJnoUDXyFaCWqbaVHdaVuLMnMhDuhryaoKvo2xTo6znZ6mTuVFfvsB0bPbzEbU3WXozlnbSNzTlTpshzhLYcP5orhmJElgHKdinAKY1ImDwAVKg06cW10wgE0pBfdfgBxEQ+AE5SSVD0AqqZat4gyLV6cQRgMYKiGQts+PFMn7/NGpH6p1CStz0OCQfYsT5H6oXFAVQl0wDH7qTjLFB1M0hqqozGD+Ph8kWcmh+y+llTwsKbGf7xZFijPNuVyBYxax27Ux4TO4OoA75FbcxX9lo8gG8Fv2bJly5YtL1BeEYKvcHRGpkCAmzuMuxLd6AIS0rFHSUnLfe3sclT24QHl7XgzyYuK2f20Hfbr6ALgdgyzwUvXit1b0xjHKDvi5IXVCYKX8LlVFkgXrknXJQB6d0t562EyR+8rdmeXEKcR0nOOASmQrmRbCSYcvfXsX6CQsIgOUqwsPZeI1LJcNZzsQXctY1A5iG4P+r5FYBjtxHhL+FI5ay9NIGUSZunOalZQg6O8Y2mozp8SjM6qo1bCJx9SLiUgzzmx9iWgmEXiPULwU2M6jzRHxx6Gz92SpbdcOJ+KJg1wttdQLYjA1Bbqay4d3QYx4DXrS0BaVjPrHoTXg4F87ogaBvJwI2AelOoMO5RcLvAAzbgW4T8TXaoBnNHE8/Ax01CtjOvz6TeXzCZ5IjvwVKnOdM8jEbxlarEFfw5fB+dIyM7nAeeQ3xYQpmRY+rse1ScWmliZzwWpJyFSiWezdq5ZA0TiJqoc7umfqXJYoLy71ipscGfxKvD1w5GcG8F/LNkIfsuWLVu2bHmB8noQfN1xmrHd0IwQIdkuVMKMHACH/vYKQETKxlTyZxquRUGHdTVQZn/HO436cuc/rVb/OBRAvzmNq9BQbYC+MSAH4pt63yaPIKbJD0AyV4hEZtL8qU7RYAZJazF4ZKUpMXpAOZ+0LKgDY/xe69IPgCqBcTVvh7WL3df/LgSxOTEFIgO40aM7TKEBzasXdGsFsgtQInaaQ8D0Pa445uIqaDwkjPe1WpRndhO6T/euQXGDKZVLYPdpBupw/ep4hYxJq8l9lBlZpDhaAxCqBKp6ZhS//EaGWpCvlEqTyYMaReuAQL025NbM+m723+C5ESJsx9mSLDGNLLlcSH6nMNLEAfGNqPqaFnggeESKZ6GJvTLOqiTLEMoQYmiSQ/MbP0CtO7dzDoqugieyU5Amt8SQAuaoDwXwTDjr4XABiCEoGW+Bdanfg9sa8/I8MpLwNnnkpPvCEvERhnMyZbsW6opYXSL0uKc4RB9CPB5a6eF9JEvcca6hLFRhoS2AeQ9sBP+xZCP4LVu2bNmy5QXKa0HwokFlQ4S++JTzx2RZ9SOnRLFaUWspJ62noSutLS373Gh/cHMVgfW+B78UwPE8nqUDEAlv/ea4wQyKN0WkhS1uzAoMworhYeXsqFAJQfjAtDT2rQVWuA9Yh6EuP0c0OE2VDokuoagKRBw6NtvaH4HsNSPgy8WRYv19KH1G/KBVLxD8rWS8sAYjB8wSu+zYoqepsp404EtshJUdyHysACr3qsEUemyn3Xo54+xPIIWOJnBPJa9V4twRyiRvkNeAYR2BNcmTk1TDCaWdGKdoeNIanXlTpPwa52pssEG6lzR22Mt9wagCuFSp/XIVgpJthSw1YrQudgVAGzzgOWmENvsW8Sm02fvkhwc4p4LjFfcO8WWQlBZxHSN2wA43JoaD+ZNuoQQLrUZgf4fFfi1CX3LS8R6+3qi/aYLI+ko83SRX5hnsPbPUkiZ40/1EOv96f7PEAwElTENJ8/wEoKzw0HZkYAsbHHzicQrWE3HafeBUb/BJyKYOagA2Ve1Hk43gt2zZsmXLlhcorwXBI6Gzg4Dxjr3NOwPfYwtfvV3rUY6OymkeNkGs/0ATYJv6SwH000xguLSdZwPw9DRwmPGqA1AVc2VvLZA6gPSBV53KRapNbhrk8CB1hy/jkmbmf4a50yBZxqs5CULDs2KaBBEfpDDfaAL6WcLtudFgGEirnlkKuAyvdT+CXGlCrFBdhaUwyJoEiWZaZ5vj414gHZgHZclxGTljCn7SWv6mj6tqeIpOYkHYBmoD/gamDJQ5mGOX9L1SM7Kk+kQmSNfDmYQG7PKjA/0YgiHmGOBBJtqDKp+qLWi8Ko3XhzZ4pg+VZmlanelBx+CaE64RLQ1q3nTKdeJzYoMsIQni4/fLLVSfTHl0Tt6F6fHAkYa/fyhUCnktLvW4eWkMT497p/jJA65CEHGS3aD/U0hL/IpAUE0yRwuAb07/PW+ud4PaqmaJ5/UVuX/uKsdUVC1Xmsw5EtMcPA8SQNCf/57mMtUbOn29J8tDsbif6h4Kp+izAOE83zaC/3iyEfyWLVu2bNnyAuW1IPglCWwxk70LsP/1CpUy7t5AJTT6hyk+mjPgUXxMpR5rO+weTrcE4s8iYzTQqqcD4yYAjmOYd3rY2tMGaQg+XJDj9/g02eAdZ/cWtvM6ACvx8spp1VpS0K/G+/kDG5z42wFc6VgrKMALWIH4QYAezHEWkWyM3EfLo2oK1HubfYBXx7vNzb2YreONiTFOTRMjSp7TwENJSAcF8MQIAo1TEDu2uYUIOo+WA1ya0BedgGlM1lbJFbeiroxtgCtykrysOIQ/kZDOVA7BdPYEJUVdhNrzq5/Csb1IBy3xAERajUoXXHaVBGKwUxE6ABsQOln6PCETfTHIz5azBADNA0UCCB6ikYKF4/cJ4c0nPvnzdW/FEcGOPmyuxG/emFJmr/H57jSER6qCiOaQwngY7HlxmexWjSxHtcs9U1AlXBbOklBxEI+R6sURy6yuh3ovSmGAr5olbclJYP+b6uKaG6+TuXR7OZUqLvemcEWLDNeLvKX/wfOqwtvyoWQj+C1btmzZsuUFymtB8IuopAl8+WXe4GLa3idgyyLfUKvvsbWkm3MKu8v2/tzaC3e43kSAynlXrNqc4U4NuJ9oZxcQBOiTO0WPy8+kYx6Mpjs9gAZ1H2YNvEP0yZRuTEQb8e7sF5F8QPA7N3v7q/UrglNvKS/1o6B6Qcf+fmHdQjErPuxAIPWAaKklmVsQnsiJ7RAJ8QKKOfByVjV1rUXYv9OcX65aYMT7uHZbOUfD4i3PXqW1uM0VUuvASalIvYZBO9++G/vV857xz6BZP+j93RZLW/sTFMCzCjkAxHgAe5sQPP3H0cSJBA27N+mumhHRNZzbe/kwQ65ADWkolQxxuogRsHgP/zZaeOnTKpw0FgAacPB2DYIE68QSnbFkCqgONv4/lyUXjB7uGzAADEaBtORR0JrGTzERv4ckayHyQVQfOEPXUyeyjwmtbd4tiSUlxbsws0RmYS1rhesf6zOptB/Ifj4vCrFdhHv4kqO+cO7alg8oG8Fv2bJly5YtL1BeKYJ/IBNuf/R78r2FLWuyhdK6L2lcpxUNAFTD89ZwBoF+SaPtLXjjwr100wHgNrQdDcB5awCe0YZFzN8cRySCJyioO+5LYc6s1yWWbT1+Op4sW91wt2fRVpjhUY1ww7UU1T8/52BG6gmj7ya1wmuUuOdAJ3FgrXkf6asl0h1Ab3d9nus3mRF8sPtVqrtpCNodrfplOhmhQA9z96M2yHdroZxIJM3+eDs+llT0AAVPSzooTOEaGk7sqqNgxJjzRrDogQZ0JtC0eIcbiEY1O9BTrjUBcOh49gR9cqoCOKDNkfp0AZp0R5IWBy/dqonImJEDzbuuQ0qlkI9Pjdx95ClCQ8D06+2MuUIbzZnstEQWRCS9zbn45XNg7XNYkstZz/PutMkMsoxDUj9XLPGRVk4BnMNbiKsZwx786qdYozBInh+cE4uyIXMGssH6MLmDy3l4PJzecQvcf03aSn1QoRX0f38jT32IalE4/CrYZX3rCxRPG1d+LNkzvWXLli1btrxAeS0Ivvhly1w+cWChmMQCTsq0jU+IF5B9bXj+kHg/oNilACxWeEhsjPXqBtGsWaeyb8pE8rOJ/VJ5Hm65NMCdO3GChgiIt78Gu1vZb18G2p7A+gA8FzuQOeLuc8w7q7zt0C8EIV1Y8ZepqFPYSoR3CxxcGhdZYUTA4jX6fA72rSnU0lSJjAnujKgOlUB0bI2QpvlQiOQ83yAN5GSzZ8YBdby+WCjTxmn2b46smoGRdSZCsUipBz+xAhgzVosQbRF3ZAg+u06HiVHajDPGnGvh2wcAabacioNK6Hus/Qojiy4B4lAejZPsKFktLB4KvTgXV7QsOScSTdl/jS7kR5l8xel3lird8mNoDYCotpz+1KMoE99Jjiixuw8eOv0AAEmzFzRw7LBf0GT1R1rlMS9IK3nqCovgH341fUJ0YoeIpwJd0TMJwoKzlyD1qyyMiuCDsW51hmfQxJj9JiTzOGgNOkL1KsiR+qBsKshX6C08D6/KgAI9yhxu+aCyEfyWLVu2bNnyAuW1IPh3C91XA7gDKMheZvtSON2GRSxsrBFr69vydZdKIB4ByszrHHY/JriznoRxnqgCmXQLQFjHLpXrerBRU53AukpwzWNuZ3U/IC6CxGCdTI1Ow+KR9EaxF7T6vQEVHnASh0p1y08a7QKgHfEwFHh4TDbnW6YDdTalo6CWKDdMvGRzbzKhh1Gss4n4xfuOGaJaPPqRs0Uoz6asHZ5xjc9wqFe86AMrYgqLpwOzJtS2H0IbYYHLV5yR+Jup1RTA0bRY2R341XNVa3FdCE2kzTBXaUPtlhwhq0c6Q69PN/to228QFVsZZQ2U24MqKjTpU1I4GqiFqhYS4TWqBFImaJ5ascDhUgfSEKZ2jPyIjnJgzjeiWBGUD9bhnOQlrn2xoAeFg1emK8bgzT/4ADlXZdkExIUrbdHK3Lumh3+JlUfieS03i5VbT57DS0ABI8MgQE/9xEgoL+X5EBEE4E/wuzgazMlUyPaj/2iyEfyWLVu2bNnyAuW1I/jYTBImrbb6ETv6oJ9LX9I8VJk6LeNZ80AAGNzqD3rupmmfOgAvmZ3FlX7aoDqhpwm/wQB+K3t1YkNh4vDh8feOGlI5ITiOAaAx3bv721/OUNa7EZBl+LsB9ybamtHgKwBVuU4AJczZHcg5RAKWG53zM8U4u1yN61Is5QbEjzuLpjHf3dqE1EM6qe7iV2Z7C5t6oGT7X5gWTGKwy6WoyKzGptex1MT2lUovPnQJMjVlDkDlJHuDEa1eAbcUECQRuAwM0qc3z+RHBJ/gUkZ4f0yT7Ej9CGurw3ShZ3gMPNjoTZFTotm9QXOeb/4hLpQ44HPbuQ7lsROCh6h76fdKIDdwsUG37lt5Q+MNcVVrOlKXkAgxgC6rhbKBP+hUUWg/loLs69xFugS41gQAriHGA3iqvPUYBAC4yj0dQQ12oGVWPFVP2uFtId2aALh0dU1fng2NGfbsb1jQAzfrnXN7TERY91FUApJzBFiCPk4AlQ3uHxTrPWPlbSmyWeWtFG5DM/l8Mmlu+dCyEfyWLVu2bNnyAuWVIvgZQCrCIK0Rtpo1M67dEb8jKnerNmjRhCZ2/xCQ3WR0Lwdt7QbH2+VpwBa2/AQNpS9OijcuwLOf2QctSDPc73W2Aydw7xrgshO7w73lUT8wtZeZUFF96eeIWI1Jcyud2eaZ7r11p8ajeXh1hkeAAAAF/h4NtwbA//YCJsLmHTijHhj52gPH39iBltOb8xNW/5F5xmzsYcis8AYF8M26h4y0dgDdRHukii8B0KkX4Z8w0td46Gi5mkLdT5tYl9hOq9ahyR1aDW/1WGlubSU891URX4Vw1hnjzNFbcNBbnlRlyULPMxbED+l0LokVqaNoIQZE3FG90THBkLq3wGSE1HFpmNolfCxmlUBJxzCv1Dlco8RraGgABPPKLBMGaNDmx4kUwKliQPxK9xHAqOkBxuoLeQAAIABJREFUAE2cvSCc3u3qX2Sk0Ok0DygfTMJkrrxewecfh6dfPbwPmANGYuz21RoMFsi4E0MhWOI1kJOjqbsadLbAfEeYanB46gCcl+s2dja5jyYbwW/ZsmXLli0vUF4dgk9CumVrT2xQrHcE9Zo2K2W0cdYdjtfNjjhuDhJHk3E0EMrHV6j3IfK+S0K2CcqzZ9PWHmUHbRnnhC1bGL2mTThYsR3QhxhwRyB4AvrGrO1C4zpck0DAyjD3JTL+vgUAg1mkeg84y2o8iijW3QzimoSh0wEQhxEAPXD7VfBuLy1UYN2C3y0mwfF9AeJ0bq905YHwAnNEcHmcpc0lZIXzxjuZ3gNXRYR97cmYdABA8YI2eHS0EcDaMTq58R3BF6UCDN0ST8dckfXMlxrDypkUjj2iMf4Iw3MGsgOK0dzfvLVia48KEL9ujEQQehs0+v7DILs1CwEQKoHMcq4BP1KZNQA06cH5QG8S96tndYnbqfrYPMy9frjrTODV2gjpDRhDERXc9E4y/KHMH08fDnUStwjBl6PY4BFR440awSE2NX4irT3hAGziqLmpWeZihq4yvLUCH1fVNp+s+DOyxxzxj7I8oid+Rs1jp/qFMWSMTLF3Kcq12vJhZSP4LVu2bNmy5QXKa0LwdZv5DhNX/qSErQTWjkK4Ew7U5ni9t9EbgNHayVzl9J93AvnzMGdjRsBfA8DoBQxrYjspVjWD+BIxs5jqQ9L878VEr95nR8+R5d2Pbk2rQ/QYycztm/2krCeKeLRXDyC+wPrWnGKvCaaQbsINYS4vaZ76LCoEbq4GaS1opocygLAAZpvnV7qUT13NPHVsKFQL4bcfXu48xKCYeqT1bJu/zxp3uNPAoN++q1QyNnyuH27VIeGpUOOYhzo0DHpz4YjCKaRK4KdWrgoD4mWpHzXhGgID6N2M6w3twgkCboH7UET6uGCsS6d3zTnUNI97ywDp9K2dUDSJaHV4Sfu4Molf1gQg2lTjTHVAbEaX+HAJFxsNCrxpDvLUFtXNhPfU8OVSNPWJRmbIQMLNNS4AIKoFLgvyaqZFPCzcBzU3AK7aNVgJYDoqAMCp6f2OsvIl3Al0WpmZnm7MCD4qSLqD1CHXyov+g2qU6bZvQqq7OqeF2B8qD937t3wI2Qh+y5YtW7ZseYHyWhD8gn0VKzs9t70eozmjaivR2pLMPurRwmBq8XFIeLObuCf5oYaMT2kADNaz3exlC+aphtvbAaBdoxHKe31643s5fZWNrW2sJvbk5ioRrmVeSFYfUemDGEGuCceM5sx0aXGXmMeYUU7enWh0CXlgGNcBhDM1xE2blkk6k8WVfak1ZegnwaJ6oDyxaZjqpcLiJzYVuoSISEicTcN1RPVbg3SKJlZk9chXRlu+1nx3x6qE4bIhDOqiZqxt+ZMPIYdMxMyePE6+Hl/CdXyUvhan6HB6j4Ot/WaAu0m36aGqwA9taFK47UQ8/F2o/brzJkkKQzpsNxTNkyAy6EV/lOet4xda3CXi5qtLTQS+hPdAHgne52ydWoeYGO/0DH8vG9GAXpZCwh0ghKoFz4g4VEi3Z21NeoUoF3FL+CVyMKmBNeVp6FTOXHhWYl1Jp5MaeLLifRRCDvYw2R59btgOvP0hWT+nLMaiWYKihIC40oIAnbT5DBhRmcz5Ch2PmDe3fAh5LS94YFm5hXCzBFqp1Ff7dHS8HJlkxp9ifoeHnhH+rriOdhl3awStkfD1OPxND+4d5n4ut6pvAp7Pfpz+preDPNBuqJXYM+66CW0K1M5SYRoPTy8f7JKrUl0TenBRRNaZQY8hcf1hhEUNAJ0Eo1oJRFDSy7bpQTfrUqcDI8Qr5iBU8aivdT7Pah5PlB2ASLqqAXhmthDBpPMf3TlTvtM9pAcSxMB2RtLRRBJg74lzeLTCOVqGVdlv/HKID4exiJwpI9hRjTCq2Gr4vqkkbrHtFF9eviVq/sSP528sRD889jz+kvCgx+J8N3HTSvrc8UXeUW05uW06WimPHWqsE9+A5su6vrbzutaJk1Wn6JOD0kNRYcBeG+5VNmRaHbnrmlOoqLj7XnxgmzwwNwThnOar64K/13MzkFlfOSLhh466AHLwTpw1fF09kbNHIWfJO3wOj01tGVQWF8W7FMw2KG6nsYmY8ULaAjDvXaLOshl7tJze4UwHf54Mz+Eb+Y69500Aj5ez+uIb9i0fXvZOasuWLVu2bHmB8moQPEFAoJZHm1Fjg/Eq4avCFnigQRVhOFzPv17PTtTkCoZVh4C+tW6G3anfdhWZzPtj9e/jkuOmdopxCIDn60BB8P0abWaO6B6yM0w7nGlKAnQmNLe5EetPP2BdnXJoKo8U116cp5AjRWHOeqb/yF1/qOuAQpqRmV2G9cQnLVhcomMxm4/ABjQUJ8HOwZ9C1V/D5C7Fea4VAJzDg5fOgW/RABwMP7rRPRCum5VgkPUO+1cH3ElsMuse2rzQRHzUreo6ABHm6hBpRN5BowsH3AogzBROJ0zXtnF3ogXBe2lq1IXZQZQl5lsnjYyzVCG0VOeXUwvXbKgI8qq5ZmZ48+DRgWbtchdtCh7flQ70+x0ZMQCRS5yeaArpKip9rQheoUF5W8oTkUNHcOf4khaqEDhpJREtVBk2yRENZqeK+QqmYXrp8ukBoIDpSz0RjtK5z3VX6hY9q3jEehoZSsr2Absv+OBarEcPn3hRPj/n1l/hSD1L4mKuHnNcBtVsUR0CVVQuwZaPIhvBb9myZcuWLS9QXguCfxdkf1DTd+QaVtf7pmAw1Exz3Uhm2nkzWpuWaWmaAEzoEqkVu7ZCECuyeq+YqHqU2iA4ETq1RepVz9l6dTkVZD4pHRU7Nulr2FDNp4Jww+EknWfrxWbW6PKmQLtNmN6tblQ2iDL9jOMub38MIX4GMOWMaaSFqa5tBy3omPk1A3MNhgCJOuLqwWsaLSMbrPgmILj9NRLco7k24kaa3s5wuMY5co8hNzO761xvNJnT66rYLBXA0SK7jE9yIHg3piYsNrCY7ghN0v8LZXzplAYgCG1kTNgx2pceznE8vNt5nW2G88roOB90k4MdaMFNa3Me/lR+rviaMNASoTyjijRpRxkHwq68SCLPxPhZKw3/qTGSelCVIfS/gLniefjaKNQ99cwLny+5etzk3SB0W+HP1CTF2BuhPIBg5BFIaGgA3JragjmZpWbMwWNdXPcTqglTEZ05mqk8s8ey600md5PIXTTuQk/j7q7Pj7ySVQGwPGDuKtcfRN319YSa50GwE4/7S77lw8hG8Fu2bNmyZcsLlNeC4JnoZTX7LVJc4e+d2aMS8EglYIQ2182h/NXcid3JXjrdp5mCxQ1aw5MqjsCnDosTwbtBk9guMWJTAIeoje4a4dzOrl1AULW00l/rCZ3bo8HrJDpx3OEaCAP0Suqbfoxe/GCvK6lsj1uJTgPEhgZcc1idJ6HJhCieNNOwxa3rk+HyOIXNMC9fE9xCPWAlBErhTl8RfKT1DNMrLfHOQtNFn5oCeOoaFLM2jnCqrhgmsn+SmNjchI1x1hp0JHeIWnqSUAfRNd2RN7Oc4FJyG3mJ9Cl6rTGzi9D2GV+J1UkEiwK7m/BAqk4SqSsdsY2Q1ZAxbfCdTvJNgp/3HTeP9UQHxuVfrWX7Ok4u3IZ2AkA7pN8AYHFZz87HlJcbMk/nVYoPf6u4tuJymflgbE5UPWeLzkw4kTVH2beMBaOyofp2dAi5bL1FDS0UFMDFVM/ROcnIAgEw0vtdZ7cMzaVrejX3ZxAPZpNidJ+dP3IIPigbaWGlhZfEHNWvUv7iThMQP9VVMD1bi56JOirXh7lqU3m3bPnwshH8li1btmzZ8gLltSB4FaH/apbMFZYPS2LJVUab9rqDhmUVCb/6ow8EiGyJoT06nJ8Nu19nqwApgsgjoaTQDCymEojUmi28Vb39Np8o6GuWgHtpXhKKAaaF9V7Qb8BVCMdttDb8QHay9vA4xpgpLSIRrakxjj6h2/CBPzJ+3e3Qz4YAA5GDf9m8AZqhbkS3gQ0aGg/1zgcMchDakqAG7r0PWG4Y2uAjr6udbRTfZhDQl2ywXnMhuL2RF0FEapR5K87tbQLo/jlpkQI1Fvu3t0g8yvkQLy4O3mIe3wDdsY2Xxn4yXN4aXcQjrNxP7QfS9K66Ing7a9ppfQVDCdkZAcKv1m5D6wDQBh3sw4LN2bIKlhC58+mU6rIZ0GsF8Y+lAnQt6ph0G0nOG0SWGslBCmARNexh8RtowNEagJPrvpOFd4GoQXUcJ7MPXUqMwaNsNJfiNFcb/+tG97fD7xGrfivO/RFZP+suwhFh6lxAf5n7LAW4L37yWo5l16dfRwwy16xPAg//i8/WLe9PNoLfsmXLli1bXqC8FgR/F2SeYe5jzqr4TtO7H+jQf5Co7jrc9D6qt7TlSK3e8nTPTuP65RYp88sdl9SodCWr6nGNcMt3Dj2vQB2Eugm6E7OmqsAMfqfZHcPeG78uRn1lag2tWV9zCAygF5nUHy08z+lnEPVtBmIqbjS9L9HqnR0OnYgj8tmzF+GIqwnNGfPvP18GJsVXNnF20MORMJiw+4kdCwhe/fk7KcliVQQ5V3Nbu9Ih3xr0iekZOx7wRQB0olWBNMnttaDB8oVq/JuMnmEejlwvnSy7HLw0uZUG/fwVuIdVHkBDpwu4u5SXDlu1Trd5D5MQQ+Qlypz2WyLjhPKG4J8B4LrIe9BhoFwV4yxTQ22YAHoAhP7+k+ksyoKTqmcLUDhJ9q8cOILhIdUVOlvJlUoKkeKtkzqtxN5eu7lCSHyd6HTRWzxQ2EIYzqlto2ZxeGSKIfW3l7jbPL3r7es3p5dfDDePp87pKipekxkmh7plBvBTFM8ydVEcVBMJzWd5WDyoeAsSvWhncRfY8uFkI/gtW7Zs2bLlBcqHQfBf//onP/7qP335xefx7Vd/4G/f/6mX//7nP/jZbwB870e//MUPP/0g/fjrEkGtsbEvqS8Wi5FtwCOFDJnsLLVME08LS2SfrOMGtFrkayQBu5nYR1rKnR7uUgCimrqEDKZVgEjvwtkN32hrgholz/MS9Q0AeonzpQ+y6bP9YIWbIGUoAAiox+WwRrpj9ADorXusv2cZiQwuob14OPv0Re8R+B4TDaDwfMn8k3BnGsb7OPzZxjJcn9EDcDvOZjIYC38XDccHeiOHFdaHEDjJYQr9Jyx8uSu6WIQ04L7x1p/AzdMQWuv86uHmyQpHo69nMiUsLnZMh/I0rvskqUzO8GV2p/oNvRGao1jcAczu95KmdxqkffQGuye8PiP46/Rq1pPz2X+NOBDEh8DudocEgtepQdrm0QrpfTuyOqZJ9n5NCyaWjJEEEpcHwZybzH2kKvyg2QFRvzckIZFR03u2G2GOGcEEZMPdBDEyTnJRslnfqP5QAHgeeLbng0qNdA/4+zRzOD4PDySp/Hz52CpTrprpXAE05LUPFSP4U/Snlrt2jSqEJQtDNcwvxnsmTbhLuLTlg8kHeMH7i/v7UfCnr/4Qb3XK17/+yc/w0y+//Bxf//onP/75Z8vPW7Zs2bJly5Z/i7znF/zvf/6Dn/3mez/66Y9++7OvWPb1V3/83mf/can4p6/+8P1//BwAPv0P//S9X/3u9198/jd5w4cxPmxnYaeeUz0yiDPj6aedrQxGuN7tTmnMo4n9Ega4A4AOMVzUr1FzcNmRADS85TGjA9rEFNzjO/GcdLh/7fGUbvzanCZbhwNxBbSQQksc2MJU6RjAfISVNG3Shof4Jxc9Oy55WNKLPbKSopwgEPyCIcJAGL/Ofg5QJoq1nJth925IYzyAI6nrSlcduxh+0kaENMKWCUfWCCd8rpOTy+VS0AqrKL7xQpu5zPbGiU6Q2B1u9ravop7obMq4lclYCf1JMxf1V/NwcM7TbZ42dTucs6xTQDnAtd2owBIt/vAoBvIxCOKYs9TD38dUP2ywEtB/WuEYPZ1BoikUDYG0xPTWrDHiNTqhPwyjL+WFy8/mpCvvEddqLWdMxoRJBbMAdCkWbK5kDaDMU9tXqWHroIKQwT04h7x1lZ7d6FImIuU7PSJNvGPPDEfIAH0+LurdFNpIiJvGK75HQeqpA5hr6F1n3vX1/gM4Cai2/C0fXt7zC/7zL7788gvg61//Nsv+9NUf/vCbH//gVwBCQV9f+p9+9l389quv8TnV9P/wD/8QB//zP//z++3hli1btmz5m8snn3zyt+7Cy5cP70X/9Vd/TDP717/+yU9+/fe/+OGfvvoDPnvXEfWl/uc///nf3oW6klaP9Nl8V6PhZ+9XWrDoZj3oO+4m+bYeGMHfvoW/xHjidIgFjnv4+NDjNB0AEVXzfgbCUlrb2GezC2r6sk8oghHYdE1Pfnjz/4eb9xsKiDNIsRjhAqA7TFJnmmsMEHiXNY0ahXsjOgoQb6WkEoRFoHdnFxe3WynOw9XpPb4G879DRJUrkFbFJTF/igsC4JmglDZfjZB9K7dAgBh3QLrAOQt/Qpi0wxicwJpM7/7V/EFIPKcP2nH8XQ9s0sL0Xo2bJRxE2p1X/DqEkphMSQsoqgTiz9R4PAPAuIql3M2q/BqzeQLAFVj/KlOP2jXA1F/CUYajPtAC2XeMUi4Nlka918dXXUA9TOZ16dU54an9e5vyJyJia5wuEHSMwMx8VwG++D0lxW/n0mhQF1t1aqFsatMLBAC66E0EwIWJbrLzeXQpzsI5cfPQClyMj3+O2eLpLqqmbOgrBV5ZD6iaBkkfnQrJQ3mRaTB9AfNY1te8PSfdw3t5tmNvFP6ifPgX/Kc//MWXP4wvn333D7/7E/D3n33vg594y5YtW7Zseb3yN4qD54v+UxjE/+4/fng3+kzy5hK7Spmr2Y9hrUXduDJ9lEH5ub5DeQk04jtbj3d3BH9dnuTteB4A+kXiqSajwNh7aBxKAtr0JYB1DWeXJeQ/BspYcAHO04fcCim0CAKXm3aBUQBMf1eyvJO9ju20GLEgzNi0nA6G5sfJFot4wAWztR/iNvXwjZ8BfM6PEpd0NhvInunYrZ2cvLPMzhjuQtxEDKm/HSLHwMSebRguye+AiaHey9kfw3yHSPLB5Y8I6nhBK9Z3CBr9ut3ErtKmhRfO7av7fWWsn3KfrxT09PQuk+hDm5UaXEHXSaM7veINkWt8ZY5AndwFMsw9Kjj+TribWBwl3mNR1CiRfUB898g40ki/6nZsAdXoX5/VHCEyV6RKaFCi6wpg6N1NpJyc2RkmCArZsvuvcDJ9aq77OBIOyE59C+4KfrBD3g5X7TVeJPt5KJiuMLuOQv7YZieOmk2uDq1qJKT+V8IPOrtqKgOnnCj6ED8152nxpIkmYwr63WRs+UDy4ePgf//zH/zk11/7l6+/+qP51v39Z9/7zX/59dcAvv6vv6W/3ZYtW7Zs2bLl/ciHR/Cff/HLr35CH7vv/eiXvzDf+R/+4qdf/eDHP/hVKfuQcsc8z23vnRkeCfTtw2RVVTJcyX04p5UPD1wdl9RQY1U8PzcAT2+vZlmdHYjTil8NetYRP2M1GE9KCAlG957gUsFY7UHbam7RFYA0sRT1IIJPij1TLRxhYrdyotiGcJ4nojOOPDXf58VjvHeH/mMobgNEVqrppD/Yr8HZQLWghxaBcHwBbJHc2qjppTH8faE+k2jHOeQZkouTZmkDwkdTEocJgEvddFp0AADw1GIEgaT9b6Z7nxE8DZCMtBYwPD1xPDvrEtoilrvRfQmgj7nSeb8efuDEr1pUMACpEp1APgFcJ1H85UD8fEtVjAH64YdcZ5Do+WyGKuB6BqAXk8sFgjfKvBisG5Z7sYgXZVQ6g1xeIYz9bn2njb+J+9XbDIR7f5vUJ2lGzvj1mC27syRUdlp6KMGDoLzpME1dY8SIqt18fv7w/BiY8PSlpJpnPngBnsjzCGCoH9ukXYVz/lkllAHOXMlTnJ5XfkXksXrYJTrMz1MSNnWr1iU7HGs9cjbCEyd6V3k+qhajQLz4Xh+55ePIh3nBf/rDX3w5f/vhfSX3uN+yZcuWLVu2vHd5LVz0o80R6mmNnMy8007TS+YKsT2lRVoCuKvCeOhEAZyXw5SLnPO3txeA4+3lu39POtemnXacPP38I+GULBVMIu+cOo5Xs0gvyevywKbtmBqJ7HPW1fNst9vIctHIEWe+5aoO8ZmxLdBOhT0YjBe4PV1UNqRiwEYcFPEoH5TRvWi+QL3dtsbBB5Nd8HxdTgZAbQF/PTw7O9O0EzHS5ipBl01KcAC4VCJHX2Wmq7qK5lZYsaHxQwB3d3ovseCJ+POi0FIuGZnAy548hl6zhq1LITxc1lECZSIzqUnelGHraUG3KSDx3Di95vXMQ1jfvp7Pk6G1IHitXvTjoh+2n1plSlwoOvLr5E4St0arJIslsD5WUif0d6BLHUAHk9wDkKDDk0oRX8YeSeZ4WZnlnaEQSS/oi4GrInQjVIuw5I7ozRv0BS5qfRoxGi43T7QonsjdfU3EzzXEmwpcnu4HdHyp+p9zFIUIvILNRKhLan0tyyn1KaWHUlZanfssDHeB0Lxwujeg/2iyuei3bNmyZcuWFyivBcEDUnfSc7z7tJdfKO1qZQCjkYKeZt5m9HCXWzivI0JoHU0acG/XMLf50WUcLZrNLO/wPXDx6p8+aDEkw1yPmabbce2ctg7i2D0x4YQlErj7j5kjTugkrwD6QRs/Te86BCzxMwLTB5JtZQStWygRzVoFDtkJ6Tr/NoIGwxmNx4VBMfK4h4svipmziU8+VQ+kiFcNIz1KvoBBW3uEPniDGhgu87sDaWvvETMRc0fi96gZJPNAmszDlO7lIo2Ifx5rXDWHTPT3R1qFEbOakC5aqIjKJWnmlEMt4EsHkdeZ1HUREO+TcgHQ85z857V02LD7M232geBbMA4SowOqSh1OXC3P35CAvtTP8IzQBIQkRueykBKCrweh/JEuMPPcBA9/kMxzLtWntCgtRFyXEnZo5eOEKgYPSUifEEuFwMGOvFn8StqEduhFhsQYEgA097I/daGbLLZzf0pQrRWW9cDZLTqCGlQSJvM6ITHccQfxFyCus64iKmjx/Echsd/yEWQj+C1btmzZsuUFymtB8HdwvGwiZargWLNlncp5N7pYsrjRpqjiQf9sFemnbdLdw/Z4NgSfm/w5shXuuCtCu9yC44n1JZzYASOYY9b2ybec5mRp81Z5qQZvs35+ehoARsLe2kEA6Eea0hHs95DY9kvLA5uMsBUbB8AzM7lZhU5lQ8O6qQ84EkgdwFEI7yKjvEN/Tz3tPW2SNkvr/tvLMUzzpjzYPTrAVH+4FZp9YIJBCFs7pDuCb+LJ2XwSaVyXBcHLjDWbdBrpbUTJT1cMne+SBFnCMdvyzCOLLzP/ixVIl3glj7mXW7OtIHia3s1d/HREroNfK0VdtcfbyrD610MEbx/IXR98hBbDMuaweJa7Eb0R2bfmJvZlCDFquSbor4MQP3IFBIe762mIddd492SzLx4VLas4vmU+xZzrjG2xChkDYw3SSC/Ohx+E7T6Rs+9QWsrv1keb9X9a7gWUh82pBdMvyB5T+YLUFwSP0pMV2c86gHTUj0H9xcW95T3KRvBbtmzZsmXLC5TXg+AXOB67SgnjqleY6wOE+DS9jy4ArmPeG7H68XylEcv2zsTu897dN7qabtO6Buv/5RGFgXCKbgbGapf8q1J7AKAfY1THe81qEQcfFkVYmDsTlDUZ0eBdwAEicdbRFMARfHOzduGqtvY4TTG9t4rgi029NQ2SrLMg+Al8+GX3v1a9NTQMAAPiCeOpbAj6P/sTPvPhLe/p1Wli17zyjgXr2ASRJ741ybZbENstlswHMv0uiZgqRTwAkX5DuQrQERkMgZLzbaWU73RAuJLJzt3pLwA6LqeoO0/3dHgngr/8r8c2SCL4VjB6v7O1h0k+lplzRVwA0JqYxmY0hmbnGvWepP6i6JT6kegyzthuMalxPmUQS2hfQv9BZO9wWZUJGx0Xq+QCQIsG2aceBdVZwuM1ikIgclMQcEfcQNybdeRk38BJdUmQ24cSqwL3lNQ4PdAKoED5rDADdMX8pFqUHmVdpI3/ry3xLe9LNoLfsmXLli1bXqC8HgTPD0Ebx50rETzqfyXenaasoEwzeNPoDE/qKdtyJ8PdTHU3jjbj5OhEcT6e+1CjToEgDic/3ZCRrPQAjXD3u2N66f8Vw5cEflDpLZtKK13gq5JfDm5X1fgKMPKe1TSZtAXApfrsmdiFvGA0us8bzqFOUUdK+YR8VnLQi95VAqLRwvEOs99i0XRIyQt+zEEVDdrp6FAlmMswAzuRFpHUJdI9V1iTXhB8zwOJsOSv+hhLGN1jSIbIr/zgHbO5O9KmHhR1dpTna7/Sfx6FeT7i3a/TELlHt48rofkoszyI5TQQ/ACAi4hfWiQtCEcSwIB4xMEDOd3lRgjoD6B1vwVaZDJT+oOI9zwArxv7aar3NHQ0xjentZdEz3a5I/bBVVOu/ZHIvBew1hbc5enmSI8f0fAxEo5MYc8GarP8uVIo6gBcEcoQRRyZ33ShBwQAHE1594tFMCxasehKUADE8ikp6ovIapUPK76zEmqluqtDnk31/jkrvEtVsOVDyGt6wa8+ZgKkM1140i0v2eCZsadfu7SNC0C/9Pmpgbp65S1fKWyHbSbm1AqimB6Lkr46wZEBQCGRQNYeSpFAdlV5za+a0IDpILek71BoDIjgtJkFdwxtzT9Iecu15iytjJ0D0Ho3JhzrhjJebp7fyDFDElwLt4v5uAaeLwEw2jRJt5Ya+PBR+v/Ye4OQu7LsXOxbe5/7q5yEjAoCr1+DG6QeFDU0mEh4EAIJUkPTA1ODDGySgQoTiGRCj9xDe2QeLmViVIMX3htkUHhQNLREQoIHphQMHhY1KAlsaPcg0MPELv337L0yWOtbe+1zf6mk7lJJpf8srn7de+7bb1ToAAAgAElEQVQ5++yzzz73nG+tb30L5mmnu3LjyI4nAPd6iy58KHHb/Prw0SgqpVINV5d0Rz91cHG8gqUl83IwHU7GVGKRGExCs6WkYwjBUYmir9PJwXi30UgOvZpIZtMQiLWbGXVe4wkg/ipvwL7JfJTaPdutra5FHPI1cYPPP9Utaam4m5hCN5YvB/EnuMI5u4Sn3VmX05FmBlyhgBGAwkMuVUs809jY27xhkRuJoMACAJUbDpd+4SglVq3veSglFT7kqiVODhe92kfhE0ASdfG/MXXpQrdnXIk3oZocowj32Pv6Pd0dI3rV+IgQm0dw48CHKPvKk9PSDXsjdMNphWzjsT49v0nefAtA0pu5qfjBG1RI7PYt2e6i32233Xbbbbe30C4Lgg87pTDNS2SzglIytgeopIvucN7Bqq9JUNadvG2RDEtFnUOXsT4Ap6WlFTILzHbUnY4Huuj90bm7jk0o3hhGhNVLkXhmj46dPmw77vHP0WDk8CDhbxH3zlmKEABL7CvMxxNh5t5o2/slXkjDv42sKOcB0d331PAYcBbpVGzBen3u+MbBXldcqb4mAFGpMCeB41/bvAoCl7s7YyT7+ObLqAEDMFmuCjLnDgO6eZGYkQNEX/Eo6kqIT6YVD4UQP/dwAHci8qSGu8lWJDQPQdnhoqdHHeGoB1pFPbDvhObeGluwTTbASrv75Nej7zGEa+zNyl2EKpD3JHA8PQRHQupGz7ykDUU8zbGlGWPfhuverz4y5mzN0pJvf/jhUEO5VjyzjtWVOKIluTHMe8GW6cguXk+2AlD1s9nRWBC52V8j2TU92htVDXCfx0Z4FsNRFJeAVWzus1+vqGN9qeqBDgDAQoVaQFldRpCcW2sUMpYJvWW3X2bbhR5OjHq4pDaOpNEUnmd60Qr5ByG6utu3YDuC32233Xbbbbe30C4Lgj99Dn2GDeHY7XMrn29ZNzQqq9i3JJIEXwuQNtpQCuNsWybvByMwrAC0ez6e0KtQWPak18CCgRISEKd4ixQlhpxiX1IoYTukciLqrDBJ2tTB4SFgC6piTgKSawZtJlq2lXVuIUBpSNhWiRViLNHVkXpXqnpaKJRrtACxoYQjhIRULFlMiKY4/vbgrDDEbjviERXRyh4euAmMteehTVlKAVAGQA9cbht6ZD1qubIeSckSNyMGr8GKAgDpKZQOVtqJE+BDH8OXYu2B1IegLJVovcHiZydkXDsRvHsEOtYjAA+05/PUmB2XIXvrOB7Hx2hQM0UC7DOwdq7JrMc+42klwi7RVYrJDFIlPTbAqBIbyD6kbzzGvw4ppdJjkFEqW168D9LJU7FQeqr8lLVspSiZDV6diJS6jgYkRppI5r007YStMh/zcFL4Rc2YeuXPDHjEThN0guoA3HQpWn+k8ZJhg2N0fX1bPv/+RIg9fpfCcbRpJxeitW/puNn4PMf/w3N1UZh/t2/BdgS/22677bbbbm+hXRYEz8ipk5k1PabK5smVj/AR981CMlP1WHs2FwfuYMs5xB77kohXMgQ9VGoMDYtMPclpKorclC/MSB3+BidoeKwPBKCPyqNZr0aVZPgA3DFISlw+ww4fE2Xlyh60efcoWFXZMagERQxkagT7p1i7ukyNzqk++aNRhVUc69tuzlRM5PTA0TD8sRSG/0dM3Xe3QRVFPDwfAp8G2WuRahlTrm/jXS5S+KbyL/0uM5TnOIxkRj/kCKWPGjAci3CA2CGGJyAXb+2hb7NB8IwuZ76zjy+T5VgqxlMJGkPslBompbvPwjUrzo/+JngD8RdpfkRs/khF2wtBnLJLcXYrs9ps9Vqcbx/6suECGhq3JkHMGL8rElecBSTHkPrp3eu1xpKRmeAta/qh6NoMqXd0i75v6sYCYhOgqSe4hffK1owyxNb+If08RBargf1g18cQWvrc6gBdqGMjwbe3Vt2vV/ykgVpPwWCPPDd6F4CR9jvcJcp1ojaMtRcsek3NIkH5jUJtlrDdsOg3P027vTrbEfxuu+222267vYV2aRD8BUZAn9FG8QBoyEGUeCal0OJGicQj640NhktAs6jOqEurZSoXm1vaLuETv2WsaqE07WY1PXlOmxVvLC6u0VWRbQtggJwAOlfvTAI4CJ+BbWaZ8Vn0RVnTBZ6lrPbmULkLG2kBgGWkrU9E84BFhZBvZe3XSGUw/FF0wg06hEGYEW1xza5MtJ7kgFkhaFB/o3owcXwUlZHiCN6ROhVqi5d5teWBzCl0k6rICM9M9DgDn45mxViJ4DdsjRhlkalITF8ZAm8XK9GOg6MPYCB+935IlG0FcH4cqjKNTa2z9Ky9OVLoZnLouEsndqRdfX0B1nCA2WgQDLYKjBQVVykCs8JrQa7PFO1LXJXMdzeW/rI44ucEojhNDEWk7E/FZjjsKb0h4PjseggGRgjahEQxzzsAHIpUY7lDqyqA1hXAShfUscuYetYzAYCmfuiKCan3FIM3ixozEYMPCYeaVmsE4mv3a4fNEqnD2QjxkUc6fk7mk03bnPzA6zyovrlITw5ht1dnO4Lfbbfddtttt7fQLhGCz6H0C6La/owfS3Q81HuKrQKQTh4u0jNqCNmmFpTStJZA31kIpR3K8awCaFUA1DbAQZmjWKFk57wBjad871RSP8sgQKMIDRGdb1b4nwzOL4BRGpVghnntvlADrgTQtih7bwJgJTo5Z1rycuhIueaxu0pp2ygCO7wnKVQ6QqvT4gEdQsIWEU2fg3wBI2w1gVBQmJnSIyNf4SK4ikRm7kFPICCXEX2HiOPyMhRnnUXPU0KvRNDpfbaM6q6Mss66cn2ExkeaeDSZLZB60Ol7G1uFDl1I2ml3QbfYPKL1eXeKAbg7sZ4Xfo3yrwT0LfkSiuNs7X0KunadatJg+IjU0aKvLxElDohnGy51HorE2wdnUpSfsZ549sVFNZLNRhJEGsz4MuaSixa4MqyosNiM/Y0rsNANJvQyKICOTozfeQTT78/oHX9Q3KVEOn+LEszqH5n47k6p1TNZdPEpPgTswkkBI8+TrWKLK1UBPRivGXkPz6WkcjX5Fw8Iqs10cjSluW+k69LP2zNOym7ftO0Ifrfddtttt93eQrssCD4Qdspztc+a6bIRz5au6vFKh/JOFFfmqUfcmMgs5cEXAL3IuhQAFnFvh2If10OtBwVwtnQQBANQylNH5QYPYHe1grNVu+neW2S1qQRHPT+qK9nX/ShR/hXActDC9X0kGG2MCqUM0kcsfF4/sLhiIz4f9S1sOdn4vselsBhMqhgCx9mGkKaavXPWPRDy9Smx3mzD7A3ruiHuuiRBSH8bgj9ICIqpK9nRmRFJ6E0N67uXYrh6Ru8Gsi8JwWM7ij50I3g7kY/ZoOogyeejGkyHsHn9kQfv6Fbjox1j76zAI/OGXTOJXYjggwa9NkfwRzZIdr1XDXIE30dcNx2ahg57kYEuD2WcFQ6FBvkiED8H36F5zJKg91ufzxb6fAjcg0VfCgBxCsYSmSRjDN2BYb6sbp4YhTC/w0+uplqu45xAbKaUMs6OK9l53vzqagvqpaFCc7IMys7wGSIFv+kkGG+ydUXWqB9dSryWQfZwGn90eyzPjgtny/PrIM+PrZJjrwCdvx55Xhb/Zz2cfl4Cx+8J8d+a7Qh+t91222233d5Cu0QIPitTBcwF/KGaFdvGI+sIIBke7Q70hVppM0leGGuXVh2yG+BezyqA9VDkAACHpXuIuhq+8Sd4pcSUh7cjmfVY1nMBUChWb4AeokGP7yayxhz0qOK6LB1AWRRAWboh/t6k9+nZf8Tg/YD5qD6HKRG1N4vmRHmZgvppeWTkp3Tl2EP6bxTxCy5BRPVy9A7UK8tYJLesPHtRBMBJBgToQcv3QCawSEcqRBu0+ZOygmgpTCtaTGlME1s+/vpWM06R0+Wa0tk1DbqydJu2sb1ijGZu8gTBO/Oit5HzbQ10xoOjHSsCu4H+QXpvnJGjfBxj8CP0rgCRPaAO0KPWKRH8OpPhe0D2BnN+OMRnzLsxUCseYEYZgylFNHpY6D5ZiN0BHMiir4tUqyPH4rCeIBAEc2L9GdmnMLP5ohwYF5Qm1Lwzh83gavBnQTuIhpVXdRcvVuGeHqiytsTGLRMXwkg0T56jrVctuV2GQ4eDly+NSGcPa3NDEk0l3I85xL7xKLk03okLYav0gLfLnty7ce2TDx5/dufq6+7JC9iO4HfbbbfddtvtLbTLguBFPWU1cLynufP5szP1O6Wt24KRvw4wRg306pCd33oLrZZO4L4eHMoDOBx6PXQAdVEPii/To2/vDqy31Peq1mDnIQTEL3w883geI9+2ZV26Yfdqf6uGIHw/FxiUL2JfgR4FHl8cKELcvi49Quw57Jae8R2XtDYasuXOdWe4c+DcGZHnNvFsEFBCiHuu1BbV50qo3M8bBk4OhGnpxWWkv4OF2wVApWiAAKTNVzbV2cUZ8dM7MQUeh3eEywNhE+ylVVkhbZ1Z7pmFDozTb0C8ray/Pmu0NeLp0rT1sWFJrbWL6PStDS16u0zOHcF7O60ZNPdIfIcXPgug1zYIvg+idvDzAa1Fgp3hc9c+eidFq3vYhD6zSOW2a7A2WZO34+yAwwEAlgMMwRtkL4VukoG8t2PLIaDSZFpiKgjuZLILxCdDV6fS6ByS7tLEC8aDeRw2WzjfZCBpAYkIw4l20aXB/hYd68eakddeJPYIYAjVbQgu0f5mR9lJRhfQBRp2/jf1VdNy4fK4KoFRAGK3b8F2BL/bbrvtttultSf3bsiNe0+mz/LhQ74Ly+tcsN30MW/54cNv5TAutMuD4ANYCYDSHCD3IoG8AfQqjKwHlOebeXmvpZfRoBYv/tZrMch+flYPhw7gnUMDUJdu5PlaPRZe5grl2oUxU4s7eih9rUUPBtwjw1kAtNWTrgUQa5CaVqS4uxS8OQwqleQTb586cWsBsKA7LWBWpS70DZTiqbelYIb4gxvsMcuxvg+7JQiYSFstLNFGsS2FtJQEvBB/BwoJATvWjvNd5yVIyD46HxHNIz0ilfnuSMWzmeeM4aYhfbr4m5CuMwTGPHhhwrBLsyF0DBMkHK4jqI6ge2jCIwH6U4axRdZzcrFt6Ent/NjaEJ7DSC5H74Mo7tkORimvA2Fl0QAey1T33Vo+rgC0sTrcqkphdAB67APBpxg8VPU4seLHvkKoLhC8n2lfbknlqkyRj5yHUGZfxDsQtedtA4/Ncwz9QPtQsw++fcgG+LkwVYNcQABQrVYY3kpGsEWBzLL7wNCod91DdejvCR+eJT82ZHNCp8+IoCsA/lCNqSwzZOdI+7xbWJAvvCSxmuPveUg27SAtnFU5TlY4KRg/Jjh9BrFhTDQ7Ut0SCV6vXf3RB9fvfvKLJ3csqv7kF588uv7Rf7iJhx9eu/v+A/3sJmCR97t/+fDO/Ztf3+CTezfGlk/u3bh2497rCtnvCH633XbbbbfLa1d/9MH1R5/8wsD3k1988uj6Bz+6iifXfqoa9/OrP/rgOj7/8smzWwl7+Jd3H91+wC2v3vnZ7Ud3//I1ofjLguALqxl7dnsRkyrrRdqSEPxSHIiXEWL3N/yrZMtby+oOAGmuMC8W+X7n0OqZImTdiKdL7YHd3YiUPaDsfG8PmdeqkRmfAXcpviMRrY5GHH9b0F0SkgYgxVn3UNTqubkZLvaI+hfP6fevyxaMqpJ6TECF8CWkQyt1ZN73DKi7F81eRhVvx/QG5DIHvvjYsH2+WWRaEgHIRI8eywMwdVf/5vIA7t29C4Jeaj5cP9Rc3x1Zix6s7x47JAKbgDuI7Ubxt45u4vOxPNLfQ9tOAbLiW2OQk/Ogzh8ZEd8i+KFkpzDnTaFge5Qc0ISnB/TjhsfVWeuE3XpuWvSMxlokfu04bwC0bVL8oVHgLLlM/IQBWBQG8Wsg+AJA3vHSBuiesSLhyVnpEqgVgFw54J0zAB565661d8kC69IZB+6jqPxkdEZR8i/qLippJlmX0PsACEqw1EPB0JYXrQA6Gp1eNitkBK+nicPuX9S5bKrQtHUVjcZcJ4PnnwoQo83Me88+jry+JtfEpjPC/y5MZ9dnuARiyZuYBJ8wvN3f/8NVAFevAnj4odz6mOtd/+AFGnvy5efAo1vycV54/csnuPkaMPyO4HfbbbfddrvMdvXOz24/+uQXTwZ+B/DwQxG59flHj1VV9fFH11+8veu+UdjrSqq7LAg+kU0VQC/FpODbUrqlrRPHu1pcQPkabwy4T6F3EB+XqgcHyh7JrouW2gEcDv7I6nh6ZN76E70H3buHpuKZK3DwOAhDbosC6M1j7bWqxeBDh44QQqMuu28+x6eV9OqgrEeV7c2zdtDvNz0Z9eM3heRjR138SFOkXDBC7KEJ79g+wS0bChttw+XLCGE6XCrReTa4cYtkUAKgqORE4hEvlOCyi4mGr9wDnQce9mdpuEKyPavJMUcjDv0k6B7kdiL1lsu6r1v2+6yODo3s8ynLPDl22kSG7wm4d/oMiEcB4MA0jBiyCNXrtKGuHV+tAJh9robgNWL2R8un1xME7yOgcQJiuZ9veIM28aq4ZyZY9IZKD+5EYjoHUxvOivzOGQD8J1fwzhUAOFt8L66Ft2bP0ZiXQ+ZtIHEut8u7bZ1HY2IReftx2DRwtXyVQraEI3heEZJLPMqYhtkVMDacYK4dO1kHmxb63MqpTpzwMEJrwI4ptAYinT2PVQbim+D9s/j9m/mopCucQvZIJXmD7OZPbt/68188xCePbv/sM7u/f/rx9Y9G7PzxF4+AF0HwV3/4Ph6NkP7rtR3B77bbbrvtdsnt5k9uP7p76+6j2z8ZNLoRmL9349bH2y14J/c1/vjuI2/ppx9df3T3j0mof/jhKQH/W7PLguBL10ZEDqAzbb0zdr46cBcCeifDr4faFwFcFa4WipYjPfQDEB0qbzPnPHSg+AyvORo3tRARZgBRRSoDcQtUqwEmhsgXj+4zBi8Bo+NR2nbknGc+1TVQeNrxkxpQlsJA8gbxj9rZKunJcCNjl5Zzc5VKYgEMhQvgmuUGgNDjK8P3HIrA+rnBOIZlZtEfSkTx/TSNj3xj/HlTmA8SgAykFgAsPD6e784e2bkQQvkqqcj6FHr30Z/z3XPxN82AO9WC0xCS8/PhbyK4HkF3JIQVivGRrRwI3pf0KfraOoEyR2HUcGPLduapOe+h96b61HtoMyry4COKrxtXTKhD9s1HBSDB/K7iHhv7Wap94GafATaxC84KALyz4FDHt9Fy6ygMsWschmHtafkQyAtiROQmGM3ex77wis8cC9/S2i6yAFDtXYzgYvNhxLjJJN/i3+BBOHaHzU/fMN5Qp0E38Dd+MygiKQHZc/+iPFxLaRz219eX4cDzDeeUiFhzQ5J/lm2VBcYYyyn6fxPs5k9u4+OPx/395v0Ht+XWNbkLANc/evzg/Wu3vngMXBtb3H/80Y1rvsbtBw9u3/pzAMDVO589RnyB7Af4tu2y3OB322233Xbb7Zl2877q/ect8IeaO5/pHV9yNb0HoAH+N1+8NrssN/hgv28+9los+h6qc50J8bZEDzgsQzo+TE7U3DYWyt9mpbg21gb+qo7Q2yCl+y7iowP0zF3PsmNMrLePSbHOU+oJmBwGdaKJ4sWkWUBqRNAd1wa+ibigr/CsGNo0DuoIqZYpnR0EWoV4RnhyNmnugSrC9VAJ5T0MPQeZRg3sCLayV+LpyDgrPXqi1FiT4lhuIdLaHOCIcfLtgPLhl7GRcrzTU3X2TG5fRwy+X4TgEbpybRuM3wDx+Gh7DAR/Gnr3wUqZ8QBaGwi+TDNySM0HdrN9nXcAet70qSXEM0j/lMUEI/GdwXUbKYvBS0Es1xzXVZ7gpQgG/EVKZ5BNfkUVAMJicVAOmn1/pPPqDBm9q4jYL15bEw6NWDMQogK6eJICtSYCvGavlsQlMtLai6Qg/Yj+x4Q5Qa/hsOsJmi9FTTriULR5AXgfY3UXkZBzIwCoTAGNmHoC93g24M7aBJKC/ZL4LnZp6LNo87F5jEt8M8s5jhIYb14I/m21PQa/22677bbbbm+hXRYE30NS3uBvKv6Wq7avZ9US4rWIxexrQLPuz9EjxMXYua2woY4D/ugrJ3xZ/+iS8r5y1n1jgwTuhikZ1I/dBUnV8W4UhSNhd8NiDTbA6h+90vxKgB4CdgN3pJ6PA9EJqSsRdadrwNIHStUkMT4ObejTRWiVu+psv6aVAasejrPib5ZCtnVUv+bKF+LvgCNFQgNcfXO+iTB0BiRJq0DVM5jnGCxL/A2o4ti0PUOxjorxARb76usPZO+YXjOCF3HA3fqE1DvbCRZ94O+xAjsXZdQBrJ0EhzJxHERGC+deBt6k6Iw2r+dNv3IWvcfgzzsAqTLY8k6/51gRAg51+uSDKPEuoG8vAGSh62IpHq2PCTo8KvTwtDmv3c5HiwuGPoZNhBnztQGkPPiBZwUuSJd9dniuhUNgc92HTyTacXQbDhcAwIHKEJ2F9MzVtNJDFBIRIy8hvG6sU5d3HO8l1uSG0QEzmwstffTTsMnqoBzk5mdNhrdjDFE+tFNn526vznYEv9tuu+22225voV0WBA9ir5COZwxeHLsfCoDjWbUnZxG1Em2qDrI32eSJUj7+AgP/xpOsbb4sWoYzwFow3EzpeFHIhBKIy092AVs+SAAMPCu8apwv7ymopiGexUR8TYT/vAIxQOrJ3KWNaafC/3AhAClg2tWLs2VKs40kIbtXADd/ScTsz8qkbbcQwR/KNlofK1SS52uKQAoh+0J3CB0DukSfrScMACtTEsiM98CyEShUe0ez1SoOcVAy4GpKfw9oDguZRzo7o+9ICJ7S8Roa8iPWHrDQMN20/hbB51A9YaMGyRwAmh9zLVJTMfUysi9CgS6Kned29Ngd2dvfOiqj0Wfh801Hgr3P+Yz7RMgSgGvFe1211d1l0pXh/BkNty7HAO4L4jSMN4Il+Qpit6VL2Wge2vJNbTZabxKMGJm/Dp8Ch8gk7Xx6aOteHr6bvoLPrnEuuWfBgZUgAHRNvkMfJbEBCPpIUDPyUeTfoXwMNegsiZhiDYb3a1PZfT7EzUhd7B5AOrYc2mcugh/gm5gH/5baZbnBn/qS6LGXXA22F1mYchZVWVVHG+EwL8XVXTaO+syVi7KftkPwylTXyz3pmZewjdukxl8AqmK/DiEv47y/E2mL5OofzzS9+w2/UIqjF5eS7d0fLIYQCB8FLuyAiExqHixQK+INesJeOrJI+Zr6q+O26k7iCgBn1DvJrngAiwyaXuoqkOh747CTSWTT8Qng4Fq5nk1X6KsPEp39HHfx26OiK32iADq6naxKjVK1YiSh7jF4gG3coacu9nFrB9CG0I2O+7r6mjDPvL2Zb2KtsfRLy+VlNeqvaPqtdelZ6tLY4CxFF3qwAVlqhHKC4phPdwRUtGt/2gH0cwAoS090QAHQw0POrckj3BQb8XS4KjrpqTQ/39pUJJSNpoCKae+ICKQBlO8VHnLEtHyWFC94tDCNrYhXklU+C/u1yWzGza1I6vyRz05CLWRtXf3Wbh/VP3aKJdtqka7pdZgE6nVzbETCFT92BWAo9Eho0+q0WnyIO7c8d3kuzpRgQmTVoY8nicm3z46kXfg52T5ldZ32pXkK7/aK7U2/wb/77ruvuwu77bbbbrt9w7b/tn8L9qbf4H/961//9o28++67ASnlud6hgcMJrEuhJmRsuIEfge83Mi8FJZPs0oN0FiEd7vROX721SkdWa4M4ll0CQcobmT6GjwmQM8S3j4YKIn+vVjXsXs3jqrJRlt2YKpmGLJrpPoYiNWrMjIQ6tAYqk0y5OpJS0VhbRINth4tAQPwNJBlvpmTFNHqGhOhkGdy6Sl+9/WVQQFxxtiC8FNZmc/mPVooXDPUjujBcEWlTZUnpbYkrl/VtQrgGw3WvA7hrAOjt+tlj01Ol1DUpyI40uRC6IWqP2jB23ot4Epr9PaN7hQhegwDW6JBvRParAliPBUDpg4A4JE144sDz4svHxARFg8yX66gPQK/eMenMuzNXU0/1bc1dISLZhxOnprUJPIbKjTR3F7ciso5NprhJdt0vuAAkD898V68l03VtunIJOtxF31RtIplnqDEmFalxhTPHfA61qBWKberpc3EyvXhMFw7J1js5QH+6pnAK6PnxAidBXp4chFOVmkg9JfE0vlV2KZbHcCG5k76R33Z8Ew8K/2v5r3/7bvz3/f/67Rv5xu1Nv8Hvtttuu+2226uzZXlrOQGX5gb/DHkFUZWuAIqBg4i3F40sFMcUPaAbm+wjJKXB2km5ZCOWiYl7Qn1Ww9Mj+YfxPNudo1Qpo516UWg8IcnYtbCl5DdIRV15aK5x65ypGRmmMWL0LiKOkQ5HTY2hqlrGGEjxDvQoaxJ9BpAi31EkJnRkjwwfX0klPyrle0uSsL3w0iwyjVKsNnwGDPp6O3AaYBEpRYAUb/fRE6NN+UlR6QxIF1cZ6QA00lIKBAdvYz3GhpDgvqU8OltvzA8bCwrdBI4PYlYE14G8gnPioiTMsxB8I4LnKFv413H8sW5JkseOVRG+gabqije9B2kOWI8pQZR79kG3ZT2WuxPLzmZDcToLiYnF/haS7xrH1Xr+tI1jN0psaRMLI97G8pbaxeQ3G0JDSMBd0qT3jjrEN8bYcOeNk2BI3WPw8bdpA9B6b9oBrF0BrN27spAQMyRpndvLj+I/SoVOxVERKs2C4dw6+ZnbZMmy6vBIXYuCTBmIp5k/Uq1yZqHMuDxv6CeNtXmjP47s+9jkDbHlsN/gd9ttt9122+2ts8PZfoP/jluKwXsYb9T39MAe4DjeosuD/U7xUV+eWxzLi0a82XdGpJ441PFUrtxuSEXGm85AeGakIyvhDB0bf9i/cHqqhj9iQlJFGJpmDN50aVQkVHcy/JXRQrS83RUZ4u5t8M27hL6vpgAEjWIAACAASURBVG1ELgi9Z1RxbL5gJXI7DMEc70CfN4zI7UYhp86rxSgEsh/AnWtWpxdYvtYmoOnLFWoQTSANzY7cvo1ZQ8ZEdeA4YvAzsB56OJwoI6FuTJ3hYAmItIFsw7MEALo2BuPT+lH+FcDaldVdPQvOAt7vOPKSs+rLW9fYtbW0dgD9qNpLOjLpjROeSJ1jZ84qht45rSQSRoJu75t0ALKqXZbauifyMf6sTQDgvMninhybSTIPAkqhN4A0hWDXM+iuvQGQTvJ8D8nbPLPYQsxdHhlXUrpwenfIvgJY1UvhNu1rugSo3AOlzJTw4AwYR0heoLVIDDI8TxPasVhPugI4dllTqB4JkXuwn/i79fmS0W10P5xnZAmMEc2/M8qTtUmuy9lx8SZrK6ffpTfFDjuC32233Xbbbbe3z5YdwX/XjVmjRL8DuGuOvksqtOEiMCen/kR2RmGp5w6kSNimEM2o4BIR9IQBUrr8hMSLOF7PErahRIL5aXoTdgtkn7cCXNZGuxR3LXgM3hOq2bHnhMgujPONIiJ0P8BHwFHDiQQvV86t6Ti0EOW4EoVM2S8P76kvaqR+x2kqVQAsns6cdvqMA0n4d4KA/FidXZ9cNwBECpeXebw2cWdACokDUZs1ciEGrlFNAjgReu9JWbb1oWCT9zidiTQzFBY4H5VdlCnyRyL4p4bgu/uQFhZuYZa5ieNq95o8o2isjr0BHnpvR2lHFj1JiC33UkMmhvkX9pGODz80+1iVtIdVUW0mCQAcHYQKhFH/Zpv44PdKpD6cRbaW96R3733r7rszjaDS3AXUNn6qQKON3oYpIV61GxFlJL7zHFlfi5SDuTGU7j0xZC8amSYJMYd8z9rLVFpIXTRA0sT1A2XwPgSC8+9McICWKD5MfH/k1ZWdXk9HqsRwOWUV2xCnmuWJB9DPbrmN9M2bJnSzI/jddtttt912ewvtbEfw332j/qojdQfuJYLxrQOQrr0XeFY6AEjVnLSrUxge4EdViYh1QPYpDz445/GR8elYP2H9kUZfq0YLeb8KJJmtsWFWgswdUOKfLkz1hhimrzYLmHDfe6JDb2Nvhl83In0RoI5BttWSKhlj3tYEy8U6CFiKZufEWVVDXL+zwJ6tg0Nt+6lpv4ZXCtFJ4JJMBu6Jiu7ZwxBYjF84FNyRRdmLVACCwoz/yiXi6xPKB9Yf52bMtmw8rz7cpIwPIEyYE6H3AdkToN9UfR2hep1Cndyhtj6C8cGfR4rBr+qaslYiSIY/RCt33Qb011X7eQfQVmmrADieFwDtXNrRT3BEW+0TxZ5zgFYQGskjySKKFflyG1Q9xjH6cZn7TUOhkH325bV7Wdgiw3GSrYVrqBmbwysGtdVPSlXH6BtF21K5xB0GEXrXCfE7t0NQqyfKe3ZEcwSvzIDvzh6gKrKz6P1QUEU18RUKAXfjCiOT3rvp689+Hqj6GiNLg0B/eAJ8jwBwhYoEjSWVVybL9EQtGo2n3x+wwU08PrKFLiYNvT7bWfS77bbbbrvt9hba4fDWFl27NDd49UdjU7KL9PeIwVvh1Lr2pQqAXqUXj4ZZWNfAhOgGICXMxo+bPPUpEjbFnyzcqJv1ZQ69e8A8NXHCU9W8PIL90eaG+S+C4hAtEqu9AaJVimR1RvsGYo4GdWzPgKYAKGOP4cMo1Qr6RLxwVHyxC2spLAZTAOBKdQn6szLy3ZHS30OMPPBHltS2js3K78EcdhX9bV2NiFgSo59+Z8sj9E68VZxvz7GMACR3PPTZYxABc9EsACy6LJ16X3oRlEcG7joVlWkRLO1T3ryOY/YIOrFYIHhTkkdTrwPLMZWI0wZPm9VlAPSnrT0FgPVY1nMBYMC9N1lXzshpYjnQL2WMgcFgo64XdcaEVBeTsInXVweVRTrawICysH6ErqOrZqZ+f6jMjz9B8NaBylz/Ulh6Z4WBy3rg2an+xlYrk89qXNskXdC3tDgbQL0WEb1lzaakA3RRh/JdFigyy52HEiwSX8HEBrrT7LuGaj4dhwTuF6jTY8T4WcFn1J7hkUzses0UmekXixwPNlhYY2jM/JF949dOn7u0ufheu+0x+N1222233XZ7C2130X/nTQho/Im8Qw1cdjXsbmG/uvZeBcDTZXGBbPVKViVkzU9VrJDz5gc22JR/jcfYaIobEvVK3lwHSE313IB0IAQPFyL1jXj+6Czxd6CaPh9CKdqLAGjNSqOmQ8PUysl+GWs34bnabVSjimsopBkyXJgHH1XjohpsLHchu0D8hAkjcJisRkx2Vr9XzejBARBgRb0MHiljn14qQBypR0J8kUGbT8AdEvkX8xBl+JOgkaYyskhdFI6aDl3ASVqsn9DsI4E+FOMdFAKABrIP1NxG0B1IxV5Xl5Qn7F7lUACUoE0H4PINdT0vANbzYtDc0t9745sOy1Pn9NRA8J0Cdp66IgJgOfNVe5NuF2MbzdohlIOhRRsJ75gcWD+YvbdU/qIeSVYRp9JUijj6UESBB2bGe8SdF1W+npHdIRvw6U6vqE6sUFdKUKsu2F0znshbPGZPZo+gO52e6eGuZMmrstBN5nNBIkjvLkkH7hLkdj/EOdbeUyg9ncyR/h5k+E36e9hQh+SEHz6neZrEUHFMwpUzOvxG2XJ403wK35hdlhv8brvttttuu51afXtvg2/vkZ2aA2IFIF1duq710gqA2jqAvkpZCoBl7SscvxJ4AoAUbCJSo/FBhp9268/UUTT9RKKOFPQAxAaCRwze22G1rhTdmva4EbaLB2dhBxy8R9Uv9oRP4OGzCNmuDqC1oXAXLWU8M3wJ1IqvtQOoi7Ks+wi6wxG8AliqP8yflYHdkUjyy6ZYnAwfgC3OUXlkn4S6uHfK8zeknvqcLHZSCLitHF0gf5FwCkj+uKmu/TyNLiGIHDF1ksLd5phnKSkgi8hiR0uSdsjs+lFozD6OuvL0BBhb3oH7sZukfG/MX3ep8F5X7ndQGxRA/8oE7LAeBcB6Ls2g9grYPHFBdzkeRw+lYF3NByaunFiEkXGxFdydtkg4A6wd73iD1agvpjpX+0jZtzU4Y4T18TwGD2L3CNU7XF851BFSJjc+ZnbWqJejb0kJPDH+BLevsnT3jHUvqOhEFe6gqOu+MQJeYDkLPsH7ycyhNqJH30MKQtzXped9uEmUE76nGoT2VUwKOlRmAjwHgKIVg/UycjL854GuJfo4QBdCvgQ03GapvCa/knwIb47VZUfwu+2222677fbW2X6DfxtM03+le8FwFa2eHCwAuvRucl0s4tR6ORpd9WAgo29SatODq4wd2GOsPVN3D0AKGebBk4eH3vkmtyoD2g1NupKOQYJ+T2dAclH4CrlD3R+opbi6fuw6Bkg8SVfSGlAtUTSsU5lO4ukeUFayL9Wl8Tz0LrA68YcagFsBVEYsz4ovP8xs+YirHmbcFUMiCWd4KfNIEJBYQQEsxf9mkTcQHlWJfGWhBL0XJxfmwTt4hTqNeo7+C2Zvxgix04GiPe02ByoZUx+x+ViJ62+UCwdYS/nxA9CzfFzEVD0YzxB7iAMYjl89NN5XsYLulJQvhrMXbe50KI6k1391jIgo+N3GDA8t+uNRj+caQyIF69EmPAUYBCYt738Xb7+2aEoBrEeX9hfxJfZDXA5aPVvdmdyydp7g4gdo2eLVSfIeoA52Q6vuHQl6vA/mqqUCkN7di9WJ9a2BcoQMKgZGoTVUqQA63O/iinVkgxRU146AVYsfKHqTlVMYm7cDOnYxpH7eBMB5l7ULgFUlliBF0OMgi1Cijh/BHtshjd+M9NVoJMj2iTyUBemUv0t9vgJOj6hP75In8o2x/Qa/22677bbbbm+h1Z1k9103GaEk/8gqU1qaBwgB1CIWoRQwDn3AeakA1hUAKorFdYXi82Ssk93K4F08GPNxVQi4uyvAB+COJ2iDm4vje8bUB896kro7iWMNvJ46Nn3tsFyjqjp7ZjtS9aieToBX1Z69Wyusc+aQnWBWB2/Aou8CALUoufHuSzCwdFZcn26hWtZZmSTlDzJh1zGGCT5Xrp+1xiO6L8CB2N068Dsu06Z59ATqqfiUshNIQcWpkh2SG2EOmxfkJYkkb+Hb8YYfx5oWLO02+OlUJaCElNceJnk5gTsT5e2jagrGzm98haa9FZggHRPZAUjRZoi/Ryq479sRf3dHToA/OnKwrgqgNfU3q/puDS1XZVRel2WM6KGJELhbH9qYmn6yvNgbAGARFHFGuLNGWpFCTG/rrVOKgfs8wgXUWat8iAfE+hb/F8+8H94UAv1GTA9IWSxzv5OXMsgZztUICs58Bgnxm/bI49hcrxZ6P3b5qhUAX62G1B3Bn3fJyeWHYON3n9x9nj4hrt+miZxC8htXVDiMUsez8yq7nGbX5TiEaGHe5UUyj6/VQlT07bPLcoPfbbfddtttt1N7i130b61E38ZK09p6bV26SldR2Kt0rau9el17XdtybMux1fNej60e23Jsy9qXtfdV+irr0V9tFW2iTRxhq0Nt7dKatCa9Se/GP5f0jDt0mA34Jp62GnO+1h4Z5P4VBig4ffS1R+kLZmjeMyCitWqtWguWYrnpjoDtVQtKVYujx5tadTn0w1k/nPXDodsSY7NPr6L2sqYG3b14xN3enBWcFbxTcYWv31nwOwveqbhScKXAEpt9k7lvtWARX+GsoIAv0SJq76voIraaxjEG5R5AFVwpeqXoIhp6aMa44CjRTMdcSpFapIoUnmEDwnrxmFsM3lB7vLFXs9cRfUVf2UDMm5OXIfLx6ulbkpWn9VPXTMlvXq7NX2iKptrFXr2JzW3Tll/PS3rJej6W2Art6DO/N9Hmjgmbb3betfuxnp/r+bl+9a/9q6/6V1/1r/7VX+dP9XjU41HXo65HPR7VgLR2sbHpR+lHaWuxjvVVorfaRTvsIkZTT+Jf+3BUgPoGNhRWhc93wKHbjFUew7airbquWI9Yj2hHtCN687PWV1/iy492lgX8SeEwpMtP3TumbX5ZH+O0yLHLscvTXp728lUr/7L662mTp5QE4KTBUvSs6lnVQ9FD0aybadeCZN2IZP1kEsWGNiQ2QfIIxa4vtM0ekjSeb1H4W2S/eIU/F89o7zVYXfS3fz2z9Sf3bvBH5cOHp8vy4ocf2oIb956M7S9c+KJ2WW7wu+2222677XZq5aC//esZbT/88NonHzxWVdXHH31+y+/Sj794dPuB0u7fBIAn927cwgNV1ccffHLNb/oXLnwJuywueuWjrEWuIumzCOztSf3wgfsO0gCURQG0KselAKjd89QrE2LLiBN7LNM2T1Xj5i5FhMrizQW1jhB7PDgX8dLRADLfPurXbW1A9umpOx7n48jKYLPKvCnEynmxgFUpBZ6aXwC0FnhX429uSaZPvnck6nuks1cG4/NR64l2XqTRLzwEi7UXjIMyM95+IYs+IoAul61iGdUHD+FLhNhTxDWfp/CelKzApfqsilg6zmuQ5KktB2DSoYu4LxL9fgqZJ8W6LDXPTPdp/XApUJres94bg/SqsQTGlme6uTe8infHgrWF1daFOdxcgeQBF0jwevCrtKYAjkeP4jcnw+vRWfQ+ZAelsjsAYF386lyPULVajgqgdjUWfankuxQF0KuUOBCrRxd+MGb8Dw0+X7MDkFr8KtVuReTGYFpQXyJCzaixh+oLY/BrYlHYtdQAiJRBRyCZw0++z8Cu2gF0dY69znsAtLnavABYec2fVV1Kj7O9djk6bd5JP/ZRmKWu6sH4GuXm4AcacfAhlJHGLDoUkfhNBP1ilxW29ILMzIj9zDt846wu/etX+s3s4acf3/6ZXgUAXL3zs9t3P30MXH3y5efX3/vpZtXHXzy6/ZObAHD1Rx9cv/vpw/s3b1688CXsstzgd9ttt9122+3U/vO//j/j/f/7P/03L7jVf/a//B9fv9LN+zpuyA8//dju64+/ePTo42tyFwBw+4Hevwnkm/7VH76PT758gpu4aOHVF+wgcHlu8EHbHCWzHNKxJlLzB+2aqaIYj51edG4ptRuUL8c60Ebotwt52oUFq1xbehTJZpU2B5G6LB1e933sUJsQ2ed68Aahpkfh0dPhQrDlmgHpkJoXD48Ji7kZ2O3qMlsiyorl1qyspQOQwnz3tbScAN2xBGMg0WWj+PrKQQ5GczCLQ248I/ics5vp/OGEGPXoCNmJ7N0Rs3EABGopdOWEjrflwZdIfydSZ6Iz2dHU+aMiffJ5BIkciAwKIJ3vnD4ckvLNEbk6xz6AeCr3nqvDZb53qvuuUaydae7qVeaITdc+ID5GR4wvwg4KQj9upCkXKjf4/COC92M3XgFAYbujWr77etSW8rJ7dzr9qmogvhSsUWYdOB4t+RxlLXmoQMgu4WBzrT0vMF+6RlF0PzpHsdR17+rYvc3ekRFYDihvB1+YJF4MmmsrAKRU+4i2MrVg9fXLDP4EBL3869oS3kUWM2TqA1eIvHPP/hiA3i+WGFFeKb6CX3A8K4XKd+fdkXNoH2SAnsdg6jv/5qMaPoB0FYOyFl0vdCSOfWkkfMR8ozLmG2L/3//838Z7eeHCM3krAP/pv/vfn7v6k3s3bn3+0eP7V4EnX36O6x89/uzOVVt+4961z+48/uIR3ttudOHCl7HLcoPfbbfddtttt1OTV14P/uGHcutz3tJx9c5neodfXf3h+48+fQxce+/66XYXLnwZuyw3eN1gOsCeL6W7zLUOdXKPZDo4OHjBeAMl0rrVNi9VaxUArQqAlegyqsDVrlN6pagVZxNxjXdHiIdtNDdVYd8+GWvUfZsj+jnEJQn35HVyTnnA4lxkqykKhcYmTStQtx9oIY9/PiDYkN9Xx7v+jK+yNj7jq0YfitJzwOM779sgfVa9jh5W9TEZNbUCJxPQlyFpl/aYkH1JPAYSnscuREpG6iULCk628aqk0O0oFhdgMbk1hr+lz9C/Dbw+l4kbofo2Qf/hD6HauGN36tZ5lffG/PjmYvUOpHrqsvfUcDyrmnVWKQyF/zbF4KGO6dcjABzPPf19XR3Br65Rr+F68OnRxaC/DUZrag6hdVXiYZ/hhpa1OvAd/fSPMmryJWgep0YzxAe0d58IWcDfq70VH0w/m/Olpzx9pcBU6I16IyWmR6BVi7Xz7xjjFM4eH4U/TQXuqLPuHOkmWSj/FzUXVvs5ol+PaeuyOGNGjt2uEfvd8g07he1GMJ4+s2fh73H0M3DvOl0ym9XSmIUbZeTWx983yuTsVd7gn9y7ce3u+w/0s+cHz3mjvwqD+O//5CqACxe+hF2WG/zWhrqMT3Bz0Yt6tdDSpVVf0v2N/S3NfPJVe7U7vQDovIf0Ukymo3cskzuwrMbmo/Lr4azBOTYFVlx1UqKlW7uRCFY0l0gRUUlqM4ibVlS1ma+/nO5CD7NOt57wY48btm/ibu2iplhTRV3p80iVF78TuTyIERKrsGBpR3XxDY4Fm407OrVxfI/L3PmVDtjgxI2ysDJ6qjyERdRcnXyCYdxk9kOK5DclL+OOSijhbH/QxhD56HB85xv2eEjharbmsviN0XzCyluOpXUh3+lnkh1X0FFUZnP/5vrUrFX6WHXqkfAJJN743szlbpqtSBPJVhBJNEHemJEUao9Hbby12+5C3dndv91XcJXYJudfdQAiZfGrCQB6ZQVdBhGUN4pBUB2yLOkYTiuuxMNQ4RialZneGdeAeOek1Gm1OAYPsHkXv/amJSjK6jK2Y+f2Rpepja1OkRvzqyVPu7JIa05vA7BAVxPGUb8BHwpZbhZGSXP+1DkPfhXHVxgUiOfTDSNXo0sBidIvDNIDgeRtchNvjMlZ/fqVfjPj3f1+vrs//FD+/D3C+Sdffn77J/cBXHvv+sd/fu+nN+9cffKLTx7d/tlNPGvhS9hlvcHvtttuu+22GyCHV3WDf/KLTx4Bj27Jx1xy+4Hev3n/8Zc3yLG7/tFjw/ZX73z24Au5JnfTsosXvoRd1ht8PI7HY6b5kFfnAXVBNXSi2lJ5GVEUS1Pp3MT8lsWBc69qTvveywpBckRbCpB2jZIbAFSlLx2ALp5Q5xlE7KHJzlgD0xEMF0Ri1dkTOr/YPKnHY3TUmdA0CEqk3smJG2CGcHnywMMVQ3uia5k5thQcisBTfYabMUYziD8Vk88gnu/j0Da5cINtB6Xr0tsPZF/S8iJejSbYeUOYNic1mdM+Lckfs+secbThdg20HUA4S8CMY6PfGXDellVc6W2w8AaCT77RPsh37o62oq7rQPaDXmcuegf00QLCs51P1sY0XPF9UAaneJA4t86kWwAEt87S4VqDh2bsb+Jr+QE1VmflQja4zVbNzuS8PillyrH3KJLX1+3dJqQ0VXO/DUA/o04RsHKtfxS64vNeI86ScgQBQLq51IWiywkDcxZtXEFqZYh9gi0ijeQ7Tb4EYbBppSRt9nKb5eVxSVZq1irE5vzKiF4Z1z8w16c5NUmxB78GS1Sd8VkUDYRrB9PZcRsRN89RlNHdN8ReWQx+irZ//fKb91Xvv9DCF7XLeoPfbbfddtttN0CuvDIX/eu2y3KDF5JJDMFpVi3xFWDL+QTtz6elqT0RF8btBtozUlIIVVianHJ51yMqgNWAuzrppXYFg/HwIrS2PTPxqgI4HMgzY3BWMHW6963ijcWbtfvBbOG7bPGQMpXLYTSXD4AOAF4GBhlYk6pmKWqtS7NUnxDU9KI1fGZPuToAVpLsECU/GK2PyqgHAip7c2AtGcbsmSYnjtEP9BA41QhjBa7vUD4S5PIRBUBPiyNkWXwFe0P3CFcikLGosqnSAuirv4mmBsSn7o2MYL/mUxs+gEyya4kXFvlvgK7dGQpdxxtkrKmj/Ix/Y/3Z5vFtbF09mt/axNUYh8JyMoNbZ8H4p71l8gD3GzO2d/XSTRUAWtUUax8+BiiHmdWMNtfs6HYnyc59GOJKTV0tQU67nztbTVRjZgziif0tQSu5yDEzzo6dZUJ//qKoqpUpYoqodv898cnAIkZqPodGBoSoO9RCoaeHxyd+moBCr4Uyj5OCTvy9gppTKk5a0Fd7DCo5s36gNoQ6zfsY2iF2y0Wda4TDL++owyvedv7IKq/3uB7eNHv1LPrXZl9zYJTBzXq58cXL6+bttttuu+2225tkclZ/+9frPoiL7XkI3nP39M5V+K3+9oYN+N2xbbwog4GMncSxYS+yLgXAelaM3d7Jlvc4m+pG4sK1bynjEhKUhuBXLYUcZhlPxehVCHyLx/MEANa11OrIvUb4LfVduzN5hSUpHZ2UHMtDNBgHGs90naCoEvG7zgxX3cbgY5QwwZ5eLCELTQaxNtYEwAK8jha6wlxiClSOnTUYSN0zfE7R9AaIzwBsKbrQtZBj8yEzO8rFckQ2oHDzzRC04Tkj45xd6qwG24+AhZddAHYg+Hw+RjpcCN3EtJixUpsrmZIVr2uz6LsPOkPy2tSbUrYRb7iHjNQNBvLdGIZBpGiIqq+bOLrSyWPLbbXedaVUbd5Ra+peCvXwb++eF3GkEu3ZmTu9XPuWSXGDLc+rDoDM8XHvU0qHe2aIdyM6xFGYNhwUipl3MCLMyTuhZTRIJO1b+HVGt594gog6ed5HVChoJcJaugCAAg/JC1NDZQ5si3hSK7Nsw1UmwT7xYkrCntp6bR49HReXpg4gXXRxDXrf6DIwuv7Iqp0hfoGvoOMbX+0Zp+e12asj2b12ew6Cf/jpx7j9szued3fzvuoD3Npx+2677bbbbm+RHco38Hoj7aVi8Dfv6+P3blyTDx/oT15Vh165+VMqH28Lg+iWqF3FOfCltEMB0JbaDMozFi168QMo9TypqqnwWLtjRm2EL20dj9DrKvaYtaDXuol/WVcTbpcRocwyOJKWIx75Z5QzhG5OY5ne8VSNhgG/vKO8lSeywyGCx9iEApzx5K6A188YB9HUgTgwQuw2ugsRvKMQXjUeoU8DE0Cc0Nw2d7r+gVA++R4UKQ/e0YwyyDukZ6c8+CJLGVsP70QJJGflX+0NgLb6x4zg8z6tjCxM3mX1TQAvZooMRruTyzf6Nq3TYeJh5sh3d6FW3xGSzbiWsHgzj0fonx0Mejy1djSviaAHGNBnnn1ryBC+95if/qYUCVEAALWKzfxTNkAqKpq/cEwoMk/rzd/c0Y1JFhLOQ8QxL5HskF1I3Da6niUCdFrg84eqToUOmnWUIPJ2uQO6Bv1jqjA1/R2Xr8j8rbLjZBG5BLUAQMtTPCN1wXYesHFvOXVj+/NE14Ivj/7oaMT3kPwmw3X0xpicvbVctOcc2M2f3MYty7EfCy0tz9L6br/y3u2222677bbbK7XlrXXRP+/J5eZ9ffChXJNPQkOXix+/d+Pa3UevvnffnPUyPzNS26wXmCCdV3SoYhH3togB97bI0aD8wYm4zEuNPPg5cpUjTvZMzRIg9iy/QlRHfE7EFe4KUS2TSr2V1igPV/Rwlp6EZfukHBZVPnN8WiIUq7EL5FHpKkPnFWPDWCdawBC7dWDrZT016lIopqxjIZT3Bp+uAuBs8Uj5oeCKQfkKAFVGMD7ktKKTsV9MdHqP0wapnrx6X54BDCgoViXy4AtZApHv7h8jL9kh1wilG/4+d6TeSJ43RK6MzWtwxyeArMfzQMpAErBbVzbVB3bHIMNvK6b0hI8mRdAEhwOyp/Ikk82LlIc8oPyqscNSpDstf4rBa2PEXDUjtiDPQ0dI0NQmDgcBsCwuB3k4yHKQ+DbqNp30luVvAhsCM0gMLwieEZtPGgM5kbwQ0Ldu5WSUyokDuOc8+JA00C6oSN3hfPOLSa1CLOeVDrwu/C2YYvDCSV7Eo+SyPUdTx5uK1Z5pffjP7E2fh0SfwZbncW6B+2QXeBifv5Z6Y75rGcveKHt7Y/Bf45p4Ro79s9L3d9ttt9122+07ZYfL6KIPe3LvxrUvfvZd5c+bNYqbR767AfdeSPNXMgAAIABJREFUpJM2D6BXRw29SoTerbqM5eqGZLQ0Z9ErGzRTgspYYojfmgcAKd12wXxSxwbCjFjPCfaCHKWgFEYc0wqzuNh4+C8lAqBe4mUm+wPxDK3xlcHZwTCfYHFWaefBbh7l1aNxMLF6JWQdEDJFKEtk5KcSGhZuPyNwj2D8Ev1LmfrPsk6lbkANuy/iNWMqhy+AFYASeF1K4RsmvgdrJiKMdow2BJHv3kYGPFKIfSTE94z5NFZY1xmgd6/NQpK8CcJNK4TCXTZ1xbqN3yhg9OlXadvpEEPDzQE0J14cXONMbrNEnYXqu2PUURHXyfB9nDk/3RWLY3fH65YQvxzEMH1dFCb07nHwGcozGizhSiIjwq+wWDPO4WYEBnBvLDPDIZCo3jvOjkIlBO8kKd9FSSH4D4GIO9Y0HEKD0jGQc0EhSYduNmrxRUczzuaRIVdwYT1ZgSF4J2aIqSG0TnE/Q/bJ2ze1rHEtbJMsYtfJqTA+yxyePx1jELL3mefwJtpyqW/wj7/4Tnnjd9ttt9122+1F7XIj+Js//ej6tS3b7jtmPQjZhiqKtOohdtLpHccHu94DRjIjckGnAJkcO4DOtFBnWwvZpN2zvD0wOdqRunQA1VSyi/KNP/Wn0K3tyJ/se0dbQ20Ntepgy9MHYF86upi4zIM4nOPurOFmYNdhdBJs9/UrW2Aur+OJTWi1CEHbGOmx91g/Yv9X6raanPlZDjWQvZa04ehSEqorKVqYSc1glwAspSwMl861cF0QrEg1ibH8BkiSgJHnrMTlffOGpy0i670BUL4ZReE2QfdIcz+atnwbK6S4smo4i+Zj1PwuDXhMyHnpBSbbj+EiokC7u26sX8e1E6l7grsh+7YSwTfG4JmW7xKNlCMoVQy7G3AvZNGDU9fOVV380qh18lql2HyqjJYHQQftYGbfpzz4iMq3rGJP4F6KE6+ovTdOd8lzv4zhm/kC/LWgFr1S8W5k1vD3YlBpxhEgH0E67VslA1qg5Lz+tCHXDJUL+7ZlYYzsFNzswBYNR+GA+GMtDm0wbzJXBMM3+eZB+ctJsqM9/Mu7j4BHrH4T9t2Vvdltt9122203AJfdRf9bVbN5Q2w9lOmxkUA8NMA3MXgwwhS0eQMrXahmD0gXAIWFwAPoewt1eAUAtEUslr8s3UOM9rcMKaqo4gXLFTbfwFq6d8ATjsWrZffADBYrNccAlh4h89C1hj1/E/VyDHAwhDQC4R5knQPVCZyIj4nvgqjk4mzkJGGeCfkhKZ9j7Q6IiOPPvGOsLsYhomthCNVFU75+Sn8PEzpYiiwOzSXjeBSpBQbcS8LuILsgodwEnNn6HB+epPJGCYCU3U6IbyH2Tda7RorCdBpGO+n0WT9GaHSTGU0BBswmkrbHIGzTY5RitOyIA2sBgLWPGLw5jWx+tuYKd5H/3zdDJx7dL8NJsPnoie/2t7COolT1a4EnYyMfpxrS7GIfXaaxdVZocC+IUPWPtBX2tTF6b4d6dvA3ntrQtKwApFQsZ2ko8UwLvx7hLM/G9LMTK4wm/bw6Xo6LaHbfDDxtlnktIRGfXQ1VMHJ3lHsAep4v8/nKl2R04OL9b7G8r7/RZkzZHHiz7HLf4HfbbbfddtvtLbVLmybn9uTeRXnvz3bR/+pv7vzJL/+7n//p7/vnv/+rH//F3wL4wR/99b0//N7zFr4qO7IYQDw7bh86PSImFLZjrF2VCN6Rda8EVkQP1hA1n8nDL5IRvFRdGHr3uu8zHGmNEnkD1swQXwbfHkCpEiRnW740AbB0schlqU48CBwYj+T2uJ/Szd1DEIjfo/U2RDKgWGDLidCeaPYbi4T7iLVjrtpuQfdaBuyAh/e8h1Grzw+BToUTUXqFJf4yBT8nATdVYXV2g3oVBUCVAx0wZWiQnWJjfgTotZAUfM0cdBSOGvFLr2IZ1Y4Iy0Bk0qeWC1sQxkjNFyMcxE0BtA1SL0lV3Jdj8ybBvLFdaoo4fqwvgFU3ENBRVIrz/YM2T3YBnR4DdLLnsR/xwRO5+OOk3CBjeS7aPuwUCM7aAHESNPC/X2zeV8ll4my9Wqem2uLvrU+HM+JfptBsigQKyTvhViGdXvM8idn0jHi0jC+2F5WSW5EdMVnhLpq2iRNXVlz+mjasMThjO/49weXTDjI6z+skD1D8jOSJrsCmYPzrt7cXwb+Igu6Te398Fx89Vn380XXcfqCqD24Dtx884+7+93/14z/5j/84Pv/qb+78Bf7s5z//+c//+g/+7k/+6u+fvXC33XbbbbfdvlWTuvz2r2e2/uTejQtKsrJQ6417T/AbLHxRe5Eb/OMvHuH9H14Frv7wfXz+5RPg5v0Htz/+8wv29/d/9eMf/8U//dGf/dEPxrJ//uU//lfXfx8Avvdf/sEP/vbR3z9z4Su083eW8yvL+ZXl+M5yfGc5XlmOZ/V4Vle+jod6PNR1KW0pbSnHQ12v1PVKXc9qO5R2KO1Q26H2WlTEgP66lHUp52f1/Kwer1Rr8HhWz6/U8ytV3xG5ArmC5awvZ31ZtFatVUtRKSqGtwRQtCatybqW47Eej3U9lvVYjsdy/rSeP61Pv/LX+VeLLfHXV3zFkvN6fl6fflWP5+V4XtparOWeSO8CVFGvj1BdvL2Ix91dJE19Vduw6RBPK0Axvn16FWezOxneHAP2ulKn1zsV71Sc8WUS9EvBIg7XbOddoequE5JyPUQoA1c4O8I0vNYua5emclQ5qpx3fx17P/Z+3tva29pb62vXtevqRymlyFJkKVLYhSKnoAlw+AfDmwXGNZeCUjxfe/OqC+oB9YDFX8IXDgsOC84O/rKPhwXLgmVBrd4Rb7wgTg8hrRRL1/eXVJHK7ov4WoakhNC/yFgyTB0JijsuMro03Oz7975IsV2Le5560960dzU0bzi2q6rp7inxmqTWCqRIHKLB99H3gvxCUfdMiE7BagWylr4CTV2Nv49DsEwSD8nbZLI3reF8xfmq/3Ku//JU/+WpfbQ5ARFPYVDFUrFU1OJ7NHmD3vzb09fpvJlHNQHeiHpfYKo+w3WLkF/UBDFxnBcUs2BMKPqDTn1v8nWvEu+DrnIShc/vNAj2Kr/hIb06q8s38LrYHn547ZMPHquq6uOPPr9ld+kn927cwgNV1ccffHLNb/svvvAl7OVcE9feu/7oi8fAVQAY74b9/p/+/Od/Cvzqb/4uFv3ql//0g+9/YO+/9/3fxd/98lf4fVy0kG763/u934ut/+Ef/uHlDmi33Xbbbbc33t59993X3QXacnhVLT/89OPbP1O7TV6987Pbdz99DFx9/MWj2z+5CQBXf/TB9bufPrx/8yZefOFL2Aux6H9yG7c+fXj/5s2rP3wfdz99eP/mtS8/x/UPrr3IHv75l/+I77/QQlq+qf/6179+kZ083959992oBTcsIuAMmcNi8FZNbikRU7fVqXwXBZjRF98E/tzvEcoqzsidQqIjlI4c3VIVhvM8OCZzZD0bo+++oxJB+lRnLSUGS186gAM6gGXxSmtLiWAuE+VH+Nn3m6NvOXgfsfNNjvuGb7+h8QumGHw+/mg5gvE+tgCS/JbFRSNUn7TosbjmPAB/D8/IB4DzXgAcKGyXqAMdQFcvwSZM8hcG16OEeQRxqS4e4VOeTs8WPwAWizVfx8LM6RWaQpCqfNgPLjh7FInJI4ieI9URiO6+iQu2DwFC7+rmZMiI1mYOh7CkWz4UpJk8QuBFcn3CUvw09eJqjOqk+iRBn4Yq5XqIE/U5vOxPTLFNT5LIY17OAQs+tlDzQbh88OxtDi0Wmw9PlCTqg1VBWADgbPFw7GHB4TC+XRacnQFIntg4VB0fv8bm08135ongP6/joFNMfYzVBfuJyXLSken3Z47ZA9trMH6vgmtf5hUKtuoXz/Q/cA8+o9Xnif0V9vUb+W3HN/GgIP/mf4z3+v+8aMaY/Bcffv1KN+/ruCE//PTj6+/9FHjy5efX3/upLbv6w/fxyZdPcBMvvPBl5GheCMHfvP/4oxvXbtx7/Nmd+w9uWy256x89fjHdm3/7/R+84MLddtttt912+3ZNf/3vx4f6omh+2gqQd/+H567+5N6NW59/9Pj+VeDhF4/w3vb7xy+88GXsBV30o7rMSyfFf+/7v/uPj/4Z+B6AX/3yn/C7178H4MKFr9BCoi7Y0SMV1eGIg4UhTU8En59T40M/8QgE133gDJm3GS05doclDfuTu0P5zFiGh4pt86AECwzpGXgNSTtbfyDMbkXeOoHUKCunALAykd7+izz4AR19t4P9DsJZ20kA4g30z5v7cI3j9lg7Uh581IOPdPaB7BPPv0iuDufeiIPL8BlAVwOXi2hsYsspi61WtLw7qX411nuROtjRwYd3VBNjm9wdwtztEhva36R+bvLm6NCUhKMQE6VHu2CY8mjmYYXjXABaiqvpxVnpA3qnzbkBT+cJc9kp5iJKvXTfKdXWhW+U6vSO4+1jreiG3Tfdl9jBvD/VoE/HMSGN5cYzlFwO2/HJgN6a3qL7KGu4dgBiSTTqFflEEc4oMRWz8PCZGoFQ3WzsKHqW5gL6hXH36aj9r56sSHeDzv9zqyC3e55I+kUBMs0em+VDwiHNrXFtbpItdLSQfQSbOWiWf/PySc/eBcVFQ5J9OaKyPaOv255Dkftm7OGHcuvzqMp67b3rp6u8+MKXseeQ7B5++PIh/Yvs337/B3/7v/3NrwD86v/+O1LrLly422677bbbbt+uleUbeD3Lnty7IbfwQEfN9as/fP/RF4/92y8/Dwr7Cy58qSN73pcf39py+38T+94f3vuz3/2Pf/LjH//4T/7uD/7ac+MvXPgKTYM2ajFGES2wVxSUA6BW003Gg6t0FTK2pWvpKsbwVlv55FF0VJHz/SSS8IDgqtJWfxltvh3FSe9deh9vBuk0ePfdQ73aRbusq5CHL+sqbS326n1iqwrUQpBPG/51lX9d5WkTe/OVvRqervJ0lfMma8faPWQ5QJG6gPozERt5005Vzq+O1v29qpPtz0p+abwOfFWBnaUqWkWN828vrtztdaX2K7WfVb1S+hX/iCsVZ0XOiiyl1CK1WPV3dTCp403X5i80fuXHlP4TgNPDGPL1MC7vzRIQUgevXoLHfjIh4hRdAOcx5u1gzheU4uR5suhlKT6sgunFFuSURT9WU0d2uWslk+cHi75Ufw0avL+CwM/mnSQvpxzzzRjEHseoq/tpRqXE2V00XXNtxoxKxrZyjHkFjbMQnHJN7HqNK02C8T/T+gH0AV91u9dTr0Vil6du5ZMgkmrNIUaCw6LT+SQrfnMOU4t+HoMev5kOmyWY37+IKbZHma2T/z9VPZT0e/jCO/r2LC7e3+Z1oT25d+Pa3fe3ijHX3rvuWWhPfvEJWXQvvvAl7DmuCXPGP/xQbt2Sj/FS0vPf+8N7P8+fnVw/24ULd9ttt9122+1btPKqWPRPfvHJI+CR3UEB+G306p3PHnwh1+QucP2jx5/ZbfXFF76EfW3swWPuT+7duEZy3WffxapykmLRmGJWSmZvrAh70ra4o1I5yv8K+fZft0eGbpWxKQtAarxx/a8UlbTiZRaJr4wqlm1QfwT7ATidHgCOxwKv8aUA+kFUR9ebWvU7rOuAQs74tthql+CiH6oAODMeQBl+Htuysg8rOcwR1StBUk50/RgqVwMrrqC3MPQe6vdjQ29wku0KTvtSWO69OHP+QC69sfGLoyIXqKsiplhnSd3jFGnn+fYeFCxzgJCjHDKD1CmTEpJnffyV+Fi5aUZKF5Co067mb4aUvx+SBfilMJJdCgCpqjaFQvbPA9sJKw2kls8HY/RzqHuw9ZN+nGvIU8nO1N56NToBD13USB29+bCJ8/xxuqOeEguEE1gu6PC8QKYJFwc0aB2Dx8/NPGMEdkhiE85S223zXLegFGfRL4v3qcYEDS29F9EO4dnWE7zO3wQeIhkf4jktPEVikod9/qGRFFnPX2giyQf73X+DZDPxEKMHG8sYTIwtN40jiduHu2TrreDKcRJi/oqXZZx+AN8ge2Ux+EFe29qFZLYXX/ii9mKT1Tqqqvrg/bvXfmNVnd1222233XZ7o0ytrtFv93rdB3GxveyTC58mHn4o8uF3qVxskNGVNNqZWuyPn1ELTgKz4YJAOyC6BfHM8tQCARjRs7ZyR1SyRHfoQIE5tyfoZYrOTX1wIKek3DtgCldB7tjTo/RWAPTmRNvQbFOGy6y2fasTk1ahkf4eutZrAATgwI4tAXgAAAs58MHgPS0iR8iuS64CF2Or0mfocUbHSNDja0Joi6gh9dhjaJMTzUQxOOPSE9qVJZC90ex1oB/xsSeER2Ztl0qhbRuRRKseZ+uikOWp8Nkpy3pyJQwYm7IaAC3WAVFYqXINzDqAuU+4CalTmMHC5OBUjFizBHDnz1eQ51k9ATXRMdrKQSZWiwFgYshwjphwvcvXq3sCauKLRH+sHVaTY4fN51RVXD9BzFuwkVVHJz4N19BSAMihen2RSOr3v9W9E7UMTQYfutm/MerfjT5Npym28/JxU78utA0X/ZmB7lgtTfEi2V+kAAo9SMz8V3MGyKYf8ozpedGSPGG7onXJyzeyDjHvxk+vXymizzq012VvGqv/m7OXvcE//FBufQzg+keP9bvkqZeumn1x6peBajjNAECGR028sqTAfkFTgpw/GZQyNlQZP8ceClBNt13EmogyJXblF68NI+L36eHPLPzB3VaU9NUoP+I+ebaTUuzs9tYEwHo0sQ+wCA7iuO2BoJRBqLMimefeiNhPr5wUAC28T8cN+5DqipoGLbK0jo9s6NJEeht/AjhQJT0DjeOOIUxZcOlX0WpfWld5Y/afd6H3WphuVACUUsd9HR1WlsY3FACi3ENURBmPPeJdklQSJvKm9DSBKjybdlY6S+vEQc/HLNzX1nXOHvCX2yIr2of0DfKElGjBK6ZI6T4U2yKt9u24hdl9vVbXd1lMuYeCNsvCXo52vO+d8Qo7MJ8nVcIVX1LwZqjFjAcC/59D2OPuOY3EIpb/Jsv8MBRN1CLmio+/lS56U7ZZ6pCyAVDrCHPk+jbPqodTKqTGKOdj4BU40uSmm+aJ6350PFa58OuT1X4Dy8+++eNUdie/4fTJXTp9P44/LhF+PQlq6fa6fu3Wv4n+vKgz/Nu1F+0VBfNvff7RY1XV72Qcfrfddtttt91muzip4yVfb6R9LYInZH8pFv2bZzKeIh1/+/JOQAzA0EZTwJPifM2NM98+VlaD3bD2km18+9ZCF6gMR3YIzY6ysNHj02aSYqiIlo2bkK7XQPCtFdDnDCQqk2xadp/D8KACANYuAIqo+Bsv0irUBTmjd9OpS3NZ2CKDTDcxmSQJ2hC4M0oQD/lc2QRqoAAa5GjYUcP96II1zt/pIOGsG73O4G71OjUDXrg+qFdEMbadrV8d2JJMZ8heB7IvbFbzCDo4bXICWzau+ITkNj7NjcmMSuNjEe9b4XyKKr+xAlhFxDecwNqJi34I2gCeOwqgUsKnVzF867TQFJoyiL8eve1K5aOs6qLqeH05eDt1kZpUZJTtiGh2xQeyHz558X6KuZQOVQ4CQM4CYdv6Ra5UAPJOtSkonIiyQfCHxX3yByL48BLY8sWdGCcXzwhjsIuC6ezaoY0FlKTlXz/5Os+Gl7bhphm+q8kudLZvunhqIUw7diETHFSFyvD/xXpC/w3CE6PTxzfNPQ+ga//6lb6b9pwbfNzav6vE+d1222233XZ7vjWsr7sLr8qei+Dfoju7SdMA5H0kRFnWKcQuwcIbEG1gNS0uUivNg6AyI/hRiibQjAFCiLombpE64MigovSI9mpqz6DkhLCjqo3H7MvoenxrDfbUQwKMgfSeZ8o9A5oSY4hiXRE2SHMB3CdfQkCuFA7m+tMxdp2QR1QGVaaAGRAvDAOa5A4AFWnCr4BFXB+2DidBZ0/cDVN8SQHQpTgpL0XiFTWWjFg+ZF6TPoZTPLJx1ulmCSFgKfCiRBwb4lPjykElCd8agGLM3sL/RnE6jbUH3SBODwfRvtI51q5FfYq6nKskzhT/mYOEFAiXNBatrHsEI9/xkHMeVJD4asWyCIBS5HBwTA9gWcSgf5kKMs0jqj7xAOPW+fwTzjw5WDDeOSByVgDIO4uL1C6zr6kGQF9wdgCI4AsJDpELWBcAshz8Y1nC2+B/h1chej5mhk4AWfNfQnhXVRrZZXhRC2Cdm5e0+/4CbcUq2dGz2YvMb56/fm65xxEl2u+gXLwxdjkR/M37L59Wv9tuu+22227fIet6ORH822VTckYfD7q1dcDRFMD4n1wcQR9R6wBMbCcwfC/ThkI07CodXXtqOWEAx+KFj72nOHvCx6NM6LQ82hlFJ3jcFhsrgsDFm35G6DRDwbkDCsshIji0vxswcVqFtqbwW27VlXZmMrykFjyNDQCwMF+gqYDHMuVGMNjfRCoxvR168UPyoL5IB1BU1NPq/CBS6LHBnCZSASyyTCFIkTRWMvoozE6SHMSMYUCiZ1dPC4tQ/Rg+5tTaEnMZKb0WrTEGb+3oKDaTBkuqaHgzjKdQ3AdgqLe0rhFHXsbgD6HepFKcziSKoC2+oWVg1qMCaMsorduTbyJ0ckRQqkN5Q/B1cQRfFgVQFvVSyJ6wRzZJ7J79t0MQuoxERHKsvRay6xl0Dxb9hrcSvYy+etB9EaYQ+F9jy5fqg2XSpMGiZ5Vh5Q9FZ7IlSR6aY/DgeJ9GwDVOAxsca1tJKv4+bJIwdPODMfsBs22WXgin9QVIAc//XjV6KEwckfjqjTKWnnoL7RLd4HfbbbfddtttYzuC/85bhNLNohiCdGarU32GYgwOkFVQosQkTBsl4km+CQCMSpfOrleJfHN/lraWi7rk41DUmbGEd5ihd2HLMtrw1UdMPbHrh7jqBcMgALTDj1lF5rA94hCeEV4bEbu50Y22yuifXrCVkoldkppqrsobjVRxJdqI8RtMWYMtX8iZYI9GfVjxSDMAdI/6B5T3kGnx3lVCqY5mzgEmyo8MawZNOwCV6kH9wGB2MkPQqhMs9iB4M6/aFWeLExlQvd/Max/+E4fsBO6Fx+wdsgksjKnLwPSAFnVRl6YwBYSmHtS3erXVHT0ONeM8bWrC2BfptJYirfnpM5KHK9d292X0PmAoDAy7YoRWhv/PzgShnLPocrB2tNqbAPT2pjiyH9VohCO30WCqrmMjIbFU06CFwydi7dvj9UOWUly+1Hj/QgGccvDlhX9LtZFjK0KkbskXLvCq6Iy1x1/lhg7lZ5p9nA3Jp0V5MiKP3j52rtCBHr6E+fg2zoDxVy9Yni2W5wa7+lW8lWvS03bojeDlvtHffe3WdgS/22677bbbbm+f6X6D/66baDw+OigxBF/7qV4oERufMnNMXYk6JdRKmwIjLT6C91oCD0dwTuARVQEgZCAH/s5AXDAQ+fZNdMYflb1LDg46P0JzVRupGhK5jjnjmTyCv/y0Aej/P3vv82pbdp2LfWPOda6UfkEIssCCKjWEejEoqeIR3HiE0gPjhnAjgUeShoUhUFbDLb/me22rIGCkToJ5vIZQwwieRAjEBKNKDG6KaqguyCCLENAf4LpnrznSGOMbY8y59y3Vfap76/qcOag6d++155pr/lprzW/8+EZBOwJgqLOonQQNi0R5K3YZU+R25ykKceBOe2EnsR2JzkhZa5eDU82GWzWGeowAOxCU0M2t9d4eA+4JLySqVRhwp5O8+88Lafc4mBxHQ/0D4WYfThb2q+mAGgld2+k29VaKwaB8kKABMiCnj1Haru25cwI2i7SZH8JBA06P4Edv3lvXC5AkNbzoj2bKCfPAkOEaoKbqsQdgT2fJ8eX6oDLCsxkddgsQuJ8MulgZasXD3wE57qwrBO6HA/eK4DsRfD+0H4NjD8ksRmQo7CJLXIe7I8w+GiJJSRtEdZKT4Plho6dxyX7Q6H4QwdMknzR+gN3+6sQJABTD7LtDT1IgB6D38V7y4wRKTkTuVBB8jPgdI4GkURc2Ld+Dd7drrjTLTyoBppDR5XieuIbqV2rmWnJZOOXByvs4ar7WEnyuchnXz7AHIo/lBb9ly5YtW7Zcy/m67Tg+O3ksL/h+jtEqDIlQ65VfaSpURa4KBCJOwzdGZ0ITlcnSJMKoZh53C9aKy5O0i1B+vn6YqMsOWXQ+AJj1riTf0OipegsG3AGb+MrRsLZqQs2KGYCNobgvW96eBnAH0Ku1bzhEdJhUvO67RUjT1u5wkX7crTirW3V2am+YcHMMzkD4XdfBUPorXDsaB44yZNKkN+lIBE+EleoOy1ITfP5tGXZxt2oi8iSxZ0vJXW8TXXEOAIzhN2UgnUbNQWPkgBU1J/x63HzLu2NQD+7trqzC4X1x+7TClA0Nauz0zyOuHrNKRxqGGd1Pb30frijynDEnmPrFAHrmmGmeT0a7++ErLJ6ARvfA7gD6nQP3dmi7SxWQPGnuM89sB3LHD2Frj9vU8ay5vZRIhDuOckTxA2jdvSgOMtxF4h1Xq3Ri95YdqAuJKpAr5/kxcAKB7x1IDzpxDGg10g+IA3edEHPg7KHrCufqDDf7SvyBodS3scL4m8h+BuhxemSJna+4IvscgQWpK+9WPvdeLws8cD7KOPgtW7Zs2bLlgcvlwb7fH80Lvl+G0Ux7UHFziCeqQt4wACisXyZT4Lt/spLEjr6zzmx1WrLMlfJY8aP9mLRfOgGoEho+o9RVKpsWnA5Ma/G8Ps9Qep7Td9z7LCSXHjTbAziVOgnGeA/I/Uj0GX7+wx23cwt/0Dq8tD2yylr5u4ZTBcCT5lcsZPUCToeQAm8o9R9ziqvWQecAD4dwa7GowZcj9Biz3VBIyC4iDHJAng9AB8osCVULKxips5zgr01fnUBugJltvdbChQYAbTDuguB10DZfTYbNM5lqFxkNgB6+IoW8/MZUlzqGynAHAAAgAElEQVQIZVy8e40MHx1wyBadBHvQHLir5SccJ+fdFsMgdhyObElyoIUCwJcN490BA+6zF73/Gl70d2Sm64HXPerdCezuun3w4zU74YI6A9BfTg4m166NfVtmjU1fZhMzsue8F/J5/p3x7DVeH/wQejJUnF0C3202Fnf6aL7dQefwD6OEodvUWg3nlUog+OZqYH1+vS45L4+E/soGw4ekDkSMpWoocl4XefaZpJP7ZHn6/jtvffhvmM7l6fvvvPWnH/C3SPNSkrUmh+zNg59WbivltmzZsmXLlscg90N++/8+6QI/+baU9zmAjz784I9/rBR7uz99/51v4seqqh/90Q/e+vZP8NyDLyCPBcELeZnJJO4byzbzNY9iMQ0jb+Wwq3nfCbyttESx3LHabr255iCzLnEnG5+rOAGZut+1JFu9b8aX9PD5NRoWLStbeNX1RF36MJwDIFim7beT4csBMsKzl1t7PS1Hl6IX9KtKn2Wi30A7PazGAwAu5Ag38HHXHKxqC99zBXAn7kvcGRNxoYKl0TnAwakSNbIlSERnkx9wWfgPFTDZSyuS2hysxv0y7ukTwHlN/2oib+uSOSqE03lzeK01iTqKF0bgX6oj6LFPZG+jPNSs8jSxM9ThaIFmo4feQS/IbHIRWuBL2tUnuDCUQA1ey7gAwOieKsAN3Byw6u3Bfri2o3Esp+xw9KJv9JYPS7wnizuK0R2QJ93x811Pqnn3P/Dy6TxfV1441VfHCGohysiUU9yjoqdRHzn4KglSK/s+bks5Ttv8KPjWzPNjnqVA6j5nZKzTVA2Egs8XdkS42E16zvh7ID+wQi8Ql+B1p+PX617nDzkUiwYoq7LH12vnRX//MuPyf/Jt+eb33/7uj7/7g29+yGNPf/6zt7/2Z0vBjz784I//8F0AePNf/dHbf/rXP/neu+/ePvgC8lhe8Fu2bNmyZcu1/Jdv/E/x+f/5//7XT3nWf/Wf/4+fpti731P9HvD0/R/ksY8+/OCD778lfwogFPT1pf/mV7+OH/z8Kd7FrYMvoqZ/LC/4yA4nRBvMGqdnbwBGa5jwemzJtW69KwW5/8wMXTpDf8BTxS8c0WGJTEp5gyMVK04wXfOfyQYmCyJ3HF4C3VmeNWRxg61xgOjErbx+2LijtWsYKAXTCVHMdvmRdI5R7PS/ZWeDkM4qjAhtFAhu7YyE9JXJLvsoCR/6rc134I9gh4sI/hoXXb56jRHUEF7QNohNepoWAcVv2vOHpkcaUaD6X/cO6FPIgQ7RCwDFuTIhhI5Co8KE/uhuRZY+4SLR9E4gIOcBK8+FITOmazqshdICuDuJPU3sagx0g+kK2yk+YgHi6uhIYszGpVWXeuva7waA1mGUdmZ6lydNjhnBG8N8IPiDCL6Jg/uwxDNQ3h3vDyZ3N+f5I5jp6BLRqHPwhA3DNRJKtUrQEUYqARvUXD8A6j0Wjw+7cTkfVPhFSb06E/MCXkzjN53kNfV2caKE23ytp36wwqHEPDX/1iteY/p6Ic0uULWQ3aCeb46DH6/A5v0i8rf/7/9Wvn3ats1n4V/8F//Dp73e05//LC3qT99/55333/rpex99+AG+tpa8efBF5LG84Lds2bJly5ZreRVOdlXefO+n+l58+erXP/jrj4C3vvb2dcmbB19EHssLvjMSgq7KZbdbcVK1pc4SVnwhxKdhz/eljeZn39g2qXtBIfXUaDJZ3wuBnVTTd20XveIX02ZyYNmW3ILahSbQ0CnwV1pAC5C/6WRJBCZ0WbdqBkGg6JxozlsHcugVUETU2xIiJhbNPs4G01HQg4NV466nnVdJe36Im+fdAEkbvAwJ33MkcrDhMkQlIHM5HAApAJWTnRAATVqqTUILZGh4JlK4sjtKDCItz2F6D2K7ZIbHCCN9zwmuo5OeEayQTupugw/dgx0/HEnFYGusR0ZACEML6Fftq6EZgqd+qGNM+bzpPCCnNup4UBBklJSyinxQ6ALDAHIAaF2PJwrjnL9TgLb2uzC9i3zBveXhCF6sAAzTMx88LJvcXZdYgqHnAPDkwN0dABwHAtN3UtrBwtzDeX5+BHhyuTtjoY9EFb5+SGA3MJSLFA7fJ6BLjvpM2edzgnI7A0M9Dr4g9Qmg61z+HHJx/J2MdQuCD4A+5uOBy5dw9hvG+FJASa+Qx2cdA8L/n1E5rloYt/xYPlf5DS5yr0b4on8TBvG//odvArh58AVke9Fv2bJly5bHK/en/Pb/vcD1fvJteef9p/7l6c9/Zm50b33t7e//2/efAnj6H39A17qbB19AHhGCN292x7TNndu1SzEPIZzhq1RMn8Ad8xY/UG8JiLctroPIttZcGNIkLxMXDQhxw+5tcIrEcxqAiXgZ04f44ubFJllSS4HcWHsPPdxaXEuhNDk2KhtOYkhyYDm3vC4VYd2ypwJgBgEnMaLb7GGU6QTo4eUAWLD2oALlJBBv7NgUaktk39Vt6dSOCAE9k6rT4V4yAp41EoHAYbv11BH8PDmYwt8deTNHelh/NVwUss8ikh7+VR8SbtALsm9BTc/AAY9KdwClowlJ2+g+r1bM191oWvLKiTRu+0cZK+XvUBVzyjg58W5SpYrLpgpckEyVnj+KeCS0OeG37tnkpGv7ggFrB+jhPJ/W9wXBG6YPRoXD0sB3B+itoeSDLzb4I0np+8RMd9vWLsE53+rjQpl3cUTWuKCow5wPPgLfeUvGcYuDH6onA9atQHq5TxErKKOc/56KyxD7YHj0MnCZgfVig7+Ogw+kXstfX5qqhakBmE73Au7Ewy4MvgX1N3mwvGJ51Qj+3e999PN36GP39nc/+qm5yb/30x9/KG/Jn5Zjtw++gDyWF/yWLVu2bNlyLa/iBf/mez/V+dt714Xc4/5THPy08lhe8KPJ6A1hZ9QAJhJ52YFietc4MjvKSlrxa3x8CY5fsRwNUtBu8FcrAEPgXVqwGc96I0S+NC5xVdSwFmcGPck9v0P/54XgR/XVohz7bjQNk/YoV+SWfW2Alt29t+hmT0rJwKjpcAuAd+AQH5zeHN4O6EUFltA9Jy19Cxy1EGOf6iXDgd3Kn+rU7U1aXxA5kVagDwAqng1MqWkh3zoLhqYnADGT+jFfmWIYWX1knzOa9DpehtFdBUQdQMHu3h4FSp74TqSmDovVjf0q/ABAyfannQqdwZZHuPppo80F5FnjyEIQ68IqnGBZKnJa05iOsMobgjeE1xraE2t5ky8UxronPRnrzAb/5LCvSEq77i0J7G5DEZDdEro7TKf65Gj80BPKA9IKcBfmi/MmBjV96aZ4VjjFGEwWF3nkACgdFlSVR2zStOBpU4ZJRfDVlB4IG0iOegUiID5H3OsBgAtPjISKC8l8cNGvSB28bp1LrFb/+EF1aonGCZxunZ8nUw2vh3y8qWq3bNmyZcuWhyfPHmw6+Efzgj+PNroAGAQ6LZiateCPGwbva6jLHfSM3U0iHzyEcfDME39TlNxnJTLW7ZcSSD345mbof1XV7fbGvtvj76MLcyI7rV0pLYnY/zHc4gg6n7crarwFxAXQ78VymeSBkpHx9bLht68V0wNonn1ONWjTCKUNdreFdCAS0ktn32nUHyiT0oQWbzK2Rxx8mNiXZFqZNMygm/Tyq00hCfcndgFxpD5Oxl/Qu37x7UgdQBynSiDM+QAabfxgA06PYld3IFfrLM5wixf/68nOXevgviYj3KM9GYGKqwCcQ+IyXBkhjCxwDWfxDLfppok9wkOCd9D85BtpCizeHU1Wxrq7QPDE7oB8gb7uB0npw9buvhsE6GFiD1f59UMndl/C4tucNa7kfS9zlJB1ssFbvkHLHVcJ4hLczrZ5/zmM7v6VXvRE3oLUyySyX5jno4CUdYRZEzC76+ctVhsQSjXV6ya5hLvACtCnf1BJRcA6Xyu53wh+y5YtW7ZseXjybL/g/7nL6K1ymKnIcDOsTBvdAB+rEbbiqvlD/RWAEpKJlGBZgGmgMDPQFadpbnxjk+9NCW41vxYjdde8ykFBH3GooRuoBRR0729TvHLhUZ+wag15v31Fec6OPEJspaB2qydr8L+jDKamM/w0uj1nJX5x7+kIew6z3xlByoBAhQZOYnpr2CBIbnGi0YM3ELyCJlVvilu+mevNz3SGAAjD5UWiEw5bCQEH3eYrpXwaKoNzPgzXAIAxMq1ccB/a30BYra4Plg+kPlSs252ZAyw5+tEYTNFgayMi7O1DdyO6A/fTc/PpRS3cXIPuHF7My18GADmSg96ROsRI5n0Ie+SPd4zuvvFf7P7hriXnPIAnh3vRd9rgw+hu0ulF33sicgBH+RrTMeX6k0wT521m+cqcUdEp70vmcfc4+KGTybsEiysmIO5pCqlJZFI4qlHCiz5ytTG6XS6Var5ov5YHUqGmR710dGRB6nmnclYjnD1Av9VfNY7XkqqCOMSV/nzvos9HPt4q+i1btmzZsuXhyUbw/+ylUHMT5rqBnPvK8Er/xO1lJoubLaMqSUqfW1otm+lybHFif84G2KONgxAuykc4exyfvejJlKfu7eze/oOkb/KcLhZv+2DyjsbwF3ahNH2QbUsIV0d61nvFqYQwqMmv1sX7McEOCds8W9J5YrRw1p+40qF2q6ohTvUxakm17dN9eByz93CUZIH+172jQ5Rj1QAohC7ElssgUr25LkhA+GgqI72UmgrwlQTu7Nis9GjMrNcxLyBxz/vIMkegL9ak7tMjB5cjpyHcTmyl+dIODBhceMwGSCN9MxSp91QS3JOoLBQ6hrAtELvTvUEcuKsy5Z2rpESco0KI4A2pO9V8Zo2zyx1detjgZ9u5SUXqvVDNV+Z5RihIhfj9oL89veivHMHrsitzRE2PjvQPsfh4PWHI/rYNviR5QyL1U0FkvxLSRY64unyUzHepGyin8MRpepOHDv5hIb1PE/vVh/g71YDbIoLJV+M1C4LHdrJ7ANLOYdkx1bWb/nbUxge0v3Ik7uDKvAk7Cflvru+r5Spxi1Sq2Hg5iZC5YnotR7bWTlYZBPUsz6zqt8GcH0Ay3tjl+SCmuti5UCXeCJEXdOogc4osHcpikmy41b9wqAjpLYIkl1/t9bkMUEloYV0GUDbRR7va9NhQtdJ/bkHiAQdAmkdBzjTBqWy+QJytxZ/zepLgxnSxHWB2TgWCjxaqw2lbtCG9DKHa+Bp0Ht55LwTq7G/JxERLr67upMdyzk/M1vxmDZ18bqC8ialat/Kx/7v4DsBeq9m0i+9y6KU3P/LjnXA3Bx2qOoUKR3khPFb1ODdX0efblN5zGpYV07TnTecv+EZ+G4bJTeUzyK0xCi5U7tYR+sQFVW3q13kzhsbclu51dFxw09pfzy4Tozdvecumlzp5d7U7ldy006+hopfIvEwqWZkKIN/cmFT3/AABcCmhaPnCrl/LbVhf/EC0ZDp90eTjagdQa563GjlAg0/VaSWL6utADVtkv+C3bNmyZcuWByivBRf9y5HH8oI/7ocTPliwXHOQ2BK7A4AKqI6sUXCTjt2LXrP4mxKYvlNJ12JVcId8dlReCFFZdsqOBKlZTbA2qOlmwJUl6+w9c9ja+Zp5SAuyVw3YEdw1rWULE/qLKw/sexPx1CC6otHF1qB0XlvSwkYZ+9qLX+NyY0UAV9RQ4+vyuldHpNgXYDwh1kEAwIHse2OgHSx8jk2hEsKz1LgHnZtJTLGTWFBV4IF2JxGPVXQxjlsBo9cWnYh02jPatIRU08+LFMGBzAHziYtKyriL4Aw1spWkMsTquVfqn9Vud9dvt9NhcZdEbQAGQW0oecOwsXjzxYmdI+OLVdMJDiWhL1UIgGsFMiNQUAeXuEl50h3QUxUvVNH7Fe/unHq2zZ6JXYpeRHIortGoMHNO6CKC0KYVopt++K0ks09uVhoqd1fRG34W6lNOEtos2WMHF+qC1DPHzKxRPzW88HhzzbdYHLiG4PHrfN5tDXyGz1E9ttQT3n9YK+TB+dfSgtfubbqd7LZs2bJly5YHKBvB/7OX4zKMVcNyzIw2DKCfd82zztj2mba8EVExJHNx1BRBayd3ohYRlFSlWMLX0kIaSWiK7bX63PVu5kBF2Y9jRBpPQqQkTgkwmhZBESlmMDf4+VlOUyMBWs5TAF43N906taHiz9L++iGQ+gK4fYQYMdbm+2gpliNTygSCRkH89brVNelC/71Wc76WaylZe8ZcYZuoaLX2iPU475D7htGRojTOgt/Q6NIR3pi8RgfgMXIohuvGBqZz1lmPONuMFqzZyjI42YlqBQbQGi4XGxRfm3d0PRtUs5hv3V1z8lpTv4yJ2QQAejPNjIXDSVKftCtzLv+xmb5jS+gc4AhePPGrL5cCi6WnmkTIY4OjS7HBI3LJPDnQieBrWtiIapOwDKdigOMPXjrvnYJCmSuodcBcc3xa0xsX5lI3A1SN//Pv9LuHySmD3DIcLuLfrIpgsCEQ51deZyBbUu+gXB3rxa0BqyndZMzefBXf36pmqnCxwWvmmOHx1w+1V7m/vNbN+23ksbzgt2zZsmXLlmu5v782uD4QeSwveFH0UxGJMYgB76FGXjvo0EvPzxbMnVEDADC+bjRRsnMA0OZ1jspYajWFbSw+2E551gtJhMV5akXiGGZYQdCpGjgYQmwXOgAC+vAMnzz++ZVhXyoef5baAje6E+oQ1EaOkKip4h0lSg3VQGM3w12/GrnCdKsVKFvTzNC4DiEbwoYVt+uJLreJf9UViEik8PFRHwKgN3aFcEcFkUB2akAi8hPeXwGC/iYuw6AMBGtukN02H+FGFwld1sdiJx5MJ5NQEgDd9aMnIbHyZlTdxOhicl4NuB/diG70GIbp5X4A0MuQk1DernB09zPOCCqH8nrO19J5OpUNABcoTeyOyB1w+zBL+Fy4zqPRbd4ZbzJDzN0dYFlfGSbnFXJBL0GlMaoRFEeALplFBoCkeq0eF2GEFyNZfJEEQ21lTxbAA0kUiGywRn1zqgI4VTx/EvmsKgct3CRvX2XWkhTPjLLCs39S7n7kiWnLj+ePd/hK/0LR7ML0a1xx+YDZ/z+IcVQlMD3l9ULM98/2C/5zkjfeeOPzbsKWLVu2bPmM5fV5tl8u+wX/Ocmvf/3r376SN95446Qrb5jSDbL3U/vlRNjmu+Py8/ACaYMHYNvViAp2eEqUauCEFKaQaiyHQprRgzQZPAIHG4p6CWdRRYtr+laa5n+L+X5OePVkWpvMi44hVRk3P9wGb5L0nb06FgMqEbJPx3D3HdfmiD9rqaiijEAv7RlaBpNHIqklyvZeiDmzPLz80RTAIZPZXuljHPNBXYI7z4sE+Y8CGFGAFs2WQCzGwOqRNsX0n85ti05SUkP2TpwkGJl+RtkZWFKTQEycabuQf9VcB55P1hzI3WsELViB3OqfWWXDrQPAeTqc/SK5nMICbaD2JHC/nG5ilxMGWS0+fii91j3WP1dcYLpzxseL5wKy4/4xNDCHR7p7gSDn6S3Pj4XVGe8eBLTxNZPKtCwQzvOquBTdw6TVOADIcYfDIt0P/+vO80elsFX2YugwbVQEu9vtPgLKp07H1ptrtRQjAtlrQ8IGfzIgPgYyCXAmqtoc13qja0HqtRKU49fMNpiB+AKro1h8CKVCvZAu7vpK9xTFrEGkUKHzmTzb8VlsFC73/TcX+ucpD3bnsmXLli1btvxGudy33/6/33CNp++/I9/+SX7/ybdFRETeef/pf8rBTyuvO4L/rOT+ie/RGi3xhumPywhMD6CpnNz7G5gY3fPMxr682KIMCwLI1DIoaNKN6Aypdps9HIk3eqqHhbvmEKnEsUKKWf/FTmy3EXyIqkiB4gV+RgJKjNFhVligH2qGOZFZSRBZUyIOv815ZtnTMLqT1C3MgZ6tNXoU/uIB2auzs8hEMFoGhYHZbGEwzfkVA75ytMwC2slJcGHGW4OOQz1cYqhPLEFxXnIh44uGBLIv8RNmlHV1izOJSZvUGiLSPBodegLE07gkpbB3ZqS1HoBSB0Xrq445bn7M4e944k7sCc2oJDhZ3kmPL5ALwm4uYroBGRqG7cnTHCUtyX3Rn0zDVNYHERtaOMnT1k6nd2pDSD2bcSbqxy3e3XnrOr8W4O7pZO4ASCijhirus+ln5Ry3pdyJ3e8AoN3BZqd1OxL5oghzz4veA6Cix1sY7vSqs0WbOFcg8DSyAuCicqEN3t3pRyTp9fMI3BkQz7/hVB/aLBRTfdxmoRbxmZnRf73QTR6OxcceRUmwqgSoU1yc54H0Kqm1yTIPr4G8dCe7n3xbvvl94I/5/en773wTP1Z9F0/ff+etb39Vv/fuixx8AdkIfsuWLVu2PF65f9Z++/+eV/lPvi3yzZ9998fffTuPffThB3/8h+8CwJv/6o/e/v5f/+TFDr6APBYEf5Lf/DT4ZHALUIaNB4nbyVDdCFv3nbLnw2BSGXFjvLYJxIQdLexUYcsfnt5GJngjCdAXpF4K+IebzqeqMtvj3WQePO0gMqRdkLbwgeMY4NZ7nJ7oZOT+WgD0TrQiqxah0cQeuLlY3iCCw22pUQAwy7cBMyE73HW/aIB0aGfHJlhI7K7RRQz2uQyVay8CC1d3/aGMS0Y4mJMCT7weuh8ECi31msHRoBtdNcIhgY4L9HhI/OuzIGp3H4MGPGfASb+AkZ7zPgQTqpLhGgJHk2dE2Bs4PlMVEX1we+8JAOfwD1mrNYzG/jGEqVxmVJqu6VKTtGK27sYgRSaAAO6tyZQDhgsr0rx6l4MqoKX/PIDjYFxAyyOtAxCD+O3wcwcH3+MRhnMDCOPmA8EbcD+I4PtdDXFQkaEnHKk7yXz22mzzTmDH9eB4XSNMg14gHHsFgIvK/SlA+I64hBf9qUULUHFzYbNHiY+vSWjiNgQwRk7OpCqISy7h7LV7FOaIUhTvgWTQm3QWGGd6FWgx24voeb5ewPL//HbC4v/mf/nfP+VZ/9f//N9+mmLvfk/1e8DT938Qh57+/Gdvf+3P7PObX/06fvDzp3gXn/rgm5+ygcDjecFv2bJly5Yt1/LOX/wf5dun3XzMZ+Gn3/mXn/Z6H334Ab72n37wReTRvOAFdHMHgCFOdSaRE5NoZwRwnz+4JV4kj0+8a4tRutrMrTARf8RwM6fcEoKLqPUa0NftdCaX86oMaBHBojX3GG+LCV+yZkb5Osgwp/oxIpzYFR2BTgM3LzbWRZYBCAlqc1AnET1eOOzG3F87Tu4+KG3nkix+fgH3b5ips5S4+SCvQfj5EoUwLgCYU9axHpIQMBNurJd52uDhzoKBoCUwN3iGhvuoch1IwHSlVTiM8W3OWBqSwwG07lC+n74y2wVgRLjXHKhqAIAFBcklo9XrYHVGq58jbeR+JMAjH4KlVTpxpJUag8CxSTLNHbO3fCOCr82Oz4L0lkd1niey74f0IwukM7zz8Mu48+53s6Brlm9UBiBN72gHGesMH7uT/MAIozscptssn8MD4skQz/D3NHVnh00hJADuT/n4dEf9FgsDuJTw95tU8MvXUDUpU/1FZEqob0zOKwTvGsq5TpG8EchDGAyLNlQemdJEzRPjZPs9+MN9OTJUJ4KAen+9jPDnK2aye+trb/82B19EXi9VyZYtW7Zs2fIqZdx/Bv+9gLz51a9/8OFH9vnpz3+Gr3/1zRc5+CLyWBC8zjhTaWI/W5OKQojsEQCdH0bY2onEblrE5WpzXX6h8bVA6hvO8L5DLp7q84Ui8l7osG3Hmaw9QuS9LZY7rnVtke7dAfR86cRdacWv5RGE7UqranN0EJHYfr5kldbSganCeSzX8mFHDE/gxSKYyDso6szQWNjj6GchSCeBbDgtlzoIK2xUO+n/RsAWAEDPKAqrJ5DZsCADIjzPJgdE5ngmigfjql13NBmqpXVSi0XY+nB7sJnY2zmrCiiNHPXj9Eu3A4COwgcY4O6kBdqrOX3USkugIwPlw/vDIHsfU3md4KGMYsW9ttzCAHcg+ILURdZwdkZr5PpwIz3p6iIgvh8ApB/ObZfZ3A9vWLALADgvdiEJ7Ugkeo/QBiOf573BW+0cntZ9jJIsbqjzTUYSuaE6ZY1jXLwy8bxr14KekpQSQzPTno3xszHpVyKSPyjoq0/7pTDJ25H7gUuZLpHi014m59qonzoGXyZ68or+E8/vfJ64Rok0G6zHHSCkiYF4VyhiJfH83OV49oo1Cm997e3v/9v3/+zd9958+h9/8MEf/5t3X+zgC8hjecFv2bJly5Yt13Lcv+J8sW++99MffyhvyZ8Cb3/3o5+++4IHX0AezQs+kLXtLolCWhxuBaYj00aBO9krvB5ba/+VlFW3kf0nCU1iU+zpWOtRCU74T6oH6XXv1Wnp+CxCgyJxb6HeQ6oEtK8jcMMSD6RnO6vL6PZicQWK63s4FduV7W8ncD+AtcvsYxgOW62QVOEB6sP/38urk4oPUoLbmV08Pl4h1VqqA21WhxgoaR7kjiHqHHYO7Jqx9qlocweHzuxe6kNrCeOLC4VVL5GAPCasFyMqwSJygYVexQA3g787o9ijfgbKcyXPCpOImvD6unvXNyL4NOHTFz01KoSHmMFgpVXrLSFeJyv+QYQ9HSfEB/H6okSy8gHcj6Mw0N356NnfiWEe5ARshS7QxupuPXGmrnNbu2rY4KsX/Tkuy101VM8JwSu56C3GBOcQAJchp1vWfS5O5e3m9SAC5adwdi6aC+PmR5YHCpQ/dYL4uDK6K08/5wL1WjD1W8wqL416dwefoTV4SKgSTUSqexCy0a+N9PuX36A33/vpNL7uXD/Lpz/4aeXRvOC3bNmyZcuWK+mX12zH8dnJY3nBC/2r/Sv3nxoM7YFqrvHxDFSV/tXQWj63ZyRsJ/HbvDEOL3fHCerY0SFewKGIo86M1ULv9wmaPw/Qqwbo5BHbeod/7DWb/WLeJdce/XjVHbdbcFcB5kYwmQuTby4bU+4r4PYAACAASURBVKqvNHYcKxSTLISJwlcdRmDOymZf6mxXxnjyjbnNXGiNd/r4SHjlFnL0RY/SPJw4c9b5pLMeVXEScl8VhnMEjTeX0CrvwM4gYAtbey4jBYpT/TKvOnIUZ799Hr44Tm2D5e13dcq8y4Up9WyMBk6mdJOiP9FYt6QhSGYy9RNj5dXBUiQ+riCxEUZ36hgaueXDdyOyuQfER7HBh9LCLfGHGIH8ccco9sYPAcTpTu9dM2+DoyB4u8TBwHf/G9R1pqYYOFGAu+oYlYseODnOEWQR1ncAJ73qL0wfF7x1AdBjnp+djozrYCscmocKKmztExAnXj/VM//VzPHLnKycd2D5clyp2NOiCbi4+oEFCNnNC939WobnjssUGyXdxnzN10Xunr1iFf2rk8fygt+yZcuWLVuu5VWo6D8neXwv+ATVAkDUXcODyS4+pMyGRpmrKigMVk+W0CwAcp0Js8IxnXwExqfNG4BIBE7T9B5AOX3Z+a9MV4p6Kk06CorNPGcje1Z31ROnVdFNZMfKlrxY4yQd9ZE+8HFaxMEn4GYN1Vs+ymeLOClhvI878ixu/kJH/yuAoFE/Eb8j/UFmO0I3H7Sg37czhsYVCXMMAZKUPPKt2ekHGkGeYz5H8eQRHKqG7DO1fWDKpfc2uMeTnImbSpvRHKcqEXzMn9nOj1wfXolHnzeuH+J1J3IfBbuXFgUpmsS655gsXvRWvoXDeImDj0D22pLWs4D99QUkvNkEpuTopKYPyD4heOaC6wToDDRAMNAl+0SLE7WgTiV1HZBh7kPPsMrXsT/VV5Yq5hACMJLead3MBv+MCP5CHVIo6jyCoWSZC8huf+NldE7XwT0RvGL6kLfMDP1BVVZY8Uk+4YqEG5zziEkAwm9/ePo4e+zoEJ2PCxUTkgvw9ULwx2Uj+C1btmzZsuXBybER/D93kaFua7evESoe0Z+qQObaRsuAeIbE+k64hLPmcUmgPdPRw3fCLf20yaY3p1VOS1Xg6ZkOHUOksQZDfk4dRRTIr9HpGXkl814ClbQOI7LPKePg1Z3qveYmnlVrDKGNvIKBMtoz+FxMqHHCCKPqQpufFkdSxPNqI7rAAo57Z0w55p7HDyOTdPnxLq5VCLL6CGNnA7x8XBplUoZoBXNNWum6+V1L9qqOgfgKs1lJMBxm59aLLRypNNBzRlL8IN2t74njCVt9/keOh10ocHMrIBEF+mUQeTlxQvDzzAfoq/Nd+RzCuN5pdLevMh93k/zhKqjW05APQMKWX9K3V2f4fiQub+UR1450TQi1VVGHKITpEIf5yUe694WLnuoZJlmgtzzmIT6vkrzFs6SFBsg5GdGLHiVugdDsyHzi/ZgQ/CiBC1bgrq0FYk6qklI0dEt+Cwyqyyz8Y1CtcUYL3dsAvdtTQvuhgDMkBkXEeWnhs2Hk81zZzszx+sixney2bNmyZcuWhyevnOjm1cljecE3hlpn+vYuAM5OJjuGgMuy46ZJN2LFw+v+hr+9/0Kjk3+naRNukQrlAZC+7sC8VR8lbTzTqnsNgePDGO9IL3E8JZUEvaungKMtX2bQWO2P1f1fNUAtSwZ+HQ4+zL0+QrgdAUbNCjLKef2By0Nn0ebBXAyHqdTI9gJAo/t9mvfYZpMyFmYBRdGhoAnd6YdYEEMXOVIbYFHCXt5a0rOdygq9KpizvasQhuSFGoDhjHancChUsjfJtJhKEXe/N0a8DOk+L96tOOKjTHOqR6sTf4fSSU4nZjd7czudn+6YeQkRvhLknF9GM23qyT/gxTqnP8CalV+cBhYELy2RvSWF67MPfCByr1WKiZ0IfrLB3znEl8oHibTls32L5ilu6qGn+887cD/VAf0ZzHSYnQ2sxsyxxvpDYzTloo8hD9xM/5JOYgerofV0jwfQyHwH+GDHV6ZDpK197pqUu2bMf+14b0u6d5/1RiY74YqTiOy32//JiLyGNojh42He9ZE7blA9os95dH5e8u9/+N9/3k14WbK56Lds2bJly5YHKI8FwY+jVSAeuLwN7iYdhIQ5Wz2cWBwRpW0sdp8BaNxJdDpekHqYTBXAEKmb/CGZHl5nqzxiQ00g5E7vtnFuDrlkpo7XwQoLIrdfF5OoqjfpOg+9TP/Q7ZqYv3fVog5pzX8oFmsHrzTq0sxv5ckW11N3MQf1lmacBQ+hbEij5qOccs/pa+xU+BiEmkZKbq4BbUTqDj/a5FcQ5UeGACgI65F98jYOBliHdiQip0lZ71BqSKxC62H3BYmi4dEcZJHOVXYX/AP2AxtCu63QBj8ugNngI1VaB4BR/oJx8zn6SpN89aIvmpm61pdMCotJPhZcWNDDDtwKgm8tALdj9zYj+GCmC1/6QPBCCrxKKe82YVNGZcMBLNo1QCqYVNraz0I+j8l5fjDdu/8Nxjqr81Q8Y3Y4IIzWhuxthQgKD12UbKLd7h0QqntJMjFYc5sKH17Gote5/gPoe+T97GwQyeWEnHehQpifNxnPkiT2vq68SXHFk34qF1vSTlen4iZ2PiVUbVZLVM6iOtnysmQj+C1btmzZsuUBymNB8ErDsi4YMPf4vi92bEfAu2xvi/89jz+PTy43qg7UfCd+KhPTqf2JuPaKfwJwB86OePcoVTy2aQStDRsifaDiKLtiixzn0z46/fk1UUU0x0t4gLT78y+YX8nb1WcQMA1J2vExGJ6+sGEPpambeDoSUQfQD03MpYbyh0FRXJdAnUE6Qky2SfVwgNbUMSQbwZb44J/iYNXskcPj6DMtWKnfiOp8qQ0ZFnvtxSB1TcSVVAcJ7zoHKFZAjI1rcqQg+5wEAiwaYy8J8YVAWYmMTbHUGmzZVcaDSEM3RroYTMQIknM4TfAVLAvg3qIlbOuUNa4LEXwy06GY3iUAOoPXA+J7S5pjdw9nR3EzqWN1ZXTHgshHTMfQCxLBMx/8nCxuVKp5V7/5lS5M607llpPPW464+wnBz2v3WglSjtR7n07vfmooIi8AgKMlZLfjVQtzLXFz3WpFXFThgSFoVCENPpNcOUFS/Qh2V3V/+w3cX708ohe8v6SqJxsArUr7LFaOeOmVAEfjluLOQPOrX6IQ2gAYdNBi/ln/zNe7TGlkS9Rc3HJU01mB3BDUR71IvrnJF2LV5pMkzssEOUXG4K078nQ2ya4zMZkMes1op7Y/31/TPiDaGUcWRaU9B6Tlm17LCz5F8mllIaxBiBI6+b6c4E9w9wSyR9Vd8w8KPqBjuiy1pWeRxVBhG7yDfNXQi2teHufQJgNAc4uKW2ritd3iZeJne9rZ3GNSq0/iHQ6KaLy9Jgnrkp8cr+FG8wPj6Dof+Wdnw6yA+ej5yOl5Qi7etVH2RVrSyE7Hy2DnWswbAVLSv3oyGAFKQGAQ4ETeW3/xz8ljYiG2u3S+i60AENFuGsmH9PYLRjGlf1X123Hg9DA5/3vhgtTqZBfRcYNJgcOx1C717HQLnM6pX5Q3XWjOQyJ6lvwzfjPTe3TV7c978BQp9ilUT0fG0UVOmsUOE/ft9ZDVz3G5JrBEt6fvdYSpZbyOAy0otjAt8C0vXbaKfsuWLVu2bHmA8lgQfIFagG+fbVc5edlIgmLS20TQGPfDqeCue+bY+0vQos4K8FYi9GblahRsRW1ZGh5wyMsxCkVIcOLxb8LcMMRXWlUCCiabyZI0L9iFisPUlbMU6zEUEm105z7/YZwyquqeH0XS2wiTpp3j3abMPU1XTx8PHSpNigIVAMVYNXXmTg8xU5gH00FVPKJCAveooUZNXjyVDKQ5x6nrJ0n6IRJ+WwbpRqOan1pfaUoNis2aOVuJyjzKlu9GUtvKU+gVSDzcquZe8lOoVQwWH9RyUI+9gD1pTEvDCRmFJweQtOXAE8j6qEki8lYSoUT9s3KrrMgWC0cKgkfvaURo1cmOOWMy/SsV9e4hVpzv3CJjf6k8gw4N807OckjkgXVVPDM+afDhLKp4utNRM6+By00DdAaBKy/1bCZ1CVe7lQBnpqRV5NdzomYq9qxFJcCvNU6vXHe9qcm0k/av5RzepN7mNrFw3xBP8dwUbi05z9NUlfS2i3X4moXJPWDZCH7Lli1btmx5gPJoEDxQMQ+Y+iW3krH39+OB0wvFDY8AGU91DQuiwjkC5/amVdTVBiSKnX7E1Jb1StIVQGsa2D36EV/Brb2wsxlOteSdDSeAArSiK/ZhzFUVY/50Lc8lc0VwYhJmPxF3pguvOqF10AlkJX2IUKB/wpGw1gOodnfW7CQ/Dca72YoSAoAO17YcElGN7h5157w32XE6cdhQuSFcx+TD0UTtlN7aoG6Ay+70C7sNvl/l0IxiMfo5fI2UJJK+aoar6JQXeYdZk/S7uW4i68DTS1oaKdF3Vi6VVZcczaa51HRG8OmLVy5EnYm0lsdbmszR5Ha0WztWBN/oguez3/O2rc50oX5LZlnnl5X5NqwctF7Ml8dESRsfzqHhXoeSbjgQ+chwOL+ELbCPiePTi8UaTB/byAEzZtv8qXLWMS7cSme519JR8sqvJf5OSgkuss7UTwNZA0qB+KAaozoV0/mISHD1uLeH0Idm4RDb8gpkI/gtW7Zs2bLlAcqjQfCBNYle43sBZlCErT02+8l4k6egxHitV1i/rklr4oPTlWC4s3vGy9UGRkGFNIPs3XF2mwlqAj7RcVdLuka0BnJMSJhc3UP+N1nEiru+4aQln47/LIT2eoVNVyut4xjS0WRMoNeanCulvFZTORvWCQIBHJKaAE+Fyr9EIZP5r7M8aImESHc3afbQl4ec0biiS1BiOwP0BxeY6JUnhkP/s9Glo1DiQiBuBpYamUZkCihaQfANht0BxeCkOLKfHCsA6eUer9w4rbnRXc8pQC7d74HEnWXUxihrrmhYdKjnuZm0SRIaoQTuMrUkA/l6jXZDo20+KGmlWOjLfRL24zIpjsiNZajAcQ4p7xG/KYLQhu708SGq9d4TuwMld1Ea1wPOTusEAazzVrKDbmtPHMy/QXu1WOvT+x1ZPvhtRLBkTsnT55syVh/DRm6w2wJQKWt4lqi5nqhXXLkx7cNPEMFzatzyWctG8Fu2bNmyZcsDlMeC4IUcohEQGw63U1RxNea5bZVwVZYT4/iMZp/XgGqsd1OmAhg0CyvjRxe7dY2P91+IZxPwV0JOcQLRMYT8Elac/tiJvwkwrs1uAJLoJuyFK4lA5XhBQdjLSKjGIDuQYBA5YUqY1Oc452haeLa7FRg4ipF3uhb/0TJU4S3fwm2fJ7a0iXKUZCoQMKWab6UQjFjXLIdro1O9qAYD0igeygNyGP7WMaX0ESf2HXpyUrROTpNuwL1L9yQ0YcS16GsSHUhUy+XhLuuqE8etjkwv657zDsTXMUXY0alXyVEo2gJ1Sz2AGuaeI1eh/JQ8piVxzYLU0wZ/RMu1Oi/kEPK29bbYYI5wjwdhup+n0ZJsiKoG4034yQMYcC96iVyrjpsDf4fTu9R0R6MAa6WbfWl4Xj+c1eOWYxx8fkC5xWIS4lc/PvKWuVSdgRRIXQ5XdF5v3rrCnStCpdY3imphrkSqKoKXtNHjA2wD+FclG8Fv2bJly5YtD1AeC4IHAgpNx0QDVkotBUnv6AmpM8y9xNp6+bRcxgVF8sAAc8w4Ve1oNJ26ZYpnzdvbyRhfKGkFGdfegpusWLwKA50CaC3D32WtmQD91oBJNqlktSmGRgSyl6vTZwVBYj0WcDQw/xCAV4l4fOqCwrYkp5lQBS+T3hG8do7cNJ6OQo7mNYcbvJ13qkQXiv8zhnjMQ+NYENbEV9LxadAoCIAu7qg9VCvvWYFMgdyVxmJbRUKavsZLOMRSt/pHRWah9+uLiC/61rnIZkCP+1SM+D+rEqkS7EIKxK/JZsaZVLKVkC6UCc9D8K0RqR8TJa2U8Hdid7jWjX0tShCE4oYW9IGT2N0I6dKR3FkCYzB5T+HKx8J+dZXAfPxSYHd8UFrlAShveg3PFwhKspnqHRJu8zHqrDC/VuY7nf7hnVIC8StG1xIfX0/UqgygsqH2FPHYyUGeKgT7vkh9jjFuQ2E6hpvRNVtegmwEv2XLli1btjxAeUwIfhUHzrO1d8UEQvhRkPpNSfrrcsj20qw49uTE7vXKomrpGYKHLoLF0yxW+eAi40tgQH5tSVQ3VZi2fCOLDvrzyEfh1+EWX7EUWxPaEjumz3lFG1HlnA22XeFvQCoxuzDZZSO+MUR+10o987TVvygmTD+9mDyn067ceVU9nQxxlYY7dEdAdiht9UPDj9xwmwbHeLQ8KBABBNHfgJPkMceMGtYUDTd46XKA3vIiLcGe+4SHzmmgtIEaoXCz52D1bslmeNrAee+DYp721SXe67Pw9zOPeFeWYaNE5tlKHY9EcMUqX3Q+0ql06hOTnbTwrq9OJhpQnqliFhk4lb7xWijlgx1dqQWoWX1rhYosiapqUj2pNgPQS7hHUMcTssedEiqBXFeNt9KljCU5IRTAPcPfW+GKQAHW5zwJAfRPXanuCiNeDnmO1ZVKwC80crQXe/8SYDJu4XswFTCAkyVixW4A/8pkI/gtW7Zs2bLlAcpjQfBpV70yNKc9D7HbRuKca7vyJwsNVllT7H/N9N6d3H4hrlM6ref+2J1XA31ema78h/V4mMxvcqXVvlyNRWDEvMCAGxJF3M9/jAmAlabQjh9WPitGfBOh4TVGtl7aacJKC00s8LnPhHdLX1CQFmaQkXl15/LVh9nhC92hG08MPQr52HghKwaMMpv3bkxGD6o7LjyhxwabGgZh04I4Nks3Al6Tf2nd11gRDjHdBg8xZUBjrARjwZuWFH9gmyQJ7AisLZlsAngn3/O8hzGaqljuA09DN9BJujYBdAJxIaaXQmmHEu+eJWcbfDSLSjImbz1JNY/Mxefh7MoCXhLAOS7R7mZjlTwIVl5LHtgY4+LqMYPjHIDiOp5+JHW652xyMRtjyJLv2Bkd+OS5sIpY+ZY+8TKIyKcByq/LFe8DwV9Rbyrr8VuAc7jktcvGU5dQ2Pps0CaArleKnqJF+83P0S2fiWwEv2XLli1btjxAeSwI/hZe/aRSAX2Dnd7/keIMm563U9r4gOZX9vgwSQMoMcQGAYfW32N3nrtgdW950nhfO8OzILfeN+Pgkdb9iMTmP6HoqODgxrDJsgenW7y4W7XlYmtZkZRiKo6LR8DiRAkcfTagsgXGJeU5RwJptZYQHBUWK33OZT4RHjTM2YiwY8bukgRgeFfdST3c6cWd5HXxP7hmSSBGDGw3WEwANBGCS0bGs/lFr5LajrAWN3RfMGJ+4/RCCLgcztDesk6+/hHzh0mi6YOnOCFZ0XJZby+AGeDVK6xAvNHEjgDoMyV8RfDVeN9ahGdMiiEO2tDM5u7+B2hwb3nTdrgN/hwXAP90OrJvIndceZK32xQoT7f5ULjYaEcsuMC46AH4bNrySDJ5AGNNGZh2bhuAJmpeHadOxmwt2d7ui0pAOCt3zIVhCoBcTqxhcEkH7M5I+ltNarKSGsSvSW4/KSG8ZHR5dfvPC0g9XRVX8TpbXpZsBL9ly5YtW7Y8QHk0CJ74ueb+cin0bAItbOrTNrPQOBGkFlQhsXGVKOlE6mP2mRdFtYJltjq5svezlHnXtzZvvH+TRHnideJirRbSAAb1umxrRtLb36xwis7n4ChiZBV+kDqDuV9x+kkDZGFKgw5PjNbDnleuBQNAMh1xFDJNJlC98QOOlgJD5eJNlajBFSRQlDDlUNwUj2BT2MwsCSUOPhpIPnwf6hFA3PUZbHkMELvsBmbzAJfbe3FlSLeKu+W777RorBf3w4+l6SucDvzB2hgGZ28G8XTiWmvruR7x40Tw+SMRfJrYZfrg7TvK8fSiVzIaBt2k8xKqu3grgevQk4qQUFI0AAPjftwDeDZOAM+GkvZgeEYAqkmI+LFYqIlZ9UIeOjqLTNJoGg+2iwyL99pn7Ykkno6hP8vwlxOL58OV8zzCr4W30mUkcL8wTRxm4M7nk7dwyR9fL1ePnxnoMiHyJVG9jnz+GVFHqc2/jlFmf8vLlI3gt2zZsmXLlgcojwXBa5PgkIfBF4KZ1fBYcBVm2zkwG6BoRoPZzAYRbAHrWWGTDIsnATpwa5MfNv+oZ+aiXw2mRI3r7wTQ6UufcF1qFet2eqWUv6E5mALih2P9ObuelaHOoFQ0cfbFD7f29HHh4K6P4wvQiXIOdGbVRFDvDaGTuZu3194HhGsIRY43dVJCFHaDuYXBW5e5/up0KRAJyoRYH0Y1L1HAQdcclj4Fu09tccgVXAIWTy+hQmEwQ9cyKILwJknwNo8HPdjDSO9pDVtB8AV19iNhqju3x5jZoLR0j68IPuPdieBnlZEmdj/hYe5M/qZOVHfqBcAYp3XNlBannvdjALC/Q+VkcoK7Ro3MvLqVUJ6aGKDkaosE8AlS+QH8YIHyxfP8BpSPQIRBXsAGHKKA0zBc1BH5WQjjUIA+ZpM5XQqyJR9fkTyKlO9Xkvngo6E2aUWJNQoHQAB6ydGgFotdTr6NWe1xM93BlpchG8Fv2bJly5YtD1BeBYL/1Q/f+5O/+gW//f6f/+g73wCAv/uLP/h3fwPgK//6L9//1pdechtUoNNmhlbXdOgl/K04Me2hiO1p/DyFnweFldDoCof7YdNKe1SBtRohwVGE13XrbNNg+PYts0ZDiQIKwtUMN39OHHzYiQnx0/8gRmeywwUFvSzEYUG951cso7NeMxtoDt6uWljKEytooIfanqW2+gFRs0+TH+nsSFWaxInhe9yKmmQRzirVJFwtngxwPqVJvYQAGOp+2oSAvsKaOOrq7gpBCgEJiD+NtTwfgJmyYcC9OTwaXrq538eq16LjwdSZRX9ypU6RSCsYy+IKME4nMoDeEX/kjz9Kovee5yXhXatZDWJ5gmEVEf5O4D4GSebNT/7ZcOVHl9PG8NQB4DIEwP0ggm84Xd+2DLLLUD15b8F8NYYBa5ks1pL54GMIgtIOmMB3jUrX4I9jgcEFExqgwcGu+eCjfh1+PLK52yAezT1nTs5SpIe3mi8aoSVWr/hXzl6oeUJ7gbnNvpLPdBoKrgg7s+gz4gbOzi4ePFteqryKF/w//vIX8Van/OqH7/07/PmPfvQN/OqH7/3JX3x5+XnLli1btmzZ8tvIK3jB/+qX//CVL//RcvAff/mL33/7GwDwpf/6X3zlrz74u+9846W+4cMElJhx3UaWfbLbqQ3mTkbPGzHugW4DDy0eqLa9jWOSe2TW6X9NqUCYJFE+KO2Itf1TEHsvEhvq8J8HIpA7m4QoUFtboX/GzQuAMTwqvLrTo1i4U6mQ42V/qDMAMOFmwuKwunq9q+7B0Q/tji1GqVhJ4XWySaWjpceBh8S+NkLZRLdldiJeWahRaexITLf4dRVAFy354/1CVuMCpITe76F4ibTx0eaqR5n0K7mc7eQTgEhryXlXBpMh8YPs9AT6E19hkWsrv3Ae243i1yLC4bv364cJ362vx8xYJ/O5Oblx/UIRnz8pxtALgFMvF1OoGF5Xbc7i58nZzAf+49FixVr5Bl2o1LuTsvmJJ4H4vVIHYCEtFuUxJ1moH2wZRNa4uBkLEPdJDK/7y8gC0aZGnHzxCoMVYUL2UrQFNpxf7DzF8boXPWQ6MXUVDMQP6vtQOZzsy/19iz6OIa7ACy3X8nihiGDxbLrWQm15SfIKXvD/+Mtf/OJv/uQP/gpAKOjrS/9LX/5d/O0vf4VvUE3/e7/3e3Hy3//937/8Fm7ZsmXLllcqb7zxxufdhIcvL/8F/6tf/kOa2X/1w/fe++HvvP+tf/zlL/Dl551RX+q//vWvf/smvPHGG4EfYysp6cyceAgFddP3eK3ttp05LUsBzGf7U5acYrtlOHAXDcumAOkxoEpTd5s554leAy/VttTeRBL32Oz7h4gl+ETRQX6655DbJzXVUOadCzScDcCVymEJUEDBNxFwUCWg/ygNdwAtU3lZ7YKVNzuvVIE+rxjxFVbYh01JVzAC/q5ttlWkYXov4yMxJk/aYFh8NEwxoZ+gRZiM7hIcfFcGcyvQMZxrDyeAph46r1DNS5iKyNbfOvv1cph/4F1zG6NJvwOQDPPtJOddYxNpjJ8ctSe8zl6W2tOLfqhZ9edRVy6soTocwQPA/ZDwXTe5HwLg2ZlascOnw8NoGOYujaNLUzrsV9bQDGf3JgCO4IIMBF/c5u3vhV2qMLcCbo6xy5KdPcSeB73g78oMH/+G20oTj4IQToIB8U5qvDGfOCtNMApJn43beWmO4M2XXmNhT2kwq1ODfW5ktAz/CivymTzbsTcKnygv/wX/pW+9/6NvxZcv/+4vPvhH4He+/JWXfuEtW7Zs2bLl8crnFAfPF/2XYBD/d99+2W70aVkkXPaj3LX6Ppf8dHJliQRAWjo/0/6NjTBxUcCx56JjzTZUc7ODP8brc0fsOdwCui1xyysKp2ew9KvrhyGMm/ZqrS1cb7dPLNhfnt+53MZrmNgD4tuf4iv9PG2Ik9lHLK9MxyU5A6YaKsAMnQKKjT8o8BYo+olXNEOjVqdlLZMekfcoNlpAunED0F57cDaX+YrAa0dgVH8MaA3ZV36QugAmQE+Ib4MvpE8XDRd0gsiwfIsPReosJms/1/FsrNdl1GNaI26e4ezD/p65WnLiM6Ylb7o4QuUFQeTwwHfviDKhwPUKUgCnNgPcJ5UDF2d586BzZegB0O5a9udCFrao9KRK4H40AM+G3J8Ckl486W6zl+JvX4F1Ew+puR+uA7DF0CRzwQXib2UQSqoCepnYcbYtnE7A49eeP7OtfXXgD2q8KFYp4i/DR+/+xLOPOwAdcnEb/KTraXwkjkiPSKWF8vgYDXyw9D7HXmx5mfLyX/B/9xd/8B++zEC4X/3yH37/7e8A+J0vf+Vv/sMP/+gb3/rSr/7vv/3F7/93r86HPu6rcBjy1egP/sXB50YF62t2lk8RVCFUCgAAIABJREFUAjK///kPH1bq1LbxOvcMF9KcOML/5qNdkphjdnWRld1WYOrSFqcJ8pVwvYFYmoj51s7Hda0+W8IMHKrTLqS+P1YNJ7cgSsLgOpwaBSJYaO5eTQtb9Ze6vpHyqZeENqa35IKw19xgdhERabembSgiWQiAcUo8ebsKgKPpF/0VogAGS5oK+0YLQy+tOKdOxAMzmgQAPRTjuWGz9gczsVr6maFhnojtl9XTpIyywjO4BF2MJJNvVB+f6gwI3/EdcgLUDrczyVO4F6By3pe6L515koaO2JpU1hal115sPJo4TxB3Y/7SenZKeMkBuKicnpolQroGa1AAR9NnDKjjLAiAZ8PD5O5Pf09zEyYHX9i+h1BZAuTiHnH3PcAqdHKg8qb3lnBoGadH41H5i+sH1NWGYHnBx9ck7SGRLevPhWfFwunP2nZ/oVfdskMNH8pksPI/fheP4KYVAOdl2klseany8l/w3/jOX/7yPfrYfeVf/+X75jv/rff//Jd/8Cd/8Ffl2JYtW7Zs2bLlM5JXoaKfrPAp3/jOj370nVdweRclscyMN0MhaVAy1J3cEof+sMhztp81DqaUK2lmAtn6Cf61ecMy0A4APdpam3AZAjFft4LXd+A+F4iNNoL+Zea2jfQ6Be9OW/JP8MjzHzL/qx/O+ovnkQBnc6AfKoYaOjihwvI3gHvCHZ00Ltc6fGU7kr6jzFIZIR/tzt9M49pChzG7RmrmjJHLUBRtsB0+WmSXKe57S9fKFyVTaWP0mi6kQmWdxoKau5A6UWshvUeTU5Qq+uiJqRa6p6vxE5WRn90LXbtxZpPKoIhG8KER2jigV2V0XKDaPuvJwrdu1LhNxWB0HKE8k8OObCoANGndVSIK4Av9vGgH8E+nu9cFvHZAD0/S2uh298R0DVCzpFzgqnhzkbtQwX4Zcj9yDC/DG9uZbCbyyXLdrrNmFpyPL76Sv0B9dcxlaIbCH3GucAXxVd8O4MLjB6+dDzwjVlJvqqlXzkgrDGVk4HQhLYrAmu8qonZVtSaPqUxhyxNRC9Ptllcjm6p2y5YtW7ZseYDyWJLNrCJlvyzP21feRuq0WD8H11dDdUGRV0ltylZ/tolazcFQK4FGF2LXiMtTLDjO6/80di4qC+AqDt9yV9BApFd85ea+jUFHQUH6qvEMAC18l8xyOXxfOZpeaNEkuFQ4/J0a6FikttpaOqfcOOFYsc/+BK1MyijHNbQXka929jMYDOpZ3QqFTlXkqrVmdLkx0XS/spa4JXyB50K634ivG0HoO4/4IaQb8so9LewAWlFwKJrz22Rc0iATzrStb/T7Iz5OaqfBrCBkurGWp0XcqmpJlRy9KgqbWwqAGoOqVJcNPelIaKcHRc+YlBNJmOMFlLdIczwtT9oA8KTJqQpS1Z4jPCRiwkNTNwA86T4LR9Ogc1la3p3/VQA8o1tZmNJHuLiKH/dBTlg71XYZjF4TFPLeCbjXlVzvzWWd2ddLOM3J7XsnUifH5XwhiTth2upqgsOUIgq5GwDOiw/KedrKj0BeqamnymMn/Ic9zjZUj7cptLe8BNkIfsuWLVu2bHmA8ngQ/GRKD1MZZht8LXDT9J5bz6F6M+srt9brFjuswrR1R1hdIcuYTFnBT7I4qy875TCGYU6Xksdno34pMJG6iBAXNSnkuGlUU/VYsdak9Hbu5Pyvm97bZBJXgow25MImHZUFteRm1dLl2iGLGlr2p5HrRa8gVNKMFMTfpQIOP7HOmnIdCNynXQnc2X8tQwiBO9WravFVzmsctG2e9FyPREIeX0eXi6AODTVThFGNMrWRlWTR3IinU0Fw4gqEbLhBCOSDLpo2+KHDEbmnfLVbJ/RdUB3BqlvD2USaOLXO5Luhy703NxauJzAEP6wNMi8ExVmpaqtJPoz0lVmoiThzcNN2VlTqZubL4O2HRr8Oi+MaPThoW14RhOBNmMXV2+dG/UtxWa8ENZ3L665lgByAo7kh/OPT1QBf6JkYCeUmvY5IzVvj5nEa7y8sEC2XHPJyiTITrd4yzXH2UOo/npykuOkAlE4iC9dva9rm4J0RyYZ8Ja8Ftrw82Qh+y5YtW7ZseYDyeBA8hbj5ikp2tnVmLsRra9ckRMczKlkvmPttCVu6uEpAM+eIgqZ2RSZjZdRvVOh4fdE9FPjvJsy5Cd61oFlNNYLjIXZYVa8gFxsgAHSEIdnBZe9xKoB0dqcmgISvvJpkC71JtaFCX/fBrKwBIEcY9djuACi1r5LIeO3CEoI72fWt7QXiV1KB6gheiG+lF4OistiJUI7IOLMqaewyXfSpohCPeqe/QnDy2Lx0TuiSoDaqGIx7d/8Gjwm3II1YpANcYBIsvOwt0fBJMKpRA4l43QSu3vdmKH+Ecor4m+oJ6+lvsLgqEXwkj+EkMxBANSC7FcsP3qQg8/FLGcvQMSYtW9yj90MuPkplwACgmfG+EL4qgCct4imcdeAJL2RA/F7F3O//6UIfdQGAS/QRTo670LXeD/fwX54yR+jtgvIhtGOOmydVgV4B+kgSY91rQfF0dSJ7ymF3n45cZdb3u8NXBp9LHvEvJ1eOCoC7u9GPgbJKxzkl5W19I/hXJxvBb9myZcuWLQ9QHh+CJz7zLX9EOqcNy/9xU6iZL6/lynZbzExaS5QMs6V+Hh/ZADKUBSeqAO5zTnRfdr7ltKsu0ow3QT1S1F7D87nn0HDAJQ1nHBc3TEqxaXsjAUjm3nBcThrLGB43iDLGO3l6GX1rRlmtnHS4Cn/PQeCHVv6iIKRzGsuUUAC4DzOKy/rS6/nE8JbnmFSoA2YOgkAiC6f9bJhShtm70am4GTNQo77G2NYUjLAX6n2GSiY9RYbLXyuamDJVI/C9Ut21MBAzeqKiZHbcSnoN5lQ/9MLh6bVCwSjsiw7y/CsVAFeJnawLJ9UYY5hKQEMTEL7uI/6GqT6A+0iiN+uCZvsKggc1Q5fiG09NTyjaGoAnfY3hZoX2e3bbVsITeDyIcmVYqtb7kYofC7nvM6Q6WlLUfXzm8SfdLxEFQq+jyK8hgde1lnQ1GP9y6dZ7N6Pqa2ZkT5vrk2V9al0vtuTuBoDRnNhutClwpB/j7slAuUcuI6JzfNCuGDa3vCzZCH7Lli1btmx5gPJYEHw6jEseALBSlKHazB3ORf5OXG2cb1+K8FermRth0lwj6dMNu1woZHLzVskTJO3NbDGBWouu5nY/AH0CdFyb2h0U8ott4TNoWWsDpkvHAS1tFpp1pXlKXG/BorTQQd3ADNzjU3QjP3A6nGacxVvAhPJv7WVqEpCahcGwYMW0TiqOr4NVo+Vn26evszOS/DoYchqvIXIolQbWYD+NgCmSi6gw2S6Lkb/eMLYx3w1CfhGH5LLu2mNBCq9o0+qTNTCKRsPg77QABtcR4fXFOOpVV+N6uOvTv2RC8LfuHkf2tiKGnlQSnHa8aQcg0ti2yQY/AsGTAy/zpvACB0nmAdwP4S2o9yPaZqd4al3DukI+Ayb/Tb650A0AANMmhbolVr9eAFuWhPKO4NmyyEljyqr74Qg+Vrhd4ZxJKerAVQhec8YsVHfBiBd38YgVNS/gaUQku2xaB1X3D+mt1F70dnYLH13vug2Rl1i1cRu9v0LZCH7Lli1btmx5gPJoEHwXKRkWJTau4ixLic8Cns4o71M61VfM55+HN8C39m1iwQsNgYpEolikhqF0IS57hXpXHL50RPLvzQoXkqwwjRdDOTfz2QCZzgg/ahqGp4awzaEFiZoKuVWpBdV8KAAaL+nMdAUN1Pyto1gDl0kKb+p6ISlDl17KjoMV8xDVPqW6BZOJtBH1NknkFflAYUgrTjRjvHjzonpiMi/XqRI4UyUAACcZGK3AwTQLDNHHMmmhJOAonxkarWWJZNI6j48P1ZUDaCXBHFZZoH+xfcfELlELjuDZ8VMNu+sJ4NRhefAaei2pOojXdXCaRgmUH+rx7gK1bLBHZVIDIG4j1xFtiOZal8dRsM/JJLBB4cBJz+Vj/Ad3pMA7mwA4Sfx+4WAF58XzHiJ2/Nnw6etjihOpFI1S5vdofon7kWTy1Y2A6gWc8/HMeneN+FmPXfrEtJKTn6Np7xotPBqzKsRdSos7r3X7cbXlZchG8Fu2bNmyZcsDlMeC4C+93Y2EHKKaqdvc49d+oQfutMu0Pa0dL8mSSgmJU27bGZFeqmH9D9NUoB22wa7jW2wivtbQerZEdU2dRjScLbgmPIfXaV/pxB7J17m1nw3/ybVXnOQNxK44vtJZ48aoBvS1MWeq+6tU9FfmOofFUSwNjSSfh3HOE2BZSbLDrZC9zVqNa0wRvukxe0sCtBjmNL4W5YRAPNiXs8R51EHk3SzA3fC3lFB+azntxyYXlVi/Zms3t/zORONCxclMU5ezOSLXAGdNso+5Iksr6MIegJMdisQBVXmkqsGRJ7mmoVC32V9lG4QD7sHoiXPQTx4G0NWgoTbJE+P4YAzBYMo8InsJhC0+YoriGR7dPBXGUjCmh4DTEQKpHxr0h6jYPdgGw0gPLkVTAPTh3iGKjHe3r6FPjDl2U/cAgGfOipCQ3X590nLEA0mj3PSZhi5WQsHrKEZ9dzpZ9FJFGp8nfivxiel3UFd7HKmys6Tqa56az2+ZhfT++cqLLZ+9bAS/ZcuWLVu2PEB5LAheZcrFdp0Lzo8niE5wUozxuAZ7mfWbZt71vPheOdL4YfQGQjozdQvxdLF+AsDIuGKDjI64JZnhAZQo8whLLkAMBrwihNqP2K8Bu1XrYIXHuNTcbamvENrvNKuarpgGPvMkB8xo9zyKMy1m4PlfBvuOhCndDYFAjQYeqGblfjXbU3h0vXTB6NFBlE9x3dRaVCNuToUj9yAiPCNfOIGaXcDQz0lGvF4A05jMwAi7cuVe633YZJ3iCD78FcLYzyoZ1qFUN1An1QLLA6FiErWlBWgLEng44LYLTTkGUilWlFHekhyRa8O9+8wDUKiZ+T0FnI5FRRQQkCH7Tqo34Jj+JJ4+CbjPcqoU1wDr2TNarK1Z/fSSfbjGwNQkRSUAjqEPaHwYnDUibwVwNLlQw2Re9E5lH9nxlMie95ZHhYSxP4JfigIAQJOq30rVUW9+V13KPQKUkId4nsTyuHUbNtJQBhf9UNx117dZS0Iz1Mu91oJcMi5dHPKBKWnelpctG8Fv2bJly5YtD1AeC4JvgSWL/dClIvurDU8UWzfA6XYaJWUpMSFEzYBQj4NvE7JuxL8ZR++x4xjNIbNlYo488RHKPydrS0ypRSVQXYiZVk6IPybUGg60tJZn0jka4f3/gteJEUm75uVnbPAcvUkOWpD4Raiujb9DwCHtGm2YwY+4B8RDkXvN/+kAcCeT1/3q7V1awi+BbbWy6UWZpFGYtTNKj4YmadA1MVO6CvsyBO7mnfgXwMmMhwR2hIYNc1ZAzxGnQwikHHgJoXzoU6aeBemeSMWCwR7XRArOdMQMYGSgvNbxUJ3CMFCrJPFcamTqTRfp3gNcwnQenoasqbg7veu6NMDu6ZHxaQu3sXJFTjiysPyiubkf7uVu03dH3/R7IvgYmDC9L/Rwyg9pjC89P8Rt5wddMcIkX0cHwGW4Yz/1N8W/3Y5T8dN4lv1+HZZgH86RWgcUrxQtd5k3slGDyEx68HULmF8LF5grBLtfl7qB9b5mC/MRmzSRyyrc8pJlI/gtW7Zs2bLlAcpjQfAT8AYAbQNApYL3HXBCNvv3Kv+Y+wbzoJneVa52poJpZ5sxwm5bpV85fde544162shiwIqGg5oKJYQfoAuyXbyS9IWzPdwfO3I6MT1U8fMvTU5CvCujdeB14tckmQeg0Jbm+2n7TiuwLEcIDBmIz+PJ004oEC25jFQggAhJBAcNhACa4AkLOEJCXKk0iwezJVLHVmuZgC+12tknWQCIRhyzwdz0Ye7l0iMzBSSB+30xu4qsgw9EB21MlL7oYNvjdB/Mmw7/y8LqIoYWmwiZ4+rA0HO9VLgMW7Ka+/ERCRJlLs0u5JeqDlF6njd4VrjmPRVS0OuFnv+VPKA6t3u+dprkW7mKnWhY35KyH8Nn7U6mkgKNrOe0tVs7Qwd27Xfip99lgoQ8PQzkJ8fkgkm3FOskbgFbDBVYW1UH+xJainC1MeIED6BvvJXGZOyPqejC4Z19h0LP2ESXrtXrgk+D8Pio1A7NF55N4iaze3WyEfyWLVu2bNnyAOWxIHhtbkRVQydDM+2XA+i5fPkcCHsqJosrfoGogbmXRgRusCufxMdZs50t9dzKot8KIZeGc2qAyxl/Y25gJGUHybbGkDDnewF1BMYY6uhT1sneVuNrJoWr8LdmjXPzrQMyCR2D0PWWftplKOv1lMdTQeBi0OoSLFmOIiJF/dSBLu61fj/yJ6xVrte1f1uZl5aJ76WOvSR8TZjC+GCaIR1Tz1oDjazqXmMkuHNGMxKEDZ0GORKpdXGr/xICHk0qGh/UEjojqi80pRU/YOSkfgmmPNFp0LR0qhW9iyJbXNzsyyBUXFiOK8n7Toih/KNZmrJQPSjz0WWcOorTezDQGYL8QvdGfXz2uJzjY8PxpzfgfuaPa1zfysQMSWznYyIjC3gbUJQZdy2p6FAs7ic/DOoS3CcAOcu8hazacndYAb+FkwHCp4BYPwJfApGfpw8OSuDJ0fQoLZQI5XeFDjpLVp/5WkN5OHgNJ++yIVmgPZd6c8tnLxvBb9myZcuWLQ9QHguCH01aYXoHkE7LmhA+fxOd8UkxaRKNTKD/E/akM7RfvPdFEeHOBLYKUznMVFJB/lza5H8yM16F/hHfnDhcvPrZF/1G2LpSuwAbskR9cLiwYC9eYyVy9+MlIB5K7ckYYgi+YfLE1XXsa3dvi8eIa6KQXuBI/doKnq5ZwtJY/ZyL1p/CIptDZ91jYAIqxCklDwlWtcmKHzPXSQQWAeMak8sFy8zfmr8CgPYyyIFnRSRaG34AdqLh2vA5d8pxGpbPXPIaThUoJnOpS3vWSdSx0uCbI3mfrMsvNVGNKwQG3GFNItwEADxpSnf6HOEIQ0cxvWdAvNXPZRC+A6H/sK/3gwhbxQbz4MLnZEm1waNA+YUgof6N4YpBPhitLtR2KFnrTxaLZTOWSrz8lDMjNWhlGiJPHQAdjsiP5hH55t8Qbv/jlHt4AQCN92y4EYTq6OCdEks9Ej3YkIYOYCwrF17hc/gvtnz2shH8li1btmzZ8gDl0SD43vRUwGPK0xw+lEzsAJC4PbyaI92xHZ6w+lSVS1t/j+xkq5n/eWxqNNbeQJMav08ofDaBlYxV5h7fCBaIFRL0eYUrHq1GVy1hrNUNuzYhz9MKK5O7XksT46uoh0arRmz3VP9V/9MrIFnrA/3BTLYKoPcwlvuPF0I2g3QGUySo8YpZ+LlQvhy6CetR8FMFmGfJbtBEgwP8LHMfqgVqT6yLpgOZpleLlR3IfHoF87nmxPkKRyYBW9am412qBAyzXoJTrY3wWjeE7ENIxN2CZI0APcaoIuxBgH4OMSN6ZiTgcvLJmkdVWN85xEBnsLm5O0JEpVMJYfD3onLtTu9ffbR94V1GLgC4qgB23HDq6evEB24UyG5TcA3cqzdAHi+cdyjLJmbkSV8DOp55djgHvMpJzAqthshTh+n05blyP0JPo7b4rQIZzrV3nmLc8vb16KnbyNGzwYFfycf86lYZXJAxJqu+b8urko3gt2zZsmXLlgcojwXBn3c9cQGgZwRtais2LlE9w0wHAOnEnkXSsF3QWimmy17aDxY1QNldZ6nrSPo89YZFWodnsr/1G4BbBnvx4zdC6jGdMqkK1H3glX65gcAi4jZGwv/1Cv1EEfrPN7ewLobnMcRIv9u898cypKWzN7tw8pRTJ6QVSbKfOPE/vmjEdi0h/jr4geypDjlrA67G8NbZLp3JtWr7B1AdJJL2i+70ozreA8pQbCXnu+QcqbfQK5zVLUS3QBikBcBBrcoZwdzmHBBjPZQ50acVfTLb+h1Gjfg4h0eld0Evmp7g4IuEeF1CFVQ1HdDEpSZu8L5o9ZOBwF3Hk0yNv8W6WijtAseTVp3uLhzeAO7mEJNWf1NOcH0MXmJQfZJInZi+GvVjkYSXwDWgj4eILcW7xi5wVLWcGCbzOPFMogyOWoyS+igBaJJ9vGNiOjvNQhWUs3NRXwYWLhHxJp7pr6zhkzcvZmECiHRciMbXMdnyCuSxvOA//mI/jnws9svId8aZr0kZOo6GWdPVLsN+AuovlOllWCTqj2fTc3S/rGeKEQp/rczgce2asmwl+P7O96LfWGxKUwA6PImIIrPO3OxBPINkZqi9UgNKvGMi4axLJrTNC01PN24R6sXr57pj0OfENGYxajJP1fuSRjbfcN1Vr40dvOsAcIie3LKs0xxv+nI8Wo5b44Z5z2S/R1LXC6fjrgQ9xqTFdcc0q7XIZBiJ3B4DbpHhlkVNwZ7vb55vfT8jwrBQ68BfaV7PWfwkQ06V000/04v8VLGuqTB2i62MDCuelZW8vOmr6EM0vUKGxmzy5iMtsSwjMd9WUmqw999JdfezIQDuyTMTVgZ/tyHVzvwgKETXg4MZrnbLOyze9EvUXCyYJb7u1FJSfRAA3DVS9zTX1SePzVxzhKLF8UiwpDEc5X3/7HTTT1u0txIGNQ6dXZd72eC+jctlAp75EejemsXwV1/w47YH7ZaXIltFv2XLli1btjxAeSwIvv1nOLUjuFY+JolqAmUAqZMNjNhPPZ6dAPq94fiRINU2vA7BJLWLASfLd9EsuTbO9r+g6jWOLUAcEuSyXk0oef0ch8vKaqc8KKHJI0OOFBV6ucrqDagTt4bgSpeg4YWkqoN4ofRN1f13QpvXmMqDQIpNDqTOkydkpqmkDdBZ/XcS/g58XM7skqr4QWIQFDzUupTxqZ1bNS6BRXKS9UaxaQyKhDlocShTpPIjQOHIVWaAyYG1u0eFFcmCyoZEp1AS9gRuzoY1RQHcF5XDtQuBU+OvL8iqUT9p7xCyvtAsIjQBKHUJPvuBhhmJh3qhMgjCBa4o7DEI44ITJSWra3R5pqrNBUoob8Bd7h3Bh+qeTnYcfG/qcPe6UPVHs2rQo9KeUhU/U4ECcycV/VWBQMyRazVWeGhceEXUK6Jczprqwz6vP2H66cug6UcB4P7E5dJ8MHtaK09NzV8sJFfms0yGCJahBm+uVhI7VXLcc8gNZeSWlyMbwW/ZsmXLli0PUB4Lgu93KokHcOkkkSHfZrDABqi1TfHlvt1LBze87aIrWHMcjwJ3gAnS0TD5vMZVTUCcqNDZ3Q8zuBQpaoeyW9chuaGejZTXugFHn5kzhnBkRuoJ7nnpSTeQx6nG0KlYlNC5tnLEHXx8LgjNljHLVuuqdQgQET5QnpXVPIZakm+gpAcdpLM9Gu7Y1F7qC3thAKn7YhC1Ym1pyTxEUqqyr416nAJWoZJYULK8IPbgmoNjCIwRVhokMK3oYwJ0VgTvp5AM9RKAbFYtaYaW5Y85JhBapv1YuLAl3m2J+DUyvgw5mvL61rZZVwRUNBg5WM/hTQ10a9JbLvC4BKrpPeMzXZY8K/cj/QM4JgBwafxAG3zRo9gQ+VAsNnWsBa688OI4puNh9Y/7wClxxHVOI9YJB3lB8LHMiqYHoPPHUFF3psMzO8Wj49zBoDVlhmUH+icfFR5SqBhnLgNlFC4wPS5C/yei9lCNsFhT4I1x02l4y0uRjeC3bNmyZcuWByiPBcE3UTMDOwzq7uMprdjQgd7VtpmX+3Z55qap0QXwv9AWTsm+qY/tqEcsLea/Gy7fz5WC2CVdxzWrumUCD0noz6+NSNqP5wBwJz7iEkCmhAkjmV0OukD4BKzRt/W7fbGjTSZSn8YwBdX0ZjfA7eiT3vI9MOXcSQmgE4ZollwslI7kyCnTRC06yHhvDg6tqucjgZDf15vubSgRVv51dVxYus8CoQqyJnS5vRwW92yvmaCtXmMwpsHd8ps3LHhYYxHZ8IdpHIqjxCJeaJDuoq2sqDNpiBDJeWsfw6f9olJ8QqoN3osGpc89jf2uBruarLhAmxiL05W9RrtFiVEAyoURAbhynkcB6K6WGw7QNTvlfw+GyV1YEp5KlRct3LdnCZNbvdm98HUcXfm5ju28osK7vjfc+Rm8SXnPnjotSDYvu2wI29RUYQjXIWpA3FVZ7tkT/j2X1FEZ4ucDb8iE4Eekyi0PCzo5ee9mVxvXDG0b/CuUjeC3bNmyZcuWByiPBcFL43bSkHpD77ZvHRMUE1zuG4BxynkRAPoMxzB+TQePq7HcPl/lsFlkIrcJg16taAZKKuEUD4KiyYt+qnw+7D0aKKbDG1ghzLolKn1SFZTLxXGveZJG73bGN7sMDf+DZhylVqqVeAKmP5Gi/xga+UY9IUr0KyZr4Y2JDgVOqi7lWpQB9uEJE29YWPxdi1Qco05OmEJPwtAJYVej7NIecSQUwLqFHoVdqEqPggUzM6mP4tpT7WWMFe5s30jFA6JkYaz5yZZUrcYlgFeoSTjmgeAXxUzAVvq0ay+AO63IsYpo/b2QF9b1KLOjwEgGiASgKLMZJVeDetAHyWT5Pkf67Vc8reH2XwPZiZjt4OER85nXFcAhwpwwk0fFBNDnNi/1RxuW2PGQIKKJv9HmmgroaEoVnPf5DL8Zq3+k48ooOieo2Fcdflx53XYMAMIs0p6i9yJE8AjjfeW0Sb8ZKhWsYfawRHnORJMC+m8E/8pkI/gtW7Zs2bLlAcrjQfCTwVBEwyzUIhfLjL7NZq9fkPNZA/D/t/f1sHYcV5rfqb6P8uTMKAIiICWLDQUQI8OBQs1ADw4EBV5AoQQlQyvYTTzhOlrAMiPDDh1M4GFgcDBWqMBYjgkoHDhYCZAA6mWKd813u+pscH7qVN37JM2Y5CP7nS94796+1dX11139nd9SlSdNhrmuXjYV2Xxpd7g/aNP4zUKQyt+6UzdSv9pUQVDpIeJRAAAgAElEQVTa6bXJSIbbrsczGD3B7fHQdew28KoDlxf2KDbwd/aYMASNyMxlVVpA3kOGyBKKVCU8nt1nwagbDQpn1hChjEENXIyAddI52C2MnZWmsZZX++1RVclBUy7ld4SokCajYmsbjZYDKZym1yUHTrxOdlLzMObNOhslKwh2113BPC4AAp2EQe5CC9IKe8OsJWpWbaYUxqfV2oBskHvaFaOnNI+rHPd8tTa/Kiro7ubKJm0ZaDoTy8FazeKBO+OfbhuKXXOsKkCydWcqcyfWrokP+VHCmPhQ90v3iHLydTXi7h8A7H2iqJ+IsJy4O+IfCCHs08zg7US2Dkfq7/UUGo6zrQqLIqyPI2YdZFeNw+5fUs13HDdbOgCdNDOJUElPXQuAuhL7iSokoFoB9MdO9wsI8skuCLSbsK4Uw2N0CWLi6SMZfCKRSCQSG8SVYfCdKAHBkrMUJb7+++7EM2PK209jLgBWXgDsUOWNmNrgY8sHJBJORwOfBWaqwiHmvJQUc302c+5SOo+Zw0drvVOgsvkS/VrOBTtrCA2NxbpO/UgyCXj+G2OlbpYwWgkQddraSSfRYOptTQ6DGRSQw9gFNO/I+JPHyDvR3JdKvjyHh7Qh+hzHkVkZO/UdMBGCepkHvhJVnh4Lz/56VYsqTQ+kC06wwoLg4PsQSOcoW3JKZ5eW4yryYY8cB4jG3XhzHzTuTN2TjvgpHo0fPrbHdPBuoz7MWdS1s8WUL1q52bRr1tcy0tlqogIeLxR5tkBms1qBk+Le+tpH5aCWFHjm06aS77mIRnLfgvH8uUyfRZejseUe/y7MmtYQbTL6pUfzgiGk/FhU5sTNLIaYiUEm4bIHLV80F1GzcI9sju9K/RtFRxX42DaqVRa8LYOVAKxrcRnnpLz3SfKwGZgGeXyqrFXN7+fYFYmnj+d9g79+/fplNyGRSCQSTxj5bH8GeN43+G+++eavr+T69evu7+6kU6ge+Tu5gJTBl4XXhQHsHy/yEryiAFixLKsahJcKoKvYewi88eoz+Twgo07cRRumEempe71PxL1HjHJFOMVfL7xQv2L/N7xUk3O4yOl7hT3UHQKvYCMyZMlYewgAGmuYWzC32Rti6WU5NBVMJLp5Mh2km9NbEK6eok3s5KtJFP5m0QI7lyIAu2DiLkr6nTMhU5BbAG2jaNC/k871xMz6PdB69EoHNFAdcZi+MAjFjEIqh4CLIWZ4IbZcujYkKlroIxTdxHvLWZckGUebArYzOJJLBOW9N39i8ILahiTI1cz112Zu4z3TqPLp7lkQpAWBB9u68bbomPeEeFJ/s3Lsqui4xsjj0/VUuQia8hajowfBTG20EgNYC4qZ0yM47rs8JtrexzbzOErR3cQlLhgZvFugc+jI0juLGph3H5mxwkKalbixLeDWL4GgSu93vwg5Vu1bsSehnd7d3NX93SLZ2V0fI9n54R5Js5sdVJMBWMPktCfybEe+KHwrUgefSCQSicQG8bwz+CeFZZnUw+YkWjXDVVG3eC+jxqW10g4FgISyP+eyLwuApSoFEg5RKh8hqlFtG0N0jVakQs2a6eCh6c4iXZ4qkO+zLutixj61ygj6HGreWIRr0Sy9t6rSe6g7dVsvFo6ch/PdsNzrHVJNdy2daV0ZLIYEqvq3sNgYyUHjHsj9pcUqF8Zj5YW4n5TO6QUnxvglATzZwRMtPziXR5gfPLlnM0JGtcp6aedbUmBxrS0NRKqz+lEPvVBXD9voYYjPfxjEXkeQeuixIDMoIXuYTFarljneNejG2KIDPXUrjjknG6zBLhuI/KC2HkhOc95bVvieTU6kEW1YvQ2Dn7R/cLf1FlKoAVgZValkiA8YBqUFTfk6WpC47txnc5hWBmm6OV13kl8tVrFQr8etdZxPe+PdLD+2CvApmJX3hGFaKUhNqjlBAL07nqvNTyu+3lRIqUY8FpbguGSvVmqtQG660n92r/cYyc5D0SEy9WFtj9b1MFmCcXcAXKl972dV4q9EMvhEIpFIJDaIq8LgS2GnmxhfYs2cvgFYdmYB2qgsDcBLL6HuGMD+XO2whT20RnsJdUfCIdoiBr1OuJkPOPZF0HddHl9sD5O5dcd3ofh2skWtn7tm1btqc2QZvVoTFYxmsX2snH9PXF/4d9Mr9MhiPZmYvturNEK4ptdStItEgy8Ds4oKmM0OH4D4lAvhXvgH6lxuqm7XkhIQGLzLRJzQx4xk15YxzJxcUa/F0PhiyhERQn9X7mnlFov1DWBvfdsVTCIBN672LkfKtlo6cD5Q3tvkkoX5I18wAkthToMHv6XsqhyiAQqVtzJOlKOqmGiOlwAM0iMO/u4l/OrW9SvTwkMLndkX9UAZmMXkOA67EYKrgvm1q7++VkxBXrKYhEYqnOLHrd0aQA+as8Vg1M02C+dVR3U3TqKb00epDIIQa64wzLt82FmXJ/rbS9qNJVfeFaxhgGqI+jdIBsP6kVmhxjwL/YY2+1dh8K2xBPd0GxEPYOd+8H7Ejksdg4VEVN5PF1Wrfr7YzyfxpJEMPpFIJBKJDeKqMPiYnBgAlfA10GJYZi2Gjs2y1NYagFIWiAGwKu815JOUXQutKwEoZp3MwPI9XlSZINrdwqxKPWnJ7kjoOnkRLubSfbxCj86GgdrzIbk3O3wXBXjYah0018o6XdeqjM6q8ENDXBUemkVkOvNmYckhQ2RXME0nFfWcVvLBbPa65uywMICFWLzb/2bBS6ZrF07vCkixrl9MQzmxYW/dQl6Me0+V4jOClf6+AsDq4dtYvvYYeXKm61yDHYUeMRqqRybVqfx3yugt9xgAO+0g+4Xkl6KEvvPjXVTCh7zyxZSwZituLTdpRFxqZP701Ug4IQROQF8njWnlfhgeha1phW7Q4lb02t0yBCNnnmcopATUE+vIHdU4oZGswGbijcnqPka5l0Fmm4KjwdTcOYKaVri3sZIJPWG1rp/mtLfNh9H+TgsGQdKAkfFz6Dt6BjZdAJ7XzgQ2k+b7IGmkm87YD07EoxizJ2uvpbXO4D2SXWxJdHBne1IFio/4tQsMncp3KcfB0CeeDpLBJxKJRCKxQVwVBr/bscVLl7dU/0XzxFuGNNfB8qLKUpRgiFuKJYxfS107aSilrKUAqLUoGWWWStWP2RWJZG/xFnnedOcs5KCJA3cjb2SIBxfg78XfournYzzFf4TayZNb2/ortnN4iJ5ZmJy9kTcXewBArexa/MhjSrEk68XzTTGAavytFA88x+omXr1LRhaUajOAk9LDwzkFF+XrNfvrDF5gmngOJFKOaDud81VmKb+EKzbQYj1V0gYdEvmwG+2lKfjZd3oXxyTQU2nDXj22g8baWONJeP0eqJ60QQLYta6TroHFljDzMWQ6TIe92JJv3ZzDW2g6eGin5CP1BhCAQrxvXSkebAJU5tMZvLBYj00/5mfgyfakG367z4LGyPPlKUKIRlj0JuIh5vyB+b0LM6qxW2vJcfMCskbtldSa8XwbTC5cYMMHlg0dKhnUb+7yUEza4idGT4GYksClDgCWEMcwhpQfruayIpUWMIBSunG7jtKsYkdb+4ILBjeY3Hm67Y9dcxpDizzvMfUOakDiGSEZfCKRSCQSG8RVYfDLyZDD3HOlF/OPdxP0Q7osZy47jXAnxGdXi2SOF+wXlQSs++KBn1ZaAAhx363Nw7JFo3d3Em9mOMsWv0z50wEJv9Azfip25NBI+rspL6HLMNCaSTEWo3CmcOaef0xKWkOciskH01Ny+OoNJnKy0sP/zQYHemFqpcEo4L5pbPC1KWV3Y2PN8r5oJLvFE4RbhR4730medlYGXxWUPU6cMP7VgpO7dnYKYFftGm503fPFTQ3Qy/nY6+idV/3ZHayVsbGZcLuu1NoWza1X06GSmzjYEDr/LtrHIeN4MTv+ZvPkoR5NdBWdLY4sNTYaag4FJkjwVeXRyNW63i5kSe+d1048g8cxdwW2y4csrZyZStAgUgIize2tJ+rB6qXASdGq3IbDXfxbZQB7DUqhczFVeNhUjOvEjQDMyESnu5pmvYQVEhdMjGlfw6OL7dLFLGCa828vYPIXd1gHekDMriM3kMVKlEtqwHm90WfVu3c+Sg/GCj23JCzUJoXFGFuYeOpIBp9IJBKJxAZxVRj8zhi8RlNyN2S3NR2Jz7L0l1NPXi4VyL+lstQpp+9Olsd/UTN7twBfFwL0a1UfYBCjiGJP+Qi1RZs0mBaT893uzno8HZMZw4cj/qefNfw4aiK736pTtulCGtLOdO1jnW6PjYKYsYrZKb5C7cONdpfCnslqVOOpIzXgMoEFAF+rGpBux+4p4AHjEFTgPbmWDYU6kVO3r9brGqmV6SkNncAAa6PHlRBC1LnGPaQ46y3fLX2QPb164OGdsXlgdqG/S+nFjgozPH+8VxiZovxYOk1CNWa/FA5NVTIHgMDVNaMh2ADQ/enrSPUm9JzlUjnHLhMOvM8rkwe4J9Xia9sn52+XsrBp98dIiMbsm9FWWz9TkrcWnB3iYO4tECFh8LZYSrdRl6W+aix6FvX0yuoa48IVXxVdJhTmhcPX6LZem4q13FSiBNsLxGk1D4LpeCkaZU/tKuw5E1Xso3G7EuvFCHcLp/ehOXgsaD/Rjd97MgUMJY+lnZQTgok/QKD5CZZ4akgGn0gkEonEBnFlGPzOGby+vU5GoZGRIqqTuTNphGD1vNQW3qzj+c5Ky74AqHsCcM5lLaqS363Ox7oOHsbpleh/vyhUcqHA3GA+6qFFncvPx2N9PKr64E1hi25tSlUis5C2+HQaZLxRC2PVx8ZU39xr7/Rai2gcbNUCurlCFJyUWhpXAGuYvqOsodhMySX2xmYwESz2LqpEp44DvzZ6bEpTzxQuX5UCjunsnJBV7rHSXAEchgD7ptxdh8TTe9NQoTe1+GwwRGvvqlmXXkSSV90jn8ms63VWTMpgxNqqKDYxbkXvNLQHfBhFCDHBPLs+GFjNJUGHkZUNW9hH5exm4t6D4fvKwaGkyup3Y4J9YPCjNYC2cOXuPg5014a1QabVW+gpCfY63Tpd6gcPEtuOfbPQhxaLwnMTeKj5o6r0huFe21HvAmzl1CAO4WAsEqd1bSHqooyU3Js2uc2Je3P1vD7xfGXHrBy1Wtr4A/7tDYDfLBZDcygWHOWPo8fV6NUmng2SwScSiUQisUFcFQbvmuxDDVOwGu1vl4dvo87gndnHNO1ETd6Ly8LVrOsfq61wAXDSlWS0LwXoAeyMuCs/Viv6gy58t/H81Gaaj49MHl1coPWHepUdaBFP9RbSfo2v45o9TF0MqjEwzR5W0MJLPBEKNUDsewfphVK3Rm7r0BYCsFgaK6fRU/KubtwOYGTSCERqNd7sRFyuf61gXwjorufmMq5mz84F/a8rdxcztI4ntjhCQX/sBsVr4HwI+uydxeZzV/7YQfk6BU1zih+N5GEv754OrYe007Ns5ENSAy1v37uIIarATWZUyDsrBN0jxuuR7vxts+C51Dxs39QjCoswKs6nJe+GFK0CQLO4CD6tPmh6ZBxMF8AYfz5YTlXp7l5Gkng1pr4GUY0vXzqg7NOsefyD1RxMptgAyzgLx6QX+tcL2Ad9XMwrcAwdf0jQ7YEWEtLHJ6FZ5FCM8qgfLnoAHcBkCUepf+IZ4Ops8IOkndQ/S2zoADM5aRZeZrJr8w9EliOVh5uwLG2RrWjheqK7EbMMr9yBrfX7bQEgoW1R0R9f5Tu29ulg3+ndfk+fkt5tIO7rfQ+180L9x66i56uInv3B5k9Q2yQWH5PehdZ6AJzmTjzWVYw3PNfeLN/4qfR5gSoRdK8SIXC1HBvVtkkJveLxQPbqikbnYtFWcR73abv+eel5ZmM2kXa4wTMArQTASwVx2Nie4yXsju51huB355fu5n62wNzgS+u0WesGg2G+/MQpJq4/8d28a2XtoyyW1UPhEknIYDFxa7afcgzHGzYthoaXaaOKxy/tg+DbsIeXuQb9oFoACyPjsv24+H1ofZuU6EMLkb/9nOtbco8vJPX4e9g+vEm46L7ZwuDwE8JLj7fDJfCahMYk5Is5ufktNrnJwcZq+txfPfWrWoOeu2+qtcffIWoYfOoBhmmaFH3vpvAEC9ZwbvzLdiQQBtvpwyAMz7dxmnvAnJ6U6EiBkOMqxfKXhhTRJxKJRCKxQVwVBo/O3eWLpUtpsBQL+p5LI//1r+Ysp1+poHTxJzRWDXByrYmR3f68SDYaueK6kOaZraWtDLfF81f7Mr/mui6g54dlF/seAQcer4V4KN5/tzYrxzIHm+NueM7t2OywPPOJkVMzVLQ0r81GXGypGsQLLATG0aHz4BvRUJAbGTtiMxFiiNTxgMW67B3A4n5Wlhtmb8TLqLyJ6JUGdfMxNcfj4Z23WVpYt4k7b+OJNpgxu4zAj8RJqJa9phl39PS1Ut65aRtnwaPKMI+UbpQJ+4nFvOaq/9Qppl6jaKSUzhqlSr9iFwbEjnTLxBnex8lVzAckpOWVQTCyaGWGC5l4mEfHqmImdcWm+3HFPgym1+/r5LHNmvN1t3CMXpFuNsiWFXq3A4Ba3KeRNViy2+7ZMqt9eIF482I8bl93tvJEy7CvvXfxA9HgekpBOhKH158SAy9XiSADIEZZAKA1Ugc5EyiSp1KWMSwMCfQUov8CgAntXfbZ3WuDUSTRd8jwk80/SySDTyQSiURig7hCDD7C4rWAyqCEc4IeoqgeWOdJDTy8HTG7Fwqr1dW11toKaFbZZV/25wuAx43dDgAAmfcSj5FuXLd6yKcDoY/d8Z+pq8xDeWYPPRsLAyZLmOuxnz3UjxsoNFe+DSKD6FGjQyG681KmiDfsVD544PQGcM+Iatp3tdYhN6ZzgzIVfBjLkeOPa49yimBbt29KwScVLKDGU0sbLNHYZAN/qYN1HqCRkapFPmmm3TVLyS6ScHaIMcRKNEsgtzgLzOwoD5qCtzRT6g82GWP9R1sC6tMUI+QEoo8wz70EX6x6dwbv2neEQR4MGOnIiRj98Rqr6KiLmqyY5xzySYlWe9EuTI57gNjOgzvLVOEQRiKuzmOsQg6zicNq8x4HeW095G10u3Wtf5c5jceLyVF2hUxIYNKy3lQAuGY3r0eLMldPuTXcAZiCRRvDxG9UtCI3DVaRQdNEUAfxa2EPppClJkx8dDO+6LhWFWrtQ5B4JkgGn0gkEonEBnFVGLzHTpEXWNdDl2LxZsRaO/iWHeXHzNRTp4xKNSfKZDVfu9YAtB3HBnAbnEW4n45J639h4IjvxKQ8k9aW+Y2aG0XnGWI+yNtpjG16hTcOt4wV+qWV+jeVJbQ2kksyLSB3KcTUWVH210bimyDOchyIlPu/1XDieVXzZvePcsctjwsrEU4Ojah1GVhaEVVL24mPq1KrahFGT8yZIro/LcGm3RFj3NaD43Ld/cFck0W8cdIZz+rVNqVi7qpwYqbyIf6MHh+U4t5ls6gwdyn2XKteUkwougAgiBCAzlknwwjvk3fBls/gCNAG9w72pnVaz71JMoYyyz+w8TypavbxF/W58Au7UyLB8/qMgYHXEKtnbcFQRoRPKkBiuzSJ+4auHxMVrCF0sfYlyDAEw9QHcYkMzsmij6T/91iFVWoDb2IzWWiFej4ktzcBwFzs+WOM/EAcZwd61hlpCUwZHx2Ge/nomDcmlTn+gOL5+FyKMT9mEk8NyeATiUQikdggrgqDX9cSY80uiwavdRW7xgct7GEi9MXWavB4EV1bHNTDGIsBWBYefLiPUTT5t6zybk51VwDQwZx8e4CIbyH63WUAnRMxm/WyMXjpUnRKt3/2zm9ZPrXvc2Kc+ePkQcsHL/vuHx80dl16wexkD5K1U4bS9YuF2GOkOJcCUN10vPXgKrHYypDkMXvLtGGMXy9H7uJsRLyaZtT5H4CXLNxrGdXDdBBnxtvgEU56CFu5hJ85hkBZSLn4ZI0/+btHXhg5WySUU0tcVqFqYA+xogtbaamHi4nMG4gWEr5OhobVUUnPJk1xr3G7sg6ae+oXQpQheSVtZHw9oy7hJckBs0OpvYOPCWvtvNw/nFe14ViK8uDulS7ZgRt4vFvdmMBjG2uSX1tv0doAmNmqWxuAh9gAOLhTigmTXtoxgMosXWjGyM9Zp/HkpCEIIRal455M2O5ZlxRe8Hyg8ZPF2ULjEhsGBo494qLTzeT4PiNkNE48YySDTyQSiURig7gyDH5fytItuUtRu9nF+bHlhBUlXFe0E3Ng6s1Yr7/AdsVV18YDwLraq67obtfiJQeFNHPR939u5cg7bo8jNr9HD6rvWD6UAYwwRVGEuuDzoC6jGLFOSo5K1tK4abQAdU/3lBusRuzGGUaX3MNW+Ve3Rh70itau3a7FaFzMPRWvlN83Y0LSEssFso46+Gp+z401eYwoZSsb87P7wN3Hjc2EoLYATMO9eM6hkSA1BC4vHRxlCUT9JzLpgvzz2HaWd6fbGcj3xfoSK+xj69IIq9Ab5tIIZ+QQyuhm7aELxE6sDyMfDtcNAh5tmGdrjT4T7AppLzlWNXn8xxMnuPv7Y+PrcuQHy+ABQBX/tzGA80pm+qDTLSt/XVUE5X4lnnCljR9UB+9ReJl1xYpA6ECVXibphWdl8m7GSQ9jRda7H+wYIavNWur5eQGwrnqz0RiQQ8fERI/ULERDGx4R8Uaf/sNMKnThqVI/uLmPM+FPQovpSRN37+4/PHxwI4uk8s8MyeATiUQikdggrgqDb5XkbaaUBoDNmZeIldnLO341L1LAuaO8y8sbdKvU46IH1uBe7B5Y26OwKWtZ3UCX1n0B0FYA2DUWdkCMZRH126Dav0gBH438L2LzsWR3Jm7kSveRRHJpnZDYAICYPf+NuewPtuLUG2Ohx4ysuHJO3+Cd+Kq+2cjnwSu9hqa35ektrysBqJ4zZlSCkmUH2ZuuVK63mgadgfOqE61nqe5fOWeZhBAWYowILy0M9NQyUq7HrTPW0kb68rgNavIl6uDtA0J0tlgn90iJKEaTKnd1fiy2UCeLgGR8GdrWzeahXegiJ5cuAC1m6JHTIyc7kAT4JV2V3l3J/XcfnJG/OoOfDLYvAo+nr6x935m9grsqnCyAeFtUADhflYj7PaiEe2EOEhc2C5tmd32tBbJuTxhKxGXlMAbJk6KOg+nc9WIGT359n2j52/R5oskyqi3gVguAttQo7/PlwUWDT6BwsbtsGL3gC4PwWIA9asgsP1RaRuZcM4oeW3OXevbR0467A713M/5jwgXPtMQTRzL4RCKRSCQ2iKvC4LtCyMynw0/6dm+f9cVZ1beVhLsL7V735ai3qJvLOp2lYuHutZ7SjDtKHrmTfQVQTNVJjUttANa6A4Ayc3fXfB910J+6gwPq736ufkVYNzTWFdT3n45QKWPopp8jc9LVo07ZieA8mJS79dFuPkbaJMZwxDqmNfBIpMqi7OQvK3VbdwaAc/2qtezbYLe/b/R41RGTeIJmNGAFVj2zFM9cJ3REqznZWYBCG0wnytMweYGqlzaObuWrlXTSLy2Z7BK6NT4BQtBNajLEog+MfBIquGxAWelI8V1bPPFpmJNFC32kUKDr/qfjB4b93kJP9eb2AW6UroNmReN4+qKYXDCc8S+kcQn/Mgon3AGh7pR97htDE+jpelPRzr4Ip+/qZL+KLQBEOxj7lUchBgfr/2gxHuU0Qw2R3doCaPMl9N6J7j+t2a0XovLpV9HNm988YPr/g0B1Lp0axpZtzWsD7YHWZ5MsKr4+IfvStT4C4GbB7dFFcyZDsCsmgX9WSAafSCQSicQGcVUYvPObbgxvqm7J5jTbxrPGkN+fK2WfGHxrzvW12iFbnb7ndv66rqY/XstSGYAYz5OlSCPmUvUDAMul/m09gvboO6i8F5MrmtF+oBej8bxX7jr4iZB0YYAFMTdCw84ngM4ZZ0995wQeR+8g4D/Zm6eIPYop+eS65481yAAVdjIHoFgy7GpMUInaqnYPIJ1HadKy07wA60pKX8qgoVx26nZ8beHJxcFLyYcQw3wo0HOQB75+WE/hY/yY+pFmeeKP8GMLTMZ+5liPyVkG4tQZPA+iHg5E3GUAnXtNunnrYzzO4eqQqbGhm8LwOWftnvq9B72P7oYuU+AW5m4c0FrP0QdxZzcarXHihP4uWFcAXddeK6lwaDRNh50oauZlx+I3UUhz0tPYIzc1dzFbCIdvfRnjSzqh9z66RwnCOmEMIqUQOoJqWEyxWhpV4IGp2+PI2wwT4cVKfOUEB5bYpEPEE9k/tAMdvNfTxnsp8dSQDD6RSCQSiQ3iCjF4fUk3f3dhWrUavxiJUa203xcE/3Whki0Y4sqJwcDYvo46KrXPX6lKhXva1QrA+fqYgD56n5sa2LVx8R0/9G56uT6ahq4Ec332YqFCkULEmo3BD19jDa6nVELjXM/YRjlGDrxHbojrTrfiR+CJ8mpV59xmkezEHoLZzJuNZ4hDcyms8+uiGvV7VlXrsnjwe2mhyiRapWoGCsuOAZRdk/InbvnvBAVgWKjwka1WHnS30rXikQQBmKrVda5ajA4YvKnMnZFPIgH/0VXsU7wwL1ZHg4P+Uq9MbyDiDqIuCEEo0MO6UWSHfSgaD1V5C31Btk589US249EE3QusBwzebTkEC+Ex9CcA51Vj+9c2dGGxh0Ar5u0AGtoWXP99RQFYCotSf2emEi5c4WBnA/QgfYf5IGaa699Y++7pDGKFHvvBBIdoRZ5LTe+dYnzdeDm55K3JzTL11IWXCL9GDM/D8WM/cYxl2TtLcf25DCHa4jB9l7dE4okhGXwikUgkEhvEVWHwsyFug7zctKa5lZZFnY49ppV/MK2tkdFA6Iea3WRURQVmsW+v3mI8v6xNdfC1QdiwvmEbezANZyfMHP51XKjH6mb2gTV0c32eX87ZregPatI/RgZDUKvwSj60MGjvMSukO42dejImyFrGmqcAAA02SURBVPLE80zq/ivfy0prUQbvUg1Vjhr1FxmAz6PGJZwTYbn1gBFuO1KKxkU42TGAXQkBt6W8f20EYOf1AACqhTzzZF5O3t1n2J3ChxHg2T++2SdjaDqI1Qiv0Nmd5YhzTXf/a1xQm+Kp7ieOb+DwV1p41G3dj7fxTA4O9FGdXA6EE15g0rhjzFLIPFco7a9tDpxQaBASsN7g2DfyCIYydCoiKrVJWIumVhs9raK0vOj9RyZ90ezswaZc/loBD3U3pLS/iKg6cXfpBbPGw5enR2XqUoFiowYApaoZQSFdsTZ0xsiLJUqIjQjK+1mg0ql/PMk16KR3/cUhNf3jsLLJHm3zIKQT/DNEMvhEIpFIJDaIq8Lgq0Wyk8TOtLD6fJu+txSCxLkzlmke2Gpnq5rORqoSW1WV5W/izumNJXDUPLVGXAFgqc2070BgvQyNPFWYAVSoTfqkAINd0ql/lwGMMB0xpFoK7u+jYtyawmY94Iz84vEUU3y2eF0cMkwD3Uo2vNz3j91nt+tayYLcmSLPs61L8ABl8CUksNJzm1S1AkBZuK7adMspQACWXetmz90JovP4UtjTCRazl0ZQeHsM+UlJ3NhDm439df7tXsLOX4+NZ+X+rl1sWblyXb47PY32/E70ybN7mcZdiBz52JsMYAlaZG1Y0Lo6Qa+RiIfeOZ+eqBmHQWhhlA5jBlR4uPs4VOGIfbc07T3DXqzfdfmFhnCBMMq+Wjx2UW/vHy8eS7GaXM0CYwwrtthsTabvs/F8nwvu2Q2iwAZd+jXx1igpQaDy65TXzsrKA6pVlsfRukdk8N14HpqzwP1QxKYE6+wo5DIwN6efpCADo5eof+FBE0Lgjc8JlwTywREv/S1PlsQTRTL4RCKRSCQ2iKvC4GH+7mQ+3+r3bGbVnWdPdNjMU92W3g2zRafu1HRRnTrvd4vVHKmWhq5bVn1V5vEF2asSfs+AJYB2fe5QLEamouHLXDJEnjdeYhWaZS3F8i4RsBD0zBe+cis3dBWm2Rjz8LPxjyOJ6kbo0aj5DoOvCbXQre4R5SVRk2rwCGWilG1tzmunNvNmfu9tEyfjAg3rVka6Um1EKiimrfOU5wjvzkMQsWBcESmdm3VHtuOU2uuPZ7mMwdsVO+/jM9jMW/Y/xLE1c/1mdNnVycrgR7t9Z/bxKkCIxcZjy4OxfZcBhBx9zuAnz4IaZQnCs60zZSqAIcPeahfaWUR3FcJBcxy05mI5Tw45afVttqMHRBiEiX8jyF288VrDdOb8sUPiGehyqmUyC1DB0qJWKY2J1yhB9Nxu2O0YwLI0k0Ux4sPEOqRpIyxCSO+nSV+k7637wVsA/zHqX+tRB8Y+H3YyifszRzL4RCKRSCQ2iKvC4Ml0q3TBK42GpDZj5UP3UKUjVf3ga6Vr5xUAmaLSQ8ovKwNohVQzLTyYuaxK8S9sJTOAE0l8dm3RtNmFLnjjj8S9F6CBz4cRiNauMVAW4Aa7HDz7vcuD37y0qHX1v7SDLRdfjP/VL2cqzd5c+24+u1a5slh2a4CovK+VmIV4sZ+o/MYsh0vhWLMH7u5EJ/jN94xqbB7PFtvORRtGWymSHACrnRg9rVuYKynpgdyDLnY44CHSxjmZOWILXz1hvPzrCm+XIqCfMskJ/Eg3Tbdfq1qeYx9kEtK2GDY/urkP/u49kfzo4s+9C17AletyXPXWHmafACHidqIQdNGXlz4pXRce5ShrJR8TVdubyUUQvw3JIa2tNra2fg7XSZwdDqKwxQQw0UGAR6N6H8OISVjlFbrDfUx+sdtpc3sXbGTW6iRdEmYWEW6JiPLw7u+3pDDyOgsvp8wXvYVhMRxienoMP41DkXgGuEQG//Dj09PT09PTO/fOLq8RiUQikUhsEpfF4M/u3fk5fnb//m2c3bvz4cc37390+6lez62jBZ41rlUaOb0qBKPb6JAM23TwHlpsqZZ12VTdWhFBwpf7W37p6a/tUkDnhFbMX6O77ktepY2iGYEhjyE/vXnToXofxw9ouZlfTy3qKGY/3RYC0MpghQBT35urN/mYK2mwqOiucx18tw9AZW6xxx4vnpfdguIBPfmbp5WzuHJucsxun28VKnH3IHrxciMTHS0hxmmyAOChvNRwYI3sHzzHWjzspdqBG3qx63rNAIgHen0IH2N2I4/Wa0Mg6MKSncG7Yf+uDAsnEnQjsvr5aCQ7k54MhFs+nJtcQtTPHs2tWXtU127HYcVcB++q7n3r7dk3iwpXtGs/eKkBWBvO2wKgLLxwA1DrEkKyg4hLXyeAR7Ij9xc58AL3QR49UGobbqdvo602JtERoJBaw/usNQ1xTyJUuFbq7qQBkAwXUSYh5gWtcQ0hIhwe4cPuzP7TuE7dCcgfTBeusaE3F6TGmPqbeDa4LAb/9aMv33zjNgDc+Nsf3fr0wcNLakcikUgkEpvEJTH4s0df3br5rny+cfMV/PHRGW7f0B9ff/11L/jZZ589kQv+y387fSL1JBKJRCLxQuCSNvivH32Jmxf9GDf1b7755q+/2vXr159IPS8ErlRnccX6m53dMK5Uf59gZ69fv/5E6tkkLklE//LNW5dz4UQikUgkrgQuaYO/cfOVLx99LZ/PHn2FV27e+PYTEolEIpFI/AdwWUZ2L9+89ek/3TsDcPZvfzR7u0QikUgkEk8Gl+Umd+Oduz97dPrh6W+BW+/96m7u74lEIpFIPElcYiS72x/dv//R5V0+kUgkEokNI2PRJxKJRCKxQeQGn0gkEonEBpEbfCKRSCQSG0Ru8IlEIpFIbBC5wScSiUQisUHkBp9IJBKJxAaRG3wikUgkEhtEbvCJRCKRSGwQucEnEolEIrFB5AafSCQSicQGkRt8IpFIJBIbRG7wiUQikUhsEMTMl92Gp47XX3/9s88+u+xWJJ4KcnK3ipzZDSMn99kgGXwikUgkEhtEbvCJRCKRSGwQucEnEolEIrFBXAkdfCKRSCQSVw3J4BOJRCKR2CByg08kEolEYoPIDT6RSCQSiQ0iN/hEIpFIJDaIbW/wDz8+PT09PT29c+/sspuS+Gtxdu/O6ccP+/ejk5sz/kLh7N6dU0WY2pzZTcBm7DRv20sEbxZf//M/vP2LP42fEi8o/vSLt99+++0+i0cnN2f8xcKffvH2P/zz18wsEyafc2Y3gaOTl5P7zLFhBv/1oy/ffOM2ANz42x/d+vTBw+86IfF84uHHp6c//+q9n713qx87Ork54y8UHj749M2fvHMDAHDjnZ+8+eWjr5EzuxHceOfu/Y9uy8fvmMec3KeI7W7wZ4++unXzZfl84+Yr+OpRSn9eTNz+6P79+3ffeTkcOjq5OeMvFm5/ZFsAgIcPPr118+Wc2Q3i7N/+qDt4Tu4zx3Y3+K8ffXnZTUg8LRyd3JzxFxVn9+78/Kv3/sc7N3Jmt4Wze3dOTz/8Ld579zaQk3sJ2O4G//LNW99dKPFi4ujk5oy/kHj48emHf/zRr+6KsD5ndku48c7d+/fv3//Jow/v3DvLyb0EbHeDv3HzFVHqATh79BVeuXnjchuUeHI4Ork54y8czu7dOf05fnZfd3fkzG4St98QA4uc3GeO7W7wePnmrU//6d4ZohIosREcndyc8RcKZ/fufPjbV34WFPFAzuxG8PDj4PNmBhY5uc8c20428/Dj059/CuDWe7/qHCHxYuLs3p0PH/2kbwdHJzdn/IXB2b07H/52UL++qZt9zuwWEOY3zllO7jPFtjf4RCKRSCSuKDYsok8kEolE4uoiN/hEIpFIJDaI3OATiUQikdggcoNPJBKJRGKDyA0+kUgkEokNIjf4ROJy8cXdHxJ98MmRowcHvwc++YB+ePeLJ9KwRCLxYiM3+ETicvHq37/7Bn7z+3Ez/+Jff/fg/R+/9R+t65MP/u43T6xhiUTixUZu8InEJePVv3/3Dfz7/4m0+z+zv3/yAVFu74lEwpEbfCJx2Xj1zj++/+B3/9p3+GF//+LuD6lD5e9f3P0hfXBXf/ngEwCf/P437/+BP//lG5fRg0Qi8RwiN/hE4vLx1o/jDh/3908+eO2n//UPLPj8l288+On/MmH+b376539kZuZfvwXgrV/L/0QikRDkBp9IPAeIO3zc37947b9z37gnYf5/QkmfSCSuDnKDTySeB/QdfpDPv/rqqxD1OhERvfbTB/2UN/7La5fR0kQi8YIgN/hE4rmA7fCTfd0nHxDR3/37Lz83Gf1ltjGRSLxIyA0+kXg+IDv8J9P+/vvfvPHLz/l/33kVAPD5nx9ceH4ikUgMyA0+kXhO8NaP33/wu/954B/XdfN3f5hucIlE4nsjN/hE4nnBWz9+/8GDcX9/69d/eP/BT18TBfzv3v38D+/jwZ8/v7wmJhKJFwfEzJfdhkQikUgkEk8YyeATiUQikdggcoNPJBKJRGKDyA0+kUgkEokNIjf4RCKRSCQ2iNzgE4lEIpHYIHKDTyQSiURig8gNPpFIJBKJDSI3+EQikUgkNojc4BOJRCKR2CD+P8YoCMBNoY1FAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n<div id=\"添加等高线\" class=\"section level2\">\n<h2>添加等高线</h2>\n<p>使用<code>geom_contour()</code>在上图基础上添加等高线，同一轮廓上的数值相同。</p>\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb6-1\"><a href=\"#cb6-1\"></a><span class=\"kw\">ggplot</span>(map,<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>Var1,<span class=\"dt\">y=</span>Var2,<span class=\"dt\">z=</span>value))<span class=\"op\">+</span></span>\n<span id=\"cb6-2\"><a href=\"#cb6-2\"></a><span class=\"st\">    </span><span class=\"kw\">geom_tile</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">fill=</span>value))<span class=\"op\">+</span><span class=\"co\">#根据高度填充</span></span>\n<span id=\"cb6-3\"><a href=\"#cb6-3\"></a><span class=\"st\">    </span><span class=\"kw\">scale_fill_gradientn</span>(<span class=\"dt\">colours=</span>colormap)<span class=\"op\">+</span></span>\n<span id=\"cb6-4\"><a href=\"#cb6-4\"></a><span class=\"st\">    </span><span class=\"kw\">geom_contour</span>(<span class=\"dt\">breaks=</span>breaks_lines,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>)<span class=\"op\">+</span><span class=\"co\">#</span></span>\n<span id=\"cb6-5\"><a href=\"#cb6-5\"></a><span class=\"st\">    </span><span class=\"kw\">labs</span>(<span class=\"dt\">x=</span><span class=\"st\">&quot;X-Axis&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Y-Axis&quot;</span>,<span class=\"dt\">fill=</span><span class=\"st\">&quot;Z-Value&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n<div id=\"设置主题\" class=\"section level2\">\n<h2>设置主题</h2>\n<p>对主题进行稍微的调整。改变x轴题目(<code>axis.title</code>,大小为15，字体形式为常规体<code>face=&quot;plain&quot;</code>，颜色黑色)，x轴文字(<code>axis.text</code>),图例标题(<code>legend.title</code>)图例文字,(<code>legend.text</code>),(图例背景<code>legend.background</code>),图例位置(<code>legend.position</code>)</p>\n<ul>\n<li><em>拓展</em>：face还可以设置其他字体形式：plain（常规体）、bold（粗体）、italic（斜体）、bold.italic（粗斜体）</li>\n</ul>\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb7-1\"><a href=\"#cb7-1\"></a>Contour &lt;-<span class=\"st\"> </span><span class=\"kw\">ggplot</span>(map,<span class=\"kw\">aes</span>(<span class=\"dt\">x=</span>Var1,<span class=\"dt\">y=</span>Var2,<span class=\"dt\">z=</span>value))<span class=\"op\">+</span></span>\n<span id=\"cb7-2\"><a href=\"#cb7-2\"></a><span class=\"st\">    </span><span class=\"kw\">geom_tile</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">fill=</span>value))<span class=\"op\">+</span><span class=\"co\">#根据高度填充</span></span>\n<span id=\"cb7-3\"><a href=\"#cb7-3\"></a><span class=\"st\">    </span><span class=\"kw\">scale_fill_gradientn</span>(<span class=\"dt\">colours=</span>colormap)<span class=\"op\">+</span></span>\n<span id=\"cb7-4\"><a href=\"#cb7-4\"></a><span class=\"st\">    </span><span class=\"kw\">geom_contour</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">colour=</span> ..level..),<span class=\"dt\">breaks=</span>breaks_lines,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>)<span class=\"op\">+</span><span class=\"co\">#</span></span>\n<span id=\"cb7-5\"><a href=\"#cb7-5\"></a><span class=\"st\">    </span><span class=\"kw\">labs</span>(<span class=\"dt\">x=</span><span class=\"st\">&quot;X-Axis&quot;</span>,<span class=\"dt\">y=</span><span class=\"st\">&quot;Y-Axis&quot;</span>,<span class=\"dt\">fill=</span><span class=\"st\">&quot;Z-Value&quot;</span>)<span class=\"op\">+</span></span>\n<span id=\"cb7-6\"><a href=\"#cb7-6\"></a><span class=\"st\">    </span><span class=\"kw\">theme</span>(</span>\n<span id=\"cb7-7\"><a href=\"#cb7-7\"></a>        <span class=\"dt\">axis.title =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">15</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</span>\n<span id=\"cb7-8\"><a href=\"#cb7-8\"></a>        <span class=\"dt\">axis.text =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">13</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</span>\n<span id=\"cb7-9\"><a href=\"#cb7-9\"></a>        <span class=\"dt\">legend.title =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">13</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</span>\n<span id=\"cb7-10\"><a href=\"#cb7-10\"></a>        <span class=\"dt\">legend.text =</span> <span class=\"kw\">element_text</span>(<span class=\"dt\">size=</span><span class=\"dv\">11</span>,<span class=\"dt\">face=</span><span class=\"st\">&quot;plain&quot;</span>,<span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>),</span>\n<span id=\"cb7-11\"><a href=\"#cb7-11\"></a>        <span class=\"dt\">legend.background =</span> <span class=\"kw\">element_blank</span>(),</span>\n<span id=\"cb7-12\"><a href=\"#cb7-12\"></a>        <span class=\"dt\">legend.position =</span> <span class=\"kw\">c</span>(<span class=\"fl\">0.15</span>,<span class=\"fl\">0.2</span>)</span>\n<span id=\"cb7-13\"><a href=\"#cb7-13\"></a>    )</span>\n<span id=\"cb7-14\"><a href=\"#cb7-14\"></a>Contour</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"添加等高线的具体数值\" class=\"section level2\">\n<h2>添加等高线的具体数值</h2>\n<p>在上面的图基础上，添加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。</p>\n<ul>\n<li><strong>作用</strong>：在二维屏幕上，等高线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等形状。</li>\n</ul>\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb8-1\"><a href=\"#cb8-1\"></a><span class=\"kw\">direct.label</span>(Contour, <span class=\"kw\">list</span>(<span class=\"st\">&quot;bottom.pieces&quot;</span>, <span class=\"dt\">cex=</span><span class=\"fl\">0.8</span>,                                                     <span class=\"dt\">fontface=</span><span class=\"st\">&quot;plain&quot;</span>, <span class=\"dt\">fontfamily=</span><span class=\"st\">&quot;serif&quot;</span>, <span class=\"dt\">colour=</span><span class=\"st\">&#39;black&#39;</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOx9eYAeRZn+U9X9TRDBM3ImIiRhXRVQSAh3giacKzdEhowrkHAjWS5RVI4IyuWCB5BAUDKZEQLhFAJJJIRLIOCKuO5CLjnDtepPQCHzddXvj7feqre6e4YAyWRI6vlj5vv6q66qrq7uquc9lbUWCQkJCQkJCasX9KruQEJCQkJCQsKKR1rgExISEhISVkOkBT4hISEhIWE1RFrgExISEhISVkOkBT4hISEhIWE1RFrgExISEhISVkPkq7oD7xevvfbaCqytf//+K7bCDzrSgJSQBqSENCAlpAEpIQ1ICStjQPr37197PDH4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNkRb4hISEhISE1RBpgU9ISEhISFgNsdIX+IWX7ag8jp4Z/Tbz6PfyU0JCQkJCQsI7YeUu8Asv23HIhId2uHSBtdbeeRQm7xVW65lHq70mH3WntfTbcv6UkJCQkJCQsBxQ1tqVVjkv1JP2pO8LL9txyIQv3Gkn7Sk/hrJ/vHTBgycN7uGnmjZee+21Fdjj/v37733szQAULAALBQCgIVLuW3QIAKxSAKzmo+IgXDHryij48+gz/1UqOjf6LKtyn0WB2rOohMl1kSt/lrIWgGzIaFVTA3WVW6Eysg+l5rLMAqB5pLU7orTlmrh8qXP0IbOIr0Zp1xRVSPVQtX6MqIDWoUo3KspWR4bO8gVkl6JBV6IS32erSjXUQumoYxKaalOut+6iFODHDbAmtOXOyiyARsMAaGkpGpkFkGuAt+R03zIt71h06f4alJjChQ0NGa6KKjfuJ3lV7iwrWpTNeWQqalEiurPxWVZUHvXZug9yRvXTrg9UB/WWBsT9dQPCIwwAWGYAoGnctcsLka0rvgqqql8WukSFNXfeiMpNfKXyzlaP+5+0OFfx3+pZ0Tjbmuc91FN5rrQ4N9fu7sgXWZO7TpdDf5tihmhR2H+QI+ZrKETrdPCtAgCWGfy/tzWALvq6LAPw1j8zAF1dWXOZBmAKBaAoNOKJZ4wyRgF4640cQPFG6L/RKisMAGUsgJZ/NAFoY8GvI5Prgu6iAoAi0+A3WN5V5F3Gl2w2MgBaNmyRNUPlXf0yAMvWygHka9lGPwMgbykA9OtnALSs1QQ/qv36Ff3WKsAvpTyzAFoy996wYrr2yy2A0wbtjhWK/v371x5fmQx+4dN/BI7ab8/46B+fXggsvGP6Q9jhc0PC4SGf2wEPTb9jYU8/JfRJPPqt0au6C6s/rhxx2KruQkLCu8aUL7e+zxoeOWu3FdKT94Pfnvqu+3DVru/3wlcI8pVY9+CTHrQnyQML/vQQcNTmg4GFAPCFzQUlH7z5F4A/8rceflrZUHJPpwS/tFbSwxo+57fqUQ3+J8EWlGTSKjpZnOYZHZW0bidORLCGx0fiBPrdGGUzAMTjqYjbpVrX1Ui04Lk7tRRJHQSvjS9+6A/nPPqtUcN+NBt+m289BQmUp0TmrCxgw7CUIFl1DxzadU/FQxJx9/BXFIgaqmvFyk9aXE61k911yfcfzEfdhGDOp6oHRS3GBqpXbV1y96gbPN105SyjLADNvFAKcayYjDYmuJKAlq5XVxk8Vx7d9FLX5FmCHVovOahQz9L1Ght+0yzeMFL8UzqlcpAqII7biMmuhFLRLVC1s7UbFm5Lv9MwyjeEioauZirVPeb+s5TbyRb9EFUnhbHRV7qchntjAMzLFUsajOwtnyIPUvkuIQloGvjLMlYRWbdGATCFotdXs6kBFIXyfaC7VhTKFgpA3s8A2P6SWb89JVpftzl/TmNZ4XtTNDQAkykARabpK72jTE6VWwDLstxkQezSbGgwWXcXZd1gxm9IVOHEikLMpr3MUgd5mBtky8+OeIJ6DStzgY+x8LId95qMHS49bU+4pf4LlTIP/WkBNu/+J7hVf+jQof74Y489trJ6nLDcGPrDOfPPcGt8QkJCwgrE9pfM8mqsR05/F/LCx749yn8e9oNefTsdeU/nz3dpPf6+jtpfu5Oor3D01gI/8+ghEx7CUXfWK9LfJeSivsJ18LVbNkZlq+32wJ6OSmpOtD2mlKIa3mjb6JfKJlxZWCW+23CWrSObtJP15Jv0T1LXrrRCUFypOsLiarOVo7Gav1tKXRROi5Y3DIAsJ2YckdPuqDZKmuDKEU+1q31W3Eo0pu6sGopf5sR+Jw93qY6refW5EANInatiU5YaZaqKPrhtfqljguUTy4mv3VHaKlnvySyAC0t6TcW1/yIqrPJ4ayM1syxWz+DreGhk8eDrR7fl6efas6ooUWRqTotLpbEklbr21yXOLU2AKgPWpT70MODRFQT4gY3MXWrrqUwh3x8Vf5XFal4B4rZai6KbBi0TTWldIT8bfhSq91fX3lkNCL1+SwYAx8zrvHLEoYfP+RWYsuNt6MyAX0R0d4jHc88cxc8aBvwOKZoKQLNLD79w9iOnjxp63hwAzRYDwGri7o7BowEA888YDWCHS2YB7jIeOnk0gG3On+MuKrdet0jMxPCjJ1+3QRwrZj4p2smGJssNgDw3mbYAGpn12rRj53UYi0yjoQFWyZOUaMUuW+h+x9AbC7y3pQ+r+5DP7VBXcofPDenpp4S+jflnjN72gkTiExISViLkGl8FLe3DL6QXUdiVDL9oVrNLP/6dwOn9y8qv9NudM+t99o1W92Pn1RP33sdKX+BnHq32mowd6ozg//j0QuzJBxc+/Ueheu/hp5WPbjVi0Sa9yiFjzhvx+LIBMeCJYLlVBU/rbbyjrMLEPwiyZjNH3C2ZlNrQDdrwWt9CDxSp2mkEXgtAKUuPE8TTAjE4zEdJOwVZoMqDhVWCkl99u0xkqxb9Zc4umVlMNazkNyUFuaTFJV5Yui5fwt+12FZfXpqvXGj7pDDDRmc5aYG8Kxamyj35Y3QhqCugSscQj3KNkbwrpiIra6lHLA0/HZSW4bID5e7JuRePs4kvUCqqHdfkX6U6M4vvsrzdWR1prsoP/CmkS87f6Ymo/Z3vbOhtt++O7p45ybzjw92R/3I9dXdHliy9SOT7IxLh+HYr3N3VFk8euvUkRaeb0qLRZG30ifd3/HTnQwGMnXk9AGPYct4oMK23flICyA013NKvANxE0fxya3aFeUQG86RNt5kigTyxdhYShKdSa2hltr94lvPOyKz3hNj+4lkAjFEPnz566HlzlBgXo9X8747e4ZJZSgUjecnjNT+2mcbkka3fvL8D0ZsYuZbXhobw0egFrNwFnvTuwlGOMXjvQ3aYMEGo1bHgTw9hh0OG9PhTryBeTaMHrSo/L635NUtS/I3nsRAC87k0KaxYN8JEVuGs2ChN0We3XhThcdOFAWAz53PGFij0mVxHuFvRWlMai26/gFf3YT+aXRUXB4M1XX3VcUt1Z4mfowJy+fQia2mwFrYINRVGfahZBeveiFUtgGb/vbgzvkvlFlVdvfKNE8618hLoj632XK7EmrcL1QEsLQDRPI33CVUpq1y//fuvEK3Tqy9T0dopLe9qF/ho4Y6uLXK+in02a3YMSkUeXLW2S/J6bWWO+Z+6TKjHH5ey6NqzlnNxrT435UVaNlR67rpfpEtbN1kVQfbcD2y0EovRDpvsyk/ePU++tUqvveomRt6OwvqJCgBH3ds5eWRrnhsApkWZtxTYSI10hbpFPAK8VzDOFk8BWPZ2BqDrbS11WF0tGkDWAIBHTh8FYMf/vJtk5kpsD11DmSKlANUgn0RDFnvUk9wWVMC9aWtvhQU/9e6VbpUWrwUS15O/pXeTc4L63rWyW5kL/MLL/n3CQztcuqC8ugPA4JO+e9SEvfY6ej9a+2cevddkHHXnSYN7/ikhISEhISGAzO62J3V7haS8K2x7wWwS12878f3K6vsIVuICv/CO6Q8BmDBETZCHWVq/5yR7J9ReajIdljS/h596AUIg5SkyfYltzATVDg49YZvrhbfuVPqiwllSHqBUbPHifvE9CTtK7pjoA3MAOpg1QxMmsySQV2TD0pIBzmTPckPsd+fbWV7Ua9xjZqnENdpSdI/KWYJail9UIOvBB490DRXiG86SnyUn5srrzPQsR+kx/qzIsEhHP7mTSA5ZKBmvQ/714xCbyFXkMD30vPtLg4pYL1ceHZEHI+uhOuMp6Qtn43MLKVOIL6ca6EYBPUwoF5NE0s1QN3SlqupnVfnr+aIc9lw0FAZBVEixWQz/qisXVboFqjJWpb5Vubv/KJ+F8k2sUORyBd3T+lppQfjHHZYjXNLsVSMOuTseR1Uq6jopR0lqXoyKGHw0S5Ul87Quqlx4l3kBOFX11j8aYPu7ZW9pAF3LNJ07/MLZj5w+mv7udOndEDJ5x9clg/dCch1UA1qE57JGA1BKaW23u2jWw6ftZpVyogUerOrDSB1rNAAKziPfFQpgyq6ALAtjpWvv4krDSlzgK27wFew5ydpJ7/qnhISEhIQ1D2QiR7yCVvdeaHTbibMfOmX0yMs/kOHSe88P/oOCashYGbqUitSeY63fo0slHm8AhU0RV2v957Dt9t5rofJI7x7BsYFYhSfIvTYo3KZe+cppT9qMDJBiWwLuS7VFb13y6LcCfa9xmPF6VhIkGCqm4HfTqk7T6XlSJeCMLCz35uI0W3OsalRQcrETtMsTdHLq04JV+YC1Oit3THAOW7pSY2Th+B7RX8EhStVGI2CjkyVPKjHaKjl4R8pbOivSptuoFVs3CaXXnNQtevZfCnpKn6u3Xg5SvUkEN1eVrMjKM+XouCSOXluciS7KAk3Wx0fSDnFR5R51x8X5e09Uu0KyFbvzRffonXheyajw/QcclwIVurTCRLdbymlU3fiQWY/hCiioDJmYueix/LA0WgwAYwqwkR2p5+lpKgrV7AoxcChIDt2p4RfNeuS03fKGodZ3/M+7wd50nrhn7iEN72rN8W04XLTyBUjwtoz1+mzWA5s5Rm4VyMiObVHo6kLH/M2uhqgimYc3WPHRlHsTKV1swruGXN0TEhIS3hEn3N/xi6+sgOitO/z47vdfSc8Y+sM5MkLOBxqJwZcRs/YKnQ+/SYIJ0JZQ7oKFytn6s9wur7w/D3VX+2PBoW0VYgrlqRW71cE3zwlbXLMu3E0WaqDjRaaoWqL1qnsiUBcbN0JEOuNdbW180+44itfv6srmk5uwMWsRzMKLO4Rpfa2SPrJ5VhZAlhni7pmIQxnJbGyNDTwhywyxBFNocFAOnQEVVh3F7uWJJHX20g/HcPSunNPSxB0qq9jFlTnoOI5SKehNtx501hWORk6EMVElDWulD35sSrQelci70WDy/Yp6G6s2I5c8Va7Hm9lL3bDXfUqhFX3uEkd83hTLly8uniFFGqgpUD2p9LKI7pcfzPLjW76tVTt2X5sUBtQq+GUNpbLVMMZyjmUq+qluJKLTnMKez6UBJO7e4HnQLwesIYLer18B5u7W4vXbz7zz6qcBDDz1ti22AICuZRp48bmLj1y8BBscc9eQf0Wm5i/85veWAsCeW/z8m58gipxbAHn+6J+OPPt5ANh92NTjP0WdVAYckQbKcW4XIrepEERuVNqNkcsC1Yh+8g9jGKBoHKIFw8XctQDQoiPu3uhdTp0W+ISEDwDGz+382c6tJ9zfVwJoJCS8Wxwzr/PKEa0U1a6MpXc8+sAOo66/YJ2Xbp930s9fvZZW6PlPH//9pTtO3Pk/htHK+c95D3347Jkj+tdQjX/MfvjD5/96j41sRYX3XtBrCv6VjbTAl0G21KoI+Qb8L5HJb/XE2gMc8VRqaGx1L26jTb0UEqiw+7aihsBuAnNSvkHn8p4VNX3l9K+gc4mVFqQg7y6spW+Bfd/JiTba+QryovhglT1b96PtiRwocb2VzgSncy2Yry9WpTkRm6/xa/UCA8ndpR9/lOCHf9JCrdjQrNKjRJO5AqvomD1YIvcUDtP7zlIN3Stc3d32d9H7Lod7Lglf3ZEais/EMbo5ES11iIKYxt2SylfpAN3Dfah9bqLWKxIFxCIrXWeVTcQo87IEwd0jRwbfIn0VNF2q6ks9q6Xx1ei/pcvpYbSrULF4LBpScEMx45eFq9WWzPUlKY8uzXZ7lj/SncWDqguME5k1WHdHnJ+CG20LoJFbY42/uuY/cgB/efi3drtvNv6Zv/3R/Qfu/NWn7jh43a/guYu//+aBU3bcaYAp6Kmc/8KMmUtnzHzlkClDv7Ih+EnMcws8tuC6u5677q5X2q7YZe8NwE8rPctkW2OMm7aqCQAkRaChINd5sGSItgjbXTwLLM+D5kw5JgyIk0g5AwrW2Ytp4SMmufEBUAq+tPKRdPAJ8NHoEt4bJo1svXLEYVeOOOyqXVv7SJrIhIQ+iOPu65g8sv4B+b8XlkbfX3n0tSV7rv3SkQ/+x+4PTn8UADDsXy6f+eWJR2H6kY/9Ji6MoVtMvW2vi8ah/dj7ZpZ+qsGbs06be+xec4/d63+jLKUvvnDpHveftMfi/6Wv85ecutuDJ+/+4MmX/bVcwe/+2LbvrWP2v3XMFS+Ko0fe0/mTnftWWufE4Mvozli9bM1MiLbqkdl5t6bvvkJJ022c00DWUGKx4lxfQIarM3lInmibJroCV29g8J4Rkv+JTAdnbZz21pVXlDgOcWzachuxlIw2v1bm3tWqLH6Q5YV6s+qw7lk4V1ViIvJfIPe1CWeVSBqhMyM9aGOTAgv4uJacZEIDAL2tTnrASc5JYvKznQ89+t7OgmrjFo02YMNgmVpD6UioaIVFdQtF/rKBXDaNyrS8qFgjW0cEJSMsRTuoYfwi3kBJF17SE0vWLmur2BwAPgGJiQ5KSDNjU0cZwZVXaa5TcKqogDtXXJotmS+ICeK9vaUCtXoV5YOVekpflehDqRI5Sb0OXhYr3bWofKVYpd6ouchnIR7YkoCyVK1I+gzIaMEIRhji1IjBK9a+01ld2gJosrE6PUH0FPzzHw0Ajf6fwS/nv3LocJcyxag3nnjg7zttssmWM4d85ZYnzv3ewi/N3PzzyHKTbbTPdlM2+tORN/y/PU74FJno5+T8YjBw75E3bPD7g2/+v32P2YC6JGRyzS5tyY3otVufnL/TDlddsvYrtz525k/+OvnEj2cA5v/vsWct3XHiLicPK7osgLfuf3DtM+/eZQOS5Bnpqf/6XQ9/+MLbvrqxAbC2aRpAaWX4TvsJTBFJiiLygNcyg9PKR1rgEwBerX3ShSqG/nCO/LzaWJm+Z9DSXpsO8uh7OyeNbB03t9MXA0Bf3w+o2m8+kNTwCR9gUNjaI+6JH4ctjx+26z7zjyAL+c8OPnsj/BEf2eCgT3weptj/0ztOevaVF/H5jbj00PVHPNxN9VtvvNsjPXfgH79/AMMnrA1gvX0/s/Pef/7NQR8fqZ6/6Kw3D56yyy4bc6lHX7j5zpduvvP/DpzyxZEbxxU8tqTj7uc67l7adsVOe2y4/Be+SpAW+DJ0RZcSmGx1xwsVDivr9r51xbrVwDHRj+z2yWDeKY8jR/co/l04asF7QyKHRHKLhnPndAlmIg9uBdaK+f5RuEfFbv2sKgaAh08bhXiZL0F2X3kqUKMhFH/99UeFa3W18B1TqkbxGNyC4+74j975VVVa9EkjnHI98qMVTWSUwQeNzF6+y2HE2vNYfqBdfgF74v0dP925FZxXSilcvkuQTB59byc4HLoxioQBThMs8slm2qWePPw3nfChxMStL41E1Zq9bAkfT88qo3UsnBV3rhvUsZgdyhhn/qCsTXZMmoJkcZ4S+tsQBsZNg6ZoXeovvR+8pER55YiHZJalozKPjtPie4d4ORR1RLmGQ8fTr6r4VHGFBBM/LLF4pdxc6afa90ntuXIYY0Of2OxA3j4qFVcnr9cbycubqMWNaGgXuVLeL0ogS4O87K2sEFPKFOqTX7999GEKT/5s9u+2H7Ae3lL4+0vPZ/mGOrNaQ2e20WKyzGSZBZa+9aJdu1E0ooStJI178Z/P2o+0FC3uAQ9yOGuczEABf31xad7YGHTrM5P95bFXF+/x4Y2OvO9EYMeJOx0yDBg25LK7hrx28xMTj/y9nrLtbhu4KJYawDZbXHvbFi/f9tvTj30AV4w6YD0AqlBeQFUYNJXyrX+Is8u8/1gF7wFJB5+wXNj2gtnbXjD7sW+PWsO5+1W7tl6+y2HH1RH3Eo6Z13GMyBp57LyOY+d10MFJI1sn1WkiS+rJK0ccdsy8jqPv7fQOxOPmdv60jyn5EhJWGJ782ezLsOXYYQA+9PmdP3L/r557BcD8V+//7HpbCq78h5teHL7/erU1/O62Z3bep2davfaGm+Ceh18VR/7xh/v+tgs+9cU7dzn76I88+L2F/8M/9N9/m8vP/fANN/5ftZb199mp8/sfab+5pPA/dl7HFSP60BOaGHwZuimVJPGmK3LXDUdcaHTU6N1lkPVwMCJSFijr72OCLrlxMMqFLCJ+yrpC/4tGZmRweyX+cj3zzxg9/MLZSqhYSRHrwz9ZEbCJJfmj6Fxmk2G7quIdIwVrE0PFPTFQ3aRN9PpdyaEja/ygVy7JMYSRfDcCExuLFlj5zZchd/3hB8+nMXlk6wn3d1A+qEwmi+SSBcWzIwveEpMWX8nb7Wc7twIYN7eTSk4e2XrsvI4rRoQ1/oT7OsC2C7mGUpjMIvrA4GPeXLp01f1PpTkp2X9Zj06XH0lZ4oPikK8n4t+i2ixW39JslbWByZ/k7vRLQ8c286JFfyOkwljWAJ6uLkWejaqlGgoWISA2qg/3Tiqqy7Ovrrw/ElUQ9TnEKhCVy4lnu7fAD22J65UjX+pAibH3IC2Ivsrr5ZtYVcz720cNydy7pI+np2PSyMPG3HpjVxe6uhS8oft//+yOi+/G4PE7X7PPh6yxGTDgq9tNeGbW9/ZaAAyY8OvNNjb4w8/v+vFdAPCV7+87fn3AZK/ePu/Eaz76/RuHYtL0c2cDwJ7f/sbJnwKK6ly3RjWbFoDe+tjtvrLfw99w6oDPX7x+9jt8bMAB63/RNJv7fmbnSX9+9QX1+Y3dvc6Gfmrn3+rcNBrl/LNZ/sWNRz+M2NmnsAp87R/i+0uTNhd/ew1pgU9415C2eGsOurP+fc8g6f0VI1pJK0kHJen3GD+3MxnnJ6w2uH7fg8bcemN0aKvj924/nnK9h13IFifsNuWEUGbL4/duPzaKQLXhv4248d/y3ABHt/56PFzQm3c2Y1tv3C1fPQJ44vLbH9v+M+vj78DfXngR2Kiu7NJ/voBGfTVLX38GH33HxlYp0gJfhqYQ8aRcL1GZqpq8h621PBE1RvJyv22087+no8RDdeyZW7cx5yoENXcbbY5MR9nfHWuvaOLnnzF6u4tmATbTgn8LP294KhMiMQsdraC82vF+qsc1Ql+ddkqkT1aqQjH4cgJHFJIG1249v5A98eIVwf6Xc8tsa7LAeV541a6tx93XQQmeneYvJrvuThD11GEK0bWXdeF0ORnApnPHzOuoqns97W7J7PH3dUh+5oPAx6KN6Kyo3e4ZfPV6PQuXCcTkT8ZyyHGhwy5Z1FOFPhy3R4nBF0Iw4K3i6feodQBArh3blhVKl3Ffc/VhqU2hRlrhFtd/WwjRy9tFVD7isuKIVEX7g2WX+u6IPtdQzS8Q3TWemdUYAKWGehAnyI75k+SUqz6OXoImA9458qojW5CqRX2mQi41sD35WwUAHDOv48oRh/Vbq1kUDQBaB3GNUhqAKdi/SE4bzhMhXds5hh1b0QNZZiVFlhHlstw0GuEVZB6fdMuF2Hba1gpY94s7fbzzuhf23nrjTzz2l/s/u97ZG2ltXdK5/77h1WEHDeEkdXnD+Nb1729Zuss+W7SQBYCpRlOgz83YGWH1ySaXkLB6YGWz51rWnpCQ8B5QlbSVLfb/68qbzpuNzY8Ydf1X16Kctevvs/0pz915+j5/Agac9OuNPwX890/uo/Rxu5wzqm1DoMBLt9//H1M+duZNX8KVN5w3CwB2/86Y4zYobY2u3rUVwM92PqyPBJ0UDtgfTLz22msrsLb+/fvvd8QNQDde7FLdJeLBwdN9uc9HXEO0yRUFyFtUK6qwoIzFOtpVa0mXKt3xcAHnqWNUW0MXuQZAf2UBcnz32nfSRkeJ1DwHpaDNlNlJ6h2ZhTv3ceFErjMrx8qFbxMe3kpZOigN150MgNl/VQcvrOitOAjfkuLAfPVnuaPhqxZ+8HnOseizYJRLjyv5wvXL0JI5dThiEumbdHt2A7B2mXx/ayPS0wh4phiFuFLREafvNADT5bXpTtXFhNfRsEQcq5Y9VKMdZPy3KX8S9RjrrlHmvc4FY/OoJskuSR2agmO1sC09ndQVBUFzBUiIkqsgI1FC8MNTMtJGk0mE4aNFpUVyhRgzuI362fH0NABvu9vnhkIGxpfq56p/OWIZsR9eSZcjbhr730vBlR9tT4sRBzPvMt3G5vAjXy1QG3Vf9sFfhRLtyp7nOjapES02edy6rOshgLeaAPCPAgD+2aXe6FKTR7aOnXk9gKLpEr2Do8Qbo8gIg6Xx4XOjYTJhOe/s5zkuBS3tJ4qVla708l0OGze3s1mooksDWLZMA1j2dgbOJsdZ4RV1hgrQ40mq90aLyVsKcPx8Okit0+cpX3a7ClrXG2JefSjHOjn8wbVzADhgvd0rt+V9oX///rXHE4Ovh4tOGr+vaOXpLiOLCkZ4Yo3xknx5Er2exFOuLIwQ/xU5LdL0ynRREJWQr8nsMr4/VKG0pFOWU8QKRyW/5xj2o9nK7SsiObaNyzm7EhJzyYvgF5OMJsECNFeH80txi0O09Cq5h6KPnHE1yxwoXawAACAASURBVBDOknJ7xWMq3jiyQG0kWm6S/kS3lHznjHdXE2JHrTB5ZCst7U6Qm9t+3S/tSoxZLLa11WJyH+jTR1Z/ImjlvBnlsJQqdJ9F6z1Zw8Wdr2ZV8Tun6HaLn3yFTsFEtdFqES/ztTaAcq/AGiVArEnSMc8ZcPHa3+D0owdu1obuceOidgAHDWoDMGNRO4DCogth7OQo+cG8fqErCb+O8qIr70vUfz4uNxayCb/VUJVzfZVWjI9TgsS3w2sowMuGC6WiVM0CLmrzPYheP3y4Z9NLLZZzxDcxU5G/bnQWXYV2tET2fG3Xui0sTnqg47Kdxoyf29lFQcFVBq/RKxR50MnEr7ThzhvGLatuq20BSKv1CQ90ZDo8cZlRAI6/r+Pnu7SOm9tprfEHc3oxZgV4LTccfqoFQHh3uT7w5YftRb8WAxbvTXiwI6NAvNqC3xgyfBOWxzRgJSAt8AkJCR8wHLhZ24zF7fJIvJy55f+GRe0ADhwUtgIdC6Z1Vyet7h8IHMxX1P50t5fT90Gmo9+ozT2zfKClnUhzz7ptWuP/ffZ7b6sWV+3a1/M/pQW+HjW5UDwjq5lJUiJYQ9Zro9hCyOIsi+gdCycBuws6yzYuNjBptqFDqMJXLsTo3sFG2utRT7Y5f878M0YNv3A28WbnC0fMo2Y8InGZN7eJpOhC3J1lNpY3WvCO2G2TldWZGCySc7iItlG1WhA978CmuhfRR0MRCefdsCgx+pGY0frrsoAjzZzs0gJo0ZHdkKg08DNnvyOqLeU8jaPQWAC5VSKxMBeLK5d0WcZp8R5WmWhL/rW2W8mw/+hM5ADEdm0mll+VBANWEFyXNURw2IauMfjy5LUmeowIa2q5rVxcNd2IlswSd791yVSvyCo9BPT/1iVTATx/zbnHL9z3liVTATSNIkL/h++PfXzUtG/sAgB45q6zRk1bgCFfn3PWXptYtvJ76c5DTv3l7zBqSvvXd1K+TuCJa//lorkA8OVTnjpiS3FR1n8oPyWB9FdvRJBz2PJBmQvHWicXIY548KC2Gxa1K+CgQW1ZSXIgP8fcujSj6K+p/FbqmLwRsQTCPeBGyECkcCJjIYShJ0i0YwDTCA/mL0cdCoBSzGVNx+CJUvscMGBLujy3pCa7fJfDAJz8YAdYRO9fOPKJawpB7LHzOq4YcegR93TaPJj1UUOUeEYpKE3iBOpeeNqVttJBrgrFzxFx934icJPmPMVOENLjRmSFIy3wCQkJHxgwd+9GMC2w9BcTj5+4AIfGR5+569brMcDFanpiyqhHtpsz7VzcddaoazZacPiXAOCJyYMvnvO1U6+fvhViW/qXr318o1nTrt1kRV1KAiY82EE6+5/tfGh9GtkKaGk/KQVsXj6kBb5HvKvdVk/pZK2MfGqrhjS8E6cNLyeP0QCaubZ56IdzTBJhSz3FUC5gbfirjKOHVmhTnSVBvDOVlVtOacr2d5WrCUw6fM2EkR2U5Ty5FnGO1JIXHH+NSGz8E3WJekvk1EYegbGSPgoxW3f7JKF3xCgzEHYDVODKEYcdd59zXXNxKlRsB+drASzng5EErqSqr+opg5WWIFuS5+l4nKVm2kdazYWLWil8Sny9oVqvqqevNLloKHLiPcxwJO0rEVD3U51IgM6pThtfQ3cOgf6BcBYDgvGQEOVrg9vocy5kM05e4mcSs8kBR3zvSkw8ZkFkdPb7uRg+Bi/Sofse/82Y/To+DYMvDf/StPnzjth65Et3HnLxs9+7GBNPHXMdAFy3sB1Al1HWPjHz/Hvmnn/Po2decta/r1+6tDBWnqXGUHVfPdesziv5CCjm7lrh4EFtZF7gZ4ipyKIiLT73rGpKCS85KDceupfJmE7iXNIRTF/YLkN/eRdK+pzLm8h9AdDg1xG988gK77j7Oi7f5dDxc11IZmmOQJDq9tN+O61FsHZpdWj9S0xctRFPRK6BhggFJtI+WeveVM7mQdyCLDNaMPgqmzelJxwAAnGXKvleNmpPC/wHBhQjdpvzu40Gn5CwGuNrg9tuWtxeCv7/bvH4pntsvXAapfh8eclzwDYAgPU33ByPLHkZm/7+od99+enfnQoAh54647ytDtys7Tqnm9/q8KemHfHsXeeOPuUc1KzxvYCDB0V2hTMWtR84qK2ze6uCFYtDBofWb1jUbi0OGdz2qxXX+nH3dVC+Bu/VVnVPPemBjpbsvU+A+jw3qzXSAl9GJcnI8p/i8itG1LMHJVjZbproWpndWA2t7aPfGj38wtkAHjm9HAp+2wtm0z6UTOWtCn+98ae8JlLSO286I1iw3/zG+/pCsGvHKaUBdLhSYhJW/kI7YnI+Iej4+Yx94Sz/laSCWoRsUY6idMir5UklnzolupGJ4BVVC3wVM62SXXrtNHFKXHepALsOFDZSzEsmrWIDDcndPQvPhZq8RMWqPFjm/EBdAX9R0kBdqvmphmZMQ1V8TyIhhOi6/xwJMJjVQWhJI4YvWJdn8Gz5bxEyyjjuXhfvkwUwADhiqBWfDxncNvbM0/bYBX+Y48waMoUhgzeAkK+8fO8jT2PBd69p/9Kmd39n14sn7TZ1xuJ2WuOpEmyyx9lPb3jN5nc8+Y0jtoqbj+aODlftb1d01aK0jg3h5Vj5wpnCwYMiu0JrcdCgtl8tmBa9UWTlJXYu7k7Jc8/9Ls/i8XeBZhUOGtR2U9y64TsCFlgYG95+3gK/Gqtbx6b1hXOgsGDB+2U7uXX95IemgV8stLFraCtjE8mr9rUto5xbNhykyjOtLCxtI466t1O50LMK/B4wRplCjkj4mGUup7N0rJ08svW4+zo4Fq8tibgg5DgyH9LbicEn9IDtLpqFeIV7+LTReE/Mfpvz5zxy+qjtL561IvuXkLAScJATTb+/t+PIrd65DPClkQB2H3PotOuXvIwRG9AaL8zvtxr6tcffVzc+aODBL+OGRe0kVLhuhTognPRAB+88VspiSDz+yDWDx6cFvgqxetZsj2MqXp6BgolQAV+NJF5OTx02v0qBMtY4t/WmAStErUITGsAjp48GQDyetpBU5fALZz9y+mitLTMGFVUr2iJo5jX0tdkMe9jYtddKKi890X1eGamLkjoqyyPggtSK9C0+ig4r5pU/6GuoBl8qBaspsUnR6ZoCMtyN1t7OP4S6bLQUiKPKeA94mXgmNPVOZIiOdZNMR3TMiU2U1Dhqoe/02nTpxS65axZHopUcsTZ9qiT3yidLrStJFVUNC2Q9iOdGJNiIY7NIWUBWa0UvzvV+/1SMlpCbF08FbEPbhsjxU+2DsaqLRSZgw/6mwSGD2771NVwweqxbpa8b+9yZl5yz6cAFs18C1ld4+aWn8end1jvxyAU49FSv8FbMUG9a3H7AZmOnL2wvoICXX3oaG7LcxTlGK2jgwEFt5JgH9geBiCojddixWMUyzQ0MWCJT9uBBbTctbpfBOYJgABbeml1WS0OqKlRefLHd/OR92RvaHjQo8llwxh8sG/LxBq5f2B7JbFQ8bYSUyN87ekCIM6/TAIAmnyMnKr0N2J8lCjMlpxwdKTQo2TDxeCdmoFNY6pBrx+OPvreT5qnOLYDCwBTOnB5sleIth1x42syArZQAFEZRXGrDCb+rs9rE7428WmJlotYlKqHPYeh5s7c5f84258955PTRtNKXMP+MmoPviG3OnzP/jNHv7dzVElft2rrCk8okvDccPKiNbMpq6ePyg5THX5w47boF7dctcFWd8431MWKbL19/y13PAM/818MY+9URAPAvv7r19mcA/OHhziE7fyUo2qcvbHdK6Hl3/Havvb5Y6eqBg9pmLGqnPh86ZOz76XBt//syrl/YPqbPd7IEygGxamvoBSQGX0ZERBB/WV6JUVkGELyH5X9StgV2Q580gKwoABhyyjRWFxkAmykA2519N4CHTtlt6A+dTP6xb4+i9G4uLhvtSbl13hOHHa9ztaej1g770ez5Z4yWLDzYBtA3YUsvc8IqbSPLUrfdpr/Kf6gtoHXJlz3SQkbpYZyLPABOPhvnhI0ilZb06CoIBrzyLBMR95xzrQY4hPXxnOhd2qLX77kl+6mTH2RxWUfX66ZQxiyw3C7zGLnrt9GJZbU6UE6oKnWrPh4cnULksrsIIdbrs8UllsLN1udUBYx1utVGVv5JMUGPpACCHcruzFjUrsXgZLqkfQ8/+eEtrAbp3e/9xYkTFwAX42unXjdxK6VsrnDgoLbvHopHR0+lNfiGe8ae+eWx7Rjyjd98/6TBbTcsase9vzh45NenALv/Yuq/fRrqmbtPH9nxmV9MPcr+AsChQ8bia6dO/8H6YDOXTOGgQW23LJlKwSdvWzIVQJdRBw4aS5FzvBWEFEtI9bxirl8blG7M4LYbF5FpIYu+xLmaH5woRZY4XfGA1jjox/fUi1788UxZd2lCasLuG8oIA3rm0LKYcpGpqULyy+Bb7SYPAGbwdFt97mDZpZKHSBwmUvbWFTBZKCDnkuFIFxxDwp5wfwelbPY46t5OhLdcGGhrlRYRrOntMWlk6zHzOptk0qSgi3A5TZEEWfvYiAhX2mtIC/wHEkN/OIeM6unzSlJWrTmgXOy9nOgpYeVi5OHXLzx8zOC26yZuBQ41P2NRu1J2a2D8onYEu/QFv/xKmz9rxuLDweHuscnuFy7ZXSssM4fftPjwplGHDG475DqAI991ZxxAJu4AvJ15zxz3PcTRIxV4HwnA5/XxfaQ/y4nj7uuA2P1cMaIVwDdWdMC7VYiUbCZC//79Dzh8OvxeWDIPG0XvkrB1K4PMQxPoUGX76Qu5TDBZ2OA1G+wH736iLikAy9bKfRMmo0DOTkpPbJ403NrYuBsA3GbYk+b5Z4ze9oLZ1XjyinfaWrB2aZGeZTZm3uLaudVq/gz6iRRapZ8c31BWChK4+4EceMf9SAbA6W5ZTxkqzzmPJBXjxLgWQKPFALjmy63HzOvIVRRHnYq5/LBMfGv8lbmh6mMkKbuxUVaVpjD0DQHYBb2WKsbM6+AFOSCuk2tH7qmfLaK3PvqYnLMyopbvW8VvIzjZy0uojcAleby0AzDsNdAifNn9wEYCGvGZEvNY9nofM7jtpsXtkhe2ZLafEHnJyUNW7m8X6m1hCE0HD2ItPqtvrf/JiSh4uklrcOlqnynbRSHPuIe+WgqTl6tApgE0rQJAp/iY+RQSn4fC+oaIm5dc4KYvbAcL52/inssr9YlQqQwtqzVWOOJulsbNlxcinmiejN187O1/ngq+HXLcmla5DA6iHrqcAwe1UeetuAWFqL3gZ0GKMehvk1MSUA9JCLQszjnk2hL9lHIIHWfu6RIPy1tF9NwVQsLgMkLxTz/d+bCj7u3saoZ7bQolXyYtmb1ixGEyq41Wzs5/rRzgaZ/xs0kJZtYSj+een0jJZtZUvPmHM+574CkAA/a79V83cQcVlr7YPn7hC9jgkDuHDILF/CVnnfkqAOw15Lz/+Biv0wAe/ZZXqO+9+Q8mfKRSv6f+CQmrCl6v3B3hW4E63RlOhb+CmcwNy1ftDSIuzXLU5kDr/fI0cQOLIvoIdfbGCn2kP+8BZGbfQ2S9K0YcduwHJMVzWuArcLSmqsSpsYRnN3MuXMfkS/WKWiNiSyoc/P22JxfssOvR56/9918/+Kuf/+XE4z7eNFD/9T+Xnv3Clmfv+rVtTG5hUDz4wDrH3zVoPQVnNArwxnzLr+79/z434ZkLRgN3fDSfoJRc8gGA9gEhJ6wjBAqs6gbzeFVRsdNVeH28NDTl2qLOSJIdOdn7kHNCwRbGSgxSZD/v6lHVcHWeGKk4CZj/G8wF3FdAqJ9Jt9etnTP/lVwh8nK28TXKs7gPrsvEIQJ5CBHlpK5RKt292bn/CpFtrCo8khb4wWS9wnVKF1l7vY4MVQbEj77Uf0sNeq5jVbErbOnzIYPbblzUTj9J5nqdo6FqzOC2GY67W9atWjiSTczb+mpjpsiPFvEtcpp3cgtvgW/hhUwKALoKx9WkfEXHE0mLbjjxnrhen5WRI0RY/5OIOSkKSNEHX5QcXLIrlPb8Hlq4nnsTCymujzpWUldHjxtM5c7KGPihPM8l8PNojTsqYzMUHDduxqJ2Cxw0qG36wnYnthHPYGYVmXU0xblSzNA0rJ4X/Nvbi8h5JSVq5QkPV5VHrkD5L1wKYOGu8jYbc3iXhxPv7/jpzoeCtfLNIDV0BSwUTcIGJ6sVr6jILKAk9qtKAVcq0gLft/CPZx7CoBPXBvCRfxv0hQMX/X7/oVurZ68/+80Rk3YduhGXeuyFWTNfmjXztT2v+fx2G8vTLR5devsdr95+x7pfvfZfd9qIlnZa0dGNhVdCQm+CVnf/VRLcgwa1rViP6jUKfUolD6byKzDUXS/jmHkdAK4cEQXXu7oSXK+PIy3w9RB8NWjiowzmkv14Muy+K3HQFw4anbglBdasA9AWf/3rUmU2oBqUNfqvv3tl6e4f7n/03EuALc4eufs2wNZDvnPnkL/c8vsrj/hvM+VfdxxoOPC3wraDLpo16LWb//uCf/+f7Jc7/PhuAIChHhi5PQ/9C4TGySNcdmRmtCLIfMkfvdvRq8vLLmUArrP8VYkuWavksEthR63Kv3SwB4Wx6xhCWwq4alfv8u75iqNNqL1f5ctxjZbJcaza9JApoknYYLrPBQc+XqXpUX453xbrOPn6kOsadwDpd9DdxaHS+SjaeZ38ICKCsZm902RrHDyobcaidggBhpTW0BI1fWEk01bidiDM4crkifvmHRAAtGinN2VdsqvJn/KW0gAKo7pE4EctRjhTPkCkAjuqyPQJKjDIcC49Pq5OW8Okfchz4r7SJIKQx3Im52YjivkUiFQ7ralukxRb39SLcEQBK4QtmbJjBrfdvHiqRpB5OMrLxTIx2Qoo8DQmsV9hAagbF7UfNGgs2KSA+t/kYHNvFQCwrABiBm8QKdqjn2w0PpKgO7V9LA97W1izLzORet6GYXNTopGh4WaXBZAVCsAJ93UA+Nkubl0nybx8Z7s8h7pm4nnTGWn7kr2/WMvvFmmB7wbuKbIAwqIgVp6apLEqltuX6lNx0dIR5eR+/T6+CX47/+WR23yKGrf455IH/vaFz2w66NbPDr398V+c/dTmt392U+jC4qP7bnXmgIXn3fj37SZ8RJltJ8565PTR25w/J8uATxywxY83WnTy9L9/+ZufcC8CC0BTKoW6GWZFRBqC1laKuH2gGDE6iN/qobD/UBMjJfYSUZXPituFfA7FOlHbf98tVT7ALwsWatNOhWJSFhm9jGhD5t/uYv0Rb7GsFJ+1tIBXlqtSPBwp5zQuLwcAZKXVV25r4E6J3MzcZbg2qVnnh1N5uYQcuHSS8E8rGQaS3pT5tOUCoWEj/vr6nbiYDCdLQy/a9cJ5fwp4fxOdAkVh4+hbnHfH1VMNYcQpatziTWs/ex5aAGtlFkC/zJAZlDS1c6mbFAAsMyojSzraoLs2AECHxMvw12tEx0rBeZzwllyqnPxfSVk93wIFMTd4EORVc51uwVNA3UYhDiXElbuCUqMUlRBvNl9V5JSo/F0OI8bsxRYi16wSOx7PZOigj4QD4NqnpgH4RxNvNQGWvb/ZhG/RyCGop0M1qiL5ivBLPt9TgHceTVMOHlwaj8L4rMThepcVCpSXVrwQlBglr8WQ8aR13MvoW+3ysNKQFvg+hk8fM+xzB8y/ksLH/svnWjfEc/jYJ/b/5Cawep/PbHH14r8uxaYbculhn/zSb31Cy23On/P4d0bByeS3/eT2v+397n/gcMQ9nVeOaD1m3hoRt9KjGjvlhshtzFGuFQsKBdOjWMT1JHsPCSESVhxWTGDgGL9aMG1ZJclen8Jfrz9n0p/3++63t4qOLNr/5G+5I3+7/pxrLlsAYMOTLmkd44MgvfzYuFPmPImhP5m274693ed3RFrge4JMbuKDPkamSbWUXfDNspeaPFjZttNP/UfctOdIhWeumPnn4Zt8TL35LP721xe13aBwUjnAuoi2WfOFt1+xa3UVuXWEbttzZxW5fvRbo7a7+Jf/WIp1bCRgr5ctuC5b8RkAVGbL5mnvJAAvFav8JMhLLBiQg6D4II+9oFNxK/ylpnIpvCWNA3ku5bkJToDM4490a3yH5JSZ7GRd6/LGqphMiF8CXVbhapyc02fSVEIwIIfCR3eJGICoweewkR53JY5b4nO+wAyvCBeiCH+Qoq5KuXKJCMa2e+WbWB2KQwa7bCUyJHAo5tiRu4Bo6EQ9WjHzpq/CljIE+XGRTS3Yv6slcyL6FmLwjhZHwgwAmVEklV0m5gFTfCtjyFCMFxkmSIXeBm7nZBUZneKCvZj4ZnKd5SdKi5w6PrOLLMrTz3IP3YtFiEAi0AZOzrFSq56VHjyojQIDZ+xTGif4ccIEaeLo2LMN81hBEfFYZjBmcNu1T01bZvDPJgC8XTjuTss9OY/RkR6MhLwJpxiJKEGtt8tzrxEFAF0k/4/FXd470VekFP42/exfXLYA+2FZ4a70zennTPrPBdjffqhhNWDxxL2XYbebpm2Lu355wJVPjjhru4EAnrhj2EWPH3Daub/YSsbQFXWHdqUGrdeQFvg+iWeumHkHhh63NYC1P7Pjx6Zd/8zWWw/45O9effKz6x+5YSi2+MZXPnvQVhxrLML/Tv+/LQ8Z2Gsd/oCD1njp2Loa42AfMr17+DgtHlXrrdoQqn3HyGuNhdul+a2nBQB/N3u+QSV3/DUHHzvk7CNx9pRnwpFPHHrWiTjnp0v4+/NLX8HAzQcA2HA9d+jlR4646OWTLznr0FWQO3g5kRb4etRw1JilqCob5gI1tJ53npK7W/8ICr5pn73irl/PwgZH7HL8V9dWsID5+D7b7vPCrKn7PQUM2OfWT69rsejnv7nlbgD44jkjdtvQWujXbv7D5ZP6tc3aFJc+3n4ngIf/uOPwAzcyxlIMS8fFM+M/l/tOu2DSF3ozQtKwus21CvX0FG2xIuXwLcZsWBKR0qZWqqmk/MCNU6jWhr/suSf7We2Q1i5UbdyQjUpWxseztMgLLi6mRMkqPJN2KTEEedWxAlXW5lWhspdSEgDDykuhgCTbpZybUC5rLQ2LGGnlGHDEpAVuWtwuBTYHbCY9s0FR4qs3m7StXsJPU4WWDa9RrphbOoMl9+CwNMmJMYTK3IsKIk2ntWD7A6XdBHDUU/B4n6gmj4QZCkBueQo5QYiQ8TBNz8gOTjzFMu9xzMtF0GlhDUC3MY5uxFRSaOZjKy0LZ3Sm/FdpouJTDUfxysQNVdzbmxdPBWCs6mHfNmYw6VBC7GQpisiEzCP4q4nLz4VAxfI1jRnc1vH0tDe6AOBtJiINptSlv5oHpxAPWpTxqM5WUQ6ptZEYwGWwNSRxiaedKOa4NQCjuoyybGXpgzTnGvj0l7bYctp1P9pm4ojHXz3lmP03gXruv/705Jc32vyUc4YBB5528fe2QhxLyt8n2cOSEGVlIy3wfQybHLvH8ceWD37muN2+eRx9LAB85vhdTzkBMuzdJ/bb8rv7dbUAmLDN+Sc//p1Rww8c1ls9TlgjQK/+A9+J4cn0YuClfSWFmkl4D5jB2zIAhcWYwX3Is65vY/3trjnt1aEXfQ+nTTxzfQB/uf+R5w4Y+JUR0/b/+l1X7nvRjbtOO2inVd3HCtICX0aFXfhtu1Ld6Ig8Jagq12shtu+hcq5Bbs8BgDkJCgpVW/FMMloxAbIA8qYZfu6sR07bbdiPZhuFwnFb4xt2lI7d3qpac/85SlQj+Rbv1HvQuLszq0PhebxUtLtqFQBrInkDn2V9n8uBblygHse6tFDGx/b/roARoUx1Hok0ukst47beyquKw0Gvitbh4iIlvWf2NnQ2ot1KQWS14Agtcdhaab3veT91SXJ9+snZ/LMO0jkyWapQAbhuYbtLP6pCVXXRbGxZCgWQHt0b58tQM7XCGAAcssYJFTJlZVF5j4KAxPVZIfZaDK6MjmOFll0x643bA1nPuKtcVfkpzZWh7wUZUYuL8GMube8JTREy1kdX5Rg4YRKyIQW/G8SlecWyEgp+mXnIC1eEATsyIXcx1roe1rxz+JUibk9T+OMpBUoII60rIo84tniQ3no+XJW0DXduF+IqugzeKtTXBrf98n+ndZnYQZdfQVSUwtbS3JChb4AaqxRjY3GXoP/eAr+Q8r+KiY+sv3pR1ESXC0hs3zaAxdsFmgZa4S/XPf7Jmy/Z/funfO/wsROu2SNT2GSzvf91ZwB7fOXAafc8+7JW62vx/HopmnRVTTr4d4fuYvCu4aAccdtfPGtVdyRhTURfC7qS0B2qxhYJ9Xj+rl/P2Wi/1vU/cc0l+shTHnhwjxHAM0teAt6LAr7Xlq0P/AK/wpPNOGLhVG7lXSqCir28EwssvEICK5s2JQ6q0n+A9VieltEGkEhqYQA0RfYJZZ2JrxYpHYzKABScI8F53GoFOD9ymwGA1pFrO8evZS2V4CjSJ75HdFsiZoci2ir/5iiOduFm84bxveXIso7ducS4RV1bNFbCV56U7hmTdbdbp5AAFNLH+usFPJcVdXtmXIjv1nFiAMh1ND4yzkmoR5AYunveMLgQrcixypg5eTEA4ttRG/ZS0oVSqtku4bts/DQlblpTU3T5zAPpuei+tDtH0Rp/y5KpiMPNhmxKKlQo7QAsnIRGWnowR3TcPeNhB98+T4+MY4SB4pMm3pbur5A78IPtwxB5IYJX80d2J+5MYfjdrD7zYTI4Rm51YJo88u6ys0qLWoo02AlfKvX9W6MqOJGuDdbGN4smvBdpZBacL8cI2wVvAeAyM8EiztHStIoTt4RXhMtDYxSAZYX62uC2a5+a5uL+KoDtJKxn4U4gAXitvOE+BglI+Z2TC9mHc2QgGSU/F7ps1RAeNDlKVvwkj/vnoqtQTQMY1VWopoIuUDz54ktNrI2XXvoDYPHhHYcPuPiWe8dutf3AYC4oHQAAIABJREFUJ56cMWSL29bvYucCChKQZa7DmVDJk6/gil220P2OoXeT0yb0IoZNnF2KQp+QQJj29LSDVj5ve0db/YTVFYcMbqOwNh8YPDzl2p8swG0X/vSiJ0pHLr3wCQAYsMdeJz134/ZjL9z+olf+45I9tgcG7HHEZQNn7j/2+0Mvwk/O2qlP+iyldLER+vfvf+C/T0fM4L160u3cq0ov2lR6Bq/DWZ6Lc1g4wFN8FeqxbGYdMQEuIAUJ1Yh4Ra6KRgavpKdomrlLNfvYmaN8NtgstwAa/Qx48+6ZbjUKbCmQmdzmCrYqaFBcQR3XLzWhwCb6jo9SXtfcugSvufE99OltqCHqWFFh8JaZlmTwVJuUB4AZvPMhziyARm4pbG1DxLHKRHSqUu+JDbs8rdoliPxQTueWR0DFBvZMhtxnGeNMBkD1yWq7yyhT2MhgWwbL88lnZTAyCQpHeuOidvIOZ5IR+HTpgkvsRyZrkaYJQVdqFeJMo6zKrQ0CGdUQfSVGzkHoqKqGMI+PCCqfJoOvefkB03rZMJFOPl0w0RK3MxXvDNLU0r3rMkoKbKQ+m2U2thDlqQmKkqZjW5ZIdOQrF7RVUvzCRM+tEv+8iEIab3eJdLd+HFj3HBrOXa5kS1F+4xgAoHYLUSFBZobdb9OvX/M/08T1hk6aeNJKC3PSx3cVeEukmcnE4Fjm+nJAJEo5jqkGzkKrItYu7q9/N8t+lqaJlKKpKImR+0tfqXv05PrnVz6D1KX9P9VL6WITg1/Nse0FiccnJCQkrIn4wOvgew+2tKmjY4Gse/v2njSUThskd48AhLmni34vz3GyA6MDQdeGBAwWgDLQLhCUgvedKwyAHLYwetjE2Q+fNhrAdhfNApA3qLdAxMJrUJvcJVyKraXp4VfEhEYS/VJAZtlQlhli23ls4g7m3wocWl+HCinUfFFoG9sJl/5qxeb0OREggMUDJcvhKF0Ed4CowDITGJ4W6kkA/RzdDGdRsUwHk3jwiJNbcJeJabs4a5ljHoEK+HN9ciElWF0ur5eLScInt/PUpo2Zt5QE+JGvhjG3FgX7CsuDiERQoYDUwZeSnyIYmUCmYS21yJcTZWWlChty3KKTHHKWImgZZF4UzFTpvNBzDqtga9yX6Ya6JzqOJy8YvC/rnlbh8GJ5blTTzDiu6bpnZY5gN+RuRCLHnSiwoLsU23QCiTIL9/HzXWh9axHLLbw9eVOmJGCazhr30OculoodMrht6lPTKB5DSfAjRi7ixAS3GmWOykujev9MyedIZqNB/FlKDsKwOL8MwL+0xbnWd1E8pFKXL+pX4GgWlCioad08lB78zjQhi8h9L8frTQv8GoGhP5zz2LdHPXzabrTGJ0hM4RSQa0Iku1L4uYMHtd26ZOqq6kzC6oQ1NgpeX0Za4OsRZYMt/Vbh7uXfbbkGwWtKO0a3n7TWSmfoyKpYxdmmAMRsHrBZYQAXXI90blbnVMBkCsDj3x613UWzstwCVolg7IBldbXYpPu0UDLAe4V5W4vq2MS/1hzx6vNMGAFIgwClkVUi7rEtvdg/I/KmZV0+e8NKXVoU8M42MnvliMNkxygjpFdaSnPiEqTjuMtKR8dtpEQkVsdu3+5zLqinJNmZwjJBJmQANWckXOKj4kvG8cToWCm7KNUmuTvlYpf0t1SerbWtPF4bszHi4WK2Opt2DihPg5o76Ugtgy9VDKuioRAtAHw5SvD+Unrf+NYD8COvM6UGrDuu2uqzr18FwHKiYmldYJjBFRUpC0RAdaPcNfJZynfMebiwANCIniu29pCWN6wyV/4yrVLyJsrJY0OugTAUso9dxk1rnid0O6hdNo93w1huF8zdpam8tzzwZgSHDhlLhac9PQ08DbqKGsN+8G0V32qUxJbjM34oE8X4qmXId2dYUHlwfLUyskWmIrFTHAKSpRFybMVfsGo/uv/ihWYsyByiKV/jXIMVF9lMDD5hxYJSzCVIUF7nNQq0uq/qXqx6PP/G1Yi3UwPWGQfgmb9PXpXd+mDi0CFjf7VgGu2ee3npSlgepAW+inqFmLLWRobjgadzRPNIKc3pg53irRREOZRwhdnE0zVBLFzRXyNIVjchq+r5stEqzy2AHS+9G3Bh2DmevAWQ5Y7BS72d5ah30tRcatD9Z35Lhmp9Ae9b7stzTHt32HUgc8QaKHvhR5IQScv8B1FeZwpAljvTelPoUKEYMa1w5YjDjruvo+QsXh3EqlpUKZ9w3aIUV05FBMLpOAWf9iHYpEesdgQuMgmWxMJrEJ0DdEWLn0URwaM42D4sFxlv0zizNtpdRUShpHBCDI4cEl/OBnoa2op8Pdj3nUl26JhiPXpVxmNYFKArYeMKTiooCavsVlB4i3YdddMagFZqwDrjXnxjSpwZQQF48Y0pADZa50im8qEJCj5hw+QMj6d3cgFgrGoIDl0IuswW49w98cbwCdyiXI4KqBifV3PfeZYpM9BLwkrnNn0iiEiYwe1SJUbIHmKpiTQ06RLOBU2jlhnVOmTs1KemdbkwgJEzCGL7FTpWuwGQUeSc0TsLNBrBTih0qTo5Ix+EWAtO11v4JyIWJCAYcNATrSLXCXftrufS6l7CP/JOqiolNPyXhsIZFiQGn5CQkLAC8el1x4MX8u7w/OtXkwD/mb9f5U9JtL47tA4Z27lgWmLtfRxpgS8jDlQXtnySoPCxMhUX/DbKBG9LdFT85NuJotDzWajsGaVhu2HVjnSyd5ypsADyLtOlMgAPTth9u4tmsfG+ht+2K6sbtFMVLJyNjd2+W+zqjeP0fAHCMFiyEGP8Rl4j3qSXRlEGig96RciSgty4EFx8uUJy4LTvik3rc0nug5DA1WPjsaq6R8fwbqyyTDNE94dmci/17lTWx6OWVDtSDWblxGUenk9IPaXn7v5vaEsSXyINHK2+kmws6MKr1gZefV4Nqm9jNbl1euXwE1fON09wWU8clXig4iQIbor4DGylPisVCV2ySvd801pU/ul1xz//+tWZVlppcSEAoOkZdHHs7QtvTAGw8TpHAnjhjasBbLyO09nTqq8VBq473hF9GNGuyWXwRBtmpvPUUN4L2/8JM0RKqeIBUQCUtpKmS0ZuQ6T9MIeZlysAOT8sDVFv2T9CK19tYeVN9HH6yq8jY+31C9vHDB7buWAafDZ3V4M7Nxedacr4fTHkQep/vyx2Uq8UqK3BX4ok9PTCaHF9jnooh6IkFagJ719l7uF+ucFhQYh4jXO1uZcDpFj0fQT8brH+s6lagzB68ovzz1Jt9BB/ro1cxbqZA+KJERsLq6Idg5MsFRZA1jRWYbuz7y5y/fBpo4f+cA6AlhYDADm9ZDKXiTKjqDJAWMUjszjpjRaSVUSLRzQKJCSXcWEpsqzOLYAsN1Hs29LeSWRxVULg7O+JXNohl/mw9tczC2/BpIX7i39jyr7IWLBe9B0Fi5XhZlVk4CZrkAsS4hiWXu5KcUzc/YgN3AA0bRSrhOBj6cSvP7eCyih1Mxa3Azhws7YZi9vlQuu9pHiZtGAhuR99uXhIu09jFW9KwnT0Q4FQMFq63H5C+btr/VUz3C9a3lMArFzwI5CLGLS5dD8TC/wAXpuJuCsoXs7Dk0yflTUArLL0Yemb1/h26VwLu/E644jiv/jGlI3WORK85PMstb4DfhSk4ZuGUizvRrBlc5cmp3dT7Pt9CNvqvAqxe1UoyYu0Ckc0m33Z8rnw/m8i2JSSgafY+k9HS5cbPbcYs3MpgLeaoZf+5dDdu8xDvjz8akR2fzIeTqm8HDG5PFsbVUifaavRZXhfZSr1xJ2M9km8X+GZX+6/sbhq19aj7u2ky226cgpAU1kAXYrF8uS/nBb4hJWBYT+YPf/bowDscMka7Sl39L2dk0a2Hn9fnzayk85sRJKWHwcNartxUbt/R++3aRt4mV9zMGCdcWRMt6LgV3cAnujzGr+G4tYlU/fd1E3U69bUxEJX7do6fm7n5JGt6JOmu2mBL6OSidTzY9uNkEmiKlVaribCqe78IDlQFhDbVbctFjF0oWLBtw01ZKwio6qGnzsLwEOn7DbsR7ON0QCMtS7BQ0OBY8uQnZqXw0cyQ2Lw3nBI0LXIg87EMmEXDFXQMmVL0XzkTzK4BKeIhW+iLOSPP0vXPmkyRiis202TY4xM8oE6nhRFo2RSQvSCkmTkOjpXesHJ+JQhUaZsAoBYy2XCdcr/oYF9Nv16i7ZNcfnU2yC+FmxDA4cMbqOMrjQBWrS985lrEcuTPWXXpapiIbCNeWGkO2CBlCM0plxAxcxNTgatAoP0F1Dil9KQsEUEuy2sktZnLU4gQWzYzQ0FNXDdcS841o5QQCmlMgC6wuC9l5pCIf4qf20WCgpL37xGiFrx/BtX+22E5oQwVTe8516/2vVEky+rRUzjcuVUT9GoxIIr6yQrgS77KcRimHCyo600sKwaiG+Bf2AtWJ4k5VJeKmDEQa6AJpJ7Cmmi7rfp13WcQql0NVnpcuQDThcSRAnhPSt1R/7tVx2q6A0Qm50q8c/qyNeRPjbFdCq1VbKqqyPeoeXxczuv2rW1sApCTqP4JbNMyAy66mWLKwtpgV/jQJlk16iIN5NGulA24+Z2Ajjynk468s1VHdnmkMFtMxa306Mvl3aP25ZM3WfTNiwfQyoFsVkzMbDO030lgdb40hGwap9A8nzC6mqyN31h+yGD28gPfo3FUff2RR6fFvh3hvNpsbW69oiy12idBJ8uHXVnun8qZJ3hilwU0sLKzbXzohFvkBAlo9q8tboIvIFYexN6+LmzHj5tNwDDfjTbtlBBC1aTU8PWxFdrgdjgTsMaBK4vy1v4qDUWiBLAlILVWC+lEOS7GgFXWtX5rxBkHaWfxOdJI1uPmdehgCtGHJaxvQI9jXIzrWNu5A5WeHmpck/TJSmv/kVcObV76OC2Gxa1a1gan9v+PBXBB0/5wrcsmVpYdeBmzotdqrpLscMoe5vkeSXDQNdzWFCKW6Fcj6/azU9XSaVYEEj4sYs5oooLsAcgdYwyFft5ahF0/67nUrIiFe0tsNJZyymbqXKtAHjuzj1UCBRfE3fXKqOvvqO27MAVBqM2iy4/zhrCMp/HJ7RIcBYACht++MhMaW/Q50dMK+9ya3kskSPMbRNHWSnZhFaDEEPcL/92qBpDGNjciT0MfHAe1yX3l2wsZHogqqGoC1MdBetFZFYs30zG1nRG0mXP4KNJGb9BlXhDSvRAj5VPVWzDX+6SqmskdKwUnKdUzEuzxs3tVMAVI1qPmdcB8bBkwkO4ThKwEpEW+DUUQ8+bDWD+GaN3+PHdq7ovKxGTR7qHDcCx8zquGNG6avvznjFjcfuBm5UJ+k2sWe9ly52+iYHrrmC9+4qFd8MD8OfVlMqvgSCqcNS9nf7I+LmdV44Ir51Vi7TAv1fUbDLrSoWAL3UEhyvwOWGj3BF+E+9M2MOptNlnuu891JSskErLFInaGgAN+pxrAMN+MPuhk0cP+9FsQ251jUC7AyMXW3GpKy0sbJcCfGyZcFFKIW8JSVo5zYaFd1rjkpG6y7gRMFGAHTlsEU2PiHLdfShxaADets5WCpSs6Gsdcugs6Z0lA9egmw0+BJOWjHb6wvaDB7XdsmRqNX6ttHbO4CKVUtD4prgRueIp4JR8CnF4EKUCbxZXQSzTzZEqQfcE2tmBu0uIeKHjsGSyboPUAa7/rjmZEtS5JLA1BitiqQ3Bbji3iqyBs/eq3BpwwFRqsWktmPJqKF2ZCkTZAeVs5kkTD/qsAJiYAKugC+bYOyqajt7fCt7SW6lYWhCEFFY33cBqs/TNKVQNye2fe51s9JSxwUKC71QQafhwRjz9rB9MQCkZX0hIwnw1eSUkEQ+L98y08J5sdEN5SkTZicRkUCx1iD07qCFHz6WDjOy/UjVcVkZcRvw4G/GsGhsLihDqKT10NVzcX36FwXtIG4tqT0qgjvnVXT7atMaTrF4rtcw9AABcXuleQ0oXu6aDVPKruhcJCWsQXnjj6hfeuHrguuMHrjt+4LrjNv3oUau6RwnvBX51nzyyLBocP7fzijjnxSpBYvBlqIg5qvgnINZmlY+LzaaK9sxRUohI3V5qRzYYGlKhFQDs5k5uu0YJYgUUWdClWWUpRobmyLWoKNcLq7ad6FTywy+cDSbZ1ootNzcsfeKtdVS7u1j3O156N58FsCbeXZnmELlVNZoXCRgFQBWuvP9V66DIL42Y36RLT1aZ+aPkOK4qt9IfjP76phEqj9TzogOIiYX3g5fRRkuOtiXPZngK5frjiYcC00dnAcB6dMspi+SVUg2yS04H6cguk2xJlx1Rdl+rIUEyT8vcbLRgbmfEvVZsKs8ypsBHtbIyFI+pNKFVFGAnChwLkCkKTVGIURq47nhnry6M6n3F8gjb21PQGx1GjNkwOZY4mm4LHrJCVCVmo4qqdfIIkRA157tmKf0p3XprwbF0LDBgnXFKOfN+ZeXdcWMgAwNn8QXKWBRKsH9Xg+fLADw35VNMdOvhz/I2+WTrU1LM+275ry2ZvWXJVHLInL6wHSLkrcvFEtqBf10Je/MoAHMs7Sw/LAV/CNeO8NfbK1SFBNa6+eyiSsRdouOa35ulc4PNgRgKwB4zr+PKEc6wjtR/ZMZLTYyb23nFiNZj53UoMW1ksqheQFrgEwBgm/PnAHjk9FG0xveMR78VMf5hPyifYrTqIxlujrq3c9LI1qOFhmwVYgxbuV+/pjoNJ6zGIIuQAzZbXqeP1QPeeJ5eMpNGttIaT6A1fsIDq0wfnxb4dwW30+ePcVZZtg5VlXBHPl0g/xAYuVPUwSu14h2yOI9TxNLvYReaqSiwLjEALySwNvyk5WZX+f4HxdPQ82Y/croLeEd47Ns16zSt6FZ5KYVvoZJmV9Jr69uBd0Vm13YLT9OVpYPNZuiYzLYiRjHcA0fTmdnX7N/5uCMiUUYWBaCwfJdUuQbL3EXelohZhrCv8L2VhbWyYwa3zeBkbuQRR8u8jcYvEv+U5AeUaTQXFftospkfKS858AVQhmNpsRU9RIuewDGzCRM+iAckyQbAQfoKPsKMkKoNvDDXJRV74Ka+9arql89QWqlaRzhiw1qpzBnJy4no+s9EKvyVPvGeAFvnHB89xTKjK1F8ZrQZmJGHs9huH0EwY12IaCceKCNTSkENWHccxcGVul8fPdY6EYgCh8r3eJbV+fCW/yGmoLxLcjysF8XBC2/EsJU82kvPlAwU4corC+DmxVMB7M8GodPdJFfwUd5iVF4hMHVynVoTdCk0op+aJqpQPqRNZ0/h0ssaUc6F6LBe3hbaoVxN1ovTxHzhHFH2uPuCzTwVlpWPm9t56U6tx87rkBK4XkNa4BMiDD1vtlzU/WLv3gLVUI19GFft2kdt5smfreTklrD8oOVcruK9/N5csXju9asGrjsewtKeFuzuEt5QdH2IhUee1RcwY1E73Q+KmvxuozEmrCikBf5dQGrB3YZO8A9/0Mjdt1e6R+aYoQ4uoCKH0agGGLGvNioQ46DmF8IAdzC2AmfRgrgWYwFkhYu/LfSb2HbiLFeZUmhaWS11zDiPfO6zEGZQxx//9qhdfnoXYJWIXkeadVJzZpmzuZeB5ywry7WmwPjWHyyaAIRXsgpUxo0BW+A32Ep/0shWEXGCKamKaLFX+tKRjCPW+fGTg9g0zkBd0nQPea4sQOG9xgxuu2nxVMB6G2MANyxqP3hQm4XiDB9BmAHRBw9JSkqe6NJIvmQiUHXiD0RZSIM4w5CFGBw5yeMgfVVvDyY3LBRh632AlbiOwQcjeQWmfZHxCk8Sur/0hmJ7bzVwXUr5qhDfAhfrjWULSlU82BVTaqF957/u2ph30cQjB5BM1i8/WwSvequcebdxUfBCtYqzKZBinh9BGSwPSuGFN66mrzLJDQXS0UrR8k9W9wPXHf88F/Z9IvuDgeuO83sFhJ8CuS8NtMzIICPKEW0trCKXjWXOZwHg2ANeQiMdIpyFiiaybilYJMXAofnZ0FRtTUT30ktLu26Ev15xzlK3cFYt6ZAPuDfRlzl+xBzkvDg2zMBQLZN7uIMlyUowQJG+TNLOqbBBGQ9+ofUa0gKfsCJB7H/4RbN6jDmxciFUYn2d1d2waE1RVb4fDFx3PEV9H8jR4Fd7eId+WuP9+i1/qoJWd1rRfUZKqdFYXUPp9X3QGo9ej56ZFvh3RsXQmxgPfaRNOh8V5SXHURYymaZjALSzp0xibDpMinZJ2f1Xo0ONpGgnFq4LK2PXI/QLQoYnr0GB7fCtKZTgifTZeiNRst0Vuv96m4PMAnjsjNFgO/wsM0rErXNEWcai15b17kDgTlxzLH1APMKFUZbU8yIPLJlVa20nj2w9dl5HruHlIKUNs4gpEJHdXDuC7mh6ZS9vYg4tSbZWcd45weMb2h48qO3mxVN1uCPRMFrraIERM0Rawlsb0Uf+ZOXXamAAn3GVY+1Z3yX/kxUsUAkm7agLbCaii5cmUFVb6Wytre9zaLc0VtLQXRIpIYdylQD49EfGv/DG1cRoX3jjapm0TdbDtNtaRT7lOQJZdwXiPHKiD95lm/T3Nog0/HNuhICC4+5RQ3w8iigXKvf+LDKom/uJi8bigXDthBd4T1NVTKD08rHq+devlk0oPgvAxuuMk07zsC78X9yuBcdaaBrFZueOeUOwYS1CLkpDd5pIBUfFKN1ToCyXkvBZZbkqoOSbzvVEpvUIHfNp36pxFVl6FiWJkG8DK0r6s8Iz5eoP9bppwAPih1bx68jH82gWCsAR93QC+MnOUVSclY20wCesAMw/Y/S2F7yz+f0aiIMHtd2wqL3vyxL6OEjlnPB+4PdJfUdVv/qBss7QWl6L3lzdkRb4niF94m3MK2t94qXBqlNTGsfYQhZsVmqS9obYuYWmfG6e0AMwuaYC8quz0rWOuwPImkZFJKvM5oE6Gu8Pk360acAMnhoqWElrZVlV/eyQZYGrZZnPyw5UuDsAG+z+Q58Uh6yvOseXanOqNZERjloPkeHF3p/PdTfMiINuvPweHOIsuKp8tV4YUojB5hhpcT9FPXR6kMg4ii+umgdBGgRIhbeN/dGlE3npLEntOPSbaysTEd09+5He9rJ7XlUpGY+0WDZQuQ7B11hlDsBHtQtigLhuKA42R2nfnnXy5NAEgt9/mNBRtnVWz2cqDxU77uVats75IgPzeI0oyYE0d2epmGuFzRFcFD7uvAEr5rkfUjxg6KuVhgFC0gNrXEA9EU2NzVa0q9b9EJ5VMQ5C3iDq9shdz0OT/lKdvNBaxOFyw8UrAChEeHy6EU0WHSmRnIKfC8eHc6GDJxCnzzTLLI2asaid3Eac71z8JpWDBJ5pUqkvRRTK3w8pEoiLqfByjdTzXnJQkzRBnBJ+Kn0Wj3YhnoUS4igaxOPdQ+DOStnk+gRKEsmewbOEs0aEKtzLguddtFg6aTyt4tpoDaCZhwnidgiZbjboJw0/I8lBrjAATBYviN6rBmRVJ96wxoa/pdkpxFLsj+cuW0q63eXwokIfpFu8W8vZwskFq+GafLs+rq0MzVH7xMhHpbTwaymcVwAwaWTrCaTiErUqITtV2kZLsrhd3vZHyv3k2yfz+wbxuBr+S1/Zmqx8Ff6h1qJGvx2pxsCR+x7DLpQuwkksvS+lo/XInYLAbQtlwFq/BKnKWfLF6sHubaGA5fd4NLZiDTa8glYfHW+7R7ph6e5FyuO6PCZ+cBRvJmjlluu3BlCgaa2QnMvC7MyoxPqtIilrlKeY/nOYW2uEEamOgvLS8um3Q+HGWSfEDsVC65Zc5rjhaJzC2k/DLRwEQv956spArojHm3cW9ELgJ3fpm1M2/PCRAJ5//Wq5kaJiMouVb0a7tRzg3ZdYOJW/5ujxsZGg+6bF7Qds1kbvtiJslQNkCmbFDnXSJs4/aO6q6G7QdBWVhUw2wjSP4Ge1TM7rRo9rq9rrlW4qb8+UPEh9KMTkiW5DCFeuSl3qBaQFPuG9QLrScWyc3p25FUyOzOYT+jQGrjueVMvSvW1AEiD3CqShPqEXxpzWePr8q+Q111tIC3wZ1dipseAQEAJqZ49jVXSisMLis5TcKlYdJaxi3zOtAZg8UHxfmAgrxY0nhXdTBWYvOhAuQPuwD8TnlhkAlEBWM5uXIXWd1Z6j+Na58OUKvO0lSThR9u0umsX7WyGmKxFQ2ncLHs/SdS5ZiJ+osFa6YqAn5WMlAlArKIs25ohqkFJ3byBFvxFvoNyOURaZmNLKEB+B81lAWAlBEIvCwvJEoKZaRLWK47pUhfMOxjJ1C2IFDrKhJPuXI+FTkmjB3aXlYF6d6F5oxATFh6YJQ+UK1MgMSORO9t5awdTIIlX1izQZI4txqscf5HSrKq6CBiQ4qjmabjVLDpT/y3llsjhIbcTdAeT5ns2umaJ/EcVjmyxhvSn6b9k9zw8kwt7Fd5m4fuCl7tnjB43PFJIP16gfyXBRXiLIbWiUp4H7HxPcwl/vi29MKawZuO74P/99srFORCGTEefactpi66uLhQ3+SuksnhxAoZRj/AiM/5YlUwEYqw7YbOz0he18EwEv2SK/uDjcrAzfVG5bzGcvMCjHNhbDQpVQW0Y8vx5yAEtZbSItqAqCDf/SdYo8qQVToSF4SUnRq/lf0gL/QcL8M0Zvd/EsAA+fOhocX7Y3QUt7H6HsCX0KtLq/T6csWuOfd+bivYc837Pompk19iwd72rOrC2f8H5AzvGrK4+nuPS9bEzXHdIC3w2qKWf8RlxwVakKAmqWPN4SWiXs8eSu1oWC8RVqACicmZsGYDmvJ0d1BYAdLpkF4KFT3l28d+L9Xc0MQN50bN6RGMHdieJbpYpG2NFadjEafuFsT3wieq3CfttapzZ2yWkMMaTQE594xgjLHaqgYL6slAHLDBz9jdmtO0i+KDoa+ura4GtgA4Lw18tIMlF/tM3mG14JTJrAAAAgAElEQVTKtglB9DkCDMB3tssAwLSnp/1/9t49bI+qvBf+rTXzRKsJtb3gshZQSYLdVnb77S0HARUsb9omrQcOQXYkHBMCYgUFAlr2t+knZ4mCSiXhoPImIRwUpW0CSRTBSuXU3e1WWpM3B1DQXdlVSGjhfWbW/f1x3/da95p5AiS8p4TnvnI9mXdmzZo1a9bMWr/78LtnTzu+0RgmqfUBAF5baOoUIW8xChh143AGpmvzCEDI7fehpRoyUT3pUTq5WdKuSGeppxWPT2dt9t48bO/I2QpNDTw3F87XFGIAN4ud+5/ceqPPYCug0Ornz93oDDZSdYiCV7Elm2EjML0AgJzfRs8tkazpmaLLDjwQ1d2VjbMLgfUURzl6jS7X9FtI3ainCC2URvEx6U0NgBDs6Xk8Lf8GW62DN8WiU4czZ/HV9Sxjs1c0X0OM7ulWsmSvSQWWHkLR9EsyA89gdx5Vk3yMa3NQzZZuk/O4c+PNR049Hikvg32gqqDiGlivpjdhnl62DS3cfkmRF2vjkniK1Zy5/JAVfRPlNQFALr4mrasXFESxx9+IMfWy60/wO7EcvGg1YFSNVnrxJT1wzgwA+18y1rj/VS5L1y1tqPg+PF2m/F2b6IbhuP2TN1yPKXI8pejMrLuren7Lq+6qsjMTQLe6e8zbNRbCSpfN40GAc8eGQQDHTJu76+Ve4gw08+8dfxDfn+BbYtbdFkP01Edr7JUUbAJ6Pc1pGbFqGzYbw2ljrsucjkqaqgAuTRG+JET0r8tsm1NSqxR1A3utH/K5ewA88MkBRDRflxbBs1u+r7M6ikqQcuyehN5cuq5NwxCC6hs8AShKvrOExZ3TbJzGoOgkx4y0ubCWeIZ0aaGdWmiMd+zdnXZyky1S9E6Qim2ta200ulEuQRn6twUi0Y21+nMvVlUqAwUiy9YtBVAqB84L8ABK48LeMaEBACmzStoptWkiUW+uaM2o8Yq+NX6dk56x/RMPgRGeKBVSsZ6JXlissbxwjiFNm3jOOcXcbWf5CHY5msNElKn5vFCS2kQ0q0qFaFl3pj4Xi7nIEUoAUBptfBheCSJQaOVKAhzq7krAFeWfVtUqa5e3pXpr7rgrWkEJAJyroQM+5PFZ1PrqUGJ54fc9xTMYcG8aQjahk1MvAaNUkM50cVQU6updZLzTMvAiuU38jVzFGiVkPm4AMgcUHrQuXt05Jy+L4Xbl4ikQOMRbzK5LLgsbyCjFtg3Qo1CrAPUqEFsFo/6wzejhCU9Z+dLTR+9f9tfvnQNg/r3LxZeCvzy1a508itKf4HcCYce6kZLxteKPnpx67/LrDpvDmZ36sl0SY9VeBMnx1B5TmFv+OD/BEPmLC0/ttfWn66mHzaWqVpXlzGpXNMkzwe2mZ8aHxXYXtsefft9yANcdNufUbfPejLb0J/imvAhe3+YpiuPzHBzbrFYAepEM7VXHc0B8Sk4DWQY/8qkBtrizSNi3YWl1runwGYVIsHswIIG33vXZ1QB+cF5mxefcca4iKGOJqddxgYfOH2D0HwOaMxu8XkdX3+m+Of1rISSOaoY3kMh35A8S3MCr/oTdJezVU8MYH29w3r3LedXMHi62KyJXq3VTLw2qRk5yaQ1lkQVTbtGknGk86Yxah2sTNNzMAQPlxC296Co6JmqgMMHuXttk001qZLzqOfjqaju3nSMu4j6hf71dFxOP/mzrDbyzETplHZIBPLn1BgXKUSxctnuUzcYFNCCgWuwlTYsB6FpP4UVjwTQO6VF4RfBOEr2wcd2kjXHeusdbI60DHLE2fiUMfAQU8tZdWOFqfRmbXndXFmXTC4+l6q5yQNFRP/wM6bvYeHNFc53kgd/7qxOcRm6TQfBiAA7U46yk74mVytCVvmIWIFFqZM/NPPGoJhNWWmOJjyu8YIYID2AehFVwtcnjbCFv4ZIHCcs3N938oX2a9vgoVi+VjukriVxpF0muAjUPEbKvTfb840Z7Z+MriFaB/Bh3Gn9YKi1idX4NOp3Rlv4EP6HlkU8N8Ew8ShIr51f9wYUDAA74zE5MOjvv3uXQfDMsCyaGO2tbjpo69xsbJxaLbWSE3WvbqvidV8TW/go6PMP9ec18NPrh71xYnxnuNo1fKpqvbxwEcPTUXdAeD+D6942bPb4/wTclGD/ItsmtIT2Tr2yjKLDthXoofN0RKA+g9gxHBKwLXmc8l2N3ABQce5IHizoV+FoEL57kdpEecZ4nJFg/AyYTfMYfV2P/S9Y+dH5MGWeEm+cVuZuuqCsDFARQuLI0TvLcCbUDUJShMI6mkrXCgKGyEzIIyffOhT1xA05ee0u8qcWHz4GqyxwkiyUv7dnUPYkRebpFxAotVVZHLyqA3tgCvctSxGYxuNx8xaExR8ux0+d+c9PNABWOJnkCMKkgIGOWdaklaeCQrcdTx8TdFkb3ECE+wy+tNj0IB3DEOZvJvVEdOeAXz930O68/BUa4WJFwuvHlthx2amdVUM6QsY71OHiNX8+8waGgMMF0SiHQJKbcQt0CPJSqNo+DR84mCeTmdpDmRw61PBgogg81QpWemTAnVwDgS1d0Ukm5kzQK6+GV/IKpH74ryhzNIxvDGZqHt8iwjcgdgh0L3HuRNJeabHewOXVI25wVEIifE0WIJZ4VQgLZ9WUFgBIEpboLtsWqDrEqnBD1YeI4kkHfPEidET8A3LFh8Jhpc29Zv9SmtrJYvOG2YXsqvmjWj70J1l3+p3kXWNoqgVist54E8fnqX6ZL+QbPuG8ZwV132BxBGsWYLuj7E/zElYcvmHHA5WtybfHIVBu3NaI9E9bA8yzekJ3RA58x/XWHzeE5vi8vU57cegNl366dRspckR7pa9zLsLWPlLSj6vsh9a9mOf2+5dcdJmrFeWOI5vsTfFNyv9DmfvNne/ulw3+CpVbi1WIkH2fPYXWtf/TTAwd9djVfls3VzoR6S/C3bjN2r6uUjSYuXblkXbtHPy0T9v6X8KTuHvnLgYYt3IlVmKC+eIigHIB64POywDlqh+ep626WbEZzwiJeyasllz+5ZScA8Jy/ROEHH6qN0ymD/mCooWK3RF760mSdWWwU9R/73jKASvUHsy79HLAeXHRNT7/2gVZBcX/qZrGoOYeSYb25U74PcUv2MbEmATh2+tw7NgymfDDGBh9zucaLB8VVLMFUW7ikEkAysafCHaVNVwp3hm4O6jT3lMajW4wVx7Ed5N4ciqZuPcGZs2ISGhevG/U2XEwswUIRz5X7WNih8ALxE3a34eOIdnf7yyCSULKV3SxN0rwutvYKAKgGIJBdQ030T74GI/gCAHyQc3uovxwA5wtWCbgifVEbUfWC6Y1WMMfx2xSTdMfqD/TWlA4PWm+W8io6gMtnzUJWZ6PnWUvPnLVVkIfIFvfIbGjF5kdI19bf6GavOq70usUKJcyEY2fkF7x/OAD6YvJJ8aUzai05llHWR68f2xq5aO4cY+9FgTtpyYbEHfaQywsEe8X85eC+YiLtLx/2kXauitGT/gS/6wvTzyWHeX3P9r9k7cMXpAn75cjBV61GwxYwIYWndn6j2tFY4yjHTZ87jle3mV2e3HrDRItHf+VSqg/dBJQYUo+JiuZjrrmNzywelwYcN33uiqHBasJ/Xl6JjLFL0FhN8EPXHLrvYxfSYqO0WrXAzcqcOk5bmY7bg3b/6Etbjxc925UsncuxPYcLvESdFAG6oagTurqOB+N4U4kkP1Vze2ZxF7Dh4i+RuMipuR3I9fD7X7KmqAlVsC76/Lv/ZWsf+dQAxQRegl0EWSr+NpoDxkmVena3Av8jwrF4XTNZJajdsP07E5vuXKYrseUL9UWwZ9m+KgrqePryYR858/5lyEPPpQa9FwtkZNmeG+fk6vlN8Z8ZLwAzDwbUYqdMV7QR6sfqvP6tTTdrw4iN7h1PFnlbJ3/VJZAdYJK2TpQEZLG70s4nWFY4v3c+qUOhdqEPSS3fyXCrqdhq60lMDczTo7xDDPt2DoZ03buOKSxDvYHaoVjcO8+0dJrRlUFkiE01JYu40xGVnZn18EpX17HN+RN2mQU9Q/P8UnXB5wp/QgEohwNRD/N80lMAVIpRX9qXbs3GM3BIPQD2xmdHvJ5mg6zdWkMw0eRazKsiKzTPkx/njM4g06KB7MW5t3mOl2y8IDvyVC+lnzxTlTWWS41eIuvJctiRAzBcO4vd+azZ0+YC+Oq/LH2+Qk1ZZjYb+lHFdLzp69vwBoi3Yxqj7W+/4K5X4czpvV2RSs8jomDjQ6pbsRWOMd4Ymwl+1YJ9z34Ap+X7vrkEh1y9/vtnTe9R3M1aotP6qgVulsOYzvE7tfDUzi5yRC/L7vjQ+TNG1Vd/LOXLh31kQuWU46n9jg2D3nwQx0z2njL/Z4YsdheWkkPUxtDK/kqE3QLYV6Anmn+RQ7uYzDap4p+vXqr0TiK/vu2ir20+8sxz/zDueeb2i5ZuPvIM3bNx8ayDLnwYwAEXP7hywVQtJbtPWPHLRUeMTEtGf4JvAXWWoXU/AvZ7W3t2x9A1Fy/BaSt1Qp+5eOVpS2ZdfM15M3ssBcZWkq09fqcFDccDlA5Zz2fnNDWcA5TJzit2B0LhKwPuHzl/4KAr13AMsfeKoQ2bvaVhrwPUi9498qkBVsX7ik2SGfjLM74RAFfjgIvXQD3nD7h8DQDvHQCvHumCt8QPgCsKAKh2weRLsooB5F6vljZOei8m0DIKCcHxhfLfGWjui5DaoLz4mUrAuJ03xWD3QinnkC4uEp1vG6a1uMfHlNgA8qTvXRIExb70hYJstrXzdrS4Q33aFQxFP+HUI6JgUEu8RQDW/aImx9cqeKhIOxk0Y+8p85/aeqN1cbc36H10M+ACFk+rT7tYWANgc5oZE7ua5M2vdCe5pg+8Ob15FkN2Nb0XLWp3RD/87FzWJ4HKkjXz5KBQuw3WGwiesbu1wVfdzNBurbiRtl1c69sInno52PObo/1sQur5MAfd9Yytt4e61SqYu87T3MXE8fykpEMA5U1L5HUuFVcyA+1lpqZ35ix+uA2OOCDXTqWukrs1H0YikjYDUV1CANAN6AYHXfsuXbcUwHBt6nGYJFdJO9ke7zM3hB56uPigxIPHagJgYjliw8y9eS1vbw1aueVyaCd9sGH6z9x+0c1fWI8PpKP/dvtFt3xxPf4cmrzzn669EBc/+MsFWDzroI8tnsFT/LfP2eO4m09Y8cuVIzS3Axj1CX7omkNnLTnk6vVfw4n7np0dWf/YAzjtwh5De+jvbnsAhxy7b9qz7+8fgiW3/d3QWeM+w09gYUP7/pet3TEMw4h/e03yfZmwYjXzu7aU2yCf2VmkEVtvX19rth8z4QwCjz87uhniI3tdvUPfq4ksvzn7ohNw0c2bzZ5jLvpvuOiWTeIbdNqyuf+Mt8+aCmD626XIxsWzjvvnix/8ZULzIyOjPMFPP+v7dBaAoWsaB4bW/QiH4GLnZvHfDWV9Bu2nv20/4Efm5P333z9uP/LIIyPe6m1J24YteFQhcStZcgbZgVxhzvgjyP5e6bkBhnFsrGJ0Yazvsie4h86fIYb2brD53fl8QJLUwSxC04fE2Jo4Co7pbqAzfYHkzFpOCoiLXy8M9hQS/paLq+s7p9GWGry1IDouqwH6abHvfLDQ3BtoHk9VgKs15bfSuxsBGATfXr9HE12WU87grVg/01Qxmo8FuINrRipcAKmAT0gLSEhC+s0bGCRB9qbREf1LGxyg9ktEk7xx0ffO7T1l3pPKDK8eI82RaXgIXSwQ+eC0SxiTccB65vuUZVU3OoDICa9nNS2VpPZba33PrOnwlkWRpBvzNHFEADol08swMx13n3rCW4u4JvNS71BRf6VDQT3qM6YIqwPI3iaEIm3LnpBVa+F+RPZBQuoBgfWKp7NHk//h6u6qopzJuvo48AGJQSAE6wYU2m1IFAxNJY5rmqibr4SLMSCtl4WizoCva98jdbsRtUhwAIZF18jDyeh2cnmt9GumeOzm4Qv282W/A0mXYNRuhe0IrdAuKTJ9QN4jzYG7jTZTr0PNs8iBgL+98rTvLg+EgP/zkUM+cdxfHkcf+s7Q1cu/duDuGFq28uHT/st/OWiPPUbY52y8vOjXP/YAHjjtQvo+38nQNYfu6x5bSYtnYv1jDwD7tU544LH1gMz6dlJ/+umnR7BZu+++u/2zMaO3nekawXKRJwTIxhm5TC1vZ33yWVXk8OinBw6+ajUg/Dak8yXTxcSwt/j7yKcG3vVXq1GRM+8ZV0uRpIZHv9F56eWcTRTBwkp+5/DgwoGDrlwjb5FxteNJl5y8W0HUsA6qdo6vmTMvYqYNU/2/fiQJkXzGkzrTbXu+JkA/zpbXIm7a6dlqe6P3n/00yNzvsxgoK6Lic2mjca53KQFuPGRjgVK1ZlTIrZCQh3QNo42qdXnCJnstYbQ1KsFYq+5LSyJzCzwr2EnXZRNtRvjqADhytnxj8bqtZUGc5q1a3q5eDR0UL0p6uMsxpywXYoCrqVeTG2jRmRmGVwIQz7jQ1d7sAr14bED5n0gFWF1f65qAz7XjIDWZleTBFOBnU0v3ezv3F6k2kKr32ZbT+PDaQVnEC6XxLLOXfbK6hjDN9GYE65AQn2AhIJIVW9DuyKZze0XI6pDPSm9xrSFzfG5bWR3/tkpyS1ZTOOo4BzVUwbxNbI+HZlmsDK1rR303axNBJ9zCdrKPS15I99irZ++gaW1SsJv1SqOwXUZkab0UnGRZcGyHcJhc1k9v/Mi3VvzjHrPc8yt+uegNTz/99MZblz1wwts//ke/vOTUxbMOmnXiH223Db4xc0UZrwl+5mIiE4ox/awLTzubDe3j1KCdUR751AA4rr16Eezal3GTW9YvnT3t+F07J+xoiHLKyrY9xFi2GHPF9TiK5czZZdzuvr5h8OhpzZDRb2zkN4WOmiqHdqkkNH++cMnhc5jlZuPilW978MGLP3bQHrMufnDlAgAHvO3MI44AsOCcEy5cNLQRR4yMrn5CxcE/8Nh6trj3kEN+f99eu0dLsqQvIoLgeunh+XjUw8L+B8BFTSXX4LOzAJAXLzyLCgQC1ZITghE8o3Z2lT/or1YDKF4Q5CFKAgP3ek77ZLEmKbGPQdI2xM719MLXwpa2p6pSt3ilY8zD2yi237lETRMPxV5TchtTobrXgVV8lHaS0tRA1QyxLhs81liqsx7eKtuj01wLwKeGyQNhIJJdIpkAYChujp0+9+syuzu7BhNiIqnBcQtZjemYwdckF44jRTXwXEXWWhuSJ7pqIgClcxknjCkS2WTVpa5VzJEtIJ6NybvU4n6L43m7cFk/JtAcgWNGTytZT/KxxrlzhlNcOznxO1PsbhzloruchLoZ77lanbMpszKkAnxuLCaYO4NhihONk13O35RdQl4ns02kqJ2axdJFzHDMKxWfu87MFAamGZZVa2g9HPktYITd6/0VyOsytzi5T6lCqYsdVH2iAJUABIXyPURvlxvTsUG2us3n3rX5ZkSblIx0uVBMFe9ApdHREcmbLu8dPz1z8UaymUxt3gj0tP9pYfsWy9Bwcgnr6BwrjFenHOI3+sOZwVQ44MlVH/vW731pwdSpKx/ErIOu/faCM4GH120CRtgAD7S8VSeI/GjdUPpj2w73r055+IIZ77x07Shlen34ghkHXrHmwCvWHHTlmp0xdu70+5Zdd9hHxrsV4yxPbLl+580W8+JOc3V31bYyvoyg+H3OHe1L7ICwzx3/G9UL/XTLDXtPmffq8dMcSxF6zYfXbQKATeseBoCpMz54wM2LFm8E8O2VNx/wwRkjNtOPE4IfuubQfc/ezzgTDK37EXDah2YC+LNjDzn7bGNxx/rHGm71oyoNSlq7x6LwTBLc541k7oo1iL1JCTVbVxVYyhwQEjamyy9hVO16AI9+euDgi+7x/96N9UQDf89ctRkGzAzAmYoiYvcDr1gjGWljugh2jRFXl3xhLAQ1Dsojy+ILymh5AhcgGLIaMm1qhL0pSa2PVxQ3qfzRFCZzbhADPjMJ5YFOugbn9gazEs8M57oGt5b7xliwpkTm24qpY61rHsy5Widxgdo0rFKTM6NdJqwNYrl0AGoSM2dhADrLJNVVqBqIB4z82hQyroXgRRFC4tip7eRwNetkJ31n+8A4zaUh6tv2+8xNEJQDCev2pWoxeS+KSDRLJMojsolqICZqGY6GbrYalv0C6E2BOgbOmUFp34i6C4Dqmg8V0xbW6y/z+5wDoN742axDVCUFxFGrTesRd1WnYjGOTnC88QH0TjzvuJeC9cWIHStm4ZgNj4lvyTWccxPsVhwfVS5WWyNvscZhNvvZO8dOmntOPhUA89dypbXeYDC/kENAdD3RdLF81iSTrTiQVMLDpjZ8tF0Z9mnEFT5zTI6ORm0sTnqX2s70q51nNvJRUJhPRDwEoySAywlxs8vLByU1+gc33vyF9cCV19LCj533hwTgwRuXf3E9cCWUye5LF3/+oD32AMfBHwFgwcoV6/Y4aI8LgRNWjKQr/ThN8NP/7NhDzj77m6sWzxQnu7+77YFDrv7aTEDt8bMWfEiJbmYtwWkrXz0hcgdcvObBhTN6xqrx7D5K1+XZfZQqfxXK7Glzx8X6vvdOi91feTrXV3T1qefxRr3+MgD1+svjzrBp0bg06UWE0fxom+SZJYm1QTzNj6XcvmGQ3e52ohyyv77tops3H/nxvz8VulQTipv7Tq3JLTl8DiP4xW2Km0U3AyNJccMyXjb46Wd9n962IEGy01aqQz2AmYtpJdwstyQeG0Mau2DIJER4xRe3mjvbJVvFEl1lMnoJpmfgTlQIKYOaqTTSrK5lCfvopwfeddE9paGEIMlM47NFrG2OOvbnLgWuUSaK5c+hgoTOQuCKsaWFvAZenpuhFE3s9l6qygPoSF4ZSZIRTFXsgEwkXvTewHqt1qIROZcr70yquYAzqBSmV4QERve3OTTgYWykckIjTM4S3VgkUWvOHtEfAABuHRqcPW3unRtvBlDGtK2mhuiHrK65vNMhZd1wUGs6EjhO26nr4KAf4p8/l9K/OufbJnYTBmYwtJFoHXdmuMK6cCfil1TeRrK5nGCpMTwtzW18r8pyZhheyRmOM3f3zHBO4oVubedsMo/O8/YsPlR10ynp9tPVqa6KaQvrdZdJ+W4VC9Q/uQQAo/koYdNVADIbfArPsA/YmOrjdduhGlRoP8p4TAWI3z1Jkuu8iRV0VHdXWsrbdD+M4/UueWwEMbfLqAASc5FNOaNjjLQ6B9XYZz2QqzE1Gw1fCGCVFR+CuV0dyDzUS5OMWfxaZDjJByaQ+8bGwUA4ZprkiXdOXRSM541B6ahCk7smFnOUYXd7Vuy1qAaIxeI46fGJNQ+N9//6NqG4ieb5Z26/aNkX1+P9ILglh8857bsLlxx+5SHLln9r+sbRprhhGaMJXuPhM2l40r/sY7u+MEU8g3h2lQfwrlHD7lDHOk1Q25edQ96cW0lZLT+hpGzOQBNRimkLX7xAve5SO52zhT5s/tzoN+0lpOqugkteC6PXzzzHjz2IZ+E88RMfx7/h2ItOoJziZvZFc3DR8rjnyZ+/EUf85p7A1FGnuGGZUF70E0Qs2IXdtmgYPQq4nmcBCOokH4yp1jNLTE0AyFFdckk+AftfspYJZw7+H/f4OgAo/6OKNbCpWQl2lBVSW4FMi9CEUpZdwzviqV386Ryg0fKNJDeiijCcNs26ASAD7rGkwpUEuAEqhPCD4llJf2B2Wg7auK3JXhngwl6i7cVKJiMOqed8YX69LtjFQVdOiz9qeHXKRGsW+BFdciW1ARaAW7pu6ZFTj799w2BBZM8tTLcEomB8yAVSiDmTL+FsAHQ0tzObTbENWB8pYBvm+Xg/zdSrJmGM5JaFt5SxmQ9yuibFa2lUs3QcO4IxC02bjbWqVtkHVZaGST6S1cBA3vg8yNhsrf+8kNXUEhAfKO6kqgJQTN2m31y97lIA6HaV98ZAfK+aHAPQWXvv3/rJVMOGK13btT779VnUh4X7UTKAbgaZq/XPMh5S4lsCuRRSKNS2dyM9FIr+9o0uJZBFu/zQBXYnpU1TH6YcCeRa3xxtveB4m5HZgvsCVBteJnsWW7w9CY8TZSBatsRebt7cYDUjWtwZxR1vN0KJXQt/x0pgOiEOPov1reS6Kt1pauO+vu6wOfPuXR7wf6a+Y3DVVe/E8b/454u/tGgqsHHNtx4+4e1vP2iPPUZBQd+f4CeySOL24VHMnvjwBdHY3x66fRkBYTviXZtuHtlq954y76cTOItMmQLZCW02VoeyHHX7sZVi6rn1hiv18o2ViqG42R6pN1wRq4g6gPGF9eNCbduX7ZE37velhb9635V7/PaKXy6aCp3fz5n1y0VnLp510HHnzNpFbPATV3qh8HyN5prFFPlmJS2jbSgcZ5Hh1DI23pf3UCF0ZUXFq9r0xSm7tc9yIACIkeDO7pWIdjGCNXB8agwvXVnzf9CVazKgbArEE2RpbBLPxJtuk77JEZdtTJoUkJvbE3Qxd1aU3AbZJUR1LUuX8xqN7AISAgCAunJdQ4qnLu7p1nwPpUPypBV4zdZ0i77Al8ui54sitZ8igjemRL6N4doBGA4C8EqXwtxjk0wWIXQMTa/0A+VmRef2mixMtIVzhTjJF7GdMJAdakfPOGVz4Kgscmn4RoO93fkyFoAJu9fdleIYrzeRqnAOQLQfs3CqGK0mZAljGuzOZNwcLHaPfvVV8qKnUAMopp5Xr7sM3eFthirzUKsq1cD0RPC8YQhuDaNh1OFbWC+3xn74vohOJalaiQiIN24bxpUrp55lvfWJgNW55GCffo0lvmewuyqEHIxPRp7tUKjuvHE1Yi395meXwIbYgDG3xfjy3IPh9bRkl7Elod2wqKbZxmjzjuw3zVrTIyMFS9WA9W1wj/xQC6Y3ELxV6fW04jcCjFoIsUkAACAASURBVJB/PaL8+o5H3zD4+Yu/cNxoU9yw9Cf4V6M88qmBXdjWfsp3lv/1e+d89P6JkjR22bqlx0w7/m8fHxkQv9fkeT/bOqGxe/XyPOGrDNaPigKJfeDrdZeNRuU9pR66Iv3BS3b2wx8rZG+56/sycWTBd5cvPnzOvKVz7//u77736DcuGH2KG5b+BN8UG9CYGd1BJt49M0xR60RAgb6a3tl2XrdpqvSkslujYRPiNanC99YqWW1pLmOcbzTjJT+cstqn5uKWILjLtZr8ciQjpyMHoChzHE/NwjEO3trsJcCd/bQFaoS2q3zqAq22Co4jy3mIR3O7Pas2ve1clpoi2t1hPPAFwRtrOkPtD08XZk1Oa11Y02DqhVRtphjw8AhQQCNZZU1uWaN1kP89HDvJxzB3QeHGuG7M9gbQWyM9vC3vNcEMYjQ846VeOo+WNIu4hGjanPDOFZ28LXpIsydlOWAikzzXQwb1BhMHH9F8XQOgUBfTFopxnb3o6/BSCL5GVUtJREioCN7i76KXod0bNGgGMLchIvuweRGgvjY+N+y2A7Rlv+qs2G+F8lErKoGOPcvo4JwSdCTljUsfGMbQSdvHrvX6wSOF42kY+EQKks4q4r784kiRIAmsB9U/WhcLG1sQPxGx1Oxpc29ZvzQQahL+CaWLSNtRbdbuv9gp9tXuqSTIFCC2WM6Clz+2/A9C9G1Il7ApMH781LMOGcXNhYsWn3nEgqnfXnnzAR98cIRn+v4E/2qU/S9b+/AFuzKIP+27y5ccPufjfz/qIJ6n9js03v2YFr32LiBlykq+c4hw1Gy4MrHPjrfU6y/nJYJ/6zkyx++Ewplkx8uXfieQH9w4+MX1wJXXYuGZ5/0hkChursNCANhz5mFn/o/b3jdnj9GnuGFxPbmKdyIZ8WxyHzrl9vhnxhAHwDrYm0U6S/DZ+lmAJ5ORebHBdycViD7wxgeUF8/IoXnEwMHgfmWz55bIstCqEDRJnTQ1mIjvUJg7cXjkUwMHXrFGEUha90YHe/tn26PeaZYzWIuyFlM4Lq2O2/FCcmpBUBY89qgvimCblGdpk8rbfgOwPO2AL+jG9805475lACYVAPCakgCUOQq3Em3wkzziWR0T3e6BTgEkBnuKYe6F1matt3ZPrOQ3ygCNAFaAnoVlqGGeoDzekwpiIrBJ3gF4y27zn9p6Yyn9EqF8B9Ef3lp2HfIw9/T8ClfmCN7mc+PC5Ci5y/XM7yJqdu6QRvJWC69tVDoIRQdQ5gSBddboXivm7sqfsRjlKNxWK7z0gbrDxfTzFbtzHHwlT8LZR2JwsCD4IN52XFVGOx+hvLh1mEP68Oy7YTs76QAKACiKYvr5yTAPoCiiU4npFqvw8XKuSTULjUKRndylzgMIzkUOHHIuUA2A2QN5O+5Rkvn0K/z/qAEQBQb0AQFAHQjA704+9adbrg9EzEanlIsA0A0JrHeDwPSqRU5XS1JbTUzHnybWdqirhs3EyMWOnjoXwIqhQc45+zwTFhjXDhZSlkTxpjDbFeW2c6sDkJMzlZ4MjVjMlNQgl9R+PS7XgjmXNLRn8eFzOF0sgMv2S/k+RkQmWja5vvRldOXUe5d/+TCZ4/uyY2JdsqteDPC9CowzYCimnz++DXhJqYeuGGPD/AjKT7dcv/eU+Y8/u2S8G7KTiVDQj7n0J/imtE3sKpo9zeD4LAG8npFlkWdzXk2eHXprAtCd5KH2eLH36DLPenhGB/u8EXzMQEUna3+tKq1CCc4miRcSMENPFtU3GYiEVOCMTU3IpbxZ4no1/ttA8xRu66AWdO/TttMrSTY5AIa2j+stCma744ZxDQzZbU9kqoVIei9qAFOfJJCuHYDgs+7sGOBUqBXVdrC1pTXiXWdPm/v1DYORJZ7baX3vRfIFvlbFa395iMp+T1BG+uhg31MoM6k6ux9pNDI6dxbB2+RvPjrJsxXf6qmIbBS7a5O3a1kbC5dREETq9RieDiR7PCPmwlC1Z57wIWOjq+25+b3aAnUFwO9zLnPPCWoXBG/8ABBxon3TSOqxTHZ8k0UcIqym4BTvLTTf6CBrnue3znt9dwLU7y+LpN94lbwahTErRJd7genmrpPlnjswxMZkmgTSoAnS+9CDhBATw5vfgPQoyQJW3rX3lPlPbLk+EFkrOCduZ6gdt23aBblHflzKJ88lQ1JfJgBtwX1N+PD0uSvWDyJ96jQ9PNJZQnzgsk9og8kORvtjJfaK7boslgIZF32mM4gbrWrjl2TJ4XPm37ucy4zx+rc/wb965Z2Xrn34goE+/3xfGqKe8Bh3OP7yhdnl6g1Xinp/ZxBxufcecJGHJzxx9QjU3F1VlDMnOIHgy5QPT59769DgzmtJjrP7uEh/gn8x6ZnyuEcEvOtRQCLgIzzk9SABQGc4ACi7ATncj3/WZYbdnXrLwyD+tN+70CpmgUQsH6RAavn+l6196PwBADzN5/Z4oYymkDClxfRoLFqzN9BZL/og/HepKc5g7nioTnUyslTWer2i2OC9vYN4PfChmP198eFzzrx/WTwkrrZqonvBmOgmxVBnUxW3flgwBwAUXip5TQEAtw4NHj1t7jc2DgIoQJr/nGK71NgvvSdajyYIhXdikreor+OSHqJwVOQpw2JgvIfPQp8N6VsE6y5zj7cQJQuRjy0vy5kclW484dmzOtOy5DchzzK1IVQZgrdZ3YJyPNRsS+40208hr9Yka2985SkAoLoqpp6nbHTDgr+5MV2D41PbDA62ecGC2uC7BkPXCtadqUGgdgEAUflknQOykHfVBPCD4PKcaC4Wc67+l0skaubNZ+c3ibBpEUyqRpHIi2cT6xn2+6q7itmE+HUhV0AVbDwkAmohmLOGeVYwUJe3exjptRsitI23yGM7sgOwukGSzlk0TILd2f+GveL5CZXyqjp+0pW+6CEmkVMvetvlVtHVUEla3ZN32dPOgtRVc9AG6NjGnnaBbZXhALlofR/jlUp/gm/Ktp7xNoSHPn8oe42M6Adn1fvmSkpyIvp/G+TmYoCcocfp0TxNwCF/iRKYAFCQ8DydWx2gFLkAgFA45r3P6pOvAFnvOWeV5HG+4fdHPjXOFGskfSHoVB1bni0mTG1EjpcUQotBTbUaBeeMpYDy2uw0ZFlsI0ewfbG5wHD81Bfp5szjUh1gkIel865btm7pUVOPv3VokJywyZYc+ux5ek49UDgqDedudOoBOJNsKtkx55Y6zbMnXen9npPn/fy5m5BU9M6OiB6ZYZ1Tipt0iCWp6I1tiDXzLk6BMhlrNp4oLg52291mLo+zuFXRR7c7mVwTy0rG0hpC9mi5cNUFQEEHunnMxdTz6vWXC3DvVlmoW7cLmAneLkGyX0gfVsGU5ydnTozC5UvVvXM93tyOjabj/YGyFLF27vfelq9//FeAvlSFB+D3OUfS2YmrHV+olIbx8t7o/+VTkvTS8kWI1ZLaSiTdESvqxZ+uho6uOCitXU7Oc+Ji1mbZjDOrfGGMsj3mkkHsEr1Rb7zq4jQcfWYLR9ELz0af2gk7bopaPt/J/WGXt63hy8596c9GmKsMRko3lUn2RcytJK2yYyn9Cb4vguP7uvodkFuHBj88fe7XxyQt7J6T5z05mhQ3iV82tL/b4y+shG8LM8PvwlKvv5wD/8KTX9q+E7urOPZhp9bV3zY0eOz0ucvWLx3vhuy4cNTuvPFQ1Pcn+JZQ+s81NPCi8Gkt/nRNaLW7GflMLGyWdkGi2uSAZZqx/krkhLAy/gmoV108hSGvS0BfF/A5LyO16gGco8hI38js0mZlseJyQK9UKvEUXrkn7G4he+pRSrg/usDY1bBdMjsh3qGkYwA4t2xsknWIUw0kmUbmengCIqtlzoFjaX1ZOl52DpvgnEnkBn+y9OhpxwO4Y8Mg3wiv/SW4TtF5YbTuPak22npO/fWFd3tNnveL526CMo2owtxlBDWuiL9QrVKE8ojPSA57Exops7uLCnbAJH2xXRa3Df5isY5yKeVrG8EH2dn2W5JLaAGTKJYHUr3uUll82Liy2kD2ELLQuOFu2qYctWu96aaI5Ml1lSAWrYcEmPImbQxUY28rFNLjQhts8KlF8E4pY0uD+L05d5ILm67y+5yb0QFJP4RsZ2bKke1udTfpeBZtPPg3WLU8i/Uz866opTx/xJL2jSiG36bH1zHuclAtvhMlgYPxv7NoWF3zuHn8wmr0XQCAY6cLyw349WFDh1mCVqZfiQx/t3aBegJmKNwO56gP0PROqUKnp7S1BT10QI0BpfWwGb6tMxgD2SGWsr70ZcLLad9dft1hYxSacsv6pZzRcpTqn+D0tH3pyygJz+7j3YpXJEvGKUCOpY/gX75IhsRs/WYMPo5kmcz7ZE2nzmnUWrqJTb3Wyq16gH8UplvGXOG+NelisyrbOw1A4nARy97TDAbkRjdWfRbocakgJjq5BcGUjcYk1G6jUyKhpzXPW/zmNM8Kx85l9CGCCWWfpJrlSLyCAHQKauOu0jwUl0fCGKUDvFNAz2DCQCyG3TWpMbK9SNdq2C2IdQqFhAgCQOEz5wBv0Dwlu13qMcXxDsBbdpv/8+duhPLYWJjuXaFpYRPFTeZJp0Q31gZv2GzkTiQ9TPTek/CzbobgG+4fOedROivqAIJx/ooctLwdGW8avzH3SXYWYstBJKDcWwRvIGwVstA4/mVEHig1Pl02Q38S+1XV5bFf5VLVHSfb+3W2vDgJ6lMsTeV1ClpDzAtlz7V6JKc8uOzQV5aAYnfNZEy2tXbQESF7eexL6PL/DQrPtRLOPEod21I4vqyxmqe23vi7k0/92ZYbGA8XrFkJBIDHCgewdYNL41ol6HukhDYJpiujbeqhKN7kv442eOtel6WbUi/ayrjH6iUygO7NtaI6wLoS2+HeVOO0x1HTZi+3E7E75WNtzKQ/wfelLyMjt6xfevTU47++cSzs8SMo/QSjDSmP/Wp120n8gS+P+Yo9VN81f3zaNJGE53gAfc7aF5fxxe4s/Qm+t1gLevIZt+tjSsUorpHtClri3wiAC+r6as8FoAC0URUZ4prgXfAekKi5uuOhlLd14cDkOWZV6HOjkCW6cdZIpi19+IIZB16xRiGvxb9Obz8taxWJycne/Gf94SObrOkJZGy40RjvzM0rXmATe6gdgMoAhWGNLSo7AXnIWbx64ejLh32Etz/+vWUAFabniXrYfH1sEkzJ1KNQXg7oJVKTo6qATOFKEL2LlQclACqMC0LE8U5S3KanE3J9iPGZj7+CyH1GNNt0lU9EN8YGn3RFhonWWewef0PTFm4MmD1VQAamt63v8RK1+dOCe1/mlRgcH6/OHc3Q3GmPI/KRVhJQl8F6NdLbGDZ2WwkpWK6cLXN5tWwuujXfWrX8BLmudwCKD1xf3X4yl6y+cSoAF02+Fprx1UvLGRyLWZodVhB5sd9b/hyJ6NhGJ0dJYRSm3xpCsaSMEPFHIafe8vxrPxSiheTBFvUIUJf7nz93U01hr8nzHn/2esR32XRAarL55ImyQ1VMtdHa8LnqRe+C+O7gzo03H8kktesHAZTeIdiS3LB0oRAHr7m4RDMoE21mswdgdK76kjYa3vIZMp/JFDVgYb1uc3Rc45OMMZT+BN+XXUq+fNhHzrhvmR/TlyjJ7RsGZ0+b+81NN4/P5fuynVIefVPcrm47KVsr9BKe3atbTwRQHnUjz/GvWuHcMzzHj57wOzWql9iFpT/BN8XblbjFdAqqyZq6zQrQBZJYdrO0E/dv0sB065IegRRg4uA9IPQ4VekBhNIzauc/q04BoOjQgwt7pyvgHHEaRK4mav6tGQ0HKFFuDbzz0maMXFwRW86c0HWxWk78WnbIGw926TBVZpBRd1jrsuzNmVvUIikL3TYxLUM+DRsmdcsnqPWdHZCvf98c3m570sqDyJmIW0H4QKS5NYvuhL6scS6/dWs+tOqQmDBD2HIcQXls1IeaSiXwibXprTutPKkxMt2RcWyGgnufI3iLGKxeKsJ3xe6t32iSz3hkIw5JhttctIYeEfBSAzFt89TzUu8xK3upL469bkhR2smHwrLPWkxfVWpxZ6CXfqlbC/K+42So7j0BdADDVca/6l3aDsS2ZVdTtXQuq5KqFSeUR91o77z6+ikW3Ik/TdCodKFzMg3jPZNKHQoGtVsveu+LqeeGzZ+TBDMmbiJ1eDAhCT4goXApYJVAZJK3RtGe9YBwS8dLiEleQhvY1B51UnAaqmPdRxxlIRsSz5L/Sa3tKGoaNxrH2E6nrYxj0GJxl1VlP9KNVZu4ypsSIdfCOPOfS95W3BUO+dD32iZtedZoudPs4zBG0p/gd0p5cOGMg65cIxnWlUuLh1174j/g4pcIcOc5HgAHy+2kcv375pxx37J2mrgxlluHBo+cKlnmRkSe2HL9704+9amtN7500e2RFPU+HlJMPU+IWvl7/dZPvnjmlWLqefXGz6I7vMNXLGd/pbrtJMSp/dYTAZ2/d1SqFSekGaAsrD4AQP2tea+k8hGRyGc3SvU/seX6N0+Zv+mZxaNUf19eofQn+KYw2M0s4jZkUo9p4hYAaeGXmWEYZQaB+7J25gWg8WPnyoN3jNo5qyxDdgbZ1aSiYvTQAYBHzh8A8J4v3A3UDF4pqHd/AID3XHM3dLLnZf0Dn5Qp/4DPrIEa6fm6vha6Ol4E2MXBAZevseHmgig4r2tJAHwZOAadVxhMOht7ILPBS4+kPxqmQ9kpJkiysfKuh4E/R+Hm1zdi2bP/IA+CN0xjosLFLrTVsSCrQDCOax4KlNLbfHi6qOi9YaMzPtQJypQuQGzwXK0DULQC1veeMo9n99rGK5MHQJojJnOVh0XwPSCDEz7aVfl4bcN0bZk41VepWLzzbTlv90LwbPBm4F6vu9SSxtfrL/Nv/aTwtUW6QcbuoS6mLazXX4a6QlAvaktXx02p1W2+Mjb4Rl6Zbg02sQP0QtNRHzGDStXygVc7KjHRmzPgXrGkq6lafoJ1jy8+eEN1x8nQF4Gt9VTVyH336m/N11h5BwAd40VflACK6ReEn14DqI+CRBBE53GD+JvgPgVUsFoxSxAMX7vEfOedGfF6T4rdmeQOcRugIGEFRnWUo9u2bie+ellMeaaK6qVasEMsKpWMLs2+j4F6kOs1VnFtHV56wfMPAszYsZqDtid8UmOYSzBJ7bjw20TpT/A7jTx0/gwABy9aDaClcHoxOeRz91RdzzXwHL8t4WmelxoPX5D4a1nt35cxk72nJPD3sy0jHAHPidvHXmRqH7q8J1Nevf4y5msDEB7/PID4Z73hysR9u/NIdcfJcSKP1vpMf+BdOfuro96ManRB/BgIm+FvHdrJ4lMmgvQn+KZYLrkcx2fm+WDMVLEAmfWbDVvnxTMAZoaXPLCGyS54x9g9KGoHwMC97vhOJ/zDuX/87mvuBlCUNaIVvMxWkoyh9ReAcXcvCMAhi1Y/cE5LgX/5GgAPX9Dc/66rVhurORUGuxf8W2SE82HYIaJ57xjcWyO9DXx3DpboXirnBLJ5yHi+phaIUNdZjfFQIGjiHSBasu1DQQ+Jt2lX31a4Hu8E1Nli0QPfRsy3a4je+xHxQ2N/vWqIFHy5vafMe3LrjYhoPsYpS8h7YaoNFo9kRHWqvrDqE00Cu4pPlkOZs7rl8I7gxWC7KvdszxQmjGhVf8D4u64Qp/afXAoAVZUZyEVfUgOo/+USrsG/5RPgpQBUGVDn1yWD2u1vIAwnBE+C4+ty9lerW06gF6p4i1Tl4B5A3UDwycE+hTlztZyVVakKAcBFX23EnY6K6pYTeDta66sVJ6ZL8Hegrl1lli+TOgDQ6QBA2SmmnhueuDpjobf6roaDfQ9/e2rTZXCne1eoGobfxDSumOHORY2jqTY4PhQkZUOmQEpvtHcZbo6eRnxtReO9xcVKKJ0V22395y3hY3R/2ZZ7DeV/NrtDL40InozJPH74W+qG5AFgD1nngPhxsNWOmfQn+IkunAnm4KtWbxdq35Ycsmh1MCORyLFavpf1fWxH4iuWU76z/MuHzfnY/cvGuyETSyxer8bc6C5TO9PFv2yKe5naX7GkafX2k7I0ZGMuMbC+oS+ubjmhPOqmeuXpo3v10bTEP7n1hj1H35e+Lzsm/Qm+KTkPvAPga/ZKFeRtjeWMyNWm7jLKOd7ODwU+y5tiXpB9MNb34UkFgEc/PQDg3VffU5QBqIsO4+YAJWD3eSpSMthdLeKStrUqPADqMAJG+g3ukM/dA8CJ5y0ASePolX1PfdopXpcBd+FlbR29/GzlVeUBlAhQHN+ggBYw4tONGBLuBAVg1uOK5ik/Kz2v+fcu/9J75wA4/b5lUDxt6a4IrjYhuaWB3d5lgKBo/TrXY6cBbHJfsRv5fruqRClMsHs213hYMCT3zvWIadM5w15nzajOefWZTx1kKRHF1p4k6aVAlFncbdbRRBq/jUVeqInIusFHqTdeBSBmN5csrpVJ4Rrt6JZTne+4VqKDwtikYTBUlWsO2jlh68B/Uh3KY75SrTgBACrCCzUFiYMmTtNsEXzUq1Aq0LTBVkbrUFjqeB00RTok5nZ+ELEAv+9VUgy40gGobj+5mHVd/benZfQAHHY/9dyw6arYPcp+b594kbXTPkQwNUJS68Q2FZw6VsPcgaT+IQNEKVYohRLjAmmSav4E/HTLDXtPmbf52SWmhqh24vqS/sqpe0qtr4a5QYotiY+FgNnTlI6epNcbz80WFsydqm7EG2wTwVPGJ9IsZq9oNRDpXPNdtTU4Z99OpSwdK+lP8BNReGp/12fZ3L6TIenxlfn3Lgdw3WFzeI7vyyiJQPONnwWy2YWn9nrjZ9U1bxxs58WRIxxxMI7i9zk3PHH1iFS1s1vi+7ID0p/gm+LNglBi0xlkO3V0Lw2CLxOOj57w0TE+nkvekUH/LCSaAK5TLM++pIfOn3Ho5+4BUEzicPPgDXr2RTJXN+mixAiYMK93ZFOuxeD4+FtVnmNe2biuhcHbXIYt7rIeN6DZeRIbPxcrQuMSUEWCdKxPFjVHkhM6Nw8K0PfGvOhMevbYBjHRtcz8gaTQ/HuXX3fYnDPuWwYFWmLs9yTYnSniLYRQ73PriGx/y5zHnq8q0e2xEnPIEoQFhfJyyLB3+SAk3k60HaZNatW2ueAskPKusJ7zzl4/sdQZ1C7VKk+chX2hC0RUrZndG8R22iS/z7n1ussASdNuh1S9XvdbM3llwHpE8BmTHRfm5+qFj91GY5MC6Ay1mRoYuFd1edRN1W0nibGcXeUZsmszBEMP1wCotuosZWq3RDdxk4cFly8pVpv0P4zdX8s1JLMwv+bOq/6nMuqBogBQzv5KvfZjgNrdWbgzN1xZTFuY/M5dstyr5qPn+slovZQckBU5RTmzqlZFog7LbKinpjEWHdJzMkQPwFMRkBIKaNhLGn4OyAejubNeLW6IwmWp5o4Ng8dMO/62ocH4zeNr18ZNQgknsvp7xLVHQ7s5K6JzarU2nZIrdJoN7qUeWHL4nNO+uzzuHOMIeJb+BN8S1SMB8kyYKbYuXC3TOcewpWleSGNKLzO90d7r3O+tWp5FNMwFAXj4vOTj9p4v3C1OZ0UA0OnIuJDJVb+BcY9zJAp5idwjIEVK2bkwu0udvFnBbtcQrKJ3sbFmorWBc0DmviODWB2nLK2NzQ/RGOsZYW3etizJbJ5wNnPtCi4WgEtubgu+KyZ5VbnLd8c2yb7/Xk0nPFvzi+FNm0NOrxGdiWC+HXb2scZWT1ni3sxK4qJjmwMQiDY/u+R3J5/6xJbrI39G9oV12YzubbIZ4xkaZ/csN+eLMNHWNtmratRtKtIGDWqccWGeOszUK0tI6wGn32M7qQfTMGhvdngma60BQ8jme1MDT9vlUTcBoOe7oo3nWTwq9nmm5yv2mOBlg+yTi4dk3ACQRyuzZDT2WCc7nhE6qf2kBh5Zq03yANxvTJKrvPa1ADCpTFcUJhxetXSlD9rep8gpUs04kaZQbReudXcVz/H27uyqVPmVhWSXebc0102a4J0T9X6MDWNL/BNbrs/IlNJZqYbmPGeeZ4ghdmaJwl1+69AgZ5bbFl1MvGcbvSi5ZbWRNsiNzFlxbrbvSjbc3TYXJo3xbrlse7aTevg8jqL0J/hxE+u4fsii1c7M9315hbL48DkAdl6HO+YP2WFym/ElsRkvKY+6sVp+AlXjYBTYMSk+dAOAeu1fvEiZet2lxbSF0NDBVy7NOX4nkQ9Pn3vr0OAYp2LbBaQ/wTeFVfS1xeIayRZUnQ4ljmUcr5heOWU7BYBQOigzzEPn9aCVfffV9wCyTHU+WF8263SWAj80gA+A1YwhLhiz9C1SyhLFZPibgQcFMgQ1XuPfuJgtn7nyxNgreeESs4p8XINTHyCCYmIB2QbNI18yJ4Bu2OgaFDe2A/MQLQegcOQdXXfYRz7KU7sDkhaOoYnE91hPuqh5j9rWRuXQm7ROefzbUXcr1hDMnjb3zo03q/6fkGwBxOikprQzKjnUKJBwob18jIXTZhog6LxjX6qYTRUtzbw8s1YsXCMfjC0Q2WnsuT0Z2q2jnNXJx+tGBtlYoIHgrZo9fr+tL5s12KQCepX4n/LRMmoXkP1CajkFImGxNZcQ9UGuzzHTiGgWIpOVOMcZRy8eB8HLp5Rd7ezgkfFE8owmeQB4bVnO/mr9dwtSgXgVbphPy5T6J5cUv/eXCcebQyCCcTLN9DSinCdhwDWQlp3qq2oVVNmDFGGRkK13pVDcsD9d7gEn0Fy+Mz1QqQ5Xg93Br4AUrs1ZGimaPlONeiDInmzuWvOd0LHgdThYjZUONDnLDKVcF5ip6Cl9JhNAf0npEaiYHovRB46V9Cf4URSOcANw8KLVlp0NvQZBX/qys0i98api6rn1Ty4Z74YkKWd/pbr9ZPr3HeeyncgixviRw/Eyx3dXRWVPZIX7/wAAIABJREFUoVmDJya4v21o8Njpc1f0uW62U/oTfFPIOMc19ljre2ShgTG6805iTtkLBg6+arWq3BVqN5zjtrHi6037avG0WbsS2VVyDvfRoHpNuD/iokY2TqQYPF2zWzYbaZgCGkEnAYopBSKQI5PGwbVUCyklROaUJ8VefP3Tci0kRLDnkOB7ZMNI14ETZ8dm/BufmwE53yxAiv0sbkjOXvaZmnbynXqHST4gGvUZ3GrQEz/e0kCiRg9Qtm1UGsqCE5U7ZWdm3V2JZHoPWapWGMAdqswGH3oheBhQniWM0UHTxt9xD1/QIvgI3K3dXdplagiaT9Yi+MifY03UDZ8oxlnDAVDr+wscMifPVQB9gxgl3k0tEI/Li05ED5E1zEqb+bGxM0QgC+iNuIY/J1viS0NcQyT3y0W6ZoiIjZ4AV//k0uL3Pk115fijXatShOwXxvxK95bgT5BLDgF8HTO7EyL9kTri6WWNb4fekP3PUtw0b1ya4qDGdZbSUxUcgI4nKI63xDUEcpSq1WBRIOHzmPrFtiR1W09xLjsqL7VRuMRENeZz27vCrB65zXQQuRKCGt/zsUV3/qWL9GX75eELZhx81erxbsWrTtj03pfRlmLawgkF318lUm/8bE/ugR2vsJejRt1dNV5kxi8ud2wYPHZ6P2/s9kkfwTdFDCWGrCZo2FsjiyuUU5Z96ck7ttw/egFnX2VwIOvTzGgka04DxaJN2pI/mKSo6GW70YwyaptrEb/E053NT2oM/PHS7SwLsR5rs2+Qf9jylewhAL6mykBz6z/fMFKQvYTWmRnGDETgO3VOYwVNoljvwHFx1rldfpG2nV45mPbHIDcu0zGm0mhiBztKmwqtPa+n1sEW8Or5zxQfRb5t7ctkzja6CoKicmdtg+mQQwMbCOKsczukZbOpNEjdHBL/+To718B6CqGYtjDxyTPIZjydsdkoi471oo+wO0P8SG2IGWIqG4Fm+GScdpZF8MxNC6BbUzewz7wg+OeTFz0FouEAiN975iovfvhk4ZhgsJoATPnSWtu1Wz4+w3Qp73PsquNKoxArPV8XDEMzXR5SDYF6sAXI1SMAE91Dvf4y/5ZPCPsN+KlZ9GihJYPiEsZPnT02KEayZRd7CdHmt28j06Hkl8/TKAMAOhqqKqjdaLMqzWNryZttN9++YXD2tONXDA3CJGzN9VvZLVlvfMo1Lyw2J3D8Ux5hI2pEznXNm9cLqQqh2ZmhQf0zttKf4PuyK8h1h805/b7lvV69sZNXA7wopi2sN1zZDJnbJWTKF9ZsOWsGbzQOPfvRI2DsD7bA1k/88Zi1kCVsusrvc+5IGeP7smtLf4LvLc4AI2ODd4jYveMBdCcVAEgiXMmDHr5gxgGXryGKcFMRSgsi218gQ7hZEHlwnFTGW5grAIZRsvMW61tvvgTHe10RfIgaO9VWROpOnw4FY7gickAqIPZEjQUQWhuymCj1Q2qeaUlvB1QJ7nfIlAqAMWsuOXwO4hzPD87WYFblRE6oaRg9G3A4yWfsN6VB8B3db03yOWEOHTt97jc2DpZW2wGunACUavtXJQHrHiL6i90jKhCSqAc5bLN2amhyABBQc8kCHXPXts8jH60xrkdc3uaxaeR4NU7ygt3XXy4UttYT3s5+MRtN2AaCbxDd6G1DwW7Mql7dfhL0IblIXmu9GCRsPZRH38RJYGOGU1uhcNN2g2wUOWoDpnxxLYBnTj9iyjVrADyz4IjYi6xRq7vZkHr2jCNiyyd/fvWWvxiAEwMuVWYAWD6EPABEuHS6EbiXqSsAs82DTA3tqgljHF9vuNI1ksayeDI78iVv4HS36pjjWiWcEN4RQDFdLGSwASCqAQQKgXdyPxPtNWXe5meXNDwCEAe8IasOJG+fbZ91JUm5lfMipo3ZAdv+InrSmJIRx1ul2otE3Lle2+3iDSVGzwB9p8RASGN2TIF8f4IfGYkO830ZFzntu8uX9A3wQLdaVTT550dGBLu/7IQxOyA8tVe3nSR/mlSq9bfmj8glpnxx7ZazZ0AndZZnzxwAQDXxzL1d6oktfzEw5Ytrt35yrHE8+9WHTYtGsM6yMxFN7315JdKf4JvSc+ELXs+KYT7BerbKP/qpgYOuXBMDx0PAwxfMOPCKNbG+6LxtGeialvgM1jMOjnvM+l2WgT41smEV4hVibkHPjdyM+wlIuVnF27/XIjQ36ifFRgiC6L1Znwal6wkhKRKyqIHoKr+NJrnIkatdZ+qRyiWsX1s0/97lREJPq9yQWYFYA0VjbgEAkxggKRa3Ye6lAdzak/EWmofMdZrlxV1fA+Utdi/VMcI70/kAFBgFuQtiCEVqlAQQJOmpKwyrKGnGT1JIhGj6tb70jUm6jd3rjMlO7e5XgEhbGdTuzlbzFvasa/WfN7+SQFbQf8yVHkVyw7xQMfLufu0jgHihFx9MycrqO0+1mjBr5qUYzyAjhhtLAMILYbdr1/56/sCUqw1M5wqYpTcavA1ilDT0zQwhyfe6cPTsGUdM/txqnuNdncaNeOM7jb2w+p9A1W0nFR+8vvr6Kc45OI4asCOVwWz0lIH5swKAEvX6y/w+5wAIj39O8smS1bBpGAC7XLSBo8kPC6DszKyqVYKJ5aNDQfC6xe6yLfokpIEqg1b4HsCu8pL0iJUm3HfamW2gHNn5nClilGLNE+wh91KHGhmh5ALczbrHon+YJrnMPSNrM1Qf0HgHlhw+Z969y9n7yhJi5tkER112+gl+9913H+8m9BBOst6XMZYGjv/oTstkB2Dzs0v2mjzvZ1tv2N4TOfwpDK8cqZao3X0kv0zlMV+pbj8ZyJeT21YPVLeeCADeMaznPLD1nadCI+DxfPfFr8izO1TBPrJeBFv+YiDO8S9fqq+fUh59U33XaTt83XroimL6+Tt8+s4otw4Nfnj63GXrl453Q15MXlKbOGbT1k4/wT/99NMjWNvuu+9undbdS9lL7MI+Qm3vzRoy1tDOEmjNbA2ONgn9VfCdtcIh/zxFZBkMno4wgMGxxNlabKPqAet7nxvQnLUqtYG+cwJsbJQnm+1DoEIQFbfWoaG3aPSEwEIHIGgWS1VjOHBq2rTAd5YzzfL3OeCU7yyPF/nr96bXbMF3l3M9lj0+d1xvhxJnoasJHGYKifRLJBhFbzQ1zDsq1Bgff9UPwAmTvARFixojXqIOxAjPe+MCLS3p1ZvcWi7gy2ysNMztGXtd65cC54StN1yRbIwRLvcob6dqLVAnBE9VKGcLaq9WnCiw3phbSXPC2pyqkqxFIhl8teLE4sgbqxUnQrPGVbecgKpObHRCaWdUC2puZS44b4arRcsxgCWDYvGQGSoyPo1fi1CwdUMoUmvZBh+58Jx1KDfZd6iqXU9NUey9iAEthx2Dfn5t69q5Kp3bdKowFQq9XeuDEu+XlXOScJYCVQBqquJOtsQHCszJWAcC8OYp8zc/uyTvcqnVglcGyoUnThSrEfAu9oq81ySZZGPPIX8ojTZn1BHUAvTmlG3pDNIpphmQqzeuQoAk9KJscOm3Dlhy+BzOaRk58LOPCYCRnraw7RXDTj/Bj7H8+/88//v3/wuAN865e9r0tP8X1854/HG88dTV+/wnEB7atPAvfwkAs37vyk+8IavgoaGz//svAGDmfl8467fGruGvRpl3b5rsFx8+h+f4CStvnjIyZuYRlGLqeSnx6ysWgdqR6GZHhWd3RFg/MQjKxRh/3p9s11nVihNEG/G3C3bsuhwcP1IpZXdA9poyb7wuPQGFM8iNqLbrFUl/gm/JtgmH3K/u+vH6Q9913lW/8atvPXrDNc9c+PHXEQEPAXj8TZccctb+vL4bvv/7kz+1evruACCmaA3f/Y/7/uF1F97z7t3lQqS4wRcZiG9CZ2ruVGZ1hsuK5AzIENfxIA6phqpcbcO9bOE5IFRVhvGEt/HezmUpXzVolQAUBZGBL72Hu1lp20B5Cs665Qv3mjnHezWw+dQkhove57fDDXNYfDinjiWvN2CZ4bvKqPaalHYro/+K5HeN3HEN8S4Ca4pnxbvMGfQobnvAO7f3lHlPbr0RgDe8XfFOuEckQScl3VLQB+KtJV625YBDJ1Vms7u66M1uXOvNo/L7nCuaed6XUd1Rhrm4RDDMdMhN43Uoj/kKQ23UMjayAW0RfK0IvpsqpFKRcVfd6WMPsnQlkpqMWwAJq12Y8sW1v5o/UA271AeWcCI2xO4L9pATyjMO+xeNS3rfKMAHPLNgYPJn79ly1ozM3MrPsg7Co2ffhcIBqG47qTz2q4g5cuzw8kq9JwT1vRV35tc4UqRXy7xjzpA4UJC0sC2NlFRMoTZ2d/tbU12HAGCvKfMef3ZJN1AVqAoe+qqWnhSOmy8GklYvtYidgZKrEQLlmgiYX9pm1pkGOYdheEiKunaK2IYOz8bfSydoPdaoby8ea2Dsfup3ltehlWRSFJnNc8dA+kx22yH/vvnvse/+rwXotz64939d9dOHngKe+tfFfwngoKMO1FIPPfnNlZsu++Mffe+pVgUPPXnnyo0X/8kP73tyLJv9KpcxoLebaBHw3erusjOz7xQ99rLlrBkcazeWUg9d4d/6yVdYSVVtN4Hd2GP3ifaiTXzpI/im5DZ4gtrSHJEj/PpXP3dhLylGcE//4N9+OgtY+eDCGTj4M+/+8IHAgdOvvmf603f+8OKTfuRu2u/QvZBW2wdOv+bu6U/f+cPPnPJDd+MfvHcvJMt0TL7Oi1wgrvso+h7l6DnaWV0GzdVRms2E5DU1XDw3I7xTA3+Wbq5Xt8TGxM6JBcWAyBWqDZ7J5tTf3rZcLqe7mivaFtwXLQVfQ5rClYjyw2o+yF6FsTu72sU1tV3Ud+u0Iq/USb1jmdNMk4LL1u9SDwDNhIEcpjcW+2rzS+BewLrA9xu4QCH+B8xw7uzZWgMDdAIQqOYyNWqoJZgL6Pfaqa2Yyc8bNvi2akhwvN/nnBQUl2WFV7CeEeTZftE7tkZOpAJU1QKy22fFzO6WjU580R0AKr17LWNZB8BNKtIhNr1T5s3MF5ryxbXPnH5E3XV63y7+5mH8EVnykDNaMdJHYCJKmvkgEAC4Sm9BmyQR/LU6B/CQG64BOA3VqO44ufjQDVkoIF9HWPyCPLjaeC1Eags1oFCoXTAE+iHy29uOhlTI52ZI0xYlAIQgEfCM2kMFoBIveqopANjwzOJKnzjHhkREW5mPiYBm1sCl1BkEoPAJ3cpX18ufpYlY7wZ37PS5y9cvDSTKGuRYvFZNEB+sW0OsoXXi8kZJhDq3HWXfuDROXbxHK2nUW3WINr/xPR9L6SP47ZDXveEt+NEP/q/Z8/xP7tuyPwAceP4ZU/7hvw89pgd2P/IPPv+Z1995x6/atex+5B9c85nXfaPXob70pS87uTx7pkTijaWMOE19X3YN6SP4ptggTZuB3QVybz7jne848tFFnEXmP00//U3+cUzZ49gtWEm/ddTeB3/5Z0//DG5PdYk9YPdDHwgFidXLGML4ENtRffQtN97gll3ORXBv3KezWF9NoJ4lbQMAeEcWr9uI9ohxe+D1fJcF9/ZcgBjQiBuBiWUvCgFStWntS1qfGthPtm1SLwAMraSX+G80bg15J9jldgQpFmpzgdd4XXGbxsalPR9gw15pID6LV2Nwe73c6N58/e4A7D1l/lNbb4QiUT3AOL4A4OFzb/kE8T28sJI1o3BBSnoj8XIyRPL8bNYR3GBVjq4GhQy7x1RvAOqQeOPjTjQ62jY512lUCUOR1QEEdYBnBPwCI/gQ+82VPtNWcZOKRPmOKnDSd8kKn+MqpaXjX5+OG2DXaD1FsjcT0yFRJDIC5N4l3IMC2OHu6jXPfWomoBnivbNo0UksjOBb5111ywkc61/fOS+1JtIfCue/eZ2ConB+BC7UQ1f4N58dNi0Syz1HudeNMWcBZq1aiCwgHoDyJAYbAW91IwHknQcw7TcXPLHleuWz444gADU5suEtSUGDGAkgNnvT+RqGbj9y6Ut1x4bBY6Ydv2zdUugIMqqH5EXPVZXmVYym964Zp/YL8IK6bbSdQyJZPWl8f/y1lRPggAXfXb748Dnz711O9lCeS77PZDfuIuMUUN0cf/UC+UBvfO83BmYU2PzXazcfvOdu4dmALU//9J2Xrn34goHfexdADuQca8Xx5PNP4bWeOMVh/ol+8t+fwus8nD7vqLJ2PaZ5GR0yn1llu2qn7VnZtpdKipaiXlsSv3G2fVJDm1vRNoki6aN0Fd+1aDILHlkmPE/VvWa10lpLIFuvmAPO3Ds5zZzb9O6OhELy6TXtr+W1j0lfAGBSwUpCAPiNEh0+xA1PXCbJl4vP5Y+LN9+R+AWxPjsa5CO/8idcqiGm9DXXlZnbFQDYB8o5JzO97HT21yrznVCXvOQXRAeHPCRutE4YfPsbP4u6yv3g8hndzvftFQDT2dr7j/Xo9WXmtur6uMeq6FnpzYr6Iqg9yMzBhbl6rXy0FQEIw2G3a9f+av5AXaGuXHfYA6iH0zSfAiDBlh9CNG+ZQ9Dll0STqkdn3O+cvIzSi8w607VGDe11NkNYJ0Ent8Nsu8WRN0BD/HWC94nxJkqtL4k41ulrFWonmWQDwCYzts7YWdzJHp8GDDPUBvHTVHuHEbYocbqawrlA9IvnbiLQm15/KrM1kCG6AYLEN9rPgiw9JWCwkM+IfDGg71Q3oGbPWRk72Xi2Z7XWlSCKdrx0KOhqwK4YQOkJvEZpw2pZJabfGKrXVq23DaPQ+ht/qhNx+jKPmfRV9Nsvm/967Tfxn/94fwCvfdt7pzx6y7/+XwD49Q9+f/d37JmKPXbHv77zmN6BcI/d8fR/Pea3x6Ktr3aZf+/ysckhe/uGwdnTdhEPoL6+dxylWnEix/rvwLnMXzviTXpx+flzN+41ed5ek+ftPWX+3qMf6nncvrvIWzY20kfwLZHlHmN3swgN5DZ/+e9WrcGbTn33x9/fqQlAsdsH//C4x/9+0QCAcMo1v/PbIfz4mr+/fiUAHPqZ9/63PQHg6Tv/10WLX3f63fvimu9dt0oOHbdnUiqxCP7mX7sCzBAZtaG2otIsQkx18l6zv+gVMoDX8g0he6i9s2kFyPQNaWFM6Rwu4OKvrIiDy1bTzcqTxg8GWErZdL98XQIkgWypanbLJPWx7y2D8Hai9Jr9xQPAawpAeTQnKZeozQpTmF+nD6KFIUEGpzWE5NcpvmmVMHeG5tPWjjGHhBVHgbuzfxqfMQeUTEpvveekTflTF719CYg+2YnKPWY4bSkuYy4ZC9BtRpk6qNdTAEfA33qi5Fah1CmAACUy0Cki+BA1pwr0SZ+BsxpYmzwkEOPm8EINYLdr1/5q3kA17AFUw45RO7vXVZXRAWTdhrpisJ51FWNddmKT+DhxXnR8lHF/qMxbMMztl9HDLnXCAktV8y6goLyjfMORAKeB4LlJKfuOydiLql53qX/LJ+oNV7iiA7ASr5CNeFc29xRAQNmZ2a3uNjfMgBuFKwExRoiiXgx25Dj3DAKAXzx3ExShvun1pwLY/OySktUhQPyNynkydyPFyEFBc4T4Srabvl1f3zB49LS5cNvgr+WXlIeSKdAgoDUAvumFFytJovWIR7Po3gDDY6P9RQBOv29ZJMyOV2/VN3bSn+C3R958+p8tOL37mgJAIsac9vGD/r9PAHjw/52x7+fuwdvPevc1H4dVie1+5P/zxSOJAJz1ni+chTHX0rzaZMnhc06/L3nO92XXkJge5rkL/nR8W/Jy5JnTj5jyhTWSOX77pbr9pHL2V+tv7UgcWr3himLa+QDC5s/t2NVfifxs6w17TR7F8LkVQ4PHTZ/LlviJKTzHA2A+u/GV/gTfFGcWdU6jVACQJx8IgK8ZMhKAgqGGGmnrKi372UIcfegy5N2yLscpP+N4yRO/BrNg9Ib2NZLVZP53im4zLsZWfhcQMictgdo9YHpOh5E1zKoiItIIrbvjNrMRta69tWC1e6VHM9KtEV+lKAL0QTAWYuD+0fuXWSzO0Tu6LXjd5oGNh4TnpoXjoxdPe2UWIX7mRmawbjyDMrMixd8ntlz/ptef8uTWG+UJUACi9Z3v18ufGXb3esgomQho5gRroBIzuCnPJAvTdB1k6mSHZoHIRJuxoto94o9Agcqjb6pWnAAiORLBvTVU1pnRPSZ4hVrTd7t27TMLjiByb1iyNjxXuQ4n4TR4KlbGNvsu7Xbt2l+e8MfVMBjB15XTADkgBstJk0RrxEOurtwffP+uH73nAwD2+95dAP73ez7gQ9JFlZNSL2qd5E30HcszCwamfGHNlo8NAIAXzisJ8LPjT7uCyphZFChctWxu8cEbqjtOdt6Ln4gdfpF92pnYOTG3+3ro8mL6BQAlCzBSm5sBjeaYuBqAvT2i2dgDICoA9b9zmu1IsD5b7vmzQABK54N42/HINN+QoB8HT7Et1tzuQFotV8iaPwegJhw3fe4t65dmlDVZDZkFnaXS4dZ2kcu5jDPx5tvFMdLxfq0DUTzVdip725luTTcyxtKf4EdM3nnp2gcXDhx0ZT/NzPjIksPnnHHfsle5dqTszKy6q5Dmgh3UCIbNi/xbz6nXXTpSDdth2e3atTDJ30YpPes77r8LwI8Pez+AP/j+XT889AM8tf/w0A8A+M/fS0e3S55ZMDDlS2sBbD1nRxpc3XFyecxX6r/Z8Ww0L/dC3VXsZDcitf3iuZt+5/Wn8PamZ5eMSJ0sOxHRTfSoH99m9Cf4bYgAMgKU6CbA1wGArz2Aog5Qk5svPYCyCjzHS6JYcZQFAOfRtuvYC0Wim/b8ROQsYnaajxUGkefYPR1q2OClQpNsI60qMz98qcfy3tilv0v2KNeo0DSP17cOqggRuCU29VALhDKmUK3VroUzBYPuYexelIwVAGDx4XPOvH+Zd5ntXMztRermST7D7pbqtMyhju2K3Nk5M8wnjYWAAweludUeZ7Ce346ReE1vQDaJgVMfjjgwG3uf7rHGyLIzs+6u6uHv27ykwsF2wpieICNTK6khVXCTca1vOC5bI71AJ9lDljPHUtywub2rnvDDYbcvf/vXpw0AYJZZIkch/GrewOTPrQYg03zmDUHh+QDOHXfawH9scfHcunahAm8gx/H7fe+u//mu94Pwjvv+BsAjB/z58HD4x4PeD2B4OAD4x4Pe770cBfDPf/R+AMRDvRTgbiuUe6oBgNs/edHqLX8xAKjd3d67jkJnkuvwTjlQ1VnUl2D1Sp+O+aU8jIKQeLKE96abqvDyVXKSgSZ9Ztj0HuBIsxIjeg/E60l6qgSvWdXjEQA8tfVGAu05eV7P9bYyMgGK2q0/vHOSVXk4uLiTCMdME+weKWts0EocYrGS2MGxM0QzFduKzO0mvqTm06Xaq9whSYMCUl9TGv7S5phyxpmdsWFjJn0v+hGWd1669qHzd9Dw1pcdE57dx7sVuHVo8Kip44YwxKVuF5LdvvxtTu3almfPOIKzsPc469q1PLu/nEvs9727ZHYHAPzTwe//p4O3CdP/6eD3//DQD/zw0A+8/Tt/83Iqzxr80SOmfHFtdCN4+VItm7tjHvXbfaHuqhHnNn5y6w1v3W3U1Q99eRHpI/jeQuY/rwQNbIcpJFrXgW1RQGBuDSempgMuXvPQ+TMkJXyHl/mhHciaQ2NncT2lnQjBidOrZb8xxDXOZfg7u4LLIFlGVuP1Dnthd26eM02yWgTZG5QTyEczUx6wrkUdpcU+aTkiD0XwggOUssYZeEMmB674PRTCvxux+0fvX9ZRmO4lByugy/NJxnzZyV3lO+ahJHsoUnn7G5PNSOZS028fVs3htzbdDBBb/Uv7vPQqjF20DQ7A3lPmsxOyxhqwcT3FwYMoGP+GDMdHk2c5s0oO89Jxum3+zEZD3LCWeKnB73NuPXRFlraEpUGQZJUtlvSmljj4cvZXqttOohcYOyuaF8s3AVmYe4T7VFGMX2clWcW8NAGh5mFDAJ45Pc3xbOqe8qW1/3bKDGhGGZhMo1Q7cXRXY3mc2rvDPD7lpai6qbc1e684rRfqORtqUZQUkoLHhZqgGWnZ7VwQKofblPTrBQMAJn9+NYCt5/4JoO9UFVR951MvcQx9oYMyci1FqZXIxjrGS+cLuCciF5TmNlTpXK7Md+GSVwfg6+GVRfmnAFhXX7giIKlthM1GBp5jO71wVqAyBfQi9uPBF1CXdL7dbnBQmD5cp+0qOKa5tTsbMajcEd6lroLZA0WufM9Jy2UGsv0eR/emkP8Z74KSWtEh1xzE22zfL8U/DAXIGFPV9if4UZEDLl7z8AUzAByyqAfI6MtISaSaHy/hqf32DYNGK9qXsZNnTj+Cpxg2dT975kD1H6N+0R8f9oF33HfXY+/bbpM8gC0fE6v8aEu98api6rnhiWu266zRwPGvXGZPm7tiaBBjPjvuArJ9E/zQqiHMnD4dwNA1C048e8kDwCGHXH3h186aOf0lz91ZxGV2mLTHB6gXPbvHO8RMCV32fFYDcwcA3nnp2kc/PcDhtgU8G2ad9X7nZaAYU8nShdmlZkp0IfA6QJnpkqOAXZkyfCwF4ivRbKwq86hv+Nhm/WBXoAaTt0tIVTmBq16XEJeubF2OkNbEude14DOIxiJpCxouBQB8IVELPLurV7ycwkiGUXvHAG6+2CSfEc12jN4ittw+f20DABQ+D5H3mD1t7jc2DgLwSkTYMdidt3+DXf1zHwt1D/baV4KKoA7MTSY7aVmzYTALCqOssQg+CAq3CUhsfthUPo0/an5HTQGWQD1WMk4PcRXRf15p7ySDMOUW1NYvBdrt2rXPnH6E2M4rByWeC7XjN6hm3M8qnwIAfjV/AEA9LOUFrAfTVy5Tbez3vbv+6eD3VxUB4N+6Erpb7iRvPCmcp7JMyoAO6wD4QjVxw2rrCmCeddDhp5llEn+f+KbU3vGHo0hoYReqAAAgAElEQVTfHA0J1162xIrSz3zzrocjhXUBCFXOF82bCvfryuzjkPciDK/05UwA3WqVHZnOfIWIchWOEcbxNQUmvdn4zOLQGk9sd2cE/3ztATzPT1mi4R2j9oik79gweIxB8CvWD3I7Qj4kYZh5bbrY+BYbwoOc4kF3m+PaLfm57UOyJ79DahySWuSLjzGUl2+DX7XAuX1nfXY9AAxd8/+z9+ZxelRV3vj33qqng5BmdAQdlbCl2yXgMi8oGF4lQIIkGAbZ1EhkSzqAaKIQwBnf3zufEVldiKOj6bAoIQ2yG8ZESSQBJTrAzOuCoKSTEJZRlJnRJGx5qu79/XHOuffcqqebTtLpLPT59Ke6nqpbt27dWu79nuV7aHQHsGLFrEmdM3Yq29+gybA9fliGZaeUX//v48h+vxnHrjv7qE3KRlPccjrR1G+qlL1X2H3P34wDiy125hjVPn3tuu61g+dCf/uq+bevmn/rqvmDVeGrRAY6wC+eMakbQNfxnQAWXzVrBYCuRd77RV0Aui+Z07vVmjjEYktvS5+VLiudcd44bzzoj+jos4L+XFa4rCizosybZd4ss40ua5ZZk3/mhcsLd/CXlj540YSiaeivLExZGF8aXxpOQuABTzHJxjtTlqYsjaM/R1ZDjda8xtzGeGO88rX2xnjynM8yl2VOp1JFAHmGZ+P1mSQ1ybeYl8vB6jBjfJb5LPOZRWaRW+QWmcE1R0whb3bKkZFZ2MzbjAuH9bzh8oZrtLlGm2s0XKPhqABdYPXP+vBnDYzBvCNYOU+nyAwyg8yizaLNYpcMu2QYof5ek+M1OXbJMMJihEXDomGTYyttzg1yw8WoTgv5M94af/Loqbevmp8ZnxmfGzSsb1gfOiFLezgzGGH9COtz43MTbmLS00YLrIG1hv4yY6wx1vP9ISfiSg6L+l10DN/DSvgr6a8JV8AVqD6IPtt/drnqSqknnNYxFuc/x4Tz8Q/sTOxV62L5UA+S63Aezvsy/rXPWfLnrvH0Ongn70JhXGHKwhQbbe3PFBvjdnrFyqYpm/yiUQ0+8KjJjTGG+4b6Y+NG/9KL7qUX3UsvuZdecrz+onvpRbfxZd9s+mbTF01fNHmdIui4kQVc07imKQtbFpZaS3/hQvg+eOO8oWw0vvC+8CikV0moQ5z3oceCc4Pu50qvJnfKw3mUBQP0oomiibKJsinPQMF/ZTP+uSZck3rEwJfNRY18orzxxiTvvwE8ifNl7c8LiKf7aUpvms40nXnZ2Zedfam0LxTx7+XSvCzkAfIUIrc+t74t822ZpzeLPm7hDtKbSE0JsTNakr6RB1M/ANxJnlNI1F+q1p/B5AlSp4u7fHBdokZSrAulybDyKeuj4sGXAQ7wi+/qBtC1aO7EDvmBruMnAph4fBeAFY+u3GpN3NHl4C8tfejiCT+fPciRu9unzB03Zcbynu5xU645YigY4IdlWF4NUt551may0/deYfc7n9MDbqIUzcWNbAcgDRyWfmQznOx4fB87pnOwG7NdiFezQYpMDXGTsscBCZ2ylE6oihqmBGBzD+B9/3RPmdufz+YQec7wRn1Pljmx04RsbKEelTKu1tRg6qFKmN/Nh8JhNspkXwnFPRcL6exa9IU229VMbmHiHKzqc8dNOee+BYA/+74F3z78E9Eer6ukpFuWoohhrQ0dQuulTOe19b3SqqRCtW5N4iSvo9gzscfrnmGnhLSd2tCeJ0Z6vufEtkHWd5LM8q5M8SDSGhNWe5N5MsxTVdTnZu/26f+54drURlq/OCGcTzwz4l2uiTaZ+9Q9nv3XeV1zz4n51u53ftl7BesAWpjJXXJsEuyesNPnJ11f3HJ65J8PfeEc+8+X0TDPVG6lb796ybpz2Ppep5xzhabbB5m9OT+hJDUm03gSY8/2eMPpYgtz4P0L/+OQyWWJZjNa9MvSk/98k73oY+c2fAhhAICCHFwMIO763lty7M+IM8MahDeXWmc9eY5Yfe2cMFfuq6IESOj5Su9131p1K51JOp/yKxptgEbZe0XWcZH0JgfkUG8mJIb8GpeA+NWHV17TMLAp2kvGOQeAssqKF73gV/XJCMZ1AIV8htqYg9KFK6MCTWdKobSDmOqNCku3EsGTIVrigwN8xe6ueiKNMlF7whZtUK/b41VfVfcZ2aj9nMKuoQPsNRngAN85Ziyw4pHHezERjz8CAGNPObYDAHoffwQ772g/qHLQpUsfvGg8gEOv2tlc64kjdsbynvAwdy3vCRlftjmd02AJDe23DRsChwUA8ItDJxP7zW+P2mSP+nXnHtU+Z8n6mUPho1P2XmH3+Zxbuw2o6QddbumdH/jsblq5/TLS71hMdh3HnjJ21ooVs0477BasWAGg6wszyZn+tFkr4mi/M4h2ctRM7sZLPCVNo2n2DYdARRXwlIa5pAPILU3t3/fFJQB+PnsCAI3mnTVJJvgs1udNSLgel5yymuGgz/PoWm/U1N+XRsB9HQ0T0EkmlwlrdVQqIB6bbOHy3eOmnH1fjxEXAQAWyIz/1P0LaHL9rcP7VNefRWRPhHgIxxdWk9wRdMu1M4E0TM/cC7k1Gs941drAEa4RvDbdWaWKQIrjKbb+ox1Tb181H0BGrvImdotNu04jcumQkKjeQ8DQ3u3Tn95wjTUmoZdnX2Ub6vBi1UtZ6E04V55PLDcuYswduiy0JYEtyTPE5VX8uneK2NyFChV2D5C97rytsXjIzq4C3BmGlmJULvRRscXBDqrjhjn+uAzECeGmBX4I6isIgo/3g66pLDlI/cD7F/7i/ZM3vuwhIe+lsJNLGLkHUPiI461FodKpM8TPAOBXhx33rgcWFs2EP1Anb2TdjjfUJ9R++iys//T49jlLnr/4GL581qF4QGJ5pN9Mqh2J9wgKyodLJZjOMeBWAD18WRr+slCPFPKhKeJRVj88VANEI6UAuufUArzRewiCdx6j2qetWdet0ydSV3CMCbwAeuoYIOWHNxJTVCoaA8krD2tw15obnDcn7D9VPmgRx5e+BfJu6auiX9LKNetoeK1VgXwKnGpYS/FA97gp05b1eH6N1JOsuDuHQAaqou+Y+d1Fj542qXvFCgBjuxbNpVjJlY+uAMZe/d2ZO834LmMUiUn3UDALfcIS0lFWpXGaQyK3cfHbYUrnKJNE5gG8/x9/BOBnF00AcPCXlgCANVbxotBsILrIUdqGUicgIVZIADANX1fV8gygL927fBZUDFb1on14B5KgtVjMyug+Y3mP98nomFkZVj0AEM1cGPu5hQCAb3xgytn3LWA2jcCquzF+TRMGX36vwvBhABSleuU8j6Os/6fGqA7Y6CpctrEl4X016igj7/PHO0+9pXd+otBTlxOi4Oj919MCK1dhNaFQDN0Kur1AcRNHcVFytvwikN5S/6poGIP6XY8H8YOo5qMxtiobfWHZezknMpHwNl0gDvZezaTK6jCfn3gdEMdyHtqF04Zzv6pBy6s0IOSPVr0aGeY1Fyx9K3WUJpCQxeqLdoUpmnj3ioUAmht9EiBHHDUFryQRZ+BROeR2CeVLTjvr/+OQyQfevxDAIx88Tk/QiTeWI0W9zDn4pwn9CiR2Cv2+cqZU5+F8cdMns+OvKe44ky1F4UbwEEqNtvF28CPB1ZWrr8r2ny1h8TzrlxzBOWIPxoctdB6P5T4JodQjqa9tMfIhFVZaH9rYlLmXkPZ6pHa0wvEMICG8ggGQW285xM7fteaG4/eb2iOJZ+hYZ5j3Rs8bw5y+nyFZS2VQh3xe0OpVrMwemJU2nYnV5xxDJgO3wXdMnPuAn4teQA3mnbNX+rk7z+A+pPK+L94D4MF/iDo6JrHfoaRreQ851m3e4WStx6ar8acv65l3xBQA5w0J0c1NK288pePU7VM5XxSLs3xi2Vy0rRuSSHHjdpcX5N0rFv5y7HEbX66B1C2WX7x/ct4wB96/cDN09QOX4o4z8xOuG4LcM8OyhTLviCnTl/WU22RIT6XfAb63txcdHR1hXTbXCiEU2yklapnitJBU9KK0dwCsM2UWNzpep6UtFSh3GaF5A+D9//dHALyFszYEzRPNbR4nsBZAwV4tkeG10VaC9dsWIX1q4KAFYKQxJWEdUuwniEcAUITpsl12yc9qr8jjO31Zz9xxU86+ryeeV+B/oqZOsDgfaw3OvX+B9/jW4TxLyIxnmoqmghQMVwhAGXJdJCtXqUBmZoxTM2qB17SL1xkrKP1Lnl4aafsJ1wvRR+yTsEwQsWHmWlJCSm7PCNltTW9P7nW0Low3dC51p0KW2L7wg9G4wCSpbAKzjVNOdsllWC6f50Agd0WMKHIuBeipYkDjXFXMly4/8bri5tM492sCzeWQZCNC5codUL1tXq9zGVK22yx2S9RH+HiJiROhAG5SsBM9rTjZAYTL6dVWvcgqXOepDBPCEoXqSw6icckz4xRXrNDcxvZ7H3Tb6m5E5y61r+7Y6FM3Rr0MJ9OWJ/3WGS7AynldOJYk5XwsMJCBiR5OslGKqimmcbFGjAzUCtZ6egBZqm8qlYI9gPUEFhsAyOEBFN40uWP4EhpWnBRLACjSq2+pnIcqoNtdUe8nNhfZmJDUylFQP51P0swkB6vLGTLpJ0xu8YzOzk7msKH1fmSY6mYQ5H1fXHLwl5ZSWB0x3e4oMn1Zz7f7NrQPRM65b8HccVNCEuXtUG7pnX/itssl82qQ3b+5dN2nBpQkZjPknT/dHFKaTZJfHXbcmGWbnIdmk6S49fTs+Gs240C77+fcE0PkZPfMhmv2bp8+BCe6ddX8Ckf9sFSkHwTfOaarqwvkHk/r/chO7UUf5rt6TkfIuvAQTxxnkBFKIA8RBQSMh6UQHaeOJWMhhfoYRvxlZiC+eD+7YMJ7L18STMiE3SPfZ5ylGpc7AD4HRElQmamT/50Y9ZNpbeJYpy7XiF3Zq32VCXGKxuF83GJU+ZCNMfRhiCjUsP68nywA8I0PTAEwfVkPkXoKboyWrdCN7FfkDWLIjc+UaZwLq5ZkqQpBT6Y9WjQJasspHVPvXH0D4MVe6KFsh1pLkSlLfM7r4mSnWWl19FEVuydoXnveGX1x3sNTlthFCBFx2mTuHSLDC5I7ZkyAwACCKxayDMHVztURvCBL7VjHDnoeQH7idcX3TkPT6WOjex1A7CehmYmrJADvve8v4S3jpNK886cLH/nAcXJyuQiTrGimuX9/3+RCmGoQsDvBPqab9Yl7ItTllpKIVW0sFeFvJWg26HXqVYn3X3wovWPnHqZX5c40AAx1VOaNBvRI7br85KmUM1rRZNNM1VqBELwZnQ6xcxBIr74GUf1kEJ5j9TT6UpXn09HjzS4IClAbcU8pFB+tDl0L0tLNTWztHmLwples4NBT8ZxVrX9FVXm8PPVZsMrVwYBzZ+uatK4o+Fh0j5vStbxH+/A6hMhG9R0cKulngO+YOXduq/VhGSJ57+VLHrp4wo4SU0dxcefct6UW8XPvX9Aszbwjppyx9KZXLHzmvT0E+mf+dNuni90JhFyxyt99afMOz0+4tvjeaYPbpH7kFZlifzmWyOY8lEp7WIZA/nPDtW8eeRaAJwaPrbal3L56/on7T/1e7/boHLM9yACd7Hp7e/uzsvcuXoyJO0nCGaMmkwFeo2Y6SRIH8MyUET6Z52mOaeW7ollKiLTBqSAtbyUMjROnegBNZO/94pKfz55wyJVLIG75GU8Aoz2+EWaENG1Uxv5GQzzE9UwdyfXwRlEGQKzymQ2e9h61yTUdUkcqjOHF1zZTu/TM1YoPrXSOqkEczkc0HMRVvqSQm0Bp6eJsmizx4RQ0Wyc7Op+BzKI88ecCVvVBQ2APrdBSu/Va4z/aMfWO1fM5F602txNMt568hevBdcGvXgfIjWqf9vT6a+TaTcIrkhrrARhJzsqYSWlUDBi+80YyFAtBDAC4QlK+qsoD0Ne8N8K9U7shShkABdlL9UhpCzF5yBeO74FT9yN6FcefWiNFl+WF67Me2VcRyt3OmoKgqFFtf/klh2ArLXwltQyA5ssuNNw7DvSCujKx5fuiACRdbKmUZNI8HwPzfPhkeMjbVPl6JJfjYlgIwAGdzAdFXvSl807fengnWo4K21Ty/Iljhw6o09qd2Hp6VKCOis8qvKfUR5zll8IKvDeIz5XkRiKw7gCU3j+94Zq9Rk7TSWkCeY9T2FeDWzm3Fw98IJJH8SsvLxp9FmKXB1cbp/SOnI4I0hPU3wqmV3SQCeWtukfOyNdVgXCvDnHSMPocdS3vCb1rAf0GDrEMkKp25VWdh81oyTffu3jOYaZz0l3DVLVbSQ6+bOm/Xbhj2OO7lm+pJT7IeT9Z8O3DP/Htwz8xb5jytm/J8+0os2d+8vXFrWds61bs/FLcfiZHIW6KuDVftvtdsDXa0488veGafXYfCmP8diUzFMfXNpeBh8mt6J7V2X1L16Lvzg1QvXfxjNMmda8AxnbtPDb4JG7cK0tOLKH2k53JMiIvcgugaLMAnLUAiJ/SWYHNZH2vaQtdwPqEodVMsvD2oEuX/tuF48m1nq1EqoDLjEBbAoLk3AsARWHJtZ7OrhJgRhsVTXJN4oprQChBheYnPqXB0hnPCoj9iVBHpnB/mMmGY6o2eNUVXur/zE8XAHCerfKQUDqXxrPSelMUJYYRBgCMyGIBNqE6rlzDdI6jrWDnBIVzVyXGTcHuAHLjMwXos+QorsdUrtRUUV19Z2SzYes7N4OuKm9MLJuLDfnJM15vAoLdveB4jeD1vQwO9mrptct91MwofBkKO2VWL8r8pOuLmz6JovRMX+O8CnD3ElTOSx9rDbCwfc6Sv5x9VGKLVf7zoXP0c8gWdIXIgxFdlA6xtHNcktRjZHcnS3z07aDQGGU8pvM6x0qIpuKgbWuLyjbnTAwQDwlLdMuN169YIr5V/tyW0lKnod/nOhOOdwl1VxIHUnHaSDQS1GZ9cqOc5AEYkyFibheWovUro3OJwF/5XDBAF3ccD9FcVppptf1erpN8brSzCyWTJVY71qQlaqnQey0c7PV+E7oH1aX13FzHOk6Eaw8fI8cbI46n71XsRP1sD5UMEMFPnOtXLuoaixXdkzoPmzFncW/v4hmHmc5J3Ssw9upFKx+YuxMx3WyXctClO4Zrffe4KfT3rcM/MVh1nveTBWfft+Ds+xbMO2LKNgH0J4+eur3lqcwbE7c8p2ddstEXlquuHPRqXz3ym8OZvHaAsu6co0Z+dft1smkWixtbpiV6cv28ofGo395kxvKe7UH7uGlENyuPn3PVJbO6Z03qngUAYxM8vzMKz/J4IskUUsroRQC9ZJhuy4YFUOYZgJLQvJia+/MMptmhSmwjZR2dlyaPB1269N//fryThBnh2KIwdFxOvLlZxa5ElxA3ybyasIupU90Z2QU9xa4hj6BECNV2Kbqb7nFTgrEtyUkjk1993krdHL9OVjc1WSZGvG9+kCPmg3pg+rKef/kg2b08VSaG9niKYF+nG5IrBM9oQOa6fGztdgWtg9Y9MIK3viFQHlW1BN/Wyl3xXgy1kXTWxCXZIE3O/aBRmkfemOgCN21JqJ1CuZtAsMSLPb5ug4cgeKaqpYD1Itt/dvn4pSiLkHimyl4nhVE6Dejzk64vvseB7xLd7nWwOxQrXJUXVEPPsKEPrBMw5zt/uvCXY497+WUHoZulpXPQ7Mv6nXNOksqomHv2hA/vG9vdI6xnKmJrAncA5BWjZUsXgTQfqE9X1Ntkze7fXLrhc0en8dfpBfclyntbHVW7UzbESuiHN62krlsIAeka0bJdnMLfiXhTNIVJQiM+RM4Xn+eggkrCkZJl+omq4Wl4cTAQtycAmfF3rr7hI/ufekvv/FJzOAclQdjSR3eGE/XVMPWpivoG3hUaqY81caV2i+o6nK0oA0TwLB2dbx1z4Nj4e8wgt2ZY+peDLt1h7PFbT0LEPKkKwvbtx+71ivLU+mtGtU/bvGO3Fnbff/ag1zkswwLgqfVDERavk9AMS5BNSBerLO6Lvnv84wTlO2/Z2VC8s7UZFs2VjZBVZRYyDwvYHUCZG0LtZW4ANAnNN9j91SjPS4mDT61ALebvAGAdJ4v0sOAxfryQ2jKXFpHcUTh+wLVQKJlzsJYG4Ll3o02gRw3wV+N3VTrOhJMu2E+12U4VQDSYqWPBBXQ3m3Ax0LjBh3ZxNhpBgBQxTz36Lx+cAoB49DzTnHPlLxcGQFvuIWC9YTGCoHwGvZHWdcS8bn+4ujrVPO3ODCN4ca2PuyoAnbx/R7VP+88N10ocPMP7NNg9bgmh02x3J+wenOTLjQBiQhUENF8AgBfzvNdO4V52OQC+uTHruKh8/LJ4VOBKKwpVbUqjJj7w+YnXFTd9Es4npl/XymAcHk0NTNVjXw81rhbTG6RbtBd9UTAaZ2oKF7F4sMGzW4BUotuoPefFSZpPRxU2GgZC/UdMlLylYajAAfctfHTc5NYJIHReZiNdAbXSgs8OvN7CPB8aXTvKqt4uHeWSYUs28RUG4K6jazQpgncADKfdkK+WirAwsJIuNiJp/pTwwojFntusMD2/2uJUb4CKub9FTxA7felD2p34qND6KR1Tb145v+mS/miJwhOIT6t9K0rqn8eBFUzUSD7xJhlSGSCC752TWNwndkycOfeBYJU3h7V0sB+WrSMHXbo0kNoOyzn3Ldjy4Pv+ZRDBwd7t0wNJ7abK1sLuHRcNep3Dsh1KufJyu9/5dr/zN+moorm4kR+zhad+av28Ua9KS/w2lwEi+JWPrqhb3Dsmzn3Az54z47RZ3bOuWjxz7nYUsrMFUipqch3s7jLrxFs+LskOJ/Z4bXen9HGefOmNkNKXHuJF71XlgMTcq8h7boPzAnajv/vBly198KLxh36Z3HOCdzfNpr2UgvecBUt4rpT9T4rVjesBZFB5H6fgPGFvSRmSGAElZI6G3oT2WeCvBvRGGcy8aTFp9mz6AgDrjaaESqbMPlEhaINeiGsnW3ubssEHe3xeK/+KIm1g+x5h99xwLri6Hv73z18LcS8I6eNsLY9cQlcHA/ic08lIAHTFSb4eAR+56BX+VojNuzIbfSGA8reXAECT7PfKVb4oUDDTGxBZ37gYWd9PvK645XQ0a4G+nunq6hop79McxX2tV37yEx6J4q1l+gCrDN6uZBM7PQA6OxyinT6B9aGrJANqcu/5scmQM3YnHZiBxMTT9kbDZLkfs/xuSjYjVm/9uiVwMri30GUKqV8sJRfZdy8FoKpTxJbSTQBTE6Z0hOXKy7LOzyMQ60sPxl7QZ/Je6Z9Y1xd0SyllgoGY58GfCHnBidgj6QKvj0S8dIRUbBq1F8yEYegieCMVcFz5yaOnLnj8xtLHyPiW9nv9zalrkVrdqAj361i/8plKbpE3c8dNmbasx0mS3W0lAxzgO2cvWtnRUhHfMXPuA8ceP+cHg9qqYdlJ5FuHf2Jrw+shkM2D76Papz21/prMsC5xYBOGoZCA4creywGwHn6HEsrC/vB7t2Lqtk2VMcvvfnTcdtSevqRceZnd57Nu7de2dUOGZShkgAN8R0df8Ly3d/FVp13SfeAXZg5am7atOJ1ojEx01gAoM0vgvhL7rrZI4K4qECarTjGFmaYD4KyaOJpwLACmhYaEcacVGgAPf378IVfdQwzzxnpZofIGCsXxGU1E3TyhL5g9jY/VaEP0AYy5uVqN5qlAsExX+zAzmDtuyrn3LyBtCPVoElMuNvhMqkKMqZVQWvkZdjEuFwRYdzGOju4Kx49Q5vaQTY4api3xmfHWoGXuijtX3wDlN6Cd8wM+042xBqPap//++esA5DHlmQbl1NsE9DMiAqsvI2kd4T2+bIXIYyx7HbuLK7UjJ/kSQLb/BQDKlZdxgY0bgZY53R0ANMsEu5da76RQuPcp6pPrS4PBY48F1wK1sf0bS9efN75sooWYFj+137fwr7MWiBrbJEI9hunU24bAPfnSCxdAXPdiLmYuSBULYTND2J1Qu1Ve9CRW8rFl5PCR+VA4NDWNRjGqs9K+0t1W6cKWpnrNgsecaUo7YS3yLHZW0GbQk2P1q2bVOuIh4Rcv+QWmmKIkDl5/PrxJ7nB6E5OL89X1SjFurxTr66hKV2nWDSpQpgZppy+0fj4kH6BwMXXlYvCFCJEHACgIPrhMmKjaGGo0vwlOdhXpXTznqktmda+gXwcOUnuG5RXk4X/YWum2BlFodN/Wrdg0+WjHVAC3rpqvv0Wm9as/1JI3BsH8le1/Qdl7BRCUscMyaHLA5maCX3fuUSO/ds+G84/epKOKW07P/u6a8vubGYhBrHbu6X/evMM3T4i5du26eUN50mHZ9AGeIfsKHtkxtuvqL8w+9pX86HvnHNb56Bd8aqdfPMNMklQEXYuSnf3s2spSNFQqJRKa+xrj1US0boOHYCxeCm4A4IzgbyrmDACrsnd7azT6Z+wuSgLSHLjcPvz58ZR7Jss94CkturUJ1xOx2jEJszUckltYRCgDCI4xFi6LzHckZNTMcofcITWTV2zbXm0koVi1836yQHO5E8ubjg43JqlW97PxsiuyYienaDmLZwwSFAO1ZTC0h5IQHP/xzlMB3LXmBqUBSc5rJHFcJYMcImmdr6sxNOKxJmdQrlO2c0syC0LtDOihsTvQyCeW5FuXxFyncKBFP6ruruyPfPIqYJrAeiXkXYdGVGKiayfUqz5urB8bfraeNhnTokL2LJHzKqiYnMFavmWFcq0P0e30/EsWOA8kHAFOv95SLeF4awIQ559ANXqcXjSr0jYaUbDxxSo0WWWKU9jXK99xdhnP2N3dqw8LAF96Y3xMA4iQrRLcFwDaGrzCmQJKAN4W5crL7V6fdn9QKcRqD3BV1JtmvHi/80/6uCW3reXtNSaC+75QeGxMqhWDMnVpTvhTOqbe3Dufu8pIZj8dmW94g35skhB52Z58Z9L2t8DuLTbFo76qH7cAACAASURBVBIeSK2g2W4RfArZAYy9euUDA2OwWzyjc9YKdFW2mUndMnYvnmEmGchA3s+urS/NthguUhnl63eX3etk7GI9PPnT8WjKI63L4tPF3mTqofYm0cA75XDnrKEvxcMXjwcP7fIdkYeUdVA0tPN7GD5MPAlA/IzEdWM86Ru1pxLtykuTO4OYatZBlO1hkA4vW6CgP+/+BQAaVkLFatMC1uqHt84nFdIyq7x2aDUbENFjUNZqgDdqLM9s8mkI13zH6huyGLmn+yeePaZ85QIeIY7Oe4ljMqFySpBjJFsrDe0ZLIDMNBBneFZfl3YxNECek+e8evR4YKkoVFVzqb3BlYh1s5l76ut21GcAlKuvAoDSJrfE1HysrOFaNLcnOXMZH29GJd9JZRS39Rtc+dDqK1fPbqtJC4yvj/Ds9sVPuLH8wAPiXRC86sR2Ea8xHWN8/QkLAWXJJEf1iu6GykZtQbCVeV3Lr3yIMJT18GwnE4IwRJTOA6aSPyasZ1nys8zjehzH6Pq18cUlyzAfDUNr+vZ7/eDph7RvrZeJ+1rPKbw8COlj4lscYuL/8F7rGYA+Jgv9WasgXlmt0abFWs0eoD6GulSYaSRT8u2O6Ka3d/GMww4zpnPSrO4VwNixXYuu7gKAA986kNF98QwTwXisdM4l3ehaJKP2xLmLutB9yZze/ne9euWhiycc+uV7xG1++5JvHz7l7Pt6hiBcbQeStevmvWXkWdu6FVVxT3zVPfHVbP/Zg0VrU9x6en7yd7a8nvWfHt/+z0u3vJ5XjxS3npGffP0Qnau5eFCMRK8e6dpu8s30h+BTyC6q+A4Ai2cMsPreOYdN6h579crv4rTOWXr7D25ZgbGnqAw1nWPGovuWH/TOPLbvXUNCeL9xF5rqAkiUMBWuWR0sF6C2T6PmIC57TsLCeJlHqpxwBu21J45vAPDwheMBvP/L9zBqt1rvxwczj41WmqUhOiZB7Qg1GOOtgvh6Ou6coWop52zeAMD5aqgllJ1l7rgpn7p/AeBFGw8AuU2mqRqms70jhRyZAsqZSXCUhhwBvNVJcgKC16hdawICptdgI2A5742TqTXnGoEHKn5xxvn4WBgXq216o4lkDByANeu63zLyrGc2XAMQ9KSb3oCgeWKiRWyOib1Uldgko7G7sSk2VEpAFX8JBCRtALgnrwZg957F3na6c4uU06ZUeWt0SJV1Sb/TqnoXYI2hd8HFyrnnW3pCpgaF5HLUrgoqSyCyrJNSXTop4mBXispM4SmdHzZsZT28ulyTUtWmMN28e8XC3x41mV8oVspoBFrR4aYnJNE5jBOXRWqwN041nRVEZXHzJ/MTmFChuPV0AIb86bRXXemQ+7gxS+2ProzV9rVsJUaexqShcWd/Tite1Ps1GLzJQv1166r5J4+eevPK+SAVjqqWMbT0rql1sK7HtFItVXaJiim+YrXKqpoGHx+5qp5jCKSfAX7xVZNmdfPAPnMzueo6Zj7gZwLondNiZ6IB6HjrgcAjA9gF4OCDDw7rDz/88Ga1bLsWzWNz6FX3tFRNbw8ydxzRwg/tM7vjyJp13W8ZOY3H+O1M3Jqv2P3O5zF+WHZMKW4+jUaPoEcp7xyo3qhcfZV986fcf35zK7VtWPqRPfbYY2hO1M8A3zlmLLACK7pnTeq+ZWzXF747m+D7YMjKR1e08rxf8ehKvLXvXeDT60H9ueeeG6Q2AcAee+wR0sNURUxDiZlcg3XBrdrhShB8dMQD4FS4XQDcOlMcqeIDzjGJBR3qHwGUYIaLM8SQ7qKvyQFn0ci85sBJ1ANVux5dowPQgANw7fiPE3bPNYQKmKkPkB3arX9qw3mwOOr2a1t4xUFP++5lrRhw9YzaGujIvSAOkSKDTJeZuqiQ7EUnmMnUethFLd/oLICGHFV6r3QJDoDzJcDBRUYuSEPzkLG0WSwWM7xEglVMf+IMBgCmAaQmVZtHUtuwSwP9LBdgXIOW3nPUVepex8s6fPaxI4xJ8uNG7A5QoiEAiZtTgom9sfHViBcqnYXWGinDx6p20U1khmlnPPvcRVjv9cORmuCtjTfChBVB7dSWpHktGpa8tsmLmCJkIXyNu5L2lZ5RuCasTeE+JYkv/3UGALTlgBDqNnI0GrFHaGNbG4Bs/9kyutc0G5swZa9rJOTrQV8n+bFX+7Qn1nU7T1GVujPUvW55Wv30+b7xtzxTyWdSrcez6i8PAHF7sJVvhdplwq5X7hgDoHvclOnLejjRkjOUIUy7QA3usIW+Zwz92OA7Zj7g/cqVi67uGosVK7ondXYac9hhM+YsfnVbw7eKPHTxhEOuXHLIlUsOveoe8pMfliETUvFtpcq3PF1mUSzOGxO3hhG0XH3VlhvjizvOyj/63UFpzybJbw6f/J6fbUJi1mHRknVctDNx3dy2av5HhzPNtJJX8qLv6Jg4c+7EmXN7exf/4KpLZnWv6F7BuWK3SDrHjG21eeyYzv52DYloaA4V7xODQGwsBoXmnRUGDxURRxLmfa7VhOrhz49/3xVLNKBJUHo4OJk9MnYH2dF4Zm+AkFXEQPnGi+koViue0RxkQ6g9Ib1BBUk7CE4KpmuNnmkj8UoaqV+HyVVwfB27ByRJ1WsLevVY3UtKnFyqNrTrMLmQLlYHuZ08euptq+Zb42WXVw3TLfdSiYdkhuXAOZv81MfyPRIeVMdO9QXA1J7WZAKDdKoTIqyND2EZfekT3Cq0qNp0mlrl2T+DYL1OKAITaE/Qd59WNoY7oRAtAFjLpvpwR51uZ/JYp57w9LpVThnvojyNcjq5bjltQlgb4kIFxxuIO7krW4T/J94elRb4ePvUdSgcX4swCBdVcbCv73rlfg5pZgBfOEOhPdQkCs8JD7cxAPITryu/Px1a+0j+EyaY5PUp0uZ6dcqBRXH5ANBbFJdzqG9P5cuVZoJR9zdUUQP0lV06jrSiStFfTdPikKQnNGRHq9sSOrTPh0dUGH32XNAMsUan5b3fWjIAL3oAPNBHRA8A6J60ZZD+kcfVgb2PP6JM7/3sGpZhGSz5aMfUV/nE3635StZ58bZuBQCsnzVh92/9eKtV/8Lil5d+svnjTzb/PejH/mgePsPde4a79zvxk/vcd8t7T33px6e+9Ntf0Aa39u//+57Jv/+3O7Yan+/6T49vv3rJ1qq9Dyl7L7f7fHaITzosQy8DHeBFqgP9ihXdsyZ1GjNj07JcdRx7ylgyq4usfHQFofR+dg2JeIp3NgIVjfH0Z0F/zpqQUpZ3EdGsfCaM88Z54zlLrHHeOm88/cF4Pqp2YhOBjAHimaH/uKw33puy4L+iaYumLZumbJqyNGVpyPATVrhyj/BH7fWOSRa9M96ZojBFwTUUhSkLG/6knmRaTUSZL5d4ucSLhXmxMC+X5uXSvFiYl+ivxEslXi7My4XZWJqNpSkcCsfhqiatp3QoXXWmnF4143suX/9zvEIlyam+zVb+PP1N6Tz1jtU33LH6hu+vuaFhfWZAdzgzPjM+t9W/NuvbrAt/IzI3InNtmW/L/Aj5OSLDiAxt1rRZk1ubW7vP7tPBoe4e8J5zrcR150v+o+YnyVhidwsgkYcta/CfzeNf2Bi2hx60Wfzjh0l1buXxik9hHxKOVfy97MKeGZMZ8qI3mTG5NbnlO2GQ/CWPegrr1S75o9ep+i5QGAGd2VoTV7L4Zwz+sHGt2WX8/BFHXZD9uad8+o/GGPPUvPKNV2RHXp91PlP+7tcGxuKP+K8350fNH3HU/BFvf7eHx399+8/Pjn3t0Xfv+Tf3rX3kofTs/Kfzwfjw2fBGA8ZUa1URX6bQT54OftYJ/6k3V90+cQ0PagR6MZg3VQ6mDjLJX9ZxsVv7NcBFaNrCvO1bbIyVx8N81Lupu8f3NF536DDpPfp2qpvMV5ZUxV+qeMrY0fT0xYehRVXJ41Z/+gYor6jWcB7yvce3D58yfVmPvgB9pUMsm0tVK6p7iaXb5ONnfqFr1qRJM44XNptJ3ehaNLOj/107p1BquEOuHOpZ/GDJtw7/RF+7tlvC2pNGT71t1fxNcSbaZCHT+9PbpQv94Ap5eBW3nD7E5/31B457508W/schr8wR+6YR75jY9AXwbvPWTjz/LAA8i+yANwDAqONw70Ls/U48v7h8ZhmeeRDv//+yXQGg/OOSXfa/3QL5nofv8tufPn/gB3bZqhc0LJsvlNP5ppU3buuGbF+yuQN8b28vOjo6ABnoN1kmzvWLYCYZnhtoPtp+dg2B0CzLKVtuav/x4lhbOcJ7tr4bZWIKBLRi2m9xQrbBa/GxYi8Rp2QO9HqdmbkSMyHH/pI9Nws2y9YGfmOgTfs69rfZtEJt6wG4hgnnDZfxzQ9+AsDpS26q9IX3bBP9lw9OAUDzWXI4b2QGQJv1mm9Nd2MmF0IZIeu2NOuDNzuQ+gGEnw0bl7lNChvglI6pd6yeHzdy5UImSt3uoylX3Oa9VOUhlviQusayddYAsMbsNXIaZ4alkHdYnWyG85Lyc8NmQ4s81KC7xAPkS0+bOCU839bAVqYIyIzeksn9NdVlMrdpNdEx6c6EGViAu7cIT1cIRQdM5jkCPiT5kd40IYuRPot+xeT1Ssz3StEAVGPf1TJhsmPrOwW5OHhiK/W7vTmHK2yvf9Fg19AQa/c8IzvqDPfUF4uf/RPe/8W23Z51z8tJd31LG5Y3n4dvBxJGgHi7ah2cbCYNhPzWz3N0GKlHn1OB8OzQ54WfwgxA4LopFypHTu0wn+dJRIFm2bV20zS4/GqTTb0Csk3YKB0gviSm+jYZGOJQdLWvobbHI+nFaLbnammj7ET1xUnaHL6Lie9HPLQKrDXdAOfw0WeqFJZD6Cjynw9vCQBjfEiDC+XVMTSyeQP84hmdk7o3ZeCVePhUJs71vo+pQT+7hmX7kK7lnC6pn3SjZ97bA2DeEUzq9Cqhuttr5LRBB+7Ngo1gNNIzO/12I+WdZ2Ufuba47Yxt3ZBXEvc8zN5vBBx2Q/mnX2Wvf1elgB31f3fZ9bqXnvwF3vEm4G3Zri2rGVxZP3NC+5wlz1/4oU06Kj/5+uKOs4bWZ2vz5en11+zVPu3J9cPJZoZUNj+b3E4revbllcNqijx44iYAHQBMwnbXwsouVVXmcKSif+/lSwBYGASmdIXjY7XeAHj48zGnHB0YxGq6+Faahojd9S4GQz4sw5xYaw5ISGfwctO40kLS20A5kMOwry8JQXyb+W8dPuXs+3rARrWIgI2Cec4zdqfjG6oAPayZ5fl7rnB8mJu3TDATKs+tP3n01DtX38A0/vp2eOOSCboB0KZAiU2TzUgb6KptaMCo9ulPrb/Ge23bpFk8/SBf+liDtXkd3PsElAiq5enU4jyfGB4NBvGKwBwVcvLkTvdhkggAJNlY+Z2qShLAagAUd5yZn3AdUarBW2oSvzvWAYHkLkXlOuxBMLr2hNdZXozxmolfgKgBQpaXxHNecjQAQOYA81z3xpHnjtgtA4C3z/Y/vqp8BgDw2k+YXQMloDF7HJz/yWaGIkyMPJbwwmPvWSUApnaknojqrnAtUMozabMHJA9NbgAQR++Gzx1dZ0qPekSts8otgPzk68u7pqGRJQA9U4qLTKjrNOeDMQCy/S9wa77C5nnVxbrRvKwrJOhG+IpP+itINVqhf8uYCR9hOoBeMVIShBZ5yNeSygo7gCfFQK3hqtqatNys9Vah/tKZyq46f4SRE4WRROgnqh/SIZDhAX67kIMvW/rQxZuQB/agS5dCXhs6sDLMb59y5r0935YxfpsIje7b6uyDJYVo7Mvmom3dlkSSMX5I5NFxk8csX/irw44bSOFfvPCn9418x980+RP77sZR8zPA/Xaq3/NDia3ohd97jAL+xoz8XfkCsCv8C880se+ug4/m2/956fpZE0w9F+GwbLrctmr+SaOnLnh8G5vhSUu/bdsQZHiArwrRPifpP3lm7WWKHSdxJpm/GT6WgYoDdAS8ibtUoHxA/++9ZEk8V6I50K2QIHvFN37wZUsf/vx4axk96MRxEQzxHL02ewyEY8ZXllkWSMGUWoLsUqUBUDRzhmcpdueC3sRjVV9OX9bz7cOnnHPfArZFZREiu7SsVfhbY/GGTfGMWrcpyV2WFPNkeqerEzwNJDTtqmPUpNxKjLu29zNukMI6m5QoJEzoFmOCbdkCsDYL6xBYz9nnuAbCSRINn6QmNQCaxeJGPtHDSOItKqAaFdB8C8gQLIrh6XSp1kiWulMq+p/EGK72gxGncNFzD4aLUlz0Ulhavv4zE9q/vuQvZx/Fl6+BJT/GJrF/WwB45IPHHXj/wv936GQAWY6c2fR9uBoyRv/+hQcf3uV9Z+coyye7yz3OarwGAMrHPvYCLtxtj1wBPOvX/ltjn+OtwS5v+NB/rf7Bbnt8xP3pfoy5aDchutCdJJdtVP9Iv7p4FUg7TLD7hvM/ZPIKy52qLiOHBmtkhTZG0rpGzmHu2u5O2D34Pdhal4U7qFVnpAoymb43+q569c9HM7x6ulqZ56uXVT2mv2JbLkoFUP2Z0HRWVuIjWdulRN//oDSYsbxnbkgzE07UQlc1RLKpYXLDst2JVtcPy6tHmsX2mOOruO2M/KTrt3UrqvLwn3909vo/37PunhOeu+fk9c++JdsV5WMfW7/0Yy/vNqf9He+hQuVjU55fOuX5pR/b+IZ/auwKAH6Pc1//xp/+1z3HPvuHw/fc582D2qT2byzdcP6mGd13dHlq/bxRW8bquEPIjOU9wetom8vmIfjO2YsWHY/OoQpNH1Ixen5nIuwGJGW2YjfjKX/pAQ55j+XTYzmPHMEa4oOrB/6mUrHicwJ1whAMZNgD32Ye8EnSd30x0tL6LopGReoSXEH/GsGXpYXYiStHabdUsZfFswRxwFnLer51OOWgg3ER5lrpWMLcbcqqqMF6pkKvkbrKB7VLmhXeAzilY+qtq+b7SMOnJ9VyFNlW4QGUBJQJAkraKEo+VvKREaE6g8w4ANaYJ9Z17zVyGgDyoo+Te7pxZIJUhnbvHXWQRQaAIapKNejhfALuyS1fult1rqH7UZpwZYBP8GaC5n0dfvWHrVpFlET3hNDmCBxVM3SxkNVLYXepkzenNviI5q31RNPnla2ZTpFlBkCeyx1J/Vze/4ZjFpcomh7gpfcH3PGaA+TSqQ1jbnvNGIiCLW9Qhft+da99s9wQoXvy1mcAcOD9C39z+GRjJQRDdXZidDeiWss8gPZv/HjDZ482DQPAtFndmXQTzYgMgNklA4CGNeopz0+8rrxrGgP3Ro6GQu16PXyFMpViLsuy/WdTOkEKoaFLic1NrfJ0NXk+sVkslitLgLsQzivszotItI9+H6tXlETdE1VlVWl5Cp/+6KsZBHMDA4dWLUWaA9aZ0sNT1WwZ8cmIm008vVGvwhBLf9nk5szAsXNbppHr6Bi8vDPbmyTqcbqpqYXMFonuPT2EBvj4HeBx3TJJLbntMNlk+sVMstEomlZvZSyxAOAkDAkA5aeJPh1Ofya8qjtMNKqDrjFJshljk6vSKn2q3KmWh1E8VVW+kngAmLas55sfnHLu/Qs0yYcMYD5TCnm9DGNHEu6id8lGPbTrhjkP/YFI0lZKYBeNndoLT/xr+B6VJhagOUdpknau/svc/f9qRuFc6CtvYInrFxaA43SxNmyBKOo9ovZe5VU1tfLccVljYmuP+ooq0Nc3mtg31vLsVU/WonaXdP70lVI6Xoh5QxmMTGCoTT7Myf0zurOk02OBVA/vrQ+n9VklWJSv7fEJH37rkoUAfnXYcbLRInAki8lJx84lCufAR6sc+nhYzA0Aa02joUd9KmBi55kWL4B8BvipFsc66cBM+sEa08jiLiujPmB2yQGYtkxsVHIscQdRKzl/TAOQAV772Vor3nY5AE49wDr8XNTyprq0lhuuZo+ojeKxB9UyBPUK1y/istpDryD62Qln048VVegGVm8oVZ9YVs9YW/Z/VOUslAnehfmEN8njOrTjfH8q+ke7J3UOJ5fZXuWhiye874ol1QD6YWklBN+H8oyr/zJ3VPv0ra2QLIrtK1iOpLzzrBCfvamy4bNH7/7NpZtx4KNHTH70iMnvemDh5p138+TAnyx87IhXptmpSPvXl2y4YDOV8/mJ15X/2rV5xxJ2Z/g+8DPmE5vFDzfvjBXZu336Th8m1z1uStfynmk7gpNd5/FdY7u7u2dN6r6la9F3W0P5nVZ0wFslZWBWOkDU9ZBdIDbY6jQ+iaNLtfc1JbFHyGGTtET0tVTSeQDOmIMuXfrgReMBHHzZUu2/w3p1mTL3BayjHl4D+nSXrlDPXbVvFoKCVkMx1XhEFWisvKXXsJHELXXsXpnLJ1k+1Wpm+lQncPZUk8z9TVobw0gAEv9GTSrZOpKYBrhaAV0E6zMF61f/ZS4AUtc/veEay0jOQWwBZGcxJmOADjoJSQnA8CFZbnLVwNgXwjoi2iCtazVBTdwiTQbXECLDbMbxXomKOXS3SlHDQWNWQtAcIIFo4kGalNSKTmpXZnypbi0ZPKzSB4h2xGvNcc7N0rgwNbCwtLWZUF+ZxxqcM1nTAyjz+MCz06goLwJbTmx4xmieEHyWRwRvcx+XmWcDk7Zq6efTesLr7V9fsuGzR5uU+Yc08KyHJ3+6tojpjYS6sXK+bk6r9jbp8xm4UyCl3fdzgHjhkSedzbhbswb/DLskczEr5+mea7sSvGa8SZXf9S0AMKp92tp18zzfQiN3Tx0T0mX5BJonJqbK3eYqeOPJo6d+r3d+oTMooVXxmogOr4/dA6ghKeNDy/lyAhfZAM8yiNIPgu+YOPcBv3JR11is6J7UaQ6bMQzltzM56NKlB1269OHPj9e55IelLkRjuU1O/cS67mCV3xpSNLfU1a5cdWU2+sLBak+QLckhu/u3NzPrzGNHTH7XAwuHBsePWXb3o5sO34de7L6fs/t+zj01xz01Z1u3ZViGWl4xXezEuQ/42YtnnDapu3tS5yNdV39h9sydG8trCzpJgM6Enq2e7xmDwDIiKWQYyeisjo7pVXUKRY2avRiLvUpWK7Z/hv0ccMSgIzaVwuQqU3k+jWe1g8YrAmIrPiXKmh4wvWbtaN1bBhBiFasMsan9Xk+0fd9WrGgkq5wgALz0AjX+rhyrAYFNz6h/6r7KTKCkjWckxFAETzrGxnS34sGBA8er+C7q4ExOV/iI3aPZHcgE7ThC7YimemtzfSKxbjoA3pBHnvHqxon7hQLczjKec0nMGQBYK0Sz2r6rIqzkoU20LgzZHaN29mZUOIH8EO88K/vItbDGJHSyhMs9WZQN+V9QrCWtewtgw2ePHvm1e5Cx5kgcOuU21xGQfmENHjti8juW3d02wgAoy/gAOOeZudZFgO40J5D3mVjcQ29n4uLHigFi0cn9mOV3P370hwFPGZazhs80oM8jjicGHqORlOFujjRPqhsTe7wRVQeFxhEfre7S8AzWCW5JpbT/bPfU14EUu7MNvkGGecbxvMykK6OLmDxjDgj+oU5++soyfEnq/nc+7qImRxyv32KfRh/TOxiYnDiPrvaeSTugrhhImtVqV0WqRylQ7pQLSHKIZ+v7NOGpVec02imvTtC7VWVAXvRqlJ81qfuWrqu/cPxbK0U6O3det7th2TnklA5KMLMN5Il13XuLPf7p9YPPYrvlvDdZx8Vl7+WD16gtknWfGr/7N5f+ZcZmxn8+dsTkdyxbCOCXYwdEgLOpMmb53Y+Omzwwfe2wDJGc0jH1lt75w7ekIgMPk+uYOPeBlWNmdM7q7p41qZY9rmuRn7tzDPA0ewx+tQAYBDufucosMPwSjKUmZ9qa7q0RO7oqX3ogBs5paz37DHMTxKOftAU2Niz40h961T1B9VCxqYtbdNxYD11DBDFRA+HFrZx/0mxUJbkxmVdaDOmIMO/VigTVRxV0rn9S5c4zy20ZVRVV0UcxH744ejMWQjyWEMDNvfM/1jHViaYkIZ3lvpJIAzo79bBKkgFvoIMI1LUFyGzZZh8bKck2sHbdPAD77D5doVAPwKE0yj2el2IEpbNr7E5gUJAMOxUIt50C6OQkYjPxPiAzOUG3qGsBAGvd2q/afT4HG3IT0YnKFMHbWA/KQGETz5ir56AUszaBe+oR1hEw57PXXvSE6QkUOkOu+IYgJd+PVI1Rk/i+egB47MjJ77j37nevWAjg1//7OADem5zeOIXaSxXFYYzR1ncoX3rA5A0AOOC+hQB+N/7DAEN2XjaSn1bvypnuxuS2fY6417GmhwG6qceNpGEFoYlAykdrbQLl+ZlLOW34WDpKG91zQfBZXCp2obwxcWP5Qx9Qu484nhIcQwH6uJRnW7uOk3tdKf4TSgvJiDZB5B46O4u2xPt0o0thuAecuKw7rQOQGhJC5/p6lSM8rrvKRn3ayniQ/ORvOmpnHDIZ8ADPaWFXAGNbI/hBbtiwDEgeunjCIVcuGQYTA5GbxRK/rXA8gKfWXzOqfdp/brh20GsuisVZ3kfI3ACkXH1Vtv/ssveKQWvPbWfkJ11f9Hxy8w6n/CvrPrX5JE6PHTnZlQBwwH0LaYzfQjngvoVidN+c1619znDAy7AMtQxkgI9jO8bu/P705CQv+FtN8MV2VGctqBZFbV+ljEK+kcs2i5BRI+lQ0ivsHvKjUGU1531FFSJQvtaiWHH12ApJIzeZzhXBaTgvb1MsMULiE9QJ1P6o/6gYzNRFw3k0FWzXXvQEa7KWZjYyDZuAm+NRYsc0ABY8fmNuvSSDF20NH2J8bZZO9bGJOcQzQDWJcHLgOUkapu55RV+ilvCearEmC0uj4Jf3XilG+JREuWONQK60PwEYdod2zL2QkN3KFSh/e0n5qh31xUad60ZTMSeGfBVHQRdJ3vjGJrvo2c64qQz+yThNoCmXKyXzsweA9bMmtF/NY7yxSmHQSlyqIGLP+QxgClsvJQAAIABJREFUNM9ud785/DhJsgwAGfcNP9sU2q476cCfRH+9342fzE7y7J+egPUA5SGonS3xDQNipZ39IYBd5Y1ibjINyys6o6t+eJxPnAV05EIj5zI6xp+IkG1m9zvfPTVHeG8SBhsuZhXNLWuG+Kx5o0puIzo8XmcoD4XjlZu9kyzXDn7v9ulr13V7iYx3MIzaFY6vhMtr/A1uA3+qnAL3FW98OrDUEB/Jsq4SqGB6r8qXaRvq+Lvi/VNXDEStpvpc1z7VW1deaYDvnTPjtFfN2D4srw75Xu/8MMYPSxD3xFfsvueXq68arAo598zNp212DXqM3xJ57MjJ8HDOkI4dQD/5afSg/ug49pPPtiBrR/s/L10/c0vjXIpbT8+Om5fkfe9X7H7nuzVf2cKTDsuOLv08tr2LZ5w2icf2nd95PkhWOAAZuW4ylxajM7bIKl4vKDNiJDVUECBCPp7NKQjIE0kfTyErCXj22sDYQrwPVFweFYzTKuy7nzlkGkwbuG995aCkNlnVdKoeXneKUfyOTuz3AEovaVRV5LaDaSpfhyRemcCeTSbLubJLVq6MERH4KAANy0HtN6+cf9Loqd/rnQ9Fe0eaBu2rT6Z6MfVJkkx1ezhZq+dqTbSSJ/b4PKpZ4txeLpCZ21jPwQESiBVBFBRcgSrsWyUlrtzmeoLVsGSuuNhot+Yrdj8Z41MmM0bkwfRv9aPPkeaqsDGhZZkxzgLwOT/xzHZH3id0ORrx+JJuAgFrcqpf/xkaIF1FYxEvOlwT3ZHSQ7IikaLeezjnfzfhw+RZ8vYf9xlK99ujJkO9BXQ2TkdLGzVYFy96LqC96AN2P/9okxlNNyuB7xktaYV36QzKiapH1hmcOgAoSul89YYYZKMvcmu/xj3SXzZYTVCgi3GnpsS0ahli1VU0vNMQ33tSMjF8j2XF/z71SJdLNEndUECflBfesP7CJ7tO6Zh608obSatAx2oPnmiDV+eqmPahuOe0OqFula9sDN71846YMm1ZT8Woz1elPmI2S+vaytLPAL/yLrK4DwP37Vgoz+whV+545r2z7+O0ctuqATevnP9RCY7fOdA8xcRvYQ5ZGuMBlKuuHKR2balw4Bzwl7OPGqw6HztyMpK5DbyHrc+WtkxodB/kSre+UFDGlsuo9ulr19UcsgdbthXFxQ4h/SmexnQtWvnqG9uNojam+S3/9hzarsWlpk0GeEYvDS/Vt0MTw0NC533NHk7Ol84aViRoyKymhC3Fh4wjjMLVKQVHIZjbY7Xqp5xOqwf0sd5XD4/tl/h7QnhOtZlJBYCSUWxE/8EAlkztKa6awI8HgMwlE232KRaIqxFIpq3ABHgsQ3lCADc+fiOAzOCk0afeumq+eJd7AA0CqGSDl8CKQlnRQqw4gDI4e6uz64AJLygn5dLm2hJ6fB+6QSTa40lH0vKuq85NbP+mBYATknd2/9du5dZCdPVIH9rgXM7LJKRePfEhaD4Ex1sLTcPuJUyCAKtCqqLF8uLaQL8MgA3nHz3yK/fYhkkwl/LwQEHP8wvdP77/H58D8Lp/PObQM3cB8KeLf/BQIA79+HuP/eLrX/jOz++95M8AXvcPHzjs9F1hDNY+8cBRv/0fAB876MOXvMGnXfXHv//Xh24CgFHfOe6dRxgGYakN3mkvehOw+2ePDkZ3IqdjtEzYvU2AO/somNhLeq5hAGPyk64v7+6KuypOFV7B+sp3ge9UPRusl8fGhLvnxfoetug700r0d62K5kMBMs87fYcjzI3fPUHYCXm7Zq8z8BrWlx4f7Zh608obEUzmPrW+p+sJQE/OyMuW+FuLr63Tcu64KWfd21Pt+FgtPZzx7EMm/QzwE2fOHbp2DMurUCg3/Nn3bXve5ptW3njy6FMB3L5TQPktF7bHb3FkfHH7mfmJ1xXfO23Lm0RjPIB+4uPXPL4G7z3mmXbc+9APpz609sgP7Lvvs5hw3KRLAe/Mvb98BHti7epVeNeHe3fFfb/517N+veaIQ/bb94U1s//w5qUfOmzfF9eccv+v75t44OGqzrVr/th55LFPjIyj/itK+z8vBbDhs9sMu5e9l9t9Psta+p1XPtox9Xu98weYbObVKVvgOrKTioBSDzGpsh7Pc4hlmVmAs7qlYD3MeRNAQsdyrdo8T+Z2RV2nzg6IK3qFfpkK6hzwh151j84dp8NfFcKO9arrg5fj6lgcnmfcCb+7QtWozkZj4QhmFY6HSlXnnPGZB9C1PCrqja5IlYTAghC0rJ3k2eWVrzcJbac3X4dbV3ZpT9qbVt748c5TnZTXneW0xqVSICmYwAKGtRLcryFZ9IHn6/CQXhVEq7bAs2t9ch/iHe9PAgpnwEcwzvOSXQbIKVxfRj3zZXi8ZD3JBqt8KAIvHnEGZL68a1p2/DUAaJg3PgllV1BcNgvXl1cuL1Qb6eptg7caFbZO/dwxZsxo7wGMH/P2g3+w7sk21zHq9RMofYPb8GS225G53+dtB4yGg8dRbxvzv5aue6rNNZ75T+z1ro4RDrvs8+l9F33jqX3Hj94NjOD/OO83axf8Zu0P3vPB73fsRg1kBN9wECeMvOEJyps22z5nCanljTa3t2UJgs8VgrdGSkZLfIVDMD/xuvL702OKWMT0r4FAPx4VXkvnYJ1oaJQbR9C+8FER1gt8TxUDLOrbJYbyJLlcClE94v8K8NWvSd0WHuPgVT2yK/nCAnDeJG7zPqk2OUVwpgEgHxOdzTI8mBVYr7uh3qSoeFLXCKWDrMTBa176IZDhAX7HE8oBjzhbGJ7BDo7cumr+yaOn3rXmhq16lifXzxvVPu2pweazI9nCaHgt7HA3GPR2hOO3vB4A688b3/6NpQMyxu8xcj/9c8NzK0fueWbFwvG69v0BAP+x4QVgt1a17Hn5CRMv37D2+Hvu/zvEMb4vGaxg9/yU7+if5fe3blrCYdlZZXiAr0qmshElyd/CQJo4mgJITO8VCRZ9o7C+TMzjvM66dG5ok1MI4vKQAH0VSE/20Aj+TLBy1xur/OHDbFtPu3X4pnNGqor2XW3Kd85I1HR1hlEh0YIuJY0Uh19MX9bzrcOnzFjew6i04usk/QfASfs1XNHVWkXwVmlWNfkWgFoUrOFJvQFg2ZQIgBPlGSNcbdwMH44twXnQNfNYOukPfgbcAgKfYBOdB+BNzCBHu6wRJxChGoiX60NL4j2q4AyArPsaCzKZHF1VQmwH5QnPnSjwTj9tYZm4VKjKvagvsvjTGJTfn5b93TXFbWcg96LhiffH66fUifeCJqvnO2ER/Oo/PV6rnTJJz8B386UN2HffjjYf6ln7p/VvHbV33uYiMfh/rzf77Dd6hPd/NRKP/OEnB+9xpPG5BXKft7G9mO/A6/b+14+/ZvZNq+97xwFHCoLP2zzEzd42vMltyAMrdncDwIwQV3nB66EAKGWc5IMHbWxk+QnXlXdNC4VhOZsc2nIAaDQA4ZbPM4HyGsdn2egLxYveQH+qSDiQv0H88yGDRsgMK1yWzin+ecHi4Rvh1UbNS8+bK+x1Yek8x8GnMF2rB1JjOb1izgAoJFzFe5zSMXXB4zdWA999As0rOL5erf5EuErMQFpMVHG1XR5zx02ZtqynlJQkapcB4OTbzth9aOFYn8QRw7LdykGXLt3J0sfNWN4zd9yUbd0KALi5d/6J+w875bIQvd1g1UbcdoNS1fpPjyc7dx/y3AW/2u2b79hVbXlh6V92mzBSl/nTRY+1zyFE/jcH3rDfU1NvW/yWW384dfVrJ79pV7SQPfr3N27/+iBh9xOuK+44c1Cq2rTz5hOLYhAUP0Mjw57zA5RhBF8VQvAuICyGDXDWMG+8nocpB9SKVD3qtX9OHaEaYVVT6JWT15lgp6+eIuI6nTFetyFM3/WxST1GTL964knH+tAB6jCvf3M+eDaFqmJipU9Z/2KXOidrEmc8bVnPtw+fAmDG8h7Jwg6kVkXHuDZhnNP9Wrn+uibACxhmn1thTAeQCQr/Xu/8E/dPaHC4pYF+nVF7xB9W8GgS4yrgHkDmTSYqkL3bpxNVbYgdcHz3YgdqJjvIfQgmQgjiMfA6H0B6Y2lp09h3KhyI4hWRmVfODNIvxuinR6FsawMciz1YcdTP1Z2jPewTYflRdwRzY19JAAGnbeSHmmLoJbUc94yNPnfrzj0K8THw3mHJT5/98HFjOsHu+qU1wPOrsl1ntMU23/vQH449+oDRgvsnHHLMHw715g+/eePTbzzn9dW7aAxs9vyav6Cj4fOA4BseYEaH3f/lxwlRnaKr48zuba0QPPnSW7G751lMGZerWyP54FMbfC67dIK4ekR7amjX6d6NTT3nf+hFO+VCyjhNToe4Hm3wynNe4K8HsFf7tCfWdTuJhuewdelQ7eLuE8grPa7uAK3SC1s4Q57zFP9ClJdFemwL67ss+zKuy3MnZ6xBfJcmwayvB2cmDdYDjndlfDV9y9Fiq8nwAF8VGtpdRp8VAGFAY8pBSeoCVAbd8CFORn0urJXzOoIuurPUPHTDiMrlMx7eIDVVYmSgcsMEZ7dX8vsNp40Prfom69bQ+2xkNV6wVs31dzr9ERcSEhbrAUxb1gOAoPzZ9y3QkwskXaXrqw7qfZ0/FNYvcyV4kL47TWdufPzGk0afGo69lXhtYUjZRsFyWTrX0SMyf1zUA1B6Lq9d2Wh/6Zm7lRTyWX2olo9pGMIAeMOJPbwag2k8psQzbIYPM0sZF4EwfmcxABxCBxNS1FCxstaPIQqO3g1Tc92KinoezuOxmS8XTs+Om8fedhl1YORpMbLuSxpWHeSxl7TLJs4wxK/etFkAmbhw9v6/hxePPujq3GHdUzPX7nH1gbvCAE//0ez3jrxBnWZWP/rgD/d931faPNavPf+pPb8yZlcA/ulH37AMN5/+17mNRrpg9rLPPLGwc+8fjXDhEm0b9AXatjicJ2w2pKJvy3lQz9QATwO24RV2U8hsXFKBPJfxm6aiyhcvS1X0Gc0YEnU9D+qalTbmlamiDscDNtEhpzlhtbpeeGd1ghlmog0vuBra9QBfpvlgEvIZDoELvEdxmARvCRUCMrTr3FSmZncL53Wpk119aA93vcUMwCf7dPxe97gp05f1lOHLQn1pWh2LFj+3tgwP8DuePHTxBHKy2ymla3lP93agrqfgWponnTx66q2DFz735Pp5bxl51jNbId9MkEF0tRt0GcTAOajYOQDrzxu/ZNnSE34D4MfXA8BrL/vY3rRr6VpMOow/4T/+2T0fexzAPTcAwGsvOWEfPPPonkufxp5ve/D0vcnnDuuePOa23405esLXzGN//aOnAWDMe/7ygV01WR8kHA7A8xcfs4UXwn7ygyF238+5J746KFVtnoxqn7Z23byyEosxSDKl89SelTc2t0rdO6EMD/BVKXMLcOoXp/CHdazeZQVj4heE6jrqYDYqcCrYHbyxuovD5CSTbF/ihVUmjVJhtblRuxKGHaVP7aPaPq5DzUytCs/TaD6qFsKOeLAUNhH3s0aL46wAwHljaxaHSm0JFgcAZCbZqNqVhM+1PDYo94xo5CF4mtoZsKrTWnHbQt0hGWwNBFkasP7fCE4oZKywBgxLFV+r1n8qlqKqajBoSgmUe5MhLUFNDJeTxisahvKE3VkHrqhvKh6JGqbHXUo9YNRR1srlqCaVMbYtst9A0gSFRyOTYEfOcEoNYxxPWnejcN/zF34IgG96YrsjWf/p8eo6/DHj3g7jyFnymHHj/zxO+5M6vP7t/zP97d4Z0r0b4/H6vZaevRfgjHnbunPeBmvIY86qCEgOhxOVu1bIQyvqR+RcYITC0Hlc5idcB6C8u0tsRRGLx6X2oaus21jS2Mzud4F78upYwNo0o0w1J2y4s/Js0YNXUdEToKe8MuH51Bp7qHV5MjVMh1r30E52ArhNKODTULcKKy0ttZZRL11FD68wdNhF152YCeTK66oFaSpL9DasQ/PkH62buAzbhhH8sLzKhUD82duOxbYit/TOP6VjMJPTrF03b5/dE7j29NaJmttycWu+bPe7YFCC5YIUt56Rn7z5mWT7kg2fPRqWv8E02G95lhotlRC4Leeg5aH9rmmJIWMHF3Ked1tnHAvW92EZoAwP8FUR63ucdhGqdhmgUbs2EfMUuALJkRSuzzZbAVJGUIHgVqALUmiuRXwDoJ3sAkCRPKYK4qvTWZuE2CXNF8VApGuQw5TtP05v2SVQLLB96QxCE+u835U2iI4kQoRaE2OFIdVsUmFSGy81aw0Vt1JY35AsQeYRQyAwkaR8JOK7ZxBvMul7GKYIrKfWulV/nkvXm1u7d/t0SA1k5qcgvWCLbJGBhuydYGdFy5xIhIkNlCXexOuTllkBcE651Gmi2Qh8QgeIr1boD6d4gBPy1FBA4SCdi9Y4nUNWErt6AIZ6J7McGkeBR3m8Xg9rAsoL56V1At/iCUmwfrcrfwSAMrm1z1kKYP1nJmgMlXDRF45gur5SPag/f/ExjOAYBse30jQse8ztQgg+AyLFDV8FBblZA4CiCZh6NvjQkbCJPXWds8okn5jYrVai2P0ucE98NdFBxRunjkr9bvK8nhmW+tXrMDmXBsuloWIegPOsnPeM/al/Yi8Fe3zqAacBOhXglUKZ2yOGVj/1Cx4s8X3y56TIu56RFh7pRRld2Khjw5nnjpsyfVlPWkf1vKH4K3spbQUZHuB3PDn4sqUPXjR+JzbDb4dy08obT+k4FTtLWpptLoNrie9LCGSTkX7DZ4/2pa8EsxHEJ1P6unOOqofeCUw3RC+/5cJD+51nDUptwzIs/cvwAF8Vk8y+ItzzxniFhZLcMANwVW9RUnvUWyM6A1UgxE3Vt2mUXAf16sRe86am+BuAcxzkpnfpyKb4U3kecJudUL6kZtlEtFIBsSGxcJJ4h8/ugenLeuaOm9K1vIfNZonJjb3Z9QkD4QpVlJDFqi1Nl8z9jUKSNjgpq/K65cxlK9tNrQ1hI0npY2/aSIoZH5ucQ5KoY71TGFuhDrDZudIc7lgrbCF0pyzkyUS8U1GpIKlFSU1UpLUq8JJGWDGl3aorkvKMIAk9o3osDEfc6Tw0AvflRQIyNtWbPAJH47n3tb6MTe8mNbHmcd1IuiEOT6UYvNIDeP6iY/jsTbfhgg/pJ2Dkl38EgCLcRl71I6KpEfcUA3DaGO8lBwzig8VEudS9DQvNXdPG5DZhHdYgz/ITrmVPujxF5Fo7UofpmY2MN3EpLxI3xkbrO+Wuz8R5nu3uFd1M7FvwhfnkF5XzmugGCBF0vnSvYINPre9KB1YKB7YOWK2YvUvVzICnTxw9tWfljRvLuLFi2ocytLfMDNsCwetiyVtXXSa946sFWoq8CvJuD22AHMnOY/t5Vcl7L1+yk3Hd1GU7cafXQsb4Qa/2qfXzRrVP32vktMGttln8MM8nZo1BSP3p1nwlG33RltejZRB5bzZbNpx/dDClb8PcMDuHkPV9W7di6KR73JSu5ds+UVb/Mozgq2JLymwY4+AFQXPCEImGR1gPOL/iL4m4r2qBRh/zuUR/0Mp7mjUHOplBqEflZs2EQAZMs5pUoSo1jm3nGsdL5YSuGBLRlXpAnK+R8KAovKoun6FbC7CbeOEYQHzpjUmCDBy7T8cajGHDtvwkHJzWboBKJgnqltBBAARHVe8DIUIbjzJSZ4qCCDkxQE/JhYHUQFzAsKWb0ZeHsG/CeFg8sa573927vPeCyAnCeukrF66U4K+VJjFq50YzEw+k45vF4kY+MfRdPaOQ6jGlWjESLU2eCNYBkl8u4Hi2CqsO0jEJYUWjsAhDWfFDeWiK285gGJrb0OcATBG1HeKS71PMpbBYQ7CKhmZFOO8L37hu2YVPA3jdleccet5fU9E/nfelh68DgFF3/Z93Tiic+fOTR37r0Z/jdVee+/7z9gCA1Q/97MDF/wPgrNOO+8ZbBbtnyVsPQvBWkduoOPiQPCY/4dpy4fQkij2Q1Vj1IGbK0T30GPeq1utI94oGKN4UzWljs0hMG5acXUZ/V0yiRVNYPP700Ze+9E6neI4lfcDHxiXWd1qapAzDeg3ueV1vpGKazUZfPSqe7emnQD8gsXzts1CB+PXC1fNK+LtPzfZIv1r6RgHh4zmkOH4Ywe+ocvBlSx+8aOcH8dsJhe0QCOH4Qa+2aC7OBwPEA3BP7Kg4ftW/rcZHjn3hHybe9bf/c+Hda1fRxgefffvZE5//+4kv/J93Hg0AfzrvW78/4ZyJL5z7pjv+5df3APjvJ6Y98uZH/unDL8484Dff/dWPtqAB21xXsaMLZYbd1q3Y8WQYwVclbzoEXiSKhpf5NVvhFBZn523B8WmAe4rp6IC+JlTeW+XLnFCswRg15yzJfhqm9B7Gp24DoCYBxKmZeKFTc308hfW0kXB8Rly8YTpKTdLsa9qfP3pZeyBhprNSbaID4Mkt6T+81XHkrah264E2tKFwLXLCcuMM/9QQSy+DUAG9tBJ9XUe5LY1tJnUvKFVAAd3kPMH/fL3UtxwiLy0T/USsgW6f4SjkoEOKOE8ILx2lqBEgQhsLgMluDWzaq6obTCaeCypQnq9HtnPIsAvHSp74K8TRXdetnitjKLQ9Ady5KE+owqYCnVnUB5jMwpZAIGtTd0VMrPVMstz+ihc1uwiYjsPf/WkPlPZDhx946NXrV+2Sd/g/Xb3kqWuXPHXHxCOXHbYbALPmuesOftuLb86BN5046tG71/5tx3O/x3sO6hiR482dn3//XZc99NaJ49qB1JGEFpnYwtui/3wkoD3h2vKuaexFT1dKCWMa4ldvtMW9rkox3GMtASbntFYPm9E4PsTBE46PKhdvDCjBTPlDXW9Yegnf0NHw/CCaYDvnknu3T39iXXcgZghsdHWY7gPhQ00XE4C+NswnegZ5fivIG7XX09eWLTfqepSrvEGtQu3z363cg1LoDs1EW/2SDNvgh2VYXs2ydt28t4zc3v2r3RNfyToGE8cXd5xF2VHzk7+Tn/ydQay5tYxq7wCAPb/xT5NfnHUAFt97xIoXAKx6bp2U2LXzjfjNcy8A+Pmzz2/h2fITri3u2N7v6RbKq836vgPJMIKvSl44CGUV89JbogkzZcOGjRy+qWbeLkSX2qrt0HhxbSYXUIXHvHCPi0qgOsszYYqq89CYmBU+2IHYJ5wDgj3UhBfKAJwYD52JvGYRuxtpi8LrvMPHLU5cllVAMBu/S8PNoIp0DH1oWG06mygvdONTmJ6lKb0DpICyhWtpCWBbcQnKWero3yQFNBwpxF/f6lS8tbN7SerjapXb6Lrh5V/iN2+k22kpaqT4EKpGl6EaK54iIX5DnTIDwEHwEMBHYlVLou90WdnI7DcrL0vuupUnCsRMV0PwEMheFNR3APtxlHeelX2EPcyzv5tX3Ho6IMHxjRgo7wtX9ZkOS0LGhWMe+4STTGpwHus24D0dHSMybtKbOpZf0f6pi1bdc8R7Oho49M3tZpcc3pscyEzHG/4KP/jDj6a8+RgDk8E0MvMaUjJU76LJ2Exu8hS7h+Qx1gixfAZI4tdMEHydok6fwvskDEGLQeI8UVc2hWxDNkPQ/En8DsF3wdORdj6KunEVw3xFvOSVkeD1JNhdEDl/GDU5nTwgGuIHPVaCpPXLqJ+1li1qaUEnSZuUVFDrvhYyt5X13Xs2rlc2AtsGuAcZRvA7gzx08YSd2Kl+uzLDf693/omjd7BUlUWxeFB86beqhNDw8s6zthqUf/ZTP26/9gMj041vPO7QPoq//W+/P3bN3826fcTM24/76R4nHdC+FZq0zaSmnN9MGdU+yNEfwzKIMozgq0KIJys9BMcHVN0k81IWcTwb6WMwO4AEf0U/aKLDI7CrIK8XHO8SHnE6mHbxLz0R9B4HXbr0wYuYifPgy5Y+/HleF+OjAYG0xFzoQ7UxNIB817MIzUUfIGlZlf+8tIlbIUb98FMamaVh8cYjJT0LU/OQrUvXqufRRBRFmWTPvX8BbbfSCeGM7KwrJ9R2W21mC2d0aXnEa0s8KBLKNTW1Dzz5clFen0Wb5UJaP0YbTFCvLlTqjTc6hVERpPHWEvH2GVfDG6x34S620V1DLp6dSYwXb2rdj/ohS+v2nBsYkKdWnLHFGB4vUIl+rBVogvRdQ+VJE68KonjLJncDKG4/E2CzvWk6T7ifSO603z4h441l3bTLFA6l/+FNfzju/IM4q3vs4w0rnzWdI/KOt7z2579+0ezyWmD9ymdx4P96rdkFE6eesnEq8OjDbb8c9ZlRgROeLxsQav0sSXPHdHWUP6YhnHSc7LUBBP95CXkPqeFQQeTga9c3QkfDi4k923+2e+KroowxST1WueXTV8igkU/cWP6Q62U7ejS0S35Iua302CjLdMgDO6p92tp13aX35f/P3rvH61WUZ8PXzHp29P19hH61USskAbIDtR5qW4MKKgELQuBVUSBISKSFnDgICIL41b6132tFOchJINkJtLCTDQmgeCJRkIBVPCHWQrFl75AD8VhaZYe+LdnPmnn/uA9zz1orm9M+JTz3D548ez2zZmbNmrVmrvtw3TESLh8KjMUzUG71KXB1TZhMCpqfin3TndQvPEeyJyLVoJ7wNqecupDU/eetRPUbiNUCqlSofyIPDciz3zoAMbhchTCmgH6XX+CnTJky3l0YN5l18T32+25MbzcBY+JHSawv/QhmnCNf+hHML1duvKTovrDsv3ikKmxo4s6FAIpjE0v/i6Gv33jfvV9542HXAnhy4Mx//v1rD/l/+IefPnb7n/7B/QD+cO+Fy356zeGv+TB+eTv++MbXyZn//OCkz+MrN+z1XFohZwLu/whlhxtx6TLctB0ZFxmzZWuXX+CffPLJEaxtypQpZZF2WGpBBxC8I1hftEuoeb5IiLxsxQygUw0AfY/GGMK8RhZ7Ogo2FjBs0B48x0b7MmrlgcPOLu6yAAAgAElEQVTcHUCJ4aPmkOUWxSffpy7IZtNA5xid23lwdN2P3VZGtUP2+wwcKTeYeNRnYKNIMN1pZyxtnljjmMfKJ9yfbXpjat+OEggtGoBe6SwdL81PmZbBZW3V0X/Lx5bD8d0L7tx0cyrG9jyG4XynkIaFgwVcFBLACI1xkCbI1ugj/7RlsIe6RA53T2xfSTfRZxH/pVReAMoTTuA+wTiHoCnmhtrritac9tA6yYNXZCjFMtKzniYg2nE0qedc4Wi+shXZUuWLH4BmjkeabQBQlgxVX27gprUuh8As9CEAKL+6GADaoTh2JSYVjFEpRJ4AOp3LMfQxm86Cxdbf1Pee+wHcvhIAplz+qde6/h9M+vzjAPDOQ3bM/x0a7+s+9X9mf+K2j2LK5Z86fOYk4JEHJ109gJl/+tO/e+1ME+CeXZrhlocu6qzHMsHurUIi4OVP1JLFtWoR8BzNX2YQmIT1Hy3X6vL7nh+2XZNuB98UUg+0BLszx0CrizXzIYYICsFIeJ346YSlLmRJEPht4wHyKAnTJi/a9FRPiJlNncdBbPDyU7q/iWyO/6zjeBb964TuBbcO9EZSDNReShXy+QYWevluH+o6jo5NZ6mygf4k//nSBC4ojpf3eRqrqpbAvFlGdtnCzncMu/wC3xFVzndktOX4bsopN0bgZ8Qj40cWx5Ornf5ZDnx2mMIvXogVB0D71ueN4486Zd6O+bmie8qBO5YdWC33qgPu7zkg/fmGWUMra2VyMdzyY6p67cjYy4RyBnqO0lngqzI0KVkNvbHEl4UjB3sL68nUXcp225F5nj8Tjkfu+J1x4ZnEcchxIePawE1axQCV956Bu7Vk25xeyQUApoB5ETkfORL92SQaRrlkQjd+55nRKxSxFQAUrWQRU50B94Twj+mmZBLPIgnsUKjRnREwtcXo30H4+/R6jWU4QXZrubcJuBOGtGAypp9idLdt7FUEn6HwiFLMh5DgYMklHwG0gyNo2Uou4Rx2ESLPDnEIyADGlsEV6sT0s6dXmt6xYdEzrkptiTGVtTWSyJ57anG88xx4DoADPDjkvc1jJJyFgMB0NdvHAkDYcgVfP+D3PY96Vj5+KZyEbgdzE/j7JHQZ7GMnPut/QnaubwOAawMov7QwtkPruBsBtNec4kS1AoCc8tprTsk89q01dSjL6VDlTzC9EGMvjY1A9iLNFctaw07yrSK3nUctzMHurRbj9S6L4AW4My1gF4Ci+0LkUvZ/hissA6ri/L7nh61XCnYnojr6FBxfdEHeGK0umziubMchCHa3F68e9RIBj3RRgqAd3LbtK6dOXrjpqeU2+VtbbPDsTi9/aj0xEdWZFxqyAhHppxO6F6wZ6FUlgX1bWU0KSUBVDZBVazG3lDfX1HAW1UY+QIs29IWIMmiFpEZytnI+1yg+KiR3DfdwNKWzwFelNPylJb0A5ZXBIW2kkTbrhJ6SJZkFoL54ujzTe4wdlTLts3VvctIg1cBqedoKUIW2sMtW7sb0rxkPaV09rX3W1dd0RDTw2iP7NjadcYC8k1utQPOeFLrEgxuymc1xdKHWPUD4X43uPUYs2tB3/Wwif87YgDjDp+NitEzaHQCbXHzWRH7ZfAorVulYraRu0qz+P9htSxrU9HpSNzbr3CdaRB4B8S1LZ9mgu82DPQ5unz0XlbSOZl1ilTR7RRlHSJcmEW9IIfuAofa6VmtOu70umtvnIr0KjN9hDHL/5E8Yx7oa22nYdjUAhOCnnxs2XR7FWmO6S2tkma3t1grAWt2S34JlivpLVbhQfnlx8d4edLVoSjGG1oPWNcvww7iUkrWmh9XhtIrxLBORd4VXbwC2GkhYKiALNpAlfvU+K0DK+S6TS6YlSntfAHCtlt/nvPDE1ameEP3UD7uuSZF9GwMADjKU7TBAqzgt57R7MAt80aXek63WnB3l+gimm40xWPZZOziSUSaI9j5tHJXZRue6d7y95JsWAaAd3VDpoCmXjKiTnc0oI68auujk7Tt35oJb+lcFk2OonofGvg1CyG5strHILjGPZ8t+yYRmxNL7Vy+bfbJWWEcgOo+I78uaJKKo9+m9WpZjGrnWWeB3YfnhRUcc+Jnd06uuUcjV7vT7V493R8ZBtgyumC7q+m3bVw5f+FmF1ngA7ZFzu1MJW6/008+l72X/Z0a8fhA3zgfYA5F88aBrvC1G8fQvWkhnUN65sCESfeTE73Ne2PK5ysGw7Ro/9cP0vRwYlcHsyG4snQW+JrThNRrkILwQzqanNCAkyGOfIXjrhedc9lOd0Tb9Vtsiuhz3m1NnXXzPDy86/C2fvbseHQMt1gTo9Sq0Rqv6IjjqfbSba89I2kHxpcJVbtFoEbyoAWhrXyYEL6FAoguIqQqFyPnVN0h9kFQsb2kWLiQH6hQ3Jsov/WTofwQNOO51BkokkNIjYW5bH02klsROWkccQQPiVEjtGnSbMtI6ACCysOmTFwVE5bV19btuQJhDgKHIJW284Pj1DrFF6nodK5/sU4gSYaXpgaEEqK5BrWkGLmy7BiH46ecU+18EgFPUkLK9yONBbT00RdqeFPKiNTKVgyG1K1z5tSWMGQ0/TPm1JRbiFe9L9GoaZx8zcJfXbrmkvANQvP8GAOVXl7BxxboEeoPg9aLs1TlkBTLHOgPui5YrWn6fj7AKJHORiwDCz69FCH7qh4uZFwEImy7TqyhmXMD1eJtk1mSX8S1WzrfmDLXXl4Z0Nkh+VMtEK0i6pAJWRc8KJ0bw6b5F83BBJvxQ6Z4pU+ogUbA5AO2cA6eRF7aOpLlyAfdtU4PF9Hr/6jw2UZ+7WhOsDZFLoaro9irH0xnfWn3dIfOW3NenlzNEBeTS2P2UnqMSML7GVs1XFGOqo+8s8B3pyPOQNQO9J3QvuG3juOW92Lp9xfTJi0Yqgq4tUH4EI+hUwtaryLTv9zmvmk5+TETBPfJwO5UKxCekntXw5bEIdfP7fIS9GXYutGcC4Kefkw5uuQKTXvas9dPq/iI72RFa4+2RhRs66WJ3NYk10Kg0NqXnEBHzk9MC+qf9HtTQbhUDTdAzga+d/mgspi4CIPa6Zi853jnn1Le1dmNkKCaI0JhUg7NtZeiwdAB8Eb3NA5sVzrthSqhFP9NfGHQL6RKDaANQFV9lNjC9ECDkyhHr5ODyE+msbO+vFtha9xVwG8N2irujyuXOEr5xkLC3mF+TuBdFGPs9b+3NOAcLIoDCdEoGTIEUIXVCS4TMUrIZQNPL0vgAgIfETREzCaWUNfeDcXxARpzCTnYtAIiB88HszKHDS4hdKCFK+7DpcgAx5PRC1oZZCnDn+so0xHr10fjfEYK3HhzRowjVs6jury7JLP2EgN+bMahny3mR08e2LDSvYfdCEvPYxK+2Y4XPrPWC3aEovKsrJXiFjDPf7GDHJ/zs83wQyYcuPyvVE51rUdQ7z7USxsQeLKBnS7zTIzGGkKH2KMOGgBhj3GfPxZueWh4jA1953HgaW3ralg0SjtgRqrqZChMO/TJ35oJb+3vFqA/UcgkNkcbHkh653MEtNVuLoDOIX1884u6alApcQCoiri06eO0hJztlfooa8EvvN3qOHICy5Ag6GoMwtuliOwv8rioa+L7TXcFuJ8tmz1t6f9+4X+/agd65Mxfc8fi4gfgtgz1T91i47ekXa4lXGRpNHE9iDfMAyo2XjFJDz0vKLy0ExnNCFTMuCFuuYHq7XUfIHWTLYE9odEAfCTlhVyOEnpjSWeBrYqE0bcoEDXj7izcAnY64CoI3tSWxu9b01cU4DLh/drGmJvvEBVevMGYu98PWacpk2VHr2+MkYiwzCWYsFw33KzDBTtEwSlys0jdxr0s6A3u9nIyjYly3m3QymAXuC33atLOtxjHJb5cNjVMLOoAUIxMTULYW5BAdt2ut+JSi13EEnQa2aZMM0ly0VywRcRwAF/i+lICml/UAfCQAFz3HTRZIgYWRvzNpCeN4ABJBBxCOZxu8ud+FwB8D+JBNXVUxEMhOBvLws2vkqqOflvTMABjcN6pZbPSFA2cvJgd7bxB88mP2wFNXn33zRx4F8PtX/P1xZ+8F4Kmrz13FR278wNl7AyFuvPOLByz/JYCDFp307fftiRg3fuXLB/T8EsBBS0769gd+F1A2WYfWtqWHfGEFALzxa9858qj0kwJ9QerapUy1laF212oB8Pt9lCPcvEXh5lPzALGB1/jSM2D0eWhc9Vx9lmwuGbXBWy/6MrRTeb0vPF0TgqelfdNTy0NEO0QCuxQLRwFykjdZlDA05YgPm+tMoXSoBLnJe+yEbnaeL8U1QHI7JSiP3NCuKptY/2TFAJe3xfKLFUVgzM6iS6Pylk/zL77Z1w4cJheC42s0l6Nxv/k7udbwaEpnge9IR563jLslfpREmHDuGu2GwhNXybcAwO/zEYycl/jGLzyEj51Z7oX1V1x7zCU/OebKN+GLP8ZHzyinuvVXXnvMZf90zBV/1I2tly7HFTcsPRsPv+O0e69+y7Fn//4Tl/bokQ1Xv/UDZ+9t6ryt/3W3nFdOHZEOggiCwtYrR6a6nUtr5JIMKWqHmpBGRyYmdqelnVJj8J56bJfqFyadBb4qLk8wAzXAiK1U0rymU5rzAeYINBq/6MzkXInz5d/FoFT9vSFs1EWGgARLI+ypYhmy2DQma7Hi8mH80qWABZFykFrkUYnpAipSN9GKc4D40AqmBLxXBglAPQxylUd97w/pHKOEWuMysFnyE/qpSYtQtb6DDe3pIK3xd2zspRHIguO5PnOPZBfPjLI2IwWbxCVi3o6fEBJ7O6oRALZuXzFt8sJt21cGdqFI5DaS3kZt8y29PDHMMyAjAOetoR1JlwDEzKm+MjmidUa200sRPP3VBgSAMqOt3By+fjbSA/DTzw2bLosQkpzSWMGdKF7sTLUMLcmrO3afeMTZIQA46qRDDlrwH/1draPmHn52jOlIq9W9bfAR/N6x0wvgd98Ah64W/m37I/i9Y6e3gFe8ARvR1UKX+qVvvfSah1dc8/Dacxd/+8TfFS96072iyA5WsDt1mLF7l9/vfAl2b/Epmec84e8E9+EcD2AQBwi9XueE0MaQ1BYtmDfYM+W6gBjAjvGA8tiEYKlqAQBlyhEMJ6mMIuL0yYu2DK7QAspjQ6llLKeNonM7X3aU6dWU7jwAsaDPnZkW9VWPrQoRQ00R7Uo1vcO4Z1TAev0sbVdVCJU+yCmu0pZezvJD5516bx+ANgcEOQBlm66Uj3DWLvOo1nWfphdjJJ0FflcVipF7ScXBd2QMhGlwRs0YP4xwmvmRpbx9/Sv2bzyy975zX/8Px1y5X3nwpkdOP3zZXkDcZ+7rvn3MlTPKgx9/5PQjlu1tz9l32QMXLtv2o3fM7XkHFn/7pFe84O74/c5/weeOl0wfUb5kKwrW1wz0QhbLMg53ytjL8kPnnXZv3wTr1HOVzgI/rGQQWoK3mbRVv2bfk2RQI//R7OzT7rJeSY5dhSKXijnYOHizKwxmI+mEZMommMkwsWRJEW2CuR7XXKHptVN4CoEcXE3YqXu1urDmSr78sq29PzvLgdA/nUOQyWXn2zj4jE1WPq2rvG01fTc3oo7LgcRK29e/6rju+WsHehEbdR8RpjlWBiDVG4TcTmIx0ghbBl9IDH0w1SJi02DP1MkLAfz86RsETZt5QtHP8ILPyOxapgFxDsZjn5OI2CFRdFgbIcSI1iT+YsvXJRCVBHHSCYK3fsxUoJW3a29tIclt6acoeh6IeT7jCNW7FQDgl7/Fnx3YTeywNAy//A3edWB3VwtxytnXvf/R2V8s3LHl+VMAwL3y7GXvf/SQLxb+/eVHpyTvd2quKLDvW779o1cuffMP13/omKO0e1686G1iJZtiR9Qbzhd+v/PD1qsASTmjkL2SHgZibld+OhOSAEs858T271IEPE8nyStTCm8dkALcAYRYWsO8lTJGu6hvHuxB0snQWDpLN0vYfYdB8O1E35Y+WS8jcfDEUkfL+TMGkQ/JRK+v9EOC4KMpUFGzZUH20l0AAZk5X9gvnK1hZ/x3TtR31qUgCnYHUk5Y+5N6y9gnY4y96MeUNq8jHXnBsmhD3wRM9kAe9ePVOlHfjLiQJX40an5WCVuvrJOxv1DZtPSm37vphP/XHNm89KYpemTj7QOvW/3nV2y6szjrxxvpyG0Dr7vlL67Y9MXizIc2NtfZfewHnl8nipkfK2Z+rOi+0O93PvkSTnyZPnnR1u0rtm5fsWVwBWnmR1xodR+NmkdWdvUs1R0EXxVnMKBs33iX5wzw4GQzJuYS3mV88hZ7KvFXhlQT3ooS+9vASB+iNwhP2qJfkikXFZOPggfDas4lCZGzfVdAm3C5wxxpAtn2MrKg88z45AVnmhQ17CVO34OzKFWo1yOAEJwYxas4PtWfg0nthKvASLNVV2ZxW95u0kMUTnjTeLAXaGJtkVPdQXx985ucRiykBBvpJw5894mvHomDL2ph74T5K++MXjsFJetFe8biOjyE3c150gcuY3zpOdsC0G6vK1pZ3jmnHHYgLG7H0ZDGVwgOOSabQpXL7Fz2DIccNL2GQfCG04088FNbDLUsjk96p/WfffzYFUd2m8Fa/9nHj13x7m4qsPXBUza88qYTp3QvPw1Lb7j0oQOXTXvolPteddO8V3avWITFKy598G3LDkKyrLOt/bePPe4O6BLTOIwl3ifM7ZzTPHvJndBZiJ9DdusDXxgEbzG9fjFmcogaxs57Jk/smjPUXhcQgkXtJuRduehFx8NvgKmTF24a7CljVE86+0g1Jn2xrzoesBhJQUVzvDDppFSbla4gn8/eMYi3CF5xuW2lyzcUs+q9ip5VdFeAuLwEo68rfAxGO0vVEjH29bPnEacNZdAglRpn0mp7AKKwQEl/MpoHgLIUZyF6vZR2OEddOgh+15Y3f/qeH150xA8+dsR4d2SMZAKC+N1V2u11xTjh+BcvG9esuvOQI48C8MSDS9f8BsDGNavvPOTdRwF44kd0BI882Q8Av330ETnt4X/vB4DfPPrwTur99gNr333wUTtvt5hxQTHjAr/fR8Omy8KWK56Vn26UhFb3cWn6OcptG3tP2n/+ePfiucri+/pWHrZLvnk6CL4qnqIfjH8427QKVxaGyc7EdjsDKvQs2rAl33Pje9/sdc+28GQTktoy1Jnxc0cAmHXxPQ9+/PAqjqdzhdrN56lVU20+C+CGMdXH4Jpc4pPOoCg41ay3lHPJY4E6Y2C9ib+Puls3hRWFW2sWj62PkGQz6igbbEN8C8TBgBUDqZjqNeq+9xXrnYXI9lN7W/DlmBqkz6SuXztgw+ccOKImag22Y2VwgSkBHICW0QyIy320kfdF1v+4ZXDF1D2YkPWJ7Ssg6hDRXgRn2qV/g+OKabJ51SFJuxrETDievw+tA8RUH30K0YYw0LHPiB6nGxNkhMTcHmOD97vjtIPSTc63CIh53pIb6lhw130qACBi/d9+6pgvAPjsCgCYesUX37r+kk8f8wUAl6wAgL2v+MJbMPX3bjqv94B3XAbgoPOWfPudLeCtN53fe8BBlwA46PzTvz27hSd+8I5j737D1X+5zK8rznoIAE6YX37ylQKvDYL3hSsKjmuH6osMFteDmXFdAHr2Z1eqlmLZK0qRfFbWH1HOCUtzLJbsP8+oPX0GSXzKSjXzEtMGSqN2shq8oHzypl3LR+CdQGTuBqCZ9iTV2h2P9x43g9f4Wwd6AeZbVEReGCRN4l1mYq/HqTnztGoB/aSfJFFf+hR6AA4FsqmfmUUixqX3r142ex6FyblJAQLQnTw/wZAykIO9Jo7jK2ZN05gi+KoedpeTJ598cgRrmzJlyvtOuw0QbVLTAk/CB40HV/QumJyw2QKPSrrY6j1OKnqT9B1Cdmu7wXoenxSq8HAuKrEd08fqw8ZvoaSJytLG+5xfh9bF9Iq2y3P6JCmK4IsIycLuW7RBSZWntpy0pT0RZRd92p+c/GktDnSkq2C6G+vDlPHRquLfLPCqnKdPWi8Ks3Z4/clnJSvneqmcEn7SwZaL1gZPSzsduX1jr1becpFHCalFL1Q/lvOHFng9i4a3i3PJp2JdKRor3ZJ99lwE4Bf/eQPkXeldwV57rgBQuC69Ku8KXuCJGIfz0BAfjs7gNAGE6u4u0LywiuJhFvhoF3hS0SsHThsAyiEAaO9In0M7/H7nl49dzAXq/KKNB80Cn9q165VGblneHlsALneOM5JU9GndHdEF3tLU7HyBz67QAWhUsRB250zWsV3GNmSBD+Z7GdvBONkpp820yQs3S7D7MAs8Ec7YHPBDHCDHp5Q2jo7vBm922VWNfTxxQvcCWuBLpsphFb3NJVPmd95+VkhvQv4njG+dNePkCzxfGjMCG9OnVi6J4U8GcNqGPsgCz05zssDTom4X+HabqWqt1fKGw99ev3EvRqZMmdJ4vIPgqxJM2qgccDvPIfL6u2zH6CNGNsnzGkzFeMGuu8cjW8uzn3KYzqs0m6jJsmWeueCcfQPE3DAPM4+tZZOjzIOERvPiSufy+iqRndwPbUKX7fqqz0Z3xMa09OlC5Thve41tuChiFl7PK1nUq4hw9DKquMfTPxKUT2MbgczCrUuWjUrXHpXmRUZiLVhRKmmZc4eiW/3YKjj1248Qz7usISdGblNM+eEtEFXPeSSFi9M3FBRk5DeY+rxlsGefPRcH476sKzRzlhHhHRPXO9YT8JvblOeICOfNfbFUd9HxVkBa7wJ0Spmwdb1+rlZs8MEEx1tjM3G9hTw/mzd3SfnEWSNV96KP+YQwN7OSsqEObJL3vrntaeeYU9MjW4PJMZBp6fTybVw7XMMC74QLz9raKYpdu7STVZxkqL2uAiSQG9rLWCrzvB7UOHhBrulz2uSFW7evKANrnCToPJHiScYEBffp0/LTaXlvtFNs8Pb8LLTpdQp3+8be47sXQILlnOxoaA9R5Iu0XbnbFW8EU8CZAqpUsLfcvIOTR4eNOZK3uqM+UFNnfms1gGsPSaQ3gdNsRlemLRC/wWIEUBSVLWht4o2mdBb4qkjMkoMCdP4hh/VGjcPzQ14Q9WkEl2dsoZ+GIZfJpoIT6hIHwJcR0Bw2kT7sol4JXLJhekKzk87Vsj7rmAAde5AW+yJol50T57gs5Uw2fbPOyDYZqkbWETH6+hBcVqEpFmM87d6+ZbM5Jbw+llmLqVL+l1dTBjRZsUzp7cW5xhSwi3SUvL1to4vTZgTl8wy5pX/VCd3zITg+xlydyNllAMD7mJGjmFEKctfo1eG5ew5pY5Rl+HAOmwZ79t5j4bbtK+kt7x1PYlbLxzIVRj4lzVTmBDbO8+6Im3AAhsr1rYJt0G1ZXagT4qZnq8vekmKZaGebAOt6FtnLLGy5wu/zEZsXNU1um6vGppzh0Yvpbpmu02hqITRKxUBgbV1wGd+OLwA47wHhpHviautkp/oxLQwv8WzWkw4yAsbnzr5edBR3lM254CIgiWHsUp20FzEGQu2ywActEMSNLvLumYclxCgg2Fl9MoN1mZMSs+4gAXJD1QU+f0JQHXgbcmv3xFClGvXHVODkC6mMWsbJTm1w1taws0HTz6Z+ZUJvqqBplzwAfPgfVgO45p3zAFAC2XZkKG+j4NSUOcaLupXOAr+bCMXE0/fdnv3m+tkn0xo/kYXgyPHdC27fvRhth8r1iKBc8nRkVFLNbrmC+GsBlI9fOuL1v3jhpX3z50apfo1UHGqvH5v1YdrkRaSc78hzEXWwB3DavRM0b2xnga+KNXVncWCQTbGzn1mBikofClvzGpLeT7eQEVVIKAXUYB+Me5XkouXv9MOsi+8B8ODHDwcSyLZRc3a7qhtSZwLkbOOqU7SgXHSThBdziG/OTrlzjASjwQ0hGeNtb0H40oySaGEdBOAt3NC38rB5IVkmM7hvAaTtQUVzSHo/Z4Ih1USXZzNNtan2lwyEmR+AVls/DSgj1PuPAEGXT99jys+RNA00YzjhrBM6kex6HbWr2D1dicELZYjeBag5gPG01EDGeFaBptuRaG5rAIfU+845D08rPYCidVS7va45W5K1YXEVAsMY4NITYqLpCq6J+VxjtBnQARCyBxAp5Qyx4fJdVDYgo2axNCv55eTTRWh+rGpBj5goQecLv+957CHPwL2Qy2mlgzb9q/NZZ8jQnszzxuJuVO4E3E0YZMNCL3A8QXMxsTmQJ11MTnaM5mmoYrROdrS6c35YMR5ZP1Y6jUhnnWtgdZUuMo6v09RoWG+wfwKgzLADvTbXi6kwSWVy0Z/WVOdd9ixYvV2Zk2s1hsvWxzfXNLKwi4wHOEn8yd7xC83eqRY4ZM6qJMcYy3cW+I7serLkvr7lh1Lq2IkuawZ6T5y5AMAdY4Xjn9i+ctrkkUwm+6zSlmyzzb++aHwftl4lm+uAEDTEfIRJbZ+z+H3PGz3gTtJFSdxHtQ0j44vd1cluF5UzvrX6ukPm/fndt4x3Rxqks8DXxEJzAMmYFK0NnsvmFm/evBkrvm4vG13rAd5JOsfpRBmv5x5AMQuxszU4U4ftSNWynwOVVDoE3ukLK0kEZL8p+WAyb/ZKqJtNb+NTAcXvzlffUbWuVgGostzCABH6jTgigjh5L5udIlMX39cH4wFk9/6Z1yEzt4ovntnmx+TFRt2oijrpWGiiA0tVDVHlxl2ucG71Y6tOPmC+hQA5TWa0VdkYzLZE19Awlnxugt2FUxVSqkrSgbB6QOyySSWQaIwJMLEnR/VOeXiFtOl6ubDY77l1v6NcbwFKmhaIidme4a8Qr0Zjh67gNEbD1oeOxiUglEodQzlny41mmS9FzRHs3JXastlR6a55bDIEL3fYF0ojzx3IiGuKzE/QYndNHuNNOF+T/50+2l2tOUPlesi9C7EE38QmBM832njSkQKGxjgFO1BV5lMUA9MmL9oy2FPyT9DPMgBsH4kAACAASURBVLp6VlaSHTlbi3W1q+R3iYZeXp1k7Z8nzlzQ17/KusslR5m6eiBmPawqCa2rvBHh3snca+onx9Sig/oJPVtUm3ovdbUiAF+UAEp5UwGIwUU7ycc2TK6zwHdklxRyYVUhRskJa5hfM9B73IwFdzw+RjBl82CPcon/bAyhfKMMCb4fcVM9wWi/73nZGj86wkSzjS6xIyqE3Ue7FRVa3cesOSuk2erbFQhrn1VOv381GeNPnWDG+M4CvzOxe3tCLfneyyJp3pnJZt9u1GQ7KYQt5vsw5phcVVC3Z2dlo3DBZtA3/Z4uwVRTaZuwuxfsjkwNUDtXts/qbq81Kq2vdWbOIu9z3UOmTsirzUy/eeucRcbALeLA0TKchSKmAuTAEGIW4G79b73L/HWtDiAFy1qbPQBrdDetsPcsx1VFACfOXHDHxl5Ext/kpizB7s7G7xBq7+LgopiPTDIYCm+xiyYyU3zVI4BNTy0HsN/vLBEAFLXnjpCMKG98pAj4htnIJS2Cl+/ejJYzn3TEu8L6z6cQu/Y6wDkK+87EPG5OELwmp9ExdSHZraV82PI5v8959Hf5+KUZJY5F5DFH8FnMiRQzAXLWSZ4lGdpZ3wVYBG/SttYRvG8Jgkc6mB5z/tJK2J16J/FsSJyyLn8bVNhnIWhe9GEZH61cfwRQhrjPnos2C3a3wd+KxevQnMQ7PGNAfPKekcGMEtpj8bcGvpcRH5y5YHX/KqQQ+SQaFSHh8oC5XfaRlMvJPq3uzb7EC8+/ZRWawi7/Ipmss3azFk0NzmHp/X3LZs9jRylShdJVIAsgGmPpLPC7mxz4mbuV9KYjL3GhtPEAxtIkvzMhU/3ouNx/jjcW+3yk3HhJ5ddixgWoaPKfg7CTPKWHydf+UZJh/BhGQ2h1H8sWVT44c8GtA70TLS3sixda4yvKxfGVzgJfEwscBZbq3/ppt41iaI+yuY5QA2JlW0gH60x2O/9Ta7AQS6z9HA0dGEZIL3LwmqoyfFJEQueLKJikAaZLJVRh1MoTHQjb/9KIkKuy98MrHbJW0kUxjon5cKcSYo5XE3Cttqy49eCGctsJMUBqQU193pyVbfBVlWKgueBvgNno0sGMC8/2TcQWgJgGmdXEqD/0MFOAmU7r9JMv6UaIZZvUTnHr9hXTJy8KtkbWAZTeuInY3DnCdxSE1NZcPIN1yGhZBJ8Y8SKC3E1nKoxDnMPmLgCuqL18VMmTZYMV/jsAodTQjjS4BkqGrVf66edWaiV3dw264/D6UEuQ45xzPhnarZO8KhXqRvoE0HOfeeR0dU6M9EjNJtoMwu7EQMcFTMA6yhyFC+QH4JzlCcjYbMSjPg+Uz0fGYHexbTsgebnnRG/ZxJMa8r/1PSf2+wzsZp+ZH36G4POcsJZRTrlorM3e9qThIg2OlxAVAA1ONlWVZJPkCgnTRESI6Dl03mkb+vS4vicDn0Y3bkz3NZ0FfjcUionf7aPhVXaJlI7jGBBPazx937Z9nKF826zxoyFMFmuZwen45ssZ5e8r+nyD6TVH7dgY2ivSas1pj6HP/PjKOKZXfglKZ4GvijPg11qXoxNOeIseZCMN2p+xKVRNawmFZgR5AipNq8N2CbVdLtsZI0Csdgleex/rKvpobeEWWUbOZJOZvZU5mQ76CGFoIup4qVPJy5LBKYeUO70676LZWGvkblY2i/CWFm2/VBbf1xcjVhw2L0ZnGfoSxkwWNfGmr+Gx1HXa4Ju9PNXTqsUmpI7JyXb8rKs8BcvFnDHQktBpW1YTQBIScEtHVR8k980BKBFNtQ6Ai+wpvWmwh86aOpkz02zdvoLidD2hQw2NgABQRMKCcm3pCqMgdULtBfPYe203RkaQ4pPh9Ls+TawdI1zLlQtVAn2xSWaDeAPEOvQy4oWSwBppBVTSF/aBbw9RzjcAfr+Phi1XmAB9MwiJUMLy11p6XYHpLveZBzICWglKyVZxUgK15tAQWRcf4YdnG3wwTvJZBdFVH5sE1ukzVDzn6TtlfGdKWobIpCUCdDrFDNBXAt9FE5bOsoOtnALWqB+BE7oXrBnobUcXtCqkanWK2buXFVC1jqm8bbUILgPW2Vst30AZjVX1aWKCCmuDb4z4z5RirufQeYs29DXYHVy0XOLDqAdGQzoLfEd2E1m0gYLjJ6gj/W0be0/oHjtH+p3J5sEeetkQpv/50zeMfR/YGL9jtED8c5Sw6TI2tI95UteWoZdXPvkxE1rdx7LFjoyXdBb4nYjFgCIS/EtbY1cpC5c5M2cw3WXZ5GD38qoDqD3jYr/kTawkmFHUnnLK1SM265Hx1cqj2YEji2j3TPNmbO2oEM9FOm5j32taCQPNq40nFB5jrbBTmJhAdAVqayX6Tw1CJBxvP0Perr0FEZnlTwLNswL2rIodLnOrsFt7vYja3aH72fLKYx8hnvNUQSmqFJ53BvFzzqEYvateb4j2iPMGf4vN3UH09gFRzPMBqY8KaRIWzGzwzlm7uyUTkxAHxy7f2Y0hI71HhBjj14kLQJEGC0UG6DGUWvfI9TJGoj4dNEw1oJ9SzabUtLy0F2o4N7huGASfEdUJgm9gr0v2eNX/EV4faq/XdnSE7Dir9R3sCU88dFUPcZdyHBLotI8bP9HZUxAjxcXFGEP+UzuH2vRdzjI4nl5I6qBjRswmmylyBE9P1tyZC27pX6XnWhxsn4hKwgj7NCkbXR5Yn9VTz32nr4hMMUDFjLIt3YhaqHrMeyg1NGzL6gDdOX5Psu5hbA1Ao+4a2pGOjKVY9puOTGQph9YNkydthOTXV73ndP+a0/1rLr1qkxzbfN/b9/6w3+fqqzbbkk9e9d6z/N4fXnovAGxccbnf6wy/1xlv7/n1CPaGPOnGMsx94git7uPdi5ecdBD8c5eYQlbpT8hf+U7cZunKYXqjMLKv7+zEzVQasftemw9eDlKBENybP50SzwA48DN3W9dj+ZTdLaWJMynYuXVhsvMZRZ3TYs5lOWGd0DYBgFDc2wu37qMxppKVys0WOEHAzKtcdvE2bJ3+oWj4ELMskHnAq7Ok63RuW7jlxQIHCF28raHmZWCrzQA912O+p4Gtzw0zJpBkcYJ+GIVTyYJN7EnbAefEtkq1EtLis5DszkLmxV2IUEAbubyDZpDjrgXmuINNe1i4FkBp5pPFXeBj0HaRoHzqmCia1NM+QucwYWiKyIdgd0oVzwVa4OP2MaGZR1z0ZXYQEcDGZd/EtSvCflj30UXHnH3//7zr8G48vPTtP577wHXfKe95+ztvP+CJE+cA8P+ydK/reuafFb76egBw/3zpJ90V3+s5B998+9tuuuqoj5+zX4XkLo+Ah2Gyq+WRU4cWWt3FEp882ytCqF2CVqhYKWHuadJJTHzMH6sqI30UDC/ZYBdt3b5CrfKl0dtZCge1jmfkdNnjKeDeTFdrum7njHISURIBDEVXmmwOSlABg6QtjwX0moEYYcn15KrTkZQutvbKrdDji5YCMHZ9e5DPMk1otXXsvvxQjo4rDGQuA92jTN07xh6cHQS/e8qsi++ZdfE9Lx1HepUK401HhpFt21eqz924SLs9uiC+e8mCc/YDgDlnnnjQg794DMA3/7Fn/pxz9gP2ff3cNz/wpQ0Anrzqvdc9/DefDJe+nk/b/OuHsfcB+wH7vfqNI9ST1qjrKia63LGx94P7d/znx1o6CL4qGZ15dacW5QdY7J7hsvpJNazWLMZ+ltUqJ7PdvUjYvcIfTqHJOWuS1GTgdEb/rmJgfdXUbbQQjXxneR9Mq6kGi/O4NgadtMklSjWxVDVZ3G0NyfFcf3GCACyXf+ZPq/jbMMDXSa8g5HTkh1A09CQrbyFVpS1rla8Afev4nMi5xHVZz9VrMfHqGfqPEo0ezHwYgktX4WIUt3ZtX9kGpfN2KEhrwrjFjo83RneX9B10R5LOQPGipV0juO8l5kKCvD3AxnhqginrY3QZe52xhcciB/AOUI5+nx20ATAAigIHTj3AFxs3/hzuTXAFilcf8IdY+/hv0P3TtQ++442v/aR/DbDg7HDZGzDjTXNnrT3mgj8NR//jw59avKybCOzMVKiDdefliyWZNzcM2FGujwKvbYY3HVV28uco9miKBfpShpQLjm+K8x4F0uPB0Nx+r4Sq63yL5khFdM5kkevIPitO9elK6SqCa1cDFanDaMkb0nq/2wdtKLBSrTRvLW3A/lnvw1DM6CkrV2Zfg/zQGZ/8EF39vaGjZ5355VpohN3yQ+dRLgxtouoCBTsUz2k1GCnpIPjdXIjYbrx7Maay+L6+62efPN696MhzlaH2uvZom6U3/gLH/nE3AOCg/V+V/XL3j747H++bc1343tyDeq9eei+AV59z1zmLb77K3/Un31n6qqbqOvI85ITuBfTfeHdkVGT5xNYXdhB8XRpR6LOXVXCbU6mLRcjWasC65oVjm7U1QStve6x2ib+pwygBuCzFklrX3KyL7/nBx9gk/5bP3k1HmclOPeG5E02XaGxvjXHwMBnlVT1g6e2c8QxPqHunA+zqm1zBY4SfIoz9PsPxFpeAQ/wrZWV7bu6ZVNCIuW1P6wcV3HhDb0efGfyNrp56QKdJO9OvALnV08lV0XwIZjJ4RIvdCS4X5naEpjtrLidu3b5i7z0Sly0ZcXXekb3fE/5GZp7nGsx0tRnt1PTrIQnTgOg46pqVN854KcRIOWnaQ+vgBAezWbiWU67hUkQl5ARSB+sZ/ZOll0+9+Rt7I7ThHaKHLximOgDuoAPePefPPNzhn/jQ2k9tfBLvfs3GZf/4hw9ecsXpF/o58/u/fmS3y4NcGs3tJrmczcMYhaVOcDMh+PSprH+E48VVPmrhGEvC7v9dlvvsuche/y/+80YYTYltQoXuEdlitgz2sHoFiLkdvcz85/m4mMYdElC2CiFpwpxF4+Qdx3cc371gzUCvGq3LiKiPiWBurcHJZ5fJ2UFoXh3mbct0neL/z13JQuqb+unNIyZPcQauM6L7TEXB5enPZYeeDMl6VQkNkJOdrUQ+n9vKMkLSWeBrYuZaFvCmYhSsQkHDusf6qqXPENcb0rkxKyD0OfoneFEK3ln/u3qimpQFp6m3VNObP30PgB/9f4eH4HwthetzEXuWrOWyaMkeRj+BhqGI1kVRalN/PVt5Rm5DxezDE3kHICeZTUbWXFZPKQrDtExBggMpPk2Vu7YSAHlETTqXrkOuwl6teu3Zs2zl9L5r81U4WxUHWyHCkN3Wz9VBd2azyIOZh8nZXgtFLg8q7RU2D/bsu+fiqXssBPDE9pXpovR6staJ4bWMbjjlX0SkkRYSJEOo4qKdgEKYo8MEJSvmHUCVMcqcbAln0qpGq0epB9ed/dD7vrmoGw5w3ftP/+5X/h1+L+BXj/0Ub5zz+xjAd/t/DbwKzsMBroXN93zozqk3n7FX9zcux5HnX/rNOcve7bPJxNw1LvVBmGgrSzvYt259dLKo831J1LMhlrKvskPuIRupoTC0I5QA9tlzUf9veyS6Mnb/zpIyBuQzWse5vqHcMriC+tAWdhrL5WKFpu5QaFrapZlYnSCVvWm2LbZOcxHZE2QtKnXfOu0MHStFe28X9XaeQqbJNY97Xk8ym9lz8p/KLAtOtlSHiBWHsVcd9zlkb272IE5H0p8h2EZGXToq+peQzLr4ngc/fvizl9v1Zcl9fRNQdXbrQO/E1FVuHuzZMrhiyyBnphkvaUti2RGUjdf91Zfes2gOgMfvWnr9r3DEmxff/OWrHgc2/dNanHjBu9D97lkH9d511WYAD3/ppplzj3wVAPzw548BwC9/+oMX3nSrNYdW9xG4DGD65EX9v81yw2x8ajltyzoyxqKr+8SXDoKvSjQRaJZZVoFyfcNb4Zmpa9SrKh5RY2ltPojmpgbWXeQuZR4gwkfKHTJb4hxnZtF3daraWNF5GfDnRFVud6aouNplF2qQZaMCPlbO2KkiIQvI4euNetGVYrZep721OgDbyQbNQnYpaCJS0RGIeUktHSVhjG3KMNAgOHYxWzvQO3fmgtA0SFYd4pFUC3oJDaoFi31Tz9Nd9uIKZHUACYTFdH/prM2DPdMmL/zZ0zfQKXLhSWls4gCrUXC5KE2Rg8BW5tIROmFny1HgnDxHNjFubjeqNGV82VSTz188gHVnHXvMjQBO7AGAP7jix3Pg91r20M/f/qd/8RHMvOLBv+z2JWa8+zu3/MK/belHgMVr/v6c/QB31M2f/v/3/935AA76zFXfObKAKyohpOlT+W0sdgdaXZI8hnFtKfFvJh+MauBjG0AIpV4hKTbKWAIYCmH65EUbn1ourmfZ8JcxaUfyG8BonrT6mwd7IqLlhyljFnhb/65fKJoudz1z+VwyDyy/pRLTkAdaLkJCQNuRwbHtDExtpTykFnB3CRS1V/qM7TN4VOxz1yg28s322bnstcoKCYkktLE5p93bF5KvoujNjIYjixnOU2MNT7U84jKOC/y6Je7obEe6+K64fE7Dj/Z4RzrSkdGUzYM9e+9x2s/Gg8IWwJCA+BHJKjvnmi+Ea/SviLINAPsd+Z3fHInQBsQD/13zw6/mw3ndMHQv/V/hTGNoH1cZRq2ydfuKqXss1FzAVJLsLJpeaMtgD4Zb70ZXaEd7a/84MzS/SOk5dN5p9/Zh/IbxBcv4LfDr7uzBwVf2f+ecmfWflrije2RZX7fEHe0wdms8w/TsuRY4leV1MJZOa27KMbRu75AdrIJ7J4wksaj95LLoDrvT5o2zhvCI2dX2hP3SfATww4uOeOsld1tztYs5DpZkH6m6FKyVY/f6kBn7nyKtem5EY/SiXT9dY/qerp4YeJzpQ5bVpqkjRjOxaEPf9bPnLbmvzzWdJZt0A3ZVAVNrINa+pJ8EoNuBsZwhFhNQDWsGeo/vXrB2oJeHN+9GXUKEjUlTDzgAXsB1ZiCutuggrnnOmFTVBGuJfQrnpk1etO3plRFRsx/nyUsgzTttpfGtR0oIxu48eTifLJmW7RMmWgQHUdSbS83URLU/tUMSI5ql/DSNxMptNMo0Vr8EyXZDP5lkr5pLxrRlv0fnmvmn07iFHLXbYLlAPnQ7jGNg4ThYbu89Fm4e7HmmRDtgKDgogvcAUIa4ZbBHFfWU331a8qdDiHEokOqFbr2DcMq2Y90rN8suo5cjEWJp8qhkKVhkXqUzgbkzF9w60JulmUaWySmjm5XmonGsszlePdAy2Y1Lc5M1gSy10jbn6iS07nWw00RembbzejkrD5t36r197ZLfQXrt4orEzWa2f6tCdFmFLxWim4HHHgHecEB9dcfAVZ/qweK7ZEGfs/yuxej51FUDY9u/3U54dX8pycS0xENgzXj3YuJKe2jdLs0MQ+lfX8CJ0yYvnDZ50bTJi569qJEtgz2bB3todac/twz2DH9KR56jrDxs3sJdxNzeKOOG4PsffQCLP9HwEA98be0DOHju/unI/q87GD1rvzZwTgPYH3kRoAxk27X61svsPPVEQ9ZoMYbu7jLsbsGry7d5tisxQ4eutvGUZtUVIGkOdCNJCWSFEdbULrqFjMu2cp1m46n+8wBizNl1831xpuZA6kxKjGPGKhi6G5cbni2Ky5QN+SjluS0SLFi4oW/ZbKKhYByfaWGa1BJBQLl+91LW4gxLe+nz2ZGZ3KSPGnpEf97Sv+qE7vkAbt/YawG6HcY8CpJ7661jhEwbx90g8BftCOgkgqDJCtZhm731PWBtTsNwu3R/a7oZJAiruNiZEDtXnRQMbZGwGqecyeIqG5Uv9YkadR745gKNYud9GMrjmUL6RCuntanrDyq5ZNYZVULSf+hFyVW3AZSx3Y4RwLTJC7cM9tD3vfY4TeseCuz3/kzw2jvBqZFuep08VaeB5IFN+HsoOgBDwZFxXbIcme7J0Nu3kbiscz32hZDjb54GIeLEmQv6+leFyFOuonsRtRxdCFVONcgMrD4QxpfJAcDLC3Ouy85tuWoNIWYuCDRiGVGuhNiVcqUrD5t3yjduHRpCjKxQZLWi6UyaC9UHQidXzHB/tdToyngt8AOPPYKD8Snnjqa/K8r6DNrPPOANwCPm5FmzZun3Bx98cJS72pFdW4i8dgKmkaUk8ePdiwkq6lHfbjLG153tG4uNsVC8+4hUtemp5WjaSnZkzISw+9DQqFQ+ZcqUUam3JuO1wPc/+gAeWPyJ+B16UAeuevv+7tG74vI56H/0AeANtRMeeLQf4FXfLupPPvnkCHZrypQpdqdt3eNdVNhsjG18hM+IO4MXFWVA9oPuAy3kz004Wfn0O/cniHHaWKSg5nnTTmjMK2O23404PreMM3anwnZzzd8N9c3wIslmUh2NBv6MHUIAtctwfepS4yXUHYyRXXTihLGS7f3za2K47LKzrAKh5mKRyqNWIKYOmGYY0RJciDFdKCOPHOPmzszGFSPkFy9KFAdgkg8SFm97G7cMriBSlK3bV9gY3orRXXyGbYH8ogAABWEqouhBCcBHUAy9uHnb2Ri5MEHM9jo0reX6kxV1zWtSK7G4ogsALMEOhcszN46XcTTYvUKYYsJqtFY6wqu7scRLsHuZ1WCuWpGl3qYQ2dBug84BPrijTA3TVbacm8REVREpwJ00PVzYcqy2BbsD2FF6QsyFdxBH90pcuPTQXpTUY9Cw1YrphD9xJlHcpJHS7xVDPuSlpXg62mKmcMxDaThdDQ2IZIei/VDBuoqsuVyFkOrVC6SSO0p347sYuw8NeQAxOPsioqfPOglVGXXo+RV2LcsRQgVHdtnCzncM42WDn7M8RuM3N/OcT3QM7aMoL0HCWiuLNvQtm5DktWsGeidgWPzW7SsoWzz99wKswiMolF+V/mvLl50VG/38s80ygth9N5AJOKWfl9z4rnmn3rsL292tTKg4+Ace7SeLe4Mc/Lr9mw6PhlgUbpBxDlU5ANoUjq4pHhgAOE+MqRa2WoDSxdLR52atialjYrLUsxzA2Ueic7LBTJtNi7dSa2ouylpx0hRc0dQdgyzt9lixXqaJaPbAN61H7dPwCgCOK7DZTZzur6ldo6VwDj2Hzju9SUWvPvAgZIl0OfanSuZbe5OrehCjaLAjHCoRGOYUZKH+jX1gXYl1Kk4Yy/xJzuDsa82EWa4gCgFqwkWIeTJGV1e0aPA0gE2DPYQCqUWC9du2r6Ri0ZSvzVwYfZQB+nTXJN0vO4vULPHOeauHMQ9iasz2WwpEOyBSIn8geW7Y6HmTISYUCJbOwFyVc9ZzXp961S5oHlgYD4lgHeaFRoGHgnUz9fdFLKMHcod5MU63TTw6NeHh6N52GcLptkkCqw0QrqVqh4IHsCO4odIB8CECmMTswKmeAJd5zvP0iwAKp1W5k/afv2agFzJPTuheQIROt/SvUixu2ehMBHk6yAmDpM91q0Q1basZhNzQ3uDSr98zj32jh1GCgaESN7xr3vx1a3Y8wyrGNiF4Yc8k8UaNEDRNk6FJieanEDy9+jgl8vDvtpGWCbXAJ3nksQHMETP8zh3uO/LcZdbF93z/wsNfao70E1/WDvSe0L3gto0TPVB46/YVAKZOXrht+8pGQK/R2OMrw3PhjUh4PST9a1sY5l+yctL+82/pX3XizPl65NaBXgAV5fwuIctmzwNw2r19zzwz3l0ZORmnBX7gqrfvf+4bTHD7wGOPAIuPnQPgmLkHn3uusbij/9GKW/3YiYFiQY1avFGz2F3L7rymOsypyPMJkLSwJv1jQKLw8Sm6ij+86Ii3XvoNsAFUMK6PBizpyYxC6tZMV+WxdxBjpTgvM7YWuJbqzhnJKl2HFtZxtIarymVHU0fGolWp1pxUNS6aFqOkEqh7wluQEZpusW7l404+KzfVYg5LVxclgY7A1gTZXWTA5/O7DmM1JGs9OwmX6TuAIjoALR8BvNzYa4OmpuWC6oBsem4tyhEAyEK/ZbDHQnMSS5v6s6dvsJeu05C+C2cfzRDrRc8tZTznFOCerPQpTYslfaPvO9GTc70OKFpzaI0XHE9sNmKJ99Zebm6gKBVoighLXYq6V5H8MUktkfCqMedbygfvXCFX5+U61RgMoJSAdTaxc4y4qDcSZZxoaHwEsEMQtgWsdJAg/lDphowVmVxAyBJPN6It3vv1CHgJ+weAwkVa1EsOsqeDDcle1TuIJyrSYOdpZ5vepfIkNvjt50esJzx/F+d8aas6q8uIZbNPXriB+emoz0Nth2Roz3qUBVvoDKipBziaP7A+i2iWxjjZzDjZ4GceM/dg9NypT+PA19Y+cPCVF8wBxB5/9BL+cd2So3uw+BNjEiK328pL2QA/8YXC4ncVy+WWwRU7O75lcAUB/Qkr7fGz0++W0mF0mOAyXir6med8Jx6wJOVPWnyXONQDmLM83gV3tOvR38aUqpYxKAQHi7gMqRvsLgeUTdzgVvv7cG3Wkz8p7hBkZ4Lf852uY7xuWPT1J8bul9wNgW4Wq1ec9/MuSRW+thXXLps+F2ZAnGxvXepyBdHmyPfZvO755yzzKx8W1Ef/2AZR+ghkW/X0pUnpYnUZitpNX8VMbs6tMNnZczNGNZchj3wsnXPx1oHeD9KLksENYVNRSFDltt3kJE+wKcKYbPWUltfccaa32RSSTpi/6WvLRW+IE6zjtymVLsdS7NX0Fmk4I5zEoVSDo8UGrzH2dH8JoJPjfbDo3zG7HGkCMk1UtX36iXBwe11BeWm1tC9AmN5GwGeW+KKmpdO3AVPBy60PZqwMmo+BdQ8W1rPewhceALY9vXLqHgs3PrUcQDsWAP67BICh4CSFGgN6CDubd84b1/pJxPKGCPG0aIMN7W3DYTckOH4oyIAndgQHeZaHgrwGzcjqvbE3nbr0TBuQR+ZlMpb2baOAO0PAWeVcAOYGVFLMtc1PLVNtelUTJ2PkSwDYfaLUPMVID4tVM2jHrJ5Ss2ha+hC6vzYvnMHl5pHRQ8YrYoxl5HiUqgAAIABJREFUPJmW5yyPKtUVfLjfOtKR5ycLN/RdPyG96K3ctnEietTvltJur2t1zdmlyfLGUo6bseC4GTudmbdt7D15//k7+7Uj4ysT1MluYkl17zrMdmynMF1s0s0FtMa6RzqaWMOkTimWeZVDG/Ke4buzALPCzW7QYRVgWsDyvCxHUUaJm00b2tRl23pmzYrpyis2MN5pOxs9T2DIyx5ZckABsnsNPgJMTQ9gyX19WmGLCdizy2VAkF8N/VN3klcWc83dDumDz2+oJY236N9pinq76zdQ21WUMtBLR4ALpl7qmPSkYc7Y9N4+Rs5nWCuo5Pw2jD5kXcpmioRQJ+wewEnfxT0jqUIivGBZZ/pJ7PREeJehDm+YFWIM4refpmMw+bkchBM+ZvZvqtabCHiy1heGSydCLf0Nc916zu8o10epnBnmxd0lDwqwfQgxo8YzEF+eW8/o2QGY5AOASZ5QZmybyAjxrnB2rEQlECD+8IRoW57N/Y1pyAtmcXcAdgR8UJTt5PKp283Vj60C8IEZ1VVci61+bFVlurWDxKM7IMv6l5SVdqwzYotcE2Yl5pngM10aAPPYckaGvHV9fwAZe6aMlTzaXQFA2Sb/+QigLF0IllSE5ph5xaXRNe8r8IvATqvmYKJRk84CXxfzqgYgz7auipmKCnmxhofIweo/QwRE829ncT6pq/PbHjRTh+e3rgAVAlsxvUeN0TKfXHGs/mlGoM6rW+maq2dJ0eSJdt8gXnhp6sdolVqqGY3gEJTqhWQDkQ2NPGxqRzAKPV7JSCHp4l98s885EDs9E9tVNGm2M7XLLZv0XXonZUSAKnWMHDc3sajdiNS6PcKnONlMpG1cEPU2m13yzRPE7S7GaP2M7JC2HHeuNAuzTTNUhoz11RJ86k41D39KG8cYmceovkd0KGWHxOsuZMEOfMQmGtXJyCp6WjV5qY6lqZ3HSiZAMBVK66zY9/q5o1wPoFUcZUYdjTTyEehiGtr1pc2fy6t4sA+/LPOUXYa+x4rG3vwUdCP186dv2GuP07ZuX8GswxTiWEZZhyJyXzZEp/tYHTHnAiTgrXD8aFgToJd7x2lbAQAfnPmhW/t7qXIiUb75X1fROTsCAPzdv6yCmdsf+oP5ugPQBRKiFX+m5LNextF3dFPkRsB8qa136Xls/Mk84G1TTC+KqzcPV6UheUBSx+Bx9rdXX/0Ozlux+L4+AOWkEhDteiz4ZmVPQRLvo686IPNDqniQXn31YqMqnQV+d5ZZF9/z4McPH+9eTCyhp3fZ7AlKXksvzdsnfNTcMLJlsIdS0o13R56rDLXXA9AVYGeBduQ5P9qdIYqhx34zntliVj22Ck18c1Zu6V8F2XPschFxjXLWP6ymC7nukHkATrn7lnHu0EhIZ4EfVgQiA3bnaf8xqEnJanaqYcrEYr7K7rLaBbOVdQa708kuCiWtSxULkygAhKDUrjGvjFSaui3Ot72po1SVvRzttAHlWRcYsVqY7mp2hNSCaT4GVR0n9UBR5INhnN9EJUAwNMtH47KeW9UZ/20VHwHOG78gQaWpIa9qQANeta4cATdcYD06RvUBdmSJoL4xj4V1/TIMuA5o9KQDgBJZMCHxuDCS9nL7uJ50E2kQS3Donm1ReZPEvS1qW1WFBHXDMu9SPUzCxKDc8h0TdndCzyQhl/o7hZqV2hkhjUm1RTjRe6eoP4X7wTyY3BbPCn2cHATW18Ugy6qaPcS2XIbTzyzNrjjZScfMZ8w4cKZPXrR1+4qhEAC0SKdlowylG+woB1V3VHE8Kfl1ltL9ZS+8RGrtAExCBPDFx29+/4wFawd6IdloWg7/3aa2oOPczkejRXfKaKSke6xjqL8KW051OanH9qUUo6qLqoNfwfQ2Io6pnFRbuZMadEz4frElKT0IAJbev3rZ7JO7WqTHiuBXqINGJ3I0ZXrzdHWFohWQPwUUsKp99sU4IPjxdLLryBgIgfjvX9gJk9tlZM1A79yZC3bp6KOt21dQVvKJKRuvvuB/nP6g+fO8lxVHv6yYM/vqn9li65cedQav9T88ozhqkvx3VlWF/9A5k074nUkn/M6knm+MdtdHU774+M27+sQbKVl6/+qVh03ETNPPVzoIflhJiA4RkePQ7G/2m4BT8cZo0ubVjiX8EqsH+IycgzPD0RSt4V1+EACcUoc4XuNDEGYVKicbyfzkhi5by1nN3J5UApVTs6tJ5fX6MkDL+NsLxK8CWLVmMWKzKR+ENlLHjcokCFXB7soAE6Ij22eFrKYSGmfFHvTmUzsbTRPW0aJyli1WGM81Ghnk4cWVDb/1pEMCymasjFXewVnGDypEll0XCPSiQNSOhRxaGb2PMqtEAKXQI3NJym5iJkqMcctgWuOf2J6p672xRjszcIpUPYXGWWWMYNxoXOpkZBwkNsw5R0ZxysSqVyyVOwCbrr7wj87/VywMIbYBBzx02UdwyU+//GH3lcNee+lVcy778EwHIA7c+ekVeP172yEiriv/Z/ur1wAO7uunXxOPKtVTLMbw+DXfO+DRW/9jRgDgwlAJNvBLJxmsh5ihdh2BkPwb4rTJi7YM9kTRQ6mvhsyNdDvawZ0kvuu3b+wV1ZHRu0UPYFIh/Dpm/KXyGI2PBX1+edPNAN67HycyjsadhHQUQ0oaAwCgGL2ihhPVYY0+nzHOEpMKbots9hWiWfpeB7kK1nnYbXmjslK9jc0D68x3La9KGyQFGP9D557xrdXXHUKZphG7AtGNc9JYqwSgU1qha1JA7n9D3ouoOBV2EHxHOjLasuS+PvK268joCfHeEJqfOIB+xtmf+afL/yD9PfCzR7HPATOBmXu/3hT7+l04Tgl558w6kr/9oh/Tu7P6fnzl+Xd//HUfPPLzvxrFTteEOGJv6V91+8be40c6uvLOTTffuenm47sXnHzASzr+7Yxvre7Zxd8SHQRfFZvvVGAmgRWXgdkG4MsBL9m5tbLDtKznil3cmT5kTLSVoDvrRJ1tyG0fqxBbrtOUr6JqutxKvJd21SWLrGm+Cc4bk6T8rX72qXi0Q1/tjD2WrlurpcvwLgr/SETlFtFFBMd6AquKyL9ZiJB9ugw3qDESpA0xFTbegcwYabb0QcJ4LIioGOylGwaf8ZxstE2mYSw1RzAAQSrkGByca0XRIeiF8PnSFzMgpcZsADEKMvB8CQDHSlDIXHAQl3UA2DLYs8+eiyu5a/MJny7Np0lJANcwIYMJX20QIHvgyxkUt0YInlhsY556FRyPUJax7eDQ/cfvP2jlsacf+PT7fvDw5edc1d0uo8P66768/+nveXTFT9HOEp8MfO/R185aihBk4oX4R1c+s/byga8c+fqPHBkvu+usVwLwkTQQSV9TscEHi+Aj/zlt8qItgyvEHp+k5SKxz1pmWRqEIaFBfYbMvUh5eOm2OIkrEGYkGkQ6XWcFDXC6X6SS+dKmm9+334cmFWzWJp1IW24dQXlG8Ka7mpmGUK461SNNY263NLOr8WGxyFsVUfYZlL6kixLmJ9UtAUZtlrsfpSZUNSIqrlTh0vtXL5s977R7++wtsdFD9D5pFbGrAITQnoQicrWH4+KK2FngO9KRiShrdpEkNM9Rtgz2UKKa8e5IXV5zxrc+8S9dn9oDf/X09a8BAPzi2sfeds3Z+MaXqkU33vXEa49+T/JsU+mes+6/Xn3e/7jrm2ed8mcvqBMvRsNxS/+q47rn3zEKU0XJl1b3rxrxync/WTZ73pnfSrE51x6Sof+FG8YhBW1nga8KmZ2c2bdrugbLYJJ5gyvorAE041f/LPu3DLDaI4G7xPto2l1aYoWoAfGMzPR7bIrMz3BUZDxncW39FHtt0K1rUzmtvMJcoRekcfASAlBD/AxNsRO/fTjnYhULqg4lU5nYWnWrbj147d5ffX699kDOUgRQT9gaTHNousdqibftunycMxdi40CuP2U0B7YhqcIaMq051rvMAGjTbJQpvQ0gjiM00SoWRoFQqXThIj0fZaYeACSqPoq2oEUGfu3nTpw2rC89HCjA3U5eVsw4l80oVlV4U1ushJijYTUGM41KGP2ma753wKNXX3za2Xsc8uc/vv+Y7vXfD3OOKeNQCcRYypsgAr+466d7Hfnhdmn8AJjoJpYxvuHoU38UQlmCM+h41o3QgASL3flTRmPa5IWE3SMi/VQaj4cuzwi++sQ5tCMrTmiNp+hKKUXDElrGDFuatDEVZ/VYewi9c10+3rnp5mP3+1CXiyWz7gDGSZ47YubnMK85+mlH4JIFsVGZKadsUdYTnrrVYk4e0RyYn2y7qv0oaz8pgK4/+1FOZl2GSzXoI5NRifgIoCjS3G55LJs976xvrfbGy0YXe2r6+tls0R9L6djgO9KRCSprd313eisU4T3evajJxruW3D71qJm/f8b911yMv79qPb7x5b+76HXH7znphONX4u/ee9zh1/xCiv7yX7F3904r+vXAo2PS4Z3ILf2rRtwYT9LJKPOsQqv78GUW39fXc+i8MTbqdxB8VdqFB9AVDP9lMnjTfs9awmgP3oh8U+Gq07j5/uwI3+WQx7RlzPOmLtMxJ7x3sy6+5wcfO3zWxfcAoD0mBWUiChoO1T2zcxlq50rzA1nQp0lc4USrQO1nPGsJt1L7afNvLPqEoZtxfAjRWQVG5UZkRLxJZwAxm9FPyw+dt/T+PrPjtxYywb5NtkCAMVORtDkJDFF5mxNHa7DgwI5bo75E3f8pLN6y32TqkPRHuhCrvXBwHHdrbrKwlsagJFvCCMuw24AqFXoeWtIkJxI1MI44VslFv3BclWAsAqzYPNgzdQ9W1Nv5YLntIP4Zlu7TyYzPO0UaF+h1R5kvEp5OTaTpBMb4EbFkT/sY4nd/3h/a+2Lbv3wXiEOHX7vmt9ciItx95kl3vWfNlUeJT/7678f3LAxxyCarZZM/Qvj61+78wJwvxxgEoHtOvcodbMLuEcC0yYs2DfYEwe42Nyv79CRaOntv+ZvkCAaA1Y+tOr57/h2PJ129lwHL1E7iSGFbsUyF1lQPmerM60xpb+T21YPdVfVpn1u2agcA2FHyT5bdmQpMMl79yGeITnXbN35yTWlt1Br42cclf3fVX7de3nj8FGtCXsBpFHsRtSoaEFqwzxTszgDepXaRxicKudYwao4Rls4C/1IRy2r3ls/ePb6d6chzF+Ie2W0g1NbtK8bZGP/1G2d9tB+45BW48D+ufRO6j1p+2Sf/9OXzABx42RVfP3Kn5939FRxzrfyxcd27X9/7+i/dcnm88feOvRcATv3Ir655VVmnVx1WyLEujNwbf/Vjq46bka3xIyJ3bOw9rnt+X8cSb4SW9tMnHiGmlc4CXxW1XleONBfOHJszi3fdHl8RWyAKW5WFnjF9BSCh7bnpEkAoPAwUI32DSwDaaS9m/e09AODxg48dTglk4aL46kcALospz6PJbYi0giaLkExhdVfImOwM5Zy2IlqJDMdZSODMd93RV3ivzDCxYZk29WQkGyYXbQPtfD7AjAMCF86Qh4niLcUUqpZi5BGxZhCqTaTOmM8cFOUFYr2wajtSE2rEh3W9Jvsu89hzHHw0IIxASSnseEUN0GjGDbbBG1Ox5VIrisB0eDwNIoxzA63xVI+EyNP91SmUpjrNZJ/Aa5oVrJphcMoO5DbRC3XMs285nxuPOOXf/s8pMuNKANPP/F9Pnim1x1Jo6sKffX7V4ZyJFwD+7PN/gVgGOmvGEev+6whgKOCUf/uvU2IM7RDKGNsxTJ+86AlWUWRKIUtXp9idVndrd7ceD1Fml+ii0p2iL94x5TvjeADArf29x81YQGt8EViBQJqVXD2gA47Gz5A7bdCUaHkHoB3U5QKQGWVBc4gC7s2TzeEnavs3D4udhBB9Q91V3gnWp1vflgdTC8SYv3eRGoIq82oiejVO3EDx6/S9q4hn/cPqz7+zQa9OJnZ10/HyJXVGtCaspWhse5Sls8B3pCO7gKhTPUH5XZqsHsDW7SvoS917fNv2lbT870Js9hWhi/rZsP2n1f0FN0G2m5v/tRlS2zW+Iy9elt6/mnX1tZ3HBJfOAl8Vb8O1DRNStDtnkdjkpGjRf20/2YDMYJQE2Q8VY5QxirJbgM+gtORQTYhcrfDMzOwZHc+6+J7vX3g4gLd89u7MQpnRxWuketrFR6MecPmA5Dg+i3o3daetuKgs0vZb7PH8f+46bnC80Kgh2gLS1E61LTJU8kU471OwLGeDpWLBAsdcyNgmcITO4Y18UpdkBsAulxlBrbam0t8cs/PIExKlNZ48nrRY3c3eVhgjTwDxVc5a45R0zlxpcABaJhuv/lMabUiIGbZjUJWlFnRsZg6kDEgFgqQ/poKyyKUWp05euHmwB8DUPdLa/4RsCDivGtLl2LDvKIHyktoun3MGPduBdvlUdoZsLql17FNv88BKPXS90ycv2vTUchqFvfdYCICMEd45jXqHrO6lEOZbYE3fGQ3LIFsEXMHx5FU+ZLBsl3MAbh3oPW7Ggi9vvtliSu4qd9xVOeBghhJE9Vg9l/wzCo+WQeHtWhPaTwLZbTMzQ8wWSFHDAILjvc5n8J8qsWLvN1BbNQe2gA17AdAyVKROsu0hPR3iUoMI4De3/s1Nm99/7oVvAuALeOC3az/595vff86Fb6Jxe+q2T950dT+A15z9ublzX80+N+6XDy0+f8Mj+KPLVx1xEA1mrlMbW+ks8C9ReYknmiOP1gmYUG54WTPQC2CtoPkTmrymd/XQeSV72TLYA1l9pxn3+10F2dPehbQRP3/6Bj1OQXHj1q2OPAf57Zq/uenqfrzXHFn7yb/PjvzkR1fjkFtX/wnWr/3g9T8++JOzpgH4yd0HX/JPx15wfs+bxr7PjdJZ4GtiN1oGV/vACDhm5ZIt0DoBI8fxWjIz7ft0pLq5s/og+VOgrIOYQsVDP0qLDRV6YxvmdvPGLKY0pk8AzAmf5QqzW2shHGN3Ytp9C9MyE6jZUFZrj0eW9s1etOlYqkGwKakHeB9Py4A3FTmNdLCXlrdUsdilJozp0YyH0+/aPbLK2t62ZWBaViXgAGBSfnXW47fOiKh/244bwJbK0hq/ZqC3rhKorPq03jvTJS9DalQMkrGbOikjJjn6Um+HxFY6jDOBjACdS98S0tL7IeeKxkYKR/toge/K408tB/fTqR++5LxPiePS0Bkqw/zpzJ7fjJYciJLODpU+ILtPNUo+9guZNnnh408tjwJbiXKftBF77XEaGeanTV64dfsK8sVry/Bau3vdm72Mrm1M8oIUpWlQGUASt7fExwLAUOlsecdaBD5ZkDqNWGoiiw4HYsTcmQtoc0n1dKUcDKkeaxEvpWME7q36Siee3ClA5lUFSUvghh35nIyP6BpZ2QYAhZenOKRLC/aWK2qHeUHJpahK9JUn/fVC9zcrN0fRZuGVJ/31Qv83KzfHSUUMEdj283/HtAOmxyK+ZgpQ+Ojcrx5afMm/n3v5BSe+2s4PL1Z4b0Z7zKSzwL905c2fvucHHzv8bZfu0hmwXrgs2tC3/NB5S8acemKkZO1Ab+O7gl7EKna9/8JuYZQlUjwAv/zPG8e7L89VxiW93s3/uuq47vlf3XzzGLc7MeX62ScDOOsfRkhpN/VPXvuGVV+45M3nvfNHT55z+hFTgW0//pdH3vXKmedfehBw7AUf/diEAPGdBb4q1rU4Og/AhwggKmBtBug1E1QNVQOCxTNTKO/ioynQWKH9iYFxGbnyrJWEmRKsqfXc+/jDi4546yV3R7uRrgD5WvS+7XiMwivmExgi4O595jPvYipgAkWrGMkoKVI3LMjWUtEUsynjYoysSCDIyMVMnV6dmQFg8X19yw+dp4EuFuJldrP8XhMAaputuOPwe81bXz21cOygPhSqBWrVV3ui/9pkgJq+PRj1jiBR+olrvcWENn1gxvy1A73eCS7nU2tKBMZPTroXIUA/iN812d1bYlbXPrAOQA4WxiNETb/1flYnX03DoYP9Mh8hnDmW0b3yHNnAM6tF0grtVStezLClnQZJlWcu0oiTnxi/BgegZL1FBNDygSwOAGJURn0ezGDQcwW703cKQHgZz64A4JmysD1jHEw4vsyuNwfHabRjRJ2gXuzx/PjQQYLvtpNOQLxloLPm9jLwF6pQ+e9g7q/F8TZ9e5Uqw6eDMbffWxY8DcGhswmLlyUPY8+h8xZu6PMOn3/nvDO+tRrqTGAUUS2fBch0OUAe28LBOUzygMPLCmrr1W9ZecFv3nbp59wFH/vYqwD89rvf/8X7p71z9qpjFqy/+bhL7zlk1bvJBi9D7ocjCh0t6SzwVSE9vDeEryLyrMe0TmY/S8iZlaSOlLdbOpjVunPJF//6uS6m4CUAjpe1CCB6NgTYc+mafnjREVk0vHnk1OHOvseijVbSNzHTzaYWTfn696Sni04HMJVjfbhrcIPKdx68sWjyv5MrZWdAIBkLSEsW7SalTnmbj8dwUpq9S8vxq2GYT6E9qb5SzUL7LD2xBVTLmo0zgLTv4YN2Ct860Ku61pZT+pR0bjbwUZSZJqOM/OJM7/kfiZLKthdUWWHulHbO8VCk/hnbgdys3OnMO5R8Z+PmwR7Se9vtAoQkx2rR80ewaUOTvkQAlm6PjOWV8Ko6H98T21fQXCrNztJGEgZgkk8TILuVxqkNTer6Uj1mzYzSOZOp2QHIOkrFVj226v0z5t+2sbelNzXda2fPha1HPilk49aB3pivxyq8mjpAYiz1DeFNVcwvK4XZoc/sAGxtiMjyAxk3YVSmqGmddw+BuyEBnwBw/ex5izb0UarZ0zb0XXfIvIUb+oaQinl5e1gDRMsBTjeyiDl1FYCn1vxoyh2XH/HJ8z+7aP6SlUd5h6n7HXPAwQCOOvj9qx544lc46NW2fIWxZ2yks8B35CUtizb0XT973gRnqxhBuXWg98SZCwCMRm6SMZYntq+cNnlho89dZQ22Cekb6XKpAP1EUJsWnn32bChMBWQ3DIgP4JbBnmfqxPfjLeSM+ZXnr6jXveDYLkmjLgs39K08jOPaz/vOC33wt63/yj17veeDr37Fysux8Pzvf/eog4Btm34JvHrEOjoS0lngqxIKD4CSaQRNFQIAEjUXDRq2OEbUzrGG/mMVqDfpairUoFIgKmCt/W4TyNbOZbQ0zMPJO9Y6H232T55JwmSmSWoJi+VSzRYoV/qVYFRICBaWbxV5v7QvAFyMNhbGMuqoZ5X1GBIDgZOO0VlZWw2SbnVFW5tZM0qhfSXlPP1iZ43G0REmaxkyTiUGUeCivzV2rA7WG0taiFNBi9SH1f2rTt5/fkpTSzDR3ECJHYqwtx5J41JxWONLNMU0BJHVp9wQK1hYx8BuUKmP+gQ4pJ+CUQ8UEW0biexT+hanJwHTJy8i1zb2SnOOXNtijPvsuXgTqcrNaFIBiCtfm+mAnB4x9zMCrEXgPgOQfLh6KzNFPY2hGDzYqJEUGQiRM1sRwG2zxiIheA1XCwbyFqLtaNdIZkRzAADtwLpoyc6S7nKo4HXTpQiwC6e5EZl3qkH5lZ9kSPnLpALIrS0QZ8AhQ49tIXvMK/Q2q03eSv3Nd/3skymhC2mD6HE741urrztk3uL7+nisSvfnd9/ii3jju+bRkVZBl5nNZHLdTLmhYzI0lBEogYd//mTAZPzyVw8DAZMPfuven7vze/PfNGvqT/q/uP9rb3+1aErGc4fUSTbTkY685GR1/6rGELtdTjYP9kyfvGj65EW0Qk+bvKgRoG8ZXDF98qJ99lyshvBagZ7NO/npuffkxZw+qnJrf++x+31ovHsxRtKY0OWMb62uHzz13r6r33lycy0P3Hjt5/rxxUsv/sxP+Mh3b7z+in588dLPfPYnADDtqDnnPnH7QfMvPejSfz/38iMPAqYedfIV0+45bv5n3noprvjrN08d0at6odJB8FUpuwwWo42n+LIRFsx2lLTZt2YxZB5tqWDm6mPwVV64HjwGoM6Vm4W9pUbqp2alXK0eCCgni1mjLqCCX7PjLv3UHH2H6rmJmIVhQVJO+DQUBn9nmWqkWa481cC4LsB7U60FFrQrD65gUyi3SNHwFs5nPEU7uSL9s5TKS4OxMrtjBIBJzCaMlxPvjXFQatay2It2WVvO9GznLgS2miSFyYehYnw2RJWS7gEVcFrMJ81QRMJ2dBNdqkdOjgZNOtHWiAtFguYkzM8K1zL6FUvaGl0s7O0JEWZlnTZ54ZbBHjkr6rldEq4WgQCUIUKAMntIiOrIqhNsNp3CNbgEyghH/YQMYTA1qMcOP9k2JYl+1iByhfTGmueFSJUbsq8eRe2QSNoypjy/QRMEC0K13pR26pLpveqFh1RAr9HS41QwvXlkearTtI/qoxDSXbZXXUHwZdPLzuoubfQggNM29N1wGEPzLpPDhnD8aff2xTxPUjui4MGMyQf2bade+N1T6QKHaIDeeuoF3z2VQXzpAOxx/CfPPZ6LD5VwDnjLaR994FTqZhlQ4Sl61qd1NKSzwHekIx3ZHWQiY+hdRWhpZ9P7OGqWX5yctqHv2kPmnfls+VtfCtJZ4KvyzMsLAK1WgiNFO8AAO6fJH8QqH1qUzUIKAAB8O2iBZlydwGxNLIiMGTPIcA+dAWFCM+tMrlup0MUfXnTEgZ/JE8o18c7UKRb5mdfsDRQgR+QnzAIUpdkErBt6GvXq7VVZIJ08H9JPdErFppUlunUZdq99ujxvb6V3jDkEclW+Vy/Bdi46gozWgTnDpoXkYJXLB0CqopaLZW7+ROUu52CojpYw7DjbAlSqcHHuzAV3bOxtG11Ul4+VYjHvRkYvWu2OxbURJvdGzvtLRaNns3rzVXvRVXA4oglX2dZz8bH9Rz/8uTfSAe8A/PqGwz/xv3+Ak25f/rdH/Prvjvir//0DADP+8qGLTpmBMjzy16+4eq10+6Tbl3/qCG6XcDBX07KBAAAgAElEQVSzQoljBENGm76FWveRobmZCpEHky/TmVES5USCv/DsVd4ccmK+W0SuXvSlMVrvCP+3vXePz6K498ffs88TxPP7es6phaJUag2Bqq1VC9EaKqBAC7Gn4hUaElDURHvDlh/eW7UFoeUbxdOqBKutJIRLxUtPDSjhaqFy8VCrR20So9RWpXB6To+n1SS7M98/PjOzn9l9EkAh13m/8nryPLuzs7Ozszvz/lwFWGxanoBYmlyuYJp4Ow4jJXgcbqncULUKUwrKVjRV28tU7mDjpu/KHYFpR7tIueVV3Fd5gbmuADD6eG5kkGTwrmcpf2tyBxD7MNtLbI209VKQkFk5MZVj17tI6ncJN6FIBMlJCzNsPXQjopTtkXKvrp0n9UjB6+A9PDx6AP645K7JtzW521767j/f9oNTv/naX6vmTcCe+59BVVXzX6sevqJ53rXr9wCox/i/PNj430sa/3vJw1ecO35ClzT8iGDKIaYPbi+wMa8wESKpJ6JiU+3D55cAuGZj7YPn5UgB14VYPGba4jHtqPyPGDyDTyI4Gs/N+SJlU9VRU1oCII4nYzgxAEtgYjYAIBMpANnWCECmjXi85Kpkax4fHwt3oegs85xItLnJml4bxmSd0V5Wip0+DnWpKQNbXuqDdfQWxq7dpCbxEjjeaqPJphXzyiXCOeQX6Zg2cX/E1SkpEv1jKw8ySsp4X8BUncag2lxNiponCKvD402vOA4FrHkwQ6WFVcHzWAijE+U24JraZRzTiLRcVOX6aWsQbGvq0LjxaVhelEjmYT9FMqSo1lOCkV2pYmquhTe2RcwW3bF8F5aYxjU4DQ4UJ9nE4LNCDb7mlifFXRc22qr+/PMJ/9pw19yGr30sUhDACdeVzYCKlDj3W1PO/Nxbr0scP+60Ik1h//waBo+VIjLE2uj7zYgCAqG4WUDELs2S4HQCU+2ybx4Ubq9A0CeSNkEOdQ54t7i+71wcAupt2timubu4bGhZTUMNgL+HTme6qmsAiKQmyjLV24qR8ssLylY0VivlmPfbJyLB3cENzp2ucIpx2hrxYDuxqYf+CSOzsTKAtFl+rPtnATnSojX79gglrlxf+5PRJRWbamnb4jElAMJQSzFEJr5L+v6aVwfv0kg6tyNtqZeAseFnl6MgFR46v+SK+uUAHhjz1SvXd170TD/B58DnFz7z3Jx4tX/2nb0tmOvI+fU7bhzvBLrpqyjfVLt4TM/LOuOB13+3Zue5wz992/B/Bq74VuOizzh7Cwef6BR+selTp83o1PZ1O6xsqr44vywdrvjyQxQG9CBUbKqtMlPyVRtqw/CQJdZV7ox+9cZauOsPu8ag9Up7K4CrNtTSKqczZ3f4CT6NTJ4C8IV719otv571xQ9Qz9nffwaGCAZhLl9IzeP1D4c8u+WUSG/MWVVSQQgF5Vr4czgc2vH4NgXY6tU0M26FSrTWWW/DTRXDaIFL09O+3UK4cgJnl2FdrOWJjuM0wrlYvV1IlkyGqSSFcDs4zR6sNbvbXF1AUzetGVUwJutxLEyWikOyiLbZAHnsEjKs7jj6JuM6bUxnaUs6uWgZbGfybpQKKxq1wPYXr1WnrCBs2lwnyoM5o9AtYZdP3UgG9sYBWtkIbraAIay6RJrB62NNmNmQkyd2FQCUEn9Y+/zuGYOvLX7wzm/Vf/Vz/3pb8ZLvT4AeoE1vqYsmngCdoyVQ+MPat4dOHBc6zuXs7JqAC2PzH8sPKNeI5XpRijMa8qrYTTbW7zK+ikA4kpIMC/qWOCNn89xMwSKRQEU7a6S84XXg2MBGkI273Q4GrXdvrEYuW3p71emNyn3QnF3mkztQRKyAsJHvaDwwAYOKRRpJMQbVlphuKZEEFaPAAIvHTLuyfrmSunwrcOX65WQk1NqqH/eA+dEAUBDaP970NL2j6FxX1i/nl9Payt6xqVccgOlPr7CnsO89KUVLiw2pic6En+APDJuOhc8lATNKSkwAYVsAYPsNE2iO9/DohlhpQtr1XJz5qS+dOx4Q4667YuUDTX/GhI8BAF66feHg+U9/jBX88+bfHz/2uo7XyH0CpIl/tOcEMVwytuTazbUw0235plpuqsbn+w8TjJKk9wlctbEWgIw+cK3dAn6CT0LHDM/E4yjIRCAyxC2NKY53RgF6aRa2BWFrALbijjJCakPPwESwt7W63u0iYRvO1omHuuJjqxAR65IT8iSMnF+/86aOpPTKtNYuyWHiS8fR6BzCzxbd0jkjMVongaw1n09T+LgD+ZXHxYT7zVBYfWtor7GbVbY2a75OJFuzSVOXNHHXkZgFOIWFw4Z56xJaQ02/WM6PQChyxqXAdlmX/QdMIhFwYw0jA3AdoGE/E0KF5D0GoBw5h/GnFzCMJ+fgspHLnPjzbBzEY1Izs5ia6GoD3Vpu7WwHJglLOEmlerKGWoVG2WwrtMYTUCJSgtq2+/d7I/UxpV1VBFW15du/Pe/pso8rEVnHcfXnRhw3TQpj4M3IMbuKjLnGNqb752l24WqjeQ8E5ll2aasAE1TwrtaCDfMzZP4CDoM3N4BTc+Lcfw8BIJTO48npctb4wYd68OgbsaKp+tKhZauaqkkyv7yxhkfOz2m+zk9h2bzD4Hk5AI6sBUgMPzOuSIyRpw9lrwvz9qBOoNmXZOPSTTYzc0MtjKDxgTFfBcnhI9iNKtIVAlBKaO4exFVcvbF28ZgSqgeurDFsEwBkpMeJrlCxYuxKJX9UoNPVWOsf43nPX4CdBD/BHxHsuLEXGez2dpRvql0ytruEo5/aPqte1lDT3q4Phkdfq750aA6lbI/AkAkjz7jtVzXfPG16/ovrH8mf+PzHAPxh8fz1F9x8BxC9Xv/9p0+7tXwQANTvRvF0z98taHZf1VSd1jh0Q5DZ2gHTqHPt+CHh6o21nZyjvTPhJ/gkaPGll2BE04lIZVQQSLirUVq/kUxeRiIKBYAdN04o/MG6jJQIpVVj5lCQ0/d4e7sPW64o9G5Fae5mmJYbKY9ouYC7jE1W7u7R12tWwbaenIt02xLXBz2puGJn57viE8UIGGcM3OuUbNWv9SZSpBQo3E1AmSD5JhBbXKtUIuByghTxDdwO5pdrCU3aSN4KBuhLP5bqinSQeQGyAS41LkxPvr4U7v0MBCKFySdN1xSKtcGel5/RaaTQ1I0z6cAKYNjVcaGJJXM8cBsfFTLZFYpfKQAFQdwy0DKDuLMCoS3tuaKa2wFESuvOeZcCwIZHvnJbE3DvGbh+1/89DSdN+PmKt84Yec0PgctWPljySWz69jVfewTANb8AgII5O8bTBPbsUxhdGavPHa0wH5lCcc973W8A6NaLuDyXEvP+z91XOfXoplq6QK7k1hHsGY9XqWptsr5EPHmu6s5q13knl2tWPx5qRVN1MvR6isEnzuukrXOvN2eoOwJ3Q7CfvKTNEwgjwoEZIZDqus3LHhhTUr6pNjbOl2ys0Nm5w7oS9FNnOmBmHIFAkJWAFsQ6vR2ad4R+BcW3zHrluPHv4lPolkqzkb0MbVN10zyD9+g05Ih104dxzUZtbduFPP6J1zvK9/XE60snn1Rac7hJfE/C+TP+fd90nhMF46f/dv/0jHljnnv3gy/dbUzM2OrhC5V9JQx7rwQJ2K7a0KnG570GfoJPQivaGcE1dpIu+2RqRQvS3J/9o3Xbb5hQOHcdgCDSPEakhEc8F5wC2lvYWf/7XG11f2kHfWE/o6xAkCQQicpcJVm8OhaBY6yevl4FCIeax6TZbb1ZwjJhgpQiLUKIV8dcPczy11n5gTlLTKK0gCEQRsqikODxuqgAb4zmLgrA1RtrA4EHxpSQaa7Rizs9xily8kLZRsn6TZthm2t1MsGbyukoHeqOjQNdm9G+27PYL8pWBaed9rtUDkPKy9KJnDule50FjNMbWDCyhCfVKhMRxfStAJCHuFrbMHvH4jYrR86h3ZqZtUSohLE8j4cBzeuW/JkbF589AZ5F3pqN88jq3LNc01DTASY/m4DJZB8pp4WG9ycGcG5pByFUJukD4pZnjaaWL0pymvrb221hT8RTzPGECKEh7vYLgDb+4Ah7OfEnWPulm2HBOa/5loPBmxr4E6HcRtqICIldmmrbnqEatI+GvgpOrwlat20YttvP7IWWJwN2+drvIAxgGLx1sTGaeAFrSSMSrkDUXP72c16eWhJG1UoexcNb0fcK8DneowehfFPXU/nuAx77jIKY0ovXzve/6Dn22B49F5QnBp3uRN4L0OMn+AEDBhzeCt3YagCzfgy0ep7xSwBANk+asvQClADO+uG6HTdOOOsH6wBkEdECU9v7svUnX+cmoFeEabqaLCbS3J2s95UQgVA7b8ph8Ueh+lIX715aUtuVOjUXDDgrdqQNB5xlb+CEnEtfUNwYh0PHvNyeTDh81A33l/oUsWIxblLMLAEYo54HxiTN7mTiGhmCQNeSl1EwhJtK2Qxa1CSuJXW6xRrYs4syDRMmmTgRBaflTlA89mmrzWhBQo6GUxk7i//itWpr90tTOI9MbqK5KdpOVdGxq1+rtm2w+mktQ9JcLaa/QmhxS9rCy56IpwLjTY1cQRi3LNFb3GZwU3lbyFG0G5d/6jjuB8+zuQcqUS2dK65cIMlf4RLfUIqIFdCXptOp6X6IHI/2uPdiBb/pMcV2OedljbTG8+S5nWWx5IR7UWn7eVtPIkQ8Bz8XkCxmByoHXbS11UgbjliJBRdL0FHXbl62eMy0QMDEBI1JNlF2m/WAu8nANAaAlCKKhN2l73UoAFAAnIS5O9fix4I/blHhyDxEmtaHkQAgI8HPSDjs01Z76PET/P79+w9jbQMGDOC+cHyOCQIjt+cDlx6ePAkgyKgwowC0tVC0Ell099PbvjPhrB+uC5GhjDW6KnLk0KegEaOsbDOB5Fsw17TIJ3WSQ+mAtUayRO5waemQMOE5nald2L0HOK/TBuef479l4tfqtuaoMBkPJ37qFXcvtNFe2XzvzOW528TOY4pxTzwdkNhGvbHvUwiS3tNBWTMBODFHAQB5gb59ZEMXMe3D0Sb5cJa9W+m7NXm7ZGjZk68vNcJ8YT/tWy+SuLyg7Oev1kgjNrcWT3wO0NMGW0nAdVGDPlYAEApCqBVN1XbauGxoKe+rFY3VEXt18h4USs/Z5FfN1Qo8oq3tH345wp19hb6J8SswUkI6E7MCn0jMKoFtdFak6YmHpky7UOYeaFr3ES/LqXw8uVoTxQwbw+5cyMZi3AlUn+CFQ96ZtNGsYNIPWnqah1l41TTU8KmXZ7PihmOhUADCQKe3bmMTPCEbOPJznhvJtjG9bkiYwHJ7w8QEz0zQzPNMw978DNmCJvH242ekGqjlmUAr1CQzeXMWVSZJq7uOF4gt6QxDMIsJ2BndTvBRfOPoO1z7O1NtfIVKOqtN3f5IV84N9Oj4wzttof0VQ4+f4Ls5OI/vKvjAtB4Hg+WN2oKPE26P7oNeHFP2YECCei+lPyT4CT4Jil2TywRM0IpMp0zIUUwbcZAgKIsAgBBy1D1Pb/32hJHz6gFkIgnoMA60fg8iLqNyYflCinq7YTOgAgFAMhE9NNlVsVjPtDRZWTvWfQdi7Pz0uk28Nk6RzbKW8Xip7fO0gJ2RdSk1nwtsBg9XjqdimzIuJNBs3olE4dBIIoWKTmzc6hR40Er3Vkg3F+dRGXNJjH8T+gWaPXNaT9AieqWTwwq20XyqRG0JBAI87CvP3mGTgfK8mSGLZZsxoWp5w5hdXryVc6aM9iS0suj4qm2gXyKflxWUrX6tmisUCDb8J/NktFRM30BDjokzxY2MbcoYx7KOdkYMwO9pDNuLTqYTbcenEbHQrTyCEAyjdZLNkMxGOo+JTPUb3L51rPnMdpKZR1F8p23EIf4Ich5MLbE6FFqBtfKYx+5ZHA2CDhakB3WG0UcLntfYES2w2hKNSbiMc3O5hKld2pmQINyN5u4oMGPSgA1afvC1m2sXjym5akOt0rJVeiHHnJ5dYvKJiiIhZQD71DOllZXhO5Z0mtYbFi6Tg43bt1oRfVo0GNvuEaE/lDfrh4dPF9sZKFywbtet47u2ATk18R7tgZxzOuFEl3aYxNOjN2FKQdnlBWWHRMQvG1q2vLGG/o5cwzx6KzyDT4KHM8xJro0LlgSQyTKWKUWQkQCOOgoAoqwC0Naqjaw+v/CZ5+ZoOXlbGFPtjJAAMtKJ2UqrwUOJiKAQr4ud9u66OV5YpJ3llBKOPZc+OwConO5HuiKrd2Q18irM4prr3bllimXh3IEk1t9zfibjE1qjG2Gyg8SHmcvRpjHc8CoQ9qLieMO6Q2LJge0KrvPTJDujAPTPOrpzLgjJCofB89wwljoHbGO/TFwPgF+9sdQ2htFcGyREZFj8Ws7S2qTh3Jm4G9uYYjIb5JANWKZl+yQ+AICxz4pMJ6a1+ELFMiTluo3ZYjzbpuP7Z7Ljcupm94FpeR0e7xTJ4Z/J98ZHMV84FqwmPnuofeGclnNyH2jbWIcKpQOfKTMauXqeR8WREFMKymrNJD35pNJfvFZtpSwZI+ax5fmjqoy0gxCx+xu4vcSvnYq1RiZybWoYBMLRfCfOaC4q965EkJws+x6LNFLCACsho7aQ4WfIetNKjPh5+fi0167NcchKMZfnWVrBpBSIwUupYCS1TjYsaVm44Bvp2DRBdy7QPTZxXm0GyP3oOgt+gu888Cy0I+d1hkbcR7Px8OiGIOPEHhokuAvx4Hlx3HiPg4Gf4JPgMVI0qTKc3hicw+4Cy4ShoLszk4kASCkBBEEGZIUrBUwW2igMfjNbz/Q0AYehCGTM2jVzOri1nhKgNBrGDFza5pFkPmFex5eiQdDRgtKJx6J7x7B7zsITrQEgFCflNoqkLSVc+m2+U7U6hqrUN0LAhJsIkkICuoaY3SuhSMUVMN5vnJEAYvOkjNT5OhViQ1mjlssoABSdnrg7WcIfFTi6c64mtEQ54BSf94r5kmEFAqGmFJQ93ryUl6GxlGWUri3C5QVltY01f2uLN4amY90gr3FvcykCXHZlKVNax0lfWyNdG78ornHMBjEpV248mcAYvXMenOVKeHPnufqWC3IimcxNEl+PGYCBTkYCe7Cwe9lF8Zg2oSva0kFXZFy5NafhVvS6osAJMJqw1aBSPA+NDctja7MPnT6hFMsbay7OLwXw6GvVxIfTtvexzyEAc1/MOOG94oDr44XUWazamLE6jYo8ZR4TVp5D5cp/ZURf8Qi0NSaIflqoYNX8/BVkeDzAbAIiZmcgWA2JCo1PLHFxU5Dxb/56sWpyGcU8no61gW7AGuZQ9tguhN9N587yM5qxYwVKqc/Ogp/guwbnVOpMsjTT+5A4Hj0RxETbC3fD4+Ss9iFxUljZVA3g0qFl7UUpnlJQtrKpuhenQvE40vATfBLZrILV8pJyjun7YOznlYy36AKB0rpSWiYzj8nAxHUJdWTEeME+6p6nAWz99oSR8+sBvcq1TpcAhIIm945bKGCUT0oYi3RqHimhySFbCvKRo8rdmDD2mtjvA6r9Uxqm5H76J7U+21E7ObzHZNjICNtyXYMb2TGK4uV5vFimPiAmbTgWqMNoSW7swONiJv6L9gKPTDNgLQmUMY9XD55X8q1nl5nWaZKREYZ/B4BxebfJY7iq27ArhSQdzKH1lBCRUnDZPw0kqa2snavm/EyaB9jh7gCY270zPTB6EcAhndSwNhnXFuukGfmzwWf4JTjKSIpvI3F5QZm1C/vqsNLI8LbAHUHpCKmREhkSA+jrFUAc4CZgG3lLLFdyqDYJFQRRWGE872OYYDsKnMErumpH4KT5a0qYwS5EsO+A9Ud3g+0QN5X6LgsAKxqrJ59UtqqpmspMYSZ4yxpqQhmn9nGM5M3ZeMP0VTO5hYCiTmiT8bOvibJ0TDe44IePNPvTgXBeFdxjIjDCGu4xz0eadAk9F0hYZxAuekmcXqTaFGj5gQIQBCI9JpXr5q7PHsZWFa4NUMIHxxRwmiGQ6nPtIu/E4dG1pY0YOg1+gu8WOPtH67bfMB4AedMdXoycX29N7bxKvlthRWP1JfllT3aYZgbAsoaaacNLl7zcTe2oySz8UcbR07Hr+RZi/MTvH2s+wLX3BfD+WdFIYQFjBYeHxweGn+CTyOQlgjXFmVK57ztTx+dmxlRLJquD3FEl2SiASS9LaMtoqQAJ7X8zOzZ6p8leSJUNJWIVO+AsE4lfCFhaQ6teE3GMjiEGzy3q9aW5zLID5H7VpNl//GEotYFkAVyDjNJfGXFQxohdEwiWmdFhTibWqeHd4CfioWoNb6DVtL5H6Ss1FFDIQIIZqMME+CSC0s/ki6UKdPrXDADkBSojcOnQMsrCwvvKRt3llFd3CA/A65qaG/IH+pw2vLS6QevgOY2TysnEyu2l41wyjMroJumzOwYf0hhd2xIZYZXrgCG4WTvkdDgBtdIkniF/v5VN1VMKypY11iiFyKrbIZY11hi3AnUZK8lnMuv2bazPlN2lc+EIPcyYpCzpvYEUrOSgTRPNuAwP32Yt800UW0wpKFvdXE2n02fkxDHXWbh8hWupLxtaVt1Qo8xG/TawcjiB6oYaqqE1pReXyqHpPKKtMjKVkLFn7pUeKSEjBaAtiGvImgJc5NPeVfBm2E/hmL7ERhv2ogJ3yCVMQBxff/aKsMIYLacxUkDrsHrVxlrHtJ5GiI4sSyNEBcwgIGHT7lrtxMeatrq+7M7LLRYRsb7ilQuehdYEBrWvrViK45PN9GlQiHilsONG77beZZi5ofaBMSUAvrPtYEnzpUPLHn2tmju125mvj4AWNwfj5L3C9MzKpupL8vUkSrhsaNmKpuqL872FuYfGkrFxPvgk9/I4EPwEn0SWMXjpRLHWC2nN11OZMDLG6p1nlTXrNV1nJlL2FFRPNi8DoOX9jCadxqL7C4ue/vX1MeE+6wfreKQ4Y3KvibzMxO10dJDCMlqVMKdP5z/QBySqgKO45ce6+w1ttwtdvoLmJCtxXsm4u1Gc8/qddgYAKNRdvFG6D702+WZUm26EzTPhas4EjBs0YE3SMwCuXF/7s3EllC5CZQ37Z00K2CmmFJCdlKJpiQgKTfY6X4swFrysl6ibVzRVTz6pbHVzdcC0s9QgUgNfnF/2s1drQpnk7mAci/tJZzPsAuGwcME+LcfiQdeJ42ZMkK+0ftdWa0xQGDUBACxrqAG7KSa1UFwiMirJjFF626ugoC5Un4CKuCo0DlEgkJB2HMguxPYYPbKh6Uar7Sblgu1D4pTktn5JfimAx5qXCn1e2wGOLb0VO/Fhn+g9PgC00tp0kabmjD4mGTwftACYtYS+m0boAnP7qLbFY0ooeVKoY9ErAKEh/RlXKmPPyFsCd7AlWTgbz1qo4Aa9133OzDhY7hw6SqR3EQun6HsVm2qrxpZctbEWgIwE7x8ebM72PDHpjD6FgH2N2yHDxTbO2yDeHJ9CF1M5HdxhZQA5EQeaiAl92kH/iMJP8N0Xn/+/zwCQkfjwbN47xH8AXLm+9l/P1dZ2HwBkYX54m9T7wHup84O1rTB27ACszICEEBSUhvQIndyqw4XFY0qu2agDMs76tU9/3BfhJ/gkslnO4HMsAHPZ0OqlWUJVDEPTbch6pV3k40VroiKe0DBoCwBEbaJwwbodN02wKnkApJVP6OB1U5Syu0SafBl04FcaMxMtIEg10x7bzq54rZySqZmOMC6nPBa9MG0I4hMr7bZOq2VBH8YrmfMkfWLlnEYgaakktAc8196ZwlzcEkRaRCNN9tLYXiF5TYCNW6czwgEmgJpd+Ds0iHWsgljRVH1Jvtbf8ztGM9/PX62xynjYHFyGaWmJDp2diRYIkaGtDoMXbJPhgm1cFap0L/Doe7xay4YVG/CkvrcqVS7hcEPImeGtg8zHGe2oRVYAoZXNrLrAhjZgN4EzyziyG7tea5LNhRlTCsqWN9bQLrJruzhfT+Q0tYdMeieVDuxgIvGLuGH6wVFon59dkl+2vLGmhSmq21wGT31lTATilts7zoMnchlAS+TcF57doM2c7qqNtQ+dV6L94CHiAqbawGQ0tpVHpiXpIPNxFjvWz4Qsk0O0GdP/gG20iYy5tCN9UXFuCNi+je0tJGftzKjCknX+0uO5QqJIx5Nvj3Mre/v4Q6qNP5z7msNRPneN+kTuA9ep8BN8z0DhgnU7bxoPoLBLE9N5HCGscO3MCcRoW6KuaFCvhtEFOFhlLANC9028rKHm0qGlvzyQp0N3xlUba+8fXfK1LT2VxF+7edniMSVXb/Qx7A4ZfoJPgpvE59TQcMNL/irIuZSzDJ6T+yDgBSjgnSIT/YgZ2LdoFU6QBwD4/MJnAFCwW4p0awPYGdYe82Ctj891gbydHZl0Ji5H5NilUvtjgq7PlUt/C0DYSICMu8d9mlry6nxfCoCSOg2X7kzWpdZO36jJJWBTOJvVNNOdml2adcqMAJBRzjI/VI6br20W11YixZsBXDa0bGVTdWjs8NuYQT7PINgvQFsgAFQ3xFMOtfH9CABaI4e780+rnc0wG2leg0z0JRuEMJn30k7MVmmdZZH7uKO/cuUinJDZzZKRtijRVzBtM23iZ7+8oGz1a3F0l0SuBBZ+Qn86QiKbTlBLd5StVinN+2l2d5KwaVd7XQe3fqdjVzVVf+Wkssebl/Ksffx6rQV+WlmeeAC4Jp5y6EkTRIGPDT7GQokoVa3tUiNUiMvzYjLShPaajbX3jy75xrPL7AVGEiEj2fwhiekyay1vEtybGDLL/0QUgUwq1UXO92TiRI6iXYmqsSXXbKxFDoOG+CnuQCNuXsJuinr+GrfCNHZ/3Zd7By/KXGACBpvkokvgs8klQXZY9BcEKghUNivpL5NRmYzKZlU2q4KMCjKKF45DT6S+26rSf9m8KJsXHXVU1P/osP/RYf9/aOv/D239+of9+of9+sl+/WQ2T+X1k+x8BZ8AACAASURBVHn9ZN5RMu8o+YV7137h3rW7bp2QyYPMCJkRUghmEQUEAoFIWJNRzFqlnKeL8sslNgIkP9ciU2X/KWded3aloIwZjmr3TygplBS6mISSFBFCKNMkKqBMokbbVKX09UoJaY9SQilBKX1lJKJQRKGQUSCjwOzVh1OB9B9ldZQSUgopBTXp2s21i8dMk0pIJUKl07NGEpE0303C1i9/cnqrFC3sD0BLJN6L8F6EPT+785rvvfD3EPT3txB/C/Hvt5cu3YT/bcPf2/D319bePry0bPidv2rGeyHoqPejvXVTSys+XVqzGe9HaJNokwgVQvXCLwtL7yws/cFZDzcLBMK5D8pMhBRRhHqYfvKbEkq0SLToCs1RSucRoVUWjSKK8JMJkAn0FuWW5392OFFJ2kinoL6S7rHUmXRp9lheXt9rXbkwJxJSiYj+pIik0Fuk2agQKSgIBb0lMU55a6lwm0RLhJYIrZHO0UImbMKKkSGotbrltgHsL90hAB5rrv7qsNKM7UnzYLZKtMr4jPw5CyVCqbe3SF3y/Qjvm8LURbbN1IGmh0XsCiji6wXM0JWIJFpl4qh4nNgbxAc5HxuJv0AgMJdGPWatIFulaJUilCKU+k6F5mbRg2nulLaupdHF72zV2BKY8UzMJfWn7AwqhH5v0FMcRUEUBWEYhGEgpaCS9OKl78qsJ/hf+rXmvLiU8zpKlLEvT6R6qUvgGXyPxNk/Wrf9hgkARtx1sIFxaI63pnY+e+xhRG1jTcmwUvtzakEZgKW/14x879I7b/9hIy51j3lz7frVGHQe/Xhh+QXbR66puRFrfzjx4UEvz/w0ALyw7NMLf33ZnHuXnp4QGv9l+fMfXV1z8wkAcNQRuqSuQ8KurTv4y1021PHl66G4akPtA2NKrtvcMwT11vedJPNdRoF7OPwEnxtO7BoTVoas7UyWglgUzCQwSYsM4yxnFpg6CoQ0ZWM/urx+EsbIjpLMRmFABcKMsLt0poRQFVU+s232F8l3S7FzcdhZnOb1kfPrd940nmZ6Cm/XgYWIUk44SkdsZam8iLuHXw4RZSAmc/Z7AoJLaIk3BlaIrmy1xrCR6tRRg3R4HLJ+Im0FhBseJ+7nOPwFa62RDQQg4a4WzivEljvsos2CnRsE2bwyyxp07hAAD71SA+BvIQC0SRxTcvtN4Z0LmvH3MPbRatqMUy/GvgitEvLZ57deOvmej+PvOPOMM2p2bZo59At7N09f+McbKu+ZNoj4GYwRVqt8YUvlht2VG175dmXZlEHcWYuL4xKiVAK/e5HSdlg6IhOT1gbmQC6WT0RV0/oRdnccfzw3mAmvIRBaqxKxxjjaGCVIQU7JeLioXCpzqawN3CpNsJsFWDPGHLCdYGuQbm6VhGmhUXkkRwUS52WqAZ5A1lbFP6nulghtbCM/nb2WFia9z8RdEZtGcu9Ka1RIx9J7I2ve9JESxiVS6UjMbCSDVWtVKty3M6cihsuvs8aqkxQQbcyCJPadY+8KyTzorMJsydiSazbW5rBLdcO+8sA11OdCIchQtcJWbmX4ImDdpF8LCsZVDwkxD08e44a5NSTefcUdZBjvzoWf4HsbODUvXLCOnhxytBs5v57meNprvx85DzpqjPW/p5+91WFvWUNN5L5228XWh1/5xMxTX6/ZBwDYv+dNYAQAYNDHCrD7jb34xO7dvz3/+ILZ3z4duGTOvFtOZwefPml7zYQ31y67dHY1Kq8uGXQELqW7YGVTNUUQ6uqG9DxYBsxRvql2ydgS7zLXd+B18AdGrOkJlAgUaeJJB5/JyExGGqUOSNvianpiww1l/zGNueGFxAFUJlCZQJH2/aijQ/3XPzqqf5SXp/LySKuorNpJiFgFbfVDhQvW0SRq1VTtzak6w42rhYqhBK1q45YrwCiiOorwoBBFYseNE0bcVT/irvodN06QUpDYYOT8erIGkFLsuFGbBWi1N1VrW5JS8yc0YTmOldYcQQFK69RdXXsUicj+lJASWokbCV4VafL++xe3A44mMlL4jx+UrnxW6y9b31h716mlV5x855Ov4X/b8Lc2/K0Nfw/31n219LpPly7dhPdDvB+iVQIK70cvPDai9LsjSu+YtX/o59GmIIHWCK0SQ048TpdUkAp/2rz99Utw8tiaBb8sHbJ64epfa638e5HWuUaDJ07dNmfgPU/9nlSY1BWAdPWsCQUqfbf6Xa19V7Gqnhe2HZ7elfhLHAVAKq02JoUx1/UmKuQqWGV183QhSkRK0Bwfa4hznJfp481PvpFtYWZcgELui6IBlBfEmXXglqe/y4aW2T868LKhZdS3odY06wovHVpmg/eRojobUMpdBAKhxHsh3gv1UKH7q80jJEKJSOlupD/SxFOXJi6NRnKbseik4DDc2MVahuph4NpGWGsJOm9SPc/63N5E/qhyq4UACIQKhKIrJbRJ0SZFS2T+QtES6u988CS8PfnzaB9S+uM3Rz/5Afjrl97V9N6mENcqNrIRyjzs+i1tMyCnbIbYizr32zLnLmYbJbSIodNZvGfwPRgkbD/7+8+0t/fgKzmirJrUAXYxYSUHlOnurB+uoyD5FKa3++DdX976qwcbk1vfXFv/KI4bSz9eWF68fURdzY1i7Y8mPTzoP2aeCgAvrDht4dbL5tzzyOlw2fxflj8/YHXNLW+u3TFk4lDg9Q7PfmL+BZ8aBWDieRfXbNizF+ekmfrpwy56/gNfncfBY1WTzorLXRlXsFDEtL1HR8XpBOQUKhC+7rrw6eh1G7xf3IeFn+CT4MFPSHFrdcxaoU6/dKgKzmKVqx/iOwQP+cJTZcQFmKKLTtSvnwQgs4o3SeuT+LFxJQBTtPONHwoqeUZ9FVblz6/UhJJ4/pbxI+6q1/57ECPuquctGTm/3q5/ieg/f4sxCDCq0EyqctYeZc9FijOuyYOxkDARimJVPXJ1COn+IykyGQHjLKcU/s+/zLu03y2P3tdo/ZekQsMWnHYJ9pKG8tfP//rSyfd8HK3BmaefUfP85pknFu3dMn3hH+dU/qhkUAvzZ2tTgHr92bs3/PbuDf9+Gv7zxRq92Hm89M/frpxx4pA3N7/zt3AQsPetBgwYPaj1D9jz+jvvR4MAtCooSZbSAiYISSAA7P2vJgxQ2mYeCbMApqm1sAQOTMPKd1H/teUaM4Kp523UXn6scwppVOykRpWAcbdTbg4bKk+7YrU9V6tTImAyj3DV246anx5Y87BKFW/kvlU2ia2lZcl6XH2wrWZlU/VlQ8soJA499Typ7orGagUhlQ6SQwhYU0maAuPQ2B+wNeRF2p/qfRn3lWmKbiR5nbUyfXYinDAlyLEuakvGllxRvzyKIKOA3h6BZAYregCIUCmYNSi3V+COgnajcDeCXQLBcUIz3ZiXYRpv4Ir65cqkOTLpYQDgvtFTAVBU3TgOsa5LAVAqgGHzgPFAy+Xma7YpcMsbal7aoS7xxmYjwVW3g2vcc8A1DohbkP59AE39YYYX0fdCkBg8sbFwwbp0Nrkuh12IkCS/axuTG1++4f7R0/T3rQ+/fOLEgWbP/j1vmq+DBhbgrTf24s3dv/vd+cc3z77hzNKb5r7gVnTSxOdqZq8q7fcijp9V+Z1tNZdOxmfvrpk+dRDOGXHm40/s+CPwx93/gdJRRTj2C2cPeeyJ594E8ELD48M+VZRL0f7cr14Zf8HQI3DF3Q3LG2suaT/oLwnJExsvLyizf4erGb0je9BVG2rj8dxtcPXG2qs31i4ZW9IBy/f4APAMPolQ267TAlAByGQAIJuV3LSehyY1PEAgTa/NapETdLOQdNZyfAFIeWuM0X6OtSHBGPmLDOWTFQJAZFReOYPYCKFIJp+zwgPSfe5WYC/VMTBmwSC1Vzurnn0zy2wlRs6vt8FKFBkYOAlzUo1MtFPFFSbar7PXsEbCdLsOn6I4TwMl1lRSIl7ga6/WQCAj9m7dM+LCEjRsgjLK0RNPOq5VIlBoU1ASb23a/sbFQ84dXVNcuvbBixY+Orpm4jnAbx7+8d2NwMIF0Q3fmDPpkmcHbzz3qYZLrgokpBQtkQJOP3/V2ysuLn0Gw8Y9evvREd4bPLF00VvzLypdC3y2sua0waJ1z9qVU6uP/dGyMXjogRs2AMBXbrjplkHcjp1TqERUGX2BmuHpa3ai2DKTZmvNrFhVJBjIM8lA+c3hDJgLCdiNSLbBlk83T7gc2vI5uu+CxSKhhDEkKqc53iZW51nVLxsaezCuaKq2iVbhWv7DdKMOmIq4K6zAQvLrJZmBbg6jjfZyXLLGz0VHHUVJX7IIorjaFgEAlOXIdiN9aY3Eg+fp+c+SXSfCkhQAlowtmbmhNgqdJ4LLKuiLUiZHsACgY+kkiqXfB7HXBhtyjju4u4W60bpjHJVVkVJ0dZJx8VYVAJj+9AoAi8dMhTkJjKxCp3tW4BTYEWe2/+4S7jce+1ZqwUCi9clXpxU3Oq4E7RRmh+lb0Lmk3YGf4PsQ7NReuGAdfyb5lH9YlPG7bh5PsfY+QPO6myY+xm9249yJByw15KQLho0CMHHMxTWb/7AX5wzCOTNv2j6zJRTQ4tDTT/qXXQA+ecOyT9rDTpj41W0TYV5nAHDOzBufmwnoODA4YeJFm+nsV123+aqjtALjQCuyPoOVJlktfeHD2zLv3tFZpJnmTNfGoKWYMDO7q+qaNOsHjDh71YbaDFPZeHwY+Ak+ibAtABBkYlWYDoOqBL1UjfNlzObJ09Rq2QXTMBnXasH16IYtMSWQXeMRhwhhCyspyCGel+eUSCgVMJWjDHKsF/nCk/TfFtRCawHH9fc58oRqjuUIKrQbdCSev0WrAArnrtPtpEsQdvVtDhHOsdR+aQi3ybcbd49OORObuebWhyUiWfL2W42po99j9WSzEozWx1TGtLVxU03d4zV19OOx0ndmV5afOGTPpndaokEZsfedRgwcPahlD958/Z0WozuXEu+FVI+gkPKhFMDeX/3bBvzbBoq+Kc0jqI2HlW1cTLg5IdaBY236IvZJkPyf6QGirVy3KgSnNuZOsUptdB2TnifuRqn07wzj6wnFLU9po9/XpnLe2nQ6E2Fpn6s/VKydVBVPGENni2PQOpzSiovi1vKMqDIumTyFPsQUSFeeQHt7ApNRpoWRddrSP+NGE44A4O9SAWg1PJ5S4j54XsnMDbVEf2c8swKAECrIqPtH68n+yvrlAIi7G0dw4ejgjeW5VEIqpR8KGht0dt4SE7GAX5SWQJibyG0dnGJuDF19pVk169fL7v1CCYCKTbVhEAFobQ1g5Kb6pREoJ5QIPRdGVipM0AzABLdjHJq9E/jgdptPIUy0fZXzyDvprZ3LMd0YxOUT3N2J9qHc77ne9J0DP8F7OPjAzvHcqu4Dc6URd2m7+g94/JFEwc01t98CAI3zS18dU3PRFwCMGPmtJ35TdvoXxO6XUHrxKPzjkLNPuPuJHaWnn3XCCw2PDzvl0aTunIjXoK/f8+VLB5G49RsHkY528ZhpAK7tITHIOgd9LRUvCd5z7rLb08kbc4K84T9wHuQPia9tWWYD0Ho7+SMNP8EnIXVihwBAEEgY9Q+UUcxnYqtTzd1JrQtwCkgrPlqZkvsm7BPI1slWJWzMnqkqZ11JS3ITyU7AiBl23Dhh5Lx1ok3SqpZWvZmMpbnJSHY5kbb8JzbfwTRv+bG1MHj+lvEj562DUbqLOG2rgmEPfAkrlE1ra7rOpfhw7bpNI3Xn5Foma6GCYOH8tKkt4lPk1JcpuuPmUYiNbHcvfuyBBmABjJycknO0KUhJVOz0SY+9/bOLStdg2PjVtx8doeX4iSV3v/WjS0rXAadVLjvteLy/Z+3qr1Z/dOGyc6KHf3rTegC44MaJk48LQ03CfnLu1KTxsL31AMyb/eHzS7hK0mqiOZt09PH2uwLghEIjZBI6ePY94YjssGETGzBgHMYYb8c12JIZJpbQnI8x5vgs7F4LVwfP+gRC6GMvG1q2oqk6Ivduqge6W9KG8Qn+xjXovEPilvNudNernMFzEcUBF7TKlVuQtIO6KCu0VIaLFvIygE0/E0FbxafeA0KAklTxF4uTfMVQzygKYJ+OPAU9eAQASepttxMI3BWCJ2JR9m62y+AFf9VxHh8ISKGu3bxs8ZhpQmhBXRTFNcgokJkIcEL72fGmKCMzyz4VsMfcttZVk+v2u+86BdjQotBdqlPj5JCV6liZWqoUj9fYF8CIK5NdEfcaDesPyn4+EPwE3zth49HSTy54P0iMnF8vhDqgizytIUbcVX84HPI0dtw4oVuQ+DOvLa27GkBN8RS+Of/Gmnz744SJV+6YCO5ads7MOb+ZCfN2xscnXrJlogLuH73+6o21+u3MuoroVAetuHJ9LYCZPSqQ+JHAYbSH9+hZWHIQmnuPnPATfBIOO5RsS1wg1mZxM2whtHqIXuLE3Ylqh21BbrdLW7lwqKpeaOv1tV5905rdVC6ev2X85+94Gm0ykIY3SwUgiCSAMMqOnFcPID1J0xaa++2l5TS5JxP3BJXnUoGdN00YcVd9IBVkOo4z480ORUisA5jSTCmYqDi2h41QJC6rhAh4JIGEPl4y9sfX8jaiFD+/phRaXsK5TpCJicKV62sfGFPyjWeXcbrZGsTHAtpJnQ8V2rJ4jJ68y9aubGmxxgSsWCiuqF8Oo96TTFcqjIt/XlbXmwmccaJcNsw71BaLdGPiXVZ9yaPBc/afyJWc9psPhBO7nlvO27Nr6sZGfdJ+PiVvsESfM3761KZz5reEE5YOzOQgrSYXiV2sMQkGTyWz7PKt3YATEZ0qZB1t7wLnwYn3Buf91Hut5P6uHJrLvRWibEwosxkV6lEfj3Bl3jD0WnA0xPa8bCzljCev2IXwLSLXT1uWX6OuyiWvjr1R8oyC4tgoKxZlg18IxY+l5a99MQrmPa8RxPc15ZoUNylhKq9sbmlDrSFcOyGKtWCsf7hLTo5ICdIEumddAdNXSZV8J8JP8D0SpPB2Il8cBGimP9Q8clxiDzPT77xpgs9H1zFoaif+LQTaWru6Qd0Vf66+847myQ/cwePt48XbS3/3xZqZowEAe57+7riaBgybsf57kz5hi+x9eursmt0478GaK0fzQ1/42acWbgSA87/z6szPHvn2dwKu3lj70/NKrli3vKsb4tHD4Cf4JBxVE9OmSxNEibIk5bCKVzr/G+WC0zp4l8FLpo02q2m9aHUM4xk7UAoh6d70p9YCZSJtPE+plWEYcBDF36Ved2rYqLR8bnYj7iW3WPCo9TCm+MTdA0aX4gV+ynKen87q4FPEAErFIWz5RpjqhP3JeF9SP8BX4jxmvulnp7eNtTbJSAKmWqP71dqSmVa38ifnOoJ6QsUmPX9HEtZHmUCv47YWbYNOI4HamcnSAFAAwlAnvBcs6pY2Ps8CQF6e7JdR94+e9q1UjhDl0lyHj8q4DFyDhlg9n4Lm1oYE5+DBZqhLxkTTmmxhsoo5DI+ROVvhn2vuvOOHjbgMksl2gj1rf7kKn/wiFKDU7346bvvn62vuxNrbx/1sUOPMMwCpXnh4+MINU+b8fPnpCohUrMvf+8jzxz9d8/An9LlCq553yb1MdY69HM5lueM4XCbqePC7FgMqdUggtBBFSz54Z0onrV/IpIa2z/NI2ZyhAQPEEkTN3bXYyc1dCXYWUh7TwCMXjEAgw3NmsjZbY3IeLkKwh4pzcThPW3zHeSfwqH+xaz4VYCIrpjgXACKlAJCziR1j5h1FRRlBh3Osa6Zj3+nxpSrnn66zHYP83OA1KPvlQJFG0UEKjyMAP8H3fiTm9R7BvN05vlvginXLBfONpDd1FVOfX2PUhPQg04v4sOD+0dO+tqV3at8HlN1+B+68o9nZ+OJmFF4OSrWHzc+vnzp5+YkAzvz8mTXPb555xpi9a6csfPPWyp+XpQP8vbDmrg0b79qw4+bKW6b3qjx7121e9sCYdg3pDwmLx0zrco8M8onvti77vQZ+gk+CR4PnL3Ql9WI5Vn8ZaP4Uiba2OI+7puyRJoLc9pVqcLOkG6qt19R8ESo0d28LAGy/YQKAz9/xNCJpybqzZKdqzTLR5pdL6t2NOXR6nZpYuraXzT2gJFZwXKitabFgdQmRXNUK5ZD7xHf6RXM8WNLbmKbxfAGJVTlb4BsBDBBHpdaCA/I1EEzBH0VCCLpxcSQ7MqQwBQLDqGI6RbG3RKAE0BaaMzIjDLrvmYyNhU8tjyUWMhKR9lRWMBwryErbyJ+eV0KzO2fDxqTA3EzWgdpVnWlebVcEPBKYcLTCjqm8pekOvwEMubfHpsUD9sKSxvyMPfGSNkh7HHrv2Yd/+8mZZ7xWs08BCu+8HufSPW44fvP6XnXi7u27zx8yfPYVJwNT5jzkyPZPL32lpvQPa+d9afZcVN5YOshejnI70GHzrlDBMcCmwq7tPaeM1l0+LQnjgQEsg+cmIoSMQAsAYydBajey2aToBZERxlyzsfbB80poyMnA9m+C+zq8VpiBqoNyBuqn55V889llALIm0aWjJjd2P6ZD+FUln+L4J+uWeIOK+4dnSaDKq8aWXLm+Vin9sPz1idt+9ealU647E4AMgiiU7z7+vcffvOiK688gs/l3V9/x6P0NAAZW3DvxouP0wBbvvPKdWbteRsGdq0Z+zrxAnQvhnZOTOidf5qlfugaRlkbyfs45uM3tcO/LgSQDhxV+gk8iPZOZ9A+BJCs2EnZlzAyJWJzL5bpGJGtmOjbf5ziRKaaXFDQbSX0gNSAMxc6bJpx95zMAdGzaSIJmRCY/1QPOOA/ZR9XGt0lFvj2AyMjxLWEPdGzcl+4yBcc5LVetrjEYfbUF9UM14q56IbDzpvEU3i6W6nOPMmZm5kxfuRA/pVo6ylZaQvvq0HIkCAWAMIgn+HhxwOSlVGEmo/hIMM6T+ijegIB58Ulj5kMb9WdGAtqqLhtg8ZiSr21Z5sxMbMpUUmR5hQBMvFLjvyScW8Dkw1COP5jTS3rl5NwiyX4EdsjxudnMZNRyPusnP9mLXnu+KURSZ9DZtGdE2Qy8uB7WVG/Y0OMQnwFvb9reOGXIhRNqps9cO/dLCx85v6bs3HjoSAUMmXjjfxxf8+mnXpw289PcDpF1YxyxhwmQuc1gIoNOxKYr546aOikKFve+45UHQs/T6VkmEM70rO8y5SiSAtBpYWEC3ShaVQcRABkGZgzHbx7eMBHodwCPzxroeV3xknxlTPO6XeEFyVK5kZjsFe8QrYRSACLNCzREgHcf/+6vHmzARN3AKMT/PP69f3uwAZOCtrYgCAD89oX7MfKhlSejbu1V971cOPfUEwD89rmJ8xsn3VzyozNtjyk34XVircYfSWcitnJ1vfyiHksekqjWXgHflNYdsIKdOrUTfLKZHo/3ds3esuCCLQsuaHyNb37rz0sm7Lp1/J4G+rljz83jn79l/PO33PPXZAU79tyid/1P57T4EDFyfj3JLfoarAV+H8Kvd2P06QcsNazggtNHAydO/MoU/PGNvbmKnH7GZYe9cV2JDgLddH88eF6JtVBZMrbkp+c5bm/HXPSDL18znJc/5sK5/8K3/Omt/8Yn/mkwcPzgj+hN77zynfn/XXFv2awzjnTjezQ8g08iYoFu9GKZQkko6ylBfEvARMKxrFEyAkdmL1pQKYWW1Yex4IjzCcvsteiYuckpZexQIgDIRMTdFWhF+F9PvvrquWfdWHn0fz25e8m//uWmb35EAtj5xndv3zdy3shrz6JzvL91a//Z9SM+CiTXkap169ajv1M/YkC8K6coy+XudLWOakDE+9kBKv4umCyxY6EBCasVN3dMrM0ZU3EX08oWRqxhccSMJhJOTEiFMdPTjoiawVPOobg2u4on6agIAcPjo1AzeBO0WADIZKWtAVZlwwJe6osNFE9cFBgDKEL5plqlDA3n4m5brSaLMWt3b6/h3EwybLuxvbhnEeNt9lhOyKQwo5edkXsJ2Vi2grXWughm4quBMDSRePwrG2oefbTmUaplVembt1becdKQxnXvKAwC9r7dgBPGD1Kvo/G1dyI5iBL4QiFiyV70A6X2vtOIAQphskccET3/bgOhqFTnRK5zHRds2OvlTw2P/GNT9fBz8doC4VgpmgoFYoM7QbN7W0sGcEI5RUa8Zxx6k89FYG522houMYTcm6gAhFJY8QMSXNAOgGTDkzZl9ufVG2sfMH4lSoowjJ9sfQrTwCgKwja0hQJR0NqSCQJgwMgTPvXI+nvOnnLOrv+6+pufGyiiN3e/8fL4j5w4q3oiMPGWkm+cQbothDnMn3NGok1LTWy6WIKOIGRlqy5RT162PVa5G9mWDmWLRwqewfcY7Lp1QjqDy3uvbcHJZx0N4CMXfuKMtX/Y9Tbw9p+W3v7+pJ+f/ZVCU2rHO0899Wbl+Fe3/ilV6453nnrqzbtz7vLoYhwwBk7vwynfrfnJizU/faVm1uU4/6c135sxCKNHnL/yiTV7gD27t6O0eDQGjT27YOUTz+wB8MK/rxo2ckwuY7pnn3r+Sxec2tnN9+gI13zwYDXHD/vhrfnPzFv5XOG4fzkOwLs7tu2fiBM+v2raQ1cMWHvXjl2HsZm9Cp7Bp8B84QTz9VJSGCoP+2lWiyJdAy8gI8ENr8jtjarddWuOHO3kax5XK7Hr1vHn3PE02mQmjE9PuUz/Z/+fgMH2WPGX7fv/OOkfBl2x/XvAyHmFk88CCj85b90n//OxV+6e8SoeOaXo43FhddaJ8+pPjHcNBiCs3UB6nUrrYu2V52jfuZ+fUA6jMWTeKrmghNDheDta0DpMyeoXhXAMXnihIEjEqmi3cr2Dh31Vzi0Lw3jtm8htw6UsjtbTgMcbMTl/cxhhaXu6QHFOpkMgk5SILlyJIDZa0k23auMIMdXjGlPbr3wJn2B4AZLdqA8xNF2kdlke72ZzYccyNbZtBe8j4gY3YQAAE49JREFU2426YVsfnr+wEVg4W82p/N7pVJtS5lNBitOvXPf298eXVmPYtGduP1aibcjEG6veuuZLpbXAuYtrxgxBtGdtZXHN4AdqpuHh667bAACXzfnx9wY5/nu2SayFjtGc8f3jsgpLuyNGx/mQS5goprXvWvVubkOQLuaa44XsFBRyRwritXpkK2O3CyCKAu7Km8OOzAwtroOP+4R9JnoJZBzAdvHOzClAS7N5i2s31y4eU0JJ39ts1C92WCAAKC0PiyAp0q0SYagH6bt1O/+/f73/s/d9beWcmRPmfTkbiY8OnjzoTBHhgtMm/vzFP70TiEFg/cyptn7V6FeO8xwZywP6VNJxkxN2VxA4HnRW328rT1SYROeydg4/wXcBbNY1ABRvLhPprO9hNgMWVcYp1pYjrM3Rx34S9c/95UuFx5ot7zVt+Z8zTxr8qafzix5/6ce3vnHquk+SMuujF59815A9t6z6a9G3/ylRy0cvPvmuE/bcsuqvRdcnd3UPUHi7buU11zm4ZmNt1dheHaF21Mwf7Z6Z3nzaHTUjM+bHiRPvaJgIIFQmAPC5Mx98eWY8I35i4v//EuXSnXn/SzORSkjv0eWwc/wHOfidX+3edsKZxcf/47z71C1fb9z95VOA//zjO0Cv8oQ8EuiuE/yaClG8RH8vr1NVkzrtzIKpQkX7GgwdG5IZE+d0w9CkMBJGMS923Tz+nDueBqAVXC0hiA2Tl1SoAJxz+9Mwq1ElhFAKYRSEZDPvvrryv3naGRe8+MO1AICTCyqOx2v4x49e8rETI+ArHx+x+E/7/iQKBhvdWuFHCremFplEx8/6SOFWvgWGyrdHgxMtcX7arI7mF5CksDmc6wAAgXSSMijj8EaJ5gCMnF8fpO6L1qPnJItmeU7fuT5br76tiQBj/8YSngwplK6BLdIDO/3QaGFn4d53hjTo8LrG9t42AVDGhSmbPNb2fMQjIrMLtArmiGmMpcsk6Jdj/m0KcPEJfcswC4BEQJIEz5PuT8DJKpvwwQMXLbhBZjhshFrH/pwVoJQ/MB5l6UQ7gZtsJmEJr5um4jYkijnCOTjFuFpd8jOaptItClM12Lwy3JSBM3jlCmBClq2AR1xQUuh4ro4sMJCM0JvLAOztMOKunAOP31luW059lDEiHO5BYHxT2+X9Fq79CqrGlmgRvRtsirc8S2YrQBgGNqmSvkcNf/xfiX+I9r7bAERB/zNGffThX7x64ZkFx+/es/ZTQxYPDkP20JmrdFTsMEGxWIYwDusmwy/K6vJzXmPiYtvtCsTN6GR0ywl+TYUoXmKm9TUVoligU+f4noVjv/irMV8SaP7x5sZzBn8E7yEptGd46/0/o3/uav7U/q7ughF31QOgSPV9h81bTXxv5vEefRk7H3yiqgGYX42btFX8v1eteej3wNzHcNvkaz6H4748YsbN9VMvAvCR6Q+cewaQ+fLoW/74+Ncv+S2Qf/ujw4/v4ivothCq45VJF6Dp3lHDrv8Mm9HXVIjilxY1bp1VkKP0/v37D+O5BwwY8C/Lfglr58wWeTaaLKf4Cbfm1pYMgLA1A7PEbmsTAMKWDMWv3X7DhJHz1uW1SgDZtjhFYiJYjTb2JMWUWRwGTq5KsGOB13+8+Rf4zM3fOhbAfz3x28VbBlx798eP3dlw18r+1y867ljDBRvvfWX/5ScXDY6PhTbRVo2LXtl/+annDLZnZ2D0UcUtyYSyvUWpcBVdThhe6EvTKolMAGPrGxi5qswIAJKZZbvu3GLXreMLF6xTSli5vb1fJhhNTHY1lTEFzEa21GfhjBLg8XCCQHGSao7S9IiPBF2tO1p4AdpFfCWTVZq4pGog2G746XlJgzvKMxs3KZeeNcPYUs5rJJC6N5tR9pBEA9KxWuGGXOXpZ4RrVM8L2+Zy9TxVSNFt+wXoFwAm4QrPXRsqEweGacQDdmycICeVHlel1OpwRQUAjsrEn4IVsyT7faYlyzLZQij15YesSTrNT6QLa20u4jPa87awZKktFICBIsOY8fbjc6eVb6qljfolQ2GUQtHamoGNYK3pZiwQyubJbJ4E0L9/CODnE7563eZl/bPUpSptdU+wA4l00lpqwmJ4BDliXyTB7+ziMdOscN64KTkORE5ECqX30dkpzFdCYmG15ogDSDgtciLguldo8n4xiq+FGDrklL71khU2jD9hWRVXa0SD3J3BBqvQNTCp1xNlnztQ/x0aBgwYkHN792PwTU+t2oaiy4fFW4adWoQlq55qmpVzhu9RSJvBf1g0/3jzL9bi4+Vn3Xih5t8fmXzG5W9uWVzcDAwqefq4Y6EaF+2orqPTn3XhYAXgL4+9evcD/aevOwmLdi6tA4DCeSMnD+4aKdKhg4wQd908fuT8+i4SfXUB0kbI1sa+YlNP9ZD2OBiUH6b721VCIJ/stavQ/Rh8isB3TOEPO4M/vBX2dPgOScB3SAK+QxLwHZKA75AEjkSHtMfgobob6soBlNd1tGkEQ6e3LzcaFxVRf7ot15vNxrpy5Cp1gF19C7Yn3b6IN5fXKdZd6R7rYFePRP1Fx+DkOzvYUn/RMXS1x1xUnygFJA716OVoXFSU4x2U60EqWtSYKPVhHpiuOq9Hx+h+IvqDwK5dcVyDbsHgm6tKlhVv3/dkfnNV8dkzVu6rHAcA62cPnLp0+op9T46jZjZXrfjE9n378nO0u4NdXYvOXn03V5U89M+nFc796U/wjbOf3FRMnbl+9sCpSwFMX7Gvchywf8e8Ff/n29PnTq2sSPVYc1W7uw4LOrlDtszIv6TuXQx/z540taXya4+jePW+RzAj/5KLLnis+ZHRACpHDVzQMPymfc2zj/R48vwsgS7skOaq4rNv24np/2MbkNqy/gfXt83dvq8CVcVnl8w7p64iH+k31eE974ABA/bvX3kkzttD0ZkMvvtFsht2alGuzUWnDsu1uVuged2TuHBCPoD8itnTl1ZWNQPNVcVTX5m7Xc/1ALD+vtuW3nb2wOKq5lQFHezqW2he9+T7Q97sf+GE/PyK2dN3onBpZdWWquKpm4egMO7M9ffdtvTue3L2WAe7eiRGP9K8uviYjrZs+d3bOP6zo4HRnzWmxFtm5C94u3j1vq2zO7GlHl2P/Iq67XMLO9rS3PQKTinIB/ILTjGbUm+qHnNejwOi+03wAICXGpriH00NLwGfGd6tLex2Nrzu/G5e9+TO6ac0nD1w4MCBs9cDAMZV7tu3b/tc3HZ2au7pYFefw4v7WpzO/M+tT+4s/Pub2Hmb7UzdXYXAq8ke62BXL8Xoq849pmHBqEpUPvZ28c8fGQ1seejZd4cf33TJwIEDB46q7Or2eXQn5E+4sHDp1Nnrsb7ulbk/qcjP+abqReft8+h+E3zBBZcXYdvLjfGWxpe3dWsCj/yCU7C0jg/S5nVP7pyO4uJ9+7bPpYFtilbU7Vtxym335RjRHezqM8gvOAU7wTvz/d+v21n4xr7x01csmlsIDLSdmV9Rt2/FRRj3T+ke62BXL8ToR5pvGt6wYOBjF5N0fstDz747HBdfvG/f6mKa+j08DPIr6lZMXzp1YF0xScnbfVP1kvP2dXS/CR4Fs24rx5LiijX0c01F8RKU39atXeTGVa6YvnTqwIEDBw6curSQpPWFw78+bhwJ7fFKk8Myi6e3W1H7u/oGxlWumN4ALJ06cODUpWjYWTj+5P6nDTyucOzXx02rmD0dHynknTmuePrHR+busQ529TZsmfFYwerVxW8vGJg/YwsA4JiC+bNnA6Mf+fpwvP27LV3cPo/uhOaquuHbt899ZapVYrX/puoN5+3j6IYTPDCpStWVLykWQgjBYtp1Z4yr3Ldv3759K6Zj+mwy70oK7WM0N73SXjUd7OoriHty3HhMnz312JTQPkZz0yvvvp27xzrY1buwZcYVzxZcNXr0I82ri1F3cyUAvNu0s6ub5dEt0Vz1jSeHT8jPr6jbPhdawNX+m6rnn7fPo1tO8AAmVcWW/t1+dtdYP3vgVKyoHAetcqqsagawvs5welPsvicv/Hpuw5IOdvUprJ89cOpzWD94ReW4/AkXFu5sGbK0sqp5fd3S4f+0k3Xm+vuePAUv5+yxDnb1Puj5fGfTu4BWyt83YwuAyscajjn3qtFd2jiPbgY9r77esBPo+E3VO87bt9H9At0cIrqFmxy5cRXO3V5XkZ/YaH27zE/zW7uSnLJiXyVSu7oNOtvnx/YS70y7kXoI8U/dY81VxWff9gYw6abpSxe4uw43OrtDKkcNXNAAAMNv0lbxqS3acQ44png1qeFtGXvQkYN3k0ugKzsk/ZJJbdEObPwJS7ypDvd51SNT9u/ff0TO2zPRmW5yfoJ34N9WCfgOScB3SAK+QxLwHZKA75AE+rYfvIeHh4eHh8eHhp/gPTw8PDw8eiH8BO/h4eHh4dEL4Sd4Dw8PDw+PXgg/wXt4eHh4ePRC+Anew8PDw8OjF8JP8B4eHh4eHr0QfoL38PDw8PDohejxgW4OL0aOHLlr166uboVH94UfIR4dw48Qj47RmSPEM3gPDw8PD49eCD/Be3h4eHh49EL4Cd7Dw8PDw6MXwuvgPTw8PDw8eiE8g/fw8PDw8OiF8BO8h4eHh4dHL4Sf4D08PDw8PHoh/ATv4eHh4eHRC+EneADAmgphUbGmq1vj0aVoundUehB0MEL84OkzaLp31AcZBn6E9BmwWz3q3qZ293XeCFEedeUAyuty/PDoe6grR2oEdDBC/ODpM2hcVASgaFGjUqlh4keIR8e3uotGiJ/gGxcVuR1aV26fYo++BXprJyf4DkaIHzx9B8k3L7v3foR4pG81/91lI6TPi+ibnlq1DUWnDou3DDu1CNtWPdXU/jEevRFN944qXlK0qJGIGtve/gjxg6fvoKnhJaB88iR360sNTX6EeAAACmZtVapqUq5dXTdC+vwEDwD4zPCC+EfB8M90XUs8ugwFs7YqtXVWQc6dHYwQP3j6BFKv78aXt/Gb70eIB0fTvTOu31a0aE48YrpkhPT5Cb7x5W25Nm97ubGzW+LRPdHBCPGDp6+i6d5RxUug399+hHhwrKkQQgy7flvRokc0X+i6EdLnJ3gPDw+PQ8KaimHXb0N5XXvyHo8+jUlVpEv/zPXD0qb0nYw+P8EPO7Uo12ZHKeLRl9HBCPGDp++h6d5RonhJ0aLGWF7vR4hHDkyas8go07tuhPT5CR6ANpUxaGp4KaEU8ejz6GCE+MHTd7CmgkSvjWnu7keIRwIFwz/DZe1dMkL6/ARfcMHlRa7Co/HlbX6F7WHRwQjxg6cvgfTu5XUpS0w/QjygoyDxKDVNDS/hgMPgSI+Qw+Nt16PhI1F4MKT8Un0YEw89LNp1T/YjxCP55nD92X2gm65EHOHEP359HTkmeNXhCPGDpw8gGR1Bg834foR4uLc6uRzsihEilFIHpvkeHh4eHh4ePQp9Xgfv4eHh4eHRG+EneA8PDw8Pj14IP8F7eHh4eHj0QvgJ3sPDw8PDoxfCT/AeHh4eHh69EH6C9/Dw8PDw6IXwE7yHR5/CmgohRO4kGLSLB+P6YPV0gKZ7Rx38KTw8PD4U/ATv4dGnMKmqrhzYdv2M5NS8pqJ4CVBex5Oet481TywBoJNpeHh4dEf4Cd7Do48h5xR/aNM7ze/l5eXYdv3CQ6HjBbO2KqUO8iQeHh4fCn6C9/Doc0hO8ZRH5eCnd5rfi06dM7kcWPKEF7h7eHRL+Anew6MPgk/xTffOuH7boUzven6//IKCSTlmeFKzcz07U9cndPBNaypGjRIaoyruXePl/R4ehxGHMa69h4dHz4HOn1JUVAQcUooLOlAfUFeOVFoNKmA2UiIN/St9bAI+GYuHx2GDZ/AeHn0TBbMeWVQEbNt2aOwdTU+t2gaUT6YDJk0uT5naFcy6jYkH5i4BihY9kkiiDiMHKK/Ti4PGunJ4gb+Hx2GEn+A9PPoqCi64nJKgFp067KAPcuf33DN8rAEYRdL/29LTO5oaXgKwLW7NpCqlvAGeh8fhg5/gPTz6KNZUDLue5tccTnPtITm/mxk+aUyvp/gOxAN6ebGkeJhVwHsNvIfH4YSf4D08+iTIL65oUWPjoqL0BE12cQw6og3N71hSzHYVLwFyyNYnTSYVO18MOCiYtbVxUTnJELBtyfXFw4aJURWHGDnHw8OjXfgJ3sOjD8JM74/MKtC6+CXFBxFfTs/vOZGY4ekM6LjigllVW5VqrFu0qJxs/bYtOXhhgoeHxwHQlRZ+Hh4eXQHHsF0pa1F/IBN2Oi5HKdduXsXW8skdjhV9jvo72uvh4XFo8Azew6OPgbF3s+ngWDyFp80lcid9ujW1I8/6okVzJnGD+lQzSPRvfd+b1jS8tO3QLP48PDw6QlevMDw8PDoRxvvd9Vxne9rnz+3y9/jookWN6Yr4cZzB63IJeP7u4XG44Bm8h0ffgQ5al9Mtnch2+yy+ff4OWA5//cI1axYm4uJNmpNbOlAwa2tjnda9AwCKyhfVNXo3OQ+PwwWhlOrqNnh4eHh4eHgcZngG7+Hh4eHh0QvhJ3gPDw8PD49eCD/Be3h4eHh49EL4Cd7Dw8PDw6MXwk/wHh4eHh4evRB+gvfw8PDw8OiF8BO8h4eHh4dHL4Sf4D08PDw8PHoh/ATv4eHh4eHRC+EneA8PDw8Pj16I/weQBCJnt6cKfQAAAABJRU5ErkJggg==\" 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  {
    "path": "2020年/2020.11.28等高线/等高线.r",
    "content": "# 等高线图\n\n# 等高线图（contour map）是可视化二维空间标量场的基本方法，可以将三维数据使用二维的方\n# 法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温\n# 线等。假设f(x, y)是在点(x, y)处的数值，等值线是在二维数据场中满足f(x, y)=c 的空间点集按一定的\n# 顺序连接而成的线。数值为c 的等值线可以将二维空间标量场分为两部分：如果f(x, y)<c，则该点在\n# 等值线内；如果f(x, y)>c，则该点在等值线外。\n# 图4-3-1(a)为热力分布图，只是将三维数据(x, y, z)中(x, y)表示位置信息，z 映射到颜色。图4-3-1(b)\n# 是在图4-3-1(a)的基础上添加等高线，同一轮廓上的数值相同。图4-3-1(c)是在图4-3-1(b)的基础上添\n# 加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。在二维屏幕上，等高\n# 线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等\n# 形状。\n\n\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\n\nrf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)\n# 数据\nz <- as.matrix(read.table(\"等高线.txt\",header=TRUE))\ncolnames(z) <- seq(1,ncol(z),by=1) #列名设置\nhead(z)\ndim(z)\nmax_z <- max(z)\nmin_z <- min(z)\nbreaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)\nmap <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数\ndim(map)\ncolnames(map)<-c(\"Var1\",\"Var2\",\"value\")\nhead(map)\nContour <- ggplot(map,aes(x=Var1,y=Var2,z=value))+\n\tgeom_tile(aes(fill=value))+#根据高度填充\n\tscale_fill_gradientn(colours=colormap)+\n\tgeom_contour(aes(colour= ..level..),breaks=breaks_lines,color=\"black\")+#\n\tlabs(x=\"X-Axis\",y=\"Y-Axis\",fill=\"Z-Value\")+\n\ttheme(\n\t\taxis.title = element_text(size=15,face=\"plain\",color=\"black\"),\n\t\taxis.text = element_text(size=13,face=\"plain\",color=\"black\"),\n\t\tlegend.title = element_text(size=13,face=\"plain\",color=\"black\"),\n\t\tlegend.text = element_text(size=11,face=\"plain\",color=\"black\"),\n\t\tlegend.background = element_blank(),\n\t\tlegend.position = c(0.15,0.2)\n\t)\nContour\n\n## 加维度\ndirect.label(Contour, list(\"bottom.pieces\", cex=0.8, #\"far.from.others.borders\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t fontface=\"plain\", fontfamily=\"serif\", colour='black'))\n"
  },
  {
    "path": "2020年/2020.11.28等高线/等高线.rmd",
    "content": "---\ntitle: \"等高线图\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n# 等高线图\n\n**等高线图（contour map）** 是[可视化二维空间标量场的基本方法](https://github.com/EasyChart/Beautiful-Visualization-with-R \"R语言数据可视化之美\")，可以将三维数据使用二维的方法可视化，同时用颜色视觉特征表示第三维数据，如地图上的等高线、天气预报中的等压线和等温线等。假设 $f(x, y)$ 是在点 $(x, y)$ 处的数值，等值线是在二维数据场中满足 $f(x, y)=c$ 的空间点集按一定的顺序连接而成的线。数值为c的等值线可以将二维空间标量场分为两部分：如果 函数大于c，则该点在等值线内；反之，则该点在等值线外。\n\n\n\n\n```{r message=FALSE, warning=FALSE}\nlibrary(directlabels)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(grDevices)\nlibrary(RColorBrewer)\n```\n\n## 数据介绍\n\n```{r message=FALSE, warning=FALSE}\nrf <- colorRampPalette(rev(brewer.pal(11,'Spectral')))\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)\n# 数据\nz <- as.matrix(read.table(\"等高线.txt\",header=TRUE))\ncolnames(z) <- seq(1,ncol(z),by=1) #列名设置\nmax_z <- max(z)\nmin_z <- min(z)\nbreaks_lines <- seq(min(z),max(z),by=(max_z-min_z)/10)\nmap <- melt(z) #这个函数来自reshape2（处理数据的包）介绍一下这个函数，以及相关函数\ncolnames(map)<-c(\"Var1\",\"Var2\",\"value\")\n```\n\n```{r}\nknitr::kable(head(z[,1:10]),caption = \"转换前数据\")\n```\n\n```{r}\nknitr::kable(head(map),caption = \"转换后数据\")\n```\n\n## 热力分布图\n\n\n使用ggplot2包中的`geom_tile()`或者`geom_raster()`绘制热\n力分布图。其主要区别在于`geom_raster()`函数中存在`interpolate=TRUE/FALSE`这个参数，决定是否对热力图进行平滑处理。\n\n这里使用`geom_tile()`进行演示，将三维数据`(x,y,z)`中`(x,y)`表示位置信息，`z`映射到颜色。这里的`scale_fill_gradientn()`将颜色填充呈n个梯度。\n\n- **拓展：**`scale_*_gradient`创建一个双色梯度(低-高)，`scale_*_gradient2`创建一个渐变的颜色梯度(低-中-高)，`scale_*_gradientn`创建一个n色梯度。\n\n```{r}\nggplot(map,aes(x=Var1,y=Var2,z=value))+\n\tgeom_tile(aes(fill=value))+\n\tscale_fill_gradientn(colours=colormap)\n```\n\n## 添加等高线\n\n使用`geom_contour()`在上图基础上添加等高线，同一轮廓上的数值相同。\n\n```{r}\nggplot(map,aes(x=Var1,y=Var2,z=value))+\n\tgeom_tile(aes(fill=value))+#根据高度填充\n\tscale_fill_gradientn(colours=colormap)+\n\tgeom_contour(breaks=breaks_lines,color=\"black\")+#\n\tlabs(x=\"X-Axis\",y=\"Y-Axis\",fill=\"Z-Value\")\n```\n\n## 设置主题\n\n对主题进行稍微的调整。改变x轴题目(`axis.title`,大小为15，字体形式为常规体`face=\"plain\"`，颜色黑色)，x轴文字(`axis.text`),图例标题(`legend.title`)图例文字,(`legend.text`),(图例背景`legend.background`),图例位置(`legend.position`)\n\n- *拓展*：face还可以设置其他字体形式：plain（常规体）、bold（粗体）、italic（斜体）、bold.italic（粗斜体）\n\n```{r}\nContour <- ggplot(map,aes(x=Var1,y=Var2,z=value))+\n\tgeom_tile(aes(fill=value))+#根据高度填充\n\tscale_fill_gradientn(colours=colormap)+\n\tgeom_contour(aes(colour= ..level..),breaks=breaks_lines,color=\"black\")+#\n\tlabs(x=\"X-Axis\",y=\"Y-Axis\",fill=\"Z-Value\")+\n\ttheme(\n\t\taxis.title = element_text(size=15,face=\"plain\",color=\"black\"),\n\t\taxis.text = element_text(size=13,face=\"plain\",color=\"black\"),\n\t\tlegend.title = element_text(size=13,face=\"plain\",color=\"black\"),\n\t\tlegend.text = element_text(size=11,face=\"plain\",color=\"black\"),\n\t\tlegend.background = element_blank(),\n\t\tlegend.position = c(0.15,0.2)\n\t)\nContour\n```\n\n\n## 添加等高线的具体数值\n\n在上面的图基础上，添加等高线的具体数值，从而不需要颜色映射的图例，同一轮廓上的数值相同。\n\n- **作用**：在二维屏幕上，等高线可以有效地表达相同数值的区域，揭示走势和陡峭程度及两者之间的关系，寻找坡、峰、谷等形状。\n\n```{r}\ndirect.label(Contour, list(\"bottom.pieces\", cex=0.8, \t\t\t\t\t\t\t\t\t\t\t\t\t fontface=\"plain\", fontfamily=\"serif\", colour='black'))\n```\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.11.28等高线/等高线.txt",
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  },
  {
    "path": "2020年/2020.12.05日历/日历_EasyShu.rmd",
    "content": "---\ntitle: \"Calendar plot in R using ggplot2\"\nauthor: \"庄亮亮\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n  \n  \n# 1. ggTimeSeries绘图\n  \nR中ggTimeSeries包[1]的`ggplot_calendar_heatmap()`函数可以绘制如图6-2-2(a)所示的日历图，但是不能设定日历图每个时间单元的边框格式。\n\n使用`stat_calendar_heatmap()`函数和`ggplot2`包的`ggplot()`函数可以调整日历图每个时间单元的边框格式，具体代码如下所示。其关键是使用`as.integer(strftime())`日期型处理组合函数获取某天对应所在的年份、月份、周数等数据信息。\n```{r message=FALSE, warning=FALSE}\nlibrary(ggplot2)\nlibrary(data.table) # 数据格式依赖\nlibrary(ggTimeSeries)\nlibrary(RColorBrewer)\n```\n\n\n## 构造随机数据\n```{r}\nset.seed(2134)\ndat <- data.table(\n  date = seq(as.Date(\"2016-01-01\"), as.Date(\"2019-12-31\"), \"days\"),\n  ValueCol = runif(1461)\n)\n\ndat[, ValueCol := ValueCol + (strftime(date, \"%u\") %in% c(6,7)*runif(1)*0.75)\n][, ValueCol := ValueCol + (abs(as.numeric(strftime(date, \"%m\")) - 6.5))*runif(1)*0.75\n][, ':='(Year = as.integer(strftime(date, \"%Y\")), # add new column\n         month = as.integer(strftime(date, \"%m\")),\n         week = as.integer(strftime(date, \"%W\")))] # 添加列\n\nMonthLabels <- dat[, list(meanWkofYr = mean(week)), by = c(\"month\")\n][, month := month.abb[month]]\n```\n\n\n```{r}\nggplot(data = dat, aes(date = date, fill = ValueCol)) + \n  stat_calendar_heatmap() + \n  scale_fill_gradientn(colours = rev(brewer.pal(11, \"Spectral\"))) + \n  scale_y_continuous(name = NULL,\n                     breaks = seq(7, 1, -1), \n                     labels = c(\"Mon\", \"Tue\", \"Wed\", \n                                \"Thu\", \"Fri\", \"Sat\", \"Sun\")) + \n  scale_x_continuous(name = NULL, \n                     breaks = MonthLabels$meanWkofYr, \n                     labels = MonthLabels$month, \n                     expand = c(0,0)) + \n  facet_wrap(~Year, ncol = 1, strip.position = \"right\") + \n  theme(panel.background = element_blank(),\n        panel.border = element_blank(),\n        strip.background = element_blank(),\n        strip.text = element_text(size = 13, face = \"plain\", color = \"black\"),\n        axis.line = element_line(colour = \"black\", size = 0.25),\n        axis.title = element_text(size = 10, face = \"plain\", color = \"black\"),\n        axis.text = element_text(size = 10, face = \"plain\", color = \"black\"))\n\n```\n\n\n# 2.`geom_tile()`\n\n使用R中`ggplot2`包的`geom_tile()`函数，借助`facet_wrap()`函数分面，就可以绘制如图6-2-2(b)所示的以月为单位的日历图，具体代码如下所示。\n\n```{r}\n\nlabel_mons <- c(\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \n                \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\")\nlabel_wik <- c(\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\")\n\ndat19 <- dat[Year == 2017, list(date, ValueCol, month, week)\n][, ':='(weekday = as.integer(strftime(date, \"%u\")), # 周数\n         yearmonth = strftime(date, \"%m%Y\"),       # 月数\n         day = strftime(date, \"%d\"))              # 天数\n][, ':='(monthf = factor(x = month, levels = as.character(1:12),\n                         labels = label_mons, ordered = TRUE),\n         weekdayf = factor(x = weekday, levels = 1:7, \n                           labels = label_wik, ordered = TRUE),\n         yearmonthf = factor(x = yearmonth))\n][, ':='(monthweek = 1 + week - min(week)), by = .(monthf)] # 分组聚合\n```\n\n```{r}\nggplot(dat19, aes(weekdayf, monthweek, fill = ValueCol)) + \n  geom_tile(color = \"white\") + \n  geom_text(aes(label = day), size = 3) + \n  scale_fill_gradientn(colours = rev(brewer.pal(11, \"Spectral\"))) + \n  facet_wrap(~monthf, nrow = 3) + \n  scale_y_reverse(name = \"Week of the month\") + \n  xlab(\"Day\") +\n  theme(strip.text = element_text(size = 11, face = \"plain\", color = \"black\"),\n        panel.grid = element_blank())\n```\n\n\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.12.05日历/日历教程.md",
    "content": "\n\n## 前言\n\n前两天给大家派送了小编自己定制的2021年日历和月历，看到好多读者下载了，小编表示很欣慰😁。上期推送可见：[R可视乎|2021年日历大派送](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&token=1431845296&lang=zh_CN#rd)\n\n今天来说说这个包吧，非常简单，比起`ggplot2包`绘制日历要简单的多。\n\n## R中的年历图\n\n该软件包非常易容易使用，因为它仅包含一个命名函数`calendR`。默认情况下，如果未指定任何参数，则该函数将以**横向形式**创建**当年的日历**，并且所有文本将**使用系统的语言**，如下所示：\n\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"calendR\") #直接install安装包\nlibrary(calendR)\ncalendR() # 默认为当前年份\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/4ff54b53-cba3-4bfa-8855-770096aed79d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gml5s7P6A4jqQATn1kadTtRImnc%253D&Expires=1608013809)\n\n如果不希望日历使用操作系统的语言，则可以对其进行修改。例如，使用英语：\n```{r}\nSys.setlocale(\"LC_ALL\",\"English\")\n```\n\n\n如果想创建其他年份的年度日历图，则可以在`year`参数上设定，如下所示：\n```{r eval=FALSE, include=FALSE}\ncalendR(year = 2021) \n```\n\n\n### 日历周开始的设定\n\n默认情况下，日历的周数将从星期日开始。设定参数`start=\"M\"`可以获得从星期一开始日历图。\n\n```{r}\ncalendR(year = 2021, start = \"M\") \n```\n![](https://imgkr2.cn-bj.ufileos.com/cc39ef13-5589-476a-8c61-a61b6faa4f2e.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mu8GKTP6cC4RG7vsi2AKz708EhA%253D&Expires=1608013834)\n\n### 为日子增添色彩\n\n`special.days`参数可以为指定的日期添加颜色，`special.col`设置颜色，`low.col`设置其他日期的颜色。\n\n比如下面就是将第9，19等日子设置为特定时间，其颜色为淡蓝色，其他颜色设置为白色。\n```{r}\ncalendR(year = 2021,\n        start = \"M\",\n        special.days = c(9, 19, 56, 79, 102,  # Days to color\n                         126, 257, 300, 342),\n        special.col = \"lightblue\",            # Color of the specified days\n        low.col = \"white\")                    # Background color of the rest of the days\n```\n![](https://imgkr2.cn-bj.ufileos.com/679fe6e8-6bfa-4d08-ab4e-1c8c5e57461d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mu%252B%252BgQHoZAcTbjr3uPtI1ZepZPY%253D&Expires=1608013854)\n\n\n> **技巧**：如果要突出日历中所有的周末，可以将`special.days`参数设置为`\"weekend\"`，此快捷方式将立即为它们全部着色。\n\n\n```{r}\ncalendR(year = 2025,\n        start = \"M\",\n        special.days = \"weekend\") # Color all weekends\n```\n![](https://imgkr2.cn-bj.ufileos.com/2a0d3258-eac2-43a3-a7ba-45f513feb48c.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=H2lVusnh33GCFRMU7hq7RFsg9iI%253D&Expires=1608013868)\n\n\n为了添加多个事件，您将需要创建一个NA值向量，该向量的长度与相应年份的天数相同。然后将事件添加到相应的日期，你需要在`special.days`参数中指定。\n\n```{r}\nevents <- rep(NA, 365)\n\n# Set the corresponding events\nevents[40:45] <- \"Trip\"\nevents[213:240] <- \"Holidays\"\nevents[252] <- \"Birthday\"\nevents[359] <- \"Christmas\" \n\n# Creating the calendar with a legend\ncalendR(year = 2025,\n        special.days = events,\n        special.col = c(\"pink\", \"lightblue\", # Colors\n                        \"lightgreen\", \"lightsalmon\"),\n        legend.pos = \"right\") # Legend to the right\n```\n![](https://imgkr2.cn-bj.ufileos.com/dfd4d4c3-a806-4c1b-b790-f7e0c9e94ca1.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=L%252FLyY8DPPAQS8IeNfMybOV0ttTk%253D&Expires=1608013883)\n\n\n## R中的月历图\n\n年度日历前面描述的功能也可用于月度日历中。但是月度日历还可以将文本添加到每月的某几天中。\n\n为了创建月度日历，你需要指定年份和月份。\n\n```{r}\ncalendR(year = 2021, month = 8)\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/b9c9b5e2-1f38-4967-86a8-5ffd1657785d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=%252FQBfqHVceMMdCIcefdoFM%252Bp%252BgEY%253D&Expires=1608013893)\n\n### 为日子增添色彩\n\n与前面相同，使用`special.days`参数给指定日期加上颜色。\n\n```{r}\ncalendR(year = 2021, month = 8,\n        special.days = c(1, 9, 12, 23, 28),\n        special.col = \"#bfe2f2\",\n        low.col = \"white\")\n```\n\n### 在日子里添加文字\n\n使用月度日历图时，可以使用`text`参数向日期添加一些文本，并使用参数`text.pos`指定其位置。使用`text.size`和`text.col`参数修改文本的大小和颜色。\n\n```{r}\ncalendR(year = 2021, month = 8,        # Year and month\n        start = \"M\",                   # Start the week on Monday\n        text = c(\"Running\", \"Running\", # Add text (only for monthly calendars)\n                 \"Class\"), \n        text.pos = c(5, 16, 25),       # Days of the month where to put the texts \n        text.size = 4.5,               # Font size of the text\n        text.col = 4)                  # Color of the texts\n```\n![](https://imgkr2.cn-bj.ufileos.com/f7315f70-bc22-47f8-91a8-8eba7c2f1c86.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9V2XPe84pqaIZ7j7Dl8w4%252FXjVUs%253D&Expires=1608013907)\n\n### 添加月相\n\n设置`lunar = TRUE`可以将月相添加到其日期中。`lunar.col`参数设置隐藏区域的颜色，`lunar.size `设置大小。\n\n\n```{r}\ncalendR(month = 2,  \n        lunar = TRUE,         # Add moons to the calendar\n        lunar.col = \"gray60\", # Color of the non-visible area of the moons\n        lunar.size = 7)       # Size of the moons\n```\n![](https://imgkr2.cn-bj.ufileos.com/1cc52168-4064-491c-ba8d-1192386950b6.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=mOBSYAaXy%252F27GUVhb7fes2z2CvU%253D&Expires=1608013914)\n\n\n## 学术日历\n\n使用`start_date`和`end_date`创建学术日历。如果你想设置某个时间段（下面是2020年9月-2021年5月31日）的日历，非常使用科研人员，学生。请参阅以下示例：\n\n```{r}\ncalendR(start_date = \"2020-09-01\", # Custom start date\n        end_date = \"2021-05-31\",   # Custom end date\n        start = \"M\",               # Start the weeks on Monday\n        mbg.col = 4,               # Color of the background of the names of the months\n        months.col = \"white\",      # Color text of the names of the months\n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"lightblue\", # Color of the special.days\n        lty = 0,                   # Line type\n        bg.col = \"#f4f4f4\",        # Background color\n        title = \"Academic calendar 2020-2021\", # Title\n        title.size = 30,                       # Title size\n        orientation = \"p\")         # Vertical orientation\n```\n![](https://imgkr2.cn-bj.ufileos.com/87407c79-7a9a-4b88-9237-645d3284824d.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=FxuzUzaVlw2XTdNIiXqMNz8Beoo%253D&Expires=1608013924)\n\n> 当然，我觉得打印出来可能效果更好用吧。这里只是给出一个简单的例子，你可以在这个基础上加上背景以及你喜欢的颜色，可以继续往下看。\n\n## 私人定制\n\n接下来，就是给日历加了背景以及根据**直男审美**把其他颜色进行了调整。下面给出上次大家说还不错的日历的源代码（具体pdf版本可在公众号中回复“**日历**”免费获得）。\n\n> 可以使用`pdf = TRUE`将日历进行导出（默认为A4格式）。可以在`doc_name`参数中指定生成的PDF文件的名称。此外，你可以在几种纸张尺寸之间进行选择以保存日历，从\"A6\"到，\"A0\"。但是注意，可能需要微调一些字体尺寸来获得所需的输出。\n\n如果想制作自己的日历，只需修改img的图片，存储的路径（默认在**我的文档**里）。\n\n### 日历\n```{r}\nimg <- \"C:/Users/ZLL/Desktop/5.jpg\"\n\ncalendR(year = 2021,\n        start = \"M\", \n        title.col = \"white\",\n        # Weeks start on Monday\n        mbg.col = \"#cd853f\",               # Background color of the month names\n        months.col = \"white\",      # Color of the text of the month names\n        weeknames.col = \"white\",\n        \n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"#a9a9a9\", # Color of the special.days\n        lty = 0,                   # Line type (no line)\n        weeknames = c(\"Mo\", \"Tu\",  # Week names\n                      \"We\", \"Th\",\n                      \"Fr\", \"Sa\",\n                      \"Su\"),\n        title.size = 40,   # Title size\n        orientation = \"p\",\n        bg.img = img,\n        pdf = TRUE,\n        doc_name = \"My_calendar_brown\"\n)  \n```\n![](https://imgkr2.cn-bj.ufileos.com/e618a4ce-2ec8-4669-bae7-47a07cdf624d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=blpbAmUFbNBMRoDDQLID3bRXz7A%253D&Expires=1608013937)\n\n\n### 月历\n\n这里和日历区别最大的点就是：月历要把每个月都输出，可以使用`sapply`函数，把所有月份输出。前面的`invisible`指的是：不显示图片（显示的话太费事了！保存再看更快）。\n\n最后使用`paste0()`将字符串进行粘合（**这个方法非常好用！**），而这里的i是变化的，所以最后生成的pdf文件名不一样但很有规律（除了i不一样）。\n\n```{r}\nimg <- \"C:/Users/ZLL/Desktop/5.jpg\"\ninvisible(sapply(1:12 , function(i) calendR(year = 2021,month = i, pdf = TRUE,                            title.col = \"white\",\n                                            # Weeks start on Monday\n                                            mbg.col = \"#cd853f\",  \n                                            # Background color of the month names\n                                            months.col = \"white\",      # Color of the text of the month names\n                                            weeknames.col = \"white\",\n                                            special.days = \"weekend\",  # Color the weekends\n                                            special.col = \"gray60\", # Color of the special.days\n                                            lty = 0,                   # Line type (no line)\n                                            bg.img = img,\n                                            doc_name = file.path(\"C:\\\\Users\\\\ZLL\\\\Documents\\\\calendar\", paste0(\"Calendar(gray)_2021_\", i))))\n                                            )\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/02f9a33d-7bc6-48c9-810d-8cdcbd749ff5.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=iiPYDJAkD5Ur%252F88xqhLUSpkuJKA%253D&Expires=1608013946)\n\n\n### 拓展阅读\n\n其他相关资料，小编收集了一些，想继续研究的可见：\n\n[Calendar plot in R using ggplot2](https://r-coder.com/calendar-plot-r/ \"Calendar plot in R using ggplot2\")\n\n[对应的github](https://rdrr.io/github/houyunhuang/ggcor/man/ \"对应的github\")\n\n[calendar详细介绍](https://cran.r-project.org/web/packages/calendar/calendar.pdf \"calendar详细介绍\")\n\n>> 各位可视化爱好者可以根据教程设置私人化日历，也欢迎后台和小编联系，分享你制作的日历，我们可以制作一期私人日历展示活动！\n\n对应代码与相关数据，请在我的github中获取（文末原文）。喜欢请**一键三连，创作不易，感恩不尽**😄。\n"
  },
  {
    "path": "2020年/2020.12.05日历/日历教程.rmd",
    "content": "---\ntitle: \"Calendar plot in R using ggplot2\"\nauthor: \"庄亮亮\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n\n\n\n## 前言\n\n前两天给大家派送了小编自己定制的2021年日历和月历，看到好多读者下载了，小编表示很欣慰😁。上期推送可见：[R可视乎|2021年日历大派送](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&token=1431845296&lang=zh_CN#rd)\n\n今天来说说这个包吧，非常简单，比起`ggplot2包`绘制日历要简单很多。\n\n## R中的年历图\n\n该软件包非常易容易使用，因为它仅包含一个命名函数`calendR`。默认情况下，如果未指定任何参数，则该函数将以横向形式创建当年的日历，并且所有文本将使用系统的语言，如下所示：\n\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"calendR\") #直接install安装包\nlibrary(calendR)\ncalendR() # 默认为当前年份\n```\n\n如果您不希望日历使用操作系统的语言，则可以对其进行修改。例如，如果您希望日历使用英语，则在执行calendR之前输入：\n```{r}\nSys.setlocale(\"LC_ALL\",\"English\")\n```\n\n\n如果您希望创建其他年份的年度日历图，则可以在`year`参数上设定，如下所示：\n```{r eval=FALSE, include=FALSE}\ncalendR(year = 2021) \n```\n\n\n### 日历周开始的设定\n\n默认情况下，日历的周数将从星期日开始。设定参数`start=\"M\"`可以获得从星期一开始日历图。\n\n```{r}\ncalendR(year = 2021, start = \"M\") \n```\n\n### 为日子增添色彩\n\n`special.days`参数可以为指定的日期添加颜色，`special.col`设置颜色，`low.col`设置其他日期的颜色。\n\n比如下面就是将第9，19等日子设置为特定时间，其颜色为淡蓝色，其他颜色设置为白色。\n```{r}\ncalendR(year = 2021,\n        start = \"M\",\n        special.days = c(9, 19, 56, 79, 102,  # Days to color\n                         126, 257, 300, 342),\n        special.col = \"lightblue\",            # Color of the specified days\n        low.col = \"white\")                    # Background color of the rest of the days\n```\n\n\n> **技巧**：如果要突出日历中所有的周末，可以将`special.days`参数设置为`\"weekend\"`，此快捷方式将立即为它们全部着色。\n\n\n```{r}\ncalendR(year = 2025,\n        start = \"M\",\n        special.days = \"weekend\") # Color all weekends\n```\n\n\n为了添加多个事件，您将需要创建一个NA值向量，该向量的长度与相应年份的天数相同。然后将事件添加到相应的日期，你需要在`special.days`参数中指定。\n\n```{r}\nevents <- rep(NA, 365)\n\n# Set the corresponding events\nevents[40:45] <- \"Trip\"\nevents[213:240] <- \"Holidays\"\nevents[252] <- \"Birthday\"\nevents[359] <- \"Christmas\" \n\n# Creating the calendar with a legend\ncalendR(year = 2025,\n        special.days = events,\n        special.col = c(\"pink\", \"lightblue\", # Colors\n                        \"lightgreen\", \"lightsalmon\"),\n        legend.pos = \"right\") # Legend to the right\n```\n\n\n## R中的月历图\n\n年度日历前面描述的功能也可用于月度日历中。但是月度日历还可以将文本添加到每月的某几天中。\n\n为了创建月度日历，您需要指定年份和月份，如下所示：\n\n```{r}\ncalendR(year = 2021, month = 8)\n```\n\n### 为日子增添色彩\n\n与前面相同，使用`special.days`参数给指定日期加上颜色。\n\n```{r}\ncalendR(year = 2021, month = 8,\n        special.days = c(1, 9, 12, 23, 28),\n        special.col = \"#bfe2f2\",\n        low.col = \"white\")\n```\n\n### 在日子里添加文字\n\n使用月度日历图时，可以使用`text`参数向日期添加一些文本，并使用参数`text.pos`指定其位置。使用`text.size`和`text.col`参数修改文本的大小和颜色。\n\n```{r}\ncalendR(year = 2021, month = 8,        # Year and month\n        start = \"M\",                   # Start the week on Monday\n        text = c(\"Running\", \"Running\", # Add text (only for monthly calendars)\n                 \"Class\"), \n        text.pos = c(5, 16, 25),       # Days of the month where to put the texts \n        text.size = 4.5,               # Font size of the text\n        text.col = 4)                  # Color of the texts\n```\n\n### 添加月相\n\n设置`lunar = TRUE`可以将月相添加到其日期中。`lunar.col`参数设置隐藏区域的颜色，`lunar.size `设置大小。\n\n\n```{r}\ncalendR(month = 2,  \n        lunar = TRUE,         # Add moons to the calendar\n        lunar.col = \"gray60\", # Color of the non-visible area of the moons\n        lunar.size = 7)       # Size of the moons\n```\n\n\n## 学术日历\n\n使用`start_date`和`end_date`创建学术日历。如果你想设置某个时间段（下面是2020年9月-2021年5月31日）的日历，非常使用科研人员。请参阅以下示例：\n\n```{r}\ncalendR(start_date = \"2020-09-01\", # Custom start date\n        end_date = \"2021-05-31\",   # Custom end date\n        start = \"M\",               # Start the weeks on Monday\n        mbg.col = 4,               # Color of the background of the names of the months\n        months.col = \"white\",      # Color text of the names of the months\n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"lightblue\", # Color of the special.days\n        lty = 0,                   # Line type\n        bg.col = \"#f4f4f4\",        # Background color\n        title = \"Academic calendar 2020-2021\", # Title\n        title.size = 30,                       # Title size\n        orientation = \"p\")         # Vertical orientation\n```\n> 当然，我觉得打印出来可能效果更好用吧。这里只是给出一个简单的例子，你可以在这个基础上加上背景以及你喜欢的颜色，可以继续往下看。\n\n## 私人定制\n\n接下来，这里就是给日历加了背景以及根据**直男审美**把其他颜色进行了调整。下面给出上次大家说还不错的日历的源代码（具体pdf版本可在公众号中回复“日历”免费获得）。\n\n> 可以使用`pdf = TRUE`将日历进行导出（默认为A4格式）。可以在`doc_name`参数中指定生成的PDF文件的名称。此外，你可以在几种纸张尺寸之间进行选择以保存日历，从\"A6\"到，\"A0\"。但是注意，可能需要微调一些字体尺寸来获得所需的输出。\n\n### 日历\n```{r}\nimg <- \"C:/Users/ZLL/Desktop/wechat/庄闪闪原创/R/R可视乎/2020.11.13日历/wechat/5.jpg\"\n\ncalendR(year = 2021,\n        start = \"M\", \n        title.col = \"white\",\n        # Weeks start on Monday\n        mbg.col = \"#cd853f\",               # Background color of the month names\n        months.col = \"white\",      # Color of the text of the month names\n        weeknames.col = \"white\",\n        \n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"#a9a9a9\", # Color of the special.days\n        lty = 0,                   # Line type (no line)\n        weeknames = c(\"Mo\", \"Tu\",  # Week names\n                      \"We\", \"Th\",\n                      \"Fr\", \"Sa\",\n                      \"Su\"),\n        title.size = 40,   # Title size\n        orientation = \"p\",\n        bg.img = img,\n        pdf = TRUE,\n        doc_name = \"My_calendar_brown\"\n)  \n```\n\n\n### 月历\n\n这里和日历区别最大的点就是：月历要把每个月都输出，可以使用`sapply`函数，把所有月份输出。前面的`invisible`指的是：不显示图片（太费事了！保存再看更快）。\n\n最后使用`paste0()`将字符串进行粘合（这个方法非常好用！），而这里的i是变化的，所以最后生成的pdf文件名不一样但很有规律。\n\n```{r}\nimg <- \"C:/Users/ZLL/Desktop/5.jpg\"\ninvisible(sapply(1:12 , function(i) calendR(year = 2021,month = i, pdf = TRUE,                            title.col = \"white\",\n                                            # Weeks start on Monday\n                                            mbg.col = \"#cd853f\",  \n                                            # Background color of the month names\n                                            months.col = \"white\",      # Color of the text of the month names\n                                            weeknames.col = \"white\",\n                                            special.days = \"weekend\",  # Color the weekends\n                                            special.col = \"gray60\", # Color of the special.days\n                                            lty = 0,                   # Line type (no line)\n                                            bg.img = img,\n                                            doc_name = file.path(\"C:\\\\Users\\\\ZLL\\\\Documents\\\\calendar\", paste0(\"Calendar(gray)_2021_\", i))))\n                                            )\n```\n\n### 拓展阅读\n\n其他相关资料，小编收集了一些，想继续研究的可见：\n\n[Calendar plot in R using ggplot2](https://r-coder.com/calendar-plot-r/ \"Calendar plot in R using ggplot2\")\n\n[对应的github](https://rdrr.io/github/houyunhuang/ggcor/man/ \"对应的github\")\n\n[calendar详细介绍](https://cran.r-project.org/web/packages/calendar/calendar.pdf \"calendar详细介绍\")\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.12.07瀑布图/Facting_Data.csv",
    "content": "X_Axis,60,55,50,45,40,35,30,25\n360,7.6821,7.3053,6.0619,7.6628,8.3848,9.2971,9.2372,8.6967\n370,7.1166,7.0108,6.5378,7.7374,8.4002,9.159,9.0612,7.8567\n380,6.2423,6.3366,6.1613,6.9001,7.4425,8.1681,7.7771,6.9062\n390,5.3379,5.5059,5.4587,5.8385,6.2378,6.7344,6.3799,5.7448\n400,4.4369,4.5943,4.5685,4.7075,4.9636,5.3273,5.0153,4.4864\n410,3.5337,3.6765,3.6402,3.7,3.8173,4.1257,3.8898,3.4693\n420,2.8664,3.0402,3.0192,3.0142,3.0933,3.3515,3.1961,2.8586\n430,2.4889,2.7031,2.677,2.6541,2.7015,2.9365,2.8073,2.5205\n440,2.259,2.4977,2.4587,2.4136,2.4313,2.6138,2.5083,2.261\n450,2.1903,2.4267,2.369,2.2875,2.259,2.4194,2.3193,2.0908\n460,2.2336,2.4609,2.3726,2.2276,2.1406,2.2789,2.1713,1.9664\n470,2.4132,2.6218,2.4909,2.252,2.0713,2.1753,2.0677,1.8747\n480,2.8172,2.9937,2.7968,2.3905,2.0528,2.1075,1.9865,1.8026\n490,3.2794,3.4094,3.1387,2.5632,2.0549,2.0669,1.931,1.759\n500,4.0475,4.1306,3.7575,2.9351,2.1385,2.0811,1.909,1.7449\n510,5.1939,5.2194,4.7275,3.5788,2.3427,2.1603,1.9435,1.7703\n520,6.4268,6.4104,5.8418,4.4074,2.6653,2.3288,2.0293,1.8465\n530,7.6299,7.5689,6.9601,5.3072,3.0684,2.5597,2.1563,1.9548\n540,8.603,8.5318,7.9234,6.1732,3.503,2.8177,2.3077,2.0799\n550,9.462,9.3866,8.7283,6.9871,3.948,3.0762,2.4634,2.2032\n560,10.2074,10.0775,9.4402,7.7211,4.3929,3.3475,2.6319,2.3239\n570,10.4668,10.38,9.7358,8.0937,4.6863,3.5456,2.7499,2.4151\n580,10.6801,10.5651,9.9439,8.4002,4.9343,3.7077,2.8533,2.4826\n590,10.8896,10.8426,10.3084,8.8249,5.2337,3.9041,2.9636,2.5708\n600,10.9422,10.9354,10.3768,8.8843,5.266,3.8775,2.9292,2.5464\n610,10.5836,10.5202,9.9223,8.3873,4.8423,3.4813,2.6388,2.2992\n620,9.4338,9.3364,8.9144,7.0977,3.8649,2.7702,2.1032,1.8811\n630,7.7169,7.6263,7.1268,5.3149,2.7358,1.9857,1.5521,1.4281\n640,5.4489,5.4942,4.9482,3.4699,1.7938,1.3687,1.1237,1.0436\n650,3.1697,3.3857,2.9798,2.0419,1.1621,0.9553,0.825,0.7715\n660,1.6842,1.9811,1.7494,1.2512,0.827,0.7299,0.659,0.6182\n670,0.8644,1.2072,1.0891,0.8506,0.6589,0.6025,0.561,0.5258\n680,0.4683,0.8124,0.7565,0.6471,0.5664,0.5217,0.4929,0.4651\n690,0.28,0.6149,0.5859,0.5363,0.5038,0.4631,0.4401,0.4158\n700,0.1846,0.5057,0.4905,0.47,0.4586,0.4177,0.4004,0.3769\n710,0.1373,0.4475,0.4384,0.4311,0.4304,0.3854,0.3691,0.3489\n720,0.1095,0.4121,0.4062,0.405,0.4102,0.3616,0.3458,0.3268\n730,0.0909,0.3897,0.3852,0.3868,0.3971,0.341,0.3276,0.3103\n740,0.0791,0.3749,0.37,0.3743,0.3865,0.3293,0.3159,0.2969\n"
  },
  {
    "path": "2020年/2020.12.07瀑布图/瀑布图.rmd",
    "content": "---\ntitle: \"瀑布图\"\nauthor:\n  - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n  - 中文\n  - R Markdown\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\neditor_options: \n  chunk_output_type: console\n---\n\n# 简介\n\n**瀑布图（waterfall plot）**用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y 轴离散型变量（如不同的类别变量）和Z轴数值变量，可以清晰地展示不同变量之间的数据变化关系。\n\n# 三维瀑布图\n\n三维瀑布图可以看成是多数据系列三维面积图。R中`plot3D`包的`polygon3D()`函数和`segments3D()`函数可以绘制三维面积图，`lines3D()`函数可以绘制三维曲线图，所以，综合这几个函数可以绘制三维瀑布图，该代码，数据来源[R语言书可视化之美](https://github.com/EasyChart/Beautiful-Visualization-with-R)。\n\n> 这是一本非常棒的R可视化书籍，小编预计在年底进行一次抽奖送这本书的活动，进行期待。\n\n## 数据介绍\n\n原始数据如下所示：一共39行，9列数据。列表示不同组别，行表示不同x坐标下的数值大小，其中第一列表示x坐标位置。\n```{r message=FALSE, warning=FALSE}\nlibrary(plot3D)\nlibrary(RColorBrewer)\nmydata0 <- read.csv(\"Facting_Data.csv\",check.names =FALSE)\nhead(mydata0)\n```\n\n之后对数据进行一个变换，变成我们绘图所需要的数据格式（这里最费时间了）。因为从行来看，数据是离散的绘制出来效果不是很好，于是使用插值样条函数(`spline`)对原始数据进行插值，变成了300行数据（`n=300`）。这里做了一个for循环，把所有数据都进行了插值，列名存在了`variable`中。`mydata`的前6行结果如下所示：\n\n```{r message=FALSE, warning=FALSE}\nN <- ncol(mydata0)-1\nmydata <- data.frame(x=numeric(),y=numeric(),variable=character())\n\nfor(i in 1:N){\n  newdata <- data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= \"natural\")) #进行插值\n  newdata$variable<-colnames(mydata0)[i+1]\n  mydata <- rbind(mydata,newdata)\n}\n\nmydata$variable <- as.numeric(mydata$variable)\nhead(mydata)\n\ngroup <- unique(mydata$variable) #每组的名称\nM <- length(group) #组数\n```\n\n进行颜色的处理，以及图片版式的设置。\n```{r message=FALSE, warning=FALSE}\ngg_color_hue <- function(n) {\n  hues = seq(15, 375, length = n + 1)\n  hcl(h = hues, l = 65, c = 100)[1:n]\n}\n\ncolormap <- rev(gg_color_hue(M))#brewer.pal(M,'RdYlGn')\n\npmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))\n```\n\n\n## 基础版本\n\n这里先构建一个空的立方体，注意x，y，z轴的坐标范围，所以你得看看原始数据，再定范围，不能一股脑地拿来用。该函数的内部参数，在[R可视乎|等高线](等高线)有提过一些。或者将光标放在该函数，按F1寻求帮助文档，在这里就不做过多解释。 \n```{r message=FALSE, warning=FALSE}\nperspbox(z=as.vector(0),#add=TRUE,\n          xlim=c(20,70),ylim=c(360,750),zlim=c(0,15),\n          ticktype = \"detailed\",bty = \"f\",box = TRUE,\n          colkey = FALSE,theta = -110, phi = 20, d=3)\n```\n\n使用`polygon3D`函数和`lines3D`函数将每一类的数据填充到立方体中。这里代码主要还是用base包写的，你可以试试tidyverse流写。\n```{r message=FALSE, warning=FALSE}\nfor (i in 1:M){\n  df0<-mydata[mydata$variable==group[i],]\n  Ndf<-nrow(df0)\n  df<-rbind(df0,c(df0$x[1],df0$y[Ndf],df0$variable[Ndf]))\n  with(df,polygon3D(x=variable,y=x, z=y, alpha=0.6,\n                     col=colormap[i],lwd = 3,add=TRUE,colkey = FALSE))\n  with(df0,lines3D(x=variable,y=x, z=y, \n                  lwd = 0.5,col=\"black\",add=TRUE))\n}\n```\n在此，就完成这个三位瀑布图了，美观度极佳，可解释性也不错。\n\n## 添加第四个变量\n\n如果想加入第四变量也是没问题的，具体不再重复。完整代码可见[R语言书可视化之美](https://github.com/EasyChart/Beautiful-Visualization-with-R)或者我的github中。\n```{r echo=FALSE, message=FALSE, warning=FALSE}\nmydata0<-read.csv(\"Facting_Data.csv\",check.names =FALSE)\n\nN<-ncol(mydata0)-1\nmydata<-data.frame(x=numeric(),y=numeric(),variable=character())\n\nfor (i in 1:N){\n  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= \"natural\"))\n  newdata$variable<-colnames(mydata0)[i+1]\n  mydata<-rbind(mydata,newdata)\n}\n\nmydata$variable<-as.numeric(mydata$variable)\ngroup<-unique(mydata$variable)\nM<-length(group)\n\n#------------\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')),alpha = TRUE)(32)\n#colormap <- colorRampPalette(rev(brewer.pal(7,'RdYlGn')),alpha = TRUE)(32)\npmar <- par(mar = c(5.1, 4.1, 4.1, 6.1))\n\nperspbox(z=as.vector(0),#add=TRUE,\n         xlim=c(20,70),ylim=c(360,750),zlim=c(0,15),\n         ticktype = \"detailed\",bty = \"f\",box = TRUE,\n         colkey = FALSE,theta = -110, phi = 20, d=3)\n\nfor (i in 1:M){\n  df0<-mydata[mydata$variable==group[i],]\n  \n  df<-cbind(df0,z0=rep(0,nrow(df0)))\n  df<-df[df$y>0.05,]\n  with(df,segments3D(x0=variable, y0=x,z0=z0,\n                     x1=variable,y1=x, z1=y, colvar =y,\n                     alpha=0.5,col=ramp.col(colormap,alpha = 0.9),lwd = 3,add=TRUE,colkey = FALSE))\n  with(df0,lines3D(x=variable,y=x, z=y, \n                  lwd = 1.5,col=\"black\",add=TRUE))\n}\n\ncolkey (col=colormap,clim=range(mydata$y),clab = \"Z Value\", add=TRUE, length=0.5,side = 4)\n```\n\n\n# 行分面的带填充的曲线图\n\n使用分面图的可视化方法也可以展示瀑布图的数据信息，关于分面图可视化方法我已经在[R可视乎|分面一页多图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&token=1309493585&lang=zh_CN#rd)介绍过。\n\n下面进行行分面的带填充的曲线图绘制，所有数据共用X轴坐标，每个数据类别是用的Y轴坐标。\n\n>相对三维瀑布图，分面瀑布图的**优点**是：\n可以更好地展示数据信息，避免不同类别之间数据重叠引起的遮挡问题，但是不能很直接地比较不同类别之间的数据差异。\n\n## 基础版本\n\n这里不做过多解释，用`geom_area()`绘制面积，用`facet_grid()`进行分面，最后就是对主题进行设置。主题的设置有很多有趣的技巧，以后整理一篇完整的。\n```{r message=FALSE, warning=FALSE}\nlibrary(reshape2)\nlibrary(ggplot2)\nmydata0<-read.csv(\"Facting_Data.csv\",stringsAsFactors=FALSE)\n\ncolnames(mydata0)<-c(\"X_Axis\",seq(60,25,-5))\nmydata<-melt(mydata0,id.vars = \"X_Axis\")\n\nggplot(mydata,aes(X_Axis,value,fill=variable))+\n  geom_area(color=\"black\",size=0.25)+\n  facet_grid(variable~.)+\n  theme(\n    text=element_text(size=15,face=\"plain\",color=\"black\"),\n    axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\n    axis.text = element_text(size=10,face=\"plain\",color=\"black\"),\n    legend.position=\"none\"\n  )\n```\n\n\n## 加入第四个变量\n\n同理，在上图的基础上可以将每个数据的Z变量进行颜色映射，这样有利于比较不同类别之间的数据差异，该图如下所示：\n```{r message=FALSE, warning=FALSE}\nlibrary(RColorBrewer)\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)\nmydata0<-read.csv(\"Facting_Data.csv\",stringsAsFactors=FALSE)\n\nN<-ncol(mydata0)-1\n\ncolnames(mydata0)<-c(\"X_Axis\",seq(60,25,-5))\n\nmydata<-data.frame(x=numeric(),y=numeric(),variable=character())\n\nfor (i in 1:N){\n  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= \"natural\"))\n  newdata$variable<-colnames(mydata0)[i+1]\n  mydata<-rbind(mydata,newdata)\n}\n\nmydata$variable<-factor(mydata$variable,levels=seq(60,25,-5))\n\nggplot(mydata,aes(x,y,group=variable))+\n  geom_bar(aes(fill=y),color=NA,size=0.25,stat=\"identity\")+\n  geom_line(color=\"black\",size=0.5)+\n  scale_fill_gradientn(colours=colormap)+\n  facet_grid(variable~.)+\n  theme(\n    text=element_text(size=15,face=\"plain\",color=\"black\"),\n    axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\n    axis.text = element_text(size=10,face=\"plain\",color=\"black\"),\n    legend.position=\"right\"\n  )\n```\n\n\n"
  },
  {
    "path": "2020年/2020.12.21esquisse包/esquisse包.md",
    "content": "\n## 简介\n\n最近学习可视化时发现了一个好用的包，可以直接使用“拖拽”的方式生成绘图，**不需要写任何代码！** \n这个包是esquisse，具体介绍可以见[对应的github](https://github.com/dreamRs/esquisse \"esquisse包\")。这是建立在[ggplot2包](https://github.com/tidyverse/ggplot2 \"ggplot2包\")基础上设计的。 你可以通过生成ggplot2图表以交互方式探索esquisse环境中的数据。入门门槛极低，有点类似tableau的感觉。\n\n\n## 安装\n可以通过CRAN直接下载，也可以通过github中下载，然后将其进行加载即可。\n\n```\n# From CRAN\ninstall.packages(\"esquisse\")\n# remotes::install_github(\"dreamRs/esquisse\")\n#Load the package in R\nlibrary(esquisse)\n```\n\n## 使用方式\n\n使用方式由以下两种，推荐使用窗口操作，因为不用记住代码，也很好找到。\n\n### 1. 输入以下代码\n```\nesquisse::esquisser() #helps in launching the add-in\n```\n![代码打开界面](https://imgkr2.cn-bj.ufileos.com/96e53f2f-a5cb-45a5-b674-19b07e840e7a.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=dd6c5rVWW8v17z%252BdHGvcGZLF4xw%253D&Expires=1608642502)\n\n### 2. 窗口操作\n\n通过RStudio菜单启动插件（推荐）\n\n>**注意**：如果您的环境中没有data.frame，则可以使用ggplot2中的数据集。推荐还是自己前面已经导入数据了，界面才会有显示可以使用的数据。\n\n- 加载该包之后，在窗口的左上方有个Addins，点击打开找到对应包的函数点击即可。\n    \n![打开方式1](https://imgkr2.cn-bj.ufileos.com/46e23df4-76a5-422f-aa6f-56c0b7965fe6.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Io88OxOXooyRyqfjqKFDl%252Bo%252BdL8%253D&Expires=1608642673)\n\n\n    \n    \n## 窗口详细说明\n\n导入自己的数据，就可以对其进行分析了！这里咱们对`iris`数据作为例子。窗口都是互动形式的，你可以根据自己所需进行绘制对应的图形，不需要输入代码。我们给出操作图，如下所示。之后对界面下面的四个小窗口进行详细介绍。\n\n![具体操作](https://imgkr2.cn-bj.ufileos.com/31b82c98-1553-4a8f-9848-80c9f0e182c6.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=h%252FoeHlvI%252F1S6VbTLI6Cm0RNZWkc%253D&Expires=1608646813)\n\n\n\n### Lables&Title\n\n![添加各种标签题目](https://imgkr2.cn-bj.ufileos.com/771f7c69-927e-4ca8-b008-a86c33c3a336.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gr0ZI5D53%252BqEXYxkNkJQFGsvJg0%253D&Expires=1608644653)\n\n\n### Plot options\n\n可以设计geom_xxx中的各种参数（颜色，尺寸），legend摆放的位置，主题形式等等；\n\n![设计各种参数](https://imgkr2.cn-bj.ufileos.com/d23c37df-10fd-49f9-9aa0-cceb3d26b255.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=u8pdjVoXk0IW3v3qrd%252FEX2Jt%252B44%253D&Expires=1608643705)\n\n### Data\n\n\n![改变输入数据的范围](https://imgkr2.cn-bj.ufileos.com/56bb4d45-fb81-4f95-be79-fc99c9d6cf5d.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=9mBnHsMSJhSjGVJoEYDIlJ%252B5fkQ%253D&Expires=1608644852)\n\n### Export&code\n\n这可以显示操作后图对应的ggplot的代码！（非常管用！）你可以按( Insert code in script )将自动导入你的代码中。\n\n![代码、图片导出](https://imgkr2.cn-bj.ufileos.com/24149f1d-b729-4508-8061-38f5851a0311.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=s6mAPWoNrQhhTJvVS0bC%252BZ2r2%252F0%253D&Expires=1608643830)\n\n当然可以导出pptx或者png格式，操作如下所示：\n\n![导出pptx格式，在线修改图片](https://imgkr2.cn-bj.ufileos.com/b83417e0-9749-473d-9fd5-44e220f057a5.gif?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=hu3nMbXOCx2Qg%252FyF77XOGsn2r%252Bs%253D&Expires=1608644529)\n\n\n> **注意**：导出pptx可能还需要两个包（`rvg , officer`），你可以先安装，在使用界面操作导出pptx格式。 pptx格式可以修改图片内部的任何地方，非常方便。\n\n## 小编有话说\n\n- 这个包对想学习ggplot语法的读者来说，也非常合适。可以直接导出你做图的代码，根据代码反过来学习对应语法，从实践中学习也是不错的选择。\n\n- 小编最近在准备毕业的开题答辩，书籍翻译和论文撰写，所以更新的比较慢。不过可视化系列一直在逐步推进，已经准备几期了初稿了，但是感觉不够系统，所以还打算打磨下再发出来。[上次里的flag](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487940&idx=1&sn=02e087fdb8c7ec192ea3297dadda702d&chksm=ea24ee20dd536736997620b3a7b6d008475503a8997476d97941e71cc5d6adab9549a8f3b9a5&token=1926271128&lang=zh_CN#rd)也会继续下去的，尽情期待。也欢迎加小编微信一起沟通交流，也感谢各位博士大大们对小编的建议和厚爱❤。\n\n\n\n\n"
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    "content": "---\ntitle: \"Mandalas\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n\n## 参考文献\n\n[Mandalas](https://github.com/aschinchon/mandalas)\n\n[R-blogger-Mandalas](https://www.r-bloggers.com/2018/02/mandalas/)\n\n[Mandalas](https://fronkonstin.com/2018/02/14/mandalas/)\n\n[Inspired by your work I created this](https://hrafnkelle.github.io/p5mandalas/)\n\n\n## 简介\n\n- 以（0,0）为中心的单位圆中获得n个等距点\n\n- 对所有这些点重复该过程，再次获得每个点周围的n个点； 半径按比例缩放\n\n- 舍弃了以前的（父）n分\n\n\n我反复地重复这些步骤。如果我从n个点开始，迭代k次，最后我得到nk个点。然后，我计算它们的Voronoi镶嵌，并用ggplot绘制。\n\n```{r message=FALSE, warning=FALSE}\n# Load in libraries\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(deldir)\n\n# Parameters to change as you like\niter=5 # Number of iterations (depth)\npoints=8 # Number of points\nradius=4.8 # Factor of expansion/compression\n\n# Angles of points from center\nangles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2\n\n# Initial center\ndf=data.frame(x=0, y=0)\n\n# Iterate over centers again and again\nfor (k in 1:iter)\n{\n  temp=data.frame()\n  for (i in 1:nrow(df))\n  {\n    data.frame(x=df[i,\"x\"]+radius^(k-1)*cos(angles),\n               y=df[i,\"y\"]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp\n  }\n  df=temp\n}\n\n# Obtain Voronoi regions\ndf %>%\n  select(x,y) %>%\n  deldir(sort=TRUE) %>%\n  .$dirsgs -> data\n\n# Plot regions with geom_segmen\ndata %>%\n  ggplot() +\n  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color=\"black\") +\n  scale_x_continuous(expand=c(0,0))+\n  scale_y_continuous(expand=c(0,0))+\n  coord_fixed() +\n  theme(legend.position  = \"none\",\n        panel.background = element_rect(fill=\"white\"),\n        panel.border     = element_rect(colour = \"black\", fill=NA),\n        axis.ticks       = element_blank(),\n        panel.grid       = element_blank(),\n        axis.title       = element_blank(),\n        axis.text        = element_blank())->plot\n\nplot\n\n# Do you like the result? Save it (change the name if you want)\nggsave(\"mandala4.png\", height=5, width=5, units='in', dpi=600)\n\n```\n\n![](1.jpg)\n\n\n\n\n\n\n\n\n"
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    "content": "library(ggplot2)\n\n# set the `rel_min_height` argument to remove tails\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(rel_min_height = 0.005) +\n  scale_y_discrete(expand = c(0.01, 0)) +\n  scale_x_continuous(expand = c(0.01, 0)) +\n  theme_ridges()\n\n# set the `scale` to determine how much overlap there is among the plots\nggplot(diamonds, aes(x = price, y = cut)) +\n  geom_density_ridges(scale = 4) +\n  scale_y_discrete(expand = c(0.01, 0)) +\n  scale_x_continuous(expand = c(0.01, 0)) +\n  theme_ridges()\n\n# the same figure with colors, and using the ggplot2 density stat\nggplot(diamonds, aes(x = price, y = cut, fill = cut, height = ..density..)) +\n  geom_density_ridges(scale = 4, stat = \"density\") +\n  scale_y_discrete(expand = c(0.01, 0)) +\n  scale_x_continuous(expand = c(0.01, 0)) +\n  scale_fill_brewer(palette = 4) +\n  theme_ridges() + theme(legend.position = \"none\")\n\n# use geom_density_ridges2() instead of geom_density_ridges() for solid polygons\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges2() +\n  scale_y_discrete(expand = c(0.01, 0)) +\n  scale_x_continuous(expand = c(0.01, 0)) +\n  theme_ridges()\n"
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    "content": "---\ntitle: \"峰峦图\"\nauthor:\n  - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n  - 中文\n  - R Markdown\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\neditor_options: \n  chunk_output_type: console\n---\n\n\n\n# 峰峦图\n\n上次可视化系列说了[瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=1431845296&lang=zh_CN#rd)。它可以用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y轴离散型变量（如不同的类别变量）和Z轴数值变量。\n\n本节使用的**峰峦图**也可以很好地展示瀑布图的数据信息。它们对于可视化随时间或空间分布的变化非常有用。本节主要使用`ggridges`包中的`geom_density_ridges()`进行绘制峰峦图。详细介绍如下：\n\n## 数据结构\n\n这里使用`base`包中的`diamonds`数据集做例子。\n```{r message=FALSE, warning=FALSE}\n# library\nlibrary(ggridges) # Ridgeline Plots in 'ggplot2', CRAN v0.5.2\nlibrary(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics, CRAN v3.3.2 \nhead(diamonds)\n```\n\n## 绘图教程\n\n### 基础版本\n\n使用`price`作为x轴, `cut`为y轴，`fill`参数也是设定为`cut`。`geom_density_ridges()`内部全部使用默认参数。使用了ggridges包中的主题`theme_ridges()`。\n```{r message=FALSE, warning=FALSE}\nggplot(diamonds, aes(x = price, y = cut, fill = cut)) +\n\tgeom_density_ridges() +\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 形状变化\n\n如果不想绘制密度图，则可以使用`stat=\"binline\", bins=20`绘制柱形图，其中`bins=20`表示每格格子大小。为了防止上下图片重叠，这里使用了透明度参数：`alpha=0.6`。\n```{r message=FALSE, warning=FALSE}\nggplot(diamonds, aes(x = price, y = cut, fill = cut)) +\n\tgeom_density_ridges(alpha=0.7, stat=\"binline\", bins=20) +\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 根据第三变量进行分面\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7) +\n\tfacet_wrap(~color) + \n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 加入统计量\n\n通过设置选项`quantile_lines = TRUE`，我们可以使`stat_density_ridges`计算指示分位数的线的位置。 默认情况下，绘制了三行，分别对应于第一，第二和第三四分位数：\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7,quantile_lines = TRUE) +\n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n> **注意**：`quantiles=2`意味着在两个分位数之间的边界上有一条线即，(中位数)。\n\n我们还可以通过切点而不是数字来指定分位数。例如，我们可以指出2.5％和97.5％的尾巴。\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7,quantile_lines = TRUE,quantiles = c(0.025, 0.975)) +\n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n使用`stat_density_ridges`，计算`stat(quantile)`，通过分位数进行着色，。 注意，仅当`calc_ecdf = TRUE`时才能计算。\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +\n\tstat_density_ridges(\n\t  geom = \"density_ridges_gradient\", \n\t  calc_ecdf = TRUE,\n    quantiles = 4, quantile_lines = TRUE) +\n  theme_ridges() +\n  scale_fill_viridis_d(name = \"Quartiles\")\n```\n\n我们可以使用相同的方法来突出分布的尾部。\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +\n\tstat_density_ridges(\n\t  geom = \"density_ridges_gradient\", \n\t  calc_ecdf = TRUE,\n    quantiles = c(0.025, 0.975)) +\n  theme_ridges() +\n    scale_fill_manual(\n    name = \"Probability\", values = c(\"#FF0000A0\", \"#A0A0A0A0\", \"#0000FFA0\"),\n    labels = c(\"(0, 0.025]\", \"(0.025, 0.975]\", \"(0.975, 1]\")\n  )\n```\n\n最后，当`calc_ecdf = TRUE`时，我们还可以计算`stat(ecdf)`，它表示该分布的经验累积密度函数。 我们将其概率直接映射到颜色上。\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = 0.5 - abs(0.5 - stat(ecdf)))) +\n\tstat_density_ridges(geom = \"density_ridges_gradient\", calc_ecdf = TRUE) +\n  scale_fill_viridis_c(name = \"Tail probability\", direction = -1)\n```\n\n### 加入抖动点\n\n`stat_density_ridges()`还提供了可视化生成分布的原始数据点的选项。可以通过设置`jittered_points = TRUE`实现。为了只管我们这里使用`iris`包。\n\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(jittered_points = TRUE)+\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n当然可以将其放在密度函数的下方，通过使用`position = \"raincloud\"`参数。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE, position = \"raincloud\",\n    alpha = 0.7, scale = 0.9\n  )\n```\n\n我们还可以模拟地毯形式：\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE,\n    position = position_points_jitter(width = 0.05, height = 0),\n    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,\n  )\n```\n\n\n可以使用ggridges提供的特殊比例来设置抖动点的样式。 首先，`scale_discrete_manual()`可用于制作具有任意形状和比例的图形。 `scale_point_color_hue()`。 \n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +\n  geom_density_ridges(\n    aes(point_color = Species, point_fill = Species, point_shape = Species),\n    alpha = .2, point_alpha = 1, jittered_points = TRUE\n  ) +\n  scale_point_color_hue(l = 40) +\n  scale_discrete_manual(aesthetics = \"point_shape\", values = c(21, 22, 23))\n```\n\n如果你还想再加入一个变量进行可视化，可以在`geom_density_ridges()`加入，例如：`point_shape = Species, point_fill = Species, point_size = Petal.Length`。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +\n  geom_density_ridges(\n    aes(point_shape = Species, point_fill = Species, point_size = Petal.Length), \n    alpha = .2, point_alpha = 1, jittered_points = TRUE\n  ) +\n  scale_point_color_hue(l = 40) + scale_point_size_continuous(range = c(0.5, 4)) +\n  scale_discrete_manual(aesthetics = \"point_shape\", values = c(21, 22, 23))\n```\n\n另外一种有趣的可视化是通过`vline_xxx`构造以下图形。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,\n    vline_size = 1, vline_color = \"red\",\n    point_size = 0.4, point_alpha = 1,\n    position = position_raincloud(adjust_vlines = TRUE)\n  )\n```\n\n## 其他资料\n\n对于该包的其他有趣函数与可视化可参考以下资料：\n- [Introduction to ggridges](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html \"Introduction to ggridges\")\n\n- [RDocumentation-ggridges](https://www.rdocumentation.org/packages/ggridges/versions/0.5.2 \"RDocumentation-ggridges\")\n\n- [Basic ridgeline plot](https://www.r-graph-gallery.com/294-basic-ridgeline-plot \"Basic ridgeline plot\")\n\n\n\n\n\n\n\n\n\n"
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    "content": "---\ntitle: \"切面图\"\nauthor:\n  - 庄亮亮\ndate: \"2020/11/30\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\n---\n\n# 切面图\n\n切面图（slice chart）可以展示四维数据$v=f(x, y,z)$，将前三维数据展现在三维直角坐标系$f(x, y, z)$，\n通过对图形的线型、立面、色彩、渲染、光线、视角等的控制，可形象地表现数据四维特性v。任何一个在三维坐标系中绘制的数据体，都可以使用分割得到平行于X-Y、X-Z和Y-Z的三个切面。然后每个切面上的数据点都可以通过3-D 插值获得，如图4-4-1 所示。\n\n在切面图中，$v=f(x, y, z)$ 这个函数对于每一个给定的$(x, y, z)$都对应一个v，然后映射到渐变颜色，那么$v=f(x,y,z)$就可以得到一个有颜色变化（其颜色分布按给定的函数表达式变化）的立体图形。给定$(x,y,z)$中的某个值，就可以得到某一个切面上的颜色分布，根据颜色映射大致可以看出其函数值的变化。在图4-4-1中，展示了$x=0，y=(-4,0,4)$的4个切面颜色变化情况。\n\n\n```{r message=FALSE, warning=FALSE}\nlibrary(grDevices)\nlibrary(plot3D)\nlibrary(RColorBrewer)\nx <- y <- z <- seq(-4, 4, by = 0.2)\nM <- mesh(x, y, z)  #mesh 3d网\nR <- with (M, sqrt(x^2 + y^2 + z^2))\np <- sin(2*R) /(R+1e-3)\n```\n\n\n```{r}\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')),alpha = TRUE)(32)\n```\n\n\n```{r}\nslice3D(x, y, z, colvar = p, facets = FALSE,\n\t\t\t\tcol = ramp.col(colormap,alpha = 0.9), \n\t\t\t\tclab=\"p vlaue\",\n\t\t\t\txs = 0, ys = c(-4, 0, 4), zs = NULL, \n\t\t\t\tticktype = \"detailed\",bty = \"f\",box = TRUE,\n\t\t\t\ttheta = -120, phi = 30, d=3,\n\t\t\t\tcolkey = list(length = 0.5, width = 1, cex.clab = 1))\n```\n\n```{r}\nslicecont3D(x, y, z, colvar = p, facets = FALSE,\n\t\t\t\tcol = ramp.col(colormap,alpha = 0.9), \n\t\t\t\tclab=\"p vlaue\",\n\t\t\t\txs = 0, ys = c(-4, 0, 4), zs = NULL, \n\t\t\t\tticktype = \"detailed\",bty = \"f\",box = TRUE,\n\t\t\t\ttheta = -120, phi = 30, d=3,\n\t\t\t\tcolkey = list(length = 0.5, width = 1, cex.clab = 1))\n\n```\n\n\n```{r}\n# save plotting parameters\n pm <- par(\"mfrow\")\n pmar <- par(\"mar\")\n\n## =======================================================================\n## Simple slice3D examples\n## =======================================================================\n\n par(mfrow = c(2, 2))\n x <- y <- z <- seq(-1, 1, by = 0.1)\n grid   <- mesh(x, y, z)\n colvar <- with(grid, x*exp(-x^2 - y^2 - z^2))\n\n# default is just the panels\n slice3D  (x, y, z, colvar = colvar, theta = 60)\n\n# contour slices\n slicecont3D (x, y, z, ys = seq(-1, 1, by = 0.5), colvar = colvar, \n           theta = 60, border = \"black\")\n          \n slice3D  (x, y, z, xs = c(-1, -0.5, 0.5), ys = c(-1, 0, 1), \n           zs = c(-1, 0), colvar = colvar, \n           theta = 60, phi = 40)\n\n## =======================================================================\n## coloring on a surface\n## =======================================================================\n\n XY <- mesh(x, y)\n ZZ <- XY$x*XY$y\n slice3D  (x, y, z, xs = XY$x, ys = XY$y, zs = ZZ, colvar = colvar, \n           lighting =  TRUE, lphi = 90, ltheta = 0)\n\n## =======================================================================\n## Specifying transparent colors\n## =======================================================================\n\n par(mfrow = c(1, 1))\n x <- y <- z <- seq(-4, 4, by = 0.2)\n M <- mesh(x, y, z)\n\n R <- with (M, sqrt(x^2 + y^2 + z^2))\n p <- sin(2*R) /(R+1e-3)\n\n## Not run: \n# This is very slow - alpha = 0.5 makes it transparent\n\n slice3D(x, y, z, colvar = p, col = jet.col(alpha = 0.5), \n         xs = 0, ys = c(-4, 0, 4), zs = NULL, d = 2) \n\n## End(Not run)\n\n slice3D(x, y, z, colvar = p, d = 2, theta = 60, border = \"black\",\n         xs = c(-4, 0), ys = c(-4, 0, 4), zs = c(-4, 0))\n\n## =======================================================================\n## A section along a transect\n## =======================================================================\n\n data(Oxsat)\n Ox <- Oxsat$val[,  Oxsat$lat > - 5 & Oxsat$lat < 5, ]\n slice3D(x = Oxsat$lon, z = -Oxsat$depth, y = 1:5, colvar = Ox, \n         ys = 1:5, zs = NULL, NAcol = \"black\", \n         expand = 0.4, theta = 45, phi = 45)\n\n## =======================================================================\n## isosurf3D example - rather slow\n## =======================================================================\n\n par(mfrow = c(2, 2), mar  = c(2, 2, 2, 2))\n x <- y <- z <- seq(-2, 2, length.out = 15)\n xyz <- mesh(x, y, z)\n F <- with(xyz, log(x^2 + y^2 + z^2 + \n                10*(x^2 + y^2) * (y^2 + z^2) ^2))\n\n# use shading for level = 1 - show triangulation with border\n isosurf3D(x, y, z, F, level = 1, shade = 0.9, \n           col = \"yellow\", border = \"orange\")\n\n# lighting for level - 2\n isosurf3D(x, y, z, F, level = 2, lighting = TRUE,\n           lphi = 0, ltheta = 0, col = \"blue\", shade = NA)  \n \n# three levels, transparency added\n isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), \n   col = c(\"red\", \"blue\", \"yellow\"), \n   clab = \"F\", alpha = 0.2, theta = 0, lighting = TRUE)  \n\n# transparency can also be added afterwards with plotdev()\n## Not run: \n isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), \n   col = c(\"red\", \"blue\", \"yellow\"), \n   shade = NA, plot = FALSE, clab = \"F\")  \n plotdev(lighting = TRUE, alpha = 0.2, theta = 0)\n\n## End(Not run)\n# use of creatisosurf\n iso <- createisosurf(x, y, z, F, level = 2)\n head(iso)\n triangle3D(iso, col = \"green\", shade = 0.3)\n\n## Not run: \n # higher resolution\n  x <- y <- z <- seq(-2, 2, length.out = 50)\n  xyz <- mesh(x, y, z)\n  F <- with(xyz, log(x^2 + y^2 + z^2 + \n                10*(x^2 + y^2) * (y^2 + z^2) ^2))\n\n# three levels\n  isosurf3D(x, y, z, F, level = seq(0, 4, by = 2), \n    col = c(\"red\", \"blue\", \"yellow\"), \n    shade = NA, plot = FALSE, clab = \"F\")  \n  plotdev(lighting = TRUE, alpha = 0.2, theta = 0)\n\n## End(Not run)\n\n## =======================================================================\n## voxel3D example\n## =======================================================================\n\n par(mfrow = c(2, 2), mar  = c(2, 2, 2, 2))\n\n# fast but needs high resolution grid\n x <- y <- z <- seq(-2, 2, length.out = 70)\n xyz <- mesh(x, y, z)\n F <- with(xyz, log(x^2 + y^2 + z^2 + \n                10*(x^2 + y^2) * (y^2 + z^2) ^2))\n\n voxel3D(x, y, z, F, level = 4, pch = \".\", cex = 5)\n\n## =======================================================================\n## rotation \n## =======================================================================\n\n plotdev(theta = 45, phi = 0)\n plotdev(theta = 90, phi = 10)\n\n# same using createvoxel -  more flexible for coloring\n vox <- createvoxel(x, y, z, F, level = 4)\n scatter3D(vox$x, vox$y, vox$z, colvar = vox$y, \n   bty = \"g\", colkey = FALSE)\n\n\n## =======================================================================\n## voxel3D to show hypox sites\n## =======================================================================\n\n par(mfrow = c(1, 1), mar = c(2, 2, 2, 2))\n Hypox <- createvoxel(Oxsat$lon, Oxsat$lat, Oxsat$depth[1:19], \n                      Oxsat$val[,,1:19], level = 40, operator = \"<\")\n\n panel <- function(pmat) {  # an image at the bottom\n   Nx <- length(Oxsat$lon)\n   Ny <- length(Oxsat$lat)\n   M <- mesh(Oxsat$lon, Oxsat$lat) \n   xy <- trans3D(pmat = pmat, x = as.vector(M$x), y = as.vector(M$y), \n        z = rep(-1000, length.out = Nx*Ny)) \n   x <- matrix(nrow = Nx, ncol = Ny, data = xy$x)\n   y <- matrix(nrow = Nx, ncol = Ny, data = xy$y)\n   Bat <- Oxsat$val[,,1]; Bat[!is.na(Bat)] <- 1\n   image2D(x = x, y = y, z = Bat, NAcol = \"black\", col = \"grey\",\n         add = TRUE, colkey = FALSE)\n }\n   \n scatter3D(Hypox$x, Hypox$y, -Hypox$z, colvar = Hypox$cv, \n           panel.first = panel, pch = \".\", bty = \"b\", \n           theta = 30, phi = 20, ticktype = \"detailed\",\n           zlim = c(-1000,0), xlim = range(Oxsat$lon), \n           ylim = range(Oxsat$lat) )\n           \n# restore plotting parameters\n par(mfrow = pm)\n par(mar = pmar)\n```\n\n\n\n\n\n\n\n"
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    "content": "#EasyCharts团队出品，如有商用必究，\n#如需使用与深入学习，请联系微信：EasyCharts\nsetwd(\"F:\\\\我的书籍\\\\R书籍\\\\R语言数据可视化之美\\\\第4章 数据关系型图表\")\nlibrary(ggplot2)\nlibrary(RColorBrewer)  \n\ncolormap <- colorRampPalette(rev(brewer.pal(11,'Spectral')))(32)\n\nmydata0<-read.csv(\"Facting_Data.csv\",check.names =FALSE)\n\nN<-ncol(mydata0)-1\nlabels_Y<-colnames(mydata0)[1:N+1]\ncolnames(mydata0)<-c(\"x\",seq(1,N,1))\nmydata<-data.frame(x=numeric(),y=numeric(),variable=character()) #创建空的Data.Frame\n\nfor (i in 1:N){\n  newdata<-data.frame(spline(mydata0[,1],mydata0[,i+1],n=300,method= \"natural\"))\n  newdata$variable<-colnames(mydata0)[i+1]\n  mydata<-rbind(mydata,newdata)\n}\n\nStep<-5\nmydata$offest<--as.numeric(mydata$variable)*Step\nmydata$V1_density_offest<-mydata$y+mydata$offest\n\np<-ggplot()\nfor (i in 1:N){\n  p<-p+ geom_linerange(data=mydata[mydata$variable==i,],aes(x=x,ymin=offest,ymax=V1_density_offest,group=variable,color=y),size =1, alpha =1) +\n    geom_line(data=mydata[mydata$variable==i,],aes(x=x, y=V1_density_offest),color=\"black\",size=0.5)\n}\n#ggplot() + \n#  geom_linerange(aes(x=x,ymin=offest,ymax=V1_density_offest,group=variable,color=y),mydata,size =1, alpha =1) +\np+scale_color_gradientn(colours=colormap)+\n  #geom_line(aes(x, V1_density_offest,group=variable),mydata,color=\"black\")+\n  scale_y_continuous(breaks=seq(-Step*N,-Step,Step),labels=rev(labels_Y))+\n  xlab(\"Time\")+\n  ylab(\"Class\")+\n  theme(\n    panel.background=element_rect(fill=\"white\",colour=NA),\n    panel.grid.major.x = element_line(colour = \"grey80\",size=.25),\n    panel.grid.major.y = element_line(colour = \"grey60\",size=.25),\n    axis.line = element_blank(),\n    text=element_text(size=13),\n    plot.title=element_text(size=15,hjust=.5),\n    legend.position=\"right\"\n  )\n\n#------------------------------------------------------------------------------------------------------\n\nggplot() + \n  geom_ribbon(aes(x, ymin=offest,ymax=V1_density_offest, fill=variable),mydata, alpha=1,colour=NA)+\n  geom_line(aes(x, V1_density_offest, color=variable,group=variable),mydata, color=\"black\")+\n  scale_y_continuous(breaks=seq(-40,-5,5),labels=rev(labels_Y))+\n  theme_classic()+\n  theme(\n    panel.background=element_rect(fill=\"white\",colour=NA),\n    panel.grid.major.x = element_line(colour = \"grey80\",size=.25),\n    panel.grid.major.y = element_line(colour = \"grey60\",size=.25),\n    axis.line = element_blank(),\n    text=element_text(size=15),\n    plot.title=element_text(size=15,hjust=.5),#family=\"myfont\",\n    legend.position=\"none\"\n  )\n\n"
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    "content": "\"0\",\"# Load in libraries\"\n\"0\",\"library(ggplot2)\"\n\"0\",\"library(dplyr)\"\n\"0\",\"library(deldir)\"\n\"0\",\"\"\n\"0\",\"# Parameters to change as you like\"\n\"0\",\"iter=5 # Number of iterations (depth)\"\n\"0\",\"points=8 # Number of points\"\n\"0\",\"radius=4.8 # Factor of expansion/compression\"\n\"0\",\"\"\n\"0\",\"# Angles of points from center\"\n\"0\",\"angles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2\"\n\"0\",\"\"\n\"0\",\"# Initial center\"\n\"0\",\"df=data.frame(x=0, y=0)\"\n\"0\",\"\"\n\"0\",\"# Iterate over centers again and again\"\n\"0\",\"for (k in 1:iter)\"\n\"0\",\"{\"\n\"0\",\"  temp=data.frame()\"\n\"0\",\"  for (i in 1:nrow(df))\"\n\"0\",\"  {\"\n\"0\",\"    data.frame(x=df[i,\"\"x\"\"]+radius^(k-1)*cos(angles),\"\n\"0\",\"               y=df[i,\"\"y\"\"]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp\"\n\"0\",\"  }\"\n\"0\",\"  df=temp\"\n\"0\",\"}\"\n\"0\",\"\"\n\"0\",\"# Obtain Voronoi regions\"\n\"0\",\"df %>%\"\n\"0\",\"  select(x,y) %>%\"\n\"0\",\"  deldir(sort=TRUE) %>%\"\n\"0\",\"  .$dirsgs -> data\"\n"
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) 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Mono\",Menlo,Courier,monospace;color:#567482;background-color:#f3f6fa;border-radius:.3rem}.main-content pre{padding:.8rem;margin-top:0;margin-bottom:1rem;font:1rem Consolas,\"Liberation Mono\",Menlo,Courier,monospace;color:#567482;word-wrap:normal;background-color:#f3f6fa;border:solid 1px #dce6f0;border-radius:.3rem;line-height:1.45;overflow:auto}@media screen and (max-width:42em){.main-content pre{font-size:.9rem}}.main-content pre>code{padding:0;margin:0;color:#567482;word-break:normal;white-space:pre;background:0 0;border:0}@media screen and (max-width:42em){.main-content pre>code{font-size:.9rem}}.main-content pre code,.main-content pre tt{display:inline;max-width:initial;padding:0;margin:0;overflow:initial;line-height:inherit;word-wrap:normal;background-color:transparent;border:0}.main-content pre code:after,.main-content pre code:before,.main-content pre tt:after,.main-content pre tt:before{content:normal}.main-content ol,.main-content ul{margin-top:0}.main-content blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">Mandalas</h1>\n<h4 class=\"author project-author\">庄闪闪</h4>\n<h4 class=\"date project-date\">2020-12-24</h4>\n</section>\n\n\n\n<section class=\"main-content\">\n<div id=\"section\" class=\"section level2\">\n<h2></h2>\n<p><a href=\"https://github.com/aschinchon/mandalas\">Mandalas</a></p>\n<p><a href=\"https://www.r-bloggers.com/2018/02/mandalas/\">R-blogger-Mandalas</a></p>\n<p><a href=\"https://fronkonstin.com/2018/02/14/mandalas/\">Mandalas</a></p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\"></a><span class=\"co\"># Load in libraries</span></span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\"></a><span class=\"kw\">library</span>(ggplot2)</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\"></a><span class=\"kw\">library</span>(dplyr)</span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\"></a><span class=\"kw\">library</span>(deldir)</span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\"></a></span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\"></a><span class=\"co\"># Parameters to change as you like</span></span>\n<span id=\"cb1-7\"><a href=\"#cb1-7\"></a>iter=<span class=\"dv\">3</span> <span class=\"co\"># Number of iterations (depth)</span></span>\n<span id=\"cb1-8\"><a href=\"#cb1-8\"></a>points=<span class=\"dv\">6</span> <span class=\"co\"># Number of points</span></span>\n<span id=\"cb1-9\"><a href=\"#cb1-9\"></a>radius=<span class=\"fl\">3.8</span> <span class=\"co\"># Factor of expansion/compression</span></span>\n<span id=\"cb1-10\"><a href=\"#cb1-10\"></a></span>\n<span id=\"cb1-11\"><a href=\"#cb1-11\"></a><span class=\"co\"># Angles of points from center</span></span>\n<span id=\"cb1-12\"><a href=\"#cb1-12\"></a>angles=<span class=\"kw\">seq</span>(<span class=\"dv\">0</span>, <span class=\"dv\">2</span><span class=\"op\">*</span>pi<span class=\"op\">*</span>(<span class=\"dv\">1-1</span><span class=\"op\">/</span>points), <span class=\"dt\">length.out =</span> points)<span class=\"op\">+</span>pi<span class=\"op\">/</span><span class=\"dv\">2</span></span>\n<span id=\"cb1-13\"><a href=\"#cb1-13\"></a></span>\n<span id=\"cb1-14\"><a href=\"#cb1-14\"></a><span class=\"co\"># Initial center</span></span>\n<span id=\"cb1-15\"><a href=\"#cb1-15\"></a>df=<span class=\"kw\">data.frame</span>(<span class=\"dt\">x=</span><span class=\"dv\">0</span>, <span class=\"dt\">y=</span><span class=\"dv\">0</span>)</span>\n<span id=\"cb1-16\"><a href=\"#cb1-16\"></a></span>\n<span id=\"cb1-17\"><a href=\"#cb1-17\"></a><span class=\"co\"># Iterate over centers again and again</span></span>\n<span id=\"cb1-18\"><a href=\"#cb1-18\"></a><span class=\"cf\">for</span> (k <span class=\"cf\">in</span> <span class=\"dv\">1</span><span class=\"op\">:</span>iter)</span>\n<span id=\"cb1-19\"><a href=\"#cb1-19\"></a>{</span>\n<span id=\"cb1-20\"><a href=\"#cb1-20\"></a>  temp=<span class=\"kw\">data.frame</span>()</span>\n<span id=\"cb1-21\"><a href=\"#cb1-21\"></a>  <span class=\"cf\">for</span> (i <span class=\"cf\">in</span> <span class=\"dv\">1</span><span class=\"op\">:</span><span class=\"kw\">nrow</span>(df))</span>\n<span id=\"cb1-22\"><a href=\"#cb1-22\"></a>  {</span>\n<span id=\"cb1-23\"><a href=\"#cb1-23\"></a>    <span class=\"kw\">data.frame</span>(<span class=\"dt\">x=</span>df[i,<span class=\"st\">&quot;x&quot;</span>]<span class=\"op\">+</span>radius<span class=\"op\">^</span>(k<span class=\"dv\">-1</span>)<span class=\"op\">*</span><span class=\"kw\">cos</span>(angles),</span>\n<span id=\"cb1-24\"><a href=\"#cb1-24\"></a>               <span class=\"dt\">y=</span>df[i,<span class=\"st\">&quot;y&quot;</span>]<span class=\"op\">+</span>radius<span class=\"op\">^</span>(k<span class=\"dv\">-1</span>)<span class=\"op\">*</span><span class=\"kw\">sin</span>(angles)) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">rbind</span>(temp) -&gt;<span class=\"st\"> </span>temp</span>\n<span id=\"cb1-25\"><a href=\"#cb1-25\"></a>  }</span>\n<span id=\"cb1-26\"><a href=\"#cb1-26\"></a>  df=temp</span>\n<span id=\"cb1-27\"><a href=\"#cb1-27\"></a>}</span>\n<span id=\"cb1-28\"><a href=\"#cb1-28\"></a></span>\n<span id=\"cb1-29\"><a href=\"#cb1-29\"></a><span class=\"co\"># Obtain Voronoi regions</span></span>\n<span id=\"cb1-30\"><a href=\"#cb1-30\"></a>df <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb1-31\"><a href=\"#cb1-31\"></a><span class=\"st\">  </span><span class=\"kw\">select</span>(x,y) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb1-32\"><a href=\"#cb1-32\"></a><span class=\"st\">  </span><span class=\"kw\">deldir</span>(<span class=\"dt\">sort=</span><span class=\"ot\">TRUE</span>) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb1-33\"><a href=\"#cb1-33\"></a><span class=\"st\">  </span>.<span class=\"op\">$</span>dirsgs -&gt;<span class=\"st\"> </span>data</span>\n<span id=\"cb1-34\"><a href=\"#cb1-34\"></a></span>\n<span id=\"cb1-35\"><a href=\"#cb1-35\"></a><span class=\"co\"># Plot regions with geom_segmen</span></span>\n<span id=\"cb1-36\"><a href=\"#cb1-36\"></a>data <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb1-37\"><a href=\"#cb1-37\"></a><span class=\"st\">  </span><span class=\"kw\">ggplot</span>() <span class=\"op\">+</span></span>\n<span id=\"cb1-38\"><a href=\"#cb1-38\"></a><span class=\"st\">  </span><span class=\"kw\">geom_segment</span>(<span class=\"kw\">aes</span>(<span class=\"dt\">x =</span> x1, <span class=\"dt\">y =</span> y1, <span class=\"dt\">xend =</span> x2, <span class=\"dt\">yend =</span> y2), <span class=\"dt\">color=</span><span class=\"st\">&quot;black&quot;</span>) <span class=\"op\">+</span></span>\n<span id=\"cb1-39\"><a href=\"#cb1-39\"></a><span class=\"st\">  </span><span class=\"kw\">scale_x_continuous</span>(<span class=\"dt\">expand=</span><span class=\"kw\">c</span>(<span class=\"dv\">0</span>,<span class=\"dv\">0</span>))<span class=\"op\">+</span></span>\n<span id=\"cb1-40\"><a href=\"#cb1-40\"></a><span class=\"st\">  </span><span class=\"kw\">scale_y_continuous</span>(<span class=\"dt\">expand=</span><span class=\"kw\">c</span>(<span class=\"dv\">0</span>,<span class=\"dv\">0</span>))<span class=\"op\">+</span></span>\n<span id=\"cb1-41\"><a href=\"#cb1-41\"></a><span class=\"st\">  </span><span class=\"kw\">coord_fixed</span>() <span class=\"op\">+</span></span>\n<span id=\"cb1-42\"><a href=\"#cb1-42\"></a><span class=\"st\">  </span><span class=\"kw\">theme</span>(<span class=\"dt\">legend.position  =</span> <span class=\"st\">&quot;none&quot;</span>,</span>\n<span id=\"cb1-43\"><a href=\"#cb1-43\"></a>        <span class=\"dt\">panel.background =</span> <span class=\"kw\">element_rect</span>(<span class=\"dt\">fill=</span><span class=\"st\">&quot;white&quot;</span>),</span>\n<span id=\"cb1-44\"><a href=\"#cb1-44\"></a>        <span class=\"dt\">panel.border     =</span> <span class=\"kw\">element_rect</span>(<span class=\"dt\">colour =</span> <span class=\"st\">&quot;black&quot;</span>, <span class=\"dt\">fill=</span><span class=\"ot\">NA</span>),</span>\n<span id=\"cb1-45\"><a href=\"#cb1-45\"></a>        <span class=\"dt\">axis.ticks       =</span> <span class=\"kw\">element_blank</span>(),</span>\n<span id=\"cb1-46\"><a href=\"#cb1-46\"></a>        <span class=\"dt\">panel.grid       =</span> <span class=\"kw\">element_blank</span>(),</span>\n<span id=\"cb1-47\"><a href=\"#cb1-47\"></a>        <span class=\"dt\">axis.title       =</span> <span class=\"kw\">element_blank</span>(),</span>\n<span id=\"cb1-48\"><a href=\"#cb1-48\"></a>        <span class=\"dt\">axis.text        =</span> <span class=\"kw\">element_blank</span>())-&gt;plot</span>\n<span id=\"cb1-49\"><a href=\"#cb1-49\"></a></span>\n<span id=\"cb1-50\"><a href=\"#cb1-50\"></a>plot</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\"></a><span class=\"co\"># Do you like the result? Save it (change the name if you want)</span></span>\n<span id=\"cb2-2\"><a href=\"#cb2-2\"></a><span class=\"kw\">ggsave</span>(<span class=\"st\">&quot;mandala.png&quot;</span>, <span class=\"dt\">height=</span><span class=\"dv\">5</span>, <span class=\"dt\">width=</span><span class=\"dv\">5</span>, <span class=\"dt\">units=</span><span class=\"st\">&#39;in&#39;</span>, <span class=\"dt\">dpi=</span><span class=\"dv\">600</span>)</span></code></pre></div>\n<p><img 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zuG/+8+fPd+9vIjKwk2z+3q6izxI2O9xjCSD+PJaTpGJXAu4eL3F6tM841pGisjeONdOdx8Tcb9dEJ05aJCP78HEKAsIXS+GCyFsAlmQ4Fe3ZG/0IqooIttcLPPoJin6TZXdvql/0iP3DDUFX8OnZzhSiO4kdPMtOZjueOSQEgpxEpjefNu7ZXOjt0G5DeKmnpmPrEmgloGMFEl3QW3aDe7qZSuaROMF0fOKtXlOJnK0Vpo7twPvsnt9p9DbJQjBLUnZWCXGIzudSuMBeibS/iidBU8SK13aYbUqIE6ckOmFDMJ8dwbpT64lt2Bh0iu8PW/AhG31konhO7Axf3ivyYIMNgJZfn913quk01mOKZAtPnIbmccopxC+CI3i1WQrv8Om73lMQ+thEkjtaY73vPVmTOdmLxzBboSXOm6v686kI1uL3LE57yER+ooATb23QeaLGRhD/9RcO4f2caSlcdiCOwNE5VG+HiyECBr+TcJ+htG0c0/o9Bsdv700hR+F2dAX6XQO0+e1IZDeoBQbE0AGenZCsp/JtzQoPOpy6HvLkPJKCnqysCzBJnHq8ITsxgpOx9iEA7LlfoLsr4d+jN05T6Lkl9/HJPnrPtlujJEDC6/h7bM1zIcB/MRUuSDFiR1IQHksqA/45netRClTB/xABynh2jo3nRHffMa1Ncuy0VIJn/N5a7QryP7tyvTYofmw3E65NJVjpsTUJi7F6c1ivBMYJ0Jiv+96OMj7WFUDbkjkVQp79VgjFH42bawppz2ckjDawgnntONbx/d///YMdks0YkZcklF4y1lmQ+edSuIgFNoW88hVPBEheJaxkte4Rz2MSP1JQ26iDGafUFduQZ7E7m2JwhL68iv1su8UTdmrTRR8bNW2OYUyYkDUpGMUsG25wbyo+sSFFktMWJ7iKT/PyATpssRh/ig8/2O894eI+/uQmchtPyvApsSq/lRnzv2OQOejdHxtgl+zh1EQnyXPoTNEiTxHL5Hg2gBUup5DHrrQULjsQxXMmAdTzx60z+2xngLM4cusFeoGIwTAg4+1Kkg7BXoI1Fsi3IfwAdjsbPX44G0fn8N63AKKPZN0O4pT1GMN4+vV2pX02nl2dsV1rvNiZAWxkvQtlHjZo52iXxAsZBz92dvAkcWrJfYWtXakecEWWClK7qYB1znQxFi70zw4k4rCptXfks6AIsxQ6PdukS0kSWxBYjbMP6a9ggi3s/VDJj0IAj2Pj0Y0da5gHc8ZsUiHOFsbksY5gqUfAxooNc9pgsSHgtGIMZ+xmwkRB+RCyIRN2ICG39n3HtA5r9BcQbTyNJU5iiN8iSMzG1qqPnWNxhB/2xjkVzalwEcvgkYQMT+xSEePxXZtcfHbXWLEP0SPbtSnFTs9CV2KL/2UiBnlaQ4Fh01BxwM8rT96zf3HR1cZF932nSLS5CDNhhDXaDJmCecbyWy9xTzFOR17lJDZNctqiXS7+CA8UAj192oBUTLkvtsA4sdnYsOTURBZkgic2eWobJDM6VsDZfI/e5LOwF09swyaWtnOlMytcWkpwuBAKFwoVJHIC0SsYGKTEgoH2kmD9GYniZd1u2iYyjiM+QCih2dXY8JudEkG1TUw4HMCzSwCUzFtJeyBivap1423Di3EAiOdoez9Qdd+jIX441lufQCDhsSMEiMcC/CayPjtO1rdPMUkO1iEQxDZanulbkJAIsvuW2Isg6/G9sURyTkRHEsWLqXChfzt07Nljg9bY04PkQ1DN0bo2tZ33/MLuv1OAnr6nkPHYGDkJOmypx9dUcqLgz0Dzsdb3Ef8wl93qdY9fWauExSYDGW7LmznZT+93diH2QYbamGeMT5tFdKJ4OgRJmPb54yMtKYTFOpjl/ZiMJFtkmV3hltgkOfMXmDOmk1MRzJ9T4QKD2bOde8ktH7UBJnGUXMPfs0ocPa2BPzo7hP9OIf6lgJNryVHEInYkh5BLtDJhexJbfzDD73tbO/NZbsAOJbx+v8tXnGzExjcRGcAXerKJgj/5ldjvcT86qxiMJ/M6LeLr7L/1Te39Vs59uZZ/Aoovp5zws5e3HZvwpFCQZ5i/59fHJLr3GycyURSSt7wv/+tKPFO4kNep7XIKzaZwocQLpXAJ2QmQ2IwVHhJqQG5XrRqB9y5GBEh7jxNtSwzfOP4YgERrV2PTj+P7kZYCoAWn8DtWrLkP8CTbgsa2CZUknYxU/8ZvyX3jJclvx4zd5LnlXYEA7xIeO9mSom147xEZ0reCVAHYS96Ag/8yTEatnJH7dj/stJ4a2HYhYJzCZVe5IbqcS+FiHXQH2Pk53npr871ETaCNPnvtjGG3TxJ8CJJAKPbtKPZsaCrxHbuvCqyxRMO68jgBnfeIPCTccHGsuGgJ/9ahWPfM99jYTh/5p0S0J2efYYTEQFK4TcK0joznyl9jOtRjHdbhJEWx2CZbyJxk4DEZSQVsateKrE9iCHPH7POUhM85FS74YA/kzG4kbJJWSSOdHrK4nUJ0LhH3Y3IFQc8GjknmYy9O/J0+kI1Hx5wM+/9UvQ1EtubEhUzF5NbWfEe2TlwkxHnM2eNIkuAxXES+hxOKJ3+MRh4DY+AIHYnt/EG7jGE8/oh387X27711wG/+wS5htOLVY3Fi8yFwcwpZA575/qlP2yI7ccP/3GN34rXcyQmkYpP/4i8nLnPOPZbCZQ9ifBQu6PaULEAJ4HYFYjiVrNnOi2RhVzImh5BQCGL7GJu+nteXYPWSB/ftogCVsWLNaQyQsCvbrrcl9522nD9/fhivxztHc3TstXefowlSwGnTfOtIgAO65LiPDMlN4gRQx/iRnLPzsYTELpi/NsJ+LgSy5outcEHswC6UIrRXNCNtFPpOKr2SRa8dO7d7nmfrtdlHVinoJRxjvG1L+qZ48HwzXOuRdvzQX9ZSgPUCvzaSHH9ZyC7sNkkZvdsM8QdN1j0aaTeYbYw94oEfxT7fshm0b0KID7KF0Xza+0OQBBpGtslWiA1JsOyE04V19NrF7uwio16bU9IcCxcyxpck2Akl+/BbJToQx7yOyfdYZC58mdtvOSTYp9adNXu8nP2wNX7ldEO+4pHw1gfxx/7tzGvf3ud7/J3cJb1+v6nQgefiau8pjpDv3fejebmSU9X8+XWPNInxbV+8yEVsUJBfe9/6FCrs0Zj44iceq1S0ngXhEz/ZCDsl0R9ckSPKcRSsikKb63RFNnTohBJ2KzbnTEvhsgcJ0ICn/l3/SgCTEzr61LZ1rgQoBpQdgF0AjJMqFBRRY496bEPmNo5HRnpA476kwWmABKOXuEhoJO3AQv9163HP87V03gse+itIjDcG7hIUoCsg7UrmUTh5HEThtSsZh84FRrtNLeU+MM6jNJXcZ0cCLrmwmQuBLtbCxVrYnWDNLuiuJW3oUZLp1FCbngzoNQmwomNXXw+RuV0zf4Erz37vQ3ghf+sQ8HukDd4FP1jNPntrwIsNEH6Nz03rhFl2weFoTy7GM9emkxR6UABl42VfmRhPMme9doFbf51K+IEzZCx5wmOPyFiSLTFjN711kJNkHG9sdA7E9udYuJAVG/MdH5W0wRg6sCPv1GVf3U4lOhWz2IJYYf5NfnJIMpeCgIwUKk5L5CGKDqeLvcIfzx69kui2eYbx6B8u2IBQxPjBvuIDDug3JmM6Eu/087SCfk5RyEb/Nrcyr4Ke7txvMcM8ZOsH++aN3sUWY/KvUxLe8KhgWffo5zGJbjxRojAVNxSJnmKhf1gdLF8Kl4l0IRYuHISjOfrrJQ+MlZE4LuVIreO6DzTzu4qxpGcTmZczem7Xbv1YQNxE5vZ4Bx0IivhpyTzWA/R7/HIAIKhYS9Dtrcl31q8I6v2BA2T8msS34+iTH8Hje1eiFwmK3RgF266EH4DvcTtBoaXMQ77ut2t23y6qYlgw21WPpyZ8XoyFC2LDfIptjJ0yIgHfEftYokm3in7FMazweR9ZIXLH11jCP4X05W9wyKMUbbIQsjaP3ghuXnttfGcDxDPlY49DhbSFoYp5rz2yNqdB1rrux774l6wcKuiSgWJJEdHDuqlEXzaqJFLWOrYOGAAjxv7yFT7gh0KY7vnNHIiM5li4hGwWeRRKLFYAiyuw1nvt9tXvFDKXWOrElB7Hnjg4NvEVBYuk1u/XnMJ4AsSFJ3xGLuGZXOVq/KOlbKAqXMiX79hMUOz0MMVnMVP88JioTUpjK6a8b5860B6eKEToWKJdx/Qe3x7Dk5jb+ORLTs7pXr61L1ZOJfNZh9MtcX3M749F1gqHFS2KJ3qhczkc2cCb0FK4TKQLsXABNPiWqApIPYPkZMDcX7hpE1EG5TuGBDyBwi4OpY95zGEu4+xCGYchKyh6iZo12n30DGvviNZ9u9N2AgUI6+utyfeSePz2Tm98Ftw9dy/gtImD94oVgEf265KjdZRxPE+eYmxXIneAMPabCPcVlx4R6IG4uXPCdBa7MrsSXV6shQs7pLcXv/jFw3H62PrYkMDIVls/D7FRwYuOx/xiCuFNcqtgMvYhxhPgPELjxHRsHfTEziUPYzbKfiU/bGKs4MOvdvzBLu9YO2szF9nhsbdOPMEThUubzOxC+hvHzqRi7hBkrXCejfP1nuzMmx/Njm2ikAe9w048zgUn5l64kL9NNcm0BN3uuw0H/5ZAQjtm78citownP3rvncCfgswvVkvyxW1PC4inMBjesS1X/AmPNg/4RW+jgQ3oLyeyJnacv7zIz80XMq7PTnidtiie/KW9nHLyu1Ym+HDaks1i98Ob1/iPkw2bxuzR/MEr/KX9KchcYr3H5Zxs7Jrj7UrmYtdkYMPZ47bPf/7zB9l4ZMwTQdVHlsJlIlGuZyKz2zcXMN5EHMeOggq/OlElDstxGUjvvuNMpwaeH9/HqBkjPvLM8y5E7hIIu5tOQnrEEfylD8Gzl0iQAz7WnSLRMbAiu14Sbw4JnkQfuLXz4NNODSfTdle56WeX6FnPeta18+xK9AdQracnF8FR8gU83GvvAzVB1S7vrus5NVknexHI6HMfvvnH3AoX62HDNlU8dz1mH3jnx04Oxk7/jCUg5P837WNriA8INBKfPG6yD+HPOuziS/zGCgn+5qRTUsJnelhtbRKM7HL2SD+bFjZJbIYYtyVt+BOZSYTGSJJP9gL0GM5uS9G5RC7/I2tfMqaEj8wkyz188NlmkOJGkmUdPcKbBFxi3hvnrAhfcy1cyIgt8bv8nwqxBd44UeVDfOksiO/yE/PviwlTic+JoxJ9eQO5sS0bDjYeglOxMa983hMV5NvGbu/J3ONeNmTldOxZbuKfpIq1IXhlbkWF0xa+5tUplMLFPL2xbZIpdsJX2hgPnsi1bLzQrbZ5RGrdae0xCF/ms3Z+ryDrYeUxyfxOq+kVpuBDLINB5J4TqBCsWwqXCcQBOMILXvCCIdHf59GfUxKlq/wBz9iz5nYx/ffj3skCcl+yNvZowLYk6DFQ8tvHQQCGatxf8+iRsVXqdqzGijVHvXZRx5IISY0kXjLS4xUPwHMsiSdH4AYU91mrsa1VUrpv4mfNeBpbsxMxBaq198jjH47ZAd2FQkBRoHnuc587PPLYW/e2RG5zK1wQu3B66Ef6dNfzYYR/px/rEmy45rRS4Ogl6lOIrPGSH1pKfPaRP+JLcEjAH7NDbcjDycy6vwJmrTBAwOwR3vMY3hiOGFti5yRlXQFB5nZ5x3xvCumPd3ZItvvqCZGZwhbmiQM9IgObGk5/rZV86lq8D2/ksc9fkTwGzblwCZExm5PcSs7YuQ06Gy820c6CxDq+pFDw/tTENsVAO/J8zavCzmOtXtl/tTN2mUea4VgbN33O45/8kqz5kTnEiLRnL4pHMTynLgob80ryW8KHGEpXKfLiE4jsFEv0DsPyH/2th87Ne0p/MReerE0uw2/3yVV2ITKTQ8k7cupCd3DIBlUrjxQu7GDONKvCheN6LtrjQYCH0VN86xhzIoZoV8IuBQNpgRK5D5gkZdbZUoq29N/VufRljI4lFTEcexcCKBxeEi7Its7ms9MFCVjvtxoIAAGOCjAh/Y0vqRlL4vEvoanPX4b0Zxce35FA7SqvjJNncneVl3HIPsfi5FcJf3QMwOjGnC3P+jjiBvJjO91zIHzzRyBsrWyEv9rF9LqrLhAZzrFwoV/rVcg77u/5OBIkBE7Y1dp8SBvBXnIKF/YlvEkSnFQL9vvIH+nPZ+1QJmnvjcmeyQLuja2DTXtsUnIiIOI1ZEz3FXqSkXqvkn4SoHWFnr5kDj/H2kwh40lm2TS974oLIePxaUlbHlfpEd61IVPy6ckddsB4m0bez4msay6Fi9jCJloZkamYBatsuOBVOwU4mYonp8432JdNT78DOYQP70Lm90dIUmAoJtiZP5nMdqsP4I+M3Gevbbxi7+TOHxUj/siEYoNs4Qp/51fyO74tFxA7vXpM2ilND2Oz2Zn/NRMfcZnT+DZwnLZ44sNGIvwxp83hU8uVjGwu4Nlpj8+n5AGeyMNsqjt1Eqv9dpndyzPkWC0/8j2n2wrWOdPsfuPC4OziSuIYNVBhdJTOOE9tfNsQvjgT52QMLeGdATkq7RmL+45MJepA1Xi7EHAxj6oaAMa5p5JxFCbAfSwRl6xISMYCsfv4aP//ilftgYofJfaSHvPbDZOMAbiWOKRnlB1v9+5vS/iwCwrsssO5C9Gfx0oUp/TocyXjOpLNDlVPL5JF/5fDTschkq9jELvEGxsVYMhfkEhAoc9d7C0018IlxFYkZvCoR/QsUNnFFUR79kQ+grpdxXUnM9uS8diORwzJju3towPEPvOY5jo8YssCoKDcm9P6yYHd8+dq197bFfWY2Lo59JNQ9TZ8kDlgHr2M/ZGPKWQdZCiRUoS2vrwLGYOM/PhZktdbq+9gkL/oSGZjRFb+18W6H/efFbGbuRQuipCnPe1pQ3xo5U1uEjM6lkQ7qfOqYLQ5MWZrxySyUwhIsuHgmD8ci8hEDLOJB+fkMl7xA1fw1xIcdAqdP6VbyWd2LAfw21F5nfU51TKmjcL81S9PbyiAnIrY7aezFk98tvEH59qYT1ZkJiGXL8otxCa4wA4k7b1C6JgUnjyCeFYneXIr/pgCji7gqad//KEYGNzKmR886lGPGl7nTLMrXAR9Rs9RCJ4xAhaVIoM9RGA+NDFSgUkQ7yUj+LU+oCDY95zSmgVoBdCuwdI4eCFDIEF+uwBg+HE6YPejt7PnPl49y9o7IbAGzqFYq0CT7yX5nKi3VomYopXz9ADTfIo84Od+K89tSB9FJIADrD0n3pbIxy4VACZzsqljGZtdCAbe00m9773HcjwmaT276OyYhD88Sawkc+fPnx/sy7qtR6BXuFyMP86vpPiU7AiEvXX6jq3zmXWPfVin3UgJfWsLuxCbYcPkv+5Rtm0JT3SqkF6XIFtrfl/T2nzI9xJZvlETQn6ib+9RFOSzvnYJJSw9HEBkSdYSzl3xrhI8yv+zUSTsisWVjOFRVIX9mJzw7ncD2nnfa+M7BaXfAlh3r81ZEh3NqXB527d92yHRjV1EXsEymy9wTMHrR+l5rAgW72tHU4ldsDv8SuDp99RkTjaqgJOLOS0RqxUYXlti1zZIe38Qw3vj8XvjkanHifm7OKgPnHfSpagxjvxHTG9jJNnAIRspvZzBfdjsCQ9PaBhPQQQ32GPGOyXBOrmezYq20DoVsWcyVUAqFm32ehXD6ab1CX4iL7vvfe879K36nBvNrnCR/FeDZQB2AiRGgpggKaAkYZ2LcCXTnF5C0gt2nMfpg2cNx4KX+wqBXiDflvSz6/DoRz96CIQcmUGacyoBFYkaQO2RHQ0gYee0x6915K+LhHwHvPST2LWAYhyO5q+L0H2Pb7vagjeZaz9FVnRjXLtEEsdHPvKRez//6pjdowmO2cNPHYutAl366N0nA4Ch+OnZzlkRHvHO3wQCQR3wOY2ryRWdChAXe+HCbhTzgjHb7a2VLvM/CMZOZsgLjsECPrSLb1Yyp8RBIuaE1Pj7krXBY/oO1rbkeyeu8MYmwJgPSTbgNxzJfQmK3Vq21fbx2diSFTY3tpmjnTG1cfK3rxwRHfNDhVAPn6YSnqzxOc95zpBcjY0HQyRe7IH+Wpnoxz8kfZKKQ+j40ERncylc2C279M9SxSfyqzIlP3FJfBNP+KINB08ZSOzGTsaORebCk1ikeK2+cipiq4o4TyDAJzHW5qFHQuU2rT/gD882SsksduvKemAgGcMl7ZyCXXPNNau73e1ug73brIMNTgWcusCx4EguPpk/BpCEO4RnfMkb6c844hDd+tkBfzLGKcl8fN3Gj/yLX5ySyAQe06NNNsU5+aRItLlVZYzoSowXexWY2p5ablNoloVLjwjaj84JVpKnmvUdI56DgBmn4zgO7jnBlifGZNcB/0CpOl+Isee/UvfubyJzAlwJwUMe8pBhRxNYkKuxpxI+nHyM/QjUTkJ9RKUl3ynkFJwhcrKTg6ee7vRRUBg3QFjJZ4kjcAJoU0lRJPlkZ/4C2OMf//gh8cmu3C5EPgAhu1KVZ++NbS6JWI/c9xyq/uxkLkT++R0LeXlPP61OtHtVKFzoRsFLFnx5TFdOXwXj3ulriO8IxALtLnbcI34h8TrUeNYh+MOrsbXCG/8EU2LR+z0ckozAomC2sZySeI6e7bdkDL6g+LOebFC0pF2KIgnKIUhRjtcxzJtKkiqJn2R0XTKcv0w5luTwMQWcZEQyOCecCM2pcFG05PcV2SRr9ekzG5Qw81V+LVG3eajY6cW0YxJ+kujbHafjQ9jgFKJDRQgdsklxid3JuSSzMLryxJ7hhN/n8Bm4xqfZustnJx8eCfWbVEXLU5/61NUTn/jEAVtsHPpNRYokJ2H60ZdLf49f04nxW8KvTQs5Dx2yP3kY7Mhj7KeUobnoTY4Bu8jr1ET+8i4nXQpRp1tkpJh3Mk9nrUzwCVvkZTab4eqc6YIpXAhbG47NoTiSIkH1KFCf2sFbYqwCvJ0bDtSCHv44pYAtWekZNEeVtOexiqlr4qSSS0GSHCWcwMZOBDD0mAGjFgS3oejEhbc26BoLv8DIXNFFiEwkeH6rIqkxr3Ecy9uJyPqMqx+A4lxXXXXV4HjWU0k7SRDdA/b2fo/MYR12PQGo8cmDXAQ2AQ4/Y0nXJjK2gto4vQTLfScxkitrb8ka7LKRH3nuwsMhKbqQoOOJrAUChe+YvF9VCheyIQOFrtPRMT8iP34uAej5DfIduZIx2zsE8R/JgRODnIrtk3zh3QaC8apfh/i3nb1nPvOZq6c85SmDnwvYfF77zE1OAicb0V5RYt1ODsiTLIxlPoFWIumx2ic84QnDo5x8t5UhO5PUKJjG+NuW8Ml3bSzASX84AZYdgsQEyd+6kxS8w2uYNmZTdMkX2R0/6dnUWRNdzqlwkR+QvwRSAUNuLfETP0BXVDtly1+4kmizR3a5D6ZNIfPgES/kKNaZ/5TEruCIeCV59aQAOfIz8RJPVR7ey2voHA54hFtfxWIuvny/+91vdcUVV6ye8YxnDDv62ovzcOPBD37wUMAoXrS39lx80WauuG2eVhf0lz/eIW+CHXlEDD6cWn78V6Hn9FuOc8ril2zoL78tgifkQC5yELlpD9fgj/uKPTznzyG3sp4TzaZwIbwULusE5h5jEPy09yyyhBxo+p7izkLgMRqBPs8Q9givHivihC1xMobFcHqPUGwiwU3yaH7j+EdDTk0AsGIKoAAWiQUH2wTK7gN+O6qSq7a9sSWZdlCAhx+NKirpQluXdQgGdj+s2XGkZAQAkhed0b37ArxnMgEgGbQy1NZjZmRsl8f468j4eOGM5uTMnqU1N3AW3DyGaA34ngoyZCHhcaoFOPHbyhN4WovdIry0pD9wIRfzT9X5Icic0QUbcrQscCseN8lZX+tiWxd74RKiK8G4p29EXvBJ0FXwjclPMmsnUZFI/vsSG/SIqMTAj7z5ifn5evX3bXSkDXvg/37MyddbMia/hSl+0KkQk9zYCIBxkgq2oR07z/9i0ceGh00o2OTihwoff2XMCYVg+6IXvWiQc+9RMfKyNo9O8W9zTCH9ycO4Od3x2IpECm4K/vvYMtLfbzONPRYPzE9vNpvM2a4TGQdmwwmFnc/78nYMoms8zqVwwQf8zUkVLGvlRi+SNH8swyvssfnHrmxIWdMpZc0u2TL/UJB7f2pdiwF8kC/zLacZHn/NqUflyat46rEoj3crdOBjveRpb/M2bzNgE7+GF2IifFQQ8TkbLuKNDUUx3qu+ChEbo/KCmhN55b82iMQdfPo9Cf+QQ9LhGD4fi8wlx0mhTE6HwPVtyfzmNDf8hbNOwuCKTY/eb6f0kdvRnXgE7/21WDg9Z5pV4WInXAIELNYp3D0ATwmMWR8KImyGc0pjqWReQYijW0/PafKoSe/YE+kHNB2bTl2HQG5ngtOToR2MHA0yaGMDA4YJ1BU364A5jmA9vR+wWwMQAvqKJYFB4AJICgTJivGBHr7ct7sFXHwvqDiSV7DYaTGetUuc/QCcE1UZ0Hf+BCv9r+Pb+BISICJhIk/860cOnv3EK/AjE49yrRuzR3izS0ueY33pO/+UsqdP961dYjJl7kMSGUQXAgS55ITFutbZIZ4lnh6vkSjvswb6vRAKF4Fagp0fkrZEBuRGr+sKYjaRHULv95EdHUl2JGx+lMyX+LjHMSRtdMufWh9eR8bEl1MIO/0tsRn+amdPYDSfExX2oL1kBDY4beGL8B1O5xQHHsAGSY9ix3tjkq8kxpiSfn+gpZUzmdoEgv1jvtcjazIWWTjxgV/wAX4lYSUzSax22u+iF330xz/s5Es9IivJnjVq0/qacfinJNqOKdnMlfA/t8JFDCIzNiehi4yj02rjkmhJto02yZ4NOzi4i/73IfOxTwW0GD2GH8ci8yleFAKKCL5BnuxYbK+PsnuVIMsRyBv2iN02Tmxkuh7zmMcMpyp+9P1Wb/VWq0c84hGr293udqsnPelJq7d+67denTt3brinL8xwSaSf/exnr97iLd5iOA1Q2NMfX4hv2ezwvd/jiD90R4f01zulPTaZT94Dk3pF8rGJbNhr/qGvzVp5D+zAU2QX8p6unX47KSdT+O3RMjKdM82qcJGQclaOQPG+YwytAeSzV0AEnAQxoMl4JcYCwqkNFz9AksML1HhreReY80PuXsDVB1DY7WBI7f0xYpQ5VeG0xgY0TgMyD3kYX2UtoVdM1MeAtGnnM66AzhlqG0mm3USAZgzJprWZ2/f4UGTQhWIOH4DHf9wHfBxD8sK5FA6AkrMBIDtk7rWPgNgZwLNEqEf4wqOd3jgrMDVf3WnQxlEufhC+/fUzuzZktS2RjXE8YtBS5ETOErbILuS9/mxd8nrqhMT81mrtklqyFpSiC7aXdpXvEN75mMLNo49+dGkdvbbb0oVSuOCT/9qBhD09IgfJr4ScnHpyIX82J7mXqJDpLmQceMkWbVrYHFA44I1vKwJsYsAdfiHg2+Bhk5vm1EaRz3+Np33WYn02YSR55od71uE+vxUEYRAf54sw7YEPfODqWc961nD6Sc9OWeAL+zeXtUjWnOA6FWWPip0qw4wvwFpvT7aV3Dc2/IAtikkFhblhl+/hFjmREVyAa2S0q074DzxWTFrHGK6QnaSZLPHZrgVm48/jOnlMbK5ExnMrXNgNvujCd3naoMqZbsQpGw3ikiScXUmA5STJQ05J+JbTKFZh9KmJ3ZMBn2abZCfuis82FsmMHOUSfmQvxmvjaQk4JK7xLbkA7Hje8543nLrwO6eoT37ykwd58zff++MVikv45OKDNhUUQPqYW4JtPnPDIqct4pZ5bH7ANfJSbPXyxmOS+eiJzdiEOLWfmp+dkj0e8kcK2LXTLZtsLdEfe6cHWG0MOLwULhOI0CWAdjnsQNkllMSMBf2WtBGkjcFRAL0EdtfAsysBSYYrMDPkFvDc59RAtXefU+bRAQF8G/6t3bgCb8BaP0HRDiJnb8fRxzwMnbMDSc6GnypvnyUjdpglJ7nP0CW6HAOfTk0ib/d9B1gEZWsBVnYy6cZn+lXYCMoZU0IB/CQReJaAeZwkZD3AjJ57hH+AB+iAHN568gN6wFZxichOP3xJ2rclcvb8Lb6qzJDP9Ev+ADWFQMiaJUYCY3bcT0n4oy/BgrzsVOFnWwJw/JN+2RD9K9JaOUwh+rsQChdEX2xIQtRbs+/Ikw0LpGOJKzuQQLO/sTabCC8wR8DiT3bZstGA8ML+FCASBT7olc9v0jn/YSdOP/QPRhiPrsxpDH5jU4EPt8RPJCOSlUsuuWT1Rm/0RsMGVcarZFzJjmLHPHwoz7BHPsFINgeH1pG1a0O+MEdiDWMUMOQW3XkPWxRK8EHwFux7+LENGYNvZ9MiGFfJ2BIyfNlcae8j8mBjbOQsdpGnkHXOsXAhMzaTH0y3OYX3voOBdMZW2BYbEUckcS1+n4LYpI0BifmpiUz4tM0oNmwzlX/zD7EcJrDHFPq+tzkhljgp8Z5d45//OlXxOxcbOR4BlVQ/9rGPHZJqf0jI7120E09cTv6dwNj4sOkCK+R0MIdv2QzgE+7bQHLSAQPljlPi2KGIfXkUy7pbTDsFmV+hSVZeyd1JuI0Tm/k9nuR19GXjKDi3FC4TiWAJWPVHeIK9xBcQOtJnjAlcY0T4xvH4DQOnPLtUHEzfU4C+OczneWnJezunz5ILTio5bwHRfQbFSSUWST7WkXWTmZ3JnBr4DvCQqQKhN465JQYSHQksYBKsAUP4BmAAVFEEbLw3tl16AAbwfZdnYvV13/fkIMEA/kDq9re//fC8qnHo1zr1xZt+AkuSePqWYHEqZDwASS7VCbNO4wENiZT3ZGzcNhnwnR0jxZoxkTEkDoIVnW1DxskpX6/YyX1rTXFWiewVNJJ+hQ8eTkH4slb6khyShV1Ia9jEA7uiG/ZC1na7BHfj8Vtr3Yfo9UIpXOhPMKc/cmvtDGmTHVx+1iP97Gh6XIlsWzvZhviZRE1xYE5YwC7pib5DuQcfnRjxeTYAI2BRbKCuxXt64f+SPuswjpM2fs3XzKGNgsN3fNlnrzYPfGeTQ5C8//3vPxQwCnpyMSb7N27GkdxIhMxtrhQpxkPawjpza1+J/NiptrDQGmEtXLBmfFtnjQfmhWf4UeSQASyCEbv6pc0ViZt1oNY+zG2u7DJbR9sG4U0Cjn8899rMhfA6x8KFzMibTiR27B+vIffp2Ym/nIFdScjZgN9kwLWzSIbZvoSd/+B9V1vcldibeJjHQWE+fsRouYLcSpHBz7Th/3zcpoTYrQ/fVfg4ifE7OPfkAJ5wgHl+9wIPfDYu2fNTJzseOVOMwDGfPUrqVY5iY9g9haa8g+5gQnKqU/oJvbAPm+5wZBcM35fkUtYv3ohLcEXBS14wv/IUe6cfm7X6Rl7aLoXLBALckilgHxKgOIrdQzt+EkBGSeibDBNgMSJASqEcH1idAvzNYV4GxGB68wFGyQCe2vvWRxaS9ATrdWQ+AdI6M1/GJD8gsm5nUn+yBTycD2CTX2RtTMmFQoWRa29M/LtvDb4HSOTsvZ1EOpOE+Esil1122erGN77xkLj48bDfhdgNAEoSX7qSBHo1niQOkAElc3Aox9b4wBee8CFp08bjNgpc7QJcVQ4h8vSbE2BrXKSN9dKJJGIbkvwIgikUW3IfKBjP/ZYPa7Q7ncT3mBRZmVMiyZ8EQydOSZZb/iq5T6Z0S+9OEeiJf+LdPd+z130IBlwohQt5SoIFbMG9p0MytSa7h+sCAd9kS4I2WU4h89p9lJzbBTUn3uz+ScJa/HDfRad83k4lW3Aayl5T8LuMHbuw62889s5mJBeSFuvTzquxJDTWwT7Ixrh5fJdu/alNbZyMK9xhjTZ0ro1C2B8DYGvhk0wkNpEhXmABHwvFDvGhEDceP5c0+dwG70rsGH7YJNFfOzqFUxKSnm7HKDzj0U6weXtER7CML5m/hxHGIWvy0ba9PzdiM3MtXFx0y4YkZmReyX2+okC0YcqXnAaIKfSY07dT6sB87A+ujv0l0mOTOcVhcYpfkwk/5y98El/kpaiBCeQuF1F0sFn9+bt2Xu9617sOucBtbnOb1QMe8IDVPe5xj8Fm3BMP+TQMgC3GM59x5BLivPFtYshTnCzoR0fw08Ymn4m+T0V8lK252NAp5zYXO4GnCmyyEm8U5zal5FdtTCEjOZrihoz5bYi9LYXLBGLgbeFCwIRKmIKfx5VUkYLKpgBPoYKBcSlPEANKAu+xAcC8nI9ze98jOxGCeDWaSvpzaAF8E5nDXAqPlqxXwiGRWEcJ/AKt42Hg4BhWUYFHr3Y5FCSc0yNSjJ+OJF52jf1PFM+rKkokQyp6j3rZ1Ve8+Oshj3vc4wbA4WCSK8UCEPJ33f2QD+jRtzk5j6NfCbbxgZjvrRc/+WGvXRcFDP7xg8bAgxwAbmsDPisyAOOYzirZ4QGWNXmqZH3kPgYA5pPsW8OxCbCyA7vbSZB9Z52bgJ5dsEFJZ4KDZKD2JfdXtcLF2iU/EmMXmfSIvdrAYFdk2SPfwwOnEmx/TBctaWde/sVP2KLv8CZwkWPv2eaQeemR/vgw/M3jnHyX3vFvPLggOeH/5oQFHnk1hossJDR2+OAAnxYUtTWGV3hnXPzCB0USO4QnkhYFi5NZmMC/rMfY+LO7Cw+NJYkiU/aCN6+KE/N53Ctxgiz1dRlnTK5kBM+yVu3wxF+cKkau21Dsgj74ijF7hKf85ajqS5WsyxiSuDGcmRNZ65wLFzbA3jxixN7IvJLP7DwbDWyMnWorQV5X/B6DwrN4ySedJmxrh4ci83tEXOHgCQI/mrf5eO9733t1pzvdafXQhz50dfnllw9xzMYHPyVjJ1Xinz5kyD/5Jd9nz09/+tOHnE4/Gxziso1XuEV3+rMnsV1O6HQFNnis7OY3v/kwvx/0w0xz2FTl7/HzU8oJfoiNMMP8pyQ4Qmbsk82SlThsU9krm21loU9Ox4KhITJcCpcJRIBt4YLivO5LvihIUGDwkldKWGeklKKNJJujCIqcx44KxziGoRlTIgDE7Sr2EmHBzWkEo+vdZ0AA1s6d+2NrNJfiwKNpkveWzAMIgPA2juU+sDcvWani7USag/wUNZJL9xSQAMf3kiQJhR1E35tLsFVwSGaAF5DBh6KIY5hHO7qxq2XXWELkWWOFWwBTQQHUOCM+JD92CwAj0EgSvo7cNxfQNH7b3uc81kIn6+REF/lhILtsic056QIMkrOWojMA0bu/L8VnyMVuPNBni3ZlUogGrLTNVUkbPCpeJVj0IPjQWRu8+ZfxD/GoGN2wF/NsstWzpMiYb8AssuqRNjCA3Slme2syFrzwqBK77+mjR8aiT75BN8FCl4DFRyS+vt9EdEr+7FFBYk1wg88pdBUx2TXlJ7DLhgIfVczABPZuI0GCYs3RoXHhgKKCr7NJfua7YJs2EjR/vAMOeExL8mI31dzwWkLJHuFieLIppZ25U1iRpfHMXWWZ10raWBO7q7rBlznhmXl6fXtkDHp2IpREqiWyJmd/PUky6nPlM0RWfkflZK/Fq7kR/sQau7z7bmAcgiTR+EjRmYsvsD/xip6qXN2nCzbmVFqMEtPYFEyS7MVeT0XmErfgr9NJPiLm2iRweX/MSx7AT/mjjQcbjP4yGLtUxNio9O8Q5B/+NYUNA3IjM4+CwQMYJEfIhggsVAzaiDS+DQKnOvQFA41LB8axuWkOc/tfL34/AyP8mN8jZub1h2HgD0wwXi5YeszLHOaUs1gjzDmlbSC2wS5snpFZTsToCza2uAGP4IqYoV1LsBN+0/mc6YIoXKoxaMcpBFVtGc+2oC4ZE1A5iERLQgdsD21sxjMXY3a8KYFsCYBKyBk942vJOjkzpwa+bbIYMjajtSaJQrsWfY0BDBQdgmkC5TpyX/KDR0UKsJGoSw4EXIWjxIgOgIYALHmzeyKJwr+A7/RFYgGEjCMI+0z+Eh+6wxNn8Vkf8wA5ibAApEA5f/78AIrm8x0nJeNtybgScLs2wKZnM+Rnh4dd4H+M2IyAJmntJYXGsUZg7X1L+FZEOAHs3d+X6Ne4Ek2/PTEPnfSSqJZiu3wM+PEzwWZd8uveIQqX2LLdOIUhe8LPJls9S2L37JNNjfHJB+lbcjymb9+ze7vE266ZDduEkYS17dkvf+H75LiN7kPG0t/pp00i61MYwGenojYe+CH/hwfsBO9szKYFDJBgxWb4N38WSI2Lb35GJlUedO57eGjzwHu4IxkmFwWGv0hmvWzN9/hi34JwtdHIcJ0c+QlcsFuK7xZj8WFOSda6cULaWBtswN9YHzIgS3gUbG959R722tCZgnOnpPDssqkoBtEPuzhrojd2SIeVT7KGM2Iru4HlyL28invwDH5qJ96Id9loau3k2MSuJZJOMvHA99i9i38c+zKf+OyxLkUKDPAEC9+/+uqrhxh/l7vc5dp/Qqs9f1fcaCs/UMA4RSVzOGjDJRshZKuPk5OHP/zhwxrJH+7wcYXJ3e52t8G23NfOb2acDitm3vzN33zQdXjVz+X9MS88Kqj8sQH6WRcjj0FslUzhoXhtY0ieZeNJ3LaRHbsOwRubRuJsLy+1EeV3SPB7znTBFS5AA5ALxhJHxiMhlvQaYx0ZR2ABVipmTkTpHtMQQA8JSBIFxsyBerux5hLQObfkpyX3Vb/uq5DHEg8gCxQE4F4y7r7Ewo/gFEmOVCU0vu+1r2ROxs2YJfN2WR/0oAet7n73uw9/plCyYveVPOlEWzu0kgwFil0rn4E9edOTsTiUpARoaQfY8qgLnuhCe+N4zvye97zn6j73uc8AkJySvOgaf61jVtLGmIDOo3QA0V8qUcD1QIbMHXWnwOuRfk5TJITW0urFGHQBcPHZ3scvvdopGtPZrmRs6wU61iuRkNAJ3vheJyv3yB3P9OKSaNIV3a7ra+xDFS4KPo8ZAVYJpeAcG9uk77MgMmOnbDhJUku+E5zZfw8LEDuAFwJOuxPcI2Oyb49X8ZV2XnbIxyUQ7GFq8ms8fdgTbOVDEgN++CZv8ibDjicfNjd8wguevdpU0JYPGYcNSmgUB3SIN2u1i82+4jMSRMVSxjI3e/SdJN/O681udrPhP+rbgWaf7se+jTOF6E4MsMHA9loZWr+ixVzm2DS+tVkz3yazdryQpA2uO9GJX+ZC5jG3ZGxsc+QsCX/0w77oX8ymS3JU1MKdsya4IalVAFf88op/hZZ46FShlT950w38YWMSdK+eFmAL7PKUxPZgh5jCtvDCp/nFqS4y8Ii1ExfFnELG45VipViDL/fFZ5s4+IMZCm9/Clmx4js4IP7RCV/xFIY+5Kq/Ndooc9JqncZQsPEXiTlcUSg4zYE/Cij/J0buqH3l2XzHklN4k+M4AVKEwa1TERtmh/Im+nDqIicRO5xYO1Wsdh+iGzkg3K0xJj4tjp07d27AnbbvnGg2hYvAAfg4CANIgKuAUsl3BC0pBigMmyEJRr7Xdx0ZnwNJQlXN2emjbPf2JfwJ1ACHofcIQNqdUxSMrVHhwynGeOJAZMaI2zF8VpFLXgQUa/RoFTCxEy/oWy8+ehc5MnBJl/Z2V251q1utbnKTmwy/VeEgArt1kruxrBUYOfJ12uR7YwlwkmEBwVokO4DIrqvTFIGe/NmBJMZOsvuOpV/3dV93demllw7HxOQF2PClLVsxb67w7r2iiJMax8kc0JRk2lHQrydzAU0QVpT0yJwKNrvD1tGOQWZsStLRI+0ldoIiPvcl45nTevkC+TgudrI4VnyF9OUn+LAudibIAEK+Ef8bo8ytqLABQN/7EB7yqJj3knxA6oTP0bYCxvf47cn+LMj62QzbYrdjuMPe+ZBEqefL1kLmdqsVz/S5jshA8s+v9RsjAY1u+Aub12+TXlHsQh9+yefowe7mTW9608Ev+Xk2jLQlCxcdaSuI+p6PK0ST8JmbzcAIRY3vPYaj8M3jv8YxrstJjnUqmjzffsUVVwybKOxB321twj3jkq1x9bcZYpeypxPf0W19rG0dGZe98sEeHiPfwUyJNb59rhcyDzsRO+DlpnlPQewhOsE3PLcRRM82p9g3OxSLFINnTYoSNqOwZ2uRbcgaYHh9hLq2oUvroifxT8wUL2GTjU7tx3z9UIQfc0g25QHs1Gm0/4XkyQExWFJ/iuvKK68cfP+2t73ttZuXd77znYffnPiti9+c+M5vXhQ3/rmkgsJncVsBwWf5UYjPyl/kEfzfevxZZP+U0pw++weU/lmlH/S7Hvawh61ucYtbDPP5rY3vnMZ438rDBoerfneoy1zWLpeAjTaP2dEpfJVdmAd2wi9YwT7YCXuVR7tfcd57uZg42sMy9sy+bUL4B6H00vrMnGhWhQvAY7Sqb4lXgH2M3CNwDiCA5niQk0jiNlHAWIInaEpyHc8LFodQGkMxrkSwtxZ8q2zxDSjb+z4DKw6vrc9tG4lOxq+kHdkAXSDOuYxjlwyQW6fkWUKRnQm8KKLsogBozii5UNVrr/AALI5rJUOKC4WAwoADedUPcJGlgoZ8rU3hoD/nwJeLnD3CJglyz1yCDSCzayfA49ERNbAwV3ZA7fKYU/KEX4/lKRisg9Ppz0nt3JCNJMWpgMLKs/KcHF8tSTgEX+1a50YCNBsbc2x9yEFx0iPrVtS4T6f7kvFyIma9CcTG3hRY8S/ZYBvkKFHiN+RiHev66yuJoVf2xSbYSU8m25Lx8K8IsC5rwAt7Zcf0xjbs0MEH3581WS852RxwktmzGRR8k9jChZaMY83smH2vK0YQzOJr6xJqY0qinVhKHpy8sjsFRQ9vQtZDtpJTJ2BwgE378btdUT5pXNjhtxz8xbPoNgqsU3/3+BtcZhftyYH3fJPd6edEBY5ZC/nYcGHT7kuQyQWWStzM7TP/t+NJpjZk2P2YzVqrNSuCyIC+4JTdResyb08evs9GRQqvMYINZDVWbOCNjcN79uy9OeuFrIM8FLBpc5ZkfnFDgmRjAfbyQxhK38Ea8qXzORQubERcgBnk2MoQv4p5cUyMbf2BL/ITRSs7hdnWbiz+yf/GbO1QZHw4kBzCqQL/c9rBZslfEXWKi149muWJBTJg54pCvPFRT0Z4LCy/cYFL+omTNobhBFlXH7I+dpXTVDoTi8Rwa3NZp9jvFN48Yq+NObkMfdiw9meWFZlitjkjl/CQsQ514Y//Wr/fwNGN3MSJB9w8tl2wTRjBDjwiJtexOYQ3J1BiOB7Yc2wapsJTfPZw8j9fhjPuw3JjGKv1mTnRrAoXwhcUBUuPDXh15JVgOCZI31MmMHHqIjA4XSB8juLeOtKfYh0re1SDATJMoLFP4ADoeAB0OWmo5LMdPWtVPfeCHUfgHAKz8cKLvnm8ope8BBQEEUBj91qgN4+1WpeEF2922T0/aleJ3IAFvsjT/HZB7YgADgWJPoDL2O6ZxyUBAVx0KNjri098p3CJHPK9ZBsYcUBJjmDiwi+HlGDgT+FEDpJWc9KNi31ImgCoJAYAeoxN4qRwsk5BiV1IzH0HTK2VA7eEpxST7lfdk6mEShDDQ0t0QCeOjnuFs/vWSI6SHDLYhfCBT2PQi8QQ+EssNvkKcp9fAHo6FJwVfb4zrvtjY/jeHOQgkJKpYkMSLVHY1VcQXaVwMU693GMXeGbzbMlpAj9lK9us+5iEL/Y5VhDQvUSIvSewtKQf+2Cb/K/XJkTXkkc+0Jsv+ECWfjTLRuzEKTK9pzvYxG/xTL5kaGOBTQlw2tKpAoa9wxGyh0WCJn/3PZ+308eOzMGW4Ay/lfDr067Ze3MKknAeZkgQ6ZPvwmHj8VX6hVWSUH6cZNOY1sD2JT2wy33rZsvGd5GRsa0ZdvFtxY5Cjmz0XZdwSH6cluVErSdv35mbHIzVI76VeMAW2IR+7UX3EkVy9/nUZE68kmHsgWz5HHuA5ewm/If0mUvhQrf8iJ/AJn6F30p0yXbhOZ3V+9bFL8Qbm4NirAKabbM38cr9YxJ5SuRhLD9g/3Amv6tgx3TCfo992VB0yuF3LPl9ifmdrJCLUw+/f5H4ao9nr3zfpocNaQl/jbnBAL7odyz0oJ8+Lv3Ff3+R1F8us/ni8T+blwoopx0egXcaBBPIQv9c4b1+d4iLj3tKxKarvASvfFVxBStg7zH9Fk7LlWGk+bKRSn7sVH5n/lzkDG/gOd9teWP3cJyf2Iz2GDA7O+Ya9qVZFS4CFkMlaEWExIQjCGyS6HUGESVpI8Hx2AuFSn4FPmNuQ/iQuDJQjskga4CZQtozIkFPwGqBE7kv2DNEc7cEvIArB6yJdJJxCXsveSEHxgo03De2BJ88yTLrMaZkChBxfkWLsfUnMzxLEgAXYwbi+HWCYax62mM8QATcBbgUKdaQwkXw951LEeGUxXeRsTnNb0dVYgbsAKO1cFLtfRf+vAqw+OB4EipFle+zRutVSEmKydtl10by3ZL2xrOjoygyTsja2CK79L4l40r4zOV9S+REzxKonq43UdZjbjIARJIgSRmeycP9dWQ9Ek72pL9kVAFUKfPUsbw3vnkkJmzaWiQE+OG3bHHT/OvIOHbO2OG6cdiK5E5bNkXv/Dw73dvI4dDEx9gdO2fvYwSTYFMPCxB/VFiw57HEyNokkuyMPnpr1dcOGp0IZop5/mZ8QU5/vEg0JQLsSSCU7Hk1v2IhsoSp8JBvs23Bm+2E4uMw3LiSLMFdQgIvejyyRTr0CKp2cIhNau9ELZhg7OA5flKE8SH3fcceJEB4F8BtPsGt4IK+1gzPycz41hB80X9MJ3RrTBiOl9a+vCdvfJHJmG+TPX3Aoaytks9kAoPx29v8OAaFD6/Whje6J0OFr8SMnuCG+2NENnMpXNiqpJYN4F3cUpy0MleAaSthgz/tfYUbrBP7JKkKIsWMVz7Rtj8UGZefkT+bUizxZzboZMOpl4KSTvDYXrBQXD3EZTzxXMxUMHiUyCOC/NuppUTXb07kCPzJ3OEB5ogR/Beuk3eILUmYPWIsrvJB8+nj8p6/Gt+JjcfjFEeKNnzADXwopOhEjmDOXOG/frfvxS/kY4oWG8E2WsRgpz++hzX8fJ2f7EpswmVTyDzWy0fZt1jMRtq8RHt+yb7dr/IPwSv5AOwWU86dOzcUzMey7UPQLAuXCJsBKGAAoSRWYFy3M4b0dZ8CGT+H4VASYQnsJoPSXxCSzHFCgZxSOYDvayK7DRlPkAWc1tQSfgQqc/QClfnMbUfSa4yJbCTJHKYXcH0naCvAzGEcsgMwQMU4LnLncE4ByFcw917SzsgBo+NYyQ6ZCrz05J7iRVuFnvGNl3n8yC5Fn8t7SSbn99m8ArgdWQAdftzjNPg0d06K8KwP8PYDPY98SCgUH+YCHvjXFqCH8KOtAEB+dGAcNmXuHulDXwDVe4Q3gUqSYw09O8KPHWRgbB0t0a/ALghm3G3JfOyPPCQSgoCCBU+xy8iwR9aNL8kdX6Jz8iCLHq+V3GdvQNqpAnsWZCSA5jX/KQsXZF7trUkSal15bMkuKxu05n34mUJkQK8SCnIZIz7jh6zk3rMB37EvhTN7avl3XyKQ3130MAXpyycUJXRH54KX/vrwOfqXCPFLz6NLAjxGZXx9rInd4YFv0TG9kzvMghXW4X7s09rxL2FUhEgqBNqeHvQxlz9rquCwnpyesbk6rlPMjONVEhUZ+k4b+M7XYb3dWLvBOTGyVve1s3ZJKGz0nt3C3zFZ+p7tsy24h6dK7luvxMD8Y/5krZJkGzDtGMhaYAR+8IfXUxBe8MxnxDx4KmbyRadreKLz6GSMyGEuhQubVjw7UYMHbLEnd2tiG04xJKXt+ugAzoofEsUUOWxfnDqGjvDAj8SxPN5pPuuALz6TsfhPZ+ymd1nrLlcdg97FWP7BB2CEosHjWfe6172GJxz85gUeKNzFZ37JFsIDGcMTyTbeI2NtyDZ/yCP25fLe3BJza4fv5nX64nc2KWL4pA1ccVkszLyZu13PvhdclRPawJH3sAvvPYkCu/guXpPTHJLMb1wykduJN+Zl17BMbNGmks/sFp9yOnJtCe7K8eAkjFfwW1M71pxotoWLK0bH6Bm4ZwoFIkVFDW7rKAGa09jRA8S+6ymwJaAEtO2wMxA8JPjhbVvDBIhONMaMWUDDnySCsxo7pL05JauSkDijNSVJq6R9+AYyjDLk+zwmknE4OpkAJq+cH0AxZMm7EwxgFIC0sy1xtRa84gkfCdjmdwFWO5zG9z0+a+EiAdAPf3EQbRWqkhRO5jOgkJBar3aRhbHwQq4A3vfW7Bl470Pmct8Opv7WzHY4JpnrQy5VL9rQmQTTGt3zHf0rIL2vZFxjkoUEkWxa0kdyA4Tpe1vSDw/kbx12m8if/MJ3vSrpa/2CoISTDQu85o+d9foh32VdZC+xdNpFj63//NfL+Dh14VIJn/raqFAMsx8FtYKGnKxhqs9OJeOaw+MC5h0jWMaebQSw7x7hkx3ZRbS2SuawA8s3+Wp7H+GFLwtWdG0eyX7vUTZ6NI5n5/HlcQy6Zi9kR9cugRJ24F8f91NsxBaMmwtffJev2jyyppa0cQosGaOj3lrwKzDbkEghxxdgORniB394tVb+5RE1z9yzdbHC+sMXPrR3Es2ffVb8XnPNNcNYPT6RMdgVfGr5xDufhHnxq0rm1R++s2345LuWyBGOWps2PXkcivCIJ7Kgf7x51Fair2hKsTKFB+PNpXCR8MsVFC8SSTvS4lFP9uTORsXAVn/W775TFwULjJdoK+Tze4KeLnel2Ci7ZcPswQaEEwnYCBfwKI7yGxt6se9DUvhg2zZH8cBfYAhbEUuc/Mgl2H5+A6cdnsis8kSOEmvt6IBfi0v0I2FuyZpsXCpIvNqoUaiJ4+xUHBTDFaT4cSIKo44hC2RMa7Lhhxd+Q/cwUMwR+2xcKPzZivyhxYF9ydrogIzJkvxho7yZHlrbxTM5a2+TtuXHfX3ogD2LNzYtxAIyPYYcD0WzLlxyETijiRNJoDixSpIy15H+nEY7gU6yygE4yzZ9M7ddB7wJxByG821rmAKtefUzVksc3aMcgIoT+FwDhnUrHlTaDNE9/HNWY4fwI4Db1RDo/WfZWthYbxzLmMZxcULADoAEb+0Ap0dfPNNK1oBS21q4kI+25Ek2SaR8L3kDOuaxZnzYFUjh4nNN4vWRFABjhZN1Zj12/ySl+kmiHWd6ZjaFR9bgezIyZ0gfY3L4kLHtVlibsQGudeMBefWZfPUzBl6MA7jMF/IesJKfnWNyb3fcjWcMgU/y194fI2Nbv+ROsFSs2dWJbMLvGJlHYinJBfbex370XdcfoAma7FEi5ZXNv1wWL+8fYgNnWbgg7a0Lf9ZoN9Sa7Y6xV0DMV/bhbxORgwRDABubB398ly2NJT10i1+nLmRSiQ4ETolW9NiS9UuwBFU2oB3fFPT5Px4qmY9fGzencoKZpMA87M9Y8MsatTeHdvpVDKqEd0mwcfFRefXefTvdxsVjj9i/ZIc8tNfP3AK2zRX+yWYkzJIqRR1/tCkCR/Aa0hf/cAa2Gdt3sAOPgr/3PcKf2KMYanmFlWQ7tg48mItdwA/898j3Tnkl/sap8jo0KQLZgphLvrAUJlo/+9hlfrKdS+HCdm1eiNt8TbyBobHhSvQjLolPZNK7DwttnEoYFUTiMZtxtf60D8W3FFHiGXuRc8AMGwviZTBOoikm1Lh7KDJW5CLf8pse8ZCNONEkKxtpEl15GH/DK775I/5afmBAkmw+o51xauwNWRNfgz3yNroUp+G5TRPJOPxRjMImeoZZyVMOTcY0Nn3nD424xJkUEjZR8SgHYR9jfr4rwRDzwyEyZIcKDrK3/laGeLaBJQ+D6+19vp6ii13ZlBU38n9cjiHHQ9EFUbhE4ASpnaRYsiwQcCJGzpnTbowYmqKA4hkAYBDkBMRNSjK+pIFzchTgLHgmmVtHxpc0cixO2hJHEIQVLgKg5BYocG5j4xtwMVRBV9CRjEqEyQPAMELBXSIBtD2DaUeIE7mP4nz4TxLrnjnM53vJlM8KBTICRC5zSwS0kQgm+SZzfbTJc7/GlMwAPDLSNoWLwgPPQNeY2uGLXsynACBT3xnbWhUDHNORsD7kY/2CbZIPPHh8BN/RpVcOKQHEV8i6JZiOne2QK6AkDHglR/3IXJATiM2BP7uR5rE+YwADiR2+JduAy86tNuEBeU+XdAHYjL2OyJ+dCBLWab0AEh913B7hjb2xa7sseAOieIq+x4gc8UZH+gBkoGYsOsOXNrlC+hyqcAHI/j8Ae6TL2PcUwoN+5MXOYYW1xGfshrlPlvvw25Lx+CdZCObk1ZL5+INkat2uFr+wu8oGMg45GFfwppNeX234TE6c6CY2oYjgpz0M4ifsm52aW4HMVvms4p5OBDU2j39rxb+1kidq+dEGv2SvbZUHW+RvgiU9jelYYFaA8ktr8cou8OIv2ZEjvfKP2Ligm5PFlqxNAgZLyATPxrUGhQ/b75F25lVowQJ9fGdNsFkCZ/6eTsjBCTL8H/Nh38EoyfGYbnclsjU+veFdcQfvxE++Qcb4Irup8xqXTsQVmMFOjH3WBNP9NSr+I97jSa5g0y72W4ktiO2wtsaKELwI1msnTktW+Sg7Zg+HIDoQd/gVXXmlL3xJ9GOzdIonWMCWvdcXH7l83vUyDzko9sRkFz/Gm0SYPfMHhZN7ChsxUFzn03gnZ2OFF5/hgJjqdES85qPwqM7rUrCQMdnCHzIne3EbBknIzQej6MD88hJygn/80lXlsc+FR/Yjv0qOQw++F9cVAPIjOQksIhfFbs/Xp5J5yASusQfzwBsFHLtm32TXEj7ZB5nrX8mY+pOx+3I3Oa0c3KN/NoQOwfux6IIqXOpnAYhwGY1AFPBI23XEoDlefvzPIGuyvI4YrKDO8TggR0miGmNuyXfWJ0kGdpX0A0jGUbwIIpwRSBhfws4orVfSLjgwOGMlUEs2ALX2QI4sFC1Axm45wAhv2puP3Ixpfhcn82iJxEsbTqJIMbZ5JJKSAX99zC6HvhlTf20AiT76C2YcWPHinuAuCQJoxhQwJVLk4rI2a66g4FVfawWWnIvTamNNijNBXjtzcWYOqq8LqOAH2OEH+V4bSQQZkFt2kdgeYDKnNdEz+XnEC7/sM2DM9swnaXLCBTStwXfGqkDBLumdfNYlaZGlZM8uG96SLPp+E+mPP8k/MAdobAUvkamrJd+5b11AjD8pQtgR3aRfe4Wsj+z2LVzMRY6SH8AvYWVr9G39rm3HD4/WhT8BUlAV/OgU4Eu2YIE2kc8+pD/5C+TZvW/H9Nk67Rayd7z11oUvPsieUmjQj+QJVvgufbzm0oav0mHWlnuwBEYI+i3hVVEjAMdX9PVeImJXXhLDruCH7/kx/rI715Oh7+GsTaKsA3kPb/hRtc+WrDX+yy9j2wKuS1JDnulPnvyPL9b5QtbuUQh60j79jM/f4Cu59XjRh32yHe+tzSs9Koirz6OM73tj20jojYusgVxhHGzdlzI3Hq0HpsBChV5OfmAxe9G2XpsoYxvXGHBUggTfPZ7s81kTedvw8qizTTOxy5p9L0Zad0sS8SRy1teShNh9/eEIX7TpCNdh9L5ErnjAqzhnHpuQcAu+wqtKeJQv2JgTJ8QhuUMun3e9FA5imz+awW4V9fBBjsKGFEsuvunxbHGTrOEBvBD3xffKjzHJTCz3o36bK7DKPesNz/wX9sMHWO0RTTbF12GJcclGG1ggVsoFtOHbeKnztmubeuEbLphLLtCS2G7N9AMbYCg+ve/Z2VTiw2zPeuV0sI3c5Tz8Tu7a+q3P2rBT+NPe950clszYjiLSRhfb8+8p5ERtnznRLAuXbYgyGQWlMSaBWgCxc9YGkB5xesFKYsfwBWbFjO+MvY7c1y4FjB+SA0UBR+DsEV4lNAzF3C7j2OETSARwDidhE3AZKgD1SIOAox2H0JZTAMvMz+jMDzzJ0XeCiPv6MnbzZ83ue9SKIzJ+/YzPaDkqmWojERWcjKmNpMGpkOJFUozHrEU7iYzEyDqtwVyeRVVcKFysTQAVSLQzpkQ+IKS/sRAd4p/zS2T9CBDISWLxQ870JiEyt7ElD/q4gA3Z+cdUHBIvHDFJLJCVyNkhSYKun3V5fpfM2BLwphegqb3vzAtUrYms9GN7QFnhZ9fUukLmTMKD1x4g0I8+xgCCgpVxyYFM1oGI+/jHcwoyIBpbXtfXPckXHbEpAYe8rAvf0cc60m7fwkU/a5DQA2N6Ziv06yRA0ixAsCuymjKPthUv+DxbVtzDDb//yI7zvkRegjB7ZM89Ps0j2ffoBz/jBy1m6ed79slfjItHSQobrHbknvXBHzbgR+n82PfapJ21Sz7YVmsX2kqaPDfNr93TRh/JAJ9ROEj8YYpkQgDlG5KrbILUMZHPTr34S5UHmcOfbBK05Dv6t7HEr8nTiSYbg9P64Yn+8MQGyQSOsRUy87klgZyPt/fYnnlskgja7I9O6nrCEx8jK2Pggb+zy7oO741pzcFbemvlE6JbMlUIWMs+ZA6882uxUYIoiYezxqdf9/FfbSTXGLlnXWwip0x8iDzJxVp9R0dnTeKfWIkf8QiPNp9guOIKtrZE7vwnxSPZVHJfIgz/YSS/YHvmkWxHlruQvvSRU1FY56928RN+JnFuMUIfGOHRHsUD/Gb7uRRpu176k5MfwIuHeFG8OOUUC/m8S87gO1jmt2X+ApjCxI/nxXhtMqb3Try08c8sjaM/ebvcNy/891s7RZJTM34hBuCBv4s1xvZ4fb4T58VNTzw4Fc2Y+154gl34URzx+5b4ufhObx5rw5ucQo6xbT66jvicOAgD4bpcg93JO83jfrU7dpTTFFjZYp32/AC25rePcMLGie/gGcze1ZZPQbMrXBjyVJIIMCi7s5xBESCwMZgWfFqiVEHHzgkgUhQA9+yorSNj45txAg0OxRCAXoJCyGdJrUTEfO4BQgkMQ5dkmtfjS5nbJaAJBIoTwCtQ+G2GxItDAQJGaqxc+HB0ahy7IhwqPAnswBzQAWDvOQCD5aj4E4QEOzLRz2mMMex+CFiCtfaKDesVyPBPDwBXQpIfztoxpRf9jC9Blpwbj34UTgBPINHfJWmQeOBBoqGPcTmYpAk/5EKeAE0QUXgKIPpIPsgUjwotBQx7IHM8sTG7DL6T5AsK5iVv68ArsCZb8/uRvrk5NjBypJpkUx/gYExgQP54MkaIjegHbFp7pBPySxFFp+Svj7HXgUf4ZTdkDLjZU5LIdeS+NZO7hMZ6JW54sYYpoBWbO0ThojgnA5/Jhg2zWbtHApnAxjYkSdauzdQ56YDu6N/a6UbwgxvkYdypMqikaCQP4N/TIbkrbvz1FgmtQKso14/uYovWLiDSLX7ph33j36Ude6Z/fiShhh/+4SS/06bObQwbHAIfmepf7ZEs4ZjCxvf0AQ8V7+Yxlj74yiNxEgWJi51fstOn6sQrm+KfeTyVbAVieE/WIXPqi094bqfXXxJSiPF7uFPH11dRgL/YAtyBae3azGlMWEBerX8Yl1/7T93mwy9fIqeqE7gDZ8gInzAULrsX3q2XD8MVWOQvtkmsbERVfVSif3aPj7E268ha8UhP1ikBh8NsC9bFro296aoUneDLGIo6fFoXX4WD7lk/GcNcxexZE73YKBIbYRvMgOfsA46LP62Pe+877fiJdVciC+tVSJCpGAw3xFhxQh7Q2tW2pB+7NI65JZ18XxyXsJq3jo1X/MEEuYFTEGsTO9moix3ueukvztqk5AdiN59l6+yKLbisHS5LoNkFfuQJ7FmBzB75Ty760E48hR/GqLx6D5fFEhhmfIULe4b9EneyIGufYYR2+NMWb7CpzrnrhR9rkNT764hsKieyleiCb8mj4DC7wxderZNOdiX2CPdsWMkfrNXmnktxTl7sstoxfCY/2Ej+FQe1g1F4hO18WA5EL3Jn/i0ewvPab250URQuiJA5dooIFT7n4QSbiDL1p3BJAvCVKAGSahA9Sl+Ga5dfwsxoJK4JQi684SWPFgkyLsHSjpA25peYSGoYV9bEOCVYdlbucIc7rG55y1sOOxGMGI/G0Z9DSYAFeYaor4QfCEr+jclYOTwnNKY5GXd26yUhQN1uCz4AlR0QgZoTGTN8ASsgb03648Mc5E4OjF+wzm9d7F7gwzyKIvcUP4KK8QAFx5dYASAyNGbmwydnNCe+ACqH5miOnvUxDx70o0cBBkAKAviXUDgFAkzaKDoEn5weIe3IFR9XXXXV6ta3vvXwD7AUQXbc6COy8GotZIx/YwIDMkR0AnDseAZkEH3hUzInECpaJM7Gc28TaQOgBU/6EVTo0nz42kTAlGzwbZ2SZv22mbslPB+jcCGrXHhzn4+xJzZAp2zIuqdSHZ/vkj17kfTyEXZIn7usB58Cg+CSZLESG5H82qXkE3yHz0o+2AJfkzAbRzJCP/xZgmC9ZCF4s0V+wH75NJ7Zgu/serK3qlNrZfcKXP5v04ENhtgOW+a7eBQwncBILjKOSx8BTnDmv2zbhQf+aSNB/5D2sAQuszsYLUGztpoE0CPMYNPsSdC3ZjqBx3ylytN6fIdf/soO+ZHED7/RL16M6x55k0GrE7K2FgULPJMEsUNzkyf+yROPdsVhL/nAEusxj1eYSoewiN7I02YT/2Rf7bwI39Zgjiq3bcmYbFWSi1fr5NcSrqq3zN3jYYzgIt8Qm/FPJtZED8Eaazcm3rWZS+GCDzqTTNMFbOErYoBiQPwg+0rWISaIK+ze2iq5z8/kKRJ7/sZW2EMeodyFyC5xw/jsHwawC/lCqzOyZ3+KFjGefuAWO3Avtr/PhSexXC7FrmFuNl/ZADklfvN7MicDNsJPqg9mPPityMCzTQQ68H1tpx9sghdyAONan4seYR+bJCPjwU15BQzFJ7yp4+1zWYf1siFzwi32g8dKZA7P5G8wmn3JReCHYmeKz1Xif8ayTphLB+xDcWiDovVvlxyQfdKVNVRi72RLb/jlr/glO/I1ptixFC5bksBBcLsWLiGKlOBSrkoSoADwAO06iqEKvAINI3UELNhyrnWkL4cRvARZiQkgkXD4Hl9eJYt4EjzxCXgAACIDRiQRB2KAV1XNcF2C+O1vf/vVG7/xGw9FjCAM6Di2YGle8lN84dmciiVjCOYMlcHjiWEKbhJWl1MQ4+GN3BQuHFZRZU3kUg056wWunFo/PEjSAYukRn+yxJOAGqezWwf4fAaMggsgs15j4BUAc7I6p/XQI8c0HpBwrKkPxzMP4DI/PqzPWgUYMgA8kl3rpM/oRIKEX/IzFllrbzwnUze72c1Wd77znYeE2ZjumQ8PQElwIT86xbOAgjeBD/AAckmmuQALWWqjaM1jBtrm/hi5h286FSiAODsF2mwnwDU2RvUN60zB2ep2KlnzMQqXdi1pwzYEhey20QcZAt3IYQrFlo3LL8jUuPyfXvmH++S3DcEZPi1oJbiEvGcTORnWxpxshx7NRy/mJk+6UhCwO1ihLZx0n5+yWQGKXuGcZAC/7IovVHkk2bEz+IQnPGGwc+tkA3jiW3ydPOEQ3+cDvmcjxoVfwRv3teNvbFLizYfxZn4+blx9rU8yqL3EX3+nZviGVQKzPmzaGGRjXH4hIfcdXrXR1j06gT8SId+Rg6BrXOsOT/iBNfkrgsHGkHHYE7wyDn+GGXiGNR4zMi+stWaJqoCvyKEXuAoL8E53bFEhZd30qQ07UMywi5os04nii0zJfhsbY6/GML7xFHo1BsBcct0Us3pkbPomV9hmTcblE2yPjwQ745+xL9/PqXCBr2Qk0aQDGAz7bSiIO9qwlZbYA725TxYtkS2bgsH8U4ItZimK2OumPKMluiRvySRbZ894Nb735mvJd/jj43SSxD2nmlMxsEdswTzwR4znA3ILfq1gw1vmdKrle99JsKuNI/bCB2ALf+FbihIxtdUB3vkvW+ZPYjnZwg0+BIP4MZnTAzv1Hdsji218aBsiR/K1RnKQp+SUAn+V8KyNDSv+mBiiSMCXPKSVySYif/bAZmEbWcEzc+CBbcb/XNqT6xiPCPbZOGZfOc0nV3HD+ugChmbTY6500RUuiBKBh+AmyAhIHIpSfO/+OnKfUdgN4Kz6A+2A9SYyh4DHgCWwHIwT+N44QNR9wdnYgruxBVpG+pZv+ZaDkQqSHAfI2nUBDp459UibI1zOygC9AmPjOnmQ4DB0c3ImCR4Ht/PHIIE5edt5BUjmEpTx454+nM4z5XZ/JSx4x6OLg+SzV0APOO22ADS8CPzGsBvCkQCNQO8zmUp0OaBgzXnwgBefObg5Ml+9rCmPYfjdi+RNew6ZJM88xjcvZ7RWduA7SRsZ4NlY9KEI9EyuIA0s8UGu9Ob3NY6JJT3GBST0ITmRrFivH7MJIoKPuehT0NQG6DrqBij4dJ9dKJbMRU5kaL3rbMs9/fGODzLEey+wtqSvOdg/O+Bj1odP8+5L+DpF4YLyHb7pUCIu2SR/MpU0WWdsqO0/RhnXpS9d8jk2zJ8kb+baVldsNQHfeCE8CxjGleSwIXZg7cbVj174IvsVZBQZ97jHPYYi2qM/eGHr+muP9DWWgMmHJQeKIpgSXs0hCad/j5SxAT4r8ZI0wRpz+06CYd2SEPPghXzhBPsni7qukLkFa7ZvHpjlPf+M3RsDRpiLLGAD3INjwa2WzOUevzeu0yprgU3ihgQH1kr2fOeV7KID/shXbchI7OkxxDeNB+/pnTwUWJGttr7n+/QmsHv86653vevwmKt5yEd/eBP74JuKQ9gjocITPsko5D19krl1j9lVbMqlHfnH5mEg3qo9TCFj45mMjWGN5AEPJUqKsCqvMcLX3AoX/kYPbEWcEXdtdvEPNmd9rcyiOzjEz1oiCzGIfzhtoD8x1gWX6XRMjz3SHn6yAQUT/2B/4jW7a/mjK3w5CeFD+U6stm7j+bwvWQP5wUHjKjTIziaAH+17GoHvim9yFdgNg+IDIeOwDTjHtthZxma75KZ9ZObVZ+OIdeaHa051bXo+9KEPHeIxf5bzwBu5Sp7cyDi7kv4uOQ17ZjP5DgbhSRHWzuOzDVh9+KP8ih5hnVxHXrUtWT/shhf06hWW0DsbSyEUvlzsUn4gp+zpwGd6gu15EodOYT8+jceelsJlAgmqhypcUJRJGYKGgGfHTxAVqDYZuHucTVs7CoKTpI8xcbhqFD0yvnYScaAkYWdsAg6DEaQZiMRAgSWoAQWBHPC7GDoQkohxcLsOAIQRay+4AFCyY7SMjzMDCGNxFg7G2TyLbufAe44AtD1u5jl7u42AOo5gPACaJAk4eSSC/BSAKnROjW+GTkb6uwCtgKeCtzb8ABUJg6LBo2iSCYWKRCl/BURiY61kZjzjGt88+NUHaAFJugCAHt1K4LH+9MWf3w74EaA1K0A4qCBPLnTgAgC+M4bn2iU85iV3wdpvVYCqAAGU/RhQ8gUUtGFXZCzRAdhOyuxe40EyrR9d0Zm1KDTCu8/ktSkhYGfsEG8CBrnSIVnR1zpwoUvj45fd0wFZ4t2YhwImfJyqcKnkHvmYnzzoTIBjq3bN+YbvN8m4JWOSDRnREflJKtiqoCVQshP3xwhv9C35sK6shZ0L+l7ZK99mN3DFnFkTHfElSbck+cY3vvGQKAiCMIWNZV368XO+CQOMa+18xmdjInPmdIpfs03zGktwhr02RNioDRI/RuWnvqNb/JBnbCfjVvJd9UWYhy++wx8U+cYjR+Pjgy9sM657xo1s+K+NBbuwxnzIQx4yzOU3aYK8uekuRROckczig82EBHF98EBXfJfs+DddxMaMRfYw4YEPfOCgE7/BUQDix3q1xav2kmO+ns0YiRWs4MuhxAMxprduZEx8sTlYTT98mR7pD9/GH+u/jsjcOtmqhAe+pqjMuFnTJtJ2boULubEbulPIi0lyAXzCffaiWLPGSmwYlrOV2EEo+hUj6ZPucvLAjhTGU3TBTlIEKVp8xh+sbrHLWuiGXdlAqBiET0mozTLrxvO+l3WSnZgOW214POIRjxgKdgWEGHvZZZcNCbH4xD7xqC8ZeMWjMWBATfitzcYdXxPTtHMv/cwtbttYlBfBDthuw+D8+fOrxz3ucUNuYl4bB+w1c+9zGQPuWxMbMm6IXHMCy99b0hZWysFgrxxCEcDH+di2dkEW8hb2SZ/yFrmQedkd2bVj4U2OxA6tod4nS/35uFNa42Sji+xgamyLLRqjtb050UVbuFRijAyRcxgfqDEizkShYxQHokDBVZIq4AI8wUcQdX8dGYMx23VTqBiDoylCJAcBxzyeIMiqqgGFfpxDIJVgC7CciaFJeAR9V4KXnQnAh1ffGcOjYYocoMHpJb4KMIWTXV0FDWONkVqPOQGjpEXQNZ42kiHOBKQBJJ4ArEBuh4GDuSRBgipnEyzM4QfDHrd6wAMeMJxuWL8dCXIB2MDeZe12xgQEvOJBgmVe8+PD2rw3Pl7oNo7qlW4VTAqYHPkbCwBKcvBGLnRBj8YjEwGbA1svHXFuciZHY3F4j+vRv3bmJVP8+wxkjQtsyFyxCuglvUDCPUmOtuTcAk8odge8JEQCVApSwYmvhHpj+E5ftgQ8rYWNSexr30MRHzpU4UIHbAP/LfhuIjLjB3QqUQXAdMHH2LF7eJ3Ko3Hxxk7YDru3XuMKBnTSjuu97/HALnJff8EjazM232cj//Yy/vSRcNM5WxWY2ZwgzQYkrXxPUgMv+AN/ZZ/kLwnjy9bKjwRZvJuPnZrL+NaSv5KDBzbJ1vgxm77kkktWb/Zmbzb4H3/iE8bVdlsyJx8xLgzjr5deeunqjne844CFeRxMmyk6wYM+1koH9OER2pve9KbD5gr/NCcZwGjrZf8KTj4gueQbdOLiHzZRvMezfhIAOEYuZOU9n4Yj/FpB6mRcQmIteRwULuljfAk8O0xCZq3wmSzI0uXxGjFJLArFLvTzPT3DP3ZD93AeT2QwlYxtXDajaCI/iSM7k9CzB+NO0TMyrn7kwA7PmmrhgvAntogrcNyaI3u+RveVrJ++6JBtkFkl9+mGLRhXHJZck6fv+DFbWkfuG8NJoYTfpg28Mh45GqPVAz7ETDFRbKpzsHVJMv8V3xVpTgH3uYzhEgM9zWGjwKagfIIvPOxhDxtkqojh3+w0fXKxd3bLf/hWyNp8Jjd5Av+p/eAD3ViPTVYblXzOJq7NHE9CKKDwkHym9t/1okOxVm5DntU2gq0wGa63OnZfDmTjBN7CB/maHEl+y+82+ZYx+Des5us2mcmVPsQAmN8S3Yst4hL8Ye8h79mSnE4+ZI02vb2aA7/BKGMvhcsEAjDHKlwQpTAYCuIMHAXYSOw2AUxIO8UPYxQIAUhrJD1yX1/BjPF45MOjH5J4RsLpGJ42nESSBTAZqh1XO7J5blnhwgk4T3ZZVc0CNb44WgzOWMaQoAiYcZrIgjFzBIARYBZ8OIs5Jdr40K63RuNLBAATMOEEHEfQUBQAV2Djh+03uMENVte73vVWr/7qr7668sorB57IAiBag76CiaCHHzIgj/BbL4BtDkkV8KRP8tAWT5wccFhv+rjnsx0QR85xVnO4yC/FIb4irzi8Mc1BvpJHQc97ACD4hFdjAhCnS3Zl7373uw9gTxb0n/WsI/cBCN2TDV4E2236IvoH4mRLrnbwfLdN312IzA9RuLA9QcwJHVDdxrcqaeuKTumLLfELQQh/koLYyq680jHcIFsJBhwxbuwNeY3/KUDoUz8BPAFPG3ywIzvoEiS2JkGlc+0kwmxdAiwxkGyyXe1gmMIdHuBFUMq6zG1cNiDxJVvFjmKcXPHC3xQ25CFY81njOFlyuqNYgk/amV/yrt+2coNp5CRwKw6c4N7nPvcZTnm9ZzN4Vxjhd1syLtkozPDLn+0C2/nlj76jazbEN8lV8OcD1k5f+vJt8oVzEoX4CN3AWdho3eRPJ8aE/5JMfgkb89iIdYoJ7IFu9Pfe93SCUpibG18uMof/df3awwqYQ1/GMR99Wrv7ubbVRYiu4bt5+QQdwIo67tQxEdvnA5JMseCsqS1cEB5hgQKNbMlUoa7whP+9dcNOfei4JbJihxJ3MhWP+Cb7GjYiXuZz64guJMjsgV2JK8aRA8GNxPFKbMZ9fJu/ks/ihI1BiT274Xf8oV70PuViIzBBvqJo8fgnnzMWPxbrzp07NzzSyh/MWS9/SlhxYW3tmuCJvM/YYjNZZl7vjfeoRz1qiKU2HBQqfNIfDRGv8cPvyV5781Xed7kUkk53bILYmIIHlXyGl3hjUy2xIzzaDJVPwGmYgTeYtA5Dfc9mc6oDG8hZEQSD2EXLD5If2ghOjhEyns8KEXrEt3Z8XqyFaTW/S+ECa3rzzIVeZQqXSkDabp0EU5CxGy6AVZAbIwEGeABp4C/BoGQBkVMySoCkjQQbsElAgblkmhEqXG5+85sPf62KwwFGCQZjlxADMYludlgZu3mNK9C7gCLDApQcyPh2ASUXjNPujaTGPY+mkK31VYcxHkeQEEkuyEKSAmgEXAmF3W+A0wPhOIVxOIydbkmS+YFxfpNzpzvdaXXd6153dZ3rXGf1aq/2asPuqFMYCYN5rQ/gCh7ZSbReY1d+rRdQcDgyMx/erNtnAUPCgGdFZeXZOPRjZxHYAhPAQk7mJ3NFkCTTWAoyPCTRMR7dGlMipL95JYPAyS4vvbAHMlT43e52txt+2K+d79xja8DI7q+1Ag1zsEmXz0BF4NVW4AQmNbHpEVnRLznSY0CPTPF9TMLbvoULwj8fUNTiXyLId6yB3Ft7WEexTWsnU34omSAbSaz37MecvQRhHcWW2CLdKxj4H+xim4pu/JqbTuPD9CpZ0i84kURHAOYP+GKLbEI7mwJkIXHiy5IuPBvfSQIMY1t2QwVd/OhnbGPAHLbLniUy7E5fMuE7HvVg7+RC9vCAD0kC7CrCOcWf7+CJR818xyatrVJsEK/8gjzgmiDMjo3vJMP3xoMT5KUN3zCPXW7jGitE3sZ1jzz4Bpnoy+/y+JeAy59hqAJBG+tgT+RGJuGPf0pK9NWHTNyD22RAVoohtsKP6NR9ctOereOHjCQl7pG7BALW0iXsg+OKzYxPZ+TNHoPdsNOatcET2VgPm7JLnZMffjbVv2CmccU3STLZ44nNZjOMLWo3ZWz6wTNZsll2zT4klPviwCGoV7iQHz+A82xPGyeV7JJcyKkl8uGfbMBYrV2m+GTjLuOwH690PUb6ipXsi2/ATzYL78QltlUTR+3xx6/cZ2st4VV+IQ6JORJw47Il89BRLp+nXDZWnHr4c+FyFnjjsW8+Z3NScWGjgz2zb5e5xUkFiSJfgVgLQOtj73BLUi4281d98exVLFfUPPnJTx7msallXrzYpLj66quHPzUOY7WHhfrueuEZD3DKeHRabQhFF9bEp1ocRPJLcoL59MqXYbWcDg6JHz0f8R1/h09wBYYpsvkUe+WrtZ/3+DM+n3a/EhnDF74AS+Rl/FTOan3mqrwshctEIvxTFS6IoihMUOHowIyBUHwFpzHSRkIlkHIazi3ZZ6TAg5MakyMYX5KQgkKhcN/73nd4jIij2z1xMawUGxJ7VT9g5RgxUGM5YfE+3zEyOyx2JQJYDF5AcRohCCsikowzSGsXjO0AO37l8JIA80mitMvYdlQEWrLRL/1deAPAnIYjCOSCl/UDALLxmNiNbnSj1fWvf/3VDW94w+HEhWyAGgeVWOT3M4AZmJnbfPjIxeHpyM5F+BH8AR5QkKBJ1MhNIpTHZvCLT20VZRIDcqEr8gYwgBko29FRsGlPx+YU6FO4kLnvJFt2mcibvnzGg6SJrgExQPXsPcClXyCGf+ABrMnAxebZhXnxTy7sSlDEe0BljLQRyIxhLexIUk5mm/oegszDzvdNWMhcoADu/JLMyINNkZmNgNgg3Uydi5wETuPSO5uTdPjOGmIr2/h/CA/aszUJi/HsBAoy1iCZsKbs5rEN30lqYAY/EVT4j11MOs+4kmRJk3t4Yx/8JbapDXmwY4kyGZmbPfJbPsQm2RO/JlffsQ3Jj0cn2TtfkdgKxjBNEQMLBEN2rQ+eBbmMI7jxVd/jwSVA8i336E2Bk3Hdp0dJGvtkK/ryJf4uMSQfMvDe9xmXTNi2e+yMDHOKpPg3F74UbZKknG7hl2yd8MBcGwESQmMbB0byU7IylkTBZ5hFZ/Rj/e5FJ+aD77CcrSiIJFfxNXrBL+wjY/1tlLA3eEt3ij8bNjAXf/racICf2kqIyIlNuTeV8IA3fenT3OwGjrFR9k4++A15Xz/3yH3jskVyYB/4ZT9wj07YmjVtGuvY1CtcEJkoDuiffbIdvsim2Xzr+9ZLb+InWVp7XRs5kik92xRgo8aHA+JvK2fkM5viB3SivcfL2CubYVvsJBS544N948PnStrwC7GEP4k5TvphguRacbbrZYMAVkrmH/SgBw2yNb68gRzFVL+NEuvYgvYuNg7fFDnsmT6sl4xdZCOX4YfmySmRPDDzsrH73e9+A6aYg+/555VOX/iqR9YUNexOe/2sd5dLX/LCh7Wyab4tb+rpkL3LReitvW9tcEVxJVehF/rBP9yk396Y7Ev+xiZgGt/Vn13Rf2uf2ssrYDas9TlkvNgnWTl1IV+nN3ItmJrxwstSuEwkAMNwTlW4hOJAgoSA7xhNgoufTYpLXyAkKHr8i4MDxBrQ00bCACgZo6NXhsQZGWkCNWB1MVIB1O5rjBFPbeGCR0mrv7AFgH3vMp8xGalkhjHaXSVjCZDEAQhKpAGKz/pxUke+eMCP5EkSbfdQP05gfg4t8fHXPRQ2disUA/oL3GQomQJmAPS2t73t6ha3uMXw6JQ+HikAepxfe7JxkkH/Ch8/lgdK5sGzpEJhCFAkNtZMLuSjTXaprSO7x3SBdwmNQGJOssCPJNgYkZV1AirJBOC3dvo1pvHIKWDje+PTo36RlUt7gEOvEjGyAlaA1ZzGSFuXBEDAVLw5oQH2+NIuoDJG7uMfcCpAgVGS8E19D0nms0YyDgDuQtYsYZT0sWvysT5yE8DYAztj55Jk96cQmRjXPMYF0uTNHuhKgGCL1mMdU9ZibPaor3EVG+wNnvBZBQI/kdizQfrm99rix3r4C0yIjUlQsuHgs7YSLH5g7b7TT/HLBvi7sSQJvpNUCsiwge8rgsgWX/hgu2yV7sKH+QVO6+CTkhI2bW2u6ISPCIb827j8Fh54zwa1CY9e+a/kjL1L0iVosMScxo1O4B5+JEaSIHrBP9tmB3zVeMHm/G4HjuhPXtYfefksYbAWBYGER4EgOVLQkCcbIBPJAdmGFzGBDL3GHtge2ZKdz15tXlizdeCLbOB78IVe2ANcS4HJlu0cS27ZiDFgjPWZ32WsKTaI6I0Nwzz6Ydt8ip1EJ+GTjLYhPGhvreyDDeGfDH2HV/Pinb7Ek7OmscLFOhSv7MkGhrUo7tmipI7sWrnoQ4bshvyqTrS1dvgkJihexCiJn8KXfbpfifxTLImT8EB7NqKojd+EzOc7do7Xdjxt+RV/kXvQCxvlx/wS/qSY2OWyLrZkIw6OyWHYK392kYvNSRhD5vqYExbYVBSf8WEjR8wnIz7BZ/gJ3NVG7LZhK5eCT8bhK+fPnx9OXcxlfgm3duKvXMKj6OSfgqe96lrWXea0TrITZ3wHG6ylZxfsXR/ztjrznu2xM2Pgw7roXW7QK4b4kXyFfOGVnEFuoDii1x4e4Et+IidiV/W+9uSseHSazbbgDWyQG8FH7XMh3y2FywSiZAYieGQ3rVXEMclcAjRDAWACvmQAH63B9ggAcULJCYcSqBmVcQENA+IIwAdwMiZ9BEoAlmcfQ+RhF0diwaDzHRnVwsU9AU/AEChjhOYVcDkBMAGgnJyDm9uRq7kBnoqdc0gaFCePfOQjh10fxs7hyMMxsBMiCbIdEkkX+ZjDeqyV0dMdcLUmvHKWPJsqGbGbARiAHTlxTgWPYC8IGgNPxuVg5tFOcDeG0yCfyYWM7Xja1bAGfazLIxDkbVy7MQBcgFKYAkkACUgBJ+ekX/Iyr+Tb6UoSBzIFDMbynbZswroEvhpEvJc0CVz0qR+gMC4eEui1M59kgpwVS8DC8XcPIFsKX+RPxgpc4yTBPAWxsQCjQCTBY1v7+Cv5ShDYYqjOA+z5JX2zgey8WzeZTp074yoQ6Il9CP70qhBmiwlIU8Y2LhsRfARef3nqLne5y/CjdD8o5R9sjy1kXGt3csI2zWlNbJuvuYeMyc7j63TNRwV068enS3s+yUck5DYLPJrKvmCK4Glu/fkKTPIKUyRbdoIzHzuHAbH98Mb28MduJRV0Dz+CS5XwimcbA+RtDOuEh+aoRHZ4o1fYAIv4DozzXdatnbngAz8zDhnwXb6NX/iqWDCfS19FkzX6MbHNFElPToL4XmSN8CoBtqFiTuv22ZzaWac25JMkGQ9kJlmJPvTVTuIGuz26e5vb3GawB5iqP1vR37rQtvamXdbGZsnJ+tiEnV3Y0+rE+1xjhA/j0oVxc/qp8GIfsNZ9awtZu8R0zoWLNeOTLdJT8FqskVgqHqoNhMhB3MqmQUvsCn6wOTEELiuUXXhgB8irz2TJFuCdNvopstl4qxfz4VNy34sP7ktczSvhZPP4pCvxTyyWiEuiXeaecunDz522yAMU3PIcST7ft2Fx7ty5oZiA3dpnI8PFdqxNbsV36AYG8W++RQZ82H2bph6lZ0fs2Hg2MT0NYaPBqzgKR332iJoTGRihPZ3C8Hr5btNFPnIMeCMei6fGkzeJN+yj2jrymd5htBjYu+8UBG9yFeslC+t18gSz+Fl8URyCk4pocc78/JkO+DG9V9swPj2TNbxviR2by3oUPsYzNl3I88yf8fK6FC4TiTML8jFaxmmHLYo9JQFloELhQKV3NNsSQOF8DC6FhB0HiTJjkBh5jIfDMkAgyOgZjwTDPZ+zVvP5DHz09z0ZtYULQNVXIhHH8b0gKyHhlIKmpM8uL5CTNHEGQEfGnEriaS6y98iZXSDFhQAMbBLcgb7xrSGXea1fIkJm1s3wBQ68SWpU+IABCPhMPmRlbjzqJxEyjrXX8V2+I0uPPAjIgr3k1fieczWe9ToZSkHFofFvzeYBThIEDu09PgX8Oh890Xn4cJ9e6VQ/68cH4FFsVbACCtk9ozNtJKl0TO/4E/Tdww9QBfKSTzvQAND4m0iCiEcyI3P6RfiP/RybyAYf7FFRyG/Jc5/5A7S1cEHGrBd9KlrIjr1KxgWECsTbkLbVvsgxhSRdsS+64Uvubzu2dgKNJEiwYBN86sEPfvDwnr1LNLQJJQiZk13xVz4p2TU3wif7lcAIqOQgyNTduKzDd8bymy3Jxd3udrchQbM2NhhMUzzDAhgEFzxS5V7m8x0+fEc/5MNW+RbZ8zsJTAqhHuFV8DZWZGjnXj9+1pI2xuLH+vFVa/aq4BF0rRFP1pOkwZVdXYWYBMmOqO+t1T24gWebTGQiKTKHNcE3uBoiX3Lhl/jEj51m9pB1eOXbklZJCnk6jfA+/mhuPi9JY7N2oSV6CkqYS/42RsyhzxSCT3hkU/zGmm2a0BUKn9tS2hvX7q+18wV2LNk37tiYsItdzLlwCbFFayJ3Gwb0InFmO+ykJTbEztgsH2yJXJwEwHT2CRPJj10k6SU3NsXOxC2vbJFPmV/sSVyvZD665TNsqSUxHN/sgI/AFriFV7bN7ozLtmKT217a8zW5GTxRaDtdUTQoGGCa92QZXGIH1pxH6OVyYia7h0t81nq056dsF/aRhdxGYk3W5hYnFUns0CPgLic/ChiFlHzCGukQn1ljrt6aehf/tD42A+/oRCGLH/kGP+3FF5/pXQxyv5J79E3+cgi5AJkoUtkJ/buPT/ahnXWyQ/kWGcqVkntU0gfP9G4zyzgt6U/mimHrwqdX8tY+a0d5NeZSuEwgjsXAY/wCjJ1+ykwSeSoyFyPkVKlYJe8SDQbWI4YAMFLoABsGz0iAl1MO4wX4JTACHicXJKwX6At85tfGPYbOeRkqGbWFC+MEgnFaZDwOAMSSLEiEnHwY0yU5sgMgsQFwCik7M5yGDgCLAMjoOYZAL2BnDkQWdAMkOZAkQR9FA13SH0ACXpJ4YK6tR9QAmHkEQ7sF+nEqbQG0NdS5rMFagJuAIDFQKAAEfNtp9ViENVsTQCMDfaw3hQs5kC/90A25myuBBcCaw33zW3MtXMgdACiIAmTGtytJfwIhW6APa2Y3PutnrfSeZEXyjR86V5CxhzE7NwZQYY/4E+RS+FY5HZPMgw/yYtsKW/wIMmzpUI+KtYVLS/jQlu3BigC+fgIY3ZE3XrfhRxsX2dMl3fFVa1SM8Q92knF7OorO6VByTUfsWTGur0DkNJnejEtW+FV4SpTIFL4IVAKc4oJd46euQTvjsr8UyvxN4aCQNBe8wTe55BEla2CfbE5fvinY8jVzwTg+bOwEST5hXP7EV1PMaS85wzefhhnxn5bICi5IfPhSyLj6WkOwrFJ0LAHlbzACr+Qn6LM3vGRjh/zNZQ4yYJswQAB2sQ/8wyU4bVxFGlmREz9VvGhD/uYzFvwjpySX8L3Vv/n1ZXs2Y4zNNq0RLtA1+bELOAUXYLaNJLzCDPe0yeMk+tJ9S+TMzuAGudIZm7EmCRH7IIupmKC99YoLYgx7MS5bNa45N/kTfmNDZ02bChdroVPxRkFK9vRuzXym7WfdZOA+G2QDZBZ5+Eznnkggs9g8fbND97RVZMABds/38cj2+BidVz/ySqbGYzPJDSrhCd/uwxoxTv7BtyTG5nH6FnuqPG9D+vA78UoMZbP8Ty7htAOOKNTZsTW7+A6/xBf/YRN8EP44KbFZcK973Wt4AsN47pO5cckHvvAn78mFfPitHIrfKFrMDz+NKXeQU4iH5GN99VpH7utjDLbAX+UsiiE5CjnKseAH/w42VoK/sFrMaH2WvOUqxhb/rRGGWK8xYb4+5CQfoS++RwZ8Gva0esezcdkPO8eTzyFt2S8ZsgmbevTklcz4eGtHkdNSuEwkwqcIhkiIBMgQGDWhE3Yc71SED8AgSAME4CZYA5eWD0qW2DDQ7Ibq670ffvuBWnas9E8l7L2+ggbDFTglo74zB6cVpH1mjClckrB6z6nd89l4Aj1HqoUW3iT22pEjJ5fQ2L1QmAiegj3HBc6AwWNX58+fH4AGMGbdeDEGYLFe+gGSwNGaU/xwQkktME8BQ34cw5wcScFkHHLxCiwUa+TNBsxjProQlDk7fjxPC6wEAQkFHUkOjS/59ReBgBwQt2b8Sc4EfGQdOZ7HZ2xLgAEc+KZDeqmFC5lKmBRgARBAbW3sN8WMOckRcHjve0DudAjP5ja+MQVQvAEo44WqrBVUTqyAkUBLzuY5NmWNdI9fNiuAWC8Z0bk18FPJ1z48GWebwqWS+chH8BB8BAYJqWSRPI0ZG5pCxrVm9iNwCxDsjR8oht0zbi464bsKfAkLe2Qr7IfNSlDwJOlhC2xdcFBg8EPyZJuCoySB/9G5eapM6YDfaK/YZYf6GYf9s01z83fz0Im5s/vGbvgVP8eLMfinEzM7p9YbmfEFeOMRM/f5AP/CA3ni35p91yN2gw/+A/v0C3lPX0mqenbjO76puMCTz14lAjYvPNbhMTx4R9buRT5+F8MOnPSSC7nyOXPRCVko4uAU/fJRvMIl2EOPZEdPXq1BokU+rS3py7fpxJwKD37Cp+3esgf2EZzjK/iH7Qpcsma/dCSpycmYcaKLXHikYzbDFukA/gQjp1D1a+syLv4lS3zbuJH7NmQc9nAhFC7kZW18jW2IWXBfvOYP9FGxGJE/eyQfuml905hsLJuU4qm459Uc5MOWxAXtxE065wN83fjteGzdfOJXqwv8wSI2Rl/sT6HEZ+Gg8di7Yjiy0GdbfWrHV9gFHzOOTQ8ysmHCRuUfeOdjXuVv+M2GijXry84VKR6T9PiY4oNcnESzOX7I92AO2dCLOCC258kTWAcL+I1TGLKTM1mz78iTjKI3/G9aq7aRUbCZnKyBHH0Pt/mvYoZft2OaE+byW2O1ZExxzViwkqyslazYk7xG7gDDYSz8sWY5HPtpid3RO4zity0/9C6PsgY4jy/2LQ7ZpFnn00vhMpEIM4ULYlAUxIiAvEBD+YJ0DPMUZC7Ohw/Bl7FwXgEEz5UoGmColN3Th1NJNu1WSNI5MMdnTIwz4BcHkkgbnwMbT0D22w4GxcDtaHAoCYg2HDxJgfkCwNl9COFXsu97/fHCSQQawAR0JPp2dxQrHMJ4LnMCEbxaF0DiABxQwAZunMV9Dm6XBB8cViAG1MYHQhzX7gwn1Qegk0WSTOvwalyAbB68aEsGApLdCu1cAgQH5+wc1fzA1LrsftKXOYzHtuxKh4wpeVJIACSfzW18PJMvndTCRcCyPvIkXzoBzJKRWniQmzHITlBRfCYw5rQN/2QHwOnQ/AEUr+SnnfkVLRIsetfOPGPgcyiKXZrPxoGgS1bW4DsXGVjHWRUuCI/hwxj0IFm0Eyi40jF9uT+VjGud+krk2BabYl+CDl+nT8kKXze/ti7+wH48XmR+2JWkKLLzSqeCpo0CPqVQ0Eff2FPIZ98L5P5TvPEUOXwaf5nXq3noBfa4xx4FePOlDfuVEMAGc9MzO4NjfkQKuwTl4AybDN/8SlA3Rk/vxg/+tWvx3n1rloC06wwZ2441PzAnImN+LnlPwkYn/F/SY70+GxvPfNFc+ntfk1OP1+jHD9PGKxtiz+aQJNldhjH6t2slD7iisPWMPh8xP9uAIcYMdrJF80lIYSHs8upe5oZzSc7oGfbAUxhiDraCj4xLdlP9Tn/rl6DYVcYvHKOnyMGYU8bV70IpXLI2emMn7Et7OYiNQH5Dr5XImZ5ggHb8qMrHfd+5nySf34gtMEIeI/6zUbFQu1zsvPUBc9E1/Cdb/IRvbX0nWRfn4KZ2xlZg0612+pABOxYr2znWEV/nm2wQ1rFVcZxs5AmSYpuMZEceCmlx3eNjCgz+Yv2KknPnzg2XP/YB37S74oorht/FKGJsNIqP8Fo8pBO/peG/YigMYZ9eJe0wSzyHL8aHrfrZjCC3bYmd80ljRQe+g4FwBb4orBQa8hZxxfit3n1HLvRQ7yH3+ZX1KUTkNHhlDwovWGaDxZyKI0WqDU+y663FWPiFCWygnQ/W6wtD6IUeyJFse6ctlZbCZSJRUC1cQpTinsDPMSkckDPUXhA5BpmDEvFh10wyzhgEJnwleCAGIhBoyxE4aJIFBiUpBgSc1057kgjEYRQqnN7YgikjlXBwWkbFiV3GZuDuCXxkAbTMzakZNMK7cSUfdigBDecHppzSbo+ikFFLvvyYl8MGKDMugOG8nE1Sw+HMm3bWr8DQF2DjT18B0TySBc4kUHjPya1VYAd45Km/sTIvYBSABHprAlJ4BKbmNSeZKLycwrAd40gcBHntJAi+lwQohMwdZ9ef7IFEign6oGNJHN3ij93lGXTrx5P10TEQAujmSbDHP77IwryKRECvv7mtPTsfgETwUVDiJ2vHizZ4FwSBddYcvR6T8IE/foZ3a5Ec4yPrDGVNZ1W4VDK/S9ClR7rlE2zeOtgi3USWU/glE+NKCiQUkkj/WE2wEUSyo66dsekxCQW78r3EDgbE5xEe9NPfIwIKEbtj7CX6DvlM/nwpjxrxo2obyGdJOdvBL17gDT1Jys2pvfXQKd+CSWQvwEqG+CMMclkv/bJ7vPNjfut0A+9Vjt6b33r4CuzqyZk8rNd8YzoxNrznY/hwjyzJ0ffwEU/8D+7AVkWJxIf/wFxjRDZkanMIdgZf+Sc8xE/IezKDARJLJzvmM3e7Fm3xYB38PVjR+om+kmOy9N78ZGN8c2mLT/fMKwmzHn86H3YZ13pSPE4hY5Ov9cJAMQmO41msoavwsCsZ/0IpXEJkSQfiBmz3KjkXEyW0dFiJfmA1P4HR1hyZeaUXmA635QJsC27a7FL8iu0+06fxxV364FNV9ublf9onNrifNua1q+5+inf2aTw4V/kyNnzi++xyG9vRF27gn4/boIA1bMUGJ7mxef6bJx7EMb4HD+VIknz4Y16nuf58siJDYuxVQeCeP9jjjwgYR3v9YIvNG+PAWYk0e7VmOmWvdc18Cr4pDMltG4rfslnyCUZ49b0C1ppho7wpp202Rip+I2OZl557xQF5sgf+TN/mtEabxApNMrUeuQy7sJlL/q39GZcM2BddVjIHvVsLWZKHXIj9wG4yarG6JTi8FC4TiEB7hUslxmLXAChRssCs3ymJ0hmTpFhRgl+GHdBnbBTPsQRTTsXQOYPLe8arwgZ+ihB9QtaoKueADFQQk8ArMBQKglx2V73nXIzVfECAowmIIf3xp+AzJ4dw+oA3c3AeDqAwACYSMcBtTGsS4IG5xJ4TAEoFTOsA5tGfboAaGTF+JwUSJc4q2RKQ8QqQgI459CUnzoV3DhN5+Yw385OrpAN4sAOy0t/Or8fgzM9ZrQ0fijpJmSTMrq3HYgJS+uLfHMZgexLcFCDeSzAFejKSNASY6B7PAp1xycs4yCvdAF9gpHAzFh0GCMiPLsjQuHjVx33rlWyzK/alaPT9OrA5BrEJfLAb8rYrNAZk5MX25lC49Iiu2LHiBZ9sjdzJOnqbQtbIP+hdkUG/xk3yw2aMK7n3mCib91k//u+7NvCFyJisJU78pOXPfTbNZtj6GP7xOW0kGrEfAZU+7ewZl93DGH4JC9gke654FMK3NRrDnHlcjWyzNuTVuDCHPwrM+vYoMvKfvulcYO+NB0/sGMMT9yRqsMA8lbRVxFmLos781gZP6Qt5DwOtwVguvmotbZIQ0hcGWX90W4ku4ZukAy71xsEbbIE/VR5sCL+w3bgSJokn/6cr/PtrkGMy3IbMbVzFijUYGzaSBTlE1qi+n0rkdKEVLoh9wXWJHaxTyGRTj+9WIh9jamtzgVxbfPad2EzGbNDJKD/wD6cVwBJ4G2vaKAhgrf4Zwys/YJN4MV/u5+I3CgQFQ3hRRLAlfbUJsStzmCtFzSbSH56zP9jJtvmfOJykVhu8kZ0YTN58WZ7An9maK4/KX3XVVcOGgsdO/cbFe0UJefhrobCH/bgUQfop9uQ04kLyA+uhM2sRm/mQQhAmwXiY1/poj/AuF4Lb/LolcR62e2VPeJAnmIOOW4ID5mZDvfH0IU+5D/lYt5OmSy+9dFgr2bEZmzRib4sj5C0mGF+cafVozckDYanNZbKTZ8ES81e76NFSuEwkALqpcKEYyhScVKV2MgGAYKH/qQRN+YyUkoE/52GIEgLfCYQMkCMxIiDDoCSinJIhCWKCnTX7TqBnWNYHeBim3TDtJCocWfBU6ChcjAnYJB6cWFvAAaDIglHbEeKU5pXkSLwZNOexM4hPCY616CNhsWMCECTa+qncJfoCqx3N7ATXxAuYaMdR8Eo+1sAByQdACAS577MgSm76Gss6jQGorccYkbPPnIkz2xkFDgAakAks+LJ2a7Ful7UmGaMLgV/SpWgAPOzMWHiJzAGK9dFVdj3o03dkYi3AFT92XxRlQM0Y+CQX61LcKZYkoOagb/ZhfPySM/kDBztMQD/FHaC0LvfwZNxNYHMo4j/WRlaADr/WzC7X8UF+7HyuhUvsiDztPtnJZns2EBT+bMW6p+CHtnQmGWB/7JufJPDwUXPwS/YRIkt2ww7bwITwyn6zE97yREY2bMzDNnt68Tm7wmw+9wVTQY+vCGh83WfyYHuw5D73uc+wOQAvjG8+/a2VP+Gbz9r9lDDBZDxYo9M5eMb++bIERpIiodLOOJVX3zkV0cZjaWyITmwYKOSNaf3kZC426T25wkG8uU9efI/8zekHwNbHjskK3tGJuawZjmRdeOCTCp1gYUvaWDMsEWtanbAddgtP2Zj7dZ3e41sCCOvJNQQPrdt6+Bvb8Cp2wBZ4gne6q2NuIjzgBa7Sd3bBjcsm3MOTtR2K6MH6zHfWtE3hEnmSA7xn33BdwUJG8Iw9tzZBtuzNLjzfF6vF5lzwHq57esFfwfKXuPy1SzFM4cLWPAaV/3vCz8V/47j0Z0tiTfVxF33hl73BHrjjKQQ4bc3iu/stGSOPP8kd1pH+bE/RJYeIr/M7cY29R3b4kSsoHOCN+MXf2S1/c7EJj4h5jJIPayth197YTlsUdrBIIZHLOPm9a06WIgc6kAcqqGxCaY9f+jOmuGsd4bNHchpPVsCtnsz4CByBtSliyNc8eGn7kIUx+TP902m1C3yKC558gXl+h0cm2ivcvPcbIHYBA9MfPrv0t15yYhet7+LH+CnetKMbm2DsQ/t18kDGXQqXCSSQbCpcYrQuICnQCLACClD22ffbKOhQZC7zCkqOVQERp+GMEn9Gz3ntGAIjSVICWwDQjoGAzUF9BrZe7chIYARWsmGIvnNxJEEYCPme8UuI9RVkJWSe8WfwjLE6GUfVl2NoT2Yu7wEnEHFSYB78Aj39AV+cQVs682ouTl2dCfDbTXFf3xQuigLrtmY73wJrdBoQlkQBR+O78GB3xvr0NaaAT9Z+/wPg8Z115OLk7KKu31p8FhzMBZAlk8YkSzoga+8VLvg0DiBQPAIy4CQRkBQBN3NZhwSDHPT1fcic7uMFiEgQJSp2rwRLNiygmI8dsadT2S8iU2tgN+xVYEniSm7Rzxhpw/7mWrhUwh8btTaFJZCXSHjlZ9biIpN1FJmwO5gVu6Y79itx98NTAde41kbG7MK8igPy7hH+2J1HKfASn0JwwYaNwBYeXJX0EeQEcAlv/IEf0ZNdTPaLz5z08Te2b2cUjgleeVyGb5hX0qKItx47hOzcuiQQsM6PbtmxoAcjJGwCKb/nY+YJr17xaY0Kd/LQzyXRyT84NDaZ8R9FCZ5hogTFmPjiy8bhz9aXZ+jxzffghoLLevz/Fgkf/QdfyIhMzedzj+gAn2zTmHUddG/dkpp8l/tIX2uAnXAkmOGSGIpfdlvpy2drQviywaEIxVcds0fkS6bGpSP46kSQTyu4yGvTGPuQeS+kwiVEbuyM/m2csXP+Idnjx+1pM31aK31deeWVg12xX0m2y3vf8X//5Z0t+0etxlagw5vLLrts2CQQA7Svl3jmHnvBv/liU/TLt8QkNi1vEMMUCvCXXVe8qET/iWX4HyPt+JGkmT9ak4JXrJI71PHxZE4+hWd+CPPEQicxLvbt9yznzp0b1q5YgAv5R9T+XLtTKU9EWI8nFYzhVSwiY3zzh5bES7qScymKYAEsF8Mju5Z8x4fhNlmQac8vfAej8UUO7AP+wURYhJ92fPivsOHP+pGdXMuGhFf92MS9733vYe2KFFimwPXen2j3v3LYANuIPXlPbvBLHlGxFFkPHGJn8iibNfJOeQY7pdNtiC6XwmUCAeZNhUuIwlwxQMKmTM7CcIFQq9hjkrkAAT4YNmdzDOhSAEh4E+RbY8Cj79yzE+i5TsApORbgBEoByI5/TjWABMcHbF45OIcRoBUjnF4byQinbGXBocmagwu2kkWGzkkAK74VKdYUxzSGYChp4ER2mhRFnBlwAPcKhhxGwPQdHdXCxZhkZW2cNvrUFhiTn7HpEhgLAk7W6FVfF36sDaBKVMgZ6HFSSZ3EOyci2oXMo7/vJHICnLHNizcJox03u7f0AMAlJYKDgs549KEIBVwAUzt98Ezm1hu5hTInOeGLzu5xj3sMiSJgpw/r16bte0wiR3IQ+ICqnSJgnTVUuxkjPF8ohQvCo3VbI96TlEt86ZZPKLyr/ffIPe0ETAl1vuO3/FCgJxPBXELnO+35Kltla2Mk0AhYkid8xG71598S+R5pw7f4JJ+TCPMFfsHOJOjWyb7xGWwwrsDJhvFGJh5DkXjwR/6s4JZ4CMwu/mntCm52ow950qPdUoFa0g0TJRi+j31oZ/z8voQfCrTWqh1fVHwIonAKv2KD+dkHPiWm1uJ7fmV+NmyN7DGbIvTsnvHwAuvgBfzk93ANdkoMzNsj48AfGyg2TfCf7xUF/Jnce2R+vOCV7iWeZCcumBNWkzkbopP4HmyEtebyeR1pQ9fGpRs6oQN25N4Uf96V6O5CLFxiI+TNluiB3fEbdp+TtJC2ZM0W+Lj4IA6xJRc/semn6PTDc9guxngMO5tfvtdfHGXb+sAd79kn+/XbB7YZHMInPvDEZrRld2wf3/hfZyd8ne3ZoOBzPrdkDrzgwYmlzUcYJJbnaYhK2rN7Psqf9OXreJHguxT8foPqpIHfOVlgoz5LtG3yetSbveqbiyzpQI5hQ5D/tUS/MBt2mFN+gl84kI3OluCL8a0RLmySmXH5LjzNqRYMEqvYQsg4bIdO+DU9k4c16OO93ESRZiObfdrc8ZdG2QedK2YUdAqq9PVK12yK/OiPzoNBKPkUO7SZpg35wQGYU/lcR8ZZCpcJxJi2LVxaohTJVnZIBFHKprCq3H0pAIdXACIocA6/Q2CYjivNrVp+gzd4g9XNbnazYRcSOEkMGSGDExz1BQJAiYEElACVZAcI2DXNyYp1MW5je59HvRRrOU7kCHYmFB1JoF1k4DMZMUz84knSQN7kjjfFlQDLAczFAcKvfoCF43IOiZ4E3JGvohHvAVfzCGCAz9wuDg0oBH3rJUOO7Upfl7b4UjxJ0Di6cciAPPAh0QYiEkHFEBmQG96tA7CTEaelEyANoPBVZe7V3PoIQGRibUBQEQc8AJCkQmFiHvIVfICZdhJCoG5uIGpca7C+yJz8YiP60Juj89d+7dcedliMS57WY63Gsk68GcN4sZFDkTHZIED0CIDAQ9fmnDqPsS6kwqWl2KPASOf0S88CNUyBI9VPQ96TF/mxsyRJ5Mou2WswwumcBMe4NhvoXNJPv73AyW/ZJT/Kzp65zENX3reEP+PZ5IA7/Np8xrAWvscebZDw+6zJxSYl/BIbPm5uc5hbYi7pF5AlX/7jvABqDRk3doNPc/DhbH5YL5/0feTnvfWbh61bl2TM++CWMW3g6MsfFS1v+qZvOjxm4bP2fNtY5KwQkmBIKvk/TMwa4QMb5bfGhXV8Dg/0JzmSpPlOn54dsxG8wDvvtSFvcsvjZy2ZG29sShFiHjhoXvxZrzHInCyNoY9XCZlChCxa0gafTsysSXLI5iQscJvNGqNnW8ci/FyIhUuIntgCPbBd9s4+6Qp+Rw98gn2JTXxNDGQ7Ngskzop9G4CSdacJ3kvM73SnOw1PCDit9M8TJazea6+f/mKWE1E4KN6am40kNrJ3SX+SZ/ECv/KFHiZUYq/WIEdx0Ve1D+/FVr4uXxHX2KpcCo6IS3ioxA7FTGsgFzZnXO28J1N+aiwbMYoWj0g5cfDZI3P8gt0GL/UPBhiHLbsvVrX2rI1TIJsrdJbH2chOTIcNlchAPLYuujV+z9dD7pGrOE8nxubv7EH+kwIi7axVTgNrPP4lb4hdeLUxTfdsQ5Fi0/WOd7zj8L0ihnzYjMILvurnlV34IwYw3fzmSOygA1jCHuAcm4Eb1ghXyXNbYmtL4TKBGPmuhUsoxiP54GiAXOJPAeuMs5J2vYuBcAIJOKcwNqMCIsBaUgIszQvEHBP7AZoEAqhIijmvpINBCUq+Z1gSA3zj00UWgpH7jFGlzQHswOT/EwjeHF4bTp1dB44f5zYWp1bYSCrTDq8cQwC2prS3TomCoshRp3Z2KDktENAHUBqXg0joGTlHyjiABO++M3fAx+4ysAeM+mvrvXVlZ8T8nNH6zE22xnCPI9mhAgjGsW46wKckBqDrn3WYEzBKGLS3dhfABoD4xxs+XOQmEGljvcYXcOxSe+7UX0Uhb6c/kjzy0IY90Jc5XQok30lk6Jq8ze89/QumZAOwLr/88uEHi/ikC+MCXnakn0sfBY2gkUCTde5K+tMRuzUX+yD3Xccl6wu5cGmJnAVB6xEE8ANTsjtnjVmntUuY4VZsmz+Th9OGkPbasmP2Rf8umCf57MlNwFa8G5+t8hM+KDH3GYUXl6DD7mCEP0maZ7IT4EIKDQU5fvFkHhsVsBIOSVrYa4oI30kqbJqwWZsy8Ids9ElgxAMdmpNtkVd8Fy/mjfz4iIBsDm34OVtkl8bQzuW94hF/5PX6r//6g8/AHjuK7utvLt9JsiRQEkr6S9EpuYIpZBhejQ+32L/x7YI7dbJWa+4RfIRZ+NXfSZVHQsxpzJa0gzmSEYkq34/M0t4rHcNSiTaZS4jYEL+3vkramw9esU3rgh82c8jjrMiaLuTChdzpV9zhw3RFd3wKLrMVumBL/F3MYQs21axbsiiBhNf6SUwVNWI/+8rJK74k7HID7/Vjg14Tm/mGGMVvfG9e84t58g8FtPgDpyTQ2rZ2MkZsUsy0Rv4f8p7fwCcnJXiWoPNz3xs//ou857/45aO+r1dwlC7MJc5ZvziegkUuxIadgHhNLlDJOORKH4qOzI+8x3ce44QfmU8xBbdre2PDCHEeHm1D1mmjg13QrbzPeuEE/skAD+xCvsGX5QeKKTrSzmaCC96ziTzCCYcVNPRN14qX8+fPDzrWnl2Yk46NaXz5i7iAf+shEzojIzjgvb5ytyl2gaxlKVwmEMDdt3BBjMxYlMmQGEMCmAA+RpSknwTWzisAExgVDRyYEdrNMx7jsIPKePM4BgORgPhe4WJOVTRDs+MoScATB7CrqR9wYKCSBIbLOIGEapojC+4M0D1Fi1MIpzg3v/nNh+c+7d7gidNyFpekg+MybKDKQcmVsRtP0JRQ4Z0jxviBtuCvn/WZU1GFX/0Al1MCPJMRsJZoK0g4r/VwXoECGHu0ijzpw9jG0C7zARPfAxsO57321ithB66CRcAK3579xIf39EO/goE2donIP/PhRxDAnwADgMnH+PhmZxId4MbB6Y2zusjLuORrZ+gWt7jFsNPrOVTBiD0AI+DIRgQRSQaZkJvxyE6Rhw9JiPXgz1qsj+wBt1dBBJ/uA27JirHJzBrt0loLfs1BjoIpOejDtrPbZf09Im+6wYfiGg94x4/AoF8F+ClE7xdT4WIN1sTO2ZlgaCctBabvyFub4A1cEBBhDBtlA/obSxuJMDvgmzYG+LmNBgmO0wnfu69P/EMftuoyh3HZi3m1cWnDR/HFjgRDwU7AFIDohZ0Yl18gdgBj2AH7Yat8lh3hlQ/QQWwbxuCPzUvsYaH7/IS9C7zWDj/4N1nAr9ijYIhv/sfW8CGZY9cpqqwpu5XauwRseGQNcI3NeoxC4mCX2fd0whf4tSSAXsyBF75NpvAcr8bGU/SrrcQKb3Y04YhEh+wkY7Afridhxac+8Bo/1kHPZO69+9ppA+P5KZ81t+QTvzZCtOfjxsUHfry3Lv6Od74EQ7OhYHy6FmvMaQOF7cAYsYVOYzdnReR+IRcukbOCkC5gt0JBIS6WiqHwk/7EC74hvtOTzS1+nA0y/mGzy+8+nE4quvXzWJTY4jcfTi/ZhDlc5vDjfX6hP3sVG93jV/xBG7Hb+ryycfaP7211z+bEG/kGW9WP7fIN31knfOILTiWsMdigbeZhm+IuftlmOz8f5lPkQ6biprHhA58gF36ijfvGcllLJfMai/x6Jwju83EbhHg2Ht+Fe3yb7KzZuN6THWzz3TZkXdYqNslJ2INNHHkFH4e35KggIy92B9/kZf6UOZ0pJlzyDjZhk0ROx27IxONjihb3bDyRkfXAPPlkHt/n+0594J+cQPwmX/gPX222wgRzsZfg3bZEzkvhMoEImDHvW7iECJ1hUgRjU+EyKgaYC6i5vJcoKjAYmecu7Y4xGIYpYAsYAgQH1idOHOIUEhr9GYw+AjrHlKAyhCSIIX18x+k5HiNknIxQQJY440Wwc6R8q1vdanW9611vdf3rX3/1eq/3esPvI+z22bnRLvwK4pIZ4wmC1meuBF/vyQZIScoETe30JSt9FVQCeE0CzAPQAZDqnyyMw0kkVXQIGDicAgeZ0/zmAdJkVINsQMA4+ADYHI9MsuNEL9YnMDi+JytjugdMJPsSKOCnHxAR1Dk4/VeZm0cb30vaJBD0BQQEFPOQtYRJofQ6r/M618qc/BWj9KGIox+FBJAEGtZOJtaRNYas1XcAh/zIQZAXdAQo8tU3ckkfn/FLB+6bg8zoidzoRAAgWzpgo/RAb15zkZd+7IU8jVUDQLXlqUSm5r9YCpeW+Asd2F0XOF74MixJ0PA9vUow2CffyS4bOyR3uiYfRS/fZndsxD192S+dwChJAqxxT7EhqCkwBEl2yc7cc9G1JIktmlO/FCjG5T9si42wL8W77/XFm0ApsbcWiZjv6cBc7FzilZ1lPGenUh/Fl/n5ou/5hMAtqTcX3DNWZKeNJIIdkxHMYI9p45Xfw0p+6xlwGAgPzUFGCjN8wh5JCflLwPigH63CAHZtHdYpgcCPdfM164js9NePzxvTHPgMHsMFeKhQo3Oy9D3+FZx0gid4Qb+xA+tWrJC5eMZf3bM+MseD8fSTkBoXP+xBDMKPDQtyT/HnYnvW4HE5NmRcfSq2nTWR3YVcuCD4JTawZ/mC4kRB7T2dSZwlk3yALrzyPXozp8KGXYgp4r88wuM/bMIjYDYB+KtCxn04ob2LvZmXzbJPSS2bYC+KAzbFNryXxEu++Qi7mmoHidvikRhDTpJtaxGTYIK1SYTrH4dwmcvFr8RJ8mpjHZ/mu/FV47B1+B77pR/v+R9fsDYx1T39K5kP9on5/L6u13zWoz/8xBcs4sPmM0+wTW6kKGvH30Taw1e6IXd8GBdW0Rvdk6d7fAAu2QwhT7gEX1zyIoWJTRZ2QMc2Y+Ae+5A/why8w1xz2igyJlmyK7JiF+JB8gnFUx5t1EZ7WEg2U4gul8JlAjGqQxYucTLCB6gMQUIkgeAsDI3xUZIgzGAkXoIpx9CHMzBYV94bL2NXEsQZlkQBkDAk42vPmMwDCIybvhmHE2oXXhmsAMgJgIhxAZ1HNCTR173udVc3utGNhuoccJlTgsOYEygrvy5zZC738CvR4uASCrwyfHICYkBCcFZwcAxAJkDbifR4Ft7MYzxrJVv9OCx+8WE+iZSkTpLteFjhIyBzEPc5lwCAH/LBhyCNd+MpRsynyEpSCMi1tWNhHuvxWbJDh8BUEijAGNf6Q5FB1asEQMDBu7UIVmRjt0TBcp3rXGeQucdU7IiRheQFCElEABL7NZaryjtz+l7yJoABcrJj63Zi3QN+ALUNQvpGh7nCdy4yFiRyOmg3lgyMCVzZB/24h1/9K3/7Eh4u5sLFmmIz1spmBYwEFLvvbDrJKluVhCskrUWyog096V/lX8cVXAVxQUw//fkZO2Ebik42xyfMy6fgA/vNuLE9V2wFpsAHtidZshNot9aYgiqfE+xcNh1gVXaP+Re9WLP2TgTMJ0GQdIV3vsquJXACs8KaTPiG+8ZQYJMLWdhZhQH6w1u+ISnIX+ORfOC78o9Xn9m1jRVzugeXFDXwK8kCv4LlkgNFIYwzt7XDerJw3xjGrHJ7uexevi5YLFGEe2RDRvCMfoxBJmRpXLJVDNWCrKcP4/JzNkTO2XSAuRIa+MJXyZqs6IdNwBz9jJGx50R0wS5hzlnTPoUL/UgY6ZLdwVLFi/jiB+XskN7sdEvw2JakmV05pRFLJLGSWq9+z+D3Dre85S2HzQAbjmIrP6ZrMZdtiuX683fzsim+yK7ETsm/4gK+xx/wuYsd6KMgYXv8xzrkAsbju/xHccTX2LL2ZOPyXvzSTiEHt3wfcp/f82cY4QREzsVP+A1/J2P9rI+d8y3YJ1eCLfpX4j/mFPvj+3VOBE/gCr3wG3rhi/IRcjWvwrA+wjuF8MBn4UvyDRukfr8kV5A3sH9xGObQPdnCUesyr5gBw63Dxqgczv9xUbx4/JbNkrvcBc5aK/nJidiHeGA885CZR4Lx4T2blDeyH/ZExlNpKVwmEuc4ZOFSiYEDVUGIA6lsJaCMB+DY7bCTxjESLBlp6xg90kZCwtiApTn0F4iTgHEyR5gBNvPok91HRiIgAjrABCTxxhg5n2AsoPsR1w1veMPVDW5wg9UbvuEbXvusJAATQAEFMOFMHCcJFYcBBObjDJxJW05i7RJ2Ywia+gvULo4g2eCAdkuAmYAK7FT9+JUIA2rGDvQ4NlmQg7kls9oaFwAbF1hzbsm2+b13H+BwUnMBc+PrSw4SBwBnDp8lgwpO4M5ugIlCxiudcnTtjCvYR+bkwLkBCdAlJ4/ikDmZkKNAZdeDfJ1svcZrvMYg97vc5S7D7hnwo9usixzpTYFAj/RJr0CAvF0p6AA5e2Dv5rIDxIa0BUZk573vYn89O8z9XGzOuNZK9hI/vPmfFn7UxxbcY9+HJgB5qMKFziVqWdccCaDjVTJP32yObtmf3zJ4pVt+I3DyO+3H1lS/z7jsU3/jKOw9WpKTNXZnXr5Dn8GqdeNnXD7JzuGsQHjPe95zdc011wy2J1jm1E7BohCwGUK/fEqb7L5ml1TyYX5t+JxEhy/AWX7OnhVXfM73MEU/fgcL+W8wAg4oXBVk/NS4LvPxdzJBEiBJJXt36cOH8c3vBHu+T1Zs32MZfFbAh6sKBD6pb5VXKzuf6Vo7sua7SRoURHe/+90HPnw2LtmSMZ20Qd9YGd9r1mWdNkusH7+SkPvd734D/vjMBvL4WHSdMeZE+LEefkD3Z03kxvboA/ZPudgGDOUndOv00aM8YjBbUkT4nRUbVrzyGTGQTyoqxBX2R298jL2yG3EcX5JP8ZLefRY3FOvsxzjGs8Hne4+TeUzIb2bzw2077XxLvsFP8dtbx7pLLOQv1qCIEv/Zt+IY7/IXJ6fJZ2K/LnYI1xQJ/M39kPs+y3fguHHginikIPE9G9YOZsgpyEYhJqbCEJ+140N1Xu3xTKY2SVofMy9ZWw/cgl/yEQUFW5C7iNXWFDnAr20v7eUfdG5cJ9ZySRjjt39ibU6pzYUX+skak4/IBayVnmEdXfN1OKkdXDQHvPTZRomciIzoRHt2YKNMXHDa7DF2GEknbCO5R9a57UW+8kC5WbBmjvQqUbiEGLqqG4AwIkWBJB+4tU7i2oY4C2NkiIzUOBwMaHFUnxmA4Kcdo+A82tsllCQDR84G8AVvSaaTDlU9Q9SGgQPPu971rsMFOAU3Rsy4OYOADHwUB+ZIggKUODtHNp65OYACBn8uAAY8ObgkHj8KAzuowARQSDoAkTVzLsmCNUo+gKjgqziTjOtvbs4I3PDmPmDmHL7HB/krfuwmATjOSgaKHO+TOAEyQQFwGEvhSS7mIVdrBSL0+9KXvlyX+uJfMuW+NdsNN7aEUMJJTik4JGf05p4LMJG5gvHOd77zELDIACiRl/HtntAXXslNILIuwGOXlG7JCR9e2QF5eyUfoMY+fMd+gD0/qEnKFDIOe7Y2ukli5Tt629aupxD9AN5DFC7Wzi+AqLUkyM2R8Eamklo25Y9xvNZrvdbw/wrIXLDZdg31fuyBzsjVbt5rvuZrDr+3YsN8yLx13E3jZ0yXZJl92O27yU1uMvy1I3Zi3CTd2gv4fFbQlozxIQGRvhUiPsMdfeiO72gvyc48ChyYA3v4Hn/iu/AM/vIZgVlbffgF3BH0wwe/lOiRh3Uan38Jrnjmq4J6ZIIfvgnn73CHOwyPewrsfN0Y2rg2yQxp49IePzATlvpT5k5g73//+w/4kpOQ8Dx1fBgbG7JZIhGCh/g15jZjnZrCu7VaO73ARzhw1kQnCo5g/5RLTJH8wW+Fip1xevZYj3tOT1ySR5tb5oD7XsUgjzspso0Fz/L0BR3DfJ8V23kvjjuV0d/4GUdS6lEziTEbVyzDFvyI/xLVXdZXL35vLjFNnIa94jc/9kqv1fa8Z5PmzaZbvc8W+DIf5pPmcLIEB/i7eyF2DTvFb3kLnEjBRw7wM/Pn0kcuAE9gRZ0baS+PEp9hiPzIRiK88de8yFyu4jJHK49Nl3WzK/KP/uVN8IiteDLGZpANZTZkDrJ1oiIHkDfhgV3I02CsfIhs5BHmgLV4ZxMeT5Tf4VcscFoHQ520KKTdg6feW58Cl2zMC+OnXjDHWPIaupwrvUoVLoycYQMBFbOE2ZwCKmeRLLm/bdDRXgIOrDm9ClcAk4AyPjulnBEAqo4lwjkmtrtificL2qdCTn+JtH6SeMWCIMxhVPUSeI9jMFBOz+CTGOMDsBjLmMYSbO2USjKsV5Fgl1Ufa1escDyOYWdEe+OYVzDlRAouQGXN5CN5+ZeXvGRwREma3WDBlkPZ3QTMgEcSAWBSuOQ7yZCkw/rsUCsKXI5LtZMYkYV56EJ7gIVPulMghVcyVFhxYGDAhshLADGeteojASIHfQBmZE5eCh4B17gSKTzY2QJKAgtnBloACcjSIf2QjR0c4+RExxoVWOayuyY5s4vqKF6wEcQUbN47ZdLHWgV+63Cf/vnEtsRujcEW8Gw3h3zoxntjbWvXU+lQhQs7AeQCM9l4T7a+F7DmQmTND9idhAWveZVceHVyyAYFomDDugTU98bVDi7ADfbCziTKdlsVosbzuIH5+Ij28ZEeZVy2yd8k74oA/fk9n8tz9ZKNYIBx8S1w+owfGMl32RF/pB/+Rj8+awMPs06vxuEPHl2QFHkswsklXPEdv2Ob4d9anJgo/s3DpgV1fpREBQ/8ziksX2Xj/M0aY/PuwUb+DzONB/et1ake/8fbOp+ossuJKcw0LgyWvLB5CQqdSJjYBD71Wzcu2VgbvmENnZCfwhS2419SkvlgFhnPxQ8iG37AXq09cWUOhYsEWFKex4a2vZw4uNggvUhMxQHYL/kUr7LxqchkW+Zg9y4bWORAZ9qQDb9SZLNtJxViriLGfYkivBC7Jfh4kDuIOf5UPv+XXzjRkz/wU+PjS5wzd3je5RIX+SI+jAcTxFlr5yOtDfNVuQucYL8tsVF9jat44zNiv5iU2J8xvZIJXJGYi88wDy6wITmE8bTLxV/5I33gP5gQ8h5umY8vwihr419kWWXsqrrf5iJvepPryO/giuJI3kCvsIddKJTIwBxeky/AHjioMIBH4r01koHPZABLyJgNwGt2ZRwbrPJIuQi5sgd+p72+iibFVDa/d7nkhwp0+ErWc6XZFC4MMEnnMYlhO80QGBiM4KUiZ1QMnLMCYw4lSFCeC0hzOo6LV23sKPivuPoLmIyLQwMqRuV5WIDDgTmTV/NIlFsHNhd+FBqMHOgybsCnaBDUGCuH4LS+45QCnaCuapeMSOzxw7mtoQbmBGFy1lYiI3ByLGAsYfQDYk4IgO0GmU8w4gxJriX8km2ysrMpCec0dptvfetbD0ALVPAk8ZScKII4tVMkn12CsQAhMbvpTW+6uuSSS4bEz9ySMuMnqacnsrF2jmt3QCEFaAGG50wlYUDdbouCytrYk/HoK0Qe5EI+xhZUJDTkDuwUJgoxcvUHDySMAIosAJf5FCXsCNDSsbXSX5uwmIO8PAICjLQDzGTmWNl6gSzgAVDGJRP6JVvrNi67YzPspLUb9shOjKHosW5tfWf9ki5tcx2SjGcuulWg7TO+dQB2NmztEjfgnYSQrshhXVJ4DDKXOc3N/vm6ZFUQoUe+LRHh83zcSSB+2Tfe+ZRgrh9bsE56y0V+xqW3JCbsOv7J5p1qSqCMaS7+yPbonKxgCjuJjeDXZ/PhBX6wQTbOLvglP8cfG2HrvjOPZJm9Wwu92iCxpiT6kYk+5GD95hdAJSdZE6zxGS7yI3ZuRxAukonPMIaPB3P1wwd85u/Wxu+sJVjs0g72Wj/cFvjxaVx+pC8/gwtwwqkzHuGKPtplo4fs6TZyw4f58MV37JiSCd+HW8a1bsmVJIRPG5dsyZgf0J1x6dp4xs64dMLPyVh7+ERHxrYxwgdgvbWTH1uga8mdfnXcs/QDspOgw0p2SwaSYDZ11iS5ZLPikFPtKZdNI5s99MOn2RCMF9fFGBjL1umeLdOXmKGvkwW6E6Mk3pJNtsIXyM+r+zAeb/rzMfPpaxz9ciqZpNSJIR9iK4oldiL2SzZb/qdc5pPMi0NOd+ANe8MPH6uEf76pCIAh7K8Sv+QbcIX/wTy+zy/Igd1oU23We7hORvgRU8mVP5M/rGNz6ecyLz7pl3+4V4mPkYtcTAIOp+U3sC0y9mo+up5yGZe9s3MnVXIwurAxKY/CEzmyFXpUBLrMx9cVnIoTOZiiBO5ZH6xJkQLbPR4oDrKR2BYZKirYIxuUw3lMDD8wHB4qpvAG83a52CX7WgqXLQkQUyaFAPlq3IcmgYOyGX+CivkZl+Cn6hQgfMeoBDxJh90SjsgBJAKc3TOoAjAw8r3EnDFyXGuRFHMkl/VJuBlEdWAAAfQZpiSeAwgMEne7jQIW3oCxACEwCqKAwZh45uD4NR9e8Oe9ZAOZy5zWmudOswPiVQCye4l3nzmKhIkjWKdigFxSQEnerVuBZZ3nz58fHqdyjOl7iQKAc3Fm7Z2sWA9+fS85AU52L/15Z0eskjbjK36AtvEBtzULEHayzZXdaImPgE++Cj1gD1zpVfKgHX1XJ/SerBQSZIoH+gasxlBcugdA6IscAJZ5ATNgJwf6MS9dWK9XNkXGIXI3rrGsgT2ROfuwfrymeKZ/awd6HhNQvOjDHgQSiWISF3OYm10KZoIYgKO/zC/xFBDMcQyyNhdZ0zG5xKZ3IToB9gpFcrIOa2V7QJwfkAMZmHOfuaYQvuwa0gNbIWv+SI/4dLFNsmYTEjdt6dU9r3CDLUtSBAfBiw1kw4ONuy+JsGb96JdOJU0Cm4AkkAWzcp/s8QV/BByJgHHNw8/5knu+109/SScMhBswhX0bz5rML0jHbz2q1tMtnugKnsAhPqEv/BAE9bEmAVaiYw6nxgpS65SYk4c52Dl8JT/y5gvkSOcKDD4Ue3P5zO49nmEdcIXPRifWGHxkL3iQFBnbfT7BJyVJ/FtywXfpRDv6VLCQgfWRTXTJRq2Nr/FVmyTuuawfTsJx9iARsU7fsyH34CUMJCProBN80aV1kBW9mdeY7sMwY0kKJSnkl93aim3HJLyKU2yQDuE2OVkbeZMHO5SAnTWRvUIfnpJlLn626dJOP4mmx7RsylmrDTwJqu+d5ImLTt1gvz5k4pVPiEPkoJ/8gQ6RV7aXjUenLXbs6dTcXtmceSWQLvbtUTXFDH/ymBqexGa2lLXtcmXN5jQPzOA3dFn93Xu2yE/5OX1Xch++8DO5A19ny3xJjIUz+sR/K/keHio0stnGxxL7FHvklpzJ5TvyhWt8vvqA+2yV7G0siR3Wl7VGzvXztpc1PfWpTx2KB7kffdjwveyyywbfVDzkyRF2YU21r404fm7+WpRZn/iuHczytAhs1N+lvbhjs5Pu2SAsPXfu3GAXcMj3nuhh95mzXj39txceFWTLo2JbEuAWSCiMsdrF8t0xhGfcJP5xUIbDARgTQBHMgR+j8V6SLImVRAhaHEYQAtSMgrNKoMMzxwHwduXM4XuJuCCHtOGw1gkszCEQSygEI9+bz/feC+LG4ogCuXEYsgRFkPferp01AEaBDa9pa/44iYSeIwik2kt2GK1EBE8ZQ+ILwO0KGR9g4VWCIMhrbyy8SaoSKACFgkISIokxPpnZMQJIknQJgjkkDHYz81gEEKcb9/UXKCU0viMPBQ8bwb9xBM2AmjXaSVU8+gwgAHuAzUW2QBI/iiBtBBYJiX7kQG/0ILiQHR1ImNmA9+TsPXvVz7z0IMEmc7xaG1mak9wlGWwFv0BXX3amL/7ZCPuREFsbOUpUJAaKSry6zM9u2aWECx/shh4yljmTYOnPFg9N5jGuEwBrAcj4bIPSFMI/nuksAYrs4g9kQwbsgM0BV7qj333mbYn8rI0NSi6ti175n2SS3buvHTnQqSTX7rzv9aEb+tQGb9bGnuEEvOAL/IQ+2aIdRjajXdbOTuJzbEnRLBizlYxr7eaXIMAG4wk+gpoESlEULCVLxDbwByMkG/xcgcBXrceVcfmeQp6toipna2XrbFGCxZfZIv3gG47zFfyaU6FucwRP7luv+fk4vftOX7hqXDJSWPBz649cEP74Bx+1drymjXWySzoxv3vwms1UvPdeO3ORGfwhMzKUFMFH/Bk3xN6sl1yMQTf82ntkbLK2NsUf2yFbSY31wDm6ZkPBJWQeuoUT9I4nssymi4u8za9/ZCW5Y5P6u4+PyGhfMo4x8cBGzMUPrImNk6v71RbhFjs8axK7baAlXm97WWOSc/5pR9upC/uw8ZZxFRx2z+2MwyR94EP6uq/Q6eGvz3zDuGwiPOrrlf1JUBXlCiAbD+QqKYaNdt0lx/ySDVT+p1506RUPTols9Inv0WmID8A3yTjbbm2M7mGbtecJBom48RUxfC222fY1F58lP+3NT87smt9kY6DyhB9x3PrF7eqjyGd8iEsKPHEy8q3rnnqZTx5HPy55ER7ZhByIvjxGJhfi9+ThMh878b0NUHZBHlmTi28r8vyvH4WWPq6MAbM9ruj0je5t8ipuYKD2Cg4bx3Cv7et9bz310kbh6bFkuku8mCPNqnARHB3pESBFAMuAe2vsu5JxGIzAQfGCdQwnFyeTbAOH/KDLZ20rP8ZhbBIqyQhDlfDGIAU+hiU4Ckh194VjCbh23jgUJzOvfhI1Vbe+kg59JPSMSZIoQTYmQxYctWec2kgQwhteEpQ5ivHNm4RIgi7gkTse9GGs2il4BGRFEUcQjFpDBsDZPZBcSEwkQ0lmkggFZCTckjffuexEcnryUxgYBzgCakmaftWx8S35UFwAfrLDpzVnHsGeE+LVeAIs+bhHju6Rh4RDUhNZGVvSQhbGk2BJmtgJWUnKAKhAQz4KFffxnmTT9/TIfo3FZtgLXqzbOB4vpFMA5jt8uehKUsOOzF1PiYxN1uRJLsYOOGaXOTJC5mMfgp01tnrbh8yBL2u1drYh4ABu6w4PuxA5toVLHc97eiLj7FwLpORFhtYZe5lKmSu2wF/YpkDCphXqdJB2mYPszU+3bEV/Mue7bNlnPOGPTiXQgrqkRUCFEeyzHderwsfOvc0U47Bfthudt2QORYDiXhLCH/lt7Cy82NXEQ/ilS3JUCFZbwYM1w2G+hM/YJPIeTwJxdhz5n4SDDLMWc7Nb/mpN5oeV9Fht1z1JOZ/Gn1NYyYAEuc6rrX7WSRaZJ8SOYCTswrM18UVroBtjWTe+FPx0p9CTFJA33+nJ1zj83jolb8bgk+TstfIYgiXWaQfdDr3xJXLkU+0VLtCBeY1LZ9ZOnz5X0p5exBT2BA8lMuyUPoyrT4+fdWRcl77GEBvEiJwU8k32ak09wrt29HfWJPGVRMIm/LjY5jaXGMtv4KfiwUaZUw46FB/4lh1uf1VOkqlt4nLGMK9YASPpKkS+5ETf8FvcxZv+uYxnV98fhVEAmU/cYp+KJcm435LCAm3Nu+tV+ZZY2/gTl2OXIXYlprcYgbSzySN+ibcwk10q7pP/ZLxcPRID+T0bww9Zs2n2F79IX6/8l4/bZIBhdVw8mtd6FIJknPVGR64q902X9uaSF9G9H8+72JmYLFe0+eE3fPCEbuo8XuUvikN20cqBX8FFuS/7SZ/YL5wSN5zyKaKd/rELtmKdbMMfcMmmavpn/m0uc1iTXLPFnTnRrAoXRsgogLrgBrjrScJUIB4jBgNMsrvuc70oDD8CiQpbwMWXIFKTDPykraBrJzG/77AGCYdEVHEBBCU4WZsig4Ea03dJLKxTO2vXznwedRAsJbyMiwMCEvLiHOYXZCSOihnj4Y/zugdUGDhQkEDo6xW4ky3HU3hwHAbLsYyrH0Cwo+FoEm/GRfgFcJJ3SYG+1q6Q8Z6DWrP+eJDE4c0a9BXoyVXBIoFSGOAHz3hxFAt49A3oCeaSDwUfueCPQ5vXdxJDCab1KDK0JTtJhkChnYSUrvTHh7HJjrOb33s8AEvAK2nizPjTTxvJFRnjD5BInuhOP2Nav0KN7RrXWn1nh9hYZGR8Y9CFIlXib1yfY+uRtVdylDRJUCQ/7IOO2HHrF3iQbErs8ZRx9iXzGFuA4Rvky37JiH8IwvvMRdcpXMYoPkIe9Mqu2JnnjAU69uWedtsSGdEPX6AviQE9sSU2leTXmNaXC9Ehm7DbGFlrDzcEXTzCATuakh3JAX8S3AVB8mzHddGr/saxHt9ZNzsnH9/1SBsJLX9l+/xDgQTrfGbffFNgJG/j0qu5YBV+KuGNTOGHgsT4If3waS5+zD/x1dok2Spw4YQ5jWE8yY3PoapbuiAj7dhX1ac2ZGiTi35aInNrgWXWgxd+xUbhgPHISFIFE/FBRzaWrDH6aCm6VnhkjXRtEwWf3rcUXSq04R1cMoaklQ74Ep1IfPCGX33wQPf6sauWzO17c+qPf0mPxIntKsh8X+W2icxN9nAOtki+6ICsYbS5yL7qtpJ7cylcyIJO2AL7y2V9my7rZFdiLZugK4/hOFWUNEoU7WzDO7FSYcq+6jwutiTGG49Oo1f2YNde3OjNqzgQw/ipwsl85lVA2VV38gJ/4Qdd1TF2vfBrbrYMA4NlLnbBvxUA1um7SnxYzgJj8oip2Gj3XiHDLto+PTKP2C/XgXMww2dxxvz4zDjhzfrlIOxd/9wjY/kGW+S31lfXmivfbXuJ5eIvOTmZ8Ic0nKzZZKFTT6dYP3/GO5mmb3yV3t2v/oln+RRsz6lr5RNmwQ52Yf5z584NcvIIocfD8AKHjcv/jW2MOk5dx9hlfU5w2PSYn8+BZlO4MDSJOCNFjJDwJYwUpBgQQBMwYsC7kvEFNklPwDjO4GJQAi3w5hQM1bPYHoGSMHPW6twJIoxPgHJxWuNzILttEgcJgnEEP8YYZ0P4ELDt4GVngzEBYesXPBQtEjR98ScRMi65AAvObmy8xPDwZxwOIfEFTPgU+CXTnm0FFObBt/HwQgb6Mn7H5tkhNW4KD/2NbR3Gtmb38WdHSPCQ0NhFEQQFauDne8kcHZOlHQj3kfkAITlpb2yXAKoY0Cc64pB4xwugV5CROV4ALfAwrn7ukUvWRb76S1zsjnlvfewsO9LmAgT4w5e5BGff60+mHJ089Q15L2jSl6KE3dCLgoPcJSQKFXzZyaL32FPIZ3olazwqvMjXXIIEAGxJf3MrjBVIhyDyIjd8KPwkRxJ18qAXfFqDRKzyP5XYwabCxfi52Jm52RqbpWe+Q+YKKnYWP2374pvdCnwCAp+gJ4HOd9Zb2/cu89O/tbNNn43LNgRwu6VshX/ZVLARwcYEMwkRfx0bl+0Fe2IXLjrg4+btkTbmd7opETYXO4StAqu+AhxcJbf4s/l8T2Y+h4xnTTZlrAH/IX0V9/zNWPErVyX2ys7hQO7zS7y084XMqVCX/HiNDhEeUoTwj0rGhp2Kg2yS4NP64A0+XPzdyTCe2LE5JATe98i4sMjOpvnrWhW4NiLM1yProxOYRH90otCAT3QMJyUk2QCKvtmw+7HFdWQOfeGIdVof3GKXSW4zbsh73/ERNkJPkmFPIohZZKKfsdNvHR/kPJfChT8rvqofb0t0y15SyLILp3tOPPKnbv21PZgOj/k/nbbEluQzfC1yJGu5BPusvhTSB46JN/Bd3qBQccJufjEATvE7xQsMYeNT1jdGbAFGKZjFa5/JAgbJM6yXjitZk3wIX/IDGwbijtjGrvmnMbYhY5GTk5tsBtjUw5NcgO/E70LGlyvAEj7mnu/Yso0iuNSjqfLS3twwh06NL+6nWGEXfFhsl6vxH7kefde5rJFtOQWBqXj1HVsQt6yXzSL9crELmA6z2Db/dpnXHwOAS3wPFsnV4Atd1TG2IfISt+gfX3Ol2RYuETbhCRgcR7JG2RS/r1A5JeUCCUmPz5XMbR7GwRgYAQdmkPjIDnz4DK8CMUcCLo54AVzARsKnr/W086FU1DleNh5nlVBwSk6taGHg3rsEv+xqGFNyLvEDHACtkvH04VBAxWmBI2dGr1LPIww9cABaeAfQ1i0BByzmMK7XWri48Gw3RAARlMmazCVA9ExW5sE7sHMf+Y4jC6LWJyEwlnGySxv+vOIXX4oBTgs8/ClJwGX3w24NMMCT9ulLFu5pE1D0ncArQceDSwKAP2OkOGQzxiFzSbzCSZLQkvvshrzpkS2Qid0zz8gKjuyF3MMX8l5AYO8pCiRZ5MxOFCb00JI1SkLsEvbu70LGEUjCB1mbJ2SNpypcemRO/NAfPdoppQ9JL6CnK/fShh4lznxJoCVLwSbrcm1ah/t0wVdhAsDnJ8Zlh4KInTC24p4xycncsENwZmvGaS9tjWsN5vBdiJ0o0GGl8XpkjdmUsaaMyd/w6jED/svOFeJ4cuVRAXy1lB/PwsLwwyfJT3IXHMgaQt7zJbZREzUyUUAkQWpJP+vg//CKbEPsXyIZ+VXyHRuSGLAn8sIf7JDoweRgCH5d2uCll5iFzIMXmIRfn3OJEzZ96GyMjMsuyByP5rU+sQAmSIbZBL1anznwBTdyErOOIgevxiZftm/NkmQJkWK32pP3MIs8YC+bwCObwZvLWNuSNV7ohYt2ZCfWwbwkigoFpytswJXTFH5BvmynJbKDnTbBFIHk6YRYjE+S3ZKTcnPz3zwKKc76zmdxVGHKTugNb8aaoqcxwg8e4ZM1wgvy81i106XYbUh7dsr25Wd5okBSL6G3Vu23lT1iQ9bIF/iBWCmXoUtrbvHQe33oAob7LF/wWf9NfrMtkQtdKurEe3gGX8VuvCqs6INd+J692HDP5lRL8jR4msLTZgs5JjfUJxcZyj9sjigOFTD68+34rXnlPuIPObCL5Fe5tiG2vxQuE6gtXCpRpKDHoBkMx81jNQxqF6IUgQFQuwSyShTNaSXzjNV7c3kV7CUP+AA0gF5SqS2D0V5yLuhIBhg8A+VMCgTJMqfCPx5cEgL3BN3MhUefJQdk4PsULt7jEbCaK8k8J1Y8MGJjAjXjM0g7F4Kg4MvR/AM6/+zMcSOnAA52irNbrR+wMnb4B6iAGlC5H2DifLVwQdZgB9DuKCfVP7ueeEk7PNfCBekrcQeEQBNPChKJaQoKa8Kn4BswsTvm2U//dM5xPueWxLAXc2qvH7lwdjIHBuzPOiT9gECgsaYULsYnB7r2ma6jI2PRs7Xhz2cXmwDedGQeF2Ayv/524q2NvUgoJT5sBQ/0AKDYGVDDR2xAIJEc1GQuhEc2gkd9diVrtwbrxpv1ATM6t+5K+DrLwqUS3oxDjmyR/PgkHxFMyJ39Cszsgj3TB1m261pHsU82K2DxaXJiA/wTLig6bViwb3bikuTa0VZMkTF51UsbvEhm4V1P1njmf2ypx7Nx+CsslViFrJMsyBiG0alxyELSwebxxmdbYgvWyo+sxxwSFImq9viu60B44w/mIHvrDVmjoF9/l9cjiRvcoNPIkC7FivhsyHw2RuAu3dMLXfMFxRNftAY+Zw0h65aUsm3ybSl6kzhmg6OuFW/siv2n+G3Jd5IKtl1PZuhJoiNZJFv2Sh9kBgNgFBuiuylkPvLBN1tk6xIj8mCXxoY7LrgvLvAZa4lNjelkjMj0Qi9cyI1diofsxeYHvfAlNieG8HHyoifyY2tsjF5b3bMniaTCA14YT6yrvoD0I3+2zRbil+xfHBD/5ClwVtJqfeKC5Bi/sclDkLiV4om989FeEcK+8GCDBc5I4GNXYlBvY2ETmYOts1fr5VewEF5Zs3mCmyFrh6fyCjkPPZEfWz6UTKwVxhuXP+FJPLRWPMIdBSp9sQ8xmp/xd3JoCdbAIW3kJORN79YWObvYD7thj2TApsUXcxrbpigcJ282qSjGF5wTP4zV6m0dkf1SuEwghjFWuIQIXwDN7j8Q5uy7Oq0+xpJwSBbbMYzre0YKlHLf94xNUOHUigBJNccFYoxYUgFU9GN81seo7awJTgBekq2vtdh5Z4DWZ3xGo5+dCztl3reFC5I0KBgqSHAKhi0YAz3Aoo3EGcDaSQIMdh/9Z2k7/z7b2bS7o9Cw06Ef8CQjSYqxnM5Yp4BYHYzDtYVLvudAjrv9BRbrBzI1cfC+LVxC1sdpFWIugV5bQRiPLrIkU/zbWXSK5Mdzng2WsEhgAIzdbzK3LjIHdMAissQvHbEr4G0dKVwAMiDUVpIHQJJwacdOyIWMjY0vfLNnbdmHfpENfUmEBCS2okAQlLRjb4pVAMnOzIHYBbmTM7n43JL7TvbYXRscN1HWwtYUg4BRoJV0ssvw3pL2cylcKkV/gi4bINv8+VLATAd4H1tXS5EPubMDQduPc53KWr8EhT5jT3RhTrJk4/ppwy4SfOuYviMDNghX4mOVtE8RodC1Bn1bucMAvqxozn288RX8+GztAj0fMB678ReLYIF7dVy8wQF+J7j5HLwwV4+sWR8YYi3GC1kX3s0pGRnTgUKAz9rogaH8km/oY3z9Ij9y5++e95ZESHTgkeQrviDx9H8djJW+/JV9RCfIax1XbOrtOqPIET7SnfZ1rSG8ayMRztywUeGS+EP/CimyhSVOjf1Y1jpiq+FxE2UN+vEDNs/2/aUsxZDkik2Y8xBEH3MpXNipmMA26dVFDusubeCtOJNij+3CfjERptI/HxYjbHhJFCWUcFLMje5zkYnEU2xQULNJySb7M2fa0YG4ph1csXkm5oofbME47Fjswg+sJWe2lMJWm3ZNu1zWmE0FuRE5kGNrd3wanwryxG9xzDphlPbb2mpL1s7+2SyfEdflR3TaxiJzkB97tslBV2RIF7317XLxXb5D9jlpERflEPRpfvFdG9+TBd7ZkuKk2kT0LQ+S/1mbfskV68VvFULwyXhySjo3L5ygK7KCYQpa2MhmxVE2QkdsbRv7d2nPnth2lfHc6IIqXBBhclAKY0SCECUlmE4lCaCkgxG0QcF7Dpvd8HpPW/NxIj/mFSw5OANnbIyL4zFS/fAsgRKEvedUxpa0cn5/tQQAMB7tjS04M9A4KmNn5IoX77UjB0ABPLN+Y5CHZx89Iys4SQDMaW5rllgLxJ7PVAjFAfGknXEVhQDcLpDnORU4TgmSiEVe4bctXJD3xpJAeTyK89G1PiFjjRUuxsWXv5nu76X7DQO+BXxOhleywLfPijXytB7FoEAgGGljXrJUWCpq7C6TU2SOV/KtJ1p4V7jkxEVb/QUgzo0/7ehZkuY/11qHIJM58UbvkpqQNRuDXLwX3KxLUiE4sSF9zRcyh3UDu+i6Jbtk1m7u2N42pB0+2IkAQc50b/3mWjcWHudYuCA6xT95AnjPibNThbS5JIzWWG12jDIO/6E7hb9inL75hLHiF4hcyFACwM7IUFEKB6JX8jIuHhTlgo6TQj7HVlu5e68vTOITAhb/YK/VVozJFoxjXPftyOHVXMjYbIzOtZEI+ZGpRFkAlOS7Fx6MaSPF92TAXm3e+L5HZKWNjRJjtIRfARnmZ466VkSe5hCw+Yj2cJY8ydl9coJxcNCPl2GateAxccH42ls7+4Ln+KMzOAHrqk6819a4NjzIhX+z0ZYyriTF/JIrvPqu6kRf+GInGc8ua4EB7pnXWN7DLIkN/XqOXUHJv8QLuoyNraPwxT5snng0mP0p6uA02UXuhyDjzaVwUVh4JJLdSApd4sK6C+56xNaPrSVv/Es84ede6VdiDBv5qE0Jf0SGv9iQkwPA5szngsU2Kty/3e1uN/CUcdxLO3zKAYwnNnm18+1/KCmOMo5Cn33nr0j5DZQfiOM7G3zWYfzMMfXS31jWz+7EL3YcO/HKbhRUTiDEQ/mBHApuyH3Ywj52BZdgh40I9stH5Tjkywf4dPUB/PAXuZj/a6KddfTWlysyWncZg27I2Z8h9odOyJ99mEdOYy5tfacNWfhjDnQuX6In81W70N64cppb3vKWg159ZoeVN5ub8i4+y3/N6Y9E2DDL5rKLzfpZApuUQ/Bz9oQX91tbH7vkKTb3xI5tMOasaDaFC7CWkFMaQK+A3yNOwXgFCLsrAJOwAb7vt3UawC6YC+rVOZH37guW+OIYgkYFe3MxEoEBWLokKwxOVU752nJkyS8D5Yj6+S7H0trjIQ5vXsmRPvkuMnIlsXYvySy5+c46FD2SM/xIXpI4aeNRMbsY2gBbSbadU4Ex7YyDd2MJ8ORKvmSd3U5tcllPr3DJPckaXqy91a2x1hUugBEAACyJFNn4PrJ1KVqML/Ejf/agsCAXBbF1pZ02go/5FBPGsmY7HmRlx6TqoRYu5tSebuy8AVjtrEkyCWzJnS61Mw75KpIcNYdv/cgKiGkrucUz3Si2tHHhy9j4EBjYj4TbvZZ8Z+fPDnr42kT64JEfkZskjXxyYpN5xsbCW3YprX2bOceIfA9duCA8kSPb4cdszSYBfQk2kkcFdU02rVs/MuBrdMjPrFHAkJyyS6eh7I6d8Uu+YfNCH+PwHcHD93zA2vBBP+Yyrrn5Hv+BBefPnx8CpKQ6vhjCFz4FGYFJYquYYLd2GVPEGN/cEgkJtz7snQ+bl6yNjy+FhXnxhhenB4pXyQI7t26FjQRGUa9AYLPsNIVNS9ZuTnahgOiRtbAd/JODPj37YZ8SBAWMNdjBxAs++So9CrjWASOs0aYRGfE5Bau1kot140dSSFbZqIAP7pGZtebRFDjhkixIAMiu5dH6nQglmSFHeM6uYBB7MLb5Fbj4ZTfiCfnkEa3YmZ1Tyb+4Am/x75IksjMYUe2syp8MfUf2dIkHffi0uY3Nfunt0ERPcylc2AmbtgsOD12K1XUXPCdvJ6jWYFNUUc1/vRqLPZE/H+Wzkk1+C8fpXzIPX3KZ13d85oEPfODQjv363n1xxWVu87hPX8ET9gRr3De/3X4xgF/7zaR7YktOfOpaKx9TL2PAYjiUfCXExviQOfkkHCAL6/AqdvQwYQrpz2f59f9j7+56ZEuKc48DNggLYWGGscdghhleZhhgeJnxYNgGg22wwVwY+wJZluwLq7/FOd99H/1K549SqVxVtbqqunubTmmpu2rlS2TEE09E5KrqxtX8VBz2VMV68pg42kUmPgHb+p6jg9H2xy42x/s4CNfTiXxPQcn+7Gs9vqYwlUvJV/g5HmKfeW392ZMNFQr8Juy4Z00XDChSzYnPcCUsmZ99zOHgA99Yi01wFTsotuEjXKz2Nl9yXf88U15U7H+K7UkVLshC1UjpHGME5lZzT5AWtIFKsBHMzHdOA3gJm3El7WMTyHzMCLgEME4h0GRU45Gk9zm3QgSxqLKBBZmbU5BFbBxLsiE49gRGEuCUQjEhoEQMkiz9avYEyAKtAEoGlzWAW3CzlqDOEcjixBCgkaX9kdM95CJIOt2hP0FUPyBfJb2tI3iaS3DMNi57XxUumv0I7BxZQJ11bD2k5/7crCMpQ1j2b+5RNr/rg0g8IqYXevUEgA7ZSgLAIfVDQE4y6Jnjc2xjyB8O3Eu37o2FS2t72id4eM9+7ItdJSrmYTvJTye27O49NqKPbCkwOU0xDhkrspBU65MLBtgN/j744IMD3twbm9dkIFNPxEY9rVq6C3vkH5/6nWrWFCzYB9nR0znjthqd3KJw0chlXwKt4J+NBUd2YCP8IfjwBX3pHpbwkmAJUwKFZNt84yW480vzsI/f2R5+FAH06wpH5vU+e/E7wce89k+PApFAMtuCrwho5qIn/e1BYmMuCRaMwx4M8Bv7sm+49x4ucpLZgQ8f4Cut1YX74Nw6+uWHTmHpgt5WzVh7JBM56WCrZXPJICyuGv/Sh59IRq3LR/i7PcMvWdlz1BU7+phFxTjZ2cmadM6nJAG4ydr8li7Mab/420EHfbKnZIHdZt8zlkySBgmnPvjVkxVYgwf3+3K8fvaDS8nnPX5kDTizL/ZhV/uxHh24vIcjRpw50DAH24ZX9+wNrhQ4bIv/+Tj86HvtZk56ewqFCx6HKbauheuti575F/3zcxf8yi0UFA4q2Jrt2E2BjJMViHzJAcGsV3qHOfbi72IcPmcva9a85rPsz4aKZEVm2LMPPqrgFivgnxzsK66Qm/zjNe9vzwVr1pQPkBl2NPf4M2zTr1xLDsOHYIsv6nONZh5YVRzwDz5M5ziAXPGVvZKJ7/Ibcvf+ffUwN/tnm54y2T/7OHD0BJrdYQJecKY+4jAeh595fv3ZET/QoT06dJn72YsDGXPLHawnr4Mn89o33MhR8Qvc4Ac+2JNdejpXD+Ki4sjeYOCptidVuAgkqljOCCQUj6g55bHGKICAoCUGQIO4GNq87m81xhL0AEdAZ+SacQDLURAGx0QkApvKGCgQGWJBXMjH6aS+EiCOJglRmEieEaFgiggEfoHQPABr76pv+9anhKPg5ZLYkAFovR/5kdl6wE1m83EYBQzdIUJJC6ciB4ADpeCqcCnoIR/AR8CjHsYmGeEk9p5cLnMcK1wQz30KF4UHYhY0xnHW1OifY9NV9rMPCRw9GCOwc3jFA6IQAOzXqQKdwA0Z2FFwMcalTwWkxKOiUjO3kzlByPwSBQUrYjOfn05TrQmPghc8k4uO2J+97JnOyc6+inbEQ5+wh6DZXGHmxIXtrWvf6UAjq4KS/OScdVwzhn0QHsKVRJEPRo0zzzjv3NzTh11g1njyCl6C6bGxpxpfFYxvVbjkGxITePQee1qXLwi6bMQmElt2IQvdwwr76LvSLXu5Ry+KBHiBCXiT8Psoh8f47Cu5gztBtnnZg2z4QmASzBx0zPzF7+BGIQ4zvgNhTf3gRTA0rz64hOw+PqEfH8FJ9gfrcOjwApboYbadddkaLsybn/l4g48t4KlVM86e4BTG8plVc0+Al2STZdXom/9Y08diYA6XShhxHBlHHdXMbW/0aq+COe5mF3xtPtwOt/yK7vktbNCzy+/4zk96hYd5P/TPnyUsMNRHA9kUF8ADXJGb3dkA9+NeyZ/3rMGPJGbFvHlP7EMX7tGveY3HC/ZBhnAlqWE3fclrLvbCUcf44ZLGDk+pcIF9+9/T6BiuYFccYRcHCDDCb8RS2JEsKmb4ISzyazaZbYYXjHUfnvyEhZln/e4+3uBneBD/WA9urDdyNTvzSblGXHbNZh9khxXxR1Hfe3wRP3oP1v2kI/LB/TUbneAceoZrPirHEm/kEvRIJnzJhx003ALb1uDbcGV9+RWbZE+Y8Z6DP33kdewuX8QDs31gBY5wPZ0puvg/2em5/l67L39zkAIX+tK5olkMsEZFrNim6JBnlOeN851q1hJ3nguXMxuCAQDkC4ySQQYBUCTsvYxwrCFpgUoSpxhRABSYj40FQAkGgNaMEXgl5BJC9yWyCENSwKHdByjAEng5NBBJJMjB2ZyqAwOwKpKAwmftfY5VcWUeQENI+kty+ryi+3QjOCJOwc6JpwLEiS556UZiz3E4iT0LjmSwB+9xan9FTIFAH5qgSrb2DNyc0qNQycQKuEjL2oKr+8a4zHHtwsW8yAJJI9CxWcOaCANGEEbzclgJClnNYf8KO98/YWc2sCZZ6EWAqnglJ3mzvRNU3xWid0RUMShpuPv/H+mRFCJWJCSJkKwiFvd8bIyd2ELiIGHx2VcFoGSZXSWZSMzvsOAe/NNnwVKBhKjIykckOGSsIXhzwOIK5/Tg/fCA/ODFiRbcZsut5p4+sIawK1Tsydr2TLfH5jjV6OhWhUuNnSVWggRd0AmZ0w/fYC9781+I2VhAiENgbku/XfREx3BorCcF3/3ud1++//77h4MZPkJvsNRc9ApTbMgvBWB4pOvsrC9OkaT2hAR+JU6jTNaOlxTdH3300cuvfvWrh4864DEYF6CMJ++plm7IaF08Bdfwu2r230lp3LLVzG2v9i3YW2eWyXx8w0dp7JUveG/Vd6vZqz3zJVzArt/4xjcO/+0aZ9tTJ+qaef0ujkheYRNf8HX2ySbkYGeHFDjRmHzePfP4aW1cjpvx+re//e3D+ny6Ay1rjHZcNfet7aI3MRM/+fPvbGse/DS2ZKBjyfy5dt/b4ONVL1w0HEGXkuSeZIqLPvLD3xQP4hXciJP4SkyfdQr7/AVmcCzb8V849jMMjQ0+8bucB8fyI1ytiMHXcg8xQoFdwsrfr2lPc7nIC5P4SozC83RDL3BHv3zDHslKF6f8fW+jI/6OS9iAHujAvq2NQ/kcHcsBTvnPfZt57V8O6DCRXezZwRNM+MSLA1+HF2KhvMWhpTHxgOan17jeAQou4Tc4hH+mw7k/znboIP4bZ34FnfyETuQbilgcxjawQTfGd53TnguXnQ3BULgEnpIBltMge6BVzXMQID1mBPcY3nxAwbgAVrDeaohEMCt4mAfoJM0CC2ApTsjAoMjCKQMwcSCFBkAhGffIYC5E5GRGEuoLXpJbp4uA5bTMF/8qdAAY2KzDSSWnHBIxIjtEgTSs4YRB0PM7mQQxspLJ/IAtkHJmiQ79SQwkzfaqzYWLRm/G2JdqftY1/SNO84+6MsexwoXD7SlczGmcZLiPeY2ymEPiRAejLNpYuOgniZBYeEIgkePscGX/7CCZUCgoyMhHPwIf3dMzQpHAIG9PxeDJRU9I1aNV2CID21tHcUlu/dgMwfnOgLURGtntO5yQD47MJbm1L/vQhx7IgRAlcOayP0Gkxladssw20+gBno01N+yRFebcW42p0at+1kDSxsNcY8koYXkVChf6pgMBiO3odmzk1wc++BEbkscfueBrCgJ6Ny7sz814hxQwBdOeUPzgBz84FC9wIbETYGCm8fYuWYIdc9MtDqBvdrMWPcMhPPIHuienwsia+rhwCAzTpactvtz99ttvH/6vjKRIsY8brHOuvfRzGWN+++I3c9OHnILfeJhwrOlvLpik2xmPXiss+K59t9dkOqdZg7/bu3l88fi99957+aMf/ejg03iTT5q/uemdz0kyvHZPwQvn9dNHIiuZYUOy6YOL06+f4UFfT40Usf7yoQMsRTKOWp3Yjy3bwqAxDkFwsWQJj/PnWXdacuIz/HGOTe7TYPZ/Q+FCP3SJu9keb7MTzCg2fVnaH+WQLPJHvqD/rHf+LZbLPeiGHcgjruP7+TBO0w9G+INx/nO+j51//etfP3xcnZ09DRIPHJLx42OYubSZm5ww7yAEJ8kNYBkP2Yuf7rfHazd+VTJO53Qnzikc6YGd3dNvtsG1mnntDadZl7/Sgy/Jv3jx4uW77757+J1vy2X4gVwonSQXbMkxyKvQwRfu4Qh83eHouA+/2xu/6pM8uBwm8brDF38URE4gz5LHwF7803VOM+65cNnRODRwUn6NshlWUu6jDRIJla6gr/+KpLXRWAIM51KVMjrAl3iMzXuSSiecwGasUxKn5hyXQQUxSU9rG1NSDDiIRPAzFliRGbldwCrBNp+TTyRgfnsq2SeTREShYU1718eXrOzbHOayD0FO8ogwJKzmk/QIZgIzQtVP0mVdsgI/55C8mFdiz9GsMzb7E3wVfSX/NTIiVEUKneSU5rhm4aKPxFKCyYmyiZZe2JScXo9zVrhItK2HaCSFsCCAlwQhAkWpkwpBSsAocTYvG9Cz9xV9cIgoFaBko0/zCZLmJKOLvIptWJAQITTJETuYD84QILnDCjmd1EpyJT1sSy8a/UmaK+AqIPiBZu8IXRLDJunJT/fI6JQKwXeat5Xk1Bprj9ajJ/h3Amm+ETN+Ny8Zt+Y7p4XPWxYu9sSugm02m5s9CB6w5wkom9GBoOIQAm4kNPTPr8xBdr/rZ15+CQOKVh/94JcKPielAk98xG58DFYUtPjE+i5JifXgmdzw15NWa8INW9AXXMKkxILMeIX/4gFfMjYOTvkB7CvCySkJhpnmO6exN7nNb89kq7mnOOjjUufMCd/0Rj48MWKLHuhWQW/NdDNeq+Z985DPUwj8wuZxBt3f3d0d9CtJ5E9kZiv+yx5kwQvwotknW/HfeJGfOlSCF/twGauYhA+X+AMPkliY4ieSYUkov4IRcvBPe2THcGUPfNU8MGc8nlXksR3ZPP1n89EOYzMP7rCuuc61895mnadSuDioY286EiuKd+de8EjH/BYvSxTFbh8vdCleYMaXsdlXf4cFjfc7XInN9AKP9O5i07DG1uM487CzGMOH/QGO1pQH/Ou//uthTfjiw7Dd/uarOS+5yEZG+IJRcoljPtIGT2KJ+ImbtvB3aTOvJxH2K27Ks+Q6YgWZFHJ9KmHU5S0ucsj3HFKzjYOo4rYnYn7SEY7ly/hhHO+1GMpnxfJRZ+TnP+yvX3vJBnJPT2sdQCiixQt/0c6X8X3HFJ/BBbuki65RhmOXWOPJojz4VjxxjfYkn7jMjdMzMPJG8oyO8M8NjPoACSKRfDEsYIygQSYCFkKp4vUxD/0FI/cBR5JQUaEfIEtGkAoiEmSNdUlGfeZQMq+/BAihAr55JaiSYg5JRvPprwihD+9JTPQ1h3UrhDrhMS+wSVoFUB9BUIxI1ku6ksc+6EwQtbYCZFW46KufICmZ8PvYzMM5JSh0mBzXLFy815fcCrbW0SQGChP7EOB7v1bhwvkk1M1RMxeilcAIPv4Bp9NwCYk9C3bJaBzMkQWGnDQpLMOevSEa+mc/ehC42ZFd4VkAlaiUAEkiEFtYMY/gBm8ClNNbAcxc7tmvpzEV1WRnFzoznoySnk590wfZJLkSbnqA02ypz6y3saUjCR5/y2caN44l56tSuNT4Kp2OuBgbPbMr7CB0e4QJ+sM97Mrm8CJAKWzZROChLwcNfBeO9cmXBC37dPpPBnpzwaHigs7TYXbtKYyxgrbfs4Hf4UZxwuetL+HClWwowVAo4S5Ju7XJ5ckv+fXHSZIR+D6nmVcw5Bd0ZI/JzAfM6/244ZxmTjhlfzLW7A9POQUk9znNmnSHi+yNfnELmeyRbemC3uhdsWcdPpfvwqAkAaZHvdgfffI1Y2DI6Wl79bNiksy4m475Htsag3vZssMhY/CiPmTFXXAnIWYXuIE378NZxaK54Eg8gVGvV00ffONQjIxd1270aq9PoXDhg2IyeeDR5b1zL/qWGPpzxJJDf95WUiqmeu2jQU7ZPQ2Z12AnfqrAgEF6GRsuwRn+hC27sLn1xstB6Fe+8pXDqb6TdTr1kUCf0PDax74VDMa29i0vRRR5xTH+aH84BM7zn1tgqoYf5G84R55GDutL4nEfP+NL9O/neI12vfRiW3mO/3/361//+oAJOMEv/gyxQ2lP5fykt3Gs1/jGx87hYm54T5zwVQF6tdZoA/yuWPIPtn3Cx9M48VnhAmv9/zo57Lhu16yX1WVd2MM9dP5U2ytRuGicgnMISKpujsxpJXOI/FgzFtELQBI5iQcDCRwSEfO6b+5ONL3HOSTc7iEfAQBAgC9gAZpCQjBzKkgeQQgxcXZVuFMB/b0WzCS/EglBEcARrBMSpydeVxiRwZpksleyCJQSYomJU3RESg6VtsfX/mO8zzsjVms6YbWWwCZwKmQUW+4JuP4c8kysGtBKVBGCAD8meO5ZF9Dphn7J63QSmcwEZh/Ibk/hYj36keTrb03z0I2CRWDPdnOLWJ1wSlzo1jiXhMvpNIdnR98z8vn/11577fd/n90Ye6ZfNoFJCZXAT2dsqpgomdVH8LIuPSMVH8mTNHrP6QdyQSj0STZFJiKCFx/fcHLlRA8xk42N2Zyd4NBPBRVbITgkbn46oFPkaR76cLG1+whPkkUuY2fdzy0ds6P1+aQClY+tdK3Rg76vUuEC256A0NNKJ/AmSbUv/kN39uaCVzqiF3bg35IKNhCQYAx+6cUceAGnKKjZ3lhz2C/bKYolRvzH3umen7ItXOAW42FGkmCce+Qin0AKx4ocuGQrfeCB/fXzu3l8PKHkXYINn5Jo9yTAiil99TmGFfJ3im+e7G6PcE0He7EgIaEfuito0h+/EUi38KeRlUz2j9fpkS954kkmMrIHvfRUm+7Zj093375hA29KEjw1w0tiDlnoVlHKPxU6TuRxMZk9IaNLRRFs4WCFBltaVx/zOMjAGcaJCX66b21+an5JqQQHr3hq5kQZVvNhujWGj/NRY1f6pkdP1pwQG3/LRranVLgoCuiOPh0GuuDhnIsd2R0GnKjDkgSZj5qXjfG8Jy9iRWsYi29xglhOF3DJNi72oCeYMy/OkI8kn8trT+kVKPyBP8GDhJaP83X3w8Y4tmvez6WXPYn5MG3vfnfJR/jFXl+/TxPH+Qsfwj30IcaTg71Wcl/7YhsYYFucDRde+0guLiSfP3stj4ALY3CAiw49zXBP3/y4hifYVI4pt5ptKTbCHF6Qk7CDmODC395X2OIXeBzXbo5TFz709Oa5cDmzIWEELBBT2DFHcE/glvQJ2owYqQtuq7He6xJABAtO55RdAmJ9QUlSLGgpLpCPeRGP5FMw9HhfgFdECD4CjQDoVMzn4D3BQCoSGUCQbEo69OdkwA5wyAgBSVZ9p8E+gF4RgggEIkmFpEeCDbCqe//4SoHi9ENwRK6SDaeJgqiTGI7j1FXxJIjTKWdxOkEGTsOpELFk3VorkArk9u3Up8SB/vSV3HAYsnJA965VuJhfckdPdOy1MeR0Ksne5NpKZBAbOyB7ROMkV6LiKRFdC+IclH7oNp3SBzmQosBH9x4FO9nw+WJzsp9gInixK9zQBfubq4/q+WiYgpSs1oQbSZAkyjj7gndPWHwMAV4UKPr3mF8fiSrZ3bdG2JB8eS2hYX+4oUfY9p7H54o7c+h/rLEX/bI3vzIXfEp4JXSzvcZmrHURLt+Zbb+n0SVypsd8eYXLazR64v/8oMRi1eBNINjqYx5Bgi+UXLAHPbIVLCmEK/xgmi/Zn715TzFhPE6SZOIXvmosLPALPszf4UJxBFO4y+98SyLN19nQvORVEEmQJPNe4wj4IpdmP8nAL/Eg3+An+AOuzbdlB+t6OiGBhxEX+1mzNfY0OoN1PpJ+/I5XYGNuyU5G8vNznCqIK4Ic+pBptBs78E969rs1HfrQa33N56mIQyl+hkPiaDoUG/gIW+IJNpI8eUIjRvDZvkQdH7r4oUMJOoIbaxtX4UQeT9ckN2GpQnXcg+a1/YkZ+mw1Y60JSzBw7UaO7EB++5dcP3aT+PlngDhbIubCtQ6QzrnwtRPsDz/88HCJk/64i++aiAeexPhugUNC/N38fuJqc+AWcVdBC1N0BWNyCrEBt/h4j8ND8dxY8cYTHd9LE8dfvHhxOMn3dMcnAxRKPh3gD4dY31rGzpd5rnWlP/NaV34jn+EjDgH56kM0/sOfYB4vZCM/k/HWl2LS9+JgwdMN+UB//IQdw4Q8LNukRzihO3krfDpA5jfhwt5wvXhgHoWIvTXeWGuyAQx64uOfmsKI/t5PNrLCo7zRBY9en7qsAe9yo5lznlJ7UoWLJxcSa0krQx5rlMroxhVYJKOCiPePtYgWkQtwEkrJPGcU0DgGYCEWhQfSlySo6iWdSAdJGy9AC0QIymN9SYqg5TTFuLGfAkBiLDFxUuYe+QUtcwOiYokMACyIC5ISCAEBoMkp0Al6CMPc9CVYS34kOAImGQVdc1tDX+TpNNG+JBwSdeSu3yqopWOJiURcolJCIfA6bXCKYH72cnpExhnw5thTuOhfoVfibF0JuSTGXN5bOZY9kgH5KwolamRU6FjHPOQlv8TOfJ6CsLlL8pNuXYIOm9KZIsCcxks+PXmhA8mMp2Ic3/vp3Dz2JFFSBPhpvvBgLafJ3YM1WBTwkLR+Eqr6wJnCyCmMZFTypDiGNZihF4knX2Az4+mDvY41+7QOvEqaYNH6xm7pucZW8Mk/7OdY31PNmvxCcJDQ0Tdsn5L/Po3csMiex06iJYd8yWmYMbMsXpPb/iWQ9M7mkn+FpcMGQZYt6BJHwI3XdEXH/LwEwBp+emLBj51wCjyCiQSbvPDAP/JrP42nf+PMAXsKM9i3ros++QN5xkYO98nismc8g6/sBZ/RVfvPxnCuyIR5Mugj2ed/5Nrb6ASOFQT0TSbzSebN37pkSEeeSCmaFQ36kcm9sDvbiw4UN/qkF2vYb/uztvnoIR2TTWGHkxSVEhIJJF/jf2xNnnSI2xWSTtz9bl4+xqZktq55FZ54m13Y2mEDjiaPsVv4JyveMR8uWDV9cILYII5Y89rNGmQUQxQsEjnx6bEb3DpwoEv6ccEnm55zsaV8hK1xO55lezGTXehd0SpBpGN5h8vhA9tJOB2AOtRw0IVj4JeuxHg84IDDCXmfoGgO8/UXzGDbPE70zcPXYLNTe+Pa33w137Uu+JX/KKjlD2If7lvh8xbNOnxGjMRt+FBOcKv9ri78jp/4u2JCXJb3KFrIwn89jWErfEFnxsGhfnChQGF7+sNXfIj8eMjTEdj1iQ05qJygveFheGR7cVKMhD/yiN304Z5DJ1gfdeJ375268IR4p7i6BV9cqz2pwgUhMAQClAQg/FPKC8wUjqABGjAkiN4/5VT6SLyQlFNmSarTapWn+YAHAQKsZFdQ47QM63ePap2OCYhO+gEX2LyWjApMkghk5ETdPBJliYYARz7gVnA4GQM6ZAaEApMEt8JFFYzE6IksiJCOANhpYKcgkhWFgPcKWAV9P+3ZODriKAophDT2q3ldYsQZOw0lu6KPLiourlW4IEPzmsva1nI6wUEVG/qvmnn7GJxCw/7oacSBn8YjGTq3Ln0qAuxNQKI/48hNTwhGsSBISFK8LxDBmUDmJMXTE4EM/tKHhlAEH4EQMQlY8GA+BExedlM00Q3cS2KyPb0JYAIevcBPmIQvWHUK52mHcfbdSfms57HRgz6CNJ2aD3YRHP00li1XmDAW/hG3fTldurRwYWtYtid6LGjDAf3FByuZ9rb2T4d8eGs+OpeI8UvrGzev73eHJvSvP+zwJ0FEoKIfiQ9f5OsSVPgzH/tKZGDG3C74ghG4YFcfV/nCF75w+Dy9JEfiWwFMH8YYj7tgkgz4B8/AVzLCE1nsl09s7dmc9gCP9kU+6/IL8prHeOsoNBTL5MERcMQ3jmFvq9k3DEjacCldmbsn4vYLc3SnuJY84AkHV2TVZ1x3thM9kY+vWktzX1LBr81tbzDI3nRgjDmtbY90TyYnva+//voheYFRtjJWX3PCVk9F2Z1s9MPP+TA9SmrNpVBnK/ulU4kGLElSrT83a7C35KWibtXYiG7YTp8te+9p7Y2cDoPEN/Fa7OSzknyYfewmloih9k4el0SfXc+5+Il452m8gwMx3gk4bCh8cXd/FU58MD+ecKjgkxcOn8QDWO3JKL3hfZiFDfyGMx1GsH8ymseTIn/1TsxzaOg9p+aeuipinLiL2+Sk/9W12tclF9nECActdCGWXQNTexrs8x0HrnSRfVfy3uKyfzHDXwV0eOFTGWwE/3IB+IcLB4zhwk94kf/JN2BJLieO4zk8g8/wt7jfJ3rkwnIZ69K9fNR7Pv1hDnEfl4mXXtPJj3/84wOn6B+WXOQ45yKDQ188t+Kep9KeVOGChBkY2XJ65IPkx2B0rAEAwzOcCzgQ7Mq5vDe+j+T1l1T47+Rf+tKXDgkp0AlYZHAhHwFFYEFOnFhyQH4kpT+ZBTrFTcm2gCXIC04IyxyCpwCkYCGvhNMeOIAEGsFZQ2CVPCgUFFgCpX7kkagCumBnvMJF8mI/QC3pteaWDslJ75xI0kqmlb6sZz42ab4+PoeEvb5G4WIs/SFrtjPWeuZ2grnlTMZJwhEHObf2azxdwZlTj/RE/35XONAnx2XrCheJJ7kELXhgB3unN6cj1iMnmwkaJRPsA8v0J/FDtIoZpNuTLj8rTrwW5MgnYUMk1i7xcJFfQuR088tf/vLh5NepLUwZr9XXNTfv6SfwwDGZ7Hseu2reZxPJGNnojb/CN6LcGndOsz7d0KFGn2xkDUWmIO1QgA1dl6xlLPvyKYmF31fNXulbMsIuMNL6Y9PHaRgMGqOfAAfX9ENX9kXffFuCY3+woQ/8kMk4QUuQkszwCSec/pqQYGh8SSLZ9U8PsCYxl0CTld78XqNfHMTefH3ew9zMqw/s4kZrsoMiH6eZ2z0HJApY8/LRS+xCdziPP/gpaFdQOGSxtksCYf84YsTC1tr6sI1TcwcQ1tH0Nzc9K9IUfBJWfJVNcICig63YkP0km4oXXEEenE5WHGFu68EU7oBpe2APfk2HLj6siIRrfJKP2xc7w9TczGvPZOVz/LC9zI1tYOjSBNNYl7WtRTdhAa7hDq/DV3p47MaeYqaEkH+5YJZfnHPhXHuTA/B9hwZ3d3cH+ysqHEgoWiR5bAibihIHnp5Uel2iLw/g7/gcdrymP/4iJuivADaejGT2Gr8rmqxjL+KMgkUB46CI/hW/K/ld5rrmlQ7JbG0/YeIhG5/0yQM+Sh77lO/cYr+ri23YAhYUCmyjQFY89LEwf/HLk1h+YYx8rY+OyxH5O34XFxQV+Eh+oy+OkcPyWfOZG77sVSz0NAfmfIEeHoyzJpyIEZ7ww9Wojz3YJzO8ywcu4YxbtydXuCAGv0uKKRsROllCjALBMUcpWAgSAo0kHkFI/AUfl+TQfQkQgwosEgEBzVoCi/+58Fd/9VeHREThoVKWaFb9Cs6MC5zWIa8LMQGNuSUUAgcyMh7YgB5ogQ8YJcDAKLiQkezGcALJrAQG+fnYiRNBgPZ0QKHTHhGIqt1Y8yFbxQNdOLWtKBG0V0BEBObiQHTl91WrH/npjC7N78TIXgQtH7u6RuFif/RoT+SWxDjpsubK/sbYO1uxjWJwy+mcBpNTMlE/c1ubnOxIzz7OI4nRR/CxRwWqzzpLVshoT0gHTsjF5p4MSITND2t0qtiRmNCV02T92Ux/e5R8wCB9em2fSA0OBElETe/I2im+RFEgdO8v//IvD5+pNR4W9IFT9uQ/Eil2sw/YMrf1YA5WBFE6se6WzmrsRM/mth6ik1yZm986sTk1x7Fm/rFwYQ86Ih/CL1mzRzayLrmPccKxlo9IPCXm5pqb9/imz7TzecUIcodL8pLRnuEGT7ETfUvkYcphgkTTHvCD/fFl36kSpGARFozhz2zHNhInHw0wxjyKawcaZIE79yVBxksgyOMpjxNAuOUz8ULNftnfGgIT2U81ezPOPPSN28gp8PIDuLZnuHTh7UsabqAzPuJQCKZwLv7FO+zO5+xD33Pxxj50uOIReoBjhx58DV9ag80UT2KDghC3sYdEie7Jxtf5GJvwWdiE0Z7I8xFxwmGG7ypIXmAAv8Men7QW36VbY9yX7JBZs09YU+j2sWRYkMBWvM7NnshFh+a8r1+2Ni6TTPF7+JQw23OHLfmqe0+hcMFteIqOyeeKb1eXe136wjpewNkOhTx5YT+5geJB0Sq282ExB35ghe3xLj04/BL7HSiRR3LJb+hRTPc+rJmHH8MKP2Z/Ra5DBt9XsLZ1JcS+ywBD9gaD8dBDXPDo4j/wLS5WqD9Esw5+4U84jkwr+93y4qOKBbbB455++Ctf3/rWtw5Fg4KCr4sT7I9D5H3syaflmHjaT/GMLzuQwi9whOtxA8zBkRxWEYwT5Uhwxe7wJ6f0V2R998lrBZV8EgbhV9wZ7bfaz3xZh0xix1NuT65wQeCRbOTL2Tm2xDGwnkpWGEGShQScWAITgzvdUKkK8EABDJyAQwggjO/UBLF4JKt4kCxYW18JtYTSCQxSIw9yt95YuJCR/O5LFHx/RTIMmIoRn1O1V5U7oJrDngQyVbY16aTEnYNIVhGggAn8EgcyWZvOOIn7nEozp/ESQU4xB+yx6ado42TW3Qp0gI04BC5BS0JPt4IjG1l7HksP5xQunFtf+rV/QQc5cmq/u2fucX77MZ4t9esEdm7eoycnmU46janfWLiYm+2QBZzAhHv6SGycasBIH10SsGAq+1mDbeCHvRUZPanyHoKRfLCzMa6xcCGD95wCSzoESXqFNUkbnPIJiSsMuS/Akg3u3IdndrQmnCuc9KVf7yEm+mYz6630VXNPH/qyvr2yN98iZ/pCxNcuXMZGDutYF8bswd4Ef7qkd/fJdG4jq/6KDGuaZ27ewz/sg0ck7QKDxFHSTof8hezsJ5ixj4QGhox3z08Ji8JV4uEAwikcHsEDbCRZF6z4Ihyb12vFr33DneTYPt3zno834geymZONBU2Jfniem4AJB3S2p6Uv89o3bpR4+diI4pkOxyc892nsbO907iMXTpZxKlyb2/37YIyuBH0F2woj1sTTbMDX8I5C1RjchVuzI4y6z//hgL+TyT3FhQJHMsLOih5fssYF/Iad+SrdZWPz4iV2MRd88eERV4oB2NKPPDgSX48Fzti8B4OKWuvs0Rn9wJi17RHX+CSCj6Mo2tjevZk34AkGxLfHbmIBna10c6rBN79iJ/zCv/ih/Tss4o98lu5xd0mq++wvxsGv4sLlibjk1mGoAxC4piNxQU5iDO6GFwUBrlGQ4lO86mDAR4QUQQ5YFMu4WHyQc7AV+87XrRq7s7WDBIc4dHzL9WpwDNNiF1w+ZLM/uMCx7MLunrqyA7srKtmup2yKWHwif8ABCgFPueV2fEgO4kv9/gjDF7/4xcN4OaL5YYLNFR/yK3vGB7gJLmFC7HFQJh/B9frJy+RNciFFMBvtxQOuMR9cPeX2ZAoXRMiREXONsiNRRC8JFMQQ9zkBsiCLSHxOXEECUJJNc7rn8rtkAckIGgKXdfxEXpJUTqOfy9oqW+DiQNYBkgoXiYt+3ufUCg6g1lc/e5X0A7RCQ1ANWNaRZAT6nNsAAP/0SURBVDpt5RT2jCAiQwUMwpLEIGbBsgAiuHYyqJnT/hCjpxFOdqy/avpJDjihxJD8q6Yf/UnABDSOYg+IFuA55ewkxpxTuLC9JJJj6qdY8Dv9z2Nq9g83nJtc1lo5KRKQOEi0/D7ONxYumvHhkT4EFHrXD07YASnZq4IWodCrOc0hsFuL3RV63qNP9kc4isjsZsz4UTGvrW8+83rSQJawB09ITLAUOCVO1pCwwgksWW+8OqlFchIn/cPnqUSQrpAuMpTEWZs8o/6scevCRTO3dcmuL0wJ8nyEbeiKD+yRQV/BhT0FhbnRHTvoQ8dwyv/pkL6djPE5CYXkQx84pg/v0Z0DGcWnfQl4dIojBC848QQWvujYGAEuvPBxOrFv+4NHGKBztvMTVviI8QoYiRJ+2tKD+e0JTuOOc5s5s0Nrwxz/o4drFC4VB3yCrK21V9ax4RUysudqHjrlI07VHVjh/hJ0a5PBOHuVtCpg2Ygv4xT3soNkl/0lIrCpIPETZtgUrzu1FVvYSWyDK8WNRBBvwJt+8IR/4AD3GI9zJEAOJ6xNR3ODL76uP9nP9Qn9yKnAk0ThdHgmj/3GG+FgbPb/qhcu9gQjDpnEc/6Jsx3UObTB+XTOZvrAlXXwMA5RwIjd+Ig9YcDHzfCEQ0oFrScv/J0N4VzspG94lwzLQ+QLDs7whb0oQB3WWN8YeYZxihm4uMQ39jY6sp64JQ61/oyHazc6gkU++JD71eCezXE2OeRffF+OoFBhb/ZjfzL6ifMdLvspDrCXHMx3U/zJYx8FUwjzb3wMK3766Jc4AEcOu7xnDgWz8fIPsQ/XOxyDOz9xBT6Rv+EoOVR+eq5tFC5iDHlvbc9L2pMuXGoUiCwFRSeODAQoEoU5iVo1fSQZTmgFL8QKFIoE9wRKIHSSwQlVssiD4f0sKY6wgZjTAixi8RppV7hE8t4DVoAzV8HXJcApJgDbvs1nvN8BWZIoCAiCAglHsAdjANsFpOSyB3NyaCRX8eDyPrntV7DkEFtkTj6AVbkjZPPOzZz6kbdHphImwRngL/momP3qI0jYA3IwbkVS1jCOfiSv+qXfcX2v2dkJBAd3ojmvD0sVLvqznWRAIuMvhUks6V0yI6DYAxnNhyCcnNu/xJccElWEbj/sk/zmhw3FMVuRwzUXLtaXpPrYCSJpvHvG0bdgaS5rS04kKjCjmPQa9vgL3OhrfgWvItM86Wi2lZbO7EXQphuBkyzpaGzeo4dbFy5js4492iv9sU2+jcQV4fYw7nWr0RM/d6hB92ODAcHK3p260gU76Yc34IQfOiVjF3b3cQ4JiL74jGywYR3j7JMf4jHjcZqkCO/YA1kksBJXAQvH2AMZYN37o2+6Z5/kcZAg2YTB2U41/diKrujokmZt9sdRkihJ3yXNPhQMeDFuvaTRAb3jdzqx3xUeKlzolw7pyNrp0Bh445d4iYz2DWcSCzaBAzGBH7I5zsATMAoP2ZHtFK/8S7yQCONtT9983MSTFDEDphQndEue9sKXYZycCqT5KZJ+xrOvPVhztWfN+8aSUTyVGBlnbbyhSLKmvZ9q+rzKhQu90ZdxkkOxhW3EQtwphnYohSuccsMpXElE2Y2/u9jIx4p/+MMfHvIOCSpsKF689lcCnbLTF127cIdYY37czxbsb5x1xR7FLnvjJLEXxygww+qWna/drMOXxAdc4vd85doNPh1IWQfuz8HitRv/cFDoI+JiocMF/lyR4j4s4HJ44P/6+x6M9/G6S87gC/Q+/ifP83FPuY9+DhpevHhxeIItR/BJCvPjVYeO8FgspgvzwwWcyA0VVp7EOlTBK96Hf/o7FxfPhcvOhsy3CpexCRYIW18Bws8xuVs1hpPceMJhLNIBCsaVyAICovG7+SVPyBcYkbYEA1A6qXeRF6EITMCBOAQ171W4GG8eSYh5AYGc+ktyJHvIEFFKNshHJnsCVg4gmBTEEKWCx0cGJDnk4gyKI84swM2FSxd5OJh59ffequmHqDkb2Vb9vEcHHAnZkhnRIuTWHpu9C9Kc7ljhQjaEwD5OlyR35Fk1ekQcdOZUYovMjOeE5uXQq34VLvqyjSQEtuiSnVzWYg+BjE3ZxU9PniQ8dKDwpTfkplASlMw9JkvkRl6CDr245sJFkmLNnnwZa5z3JVbk8jsyt749uQQ2+2RnhQwy40+CnwTZCSCMrmxas47iSDCETwnf6kR3bPZgHT5ybO5TDQ72FC7jRQb470SKngR3Pp0Ou+ZmXbgVbOyVDfSj477/Qr/mkigKEOM8fhdA+KQA9Od//ueHzx5LXHEB2ervJzywDdxo7OhEVpJoD7DjvievEh6HAdYnJ1kENVgZZaiRnXwOFbaSdDZ2QCNwKqyv0axF3wr4Sxpd4VQYIOelzXx8ld96WhgWxpZNFCR8p/fmSywwD4zpjyskUg6R+CHu57N4X6HKF+GJDHjA2LDVGmyKG/mm/7j9uc997vCnVRVBbEwH9df4r1N72LS+eSUw5tH0hycJrlgX9sY5at5zH/840KBzscae4LBxq7Fz0wdGzfGqFi50wRa4n6/jNHzUx3LYXHPAWOKqr5NvXMn2Ygd/FQMUtL7zYCyfdcJOP+ZT1Pj4HZ7V3ziXOOTpjbUdAiiYitdwpb/CRk4h9pMFz5Blhe1bNliTE3UgOmL7mo0NHRrhRNi+Bi/sadbDIXIdccJPesffYgZeyI/6WJh8SFEiJrKv3IOOjFGEOHDkJ57a4Q7xis0VrsaJ2cYZAxfyCk/o4A3/yCHwg8YGYh6/lTuZy3pyCAcrZDu3PRcuOxviPadw0RgCcUvOJJMewwMUx+Y8c2MARhZYGNfvDKpgAQaPZI0HQH2RFcAFSH0lMN63LiC7BFiAsS7SRnpj4SJoKS4UAjXkh1CBStLAKSURkl6n5UBIPrKRC7CBGImRR2DlFBIl4wAUiVrH+shM4jM3+yKj/RsviHs9g9NrtlAksUWJ16qRXaLCkZFyjy7nOY0/p3Bx0sBBETpnNf+KpOjW3p1SIzTjt/pZT1JIJ1vzwZDCULBiF78r2pCk/Qvm9MypvZZUwI3PwCMw9mIPSS3b6gcndCyoWJuM9OKCNfamf7qZCxe2ptMx8TCf4oid0yH5EKC5vYZDdiOXIhI+YQxuSmbJNjfzG6+fItDJIV+01+Y+1sgIK9coXBC7IE831j13Pv3Yls3pUREAiwIJvPNBe4eBeU7rGMPu+pnD+uzk46B0aI8FCIGDrOYyp+BiDcmioOE7DYIRPUoyJZcSQb5vbuPgp6cT1oJ/tmUDczugcIqrcDSnJy9sKyGWYG8dPngPDip8zbXqZ037heXV/b3NfAKthNEeVn52qmU/usMX95ljbuTCq4qGsDzvVx8+KElc+YdGFslhH99hF8WGhJV9BXvJDdvCiWTV1Zr4iv/5aT39+LEk1lgc3pd7YclTb3Ozt/noxTzsar1eO2RoXjJajw9JcItVY/Pa+vaJO8VO6/md/PzAfXPvafrDN6xK5h673adwoWcxCm+KP2zCt8X+il7NPulX4glb/n0BvhW7XOwhAWVPT1/9jpPffffdw8fEvPbdRAWqIkV/XAF/YpVx4iq+9/SVP8MsDsJFYgWeVgSRE0/BCvmv4cvnNOskkxiIn+BtL25ONXgV5yTsDkqLhw/VrI/PxCS2VjSyPb902NwnJ/SzfzmifIE95EQOY8X67MvevufK9nDgY4T+eaU4If9he/+8UnyAC/mDsfDlqw5yFTmeGMRP6cLa4gHs4RSHEApbPoCn9Du34YXnwmVHQ7jnFi61wCLhZFwGQ+AFi7F5LYFETBU4jIQYfOYQuJACUiLHSBj6SvwYVNAyv7WtJVnhWAhSH4WLggpYJL8ArJ95EB+iAUZEyAlzDMHDibn9uEcefw4R8AQT493jwJKrZPATYQmAKnUOgHit7/7YzGEt+5O0cLoVESSTpK0vic1zad4jkz1yRF8041DzfNY4VrjQnXEc2uNzgdea+s1zeU0fnFoiR865j0Y2duHw+mXLselDf4hI0eVRLd3QS0GKDHSAkEo8ySZ4IBp2UmCYH/4EHHNq5ocJwYpNvU8GRY5A5qe9j4WL+elC0PLa+sbZA9wYk80qXOiAHejYfv2pTkG1JIy8cCM5toeaOaxHBtiHQUQL4/ZzbiPPtQoXSRndKBTtNZ3PmDmnGadgENjpRVIgmacve85HyOxie6d65LB/Acij//yUjgUvAUFRxw8lfuaV+ClE2I2d6NshA12yLTtJZOlZ8JGAkkOzNltLVs1hr4oAfGNNdhSYcBzMCX5kc88eRh/203uCvMRmyz+MiTft1/4uaeYjO1+xNt3vbeaQBNl32L2k2RM7SxjYgh66an6nI3qADf1r7pGJPvVhU9h0SWo9/eBbHVDxrzAHEyU1LvYVU3AN7PgdXxsnCYJ3BzH4Hp8qXGHP03UYo1M8I1lykJZ+zY138D9Z+YxDD3O6Zw9+2gNfIgesepondsHVFtcea81LDvPSD8zCp709dttTuLQXsVmBS3f0jpfNQbcjH+pLZ/ooXvkzX2IflwIQFnCO77I0r8STvelJQeNQUj+xASaMwxeeoIkrnuRZh46zDV+VlHq6Kb9gR+PtF8/BwB47Xtr4GNuLNw5B7uP3W80+4Ja+HApucdmtmrXo0/p8UDLP13CJ/IPt4k197T1O7RCXTcOFvNAcvkfnI10Opx3SKlL4vIJfnPAdO9zCrnzdWAdx8jqxHYZwLdlqdCOnMyeuUGz3dDYeOqc9Fy47G4fcW7holAs8yJPjOGH1uIzRvZ/y/ZTY9RQByPwEAkBE5hIHyRcnAaKRrDiQ0xdGlYwCgvuScU9E/G7+ChfySEC9lmBYD9FIYgSp0cGBTrBRzVtf5YyEzMcJBCPyA7HXgFjzvrkAm8yA7dQLWZLJ/XSg0YlCStVO34BqL2MfzWtFhkeSknH7mfto9aMXH3XgXHM/DnascDG3wsCffJQQCPwRwtjMS17JkcSN3lb9vCdAI3MEb/7VHulRMYuUFIl0j2jYepyX3ZGCOd2ja/MiBuTEXohbggSD+tTgGr6Qk/5kEEz1FxAFJgGSzczPZn2GmtzGs6WkCZmMZKVw8dl5eJIw053iF/Z9TMF9DfHpB4fjvswlUEqo4YourAlPK31tNfNcs3BB3vShAIBRn+nmA3sbWezDZU/8lm87HZRAsqt50wn/kojAGB3qK4mp6csvnbo7LcM1ZGNvl/uSEX7Ov2CDPd2rqBFIFOf2hX/yBXqHa0U7fEhcFEbukw/W2Z+O/e8ARbanP+Qhd/No+uMgwdCeVzbRB/7JZA66v6Sla4k3/Nr33gb/MMxfRn68bzOHRAKvsykZu2r0QEeSUHYa78E1PUpQcJdCwwmpZJaN+a4x/Irf41/rsCF/oA/zmQdnKIp9TESyKdllU33MgatgAt48UYMN88YfsNb/dXDaPu7H3PybznGBJFkx1X2cawzOoQv3+b11yTZi59wWfmCUvSVyYptkHUYfu+0pXOwfV5Jb/OKHxtMlu/MvehobbLnnI18KTn7PrhLSPv7juwzvvvvuIbY42MNt4rPfxUoHffjAmMY1l8NUMsx+QFZcwvfxNVvCFa6Bv568PWQjk7jJ/n5eq9mH2EWvMMwG8PxQzb7o02EB7lWQw7pcEU+VX9X8jsPEDQX8aFuXfYht3/zmNw+5jid1ilM482evFbk+Yuwn/MCBAxA/w1T8M+rCzzCsYOKTeF3eppARB8h1TsMrz4XLjoag71O4jA1JSTg4cqelCNzcnIBhFSoSQYmCypRRAVAQAVKFhs+VS2YllPoZCxiIHhgkVAKicRJy38eQbDstBBj3vJbsui8ACUQC0vx9F/eqjIGY7E7bzG29TsWs79TdffeMdZ9Dm5PciFci5VTY/gUqr+1tJEC/k8eJrASZfsw/N3IK3IoqurTeCszmQ+6eLChAyDY28xwrXNhNsOfcdGedsVnTZd/I36nHKpjUOJ9TK+RuPfI1h9/pXIKPiOhJUJcwIH56VHBKQunFGLiR2NMZXdK3xNfvZPK+vTlRK+koYNKFeSIQ+LQ+XCJE8sGZR9Hw53cyhBuBQPEoeUqecGMPkikn8GwPNxU7EgkEpvErhZa9hxsn/3Rp/woXdiZz2Ow6p5n3WoUL/HjyIymUUMKwAM5WdCiQ2Xty7mnkZDOJnqKxefkA3LGFJyHu8TXFh/es6TV80CPddoBAZ3TqJ1wYAzu4xVx+h/fWpiPfLdEPV0j02RrGFBASEYGR35ufndjeOLYyBn/Zg/WsgRPMg3/ojR4FKnvTx9orXelnbv7EZ67R7AWvSK722gcPKv7p6RrNPPyxJynk6dL8ZBu26MCBTtiMb/meAt3SucMpnMNGXocdp7F8mexsx+6SB5wtBpgHFxlDLxIROOan4QJf8B8YhAP+yjfDFpnwgGLWIcnsD3hDYWMP3odf78EIfyoRhxFrWZv/mPtcG+lnDN/DYebNH/AH2eHVgQOffexmv+cULvbF7ooAOBALcDFb8y1+P/sPPcB3p/B07WJvl3sKCQckYiJ+vru7O/yu+JS8emrnvliDt8fx9Chui4ewNcdLr42zR7jARTAu3on95H3oJg7CAdz6/VxcbTV7ZDs+JTewJ3h9yGYfsOyQwUEFXOBeBRq8j7jIP7zPlx02ZE8X+7Iz/vBERSEidvuDHAoSuR9sKPwVr2If7jdODHJ5DVfszO9HO9ON13DhaUsHGA4ocIOYco5NnguXnQ05X1q4aAEI0ASdPlqFnHIuQBScOL41CyCCDiMjMGSvquaIAqA+Lr8jbfMCEudyCgbQgpekE3gRjwLHukCHXAQOxJ+c1jQ/sreedQVuoDPOPhAnRyF7wcoc1tVPdW+fAiUdmtclACo2gFjQ5DTk775GXkUX0rPequnbZ1iR+ugsYzO3IM65W6dmHxxRIOCI7o+N3O7Z2ziu5j1j2Id+zLXVrMVJ2ZEdOLTLePcEJAUlnDldo6cCOJ3CimDMJvZtDH1LYiQEbOm+AGFOekNGkm14YkfJkmTYfK0toTTePs0heYAhOkMu9CbgkQkxWRNG7SMMktFlLjg2XkKrICIvsqRL65LPPr02B38gq3mRH0whZRgYcePa22DimoWLvSQL/dOXBBuOFdF0KHlyL/vuXddYAaWTSvZTfNKbJxrw7nTNQQf98Wv3JBKKPEkFLLc2HDl8gC/2ZgOyCgD0ow/9K3rYQn/2giuFKcwI0H6X4LCv9eGdXPQiWJm7Nf3EC5JjhbSx7IoXBCB+AI94yrhVK9AqmK/RFEwSdH60teZWk2jiZrq7RoNthUKHT3TWpXmPbhQD+JHs8MAX8Sp/ZasKqXTOjjiIX8IibtcXbtlL4Gcv8YOPKlzwM7zoJ+bkc/bqtYsti0N8V//WpE/xQeFBVgUpnhBjYJOtJT4wZy5YsC/2x+/2Zp65pYut1vr8BUZgkX7Myyf5Zo3s9MaOj93OKVzaGz5VhChejJP8ief8PhuMDYfiojh+vO93uqYfPkxfvrcgWf3a1772+ycucOMQ1Hq4YF4DZvGBw4rVHnAXPyOnuCzOswceId9DN7ZXRNmvwwt4uaTxMXuzL4Wc+WYd3bJZSw4HzzhSjMX/+FrcnHWsPzvJ8fg7Xx5buMD/fJWePHHxJ5D9E2mHWf6CKcx2QFXcaN9+WleclRfCSPc0fGsNfAAL4pq4QI/2Ua5zrD0XLjsbIke4lxYuGuMAvsRP8JKUCOiSHeQrwEgWPV6XhHjfe/p53zjAk4QgIEkMEAMNORGdIgIwOBUCE6AkuoKJ03uBR2ADBHsCSAEQuM0jICoazCMhMad1yWIuDuvETDLroykSXGQnYJJLH3syj7GBXPOTDsgq0RLkEDOHIQPdaMbYv0eYZN8CtXms6xSYPjnIDGpjkSniJN9IXH7fKlyM44CKP+NXTR+kyLmRInlWzTpIxpMMNqBra9knO8AXPSg0BFz3zT3qTd9wI3CxAx1JGHw8x/uSBfshk0RFkgBX1mAL75dk60/n5oUVWEBu9qpQy6awBwfwo+CEEePhY8aN76EgUDK5R15EAw9Oz8lLHrZAcNbUF7bhRgLkfolS9uza28xhjVsULq5sSLcVd7DIDpI8ejd2z9rNaxwd8hN6dxrq4x0e57N3BYOAoC+Mwauigm0KHgKFRFUfmHLxO3vxnkvyiRcEEPO42BQWFRuCmn9U9+Uvf/mQ3Ahg8Ky/dUa/0dKPedjSHvCUNZ3a4Rx495E466+acYIo+ef579Nwi/XgjL3OsYl1JeOwC99sfWmjf/P1RDlddWl0j8NdeB6/8Ee+xCbu28+oF2PNTUb3+TT/dZjhi/Vvvvnm4T9Z4w7Finno2Dwu/E7ffpoXDyiOFRdeu2CcPylIvTZeEsvnrRkGYRI2FUP+QeHbb799+PiRYk2SZR42sG57Prfpbx2+JfZIZsRByf0476gbOpEg8Y3HbucULhrdsj/uojO2wY2+e8D3Vn7DrvA9Pw2hMzoxvtjsBN0BBRsrWuCrp3JiLj7owGm0kXnpHqdYb250LT8wPyzZL/4Sd8Q5OHnIlk946idx3uKbcxu/ElfpiE7NvxfD9210LybATx+38rST3fg5+/KNsRkj7soH2G28n+zmYm/jFZ3whnNwtDyDf4c/uWH4a9/msY4cCL/wf6+776d1xQCFFvw5YIMTxZT5T/kD2Z8Llx2Nga5VuFB4F0MhJwkcgwhkPscOKJJlJ3ySIPclCGPQBBTjEYtTNYQswAK1gAEc5pOoAwhSR0yczcfNANmeBFAne8jeOAlPTw4EIGsmr9+BB8jJ5acEVHHhpEsiJbmSFOnHqY81wDYnGZ3AIQMJLFno3HhyS5Ilt7NDauQiJ1ISmOhg1JOmDz1Lyp00jQ5izq3ChQySJsS+Ilvr0B0HF9zT1dy8b27JphMoa5ibjhCAfbMXPZQErubRvE9mxGA/iMVHRejdPD4n7tRF0FAEjQman2SxF3plQwUs2fSzPvzZi8QEDscnRO4hJvOTF4EbxzaCEwwrysJNjR0lQXTvMXQk6fReUgaHngg4TaSTxpJ3vvY2c92qcJkbu8EWvdCbIlUB41SJjksS+e6ppo++xtAd3Xhs/8477xyKF1jiZ/ku+fIn2PA9F4mpOUpUR73q42Mi+tC5R/dh2Nowxo72gjNwjL8oU+HCbuxurD2vfHNuZMFjgpcC118vyrfHPdT0h2FFEjnO0duxZrz94Ss8M6611cgg6cKLivBLZbAme7KJQwTysEey0SXb0LkE1amngwOvKyr1DYMzDr02D9uRl9+ylX9y7L9gv3jx4sDZ5qNTXKC/eV0ODjzZIYfkgp9LgpvbvHxJsSLWsB1+pE+6Moex7sEmfnGi/9Zbbx0+Oy+uwVlFk7XP0Wn6IS954i8FtRhovXSzasa+KoULPduHuOT7gHiZDfm7nz7FwFdG2+tPn7hZvuD+2NynI3EO/8IzXUhExRD8UDLOvnyuYgMeZ5yxdTmFeWdf8tpBl0KhjxDBIl6CmXm+h2j2gXcUU/Cwt5HZOPplB9znva6HaNav2BSL4YMNJf8OCdwffYAd+Aw/4XfujbK6LzbhBAeZ/HUsNGFPoSIfgA9zwAr+av81v1uLzc0D3/N6xsCW3FRMM4+DMTGaXUbZ52bsc+GyoyGBaxUuY2MkwAE2JAVcvvwkOfAFOKdInB1J6DuDQPOaQcnnJEVyhRgkB8ZLoAAFaZPfCQ6wSHYLYAKoQKQPwHHwFYCs5SKvShkJSGKc5PlSr+ID0N0/F1j6WcsYCZ7EECn09EKglKCTTbBbNeORp705FRiT9bHZK+cTsGuC3apwMV4/Rd+WQyn0nDoJKGy0tWf6pRtkYD8cGhEIQGzAca27su9W049+2BmxSHD86UL/q6MnJCs7eG0d60lsBDpFp2DGduRyIu7JAdwgSIENWUsyESSZ9YVNJyV0Sgf2ttqD1yVGcCNxJGc4pxtJTonHPP6SRge3KlxW83kv/VpbMHACSmdkkIjDlftbcxjPNnAhwTEW/tngP/7jPw5+x24KSzaB++alQ1iUgOIEyaeiRLIctjUY5FcwyC8kqHBKHnYUHARCtsUjfod1n3dWgApODjic8rG9safsR4fkcSADc3iIbRTbEhuchLvCrbnI0smu+S9tdOCQA1eO+thquJVsWx8V3dvo3f7psITPvPkGjuBzdAs3YgIdwV6cuCWH9+kOX8EKLjQehuyXHzvQsDbdwxQM9ATYeJiDN0WS5GJOsMnJ7+FFsUJeWLMPFw6VkMCkYpcMDn98IdhrHw+BWTbFHZ70bfF1DQ5wP4zDvfGd/tqvdY/hTtPPfl+FwsVeFCH8Gw+76JvvsOPsyxqfl1Tyy9V9dnOKzrZ824ElbqJLfqe/GMCW7Np6Tt9XTzzJaB12Ni8/HfWfzTwNEFP4kPnh0r7gWJ+HbHRg72Qi2zG8rJrx4p6EXvFQzO96iAYz+AEf4G75Gl7gT7hz1qn+7MuOiopZThwkLplHHuSwDQfoLy4Yg9/xvPdxEq4XU+BiXs9rscsBBXx5Pa7JD+EK/uDMwV484ydcbLXnwmVno8z7Fi4UDPAABCRI2omRQAxQDMaIiFhS5FH+F77whcOf3/UeonYJdk44GFtQMVcnVi4yCigCA0ADtgAtUElAffQMSJyyClwCo2JF4EIqxpmbnBzSfEBrHQmRICFwALi+wM6BFFqf//znD3+Rxim6te1JP/2NUwiRFbmZe0VY9GRNCSy5yWweToIsyGhN8qxA6z1kRCb9rDc3HyMgI8dpDrbZKlz084SAzGPTB2lbh4zWWu3J3NZkXwGT3Ur27Qch0M08v2Z964Qb/TxNUqiEG3PYL9w4zfTPBX0kBKmQDW7YwVMe63J8c7ErXWdj98xH54KLEzJJjnklyQoZHx8QcGFIEieRtS8/2cg8cGgvM27glv/AjUDHj+Dmz/7szw4fUUheOiKvACyJQ4DmoV+y0ufK9scamR6ycJmb9eGejvmbxIKe+bP3Rr2xNV3CP9vyJb4qMMEmfSscBX8JBh4xB902r/507hCA7ehWcmnuUV7YYh9Fr0CMa8wlCbG2ueBUgNaPL1vTfYWm92C7JFVg81MiCmf0FS/xSwco9mNedjZvPOa+eRxYWBtu8SOupBeYIN/KT/Y2fkqXDiR6OrtlR++zh4MAHHSNZm14lATANYz7+JoTcHaAU7qiE/fpiC29T3d0zD/ojT7CDl3RafP4yY9hBH7whHU8mWUjcnhN12xnjP78zse5etLqyTy/q9GfNWGBP0hQjJOk8G8ysiMeUUBZh/5gn+zwiTvJ5PdwC6NkhVPzW9MeycNX8gdrmje/Obfp+6oULnxGscIm8CfRxqF0IFmcfZlN6IqP8FOvx/t0CU+SPh8pgiU6lSjiM2M1McaBAnu6xzYwgkf4+1i8mN+8sImX2Hn2I7xHHtgjf36kvzXs8yEbvVjT+va/N57AEG6GIXujD+O7bt3Yne/jD/pUXOJ6r/HnXDxqihnFageDNf3ow3iFJFx0CMtX8Qc82LOxsGhN2OXH+Ase5wPS+MFHOB16wda4bjbAO/xfjqUYMp/+8K7Pqj0XLjsbRW8VLgEAiFcXsEmMBV8GEhQoH5gEDIUDsACe5FlgUbwoNBA6ACAryQqyk3hLHl0c0LhOKcmB0AUmYxEecPWlO1/E89MXJQUepy36ISdBEsDMAzyS4xJVl/7WJwd5yCWQ+BN4PobgYw0I0T2kJBABJ6cwXhC0P/MKhnRD3vFKZwKxU0BByjgkmgOSYSuBMYfg1xdSzTU2uhFUJd6Rlp+rwsVcdG2/s8ObBx4ECgFhXkfTj6MhAHZG2MbQBdKk42w2X+Zjjx7dcmgJAtwILGxqLvuga3L6h4A/+clPDgmZPSIZtoAtdtCHLq3PrnBDJjJYD0khD3roz5L6/pK/RubPanpCApt8gH5LUpEl/bjYlUwClXXgyzwSGnaXDMENnfzud7874NAJvmBAXvfDTTgnczhHktYh73ylu5nQ7I/M1yhcELoTz2Sw3p5mjASAngU/hwf0I3hI0OCJ7rNf2CI3/LtnvMStoNWeJIOSCPM6ZXdyBhP+lK7gsGq4iZ59dFSB7qOA7A9T1uBn1odFSYz9e09R6xSVHuhAcFJowCp88lPYI6v38JB5JWNwv/Jf65hHYY4TJVTmEahxgX2M/nnfRl/81xr07PctXJBTAm7vcH5poysJAn92SERHdOl0E9fZpzVXOPY+e5MHp8ICX+HDfM7HpuiMLykK2NY8furHXxVJ+MB6YRiuyYLnYMG8sOPJHqyvZNHY30fA8AP7Kmbxmv3hPXMba24fa4QhSWyc6Z4+ikd+Lw5Zl68b47TX+ubG+eZVxN3H/uSnB/s212O3Y4ULvdCJeAv7Yiy+xH/sI4mcG1uK4fILOpqbdeiX//IvCarDLDazVjo1D/90mKEfjsHXYo0YtvIBcQOH4TTjZ6zAHJvDLQ52iRvmtvaW792qkRGXbj2hONbgGrfSkUb28bplMz/b+2gY34AJ3MqmCkI8Nu5Ff3YVe3EdPx+bvvhWnsRv2TheEi/joubhu3wTLuQhchO2xDd8u/3Xn65gV9xf8b1YLt7QpbUdyNkLDmvduT0XLjvbqnChOE6AFChU4iBp5pQSL0mJPyvorzEIKozIuQVzzmwMgwOcCyB8JEMAk0ggIkGtQGAdxGGM1+aRkAAmAvI9CyfjyAtQnb4JjJJDf9bOafxnPvOZw1+J8LfbPXmRoOin8jVO0msOySIgcRBEaD0gtz5dON2ztuIKuXqqo7+ghwz0JS9Z9UVu5DWffubvb4ELJgoTDiOg0Y0xrdcTC/rkPHQqqaOzVWMT85AF6Y4O657kjtOXBHlPoB+LEBcZ2KOTCvZ2ZSvkbq/6rpp16ddpFdJGOAoOa9ib+e2VHPbIzuRCjPbo+x/GViDAFjsYR690QxZ7VAzDiy/BSg4lj4hF33BjTeOtKekocWYHH90SSJAQu0okJL3+EZmnf3/yJ39y+Hw8HPkuCv3DDcJDNDADfxJNfmJt/sDuZCAnfVhfgn53d3c4zTfWT4kNvSSv/slrHvrWD849+YFTeKVX8+kDp/RindEm1ypcyAW7Ci06tzd63RP4NDIgZnvkE+xlT56Y0YenHvZi7gpbDanDgv3Zp2RasmkufezZ78YZD+cOEySD9LPau7nYWvIpiZcE07v3w7x5vWeekhz7h9P03NrspY+ACiuewkp4+FC2ad65tVa6gQUHCnTic/Hw6NTPGqvxe5p14A0v86uVDa3BxoJrHHBJs6Y9saGPTfEBuBS47Qm+Rp6Zm/fggX3NQ884yxNWfM4nsx8dNoc94E8JBP3Dhj3hHPPpZ13rm5c8ikx/iIGdyb2SB6bwFF/Urz00p8tYSbr44n1jJIzsSuf1SS6FmcMK/zfCvDBMptkXzm3mNw7n2T+c49jHbluFC3npgi31wfu4FSfwT/GEbefGxg6V2HTGqTnFXXqXLCoyFLr8Et9bT5+a+cUGvubpqrzFOLnI6gCEft0Xb+l5xov78IGvxHh5h/jjNe7b4oNbNWuRqUQctk41Y3ASLlJA0tlDNuvDfr5En4pYsU88lofY06hHdhQ32Xl+aqp5DVd43E/FD/+DM/Y3ni0188KYXE2eIVeRw8GnwwVcOrZ0bD73V8U2HcqHHUyYx2EMeeFMYbLCOY55Llx2NAQjSeP8Ah2gcEKKFqQpXSIrkad4pxtIRj9kjXwj9Qi7BhwMC4iSPoFDQAAUJ2UebwtSK0Iy1rwMah3EJRFBOuSQEDkV++EPf/jyE5/4xOH61Kc+9fLDDz88JAISG8GbvEgKAZkHAXHoWVbNmvoBrlMbwdJanIgunLJLxhvbZRwwmpe81qBLwUlSaZyA6jRfgo8gAJ9Tqf45rP1IsBWD1jbfLJ/X3pc8S3QVJemdDNamU/e9T69z4WI8UhVcEJb3jNffOHJ0qjm31pfYKxoRuiAsyeRwgjbntx9yKPoUD4okZM5+4YaOJHzmm21BbqfiyISujBEUyKtw9tRDMkD2sbU/88IYfSA2eEWMiiW4UDh98MEHL//oj/7o5cc+9rEDbj766KNDwiVIIrzkhQFJuKSJfa2RrH66vEdG+0VU7M5v6EMAVUTxg3mfXtsrXZvfnqynYJYESnQk1fCDdOmCb/IjfmNO5ClhGefd26wP32xvLfinsxLPbLSn6U9fdCDJF4Towbxswv7s6ackt6cccCjw2m+YHZt5ySNhgQ/+s5JN8BCkzJPdxkZf3sdDfJIs5mEDCZW9wxF+wz3wLRlQpHsS61BlhcGtNmKFbPRKdgWj70LxJwUsroE3fc6de272Ar/2RqczNszr4Afnr+6favobR398GSbpWuHrYEohitvCzrmN/snm4r84hK/CJRuxOczbH/3QFbzCSX5k7a0Tf/MaS8/4wJgVduzHmmRYyW8eNhJf/PTaek54cR/MhG1Yx4/4X1zCEbBOd3ubdchDr/iRfcmAJzy1xo2P3bYKF7LzIzFOcihR5ucKGQUHfelTYxdc0JMMthqb+/RckdCBg6ej/JR+5jHmx5twxZ/hf5QBB894MAa/4hF78jp/0ZfcchIFExnEZT8VyGLCuKeHanzEfsTpY+vHR+wBn3xqLxdc2thI3qG4YBtP48RMOveTTeY9sDtMONybY5O+eFsMgwu+LvfEHQ4x8ZU9jvtkV/muWMpfxW5PbxW2+GSlF3LBed9dGe/7nX877OT7ZMULMCeeyyHnxqefC5cdjdEYV9LGYJxZgs2IgrMkjLEBjEKBpOuYgt1H/IhK0LEOh0LeCMk9pCNBY7StucyDHJAAWQBFoVViV+Hy8Y9//OUnP/nJQ+EiUUbiwAL8EkGJCDC1j7kBPEApdOwbMAUlc0kmOQhSth+y658O0ku/a73n0te6Eh1BiyNxUCfrdO7kwNz+MZI/ByuJ4Wj0tGr2wSGQk33p13oI1lMp8lqzwqWnMNkEWSWf8ZKonvw039zMx8F/+ctfHuT0FAROBAIJBNx4JAtPEniBOz11ed3Ve+mtC97MyX7soAhUOCe/QliwQGCrZg594UZyoxCEG3qHG8WowiXcfPrTnz78VSnys70ERH+nJgpZ+ibnqlkHrtmRvEhM8PQaEcM9WfkAnG/No6WP9NM140bC7ies+sgbm9vzfRuZETTbWZ/OBF42JbsAENmeu062hCV+yg9hEdb7SKAnsHSNB8YDAXZTzCiCV4mjPnTJZxSX1mhsTfASCOxlfL+mv4TXk1H+XR/rKRD5piJL0CELnEtwBBbJYvKe28zvomunuwoW89KFIAlndOG1xIs9+Daf29vaA3wU+EcduF+R7d6eZh4yOXiBRxzEvuT1HpuKId63xw5XxvW3Wjrqwtu4ih4khub1GrdLDtnGGvxTf/bohFWS7L252S+/lNhU8Mz9YBOH8t3VHGyo0Ofv+ljXhaccjuAN42HdOhIguvFeCfBq3mPN/DACK+wq8ec3EnT+ghMUj4/dtgoXGGAvOmFLiamY7lQaX8760N+hB1viv/k+DPPPdOtwtYNNsXFlV804B5nkMF7CrlAlT0/uxkbvfF1RRB7j53nJT048wjZsRA7YnOd7iGZNOmAHMXgLa/Ymx8E5eO0+XHNps754Lu/I3/m2WMSfyD7K73d2cJ++7WG8b+/xt1wFx5pXEVE8mfXhtb3DLhzIF/wuJ5IryMVmntS/eDYXyeazjoKMnPr5KeeCVzxP7rE9Fy47G4KhSIGMAyJHyWGnRoDAKLOiTzVzCMCuqlxAVLiYDzF5XQLGcIIskkIkTgQlwMgZ2XNCBYvAKGAAplN4HyV47bXXXn72s599+cYbbxz+uZDPjEt87asAgzSBRlBC8MgL4VnP2uQVEAGxk173FC4Cnf3r5z5S0r+EoOtUM4d9C7R0S8d0ANj2a3/0IdgJRIiUTubAb13r25/+krQci1N7EsKuOY8nFNbRx56RA2cjD1tIDKyH7Nh7trV5kLdkXj/zm0OSL6mFGySSDa1t3nN0Mjbr0omkmd3hxt6tzabkJwv7CBT66O9iK3sXjNgXphQpI27oC24UQU65fVTsT//0Tw8fMfRUwGm6Ig9eJGLhhr0LRJJXyZGAYF37hinYqVCTmCpcJE9e24c+1jfmlF7CU1d2Yhu2pmfFEr3zW/s8NeexZl77ZVstjEosejrHJnRBx2SIE06ta//IGCnDBtybV/JCJ56awhOcmpcsGnu53+u5kVGCyB/HgxXNGL7DN6y3amQng4BmrvbMZvSJR2AFfwhcdG5eyafgtjXv3KzDH/hfMsESTAqs/Ni81vdTP4GbbwmecCzgtf45dqZzuOQfCl5z18zhPSfDsEi+Uy2/g2+yKUrYhn8I8vTvvrnpkC+yDX/Rzz6MM16/c7lBUsM+8EPf5oV5usPvbITTxSzYsU9jSkrJPWPU7/ZNdoVXRY9GLnrjB8eKRvvFf/zcftjX3uzZoZOn0PgAp5vPGo2hr3P2ro/17YvPwRxu4osSMzHJvOEGVp9q4UI+e5D8K5bhBx7YTt+VL9EnznFYOeOeTWHBPHRhHrmCJwx0zIdnu9e818m+hJadYBSv0y//G31Cf+s3N7nmee2Vv5KDr0rEze0gkP23cHSrBvf0I+7D29b65OYH/IkNzuGCazU6JBcc0D8f7zBFnPE0ccSQRj6+K7YqdNyfbeGJiWIY3vCPvYm7dKE/3ayaecRUBw9sLed02AZf8jF+aGzrkQUumh8PjE1f8a77ilk5i2IIbsW7kZfx+3PhsqNRPgJXmdYobnWd2xgNIakyC2reQyg+KhbhchaJkQDktFt16zRT4EAo7qmSBSvFDFIxh8AEBBIMCack1FMAX7RWyCAlp4CIRCADSCevEgXzKVrMTz7rIVQB0hf9ORLZOJWEGZAlWl63B6ddEr32tVc/x5q1kY6gqOjjhPbu/ZlQ6dZe6SMnR6z2xEG8NxYu5rB3waXCS/8Sc85TM7/79ogE6IczcyzrJo/rGns3j7XYzFrWgE17YD/fFfG7PmRHbD5GAjMuBSYMCyDIRsBjb3ZX6Agg4UJA9F/afYYebvzBCN81MF6wMYc1rEuP4QYeJRDsT8fWhQ9FFcKlExfik1iFGxc5zA1f9jDa8r7NWtZB9JfYgJ7HwmVsMEAX8IiE+YokCSbYgwz5wKrZJ4zxNQkzXdQUBL7DJGEwL9vQtT5057tFfGFLV/ToqVAJJBlc1vNRn/Q/N32877TVWPuAD0WCk1t+h1/gPVvZI//QJxtuNfMbYw2BVuCyFvxJkMh1bLyGG+kYzvkDfBuLO81r/i2da+QVuCV0gm7N754escWpPZijPSjy4L3PwsNzuhnl8Hv7p1cHJPxG0ieh9/EOeDK2fWw14x2W0Lnfm9vvfJjvwY6f/BLXs5GiV/zx+wqb1uSj8D4mquaVyClO6WlLNgVKT1T5jtdw6OKLYhAMtrZ52Jz/k3Gr1dc487IR/md7GKqAnvdDl3gAbz12mwsXspKZf9O5wzF9JNQ+NlMcak/pANbFNfqa94tz6BxfKw75Kv2Kc8bR3bHmvjgo5hlHNk9rjDef+/OaklDyOHyYMeV3PuIjpuTCG3IqfsLmfPYxGi6FnXk/fnc5xIRjB3G9/1CNDsVXhyuKEPyAr8Rtvu0efqiRjd3xkPgDXzNu9DefWOYwWMHBZ3CWHGqcb27G8yNxXH929IQXz7G7wiMOGhs56Vi+Qp65GY/34whciF/krjBT4yPPhcuOBtRj4UJpW9c5jfE5LKNzCImHxFHC7xRMoijpu7u7O/x1Fx91UTAABxIDTnOQy+V3lzmcYvgsLxKXUPoiqALER6wQhL8qBhi+VyFhR6ASGImGQIJAXOO8fkdKArzPZwuuZJI0CRo+t21OAFXIIFJzS7LIY/we/ZxqBS56MLd9AjSHlFyRucYR7YfMnNVrzmW/khP6rHChf7Lbp2QoPThR1pcTjY5nHjpHbHQhiTLG+tYh515sHGvWQw72igTghr6dpo648R0gX0T1XRVPTuhFX+PtZ7wkf/YgWMKN+SUbxiI1J/2w4iNvgikMIQ6BRzGCUOlwnNM64QhuJEzwK2jBje9ySMatQW64IZ9LILGePbHvpY0dEP4tCxfzsjW727uETtJhj3QKa/ZGH1uNnJIWOKuf+ZC5NRvvgKLvTUlwYY9+x8R7bvrRgT2ExU47rbHSi74CJP8ml8QHT9CBAMIXJDA4qYBkLnhQWMPUKkhp9mo87nOy5gRe4g0H9GceY0/Za9Q5HJsPpyqwJddw7N7WPN6HZR8D4Uf1k6zAvn1tjbW2NXEGfuVn4oMkz5ou+zzVzKOffdA5OfgZP6FbfkDfW3ORjxz91bU4x1ye3EsK4AavSXrEAXiSlErk4cnaq6ZggS8Y1MxLJ07LYWNlI6/tXYGgYBI32QOO8CxZxBmFJgxa2xh7lIyJI723aua2T8kZPsF3Ei/v2zPOSAdjM+dTLFySk4/Cj9NwSaE9wSDfy29r9E4HEjtFyGw/ffGFmMT+7CDfoG9PEc235Zs1c8Acf2Jrfo2bxDg6r+gcG907vFLswM6MWa/5m/GwAFte4yHzP0aDR7iTKMe7mr2xjwLfUzr3ZkzdurGrYhF3w4Gknt29xr30OcrktXgrruK+/LM+5vO0C69UjPIjsdzhhz3ynWPNfWvgbTkSrndYp+gxHz6c9dS6PcGeGz2LYWxALvHfYZniS27XfM+Fy87G0StcAsLWpfk5BySgoHiG5aSMYz5OAQSepkhynLB4uiIYcm4JAFA4OTfWfK1lDa+RSE8fBATjBF+FA0ACBRIEeEFH4LIfP83txM9pL0ADBTlbp8YJJKg+KmSMAOajAsAmSSY3+TutVzghuv6KCEcipwSLLsyfY+1t4xi/m4tu6NKeJIqIM4J2eU3HHJ7jcRbJgT1UuCh6OmGRRJEVETtFlUDQd8WS+4I/YqfLkgt9yLS6zm3ktQ4Z6Yre7Q/JSALYEz7gBulLYgUDT+oEDcHFfu3NSQf7kjnZzE9nght7ww1cGCf5czpLl/DgfXMr5iR4iCric2IDU94TaOl1TBz8pBPJUScz1hScPfqGaaf3cAOfAjoSFLgV7GSjZ7qlB3swP/nPbdYn4y0Ll1p2drEduRE2PxB0JCd0zA9Ge2j2BGP60qvX9El2c+if3cyrMIJLBaUnYwIbn3R/nFeTtEpscYn7ZMMTPbmpGee+4CJgCDR8u0IApow1xmU+RTFZjCMvPeMEdhpl8Jo/2WPJDX80L/kk3+Ycx5zT9O+yhnngHr5wkWQd/vhPso9rkJnOcVT4wl3kgrmaMfRvDlxG33yQr0gg+YP9te9xjWNt7scG5jGf+SUD/AsG8JN78x6M0YctwogYI1GHNX2NsR+JD98WfzxN5cv43Lxhp0Yf9MeeMOG+feMEflkzv3X1Nz88+AMenvSzQTEl3egHX+7hNmuWsJDDXPp5P53DCL/w0RS+JKmBX+PNO8q9amR/ioULu8AmP6cn/gO7Ygtc2Tcd1OjFfsUx/eB3vm+vDgKMhwP9xDgHRnwj/Z7T2MmTUHERxsjlJ/9lR3pvLvOysz10+j42/fgneRRS+tGDfeMjfEIfD9msB0O+lC5OauT0Pq6WF+Csx5ALn+NIhSOfU3jKbcRSdp8b32FzcYqvZ5suXAAXCg5PxfB7/owD9DmnwZfDDAUxHYnhbChHwJtsPPqj32HFXuBx9lV45gP8E075KHzJqxRVzQdbz4XLjkbplIhcRiCsLo0DAxEwqJglYxxUwllA/cUvfnFI3AANIAVDBuTsEhzkJlgwmDkEMIBpDU1/CaOAhcgFDIBFdgISgkFUEhxE4T0B2kmcQsdrMnFc44CqRBQ4RkLUyAasyItjARSgIiC/6y+5AWrAc5LqT+pKRL0u4QZwe5LI0tMM5Ps0c9AHouWICMfpnb1pZLN3zuNRp71I2Mlf4YKg6F7hRc9klFSb09w5j6cTipv0SxejXS5p9ChICExkgxvOi3AkSf4xqUSSziWAEgV7g41sTKdkkuwI8n4nexhla0GfTZAFYrQu3CimzeG+x7dsLaiwFdLwvoKJHeHTWvRsHDklF+NaGkwYQx7vWQu5ShDZQX/EBX9w76ngz3/+80NCWQC2Hj/SR4AxrvmPNXt/qMJlbumADHBCBn4KX07yBGp7ry9s8RFP+KwHlwpPttUvvcI0nZvH0xdFn4MHvgm/ksrRd+mKruG1Qw74FRjHfoKhE1wcZZ/sLiixrQRWUTva1iWptS9j2VlQFczc0/yEN7bHU+ayNt+y3/pdqyUXndsf3iZTeyAbfdb4NdlwlP54GOYEdPdq2QMWzeWSALDFqJNrtOYyL73ya7jBa3xMIUa21vMTdzlUolP74PfkpYeafmISPdivuBB2zIsHxhNR6/cXhIyzV4WDxIJNa2RReNA1HjWfpMihCoziMjxRI4d9sQs9wyDOUVCR132X92GZP8A2PsADZNqrczanQ2s8dhsLF3qgT1zPrg4A4REvi1P2OTav6ZJ9+fG8f/qTE9A/m+FwRQTc8tX49tymP/mMZ0cYtK5YAF/FxRp53HfIxXazfF6LpzAiX4ER/e1fXmBv59r0Ws0exHTYLcfyk+4c/D50s74YySfwtiJDrgIP8MHXVjYUm+nd4aD75nH53cXXxELFj3xSTOez4qr1ztW7ucItPqAjPMKOYgeOHXlH85rd+Tx+muW3NhvwcbjFG/JIMUjMN/65cNnZ5sIF0CMdZC4B4cQCmeAM8BQvoEt2BB6BGiFJShGvBAEYGQrgBAxEzUDmdAJQYqOP4sY9rzmVJAGZlKALuGSSmCAAwVcwQzaBSqIKrEgPSPpOA+AaKxh5j6MIFIKZhNL+W1ciC5z6SnT9xSHr5PB0goARLx3Yd08yJNrkoQ96EYQ4p6SWbPSHdAG7pJhc9n0OUMnINmQQhCWBggTbkNecdEU28iACulHQkIGciJRj2wM9sU1zciQJi+DHluY7VzZ99A03xtIfJxRY4IYt6UNQQwh0HW44s8SRbeg13JgrXEjoEQNskLknTO7pw45wAjfW8TEtMnjfTzKwCdzBBwwoRMxpnHW9z94+OoRU6dWFTN2DafoRtOwVppGiPSE4v9MzG5DXfRdSEsh68scG+iFBercPtoMbfsWubGYv3tcPbvgOncBjeHiswqWW7fMvNpU8logi9DBR0c/WSFshYg/0pvgR4OmIneybPxtH3zDvPt3SIWy5Z1268dE1a3Xi6X3rkklSQ0/0CWP8gy75P1uYVwJL77gk3MGlJAkmcBqbGMf2Aigcm9ee7IUvsjV9svutGp2bPzn4OvvBNL2TlZx0gGcERIGdXvgBf3DPWPYyxt7tB194P4xdgqtTzfzWsR6940i2laCwE7uSk53p2B4Fekl6vm2fOIRt8L0+cAYz4gUf57uwCDv81FiYM57O7BsmzSt+0Zk1YYw8xRE+3Lx8kox42Dj34cS88GNOutbfl45xCHuZF7fhEvPCnvgHi/ZKH3ubMU+pcOG/dMB/+Qf78CO2Yws+Sodzs3/6nIvHGpywIZ+FZTYXR8IDPezBKx8nB77HwWxNPvKKC+7N87GvHMTl9/k++3ZAIwY5nGFvPoZ3YP4hG54o/uEF8slVxFi+89DN/nGR4oJvirvsyZbiBpuMjX7JDNv8jQ95r6v4ypfgC7bks/hEPHP4Oc95qplXriYWwbC52ZOvivWwqU/NnrzH57ewS0YHKjhOP7EI98qF2YGNnguXHQ0QGJrzc3wJF3JHOB6jUiYDAhXiFpwlCJI9gNpyREEHkJx8CRoMJ0mxngQZQSBrCbRk0vsMzrmchAKKQBBQ/bS++QoCTmT1BzInroIPkjAnQAtCig+BrIAgWJkXCJEKp7GuSzBSFAGrea3lfXLbq3v04Z79ed/+ydflPaRtbxIfzml9RMeRjPekA3E4mdbHXjjXeJHfNc5fswaHTEaJK/1al+MoUCSCbCgB95McPscvMaEfH83iMPbBPt5DaF5bv/Vamyzet/Z80S1MSBDtS6C2T+vClv3Djb0K5BID84170rwHCwJ5H7WxJ2vYL5ITDAQUT9b0dY9NBR4fx5C4wGZBzD3FE9KwtmAj+OlPHkmSeSTKMKQ/3Tn9QSxeR0zwRNdko0d2M59ABxuSZLJbK13p454ikj8IvnMjp74wRvZwA5vsSUY6lZzzHQQOw/TIbx+zcFk1WORLfJge7b2iXYEAE55Y2qt9sKu9SXjgBInT39xKhpC/j/ixQwWuhIn8dG8tGJGImJf+BbKwPXJWtsV7ig9P/mA3X5DACDTeNwcbswlfEtDYXjENbytffagGh3QJYxIxHArT8IZnyE/P7Mw+OB43dnDEFrD6GI2+6I4PkINO4QTmcQA7wBO+h39FAl/hH/YFXxJYfrtKJuMPPqtQYTvJJYyws+SBjvgSLIgzYp/4IE7BHIyNzRrN6yAId+B1XIpnJGeSVZeksfUl8LDbvCMXdu1tsPdUChcFGd3yYTmDBI1c7Mk+cgnJGTvxxS66ZGPfYYBZnDTf7xMD4jyb8UcJLz6AB3oYx3TR7+oyJ/2LKbiDXBJqGLQP8cyc+jUXH4FHTwDJ6/44p/vmYXdc1kEMfOC94tko3y0va5HRwQ8bkMnH6ugQ1vVJ9nnstS9y2L9cg4/hoDjUQVY8OuuTn+NyOZXX9tRlD+zPfvIMeLBHvG/cffZljNzIYYn8STzht+SUb8kDZny6xG321n+2MbnFDpyGB8R08uEL8Rt3k9k9/Z9qe1KFi0Dsv0FL/HzUK2NxakBaXYw7JgBzc59xAVXwQdYFSAADXsm8ewKI9SS6Eg8ADRjW8NN939uoaBEIfMfET/cQA2IEfqDwO/mtpx8yKbEoSAKgxIRDAw6yRTKCGIBKbpFUxVSJ9CjbGHCaO2cZ9WVMF9KW6AvAHBYJ+jIm5/OeZIz8yaS/OcfWGhyXzIImeQVNQUyAdjrQx21cTiMKLIhLkmNf7D0Sf/vS/LQGPXA8cgnUxkh6fAeF/PQoKRKMyWuf7T2SMfcx3KQ/fWGCTfviq30hfkkA3AiIJQ3WpjtjWsdcfkogFHL2CzcwQWb7cQJmLB37HVH5SX7YkMDCVjL7aT8ISoCDV/qQCNIFu7FnRSF7CMTk1sfYY3tv/+QeL3sylvzkqTg1t7+kRydb857T2OqahQtZyGteMiP8//t//88Ba4j7N7/5zcG/FB6wI4DRl772Ss90MTfvuacPTLI9XvFFXQHrH//xHw+2NS+uUDiZNwxmx3nudJ6O2Rc/mYuNf/vb3x4uBS95+RV/yZ+NXcn7kK092Kc94ElJPt0o6v75n//58D0xgRVu8AOuJL8xxq5085DN2iPecTfOYkty++kPskgi7IGv8Vd9RxvPzbyjjdmOD0ngxIrf/e53h38qag3FnHlhgB6be4XJUef64B8xwp/4Nges+/9QdG1emJeY0Ln+6fxSvZvrqRQu/FAyKlH23TMJquJScaDowJM4Ez7Zt8trfOuQwH2xpnviIL3hJ8WEwtx+6Vq+gAfwtX7jnF3mmi99renAwtz8vMNFMuJwNjS3/nISMch81sItiqfWbV5zkr8nw/SAM8Ra+5P76G++8ZplvtaVXGKoHEDRjhfsO7nnvre6rCluK+rkY/Iyh3BivOJD7pA+uxSH/FK+4TU505k5+anvKoq1sKAIgBH5lLGt3R7PuZrb+P4hOJnhWm4ojpF1ntd7+skjwvi4F/c7YIO1Dtnk3fTgu3OwiA+eantShQtQSOAQL1L3XkS9dZ3T6luSgRwAl9NLqAUOAaSTeieEJRqN9TuQOwGTGCJ78hlHbgkhp5TkAj35gctpTwm0UzUkpFgQZGp+t2egUbAZJ8hIWnxHxymeeX3MiQySXusn230vwLQ2vSiE7FkiDbQSSDJwVgWVIsMTA0WfBNBe7VOyTR6X3zvlkcQKEvRFx/6MMMfwJ6PNJ6kzHweyBp044SKHixwIxmmW9RCvxJLdEB5now/v6aN4ID85SoK2EsQ9zVh2phO2sy+kxDYSMDYTuMgkKLBjiYCx5EAUkgV7IBN9w4xCD26QnsSF/PYu8DsJta5L8IrkzV2DSThzAmi8nwIpffrLdF4jL3ZEYCUp12jmoWfrsxW/hdFLyM581yxcxkYuuqYH+oVD/2/ppz/96UF3MFTBuUoOtxo9sJmAgAv+/d///eVbb711+Ie08CKhZVd7G5v5j61hXlg2L17yl6rI6x+uKlZ9LIve4cPeLsX5LRo9ko9v4lQJwje+8Y3DHnCugxicwR9G3JzSzUM2cthDvChZ+Pa3v/3ynXfeOfg/3+wJar51jvzNi/PYEj9873vfe/mlL33p8NcBHTJIQmDLvNl3Nff4Hp3zc1iGPdj+8Y9//PL1118//HlkWMLbfKF5r9Ws+1QKF5wn5tzd3R24kN0kf5L2X/3qV4diTlLvkg90if+4gY/hIfftySXmeHJrHj9/8pOfHE6nFYievBur3zjfeLXe6rIWDpXQk9kBoPnFTjkAefVpruQhp+/yWru5ktdFPn8gQtHmL5N6LcFWxBg/yucaZbrFpdijv+9///uHPYlV3XsoGegZJvqLef5SqILeoQE9kYmuRz06pIALTz9HfSUvPLiviKRfPz3Nx3NyheabZTl2NTd5+gMuME1/8if/PsHPeRyfd1CEn6w9rmtO98knVinW4MMBJHlh2YEeTsIlT7U9ue+4SNBmYr52E2QEHE6PxL71rW8dDN2j9BKXMSCoeiUiEvcIXzATcIwRhCTz5lDh6iMZApbm1EcyyXn1mZu1BDKP7ZwS+a/qSMm6PksriNXvWtc5TXJqb04jFA5OcHzsxSlkl8LGfZW7jzzRbwUKQvjRj350CMwSOqTBYZwc0Kcga5xk3fzmbn4/Jd/mN6ePLqWHY83erpnMmQdu2I/Nnaq/9957h0AoYWW3MbmoSR7slewlr3DjVEPCI1GucKFnOJHk2beE11xwo9CBJQnH3Kzr1AX2/MlYQeHnP/vZ4WRLcigBvpYeVs0eyMZ2lyRCtypc7J0O4AvmcIwgwLcEc4GM/A4yYJfvnqsv9uCbniCSu9Ntf4mMn/MB+Fj5+7HG5k7YFOcClwOA/tePQGReha956XyFvYdu89oSWQEQLsksiL548eL3AZPO+baDIBh6TNm3Gn90qALbdG4P/iKl4C74s40k2aEUXjp3D/opIJx4SiZgEB7FI0krLDnU4b94Z7TvsTVgFxc4MKNfySks4gRcBfueRsAs7F7ir3N7SoUL3fF1fM0nfdzWoSAOoG/3OqGmry4HG5LTCrzed4DgNTzbo8NFsc3v6RqO+WPzjte4xuoyvxhLLn5tPsmkwz1FyfyExBiyigv251BrntPl6ZK52FuSLrbIe7w2fu5P1ta4xSXOy2fkN/yJTr0/rjvq7RaXTz10EMyeiji5J3t60kbPrlEvbAM/Pokyz+e+XE9RAW9iucNNT8RhSc7SfI1pr6cufY2Xn4qNDoXZVKxid/qcZcVX+sghHJqO983nPfdxjP3Al0MZvGMNxZYD/mtyw7XbkyxcrqEwBI7wJYMSYyBw4t2jMUZySuLk7POf//zhrywJRMApmDpJZ2BJJlJgaGTmdLDgIbEgr6RI4K1wARQBD0gQEVB77ZLgchprSFwlSoBmPe8J6D4aonp3SkY+jiUJFSA5BrAqhuxLsJTk0Nkc3M69TjXz2p916NSa1u+JiKCtABmfiNCjBN+phiT/M5/5zMtPfepTLz/72c8eArQ9IVAkpn+Fj3nMZ49Ijb6sSe8wQo5bORRdwI21wg1bsrPkU5LixMrJxFe/+tUDbiQF7CWhUXB6yqRYcZJMflijF3tgf2v4TKmgZC/W01/iolCxN7gxH3xkI/MpGCXIdO+CN4HY+3Tp1Eji4z/xv/322wcidWKHoAQ4euUHdGp/7LknSd9q9iEZYMNL5rpW4UIGMvFLGJUICCZ+SmBgVsJBXqdS9Igb6IjeBTQ+CefmGRv7sCW/7SOW5jGvww08wbaKFnYWGO0Jxt2zVvOOuoIN8rK9efmPwGPePvqHu/CW99gSF9gHXMIom9LhrfzjWLMX6/IdWO3pMc5StPNn/El+CYtkz9NZiZREmp/4mIWx9kAfj9HsQQJOlzAh6SMvXfu4J1s4veevEnSv+TwflADwdbzBlqMdmpePm4e/2LNESdLgqTpOhFH2hD/6ECuc9tIdLoK92WfN6306pm/+I7GBCVjxGn7EJvhrXlhnBx/VY7dLueCpFS78kp/SqX3yn3ybXfnrGDthjg7YFKfzU6+93z39FRNidZ/AYE/z0jU96tuc49U88+UezPNp+vOT7zjQYjO2tC7OaExYgkXyjveSU8LLt/Ad/mEXcYwujB3HdM0yX+sytz3iQ8m3PeIChZ7303N9b3nZt7wOR5OHb+FmhwR8T0FPv+MYduXn/EZcnu/bg3lwhU9e8EP+qHCR01hzxNI5F13wawWQS57Aj+mO75o/v9XfT+sozNib3Vu3Cy7I7wkRfsNDcMtH5C/ijwMO2L6EC27dXsknLu6PhpovQYMzIy7Js49ZKFIUBYgAABkGuJxg+ty1hBQBlcAIQk6vfe7YEwIGlvBZV/PTXEgsxxsLF6+BDtCdbgRE7yMNcnnk57PSTt8FMR85IgOgk8dTCo/3OXrJFeIBWkFOolrwQwCcyUWert6zbjLQ33id28Yx5nLRt/k5kH1JjDmO4MCBJdTvv//+y49//OMvP/axj7384z/+48OTJMmd/SBnBSJnQhgjQZP1Wo3M6T+Z50ugctIAN06nPKKHG7YSqDi4+wjvZz/72eH/oDi5954TMYFRQsOe/u+Hk+WenFjT+vYliUaY6bDCpRNQuGFzOCCv5n33FYIwY36nMIgWYZqPfHd3d4fTVQm5hIVs8EcOe3LfnuifvO7bt/2TcbzCzCmM6Mc/HvPL+dbNtuaBQ8mE7yAopiUp7ACnEkV+CndO2STWxtKvU0hJN/+TPBQc8iU4FeR8z6ETLfPCLbv5qIe12A/+jfG+0y7Jr8MRQYPem9dYibLA4bSL3OaQxBtPJn7kJ+7CkRUp5s3PzKtYMl/2vKYPrVo+ZT36g0UfCWVDgVOBbZ/u4ysf+8SZeBHu3JNE9H1DvOYAg86MMfdD7YG+yKSYsgd+zwdL7Olb8sdv2EGSKSaQk61KEPkXnrYvtjDOxUcdJPBDxYN52dG6bI7HS04k3MaaA0fCHH+HEWu55yIXPJsTZulOLBAD3Yc5c8Mt7iiGmUNM8VTbAQcs8xly5vt7fZkenlLhItmjB7EIP4mtTqkl9DBoz3A2NxigZ/mIPc2NDnEDH1VwlqzCfBy+p+nPVgpU/utwih3FUXixHluMl/7yFHv0+2grv9sXO9gDXOIVvkdW+7+1T83NenhSjMCvivt8no1G+R+qKQD4DP+CF/mIeEo+PjDKxB/ILG6IszMu7I+vGs/PHViLK3QuxsAUv9vTrE82uShekIPgV/wjR2HjUcZwRH4HKdZ0f7zclzPqg2/wiliEs3CRPSiGvbdX3odsT7JwOdb06yQccShQAIRRAUSx4bPUfmdgTsvAgAhsLgZyGsWZAdGaEjlGNLc++iMMBOcLscjf+8ABxNZHfAogr11z4WIOzokEx4+gAASH9REqBNWaQGVNxAWsnAohknPs1wWY1lTwSKYBDtEhJ6c0nAZJSa4KvuSfwXyfZh6EI9hxLusI8ohUEu7ESnLHIXzG+pOf/OTLT3ziEy8//elPHz4u4rOUEnyJHMIX6NmEPs2n8mcnOrtGYwvzmTfcCDqSWkFIgkteOCAL3LCHcWEH8RqjAJZcOLmS3CA9c5egCF5s4rOjftI7m9sLfbE9gug9gZX9zN971vde5KORgY0V4p60jHgwL8yQRUEMFwIhgkqPLnuxDnJFfvrwGUmR3+HXGMkQPcGy/sZtYcW8j1m4RNiSeBhEzJI0xEx/6YhuvWZfTzW8pzBhQ79nZ3LoJ5DRt2TaGPqV+DhVp1N97d36OAZX4An253v4hD83t3nJaU385PPUbOBUTpEMTxJKfZuXPvkyDrEmO3v66mme+8nsYi+Y4XuSGq9hpHmu3aytMIFVCbDCBCfimlE3fhcIyU0fZNcPxtzTR1+/w5tTYoWD4oaurMF2t2jWrjgQL5ygiwdkJI89WttFDoUpf7APPGCcfvmt39mfXfEJe+AYNsEz+C1eMUZ/nCThx9PsD5t4A6b0ycbuwSGu8sSAfnAo/LABfOmb3mGYL+FV74sL/Mv9ZE1eCRFskwN2O1U2z7lN/6dWuNAhPThRll/gfT6N/xwurE6W7ZmviaWe2M6NLmFXnBP3cJ/Tevr1k3336E18kMN0WGUecuIH67ArGcdLUQNPMMmWY3MfLtlCsWYf9tknIh6jUKAzhYLE3n5gxb69Vqw9RrO+WCG3ohvJvljMb8vXaundHtgHr8461J++0zv78W04xOHWO7fBD+zyd74JF3IlnC6u4EU6HGXAFQ4t5Bez/DUxzQEXfOBs88KxgxQ2wm048LlwObNxdsTC6AzMuRCqQMZIwM25AQAJ9ZlyCSRDIuQIl/KRlXkYIxLxk7EVE0AFpJIbcznNrNixrn5ICMlZ0xpAWLBhaImatcwLJEhMMKlw8T4CJKdE0jjySD44DDmclNkbZ7auoOWeCrvPRyJP+9RfPzIArPnNR3f0ZS1JA9LzGVz7QoZIXDDhnOS2L8QNvIiPrjiJuZPbT6/p0L2COx07cXDiSOdsYX7JhuLRGpzDOl57HOy/O/s+yJtvvnn4YquP5dErXXEcF3kRh3nIzdnN72kBeREBsha87XWWd9YDHdOdoC3JZ0M6ICt7cFZrIQV6lfSGG/OMTtv85lLYIA9Ob07rwCxZ2Y9M9oM8YMl6ZDafe9bTl/zpeS5cvE8GyY5kxmvvW0tCBy/wQCfmsWdJL4ySzQmqNZFs2CP/2LxmW7pCwgiP3fggvXgqZF/mZAt+wv+sbT6+QN70DbOStZnM9zT6oRsyH2vWSJ9wSQ/kCz/wIrGkQ7prjMY/7Eui5x6b2SuiDk/NC+v2LpD5CB4ced99Y8e90rfAQo/WZRc24ot0rW9y4wE6J7dk3xcvPf2FdePZJRxYjx/BlHF0rYjhZ2G/ubuPLyQokluBj6+O845y72mtYR5coMBlc8kH3MDnqPManxLs4ctY+sMbuE7/mvntCab4I0xLCu3FWvzfXOnzPo1sxlsD5zuIYGN7IJP32I4cY6M38ipM3bMPeIC30Ze9T9eSIfrHfZ6G4aLmHWW3f5g3N9uRDe4UT5KPsVnD/Dgb9/gkgCKGjeGMP9JtvkFfYloxis3gjQ71qfmdHLAGg3TBl/gUP+mA5BR29OFbT6lwoS86xaMlfE7DcXMxgd7nfRmD7/garIz6CkP2qsCkM/GADcQ7GDqlq5o+/FXRKA7AuljIn+Bn5BqXecnD5vrZ37hOGMm/HGCwpXyCXfnVjMFbNzLDkEIfnrwmJ7ywBx/Cy6OOH6KxoXX5pxygwhG34mN+Mjc+TJ+eyvKZWY98CPZgB7ZgrmIHL+hv76ca2cQkc8nVHK7JP7Ip2UY7mpM89kD+MFOjWzEKZouX8izYxWlxgnjoo2LFx6fanlThggSclHI6TuuEk7MhccqlaMUCZQsECDnjdR1r+hpnXgAFDgEREJGBgCDZAA5g4WhAx+kQiiCHSBCd1wDSmuYytxNrALVWjcyCMEJxIRxjyS8RtEekh4wAlDPl6EiNbiRjgngF0Kk9935glNDSmzXoV9GAbDkoIvc7mczP+RCK1/Whe3uzb+PtRwGDEIEeyF3GWgMZ2beE3Gn+3d3d4SmLp1eSf+8LCvYkwJAxQvM7EicvTCAAc1mfHORJXidJkmh2IhPcKEok0uYmB+cnt0RDf0mttbZ0NzZ92JYjO7myjvckbOzINhIEp8nkLXghGVgZi1bEQSaEZA4XnLAN3LF5cnmfHsnu94IR0jIP4rRXYwVh+1W0u+eLj/Ds8hQAWcHUHiIiB3ySHQnbL1n4IXI0Jz3bD9t4KsGeZL9vo69jhUs6C9OSQ33xA/nIyh5bzVj24f907TXbKrLhxH75NpzYl3k9GYBXeCtJ6Kr5HTfAdL5J1+axljVq43j79UTVCRjdkaHEjy3dhyOFS5i1P4UIPLN1eJkvmOGLdMRfcIx5JHDJuKfpb228KQiTkz/ir1XiNza8SDf4VaMbPgA/7LjVzGmP9ouj2cReOoXeg+caW+A2WLYHiQW/LblfNXKwPb+mPzrX175xm4LAvBWMMBlH4XCJAp2vmuKEPynqamxvr/Y56rXfrY1zYQY26YZNjKErctErfvA6exuDJ/oI46rpBzv8RIzI1s1rbX1GuWrmDL+P3di3wiV58XWHO+TE6XRG9/Y8NvtkBzpwH97GPbuP98VrvIMn4EgMwhvmW+lobO7DDaw40BJP8asYxV8cgFpnbHTMd/ie+7A4NuuKUQ7OKgr05/+S1cdobMAW4kRFvr2TXYySb9m/ew/d6FORiBvlWHxRcVeOONvQa7HUwbcCdb7PnvACD3Ij/omD4Uiuab04e6u5Ry9yHfFQzBff8Iw8wc/ZD63L7vKQYlvN72xAx+Yit1yGT8NqRYp+eOO5cNnRBEUJmNNNBMMIHE4Cy0EFMMQJ3PoyVAEkA47Gmpt+ACmoSPoiNL8LMJ2GISFPOnzEyb0qUU4vcQQowcYpLWCa1ziBT5LtYzeAalzk1Sk4kEhSfD8BECUjxpnbI3+P6Mxpb94H+k5vrCtRR0ASklPgr+lDRnPaM2CSX5AFTkkp4pR4+hL9P/3TP/0+keJogi4iFbCdqJKLrszFCe0RqXsP8Xp8zX70yFkFbnv1hMU9ToEk6A+ZmRtxuGc8+cZ52dpa1rU+OcztVFJiiCA8mfiHf/iHw/eV6BH5S9gFfv3tN7kjjnBzqumHKJxcKmbJo0lQKlxc5KE3H/lBeLDqQjSw5MmaOeyzkxd7hAF6gAnECTdhW0EmMSI3mzsZogN70I+/uO8pjH2aj5/AoLXN4yeygqWwfG5LRnaAP3OZnz/CKf+UWLOfovSWhQs56MHadE+fEiR6p6dsy16r5n174IMlbt7zU2KNyAUrOoZXtrYWW/ENwYo+5/2lI3jmK37vfQEEHiVAvT82tmATvkEGa8A2XAlAkhf4wTt8wBzGwIJkFUeWUM2XvbnchzE8ytfxh32yG3uG51WzlvH8x3cl4Ffi53RS0sZX3d/CFDnI3OEDG9WMJYfkaqWbmrnDuzVxhGQar/B/eCSDOaw3N++5XwHE/yStilV+zUbHcKO5b6wDHhgyJ7noTuyQxNArDOB+tuSn7AiruGc1v/cUCPDO9jXySoCNpzNrWbP9+Wm/cEwXuNxH2yRMcEE35OKTikW41YyBA9iyj61GLmPonA/AmX3wDVjkE+41b42ennLhQhf2IC542iIOwRPert/Y7M99RQIfGu/Tkf3CkXjDFjAgx2CTDq6ONfeNYyvjfFTXel7LPcw/N3YTV/nyzEdwYh/iofUdfMERGc3N5+Y9PkTDabijg7kaWehRfkRnMPzQjTziR1gQH8TL7II3Rpk1fiQ3cjAw28B+2IgfytdgDYfzc4cG7DyPGZv32R0v4kc+jR/oUCGDh+FGv/ESE/h9H3H13thwhKKML4srck7ywTZ+qj+MPH9UbEcTBCRAjMQwADAa5pxrq5lLAFDxC7wjaTslAdACBHIDmF//+tcH4tMXEBC+wAR4QCQAIjNyI3GguLu7OyQqvoPgJAfo3XcaK5mUlAEkMHst8FpP8u37ENYt4QSgCpeadZygADB57WtsXnMq48nsoksXORAHR0Dqvj/jDw/4nwGKCcHXXzIjN0e2vjHmpJdZx363lrkFMvpQ8HEce7BnCZ/XdG6P9iep93EFcujPoelYIoxcERgnJHvrzs179mhtezJWQPJPEDmlx+4+RuHL8/ZmbQ7LQe0pffS7tcwX5mrWJwudOBWi8+4L5OxpDnpgRwkoGQQMyZ4EmDwSLsmMxN7HiSS45qJnOlF4+DK9j31IjsKVvdGHpKnTIHsgr3v6+gMA1vCa/MZWuIQPvyNk2LHufdrKDhodsZ+gfc3CxTz0GoZhjC4cAPBjiaF1R5v5eUxOvqvQow8Bno75qACk8OVvCkD3rWlMhb2+q/nhRvHNNgqUdK4ZI2mXaGzpXX8Jqj2by56Ngzvv+yMQMINzyEwufSSHClc6SK75Gpt12q+kRqHm+xqwFce1vp9e0wPc4zXcRwZ9R1+Z1xmbPvzA/q1pXM3vfFJQbw+nmj5sggfJAvewTk/5TPtoD95jQ98TxDcCcj402upYowdJgYMe+neZl/wOJfz5azKwmffJaA3JkARn1axt3xIRfsO2NePhnU/hf3uZde136+EP+GM3F1nFGHzirzfiKdxEZntmf2Pw7jn7t471zUsmXErnYgjOGTFp7woXcfOx26pwIac4zp/wMf70xNBPhYz7cxMHYZ+O6S8b+Jmd+b48QpxQcDj0MMa9+s/N+2yRDApe9pY842lcMjc6ZgM8Ze650b992HsFsZ/irVhj/JY8t2jwBTtij8MO668auXGEfGjU8UM0a5GL/STx8OEvhfI7frkqII2hf/yDT+Z9uc9OCkhxCg497VD0W8N+V75nXLzgk0cwRy/GwpT8orH61t/6YoF13O+e5r73HOTaD3zJiRzayMvIP+7vuXDZ2ShXkATylH6tZm4nDx6fAtwImrFwQVySTTJwdCATsAR3hnZSBcgqZ4EK+SEKgJJwIQlzeV9yJXmWIPiIgbWtAWScWXDx+WeJmMfNTrYk7j1OFRQk4yrlWiDUr1PYQOp9p3ZOL5xCA6ikSdKF6CRsnEGSptKW7At8AOp0wN7Ib06AtVbX6Aia3+1BQi5JN1ZBwgHIXkJCD+SxPpmQNIdWWNg/GRE83dI9h5Vk2DcZ9V0Fkxo5yGpdcisIzC1hsC9JkwSH47MZAnfRH6dlOzKQlz70p8f2Kliby+X30ZErXMjHVvTMdpJjhYjvLcAF+7G5yz148r5TPl+iJwNZ6ZD9zAkrTkQ6RVVUSghgTmINs/qwrwDX0wc6EAx9VAwBhXM/3bNvuiDzaM9LmnnoX5DkO5fMSy77F8DJCxd8z9x8RGBjh3yo/Z3T2E6CQU9wa15+zjedsNGtICHQmD+dSUY6PV3tDWYlChJUMo19zCHJFsDwyarpA3f+Oh3btbZxTv0kmX7CB+zQjwRaIsCenQSvrlq/mzfd2Q988Un4EvRgDb7sFycIYIIduYxJtnHuYw0u+q4bPM7jzIeXJAvmXbXVXtjSWBd7md8eyIpn8A6fhkf2lQjyw/Zg/Ll70EoCcRlf5XdsgbtwjnVhqALMXiQ+7uOw1VrJ7uNy+nhda4+4mq/TY/OODVdJWGAXVo3RF576a0l0IAbhJxxtD2T1/jk+1Lqu9I7v8JY1OliBJZjFU2LCY7cKFzqq2YPX8NATLXyAl+0FL8yNjtgYDygA6aBGN+ZjIxikb3h2MCkP8P5o17GZR+zxtEZS6sDT+uK6YpYd58bXi9PkmnEF42KBOGJf7EwHeO2anH9usyb/FytxyBbWvC8XkTOMhfpDNWvxGTbGq3IPdsHpYga7z/KwK56RV5B9buwn1sIhXjU33paf4Nh8emxe0wW78Sv+6pBaDsOuCplZh3RlPvLC34w3r+U25OT78APP1sBb7o9y4Onnj4rtaECucGGwazaJtNMMyTNyncFS4QJ8ACuQ6AcQxgKDUyYAkmwyvCAp4OsLmIoOCRWAIQljBDhrInmBafxTlJr3PMqXlAhwAi7ikbQjVEHNCSEyM4Y8SNI6gOrUqMeRnlQJYIBIHrK5kKBCAAiRmfUBU/HGCQRggEeU7s0g1rzu0jgcojeG3qwrKSMbG9pDH8WwliCG1K1nLbpiZ7Ia43d6tF/B1xzmd9849iG7/W81snEy8tsbnbAXvdAfG3jfJdGnD0kwQk1X9GZN+zFO4eC+5E2RJSiTN1J1T/A2N11Igkv6JEv23aPhZJRMwTcdIXJPqQQtiRsdmEvyg2AkGpIjOoA7ya/kic1dsJY89mQtpyr0q3CBk2ymwZB1zaH4hu1rNTKw+bUKF6fjsAlfMMNH4LPkTDt3HbigJz7t6ZSkhf7gw1MsmKBzGHNP0mE/LgTv8IIN54BgXvIKCgot+F41ffCHRInO53nsw77cl2yQ1dz2K1ixs3HhAr/QCfmdDFbM0UuBxpyn9JP81oFLWPNXDv3kDziTT1pX3/vY1Vg+xR9X483bd7DoO/nHtrUX7+lPn/TDRrDDv+jNwQGfYh9+Zf49zfzG8HuHHPwPR8AIXmUL2GE7azhIkOCQiQ3xLt8l2yx/857au5jA98nPVvM89o57JXwl1dbGXfgHLqyPH+AEbsgvLolh9mBe41Y6rq3WtQ5+l3zDYhjHffTz2I2t5sJFIzsu4LNw76dklc7sgT1HW4QDGJCIZqt0QndyBzwh5ukjSfUUhf3ZJ86qGc9e5lT08TcxH895TxI9j/EavsUFMXi0mfn4moMz84gp8Gh/5haXZt65dSObPbIDOehtxtHY7EGhII7B7GiDh2h0ymb8g83ESbkH+fkOmcZmL/yf3cVT+xtbNuFrcMEn2E8xIoeEmVkf5mArtlPAiVPGyEfY1nzzGDzC1+F35hoywD/MiKfmkreIVXherjDP91y47GwI9JqFC4PkDMAjaVs5L9ABJ3AwmH4Rg/HGMCYCEEQAT6UtwRUgvSa7MZIMQFa4+GlOcwCXJAy4AIlskl6BBNh8rErxIsl2AZWxChdzSXKRK4DSD+cmi//xApQSHoSFIBREyLdmrXRhL4KZtTmmNZD4TJJza7w9dgLOETlABNo+nVAIYJyaI/bIU/JPVqTAKST6+tCtACOAjycbxurH0QQXZEz+7h9r5GRH+6MrwcQ6ZFyNRwj0ruigR/KQVSLnJM6pt2BkX5IJcvcIF6kpQBCOYlVCIOG1T7bSXyO7Akdybz0yem1/8OSxMxnpEybgy5MbmFHYSHKcyiMf49O7y+90TA5z+p4YW8xNPxjhD3S78of7NLa6RuHCN9hMQY9oJVajzffOHSbZyBObv/mbvznohs96f8aDUygBgj+7zzb0bn/6jpf3+ILCFg9I8lfymYt/e7oGM5JIdmhtl98lK/y9REcSQ2a/j826FaC+00VXkh2BLl11rVr3zEs2xZu5JPk+9gSzipmx797WOHsqQK6afSsY+cyqONS2ZFi9bz78hqt9VEqCAJd8sIR0a76xuU8/OIFuff/Mx65wgr1kP5dGXxXY9oAf7Nv49mTOxrCfJFkhzV+bZ26SGx875q8wizP0Hffgp2QLL8CGMTgPp3S/tfFGWPS/y8QQSRXdtKfmPdaaUzPGeHyCs/qu22O3rcKF3N5TWPJv8Z+fsYlCQwyg57nxL0+u4JSuZj3BmDgBH/BHr+bD3/xsbOyE5+QE/E+CKm77Hoj150bH5sXv4a9GDvKKjzCliBaz7AuXOTQo53ioZi0yetLAZ+KTY80Y/XrCTEcP2axvTfKyjbyR78tbHCazPzuMLb3L9fD2fJ/vu9/Blieo7AMfcrDZ3/CG9fmop2pyAsWU8eafbei1vIPO5HFzs5+K2J76VKzDJ/nnOZ8Ll52Nc1+zcGEUxlcQMAIjzkbSBI4+QuS0VbCVhBgreXKa5QT7N7/5zQFUnX5JbADMI15BH2krIPxuH747AlSMb20Ji0QUOMkmGHEKQLGefQt+ChSAFdR+/vOfH/4njY+EIFmnW5JYl6RZgBL4JbrIyTpbYCMvEAtWEnCOYNwKvHMTmBAmx+PM1hcES5SQlPXtQcCwR3PTuwCOkBR19EdeupDk2o9+kg3Oj+hLyu3D/PQjCEuu7BeBjIXZqpHJWHNzaAGMDjmv9+b91p8uzE0GCYBAQGbrIxz2JqeEyJ90ZuOf/vSnhyLM3AiO7Y0lt0JTIWROAc9YxY116Iut6Qd2BEVBi10FMAkxsiOL/ub0lA0uPJGgD8kKDClsYcgpuUfRvqtEp3OjU/uXcFu3U9pLG9xdo3DhV/aPaO17y2ePNXakb3ujVx/LY0f+LLmAXzpSRMA0fYRjWNGfHH5XhFecu/SVYLCxE3i+zqfxA4ysZGVnCYrkV4LEhmynKBux7ncn1xVK+sIU2cImP2I79+hI8sX+CjP2xHXwxfbWnduoG8EZ58G3Qts+7+7uDryg3yWNHugKt8L8Spaae5Ir/Lbya3OdiwH96JMfsQ2+Y3PYVBzxD/xjzZknjWV3/K5I4Mt06tQVj0lA6R1/9qROf+u1h55y4DXre999+sS/OLBDJ7YSUyQT7pNn3qf+//3f//3yv/7rvw79cQQ+4e/mbhxu8r0T7+EUhYw4lu7IiTfhC3fwB3qhHzEHJsU6umEDMp/b0jl80onY9ZSfuGhkxscSSImcvmwnWVVE4Nq50SFcwBJbzrbyXv7LBpJDOnao4V465dvsR+d8FR/BC97ji/xmbt7DRZJYeh7XZv/iZxi0H/5MBhw3y3rrZq/8iA/Apj2f0+AZBukMRz10Cxf4XI5DpzhDkUGXs1/oD18K9Xh7bO6zvTgkZvAxmMPZciW2sWf9/MRb8OCw2veD4UNOKc7zy9GOfocLa+N/uJgbG4hnYg3ewLEwwkfJ1dpjey5cdjbEcI3CJRAIwIIEo5p7ZQRARBj+QaJCQjIAMJ4SCBROYJE7AnQaKuEo2bMOAvIFV6Agt9Pcf/mXfzkUM4jJHOSQlACEx+hIClmqkgFVQPS7U7C//uu/PpywCS6cR3/OI9FHTsBpbXMZ08datho9ALykVnIt8DlZIg/5V8Gy5p71rMVx7EkQ5yzjGL+bTyIk2SZn75GR/oyR2CFmAZVMCEFCpx85OTEdSsbYa2xkERQVDtYwllPa+ynnct96HFHy4hSK3awxE5FGdpd7SEyywsk1ctofe8CKoA875mW/X/7ylwc8KMCcGkky6UACIpgZYx90ag5kCG++5+QvukloBH3vS6z1Sx5kBoPmQ34ScUHKnL7Ubx7jJDDf/OY3DzZfEal9eV/xCg/2s9LDnma+axUuggS/3TMP+WEVlhQWdKfgF8C9znfyB0WJPvSuj9d0DRd8VgIjSfA7fzcebu2Pb9K/QwkJPx/VDzbpYZQb9sikyBGQ4B/OBTnvCSCCWJjgX3wAXgQPQdz7Eil+AVswwKdg0nou/qUPGRQj7Apv5rW+PeuPi2DGHiS3ZNGHbviUJNrvl7b81ef9rXkMX+5Jqh0s0PHc6HMvptis00W6IQs90zc/wdeKwHTjp9cKEvzSU1N2h0k6phecQ/cKGsWvU0t6Nz9+YWMJIztlI2P4qjHWZkccDDdik7XMAZvt0096MR9exb3mswcxyJMxsZK+wiZ5cI5YheOs65614FWBzfYwbs/mZyd96B8exD3jzUd2ex/l2mrZSH96l2w9djtWuGhxjaQSV/Ib+nU4p+Bg87nRlQICrma9eE3nbCVZFwPYzvwKGjjUYA1XwgGfo3eYEe/5+ugr5mcjttcPrsc1uy/Jhm0YlPTjDVwlbtvnQzYy8RX7xrH2M8p8qtG7nAxW/b5n7KXNWnzD0yp2xqf4GNfzX3Za8SMepXf4YI+x2b9x8i+FCA7w0x7F+nwR/7AZTvBTf/mdQpp/wtfYjIEz67LxqCe/6y9/NZd+4cM+5BLpdtYvDD4XLjsa5V+jcKFsBCLh5zgIeDaOph+ClkwIHkhGco3ogMJ9xkcWkkXBx0l7j//dF7AkINYzjrGdZgkWgpH1BQxELhkqwZU4+CtG7glOHEWi5IRCsqyvYGOtEpiARjaOZN0ZsGMjHzk4iKdGHAJ5mqf9rYCrpRtyAbxK3V7te+5vPiRsb5zP3JpkUCAkoz50a6/06TVdsbX9ti8OLRGXNKwc1Ti6UPAIkIpAcp5q2RIWOLMkycmDpHZLf95nV8UAuTTzsDMsCEwIQcBzEocMBEvFqISGjBIYfy3Oo3uJNFtKThGWAhU+4IRu2F5SQU5+oA8iJIdLEEJCbG8eekOi+rsERATZU4Z0Pe/Pa/tgF30EVTJc0tjl2oXLOc1a1mYHGOQznlaZg61hJqy72rv36AxJeyqgOIVFmPCabfgM+ygycASdKvboyppwgGOceLG5AAOL5q4JSgU/yQ4dCUL6GO8967C9ZLrTMMmPeWGFDA5Vmj+bj/txeY9cdAhPEjBFDj7CAXTjdXtID8mi+IJrc1zazG3fPtpivlOYwBt8h9/MzdhT4+dmP/YJB3yofZJFksF3FXd0ivclhfhLsgJL+tGnMek3Hadnfhje4F7Rw44KCwWxvUi+xAzxQ1LcnCWY+MUpLBlgpX3q42CDTZyWus9PretiP2uyp/clwPCjoKFz/fEF7tKnA6f2lU5d1mpf+AL30A3M43887p79n7IDbMKdguGx26nCxV5gg7082eR79ux3OKALuhkb3xKncTsbpMua/g4iJZ0ODvRzCCFW8y19je+jxWwndjgAwzvmGxud8w2FKBtaf2zukxNOyI2P2B0fiaNsPu/h1s0ecJwEHV+dwszcyCuWik14bNbJrRudiqV0yofZSJ7Bf3G1/GNu7IJj+8TI2OxfLJJDwYYYLd44ZBAX7NV4h5xw4L6nvfwOPxlDJ6Me/Q675glXY+Pn5o3P/JRPyH1wAxntc9WeC5edjfEuLVwYmLNyYCQUucxNP4ECmJCLIOE1gwcCP5GehF8QFGQEQiQCNO5xTEmGU0XOCrxO7AAQaZrb//VwuiYBkZD1iJhjCJiSVZc1JEnW4QARmcCH7JKN7JxJUPT72ADNGHIBvHkksgog+nXfHPNVMx9QW48O6Yes3ht1UzMnwpSQ6adPTUCWfJVojYWL1whZ8cHxvDY3nUoAJG8C9ri/ZNWXPAUGMpIBObh3rLUGW7GjPQrOdE7OeX+wxNld5qY/OKFb9uLkAorg532JjNM6slhD4kAHTsx6isduMOE02voIBbEocuxdMSUpkXDSFXkVjWzJLta0vnHkSy/ZXn94MqeCWf9ZL6MeYZCt9LsvUbG7hIVOZx3uafDEx+ht1cwNE/rZI7vTJ9246M/7dDD7xixXOmM39hfs+ZXk8zvf+c7Ln/zkJ4cAwpcFFfPqm56NkZCwMzsYN/qA+WFcH0kKLioxMt59femdD7A33PsT5d/4xjcOT9/4B04RxNgqO4578XuX+9lfYJS8+PPcCmeFHTzZw6gbuFdASfTmwus+jX7Yh+4Ev/Z6rOlPPnjFrTMOT42fmzUVKPgWt497SkdO2enWd2H89Uf4hQG6I09jRv2OF73xc7bBD5JVT9w//PDDl//5n/95wDBukojox9bmtD5b43dysD07j0me9yUY/JI8khAcgxuMd8GDPYhPeEdS9d577x2e+LM7roW1kdu6thr5rA23kjUxK47CYbA66mZu1nlVCheNLmBf8SnBozN+w0/pn55Hfdk3P8Zzklpzj/f9TkdyGHGF7uQ0YpXiVUxja7qEPeuZx3p0PuPe+jgfXqw76j0egj0cDrv2jIMknk7r9Tlm71s0+4cBez6m+61GXhzFP9iP7zx0g2N6Fw/EcwdHClD+TK4xz9HYhZw4T/9Z5+wkTlRIKEhwHduJXziK/zrssSaf5rsww//nxgdxgzxB7JkbDoVRhxliF/lhDi49nbO/rcZ+z4XLjsYYlxQuwJJBJRGKiZXTeo9DSeglkgKc98YrUgAohYgq2xiBX8IJnAItIPp4kNMzyTsCctrmJ/A5/VAVAw3ZOIMApAl4ihIEKQAhNGABHElLTyL6vDWwcRBAJUMnYaPMZEQYnX6br2RLn1Ub9+sJDTkQKlkFvfrM472mO6QvyI2y2Cvy6smJS+FCLyVH5GJvF7mTESk4QVTsHUuk9Oeg1mYHRQybG+/esWYdayJ5QYFNnKg01n2NzhGIAFQRjEisJcDpDxuIgaww5RKEBCY6gCHzSKIk1uamW6ejghacwKvCRgAiy93d3eHJHBITABW01mRzepMIG4MEyURH4579zjbmUwgY057GZpy56I8cW/1ONXqw11sVLvbjslcJG33QOa7gY/Rg7J619W3e/Mpcisq333775Ve+8pXDX+0SYPiBPaZretJXsVTg6KNWyWFeQcOjefb3PizxrZLz+oUzT2Ilvq+99trh/0hJhtnR2tl43mNzuC94wpQ16VDCqXDiI9ZYNWMEUjgzxzz/3kZHknF4pzdznmr6COwSLwmC/V7a6ISunaSv5rNPWOJXEn26IjPuxO/sOvqW/l3p231zk91Y/7z39ddfPxS9ikXJI6w0h758mF9KIIzHG3CMt/UzP06BJe8Zx0biGq4kV2u78JECwx8S+dKXvnT4mKgYJD61h+Q/t7VHsoshYhR5xCsHNWJU845zw9irVLhodAsndMsH+AL7iz+42/7mJq7xTTFhvs828E8PiglPr3Csefm35NdrftnTFnamz7HRKdklr4qruVkHN8gvxDE4xosSYWvY1x6bX9qs5YI7cQvf3beZR/5j74r2h9yHRrf8Sg5lP2K6eMsO8gX+vrI7OyhA+N1KZgeOYq3DjHye/eyTzsQQuMKBcLPFhX00EE+s8Cke8lf840my9fiw/Yg9x9pz4bKzAfp9ChcAARoGVrECggJjZXCG8D4wIg7Eg2wDmZ9eC2juARkgIQNFjs+h+stEkk6Eo6L1voSNozkZJ7/TeOBBJsYCsnURmcCoISprKIyQGvAqcDrBj3jMqVABUvNIfgU+9+3bPGQ2F1kFUEWC+97XZ+VENXLRFzl9rIAzAbf3t4CbXJxHAifA1YzjMJJ9MmcbTkhnFSPkdt/HtszlPc3c9M+JyVPxNDf9sqfx9OZpFptwvlP7bqy+TsLgRrBR/NCb++ZAYPaCtLwvWCAnOtOHTdhfgsu+ChA4VHyaK/uwrwSSTLAuQZcI0ItEU8JpTkWsQIrI6BYB9VeAPJUxj/XoRoAiX7Zuv35a176swR5er/RBB4o2+4eb+wQcc9yqcKFj+KJ3T6/4At0IaApE9+xNvz2NbY2FRwmOj/w5uYZ/RYPEU8CylqeqfE7CAS/GsAWfS69+9/0i9sjvfCSEfXttPf5uHj7G3mwsoeEHeEmA/Ld/+7eDHhS/EijvSYbG03PNT/iTYEls4R//0I33FE64ZSsB0xxWsB2sXGK7GnnwF3+Bi3Pn1BeeJV+XJD1jw8HwsjUf2/FRgdpP9uAr/JsuFfPsOvIbO0oo4UCx6eCGjcgtFsAR7Pi4r3vmxk18FF7ZyUUmujEXnLG3PvRABnaEM33IiVtgUJzRj930IWdPRHws1SEaTOEJBYy52Z9dyH5us67+8Gb/ZIcrHG6/5qUvXB22yI6vcOFjt3MLF3vkV/TIx8RQBYWDKfHN/uZGl/wKZ2fHGl2IR2KGE3VF5d///d8fPhr+1ltvHf7gjsNOSSTMsOkqxpEbbuh7SwZ+Rk62xxEKLpgb4+lDNRgVu8hLj7BwSYM78dJhJ660n3O55NKWz9GrmAD3Do/J47BKjjFzijH8Uu4nj11xDs7A/+ykAPKXCn/4wx++fPPNNw8xx1+JNR525I7mGHnbGt6DHU9+YGBs7tMVHhVL9IM/3O5wBp+c4mQyPhcuOxqD3LdwAXLJl8DBabyem34IGBgllQyDMBAygyJ3wQA4AQdQPXYHKEEAWFW6ApPEFEAKIBIPJG4+BYDEw1+D8b0GAdy6ADMWLho5JRc+HuJjBv56jL80U7BBXkiLXrwniCpMANGcgGtdJ0UCNBmRFl3a73iNDSCtra+5JE2IXlBs7FZDIPbNoRV2ZBxJ0msBAynnJH6yD1KrmNHIITjQ0TgH0rBvwYEtrDfeXzXrsmMng+zLCVdYmJs+1qC/niDRNV2QxX6cWMCKYEVX5DPOfiQiCgt/OUqy4B+Lsmt6tH92q3AhKzJ0Suq/+yMs81pTUots4NgaMGV9+nZyIilBQr/61a8Of9kMTunI3LAINzDBtpJoBEcfPi5I3hUZ2WdPB+jwlK7nZn/XLFwEYdimb/qwP8kSQoZRemGXPWvpS066Ny9fNq/DCEGE7uhL0qVYoTd4pUsBmQ867MBP7M9n2IbMdEpeY82JG+APFtnUuuTVxx5gTLIpiPEhWNBP0SRxgjV4sTZ7CEL6srukyqk93Dn5kxxVdEnUrZ3/sIcnDl6vGjtLfIw17hqNXslOZ3sbneEDvniNoGke/sGXjukAv0sS2WnkRftgc5j0MS22oHu+yobmdpAETz1J8TTVx8/owTxOYe2Jjdgeth3KwAKZXLDHBn6yG5uJI2HLT/cUwpIRhb0DElzAH+DWeJjCHeSFJ/dg2Bf3cQgOgFk+YK/mPuZD7o33jcGp5uEjindr8lE6gCGyWeux27mFi8YGYihf5F98kH+J1YrTmQ/pwX3FDt/lp2xA535XBCt65SPyAAUlrPhrj2zho4me5iqQncLrb3yXeeBH/gBz1quxB3nIhYvgEqb4sQMvBTO8PGQjk3zEXvDRNfzXeHbBs/Kmrdh1q2ZPfJRfiY24SRz1tM1PcXbGBXnZDZ+I1ew42pU/stvd3d2BV+R7vk8pjjs4lG86kHQw7hARxszRPF6L84qansSPjTwOyvAE/oJfP/ESznH/lM8/Fy47G2e7T+HCGJwXSQjwXq8Mg8AkBgoECQDS52QqaEQFbJIvFa/iBhEznoAjMQUezuNEC/iA1DrA40RW0gIgSMTfskfuAA9Ego5+q8LFGLL43XqAjPwFHEFRMAB4xIdUOQ1ZyGe/9CXBB2xrjOAcr7EhFsWCvdPFeCo8952bYGyv9O33eQziJjsd1si1Klw0hEun9Dk2fZCDMU6ekcixRgb7To9Oo5zQO92Y5161xrOVUx6BWYHKkZ2AwodEkT0LVuzLfshGIoPUyMkuklaymNf+JUHsqUkwkYO9s73HwuYQeATcTluMFRB6zGseOucn7GafsADXbAK/5ES29q6fYkyA9WVJcptv1ehIsmR/9L6HtMh1rcKFrSVl9gzbiilFsIQrbI/4PrexhWAgqZOg8NNOrezdJYHwJfwKG74KD9azNl+UGCg28YKkDb4km/qQkezWYUfJsHnpxzxsIYn927/928N96zU3n6R/BxT0oFDjl8bbpz58inwOOnwEgK1wE3lH3bhwIvnNsWr6GFfRSz+XNnJKumHVfvY2+pWo9LHNS1tcwDf58dzSK/9mt3zOe/RuvMSRb9E1nbM9vjJGf3rkUxIDhQkehgs+bA73/VTIsYeDKb4C03g9u+E5cygC9GM/OqAT/s2fHYpIehROZDPOeEWTe/aIs/AXXrAXffzOn+wBx0gwcVD7PbelGxfZ4B6mzYsH8Q180sNjtz2FCx3hcwcYYmx5gDgvDtCfPdfowf7FaYWJWCOHcOEs3I+HHS794Ac/OByOOKSCI/0ddHrfUxhz6N94v5tPHuD32UbkgDF+Cwf4Al7IKp7DVJzxEM06Lok0efHNtdY2Dy4VK+2ZnR6y0TV94g+cJM8Sn1ywsuI4nCrWs7kcCZbopUvc+eijj17+7Gc/+70/yws9ifPvFX70ox8d/nku/ISJcGU+n77gX3Qx6xlX4CZ87gCDn+MOMVRsOscuz4XLzsZB9xYuFAvQAMGYEi7GYyRJmkTRnBIqF5JQ3UoSvXYq4jQOOCWEgCiRKSggPQQPbIocQUySiSQiMiQhURTUEInEHLAFCPKQC6kjLZ/Ld+LCCZzMIUcAt771kK1EBIkiUAkAZ/E70FpDH4lxxCyZATZEGphXV4FYQUUmJCfxtu9x7Kp5X3CWfCFyTiXpmtf0O+LirHRV02+rcLG+v65GB/olg5/6GYc4BG7BP9tsNffMYx16sk96JLux5myNVaMnAblkweU0jt3YRsKKaAQOeoQFdkYUEg9jFTie6rGPvrAoMbIP2PCT7cmoj2TPHIimvaZTGJRsWws+4RbeJdZ8xn7sSz9rw4KkRxElOTK/03p/blkylS+QA646heU3bIaQ+eBov1ONvq9RuNiDBMuJk4DMBmSzT3YZ26l19DefpEOBaF8u+rBv77uvn7ngBh9I+qxnXf0lguFVf3MJJN4nI3zRq7GCNy7gqziD/A4IYCXdK2aMcdG5Zn7r4Qd84zUsKXLwER/h53zPvIKd4Ag/ZEq+mj1ZV4HWGnMzBoasATPzHHub8fkBzMPE3mYMnfEBWL4ESxqZ6M+hjycfq8bu9u/EUxHKl6zbZQ54wHfihlNS/sGmuIlP4ms8jSfonv9JILxvvMsa8FKBUnEDE5IEyYZiFf4lrvjKoQabwwrugR0ydNqafObSh+w4Ej+zbTypD5yIleKO/j3NZiuy5WOndL7SDa7gU/bCd3HVqXlu3fYULhrd0QWfV5Q6EZcb0BV7jfPYN3+VCyhCcB//Zx+Xww0cLrb73pPfHTj6VAR/4yM+LuaA0+/6N9ZaChHrw4N12KVG3+I2OeFVkul032vv2we7P5T+rcXH4NbBy9bB2H2b+eRwMHXtuU81OqR7sZLP07e8DpcoBuAlH9P05/tszQ/kHXIQvOBiZzqCCbaHUXmBvM58uN2TOH/cg+/r3zj44rMKF7wRt9Toxtx8Gm5xvxggpuACuDin4YLnwmVH45Bz4QIILgpkJCAaL2OcPDM6xxEUBD2vJReIC3CQNdAxhmTOGERf0m7unN01Nmv0pSvO4/QaSTfOff9Ho48jeF/VLblwElbSKyGyP8SFlACMLBJhhCPBQIQSeCQJaCXfgqCA6UufHjOTRaCzjvXSz7iHUW/6mF/gpBvkWOFxqpnDHiVqnMopkpNqjjKv5T3yImNkVrPOVuFijKAs6DXn2MyDAF68eHH4z/GK0VWytmrWpU+FAVJwOsgx2WleZ2z2Qv+SW/ZDRD4G5rPKTkXgh80EbLoho6IV3iQOCE3QUZwgGx+pkGgiQGTlIwCKGXiUmEhS7EmyhqCQI3vZI50YBz8uxbex9pbNxzbanCzWkwjZh6KSH/AHdqJzQdcpt1NTffmR5EggJI+55ssao+2tda3CRZFGv3sDsH7tnX3pTyEoIcUp9qugZK953vbggAG+/K6fRMKe+JkxklR64T/ksxY/Ynu2EYT4usAkcHnKZS/w18c7jZGMCA7GWttaOIUOyZ3tJD7pQzHE78Kj5GqlG+9ZB+7ay6p5P3tfo9GHIp6P87GVbOc0GHCQAat0fklLtwI/P9uSSR/JHx+g/1UzVgKAg/gQvenPd/gnXsU1YU8SAX/mZnMY4leKO7qnIzFDgsKeDjp8xNTHjH1HBj7ECDzfgQ0Z6BeevLaWecwr1niNi/AQmehy3jOdwgc8whM+kTjR0RwPz23WcJkXVvH1vO5DN9jeU7hobIQr8CM/w5u4nF/mqxqbiln2KS56iub7beK6/AAPiNNipe/Eihme1H3ta187PHnx1MX///LxYu8rfox1mQcO2FiO4sDUehqb4A6xBC4kpzgKHhWr4pWWPW7drAFz4hf8w8+lPjs3a9gXW8jDHmJfY7MfRYK8DUezOd9ykMRvxgMWdhJn2M3hopwBl4kDLrmePE4u4ysI7osV/hiMfNFHxhW0/p+fpzH6wxNcyCH882m8I9Y4QIsTXPgSH8GpPFWugz/EL3Kdq7fnwmVno1xG4Yw1JIp4JAxIRaJIoQhJJSmRfueddw6fKQYWDqwfMCFRl3ld5mo+gUVCoOBAAOYfQYC4XcZxRsm2oGw9ZCNpdc86iA0YVbf6OdUiQ4mgQgq5WENQESCQoz5eC16SqxJTgY2j+mmviEtQ+/a3v/3y85///AHoiBRwnbxxKmRmLnsANnvw00VGDucRPqezH7Lb3ykw05UTeQUVMuZUPhbnPbpMX+nM3AjUqbH7Nb9vFS5+5yyc0z5mmezBGI/Vra+fwk0SeiooJZf9mkMyJ8ivPibhd3LSI3nsBQmwnUChaPzc5z53+FO5bAqryMK8bKWvoC2pFajsF/7gAanDSRgwVoCS2DhdgwV2NBd89PQuWRRAff6V7vXL5gLHSDD2YB3jJdECIBnIAh8ue3d5zxz668N/YNaJjy8N8itJlfVh0bpk1Z/uw5A1EbXTydl+exqZJHKSKfN0ndPatxMvdhYIFIDswgfMPeuqRif2juglifkOnhAw2dlYc+MnH18glz7eNzf9SWwVLH/xF39xCE44BjboON/U6I6MbGO8sQKUjwmxN/93yPHb3/72YANPz8KOBJtNvF7phk1gie3Jtdqv98xFR/qeq+NjzZ4ETHu+pLEjH+AXI0/ct9GvxMpBluR+NSd9wIiYQr9bDR5gU7LKTl4rINncx/f4sxjFr2CEnfgpGfAIH7E/65HD7+aAObrzF8HeeOONQ5ICS3zNXI1hJ4cWkhp4YkMYwuvkN6e12FTyYe15v+Zw6WduOMKlcG1dcsCj+ep/biPTNXjgGu0+hQs9S45hTxHqsAsWzaWQcV+DI/fYCK4cRDnQ8D6OoE9xwIGjokb8xS2KUqflcg5/ppyNHFTBp7H83yczOo0ngznMR5/8WWKsCMfvbGYs3CpE2fQhG5noxD7kP9fw11VjQ/5DxzB/q3VWrT3yO3oWBz3BZRsHwS5+RCZ+I1cTox1o+MiXwyOvXWzlibJYTmcOruVleJjfwQbe9lQFDsMF+4tlci8HWPhHXJKjWJffwQRsGEc+csoz4XGPvvDac+GyowGnYI3sGZkjdrLVR7GcTEjyFSmAzNERC2Mjb+CKwI81YGRsoFAEmC+gSGqtLVkQhK0NCJJ3STkiEThUtAoNVbE5GFtyry8yk+w6OeFsglNEjrSc7JKzgGmPTmdU9chIgqpCN6dAoD8CVIkrtoBcP2txJjKSFXkgUxcdCp6CCLALyOkmWVYN+bGFosgeJensYt++H2Q/CLR5mss4etPf7+MaHH+rcNHMJ1gg+Fk2r93n4GzPDoKAfXNgQZ+8p5ysQO2khF6diKYnuGF3pMIWHJ4NFBxkFkR85tj/12BT9mBzJOVEhSyIxQm5L2AqKguYdK6IleAU+CQL5tWXXtnSXD5K4Kf52BkpsqtiDf77uCAdwx77wC3Z7cFevDafwArP5DjlD3RDJvaDFzIozvmXwIoUrQuTbGD/Cgx4M8b69sgHZ/vtaex8buHC3vRJvzAAX/yYfPSmYLGnc8hXMJQw0DVeqNGbgwP70gc+7Z0/u+c9iSodKO7wExv6Doviz2t2wmMCCHno2j69x06CHbxIftnOenTtnsMStij4WA82JS3smm7sMXwLqLjHfrbsbn17pS8yXWIzjR0UW+Sm90samSXcfNS+85n7NroR4HEY37b3udk/3TlJpXcc5fXMY7CBUx2cmJNs9ov3JbY4DP74JZ6XvPAjOsYNcGJ/ZJAg2B8fwvEKVxzvqa758I9CiG8pRJLHXHjJOLhna7ghi72GBfwkMfL+Kfs2L76ACU98YJ5P0QXcmYfsx+Z61QsXMtsDDOAhT17sx8EFnhYT6YKP8h8+LP6zk5xAHoKb2V1/h23+vLm4KRl1+s3PYcF3XHyvgZ0b4yO95ineSh49FfMJBvaRr1iPPOKN+Id3cEL2f8gGx3AOg2OOc+0Gdw6A6EGMHjn6oRr9+8gfXMgd+Al/VkCIvXABK+5lM3xSgSo+ym3xgzxOjFDweoKL5/m+A3B48cccjIELOR4+8DTGIbfcUjHEv+me3o2Xl8EF33VIyn9x0hgnzmnPhcvORsGM7jGqBJ6hAYFjImkJwkjOwNwl0CETDixpQbanlM6YLokPAhAMPFGRJAIj4DE8MuegyJ1RJS6SaKBzQgWMghfZXIKJBAewEZmCwfu1sXDRyKmPBEWCCHSSD85pb4IbUgNgiSvdIC17NpZs+ijkyCx4CoL2gvwQrLVO6UOjD3vhcBxDAOKUEsmKKk6T7sZGP/oJlvNa9n+scLFXJ1Scb76neU8QYRtz2KcEQlKoaHPq2GPyU43ckgBJDDt6OqIYgjXEJCkUoMhEbvqwJiJwYqYPOwgi5Er/9KKfgiN9WwvhsZ9COBy4JxlBVOZDPOHbVXEFE05yYUwQhj965iuKCrp2cg+vApriCo7hj/zJcKy570SHn8GOj0AgYXsz3uV3lznZT/Dgq+zNTyVy9qKgPLXesUaX5xQu3qMDRC5B5/cOFfiAhPLY2FXjs+xvvnGM3+kGTmAC7hQZ9ON9nMPm1uYzgisM8xXz8Wv92cdriUt/wEFfyRVcCV4+BgAn9A/L9M3e+vANdldokwV+RznpjZ8aCysCo+BpjlWzNo5QNLHvJY0cCmZ8hx/2JIqrZj44g0d65VvXkBFm8S6/WzU6xZXsAM+SR/sa1zYPnVUssIlTTvZmg/iCTfmuBAS3mJc/SzJcMMFX4Bx2YUKMczBg3+SEST5Plg4pYI6cEiZ+Lj7Rub6zv0o8cMKMla2mT2PJCHPkMz8fkxSxrT5b87EbnL+qhYtGbpjjH2KWOcQXvuUwCDfzcffEZPFCHwUHO3UYJs6zuVN3T1DwowLGR8jkDRJRSaZ+HZ7hEk/i8QxcOQjwvvVgxvpwyTb8jQzWIC8sPrTOxUDYh/ktrrlmK8bi+IdufJtPeSpC72wj3sgjxEJ2VJS4p7hwYCGeOsjiQ+IHfYn1MAAv/UUxOPIRQh8d81TFgZeDTrjoEzkOSOW2DurLf/rTyvgCXnCHfACf8EO42NueC5edDcFIBBCyxJ0CBXjvI8SImXPOF6eVLCINiY9kVlLnUXvJ4lYzp/klez6GhRyA01xkcM8a5kfIQILEJfYCB7BwXgGNHC5BzFgnMcaM5DkWLua1L/MgWkHVemQiN/kFD0FOcaWwAVigRFzmNd6anJljeBojAfIEB8kKXMcaGaxpPKdAvkjaHhGSNcnmtRMAievcyKuQUsxJisw5tpye3OacbWIP9G2dCoKxmY98kjYFDGIgI/1IDuHGWKSQTo418tIL23lMC3OSSrgTtI0no98VFnQqOUA4TkARh/7ZK5t36oxwwo3CZX7iYm0ymzuMNYeCBNEhIjZX/FrTewKWPuGMziUx7llXYKQffezxWLOm/QnKbM5v6L6xow393kU3+vBN48mguFck2884bm8z71bhYr/0xs/glI/zAf1hi56NP2X7sZmbTdhGkIGnudkn/fB331vKR3CEJAJv8An+TBeKa77CbwQ2470Pp5JPvg8PkhFBSqHbYQO/1QfW7MV+YUBfha0CCKeEFT5hfbqAf5iUMAs8MM3GKxzAFsyYY4+99DUfncGt5J0eFPK4k3+zwyXN/HRpPxJ/Tx/5G5x739p7bFxjIzbcCsjsRL/91Um6dEBEDjwKG/RpfwqRDhLYW3JBl+7ht07M2YJO8IbEA48rRCQX7GxPfIidrWWcRJUcihT3yM3G+E4fY/GcpBcfS5rInq9kI+/ZBz4g955mvDF8TRKkKLeuIktChvfMP9vBGPt9lQsXzRj7FGfoj3/KJ2BDcek1/3axoQJbDHdowHfphw5cfMNTegWJj3uLN5JVc7CdIlfcwL3sKT+AU76FZ9ic3nGFi+/KUXALLsEP5GWzh9K5tawJk67wd+uG7+y5OP+QGMunxG3+wCcVDrjAT0/YFRbsia8dHMEFfLC1n3BR8cmmiloF7Ne//vWDbRUuYij759fwgKvFKJzCH+nAfQcoci45Arn6KDmegROctLfhtOfCZUcDxD6mswKk905dFC0ISBwEVCCR9Al2p4zAKYAHIQCFKtgphyQBIUsakRBiQjTA45QMuKwFbPagf7IwvvcBtjYWLvoITkAngRyBZl7BApCtyVkFcAkMkuMU9GW/5ANowJZkS+YETfeONevTjfkEHHIJwmQii/klkSp3xMwhVnNav2RI8Lb/sZnLfScMChT9xz5+tya9+2iS+3OjW0mbPp00IXdzczZy0wmbVxydsjlZ2FQySm+SPUmK9a0nWAkm5mNX8ivMFDhwSh76KICTpaQ6PbDzWLh4TyLrlF1SYl7NHPbBDgIuPLI5mxoH0whSYpN81ncaAztsbv+r5HtsdGJNiTQdSv7pwT5O6atmD+NFHjiX7Hl930Z/FS7Jae8uPqTQlLDBaU8m75PE1qwBdxIQiQqdzo0M8O/Rvu9YwQtfYAc+WiM7ezgRlPA54TcvW5vX5Xf69hlnyQz/Zj9BjQ86UYQdvsv3yOZ9yQss+ohJxTWc6Sd4OfXVj5z0g/P4gXnDl8Y25BRIw/k5jZ7omR2MxzUKCv4iASefjzNYm69c0qwD+06lPa2QvNGFwlxhx+fpkiz6notZ+JZYSDr8PuLUPGxTAmBNJ6QKcnpWbLAT7iYb3rdnWPU+bPJlTybMIR70US4xhN/qq7hzskoG89ClvejH7x1K4QD3yepePgZr/BSPWhMWneLTx8qO9MP+bAQvlzRy0gV84j24k7DRo3VgjB3I8VQKF76Hj/AwGfde7MBPJOZwzW6ub33rW4encj4GJnmVpOIlcYH/i4Fswv/4CV3RmyLIE3s8qdjwNEw/r80jRsg9xHd4lwCLGb5bKblV9PhJBkWwPubUn95d5GaLW19wKX7gr1U8v1WztsTcASaOte4s260vvI+L+KpDJ/bw8U728dpHw+DAYTbfxw39RTAc4DCBL/ETr+UBuM5hhbiPO+DCe2I+fMk92RluxD7YdjAtHvhenDW9hjlYU0DjM35LZvzm54zx1SV/wGFkOJdbH6M9ycLlksaBABqpSmYFDSTAIAy51YwBEMSjL6AIWBJHwRmxFCQULhIMwZ9xJacRdgmrS0AyHwKrjYVLjuC1wGeu5JeMSkq8L2gJhAoXa7svsApgAi05FVrWo8dOou13i1CsxfkFSI8mJYIB3RwSL46AmOiCszoF5ARzExiRO9mMn5sgQH/vvvvuQbY5cfA7eSRnCrVVoHXfnhUS5JGk+Vih4EwfLvPSk9NrAYc85N3SAVtJ9u3VWKRofhh0YuJzyenbpXBBRPQkgbQOAk/PZPS7L1Szm/esMX9UDG4ENH1LvuBB4tPnhfUlF/xZW1/4k6zAkMQRgZFHEsTeAqh+q0YW9ySWsAs79MnWbEaGLT2damSF/2sXLsgX5vgHkoYNmNXPRe5L1qN7dnRqTg8rbFsDfp2as3VYmNdmA0GogEonEm/BRyGBG+xB4LJHCSV/ZW+JC5zAoItNBTr4liDzHR9plEjRgdNb+mY//ZNHkk1XZIERCfhYXJHJRwscQPCh1X7nxg7m0B8PCs64ht95n9wFOkGXDx/zuVPNPhSRgi8fhil8bC0HQXjIky9BnK69f85a5pVc8pvxwECjQ9zqJFQBaG28DBP5ntf40HcUxBPzeCLElhJItpV44AvjjKHvChz6wyts4mTUIZn14NthDB4uJrCRvnBgby52MB959MMNkiNPaK0164AN8Ii90NslzdzWto69wW349JMM1nLvqRQuDhDoHqfzkz2X2GKcBNRJOp73hzJwgPggWXXS3Ue/+Dd88Akf0XYgJH7TA391H9+zg3jjJ705LBO/JeL8yhySWj9xibkUKdaGe+8pWD31EYuM1R+HuXA729z64iNkh194vMTf97RwiBM6XGjvD3XBurXlTPxfjuDjYJ6esCG/9hfDxDDxgn97asd+HdziA/4t3/Rk0D4c/MhXxQP5gQLEExg8I4aws0JWrLKm4tnv8OEJnt8VtuRQAMNw1x49wbCiSb6Cc55q+19XuGgATukcStATaJC8YM65EfBslIheUBTYvBaUgcDpqORBle099ytcrCXYcGiBTdAVWIz309oAysH1HQsX7zktRbDkKTgJSgIAgitwjgmHYA1gnAO4ndCR3fuSFmMlEuYam/XNZQ7OZLw5BduCn/U4m4DnxMAc5KAX+9Rnbu7rb+wK7Igb2fq+hsBP1lU/hC0540CrdSSyApLihCyeSCAE4+CHzu1DguKkQl99Kj7m5j3JHhvRnbH6Kq789TjBRQCT2LiHeCRL1vYeQkE46Tr7KVYFPa/hBXYQgfXIyD6IyWtzWVvCJ9G1R3N43z7tz9qSZ3qWJDq5c+rvPffI5EmAgGLc3PShU9i0ttPSEuyVnvc28pr3GoUL2ehBIU3/9i+BZdNsrF0qt/H2z48lACs8auwsqYQnuN1qTsIdILA33xYwnKJJcCQtEhhYso8SffMJRjDvd3sjF1n4sWBlDv+s7Atf+MLLv/u7vzsUwQIcXgtP+sOQgEpv9CTACoJ8ub2RS4IOa2y2atZ3z3x4zlpsgj/5E96Ju9jLffKTB6coptvL3kZO+u7jV/aAG/l3PkEvdKdY4FeSAH4hybdvfVbYMDeZnHCz1egnuJud+JO99WSGDjRj7ZfPWEvh5ntE/nqcRBKG2Mq+s5/5+Rx9SGDMi8PZxPv2ptCTtP7P//zPgXMkGNb00/70DxNjM7+14LYnH3Mfr72v2JRA099KL+c2Y7vChzjisIQd4Jv9FeSX8sA1mhirqPBkg7/tvWDQAdQHH3xw+B9YONehkRxFcqig8cda+KY1xEeHd2KEe/gd1vIPhQxbsodklf35lNzAvDBgHnO4PNF56623DgcW9sGG5lbEeHLnS9zkI2cyw9ItL2vYp4+79ZevYNH1UPa2Fj5QNJCDrlay3upiI3r38S62Zw+Yx71woqj1543hhW2MkSPAhFgmt6NHuJAn+Ikb4AMu5IM4AmfAmT2awxhFijVwj3X8cQfjevLjT2x78qPwYKeu7HbOZQ37kts+5fa/snAZG4cSRCQNAoX5JX0FpbGRQaBRrOSQCh1gNVZyhqh9VMk8o3GRuUCDpPwU1F1+l/RIWBDXXLg4gXHiqpFTAHSqg/giA/2spciQODihE9jM2+eYye4yTiIukM/Jg/tOKjmZJEQhNgZGPytaOAT5vWffnaSumkAl0Os/N+PJRIcSUl9K45Rj4lATjK0lmVutJRBLYgXsEhnEr2Ak99jYTrFA15IfRRg9jo1sEk1PNSQv5rQGHEpGYIGerSFpQdZ+pjP92ZO+2IU8LusKSn6vcLEn+hHs2QwejGcTxZP3JLvGmNtPhQudSWQ8jVFoOumSOAl8+phDskoHcwDxO52yJbzCi3HXbmS4RuFCF07xnOSyW7i2r2s3cypSEfVWoazxI8S/8qcaXAk2MMbOkgxJi0LGKViJtzX5sYDWaz8FPjzhd3KYD4b4O7vRh4DkxBW/4CIFOayaI56R/OIrciqAFX64LEzBFzzzv61mLIzAGxsonHAeHVkrPfkp0ScfO5EZpsnc09stnW41+6c/TyQqAhRaDgFGbvG+Pdkr2egEn+EGvkLOVTPO3nGRtXoPn/J1c9o/W+MEhZu5XPyYffkQrEsUJK5O4732tMVBRjZkG0VVT1LMS/9wUcKnSWT6Xo1ERgEAIzCEh8xjvlWjZ1wOx6s+ZFFoSYiy3zWatVzmsy+6gTN7w5H2sCXzQzV64ZOwyTf2XmIyf5JwSjzZ0RMQJ+34Ht4UEooO8Q1Xu/zuabanosZ4zb8V3JoC33h2V3TSl8MIh0rGW9sYCatDyfahsOb/xpChp7HkNKaxsOPqvWteZK445R98j9/DwUPZ21r2LUeiN/G3/Y77v5Ue7F8c9gcXFJs4Bwd4AiL+wYUnLjiMT9AZrpZ3iTWeyPg0h4+P4RL5BN6Rf8Abn8ZnPikkTrQ/lwMXuLKusfIAuHAozOfI5P8C4e5x3+nmnIucDpcVVI/tw8fa//rCRWMA4BAMBUYAQwZIXVCJ0DkhoHAMAQMhS6T0Rzge85ERKH00AMGYV7OGMQiIQ3UCaI4qaIGkwkXQF3BUuQUewQVgAd5cNfNwGH2BFWH09IfjAGryCngS1QKowGK8oMohJL4Aaj/10eyDHAqpAiZ9SICcahTEx+a1MfRFl+Ybm/v2ST5BzVMB+vRzVThaz4mDxKzEZWxkZD9OSlfWExDseVWsuU9+xGGMRE4Sae3mJqOkReFlffORj73IYF/srzBwitLTOM0cdKvwsL9kIocEkc7pGR7IbT4nbmzGVsZKUBGhZEZyRh5XupAgWZt8im9zID0JHexaz+NkuGpP5NOPnSVD9G/+iuVrNzJco3Cxf4HZftiBrJfMd6zBtwIYdthh1diBbQWjrUTcWAltp1wKXkU+fcMKnbAVe7ML/8IbdGY+77GdAlXwsh4/lWxK2M0NWwph3CCwmV/gMhc8OeywDt9KTrrDRfxCsex9fgAPJVFafeFFkCM/DoY3wVJgNXb0qxo+hF0JOV3ZE3kUAva713bGKPbiSo3/SfTIYI3m9BMf0L+CqUQQDiV7Aq89zXazhqSHbxjrtUTT08r26Cc9STztjV7tE4fwPcEdpztB5efsad9wwlZe81VyFCPIzv8UOPrCHx5iYzZiezJ7bQ5YkqTQMTmNnxt8KSzFgpWujaED2Nj67uClzbrWoee47lIeuEZjCwUqG/AVl8Tw3EtMMF7hgoPFXYdu/r8VP1S0SBg9mWeD5hdj8DXbwSOswUJ4ZgM29RTWgZh+fNs4480DaxJhSa7CBjf4R5YKF/7hOw2tL/Yms0OD8er9a104iUw+moQb6Ggs1m/d6E4cVeT7KYH3NIJc9Dbvv2u1l/te8KQ4YD9PvtjKkxH2wdvyQvphN35sDNnkNPJGdu1piwNwuuM/for3cAAXnijhd3trDpzD9p7CKWoUkD15U1wrohUvYpH+rlH2cy7c77CNfh/bh4+1P4jCZW4IluMBEuOWxCNfp43ek+AK+BxVgOA07vsdIH0ZSoJVsirYMbSkwEmbwFPwFgAlSOYAToWL5FaAF8gENAFGIDSWLjSAFvQk30AscbZmp/XukRepWstpo8SDQ4xjrePUX/FDPu+PTV/jkT159CE3eSVJih3y6Tc2MgjMxvQkYmzWoR+Jh32zLXmdPDgtnZv5BXMBg57n9chEDs4sgSOfNRRPbJLuxnHGeE0+hCDo1I+8Lh9l4+ze47CSH7Z3zxqeppCJQwtKnZK0Dt3BjIBNPnuQKNCbeSpcklOxInGhG4mPkxQYMCedkqOP13kUbYzEpiSMDgUw7ykMBbRkMh6eEax1ncjpY1zXtZs1r1W4SOokAreQc2z0Jfj4uVrLe+1LUgwH3mNzevaaDdmO/j2m1xdv0He24H/sCO+eCgq67GOv2ZrfSo6cvAtOsA03MCHICXrw4CMBklTrul8AFLgELAUQuczrp3VgB/bsE0Yl3cbDNhmtgRf4hQSen5KHXFs28L7x/b8Cc9QkZexnv7P/nmqSIFwxzkeXArR5w/+qkYcf0jGbOBHO79J1+mYj9+nH03cB21j36UR/NpeUSkToXyHKf/VTEEowFDUST7agU3tWaOJqJ+Lsbi3zhRfzsIOxeFzSSmb3XfCCv+3ZRzbMz644xX37TAfmU/T0RGpLNw7CJLjmvkVrXfrD8eLBliwP1cRY8Yz+6doF1+deig169fFmcZlf+MiW+MtHxVTFg+R5XMNlrIQZL+BEtqnRi9eKYP5MTr4NA13mwwXWdrjCx3EVLCq4FTKe/jjAFAu29jfKdI0LLuEah9GN9cU4/rrX1+/TcIG1HWY44KF78ZiPk088XV2zXi652Ib/WpuNyCGPVODKleR6eAPv4mpy9YdT2MqhqKID5/DnUW84Wlz3FMVcsJDuzSFvkIfge7jQ16cBcLd8w1M6OQqstiaZm+Oci8zyMzp9CJvet/1BFi4MgmQlmU4WAU8CXrIAFBIoAEASgpkgJygpSAQyQcx9X8RzssJBBBWXgKKPCtwaAKsitkdjFBdAIiiZRxGDwBQ3SM0cfpJHcmAsB/EaEQKvhNRYcjnVEfQlMoImB/C+E1ZEiyT1tec5oHhNXkQk4ZAM269GdsGfsxo7N8GUzAoBOp3nNoZezYv0BBKOJKm23qqZpycg9DA283sPYbJRyZUx5kUIFQVzy+aCtyDOboKA1y5j7dPTEOSDJNkFIUjo7JEOJUUwoQhSXNGVufWXhMACuTg+LAtiJVCSFu8hFbplc/ZEYGRzuec9ayJoa9KHwlMyZR04oEMJFD14ogK35CWfhMlTtzHZuWUj97ULl1s3CTwd0ttKZntiLwGGb7Oz9xT1nnDQuS9lkpWNEb6gyqf5hGJXX1hQaOsvmWEfdmErY9yTkMCjuYyxjvUkyezbUyE2lTS576Iv8is8nMIpvnvSZ359jJXcSo7wnLXhHc7o2tNUAbDCwGXtY3aEd9iXtOBE69TM7xSSH8LeuXjQj5z2yH8bRx5+SnbYt/bWnO5Z0xj6IoNDHR/l4L/sZg7ze19BIJHAm8YoJKyFQyUl7CKBMCY/YhPFWk+1JZU4wz2XfvrTjY9cwIjfYYRNyMVG4kBP18zD7mykr8SBDo3BSWTE+bBgD/q2HmziBpzg9aq5x8bm2+pzjWYf+A3WH7uJsT6yI6GUbLsk+07Cz7n4k38K6COanrL4h5HeY3d/FdD3zd58883Dd2C83zhrWMvHvJzE803YGpvXfFSSqZ/xTstdxnpt3jfeeONQvFqP7c3//e9//+WLFy8Ocnnf/sax49X717j6DoWDBXGJ//AbPyXYt8SVxrfF4g5x5GHyG5xHR56CrOR2zXq55FK8+sfRX/nKVw42ctEB7vrOd77z8qc//enLL37xiweZRtuEKe95akKHdGZfNVwut4Eb6+gfNqytkP3oo48OtmcLa+MRPOP1d7/73ZfvvPPO77+PlczJ0OtjFzubFw8+5fYHWbjUBEBBDIkgOgFJckgOrwV1CTYjSgAEKU4jOBuLgARwCYkkFIgk/gBYwolwJNWCC0cXrPR10qeoUGQoTswtCNGDOSX8xpvba+8LDOQVgPSXVEkckKkTRK+dBiBL8kpyOIgxFSNjsweBVhEkgNKD1zWvJVMC3ypZIJcgrCiZW0mEosr8kjSJolNJT7skuuQaHbdGxwK1Pc/reu2+JFHy33i2kHw5gWAvulrJ7H32kZSysSCLACU27EGviiZFAZJU5NA3WY0lk9dOXOna3iSC7iFwJMYmEgx2Zkc6gCvFjjHszK50y/5kpxcno8ZIhCRRbFYCKwmja/fIa7xCUFFr39Yxt8va2Xylg2s3e39VChd4YX/246/WXMnMx/mQQrREn12dtEpsBSp+rZ856YCd6D7fc/KsUGEzduqvDbGzwwR9cA1bsz8fdqChkQk2JK18mC0lvk7X4Md6Ap8kGWYVYuYQ3CUX9odrJN6Cu5NJ+MENCnPz4guHKvRhvtY9ZUOywCP80c3ow3we1ju0Wfn33OyD7Io4XJIsmvFsZA/4SN9j8iW/iyxs0ikpmfAx3/ba03S8zEZ0ZT+wR5e4DUfQpUMg89k3Ob3H9mTh8z6uYh2yuvClww08BQ/0rXCAGcWSOINrFEfs7z2+w0b4CNbMZz1rtAc+3x7YkQyKGOPEE/1XDWeFZbxwTH+XNHZ7KoULW9IX32K/DvPOvdgQFzmIkCQrYiSsDoUknk69+aLvm4htfJMvNN56bCuGjHGOPb0WC/GCfl3GdaBhTfmH4lmB4umMxPUXv/jF4Z514Uq+YZy1XfDg6vUlV3uRy+BKTwHJIb5KsPEbLsOH1rxVozt+x2fFWzGZL4mnnoZ5GoXH6G7UZzpd7W3vlS5wL2w5SFEMKERgQbEg8acfTz48cRGX6Y4c5vCTjPI9uR3eHX0W5xhHv/q1pss8+A+vONjo42jWhg9PaRQw1jd/647XON/WJf7AuIPWW/HENdofdOGiMQ7HkIBITCQBHNTJib/sIUEW8AWzCpa5Ge+EFRglw5zZfgQt4zmYj38Ikgohffwu2AiYgiVQml9SSgdIF3hK3sdL0xeZWFMC5AQImJ3u24NgPCYAq8ZpBFdOKBDOjazISeCcW3sWNAF+bu4jVQUIJ9QX4QgknAOh+yy75GLWKRJkAzpZBWMJEb2ac9yjvvZBppKcVbMe+cjNNojIfE5LPP73OxuYy5yjfP3OLhI0e2IDZFOCgUwljBIUmKZf+HGa6n0ELJFBZhJPySZMsLnxs9zWdCE6iS88KtCc1vkomf1KVMkTRsfr1o0NXpXChaw9SeGfXq9kFiglifCvr/2xM1sJAtrWXkdswRE88E98Agswo9Acgz27KWbsXzOHgw4Fhn5eS3gEJUG7oCfJ8XSOH+njsieFBR+TZFv7e9/73iERIk+BTd/7NGuR1Xc/VjpwX4Dl3+eswe50BT+rYsd8uAiv8Y37YMw4fERmdvUUhk6cbnpSyifhosTeRRZPPhy0kMk9/qcv/ZtTn55+ktN7DkH46CgnbsDX+ipQnZA6xWef8GBO67T+qkluzM+OMJkdJLIjF47NXMZJtCXk97X7qWZ9cj2FwkUR7JABtva0dM/WeJWvVKBIEt9///1DgihRFR8Ug/yR/47NHOKeORw8xDN++i6ZOI1HvDc28+Ac84r/iiQfOXOaLlH1yQCyiCPiPp6Z57hmgxUYZ1O6gH9FN9/OZ8jhsO9Wcoi16VmcFZ/xHj9W9OcDbDb6T9e1mrnYUuyWNzn4UEQqaD1twcM+shtX4YTZJ/EDH6Q7uV4HZ/rJ2eQA8oJZbnuzR9iQK8hPcaz/41LxhMfkmRWScWXXOc04eGffc8c8RvuDLVwAXKARLAAFmIBS8JS8+lOEHgcCBIdRPSOJPmoQEWl+eo2IJBsSVoTl9BBJScKdFCAcwBS4nBJ4TzLhp0RbsEO4kg5rkW90RGsgNkQi8SCTUxdVtseTZPYFQqdrHLvE2JiC6iizkz1EKGiOBG9Ne6ELxVzONTZzWluitQoO7tsrQiMveQRtAZTOBV+BRb95bligN4FZ37nZC7kkuO43ntzmUxD+P/bubteW66oTeAKJlMhISYA4VhIn2Pk4jmPiJHZIOCYiSNgBBcQFV4C4Oy+AxBW8Rveb9HvwPn1zml/R/+7Ro+dcu2pVrbXrOHtIpfVRs+Ycn/8xxqxaeztnR7Lqz3u8GkMvEgsbsa9nVl9//fXlNqzdcb5oN5Ue6NE19G+OrOWzQAdAig+ForsxPrvLolFReNohISvA5xuARdEMHMhofb5nDTYKZQ3+hl8+oVkD1HZ72Pvtt99ebEifmh78Gksv9NR1ewvC49GNy634pmMJwK41P4s9kfd0Rn92Qz2rLFYla7aifzzWOBqRhoL++Z9E4zEGhQffknDYKnGQ9emQT4t//qbQlSBhgXPWs66YlbgV4T5L3hJlGmzzmZdvawbggVv/fMWOreaL7/HbzLuG4ov4gnFiRHyMyFhr4x9fDxFeNN7idqRbc2gk3bGSmNkHLw/ZoRK7wiHxyVfZ1l8F8xiQ+FGEip3YxLzijj+KN3Z3nty54xWdwBBFA7sZp6ARp+HNK79jM/5gZ/bZs2f/xx/4onPBGONncpE5vCgaFXT04hGiPNbmPN4quU5uJU8/dxThXQ4j42PTnsaFD8AIGxY2IemNf8gRGk1FKT2KPTnYLjn87sTn5HWbTHzc3PK2jQyxQ1+VnJcnzc+28gWsELOKYn4lL+JHEyG/Os9vZv6yl8hgQ0x8whQ+Ls/Jj/w2tZF8B+/WxPsWMh/chXk2EDRJ8iu55VJ6pVMNhGYisVuPI8i8/EKOkw9sQrCJZsMmtOaSTzjYD5azn2sS08grn1Rz0ak4JiNc0sz0zVjkGneUxDm/UGtYV42pyeBPNsCdZwN2SSMXHWT9h4g+nxqXDXRk40LhgJpDCLzRoVGxC657dWvMs4RuywoQQONZQckFKBnDKTQIzqVbTnNRDyAiwJ3XlABPu7Qc22NAbj3brbGT5zlGwMjJPG4gcStoOHad02eAAbQEKx48OuEVfwo9jYuuX6Hlu4wR0EAcP0msHBqPHHR0Z4beFCcCCyj4XJ1YEJvL3HTgfCffW1+hY83ccaF3tsEPffjcA8T8AtrutEAcBRB/UQC4g9PPS97AXuIxP3mNxxNAwYdiRSIQ9OzLHnTorotmQ3FjDBnoVBMimAFRtY3Dd+ymScsP9iQW19kl02QAdnORKXb0/Dt+8FjnZQ+vdGBHif3Yiv9pePHr1W6LQwJjc/5GJuv6DtADou5PWYPdAvR7yFxHNi5ssZenEZGVnfAqAViDL0bfYhcmiFGbDGIWJkhS7IM/Y13T+TN3tZvkwRf4ElwzV35rAWOswUdSaPJPxTmftZGhgGFLc1ZSfMMf14pNxZRYdn3m4VPW4tvGuTNnPXPzffMqPsSD8XieycRH8CCG6IyPKfr9dRyxNyLzwAf+jjfzzMg5zZjkP4plhAd2U+T7PwZ2qyVyPMUmdQ1zOFwXf9ds2DjBE/151MVvDDQc4pZcdEZ3bE0vXsU8XdkUoMs0Y1nDQUY+BdPskntcNFhrbTbmA3xJ4WX3Gh8e8ZAbFCByUIrQ2HJkk/ir82JbgwgHzQfzyaHIDp5UXhVHeHB9n/cIsh49vOqNi5yj4fcK99kFtsmzGhS5RUMCZ2GD5lFz2xsI7+lE3IUX4/hej2v+63prOS8vw3jFOP+zgaE24jtqBnEsBsS12Bj5yh4yF57kbzaFSch39CIO4AEd4EPtIr7E5JEkFsSrTVIxRJd04kkO+EWPfFy9YoO4xk2OI8g68inbW9+jWWzCHngLXmvi1DVsJAfbABv5IHzQ5KsFxKoakI/12PTed2TT7MAg84YHuqcTm1caKrmTXcztHF2w2Vo9PDUuG4mDHtG4UDYAEMyAmhHt0klOHMrtNQWgJGUticOjS9YXdAJAp+w5RU7KOczBuQSIYkFy4ywSlIZDIQKQFKyc2RwKdYegU7QqWN1StDOm2PTYiGTNIXXoxnI010p+HB9oSmrWABDAWCErUQJEPBsvadnFl7g0WIKBDozBL4fn7HgwBiAqeMhA5pr0kUATIHZTRk7vO7dBJWGBMSJ8Ajw8GJPGBT8+22G0Y+X7TtYDmHiWmDt/iC3MBxg6f+ZX4CncJHI7I4KRHdgpTRxAwB97alwVMQCRPfkOPTgUlIDZI2XsYC4FKaChB/4h0BU3DqBO13Zn/+Iv/mLZ2fXnEhVK/I+NyKZgA3Su5zeSlWaRXxjDvzQ4+AXSeGVXO2Dml7To0KML1nUevwrAPEaIX/6DJ/ZUmPMZideuH3uMgHUL0SNbn7lxMR+fsTsuphST/EQssZ3Cgl3FIt8UFw4JiZ+zpYJC4cIW5uKXjvizxMZ+9C12YRA/hD2KHge7iFvjjbWmsWJeAysJumPKV0e+z8Zwhm0VCnhibzYlA1zTxLItHvNICR+HNXCMzOJGcybBKXTxU5OmV0lMfCqm+Ju44D8aCDvPEuWM8Om8eLtUzNBFHqucjaNfeKL507TgR2yLDzglhuBFiM74Eh2IHfqAecayOx3QSfBVLIgrxQDfM7dcxPZsKG4UFpoLY/FTCd/OKzDFLj+AGdZmY5hCD+xkLNsZz354YCeNlbE2M+SMPOceH0NefccHNQdkksNgFT3SOZ7lAjijoPaZv7qWjuAW/DD2aDLnq964kIFOYTFs914M8Al6NJ94NjcsyU4/27N31ytfcR2/Nad4U/h3HxJ7YjjriTev4pT/Oc8X+SB/5yviRsEqBxxtT/HPp9QSYkJ85Hvxgy9Fu4YGLogLsvHNYMgRZN7ITMfW49/yBJ3wa7oUV+JG3eQ9HnIcQWJHvSSmyM3H5Q2YDmfoX33ge3ZmM3e21Zpycye8062aVP6X79m388vf5Ax+YR22huVeYYT8Yh68qR/hP6zkn3yD3ehnrR6eGpeNJEgealw4KZDnJAzCaHY3GYwRFZeSpaCSKCQGjiVxpbEA5Io1juiwLsOaV1IAvIwusWlqOIkkDUSMc1hfcFifoTmU5kARkYJCYaoIwYvk6C6Luzhf+MIXXn7+859/+Z3vfGf5qycSnTEBKIBrDjxwQElJsnWQmSPTA8d3jr7ILPHbiVfs+C6Fd+XXHEATf4po15nTfJV8B6wU0504s7UlTcVWd26f6VJA0p33eKC/2rjgB8+ABn99HrxLHAISUHYezQEQNCXmqucjMxk8/wnkFQ0pWqJDY4CGRlVzay3AowCnI0BiDBn4iWvNQQbJCn/GG8tuihA+wHaKRPb95je/+fJzn/vcy9dee+3lD37wg+WZev5oHP/gJ65nc9/hQ0Mj2fEttggP9M7XNVjW5ldATaHLzxU/xpKfjPg1hyYNiAbQrOUgp5jj5+zAXgDLPNEV3WbOGZDh6+yNC3uLdzFiDQkRHrCfhMsW4pi+yRoiu+/YxFiNjcTl2jSd5mNHNlBUK1qtRR6vihCxJMGITbrlT3Rr08TdGdfyH/zZiFCks93I79mHjmAKnbuODGwbGazt1Xzsmo0ChYV1HbBTkiWTpI93OMq3+ZX52FWc0hd+FP/kgXdinHwjW+ETnoot+hiR6/AghvHNjzoZQxbNIGzLbiZe+KqYxT8d8m9+TgZ+LSbxr9myjnkc9KdQ8EpG9iOH9elMjiCza9lavNlNxSfM7zZxXXDEXXS+BMvZOv7A3tZmbwWONcQy/CcLP2MTOOUa18pZeFPQGp+CSG6KrcUobAh57zt6MJYM/JYO5AT4Ip/BhKOJDFnvsQmmav4V9/xlzSGmxZO7UjDVZoWi3OaPV/lSjcE33E0VdxoXsaiJFhfig65zGK+A1Li4w8bexlirro1P/qVBymYlG3tsXWxmHrbDjwbUEwH4tQkZTMq6de6th7XMxQ813fymxiYf47v44ve5aymPsH0a5T0EP8SFuTUreBEP8p7vYGhykrXwp5FkB7FPhhwjGbcc5pCb/X6FX9E3OdmAndhFbLIFe3m1KaTeUQfwkT4n/bK5OV1jHL+ofDvolT/SNeyTG2AD/9NEWRdGaFjkABjvbqGax3d8KPl9zWFOd3BHtd2Z6JSNC4X1g3MCfgEjMCUsu8gOSV/yYiwFhHnsfuROykNkfsEGMDikRMNJgLyExmk5rB3RHpDhzSGQHNY0h50twWb3y/PlX/7yl19+9rOfffmZz3zm5Ze+9KWXf/d3f7c4NafEr0QqAKvMed8PiZjMkqQkxEHpxNqaIHqRZIFMrjE33TlnvKLGtXgOWRMfQNQanYylVwFLZ+at5HyaI4AXnfTGBV+SsWDrPKCAI5lc63Ml44GXQJXYJc3w4hz98wuBrckSlL6vOtX4AEEAYS6JCEDSm+JCQlLIVBm973Yxb4pQIKspAPgff/zxYnP2Znd/3tKfG5ScrGVnVqHCrztvOULO87/spPqsSFCU0h+/tS791utG5DxfyF0CgMnHJT9JWcHHNxzmtSOPz26DkLk0YwD7obUvET0A4aMbF3PhnR9pZBV+DnghEa7RGWIfPs+XXCtGgLxXducrsaODPOJavLnOZ8WU5oHOQtbmf2KTzj2Gxf/41Igvc7M9XzKej/dYNL+4gIfiEW/8XFzyb2PDJ8yAVWLBnO7KGie5aj7IZLwYw78GTCPHV1xXZalkTetrOEZkXkmVvbNGJ3MrjsQMf4VtME9xbjy7inXFA8zGO5vA8uBCndt1xvEz/PFrMomt6t/G0yneFSj+mhM946fz6TMdKlzduRWHeVyLfjPeGPiiycqmlwLZGnwj5Brn+QO52USxasdZw8hXMm/lpfNlDN9md/7K5/miJkzc9/F76UyNi7iAwXKO/CqPPHTAPbvNnrKQV/zhE7ENMxSW9M+f5Szn7PzLg4pPd/Y9FixurVcPutcoe9JCMWmefuDTI53mcQddXWAd/y+OTjOPBoo/4Mt4NlVIm995/DnI0/lYe+DHXH6b5+DHnfiORs464pPv29wgB9+fYcJaEivqOPGhjtD4wWpYIW8HZxMDDvUCG/hN6Ra7P3TQB72r4TRG/giS2sLmEjtZi77Yyzl4YQMDFmW8eUZ6hhf8wvz8L/6Qg7w2mc2pkfaUhTtQfnvN14wxF5/1vfj2ZA+f4bfmxVuXaXaIG3UpjKffs9JpGhdJmqMykISRW+I6TkYAKHYYJFfNgOJBwCh+7TzZHQfo5uH0AmeN4o0xnsEY2BwOTqEYAcYKQ2sqNmsysobPEhCeFfwSi7F234CJHRb8Aze/oXC3RRH7jW98Y+m0AaQxEpQEynnsdtoxk3SsbQ18WNehmfKogQRsjKSX3Xt84R8PQM1737keANChhC4xAhvFNhAI0R85Fceu6eS8QJFs6c3clfAiObOlNfCrIGAnxRC7+hy90xO+atGAzOu8YlhxUxM7ikx2WRUKipCQsQoTBQK7kFcAe41vKFQEPb3RJ7mspQCiD3rnE3SRufHsnM/0agw74Q/okEWTBDzo3m9bvva1ry2Ni7sufkgPVJ0DaMayvZ1RfqMww6+58RM9kVUTSQZyZZebX6VgJg9+6d55110i15OFDumLXvgZf1LoSgJ23s1PRxoT8tnxY38+mjtDbEomujLvtRS7HdW40AH58KgIt3P1/PnzRW98gazkrv7/EOGL/2ROCUozyZ58ERawB9uxo0JEw0Q26yh++Qe+zOXAp/F4YWfzwjlrdDv6zL58XoxFhj6O/9icoM/4PD7gTTYUfI9fNoY9MEgChEF8G4/GmdtYTRgfc43PfE2zW2OvkjXphL+kWQqZ0/x2Ju2Udv5DfFKMwA8ywSsFguKFzqI/+pUDyKwYkC80L+7KkAW/xmpyFIZs43qHDSDNe2yCvDpnTXEJW+HDKLaMM6+cAQusF3sbmznZlt1sNjhHP7kLR6fG4ZNN+Yn8kbs4+MNztUnmvUTGmRM+aFbozl+ZUwTZmDNndLOX8Ca2ztC40LPGGjbxPXI+dLAB/8rTBPCMv9sZh71w0XxiCh7yLc2R+OFDicc6pxh2nbhTfCty5Qzf13Guk3s0Qwp/jao7uq5TA/EdeKthwY/cxSfdXRRD+OZ35lor7+wgo6YZv+S3vjjohOc8jYIn/q+5UMPxqT2kptMcwGl1A93K5xqZxCCfdQTL4CasEPf0QmfkoY89hznSPKk7+QI5rSc+2YUu6EBNZQy78SH2gpNVvw68sS9fI5PGGIb0cTCNDfI0BoxWK8A3+dl5dbK45of8ko+pD9WzfC9+seZQw/JptfURmHArOlXjwjF1p5Kn3QRFHiBUJHMc4CvgJTOgfwRJMIKCwQGXIDA/Z1GceY83gZTnBzkzPiQCDQLn0wkzuOskHeDEETmTHS4FzIcffvjyzTffXIpZf81GR/7ixYtlR4zzS8aCAQDo5N0OtKMj8dvZjOzGW89uoc8OycK4BDKeBZWiWGHBIc3nkZUkaEke/xJviJPr5s3ZEzQSoPgWeKPzrpc06CnAYv40LtnZ9L3rNUgaMDyNiA0EoHn7eubgF8CeLkLsgwfXusbcgBigCmTn7dwqkIEgfgAfXQFp76MzRSl70ice2NPcdMBPs6tnfj5ifrLTtWbVnz1110WjavfMNUlKbO5VwgI8nm9nV+clTQUdMMGjYlJypHe8kAmw4we/PltfAQ3sfR7ZZyuJM+uLDbYnH+CkA+AZv9ek8SU2uZaOaFz4PtnphO7Yzk6UxCCRiC9FG7vSEzsbH59cS3TL5+CTJKaohwNi3Rr0JWZ9Zi/jHXzIrqDiwLrIuvXAC+zjH+Sp5JwY5g/sPcJBc8AeWCSpIWvDD7EmwYoB+lAcwybFkmv4rEK9EzwwHzmtaT7+CTfFyYjw7hxeYSXeQ+agfxiVon1EdnEl/9oIwBEyRH8h88MDTRje2IEeyaiQ4ceKPUVf9GZd12hqY6d8n8O6ePSYhhjovoIP8Q8/jMv5HOY0Bwzk22yfcwpOccxX6FiuwzO9KB7kBDHGdpVy/YysiQ9xQG52U+A65A8xzF/FArwSK8bGtpfmnhE9nKVxkTPkN36zhcjNlnCUveRiMcSn6Els8B/FKbwTT2ynSeLr7BzdeaUT8aqg5htylc89bl0n/swD063Ln/it9/KmNeGYhknO4R8+46v75B6CDQpv8/NVWAn3ExshusWfYt1Gl/zEr2xi4anH5xoiA93IzzZFNPYwVHNkcxBmZV5jM158yEdswC4Kff7ceb6WyGpTQo0lZ8spdMIOcEb8qOXYhg3lMfqjO+c64Y19xTsd8wt+1GOPXeV8tYF8YPOEbNaEJc5pUvg6vuC31/il67fEMx+lR3KsveYx6FSPimlcFG3AQRKh9H4keR1B5tLF6qAFXubmDByJM0jogN57CdQjAxI9YBNIihZdr+BxPR4VQ9mxsSMByCU969g9M4dk6llECdgOgWQsuaeQzlwSpaQjGFwPJOyY2bW1C4c/5xW+DkHN4VwrIIzT0btWgZzzDklTgU3XIaBtrPUrGc9GKSxnxQY9Aj1FVQgvs8YlxRK5R/M5L/gBmPX7GIDFDvQQwgNdKEiyDvCgQ8CjWaJ/dnMeP+QR7K4zlyQBJCT2PN5lxwUo8hl2YXPXenU9HnNnApjwZbdrPfqjuKdrc/AHjQ+70IuChV0cZASI1qcv9sYDv5EQAB3+JAx6SRFKRjbjQ5pd8wbg91D0Z+4cPucguwKefo6646KQ2zpP7Mim4kFSYGOJJQW+IzoWn+5qiGMbF5KMpmItWc98GiNNnGvNLaZ8ZhuPLNlNxxPe6MsYMip+atxVMk6siqMeh7AFFknsiaNOruGL4jRJ05zwQBEk6doYIb8EhSfj6EEsjfhSQPNtsWUulAKHz9FFvq/kO7zC9jqv9fI4Fzk6xZ4aDTjHNyIrW4ktuqjyW0sspeiBKfhSeMJz+C2WxRbekeu9d05MGd8pvGjcyNH1Tha4Ijbx4HA+B3vwOXLwy2wKOcc3FZ+xCd8R/9Ywr1jgY1sLcPjELvg1P19gQ7YmYw4YSS7ywyWx49rud2vI3J+GxoV94K47Y3bL+SBdKqDJZm74Cl9sUIplRW1sGqJDeUG9oAFnUz4mP1ijjo2fKPxhlvnVDF75DB4yj7zEv/EnhoL/db5rCQ/yCxzgB/zWI2Hk9r4TmfBgw4//441PZeNlK0980qYDHYhb+KwAz6PMPfa853d8WNy7Ht7yZ3Hk8xFEL/KcOKcf/LEJe6kb5Gb5HO6KAc2pHKCRwnMnd0vkBv7DR403j9qzxh75yKBmgb/8kP9pTmw80InvNbtqPefVpObaks9CT43LRhIUEiejM9ytlGZuji6pKV6ACAdkbIklj1wpFiUThayg8B1H86iVYpqBgQ+nS7Iig6AlhwQkaShyObXAdq053LkBjABJYHJ24MAhXWN3QZI3J345riRtPYW6HTjAIlDNoZi2k69g5eAKI8nO2ubzbKZzkiTZA3IATyENGMghwDRa9JLgMRYv5BKYdhU4Nvmrjbw3twAS1HgNmRcAjxoXdpCc3R0ZJRmB79EOQCgRKZzo2bXINeSlXzryGQ/WMnfIOQWPZlEjIdEAIHpyPWAkl78CorhzPd26SwMYFH3mszY5yeCgG7K5XgLT+AFcIEIP/EsCYiNAZl47U/TJNmxufXKam97Naw08s5FkZT4+xQbmshab2wn0md/yX36sQeNrwBB//Cf6OprYkc4VsJLWHoot+cMaIhNd0ZNEoMjQPNG79+I08o98lY+6TlKmW/a2k8UWrksMXCK2h1nsGX7EBr+yS8Z/2INtFI7sqSBQ9FhnRPgzRqFiE8ecIUlZESqxWK/KhXwmF7/CA140U/ydfPDHHdU80hQ8sJ7Cmc7qeoj+6IUciums6VqJW7yQy3WdH0RHGgdr5LzvbJBI1CPfNBe8koDhIR5C4hBO8W88ZE6vbKY4hNt0FfyEO/wC9tIBX9XAKG7wroESYxUzKuGRjV1vfHzDK/1qAr0ifNTDGLqGtXzAezFtc8PdKOvCN0UH/ZIJKWpseFzSLfI9GdmdzhSQcIK8cImPk6vqMOQ78eM6/LgGnogfvDpnzMhGnfD9qjcuiLz0waYaORjORn5L4g+uyKVqA5tGck3yQ7cPDMEDX6NHOmQjtQTsd75fI0bEkzxifnfI5CV368Wt2Pc5dQUbJR/O/GMt8VNxAkPJxZ6+EyN4lj87NlgTjtGRuJPfNFSpgZLT1pC56MT6rpfTratI1xyJh054tK7YUhvRgzXldtgrno4gvJGDXfgWvYtZuOYO+vPnz5e8rq6LzeEt/6ux4714JpsjeQbvrhO7+K/kGjhOHvmdXtWNjl/96leLX2rU4Cm/FYNsdY3vW+epcdlAlMyQEs5eovDRwckVAgJL5+zxFkDE6TmdxMx5FE8aAIlcly1YAQ9Aca15zFfXAkiKE05jDg4koF0jiUiMHIKMEienAw7WJLME7hoJQ+GmEBV0nDprIU5sNxZYAli8SUruWuCZ43tNIa0Q12woLAGxgoGuzevV7obiV+DYIVEQpOt3kAvICgp8Kog9Q64oiOzhCx+AJqAXwqN1e+PiIINmhD4ETSdAoeD3I0FJHlhr8vBlTdebm6wKC0UBwHBd7GQ9RQ6d0b3GRUIwn+aPPs3JFsBB8jAvgAFAiryQ+ejFeWvxI/O6nm/Ro6LUTgg++ELujCgKFIB8xHvzA2X+wTYBrWrz6CjFr/M+K3z4Cf4VHPyFPGzEngANoCoiJDl6Ny+dmbMee4l+yUSOPcQfLzUu4ZcMZAH6mmy+LYGwlUKDXi/J1eXGvzjnM3QoBiQH/kTXl+biA3lEIrrFl+KPX4kTj03gjW8pxPkiv5GcZ3PDG3FsHu+NIzN5FS/WtV4nsihU+ZzkI8lK+uZx55gf8Ev80Ld5zZNHD2rcIucUZYoJcrgm5JyYd/eQD4rJ+Fil3DlQmCf2YYrDNdFBeHGIQT6Fb/asc+IRnpLRuUrmYEsxSVeu8x1MM59YgMtijQ0UpPShKFJ8mXdGZDVHfQzGq7n4DTt1srYxYpf96QAGwTEYkTscmZd+wzOdw498V6nqio/yVbkGjuDFXObt110itoNffJlO+A3MhjVk6PjRyRhynKFxsbFHv2kOqr7WHK6DZ3CFf//t3/7tctf7k08+WX4s7akJ87ubxaYpQHO993wJlohVn/EQHStwbXCJh3qdeWxksqP48Ade/CEA66lXNDByFx+SE+TMzFHn2Xq4Vm6BA+LC+9jY+WDjqBGo13kVUzZA2MCmaGLlElmLHHK569VZ8MimBcwUY7FlJXhmvBpCHODVGDzJC/LyXt3UA56LC1jKHu7g2hj2MwCbGmxlXXFAFnzUtcUyuztvruChA177XqNYr/GeDrMhKr9/9NFHi1/+2Z/92fLXJfGgmc7Gg3W6b605+KrG/KlxWUkUvadxoXTGlTwEs0QFzDmYIkSS0zhwOoCjI+fUdsPdUZHsAYrrBYiE6hqBwwEUpRocr5wBeeWYxkh6AtuakikHUHgAGA2JRKpbtguq0FFoCkxFAUdT8FvfuhKrQiCgmAYGkVOweuxIspWkNQqAVXGs+fCdORROHNo8iiXO6FlbCc7uIN6t55ajcynSfS9Ju8a1ABQwaKrwLjABlHFxbjoCVh7hoD98hgJIo8bFODqnW0V8JeMkUMV5fp9jpwNASgj0in/XC3hFiaCXFOjM93ThnEJBk4YPf5LYXStFMvBgU/ogp6CVuMkSu9JpiB3ohi3pDaBagy74XPTFNgCX/5FNgcIeko1z9MTf8emwhiIzP0QWD7E5PSlE2M3a1uJbfIivkZMeFCp45uOaFuvx8zRGZNMgavLc4aIrPFvfnNa7BqzYiY5v3bggsoot/keH7CpG2JAOkry2EJnJYH3YwRfsxtOVhl0BTO+jBGw9SZ1dxS8d4kW82/VyjXld73uxanfMX36xmy5O6b4Tfvggf+Jf5mVrmJE7pN1W1mJP9v+bv/mbJbGJZ2tUG/M1d5Tp0jqukxAVWuSp5Dw5FHDWj08ievad3yPaAFJQiVd+xRYZa046xBcd4UcM+87a5rGOucS0OLDZ4p9N0n9v0ryHMfwZb10P5BSD8D1YJPbhPNvix/fm9Vmc2D1VdOCRDxjT58Wj2BTP7Om8sfRMpqqbEF5gPBxW+MItOMWm9E9+14lHjY3x1sEbXuCuMZUX8vjOGFjFn/gqTMIHPWbeLsNDlLnjrwpIvkHX/Jx/4XE0L53BgTM0LrBXfMn98nIOfrfmcJ0C0f/h8riv/7umSJUjxZZdbr9ftAmmWDS3HO6Ar3KdO6bJk/RKZ159hudikK9br16Ld/7yk5/8ZPnLVf6Msu/xoVBVMGtk2L3Ll3m2HnggB574eCdxyz/l6I4R/Iw/kzl3YJN7sin7ENENf3aXGi5o2OU3evIYMn8UF/E7rz7b/GETsUS3OceHxY5iHl/R897DXOpC/5ftN7/5zWIb8U83mhd+4dWGMTtm3fgeO/JLfoH/+IVD3Boj7tki1zlcR5dqF7bnd/5ZNtvZSPY9H33+/PmSE10jJ2098KwxV2tF12ekV6JxoUDBIWCMc0g8nDlNCsAWKIzN0QWAV12/pACEBZ/xruXYAETiYiyJmaOb0zrWU0Qp0o2VFMzleg5nDkW4AOOIkn4SEf7cvrS+dSVyjmw9vCSB5rcd3iuQFbkJUIlfstVsASdzmN8cinfPz0vunNYujGTBYd0RcQ2+ObP1AEnuFCluBbviXhElqXJYPEnIQCO7srn9S1ZyK0xdS0+CVaDFufHsLo9A6g7vnF07fPRdBgeZFOJ0GfDxSi8CUBCZG4+upQe8KzY0oZHJeXMoGtkaOAAuIKpws5Z1FMZ8xfUKCXfW6M33dOAPRCj83ZrX4GgG4nPswF/oGoj7Dq9e8aqx897uiKbVtSl+6TK2Z2c6BPTkd401yKUIEQvs4Du+6L07OWTl79ZSXFubXc2niGNXO7Xk50P0ZW72c62iVQECGM1HP9bTPCuojHEtfbkeb+Z3mCv2qeT7WzQu7EV2PJCP7zjPbnRKx/jEm7F7KL6IYk96kDjZSSzTj+/4XwpbsouDNN5k4Ftili4jQ2wEpzQT/qKg4kRBCFvEYRov4/FgXnbmx77X8PJTa1g387oOX9YV+3DB3BK6jRS2d014JhsdW5f+YBd/gFWxsbHkVJDAZEVEJ3PCLM2Uol+cSuywF6aIFfMbh0e/EbBBQh7n6cf3Cha6E1dsK9bhlkZI8QgH8VWJ3DYN+IHYMI/vwrsYE6N4IC+d83Hv6cAYfkU3dOScYoBd2FIs4YsOrB3foGf4Qmf0j088WLfbmu6yORY8sbmkIQ2/xruOnsQi/7AmfukcD9Z3RI/0Bs817opXuGIcmY4ma+ItOKYYt/lG52S0JtmR92dpXOAsXtUDKcoctVi/dCgU5YbcuVYfuOuvIbaz7tyPfvSjJVeoC8Sp67z6bFNLAQij+Vn8w6vPcD7/DkFx6zoHfmGHa/3fN02jufDuH7+KNcWxIlV+zto5uhxrD/PwfU2R/N6Jr/J5PiBmEg8hMolHcaQ+UdvwGfPKN3yjXxPyPf+Ww2GNXOlVDMIz8yVOMofxchk7wZTq+8bgFwaoj2x64mMk95aDftkKJvlH4vxBE6PRUG/Bcp9/+MMfLvUeO1oXj+zqvZyr8YC9ZIpfOMSTce7qiaNuTz6tpnMXDkaRDcbLTz5rpDRU8Mk89dq1hxrNHRyYkrg+I70SjYtkA+w5tEJCslSAMiwjCSZJSuGU4osjB/AdnKQ7ive+c96uLeMznoLImhKP30SYj4MomiQVCT6PdLkzYmwCSwDbEQVIkrG5Fc4SOhnxL0EDft9JUJKR4AREbhPjPXyZG/jZiQea5OP8EqJxnD3zpKBSDKfoFryuo1tjzWmMoM/dE9cLQvPSqbWAiIRlPLnogJ7xCRSAt4KCDh1kk6DJbHwl4+kX+EvM5qzkvKJH0FjT9b4T3PhRwClaNYjOx5Z0o8hxNwGgaOYAL3n4BcCgZ3MZT26JgB+5wwZogBp7O0d/GiG68t7BzkCE3TRF1gLEzoUP/PFH+sQnXbAZ8OAr9GnNFIHWw5Pv6ERC4Dfmohs8axz5dO7okJuMij4JwTm709biM9bijxpEspir+7q5w3PWMhbPCiENDxAmo6bLZz4v3hRo+CYPXquNzXOLxgWP1rM+PxAfEiP9RJb4X/e5PVR1Fr3ROfuypcJM4aFYjC75IRsobN0h5A/BBbGjqYAtkh5d8T/n2VBC0zwqTtgh8sWPXMfH+LR5zem8BtZ4eldI8S32Zx8FO36s53qYIPYij2IdRsEH4+g4cUIGOGNeY/wFRHHRyTg7jcbyVwedKGzEjWaPzjwmZRcX72woLuEm/ICN+KZX8Q6jxJaxbK2h8p4uKuEVBtqBtkvMJnAXlsZu1tYo5C4ODEps1AZeA8C/xIG1xaO4pFMxX+96hzfr4RVmOs9f6JW/mpctyerVd2xNB/DWvLnrzzdcSz6xrjjBr/hz3pxk0djCIDpm02yKOI+vI/2/UuLAOniMLvEAH/gVveGBbs/SuMixmrrkPoectvYQz+xnA0AulZv81lLRKFY152LDpiE/dU3WID/fh2PyjXigQ7p0iBvn5Sk5iE1hhgO/fEZDJN9p9q2n2FUMix05TjOlSNbc8sfIeM2BZxigMfIeXoivTnxATLmGPJXYX+wr1uUKcvBlPiy2xIAxIzIvv6KrNCPijg3ESc83SJyTHb/8sp733jVwxUF/eIiN9hz44geaSbY3r0byZz/72eIH7GoDRF1Bj2yaddmZ3WASLIffVY+wG+7wC5s2dAgnc1hLncZO7sTL1f5QkxrVe3fknCNv53vtwXfdQQ+mnZVO9eN8xuG8kqrCQPKLAzu8jzGBvGI3u3oSu6TCiQVIBYo1BrC+eTQAHETASKzAR3IDhEBaohXkzkscKWCt4VXSk4S9OmdecyRppnER5IKUo6cAknh9ViC6jgy+l/TstiokOLS1JU1rCnrnJG9JUEEkWBWA5lIM0KfvrCfg6UrQaELwItDs7ihQgKMiBtDgyzoO/EtKZCKHYkTyxB+dk9duf/iKPqzlWgFpF8YcxpIpNjLOehKhR6rM77xAtw4ZzQPQ2Md760gACi8BJ3jtdLz11lvLzhEbko+c8Q/2JK9GgC0Fu3G5E0SPeLCDY018KRicJxv/M8658I5PjUOaBoBvTQDue9dL8nwVz8CWD3mPJ7pmG3K41rzkp2fzAUOHcXRjR5pt7bzRK6ABkApc65oj+u80+46criMLvuhLXOGZ/hN7eHckJsmBJ3pWwNLdaI21xI/EvPhhA+uQzcHuCm880n9fZ8+6nUZz0Q/daMwlIn4DB/ge+WGBZEUfeaSC/eCVsWzIbmIwMtC7g61tzEhoxvJzCU+h65DI6FqxJMHbEaUnY+kGHloPf3TIh8xvHTozr3H4NS9e+aXky38U03bY+KBmwDgHvBEjeRSCv5ob0ZE4ju+KCw0AOYyxNn9UuPAL8QAD/vzP/3wpBjXH1hDj1hbLeMe362wi0ZVdV+/NF7t4hY82TyRw/mKe6IMMrmULuKiQIgdd4Jlc1mYTGwHwIDjuwAOcpCe6je7YgP7Nqyhzjg7JDFvERHQnbuil2lpciy22ci0745ktYTQMFN8KUXjC9vDcWHMaiwfz4pdP3pOqX+GBXymy469ilp7Z4rEJzruDxU7ydw6xtOZQoLtjYxfbLjcMVzR6JNK8/jCKPONRstQIfNy67ooq/vgh34Fj8V92C6bDNLGgQXFd1lbUWlcRClOctwtvY07cyNcam8hn48Pajiprvrt0WA//+CATzNKYZgO1Ev75upiTK+t55/iGeJebxJr6QBPPP/htny8kJuhJzFiXD4kx14sLsRPKOuIQbuEjmBSCDfiw0SqHwU3Np9pDLo+erzngneaDrjST7OER0Of/+xEta3nE1Rh6iB2sq+F0yNtqODrhF+TT3MqtsEBOcQc7j5uZw9rw01069uebml533vgFHHKNuyXOGZ/rthx8zzx4pOuz0qkaFwHOsIJRQgWMCjI7esBeYc5pb0GMlOQiGBQcOlfOAjT8nX8By7iCSmLDi6DJtcBccOCbPObCN8fNjoOCmTO6XuKRlO28m0Pi1rDZ6RGwmd/hWgkWkGl+XJu1jQOWilu6o0fJ31wCCy+Cik4FjIBXjHB2SV8Qvv766y+fPXu2fGd95xXrAk6hZhwwiFwAzFgBp6BR2JCDDHjFk+/JTodeBVqKIcCNR+OMJ4918Ec3wIxuAJTz1tTQ4Usip0O3kXObFLj/0R/90fI/cjRIGmC7oQ4AAhSsCxzNzU6KlvDoHF/zPmBCv96zOXDPzln1FzbnJ5pOuiEHm9OXayOHgoluFFRsoXAkk+aQTuIT1accilF6d9482QGkS2sAuRSL11DWeYjw6k6AxIYfDTFd4p0uJWpFzJq5ZkR/bAt8+a33Ek8tmB+byMdG7CeW3YkVL/xfwnCX0yufy469wjw76zPKOTEB7/ir6+mBD9tN49N8R1wqDtkhmx6uz9HJd/xIEwDXzKkQgxWKMfxK8hqPzKsRUXzAPdcpZuAeP0DWVJjBFMUY24kdG0pslYOuxAAd2D32/4zEqTXZNrGWeDPeo0jizXsxR7d4EFtkwYNGwcaMpoT/4RXPsJEfiklJGBbY1GAj+oRFigPz4QuNdIZ8jye4ax2YRffmgDse50lc0p/GhZx0PSNzOsgmnthaY8cefMY8L168WOb3nZwDE+FLxZ+zEJ7IrHHBr9ilp8cmdvEEgFynCdl68B0YqzGBue7e2GlnKw2zfKsJt+vOV8WSgw3lIsW6mJEL3JmBm2zH59lUHtBwKIStI64V6llbgeo3NPzAeUWoWOTXfmujOMaDsdZ1nbnqYb6HDtfL4967Bl7Ji3iHRT02xLm6wppwuZ73nj+oGdRAxuELbpDB5kT3X58VydYX03CBP+EDBsqJlawBb+hO4+d9nxMO0JMNJZtF8AG2yZfkrfJvPdjX5jHb+60TDIKnMIGMbO+3SWKYX+Q62A07yCTGNWj4UufRKZnpip+Yk2zw0nqxrxpFfGmY2J4/wjr24ye//vWvlzvQ1rGmaxz1/UMHrPX7SzY5G9ZUOlXjAvwoTkLhsL6TrCSqJOdbkfkVloJRQlEwK2SB1muvvbY4o4DkWJKkgkHSTQORgJW88O47/HNUCd7c+JdYOaIxrpWkgZzxkplA1Ilzanzke8lQYS8BAz6BaTfOeHNbQ/HoHP7ML+ECRNdJ4BwS8EqCzil47d7a2bFT4Hrjya7R0sBphiR8IArYfE8uelK0KlTwoYnAh3N0AfzMST5FnqZGAAFsayu4BagANhcbB8Dwb/cOGGQXmS7wJmjxo5AwDj92JhV3gB3wacTMZR1jrCEo/QEDScTcdjMAHBu6uwAI3PJV2PEFeiW/osR6kiBgxY/z+OUDfjzJpnhkK98DE8DrM9kkdAkrfgy48MD+bMtWae7MY5zD98CenYwHhF//+teX5swuC90CPrqxTnzxVhT5rBNb05MEB7wVvXti1Hy540K35r61TNdSxQt+xG88D//Vr3518TNNHd8MRhi/hjIv2V0vvuywfeUrX1kSk7jh0zDSOOPX6Nw4tjOvuNB42inErx95akA04eaNX7IFHHKNQgR2pNEwFm7xy/iEhkeBJUbYkv+Kd42/DRdFg0dtPJsNpzQU7iLgxxrmsa6dZPNax7ziTlFA12Q1Lx/Jo4vwVAOQ8caJafy99957L994440lXjSaYgVv1lrrV3giN92QR2zDIf9YFmbaVYZx8dc1NnHevPEhNqVv8+LXjjtMj63N65o1tr4nBRPolA5g2xnuuPA5xaI8oXDeevAVDZjdZ4WqGBTX8rkfZMs3ciIftgb/89im/ONuiEJUvpQ/nZMDgpPmN1YhKnfwIXndd2LId/KTmoM+xb1CXS2CH7lKYyNfiGMNguu8WiuHz7MjY2wOiQ25VS4RZ3KVgtlmGx+txN5ykji3Zvd1viDGFNVqJTyqBcSINfh6yLU2dGz+wDmv8rR1NZ5wwZhK1sKfx+TgAP6yPt6M1+iLUfKoMdw51VDQH16qjrYe5HB3yF/1evGfzYnaic01C/yBHZ2DT8bStTzNZmxKJzBVbIsTeUKuoxvj6ct36jKNEZyMbc2jpmJ7G2TWg5/k8j0flYPMHRtXn1b3PXTQj41ROu62PxOd7lExwX40cWwOHYCVhASQoJH4JCMOJTlrPnSwAEkhbHdDIvGjJ8W/4haQASDBwalc673iU1CHzG8+ThOqjQt+BBWZgT5H8R2AyyMHkrbrOTkn5pAKJSAnWDQlAlSHztEFKNAGmADAjhMn5/R4BwZZ23uFvKLLWkAZINulSCHhVQFgHo0KIMUDcNIcCFC8GYNvTQTQEZwaIAlXcvY9sMO7z2yg8NAYCFY2kKR9Bvpe6UoBIjFojLIOYHI9fQl6BRVgF8TWVtzh11rszuZ4Bg5k1Gzg2zoAkz2Aj0cCNJ74VsjRheBnG0EtoK2tEVJYsQefpSNkLe/pW7OB+JmkpXELCLONxEAPfNNBDknD+vTCvoBc8gVK/NEOjELs29/+9gJSvnMAZztkZKFnurQ+/1P04IkO2CJAH7A/gsypKMXvnnnxmMfg0NF8HkXsSJcadL5SE4rHFW0Q2FhQVCjwjTNeYp3J43vnMy+/d72dQkXsO++8s+zwS4DsXJuXhxKMuY3TUPALRRn++Dt+7R7CDbonjzjmj/DFjrH5+ZWiAl/mMoe48T76kGzFOP+GW2JSXMFG89qQ4ePwSdzYGBC71tbswytYl8dezZuY4vvmJAvMNI/v4YCYMR++HTZSxLlmSSIXM4pDhaI4pgO6eMgmyPr8Eq6LKzLgVwP2gx/84KU7YXbjxSubZKPlkk2sl3npmY/Ab7piYz/6dpdfccYH6Dy2xu9ZSb5Innhs4ofyBfxlZwf+Hjoy1nXyIvz1+J7ikD+xuzzAPnwsv/VkR/4PjxWecro8AZudVxsoasWQXMCH5E982rA1N/+xrvFij197NE3etp65g/vwlnz8gq/h3WtixuHz7IiMYl7OxCe+YJnNQ/7MluTq8eFa8S9mxZu1KuFFLaUGkMPlJrFujezkm9M4OZgs8AUmZUMEX31e1+FZzSLGnTdP+DOf78kjh8r3dGsuvKohxBPcGOlkzUFeNZACX23INpoXj4rZ4NTcqi3ojj/AAzUEXfAl9Rr7GkPH8FxNoeake7pQT9qkhJMw1nh2hnt8SG3kjhs80yTBNnfO+aJrNNz0lJyfg34eOsgHW+mv2/1M9KlvXOLsnIijKGolUkUuEGF8BSinYnABKbFJEoIPoHAut+I4lSKWM0qy5uJgbicrWqzBuRGjczQODJBCtXHBm4SoSHZ3hw4SiALcY3NuDXJyAGd9DsUJBS1QUSwCQQnPNebDWx4J4cyu4fjkEziSPQc1n8LBeyBM3gCMRIpHQU5+zZF1yQTYFCUSFIBVtGl86JS8wMG1ARYBIRjJoOHJ917JbKfBnTbgAhToVEFg54CuXKfBpPc8sqGw8VkRp9AFWPzHHPRtVwSw05E1zOU61+BBAgBs9EE/xmlCNR3kAXhkxyP+ye97TRaZAZQdF74VMk6y4kvWQc73xkVDZR1rphDxag0NMiDim4okgI4P5/gBmwJKxRJfdU5BRYf0T3/8mT7pjj4Ao91c8gVMs+4RxOf50tGNyxmJ3vii4pyPwQ+Jlt3FIR9jfz6VXV824Ld8dqZ334tdNjUvO8NCMS0RB6c84sQHnTdOsoNXl+bVCFlfPLgOX/jDN18WBxp75yVFmy3WzkYMvzWHuFf48znnYVbwCjZkJxRumQdvfE1cuEbhoDERs3yV//MdMcLXxYkCUXKGYeYMfiSp+0yf+cG+c3zaeuTBO77hH93ZbKIn8YQ/2CI2YK6CwtrBo050R7fWhncKE3zTgfnICves4zM9wlM2pLfRnMh65oWxrucjePRoi0YVj3DUPHROHn7F5+SfsxJb4PsMjYsYyWOM7OhgjzUH+yig+aG4YHc2gssaF3lAXoW5fIHM8iL/VIizrZgVH/zCTrtm3OaDBtyGKBuLSRgt53rvOz7gWnWCwyaptdyJE1fGKHKdE8vyAqr8h+p3/Ujc4E/ulBv4s/X5It8jj/yYvBVyvbiJT4rDSnSt3jA3/bGFeBV/dJW8BxvkNHOIXXEG3+gbVnZiS7WbWkUN021qTRiWXAfb4Bys8mqj1mYkfV9D1mNT9rFxqSZkA37hx/kwGobBMjiBV/EKr/gK3VpfLaSZ82QIvPvud7+71Fx8ROzgH9aTkd7kVT6l+aErmKHJs74f57tzznZwwiaH/Mkf4Seeu54uHXSIR/L5fFZ6pRsXipX4XCsIJFdFq6QnOXIYTqbw5SicmjNJapIGkLETKMm4nqMwtgARuIpAiVZC4kzmMYex1nSdwAYsgk0XLojxZR5ODgTMF6qNi3FJykAAGPjOwdGtz5kFgubCGO/Dq/H4VDABOMU5vszpVfArWvFlPU6NV2AnwZqPzgAJMKAzjuu8IBAkmj0BE5mdN49dJcGj8xe0eDCn4DRnBRb82DWYNS7G29llLyD//vvvLzsJgICdJBGy0qMAZku2wx+9AwLn2QIP3gMpRYxdK4W998CLHF7plk6rzYGEJk1i0DCYx3kyK/bYiX8BF4USXmvzQcf4MX/8wBxpXNLUWosMfIr8yGsSlfESGb0YS2eAkL3Y245IdsTo1VoO61uPX7KhYppN6YAfuD7FnbigK/qVLOiLLK7HC17xv4Y+7Y0L+fgAn1AMKWK9avTFAhtJlPwGnvBzRTF9+kzHdON7cSZx8kPXeeWfbMQubMJmrmN/RTFfsbbYZSs2cg1/NG82ESQrfJrXYQxMtW6wQ3zGR/gav4RH/In/8AOxzbfdAVB0GGM8PPPZOpoQ82vCfS8+0iBkR5RPig12Fa8aBjKJZzKZV+wFwxWBHhMzj+TswJc1xJ24ECP8XsyLX4UR+TyaYl3nFQvmg/Nkozv+Se/W9zm7sPgWh9YQ6/RGP2IYH7E1PKYb58SmOKIH/OKN3hWCMJgN8WTzg47o1dxsjW+2oke2Y0Nj8GU+MrCxhkWhFzvyCeNdJ4fRr3Nb4vTWdMbGhd7pZ62OjGMn+YFtbE7Su/jWsJCNbfmdzSN5i49ocsjOFxJD6gZ3azzqZWfeI2Ze7Y5rXsQDP7QR4hpFsAbJ/HyAL6g/+KFDPMIDcsELeV1u5l/JQWsoMWlOOY/dyCzHyBPyauJTQQyL+Fkludt5jRTfr+vTobiWf2CauFYr8G/zkxmeiE/ryGnJzzC05s+Q+cUtHasB8Ow7Y7z6zBb0YT35mQ7hH32RzxixLu7xl+vXEHnFIVvDdvmT/mC7WJWznOMXalg8aJzgbhoKdrY+O9qgcMfGBqrmxSaoTWr2V0vRnfotT3aYWz0UnapxNTuwxvcwyTo2jfktfJJ/yJdjDcE3NlBXnJlO27jEIXNwnHpwwiQYQQxoJBFOwfgCQoBIZJImoJfQXBcjdqN6NbcAUYACCclRMHBIBaTkqfiQNJwXkJIRR+W8QMQ8eONgSc6Zn4NJ7gIpJKA5v4RufUGrCAeaHFUxTU4BDtwkMeddo4BQkBgnUDh0khlZXSOIyYQ3Y+xyANUAAOCRDBVigJ4tFAx0AKyBJ9m9AgHXK2x0+t/61reW5+Q910v3wNhc+LCe+c0HRAS74CUfnQAvtslOsMLMrXHP85sXyCuu2JaN6VIwA1gAZseCXqIz+hLIZECKd+CvOKEDtjRekgE4+EJ4pB87GBIU+9ZHWhz0zD54ADr4Zl9Fm+uRwgXQ12SpILEeX8k4/CqU8OY84i+uNb/EQcd05TyAl4glRfOwIf8jg/f0bM6s+f8c/zt+8MT/gTtfFRdAFMCJF7bjZ+yn4AJceMIzfzJ/jg74zn+aGhcykJNc/JTP8B92pBvJRPxG5/TBH8U2uyhaFN3GLDb4z4Ovs53Y4EtiS9zSm8TEvxWq/DvXsCvf4490w96SU2zg4B/4gwewSRLjw7CBP0uc4jbzIjbFI//mE3Ru/ciUAsF6xvEP/igpwlgFG380N58V12SHJ+aDLfWOCTl8Rz7f0Ssc4dfORRa6oV94pEhQmNEPfZEPTrjbqLDzmXx8BR46xBM/Nz88wDc/pnt6dI3zWU980RVbwRlysAk9sg88dD0dG08WOkrBYh28kIPekHFwRozBIvFp3uCpYsN4cU7O2IQe2Faecb01yGQNc3rFBxxlK/OykdzDN5yPPz4W8Sv2YtvHpmsaF2PoUOEnH8nL5uHLYh/Ww21+L69qYtQr8NoTD/mtgc1Gd9T9dsxdFrviHuMWx4pVjYy/4uk3ChoYMcX/PGVgHs2vGsH88oyiVf61DrvDH7jNF/i479l/re3ZiWzmMi9yLf/i+3m0ic+Ka81JrVeQ8fKDJkDM9EYDiTu8Jz7FKnn4LRkV6HKmOBFr4lBuhxGJN+Q9XE3TI1acy8FmvnM927EP3dmooB8ymsORjc/UZZ3nGYmxPLUBx9QA7KAGoCP4yu7qMDJoCOnFpq5GVW4lr7rJ47kffPDB8oiuxw5db6P2F7/4xfLPK/2lML+pNZ/G16PCml75g65gGX3yERgGT+gGVvEjmABzYBDdVF0+RGz61LhsII7BIGlcBCLQAe6AnIGAukChWD8oc+vUewUTR9RhusZcOVIkrwF2Yzi2pM/pXM+QeOKQrrcGZwJCihkFoDUlWgEpEI3TYaeIjeMADIFlHLlC1pDYyWC8gJOkFZkAQ2HC8ckJEBWXeBQQHmUDQubDo6LC2ngQTHgHjABVcwEs/TjWGvRinKCXDAUDmfFLb757/vz5EjgCjLzk1gQCcMFiPoAtIVuPnQC8BGZNzYUCQ+HjNjk5ffacrsAU+Aoi1yXwrElOAEMW8ktCgNafoBTYgtz3+KVb/CpMAAsdIvK53m12SURioEeFItsAVrJamy8p2MwH6IBKfgCpOSW/BCbR4NMaGhkysR8e6CNFps8OOqEbIBK+fG8MnUg+iP/47Q2egTR5rcu/8GpHzjnghX92yzggGh76EfLeNXzcPPSFBzw5zCVx8UHgKK74Cb3wKXFHBs0T/RjrGvOlYPk0NC78AR/kE6v8kf4lg+iMvMYh8ruLIabFhM/G0ocYiz5cQ8/8QXyIRb+F8V7smtsYNnKN+fmnYtZ8PsNBsUz/1olNXYcvWCGpmtdvO/giPPM9m4dnvsJvzeM66yjU7Kz6jCcFue99hg/8QnPrH6/9wR/8wYIJEie/rDqxTnYd6dB38Alm0Q9+HWJIUQFPyeIcLMA/XfjOK52JYZsXCkC/N4QPCiwxE/81D5+1lnVhCizwHm8KTbFMJoQv89MFGcQujPBoWRrTyBWbeKUz8QYz8CgeFCPmiq2rTdhWUeaH1n5T5M6N5o5OXR8ii8LGNXQI+90twlt06/CevPhTYGrWbLJoTsnq2sciayt4z9S40OdaYjd+BK/ZVUGoGNZAmy94B5P5KpuJCbHP38W7nODgQ+zpkWJ5g697BEfM+Ow3K3K3ODBenNpo05DIC7BfTPABPkO3bA4PFLrqA42+esM6/Jh/rCE+CUesV/3Fe3FDFrlVs0bGbNzGv0N0YS65mp/29c0nX9EdPuV9m4t0oRAnQzYK5F75nQ+b11pZzzxqG01QMDbnHWICPol/+RR2BXNgeOInY2GBMXjPGpfIerA3m9nyL/2IOfUMuxjDrmRhUzkMTuBZfmBfmEEPsJPOXK+e4KfRjc1g713DL2Afv1Jzqsf4hfnJKpfwbzrhV3iTg9VNfMZ7eMWv1xKdPD0qtoEYAFBkR4FzSbyCRuEAoCVXxhBEjMqZKFoSiHNeS4zLARVtwMqc5vMqkO0U+Ay8OChAEhiSvUQi4ARidvIBEEdzjaTGuRR+nNKuoV0SCUxQmlM3T17ACRgFOzByjsPjwV8U0dyR3Y6CxkDgCNKACP0ISjq0awforCNhexUsAhAwR2dkrI0Lh/W9HXkFGIBzPX6MpSfgCTDxRVaBI2CsYQ5jyOIzfQlkidtYvAko5zMnGfENjP0NdAAkSAEpuZKs2VzjpOkBquRGdEw37GcsGZyzhmIEDwKdnbySCy8AU2EO4MzhGkChwXEHyHxsTDZ3gySSFHzWYQOAgTe+ap2cA1Z06HYwoOG39EHvxiho2Mjn3Kb33rUAS9HsWgWK7/iowk5CYR+fzUO3fLaD/hrgqWPNyR+tRed0zU/4Iz3wGzoByOzNZ/AmadChMWvWnBFZ7t244JfOyExeBaM4UCgrSPgh27LLiFwn7nO30nzG2/Git/isefkYHcI4cSl2+FxPLOZgB8kR5gWL6MdOntivhTLy3uF7vAQbbDYoDiQ7fgtPJDXnzGdeB9tJoOwtLuy2ik3880V3JujDn9v0xyFsILgGj3w6cji8h9eSr/fuosB2coRP85INFvA1PNEjXdG1I9gnpvAEP958881lQwFW8Ls0ZuYWh/wQPios8Wwe6xkn5l3DTgotvNvscA2f8wpD6J4crgvRkTX4Bl68NwbveNHsua6TMcYqaOCZopTt4T+fgE/40WSJp/DLJ40TX+atvHjvMNb6cpNikG+xPb2bNzap196S8PkqNy7sIAcrINlGnPIXvuEzfSP+JhbEN327g8Kucq9DXIg7m3YKdLaH/x6B1mTCb3dgbFRZg+3FNH7hjjyDBzWQGGHjYLNcZIyYcM6mhrGuxf8lil/BA/rp/spXxImcJHbkOvOm6Zc38VHJfHI0PJF/xCw+HPzed7BQXIk/G44eL6cLG57+d5w6yhj4qM5wnevZzqvvxJwYs378PzEAM9QiciMMoQ/8a0BhWPd/crjbLG4Sb5eITPyCjaI7MSbm6D/xiW/z4pNMGhR1A574hVfXxPbwgI/4Ix9iXTPpkVf+ZB5+ZE3XsjW/UCPwF3hKN9alE7gEh9iPf/Bb+UXNEb9dQ3ztqXHZQJxU0arjZFyOrOiTXDh/d9gc+X4vcU6JA0hpMkKckQPo+K3FWTiSZspYziIAGFzTwtEkf7spQMa8inyJFBCQC3iZ07V2cwSm4ANoAsLdFA6PD4lLUSronVcwKnIBnqJKENGRJAVEOC7Hc94zk85VXQlkhYRGKYULIO6NC7nJCUzyHbKOYADEmiA2E0TklzgFmzHGJ7F73ESxq/AAcNZh0/BEp+RN8W0HU7Gk0XObmn4DDsbjBzgBggSv74BjvkOuoXu6I2O9XpGT2/QSi0TvnCAX9PjUIDnnboNHcCQrgET/rk+RJ2lJYgAlDRm9sSN7k8ccbM4P+BI7sA17asDok8y+p1/AZS0gxKd8T594wxNeq47NzYbRa45rqF7fD+uST+JURJMHj+KWXoy5ltjlHo1L5THJmizikd35EJtUuWfkWsUaW4b4kGTP78QQHyAT/7AJwD8lIn4wIuuxtzH8TFyHDz4nzqw7IvaBJXxSAw6DJF2JkU/xEQU6/hKnDr6u2dGwiDmYBHvEM/n4F8wwxvVky8YIHxCPGgb6NB99khm/fL/GsMN78sN6uhb/8NP19AcH8WpucUAGWPfixYuFF7FubZhKx/yR79sYUmyJVbq2FoLD+LADKp7EpjHmgbPkhemX9Goc7CYTPpFXticHvY+I/Wwu0Ru7aiLJwtfwQza7uNmAQvhmIwVwxd9O0afryMte9G5euItnvN+D2PRVblw0J9l8EnuaaYUqncK82IDNxYvCmH/yQ+Ngfh6vtL78YoPTe/nYkwJ8wGfFq/PeG68OcHjPL3xvc84OfYprhzwGn+RwBbL8wW9cw68uEb7Fmvipm36dFP0wix5sQKgzxIy8ndwa4ltqBXrDM75ggQMGqOHM5RE5d4HlCXfy6c5dB3/sQIFvI0Quhf/1cL1iHQaI0Ur0YW28iSH8wi78y5HwL3FaiT6Nka/YdTQmZA3yy8/8Q40Ct+AV/KuxZSwbyIfsbSydxbbeywFsrz6ExxoYGzGugSEaF0+GqKn4An+KT8E6zQ6b84tqP7qhIzjLb/mlzRE1I8zB2xp6alw2EoCx+6VAlIA45D12jDgewBVonEfiqg6RxkWRjh88chCBbzdQchEAnFDxin8FEMfl4IIZ0LieA6WgtePnvKRrvAJF8nLrWVBbQ2K18y+A8QUsPe/tzotdDgFlLOcURBxVYHBsRb/A8T67mPRIn/iU2OiZ/PhK40JG8ttJIBc5XWucg0MDdSAh6AUYsDLGzis9+GxXg3wAjYwaHPoVgBIBvdghcQ1gohNJFn8eY6FbIJ3di/rIhFcNgoaAbL7jP2lckqzIBXAUQWyMXGteyZ18ijMg51q2Udwo+iQNhZjv/dUQiQEPvpMQJSBzsJFiyTOp1lb4+Y4+6cg1fIH9yed8mhhrsz3AMpcCiF2c17yQjf7pBniRh5+kcUFsmqLFuvyR/Dl/CzK3NfmKghBokoFd98Qqvvk6XdyK4j98QPzwVzHCj+mcjYI7D5ExrpFQ+DudAH525PceB2MTsSp+6cohASpIxc+I6NAc+c0Gnn3nELP8lq/OdE2PzsMCPmMO/PE9DYc7vuzF/+iBvPjGq7uT7qrxOZhiRxkWuB4+8FdJE28abf6tCKA/yVLxIa5hlSSchoOs9BM56I4u4J3iJI9f0KfYMBeshSFiHR6II2ulSON3rtFQwTrfG6MwoiMykc3aME/SVzjxU7KTia6Cf3CKrkZ6ZQMxqEglR8hY/NGr2Ix8dQ7X0jM94MN7svMJdsKzhowf0bVz1hD/8B5+kWPEV8g5fu1aGGXDBubybXjiznJy6qV59tCr2riwD19J0Q2D5By2gMd0R7ch+iOreLE7LhbkYHnDAZ/lHf+c0qOVNscUqHiSJ3z2J689wix/u0Y8OmxKmE8xS5fshb8QPjQq+LKJJadqmvLEBnyYYRf704mY5id13kriRbMhlyiCNQOwQ1zK15Xi2wp5G5NwQz538L/EvwbFnZbkVvEpFn3W3MmxZBKHOVxrXjiazcdK1obh8gUsgk1kg3vWn+VB39EFvsRftW2lyEbH5Kdnc8MkWEIXfX6YBIfwrAEgH5s6vLeB67FRj6Lzhfz5bI2dx9E9eq+xsYkCE+v1sMAdPPiFf3ZOLJMBLttYUaPJazCAHsT/mjhAT43LRhKgOnTBcU+FASDJkmPYnaxJCaVxkTA5L8cwjtM4XK+z59Scze1POwuACwAJJAmKfObmTEBBsuRskp7CRvJyrUJd0leIAkzrAVRAA6gUyYoDO58cGW/4BzDee7xIwYwviRlACDYBmCDj4MZ6JUMSt8SMT9faIbCOz8bgXZECQHK3RpLHm8N5n4EqWfBHp4LcDiWegJqkSh9kDsADcsBDH2wvoK1lXYGEN6CeYCUHHs2vWfMdfiSd2rhIPOb32bwOPAJIhRHgcS3ws+sjyNk4j2O5VhEniXnPVoAJcNCfoont2UuRp1ADaGlSyEO3ilS2w4eDDvDNtv4AwfPnzxcwpzO6pQv6jJz4A4Z2UxRcAA3fNU7YSeEEZNk1urwH4VWStfO1Z026uUXjQo94pBP2Z182oUfFuMJQfG3h3Vi25TOaW37KTubV1MIyiVGha91cw4f5FjxhsxGxrQ0QMRg758C/OOWDNaY7kcnaGl9jXGs8PBDXErb4UTjBDwWPhsqPQ8UaXxOn4d21bMy36UqRjT9kfufFAd7EP5xyaCLEZXioh7klbQWP3VexhicYCWMTt2xnXut7r5ihX7GReWAMOWCK32XBUQUYXITDYth34tN3rg1pEMRu3eCplDVsItFR13lwUKFm3sgaigx0qgjkNyEYTS5NkwYOztC/xoxPwQm8u6bzNaKM8cqmMJD+5SYbTnIKHsmDp87rHjIvWV61xoUu5C0xRz9wwavYdnSfoDOy8is2Y1fY64Atcp56gE+rBeC6wlzR6hXuw3yFqvrAeNc55AkH/+ZvMKKu7b21bRTIi3KQuOGXYhIPzneb4pkvsI/8csnm5OV//BAGyJXkhBtiqM4Pn+Rqv8GA2+oD8SWGNT5peOR0WIBHvqgxUw/YcPB7ZY9MiQG4mFpLkw8rjYOr8lrW9SruXA9DrCn3wWD5HRbx7xm5Xu5mGzhBjk7klI/VT+aDTWxETus43+PHBouGln34RT3YmU7ceaMLmEMP8Eyzov7jH+7G0B+sjU+pn1wPvx3yCPnq2j7DIL7PThoWMqoT2b6OndFT47KRBOg9GxdrcDoJXjKLI/a1OYhg5AQcgCMYh1+JQWArDhha4HmG84tf/OLyv10U0RxPEhbcAkQCJqPrFLfmAz6+U4xIUOYGHtZxjaAHoBKPgkPhYD07FUAW73gBtgpcwIQEo/cAGQgAcTIrMoCBwLCOzwLN3NYGCOnYUziZX0DQQxoIfLhO0nadJKyY8RsVfFlbIAMiAOH5eDusgJnsGgE7UYp3wKMpMG8aF4HowI+Er+GzpjH4FswKFWPIVhsXPGsmAG4AxjU+Sxxk9tn3ChY7HtbRzLhb5a6P3Q0A7jtFOWBkP7KRy7zmILuAB670IwmyK/nYWSGrqMpn54CRRCCxff7zn1923xTA1uMv5md7c5OPfszDRl7xXYm8xgEswDMD41uQdSXEszYufIY+FQmSPP3xQd+zIf63kmskM8WFuLdhoGCXIMhhXn7Dt+PXbMb2ChbnR7oyL17xGRsal4O/iSvJTjLkH6N5xCv/4je5VnFvB951fN2RhGzzQNEsnjTW8bv4GbngYM7BDphljshmfOKBH2uKJGIYGx7qYSxZJW8FirhxbWxiTnP7TjwqzlxnHB+n56xtLrhEN7BL8lcYwA0xZyxeyU635kTmU5woNqw5IvzAcdjKZ1xTyXXkVUThbWRbn+kO7sLpkPfRq+vwCH8Ud+yhcXGHjJ36nJfI2OiFrOQnuxymoeQXdO9744zfMv+I8P+qNS5kp3N+ogBWFPIHPiNPyFPGVDJenpOP+Jw1yO6ga7HnbgF/oA877HLi9773vaVJ9xelgpdyFHyI32cOdYC8LQf7rhJ+2E6sii15z1r45vt4qjyzq/k0OvAVv5ds7RyfNhcd2izTvPBVedza8Rm82iBxd5TMCnBPQsBC+dAdA42Jotw572ECnWjsxKg86zC3axxqMo/SuQOhfpC35XbriXky0I18K3bcuVATaTLgPJ11u3UyB33wFTrrBC/lfDGdx9HMbTwbd2y2Jl7FeHJ37OlQZ8BttoI3/tiIvP/Nb35z8Q937/CvRjIPnjKHA79qCHPAX+esG8IPvvBMF/Kous9Y2OX6h3Ty1LhsJEZI43IPYmTglEcSRgkJMaQg5FjGCmKOoEDU3QIvAGNXTJAKznfffff//ONIAahQ4kScUvNhd917wCDofacASkOTYgA/nFdj4i8FSe4SmAAAstYWSNb2qqi2SyKQQxKXIpnjKvR95sCAFygBNM4O1Ow+04P57Q4JEnwo8hXVZFCM4cthLB0Ae8lWoQSQvSeXOckokIzxCmQBk10RoEMGRZFABuIKSoUdPeDVOglIesruNmD1nbnIS0/smNuozuHXnSvz0Cmgyy5S5hbIZPIjeA0kniUxdvaenehDwWlXCOiyP9kR3uhT88qGbI0n8gM41wNV8vnMV+jWOInHY39+nOfPYLKRgz3pDVAqMOhE42NejZ6Chvz474QvIEsGSXc05mjCy1kaF/bED3vzX7rjF3xH0Q6Q+bxEsJXX2Nq8ikB4INko+iUI39fkwBaSrTjzPsUAGc0V8h4/Gn+7auwslvDpHD5zZKy4EU92JtmZvHw6ZAxMsOHBf+Gr4tuOp0Ip80UesWEjRGHL7421eYBvB7zhy2ILDxoECZh+67rIvNYXV+SFS66hF+e8OlznziuMjE0qZR5riBc45Duvmi028B5PYhru4F0cwhA2IXts4rCOnXJFGJ251vqaDXBlAAD/9ElEQVS5+xwKZtCBXVI21kSYq5N5zcNvxTkbZl02DxkjRhSYztErzIVZmddcvlccwmgNmB/lii/4RKf4Mm4L0RsfwIN8wg9gDbvKC3wo/Bp7DbEVPl+lxoVOFInwgT/RCz3DYL5J11Uf3vN95/MYdT3Ph9Kg8hvzwgB5x6NjagJ5jv/yE5ik+XBdnce84lLOV0B3Ihfb5e6C5leOkF/MW+VmUzbmn8mdDxFe4BG/dy0Z3EWCpeoUPko31iSLTQXfa1rwAnvkLjqEGxo2TzWQXT0FZ2wA+qypEa/sZbzD9fKcIl2jDY+8hwVkwxvZYQvexJAxeGAf/Fd9jkgMkQHemjfjvfperQFP6BO/9Myv4AxfzxoO9mMvOu6bF16tZXNDU6CWkDv4mvi2ceM6vNOH2sN6ZKy2yjywmi7YsvqNV/7MJs575T/8S/3FHs5foqfGZSNRqMZFwN+aGASAA1nFsyAcJQLjOI9dU89+czSOK2ErwDUEEgnnAfyKRUEs0LwKLHMLNCAXIKuFTtZQ6NphIL9g5EDOuV6ys3uJX6Ag2dmRU5TTmyQpeDncixcvFqfrZKdewQwEgaIdgeyuKmSym2BdYxVO1hY4AlhwR1Z8OYwlo2IakCuiyEqXiirBB4SME8yeQaUnxYO17CRKzgEQhQGAd7dKc1DBAeGdXvFHBufNY8ebDvAJwMhGv/neWLYClAGozBlAsbMjyO3spGBUPNCzu0psT//sl4YP4QE/bO9HdQCD3tgFvxpcoKygYX/fuYa8bG1+hRPeFBK5E0RPdOBuFluYV7GqwdEIWR/vkSPkMxsYS9/8/NaEX35J352fLYTvvY0LXtyxEjMSAZtJtvSZoqwea4mu2U6jrtggL+yABd7zD7yLpdjFq5ixwy+ZphDx6lzIOXgSf1Co2u2NH1Z+Xcem/ND/CBCXrtOgSIiZ16uimw74vjjAO+xI8jKf2BSjiglFB93BYTKZl6/zQz4ooSYm6Zm89MxnOxmTmJY0jYkseHOYA0YoOMJ3J/wp9sSo8a43j+Ib3rkWD3hlazzhU3MCg7wPJjnwr0CFV2yjaNVc2egID9ZQpGReG00wyThzdXIdXZPFHVT88gc29b35kLWzwQN3NVtwn91SoBjrGj5rDnYwXvFHPr4Hj2BT9HkNuVZOYl9r8BP88nHnriHy4dGcj01rGxc6kFsV3opUuqV7eYY8bFt1TDdyBN8Qn13/1hM/4jFYxr4aCvplPzEmfn0Wk/LWqAaxPp7k5m4T64pjOVxMW5O84oIf8g9kTnih8ed7W4hPin02hRF8VVyQSdxo9skht2tqxLkY0aDhy3UOGCW//vznP1/qE79tgZ3uNqgBPDrljotxucYdbJvAmga+Twf0Dm9hrLhW/OMLD/xWnMi39LI2LoyVl/kLPbmO3DZT1Tb0KZ7hGHvRcWxlbA7XxOf62rEBLFHv2LgQ2xoLsQfD6ItO1QBqFH6hprNWJ/UNHbk+PloJfphfg4MfNZGxrklTNyN4+9S4bCAOdK/GRRIVYBxNQp8ZiNMIFKDOgRU/rk1hKpE6GJuTCVwOrjiRmMzPAVOAuIbjms81ruXQdk40Fa5TcJtL4WmnA3BJwMDO+nF2xRCQVBwIZGArwUrIQNI8zlsjsgiY2rzkx2y1cbGGPy2oKcI3ILC+zwJEgJJDswUogIY5HXTkewDpdqcGTVEv0AABnUvWCjdAp1ASJIDPdQDELos5gaSAw4/gZCPyCGrAqfggu4TjM31LOkDO/GxhbWPIaz62CeiQH1/4oDNBjWef0+woZgLA1vOZHjQhdEVOvLKXZtNYj7kBU+uyubk0JoCWrenPq2v4urGaHnIBSjbhG/EX/BrvO3IoQgE24OeL3Xd9di0dBEhHheWRRFd88jEaF+vxDzoSL3QjviQ5NuM/9JhEs5biI3RnXgUHO8EoiUbRyzbO8z1xKVbFHt+zy4UnMYSn7N6JU9/zPbv+bG5e30vSdMjfxIa44C/4zkFWchnjzgLssGFAXt8FN6xLn+JBEYUfzTF/xLPCBj8KNHFpc8bOp/PkkvzIiW8bMc6LT3zTC/3YmebD5hrpFu8SJQw1FztUOfCpWBn5ccgYhUPiyRziEL5p8OAOnzEOjkU//koR/JHA4Rf9wB5r0b9CLkWZV/pyPRvwH3LBSzEujtifTXrTiejDbiY7etyFrIoGmApb7KLCb2uIY2uymVwAS8iEJ1hmLVgFy2AG3cbPcleJD4oRWKHAcC4YuZaMjT7FCJ3xQfPyIY0ifs27lowlG8x5bAr2iYERsSF+xarNIfjLFnIYf0kzWXXqMxvJe/kdYSVzuo5vmY8e6VV88QV2wpOmR+zQNTwXgxoC81diH36DR+NH52GIzQ751Xp8Boa4xnh+RT4+6P0WIrtrYJRYCO98UNyKCXnI2vI2v2d/csmFmkJ5nV+pbTwaZnPP/5/zBIn4FMN5zFZMwAu+JwZzR4auxZFaxLpeYZK4FQPyvNxubT7b7XaJ6FDcw0jz8hcHuejNAUPJggeYbO56iCE6IAN86Gs7r7nMPOwFS+UFeQMmpFEhi+/5ET2OGgh+xy9sblmPDHWM9eAPH8Cvxpxu6dL3qYFGhJenxmUDUfatGxfGYnRJQQBIwozeicGMk7Qlxtk484VvTmscoJJoOYdiwXtOk8TCcSTTJAUOIsgBGIdRECkejPODNwmYs+Mn/CueBTtQ4ZiA0pwCPAWROxcCX+JzDf7xpFiRGDkoMDSP9QSOIgjwKeCNBeLksobCwTzktTPgmXOApMBxABENDxlcIzABkOCjAwEJWHynkPee7JK3HRjXADegKBgV6cCeDHSa9YEK3VhHASOZk916QEazk9vW+HcdW5Mb73TuFUACIiBBb3QAfPBFP+yAD+9rEFufPd3iBZ4SlMRPJxIGO7o7Zy46x6PEZFzWlwTomK3MLWECIgBqPTs7sZl1cyA+QGYNEzCMX1Xy2Th+U/3vVoTXezYuiYPYks/YZQTQCki+w3+38mJeOjc3v5GIzZk7h3wmBXQl60iWChMxzw/FAjnEKn81hzg3pzF8Mo2yefkCTFAY+E5s8U8y4ssaDjEvLuJr4sO15NVA8QkJzXqKcP5lQ4Ofu2snVr1qsCU258WmoltcasjZITKZG5Y4Z05xCjfwxV8VeWTqOkHmME7Bg2e8Rxbv+bAYnfmmsQoTPkG3DnHubgrMU1ApAsyVWDEXHMMzfslnDfJrKhQPdJCGjI7ZCO7BFPMqYIxha7GmiFFgyE1wpBeQsJQuxS05FSDs4Xs2cY2mh81hk3xBdnfY4Qa8FqfspMESt2TqBVj80zljyEkXrjWHWOC7rouet1Dm5UN0weYawuBW5nWMyBj4+So0LvTEjxXf7M+PxYG48sr2VU66ZFP2Zef4W8h7c6oXFNH8SzHM99hVHBkjp4g9es4OvvFsiNe+Jp2zM1zg/5Wc9x1/Ct+aF7mEPPK23ManU9BvJTaV2zVfmit5H1Z4dN5fCpOzPQEAs/CBT/lQbPFtGGE8LBLL+Y2L/x6v9shj1PxeYS1mbEqwC11piOAcPDSneH/+/PlSF9kkEpNiylMf5I5dHGvIODq3BoyAxWKWHeVsGA6vxTAdi5FQ1sGzWOFH9FXX9l4NRh55GJbwCxuddEo//IZuNcTqIzqFN+LQpg+/G/kFnfBza9bz3vNfNZA12YE/mBvewbYRXiOyiP1a85yRfqsaF8ZU2CgkBdcskI0TPIqWONbIiMZxDMGs0OUMnN38znEoSV2AcRqf07gAMklRIHIs8gdsvDePuyqeDQVIKSY4pWJYEgQInFxy4dzmJxN+jU/hpSmxVpKpJKt4EaBAlJNKtgpqvAJKwSoJCTCyAEgg7DtAQy9ZK+vRqYIBCIQf1yo2FPp+zwFgAKHxuR5v0aHv6NHhPT7oRZB7bz7XKg4VgoAZoACW3HGxnkIaGGRHO/wAJskCCAJT61uLXq0XfukeyCjU6Cz2B1zWI4/CCAjQjbXpDW9sB/gAqYYI/2wkeSiyJRI7UdZWcNApHeCDPPjFg7msmwN5dR1/0uTQNblGZBybAVXjyXcLss69Ghcy0HcKQHbkcz7H/+nxGj7YWfKRMMSlYkJxUwvCaotKvuNDxuGDv2XzQXL2WxiPUPgxKp75AXmNxa9Xtkpc8g8xIXlXu4kR+jEWX8Zoko2xdnilDzikWFdg/PSnP118WqFLx/zYmrBE8rOBYF3jrU0WfPFZ34lt88IK/uRuD58XI/CATVzTyXd8D8/BSN/hT1xkrU7GpQBjZ/ik0RMbYoTuxLIGwO6k8Q5Nq+KN3fgkWyb2YZai4cWLFwvueXQlf8UH7gVf6JA+vdew0g/fwA97WhvPbG5N68Njxag12JjdnItNHHBeIaZg84dK/PM9RS1f4w+xSXw4a1TynSP2Nh4GaFZhPXnxY66Rn16iyq/rYRaMxR//yLzWHs2Nl1elceH3CmlNA6zlZ/SIfxjQiV74lo0Ctu3y05maQWEI0/kee8N689EtoiP5z3qaZfUOPfusljBPJ7EH6+WzzBPiC3KHdeUdWCjvW9s1YpMPm3erPyDzs7n405SIHX/W2F/u86iXv471y1/+csECMmdTQyzIj2oNdsC7mCMrLJBXyU5XXmEEXFGw0zF8o3PziTlx63Eyj5bBMnHrrg2fN5/8L84TM1tkdQ38ghvZVBSP6iaNKl7cHTI/3zceWYM9xAbchhc5h5wnA/07b07zwBENCVnpKuPgsvqFH9IVPKK/WguErItnOQqWuj5kHD7UkvAGTsJpm1LqIXJZd0RPjctGEli3alwYgKEkeMFsB9961clCxgkuQGC8z32czxzFPICdIwYYAkIcjXO5XkFrXcCnCdF9m1vStVMRh+e4wJQjAlCOZj4AANTivIoJzQ8eJXHOhh/ncpjP2gprQc9hBZju3/wATZBqADxXKZAAk51icwJ+YOV6Tsw21lSMAX2yWcNhPesrQswLjKI3gW68tckBiAIAyLWKewnca/gPmZe8rgeMdmbozlr06KBDenMAH4HpzgvABOQKJkWZOZwHRtbCYyd2BNRsasdCQaLJjD6BD1tKEgp1BQ9+7BYBGOPwi0++TNd8xHVkN57fxD+Ai3XYBZFX0mETwBU/rTrx2feacPORbSRL7CI5a3AVHqNxe8kat2pczMdXxIj4EQdJknao6A+A0+VWco01FQaKCH5hXn7Epuy4VV+VXz4k9vn9e++9txySM3uQhZ+Fb/bkx3SY6+3a+oxHRM/wgG/gzTh3BxTz+DSX78QXn+dH/MNvYfy3e49q0C8/ML/5FFX8T8y6Dqbgz1zG0IlYco5s+BQP9CP2zOlH/eQkr2vwED/wyj5sKmbw6VBokI8OQlV3MAhmKYw0ft6zdeQ2Fk/WhW905HvFAH2QTcFB9+ZzHh/m4D8KsLfeemv5TQqsdR1MSbwhvMFFOEwfrqerWgiaF6YrMPBjXfORzTwO35lLMWcujes777zz8tmzZ0sBR9/i31zWMW89KvXPCC94gy/0hEe7sQpkvu18/OwhyprGsxO+FZU2jsQG3YmV8Eu28GQ82c/cuOAVzzDYGK+aPjLyI3mOvjrJYeJu1lwoDhWR/DZ3/t1RUbDiITriD/zEXHwhxTG/5AuakE70Ku+6I8D/M1fI/LBD/ta0kEesaQA00b63AcnP5ECvaw/jHezqD9hoHMS7ZgK/n3zyybJJp4mxAUBm8UCPGgB3GsWL6/HgHB+FQfgTdzAJzslnHiVTtJPHeH7sMVVrwwKPlqlLPNZqrDs4Gik6ND784h02rjmMZwu4bz580R998w81BCyFL4lP5DO7k0fs8atKxooTsotF8aNGsBbfg0vBGmQusatxISP/JZP5bYjUsXjgh/hT0zjfyfzm4Yv0rH4jl8ZEXcKvui89NS4biRFu1bhwIAU4x1GsA3lG6YYxTkDhgTMzfCfXcFCGTxKrwFQbl6wB/AWWIgJQaViMUexyfsQpgR6gESgShPU5F2dTeHpNMAhu8ygAFMUKcXNkzfCDvBdYCj2yAQ/BYUdWYCg+FOMAj3MLBK/GCB4FinWyY1rXyIFXhQIw4/TsSTa7U4LWfP4qlyAiR/g1H7D2eBUe6S1rVDLW+vRHBq8KAbwCGGBjbToReHYYFEsKMkBhffq288N+VVfIK341AcDFzgc7eJxDcecc/qwjObALmQEQvaVhM094BYRAR6HC7wAYYE/ycfTGBZnXeLL4fqQPZJwGSXMF9EZjfCdRGsfedHY00afYOrJxMY+D7PRvbjpmb7JkR/taYiO64I+ZV7KEE2yd9XNcojoOT5oS8cLefFUz4HFIf6SBbOJJcc0X+AleYIRxYi4kLtjWq7nhFl7FE51bS8LSDGkafMe3FPNZ21qSvB1Sc5HTOfokfx7LMI+YleDYkm9ZV4GSuy34dPB9dysVs9bGt7XwJm7EWJKiw7USLIzR9PBpDUHiKOQ9DBHP5jKnpt8hllIc4CEEa4ylc+f4uPi1rrVgAnwUm3gzlvyKTDu2NkzIKwYdCkS2sAbbkC+x5TsNG+ymL/qHYwo3uMFvHOS3E0yHxtAfHGXz+IMC0I+Prek7PPBr87kmuttK8T/FM1ubly+ypXNbqK5PdnMoJNmdLIoisemcsexHt2duXOhAE8L/NCH8SmHPn/md5rXr3Wd4zH/FSD3vvTnpW0yJX7mab9MT/+SXlZJL2MarJkZOxIc5qn+HXAM73FHrdjSezeUavq6J5mP4lcN8r4HySt6th+tsAPgRPSwxt5189Qzfd0fOj+89HUImcqfRlS/hi2vwaD66lpPkvmzqkU1OVyOwnXHm4E+aFo2RTVV1Dx/UJJkTP/68NH80vvJtnjVHHct2sIG+4LP6yHvYywbxdQecwAvbkyfnQuzOz8SJmottbUbSl/qxE19Vq7gGNsv/6kv6oyPrV7IWjOK3cLz7jfP4ZiMNGjviQ/4wd2qwSk+Ny0a6VePC2JyKM9oFYxjfdaP4Dg8CgDMqAEZA7zsJUcJRZHHeOg4ASWzmCjnPgTiwZ0PtGkgqHKfyIcFJZvnzeIBXMtNE4cvjGZxUcOfxArsUmiL8WBdYWccOAefHK5nJA5Txp4AHKq5TTEneCiuJ3KMkQMruE+cmo0QM7B3mM48ELsiABXAEYgBA4JvPd4oI9gTIwFQyl0QVEAAJv9bwKIrr6MQ8rjMX0BDo9GRdxYL3+AEqdAAMFGR2ZezA2J21a2It6ypcrKcgUpSSwTxkSFMDsNzSB8bAEK+KP7oV9PjDDx15ZQ++am369ggQQAXMgMABtBRoKZCcVyRbvwIQ8KYLRUHI9fxK8+p6spuv+yy/4tv8Gh/k6uCGfMf/6J6OZ+OuJbzxJ/rYA3aAm175Dh4BrPd8hJ3oyvdiqwPuGlJYiQWbC+KFzhQCdluz206nW2TAR/TLn+gXz+Tgc3zAe8WJg0/xE3HIHnxXQvE9n8UHwoOYlcTEgTXIL6nQk/PWtq756UhcKwTYQaNgHfKJM/qzFowhL1wRG7DEGhKgQltDKB5gC/8jj+/5mTHmESe+lyxttvBPcSkZ4xV2iSP2IwO94hnvdC+J2lhwHT93LfnFviSteRDjmZfcvmMzclo3foBfmxLOa95sEuDTObqEAQofsotpssMB68BStmILBbg7CXQIi2Co93TDZ+iazvGj+aFfvmJnl6yJT98ZA6/hiNi2BhxVpPIHGAJf6Ql/Yj/+oNCDqfCVztl9iz8iPLAZPfBBWAlTyUyX5MHvVsKLeemVHtlaDNGHddiCLsj02MSHR40LGfgKvNK48GN+Qw45k+4q+cxf2EUD28/TI7uyKZvJSWJObqX3FLSVEtviFo/yCf/MXcbe0CPXyOfuYtB/5cM5fLAH3yGPfONxKnbnizn4XP285mBfOOF/nfnfI3DKX7fUTOBHXvQYuH8BAVNyDV/waLhNE7qVp8RT/FqMijN4BY80PvhmG4d54JmmyBr5gxv+67z/TK9WwZPP3sPXzvdDh3HRiVf51qO18r4Gy/e5OxEM8OrgD9aEVeyR88h41/EHsc3PbKiQi3+QvZM5xCa9aVg0GfTlevOoHTJ/yDqwGp6ONifoGQ7jE/byc1jHz2C5+eqc/BK+i+8z0+kaF8npKGIQQMtpzBvDd2MhwQR8JBrFQD+POKZEqwA1HyN34mQSVXdM1yoaBIQABFIhawEeBbYABKyaJ86IHzv6mhlND8AAUHYcJAs8OIAZgDWvwkBSBiruGHB6ASDZKgYkGE7r7o71fvSjH718/fXXX7799ttLEe65edcAYuMBpvGc3vPZ5rQTCXQEtwLJnHgAumSUFMjrOwnU2oI2hY7vJXE7vEDP9YoRYwWgYDQ/IARm1pV4gbzdTXOYi240LX5D8MYbb7z8+te/vjw/riFzjaA1JzCnN/o0H5Akh4DmdxK6dY0FHnwA8NjlAZ6KLrp3a1wSY3/82YkDRPThPSAJiDnw6Na668nX/cq6vXFBrmVXxRP/5Xe+69f7jF8+aWeIH4/GADD2AFgKi1FCvZb4Ltse1bhIdnw3yUMhOJLrIYrc+BOPfJNf+62HGKMP32+dl94yJ3wRJxpfcaM4Nm9NznzD2pK3wiKbHcawuyLWbqbrFUHmdPBBycudUr4j/twVsjY/oy9x43sFhQKen/ve2g520RT4XgMjll2LB1jl8QuNuWSFR99bw1w2SshmbUUY/iR3mwmwNBhHf3TisDZ7mcucNjHo2njFu6JHUcnO1oIr7uqIMfykIak28Z7M/Jw/BBPoxlp2auGb7+kfttC1tckAH+ARnZrbumShC9fBBd/TlzXEvQ0XuKi4MY5c9CYmYTOsZj+FFJs557CG6z0iA0sVa/DR/M7zDXOSVcHDb3LOfDDR/LALNtW16WBtzNJZ7EJeuYG+8GVNuSPzkrvqew0Z73q6lm/kETFLH3DgsWnUuIg59uE7bGYM3+QbYmOUz42HmWTsGES3YtQ6mk5xplFgWzHp6Y1LJI5gv7s0GhfXixENL166TfAi/8F5PlPJWDEG3+RatiAfvviguwI5fF575BqY6fBYFgzTyNiAlZNtGLK7pybUHbnGxodcI4fK83IyP6Q3/PI7McDPE78avrq2HPvRRx8tcvFd673//vsLZrEZW2pqNBxwQOzkgFmO+t3oqOtZH8/kUX+Zk43Ds8N7cWNuvh6/yIHYLzUUjIOn/ExO45MZ18nc/EYOVDvAflgGb7yHAfVa4/mC2pUv46uf5xf4xAffMg8f1ZTJH7XZwfdT47KBKP/IxoXx4lycWtD3bhRlnCBTBBs3AnIO4JxxgF8y8F0nDjFqXJDxwAlYVZDk+JIK0EtBwEEdxunUOby5JQidvvf4kCQzFt/9ECSSoV1KAKzoVkRbCx92LL70pS+9/N3f/d3lePbs2QJQ7ABI6MQuJ7mBvcSeZOfo6yrEgTa+UsC7BjAoUBQeeDLWPILU7V4yG++o8zmsQd92DAAJfoCCnVGFFBDDt/9A7/jqV7+6NBlkxb8GxasGwA4pfURnOfDiNevZPQde9Aw8ya/4M48kxi4ZSx7ykhtAm4vcDvakRw3UCLBmjQtyvcIgBavrfdfJd4ojCYAfjcZZl27JQH98is47P9cQHRzVuPAT9qLP6hMjuS8RPtgoO85ixY4+H058mfcaflPcalhhi1hSLNR58es9+7K/a8gnjtmA71jbq7lsZvBNPgs/FCiutTumAJF0bFbwXd9JUhIPf+Sf4lUBnvX5oNv9ihwxBrvMIQFKxHhRgEjU+DaWHBoLr+LHxga7KoLMY21j8eyoNiGLw3fWd97YFGeSoaTp/6G4I8pXzYsHeBZMie5GdolcDgW9IglWmd+mzvPnzxf5yKA5UBSR1YaCdczvs1imM/5BN+TVMGV9fNsBhi8KP/NpZMQMW7G1+YxhT80IW/EHDZhGlR/bOeV/bEImNhDHCl3r8Qk6yePG8Rny+Ww9euNjfIMu4eAWnzWWTOZ14AGOwgC84gfW0cs1FHvTmXkVpzZRHptGjQsZFW3sT7fygQZbYc232b+TAk4M2CwjZyWyKy7hs3lgDJ8zv4NOLpHzuTPDn+V2+ZE/w6luZ59t2Ikd8doJ/wpldxg9/g0P4Eh+76H2cHi/9eD/fhwPm+Vg+Zdf4gU2+IG+xkKsWa8e/MxTB+SMn8dv+KONGxuP5sFzvZZ/pkkSTzBDcwcv8aHAt7a5yVp5Xitv1nK9w+aHNfmFeBc3dJ8jeqYDPkWOep5ssIDOXK9GUUfg3+vIz0Ku5xd8l17IrymRM2ANnOnXW8/3cIx/mKOSz9aNX9nM4dcwig5hSggmPjUuG4ixjmxcBASnBTpAYeQsDJqmQVFjN8A433fjG5dCpe4adOIgs8YFAUzdN4c3hyQF9AAA5/M56wuY7DByJAnSY1xeOZvEyakVrcZWMjdZ8GEshwa++COvHQ6BZ1fwC1/4wsvf+Z3fWY4333xz2aEUZJxagSRIAbsi3Fx4jPz1sJZdBcWUXQsFluRsDs0F8AI8krm5JExrSHa+M28lcwIF39O/a/CBH42Y4LZTZcfnG9/4xsvPfOYzLz/72c8ujZgdbAFKPwo2u1qCXyMgOPGaRFRloEfjgAAQpUMAYmcmPPMVeqy2Mhfb2gWSvAC0762lAGLjjK/E5rPGBeFHkuLHdFsTcSXjFA7GuVvlMxt1wkN81Pj4+x6ylnWPaFwkIL53DV9sYA6FsISkKFDc4oue+dDIBg9R5rU7xQaZV3HEb/n4aF6214hoWnM+GEdnPptbEaxAjo+Led+Zn+95/MmdEfGqKLQB4Rx/M94Bm8gpzs1pLnxaz3vfwRBJFB66U2FXn9/hA69wTdK2g/mtb33r5Y9//ONljGZc3FSfWqNDNoQX9KbY8MjI97///eVQACnYnTOm+qq5Z/NHZ/AdFig4bIiY0x8ggF104Zx58Sye6RwewUB3W8jqHB75GzzxHh/0pQilL3rJXQWFCL2zAWzxaAx9+ux7TZPCwLq5E0RuWINvtlGcwUNrWd9uqnzAhpEvB5/zPeyA17CMTV2DJ/OOYrxSnc9hPF+Uf/iuQl4Bb17+RG/WpeOt5Dp5TFP42MQetXEhO9yTL/i884pU9oDtPX7pie7FnzyQeUI5zyfkU2vRoTwjByQOL5Hz/FCTzW9c7w47/8VfjwuET3FDx9ao5/mTRtyGA5/mV2zrgFcwxeH92sO15pG33W3x9IDNB5t61vDHLfyhC0852IDUxGXNrEs+jRTsjEyOxDGMEk82YirP3lvDExnW1jh5jIuebH6oXfw3fo+SyWdskTUda+XNelkTVsBtzb3GNL7hwDNMYGPy+JxzjuCNO0Ns6q4NXICrfITNjLtE5jSOLvOX52CGuMebTY9O9Ahv5ADv+xp4xovGEtbRq7xkPjJGDuOeGpcNBISPalwEheJWpw9IevAjRpI8OIZxnIzxjHWuGt57oK5pkfxc18eEtjQu5gE0AlaTVcERL4otAeQaYwGlxsXjTM77LCgCcnE+MpgLr8BZ0qNXwQSogR4HBxZ+U/OHf/iHy52Kz33uc0uxApyAjeaDs0uYrhdAgJzDKzgEdHSGPwURHSnCBKmkbh48Awh6VnQBvfDj9qzGQqFA3ujfq7kBu8LJfMbX4sF3EqUC4rvf/e5yx0jz5XExOzR2KIyJDK51SA70QvdkqGtK3IpwBXRAFiD4DBAUNRIZ/smLx5Dz5tVoAi/XKmr3NC6IbumAjvAcfjv5jq/Qd57Hno0DXnar+chs3FoC1kc1Lppx8bB1nvifeADK/DyPbVUbbSE8uJZPkE3Dyo/5a36MfYlPDafHlZJIHObyOywxTG8KUDv6vg+fxvEVvmFH3mOiYlSs8n3z8s2sHR4laTgGe/gTH+V3IWvBHsW+HWFxwZ+tpdllQzELc6zpH8WxBX0qcPk0mVHk6ZTv+Sg+FMf4yu9X3BXxmKbPYpc+YfVDugyZF7+uEcuKCwnenB9++OFSfJDBvHRB93QFZ8QxbHI+RT/d0REsZA9zaxzpCb46n3F0JZ84p3j7yle+sjR55qUb14e8Nw8dwzDXu2OCV74TPfFZ8a9QqPLnvCO6FPsaIfpkE+uae63uQpkXmZvv2NnlW+wC9+GHc1soOABzH5vIgg+YQlY+IAfwfTrki3yev4iV+HWILM67RhPadcGe5mFPfgZvvMJeNcVaexinEZEb+Yd5rMsnNDDVpxA+5F45TCHLX0OKTjEvB9Tv95D1xBBfE1MeB9OkyLmaCYe7s86JZxt7neiK7yqI+as5yU02m66u5f9dVmNgoDsf9GLjU163KenQxNjMscnIf9k6c6/VP8p4h+sT6zDSXQm+ExKH7KKOoe+6nsN3dOU6G4OwPc0H/zBmDZFF/oL/akp3cfkHf1UrdjIvvKJjeEaGulbwR3yyhXlgoSbQa+LkqXHZSBziiMaFgXSkikVdqqAbEaBiQECtc7b+iASTYGRwBbtxl5xvbeOCRwGRoBMoggCZX3KzOwScnItjupsh0XA0RbFbkXZkFWyChf4UuBydcwJXPOmqyatQTbMGaDxiARA8N+r/CgAEQMJ5FSsKBMlVYnYnAT+cXUGjMcCj4LIj6VEW5yQ9PAId8/hOMOENH5of/OHHOOM1Igp+/Eo6iidjND4AhO7x7jAHuZ2jD+Cl2fJMOhnszNiFdT3+AIjryODVZwUy/VjHumziO3MqbPBm14OeFa125fgS/9J0KoSMZRvfIT7FpgBHQaU4lfTSQHdgRmsaF35hbb6jEFaMx1cq8RH8aEqymz4ah/gnv6c7yYMMs7EPEbkeo3EhgxhIzIl5SYV/sJ3z5NrKE3nMq+jGC/9gU/7Ppvi8VCz63nnFEX4UEb5zsCP/t3mgkchjJYl/13nPduKKvf0FKhsK7hR4HIOv5jGoyIdnc/JTPsjHFVVwJnzSB/2KL49a8D07cOJYYuWj4oNvs6di3mNRYk3BB6vgJb1IcCOMoxs+bxda7NMfzBB71lbYiTsy+44PihVx6D1+ydJ9kd7gH0wXT2TTzONNoa2wIQf88J25rK1wwLtDXNv5hWlsYg0HW+PV99Z3naaDfM6Tibx0Ti82Xuwuv/vuu8vjPe4m0z3Mxqdr6JwdyAq7rGFTI81iyBhFKH3Nik1zsTO94IXfkFG+JDN/gNHmMmYrmRtP5lXc8Fk+T1+KLbxHX5fI2vRztsaFHckkx7AR+/FhPgMrE58hcroGvvOxjiFilK7kXQWeeFb8KVjNKS7r+EtkHP3StQ0xtYCNRvzJYdaxfiXry+F8y7XOO/g8X+Cr/ZpriU1hibXEh00XuvXbEhspdPTixYsFH23uwQ7rV18hIx2rA8SJzw46dg07df9ynpwwR96DHXK9mkWOl9/4mtyJP7gojyX2HNeSa82jBoAzapToU60Cr6yFv6zj1RjX4ElOUoepe9KA8Iu1ZP00qDCP79qwYGP+kfUr+cxv3A0Sz9UH8MeWcNec4jp8whBx7/qnxmUjceK9jQvFcw6/4RAkDFWDIZRxDAxsZkFunAJIIlMQJJFdojWNi4LGzoNXOzoSTggvEr/mgbMrlPBqjGJGIWMO/FhH4+EH426Zcmi7NebGN1DjwOQADJySjt32BZIaE7vuChRgnp0TSVmx4b3iX5LEA5nMY05zAVXFh4RMRx988MHCj2JYghZkgkJxpBAANCmWjBHQgkfD4xlXMgBwyYQM9KBYydrW9V4hgGcgmDUAqcJOoeJxEcUNULSGYkVBQ4dJZF7pR6GiETAnQPbYWf5OvSSgIeOTiqM81oIP791dokPz8AsHXfsMaJwjR5qnkZ+taVwCpHgmE3lnyclYNgL0+JuNQ8ZJNGzHX/B+DaVguWXjEh1YS3FIx2wm1vkzP/Ad227hoc7rWv7F39lMclRQ5u6IcWuIvvmUOby61joOOuY/4s2GCBtJNr7jW+JJglIgSDDGiAlYJWYkGEUS3uy6S6p4pjvJiP+LS7YQZ76PPvBlvEKDj4tV87rOHPSKX7uJcERBphBLvIhHuCD2ovNswDicV3iJRQ0OPbKJa83hOmspIsnGN/kg34MP/FoxqCjzXeb1XuMB+xQDGizfudaacNQuJN1Zwzm2sjbdamhhgkLLebEbm8T2+IKn1qZvBUhwLr//URzZHDKWj2rC2EqjTE9sSi46cS2Z+bMxbGJ+PNY4IwM/hhWzDQkU/6lEr2SB6Z7JN3/uxlnbOv2aGWV+PoInMsB1+YEfK1rjI7N56fFsjQs7wGA24LvyLcyTY/gNu3SiOw2L2CBzl5UO2Ft80YsYVqBq9vhLfGsNGZcY4JsKRv4n/uQufJOhk++MU2/wA9fL12m4165/ifgiX+Kb/FitJOfyMVikifAdHfB9uYxOydBzSbCHf9gcwTO7kBm+dqITuZf8YlH8sZ+87nv4mPXcsYIp+DGvtY6Q31wKfbUku9Kr93ThXCW6YhN1Gb+DE3zQhoLmx2vXyUNkPNnMAzfceZIP+LD6DT+VgnlqFXmLH1aiE3Fto8U88oix6klxzq/5kZzKxkfo8Fb0qWpcgDhHT7DPFC/ZSc52ERhrRowsaDnKpfkqPdS4AExJT5MhIVhfoCGOKpitpwgXJHSiCRCUigUFh6BNkyIgjAdyeOSs5jFnkjK9CHZArKB2jQID4ErCnjenC3w7nOO8gCHFlSZK8R0gN7c1rOUQyG4jGw+ggAzd+eGcZ26t4dETxYfnU/2AEOgoSABZ3UECWpEha/geKJPV7rBrAKaiRIGlUAJuQIIMvvNZAiI/uend7oUCwZxk6HJk94wdFEhAiFweRaETTYa7W+wCwDxSk2KFrjOnnRmJxVyKSTvXHUjQmsYF0Tk++Qt/4OOjpIuMlXAkGQkWGI3IOI0h25KBjq8hct26cSE7XulR4SGB2e1THLsu/nLN+uTm25pCPmPu2MucW+fFTxIce/GHej1sYD/xz/ZiRRzQobXFcopEPg+r+DtM4C/ml0glNcU+nxezxiooEr/kqrx7b16bEXTMx8kY3eGLz5sPltjV0/TDy4xxiE988G8NFFn4GV6yG2iMsWQ3b3CRLzpvnLWMw1/iTxNAJ+aCH3ghN6xQwBgfns2tSOIHMM067twoIpHzeDDe3RJYgU96cb11c5DXTiOZjNGU2eywu+rQXBhDf4oCvCkmFAFi3RowgM3NA8sUF2yr4DAX+bNuyHsysQf7/c//fD+jfl3ki03wiFdYaD3Fx9q81Snzm5edEhcKOc3gqKCngzM1Looy/pbiD/bzD/bif3IDnju5K5M723TbiV7lY9gjF4ldmCzuxCy9rNV5xvJdxS4cTsFufXoXO53whU9xLmY0UWzDLs6tXf8S0Q1ctEEiP/MtORdvYkBM4c9mCt7hPx7EBr+phB++r0iGZ2KZn85sACOsK77hMv1qTjRncIstbSziwzi6UDviEz4eIb852NNmiVqL38uV2Xyoa/jML8QIv4BXdKVekQvE0jU8yXnwzfrwAT7RIZnlf/NW8hk2wxM67JR4lnvons7Y2Lz8WBP51LhsIIF7beMiUCUjQSNhCN5RMDCqhK84tY73I2DynTkUfpwG+HHMNfRQ4yKZKeAVGJzRIfgUuAJf0vQjVo+HmAfgpihRvABhhUd2gRW8CmPFhc+cjZz4JZ8ELOgV20CALBxWQaA4AwSKeoAJIDRHAs5OirUBgrntvgEkOgZYAjrB67C2hGUt30uYxik68E1uSYO85gf4Eq0C21jFqOtdS/9kcDgPMOhGsrGzKmDxpaECCkDbOfwBVcUDPVrTGIGsqBCw+CMrHZi7grxXoEBuMpMRSNGRhhPY4t/8xgAPd2j8ZTZz8hf8SDzGKUDtyvp7+mQFGlkrtLZxQa7Fr4TGz/HBxn1O5DvABUABPllH4+iYXICOH15T6LD9LRoX+mIDhUYe9WBXBQm94ZW/kGHLunRoXvrhI2KD78AFvkmns/hdQxplNrUpUP0rRC5x6X8R2KnkXymW+R09hEc7eLABP/wcHtE3uX2HV37N98yjaPYcuPnMUcl1EjtZyW6OEB6ti29+m9ijc+vHL/AOG8Wkc/DCevxe8xCbVJmNNy+8IZM56FrMWzPkGtdaVzEGo8WOwoHN4Zdzrieba9kPH1lX/MIWYyoPPuMPHoifYGXGmMs8fghMhwoOMQ3P5Ra6tqb3sIaOzKF4gonOWd86xuBDkcMm7va4mw0T6HVE5CBv1UenKk8n82bt4Bw78wuYqdgzd7X5GrKmefmZTSl5wbxiHc9wi03JHxw4S+NCdrlHkwF/YaFGUhw5H76j1+iQfOLO+6pz5+URsvudW5pRMSXe+NdW/YZcJ6fxO7zSIz7le/xYt/pO7MIeNiQ1FWST3x0K3r2HGPQoJB7kVXbVoNhU4E9sL4atL040InBa8Q7L8MFvcpgTbsub+FU8q2N8nzH813fwgdzqDndl6ASGyFHWlrMU2+zErmINn+oafNd1rz3wgje53x0JPqMWCxbEN7yHceobuR8eONjDXTh6uJbYWBPBzzSI7EDHfBsmjvIUXtjKODHffZjf8zP1Hj7NQ5+aUE2MmuWpcVlJDHBN40K5DMFIHPgSeDCiNYxTDFUgqGQ+AYoXCX42bkSXGhcOLiD95S7NiR1AwSjoOBAgFHxega0EDBxc58AHGewGcWKf7TykWTMmyVPwAhK7G4BVEPqefgS3dbz35wS9xtElZYfkji9FneAlD50JXKCjUBHc1sOHpKXIF6j055y1FaICVyBI4kBH8eF7a3pvvKBXNEZW87nOmBRTedYUsCngFFTAHr/sCkQlbE2ea+jALgKgxTtfwZeiQuACAYnNWmRwnr4FMLvTF/ChA/qztjHGSuKAEwjjjR3YDU/4kXjY1++H/OEAfHQQQVsalxB+NYXWlyB8HpHv7f4kuV4ahw+209CxaefzErn+yMaF7+OB/SQL9mADPsv29L+V8OUwL9vyE/PycbbhW+wdP9gjB/9T0OJ1NA99uSugKMAHXxJfdaz3/J8uxA7fE5OSihjyOWRs9MUHbYwoKvvafJ9vinPjK5nPhoBGyjr5zlr4zJr45vMwRPLku3is8dFJXBtf9eE7fglbRoQ/eMJGeGZ/eEavroUZ+NTk8nHjrS/O4abvfK5k7WxsVFsjvkdnNlqcr/nBGIfP1hTXzpufzjVvGqfql96zqeZVYeB3d2I863UyrwLNa52n0uzaEfExPMImOKxhU+SyMV1ZY8t8VQfiR4EmdhStXq1Fh+wBsx+b4Duf4Xf40WjAJ7ku+bMTvSjc+NhoN5vPwEf6FLN+c6Gg5p8K7C367ORaNqNXBbt53c2D8zBC82n9kPH4FZceE/SYtTjV5NvIdMh71x6uf/HixfKbVzFhkyWPdHvE1Hmbr9b0f1bw4Af7NvKc92QIrKp8eG+8zYGf/OQny+/s+hhzukPpcXWbt+YxL148pSFGjHGdpyAcHuH0f6w81eFxUHxmTq8O11xz2MTI5gxs5OeJhdhbnMNd9Q18lJfZT2Mmr7LTHoIzNlX4nTqDv/E/j9Xy1+6nwSV+rD7qOMjP5BTnvcJYOKV+go+aNJ8j3xnplW5cKNZ1glvRNXq2MsRYjAxoGR0IjAzjekWeJJmidosBZ40L5+FEgskuAccCOnZYFRQSn2sckrLvFdgCXcJJ84A/zQ/nxRuQA3bkcSjAJBKNgF1Rc7tOUtEkCGLJzHrAFq/W0ogoaAC6P0/svLnoS7BojMzje8kVT24pKuyzC2otO0/Gc3yAI5gVBOZW8Nh1ta6gBggCm57xleLHGgIfaNCV9eiEDPggn91Q4+0UKJrJyn+AvO80c7mrYm0JTCGIT3qzg0W//MYa9Ot74+mWDNGLJhZfzvtOAqQXIOUzvswb2+HL9+zLN60PVAN6la5pXAAVXuiYDl2Lh07WMk6hB9z5Hz47mc/34sKjQXyALGvJGkc0LnRnF8hvJBSHkrBmhR75Fhk7CK8hsrEv+TTdYkoM0AefMK+4Mjf+c2wleuSfZOBTMx3ix86h2DDe55586BS//NYY582nqM4dhk5k4L8aMXJWXZFHnLETn+vy0YHYsYHivfP4srbGROErHuGiWHNXiV3ghzsJeMNjndd732nmzYu3nDevuWC2dToZRwcSqc0S8QSPxCzfUFTAGnef6SK240PijY5GMQF3+XjXubGKXQWIObqfmds6/IaeoiPf4REGV3s757AOPIB98Ml3I3Kt8/B8ZFs0u7ZT1iYDPq0PI/ilol1hpbETU3S8hczLz/Drek0MfcsFCiB4pKF9bLI5yU/Iy8d85jua2uS/rk9YYFweE61kLJ9V7Imv+CUMFhvyyRGkuWcfdYT55Qa5SD7TTFViX42Y32PCbLYQI2y99zCX9cWKekhD6rNiWSMIO60Nozz67a4EvsWQ/AwX4E2fk9/BJo9YwwSbE87hO7wrptWB1oF/5hKX3tsUcg15bWqxp9izAYsHdQ9s7XNee+BZ3saTmoW8ia/4j/PwSN2BZ9ivRlOjiJPuZ1sJTvELNRI+yC3m4C78G+GVGM3jeyNfhsXwAL82P8UJ/vkb24iBPueZ6JVtXCifcSRPBY4diZo4KjGAQJMogUCSTifzCSwBKAhm812iUeNiXqDo+zz2scYpXAcQgYGgMbd5OJhCVBDpwgGzxC6oJQ9Bo7BwPTk5vnk4paI8xRIA8ViBc2lcBAggNpdx9GanQbESvh2CRRFgrGRrnEQG1O0U2RFJ4Lh17P9Q+BOidlkU/XYhBaBdK82cggwwAyU+oKFQ9FsntpZs3D4nB7BwDl8KK9fyH0mTLrwaSwbn7L4pZvCYAos8/EcBGVAgvzkBIPnYTMNL774HFHRIv734mRHegT7gpWufQ9c0LiHyaxztiAHomU8ZB4jS5IzGkQOYacbogn7jPw8RYDzqjotdnyRCzUaN1TVzG+PAE7lrXJBfUmNjvkQP8YXRsZXoi/8qfsRDtXPIvGQSe3Sd7zrxU/6iaKg6YD++yW9HRCY+BhPDg2v5qrkUXuzciU7sVNI53Sh2+ZSdar/xEr82E8wdfjTEicWqxxBezCFeXCvecp59+CQssFa9rhLMYzu2JIs58CjZ/uVf/uXCM+y3jljHG3vDEN91olf6My+dIHyKb1hEDyMiH1+S4GuskQMO45EcnchlDB2JK/yNMCPx5/eLM9vOdLSGzG9t8incNRqKUbyTi83XxHs/Tw9sQn/yh53yMzQu/Jx/aCxslPlsI0suImv8FcVGuRNPH534Cr8Sk+aRc+SHFNPxpb1kHtiHDzmGX+GLP8N65/Ebe6pp+P8a220hc4kVzZ+aSX0gf8qR7ojwdfHG1moaORIv4oqf84fECDKfz3I6fOSDdvbN08eRzYaVjQnjNWbsqBGwCSpOxJM6LdiujqEHuRxWHE18PHft4j8O36stNFZyubolOZ1/jHLAVqITtqZbMQtL5Q/zaxLVXCP/g5MwbZaLNJb8iu3oUvyqHX/+858vej3Sn46mV7Zx4ewJbIEzcxDOlXGCz3UjgxinwAVEOmZANht7iThXb1ysDzg1GYJq7ZzGJTgkGAEvmAUoeTQzdhw4LqBTPGq88O66XC/QgQ7ZBAAn58w+S2SA2h0UgQmsFOjOARXXc2wBALQqcDoEMRCyo2gHheMba0eETfBiTcBDB3YwzEG3zgtyt4Fdh0fgB6hdE/07zJNCB38+4881ilHv+Q8QEbCKKjohp7EKSjxKYmxgbvx7dQBIoKDIkpg0A/xKAyagzSW4I5dr8bWGjMMfkNes1cS4p3ExL9/AJ95mj5lknEaSjyi8RvFiHHuyox1FBRTdPET0e0TjEgzge7HPVnINHwbu4p0/0a+45FPkztx4vXRsJX4thuhuNIfP1pcwNBZ8YkTG8V8FAl8Nr141DjBAYu/zh/ChURYLYp+8Cgn4Qy/s1YneEy/mthkBX8jiGmva4aPD8CJZ82n69l2OEHsqetyhMAYfOe8VH2SEK/VcJTpSDMA/clnXvOLGhgpMsBsNF8ir6MsOu+Rb7Yzo31wKMjjhez5vIwUGmXtExsFZ/oTXkPnFnUdX+diIjLEG/Wq4osNO9MFG4m9Eo2vWkmsdeME/fthTrhJvYoUu2cmYtVTn5KtwiC4fm/gtG8uHZEz+0byzcfTh4BNydLA/50Pey1vuNvAdOcemmE0WOaP6wxFkPngqhuUrfoN/8rjL4Twe8cp2/PhoHshsTrkCDmQtDYKdeljvO7qVu/itpsL39NJ1mBiAM3wNjsEOO/6jRsN5tY1mzTriS/zRi6ZFzhMn8FazAg/wQj9b/HctmVM+hGU2nGCReBXz/Mb6NmDVCuSCjeQ9ihe6hOH8mh7omfywU10Dtzuxn1qJ/frdOsRGbEWX7Ix/+OmPKbHzLfR4FL1yjUuCQcEtYXESnX8NkkoMxtk5nHEzEiiCAygFvM05m3dGtXHhOBIvJwM+5h0VjA+ReQS3wHFnAlh69MhzopxMkgakCmLBVB1O8LgGaKcQlTg5PWCkE8AHTASd8Xi2M6XAQmTRTGSXKbpB5jOHwAVewN285jHGWDwpxABML5rJBZw8F8uWGoMUa6EUG3gCYs5bV8Fgd4ftFGgAVfFCz2RkdwFofUAjiBUeErbP5o2NyYg3eqQrPwr2j7Y8Z8svJBD6NW/lbS1ZS/JjK2AbUNvTuCBzkFfjqdgcARQyDvApKuiIzkZy4JNejUlRSteX4oB+j2hc6JavKn62EP7YT0JjP4nE7hxQ5jd8I3ERez90bCVz40GRrcgYkXnxAh8UsHxwROThE2SpPuqgI406Xc8KFrzkzjGfM4diXnEvVqvd6YXPeHZcMyVG2V4RQp/s7xq88J00Qs7Z7UsshT/k1XfihS1cG91XMk82PayBz050Ch8kX2OQ2CEbTAp/Nib4DpwXYx5xkUvkCXNEZq/wA5bYkMETXDSe34/0aYxrYA09djnwkwLAWv28z/QBuxRjaZg6WRse2E0d2XZ0zbVED2zPnrAZ72wFw9mETsg8skknfEVGuUkx+tikwOOf/AQ/cptXGNhjCt/83fj4t+9R9GTDiY40Pt5r5sW6gjFjjyR50B0JOQuWKVblaL6NX3EtH4tzn2/BA9lTK4h9OuKbNgQ8Noo/zb66TV7NBocnQrrf+GxzUJ6QZ8WJHCSu+V/nn0y5i0PHcAkfrvedO582WMQTDHLextwaf72G8Gdum8bsoUZywEr1jrXVDewFd+jANUfahU7Ian05xl1TOpf7Yax4ret5T89qzxEG83PYz4/UreonY/2lSJ+NPyu9co0LZUoUAgk4cf6Rc8RogBjQpADrZByHAAicfzZuLaVxEfAciUwaLIn+oaDCC/n64ToJWOIDru4G/OIXv3j5e7/3e8ttPUWHALJjJpgArWvi6O7OAI3IxVnpGTCYX+IHygoiOsM3QOLI0S2dG8Ox7U5lDXIKUtdKEv6Cjh0JcxjjMMbOQO64+Oza8AjI/AdePAEjn33vvOslUb9LAN6+xzNZolvvzekzPUg+SLCyP5lyDR3Sn6YsPPpeQpOUJG+B659Zvvbaay9/9rOfLSCpiAcSdIcv19TD/CM/rOQ66wJr4G9thfWexiXEJkCdDsiFnxHRpZ0zzVP00vn2WbLgN5IVnV+SjRx7GxfXseHaxsX42JRPadrsGClcNQX04dy9iA4kYAWxGBwRnsUh/yPriPCsgHanT8x3O/Ihu4oKBElyZhu4yPfFk7UkJwnWfNGba+EVO4s/j+hp0K2buHCYX6JUJNC168SCz2ItPpTxroUtGiW/l+PjIx6N5Wd++KvgVYQltjKnA/Z4/DRNAx1qIlJkRh7+zO7umJvTH8bAA+yhg8jkveKcfnynIII93o/I97DVBob80GWJvLBZA0WGEZFVrJMDz518B4f4MLvR8z2IPHhmW4UZORTl+PBdtcclwi8dnaFxkac8Eqi45uPyu3ghQw4ykZtNxRtsjpyRNbYllzzHT3IXxO40rM3YI4nP81H5zV1L6/JjuUNO18SwD97xzEZHH3QjVsQlecW8R9T9FU2P4f30pz99+U//9E/LD/hT0Mu5eKe3xLLDPJoe46Jf8Uo2dZxr6niH8+ZUv9GFP2jkD498//vfX/BKjMtl+PIbk2zM0kc96px7D9icpz7Yw/rZWFHjpZEjz9FEFvPaXJeX6U6+kXfYR7OLx0o++57/q6vMUYkd6E+cwFX45I8gPN1xWUmclMEF6CUSAHZPAIlij7OPyDgJSeCPCoAQR9AEKarTtOwBIiDJsRUvkn12FATyaF7fAXx82GWzSylZ5DlXj10pdHTXEoqiDJD66xmaFj9kBgYOAZ7xErvCQsHPCa2R9QVdHNl35NaUpEjFq2A0H76MoT8ysY/feZDL2sDE4x7WUnTSt8JTsYInBaWkAGT85Q/AY2eXLAJPsnALXMDggV0VKe54uKukCfTDO41R3Q3Do2df7VD6jr1745Kd1FrIe6+xcicFz3ijL69AUsFAx/6ayR//8R8vj7H5jFeysIN16VeToxFiLz5GP1lr5G/45q8Awm99FDJscUTjYl58sA0gSlPSia6MYzN+RI/GxTdCvlcE+g0Bfc38Fzl3r8YFr2QVK3TnN1YaTY2Y7/gr2ck5kv9WpPnnE5IZfXQiH37gjA2BmT75j8RED+ToY3wmPz/PD8lH84hjjYrdOI0Of2NPOtbUKAT4PX+BWcbAQf7uDgjMEkfWwjff1lzwBTqGTeaIXNYzr7sgfkuk2Te3uFfMZFzl1dwwQoMhpjz6pWDmlwp4cxrj1Vp0x64KNs0JWXx2sD2e4RKfhf0KCUWeogZP1oKvGWs+RZl1xYS1RkQPcEjDMbKbz3ihEzgxshsyD97pw1qjMb6nQziaO0z3InrEO/umAYTF2fWWJ/A3i6vggOLtsQm2/+pXv1rsbMNLcUz/dF4POpYL+Rcb1nOIryhObTQ5xAY/kgdtYs10sZfYQszRvfyDRz9C54P+glcKZnlHrNziEM/ihS/YuLPBIX//1V/91VJXwBSbHeJNvIsz+Zhecm2u59OKbD4U/fIlY1OIk8V1OVzrCQ+52tr+GpnfgKkHPKGR2sN7GwuaCtd1GS4ddexDh/FkgSmwk7x8wiaS/KPmUdvwKfY7muiMv8Es+lDfqEHkCtitmRW/lVwDP+lXfSNfdBLv8Eb9ZB6b4uS6lW8fQa9M4xIDACLOIYA5h+87GSfBaiCMnxnAmgpP4KCAniWTLaQIkDx1uUBOwS1YrQUYgacA0DTYnbOuQlOBoWBT/NgpshsIqIxJsnVIsMZIkgLHTqr3gtb5zC2B+usQknICiWySC544cZoS/HF6zo1PYxUyxmi+6AXRowBV1CswyAps8CewFW2AxI95FSN2wAEeXswt+Ss2fVZ4abLsnHh118j1+DefoGQbRam/ToYfdsUvfuhNMQLc8UW3vXHxqhEC/CkmjSUvwAWC1gBG+Kc/QKmRUlxaN7fn6d111sOj9e28ae6MJ6u13DIGos7jmRzmTkGNT/awrtvt9CLZm38P0QvZ2EtSAW6RuZOx4sJjhsAWX52M4Qf8OL9DGI1DKVhu1bg4Zw36V4yKbX6vMNZosodrZ3F+S8Kbgz75Ix2N+KBLsSJ28TvDGr6oGSPn6Hwou318draeOfgk/1Sk5/l0vspefD3YQL/iRexpiIxxB1oToan2PdtIlHae4Yrv+bZ5FHMKE7uqEjv/hwO5M26dyqf3bMZXFcgwnS+KVWvTgTnhn+JGHNEdfqwBR+hRQa2A4zPiyaYWnZibTOyBR1gF5/Eo5nzGFwxNk0API2Jb/sY2l3wMXkfWke3gF93A1dljaYjMCm9F4DLPBT+4FeGNDuleUc9OfMZdaX5FRvqt+ggOnKFxYVN+T5fsDovxR58OfJORP/E9svpczxvPpjas+ACfF0NyM9+c4eFRxF/kCbGr3sEnn4Az+YfIMJCcaoZ+OLf3yDw2ET2F4M8uy+10AR8++eSTpXF59uzZcs4GoBhz4Msht6tF6JCeq47pUE3hj3/wMXVF1vbeo+euff/995d8rBmle/lbI+MP/vz4xz9erg2//ch8o2M0/qGDHfx5c019Ghd4rI7hH2S7JWk+bIB4PJVfyAPwED7yd35biZ7hpJjIRm8luQhmup6/2bySY2tsn41emcaFEhVlnJlyU8R2Mk4ClNwkQfOOxjEeY9tFBMbmO4I4riCSfCVtTYR1FLmKBjuBdhgkODvtDgWfHQnJGV9kcHifz2Qwl4RmXgWlRoBOFNaSIeczTkEhkfuePvHEmc2leDJWs0RmcyvGNU6SfvTFmd0BUVSkwTGHOyWuB1zZOTSPQkXhoHAnn1vKgE3CEGhAS+BI2NbAv4AHPt5bhy4EmHWsSVfkABSxOTIf/bkrFRkAYm9c8KaAV5zQDTK34MW/hKDxoBPzSNLk02yRx46Sgs13eFU404MjNnLQq+vxrilT3JGF7s1PH2TBHz9gM2N9pyngKz4fQWTmz5IaUJuRcRosawPdEcUPzCcBiasR0emtGhff07viFFg7R69iahbb9yTrO/ijeJjxRDaJhp/zlRnf/MdGhGI7c49IzIp/tqP/0Tjf0ZUdNHiogBPnuVPNd3Od1+rPYgx+KtjZ1uM/fNdGgkTJDhocRZy58Zxn210PS2zgSKTiR6OAz5BxwXNjXG9nPHgnRuGHzR/xZLNFvMIVzZhiuvqD+YMF/Yhs5hbX+MGzwuOXv/zlovM6tpJr2MwGBky6RPAbVnnt8yDfmQMu567oiOAXvCEzneHrsSi6w4cijS/QOYyEC/FTFBw4Q+PCL+UC+R3OdtuyK97htCKNP1byWZzwMdgjd8UP+Y9rb20X/GrO5XuxB181BBoBOEn/MNzBNv3QJB9xmAu+ufPhbg8cw4MGSoyKP3dWxRP99PVtIDuXDYIQ/cF2OtaIaBDpWmy4zqv5coeFLWEK3LMpwq7yuI1U+jA++qh6CR97D3OZ0zrsYeMTplrb3W3ypVa6JZmfHjWJ9KW+UFvwB/6hZumU3Epn2eytBDvVbuzMrvLKrf17D70SjUuKQk5rt2wG5r4HNpIHh2csANUdyTgFdOYz7ggjWYeD23mwO0AWAM9ZOLadBbsFkqyCXjEtSSkSyI8Hc3R+gSiglBAEt+tcL/kqFuwEKiYAnPcaC+CqEKcHgGsdcisIJB7OmyIBeChocrs8pFkQjAIBD5K+uRXECkcAInA8+oQXhZsmQMKwJn6M0WBIDhK23TrXKELoxKNc7jbgm54kHA0akMjz6fRlBzaNg/MKHjqJffGtwACCKTDI5ntFvHmMM4diyE69nWI84ZuuFGf0a37Fh+fz+R6eJTj+yVbdPiHrGW8MYMmdGWCnKGN/4KEIokdFoeeFgTZejvBBc/AltlREpBHsFN0YRwf8aDaOzvCtCaOLnuRdd2TjIhmwobUkSfwBZ8UwPukXD0foay/hg44l1UuPG8EbBTc/NKbrKXrmg2J2VtSG+JkEKi7E8kjvvhOLGnf+J8boeLR+p/gR3NCwsoNNE/+A7t13310eDRGr5iU3m4hFh+vs3mlMrOdahzEh8QGHJFtymwe+wTZrk49c4oeP8j07u2+99dZSxMAlG0Gwx/XGkynHjPgqnsQ4X7OTDF+ChfVa3/FBWJrNn0tkXjgK8/nvzD/5sQLBeNT5tY71yJzNrMcmPMYmcgjMJCcch7t8AB6z6Rl+48I/+KADX534KBlgcTb8QvE/dmJ7m4pyhjgXw3ITm1zys6MIL/IHPfNTj2op1tUWZORHo0ND4fxRh4bF71f9wRqbWPjw2xbFs1rHOX8KW+FsPB7ghY0xjYecb2PTHa4QHFJHyINqBXOoI1xvI4MMPj9//nx5OkCeNIdHuGGAvO67/PNN17mmHl0v/ejjLx3Ghy/614w5YJc6R6zO8P9I4nfWUVepkWAFnmwO07m6qvunz+o4/kvnnU/n+bc8zp7mnOHXGejUjQvFU56iEiACmpr8KhnLMAIH4EhmrvV9NSACZApf62Unfi9ZSyLi3J4/tFupWLfjn4LhkiN0HkO+d61iRvGsYeF0Eq+gljDidJxYMNOhXUw61WRwRuOAMaCWXM2LH0mS3gSkxkFBYT5E75o71+Ihuz5s4LPCSWLQoUtivpewvbcbywYAy+1Vga0gBcAaS4FhDtcKeHx4lSDwq6GhP/PhG0C4M4M3fNiRtB4Z6EOBpUgTmLUpc60CRSPlO42tnZI0jOZ0ixdfwI/ejNOAaVzMjTcFDJC2O+vzzF6V+hj8WFMRq+AiH1n8MO7S7xW2knXIp1jkj+zru05k4/+SBn2Kiz4OP/RBp5J4muA6jj6OalzEZJIQ31LMStjsMZLhsYnNxBMf63oJ+S6bALBgpCM61NTQr1juiaVT9OUOiEKyz+kz3vi1wgcWjNYdkXF4Znd8udYcmhV/yfD3f//3l9/YaSgTu8a6TryJbY0cnzFPHrViw/DgO3iVx+bgmUZIYWM935nLIfbgh8dEvva1ry13kOC3JtZYa1vHsVZGRD+wSl6xXr/e2uIdXzPbVsKHWFOo5VGwET/0YMda7I3G+My28J0u6fdshG++bKMOLsg7Gi12OUPjwjf9FkNsjPQnjyrysqFViWzyIPyB0fKu+JWDyciv70n8RBzLjQp3hadNAYf8ToZ8rodxRxxiEnbJye66iBf5WH6EV+oNj3r5zasNQXjtGg0ff9Bk0J2aSB0n7uiYj6uVFP42DM1JPo2hOfi+4twfADC3a9UQfg+HD7lC7mRnNZ9rsnaOyHCUfujaq3XUNvIeDJa/yXMvgkX8wgaozXcxZ3NPvQlXZxsedKuZHOUDeGczyG+nbSg9hHePSadoXALUlAYkolCKV/wyCHBMM9LJd4BGkZYdd4nIPDmQ+QQNwBIoCaAjyFxutXFkSZg8HKEm9WvIPHYdPeup2DWfuRTgkrmkTn7rC3I7onSAB+tLpLpxTYYxgsx85qBP7zUPChGHJC0grGNtegcsmhiFqbsG5KFj/LgWqHhvPd8bB9wEhzkUYnZ97ZwAH7aKXhSpkrzPigOBRwbzSi5ppOzqSUbmkzw8e+y9NYxTKNkpcNtYg2GXOE2P5kQRIOFozlzvOvqwtqTg+d3oiG7SuMR2/InO/ShPssi4reQa9iIvHvDIhh6BAdTW2UvWwDfb80c7MKPYCS8aFmAnudD3aJyD/iRzzRa+Q3gWtxog464h19GpxxM9v83n8ExHKSrPSIo3iVtCCOZUYgc65Xdk47Mj8r07CxJRYvwSOW898cj3rVPJHBIr7MRjP3+J6JrexbziQ5ENfxRv7lD4DGNsFChmbBYkVmGHGOfLiR1xreDIM+6+l2jFdBoCspgvGx58UnFg48RGg7EaRH/xBnbzaXdgFDoKIuPxvMVPrCnB17u3lfijteh47bzwhj/YNBj5A8InWbIJNCLXKUjo1pxnI/zRSXAMNsjffo+gGXxsUguIixGe0acnHxTdFcdCZLGRxxfZXk6UTzye5NyWWDqC8OuOvMb9448/fvn3f//3y6NZ7tZ7vfVhPb9F/fa3v71sXPiLYjb7xIb3Hrd8++23X37wwQdLDWG838G4I+NOjVydDUq+IXbpUdyqYWCnPOWz3O/3I/xIrP/1X//18tfL3nzzzZd/8id/8vKjjz5axsEhv2955513Fr40LzN94Oeow3zWceCPPeCTmuCecSr+xJ7aBrZaXxOnlpCf2Wbk2/AGpuRJo0rmZCO4Cr9H2HUWOk3jQqG1cQE2FEvBGo3caRgRA+m+gRXDJfH3A4hZQyG/NZnPyLyA286M4pgDzRLWVsKfxkIhrmggZ+ZN46IpIC/9eGxEQUBfZMSTuyUSpKLJrqm5OCcgVExJ2oAFiNvdVjwoFhQ95rQzYTdNQ2F+AO57haXP5tBQACQFB0DKLmbswLbWVtCEX8TGGhf80CGbCKoUeeQThGzqTojinkwaDzsfvlNse0zONYo+srtGMPMJDRUdOIfn/KZDY0en/I3swNA5+safhsat6/hI7CygFfm1ibyW/uva//J1RZ2mkr6PAkD80RE9amrx35M4sh4dk1+hOhtnPnoDkOZzHRn4+xGNi3h358LOIh7uXSBsIfzijz/yf/4ykp1uxJp4lGTi+5XoWtzAD7t5a3VofXcsFBHWr/ioIdBcJA5H9qxkLjo3HwxTtNkNFbN8UhPs7oMm3y62QkRcafCN1UAo9mCITQoy8w/rsqtYU0hqbGCXxAoTjMnaMIcsYlocwyGxzOdgBLyxs8rX4JBmw2aDWFf4KEbx5vs0SJeInvHnbpB1Es+xLZ3CR69ryXViDmbCwxEP7ESfmj5yzexNDrvL3bZnJDLIT3R5hsZFjkhjWPXL3vIXf8lvsioZq2HWnPNh8/ANPieWHvKpWxAZxJUjMWdTgQ95veVhLfGnbtIseEzNX/Piu+6QaBjo0nfiVV7k02IAfshpYlaOhUdwQI4Xt3QsjlOfyNdyvJije/J5Nc56CmqNjadq/DZWI+mcOzJw2Lp4dl2OLs8RR52bPWD7Pf98eSU1rU0QGyXwjw3gtZzktfsrf4fx8Ekt1kkegsGuneHSGei0jYskohP3PKVkPku8DOO2lqQm4Rrn+nr4zjiJUTAJrtl8W8m81lUc4uMo5zUvxxKkirmaVFEaFwABWAS7XUnn8aDABih5BMx5STA7fc4BYo7PWT1CBmAkfOvaZdII2gUGPoBHYQs8FRW5q2M9DYeixNze2+V1XZIC3hXm5qsNo2s1LmQRTK7Hr/fGAAWNFJ9wN0thpygCfAIVH/hSdCvUAB++FEdAhdx2PxXU7hxp2vgTfQpsMrKdoon/uR4IkR8Ivnjx4v/zE+vgBZ/GRJa9FH/3WB2ejiI6dss8jz1Yp5Mx5ABW9Cu5zPzYuDSxko/52Hlv44LYwW4cXzo7kTONlqTLf0ZEJrHA1+ip68dn32umxbNYXEvhAW7asKg8iAlxMnqMbESKufw4M7YUB3gTA4po8cY3rMNGGhlkfv4CXxUz+VPjPmezxTXu2mhWFIIwgqzmFq8Sv8dRPCevGOFbdOe8ua2locUPXdoJ9725+SR88b1Gy2OPzsOaKvtMD7DSH8nIfOEXxly6KzIi12pY8JLmfkTiBgbR+SwXkZ9tyQOnZ/yfhcgKb+WIxyYNBz7ERyX+w8+i96rT2E7zxc/lA7pnR3Lx05mtbkmKezzJk9avB55vedCXxtnGnxgV2+42iFN3WjwW6d8c8FN6tfHFD3In2KaoHJ4NWDneJofcKoeoWeAN/LMhKPe5DkaIa/Oq2WyOWFez8p3vfGd57wkLhbqcQT/sg+dOXaY9RyffwQ+bJ3xnNOaWBEfkY36ax2/pTG2npoOxnSfX8G126fUAGdiHPc5Mp2tcFNSChQMraj1DC3xGDsGxBbViT1EG6I3rB+MYpzCQOI07gsyruFYU2iVMot1LeNYUCGiBLWmRNfIgIMZJyZ07DopqRH90Zoygdt5t1WfPni3Fg6KEY3JqIFMbF3Ygh6QPsBUybtv6k4N2NhT2bOKa6NF69ODPC+bOB6ChH/w6T//msoNDFuScnVvNlwJQsZNiyTnXa0DcDbEu0PTDYM/TSkyA1M4HGfAtGHPnx7WAjL0VH3aL/KgP2CmA3MmjYzLgx6E4S5Oj4PNXxfBSiX01VtYxzrqxyR4yL5uxV5qn6G8vkdEuIvtoTpLwOlkfCIsT42KHSj6bT2EpWdCT68Ttb1PjEp9XKHvtfoLogm40y/Q60o3vxK3kLK5mhe6IXMtvJS5NfTYS+I1NFIUE7OjE/sY4x86SniIa1mg2FCr82pjgmTXEp1hlJ42E5t04PODb7qjvNHKwRNzlDqrdbcVi7szAenegNFx0yHfcEVXwKHDENZ2amw7hhMbeZ404zLdmdOq9JkncwC0FEp/UINmoIqsxkacS/dvYyR0gY+CUJowurLmF6C0bR3ByROYUO4q3iu+VfAdr5EGNaD9/NiK3nHXWxoXf2gSlc/7V9em8HMInbUqJW/GgaYGfzt/TBtbiG+JHPj0q12wh8SAu5TrxZ2NLrraZ6YkEeoYLcpY8bRxd+T2cuJODjYEH4sHjVvKqx8pshPosH4tTxbbcraZRK9C9jVu1gyLbOuJUXGp84IzvsumjSRrF0a2JXeAXO9WnYu5B5OWX1oa3MIUuYKRYZItRDcO/1Wlsysahp8ZlAzE040tmihYJOE1LwKI7A2Mort1alKQZkEEyNgcwBVJ+h2E3YJS4tpJ5zWNegaUIZvCj5gZWAlnwSlzmrbIhxRJgtb7CQ9FNVnoRPIIdCBuj4fDXePwneLsjdi0UKnY1JETXAnk7w9YD7nYbXeuvh3z3u999+eUvf/nlhx9+uDQACiLXcn5Ob10JQnHjMSJFBlv6Hr94mjUuAA7guA2sWY0M5iUj3fIJPHvG9Ytf/OLSvJApOzbWZQtABjg1OwolQKi4zrXk/+EPf/jyH//xH5fgBsCKJ/qyrsOaGlGNiQbJdyOyht0ea/DRI4he+BHbAxWgH13tJfqkZ3JfulNEFsUj0BOHs3F0xkf4ggaZjX6bGhf6tFmhCK9xWYkfK+b9MQoxNiJxLcHDp2DdFjKe76eRtyb88EhXTUrWcY4/W0eMKxwkKToXLx3DzO0zH1C8uJupyDePHVSPe4lZa5hToZFmyXewRAIUS+72wiqPlogrjyO4s6JIJL8C37xwRFHkOmubB3bIB3ToO3GhgPJ94jM2cEQHYpuPutaOqHXM7xoyGYusAUM09vhwDnaKgZn/P0RiTME7e/QvcuQxVTyMxvle0aYwcc1ozFmIXumMTR+beuNCb/xHUywm+WsnTTn+FX2ut5knj8kpbHVvok9xAGMe43FB/gYT1AGaergsxrNpJX+Kd8WyXKhZ5/N+1wJT6Iz+xQB880+lxa1mxaNfGhi/IzUeRopPdSA8gzU2KuUi9tKwWNdmrTsLNmPFjVyZGsAYNr42Zq8l68ENtShcrNhyD7IWvOHbNm40ueoHm0vswgaVH+/5knwBw1NjoqfGZQNRZBoXO2kKUkWB7+IEVfFI0yLRKMYuJXwFpuf58hjUEYQniZHROYgiLobfS2TRQAAGMgKvJKyqBwHiB3CCBYAAacnZIw8KAruY5uGAGhs/WnfnRGOhC9cQaF7sXgAUP4Jz+POCChqAoCHk/G7L2iGxllvouYtB/4oM+jUXsHKdsR6VSDIWJKPGhVyaCneDgI8iwzzuCil0zG/n1K6wIoy8/hyjNQUd2wM4AYtvRZEf9pEn/6eFH+EXMLq1TUbXATrFid0eOtNA0bmkS2/mMXbmM2QCVnZ+7PBWO+0h1wNuMYBPheLeORHe+Cx9Ss7sMJrXd/xZscReEsFonPmMk1QleXcM+MQeXvn+q9C4kJ1f8FPxReaR3JKAYl0cjQol1/Aju4Z8LnGxlYIZbMteCnYxGZ908FWbFMbhG18KAHIYhw/jOuFJA6t4yx0h46wpZq3JDxyKcI2889GJeRUw4g2uvPHGGy+/973vLQUIPtMs5cALbBKL1sY334JZziE8/Md//NdjlTP/zNr45PdwC07AFHLk8dKMlWvymKZrbL54fA5P15D5YImGA7+dwh/fUOyRcySHMQpD2H5knrkFsc9ZGxe+lCak65HejRMbcI9/KKzFh/EK7/jePQlPcrdNQvyO/ONWZC16kpPldBsX8qk4hh8KZHEtf2oo5FjvNXt+tO4PrNhclJf9HuW9995b/gCOH7PbpJSXvZezPc3hR/9++O63Mg6Pomlc5H0xq2GyMeaurXU1OXAhj0HbPGM/9YDYuzfxDzxqqFL33JP+CxP/Y9EVrJRTfHbH229fEgch/KkzjKPD8AyH5A41171l2EKnalzsmHNYzUASUj0QANIhCiY7eRQ9CmqAr2nRXHBmyegI0DeHgNbVKtoE8LUFRyc8cjo7HAp9a0W2HD4bJ0j8mUE7lwpMBZ8iSrNDJw46dJdJEQBUNAWSJP3q0AGAMXZb3eoF9HZ2FBOSvfl07ACBXchsDjsgkr5xbOEuBbAHchKsYl7hrThgB0HdGxffWddf6XIoVoGSYMNbZMCjaxU97G03wDwSJN1nnOIKyJMVP65zWE9BhS9AZ448Q+88ObzyFQFMRrrQ4AFO/ND3iMimgVTwkB84dD+8huiG7gBynpM/wnfxxj/YkK1mTZG16C2P+fCFTq5jR+BHr3xR0t8jPz2/Co0Le4hTSSLF74gUnBpw2DayHzvzVbuQ9H2tjfmhWBIXiiz24Md4ExeSvybdZoYxCjI+T46HsAuPHt3wmAc/MJ6NHb7nS3wVbtgcyN08sQBbJEDNDJuS019GUtjALUnThhKc4Ufs74Ap4p1uNBLew9z4lvl9toFBlks+5xy9hh/xjBf4QUdiXuw7JyZghU0RGFDX3Er0ZgfafJdyRHb5yTGyv+/oRmzZRGKzsxKZz9i4iA965qvwuuswOK4w5x/uSuc3LvIFn7w2Nq8la/IfhbD8dm8ir7xrU88GgzrKRppGjk74M3urBcS1GIYt8oBXxTJ/lb/4rvrB5qn3Yt84d0jM5ZyawSaI2HOdRsk4Y+BZYgkv7Kf20MjYPGEntoMVMAXuXhu31xJ9wUd+504Q3dyTrM9n+K56jU7oJ42mTZhO9KgOYi/5wBx8zQbPU+OykrJDZadcEpwBhWSsmKq3T0cKZgDzKWYlplni2ELWsZ5CQGEuUI4icwtOwaogmDk+Oaxrx1TzwvlcWw+ET2Di2Xe7ekBB4CsGJGVNIh2Zz6vPdjo5v+8UPBxaM6EoclsXWNCp5DQqZhG7sZG1NEmSP1nSuCiczY8PhbHiITKMSMABb8Eo+QAx8mtc8SGpW5P/2FHzfXZc8OizdehC8SUxaTIUQ4qWSpWH2JmeZ0HsO7KZL/4w89utZB5FKODG81FAGN1r5IAbOTuRKz5Afr4xkss43ytW7ZJJaiM9rSW+9yo0LkCeXfgFPc1IEiPPzL/FhuTCDnuKZIQPGzTudHr8gu+aWww6FOUK6K3E72Ct5qPzJ/ZsFEh+dlrdcRP7YhxuKCKsrYCUQOlCc0Mv4l6xrpARx4pK+KNpoYskXviNd76R9b36jCdYtjXm6Aqm4RkmWB/vdrbhpUfL6O+SbR8iPCqayZrNhxHBXlhp7VEsIvIpBOGnBuusRMYzNi4pcj12NIpF34kbOVKe4Qvyjg0phfljEOy18239PX54DSW+bHTwS3qD7eoIeU58J+bwpvkQ5/TnTpV87bP4F9e+83io35d61Bw+eWRbQZ3/fO9ui406413Lj+R6T3vABfYJBjngkvwIH+AIPIYvagB4sRdPryU1AExxB+jeBD/4i+aFTugfNmvuNJ4jP2JHuGKM658al40EYCQ7RQsFd6X5zBkFD8MwEEX3cQwh6HS9kqoENRq3lVxvHo6ZW2nWOYIApx0fdzc0I+YdJWPjFJ0cK7f/ZuMEjnF2L4zT7AEfRT3dARdOTaeKBaADoHxW8EumAEnTp0HMnwa2M6xY0BAoUkZ2chgHhMwj2Qok1wFkwCc5KFqsPQsohZFiTNIRUHZ2PWKigXEdMPOMLZ4lJ8FnTu/pgD/xAYU1fQBDzQp92D3S0ND7qCkgA11JZNaQyNi/Ez7pSyFmHPlG464hMmhm+Tu/G8XFVnK9efgA/xAn9NHnJRe9iB86p6+Rvxvn+vzAcg9/5n8VGhcJUwHO70bxR7/ilE743gx/nBOHHns4wrZ8xeMZniFXYHiElL+LRb68tfnFtybNY1N47cReChsyWFeBLgmKOQWEDRjxQE+wwjy+c427vrDF9+Ja86/QhIHOeZTE45vuPttB7bzjjY6tnTu7a4mejacTuIAnWMGmdKe4MudeeyCywgWxPJqPXJokhQVeRliI4JSCSAN4ViLjmRoXfMBmOlMMa9y7DfAsl+c3Gmxlo4hf8UHn70nxTXlMPFh/hDG3JHHtTqj8L8bgvyc3+B8dOR894k2eFjt+x0LvcoU7Jl7VKfKCxsSj3moQd2g0LOoose6Oi2Ymd1xckzlsmvjLZfBMHRKyvvh1jdiFyXn8la3dEX8M28FLNQveq57uQWwBa+VQtsOD2oEN1UruIo54gnXsAePZkl1s9N6T9610msaFQhWenK4rzGfBrNiSVEa3e5FxEgFD2TXjxIqqI8j6CgD/bEii8XmvYV0vUZlXkQMoJfKevIzjlIp1AGscQO7rZ5xGAThkPvQ//jO4gQS+6VpgG0NXnDV/VcwaGhg7GL43nwIDsHiPN8Fpt8QOyAxY2cE4dzZSQNkB0ABYR0CR27iRHOwmADU/Gh3jFEEJRHLg1Zx2WKyVOy7k1wDaPU3jai1JVZLCr+ZGM6fQMs/InmSlZ3oDRt6PZDUOYAAHB0DtNryW6IH+JA3AfdS8fABgeWxOU9TtEF9iX4V1nv/v6xtnLvx92n/jQjby80kF6SwxksNdKncN+JlrRrq1qSAxi7MjSGHmN2MSliKY72TtHFvI9eRgV/7diQxiwt22/AZOAy/G63ixpTlIzPM7ceI7Oow+xCFsV7B5TPP9999fdl3Fc/e7rE3HeKTza8nc+MUXeyjUYM5WfY2ITGIMPtFDJ3LQM6yiD3E4IjgOqxR20dnZCF9nalxseIkt/PATuFyJDvmQx4JhG5xVWItLxZvx7HNP4otiQKwc9aTIFqITeRYGawY0BWKCbmxC8uPqe/TDN533exV3V/Kb0RxqB7HsP7JrXjwm5i6Kfybpd7d/+qd/urz325dcq5D26ju/WYVBqWUqwTk1BuzDA1vDP/lITAf/7kXiV90J3/B2b/8hq41ddZFGBX7msT0bx6npKvFzG5g2ctnehibb35v3LfRKNC6cgWNKAArgUQJAnFRRq/NXuLruKOVzQrt/ggJAHzEvfgGBXYPcmRgBVcYpoAGruw+j9X3nHKBRaAvcjEvjEp3QoQbCLqiGEDAoGjUtmkMOnrG1cUGuldizY98TAjLW9fTmtwDkk5ytCZzY05gRqEjkdr0CSBlnR0BRYU6f6SWPj7ELPZJR0PnO3bnYCr/AzDnXOhQVEi250zB2flxLFxIJsFWI9jHhhe7tDEmS3h9B5sYb/Tkk2r7+NWQOMWcnTdM8u/NEfvZVqAND+u4+6jp6VCjs4e3sjQu/549knd2BQ+KFr81ut9Op2LRZoNDn70eQjQSPTvBRNrH2HnuIGX/xBy6Rtc/FD/gGXBbX3hvX44jPejRR7OW85kRRRA/h0/f4FrNijixpmuisk+9swLCHhuhasra58KZ5gTGepY8cVZatRBYbJHbQxXGfK3Kznef0jR+RMfSn+GAXn89GeD9T42LXWTMob9N992HvbXYZm5169YVNGndpjL83yRs2RjwNMYqlW5J12JAvsiFdaBw85i0W5dOeI/BnQ83dDhtcYtUcxjnIQBYbye7K+l2aH+t7dMxvW93htBmcuyW5JjggJ4hJtYC838l52BO89XhbnlJwF9f5e1LiWf0F27u+7kHySTZ0bNz6nSXssPGRmqj6VfKRO+XyvJ8rGH9GjAmdunHxyokVC7pBxeksyVO+poXjCjxJdJTsthLjKUSAoCSqgThiXnNwII8iSVhk5OSRPWQcWRR0ktalggk406HHDvKISiiNizVDnBVAKbbpTbD78btGJk5rffK/ePF//xkjHs1jl0rRCzBmvLMr4MrOiyByG3IEKLE34PG8q3nruNq4hPiDR1TwoTFyi5+vuAuTQsHRGxeEZ3p3nbtT5hoFq3F8y+1wgY2n6CJkTuOAuLtVwNR8fdw1hCfzKugVe6MC6Boyr6KUriRvxZ/v6tze+07xCQhTGFc9kfu3oXFhT/Gl2LlUTGu6U2COiI9L8nQusdDfHrKmgsd8EhM97vUP19vEYNf4e58TBti48HhN94lKChLxyYczh+80M/zP3H1+OtFMk4muZphHx3StQO2+ew3BWlifHLJ3TnKJK5tBdjNHc/kOxiruOm6HjGEHWKVZm+njMYk/nKVxUaTZwMvjibFj9E9/Yk9O4F9yI/wS3zYURhtUtyRr4UmxjXd+PYqLW5L15X7re6pETWCzTix4koFO8BPyXpwq0sUM+9d48V58i084obbzyJimxV8Rs4GpmYH3couNR7mtroHEoRzFNh3bjHWN5tTjZbBZnabp8lRINjnvTdbFU54OuSdZD46oK9mRbt0BcrdWTNgUMqbqhR2NZQuNpBqs2+FMdOrGheIAusBwzHaxjWcMxrGzxdF9d4TDmsuOmbkl3m7wa0mQKzLcjZDER2SdOJQguPTcIZAADgp7u0V0V3UwalwQ+eyk+/O/njXNnaqQ6+m2Ni4h1yrQBYhdkZmjsxu7+P8pGrU6fyW6lUQ8DudOQNf1qHFx3nx2GPy1ol//+tdLsROAy/V5VAygVXJeg6RR6rJXol+NGt7o+ZIMdqiAPhm6vq8hPFqf3PxFnPh8FPFryUXyUZxHZ50kU0Wqu361eaKL34bGhZ7EikJoRGQXEyl+NDcjIqfdOPHY4/Qa4mM2B8QYH+GDewgveFQka/TNP+LRJo7CDz7xgchSyXfw0x0WPhNyrU2G3EEfXeszHSmc8DOi4LP411yLi8zjdXbMCC+wDB7w945B15A5YKRNhxlGkoPP5E75iPBh48QG0wx/HpP4yVkaF/q2UaYIH92llpMU45pz+G+zyeM0Nr3koHsTv1Dr4EmBGX/f63tryBoOa1pfrPIzd/jhiY1NtUcnfu33Z3KS3Fx59V4uUR+Yz516TaT8b8PCo2Din6xqPrkV3iiYgzchvu4OKH6sZ91KYl4NhU/zsD2Mls/gxxExvJXIzp/cxdDwPQbRLayge/k9TbHc03XM/7L55d9GyCdH1hhH06kbF2AO7LN72R02xDEUTe40CP446h5ndS3n85ygYpXxjwgA13MSAWrHQZPBiToZw3EUwYJcgp8VlfgCtu6WCFy6NC4HmjUurnXXQWOkG+93lAKo9VGxkM90L+Gy24w/awISBakdiFlA4EOguYNiLvPX+WaNi3EaE82Cx9c6iKJZ44Loi/4AsCTW1w0ZZ7cTD4CyFkkhn/EOJACxcUf5jXnsEnoMDihZ5wgyr6KcXGJQohjx63vj2IjNU6x6/bQ3LuTiG//9v/+3xZdGxB/ghFgIXowIrsEUTcHM17aQhkBxYLeRDs25h8xhU0Wh4HHKWbzCGrIq9BMLVRbfiXeNrrjhJyGYIBY9loP/USwhOqcrxfzoPB27C62Bmt213kLVnzVMR8SuOfK4y0wO8tsg8viMGJutCYuNgYF7ZT2a2PQsjYsNFn8J012uGof0Rdf8SoGrIJcz+LtaQxzNGsdbElvaOEujtTeGtxCdiHmxqoETlxo4d45hgI2BulEVkvvdSZXj2L6f56PsQLdqOPPI7fRO/35bRP9yicM5dx3hY407upDrFNzuHtQNEGQcm8E/zYpm1BpizkaTWot8nb9bEh/Dk8ZFA3PPtUPsw46eJtFAeloCDtOxerriMf7oWcPym9/8ZtmMuqcPbqVTNi5JHhK7R4uyizgyvoBRJCrajXOtw9hrncX11lPM+iGZABgF5jVkXmDqNqbET87wW8k4zuUZc4E3G+d749x+NS4FQD3QrHExp07bbVoB34th52eNSwjQKmYlrFExTRayAmVFX58Hz3jQeNpxDEhW/tGocUHGAAn+M9uxJIOkyqdGRC8AhhyX/I2f1h/Lz8YBX0DNn8lMxr1kHXzSpR3h7D6N1t9KbMKP3CYWTxXUKpFDMWt9d57oA0+f5saFbtiT3RU2s4aRzjyuIFnOYoWuPAJFf3x+r+2so+DQtCsgZutuIfEn8YuH4E4lPPs+P4YVb77LETLGHO5Wj+6G0IXfI842ARAd0RWd8fVOrsGf3wXkUeLRPFvIfDYIPN7FJ/fq1PXkp4fodETkM8bdI9eM5OCHGio7o6M7CY9JwYEzNC71d56V6FVBJ9/lzihft4EId+T6kZ/dkqzHrv4ggDsL9yY+JOYVtApbDRzdyZV+4+Icn42veRWv4k3OTiNRz/MFPwoXl/KJ5lC+NC+MFNfm9/i1NZObFdr8exTH8qiNjmxQVGJXGxjyrU1SmwTm0wzBY+vtjeOtREd49fsdPufzPYlf2QzxBxPUCvQAfzQy8sWoTvJonztjdHdvfW2hUzYuDKxLlzjsXNagqUSxdgf8mAjwZFw9riG8ABB/dUsQCEJrXTtfyPWaFrdK8xuIWcJWjNhZ52TWn60NaN0tcWv3UtJe27gA0DqH8w81Lr4XBByeDTu/AmjWuBiHb+uzvaJ4ts6scUFsZsfFre4AaaWHGhdrhg+JzPiRLsOvXWTJUfEwIuP4ozGKjCOKVGQONlLg281i/6MAkZ006xKoZDKzA7ncCZR0xB/da+Q+rY0LvfA5cZsd3JGc/MJdOxsIMz1IYB5pkMwvxfUaci3b2xmViMy9Z74Q7BMrfIEPmLPOS35+pwgha+To42CCOIEr/RwyD3yzKznzYb5GV+66uBvc5wiJ63pHYzZuDbnWPPCXbjtmbiXzkU9M8W1yjIg+6N66kaOT7/hZ/QMnZyF6OkvjYnfZJgNMqUTHcrpYhmHuGKgd8gjNLLZvSWztNxziiW3vTeT1WxZ+J47kLDpScGcTKziA6AgOuhsD/3t8OK9+sQloU0JucDdJw8I/jDenPOazx8fYSsOkPrBRoe7rdnBd7tjkt3GVzKf+MJdCXYzgjzzuJDxGrLCnnIanUV1yS6I/elYXaTA1LWzGNvDUXZVO6hT53x2xo+qKW9DpGhdJjNIUqLVp6OT7FE8e56LkBFc9tpDx5smjB5IqUKnzXkvmlbAUdwBKJ1x5DvlO0eyWpyJ8FKAIOCjkOaJEIWhdO6OHGhc7T9c2LsjaKe7sxJgnhNdZ42Kc8R6rsst5aY29jYvGaNa4hIzTELCTeUY2x6O7E+ab/ZnGkEZMUYkvheURVH0pO16+2+OfyPWA1k4Z2RSubNfJOHYTH3bUjBO3dLGHh7M2LsBfbPALemb/LqcxkqQdwUsNhITqUR96Hs2zhVwPn9hKYqa/PfO5Fj4oMOBK5OhzGgNz81hLfK+O8514lSBH8YrwD3s8npINqk7moSt/jQiGdF5CYkChpTmIbq8la+AFVsAzPB5Bmj2F2uy3iniGF3BagdaxOoQ3eOKxGfE508m9Cb9naVzgok2o2rjwJbbkJ35/6LycoaCTV+Tae+vSeuoYm6RqghHe3pKsxy/93gQ2KbDlFXc9NH81t0Q34kuzwf/o0/mQ93SuWTCXHKHeiYxeg3v8WEzzGRsE+U2dO47Jv31ueUezCaPqOeQzrLFRqHb0mz8bJ/IZPJPb9+DCNUROmxHyZP7oUfR4DyKvO12aPc2jV5vmmhY26k+DwHxPL8Goe+tqC52mcREMGhe79pIFMBEAcfJOHh0ANpzStUeQtYAXh3fMdsa2Ev6BOqB0u04AxYFzZBxZ7L54JlTinxWOHEyCSEA+lMBu3bggPAAeO8p2YjPe96PGxToStEBhzw5Ene7RuOBNUwIc6XfEE77JZNfJLhW7znRjrIRkviMLDXPQByACzrXB3kOu5wNiUQMhHmZE53xVccoumu0965vvbI0LecSM35ldKpwV8O62ZLNlRK5lr/rbiT364pt4si7d7ZkLmU+cwL48BjgiGCWWJcAR4cOYxCN/mpE1zAPHxO1IBgUObIKJM4wI9miCLt2Z2UIwVtxqSI+Yj4/Qh6KPj4zmJEfdVR4RffAzeHaUrEcQmc7cuHgPr/iaQpn+vPIZmy/0OsPxWxC70ZmaII3qvWxpHQcf55MwSeMCSxS4+SMFFadywIjcqepEhzYPcjeebLCEzt1RqbmZruUtucZj1XBAc6PQN56Pm6+Sa6zLZvgY2UtTY+NZTaFhUIso0vP0yr2JzHxRbuv11T0omEIHNpz8+XobRRpLWFRjRC341LhsIImOIv2DIgUekKe4buQkV0bg8L0r30MMaG2AduTuB9k0Yp7zVHQHACr5LIjdmnWr9NJjAJwfuCgY86jQQ8Fwj8bFda73w3F3GfKnmx29ccGH4PB4R5LGQzLco3FB9Gktdpg9gkcGPBvnlnj+TPKIzJc/vXnpT3pvJeuTiw7FAp0+ZKOHiJzmUDR53p7PSG4z4jt25j7++ONl7EM2vET0d7bGBU92p+zS8u0ZeSTCM+JpIDvFpzzWyWaJ2Wv15TpxZZdSwt9rd8Sf7IjyZzbHYyfrsLkNJrE8oviP3yfmd2AzSmyQwyMMI30YQ2c2RGwWjGR1Hfsc+V+fxanf8WjkYq89RL+KKBsNcHumF/rQAMDFEZGfPtzhtHE3w517E/w5c+OikFUYK9rwCDNhFj2y9RE23kJ4kw9s+OSPYNxrfetYT6wo7r3SjbpCg+GOBwzga8ZmvLpM86dOGuEhOTQt8JBscr7GRUMEE7rPm5+fJzdrmOQAj+3J5X3zGB9spQlhY3jbdeYaeJLH09SKmih/DAB29fG3psSru1pi+xIe3opgNTuzhTpJQ6nWtEGingqm0s/To2IbiDMCGsHD8UbGpVyOqjvnjILEuD2O6NoEg90Xz0VKjkeACH4lK07gv0orXHzulHF2PPwxAM492hnAD3ntOHjcxK3H0bgRrWlcFED0UOV2XtCtaVwQvZlDMshz9z6ncXE3Aw+KFE0IcHF+zdyXGhdzpHHhI53IsLZxIb+korjQvLgGz1Uv3uPZOPZV2ALrGSjRix0kd2jq77H2kvXIpnkBREcUMfgyLz0qUCUR845sZBz/8Xs0jwTskckaZ2tcyMYH2GwWa/TiboDnxEfxjcgm2XsMIs0NXV2rL3q3ueLHs3Bwry+5Ho+KAXE0m88YOK2wGcUZ4us2QezKzuIhZB0FjR1Whc4Id+lKkaSJgP14GBHds4FkbN29OjGf2NeY20jC2x6KHPz70m9n6FV+U3TNcIJ8dMxelzYW7knkOWPjQn94k39sMMFpOAy3Pd2RH3sbt9dn1pJ15ELFpDsU1h/h662IL/Mb8ST3w3i5lW5gmdqi6gRvfM73Htt0bY1tY3wWoxpzOTp/nthjxJdyU+oFuKPAVji7Dn5oqHoM4N04ccSGPdaNV1+oueA2u2vwyYk36z0GabY0ZjDgnrZG/N/jkXK0+lYj6e4LnKmbs2rvp8ZlA1GcTl9xyajVEZHPAJ0DCjC7+aNxW8n1DkbUDFQQ20ucRTOi+/cM+ixRWU+AeT7ejpzPI8p8dnbtAveAvUSXGhd3KGrjUsn5LY0LfjKnrh5QaDgAkd8IADSffQ+stuyAPNS42EGzezUqqLY0LghPgJa/uVPi9nUF6hBZAZEiQsGk6ZwFvPkU5sblTtlewie+JD+7+QBnjZ3WkHkkV02RopJNu62iJzpiz7W2HFH0c6bGhT6BPBuPZKMjicBuGr+c2VSilcDrM945riH88HWFwqwg2EJ40gD9y7/8yyLPiPBKRn95Lr/R6/z7jB/NHqxa44vGS+qaIXgwusa8iiqbIe7ijAg/8oIxR8QXPmCJQtwjFXS+h8iAJzirmJrZDf5rbNzxhFsjffgOJimE3Ek4A8ktZ2xc6NMf+1G08Q9PKng0yhi4dlS+X0tsR1caU01xHgm8Jw9kljPgtjseGjmvNv7ojZ/iE08O7/m//KtW6joz3p0svy/RbORuvbiWF8g48mNkHgWzJhLeaqZgm3XUEP3a8CKO+JomyvqVH/yZAw+ednHXlO1tjJP7MQifNjjVbkdg9haiGz6nUVaruPNCtzCETWGIOuKpcdlIDKnwpMxOlE6pOkbFvQTWC/BryTxuIwqaPJ8+C7C1hF9zSPJxFE47mjcBD/Av/WUw4ziXpAzwZuNmdK/GBcVeihDAqCARrGxLViAC2GZ3oGZ0z8YFkZfd7JJrukbgW2X1qKOmJD9Y7hRdA1t6UShukX9G5rUmP6PjPKJ3BPEXBajCPLtmXQdk4L+fpsYldlXc5DcJI8KzRydsuuTx1k7mkoQVq3m8ync5riE7ihoq8107RyXxo2BQ3HUMCJFNjImj4GRf2/f8hD/QxxrexIpGAyZkh3VEZLUbPPsLQdYS+3hkj73FgfkcHn21Myn+18hziVwPxxQHXkdxSjY4IeZsZI3GmMcYhS/+6H0vb3sJD2drXPiyJpue5E1Yjke1BKx8jL/Mxt9tDvBlBeIROWALkZde3NHQWLCXeFEHaer4JV+qh/HGioNRfhN38MNdZU2iBoFsMC+/Vbnkn85pMNR3Nnc0QHAE1sBfMZDrvfqMTxuguXtZ5ycju8uznuqQv9VX3svRsw2SWxIduGNFxtnm0C2JfjQqfsMip2tS3aFme3pKrMAmddKohjkLvRKNCyfVrAiGrXcaLhHH5UgC+KE/BrCFzCGQOQYAVTSP5vSd4lVD4cjOSyfz2fkWzIpjCWsr3bNxCRmfpkxgaA49EiJwfb9Vz/duXEKulfiA8GzXlSyxEZC8ZCPgoEjLuL3+hugab/yDP9PBVnvNyLzuPLFjdmYq+cy+n6bGBWgratgIyM+KC7v//EqBOZLdd2whOThm/rOFzJnHhK7BghHxXX+AwOvIb6yJd81S/U1al1lB4M7SrOCekXlSQFmnz4t873EScT4b4zuFjDxCliOIje2Uaq7oZrTuFuIP8BYez/zBGmzsjkrH7BAbwDPxYqf0sQuNMzYuimw1gxhVCLsTyH9tnsl3bLHXnlvIWvRkMw9eHlXLbCF+owkRIwp5NuNHfptCV3IpnnLweU8SiGv1Ej+rPHsPK+UdTYocrWnRKMqHs7qmkjn4MF9mF3fEbKJopvJ7upF/587BqJmiW+c1W5opvzEWw/yT/DNMvyVZk1+qU47Akq3E3+k2OqE7d6A81gc36fnpjssGmjUuCQqPq1CscUcYm1EUkArpNAM9IK8lwGT3we3X/ixoJU4sUdtZy+91RiS5ATk7NKNd/zX0GI0LIqMg8Lyp/8hqHgXFNaDxWI0Lv1CQeRQL6NDhyE/IBHzZnU+x50hnvjdOsU/v5rtGt53wxN/IaVfJ+yPIvHzQnQV3/PhL9VXvP22NC38iryNJscvGZgp0d1VzJ6UT3XgM9KjHA63Jrhpfmy09nq8lsa9ITtHSyTp2qTXw7D/zV7ulfGH2Q/tLRE9ijL5HZE2xT5ca6Jku4YsYUES5Zo9PIjqB55d8YQu53jwvXrxY/GamSxgBN+Wp0Rg8sEt2qWtMPgbh5SyNCz/VHPAXBbcCmN/wc3WETTR2nen+VsRGcpFHMtl+r29eQxrx/EZWnpI3HbC964SvwmV1ihiQW+t5/PNledfvZIzTNMrVYnnL3X/j1EH5q1YesYevcE5dOGryYSG+Fd8du/BJx4p0c+DfRpS/sOUVj/cmPMFGPF3aELsV0TF7sY2mVWOpiWMnDQtcfWpcNtCocaG4OJ4AGxWk15B5zWU9QKsoPQr0BZcEL+FIoDNgIi/H8ccABNBsXOazG7Ln9uJjNS6IDHZK//mf/3lpHK4tth6rcUFkB6ru+nkuegSiiB+xk+bFIxzGjWxrnF0soMu+Rzbk5LezKJFY/1q7dQJ4ilu35xVVmZcsn6bGhQxwR3IToz7nCHlPt35jInbwPiKJib/YdDjCDuZTkLOBRmgvbuFJ7CgS/A5tlkjZHlbatRa/VRfIZ9eKQfq4BqvhkPgWozN94pcuL/0hBPLIF2yDj716p2PNgztS+StpXf6thHdywEUxO5qPf9Hnpcf3XOduqDtCI7vck87UuCjIPPHg0SVxrFnxnSc37Lzz9ccgtYY7E3a7Z7nhVpRYpwc4q9jX1NGJ5ldB3YtVvu4OlWbExkKPOePpUr7xGJJNHLqGT/zAmltkNL+YsKHs0S71IJyTJ83bY8V45+vd4noejrjzwxfUD//2b/+23FUij/w8w5lbkjghm7iGq/f0AWtZny7lWQ1i/jote+HJX1/T6HdfOBOdunER2G7p2j259k7DiCQzt+GBvSLzKMcxj0LHjsGlosI4Xb9boO4mzRKh7ySlf//3f18erdoj/2M2LuQDbh61SuK/hh6zcUFAEjgDec/ZzwLb99bhX3aMRuPY1vcSmNu0a3/IvIbol/9JSvznmiJyRHi2w0V+IBfQt96nqXFhF/5Kf8GHHCFj6Jjc/HFmO/HGvpqNPboJidXskh7hL/jjy+7gXNr9t6MPm2c4RB8KH4kvj/NuJfGlmLJJo8Cb6UtMpVAfEf7Yxp3sUTG2lfBBHkWZogqfe21pPoWgooFN8djnJIfC5l//9V8XOWakqbJRRua9su4h9jhL46IpFL/0AosV65pdeU5R/BgFK1JEwzfrH4EHW4h9FKw2gtUd7jrZ3FJj5Y9PdJ58x578ns/yyYzxyj/FqzyjSXRX0m692kbO3Soj/4Ux7AUz8WgDgt6yYVZjxat4lHuc977zSNdqD/a3SchHFeywmx5GeHZrEs/wFB/3Xt968JVd1cHsr7lPzvuHf/iHBWMfQy//i737DNZvu+sC/p434Bvf6IwDMgOOCggOiZeYEILBJCaQXi5Jbm7+SW4KAQPGhoIC0pWuFAvF3nvvqKAUC9hAbICdIqAU4TifLd87i+X6rWfv85TznP/dv5k95zx7r/Lrbe3nnLVwlYULZWMw3sVj5ITsXpTxtkAQjJcRSICrPzt8G2AwDJZRCNzVuu4Lzjne5AxGdMXJCGzGHRss77Jw4WgkPhymrshtg+uhwoVzPWfhgn5rKzrJWaeqStCMcwTrhMIxLPn1QJ54L2jowinqTpF4WLfVM3qJP7eVXwvWgCe7FFDYKdrwQ/A4Bq6hcEEfmuiSVwvwDT972yN38hUQK5nhCx1hw8e+thcc2EC6pKcAiQe/gJbQ2gO5CHC6lsaPgL75AwQKIEHxNrqGj3jvrybq+I1wAdbHU4G292cB8snJTGWjWwBumhYSMvsfa6f4oxknic73K0f0ok9C5hWciqfmkp9kD//uCuB6LYWLROzlL3/5ore+5+Azv63gFQtOoRNbwH6aIPCYxY1zggLXSYNuPz8rcXeCqiBXLNDp6KCf9MqrQ2ITv0P/3M8Yz3M6kldENeL4J/br+VawBz9EnyXUEmiNW3HbPqO3E8xBm7gh5vXP/Y7nvp8n58v3m1LEjmLzuYGtyEM1esSGyrbPAfhhP7ziI/FW8clO8OaZz3zmohd3oaNr4eoKF8klhuWkQdFyCgYSFgUlLFU8YZ1KMJSAU5IkjpL/gHEcBEdQBSufXYKk4MwRUHJz+7Fb4GEvXPCSE5VcoaeHUxQu+I8HkgN/nYZ8FCgjubiHlxIOTiFd1R4yTqfKSd1tk74R2FMAoG/4Tv+P0aEA/MjTSQL9hL+g5Z3yY4AM77pwISO6QmazQpIdSbAlzxVPBSTBUvA9Njjag7+SEEgMdB6PBWtKTCSbTvxGdJA1Pkie85pYD+axA0kAH3IMoFFywkdXdkBP8JSMqoIQTmRDRqdITOBiLz5EbILDMRB55k++kueI/3CPzKsExzzP+A8d5LsCunEthYvvsyR/IDNvQuCzZHGkw+cCsnGJGfIbcUPxf9sYeBuwv/1SpGissgs5hrgvRokVrf7RM3j6LkQasaHFMz/NF4/lNGTuxMV3QBVHx9qHWC9u0X3FpwaRnIl+9d/5ggtb4hPI2CvdLS0AfWKst3fELOOM991pvu9UueBagJ+4qbGZPy99aeAzNJrYBB7IZ+jIo48++uRredcKV1e4cNACzrvf/e5FeRnMyFlvAUpCCKptyRYnRmligMeANSiAwC5wV4mscQzKOEZu/9E4eHplg+PFD+u5Z+wxuO6Fy/GFS4AckqhxPFUXOuPI3HcI6MkIjIOzEwuvt1TrbQXywhf6JgBF548F+NIjDleCqZvFbh+GwkUAg4MAL5iM+AXPdKlGuhbw5Vevqhh/jO0CeNAL+uEVimMDrfXQp0gQxCuds49mjyRipD/oYs8Sb8kQ33UMWEvx/mmf9mnlSSUcJIL+M7/kqQL2JjFx4nhssho6xRCvSp7CRq0p6eOX+MeRjtiT7+LX8mePR0DH+A+nhHcFcLuWwkWs4Z+dCvNRThl0k/FyZNPnglZvxHSvB/o8kvW5wH6Sfd11CSnfxsfxYYlLxgSCs4Q2dh+cXXwCu8wX8vl/39VFG12Wv7Xr3QbokldYNcTYCL/HVzmxUniw/5aHfpdfKUycrAXXAHzEYt/f4D/phqYenH3vpYrN5wT48V/vfOc7/z96LgF4IpeSG2hO8TFkikeKumNleE64msKFonIymKZokexRUEGTgR1zqdoZrCTTl8gciVnX/dH4LZd1CPyJJ55YDItRuC9YtpdxjgX911mdA4rRj8k4R/7+soPOnvXgeSyunBUHHvxyWTcOgtHjU/scPvj1qle9avm9fTa7eprIVkWP9i3rtBd+SI582bl/Bm+BgaPVNeifc2aSIV2X/tltLjTQz9e//vVL18K9lmaXe/BSKL/97W9fxvVjctEH43zJkBz65+3eWy/ysz+nb5/RmK0X+nXiFJISOQmibtsxcNeFi4RGcPMXd3wh1edRMBHkOHs8gHMP5nD6kievu56im2Y9QZZ/tN6xQc56ig2+i22M6AD8soRH13ME8JDY6Nryb/h3DFjPGl6fnK0Hr/xZzyrA4hNd9XrQqRITr3eJI+zoFIA+CbVX2kYFrs/u833igqJuBGiFEz+f7wFcGq6pcNGhl0S/6U1vWmKcz/wrvhxrO1sgRTZ9Vlgeax9bAa3kQn/EePFIHOBLxEo8MablCV3SJBQv/QzPclmPXTlZEZv8sQMxRuNQs1Uz59ji0J70XsEi3rvovyJJoSHR7veAtxxM8q1A7W1AwYX+fNHfTyfOfIgCzPOWD5cA+gBfp1V90/gSgIea1nggnilk3/CGNyy/HyvDc8LVFC6UjjIpLCSnjEHy7vjb5XeX+7e5zHV0bG0/2/vHXtZ0+X22LoPL3qPnuTJu9Oy2OFvPuqNn1vTMFfz7y7vdo/vV1a9v/5bvt7nMx+cKz3aPdn+X+2gIb/trNOfQZU5kHxxytWPsaYx9os/9ZaznwS/3+nVve2Vtvwe3Y692facPCrljHL9AdZeFi4Cs6FWUzF7Zkwj563JVsi9genVUEPCTbzs2IFrTqRb+nCKgwEln9PM+7/OW4JnkpAX7SAAUSxKYEZjjma5hXq07llbzJRQaLb5wPlpPkoG3xvjZJykBMhVTJFqnALySoDldhMOxQA6aSRp1SRJ7QL/OrIKpOmEypj2ZqXTznHBNhYuixXdcnEBplknWdeQvDXREkcB2Z3/84hxAJ+iTrjp/rUGpCaEIUChL3BX0vX25p2mgWUGmPc6e01fNSAm/pFdBQT/zR2ZGNrsFgrv1xBc/4eTkjH6R58j+JP9wc5oC9xbgpahXKKDNOLjzg9ZzKnYKX70VnN5qrigCLw1opZfyDY1IxejTnva0JQ5emg9b4GoKF0rF2arWJQ25GImrvXebi8K2v+dqxxx7BdfqavedXaO1cx16ftsr67Z4HHNZK3Rn3fb3217mj672WTWuXae/RuMPXTOZ5rmf+Ty7Mrb//dRXi4t9bnu1a7o432M7RgKNzuTjjz++vL506Uvg1aF14qvTJ/kZjZOkv/Wtb13Gj55L3gRbY3Q1dTqrtVyeHbqs8bznPW85tbT+aJ1co/n9BXene3htvVyeZR17Sni88yzx6tfIpTB40YtetKwZ3EbjtlwKKq+nWtspgjX9dKrqpzEKQyfdinL7ut9f7pMDmYWuYy40agDYl46Mxmy54IcOfyqebKMr/aVAe93rXrckGD5HVrl8loii06X475+f+4r9+Kt3dw0S0Wc961nLq+Hk77VC/MGLkRxaXp3yIk8nuG9+85uX3/FpNO5cl/34M28GsGU/NZle9rKXLbY14gnbM87386KP7XO66C9POWX308kMm6Cf/F1LYztvyxXc7fXYY48tTUcyfNvb3rY0lvgbe432MNapQe+fjXXP87e85S1L84lPpSPeSNCg8Ty4n/Jq8egve3qluPdRo3VOdbXr8+3+cuGDBw+Wnx/8wR+8FDHXDFdTuDyVQCVbXTNYM+YccFf77vDUAh0032vwBVZd7UtfXneSdPmZL9K6dBZz+exoPV9qNW60jk6vK/eyTkWbdQ5dcPOq4+hZe9l/zWU9dLTz/IQPPP1uv5aO0eV51jlE59rLWl6fyN7Bq33u8twrinBv77eXdVo6Xe1aWy5z8SS4hWe3vTKfLKzn92pfdNrXnNG+xoUfPkcG/bhzXfayp+923TX4zlD42epHxY/w9NQXXYnc8MY12v9cFxzsHTsOL+hb+BG55TI2vmb03HynHmzKT+u6WptwjfDJ1a/ZX54b53frwtt+Lnv42eLXrm1vz/Osv8IH66LTnOxhrjHtesde/f6jK/xr6XeN1jvFNVo/8cBPp7+XPB3cCnvhssMOO1wFKI4VL05enL5e+nJi1F6jMa5DY9rn1Zit15Y1+7GHrnbebJ32WXutGbP1WrNmO6Yad+j51qtd71zrHnpeXf24rfOPvbLXKV6hOxbgMMOxv0ZjT32N9j33NcKjvQ7NOfR8do3mbr1G6/bXbE7/rH8+ukZzznnd5d65Whxc1au31wJ74bLDDjvssMMOO+ywww47XD3shcsOO+ywww477LDDDjvscPWwFy477LDDDjvssMMOO+yww9XDXrjssMMOO+ywww477LDDDlcPe+Gyww477LDDDjvssMMOO1w97IXLDjvssMMOO+ywww477HD1sBcuO+ywww477LDDDjvssMPVw1647LDDDjvssMMOO+ywww5XD3vhssMOO+ywww477LDDDjtcPeyFyw477LDDDjvssMMOO+xw9bAXLjvssMMOO+ywww477LDD1cNeuOywww477LDDDjvssMMOVw974bLDDjvssMMOO+ywww47XD3shcsOO+ywww477LDDDjvscPWwFy477LDDDjvssMMOO+yww9XDXrjssMMOO+ywww4PFfzUT/3Ufu3Xft3iiv1cK+yFyw477LDDDjvs8NDAv/pX/+rmj/2xP3bzh//wH775g3/wD978oT/0h5bf/8gf+SMXvez5B/7AH1hw8PulL3TbOzwY4ZjL+N//+3//MtbnP/pH/+j0Ms748Pbcl/3w0u8jfNoL/l/3dV93kGaX9awbWnLvUpc97Q2HHq9LXNkPHnDw87/9t/928xM/8RM/bU3XB3vhssMOO+ywww47PDTw+37f77v5xE/8xJsv/uIvvnnDG95w8+t//a+/+aIv+qKbL/uyL7vo9SVf8iU3r3/9628+7uM+7uYLv/ALF3wufb3rXe+6efDgwc3v+B2/Y8Gnuoz9rb/1t9687nWve3Lsl37pl5bX53zO59y84hWvuPn8z//8Ze6I/lNc9oLLJ3/yJy902K/Fu7/IGf7Pfvazl7G/83f+zvKytjHveMc7Fj6Z7/4Ij3Nc9ofvr/k1v2bR0xa30fhzXGgmy5e85CU3n/d5n3fz0pe+9Oaf/tN/up+47LDDDjvssMMOO1wCfu/v/b1L5/0HfuAHbv7+3//7S4Kmi/xjP/ZjNz/+4z9+set//+//vZz+/Lbf9tuW/X0ejTvX9aM/+qM33/d937ck5t/5nd+5dNGrC2/+83/+z0vX/W/9rb9180M/9EPDNXN5js9/+S//5YXP5+ItGv7H//gfC172Cl72G11w+fN//s8vRYGxI1pz/a//9b9u/tk/+2c3X/EVX3HzPd/zPYt8rNHjcK7Lfmj7rM/6rJtv+ZZvWe4Ft37sOS60/vf//t+XQo/M/8t/+S83v+E3/Iabb/u2b9sLlx122GGHQ/B//s//ufmP//E/LkHnlNef+TN/5uZP/ak/dfNn/+yfHT6fXeb86T/9p5c1/P7n/tyf23SZ5/L7aH2X5/aYjXEZ4xo9m13Wzdzs8Rf+wl9YdRkb2kN/u/ZdXHCIXELTlstc+oCuEc2zK3P9HD3P5fmf/JN/shwXOiJ743o6Z1fm3lavj5k7u+D1b//tv73zpMeJyx//4398SUwl47/7d//uJTGUEF4St5/8yZ+8+Z//838+qQcS1UsCWiWof/2v//WFJ/gxo58P/kf/6B8tBYlCy9hqPF4a85t/829eiiK0ngPg/4//8T9eXqdSXNgXBLf+on+f8RmfsRQE6KnAWPLwStlf+St/ZRmLBvcvBfZnM1/zNV9z88M//MM/ffcygE70/r2/9/eWwgku3//933/zm37Tb7r59m//9p8edZ1wlYVLFHC/9mu/7sd1CtBZ01F7y1vesnTARtdXfuVXbr68ovGyl71s+bl1HUf2TzzxxM0nfMInLB28L//yL199mfvOd77z5m1ve9vye0tHLuO8ZvHc5z73SfxGeLj/G3/jb7x59NFHyzHVZY9P+7RPu3nNa15z87t+1+9a5n/VV33VqstYrxG86U1vWn5ay5ru3+WFZ895znNuvuALvuBJfq+9dN+9cuL1oZ5Xhy770k9zR/xyGYdXH/uxH7vgV42Bx6/9tb926Yb7fURnddFFOumiW6M9qsvedAkd6Bmtf5sLb+m6k45zJbFroS1cJGR/5+/8nQWvJLOn8llrwF6SaTKW4F9yb4BedEuOv/Ebv3EpBGbghEahp4BRJMySeWv/3b/7d5eOPf99DtqcmtDZr//6r//pO2OwN3x9R+Mv/aW/tOAzA7g7bfncz/3cn1GknYOGEdjnX/7Lf7kUCnh+6aLJfvb97b/9ty96iR8/+IM/uODzz//5P//pUdcJV1O4EFiOrjia/dqv/bo/l4DhOsbxpnDxni0HOrp0L7dejr8lejpb//W//tfV6xinA+V9XwmZ0yCvIay9BAWB6Vf/6l998x/+w3/4Geu2l6N6SZ9XIeAqUPeXcd/1Xd918/Ef//FLkJGItLjOLrj8u3/375b313XStsy173/6T//p5mu/9mtv/sSf+BNLgM/9u7rwCC4Crt97vs8uMtG1JZPv+I7v+P/orS770gUy0GEW2KtxcNLBlaj6fTTOWmRC1yWI8GppnF3k51UORYdOtLVGe4wuPGADX/3VX72cCMUeTnHBS6HmC7/XUrjwTRIyNiQZ/4f/8B8uOcYlE0TwIz/yI8vJWl51uuT+ya2++Zu/eeEBOc2AH6fnEno6g38VkLOTAv5BAXOoWNgC8Lae4oKu090ZkOs3fdM3LX7UKdsMb8BuNA7+zb/5N0/qRK5zA77ZE10pEC+xbwuKen/Agk6yE/uzY98lEicujc8WuJrChZJx5IeO/h1xC1qHxjma5ZiNM2c0xmWcI3PXmr2taezoeS5rwvFU40LzmnHBbwvNPo/GudbSfImrxRteFd7ue56xM/o8Q59r7bjR81xbx63B79ppdtGjY18REaQkfL6saJ3RdRsQICTfio90ttaCPc13lP43/+bfXPyU+T1eo8s4wUFR4lWN3O9B0JJM6IJLJCsQXHQSJQl+XwtJLv7qX/2ry2sRcNoC5uvU6qrfRWeyB4WeLzvjWQL+mgsd+Cah9Vd0tryaYb7k8y/+xb+4dK4rHtpDYvpbfstvufn3//7fL/j1YC2JpCRa4WKMeS2us4ud0EXysI57a8FeimmFiwK4T9jaaytYS4Pgmk5c2kRaofqpn/qpi9wPJbWnBnxn23wbvbj0/kDi78TtG77hGw7uT35OLlzRkRG4jzaFjlMX/uFUAAeJtC+uyw1nJ0XGKkTo9d/+23/7oP6hn374S2WxofY6N9iTHOAL70vaC/rQzx7EJk21AB1RuOzfcVkJHIyEy19W0KmqLsZhjON1TtKrD5jfXyrZt771rctrHl7BMNeRen8Zp4tpTR28vErRX8Z63eIFL3jBksT4PNrXfDg+61nPWpykz9Xlrzn4Cw457q8uXSx/mSSvnIzWcnGKjv+91mGOez0dcMQ3r7BYE81406/lspd3RV/0ohfdfOZnfmY57lIXfn3Kp3zKzWtf+9rly47w6/nvgqdu0ctf/vJlfPSkv8ynG2TgL9BU67k883qJ121mfICjbq41Z/ppnNdNvPoDV/cch/eXvXR4/aUXsvC5X8sVmr0SZbx9R+sZS09CMxzRNro8YxdwrOhwGUsmgtwxzu5chYt5AgUaFFhbkgZzBe5/8S/+xWL3kp61eBiHpm/91m9deCko2Luf77OkWFGh+yWIuUbjFGCf/dmfvSQ/W8Dcf/2v//WTidNWkJCgQUBDwwi/S4HvCkhMFSHBY81lLNr5UQnRFj0w97u/+7tv3v3udy+FUzVXEhcdrooKayl8fAnWicloTAXm0kG+4DbvodtXg8OpSIXfbeHaCxf64nsSGi1bCv9TQPSPfSseWrwuBeTt9Fiy/L3f+72lDgdXjQEnEilwKzDeyYxmwF/7a39tsYFT6BUcnAho1Ni/0il7oS1+9lDxhG5NNjmSE9Hg217nBvvC1WnSFj90CsBHzRXfSeML2ibMXrhsBI6EkuqkCbCqUMztL/f9lRBKx+ljtKDfXwITpdDR+gf/4B8s4zj8/jIuX+ZidNm3X8893VpFye/5Pb9nCV7mtri5rMmIOUhFkWBnTff6se77Sw6KB68eWN+4/nIfDfb+J//knyzzejpcodmf98Mjn3s6XNYMzTrBeBPc28s9e0vufVkPzaNxl7jgLGnDe0fBulfuhZft5ZiYE1WoeS0EH8LL9uLgBDEJv7XNHfELvx2zc+Icf7UeXtFdCYmOaLWesfDHVwnEjP8cHN6jGb7VmtZDs0LCK0326NdzkSGH5R18NNu7pSGXcTrCEjU6Ze5oX/N12xR0dP4YOFfhAjhrnSVFHf0QrNaCwIJO9LFXeK7FxT54Sc7es6/mCrz8GVnzGVWXkxwkhjr/1ZgK6C0a6OYWGoC90G4+/cCTLfNPAfZkDwpl/jL0r7nInywktE4Q8XstmG+uBEpx6fdREuW+ZI/N5J1xc3sQ6/K9APLcAub+jb/xNxYesL8tAGd+Qkzkp0a4HQPkcc2FC3nw0RqQ/tLXXYBYIt77QwH4dUkgE/zQYFL8z/yH+8bCk0zhPQO67ySPfVQnjVuAfVqPjZAZ2VWALv5ZY2oNX/kvJy1skD2Zj972OhdYO/bPl+LrOfcbQWSlgBZrWlnh4164bADC5Gj8LWkJGOUaMc69dI0kadU44L4vdDm2FyAqZ2qcgkAHWnJTGZ35kj2Jrr/EUBmI9RibVwokxgLMaG/34CWQUmJKU43jRKwlGTeuopmBS5Cc9khyZ7yRKDmGldyP9gXue45mxdCWgH8qgCv6vQsq4W6PNlswDv2MTnc8wbm/0GSc5EcQ1013fwTu604Zly82VmMlhoqRyLwCtHgNS4IuERuBPeihgmlGM0APWpyKHaKFw0ILHpnncr+9InPj8k54BewPzf6ePx0+BvDlXIWLudbnNzRI+Ju1YG5kIYiT2RZc8JO/ECgUn6O5xrBrAVUzgY8bjaN/OouSaEFnFtB7IMcUR/zYlrnwI2t2pdM/S3rOBWTmVTn+Eq/gD4c1F/nhl6bOrNs8ArSTm1f5Eh+s2UN8tORtFm+sJTnQYNiChz0VHk7oZz57BObSnfx1KbHh1EAnrrlwAWQnjkly70KH7a9p6PtmZHkXoLDIa6mH5CSOaRZqdhziFV12os0+2eoxvJXQe0OAz63sDUSv7csnrrEnsU/uyAdeGuDH//iu0V34UXuxCW9oyBP7vffCZSNQdMmcv2jgVQgBdqSwPjM2nS3JtMq1CvKA4TEkydXMWdtfYirht/YI7MFIdNO8quRLXe7lagHu3iF0MiSprAzKPB3gdGFGBp/1GbPAS+FGxZAx7qHTd2IkgJKNCigwx0BRFVoVoFlX3QkNmi8NaBLkBWydsj4YBfAYHU66Zl8UJBtOW4Lh9KE9Km3BevisQ6WYxteRo8d3Y+kZZ2tcJW/g1MbJok48XEZgTcmJ1xjpERmMwD70lZ6loz8CeEuY0MzOKprhA38nBJLoGc1wwj+2pbjCp2MA7goX8jsH4JV3peEqid0CeEBnNA4U0OjfAubmZNVa/fzokKRKUCXT0R7mkp2miHe5K3mPwFz+RXLp/WpBcwuYrwDTsT42MbkNKBjxZs1fO+oBz+i+U/WtuJvr9Rpz/V7tKxHSJIMn/Ko96Dg/sRUPYyVokp7KH1QAZ77Ra6xbTxzXAn2SEGkMbKHrHJDCBY9bgJfkjD/X1NxiP6cA++OT+KzAuwugw97gEDMOnfjRE01d/oZ+z/QGbXRfDsXfbdVRYA178DMKEX6Q7laAl/RZbnCooWQdORS+59T50oAnTrvEVro5o+0cgGY5nzxSo7Xn1164bAQMTRKEsYqX2REhhVW5+h6LznoVKCgGQxVQBB7zRspiLkViLBKwBKgReKZzq3PJEKq9racDl9esqvXgpFBTGEguK+dA6Rnp29/+9iWB97lfEx54Bkff79Bhq2g2Dt8FZa8izQKpZ+QTmiu5nBrso/jSoUILmmdylnB7jWLEG+AeWugCXWOoo3EA3zh4wXh2Emcvr1MJBAo8n0dgPvkpzp2MVDyEj4LBa01eCzmk24KwZBS+o3HueeZVH0UBfo5ozjiJIZpnX4h0nw6iWecILwWE0f5rgS6es3AB+ApPnfuKryMwjt6YJzEbOf0ZoE3Asveo6RCwLt83K1bty0dqTBhfrVUBX+N0Dh5b7Ni++Kfozl/iuQTYl6zgTTf4H/dyHQL80eU2V9NpK7B9xa54Y61+z+DH10tI2OQI8Jq/8f02/qKS7wjMRfenf/qnbz5pC34Sed1zv58D6EMKl7uGqnABeCcZd+qy5tTh1GB/uoRX9PJc8qgAvfii8cvXz+inO/wEmfLNI34GjKX7mi+aNOJMbyuHAG8UK+K4dfjNGfBH4r2mM9xm++GzpiG/Cc9Ly93+clpv9lTN8XODfT/pkz5p8SGjfGEvXDZCChdKSMCKh3QERgqGqRy/zqGkdvbqg/UcjzJUAqkCrnE6+7p6Kv7KaOxNAXW+0rEfCdk9z6zH8GdGiEZJTSrhyqjgyEgFMDTjwWiscQm4jPUQzfiuA1U5JrRQeoFPAlYF51MDXnBiOj6cFBmPeA1v+Pt+ieKvAnzwupSiLidmI3BfcCEPOobH1VjFCF1VGOBnNU7xShd8P4EuVOM4DwmQIlrQqMah2ckSR4PmahycJMIKIYlbNQ5v6VReC6x47Z6ALwFTWMExr4hUa68BPDl34UL+grXX2mY+YwR0gN3fJvE0VyBntylKRrzCA3osqZoFfnYhOZCA0YPKX4zAuklaZj5pBHSJP+FXJNKXALSxfT4AbypfNgL8w2u26cQF7VuAfvDxOXEdAZ7QJfHAa3wVfuTEXsQCerRFZvxt/srfzCeMwD7s1etBfNoWnd8C6L4PhQuguxp2/NwWOz4V4BXfDc8U4pcCe9mfDyOvQ/t7Jmby8WLdobHW9kYKW52NHYG5TpLFviqvasGfP4YX2zyEF5+pYcNnbsXrFIDPiqZZbnkuQC+7F6PZZ2JGz4e9cNkIBClACNgYxrFLYDif6jgzAc3xuYTanBGz3SMoCmP9dM56MI7hKAwkJ7POlvm65gK4zrN5laAVBgwmXYEKKI3EVqJe0WyPBDE0Vyc57tmLY1aUeO1oREtolsSgefbetfv5XsihvzRyLMDf+vBn7ALvqCjw2UUWeHyoE6yAVej67sZMFhIcrwUlURgBHD2jf5KqKpGLzCRP1uT8RzIzjh045eGMFSOVzMznXPLK4oxmMg3NM4cJf/oXm7NPD6GFrQq6ilmBXwC8D4ULfrIz8pI4bNHh8N1fBiJH+rOWXuPsba4vnKJ1NNf6eO91LCcMIz0B8JZ4kCu5VeNGYK5T6vzjsy1gH3TzAZKGLfveFlIsaSKwy8o/jcBY/l6SmteP14K5/AVdkezg20hm+KEg0WyjWyP8zGPPfLY4MPJlM0BDCuYtNAD2yjbTZDuXzPDnvhQufJaTYsWmV4wuocc9kKnvvaWZukUfjgU6Slf9JVc+aRYXAD6Kc940OVQ4W9ubB5p5W5pD9Jp984/iOZlU+1hTTqAB5fQZ/2YQ/I2vcqtzARrg61VucR0ul9Y3+o6nXlGVf8T/9PzdC5eNwHBSuACCxWCFgeR1psQcj2TH/ykgoGqcIC3hkPRXDs1clTlHf6jqhzPc/FlbQalSRkoi0c8/O6rWo9wSQTRLqmbKbT+BzKsrh2jmHONwqjU5DDTrSlY0u4dvaNa9n9F8DNgHzxyjS6QF+sqxhmcCJvlXDsya6OdMOetZoLCmDquCqerkuIfvCj6v+HHQVUJhPfKXeHEI5o3AOPrBBqxb0YLnoVmiPws65EVPJBMzmuGOZvyh/zPe+C4WmtO9Rc99KVwCdEoQqQq0GQiYbH7LKzv2MNZcfxCDnKu55Ev+CouZbZOnL8tLmq21hQ5z0Y/fW+k3l3/iV86ZCAO42Y+/ycnnFjBXwnCb75TQR6+U8ol8x4jHPucklU5VYK6ig33xm2vB+q403ey1hQag+FJoamCcE/D6vhQugEw0CX3fpPLJ5wS4OV1Q8PK5lT84F9AjyazG5uyPvwBj4Zi/yDbDlY2iTUEkJvARa4C9Ga9ZeOgExfq+p0J+GjeHeAdnuMuHLs1n+7F5toHfl94f4JEGTE57yAh/ex7vhctGwMy2cME091TTEhnOt0oMOR2Mlmg60qyckPkSTN930W2pFMh9CY1xSQpGEBy91mPvCr+Mk/B7f33mpOCed7l9x6Ba0300SEoZwyGadWZ1LatxoZlxOxkQhEZgHEeEZjiiq6LltsCoFFKCTxxflbDAmfOCz6zzbA0BShIy4z+68Snd6Go94xTMjz322MHvWHGyEn1JEH6N1jQXLTqy7VHuCCRRigyF9cxp20uXKeOq9YzziguaJdbWG9HivoIpNNMla/p53woX+kUm/EWl6xXAUxKtECWztYA39sJrieQsoAvcXudyqkEXKt2ScPjzu3DaYof0VTHtxG7rKYZ9Fa0CYU4uj5H7DNAl4fcK6KFkpgd4wi2+byuO/IRCgz8egfXoPh3SbDJ+BPCAu26vZGuLvpELm+S3xA/8WEuHcfYWVyXHcDgnoOs+FS6AP/MHC/jyKjaeC8iGD3DCIH+pYv05QSwRO8V8v1e65T782IImAr5WcQKgTc6mUezV2DXglMaX7PnUmT+yJ7kpuPDNWPuNwFh6aaw3GbbYz6kAr+QeXhFHW4XrOaCVm+aNAmpG/164bAQK1RcuFDInAb487vcRM93jdMxnKLNATGkc+zv9EBAqZ2FNHSpfhGd4VbAxTsDy5eTZX6qAj24ZJ7Hm9SOnAhJOa89o9n0TPKteUQDW1CWX4MwcdGjOHzyY0QIviRWHdyg4bAE4cOacjMSSg5rRL6mRQDqdqYCMveImQJClz6M18UkX3StnDHeWYMAr9M8cPkclgErGZ+PwUDcEzQqmahya4aYA5uircWiUUKHZz4pm97xGaD28hEe1Jrysl++1xAHD6b4VLvZSQEhq+YstgG584kPo1VrAm8ylY/hdgTEaEnRM0VnJDu6KXZ1bc9YCGyZPRf/WZBqQudNeCTXfE104NaBPM4U/2oqj8RpfioqtSbu57MzcqjjFf+vyv06gKn/J9iRxvlfX2s0awGfxSgFlr8qOR0DGmlbiiALYWucEPLuWwsUrYOjG7xmQmeI7p+t3AXltkx2R7yWBTtARfv3Qn9j2DI/wNn/BcqaLnsvJ8u8BqrXd91wcl2BXeQywHz1jb/5Q0CH5ok+T6TbfaTwW4EqeeXNkbQF3SsBb8YOfl6tUPiogR/XHA27T6LkkXG3hEsB4yaQvojqKnzlfybTKWtI/U2gB3utiig3FSwWUXHIjyXD6UQkSjpTSaxuzRMZ843RbDymGjiaa02UbgfmcnWJNMTTr4DJ2DkQAVLxUezM0NEugZ4lxaMabLcnbIbCuhE5SqIiqAjXZ4BF6OCb0jcB66JXcSbSq5C68ZOAKDQmTuT0YZw06poOB5xWPAGchYcmJRzVWYekVohnN5rMFNCvsZjRzQBy7Lg9bGK3nnkCk4ED3rEimg143ZJ+9bbHJ+1a4wBPeZHPMl0gFpJlPGgE6FX++d+H3am/y8DoXWY90Ebgv8RH0D31xtgdz2bo9+MGtPOAf+CivvVX4HQt8Pn1nc1vxYwNOamav5VWgqKMbiscqmUSzwkhhNZOj+RoDTly30kDP8JiubZ2LZ4pSCTw7P5eMAnC9lsIFHg8ePDj4ap1nZEfH+IG7APsfamieC9DPNrzWJXeqYkrAeAUAnXTaO+MtfcN/cVK+U60dPVVcHIqnnjkpZnPi+iGd5tfkRwr32brnALhpeojBcopZfnYuEJs0RPGAnh0Cecjzn//8O+HXFrj6wgVQeMmxZHoWhNwXvL3ipSKvxkWhdA4c4TGckQEQHEfCoUjsJJ9VEKOUvmPh9Ynqy9cAnXCTLMw6ABRO0MxfITJupEjGhWZdkDU0SzDhO1oz4/CFM511U1uaT9HNQItiRJBXjFQ0u08uCk9BEk9H4+DtmdckJInkN8Ix4yQH+KPzBJfRmnRRIklPJW6VPkRnJeFJfqzXrwkfNCt6JWkVzXBEs85gXoEYySW0GBOas3cPcKQz+Q+6aB6Bcb5zIUlHc89D8+5b4QLQEb9C50f8rMBcQRnvJP94tBbIQ9D3Oouu/kzXFL5Omys7NM/pn8Jc4tXL5hAI7HRZAl7pXgX00SuL/PahhGMrwAXN7JG9Vbo5A75JMra16DFWYem0i05WfLeu/1nBNiq+xw/oqLPFLXiY63VgMUAhtRbsAWfFCh8JP5+37H0bgO+1FC4StTe+8Y1LDDuUMLJHMqJrs9h9LqA75Evf4HFuOfVgP7amobrm1U/81MTKSUrlN9zDS/lO7HDEW7GHvPJmQAXm0jHxQaFzSE5sVx7Bz7K9SwNaFFnwFeNntJ0D7Eev5IaaWzPASzqgePyIj/iIpYC5tB5ugXtRuABCEMR9KVaSUQUK9yWKhOV4vhpnPUkYg2KsDGIExlE6XQMGWBkfIevSc35Oc9BTgWccKifv90pB3JdM68QLQjNaGD2aD30vxrElmiUDo719liQoRNAisa14gw+6wsZxYlsThBayFj7nlKACjtN3NziEWUBHr87Br/t1v25xjhVu6PXupz8XKpGEi7Ej3ghsutu6GOZVumA9if6h13DoDJq98od/FZAVmn2nadZFNI7T0WkOzblagDccjUNztZ5xOls5/RvpID4rxCWx1TprAO6XLFzQluSYPGdy6gGd5M8+nWyxz7Vgrr3IXEOEDoz4Bj/6pjGgOKrsmt7QszRXtsjAXHhINqtTxgrgA7/8oY7KP90G8EfCrelgjy1ro4E8JIL84paiB+/4F+95C/ZoHPHTmhoN7ELhCL/RODzVlHCKvAUPgAYFz6xbPQJ4GI92J+Jbip5jwJ7XUriwaZf/OaTxN9Nrz+QUYpiLH7o00A2NB42QKsc4J9hfwy7/PHK2P12XR/BLci1zZz6HT9JwG72uiNeaBPkqwAzoF5uTvxzyc/A3Rlza6gNOAXBDGx8kb5zhei6wv9NWRSb9ngH+8FFeGXziiSeWeHNKf35quBeFC6FTRAatq6s6P5TYSri9n3wosc2XbKv3O+1NqN4z1vmadWSM9dwXzBQQVbAxjpEqSCSDs4QVnU4/GOBM+ezFQNA86y6ghfHrrqB5ZFDu4Q1F9uVdRYz10d2Pdw/N73rXu5YC7zYOwhrmSQI5JY5zlKQFL7KQcEs6Z8alq/2mN71poRP+Fa34RRZ0ppKFcWQmOdfBMW60Hlo8k0jqds4cLNztKdhL/Cq9cl/ykj8YMOJNAK1ojsxGAB9Fou884XkVqO0reXJ6I6DSxREt8LmPhQtc6Z3AJsDgyRbAH80P+Fpjq6PPX5rTYJnJXuDB28rnGaOY5Tu9I17JfQSRsQTXafYW+8U/spfAaMSYe4z8WyAL6/qns3Dcsi77y1802lqMmWtPp5Xmkulob/j5flT+SWuFo5MsJ8hktwUPYK5mlLlbdAse/I5/NJc/1X8JsM+1FC58tKJXsoz/9LTiP355plDVbGKPlwb7K4DZuThe+eRzAR7Y0yvQ/H2lz8B9Oi9fk5tUcSFAd9EmIW7zMffZj9iC57O4Bsz1ipjYNhsLd8/5AHpQ2fA5gR/RQMxp/qX3B+KLBgxcKt0HnpG9vFrT2OGA3O8ucF4LV1O4YK4ALQmgdD3TfMZgyut9VAl/lYyCvMZBeRUy1ThBW5DK+6UjAZvrGSO1pqBQKQI6JNYSYYlzNQ7uEnRdfl0L40Y4MjpBUiXMoVU0hzfwyz+IXEOzcRWOnnFkoblyADFSNOvyV7RUQN6SJomXdRjRaL5xvgOSE6MqyQov6JJuDp6NcEIPpyLAWdO+I16YZy8dMR0pvBjtbRwckwgLhBWO9v7O7/iOZV8JChxHYJxiRGFg/yqgGQcv60m8qvXQR5cFyJz4zWhmZ2jGT7SNwF46z/g4kttagMslC5eAopGO51W9tYBWvER3/MyIlxUYL7DS0UrnAZuSfPlZ4WcuHHTY2ONWOZiLB4eCXA/G4p8/6OFnpSNrAd5oZDt8nmbIFjAfHrErOryFF+YqJu1f+Vr3NLwkuZonuddC7Cyv8FV+YASZq0vNH1a2XAH+sVsnYTO9OjXA81oKF/FN4SoPYBNrigE8N4f/vI0NHQvk5pSRT4D3Fjs8BSQOO6k61MTAG7bCL2neVLYSsLY3UZwKG4tWP71FIK9C64xeY8UG8jl0emAvvvKTPukTp9/nPRfYT26n6SB2w+eSOOCjHE8hwgce8snkzM+I4fIrxY5XVC/Nty1wNYWLIM7hOa2gdJWww2RJ7qHvuwg+kiBdyMoI7aEbIFhJIKpxlIHjE0wPdRc907WA46zaZow67oKsU4s1NKuiK0VEM554ncj4GS0cI0XlpGfjOHOFouTsEM2OewWutV3OrEX25MSJWcfc0T4KOEGInphT4WJ/Di7fDcAXY/vx1nAygl8pzEbgvs5lXh8Z0WZt9+mSTixdqvAD9MK+ivUZzRyQ904VGjNdQrNCFM2zwpoO0zn8hms1jo55xcIpj0Ks0k3zdcAeffTR5S9MzWg+BHC7i8JFkuKL04IqfdlCA93QNSTLNV3DFvBOc8NpBx5W+gc/wZ1PYY8VfnyIYCnZr+Ragbk6bfzHFhrgwo4kJfl/J8cAvNHLLtBs7S1gPt8Xn7pVHzVEFBtsYzTXvdhkCs7cb4Fvh4dTo1nBOQJrKqIl3/z0lrnw4CeclDul3TL3WEDzNRUusRe+m0+RlOHPSK4AryTjTspn3188J/Dd9E8BM4u35wB7oZler/GF8HOiJSeaxRKAt+xRsS8pJhe+xgkKfzXbB05eTWNza177wkONPjbEli7NQ7SKxXIQNnFpQLNCXc6g4XiIfvz3Or34ZbyYvxcuK0Gw0t1/+ctf/uSXwkfAODhmgTLvlo8Mxr0UBhIDSe/IEUXRGITALVlLMGoh4yQYihyKUXVl7A1/QSsnOSMwDl4cPUfh92o9NDuaRXNeYRgBGnXb0MyAK+drTQ7dK1ehud/bZ/Mpdt7VXEPzob/qFrCOxESCojsQI8sV8Dv8OHNFVPWKGyBzAds4RV5bFGSOn+hCvyDFSM2r1kQ/mec/z1fj0M9hzmQOPCNvHe44/NGaeJ3AIMEl836cz3BHQ2gejQPuK7zQ7HstM6eKZoWtznIlc4Bm41772tcuRVM1bg3g7V0ULviiMyeo4ktlMyMgOzrGZ5F79GgNH4wxV4eVD/D7aB78JFT80+wUly0pwHTq+Ytq3AjI2Pd8vELKp64F+OKXBFuyrIg7BuAswfSKBZrRvhaMpY94qSEz8uMVhMfsUsyo5pKRLiaZ8UMVfny0tZxkzXzGCPhByWu+d7Vlrr18FxR+axtIpwK4XlvhwibEVY03TSq+l8xGPHUPv5yQsQM2sUX/TgH0K4UWXb70/oDvF8fkBZVPAu7jkSaBv9jJD8zG0k06rfmoEJGj5A+bzOalMaKx6feZTnsmTxFbM7Za+xyAX96gQWNi9iUBvWKZPBFvZ/ID8JOzpYEqZ9z/j8sGoNS+GKfaEwBzxDVinnuO4ig+Ja0SMOPitGbfizGOAeqQ2T9fTmv39rt7hCv5a//CWTsu4J6ujYTXq2MzBRao0exkqDI09wRWSWIC62gcyImDLvCsS28Ne1Y0A5/RzIn5js+MZvJCMxzRfMiRWYec/QMwCXzG58o49zlT72wrMCt5wz+JNAfJcbXrZU3jGCjnomiqEjXj6YWEXLcHL0dgHBw5VwGnKkCBvRVg/twzmiu9ME6Hyt9UF8gq525+aKYXaB6B9SREEmyJxSGa2Qz+zJJYe+MfPVPY6vBUdK8B+ngXhQuAt8aAhAceW8BcQcr/fEpihN9rwDhz3/zmNy9ypEcjsGb+Z1NlV+7R0Xe84x2LjzJnrTwyly9guz5vmUvv8l2ctbSPAG0KSEnKlv1d/BRbkXSywS14sC++yN7sBO9GwB7YkETN+iMc3de9ZJNbvy9hrvfLJY583lbgUzQwJB5b5H8KwMNrLFwSv7wCxdZ8rvjivthAh/jpKtacC+zPBhS9fHDl988J/L/i1wnqLM8A9BVP/elw+mo8/o4utCj2xQvfxU1+4r4r49Dv8jv5ScTlHubOAC7yGDYsr7HGJQGf2iL5EO9ODfbCMw1WhdssDwH4I58ih+R/5uyFywYgZIWLbiGm5a9bVMpHQE4/Hjx4sFS4FZPNp/yE4c9j+tyP9ZnQjBOUGCzjda8HYyUnAquulmRjNA6giQJTorzrOAK0SBYUQ4x0hCNwX2fTl9yqV5ZAaEGzRLAKwvaAo2RDhe73imbr5ZRr1gnigHQ7BQk0o21Ei/ueH3o9xX2FGFp0k82rcCQzvBa0zKv4A0d8cZIxO70yTvdNskjXZuuRn4QhX9YdAZy8NiI5zLgRb+DDoQsGju2tP0pCfPaMTNA9k59n1kpSV/Hbel6XkYAoqKtxcEcze+X8/FQsj+hZC3C8q8IFsC3f05oVlBXQPcmGi62sBfwyXgHI98yaK+TmDyrQoUp3yEszR9Jcya4Ca5K9QOZ3urRWnsb7U650kW5v3RvgOTvzxyUkj2sBjnCFg4Sfn9+COyA/uCtIRrYG0AQ/r4JK0ipbI0++SgJj3bVgPXavQHVqzR62gPkaNnxvEpFLAt9xjYULPtANcc6pKL7OdIOc+QDfP+UTLg1wE2P9RTR6BvdLAn6RpWJBEX1Ij+And3vVq161vMHBPrwC1l/WE/te+cpX3vyCX/ALlu/FiXHGZ05+98qrHNArTI8//viy/iFb8lyOooHi9y32fyzYi9/gA9nAXXy3xv729UaFYpIez3AQT/h6DZvo2F64bATOhHISOoYrDCSWAtgoicBU41SXDEJA8XkE7kuOfZGMYlWOwDgdMn+e1utWVeAwTjdcgJh9ERreApECR1IXJ9pDHIWkhyId6rx6fYhRc2qzcZRXZ2P25zSNk6CiWZJeOQf75AhSUK4cA1rQ7FUHQcK4Vn7muAQGe0oUJGuVkehw4h1e+70ah7eKRI5Qx7QCfFD0MU46Ua0HZ4WVYmT2pW304osujy4Vekcyto8g6ORPYgXfau+cmAm+aLaesf14a9hTgM1rdiOgx14hEzT8HK0FyJhOKUJm/0cHPvRfQeevksFXsXHfCxf8lDSS0UzXRoB3fFX+ceoWwGe6IXCbW+0Lv/bPL1ey4T8ULoJP5cNGYC4/in5FKVtZywNz6YECzF+mgetWMMfeaNw6n0/nuxV2M/sfgbn0XsLkVHsE+MC2JeVorHwWWeKhJtSsWTUCY71Cw+fM4tkI7MunSs75wco/nxPoyzUWLvjgUsyxMX6/ygEC7Eb8kof4/dK8JD9vgMgHthTxpwL0akgltlTxzzh2L1nWRHZK76RKMgz/9vLqkhyDnb30pS9dXjEz1v3+Ml6ew9d5DZlfkFRXwP+wN3E4jexclwD788ma3mLyFr97CrA/nRaD+QC/u1fR77nc2WtiYk/G4XH+LcSleHcbuLrCRTKEYYyBEhLCrAtpngpbx5njroCgdCIFBQpegTUZjL+9L/GvwDjdCEWTDiMlGYFxOqRea9MNq5yg+RyEpC2BsYLwSuI/6whZUwGmy36IFo5GYTcbB3yXwmttAmzlzACaBe8ct9sjF1ko+NAq6a/AWDKD16Ev2nJ0ujQSvxleZCWBJ2N4VCD5o39rZZEv9FYgEClYBKJDNNMTr17NxgHH4YIAfa70D5CpgK0Am9EsQLI/NM8SR88E9dBMvui674UL3BXd/rQ3PZ/xdAT4IGnTad8KdEzjgi+bAZ1gzxowFX5knJPCFLRr5RLblDDQhy1zjaOTh3S8AjTxkzN7q0DSoFhTcJPDWoCzuXBuv2zfA15LeJ0Q8w0V7xUbkgGnZzM/NAJ4S3wk3Gt5HjCXD5Tg5btBW9c4FuBwzYUL2eAR30qGMzBWrLCO18y2yvIUIGaIQWLgpQG/8A4P+fUqHrADryjLl8RBY+VulX1onLE1DUtNxvgYEDnldzIS++UQaSDmeQ+JHXxomjrteucG+kJOTnzhcql9A/bnn/wlQb53tj/ZGOMV+P4PGOC5wmXNSdtdwlUWLoAgMFclL5HzeSQMDkUnhcPO6cdonHuCOEekq0q5K8F4JoFIZ7Max5glCAxQAVGNQxul0uXXxajwkzRI0BOAK5rtYz+Fy+z0A3AMvp+BHg6lGidhc8zqZEPVXdFiL47KiY+ktVqPHDj8fPk/49DkxIMjwosqkTbOPI579q6xcYokATOnZBXPyJJj8V0GdIxodM+agq+TB/zzeQTuC4Sc9qy7SkfR6vtO+YddIzCOntBlhdWMZh1ZvKF/Fc3WkwQa59UT+joKwObCSdBBM+c1Ggfs7XUixWteY4Dnw1K4oE/HDA5o20IPnikS+TDf9arkPAJjdbmczjo5qPYmQ13gVp79ODqsgKfrKcBca2mBO3kmYdvCA74D/RohlV6OAL0KPvKv9H4GTogU5zN/PQL4KZgUq3Af6X18Ap1wmjSzDXbpO3tsYwse5vL98LDGVsBrsQ1+1tqy96mA3K65cMETfp9/FTdnuumZsZpN1hEfLw1sQgLpFGPrCdwpwH7yDLFa3OqBHYgV+aNG8jDNrFFTEO/lH/IQOiquKl74OzLqwT3NMbmaeWK7PAY+vf1Zm79T5MgZ4ztznRvwCR+8ZhyfeWn747u8oZQ/NDQD+Mrh2Ah/2fJzL1w2AsfbFi6AAFSEArBgXhmucV6HEmwlflVgodASTKcks1fGCIyj0v1jONYfCZFRwDuvePm9Era98te+qgAJQrOCTUJe0Ww9iY5AoRCoxrnPmO0bmkfG7J4k16tlHMohmnUUBXLjqvU889qHkxfOx94ckpMRrzNUvDIX/xN8Kr4ax/DgIljOkha40C9BYHb0jV+Mn/FKJHwe0ec+J/r6179++UJwta/7aFa0chb2rfiF/7q1aJ7JCW/Qi260VDjiG/1VjOrCzmhhP06i0DyzHwnZG97whie/Y2U9vH0YCheAdrwSrMm14sUI0E7W6TxKfNZC9CTJu98rWbFjAZ+vq/SELtEjzQi/V3o3AuPZieSuwqMCuiDJUdjOGiAtwI0+sXeBH41rAW7GK7gV1PZfC5kr4fHaRMVL+NF7TRjNmoomfMurtH7fAmyanWqqbJ0LP77Ia4p0N3Z5acD7ay5cAF5JcjUHZ7IMpJHoDYM1unxKsB+9kMCLyfEnl5Qtn6yRIy7Zv93bZ7lPq7OKFraI7y2/2Bkeat5JjtmanEBuwJ+1YJ7miTiQvI8v4Rs16lobh4/P1pFfxc+117kBrYoqBVlykEvsC+yDZj4nMpqB8cbIb8ijh71w2QgMpC9cMI6Ce3UlX4SvgHIL0ubPElhC1iVXHY+q94D5En5/KWjWJacI9mOsTnwo8QiMY6hefaPk5ozAOGtIfDhfBlsBmvFMsKiKIeu5dCT9haoksCNAM0f9tre9bUneyaQHa9mHQ+WEvF5R0Ww9dHI4Ll0jx5Pw5eg8t14P1la8SX5mMoeH92Ed/QtG1XruK/K8YiMZRf9oHFAs6txyphxitaagJ+FxPDxLlsicQ5HQVHwC+CHxUlTrRlVAD8nSKQ/Hg5YRjj6jVSFKf/FqRrN9ybxK3gB7wRsnoC1v/P6wFC7wxwOJrBPNyk4riK55fxtft4LTRT4MrytekrlXihSldGo0jrwFf6/QKgoq/zUCNEgYFG/sasvc8I/t4uHIJ/VA9yWZTpHo0hYdMl7Rjc40GtaCuWSlU8l/Vn6BDujSS4z65K0FPPPq5tqCrQWy8lcTrb9lLlzsp7nHF8/wOzfg5zUULuiXL8BjlMi5J9GbvYYeoMvpTt9GrseC/ZzW42tO8S4pX/YrZxC3+8aAxonYK17FzvGT/1bARBc946fEImugwcXfG8dXtvmAsU5mXImZnsnbjG3f9CAfuYLYLm/IGrkuAeTD34WGSwKfRS+8VbGm0YZHcjG5civLgHxr/3L+BhgVLgDzBCQnEKrtkSMKxIETyixJ9EwXgzNgBBXYS5IoQRWUKkEyHu9u6szMuu+UTLLJsA+9SkEZ0czQZzRTNDQz8pkT9kyiY29JUQVworROaOA6wg99aAnNDHdkMOa67zke2tuxsoIwTq0Ha9s/f22sSpqsK6lSPHFcFY+s5whbYpP/bj7a13ocsORboazgMNfYdrzf8TKv1Vmvoh2PnCxxapKf0ThgH0WDjqkEZuRQgHFODCVa7f9X6XG0D5oFbzZV6YVx9EeAd8GxXSfgnjW8oiSAh5bsC9+HpXAJ5PVDRd9WmvghvJfo4s2W+eai33dFZnMV1nSf/6r0yn2ncjk13gJ0TbFNf9jgFhrMVUxIxHMiWIFnXq+j02jaymt2qojLF/q3zKf7itMUJLH3FnzmvxRx4lA1Bn/5DX545q97sJ65Trh0bLfKyd745vW0mY+5BMD9PhQuwPdX5BpO+MigAmvRK/qpqXAX/OV70cKOo6eXAvSjWbNKA06MtD9dgxO747cD7JnvFL/Ztfmey93EiDYWGSsXETfy/V97yaHsJedq+S3p5pPaP94hT1K02NN8+7XXOcH6/I9GdHz9pQE/6QU/XcXvgGdyK81H80Z6tBcuG4HSVYWLwMnBYLhEojJc9yVsGE+5Z0BwcbIzR2CcClVwpKSVMBmVI1MGp0MxcnDmcjyKEckJo6scofsCkk64AqHC0ZqM32s+aJ4pGx7rWEhmZskEp8CZCKYzmo0TbENzj2Po5bT8acenP/3pyzGkLgsjM96YrO+zdRRNaKl4Yxy5OHXQqUVL1uqB7ki2BTL49jgG8EbHsu3cBrd2XesJYDocs1d1zKeris+qsAPu51U+nT3rVYBmBSCdrdYDHCgZo8WcEX4gNOsmo7la03qKQ+MUsz63svP5YStc2DN66De8tgB+KDw0FNa8jtIC+TtF1BCYdXjZJVumM/ar+M538A0JalvkQ7+d0q7pNGddP+mRuf4akERDg2EExkpu8EninT9ysgVHcYG+KPi3Ql4vlvTYM1fA72TvJEiji06MAL3sgh/Mie5aIG808Cfon9n/CPBLUUUPtux7DqCH96VwEQs0n1zkeki/+XLfZZ3Fw3MB3DQAxIhZLDkn8GNyMw1XvOOjnMLIYVq9wxs6LJfzfRfP6LXcTYzp7QttfKUGgjcy2Ju8w3eLekC3xp1XzRN72SY/PYtz5wL6LpeTH8rB7kIu+MGH8SEzwGcxQMzIHzAZ8WsvXDYChR0VLgADGQAlwdRD31EgRO88CtpVIDA/XZdZtWwcg5IEehWsSmIohm6ELjxnaNwIR/cYvu6cgMuxjhTEPbh7FQnNkoeKZvdDswBe0QxHSYQgKdDNaJFMo1kiWTl/4yh6OrOchzU5OY5NosvZSqIdy/uLF97VVQRKwDk2BkKe5poHt6xVGQ7+WdO+6QAZ24+HNyP1hwTIMON6wC8Fk3FOPuhQu2bm4LNx5KZDZH33RmsqoDmJQ3+alFzRnGPxapw1vE9Mv/B8Ng7vJauzV5XoO1rIRIFc0eE+XdBxVLyYl7G53HvYChc6wH8IqIIkmtcC3SEHr4/Sv61z6b6ifDYXfjqSvsw90wdrSQTyHvoW+UTn2G6VtAdCs+SEHkhUJBX21REV3OkbfRdA+Sm67PURxSEcvWbFP7Bpa80gfKIv9juEXwvmGs/m4GidEfALdEDjg3/F8xHAFQ0S5a08Nh5v8h0GuK0FdudddHqGZ1v07BwAn/tSuJClZBPvZrINRMa665W+nAvQAz/+gF3xk1v05BTAFtgsG5VDKNLzmnSPCz3kk+QPYoY4720HY0fAJ1hPszJvZ9DnHvABHpptXlGj+3KAFDGXBDTLW/gwr8SSzxa7Pxbiw+Sa8iW5wwzoDB/DJsim0h/P9sJlA3AMVeESEPC8riKZqBwSgXjGCLxKQbkqAVB2RYuueBLbCnRcGK1xswAhyfv4j//4pQM3MybGKlGeFUMAzQIbmqug6J41OBLdN8pXgXH584Ic0IwWTgTNEuDZOM99F8hrdQLGJ3zCJyyJF6clgScDBYFCRofVZ3yCLx685S1vWWjUwZEge1bJAh7wV6ThTRxGLpDf0eefXJEZWeR5C+7pojFW7z1bM/P7C+8kOhxyPy4Q/UsCPpMFnHzxUcdZUlfx2Jo6XU7f8G7EGzi4rwODZrRXNgLQ7Hte7GQ2jnOUGJJrksOWbtfDWLiggzwERgkLGrfQZqyC8J3vfOfmufblbxQleFLZAt1nx4oDc0Z70DFJgD+oIFEzZi0u1jQXDQqSHqwDN/TxTU5S/cM4wdx4+kK36KTk3x+yUKBIUtDmFQfNCkHXfP5GN5X+Og1BOxrt0eMMN3vk+20zX9uDNX3vhz+anXLAHY5wrvwH4OPorGC/BdBlf7bjpGYr4K94KXHCq7VyPRfQg/tSuADj6KzmmnGVnQHP2AJ9I6tL89r+Ygk947PJHr/J/VIXG2W7bDR/drd9Dh8XXhor/ssFxDdxn92NgG0pimL78gjxKeu1F1/B7r25AQdNFbHYni0ua67R+rlG4115jj7fueF/ExcvCfwfv+oPhqDf5xk4GeMrZm8sgb1w2QgUT+HCiWPsiGnuSdy8RsF4K0MgRImgwKTSp3AjsB6hM0YFUZLREVhDEGOESQBGACdJulcsZt+LgaOgLXl0VFpBaFYIeOWqCrJ4Bi8FDporh209hqZzJNGcHX0zUIkkmgXnSuE5dA71kUceWdaFr7l4AV+8YzASfk7HM/TnyknLi170oidfyYsOtLhZT6AXHDmNihdZk1Nx4lGNszZcnN4ocv1e8cKaCi0Ok3HDJfj1OJKnYDgr+IwTAJ3KSNJ8HoH5HJRxOfEY4WgcOeANmeH5aJx7nikUyWwWsOGksHZ6Q2eqcXDSubRmNWYNwOtaCheAV3nVik7i8YinFfAnEh12i7a1YA9zNVRysjcCvJa053+LjPCLvPk49h67WgPG2ds8SWA/j37ws/TSqYrTk3Svs4+f8KIjxuIH2xBI2aWxnrt8tl9ey2GTknK+p7cPYxVsCkvP1tIE8EOs4WsqfriPp/njFtX69mbvdJbM1oL18IR80wjZCgrjJIXwvWtAz7UULgplrxFXtgOMkwiLE/me0wzojbxDvDDvkjyHK52ni8997nNvnnjiiSUWSTIvdUmS3/jGN978/J//85dY7Tts7XN+0k94uTQ8nve85y3+A28rGwLsiG944QtfuHzX015Zp73clzO97GUvu3m/93u/pRmigHF/dGVei2eudt3+Go13odFz/H/JS16ynIJt8e2nAHzEzzSHD/k/dukNG7nZIT+joY5GOexszbuGqypcOBrvOkv4+kAVYLy6zxK56s8AYzhhMXKONK//jMB9HXnFkCBi/REIrJI3+zJEyjpyXNlbZ17hJIk3dwT28vrbmo6EzpCK2bFoNc6+CWZorvY1X7ekTexHSoo+clFUOA3pnbU5ocEJiGAhgEtQwseM0U3VibVviz8cGYuCVfIjWZHQm9PvZZxkw/vmDLAKHJJMY7zmgv/VOLQpRpzyKPqqcfgKJw5SYgZn+LQXgLMiw3E+nmRcD/aBP/2QfFl/Rgs62EaS0x7MpZuOjQVVRVslezRzYmQ/GwcnBRWaFYsVfmiWhAog9q7GrQE2dU2FC6Dzurd4q8s2kmcFeOPklT7Qr7VgD/yna4J5Ndc4clcw0iO4jvBjb2QkCZ/51hGgAR78nrnwcikmFGRkrlniFJROezYCeNE9BQN9Nta94Jvf6Y81FOFOI+3Lr9gLH7I/HyPB4Te3AH2n92TCl44guIoHfB8ej8A4eOIrf1vRPgJ4oIHf093cMjf2TuZ51S18vEtAw7UULorBnMrPfBJbEP/pgybAjI9kxo7EQjnIFpkdC/AiZ3QpGMQO+Q/fzAbOfWnCshe5hTdKxAUnI57hG98g9vmZ3/k+/pz/pOtVrCEDa4j97KiiCQ6eyXEUD/DgG+Dl2WiOdU95WdPpEL+skLE/HbskiJP5Pi4/MNNZuk8WeeX5kO8ns8cee2xpDB8Ty88NV1W4cHiqWIFYkloJhOC8vsFJSuwqMF/w84qNpK9az33vSzoloZSVgREkJ8fAZqcf1rOfVwyc5HA4IzCO0gs+EnJBsMIRfwQqDgtvKjAfzZx2FXABWjgAr3YkGR+B9UJzvggcMAe/ODNBnnNS1fc0MxaJv8Il3VPruowjdwkKp6DDL6lGYwzHOHN0xRQ3HEgF5uRPJMPN3BG4DyfrkX1FP2D4xun+knm1Jvkpgrw7P3Mo0XVJkSSqAjTbEy14U62HZsmPDgxZzkCws97s77TbB4/RrGCOvHpwj5ycDLz61a9e9L1acw1cY+FCL5wSeuWBHmylD030Hl2V/CrAW8FJcV3Ndd+pEL0jM5/7sXBW1PAfa/4EbAvm8lFsml5rBOgy0iFBm93HVkd7B9znO7xnrQhfiwN/4RVOPsae9lbAiBEKIHRtAbaHFv62mosW+0oQJUSzeECueN/6tDXAj/gCshg28ykjsJckRCNLouHzNcC1FC5APHEqIBYc4g+8vU2hIDk0li6IQ+xyFq9PCfawr+aAV7c1lJyg8vWXkj0dFU/9hUyxBv1sH14VD9xnb2xNvsYXjsbK4YxxmkUWrS/pL/TKEcQmxSM+iI2X4oN9NMSdGsuf2CAduyQo3uRjiujKNwXITeGoYTSD8FaDzh9RomvkcK1wVYWLwOiYSoCiDALLiHnuCaC6VV6LSvduBJ4J1sbOgiWjIiydhFl3lCJI/nyBTMJQgXGeS8K9SlAZFlpy4iNJhMcI0Idmr+TkHdcZzYI6ozqUQMPNkarkrAK4C5RoRntAceZVjSTqaBZIJbLkF6Py0zxf0ms7L35KzB39uo9261iTY+TEjHExVoUlntpnBHipEODUyLLipfWM0y2WxFQ8co8O4mW+oFzpIx4pQHU2OOKZvCVfaJYcVbTYW9IkeUIzXowgtEie8rrDiBb70h8J36ywsh79Mc7J4ow3cLeWZIVuPIwnLujEe7ThyVb68EiiIfEmp0ovRoAfipY0Naq92aExEnzyG8nLvk494SHQrwVr0Sm6xU+k26szih44Wtu4XCNwHx9TuKwtONBjD7YiUWHbuq2ve93rlkKysvERwIGv9R79TBb4zC9IfuE8oil2ouvLPsP3iv4WjBWL/L8sfnHNnBbgJMHQJPL7MTZ3SoDLtRQu/LDkzis9s6YloAd0SRIsbqGjArxmi/IJsbPyt6cE+sIPK1rYsN/J31sFW5oQxwBdJ1s5it+9oqRRJXmu7Ahf4M7faJDiWz8WP/lH8Z4MjHfP3P7iS8Uwr8Zq0qAdD1IUXQI0aeiVxh858M/0HV7ntkPr003+U6Fd5ccBY+VtvjvE382AXORu9FoTkkyuGa6mcKF4qkKJPufBSCWzGEppW/CZUAQzjimvgvXjQIKEYz2vRVXjABwkJxzYzCF5xnHoOFJke/RgrnGSCUY5+8I5WnQxHFdTsGocw8UTX2YXLHsnEDAfzV6lQLP1R2sGR4agKzyjGW84CLQwUp+9DqUD0xZ67ks+JTeKIfvijwCtIEnhAneO6t3vfvfPOOWyv/Ul9pIlzkEhYFw6PCOwD/wVa5I4vKpohqPiT7LPmVZrWoODtuasq2Mc+UkKFVjVOPugmRM3zrwRvyM/4zjGSmetx3mhV6BGl3ujsXTAiVn+zO6IZvOMU1gJUgkkI4ATmvFQB4rdKvBGPF8L0Z1rKlwAWiW8umx0veLJCIylY4pAvgCNayH78jP0kL6MgMzYIj1gX5XesyNrOZGtdAq4D2/rmiNZ8kc0nvWsZy2+go/yPPP9bK8RuG+9rYVLC3iH/06TX/CCFyy+x6kpH2HtmVzsj2Y8IovK9iIvBRqfXa3J19BV/MSjLXqPDqct/IokcAvAB73w80rONQEZXEvhosus2eBU21/wnOk72cGdrzVPoVPJ0xrkx39rctKVY3zeGqBrGkRpstEBukn/+Qe0nQvQaz+NQDmC2OwzvVW4iCnwq8B8sSw5AfwDnlmHvsgt+AT3qss69nTiEv+BD/ImeF2CD+ilI+ggd/vKifDnnPsD6/N3fNgsfwjwS74rqHih3xVYI7GXjOQdCpfZ2ncNV1e46HJREMosKaqCAqYSpPcsCYcTrwK79Qjau8iO92bjJGuSQIltFVztzWFR2FS+I4A3uoxTEM2Ux966B3CcBUI0O6WQSCgMKmNxX8c+R5rwGCmifa3DKbUOoYfQrPhwsqDbpJLvT0CMs5f1QrM9+sLFT4m+PXsafE5gkERLDiTFHBe+jOiAt7U4sSTmo3Ht2o7aR2OA+TkJm31fxT1dJydhEsKZM0GzwC5pwZdqHPx1UznpBKrRWPywp1NKDrQCNHNeinc0VzwMLXSQjlU66L6mAZq9ayv46NQ4lapoWgNxntdWuKCJfnnVShKE71sAv+gQPzA7ze3BvuY6VRasqiTXOM+caCruK//mvlML9jTzHRIRdq3Q4oMVCuxQI4F/TuGyBeBI548pXODLp9E7zSr4OOmje/Sw90UtmMu3o2XWJSZbSWJwrGxFwmQtnd/Y/GjcCOyfU+6tfISTphnfcBsenhPI95oKFwWqvECHXDe50o3IjkwVk2JbJXfgvrXyBy8qXToVaOyxQ/oLL4DXCi2FmXixVve2Av1k7+IRe0se4b5TB75aXjDb3zPz5Gm+O5Gx+CbhlyyvKf7ZPNtvTw/wgZ+Ss52TD3Blr+wup3LwxQc08EFtUXYO4OO9zih/sO8hHTVOzKlyv4BndF+uqBkpT/DzXLw8BVxd4cJxAEoosRVAqkQBYz1TPEiUJYYjMI6TZ+jWl5COhOIeBRXcGcisy26sTqTX2vykyBUIVL5/IKmYKRHjlRjkFZ0RmItmwYtzlrBWtKBZh4BzQ/PIMRjHCXPWXvHiJEcOnqHYl9E6yXHqo9AiJ7Rbp8UjNOM5WnQKJFaM3xzOJq+O9fjbn3Mie69TKJAkLAwXHv14dHGIDI9zDz49uJfkhRPGn9E4wBnjGxzgPxpnX88ksxx75cAzTmHFkXASlSzoB9wEBMUB/evX9DmBA81JnioQvNEseZaUjnB0jy5JPPIq2QiMS3InASYn/DbvYS5c8JsOozPfJdkCZM6f6WSNZDoDOquYVHDDowK2Zo9KxtFDNPAL5E1n/YQfm/UqiCaBAK1jDV/2ylbIXQJ1m+4ifOjJMYULuvg9ugdvtOiO89cCukSSDvIFZMTX0E/08TNsVIwxr+KjeZoVGi1oxLOel+7xX3iENxlzSKaeGwsHuBxKKEbAlp1Gk9fM5u8C4geuoXDhm+gZPfF91MhqBJEB/DVsxFVjZ7ZmDt1SOPML55AFXZET0Hc5DvsLwI0eO+mevYlwLOCJ9fkC9GYf9MNHczLN25kusyW+RH6jeWNd8VxRyX6tW833jBzlRfwWuwngETzkYeJmbPHUwPdojtBtMgm+LnLQlFUoHOLDbcB69Cuv+vJr2Xu0F9zECgWhhuXMV5ufmODtHH5lL1w2AGVsCxcKSEAUkuH4PALMFZRUvJKemZAYnsqcoVHEChgUp8eBMYpKgNZgLN65lmRWOFI6CuTdTKcflXG5jxaB04lPtZ77jJ8Sn5Jm/JdYU+Tg6Gcch86vBOqVr3zl8mcOdVA4tBQ7xmVeaFbkSLC9TmRtgZcDkwDF4Rvf7uVUQFGDX75g6X9QSNA5jjgNlzl+KsoYnsSNQxyBtekYnsHZ78G1B2tK3hRy9huNcQ/u5K9AI3/zRmAc+tHsJxyrffGMDUgOK1qM42AEgbwKWAE6jRNgyH9Es890SFdf0UD+I/yAcV5bUgixT7igD/8f1sIlIEmml9GzLbSijazoHp9CDmtBMHTaI9mdJfx8ghPHdJdH+LkHjze/+c3LqaOmgNdy6bDrwYMHS3Hg9GK0lwaH1zar9SswFs+OKVwUVvSM/wDWzGU9/kRiIbHnm+go+lxef/DdFr6IDvd24Hf3+BhJLn/Xrh/wO59qTNsI6MeNwPrmsm+JAR6uBWubr0A7dHp/VwCnaypcNNbIh6/UhGq7/S2099AgXuZUfAbkp5FHnnzDqcH6ThkkxXS/jS9wpsfihT8+lAL6lGAPeYF8BB497+zP5zg9FLPhN+IvgBscva3htTD88lOzoYqxAXLQUOH/7NfyAVjb/vzX7DW/2wLc2K0iS/HQAz6Ihe0f85jRsxXQg0f2p8NiifVz9SB+87HyNX7R/AofzxT28myy5vf2wmUDEEZbuACGS1CKktlfTzFO0HJKIjGsxlEoDkBAU0D0BhAgTMKXpEv4rTcSonscI0dtbwY2Mhr30Kebo0OjAwjn0Zrue/1N8lZ9j8U849CKN2ipaHY/pwz5LtAI4Cihkqw7QUiXUodDYqOI4HTsKUEVoHQlJXFo4rwECoUJB4UXLjR7F1eyqwNAjgxDMeq5PY1n8AKydeAgKSBT+iBps69iQpHIyKyXTnHeW2d4I/4DcuIoBQHOLzLtZQAnOifxsXbG9WAcvj7++OOL06zG0TE0Wo+DoAeV3hknMU5iV9FCN/ECv9A82hfYSxJqzRnN0U06MqOZznHQTtvIJg7a/adC4YJOuoGfZF/xaQSxL8WCbpi5awGfydmfincKXO0Z3bWHvUbj4EF3JHbWUvS7yJXtCr6Se7SO6BNAY89sai1YB363LVzgInlRhPMXPaDLmnBHv5OT0OWnBE/gp+do6+myvnGSI2PgOgL3+Tz8g0dlyyPAL/GEfzW3su8RwE/s4vv6JPZaAG+usXDhV8Qx/mmU+AboBJmwg3e84x1LEkdXKjDWGHuluTqyudsC3MUrjYaRLcamnOSnGXcqsDZ7oq/iDH8yArzUZGNbI7tqwViJvyJSw48/U2igbQZ8n2KSz6n4gFcSdXmA32d4bAHroJ3fyto9kDs50DGxC45bbPsQ4JsTXrkemYQHuVrwTD7CT/KB8Kj00rr0V+ErnpEfeeyFywZgdH3hgnEElVdxZt1vAqNcHKdxleIY62jSX6hSRVdODAhk+csvlQOzHmErXHTrRooNoiScusShSrJDsy/i6UYIcDOaORZGfYhmwVjxIdmf0SwoOlGhzHjEABRGcCcjSbPkkjzQ4J6fkjEJiT385R/vxDMG9zl238mRGCsGHW8zKpfvaEiEzHMkLmhYD7/hqsPIyeGJi8zIz+mFP1HLyBQ7VULnnrUk2jpHSbhjzJmT31Pk5XtBlYzg5hSIM8WTivdo0Z31KqNxFaANz/IHGoJXC+5xkJIzPJslsWgUJMjCTzwY0WycddCiOAzNo3XpuSJV4gbfgLWfCoULvsCR/tJBv2+h11hJCF7zE5XOjICcBC9+ZuaLrMtudYL79a3hZBD+fBpdMieXz056JRZoS4Bswd6aDIqDUQFRQda/TeFiLv/DF+G7dXpAK9qd6ooj0eFc6KG3fOqoK2tN9seXVvZsHXMlk5oQubcWNILgxxeTxRbAL7ED/+C3Zd9LAR5eY+GC1+IjvWYXlf0AfMVrtuaNi0N6imYxQDycnbpvBTgqtvn5mZ3ZT8zi5+Hhd3nL7BLjD13W0UB9zWtes8RMMaIa57lTTYm7+D3a02W8pBqucgyFAF/Sr9le1me3Tkzt43OeZU0X/BRQ4mf4kGfHXOjBB389kA/iE3ugM+6TEx9l/GjcbcA6moneXNGEiU9urwCfpcjK67R0fzQuQLfZiIZu7IR/2/8B5QYQWPrCJUBBJaq68LNkIc5JAjVLEq1BuDr7OjAVECQBSuK9DlUBhYmTmQncfeMYru4hp5cA2wNllaSkSz8C8xiLYEFRJcnV3hRTwuBVqRnNklx/qe3tb3/7UnXDL5e1raOzwVEzktx3+R1NeKVLo0gRqOHn1bKnPe1pN894xjOWZ+SsSxPnxVHjd9bzO0ehkyShsC7IXsZLvvLKi9Mp93uwjoAikOFRFYjsS3/ojsDr99F67uGBYloCYz1zR2Bvrxxw6mRTjQMCqldwOFzzRuC+QlDXNa+SjXC0jyRJ4GVT8B2BceEx3Ujxa81+XWvkzz333TfO9KlQuIQv9FHxQfYjXlVgnMCg4BaEZwlUD5EVXaLrlY5Yk27QEfxsdY4M6YOmCL9hjeDvIkfBkc0qjke+1nro5kckFGvBOvT1NoULPPEcTejrcQLGOM3VAKH7oSkXvNFjDYlFz3u8QVMKkhHYg9wU79ljC3jFTdfU3K0gMWMX/HMr02sC8r3GwiXyJ3fyn8VJ4Jl54pNXI2dgrFyDD1XsmHcsWFPMckovX5glwRmrMfjRH/3RS4JvXl6RHF3GGjO7xNSP+7iPW/6nh99HY7KOtyBe+tKXLv9VXyOxH+cy1sU+X/ziF998wAd8wM2b3vSm5Zk3OnL1+Nnb66v+o765ee7qaXJa/PznP3/JXUZjtl7ma4r+8l/+yxe6qngL3Ocf5Ez8CJmcAvhpjU8NJbpFj+3VXgF6Yn8N2jQ3RuMC4gh/JKfIc/vhucbtNcO9KFwEGQ5bJ9zPNuC2QHASfgagk59xPST4enUpzq0KBp5J2HT+qxMae1hPQp/3UStnYz0nCIKQar4KxO4LkmjWCa/wM05hx8gEXftWNFPmFDn9vuhS0FB6RZ3khsH0R+vwRyd+JHFrwWdrMwAnKhJ3R8K/+Bf/4pv3eq/3uvmgD/qgxQE53haMJcsjHvgsyVPkSFqMCcBH8MlrbS6BQ0LQ4opmeoUWydhMfpwS52Cc4qGSn/UU0HRHwVWNs55ASc6zEzvz6YEiD53Veu7jKfnQWeujr+cbgKPkgS5Kdmc0OwFAc3TW/X5NuEuG0awo7XH0+alQuATIQQJUdf9nwH68zqURQ/fXAr5G9xSP9H8EsXMNHPpkTu7zE+Yqksks+pMr9sI2vX6JzpE80Yx2r8w5eYzezMBz87YWLnAyVkLGnqp90OwkKY0e40YXnogP1mQX7hnP1ypG0TMCeJAX3fRaEDmauwbMhZ/THsVe8FsDxuEb32Q+e14799IAz2ssXACekYGG26yBFYjPw3Nxgwwr8Eyix6bJZzb2EJhrbzYipvm9Wg9NdElB7KTBdzzopjjIdmeX2HroEpd039mURBxt7WUMe2E7kl1xLvdHl2eSZXaqGawwtK5nPW7Zw/p4iz4/2WB/mSNPkVfJCcSofs3bXHJJuRC5ysMUsXR8ZH/ukRN86Vhehb8tWM9lT748eVju5wrwwRqK+Cq+tNCOA/BEn1fw5dzsImPc3wuXDcAAq8IFYKxiRADD3AixB/cThCjwKGkDBO05R8tBVYml+RRYcifJiyPsIcogWeQwOcaRw4GzvSTbTl4SQEdgvkDny6aMOOv1dKOFognIs/fn7YNmhkC5W5opPYPzGpu9dF517CWScMyeo8IlF/zsYYzEpnVQukHv937vt5y8kLH7jlV1COABZ3NbGjmfFC6tE6Ar+AcHjoLzI0dFkmdZw7o6x3RGNzY09GBfwSnvdlZgXQm+pNXrHtmnB/tIEnVe6GxwGu1PZ+iW49qcYI0AT51OKR44sYoWfJTgoVlhWIF9FGi6ZU4JK7AP/pKhpBmv+r3t+VQqXOiiVx4c37OVrUCWClB+Zyu/yEJwnukpeTjRdWLLPsiaLbBJOuneDNiKThw/OtJH6/OJGht8osDnXqXjwDN0e7WVHrfBcgSemUMn+ECJQ2jp5/msGNMlZUPVuu6Tl3Hxf+5JfgRr/LFfD8bQe0mERK71h2sAb9iYxDI2vna+fSWi4gp9GeF3LXDthQte0iF+nv3OZGAsXaGrdPxQEqqRIHZqkkWvbgN0xSkL2xWD6UoFntmXb6bP8BQfDtnWWkCHfEFTLTGnvexvDH8t9iY2jC5j0SZuap7gvxwtxUAL/Vw2o9gkzxFvwwc+S+xjI/2YrWC+xis5wFV+xWdXr3EH7M0X0T85wEx+MzCPXxJTyfWQ3eNhGufkPwM85NvlX3BseboXLhuBYGaFC6DAHIPEm1ArpSBEFa/CYBakCUyVLsGeddkZpFMPhQFBV+Pg47kTBt34kbJRkBiaYCxhFQgr4HgZLedQdVmtaS/vmKJ5lkzBXQIamuGCPsWBzgJjxRfjnG7pdOi8hNf2kVzqBsHbffcovKKJ4/QqDKPQXZHg6B55R9SpkO8MKToUAJwMZ+AeQ+ewrYNm+/eFCzrdl2BEtu7DV7JlX47FZ8Dxe+3Ns9zrwZp0yWta6YaOwDgOQXEHp0pmkQUe0VM04C8+edYC3DklwV5yEh73YD6aFXsCSEULoH9ozmleBfDyKkDble8BPmgWvCWpVcJGJk+lwgVf6KigRn5bgdx1Mfk661RyHwEZ8JN4bZ0RkAG7kyDQG/KhNxoR3v+u5B3gZzUFnLz0SRBc4aAgZ9MPHjxYEif2bCxd7cEcusheJbW62H6HV0+7vYy1Fp/r9Vv+9LWvfe3iq91v9Tpr82ezBlTAc75Zwc6f2s88r4Tgy0h/s4eEV1LS4zwDY8lJMtAW/qN9RmBfRahi1e9b9r40oPNaC5cAHrIJ+uL3Sg7u47UYxY4Ovd5HrnRTLBMHbysnOskvwBF+M4Cj79Xyl+gUOzXVFMmH5q4BNPAFfI08pddb8raXRoC9K14C/OEj5AXyCuuKVWnKzoCfYLNikLk+B+yJVq/pWRsfRj5oC1gTbU4k5C5kIkfgExVoPR9aiL3DFU4trlvAGvySQsTeM94CvJVf0cGZ7gU/eRofHh+c9ffCZSNQuCToleK5LwBLGCSZs6DPGLzK4NWEKuEClJJyOBJkTNU4z4zhVATdal/BjzFLoinTaD33GJsk22tTnCMFGo1FM2USuNrufQ/mWo9CHqKZIfiyuCIQnzgFSbZkKo7eXL/rkFoziYaEVyJLBgoNlbv9vPetyOFIk8S4dIZ1Vhgx2XI8vjzseBuO9tdF5hh1JBUyxip68JEz4izICe2KLYFEhymJhstadEJHlEOEJ1nBv3Li+EiuirA4/4q31vC6Bic9cw7kBW/8wFdOIji6AsahEX2SoQpH4xSTEgKJG5pHED3RUdd5se8IQrNXgcgczS1eAeOsoQHgXWNrV07YOHJT0I3WWgtouw+FCyAvctaNpJv4vxbw0WmY5IrsK1mNwFg2xQYktGyHPpIjnIIHXgq8kjc2xt7YV2xmBtZh23xOGjUu+2gCCODo1olUILFdhb+OM3/AVxjLHymgdW3hSj8U1d5t1wBgw/Yxxlh8RI91FTdsjc+zh30VSJIoiSF7p6NwDT/4ncouA/iDVxJX/s6eiiMns57hDVrx2Ti+Eg8U+AI6PA/t0QL84vP4JHPtscZO4IOP/Cf+XTvg2bUXLvhPDvxff5I/AnomP+B7D421dooictviE+gDXTHffuxgBtbWmHPqyz58Zttszb1D89cCnCSxmh7iUGIAfNmCQkkcm/kV9/FOLJbQWwOvzGlPqSq7Mt8cfiaFRMA8fGBfOaGq8FgL9uKX6IjmCdztw4fyS3KQQ7JNIYFHW/QAGC/eyl80mnyuaEIvHedPNZrgOQPPk5OlIMoF7CuH2guXlcBBSIh18QSyGEgLmEuJHC9K+GbfHSBQRuz4MF3HEVhToNXBd2JRKVkUxKkEhZ4pCBw5TTgysihFC+4xXM5TotY6hR6ibBJNQdD6ozXNF/Qlr7NkGC0cG5oVBQyEA4nzydp+kgv8GKxkS7fXPJ2Sd77znQs/FC6chv2tjYcuDjXfN5LQKJasocAgl8jZPuahCx8UsJIUf5KSEQkEnJ7kRZHB0Um+zQm+foe/YtXaOSWYFZnkCRdJdwqz0N6C+wxZ4gKH0RjgPpol+gJdi1+ugMBGTorhCj/AueA9hz0rrOmZ72JxYIdodkqg8DRu5BR9Ds15raLijfkc+ate9arFLir81gD53ZfCBX/xTzI++2uCIwh/FZjkJZFayzc2Qi6SbcWDBI1uKFL4kviRyIVt8wVOFATBtQAfekenyIV9s0vFDHt3L3ZuP7qJHrZKrxUYChxND37Q72wCvxQZfALcjfWZ7uAF2aOLX7Km9fHahU+SF36QPxBkXdbhu+GxBuAev6tJYj1+Ds3kyMYV7GwTbyVtTsf5sjXFUQuKRvQkAbJHrkNgPJmZj85rB/hee+ESUKiSO72eAVmTuQRd7JrJnkzprL3zXau1QM81ZcVhc/FyBokL9F6cj42YJ0aypbX2MAM0oUPcx8/kM/BlO+y2LSRGYCy/5JSTT4ruw08TwqvSih90zOzCXP5HDhQ5sC8+RlMx9nUs0Bn+K7E5Fz54W4Tv4S9mABdxgY5VOeAIjLM/HTA3xUUF9tH4Joc1BZUmjJyKjwsPW+BnxHxNuWuGqzpxYRiveMUrluBGIUeA2ZI+gVqSSrAjARA2wxCABHmVb5XIGyuwCO4q7dG4rEfwEl3JX+UU4QMvibPAD98RUDKKIhHnbCRCI7AehyhhYOTmmNsrdHCk9BwtHvrcjvMZ3vjBCP2FD38NhCOHc4w/c+xDyRUpOqACOR5xOAoYBYJ9enwkGQoIxg9fhQt5CQIcNFlLZiJnc9EJP7hxDuTGoZENnZA46GLY237GB1c02YfBoed1r3vdkgRbi8NpdcR48xW+khe0m5/1euB0FWi6npXMAVoUBBK8FBn9BTxDD37gUwWcowSLs1bkjfDzmb6iW9J4qMggB+OMrxy9fThB4yROM97gL3vAb8FyNGYtkNN9KVzQic+O8wXOQ6+S9GA+PaZXVRBpwXhyVhizK/ZBLxUx7F23jU8UcCU0bEtioQHgr+285CUvebIzemivgBNDrxNaPye0cKYPsXeX9fCCvrIlCQq/53f+kl8RvP0MjmyVHfCNfIJmFH+p6Kd71up1zp72psNpDPlJX2YNhRasYV/FHL54BY0tKojwTAIg8VaE4Z/4IVGCk+cp+NcmSezXnJaeNfMAf4KXbLqKXdcEeHJfChd+l77lFHsmEzpDHzQByGE2lh3QT7pJr9eCdeECb7Yxs1HP6K+Cv20+ufwuvtrfCcEp9Ma67J6diUN4qinMz/A/aJ4BXothYgkdCaCDLcql6Ay6Z7wlJ01oa/ElaLOmnMjn8OEYsAdaxbQUteEt4EM944dnMjKeb+N/4RjbPwTGpMhTKB+SHx8h7vJTM5/kvrXka/K+aiwZ7IXLBmAMklzVoNds2i9PjoBxSqwo/Uy4BEFQxlKkaj37S0Ik59buwTzKZy8Jr9cu/KzWc18wldxzMjPj5uB01XxxrXKi7sFLoiO5Y/CVMaBZIaRiRzOcc3lmvte60OpL83ju4hDyChbFNt7vup8SUycgEgy0cBICKrwpeXBxeY7nHL39rcWZOwJn8NblAM3Fc8/Nd2VdhRLDtZbLfH9y8V3veteTRWNwlBRwDpwEo1OAKoAlQb4XovPBwWctcxi8REQhxum1+Af8jn7jJFsz/TGf8+VEJWnterlCHyfj1RpJv88jMJYDdQROzyq9MN9+kiPdN7wYgfXsJzET0KsumXHWQLMr3aJcAb/bG8265mRp/AjHtYDG+1K4BOhzTjgj87VAn3RYNSToczU3vCYXNkWGfZPD3mybzXz7t3/b4kvo9mOPPXbzyCOPLHbglJTtkKn1DuFqnCAtiPILlf67Z3+2xVcoBNhUnuU5GtEKr5Geskn+kr7HJ4z2g7v1JTF8mGRZcjQaG/AMfj/yIz+8FCSKFt+d+fAP//Dld0UZXyQRkziE1qwJF/jxq2Tg9xkP3Ucjf7Cmg96D+WxBArp17l0BPO9L4UIXNMQ0HcRV8p0B36mRJtaZOwO6yQbomVhT6UjA3nTFib4ieQbWSrKaP5HrXi5rwY+9Rk9H+7dz1lzwY9v2xa+8htra6ejyzOmqXIR/H4HmBh8qxsN9tI7LWtagY/yIt0YUn5oDiY/tFejvV5f1yU5uhH8ju+MX5Kn8QEVPgH+gY3I2vtn6M4AD+vFCI8r8Coz1nL8SL+nnDKybgloTptJheO6FywZggBSCECSuvvuR149GQGiE5X1pAqmAsjA63XpdkxhGDxkn+ZIYVA7HPeMYrqDEMVQKSfEZrc7ITFmiVBLuGc3ucxpoZhCHaMFDnVlFAjw4RsWHxIGzYPiSEkeNDJayMjInFhyNZIyjkjzD30kCWuBgb4arU4kPKUjI0TyFoo6tey70pXAhO7yxn8TDPeu5yNR+cDLPPc4if0KZY+dYOC4dXQm7IoeTVigq6DgyRYy18JNMfefI6x66WmgRVCRlSeJGAEfBgaMM3iMwTtJDd9MlIZf+wi9JEVoUqxnXg33gJdGU9FRyBvamrwJwtR59MI7+44u1qzXhaE+0wDHjcgWMQzP+60KRtyR8tOZasOZ9K1yCsyCeZHYtkAsdpQ9+Vn7EfTagw6lhwsZG+xhHXi7yZtPsQiPIT0FOAaNJwb6iBxXA6cGDB4seWK/CD1iH7rO1+KYW6AW8+U1dP/6pB3s4zc1fa7PfSJ/cQz/eOwWlg+yz0j3rKGzYPt9ivKaA5MMJriTTfnCCA9xHtLqPJxKLnBpV8maL7INt4t9WveDHJMo62zMZXRPg3X0pXKJDEl8x8hCPPee75RHkPgPrkptXDNfIjx1q9OEbnZ4BHmt8wXuWDGts4IGxdNG65uZyb8tlPh/kr1VqBIqlbBVt6IVHf9lH/NUQFSMqGzBO043vQJM1XRUe4vdb3/rWxQbxQT7ifuZl7hZajbMG2jRP6M3In6DLM80OeWrlc4BnfLDX7JxAV7oYsL/cSQ6G1/aqAC+tzZfxtxVvA9Zzas4HorfCey9cNgKhKlwEBYz1u9edBAqKOALCcgIg+MyCg/uSaAHcd0Uo6QjsI6AJ8jr4FGkE1vNqCEWQDFYKSfEEQwk7I8spSQ+UKDTDEc0VLXCUsFMujtS8EaDROK+I+N6Q4kLiIoDDCR6UVBGQ781Yyz0FjUJHUaMowBOK7z6adV7iKAR8a3O81sc33XeFAXrRYV2Fi1ejUqTY332BXTIFVxfe635y5sb4yaHjIdzMkXR5xQOvGCK84IcGcxg/g2bYPpMPPM3TcfBPK30nQ8cW73sjjjzwlxMxDh0jY3dfEWjdHNdmbHvBg84oCPEmMhgBvBUBHDn6R/sCMlBkJPGtwDhdbIVxXkty9euiWZGroJMQtuMy1k/yC832x3vFBh2vcF0D8LxvhQtZSxLoCR7jzRbAL7KWZLRz8d1n9iowGePUUyJgz0NgjELCPD4vDR7r0VM+k02xFfZDb1vZ2V9zQCLaP+vBXnRAUZJOZD/eevhEtvw6PHowhw4ak9ctK1qNdcFd0c7vtHj66TObQ6MkUnHNB7AVvob+eiax4h/wO+tWwG6dZmm4oJVs2GvmBtxjv/GVszV7MJ7/TUGFd/cB8Pu+FC4B+iPOrSkuyZ49HfoSNFnTB/tLWiv5u0e24q+CoMoPAsazG81DRbGxlV7ZUxLOL7FL+qQ4cPle520ua4jPTik1APjq0bhc4pwiAM/gXdHmPtrZlHzAqbCGof36yzM5l/znV/7KX7nEeWPtJxbl8nkLvdbGJw3OvOlRAXz5VE0k8XmmN8byu+K+dSt5AX6J/Sga6Fc11ppsDR/4vt739OA5vyd3k3/NYC9cNkIKF4IjBI6fUAQHyjEC4whQ1z9HvhVQBH+OkiFLSCsgZEFQcJXEzUCirvOg8p4FWE7ROAbCoYyAMqKTY1hDs6RC4B11Gn22D57o2ktE0WwPl+cuSiqBEXxjKO0YQVfRE/rcZ7DptmQcuhQ4Chj4U/w4d8/xlHNmODlRct+agrPXpjyTQOikWDu4cEJkYZ7PwH6vf/3rl4JqxHfFrORHMtWDAlai7/SJk6R3oSPgd05EEU0nOdX2eQuCmQAlUEoyQD/WZ/LAE/qNlmo99KCPzCRd1TjAGXH2aApvRqDr57W59vXLXAG/wx9+6EHXCOyDH6FZQLKm7108FQsXgCd8EHtkm1sAv/gZry21eoGn3lFnF/ihMeA1STI/FKgAPPg73WR+MaelcKVjZMi++UOyU8xE5tEFp3Npasz2Y0OSB76C/EZgT35G0qOZgo4RGOfE9A1veMOSTFR6GIhdSZbhbD5cxY98OZYd8yOxdT8VFbqgeKlxwtZa26jAXMkTvin88EhDRiHU+nYNAjbMR83WGwGfDmcNk/sE9OS+FS5wFu/FHvFwBnSFDjv55HPp2gzYteaa2DsaSy/YIb+hAWf9GcDVOD4BrjO9MpbOi5O+p0ZXxbtc3qTYcpmDbgXWR3zERyxNFPG+WtNnDcznPOc5S9NgZsfokO/IRZ7xjGcs/she7dq5nLCwt8cff3xZ2+lsnrX7u4x1tfOrS66EV9aV7xySBXrkLvyd3ytZuE9WbIKfoo+jse7xwXg1a0ICukSn8j2Y+LwR8HdiNFwVcfRiBnvhsgEwnUB1JxgxEEQEExU1A6yE457ALCgLxJViAAKkyIJ4ZfgE7ZlEgbELgO6NwF4cCQWavaqAFkelFILjG61nLmNBi84gJasco/uUmwOTMPWJNUMS9P3JO9U+mnUT0JI1jUdnChcK7Z4LvgxC0BegBed0/gRnibUjc4kWGcEDT3Wv8FgXEw1oRpP17GGcTr171pGgSMrspcjg6PHcqyS6s5ImOAjgxltHEk5HBHZJe/+uP5AUcX5t4QJ3nzkoTsQ6fsfnVmeMg5/i0T5JzFv+BoyTHCqsQtcI3Ec/XobXIzCO01T8heZqX8fl8JcwVuPQQubWk6RFxj2EZoUHeaB5RIu5CYihmT4Ze6rCRReJzR+zzqUBruw2J1r0ZQvgKd+VoEXvyVZiIPkWpAR2fPf6EH0lsxnEn0qy8ZVtskn+wGdyM4YNs2d76Y7q5MKBPdMZumDsSB5Zgw/SzGC7Eo5RIMcT+u//XPjejcQidGSs3+FmLb5CRxP9cKjodZ8PQJvigT/iV+i8YO10xHPrGgsPvMgfAXDP3DWnZeiFC9zYHb9L3mTHV0iorIN/4sesATACfLAH21cAwPM+AT2+b4UL+ZMRe2MHIz0PeGasZpZ4emhtusae+Er2269tPv2mp/Rqtjegr+K1OFnFpADdYY8KdE06c8UCOutCx5ZLrsBXeM2UX1GQsbfReuKs/b2SLi9jX3wa3R4BvZHUp9AgO3Zsnf5CAx/mVa2cVPOP8BuNP3SRC97zE4pXcZ+8qlgZ4Cs0Jei7n5V/AmQFx4zt/YzPZKqAkrsYPwM446t4gN+zvfFcYW58m/9VgI974bIS4hDawgVQHq+sSIoEiCqwEDRGS7pm1bL7ki0dtnQue4CL+wQocEqkZkosIDJQyb3fq3HW5BgVE0kGRhCa0SK5no1jwHjDKYQW+wueOiMcDb4yGgmPEyLzouijwsV+DJoBSy4kZHDhAPHZc3RKUKwpwfG7cZ6Ff/gmAbG/8XBM4eIznHQkBengY76grxDkyBQtgqA1IhPGzWFxbJ5bs3fio8LFnoKN8fhijhMITlX3zPrwCP9TmLlv7XZ94D5afAdAgdY/D1hTgEIT+YdHPbgHX8mPgAtfY3vIODpHRngClxGgBc0JBJWDwwvFJrtwOlit574A6DtQ7Cx0uH/KwkUAOWaduwC4S5iTpKwFdOI/XdI95B8EDolxEuuM0zywPlmZMwPJhOSZzkWvrSmZ8awF8iN3wdve8OAX2EFsr9cd+KCZz+Yn+So6Qdf4mVZ3zfeMT7K/11P5efda24WjdZwsCeSSPnjoRNprpBPwsj49h4fEyk+0xCcFjCUbSaqTKLS5x97Mt84M4CoO8XtkEYC35otXXBVmEpScoHlmjxHuPRhnXXakGELzfQK03rfCBZArnaWTdIYcZkA3nSZoVs7Gkrm1xRiNxNb3+8nm6KJ4NfL1LdBNPlah43fzRzrlHjnwEcbDVaGVhsJtgS6yEbEX3X7HX3v1eKAFbexcAcN+NS1GNuyzsXgvR6D/iiI5DN7GdlzWtQbe46e1xXN+ol0743NV4Jk15WOaNGjic/lYucEhf4Cf5vGBI9paQIcmkoYKnYzemGOufEdu0/rDHtw3TyFCb8T+8GcEWdtbN3KUNWDNvXBZCRhMCfrChVK5355+VGCs+UlYoxg9GKdT4NUmSfJIUXw2TuDx6pICwbgRGCsY6hLn9GMExiXh5ESq5AbelE0QlMSiuccvAEdHlRyjcQwNTZyEapyBGAN3jsx7qZ7H8aVwaQsac/BHlyaBX8eS8isyyMM8PHnLW96y8IeSG2dNl3XwAV4MmzNK4cIxWE9wUxjhQ0sf3Kz9whe+8Obtb3/7Mg9t9kWDpIHjRJP7Ej2OsQ1QfeFiTetweIo0c+FoTQWZjpe18EACgieRuXGhqwWFgK4tnOzdPw+kGBFIWxx7gIvTG0lX5ZB8hhN+ozuvyo32dg+tnDDaI/MRCG6KlraAHQF9wBtJYUuLtZ/qhUtsR4GKj2sA38haMswnvOAFL1hOEdlo7LEFsmerTkbMmwEbo9dem8RLaykKnFR6FbMHemRPQRwuH/VRH7X4NAEvPiNgLXbrdVXNhxS7LkkS2279INvKyblkR8HNJvhLeh9ZW5O+5p/T0gevpvqDIuaOCi64SVw0Gj7swz5sea+ezZmLphbsg9eaDa2e8xf2bWkcAfw0cfgbsghYx2d7shHfofuYj/mYxbfgdXxz9qvAGvjNts05NP7aAE/vY+FCn+mbxFWu4fcZkDPdlWgekhMdZB/8K5ukB/bDK6f9ThjY8mwNY8Vkdk+321jbg2d0UA4g+Ue/S/zWKLgtaDTaX4OFnuaEgK22doM2++X7p/CJXYuVrd35aS1FCF9lHN5qtvAP1mlpNVdBwefgg7lsn86ZexuwZvIFvs+eie18ywzgJl/gL+ULa/yHxkrGoslP+ZF8y1rhzQjsBye8UbzgVXgzAjzCV2+wVHlpD3RxL1xWAsZzFn3hElBAtMl4JShMp4CcD0FV4zzTcZMgqdpH44ITw5RMMtxqPQolQDFsAmdQo7GMWrIpiOtmRvF6cE9yLkjmvcRqb45T9U05ORVBQwIZJ2meCy2KIRU/x+YefqVwgYvxAqfOKKdnX4DnkjEdFIkB3uEzZ8zA47yCYxIbCbN3YQUEBYyArJMID4aKB4zLeGC+dTgSBZFCw6mKBMm+cG2Tazj7jO/wJgfQFi7WJmOv4cAjepG9OCvz4YXniimnSOZmXC5gr6LT6AAA//RJREFUD7QpQDlufLVHngd8pge6LIotcgp+LRjnvgTNd33oWfjeQsYJghy3oFDZAnw4Xw4uneXROOuhn3NDT5VgRZ4puvEwMgPmPNULF0DeTjUcy0cvesArcmOrgi6e0nN2K2CzZbpsXA/kxXb5yPakcgT0hM7zN+ElnU43Hw49WI/foNdslO7ATQLuNEmyYg1+g69WBKXAD0iO+DdjrWdvPpHO0m1rolXgdS9FkXGSEn7eHu6Z73d7SLzMhR9dRJcCQuJnP8kP+6Y7o0TQZ3bllThJZ8tfPNUkqfw2cF8RqDCtZGu+5MN3Ctgzu9K8kUCTtTiG7yPZWt8zSRgbuI+Av/excAH4L16JN/nOYKUL7JAO5s0NnyuwhrFiGJuKbrMFumjPxLMK6CdbE6fRE9z6OT5bC938tN+NtZ+igWzYxkh3K7AmfdeQYH/2zz5ishMYNAXcz+vT6DaWXaA3r2cGb3zDa3EKH4yDK9+lucEfiAmhV3xmT3kTxT17yGnsae8tYA24y7P4o+yDRjkkeq0ZfEcAX76Qzlf5XMC68kRvoiTvFEvJCn/xeQbWlw/y4a3fHO2JFjyVsyWurwH6sRcuKwHjCa0qXCi0YK6rJiBUjsI6BCq4OFKtDDT7JdmKAoyAYjEiQShJ7wisJ+BIFhhYtSZa8t0Owb2ixXzdQUqu21KNQ6OKnbIxasUBXo2A4qOZkVkvhYvk3zMOksNBR78fvuqieCUOb31m9IwbzQwx/ManHItyShIhybsv3KJbUHGCxhmngDPXnhI6Yxmo+3hkD8WF7xD0BshxKYoUHJIa6+TL+Z5xPJwrPOA70gn88noKfCSOs9eq4KRokJxKUsiTnHtZw1OgIZNDxSyaFcfpUI3AXI5PskcXrW9upWN4jscJNCNACzmhhc5WexsnSUxQ72Xg8zkKl2PWugvAB/rqZFExTw4toAf/2B9dk7goIqK3fIhCJgnOCNgWHqWLVvFIcGd/1s4Y+CkC4CeR6OXtuYJIIWB/n+2hGDOHTdEr6/p9dHJjPH/AR9A9F3rRBBcJnM+SEzpD58ndODac7+SA6AA8FER8E76xZT/hJOEwN/zLn3rubSNrsMde1/lPp9F+mjcCspSoOm2s7ATP4Eam+GAteymU+BWxDR/Yey83a/L3mkZ86X0Evva+Fi5AvIA7HaVLvQ61QF70bO3pGLvHG8kk/dYMgJ89Zvu4r6EhuY2fcC9XC54p6OU/CoQAXDWo+Gf2tSXBZzfyA/uLd9F9e9Fj8QhNuW8MnrQFCmA/4jS6Q0Nis/vmZ7zn/AI/IifxGR78nlys9Y3u2wvNfNoWIAf8ENfagg6uGrdo5jNCWwX4wDfxh7Ox6Ivs0ewzfyKvTZE3AzkXv6sINba9WvDZevwj323PfkwFe+GyATCVo6gKFwpFmVTVFJQyU9gRUByJKOMRpCuhmU/RBZQEzxEYJ8i2SjACOBI6h0kpjYshtBBajZEg+H2k7MbZW2CWsKfCDphjrvvG4Msv+2W/bEnAFUbue97PQTNDNUaA9GoKx8MpchScaYJuwDp4qnjgJNBmLWubJ0E2j3PkOCVkjFPizoDch2OMm6M3j+M3VwKDx4I83OhBcLCP4OM5mSpc4ROZwoE+mOe1OXrCsHOapjBSnLUd4PDWOvbkvLyb/sgjjzyZ+MAZDvbPHL/HWee9Wvd6MNa6dFkn2D4jXQD2UTi0wXIEcJGsGku2LS0t4BU70WmBawX4BEfyJ6fQ2YP7OfHkZEd6jZenKFzgrvP06le/ekkAFa9okFCiH84Vf64B0I4GnUgJPn8AZzYgEPIhEmd6iudswPjw3k8FD7+geHav56cxs+II4BGb5tsE+azhvv3YuXeq27mekS3dxndz3LOfOWQAZ4m3fwQrEWvtMBD82By9Ya98ogQAH1K4oF1x5fs0aGWneGauNVqAh73QS1/5GvzkR+AcPkmEnMrzBXSyxY3+SETYY+YEPFOMaXhYo6fJPbovabAnfEaQ4op9RlfhYT/0wt+pEFtyUsQXkw96ret0i580/poBXXgS3y5ZpTN4+Oijjy7J6F3DbQoXsuITNefaptQI3Kc39ki8nwH9JXvj6ZnThzT7rDXaJ7YkWac7ra6P5tAlcc/6LT7GWYv98gltUTMD8/DP/vEX2dNPOsC+FfSxRY0X8bv3DfbHW35dI5bu0H9zR4U6f0OfjEUX25JTsPvWP9iD3PgVeYdn7b4zsL+8CV4tbfE3Gg7kRc4zMFeiT6ZwrfwDgCvdIgdNIjmVwvCQrpGDZlD7en17tUAuOZnhW3tfOIO9cNkAmEpRJC2jwgUYQ2CMQpLUBuQeIjhJgiDSKnrAXMoiARVIKHGl9BSRIFXGEmiKUIFES/EgQZ85TQ5fcimJmY1jNAxIMoN+tLngIfjZS7deN0LyzYA5Xt/h8ZyTyhwXmiW21uPIvLLF+SjK8Mt4Y9o5nIeEXicR33vAO69aCFhe6eDkOJ7w0nqMU7KV0wzP8FFAZ5A6nk6X4CDh6YF+KEj8NRH4+NzSpFvpz7HivyTDOgpXr20opoznWI33E86cqL/bTp+cKDgRol+cqS/XpjNsLhlxzpKrOI8RoMtY8pDo4MMIon94pXus+Brpnnv0UmJHX/DLPPdzZZyLU0SzgFfpqXHk6NU03Tz0jYCckvAJGnGYPdjnVIWL0zXJqQSOfOgFGfELbJUMIvdc9o9OXQPQRfbFZ9BHepBXnXye+S7PJB/kjbbROLRKlvKaRj/GPM0YY8i2fe53TQgFA35bC9ApNgFPvmkE5grygj2btTbe9/tbU2CW8Ch06AWbc7ELyQ287alQgieb4v97mv1O/81FEz2A30jm7tF7hQt/E79vjISHnSm4esAH+oaf9L2nh87xsfxlJRN7mS85jT3144zxjA7gi+8IwgnOXovhiytbvDTgLXzxlJzQnQs/2KITBPFHbCA//tmp/H0tXACZ0TNNMrKIDo3AWPFSwZkmKf7gV3u5R6/EEm8T+B9keCVJjF1kbub7yd+KuRoNwcWe7RU8PFO0a2zZa4Q3m+PL5Ub2yPwKyJ69iIejOEZHrMMmxU32Yf3wAg7GRJfIQpxVYOAbP18VZMbKS/BWYeP1ba9vhQctWJ9P4HPZPbxnYLwx8MazEa+APEQjgn2aUwF8rMfP82H4DP9eF3x2P3ksPWA3fCqZtTqQyz2yF9fldPIn91tetfzwO35pCOcPDMC9HTODvXDZCATKmUuARgIBBKAQkZTqnFXGZ5xEMJ3sGEYPxlEYCb7gSaEqBYWfoKQLUCWZAE4MVxHB6KtxjIUzkChIxCsw3zg0SwTgyeAVUZJeiSz6GJmEgmL7zHjwSDFgrHkcgSSeoXCG6JbscwqMXsJP6RVdAhMnRIklUhJXzrzvjsAPnYKuv6vutIJhtXxkPHgiEcW7PDMXH8iAA/FPIeGBltaZGAdnSQ7noDDT3cAPxqlza134K2xSfMCX/PEPjjocgqoTLImIPckcfoKxYsU68DdHkoV31sEjCRgem1M5O2txxvYXqKpx9ISjF/zJxecRWI/j5GQVnJUu24cu4F+6yiNddo8uc5hsrRoH4OT0gGzpUzUOjvbF2xFuawEu5EXX7W1dP/GbnAU8/CcTiYmii+0oFFLQXQPglSJdYci+4UnH4UdOFR8BmtkfnUNTpT+KDHLxM4EMRL5kITka6QtcJMwSGL8D+m7PFEwVsE3/B0kxKUiPcLSf5Nz32/ifvG4BL00YDQG/uycg+/PIbJpv6nE1hs/nv/w1O74ozQ9XO95nPEYXX8OmPXePXmk65F4LPkt80N8+99Oa7I6d8g0jIDPFYE6WfB6B9Vye4zEesFVyxKutr/GcE+CIXjLhp/hK9PFr4o/f00Aylpz8zg84Gb5rOKZwYb/oVMyiKfrQg/v0X1z6yI/8yEU/xX1NPN/RdD3xxBPLpUj1Rxue/vSn37zv+77vovPuGZvLH6Hw0xrm+r8i/smipiL+Rt/bC3hGVnRe/K38oHF8JT/qZ6WnAXaW78DO1uQTxM7Inr2yJ7bUXnwM36ZR7KSBbzDX+P5Cj1xDIcxujRVPQ3ML7sGPPsoPyGQGbEw86fORHvhOOVb8RjUO2B+fnvWsZ9284hWveFK2kWWrD5599Ed/9M3P+3k/7+b5z3/+8s+BPYvutJf7Dx48uHnmM5+58Iw+V3rgJ9q8TSP3JYN+zCHYC5eNQCA6cRJpSSZFGDGbYByF6qrpolUG5b7qW4Cm0NV6jFdSzJg4Z+NGCspABXmGAU/4VsoAR0WOZJMRmtuDudbgHOEoAakcCUcqEfGXajiHvHYRmuDLAAVAziGKzfCM46R0ezmsdDZ1LzhSjpHBcFCCO2evWnfhB8fCaDkxwQBdSdytz1jRqbCRkKAFzS0tfvfqCkdhnZYffre+NRRKxtg7OmCu4OF7HZIHhmU8J6brK0GEl8IC/k4ldDIEEgmWhAe91tYFNka3Bw1tUmcPRQ/+uQcvcrSfwkJS87znPW/BY5QMAvgqMiTWnMdoDLC2dRXB5ILG0VhyRCvZ4605Le8C5qJH4U8Olc4BOiexo++zxDh2xonNCnC4o1nipZCsxq0BfM1JVQvWxFt2gE6y1xUlC36A/NFOH3T0OF16y17JNXZyKbBvmgxktmV/Msd78kFPdMN9/KEPgr9n7J28BVf8YIsKBvwR8Nmatfq9rcnfOHXBJ88VORLSSrfdM4++KnjhwFbRSf5obIGc+CpJHZyDf1u4WBN/jCNzcwLGG8PXsV3FLPr8Tsboors9rubxd3Dkc4xDK9sXD0b8MIdupRDEQzqk4FRYwA2OEkiJPHqsYx6gY3kNCM65PwM4GIcn4g95SaZ6Pp4L7O+yH/zRhG6+jt+nX3yqeMHHSkg1s+iWsfhFH1qwFr6T713DbQsXQLZiBJ/vZyVP+qdpRm/EHXopDksYxREXH+tii/IQJy5yHHaAl3nuynj+2754qUEnHlozvqC9ABo1mNBrXOXTjfdM7E8cG9FmnL3IXwKMHxWYTx/oyStf+colpllfjuRne9EL49D/yCOPLL5BbOvHmZvLev5hreKh9Q89wBnfjOMLR3YeSJGnkTkbhzZ6rmCg+/S74hcb0nwhX7kMPzLSA3uTrSYoPhireQN348xpL77BqRvfrGnHtwXn9gJkq9jDs9bX5fka4I/3wmUDMD5KS0mdBBDyiOHuEYgEVJJMuDPBSNI4FUpRGTRl5FR0RwXk3iEHjKN0ji8ZRxVkjKMA6EAT5e8BzvBh9JIuY+HYg7UkjvZEbzoKLc3GtIXLjE6ONsfPeP1e7/VeN+/93u+9JO2SIMFaMmMNe2QfPMEbRpFOlO6OTojX3dDb0oyuzMUnhiQAMqwWP85TULQu5845mc8BCKI+S8wkcgJrixd66AKZ6FZyik5Tfu7P/bk37/me77l0KuAjSKAbHsGpB/S0hUsLnBCnBC88br+M2AK5SLCc+MwCpr3wWiFpTgV4jtcc56x4gAtZOEVSzFfj3Ic7XaLv+DcC4yRokikd+MoeAH6RzWte85qF19Xea4AujAqXGcCNXggs+C4gpEilUxJrNNDN6M65ge4L+PT3NkAu7MWJjbXorQAWn0dvJB7sWLKU00B0K+7JTSFJJ8y3Hrrbi91IiOg0vuN5CoIR4B2dkMALtD7DSeIhAeltQvEGRzgnmbePzxIYNgAP9+yLpvhJ980hU/tJYDyDmwKLntm70ks+A174Iz4ottCKj3Ds+WG8JEYjhw1JavgS+5orhtBL9PBhimWJfRo0/CW73xrs7R0QAxQ/Ff+PgZbWXHiAN2wDr9CE7jSg0I3X8deZNwP8fRgKF3Sal+S5iht03D6KBnrtd/dG4D5bw1encwoScQW0vPUTv8lFrBG3JONO/eCUse3FFtjvWv1TMEmErU0PerC/BNlrUsaOxgQ842PoD90RhxX4Cn6X33N5JhZrmPienPXFds/YmNyjv8zBNwm+fWbAl+Ab202u0IN7Yir/3L89MgL08ePJrUa84AP4MDLgt/lgb11EPi0Yy4+KTd5CYS9ky+77sQD/+UDrxk8mR+kveioGyKdmMpvBXrhsBEYpAFMQSaJAQhFHQCgSPsrMEXAKBDcCzzgfrxhUim+u/b1qZH+OYATGUTBKJDnggCqgRByDgKZzTWF7HH12n3JK/gTqOKc8p6Q6+HATINEswKEl4/DjUOGClwIVnBi2Is0x5c/6WT/r5n3e532WJN8+HLB9KHCLiz2swegVFcZ5t1mXUOBHR2jOFwTNMd/9vnBx309Gq2DAc7w1h8ysqyhiiJIJvM5zc4OPvTkC3W0O0TGsQuxn/+yfvRRm1pZwcMTob2lqYVS42EPCJBBbX2GlG6mQaHXEeLgZJ/jjHX6MAM3W4BysV42zpk42nDg464/AenCGk1Oemc3Yj+zpD7pGfLBeEr8kjOb24B65CioctWLoXCcuM7AfHppLtvClY3RCMo0naIGf1z/oEzuj4xJPcyoZ3BbYM11X6N8G0MQGrMEvCPDky+acOtAL9g/3XGSG7nyJ1WkjWvkp9qOwUcgYQz/NZ1O6ytZNIB/JGuCrxEyCYT9AV6yFn/4alkTFOPetz/fx4/TEumRkrxQuAO/pNxrNif5JZshMIpL7LvPomWZJZRPWxCNxBI1siC7gKR/ADiTl1rGvcezCfuxXk4A/tVf4G19jXUmJIhlvFW3W4Zc9vy1IFMi6ouk2ENtABzmRL3sgR7jz02RBfhoe6MYbMkQzueG5ddbYtfEPQ+EC8I0PYUNsDm0t4AfdxEe8pS/0nZ71MrSWuEhH/IQT/bV2bCMQ/Wez7MJYMVKjUc5jbORhLJ22Fh/n9zUAPzSJ42yrB/Lne8SpQ/qINjrFZ1srtuJnfs9ndGmYScQVe14vo5Pwzrj+Mo+f1oiSA8300DO6rtDji3rc8Y7f18TgC9fYmjXh4K0NPnTEY2vyo/w+XVCMiZ1pbARn+9uT3MU5spcX+Z4Y3rR6YI6xxlkrOgZ3fDC21QVryXHkQDM/fgjssxcuGyDKwXmrcAVeQWYkAIKiEAxZosrptgrSQoKdV6EYeDXOPThwAKpav1fCt54OWZLUapw1KLFuhIBQ0cJAORIKgyY4uyguB8wQ/e7yXJEhKaKs5htrfYWL58Z55qe18VH3QqHhPVoGqDDwZXSdD51dAVyxoRPgnUufBWgGhY6saS388TqWjhF+ois8NRafjWFAcIuBtoWLeQyeoTE4n0HoMcf+3vNUNHCKcLA+/nMg8OUwfBnd2nioI+39Ya+KoYGjlJQ4lnXyJtiY29IEf/fyJ1Hh4PJcoUbOOjg+e+6zoIi3xllDIsQhC0zGhR8toBHPJHmShNDcA3zgRy4SuGo9fDIOrzlDn0fjgDV0r9gXXtq7H+uzcQKRYmTW1TaO85f84Dva6VW1/xrAz62FC7DnaF/38IS+kBsZCmiSTXqleGfv/Icx9keXi0xn/KzAeP5LokG/bwPWgIsixDvRdEsQhGPlawLkRcb0hpzRLdnW6WQrThX4CQUnuiXd3snmp9A9AnuyNcVNTxNc6SD/w38lyLJz+sse8NEaaOoLF/ON4Z+TNMNNscHHGWdMLkAvPefrRvqZsXyC1yvwQ0JpXT7Qq7F8A5rZIx65nKLzt2t4DE+JBL6kaAtNtwHy5X/w8tD+PRgPJ/LDY5d1+DQyFj/wi84rLHRu2QKa+Xe63vI411awzsNSuKAfT8Uyfg2fwpPoMnk5fTQu9upeXwyQA71nG3TEeEmuWCsu4lvAOopi+UXs3XqKIn6FjCMf65Aj2YrLPq8F6ygGnEqyz4D95AZiCnucgf3Q5nsY/St1wTHgGZtBm3hO9zTQ6OYhGcFV8UamrRwqYNN8Edxa2vBZ4wVtW3w73PkzjQ2NkHZNv9OPvGVhD7JgB+Ji5AU844vlPNEROJrPZsg+gLcKZ00oRYR1XGKzRmxbFLnwRWOODrZ7boW9cNkIhK5wkXgSCKej60DArUG0QGkwWDBiOJXhuk+ZBSvK1DqKFuzDEVBsrwJQqpEC2JfiCOQSgsrwzKVEAigFrMbZF05OnCQXaGbkAqmEkDJ5bl/rCZYKO7i6jz4JhQAqqLsk/ZIXRZOTFMk7J2s+I8RnSQunp/thH8HX3vDkJM2V7EuKdV/wmsNFc5wI3FsewdF8vJEU+h2ObeECX0FfciwwwKmVsfWsYwy88QGt9oejdRy1+2lde8BbYi4ZtSbdwUsBAC+NEcQllBI3xQf+0AcJEGfjr3bAL/vjIwckcYSfC66cl+4ZJ+IznkjgOcXQMtIbgQCf4clJjcYA8iYbPIZ7NU5gsxZHA/9qX/yHM/2QJFbruY8WfFPgVesB4ySEaGYn9OmuCpcZwAcd5EnvckW/6AA99OoEeuisIl/wxit8NXYN2IueSVL4EHK8DbTrCIh9AJ4B/dPMobvmtHTTAzxWNLNh/tDYj/3Yj138xWgPvGMfXj9ge21wbcFce7ILCRH90UDgl+yNJrj1hYv1FRUKDHbO3iRp9ozPMzcXgANc6FzFY2P5i2c/+9lLMsnuFCrwsWd44rKHNfk8/kZSZUwFP/VT/0+f4EdOaI2t3hbsyT7JgbzXABrhQUfpKp2lu/irOKfTZCHpCz9bml3oDF+PBes/LIVLQNyhO3Q0vCJna2d99130m92LOdEfPFaAOzEVo8JzMhA/dNDJPsA2+T/yzBr4yn+zVzaW/ezNVtrvwq0FeGgYkhcaoxP0hPzoEXpmQE8l0QoQuMz2tz57xMv4VDZHR92fAVrxTnyZ+aAAvPlNthybtIbYLm+SC27hlbH8lSIPDonJ5CJ/8OpqClj30eYUk2zaQkvOKJ9qc9Cs7b4TtozFT/mgZkvWRYP18FzcMi/jyYvu4Y17ub8V9sJlI2A4ZRP4AEX1e15XqYDwJDuSWIoxEph7xhGuNRkqJRgBpRMAJKIqXgrWr+mzcRQwVXhlTPYRLCWDlG3mSNEsiWIcihOBmQODg/2s5SflxSs0o4UDoswScsUGQ+K4Jb9okTBzMnA0Fx66PZyxOTrnghungmbjGL85HKVnnIDuiAKMs+FE7W1MjBCENzoLjqJ199DFKBULeME4FEsMLY645bH1yBxdChBOH332l1hyCmhCC1yNl3Qwfo7fKQn5MT4FDvrwnQOAr0BhDXxGF37BJV/mt4akFg+9ihinDE+X/RRyEl0OS1JnDesalyvgd/tyfAIV3K3Tg3H4noI1yWcPxpnvNIleKdrxod2zBTjqxgiUlf6ZC386hwfwHYF9E7jRLAhZk96STYXDGkDDqQuXGeAt3NGDdrbGNqJr9AMvBGYBk71H7/gTsrJGbNJzgUbAIhe21waXtWA8m2FjcBrpwAjoN911egGf0b7uWQ/u6KYbkhe+sbVjQNbwRzt9FPCta9xobeNT0LI7r4JI4tiQ9ekrn6SpoRmA1+lo8ymaKJokdAot1rNPLnjHN/D19pC0kWHLo4wVO/gcyZg5GTfC3X32Rq/h2fOihR4nvk6hcJtC1Xw85U/wQHzSwNNkgTM88AE/yMI9Osjf4Z9C0HjJJr2lf/w1udIhdkzW4eU5Aa4PW+GCd3TXevhJDmIZOsWy2EJ0gV569TCdb/uzYz69lwF9E8PZgnH0IPIk50Dkj6/sJ+uKc/w1vIzpIXi1ewbcM89+EuTEca8hsUH+ED0VmK9piA/GjvYPeGZ9NkI3gxOa0CAmzhpExlpDHJe3iM3uVWAsXsoH2LKx+KmI17y4jU7AlZ2LC/DAK/wT+60pdoUH9kMv3yPms1u0aRbhrf1b/K1Nx6zNh1iLr+VH7dmCeWyf3Pk1axmDh+KPtYxp198Ce+GyEQhLMk4xAcZz0JwABz0TBGfB4Ut6GHU11jgJapLRCigkp2cshR8ZpT2sQeEEHNV8NQ5OknAKoaM2AuMoN5oZnO4bflAk913WN85P+0kmJEiSdM6HU1CIuC+BoMQt+Kz7wyDgYQ24K2DwhmOUjDL63on4bLyCwxexneLk3XCFm7WDo4thS7oUA/ayByfNyBgeHDm+GHxoM4/RMHDrO4b2DqjAPuKxPfGM3sANrwUCPGHY9sJ3CV32CMBZ8PEaGZ6YhwcKJU5WMs4BZV77k6PlaHRz8UIRB/dWTtnLPmiS0OKVcSMwF8/QktOcEVhf0FOkStzwsN2vBTrKNjjYal9gTfSgmc6P1nLPGorRdP/gDM/7WLjMAE2CBzkI7goYyQu9dUkQyZR9kimZuS9RV7A4CdPUELgqOVaAh+xR4o8na4Gs4cUOohOHwLgETfZCD4C5dB/dfBE7ZlPWFyDNa/U8F52g53SB3dMV/FNESBzYs1MevtXanvFbxtJTdk//6FV/wcfe1oKLUxqn7YqfJG9wMBYebJq/8/kQmEdOYgMdnMWHHuwlkVAsWGcNGAdf+sN303lywIcUv3jBX+IHP8Y3SRTtRV7m0S9NGM2LNXSeE/iGaypc8HGLHEcQvcBzvpbfty69pY+tvP2OB5LxfGfMiTTZmteDdem+pNNYBREb4Xf62E1XxCrNQOPEMzYgXkTvW/B5dgXkJXx3Xsv0nR2JeTumB8/kCBoO/NwhHqNT84OPpO8B+srniE34dMjXsXH5Cf82i2XA2r5fRx/JiR/Cr9s0kgL4LA9JM0Rxak2xvwfy4xf4J+PIlH/Db7i3OFhXDqPxQB/oilhir1Hs4A/kXXyVOMEPpNmIbmvn2gp74bIRKG1buAACpnwMa5bwURLPCZvjn40TCBi/Srxy9BSJkUhedT+q9YxjCAwPjpTMvR7cQ59uhqRYxyAKBifPdC0kzhJCf+P7wz/8w5fXnvCEUsPHOHPgYw0O8WUve9kSvCg7hdapc0rhxCbjgwPF5pgE+uwp6VZUcD6U9nN++nnrOP2ONnswEEHVyYxLYofvDFSxx0AZNb7AkyEqrgRXibY5iqW8VgUntDFoyY516AC5kxUD8pfC0ANfa7YGiWaBktHDH50pXOCLLkFG4IlhB+DoGfwVesbiNXn6M9GSK3OyJ3zth0bBy6tlH/iBH7gUcpyS5AKO5uCZOfbzmTPQdcv9HtyztmQFPeaMdMk4QQO9obnSY/RIpAXxWVeMLqQYEUCr9dCPT74jwBlnHJoetsIlskMzPpINm5Mg0mFJJpkrXL2zrLniGbkZ7yedZm/GCTBreWMcvffHKSpZjIAdsWd4VrLuwV7sgK+TsIRe8s1JJ3ol5ZIkdEuu6DP8FBEaEGye7rAdr2OyI+uQqYvO0p2cbrJt99oxEgC+WYJoPjr4JmvyGU6SJHl8Clwkb/wd/ZYo4r/10MM+nZTZYy0vgATLXPPWAj/jJJqPWyNj+LjYJHtnO3gMb3ygO3ghQdO0cRKFZ3wtncJH/KaT6OWfttB4LoDHtRQudLD1o7nIZ3S1Y9qxfrJBfPd9M3auAUcG4Xt7GUtv6anYLZ5JYPu4BXwmc/GO3rErsTcy5Vfby30xRnxXuCoc+P/RWBdcRleLLx0SIxJD6TGcZmA/uih5Rqs1Z6Dg4i/YtLkB9PssXtNvfJoBmvhR/taaPT9b8Axt/JNTKv5NzoeH4Y+9t1zZX0NKLNcYkdNZs+exse7zl+IqO49sPe9xd4//iN7wg2TLH/fruofv/pcWOcjHFLOhy7hWH3O1cq8ufkVDiJ5dM1x14RLDlhAz1CSmI3BfAk+IAjgBjsCaFFiXXeAg0AoYkiSNUlCWEVhP4JIocz4Us1dKn+0Df4YkeFM8+wu+AjNjlOBTdMk0ZReUVfe+UM/wdHLNY7SM0RjOQ7CAA+PCB5W6BMb6UXxGrDIPf4CfTnckB/gFTw7Zl/Z1YDh98xirzwoP/IAjOu1lT0FeIoIOX8jNK2KCr4SHU5J0cLjoYPie4a8khaEo0jhQyYc94zzhwOAfffTRJTkhiwRtY8gS39puZwqX6IGESiCzjvVi4IpduORY3z1FCd5JjiRLHAk87amT5hQIrvhu3ote9KLFyBVi5qFfsWsd/Mc7eJO5vSvAT/L1+ou9eh0C7sFTUovHeDWC6Bte4beflZ6TIVytFz0f2Zg10UNf4AnfAL49bIXLDPASrmyOrdL7JMjoz4WXxtB/ekNe7s145Bmbol/Wpef2Mi8B1GcXGdApe9uHv5AoHUo8RhB9Zyvsmy4LztaO7sDN2vwYvWY/dN1Ypyh8KhuX5I3odE+XkA/sbcFYNPEL/vGeZoUTJ+t5xSLJur3RDafwCo58vyRfg4Met02sHo8ZONFhr2jDUz6EDOzZy8Hv7sFLQ4f/GtEdcN9lDJ/CliSr5luvB3SxBz6I3zdvZJvXAmi4lsIFz+QSfDM/7SLT6sqY9mqfiTNpKorJ7rG3dnwuhbz47LtjdJ3NRPYjELvE3AcPHiz6K9aOLvt57gTU98fom9jd0tFeozVcin426qc1+XMnoWIs+6piBUCDMfIxMf7/0TSmi67S2ZySjGzDZ3rO5iThM/sBcFM8iu+HxtJHcuNPNGF8F41vIKM1fBpdYi8fJu7zefzFSA+say+FjQKDzPAcvrl64GsUQy9+8YuXHM46ZNRecLA2uSsy6aMGEjrbccaMcDp0yZ38sSO+c8bbu4arLlwA5knUCZShjBx8gOB1N3Ui/F45+ewluOkCVMBIKKakm9JVYB/KS4GMrwRuHMUT6DlByYzAzKglhWgTJNGpSDAeDgyUEUi+jWcIDDcJi2ChuJMM2JujVOFz3goKQV+CJam2TxwTBwSXFC4ADpRXMcFAdWEUBpJJpyBwSOESsCdcXebrHkq88EPShpYXvvCFy39/delokjNaBDnJinnwyjrhIVokxHhkX4WXZFxAENR1tCVdrV60hUt4yLCNxVuJOp5wvhIvexiDNwKOtfFVcmYdRSVadHbJOYUPHrz61a9e1ss9c/BNQQRXNDqZSfCqAH4SL/KKfHpAiyBHdzjhyKwH4yQ6dGD2xyOMQ2doxq/R3vBmM+Sm29gnnfB4KhUuIPoiYRX42Sho6cdfvGHT9IxOkBtd9WwEngkgdIF/kpzQfYFM0mIvgds9Pox9Cvp8CfvW5CBTeKyVBVr4AP9Ajm7xoekot/pgPXi7F33PRXfgy76MGe2NR1XhAsyxp2CvQ2tNc6xvz1zt+n66Zx4/An/2qvDhM8PrET4jMMdrcfYXc/gv8cTpDd3kg/gXl8JOwcT3Kvz5Jf7dniOABz9gnld6rdcWYT24jyYFJZmGjrW0XBrQfS2FC7n565j8Nr/vYn/0Y3RlTD/WT5cik6//4A/+4KXIEBP59tFlPn2QAOY7FTOZsTVFP1ytm/37y32NLf/GwBsZ8gBzKhyqy1qhzVi4shf+I42KCl8y5oPImf+b0cV2xTVxE43seDTeffmVNWfjABuAI18TX1qBNSTxbE0+E95G5i1v11747ZSdHjznOc95kv/WHfHennircNKsgFOuHvg7vNJ8Ms9+/brBw3N/IfJDP/RDb171qlctPqIau+WS6z73uc9dcj28vla4+sIFUE4JIwPTzWBYI6DwOoaYr5Pv84j5CSASaomHvUeK5J4A66RCcicpHe1tHGNSHFA8Cjoa556khPPxepEgz/gZeJSZk9N54HgD7uOBBMgJjdfDnO6gz32FHZoFVXu4pyDjDCUhkh97SnDQHZ4oXCh8W7jYy31BmPJbkzNBn3XJSDI8SjxAcBVoJdkCOzx+4S/8hcs/u/ygD/qgJTEgHwm/ddFfGQma4SKhxxvGbZ75kjX4k187vy1c4OOZbjcnk8Cq08mZ4J39XdbUTTMWP8xXgHESaIEL2qwJ/K6rhj/uufAfj/Ffgsa56LYoJKyZuQH37AcXJzqjMcC68EEXncWLEc/MRQsaFa5todoDvqGZrs0ChnF0km0KMP165p2qcKFb5B09PWa9c0LkRl/4hUp/4W8s2Sk+8AmN7IP+BMxnC+EzGUt66JACWxHqDyzQYb/rYjqRkfyaw4YlzRIsr0UqMhU4Omd0N6cV9qDH7JfMnRTag56b5x9XphDv+e/3/jIGbZJ3rzTNXuGgl7PCBdiX7UgO+F362/K23TuQ360vUVG4KDr4bDziZ+PD7OvCCzzhTxWE/Cka+BS2g5eaUJoi5EW3dTjxnj9w4RnZGM/PGYOH/E+brPqJLvuhqR3DllpaWkA3O+A/zDUu1zUBfOgyPdCsUejdNWiwkJd8gK25yLy6Mqa92mf0wQmgGGptekM30dxf9MxPyR+/7nfy7+Xms/tsnV5lPfP7y3064ARJzKOn4rq4Mhq/9rIuGq0jtogHdG6kY+7hpzxMPsbeRuMC6CED9jfTdXpOf/gs9iQOVDHLfPvany+Va43AOHbOh2ruwBudI/q3XBoJ9uV/FRiKJ/6qH9euL07ICzVk+lwlAF8+3Np0jY/u18xFD+CRnE7BgR+eRWf93tK59uInFbAKzpls7xruReECMFHgl/RWBQTgCCiAwEKYbXLQgvmCnP1m3WvjGIcEhRGMjNpn8zk4yaLuKnracZSVYghulN4Fx36cz33hAozxTIAVmL2uBC/r2puiSWwklsb56fgXzgzB++t4p/ALrfDlBHUZcy94MggOB9g7OAryaGQYLXhuLn5xUhImvEWj7obvjLzv+77vzUd91Ecta3PoTm/gQ0b2N7/lBWCgkhCOtQXjHdtK1Pq5beHiPkfnS4TumWNPMpIgh18KI13WnJi5JHCKLqdS5vW6ZF+FCxxbMJez0hXSqeW0FMlJxlvgZPBDQlMFDICnEoJDiTIc6TNanHxV65ETHI1DWwX2kYxyjhzvaF98OEXhgkZ88D+CJLfsl2ysD1975zpmn1MAPrMrhfAhXPLcT7pHFySvaXCgB+2Seokf+6cLAWPoMh1WHLBPPOn3NQffNDHIgn3RQcUOOQt09pXQswV26Xc+Al70yj0nAmv4DC/JgSaARMoFd/dH8w4VLuagi+/QpbSeYkIQn+ERsD7dx0P+gl5LxCQubJ2OotdPSUc6m2SIV8Zp1uAHeQD7thdb4LvQQSah1cWW85qu53TEczR5Ru58orizRoc9p/9kouGVfQ7NOxfYN3qBrvzEK/pD59guHt81kKeCMj71EM8ypr1yn/wUqS7yR5/vI5DNDOi4xL3y7XjHh2guai7YZwbWoz+aDWyETisO+nVvA3BBmxwCLiP9NEaBJQ75fQbm0gm2xs/5nGsE9rM/m5zlbcAabI+9atyMcHFPTMcjcc561d5rAY5yC28EwDXf25VHzcDe8i86WcVusqUn8oXW94+AvYkF/C4a6aUTePuM5LYF+Cky4DuP5dc54eoKF0Y8AkwUsAUEiiOYjcA4ghd8OXyVdgUELdAK4irNkUIBRiChFZAo7MjB2Nd860guJMSt8fkdTgIjRafA6ZpQ2igJPowKF8DJOfYUxBgso0xybp6ixpqeK2wUGdaGr+fm+ydklN1+beECP+Osh0a0mt8DfjqKjMN0MSSfJcq6UfbmYBmrvTg6BYDEyXuhAoqxSTK83iK5hzfDaQMCR6HIc78FuHJIvjAJJ/rgHnzawgVdEgUJivc8re0evuik4jNZwcGJB93BT/tJ+OgbvaRz6XoG6IVArXDJfXOztgLPHHzGM84lOLokWmRG5zn3PGvBZ2sqGuiO7s5oHHBPJxBP0Gz9CuDkFTY0t/xuwb5sjsMlQ3SNAD9PUbjAA7/pCb6QodctfLY2G5SQkg2dOxQ8zwloFpgVnVuA7OAvIaeTbI9M6QAdodPk1vMRrXRJAamTO5Itngh+7IV/sQ+7xDN7sDknEAobjQ73yNc8eOE/m+VDwt9KzzxLl1ZyZi06yv7dH8kGzuRbFS7msD96CT+NAz7O+sbjeaVfdBXOGhn4Yy3j0cQ3wMnpiddrBH1+CT/xx8Ue2CA9y19Kslcu6+MRu+IbQ6P7GeO5efwnvPlA8jTHdwP5ejKZ0dEDnpGZ07O1c04N9kUr2uCPb5qD0SUNMQmchJqPcv+uYWvhUgFZiZvoYlM+i8/0PDGwArohBvFh/HJ0JUCn4emtBno6W8sz8VJhQZ/ZLfrEJr/flr6A+XCQV0iE6V1rw3RbfJYvoBstFZiHbrE1vsr6uUbgPt6KxWg0fwbG8r8KKfYFvxbsKbH3FoyxPe+3AprovhMh8qJXfIAYz6/M1g9tmo7suMfVZydpbKjynS3IN/CIv4KHGMJHril+DwEa98JlAzCaWeECMJLT1CHTSYtB9ECJOBmGI1ByEJWhcQIUynGtcZWwKIhq2N5wqMahgxJyVklo4cn5Sd4ZJsU0jpLoLEq0Y1zu94WL8ejhLNEDZ59jmD7DLx2GN77xjYtDtJb9c9mDY5JsMBCKrxPLIUoSFCr2dSQuGEmCrBHeWUOAlxxwFgK0ZEiS7h6Fl5y7z5nCS4BjZL4Yr/vL2Uge8Ai/OShJD7okBOl4MkL3HR8L+K1zhg96PfMeMxo4FHspHDmWnNLgk+IIjow6awD0K07ICk84ZOvCS1DQWSU7NCtcrIGmALl4tSZyDo/R5jgdLj7bV9GEtnSTck8HnHOPrHowjmzonSIDPiOACzzx2Gsr1XrhHf2gT3CpxllDMqJYxv/KKVrjVIULHpNdkiQyETD5Bom+d5XJE434h8fkTI/MsQY84X8MLocAzeyDPW0FuOGnuXir0CQ3tFp3BGjxjG6hH296QD8+6UT6vaef7nrGj9HxPkD6jJdsSTfRGvbEU7/D2XPJOFtjM/w1OzUG/uyG/frcA92dFS72YOs62vayd2yU/rOdyNtz461J3n7nv/ghutDT7rNxmln8a2tHnqHdmnyKotl67uciMzxXjPAv/fN2HNrxRAGoiaVowU/8NyZ7rgE8YPeSybVzbgtwxyO8QV98M56LX2yOH6Sz4ozYw/+Lh3jnohMKl3PjeghOVbiQuYSSbpGrdfCFvbqHV7O16ZVTOHYSmzMenxXnGh/4OwNysSe+i/F0wjruea1HUWS929IYsIa4q/EhTtsngA9sUGMjPB2B++Zp0hqPZ6E51wjcRyd+kh0fMqPJffukeIs9AmuIDWKFmGhdz6q11oA1+Q12yCfZG23o1NRjA+icATl7W4afzFg44acclN+ZgbH21PTiw9FsHbjxmbO4vxbIeS9cNgCGHypcAIXJa0i9cQUwnNJzqoynrfp7cI/S6WTqtFXKF6PisBh2pSDueSY4S+Y4F0m+zlTwsJZ9jFM0WDPVsnt94UKx8caphETcOOvoQKMP3go5Ci3Z9lc0JJwUO0YL/J6CR8LMOTz++ONLIiQgcaJw5GAZPmPiXOFkLl5LWCQHigx7C1SSTQEujsbldwZunH3MkygzXrjZX8JvTbxw+d1zdEgwyNi77vYhy8gmPFL0Gc/YdD3ojrHmclr2EXTIVlDtwT2OSPGT17UEGQ4AjWiKrCRL9uNcwk/328IFzQKLvSUb4b0LzuRE1vhCL+AqUbNOK6cWjCN3DppcRmMAWvANPXGio7F0Ah7kbe3wtIfYGVrwPrIdgbGnLFzoVPge3uSzMQoZjtuexup6sTeJlNcbJVlspuX/qQHNbC2vFt4GyEkx5q8Jka8gO1sLPXQyxW4PZDsrXPgYck9QytWCPdgW26fLfBeeKrLoF12kwy6FOFtJQubiL+j1KBnjs2aFC7zYKr2EBzDO3k5xFQJ8FXkrMMib3UvcFPcCLr8101VNIk0Z+tGC/diQdXVFw79c1pS0aZKMeNuCZ2wcr/gHXeGZ7c6AninY6Ppt5m8BOGqsRd54QQ/I2v7u4XXigQtO+R1fyF4i5/NdwrGFS+a0p4hZgy7wMZpTmnQzWs2hz3Q2p9vGS17ZQRqwMyAXsmDbmW9deJAVH5j7x4A1raEwF0fg6J6LnYUPs334AG9AiJV8R3BtrxHkGX3nB8hPg2Rmy4CPEu/Zf/Bie+6xm8zPdVtIAcs3Zi37ka1GT/KtGZARn8WmrAfInk+qmlEtoEW8kX+y09AGD3ZJH9E+k88h2AuXjUCoawoXQkkwk4Cnou7BvRi25IDBMYoREJBOJmOj7HAZCc2aFEMAzesEI2C8FFrxQcnQxIlSUuvmMg7+vm8RR2HvFC7G+CxRkyTluyjwMNczQQJ9ulySPsmmnxIARUr2RLvAzFHC3+s3klJ/KYUhUXwdQWsa63dj4a6z5MQELY4kGepLXvKS5T5Ft0cvA3RJsDhnv8Od4+PoJWxOuOBuPvzMD13kS172lCgoKvDHvnDiJBxZo5UMzIMD+jgw+3glLl9i5BQkI/BAW3gPJwGZ43Dhm6RPsSQxbBMUfKcjTo/Ms4412sIFvwWRdLR7QFMCKl4qzMwxN/sEfLa/jo41OfGex8A4uHA0xs0SN/fhjh9oiW70YB/6oKuad7lH4wL2P3XhYp1cLfiMDmPxGP/QpOCUFCs+6RU9dbFTPKFzxuMpuR2Dp7lolkxHflvBnCT5cBcU6S2+j+wpAH/FAntP4Zn9PRsVLn76zAYk07G5XAG/460E3ekw+eOlNflbupjXOdkaO+Q7ktS5+A0FjkQrSWMAXaPCBQ3ko2uIF62+KdQ0P8jQvj5rWKEFXgp/9BrjNdT4vIp/8FO85VQo+xjv2ahwgZ/P/CzfXK1tLL1gq2xbwWIfuJFV9toC5KFxQDeqWLcWzEULutGK5xpKcGS7LrLr5S0uGY8HM9vBd37gYThxQadYhSf9Sbe1yJke8+N+n8nFc7GSDNPg07AU18hgNtde/JtYKra1/DcvRRG7aW3+GJDj8EeSZPyjz+IvnFubGQE94WO8edHav59rcAtNdFAs4Cdm88iFH0ojDm/Ng29wXbv3CMzDV4UbX8fvBTwjD3mImOWZzxWgDX8UVf4ym894rVBQ5NGTGZjLtvh/dIcmP9GK5/K1QzybAR+7Fy4bAOMp66HCBWBoOo85MquYTJk5GK+fEHw1zhq6K5LddFFGYxmG4kZhwKEYl7F+UlxjKKGk7xWveMXyuoDgQ9k8d2WOS3dSYSIowlHA4NSM4dgoEsOJQrpvD585CcmAgG1dzwU4TlunjDHBRYUuEbEuB6RwUPgxeutLvq0Z3AUrQdyfMfZXbfyOP+iwphMJnRn79QA/4wUx9FhTgLQ3Q4WnTgkewsXz8AP4SR5ONxQrZC1xwQOBxPdKFFwSDDwIT+EtibKuzgYHITkUeBSCnLF9yQFfJWUCNAebfckeXwRzOISnLuPIBh91leANF/KztwTKPvjT0oI261hPMPDnNP0deF07em8dz8MDl8+SWEfLVTGdcWQFJ7IMLT0Yi2b8zKt3I4ADmUkiJf5+PwRov1ThMgNj2TtdV4jSF3qoOGDXCjbFcOSFd+F7q3+HwFj7KPw5+tsAXWCDLrKgT4IS3eU3EoR7HOEtgZGY0e02gFmTD+0LF/P5B/RL+qNLuYCf9jOO3/BHJ3KiBD/PMjbALjUG0tkMfvSRf7CX+wG4jgoX+NBdOkdHs5eLvCQMxrTgmf34OnzwvRivfJK1NeDheQ/ukT/ZwT80uY/OUeFiLYkn/qHN2Ozvwl840wX+LH8RjS+Cn4RVUtHTsAasDU/+vHrDoIfgFt2BWy5+ns8jY00YBZbGmdgjAXMqV/mQQ0C+/ABc7xpSuNCzyMkVmR66yErT0zrkio/tc5/FMt9LSyPL/RG4z67ogQKDT6V/h5JDz+DPX5NR4kro8BPPFZdpbvV43uai+2KmOEjfxWGxILG2AnM1/OjWmrgxAz4cTfRxtieAl/inSSVm4rPGXGylp2/LZe+cIPG7bCjgORnQD3FVLsp23KsATnwEOzGPfmkWzPQHwMP+cho23ELwwHN+ly3DM3oS8Puhi7/SLMY/n68V7mXhAggSk73qJLhWyuI+xfZaA8UybyQQ4yid5EGnjPGODMZczyRYAiqHREkoXhIQBuR1BLhJsHUCvY7EyVFazsAaAXMl5t5XlVgrCCT5eMIJwAduUUa/W0Ow94zD4sDg67lxlNipCqMQRKOMeIFvDMbphQTJGEWgpF1ibqxEAH2CG2drHpxDq7Ukg/Ztee+ZpJEjidMxh3NP4RIcraFoEuB7w8VLjlPymTXsBV/BVpKDN94fJ1tFlQCv88KJwFUSY19rufCLXCQmuhbG4lPwtz88JD6+h8N54pV9PTOOs1EsJuH0Z62NgycekgVcWznQEfhxKv7Smzn+GACd8AohntvTWPu78FvBm27fSG/tQUc4arKqdBZYQ/KUAr4aZz36i3cpMg8BfK+hcAF4Ah9r4QdbwUs0k5Nkhn6Tg6N1iTGbIyO6ZW70oQK8JE+dTutuhdgDuSUY2hO+8PuQD/mQ5W/p+64ZnkpY+RX6Zgwc+Sl2jbbwyLNR4WJ9SRibrOgznv1Luu0JL7qJN3hJD/p51qJT9ktyZ4z9NCr8LxrrZh6+9YWLOfbl9/mN8MJPa9JD9+01AvLNaTDbgzu8JTDwHoH7afhEx+BB/nDrv+PivoJSnDHX5R6dQaekDq3+t4Y/+64AgJc14c1X++d+bDwyWQvG4xVfjdfwmkH2pCvwIz9NHLruFBpv0M03W9d6ock887fiGDCfD6Sbdw1sU2OKfdIlurf2whN+H69S6LuPvlw+4zFZi+tiWHjZrpULr8V+MUf80kSjQ56167aXOXTfeMmq9ft17Uev6CC5GmNu+3zrZQ20xT/6KT9hl5Vu0Bu8FuNn9roW4KFgi4+b6SX58iH8pXxIPgMXa8DD89tc5rNjvgkufm/XC5/hx8Y1JMWY7Du6PKMrchS+zff34B65jS57kLEmkTyDbCLf9rKGkxtFcfKWFs9+3dHFZyjS6DWeXyvc28IFEIrkl6B00XweAQEIxByMLgchjsB8AY/xqVrhNALrEbDuOsMSvHRJBU+KKImUmAikHBSHQvGMoaxeOWBccHGCkaRJYYUWPyVvAiinIWmhiGjkQBQ2AoTkmjLHWOJY4MeY0OCfVUmY7WMMGhmZefDBC8FN8i+wwRdextiXPIxrHYd94IMO3R5reOY+I0QjHMM/c/EDv1K4GM9QFABotV/k5yfeKYzwrQV7CUqKDw5AsaLLIyDgh2Q/CQLnkKIrhglfr8G8/OUvXwqG8C1gb7ySiDjtUDxJ+vAd7pIpCQz+C0TW8RMNcCJHeJEJBy6RQofE3jj4mktv8FcnF88ViI63HSFbnw75nIQQPeF/AO74DE/y6J8H8Az/nZyxgwrwJ10deI32HIF511K4gHZuLjKOXeApGZGPgM9m6TJ9oRPsmfzaYgEPzQfWUPiTZ3R8C1hLIJRY0Tf4Bez74MGD5aSW7gnEbJMN+6zgZp+KcLqepoU14NkXLnBGr6DuZKTdC9if7giIinq6jmbzyNNasZF+rjHsVqCjs5IFuuC+NfETnfaHo+c+Wzf3jJMc8blsxz7W8Lv75MOnxDf0wNbNhT88s57TIvIlq9a+gbViw2zafsag24lLChd0wJFOsE/yxnsysKd79mGnfJ7XZzWf+PPwyxX7YPPW3ArmwIG+SJ4iV7j5DG+6Snck2nwe3ot3Ci7FGHr5HnxFG5x6eR4L1rymwoVNK9pS9LmcpM8uY8iX/9VYctJBH0aXBo84Lu6wzdF69nb5HS7+cM7Tn/70ZZ598nx0GS928E/Bf0QDn+k0UEMGvu0a/dg1l3n2kc9444JM6c1MX9gevdN44Vtvo+ctmM9H8Hti10xf2ZqxmhH+ia6fob3lxdbLfPz0Dx7ZEL/Srun3yMQppldCvT5Mrrk/ujzXRKIH8jv5nnsVvuaQBTzoRPDI1a7Nf/HjcoqsmWftnOqSj3mLxOvCp/YPp4R7W7hgaoIcQZrHeVfMFqwENwojUa/AfIm6gKQrVa3nPkP1SoXEUfc0f3EjIKBwoJIMuLoYJCdgLmcHb0FGUBMYFRQKCE5TYDSX8gpa9uBEBH5JiHWcPDBUSUGCJV6ilVFIQq2JFs/Nkew6UUFn8OKEGalAbw2XNXV7jGsTLD8FTUkO+qxpDbQzQvfaggPN6TBLQuLUrGOuooNjwK/sK2GCD1pasK/10WC/0Gwdxqp4MMc9wYHTx1cJjbFez5JoOa1x+pDvsgBzdLYkIfgBH4kAXNyTrHIM9oYv+fyKX/ErlkQbn8xh+JwMuUpunJgkoQuugiKdhad7+GEMPBV3iib/ZFQRnTm5WtAV0RVU0BpXgYTG6Vv+w3u/DnBPwEEnOwwP1wDarqlwmUG7bq7YJH4KvHhAfuwKTWyTHPkNfBYknRTS8RnfR2A/uijI0cUe6CLdoptpgqTpoOhmX/Qcj5wa0TH3rWtuX7iwETzla1rfFOAzjbceG42dowu9iqNKv4zlVxTEEgz2miJDAUzHfQcMD+k2/8j/8q0KY/ckuU6mNXfYHl1STFjLuuzU/fiMFuBJZuQFR59deEVekjk+gGzdD/idDMgajvEjKVz4EPxjKxoWcOYz7MM+JQQ50SFDvOVnyZRvMK8F/GCDeMTGbqMzeKWIo3t4gV6NKYUgfVE8wcPFB+WkwF7hS3udA9B9LYWLWENm+CJ2rr3oqphKd5yMsz/0jC76Ixb5YzjGu9evl7F+Fxe8gfHII48sem0f+1WXvIAN5BTU1a6ZddHoL45pyIlRGXfbK+vSN36dzrOhGSiK+SJ2fSqgu+xYAco2K7uhz/IWOBuLrz2PbntpHL30pS9d9Bo/IodWHi7P/HVI/tLnflx78beKzKc97WnLePJz37wW71ye0RevlycmZd9+L/rqn17L+cKHjGnnVBf90TRL4/Ja4V6fuGAsJ67DRWCqXglgFeQEKwqWLmAlGGsYw2CTTLfAgIzhuFJoCGR9V8CYFC7ut5exAqagrDCAO6WUEL3/+7//zfu8z/ss3XxJvcRJMBWMJEvmCRLo1HGFQzqlaJTcmGddAY7D5IAk1wIbJZaoUFQOyTrWR7PP1reugMhpSo7z2pAL7vino8mx2se+uoJwMVewDpgzKlxAEhWJmi6WDi6jEQx0N+ETvvqpcygg4RN9kXhxWhICnQZ7G+c+Y+f8OQrJHqOUPAjsdIZTNEayAX97SfLIISdA9NL6xuN/ihOJykd+5Efe/Jyf83OWQAQnTk5Sgx/kSh7moxf+ATphXIoIF52yj0RMl1TnVjJHh3qHDS900k9yQ+8I7IsOtKcIbHkfsL5neEU/ohNrwf73pXAZQfhPHmSA5/SU/mkw6ESxQXYkOOORV38cy7MDc2KPI3zDX8HdWoKrJJfO9WAN4zRD2IFEmvx8j4M+wY182IzTZjoCJwk4nTOenXlO9ooauqT4sV98hOfWT0BWaEQfgZ/8AL9Ez9AYCD32l8zzExol+MP+2TIb4k9cioc0UNDNZ/iJjzqIeGsc+7SehgvfxG/6yUb7V7/ggA/opTNpPgC400m0w0PhAVc+wpzw0Pr2U1TgFZtiA2yT/OEER91Kts8HmGsvfPS6ILqM43PtRXZwa4FMzTXWKQhe+QzHkb6AVh/JC66SHXYm1rE1dGtqWY9/5fPgF33s8Tg32POaChd+X7zEj0MXvrUXOSqwxTSyNoY95PKZzpALHcH73G/XyfrGsn22ij9sIfKqLr6F3ZtD/+JrWhxcdJVO8AHB9ZirXZuekilfQZ96fY2fwAe2Y/9TAj7wK/xEa+MtwAt/2FfibkvPbS902V9hQQbsMPfbi1zEAg1HjY7Mb2WZy1i+mH5aV/PTZ8/QN7rskZNlcTw5YH9ZIzlYdMv9lp5Dl3n8szzk0v5jC9zrwiXAYXq9Q8Ij2WgDcAsSPooteOg+t8l1CwRG6AxBQOoTQ58lvQIthaWAjLsPRNYYFS7AT/vAwTwKLRBJ2CXD7/me73nzjGc8Y+nkpOOKLmMFwjgRnwV136OhbAI9HjJk4ykjh/YxH/MxSwIi8VfMCOJeQRH0jEEPR6+DiB6dAA4jr2FJzDlfAV/3z1z3JAWMzxGpDhUcQmMAvlXhAtDG4BRJjF/ibk+vzfkJZwZsrsIDfnCwnmJFIOAw8CAOI11I8paQ6PY6dn/xi1+8nFbhm7ESGoUZh6AQsx8eRJb4DF9y8hMeaNEN/6W/9JfevMd7vMfNe7/3ey/db4WXvc3N+J4XoCpcOFwykozBAT/I0VpZx8/QJ3mhN6M9AFwlVQoqAajnewAeurfsAr+qcRWg974XLtUF8IPus0P6Qn7eZ5Z8O8lSWLAjtoEXkb+feEvPFMeKbXPZmmJC8TACukAf6aIgxX+Ymy/jwyd6yXbpPlsQsMiQjtNHX1Znkzpo9EDw17xQFLEVDRe2K0mDbw/oR5OE2R4+25Oc2B8bkIwZg0Z2oWiJ/tJN61pfR5iN0xE8EIT5F2PQhL/2gCPccspDbyXnvhMmeTLWfesqOBSBM922Nzyd6uAH2vFAQQYPr3eRpfV1TBWkfC6/wh+SOR6jPfv6DE/y5zPgKmlT4IwAbuTIX7J7PpMfE6vwrfUXfqKRf6Yz4gr+kz/8FIHmOemSSBkfPQ34nOuSAO9rKVzIVmORD7wNoIWdKgjod69fdIFMySVFP2h5n8tYCSEbtSY/L8bxGdb1fDTPM/jzBS660oIx5irQ2T6dyf1TAd2Ep+aG/e3XAhzZnxjqhJY+nhLsx0+ib/SaM1rhKNlui6v2OgbMt7/cQa7X6wEgIz5F85hvBD0OLrh5LtaKGfhJrvQCDTMwH/18h58+t+AzHfRKsJyhlVP2XwP8MZ0W53pZXxM8FIULBhO8wKFLwjFQsJFwGZZEWKASLCuFsaZEWRDNX3UC1nBfp05S775E0mdBqXWUVeFibc8YPCWj9I55FQMSjEceeWR5/1HAVRx5xnB0+ykt+hKwXHEuTmgYOPzcwwNJOycpiXeE6DOD8cxajz322EIf3kt4fPaaksJOYGQMxkuoJUESonQp0eqS7CpcJAESml7h7TUrXKwh6XckS/6cPBolCAoOBeSDBw8WWXCgnuG7n5yVpERy9uxnP3sxOLwwR/IEf+vojurk4rVkE01JEgQg+EtgJEl4hwaX5+TEKSsAJR+STkkXXn3AB3zAkuzotnLekkEyR69AYh8yasHztnCJPujuk7lAiT5JkVf64Bec8Ir+GjdyYAFjJTjsgXxH9hCQnEnCUtDNxo7AnPtcuKwB/KQvaKWDEleydbFhxSG98L47+2Dz9E8wkwSTAz6TH72NDli3p9FnAcRamhJ0Q1LP9iTc0V3jghcdZ6P0M/ZjHl2S2CgUvNbqhE6i7Rl82ATdposjcJ9NsF16QRfhIECij8zctz9aBWR7wgluLraHJ3wPPyOxZR/shY1E5+wFLzrP7kMbntF1fHQ6wy/ZV1Lp9LPSV/PhxX8GV7Tiu+8awNVnexqHr2yOn5Rgut/ag/XsDX8FE/7BWULjc1WIms93sxH7oIfOSLb4InuxPXjx3/DVtNGc4GfhLVbB12muz/CCzzUBXjwshQve4jla6H+7jmf4zybEa3RXsqA3npOb2JNinj2LUfTJWsb1lzVd9EwxnVfX2+dik2eaGtY5NdiHPfPtfBrdC3jmsxMndh06Tg324G81ZfGy3YM9icvkzX7xxPP2OhbQxV75cLxowfpyJTLI/hV4Bl+FAXv3mT7wpXzIIflZn765el5bl2/UQDmExwzgo1GyFy4rgXLetnAJUCrzKTiHMGI8YROsVywoW7oUI+CsOATBndPgcCQEnBmnSFnswZg4IootIBtnH0ogYVBQ2RNOFFRwlKAJ5nBIMmGuZMWrEQKa6tqJj8JAEio5FtgkToKdUyZJCrrhJ7Gxl8RWwsPhcTiUWVAVGOHo9xgNZTdPIix5xzv4+CzRAOixvm4lHPHAvfADLyQRighKr7PKmPHOeBfnz1ErbHzmCDzHC4WGxFlChB86jXgf3oZvCgsdVEbrGTp0pckSL3WbJQMuOAVP/IOf9fFLcFXIoQmf8FZioRBBs4TFHPhx1vgOd++M4rl5ElXO0pcXORJ70wGFGT6ToURDR1oSZh9yojMCGJzpvKQGjk6q8F+Clvvw12G1vyBqHD0hw9kfj8Abeo1m+1TB2x4CH31CA7rtQXdbp3gIyONhL1wAXPAHrXQ4vEI/3rEn+kn38cJfvHNyyS7YfxJgOk3vBfwUpT2deEH3+A96YExkz0ajJ2RtXevQN0VCqxfBj63QYXjTQbjTR/6FjrnXQ+YKsuyLjdBFug6X6FV0lV4rvkML3Nx3cmEu/tBHCTl88SidZGPNgxfeKATw1NqeoYk/Y+/W0zTit9hp5vYAP3rNVu0B8J4PYGts1X6Ri8ue+BuehL/m8UH8Bv+BfnOtKx6IXSMehjf54wjteviBXwoVRRmfxVfAj8+zXnBDC1nxLcHr2gBNeIOOu4ZjC5foA1vWEEjMBOgUY51WklNkNAJz+Fi+mF6b6x5/TjfFJPLMfv0F0MBO6GT8iDXYpriK534/l05YV+HFF4krPsONTYqdTg/Rc6790auAl8fIG9gdgAM/qOGLx5FDf50C+Eo6xV7x2rr2ww8FLP64P4P4I/4rvgIP0cQ3o2EG6JZjaJry48ED3zWn5GzkQMduSzeftBcuG4AAjy1cMJrgvJagQp85FPsJxoJOJWj3BDIBVsCyNgciKW2DrUtwEljyWg4lE6gk0ZJxwdrpCSV35Z30zLcPB8Q4JCySAIFSQi/xsZ79KLwkl/JzqBwigxZsfRHWXuhySXIkTpIpc9FpXYmq39GD37qWfpeQSxgULuiQqKMTfoK8Yoqj9hngoaJDAiYxl4xILOyJR+ixn5+6q/BjoBI/+/oJT/LifKzNMBlgOkjwRru1dZMlDOTKCdiLXPASTzkxdJAX/M2BI57ifeRsH3/8wJ7wg4tkWWHEKaDbevjrd87ZXLjgB/mEZl1sxYz98DHJBtx1cyVq1oEDOXFckiZyJX+0oUlxrBiJM3LZj+wUsAogTkXhSvbmRQ49WAMdaEB/Nc4zhQL86Kz9QDW+Avs9FQoX/CErukTXyABUeNItRXgrU0A/JEKKAH6BbYX3LdBfp6VswVxjyIn9sqnYCL7RSbpKX3q/53djNDX4idg0kICxVbY/AjhoRDgB8Bd77MHXtXvAgQ0oaOCX+/jDV6ETv+zLR3qVjL8zln9kR1nPT0mCIow9sLuAZ+hjS77k7LVSY5I89oAmvohPCH+jY3iY5MG+wdlP49mnZMm65khMvAbX0o8+Pl4HFQ69DH3WCOEDJHfoyl6e+ekeO+VzQZ7nCthPPKB7+Dai966BrPjkh6FwCfC5eM6O43MVluhMctfKqQe2Ia9hA+1YPyWf7FhB7vPoylj2InYqpKwJF7rlTQz2k7HnAnygp05VEwv5LTYh8T73/vRd0S6uwcV+ZMt/8ikzORu79eoBzXgvhidu82cKxzRiZjZpTXkEv8J/tHvwL/IHRewhwHf+ix+iEz6jXR6UIviQTs5gL1w2AuEdW7gACuW1AEaO+W3ga4GSCWySC1W0/UcQB5FEm6PgaOLEcvms+taVkJwqnBQRoUngZ/TmClb2MwdQNsGcc+MM4CMx9lxgpkicJYdFmdynrIK4ziO8vF6hq2e8YMpI7CfRTRfWXLSgWQdUEsMZwQefrKsrJLF2MmKcgAo/iYdXYRgtw2M0+CfJNBY+lF5SLVDDF37GC7iSJkWRBMBn9/FLEPaamETFumiUKClSspd1/a744SThi8fmSN4k9n7CAQ1oFBAk++jUGZNwxsjRaH374BV9wUeveel84olikTPinOAUGVsbT9HhmdfE8MalCHMShtf0y+V3+EqCOF7rS5oUwIIfpytxdPJlPXNavQq+ilB0oCuJi+c9GE/uxsJ1NAagCU4SqnSCq7GHwFqnKlzwBU+tp8OoA01PYjMV3ZcANqJAIDP4tLIagcJYYUBvesAzAU/RTo/pjqDM9hW8EmE2hAdsilxj+xJriZT12bvxEjW6nqSiB/f4BHxVNAXsk7n0PT6FLPkt/kxBzlfwYaGnpdkanlmD/2Jr9mLX/ANbhT8/wjfkNMM6AjC/gK9s21y2jj52TfYBNOA5mp1MW0vjSeGNf3CWXOTEAj/xmN5ETu6byzasNQL7kLE12ZpEgY2y7dioC6/FGT6eTKwtiSRDe+M1P84v40XwAPlpPX6fr2p52oNn5tsPfyrcLwHkRn5sAH/4XnzGI9/RJIe7hlMVLuyUfSnK6RWZ03OJIvpnYC5ZiTXiQ2+XYigcNd5Gvj/64KdnbErzUDx0aVqyEzidG8gcH8QVNPksnvFN6LgE8E38ieamPcVFdjazZdDzdMuV+dYnb/Jiq+QFHzmSPLP3iS2Yazw/L+egFy2YKwYrkOVGPs+AHsoH5JL4IP+SU5CLtSs81sBeuGwETvkUhQuhMWQKIsEkZALohekzIRO6ACkJj/IbT3k8t5aALoj5MrYgKzhxiJ65jM14HUZ/tk6SzcDQJRj7orVnrVLCwV4cP1wlkhRc4ilh8JxiJrn1e0uH360tGZbgcSSKJQYFLzgKtroijBt+1hf4fVHVPPOj7HCxhy5CvjyoQ8BAOFcd17zKZgyjjQPPXIkEx27v4IpmhsnRkofPnrnsDR/0ws09CQCaBYu8PmZf33HhOBQNAjiZ0RdJf8sbexuDPp1ZzsBzssAHr5g4WhV8ydx+XtcQBFIQCcjBEfiJpzojkjg0w92JSxIJcpSkWC88beeSD96RD3yMVyT60i0co0vm+h2+cFRYWZf+pbvjmTHZx0UGdFMRK8h4PgLjOCg6oKi1xzEAh1MULuQmSaRzEmQ6wDbJXsCW8EoAwqdceI+mcwP8BGq+xX6u8H4EbZNjBPydYEUXXWQhEKNVk0CQZl+KVfbT6hRczKEX7EQBSreD0wgULJJ8AT9j6BE7d2rABpyAGINGepciij2wUb4JHsAafmc3z3zmMxe9830zdqog49P89JyM4NwXLhoEvm9CzvZ2Eurk0xoKWI0T8g6/zVEMKHQ0NdgCP8DXao7gFd5ZCz34H3zN52ucblYyAcYpwNgl36NAVFTBH80uOLE1zQ40kZVXaclBMab4wz9yk7hWCQk+8Gn8nDUr2QHP0G7tGf6nAnwgN3vlwgN0pejG5zSJJNZ4i/4ZHZcAsUYs4mfRcMxFfzQi6Sg5igHiC15EL0cX/aQXYjA59zzBz7zCyXbZYstrl/39hIP1+B9NTjabJlbG9fu3NJziop/yADaBDwpuOY37lwD8keewR3kLOYip4dsI5/DxVFfeCBHz5UB8oqaFZ6P9XfSE3yQ7sd+9FsgKDfGN5GxML89c8iT6l9M2fEiDmZ60e2+9+FNNKLmJva4VHrrCBRCAYCspsGYUYeRMCZvgBQ9zKBnlkhRwxpyKgMQBSfRUtipSgZ6SSbAYEYE7+lXYSE4FcAGV8CkTx+Sdd8VJwDOJvO5QDDBKziAAvBmGNQXmNohzypyHjigaraWY0LWWmBrrpzGSSsFXNxCOvsgvCPaFhN/xwTqCE2PTZUG7sdYToByZoocjYUjB5xSFCz7gv4TG/gIlOeEhXBiWxIrMODHjW/laU6dWAuyfKSnc0O11Fk4Hr+FnHrkJAHCzB147CeGUPG9pkFwI1u6j1z3r51Ufa6bgElAC8KEbuiR+mkc2nD58FLUSf0k5/qLTMwWSxEAypJDkqBRVxqNJ8oY3aAju7sPdZzj14J5nZM8+qnFbAH2nKFzwVYLA1vAnV+xHUYNfElJ2qciU+OKBQEJ/6MG5AJ/xLIl/e/XgHl0hU/iPgJwUJgl8xrnQTC58i9M/STz+0inPrU1mKX5/1a/6VUtyLIhVfg5Yk4zoMV21hr3YBZ03nwzgkivrGccm22aQ/fkq+ihwkoE9IjfFPZnlC/TW9lniFb3jY/iM2KOfLgWHcXgoSFsP7XDFE7j6bI3wLvuaTyf4ML7Zc2CspEsiaXwFcLU/v89POsVp7cRzcuWL0C5+sGv6Z9324ocUdOaPAK50ni6z/Zn83MdvdIWmcwAcyFZSLUGlY/Djv/gjCTwfL06iMXw3hx6RZ0XDpUDxzVbJCR3HXPw73WeHaNfEpH/uj8bnIiv+OPlHD3jkvjxDDKKr1myvrOV3PlAMp3NiAbsKfWySPHL5nLmnuuAgf+F/+WhyJvfYxbnBPvYjA/KVB5DLCNf2wotTXeTO/sjrwYMHizz4PXbeyq29FDvyWg2Hyr7d43PolpzKmqO1XJ7JLfl8eaEmOf9zCnrJl36x+WuGh7JwARwqpyvgUxjrjxRGAGDokk2GqKMguJunc0SAEgsGki/pC8gUOPjqkiaJNp8SSzol45IdezA4DoqiUTxO3jOBW9JsTUoNT0l5CheAFngwGD+NhZPEh3POqQkHqZji1AR9CZa9JBWUXOAxXrJHQX22hrXgzDHimaQZLyiwwsF/bHUyhQY4Zi+OmQPL/taRiOXoO4AmR4/wt4d1yMIloEvA8RtdnJP1JOvwtg/+uW+stRUkeC5hU4QopCQq5uEpeSogyICcFEaKQ/JDk+SHA6AXxni9Ac1ogJsusz3QTE4SCmPxkPyjR/By4mIt93xGt+QI//AjBSW5SxjDP2tyfk6LJKR0TVJOjyRmHJgkD03WICfO2lrWJduv+7qvXdYwx3NFDb60CW4PeEgPJRhJlI4Fe52ycIGbdXKB2AacyZij1qFSgOGBvekGnaTfggA+kYfkkGyOwc3+im9Fv9d+BAhysT6daYO3fdyX2NIle4+AnUt64dmD9fCV/tFhBQPbta/7eOAkg86kwcC+2AI+jeRqHv9hT34hPGV/GhF0q9IHNJlPx/g9ONufrtsTnv1cfCEPsvG7vVK44I/xeGQ+eiPv7EVX+QA06jCjHZ1s3rOW5yBzrQsfuLFXa5EB38Pn8XUjOs2FF7s3ly2xT/rI/u2JBr6Tf2bjbM1e0S9XC/gk0TNvBO7TA34IXq2eRgforz3Ix1hNscSLY8B8ckEXPNHFr/CXeMwnsS/+iG2JgZpacIGT+aHZZS1+gD/q+XBpEFOdZvP7YlIuer71ogf44DtVTr01Lt2v1tMo5JOcHvK1+Fvxw31y94dh2AJ8R2vCwcXG/XVP//hYPMt4uGiq5rotrYcu+8HT91UV9NGBS4H9xG+xX1Mj9PNp1RXeneKiV3KeX/SLftHyl0z55DyDR/Qs/IefJjcfJk+YAZvmX+kt/5I1+8va9iWHX/JLfsly6pvYfyy99Mtfh+UHev96TfDQFi4Bjl4in2A1MjICoijPec5zlgJGh08AawUn0Ot6Cx4tGCNJ4uTtI6FwT4JFuXSqJDjucfqUg8IJhgwgVXvwSiBrCxfPBAVGoONFuTyX3EfBXLovAq7kLwFXx8d+CpoEu+wlYdBFQhdcjBd4yMAlefeqFaeKf4Joyz/BDk7khjavunhNI6+9GQuvJBw6jxIXPAoeElAFn9eBJJxwhAcDbRM+Y+0veZQIcBroCq4ChXkKBHwhC52O4GAtMkQnJ4Iv+KiYIDtO0N7AeLQIVPAwjpwUBy1OaMirYvZxkZN9ORN4CfqSJT/pBKBbSQrw0DzPdHgFPYkqHNwPODHCa3MDxuC7RENC7XUdsnC/nx8gc/zG99Hz2wC5nLtwCYzWxxNJlSRBMklmeKXQw09Jl8K2ld0WsKc92JAunyDB/ly6noqB2LixfgpS7MBPejICyTC9UGhXYD3z2R/ddtKEFry2PnnSOfhJNtmyZB8vR7wzll7yG36HN37hubVG80DwYH9etZRosyW0mzMCeqF7KJGV4Od0AR8lc+TBlvlUPAu0+1uDP84rSfaEQ/xHCz67rE2/fTE/3+/DHz6R/6kSSWP4Azzkg/HHxb7w3Vx+Dd/Jnc2O1mmBbDVL/i9799JrS3YVer6DhAAJsAUSSBYI0zBgsPzA8hun7cROO7Ez004bTIMOR+Jj1v0+xW3cxm2VVKf8W6W/a2gqItZr7332ydpDmlprRcw55ng/ZsQ+50hGZMhONbl+x4d45ECFvNizwV4coPTU6tz+e4Aeh1Se4tsbbkWPuERfnqiwM3uAaDqCbOs5NC7kxFYU1w7NDHK+ZdCBp3T02P9Kzq635hrslT95I6En+XtATvIe+4abD09c9jb67SCA/5nLb8WQeX+O1j7UgBPf+BP7xNstP3xMsB/b78kEuo50Yax83DPIW15RN6mLHKi0f3Ymjhqu8yOvnHoqouHak5Xr6ggxvn8kSIzeosFe7Frjig6vxLOLef/WgR84fZ8x+bnBJ75xkYx6fWcr0Qi2jI0ROMFVPGpEOMh0SiffCj5F+ATKVYgzZq82dQohKUsMGgkGiA6JyB4SI0NzssdQOSI64CKHtXGRzCRMBRNeBDoJh8FK0PZSgOjEydA+9vOqifflPTEReO0RT4YTSP9ni3fJFeUcS7JSODupVAQq2iVtQTIaA3Qp0jUQCnXFq2CGRrLySFeDoHAXyAVnCaV7im3JRbC2zjV4yEzCn4WmfelPA2QfelKgoVXDRtaChdNwTY0C0z18WgsXehVO/gljSQiv5KR5iFdyJCP8O1XqBF/RoxlSgGUT5tW4BPSn0MAzegQ6DaBr2Z8Tbtc6PUefPeiJPWhG0lF7aVw0wugN3KMT+8PHPsh7FkAB/s1ToCjkj5LptWCvp2pctsAcuiALxTF9sgkHAuSqGOVP/E7C81ujYx6dWDPtegWyk4TYKzuDm6/ATZeKPcUMP2Gf/FoCoEtF+bTjCfyTzR2dxOEt+2UvbEAiVGyy0XzaoFN0sQH32RHdrIB/BZVmSBwhF7pTuOID3QpzSRBt/IT9iVNwv/fee6eGg32vMWGC6+KmdXjlW2KDwpbc0S8uu76nZzjYBfnjm2+jwVMmcVL8oQ9x1zX3xA/xSvJ3kIJfsYCN4oks535kxK/M1xyJqT1RsD+5shf3FIxigfjU/SPAoxN6PGwBWswhc/bEdmpK2SpZiUFyFBrEbLywRQ0VGvZk57p9yZctWE/++BBr6VthBTee4ZY78Gsd2s7xN4EtPJfGRZzTXPc0zKDnWwa+NMDyLZnL9WILm2lOe5AbfYph8hP7FjPMWYHuyFsOlJ/pXfwgf3tOuv2mw56sOxDFo3zkXnTM4fpjDPSxGf6CNzQ+BdiHr6tp2CpZX8LnKpd7hhxKpwp79ZY4SYfupa8GObFBsZi+1GZbNSgwX4yF05NsT13UleLQyg+7I3f2qLaUQ9QZ5BG/Ky2XDvbkYERsf9M+fASf+MaF8ClaMpNAKbVg7J7kLYhLDgoZhiYwMTqKTHl7jYs5kqpEroBREPTUhdEpygUaBq/5UCRIPBmZZCLICQRoc302LvBIPHAb6EKTawKd4MHYrIG7oAePJOjUUYJCA7riX9DkdOQNL56twY/1TjU1GQoOhbCmQ2K1PjBXoaTZUHQLJhIFPL5rLnrdiYPZiwz89jgb3+RiDR1xXI0AfHiyF14NONHi5EJiQJ+TCfrAi0HenmLA65NO49enIhIfko/AIGnjFR9oUPTQl++CoyazAsY8AYJMosv12biYI6B4OoNHgG68+A9PFQd0X1MFDxz04o+snWRpoMkhurM/vAiA5BRYS98KMjpmEwq2Ghz3QbZIF4qhee8hAK1vsnE5AnzSJxkrwhUc6KRfJ6J8RjGgEC1J4MfIF+jP6zlsHT7ybFgjGbFDOu3E3uuHGl02B9cWHxoCvPKVAE77WmPvBtmwe37Tv+43IVlZixf+zJ5rjuf+rnUaLAk7qFEU4dNadInFnjh+//vf/62cxBrFGDk49VVEwE0me3pCu6bdOnvAq7ClD77m7/HIcF0fP3C7j2Z+wzcccrBzvqKZ0UhJ4j0RIXN7iefkRA94USBuydpcc2o0J0SHefj11Bj9vtOJkY6yGXgDsvKE17wtMF+s4P/imTzl8IUt0QUe4IUTHdmcRo3urIXbnHWQMbk7RBF/8KiAQhNfcN9aeB8CyECM10C+aRAD5fs9uV8K5I0vsiM38ZY9s1txZZWd/RSe8gR/sobdKm635jo4QKc6hN9Z53ONz36bw/58Guxf/YC+LR269hgDLexLrcSHt/z3MYDs5S91DdnlE2DSt46HAHj4qvhFX2IGHxVjxYJ1H3P5b38bzBYcRnQoPgEf4pKmhV/zeQ2svZJtw1x2J2+ox+R8OpDT0OT+PSCXebVN4/Kc4f8XjQsnk8CciPaKhOsKOMlOAi9BMCBNg4DivnlAglgbF/MVE4KJAMVwFMySQwbHgOF3OuKUTAHqGuNlZL4rnCTViidz0GA9uTiNkaTcd80okDFgBq+4QKPrDNsJkQJa98zRNWcCLudHt0DqlF6i15gImAp5axmvwsdv+NCgUVDsKcwBGqyTFBX4impBJRrh0ByQDzlqOuAkP/gUH+RCZvSBdjwrXBXhZM3Z7W+gUwJAh/lw0J394UC3hIFfgYJMJVDJ2XqfbItuNIkKS/yQTTJBIxsxNHquFwjwZB/Jwim7e3TnaRXa3RfANGsaFPgC3zWWXkfzdApt6IcbXfbCN52wT02A5DUT0lbjggY6Ihfyhg+N+CIL9wE8eCMb9m1eeB8C8PdcGxeAXzZAX3Ogm64kYA0xufFfT6ToQZNj8FvxYOoj8Bv+idc8CUXBKi5srQPshl9KgAAe69iRdU7RnO6LDU7t+YTGxSd9b4F90MAO8GWt08Fpj/yKzYgb7Mm+yciAm42TB/7pJXnhhZ2KF/yZXSmCw7MCevBAr2gSK/glH0WbwgvuVT7ogJOM+CteNE/iY3I22Dh/mr5sbTyZg1561RTwt/aC3xMYRWkNlLVbABe59fcNbIIMxTxr7U0WdAdveHptiAy3AL3imGYIfjK2Ft3xgd454tu+YoeYyI7ZLNvFK57kDPFOzKa3ZNbY4/VWKA7g5U1DjQud3gN0yWbxRObkxl8VoXLbBLoSX/m9PEjm9mcjisvsLmDb5KVIJDs2Ii/ycXFggvt8jq3kh2IEO7QXXa74HwvahxzkX7UV3h8T7KmoZ9v8ZPrFUwCZ8yOHN/ycrtQcYpu61e8pA3FGU1O96Z4cvhUL4Oa3cPtuVGvYMz7jVUzh92yE3sVr9WUN3T0ygUvjgsfnDJ/4xgUwGkFHMe8xeadlHEFQkTwZC4ULTIzN6Zvu2HUgyKyNi4Qt+UryApR9rBG4FB0CCgPwGpKTeU8/GHROF2hAnOZL6HBKxAxcwkWj00RBaho8HGhVUHl9QgBBhwJfMYsGBQtceGbUCmdNEKdwEocu3/GIXondPp7EcCKOCdDqu9cMFM+Cs2CF5p74uC9RVgDg0ykf54JfASTwFmQV8hoXvNMN3uGyTlEjQBmCI9njj3PDhXc44OawEhS+/G2LZkqyqalT9HBG/PZIlWMq7A36V7ywAUlDwvf6nEJgDQL4ok98djJiriYDjYIJmeJnrqMnMlMcaybRS85kYU98+m4engRDcoYfr0DQ60kK3Pj3pI+e8Sw4AvJh3wo5980T/OjeCc29iXwL7PlQjQsbE7DZAV7Ri4d78O4BnGRuH37At8lIg882yZvPk7E56LgE4KU3T1/EkWKD6+1Jz3ySb3vi6ZSdr1qTL7ABv9kieYgHiiU2uz5xWcEe7F7BZD7795uu+EoN/7QHNPJjvplf4DsbDFwTp3/xi1+cmnw2j05xR1IVg+CGC357O0Fk44pA9s9fyFW84y/kgUd+iD7y0OxoWPjx9773vdMnvqYtoM16fPJ/r+itPLErxT1axUT3+VZxVl5A68rnBPfQ78k6PUSveIBOtsJm8IlHfo0mMYfvJctpyz7FOgc25Ia3c5ANGWzB4VNxmL3IoXjMXvB6Cd5bAA1w24MOxFyxURx40/AQjQv+5HO+IA9VgMLJ3jWwdAqyQ35B7+Timk+2Lg6I0103l7/wfX5pL9fNoU/5Mr3ZV1xiq+aal+zZnHxRXnxK4FcaKQeN5ICmxwA+w/fIytsS5GGvx9pvC+hLbUPWxUTyl1vxL0b7jSb0qi06/AzEGDUWHqrlzBUD5Bm2Fl9sjB2JrWJN+5lrP/UHnQPz5RJ2ao8ZY64F9vXSuFwBFPlYjUsgWQnwCgPFndMzBlYgCCifkSq0c0iFghM/hgPQy6gqJgPGJEl5AuJURmAxj4P3upX92tOILq8LOEFRzHiXmizQ6HGjJFVwssYnOq3jIIotJ6i6fCeBkj8aJRPFvfsaMkapMFFo+s3I4VJoSDj281TAOteDgodTVgFUMva0QbJUuHM0wd3+krCCBA+CDIdTqM/GxRy4FPScUNFRA6nZQLP/N4Y8JENBSyKecgP2tJaMOLpAEU94hocTRpcEohiyD71ZQy+c3h6SnT3pi/zaB+BDAeRVHXLSEL7zzjsn3GyDzp1wFpQC6xQT8Dq5M/BujeKNvdR4ALZWQ1xw8ooMWyswka8Ep/iaa4E17NppPt30muRK10OB/R6icWFDEoO/LaIPBR398FV7pPd79rgU6Ixf2l9xyK6u2Zf90RHb6ukZvfnky3hLZuzOH0TSb4cPdAVHw94+FaQKJjZzDqyBh+2xBf7ndNeTN08GxTh/I5Kdk7GDELan6aUP1yffvqNBAZ9dmcsO2b5YJyawTQ25Ak8Rz+/oUYwXf/k4v5FwNavminfWwmmewg69/FUMcKjA3+lmBbSKa3ijs+jm4/xfQyEeadjQQRbipnuX+AVc5Kip3tofKDDEB7Lg32REv2K0psk1uiVnuoSH7NiBuHgppA+46MChRrHxqQAN+EADHZOnePjq1auTPb9puLdxwR+76GlYDUMyhpdO2ZxrZCHn8rP5RMCnuWyCLbARc9kgH8kezDXEGTFAceq79Q5UxEM+Y07gu/zJNxS9e3b5mMC/5LD+3uqhAY/0wMZnDfCUgIYOT8WfqQO09LSEnulA/uYP9DV1Yq7GVByjN3joWNxb8zhZ2hNedRobmXZEDpMO9imnOCQqnt8CL43LlUDYj924UDwHV3R6Z10yk4QZ1HQGRkOBkq3gx2gYkUJTEGFgApbAowiZxmkuo5QkGa75nNtwsifYuYZf9AhcGgFNh+Jeomb0CguJUjFvL+s4SK8TWM+IGWtPGfCioBFIrDUHbWgX3JxGSuoKbMUJWSjO8EAWngh5gqB4QBvjzwHIx74ClMYKL4xbQS2oamTw4GmNxkcQ9/45WaFhbVzQSdcKbIUq3gRrRaIixnqnTpIifaG1QD5BwyVh+tfPNBQKJ3M5tiDj72XQJgFVVJEhGsiMo7vm5IQsJBbyJH9Npnl4YA+Kak2SIKpA0XB5v94JKz3RN32wEzisIzNryYys7GkPOiFLxROdz6BFXnigj05yyEkTpnHBG30p6LYCufnkrMD1NI58BFO8PAbg8aGeuNATGdMHudA/OxHsFbru85l0Ze979twDOMnLPvQpefCJS8F69srnFDN8Ht18gI2zITZCn+xDk83++TCe6BSOht9sxBpP7tii6+cg+2VvDlEcBijq2Ha2LzYY4p0ndXwVr6u9xBM6+ZICi6+yreIZXvzGK58Qo+jNP7FJj76LT3jwfyCxG09X2Dmb1+yxabjgFEv5v0JP7JLI0bACWl2nJ7gdErAjjQY/4D81Cf4pVXzDTTar/2wBn8KrRpRN0NEK5OO6mMRO7ZGdikViHN2jR8zFK37QhtdL9Anah2zpwn6Xrr0WyIZc6YRt0qk8zabxIn5pduUCTTC5s403Dfc2LuTLJjWb8+AkIBeyENfZPP/mQ/SaTZnvk23CITeIaWhiA+QIr/vmGuyMvcjPcqC59Oz1MXqeYD67cSCnFqGjp4TopXfxGd1+u/5QQDZ8SI2kpklWTwX2whffl7+3/BR9dF8N4UCIbvnNnMsWrBcb5G+80LF4SHd+B9axDXuqq+CV09VafG2Vs9/u+1s+sT77uxbY8kvjcgVQ6EM2LhRHmYII51cwUqig6hUH/w63QtdpoIDsPhoKJNZ7uuIESSKXVD3Og0PC0X0LGMB866yHSwGgMLF3wIg4QE8BPNFQOHhSoLhRwKAXDolMQvAbHRxA4CMbhqsw4CScZT15NV9QNDgDGuCRtP2rZ4Kt4kFgVHAr6j3pcVpqDwlWIyFokwnZ2Z9Bk4MApfEhA7TCjy8FlabFPf/GvOJB0+Ce9RyhIgdOAdwTKXsp/v0rXxwaDfaCm1w1A/5olbztAZ/17hvwSqCaHGsV85xXMFDo4FUxRtaKGDxkE3RFzvMxL/CpMbEvPq1RBEli9GY92nx66tTpid/0SJ9sS2FnPXuQeNwL6AkNbN6JnqQ0bc+nIox9CFqKO3aDZkUnnu0L/l/7+//snN4VE+TPzv09T68h0oV5aI3nxq2A7odqXLx2wzbhwRd6C9psXRAnW38cTrd8hU7wbW42me/cC/Dwfafn7PISQHv61STzL37rk4/QITqTlU/XPCnFy5Sh72iASzxSxPMVdqtIyr/cj3f6sIb8DN/ZBH/QQPB5fo4f9NlTowGnwpO80T/pAH7zMf8MKHlbyy/nay0T0IE+Dbp/7pzu/D0gmyYPjbk4hrZVV/ZCuwaNXeHNkyu+JIZt0YZ38UWRw1fojVz5FjrECq+vOlQSh/GfjAz0wmHYj0zJlt8qTMVPNtcBlvlbdOBHriBTcmkOXHSmgEWD4oUto9FeaLgEzMOn4twTK3TcC+SP1sk7HYsZYg+e6U5RI16Jj/SP12i3ll7df9NwT+OS7zrhl5OmDgO/yYd9yjniu9qCrZHfOlwnF0PuY4dimnv2mnP9Lv/41NyKfVt6Rgf/lZsUwfSw0vqYYD/0imu9Jv5Q+8NDHnK32ETeTwn2x588LO6y7y1bQCM9qTvUK+KPmsJ8OiuumCeWiAv8xGvrnhBr/PBmzjroVt1C/3I6u4ZjS8ZoU3PIxfZaY+olICa+NC5XAEE/VONCqQyBgXgywKkkHElWo8IAFOASoeDkCQHH4CAMRLBmBGhigApfjYhkJGlKwJIpJ2UcEr3i1qsNTlQViorsNdCYy8AlK8mbgVnLOTIyxmovRb5rePGJHvspiiV8RZC9tpwZX54o4VViUxzYF/3WKiI++OCDkxMoftFpj5IPXjiqvRQdgi3ncUKq2RBIBUqJy7rWRqOCAW/+oyzNXU4n+XFWJycCvsJagSEJWxuuAoZCzV7k6mRLU+SkT4AkIwW8QouOc9R4IAM6/clPfnL6RwI4o6dWggR5kIvCwasWmtEZCHxXZNCxxkSTgAe8TTrJzd8usRdr0hUayNUpmGCm0JPgrW2eYZ7Cm06cznqyxt4ED/fRoKGkZzyTuYCiYcV7QRH9gqsky380WZI23DXRmlQ65Adw4V/xmg3b71ZAw2M0Lg1yMsivT3SzU0UDG8AbOZGBUyr66kT9XoCD7UoKl/BHnvTNzjWMChD+Tt5w4WGVOT+m31496Z5PRQ8d8knNh0IXPYofCVA8olN+gnc+xo7EIHP5KbnyA/YkzrJp/u1+MmVv7INM0eP6pNFvMYPv4oO+7MeH8bvKhl2IO574KX7R4bUw+Pve6xd4nmAvfsWeFXr2sgdePYWz/9zPdzSjryInGvFYzCdffiE2kaX4ixayYk+eotKz2Emm4g97FKs0HBoWMhQ3yBn+CfhwSMQHV55AshafHNjQIXnSF5u+BPBF7uKL/Ve5Xwqtg4OvsFc2wVboVJyhH00eeu2L9gZeslXD/U9C44IP/Dr8Ksdtgetir9wkzmtc5CI2tA55kE3Rt8MDdYdr4vY6l13Rg7pDzqMLvrQH9Ge+fMbW9+h9LLAf/5Fr5NZb7XEFeDX63n4oTj0l2F/sZQfqhj25ZvviBFodDInX9DiHnMs+NJhi4ocffnjCLR6xBffXYT69epVYzXfOHtUO4hQ7QtO18NK4XAmc79bGRRAVoCQ2AUeAELQUUgKEBMWhdKqUySAUOhK1oatVCCoyJEYBSNJiYIpweBiYApPBaQDgYxyCfHsZmh97SmycLcioJF1PIzQPunP0TEf3W3JlfCWEhnt4RENFSwkPfsMceyjmFAj4keCsURhxAqdD+JeoWxvYx+mkwoizKEjx6FQVX3jEB/yaKwFTg8jBFQvwoQFvTsUVk15R0TT4Tb+SioSokNAIWkMP1iskyJbjRy/9tLf5Ch7FiYJQcLdu8pAc6Eexhn48w4cv+ytIDLo0T2LAu+DoO37R2EkxuprT8FszV+Myga0pJAUyJ+leE5nFnX0kPbJRGPQ6AD7ZoQKcTbJnxaRggnbFK3rYpYCniFIoWKsAEnDoB/9kRX/07LfrCklNjH3Yh/WKSsUjvfND8lv5OQJyeIzG5QjQmN+jOx9mG2RhkCOZkKUGnJ3SAd1YZ/0l9JpHhppmdr6VQM1BPzrohSzolm7YMntV0NIDWPd1vdjBF7JDtkVH/FcB75694GO/cIt3Yha7tqcm2D124lUi8lRUsDN6UmTwT9fJiB3aC94aboX+PNlDH9shAzyaSwd8TyxA2/RB981DJ50md9/tSWd4YYN46wlGQD/itrkd7riPboWcJz/2CNBBTuhzkm0+fcBLF3xfYe6QhD+a62RUDCUrcvdJdub6JFOy1SSIBXzYPmzAtWIjXuIdjeRBL3u2RZbk5cmZgoYM6M6nnINfc7bszD7kJp5aeylYBycdwM8X+AT6xQOHe/Y35A+yoj96w3P6NvC1xRv8b3vjgi/FocbT4Qcb2tMjMFd8VhvI2fblc+tgs+zKq7v+t3M2vzfXcE+u7H/fP6KBTuiUv7PL6UdPBWySHamb2Aya7gHr6U78EIumrz8V2F+cMPjNEaBP8+EgU1wtDq+DXn2KQV/4whdOdqOOYh/rXMN1uNR7bLL8vAVsxH15UN0lDl4LL43LlUDglzQulENxDcFdgFWcSM6SiSCsCFC4Cbqr01Os5LI6OHySkoAloCt+JAiNhr+hEHB+/OMfn57QKHgZFZolAAkxmhi5AO51ra7hj9GiUVKQINDA4d2PRsH/qHFxzzoJXGCGj5G5Z/je+6D4Y4gFQ3Kyl2uSi6ZgJl1AnooCT4WcHAhG+CEX9DuN5KSKfUU7PgQXBQPdCbKdstKDwkuRLRn2ty/wu8a5FAfdh0fD57pCggPRI5qcTOELrQIK3XBkhbxGZyZ5stI0mo8eskeza04tnGLgGx700KWnO3CQoQAEN92yFXwpOld7gXOrcfG9EyhNAVl5UoUnssweFFSKJYHe3vCxI/YkWJKDU08nel6/+9rXvnb6/3I0neRtPbkpguBYQTOCds3PBPSxO6dIFb30JQF3oo8WdBqTty0gl6duXPbAOjRL5JoN9gon2rJTiUJTSW9oJ/t4jd+5v2vky1/oE52gvawnM77igEOBItlqArIperJ+S0+AvOmDPYhb9lB0iyPijELTnEBSw8NamNmPf9N9f9OheZfENCiaEvYIFxtV7NjDfGsN9sTGxEHz8Mlu0WFPtOE7EJPEiugzH5/kDb/izjW00oVmRFyGA63kAjedmWfgHa0OmSbYQyJnp/Alf/LmK7MJE4fESroXc8xFv3nsX6z0CiB68Op0V5yLjwCtaBEr3IOHTtkXvxFn6N81NtXJ6B6QDRrsaQ16+SgbYZtsVNwjG7zZL7nYX0wST8hrC5o714grbLcYy0bxTW6KXbZnv/YKVlyTlhXw8jY3LvEnb5HPagcr0A2bUSM4QKQP9mOQZcNv9iAn07vcag/5eM6bg41YQz9iAnvdkvkENQFaLpn70EBW4h3f3Ht19BrgE/IwfyguPCXYj3+zZzHl3P7odYjq0CodZgvTJnyKbWKegwu+LjaJH+t8A+/uiQ9qErUSuzsCdkMP4rK518gOjS+NyxUgGZ5rXCiBgXBqyndK7dSewThpVcBK0JIsfJxHIFpBESwxC7SUmmJ9WmNtewjoTir9c5y///u///pzn/vcKVlJ7gzaPMHRuvDYUyGsEJQUFAWMjjFVLDFGTiF5oLe19nZax2GjraF48YoHPiVTRsY4de+CRYWqpA6PNeYJfBXwrpFjyZOjkUMJCV0KHXQrcMhJgUMWNV3ox7vXJziXACwxci7JwrvQCiZrzLUvR5A0e33HICNPNOiQ/gR/eMInwUrS6DUXn/AJaOhCAzolcWsCPGtsNFVkFG/w0IOAlG3QHZo1VXgnJ9/RSy+SgOIXLXid9gSfv9HBZ5DM2XLFGP7x7beG1HdJBj/WWhPAaT79mMdW8fd3f/d3J/vzegnc7BLP6Ju2NyF7UAivgA/rrM/W2ZWEKnCxDbYlCGry9vYA7tmHvPfmXAL4vrdxCaxHF5x4ZAfkxWb4Cj7ZkCKTn4olEgh94bfYMHHxB77fiT77gE9hrLnUAPIVONiVOQHfwJfEs8Wba+gkQ76ARkU1XcNn/wkaAk8AtgozuuUn7FCMwRs5mOuAp7/vwCObVjDzQTZlDr48SdXQ4wcu9+BiI//Xb35PHuASg/kqHszXdJNVtJuffsU884B75Gm+01qyN098pRf0rcA3+Q4c5mrQPBERP+yNh161cUjBvs0jD/7PH/Duun01M+mczlb9uMcHxY0gGYsZbIn8+LS4Q2f22wPNGHrRY79w4Ye/4UNjyqbIxB7hI2sxk29Pm0AzHHRHZgpqRa+45T16BS27osdibHKJ7xnbbgF43ubGhfzIrXwy/XcFsiJr9iNfkyMduG74PgddixP8zx58W+wA61wjm2APYoI9yPcI7CEuOEBhS/fq8xpAM3nJn+qNc7SeA/J3IMDnyAH+p4J06/BQbCTLo/3pXo0gB4oV2cA64MCLOk4u8CkOqGfywb3Bjh108C+0HYE9zHVY3YHspSC+vDQuVwDlr40LZVMCpRGoBEegTr/86y5ORTrpMOdSBSkGnYzaM6OSBBgPoxAsBBUJ30miAt37iH/+539++udvBR1JxWsogpD51sEHT3RLqIxAZ12D4jowh5HDoyBy3TU41sYFX/gXJAtK3ROsyMQfjmoWZrKHzzry4lh+B9YqorxuxYHgQZ/CGh5JDV8SJyeT7CV9hQweFboalHD6RKe9OYwCxTz7+HT6SV/Wmec6nj0x6YmB364DdEu2Ei3ckjed5/SKavTCh17FQ0WPAspJRqfIAdyaL80aGUaHvenJfnhUgMDjnmEfewhkMxFavzYueNXcwaNAYFNwuC4Yw4N++iKT9nE/+zOPvBXCihT2993vfvf1X/zFX5ye8PjtJBkf9iBzdGVDwVHjEsSjYT07oHcyFCTZroZLIZ2dozPdAt8fsnFhw/ESbQ8FcKGX/sUNPCkkFToKPKehDhgkE4cMdMEWzWf7ZKLQJXf24FVARaYmk+ym/CdYbw+JPf/dAqfinvD6GzU00OvWXLJWCLE9+MiOzPCWHvkVf6Z/19irGEq+9Ak3XVrvaQJ/pvcKWnso4uwhNm89oQX2ZMtiKpn19INdZidG+p2NC0Cbxg5dDiL4Orr5OPpWQJ+CnKzEMP7EF9gm+jrQ4oeuJQ/X6SC60INur2CK9+YkJ5/2JiP3xAc0Tl34ThbW4Uuu8PeNbGbLDpIBX/FU1fdkM0f2KdaJ3fIN/GTrU0OJT4NtsmEFiiaNr2rq0OuJvwIGPXi2X/p4aMAvWYmv4tZj7HEN3NK4kCf5saU9vwPpyKt6DrnYyp7fA/fYGb2rF8wXB9Do+9Y+rjU04XL/nl0FaKJnfuwQpvz2lMAeyeR0wPEbeq4F9PI78hHf4HtqHuikwl9c39NRIBc4JCmu7AEc7IouHTqZy8cdnuHTb3P2hnylhurJr2tbwEb4OlsWI8lzb+4K6HhpXK4Agq5xIWTCZwgMSCIUhCVR9yVXCckczunzUsWAtXFhBBxdgECDJwOSmcDHsBiZpOQPbCtUGKtuXMfsZEATI+BxWMYJp4TivlM41+w1wRy0SLydoKFJwpa4A0nMaZlAB2cBKTlZozjxL9VIrvC4rgCQzNBMhlNOPq0TfBVKPvGuMHYiq9Ehe3jJiuw1WZzH/oKoojqnbtBLT3jiFz1r4wLcJx/65FyTProXABWL5hcQnZIrLJ3EKEy8UkFfkjsZaTokTzJMvxPcMzSV5OM++hQn3k9V0OEpWgz7m6vJUrRGo+s1Lq7Bg16FQ6/jNDc87EBTI6hEn2vkA7fTSvaERr8FTvwKbvTgn5h2nZ7ZlSKGXJ3kCWhoiedLGpcJaLTWQKvB9hT2CnaFpWKWbslZoo9v+9zbuJADGxQ40e43fPfg3IMtXuldUUx3Ti41svTAnsmXv2sY2b14oADnB/iH54hO+BUt9KUA9XsL2LUnnmwb3i1Ap1igyWJHilkJyhpxScyiI37NdvLZZCl+0WeFPdrh1AQo+PGFRj5onljIJh0SbSVBv+HRTJAXGVWgJReDvW81LsB9cvdEWUOoELDXFpAL/P4Gj915BROPfJjeHFqIw+1vL7jJS4y3V3ShiT7JSSyBVxMg9pEpet1z0GDtnt5cF2M1v+S2BejhS+wJrcWHowEvevAobohRCjr6dYiluJI/xGfX+eVswObAc3w/NNhTTBIj0foYe1wD1zYu6OUP7EkMP6KfDNkbG3WwRrZ7AA/fkFPlJ7ZI7/xLAcvO6OoI2FOvTZp7RJv77BgfeEfrUwJZyGXiCLu91g7IRuxn1/RxTdH9UEBu4qo8m9/s0UDedOuAt3y1B2INfatj6BRefuPQ1aEQOzkC68Ugef9cE4sO8Vw8nTXXOXhpXK4ESqd8SVKSEXQo0+mNpkABzhEKyvcYM1wKD0lKgd7ppYQqGXS6zLg4EZp6f53hSYwSh/uMTZL023qFJ1yKHI4n8a9NQ+C34k9A05gpjslB0a0YdZ/jCgQKfEUNA3R9DvIQCD2REEgVsGiRQAxFhyc4jD0Dts5e9lRoS94aHAFSYkWP4buC9f333z/JjGHDAZdGJgeED9CPNU43uyYYbTUu7nudhDOm09ZIDBwoB/VbcvavoSkoo5ET++5Jhj9kpCt0KQwkieQFPz25r3BjW4agoXnUIHiS4UQ6fhp+xwMb4NTwwa1xIXtzOs1W7JBDvICJB8+CHTszFx1shs2zIzangKYb9qkwYaOdQisMFZF0YY5iRcFP5/AYZCrRKSbZjb1vAXSjmZ2TKbxsHD2GfTWB+MbH5PlaEJjh84842IPvGxIxu+aT/IVe740BewAnn7MH+eJZY82fFcUaZH/vxhbZnicE4oi5ZISuLVnD656CCm9kugWekij+a4ZXYHPsmC85tXfg0Otf2Qh7kfjtw9Yq4q1Fo0JcXDIXD/jjx2wWL2KGOezTbye4/mU8c8kA7uKD4ZpErzEwT8EPV74HfB41LgBt1vYvAdonP+/TYGdo+eijj06+7tCJD+PVPb4sttEJ+aAPrw49evUDbfTMd+hSDLYfHHghOw2UQyu4PQXnd/xySy/AAYFCwb5bc1xHi9iIPnZiXqP44B47txfdORCjE3HPq6JiEPrQxvfYSn5RobfuP/e5BSZt9IQX8ZC+2B56xB1/RMwOb93noeCaxoUtiKPopht2egTkjGexCv4jXslNHcCn5bLig3X8Vy4S147AGrKmfzkGvXsAN1vA/94Ty8cG9PJTuZXNXGML1qq1ihEzhjwF0L14KA7kx3v7400tIDZpEvZiekB3mha52z7w4tc+6ju15RGv2akYJx5ZewT28GcDcgWfvQTQ8tK4XAEcmbH642OCVuxLOpR9ziDOAWPg0BTPGRRD/hMyiYwRCL4CA4fPcHxSoiRB+Qonrz8oZHwXRCR288IPNwNW3El6Ep1/+lDShIuhocHc9gFOSTVDAo1kxSitMY+hOtlxryDZfnAZDFQz5j+PdOLnJAfNGkC4FRECpOSHRvI0JD2neIp5ids+nNYc+yoI8Igf9yVlBi0Y0gvZON31O1rQby8FeNfc56x4UuQ3Hw103d/kNN8QzDViGhZyU0ApjBQ+ZKHgcA2d1pI3PhT6imi6VbDby3r0at6sp2dyUZD4jS7OSldkZn4yMsjasI+94ceHeZ5QwWUftiRh1LS0Lp7MV2QotrxSQueCO5zsBo7swjonmPRJduSpoJL8nAQJrGSUPcDPxuhLoS+Y2sc/50y+yRze9rgVyERxyD8Vgk73/Gs5isl7cJMvfdAd3jVeijYFiMZU0FeU25NtkmcybtzL2wrwwWsvtGiq+kcp6ETBhjZPUtgXXVZcTLr8Jjd6hmMeIkxgf+xxr5gRA/iiInE2x/RLJmhwUFGRRCfmk63Bl8UBp8rsim2wQzxYDw8bVZSRN3o0ERoZDZL5ijs2aJBB3xUcGmUysNcEdKbfvcYFvV6P4/f2Ce866B99aPKv8sgV5I8WPgC3GM1WFIzo1VTgWYGAT7oQqxzUiJ3W8Gm/NRd8euoHbeKLXLGlN2CNQyA0rHboN5tGj4OJ7GEOe7AdPPIp+YOf16SIlRpDo9N3eNa97gX4pu0a9hJv2QAesneHimKuWCCHki/feNNwTeOCN3buyT2bwO8ekI0cqPG/5IkC3OKwXAOv+Q3r2Zu992wqsBY/bJ6PHgHc8gCfZtNPDWi1LztRH5zjbUI1F1ubsnoKsI+46QC3w54jYCtyvrjDf/fsBl73+LFRnm+Ii3KL/K422cMTsD8x29wjgFtcU6uKcfC6dgTk/9K4XAECjCcuEh+DIHAKPqfEI7AWHqeJCl2JzJMFid1rVRKawtfeGR7F+vTbqYEABYekJ1hL6uYL4Aytxioj9J0hKiokF07AyPw7717dEOQFLB1wSccaBY0AJ6kxYkEUHsFH8RGN5vuOJ8lZk+KUC21oZaRotJbjtcaeXnVzyqMxUhx4rO9dekkHPp+eqpib/A37MWhFh4TqVJgMNUqKGgWLQlaxpfHxb8+/evXq5NTk9t///X/+9jG8osE8SZx8FfB0oYgSNMxnB055/S/fkrXgR47kSa7owZ/BeTmlZoI83Fc4eaWKzPFoHX2RzywqyQefgqz1mjhB0zrFiyZV42QOIEe2qSmQRMjMqxuCLJ1JQuiHN/zmV4x4fK75kvD9PxLmuo+P9ATQ57qijL7ogN0U2NgsvXd6nN0a8JAPuvGsGWUTimW2RHd0ac/2uxbyDzjIm97YuGL6VpwAPjTzU/jx3R7kgSd2o0klS08/8GYNu3Ifb/fGjQnJ0x58RjOsWNNQ4htdfNkn2iQmMve3QWxKocuO6MwcQyOhud86BeOb7uFlC/gmO3boUPwA+CUnBX0HFO4VV9gNO2c3/IVtGuRLZuyMzyk2JG0xxDXNPNvBA5zG9L/1u1hgfzgnoMWco8aFbPirGEg28DXao33MxZvY50mr2EXGNQfimGKRj9qLTdAlfbBVvMsJ02fRTH8KEbKctuy75kdsQMO8F9AZn93iHT68K+rRhD+f9rafgl9uUjSwbzEPPnxay+7YvEMJdHh1FD6yeChbZ094K8agga06SOBrDu7EHDTj0TyytYZ80Skv0O+bhksbF7Kje0262Lil1yAfo0NyOIebbMzjc6s9AfvKl+6Xl46AzNkH/Vt7BHQjh9VoH/H1GIA+b37gbcvXV0AfmsVMT7Gtca3xFGAfsUvdwp6P9hVLxB9+Kw8cgbl8SaxZD0SA3//zf/73ya7UEed0y+7Q6TBjy64mwK2JdegillxqYy+Ny4Ug+Em6ittbjZVSGBwHkGgrMnqqoCiQ+CUMxaYkI9EJSHM/tEjunjBIHvAyODgKMBWPTvs4GeNkcIyKITt9UihKQn7DU6JU2Ch+PRqUBBTyaHbKqBGQLPrjU6duiggG5ZMDSFgaFcGW8aKNUSo4OJJr5jLqijg8O231jwtIvk42yUAx1lMUPEhSGhy0wIduPEm0ZKVgU8xI8JoVfGpAnDQqOgy40elxugICrYpu8uSceKITfCo4FWHoUNRLzgpgtBnkpIGBH0/oTMboo1+FiyRF/vSoYFH8koviyytgnuRJIuS82omiVNGGV3LCF1ukJ/LU4OI92yEvxZHA/Mtf/vLUGPmtuXCfjSi+0CGBkokGjP7tgXY6QiO81uAp+tEleBoFcMG8pznmocFprGRLbxPwR/4KXD7Avgz2xIYVIHg2RzFrPzZv3SWAnjnIEi2eZN4DaEAXWe4BWyYTMiNHtiOIOxRgO4p+ds42+JY5FcHkhkc0nwNzzGdDXiFiC75XvNiHf5OrawY9uMdX2YqTVPbL7tk8P9FUswN/n0GHZI+2/JRd0Dt/XoF82By9rgnLd3jIj//47hpZodWTPUUvO0TfCuaLaXySjzvZpVNNi98OT/BEJujcA76j+GDX5BGNPtEMvxiwNm3u0xuZmncEZEUX5ClWaRLJ2VrfyZBeyAvA3SBvcZMeFNjkYj924Z6YxU/IYwU0k8leccEmxW8+jnd0ooEuyQNt9hUn2JQYiBb2KxeUB+gsO/Up7vF9MTdfVTTJEeIeG19jwBHAiX548GQ9vaGRjYjVaBO3yUjhgzZ2Qy7WbwF+2ebb1Li4L4d7ynnO7shMvKE7seVI5nRnjriRPaxAjmRmb/Edfuv2AK10IW9sxYcJ0cov6HZPZ48J7Fa86ZBlD7JzNZd6IRtzvfHYQD/yoXzPj8/Ryw/xRm/FmT0wV6wSN/C28uO3mKqOEHfVD0d8oxWN6jg0H8VjONiKOumSZvulcbkSCNRJu8blWqAc651ECEICvAZBEFWAC7qUTcGG+QKGBGc/gTtHAU7uFfCKwgxYoqxxAeYKIhKP+YKT5KNwUChY71RMMRAOa+wvMXFST1XQyKg1OBqrTsc9CemELZ6c1CvqnGoKCitPOUin+pIwnOYyxAoogU8Rgm+B1T244EA3PQj8ChBBWuHuiYiCw3xPP6wRHPFmf/JEt+tdM3znXIoKfw8yA2n3yUNB5rQOztaiSZInB05KRhI2nWoaJVb0WacgSn8CJblyWA6uUNf4SPQaCgnFE5DsxNMhMrYfgMfwW/OkuLFWUKmwURwqln/wgx+c5IQ/OAUIOrA/GjUvZBNP8UxXXuPyfr3CRTOqoHVdgHOdfoJO4kuu6BOIzFsLUTyTiZNc+8VPPLEHjZmTZzao4aBjsk0G1wCd4f8pGpcAL6tMFWFkSK9w0AGZ+c0+2JhG2T7WH4E57Igc4ZgJx35sFn4FPZ2tcl5pY4tOVzUlbPHdd989+SJ74s+SliZS0WesRRScYo94wxe39IRGuGbjYn94vV5kP43s1lpz0WmdmOOJpSeqYmrNG5n6bb35W0Bu/JTMyQXOSQeZ8Rc2GLhPPvzlXCFgLp/ijwpsBw/8wFNbr/CRMX8zb4tG9PBpsYrvsHnX0IcGxS4/nvoOzBHLxBPxcwXrxQQ2JsaJ8WijT/T6J/U1geKo3MJeyRJeY4tec8gFr2yieT7x7p6hUcfHOSBbMVCTKObQlXglDxp+y2czZsGbHo/AGvp9mxoX9swfxcxz/NGFHMA/LpEFP6T/PXtOj2xKXuUTRzo0V6xHg3xwBHCzLbYm1qPnqcGefAkN6qA9meFLrtM0OAjdik+PDejTiDh0PYpvAL3qEj59zufgUhPxLXXHHthPDamJ5Zv53x4d8Mrv8B7Fy0Dcd+gi9h3BS+NyJQgwlzYujIWyJEaJR1EisAsUvjMq1yUSeCl5NQK/NTSKRoVpSYQDcTQFKPytWRsXALe9FPIKQMlaoPLagv0FT4WUwm4FhmlPwUrRIomY71Wlz33uc6//6q/+6vQ6UclEsSzIMixrtxxG8PV4WLAQABSj1iq8nPqjT0LVPAjqHEminY2Lpkfiso/7+LNOsaGp8cpXzYTEmXys9y/vcIzV2ehKU8dx4CSPOcd9MtBsJSv48IB/BYB9ncYonujHyaXigQ79pvdkouC0F9mSqeE0UfFFh+RBv/iQ8Mne/C2ZokORKrBpVDU4ErPmz38K6Z/I/ta3vnWSB7kqvJ20sxP63QpAcCpuFKHo8lQIDexX4lcYKbKtDdbGBdARniQyNAL30UEma/EboIftsm9JU6PE9xTV/EhDT4/u082qzxXeROOyBfhCC77RTv8KRIUG+fJzPJIjWSuSFXHkaA150w29kYmmxVp+tdqGefBLdvCKNaueJ6DLHHj5orgjScKv2dDA0JsnfxIH2+Sr0UZfZIMmdG7tY87auBi+k6lCffW9IJuQDNm22KHR0SS5Li7aX7Lmq3RlzQrk1IGIeFWM459oEF+8EsVe7CFZky9/7qkO2a5gLzpFH18lHzbvWs0f+jTj5hTPV17hIU9+hob4MM41Ln7TB39Duzn2L1Zap4ESJ8VhMYbdoVWzRee9b27Nni4CPMAjBmr60DjnW0/WeFf0iRlkYh4b8Z18xTh2xvbZGD+lD7GM3BU18NgPT/DCcS3Y821pXPDnnnhHb0e6cJ2daIrZDJke6Q0uc3vdeCuvBPCQGxrEkr2YHZjLluBmc+dwmyOfvImnLmRMVhprMWNvf3bKVzRYbPAW27sVyIi+0MevZ323BcV9+V7uPqIVbrFN/cT/2NAR0K3aq9f9t+JX4Lp4JZepDaw9AnLl93yeje3hFZdeGpcrQBDZa1wImYEwfMrkDJKTxGowes2CIqTAHVg7R9BvydXJfSdgAggaFMTT0bYaF+sZgf/jRfHJ2BiQ6+iUsDQOW8Fz8sNxGKEErFn5zGc+8/qP/uiPXn/1q189NTIKeHPMNyYfEzqB41DAXI6jkK6QgMd9wUzS0gzMxsVTIrz6DVwjb8lIY4FO/PgtWcKHF/M9QZBgV/rcw4PATEZTroDMBHqnDQoB+HzaTzCrQJF0Dboxxz7uSdyKx+QMlyJJclZEmE9PTjLQrLGh6+gU1L1OFs8TzElPBjyaKKd0X/rSl17/3u/93un/V9G0KfjhJRNzrYvOCejEV7y5TwaSjCCHdnZmbbDVuFinodJ82tu+GlyFJ51dA2jSbLMVCZ99KHTZMJ7wsscPXhWTmqX13jVwb+MC7L9FAxr5gkZN4seXQhmffIDPa2TI3XV249BhynuCPciCL4kfikbyX/f2m9zoWcziz+aSd3PpXuGokYGLLBVUYpsmh1/wVTbKj8zPHqdO8oW1cTFfwnK4sgXW45N99VQGL3ycXVlvDvwSvIMasXruMQFd/FG8tN5rR4oC/ud0Hx34wZcnEOaKDZ6kFk8CuPHINsTSDk8qAtBNXzX6fF9zJUnTN1omfb6jm62Kt8nO52xc8DrvoQEuvkCPCli8+eR/DnbQIf56OozG+bQdiGE1hHSNjnl/guvymnjVK3pombwEZIBvxZSYIn4rlPgR2siDLu3pAImfK1Dib8W5t885IP+3pXEhT/KQT2qW93h2XTxlq/yd7Z2by87FGDK+BNibuM2Oj2QPH33zcT59rhjGP5x8cS+WPTaIow7ptuSGH8W6mCA208NTgv3FXvGAvvbsJShOigF0dqQrvMg1/LAceg6s4fdiI3kd2Y+5Dpb4nLrqCOytvhALHJru0fLSuFwJDGZtXCiNgCldcHFSJQl6r1mSEKAZDwWXzK05MqYJ5lkniSpgOI7C3QnVimercYk2rygo2O3fGus5LAM0Z+JyjwNwGIlEgejVBYGLQ3zta197/Y1vfONEk0LWe9P+HkdRMk/CJ8AvACh6JOyuoZGM4OoVAGsVaWTphLdX4tBvHzz6Hg664dSCfA4oydMVJ8MLvP5lKWsnrwCuc40LGhQ3ZAIfnShS7GO9a04MycB3e0QbXgQea11TXOFD0UeX1punqFfQ1PgE5OUU2Oe8Dhfa0MDpFU7mSWD04tW3L37xi6f/xBNtZOzvbZyK44csonWC4KCgQNssSOhFoJN0BZd0ALYaF2AO33CSjT+FjgZ+rr0UrMk+2Aw8dOIUGV602WsNwtY9l8ZlC5KvgW4yj092gR/FumZBA+rvrfBpzrSHLTCHnzsYyNYm+M0u2Z3kTa8rXt/JWyElgTuAQJt5fJ09w/+jH/3oZGuKZU2AeXCj3z5sQ7PuZH8mVfw62FFcr2APjbuGin/ClU3aly2jxW90+s4fNFgOFuwPR3u5r2D3pMHBC57ZETlbJ16wEz7gGh4U3F6do/sVH1rIQMKFC+7oA347fRZH6Tac5tNnJ5cT0OieRik8eENfjUtyIFN7kJE4I0aL9w6rPN3pwMu+Blod4MzDn8BecCp+xRH7k0M0BPCItxqcrXi5Qnphf/4hBrlEPNKgwOUemTbQAKd9Gw8BcL8tjYsc4DVhfpEstsB18iJPfkWWR/PdU0yyv/LwOYCL7MQRsW/NQxPMtUcn8+W8PTCXDORSMeNNgP3VDnK3uBTg0T2xwiHCjFlPBXSrrlTL2P9It4C/i6PkvqcjAAfe5GX24PclvJkjJjj8cNDMLvbAXPGGbToQQfsRuC/GOlzB9xa8NC5XAiVLahIlA5IsBHiBRaITxAUaJ0udoG8ZziXGAQr2FCWxMjD/R4hk7LdkxGgyBslQYSqBoVVhoxjndP4VrR7lT2B4GhfFCp4EJMlEksUrfBKhgCJBwedfyVEE+nsUwQ9+uOFioOSgmFTYm28dWsmDbCo+AFmgiZEqfBS2+JCg8Q2/gkHAxCtnZLQ+4xsOcpJIJQNrw4kXwdPpoj39ET25rDqQwI8aF7gUjvCTvQJZs4bvdGBfzSrHK8AZ7nuConjpcTh5ep9cAmUrnJsOFP6SGVonjWTnlSunlOa6b280KVYUkwI/fIoD+8AlGP/0pz89FYuaWgFKs6LJYMcCnGBNvhpOMkc73P6GAO1kGS3kYK6CyGkumaHfGrpTbKHVfOusJxcNN9n+8Ic/PNkbveAj/LeAdRXDaFDEKpjpmwzJ3D5sGg0SrlPAKddrgZ09RuNyDrJnfJA/HzsqHlZgc+xEAV5jQg748ZvdsiHNr3srmEvHbEWsg2/KkQ7cE5vomi9KPpoN8YM+ND38hh2wgezGfuIVWxJTgvaUiOEQj/j9LLbZqT8ox8ekxxz3+Jy9FFFsm42TA7sXW9iDTyfQ1qMDH/zI3uRrHb9n82xLjFdIwGWdBOpgh3/KCatsyJcfihuuG3g2l4+KpXi3X+vw0xM2+Mx3X2xCC77YN5n2BJR/O0UXW8RZupCb6AYf7U02/NQhx5792E+cpA97ZTPZobhDhvaB/xKwL5nZ29N/coUr2iZE6xwPAXh47o0LXtHpKQRZnXsyba6YL1fnH3syc03cZRviJTu7BKyDFy1odnh5tNZ8dqEekHvO7QO3uMHH5Cc2/5RgP/boSS1ZJj90yZnkJTayV9efCtAlztOtOOP3lr8A96pBxN9zMi/WsbHqsUvA3nCLAcXOIyAztKNLHDmnWzyoBeFn2yvQ00vjcgVQtGCvYO19b0mR4iUyASHDatxq5NZyYIWq5KoRUHR8+9vfPgVeQU8BoTkQrOzDKJySayQkM3MUI5Kjk300r0YjuSsw8KUwkKQ4qeFUT3KNF/zZG9/uSeASEbokyAImo+bs6JN8ycdJEF7g13mbY67BsAULtAtc6MYzejiW/0yPfAVCeBUPip54gYOBe53DKzX+zsNvNFsjyKLBHl5zs9a9CWg/alzc16SSpaCAVomleWxDc6R5QHuNR/xVuPcqnL8jcDLtCR4+rcG3Qs9JMb1MGuzPkeF3Ckqe9Ea/5Ix2cwBaNBH2Uyz518qspTdJR2DAP/x49TdPdAlfjRcbEjzSaQC3pK9oIVN2El7f0YP3cJOV5GWu02j/wpmnaH67roGq2HsIwJdAX+HMPxVz/MFTCnKb/FwL7PZNNC4ge0KDwwNyu4YXPqGI8GSKPdKtAtJvuoDvCPg/vSuY133hFhPIesZBn/yNr5C9Ztk/L+6JoLlsXwGl0dZkug6sTdaSI3tem3kgiTn5s+d6L1volTA2bn/2WqOAZ/xrBs0vmde45HfiirlowL+4xgY0VHD3SgwcKx3Wixtb8rUHf86v7W89XOKcxkQDIf6RFRl6EuKJvljM98XebMFAs1wg1uJLvJlAJ2IZ/94qDAK8kC/bICPxzXwFHP7Re2nTAtCl2JDLetoTzcZTAPqfe+NS7CQnB4jkdAQKR7GOreAvG9ySqXvF++R/LbADzbYDxiOAW65RH8jJ9t4Dc/M9dco1hfRDAT8Rw8g9X/Ypr7H1I7k+BthHfCCPWVPsAVrFCf669VbJCuKSXMzHb+GJ7zusUc+dW+8+nxPD0HkE7N9Bk7gqTqzw0rhcCRxLoPHvxSuGvWuvwKTAksMtBmCN9QwTPqdsApHCU2GruKMk+/mXvChT8FCcMQRzOZvk6B1z3yV6CVbSEzwUKpKPAoLiDUW0RKiAZsCKBIVMp9QMjBEBvCsA7AGnwptDC36chSGhHR/xw+k0VU5MNS0KFY2D00kNV7QpWvAgaGmE8IZnxYDkS97oU1RL5vZSQEnaAjs8Tv8FSHPs4+RdgYJOQROtEhZeFVKrnvBxrnFBGxyKBsFBIwS/fRTI9jY0HnRIHooKc9Gr+OGMePJb8Skg0RV50LFGI3x0pCCDxz6KLq9/SHh0bz490U3JDd0KCw0ge6ED/4+LQsZAPx2nJ/Otz0bc07QojtiaoMlOshm6UayxEfZKVk7RBS+4nX4rjtHPDtkn/tgAevBNJmhnExpmuscTO1KAoRldJYprIJ7YLvrYloTOPryeQt7X4pwA75tuXOjaa4b0fA2QJ13TKT2wVTrja2SVDW0BmbIfBbZCaZWhgome+R5cM8n77r44yZY0GuydTSsUxBX24nRfk+2JAftAm8G+0A3PCvxDY8PHt+h3jW3D72m1AwdyK7bBq5Dzmhz62B77FefxwU/EK7EILmvME4PFpJ/97Genpthc97aAz/BpuFewxnXNG78SX/FClmQhppGpPCCmkA29mSseWVuxGsCJR/O2GhdzxQ3NB5+0ftUncM3aDgLYjALO0z5+nZ4vBbh6KroW2Fv7PwbYl00958aFnNgg/bHTPRmTGV2Lp+ymtxD2ZAmveOgQky8n/2uBz4mjfBftRzjsoeZQe/CbI/rMlVMdfmmsnxrQJv/Iz/xM3CDb+US28djQPpoLMYtuVz+eYC4/lUvl0T2bAebShZgkB/OJWwA94jQ8YhH5HYGYJH+InUd7op1d8VFxT9ydtqoOeWlcrgDCFLAlDQElQV4D1hiUQfEGXJIVvF4HUzwL8L1Ok+I4s1e+MmDXOJSC03wn2pxOMckwGJK1vqNdASdoKSidbJqviNE121MDYH40Ap/28UqAxCWwoFfhYY37Ephil4P73trAejQrVDytUigJToovazibwMagzUNDdHNYDYViR6NCTrpxT14kUUWFv2/wh+jukZeTIA5vHyf77mkSvvOd75waOw6+0mi/c42LoI9m/88MPXhXm+wEOWvd1xT5jU97StKCA9rMUcDjw98p4cF8uNMV/tGPZrRoQOyDD02Fxo2szV158Jtu6FdCFHjN8747nPRgf3uTQTxa17C/0xp80pei0lMgp7OKO00bHHBbb036xb+CHo0KIvbKPt03jz0rlnwC1yUK88iMTZCbT7KqKIN71ccR2GuC3wI1+32bG5eATp2Ysm/fk+8lQJaaY/bFhhXV54qP5KfQwvuWj9PTbFzW+37TofViFN2i2zU+oKHBk1c17OOpzHe/+92Tf4tb7A3t+DWsM9DlEEkco5sV7Gs+u/NH4fzRPvkdutmaws8nfA5Q+I9YwlY9qasoNJ/NSpwOEhyI8Ee8rGBv89k2+uCO5/gw0K3YEI/FKH/TRwaSvCJbzLWnuQAtfEwcdG3dG/6jxgVddG5PJ+cKAWtWnQXuoZGt/OIXvzjFgOLJJQCvufhX7GoiXVvHUwB9PKfGpaZ32oI8SMZ02PWtgRex2sEYuVYr7A332ay4vudPxlyzNfgu2jTu7ADNc/0c9mCveNX84/VoX7YoRvPBlcbHHvYiU/FGjJCz5UH1FZ7db2ytf8hhD3IV/+T99t+aa+TP6imxbcp1HebKHRoOerTPFs5zg6zYlLpAbKXbrf0a5qqFvPZWbbI1D5/4JX/xlfzRHP/4k7/EtqeKG7fAs2pcKEgSvDRor0DQAjiHlwgUpIpyBaqCkBEZjIKS7FNgl8D6l6W6RtF+U6rT6woL1+CQ8JyuOBFXFPzkJz85JW/BxH17MQT7K6RdCzeAB62KaAVCazQuGg6ARoUwvE4v0dL6wDoFAOfy3RzyFESdLPo/HOBTPHjCMJ3AdwFEY9Xa+LOvIKM4gA8tGf8cZKAYsVfvmk+Az957jYu9PN1x+uEpVQE7XUWTT06neXBCylm7b6ARH+RNB66V1O3rKZnC3dOOjz766BS03LMPOZAXGuN1gv09WdBkuJ9t1LjgGS1sQdKh9xWso2dFD9sx37De6e977713wi9RkiP5u58NeNok0KAXb2hwz96uz8YFuGceHNYIaJpwp7qe3ihyNaFk5v6qt0sBfkWapLjK7RpAw5tuXMiL/DViZMlfyO0cX9axMcWLBtETwPxxby15sz8HNv5pbUW63yvAMRuXFeBnb3zVExS0wJ1N0q+/qcqvFA/iBZwKTbbgsMVBAfvgz/hmc9awu6194WeDbFZjzef4r4MS9pjdSb6aCzTSr1iBJoUAWvgFX2KLZMdHFFmKMafj9l5l6Df6NP9OnfmTopTO8KBZwpODBMW8/TUvThLFEPGYv/Az8grQ69DGevFljVV+n2tc8C0+iTH4ZT/WubcCPuDAJ5sRO8iF/OE5AjjJBt+dpivW0/0cTwHofS6Ni3xHh+yBbRoOcTx5ZDPskx67tw52Id6zfev4xNa8hpjBD9iwE/KJ23frG37vDWvRzNcdarXv1lwDnezdoamaxzXz+S27aPgNF/tHpzXWrvgec+BNway4doCgpmG3q0yS22MNdPA3toqe5LA1F21ihX90h8zoZtK6DvqHV0yPry2850b41Cj0pXaBb2/ggSx7Y8na1QayA/fMF6s1L9bZ0z0HNmLm1ivLzwmeTeMiYWhcJN9LgFAFbklPkiN8jx57x9QTFomPMjpd2FOE69bPfxK3uT4lF82ExG6eAKHIVagpcAQZCViCFMBmsrNe4pZQ8BheONEuoToxqTgS/GfjAtCksOcQa7I0H00SPjkEaGCMThU0HooiBb3CnFyc2gveApkAKfFHWyApSoZ4Sy5bgG4Fl5Mc8lDAzPm+7zUuvitABDHr4dnaC20G3iUkpxrkN2kOF4ekB06YTeBDca2xELgUcrO5sKfrinlN7ipj8vd0hkzxC6yZjYvrdGwfxRleJq9kjE8BcOI3B83sxz7sjP2yG0UZevAigMFhfvKI/63GZQVz0cfWBDE2YR/7KtbISkBjR+ZN2o8AL/h6iCcuEoqCk52jhT7JseL2HvyXgD0Efq/z8Rd+KC7xAcGcnNkNmtBr8BN6Y/vsR9FJb/wVD1tyZCv8xON6J/xwa16d8q7A/o4aFzSwd/bCPrJPYD6/8zqS7+7RvyJeY4UGT+4kPyd2DgXwgW+0Wffq1asTfWiLbzo3XEM/PaGT7ZAX2ZGB4p8uFeNsk34VtnzAq501SPZhiwp+jQVcbJ4f8Tm/DffwYR0cClGxVTHqoAoO8VR8sI6uyEQRSn7oIwN8sH8HUlOmdIUeT33sv+rOb4n/6FUx+5EJmjThfIsctuIannrirkASH/HTqTTazCFzNONHvKcP8iVP+rWfxgc/xYU5HhLgw0s04Zedix9Oa9nhmwb2K5+zQ35oOLDxHwazO3bimli9NehDseoV2NYfDW8m+BcJ5dtwiImG73Nu97eG+w4/NLD+ZVH2szXPCJ9GwKGhwwhr26P9o8E9+Pyrdw4ryGDie+yBLocaDlK//OUvn/KpuLDOm3Q/xqBbNCjQ0UMue/uimV/5Fx3RSn7m7w0x3N+a+rQe3vR0zUgWbPVf/uVfTvYA/9Zcwz18edJPv+h0PTyTDnP9Jn+vumvO/UavOPL++++fYtJDx42HhLe2cZEwBHCFvyAlePfIVCEpwcwEfgQUtDYuARySjgQhkHF2e3mE3PvhQEKUGDU0Kyh2vKYGT2AfxZBXFgT9QJJaGxdgvkTLeSTWQGJjcDrzmUTRLQkySPu7B4fiHB4FimKe82pc8E9m01jhrnGx/siQ3cMfGUhciqPm23evcSE/RQB6FFBHAB866FmBs9W44IOONCbkAi9+FRsgOp34TDkC652UKJZmMUN+kh59T/vwfTYuXVMwCRCKJ3YdKHTogw4m3XRlT0Uv/vy2v8LSNTxITv6GyDXF2wqXNC4T7G/gGT1OhsmVjuznN5lNfvfAHHvfW7DQJ/l7IoaWhkZO8efkc8rzMQAvAns2T5d8mh7QomlWIPteQ6NY5JeKb2sA2fYq4DxQAO6xdTHEXvagbz6vsV5BTDhqXDQf/JRtrCCW8Ac2w67sjUf+qMjUeLg2wX2NrT09SVYUOZnDu1cg8OXgQ/NgT69noost2cN6TYVE6h6fZFdsylr+Tn4aIw0TviRa/mE9yAfs6bq4ASc/MZ9NoElhiUbxgBzQgJ8GffBP+/HfaT/osi+fmWvQr3mV0OGb4PdR49JaBbPvmhG2wX7ExBXgUjyxMfPxzV7Yu3Vil6bYoQ6eG/SgwaHfnpazRbJJz3M8JLAlTR0a7UdH+cV//dd/nez6TQNa+BPddtrM5jXsYoyYx+/2hnVsje+IO/S0Na/BtjXR9vCbzTX8tv4cjgafYF+KX7WBaxPfHHDyC3N9zrnxbTSXzYpJDinC/ZTDQSl796q3Wo8eJp2GuPKYA990qz7whIRc9vYlN/6nTtKYm5t8t4ZDbc0Gf/V7SwaXDuv5tzdRNBLn7IfNal7VxOfmkoF8TQZiVPT77tAOH8Xi5whvReNCgO4L/Axd4BFQFFhOGp0+UYT7ncwWvC8B8yQx3ar1ArPCgrFIeBKIwMzZJF4KNqci03AShh7JfgWFssKOMVdA2M9TEglA0gn2GhfgZEsB0CkhPgXKXk2aPLvHeN2T1CVDvJGjT/xxRH9U6wR0PpGQCMkSzksbF2BPzkM3cLcmOmtc0o9P+uOYCqi1CNgCc+gETxoPdNqTbO2rmPEEQVGG7mzCOrL1m1M6HcPfBLSyJcUM5yYre3gaQu9kju4A/Wvj4tM6/CtKNRTmkb8iRELaKmQ9rVCQOMFMPui1p6CqENK8mFMhJAmwCTbV3zGQxTWQfvIvdqkoVZTQl+KzJgYtW3Zg/UM0LmigR4mNbsiODyqO0aNYw7tgax6bUqym44rWewAvmlpJKxtFFxnTGzlI/ooEp/jooRe6QEP2gQ7z0UyOZAfQiF6FlaKPnZGpPfDJtvORAF78S4jiXDpnH9bam83Oxt89SVnD7WQeDjQZaLCev/SEeMrN9/CSBXtQTHkCwK75Hn9g3w5zFGyu8QP+glf4xWpFrLhiH/GT/Sqy0KvA5/uaG3ZrHfsnN/L3pNirD06HHQSIe2KjBgRvZGdfckEbmqd/AnKAR1yAe/Lqt9iHPjLPts0hY7R52okX99DHLtkkesQK+AM6o1tFDvrQQq9kJ3+wAzoyzz1zNR9kVEzI5tDGzhTafJCs6IFO8C2noAt+a9CnwGJvXbsFkg0a4EcjWZCv/MzP6b2n2uxBPGWXfJFe6fNNAh74l3hEjmRhkAs780S3A7TurYM8xV3xmv2Qhfl7g17FX/ZpT/Kb9/2+ZPBTftRrweiYeOZwj7/JBeIDGrd4Mcxnv3zIsI7tbtHwGAOteOO75OSTHtgNvUy+tuh/yIEW+mLH7Lf9t+a6jm56VQvx0y3+GnTvwJJOxDAynrxdOtCYvsRa/r41r2FfNKoT8LY1x8ATmviBOkmcmvK3ll7EvWLBc4Rn2bhkMJRnCJ5OM73HKYlRpEKNYTAq8ycQeOMI2ociFUEe93ba4e8+PDZjhIoVQcypGGNHjzVoQzcaBA7rJUK/3W8IEopW/8+JIKhIl7yderg/6fR7r3HhGJKIJ0OKcoWBpC25oa9EBgdDluzQRGZ4YaxzjoIUP+gTyCVKCVaxoBhDh0RUMLaGvI4A7+REjyUHdK+NiyFoaTCcaPl9BHRlDjo6mcafxswpgyRlD0EfXk6pEJzyUGiQu797MjxFc48efRpOHMhX4IHLEzyFcnRP8HttXAKBRpGmKKFvunYy7KRHcMi2syGJSjPFPgTH6HGPbhW0ClDf2aiigd4VSYofhbRiSiOMrtUnLoF8xlry0sBqZPDgiaGng+wOTdFOv34r/u4tWOBhkwrqSYs9BHFyrIAmR7pnq+jyOqB4AEeymzSu+tkCc8x3ik9H0bC1Fk42ofmwN79aAe10yZfEBzTBqyBmn4rg8NOZe2KPww9+FNAF39GE0Dd/dkjCnq1RTPI5vMOFBwWugxFPHhWW3Wugha2THdniJ/Ad3R16mOsafgy/xQy2wZ8Uy3yELvii3/ZPZ04AxVM25OmNOIgHfyTNjxW85qJTwekePL6Lb2ISvaMp+gN+RmYdCKAv8J0cvWKpqJ/8AzLh3/RNTvaIV+vwwC/pWYEjpuFRzEIf2qee4NPMapLkiAA+PLIVPuo7e/GUnA+TJVq36OPX6J+20v0J9lDo8kHxyO9zAI95+QneDfvimS+yObGSHORnMiEneq1Qjh5r2aJ49CYBPeJluXgCPvEhvk9b2QKyoSO4kv8ewCs2O62m33O4twB+vs6W2Rb57u3puphBHw4O8HlEH17kf8W3PGnuUw778095Gm++OwAQG/nwmwC5Ulwiu3P6Qi/Z8Ue8rP46+aRDfqD+4BO3AN2Kz2oIcZZ97YF95Wpxie2hYQ/MRZPDf35qH9cCfKqz2dW8/tzg2TUuArviXDGrYPabQiRDDQQFErZBQbcKl+HZU7CRMBUCX//6109B2mmuQEXB9rGnYb6nMgpIRb5i1GmYYCXYaHIEeI5J8YKehK5gZciKAKfJ/tNCj2vtsRqZPfcal4zOPv6YFy2KAkUcGSk0yBA/aFMYSDICoIKTQZojWeuoddaCGN7II7naQyeuaPj+979/olXilxgVSuccw33BnkwEBfg54WxcJD6n1Yq0owAN0MYhBRoFmlNe730qEDqVasCtCOokU3GnaFRgScL4RhP70swpOgVQcjRHEtBkKgA0fR9//PFJn1u2Zq+9xgUtbIuOfvzjH5/sWKOlKFfA0a9iiJ34bm/fFbTePdUIaGLQKwGTpwInHhtsiM4VX/75WCeh8JAzud4K6XHK1mEBmSueBXF2KBlqGDS4nhrdA/xrbVwM+kdLPEcPO7UGXRKiAo+voYUOyU7QZ2t0fmS3AE6+Q/aS6arTCeayPSdcW34M0G1fvqfgVSyRnQJ4TZbJW0NK1/ws4KP2oVP8Si5iBDl5IuIVQjYIJ7rphRx8ik+9IjVlai942Dn/sDaAQ7NAn76bO2URDvGYrHqFDw6yFufQpigQb+Dy+1vf+tbrP/iDP3j96U9/+vS3BmyI7WvkxCK2JdmzW7o1FApiaM3VpAO4xi/8bQE+6CVAE9mLY+mzAezjEIzviyt0I7a3vwMQry1qADVRYgn8bNDTDzEdv+FTPHgfnp7QNcFvc9mmHCGf/epXvzrZ6BZfgH2gBV0OqVb6J7gmbzqMITP0H0G2KUbRH96spSe2o6Dki2QA1+p7vq90mEcm4tWbBHTtNS5kLZegs9x3BGxL7BYXtngOyJO9sSX2fjT3COQ4PkXuqw1NgFuBLObJ1ZfM1VTLiatMngLIWb5j/+wEveRFtvLcLbK6F8iEH/YmzBGgXz2npmETe/LGh3tysEOJc3lkD9Ajp8ntR7bkuvuezGTvM6+sYK58rRYUj9a5L43LlUDghC/ZcX5JTABg7AKyxMXgj5SyBxkTR6EYxRYj5MgMURBT6EqQHEkRYB/rGhTOYAUKitUUcEL0uc4J0CdRKlYFH8FTEmEgDNjekro//NRoaAZcc8+AAy5NB1rQy4Bdx79TPIlGUpHsFTMCnDnu4Yuhu+4PtBR0ycw+1kruih3NllNGNJgzwXz7KXwkfY6t6VBk6NTRx7Dpxd5ks4Ik7jRXY4I3ScJ6ToNHhaVmkWxmECBrQYI9uKdB9LRNc5ZNOLlU6Avy6LQm8N1+bIicFfR4RTP+3bMfujUk/atzkrZClJzgJEu8O1nFO5tIV/Rhve/9S2rpyHCdbDRFCjevyKRr9yVOsiUPtsQWFNitd18Bha7+Q0ENDXlMWeOV7sgUrpoHNixB0TX71Lxmn7f4T2BvePCGProgGwFTcWfPqYtrgb1vNS5zrOAautgL+bI7RQY7rTDm22ShOFXE05fGAC/2ZA/w0CkexAay2tovYKMOAOAvVqzgOpwdyIg3/NYpGntaiyY40K+h91QJT+yB7Wt64Wof9GUrdE4PGvCeRFmPNzYhBsFlbQNt5MY+xD/7umYN3Yo/5GROe65AzhK/teHFU3ThUYEPlz+G/dznPvf6d37nd17/7u/+7usvfvGLpz/659PmaIKsoZP2NNg8maE/Pa2AbsW32IwmPLTWIQDfSp8NYC+6EBvQjA56YoMOWjyNZg8aU/fxZi/gd68Gw+MeGyMPv1ewv/jCJtm32EM3ct6RjK2jX/FspX8FeNDAztK3a+SBJjaPBnyKP+xX44oPjRm+FcH8Wz5J5luwRQNan3vj4p5rGn+6nwcEW0CvbIpdsM/0vwVkRXb0qyElj0vBWvFLIy038vUjoFP6Yp90u2cTAM3yGR46/H1KYIPkIi6pG9CTbfIzPiGH7dnaY4H91SIONMWPI0CzeOJgkO0c6RZvdKK25N/n9LMC3OoleaDYugd0qQ7iv+q9c7rl1+KOGF+MmMAGXxqXK4AQBedOvRjUKji/LxVmcw1GJ+gI6pxEkmeAihtKL9EpOBWLCnVOlIMZAon5AjOlVrBVCJsDBEJNDQOwHrjHSQQNSVwBpRDR4DgpFOjQJenpsp2SauDQUWLRCKHNaRgcEpBrimO4JyjQFcscwN6SrIYKzZK7Yl3iUuRsdd6+e49aYKcL8nHNPOskUkUrmslxK0ibj0ZFt+JIgFLkwat4xDPntM7c5Gwv9+2tWLcPmSi+yNQcgUAx6ImJomKuNxQqmjsFiRNsT2icfggkNWrW+I4H8gqsJ89kLuBzdHzQFf0LEmjyx8WefJEHW0Cr03S8ado0PXjXrPqtkItGgAY2hS466rpP/mB/MmNv6OwVojkPL3BISmwPTnN6J12Tpzg0yMNp8EMCm8CfZhgf9r8VSsbkh7etcQ2Yzz/JD16+RB70R56KG/bBL+mcfUlibMrvo/3IXWOssd6bRxZsS8Jmk36zAfvikY5cm+vhJc9eJdOMiAvRuLWXefQrNvDxTvzNZUOeYmQ360Af2+Ff5I/O3v1GyxE4UNAUSobhI2/xQMJGC1njV/PxpS996dS0/OEf/uHp6QtZm5N98icysS9cZEMnfGfytAVk46mBJx5iurV0jv/iezjDgVa+rZCH25D8+TB7Rvfqlw1zxTJ8iRHkiFdxdpWbufgS19lgTSKdkr046fcW4Is80SnuTfonRJeGiBzNtRZuDQpexBJxVbFbM0xW1xZWe4Dv5964APf5IT3zrz3ZB+aTm5h/FBdcVzjKX3CLjXtzV7BOPuqAis0cAb0pbPnFuT3QzKc1tHv285igdiI7eXIevqCFf8lTlzz1eAxgs3zG0/DixBGIi71efwR4k4/VW/z3UrmbY536ZT4p3wLXxUf+rLYtbu6Be3gVc+Ddogm+l8blChBgBFxCPaeAI7BOEBCMJRPFmoJf0uAcgoPH7p0omZsCObgTJ42BJJhyDYld4SpJcTafkuLauTI6SW/LQeGV6H0XcPyHYwInmuDX0JCBRO3vLHxnaDp8ydVJsFdOFPz2RCsn4hh12vaC2wkn2hTgnhwoKhVEmiUJ3XoJVEHvnXKFhkRKboYm0ukPWpOPYEo30W9/jYwkgWaBSeInW7KDB81ejbA3eUmkiotOLcwRBOCSHHqNQoAQnNFoDv7sHx32qDDCC4dDPxo0ZgpDCQCtEphGUJNHHl59s84+7I2dpCu4FLPWu+63hgSPaDI6rSQjr7zAKSko5OiRPt0jdydJ6Kd383wvKdmz5hSdkwbNCHtRlJIlnOjGE7kBNqYgoj82DS9d+SQveNgjW1Vgsye+wGboA2442P2tYC88kZcAav9bAZ8P2bgA9JAFPtHp5M8TKI20xOB0U7HAfunLk0r+RTYK2xkf2t81Bb4Af5TArBcz2DpdwEN31vB199jS1L05fntSwMbYrXjCdvgIu4eD3FsDN3yKRromx+jlI+65FnTPsFYBK3ErxCVMMiGrLV1aUwwQH8QVNs++vQYrzvJBsUdcYnf8T+Mv9rz77runVyf961N44zNsW7wVq8UADQ1cims64qv8Ae/ZODomRBc/RIOnRfCIIa5b02gtfHSOTtfcM5ffexpUsxjQO7mwDfRqtrxK5tVPdBt0Cw+Akw2QL31qasSpdEcn9G+deVsAF58nZ+vTLZg00wdfVwR6got3umQ3cgg9kAUdoxEfdI+/6L0XyIcNsoU3CeRy1LgAcuSXDjLI91wMlO/kLXY1dbCC6/QAr1eg6PocWAOn5lS+zMa3wFw8yS89WTsCePknf0LXHt2PCeKW3CX2knM0+MSneMuXeh3yKYGs1RgOYHvSfgT8TD2gRjjXaPEHh0byrbn5/R64Z46aQNzmo3v2Yy786il2oxEnyz385C6umevp0p69vzQuVwIH87qOk4prgLIogcHBYSgqFD+efMBXIcLJV2X43TWfcEk0TmQFNPgELUWkxAoHYzJPQFD4w82IQI3LDChwWCt5uQ98+je0pwHhheE4WVfkw4Ev+3XPK1KST/MVNxK1e+ZLtoz+m9/85qmglbQYdcmP4VYoue79ePIR6BUWTuU0U4I0vo4AXXizN1z05ymDk19BVYJUuAviChdFgtNn9MEtYAjA5OW+YGA++vCCvz2wt0AoICqgnD55siIASRgSqCRDfp624U8hRJcGuSme/KehCm6FA14kd6eonNtaNJCb+QUfOotvf2isWEQvG3CfbMmQzRSs6Ywt0i282Z1C3WlUhaL1ggY5KbDDZ2/y9ZSpgEZ+dIxm+4fTCHyHNxwVXexA0aVxo3/8oGvycClYA4+C9Uhn5wAND924gL11+CTL7FRxTCYOB7xiqMljS+6TL/oMvyUjhY/fK9gPbkWp+GAOuaQH98haManorXlx3SBPJ2j+voy9SOj8iY947ZGMimdwW4tOdszWJx4NCbvJvht4MOCBH26HBnhm19azW3Qac60EqDj1GiP/8XdhDgXYK3vXELA1dKFbIkTfr3/965PvixF0bA0c7pmLRnvjQQwXG8RMfxP47//+7yc/F9/gjvfoi148OZCgQ3uhCY+uR1dr0aq47+8Xpn6s7+Ai/sVQBzx04CSV3vi1GIs+MSIc8OGJTsRSa9naaotikca5pz5bYG97iGH4iO940PyJCw67DK8Aa3LI0X08rPs+BrC3DufeJOD1XONCT2QoDmqOfT+SETwabP6xpccJcCmC5Q36tdcRwI0G/sdGj4CNaLQ1u73KeQTqDAcE6GAvT2EHAb7ZhFpGreS7/Vca8C9vq7uemkZ7kSl9aUjEiiN9mcun1Jb864jW5uKtw6Oj+XQJpyf5Pv3em49G8UWsExv27DxwX51RHbOHV/x9aVyuAEq9pXERyBVuighJWtGpkFc4UpAgwmEY0aWKgFMAdvLIgBge3BWM8DAc+CWmCnHXa1zwEygUFZiCh/Vgq3EB1kmIGid7TJoZ1Wxc3OvUUCHllM1QLHAUstBQkIETdkWA4sBv+9hDs8CoyQhNigN/NEp+5h1BcsCD9YY1FTYcQIGiGfr5z3/++mtf+9rrH/7wh6c96EjTQcb2TUfGyvceWOMJyH/8x3+ceCYf+/vkpHBzbvRxRMld0CcDQd9ppOJJgaQQIUef1loXDRKQQjKHR59hf3+cTx+rfZGf5glvrrsvOdmDvbpmzMYFPvpiT+trYfbDGx7YnIDoJFozNudtQXvBgQ7yMNiAk3E6ogt2ruDRtJ1LoBPgkpw8OTqi4xyg5zEalyOAM7mwAyfW9GxoLDTd/IjfsReB3yGEJpmMrF2BPMSNntDS68qHYZ6mQeHPBsQH+7KnTuj9tt4gn5p9ja1mnV2aR/b0F01sxd7sRYOuEPcaruGJpIbJAUPDHA0Cu7IXH0Gb2CWp81lFHv9QvPEbfsGe7YMXMsy+rBUznfR6umWu3+KCotIJK591YogXtOM7PD7xwF/9/ZR45okhftCgUUYT+0Ujf+b31iow6cdBjLV49amAw7/Ggzy8TqtxouP2phc4rIVXwdWBAT2xD7olo+gUK9iFBgQedGuY/MYnnYV7BTrl65r+vaLJNTyJmeYqVPh+T1XIVEyxL9rMcxgRfVs4HwPI5G1pXAJyUijK+UdAhuxLzuQPRzK1P712Mk8uR8Bm2Ki8SV9HIE+Uj9CwZVMB/Wu0yUEcOTf/oYHs1R1sW1xBzxa4zm41bpc0Yw8NZCJP060DBH50BOgV/+Rpet6TqevmyiNqAYdjR/Jng+zWQQQajvQFr3pCDDD3CC/wJgwa5PajuWz8pXG5AhjAUeNCiAIAZ+AEBCuAS14SouQjuUkSEgl8lHuLs1pLwRIkQ5JYew85XD45WI0D2tGHNkmRMbkvgUuygp37rd9rXKzThEl6KzAqBZTiFsDF2CVHnTfjNKfCy3WBS/JXbDhZhB9/ih28mY8v4NNv8sQPOZ4DNMxB5vbAn0AsoSsC/vZv//b1H//xH7/+whe+cCoiBHSnp+R35Px7YD4eyYrNkIO9XSdncifDHouaK9gLNuTnO5tRoOET35KY4KVAmryvjUsDXk0TfMkwUPSgixwCOBWddIVe62tc0CEpsWdFnHX4CdqPzNilQqpX784lxj1AM39CF7z8SXFOLuyGjno9ybxJzwR0va2NS4A3xaBGOD/1Sef411QoYunPIQEfV3BsyZ48NZR8yNrscuXDp3tws0sNvhim+CMHeIo5jWhir2KgfdisGMXfneyzJfbnukKbfbInNtcQp8y1t/uSK1tmf/ZGu4KH7RtsQ3FsLTvl43C4x17iE20SajFJQcIHa9jRw97ZFj7dFyfMJX8xjD3hUxEDD9/LP+yjwMe74qFXtNge3GgnRwW9uEdHCgb7oJ3cGmSiwRAH+ar4aA926MmFvXtNTWywBn/pM/AbLgUa3/V6iCLBbzJ2fw/cgxfNirwte3LNQQi/JzN8kjG+8IQmMmMr6CdLduzapPOxAZ1vW+NCdnyI/ZDdEbB5hS35zkPIPWAz7H6vaKcvtsaH2Bj5HenLfa8GqyfY8xHYj7+x33IE3E9hD/bAm1qsmJp9boH57osJDmPysacE8nE4UM7eoxWgVzySB9gDHR4BH1VTie3scdVB/NtfHuDX9l/nBWQjBqoF5ecjiFYxwzjnD2LRS+NyBVD+VuNCeJRIsRzRabPCRoGliPB0QuLbMjRrG5dAcykPXo/cFdpO6hTICglGY077+XTi6LRAcpGoeuIiYJX4BMWM0ad73qfnMBMYlgQmaU66o6s/BiUPtAgKTkIl9Ob7hNfrFgoaCU9CFxDsTV6KGok6PgLrnGJK3D3eje5JT9B1cwx04c2ptGJWIhOMfvSjH52aF6+/kInrGgty5ljWhWNrn6C9JBH0KVA0GnhbwaNn9lTR7VOR4yTcKVunD3NIDOyr4GFIVD29gceIhp64RH/0sQV7k0XXraNDvJdUvc+qkBMsFZuefNDbpMl6QwBUQHp69/nPf/50Suc1m2yrfYxbAR/4YYMKEAWVAI1edoeHFb81b3PjAi+/VYB7OoCfI2CvGjvN4ywg4KEDfuXvDBwOwGVkN9nOBL/hFM/olN30BHdrgPndWvoSBxTr4o0nHa65Z177+u6T7bF1c+1Hf55EsC3XxDtFPZ20bgXX2K21fMRefI5cJOkKEL4jcSv26Je9w21ehynwSKxerdNEKXoUn2yQz8ATDT7jw3W88BknlZqFnrB48sB28SPBm9faiQvdmiP0+JS0xSw+jId1zRa4p9ESb70Kp6lq7TnAA78mf3zjK/CdjfFDsQKtYI+m4qIGqrjwVCBXvG2NCyBT+bt8uKcz98zFnyL7HG7yFyfoYis/iQuzWD0Xd/gIOvkHPR+BekA8Rie97PH0GGAvNi3+sdti2R64R7b443fqD/w9Jc2A/MUpdnNOF2gTP9k7nz0C8jdXTUfPZDPBb3WCfcWBc40QW6Jbh0xbdjUBbrFPjBZHzsn0pXG5EtbGxW9BQjKgUAW7kwYJUQHFWNxn4JRzq5AZKOUrCiUqyV8nq0Gyl/8vRCJg0GhwkibYMDTr7I2G3lEVVBT9DKDXcApK5qHZ6Taj+973vncq3DmsohhOrwJIYP5wX4D05IDj+/ToufeY0WFPxb8ig7ymDAQCDqNQkEzR4LdgK5gJKPadic16xm1/xYWkTxYKj8lvYL71AjQaBSr8eqUCf5y110S8nqKYU1Dgw2mUIqt3wMnd6YV9ZpMXpCdFtUaFLvBuH82LomgFOvVH1+RU0UL2GlIBYiv421sBaaCPPTpdJ3N2qMCF18mw5PjVr371RAveyZN+yYL8PGlSCFlDv/bDFx7Ig63YRxAiD/MrsMg5XMnVPmwCTsURf2GX/j6BPWh8yM9e5wLvHpCRtexJ8mM3eFUUSipsSSGo+XXfPHb1Njcu6CdjumD/0ye2IN2wYwcobAYO+qU/9u8JJ7v0yhd+2Jtine2YT8b4mf7D973KxL5duxTgYDNswbvX4pBCnh0Y9mQX7E1hzyc0CA4QPAkWT+mYzaKZfWZDRzJ3jyw8sdEsOCDpyQpdkkevjPFB9iKWsGE0iaPocD2Zuib+9I8lsDP39uhw3UCr9Q4j2KImCE9+e+rroAZ9Xq1wwk4WdC1O45W86c6hhtfM+BIeztnCBPtr4Kx3oCAehMNgI+bgnVw0N8VYTRa66V/Oaw29sjEx1Npz9LhPnmKF/c/p8CEBf29j40JGDgDFUDLeszfXyFdeU4uoQVzbky88dC0+y4PrPPfo1hM6stvDA+ASd/GFpyM7gIvto1H8fmqIbzFQLBAHjngD7tODPC5++jxn6w8NaKBT8UedsFUfTBA7xDax/ShG4UMckB/pkH4m+M3+NLDkBtcRsCW5WPw6moseNZN8wMYukedL43IlcEbJRpJX6DmxF3wVeAKQpE+oOS2hXitY6yiaoVCoAC9JKJ7tI6lpNhQREpmE7I8u7ek3ujQaHKvivmKEo2kGdNX+4J3DSpSaHvgkEYU5Z7aWkSqW8Ogdc3wrOhQO7klmDN172AKfPxZ2Imw/zqUAcA8tkmBFsSEAcBQ040cAwydD9xtdChgFCtolbvIQtAXv/pUb8uYkkpFkbH+FAH7IzloFkmDqKZNCA0/2sTe5Kf45AueWpOFDd4UBPPChC+9OfJ1Qkb393Vd04RFd9KRg9BuN1vZUKz58Woc/jY0/uKYHOiA/+qmZQAN55dTkKNh7jc+TNsWk4sk6coDLdY2DPf2njwojp7WuswGfbMnpM9lrSKx1oocn8tGoaIjYi+SFb0U7mZoj8ZD3lGs00wE7Ivtk4HRdsWdvhQMe4KIHPF1TwORbDT5DTuhS5ArW9ODpkMLXXgovNnXpHltgD6fKcEXz9PV7cB+BfcUef3NxqZzIhF3Qr0aSbdGpBleTR1au0Y2inV1o2umZvYoFYgYbdSpLnhIMH6F367PJI8jfyctBC19jq3DTFXlqNrMjPPIdtOX7bIi9sdl33nnnZKtwuHdUUNmb7OyFdv9amPiIFvLh/+I4f7UH37S/xsF38zQ85BOvrvvtX+tTyIt15u3JwnX30YpmfvjRRx/9toGyr4FfuPgMv00mbJiMrOWX7Fl8rPnC/97eK5AnObCH+eSFndhbjHPYUvwQ58QE+2Yv5OWAhz5cE1sc7Ph+KaBXLMDrkf4eEuxJd/xBXnyTgN9rGhe0yzHm811rjmTmPh92AIhn6/fm8wM5kG9Zly25LqazB3sfgbl8jM2wMb/3AB18QSxGH994SrC/PR0CaeLznyN5TrDWgZwYwf+S11MAGuVLB9jqMd+PAK1ilXgtjp/Ti1zgYEIsELMB/uhLPofrCOBgb+TKTsnnCNDHxsRE9dAlIM68NC5XAKdm7ApGyUQzIZkwfAZhUHJO0LgEmstAJATJiWM4XTMUpxTGmNrLUChqXLqOloZgIwlLVAotwRpORepXvvKVU3EueUiKClMnixoSBsGg4ISH8QmYXu2wH2dRhDohdB/NTts0MYzPutaiWVGgiSmAkqN3WvEoAOCPs0iGXo9SQCmoGT/6GLXvimYFqfkSfTy3l4CpIH716tXpiQVeFAccTrGCbvNa57uC1p7uO6USpOlUAiezTjVWniRrTQzcCjlNm+EkOhnYA7++w+kEghytxQf8dNOTM3vTgRNwPMCrWPW0SiFXssKnNWyRTNvLp99kTZ/ooLteFUO75tD/ti/wweeagUeNE52iCX3Zjv8H5oMPPjgVM65JOGxB4Oz1xGho2Ad/+I62hiJI4QQ3mbBJeDQ78XgLWEfe7QeX4k6BoMnWDLLTW/EDOOlYoy7ZC/DkN33+MYC981/JoL0uAXOd3LNVfqiw8BsfUyfpzXfFKX/Ao/+81MEF32NPeOUTfFDTAf8RPe5Zx98lKDT4Y3O2gyY2zh8UzvYOn+E3/2QbYoJi3QEKfbLBDk7ix5oV+LwDCHGATyiU7Il2vKBNIevpA/75D17FEWvRoIEjA/f4ibXiVU+F+a3YAd8W8A9PadgemWoUaqzhVVSQTcUCOpKB/fgSnyZz8VC8+s///M/TgYcYSrb4uMQmnGqiw3y4Hb6wK4WApk6sFRvY26TDgN8nPtkHWuhTXEC7e5cCXGIb/eH5Unu+FfCqsGYL8oz4+SYBv9c0LsAahSN7w8uRzOhC/mdf7IMu9+a7zrb5VK8BWW8PtikXkt8RyIk9HT2nT7Q4vWfP5wrbxwD2y6f4n/iSnR/RPAF/chV+HXLu+f1jATrFJoc35H2ObvTRrbh2SSMhRojPdArYp1innmInR2AvsUyMIdtzMUEucehR7LtEB+h6aVyuAAp08ijh6kxLZKvw/J5jDygKDopQYEloEqZEIqALIgpWic8+a/BhFIpAjctMMub4dA3N4ZfoFJPvvffe6Y/QP/vZz54KAQld4cx4JCzGvdKv0NQ8oMV9xQRDdt9vwc0rS/ZCKzyKZQ0OJ3CKqxmRgAVrfEoeGhevPwjIkjrnQgc8nBMeyZUzkIt/WUjQsycnwZ9CCw64BFr68a8PKew5BhxbeiI7RQy5mCMI4cl+9OvUU4EhAbTWJ9na3xqFuWKfDpy+VvBrPjUY5tEzWjkzh0OXYohjkyc8+EUrHjmyk0iOqal06skeyAxu8vMkRNGkgCBneMjCp7nk6Le9/UGzwhDdeFFoSEoFingiI+vxpJikTye+7IS9+H93FHloQh+a8bfape/sVlOCp/ZoZPf4llTxxBbwromxt8SguII/mzaugXhiR/h1wmyfa/FMIFN2ofmjD7pAM5lo/PCLbnvaO9rvBfTzU3Z+LT5yZkP8S5GPrnTWAH1HO7vSnOKPnaYHQAb4lQzZC1zs2xy/+RU6HbYoTvkXm4eLfyi6rWcj7LKYAT/bgJ8c2bDC2FNMiZNd4sO+fJ4N0gGf5Vt4gwsOuHy3js3ih16soz9FI748TWCnruPd3mIUvGzUNX7sKa+Yx6/N10CSqzhhfs0P2VmDBt/RhF774cee8Mkj7F0BJe67TzZivjn2xoNBrtaIi+aLN+KIeIxOgw2Ss6chZI+uYgJaDDjsqTEhb3TSnb3EPHjFQ7HWWvf3AG/81hN48Zj+rwU+ziattdfRfpcAWeEL7XQtLqKNfsibDdKFJ44+793vHrD3tY0LYNNyHFtkF0c80G1vEdDXGqcnuFf+Izdy5Dt8j324vwXwGWxKQ3WugWWHcpDmW7N/RP9jAT+Qq8Vrso+HS2nBH5nIU/yJrT018Bk1k/gohu7pJ3DYolF08EwHe+Ae3shH7YZXfqomqWbYA3OzT2vN3ZOp62xM7FK3Ff8v0YE9XhqXK0CgkIglMUBolwrOPIoxGBmlMgSnt5oJOH0XkCRoijwHcElQ/oC+oLTuYTByCVShq3jz/xp8+tOffv3Xf/3Xp9N9Bi2pKjjsa4318MRfTwkYtTmSNHzAPAkVbgWHIgFPCh8FBcM016sVgrXk2um6fQwBjzE6RSWLTnOnfBmsU75ONzmshOSphyJbQdKTIPsoLPb0AzenMVeig6/AnYPSAx7w6v4emO+kEp94QxMekisdWV+BQYbRhQ7yJA8Jxit6iiDB1T1D0kGbRNKrYOTJXiQWe+Nf80iO5vhuT7KdjQu8mlW69nvajGGNxCXJ06fChJ186lOfOj1Rck1g6jWV7CRc8VXjAtcWNLdBJnSuICQjMqBXRYdEly1PuzTOQfPImFw0dZes2wM2S6clASe4GnKFuEISz0736aVX/6K7cQ39AdmwD3q7ln40sx32x4fsvwKcrmennig7eVt9yHf8sGn+yg7gZ79io0MKepNQ/bYvOYgP5vE1cmKD7NZriOYo1ukIf4o5MhQ7FNH2si8bYZfsg+0B9NrbdU/w+K8YxGfYKTuiI3sH/FVi1sQ6kXaIkC1bR5fsBK1kYi0cYg/+FL0Tn+8KZPyIkQoZDbkYwB4c+qCpPcy3B/+1H3nim8+yG/uIZfa3xlo2xn7JRczmF9bCxTboyj7kIDaghb2IST2RIUuyZbfklh0GcPE/ezjIsveWrQB8aHbQxMf9vhb4hlNvnyste2COYX7D3gZbJQe2xwbTscYQX3IgmsmFfvd4ewrAwy2NC1th7/7eUlzckzv87pkjN6gr7ONackuWDbmBb7IjPsJHHQ4k49bNgR57kCc/nrltC9iUYp99H+XnxwB7oRmdnpaQxz37s3v+7TAD3qcE+4nlZCk2nLMhcqdX8ZaO0t866Bku8Y7diMv8RQMsPpPX3rCHdWKumH0E7IY/infqLL8vhZfG5UqgmNm4XAPWSh4MQSB10kT4EpikzvklIsbBeC5RiDk1LhTvN+ODh3K9zy6JeW+d8XmsqLD2zrb3xP3tiQDI8QQSNLmmSRHgBRb0wMnoFQJw42U+cTHw5UmOwlvwY5SKGgldEvEPCHjVQyNADnDEJ/ySqSDA8CXYTscFUI6EDkW5QoNjeOUFPrKU2MlBAnQKgS577zkDXGRBj5xGEjdqXOyDNvOcXnq1oMJ/C+xDhr0ygT/0wE2/TpfQrQlQWLhHjgKI4Cf5K0YkV0WxU+UaF0BG2Y/A4295vP5FDl5BsTc5O/HyRMrrPfjHE9rMpU/4ZuPiHh7hJedoVbSyAXbJbvwjDJ4wsA8y0shpTNkRWhVO8JYY0WvtUeOyAtrQg08FGxnB4RTZvuxBMcVO2LZ99vSxBWTxkI2LAjYbQbPr5EiubKXTMD5Ot3TDrtkH3vBo/SVALvwJjluSPdoka0USPaWjwHcD7Yo9fsFe8NZcn+jAo1jl8ABvv/zlL0/xxTqxxTo88kU85rvWwuO3xl7hwD/YrpjqKamTcM0xH9H42qsCG1jLHvBB5iC74U/krhl49erVyV4dSqDF/tNWzBevvDL7T//0Tyfb0vQo9jUN1nqdzXfxSiHvxNH/qI/O4kOAL3TyIXtaKz6RB3tFt/loMPj9bFziAU/0Qy4aIAcUXklDQw2IIb4qJOBNN/b3m33QD1oUnfxYk41XeQKfYpo5/Mie9ofHJxx83Cur9ITWyWtgLnnzKfF3a845oF8+UlGNhnNgH3PJiWz5lCeRbJGOxCM5BG38zB54sIYd+M5WFXx4eFOA11sal2zN4YL8iKc9ubnOrsRnNkRPdE52W4P/yyf+1kVsp39+4h5/3hrWyKXyJlnTz5Ee5SW0sD+0PSXYj1+IPeIpOd4DZN8BLr87x/tDgn3sp3hnz/RwBOaiUS7iM3S36j898xt6d0AjjvXKdzFja1hLtnK1uHNOtmxFPSmnm3uN3Oz30rhcAYL8JY0LYZZICFkQFRB0sAQuYDk1YSRwMqpbwD6Ct/8ZneH4Lkg5jfPkwlDYKx4zLEHeSaZTeIWhRNbJMGOS1DgiA1cke5VCMeKUE92Ml2FrZBh0hYomSdHskbF3/wU9+0uAErSTNcYviSqiJOyMzifHU4gzenQIsmhVFEnSgqMGyN8XeU1K4YQn8rOeDMlaMMf/XhCRrNCsIPdkiI7MpS8JG+/kmE7MJ0MNXYF5C+ylqEIz3gRJoyLcaariwetWmkV7S1r+zkiAqNhz3W+8zcQqSJIB+Sl62KDkxZ4kGjTSFflrbCQIwYccNTLJiw3QBfujj95nJWcFkXUFKM2QQOQ6m2ErEr77cCku8azZUdxpvFyjS7KUoOyxpYdLgPwUGvBJNPxHIJV4+FNJlQ7p76gQcf8hGxc63AP40z1ZkQmbt0bTSF/8SeAVG/DHtuCmZ2snjWTQa0DmXAvW2N+e5DV9g8zsyU7YlMKbf7BhNJXEFMxO9vhoNidh8nk2xV74YrTPEfhOJnyUn8Cfju3D99kSmaBpBXxoambjAuDFh8TMB8QH/7Q5nFt48I8ONg+f5Kl4NtiwOKN5JzNPcA2vvbEfMXAvDtiLXTooEPfEUrxNffrER42L+67NAQ/avV6b31rDhl2X8K3damInnmRrvfjs8ERzJi6zQ/rT2IgFdAw33uBls+YpSOnX3nMv8qYnNnPrExc4yUgsjhcDbvfIAf1sS0y2nzjAjjWb7JC90g+bFT/Z7bTvINzs5m1uXAIyYedy+JaNT6B/futgQJyWQ9i4/D2H6/zaGxn+KXv2Yr575rs/B1ti5xp9fkOexip74Dpdsl05bvrvUwE5i918np3fq3/ryZ7M1FCrjzw22CuZilW+HwH6+I96UT2TLcwhZ9Or4RDpb/7mb076dWDjmvvrcJ0M5AI4yGRPtmgWl8rn6p5rge2/NC5XAGc717gImgTr9QMFtKKPYiUABT3jotQ5bhV+BuCPpxWxDEGAcTon2aAFfp/21QAI2v3LQpK3RMv4FCbNLeEpgjUvApeCkbHjX2GNJ9d6fcLpuj/gzhkECMkGzk7f7ScRWyNhxbfP2bhIoopmT4EUMv43+05JJCdO4lSNIyZDNJO54gKuLZm6hgaJz4kmPVnrOlxbjYt7kqEEqeCyZgvMc498NRiCpGvtSSf2RRvaycj/0K9AIlMn9BJzjQs+0RAOuuCsZINOeqmgkLQ1ivRI/u+///6pSKY3slYAkT+85mn+FAtsxYknfILNtBcFoNNejTD6FPy+O9VU6LGPaEODBkVCgNOTAUnZ0ze80o1510L4s0mDLsiKPtidhoqf2Ucy2tsHjY/RuOzhivbk2cADHWpC8MCPfNING1EgixNsqfUKSvPWk/5LAc17jYt7TuDYjVjFx+iXTBVV/IHtkJ1imw1YQ54ShwJ42k7+NAfwiXeNMp/2dy5spvnW55uacHuswObYmFg371tvLZnyJ4cqPtG3ZXvW9kopOePF/oY96IGdwJnd4ZF/arjEGb9XsJ4digFO/iV7/gzPBHmEDfGr6UcN9NiDL9UkdV2cFofJXex2zb09cI+MxYqe0OKHDHrS7jpfkqPonPw1cWI1ftEhHvO9AM7ZuMB5LaANf/gpZsMr9tmbjtCiySgP9YSGT6SzOaw/Anpjy2zo3NzHBLze07iQN18lE/nqCOjJWwPiOXlaJ/+K+3PwF7lLQ//zn//81BzLA3Th3jrEYbbjHz2RG9k1mW7Zo3sO78wTx55a9mjq7Qb8PNT+8DjsE9McUj01X2zevuIdOvzeA7HCq1kaEv4uxmzpVf4RJ+TxDz/88FRXNJfdrIM9yQ0OZT2p479bNgDQ575c42nVVhw9B2L2S+NyBczGhcAYqQAiMQm8lKsAURQLjJSpExdYJChz7xG0/SUPp6MKEXsJ5F6NYLiCjMLEvAzYfvZljIpYyUoS89qSgClxCJ7o5QDRaDB0dJuPJyc89lMgaEbMFwwEAk2RpwcKr5UGBir52I+saugU3PDjCV0aIUWSgK6AIWsFtmvoZOTok/TtpYjBF/k6IcQf591yBrrCj2TIaQXzkn4yErwVamuB6B5nQ4eTJb+3AD6nl+wDHZKrQgavHI0cyMUgP4WNYsFpOvng2ymnQEGuZEU+5ITmig60KJzZQbTDaT/JwatdEhB+6M5ppoaQjjSAH3/88Sn5b9ml72Rjv+hlxwo5+9KTIlfz25pooF82IaGZ4192U/h0IoQPPNFPay8Bcxv0Qs7woFMhrPhiv2yUTfADwQ1v5ltnzUM3LpOuI1jnZIvsHp1sgd7gVSzwR3RK8nyA3jSMZHwLoJn84aUf+5If25DIHDjwJ8UhOWpu7ctXsuNsEQ3oNyQtT2bntSmTyTca8MifNUYKHk2ntc3zXQyVMMllBfuvjQs5ig1kxZc0+Nayf7xZswK7Z98SrvXR6JNt0oM9rI02gy3V1Gzh5St0RXb20BDiE83yQ/ozb69xIUP8eA1WXJ2+4rs/iCc3Ni/e2QM+644APRoqPMcLfGxBfHDffmI7utAsv7AJhY6ntmRSPLFO4wmnT3K8BeBSfNsv2YqF+PMbv5omsS6fxu+RzR0Bnj8JjQvarRPP+Xa2tQJ50SdfYDd8m+6m/Az24Lqm3zx24KkW3zd3HfQtv3g9Wp5AB/vZsgP4xR00mGv9OT09JNgrObAzfD4UwE0P7HUvLjwm2J886Ut9shcL6EW8lkfkTH4rl7tuTN3ih0+Lw+xBLqdr19nJOtiImkftRwbqGXRsAdrUUGI1WVl/LYgDL43LFUBxjJ/SKZjCFYScVuNAGYKI5DkT1S1AIQUVeAR4xsmIvLIj4AkCDEqhpuBliKsirWVYmg7FaoamcRHE4S+RM6iMs719d81+CmINBYODt0EO+FVwa9TQ0ADWdEpvriDmtFPykEg1QxI1/BK9ROVUWgOk0HHKa59ox6u1CmpJSIB1uq8YgH+VAXBdgdhrZlty2mtcAFkpyD3V0pys9wMFHn6dcDlJxCe6OLbCuhNvwdN1ctUQaFQUW56OeKXMCY6C355o0uzBba3ChpzoOzrwgwcnsgqNdOS+4Tu5k7W/N3ISnP6N5MHO7Cv42Me9WcixBQUFmvAy5RgNeBEg2YLGsqdIbJedKWBXO7sH4FFQkZVAq9hiI/hgO/i0H78VWFfdXwPwkMc1jcs1gFYNHp/m3+KNhOT1PjZziazQY166t85rXnjXiCgG2Y/XPDSxX/ziF0925ykB36DXbCf9xGe/+ZPE4bDB3HXenO8+3fAHNsGO+SIbd+CAZ3uZx375isZ4BfZX4+I73PSugBeD4YoGdgw/Xia4ryETq/nDBOvQMu19BboRq9ZTY98lVHjFh+QnPyhQxQ2NtuvZEN22x5SXOCROuWd+gx49bWbT7onXYgWZotv6PUAvnXf4MXWb7A169SkmsQWHLuKJ/8TW39zwa42GV8wcyvADusTTxHtES5DMxG9PoNmlGMiu6BXOPUhW7TXHEZBTjcu5uY8J9r6ncbEeL/QJD9siiwl+s0U6k5873JPv17nwiduKVTlZ4Vne2QJ6Zk+KVfQXX+hzytV3uPg/v+Zz8/5TAFr5jHhnf78fEvCjLuEL4vZT8mcv/PBX8pUz+PAK4iAfE1fNFUfplu5Weq2X4zWwdKduZDdbc/123wElG+O38qwmKd8MfBe35DJx8VZ4aVyuBIrTWXv3UyEmuSiQFGKEKbFQNMWvSrsGrJXcOZkmiMH1tEPiZIQMoL3sywgFHfemY8Kj8KnwhXs2LsA1AU4SFNTgM9d3ycRrCZKX7xKwdxkVo5KhE1L4JTWvnykgFLySGLw+FUROBtHGaSRmr758/etfP8mS/KwRMP19BrzmCYboRg9cZNyrcOa7rkAx3z/bq0i235bc7e10WDImt3WOdZyaA8K3Bjf7V9QoTuDbAjZiDjlwZPSRpe9o12zBQ2940OxpQPBClgIEh8SrAsvjff8amwZIYrGv4WTe6S5caDPsw1Y0cU7QJWgyFUw0vV4BEIwku+9973snedgjOslF4ENnp6jkRAcVcuTCfiQ1hRd+AzJEg3008NknGq3FLztjp96b1ZQqfuDD/62ARnTZBz1w2UtA5Z/sV4Gg6GOLt/olSL+P1bhMXsiP3PgCe3JieU5O1qYH6xwAKDI9wvfuuqTh1JV+m6PoYUsaSzGAXXiCwZ/Yhjn2Tb7pkr2yz3xuHfkMe2aP/AtP5luvKPbH/WIBX0eHwb/Qs/qgfWtc+ABfZmvmoi86gH0d8MwECR9e2ED2OcFa644aF/yLP+LE1AXc4iV7y28AfPascdeY8W0+1d+4TLAObvP5LZ/HHxl5FUP8JTvz0KJAoDN+ay8y3wK4xOmZq6xHt++u0TWdO2Bx8CJHaFLEDk88yNw8jYo5YrX/+0UM00SxNb4slkxd7AH7k0/FLOvQU8xA17n1twAZ1bi8aZDPHXLh+xaga/ZDhmLxtGeyowPFumbQd/fZCTtgQxO6pwnKl+iZbtig3wEZsiN+yh7oin2xUWvMRRsafJojz4v91j4l2J89OWxwIPqYdsVP+AX57fnhYwB+8Ch2ynH8eAJ9iK8O8tiauepIftBTl8B3+lJD0Cnc+KFbOp82hkcxT03BxsjAfXvJH6sc+LvcL57davMAfS+NyxVA2Aog/zqLYCGYF6TvMVTrKZxC4JQ8JGgnlAokRqGYZggMa1WW9e4rbhQpgplr6GVEOmB05rQaotm4ALidnnBwTQkjl4QlUaclAqzTbE4hALmnWXGC3mtHGhLO4OmK/dDBQRTdBVeB0CtSXgcgR7TkDGhQFEiWHEpgZ5x4YfTuOU23V7wYZMIZOC052BdvdGJYy5E6QdgKXubbC61otucWSLACPx7hag80kLfAjUaydN1ezaFbBYEAgEbXFC8SgD19ag7hCad3kgUNxZbgT36aH/bhu6IILvK2p2tkYC+B1G//xwyZsyvy1qywCXpFq+RGH+yM/iSwGVhm4wLITsGkMKQnsnNNMuz1EjJfZQzIFa3kyM7Yt2aNHNiPglqBZP9bk4w15IcuPqV4RZcneuzgFpwBuhS27B6exmMB3ORALvyCr9EDOWZzZM0vnah6osK+8EnnTrP5Mp2yPQcI5qczcvIJF72wD/7Nxtg5m6Mj9mdvtuC701ZPafwLWmwXHWiCrwEfHWtu4HS/Pd1zQug+2jQxYgp6NZrws2F0JV98i7/iAltnf9Zu2Zp1xSz34tFBkDi5dThhDp+mX/6QvU+Idq9BOsUN4CJ3tj8BTnyLbfB6CkQH8Gsg2wNeeysSyQv/4hke6RLdbICM2HRgDRl44k8/s1iwN7roRpOnaSVbPs4f0KOBc1LOtsR+hwlyiDhWbLEeTgM+cuS/9KCZpgsHGWgTq8QKT4HEBjSzTTrKRgx0i0O9Ew/nUwC7/6Q0LgE5i+f8P5vGJ7tgR+wl3ZE7H2Or2YlPRS8/FyutdY2dswW2yq7yI/mXDNlUPo0H+lQvyAP2ggMu9iUGTV9+KsCvGCOW7eWkhwC8KvTVGA50nsqeJ9CRNyXEbnzjNd2KedVSrhtqDXGXj2c3ZCSGzeYCL3hjN3AFdKvGwDNbgQN+MQxeMSudu6f+kJNmjLoFXhqXK4ExKJ4l3BzzXsExDgplbAxA4cxwOFvG0Djaz3Un6RKdUz3G6LGlgOaTQQUShScUDG8CWpyMwsH5GLF9rZWcBHtGRw6SpELbvuhqnuKXcUrOgh6H8a/sSPRkx6jNR4O94Arg8lsSVUwrkDqJ4yCCJd7sNcFvMlRESaQaJKcrAih81pKpAosjrXL0nbzQ68nWKq8J1krqnNjTAvgUe/gVNOhPgWj/5jfgVOx4vcYJJbqdUjrdwJugj15zA7TjibysV8xJBJpGf2RPRnSDdiefAot5cMODPv+CXKdmZCKZeRJD/3jXKMCBJ7ozb8pnbVwAOhU9ns7U6GnmnCrDN3k4AvPIT6Bkq06U8cQfNDLReCtYa6AXfrK7Bx9bfMrGJUC/d5PZCftkB2yMnNid2MHGyU+hIWnwX/JFn0/60sjSYzTPz4a5Dbqhe6f9FdF0Bb/Glw2Khw5IJDd2YJ09JEZNKZtvH8A22Sl7I8/sFbBzfIotfEnxhXcAp4LbST9asmH7rcDuFWdOkOPLevEIXZOevvu0lybaPnuFDlo1HejrvmsdMm2tcQ1NYrT44EmRffDvHtrEfI2DpE+24r97RjLtCW3gHvlZq8mcT5Jc1ziIh/xUkyBOsR/z8EgHDrHYdLQ0JnQtu4CXD+DHb/fIgL3QHx7wwh4VrWwSHfKKAltjI9YolLf091jAXj5pjQu70NCTr+90IT7QuVw15UtHDjf4j9zqnmv0BQebSfc+6Ye8PC01l/zkLvPpOjDXWrlFLvKdvbNVB7Big33C/VSAfzlJns1OHxPEq958eWogc/KmGwdF5K1eE6scHOffwCf9k414UJ7luw5MZt71aa08Y2RPYrWGUO01bcx8dYvDjHKQZkMsFqfEp3DfAi+Ny5VA4DUutwiMITEAxaMTa0ZAuYK6ZCrpKTApm+GYb+RwjT0QtAQNxshwJQ0njgWz4KhxkVAVJe3fnhKN4sha9zQuaHePXNAsWHoS8Otf//pkWL4LcvgUvCTNkjE8XnEQ3CZwPsFG4yLhWa+g1+QoPO3BGdEGyMbeCjIyRKdPQVhhQVdOI+2N7ulgAC1wcXj/9K9/TcWnUyW0bIHrTpf8YSLayEFwsK99BAn749Fcexr0oMjn7P5VJfKER2Lwek5NXTIH+CcHNgMXp6VTwYWuFYySn+ZH06WQVRTWtJHv+v+42GsWZa6TqUJQ4omGQOPCRtEfkJk1Ho1bo4AhawUXOc/158Bca9CkIOIH9lRYKcbJVrJFO57S/TVADuSMj3uAj7yJxgXP5MO2vLPOJhQqmgi+KIgrDv73b+bQU7Y+aWOPTsXpmRyPgK2Zz6YV5JpS8ue/ZGAPsbBXCOjKPL6uoWKT/FWcmz7uO1tUtLJXBRF80YpPtLNfvsS+NLKaHzx6YqBpV6TnP/yGX5BPfKNf4yuJF7PEld7xD+xpLn7soTHzWqyhuIXbPXTDC3zCUZyF230xqgIpPvgIuTlwcTigWNUsaARrQti8hgo//Ajv7Zd9RR+ZFQfhB90nd7ZB9hoEsjPXb3srqOiT7g1rPA0RQ8RPzQwZHgGarOWjmkuyneA+2uFmK3SNJ4WMvfmyeKl5g+OcHT40kNknrXFhv+yRPfE3NidGwT3jPPDdNXmCDOiPX7HJ2YgEaPPUXy43N1sVQ+h5At2zUb4pFrEtNYicQM+TjscGtKDd4YA4ea+MLwVxiJ+JXfZ8ap75HX75Gf+jL/lZnBGTJj10IkZ60kpfGhk53IHsmmPZmAMHh6h0zxbEejZkzxXEPLrvAFn+kHurq+6Ry0vjciVQ9KWNi/sMiZIYASf3egKD6g8cFWeCuuC+BoEAnnWs4NpM+OiTeCWjeQraWkalcWFw1hnmmOv1LYVv8xsCkoTDeK1TMGgMBEldtEJas6GAcV0QFbjgMUcz5FUFBoxXToLGmXzjgcMr0DxBUbhKcD2G9qoFxxR80Zyjzj8MhB8vgoeC3tMejosWY4LfnFAwVtQo7DicooTjbwE6yeOnP/3p6e968IafdI1v+Jxe4B1PkoJmxtMWzo8nydyppPf88VfBAkd01rhUtJtHrwIC3s2nE4W0Ik2xggenbfa3zqm4QsdcgUqxmdwDtJMRuyy4oMOQ5AQdvJAv2txHD1uhd3IQxPAAt/vm3gLpUNDlH/ZnT5pfCZm+3I/GS/Yx921uXIB9+Bod0RXdrvZ8BGQmKbEPetoDMmVbbEmTrRiia4AG9/mfQw4nsvDyN/an6edHmmUFD5sh+2xGA6qwUQjxcbbTk5NVl+aLjfwbXjHLd6+oWON0U7xxeqwQ5reusQ/0snlxQeHEJ+mMz7kXH2hn72iXDM3nk3AqLDX5bIb/WocmA19imfhkPw0M37aPeeShWVA0KAz4LP/gk2h3yqlJ8hREM0JW6F1tidzQaD7+K/z5d09X48W+fMXfnYjH8ozDDXGR3YoddIrfaTd0rYFUvOIZb3t25TpZmKdhXuPIHliHPnGST9M7mT01oPe5NC5OoNmWPEPHR4Oe6XgdrrtPtmxLY0++cqXCjm6z2XCYr5HmD3TIX/ml6yvQm1jB3tgguclHaLZntEUHW9MI8wv1jSKevUwanmKwd/zLtWIUvp8Ckq24wdbZm2tPNeynHpAr5Qjxkc7EqGhJRr7zdTGBLYodmhH63otDDsocsDggVs+pacm6/RvwsilzPJHhc+zTvisd1w5yJV94VzqfE7x1jYt7FKOwkIwkD0nRCb31rqc8n3tJ4hKwF7okI4bCCBX8//AP/3D6A2iO63qJt8TjdFBRoAB3KiHpSowlR7RNHhkz41MwcwxFuH9G1R4SqZNJvJjniYBrihv7ZfRoZHACmqLUExc02cccQc+JJeeQdNFOVsnJXI7CyRRGAqgGQCGOlxmcrFGk2I9MOHAJIr58cjonU06AFXWKHzJRvCgs7DvlgE5rJF7rFNR06pp78QKXewoCTYzXMXoKI5mYD7ff+NFgsZNOjzkoXDUu5CXJKHQERutLXGRSM0Tm5GSO01av1fzjP/7jSRaKKYEJHfae4LfA9erVq9PfLii26AE9ijlDMLInHdOlBMkmfvWrX73+0pe+dGpeFJZoFVxWG7oGrMMbugw8sQ2JnizRpzC0V/I6Avff9sYF0F1PUbO3S/c337qeQK4AD/2Ss+ZGnCtWWRvAIWZkE+75VHwraBXvnsJoNmo06UhTIQ66Z76BDk2He2x3+rDv7Ai//tM0T3TYMFtgWw30oUlTLqmKZf4BEYcP+OAb8HvKQX/W25ttK6zYLR9GI9sW/8QLdBv4LJZmb3CIE5pIuBWK+FX8a0R8t84cc9u3gV5x2N+ziKNwwDt16Tt60GZefi+O8TN8dg2d4hHfFXNcp4teZ4XLOnGZzMkscM8cNLmvaRNPp84DfgQn2fo+6T0C8+CjU3zSpYZ46vspAN/PpXFhe+TNRtQJ5wZ7pZc5XHOPXcrzNfjymCeMrs/1DU2jvMT22S7d7+nSdU0LG1Rb+D5p0UAbvttPw+BQA25xAh3NeaqhRvGk1v7ncsNDQr4kz5AreTzVSMd83/7swP+xo4HJxqYNGOTEnx3uipV0u+fX/Fetoa7xd7PqA3vB3f4Nv113cOOAWj0nPttzi45rBjtniw6AnjM868bFJ4UKiBIvp3EiKBE5xVMQKzwlE0lIktlKCJeAvQR6dMDFOChPcSsIOtVTmCoonbIzHIWD64pYhQXDdALpVSV8SEASrECm4PDJMBmzRIdexYN9FDwKdt285MbQnZpKwOhCn4JAAc5pfY9Xn373WJpM0CioTXk5KezJgeKQsaMDz3MPDiT5KFAqoAKBg7w5CxnZA68Kf3iiyRoFvcJJ0JVIvf7AOTgkp3OqSgYBHSsQJBxy8bRFIuwPFQP60VAJHnQCv+SQrfiuKCzIJzO6UjRpEOG0FzmgS+Pr9Ig8yAIun/RW45Kc0II+e2t24CVbBZjiDm730UEObEYh1Ek+WdCR0zjr2Q8bYBd8gGycxnu9xMmHgoldoZ2dSIrs0j58wj74Tva3QDaEPntq+MhYglKMsRV2YS/8z738fpsbF3vggVzJmY3fAuxD48CP6ZhdsmkyU1CyZzFBwifrit4JGhsxQnxhawBeNsxvixvFErbJZtgfuy4GGvDzgZ6m0R8+4WM3CnSxhh+iy57ub4Hr+RW+2Kj4wB79E+CaXTbstDA7xadmCK94sR6tYkvXfJrTQYV7cODFwYi/IWOHXpHlw+gmS7isT0Yge+ELTiDpk+2KpQ4OZmMGD3vlZ/iGz1r4zPH0zf7o5QvkLBbQpxhGF/id8tLs29OcqdfwiuX4U/jyMXvSl3gGp/whroo110B8AzjJUuwlh0nHY4P9alyect8tkEPYDh2SaYOdrmPvuuGeNxHkB//fzt///d+f8HqyabCtBj8zrFH8OXRKD0fgoEoB+urVqxO99g1/I1rkC/+58je+8Y2T/8mv3V/HiuPeAaf9HNg5qGCvT61nfsRfvZHhtdZVv481kqnm1eHe5z//+dff//73T/JI1tMGDNfQpyF1T/zdk5frYrtYY756Ye47B7xsUr2BDq/gq3NWWm8Z7Ny/iipXvWkfPoJn3bhIvk67BHIFlGCkiXBaL2mUbB4CJCAJ2aNYSam9FKcKbfSZw/gYTAnL/tZpXKxhyApSxb0EWYFnnjUKfAm6v22QED36VfwyGgnVPMlbY6Yx4Kyu6YYVDE4iGXm8+zSP/BRNTiW/853vnBIk3PhRPFeQoY1D2VMBLalXtMyiRwJCJ94Dpwj90Zj59lYcaJgUXa4BiZ8jOAGgJ0U2+ZAffBxD0aQgTkaKDTxbE8/294RGwRTApwFTKMA1aSYjBRm5KsYEd8UBOq0jVzyTi0f/ArBEZI9eKUkOPhU7+NXcaNTQBZcmQ0GFH/jZo6BhT3J3IusEH07BTpBP/tYb1il0JSN7KMyctJBjc3wnR8WN35IgWbId9qlQ0ixpZNCSLO8Fe5Gt9/nJGv/o43tkSB/mAHxpvjS5XbsFyEOB7cT4KYFOyQ6f7DEbvhbIQcOi0fRKJ3mxNa9u0RF7V5CbR09kNeXlmicgbEfc6x49iEtwKfTNMxTwrkl0GpStQtU8vLFvdqnhYbt+8wHxxhy887VzRRba7SUR82G4NE2aTcWXpz5oFZ/gjUff+d9sXOZ9Q2NA/5Kwp9ZeoWEL4pW9+Bv55kd7QC5kL9ayKU0BP5FbxAcyEEPJmX/T/wr24GdOP8mlRottkBl9FgMDcha/HFCsNpQM4CUfMhMf5BexSmykH/FILL4V4Ccj+Mhhi7fHArYjDohNeH2TQLfoYId0YZDF3nCf7NbRdQdRdCvPiXNsufsrbr/JQn5ic+d8ysEB24YzfHuD7ckH/M/riexya54xaXqIgV/NtxynMGe753h7aGBXbFuNJWfKj2jb4t9YebhnwOdQQn3Bf+V18YMM3COfrTVqJn6hBtvzC/HBPPFOjIp+Y8UJhz3ZlnpPTYiOOd8neubo/tGgXzWsegae5wrPpnGRYBgEY3QCoRhWmFGO1ws4qUQhuXFWirsnOFKkoCapSST2kdwkTo0S41GcoYtC7UWRaNO4TKX6jh7FpcIOvYKQNQYc7kkoktOHH354SsoCm8Sv8REYBUVFAdo4iBNLRqnwYfi6cYlfg6PAkuAkVadcaLevP9I0nEgoOtEL15SX4lzidoqrcFZcw20POCR2jiCh0klFBh6c5nkS5BpnA+TkOroUDIogwY0OFV0G3hW/OTrZK9IlAye07uFf4daJpUEu7uG76+hQGKJDIFdgKIbISpOGfwWiNQKs/a1LT/TBjiQ3DZw96R2v6KcjNqFRYQuKvBoFgUXiIis2CTdda/7IQPHCppzcw+HkzUkdmaAzm8o2BAoNJFmRKXzsLRCYnbDgGeCBfdjTWnLVJKIdfWSliSE38oIrPV0L1pEXuvCl+ZJkyZq/sBk2hkZ+y/Zv3QuQy5toXMgSX+y9xvUWoBd6Zxv8jo8qnvkX23ad/Zu3yole2bJGhC2Qe8Am2FKNS2ut4Ud83x7ktyV/8zRCCg1NAZ/g+2yxuMCOnPLt4QDwOCDhM5pXNsFXPNFFu8YFDn5R4xLACTc/YT9kPvfxnY2jy2uur169OtlVvCnC4SXDDhD2wEGTmCiGm0fe/F2sdLjgxNR3PuL+Fr/oq/AkO/Pwwzb2Ghd6Igv+TE8rtJ6NdYIt9tC35ow8NZNs8VZAJ1lpnukKH1MPjwlsiX6eU+OSnW3peEJz0D2Ha2QqV8nZ/ATuDqJa0/qJg3+RB19hGyvAS+fy1tGBSTj5Dx+WL8V4sX7a5qTBeEiAzz7iEPtm++K/nHZrvLwW2Jfi3/7ejrA/Odh/i//GQ0B4xBa1k/3VF2yMXsC6b2vo3pM3dYOcTY4TzGOn6haxURyMny0gB/aneRI3xMj+xsU6MGloXAJs9uWP86+AnNIfUyuAJR4OrzinbEHiVuc0j7FQLDyMxAmeolkSloR0mApOCWc6QuvDsdW4TGBQghU8DMx8zYCCwSmBZkTC8hrQLGDsJ2lzSrJg7IKaBK4gZEjok5Qkco+hyUkxo3jkSBKgIOgVqnffffdEA7xTbqBgiVY0MlZyVhh4d/add945PYVw+ucPwDgHXJIyJ0FHtBvuOXFVcGomFPpkq8CA31wBX2GtGPEbfxokf38iEZCLUyc2oOBGM6AL9HqKJGiRDTrwiD6vxL333nunR6eaFckaP9YpPNiRvSb/gSAMh9NdBQMbsJ480WRPj8U1BWzQPTRomvDrhAR/6MOb4JFdoN89QYBeBTmvzyhYJEF8oJEMFEdkFc8T1sZl5QG45j56+IwTKbZBx+yGvZO5/dC3heMcJD94FIHoVmBJ5uTkH57gR1s8XApk8pSNC1qzTYWegjmfuEVGcGle8ZC8yBtfEp4in046SJgxxG9NssZ5PSlnv7NxmYAH9PMdemnfCXCxAXbsj8vpLD9svgJIUc8H8pcJcKCbDSu2zRFD+ZeDF/anaWHbTiL5gjnWtYffZONJA3qA6+bgywGNk2Q2LCnzCQUSHzIfjZpLyV1jtNLoN5408mQtDgRsXywW37zaxicnfStYy67XWAfPXuNCF+gU69nRxGsfOuY3dKXggWfKwX0HSQqyLZrOQTjEce/Vy1PszrV7/PJSsM/b3risg37zDfGfzSgwFaFscK4N+m0u+5Hjpi0G5CXfaaIrVrcgOujSWwJwyUd8r8NBa9u38ZDAfsUlPi7P2FNO44sdKD422F/sqh5xKMQPHQah7zGBfOmLTeFbTOe/4ozm7Yh/tLEVNcNWfnGfbamtxIfizR6wFfHRXN/5O3vsIAZYv45LgIzVUi+Ny4XAEBS9Cl7C4xgUyGEpY8sxLxGstZRbQaroVNQpFiX89mmv9tkCuPYaF2vgEFwUQBK0JK8xYuiCH4P1qXiRoCZPvis8FNIMEM3WKVZ8R6cgjA/NhFcY3Oe8eJKQPeXwBMeJHhqsW+UGBD5OpLBn/E4DyB1e+zn9gcvpsVNLwYqDchYyxHv48OQU1etv9qNHA00aLjSTy2xcyJkcBSENHKfGn700d3jwG370W+8a2UiM/Q2AZg1t6NR44Fnjp6CmB09fDDSuALdi0KkVmshR0aSA9XQETgWa90cVGvbCC1ySBv6s8VvgcgojgCbjwKNcBY5Aa669vPao8bIG3Qp1et2yv7Vx2QNrrLcH2dEBmdmDjBRDgqZECpe5K62XAv3TiX0M/opG+kb/rYDmp2pc8E4nEj+798luFChsE3/XgjX8iP+sQGZk5YTQib/mmx2lA8lfTELDagPW7TUu5okbGh4F+6pX+9qHbiQkfsjv2CxbMdeA10mugyPNR7EjQJ8ige3St/uKLocy/FcxQW/iG1v2+qP7fhcr7adI09zRNbCHxp99ikns0xpNFjrEQrFHo2EfuuGP5DR1ZA9yUFR6PVQTVFMAsi0yVHhphsjEmugLyENMw489Vnlq1MQVTd5qJ+QvdvB734H19sGP6w5Y6HTVM1zisRi20nQO4LGfgxW+iDZ0sG22hv+512MA/b6tjcseZPdyJJ3Bwwb5uZxA5qse++6TTPiVwnKViRyl+fBq4ZG82AL/ZPeaensa8rzYzn/8tt8cDwl8j82zYTJFL99U4/BZ+z8m2I8O6EL8IRM2zZ/VEMWTxwJ65Fv2i19yEFPEs2SyBXSBXvlYjYFWc9MRvxdHHej4Pu+tAI9cQBdsMzmoYxx8ofMeeGlcrgTCd+oueW8pzu+tMcFvimREkjZ42A8AAP/0SURBVLUkTZkaIngViYpNgYfS16RzDuC2VuNirUAGj+KSUStKBExPKyifoUrCeItehuGJBLrgi1dDcGC8Ap2gJlAJavZxTwGs6JD8JSWnagp9jit4oYVDKRw8KdDYoAvN1rsv6ToNFfAUaZoAzohWyVUwkvRdswY+id4fwlXg1aC4rwGT4DUdnCZeFPGeLAl09odfUoPLPmjTyKCbHAA+4a/4E6zbR6HiVRQ0SBj20oBxeKc+9vDbKbBTWa8ume+TrNCDf8P+rmk+yJNM2Iz1ZCcYKWz8qyHooQ9PMeyLJjJRSLkGlyJLknQP7xO8IkRv+MBn9knObFGBp4mR2JycmIvW9kFbf+NyKUQDG7WOfbJNSZIPkL1iRrGZfaJrpf0cpGt0asbxcC2OCeh47MaFv5GL0zK2Qz/k4BrbIBt/i8IOzL0U8I1+p+1izx7QK53W2ItR7I4fefInYbB3vjD9Yq9xAXyBfiWz/Ant1uGNn/E9PuI6HvmI++mLTfIjsmcnvvNp+xnshY49BbGOf8LbqR98CgsyIEt02FNDYo7r9qBfMY6s0W0PcyReOnHN0GS7xk/owqk1uaGXzNIbnPZXqDtw4O+aOLp1L8C7uAsv+hUfYjW64bcnIDt7ep1OrFptwH10zMYFTebBQZd0iF4xS3wwT8FFXnOvFeDmq8niGvvjg+SsyRNzyRef+MU3ubNL8x4L8PVJaVzSqQafTOklnvApdrvO9uhtD797fIFf9bdP5vKpXi/e8ukJ9MiWnMiLDegw2J6YyxbNcS1e9+i5FuCBV14SF8ghfslBfmRf8shjgf34a3UKvgO+xuf5mXmPAXgld/HaYSYdJv/oUmdNulYIh5rB4Yp46Jo14oyc08FnYwX8mcu2+fXcT4yhh5q6W+GlcbkSOJ4CUINxq8AYA4FzZMHf8F1C5Vg59rXQGgYhAb569eqU3JxwlnR9SvZO05yKUH7ObL29ObqmwdMRQRW9rjfHfQ7gyQIjlIDtp4kRoBSwJX1OYD9NDAOGx0CjYCiocijyNDidwCd5ch74df+cxXr7W4t+SRvtfnNSMtQgeUVNkJAEFVoKBU8oFEI5MzyG35zLK0SSmSZMI+G7ofBQDOF5Ahz2IyP8kZdPp5D28QQBz2gzVyGloLKXfQF+2AH+3CNHOlKkGK4Jwr57smSPZBjt7uPXPooRzYsndWTrpKv31BVIXimhczYcwIM3jQlZCywFK8N+aEcDOWhaFDfZk++aMMWIBlNAuRfwoujqxIYdGE7GazxvAfKqYEkHtwD5PHbjQu5kqoEjZwkvnaQXsmHnfJxNXAJkoDBh53zzCOCkT80vXj1p4A8++Sc/9TTU/mhiV0eNC3yKUrp0IEKOCjbf8UG/U7eKYo25pJu+rHFgIb64L0FrruhUscBnFCnh5pMVqGIF3/AZPkAO/IX9W+s+H7YOb4ox/seH8JX/GQ4J7Ek/+CcXe7pHR3xWPCw+VLDbgw3JJfmjNWQhDlYcALjRR0YOMuyPPwdd5IBP1ybkQ2ykxoXuk1k+BSedilt8A694bO8tcE8hbH/zr/FHOcEhjLX4TpY+2SX5sXk6X3l6KEBvceCx9rgU7m1c0M9++YR8s/odvbNPRSj97+F33VyNDprYp/mabvZ4SYMqT4gNfKK58LJF9YHDR/Hb74cG+1Rr4HW1YXuijYz2ZHAv8ElxAw1se+7D7l3nx/n7QwOZz7cqVuCrYiU6jwAe+hIXesqmtuhJWrw1ViB7h/vi+YyXgI7UDOLeOTqO4KVxuRIojdAloEuAw1hDYYIApUkSimmFoKQiwbhPkeZPRV8K5gs8HITRKSj8c4QekQqMEqf9JUFBUvBmVN5FdU9B4XQSfQxU8SGxag4YvPtee2KM6JdcJEVFsdfMvFrAKRQ5JSY8CX4CmSKHkUl4igENhcTpnnlk4DSWs0isHBAfAqi9FA3Wtd7rYE5X4Cc3Cd81nT58imm8VVwptDUGTpXIibwMMnBdw6UZElj6Q3i/XZcUzAvsCQ9aydaTJEFZcSMw0wFeJUf6jUYFi0JDUMEHHOyAPZGv63jSfJAhniRwTkoemjInSYKIgT4FJRmxGzgVuZK/4C3hOCHxFKoGEU2KFadoZG5P/ElueCYne6NB4UwHBTwyYMt4QbsTZYkODwpMrwUq/PCyVUxdCvRCZvayJxmQIz3SJ57oSVDFc4H0HMD53BsXdLEfBwP4pBPyXJO93+yip2zmHPHknjXsUxPvNaWe1uytc53M0MPW/Csu7FMMYRuu8XPNM7k6eCFbhY81K4SPv7BTtoW/CnK6nHzSK50rOPI/sq9x4eeuOzDx24FDxZu18LFLfMKrifePUPD/CWRgLn74MN/g9+KOeKihoY+Ky8B3BR2bZIvtQW94E2OtETMdGvBzxZ1raLdPjQsayNXBjTiIT/gN9EafQw7rxHIFCn2S6aQLRIvYpplkr/yannyHi9z4sTiDRzETbVMHW4AecdZBheJBDMfrSsMKbEKcVMCJWdleAw70sAnNnkaUHB4aPkmNC12wTbYqlq+6wx+bYzdyB1vZA3ubI57wR/oS48T/IzlZx4bZFV5W30+30fkQh1srkAOblr/IYfWJ5KBOOSeHW4Dc1SXewqjYn/t3n79svaJ7L9hLTaDe48drjAPuy/Neo19j2QS006c4TZ7mqiesPRcfrCVfMV2O8HvKgtzFObWZ+LNHwzlgQ+KPuug5w1vTuFAS5TAcQwJTdHt1SBJ1Os6BFBpwAcrbGkfgvn0EBEOAl0wEHe+mS54aDMW8ZIAWtE2wDi8/+9nPTv+niz9WlTQk405cOKFXypxUOpVXMEpUcDIeHbhiQBNmHTokVQ6q4LSHfa0jM4WApORvMurI8WIOnAKlQiPH4ySaHH+o6lUlOCRgxQsazdNQCcySOMdyTXDwihpa0WmuQh4d9jQHbk9AOLtraO+RO1r8hlthBo/56IRPMeVUQTBQhPiuyRM04cZ3RZlA7jcaOL/373165UZTQYbWwG1PT8gUpPaxp+E6On7xi1+c6CVHhaQGiR3BrwBReKHdnuTfkxbfBU5BBY+aTnZCRppPQYbs2Cue8cNmvYamIWEn6JhAb/aFW8CmV7jYg0ISHe4bbGm1v2sBn4Ie+8AXeyUDcsYvGRp7e7n+nBuX9MyX2IZEvBYBK+Qz6NlLRmRBdooRCUMhomCiK/ZKP3u6ScfWKubR0x4+2T4c/Ih9+n9M6GSr2QL2QTMbUcSaK27he6XdbzyxU74N/ObDPWkwxz7o8rdjinXX4BNfPClEozkODsRHdrAF1qBFY+c/t/V3NA5xZjxagY+yJzrDmziisUAffGQnQbNVNCYTtJvHz8yDX1NIJs3DxxzmibH05hVbDZOY0v3Ad/jsK2aL3+aSIdkXc5urKRJPxIU9PgOyE/O8aki2+ESPnMbGtuwoehRB4h7+zHN9a7jPnuzhCe+Rfd4C8D2XxoVeNKv/6zcyJb8GGs8N8+hOA1uz21rybg59OVTQxJrfHlvDXLGebsleHmcz8O0Ntsy2HEY4YKK/7kWDIU44VHMYMfdE9zXDmmTQ4Ifyu1ph2lbgOzrJSeyD46Eg3PTYU9m5d4AuPqZOwAe6t+R0y7C/WCIXiHeuhbtBL2KZPyGgiz1Zumau+KHRdPAht6sL8Naec7SWfald+BZbmjprwCEHiXXrPThWureGOkBMFTO3ZP1c4K1oXAhdYUiYOlRJj8IV2RJ5ShH8S0y3AAdgqJK07pmR2EeTouhlPNGiyOesfs/97M94FRCMSCB3Wsno0ei+feDqiUd43ffptwJYcatoUfRaxxgVkwxT0CMzTqVoYtROBARFTqbBgM9e5jr54yBk5Lqi2JMNAZEjKM4FUye9fkvcniQoZjgbushF0+YEViHPyNGFPicuaKMPA99w28vwFEFwJRu/8ULf+LeXPe0l4dR8enIheXg1z1r74Qc95BKthuBqLr3Bp+CUJDoFgo8OBUG6gMc1iRzfZKOgxbNGTtNDDxqPArf5aFfk0IEijG7Ns0eyZUP298Sk4s51MhRcXGc/Eg2aFSfuB+yJfOhWECmo4RNeJyJs0uNzp23kZP6tYD80Rr+BJgW1v4ViT574SQ5sybwJfj/XxgU9bIA/kVv2R/9HgCfFNntnN1M/AF66lXwkTL5HRxK9vcQo6+ncXltyYSdOyT2ZXWVqzdQJG+NjZIMf18Lp01wHG/TlX9lT8GeXW3tbr+iit/CjxahAMNAvltnTb7bmUICPwW1fRXOvWm2BdfbQ4JOVkR1t0QayfzElOYhp+Qu6+KziHu7AOn4hN9APGcsp7Nde6aIBXDPX4ZcG0eEU3PBO3aEB/2SmIbHGHPQZcy7wXTwhm0njFjjAYZ9iIprNp0MNkk++v4L9zXeIw+725B+4T24Ozdin5kisnTTfA3AXB87R8tig2JfzxHFPmAw+esmwRvPLzvm+vGDILXTZ8FvTrhFkq/PeOqyHy8GJ/+CP3vh9uLeGnCP3eCPA77m/743esnDIxVbWudcM/PuEw1N+8RhetsZOGhPoWk5EqwO/hwJ44VPfFEe3AD3oUzvwS3Qnw2Rx66AzMUHdCdeWHZEXXfJf+cK8KdMGfO6Z602LH/zgB6c6IjtY9zZaJw7+67/+62/tcZ1juK7+I6/yimuNuWZvwO/wBp8PFRceA55V4yLhSIK+S54KZUbIaCiCsn0qoARyAV4yuSVIUopkU7EhcWhEBCzGL/ApShX4CmUJseRkDSWjR7EgyaRkxbVEqSiAF60CqALZ+gB/go3AV9EJBwdlPHBYI4miyZ7uWWdfpzwCFtz2cB0d5mkG0F9i1VhIKE4F4FBAOB2BF29kiAaFVsFPQYY+/LunUFGQkY3xwQcfnBoN+9GDYsLTGYEO3p7ckBc66NZTCI4FH7ro0BOWAiSaK/bQRddevVFQCAieqCiw7Knw0XBwaPedOtYA4Mc8gQwd9kOj1680Lu7RBfuyPzmSn3nu+S1ZCJp4luBdDy+crmukJS30u0d/6dDjYHPg7ToaNLN0poCmB6fX5EkW2TH+BBryJH+0Wm8PONiVkzh61vxqMPiI5pP8ajRv8YvAnvbCLzu0B3sgC0Wg5C5Z4c9+eDIHnbcC+T9U44IOclTsolsBWUN8CVhPBhpDPkHXZArYmEJegaaB1qB3j47YJv/K51zb0gW58XMncOcALejn+/YUr+yJFjqQpMROBREbU5jyDb5vztRLsoFDo8MGDbFGIu2gxDxrPSGka3ywVX7ousE+4Kh5tm4L4BIP2Cwc1pHJpCuAQ8xgZ2Qsptrbek0/3yI7ctMEhcM6fkXuDlbIQizyN4NiQ3ow3/A9uaLLYYK4J0bQS6+x4dM8zQzZuz/99QjESPEcji2gQ7aFZrZPL/Fj715dox8yxgMg63RODl0/B3Dbk12KHeSZjbbvrQDvc2lc+LwDNnlDPLlmkLcDI28vOCCSIxrrXHlCQalY7Nqc39BcouWjjz56/ZWvfOVkt3zN9b3BhxWRTvLDs7WHeZ7+e7uDD1s7718zwg8HGXgt3gEgf81vtuyE3TqIY+/y0722xN/4GJ9Ua/m9B/YSD+Sm999///S0ORlOnq4d1or9P/rRj04HJOxCntuai0668t8z2HdrjgEnPA5Hv/CFL5zW0N8enfZzT63jn7JnM9bHXzyG1+GW/5CYPUZH91fcW4O9/+QnP3lpXC4Fhq+4JTiJtz8mVHBIPopzwXoVpt+XCri5BkdQfMAt2Ap0nloogp2yC8JrYp3r3beWskvCkpOiVjKIVoNDK0Il3kCxYU/34AJoUvRwEsUwnIoLT0Z01GQkwUhggop/uUaiXZOERC+hOQWyz2xc/LYnuuGJRgCPfbxC4qSvgCEolMwlbwlWgIxO9JvnJM81r+5JxvAZ5nBUQVsjAm+JUoFuP4GcjoF7ikE0sAcNB9qd6Ejg6LGnwkIz5KmY+StU2CiyrdGQKbroQWEEl4anU8eG/RUy5Gu9ZgAf9It2wYyNmAc3GmfyR5vGVYFG3sB1T53IDa0FYgWVoKfpsB6gWzATPJpn/QS/0cQmFOd0Yj8+o7jSEGuM1nW3AjwSEprIhl+yB82hhh9f/Oee/R6qcUEDubFlhQWc9H0LbXSukWVj7JPM6VEC0ZjyoRXsLSbQBz81Jz1OYHd0xb6OAN2Gva0hc0lJbGDXCnSFOlvMFxW8kp5YJKbM/X3nA/Zmy3Qp6Ytd8Gi63LcffBoXDTQ+2KXvruPRU08+LTGK2+izzpjyTm72o5P8Z2seHOzXXE97xGRFvKFYR69DB/zXuBhoFvckcL6tgObv5tCFQgjv0Ue38FhDFmIbmlzXHOFVDEWPvdCtMEOH9ZeAgxpr4FzBXmwKn/Zj/xPwZA5506MY3pMgMYSf0LkYcC2gnz87HMKnOEynUxfXgvXPpXER19FBV9nHpYB2OVRMm39XseLIz/iixv4I6NFc8iFvvkZv4d0a7ssLdJ9frWCe2Aaf+imc94D1eHM46jBRfjuSA3Cv2CRHbtn7NVAu6A0J9OzxZW8+4okHXTiwsf8RvZcCmxZHHQzCGb51sDN1j5yzpSdgHj7ETfFWkyH2kBv7cH8L4GOPGl+HiCsdARz0JP6p+aJjnXcEYgI5Vos9V3g2jQtDFeC9NyzpCMwSOqW6RyklnWuB0ji0JC9RMULOqGD0XUJl+O6bZ6+Mfg/cUxgqpjUw6FQYC5hwwRHAK7AwKE4I1sYFPgYtyXI8911zjxExckmKMyuinLB47MxRVuBE+FRwoE3AFIQZvUTqbzkEl4rqAM8CsEej1mpCFKklf/fQT2b4dJIo6Va001NFrCKCgyl0OYIApNhThKBdM0OfZKipEaArCOardk573LOvfehMgaxoVBT618f80b8GYNUX/sjOHLTaW0PMrsgF7QoodFvbwAtZ/fSnPz3Jga3gWVCwJ56sQSuaPZmTkNhAOpuNCz4FHgUXebKH7JiMNB50ii5ywx9fmPO2wF7u2w8d1tK7J0UKLkUoedE52uDDm3W3QLLhI/aiD4/K2Yf/EBW/t+IGZHpv44I+MldQSyR0hW86uAXIF13shz/wYwUsO+eve3itc18xwZbZE5mxY/Jz31MzfsTeLwX88Rk2wq8MT2EUtOHNBh3MSJDs13dycV0Rjhf3invusSHNCd8Sq/iGpod/aqbplx/gC81o0CiLQQ4RFMFkI9E7bJA8xVb4HL4oKnx6QqN4MxcN/AodmhXX0ctP4OxgQPxIfhpmNk1+TlclWzjo21M/PNAZfvDFTslZc2If9wx+IgaIC2RKtsnPfTjJlqz4vViN9+ZcAmQoNpC7dXSEB/GAbTrQOrKldIk+sjdf7MYrGcB3i89ZYz+6h4/c5R203eor6PwkNC7JnD2y3XQ3wW92R270aM4RmAuXg0Z5VM7ih9G2NehBs82mrWOTE5oj9/EtMSDbNG4FvOBJUS1uyG9kaf8922hP/sr/b7XLgM/an//MJmQL0GuunC6XarbEqwr8W8Fa/MrL/F88EyNWIBd5QSwTb45sv3xPX/SGVn59RKvrrXNAxCZcawB7krnaS66asWzOOwcvjcuVQPmKDYqh/C0HuVQB1lI0RcILnwSqiFPsaxgYNgMQ2Pac8RzAz5g9muPcaJewGc2kkwHZR/MhSaJNwqhxQSPnk/gV9wXKcEgmTj/+7d/+7XSiwZEk554YlEwNa83nRP5GRqLzrq7Hf4pLfyAr2WsI0G4+/tGMH46kGZMoBQ/B1h/wczJz0SrBKWIUL4pWBUwOq0AjZ4mZzP0diCDoOh7g1OB5xUrRC4fvApT9yUlBo/BGH/xOvN2zh0LQk5DuK9QVT35biwb8oBUNgqj5/hGEDz/88PRH+B7nepQqedAbO4A/u7GP+RonhRsnVixZTz5TPz7tI2F7tQUeo8aFnv2mZycmfk/78Om3tV4JEMTYkeSGlksAjjngYwOaFbKH79WrVycaBT/8ZXN4tuZWsJdgJxDjlzxuBTTd2rjYl7wUt4pNdr8Wo7cCvOyA/PyDG+yxgmgP7EnO7NpBgD9wZ4d8oycTCk/+xQfowj7n9AEve2KT8Ipn/KB161o0sD2xQCPgQIjtO+Gf+jd8x5enpegVbxTt/hEJtLJf69O3RsDe6EE72yYn/PGtf/7nf/6tj4lZ7knw7ERBJBaIU564OiTQ1LBX/ggXnGzCfhKyJoOvibEOUsgAX9aRBf+paZl2iC84PUmyzt72EQcVp2S0JXN8OfTReBroPad3YD92R77k4zUPPKCNf+PDa0xk3D+QAK91k+7Afq6Th9j55S9/+eQjbN31c/ScA3xq0tAlB4sd9roW8PtJaFwA/cl58o88NeUBl/uaCXpwb49fc+lVk8xf2J75Cnxryd79aFwHvHKn/GPt3Md9tiv/Ktrx2nXjFrCOr/Bvvg6/WslTHzzg+wj4rUMPT/Ci5xrAH/mwQ7Ei39zjic2JV3wM3X7Tu3pgjQO3gPV81+GC+LHyhCZxQb7Be7rcArjQRD782DwxUdySD87Jll+yR3ZHRnMfeNmp+Gaevdyf4xLAq8ZF7fac4Vk1Ll554NwJ/RawTsEiqTE2DseodOSSlX0YSEniGqWuIOgU3N55551TQcDZGTDcE+zntK/HfQy1xoWxeAVCcpaY0Tdpwo/iE/3uMVoOCocnCJohfPmNZ3sIZpzMdY7l1FLR6jcHd6LJgRQZ9tT8oMU19+1j4FGDpJmxp98aFwWXeQps96x1T8ByGgi/JknQE3w4gqJfkYYu8zkIXF73QptEbJ3Ckwzx7aSVk8cfmWkKyAB9aFIcalDsbZ49FMCeONRMcGZFgyanYksikOBcc+pIBvZ0zSkXuqccnGixpfV0FC2CuqSd/mtcyEjxo4DrRGzaNzzw0a0i1Pu01rEVeG8Fe8Ad/eTkKRc/UKCwDwW4ogmNt/oAIBt46eoePHR3a+OCPzbEFnvVCf/30APgYC983KuLCnk0suE93GRPpgp2zYqGgYzwh0ZP0+jAO+mePkrQ/J8NKG7NXeNHwCbZlljBd9iVE7Z4XWmCJ9loxtkYf1oTn/X8RixxeAInu1TQ8g+NgUJfQeETD3yKbbW3vcQSfuapiiel9jbHfvzCwYMDmta67jtZ8VcNUvemDZOJOCSOweHJvLiBNocXcBdTNCMKAmsB2nxHi30c5IhR4nBztsCeZGwP+negIk7As8oZoJd+5AQ6J2e8agAdzjidJz9xAA5zyVlMEbe38kbguvvymQMwuUbTxzbJ5x5IPuKhOOqASdzeo2UP8PRJaVzMxwN7JHPxJGCz8h4/8El2e/jhYM9okYvIyDV5xhN4Tbi4EI1bg97FNLmaTQbwsDP2g0+/57prwRq8sUk882W8sS8+JS+JCUdgLjvS6PPLa+mwv3zpYJWf+H3EEz8XC/lcfmB/hxn2d+1aGlYgV7WHGkx+D1znwz1Jm7rZAnqWg8UBNoEu9kC26jh14BGQheaEbtQH0+/Fbm/SrM3ttfDSuFwJlK5xUXhcAxyLQXAyTizxSqqcTCElMDBuzm+PnPtasI6hMFRGIymhVwEoWPuPFhUjHE4hIuFVFNrXWsFFIHR6oiDwaR4jUayh3xzGjCfBUgOmiJZQ4AI5MZqs0WULihzI/q5VXBuSm8JSIECHa/aBWyNAVvhQFMw/Qgb2QAdns4eEhj+OJlhwZM2mJsX+TpQZvt++K7I8EtdU4AENcOBb8kW7gkrDhBavdaEFXus8YXI6rPkSHOCg6yC92KdGhQzonowrBuiA7F0nRzIQCAQQBbzihJzxIEApIM0JkoP7aKKzdGuQGZ7oURBBh8SkocGjEzYyT/bWkr817NVc+/rjQj7QqZHij72xO/vcGoTRz6bIQ0BXKNmT7rNXPLEb82ZQPAfkyAfo7Vb6gH2vbVzwRTb8nB1azz6uoX8L8EGn/IFd9oohu+wQYdpHYI157ESjns/lr2SFXglGMWyOZl0yYpuKUXp3gsou+QpbYaf46hChRluMkbh7Qrclf/uyOUmTj8yCAg8dnKAB3WjOBnyyU4UAH1WAa97YpP3NyZfQwJboQfFhbfv49BtfvVIRWO83fq1V3Fc0hRv//Jkv2d88DUh8R6s4waYNcdC6KX+8e/pIbvxpD8wVN4vLcCvoxDSNUzmFDBycyAdyj5zAn+lSPNEo0RffwBM9RE98i41ygSbHHPysYJ0CR/wje3zSlwJdnETfvWBftoYONNOHfdPhOWD7n5TGJWADcoOCEX//92/4onM5Rs6ZNr4FdC32kwtbJBfz4fKKpdhSsdm9dZgr1/JdscU1tkNXmmm0+T3X3wJwsC15Ac32BfCRA5uQz7q+B+yIH2i2yOdSW7APn+Qr/Ibs9nhyHR0OSaaeARz80VMjunoIgJP/82lNDFnZXxwWa9QL6D0CsUocpUNr4wvt4gx/W/mcgGcxR/wUa6oH4CJvsfecbs7BS+NyJVDCucaFkihvDslMkpBUBStJnwEUDDKOOS6FuUaQYLgMTOGi6FCcKmw4u4RG2ZKYkz8FhoDHefClmOXQAoBihbHDI8lxcA6hMDZHkJK8rVUwmS9oOaWZ9PvumtNDr7AogDhViRGQgTkCp2SXc7nvHscmM69PMVjOte4huUqs9vE0x2sOTjx9RzteOY1E6rU5RQuH5lyCkGRrrRNmjquJwq+goyhw8ul1FOvhkjgFdI2gfxHI6Q0cdEz+azDCh4LEI1cNpEYHzYJLvAigTqxm4+JeQ8GAF6+SsSNz4A2SFT3TBTtFj0LM3vTW0xpFleKG3shIYLW/Oebiz3X38Ysu19mGZk5gIxfyYyd4ZweamBJl4xqY6wy2wNYEebZD5niiMwVzdmQcgXlvonGhE0GfjOhEASiw30NDgBb+zkfRVNHBbjTAXk1Y/ZEcFMRkiCY+v0VLcmcX4he7gJd9aRLYL/uBg52xFfJ1suo0fDZD4qYkb+4R72gv9ihKzDMU4Na6jt9pX0AiVKCJZWTtyQ0aHHLYV7xDN175Bh/GG/om2J9P9sRli1Zr2J29yF0skkjJVDyk454GwOXJkGIe7jn4Lh15cqsIgwNuMhazybPCYQ/IgT0rEtAKrMc/+0QLnYjRDjLEcfKwL52iD6BHfvCOPDr8DnxP1ngiO3jJ1F4T5BaHR+ICe7GW7vitxlUMdf1egJdNyaHkiyd6n3TvAZmSySepccETmdMzuyJj/iMey7VHeMmAb3gqOk/qA+vZM1mn02hdx7Rd39knn2Cj9ngIedO7Ayy1zVoAo0+jRQ7sGE17gD6xkYysudQureMHfEA8OQL+rGHnM2Lm6s/kI2+KD0e0Xgrkiyexkr7Iym++79AM7elqBdesJ1c6W+XhN5zVHefoFR/EX/5vrfiodnHd73v4fWlcroRLGhfGSLAMlsMqhhUJDEdyEAgEKsq7x5GtV6jApwCXqJzyKXolLk0ROtCDbvMEFAnLNcN6hqVZEHA8xlPsSNyc2SskXnXwSoWCxKmJJOTxs0Tk6QInVmxwYoWFBiPefLrHcdBXspcwK2pADlfjwsEC38nLvmhQeHCAWbSb4zTWSaviGk2cFz56QANeBU/4PWkRRMjFICfrBET8Ky7pynxyMtd6T12sR497ZEfW7AHf7pEvGjl5fOAVfnbA8e2hiHB92oC99hoXcxUngrJg51SX/Gexik547Y82QwEpsWuwNGR0q5ElA8N/tIcugTUZC8oaW/zAYV+00Sc7Yg8+7ec6WtmbxCdIa2w0fuxSo0teyeJawLu19mY3mjdNEznhk81rSCsg6XMLyI89PFXjEt38gnw1XGhE37T9W8Ba9qdpVCjSJ9lMGftN10472QigB0806F0jCMeeXsibPp3aWsfG7Gugn+5d57eaAM2xf5pVXMCz9fFoD/7kYMFTILim3U8gX75k8FsFmbiEV7bo/lzrO5nyh3CjjZ0oxhRd9lWUO/iYT0omoBUedi1581PzXJtz4988uPz9iiSqAdFAiJls3hwycEBCB8kwgBMvZIVXMRWvfI2fiitThhNcc8/JMl/jf/brHpolda/4eeIjnjltnflg5Z/MHNrgeWtPYE97iW3ihJyWb+ODzPiGa3Dg1xr2x0bFT3aS3Zmzt9c5SO/iu5io+SM7+x0BPj9pjUuygI8N8hM2rMnH7xFYhwaNeo3sBPJki+I5HRe7tgY62IfYKL6wQbFP3GvdPQAHW+JvcsCqP7/JUh0i9p3TL3xiBt/lz+cA/fYXS8gkn9sDtMip/HuNW8B6fMi3dHYO3zlAHxyaVgeTfNlhAXvn+0fyZyfmylP0vMYHa/kt+1InsZsjcF/dofYTe6xzCOz6Ob2cA7y8NC5XAKHXuFAk5VI4A6VUCncSKVEp7hRUij+CNu/IcI6AottLcOFkCmTFuX9RiuErSBQRHGt1AOsFFI2Ljtd9tDTch1fCVfQ7hfCeufee/+RP/uT1Zz/72VPS90eaDFExDB9nDAQ4hQ7ZKILh44zmCySMlyMpusmoYA3sv9e4wKvo90RDkiJneyiQrIHHfa9vkQne0KXQEbznPu4JonTTtcA9joB3c1bHpXuFkAIdWG8O3SqGBeqKAglDMESX/RV2PcEhA0WcQqtTmMD69VWxqR9yU8Qp6OAWQM2lN3onf3Jw35poRAObsZ/mUYHi32b/zGc+8/ov//IvT82LJ0GdiOCpAANHw290kYNP1ybgRfGAHk9enLgoyCRF9LqOD/Pi7Rawjl2gE73kzw8U8fQwm3bz0O3zqRoXvOGRn2jUNXPkFS32v5UGOOhSQ6F4ZFtoWiEaFA6aWbIXuxTW/GhrzQT7KEzRjuYt4DP0TdeacfqW+Fyb/PnOBiUu98UYfkBH7IGtZRM+JXO6knz5vSIBvcWtCehUfPAn+wbm450vihlikQJNLCILe6LJPb/JkX+JpR9//PFpiJdOQ8UDsSZ6raNPTyLFN3EfP+IHnwzyF6+2abTxtgKe8KCw17DgG93odd1964zopX/0+nsm68ybciFH/oZX/PgX18RG693bAvuwVfLas03XrScDh1j+lUGHJ2SjUBTb6aA9zG/Ym4z87Qt5WIOnPdu6BKIHLrry9IXcil1bYE8yfg6Ni6ZaDkc/uTXI99oh/sud3jLQUJd/2MvW/PbSWCsCzduzDdfFG0U4e7bXpHcOeJyui/v8l4/gb4uGawc/JTONAHui/xXQygYcYsqDezwF+JH3xLrVjya4zkcU4+qPIxsD5opjbFLtswXW46O3PsqP9ww6wL/6CF5x2QGimLE137CGrMjW69l7vJFBNZ7cZh36t4Z7+FFHshsHp/LO1v7XDvldDefg5jnDs2xcOISEprtVyFC6JCEgSioM1xxjzxkuBUYgGChqFYESmyTDyASK9mJsewYn6TqhZjzmr2AOx2UUmiDFjleqPvWpT73+sz/7s5MBChicgJGba69484kGxYmizhMQwVNQ4DTmuo8PjR2ji1afgttW4+K3IOgJEPnbR/IREDmnwkJw8ITAdffNQ4ekwNCjEV78Kby7FpCJwkNhtRXw4OYsdG6tYQ65zsYFLz5dwzudSaqSSU0lXE4t2QtbCdC917iQpxNUyd9118xxmsweyUHxRq5b+qUvBRbdCiT+taA//dM/PQ142bB781RtBdfwqzjzuUIymQO9ihu6cjLq5IXdajgKkFt7XQLWohVvZK44c/JqL/7IBuLJXprHp2hc2Jx96Ve8EMjj8569ydOpGHvXYNOBa3s4yUaTrKHQ5LAVtr2n3wlstFPbvbnkWlJyqk/eGh38r2BPOveErP/7SNEvpnjNSyGs0MqH2LLCB64jG8EPf16fUNC3E2i2R0b2FyvFTQcI9rQ3O+Gfim66Usi5LnagSRwkb6OiydNp/owHOoDbfmKj5nmCvckGfvLaAzzi3xOUbIYs6M96jY+nFWSGXgWJJxjipv0n2BMuviBuaizJU9Fl/hZYU8xGyxG4T9aKF3LhV2I0WvegNeIBGxHr5YIj+70ErIWD/MUWPocPOXML2PVzaVz4sKZBTpfPGg48bxl4kqO+/e1vn5pKtns07Etv4hn5bQH5kpPi+9WrVyebsnbS2xALDI2pBsq/9Mnf6KV7tw74xS8HbjOeruAaXvgLf5Ujj4Dv8Fv+zj/Z0ha4zr7ZlxpgxpoVXBcX2Ln4g56jueoOb+agd5XptYMd0Cs//sY3vnF6rZxduC5mbQ36JFuxqydZe/SqYcylV/vAe0SHWu+b3/zmqX4Ud7fmXjvoymGr+LxH53OAZ9O4SCScyCkgpVAEo+j1GwW6OXvGfwkUhBmIZCyo2MewZ6/wCMyCsPnnlMcQOVKNS2vsIwhwWIW0AGMPBQvjcHr/7rvvnk4fnWC45h6eGVBNDBxogZMMJAYBmVOU2IH75joJUFT0ZAN9Epp1HMc1Q9DxhIAToNU84B48ig8J3LoKegFFgWJve5Bj8nG/xiWAs6JG88HRJFS/4QrwtzYu1ikEBCi04NUaOBUOigVPSOhMAWZ/69wnJw1KMnINjwpFxXWNi32dUCvGFSGuBfZ3qiHwoI2N4BcudNjT6Ysimm6zI/gVPR988MHr7373uycZOlFz3f10Czd8aABoRyv5+dyCZB2gl0/Aw/YUfWzMXuyILWg4yPsowO+B+ckUz/yCLaCfr8YzWeOZ/V67xwS87DUu9rc3e2WTCkY6uGc/a8kQX5JxTeolcYZMzJM8FTLsiC5dPwfmaUhmYw2sdU+B70Rfo6+AZQ/2UbRsNS7W8T1r2KNCyJMS+mebxR6DT0h0dMgm9oBsPCVxkJPtJGu/2ZdGo+tiBtmxXzry3VNIMYx9kjHaNQjoYJPWuGeOptFrEmiVOJ0+0gHcdI8X8pjgHr4VEhqQ5m+BfTRrZMQH7cO30cqW0SvPiEFsS/HAR1c78Fv8Ea+9foYPPqYw4ncdrE2gHzx6InLOD90z3z5syv/KThZHugLJScyUMzU88o516Dnacw+siR66go8MNdFbTR3bfS6NCz3Lk/TMTu8ZfEaM4zf+llQhnN24tw570rW/tzRPnNgD8hW74aZrPr5FgwE3u3PoqW5Qc7Bh9zTktwy+ID/5bwu89SHGsqM9e8nGNIZiyJGesxt5Qfyydgv4Jt/hgzOXb4F7/NMBA9/bm+uae2KQV/K9NUB2+F3leskge/oWu+zNDuigem1LttbQJ33JEWI5uvZ4U7fIQV4LJl/rt/Aa7Ip9a2AdlMrDW/OuHezc20Bi2x6dzwGeVeNCGU6oGbkkImHfI7yMpEQgyTEGnb0AyyA5gWalAvJagFshzJgV5fBwUIUD41MUO0nhlJKzxKjAlBi9vmCO03KPUyVECRj/nig4ddQgOOmHV/KXwM2XVNeggUcyE7QFtAqwGpeSqmSGdwFLEDJnArwCCBrIS2CxP/o4qlNdr0hIxtEAL96casLXPp6a2Vth65U4TxQkVR29+9YLbnAquNKX/RQEnNNatkF/ZIsecsOjtdNGfI8WMnZCCJe9NE8CJH2ZQy6KHjJdkws89nISZC947GWeQiLddvLM0TWpnuo4aXN6K3k5jcerexWT1ihc6Sndkhmd9l79NYDWBvrYMzoEWqfRbFNDIDDaxyBj8y+F8AfRi28Fp8T7GI0LfPZie4omvtSTzXv2Cq89JR76wAtdXApwoEXRKikl03N08VF2IfEG1rA3/oIWJ/jsE41sV/yQBNngCtbCxZ7ED/OjA056d/KocVd08idxz70tsE6sURixTzRMgJ+e0rdBjvCjYcqw+2STfhV2vs/7DTRJnHi3BtC1AwwxYAXyqEEjg9asgB++7rBGQ4b/mvq5Bm98R+zbwpctKoLEc/SiT2wSG+muA6IADj5tf83IHo0TyMI+Dk329LQCvAYexCsHReQiXqIHznsAXgd+ToXJcS0y6f25NC7iryKz/HAr79bhRUz1KqimUN6U++J9DnPd02TLNQ6upi2vAAefEavlRbWBa+avuNkBm2Vj+LOGbu+RNT/EE18WX9g1vdpvC1wnU4eJilyx5Wh/88VGeXyrZvFb3GVTcu3kewX3xE6N41ZcmmAuPsQMsvLEw4HQpb60BWiyv7pCPHaoI95u4TSXP/RWCH+Zb66s4Bpc5MB/PG2Fd2suvsUmNZPYhx528RAgTrFZDd9zhmfVuOj4FC2+U9pq5NeAZEK5CkbJmjI4j+8chIIYNkO6tQiyxlqByhMAp4kMVIAThCQ2jYHggid7CfwMuVcHGN7/+B//x6kZkfDMkSCs4eiMmRF7EqXglVAVcIKhvSegh1Er2hUnjFnC/n/Yu+9n/7a7Lvx/AcMwDjPAD87ATJihCFgYGFIMEXK9uekkgXtJQspN7k2VGo0RIkRponSUplQLQhQVVMBOUZAmxYIKFnoRUAggcOSx4Xm/67uy9tp77f1+v8/7cz/7NbPmnLP3Kq9e1lrv99FXQMn88JKU5DpBDTF6Dlrj0JwK5d61AijOAF3mIC/O3UmSRB2OkllOmWNCrySaQeQuJ8Pj4CVVdg2MNw8cyUcfOwnW41zJUOHDsBWfnqGvlh388cZaCiwyQI/CxVjOB4/RogBJEKoBLnSFTAVBtOArR4FGTpuOceRwlsTatZKcSOYlW9b0ge30wy+/w8kpDzngrb74Al/yAuga1cvoAPrQjI9oUKgqihTLgg67SEKzBayDz+hGD/0VqEfxLQFvysIl89NjRbwCg03MOfQRMAfeKC6th+fWGuUHXWKXdBxOaXNgDac7KUL0tSZZkQs/Qi+8C53GSLYVbfq15jeHoEffyD4QWzBXdIKfYlfmbYHnArNkBo/MUYJ50MB2Qi/ZtQqXgD7GkS9b0r8FcPU/Y9hH1vWMnQj8Ne360Am+iJ5Yu8UfdPC3Ng/goR+e1X2941fomt9r2o0pCxfz6OM5X6Qgcu2HDPHcO2v4nQxtBJHtGkixWvv5HljLmvib0yj+K5s+e8C8cMFLumGTUayxpoYX11K4sMmyQN4K4acklX3SNbZDF+lHCeGB2MCn8LEKEXGsZUfkIb7b+CAfybi+fq9lbiw/KwZKwMVENw6c+s/p/BrIhpCNYrTZUJEX9Oaj62IHf2TDteZDDd7jF9rYSABN+KK4EIvJqrcuu6F36dsDOMp3FO/WUECQCVyssYVfxsTH4Ts+0Qt8q8H6fC0b0U9OICe0OUvu5fr4IP6wG1e9+T7+LJuuNeADv8BPWVsBy6e3/NUo4NVRuAwAphME5R4FSkAZKDNBMkZBRVJMCBwD5SEUQucUtiguoJCUiQFSXgqp4HIs7FtwJIicOcMu14mRMnYKKlmW/AtmFDV3MOvEBF84LMmnxNAx4kMPPTQVYNaGA1zgBDdj8YETlETb7eBAGY3+cLMuRY/xww3/rA0XBs9AGZ3/Fi+p9gz+xuiPzxIXjsQaHJ+E/o/9sT82fRidLBlpEvZ8xgUdocmcggvePfnJT54CLLwVMBIDOFszNPmdHCVortk52YAnecMb/iUf/G3nOndnJVt2ifDeOgopCWcMnrxK2SpEJBqO0cmWnPHS+zIJsB7c4MupWleQ41ysJdBJINAcgJ85zBU+uLuMF5I7sspa8Nujs1lHocUu8BnvBHfFUgp56+g7ug6eSVjQuRVHQMZop0fkAWcO1A5b+IrXe8B48wj+gpokOgWcNUfxTzAVwPFhaQ40Wdf66MVvcrGBQSb0m6zNEzBvWbiU70og1zqRgEvw8ROd1lbAlv1KYEvkKalq6Z1xgip9z/xoOWfhgjcSwhoXf+vHn1nbvC3+4Cv+sLUe4C+7VTSgo14P/+rCJX2887e1XJmzecFPkrHnfA6e0bclQINYaA30jwB8NGtKgukNn8mnkEFN0yjAB5/wSHzlQ/yN99dUuDiRn9PxtYCHilBy47/RlY03/rm0D+/4UXGF3Ng6/2CzTcFc+hiNbtBZtoR3NkHkD2KncQF9rSOWiSN+915RJKYal3nXgv54Q0flFXBFK/9hTn/Ddw6shS+KHv6r19c79Nk4licET3TgE16ioTcHfNmV4kNM9fccmJ8+2pSCo75sjt+NzY4C3BQXNo5dx2XT5pRPtE5S8A9fE1+8U6ClmC7xxwcn7XJA+kO2cqcUppnXT+Ns0Mhp8EzfnPrbFDfXHrD+UbgMACPaUrgQJgG6byl4USwnFJJzSkqwhK1f2lagvJSEw6aQnIidZhWvZxSN8uiXNQMUWz+OnQPUKJtxcKQwqmx/twzYXJyfUw93jRmBtQVjv6vUBVSKazzjkGhzonBm8AxfEWLdOHR4MhIOyzyclhMhRifJsNNNieFf46XAcJpgl8bpST6f5EoYAy1PUFK4ZMcBzQyQ4SvGJAISVPNJqiTxeISeUnb+lsTZmWbsgrJdD/LGXzuegjW6NHzAA/2MQ5fkhY7Ql3JegR0f0YCv5lW4oMW1sFy1q2ULT0FSAivhwluFizU5MDhwNPhZ89A8nkkmBTjrW49s8QtNHGPWLtddC8aEH5r18J/NoJNuSNbgx9HTRf3WAnkKYBKWLfgF8AkO2cWij4Jdgs2euQPkwcasoZDFB/yITEfXgJdARv5r+EY/FPbGaWyAH6GPc9cu9IMz24V/cK1BEkR/9G+BcQI6P8K+2ElrHjpALyRmLTA/HySZwztzkF2vcAHsgs3RE3xvgflaV8WcYrDbFr6ewdWJoj70sexnTjYkcVE8wK81D8BfuqxIasnCXHOFC8jv8FCAsyu2Cyd+WAFhE2UJ4IG/fKg1twBc0CBxEgv4dfpBDnP0r4HMi3Y+hG/M1Ubxjf9s8e6ScKrChY3wj2IgmjSy4dttHtLj0EpP6bYcJHGC3OkJXUgCq3mvYBGLJaf6ei4O0D9yj4z0pUP4nNMY/dkcnXbKGjtcC+bge+ijjUd/mwMuYqQ4av7enPgAf7Fuqa/56wKQL8IXfOiN9c4YvFVozfmOQOKvtaxhPNps0Mmf2G9vvRagz+YZ3TYnQBNfSheSewXIXD6ob2RLduKZL1KJj/QcPeJRfI6Gt9aSE5tXP818+CCXiR3rz/7EB3jugaNwGQRCWCpcCIkCEh4nyWlKfAUHSY6kxy6hd/oQOKFuAWvBiQJxGnY3KCijlsxKJBQCiib9CNxz+Fi/DDaUyc6+BF/wghMnJjE1T4xe0ZNv8aHM5mWwkn6nMcZLZK1pjOROX7h5Hl6YU+BkqNaQhBgrsWY4nKN3ghiaBGHzcwp2k9BifjgyCGMYBcOyJt56J/FzhYPxchR2OPx0nYpxCR5kKqlgyBK04OW5Oe3S+38Ikh7FB5wkgvB1vItW60nqyBu+vj3J2mSDT5JB/IUrPcADtJpToUROCgeNfglEfsKD00Y3XPGPDDkFY8yPD+bnoN0t5kzIIzIgIzzjgDkkchesUrjAzzNyF5DQ4H3AO7TBJ7pgDD5bB25kS+/MJwHjJPGf89uq38HTPBwqnpOJYO96oOKJXOERGubAPKcoXKwDB0UweUgA9hRsJeATvcZHJ4X0xXrhn/m3rEFH6KyiW9Jux5Sd4Gn0E38yt3UV3fQHz/FNUgm3OR6T85rChd47udHHGLI1rwTFeLSzdXquMBag2J/EgS3oC+8kCHSxBehBo6QHHWjkp9iNee2Ixv+WNLGVVuGCFvjCmy34Ygv4+Nta3ilmkuC1wNxsg22zV3jRHRsy7IZvSiGBj3hAbuwf3fC1jt/5Cfbf4jN6eoVLAN5o4I8kUPw0fNgWHQDG6Rdfi/f8Ax2iS66Q8i367AE4W8NGD31DOxlZv4X7GshYclQM8t3sSiJt/jk5XQr2Fi5oI1t+XCzhB/ERXeSh+EQrn59n7Exi7F10hw3QY3qg0CEH89BlPGNHpXzxUwyiA3TSPHSZL8w10MxtHnplc5HO+3sNGG8ediBnin/1HM38kvWiI3MAbzouHs9tKgTMzbbIBB/w00/roLMH8GMX8MXbXjzwXNFnE1jf8DY04yv9RGf4uATWS1FO1zPGT7iLlfya+ckAneiyuVP6B7InbzkTmQLy5v/hRE/01eBNtvwFXwQHc8lv+TC+Av4B8lf8wK/Up1GA+1G4DACh14ULAVKENMITaCiFalZSzrFw7gS3R2DWoghZRyAR2OxycAyU0y6EXXnKGmUMGEe54Q8n9EQJBToFSU4uPDM//Mu7ncZwGhIbSSt++N2JAcPwUwJC2ev1KT0nms9xcKoaXvnchCtcz3rWs6bkhYI71RB89WcgDKk2ZLgyvny4W9AzzhwKh1xtQQu88UqiYH7FigJJX3hIkBi+xIphKACsywAVOtZhkAwTf9HPIaJbEPK7wgdPJAJ2b2p5wz0JPxkwemvlZMrJzr333jv953+OglwlKYo265ItPtYAHzw1X3YujVMwCkAcEV0hR0B//I2eJGh5RoYcavhsjLnhkJ0o79LolTnghkf4qYCypr/ha25N38y7BOUaaeYQrDhXiSue4R9eSbrwu7WOZ2S796oYOgV4iSue9BLVtQAf+KKLLbGH0LIVzAkvsuMT2AT7kBjSCWuwhSRx/FPkFB3CX30U5+QOR3NG/vldwxf9zctO4a5/5JD+eMeu2J6iPfbKXnIaKWlWRLuy4dt22JOEAC7sGc6SfLyf45HnAqREFS/R53dXU/kaGx10h28RCKM3eCBxoMN8RmhQrPAb7Judw8vpI17xafjzvOc9b5IhOltgHrzxuTpJCxrYKR8Xm8UTPkkShO90jK+yJl7BQQKGB/oF7+Cp8YkSIzxT2JNN3aeUnbikoHKSjNd8Ap9vTBIZhZU1ycC8fDx/KskzTtyYk8UIwI288FnDT3jQny1Q6p558dqVX4n+nJwuBXsLl/CKzSlQzYOm0Ixv9JvMyIbuKYzZV9nXT3MpauQGbI/ck7TXctU/xXOu9vHzcgW6VIL52ZEYye4Tf5bAmmIv30AHSjCnGMqexUS4ezYH+CDWoh2utS2kee49XooVYqENKvEf3rG1VsNb+PBf+BPe1uCZeegf/lov/fKOTPGSzyqLijnwHn42RVNc1MBn8CHkwx/gB3nwa/Av17CmTVD44Qu9wIfkr6XeWJfPkHvygeYzlt7VeFuHTJ0667sVjsJlECgZIaVwIThCJgTFCmUU0ARFgstOsHEETgmWlHAOrEUhKY+gxmkIwJTEjr11KJFGQfSvwdpRRMEm+El6Jc4UvzQU65k/u+wZzxEJ+q5bSUQoa9aWfEiQWoUTnOCGH5o+cJHk/sE/+Adv3vZt3/bmMY95zLQzzBHWvDO+nhOOAkCKCTjgERx89sXzrOd3fEvhwtkEH8kHg3rCE54wGYXEPWvjk8LFrq/Ah//eWdvOk0REEMcX68NbQsOJ4WcN4aN35oGHvr4w4Z3f+Z1v3uZt3ubm/d7v/abEX4FjnfBiTrb6KHLsLumn+V0Sgg+KEbhFB9HFeUlI8C1gHg5Y8YYe6+GbpIme+buWgb8179BkHXPm2onPO6FFwMxJY4uGtRDeZR14SrwkkngoGSA/dhnd0V9yLCGt8R8BfBWEXZsKP/dA+CbJ9nktJwLogfdeHpGlIkXQkeTCFx/S0EK36R7e4J2+T3va06Yg729FP/00F17jqb/h+Lu//+IUSBQRClu+kd6Rs+f6piWoKc7RShZ0xLxwi95q+mfTxDreswFzwNHXEZODZy15ok8Cggaf/2Ib9Dh0+Om9NRQzvrADDyQrbFzSzD48Y9/W9Jy/govx4Z1i0z91hVOvkLUuv3TfffdNPgaNnqHBfOhRWEf+4YX19FPI2QHP19TTFfwwD177XZPo6UsWfJ2/+UR94Ky/NczppzHesW9fyeqKnp9kpLFh/k0f+ER/4Ah3OmKjReHq/R6biD2QuXiKt/TJenvsFpgXvXQbn28T0EKWEj7y3QJ4gucKyZa/sAY+0gN6zRc4JaNr8eNlow8SXzoqWee3/fSuBjJmT75RjnzYhjXgVIO+dEuOIc625quBjrI5eRWdqseYU4KvmNa3p3P4gg9OOvkT+No4aTX0WhOuNlFtKPT6a2INP6BAxFvrhac1kJP8QUFo3sgh4Hd92C1fibbWPCVYDw7ywTlekIsYyZ/xuSn6rB98A56FBza05Wc2vMxR0paGB+RgXjFerGdnNRhrDnovb4KnZ6PAD+Vb20q8rw2upnDhYCgywTBE1SNlkNQRLmEQNOWhfLVSrGWyfsZbT6Cxe2bXy+6bHQgGKLEXkASOcp01axjDgJ0IMX6VteS/DrqMkPGUdxU1Dk5QyQ5KDMU7eJqrZXDw1J/i2QHAP8mOk57HPvaxN+/4ju94857v+Z6TAUq4zcWAEjCNDw4BDok88D088BOOihMB19/4WRcupXHhCYciqDFA62U+tLgSYS50O0lhgMZI8NGb/sAYAUURVwcl78IHCQP6JFAcmaL3Pd7jPW7e6Z3e6eaJT3zilNzY/WWg+JUELnwogezs2OJV8NDP34pLhWY5Dj9ahYs+HKrdYM6KfgiMeIbGtWAdemFuSaxCHm85d/igGU3wm6NpCfTPOnAzF9lJBlzjk3wKpvQMHfREYB5dpwTrCOxo2QvwpoN22RVCdtjpVPRuCxhLN/kntNpEwec5/qY/3RK08csHz/3zMrpBVgpBts7/0FOBB28Vw3wRvvJLDz744PR/HuzskbP3di75EAkTndLPThm/hlZ6GH0NwJNvY3P0Rx/gJ7oUGmzRqQUbo1/o08iHXUnc0S7pLm3H3Jo1zZcChM47oeXLfcmF/2sAZ+uYnw8gK7wKH0ve0S++xYkQezUnXMg4eJOHRNwpiASiPN0G7E1CYb0a9NE3+PJB+MCPkQf7NieeSyL4VBtLChA81887zRp8ls0VCTy5shm+Br+e9KQnTcUR2vkV9Fm7llMAffyYNegKPSrp2gLGopWt0Se+GR5kvAfgRSfp820C+vYULvhANmyK7rbAGvqRDX9I92xOyifoY93YCT9vTnrM7ztRkRSTaRJ1v3umYKVndMoGwFzSDNAoVvIn7GhON4IzPPGntNkS0i95SE/f6C1dkp/5v2U+v2eTUmGk+b1sinX/e+R93ud9Jl/IDynQWk1/fo/NsB/rBN8WPmIUnOWN/APcWv3wmZ0m1+jRxkeyffFjjv+e87fkynfzzzZhInvyTKNPZEum6ONPbKDKsUp9iV7QEYWrQofPFW/ndBoeyVXoW3z7CODhUbgMAGFItO1EEKqdDI46iQEliiKWbQSiiBI7a0ngKZrAxKkw+nKNLWAcYxCkOClFl0BfzwcPziOFS4DiSFYYClxKoMje2eEr56OgFNquIprQgy7OjJPiBOz0Sd4FUrtDDBeO+isY4ZjELoD3kg3rZj0/ycMYPAy9EgPJjiS6LlzgxzjJldOIAzB2rnDBH/LnEEtaAVrhrk8A3hybxAVeEgd98EIRwfglTL52mY5JpJzw6SOJkvjgFYdTOzM7HZLKkiaALnSTM1wyBn8kqXiLpwHv4el0yakcHSePnvPsgTGZ05qSSk7bvORrfQ4/SdweyFoa+Sli4B7+0S3reb8VTlm40DMyNRd7x6M9uAH40QX8ZbvxF0tgnM0QJxPwcV1RksGGFOFkRkfpkg0AawhudFHSzg4lYZJCgcwz/PeevpubHMiDH12ilW8hsyQugB0K0DZd6Cz+8SHwZW8CK7+B7pxI0y0JL30o12utDSfz5uoDWw2eWvl7CZ7DjW+Q9PAx2fBRgOGVQE3W/CKa0MBejQvwYfjndAyU62TdNLZEVpI2fsm1EvLQ8AHP6QAfQhaSVbvd+OSdQsA4coUnP8yP4adTKjEBznAvff8cwEkyg27+DA3xoXsAb+Er0UIrHu0BPubRULiwa3ymR0vj+Vy64nOabCUxp9XI3zXYd3u3d5s2JZJ78NVl88w8dN0/ObT52QO6ICHO9UX62wLPE9P5j8QdrQR/0w1JsM/s0Pu5Oa3NJiTt/AJ99yzNPGWjY2hT8NPnJNfBo2zGo0k+wWbmikhgbn6eXZb5SgvQLd8zt40KY1vANm18kQk/N9cP4I+47kRdEQuPyLeUv4Z+8f9d3/Vdp80P8ij7pZW64MRZ4dLLFTyHM/+OttL/rYWjcBkEDsIOHwMQADDQMwpBYTAxbQ0Yo5nDXBJngV3AthPGKUkUGEO567VFWMbBk8MTPCXvApOrUS960YsmR85heE+x9Pc7xczRMYcj8aPEeCCxYdQlPsaYlyPhMCUuknTKLXALkNa2FqXNh9QkGJyrZNrcgqr3gi5jkzwZbx7OPvzX33O/Bw+445U581kRzkpgZrDGKK6S0OhrvMTM7h45ZMfUXN75NjLv8cM8rs8JPIwbffppwQHtHJ/ExVwSC3hwCvAnWwmgYG9+xZCdDcmM4gmf4JITJ0kHY1e4kIF5JFj4iI5c0auLELpFl+ws4R05M3i7uoKNuVLQoNdckg64ckQf/MEfPJ0GwA8d5ifzkta1oD9+W4eeRLZokvRxpHhJd9Ac/m8Ba9F3+NJ3/Ld7hK5RvEuA097CxRx4LNi5LsUG4uy34oZPEm1BTHJPx9nPEv/CIzKwcypYkrWrT2REf+ALv7oZq5EpHVI0KMaNoyOee+93a9h48F5hU/sNAFdrkZUCAC7WCQj67BddWVfxgodOH9gP22EnnluPzjs5KjcjeoBemys2JODck4d35pQU0V1JjjXZpIIArTY8BHNBWpKFbrizZ3Ki65ERvvGt/CDbQHtLfsbTGf6FHeOZZ2nGGY9uhQc64JmmDzwiR+PRrR/fQC/xmr9j+3QDbsb1wHu2Rg8kcXSx3CzZCnBDL18lNuIdvLfMa9yjoXBxNYlsluRC3nbGxWQxh1w1NlY3NxzESTHN9Uq+Uv6RMXWjWwpgG25ssCcTz+HJL7EFfVu6jZ5sXJYbBy3w3DzyEr5LfKpBH7pOb9i0vuySfcCnnNta5rPJQjZyF4UOWs1Rg77sxdr0k07xHS3arIM2fgJP9emB8ejnz2w+kGMN5pQ/8BnyIXOW9JTgufcKDHaED+SHtsi/lCsc4erEyaYBnhhT6ovG32UsPPk6fm6NTpKJtVq09UBucBQuA8DB24Ek0JJhfq9bCzwvA4f5CJDzcuLASCSslGFtoO0B5S/XkwQLrgoDzptzEPwEOMmtRNnfnJfkgIJIkH2+Q9LHQCUFdsclFQJvDMY6msSXExO4jHEdjTFTbgUYJ40PcEO/BFB/78wvkFufU5b866e/vowUb+DNmNAhMZC0WxeNaIUP/tlhhy9cJcZ2Znzzj89C+D1FgyTDiYQix/8oUaT42zjOUKLl+ghjsWtlLKeuAJNo44PkBY5w0IwTLOxaKRoUSoodvFPMxHHhmeCOxxIF+AoECkU/s+usH1743Vr4iW6JHLmhCd84OzzQHx7kY8eEkeOdYgXvBCdX0bLrAV9yMh/ZKc4Ufu73S9Tpi2sBZEWP0IqGrDWn8zXoV/YNTeQPP0kS/PAYPnCLXLPO2rVKMAca6PmW8QF0by1crIsOCQKZ0Qd8H+FfDcYZj4c2EuitIEIPlsA4dqMYJ3O2R6aKKid+cF0LxqEnVw/JtQR4wpE+syu/h2Y/9TcH3PFG4oRPsRPv2ZJkOM+Mkwzwm+xRAGcT1gfGsHE+TB+2jqas24LIFy97hYu56RRbMLekJQWVMfhPf13RkYjlNNQ4Dd/ZXnbMQ4vEgv7zXeRgDf1LPPRnh/xgjaPf4cBH8B/8RPjVA+M0RQE+S8Twgr/il8mVrq7RCTjxO3yJZBG+e3Qc4AO/Sb58A5+Mrpo3S2DMnVy4oJdu0RH22pOHNciQLtMX+tUDeNBZ+mrzQvym0+aZa+RCV+gyXSHnHtAhG5U2B0u9NJexnpOPwmIt0DfFCFuq9Qx/6DS/SH/wg68QZ/xe9jVW4a1YQD/a6ZlknO+owXu46svnoJ+dWK+Wi78VbXwUfMlxDfAv4nd8RyC8F8/Jlj/pgfX5E3kCGo1PC+RvfbMZo0DGOzK23hzADV02mPkdvO1B+tJNuJd4LMFRuAwCYbQKlzWgP8FTGkZJ4Th2ySAjYgTmZ8zakgPoASXihCTvlJUjcvwviAr8BM/QVMaMlHL627oCPSfEWCiHO+sUDH7eo0GDK7wZIuO2y8gw7Xq6H+34mAFT0IwrnQrny9FYw9xw4QTMpWCh0Nm9BWgyPuujz1Gy5Nu3+XAuAjlD1xibQsA65Rg8kWgoRJwoODa1jrkkTRIH4wUFO49PecpTpmIF/+CKP2hCv/ngZwdDY9wavujr7qtrX4rTyLbmA1mgm/PBB8WQ5JqeSOYlXfhkTWM0vAhN5EZ+dpwle4KC3RF4ogkenFD6wwEunAW9kKCiTSGD55y7fmjjiMkTLllLcYTXCm1FG3ll1zc0jUBJT3CzNn2Q9Cic2AkesJvo4ehanDFdRfMWPAPw21q44B86JOZ2r9C5x87RYbwi1/1ixQfbwZ+ledFBV20s0DG4kYFxdF/iXAffHlizLFxqHkfOOaks9dma7Ii9KuAl2xIz9ggnDb8UWPQitBmr0EdDxvNn9DUQX8hvv+xlL5sS6hZ+gci3V7iY0zv4sS80GYdf3hnDJmyAxCfwK2jwXtOXz6RH7NzfaCALPloC56obn2otYwLowx87wi05e2ZOCQQcR+yFz1CowM2axuI5/J1eZae6BxnH16JPsYofJQ2jAH90kaXdYL7VqTxZjurpnVy4oJdfpKPG9XjqHTna3OPv8WlODzwXJ8jZBpK5fQ5KIWMeTZ+6ec4+5Qg2BxOv50Bf8VXMUcSYA5jHWBtmbhKwp7VgTnpuc6De8BVf+cXyREJstOlpfWODg9/5EDzwu8b22BF+px/AS7cFbARHtz2DP99BTgHPrcWnygV6cqgBDjazyQJ/jNM8Z/9yNTmeOXuQGyByMmNLedbgPbnLachBfiEPWPIj5uQr2DyceqAvHtkkceqyhH8JR+EyCIS4pnDxjmAImnAkFgzLURojcJrAyDGeQpu3nG9UGAzVHBRbYBEgJZgCPcXgCAQySbx+lCTGJGnPt0tYN8GBokvyjGe83qOJUnvPWCXZTilcbRBIGZe1Jb8qe4GldCLAGubSz84ZRwMnYySWgry/OVwnGYKUv61dAjzgLSgKtpIdhuaUQzHoqgnHgvdkoL9mfomjn5xtdmLRJFkwH8fmbw7cSYm54QdfBhl+m5cjMo+rNQyWbBUeHCXaBFj41TKNfii2jMFj80la7AhJTqzhKJ4MFAjwr0Ef63PacMEvyRs+CA6SOviTV/hgbTTQCXNn599ckZefElj8yW6OcfrgF5oULuRuPQ7fOua0jn5r9Vi/ulnfPNYmB7LN1Sp6x3Y4SXwLTcbNAZ07VeGiMF1buIQWuoBfZJ3dxlqnR8BYtONNCl/8SnCZo9E4foDtKHYEZPKM3L0XEEcLF/ZCDwVS87fW9wwf2BH/gpfwlTBJJG2s0CvP6C/ayJYOSPL4tOhVbFk/iZC/2Tpe1NcP9Ocb2RJ7oKspxtGrrzXxE14SQ/oWm7Fm/J9+xsJFQsTHGJd19NHwQhGiv/EKVj7fnObS10+2Qwb6oVNxYOMBbuyPnjn5pOueGcevGscntQA9eKqItBtMNsatAX6G3xVHjNHgZi088U4sM2cPwisygasEj2zxopTNKAQfOkqW4rE1wpsl0O9OLVzwlHwk1n72+OgdO2QfEk+65lmLR+alw2KR2ON3fROD2Vn4O9fop75825LfMBd9kODHrvw0VtHMHy3NUQJcbWiZj2+GvzmtI+cqN0ABfisGxHrP05ceZaMvdHmO12XRbj12zOc6PdYnIP6xWWNCAx/CdqxpPePXgr7oEWflTP6GFxrkdXyV9T2bA2PEeL45vqBsJZgLnXIk/PAe/mhCK/vtgffWotf8WY9Wc6OpvLa/Bo7CZRAIZU3hwhAwl6AprISekXAKkjuOV59TgHkYC4OlmBxCPofB4cw5AEoCR2ME9FY/CmwnnjJbhwEJhOiwjgBgp19CaK7Q5Geq9FoZveMYjLVbihf6cHyeSUI80yThEmv9a/wYoAKBPMhCC46ctV0meAmaAjh8JBzkIGjpKwE2P7miUbHhPYP1XnAUJDjwyF7yYR64k63gw2G7YmaXQr/gY04GTP7mK8E7upDrcehDs8IFXfhpDsHHHAqTVkKYZIeuZV38hKNEkIHTV3hK2rObpg/6PKc/NXhfFy4tgLd+rnCYi14IFBK3evdrBGo6/c2BSrjxnZysZSOAbEv9awE8T1W4CED0Zg1Yl17pL5Ellz3rA+PpiuSZjfmZYO1db3796JOihc7gadnf7/TChkRtcz2QOLA1hVmSgRbQYf0EN2PYALmQpb+Di2CWwsVmAXztFuY9nVXIkgWZWC8JCf9U24rf8UxyxB7oKP2UgJAPe5YEsHcnHU4i/Y5Xkh999MUbuJJn8CkbPOgiX+aqG/wBvqOBL4JbgO3wVQom9PPdCpPMxwfjF5wlQ+Zha/w72bWA3PRlu/q2/MYc8L8pXEowHp/xgs6t2RUPDeQiqZR8uZbr71MA+bEpvgDea3wNedyJhUv4KM7TUzbm2RyQjdM6ayz1pS90WXLKrmK75mCnYl78q3lajW0p4sljaT1yys68Ytb6dNkGaKn7o42NmtPGozn9lN/IWeBXAjtyK0O8RVfiqA0GdGdOuOK7eEZn9AtvzV3boHW8o5N5l83H8CV8XAP6WU+uwg8kwScntyvkeL25rIUP8ge+Nv5oDsiZXyXL+H9zkBe7Z2dL6+mLN2QZXs4BHvHF/Ak+r+ELHI/CZQAIoS5c/CRghkBgFIwBCMaChuTAlRtBTx+Ks5XZlIJwBSLK6LoZg2AkfpfUcQQxLv3nwDuBMoVLjZff0ePaFyXmvAQ0uwyCl103RuNvdFLArOenHXyBqjzGZHCUzlx2n62vbxxZChe4eI4Gu40CMJr1A8Gd44khaZmLs7aLKfgyVvhyOpIqCRne6S/hxT/OBo5wxUsB0VwpXMwDJ++tyWEIxHZGyRYOnmVHJOB3RZDdi+y0Amt7zsnb/SVT68GDvqRw8cwYSY0CBy9Kx+MdB8bhmz9gnDnpoYSMnCRRkga8l0BJ4MmALpvfmBLgvqZwQQs8rEfmxuCrACsJM791o//6GrMV0E/X7ABLbK0lkOMPx67Y9c5apSyse8nCBT/hyR/gt+ICH2s+jwIdoRuKYfbodCG216MrughviTfdRkvJI2COkcJFH7ZLZ/HXSWvvA5fkJ6l2hZJeKFDwhg6W69l84GM8zz1ugR/9xrM3VzgFR+PQ76f+Aqw+NQ7+pqd01HVHJ7NkQ2/oBZ2Fi8BtDuvyI97xC567uoiHNlNSEJQNTyV7xsDFswB7UKSwKT4ouow2/cmHzfK3GYcmtiNxci02GwS9nW00hrf4ZEMlulfi0wK+rFW4AOPhbG08s4EQv7Y0L3vkH+kIX1zLewsYT/fICU/5YvzsAfzvxMIFrXRazKB76OjxXB8+UT6g7xyQqfXptKS9nNdP84iBkkS2Fx2qm+dkzN+bx989sA4958fIkI5KYJfk1wN0iJHigQ0iJ0jsKnlGCXwBP8DP6CvuKnr0hVvo9FMTQ9GmONKHXfFP9bz+5qf4CrFdLMrmGv4B844AXOFIZ/kWv7NtOPM3vfnwBG9tdMCrxrcGOLNRsiznhTtfyM/7vbcmXRX3+Ey86oG+eOQ6uOJ5DRyFyyBQAoULx054GsXhHDhypxOcogJCACA0SreVucYZT7iMyfqKDEHVB74ZBOEJgt4tKWUJ5oafwkWyZzx6rOMnZ8Kp+idMvqIX3RQrAcd4TkYCz1mUyZNmPrsZjF2A9w6v4C/Rdq1JP4DGsnAxf0AyzEmoyLOGeRQN5haQs6bmvd0FgT7FjvkEWAmn6wXo1o9xcVb6wRcvOWnFgDU4KWvACU/gzNEKlIwtsmVIEqA6YbOGuTlSPIqM/ORUzW1swBp14QKsASd39DnbBBf84Mw4JY4m4J016CQ+JYkPH+irMa635aQInpG/fn5XqC0VLi2AN9zgjCd4ZhebHBVQ3llDCw+3gHHWopNowjsBw+ezBEHBJjThAbvF263rAXpB/r3CBU34r59TL/7A33sAzvilqGb/Ehj6WOpbCzJOAgKXJI3RrRr0Z+dLhYt++Mo+8VVS7mSSvBUEsdUW4KFrjE41JLN0r5aJhIKusD0bDpI2/eDmlFdCQjfx1jrGa34XfF3jIvvM6zl8BX26YSxczYuP7M389EQBw/f5G65oxHf6RMd8vovf09ca0WFr8GMCNrur6Vcw8gWawIsWc5CHq61sOycuNUg6FOq+ARJvStpKsCbfpA/ekru77XyrOci0NS7QK1wC5sAz/osd4Cne1vTWYBweKs7wwjjPlsYtAbroCp3NjvncnPC80woXtKCJXims/W18S46e4al4mQ3FHuAHvcr1zcwdgFtOecSB6HrdgJ/0jW2zpyXfJBaJUfCUz/g7NrWlGUv3bQ4oivhJtOFd+qBX8wyONu8U4OylvA1T02ZuPLXJQGbsFL/KOdPwTOy06Yt3ciybEntogy+/oBiFM9qsE3m0wHMxWJHDz831A+bh59gzv0SHSojt4pU8aUm2bFJOhKf1XDXgGX+pYFrjD8jtKFwGgKJIQCW/gp4kyDdv2IWXBBNWjGSNAHpgPAFRItW6IKEw8tOOKFyyFsFbq3Y6PdAvhQul4XAETo5fgunDa5yVQMYRCcjlOn4XzAUgxizIl04N/vC0s2mHDr6MSBC1M2Z8cDVurnDxztyckeCEXn3sfNrx4AyyJrz051TgBD/reGZ9zgxtHI7GUCTvaJWIKM7gp+AyXgImsecoOFUFohML81g3fBDk7RjQAfiFLj/97ahaYNCPzBizJNKc5giYyxytXSLz0AUO3jzokaz73zfoLHlmXeMVZZwtWqOP4QV5e+9YWwFMtzg4O7XkRh7wlACaaxSsZU00wd2a9IBjxFd6Q56SXXYDry2AVmOtZR1NgJW8skunXYo09spuXSOKfLYA+fUKF3PjHT2xfnbWS1luAfzDL0V/ivhS5i2wpnH0j36SLfzxq8cDeuVEpCcTawvO+IundATv4cVv9JIm/CB/NmeeFi4SCZscbEFfvtBmhGSAHuGxII4249PQDBdJbApXz+iEJIY+JwnzDi7oTPPMBoc+eAeMz3v9jVWMm4uN+Dt9+CR2xSZrurKhor9TYLzjS/RFp0TWxkmrcDE/ftI98/i7nt/f+iRZ4jfh65krapIdPgSN9djAmsLFWHLjAxUAkgg+Y07eJRhHlmhIQgWfPYAX6ORP2Zy5JbAtGvW70woXNitJY8Nk2gP02XAjf3ZMJ3uAZ2I0P9WyxeidzQO6bn595pr3fC/a2KFnc0DuCjExjM24HSAX2dNsLPAXH/RBHzTlDHxe+Z69aX4Xa/nppz/96VPOgM45fuED3ou/99xzz3TyYby52Gta5mbfTkj9I0s5m77yuK3NeLRY3xcfsdNSFi2Ab06glnQM7XTRJqbfW3PiDXkpXsTsHtAl+R5852wxYF5FU3JR+t6Do3AZBMIXbH0jlQAjQNh5IkRKBLYw0hjCMz9HIrGXPEuY7azalaC83lFGSqG/ca3WAwbIYZhHEBPMJACUhkOXsAvwkl2BiRJ7T2GNgYP3DFPhZoeb47PzKSB7L7HghPBKIYBXki0FAprgXwZetMwVLgC+5jOX8ZIG39jjd4k3BbZ7R6ElDnZTJamcEicq4ZOow9kcEi6BnAEyLg7eXIoJTtRYBgwXfV15k9xz3ByyebxDs0IvSYF+aKcPSfzhDQ+FC4eKD04EOCJ0l0CHPG8VLnhFFvBQfHFIuZoDV/oheSADfEjirq/54IxP5MNJ0C/JIZ0jV+PIVRJKXniDVs6fnugDv1Jua0F/4zgk60jU8IVu20VEi7/xjt7BKfq9BawVusjJ/JIxu9VksXVe0CpcQh97wUNBUMPn+IWtYF50kKc1yZF+tZKMEqyLl5JwSQF5p9jugXkVAw888MCUzKA36/hJLugSRCXufpo3Nu29cXwG22mBvuQgeZyjgS3xOb4kgG7ryz9IdOGENn+3Chf4CIDoZofsgg7wVXzBXGA2lq/JDjMdaoF+5lBY0WH9Jfr4wm6cphpbr+GkR8JjDe8lOmSqyEMje+VLJV8tQLe12GgL4MWGsikSeyUTdmcsnkmu4F/6WbjCC3/4SMnvko4BvKaTdAFt5FL7tRq8tz7d5J/Yfvzd0npzYBz+2NkWH/iunE6X8/obD+6kwoUOkwk9RuMcmJM8xF2yLuXbAu/1E/PJnlzm+E8vsokVvShbAK/FH/Yv/vXmRAtdV5ApjIwR08hnazOHjdbHPe5x0zeimtecmTe/p/EJvskwMXdOdz3nh+krO7WOgqTVrGNduYT/1O80VfwJDlsbXyFmy334Qbpc8z9ADvyvvIRfmtMFY/U1H1/GZvw9B3QRjXKJnn6Z13u6yNeT9dy8nqOFL5fr8l1zOgOOwmUQOBi7twJiDHIr4zJWMy8hEDDl4dA4E0ZNiLXAy7GtNgfmocQChSRaguragytIEuY62PrdM/1d9WF4xtjRUrQIvJRI8mIXXRJKqf2UzEvqJY6SCEmjrxaW7OMdXDRr+JtBMG79KXEJ/pYQKhjhCh8Fo6JFEMcvfIMbB29ddDIECQh8GT5anaAwfsFNkLN+DEfQNw9cSufMqDksxsIpSubhal6GqViSGFjLLgt8JK7eww++CicJjd0SDtWc5i5B8CBzThRfSx3TyEKw9xXOcBFM8EvwwW9GD39rK77wWiIhKQqf4M3p2pG2+9vSLQ5aMvLMZz5zWgdfJV0SHu+26n7GpJmHDOgPfQ/+6Od0e0F6FPBWMLHOKN4lwKkuXPCQbBQXimZyPgXu8KQH5Mnn0LPaNlpgnESa3Og9vQ7Pe0AeAhc+SSbYO9mQORq9p094yIZssMzpDz2VlCmy6nXNw4/aLJnDSXJA9xT8bI/+KYisZ8xc4aLpI4ETtPGAvitcbG7U+JZgLLm5yy2xNUcP0AEndu/0wE948wWtdVK4lAGfH3B1SqGG53ykgq0FcMM3PqcGuNNB+seHlP4rPGED/Dxc0UdOee93ek3mfCgfboOhx68APuRaLRr5zzVgHL9O39g8n71mvR4YLwElb4k2H4hvnqMTD+jNnVS40Cs2t+RT0EifyJde9kBfsQMOfO2SX6Ef7NWthCV8vafDcoSW/QfYozxEfFzj15bAOtamTzYQ+A2bknPrAzooNos7PT7QG3GUfqfAMW9r7swpFxDT4gfLMaONvDRz8AHsFE55X4K/6YrTeTE767bAczyTQ8z56xLiQ9DFz/X6Wlf+KlbKb5dkHH3w0QVj5+AoXAaBgMsP58cZjgCBUCrKbydbsSLYMAgJvgAgKeco9OsJsAfwErwEXwHB3By2hMMOn4BvByeBg/PQF00Bv8ODkkqcGAJHx+F5TonhR9nN6b15nWJ4ZlfNnBI57wRGO4qSI0Exu/9+cjCCt2TI3xITzXtJmOQDHvoIzJnfT/yyu+M0QmJuHLwYivUlxwzC1QzFhF1cxsG5W0sTbPMtZpx/1vZTP1eNFE1kr3iRmKHB/NbBa305FDxiVOYMDzTFAxkI0pwq58ZYjWOMjlQVXcZ752/v9PE7vPAPnZIL+kMnzc2JuHaFBgkbR4Q30TfvjZdY4QVHDVf4lTpG5uRKnhIpYzh08wpE+C8ZtNsML2snSRoFa+EdGuBKb/BN0ZSrOAI2Or2Po94C1sH7UxUu9BGYFw+TvNJDf5d2NArwQys7w2/6gjfWWsIdfnQVLk7i6KhnvXHewZmPoJcKMOMUSvjF9hXJ7NDvkgL6CZ8W0Ae6T88Ee7rNX6TxfXBjq3N8EvT/8B/+w5OtSoLoWujX2IXxrcJFo5ds1DwKN38v+VLj4Er32EYSac/nwHu6iVeu16FrbkyrcEET2uiUzxK+y7u8y3Qq2xrPTsQLuoaHZcMPNks28AkOdTMHX4tGsnSdjozcR4cD30zOkiO+wntjloCO8cMKAvPymSWdLYAP+vktSbECjt7ssXPj0E7P6AY/GX7Bx9x3SuECV7rLjvC2p7/m4+P5C755zjYD7N0tA0X6Gn+Fd+Kk2MIf9wCedJqu2txs4eJZvqzBvFvlXQIbtxlHt/kv9kCP8XeOPuuijY+xQQSvFi7m855/1weNrX6eswV0yRX5Sfjwn0sy6YG10vDWxiT9bumOZ+I/X8AO5vwRQLsY7+SJ7c3RH0CfOeUA4sSS3pgP7frzpz0wN33nB+Q7c/7jKFwGgUJSRkoZZegxzjvCIDxOyHhKZw6f0XACILhKSghDn56SzYH+xsUxa5SXQkpQBVQFC0VngAJbcNdPBS1hr5MjjsCOlc/xeCeIOVnhRNNPs57K3lrl3PrAwdzeC65+d2Lgnz/6x5EPPvjgdJrjFONDPuRDpmtU/hbEnY7cf//9008fyBUMFXbwKY0sOChoJAcccQBPBGPBFC2CskLR3VPr+0/6vp3I/Jr14OJ41wmPKzOewU0iDwf88rtgGAeG//glIJJpHEr4IJgImIoAvESD0xfrm99nbayPH89//vOn93QEH/SBxzOe8YxpJ1QhJkGSnJZBx0/8V1xZD0+CG91TEJCRRAEd+jm18k4/AFeBSYEUfYhuCYwcimAkQJKThINzRq95rBmeLIE+dbOWOTg5ONo5xQtX4hTAcICTBqfQvgT64r/iyzpbAY2CI32CA35L+uhDTvD2QOjnwBXaAhCa4d+DjGPjnLqNCLoRWczh5T3ZuUomyVeg0mPzeWdtCQZf5Ys68C8J/RwYh08CGzuS7CiI0ugOXZdUWqukDZ7Go8GJH9tFR2hIaxUuxpGJtdkmmvSJHfQgfHCa4XrHvffeOyV2gvQaPbMGHvENsQPjynVtmihc4FhC+AVf9+fpfI2zPtE1p6UlPzVFhs//4Bc64Bu+pAXMJVlUEPMt/IhiBq3p53c+23UXiQf8jOuB9+SpAOJT+Zk1vPde/HNqzM7ZvfVq/o2C8eKdpEzSKfaiy6bDnVC4wJXO8Ns9PnqO9+zNJiT96umrd+IyXeLj1/LYvIpcMQg+vTXMKU7gO9mWfb2jy/IovqW2hy1gTv5XbBeP6CEaJe98aeljWoDXTnfFvhIf8xorTpIFf5jnWg3GWs91TT4Kn/hiPFtTIPYg67ENeYw1+LkS9EE/n7tUiMDFXDaV+R5/r8FPH7zmh8hxyS+QP97hw5JNe88uxFi4tfoehcsgYORI4cIhSfLtxhGE4kEyIjgK/pSaoDTC3yoEuDA8wYezEJAEMgmCQOqdtSixtUrltKZnDJyTSdLiud0FwRANaI8RKl4YpX6ad3ZrVcrWCjAqu0V2Phgto5cM2FH0u/nwgfOEp8/A4BOH47158dDOs0SawuKTXSKGaa3gGtwYCGMKL+Gp2rcm2swh+XKyASe0w0GQNS/5mMffcMAz4xRF5skzpz/4BT9raX4XiBQucAfwE4glugKQ59bjyCRd5oY7PDlNpymcr8IDX9DiPVrhjZd+N48gZb2sBQf9FSTmNs76eKbolKBITOGv0UunGoop/QCHRE/Rm/HmzTwZ6x1e2Kmiaxq9VhziHd5uBetZK7ZhPbyw+89h4aUihuOCr/dLYJ5TFi5sy2klutmO5+HhHqCfgqSkngzQtjQveoyjdwoodks+ni/RSjfxhN6zO3wq1/M7HOhcTuokg2Bu7l/7tbdMukWP2COfRB/T6LakWMLNDgTa+D9r0VUydiLMpvKubHXhog9/wweaX2Lqg6GSEfqIrjlAoz7sxk4mG7TBwjYk0+jtjfcODU5k6addXsk+OlJ0ATuv/E5LX/VBq8SQX+Ej0JR35vKcvvGV5i8b/4tnkvJceTGubAHzSmrorSQB31pyp0P4Yk2nFjYtlsA65mIbNoCy2VCuX4N3cGJDfK9xdB8PeuPWgHkVLPRQ3HYaqZC59sIlPBRzJKn6z/FCX/Jkw+zZ33N9PaeTTv+zwTbXtwa8pAPsg/3Sjx6kONG31HnzKODZm9/3AvzNbzNPDEoy7RlbJOuleKQvXWe/eBnAS8/JijxKv9rimw0BcZkNhjb6T+cSk/cC24wMxaLYLXzYkBxLnlrmRy0ge7LJZ5d6fUvIOmSYYq43Dg/FAPiK1z2Zm0e+yXfzu62+R+EyCIQ1V7hgsPeMRhATgCTCki3BmfG4BkVBJKs94S2BsZSOABmIe+gSc4FIciFYSPQolH76LwmYQ2JwcDYvGuzICJKlsTIGwVwQFljwAd1l4aKfdSVgeEVZKS+6ORK7yH7Xz3hOg7IKvJIn8wXfOA5OGT2A0VpLwoXf+sIxhQscwqM4SM7IWvoLFngWJxLZcUwSs/JECehX04dH+MX5JzmxXlm4eKa/Z3gGfzgYS06CaNaBA57gGTlKzMoAZG46ZDc2MiUzSUUKIn3RVxYu5qBz+AKP4B/cyMM7DhdenL9TFMG+1PG6eRe+mV9xiW56wUGxE3gpxtCtn/5bwVh0W4uc8MJainT6xAHTebKIvpaAD6cqXNiIr/KVONMjOO2ZE9ADSQd62AEardUDaxqH9/SLvfrdM/LpAV4qcOgh/0T+sYcW4B85sgGypSu1PK2J/3RYAs32+IjoYdmsLzDFP/KN+tFfxbk1/G4NdNaNfuKTXWZ0WCs+0KYCGQlsdJ6esJUaX/PgsSJLUUCn+CqBnw4rgtiGd2XSUgKaJZbotRZ/DA98dVWGPcHRc/riNGKOz+bmAxR8ii98wk+bAWwar/Ad78i4bOjAL3xTdLE9f8eGA+b0HG7mJHfPWhD+JHGjY/RL/55+GQcnNEu88YVPRffSOLTZscYrehBfvAfIHd/IBW+cAvIDtwlonStc8Ai+fIFCt35fAz+evsktSpmXQHZig9jFnudk3wJzkqHchl2wwbl1gL5syCYMOQLrsRcna36eAsxpk8NtCzoXO9X4VEWDODRnd0BfesbGFD9+Jwf6z6boi99rfxTwu/nFA74juQowF/yckPAveyG4shNyiE3mmfiN7/4ucawBb5y2kAPaen1rsB4a2ZONk9rPlOAdn6Pg5Qvh1YPkQXKxko8BtnEULgMQJZYAUeA0gmHEhOKdZJ2QJFQCXpL0U4D1KBqnzrmnKGIwAgzjiRKVbQkoomLFqYdgK/AyYIHTfAFzURxJkuSAEuJLmdjDUTLAYZWBDh8k3vjCGQcv7wRQvLOzWBqcuerCBeA352vH33h0lycu5nAMDQfjIyfvBIuycLGW3xk9fkpySprRp4DLVbgAftl5l1iEPrJI4WJN69hZNqd19JPMSa7wCG+Ad8bYaeT46mTY3IKTQiP6ZC5X5ziffFAafilczIcuu6USK3PCyVjN72SJLo7ZcbFkMMlv+q1pAetxmoKoOSV9nBudwq+y716gD2RsbvZGf/AVD1OgBdDj/d7Chd6yvZe//OWTw2YfpwByZFMSSgVY+NrDFU2K34zLRkIPzGccHtlps2NHZ5bGAfrCPtl5kqSAec2jSKBDeFO+b4E1yUkyz075EAkGv6YYKXlQN0FX8NIkgHRcso539D79JApsWkJS+hzA9gRIpyz8CFzgTJ/MR7b5mw2zD3MHzGWDgc7hf+xS8zs7sonEbvnTBx98cOJdLxHFk5w+s1e+hJyszbZK/GvwjozYBN+Yq27lGDTx63iMN735Aubkn/k2cuIn6RBce+O9Z/MpqtckU4Bc+Gy+nO8g66W11gDZ2fl1jY2/uE1Ay1zhgkeKA36Z/NA+B+ZhKwrEyDOtBvOYj83QkS081Z8uwE3cW5KnNeghG/Q7HwFX/ifxdy+wNfYtr0h8C5A5v2IzYGmDyVh6J2a5cmks/bU5KOcytm4BOss3KhrE9Zo2c/Mpiqty3FYwB/zko+YVC9FHr/lT+Myt4zlZiIVimXm24GQcn2tN6y/NwW+4KUMHlvryU3yU+fUt+x+FyyBEUSSBHICgJchwrgTCQAmHkmOu/j0FWgOcmrXMacdIAs8IJQ52Fzh1iqBfGUzqtgTwpPiKL/eerZOrGuYswTqURoHGAVrfjregjG588RWDdg3j2MzBwShcBOPSWXs3WrjoIzmHA57DgZJzDHBwwuJrAyVQkYF19KsLF2A+yaMkpFW4lIEkEMNFNxzhJ7hzHNZxeuRzKvgYHNCDPxx5TlwC8KFPnHDtCPCuVbgwcA5bYmQHG+0KF7w3B/mQBQesfzknXMhB0ePzPJpEDo2RwdoWsIax5qVPdtrQT+auqin0JMt4bB083ArlWnhKBujlkF0Rok8SFUUuvkio9xYu8FWwSwz9XurJKMAD7nB0PZDtkRld6YFx5E4nBVVJLV6TZ4827+kOP8JfCbDmWRpXAn7zO4pldocH8FUwkK0gxifpt4Y3+sDJXD5jIyDxb9mhN7c+aeb1TvLDVm0M0Xu6pD9aynX15wvIi+82n2f4LKG2C4oeOBgX+UoGyAZfPOPv6C//pUC0DhunT0556FhsXPPe3+awlqSSbiqA7ADnBKjku7/JA/981k9BBe8kUSVdPUAfP8pf8TN0P3QrpNAxcpIBR+PpGL/iGod54Op5D6yROOmkI5+hWgK8MI7fcHWMfpgLD0qejYCx1qZf5HabgIZW4eI5HPFYPKp1pATv6KJC1IkjHemBea2nsZetfGRn5KEYtX5PB6zBNugcfyNe0kk2Af+9QCf4Hv6ITdUQmcvZxPylNfHQBqg8Qsxkg3Tec7TULUCG/IYYbb1abn7HK8k4X1LmHnsAjvTZpoCWTdKer8AzOKAR75ZseA6swUb5fIXokv7hkVzFung0B3iFP/IjuZXcsJQbf3YULgOA8YKf3TA7iz6cLAhxxgQYhd0KFIESUQBzMW5GI6mRhAu0ghrBwUV/glvTSvA3PCmHdTgx80q67Qr6502+llNyY30BS5CyJqWHo9/tRAqOxlJcyYQkQsKIT0nCjNEfj6wh+JvT+mg1t6STAeY6nf7ee8eBU1QGqb85Nbhz2gKAQMx553dzCXqSC7TCQX9GwNDyznNzWlMhxLEI+kmENHRwet5xwsHBWP3IRaIDPwHR3Hgiec6uvP6hB34KLsVe1jEnuSpI6ZZER9/g4J118I8DjCy8w1fBwLpwwH86iQ4fGFZARTcjC+viBSftM0s+jOxDyT40zQFbH3/gADfjevoWvSqb/tb00/rwFPDIyGenONkUvt6jBZ/03wrGmw+PFAQSP/9c0+6tJNXOePDdAniBzzYr9syDRjKgAwpLfCCXJdrx0zh6gi72gnc9XLzDFzpOT/gv8rXWFhrgSb/4JDqmMOUr6CbeW2sE4EG/+ABfxOHLKcznFIQ+w9u87FBwlsgqvjyjM2jo0WF+uu7DpBIn65CfVhcc5JvCxe8B+LEjvpgOmY/PUzgK/uSSOeaaOdiT5IZdKpjI0jO08Bk2PejpC17wgmkDSWJi7CgYw2fYxHBKQt52yCWQufY2AqEBT+Dks1R8nTXQ1QPj0IhPZCd58Tee9YDc6Bof6oQTv/Er8toC6OYf6eptAvxbhQv+0j2+Ho96dOK7YsAJATks+Q76S1/1XeJ9D6xDBvAXy+HcwxPPJZl0xuYGOfLP0X3+q2yerWnGs1+ffxXHkoPVjW05/RTbxF7jWv3S9JXX2ejCL/PyPcaVrcSDbrv+Jlep+5jTTzTn9EeelHd5P9qMQz9dluCzczZOn+bk4Tla5EDyJTrUk90akCPaOOUTezpI5+Cczdwejvry+06E+PrSF9Pfo3AZAArBqbjfzvgoAMFrGE1oWxlpnJ1BCYwdaYrA0O0qSk4IzjochrX2CAwdTnAYkQLDzonrCAoRVzUEJDhI5K0PD8m0ZMmxNAWFA4MWBCiW3WKJAEWzu+EdfCmfAG0n386knSQGJvkzPz4yPM9805f3jEqCwSAYl+ST05F8SmYkv5wAfmc3Fb6CPieumIGLteFATpRc8eEo1xoSI7uGcLBWcPPNXQoOyQXcJGgSGSc+gq5rXGixC8oh4oPkDf/wE+5O3gQeOwvW5rjxTJCBv+Qx/6ne3wKzOdHnG+B825kdJHTAQQKOr5Idu9Lkw1Ez/uBAR/AQz/DfvJyZcZwF/eEs6Re6FZx2k/ADP/GPLMkK/vijUKMTnCF9SYBqtTko+8CT/mp4kqCLVnjggV0WyWpvziWgF9ZK4+jwwze0kfOeufFgb+ECJ7KQAAu6eIsfS/7DO4k7/SE7fDLX0jj2Tv/ZuWTD38btATKkG3iqEMITehZ8RsF8TpAUs4IyPeRr2RVdRLNi31eRSw7YtXWWaA+YX+LmVJGd5/Mi8M14P8m3Vbh4Zw5+zQYNWxQH2GApu6WmH/6zaz6Af2UDaCQfPJU48V14wc6NGwVj0IYGPkDRptDkI0uaRyA0GK/IIg8+Dl8964FxZEbX+RW+ThK5dhye4JFEP59X3AJkdQ2FCxBT6Rl9CNB78Ta2NCcnz9kJGTjN6tlB+opd/Ly55/quAWOtRw50mEzZxhykL1/n2zPFJXojRvLHW5vxioAP/uAPnngphnhWN8/ZmYRZcaFvq1+a3MvGmq9jp3P6Z72yBQ/v5Qz+Vx0/Zb209JMX+J3u4YENkMw5h/dSy3j4+sZHG2CJ0XNATmIBvVFsbbWjEuQgfKJNuKX16bR8R2GosJvrGx2zyWJuOpa+R+EyCByMqzoSQ79TAoxLWwPpyxlzJgxaYqiA4JQ5Fz89y65EAqO2dp0ARaFMlMtOowJDYir5ZUzW5SwFSvgIzBJmiZ4xnqnqJc/wEvD9NE6iLrHnDPBEcSGhkoDCXxDW11roUiBI8hULdgLt9MNBMJVwS1Kso0BijBydftYQKF2JUVDAgcM3v/4ct2SEMQjOEuEUHYoq9BgDB88ZgXGSTw6H47Ammu2ywA9OknsGIrFQsNlpREPmMq/dQ/2N5zgEHcUFx8SQ8YjORLbw9wwOcOF4FRwckPXJBu0SeHMzWnyQcCpO8M9acAgfBCP9/e6rg/E1hQcnIfkpcfDTc3LNzhC9QC89ENgUXNazo0ZGZIhesqVHpV6O6GT03zjjrcUG8CQ6KakgN3xlA/r0AuMSWI+9kjee7QG2tLVwQTM88FWAk7ytKT48xwM2Qh/wJ5sm3qXV4Jl+7Eagol/ku5QsLgFZKLzIir2xPTqBhlF9APrTJwUQvfQ32qKHeCSAsUP+hb7QHf3S5sA7OPFl7ISNshG+wfwl6Eu+rcIFeA9Pxb9TIb6BLcKxpH2uZQ60KXjs5ipebRwIxnCEE/noQ270hMzMvQXMxW+5eqaxYZsoe+wJwI8OmE8yxp/W/KohtPPl7FACm025JfrIW/FC5ySK9G1pvRaY5xoLF3KiRxJ6csenUm9qMEYsYhP0pgfm4kvxTrKaufeA8fhP7nwSHZ0DtClSbdSJPzYj2Y2ffNLWZrx4aqNP3GTfntXNRp3iXX6C52W/1rxink1KX8cuJ9Hfpqd3ftbN/ORmc8A6aEsr19CPDNzUwQfPyn6jLfj4SZ/lMvK3Of8ePyjuyAnIb68eAPrENvGKbfds2Xr0HP3oXrJh+ad8RT7md3MfhcsgcBYSdsk0BmJa2XrgvTGUirAEaAkqY5Y4S4Y8E1Tquco16ncl5L110sxHua2juKDgFF2wRI8+Ab9zQIKRPrVSmdt8gr6igROgrAz2vd/7vW/e/d3ffbrqIbngUDkAyTllrp0lpWNo1gieEm0BCY84WMmh3dVAaHPtQVEgucVDhYrdz/d6r/eamp2P7NLCU1JsTG3QKRzgIJAr4MwHF32tDQfFQglw4IiNRyNa4eD/sbzHe7zHzR/5I39k+mpUvFFwKEA4eAlYyW9ANuQCB0HVrrMEnhzQi08CbX1lBL54pZDgtOmkosmJ0bu927vdPO5xj5t2jZxG2ZlJccPow0c/NWsLggoXzqEGyZoEg6OBG3wFK/R7h1da5twKxka/yJZ+uVpAJviAP1vXwS/6JdDtAbzaUrjA2VjyZYv0UhBZmsN7uAs07MpPyW1vnHcafkmM7eQrelqyHQXz0js6KVg7NVMUJdGv9XsNwBNdbFFQa4F1BWUFPt1LsRBaa8hzffCLLrEFvoAPpFMK93Ks39HQK1z4ETopcaD/frcpQJbkVOLUat4rxtHBlthjC/TjY9iujRk82gJwQit5Kbj4KgUDfK2xF9i/RDt8EFPQ2QPrGieRs8nDh9W8ngM+lG9wAk6n0TcC+HiNhQu8yJm9ZjNjDvBXPHDqR0eWeCCWsFU2Zt7o4ikA7uIsGc7NiTbxR9LObrP+XhyMp0d4qMUvlo2uyT3Yqk1Jeub5HHjnNgWfJm7bTImfnhvnOTvOJmsdD9PgIQEn4/i5vNsLfDseyN3mbAlONijRtsSHUTC33FgOgc4e4IvcCr/Ekh4e5rVRJTboS9f5T4WLzaxT+LBzwVUWLlG4srXAc0qC2ZIvR5aqecFDoUIoHIs+BE5QW8F4CsyIGJBAbbeek3YFzA4ZI8xatdD93StcAPxCj4SeM3S96ff9vt9383Zv93bTTqSxKnBJKHyyVsknimvH0Vze1YULh4zPiokSjPeeLNCpv6TbHWjrv/3bv/10FOsZ5y454dA4z5pegUIS7h2DSOEicPibA5MkOR0pITiY1/yu73BI/knl27zN29y8wzu8w/RBVEkSHuFV1qhxKB2JPnXhomDAB4VcCd6ZDw7wtYOrgHniE59487Zv+7Y3v//3//4pKXDyhZfma/EAeE6OdNJ8NWQtOHK41lP4csA56VKY4QV567sV8JW+RL4SRQkK3RCo7RbhaWwGbmsATrdVuOjH7oxTrPMD+IzWHuCDfvgsUXMlh6yWwHr8ikKH7uAhvJfW60HkT+6KdIUX+ZATO5EM0jU4jwL7cArCXo2f4yscrKkQ46P8Hb/SAnOxXVeu8JB+4h9fIxCyPfhnvJ9obBUu1qHbNpnMRf/8bX56pSBgG8YEp7p5Tp7s1BhzzMlEf/grkCS46B4FtPGDPjNDH+CGdjuYTp6SNKRtAfhbx1x0QHxTYC5BeCFOKHrYJT1Y8h3Ww3fxg03Y4U5iuQbI/xoLF3LxN/8Pxzl6PMcDvtpGEn71+pqLzbIZOrSWT2uBjtq8sEEnvyDXEqwnXriVgcZavt6PthLwgr7Jdfih2vasRz+cNvKfNX4leMeGFSyKcPPK92wyKJDmwDpsSz4hPvEzkUsaPMVhOssW/V1C2XdtK8F8+K84Q2cN3uODTWY+K3w6FZiPP+Nr3U4pfWcN1oVLTl1KH9wCuNscsRFM36wjtzkKl5WAwb3CRaOwhEbRKbBgLMhTWDviBEWBGQghbAUCy1pJJiW5dujcOy2TSbjUDqMFUb6ycEETxeEABQzBRSLEwboOpQiTjDnpcOqiiJGEeCYBENDgRlHxD83mFHgkVtaw7lzhol9ohQNaOBTvnVZIolyNoshOO+DgSFhCrTAS+FPAGGuO8MIujMIFTtZM4cKx6MMR2kFI4RI+BAcnSZJRxSEcXJt5zGMec/MBH/AB0++cpZMfeMLXmOBgLnwYKVxKmeMnnqGNk7UzlDvE7/qu73rzgR/4gVOwoAuSbEUUx5ZkMzgAv/cKlxbgGRzw1emXRNB6kjqOE25J5OAM961gvLXQSqfQ6nqdHVuyoZPwLvlagzkuXbh4Dye8sONpl4gemKMHeGUcO4OzYEB3ltYzztwK6XwegL55tjR2DozDU/wXKNBgd5UviEzxVoIsOaJj/l67njn4RIn/GjzNzUdIVNgK2dNp/MFn6+MbHYQTPtBJfYIv3aU3knc2Hnw1OLQKF/IQPPmDFJDm0+DgOb1kC/DjG+DB98GNPcMBznwDPNfwyDx0jR+Mva4BfW1m8AkK/Yz1E138Dt/gFIaOjMisBGPwwLz8Jn+qeKHnscce6INfxtmRxxfPemBNffgDesBf4rO1lmgw7poKFzZKN1zlEgfoSA/orsQwfaPTLfCODxanxZElvm6BrKGAdKpS2gxZ+Bu/2XhLNp6NthrQxdZTYEQPNH5KPsOe+bDW+AAbYDNOxNksXouVCv8URXNgTbGQDvMT6A4O3pknmyWtedJ3pNVgDbFJjBMPyz74wA/KRet3pwDzoVmRjE4+sQd4y4/yFaV/mgP4k0M2+8UhdkBm1wp3ROECMJEwKCdj4VwIUSAUxAhHo7j5uQWsxwgFM4k5hZQ0wEtgpJjmh0/WCY490K8uXIyXNAvIuRZlh8wdZYk+Q7cTLukVkPPZDjtpjvQ5AXhxbBIe/TkaCq6wwFN4CjyCV5RYwMY//Si5IGVHwzzWksxx+k6S9FFAcZCSETy3C0QW+sAB3vCXoEtarMHBSArM7++ycPE3Z+XERWCFs4SQg8QfNKGVPljHCZdCBW8UqeSBVnMIkgIsHPRXMOEpuvHEXHhtjV7hglfmNAZdklrzclb4LZHHB0atoMM/fJNwcur4IljSRzJK8mTd0cIFGBtd1jh7up9vZKEjijY4j8zbAmtlHfLCD7qAfkkjXSNPssXXWt89u2Th4h088R7fBbQy2e+BNZwysQcBVzBeM45+kKOdKXwnV/xaa/8tMFZApm+S4CRA9ZxoQ6Mdx6WEKqCPAEdP0YvuJTy9J0t2Ah87vdakA3SO3tvE0NgCXuBLjS++8Cf60SV/ew+HunDxjl9KsWT9zOenv9Hv7ro12b+NG0UTW2BX7I9v8eUC/AndWANwd0IDH2ss8QfASeHA39BVc5RgDjTxkTaP+CQy9mwPmBeO4hE/6ARuKVkE8FV08k10QTG1Bhfj+Gp85SP5QHztrUd3r6lw4Y/4LPizrSW9wCcxib0s0YqHdMdVrpZPPBWYN1ea5TkBvKYDYjU9OBegUyzjf8Th+BFNTLc+P0Nf5kBfdiJu8536eoYGNi/BRkNvDoBe8mFPmQM+4i3/wT7OBfgg3vM1roXDPSAOZf0lGrYCWvl+p9JylCV+e8+vOXkpcW0B/eXT+FR2clwVGwCKzclTTAyTjNltENTsKEg4NcKQRDIagjQO40sguLVAIc1hLUKDg6SQ07OWZJrzy+5qhGmNtDVgHY6RAzC/4sVPisXRC+iCHZoYgIDP4Uoa9WX0cIsDgw+HgQ8CqITffBpH4JQCXVm3PHExt8JAYiIQGiN5ciULDyT25meoxuF3PswuofXTnPrBgWOFPzoiJ8mnNRiNNevCxd8SH44AT/AbDYpFRQx50AGJueRU0mhn3Rp4xoDDK46ModnxQIe5NDyTcOFDnGRZuBgjuTCX0zTvgoOCKTiQC1oENIUafZTQwUUfvFIc2tlTwJkHX+EC71xf0XdEN0vAMzSgl01wXvTTWvhHTxRyAkDkvgXgZ6w58Jc+0IvIFu/pI11CtwAPr0sVLnAjE4UU+yALfF2i13zw1d84BS76lpIO9NFxNiJZFTytb0zZRoAPwTMbA2xQksXW0NCaD450nD7TJWOXgoo+Cl14s9WR5IqNsgu+hqzpNXsV3PDCe3Mr/lr4+pueske8tr5n5FsWLp6RgT5ONskn85UNLU5cyDw+B79sLjn5hqOihv6RzdqAi9/sVsBm72vGmZ8P5LdyEtECOOMZn8Xv6Ruat4KxeIQPZIO/9KKOfzV4j29si8z4c/Ms0Ys286NXkWhd9M+tx1aupXBhV3Sfn/YTbnP0RsecnLPF6OucrMwlRink2cRafdsKbInNiP9+hxcb5CdtLML9XGAt9PKXdJm9+ps+sTe6JFbM8Qp4z0YVzmUMjH7RF3GSffRAbIMDH25OfLcJKyaLiUt2sAfCB3SIt3CxHl8k1uMPOfT4sBesr2iSqyRe9ICOyLH4SWN7gPd0jF9zq0fOc2693gNXU7hwiJI+ys04GATHQCkFa4rBWdRJwygYQyAaxaMAEswEasFAciK536OIWYNyaRRD4uxD3Q8++OCULDNWiSHlD1jPunhhp9Nuhx0FxYICiiPmMGK4oQdf7IoJLvq41sS4KKw5cj8THvho51KBo7+ENw5Fg6/5BSxBlyNXwMFZwgov72ocBEOOFF12ouyAcrTWRKfEj6P3NwNU2TN6fIlsA+aEUworNKBb8EWHYgbu5tI3Y+iOhEFixbE7sYKXufU3X46q7SB5LxmHA/4Fh/BBXzygF3BAuyNVBZWdXzuYCRzG0CljFHMKIHzwtbY+J0Sv8EwfcwfvrQBXDlvAVbgoHvJB3iQ01omMtkJkS0/YoUBjh0kiTS/or7X3JixkOVe4RB5otg57De/h14Pgz74lYHSUHMyZVoNnxvEDdhXZ1NrktgfGw5k9xLexr968cCFL+ukklR4tBSLyp7fkZe45OltAnvwwPa/H+Bvv2IuCowdskc3TRzQbl8KFHNHAp7Aptj7HA7Q7AUL3HA0STjEjtrgWrCkhlNBbpwfe2+xwLVnRuYafEiubIQovfgnNPVmvAXhIRvgWekl/2EYP4Mq++CWxwCaE+LDGDxmnQORz+X3jWjSg7VoKF7GNTF1tJoMejXjAt9NFdJYxpQV0VTwWl+NHzgnwI2N6xwfjcz6IThbnXh+Iaexd3Ga7bFGSyz/O+SJ4afIWsVIiTXfzXKNH/LiTU7mBZ3OADzZt2ZK8zbp8C/sih0sAGtiB+C+3SU5hYyK0nQvMnfhXXx1sAZ648uUaGF/cww0v0cR+fZb6KFxWQgTi/424kiRJpcgMkwAEJIqxh5nmsFNgXgmSzw1onACFlNxYixCts1UJGZh5KAsFkAT4fIqETLXMAQli8NFKmvxtZ19SKJlnHBJTjtJ8kgrXJjzDjwQec/idMqNP4fLAAw9MCfPDDz98c999903PNN9L7gPunCC+13yFA8fE8UuM9cmJBxw4MXPCs1xfM5fxHIuvUvRtYAo1/+PBuj7A5lvB8j9d9Ct5HjCHY2UJMXrIjX6Qm8DhmTkZGxwC5jCXOSV6/tmnb2PzzWgM0of84Q6n/CNQztB6teMxh4JAEJQIapIsO9B0hXzMyXlb11gNPuYiC3L2z+7sEkmaXfmTcCgA8TE7aFug5DcZhS+KLDaEv3aIBGXv8GXrWgBdWYs8JG8SJ8X40572tGknag/g11zh4p3gbTODbZCFZ9G/OfBO4qJIZnvwrnWtBmPInu3ioQIU/9C+h3/G0hu2Sw9SyJb6OwfGWp/N2nSgP3O4eC5BVVwKqP5OWwO9wgXgOxqWChe6yUbYsKtjfAd7YsfWUFTZ3Vsq3MyzpnARzPFoBMwHF/6BntCNFujHjvhehS8erOEn3Nk4eZA5e6QDpb8YBWPQyQb4ZD4siVMPrIk+/o7syAXvl8YB6/FzxrD51mmTua+lcJEIP+lJT5oSbbLqAbxtdCmy9e3pIprpvr5LensqIG8yov/8ER+gyLL5sUZ2pwA8Ek/zuS46QJetP8eDxAt9xT+/l/oe/WcPYiIfsWRX1pMXuAHBpui/DaU1PvQUgAZ8dw2Wb4N3crHY9DnB/ApBNs+f9uSPJ2K1/EVcXdJVc9nEf9/3fd8pxzk3LXvgqgoXuzl2jDkEAX2rU8BwYymZeTh4RsfwOTSFg90Cu1aEr9+etRg1g5MUCX7mtYvnmM5urWs9HD0DhYudUDutkqFyXYojmZW4SZb8DTe7QBJmRZxnDMbVCA7E2lEwc+GjXQhzKMb0sTPk6pg5/S3pyemS/jUOcJUcKlbifFK4CMKecZ4+pC+YGVPjoPC0hmAPXzutKn/JChwUHBJ58yZ5M0f4qaDBP4HH32g3pytr5oeTExNzWgMOAXPhreBiN8R7BYTfycMODxw8o28cT+0wzQ9HO/sSdPNLcHLior8+nKg5yK3EAR/og+JPskb2cEKXQhne6EMPPBQcaDRneLEGyn5+hwPcrMXBSugkuq5OKDTRDVc8x9dS9qNgLJzpCz4pXNbi3QJ414VL9IlN2emSFME/uM+t5x0a7Tg52XCCCVe87UFoYjPkSj/JLjq+hT5jyJU98nFskU3AbwTMAxdzsEW+BL4lpI8ddTZnjeCtrYE1hQv+LBUuxpKTUzm7/Hww3wQ3cuGHcwrcA7xfKlwkR1sLFzg6QZKUk30NdEacUDTzmfqP2CgZ8Zt8JpvnD8Q486ydowbj8CX+XgHIV5J9zy48Dz18Ah9MF8mg1qUa4MtPSdbEUXGk5Lf311K48P2SS/zo8Rgv2JG+fONSX75HHJFkj+raHohf4svYDdtjO5JYMViTe5Qtz0/RxC384ROd+or9CodW3zT9+U8x0/gyPpaAj2IsXRSz0FTTkmYefdzmIDNJOT5Yj27WzZgarz3N+vSePxLb+Qyxlt6knRvYvHjGzvzeAzYJXzEVL+hwD/gCG61H4bISOE7JvpOFvQrAyDkYwdWcgjzB2aUQMAURfQix5+TXAMWQ4AqarjRx6BTa3xKVeg2/C1qSMAWN8cB7iS5cBeEkWYy6LFz04wA4hChj5k5CIengCIw1v8Q9V8X8bV7OQRKvf4mDgMYZ5B4nfM0LV7glEMBBIWQt8wcHiZLihGO1hrnhwcmZ125fntm5hANH5Jk5rGc+hZPEK0kNmTFUia010OCZY2IBmLwDjFlxoECUVJvbMwkUuiRm8IeDYA8H/CpxYOx2NbILZD3j6CejNjY4CGRkT34BvIObAjnrmdcYzd/olAjRF4EWfa67SPDgshfQoqFdEmoNjhZO7IJewS2y2wp4IWFRWO6ZC5/rwsXcdvY8y9380JXWAsGEDtiZs5sa2faAfOgRHXFawVdYf2ncEpC3wgvv2cDSTlkP4CjRUpCSYR24rKUQVkjSR38v8aqGUxUuAbbA90p42Ylx5le0s58lvPDqXIULMCc+SsqcstVr4KPrpPyMfmSwhHMN+qND4sOH8OcSIXPtBTTbjJFQKorIL3LvAZsgAwUZX04vl8Cc5KlY4h/Fhfho811L4cI++KOlzQH6R3fwAR09nnlvo4p/oBOnkN0IkCmf5PZEmTiXDf/T6nd7m8LCNS3f7un/ufnb83LNstErtyzEx2ygzAFe2sxzO4KvNy/66uad3MmtjT/wB/7A9KU9wYFPabUarz0teNkMfM5znvPIxiva0s4N1qC3+MVH90Bf/kHstGEkLvaAD3ALSb52CVq2wtUVLhz6KHCYBCmg24WXYEvOnGoIZpy6hISCCR5bBWIcPDluRYCkk4OkEAIGJfacclAWjqYFjNRdaTsG+guGaJCoM/ZyN87PFC6SNuA5BXP1ReXNiaJLIGRYCgf8sL6mgGLwdkCCk594BQcFBro8E5A4hiTb1kJLPuNiXuA5nqKf04ebfhJ+hg3nGDQaFCkKCcm6dfDAc4GAEyY7OBgjkEhyzBl8rVsWLhmvcJMI4b3xcJAkoldBhAZ9vXMfVQKSoBPcyBHf8dTc5IeG7GiYAx54UhYu5vBOMsKp5eTGnBIgOCiIjLVWCf4OXua1G4sX5Ckw4hVayTS8NaaeZw0YA096i/9OXthFdu4lkxwgeaI3PF8L6EX/KQsXvIEPXQjPS32YA7jYabOBoEgQ6M3bAzjjrw0IumRNthIZbwVjzSHhZpfsBE1w3MMnPKBfAgwf4Hg/jQzsvvOl1oGDtdLWwKkLl8jENRcJz4d+6IdO83u2hhd095yFC2D3fLhkgN2XPKXbknQbClvnB3BHC3/scz42TNjiqfSMD1Fc4YUNovjJHvCrkpRsnPAPSzKhf+YmD7aJFv7bM3p+LYULPOA0B2jkV2xS8PktPx3wjl2ICza7YleXAmvx3+IiusRTu+PiS5q/5RNa/e4ULRuNTlv4Hv6hXLNc2+aJWPbqV7960glxmu63wHO25TTSlXp5kLWsySeXzbzWdeICD/wQ5+l+je85WvCAHzzFGPkE+aRdAuijHFKuZn1/zwGc5GViIj71fIK5FKdH4bISBMO1hQvGE5TGYVMkClx+4JxiJfneCgSXtcxDqO5qMjBNki/wSFx7DrIF+iswctVJsm3HjOEHZ+sLIooASpeiAejDQXCkChCOVwElaEnAjdXg3ipcgPnstFlXsSQZEqDLnUBz4LHCRTFY4gA3eDsdMRZ/JMF2WIJD+pFHWbgA742Bt0RGUmcd80lqSgOrCxdjvfc7R4YP9MAceMWplLiiAb9zMhLcAFwF/HwGQkJPn/AErvr6WRcumQMOTmbQwEHgt2QHb0f0Aj1wpgNkLmkiD6cGCrMUxPhJ/iV/RsF4vJLYO9GQhPociELG+tZAs7a0jrkEJ3Is+ToKfIDk3k4eHtoBJxf2FZuYg8iITShC8U6wXMLdODylv2xaEUsHIvctYFx006mPIE+e2a3fA+bGCzLzeQunehLuND5Ukk9/8RMdGbeWnlMVLuEDXWKbPl/34IMPTruwcGQreB8c5wC95yxc4Mju+Df+WMKFj+EpXkqUJKxoWcvHOTDeeq6nORkTU7ZsGNSADn7Txgcb8Psa/qKJjbF9J/NrimvvzMtX8HUKBesZr3i+bVgqXOBvIwS+fGDobdHsGT6yZTzihy8J4XVO1eC914+MgvXZq2tqYj6bcNrWwgO+KbLkYopiOs5vtPiLHnx1KsDGxDs+uAZj+QIn6PqIw2wWXp5732rnAvmBXINvxx+8OOd6JVgLj+UqYtaS32MH+unf0x98PwqXAUjhIjGcA8wmAIFVgsJB+7C3gkXyTiDmIcQo8lagiBSDclJMAUaiYMdeUgWPrBWlHQG4mQMNEjUOSSDzLHj7yaG2Cpe8S/Fjt16CBGe0B+DlupTEnoHDNeCdgKnYk3jm6BFNJe/MJ3EuT1yAPt5ZOwHYCY4+JQ7wbBUuwO/628mBB8doZ8OYEvSpCxfNePhK4BRn5OMesGelTMynuMhxaUmffvhGj9Ao6Oeam3dZR4HTKlz00ZcsveeE/aQfJa1rwJx4Zyw60a3gFIjtRtlhsb4dTvo+On8J1oI3WqxHF/BIkpgvyJDQWKvmZwnwPUXhgl6Jon/ep4gia+uicWle+OMLGUrqkwj2xqEHD9mXb5ezCRGZ7aEDT+mKwO3zLH437545gfF014aJfxJq9wyd5k7DQ8GHPxHgvR+FUxUu9ML6+vpgOv/GTtmYXXonQxJfMujxxjznKlzMZ33xg//kn/Cw5CkbFMgVXuEpGe8BugdXts134oeCYas9hy/Gwx+vfZshm0DfEuAxOiU2dsmzgTZn88A7fczPt9N1tNgcum2gDzYvyKqlM3gk35DMwX+JTjFfrsE2tspoK1ifbNiLvOfS6wPryyEUT353u0DRgC81Pv6mf/Ia9iOW8ulibAt3m0aKG7GGLMhOTlEDXeP/fFOcvmRL1+qcoG7nArTZnJRz7LHdrWA9J1X4jB890Bdvbc6JG3jZArHqKFwGgBKUhQumYbbnGO4UwM6IKl8SL4Gzq25XlTILJFsYbQzHYLy1KKDdQUGSUUhiBegki4xW/61CLWmidHZxfPuWwC5pVGRYIwkbvFK4GGNdzxitokpywSk8/vGPn67GKU7wwxqU0xwpXATJPCtxkCz6ti2Bx1roTHDOeilcgoN59DE33ijqnvCEJ0xzWUfyxJlYCy12aQQ3js5YzXu4KlQkyb7lC7/tuqDNe/3iuFO4WLekQbIhOfINX/fee++UXJRXrIz3U5GXwsV4c5sDro6g7e7fc889UwHEcAUpaxmrL6OmC3huXHCAGxzM74NtvmHLThNnwlmn74jO6KvB3drWIBcBQ1Jpx0kSCBfBjN7CVd+M3QJwRSe9kMRIgOkeu3Mkz96in7ED+mHX9RSFCx1+9rOfPe2+WQff5sBa3sOXfjqdSvEPtx7AmcxCo2N3tO8Ba1qbPgvECtg1u9dLYCy52tFmAwIVG2vN62902ChwBQZdcPJM83safUnD6zTrWIM+s6GyeR9+K1ZbdHlGP+irEzQbCXwEe+Kz2Tq/QVZ8Dntl7+hpgbkEU/6xha8mbogfcPN3cNVKOkN7yQ8Fq29Z5Ds9q4Fc4SYBs7GS07zYwB4wh3XZtBNfcoVDi69rwVh057NOYgx+06EesCX84GNc6+PD8LNng8B6+O2kyucvJHJ78D8F9AoXf/PNbnfw8z36vMNLfp+eLvHwHIAGcZXtw2Wvzo0AXuGBwt7GYjax2LL8yA2J0mb0pWtyEvYCVzgrEOsbG+Y2Dx9MR+keOxNnrOW6YykbY/kOPoS+eWcNm57sMf6wbucC6+MHPy9vmvNf5wQ8sZGFJ/jXo5cs4CnHSg5UA3s/CpcBoPwpXDAMUwVQCp07/5wHIXH0CRraHgYbyzFIljk6ybmkww63ZDgGcgohwpWhSco4dzuyjqpdQ5D4S5g5XAmgtSkXOjkNyaMkmHJKAuDKYAQ8JyUKD8WcOTkICX6CoGLB3JJR8+Gr0xGOh4PwzvqcjWQEDhyE4g39xnBQ1oOTZ8FBX0mrkxZffeyaF/7BwQ6oAgIOZeFiPrgxNrLVn/Oxo8Vo0GReibndGMaJ9hQu/mZgeIRXZAVvyYc50IMuPLZzw5HiW3lVDE52HuichEEQk5T4+miJrL89p2+SQDpiXIpY80m2JPQlDk5F8NLvCiHX5uzK4HnLUWwBeoR+MsFTMscvu7aKe7jpsxXouvHw1STi5ian0ER/cnrGYdMBOrPHTshEgJPoxq7n5vPc2gIWHtAfeK7hsbGCIz3jbwTIPfwK4IOEng1I4shoDz8C7IV/UkygNbu+c3OjhY2yP3LhbyShcPM7nyoRUiSyYXZC19ks/4cnNgHYA73S2L5GxvSdr2EnbLHGw/p27Pksc0UubIjNJwHzjE+yqUKv+PUazM3u2DVfCWfr8xtwhRP8HnzwwWlHGn/yzE+6yo8Yx9bRHn5o/kaHneQl3YELmbJ1tNEhstkr48wLV8UmP5UNiD1gDvxlt/yZ2LM0p/fkhza40A/J2Rpc8IKO8rF7cd8L5A/3unDxO//If9GhJRvlkxTYeKhvz+7OBeIUn7JGfqcGukD+fAR7KWNLfBK+eAY3cZp98D14FyAHG0tyigD9FMPFaj4tc/BdbJWP4icCNmH4CnE3crCG+CoGWvvS/AE2PuQcJa6XAjyUS+G5jYpe8RTe8l3kU9sGIMujcBkABiFJFEQ4FY5WMBPoOHKnIOUu/hamGmNsklCGIFgQpGDqignDkmQSMCXYuhYjZFQSfcmVYClRQhOD5AQ4JGtJBiSGHBNnAA9OFw8EZwmFawx+90x/uz922xUGqmiFg98lCXYmJfgCt8RFIHEVBq0S7/BVP3yFHyfumZ0oQZwzwJPgwLnYhRMQJCPewUESD29JNNmZC13mloRaK2sqsDhAOJnHbqx+cHZqwPjwRQJDDnCQpOAbHgikSd7ITDIiGVPcwBtfvMNThZK5JJBwhzOnZw78gINkTeLCeUq08F6AMh9ngK/kpp9CxDhXVtCin2Z+ODB0/McrfJYM4wPdpdccK5zxTMLPye4JgsZJFOgyevELruEvXPFAQNhayFhD4xytYy5zKtYEFnzV2KsjaGvuAfZCJwSBHl/wDU14ri9dxO+lZM87wUUiQhc4b34AH7eCOfHWPHRVck0Wke8eMK95+AP6h+/+ToDvAV4IQgoMNkMn+RT6ym78ZO/4jQ9kaB366Z1NADrLBtNsAqToQSs9g5fn1iI//GVPORHPRgWcycwz8krQ9E4fOLFzCbN35mFP8MNTctbH38EVTvnp5IZtswH4eKYf2oyxLjvU0AcHtmrTqE6yekCmTtHMjUayDi17QKwRc3Kiahe6TAi3gHH0gL9Bt8SR/UYePdCHD4z/NccSj9DArozZy4+9MFe4kJ94BU9FHZx7uPIr6M+J7B55jIK12AF95W/px6WBHoij8oNsmATgxq/wCXDzThKPt/FTAe/EVT6D/ZiXbGw4ySHKvn6XhMsbxHU6HP/K3o2LHMxrbfPIJcp5LgX4IEfIbZBL6r616DAfRkZ0tJRRDfEHfAH9r3E9CpdBENQ4GldtCEASibG1AYyCsQRJYIxFkscAfI2eZEsAkmCq4uPQtwjMmKxDkTSJrys9FAFNgjKnGSO3FqXnlCT5eAC8s8uhqJBI+PYMXxOoX3CNw/WTU1U0MG7PMq+1OAUFzHOf+9wpgRFog0NoVVzZlRTsKX7mDQ6MAr/8w0d4OoEIDuYwxqmQxAEO8NcYseDnnbV9m5BElwOTlMAx/GJMkm74JUB4J6nBN4H8JS95yZRoSE7gxcj0ga+1OHh8Rptn3qFNUcKpKZx8qxFni2d0IYYOB7xVIGWH2Bze6+ckQ2GEBjvRnLliC71wBeiREEmwSlly+HDg1AVU/4MHrxSI+lkbrllzK4SPnD4e+XC7ghdf8Ip+n2Id9KIbTblC4B+Kkt8ewGtJpsA3hx/c8Yx/8DkPQRMuSxA9ZU94Qs/Iaw8vjMNPvLUjmkBLDnsBXvSYLkmQ0Qv/UZDws2t6QQ/5UzbBXswH1+gfHqFJYiHB4Dv8XfJHvyQd7IJO29zwT0gVNPxDPiuHv5nTT3IrC5eAd2hVmLENc6Lb74ondsIXmc/aLZlll7fkvX7BF63GWwf95uMH2TJfMAr4SCZw5JfNj46tEB7Bmf9hu/wUfNFQ0zsCxqJdUeTzKzZT8N9aS4BPEjKnyDlhmqMT7xWy/ORtQ6tw8RP+NpD4DXR41uKtZ/jDdsQUNp6+rf7nAPzEc5tx4qW/L7U2wB+2oiiARw3e44tNDrakr1hAv/C5BHjzP+yaD2A/YhT7puM1eE//6Z55+Ws2zo/FHsILchKLfb4x+cclwXp8Jtvi/+fs45xgfddCbfriTw/IRgHIRuhUiS95HoXLAAhqdoU4PozF0D0OmzAEEwk2YXJUEh1C8Ttj8D5rUb6taxlrPkrLwDhGgdxOjQQbbUkS0AS3GJ6AksKlDub6esYRSCQYfuYIvv6uCxfNeO+sa9dIYiUgWq+kVz/jnPgo4vQH3umDP5yId5xScCj5pQ+6JVj4EPqCgzUl/hIcJzL4EdmmDxw5qhQugby3rgCCV/C1Jhy8B/hUFy7e6WMtz+zE2mV1dSzjQ4M+deECvPcOznSJfipI0VTKAVhD0C4LF+/0QQO6NLiSmZ1kiQ+HjD94YI6svQXQDC/roRGtij7XAOmI5I6TjxyD+yhELtaAc3Za9wDe9AoXzyRSTrh8eQCeraEBP9CrsLIJwE5L/dnCA2PMi5f4inZ24tmW+UqAE10Q1NmU3+Hr+SjQV0U7nTM+DY5poPw9hYviYw70xT94aXCUbPusHp9HlniRtfxkE63CJe/Jku098YlPnHyoOct5epDCBS6B0KQFspafgrSNpVZStgTB1xzohi9bOwVEXxWd/LZkDR/2AHrhay4JHh9ljSVfg05rkwtfhcdJDmvemv+aCxd02NRxOidOBf+ShgAZ2DgVLyRx8TNz/c8BdDk7+daP3l4CrIN3igu21dI/feBlw0FxQzfYdvSqxtUzvHT7QQy1Ccl+WjqI/975khZxmS90q0Pf8KFscI1vEY8uCdbHB3FJ7IK7Z5cEa0a38a0H+GfTiW7bJCp9prFH4TIADEPiaddyFDCYQptDQml3QtC1uyoRtdsvsXBMTjAC6FYnkLUYigBPWexkKTwYpOtDduOzQ1gaWg2e6VOfuJRAIRm5pD8GaVwag6kLl4DfrS3hlog4/SgdMPCeE68Ll4B+FJtzEgxaTsF7BuAUQfJW42BNvLdzJLiXjsp7NAqoEkvztHCwroAosU0wSgN4h49l4VK+hyPHYg06gO4S4CDhqguXgL/xl4zpFppqIEuOU6EQGko80qxtPDwVcgoq8jE3+sgbDtYzj7WN2wLGmQPNTspc55IQ4YOC2K4xJwYXONV86UHowVv4701Y4NkqXLIG/YU/XXfa07KXEkK7wMcu2RC7DD/LNgL4RNauRKGbvOn9CO9aAA+6Tb/ISMJAbmjfCvDjn6KPPQgf1hQuJRhHFnbx6HD8Xnir4Y0+rcIloB88fV6PTrZsbA74plbh0gN+b243eQ2YH474JcHk38QDOOzVBcD+xa18uQEdq/3SKMA3n3uBs53yNfpFLvyjwkTRw2/VdJr7mgsXsUehmutynqeV4O/4Ivz3e6nP5wZr4KW4LndRCGTtS6wPyNWmjCTWrYc5W8QX+u/qkZMXfLf5KI7zH2lOTf0Ug123doNEUS7WlX3STzMPfXLbwwZO8pu6AfFbfLBZLHaewv5GwHrws4HhFsIamzolWJ+einF8bPR1DvDLhpZ8h/zS9yhcBgHTJZ4C3ygQmuRcoeJalOrc6QAHalfW3KcSAgMWnARfa2jWFLTrpHwJ4JTChbLViZj31pMglYVLCXFwrcIFhDcSEYZNYUvwXgB3VaxXuNjt4JTgW4P3HJUkIEVDCdYULPCKE6x55G+Fi6RUgK6NPnzCc7tPrYTVM4WvU5sWjugaKVzm+EQOcGw5cusKlmXhshbQiAd2uOhTdEuSAJ/R+VpgDU0wRwNeWYO92H3hyFo6tAR4IWCcq3ChH+xYEFeYwrGWXwvMhyYnW05zW3oxCvDi6OmqQt38cAlv9wD8JN928Nkb/PfOOVK4BEYLF2B+hTBdog813v5mo+Q7V7gAdue6MJ81AnwT2xlJGPYWLgHyR4/NJ9ePbVydQnYAL/ktOsy/SWL3ArzEKkl5Pqezxp7YoYKFr2CLNg/KcXClN9dWuATEWb61Zwt4gya+OJ8DuDTAQZx0MmazqY5F5wbrR5Y21Xo2lb42Pl3lphc2n8VJTSLvp7zOcxsy/uv9e7/3e0+nKeJG+uqTlmf0/qGHHpp0iiytV7fgobAXe11Z5Wvy7lJATjYC0WTj5dLrs0+3CfAsV8Z6OPABeCx3M1bfo3AZBM5kTeGCmQRCiTHeB20ZRJITSb7CQvDVh1GtccotMI4BEKbkUcAV6DhFd18pKUduHfjrPyJsfSUrlypcOOHaCXpv3N1QuEgozlm40IOthQvAB+M4YDuh9EtB7MsA7E7Rb8/Ja6te46VmHXxiQ4IjHYM3O5J8SmboTWyoB96fImGBU1m4aHSB3CRYTv3QXutPDd7TQ4mk4oJukt3SuCWAi+Kfv7FbzZ7gTA5w3Qp0DV10Fx8FDXzfiy8w31G4tOFUhQva6ACdwzO6ys/ETvfohnnZqVND/lPiveaK1xLAC37sHr6SvVZ8qQE+Ti0ltL4QRvzFc8/NKWkj39uGsnCBF5/uREyxtcQ7fJCAKxhH7OYUQFfYAB6KRWwGby8JaEa7ApWN9Nb3TgzxuTZ2JI77m46UjT17Lg9wgqgo45sl+J5r+qRlXD7jyk5TDNQtgG/yMT6IvZD7JQEv2Ca5iVV7bX8UrGXN3DSqb7/UoK/PG7PZxMejcBkExjJXuGAoJmv6cUISSEeTkhzBwn3oJSPrASEZaw2OWCN4BQpB2h2QODo+ZVje7xWs8YLSUbgchcscoAvP4GZun+2QMKCF3PAKLnhY870H+pbNeE6X43d8L3D7vILrSq5eZh18wYtyLc/olx3c8vko4JnEVvFkTvx0AuEuNP2pZdICONJBO3tsAj17ZYFe9LOf/LNAgTZ6vJVm88IX382rOBWcw989vAxcU+ES+T7aCpcS4ssUt3hIT+C0R5bGmgNPbGKIR+bFq63zGkfPyMTurCtUfGcSmB5knOSQLxK72ByZovmaChexAG5iS2Jsj2do4/8k4ujx9yWBTMnDyR1575HxFkCveMN/8rlLcYWtS3wVvkt90WJDTAyVR/mmUzq05JvITO7l9oH14GidtBL8LWeTa5j3kvKztjVdd6N/8eWXBjEPj/nvnv547j2fLc7irdxIrnsULiuBwOvCBeM4D7vMkkVJJ6YyasGYQ8J4BrNkNEtgvASCsdpNkjz5oJOdVcdvjIdgs94phGqOo3A5CpceoB1/4BP9Yw8KCgkMubquBGcJZwuvtYDGrEMGZGn3xjcv2SFDG6eskMGLgDHwOFXhYgc4O2cSiLV0wZkM/YOt7FbWujYKeIIPkhk8EBTpF5r30ArMY8dL8sf+6E/mTdsL5HIULm04R+GCTjTwMfyZjTW+b49dAvPSZfQ5hfU/a9ylx8fal41A8CVzvk385V96dmOMNdEkyWavaBVfJLzXVLjYfJQc02enpT1eoVl/+YW+LT0+N9BJNuQky/rwvRQO1rEm/ZqLkyXQRQku/5WCdw5X8yi2nRjyK/RWzOJT2XqPRvMqdPh0/t26+s+NIUPXbdndqeLwWkAne3Ja5Uug9tr9KOAJmuVyYorcbsmW5WT+/xw5OOUiz1P6xHPA1RQukgwJPMeH2RRVoimhp7A+TKgqlNBQTP17RtUD4yiUxEHCzJAUKyp6dzIlvxI0hsvACN6YnnFtAfPB4ShcjsJlBKyNz+QqeSEX9kGGgo4rZt7BR99aHmsBzehAt2SGjqJfMe93wZVOSXoU+ni7dS1gLde7fBgTPQohmwlk0ZsXnmSugPNhznxFci3DEbBe9JJeKYb4CTgu4dODzIsussI3fCRPdHhftr1wFC7zcI7CJYAH/Ax66STdjIz3AN1Bo51rukPXU9hv1RfjzGse/tMOuvn5VM9bYIwGF8WLhJRv8FXj/MBtA31wdVUi5ic5tOJGCd7zP+x9KRE/NViH7ss7XGlzg+SS6wPr8/N4Z0OFbOfW95yN0kH2w57jv1pAP9knPTGvv/mDxJKe/nquL9nI0Vp5UAnml2/Y9JJv3Abgo89IyVf3xKFRwCvriS/yLXaMtz0gu1xJtylpQ1ROuddXnROuqnCR8PnglmN2TJfIKlaScJeK7fc5Re+BMQofO6gchLUYqusfku9W4n0usM5RuByFyxYoeex3SYfkiB47jaHXdn2yu3UqIBs7mBImQUeBYbPh4Ycfnj44X8t+BMjQlYAXvOAFk5zxew1EN1x3kaST3V6nSyc5cfTZhRaA9tAWiL2xJTJiu7W+nxKOwmUezlm4BNBsc8GpBPsQe+jAVl0yTqOfPpvmRIe9S9BrvzoCmZfsnLK6Kur6Dx6twZVPFcceeOCBic7bBvognvCJkmt8X6KD/1e05UsHwpNLgPXIkG3gP19Inpda3zp0VWEghtCDOT31DG7yBYVIeDuHq+cKMTv5TrLyzBpiCR+7dBoGFMg+HyOPmVsrwI/IBfBzqe85AC/J0abF2jh2SkAzv+P0cCmn9Y4/5vPl20fhMgAMVcJHuR0/ShowXCDa45AxnxJxrIQhebZ7miRZkuoIk+CsdUlhUZijcLlM4WJOhYs1zlW4BEd63MLxnICHZEeP6TNa6A167PwoYiQ6aIDbVj0nC7zBT3pjZ8k3frmvfIoTF3bAPuG5ZPdkICBKsugW/TbHHhyMNQd9lWxKtCUUtT6Mgnnhi1++DpSeKIbwcass1sCdXLiMyPFaC5fYpXgmoZfI7N2FxRc6g1b36CWbTjr4rTm+rgXzwpf/kFDanOCzl/BFpwRWouSk9LbBpiTcX/GKV0zxEX5z+uQ53PkeSftSoncOYDN2x9kPvcTvS+JA5uTsWjCfSg+s38IBbnyt2MJ26OFcX0AnFYT8g3UC1vBOsswntPKbEow1h7nwqOc3vUOHvuLeiF84BcQe+BfF8yVlGcBPcd9JVSsnKgF/6B//5NqYWxu3gfNauKrCxQ4PRdsDFJYTIAiNMToq5sAURb7twTNJVyvAXhKsTaGOwuUyhQund4nCRSLRwvHSAEfOUyFDtu7Fu4aFT5JTPNHQir5abmvBOhzeKb5VjHyd4PRw8c6a9FkRoFBsyXQUjIcDXfWFAHR+KZguAVzpd+b1j2l9Y4+AvZXfI3AnFy4j8rzWwgXdafysZFphrpBp8WkUjEcHn+Q/d/uJz0nUt84PNz6Zv1PAW8OzHnhPb8j3tkHh4vYGfeSXe/4Nr/h8fkchuETnqcH67EEMl2TfBuCRE+tcW43utHiWXI2cl3TNM7mBz5zo25IDWxA/fBtsz+aNY9+KKzdmyKnX33rokXfs9eNbgC8To2wcsh98uhTglfXRzS8s3RiAGxxdF3vCE54w+ZFL4jsKV/XhfMbAUW4FBpX/Xm8ux4qURlEgaFiDMAmEwvec2SXA2hzGUbgchcs5AN/wVkOPhv8Sfc6ffdA99AgeeFTLbg2Y15WVSxUueOv0TDD0YXk6V8trC9BBduA/hLvCsBQY1wDeu28MTzyPfpt3C69H4Shc5oE9n7twKQEP4GeHmS7wmXDYC+bFczcVJJ90mAzxb6ueGUN3ycn1TRsEdLenR3C4lsLFZ1Xvv//+ib9LeoQmCZvrr/RvC7/2AB7bFafDtxE30Mt2FdR40eOXvpJg1+9cqevxyjx8n6u2cpS5+IJm+Y+4mWtnc0A+Pv9MH83d6+udQpQu0N0lPTg1WE9sIlsxt5W/nRPQzybFHn55yTfqL9e85557boVfI3B1hUvr65BbgKkEwehdv5DISMIcU0uQJbESUY6LYehfK3lP6S8B1j8Kl6NwuSSgi83ggR0uSYl7yvhCD9mRu8TkSBdqWbbAnJc4cfFMQuZzRPTZBgWeW38NnnNANxQqjtXpKF1cS3sPzEvf8VbwdKUuvuhScBQu83DpwiVAB3wI28aBhMaVlpY/GQH8RbukLle8ciVtj77xzeQmBvm67lydrH04QMO1FC5OXBQjc3oWwLPELj5xr82PgvXEHYn4Ka6kjoL1+DuyFfdaci1BXODn5WpLMY7eKNLFQz5lzk97rgjh930pQE9fvePzxSpxYA0OPmsiL9z7ObBRQKv1xBZ5bf6H0CV1DL/ybW7i5ZJ/5HfdNliSw23DHVe4ELpGGCpJO73GuF/vb0etS87qWgAdMcKjcDkKl0tD5ERmkjc7jvinCPHBcd+0h2b9apmWgFfnLFz8TkckeN4pAnyYM0FgCb85MAbuEnR38xVxgtteh21ec9B580oiFYOebcV1KxyFyzzcVuEC0OZ2ALzxmj6fQi/My8ficf4pIHlE97aC8RIwdi4JEn9r+VjnWgoXX1ggsV3Se3HnjW9840Rb7fPPDeRBX8UU8bUVU84NYpbP9TgVYJO1TEvwTvLrFouY0esL6Dd9yP8ESWuBucRV/+5iKXaaQyzI3EsAV8U8/4TfczicC/gZsVF8XeLxOUCclJ9lU663PlwV0XLKS+M5AldfuFAyBk3gAqodYR/G9Q/bJBp2mBwHSr4pPMW8ZoaXgDaO4yhcjsLlNgFt6CV7umCnVkBAi8+F2Y1zV5geoUvfUsb+PlfhQjfITgImYHL+dudqHEYBzWixG+ezP2TqmsLeeY3lq3yOxf8oYLv0DQ3epV0K2P1RuLThNgsXgE94Le659ugEdEROc8Bm+GEfsKWDdJxu1353BMgPb+OnxRu/l/xGD725EwoX9OAHn6XfiN6cCqzJT9jYaMWkcwP6cxogj/L3nG/yjs0qLGwO93QJHWKhjS/xuJW31GBd8+svF1mKn+TK3hVdS9fLvJN/iGVw4WMuCdZjK+KoDaxLrw/ol+JNPGrlLoGjcBkEisiB+3A+wTJqSiZJYVyOffOf8iXJHL73pQFR0J4CXxvAlYEfhctRuFwboE8goRu+VtFmwete97opyPo8DLska3zVT7Fx6sKFDPBUAi0Bo58t2Y6CeemA6wMf8zEfM+0MopeMa91dC8bRY0EUnna7XQ3DG8+9L9ul4Chc5uG2CxdAN/BFAvua17xm2jCg43t1USMHSaaC3446W27JZgSMZffs1LcP5YYDfM19pxQu9EyyLp+w8VnHpHMDPjq1Ek9yLWePXEbBevRM3oFPc/YYwEd93WxhNz3bxEv2S+9a8b4FaCcTc/P1rv/3wJzmZvfisbE94JflBnKtS8dmtOGXuCAHsn6Pf+cAfpE/tz7/PqdrR+EyCEk8fbiQM/TtXxRNYLGbm3/gRUEpLaZe0tDPAfDnPO70wgUO5yxcAMfKwdpxbwWj6M85CxfOzzUlDgDNNQRH99bnAuadAmSokU2aYoVdstHP+IzPmAoVJ6D0zzcP7f3Hc3hWFi50wqkq3bZ7DIdat0aB3GyEkCOb4qgz7565jXV9wVfe4oX7zNbai+9eOAqXebiGwgXgDbz5H19rzH/svY9vTs0cdnkl6HybGDDC1xYYD19FlpsP/K4knNyvpXCx0QmPuSSV7H2mLVe0an09J+CfNdmLa3f86qXB+uTHD7K1JfrpDb/mND78mhvDzp3iyGnQOsJbY135F8f93htrbvFIXtHKjUowj7zA/yiRX14arE/nFHMKVbRdEqzPF5RfQtECuaCTKTF9RG6XhqsqXOxU+9piyaVGKe2GUkrvT5G4XBPEmI7C5Shc7gQgJ3SRsR0x/23XqYVknd3i/R4wt8JFUeTaDCfreFvi1dKJUaAX9NTnd5zuSrbIudbXEQhPBCPBWjJkB9e8tY7dBhyFyzxcS+EC0Ap3duW0QEKr+MWzvXpEHvjJ99F9XyXLX+7Re2Ph69TCB+H5APi6DrP35PUU0Ctc6JiYSVedHu/hwxbgM2xyiIdOZVux5Jxgffbndoscq6dfeMNviq1O7fh+/T1v8Y2+8jeuPrKvUd7Cjf+kT3z1kj1bQ24kR+r1hQe52wAnd7HcWpcE+MktfHubPOg2gH9xG8BP/KjhKFwGgYNRuGCqYqUMtDGStEcLoOUoXH7XWR2Fy50FZKzhuw8Wu/vsGtkewDN24P8vCHzkKYmuZTUCwZO83IcWNNgTWe4FeJG54G830k6hv2v9v004Cpd5uKbCJYBPCmr+mA3ADU17bCBA58kIzeTAdvfOG9/N70l47GjzsbcNc4UL/op3Nlx8BuPS9mot8uSL8KmOM5cA6zspd7LttKdHP/ycUCikbSDFttNqoAt8bCvXWAt0SoHBpp089vDT11r+AbK+PdBXHzcD0N+K4ecGvKeX7IXt9Wg7B7AHPsAJbCufPAqXQcBQCXw+43IbQr00oO8oXI7C5U4GssZ71zn3XhUzD9498MAD064wXSKHWp9GwFiJOEft62fpH12o5T8KdJYeObnxzWHlXf9rgqNwmYdrLFwA3aL7vjL5ta997eR7Wz5tFMzLxpyM5Gonn7ZXZ42Hr8/CPeUpT9l98noKmCtc8EC89Jk9PN3rX0bBemLdR33UR00/bwMk7/4RcXntaw7wz+mHrzXWl6z1Twt47j3/LZfY4wvNq6Aio5xIzeHoOZu36e20O/jNAf7LVfgpGwSXBvpnY976bhWM+KtTgPXpvXgtLtRwFC6DwEAk8HYBOME0TH60NvQxHg6W4UkY6j4UyY4uvujbeu8rCu14c4Q1zwQmx4ICleJB//K9/o6tBRtHwRxG+V6ToHFcuQ/rGceUZk5Ji+KphaMEQRIkGbJr4+/yPRzt2ks+7LRYr5xfH87Wjr7PPMAxz9PwzhUFASs4ls2cdsStgV5rlu/RwElyKIy25pP+kl5ygGP9XguOiqMWHx+tDS/w5RSFiwJewOLQOdmtzlMAMwfdp3eu39ARQXAvmIMu0De74nRXUn6Njv4oXObhWguXAHuQXDrFsCHCv/SSuCUwDk/ZlVhBdyV7ft8zLzAWvq6KXeuJC7r5cYkZmcM57VJA5/DIZuSITZ4C4hPJRxz1ey/RZ4s28XwuIyczdQvoyw/iuzxjr5/FG/GYH7J2zxd4J77nivGSD6AD/AV/ZZ2SjnMDXPk+16zlCvTh0kA2Pt/kkABv2UVALD8KlwHgYFTqvqmEYaU50n20NvQxIN/W5KiTItd9JIN2dB988MHpuLb1XqL3vOc9b3pf88x7ycSLX/zi6Q6+AqV8r79xvl3Jt0nUOHjvmSt8r3rVq6ZAZ07P0+zIwNFOeQtHa3LWDz300HTqYkz53t+KIv/4yAe/k/xbRzPeSYhdIkE878s54OBrbe1Q6ut92bx/05veNL2XxNY0WMOu/Ktf/eopCbeGZ8FBM+6lL33pdBQeHMqGdt98ZzethcOjteETvqF5Dwgiimf8bwXHtWCM4OXURsFuF1CBVTrorWAOO+H0mS4nqF6rkz8Kl3m49sIFz/CDPHyOhEwkkiN8mQN6TAb8ufhhF3pOJmsB7yVDiqzbhlbhwgfwBXy33/E37VLg2+Pw+9IJMyAfJ8NsNP+cdI5+z2zO2Iz0+T1j07dsAcWAjRyfUUzfPWA8HYWrImvJd6PFbQ2f4eI7emAuxZXPT/qM09Lcpwb2a0MNrgqIU9jzCFiP72MLNgrZQuAoXAaB4lEmAYhjSZNwPpqbwMHJSoL8Xr5Dv2QQT1rv00fCzAj9bL03zvjwttVHYJxbAw6Sdu+toX/Z8j44eFaPt7Y1Wu/97Tk+mEf/zJ0WPml+96ycw5i8b40PjuX4+j0cvfdTn3qe0NDD0XuttcajueEJZ7cHBHOOVAGZwDjqPPkRztcmiMCQbyPkrPc4YuMFZ9cpJZD+NwaHb71rhqNwmYdrL1wAvuGJk2w3EtiGwhmdexMeCRsdJje2gn/1DuwIwMk811i44CM523TCy+ho2rmBDJ30i5GS1b0nElvAqYRNJsUTvsQn1vT7G74SW349upa+5Rg/0eL6oaJVQr5XLwPmVmDZNM23NM6Bvk4k4cvfwT841hCcFVp05BSF1ghYiy8UQ8iDXE7FszVgfTaeb5Vzdc0zDS5u74jll8RpFK6mcDnggAPubkjh8tmf/dlTYEkThNY2ge5zP/dzps+E2V00p+ej85TNWMWPhMzXv0roBf49c16q5Wpjiqw1TWInQXYFtfW+1fBHQoTvipKaN+GhzwQ5IVU0lO/T8NXpcT6L1OrTajYL7PaO0ClJd13KlRSBvNXnGhre0WM8U2Q4oc9nIuG9pWXeyMWuOruzgaTwr3FY08hdwSsZvG0oCxcJmJ+KBteu8TKJWtq5wfp0X/GJv5dOCq0nGVVYLn22Rl9XuvP1x/RlDvRlczbyXH/ae2pXArnwAT47Ra/of09W5KpocTsCjT28gdMWN10UW0t9Tw34hlf8s5sB7PASeliCjTi8Ijfrw4lPdKuFT/T3tcJRuBxwwAFXAQKP4O5KoUCY5vRqTbPr/vKXv3y6VilAz52MjTZzv/71r7956lOfOgUaSbJ5JXk1DtfWXG103QMvWu9bTYKHh3anW+9bzfyKOrLzN76VJ6L+Jh//p8R1ypx81k2fJzzhCVMB5O9SD3rt4YcfnuY2vvW+1Zwgv+AFL5hOXaxFntfe6J4rv/fee++UYJS8G22Zk+yclPnA7jOe8Yybj/u4j3vEduZaaR9p5nRl1E72pZOwGsrChV9RVNvdVhBLUuFXtnOC9Xz+g9x8BiPrXwqs5zSCHa75XIeEGq6KgKWE2tySXD4mhfQpwdoKFv4APj1cvFPo8F/o9HsPvHfqwM9J4i8JcFUY+EIgV/DJxN89+k4NZGWTyj/8dMJt8+EoXA444IADBoDjzB3ssglES82Rt58KFqcFfnp2iiaIS+rs+gmgnlnL79fe8MEVDr/XdM019LZ4WPK7bubHH2OyVqsPXMzvfS3nyLrss7YZQ0Z+b83basE5a/n7Whv8wsfoI5r93aJtqWWuNPOTXebNmiPNGHNdw/34FC6ScEmZgi+JeBLEsp0LrAUH34apcHI6cUlAG5rt6rNpuMwVTp7B12dFyHHpZMY8El0bBj57dS4+Bn9FtgJsqThSDOQLBXp94avYou8K2zm+nAus5bRTQenLmejGJdcH1qObNuSsj79H4XLAAQccMAAcqcZpjjRBp/X8aLfXflvbKM+j3bmNvG8bFC5Ofuyk+wYvhZXd5ehj3c4FNmN+5Ed+ZLqGZ338uSSgzWfW7Ko7XejRCjefccA3/+NGUt/rrwhyfVbT91wAB6cu1vHNoEtrea9odQ1KYdADRZFCWzHvM0hLRdGpITxXaC3J51zARpw6KQ7pqA/n3xYua+EoXA444IADDjjggEcNuOr2sR/7sdO3/7m+5hsr7So7rWq18nTrlM0plquRrlA63bO779mlmvXe+MY33jz3uc+dvs2sRXuavhLol7zkJdNPp2+tOdP8X6z7779/+pyYvuao6a/XGG2ZA26+NdSVUDKd46O+cJGI+59CJc2tvuZxVdS32fpQur/T/1INvq6L0dPgdclmfbS/8IUvnL5S+jnPec70mbdrhqNwOeCAAw444IADHjXgG0p9Xs4/JXYNx08fjJ9r+p6jmds3HPpKX7/DY66V+JyyoT/r9xocfJOYkw1/lzTUuGrmRFvZr26tcSOtnCe4+d16vjp/rhkDt/St6fB7nmuhJe8v3dD25je/+a3wukQLX/A2eDh9umY4CpcDDjjggAMOOOBRA65o+cCza0Nl8+ySrbV2+axs5bhTtZE1lvqV7+vW6n/qNrLeXN+5sXP9L9Fuc+20EgeN/VwzHIXLAQcccMABBxxwwAEHHHDVn28BR+FywAEHHHDAAQcccMABB1w9HIXLAQcccMABBxxwwAEHHHD1cBQuBxxwwAEHHHDAAQcccMDVw1G4HHDAAQcccMABBxxwwAFXD0fhcsABBxxwwAEHHHDAAQdcPRyFywEHHHDAAQcccMABBxxw9XAULgcccMABBxxwwAEHHHDA1cNRuBxwwAEHHHDAAQcccMABVw9H4XLAAQcccMABBxxwwAEHXD0chcsBBxxwwAEHHHDAAQcccPVwFC4HHHDAAQcccMABBxxwwNXDUbgccMABBxxwwAEHHHDAAVcPR+FywAEHHHDAAQcccMABB1w9HIXLARP89m//9tGOtqsdcMABB1wKWj7oaHdf+z1leKvnR7u9dm64msLl//7f/3vzn//zf775mq/5mpu//tf/+vTzb//tvz3cjPuqr/qqm6/+6q9+5O+tcy018/6Nv/E3bv7W3/pbU2v1mWt/82/+zUfGBselpj/ejIxJs6afI7jCsSeLr/3ar32k+Tvzj+CoH7rCi3qNVgvv/GzNWbbMnzGelXivaeagT+bw+5oW3vkdzvX7Xitpq2k/ZbPWV37lVz4i4y0tPPHzP/2n//R71rwNOLz/83/+z80v/uIv3vzsz/5ss/3cz/3c0Y52tCtsP//zPz/Z7q/8yq/c/NZv/dbvWfU4/OZv/ubNj/7ojz7ie6+l8XF8c/xzq885G3/rJ3+d+KC1fPs5mrVCf9a+5PplCx/8/Lqv+7q3itnnbNbHg8iFLG6jRQ9LWVxD+zt/5+/c/MiP/MhZC5irKVx+/dd//ebNb37zzQd8wAfcfPRHf/TNZ33WZ9183ud93s3nf/7nD7XP/dzPvfnjf/yP37zkJS+5+Yt/8S/efPZnf/Y0T6u1xo80c3zsx37stNZnfuZnNvvMtfvvv//mT/7JP9nFr25/4S/8hZvnPe95N5/+6Z8+0dmad659wRd8wcSP5zznOTd/6S/9peb8dfuMz/iMmxe+8IXTusaXbW6NN77xjTcPP/zwzed8zue81ZhWwzdr4OPaMa973etuHnrooYl3LTzKhg50v+IVr7j5M3/mz0x/t/rNNf3/9J/+09N6dLKFT6vB7WUve9nN61//+kdk1epXNmvh9Stf+cpNuI40vKYHz3/+828eeOCBR3j/l//yX17d9Ce/V73qVTfv/u7vPjmtPc7K5sW/+lf/6uYZz3jGzdOe9rRme/rTn36yZr5777335ilPecruuc1hrr3z3HfffZP/eupTn9p8v7YZDx/ztd73mjH33HPPprHoN04boSHjtsoCrXDewresa+we+ZkHHqeYZyv/ywYPuoS21vtzNPHlm77pm3YVLnKBf/AP/sHNgw8+2PQ7t9X4TfHt5S9/+eQ7/d3qd47G11pPfPiQD/mQKb4kZlyiiWFixCd/8idPchYL4eCZd60x52jW0vDhpS996c0nfMInPMKbSzXrk/8LXvCCm0/8xE+c5OM5PC7R8AHfn/zkJ098KNcu8byNBje5kmJyjw9YgqsqXDir5z73uVOy933f9303v/qrv3rzG7/xG1NCs7b92q/92s2P/diPTcmt+ewEmaPVWuO1Vt9Wg7P5Ceq//tf/Oq3d6tdq3/Ed33Hz8R//8Te/9Eu/NI0z11L75V/+5Zu/9tf+2s0//If/8OZ//a//NT1rzV03/azxv//3/775mI/5mC5PymYNBvo//sf/+P/hMbeu5z/4gz9488Vf/MXTzlurT9n0/5mf+ZlJVj/0Qz804diSR90UuHY98KM1b9no0H//7//95q/8lb9y8+///b9/RKfWNPiQj7Uk1E4DWv1aDc/+6l/9qxNdrfetZv5/82/+zbSTYvdyRJ9GGr7/9E//9M3Xf/3XT7z/L//lv3Tl2mpww/9/+k//6RRAFJ7w3uOszCvpoXP43mrWPFWj33bQOHz89oyNbGlf9mVfNp30/sIv/MJbrbOmhTb4fPmXf/nNT/7kT75Vn5H2/d///VNw+6mf+qm34uFSs1um2P/X//pfT3bc6jPX0P/P/tk/m3Yi6Rje0Otegy+fhG5rkkvN36XG1//ZP/tnp3lqXiw1PkgB/m//7b+daGjhuKaxeZtK4Vurz5r2P//n/5ySQrYFv/A2+LbobzUnlPSS/8oc52xsCN788x7gh8hTElT7ndtsfB7Z0NPv+Z7vmU6WWvHpHM36iZeSVXFlxF+foqGffdlw/W//7b9NsfTSOFhPE7M+5VM+ZZLHpXF4y1veMp0I2mBnZ/hySRzo3Q//8A9PcuBvs/6l+dBqcBNP925iLsHVFS52+SVUn/Zpn/ZI0jcKFMvYT/3UT33E+Tt+PjUQDKWB9xd90RdNNKwVFoVH67d+67dODsC4pYYXEhonAJLxkSTReLgaK6CtAUHUboJCEP9KXFrguetCEnY8XwL9JSkf/uEfPgW+tfS43vQt3/Itk5yXAM8UlZJhycQIzwQMxY5k8sd//Menv5cATfRA8mIcOa8F/JbwKWp7fN4DaMBzOyJ2q8gWH0fW0heuijmJ2rd/+7dPiR/c9+CMb//oH/2jKfkhp1YLX07R6DS9kyB953d+57T+VpCk242lL+xsBPe8BwIxfDIPHMs5ytaaK821W8U6Wbfe95qgbLfbrir+1LZfthroF1tRNFm7NzaNjUr62Yuiyd+tfq1mPf7MhsxrXvOaSS/xxrprmvH8giTET89a6/Sa9eDMHvg+Nt/qt9TMQ+bf9V3fdfOlX/qlU9FZ0uJ3rTW2bnTZJhK7lOxm7Dkb3O26KpT2gHnEVLHnmgBfyfnv/t2/e/P3/t7fm+L2JcH6EsO///f//pQjxU4uBdZCs9jrmlbylktCePCN3/iNk56tyQFODXzGV3zFV0wxNP7iksDH8e1yDPSzPThcGo8W0El2e1cVLt/wDd8wEe13hvlxH/dxN//u3/27SVFGgfP77u/+7qkAkgz5+9SMNF8Cpx1KSTtFXgOUTbB26qIYWQvmx5sv/MIvHDIa/fBVMrL2swgck90zibSxa9aSCNvlE3SXwHw/8RM/MV1XovBrAM2SA0kCmS4BvO1O0AM8HwEOQpD65//8n68eCz+7IHblyiRsCcyvGJXw2dU6B8ADboKO4267Zmv1NWAONHGaEnW6gU+S7b3OijydJuLBJQCu6Jfg4YeEewuEJzZL6JlE0bO6rQH6qojCz6VEugcKIHZCp0bhe7/3e2+e9axnTdd+yDe+s9VaAGe6QY/5x9a4spnfDja/xpdmzBrgo8QNV6JcMaSLI2AdJwWuO47aQsA4uiOZcVNgrS8rAR7mQb+ixelPfG7Z1gBfQgfJ4D/8h/+wCZ8tAF/xWwG6B+hDcoFrhB/4gR+YfBS9uTTgMR1zuivGbtXZrUAH5StOO+Qvl4botqvUNotGY/peQL/NHDcM+NjbWF9eIc6I5daPb9BuG/iau7ZwIQy7VnZyHYcJpAx0hBH6coCSVgWQ6wsju99rwTqqXomcYsJuwFqAHwdkByUKuAT6SQg+6ZM+adpVxbe1QKnsUvyLf/Evfu9JH/RX/I3srnCmaILbEpiPbP/Un/pTq4pTtDNWCYJxa2jXhx6NBkFrcZB2/9eeUAF0fNu3fdvEM/Jdk4R5j7+SZ0n0iEzXAvvJSYviVdFCvmtkGsATNEms7XDTDfrumZ3WO61wAWhyOsgu/vE//seP7GCNgjHGStrweCT5LsE8dMF1S7vOW+fhI1zds/EzCooVn7NyYv3qV7960pvoSt1agIY3velNU2Bfk1iRu5Nx/t/vxs/NHfBeP6epdM91XXfOUyytBfM43frzf/7Pb04C2QBZKbz8voR7DfpraDcP/7lVD81DVjaPbDqFn5cAfutuKFwkrm5L2ABE86i89wBZ8le+LEacOUesWALri2//5J/8k997cjlgo9/8zd88XUei55eG6KbbBX6/pOytRf78zG2c+K0BMrlrC5cQTDEYh1MJO0f6jDphAUAAfdGLXjTtnPt7a4CaA/jacbNLKlFYKzAB9j/+x/84JWqS4zV4mZvCSi4E7JFCiVJxdoxuDcBHoSCoS17W8N4usfntCi0BWgRqSdqaZAP+goXCxc81jovOCKQj967hha/GcJL0cA3gD/1SKDtJ8re2pA/62K1XjG7R8SUgR/QoClwPsws/ktwBNKDNTrDdePgGTzjfqYULoEcKbTvd7k6P8iZgHD/gGN+u2NZ58JXdfeRHfuSkR1v8FXn7dhcnkyMysTYfoWhxzYitOT1MQl63FpjDdU4bJGtwp1c+2+Rk3Nje3AH94CSA0z0bDHaB+cURvltHwcpHmHMEjLUWe8IvstoK+CQGOPkZ2SipgS7brHMDINf8LgX8wN1QuMDPZhg7z9XEJX09JVjLdS0F7jk2Y5cA/Wxb8UTHRu1mK1iHjn3UR33UZHOX1O0AvyyG2qC9pMwB+uVh5L524/bSwP/c9YULQQlqnv+5P/fnpiRfoBhliDF25iSGghRjOzVTFRNOTuy22pFYY8xw4HgkGO5srq2gjWM4EgsJ5FqgVHjI4a4BjoGhPPvZz55OAoxf4ptkQuCSQCyBueCiOF2TbNAFJ2eSFMfUa8bQK59vWYNPgOwksvQlgWkNwI9+2b0nS/SlzYG58YwsR64ajoArDXZpfL7JVb619JQgWPk8lpOWXD/JPH6/kwsXOKOF/QrGW3eyzIMX//Jf/svpSzQEua1gHsV/vjhgFOiiTYGRa47wRzt75HMl4wo5pycCpXn0KVsL9GNvTqDX2Cgf6Eqmgmlp7gD+2CTyGSunNb4WVPGD7yPXxazDB5WF+FrQn4wVdhK5PYkEGSs29iYkTonZj1MkvmSJj6cEeN8NhQu5sy8bVPKKLTnJXrAZ6xu+cs18jc2cCuiVdUduPpwCrOOUn36z8UvzHPBr+fKhS0NisJN0edlt0L8E/PJdX7gATkIhIAjbUVubQNdA6O6m+kaOfGB/NFD1gPOyC+BD+hK7NQEbwIHxC/KKEX+voU2CYYdypFDCN4mIQL3G2eqD33/oD/2h6TqXBHspsYaXJNlObw8na3vv6wQl7Gv4Jbi7t/0RH/ERq+9u0yvJtgCzBuBEV+ih04U1fArYcaVfrc8mzAH8OCPFzqmdMdzxjBORhLoeNhpk4EOmknG0+eCwoF3SRQ53cuESwCtrs0e4bKGF/rBjny8RZEf5HTAPu5YcsKVRnwd/ybRCeq1e0Vm7/XyKRJ68nRzlw+LulAePtBbA3cmTb4dcY9eud9lFlGwvzQ3MKXDnhMWpC5/O/uyG4tsasAaaJYBwWPJtJRgLDxspOflG9ygYg6d8uaLL71vnYaf5Rjc+qMfDcwBdvxsKF0BX5CTi/RodPzWQteta9Gar7m2F2I04b8MBLpcAG86uU9uwGPWHewF/+VEbJDaD6OilwQaP9d0CuaS8R4BcjsLld8DvGoZIanx9qwLE36NA2XwI1OkN4VvzlMylTIKoilhytxYEYYWLz8lwCGtw4iydOkgoXA1Yw4+McY1DoF5SfjSQyWMf+9jpu9vXfPDUGMEz3/o1R4vn+O8UwO7NkvPX32lR/k/D2qRQH/9nZ+2uNfrwE1/XHoPDTePIJYojuilRtNaazwSNAHxyrOwKo2+K2uLs0OIrmt/whjdMSSIbMk9oTp9HQ+GCDoW9hDtX/Ubp0R8dih9JDRls4Tugu77WmL2OXo1Ai+JeYqHgWDOWDTqR+5Iv+ZJJ9+mOoontmMepsCQpsp/jDXrR7QOsPR8QwGt+H55LcwOJEh+rwMpneegofokPa0+64CnB93kFY0b4qy+8fQWzTZElGucAz+kcXeEDlvxgC6xtHDwUP2s3dU4N+H+3FC50h776DO6aWHpqoH82B2zKiSGXXh/Y1LAhdqki2WaKwmXPSfZWYF/yDz5RLB3xFacA/KVv+M1HX4LfW4DfuasKlyVnhQkqXh8MdkQroHs2whx9OVc7/O4S25EUiE8JdlslPo7s1wYP/STLrgtlt34J0MKYfA5DAF9TKJlXEmLMmmIHvxVU6JG42pmEXw/M6ZTG9bdewhTH69sxlnY7OWW7Da58+TYRJy70gM70wJxw4NwlYmsAzU6k1p4CAf04E9eMFIY9WgLkp59ilTzW7hKvAfOSk0JKUpYP4o8CuhSI/qGoolXC2LI5cz8aChd6xj/AwXUAfNySEBjHHumDhB9/tvDF2uZhS3zjyM6msXQADvl2uyXQRxLteprxTtlcgyIXc6QABj16vGNHrj6xi5496Ovai+ufa+7r62/jit9g0/wZGhWK8Hd1DA1rQH/JkMJw9AQervihYMKfLfIF5rFzbrOHrEf1zboaHNifjTM6PDrPKeBuKlzwHL3sm/+G8yXB+vTXF62MXAc9JbBx68vF4LLVBpYAbcl1XOe91AlPCWzTpiQbI/dz0doCa1nfzQxfunEb9K8FenDXFS6OXnvOigIzFsejPn9gZ8m4UaOlBBJMxYuAseYK1FqgVOa0M7C2KII/Y5CYU84RJ+C0QjJvF2Ip0Y4BOA1xtWLJ2QqqTqckcr4ggWx8KBGv5vDzzg6QnYneh8DR6PRr6WTDOuZUaNndgoNCSlK1hD+eujdPFktJkXXgACeJxFKBVgKe5vME5N/jT8Ba1pBoSH7w4xSA32jlZF2bkWjiwyigyRUhX2qRIneOJu9OVbhIWFxRCh8vDdZkS7mSsIV3gHzN4wP2EvOt8wCbIRL1tZ/rAtZHix34td9UF5tUrABFBx2yJnnYIMmpy5y9AjqgP1nyhXN2qp+56ZeAvOQv0cNm6FpOf516uE6X4gyO1lwD8OLbpqsfv6Pva3RXH+vw0/FdWyC0K8JslPDjnq3BoQT9yZaO8dG3sRsNyIbfsfnGD+yB+IFrLlwAG7D5JkauPdE/JeB5Toj5655NngPYAf1FP9mP6u5aYCf02wZFiqRLArrYlQ0dGySXBnJ1gu2avE3ItTHgNoBsjsKlAoxgrOnrQ6PuRW8xWMJX+LgnLck71Y43HOFnl3/kG8b0g49xgvNamuLky3981gPzSubXfKaCM2YskoTXvva104d27U728DMf5XX1offNXxytpJ0c/T6Hh+eSICcHihWB0VUWpwnG9cA4BZRka6kveiQhdtBciRpxjpInCaKrHvBN64FkQ2GgSILnUv+1QGaSN8FMUKPnI3Pri3ZXhHyuSWCCa08f9T9F4UJG+O9rbRXXW5PCPQB/PGO7aFI0bKEp8ygabEg4gd3KG/xns3wj21szjz5kRr9yOrk0jhztauZrTgXLP/En/sS0PlrIIx8g93cP+GlXK9jqnJ/xDF6KB1d3l2xUYcNXs/34OgWLosfJKjzRqpBZA+bjN0c+mI9uyao11lydnQP8wV9FolNXuC/JpwXG0FEF5m1dEQP8jg3FD/uwD5uu7u2BxLRrL1wAGdJxG3qXBjqbU489n6fbA+zBlVB+bq0NjQJ9EPvlHuz+XOvMARsTS13Tuo2NAb6Cj/DtrmR8afpHgP85CpcGYIam0NDfh7tV4QxohFH6CpQSFMHYrtupjAIuFC3ffkHxlgA+CaSOQ5eCeAC+khnOQwBewwPBXXBZKnRU965eSEDMb/degm5XtuckzcmZw2cukKJVH0VCT3b0Ai/hK0lhvGSFTxL+HtARhdeau+Pw5Hx9+G3tnWU4w0/QUiygybO0OTC3cYq7U+3goE/ywGFIlJ12WaOHRw360ju7yWwC39G0pL94d6oTF0WmZJlukHmuG17SWaOBTaHH7v3WwtIYNNkplJyP+qgA+snFtU38WTuPPk5WXTVbowvkSO42HIBChf04DYCDxtf6XIcA3rMp8tLH6Rk7bK2tj3dOHFJkt8BYuNFpPOCPog9sVuHCl+nD1l3h8n6JXmuzwbVXy8zHHsgST43fopfmQatEX+FCv/B2Cd8arM0P+zA+v4X+LfhsAbhq9Ar/6LgdaRsmcNkD5rxTChe4sjHX4+L/LwXhv6IF79nApYG+iceKJ/p3DvrlUDYg+eI1dn1KsBa+ikdrbnmcGtBrTTeMyHlrDLkU0IGjcOkApgjmvgKz/MD+KLPMYedMAcQwTlHRCkISH1cJJH/wWgOU0s4sXFwLWQvG2SF3KrFGsSUfdmLtFvVAP/JQwDjpgJNgu+Z0x4mOXWL4tEDQlyRZozePZJxTdm0FHpImBYyxHFoP0Pfyl798dsc34J3kAU/ylaxrIHJ2rchRdvQvbQ7wxEkh3p7iVIG+wkOir4DAnzm+94DuK0qdtNBbc6wJFOg+VeFiR52uOcWSkPnyhlx3u2TQQpMk2Gddlj6n0QM8JA8FvwRv6zzGSdydMq/9HBWwecB+1gRcPJZ4KtyBRExCqpA1Hv89k7RLJJY2V+gkWSo+W4AGxQ1/Z6452XrOPiWz1o7de67wz6klXvMRipE1SaSCjFzW+FpzwZedK8assxXMQ79dfSXT6PUSvjXQUf5T4SdurNWJvQBPONMXhaNbC07B8FECy/fvAbp2pxQu0TmnAbd14sW+bJJa/9JAD+hevrVyS9zpAV0TxyXuThYvCbF58cdNGDnLpWwsQJ/IFX/lQtYf9ROXBPgehUsHolQClBMExYsgzXBGGKYvB0w5fftUdlj3gDnhIahK6JYKhIBxArjgzPmvpUUfhQTnpQhbSlIkUByt4NkDOyl2BAV4JwpOR4yxC+snvs2B3YnQ0ALJkQ/kzjlbNHGKCjK7WXjodMwYVzU4MrLvgSTD55iW+MHYOH8BeKSQoH90Fn7oLBOQntzIStKCh3sdoTU5VKd0TqMkQuTSW78GffGAHX34h3/4ZEdsYImOgLGnLFzoMb7gE5m7psguyXsvv9YCvqKLfO0gL+nQHOCHsa5fmWep2J6D4MO+2cDaediZzz4s4Q9P/FYosgF/0yMbQjZgsl7kInn32ZQe6Md/SG5bemEudsef9Hydd/k/XHDDC8BX2u12rRCunptPwbumeLd5oNhPodYDc1tbfNr7P1vwxRed4O1WvQKKPna3ddNuK5Ab3vPx/hmg4olt8hk2ce6GD+cH8JyeKfBH/lnxKYFt4jlfFdu4JLCFXCPfmzuVgLf0ii7wnX6/lI4DvNSc6LrWfqnYUwLZsnGnLXTrkvRvAX7oKFwWAGOi3K5QqEolwqOJG2BwEmLfWJZrY3sVVRC1++f/gZhrDU4EL6A6mrTTuxR8A/pZxzUJO509sHMhoVCszQFc7aIpWBQNduIlBII8/liHUc3RJHGw2zqHP9oY5BwO+GUt61vbWq5oMAg7POb2s+eoFY52qHtJBvzJ2q5lnMMagB++2ImxE2KeutXgGXyd6rjysla2c2C8ogVfJKd4uSVwsh9FqQJB0TJqP3T2lIWL5AeYyzNXgyTKdIFNbbHvrSA5lDD3Pq+1BHBlc3xbNle2gHnonBNPpyBr+MAXKAzYT89W0MZ/OIGIXetPN4wvfYq+bMVpFHzm/CSfqqBWdLbwNI7eKYzm/KPnCgZfQKJoSx+4sbsUKRnPlp3C81FLtuBU0Wd6jOmBecnMlVBXZ9Hf4+UcmMdYuJFhzy/1wDzGKqCcfC35+1MBmq1LlyWSitzssoc2fuBuKlwCdFNMpEt4cUkgE5+XVDyRzRbd3APWE3v4aJucpwJ85H9f85rXTLGefl2St3wK25ITykUuLVfAvtykEfesfxs4jAAZHYXLSmA4xku0XTtQvHg2yjgO2O6g4CQZXApoSwAnQdTJw9r70HAW8AVUiZokYg0Yl+RIot8DuAicCqQ5YLQCkAACB6cXdsIZMrpcKZHozNHkm8XyjUQtsEuoqJtzdNaQKORqCJz1l2zAAW5+7wV/MpSI9frgG6efhGStzsDHFRXJA1yNq1sNeCXRIyPXKrYmsAHOlG4pWnKdqbVuD9Bs51RiqNDbOodTgFMXLsB8+GZTQYKLd05h9vJuLaBNYqx4cUK2lT46SF/ZnWCMz1vAPIoGCSubXOKDwkOR7HSkZwf6+cB5/h8KQKvfXc9kzwHyUEzwUWwshU4NduXZBxtuvccDu5n6zPHD3PwgWyuvk+lPHhLlMlmEGz4raNDUA+uKGUu7xObkp3x7kpMff28BOLNTBZ9inG5tATKX0JAXuZ7TFkq54RMf4eqh3XWywYv0Qc/dWriwByf2bjNs1Y+tYD3ri3VOzbfq1R4Q16xvQ+VUgC75k01KfqjlQ84J/A09pM+l77kUoF88FFflG5defwvQPXZ71xQuFEOAUl0LDqPGR8iE6zMYkmoO1hwjTgSj4eHo2+5BTl5G5ihBoJJccvQKorUBBh4SCF+l6qqUALxGCeDO0CUUHMkc3gIQJ2P3tzWvZ3gp+Avu5rXjofL3HB2SJ4Fc8GolHYoLchCE4FGvY6dVEgfPGvTFd6dCdrLML9lTCEn48UPRJHj2eCPRzTWSFpgX/hJln2do0VGDPuHNi1/84keSUDjUrQa8IB84kcFWvcJ/u82uueTzNaOOHc7kSr/ouoC3lOi1wBySp+c973lTcjmCQw2twiWAPjTjnQJaQBGsrdfSr1NA5ra2bztDn9+3yA2/8VfxzbErBLYkGNZmG4oBvg7/4TlHP3zZsEDSky+ddrKCxyV9cJSM2uTIGn6ixwZRinB/12Btfoxfb9FqHQWz8TVPswZf7HMT/EQ5h3euRUpqjC3pt3Fj93lu40lffKNLrrjQux6YR5Hg5Mi4LQB3+CrwXTvZGlfgDl8bFuf+ny3WMjde85OKVLHMhhLbq2UeXbkbCxd64SSdb8KrS0JsRWxJgX9poIfsg16K/Xt10nh0iPlrrn2eA+RufE/WL33MuYE85VxivI1V/L0TgA+46woXCfczn/nM6VuR7HAR3lri9dM4PYE6X+WKkaMMhItCw+cQyvvTW8GOtgALt7W4KN4kg09+8pOnpHINDlH2JBploC9B0iy4mLflEKwjGRb88cE8jNhReI6iJTqSMM66tY73EmLBurUGJycZaDl589m5QocAaT2J3kMPPTTJxnyOTu0EW6fFU8+cotidnzN668A/pzpL/PVePyeDj3/846fPg1gfWK9uJfg7pwaKJHO1+i2BcXiicJLg05MtTh0f2Yc76opQcwSnNaAffXOFyhwve9nLplOAUXpKYB9zhYt54Ud37Vbrk9MLcvTuXIBOek92kuKWvq8B89hQeZ/3eZ/pW/rybXdbeMau7PCy994c3sFdAiD5n+sn2YBTTZu/nUrwg/VYhRCfzR5aNmZtmyP6tK5a4MdHf/RHTzKs5ecdPSdjiXI9v/dkYQOmBr7PpkZ5SlSCsfgm8Y9/mwN4Kb5c3VXUjQKaze90yKac+MbvzsmhC78zBk8lUjaN+MBzAhytx8eJyWyOzrLTFqDzbi1c6JTY8OpXv3qKXZvkuwOsR8dsBMDj0uuzE35GTrBkU2vAePatUE4OcEmwnpNbPj92j6eX4it/Jy47ke5tQl8bkNtdV7gIgJJdnzdQ6W65UkHA5hLsfP5AEON8RwSP4QQgOfA/JfLB4K1gd01SYL65JLoGiQQn9P7v//43T3nKU6YThlaSXwK80WlnjMLPreW5I11JIl7VvMEvzkdhkQISPua0u64//gigPlfkXQ34JeFpfWDRWGsLcBLREtCAXwJzPuNjPVcjnEDBJfKRCLSSMX+jkS71rmTAm66hI/POARzw3wnF05/+9Jv77rtv+oDtGr0wLzropLvh6PMsbS3AwVjyzekTOkfm0Bc/JE9PfepTp13zFNQj81hXEkNHyVhig7aROWroFS4BcrK2YCY59YUECkLP8GfP+nNgTvJz+icx25LAAvNImJ/4xCfe3HPPPVOSw67Z4CjgFX3C9+hTCzzHm4cffrhbuJClQgidJdAVCQRfUI/Fb/r/hje8YbJVf5fgb8mMAoGelXOTI9/Cf5BlPTeeOFHiI1rvjadvPpxfg00X+M59fg4e6LU7rbip8Q5YQ3FAz9jLnB/pQekznv3sZ9+88pWvnBLbLYAHighfF47f5HouwBN8sjHB7/LlaMGDOX55d7cWLgC/4C3ZnePROUEstdlYnsReCqyF/nL9PUDX5IDyjaXYfGqwHl46KRZr99KyBbI+u8PXS9K/B/iAu6pwibPK5yjs7jtWt4swGjA4DcFcwHS9acsHjgGFFSDyVcnmpNSjgDZXHnxQvJXkt8BakhInR65w+NY0O38Meskp2t3Mh7VbvEMDugR3+NQ04ZXdBruZEhN8gw9DckIBPLMOJy2Bqo3LHGh2v75M7vWxpl1YSUfpFLyDiySB7DMnegV/X4HsvWfeKQYlRuW6QB96w4BaJz4Zbx0GtiQT8+G7kzPfUuYk56Uvfel0UlMXXi0wXsJqLTvu9AEOaWsAvvD0JRSKhXwQf+34ADrIy2ni0uceWmA960r8nCLabLDBwG73OivzLhUu4ZlGJzl2BaTjdPwZ9RUjIPGXxNIDejuanMAZjr6imw4prH12RnBkS/RkLVgbv8zhSmtspQbP8IQfYQutPoAtuuZR88+8Nn98LiO2V4K/FXR8JNpqGugXP8yflHN7jo9sKD4G+GkORTnfwXe3+KyPosfGRI0TPPJtZi2Ah3ltxLRoCuBv+W1wNW09wDdFjyuGNhn4WsWjuEaPRgEP8Ay/XB/x+xzee8A6+IOHbi7gMTmRUY9XwLi7uXDBn8Re/n5EX04BdM5mgBilID+HfvTAenwFnVmTp8yBeWx0iCnouTTgo/jGh9zGNTV6I6aKazZsLy3HPcAH3JWFC6IpvF02x/OS99EdKgyLAxY4JP4Clb9HwZj8k0o7CWUSvhbgUyYZa8A6AqvE8BWveMWUoEl2kjT1AM4SOTuoAlALJNyKA4m//qWSwVWhpnDJc/0llZKsgH7wsXNZJ174L0D7nEuJg+d2RJNYlAHYeDuKEmKJeeYzxjUkAdHvwDs7MuU3DQXM6ZjV7k8rgPjbrjmerrk/Shd9O5IExJUZx9d+xyN8qdevAX+dcMGHE9S/bGsAvhKWnEStHRfQH52SEYUPuvFhdB5jBBW6TB7khT4JyyUKF2CNNPxUDLINeki3zgXoFJj5pWwKjNCrL7tGnyIBz+gFfZfQCpa1rvZAX8UI2lsFesBztmzTocY3fyvInP6gqQRj+V+JNzuI/ZUgScH7FCElmF/hUn+WJP4tHy43r74am2Jv9GvO36LdSQC7qHEyhn+hFy2If+hdbTQn3yj+5B9yrgW4KagkXmjEV99MRLfpDn7NrTsH5CKGiY/nTIrN6wSTPvCv1vQs8ulB/MDdWrjgj9hMd+n8mthwaiArcnMK3rLVcwMfwWZsWta+ZC3wB2zFdWibU5cG+ifO82lwuaQMo0M2F23c8BV3EpD5XVu4IBgDGAEGcIYShaUEswRzaBSfEfvqRlceRo05iqR4sZOg8BAYR4XCAAQBxYJguxR40CrYSwacdFBia6vC0QGHOVo85zRdo7HT2Qo6nkk+7WjArYQ43zIAkY/1Pc98xtkZEIwlwqWj8t4Yn38oCxdjJEKSf8VOSYP+HJYCEf55h1d2ppNs51kCRE0b/J1MeEf+5RoAnuQpcZF4z8nC8xQtCld8Nyd5SDSdgMG5B3BDC17bvfF3jW8P8Iu+SHQlvHaiRwNCZCFxVczSiS06bA4JGVtSUId3cLxk4VKC9chbkklf2UkK4ujpqcB85kW7xA6+tW4tAV45PVRI0CM4K0TRTacUlnR2TidLCO02D8xHHi2wpkCocK75ER6RH7usdct7u7f8UE7o6jmMYct8m89N1eA0h28vixAB2Wl4fIA5NXRbh71ZF+416M8mXFHjW2oZ4Al86XsLX8/4GD6ifheAn/v6EhjzrQG4o9GHpCVv6Ba3JJJkxH/aLNmS0KJXERSfMzp+CcwHL6fQfBX+lV/6sWY9enC3Fy50wMYOmd/GqQdbsAFig4s81/iRUwIdkOc4deKPattcAvi6LWGTzuZCy37PDWyfL7Pxcem1rSfncCOCHNF/JwH539WFC6DEdu0FXI7ATpNANsoQcwscdv+3fnBM8OKQ3Eu3006hRowSLYKsBFSSAqcexAH5DIaEQjBhyJqrJoyqhwM+ofXTPu3TJtxb/QSZ1lEoh2eHVeEUsJZvApO4mw89Gl4KyJJNBu8ZICNjfIBesh2IY5OYlOvCj8NCbz5wbi7P/e6ahUAaOryz+23dWh/gAX946Ve+9zveS3AVL3O6ZB18ICs8tINkVzv/uV+xli9/6AEe5LRFQjq3XgvgbowEVyK09dgaDvQFD7cEA32ta+fZlSPJdZkke3dbhUsAr9hXTpQUmfQUbi3d3wOSbVejtsoDnk5J8JNfY+f46fRQMi/xE7zWzg0fhQv7rPUdmMcuOpureeHvJOn5nxwlmIsOKt41v+tTr0F2CjLJda3nbEcigqaARBw+eKGvBnfzuALKtuo1gGfkajOKrzBnTRP9luArEubes194tQAeih4bVWjx9xLoI1bxK7nCxn8oYvBE7OL7+ZyR+AP34ONaro2iU0L4Dlen6fSar4hcRgBf7+bCJcAeyUoRWOveuYHM6CzbcNJHJpcG8vP5YvZFt0b0CL7w5o/4xJavOSewTXmGjVI2cGmgL/SGHeLFJWk/BeDfXV+4+ElxBR879CleRp2B/hyzpEbxInEGI4wNLpRaAiipF/DXQsYzSjTajVkCu3d2TiQddnklv9aEv+tj5kJXC6zH8J0qSCgE+xrsqkkA4FWCdV1fEWQDKThcvbO7TV7mV5TYWfQNaHYnBdbIxxjfCmSHP+CZpNI1igDnRq52sX2WRwJmDbTpDx88z+5sxrhDjj+1HDlu67a+U54zkCgqBMod4BLMh8/w4IAVrHBxz19iLcGEK9pr3tWAH66J4NWIE0cnvbeeE47eVaA5sBb+SeLuv//+aSeb3My9Fg+AZ5JsJ3j0BS8iBwCv2y5crAsPc0jAfYBZAiX44MEevGowH3mQqwR8ZG594Sgw8QGCFL0yDz57JqHn7xTya2QOH/PwD60x/javoqgOht7Rb5/jaF1B8t5Ggm+N+6AP+qBJPnS5ppk+SNxd7+AbS38Dp5ywBqypeKDj5tLYo/ESfDpWrwE3/eH6uMc9bvpWP36i1md/O0lWMHhf04QH/IZ3NehLFmwup9E1HjWYT6GCvwo0/gf+dIQfssGCt/n85tJ8JVgf/yTCWzfdemC+nObYoMmJbmnfawEfjsLldzc54a/Qp9NbeLkH6LANKjY3kqOcCuis2Evf0V7bZw/I3hUtfsY8aBmxlz1gHb7NaYsTavp8aWB7Pgstx/D7pWg/FcD5ri1cSkA8ZlAoTpFSca6jSsV4BBSJtwAzd+1hCTglia8rW05ekpCuAWsJEnYTJJPW7zk1/X2WQoAVGN3blkBa066YRFLCwjm26MA3DmyuUDIWLhxEwDySA0mZNQRvQVeRIHAKcBITYzlmSaYEhKGTDRwVJmSkUWDJCFrhA1cfzLcjZG594CjAKwYUQfA1r6scrocpvOAjgYIHfiggJT+ui9TBQYIkGTRvDZIrfJNUwKcGsqQnijCnC2jGb8mwceZ2kiN5Mtec/PDRXGigc9byrCWnGsjD3HaM0Td6TRJYJ8WXJBkd0fc1OAD9jKHvNg2ctrX0Hb63XbgAa5MH/NgZPaJXbIaOrLXTNUCeEmyNTxiZG535HJd54Mmm8NHf9E/yp4BhZ3iOrjneeo4+SbPC0vyepT/cJMxsty6A9bU2facvpT773XtyZX9OEtgAey6/1SprmVfA5VPYduaCv40CdhucnICzi6wJRz6Zb4ZriaMx8MAvJ6l00Vej8zU2ZsxR8j+40CN+InN5bi39+VV41YD3Eid+iFxLftRgTXOwMTi5fsZ3Ws88NoacYvid3xIzRvTE2nDAJzw3bw+fEYCH4ipfvEBmcCz5Pgr04Shcftc3uCXB3rZ+Xf1eEK/4D/GT/sHpVLqzBNaRbyx99q4GOmmjz62G+A8tPuNcYP74XTmPGy11TnEJwCexltxacfZOgMSzu75wCWCCoMAZCG4cwwgYr1GOJISSuS1OhXAEaAom0RJc1gI6BTp0SK56yumdK2Kcn6AgcZQoCbzWdH3Ae7sbcKoBvYKRIkESXyuSogEfSh74nbOzmykYG5smEXDSI+i7EqFwk2TAU4Oj4O10BF5kaffAfVeFmsRF0u/+pnXNYZfPaYDCB02RkwIBbwVV/X0tNXzMCRc//e0rkevdEXR5r7CpwfoSnjm5cxgKMgmaREo/eucqDpn7nf64vuVdzdOAd07KJM5l8rQG6IXkCW/o+RYHih/0Au+dzJHPHK5zYF0Fs4QrpxctQNs1FC41CD58ioI6iSjYg2MAb+iznXAnkyPyJRuFLz9ALoK7YC3ZiKz9FEQVCwp9hay+PRD09Gfzxmto9VOSyjZtAuBzeOC5pJjNl2M0PoUN0EPFDZ2kD37yfexOn3KM4E8PJOzWAfwVeyx1mQ2RifXRRbdsXLDPjAvgLV1W/LC7XFl0EsE/KO5KmoDf7TjDN77RM3PBg++Aawne85N0uSyyWoAOGzo2beBFVsHbOOvSO8WWNeFsB7o3Zw3GKSzYH1rByPgWZDx9sqlCjmQV37sH6PVRuPyubpCdOJPPSO3l7QhYS+wRN508yzXYcStHOAdYnwxdk+QL2PgaMEbs5V+yoWGuc/IuuLqaahPkhS984fS53EvxqgT+SL4gr6M/56T7XIBv7PYoXAoQ4DhcQZ+DtOs3qmCYKTkVIF0bk4yOKkmp7O5CmkuStGYONFBQQUxCae05YLh2Uc1vHHqTmKDbmgoCyanA6e8a4Gl30zx2Qcr1JAs+s2MuuFtPUHc9SoKDPg7PT/PjlZ1iBZTk2tzwiqNWbChGJJ4SIYWhe64MUeLv2904UX9L9sytn+LGjh/ZwkMzn/klEIKrIKAvPOCTuQUGSUiSImMlCfRDwRCAp90cRY8E0d8l+JteCOZolHiEPnTnW5XITvKEB9abA85aH9cT/d7rG0AzvjJ6H9SGJxzW6FVAXwmEpFgy7Oda3SzBunigwFRM9HaA4H2NhQuekxddIXc6iL/0fY08lgCf2R/dNO9aMI5N20mHh8RG8s0n0DEQObJ5gZxfVJDrO4c7/tE3iTR56Wcejewk5YqUMpHAH1fMnJLqnzFAYm6Tgh5IwNFpE4U+sUVyQgOc4WocXTDOl0CwQ/PBy1XXfAufPjZg+AH0aN7x6/gI1+DMZtkem+Qv0WVem1dwh7fikZ+o+ZKvXC9jhLXgooiDV8Badnn5yXITpQZreOfaLD6jn59Bvzm8xw9FE9vTF718gfg2AnjhtNRa5j8FwEVSS5aSJJs7cAzP9wAcj8Lld22XHtBNehtd2svftUCWPn/nfwYp8NkVfVRI2wCgV+xQv3MBPaNb1mWbawBe8hF2mJzkHHxDN5nwLfGXNiDEr5e85CU3L3rRi6Yc65z8aQFfLwb4WfuyOwXI7ShcGkCZJMgJZpJdRjICGMpwJefuMkugE3hHgPLb5XS0KBExx1qFk3TbETHH3BjPORpBBt0CtcBqVyKBTBJhruc///lTUl/jYJxg7wQkdAYEWEUKA4YHx+E/WVuz7FeCAKwQwr84Fcpqh0SBoZGP8XlvbrhxnDk9Cq/JTkFlV5FM0eNZ3qPZO3xGS+bU0IkGjlnyAn96lM/QlCcE1pQUz12JgaPkyd13iYJ5PPdTAiTJ8rs57cyQd3CswXNJpkRZQTXXrwS0oV0ipDg1bk4GPQj9Tqjw3LwjAFf8x2+fN6ILSabnQP9TFi7Zmd4zVwnmpRvkweYkbngbfdoD9MGuv89xlXrbA2tLwp0wwIGe0Un672Sj1E3zsU++UVBPotlaK/rqigZdTvAHftosYCfWCigy4GFefTTzmN9zvsY8/Kyxihg4o0GAVaBIgM2pn7GatfCaTuvL9+QzLehxjUoffsRpgiJIn4w3l+TBpoR14Yk27/htumZe85GrBMTfJU8UajZEPAfewdMJkkKstA1z03Vz6dPir2fwJWsFBT8YXoZv8FYUiWNZ1zpkgr41YB5r8Wuhcw+UMpUU2igyN56GZ2l7AA9tuChK94B57uTCBeAl36Dwz7XkvfxdC/yDgp3uiyXiAVu12UAPFTPZbCX/2O2p8eNz2a7NnaW5vReb5QjhVdn2Ahs0L3r5MjYq11G08EM2H8RePk+jw/TwEoA+/Of7bSLxaaeg+TYAj4/CZQYwhHAFO8WLwEfwI6B/DJxzcSQ/ymj9BQNK75tw3C9XJKwBTgPucSAtMD+n50SF4VmL0bkeJfnyXqMsgiJD5CTKxCR9JN6KitIY4QoHxYjmBMrRsoDP6ZSBPSC4SzqskbklGGQh6S93egPww1+JC1nFMQF9ORM8VFwxXDRmDs6/defVT/xQJDF4SZ1kgvOTbNVXQeDIiUrQjA2YSz98U8TB07yew5NTs4OouDLOegKCYBAaSvAMPeard7fnwJg4U0FFkCl5tBbgbZfXB6ntuCUpGQFjJKcCnNMncm7pQQlwPVXhYk276dbcM1cJ5iE7sqC/NivwaQ1tS2A8nXLSSIfXzEdO9IquZwMADxXMioD6Xrg58SbFpABLD42reaSvhFRymp1LoB+5mp9tBxTICib+JEAHnGraLVVkmIONsB/2YQ38zHOnqHhqfjwNr9HHr+gDf34W3vrZDEBn9N6GDH9srDms77omWo23ZmhlV7mmhk/4onh0Qlnyjc3zaegBxqOdj7NW5jPG82yQwKHmq2fmgw9/hA/m9RzoDx86YH505h3cbW6VfO+BefBIEWC32vg9AA88xDOxgw3MFb97AD/Y7tf8jnz2wEgucM2ADkWu5Fi+En04J1iDnqU4Fwsl4ilS4CS+8FeuRPHZ/A47L23sFEDHyNG1UPo3N7fn+rq9kquVpwJzo4tP5KedXvOhfBZ7jg8R3+VHTufxwk2Dtfa6F+BnTcVSfZX3TgP8PAqXDhA2Z6B/jtcSoEaAQTEsDjfBaJThcDdWsqcQkbwvBRvrCHKC+dxRKjwYj4KEcXFKfkoGXH2hJPpoHJMdFTssAhTewCvO0t+5OgQ/iQVDlijbNWU0jFbxxelJDiTfjBuucWqcnF1Jc1hfwsMJSFw9a8kAzkma6kBsTjga5wqI4kVyoGDxXHKDJo7H3xonpwBCp4TH2oKxK13wlvCYx1h0wotDkEiZt1xT4uSON2eGfv2DlwRKgmVnFQ2eSQYVe06OWoA2TlAyqHjAox6Yl9OyjgKV44aXtdYCWuAKT7Qrqkbn0NcYdCn62ATehO4eoPEUhQu5CrQKLwGfzloffacAuFlDYS4pRCNZjfKqBjJ3fRD9pc3Ngf78lYI41xaNoXsJ9PVmhvdw5zMUzk79FB31eunnxEWQxkOAPmMlMXQsoEBwossuAniiSOFPzA8/+LBLNodf5OIdvWObrl9JhGxqwEvijja+xKaK/nwNf8EHodGabE7Coi/fwE9Yh88zl7mTdKFNswHCD5kTwA/f8CT8QC+7txlhvL819muTKWOBGMCH8auxf5AxnsGT/1LMmcM6NbBj7/ENvgAu5lfMlDzugXn4D3yyDhy2AL4YT+78i8QMfz07lV3BzTposwn4ER/xEZMP3gPwezQULvhLd2yqsYlT8HsJyELcFqP8zt58PTx79Dfgr9mEd2yNzvJdche+wTt99uJLx+ieXMW8Wb8Ga/HJOZGd67cW4G1Ovgnf0SVPUEDyO+j2Lj6ADouZNj/kO+jnD/jQrbY3AnC1kWV9vhdel1j3HICWo3BZAAoqKAhYCg9J1yiz9BdkKLTk1bUrzB8F+FN+ePhQmr97YF0JhgAlYLZAH8mj3UxB2N+awJY75GUAZnAKD1c0FBcS+QRtCYCkxYfuJT7ZOcw1D7zULzwVhDgSRSFHyAF7p2DgFAUqRsYpmcP8cw7Hc7jqxzGUOAes7TkHhzY44g95SIoVcNaHB4fihAd+nBL8S4CLHSWOgFz9nl0W88AHX8jL3AILx1EmsPpwutaBc8AVMDuzAkEL6I4dZElp5poD7+FDfzlVtMdpLY0tAZ8klIpgyc4Wx4e3khqnVpKctUkWQPOpChcJsMKFHeEhvSeLPfO2AK/pI77nFG6U7yVIuultmVDPgTXYNVrLJBdI4BWO8INTCcbhBZ2R1NNDton/Nd7w4UslBHlnTUklm/JMy+e32JC/4W4TgG2xIzhYk2zgqkAjE7bvbzLCR4WODQaFirFsTis/v8ZvKEj5FgWqJEWxQ9bG68dv4aMC3DNzo5Wtoptv0ofMSj/NHs2DnvADnjYwchLjmSt6EjU0Ac+cRuNleJDn8Td8qnn4dX+3QH90wZdvCcBFcahQjg/vATx93onvIsOl/nNgHL2yNtvk+8kMTWXbC/hITxS/5P+RH/mR0zW0PfBoKVwAedJ1sQe/t8pzLZCHWBe7p4viixyjzkm8hxNf76ol3WUb/AE/pr8+e3A2lu45SS/9XAn8AF/BN7R82RKkf3CFNx+HFnaH9+bmO2oZ+D1xR06EX8bzgXQ6+dO5IPjaOObn/J12JwL5HYXLCqCIEnkVNSWtr1ksAeaaQ9CS9En6BclRA9IXDQzeznkroa7BGvpL/u0CtPAW7ASxBGrr+JkAHGPkFAQ6zyTbrk9xGBIA+EgoJKQM0jt3SQW0HJeGVj85P7RIBvBCAuEamICqcLFDmm/ugrt1kxjU4Bk+cIx2ftDBGSRxKCFrmzsJEIcrUcEfSZpTLY6VYyJ3eNbrokcRC2djyQLddqDhrklU/C3ZUMDhGweOj9EH/ENzHK51zCuxIpcajPPcMbNd2RY/AuiEPwMnC8lVKYc1oC/cJJDWJCuJ1cgcAA/JRYHsqh0aRmwI3qcoXOCh6MtpmRMyslJAziWMWwF9+K/gowuS0yScW2jAAydsdGmuqC3B+tbU0B0gT3rOl9HH2k7g5hm7l5TSRcUImZX6Y07+jI6zT2PotL6COF0VsCXt/K7++uA7/+HzURJdSSnbMxd79NkpdkxnBXq650QF7cbyJ37SJ7bDhugVPyERUJwI0BIVhYzTD/MoOGxuWMua5jCnua2tYLKJYi7XrvgkstNfQQNXa7oaxkfhA5okjeW3ZvGBChC0avDla1LcBMhBQgV/vsGJOh00pgbj+AV99SvnsS7cnfj6vQdkAh88Ehfid0YBnWSdz+zhE12nH9bQ9ImubIHoj6t15KiAJTNF9/EZl/8P8Ik+5tZAqRunBvJlA+J9Np7oK9uwOWfzoAY6AEe6xoc4CaYvdF6s9btxsZ8tYEMMTtawVg1ORaxn7ejmWoges01+QPxiz/wDP8cu2XGp+yWQh/UVbfrilz7it/iTjZBzQXDnU/mgOx3w6ihcVgJF46gFVsfhfh9xEFF+OEiwJYGMbYvCmoMD/7AP+7ApYfZ3K9gBeHMWArmTgVZgM14yZ8eifM8ZSTAUMJ77W5AS9PzOSXA4AqakQfIn2cEXhsJIBRlJlmdzSgZH8wjIkga7+u5JMzRJEwfZ4hOa4UAWlNhXHj/jGc+4eeYznzkNXV7EAAD/9ElEQVTRCxe0tdY2VqBFDz6SqZ1Z6wvoSazncPbO1Q6OB93ms5ZxgogdfXxDO9z9lEg5beCw8MeON1rL5ME8kkqFZJLcEvSVhEmMejs15rEGfZeYc9hbdc1J0bOe9ayJL2Q1t2YL9LUux+0aD93PUf3IPOY4VeFSfjgfLq6/5URsTl+2An6xHfwn+5zwWMO7UcAHdqxYWIOnDQR2TRcCxtAjtum93+dwiZ3gvZMjBQoe6R/a6Krkw3M6ST/5BPMaK6kWnPX1XnHBzvgR16zYn+s/CjKJua86V2BIEuJngx/c0zyzBjtnU2z+6U9/+mR7NofMSbaSOfTrX44vmzWsxXZtZKCVvbJPhRTf4v/MeCbZ4u/QY166jZfwMJckW4KAd/qgSYLvd+/hQY58nmQLb/GpZxPhK9xqucNbImU+c/QAv8xhh3yN/tQAd+PQLXEjP7HI3+ZqtS1gDfwSE5ywsFn8hb+YxA/sAbr6aClc8Jh+kCtbR9u5wDpsQ+wjiwB50WP+JLbaA/pP5xVBdPdVr3rVVBAkZns/ojvG2UC10VLiBeAj7rDD2OgS6MOWzKWhm76IEzlB58fX4Gm8AofPKHmDZ3w5/2j+c4H1+WcbVXzFnQ54fhQuA4BJEh1Jj+Bk136NkZagP4esUJBQcsy1oa0BSi9gSO4lsYyoBTHAHGsKzC2wqyUol4odPBVrHJIgxblQHHP6KSGnQHZF8TeKhE44ulqBxiUDD54CoCDvupmdTXSZqxzrd2spjOx0KgIlQxJ+spVIOAXhyOAvicDjeh74SSg5I9+YZrx54dHDFeANh4Mvfje3Fr5woOSLPyVt+tsZ40gVZfoZF9DHbmL5PypKUCS5GqNfD09BwdE03tATfUcBzySg+BM9XeJLDXjByXP2dqY56NE5gHlOVbiwFzYM8MUz9JGL5FBRbo0965RAvnTNvPRTwk53t9g9fJ0AmKNOHlqQq1aK/5Ie80j2Jfh0Cn9bYIy+/J7CQIGvIMezzIcWOknX6aXTCwkUHeRvJLho10/C7ASELJ0EKd6NQQcczEtXFC6exa5asvDMO/2s4wSHf3/sYx9787SnPW1KCvg97zKXMa3mHTolNujkg4KTRm/hn6/SZleuK+Edn4mPeEzOTvOcaqBFMUzm+lkH4CW74t/iP6w9B945GXICRWY1WBf/S5m0AG74DVfyCG/Xgrnxgq0o3iSCZGfekXnWADr5V8UiXbBu1qc7dHoP4NWjpXAB+M8f0E0F/7lAniCuknupa3SUnxfT6HNPDwOxudiv61Y2CtxssdFBR+nWGqAXTk5tfuQkCFjDPE6Bnc4u2QiInrFhY+Bl0429m8s7LXq/NB/6+DS+uOzrd/FfLLLWOcAa9IHP3XLj4hoBDez2KFwGgLIKPBIHFeyWBASzJeQCq92GJCCjQkCPXXDFgZMXQbcVQLKeo0pJRQtfyg0XyQanpL8TIQbvP0jbIYUnpxTAC4mBeZO8l2BdCYCCKMF5iUbv0aFAcpohKfBMMx5uChNylBDp42/OiuP0TGLnb/h7z/FImpMkBQ8/ydK1MMmsdT1bgjhD8+aerwbyE72cFX7nvbnRY/dDwekfbUre6JC1JUf4ryjm0EqnbTy5uYbi6tmcvhhDloK+wknRMqqfAC6KKwkKh7tFP41BG3okg2t2hOeAszpH4QLIk7zoh4JXQkan4N+ypy0AZ41ekqEk1NVCf5dyXgJzoIEOKdYVCz2QaNDv+rMMfkezeWxM0Bl4lH1K8I5PgLPkiP3RVfKkKwptJy9kbDOA36B71neqaZx12KG16Ds6jC/XxG87x4ruWlf8TQ/YsHUktAoAmxP0nezgILHir/gdPKJ7ZO7KWnwFnM1lPeunwUtSHLsN+B0P6AQblmiZF2/xUBEiMXC6Y2eVXCQjNiHYEV4bzzcZZxOJ74ZHzyaMQauYZZ2WLeOzEx/87AGcxAo+u048lwCv8EacgQu/RR96uI+CufAJX8hSXOFnS7riB05RuCiuyerRAvSNzpNv9PpUEP2nu2yz1kPr8QfkZmOjpadzkLnZAhviI+LrFfZsls2R/RyYw/vkKQHz+pu9xde2+AJ/OMsH5BF8igKKz+Lb2HtsOGCe1lwlwElcsbFg7ho8Q6sNjnLuUwH8bMhaH32n1ovbADw9CpcNQPictgpcIBCotzCQInGe5Qf2R+dBk88dKDok++ZszWFuO2UCHANGQwmCoznsMEg6JHaSgNDIwXMsJZhTksAxtNb0zM6ZY2CGKXAyTmvHgNJKEMAEYzuXDNvfGsesWEADpc1OnLn89Ldgp1/5XCBEk2RRIpH3moTOtRu7tXhZ41U3eKBDQYZHTt08r8Hckp+cYpkX6CsYK2rsPnOO+kgGBWOydGVNEMgYYF0OPLu85q/X9bdx5lVU57rKKJAR3F1j4fTmnP0c6At3haPdXYko/RqZowb0nqtwAfDV6JvAydFLtkcC8FqwjmKQjMhKsuvZCF1JkM3TkzFdpV90oTU/XyCo5/51glvwKcf4nS7QQ74B/nTRM76BveYEQcBnd4qFD/3QD51wUIyXetvCxzP9+JzoXfAx1q5qbNnmEXuWZPBBZAePFDISbfTZvZV80B1j2Jo5JCjoxb80tskf8Mclfi1cjXUFBC6ukimiJG1O7eg7H6GwYf9oESfsSJuf3wgvylYCPvLZ9J6s9W8BG0Xbkq7Cy0aOfi16WqAf3uMLGZI53mzxKz2wBtt0tQ+PUhh5XgIe0NejcHlrUIzSe6eCdKfm3R4gb7FS/GVr9LkGz/hMm4etJH0JSjswnm2xU/ZK5vTOGugq+wa8Y3NsPEDXbUSRdT0mv0f3xF76IDawJ7kQ20V7xoyC8fIV8dQaNcCP/aKP79q6zhygTa6BtpbM7jTAHzzDr725wBI86goXzKIEkkRGkQR2VDHMQ7EZiKNIykV5R4JCBCkI+3pPhtsyAAqchMKOSR0EBVrH8pRc8HA1QdHBgdgllMRyXBlnruyioqFeL3g5Hfijf/SPTl/V6bjSfWXHuZyb4CS5VUhYP7uw+MgJS+C9kxxJ2I1DnyRM3wR+YIy5zBs8vdP8jifGSaJ91kAQl9RxwmQoqXjLW351WhfNZCtYK4YEAnT6r/xwsruKnvvuu++RL0iog4S/kwTlvj9cyNbOaY6dBWd8lgyizXWlF77whVP/ck78llDbva3fAfPiod18MkRP1lwL5kC/UyR0KuasOwpkSBau08C3ltUWMP6chUvA3HgAf9eiyMkJwYhNrgF4sCv6IfGlmyPywg9JPDoUMXP44T2+2fWrdQZ4z2ae+tSnTj7IJopdU8k+uumntQLwsxYbYb9OUyTrCgRrKDjsTko26JHCVfIBxzX0mZvNO+Vlg2yDvaDTKSVc84FYdl76DGPpqyJFAsfm0Qx/z+FsPnjyXRJxNi0e8MHoVWyxdfObr4evd9aOf7Lxg3d03lzmJiP+gZ947WtfO60DD/iWc+ONfmxYHFAY8rna4x73uCnxmpMxX8Z3wKWFL/rRJaEbvTbKFvhIvpf/yabPKcF89Azv+D8+17pztJyicKEP+EZf8XxJ1ncCoIHe0ust+UgP6Ke8gC3T35Yv8Uwck/zDYwuQgYYW+kwv0MKX8AFuEcS+yLDEw+/sm78zxnv+Qc7B/wQiZ+/FYH6M78tGEL/MFumaOffoBb/O7vGsZXeeWUtOsXUDfA7gL46xl7n17zSgE+zVTRCbvqfkVw2PusIlwDFQCIrhOozgPerUMZ6TZjw+TGpHjPF5RtH+H3t3tmtJmxZ23mDLHCBZljkws5hsKAQ2FFWFmWqmqqiRmgdRpQISy1jmJnzmvqeW76F9Pe1sfov+f/30q4i1Yg175/6+zEcK7bUi3uGZpzdW5lHBMEIBVMAXJH1fFZURGmMfBp1jc19AktBL5jmp6RCsJXAKpowMTugWzEpsVjzxmhOQuKDN2ozYPgKhIKgTwaj9vsTvKAQRwUSQNe7Vq1enooFT0enhUOAycQvQMguXNTHw2Tx8xQOdQ7ySpNAHjlYCJvGXgDgl8jsb+BmLfsUh2XCKOqnGKfYkJ+hd8YKDjrNutiSMPBgdGnVgpq7Azxpw13G0V889k3BYx98V7CNJk7BJdCpajoC14YUv6MIPSSwZbPH5HFjLvngpaVO00o/02HUr4MVzFC4BntITti3he4qEzR74Q7cEV/pb4+ISjWRjvsLHqQO6tuaQq8J8Pe0I2DG7ZguSEvpVQQIn+kAX6S3bZ3/28tc9SYRixxz8wluvZ+Gbf2CDTeMdPMxx+WwtPsiFB/CwnjUUFd/61rdOOLA/RVDFkfHndBtus3DZAryzhrXYm8LIaZFXN/2OTwMIDuwBTvaFHzzNsa4LHS73+Cb+DN7mSoLoKn7yFXjktTlrmRsfzOUbNAnQGc+skR3RPzJa/XlAvhK7LR3wHe5kMhsolwCP4Kkb7PUb/gr9R+YeBXvAmcwlpPywhPTcHnT4UYWLNT73uc+996NufCITe8DtkbQ+F9ARvk1xj7ePAryRm/DpYIs37tERcYr+3uovrTOv9ITPICdJKzuV57An9+k1f8ie+CKyZVfyPXjT5ezeeHavgatBV2OndR4FcGd3fhJwbl0yk+fwGXs2fgvQZb6B3W/5hpcOyR7v0EK3NH74oz//8z8/xb6npOkDW7gAjGM8FS+S82vAfMrK4ATqn/3Zn3394x//+GRUuhcc7FFguE5KrCOBIOgJ9sID3UHJDkN1z18nEQL+lrOjPPCQREjSzfFamiR/NQifKZqAJwFXpKBN912AQuu84MzB6XwodCQcTgwUK5RTl1UAt6bxc68Jns1XxXxfwVy0tK9EQnLmh7z2k3jYv3+mFW3GmtMFD3RILoyRuJkrseIcVzBGQoIPnqNRQhPvV7C+Ykeh1hh4CAT4vaUPgj3nqGuDV+f4tIL18E3S6l8O+6Vf+qWTo3f/6BoBOUpy+02LYBf/rHXtehOs/ZyFiz3oCNtmK+RNp8nnkYA39sE3RT456g66dwQ0GtgIGW4lCvAVuPiTLd2RmLM3zyXvArhx9me3uv/8JZ+iKaDIkjDo8OcD8OYb3/jGyX5cTiMlFt/+9rdPr44ZL+GRUGhY8D/WIgPFvwYIG6K/PsPDP0fMV/NhcOm6pNvke6lwAdbAe+u5rI1X6HPiGV5kAjefJUx0EO66fXwmXYpG97wWZ65/3YwNayRoFElK8Bgf+AwX+hQl/qlsfsvrtPEeHcaIK5oI9ofnCgK61w29RmPOyhs6wZ9VfB+xHXyR2PEJaO4f5nD/kcC/6eLjFz6iZYvGCeh5VOFCHk47+WZ6Cw9NJbxk9+RwhF8vCchIcocW+vAIsGYxTzF/DshH0cTG+aZH6cy0V7pY81DBS/8V1pqKZEZ+dIRcNSH4U7xAgyaF8eaJJWzCeta1/qPkbR2FEznAFV57YF95Ap/p7yN4Zn/78ldywkfJ4TkBzuxQU0ac4Sf5Uf5I7KBjT2mfH+jCBVB8DBaoGIzkcSuJ2APMd0kwvH4kCRDEdTslMv/0GtPld1atIVgLgJyM5EJwmMHAZwYlEZG4w1Ox9bd/+7fvnSowHgmDxMW+6DMOfr2GxGlIcuba8MNjjoticaDN5yzM3wJ455AELzhLqHQZBRdrrkHEXnCyPjwl73VOKbbv7lvPOOvP+cA+EmyJCPk13l7WX8cD63iNximM58aj128iJBqrnOBODl6D4EB1QBQv9oCX5/a1v2RBYSh4eq2OHlnffTqrqzsdoM+CAwOWYJCj/S8BGugJPklOrK0oVbg4ZeLgV35fAnTQJ/yH/wz887oV8Oo5CxdgHzQkY0kO2gou9+AxoX28YkU/dOmcvth36tIWsBmJrWIRXiuYr+tPxmx6XY/+SIrpDv2V4NJHOBlrffMEeF1BwZWu02c2rUBQKEhA8UhCqRnw8Y9//PV//a//9eTD0IMuHTL2hv9slR3RGXSzd4Wuq06pYFXxPq9zQL5HCpcVrItWRYgiAi/hQo/hIymCqyJAE8HvaNDKD+IF+vBY4fLJT37ydFKCBnyxJt/K1vACTxRDbJxPtBdc7Q8P9klm1lQ44r3ilG1P+o3jg+HBJ9OhCeRnbfsJ/tY/B8bjn/ghQUCX5IftrXpzK8AfnvDSEPBaE9zQj55LAJdHFC7oFFusBR/7S/DYN5kqQsmN3D2z7/sB8JfO+te5iu/3gPX4ITGMvft8DugJu+ErxZdVJx8B6RC5iIn8CVuBH/lp/Ml/JLriIn3WZPCc/WkesKl0btrUo4CtadTAh/+8pNv4Ckd+60gMPwdkQO58LBvmW5+CxkcDHOFNLvwZubJD9igWaM6JB3hVLvCU8IEvXADF5DAwU3Iv8FCgaxSGgklAGJxXBjhnzt17v4KeZPyIoVF8Ac8ROOeL7hl4GJWCSCIh6dEV0HES4ODutS6Bl3IwJoYPN8HGe/+cAqe/OjHrMhZdRsGoPSmjbjWnMfHYArSlwAofAdS6vnNW5qNHsmNNSTt87KnTK3FQ9An0eMkRWEcAsgb+uazDoTAKDqZ7l3gLB0kv5xi4p+ODjxIR+AXWlAgqFCVt5ipgyFJnCH/dw298x380c7icvwQWjhKliZt1ycTpiBMdp3Po28M/vqLT/ubZC054KInCQx1xcr3Eh8A4uMBTd1qCxg4eDWh77sJlAr5JEtkI+dxS3B2BknYFYCc8l/Ywhn8gS7JYwXMdxvxHYF12JMmRbOAN30iG2ZrLfEGDPrKnGjPh5a/x5rMlfgd/2WCvnl6CSaM92Stfw0Yv0T+B7+Ob5onyEbAnO6Vfio1pwytYc17xSNOnrqATZuvAJ142nn2xF3bnVLoEcz4nA/KEE53AT8WltQJ70hH0sjl7BNaxJr0Q/M/5BuCZhIH8+AV/K2AfAdaBH/wlJfRRsokmuB0FYx9ZuJQLwC8c2YQino/k08UtONv7aJx4k4A2cZgc2fU9uKKV39DckNNM/TsHeEu+R2z/Hkhu6JRzsBk25f9y+8hHPnI67ayZJt5e4xPuATyjW+VB4bkHnmkS8J0alfeA/eQGkn2n4vcWQk8J6IavK9+g2JLz8luaRJ3cxT92iLfvCpcHAKZyaoIMxgs0qvqjhg4Yua6P9y4lqhJxgYlCcwS6nQzQHmghzC2AC+EKiP4zN68xCEoBnARZzkiyqnih6BUnkgaJmYRfAtB/zugVCv96juLAu6YpEjwYhyJJ0SCJnkUNvrgnAceTPbzdhzdcGT7840F4+O2J/2DOe45OZHRX4c+pctYKMLj57i95SyR0OM3TDebQJWASCzzwl+MTqMkgurbAc+/C41VgPHkoFivafJ/80Y3mSP2e56//+q9P/FRYkacuLn5zWIzU2mSsOPJuvCLW/cB6xigyOmlJH7Zwx3944yln4Hc5+EKv6AaZKmQFmgqgo2BdSRh54KPAcI3OHwU4vcnCxZ7okiA6echurinyjgBZ0X+FguRXc0Hyfg7MIUsnvpoMZDIBfgIiu562hx4Ft643uXnGPu2rkwkPe7MVhTV6jSOLSbP92A87E3AEH8kfW6O3t+gDOdmTTR4B+MCDLfi9jlfN6CVc8ecSGMOm+A82cGROgG9o5H/4JHwmD7wQC3pVBY4ua8OLLvHDEi3+Ha/4UTJQAPEJ1iYH9qlTPJMQa4oT9NmaUyaewYGt8yXz2QrmwlFHmk/id+0zdeVewB90SET4Yq/W0i33z+G2Ahk/unCZ+/sMJ8/hR4/5SMW9+MMuyY38HsmfRwK8+CWvSYmD1/B3BbohdokzeHKUZvokTpL5NbZ0K5AHebF/NufU02vPn//8508NAv6s+P7UgN9eU6Nb9Oco/+EvBiuagXm3yA4vNJfEOLr6HPy/BdDGz1R0au6yM81f+kNedG71bejjAzRlnhJeTOGCSZI9RDPAW5TiEnB6BCHJ0qFUCGD0EYCP+RykTjzHKuHw+pCkU0IgORDovG5EuBzUnmJyGoKf5J8xlFQyXse4XgvxusZW8mVNeHvGoOwvsRZ4JBQSnhTKGEmrYKsrTdnmej4LyjoA8LAe3PBJUEWHYC9gWkcA1ZGT5PsXdXRuJWXmcsQl9ym29dEkUenH+XBHK9yMg7+EQFIiAdBFU2T94R/+4akrg8+Sevszeg7HePiFr3V6ZcjnlWf0y3xJLf5mdHRNUiO51/WpQOFU4AfPdS3gmXW8xgcX49Blb8kKvkx+dwF7Gg9vcx31O1XxGoz98UNham0FlwKQbVyTsMAFP8kdPuS5OplHgPXQ6GRJELpnfevcUrhMIBc8k5iTN108auNHgT5bVyIJV0ko3Ldkgx9kDRddYnq1jtM0UAjP+3SDremuWxvYl217FVIRpFvJVuirZ3M+WeMFHWCvdIsOGGNtPkzh5fM1YD5f1esu1qTz1rEfHYUvfPx1nw+BN5k4udSwUYC555n1jHWZ77KWufkHTSONAvzDz6MAX2toBnRiCTfJEp5IYPju1bbSaz7aOP5YYizxoqPWMYZuacIYM3lpPTFCw2iCOWjg0/hE4/bA/nwv24InXsH9URD+/KhTpH73CCf6cy3A7SkLlxXIC65sUQGjEHQKo/BKr9B3Cy1PCfQXfWLarT4Z7fgkzmhaXGMTdFcSzh7w6Jb9z4H14AM/us6G2J9ijc8Sg7/5zW+e8gbyEuvZFD/Kn5h3DT3XgLWddIhV+HAU6HY+HM9ulZt1NDok9uY/mve3AjzQBD85Fb8jj9Xg8fqxfORILPW8wuUpaXtRhYujxK3XJh4FGMngGZPiRREiET4KcJII6EoRLAWEL8dBYIxNl04yI4FmjMZt0QIXc4ynHAKHxN/6EgqCtx58txSg+/667MGgOCPzBSM8VSFzFpJsY7bWch8tklwy0MWVEOGRbo5kQ1DjaBQWAq7iAf8k2uEZTvMCnptTYh4/PJ/4z0syJhDhibkSOEkDHBVneAY/DljRBWenXk6A0N3eE+zD+VhX0iShcTnlqciAazhEzxa4z8glFZ2GWMOpA0csGZrym2u5T+96FQ1fOHfrwV0RqJBSuNIfSZPEbQ+XFeCOZ72OxxEdnXstoEuS0AnEPfvg/72FC3zYlcSUXHXZ8Q5PHsUD61hP4JOA0En6BP+9PfCoZgQcJ9BJRb01gTUEcHpOT7qPLgWLU0E63D86MfUsMB8v2b6GBt3qubH8g99OwesaUGhLxhXV/ILiiq/iN+izBIWtKsSMc2qpyFF00El+jo1oFjhlJSO+R+LJV5mjULeOudZm9+zdaTJeaTit9O4B3rBNvKcT8QpPBWJ7wMG65DfB+sbyq5oLCq4f/OAHJzsnC8+tI+Fia+wsEGPQSUaB8S4nMfwr2Vl/BWPsiSd4jGcVSo8Ee4sL/I+mG1+LP2i6ZS869ujC5RLAEx3wdim80EIe8NDEIOeXBPgrPjhBFOey72uA/mma6oTXkDgK+CQOa5ax50frFXroklxBziCHYAvkwBdrvLA7cZKesx8FAV+liUPv3buGpiMALz5ATNB0wYejABcxmr+jY/h/C7B5r21fk3c+B6BPTFCgKOrITH4lPy0vMuaSruDLW1m4vHr16qTsAp+kEiMeqcCYaU1BRsLJiAS2I4oID4rH6CSEAqgAq4OgW28Nzpsx+q6jghaBuG7ZFKb14CEo6kbAxXhdFOteG0Aol0RX0UQBJS4Sa8nOGpQBfCircapqvPdqikRCEqGTWBcEnmg3J/5RcIHBvXOADt1KAZKjvOSo0WFfr4rZ23j6YX/7wpnjlwTinYQJ7yQ2jEbitMVvYB3FDYePbkkEesksfs9rDzyzvrkCAIcrEP3N3/zNiVa62xrkjCbJB4el0MILzhyP0UQ+nldoSoTRiT66drQ7hD776+jS02s71Ecg2vEYfgKB13/o+zmeXQI8uLdwAfgNP/wrAXIah4d4cQ+OgTXoC72xtsS44LzlS9zjE+gnmUyfJshrhiQnzyQ0EnvrkylfI4GVxNN1NPFbeJaOuexDLnwbXyK52Up68cc6koRzYJ49rMmurOkS2CSH9JUN8d3oF9gl216hFLzYR7+7Iw9j6b/mlHUkmcZI5PlAY+mRpJP+0geFPP4qjq2PB/DGxy2/NgE/zDeHvPAITS7P+BS+Ej8V3vmn+OUv/rPbTpfZuqSsE3s+Fy/xKPAZTWQUkK/iDS5bMcdexlgPD/Am3w2nRwEe8E+KRHiLT2STv+y6Fsx/7sIlgC8ewUHM4APFUDGKzOidxJMt4DsevCmAK59Pv9kU/boWyA9/vMaT3I4C2pMV/528r1kjMMd6ZMb3SerZPTvlD/gHRVJ+TMNCk0YBoRnhVWzP2KF77Kq8iZ9gLzM23wNsi6+W48D3Gh2wtzliMb06Go8nmK+JxObQ9KaBHcDDK3xyKDxXQLIV/o5M8AyfjvLemm9d4eK0oP8Vm1E6zZAYexYDHwUMQfDk2ARLRnPJODyDQ6cNqnbCZfyqaEkoPAPOSXLNEBU3jNJ4a9iLkK2FVsYsgDJ867knoTFuhea77LdeDN7/vaIrC6dJUzTgqeAvGAuw9nSqgqZLPLAHw/V7AgrKYYVPlz260MkBzVfFGrfi7sI3wRQP6iatNMzLc0HY+nRHUdAre1u6Y3/Fi1MN7/rikXvrZV60tFfgPoN3ykPG6OOUOSUdePrUOgVTCZqCQpLXKxmBcXRdZ5oe0BP6yR4ksRP/PTBGB5Uukg25TJzvgfiMJ/AWMAUeP4jXrfZZoLpnP479EYVLAF98xE++hHOmt+SRbB/BH2vowrMjsldUW3sFe5bck3dAzhJ996xlLn1iw/DHa8Usu3a6S5esQ8boQac55rNfRZAA22noFuABfeP7zDPfOvNK1hJ/dEm4zZHc46niCL5d1lCcaTjwdXVY0atzJ0iya3T4LEFXrNBviTO7cDqTj7AmsC7bhodiwSkGG3ciJTHCg+Q58ffdM7jDC0x8XcbZi73WfOB33fPMGPxWONkLTYoJNHVir0gR9NleYA3+C12BQktxhDf4b/0gPCTXToCs5/MjAA14gUf2hYe4p/BEy+T1PWDtN1W4gGQa4CldkqxKyPhSMhZz0Ywfyfi5wZ7ikySa/l8L7IrtTv26BtDPljU52fi1fDA2nXIpQNgDnCT2/JQ4Hlif7bAbMZpN8XGaHHOcdX3XcOGvxXG+z/ds5lZ5oVmc4rtuWcd4dPERaLHGNYBm+QLfgGfPDfCHs73xQqzQHJGTsFlxphO4rmvBum9d4cJZSfwoKOUVFHTGMZbiCnaPEnhCpEyCon05AcZ4DsyDhyCrY8LJwp0T0nmHZ0bhL1oET4HR+5z+SVvverskK14/kHRb01rGS1asLymEXwA3z3RbGI6EhwOT7FFA+/vtyQ9/+MPXX/jCF07B2rpwcZlvPfcd0zoKlDwYg9+CmU4/5dsD+Nkbfv51kE9/+tOn/xNCd1hyKDBzDOiVZBjrkqhz0oIGHNzzXHB2uiYJkvA4sfAfxcHf/7eAx8bZdwuiS2LRj0pdOm6cni4tPZp8IBcy4WD9+N2FbwoZPJdYSD4lJHQCv8nGPuYD+EuyJGTkDz9/JQXmo8OrOC5yJpNOnMjQ+PTEupwGPPCpvcgDjsa27x4YIxBIWiV49mArl+YdAWtYCx8kOoordsnx4av7TvZeWuECF5d16RD7IwsFJPmSIT3A63uBzeBDv5diR9ZewZ5+16bQSa7sAT/ZBlxcEnz2wGacbtBnvLYPvXEiKejTT/foPD/iH5ZoLJkZuwWewYHPIDvFOx510X2NDaewvUqJvvRcsS4JhKs94EB3azyhnQ5qiLBtc1wVLp5bC92aCPCBs/FsR1Mg/fVXMe5ee1lbAiBRUvxYg23B21+Xe5IMr9ahaQ/QYM325zM6CXVf4qtow2u40Cf7K1TIWvLnubkBXAVw44B5kh66DZdVLsbxAfwoHYLLI/QS//CZ3sFJgtJ/7AcP9O3pyLVgnzdZuKyAdjyEF/6Sn9838AGKWTrM9jx/FA+ugZqW9OJaEH/ZUfp1LeCLoo5t4gMeHAW8Sqf4a/+0ulgr9+Ej2PmqV/RfHDbO8+yIjzNvQjZvHBvEH35CzsI2yAzdxh0F+PCZ/J0Yfc3cCfCRj8q74H/NOnJEMSh9uxWHW2DKjG8TOzS25V7uJbN7fY413qrCZXVWMZqzEUAlxf71KsqP0Rj0CMYwEIrMketASeStfQ7gJfnXYRU8fWdIkkxJBkNzL/z8pRiMnOJKLCS8lEYBg8YVBGBrSUYaT/GdSklmGLKALXAqutyHE/w5JN1BSYS18VaRYx0BttMIwTEDgi9jpHScz+QtZYZ/6zJcxZ6Abi3Fh0SQU6GwOiQcBMPwWffQvnAV1CTwaPNcB0JiJOm1nuAqSfLMkSXayKVuQE4vgJv9dXJ6/QJNLjJRRElEJFnoJ2trCNq60h1luzhwfKQLCgC44TN84YK/1lAg2I8+4q29AvjooNlPx4hTluDUHc45TBzRbS+JH9o8J0c6T36+n9N1a6IdT+kgx24dc87NuwTwQx97QwOa8EN3jY7AC9gfzx5RuOCzZNTn1n8E4Ac66NWHPvShU9FNj51akKmgNh34lOlRgK/59ERihJZVfuhiZzp/9MR9OiARoR8SemtIuBXTiixrrDiZ4zUT/pLs6SI7UUw2dk8W7rN3vsW/Rqi4xgs2qwkhEeFbNFnYAT/F77Id+MMPDWy9Qpye8zfGJjtBnp9Al+/uV7hYwz12jReekRG82AObxUdrG+tfLIyXxtBxOCkW4Oo3N3ypRhC83eNrFQIuMWRPrvjRhf98hpNo/hpdfJciyL6NB3DBE35EsZU/QAcb0XiwnvHw1VyxDnraD054SM6SOH7UGteCteINnuEVnear+AT+x3/m+9u//dsnG+D/ouMRYG/78uX4cA+g/xGFywrTn9FJOi++iIXiOH31HC3PAfTA2xjsEO+OyANuZCf+0NN08lqwF1rtrdlo/z0wlk7Dl17xMXwP/pFRDax8zkqH7/CUL4mv2SDdZ7/sq3i1BeayM00z+iUuwluekE/Cl735AH1yG02VW3kG7FVDZM2T9iD6xUi6do7WR0L+gF7Lq/hmsZXc0MAnPVrfyfStLlwCxGMsBeXcMZ4B9ArZI5hjDevbW/AXEM+B8fAVmARiRpGSFIAEvAzUM45Rh16SkQMVTJ2UUKyVDmsJ6v7jQf+EqEQIftZ3vwLL2gzB33nhjTmCVq9PKSJ8rps39/TZnpIPSu55AHf76sxyMvhjb/cZo6IITTkuYD6+cC5OIPBV59OJBhwEaDg2p/nmWc9vJnJKijZ7o18y6DSnfeIjOtcupTG+czBw6BUMQYvDrBPc3hPMxVddMQHCyQ8c8EciBD8FqjErhBPdqLOMBrLGs+TmvtesvEaoaAoHdDB8heAlJ2eOJBJ/JVCc0bnxR8G68MArPOOoBQu6uvIKjo8oXMhCYMNfjtX+jwJ4wVsB5rdQklxJiwQA7mQjgNNze2sc7NFyiUb7SBo5cMkqe0kvzaUT9qIf7uMf/WBbdJ5eSbrZTvJf97SHsYobRa+5EuBLuAFrSmj5I/x2CqD4N1+Cy5boJjydYrI7Y/CKPuCNy1zP/faGz+OT6XY4sB32oikAX+vNwiUaND/4xWjFE+sqku3HV1hf0mQttgUXJx34q4NoPTgrAOlqzRSFDNsiC3YCv3PQ/ubzF14l5Tclu/BdwXj2LqniY/EVTWiUKMDJevyHIs4a5rjQSx/xVlNAQpqeXAvm4beTPnt5NUqDK5+NV4o/p9kK9Ut8OArRwtejVwFHt+8B/HyKwgXAlQxc+K84Zv/4w+eigZ4Z99Rgfzppf7a3pV8r0Cc4il3wPDLnHPA7+Mxf7dFMtzxXyMu9xBoNSHF46vPefGPYj5O+GbvRz0bpqfWz/y1wP5nJT+gY+4SPU7R8zB7wwQp2fuIegAf91Bhh60fsFc6KO76Kjfr+HMBHyy/wKj/A9u0P73i9x/NbgF95V7j8v4ABGE3pKKgklOEKFIKWBAfD7jFiwpSU6YBwJCXne2AvxYluIQdivnvW0MXVMdR1g7fOKscoEc1wBHFJ/N6P8zyXiArCkiwObu0I44u/fe4yxlg8+t73vncyGAmAguAcn8yVfOqYSfYVRxI58yUEnPzs5pOFYMyRrOC5fYwV1AoQxvreGiu0rk5wPPUXX/ETTzg6PMZ3wR6+DLTxc12fyYB+cZqSHnqjiJL84JN5e2A9PIunnJUkyutS9hRIVrCfJI4ukaM10MzB4q91fDdfgqrDbP3w5lzgiDZz9wBNxip8JJt4ZO97AD0SLzj50T0avHpGJvbbWh//yPYRJy7okCxyfpLc9O1egBde4hE82ZXCRdIseRRAFSzskc6jWzJJzyTV5pEl+s/JJDCGvaFB0CBrfgot9Mma/S4insOFnfOBdB0/Vl02l95LUEtM+QfJOZ06x384oUHioxmCNvZoHfpqPwEW7/GBD6CXaIcv22FrZC1hkHBLBvxlF9Mv2Udhw16zAX81HuY9e1qXr3IPNF+DRnLiJFGXEL/opPm9zhlP/UVPvzvBUz7LHP5XIu9k6X/9r//rvb33wDO8JBt8Qh975MM6aZx8Nh6fJHaSBMmJ5M5f99moVzP402ToPrqN4xcu+aEJ9kcDnZF4shl+mD9zyqvBgHcKX3/hL2nht+i+/c/pyTWAz5JuiSx7UkRLWu4BOvFUhUuAfpe9yJTO4JtmpH0Vf5pM6dej+DWh/dmb5tgR300v+Sa6vurhLUCHxHeNguikzz7jCxuwH/1SjMthyJv+HdVXY/lUNoif4Wwfe3im+TFj4B54Tn+tKZ7La+CFBrLjh1aZ2Yfek6m59wK+8+vklszO4Y0u9sE/XPI9t0J8QTufzvfJL/lrvtK9+NL4pwA4vCtcNgAzMJ/SSqgUDoIVJ22NqbDXAiWUFDAAAVOytucc3BPQGTSHZ28KaTzldKrgNQsJhaBMgdxvLXO9ty0hSPknUDQKoDiSAEgWGLk9Jj4+ty+lEUg4G91KnUL8YbQc3oQ5z4Vv5sOn390ItpwNHCi9ce3tr8RaQBQczZ8G6XljPeNQGBKe7snIfDhIoiRjaJnjPIeHV6rwXfHAGemW61KTgTnWsAd8zQkXFxmgDe/xRkGJ1/FhCy/gPh6il1HSPfy13grGSnIkvpPvHL5EU5BAo3etOzoOfObEJTMc3h6gS0IkcZQA0Y1bAb5oxzt2RPddFburXFeA86MKF4ktvtJDiZCktcbAPWsHdIO8JcDok/Qp/HTM7e+5RNf+ZCMAwEcjQlIqwSHzdMyVjm0BnpI3fvIBEgVzyZaMremeAo3NKgjoV/K0rvXNcSls4EOPFCD8oKKok9tVVuQanmxHQcKuf/zjH59oLnEQVOmqPdhSP2iHf2tajz5oFuGbpN7vderixQPjyUyhwEf0DM2zcIk28nCSjpYJcLeXUyVr2ZNvk+Rnq+Zbn017haQTTXtpCvA79hJbrMFe2N6UoTXCHfiOL+IK3irmnNDiCd4rmIo1cy69UXzSK6+aWZ9c+RkFRnPIm/8ib/QZtwfWRmuXsfARU8iRXqGND6az6YC9rO31XHzDH76O/Catt4I10KI4Y6cKRLxWJL2U37hcAjTMC2/JEN/YIN4pWJ2+eYb/ePtIsC/ZkeU5PQDGsit6s9rKrcC+xTLFCbvynW3wDfRWsatI4nfKAa4FPHVC4S+dQUfgMz8rNzh3yr0H8KmAFzfxhj7CFy0uPojuu2f/e8Ge1hQ34HyJJ2ykHOWRgFf2RhObKWfke8RieRJ9uUVmtwIdfle47ACGEJbg5KL4ElGJoiNH924Fjonzp2i6shz9nuCN9fzVq1cnozTOPbgxfsHuG9/4xkmRcn4J0zwBeQtXa0hqBHm4UD6JiuBjDc+nUvjOqQiocOZwOR73JE3uMTAAR7y2LuPTBZSMKbTwDx//6I/+6PQaScXgdNr2dUlyBHVjvc4mOOLBlvPFj3OFi8/4ABdF46c+9anTf26p6zwTf+PgYA/jFQJ+sPeJT3ziRLMkUDKHD5wFfCQOaDAPcHLe5ffcM51rBQRjY/h4tgVwxn98IVv8k8TAkXNcQYIlCSW7wDhBkRP/7Gc/eyoI3YsX9uCABX682NM79EsW4aKbghd7Y48AmiU9EjWvk0hYrQk3OMW7PTDmEYWLPem8IJOM6ZWEEz+nHG8F+Fm71x4KevTHPd89RxP67emiKwpEybFknZ8iezYkyd3jv/2sQ1fRRc/MsQfdwHO02RcOEhi6GZ3+Wp/9SJz7LRQdTEZsWyKsa2u8PV1osIckhy+ShNAZ8rUP/UMb30R+va5hTT7V2AqBAJ2+m4cfEh64T7nYm29gH8aGT/KdhQuQMEnEJfwr8JP4ggfwsh4c5prodiLD7uEPN/esCTfzFGySa37Rf2pLjhI2vGbL1pm84w8VIe5bz18444viR3zgN0vmzPHXen5jI6mBq84sv2SuyxxyhBuaJm8nRBvc6I59+XG/P4K7zrdnaIOfv9FOVxXc4hKfxpfYs2TuXoCbfRRQ/K+4jQ98taTx/VK4rIAuOkAmeI+P4rBXnOgCGxRT4PdIsA+fQlbZyxbYl83RY7r0CKCzZEeO/KD1NVzpmBhjTxfd2sPrErAzssTTbGUC/6Xg5QevBWtZE9+yBYWgPEZj096amzWNbqVhgjXsw5fRdXTtAX2CD3/Bzz0K4IBma/INfLvY4K0BfoF+eL7F76cEe74rXC5AjKGwAoaAqMtGoRgdh3CLwZlDyQlAsiHwE8gWuK8LL+EueKRQOhWSLoWLhDXDBfD1PrDxEyi6RIJBcJQZo2QDbzgX96zjLzyN84qA4I32nCu6fUaH41SdRgHdGIHTawqSE0EWnfjnFSEdWEWGwAqs0wVfAUqBkEN3TOvSHecoJBDGxHt/Z+GCP+hsLUmzAKF7qHjiNHVOJCyM0hjjJ/gOP4mB7rlESeIsuOM3unz2+oS1rYOHkgh7kIX98Yms4p/TLQF+JhX4rFui4CEbvDcP3u7h8QqCEB4bD/DBmvD0KhQnA6cKY/RYk87oHG05HN/hImGQ8MIZ7unUNYA2Dk6wIG92ozBjM8kH2HPFYwV8fFThgn5FOrCWe4pLSbCgKhFPLvcAu6EfggqaJfH2ZXfsHZ+jxV88hot5dFhii2Z40Sk4WoO8rZd+4aP5cHafn8Brtkhv6AC7ViCwQbJPL9HKb5jDztBP3nBrXZex7FZCQzfYH32Fo3v0WkIpuLF/hZNEli+BV/Kbv6lyX1HMFjUw7LkCeum49QJzje00Id10H0+2Chf4s1HJhSSq+wCP4LDKHP3WU8TxY/hpHXOtwSfzUXBBE912emw9eGsameekh49Au/vkwK4VdrPJ0mU998UqHWm2g9fs2P6eF4f4ET6PjZVgkLFi3Hc0WRMtPqOHjigg4cJX4C/5eZ2PjCV5dND4fITLZ36S/O1ZgWw9OkWv2fs9AE/8wEOFYP4Z3fZHOz16vxYuE9Dqokv4qKgmB3wlFwVohaNx94A12Kq1xQjy3AL+AM/FFnNuhfSFvqGN/vptm3+Yg00o7unNanPXAr7QSXR1epu+ToAHm6GnZH8tP9f1rAF/dPCbX/va1072kx3u4XEN4B87lfPUFF7B+nRETsHvwesewBdroEF+Sw/Zm1xAwcIub+HfI4HOkPe7wuUKoEyCikAvCRGQCs63MJHy62QyZo5qC+ypCJEICHroEPgcMUuoBSCOXCLNQYSLhEkiIRBMYFQSBl0Czyihy32GjS4OTOC3hsDmng6b4LkqLcckOPtBsh/KUnROQqCW4HAa7YEWIDBKPKy3AkeuWODoBFKfJZvWQbeiQPcE/gzbmuiocNH9wwP04KmOrWBunu+ecc4SEV1iiSFcrT9laF14ol2yOBMMtFQQSV4k1IwJvzkxnz0HjTefA4K3NSV8nK1nAoVEpPeAA7gaS29XwHeJErrtgV5FnVeO0NMPhhk4PnnOIeGd/SatAZrpM1zolPFb4y4BvNFCR/Ge7sA3+V8LeEKvHl24BGSAj5JvCanCwr17AK30X9EuMU/+9JG9K0SMQc+87Du/sweyJEfBEU/5HQltJyMTV7xnt5JezQTBBk0KfzZHF+imoGQ9NEsS+aLwWcE9+kOWOo3m8T0KAf4HjgoW9odO3+2Pp63puSAPv9bky/iiXnVagS3bB86B9QVVdss+o9165LtVuMCBzrM5+0wbgz96witoH/aioLNeYC366Ln9rY8OBSba4eG5YgaNCizPyIDuOXXkBzwzN5kHPrvH/jQPzBEn6Aw5OSFxKgNv9IpJ/SaIX7O/+eFmDp3AF/6cPiS/qUPhsV4ArvxlvteaeCRZszdfOPl6C5ABH03H7UP+7Q/oIL5+EAqX6Jp8Rh9dEavYJZnXTCCbe4BOiA3ktAX25xP4/nv3Mr/im57xG/763eekJbpvBfoihtLptQkwwX05lCLgnjgUtL6//Im8h766yMzJun3u4aO15WFsQTzfAnSILQqLrdzsGjBXEcTG+Qd2Rg/z4Z67tvj7nEDm7wqXKwGjGAEnICkR8AUjgqZAEoJrmGktwZcQCMOaawC1HtwFRmNU1wKWJFVSYjyFEyRLyK0rsYGXzxPgyMDgOxXRZ4mVPRhfRmgfiZzgNtcqIApieOoomONDj7FwhlsK32UvwZ0j07EIrAc33cJeb7E+o62DaE1zOQv3OERFpIKOA+Ps0VXBAi+Jqi6htQUGODk2RxNcra/TBZcCePhYS1IVHRPQ4T5dMI/jkJwI4pw/h1Ni1WVNY61LXuTIQejMS8rRO/cxXtIGh/XVLp0zhUuJiiJPYiq58Qyt5KnTjA86bnhCZ41fwT18FdwUiyUmRwHe9iU3CSw+zNMxuOPZLQAPfLq3cKE/Er21cAFkSSckivgo0c6eb9kz/aB/5Ewe6S9ZsTOJJL1B394e+GodekOfJB5w4w8kcF4RYKcKaMGZLyA7Oi+ge24/J7OaAeSPl3TVHHoFr3M02l9CpfDQoGAzAjP+0Bt/BW+JPKAHnbiks+yXf+rVMPt5Zn48YheeBWiB62zq2IttoQlPw9tfa20VLnjomYKPTpoX4COfOu/5rFnhVKSiJf0lK91HPEeT+3BmZ+wRfsawSzSxQfPd56/psFdHndqybXOnzU8Ib0kZWdW00Z3nI71yQ54aR4oQtpb86QLc+Vn2T1fQTneMy0+jNdriV+C7ZyVQ1uEn7YFG89EMB+vs0XEJzGMXvXZMhvQruwgve9CHD0LhsgX4gEb2wxbImVzFNc0fOpl+rbK6BPRMTkD/7DPn+2xNJ48K2WvlaD686YXmqbwDznwpW6GL6ZC4jQZzum4FOoMe+keme3jbgx7L1djp9DG3gjXZJhtjA+hDp/grJ+LfxVu2Vjy+FtCUny+XmEBm9rL/KtNLYCw+4KGYUjO+fyAhXTPmHhk9GuCDt+8KlysAo2KWv5RRssMYvNIjsAhm7lOqI8CgKCXl04kV3Na59mIA/qdrrzv1LrFxFNblO0engOB8OA7J8VyL0Bk5JzidR2swMEbysY997ORkKlhAYwGcGS0ll6QLrBIJjs/9GQi3LmPgZr7vrcdIJTyCo+/wlQwxzq3EXUJjDa9FSaokRpTae9qcNJ6FP7BXv1vBZzR7Lph7vUrSjY/u45vkjs74vgXWi3cSMh1dHQpBFz6Sd+snp8A8+5gjsXRKI6ngROY4n91zEsYJ4klgXfgpSHRT/T8TfhOxOhp80lV2GuQfRfB98tFY+NEp3XTrSXzmGntgjLXMp8NokZCRIz57hgbjjqy3B9Z/ZOEi+dsCa+OPJN1JCb5vye8omCOgSWAFcLJxj+zppsJbkVcD4ugerYHHbKDTLYks+0GjhFnwkWD7zYXfRXzpS186FRhev7GnNS7x015sT6LMFtitPeglgDfbtw+9AXi4Fi74yE/Ck+8JPDevhgy83YOXcfgz10ETH+jvBOPZx1bhAnzGJ37Rs4AN4Ue8QC/75Us0YqITeCY54z/5Ueu4Rz/QZq3u4a+kU2JjbfijzRhj+Se/RatwPQfwaj5cNRf8P0Ff/vKXT/8IgrWsww9rOii42DJ/hGaJSTq28mS9um8/4xVXfIfYRt/C1TO+jv+n47dA+5AbGvhl+NKfLcDHD3LhsgL+yCcUAxI1cU4hKi6jgQzIY8puD4yzjvhIF7In4JkYwR+xRfp7CZKdueTCRjXYvPmha88vsRVrGWscPUEDXQ3nS3jvgXXpJhna+who5sih8O4IjecA7ejBT3ZAFgA9NZjED3aIL/hrzJTZJTCuvEJu0h7AfHyX68wT6XNgDrqTmbXZE7tjf/gDd89eKuDBW1W4cI6qfcnVowDjcvCcrQTbD7YorMDKQI6AdRg5JVK8CBCrYQlKcO9Exb45hfAQNCWNEhgBTdJvbMBh6LzWMZtz8Ufy7F/bkpwYxyC2lJgzlRwI7owXHpIMSVGdamu62mNe+CLJ1hHmKCUHkh+8U3TZs/nW1w3gHCYtnlnHXvaEr4D+zW9+88R7a5JLOADfnczgM3nB22U/yariBX/Ndc8rHYJE81eI/3jHWUusrWuuZE+CJVHhDOAx5YUWjkOSQeZo5+zQNPczh0OxlldUAnvSN8HG/4jPeU/HFphvTc+dNK1j4EpPFFr9y09wPQLG6gyiUVEkmUO//dA36Z00XQvWovtPXbgAtOOJ7hm5SLTZo3vX7m083MnPWmzYPXu4T880LBT8a5F9BPDXWmQKP/SxHzqhSPEbN0GJviuOFbcCqLHmmH8O4CqJlxywd/KGoxMpTRLP+QL41yEHdGAtXOxlLpuSgMdLfz3jkxQ/ktKSJ+Ml4p16GFuHk01N8IxN7BUuwBw+Aq7wsofmAp+Ad3gCN/zjw603eYQ+NupUYCZ//vK7mgvta55TEt1nPsz6cPcdH132kNTA5xK0Jpz4Qv8M/Q9/+MNTQalpxt+TMd2WNKYPyTp6rbPyZQXj4KdI4uPh7bs1mssmxAo+afUpR8A6+CnG4Cedquk3eT7Bs7etcMGLad98rN9+igdilTiDj8l3D6xV3Bbf6VGy5IfwVUyeMt4Dz+m5OKsIl28oWNgRXCc+xrp8NwfPNce677oWzLEHfCWx/M0R4GPEW/Pw8h6wP3oV7miO99GavNBMTvaVH8lz8G3LP60QnU6KnUpPnK2vscJXkd+RtdgwfdEcgLfXTa2Bf8ktmb1UgOdbV7g4umRkErWE9SjiCdwekhQVciceAp29KPM58FzA5kAEH0kt5QwECsmb7jqlp6zGS4bs4ZTCMaVALwn+/Oc/fzISyg4363NagiUFRbfLfQpNgb0DqggQ4CWxujsCdDwyT2B3ysFgOEL3AJy8i/+DH/zglGByTo4cGTf84GldvIA3+vrRap0JY3Oo4QZ/wR4t5sPVhX5rMURdWUm8PXQ58a/ipXH+SiDwQODzPWfjrzXx1mtjeMG4nR5IKtoP3hJaMlbo6KIbq7DiuPHf2PhtnNc46J257qHLc7zTYe73HxKunD+ajQ3gb495JG49iZJCjSx6DURXTTFBThx1PJCwKlySFzzoFxzN13FJnsl7CzxHIxsiN3pOH9BAdtG4dV0DxtsLDXRbcoYH164zAe8uFS4Af+2Nh2yZfsEB3Zf4swXkh0f2JospW9/pkKCm+MVHfukShKOx6MInp2ZkIgmEt2LIehJ9rwGwaf6FzM05R4e1+Rw6qtsOz4IbGxD4rM/HCSLWC+DCjnQJJ63mwwuO8J/7W1djpdcV6Dd9UvwoLti+i2/Bp7kfsJbx5woX95zsKLThyLfRKbrsGV5ppvAPbGHiDuAvgUBDeg7QMguXwHp0TaIkQfe7HDIyz3z6BBd70jU8OAfsFa5OQCTe5IKX5GR9aykC+An+lczgYx7cw3nlywRj4W1duPNx+TTz/MV7OsXXWte9ozBpl9DxH/jtu7Uu4fY2FS4r4A0dZyeKYrzjmxSW/D59RtuqtwH9EpfZE/2O1wpdshZL9/if3OkafWZn9heb2Sh7Ir89sK759MkrW+nULWBetqOBYt0jAD98EpP5l1shWvhZ+cvkmc/zIgs2yHfhG53Da/zzna2RKdnMdSbINTQoJs+spxjSOIDL1lz3PCNrDS1+QQ7JhjQlrGHvvX1fIpDhW1e4SBL9QIzBCRYC+K3Gcw4oq+SRg7AXJgsslxjtOSWDG0XlUJojKReUPJOkKyoEeRdFJExKyZHZz2dFGkOlvBTcmE5IgLUFA0mvV2Mk3hkQ52g8AzPfHPcES3RxYNNBMk789Q78q1evTt1TODBUFxxdEiEBr0LJ/9XgpIMRW8/eXZTUiQfcdErhw+nZX9EDFwkSntgfjnC1hxME9wVYztU9vNKFJhPJV/sBczkGSZEiSgcT3hIz+NrXPDS4bz30uXTAemXDGuGPl2QoYdG5p4PAvnBTMOR0jJVY+h0C504uAT5wXuhQkFlHAMIXfJDMwQ8ueARHuLoq6tDgmNxa9kOvgMP5SvQ4sUtgjmQULfaQ9NEruD8a0A8/fOLo8RetyesWwGv6bL1LkAzNIZcKUPYTDkdxwR/+gPzWk0NAH/giesWu8RSvt8CeElJ2wbbZLPzSJ7YvabUmWStY+Q3+Q7CWsPEtkpS9PYDndBzPzTPW3v6SjUTdKRKbIid7BRUu9p60psfwzF5XsC980WEd4/gJ9ir4ohPt6JsAN3ZxrnCxHx/m9Qo8hh/bsDYa85tw2AL480PonWDdrcIFn3SkrYnnmkr5ALjhTcUSX3bJBtmegpGPUgz7bh7dspb9NIn4A7pEdvbU9KF3iiP7b/EdwAlPyIffKP7MCw8UHHRDomxf94+CsfSXr2RTEvDiy6V10MfvvK2FywS8Iks+QIxDi4tuZK9bwDfwf8nWODZDd9nbFhjDJsisGM4Xymmyb2tdkp+x9qezq2+4BuigpokYzw6O6E5A9k45+ZKjc1aAN/+hmcafXIJ444KruXwUeYnXNR/2ZMYv8IG9kmYdjVV5BXpa19/AZ7GKbNMNfsEa7Cgwbs576YB+POOrnxLvF1W46LYJipSegTtulSj4LuG81ZBWwFBrMSodMspDyTgYCcQMbisQDIWjZJyEoAQ363g/lAIKRip9JwQcl2DFsVBwQcHlc795kdzYeybEcCywOqFxWlKw75k9Jb+eCVKURfJlL/RNxRH8dQLtKRk21xwGiQZ4CnidCnGekgBB25GlxFrRBm/BXGJOVoK+V1GMdw8N/koUdD+9MlEiw3jxDz/w25oKD+OdRpC5V1Uk+RV91rMfpy2wC/Zec/HXP98oSZcYCv74zeGiB13kZA4eS4DgiA8Ab+CTk4OPYs48RZukbxZPxuI//vTKXQG9Zwo2vBPsdVs4LzybMscLSbATAl0VYzlGeuO3DgofesmBeQ2k17vWRDCwt+cCjv05DR0+gc+eqx7cA3Bgp3CUOJGPopp90gUdtnv2ws+jhUtgP/PwFP3wIk96hjdH8DGOPdE/3fqS16A96AZZSU7pNlnzA3wU/WEHClv6SX/ch5MCFk30Pj9mT0GVrmsKWD/fkq56blzgs/loFdw1Idore3GRBVv/whe+cEpirGnPYK9wId8SdbYEnxWM5yOtqxFiLXvR6xIsvFz57ju+GrNXuPhuTzpsbT4Ej9kN3eJv2eTEOTBPoVCyNNfGt63CBb3sk3/1f0fhCV7iveIAH313suv/4+Lr7G+NiYPPfA4bZq/odI9M6Qs/55793CdDxZX1nbLSEXpgL/jzHXRJMcUn4QV9EyPoGf9EplvyoaPwlPh5vvL4HMCXbRcL6Su88e/IOuT+rnD5/4C8yYC8FRHiAX8h/orV/BQfbVxgLN73+p9Y7rv4hr/AeDxie3Sen6GvbEVc4Tc8N+4a+RtL3l7Zp3PTVq4B+4rLCqmjuhPgFxti71t2fgTwje5UdF/Lg2yUn9PIwQ9FPJrYLf+S7RlPLnIncvXMfPbMTu0fD3y2rljNF7Fvvp7/5z89u9ZmXxrA/60qXAh1/sbFd0brng47ZyiAURKKQRkeAYyMsQpIAqrEWbGQEk2nEthf4JDkUGgKTlheochpnMPPs4zDXv5PAQkI55azsYZEyr9wI1ExfiqCNfCCkUukFTBwWQOaOcZSJEVCBQ5c7bGC/fHZWA6RcyQHRutVKfs4GZAgCY5ohyfnytGZb39r94NyHSZ4uDzHIx0hBov3GSvcBO7W9ZxD9iNZyZQESSAnKw5CR0UhtiYSgfsCIFrNsZZCyJ7h48JHONmbHNDJQZmPd3Ose+iVoAoa5jZGMejEyH/IKUjhQfIM5lqu9ADtkis4cJaSKQUd3m1BfIYPvaUn6MQ3OM0974F01ZqSWt04p3D01l5ohIeE5V5nZa1rC5cAjvCTJEr8JGDwSjZHgK4rCNYTCmANPMdvtvGf/tN/Op1c4ofCma5K9CQj7JjN0Dk6IjmFT5Dc/89/5GEnJvM+3bYuXfDMnniTDjsBFdgFZgW0Anhe7mmc8E8KOXKCe7BXuNgf3fRQMm3fLTBHok7m9IC84CLh1UTZ4rd77O5c4QLgqRD2+wz6rJklBrAHNrI1B0gA2e58nTWgA3uFC53RmJEo8g3s1hUvfZY04iee/93f/d0pRkk+2IS9rMFXoost2y+As8IHTVOn7E1P+Bq8oy8u6+It36l4xC8+zz/eoHiCz0of8J1cyMNJKxkfAfPgQr/yO/RbHNnj9R7gx7vCZR/ohUKZjOkKHZfgkjveudAu3iti6Q55s2HzPEvn6LkYJL6KgWyc7l0rsy2gB3QcXtesZ6yL//rRj350wulafOgw/0vn+czpt46CIgBfssV7eBI9bJWft67GFRslB1cy4ZfhLg7AX/MQDuTmue/kapzCUKxgp7fQ+FKBj3urCxdA6SiywCDo6EDrKggCe0H1VqA8FJCxcSY6XrrJFHYG94BQKB1HrdihzHAydgauPTCW8qu4vR7nx+scBsFTcnv3e5+t/eGrwODYdOr9doWjcX8qjLkcgJMYiQ8aKwgY+BYwNsFPALOeNRgvZ8vw7ENecHU5HVDIuAfQj4aKH+sFcPNMgK07GXDY8KqAszZ8yV9iLrnwPb1QQCikVpoDdEti7eGSiMCz99XNcYWvgskrTxIZPCLfdV1j4YU28pFg4A3a4UMO//k//+dTwoGOvQQtCAf4kKck0I+0BW7J2JYu4QHdMYYjVTQKbO7v8eJWaC96qnBUcJKn+3COH5yVbuI9gIe3Fi5oRjs5SjSdfrIhstni4Ragh/33z3GvfExWEgoykuTRb4GKruAJXljHxd7oCLvONoAxnRApHuAdWB++eKwjTx+96ggnP+5Gl64qm0CrPbcu+9Ef+LG1uT9ctwoXYG+nr/zwLLYmwNE88xULLq9KOKG19haYA99LhYv9yUwRZD2nVRJ/tnFOjpIAjQzrrjZg3lbhYh/FHf6k02Tj2uKn5obOKrk77ZV48hUaK7qnbHClyVqKV7SshRce0lXFMl8xdcdnONET9kAPxJi9ZNIcsUKDiT76fgmsgzd462SITmz9hu8o4NO7wuU84Gv6VYOwE1m6oKhhJ3RCIc234wW7EmvoKjmxD/GKPqc35+zjGtAs4Xv4gS1dOwdwFyNqZF4LaOC3NC0UCta7FsQqOjh93j0AJ/wlMzapoSCvIAc5jIJEoS839ZaQ5hmZkS95akDyjxrMmhRoerTMXgqg660vXIISEkrQ6yCOzilMgeoRgNGUSUDjwAV9gUDQs7e9pqIREoX1z17CxdxzwvLcGoIR/K2PHg6IsQlM9pUAc0wc1zS+8ONYGA/j5NwZiQSJEcFp4sjQdC11ZfAQr+DsVQ9Hme4xSH/dhxt6GaZgGj32lRwogOBoDvBcQGaU8x7nY4+KjQnwY8C6DvYNJAWKRklZYK79rIXu8LGG1yIkD9bCf44Cf83BNw5E8oon6PZMgoG/8cl6/nomqVKYOd0RgB1129u+W3w1XscUb+0jgdPtFmDIVdeF8y0Y4eEewMMY8/FA0mvPIPwVEda0tkSsIt7zeHMvWIv8dI/QyN76vYy90vP2g+ebLlwCMiJ/yZsgwoHC270pvz1AO3ty4hFfV6Bn7I2t0DF2THf5DeNdEg17s2t0uZeukbPk3TOfV7zSBU2Jr371qyedVSTQcTzGa8kBv2FP61sjmXQBc/gShVj6VOEiod6ijx/gh/mY8N4C+/JRTmD/5E/+5GyiYQ3PLhUu7pGVBO2Tn/zkqaFDD/dsx3i0kwN7hZN7c23PZ+HimfXYNt1ma3M8aF24kLeTJH6UX7AX2eMfP6moIEs+YYXW4FMkY2Qw97K2UzfyJBfj8ZzPkciyBfspLOjZVmFoPXuwUclT9nkOwov/cJJEp/itLX08CtZ7V7gcB3JGKx0gazankCZD/l1DVHceP+lXz/kC/hm/yeqSrK8FOInb7IVOX7O+pqAGl7/wuxbshS+SfbRuNQP2wDy+i52wzXPx9laAi3X5XQ0pcUK8lsuQm7dnvJoOdzosX+JjNE3Rco99vR+gXOBd4bIAxujwc+a685y+oPxoZcB0OOm8U0IBhPOYhuSvQOL1IMn7uSDvmUSbQHUzBJk6nuagS2B89erVqbvq1YR1PfgIoHDh1BU/1jROt03hs77mItHlBCXPwHocCqOT2OmqWlNCJOgwejztn1Ke+/vMOAU6hhsIspzVTFo4YwZs/S3Z+K0IhyyRCDhLnZoZnCuyBNW5js86oE5iFJecg9MX/NUpoiPo0EHPgZlDnvhbkumeS4InCZEM+k6nCp6924r/eBBP8FHHzKtTCig0WROgS2ec86en+AsXa0+eTiA3CZHEjrxmAoKPeEP2dCd8Hg3wl+hLtMIbL8N7vUDO6iUULhMUwOigF2Qx7WIP0ER3yVwysRV8JQ2e0yX6qXBV8EpMPSN7TQTy8jydAGSKn2x161QHuGcN/GRr7JJvYBfw0VAoicZ3tFk3XZ6yYZPsCk9LrOm+IpsOTdwCcyTKCgFjrbkHnqH1c5/73Nkkw33rXipcgPt45J/w1pTYwjGwP3mhjz/FH/Pn2sbMwsV6Eivf64BOaL618JZP5AfEp2yYLMjXfQ0gciD7LbAfXTQGjpMen9k6/CU39hRL2LjTYYUhnLdOjAKy58/ZK7qOAFuj3/SQntlz+ptbAO7vCpfrIF2jB/wT/SB7J6u/8iu/8vojH/nI6fSF7vEXU3eeCuh3MV0s8P0o8DXs5Zx9XwLzNBM0bBTWR4H+KXjkhtcUPPcAecBR4aJBRF6/+7u/e8p92IG8hVyfA5eXAOUC7wqXBTCDgxUkKIzEURAW4HuF6RpDOwfWIQiBUWGgcGHMArWkWQIhYfbusYST4cy9fYaPoMWYHAkrNuAtIbC2MZTfOF03v4/w6peA5TlaJRySccFNMFNoCKACrrl4Ym+Js6JIMWNdzoMhS0BmwDMWTpyj19R0DARwRsY5emYdAXVVPjz2bq094AcE8V7xin6OQ+IBxy0FRgMHjbfAGMEKn0qw3FPASXQ40gn2EWwVIclCIoZewdM7tk490AJPa5mD7wKiNe3TOl4zEcjD1xw840CtySkpoObpCT7imxM3nTHj6KU1XdbAG7ohEfSbBQmn/bb44p5EidOjz/bGZ7jaXxfaSQIa7G+PR4B16B+dkugLlE6l0IpH8W8PPH9JhUuyhpfCgS3xEfiHd+doAeaRA1+09WpOtoZX5EtPahDgnUKGjq1By1/67vUiCQp6t3CBOx2hB3RHEu9VA+vSL7K3J1t1Qsf2+AU+Kt1oXf7BHM8lFe7DuRMXY1cwh93QOfRvjQmsB1c0+bunk8bR7yOFC0C3YpBtn9NzPFQ40D1j4b6C+TPxJxMnpPwDfs05ntF5PJc4KXrxuNhC9vjHp3YyCldNDzzbA/OcYPEhdDLAA3s6XcND+5Knoij74/skRvQXDiug2+uE4so5WQHP7S+W2Yee04/iyD2AxneFy/WA7+QiZoqLfJUG5m/91m+dfksnttBVPqb48pQAH/pAjpp/Wza1ApzYt4YO37ilp9eAtfgJcZMNHNFNdsnG2folO3gE2IP/4Hf5CactH//4x19/9KMfPX3mv+Qk7JeNP7XcXgKg813hcgEwhlJLuBzJScQFZ8kBJfaMAXCoGErRKM8tDDXXepIFyUm/Zehol3Px6kH7oQcO8PG6gwJHgPE8yEEIpo2T7CiMvBOtWDEHbV7HYJC+o2ULGIh9FEAcnKQXP/En52OuwGWcxMj/M6JLN50DHAVOr7+svIIrOUnSmmO8hBzeaAdOCLz73fcVBHtOEY4AXoJpRRY54Z8utsRzXQc9HIZXtXyevJSQ+Rc+/A4BXpyL+da0Dz75J1clHXCXKDO06eys12UMvnOifpyruMJTycYXv/jFE58kFQpTdLVX4DO8JBYKQgmKAIV/9MR4e0haPMMDiSb8vYYiGQjXaL0G4g367GVPa9kfj6NNsMQHAdTzo4CnL6lwAav88A9vdQPRPWW9BexH8ej1p1We1uRj+k2H79ajZ3jgf02n2/QseaV71vSsNT2b4J61JMuf/vSnT8W0PdgT3es1zeZlz/Bkb15dgAeaPWs9ibdiRXJtPPvoxHcFa7vwis+x3x4YR7f5QYm5PbfAODgdKVzgrEDnu8hhbxxAq2RJUbfFT+B+hQteWtNvw/AUkBG80xMJsxNUfocMo9/adFSDQlHD1s1hy5pXcLDWHlhLAqpoapw1yYBMFEJ//Md/fCpS4Ghfz+AkBqzyai76xSB07oGxaKS3ZCWp6hXKR4G13hUux4Cs0jk6zPdrirJhTSNxj7/SmKOHmnzigcKXPuBR9v0UQF80IsQfOF7ahx7KVzRzFNxTT28B8+UlYirbOGdXAH7FV38fDfhhD3gVP9mPOC1v49P4A01nTSb2yNfJMzQ1yJfPe2q5vWlA27vC5QBgDqWmUDoSXn/6zne+c1IgHW4KJaA4hmdYOvOct/HXMLZ9OGdVtH/BS5FhHcISVCQGnLZje85HcCNEexpn/lRYnxmAooWBek3JWhRcEu80xP++jAZBzzNr7OFtD05D9a/rwfnDp/Hm2kNAkODAS9DrFZfGJQvJ+LoXnD1XJCoQfIeXhErglRQC/Eb/XmCU+AugHUVLlik7WVnfPGsoEtA++QbQ4vSKI48n/sZPCSRewEEiTFbWcBkjecQDa+CX4LHuEbiPt3CS5HPOXg/7yle+8t4rW3ggefRDYnxxbwL8WkdSppvt0i2ml+jk4PAj+skKnzk9OK+yOAL2o+9kbT1BUOJEpySvOkP0TCHaKZprjxdbYPxLK1wmxHd2pSAXWPAc3ns8NYcsJIxkTB5zrLlkQ28Duk+O3k0nS6fBgq55xvMR/BN50NUtcN+aCnLBD66+u69Q9wqnghktQfQZp+jtNQV6mF4bzy7ovOYL30iv9vAAeGS/PRsOPIeTwiD7XwEP6PClwsU9esAu0GK9rXEBn9/p1t449Fe48DuSa/7ZPubwL/wXu2YT5KbAITN8Nd84370eqCPeaRX+2t/phfmSrT2eGi82KF74JuNcfIoGiwSI7NgR/iRXtLEJ49wL8B3e6DlX4LmP9wqs/+N//I+T76Mr1t6bcwvA513hchnIkF7zB+TOFsUCp1/kTqf6J/cVLHIBsiJjMVmBw9/y4/Tokn3eAvQCLnSVH/L5HKCHXcsbzvnVo2A+muk9X+DzHhhrT/zSkN3zQfeAPeil2E0G7N2bJ3Djz/FHw0Eu4S8bYGPu842a3XIRc/PLH0Qgh3eFy5XAgOso+bGoS+LP0CkUpZa4CAySdScTjJ/zl6RQNEGMg4APY+FkXNZ23zjG1GtFAYF5Rmg6ozolXiMSALeMzprmSyY4LQmlPXvmu3+9xitMDHIrcfU93Dy3l+DGcMyTQFEgDlF30YmIDgFHhFZzdFUkWRKAwHpw54i2lE+w9QqXJIxhos++frCcQZKlRGHPqcJVsiwZM58DllSVJOI1p40PaFzxgIPA5qSg5+7pKCtm0MuBkacgKKAqinRjFET2dkqGT2iVYMIdPfaH09wzXltP8mWeE4r5Ko39Ciz22HPg1rcOp6fzS06SCTiYU0IDF91dXSS/wbF+eMHFWDJ0P9mTo+SKXtNvMqQDkipFKvlLRr///e+f7ENXX4IIH2vbdwvnc5CzeqmFC0AT+vBJ0kp3JKhknvwmNF5hK3hP3gOfJRzkAuBO9gKU4KZIVMj3yg99xH+nc+S/8th3vJf48l/8k86qBgkbAfaQ+NIHSajvcx3z3ZOckz0dJ3/2j07+iQ+k7xIjtkaP9sAcwTmb3AP70j1FVva/Ajzp6qXCBT7WcDK1ni5NcI/e8cPoJNc9sCZZszH+wmkD/4fX/Itik8/wzD08zA5cPturBCQZWpdc+T0ypid8L1yMX8FaeEC2klHjFCXmKZDpEv/Ua314ZB26RA/CqbU0GpyKsfe9/egpmvhAesMHrrr8KMCTRxQueARfPuXROL4JSA74rmlQ44jc8UoDgf8lQ7rHXtEulrF3uuEz/rrEbHpvPr9O5+ik+LH6hHsAzvRbMW/PPbBfb3fA91H7sy9JvmIe76b+TzCOLSkmNFv2fMa1YD+6iK9yMHzGc3kKXyDXxG/7kZ9TWr6SHfP9/npuDf4Gb8iLPMldDoVvk7ZH8e5NAV7wX+8KlysAoyiApBfjvvWtb52SSIYnEDICwVDiIlkTsCiiJEY1bJxumuCue248gxBAOBMBUiAXvAiIwaxgbQqsGJDIUcotAZqvCyjoCZ7wBsYK3BIPhYYAp4CxP8fmucveeMaxOF2xl8Dk1Ecn2BEl549ONDrR0dHzOgdDCuDBIBUJ4ekenKy5hbu9GaIE03ocnMuejBkwTsHXWluAL/jM0NHBqPE3B83o6QKnsQX4JenHl3DEHwGBHsFx8knARr+ETTKJN+hTvHCMZI8e8wUFJxTWM99lP985MK8ISYTgS88mP40RmOiRsb6vAC84kY1kj7wkuz2bVwkhHZB4SWKtic8SGomg5EUSbU9JGafJuaKFHpGDwCZAsgPr4Z3/oJCOkcU9AJ9HFS6KagEovrvw4VFgLbpK3+DsOvfbDPqIpwpRehkuaEYv3gKydkpCjp4ZJ0mRpJIL3WIT5LmCsfRLgimB9i+JkTO5sE1d2fblixS8EsSK/i2AAx2mH8biqWALX3al8Bbk8ztbYA1z0H+uMABwZUN79gp/dnKpcIGPNTRzJAN78ncfTvyW4p7u7IGx/Dm977SEjQuw+M0PSyD2eEHu9MVc8/A8mbEnONiDPWtmsD/2uadT5CI+4INYwkZLDK0pRrFn8kGXmGLN+OAv2bjPt5/zkYpf/kGM69R5i5+3grWs6YKrBqG4eg/QEzpPXvQKHY/G+zkg3tAXcuC/2ZLklc7xD9EVbWK/uMif0DtxVozpTYTAeN+d2ohl9Fj81zxhW8mktVv/WsB/a8sx9tYgHzbFx8H3UWA/PsA/IKE4R+8WDvirkWMcnqUvt4D14x1fLR7xRWTic/493gJ78sl8bAUou+Tr6HLruTzTQOKPNLKsLY7YC94u627R+VJh8oxPFgffFS5XAmZRZAakqpVkCmyCi06zrpogxUFgsnESBk5Et4tiqoQpEyejCPEqDYcsSedAGMiqvIFEhdAk3jqnlJnTKti5zOVg+s8lKXfrGCch7bUDRRgn5t1XAQEdum9+jCkR9//NCGqMRMBDA7olZIzIfEGR4XtHVqI6kxCGAldGBBe4uaxlP/PROQGujFXRgEeCtHW8k6uQ8tx9z43bAjTjMZ5yjgolCZbx8GbQAi1ctsB+/lM23WX7+U6WCkHJZrLBT04BLYo6jh6+nDu5SBA4HTyAL/3gqBWoCrH+jwY8lZg4HZHE4IuEgW6YG9iTLqNHcsKhTh7AxymNAoos6RsdscaW00ITfARySRO+2JP8zYOX+9bhENGEfvPw0UXfFcDk4xRMMowOyTjer/K9FsjoEYULnZDE+R/h0YUnZAXHPT24BdBLJuwLX/s92UwMAvvSaSeo+JheuY9/7MYpF5/QCcHUPfhLLPwWSqFLF6eM/UVfnU1jFNI6+PBROCi0K65cxkt8JL10MN8yof3pORtRuMKRvtNpRSt9N2YP7EXP+U1+Zd1jAtnRAT5kC8w15kjhglZ+bcseAnjjmWKCP4PrHnhG/z/zmc+ckjs+gk3zifbC5y0eAjhorpCN8TMpRA87Uoj4rmHhFFqCyn+wydX/Gcc++YY//dM/Pfke+E852ENjhD/EdzpGhwJjra0g8ZcurrjTEfajYcWn0bt08xFgHXjYx9p4JGb5x2XEv3sAX8nL/5UDfzZKz/Fl8umlQryRY4jlfDZ9JlO+gm9O51YgT7kGuVuH/skV+G2+PCBvz8kev+gUGfAVchaxhV6SjzVulTs86bVE3GfrTF3zHZ1yHSe5q77fC9bTcKuI3QJ8ZE81FVYcj4Dx+IQW8Rm9EnB6yKfRcXw2ZgVyoadySPIwRo5A5pNnU2Z02ZpiNp/oFXP+s2b5Fq9fGkye8cVyX7FFXqah85S4v5jChVJwzrpgCe1WwDAKbE0GLzGRCFAKTkSwo1g5EMyPyfZtLsXiaMyX5OQgdL8p35ZiqcwFI4pJATlxyYIkXBJAyXU9dbxn0Omyh8BujfCS/Fjnj/7oj07GoNNG4eEk8FrTvPBRzQueaAs8xwO/zeBAZwDmEBSN9vUZTuimiOZsGSvAC0l1r0VJCnxGp8/47f4WcEj4IKjGEwUY3kv2JQDxeAU0MhYJPPp9h7fA2b9o4p75xnndh2NA03Ss5tqHY7QPOs0lN7qBv+bgpw61f+ZQ4WddY+Eq0dZpsq49A/w2z75k7zscFU7ucfLuka1kFa/AXAPAMV4aDyf6q+DzjBzhMx023IwV7BVQEmFrSGTIRyKqGE4H7gH74rH1Bct7AM70QcBQJLI7xZZ18ZBM7GUcftt75dcRMCc+kTWe2IvukNHkiXF0giwlivY2D88lF+xRgim5WIt8Y9gIfksojFVAowP+LvSQ5atXr04JQj6qZgd5s2VytF4AT8UdXeCnzvHCfc/tiw6F4Yc+9KHTvtNHbAEc+QFX+rUF1kEbvd4C8/DuSOHiJIk+ed61Arysw79vPQ88M5Zv+M3f/M2T/1PE8TnunwM84/8kkvDGc2BN9JKJRpu18AY/xS+y0yyjx/ZCq7Xsp4g0RtNBB9cYMvY8sD5ZiyWKFkmRdYB98JFNsBHf8cxfl3XolPjhleUaQ8C653i1Bca3LvzRwmb4Fr5RgiIe0m1JnuL4EYULP8A3s3sxxj+Eo+EiQSqxg9O19DwVkEH84ZcVuRpl4rxmJz8PZ+O2AB3o0ZDgK8gwQDN7UIjszQfm24M/IBdJZL9pa39jyPMoGC9xn69xT57Dh07z04+IJyvYC+4aDfZfAX7218TNDs05ohfpNX2zh/goB6FrfKtGVf5+D+yjuUQ+xra3WMBu+bJ4tl7tTy7ihDxFniGfMK9inS94NF9vBXiHM37jPXv31ggdkZdqWrtn7FPBiypcOCav7kgCMOQRhFuXEXtlTNJICXUaORXH5wItBZ1JgX2NozScDkfC+ROY4C8Rmco450hiKDLBppgSIh04wZ9zp9CSc+PnGr4LeozUPhkhI5AgOzHRWVB0wc3a5rga67LPWrgA4yXpuoeCJsUD5uKNgMrJWY/zs+d6YjABbyXyOu3wVXgyeMoryZPY2XML7CEowZPRSkLIH58FrObGmwnu65A7tYgGazEYfLG2eQwfLyTunNK6nn0FWrRP/vnsso4AolAUBPBN9y9+W4/Dc1/CudJqDD4L7PQIfXCsoLMOevGtVwFWeumm9f0FeC644TO9CN9whwMdMkYHJydorI4UeiWv9l33uhbsK6haz/9lxE7uAfRz3nCOv5IUCT3d1HWTgPIPCi+BfE83j4J9rEF/BB++gu9Jh+Ipe5BIZtvmSNwETPcroAOfrSmhowPkRlcERvtIbCQE9tPYoJ90gm7wIZLZ1qFz9JgMA/fhYG9ypstoOQfmoMtY+0rI4XQO0K5rbCxZTBonwIWc6PjWGPfw4EjhIiHne6YMJriPF07ByGBrncBYvFQAwG0mN+fAGPPQDV+4N8df6/DTZFxiIbFiX57RESdwfJn5dIru0GHyhD/fwvYVMfyudefFp7J9rzyzaYDP/BWd4d/s20VW+KERws/Y0z1r3QrWRbt1FWrkpxuNDjYpfsITD8iU7vJN9wDeaaRZC/6ufLlC3SuXCrNOul4C4AGbprveiGDj4nS6kUz3wBg+RMInXyDngK+mv94EcX9vnfag83hGv8iLD5LriGP8DHkeBevZE+8Vk8WcwF70zfOnkgW9Yifsd/UJ8LE/2ujNEV4H5oqN4hcf6gSbD8Of4vOldYyTsItJ9g/wTNNAEyq/uXXB15We21uclp/yC3jeGx7Gv2lAl9ysRrNclmzEEfzCA35BTviU+L6owkWwkiBKgCgDo5OwYcatTMBMawsenLkgTJEYNQXRsbMXhcV8gkmZjBEgeoXKPXMlVjpxFC28PONcdYcomnUC+HMYAp1up6SeozO3+f5yUAxIBxjOPbcnnjBcDp0jUnzBqTHoNF9CJ3hK6n2fYJx1zeVcJbdojFbJTL95MI5C4ss0yAnod6wqedIRtC6HDX98lSDBawvgYk/GKSjnWO3HCdoTXlsANwaNJwyaY2Mo5Gke3hcEBFiF1JRHwAA5CAGSXlh34mtdBQW5CSa6qDqs4eWv/RgxuVdETYAHQ5Z8+AuXaHPBS3EIV3KeNOOR4EyWngE8d1JnrfYzB+742I/JFYTkIOE2ThKFVk4az/bkcgnMwxd7lcS4dBbJ/B7AF2tKFoN4TFckd2hiy7r7LkmSOZI5tMItWz0K+GyO9dFgf/xKH8KBnARJOm88/ZGYVnQEZOoenZakwoncrIUOQY5eOnkzpgIMHuyIPaEl0Gywj3vGBD7TC7pDBhJVuM4xWwAPNs722Fs0bs1zXwJvD/Y16ZzgPtnwIdZb1/IdP8nqUuHC52pMhNM6jiz4OXZJl88BvvJpCt6C6yWwn7jDL9CHreJOkcLXVTyYIyZokGXHeKKwwWfNE/jyIT2Hi/l0OH9JTwK400kd4ApZusf/K3Dxwb7m4KdEiT7xn/yO/VfenQP4mGMtusafK7rhx9b4WIm5feCK93BI3vAVv8j3HrCmOGctfHJFIx2ns3w/maKV7yaja+m9FeyBZjZEHt5goPfwQTvdwJtr8EEfOiSDcgM0B55JsCXWxesjYG82R8fYi1M6OJKjIhrPPLfeJTz5JTGuBo0LXr7L2Sq8nwLwEW/4OI2yyVc2J66hET7htoVLMoOzeI4f9IhvQx+dz+cfocV+YrHX5Pz1PcBTa3ktla1OvPaugP6L2WimT8VzeQb9PyqzR4A94EMny1PwDO/wEM/wNJ0kG36Tv3hKeHG/ceGsGD3niEGCmEDPYCnGUaOdYI41JeI6WIJJysl4GTXlcJqhg0QQBQ1dvZIKl2ecpXHuB3DjXBRB60lDwqfgCgaJqI6R+fCAn0vBo6NkbPPCEV8Yl2eCv0Sxd+rtRZkpi6RLQeLVFw7U+itYgxIKgHUKrCOBcq+gRx6CvrXPAXw6YXGyhM+K0EtOFm3ka1+8VyRYR3J/DqzLODhfwQzfGLhkGh2MjO5I7hWdKw/si2cCIEcu2ZdcWENSYbx1rC/hoB94wGniz0oTPTEfT0Fys4bAr+ui4IFz8m6M7/hdYjpxhaMuHpnOBA2dgojk2Hjy49QENnosgaSnnlmDLTnKNWaucxTgCWdrmU+27EiwltzgNxzv7bKk27NwmTB5Bg/2RmfoKX7QJd1YdpEznby+BMaYx37IDC72aR100hd6Yxz+S17n2saRD3sle7oK33BoD3h6h998SXJj0GOPWQT7LOntxHgFQZvdCCrGbo1ZwRj8YyPwJdtJxwTPJGfwQs8W8Bd0QUDbsntrs6FLhYu9/PavBGkLrM+HrsnCCmjEW69jXTqZCczBQ00uvnyPFgm803N0BHCWqMzCxL58m3+en/2vfDaO3bNbRbH12s84NiFmVMjBSQOLb/HdeuYokPkmcSmdPQfWNgaOXfDmK+gSX6KAZluKLX40usydNARweorCpf263CMX9oBujUT+U3ykh2hp3qOgfa1tDzGLzPhBsUYSp4iZ+hi+RwDvrMNnbIF9+TenXvA4Auve9ETuI6aLR+VW9B3e9tjDmY5qiLK5+Gs9sZ7f9/kordeC/ege/ohh/Eh40hN5T/6++13JDH10Br7ooOP8fE2nxl8DaNZAlQ+G0wR72oMdTd7uXSu4Ry/oGrrlj+xSI0Ej4yl0PVysCX++REzCM00RuZJGmvuNbby/cGK39566XoIXV7hgTgzAHI6fkjmSkpBQvukcjgLGWk/AUaEXrN2nHIxXwiyQ65pxgpRcB2QqtrESQ0kCYwnc57zcF8CsG3jGqQlqOlWMRZCh0AyS0hsjOfEMXu3n4jQ4GfhRVAYjmfY+JEcmAdH1YECSK3QqWryi5PMK1kSTvXTzGBc87KvzJyH1nFPjSO13DuDnHVfBVRdZAKbg0XEO0MyZ+iG8gCwZ2MJ5AlkpzPCUPpgnmaRDijzBTAFId9xbcYAX+ZOVboj9yEXSJWHBOwmA/3xLMoZ+/CBX9K10eQ4HDhxu9pQw0R3OBp5kR4+tR9aBdaxH52bh4n7ruuhIYA+4CkASDa8rsRv0tr91zPFc8TVPEK4FtNMrCYwCiH7gH51Bi/0kZU9duAT2yLGaww7hh+fsFi/whM3QC/ezM/SfwzF/oAiXEPUqIx6Yq8NXQVyREXhurAArwaSLW/vBVwAyX9Cly/ZDC3ujl/Q6sDefg8fur+vZQyCDL591yV6BNfBjFiR7fMETwUpAQv8WwIGO80H5sAntd65w8R1v+DJ6PH3oBDYrYbTe3hhgDb7eSRGZ7tE3wRz6VxG4ru87XpMx2yKbgLz5QbqIDo0Pds0X8nFeMSbjrTXZvmKZLOhqAG8+0UUHxAyFKn57Rrf5UH6L7lkfnXu0dt849KGFDtBHuFufH7eW5+RkLHme4zWADx29N2mxX7nAHsAFTnQAvxUx+OAfLcFv/OTXL+F8BPAsfmmSkRNZSib5dvLyfMvWj4A59AXu1tsCYzSx+Ar73AKTZ3SVvYpPfDp9J3M8mzodoC9fgQ9wwGNNATp4K05HAO1wl6fwq/ZHBxrgz87o3gRzXOgRFxWZGiLyG41Ga+CDdYy7BdAvpsvFtuh3z170pdc2rwW4WQeucBZ35bD+AQbFJ39M/7Zkdgvgs73ogn34Yj5BwcrOPLPXnl15xge8dScuq7PCIMonSOjmSxoELs6bsu4xcAtSAMoroFCCqfA+E45iQJHhf0OWHEpYc0wpIzxmcsHB60S7rDONAf6CoaTV/nCGvy6xBFtSwAmW+NojwzOWEkmYGWpgTUmC3734ESanBp/4QYEkbpKjPR6hySkCx+U1LfM5of/yX/7L6a9CjALOQLoF1rEPp4oe3VBO7ghwhOhQJOhCO7W4ZOAchiCLHxI+hoUOeEqM6ElFyxaQj8KkAg1/coTmc3Bf/vKX3zvRynl4zgnRB98Dc+mS7jn8JbWMV3FBn+wBFzzmQKfetHbFdOvCiS5ISMgWHu75Szb45v8o6igabuYa4y89E2QVyAoO+rKnB1tgDXvhNb1FG9rpKvynw89ZPVfhcg7gjFb2RRZsTJJhTTLXvcK/kg10bPHFfXKmW2gzj42jlY3TVb4KbwF+2dczvgHfrLEC/OiqLnwJrtPX//bf/tspMRWYJfbuB3hqnmAlUG3x2D1+iZ+QqJvv3jl5oNucWVDvjSdz9OJpOjrBPDzVNBDIrTXBczp5rnDBLzqrQLIW/OYYn+2taPDK4559m0dOilb2Yy/zzum/tdGoQJAQ8Llb460rPvAdFRCB7+xRkOfPyRgvrMs+NXR8t8YKaDeO78JDn62Nj2Sj40mXxUH36Bq7Z5P56FUu8ct4POBP6L7CEn54o7nVKSV9tK7x53i1B+Y9V+GyAjrRp0Bk63ivgGEvJVzGrDq3BcagHz3kIOnUEHHyxb7wTsHHxowz/si6e2AfdsGn4/8e0KH+UR9z7gH40kP70Qc2pXASszRC+S88S1fxDi/4oF7X8psbvsP95wC6q8C2P33ODskCfmQBL8/YL13AU/6fzyFHMmNr98jLXPuJ8+yP3W+BccUE+rznr46AtbqsiRZrakryV3wEPqAdDxp7CYwxHm7ooFvyMTkhf9VJ6+TZuXWtVeFyZP9b4cUXLgADKApjVV1zuH6s1vvCRx0SMI7y6jBwbrpLEyg/B6japPBOejh2RlIwZUBwMC5oTR3TFRdBQTd0Ok/roJkTFKycfHCOGaAxLt9LdOwfmAsnCbxg6X1I/GkuBUIbwwrvLaCQulXeua9TxYlK2CTMOhruXQL46HL750EVUpzaEXCqI2nz2x8ytc7KvxU4Wh1XiSVcJTGSfokzmSlmzukEWenOcdDxy3j3FRd4qnhx8pLBes5BK3olb/g7wRi8Iou6vNbuMh/ekloOtX1bG+0SV5+B5/gumJQEk6Pkm0NBv6Sjk6YJcCM7MsUbdNnnGuD46aZTI+tImDjF6Jnr5azedOESP13hiZ/sjgOWUCnAOHq4slW2uZekAjSzC8U4XpIBeSjQNQ0UyPZzX8IpsNLFLT64R4f4Ak0BfHOPbHU/7fHq1asTnvYNGiMYOAHaW9sYiZpgBtd06RzgOTnTa3TtzeFbFFgSBTTs4UB+W6+Xeuae53uFi3uCpn2MXfXMdzYvRpDnXvIWH9ga+Uy/uAfGkF+nSmSzpRNsCf5i1bqeeKAhpgtNP8gULtbxl7+me3uJHt7ruNMP60tU4aVZwK/yL5ITiQtcxSCy27Nv+xrPdum6pBR95jnF5SfjTZd1zvHpHFjrTRUuAP546FKQs3EFn2aROOOeZ5dotA4ZKTLZnNeA4SI5JMf2mPy6lWeAjBQEktFz6+Av/0+HZi5wD9gvOugV26Uf/D6dURzb13O0s39FjrF8kQblautPBfTc3l7P4rPpM38CfwAPuYxTZ3pO9k5pyHLK616wxvQDa/xdoWYMnO+F5JUOkgM9ZXcujRf5ifueX6LXGL4UH+UVZI8msaB9ruFbucC7wmUARmCM4MWAKa4EW6Bzj4EdAQZI8VTC3o0t6QfWl/RJcCT+AgnlV7kLHIKAYCi4cDQBR8KQJb4BoTMa92f3PvBdhSvIOeWQsClSGGBKZwxadTbgDNxnBJJduOGbAgveBVyXwMd59zuWLbCWZ/BT5Biru8zoBUU4cayXAL6Kys997nOnkyrrHQE8/uhHP/r6O9/5zqlwm/zZgwxV4aELhoccGqODO3r21sFbdOFLyRNeuU+PJOmSSokf3SDXZGGMQoHj9DkgI0UOXkk8Ja941tr+ushPwsCx5kzd91fAUPj6DPxVIHs3np7Bjf4JHHCwvuKQnDwnd4AmfFToWtOerXkO4IFvui50im5JZiVb9Mj6e+t4Rh4voXDZAjKAO5mRJ/t1Qij5kyij04WvklYJaDpkns98hISBH1CACuh+o0JGeGwtCQ77zZesYD3JrCSU/K1rLh/D7pyQKpj5A3Yxk3t4WFtRgoYtWtFJFuyB/+KLpp5ugXXRqznDLtKjFaytcw03CaDvWyCRYR/5qgC+cDlXuOAJm1P4lfDPMfgKRzKjk3v6iD9sCU9nAN/iGYAL+7K3IqHkdB3vuzhjHB4H8EQvneKH+XK+CP/ZJhysSfYSmDl3gvXpRKcG7JtsjOdTNEMkGZJK9ilJg3u8Sr8VJPamq/wFnyNGWM8za855jwLy4QfeVOEyAW1kQl4anHyTAkZCxbbwKZ+GBz4bT0b0lz3gnaYFnpPhtI30qeseICvy3vMbATzFfTFqT4fuAbygQ5JWsZzeOAF2iQNiDt+pyKJL4lHF+XOAfcR3PrBmK7+cjOFD5+EteSdjev5IHQf4xEc5fSMP388BvrFZhe+9uKx6l+6ilV4rXOgtXojj4hN7yp81nv+UL8qbyJddkDFeGh+ec68jYO13hcsZwFhJli61I3Rdbgkb409I58B8Dtye5jJITKeQgoIAzJFZx1j3JTaSe111gYSiJGAKrIhS2ATWk2QrkCiE7+EFR+vrCnJa1jKOU+h1AGM4TEmVJBSPzPdMYs1IKSDjFNAZNEOe+zAWiiTZOMcTa5vrxEKnBS5kwTnsvXc7wdqMx+tiP/jBD07J3RGw9sc+9rFTMoCWIwA/xSDcfMYfhWRdWLjs0UpO+ER3OELjzMEn73P2KgdaJD+SpJI/8pBUkDO5kL016B19kChxqBJQY8xLF13W5UicltTVdd8YCRM99Nm6cNKpUdBJFslQsKBH6Rz9oae6vHTZegKOEyPFl3vnwN7RYD/OX+LFydJBPGqvc2Dfl1C4XAPwRLegQmYSV40QOuX1OieubB5tLriRGSdvDD340Y9+dLIrtghn+mfNLYjXAgXdpRuCMBsm34pENkim7MhfcjHPxT4kX2wdPltgH/haR1E7T/H2wBy4ZxfnxguKgtIenfRP4rrqnj3QfK5wMcepkwLGs/U5+gVkBcLWc9/hrmmiQMjnzWsF463nFRw+a/qPdXx8VRTC1XNy4QvEET6MjRrDlnQw2SaZ0zOFH/2RqG7hAtwnW7bPj0gu+Ab6xb84naebCiL8JAc00FV8pUuaOT7zARoqnaRt0fRIwLuXUrhMICO+Ei8UJfjKhvGW7PCRrihWyIvfZzdiE5k/JeAZO+WD9mwqIDv4sj+NEnQ9lTyty0YVSBJg9sQ28Y8uipV8AZyfUqdWsJ+3JeRhdJ2syExTiR0rpPItXY8E68HBvnxxdnUO4MMeFcHllE8F9JUv0Oymz/I5+sJfsCu48AtyEH4MTmLZo/Cy/7vC5QIwXPMYM+euyyyB1Jl07xLjzE+pBATOS5ARcKzreUBBjaUAkg3JNucn+bBXryZIAALJr2ROMCfQ6Wg8E+g40ZJODlRi60f3nL8kQqLKYejwFqTs5TUha7jngpuk21xOkEM01tpOayircXs8gZs51vbaFqMUAP1o3smGtS6BMZJ4RiFYHgH0fe1rXzvRPfl9DiQvZOQVM/yVbHNY0YzGlU7frU/Wih28iGbygQO+kbuxnks28EAiYl3jyZucex/c/jov+EZ+LjRxrgpgMgof88lZ4kHP7d19/EKPfawryCpi/+AP/uCU1NbZn3Lw2T2Fu/UEW3qh20Qf5tgtaC90o0EwYAPWhCfcjgBevR8LF3zHg2wPz9AuQaYjigcBmi1obNAHshUgnYQ5XdRQoO/klTy3wD4ScicqCh3BVqEgwNjP/ubDxV8FtqCjAK7Idp8t8nHW2gM4GMsW+Re4mX8O8J6PoLd0dg8ERf/x6+ofA8k/+4T/BDih8VzhwoacWhVE1+e9aunUYAvgg1cKUIUDHpwDNCjQFfqdWpLTum/Al8Kvk21jyU7SRCYSXWt65vI5XfIqsIYXn694OdcUcN98donXimpFcr+D4iv4A80uTQY6RZcUNvQTb13hAs/nAPx+iYVLwAaszefhnXzBvxrKZ/rfvvnlfg+DFuPJYk8f7gV70D+xjL3Y79Je5GkOHTLnKWWL9nhGn8Q+MhErP//5z5/yjafizR6gV673kY985PVf/uVfvudryCxdf0qcrI8f/t8yvvDIfvgorvJdeAjPp8TRfnSLH+VnFJh+Q+y1c683a/LCA88ezTdrvStcDgDmZGCChO4AJ05YArZ75xSFwAhPwSLpN1dQCFJUxiFgEYpCQvJCOHVnJTD9wDp8BB5Jj2BifxdcJECKCYkiJ+pe4LPCSzKqYranEwxJCHoUMJKp/qUce8UDa3kNSUEl2YY32iQk6KvztgfW4aAkBnjhlRavw5To7/FwggSPI548PAcShu9973snWo6sD3+vYXzzm9880VmnFK3mz2sC/PGdfsHNOgwbb9CKXxMHzyWHEi0OR6DjMCWaOj2KHUkDOZVYkoGLDPFB0kgeEkHrWdtzcpBsSNTs6Rnc/K/eig6JF12iU/aTGHPQcDQ3PK1lX/t7r15iQ//h3X4rmGO+OXRJ91/SpVNs3jlb2QM4PLJwwe/nBnhPnrIbiSoHr9DAIzLRaZNI6nh+4hOfOP2uS8KId+au0Jr8kFNMMrYWuzYnvVnBfXP4AHqmK8a+JccCD308x2trkiXcKkituQf0Bc0Kl5ocWwAH60lajF9B4qfw5MMmwNX4c4WLpJzu96zn/tJZzxVy7HYFY9DLL+OXMWjaAryha/wzWpxu5D/2wDPNEYku/MiO/bDxChH7rwAHa5MlvooXdACe5/Z0Hy+duv3FX/zFqXDBN6f+YhufoKlCvmTLx1iP3Mztek4go5dcuAT4kkzYMjvWJMJfdsbH0tV4+VTAltg332s/enlpP/oEP/HcCS2ePwfADT/omdjGh9Hn59Yx9DtRlYSzRzx8ThzoTT4GL47IzHN4s40aKk+Js734KHmOnIG/ULAoXuSRxZ1w8PdR+PCB7wqXKwGjKJKgxaHX3RLwLhk4A6CMki+fgbUEDwmkZ47WCJ5wKIdLQSOZ0bH5q7/6q1OiSWmsoaChqGhLORQ2jl0VLoKdNVYwzr5eU2KgAp2EQsIu0HKylFHgLXmIdrjZV0dXcuM7fPD1iNF4Zgzn4F3t3/qt3zoVCHh6bl6APsWak6cj472ipXDYS5QmWE+wQf+///f//sQbThQPcyDzmkAe3n0tYXAJWk4ayNQac048UJg40eGoyUCiokAwzysfyXYCXMzHA68BlIz1jMwEKwk6HeBIrOW1MJ0cCa6kOR2Du+e6vYpCyUvFkPXg4Dmdwp/JixXME3AUQnRJoWOvrbFHgeweVbgokiXmbwrimwsfXckBb+mPxoFE8gtf+MKpk06+e6ca1iFfhY5TXcVv8rkExtAVPKFHZCUYSQz5SjhdAvO93mH+uffi7UVnJeN8hWR4a3330Go9/FgBrZ2KTBqjZatw8deFJjTSgwnxnu4r7Lf4bIyijt/L97T+CuZLUPlhdpMPPQfWcoIKdzZLJhoY7Bi+l2QaPsaxX6d5506F+G20Ot2XdHzmM585xRinBOIZPqO5fc/tPeGasdcCWt4vhQu+ufg+jUivzDjZFuPpmf0V31s28CgQu/rNE1yuAXYk76AHTyXPFdIduQD95+/J6rn2B+QhH2FDPifL5wJFo/01S/mRI3vHN80q9svfPBXP8ARuckBNHjmwPJLfkpfRl6eEd4XLHUCZOFFJtOSU8+cgHLFyVJi7MtU943VVnZBQUJ1WySIh6DAqUprrso+kUmIgmeFIOD2vDnAs5gq4FNxlvg6LACjIwnHFg+IJ6oIvxfvSl7506u7ryMMJboKu9a2j80JRGQPcAOVUbDk9QrN9JOG68vC95IzhZD0dHac9usolxJdAUFdUMdKVti3gAMlmKxlZAd6cvGTRiYXPM2lYr8BzQYLhSqjwV9KgCOmfjrU2HDyTmCkC8JcOkEWvCUnoBDinUfRKIrk6Ip/tSR5kpTNK7vHQPvRQ4gMneymw6RmZCaJkaFxrodNeig78VUhLmuBEr+gBnhdIugD5W48O2wv+EnBzjb+kD5cAnu+3wiU54A2Z0wE8IiNNA7bmtEUij28635w/3PgoeqH7rkHCFtm9i7zTJ/RYh7xevXp14j17dNLmfnspJu0ND/jAC37J0Hpkaz5bwSN647Tykuw8ty7fqqCwd35iBXvCAz2ST75iBWOsp5DXOFnBM/qV30kf/GUn1t4qXKzLf+K1eRPwg49jr3AydgU8dNJTM2EPrE3vNYH4SPRurTfBc7Li4/lRflnhrwi1l+eufAj+otXa7NxlPt/I/6CRLLySK8GAT3I3h85Zn89wYturhfyCxFozBQ/pJD57fVA8sb7LXtahN/CYOtUV7x8J9nk/FC4T7IXfmgr8KD7ysX7kzMfyyxqA9GvVy3uAjvDf1rf2teC0QfOA7pDncwJdonNw99f35wA8Yz9sgZ5H91Po8hbYh73RcTZ2rR2RsxNX+cWeD74W4ID/1uZXxIf8hoYsnVaA81maxI/adw+s/65weQAQKmckCddhVoFS/unMAzhI7gVXlbEA4aiNkq4JAqEICn4o63cglMJ3SiKgKDj8W9i+F8QEHQFLEjyN3VrWJ3QBndPkSAUnAVbAWp0T/CXjeNarcYzZuiUruhJepYKbZ/ZXyB11wPDmtCVdHOXKgy2QWEhgOeUjiisZw+/Jjz3AH8YpoCgm0GGPvQv4a5xCRWKED50mlSS5yF4QYPRkr/NbktdaAE84BUWNBM3pDxlIEozFo/YnM2sriL3aYU7JhKRYQagwlJjQUTKjpwoaBZVxK8+tKek1jm5whApbcypKjfEXT+HEURrnxMhYzgxejwL7PLpwiYd7MHmM1ui9dOEHvuIBvrAH9kY2EoH+KWynat59143FZ80Iz5y+8R/kqHChM9ajK4oYnXS8MIaMnApqhmhiuOiBdRW+Lj5CEcAO4EFP4aWgISP4uvDGHgpUc+BBR9y/BHgksDn1Edh6VXIP6CYZKEzMnYDn6JX0w2V9DmeJH981i4zm7RUuntEftpluuu9CP/7AvzkT3ONz0WbdPTBOEeG3Q2z2aMKIx/wBv+D0m92z6VWn+HVJMJ1yustPkT0dIGs6xR+zVUUWfbAmWaKbfMUniRn+ih14SB4KGXtKFK3F79Az48UKe4hD6a1YR6fxjC82j87DE3+nbu1d0ZdPcSWTFdzDJ3RJWu4B6zxX4QJvPGHnZBCI13jGN3umaC7xiydbfDgC+CgO0SOySd+vAXohP2n+rbjcAsnaa8Z8Hv49B7BtDRs5lz2fk2YyQ7PTdfnYLfvTHTomztCvWwEu+R16wN/yGWyePsjVPINfe/ITzyEn+70rXB4AmMfJWJ+yeJVL4JegCgwCWWAMB+XVJckDQRPE6qR85iwYkORFwMn5SGgc8QtIXvkRUHQmJI4CjP0o3gT7SoAEXz/GFKQkF5RS4cRxrnMAvOxrnKRH0JIUmItWl866YOk9bh1aQY/TPAIpfv+SCGO5BJyLjpt94WyNybsV0CbYH1kbnY49dVXRnVz2LmAMOZO3Qo9h63TWGcd7CYGiRsJKB8ko2bdOIKGUsHldwxi81GmXLEg6ybF5LjyAK95LUCWEEiyvhOGr5EKBiW/JU0FFZopGOE4cWtMe9ueUnEDRL/M985fsFXf2UthKQq1l3pYu3QNk94jCBT2KQidUPqNjgrXdIzO6oKEgGSM/NshhkzFdx1PdbHK3nh9G+/+FvM7ZD53ZKH9DXyWbbJ780gtX9qVI4BfoElmxMTyX/OInHhhPdyQ7ClI/HjVPoTnXRJu/1qmAYi+CosaG5onC2qtB/hEAuLqHDgm81xT9ow10iT7a33WO9/iGXwK/hAmdK38DOsLG2ATcV33xnD5JnNbnPqNJoJy+Dm7o3ipc4CE5d5/8pi/wjC6TmXVXMFbRr7h0KrNHk/vwkTQoKshjb+wEOOIV2X/4wx8+zUe3JgA5/P3f//1Jp1w+k1O2QAclv/hOj/DN3y448Av47Hcs3//+9987AcYr411kTJedutFFp770Y45tTXy1JhsQn9IpxZHChl7CVQHGJtDBB4kfdFC8yZbIxFroJ2dyoLd4vuqae2zBemi/B+zxXIULwDe0KzSzI7qB99krfvIXTt36XRF+HNGhFfCKb6ZL1l55eQTsS04aJfTrljXuAfuJUxo6+PAcgOd4xveuPumpgS54w0PsZ9O+X8tzOJO32M7PXTvfeHLHb7bJZjU/NUCsR1fp8rRPeNawvna/W8B+b1XhQqCSFkRvOcZHAMXBWI5YAqz7qbhw5M/pCmwCjwA4FXPiYg3BgaNjRDrYFKYxggxF0XG1JoVSDftXOAQfCbPgk5IxRgmvBJOj5owEIzygpIzUj6okTHBawb7GmUOZFUkSGh1Chma+hMB7mZJcQcp36x8xfutzjIK0BGsLhwnkKHGzPz7ZY/JvAhwET8HR53OARrzkKAVRax658FgSphgkM3Q47qUDgofuiYSQQ4IHnO3V/KC1BDBdVIEdbe6Rp4CP74oYCQW5kon16IFEz3HtV7/61ZMMJGGeSW7IjN4EeMjR6AxXIMFphXTRXgpAfHHBUXKGLsWRTjB921rjXsAXa0uOJGCTZ9cCOnWIJVU6zmxLE0BHiY2QH35JHDhtHT/JhqSVTNhZJ0sSfEmbExzz6e58nQa/2bsgUPKXTURDtJGtYNMJhHvwkaDh/6TZZ/ZC1hoV9E0Spji3J/tpj/6SCz2CA12Cl7HwJDu+BB/Qzm9JnHXtFcv8F70133rnAH30znw+gF7u6RU8+GK8M2/S6DncBCd6au8JxvOjszgyH31bhQueeK2B/0f3pAOvyZs8txIk9+DB56Bn4hlYD92KNjqKv5dswTrw4lMVehJETS1yxXOy0IHnk8iI3aVTLjqFL1v7WJeOZKcaGn7fpnGF7/C1v8tnPODD8A4Pfccv9k0n3Zt029MexnmGR/YjM3wgF3TRazZFP53Yiz9wITu2xKZ6NY1/ZHNkYY4GINnzdQoqtkp+GjLs7h6A93MWLvSVjYkt/uLd5Ge6QKZyBTrA5/AtdECMhfPU23NgLbqIxrnPtWAdxZQYcUmfnwLoe7EQLk8FeIQ+ukdPt/zAUwP5sk++gB4clfUK+CQms6fVr26BfYzjL8UvtiYGyD3lDfSVz9jiv3l0U/N8q+nzFACPt65wkdgL9hJuQehW5TgC1hZwBA4Vq86sZIlCCDxbQBAUWCeLY/cqiHXcT0icOuWeiuS3EBIsDs/JheApeebofbe3ILYmQYxVsNIpE6TwZQ/CAT4ScslxeymWKJLkS9BFr2T3iOEAtMC3Du85EKwFVbJck5AJ9hVAOT5GBZdzgDeclkC5Jkp7YG+yFIg5il6v0THxXbFIVnhxyfF7LmhZy2sa5Dxl74Ij2q3rEpic8HAwZCFYOc51kgLMcVqgO72eOtnPHnCWJHGWxk/w3TiOHG/IXIJHvvhPvxs3r0eBtfAXPxVZeHnP+myLU1fMSgoUgV7P+p3f+Z3TPz/upEzyWKKq40e3OWf+Y0/XbgV6LymQHCj6s2mBQjJHjnRxpRkd8BNoNRwUOOSPHnZOzvfiaj66JXfsUhI6fc45oO/m4CM/uweKPfrOPrfkKpmu6J7PfZYAzxMQ98hoq3BxX1KgibDaoWQb/zV01meA3eCtAL6Fo3t0tIJPkbc1bgUywlN+l23hwVGZWX9vD7RK8sUCxb74Ayd6bb8t32YtfosvL2miY5pD4lUF9aPAfnhNz/kn69ufTtNlTZJvfetbpxMo/3mwAprf4e/ERXp1D6DtOQsX9PKhmibkge49+QG8lkQaK0kT283Fq0tysC6d7vT23D6XwF58oBjK9u9Z6xYgJ3Yr7uw1DR4B6LS+glhMtO9zAx9CzuL5PcCviFn0Zqthu/IQreKr2MoeFNfyVD53yx9O4CvkHGIqv/McQA/fysLFETinLsFzIiIBFHQ4Tt0tRk+JOBpzbnXYmErwAqigRxkkiZwzga9gH0rkue6bRN7cgAJKxCXEHP10JLpW8KdslNYakl9H9hI0Dk/AmrT4LlnSWVVlU1wKgQersq/gOf5IPKwvsOMp3HSzdaX78e0R5TJGR5EsJHLneI6n6BHsOyXa2gOO6OMMLhmh+YwXH9F1yWAD9KFZ8iVQ+Mz48YPDlVhZ7xI/6ZkEidwVfrrt1iDLElcXvOgI+iVnP/zhD09JuGTNXvSjIhUPzTFeAqK4hcvklX0VQpIT3U30rLQbbxyH7jUGemx/6x7l0wrWJDfrwol84K5wpsdoowuSLYmdhLB3/yf+1wKcOVndzPZWHEiU2Aqf4OTQvhIozySUxm0VELeANaxHtmzm1atXp6QEPq0viDpJ2WuuwEVCQUcUCbrdEm8FKH2nx3wEP0NG5+xpD+BiLl9oXUmwQsJel9aj73QEfnwr2rbmuE83yZrurcBv2lfHdfLeZ7qgcEsH3bPeVuHis/t0dwV+lj6UsAc+00t+hm1YewV7uy+u0FGyoNfnIN+pqaT4crJwJEk4B+i0Jn/H/3T6Cx/yo2uSTz6G7mwBP4CnxlvPJRZaz6ugEhr2GZ/i7a1gPl7RcTbmZMH+7I/N0+EvfvGLp/ikoQMvl4KYfO8BfuC5Cxdyp2tsXkPgEv/wmG7hDbnyW+yEnilA2SE6VrtiRwoeReiWzl4D1sZzMjnyNsSjgU2gU9yhyyutjwJ04bEkvGT/Xv2+BuznhJI/IO97AN5kxif1W5kV6A2+iiH0ScwwFg/kYXzEERvn4zUUzSGr5+AZWb11hYukkFPEcE6YMUoSVdrue5/Pa1MMBXMEO8FT4CPMLsxzUTgCO2JQHL4gLmDBZWW6e5Jd75tz1KuTsK+TGEmOIJWi2JtjqTvrO8VUJHHMfldByIKQ++HMEUhYvSLC2XGmXo0RMI4GUnuZi0eCiX9WUyLtP85ziuNk5sg6AP0KLokC+vZ4imZ06i4rNnzeUmC0kh8DnvxawT084TQE9iOyBObRIf8PjX8owUme7r1CjhOy/9Z+K8BLYqGrKNnAe3PpCR0U2NM3tNIj+kpPFOFkFn30FL2SIWsA9JgjEd6Sh3EcmORdYPV98sBnspG0Sl44xT1Ar/H2cOFruK804B35oNFRc3bnNSz6qvPEPhVkEldFBbqP8HQP0EY+kmHruODk5EihTX78gcRA8McTSZuEikzhbvw1dg/sY7y5+CdQoNmrMRIz+3kWCAQCwrw3AR58gWJLMkPu8Vrg9foVeXvG15GfZ0fxXcF8OPIXNSRWPVrBczyTaCow4LwCvpAJvaL7K370hH44QUBfYJ4GB/rCwz14bhUueM73OsEOPLOfbi7dzl4C+ymiFV/W2qIXTeY6adZEuQTWVKCj16klvbbGLXIxh0zRTMZiAJmzfcmPddtPHPBbJnHF/fgygQ/SeNHpD/BE0SrZpquaM/xzyc2UySWALx6a47I2f6TIsq4mIt8lHrFRvk08FLfMhbM59IEs7wHrPGfhEpAL38OW8GJLDisY42ILmhPsxKmTU0K8In/04Ck+2UPMP9JguATxnN3QWTg8N+AT/+s6yrNrAV38Pb1qj6fYZwvsQ4Zim9yOr3kE8F1yJPaPJr7CZX06JP6IPcbx0+5foy/whi9/kE95Dp6hgQ94qwqXksEEySgz/C4CFIR02ThViYuEXKKoEytR5FB1tSXOnIlkQfJozT1mWltXyRrWz+kbDwcBQgDs9y8rcOACC2dFwVzooJiCoMDTPGtyNLpoApF5nJ0gS0klajrwHVtbBz6SZM5c8SIYHlFke5lrb0m3QP/pT3/69S//8i+fkpwtWrbAXoopPF9/1zPBPWMlf1sFXoAuAVcCnDPaAvvgqc4DOeyNm2B/+0rK/D80/qdfe9UF8+zIOsahQdGko8aBwhX4TK50VlGpOJSUcTj4ilclfObAyd6CDLnWrYcH+XBQEhtjJj+iBZ8kEDr1eNcz4+mMBE/ycs6xWhfegqekjH3o4khcyVVywo6cniisvQZE7+mafTjA9YKbNdmtU5AjfN0De+DhLFzQiD/okvA7qWTf7Mq+7E6R0L/W5USLfjqZNAaO58Ae1pakwV9SQdbWrcu82poCGo7W3qLXeuaRs9M+3dW6XmgxzxinGb1iZ386kX5dA3CwLpzZN309cjJrLzpgvKJ4yy6sYQz58qMTP8/YJlvnX5vrLx9GXxvvHvvdKlzoI13jIwNrsyF6vfoRa+KnQrrTntYKjBEj6Et6MGW4gudihsJcsOcr4bC19iWwD18jAYY/PrBvNKPDui7yoiP9gwv2ZwNb++E9HtGTwD74DGdxBE8kd2KipAVv7HmOboB2MuDDNEkkVGxJIczvKCytY4z16bRGXL4qsA79e7+duAT0kw9l/+eacxPSD3wgWzzgl8ieXfGj/CtZWNOporiAxkcAHOUrXjeW6zw3oJtu0pdi2iPB+mwRL6fuPxegh22JC+Luo+izpsYOO+Ir+Fh2g4/0XkynI3TKhQ9H/ZBx5SdiFV/T/acGuL5VhQshSQKns0L4elEcgjCeIlFqAYDRCr4cnmDhhIZD5yQUBtaWKOjYGicYUh5Kw6FwLOZJJAQuyZDExbqO6VS/kk7ObQX4cFSOMq0F4EqIutKSKclPCgTg40IHJaPAaKfM/iqgGKr90Gw9861PMXpFwx5HwVjz0aUjpBCzr/vWPwfRI/FQVJ0zYmPxUNd1Dz94eAVE4jyD3wpkS4YlYpfwhBPcFAwSBicBnIL9Ls2dIAA5dYCj5AIeU34COd3QKVUUKazwpRNA4yV0Ehb7uswXZCQGnueM4Mzpc87GbxVodEQR4dRBgWtvrxDSU7wmT7pTgKTXEj/6R9/xg/7rmhpPx9iIi94L1taDH9zgY098uOQ0jbHeUxQuLvyBA/6xCbaoGCgxgyeZs7E60PDhPPEH/fEFb61jL985dh1kvDMezzxDP7v3zPoTJB8SQ3Rv0SvRoze665JJfoitz2QYTXC3H/2SeEgKFQLmsBtjttbfA2tbkyzJla+z5zn7QoNkVZDr9zdzT5/xyZrww+cJeKXpoqEDX2AO/aOr8PHdhfdbhQse4L/EOLAufcUb/GxtAB8yJocpm9ZzT8KNB/Ql/9bzwHfrwlWRoyFkTd/x4RqwFl6SnYYUGjVOyF2cQXtysDYaJMkVhHClu1u2D4zXXBBnAuOsKVZIdNCJr3wlX6SIwG86jibPG+M7mfHR9mVPbEasorcKIjZgLFlYU4ENX35xS/fdez8XLuRCF8UMMjtnN1uAH8kEL/BPo4nc6JaGln/RFL89u1bH9sBa5Ed3H7XmUWA/dNOJhP3J7lGAl/goLokJqx9+akiW9FnT4Fp92AMyEmPZirxB3OR/8bD8YcoxvXIdATYuV6UTmjbTLz812Ptd4bIhrO7tXYHPggCHrgMrYaN8kkYJpX0wWHBxCb6CIYcl0eFkjGcwX/nKV05KVvI69/KX4VpXETAVDk0SRopp7gy+jNH+BN1aPisq/DOqumYSBcGcwUzlk4woxiQ7AspRaB9gL4FT8VLCcgnMlxxKHCXO8JprTuDEvPpnny0Q4CV3AvHeGu7rsuMf54wH7p0bjw5Ji9+j9C8iXQvWUdh6FYkM8HvdUyJMP3RIvve9753ooG+BwK/gUpiFM/zphETWKU2yd5+DVmDhrbnNAY3BM0HVq34CoYAIP/KToNFj8z3zmc7SMXjo6vYv2UisBOipe/eAdSSeHGV6egvgzV7h0mdAHnhvT3a0Jns+0xdFADuWxCnO2D1+sVPJOp7kZOlzumI+m0YP+13B3vbd02026eSK/hnjuwAl+YdXtLjQxo4EGP6PrNDFB9GJcza2B+igd/RMMLT2paArcVf8OvXYshlJLp2ioyso4tDXHvBFp2BML6OBnLYKF7w3Fs+B+/wl/ZZErwBXOtIarYOXLv7eb0bIKN1ZwT148ff0wv6STDy/BeDC74gryZq9bvHdWHrI/2kUAPQoQCRnW/i6T4/Z7nyOPvqoQCc3z6KNj1JsWJv+o49e8KdoFu+sqTh0XzETv9qDTWqmaKpYC03GbAHevZ8LF4BufkHDYs++j0J8xC86zi97OwQvNY/Y1JZ+XAv4xU6ST7J7LqB3GmTyEb7mUYAOOskvKb7vlce1YH92J5dQUDxCVnSh1z69JSDXk4fNAuNe0OgR6/MJwXPoBRm99YXLI4AyYKagKWAIqIoBjkRyquPEsXP+ddo4YApLmQQ/zobj956yxELSw0FYL8cuCNhjKl8Bqq6tZwlTwPR7k2mM9paUKVrsK/hRboEFztYz3xz7S6wkOQLUVNCjACeJGTwkeda4pGxo1DGinBUTWyCZxqs9ZwNnfNe92wJOAv/RXpIIt64V4EFuZOO1C38lQpfoWcE+nDBn5YjfGnCxDtoVFYySk9aRV7AI1gpOuhBwdHRZ4hZYA5519JKZ+/bATx1PvyVCS7oqIbKHPSVzfuvl1MUzMjQWbopKTp4jlOBIMum5cejQLTcOjdH0CLDeUxYuXcDf7ERXSacav/CqwGIMPIxDL7vGB7IS4CWW/s8M/2cSWUoerWdsQM58wuxwB+bjLb6HF7CnNdi735Cx33guWElU+Rt2HJ8mrmSpoDTGHhIcSbj718oLLzVt0Gcdn+27B3C0L9tBG5zs156es3sBF68nLnTZbx6SgWf2Yh9kAxf3rDELl+hmO3iNTuPcl9RJ9ErsgbXxpyYRvQtP4Ds+sy/2n79cwRzP+ChjJclshDyP8tkYuLMrvpNt9poi3UGr53Mt++KFQo//xDffjaUbuvHhvYIx+MnufZ6gyBWb0A/sYwy951+9Pu3/BPKbK3GMz8c/NiHJMXb1CXBXPJKVok5hQ5fP8cYa6Hq/Fy50yKtB+IKX94D1rEEWfLd4Rt819sicX+dj2M7qT44CuckHyFasJLvnBDjTecWwuINe926hZYJ16J2Y9ghZXAv0UPKvuN/zJZeAbKxDPuI4X6wB5DRZnuE7mzbmXn4Ba5R7sP255iPWvwT5gHeFyxMCxgo89hR8thjNsftXuAR+QZNhUjyOjaFycpIUgZZxcRoZGEcvmFHSgkJ7WNca01nhgWRZsVPCI7gpirzP7dUPwd04z6whiHOAM3G7BqwlkHlF7khXA31wgLvxM9hN4Mi8C723HmetsJFsb4F5ArKOMTpLAiYPA9/RwWn7ZzolJLcEAXswdq+44EmFLFysL9iQtUQA3pwZXnBIkvaZcOANA17pgxO96ZTLd/piDzK0L32soPafS9I3+mktBZ9CkyOcOtWFX3QIvr4/B8D9OQuXLvxml3RXok8WcGnsFtALfBQwJGQSV/8pq7/0zRrGCCpo8hrP3Dubc4qy7kWedVYFPHKU/BW86ITkFM57eLpnHfLlaxTQ1uN70Nu8rblbAF865R9XoFNo25KRe3BU2Dl9Xk/k0MDnKBTxCY4BXyUpm3pnPFvCgxJe89fCxV8FnqKouf4qSnW+zQ3MN1dSy29nPwCuik//ITAaPLN+zwP37Wl9DSK8IXNjt8avYIy94MgWnco6tSar/EVr+Ds/m2M/uoY31nHRNR1SCS1dJPsVD+OcmEgI0Nlz+PC1/oNOPMEjfgQPnDpZz8mb31rQ23nSuwXRBwf8rzHm3iUwhs8Tv+4BfHqThQt9wDt6Dpd7AU8lk+KdpkD+gG/gJ/jzmofus9GpR0fAWEWRE+ZH4Hwt0DtvWMDB52vxXyE95KcV83j23MCe/M7r2pMxdKOfHPkvtiin8DaEuE/ueIRG38mezHy/B9qX3oqH03eCe+RxFPIB7wqXJwRCduoikAgeW0D4BEF5jScYCik4eGXBKyFf//rXTx1DCiNxKRBKGnRBFBy+E2TCNF/gLAi5jBdgJDYpcU6O45NoSRwkIAoauFiHQ1S87BUB58A+jEg3T2dDJ+6SkeIDHDh3c7cALQzS2C2QOAikjHgL8F2BwGlNvq3gPnlwDmQBf9/3xu8BPOmAIN+/dIY2shBc8EbBoTOL73iEdy46REfIIpDI6KZJZibAi547FbGOfe1Fv+gPfdEd9Qoa2gtkxsKHjP3g2LypU12Cn0Jb4pEOPTXA7ZGFC74dAXvhA13RFZNESu7gs4UH/jSH/rIl8jSf7DUZnDiwJX7Iv0TnPrmaZ10FN6ds3mon5EN+ki4y8+oPf5CumE8+in7roncP4GgNSTb96x9LoN90aou+LTDOOk4unN56nYTv2JqPP/ZzYsQOzIlGz3yWcPsHKCTLreGvQkejgj8DxsNT4Mc/3/FnFi7W4yfwmp2h2WUNvo5dzf0lfcYqGo2zr/tAck12CiD7uN+zwHf7KO7Jls2RSfLZmjPBGLhpkGgmoZce2W/FZ4J7npO95EUX2b7u44MTGP4Xv/geukxmcz2f+Qn0p3su+NBJhYvi0ckLXfEqkqYaHVMw0/XkuUej+8ZbzzrWIB+4782ZAGe2+34+cQHoxTt+FP33AL65nMRJJrMFF5naC710m9+hU/SBfyg3OApyBE2Ha+c9AtCBBj4q+7wH2EeNATLAq+cG9pq9HeUnPNkxP62I1NSwBtmQM75kgy76INbj3b08s658qpM39vjcekBu7wqXJwLKRaiSHEzWBVX5EvwKAotgL6lM2VzWoCTme2dYsJf4GOuzAOoVMs5IAkopzUmYihvBb74GJbB4DQgvGgd8NoaSC5T2lNRLaMyRRLgneawTfA3AS1AUNAUsRnduDePhomDyHj9eruC5wmYrwUOP41Fdxq2ugL0lLda3zh7Aw3wFpCRRcMC7awH+khidbc5Glx0fJRNwJFfB3F5bzoUMyNIa8Ecv2a9dD8/MV7ThDT6TIb0nT2s4neN46JC55nSZKwkR3Kbzmxddo8+KQkkmfmzp9SOBvN5E4RKgjz2xOXjQLcFuT4fxyXiJGZ1PrhLQTjokwS6BmHycJpCbpFdi4TVGc6wFzGUL7N3e7nutyr+Als60LxtWGJC/Z1s8M9blGR9EJ51M4A0dpev8Dxov8dw6dFzBgh66QY/cW+fipXXxRgKNH+lP+Dt5lCTzGYH7TgbxzjiAD2yq/xdjLVzQbi9FRD7PPXxhH2hzz2UvvLe3deDtvjESHLZGLp5NfTfORcZ8reYD+vnMfEV7uFZwzzh+rN/bWQMeZBIuexCOElG80MRCe3vhEdunW2gnI/jhYzxxock8hY9YZDyd1OXmnyR4CkpFkcTVWPPx1wlIp2FbYH1702ny0chzMpSMjgJ9+iAULuRJrmKKU7B7wFpiGBnvxUpj8Nk4BRM9d9qFj2Kb5gf9zx72gA2yA37Q53NjHw3xjC7CeYvOa4Du8Z98ubWekxZ70UG2xefC5RyQnTHkBF9JOx+hOdlJOf5s0UCm4oFGzaV9LgE85LROqcifz3hOvgH0vCtcbgDM6toDjl2CqKPCqTMQR5yETcEmwIsz96520NqCjk58XVWXIGptyYUA4IfbgrzgTbFSYEbOwRjns3v2QX8JRXT43PouTkziIsB5rUaSqxMv2bCnpDscj0B7JAPHmnuFXGC8BIPBCeor5Hgk0tOJ2QsfFAUcNJ5P8JwD1yGcHdctsK6TFom8QoHRXAv2k8Q5HeJo8M5fSZgiVNdlLSDWS3Kn2OgVFfKkL5Kc6Yw8oyuc+6c+9amTrI2hB7MLTg/wZ3VkeMGRKnB8njiYa7xCCx54K4mWKOGn8cZ0zbldtwK+v8nCJbA3neQ42ca5Uyf3+QEdSglt/PRXAtfrWYpR8uEf8P3Vq1enf62OzbHp+Mru6SH5lmCwIacc5A7iMzoFODyTqDR+C5pjD5c96YbEho7WWdujc4J1+A7zFQaS7zUp8BlNuoZ0iSzgO8ewWTxWdAfG0Hk2Ey/tpVCS/PnOLsh3Fi5OQCTdzXFPUYc/7emeRFojw7x47uL32BGbtd/kg/nGsl80C+hwbo1zYK610OVVUPogKcQzxUD4dq3QPeP8zs0rf/a25pyj6cEfGOMeefDtaKV/aLeGz3yz36nwVWSj6GbreECX6d/sDtMrSREZuD95ExhrD3rLZuik7nD0XQNw/yAULgAOdEbxfgsvArwlW7pXHLkExiiK2RM58+NkyMekP1tgL7LzWjkftiXvpwR4KajlJOU0twK/4Hdj9PK5AR/tr9jsBH8L0Ges/CF/LOdhk3zEUcArPlC+cw/Ak+/ULHtu2QdweFe4XAEpkXUEMI5cV43TEAQ5IMFaN9qrNj/4wQ9OSYOxxiliOAtOakLJcc54CkNwl+TbKzAfDooajscJhgCjoNAtc0+wMkeSLNhILim9teDG8UioGIMEifFIbCVi9uQAJQ++e6ZgQZekVnBl8ILZSss5QBdlt65gJzhKitCyF/DsL9jhz6qkZKGYYMjTYVsLzxWMdU8neM75UX57r+sCYxiIcejWgVyT/CNgffzDb7on+aEHklRHt3iNH/AnT85I4ighxWtdFfIjMydlcIG3RKd3/ukW2dEBr82QM5lKZCT6nuPBDI7mw8G+E4yxvvmN70KLefaRpChWODC/yYEb3er1D/omIJAfushjxeEaIIuXULgAuJAReeKxQsa6W4BeJ2nskj367uIzBABrufgA/BGg8FAnjgwU1woTxSe9cR8v6T4+4q/fGNDNyZdsQGIkyLPV5uxBcoaf9eiN7jp94tcku3PvPfDMfL5P04H94hcaA2PgSI/oOJ88ddEe+GrufMXLmhIsyZPv9pGA0kNr4uFauChIJO7o8h3P6Ky/5uC/JMxpgiISXi5jyUyBzrfPYgL4iyY2rOCkU04g29cajZ3gnnXYBf5KOM33edpq87tW6L5k04kSX2XuHGsNOoXH8PcdzvRBoYQvLq9+kTN/yb7ZM5mhhW7HazQ6qbGOiz5IevlHfFzxNMY8PlDixKeRubGebdF1DuD+QSlc8JQf4MfJkMxvAeuQL97i6VGwHz7QX3mCZihddGkG0k/8nmuSl3t8ktOda/Z7FNBjuYuYh/ZbQc4md0DncwMeymnIns2uPEaX+IrH5ME2xV5+z/3s5ygYTz/4MvNc19oefWG7XkPmO98UoOWtKlwoiKN+SsCZ+s5wKRFmUBaf3fPMmJJKDloQ44AlLJy+hMFvBfxrN4oGJxsScZ0rla3/f8N6gKJIOjhK60+GG0NBzV8VikNz4mL/CcbAR2CXAFNmDohi66BIdCWxOrmf/exnT4FNsSAYCY7+erWAMlMCPzjVafu7v/u7U4ChFIIRReUoFFxo1wkW1NBunGfXOA94u/CX4+gdfzyYdAd4JTnXZUXvHMOQ0BF/euY+fDmlusUB/lpTEmjuXrAwxknId77znVPg3xt3DuiUufglGcVfRYiEjAzIC3/JBa74+Td/8zengtc7qQpERahkTWLFWZhHpt5rddKma4qH5MEpSbzoAn7QQ3uXoE2Al2QMDhPQSRfIxGfzXPhmLDlIYD1zT7IlObWeZEjyZIzA5uQALYo2yQZa6GeFDLtiXyVHruzQ32mLaLJPjvdWsN69hQvAE/ijnywEFXSs9uszWtm+otX+6GOjiopJi7FODviIEli8klQ4bfviF794+p2BLh2/YH/6Y7xC0fi5N7C34Cix8TmZHgHjyIDcJav+gQLFo6aIvcno3FropLdoVYApZK03aTYG3xRxZIuGeGgPzQe86z4aFHGSrHQG7cZ5btxauGjOGNNzY/k3zwEdRFevLhlnnv3JjO3SP/cCn62lqNJMkPzjk7nTbiZ/PIOv/diB14TYGt7ay1hj5tx5TfAd//k5sQc/pp8D1nJPbBEnyAId9Ik+fPOb3zzZp3jGV8AfbnTq7//+709/0QLsB3ev6NC/6HfCU9E5cfQ5HuKr/3CWn7O+Z13XAno+KIUL+dA3DSRxGE7XAvlYw79eyM9b8xYwjz7RAUWLOOXqdVF7eG6cPeUC4u5qz88BdEiBTXZ08FqALzuQb/3P//lPfuQ5gd6zC/GdXwJ4ir/okeewKwWCAk0e4h48b+U1WyQzdr/VPD8CdECeQlfJ4E0BPr11hQsnodDwV0JMcSQfOh8cus8cvUCg8yRhEHR1ViWTgpjEjHFz9pSJUlCEeVEOiRsmBwKHBFNyaU7gs4RFQJWoTOWktBI2ij7BHk4bBMy5f46FQ4GjwCRx4JDgYqxrfp4X56fbL1kQICQrfuAvyPqu2+mzYuhnf/ZnTzzRZbwW4EkejECSK1G2/woUE090JAW+yU+ATp3SWdQYAydJPTlYt2f2JHdBqyRlVX4yFaAlJBJy3/H0GiOxJ0ehEPzVX/3V11/60pdOyZFiQQdckeLECX99hw++owP+Uz7J1uU7mjleCal/EYyOwNEcY9DkQh/nKDFZQdGGP3RygjV++MMfnp5bI0C7gsuec4579pDMhu+8rOeZ5IXTI2uJvqCo+McP+OE3fcYD9kEfFJ26UmxRouKfDLfGxOtawCcnGfcWLgBP0KewdAIJt17Hm7riuwTXnsaSn44//xEtxpOXpJsf8Nk98oQzPcRnfFHw8AkaE2ybbeAn/7UmEj7TeXsbV6NhjjkHcDDWHDg52ZCwkBv5lKzvAfwVG3DmR8kz2oC/1sY3wYguGA+syy+7L9H33XqCOl7bG2/oDZ6RRTykL4KrtdlZvKa7eDWTPH4SP83tnrX5f3bKJrsfuCd5os9oykcY50JXF/DXmhUR5inyzUsejb0ExhnPt0nexLIKgsBz9NA3RSH9lLSgx2c2pXGkCGHr4YEG88hLbJlxB3/Zu/vo59/4SDw1z/5dvldwO20kX/PhdQ/Qb7r8QShcAD7RH3xim9cCWdFTJ9F4M3XgWkhu9AB/+ByxyWkrHRNn2I0xcGUzGrTk+pxgfz5CMayBcy3N8BVn+EMNIus9J+CvJp9TTrbDJuChgaEpwzfQh2wGftf4hy0wF8/4ST5cTLgW4CxfkY/dg8u9gCdvXeGig+bUgLFLwgQRwZ8zFtD8612f+cxnXn/7298+JeWUm4N3qiH54OQ5bQ4DA/cYZ4wuCCUNOAPBisFLPAOKKTDDzd85R5ItSZgVrvGCIIdSQJ/gu4Cj+OifyNPtTPnnFcx76EKfPe3DqARACQIc8UwBpyMgmRRIdJw5NTxe8dkD4/DBKVg/Upy0B8ZJfOuwTLyNl5iQSwB/TtcrDOHTHOPgj2/GzWf+ollipIuJXnzAb8/mvltgLTK2h9MFibnCRVdV8mBfiZl1JWScP/o5lPQpfM7thWaJGAenqMR78nZ/zrMenjJy46eDti/+TL7Zm6x7jcb37ltf8JJ4WTfwGpSEaK4zAT7Go49N6OIqZCQ9eKSYk/ywu09/+tMnu8N7Npld0n8Oly57De2SHM4B+ZAB26mTuCb710D08Q2SacWgoGifuSY9ZMucLR/glIwMosVYgYrOCMaTx+TK//BFggcdJQ8yZTs65wIK3rBP/oq9sl1j4UKeCh88JeNVV44COsxHi+CqiUOf8RLOW3x0Dw5OcHvdDx3h4CIDSRBfQsfgTV/Jh57YB83u0R+6i+fwYet0kw4ao3DRTfXZvvSL7sEDrvTVM/jCg46xE9+tzx41VMgCrass8N3pEHtWSMIxXkaPK7qtoUGmSLCXxEHRcavewYE8yZJtoNtaeIZO/LCHGIdvGgUaHPhoX/TRPXpIZ9fCw2fr8Kv4E8AVLZIrOqYQxc8pR5/xtt/QKDCsf09Sbd/osz+/B4d7gN6wnU7eySk6nhPsZ385gZiA99cAf6orjy+Pwt8aLjyhK+Um7J2PoUf8i7/02ZjnBvrABul3+B6lHV1yo603XJ4a7MU++A46LH7yV2iRj/I7/BqfcotvOAf0Qx6l+Sw2XAPw1szgb/D+ucH++IEv/K9/1CbZPxW8uFfFONRAUGL0nL1nHPyf/dmfnRgjUdad4ih1GQVLjpjDl2QwAI4m5Z+XQO5VsRn03IeDgKjLF7hPkew/X21yX5DvdCCIDsbHmRs3wT3JmQRQAJEsCVBb+N4KcBQ8JVReXZIMcL66Bp4d2aPnxuuCKxQlfVvO216Km15hCnz26h8HEOA5PPC5PcJHd0sSXNI4L/PIVhJYgn6UV55z4HhBTxi4vZz8kO0WTbeAfcjR6y+CiM4p/ezUBb6Bz3CiQ/Rh6pD75CUJDMiB/ilKS/QAh0fOkk66N3lhXwkxGzJ+Xmg2F16cjSSHLuKPxF3xQEbsUXL5iU984pRkSYoFRjI331pwsz9nf0kW5wD+7MapksSLE0cD3sDXXta/dg/z2J0uvJMQTl6Aah1/+QRFpIIfL6ZvKKig0VoT4r9gJ8nxHa7Wk+izPfxCh2SCfruHPsmyLjm6JTnWd0/X7F6dJHMJMvnTGTTFx5V/vrvPb/JF5N4pq2cuesL+raXIIXNz8BXf8AfP6C65KdjRJRlXuPDh6OJL6Bkbp9/8IH22Hto1JuBpnfxo/HCfTdFJiST8Jv6KK8muOEAWq6yMc89YuDlJ1LSQ4NmXzNY514B16RX+1TGGO9nTD3u5L9EkGzzGUzankMETa+C12MCHwMm9CXjFh9GnCfiPZ5oxeImH6Ilu64pXfBJ5kV3Pj4Bx64Vep+3o876//y9IoncPkA2dEaPpG5zxkbzhew3O9wKdY7PF+aP7GqeAVFCgJ349BeAL/8HmyB3PxEkxm508N8/go8GjKVt8PbK3MXjFhuH9XGBf/IG3JvLXvva1Uz7DXsU0toiOp+SftflDOS3eHQXz2DG/As8Zt54S7Pu///c/6RW+8beKTXxzKCCOPCW/XnThgnBKTxichg6TosIxnmKFoVJwr68IDh0v63TrTHtNSVDgBCV9gqgOE+X87ne/e3K6GN9eHLsAL9jbr2cEAzeBzphAwBNI4R7YgwMXNGbAsRYF48wYZt1ANElkBPSM3NhbhW6e+fENvhIDAVLSpgOko452dF0C6xmL7xyh1/XMmzjax/pe2RN8AzhIPCSKgbG6GBys+S7r4ZsAobsqMPfMGr4LjgpX+xwNINbFc4WS1zDwWXIjCOJLdNwL4Ul+kmP6yZDpBWcCb8UbOibYuyIO/QH6BH86DUfru0ePJT3ppvv4KfnDt0mLZ2Rmbfah0FCgwIeDkTj7jY335RUqii1ywV+vkbArtEjk6StcBEc02XOV/yMKFzKhK+yHXbBZgZ89c+g64/jEtvASb44AnMgH7miDq+S6QgMkC053DVLw0izBg5U+8ytc4AYvuMO3jjb+uG9/uLunc8fXwEVBaDzZCvZkD4fJ42tg0kvudN5rJWigE2wUTnNtn9HimaI+/5ofyw6t5ZVaNNND9wXcfq+FVxoCkic0GMMHkJ3vnbjwSYoFRTt7VNyRCb2Dm06y4p284GVd/OfD+E7fw5mN0xt8pL8l5BMaix42gBd0Hh1wsd4651qwDnmiHc1sTALZqZHCkOzhgC/otC+90eDR1e0ZHHUt8XTFyzy6qmCMD8A4snMag5/JmNwUUXiHp/CgG8a7mn8OjLMXWcFXHJOk2Ave6CMzNJPvPZDO8GvWFBfEcv6bLxOP6N1zgH3IQGwj3yP7xiv4VuTj8RE+3wLWzQ74NPah8fSnf/qnJ/nAIR1/DkA/P6JBwd7p2hHa0QBXvooOPAfA1V4aBPI7Rf+HP/zhkw3Lk9iN58+hb+TH5q95XYyNy400Odglep4D6DRfVTxlH3JYvp8PeKtPXLbAOMokqZJI92oDh4qRDIajIUROTnB0bC9h9/sZQdfraE5udI8xnrFb08XhczgCAAdJMSitAFuRQiCU2bq6/wUPhsdYjTMvMN94CiYJk/znzOBLyH5orkPLUQvwnlPCW4S/NSdFk7h47U5CiD/uwfWcwnuGF5wQh8ig8aE50a44YPztj27Jp4QksJdAJ9EwzgU3pymKGbw0z33rV3j4YXxd4D1cm2MPfCVLXRPJg6QnWsPvHph70T24MVhFikSFDAE+Cere+SV39E384WQe+umIdY2R6Elk0zf0KEIkNMYZY66CiDwVJZyvxIg+02v66fTCiQm8FClkhM/kpLOjGDHP+nAW4PBNA0ABhm+XAjYePKpw0UUWuOIDWtkzHYATWiQ0OsbsHu50kw5d0g2XcRI3HSpJHL4lE2vRFd+jA23G+EcMyHmlz56SX0mwBF5TBC8EEnxbxwf2I1vyQgc/pUD86le/+vpzn/vcyb+5j3ZyNhYue/Ttgf3hCBf6Zw/4kbF7W34Gz+lswQgOxgF4KF74XnT7Dj/y4Dd9th/dccriM7+OHvvxIQo6vhbPjDHHfQkqWdJFfl3ymmzxVTFFD5KXv3QdryUa9Nl+E4wzn27zW8axF3Zj7ZX2IxBP7W8NdNERXUavVHo9kJ/EE7pmb2Pza12t5TKGr9I0E7v46XRolbl12g8dk2bFJv9Dr9wnyxJZ/MXb1ltxAT5bn64l2+Ipn8H2JMNkTUfgZ4zxLsUU+d4DcKYz7B0ueMxPucc+5Qjpr/hg30nDowG/7ElHV/3aAvgooPlZPuGp8LNmekh/2IiYIcfhQ+EgP9GYYo9iCr3IpvB21a1HgDXhRT/4WH71Ev2e8690i1/J3zwa7INuPKO3/Ac7lR/yDXDmJ8RkuIhH/DC7yYafCugJH8ZHH+EZoP/irnzqKWQZWJtM2Cbckq1CT4zAR8/4DHnsvbnAJXjfFS6YgYkuiueIXZeSk6/bxmga46Js7lEMcwRR/+IY52uuQKbLy+k6guccde8oAwHpjsJN8WJ963FIEkOveljX+ooO3VIOxPfAnooZa86urXUoqnXqcFNaib4iZwaZR0C84yDQxlDhKxmw1x40j+LiARzRPWlEi2CiC8eJmuMennCigTWMmXwge3LnMNzrYgiCJQdSsWfN5q0AR8FMJ1sHXBdbAkA+nrn25l4L1kGnwKAQ4+jsy4D9D/7+GhPfyFPyILGYCRPc6C1ZSObQ56JDHABZ+W4veqmjoSjBPzJ89erVib+SFQkpPRakvA4CJ4k+HuKxPVu/K7uwT7TARSJN14/wzBpPUbjMK9zxkq3QW/TZt2YBW+VUjdvDw33P6Rb+0WUNDDLBA11piZr9gPsSbK8Z2ntdF+0CLf01hm7DB7/t0zorWIdM2RHcnaahBe/prstn9zzThTP2aEBbwRy4wEugpif20KCJZ0E8oo9OAugh3sZXcqJTCumaGHwcuSnAjaM/7M9n3XlFiKSpwkUijD72an96J7HCYziRLf3zXeKtiPIcv+Hgb6cO5sJ11VV7S3jJRaEKnwqfaLkWzKMfbJZM0CLBTm4+s/MSeHzCI/wxdwviNz7C01sCTi3EBTzbmsdm2Qqbw6fAPY0w+7m8l68gIvPs+RzABS/5F/GILpMrfVH48OfwxH8X3FzmkRW6H3HiIt7Sffhav/3EKT4Uv+FFr9gFnC7RditYl41reMLtkt7wGXhAFvHmKcDa5EG/6QrfRV78ypQNXZFnsBOFpzFiFbsz7tH4WQ/PFNcKcbZyaQ/PNTnlSFt+9lGAH2IzX8JO+Gt80cTFJxee+CvHqykiL8RnND2VngH4sX86fmQfjRH+mY98KoAHnSYf9lZMyJdmm//3P+LuOx/wrnDZAUwhZAxVEXPQFFAXSpLr/omZ/8j0GOivS9KDsQycIxSIGLZjSkbtR+06rE4ldE1cihuvQ9hDwiMocZw6XhxAHSFJEOdm3/BTkTKQHGwAP3sKeARuHlwEesULY9Fh36LlHrAOPOKbvRgoXPBjy3H0XTD0Sp7up+DGwIHnnuEr/qTQij4BqDX99c9Bu++7cZIdOKA/8FnQryAoOK94ocU+ZGk8mdEBTkbQf6QTtA56rcvxkQ/dIC/OEC2ew9nz9oUjGUrIBA5FjO/0w4VWSSCdkjyjhWx0MyQc+Imur3zlKyengFdoFKy8yuW1RwU8edBj+mgdTo09Kd7waPIBTnhD3+ruWk8SLXG5JhGwNhk/unDZAvftRx/Q2KmqpEqyAAd/6YLijz6jBZ8Da8R3NolHgpjkhH3Tx/Ra8FIISrDnGoFxJdbGSczZgT1ceGgeHNwX+BSIZB2uChNJonXInm7A26UZIEE2xli+y1yBDW7WvEZWcMJn9NAt+kuf8Ive0InWQhtcyITewQNO7sOTryN3OkceAhkdYtsKEw0HOkgv7eEzXwh/slFI2NNnybW/xugQO4mxj6SfzbAn8sJLe/O14YQWOgHiNfvBU/iwFfxit8Yd4VU6ktzghl9OgsiALOgcvmkm0EXj0kuytH9yI2vj2Roa87P2sBeINsmBf+yCTwjnFcwR5zTf8DpdMxeO9q8ApuNkPuk23rp4ChcJMD8GV/P91b3Hd40SY4wlk3CeF0DPIwsXReC6j73tAx/0428xmix6nXWLZ7eCfcmUPMQivHRvC9yne/iA73i+N/ZasBb+sxmNBDqFbvqIdnqwVZza3zzP+Es2yN+zSXx2j53j66NwtY796JD4dc5HhZ8OPt/4SNnFM7bJl9IXcZu/5zvIir7l9+DSBQ94i+1iTDxjx06d0WftPbpuBTkl32nv6R9WsDefqzhE3yMBTfhCp+QEfKgL/+g1e4j2yTN8fEQucAne14XLvDCQ8xVkBTpORkDJiXnuYqAMhBJjfJCgdK4lTtYSRCQanITAKtASnCRSYeNfWZJcCrT21C0gZHtkLE4oVOyCd8EjgRKyRElAnLQACsNY6nYJ4OZHy6OAYUiABC08U00L0nCbASrwGR7oZFx4Ex89g6c18M19QRpPfbceY/fKXl1tPJb0oRdt1rC3IkrwlkR2P/DZZT3yZUjkw2A69XgkwNNe8LAXWeuMKkTgP3EzBu54aY65LveNJdNeXYM7WgUiBaTCRLBWFHoV55Of/ORJ9hytwoSu2VfA5nAlbGSlw+tUgP62J5x8FzTYVQEtWsiG86Wb+AavTnyuBbQ9V+GyB/Amd/pIlzUg2Cv+SeboITyj3+W7fegep6xjKaD5jHdAke4eWW0B+ZElH4CHyaALn+k6OeC1q2LXPHKc9rMF7htjrK6XuZ04aYjAMfuxZ/K/BMZI9NgMn0bvfF59Jhrxld7jTc/pLb0kL7rtkqjAJ50mB4kRn6CgcU/gRwe/YBx6rEv/BOEKJOOtrRi0J9qMESPYiUIz3sETTsZJ4pykSzIUCuzE80sQvfZBM36jB926oCWI5EbXjLsE8OH7+AR0kT0d4K98rzCxVhee4JFx9BaNW/I0Dl/5XCDesEFreyWZbMLT/OhzWVPBqYjkP9Dns4YPH2cevMxbL/PXewDPHl247EF4wLGiUpHmlR8xOl/mMu5ewC/6xt4kbdE8wT37STx7RSpe3QqTTpdGZvojt+hVS+O6zoHnaIGbBhpZoQnvFKiexbdLa10Ca4kxfIscA/5bYJxcjQ7eGoMmwBvPrMP+8MgJAZ/AntkvGa56Ef+6Jvgur2Mb4gn+8wv82PT567xbgB/1SreTKja1tWb4aFzwLXh4D1gvnpETv8SX/Pd/jAn8qAZGMeYcwIPdvitcrgBMl/xQSh02zptgBSD3KZjArKPEcFM0F2VhYE5WGA8BESTF4bAEHF0sTkIiwskbT8CMUqDx+plk2+8xfPYaT11ycwUDa9e9cklQrbEKub0zFokR4xNsMpRHKIY10BrfODMJMpzby/P28tfeEmFjFWscdfhIEiTRkhEy9Z2zYFy+KwgVKu4br/sjuaHwaPbXuv6PGw4HXquDgY/9BSyJqdd94EA2eAaXR4G9rSs5kNjpqqcHW07FeMkGJ4x/ZE230EsPOU04//Ef//HpB6d+g0JP8NFvGyR26KITXvfSUbGG/bwi1X8+Z298cPnOidLHgio8PKNvEvgKSfKEv2BF99Bl7S0+HwX7vOnCBZA7GukZu8F3PJf4kp3TKcmygEAexsAd3fRJ8uNfRVJYeo5XEpBZnK9gjMLSHvYjK3M1RvDDq3oKfDZAruSD33BM54/Qagz6yD36rEXXJDD8Fh1SZLExOKAJfefA/vkZ+NFb9tmpYPjBFX34hyb7ot0eig5JAbrQTd+sxd/BCz6KQoUIe+cre2WMfNiv8RoBfBzc6a3PaIMH3aD/xprHf8MJT+BoDJzZlGTentlovmkPot++Ei3ytA4+0Ee2g9fhcWTNYEtu+IhedNANccpn/KE/xvjLT5xLXuCiSFSgpAv0nE/J3+KRceSEPn5ScajxRmadTEffqpddRwCez1W4gHCj42gkb8kV/UKfZBxP8PKovPbAXDziD/Bway08g4NTPicH+HEPwBkv2AI5a/hJmNkYXDxLVteA8dbGMzJnS/AVD/oHPNzb07ujYC78+HO26fMWoENyTBfp3z17AnSxMU1P8VruZH95G3rRlX5fA+agAb74Lz9RRLJhTSvrw/9egF9NRXa7BXDh4zqZuZdn9sSz/Le3i/hoPgPP8PSIDeHPu8LlRiBU6wmcnFgBiDIoZHTNUl6MZqQKEwGkgBgY53RBcHDfHMokEXERjvu6igzUX4GAsxGk/StFJRV++8CQOHddIUePAo3CRHAsgDAMimJvl/3QIqk1H590RyiavR+pIPaFA0ep6pc4oIlDtlfK6697nuvYSYA9N79Xv+AMdwkhI2hdz+Au4eaIGbz1jFVYMho8Kam0X7IyT3dGcFQkchjWxadHgr2sy5AlZnWj7W8vz/1FL10THMlPEVtyIIFSlJE3HVQQ+ithUNDq1gisdIUT8g804CleuBi/103iAX5Ya+qi+/BQJHW6AEdj45tgi09ooXfkxQYepT/2egmFywrRTz5kKalx8qHIo9s66BJDSSTZuZxmff/73z/pvOSP7M2zzgrWZ7dOPqyrwUEvyZcs2GlJL5sm02T2CLCONfGNLOkAe+yfiKd76JMQs7H8ijlbOJCjdRTLaNCAUOTiS/PQoikhwZEMCGx8pmAlUUQvHDSH2L8TQq+Uec6mPeOTFVjWkSTpkrMDY9kYe8B3+9vbHnAiM/rBRug8eowlM0UOutkeW8x3TJj84musjTcKU/jimQKLLNFhX2OzpUeAdcIDHfipIGOX9kcDvZEUsSd6BFc4rGAN9JMVOshasoOnaMN/fgXv8FMCZz3xDL/Jmm2kD123Apk8Z+GyBWgxH+38ZK980wt2LAnLd14LeCWO0RW0rryyNxuUR2Tv14D1yNlc9irO8ef0nm2QJX25Ff8VpszRxo75PDrk0mWnV/ZEb3pyLbBpMcgeW/PpIr0Rn64F68ErHwhfBRCe0XW27b7n0XoLDSvgf3FF7oM+sV3OR058Bzne6jv4MHYtpmyBdfGMPl67vvHwp0f24evQIGfhI/jYirBr17bmu8LlTiAcSs25K0wkbJyKhDHD5+C8ruXVnhLoCZSegigUAspqPclgwClaRxCytosj0KEUKBg+47GfQML5OZnxnyAKVhRfoLYuoStSdEEoLrwoBNysqTsn4ReMBKuM8pFgPXwThFTgcOL8BVHPXPBBlwCBBkkOgyJLRuDUy3P0SlTQjpeSZoZtHh4ajz4FjsLMqcQM1PbiBDhV3VCykgDBBZ8fSbu1rCv42wufOSNJBpzgKSlQBMOXzpKHIpT8yFHyo0glX884T0Wa+XiGp4pea8VHAe/Vq1f/v3sKQGumq+5xzBIcsvHdWM/Nk2xLyuGChkDxoxiUYAuEdMjcRwG6XmLhMiE+dbFrr11ICOgTO+0kgC7DgX1VOG4Buj33T68LMnSeTrBX+7XnU0F7uMgz2nyGA1zoD1p0gdmbYoGe4PUWbq1Ff9gpfbIGO2WzaGbTfAFd56+cmvCJdIBf4wslPnijaMFbelfRomDEW4mRpg7/6Lln1iITRT8ZwdVY/M2/uNgTWeE5+ZElmuIDOib47rnkCE724q/xRmIruUKDta0xr6317gXrdSU3e/MtXiHDSzx08ow2PnILzBMzxQt8EuMUXuhDFz5qFPHBZBo985q4uG4FevGmCxcQT9HKL4qj+ICninnxZfpHcIR2a9FBcUGzg7wm+M5WFKH2voaXxlpf0kuvyU7ibS02kV5OmT0Sot8e7ER8K55lq1v50REwT6MDz9A4wZ5sj48w7lqAD3/DpslFriL+iaf2ejSfJlg7eYQH3ZczoIccxWuyuxYP9i5HYMft0xr2Ev+dCvOD165tvAKeT+CL+UB86/fU7XXtugDP37rCRfDSObtF0OeAIDhDRYD1BXQCInxKjtGrgvkLJ0GV82DMgcRPsOAAA0Fd0FYUBYRoDCWWtPsOF3+twZEK5ipeAVUHU9CSYFBY6wk6HAgcrUOZ4SSp4FByyAqYuvWP4p210M3hOCkR6CUqnDd+5hgEfMm9ohC97isyvI6Ezz6bh06vmfgrIZFM6SoJIhyjQM2JWdu69keTNTlya3BQTjXI5lG0WoPsJUqKLTzFb0aN12RCHuQgUZJYcZLkQzfwRpeHI1C4csASROv4POVOz6whQXGv/cleQRJP0YZOHRCfG0ce9lMQ4YF7LmuhgR5xnHhLx4yHK93hzPG29R4B4cT54Zfvt4J1nqpwWSF50E/OXyJO1myP3Px+zStj/Y6JHrrwT4JGZ3X66SxZ6xpaCw3J9U0B3pFxeKJP8UyP6Q69pR+SODTQRbine61Bv+gQmyRbeo9HdBofNHMEKPxymuI7G8FDNot/7Nopt0QUr+ztu/3hIvjyXWTOBugp3yqB0zzwme3xn3UHJenWgBPc4I9WfIc3OyAHNMGfT2Yv9rS/+fwImZubTJsf+Nz1HGAfcoM7XZOwilVej0OD+y74os0Y9PFZXg9zus/28Z+sndqIS+SYD3pKeCmFS4Cf2Tkb1lWmc+Km00G6LAdgI3Rmlf8KnuM7HWLz+V9AbuyCPvMFR3gdbsU4flqMFePpvvXsMXHrekqwF3rwRQzCJ7ZWUSaO5C+OAHlqfJCnNQP7kAv7tgfdvgRoj2diHV2TG/BlZCK+2g/PnhPgZU/yIn82yefQM3kLX8N/sZEj8rOWOfI/Pg/NzcND9Mod0XpE16xnnuJKjOU/NZN9VqTCDf/vzQ3gSc5vVeHC4Qp2kr8MA8PXC3OnIR8F60m6BVqJs0Dc+9ieTbCugO/4j/OfyuG+7jUDDigF/BUU4eQvZRB8JUA+94zCMbgc4ARj3OO4BHAKrPvOuDlN+MPL7x2+973vvf7Lv/zL13/+539+Cv4Uk3HEp3nFs1sAfyQmFF1HWvKND+67yItz8kqcRERCIIhKXCRHDJCj8b8hMxJ8Yth46zmHX9cU7tYkF0WZrq8AzoCj6xpAM9pXfnTZi7PR1f3yl798eo1Lp1MnXeEABw5ScSJRQh+5oNHcLSjpwpeATNFMF6YDB+4LqNaDL7zwxnvHaJ5ys7cuM3msdmCsYOPUh3PiQDlPiV/rzLUugbHneCeAcHwK0pdQuITvOZxdeOHCb/ZCHngkgRBo6Okf/uEfnk5E6aaTAb//8ExHmz76PQx8zbVOa7q29uwKv2i8h2dbYL2tNe3J7/BbEmHJuyCDNvZccgzHlRa2rjDnM9k4f0T2fBQfhkfsn3+VaOOL9empOXyjU0iJmU5yHWUJqnuSF3qqsSRhN5ftsRXJOR777J7xcNUwiacTX/fZHz1ygssOJPL8DB+y+tunhGQx9XHieulCt84tf+8ES+JNB8UttPkXGunof/yP//H0iiMbVKRKyPOlR/dtXLq5pUPn4KUVLiugC0/4R/pBB+kT3yuB9gwNeHCOdnpIB/nnxpknkZQM8vl789MD/DZOzHNKJsbRUTGSzNf5vq/Xc4B94Euf2A9eebVTTJSn5ff2eNZ8voKuzpzJXI1LNs+/GLcFrWEfc/CInvUqe0UB3r4UgDOaNQ/4QvFY/JA7lJ+5tngG0IIm+tRbKY2V4/GxeGaN7HWC7+63D/nxDZqj/IdmsnUmz8zZw+cokM9bV7gIXhJGgZET9gN3R4wCGuEpNjCbEAQnc/YMZg+M13H70Y9+9N5vWrYUiEB1QBQLnk+Q5FIAjivg9KxrfDh1UV4/oiqQALhTSoY3lRL4bP8UzzxKDFfJleAreeX0BGXd4V/7tV87/Y+v/gO7v/qrvzrtxzHDh3PAM4oe38LtKMCFwZlbkeI/VWQMZOKeS0LjtyecCVz9x4ccHl4qYgRZ+DAejk9i/bWvfe3EI/MpvmQI7l5vMqf7eAGPo3jHP4FQYMIDuEquOBMG5t/mV/zB4Xd+53de/97v/d5JN5zESdTwGb/JHP85I/KAz57uuY8uHV7jG8Np6ygrOOY8nyV45ljXd2uQlaBGz+Ze+IHvdIdM1rXQjIcKTHbESe3hugd4h044KNDwzYmlhFLnjX7Rs/5BCom+ovqaPVaw37WFi3FwRR/dxG/8gjN9h7dERXGl6JT4kYFAQvbw9h8G+n0RXUWXe/yQ4Kh4hpd1FDWSSHKV7H/kIx95by4dMl/jpQLHWAkSHTKfPZALPYIjXOkmmT8n4JU90eXCNycm6PVPwdMbRXo6H450zYWfiny86keveI0nEjpr4QXfIymh2wojhR5f7ncHinpFuWTNd51cjQ62Sb/Ixj7WZReKHic2bAi+8IYT3PBSsaOglMz7D/iswc94Psdfawe3QnoZjuyID6KX9MBFJ8Q0fpyu8JF0R8zTlGK73/rWt15/4xvfeP3FL37xRJsGkAKGfyUfSa51skN6i69eUzWXXvK5YqrkmO4qCtiCBJBvwztrkCMc6SWewf0af0s3XnLhAtCCrvSefkvG8UsyTv/EWs/2dIVOshH87zn5sn08tP7WPPf4bmPIW7FPJvTUfrfw/DkALuUhcKQfXr+jb4qYfkdC/nCfYK575jolECMC4yvErb/HM8/YDV/K59BlNgOX7Pql8QzAKV1zOfHjI/kycZT+0HV6tgJa3NcU0tDE3+jjV/kKcojulXY8o5NyDa/7acLiNV8Zz/Z4fg9Y960rXDgrQQ7DORSCFRh16zgVQldsMHbJB0FQZlWsxKQEwXwO3ZqMg4CmQTECCYe/nq9gvKAZLnOuz/aRUHJwgXUolKBi3yk0CkaYlLX9jHEkTMCUkgLD2VgJtiNPib0kQOKvUGD4AoOiifHCQYKvYJJUCUJogruk26t3EgbORTAU+CUO+MzxcDgcdXyj1BwrXLb4loG4Z5w5nI6EA90CoYAsCbEXvCUoOq4SGntz+JIVeCtK0AF3wYAD98oZ+jg491ZeThwYiefxDv50hrGii15IeuiPRAa/OD7dCt/thQdwthe+cYj45rmODn0zr1dWnBRZlxMin3hObvAIF7rQb03gyllIBuHi3gTP8WG+ruiecfiGj+QRH9BNLzxrzgTjjEeLBN2YIL7BB+/sQf/Ikh6wIzyhH/SEzHTGBWVyocf4Q7/Qzk7xTgf4EScuArq16FcX/PCYbBUS9CT5whUuTkD4AziQnTXImfySOXvmN+BPjviKZvZGByTF9EXBzK9IEvEDoAvf8BVOdE0XXKEFL8kffVAk0m+6QsftqasLD+PpP9zYRMUnvXCCgB4JKdrYEtzoJNzwmb+JJ/SM/OAzbRWe18igsYKk9eyDL3SL3uMleuDGrsga7XDSpKBfxviMdv7GRY4SZp/ZkW5r/ziJIKrpYa4EXRDm2/lDvkTyTr/pIL/Pl+CLffljOEj44cTvKI7gKsCzSzTgUXZxi07iJZ5YA3/pZrZt7WIUPrADsiJ/DRi4wg0/yJZ/5s80megDfrAnV3qJ3+g3nj/Ea3pJr+gYHvgOhxKP6KIj/uEPcvN6rljB5uHGbjph45PoJRugg/SS38k+pl6yJTaFBjaWXrI968ErXeD78AVP0CJO3QNofKrCZQXytR9a0Ix/ijs+BZ1oI3/6ELATMa6mo2diGV9p/LTD7Ios6LQ55M9m6Ivx5Nn4rpcMaIIzeSuA8UxyTHf5rYoY46KHvvLd3lRhN56zI3ZPpwJjycT6CjzNMrzlE7xpQsetH8/eTxDNaCJ//o+fZFf0D03pDvCXneEZ3jVfzsU3Tr3Bazxmh/SWXOgju+Yj8ZKe5xODR/MQDm9V4UIIjBrRCWPrwnjClFhIAo1PqSX1ggOBSTp6vUcwoxgJjTPX/eNQrLkCZyLplPjNAAEoD6Xh4ClRYJyAw+Fafzo6n+2vQyHIAPRyfnCGI6cp8ApgaBCw0YQ2wYCyKnzQUldnOshLF7wlpwIYHBmOdTkcHW6JOb7Zv+NJwUqwXJUdWNP+DKbfq8BXgJbs4b1EDV0CJOPz3b4KKeMVUxIehkteEhBBk5O3p7WnIQd4hxYBVVC3p/XgL1GyDr6hTfKoE8vhkUHyvHTZN/rIUyJiHXiiE+/sYz/7SgbID285DTj5zpGksxI0Dsiagb3Qo8MMv565T2bkwC44Hfd6ho66+uZ3P/CZ/NiHvUG0CKA6OTqN6IE7udA5PCuBkeCzAYkZGvb0DZ70+F5nxR7pi2SW/cGNXMkQPtMu8B+e5Oye8fxBhSXnXYBMj7rgOL/jHx3GT51XuoJP6NE42QL8oH+CD7sn57l+1wRzJHp0wB4SQ/pCL9CQraPPhafu0zH0kxP7zLfRR4WWtSS6cC6RugUm3i46hi9wzK/ikb35QDaIbq/O0Bu6JmklM4m2e3yeQtBJgNMcpwc+f+lLXzr9YxROB+i+8RpS5OhCt/V6fc1eaOUfrE9f4aQokKST8yX+HwFz2Bq/h6d0Ca14jW42bX9yoJfwIB+48gfpZvxAC/uVfFkLrhK+7Gm9wttn8YX98Zf4MJ+7wpfOwwXebAA/+R82QPcb3x6rPaSXii+6zOcoougZHURP/hSN6Eof6CR+GEs3nHaJH/cAOuicfZ4L4hF9pmfZHh+giHE/MI5+aEbwrfAlZ3G8nCK+koE4j1/iLH0Vv6dcut5vEN5olYzTAzSyDbwQz6LLGHkVXkikNWHkcHTL/QBfzNMsoV/W4x/5oRm7368Q/nRDXNVI4DPQqpHq3qST3rEneY6CTcyha/QOr4L4iVd0l0/SdNjLcZ8K4M5u38rCZQ9iBIEJVITCuRMaR6JyF8QFCYovqaMYDIowJToMQbDxuxDzV2BgugbmCJa+T7AfxWF8JYwAToKS4MZZUcwJ8KWABOqz5IXj04G1niTRmhJKwUrnznqSrzqt8KUY9lrxugTGm2d+fOMwOAkBGt8kIIojBoRvjIA8BC3GBU/PBWC8qQPKoOqOkGGnLIKpV0YkJ064JC0SGd1XXT6Bjlw4deviu3Ws6UI7Q4YbWcIHLuRoD0mEAItvgq2CAW51htHH8CffrjUm4/EuvlnPuvAkQ4Ukh0FukhtylFyRq6SWgzEHjnC1zsQBThIGY/FzytVnPOGEyGsFBaKOivXp20obXuhwkyG9xzef6Tb54im8BerJN7xf9e0cGEOO9zqrfIBEl6Nmw+EneVBw6VIq/iXp8GQb5CF5cLEtvMhG4LOFkzH45rREkkefBFHFtPvoxyPFxdZ8a9NPei5RxFfygMsq4wnup0/5sHTKhR60oRGt9EvSSrclA3iSb6NPeM6O4AEHNDi1IGt6w2YlgeYKZGyU7cKd3eKhfdEb/7KT8HQfXvyRNSTK1pS0sl14kA1eSWDpslM6J3QaRF5Z8hs8r9b90i/90uvf/u3fPr2S+dnPfvb0OpRiUXND51YBgx748oMSHQmkde1pb/zAH3jj35R1fIUzmtCHTraFZvJmU9awlkIZj9CBd/jG1+Ed/8XHKPDtzb7powutmi4Sfb5aYsXW89eTp/EVbvQOruE7L3QYZz7a0e2VPJd7E4wP2DP9MB9tuthwV2TQbbyCQz5iveBirudwZIfpJRriI99gLXGWvOkC2+R7nebQSb7dnta9FeD63IVLgBdoJz/2zNboI50Qo+QZnuMR/BSKkkl6Iu7HK/olGWWXfBpeaciZl75+UICs0YVusUz+JQ+if2xHXPHMGHqNL+U/eJyO8Xd43Ty6Rd/oIZ7do1MvDdCCJrpOL/BF7kCnFH78Ph1EuxxI05d/YRd0jg7yCeayO/GrhjMfwN/h93PzjP9Aw7vC5UbgGAQBQUvAZSAcDAePqd4Jdh+jKVCOm0JI3CkIYyvYeO6zpEKAlejlhHpmPKWzT4FiritpcPJASXXRGDdDtyYFNW4K2+d5PQfAF9/QIsFhBIIUR6xbxOlIynXW/IdYkhOBX1eSUxe4jUeb10F0z//kT/7k9a/+6q++/oVf+IXXH/rQh15/6lOfOr02IuEjc46NkTLQkh/B11yB0GsgkiT7CwCMmpODn3kzCJzj1yN5OPfpogMcMHmSNV3mhCVLCjVOOH1KN+gJmvGVrnqeTrk4K8kcR9S85hpvnsTRvnNtn+m611Ekf/jmBIY86X17bQFargHrPKpwkRxKINH3aICbdeFLT50OeMVGIJCM0THJCVslFwFBQrbHJ2sJFDrR8BZcFIj0GC3kcGuCkk5twUyuFND0ix/JVvmmGhF8mURSIu5Egz6wPa9xsS9NhV69FTjpDNuiH2jgC9bLfUms/RTt1u81Xk0hvKD3X//610+/g/KPHPzyL//y65/+6Z9+/S/+xb94/S//5b98/a/+1b96/Su/8iunYsa/huWCY40Pv3vhI+kUf0k+9kT3xIVsXD7zWU5vJTzW4YvR5nUovoo/4k8U8/ywYsX6imR04xsbyb9IRCvysq979Bsk1y6yTCfRRnfokNeJJM3sih6ib4XWUCjSAevQCbznr8mHjpMNfuCPdfIfEw/XvQB/e/P996z3JguXlSd0nQ7QDb5WzEJf+u8UUZLtnhiumBUT5Qj8utiGnnv48dJh5RndYjMKes0f9qcIF8v4Kfqt2GaHdFKs1JDUNBLj8VW8m2t+UGHSx3b5OTFII0eexY7plWaUhii/xbfjryKPn8RLOaf8C+/Bm+IZP/aucLkDUgiBoUSuiyMRDAUHjoXTZ1S6ABwPw6IwApdkUzBx+Sww+vEuY3PP3Ob7rmNH8cx1r2d1qgRnpz0SIsFwDSJvGibf4DX5VrLQBX/OSZCRvEkCFYSKGo5b4iA5krT883/+z09Jy0/91E+9/vVf//VT8iRBYZySKAmOpELg0wWtmBEMXfSjawbfWxPDR8PkGdzgyOEwYD+QlSSRP12YF0fumeQPzXRoPpdISTzo3bxvHH113z8sILAqSOiZi/NXAOpcwwM+8Y4sH8k76z2qcJFo624+2hasZ328o1+CqQ5VOiZQ6o4KAOk7HyCZV4hu8cqa/Ijudh0uhYxigO6TN94/mhZgTTLsCufk2+e9C678ID1yaiB514BQNPY7FKclfJ3P7vFt6FIIo7NTML6QHtJVaxjvH7hwGkj3FSiKlZ/4iZ947/rJn/zJkz/4jd/4jdM/bqCgMsdpmwDduhpO/sJRIkge9BqeCkW+RtEDV/sK+Pw3e3IyYh0+GK3xBO1dK1/mZXx24noKOfIVJSz8IRrQaX96hy/w39M/ODrdUsAagyaxhYzSbfMl3U67yU0i7r7563UPwFmCKom/B95k4bICnubP8Qyf+Tmxjp/yRgEdJwNJpCIyuzdnxve3BdA6eSZPoNP+MQL2qfj7whe+cNIVxYqGBz9KX+kvnc7eHqGX7wdA49Q1NqCB0u9LxRgn1PwcHeMnNdycuuBXevamAS7vCpcnAgJmJDpwOnL2ZESSb51x/ya+IC0ASsg8M8ZnCflnPvOZ99739WxeFEviooPguTkun63nmTV0bN7PDo2RUVIOWtImGPbqlMJNEFaIeEXk3/ybf/NewqKA+Zmf+ZnTv5LDYRknmEp8JN51OHNi8WheLxVWPPFI0sA5++eC6cTUB3/pGZ2RSEvGps64OC6v2ijw6GC62iV4+pfkzE9f/e1VHfzkCLfwcz0C6IF9H1m4PAroDx2VTEhk8UwS57vgwOHbV8NCd1rXqiBAdoqbrVc/A90vfFbA47MgbZ4uLHnqIErodRbx6VE8B3OtW9alnwVLPMAPgRCubJodStQU1+iUfPDRihN2K4Ci3eV1Jt1A+kzPdQgl4Z/4xCde/9zP/dzrf/bP/tnp4gP6rHj5xV/8xZM/ldSYR/bWsJY1FT9OXuYJCR1xmsTX8Bl8KVxd8EYDmaMJbfHG33mdgyNjboV8Jz4rlBUZ6JOso0GyR/8Uzn4PRC5bYAzdlNSgOVkqeDTlvEZiH/es4XU2+s+H8Lnm4pMx5t5LL/23/gepcAHpAn6jkYwU7ZLw//Af/sPp9Uc5g9eB+dtk8TZDPHOxQ7quMadxyyd4+0Lx4rTAfTzDW+ODucbbBmwWTzSW+Aev2v/8z//86YRabBdTapa9JOBL2O27wuUJgFIIGF5B0s3jbCQbHDmHrpjhmARFHVrPXD4LlrotW91x3ymVwDqf+6sLbj3dQicM9ilAvR8NU3BBo1fEHAdLKiSvEu8uCfuf/dmfvf63//bfvpew+Mtp/cVf/MUpKTGOzF2SIV1vrz5Z1/oKmOnAul46wJF80fIP//APp4RM4pAuueidpFAXm+77TF/mGCcpOqX01fh01WWskxXrS3LSt04Gvf7jFRi2tfKv6xHAWb3EwoVt4anuu2JOkFRgrDanWJEkKzb4BklHyaWk0ByftwB/FZ70V5JvfrwlB8WQvcmCjPaS0PcjSDQUDXwof+Y9/7qB3/3ud09Ni9///d9//a//9b9+z/bnpXDxzBhjnbxodkjiJb9e+VJgSsTxH0+TzZTf+rlrha17bwLogFc36Qy74SMUGeGHPnauweWfumfrW7jjP757lSwbB+ZrwPErfNAEfJT0+L0gvWQb9NSceyE/QOfvgZdWuARsW0zKpuUJGnD+3y8NSbSLX3wCGl6Kvr1poNv8OnlqTjhZ/fjHP346oeLr8cxrkRLxd/BPwG41Z8QNOZJTF6/Ye1uHf9QgkHdpFr8kPeMDyPld4XIlYBYnzKkTvg6o97UFP68d6LrquDptkdzZ13gXx8R4MB0ukmb3JDrzuU6qgGoPz1w947wkKPZuXc+NFYAk547yGbBXIgR+3YiOltek6rkhXPENXrpLnDVnLLFjRAo+CQrjQYPXTCQZxvhXmPzo1YmUVzkEUInJv/t3/+7UMfDayMc+9rGTo/dcwSOIu3S9GatAXLIpeDttKMAbw1glifCbJzN4/SZ4Z09XfKMHcJQ4eOff7wAUH+5NnfAX7vFWh4Xhe0afXIpdr+mQQ3O77GUeXaU/3Tef/vmRJIfXK2OKHHyDH77B1x738gzOdOHehAUv7i1c0I92RZzkzFo6oZ2wwDV64yEd1jGFP364n0wFDqcN5q1gHNmxaXZBZ7Nhz8yxJxniv24j/ybRt+/Wms8B6IKj/eELR/pA/+iGSzFHXzRw+Ey2p/Bjk+xf0+H/Ye/udm7prsLOHyEllxChBKGgEIRIJCsIY4OxHWxsYxvbYAzhI4C2wkmkXEhurY86R5176JM+bUVv+7fCHw1mV9Wq9fW8e797D6n0rFU155jj+2POWntr9pyO4J+c2JnTGHL3TNHt9y38XpOiWZkNjHtOY/wGzo/0+br54eA/cIuVYoBTBGPI2lpeG+vH+mKUGM+++YqYjydyxiNe8fyord8D1rQ+H0WrWEZeNmfYRqce0YZ+z9mT+MGP+cYK9EZWXqObvLE9RaETATjgD4whD7rWkIu18pmTRbSUt+4BPHzRGhdyl2foTSzBn3xnI8k9r4qxVb83UE+wR7bJL4zTgMLxjDj7IQAe8cr3+CS5qAXoU20gz5MN3XoF2saS+4pwBblY3ElgcfiLDskMzzYRyEqc45Nt3pCNutV9J99sT14TC9VQNiPEPL59r/8+A8SAT43LAgRBKTPprhc8nMa7zZzBroj3oL2rrVhWYDEIPxzdSgZAUBKg7F6vRsC44FVgWy9AE4eTbBma7wG6jbWrYCcRTklC4e4I0Gs+EhWDdB9dk6cuPN+bfKfsOAlc84JfkncqIJkpGLy+YXfEjr5GRRKUbBVeeCUfRQ6ZuCRDzuQdVsFcYeH3F06hvv3tb18+2431ComA77l1nECRp4SV/uCFX/CTECRvQVBB1Dv09KkY1AhqCiTdZDd5wy++73XoI7m5rItWSUsQQZdXPPyoXuLCl8JPUF71ly2QgwTZc5fPTvgELbKd4LkgR7b0toLnCm7FC9tScHpdzemA3xfxC/JXvFh3lVlXcps0r0Aun2fjkqzQK3jzTzziTwNDPyvgyXpe5dJcOy0QbMly2okkogCBewV6ZXtsXgJ2EiuhkOUqL2tpBiQifiXhoM3Y1SbOgPHTLvdsc+tiS9ZmH/22RTFBDjZ0/N6MnSh+NShsh3zEBRsHNoD67Uk/YmfDcDll9YNlr4qxf6+B/eqv/upn/+yf/bN/bF789croL/zCL1z+09dvfvObl/iiMelfGOt3gi4xSQywprhOhvSGJrbiVTKyR7MG1PoadgW5MfSjuLdxxBa2ZLJ1JdczPrAF2SUda6jkFPKxkTFPkbqMZVNOUeiDjuQiBYv4ugJbFW/IJvrCI2YrZujPP5bQWhPMgZfu+K48RMfwGg/POucIzPmQGxe8kgmd0xk6FNVsyeuQfochzpOZMZpKtQSf16yQozl0y07lJrmKPDSj2d6tcn3fYcoMj3KxnMUn5T3f2ZTnZCjXiRviYPFZTOLrNuKcusonPTMPfut8USA/ZQ9yg/ggDuNdvhbz5DL25JncTT5yG5lmn2RkY8nmZq8su+/552Fn+PnUuCzAeBW1EhDDF5gFFvMEcEFaweJfkNGZChh2DOtECdXl6FKC83kLrCFZSzDrGDTAqQidySSnFMjgn07GgDimpNUrAb5rYiRWxankinc78+hX/Npp1CwoiCVeiVsQEBgZ51nDME7gsHaNgGCr0LCLSlaCiKIFjZoOO8Scgl6SWxcZuHzGd0W7RkziQqPvmkXNGvycCm7yUVhwTuPxpQBX6NjJNa9dKrj3Ls/xIsk7avZ7Gc2QxkjBRK4c2qtW+FVYKhzpjG7I5Eh+ntGb8U7FzBcU4LMmPtCsuKMvRZ9ERY8KOn/JsSLAfDtPeCW7Cb7bUdasWRNYH48KeTYw9e2vOYIYPcG/BZKnBEAf6QpOdsC2NYKKRY09ufWvtyl07IyZrwk3nk/srYOWz7NxoSNBXaPCxvhmPo+2LT3jh33wMwWHTQPzJInpu/zE62Ir78awJwG6ZEG3kjU/XQEN5tAvPjU8kg0bMR4918B8c/GLP3ZpTToSQ9imZsTmAh2ijU1qUsmFjvm4Yoq/sF/+rwkQRyVLcSk7SX7zwicZ8T2+K4mKuxKutdgjfOKwtWxKfPnLX/7s3/ybf3P5bZsG5p//839++d3bv/23//bS2IgHxTwNC//S2ImFGhr48Ic+PoK27Hm9PNOIyw+aLLnBZhIZKMz9YyHWQq8dXzKoAFW0a4rwJbdYEx7+I+dYWyFFDtNGtqCYzzbEJ3aBr4pX813TNt0Xq8RLf+OFnfi7Aj30e63wwMmW8CqOWl/csfFj7RWySzzhEa3kzg/51NacPUD/h9y4pDM6ZzfykxzsO9siIzy6jGNDfJC8NS5iCNrnOA2LuKLwFFs1ouJpcf5DB/ZT7GPfNtJsPPC9bD15sGE/KGdb5Ede/Jz84DGOnD0THzWMGni5SC0y660PHfDJ3+QXdq7+Ihv2kq2RCZ7FcfaollBjqm3YULI1ng7ELHWEuCaWidFk+Za2hqaPqnFh5JSjaGHIFChhKJ44AadQaFOuZK9QknTaVZPwKI5CJRg4KJMg1wSh4PA+tQaA0lcwBy5OA9ecC5dCh8MxpAD9jMUPqVYH8wzdnvfMX2M1L2g1Bj6f0cXo8K0Yk1jxSjYSlUJWYMczB/e6CnrJSyBFn4TFyJOb8eSkwSMzl4SuKBAYzCFzho4OctkzeDJAb8FK8QMvXUmWmg/JF/12UDwjL+MkUfcEcX81GYot89qNUVArKtvBQgcdzAsN7kvu6BUUsxlFOX4lYTs/eCVDOxrukwe6rado1HhVjEksTn/IVvNhvKIGLnjYBB24r9gjAzYybc6FfnzQM1rJlX1v2QdQ/JFBMm8OuukXn9mhYKURkhAVF1t6oj/yV/iRYwCH8Z6jg9wUwYIo3smGf9ETm6MPdqNAVQzji3zZF7zxTjbs8hHA49nGhQzoHR1sWLCkX/LAV4F/Bfc8p2O2UUJUpOJ7lSe5aOjEhAnG4J9t45++yJTts3V20RxrJnd0sQ9yV3CyETqiS/piv2hglxoRNqbo5Odskn7YkbiBX7aoYaAv/LDPfEjs0EjYaPHaF/tCK31b30WG5I5WtKERLy7f8WSMuWKMYoOOyMq6mmf+60JXcdlfzZFGwQnO97///cvv3TQwXhX9nd/5nct/QulfZNJQKRLJTdwgU7gkdbRLgp7hydrkIdaI48VOdE854wftZD3t3MXX6ZVMxD6yFZ8Vn+gmw+ItWYpVfKDXMvmBDRm+wI7EEbqyYUF/dK95JCO6NU+MsTZakzUZoxegFU0aLPSkC7ohR3F9hezHvMAc65OjOebjkX+i0fc9yDbxQBZ4l7Osg27PjwBfH1rjgmbr0Q+dic94IFd8F0vSlYsMxRx6ZX9wsAX51PcJ9AEHHfFd9sWW2A97YRPwTVt434E82IM6hY+ShTjEb9maPJxPAnyRkXgpTlV0kzu/Z28rmA8PndCHzSO2yIY/RJmhFc1yupzFFviYuKYWEZPwvIK4K4aQkfnit7iiJhAzQHZJpuxP7ig+8yMy7KQwnbwK0PDRNS6KI68KSBgMnMESPqdQ9CqMKFySFAgobgaUM2CcxGAnEH7BfWsu56QAil8DNmOyEyMZBpxQMSGxrfjs2EhICoCe+Uy5igsGtUWDe10MjsPCxRAlWvTbWfNakh8I+mdB7aCXcBXbgqlA2S664LG33jUwh7zRoWCxDr3YoeRU5GTH0o4LGsnWSYt7aFXUoZdjoc+uIp27p2hm9GjjlAp2+NFNzvif8jh7oZesFfqKQEFWUlKMWIOOJXWvmyiwvELoNTS7FmikH/RoapLbvFZwDx8KPLK3doBfPClMVyAfQbnAgm6BW9GiECHb1oPTP+krWVprBXPZqOLL7jr97NG6B57xSXjM18TxPzzxR7IRdBV5LrankH4E2NCZxoWMFJ2CMxr4Hf3i+xpYg20qILI588STfog//Z2tS64licA8a7Id9MCRjyp++IXETo7uS1j0qOC2PrsmT8me/funLr1Oye6c9qKPjNkousi9GEj3fIk/iwd0JP5Y50inKxg7LzzMS7JTYIkj4rBEqyiVaPFCJpoHNInX9IE2dLdh4BQFf37DoonhW05YvCKrYbFrzcYl5zYF2DX/hFP8ldStxW80b2ggD+sYL56jIz3Ma8pjfr4GbIC/w0vGGig+Lb6zc7KgD7pJPmIJ3dn19PobnTqJpWONqdwllrABcZgfpzO2wu7ZMluJfj7Ij9ll/MRHTR2bC9AtP6JF3AyHBkszo0G35jXgJ+I0/HiiDzHwSIZ8Qhx73xuXZOhyiicf4JE+xblsaYV4V3jbcKNDQMZ8Re41dw/MV1jyW2uSFbuxEUTet/rvW0LywqvimZ+LvehXC2jCon+1U8B/xUW1SMBe5H8N4BEYJ3cal4/xJXa/ymx+fh8gefB1Gy5kRu82QzV97GGV1YTqiDbHjGOjYpD4tAfG2ZRT74hR1hWDxO3wvALoio4+usal14d6NYDwJS4KFtCMIxwGSwFbTrIHgjqcDN8umYTCiBTYcE6AT6J0xK+AnIFMklFYKBgC9DEmiXWCNSV0xYo1opMhM0iFAb6AZ/gxzj04GSz+BQZjzbE7jy6/JXGc6vToS1/60iX540tRR274kgStRW6CB3qscQsYby6+7Xr4nQldWYdsBF2XtemPQ6IZrZxVoCYvyVehUuCT4AR6SVXxQu94Ryu8Ejxns55CTSL2PJ2fAePozjw0ws3hyYV8JB8JWvIgS/9UoyIcPWh1WZ8OFWx4RjMe4MgeJz3os3vOvsg+8Dk89DvnaEQVRdkZHOT97t27yxrGdimyFWyC0Armo03xJBHTFz3Qx7ThM2CtKbtsEi72ZUeSn9K7ApWuHgFr7DUu6CAHO08Co8LYaQL9oesMb2xYEep1BjY65QoHfbmPX+A+m7PpQO/07ZIw2Se7VWwr6MxTcEvi8GuAFeYKV69p0YcCVKIXJ/iPWAGH5KJpNgYeMQpf9MwGrMd2XGwXrWSFH7TiHa33ADszH068WluBIJaIk+1yZvf4ZwP0b5wCzhinMYpyxTG/lrjwzwYVdHyrV3g1016d8dlrSQp/p1pkpyFwz5rihXs2P/jGXN9nNIkZnmmSyJ1c2SUfwNMtsWJCdpF8yDofoIMuMqMjuqIza2uI8cN3bXwoPjSqFW5sgP3a4BFnko1/oUqcxLN4RwaaY4WHuXhiC2TAFmriyCNAozyhmUQf2l1sXUwSn9GInyPAO9uCm33zNTSLJXww2U4w/n1uXOKJHPmZxlisEY/lVz5NTuR1BOxc/DU+OzGPjtgf+t3bAjJLruoLm2maQhsW2fgZGt4Ksn+2JDbg2yYfPtmnJg696oojMEY8gCPZmKMhYTN8aI/n9IYGDaJ1Nfh8TAOQPcK3J/e3BvSiSa5ky2ITWsUrcYL++SCet2h2zxhx1MYM+QVir5hYTt/j2TPxgJ2SsxgkXrN58Rp+dK5+/AjA99E1LhQscBP4NWESyryOoGBBgXYwGYLAL9lYjzNeuvfF+YwRjCSBaTjGSrASa8CIJBjJIcCHJMFgORxD5Vz+Cp52DDtRYGDWYOyKQYWs4kHy05h4Z1ziUCwoFAWAfujrHWjf4Zly25LPGXkF6Ecrmhi6nVMFAp7QmtH7bJdPsYZ2PBqjMPFMoCdnDitRk6tmQWEkKfouUUuq1iELeF2cS1LhqNbngOhp7TO8xPPRWDTTCf35YSs6NU7o1HQqyvGjwKAPvwuhD8kGTYIqutOjhk1AhhOt8HtGLgphTRP5BuTX7j3wV2JT5HUP/fSBRvRYZwJ5WE+xnE0bb7fFLg05owW+M3I7C3izhmD1CJDPbFzQiFY8SGwKfvLAC96MPwLzySQcfFDDr7ATC8x3nxzpj/z5VEnFPbZHX5IkmQr85Ku5/frXv355/YmPuscexAB0slV0snkbHOinG3EC7dbItp1EaOTZdbreAvzMq3tnwVj42QDeXdZkZ2hWWGgaxB5+iV5yAOY1nozoQSxVWBvHV/gNnvmL35sp4PkMPxAv/XheI2MNTaJNAacG5inCyZj/KyTNF+fIi97YhBgolrBjtLA7vPiOBnwomjXR4qLiRmGF5vjN/l8JxRK76ppRMkJjDYe//B9teHNpaMQUNLMhtsSGnFB95StfuWyoOKnCG3vUQPgrhoop8LLjchM/8hnv7rvQRHc2vNBEt2RxLY56Do+iWlNFbxoy92Y88fl9a1yyd/zDy6bwT45iNJnQFx6vAR7ZkHxuxx/O7luDXumohuYIPLemteVtcUKO5Auaghmfku9bQXQVGxXK7AxtYgPbRdsZmQF4yJ1Ni3nxgi/yZ/N8vlhzDcwXm23cyTlyMluZvo7+t5SZtZKZ9fmKmOp3vuKk+oyPspP/9b/+tz7ntQJcailNrcbM94BObIDKY+RAD1s4As/MLwbYLOMDGlBNIBzo/t+0PdbEwPFRNS6ULXlj+pkMU5gGRWEsMXI+STZlcko7oZoTDjkVZ4zgr2gW6AMGKIBL0IE10D6bGeMEcckCf4xGgLK+oKnYseum6EMbY/Jjb8URZ3ZSIXGjycXwFEGStwSBLkUsZ8XLo0YXJBtroV1B59TBbnUGLui48GVHUTKzA+q5SyJ2ioFmjmJXlXw5sYJGsJZgcz6XJs7uLIeNl2ixjuJeIUDOZJBsngHWgau17DBWYNmdU3iSc/yRDf7YjQLCDrLx5IRHOlR40DN+FFWSkbl2XBUW1grgo38yBf4K5oJ9PLpHhu6tvMMlwLFlBTZaw0VW/hNLxZ8EcrS7dQ+wB0n32Y0Lm1CUSZb0rmknQ7we0U+X5KyQIFe+y+cUzjYD2Cu9sD24JRg68zqTMZobRbziVzOtYGbf4pNCmy7JEE47adZBuzX9ZRfsPzmjN5pd5EWPeLXT2knjs2x5D6whTmkO8M//xB325LRIrJt0osdlHttSrNGDeCVusjF/6Z390xX5iGnsTIEn8bJHhS4Zalys5z4/VqTTgTEKNr6kaSJbJ8virAKFLUj+YhEb559tBqFxytk98tVE4U9MoWM8i8+KCuPZyasATWyBXMnD2mx3yjV6XegVL8TQ4rkL38mTz5OBzzbUxAu26cSG/MnX5bdDnvFJzRPZiyViOXrokF3TFXwKPjZMz3sySb7GGM924GDD9I5mz96nxoWM5Vy2VAxhu3iWl9BrzFnAv40sRaOY4ntAbvDBTyZn8c6842Kb/IlONadiCR+L1lfabKBuYYfiJH9TZ7Bftsi30HkLHXQgzpF7/griXX0gLqgJzsCUGbtEl81ofqLBQneN+VvIC9CPvKAmVDOQmU1NPCWzW/QndjjJVl+ssQoesU+tWI65htecZIZWNMkD4q0cJlaKjWd1sAdw89uPsnF5FCgIPg7PoAUaxSKDZlT+FggoUFJ2bNwrNZ4FApygJ1H4TBn+KrIZFkOAw6673UU7qIKPIt9zCcXJiuRBmWhRxAtwErOdRYGuhNpubIkgQ5PYOLhiQEKSgNCKz2cZCDzwWUthINkrKBxno2k6n0uAM04xKMCSg/kKdckLP8aQg0ZMAFMsKkYkSglPEaKYMtcaHatKNumo9YzhqJpQelQckgl6jTXmWUDH1qIbRS3d4bHiroue0M3hFcl2MMyReBQPAikZ0juZKM7o3umUhoKdkJPijC2QCz5d7JHckgEeFe8ConsBOjTE7IzOCmbAPOvb0aYrhaLTGgGWPpLxIwBHjcsjOmA7XpviR4p5+BRCilz2oGBi8/gjL8mVvOmFT/NJsmFDfJa8BX7FmwTsB+F04jN6FV9sUdBW1Cly3SNLsnGxLbj4bPKqiPOMXhWS9OW+4pSv5w8r4FG8QRNdiBvuPRPoAC1sk12yMfLJD/HId/DM99gvWtOdv2zCfMlYXGaL5vFtPg2vxJdtZ/vkqkjmo5K3NfgqmWQj+IaL3O2Wmsd2ja15EUdd1jVerLAmHbAN+mAXeFKkoLUEHv3kilZ2U+xEKx/io4qx8PJ38/O1ZwA86BLb2RzeNWVbduGeeCD20Qdga4o9BZm4Ax8a8SVm4Idu2KHnZAO/02I2S56aTfySo9yKDnHGRobfaZKHeGAMHeU/fIkOxX764Wt8ju+xcfpmC+FlVzakvDrJ5x4BMru3ceGXdM7H5Eh8sVF+TpbiqzH36JjcyZO8fF7jJt1YQ16nw3viKtoU4mxWrpR7xAp2LgbC+4yYHWRT7J9e6ZsP8i261dzyD/wae6vcjIdDTE9mKw66Euf54r3+V76W1zRBaGfPbTryr3txrwAHXPTA58RBeYsviVXyLP9kx/R5j67omX/S+QrWt7Y4ycaLe9dgHUMfdCs+VevxY3FRXiim3kI/uj41LjdCBlVjYdebETNmSiBUCVxgFehTiHk1LxxIIkjo5lAsg+xEAS6FoKAoUUg4krJ/OccuVO+H233v/zGREKxdgwIPo1HgClJgVXS8KIIU+AoARYF5z4QpN3wlN+tKAqvhGs8h8Y1fCbJkIDGQS/fQKtC3Y6MQwotxnIKzS8AFFn/JylG+AqX7E3wXMBQ+Chh6Q8vW2EcBT5zYWppfwUQgZgPW2tIZfbLjdqXpW4FK/5IC3hTRxqCdffgdhB9nCyDsVQJ0jw7IkawUDnYN4Y9PfxXzChLJxloB2tg5WzQXHnITWI21roLu3iQb8JFnNC7sT7Lxrr/Xp8iJP0pCTmEkP80Fuu3au9cPtCvABF72J5YI6poKPqcIZmd2351SspUVFDcusShAE93Dz34DfNIJWaKvgkKRR7b0PmXRePJGg40Qxd8j8loBLnrEm4Sn4CEjPkIm+JBk+Z6xW2tHpxjIFtm7eOm0s8IDfhtCnink2BSbZJsaAv7iM9tl72RHL+bQr4ZS40R2YoExdEU/mkbPxSC7ivCKB2KpMdZmx+QLPxrD0WnckUzRqsBGp3XEVHywHbvp8OIfn0d4bgG4FE/WU9grptC5Altj3/wfiL3FNrYVoEtcZaviNZoDuuUnctUEc8hOYaoRIi8+wU7ck1f4DDvhx3ShueRbfEwMQTu90IWLb3rOxpwWaYh+8pOfXGT5CODnbOOCLxcZu+QX9kZu7B9f5ddHgY/DK/5bawX36IVN7cWYWwA+MadNHDpgq/TFNh6x02QGB7+he7rVKGmSNBHRn4xvhdYQfzTB04YnkKsYaow1712ri/3wZXEDP3IjP6nmeIbMxEKxnD2wU/GcL1k7Oppz61piA/r5216thwax3Ote5HrrGtHlwpM1xW3+QhdiKj8Sj+FPZtfWMZY8PjUuJyDhcwDJ0a6qoCV4S1RwU7SLISh6KuQCApesGToaKNFY843NAQR0icG/5OW3DhK0QkWQd7qiONDMSLrmSQQSBUOsuLEuetElKShoA7wYI2B5VcxcO/SMVPKreHgWoAWPkqOdIg1LchNEMtjAd/cVhF6nkYRzVqDgZviKMvL2V8HvPr6M1awpqD0nJwmvZs49AUZRonBVMKFx0pC+JXoyaceWXsi5ZmvOeQTQZC00Wos+OLaCuAQS+Oy+ZO4zOlx4Z08Cg1fK8Ec2bESSkOjYlMJOoLdb/du//duXd9wlK3YkSCneyIx+4MO/okGQ0cBMmyZP/iCAsz30eE4HkhVZsWvFkmQI3+TlLODtGY0LuvD5r//1v/7su9/97kWf7NHOIztTaLFN8lKgVNjimz3hkd+44OIraHORBbtiTxK0HWkynLZl10zRZWwAh6THv+l6Ar1qSBVw7aT7rDAOZ7ZqLbFD7MEDXFNX94I18U0OCl7r8y8FJZtCl2fWM5Ys0LMCOvEqBiqI2Z/NH6cZxS3z2I012L+THLIWK8VcTQybJiuf4ZLYbWLwB3oT4zUxmiGxkj/xc/IRRzUz1qALdg+3NchM4WZMdk7HbBaN7IYf8B+40Ltli+6Zi9fkZj7Z8YX+NUGxN5vaw3UWWpP9wcvf8I6vaQM+sx2yFW9spskpxs31fcb3bFx6vtW4oN94epFPilvuu9gFecBjLc/wjX+yFvf4miKG37Ep8VwBT8829TTjfvP1K7/yK5em9BF5oeNM48IeyQmd5GrDUEOlAEan+55PGd8LcLATumNfW5CexSq2So6PyMHccMLFF9gHO5ULNN/0Q2dbPr0HYrw59Mmf2DxbEl/5A/lb8xHaAdtih2KRTby93GKctcUIvn9PDppAFugvZ7N3cV3eZZty7up71yCZ0YH4Jd7JueqyYhR8xln/EdnxPTlKw8ovt3C5Z021CJsgw0cAPrRbjw7YutiivhWHxWl0iRVHvKHjU+NyAITCQAhSsFZAEDKFC6wFjYTnL8UwLs4qMJrHSY31WRGoa5a0BQYBwg/f3717d9kF/uEPf3hJ2gKjAlQi4HDmUjZDhjdjyxDstEmqEvlUvM8aBjxTuLFwSQqc2NoKLQbTnGcAuqwNr+RoBxvP1lodeq7rM3kZ5/cAAoKx9OASKBTKig68eKa4gd9aycOrDGRtDtkrkOxS+eyeceiAix7odsthfDfeOuSmmfIDV84sKMOxNe9egAc+RQC99c6+Yi25ea6I14y2LhrxTW6KSnTiFZ+e0b1GWyIiX7wIhv6TVPbI1vCkmPf/XvgBqAKEzPxmS8Mp0Eio5sJNF5KaphGt1m+9ZOwyVoBEF5tToLiHl7NyQ/+zGhdNlkYWf/hUhNnZklDJnZzxYk18nAV0ucxVQGsg+GubAZ4pmjWNdBh4tte4mIMmekAjWxd/2DpIt/xezLGDxlbo5hbagbWyL/OtYW3NiQ0VduCvBlQsyr7M24NwZnNORdm0uMeeotM4clB8iBMKVetHh3sKH7ph42xVXFEg8F+2jHf3xAaJn0z9JQ+6YOcaabaMfvomc37Brq2vCGFn5E2m7qEfjdaGSyxVoGhEmndN1vjLr61LDl7D8A8QtMOOpng2NtkeyXcFY80jB79vVEzxUfg8Q6d18I0Gp0kaPDxOMBatW40LubBBOQQ+/PNpfNCL9dLpCu51rWDOtD/2Qh98RtPCZ+VfhNwj2gAA9rdJREFU792j+xHAz17jQn5oMIas2tyTK8VVcskmjvi5FdiXZkRcJ9MjIG9j5Ud0PAvwYW12rSFQi8h3dvvpgn16vvq9z+JQelMfyQt0xob4KXmat2cb94D14JfXyG9PFu6jT9OpEVjt/VHAD7sQH+QoMc7mKj9MZtacfKPJvWKcuGAu+xb/xMJi7AqPyo/vij3sHB17ciMzsYkd0OuzAP0u64sn6g+bS/QjvtsQYC/RN/lF06fG5QAITBBWMEhkdlsokTALXBN8Z7wKWkHRvwpkt0ihJDkK7JKspOdfgtBRczhFDiVJuLp1QcMaDFdD4j1fynJRaqcLOR/lmW9H0Ks/ivAUaoyx6BfkFJjWkAAYC2eHdw1EjwA85GbH306IoO8zB12NcIL7+Ba4yUjgRJv7LrwIBIonjo5mz43DT7jpWRHjvu/uk4/ihyx9dxU02ITESG57cphzyMyphDXptROhZ0D0utBCHmwJ7fRG/4o0J1ECXbSSg6SqgXN6gC/gOZolDvYnERtLRhpiRZzC2j12Z7eP7siX/MjmZz/72eX/JBJQ8SxhKsg9Y59e5dMYslfzosnfLrxYk80pAAR1vsRf8HoN0PeMxgUNXlMgi+TQe/QaGLGBv/J5/B8F6z063CdzctR0KvKKG/yA/HwO0LDXuAC6tHvOlzTkmhe0A3bHJuxYKebONBNbQAd0xyacXrA5yUoiYeNiB5rx5WqNa+ugU3xz+kS+Ypfk7j484eBTZM4P2a9naGLr5iic0Kd4UmjzP3ZLvmSjITGOnbNLDQu5iHvGiXuaJZ/xxpfa7afzZIcuMmXXfKlXZvJJF9rwZJ44w/btvqeTPcCn+WTngofu7WayP76HBzlBU0TPeDbnFmgdfGiS0WgNMgbWJg+Nk+Zm2lMAx17jws+d2Gg80Y9WMZmO+Av8twLcYhM9sgM60gSJT/yC/dGDy+aHOPAI4Mcaay2ADjbCX/HDZvmBGJH9Z/vPBsW+fwjhqAAP2IWmlO3RnfHPoAkOV3aKX3phB3yTnYrhbMCzQIySP/mi2KpWsvnq/iqzZ8pODuTX9HWkl3hi92xq0v4sSGYuuZTfFPPITMxgQ9FCb3Ru8xQP6iQ2KSaFy99nA5xsmq7IAT1Hcqt2ECdeAehxoUVTKZexHznDhmcxJTrJkN9+alwWIJh2eiRt3e/6+oy/jI7DMDyJUiDxqoYkqMhyrG0XVENhDCWYw4AlTgWCAFTggcNrCCUJTi+B2vExpnuSteIKPwGlm+e9YkmEARRoOQI+OKxEpnBTPBhv3QziUUCPYkDjgT90kqF7aLHWHsSbuRKqAOM7g+05GdAFWeILPveMleTiAx10YUcxsL4mEl0SQ44AB/o4iaLccaWxnk2Y331GG4cmS8Gc3jUONZ3r/Huhtdge28U/xxZ42KHn9MhG0K/oS6+BZE8eirD0ACc5sgvygMc4hR77JFcy9oztC6qaRgUamxeUBQ7Flv/Qlb2TAfn6yw4Fa8WjOWyQnBUFeElufEXhIzhZb09u6Hlm48JeQDZEZhK0AknhaB3+KGHzNbKTJPkrHCWVPVrch5f8JHD6oj/+IJlN3/X5qHGxll0pDaIiFK30yIclunZpzbXukXw8g8+axotz7BYevIo3eCcjRSSdsZUj3axALmjUBMEnDvjbyXG+50IH3qzJ9tCSf7IVOugVRTjxyWbQbYPIa070Rj9s3H1yFnM9F0s8V2jxG7aNBk0Ju8MXP6Yfa+EXXWjwnJ24z2bJPPAZHnGWvbNlMURyn3HrGlhr2p8Cmf3hhczwISfwOfaHLuPP4KcHOmZz+BP/xCdz8WMzRAPinrET0LXXuMhhcox85rnX3vLha3TBYQwZGY8OJ3p4JGdNKVtQVGUv8YxGcqcTsnkE8MPmxRQ24Ds7ICMxkS+iyaYfGab7ZPBswJtagQ2h5xqgR8wXb8nwrL3dA+nLJoJYlnzogN7dozN+ym7RxZbzg1fIDE42wf/Uc2LItXU859+adXo1/1WQzPiWzSp6Irdk5h5ZiY2eqVWKca/UJdtiLzakrHdmLTmPX4oDZPYqHwDw83m+r4YgM/HV2uoPscAlnrG5V9LyXjcuGBc0KJBSGRvDolhFokQp6XnNQFDTGSsQFXKSIgFyWAHHiYnxxnIor9xM57AWGiQhxsogrGt9azMOaxgTUA56ZtJUUNh5XROOz4IsOtAoiZrXriUjwAs6tq5boXnWtY4GQlFmZ5gjKiIKwtfWME7xgS94yADe5njOcPElKPaMfEvwrYEWwVRSDcxXQAsUitNJF1wChmLNcaWEfMZBPYeHTOlNElREcjrz0+3k4xGAAw8c2jvD9G999kFu/nLqFfCrsBLcA+PYAznFa/4hSPAD4L7PTkkEWzy518WW7cZoUNi8IknhVdAhEzaBXk0O+cPPhtmnRksh4pmTK/aLp2RnDYDGVzQue2B9AZ5N8ie8KNZcGj5BH01btE5wzxinN2KFYtFpWfIFaCKTvcYlMFdRwFbZAR7otdPXvfWzQZdxGh5ypzcxDG9kQr+KN3zdA9Yyl9wUYV7PUhzS9Uqfz+TGV2yoSE4K4ugkE/6rmRG/4CUbp1fszFzPreMz/8MDn2B/nin0jReHFCxiLr7FBz7BLvkE3GSCXvblOxr8dapAL/QefQEe4tmGhx16DREcYhSe5/izACcaxTuxEG9oQJ/cxWc1FfAf2V7A1uQkc8UnMjJfLmPb6F8Bvr3GRXPFFr0xINZfKwLNQ6MLzfTipEAjSa50r1Hha9nfHj/kwo+e0bhoTMhDM2tDTwxiI2TOv9H6FkAu7EVNcbYJMYc/8I82Nl8N1kQbu5DfbRr4h4HYJRslxxnXXglokcPkGTmLPs8AOcmTbOgo1j4T2DK7Fev4m9ryD/7gDy7+Q45tXpOtsXu2/wygH77D/9B0Zi3jyEx9Kpaesc9ngHXECjKyWSWW808x2amrxu+VsnpvGxeCcY/h2N1zAuL1rm9+85sXp/ROtx8u+9eHJCQBVlBTNEh05kgC7Qq5GAYjpGCvg60BxXPB3s4i5ysYCFyUs/5LIRxTokJnYB3NkvE+B5QIF37wKblIKoxUYBZw0GPNHGReZ2HKTYBXSHBGu8GKFDLxfC1W9gBNiiY79wpgPFkjOoF7kkyvNXTfZwZNL4F7jq3xHBhPruTCAdegZS3BT/L0mho63LsGxuDTXPYgiSjSFYXshCzIydpn5XEEcKCdDSmQ2Kz1FE5kPmm2FrokFcXHtCE2q3BjH82BuwLH88B9+CV29ks3gSZPU8zeyd0axtAXHOzaMwlZYmPLbFcD+vd///cXH+n/M/nWt751kb21BCzz4SI711s2LmTiwhMZ8h1FLh4UqF7JdAKisFPQem4cWW0BPGQh8Pp/XPAWH2g607h4VYNsNHiKHAU6nHCvMkE7PdGDMWzRjrbfGXld1UYLuivq0Z6NTv2eAWuZSz5ipDXw4zu80ddlPLo0gOSIH/IwzjNzNBgaGk0LmviQBgvdZGSMGC2JmedUzBy8KA5sZPBHczTX8Ds55PtiFn05EWSL8MHB5hTS5qMvvvgw3Ghl7/lZvJCXcfDAIcb4J5jZKho8u0em5qDLeuiHS/Gj8LBRgR75gR7pmK+YtwI64cE3OXkdSZyyiaBhMG8Fc8hoq3GhL/yxv6m3FejNPDoRZ8iDz8hLGlo2iSc48nNzku0WGKPoZMuPALrw5v+jYWdeT2NraCVvst+j4dmAd3aNHp/PrGuMS26nR3J5JdALPdGjeN9vA21oaWTRvWcHrwD08DW6459n/cs4sUT8ZH9vBXSVP5Ch3zOrZ+Q9dRzbQ9erbU68t2GgjjqrKzShzcmqV05fbWuBdenLemxPDSFeafj4Lbm9Ul7vTePCuQRuha6dHUWa7wo1BqQL/o3f+I3LPyMrKQgmkp7dOUZeYJPUBLc1yPrbZ4lkbVwowOsLjuczUpcxko+ixlpzjkIPHQw+sC78flhtV641A8pGo6Tsh3V4s27rbV34cMFtffyRl2SMZwkG/woFxb0ChYzw6IdoioKCPnygv0eATk2HIlYDFO8rbRoJjQgZToczXuGLrsA9Cd74NQHRI/kLHlvBjpwVtor0dHHEx0pngVHxIrhrxgRJiVbRSV8cUFFHXhVh5Gatld4tMMYpD7krQpxycOwpF2CcAKV4o7vJr2CPLvQE5qPJUbrn4TMPfieQGswZuPBk51ZAXAEf5uKLLdE1vGinL02uBl4j4D9k/PKXv/zZl770pcsuHhrsJmq6JEb68t7ro7ss5HymcdmC+KEvRSAeFBsaAY2YIlJjI6GSo3HZa/aBf/FGMZgc0TQbl5U/3130JUZUeJIpejzjt/CRsbXRQGZih+Suua3IjTZz9ta6BsZYG71sS2HPD8Uc9o22icdn/FuzAtpmh5OoZOQZfIpI9mBziGysI1b7x0fgdQ//Gll2iWeFsCZesV3jQtY2gtgMnsV8J1RsFh50KsLEFjaKDvJxCsXOycja6LKmccU6POSv8RePeODfCkpy1yCRUfgeAbTAI/7Tr2aAXYhXThDYH18kBzxOGtFGVnxO01fjY9wKeFkbF3iMJVv38YmedEdGnpuHBv4t7llHgYu+7C/apo0EW/cCOnpW44L3Tt3Qgz+8uI5oeDbQpZzEF8jxFiBnsZLM0f0swH+2JvezK/mUD9I9e1ab8B821Zy3ArZDf05cb+EbjXQtLso7dP6WdAP1ARsWs/iD+OT3o+IFObMHcif/Z9EGDzmJkWKg2HgLmC9mo5X83lpmwJrWZo/y7UfVuDBWPza262N3jRNKUhWTdtQYteLCDqdxgkqvWhmvKHV8pbCVXLcUCZfClYMBzxmLZF3R7J5LwLeLpXAxbzqTYp6hSRJBcyQtRWPJI/BZcmCk7969u/DCGcK5gvtwGCN4koeCUaKxQ2l339Ec2gVJzYrd8V/6pV+6/KshnM38Av9ZwIOi2+6hYmIriMCHd0Yq4VbsAWPJHj1oD+AREMiTzCde4yW9msEtmaBLUOEcayN5BOHy1xy6Y2v++c4f/OAHlybGbrQiw4mMRsDuqQCsOPg/ft4QFriO5Ag/mQjaCjyNn8S1yg8N+LQrt/KpkXWKKAFNMAdOwQG+CWyUHcwmRVPBBvnBBOuRNfyKTq9QVlAqbtmkApF/kYcmxXPyxn+vybA//qc48+rlo8GKbu9tXAK6IRt/u/BvN4hexRUNhh1pR+watXzUX0WvVxLZaHIyV+La2nXznV74nh1rMs3fPPOXPslLcrG20087ehoKujR/0vyIDAF8YgVfsoOHds2S+62xruOZYlbD4YqPxrFpRb4G1QaD8cnL6yhsDQ+aFfNtKtEn3sV1uOHIzshSPNW4sSlzbbh4bhz/t544ag1riS+aIoUFe/cdbS7P4UMf363pCoyJb2u56Jh/sAm+KPbPObdCa4TfRU5kz8+twwZ7/Y9NsD/j4sNnslIslVvcn+D72riQdflo2jR+NPHyjTX5NxroiHxr8oy3dvq+B6z1jMaFz/EVuFa6ut4K2JscS4e3rqtpUEzSEx09C+CSk9UA9GizVwyXT9kBnYqhYjvZgbeSGz3h20YJf7p1TbbIjsn8Wq59BZCXmCUuiSfsT0xzAi0nqjXVmOILWT8D8Cgeiot8lP3fCmi2ESMuvJWuV7AmmbA9tcEraXivGhcFqUKIAzKWAjJjZlD+Co6CueeSvuQjSSoCFJmKTUJTIEjYCkh/BQ+FOGd2eSUkwyNgOxR2LayR4l3o0ghJCNaeypDcFSGMbgJjF+gkdDuB+BFM0IkOBR4aJTCFPRoFHgUWZ3EioagSvBX4EjEejBcIJfOM3DzBVXGlSFFoeoXOaz0KbTxOfq6BMeaQB/ol1z1HIitrK0zwW5AEnNEuql1QjVrgvrHksO5ime+ZgpkjbgUt9NG9AsdOoQImnZ0FYwVXMnaaZJdc8Ff4k6OdYPwLUJIwXSmqJH12QF/sy3Nj8aFhUNCzQ0FXEIGPro3Fr8abzaJfcUFf5q20S0qc39gJ5KM4ts4qG3ajCENP/gIPGqzrs+CLn3hgW2jDH3viP4oZ4xU7bBat7JsvwmtdOvOZDCtAyY+MbtHDCuzu0cYFrDSgF2602gTR/LMfOuFLfKrGjD581ryxezz2OwbzV7mTA381XqLm6xIuXPCQrzX4CHvR4LNxuOCnpy07vxXgoCOvYWanChunetZKd4B8kpH7kh271Uxp6GoUjHeRAZmxJbGTLLvPnsRtn+EUc4wjZ+PYHTmzEfT1GxcxXDx14szO4RMr+QR6yYUcyV2MM9Y9MnPqo4jgd3DGD17QTu5eneAL1ln57kIf+9Z08QEyww89wksGxt0C6xrWRnf2x+/RJbZP+2Nj+KELNkTOitI1rgJ4Z+NC9uiVJ+jCHMWfz9mfxoz9ideekwtZxqPrUXhW40JW5MOnkmE0PovWa2BN+uJDmk4yvhXIVk5XxLP/eyH7YdvyEjulV/Gaz7AFz63HB9izjS/33lpu7JC/ao63mu5rgFd88kd+zqbeGvAgB3l7Bf1sIflX74hLLvWR+JWs7wF6k0P4r5rJ91sh/xdH+HUx762B/YkB9PdKe3uvfuNSwsHw1nUGGL6Ep/Di1HaaNCwCt2JNYyRZ+j8xFMccg4PZDbOTuCqcEShmFQMrCBgaBQEuMJ/y3DPPbjRFemWFYSkeFYYKJInKb3WM05TY6VXoCzqKUMWkgqLmRBEi6ZRUrQOHZK9wlKg0Ui5jb3UAMiYPa3ldQYI9woF/jaIGYJUb2gRZyVdxEFiDbBSoW4GJ/iRYRZS1t/TuHhmQDZnRMzxnbQQYKxnRBRwaQ0lKQURP5AwneoyjL8FF80kfAoSCQHJ1+kOPGikOS8eaF/JjY9ZQYNitcVLoBMNrEPgnC3ySX/TDjx7ynWAMvOZt2ak1rC/A+g6P/9RSE8tG2LCCQHNCZ4pKTZB18BkN8wrW76B75MTGyWMdcwuIAc9oXPYgel1kTvb8R4Kie+vyU4W30zf3Nd1omo1LF5mJHXzAGO8YG8ce2C9/lNisYe4rAB18zSYKu2VXCi0NhPtBfAc+o1/SFXvQu55ihFszJKZoXtgV8Iz9sH/Jtjn5BbnAJf4qZOHxnZzENT5FB3Y2yQc+Bbt4yibRBp/TBzrJRt2D22aSUy5NRv4TeE53Tl+shWZzJ0Rvn8UTzSad9aoDP0L3xP0sgFNcxDtflJ/YHV7pQ6yRq+gk+ie95FHjAo9479TYZhOZyqN8nc7IiLwnH5P/Z0Fx4IvQuLApMhWHyW/60i0Aj7gix98CeKZz8+UexbINPTUM2+cjniULf82hA/WDMfNZ16uBD7Ph6LsH0CmH8oO56flWQIZqC7F8yjjwXHz1Wio/87aLmOs+nbmmr10D9m5Dx8bfLfMmWFOt4f9/aSPo8wB+8lE3LveCuZTG4BiSC27BX3ISwDU2Cgr//4UiUJBULBu3rm2OBKGwWUExomGAz3ouSUQil/gkEv+7vmTNASVUdFgHXsHMKY/5nlnHbkpjjWk8PloDDwoVTYHkLTn5LklrlDRgxt8iR2PhtiP8J3/yJ5dixdp7DsBRnHahgaOsa6FRoYx39E8wV2CVrK0xAR7826US/LYShjFwkA/eFeZ2GN27BYxHmybXf6gpeNgpphcnMXa17JjRBdlUfJkz7UmTI8gJdt3TtLEZNmKeOdme+1/96lcvQYbO8clu4CZvxZP71p3gmUJFobzqBS9wsMeaK7bhszXRYf1pU+jCF1xkuurwLBSs3vfGZQI68Y1/cicX8lYQKiQlX6/raEbEJQUDGZpHfj7bRaVn/2iI/5xW0S2uSN6ew5nfrvp6FOCDW3Ftx96Gh+TJd9BHJ8ZsrYsHzzsxxhteyJ8dJRtj4ONfmvDs0xj3FVMKjO6jhQ2Qn7nw2TQyznPf+ZjCNp8SJ/mN5+iuGKhRcY/8Nf3moM08siVrxRw+0BCY5zu7rxngo9ZwwdEF/G2O9azNB+mVrykc3TfmmRAv5AI//tgOmeHrO9/5ziUOkacY6zkazatxEas0OTYoFKziId3wdTjpIfuL31cB2p7ZuLAD8Gq6twAvcpT8TH730mCugpIN4usaHs/NoUPzyMDrwWTKDtkAvW7RlE+zGXb0echNvrEJjNdHQA2lcVfLALy8FT9kq1mV09VB+VzgM1nTgzhPT8Z600XNgHb+d8bnPBfL1G02Tu4Fa6HTxqvcL1a9lbwmkMtH27i8GgiUku3mMRg77r2jvQIDFLzsHAbmGyu4eCZ5m6+IkSjteCnKFT2e2zUzZ14MnoIlKbQwPAWsgp0sfA98ZhDmKLLtavpRpcQlULmsZ5dYM3Fr0ECPgIhHxRpntd6e47nHaQVTTrK1niLaCQR6t+SqYTSffCeEWwJ23CxBb4Fx0a3p5LAcH923ABzoF2zmv7ikYdF40aWCkG4EGPjNmeD1D3pWQADP6VwQM2cCmSq4/HbIro3CyA4vu5co4XAio9gjw9UOBFQBfd4PrMs2FLFOzOwYpsdXgjU+hMYFbWTBHtHM79iaix2yJfZG/2SHDjbhr8s9Fz9XSLNv9332aiBfViDDAR/dw40va1nX+vfKyDxJtTjAVtmRnUkNt7WMCf/8DHxGA7tySimGKBDRm1270Oi7hIwvzcS0Y7w40XD6p0AyHl2dOJGfdczxOgt5wUsOYvxsXBQ6bD+9uO/3h3zKPRdZotVJcDJMDuzFzrwYOG3der7DZ6NAUY8+cxpnzBa0Jl3aSDLXKaUY4D4a9uZeg+SLfnRkg2SF1gpWNNt4UACIg+KQ3X9NjFMaccJ3G0cKVZtWbELcqnGBk8yzP+s9Qvs1sMYXoXGxHtkpwMXxR4C8NdA2B9bfgQbsIVtl907o5RP610CzQ/SwGbTtyYMd8T/NLBm+pdyshTf+Ig7i5xFgv2QgJ+LryF+fDfm/HMwO6WVd2/cuekGjuCef23SwIaMmFJforpizAj2pt8QYsekRQIcayEmzNbfWezXg81Pj8mIgWAnArqMickvQjElRrMkJGAiDlNAZnH9BTDEpYNshKDkoEBiw3Urf5yW5aBIkKfgYmXv+ZRDBy71AQGCQ8AumjMLuizGcxXe/fdC03JqYjGVsiguFmELiWpDwvNdHzN0aa/fVP8spAGw9F8TRrWhan/vuvoSNniMwlu1o3OxqCRZTdmfBHKc2dK0Yoju8aWg0IApUgYy9zIBgfYUEZ1XEAc8VhpKIoDfBM3g8Q7fv1oHXOk5MFCKOnxVL9BwYa+cVn5OGCV7fUWyjZU83zwbrfAiNi+SiiFCA8ifFt+JfcpJwHNXzVw2rohxPbPjHP/7x5R9yQJd7NgzMz29d7Flx6VUdBSU8/FVM41ueiyEaDLZ9j42SMxzotEGiYZknCV17OhAbxAt0aZYV8lvxAm3s3hpsVfxpDPxsC5/TN+lOsa2AjgY+pPggK9+L8dbnF9YRR9kzOszDoyIPnmgzrnjDH7pvvDjDb1x8w7OgMehX8PNthSD64fR8C7rf2uIqmsgD7fx9qwC9BmizNtnih00o8pIJvYoLYoA43yt06FCIkCMfQ4fY6ETIP91qk0ID41VBtkcW/RYSbnnKRpDYqLkhs3vovwZw8g+8PALs5PNqXKbO2WUx/V6Aj03brPL6txy/ghzJl+RDetSI01f5AY4zMlBL2IgVX5r3VkBmaiiNS69vPgLm2yTiC9f89RVgLRslNhHFzGtre+7i43TsFJkP06XahO/R8wTjjRWL5e1HZWZt+Y3fqGVm3H4rqBb41Li8GBiOYKHQ3DIcTtO/MiThSAKSseJGorYb/5d/+ZeX+QyT4lKYZKMgkjTdmxcD88N8Y3wXaHwmA8WChgk+zisZKaQUdegwjrxqWhRUgpZ7cJ0FYxm3kxZduuC6OtcK5uBREWFnCN1bQFZ2/vdokrglWIXX1nPzFO4KwALxHnhGXnaaFO0CDhqP5qyABmsq5gRLa8NJPgowgUcypQcFhuDsOV2QmyJBYRwu/ClCFFMTPPN7AjtJs3hALz7pQhPsFEnxJhHA42RL0UuuAh2aVv58Z2/m0iN7vkUG90LB6hWNC3x4wAt5GYM3hS958V8+aqeZr7BJp3nwKKAUnJJHBYHigUzZHt/ha4pZMvMaExuie/Lm62jxGph/NU0yspb10TJliy5FoX/Hnv2wFVfFPJsRM9AjGcLLDtDFv3vtR6yQdDRXCkxxwprsT9wxH514rfgno+S+JX90slNznA7hG/6Vh2Rt44ScyGXamed4JztygzNAixjC7qOBbjQLJX4yWhsX/Pndl+/WkXzJ0a4l//I9+umUfc8YBYcYgiZyNM6cIJ7cL+Yo+ouj18B89o0+89lJMYA80e/CI5wKEDp08tkGhZxG33Sd/Wk6sz92gjd/2Y5NKH5u3QAd+CJDdqHh1lDbNGNP7JXds11rsxE46ZBtTftDA1r4rEvDQ67Gi0tymSKUXNmei68V78Q5toO+dA3c+yI0LnTtN4FiMZ4eAfhcbIMNaEbZLBmKWeoHfk0/9GZjgqxX3zwC+I2lc3j4x1vKDODH+k5YrX+W9j1AP/sSPzR1xbm3BH4mVthUpI8z66cLNiz+qSfEPLFJ7OVf8PIjPIkX/J38HuXPfHSKA9bjr28ts2qBT43LA5ARCfguhjIvShZQFIoCN+MpUTZeQ/CTn/zkspMhmQoMCmOBvV15BQcDN97cFAafwsWOa4HIM5fCwW8cCv4uazJqJx/wSzTWFdAk8wKCcdaWIBQ/iuizjhUYa22O44fldp+j8QjwqLAShI8cwxG7HwrT6xYojiR1ctjCgUe7sRI8XNbdA/Nd1iJrR+z37jjAYWfTLqYiwPdswmeyVgywBQWpgkXiJ0fyDPAVfxPQQ6fwzPHu0y+HVzBbT8Eg0UmgbEFTZl2ncoIpesgFfXBJeIpAdqHJwb/Lsy7jm5M9PgrwPrNx4UvRi36FITkoCBU0ZMFnyUNz7PVKDQPZaKgVcnacFZkSBdvms5oadgGXwtDufcUYeXShQ8LRINp15aeaFsVMMQKfK6/otQbfYLvGKoIkMEUKW1Ds+hejJGN04UkRWpFLjvzGZon/XNdrU+/evbu8siXOONGlv9beoiNwP37wze7EWM30lp8by4YU94pzsWiuYw664RF/eoYeBZfTZ/Pdd9EbXrqHDsVNjYv1yF/hRi6+W8Mz69t1pv+ATUv+fG21XfIlOzJciwCfjXfxV7psgwN+9/fA3HnBjVa2BYd/ct5vGf2fXL5r3jQFZGinlS/TM7mJY3TP/tiCxoBtkAu85MWONUh7gAbj8crO5QrrKn7ELc/oA0/kCS9dsXW5LPtDC7qc+oiZ/MomEZyKRToWA+UG/mUdO8jki3dvEdS45avil3wklz4CePg8GxexGc/s8Blrw0HP7Jzt0YU4T28aSbpg22S42vUZMIePwdVJzVsDv8Ife3qWvuDhN2zvmp++AviO2otfrDHlFjBX3BbrbZ7xcXFYfpevnvFq3QT2Kz74+9Yyw4e1PzUudwBlMXRJUZJWRAjaArXiSmD2CohdVAnH97/4i7+47H4wJk4omChAnKb4kaSCSiBjhHAXZFzmtNsoaQTGKTrxJbBIJJTpEmAkdXh8h0cBJYD5kZd/rlkxY54xzTUOX+jh0BwiWs4ainHmSD5//Md/fElA+GqNI5AEvSaCzsnrCgoUXT8ZbIFAruBXgO8BnsiOE0iK1yAZ2nHQvEjQtwYcONCssFU0Kjxq0FxkxFbZiEThP2RUvGjErB2wOwW2YiIwHz2K0PV1QJ/NUfyxJ3RYy330WBMdilz/CavXyRRx5KfAZOMKpoonRa8f7LJvRb5dV0lFwWRtc+gSXms9AmxJsHq0cSEbO3ZefyF79PNRjQOZKYo0JGSgeFX4sQvzXHghq+S19b175Eq+Xb7zNfrSrFQIZscKNJsW1qbnLT6zP42tJocNoau1/HWRlyu6Jo3mo4Oe2Jf/eFdzZuMiXNnikax7zpbohb3YDAnHCmgX34zl32wNzYHP7pGNzR70twY7ame0+9bQLPHx/AKP4paGn96MwzOZuo+25tYATr+H2+8J0chXkgXwGf2aV5tF1kKz5/OCAz2aB7ywN/RPXEcQDnRrlr7xjW989v3vf/9S0MsxcGWTxnShx7x5oc/lszju90pkYe4eGCtuF4PJyAmJOOB3QPTAbskinoolrZW9zSsaty7PxAw60Twr5PiiWMOu+KmrV9f46CNgvc+jcbGOy4kYH8b7owAfPYi3Gjq5nazYnZiCVzqho3v5RCebEJ96lfItAd1iC9vD0zNBnWCjTsx5hj5ugWKif4WW/u6VKzxsAP18Gy4bM3KcfwhJnYJPdvAMEIPkLBsS1n5LwMOnxmWBAnAGIEFIpopISUMyEsTtpJXgFHN2oRUiCjgCdazOERiQxM5ovIqhmdERKzoEZru1AoEgo6A0Bp1rkKEsTRHaFR0BY5VQBCxJrkTi0pTYlcMD51CYWE8zoyPXiSteprPiHa2CqiSno0ZPOCdNK3hmfWvZHVVUK4jmycS1+fj2WpTENAuKLeA4div2go35nFeSPVpXQS4gks81x45GNqF4EfDIWVC13qq3PTBOcUO+CiHNC726H7A/yZwe/KtL7EXDpJigE3qiX8VcQH90yr7YLH0E1lP82JWBe9Lps+dsWxClf2uji017512wcEKAZrS6FHd4F8AkNcWO4sY4dm7nm59oaJwwsFV+ZB4Zkhla6BDtk/8JBatnNC54s9ur6CcjPioZkiea2G/JHk3W67oGc6wLP9Zso8LpHj9XBJABXWmYbGhYl/+LJ+7T3ZY84BULNI7kLRaJIfnYHsBHZ2KYIr5/Npt+7XAXO85AfNE9H0MzHo9osLZ1/E7H2HUcWxAH6UdiJHtgHN8UO2csYq+a5U6bgb+zcQFoteFjt5aMA3aoKGfPQfEPT/wkfqKVjNiNEzsNADrhb0yXcebKAXxHPDPPvS2dbkF4yIQ/if30bV1yZ6tHOrMOeWm+xX2/abMJYt4eDe6zPQ2DOMMv5AkNLp25nBJ73UzOES/cs87EuUfTEZjD59gV3dEVXaDBxV+Kl+LAI4Dez6NxYV948a8Dkls2fiuQNVuiSzYmntARO/EvmbLdW5rla0AX9G2T5V6a7wX0swn5Vv1Gd88EcrT56/XGGR/eCshTo65upK9HgW0Ux8Q8mydqCGvIG/xb7ORr99oGHTjFQTN73osnr4BqgY+qcZE49xoXQjCGYhUDxio0JDE7z5KzRCcRuRRqCrveGaU8OPYuAhdQFKFOIBrvr0DNMRUTCoutgKMxsb5dqcAYAaofrM1Ei17GZafNKwVotjupkVDoKHolePMDRsjANTfwXTPIaPTXWMWA+U5zfvmXf/kS7DhR447AfM6m2FXk4eUIFOeK9r1xZKpQl1yP1qdzu3xkQxbXaI1X6woK+LRjj9dOOc7wC+DAq4JEUhCgA7LQ/Ak+9IWPikTFHf3QoaYggE8SY7dsMtm7nCDQqzXQn249Q7Pg7YSNzcDTPM8kRz6gwCbX5nc1tvGadcWiIgstAk3ND/+TJPDVayH0SHaT/wkFq2c0LuSs8Jw0P4JzD+BUWJABfvku2ZIfvdApGTiJlZAlEzrDo4bc9z3fUbhq3Nmt5gN+et/zBTrh94obcuSjmgG2oyFWRN0C2YuTtxoNOsoWVkCXApz9WWtrjA0GNiJeTYATr5oU63aPrBTj7MZ3YJ21cQGaBnyaE/jMNjXWkx6fNdkaTD44efLXd7aNdzKwZmPm5R79kQ0/tTnSpsFZgAOdNgE0aBpbPsOW4PT9SOc2OtgG+WleNOx749HM5tgTuzLO2uj2j7yIPfk+nsQCfmQ83Io+z54NU6Zowrsc8QiI+bNxeSsgX+vyhVvsYAV6KM6zDbFZzRJfcll6JrdHQe3hNT4bT8/AdwvwIZuLcq1Nplesz881feLqW/PHZ8Q8m8n8zfdHaYBDXOSfTlv4q9zDV9mLjZQzNdYeoI/M+CL8Yv9bwUfXuAgUkixlKqzsKGkCFDISup1HQVowkxQU+5Ka0xNBQbJSmJsrSAsecB4l7MAzxkPYcK+dfc8lXSc3mhdGNROBoKSgscM/E6oEhWZBxRyOji4Nkt0yu2OaIkmOY3hmHCfFMxngxY4Wg2aMihpjrhmGtQRjjqeRE9wEGK83eHf5lkBAjuSsQIm/LYAPDxqXEvfWGuQluXrn94gXPJCLkwJOuLfuBLiM00zg1es2igL80w9ZWh/uI/7jRaFkF05xwjbcQzu9CtZw4QGd1pT46I6MJSo6QI+/ijZz4XAPDeYrCjXZFX/WdnnmtUK2PwvrwBhrK9I0UuzUc7jDMa/WpE/0KN5dmmIFpALVzg9eFfX8DF4BVQPn4o94wKsAbNeYvtmYNe4FvPIxsngFoI2s6MzOtHU0tOQr+XtGNvTIzhUY+GWj9EVmNk40pzY3fN/iV/FvrmYULkWMgpZ8JPnshS0Zw47I1EYL36YP9LBZ6xh7Rq5sx3oKJnGCv27ZwwT21IaROJH9BeaJqdktmwng9cwpjXjtOzCHPfmNBF4DfGw1LmSiuPM3MFbBrVHkr3AC9NOFvKBYJ6vJU8/FTT7DRtEQ71sXPbABekUb/a1y2AM2ILaTdyctdEaX6KN3+QJN6HDZaNAU8ys6IitNDBuAbwvMg1N+IvN0nX3yT2u55xk9ab7ZHrtTTLTTb43k+UygM+t8qI0LvZOjeH9W/4Ddk7c4iO5iIX2xBfZVrOAn3gRIh+7dC+aSOdvVAK++8BaAd/mO7T3Kzx7wHfFYnIL/LXm0Ft3yVRsGaHl0fb4nx2pU2BwZwssm5F/+bDNDfpKn2JXnxddrgD5j1TriJxt5K2DnH13jIml4X9h7s//1v/7XS7HIIShSMFHYSa4UKRgwAMJZr2B+XsEzyiVoirWG33tIrDM5B8YLqJol9Ek+5rUGWtAl+TG+EpDngqEiHs2Ci2br29/+9uU/p8SXxGw8eox3oUHSdYyomJEMJKlObvbAXHiMgUPx6fcednE1SD/96U8vgXUtHo4ATglRMOZwvu+BtQVnTimRTxlNEOTwzrHI5CiRwinpeg2hBHAG0Ewf+PcqiyJcQemzwty66Evue0DvihPz2Em/f1J0pmeAB/gEIDoma7+RsnPOvjV8nFqxYmzjFRccXVGRHNADt8SnyZUEC16TVp+Tj3+lzqt86HWVSM5cK7hnfTYiuCrYyYBNCuCKMgUrX/CurtdcFFVHerwGaFYcP7txQRNZkLWTDc27DRJ6nAWEcWiwgeL0QeHH5hUjZN8YBbX5/IvsV+Dn5MO+jTdGbJDg2SJ70PApzNCiaFW0oyNa2ZfinA7c29JR0Bw4nXKImZrQ8DW/C/jrOVrEWbpFp7ETrK8JQA9/ms/N10yLLdOH/bW+TZ4pH/Lfalw0kpotMTJAH90oyPiZtboPPxmjWzyyxuQr3tgqn52x2tzGTMCneEUnrpqXddwWoAVPCoXsxHpkwFfs1uKDD4nHdM6fxf1o10CLSfE5IXmiq1d73aOPNrfgamOGDadL+DxzauoUymtmNvmMwfMZ/s4Cuj7UxoUcxAMNJV+9Jpdki2e2YlORnr1dwY7dw8eKh63wF7mIrTwC2YDNT5t6W+u9EqyPf280qMt8fwWwU3FKXHvrV58Cviu2ikmPyBjtYp8c41ptAG78sh9+rX6xAcQf2A55b8XpFeARM9ikPPVWMkP7R9W4EK7C2i6ZJMgJKclFGC6BgtJS3COCgUPCFNC9AqaYV0BKBILBFlgTXbpgBqGRQh86XHAKSAKTcYHih5EqUBTf1urVEP8rrldRnBaRQWAtPHvm1S4NDOdxb49v98nIbq2Cwr9MxIjgRrNGSeMliRpj7DUZeo5HyV9BSTdHgD4JgAwkADLZAnjJH32C0d44QBbWFaAVI9Y4AyVt7xUrqu10kbsCz2mXHW06QcfR+snVPEU6nSlAVl3Qn2DBjv1+yJpkwb5cdKLYZ3PZr0Bot1lDmj5cZK4B9uN6xU/Bp3mB7wKhBtAuDfviQ+YW5MN5dG2B++SCLry68sku9zRkgjo7t969wGee3bignxwkATR2ckeeq/7YmFNT+mUT+FaE0WcFqctcPqxZnD4b0IdCnJ6tQSZkxRadjH3961+//EhXY2KsMdYyzjoaW/jRM21iC9yHQ+yi+2wlfFv6B9ZUwHoFQ2OAvi3diYUaBGPIsvkAffyIT81n8LB1zdukn37xTKaScOPhIWMN4QTP2ZRmB32B+77zGa+MpatgrkfX/oUoGwBodE2ZBD6TCX4Vob0WDEfj9wBOMVbRqlmFp/vogkP890NcP8wWc6zjeWCjh89OPoC16Zde0ZXcXHDIV9Y0T+zRYNpcaZz51kFT8tA4sS8+wXc9P+LvLNDJh9i44J3f9cbElk9PQJ/CUoNiU1Jh6TRALPds+vMK7pG5uC4ePAL0hga1gTWfocNbgMxs0LFdNvqq9clMDBcL+A4ZvyXgi87YowbqET7Zhbji1Vj1xIwBE/BsLJ9SR8ntfjepeRO32c4aK1YQA9R6bXq+BbDDj6pxIVjOLxBQ2iuYhpfRMwSJvlcqOkKXCOwSrqcpE+BgEBKR4N/pSjTbpZUY4IBTQuPYmgaFhULWfMERz05TGCOnkHDsXMzCQ4D0z23aDTR+jybPJC6BRMGgU7eLWhOjYLY7qUCw8xu91+TsucJCcS3RzQJiC9ChONHA4dM6e0BuikkyO3JCNNCbBG1XUaFyFsyTSDWJdkWdWsHD8TVBvWLBHux67+kdeGau35qQtXHoxqfCSOITkKylyRTMyYNdOc632+6f1kYPG/TcCQknbyfHRf+KV7ujChY8VFx0gfTOhsgRTgFWAah5gVegZUPGrTjm9QiQAT/SPB3p+xrg89HGBS9ogItdkbsG0gmJpmHrVRmf6cKOt0K417nc14B7TYBOkhU7MMYP9/nrCubaKLDpIOGyNb7jdQdNgAaandDVBOtJZvjn79GwpSPf6ZMviJtiEVtiiyXD5q0XvJKf4ly8II9sYwKe0UwG8R/4LPbxHyesE9AsBiky8uvWJZO1cWGb9O5aAZ1Ov6wPLzAPvXQm1ohn8K1gvDVt1tC/eGitI5mShbX4EPnYoKhIWMdPgFOcZSviZbQC89DHP5yKikHo1kSIDWxSfKXzNQ76boNMDllf5aN7OUWDTB5o10TiVfzAx9Rr8jAPrcahSYwQO6a93QP84kNsXMiI3zltYSPkOCG7oCdFJ9/WpGoa5Wz2wa4mHMkQLn4jtt8ra3oib7ZEl3h4a5CzbCbaQHjl+mQEP5+R07Z8/dVAv3Rt/RmLbgU6g0eNMH3zCKwlpqjlxHr5QRMnRlVnrjYLrCVmqDvY6FsAOj6qxkWwohTB6lUMU7AkJklIBBJrCdQlEdqBVPRJBmswmsB4Fed27RWOOS5jUQxIKAplhbYmQpHj75YBUbbkpJFS0EgmHb1K3OYaE51TPj5LfhoScwVfzRF5uvCo0NacKTAUTr1LvuLaAnwpiBSmZ4ITGSoUFfGS7FEi9IysFflrwt4CRSC9KUDPArycm+wVBnTCkXvlAr1kBC+Hk0zIcy8woUHSUogaI+HZCaVnerOWNadsyY3MBRzFED0pGhSFdN7rXY1t91xCSu9bEO1k2Gs5xvrL1tkhf5IkfV6Ltnk9AmgkX8FqT25ngAweaVzwYX06UviQM18X4GehvAK5GGM8u8APIEcNgVch4Jyyi1a2wI4mbvpna9ZWqNM1HfBRts5OFNszvoTTeHJkg63VNQFtbI2s6NjnI1uZgEdxDs/W3FvDRodxCpRVr9bqFICtBXCgjZ3btPG5+/G3Ni7u2xX0LNkH5KBxEQfXZ0DsJQM7zyv9gbWc2tAl+U89boH76DaWTSgybFTEyx6wEXoWa1eb8Nl98Rmt4pCxdkTZBTsSI+Ya5pANGhTLK71OwZwAJzfPrasRUVB24te8Od9nxZA4zXfFPgUVXaLB866zYO0PsXFBt1g7TwgDtsJ++Ap9ueS3a/XBEVjDphl7nDH5FqAjNukE2cZUOnsrsBYZqAvY0auBjMRUue6tivAJdGZz0ian5tX3e4CtqedsWtyiL2Nd5CAeiyVtUHv9XT1SLA98tp7mVs10y3r3gnjzUTYuAsMzGSZIjkXRdkgkWkqWZCh1BmlBRJEvkaBDwrCboEBcDdV4SVXx4l91sVODB06lofF/bfTuqXvt6AjGdrxmEoYLfnRK0HYWvILhvWfFD3o8N47hArTbJZN4vLusCJbI4PYMLQoPTiKRoUECVuiWzLr2AB7BsX+QYCbVPcCDY02FCFqPgrL7xtoVPxMI6AdewbJC7RpYv8KFPMnMzrrmRYMHB7x2v52mKCo5ukIfL3hO5oAt0IeGQxAz1q6soGY8fPHs8p2tKb4kPMWJk4BeB7IDK0nTm/l0ZdcfjZrjVXbwKS7YsIJN0NKMO3FpvAsNxkpqEgx60aBJwivdPgus84xgxWbvaVysiR+88gfJzSsf9OmeZ1OHgXl0z0fFBsVEu2l4YvPiAD1pNOg+YBd0hmdFD9qbJy4oNL0aRC9iD/+nX7IXV2oaAvaPDj6qGIcnXXYB69awii/W5tvmb/E4AU52an32vScXuCRH4zQ5q2+igR/hnR3DG8BHpjZ08FrMQD9+txoX88UuflURFniWT0z5B9ZScGsE0DznBmjyTIzkL/wRT62/BenSLiea+Tn9bNEQGK9ZE1voe+L3GY82OKyPbmPYSf/RI58mFzwkL7zZVJnNIdrYKbr4HdrIGk/mmkcv6NAkbcUR3+ExlszFQrlRLmFT1sNPtJwB9vShNS54owsbemTgO57JTKzm9/zMqaN8Ku/w4z3fOQPm8Vmnc3Iffm8F9Gl25Qk2CedZPT0K1mEb7EXuu4f+e4BvVMNs2fQrgXzZiZMOPunzreuTEx8Xy9jQvYAW8odDM6cpIRf5nT7EF2uxY+PYtbjj8702exas8alxuRMoRxAXXARuAVxg0hRQNuGuaxgvgHvNS2EpsCg4/CMBGg4JjLGWHAA8nImxvHv37lJAKvI1HRKSzwU48+CwwwZfSX2lw3dO6cRFR/37v//7/+S1I5fnXtPwbqviQoGFL+u4jFGE/exnP/vHd9M1YF4XI2djrNO1gnvolYQ1ZXjjDOZdA3LDnyB/tAZwn+wEQOtdA2MkDj8I9xsjheOZedZxomV3okZUci6xpyOXopNsFb9+I8Rmkj3Zea5g8LsZTY7iw/PJqwsutJEbWgUWcz2jD3PQA4ddbZdiw3usdrLpeMrbHHTCZwef7iU9BaR7GhOFOptcAR0KEbbABgROxTF+jM+e74VnBSv0nG1ckiOZuPCWP9Ap+R7xZT66FSde3dLQzWRI314HVbgY40fe/DdovkQkZvFR8/1V6NgwUCT755TbqKBPfu8VQPaY7cKFXjqke3Qb676/XXDwc/anIbJ2dhLdW+CZtdiKpt/GyOR1Bbxr/BSP1lvHoaPXLMU/6wfWIQP2wOZ6Bgf9bjUu+CVbzYfGKrkAY9DK38SjFeBHI5/I/rf4ygdsBCmIyXuucwTGav79LkFcJ58t27Ku9cUWzQS7nLRofNxPpsa6Z9ecrWheyI29WYMc/VauJiJAt4IFH/KE09lOpOB1mc/GxDgxsxi3BcZ7Jj6ZIz74B0HomJ7obdrkHljjQ2tc8MWu8CvvW5ufaCrEYhtrZEIH5H5NBmcBLpsHcjx7uBXYiJpGzfIsms5C9pdspm2+EqzDlsXPs/XIs0HOtZEgjt+6PhuSF9QXfOUZQO/kQh7wioP9a6buiV1qFDR7/mpdwf+pcbkDzGVUkrrApwtVyAjkR4HHHMLWUQskxglqkiYj0NA4SanwhcNzRYkiXYPz53/+55fPnjMaxaodEWuHT8DR/dr9N9e9FeC2Q2hnQdOgeZBwFTZ2fiQjzssQ4WYs8LjI0Tyvt2hoOIhLkPEKEhlcA2MEc4Hx137t1y6JNZlcA05C7mSOj649wAMdnaHL+vRkh/Jf/st/edEJOs/QRSdOSDSXkrHvikQ4FA9kh064yJMc3dd8kgPZuzQbilg6mb+ZmLy6yJwsNECKEnbUGsYr0BQxigU6VOxoUiVLemI/jUevEz3J1H9AiQf44XHBbRdIEqw5muA7vsgY32yUTu3YkT9bewTQ8JaNizXIRoFBBwqt+WNqfHp+ZBdoVhhqTpNn8gZ8k//ShWdO7Mhr2qmxZMfe+ZfYxS979Y+s6cXpQ3aqsfebtRIvHGipqE1/XcakO0kbTTZGnISaN2neA2OsK3aw4QrnPago9ndLhuQhfjnBWNfHt1NRsXw2R/56ttW49Ezs1bzDPwGf/dZmrjWBHWv+t+wfuIdWz/mkzRI63sM3gQzQwN7Ymc2nvbjjHr33SisZBGh0ssdm0iddsj9zxAExiW/ydUWOjZ0teaBfPvLZfHh9jh90kKlGiz/JW/Ac8RufxilIxWW5ij2zHzQbs4fDvA+tcWEPNho0zfRlTTHBpoVneCKTdH3GXs6CHCCf2ES6BdDgZDlffmtgp+xSvKerZ8rkCKxDF3K2jQT+/NbAd8WZs3XHBJsL6gm+fuvcIyAX+MiDj/JVb3eU4+V7uvL51fZCP58al5OQQSsGFBq9PywJUCLce4biviQqodoZVzxoLgLPOaoiR2IV2CRRry9oECjIqySSqiRk18YcAa/dWPfiyX247Aybg+aKIWN8tj5ZSJA6fP+qGF4EKwHVzh/c6KpwsCajlKgkHPSVhLzuIdG1O3MExncypIj2Y3J8W+uMXnoVhWMbf20OmjR9aL021nPy4Rjf+973LnKQsEvKRwC/4l8Sxx+5aTokf69HKComDf6yCwG6V/EU1Qo2jZOdeHyar6Apqad7OmYrdIzHgiy8mi84e73QMzjszkv6dk3MUzA45mXPEpy12NIsCM1l42jR0Eq+6N7Ts/toFYAV43bg4bdOiTrcZ4HcXt24wIt2dkh/Gj2JW4EoibH9fKHxW7S4ZxybIWfrJa/muOgGT3zRMzK2lgYDnWTId8mb7P1T6uSoGEZjcjaW3pxCsgm0e4WsmOTS4DoJ4zvZoMsztOKNLZALGdNdeuraAvfhY28Smc0P9KFrC6zHBryy1anMCsZIvJoxhfcKnmninH5YJ9r8xfNe4+LCG3kaN4EMyMgGAt7RsAKZOyUSq9CwBdYgNzjYPT593pPHCmRpfK/w8hnrrnwaR5difwWO++6xOack7msK0IBfPLrg80xc8/sVDQz7phd4PTdfjC+e+ysWiiFwBD6jVxPQWwNwXAO0Wod9y3H4IFcnBBpmz7Z0APeH0LignR3wBfx95zvfuTSzdKPpJkf2iR8yTLfPBn6tmETD2biLHnSLfWKCeW8BZEYe5CIGsk+vu7414J+85LreNEHXlj2+AsREOYP8zzYg9CpG2HSykfVqnZEROv2mWXxRa5CXfy1TXZhtv0JmxYBPjcsJMJdCBDrFhWLz7G4aJVKuwCVBSGiETgErULSTBEag+LBTwxAlFQGdYUoqgYQiwel4VyORnKylIEUrYHCSm4QmCSuaJA/rSNxwuCQQ9xQkvucYHFqXXUBz3xhJ58zvSOBi7GRBlujwigbZou0IkrWGymssCr0zgHcnVSXdazrzXBGmiHP6wF7Qq4i6Nhd/EoUCk36N91cz2D+VnIw8k/QlM/JEm/nWseuKboUonaNFoWuuS3OhmPUMX5MunyVHhUQ0wE3X/olc93xGk0Ltxz/+8WU9+NN14DM9s3nBQmOroDmb0MynJ00xO3QpxtLDXOsI0PzKxgVOvLNlcrFB0Ami5HHLmslaY84O1uIcLvzYAJCcKrDdI1c65SNeI0UHPP3OTDOFB+Oj2Xrig0bXWPFDk5N/eu6+5mb6Z8/wLBbYUBFH0HEW4LA2WfFndK82NOH//TluJx7sqdiygvXRwS8UDSu4R398Y67jM9kcNS4aaTlAYTzBM5sM4hgZ79k2WxAX6WmLdgCX+eI0uSuQ4d4bvwX4cJJO5+bjJZ8JyAGfeDLeczvr/ET8ZjvyDv2sOjGXnZlvDH3II+Kw5gOPPmdr+Laxwp63ZMfHbYTgF+651hEYhzbywS+a5ATxAs+rzIoD71vjgo8uNPN5uVKMtunohMUper9BZB/0ZWzXK8A6bJV/yifWvAbmqFPEphrXV8AqM3bldVw5wqnBj370o0sMfGsQI73C+Lu/+7sXvbFJp4PkEr2vBLJQF9i85M/oubamOWIv2an7Zpx/FaDJOuKc2GET2qvU4g4flef5QTJ7ltyKAZ8alx2gFMFTIaBwNU8w7z3Va0HAGuYLwuYzfglAYSpRCNQ5g7UEfEFG0yJhKXQFPw5NWZKDxGqnMfrNVwA57dCAzCDjGVo5HoPW+OC//+dDssADGiUkO5HmAGsqdARaz93XmDgxIgN8mItux5NeVZLwjuSKB0Fbkaw41PxJHIz9zFE23tCsoPEbFI3WGTDuS1/60kUGZ5OqYkCi9l6yQkhS5ixkeBQU4Lae1+gEHzJyjwwVwQKhxoss3JOA/QbFZ+OMFzQ5v4Jj2oVigszgF9glFjYBVzwZXzMkgGWjZCcpsCnjW18R6bUi9mlNp0Lux6PAw34dXdMdWtiB19gEJnZwDazNX8xXkCo6Fcl21eA7kmeA3mcEK3TMxgUuuCX1+NQAaOIU1lO2ZwAv/JA/2DX32b2Jg07gVtDY0WsNf8UG/sl+JAM648P8kc/6rQEejCdXf11w4kGzqvDzn5Ia5+LXa6NgDt1qem1uKGwkIH7emGtgTTjoBL9wmQ/3FsDLXpyk4Cv6V2D3YqANli18iimxWKKeYBx+txqXgO95nRPdK9ATGcMtzmwBnuUDBSnfy79WwBt9OnWzc1wDfFa2AC/m012n9MVnYG2xwOub/NYzzZzcYcOFHfOv7GvKQrzFp3lkIXaL62KkgkmcYnetZy22LFazsxWyezGJPcG5J5stMBa/cJCvvMMP5Mh4IDv0vI+NC/7zJ3mz+CyW05H7ZG1Nm0X8lH3jDS2rnT4L4EUX+/FaJjqvATvFg4Y223kVWEtc0mijkf3ZqPVqLjuUG19NwwrWY8M26LyWK1+I1eoWfsxGyRRNXc8E+OhJTafe8/la3Khec4p96ybJrYAeNYZ4QS7qEjpTB4rJ8kibU2zdpptYQmbPoIt+PjUuC2Q0xipSFYCOvyhCQt1KpFtAQXAwdEm41z/M9YzSKVwC4bwUrpiS1AVuQUYCkoiAeQzSzqhmJhoEfHQpPK0xA5Mx1lTAeu4ERxJldDMY+CzAchLGBTxjbAzEroeC0/9cbg1reh6PinwO4/MWGIsuwRtPxkqUkoamy+mJpHwN4MDL1772tQsvEsEZ0Oj9yq/8ymc/+MEPLiceZ5xHAaPAwa/mBX3oVRTSA5ntgWSkaBV4BJSAfCRiwVDhRuaCc3YByJ88OSW7CIzR+LIlu+kKJwVcumg++xQonLhYO1791SyupzCKED/O9dexvB/d2TWlK+uTl/9cbzZI7rNRxbHmBW1nZArgKFFpojTV1karZ3t4PHtW48L+BFyfyRsvaHGP3+HvnjXMUSzbBOBnEhx+XBMfO9awSIT4RkcxQML+4Q9/eEngCh76pCfA7ujK/WS+4rWmotUFNz0qCDRi8JhjnGeaJPpmqyW7s3zDhWaypBexko6OAM0KTkVpcWYF6/NrMdf47HsCXrzexCYnGGcOmvYal+Th2RaIL/IDX9uShXv8W3OkMbDeEZCJApWfkzd9kvNZsJ74zneddPcbq2jzV9HAr8mUHmwuyVvkQ9eN6y+Z2nzQGMPtnst88mE7XimVM8jDfXzQN7vUCIdvgnvwWZt+zNsadwTGo5k9atpsJGiWxGE6c1/s+7wbl+iEJz9j116n00jKb/nUBGM9s0lih1phx97JFi5yvlVmZ0BOUUheq1/QKx6rTcTCZwLcbA8N5EA+5OAkSg0gJ7JZ8c0rjOocOZhNm/cWQKfqI6+Wy33qFXJg1+izccQ/5Ep5jI3n08/UG37zRWtcixnkxpbVqc+2n2wdn2hRT/kNEL8UC6zNdqPReN/pVxyVC/kFfyNbuvd8K7afgWfVAtfgg2pcBEcGa4ddx60AFFQo7hbjpDQ/aFT0CrozYflrHbucHEOgYwiKeIZBoZzVPYoOPKN8eCfA16sB1gnQykgU4Yznb//2b//JazqB+YKponwmdLjgVSz7DYwCb+KHR2PFiMzbkw3ZadDwqUgSGAUBiRNt1lWEH0G8CKhOCP7Lf/kvl2bqDChk/JPAXhej+8nDHtg1sOtCLmxB4Ne8SNzkLKFvATrxS//rSRI5s0E6VCg7NVKIZBfmKorsXq/v4XtG3nZ/8C6A0uWa6NBIzhMv8Flgc2KDvvCxL7uD7WqyMWu4p4Ghe8klu2we2uxe2xUVxPnIGTAfLdYS+O3qKcicbkng5LYFzwpW6FZ0WpNvK2IlTcWwZ2i7B785ZKCgUozgg5x6NgEvnZ4J+uxYUeGfSdfY0iGfsIM1bTV/ZMMSabSG31+6Zbtsi3w7faBfz82REMVBOuZ/+E63ZwAO66MFH/SWTR2BhEfu/BiOLYCDLLLTLbCRYgNntRVz8cLfNY9bNoluNitebemanOyUi7F7PJEV/6IHf4/49gwevqIIIW803ALWwwu+5Qv+iXe4XWgWk2yGoUfcUjBs6RTP7IL85JtpX/TiXpsJbSr5jn62yj7n5scK8GuebQJotrK7WwEecsODvKOJ4v98ln/ZiX8E2MlsXG6h0Vj88zPFNXnYcBTLyCi6t2w8vsxHA3ugO/lZnK2JuUdmRyC/KsbpdYsuYE02QNeaz1kLPAPIROwR7+RU/zCExsQGKVmQCXuUC8iU7RjHDj1/C5DnxA6nF2gVC+R+MkM/+jTU7EZ9pp7JN/Z84h5IFzaTxcJr/Iut/F7ueKbtwCXWqInJxGYb3+aT086Nm9e0cxe6NPbiv00NscxG3Z4tHgF8H33j4i9BUI4gwhAVfoIuwTIeyjkrIIqAy3wJkoI5g/nwWEtg9+qPAvWnP/3phR4FsrWMg8PunADNeQP0S1p22BjFpEky0mgpZuHxnDMJzhKP97YZnkAgMK4GgyZFSMUA3D4rAuy4C8yK6RoUF14EHrjxtgXG2EHh6JKlACogoFVRr/gRpGaDtgXwOKFAh4BnJ07SOAP4J2sFP+deT6a2gDztfisyjO+denSiVwMjgM3EP4EOFI2cld7Ii4zMl3y9ivHu3btLEA+HMZ7jTVKcNJpLdppdQUoww4ugSs/WsCa7Uqimx4C+27ENb7aKF/fTITwaM8nUbpgGRlFkLD2YZyy7tp6iwg6u76tdHQEc1uJnkhUdSRrkDZe1Ap+fEaysxyc0smyXz+Nr9adbgZ3YgRfUBWn4tmSBD5sSNgIUOuxDvFEoszm+SS50iNdsB8DH/zS9bNF3zybdjeFf/I3u7Phbl53ZPZSMrSnGbMWCAN50BCc/doKMbrFJXOjE7Uh2cLBRDQl57/me+2SnOGfrW3RZx4kiu2cjEzxDy1HjIp7yG7FnS+dkJK70D42gfQVzrI0Gdkv3W+MmoIWP+D0fG2GDZGcNskX3nh5AeuAbYinb5aN4QDN+nOSKOXTOprd0YrzYojBd+ZMDbCah01p4lJfIWwxlN/KHplqjP/1zgjXYXA3okY0dQfRbp5yJPjLkG48Aed/SuKAfHeSqiOMDCjC/Z5T/isHX7ABYy7XiJFvxne/bDJIn6WHLTm8FOmCrGr7VbwJroIVvs7M9/Z4F+OJPfuGX9EZmPoth+CMzssCnseyYPbtPtmyvOuhROVwD9i6fidHkpMCmF/qNTjzxWfmfHYil4rjajG+RNZ98hFZzrcXP+Bv73wLjikXqO58fWTc94EF8UauwRzqTT3ojwZgtsPbWZTzaxHcNOp2SKx9sw5LMrH8NyP+jbVwIKCMUZAV7wmAAEvQZAW4Bg2ZAdnIFowyY0ftMSRKXYtFakgElFpxcxkmeHIKRBO5zJMouWaY4fGiSFKcSEscXlHXsDNBzhZGghAbfp9I9k8zq2MlKonBqIPmgh3zQa220cFw7cnZxJq7APY6sgROoFOX4pAN40OC+ImE2aCvAY812FZ1iKXLP/Isj5uJZwYQXxn5tPeC54s+6+KOjdn4EEQmAHvG3ZSt484wOyZu84JLs0E6PmmSvdWjIyAUeulMQCuhTpta1Pnlxct/tqtKJZE7HdIc/dmf9CWzF71IE5oIO/PAIuOuuDv7xCB87Za/WsqZx2R5713zaiXFM7dk9voNeiQx/AjUfVViXLMjvGcGKXPkPetF+r58HaEEbm+Y/yRfe6PTX5R7d0f83vvGNyysvEoMdx+TZRRbsnM9MGslJwJcw3Z/rAJ/Rw6b+9E//9MIr3C6nnHzBpgqbdm+CueH0zFpsin2gFX8SGL/46le/ennVZcWxBRIWOjR24szkZwLdoM21l4DRxEY0etNegfHueb7XuMBLV3gipxXgQCN73/KjAA90yffafNiid4LxNiu+//3vX4ojdkCmGil2QCfWW3U6wTP2InZo8GxisDfzNC1+GyVOrPHDZxc6jROHVoAL3+2EdtFxDYzNjD/7sz/7xzxwRKe4x97E7Pi6F6xjPt3IJ+LAI8BO2ORR49KaLrGiTRoyUsTxJ/ZEPntyuAWso2BW2GlgrMWOZ0xxWevW9dAotzhBp98tgNvpgZNf+rpnHeOjEw4NkAZW7lYA2/gQ09a4YR6ddIJF3u6JP/RNBvH+KoBbLrMpwLatJ9fL/eoH9BnT5TnQ1KhF1BXlLrUY/vH5CN1igtrUJscWwE+f5Mt22Mmta8WLS3yQe8QH9ifXaB7LUc8A64mF4gL7tinMD9keGUfLHh/k+oxa4Bq8d40LpgUcCpKQOYZCknIE/HuVL1EqAO2Y9+oJXNZS8DnR6FSEsSsOFZzeM29Nl/EaDskVjoDhaLIEAMZqTkDR1hdUrcPo7JxxQOPM9Zdjos/naRwMSVFSs6UI9V6iAkgQQQf5kB/cgramRQfOkFZwT+FuLU4NB9kKBJo6uNCAFwUN2vdk7j79CCDW88oF2ZSgj8BcO9RoIXvJh5Nr7PbWA+gVcMkb7wKXgEbu1hRQJG9OJ7hOXQCyNa8iQcDWpGgC6J6OyVzR4/SFPI1nD/hbg4Q5gohxnrnoSTIiC7qy6yPow2P9wFh0s3N0ozXePaNzup+FnkQhYHpdCa30hVanUIo1QdJ4a/lLPk5n/DX+VkAPWsjWTiM9aS7ogEwULHxVM3Wkt2uAXoWtgvERPAF62SF8nWxMvPTgXrr2ih//0UR7nUui46f0KB5kR2TKNyTDqRfP6cu8xq98WNMzjQXZsWWFFrsy1/fp+wAevCgqbEiwNQUMn2Ff/I7O6QUe9iKO0tk1MFdiEhO36A1qGNj4tN/APH6gaRND2f8Ez9PvXuOCXvQowMlhC6xtd9oF3x5YD0/iy54uJpAvubEBvqi4oR+5gX7YO5lbly2xGXNWGaOPj4nNGgP6EVf5uN3qNTcAdJmHH3GIDALPyEohjw64zTeWjPijGK5Rctphcwyd5TljttYzHx82YtidMVt6PQtwkge74xuPANqOGhd04otfynn4FfcU4u55xv7oxth1/j0ARzGQH7INa2s28EzWbMK6t65nPJ41yxrQLaAf8V2uWvV5BtBuDT7qlUIxTq5XoLJJPpLMtuhXq7AxOac4Shb8gV2S+a18n4X0re6yiVBsQauNaOvzEeuvV75CN/gUz/HBP/BOl57fY/v4Z6doWOMdQJ/8wzaPZLsH0U/2bBvdXoHzVo94n7094rcrWBM+eMlUfFE3yGnil7X5mDiUjCdPbOOjalwYgd08yZghCuB2NxgWId4rBMZCyJK6IFNxkFIEWfc7Ui6AuxiG158EKXjcQw8DktBmAEEfA1NUeEaB3fcZbjtxftwr4BmbIbvgdk+h3ClAgFYNBKPRtPSbFus3Hy7jJDHJSNIk0xXMYfSCFnx4dI+MFB3mu2euQlQxn5GugGYBzw4U41b8tbuvKcDP1jxgrmJIAEGP0w+vu5GRYLBFeyDQKAisgWeXnRSBiNzI29rucTiFQ/qY4IjcD6rJS7NkLjkG+FZoKWTYDRtR2KId4I3sBBVJhyzSh8s4Tk6Gv/Vbv3UpjPCMluRiTfKSpKdNAH81JZo5NgvgVNgoxq0XkBcbIzs8CaaaNro0l714Hc9axsbDPYBmaylA+Q575hMK+Wi/B+iVrPpXxR4BMlZM8F+6QzPaXHj3nS40zXbVFT8Kbv7tInf2yE80A/RDdvhmT/TqR6qSYQCvmKJpNrY1V2BX3mnn52zUGvCimT2RgzHsQTOLD0lbEe63L/wU3ehg+8Y7AbMpIlFqkJ16HOnYM3Yh5io+2MSe7tzXsGiWNc1bwG7ZJZsoXk6AA51HjQuajLFZhPc9gF8SRf8Rj9bgBzYN8p89oCtFETkbr0ljB3QCj8/WFZ+szV7kKY0kHfFFtBsfH/Qmn8kJZOM1WmO3aLa+RkLjwWeD5OoEWbxiJ2xPvFUgOKkVHxRkcGe/YrE4YIx5cEYbXbjYmlxlY8UYNO/ZwDVIv+yZfh8BeGbjgmZyYKP44HNkz07wR27sBX/RH49dzwY4+ag4odkQB8VkhX1+Sb5n10a7UzWxz9zpPz7jWR6i2yObD6xrHrtim+gUG9iXHCZnkVm54IhOzzTifILt+R5+NmcTBv4zdN0D2am4akN7ykbcU0OI5cl7vQKf2RYb4i+aAPHUSRK/EiPIC/45bw+sR442TcXjSVdrsQu5d/rdHnhmHBrQQtf8HH/R6d6j+ftWQA9b4WdijbcN1OZyAVniE+/R/tE1LpoIR90KZg7BMFL4vUDgilzCFhgYhCQsAUkqguxcK7AmhShWja14Z+AKXQa00mW+It6xGgV6LiBZT9fN8SVFSa/jyvhzUb7Ahy40BWQjOJpnp4SzmLs6WDgka0UkGiZ4xtEVNoofRpfBCQh20DUR7uGTHiRAcpjrBPD3Xr3ARZaKcwme4XqtD64tcB+vkpMCGw48CvqKTfRMfUwokNnlF5ThQmc7tclUMDNG0t86xRGEBXF0bK1lPL0r7iQNTZkiJJ7MYV9Om9KnOV3GCTZeEVHcc2j0KGg8Mx9++sK/z9EeoBH97A1YQ3NJN+xigrnwGqsAs0NHDwpkutbYCYJO9q4VckeAvtaiO02n/6hUYpm03wrs7BmNCxr4HDta7RfNfMspoUKcXsmD7NPJ5M9lvqaGb2sO+A+bEFPofvLMNhXA/Hw2shPI3r+8x57EFvriS2jgnxomcoDbeuIGO8pfreGKPklTg64pgFuMUFCv605gR4oR9shP4NkaTw7455tsac9u4JNkjdmKF767f9S4GAOPf+Vrr0EC5MrXt052VrCmeGrsEViXfyhC+BUdV6it8na5rznkm2II2ycjBZG4Z93GiXGaFgWeuEqmK9AtHaLB3MBntqR5FifkEX7NFulv2m1/Xej1TP7SGIvLNu/ouvX9Jb+KozbD7gW4yOwZJy7ipSYIoEnhzq/w4W0CsmhDKr6zuS07fgUkZzTwC7qzgcM20ScGsdXkfQTGyKHitpg17RpudkmX1jrDH7rgo3M4bWSSnVhBvp6vctsCz4yVn1zFtJ6hR16jL/heAexYIyu2rGuQjRhG9miJn3lN8D29wSXeygPiLPzwyA9ndAYXPXlrwynW1JnP6jR+r0bYo2eCNcUBm17sh9/yAflbTELzNX29AqxnXfLClxpRTBJXxT35Sh1A/p5/dI0LZUkEjPGsg+4BITMYzta7vAK0QkHQd4JBAWvBYl1JlQEJ8ozGLoWdVDQqyr12szpQQJHWwoOCxKmGwo5hwytxxacCdjobQA9jFaS6757g83u/93uX3T5FDV7adcWn9dCHLs/xOB3JOmi3uyzI4M094+GXFASHdors8jFOO3HGbYG1FdD4hUPAxi8cii4dOlxbgDZjzScTxq/ZwjM6GL8xWzZgDP04nfLXdwHVjhc8aDHP2vgUWCRU9EyZ1BTUSHg2dU++gojdPf9a2le+8pVLUiYP+M1RqDhtqVBx3wUPHUpkik70WU/hRr+KHuuQIdokq9UWABtSRNEHvuAQ0Nolgpfe06X5PsOtWWNrElZ26XVJhY+EhkbjHwEythaa2Oik/VYgw0cbFzqUMMhdQ+I7efhL1nTCN9iEz+6R354c4gdt5CWpKfDYqEZf8wFPBQodWL9X9tKnyzO+yp7evXt3OVWmF37ArgR7/iBG0RsfF3esi/6VRvgUJ+zHqQmbRacCGV97uogOMlLQTxpXMFYhTl42fshyC9DHF/ZO3dxD21HjAthThcgeWMspmte62N4RwGcjiQ+RJX62wDiFpo2J/F+xaF5JecofHnSIu/CiVzNLdy66lGPsTIqjdCJv0CsbMn/KSQHVKZJn1mJT5KX4QYfGyIYN+sSCLZuY4BmZo4+N4Udc5hdoMB/fdOE5mz2K99cAvmc0LvyRLOUTemYv/ER8kUc1tfnslq29JVjflT3QmfjAx9HsEuvZUPl5D8idHvgQPIG83e8kjvTd+v0wnc+62KDGj57pO7nNaw+Mtz4+6GK1DfzITU4v2O4RffcA2viipp5/roBnNZpNXX6x8rYHnhmLH3KR6/laOhPf+SR55vtb+NyXS+V0+m0MWdjQsLnAB6NnxQE3X6Zb/i1W0Jl4ri6BJ529D4B+9KBZXlBPRLPYxkbo6tG3L67Be/UbF8bCAPaUfBYomcERZK9PSIYMiUNzNEZGAV0MSFEoAUk8giZn0LTYMZfkGZbA6f6eIeHBCY9ApaDVzTNADmYOR+comgdH+ZzD+vGKDsW3ZkfxYZ7iwu6uwC0ZMhKFvV0yyc6usbUEKMW5V6gUVdYpCVpHoseDQJB8rS0wwU0uvremgndvhxBea+EvOp1EdXoR3/jZArQJEHSjiFKk4QtN1sQTXFM2E6xvvQo04xTqZKqYm/zRK0eiw46aXRI6PStsrMUJFQiCsF1rO3sSsWKG3P1VbAqg+HWRP7klZ5c1FXFsx86+ce6jhzysBy8dKhSsI/g2xhUIxopZumUzaMO3o2P4JTS09RoT/Nb31yXASKQKgXbv+YUj7Hb25nr3gHXwwm8ewSUGPNK4WJvN4VNjl/37y6fYP/36zCbS2T00myOG8JN2gMma7OmBnCWtaRPsDW8CPRuQ8CpM//qv//oSNxSmkhWZmrcHnolxGie+W4Jl+2yS3Wzx5R45sykFpnnu7cmAfWSv5u2BcYouNroFrXutcaETcmBLR0D2fJRt79EOyKkNLD5AR1tgXcULmYiVZGnTQJPr8tmYo7XoWGwVS6zFT20SdBLjsnGkedA4GI8+OMmkV/asg2a2amOlHdejta+BuexP0yI3KC7YLnnglb3IG+KhmHpke3vABtjJMxoXsnAqaTMKXWLuKwrjVwEdyqtOxzWMYgQ/pQM8pPcV5FQyNNdzl8ZHbmQTK3gOF9mTEXnJc+WEow2MM8Bn8cAH2MqKy9piiHzPvtnSM4GPkInajW9tAd7FULXbLfaxJRexGx72R46aeY0gvqa/Bj7LJXKL5tA4Y8QlsZktTzDec/YBHz2zD7Wc+oKsNeaefShAL+o3NkdmP/rRjy62viXfZ8EXrnHJkSQL76BL4nY3JVQJSXLS3RK0YkNi8K6s/0eld4LRIQgIMhKMy1y7aP41IMXinmEpVBmxH0oqhAWbjDmefJa4jTVOsdIYF+cRqBSriiz/mSBHaKcNf/AKSgoVtNn1NUfB7z9BxBOHx4tilyz84JijSADRwumtgTefrU8XCj/NE7pWPfgukWtsyIksjCMbMuOUnFHB4/4WoEHybEfCLrXmwn10+O4ocq8AQye+eiXGd0FCUuZAwH20kZckbdfzr/7qry76URwJtORPvoKMgpIsNDeCEb2Ts/no8tercX5rILhJqByULq1vvXjx7jn8s+Dwl2zcg9t8Nue3J4oTz7sC62u6jVEMCWrmshHNMfkLsmQJl98/ea1EAmPzdkDw4yIrY/2+wmtU3/ve9y407hVzZ4GOP+/GhZ75Njr4PruhM8mbncDpOR1ahx7AI/Say1bpRGK1hnWtoYhXjArq5Mt/JSeJCV1kZq5ndEl29EeffCs69+iDV4PG1o1lf/kyO2aTe3MV1uKEeclhD+C0KcJfjN3DiVaJlz1tgXnGnGlcxCi8HQF907PiHm78b4FnxoqFcgsf2OLZODLlYzX0xvE1/Gv06DY/dwXuGZsuyVeM4Gvis/vw4Z9u8W/jIXskC/4qBhiL/14HY1vRMte8B9DJ7vDBHhXT4p78g3e8skNvKLDRW9dD57MaF3rQRJEXmaA72X8owO7wQt6KWvFYbC4WZU8TyJ2dqk/MxT/bW19FIgffPVdH2EylN/FO7mJTz7AbeNi/DcYt+ftON+IWvujqWQA3GbGD9fW5CXjEt3rhUTvJR/DBH2wu2SBRR8r98vEqV34t3mve0OuZzQlvscA1gU3wNafX6BUHyJbOzKVzY1a7eJ8Bv2TAdtV/3r74dOJyEoxlJJSviP/d3/3dy6s9HLpjW92gHXM/RlTMcEgGJ6lJMBoB82egDBgTJ5ZUBB+Gx8iAtT0XRCSrP/zDP/zHV6UkpulMXcbjWfJFS6cyPXPfrrj/S0YQQxMDWaHxaLEWZxPIFOheLfN+tSQlAQqcnFBDgU/r+9sxNFwArnZ5ujcBL5KzgGJt/HE8r1ORgecKUIX5XrDhvJpGRT4c5Ep2Tk3wiS5FqAbB85UOa/Tag2cudOBDgYgeMoNH0yJBK5rome4UCv6hBA2GNRQcHC8drOt1j4zR6QRMA8BmCmTsg6w1Q3Tg3sTjM7qNFwDJUMH7R3/0Rxd9k128uuhHQNZokQu9TBuAy3f38YkWfChENM0CqQaFvbN7zY3iUmHhdwT/4T/8h8/+4A/+4CJDfG3J+QyQ++fZuKCb3dsdU+wpCshWE6tAp1/2ZtyeTtz3HC/0Q6YuNsQuyJd+2BVfckxeQ+jETcyhd/JmX3TgMzoUHYL52sgG1raOcXRlHh3RPb+wFrvFAxtDh8SgaC++AHTaMGDveFnBOnDQlTHN2wPj8a0Il5COdIsv/kweW2Au/Z5pXOiSv/q8B54pBsV0Nr/F7wT0yQNyjLW3eGEH/FCTNvVE/op8uaKNDDqgE8/YG10pMBU44qANGXa0RZf1NZhOZMTZfJXdKADlAzmGTWm05oYb22OD1qcbuNDqIl9r0uuWrU8whr2wSSe3cqFNDjK1UaIBEU+uyXWCtZ/RuOBj1gIfKqC9i7zpiEzZl9ec+aFcTKf0SNbGKJD5Nl/13AahMemX3tlCb3/Id+yvopodB61/D7AftiYe8ck9gN8mGn7kv2cBPti9DUfy24sH1icTzT5fIYN7eZ4Ah3X5uxoMfpdNXXGZvOnEGPrj9+glC7lITTd15j59ysNkyr/J1Zh8dV4fEkRzMUAsfCUPX4jGxTjG47UbQdMP/J06/Lf/9t8uTi0IKIjsaDE2RtLFOVxzTdd0/qB1JH0NAHxwCDgSjOCvcJIArKkhUORqYMwLd7isweDxbbek5sUlwfvf5Bm4hCmINXeFcFlDQJN4OIXCjQFFIxlLqBoOdEm6TnV8FjCTgyKKHO16rWAMPApuidZ4uBWHighObgwZWR8vW2BtdNIZ+uhFo2NnBX70CMwaTs9W3q2h4La73DP3kqfiCG7FtGCD73SOP/KFG+9br26s4J65Egi5KCwEKjLAI9nbTZ+vYK24fLaG4E6+dpXRIFH57ISrxs1FhhoOa8AP3xHA32VsF1wSpnXxLagqDBVOP/7xjy+nc+gR9NF3K6Dt82pcrKdw5XdkSDf4o3M+gLbkmTymnComFNyaDQWAHTC2qNB14Yud8W/xSeL57z9fS2whR0UtmjX6GgprKyhsOtCfOJBsW38F99iMzQX+SQ5w8RGvF4kn/W7CPXQoMvEVSI5oUZBurcE3FE1OmouDW+MCslFUa4b4zBF4Thbi1BZYh37PNC70gUd6Ja8tgA+fcNEVfHuyBZ6JTWRJx1t2bq5YK34kQ5ex4iqd8lGvndGFz/QOJ13RnSZAkXPNj/AJp11XPihX9XqqzQUyh9tzucFfdkQuNqPErDYh2Cidki2bx5+GmU1rcPjAnlzcpzu7wxpPDZTmjY3zKc3ZWWC/nxqXfcALO2QbdCJn0ic9s2N6Y/v8TmwR9+Un8jRP4SsnikVsgX4U0PAV354lr+yi+HfN//kWOtngVu67B+AQ9/g3Ho/weW4TgN+g5ZlyQIcLX/yKj5A/n1eDaO6s7zudoJc/ioUu9YBnNkvZtDxcPo9Of59F8+cJ+MLjo7XANfigGxfOKmFxXk4jmEvsdpE0AQxq7kpVxJwR6DqGYcIDr8RhF1zAUSgJ+ujWBAg8DFzi07BIcnZMvFKkeBEAcgRr+KuIwLvkpOkRnBRhigaJVHGraHK/3cUt+XASDiLYGKcIlowKbsaTsyZL8EO/otVzzm4MevCp6bCzsALeyBc9eARkK8BaF3/WEWTJyPgtEADIzNrG042ESYZw+K5JwPvWMTE67W7YyTbfd/STpcJBESDh41OBgEZA/+Sk8TJfYqdPicOaq0yB++Sp6DNW0aJJwbN1HCcrdtFiJ5WM0y/ITtkOOxDczPWdDPHGVslPA6AAsoaCSCGEB7YEB1z3QPJBmzXJwJqKdadD6JEk2A+dkNeWLLaAfD6PxiVdKrK8pug9ZzYlcbBxhRufs4uNR75ExmwKry52rgCV8BSC+NfsZ8/8F00KQnavmNAQa7w1u9Zncz4r8tgCn7Nr/u7du8++853vXBJWPjjtYgXP2rlU1MDLL9koPvijVwsUuuIOu5m4xB7NQ7YeGENW7F1TR+dHdLjvOX406PyHjvcAbnRL5sWEFeCk32uNi3U9E8f5/bV1jeEnaPV9jyfgGf3RDZkavwIdyyX0arzLOLJmC/TJ5/l58YOOiuGa1z0ZTGALbMhmzze/+c3LCajGSCFrI8MJV7jlMJ+NZ3t4YGfyDnkqklzkynbZMDrYtJyYnfsr/uBDkWcdJ958hX2yC3bP/myY/af/9J8usYpdofcakNGnxuU6xBPbZjc2QMWsNkfEG7GIrzvZz2c8o19655NsA458+Zmygkv+9fpqr38fATpsDuJB3D1jL0dgvo0Z8UyeurY+esVIuYBfPlMWATmzcf6NR/WZPM3H5Ar+5HTIxhUf4GeaHP6otnEyb+41Xj5kYAf8Vi3wSvjgGhf3KJ6AJBcC4uAMRVBnWAxshSOce9BanEigEPgVw4K+otOxH6P1esfeqxfoUXTiTcJk6Og2Fm5roFexboxTFv/6jELLXGMUzb4rmiU6CcZ8V0ELjZK45xKc+wqAiuBZzMAp0ZObUwMNhsSn+LCmYGrnzzorSGKCg0QXvwKoRquTAQCH0wd63QLNBXkKSkE/QK+5w5NiU+NHZlOvPpOLV4GSBb7Zg1dbvH4lieKzIEpOcLOVXgcBeJEgpg7hJyfjNSnWkcSNQUvP2ILX8gR4uy9wmtt8axuvWLaLpuAUYI3zfNojfIKh100UHZo7OhAU2Z3kJfDBuc59BNDKVuwEKVwULeikQ/Kw1hEY85aNizXQpKDjf//u3/27z377t3/7kjTo3msYdrCddigG2Sb62BH+JBG60ghINhIuf5aMJGl40XJNxukY/3xc4yppsVn4/eBe4kWjol5CNda8LbzuWVejBQ/5u+eic80T3jQfbAGeCWgQP1efM45t8S38HxXWrcXP8eN3cWsjtIL10MZO98bG27XGpfXFMkUauo+A3sRfxUG+AcceWFesU9hv4SbDXs2Bpwtedqnwt8GE3+zDMz7NZ/mQ73tgPBswni7FKP/4i1gptvB/cqdjOQcu66y6XsFzshdr2LBNKwWxHMXG2TqbFPfEP7HM2nxDw89XvDbrrybKa4/+76kvf/nLlwKM3KIDD1sgd3xqXO4DspW/6MpGit80imu/+Zu/ebETPmNTUwM7ZeLzvJ4B8KBHDlBzHNlzYA77k9PxwBYeAbjEKnbAHs7wxvb54PTNZwJ88+LH4iQf07B5g+ErX/nK5RVs+pPzbYSIJR8LkMmnxuXn1wocQjEqASpUFOUKDYbOwfYC/Baua5BhSoqCuoJEkV5DgWaBROJUgO+tAY8kCReFKqoUT+Z6Zh7aFchf//rXL6+69dsP/FjLmpKHYlZxptmQtOGFw46vRqSmBsBrjB1JRVkAr8SpmLOroQFQ1MCLNknNX/cnoEWhLvErEuJXwLAzSw/JX9GlUXMP/SsILtaZRZTEKkl2Dy40wKPoQ3drekbmdj/tajg5UvCTq/uKP7bUvEAQkZjZS7SSF/loPhQL1iBrOoBTM6gQcI/O8eMyT9FrF1YDKID1jL7QIfgqYMls2qk1ugL4FBvk0r98Bw8dkwM7tI7dObbj2bMAHda3prUU2+RhZ5l+PZu0TvDsrRoXsuOD5E43Ci0nHHYo7USyd/TSFTvyl/zwNS9rucgQ/S646Y5dpJs9foyBn93wLzalGbe2HWyNLvurARE72Cq7RL91tnDDiyY2QN9owoMTGPFOk60IVRyKJ9kw6BQQXxPg4O8aZzTPORPQY66xmldF6/e///2L/PbkAOCkf4WVtbbAfHzRrwYHzj0wlv0rgosFe2CzBI3f+ta3Ls0hWdPvHqAPThsz6F35IlO8tGvbZZ4coCng55oLf/EEn8KyU7A9+dI5XbIFutTk4JP9wO20Bf182wmuQsguO39cdboF6LQ2WtkzWlzZumv1AxdddLFfMQiNikAyrbGSe/C6p2NrfWpc7gN6YxvsSw6TM37xF3/xs3//7//9ZfNFvGOb13zxGYAWOrCZZzOT3V4Dc1zsRuwy/xFQz3grQiyC9wywefWJGMiPztD9CKCLzdOL1/zI69d+7dc+++Vf/uXL5rNTUfI7inVfNKCDT43LPzioQEn5imwnDwoVO1KCvvtnDfsMlFwUJIprO92KuE4jZtBAFwfzLzkpKI4CChqNh1tx1W6XwhAfinY7K5IhOdjVkyjIJf7CoQBWpPQ+tDkKJUW/55MOiUbnL6DA5RkaNHycjaHB6xm8dn28pqEgJO8AXkW/YqqEHWgkjZcEA+vafbHGHAusZ2fRLsV0akUI/AIBQKvnAjl5SehTFhoDCVUTS/4V2Gj11+sRXq/zGS502Gm1I2TMBOtoLhQPdnvtemmY7JiQi/HWhMeFP7wJUHgxlz4VpoKtwkPDI4izHTSsgTRcAfoEOzSwv+zNZS754lOj5tSL7dCRcSs/94K18Ik/BQxf85sLMqN/ckLLpBtvr2pc0GI9z9gZ+fJHJxlo4n8aAXQl465kdy9N6zzfrcHO+DB/Ex8UrGwRDfTRKx0KYzvXZOnSfKCbLypQVr8I8MzWbUTAYR5fdxqAV2vwX/qZepewxYWJ13O4rGtHf08W+ILbWuzWTivf8q8RopU890CRoHEzfw+/++jiq+gkqyOwUSAXoH0P4OSDCnw26pU8+iCzmsMVooMs5BBNwRxHTxoJzSc9ADIUi91nb/xQvOYbdGI9/sjPj+TruQZHM8B+4PGWgLghr7F78bxGhU1pAhSuNpbkCbJGf7RtARq6bgHj4cWvNdBHHnQgDpKtZkacJgc05mfAnE+Ny3kgO7ySLzuy8aJYZx/sQPPC/3zmv+I+/9RUy0nkDQedPVNObJW+5W/rHNnaCmI0e5XDt/zvGuDDPLHdyeeR/69gnhyFbpuO+Hg2WIPcxRcxCp1ipU0hNYATag0MXbknRqgj1QTyBn1/kW2azD81Lj+/OI0iTnHgmQJJ0CegZxuAtRijHUFJRNEpSOw5LucUVBQnDHUW7iusPKFfsLIOQ69DlzTgURQJVnh1b/LqMwfiCJxFgPPeu6J2BePgUMR7Dpd1FX+alC1QVCjwFf0BR/WahJ3GXsmLJgWUwooOA58ldwWc5NdYf+Gna9eUGVlrGObRKjzo/dnPfnY5abAuIENFiyJ/Sz/WUVB4DUKyJ2+nUZoleltlCq+GTKIgz14lm+P8dblHNn5/InCZ62I3TqoUnOxBoVHwbO4RkJPEDwdek9uc5zOZCMwCtPFsxUnRM6F10W8twVmhR2e9fhddxryqcWG/CjnFPho8U5zTTXp/ZM1bgD7om29ISIpQtKEDnexMjBI3+Isi3b/aptnCm3F8UIFNluxzy3YBfHZZxQa+5ZQFjp5pXNhg/kAGNiic+mRzwCaFRkHB6fOEKTf2Y9OAf3eigz8yF5daewsU2E7n0LUH1oKDXbOTa42LIl/hIh7vARu0yUDeNnrEA0WCwk9cbANkBTJXkPFXOqLXAF3kKBbA3z2xz0YBeZvvnrk2jBT0Cj3PtmzReLrkq/QgvhlHXnA6YRcr0aEBslaNFxr4G32wOfMVr2S5tdYzAN6ti8zsaPN1scdn9h/g/1Pjcg7olh14PVouFtfEFbmOHNmBDTFyFhPFO7FH8+zi/+yAzbAv1zNkBYe6guz5ALy3ADrpn2+t8eYMWJ9v2TyVg4/izh6oOaotnm0/GimbDGIEPcjzfJseNJ5iNf5tNLhXzVE9KY+JS7fK9UMBuYftfHSNi0TJ2BidnT5J1z2GbKengP4sg2RA1lZQMDAFBaNT7LersYL17ThxEIWzAoQRa64YJYOF07g9kJA4tqCkUPYaicLdHDQJHpK85kWjYPw0dvwzEjv7xig4JBRFxCxcjMMDp8EXvALi/I1HAL91jPFc0PRZgyO4+i5g+sxhObCkaqy1yQvf8NKTIGzXQUEX4A+NZK3I8Mx4l0AuYGuEyFcyt3stWUvm1veXrtBlZ0eRRd54xmsXcN86CjB00pdgb+yUpe8SMBna8fKaDBrQNMf5LKiiyzj8k6371rQr659I1mRVaM75E4wni2TFbiQxRZfgJgDSLRtZcfhuXTIQNCUJwUID005+MngU4CEfAVtRXFNvLfrCo/We2biwgXQiJvAxcYDuyCmZvwVYix21Q6+4oBc+QXd0iGfyIQMJja3hxckFe/W6GB0nSzbfKaLmg47XWGGcXX+xQSPLd8kj3UqSEiQafOd7fJMNmuseGYlNbIrcKsQDz80XV0s2Ghi8+a4pIHOxgwz2QGOL1xX/BGuhwet9LjI6AmPxo3jaAvyRm+aBP7IPsidbcYeebILgZaUr2YjxdDA3p8gXPvadbOHUNHQK6kK/nPQXf/EXl/8DTIwWG4wH/tKpufKC+XTNTtATXs2ZDRC4+HMndnTrOxwuvNq1FRvwJieySXo/kvszAR1kgncNl7jKTsQtMZ2N8NVPjcv/H9gXPZER+xQXxBPxVK7QqNOxi9/LRWK5HMOO2YJn9M1u2UEFsVjCztmfde6VmXkuMUQsg/NWXHhkH/Kgv/fM50tqjOqhW4F8bSiQxz3zJ5AnndGFeozfiUvkb2OSPviEy6mzeEV+6Bcv0EBvPosrYiy7VougE+638t+3ALmnXPJKeG8aF4oX4DkMRStgvALAOTlqDcuzoEAiICgK7MwLlhkTyJEn+I4WhqcgYLzoZax+68J4FfWag34XQZnW6vIdvwoG7zpzAruwdjY9QxteGb2g5DSGE08D91kik3jN81zCYzSKfvMn7caizw6AORVe0eYz59JMaBC81+xH5xKR9681J5xXQSGwKmKd4jhdQB/50ZkkZreT4TqNktQl62RpPfM1W3ALAuSvEBMYFGmCjlfPJHW7qGgnc0WVpOi5APLVr371siNtnF14wb9C2jo+oxnf6DLO9wA95IQnjaNgQ2d2Eu3gsoPk6IJPkeaVvpqWwDi8WIfu/V7IWHPSe1e0sTXj8eI3Qv/5P//nC48KGu+/s39ysQ78E6LJfXRqtBSY9GAO/J6xpWeB9azFhhRPGkfFmOAtgeLfmHsBzQpRRTB9si3FqPX46SO4b4HkSlfWZgt21e14sh90Rgv58p3+1SZ26jl98EU+1W/P5hxxgf/7rZq4Z471PHPhly4VhwobzZwCV2JkD3zI61EVrmxJ8d6pKvo9Q1dxJUCHNayJJ69E0qM13UcXm+RP+EEjm9wD/o4Ha2zpyD3PxDn/6anGXlxD49Z4QD7wikdbgE6NnE0NvJMzH6IL8hEDxXPxpBPidT22TGaalHzZGD5oIwrv1qE7RSZ5mON0RQwiW3HQ/ArNdIhf9BlDd/TVGsA67sEjTsAv9hhDh/CRF37gCzy3YaBx0BDzO+Ot19qvBPJzoV9cd5qIR3yIiXh9tGhhl1+ExoUuyIlu6FEdo2YQN+VKdsmvjAnkfDFPDuK/Gll5VJNK98mfjDQGNibkfZsBciOdGFcMuEV+6ZVvOn1l//fI39rih3x+qz3ii93z63ttGd02CsgMvlshOeCDjOlDbEETefNrzwKf3adXOiN/NRZ/sD584aQfud1GhXjO78WbdIbne2T+vgBb/+gaF4lKwazowzwlu08Yz1QofIpVydp73BoNBnom+EvuinhGWjNhvKSm6VJ4cTzBHG7/K/u7d+8uyZ/hS8QCmMDkn5v0WZLWAHnXWSCDM2PHPwfw43MJ1DPAQQQsiVew4yASp8ShqCyJJjN4FFaaEXQqbo1TkJG5olmzIODgT1DlsP51DM8lVWvASU4czYUOvOGVztAk6NlJUnRpCHw3luPiVdOnmPPcOM9rujRBArBx1ksnzZWwNStkSn+CAHo1QV5XIVP0OhGB1xhNoQZKICWHAE3kpHDTtGRr1hV40OQ7GfprLfJYX51Bn+JJgtEQKm7srkgmCkN6s0uvsZDgvVpIDxogOqELxSn64WJP/gpo1vMczi3ITsgHP+bgnYzsmLNzuJ4F1oIP/4pFa/EF+rfJkL3dA+SvCPR6lcRDltkA/t4C0M8HFZX8RPPgVIuPRcfkUZK0e8rfFcvGuMfX6RwujTlfotfiWPqiHwWoC7/mintskm+z+Xbt2Bh/RRP7omNFLLmxH+tUbJCbAgQOPjpjGv2hS7Mh1rLPeINLbGLL1kUjX0XTFliLf82Tngm+W8+OvBjGz9iLhK0o27NNaytexdJJe4BW8xXuaMQjnOKruea4L47ZuBCXjZnrwUGuZE226cY8p670abwiE91ws3NxGl6ycpGNWMO/FTV8lf04NeUTdEI2qw7kHfGPrvAhVvhsfZs14qomydwJ8GRn7EBRxf7kIzyuOngFWIP80EEG5EFm/qES8f0RIIMvQuPCtuQXcZgu2b1Gl97wyAayuYBNy2XskXyN4ddyIrsy1mVe8oeLDRrDx/iBekJs8ewsWEsdw2Zno3QrwCM3sAfr34LDmjZkbJ7cC2SCf/l1L27tAXrJzfr0YCNa7GDjnsFN7pMnPmejYb6lIR6Li1P+6Q2PLnHKOJsrYhA7UYeG40ME8vnoGhddqMJVocDpGcjq2PcCXIxIYacRUKBLFhKugMDZjsBzTYvmSgCSmKZRClACRl0+nIoDyY8TM0pJilPacfSvh5WoAPxosgNP+QG6yUKRYa7iWeBTzCruGHrAGazV7jcHLPhwEnP9CzwKHoU5etBgjrHoRTea4IWDYyliOJYmi54CeNFqd9IOOfmg1WU9dMKNTkUcGeFPQFOkGeNqDhrwqeBCQ3r317qCid14jQp5+oxmY+GxvsKBbtAtkGvknBgpXuimoIButKEdfzPh+0tvToUUBnAqIASxfvzcWGBtycVYtNAZWhQTinBNkyIYbxIX3uFMXmhSPCpAzAXwwyWBoYO+8XotoLEjcrcTincFFx0rHNH9rICIPrispZHzyozibcrlViAHzaUTDrKho0fwnQVrkA37IzfFvkSkYKUndKWXAO98hV7oVtHonkvx4Z7YYq7dOI2J79l18oOXDMUV9uy0rsDPJj1vLHmwNa9voe/dz5t3PspWJVd+Ew3iHPuzm+87HHChh005fYQDrcnZOD7KZsjBfZcEzPe3gL1pxsnJZ3hWwIf1aqQ0dYojNONnaw458ScF7FYxjhebKWzcZ+PpwEYJPQb0ahyZkhWfnzowV1zRnJmX/DSI8Llnc8GmAv2QC59NZi6fFXwaET5Ah5obtky3jXMBOmAL9Ef+1uNDduM9iwbx2q6sU2c0r2CM+/TPfqwpptM5/4nPtwBytiY7J+dHAN0fWuOCzuyQPhSx8pTcx3/UNOUq47aAPm3MaYBnwc1WxBM2ZswqE9/dZ5fytrjFt21Mwsf+xaryVtcK7Fh+UsewW3RujbsGaDHfxt9W470H4ofc7fVy8+8FNOPVpgzfPuIBrcaKMZoddYP4x9dtmpBnOtvDI3fLWeRvjIv++C7/XuNiY6xd3SKmq0XYvFqJ77Oja2u/b0DXH1XjQkGKP8X7hGcoDA4JCH5FvUQ0f3x5BhiRXVOB2W4Yo5sg4aMd3i2a3bOeEwCNj6TFUSYNCgrF67pLYC4Dtxttrr+Kf+Nzirmm5CyhS6QKVs8EAoWMQMJRQfPW+Z5zeAU9WjikxKhAmYGADOC3joQeXmCM5+iTyBXvigH/vwonXWVvvPmSLjmjNxlzXMW35kkwQY+xXqXSBKwQP+abS3aCuaJCAHfPPM2bIDULncAYAV8Q5Yjkje6tIAyX95CNB9ZGI+fVJNr5wRecPXcF6LRjqTiCfz73XcMlESmE8HIW8EpuEhFeFY637kCdAbqQWO20Tr5uBTFAMSvRP4LnViBTmxl0KD6goSJ26mICvUgwFfXpFrCTfM84ydhOvUSmoMmuJyhYNfVOaNmLk5QjGdCtJKtQ51/800kJ3PSBByeaxoXH504gNRDk7Vl8mqdIJgOfAfptKGnQVjCHPdlQER+LRRN8VzyxD0k8uiRm88Sq1p9gXT5h80ETtsqMvPEvVpjrQrNTDvJfgS74v7jIJ8w3B16+ah32Sw/uK/rI1aX4ZB+e7YE5eBLnbEyRo3srWJfdOI0V69mZceijSzLKlshA3Og1rCPAh+ZRwa8AkuPwCcdbAflbmywfAXb5oTUudMZn0S3Pird8TE5In0eATzmPr7G5/A8oqslVbZHdXgM2bXNPfiNHsY1fkO2WvwHPjNdwPQJwszu1hrXFiDM0498JProftVvzbXyIO2vsCNBETuKTjRUy1jT2Ou6EI/rFM3qfc8QKtQo7qJZZcazfyV8ucYJvHjuSs8XWPR7eNyD3T43LA8DB4ZQYJEsdsUCiwNbhMrIzziQZSwiSnoRuF26d67P3jO3aMX6F7DRiz33nIJKQhILPtYBXQEmeJdAJcKCDUX/3u9+9NAGapQLRdAxr2Z1RsDIiSdGur0KBo14LpHAqVMgLfS7ORz92BRVf1mCkdoPsoJJLeNGiSFJwKADsBDp2JR8BFA48tvsZ7ebTl/EVjuRgN1LDRn5kZJyrIBvfWxBeuyJoEBgEUo2AxoxsonuCdchQIeR/Zcc7Gaxj0acYdJqCl4Cc8aphUlAqVDQgZBa/LnM0sOzT62PmeR40xo6dkxdrGXMG4LGewKeQUlCwYfTc4gPXwBrPalzI6y0aF7zTpyRlPQmLffUPDky/XIH8JVdNbQ0pel2KDElMzGEbZKNxYXvszRzNv/XTs7/m9UN+McLJ3moLE9yHg03ZWZTwFQroVvDzdbuFvqOBzUuw9ITnmUyTNXwKXgm3e+biFe0reAaXHXb+nhwCa4uFmgu04bHGhX/jUSMRLRN8d1/8EG+sNQFuOuMPk1Y4xbyVFt/teOOF77NVMrGOeehJNsYqerySl77ocqUxMB5v+MKPTQ6f2VEy9tf62beTkV5l8Rx+GxMKKLIE5tCJXXB+6+8qhwnGW1Pc1dTViLMR9B3NfQag9WNpXNCFXzbK3+RAspYnxFq5g07ZxhkwTh5QF4hBcx5bp0OyZUOeXZMLW6BverfB6TVHfohGjVEnCXDDBaecIE+h/VG5w6cmoEM59ww+dNqMJNPJ/z2Af/USfm00k0V8iqt8pNN9p7/8RZzme429BsU3cUh9MWn2ma7wQw7ouYYz+tiNDQx1jxpMvST+qjGj730FtH1qXG4Eis9hKy7sYhEk4z3b+QeMiKFIMgoKgUWwmgCfewpLxQlD4/wV5YybIQo8/oWgunkJvMYg8BmNEma79xPwxBElc0FMIwAv/K0F0OSzAKRrx79Cxq4dHNYnI2NWh/LZGE2BRNkzshBQ7AiRqaBANgoFRYDvcMItAErc3rf1Fy+SP1rIRYCS3OxC22WOJvTAYWe6wIoG4yoeogfdAo0GbgaMwPPwwaN4tz76vTrmGFuxMqE5aFFoCTrkTPfm4deYwGfBSSGE//nMfesJPugmJ3LTQPqOLn8VywodOyuKT7ux6F3BWAny3bt3l6YSjVNv1wBt+NW4awwVZOw5u/H8FnwT8PIhNC7sJBuVTMnAKR6fpD/3j4CMyIotem1QTJm6gp99i2H9SN7FThWR9Mdf/ZPb7KEm3F869R48X+nk1Vj4p12tYC6b8Spk71izb3q2tvn0zsbEAQnbmFW+0QEPWQRwOElA/zrHeH4h6ZPrtCF/raNIqrFJv+Keub4r9Nm++St+38WhYuYE48U/RUGAVvJni/1gfQVriotitVjaqRh6+Dw+8U8Hv/d7v3fZXBKfVtoCPONTsweneWgSl2wQwIsuY/BOx4ocl3hQ80T+iky/aVO4Bp7hXezmt2zO2GtgDt7wpBHmU2SCf/Ts8fMIWPOL3LjQBdmxFzTStbzqVF0+p5utJvwMwAmHzQv6XXFYzzrWNPYWuRhrDrsWu8QnvwNV14gNcLM9323Q4uFRuZsPj2ZJbLtms8bLl5oJdvTo+gBf4g8a5GPf1SZ4FF/VAG1k3uMT6BR72fuWzOCUE8W/e/AbjzY5QZyVc2rErE2n8LKVZ8jrGYCmT43LjcA5Ks50qTUbU8FngSEwcsFE4SwZbRkfnIpiSavApVDhGF7fkCTt3vlBtsIFLQCNkjacAdwSHOflXMbC72KoAgDDtZ5x8HkXE6/wzSNFuMxXqGiEvL7AiSRiBY65dgvaeQ/Mx4Pd/VnYw0e+EjGjJBOnLRIMPt2HT7LWePSeLtxotabXZ8iDrtEmQdO3BG7n1HfjJX0ylHSdeMwiL0g/mgY4e5YMPUMPPJoFuKz/+7//+5dXP/A4ZW/+//3zwK7ZxTu8ZIdWOBR+ZOZ7QFZkym4nLhBv9AI3msiGLtADNznSnWJLcZSunKStMPWpMFXsSTZnwXw0woEHxWD/67xTA7imfG8BON/3xgVvEjfZ8yH6tDPKrtB/hnf0KeLt0sHDVvM3AI8Ck967D6+4oACXgKzHT21u8BsNOl1qkn2GEx4JVePPfszZA/gVqJKw00cJzu4v+2ZT9NwPTNutQ9uUr8/8Rdzht3M9+Nl//jz5hQ+viqlwdpmHH/ZdsU1+1rCZkT8b07vgaFuBTZHpjFHAd//kqhgVL2jAs1dpFRNsumcT+AHaNVByhCYCPWRgHhvkm4pwfmv8Fh738KTB1ASTg7FoIzNNCtrxaMeUb9cgG1vjAo/LWoo366/reYYvr5CIa2R3BOYbQ6ZkIgeJG+azYes+G/D1RW1c2BYbYcttMogh8gXd4J2syfwemulITJBjVv8E7tnkE6+tc+sa0x7Qym80SZohsYmd+odt2Ke1ngHWEmPZvpy2Z7PWQ48G2/prLr0XrKdRUyOpL7yBImaIkdZjZ2RhnOtWvjVDTms0L1u8uWezQL64J7+ms/I2G7HRKY7YxFDLqR2KpffY3bMBnZ8alxPA2Bif5OAVLM7IUCmYYXl2KzAUhi3BekVJwbGHx1iBzCmIz+hhRIoJr13YFVQgKpzxmHNwZK8KrE7qu+ClEPG3QCO5ko1CxD1G6i8e7Xja+dHZM2ZrC6YFBK8fKNoVHwKfnX2Nlu8KA4UT54PHCYcCSIKDewX0Sfrm2a3mlIpWMrCGxKsQNsZYdPqrySEPesrJ3OeMnuENz+hQTNj5hFuwrsmYjumzQsMukcBLvhoc89FPdwo4fGpCFMN2XtBsvHXJj5zIi70IsJxOMsp2rENvTk4UJxqHaNcwCSIVHxMkODLUONODS/BSLClY2AV6pjzIDA2KTPhXMAa9bIGNa4TIxvwzYH4XPOjxCgE6yYvtkCm+s9MzQI7vY+NCLvgkYzpFH/snN82D9c7KDo+KUXwqAtnPlBF64WNrfCiAX/xgc21+GMcuNFBsnD3AbQ0Al2SkaYHP32xxBffwiCd+I9bwJxsFEnSNPxtlK2yMbSt+jaNzfs8ubW7gzdoBHjXh/EzBO+0SHn7L1tExL7Yl7vBF+NzDA9tma2h2zzO+JxaxvRXQY7y5AZmiSeE4mxO0wieP2FFW6CvIFE8aQXZQQ2ccG0APuXnVEx9OTujEyWbxN54m+O4ZXdtoEZfpNYAf7eRvI8QmlfXRiH76V6hOnuGjN82e+dO+fCYzNsxf8Z+9XANzjbWWeMruyBTt7GLPtm4FeL4ojYt16QktcjVZiU2KX7lJQfz//Nz+yZV8o9PfW2m2BvxiE//cmy+X8295Zys/nAX40c3G4OIvTvr8y6LiiKa6/EcGt/ITkAtfo0f87dkr/tHAB4235iOAbnGBz4tbfndm88bpi1xPxtZIb/M6C+aK53Rm02UL4ONfinj10YwP94A18UY3Ypo4wB7ZpfjTpsij8nsE6PhT43ICBHMJQoGqMFaIHTn/GZDQFJXtFjKYLbAGB5FAdb5zTQYkCfpfke3OCBCU2hgG7Z/B2wpA5kq6igmJlqEyUkl24gh8x7NdBfIzlhzgqQniXMa53GfkkiSHksRqbsjR/8KPZrvKK+/mu0fuEpR/pUyRoDBrFzqITjQrkgT8CnXQc0D/nA8eQfSP//iPL/ITyKJ7jkeDgs4ujYJLAKF/fJAbm7AmXc7gq2Cwu01fnknkHI2MFHD0Cdb16ElTIekL+OjSGK0NaaB5o4dOTzxPDtb/yU9+8k+aUOCz3Vu0sJcVZ2A94xS1ms57AmL8uQRzcqB/hbkmDY9nAd3vU+MSX3TEvumXX2hqZ6F7FuiB/+CRX/LHFdiY3TWnBwqMwP21cbG++xqLH/3oR5fmlw6mvo2hAzSzI0l4r1hxX+PDrhTcimSbEz6LTQoCjZvTDv7BT9AjxjnZlWQU2MZrqPnGBHJkHxL/tDVNPDyaoGTuwoexFeDdFxtm4wLcF4skYP632rwiSkyacYW98W8bNeEB6UBM8K/c8VU2iUe8xju/FVs0THTg9Nh469gAUuj4/7XEh/TlAv11nw9qSvhL94N04vdH/mXBeSrlr2dkPW0J/eIFmYpt+FzBeJtzbFGMuBWszz7xzyfgIfd7YsgKdPQhNy6t5S9dyFXoIKNeM86HJzxKo4ZS3DjycUAu/McuP598BNDcRW/8n97kaPFGLlAce0YWj/AoR/G7PRtj83xVzpx03QrmiDederv4PJ/GG5kVX+Y68zoDxpGL3MIX4d2b677NIbSIc2fXOAN4Uc+Ip/TF92wUsVvrrGs9c+09ELM+NS4bQPgcSUHvhEXicNqhCOykwfN7wDxBndAlMU0LuvYUzkkEM45pXYbkMkdBa0fNbqfdRMWD8QUB9Csq2oVbQcMh8Ut45CKxS6R7tMDLmeAjCwFIMFQEWruCrcuagiRa0cX5NFOSscTNKQUTRbQCBb1zrvXct9souEuojNbzFaxT47IWaIF56MejpksRhqbk5YLHPTLhpHjzuo3TB8WDBOA5fugDPeZbL7qMsQvjX/ASrBU0ds/ch3+LNuA+2gRCgUIwohNymTS2Tjsiyd18tkWeigZNpqKGvNHpuct4wVChuBfo4fNMo0jH5IXnewH98LEd9NAT+1c8xd8RkBte34fGhQ3RpeTFNvs/Cdgd/Nd4WQFv8DltY+c+b+Egf8UN3a7F9GxcfEej38RpLuiOTTjZbXMjMJY92AVtx37Lx6xnDDzs0omc+KEQwDtbUiRrkMiFXtGkYMAP39UEo0WCz67xbi10sFd+M+2M/WkK+EVgDj7IC7/Tn9BOvzYWpozcR7c4LqbAEfAZmyhTd2hTCPHb5OWZMYpMeCRya+HNhVfNArnLG3wPjYp2MrKJI66IU8bQB/6SQTJPHmTrpMZYMpk68Vzzp2AiM/nBa1rJw3NrrScucKRLcUksWwG/+OFr/PTWV2uin6yc2tgJ18jRfyfNt+CbwK4/5MYF3zYA+Q9flh/QwTb4Ht2Q3bNpkq+9CQD/9JcVrG0DxYngPU3rFtAZ3xM72BJ7VPdo6vkQ31MMP1JbsTMnRXx5tS2yZIP8mY/63nUG4OMnaKcrsuE7bJkPip82M8RPMa/4ecsaK9AROdkgoY9r/mJjR137aGO2AhzxT8bilVpPPaRO0dSsp0uvBvL91Lj8AxA4wROKJMEAJQ2JmDGUZI+c/hpQLtwUz1klGU69B2iSZCUnRmztcEhmTgwYknuc3m9ewmkug1JUSaLdm4AnwZMRaCQ4CCcUPMghfreMET5NiKbJbzo0d4KRuZ7tBR+4NGJOFvAhGPjuBESDIKm5H72Cp+bGTtEWTJn0o0B8oAP9nq30uyfAaSx8duGXfeBJoeQVO8nddzRyEkXXxOevCw3WQjMcCjrv9X7jG9/4Jw3XCnNu69OZYECedmk1S+631lxTgUh/9Bge+pAg0O2+XWOvCUmMydQlkSkA8Ob7HpAjm/JuMv7RQgaPADrRxy4lGw0MOrKbLXrM+Twbl2kjCk9Fp6SiACk23ANo4HMaIMUYW9mjS9L1f1Ctyd3n2bigUbxQONvcQDd5a4bZFDyrnPFAtmJehWXP2RY/JH8+tgJc7Fas8l60poQ+rQsHXOjjm+iTZG20OJ3JtgFbRwNausd+xYVZYPusCFH4kMUEa64nLgA+a2k4nNR41hr4FY/poXs+07G44p6LfWoQ+CYZir3owCsZuYwha0Wi5I7X7HqCsXzS5ok1krfLZwWRTSW0RgPoOZrlJrETrfjW+Ilb6DFHzOw3LhPg8EqeBsw64Z4AHx4VJ+Ihnaw8nAG44TJf8YhmNsKe6GBLNkdg/IfSuMBLz/iPV/FCsyL3sCXNCl29EshXjLXRhZYjQDN76sQQ/Y+C2MD3xXtyDyc7loc12OjjC+yZbRuXbZzRD/tyOsou1pgAV692ix3wdW2B+2izvrlqKxt9aiSxjV/lc3M8X7FOsWUP/xnAt9in9jvjH2xIM6Vm8ZmMH1l/D+Ala7FeXPYbSfWZ72RlbbJ4ht3sAbl/alz+AQiaMUpGDFCAtVPG6QiK8TxiDOZxUsFSQSGRU/AePvc91+QwxpyBM6GLk3Mg34GxcNr17LUKzxQDmgR8rGt5roD1moFCm9NLfgKcZg0+SbC1A5/Jw/12HdEkoVuLDOcu3wSytAtphyKZuie5arLIXeGhCXJfoSPRSaIT0CAIuW+sHU2GTLZ2f33XAHieIwc+W88OpWdkY6eUbdihVDzhm0zjUxBXaFlz4gK+062ijjwUoQpGRQcHg3+dAwqK5ExmaNYcCJISmyJVgWEcftOBv2gSJO22ooms8KuR0TgVPPBHngophVI4BB+vvl1LZsanB7ZF13u6PQtwkisa2SD7Jne2oylAW3QGxn5ejYtx7EjhRZ/tgpN7cr6HJvMkacW0Qn71sxXYp9i06oss2ZDGxV+FgAald57hRCe5kl+nrD0DaPHdDjvZzFeYsn90sqcVjEEDXbJnCVQh0mkFOfHNinDjxBdJOZt0+cwGFHPh9RobvtEA0Mlm2blxvk+wlgIFHp9XUKjTn8IZzQBNeON/ZAD4P3tP1mixwypOih14cIlNePGZLsVbvsbn4LKGuSu4hz5NQa/rdp8MNRWKODgmj76Ld9bwSqF1PTfPM7btZIMdyA94kNdWwIMiTJzaotF39xUk4oSYisZV3mcBPvZE1r0pIFbyqRq/M4DfD6VxwRN+xWMxjt1pWpw+FDv27ONZAD+b549imO/XgGwUzfx4q264FeQlcpZjiyku8kEPH3PZcOQPmm3+yM87bbhGA1zykh/GFz8A/HyBrVnfOq2/hdM961mXv9sEzNfozHzPrbfOl7v4WzFyC/8ZME+8Iwex5YxvoEn8tJGi/ovPV8DUG5mo/byVIe6I8eT8DLvZA+t+1I0LBVC4opOTSHgEokCVPIyfwr9XEdZQCAi2dvY40op7BXOMM4chCNiMQWNhJ0/wg6NAhBcOgwdBSmGlKVG8cDzrr7xIGBxasFCYmOu7AtFfuy4KHZ01ucEluQvEHFthIhiXyAVGjRYZotv6kjlDAwUR9HWCFOCXE0i2AqYiTPJXuAgGdqo4pl0bzkFH6FLkK+YkM6/F+fGf5CDxu28XRgEgSUm+5msGrGNHFX4041ewFORLKoHPigXFkL++ox1f+BDgyEnQsItGrnQl6Qt8doZ9Jjd2Ro54EwzJ3rrW7z1nepDofCdHerVe+iNHayg8CvAKAAVnRR4ajTdW4UIG7KYfP7MdnzVZ7P/IFj2DUxAVnND2SBEzAW78sR3yIEPNmFeN0oMxZP2WjUv+JAkKzOyIjdEdX2LvZHsvLeayHbzyM/zvydMaZKHh4AfmTvCdX7EdujEuOyA3z9ELB/u2HlvgT+7Fh/VrpKLJfL7ilbOjwsd8F32xDU01PxdX0Czmih3mJ1t+zO7J2H0xhc9qkuOZz4p56DdPw8EPO0VubeuKQ/QjXjh5cPq4xhkyMYYt4dVcNgGf5tGa7pEN+vGfDLwKIq4abz148cku+LcYw2f545nEDQdb0hjwLTKQd8hEDEQfsA4a4BT36Y7dkBs5emYMefBltip+ahBs5IRnAh6KoauMJsAt/6ARr9bbs4EzEJ1oYhtslk+TPzrQdSQ3c9/nxoVO2Rg94knclQPYlnzIN8iUDJ+57h6gRT6Rl9j2np4noM3GohjALvF0D+APr/yXz+Zvwcq/ddi4OMPv5TebmeK1GOLZlux8zkf4DjsN3OtV2Tm3C7jHrsQP/JIV/2Mb4il6svu57gp0yzfnBsg9gBYbmV5JI7MzOjPGmnxDPQLHEa3PAjKxLt9Vg7BzMVxcEX/4ARs07llAjx9l44LpDF2XTNnuKSaPCoh7gPFIJooBiVBS5qDTcbZAkOGwnAidaLebp9j2NxxodfmuMOY4ThIkBE6sYLB7L0jD0boMG34JHH0TGBmDIxvJX2GvwBbIBBNHg05XvD5UURFec70LCTd+rQ8P+vDE2DQLR47F0AUQwU7i/cM//MOLE8MlALkv2QlQnFTRpgCwjsJXcYMn9z2nV+MFQc/R5dTHP2rg9Tq8CYxoAvES+EwHdqMlI0HMJRGRH7roie0EzVFg+L91rEdudMIWrMkWNTWKIvRPu3ChXxOmWIIrXdMjfdqRUUzRtcuxLVoUp42HZ86xY6wpwKsxxgsyxlwDcwRlzarEcqTDWwEetiNhkktypVProF9RSI+PADx7jYvvXcZJWsaxGc2LBhCNc5zrVgg/+1UQiglHwC4kBf63dcqAJg01n/Qv2/BXvmYNfgC/+fxOEWNNPuXH4b6T+YwnmmKytynAl+AVB87YSEBnfFhs9Tqg1+o0A2hCr4sP8QVr8XmFA1nzB8+tzX+KH+ayO3RVhKHZM7HPXH6gGbejz4bEC3459WQtelW4w2EtPPMNCdhYRTW/sKb54h67sa71XPAoMMRBa0rYdpf51VmAXyOlULJTqukUZypYoo8OxYy//Mu//OzP//zPL3zSnbnikOfkR6Z4wbfTa/awdeICJ76yP/wcgU0OdpDfPwPQIO6xS3FRc2ynn1zTbVdg7fetcYlGNNMh2pwm0oE1xON1jea8GviQuMlOb1kPL5ousYRN3QNwsEl2Ozca9iCZuMzlazYBzMfD3BRgO3O8i9/JbWyaDblHz3IvH2kNuOcFn/guJogZapQ2zow/C2gS42wUb/ncWdAAoQXN6EfjWTrwwY+a95aARnHEab5NSD6NDz4gDyX7W2S6BfT8UTUujJkQFXsMTJJQ6CtuFdsclOAfFWzAkClMgrGWQpTzpry9ddyXVCR0ScUcO22KbMZc0obfZztixiooBQhzzFfk+ItXwV6yQ4OmBD5GJdHCs4J7DIQDkBvZSKZw2l1UQGqQ/u7v/u5SaCQ3l3nG20FFDxkLJmi3Y98uzhH/cEhqOnjBRPECn3uCgmCDLrKBC53oqFBxDw+eR795krrkotBRIGsmyOMaPXApdDQ/1iE7cwVS61kDjgD9HFij6X+rt7ucXhRHK/3wr+t7pqHyyhhcaKc/tiQo9CpclwSpkNHsKO6Mh4OtuNgKW9cMakCsjybfjd2ygwnog489KYishY9nAfxkgS7+qEFnL+wWr/iWxB4BdrLXuHjGxjXjmkK2JOEnR7SR4yNgPlxk5/RV0+D7SssEtqtpUdShcR3L1tjJ3/zN33z2rW996/Iv8IkVdCSJind4sROvWYLHbpjkrDnjmzYGJBv0kL1C1T3/WST/q2E6onOCsWiFz+tU4gQ708SIG05EFBSu7373u5ciiX9rPtDFBvDE38RQuBRBxmvS8m0+zS/xwF4UanDZ3OAP7jmJtbHQbjfa8CguiQVww4XXEqyGym/I8K0Q9S+CWZvO8IEHjY4GySmvYgH9W358BNYVTyViGxzitNiEPj4vd2hA6FAzxW/Fbjq0IYNPTRXfEGvo3eU3g1/72tcu/7eXmLkCGtkzeYtN5HIE6KRLOUZx9AxfAOiAm4+JK06KNIHyhjjZOsnU2PelcTGP3Nig+CROae7oxXdyf3ZNcQtYk63gccsGroEYwK74HVy38kA2ciXfJKNr+WVC69E3v+Vb3vBgr3yQP7MPcQJeNkLOcjnf5fdyk9pIDBTXA+M94/te03RCK77CL354do/OjKdv9Ilh9wKZyXt0Vjw5S4v4Je63GfXWgE56J4c2v+UVr5LZkOk1slvj5AT4P7rGxS6aH7VLPr1KQBAF4VuM5AgYjUJRkSTxSLYVHfNawT30KDAVaQxRcfzTn/70UvB7xpkrxO1ISqKSt+BvjRkg4HNPwvO/af/RH/3RZdfOD80UMXvJZ48+4+FjhOiSTP2Ty5ItuprXZaygYZzE7JTDLo5ARPaCEjnhh36Mh8clYNrFlCjdn/Ssn8mFw0t8HGaPL2C858YpONBHX9a0DlrQxMHIGB2KIMnSPy1Lfgo+96OrC8BDb2xMctTksbuC7Bx7DfDFhtis4s9OuQJLYQPfCugWfAVi4wvweMKjv2hXzElMeOhEbQvfFhin2FIcOYaPr2cDvHSkwbQWfp7duKR3utakkAub0NC5V2w4q68jgANPfJENSZzhPsLP9gRqhfw6znw6JxeFtZOVmg9FNzukY3ZsHF7xTF8+swfjbCpI4P/xP/7HS6wRJzRBv/qrv3rxWycCirGJJ9lsATo9ZyeKH7KNJk02GhUKYhDcmgDNoqJUIS0ukBN/Rq91bc6wVfx4Djee6ZLNG0e/CkcFP/2hU1y0yaAYEIfN5RMaVDh9R6vCX7Pgs2aN74jd/FiD4LQBzfSBB7YJN1vxm0M4j4Cs4C7G4IlMxThx5dd//dc/++Y3v3n5Z8z/5E/+5LNvf/vbF1tU0Cti2A4e4YEDLjxaVwwzhr7RpUBQ/GsQycLcLV2JAWx+7/kE68lBGr5emTuy21sB/vTuhE7jTNbszVrp9/NsXMgIHeRFf+yFjuhfXGEPZ2T5asATWYqd+ETTrcC28CbukBeezsrKOL4nD/HHR+zE3OTOztk2G9CUazzIHK/lbU29JlK8sWlik8YzPHgufsjJ3kRRx4hF5nv+CJ2ADRdXbi3OjTVHTUdnaHbvFhx4kN9tbNLf5w35irikBurH/MVh9JLZLf5ivPhMh4/q6wjeq8bFbpyOuGL1VuM6CxTFABUDdsmtlePPawVjOJxdJ8mRE0jqOlcKw4MCXRdrJ0FCtdYeL+6bq3iQwDit4oFj+W7OLSAYafjI0bqcS8CuEPZ85dN3940je0FHQeAHxBKUV7fsrAqyCmEJ146FhG0HS6DC+5a8AnwKsHaW8XeGL7KIbgnZa1ucHh2KKQ6GXvI3Dt0cj/zpYc92FDf0rlkjHwVFSRidyeUMWMOpkwCrQCFz8qJPBdOEZG2OdcjBDpVCQ/KwPrrRhMd2hgRu9LHTswC/ZpV+FJ+S+LMBP9ZxSU52650knZXdFgiUZKHYJSsNtJ11gVBh224/GU47fhTihW3bpT+7I4YOdmlXne5XWsQxsUCBoWCFk6+4zO2Kj3m5L9Y4/WHbbIDPsRP2Jfm9e/fu4reKeK+/sCU7ml7vUWB2YrEFaMAv3NHkQqO1p62SOZw2QcQ1iU2cEd+MERe82sa3yANtGgobPNaPR59t4ihs2Tmwjs/wwc23FTO+45n+zbMW21CMaqLsXGoEPRO/Jv3x4EI72eB1D6xPrsZozDRkfMc83/21M80n2aJGzFh5gO7xzAfiM8Bbcuxz39Eq9jitIRO2sgK/FYf5Mp6OwLpwkxdfEW/JZdLzCIQfHeRF9uKuXGVN9u0+e/o8GhfjrM9HyJPOnEChkw1kE3j4vAEdfEiuILd7aMIP3tgl21lt7wiMM4cPseVHAK6u7Jr+4FfD8CMxiY+Iq/RDr/xKQ6mAl9v6rQt7V0TnT+nsLG9HAJecwof5O1rPgrlqPvHJRtItcwM8sFFNmY2dzxum3tgTXWgsxTV+rEYSRzQxZ4FcPrrGRbDC9KuAcjiEHVBBTXFZcN+6JvhOKeZySI6mIOGQilYJVcMg6RmjoLeDwChWXIBz29GDw86EQh1+cuAYApKi3fcZ2ODCh/kMjVGZy/klEO+aSvzGuNAmEMCnSap5CXyGQ4GlSFKACTpwcm7B0W4mQ8SbHUjFuuBjJ9JnTaC5ChcyNRdO/KPfZW0FvmIM7S7PJWfjzcM3PORoHa9xWacflQls6BD0jEVfO5pwaCQVTdZbwT07kRoeRZj1yYd+0M8enFKhgwzQRb6NW3VIjgqKv//7v7/QkfPjm5zQzQaMA+t8uka3QK7gxyO+2YRkRmd2rB2r45l+8LBlSysYg276UOTiFy/3JMgjsA6c1hLovO50hr49gEfRoYAVNCUJOudjZEVmz+YBWJed93qhda7x4TkbYTv8Y+qGLeRT5C/hwRns4cYbPOxJM4J/SZ8/K3IVGQplcuYD5KRxN579e65ZYC9ikON69BkrARnD5/ArgXptEb/BEc/mSPpsm5z4Gfrwjga7p3zUbqoCX1OugBUn+QE/tT67RltxRgFnDJr7fRudwwUPGtHPHhTK+BcvNf5oKr52rcAnxUV0GU++fIE8NcLoJyO08kP0sQXPXT4rvmx28FV804mm0HMNlrhmcweNdGAdujySJyiGiNkKBjYz7RsetJMLmo8g/unVZgUdndlUugfIHO30h3+xC/9kKY6yz0cA39caF/fRgV85RexnQ04R/HWKyObo/xUx4xFAk80ptJ6xky0wD++aeLk1PzgD5Cs20Zl48CywvgtteORr/EFOxyu/ETNsanh11saEGOW+iw/wKXyZ/2xAGz+SY/p90FmZsXe+aANFHCPve4A/8g8x7OzabwX0Rmf4s/HFPvg2f5KDxWy2c2Rr+BPPzHklf+/Vj/MZlB2cVwClUAhlSAQzqO9dEziSAoGz2fnjfF6TksAoV+KW/Oz4HyUL99Ei2HhVQAATYAPraIJ0vXZ/FZ+cxn0GI1koQiQnCV5idzIiYQhi/hd5784G1mOMHFWBoTCeQQFOshBQJIAjh4RLoCMHSfpP//RPLwGQEwo8mk6NmIIDPYocyUzCVwAoOqwlqbgv4StUFGDmSVToUMDDiX+FhJ0a65Kba0u27pONV7UEv8b4i19NC1lJcOQRHvxqDn7rt37rIj9jKpjQL+gqLqxvrKsgZmfT+/Wa4QBOdiYge64R2qPZPRd6NGLsxxx/BXyytTsjCCiYVt1dAz4lALElxR+6t+h4BOCzzjN2WbJTP3CW6NiMYu7ZNE+gG8Uzvxac8XIG2AHdakzon17SM1up8DLmmk+5jGG/+Hdyori3q49/zyRZMYZcJBB+0hpOqCTkaADm8TtNK39SuNuFFjM0K+KUUzINwC3yxat451Upv9fx2RqaAHLUdIsjdKf54gcSGf+WsNkJGxcv0IN+G0HsU6yD32UjxnenrGh22uOfhhe/p79dAzKhA/zDp9BGA1nyb/FXwcTH/7GQ+flFjvRBtnShETAfb3IUH03mNTBkUaM19XEkX/ZGdmSh6eSjzXGhyS40Hq6B8ebyIydx4pk4cmR/90C0ueCWv9iqZtVrdIo7z+4FMtlqXHzGnzWTu5jDtsie3PO3R9Z/NagP1A9s7hHApzxs04g8zvLMf5y28H3yfBVMfdnkUZfwOa9U/6t/9a8ur6bL8/yKv6S3rleBWkAtwpfProN+sVfd8ghtdK/+UYOqE14p/3sh+fND9Rq/UquJbTZ5+B1dob2xwafG5YlAyBxDEawokBStNwW/dU0wn7ELzv2Owg6d4rqdNsZ91LQA62pazJMwS3CBxKWAEoglf7+RURwp7hUKErikqwjQODEszZOiwamKZMW4Ks4BehRTkqsTAkVLQQK9ChmGeSb4NUexojmB14V3BZVgSBbowZ8CReHs9MR/hun3O+ZJbpN+u9IcQhKEDy5Fj93YCoAtRwncIzuFhh3qxrlXI6gwKECGDy+KBsWJQs5Y9JCRJgf9HNa/Qe91GE2NyyseGkhFmAJtAvyCkrmSCt6mjiegER3sQrFpV1sh15G5BpXe6VUyV8icBbjxhye0a8Ks8WywhiKDvh6BYgAfVTTCSzavgvTk1UiyYXdkdgbok24kM3TC5fKZ/7IpjeZRs5h+2Lz1xQR2KD7Rk2fGkIVim89rDOC1roSv8P7Od75zSYR8JruOFjJl83yLX2pk2eQPf/jDyw/FrVWxfAaSmbjkRBht1rAWmXjur3tsVVxQKHWK5PUQmwvipw0J8cIYY+GI9okHX4p7Jy78w7gzAAe8fJn/2mhxkuPVU0ULPugc/61N3v7iix1qosUgMZLcyBHdPrPV5lvHHHxaQyyR8K0R7i2wnvloog/xcG6seCYeuM6AOfGNbrYt/rj/Ckhe6KRnsf3RooXO18bFX/bAL9idOG/jjw7og+zJjaxfxeszAI3yk7zBLx8BfIoH4gYd0/sZEGvEEzp7JaCPbeCTn4tffgvpH/z4jd/4jcvvYPw+0gad2ETvdJztvwrYKdsSP89AcnbySs6PAN7EELIQA9nD+wpopQ80imPqIrUlv7MRJA+JM9OO+N+nxuUJwJkFO0VGv2khaEK9dgU+69I5mh/Iair8SwyU12lA4/bAM+sqIudvD9DXvJxcUEanZxK1XSxFtPciGT0jKtGba1y7xpxRUrUOowuMsb6g1Q9dfXdpwNzfAvjhMY4BW1ehxTgVWXuJAi/mGK/wcKqhmFNE4KGED7f580oOdjfQRk7Gw8VOKgTmuj67rzBjQ/g1XiGgyFJMWi95u6wtCdrRVcjQi+bGmOSFDklR4BJcFV+aiOyIM5MnnaApgNs8CUKTYz76tiB6zHehmz4lN7QrBtiAoI+Guc4ZoDd0ano1nGSJv2cBvp7VuNgpZr+vBvphh/xYg0iXdHAGyB+tmkubENmSewpQfmjTwfctMJ9OrG8Xkv0oktkqe8snjKMrycKJGxsTyxTEEoPvPitOrWkzgK81fwX46Irtvnv37uJbdn41P+zDWvnWEbBPTYQ4sLWOe/MK+ANenJr4bB3XOm4FY4zXrNkswN8eGOs5OYmdZKJZ4kM2djQWe/IBnuHLRg65illOV9kHXyQ7uvOX3uiP3OCLH7SKr04CXGIyXbOH+F2BTO3E+p2enXjrGUsfeEYHntw7gmTJv9mS0zYxSPx5ps9vAZr5riZrT75ngJw0LnD5TL7oJx+5UaEuN9ATucXXI2u+FbANdkWne/HhFiAbb4CQ+TV85GNMr40f+dG9YA14+YhYxE/Eqn5s780CeY3vyItsXb5uY05+E9fQSa+v0Cnc/ILcyO/Ip6zPrtVHTouMfwTgw5dNXfJgDx8KkBPZ0ZFm06mdvM+WxDe+KObw20+Ny4PAeTi1pkUxeKswjacQzvZLv/RLl+LRCYgio+RizDW8nNmrG5wVPQw2x2wuGSjwFRTWhFuyknw5NaNwr6u5goRTGMWSRKcwYDjwTWgsOSvS0cMRneZowFZAs90JzYbkrdlS5MHv9y2SiMADZzxMiD40MW7FF6OvOJo8NN9fctVQSPoSud0J/EtWdqusyVHgaR5Arx0RupLUNCECpNcY4JxjAdsQPNBGtoITGsl8Bk1/0UqeChVzyM19elKMCYQ+T/Bc08WRFR7s7xqY03po+J//8/+62NsPfvCDz/7Fv/gXF9mvej0DZEOmCjh6xOOzgB4+tMaFvXv9xys/6fIssCWFB7/hx+a6x474lSZ5L+nSqyYBn2RmA4A900/+ELAnTYWYaNMCTus5ydOAmsNm+aF4xMYU92zuiB++5FQAfjuh4gufIQs2strxBDTg3ekzGzort2Qk9oh/rXF2Pttwsi0GiDdbABddKo74iWZQvEInmTuVEvPwsMo6ED9sTDlZt5nBFsURspas+b7PcGhO5BW68B3Ame+iWUymF82DHVY4zV/BHOPZDl2wJd+NFYNtEnnm3hmAz1wxjn4VXQrCVwL9khf9PgJ4ZKNymo0ocmfj4qD4S//p4Kz9vC/g5FCM2MuZtwI9aw7kSrZ1BOI032DfGkFznw1sAD02+ORSPiiGsT3PbACzEXq1oSD+0Dd65A+vvYtNNmrFaDQ/G/CtDtHAtcaeLozlQ/xcDN7y3XtALUAOTi0+NOB3Ljbc2wViq9ecnUyTK12+0je/sI0LA1PcKnoJ1k7b2aAfELwEKzHpLhV9AqdEyNjPOr51Ff9eV1LgKqrRB3/K9ZcTK4Ykz577y7gFO86Gntb2HG5NhdOfCjCBw66UQLY6vueKH0aGHwWU3VOFj4KEIUoUEr8ApxDyWfIjS0nJToXjXmtK5Ap3a6Jly7Hdw7+x80gbLS68uIc3Cds6xgrGfgPjPViNmPuaOu/3u9DXu/5koChBhxMUBSEHqmlZdeW7IKogUBiQk+JGgtccmROgn2wFV8Wunb/kCg/eOC4bw88Ec8kbnWiiy1UnewA3OgROgdwral61E/ST4VlAF/m249XJyyqXewA/H0rjQh924umDPeXLt4A5ZEgPFVFOWrxS6PXHWZT4m22zD4U0+2R3iloJ3fzVbuCwK6dg45dw0BXa+YWE657GhbzgUEg7eUEbH7UeHbMh+F0adHb4P/7H/3nh2zz32KXYxKf4mAILnyttdCQGeA3pluLLOHSgiz+Txy2AV7ZLb+tcNKJVbOa/YhY7UmjgP/lrzJxQods9Fxl4Rp7yD9lpysQputFwwGuseCoPuG8efuidPryGC+8EPJOXRlLzQ66KMhsHYrn58AY+40ODQ0disvnwkrc4DNcZsLYLTjbD5ugN/rM6uxXw84zGBb/8xP+RhWefxXa0WwO8iodXAXrZqbwm9t4ac46APbIrNngkF34uj4sZ7OoZMoSDvuQ48Uxcs7FiHTlZzvQcv9bX0LAPOc1Yn/PRcqwaRC7hx8bIt/DT/bP0LpaqX2yEHOE1Thzm48Y9I18Ctow/ef1ZunhLQC9ZkAndqKXUgezQ639y6yt5+kI2LgSqOZAoBNL1tamzwNkkHO8rMzDGdovhUhyHVIx7JUMypeQVGqc41zSstCqs/TbFay2SryRLXuiTYDVmklPzBAB4FJNbBQIe3Feo+MGr/5ugfxnITqPCQoIXCAUjCVQTmOMKQApohb6EAo+dEvR3EoKfDBddinvJU3PmGYhv4/EoSSm82IGdX5fAhjfrGgc/ehSNAorAI8gpNuzueP/SP1Dg/1bxfCso+U6XiihjBNbG2NWFCz3oJmc74mijQ8nU+Al0quBTcKBxBTwK1JyZrhRUt9gRMN466MOjwvIem0a7V1e8roDeZwRjPCvwHj0eJutXNi70IC5IqgpgOs4WzwL+FPk2BkrIds/4jhiR/o3LZhUW7FgTzlYFef63Jys08T0bCmwvnPStGFCc80HjFCGKXLiMk+SdhhqDJruebIWeyVcRLmEqnFfe4ceL4sLutljMx+HFizX4jQaaT875nq2XOS7j4MazTQYxDB73XY0z5wjEUfHJ3PDjy32bFC4FvoYN3onPdz/c5n8aELKAhzzwKu6Il70uCi8Zu2cMXMaTXac+1k/mYo/dxnXdwD1NB1xiiQ0qO9F8mmymDNioGKywUsjBqXlFyz1vDcAvT5AP29+Kic8AeJ/ZuGRn2f+HDHTILuUWf+nkWUBebJLc2Sw72gLr8mkx5RGAH/14sra4x39s6okdbJV/ZWP+0iEbFKvYsHlsW23FH1d75Gvyv3zAbm2y2lA0L395FMQNdQ9at/ChyVp81QaFz8/yG74ibvBp/k6WHzrgiY05nRJnniWrLfjCNS4MkNFLkIppyerWQA0HR5NkFMEciMO4fwse6woSihwFssS9ZaAcoqPLrR1YBRZj4DwKVwHdK1SKEwWzXQnNSnPQ6bvX2jQMW2CMNf1oXuCwriQq8KETv3SCB/Sg2xzfO9Exx3fjOZ+O2+8oJGSFOhzmkaVgVuOSHN2HQ+ODH/pvhwZega7dNmPNMRc9nqPPWLzSucIAHZqtTkCMX8F8ciE/85K3i0zwJmBKMvgRNOkP35pHayZrYL7dYLsNW8fJxrqHV4WXAK8BuyVYxTueBXABV0F6a8ALB17854XsiQweAbp53xsXfLNtCV5BT/7u3UJvOtCAsln08h27cfDm38Zk22yabWtY2DNbzZa3gG2xJe+Es19joxM+zY/G3X1raVzIPtrQ4MfvmiO+4K9TBP/Ag2ZNjEET2rdoYKdoEAs0SX7U7tRHHIW7TRTzrTchGtDmOTxocJmnCBWr7DpriNyjB2uRi/GT3xXwLYaQo7Hw4YdPkhn/L16Z3wXgRJPGRHHFD/1mReGgERWXnJaRJTsxbzYuAG1ooANrGQOvNfmkBkgx5PsW0Bca4MU7ffhBPnrIIZm6yEOD49Xi4qt4hJZ7fB7t1tAMi6loeDbg+1mNizcC6CJdfuiAJ82ozbLixLP4gkvekc/kyjX/ADagsWZD98Z79LIj9PPf8raaTT7hE9OHJ7hnQ0Be9hnNxsqZYuc6Pps1RnyQr/mCRoe/8B90bPF6FviYpkSM3PJZ9zwTY9Cw0vgIxJ9TerXRI3y8L8A+6IuOP5243ACUz8AEh37TAu8tQNgMWiJSPPRa0C1KMNa6/UATPejaSzgcULD3WpPPjLr1fJaoJWfNALySmFMXhaJ/jpdjCRyTV7JQlCo6wjkBfru6dkCMvcaf5+iX8PBDTyVvAL91yBxOO5tkqLgwTvOnWNAYCJzuW18hAZfv5B4+f83jAIoz6065HIGgpqETdFa+gYDkNM7ubDjxZn3NoR9dO7GRPCWECquKBzxOXZrPYe2Q2n3aCoLGsCO4yIdeFL23BCw4XOSsOdRQofce+0Sv5ONHkU5e6GTPPq+B9d/nxiW79YqG3zyxQTTfCnSleehHpWxW4vVqRP5tHXLNthW+fJW8j2TjGbvhJ14pVYDMOezUM3GNnHy33mxcAL7w6Z7PcLBbPGsYvve97118k92wP3a95//mijlsTYJXePEZjQue+IbmSjEv1iku+BzexXK7bnxC40Tuihx+5fdamiv32DBa+aO4Yi4c5AsnX7MGmbqv6feqlbigiGEvFf2rT/pOpulE4cTev/a1r102tuiP3ZtrDBrwmizcm40LIA/+q1GauiErPEva8Fp3S6buucgcfk0Te+/3G3QCF9rZFHzsi/74KR+bcfcMGItGNMklTofEKbp3/1kA/zMaF/qgH7L/ooDGWO7oVI7cb9HhEaTffIgeJngubtk04F/kexbMZatyjk1F9qrx1QCLJ3yfHe/FEJB/mONNCeNc4pPTfzZ+ZIuNZfc2BvifzUX5WZzg/3gm1z0atgBOcVAMgnuF/J+vH/F3L8BHH+LjXrz40IBM6efRWuAafKEaFwWrhCY5SXj3CI4B2V1XINi5zyFuAUaui5aMvBahcDhySrQqgCTX/6+9e93R3ijyAH5BSCDBtyiCoAgQAgmSQMIhEFDCSYAQEp+4ib2Q5R6z+3vCf2l623a17fHMy7ik1sw87kN1nau6/UyPs7kkT+aKw9IYEniq2hmbqyoxStZjLBkFCYTPM3fm8EwwUNmf/tYXuKjCCmQ42cwJ/G5dODMIlF5QwgnBzd8MlwCIcLvGwvAZo7Vz+ZuTdbokaVP9ZUj6fiNgBClO7hK3/e0VvQWfAoMAw6ryIwl0cuQ6FieDbsZo5rUXdB8Zfycggk54jiD0IQ9oIYC0/1kwj/U5EKeKjPkewxo+MZwCwCW8twCNX3Ligm94K5HI/3nYA/Aj//ZpHjzM1SW0FGCTHfj7nJNv5dXPJfoYH6dMbsljC+ZRBJGo6Avso09cfCa4dipAf/Q11k/2jB0hw+Qf/nRR8IBGIzCv8ZIU9uIHP/jB59/4xjceeNonfWF7XBURAOjDJrmqSufNjVZknnyx8egHR8EU+WcT7FkF14mK5MScdEmQDWefqU76HxC+ylk/+huajujKXtFhPgGufAucnKLCidyGP+YSWPk8c+Er2raJCzpJbgTnsS1p9of/cFVIMncPbf80/dh4tMMT80sM+R5yAHdBKf+AHn3hZAbgTOfN6YSJH4HDGQBf856RuAhK0eI/AdAXf8mFBLTl/ZnA/7DleNoCWWFb6BSZHsnlErAbdFRCxF+bn9+RxHhm7ujQEpC53Dyh8+lrLBvlZJFt0m8NrJM1jSNn7A9bwG/QXc+rdNWPrPELdKIHOLOTfZxzFphT0pX/pA/3Nx3w8E5cioDhkhYVBcYhRn8GIsQyYM5hJlBuwRwqj5wg48shLgmkuTlClTRVxD5YAYyQIMBVk9YgEXgKrxrAGElOVHQ4acYR7hIXRkZQwdlx5NbwTFBF6dFtyZDZNyNhnBfmGC4VVP84SuLCqRvfGxzj0N86Ahd8YRxcTWL4BEuqx0u0MR8D5+hbldZYxols4At8jFvijbUZwjh7NNbQiRFW6RUooSGaopP5BZvoKciyjn72YC3rWE+iZz+9cwD6olFeAB6BeewPLirSlBxOW0a7heCCDuRGog5vc4zosQT6kld74tTQZNaxAeu+xMSF7OKTRNOcHJE19uJIXlW/6TW+0TfJCl2yd7oh+BakREYrgH4CAadwrkEY2/LAPug0x09W88znfeLiWWyGRg71w1e4kRfza5y1INmeBInkVsCt0U9Bv0TNGM9da1JE+Na3vvXQSTLH1rIB5kMf69A166I1GlgfXj7DX8UcuuJzzxOgR0c985Memtsa1vJe1s9+9rPH19JbX8XXqTL+whXOcMdn+8IfOgZ/Njk42ifbYj1081NCg3/5DMChT1zwCt3MC6/w2Bi/GyN5kYTgWUUnPUcDY+ki+8iO4Bf7JSBjnwRqbCD6VWWrB3zAF8ki+28vaL+FYwXs4YzEBT6S6yNFzJcC6E2e2QzN3p4KyLdiWH9dkay4gcAe0Lk1Xkc++F8+lI7RFXEEPYjP3JqnBTpF1lJADUTu6TCfik5bYIxGr+AhqeCr6RwdYcskcGhR0T3rK2oZFzulGStZY9fpZXWvs4Ae9o/O6PSmA7rdiUsBCBkhRShJiwAd8WaBADPmqnGubMHH3FXAJILPQbpqJKDgeNeAQeFoKY3rBaP1KDMn6JpDnJX9MU4CbY7Y2p4JoggN48WxSi4E36qvnD7jQUk9844Ep6zyCe8Wshc0cXJkHkEVutifu+GCM+u3c4wE1WeeMwJwaRPCHrIufsIVXRhMjlBwKHlK9RWfOQH7Nle7ts9UZAT09k42OELBuYRSYuqniq5ED78kgYwg2sIBXe3Zmq0TEKxwziMja13BimTLzzWwBucgubIn/ft9rIF++qOB4NIXBzDYe2SfrAtgyAQjar8j/iyBNV9a4gIP8wlo8VHgeyRAQ48k+05j4Uh36RLd4jjp5GxgYl52wsmiwGJkM8wpWGUf28CaDPWJCyCHgnhyJdHBH/LlRIQuhrfGcP50SWHEaYZvzaIbdEWRQTWQPEsEXM2SBHh3Dr4CBfiSlyX9D3gW/tLn7GPU0t+ccNfXHtlmRRhVUHZOkieZ0pxGOZGBu2ton3zyycPmCPpbW+4n3abzaGIdf7Np6GAvwWGUuMCJ7jtVGl278RxN2C04oj9+ZM4tgB/756ogntNJvFEAsj8Jzaeffvr/1p0FNOVTzE+O0bmK4xKg3VmJC3n7T0hc4otyAko+ngrIBDvnFJ7sAjwlT/4B8ejkwN9aq2tO4hQG2DoJPb04ErwrJLCZbM0IxBh0RfxSgeAc8Ls9WodN4N/RWzxnT5r9xQa04JmYhl4pEuBXeMY3Jxbcu/ctgBPailWq+3/JgJ534rIBcUKcoeAhx22zBCPUnNN77733CD72CKoxFEBwLpigpHBZA0zWV4Vt1BcOAg0OW0XWfrNnwqFS3xrCGB1OWECuWukzjWLbo//6zxEKyBkzzp2jiFLDw17MzXCp8ua+PcfEyTGM5tY4KlfVEiCN6GacQApv1+5/WxueHLQqDxqqQKoWWQtePlO5EUwJKsxnf+hgTj8lBAyRJlESZHDQ5ATtzAMHY82BXowW/ENjvwt67DXBpPlzZSaOoQXPzSvgUEHaAuuYR2VYkGmPLT+3wHrmsB/7FFiic2hRBX3tV1Lm2+KcvKBpFYxFx5eSuMCBLOGfpJu8hLd7IPJAV995553HqYPqvOAYryNTs2vAEf/JOydJtkf8J8/2IdBun/t9lLjAQZBC/jlvuOGpU5PYGf3xWFAisWNjyBAcyLCf/qeUEw5yIQEk9wJoX3cp2JDcfPbZZ4/EOQnM2v49l/iwd+ZfAvvSlz7oDwe2SBDhp0owu8Q2Cr4lUbnuhgdsiAKNLyRQyUSLVl/NrSgiyEEHRRj2AZ1a3TEGzdrExTN7ND8b2xcwPDcHnuXrXNEmdN+CzK8/PO1FUYWtxk/227fG2bfneyE0xldXf2OXj4Dxd+Ly74CPikH20srWUwC5wQNyT6+tRc8k0WRopHPGoLdAXXGPnNExttPn9Gkv3tEFa+Mn/RrNQ8/IDVsFnz1rRWdih8UvCpPiBHoufrKOfi342+dOw8SfaGSOnDbRv6fkm3mtx4/A4anWuQrQ/k5cVoCQcgiYLZAQUM8aXoTleAT7Ml5VZ3jMgDmsm2osfAQy8FtjHGVw7Ko6wGiMwHjPBJPW8LdxHGkbhADP4W++vGyd58b5W7Blr5SRUZKQ+FYxjkaCYA+cuMCC0gsiJSTmjsLrJ4Axh/kF8k5AOHHOOni2oJ+kjiGwn542fifw5oKjKjYaWlOwxqgJYvTTGBe0U6EW0Kl2w9saKk4SlXyjHH6aS6AhwfJ7AB74L8AQ/JAh/a2r2aMEStAX4yUIgiNHPwL94KwCrIISHiyBfcOJspMfCRQazoJ9cFBO2yQvIz5sAboy+KrWZChGewvs4aUkLtZHc7x0wuBa0MhhzwAasDVk7atf/eojWCfLuYZIVmb3bQyZbl/u99kIBOzWg0O7DrxGiQuAFz0WjNDLXCdEY/NIPDxjL8gNPY/ssw30XEJFltAvJ7VkWnBDxgVk5kUXpyBOIOgg2utHnzV66DPP6JpEXYHIZ1r6GaOvfiq99MEJiy8gsZ5gwmd0T0XVHiQvuRZmLFnU7EWwokhDt/Xxsj+ZRhtBEl/jb7izi8a1dGQz+8QlAFe0Q7cR/81lT/DKSVlFl1owrzF0WyBqLqdJX/7ylx90x9cjAGe8JUNO19Cvt80zwObcicu/AP/wTmGMTO+l6wzgHxsqySU7bIwk38/It5/4zhfzm2IW/tIJKvvPFm35rQpYn37wbWKLXr8C5IaNYwfp45IdrID5jbcW/UV3/pp800P2jF2w/8i6/oo09i+uoKt8GRn07Kn5BlexG3mn02fQ/rnAXu7EZQUoIkfKgTHqe4Sd8HKQTknaq1gzQDkdUQpUZc2Cggj7GuMYZ9U4gZW9j8B4c/sPs1Eyii2gYGAyv/UEJZw8o0XxevCc07eu/pq5OF+fEzYVVNUaiQAFyvx+5nfBisQga/icw2MYXB1jDHtewJ0BSeLSO3B/c/KeE/g2QGNE0ZYBDB5p1pFIwMcVFzgINDh4uHAaQD8yIuGKA9GAn2jC2UrWGHL98zmDxqDmxEugZD2GJnP0gJ+qPPjLeK6BOcxrn5yNkxdGfhbMwwEIQiUe9rFHnhkesiIBUu0eyVIPxryUxIUskUeyjBZwO4ITMIfkVWUabnSyp+3MGuG3xIKsJvgdgXXQVeLAXrVgr0uJC5zJqMTbOyBO4wT/gnDJg1MWMm9t80TmNQUNukD2rakPWRZcmEPiQh8E73QOPcistSQJri0qZrDlafTSmmjoW8k8t3dNYOFnTn7M6ZRVouE0R1+66CqkJENw4aSA7WIryD3bi+fsQWtf/A5XtDaWLkta4J5Tl5wg9zxdS1zoBX3Nad6I/z5jp+mSk220HPWrgrESPvRAQ7Q5Ml/APtGHzUQXdNgz7524/DugB9nhl/naK4C887H0l64qQJFhskfH8ZYPpC/kF43Rmk7huT6xBUcBH+kX2VpLSKylL30e2dZZMF+Pv2IHPOgOOff+mJgj/dDKMzaHTWHH+JEz6LAF6IL+ir9uwizFg28CsIV34jIAQs3JIAwjydAyEDNE0td6HDOj4qcgd0mxlsAcHIn7m5RfsB8nlrYEHL0EhEK3jrYF4xkewax9U24OEJ0SxPiME3fVIVd82n343WeCCtcpQiufU1BBiKqngDx7UX007wj/f/zjvx+VWs8D5mQE8E4QJdBpeaKvYCH8QjfPNPSytmdOUChwSw9OVcARI5M587s9SLQoiwqs/tZSPUniAtBAIGuN3ohaz3M8RGtVGTiiOdwEOKrJ9hQnj0bBpQefM4BwahPMJfDcWvYiQDROooU2VTCHPdlbKuEq1ObdWr8FffGLrNANwZE9r80Bz+dOXLJ/8iVB5TDhjbdHcKJngg93vhU3EijvndM49CVrgvU1+wV38iNRJk942YLnS4kLWpB/ztgJEd30O1nGU6dF6N3Sx/zwQX8yRJb0kQQLfvzN9ipuSAbQVxLE7nimoZVv5OOE2SU2xR7Is9/ZIXP5nd0RHEiUJMvWFTBEj9FeMUPihUbwYAvwwHPJGDsauaeXTtkEIQF789xccLAvc0qSBEquXrEJvd0E1lhKXOADT3IhCR3xD+hnn9Zit/EezfcCXYMXn4VeR6+MAfjgq4RDUIe+M7YnAA843YnLF0AucjJ4lEdViN47RWE36AvfJZnhFyUrmjjC3/gemTSWHC/J8izwnfRdMN7amRF4lvivt3NngPXZMvtll+i8tcg72yNmcV2XDUE7eLMJV4C90ze2id1ky95UsI87cemA8DHaDKMqFme+xyBYS2Dm22kIsTl6p7UGGEK5VC44aac2kpYoZ9t68Jl+nKe115RUXwokMCcQqokcbiq0xlI6SYufFK1fUx/ODc1yDKmP8ZKEDz/88OHUfQ4vVVovf3K2DJp1NTTS4DMKCv2uqiEY47wENuhsDoGJUwBJkkqQO/M+jyFx4qUCxNCB4AcnTlT1VWXTGDjAJ3NzCpxD3u0Aghlj4BAwF/pZPy/htWAsRyO5cfUl77zAxd1/wSFc0IqcOo1YkxnOAw4q5XCtABzQgMMh32jT47kGcDUHOSCTrgigi33Mgn0KLt0RFpiu6Yj5nztxsW/85ojIOl4eAftAe/rgRW66Tt6O7A/Yo6BTgUCSgKbaaF40l2Q4WR7R356XEpcAB/21r33tEeRLtumAuXqI3HhXMIURn8GXM+XMAflM4uIZG+yEJCe0PpNgWC/2OXh7JmASHKAlMEYfOibhJHP+JlMSHlfp6DNeOHFxOsyxey64lYjB0xo+x3vBmt993oO5JTb0g/1/6623Hgk+6Gm4lrgEb6dLOaVdAs/wWiHAuuiwxK8qmAM/JWpstb0enRNP+V++aeRLtgA97sTlC0A7MQb+nGE3qmAdssBu/f73v3+cQoofxAj8pEIo/8s/nSEzS0DmnZ6QB760AuIa1+L52ZHungVws39FE/ZT8YHPJ3Nut/z4xz9+xDJPicMIcisEf66Sl7OBXb4TlwYQgUALbARkHAFnNkschOUkvduRSghBnpnHGGOdmAhMGaY1x9WC9QWTKpX2szYOThTKfXP0sXfKJajwt2RFhdJ+lnDgwAXCkqTWUPlJUbxoG0eqmUeS4B/V+fYgSYyvMUYvL8l+85vffBgXgQweCBDxwdrW4uRVeD/66KPHy8vvv//+Yw7VHYGAn+bzuec/+tGPHtceOF/PzQMftIEfowuPd9999zGnsRocVEvhxPjgSYCh9EKgOQP2Bk8JEtrrnz3nuYa2Tj3Q1SkPmsFL0IFv9ihp0ULPzNGCZwIvhjuyWgHjrCFgEoAJsqqyBeBiDnwU9ObLFWZ1RV+yan3XeySr8BqBfs+ZuNgvecFXQReZ9NkRgEt0XGDrb3Pu3V/oKWkhv+ganizNSe4UCpw4jAA+lcTF6SOakPnoag+KGGRckkIH9NHIkeA8zlQ/AZEgw1z2lFMS+BhjHUGbfnTAHMb6ycYLbK3hs9hSBQX2LDTRl61jZxJk5aoYuuhHz43L33AhBxI9189SCGnB3ObCT8GJwFKQ57MezLuWuFhTIgKn0GsJ9BXIuZYWeTLHXjDWHGhk/dEV3FmAo4QFP9HQ/mfmxMs7cfmXbNDdFH2uAmuTQ/ZF8cBXlzvtE6Sj65oNOAsim3RDYYFMVQB+ihoa+/CUAEf2Ap7W5SMldm+//fbj2rnPrwZ0IvPsDT7B8U0DNL0Tl38CReOEBLFexBfQ7hEsgkGZOA9OEpFBlcBRSBVAzkJ1PictVRD8OZIknBRmbW3z5htfGB4OQUWSI1flU01xvBjH3oPPBNuMR3v6AKyLFuZo6cCgCXKckHC0AhV7NN6aHJNgQiU07/WYxzNBlIqBqo7+mkCIAcdD8+OB4FRiYW4BEL67xiLQxheVRC/V+sxPgRueSQDM6SSCYVYFdyeebLT7hy8aC3RasL7xXpZEz5FDgSceSV4EcmiAvgIxQRJ84Zc76+g14qHP0RWdXK0LjSsABwEYA+CEzb73OD/7cJdXRZxhnjWG+pJ3dLZ3fB0lBfb2XImL9eDE2RmHP0fAfOhEPgSt9pwAzrO9+yMrCgJ5J2+kry0k+JA4CUpHYI6lxMV4eLOndCry0/czh/nJKZ2kn+QP4AecyWESDbqWxMUamuBMAkAOQiP9FVTYF/JjfZ8lcck6TmXYEbrPHgbYBjiZI3MmcaGP1iXfZI79gavPzOlUyckvO6WPsSOADxuujXRjLXEBaIceTs/wKvtfAji6tkNO956EtmB9e2Dr2Ep+4ow52VcnW+jHlqLr2r4CaHgnLl/Yb/ZDwWB0xfOpAa8UGvCQr6IDZK/CwzOADNFfusp/VmWSnJE3sQeb9NT4mj9rwJG+izV8BTz7dBW9AnBg49hbPg093jSwhztx+SdQAk6VE4zTnyWKMZyGa0lrL1SuAUFSHRTQMs6UbHaO3PNWLVwb6xmDo2rCCElQOBJGnSMVyK+9PG08By9wpYzo24LngnOJR29YOUMVSwlaQB9KhQ/w8TflQlN9KXs/Bo05MsFfIPOgQcBn6CrpcULA4HsR2F7xDe6qvklQ4G5uNJSE9HREE3tzRa4HzpUxZ9QlBz2YC5/NIaARsKvIwlGARUYFHei6VeU0D8PtOqG1ejzXwFj8d4og8WjpOgPwsw90RXf7mAW0dlIGD4lFG2ACz58jcbEWXJwo4o2kdI0fW2A+483DDimUhG9H9mUs+pElP/Fgaz608MUdSSZGANelxIWcsxOC2jU5FaS60oiGvYz6W1Ceq1pkks2TuKCRvpokxhULCYM+gSQqgn8nL+yRhJ/tFCSw5a5p4HmPn2feNxOMByfFEYm8sT6DE1suSYkt1szFNpAjeko+RwBXtMEX+2lxB1uJi7XQWZCdZCy4LgGcndJLSBUk+jX3ADuR5MUp8REI/eBG3/HLvuC5tbc7cfkCIvdkj8xv0e0pQKFNAMlnXr0+GVdwkLgs2Z0lYPf4fzHD1Xhbj83rC9tXAR2jQ+JLt2ys/xyycwTg/OoTF0LPIam6M4iqGLPMNAdDKBB2f9vPioPpgUBJOrz0nGszFLQKhNIYyQal7IO/HsytSu5dB/tGF8IsGBcIC74ZyCXH53MBh3Ec8KifwCAvcLeAF2guKAjYvwTIFYJU3VvamouRbJXd7+7WolvAWv6W5AQnP0NfDl0Sk73hkzESlzYw9RnjIujoeWks/F3L6Z+ZE/8Fumg5oosxcEc3pyWSHEE//ARyqmgSo0o1CX0kDk694FWF7Jv8W4v8u/q2td4IOE9VaTTE79Ge1wAu9oEGrpGosCd4BHB6jsTFvvLtOAJc9JrdWwDeZBC9yaDgixMz5xFAG/LMiXPmazobgIu1vVsjEF3iOV1YSlzIONqgp331AAd6TL69F+TEILoVwG+FHnzRXxslLp4rqgierRvQ3z6swZnhkatrdJNtc4WFTUGT4O8nWWMjzdfqTJu4AH3pBBtHF63nMz/tRfKj2IUGLV4t4K/Ex3r9/rcSF2AMOyvID022IPqYK8/wPQLkg28hswpCEskjcgsfPEVTyRB9j9yu7Y+c3YnLF4VWwTd9Jx8VmTgL8Ijc8jeKivh4JVgf//b6GjIknkC/50j60EuhUnywZDOeCuyV3uKbuIZe++xqGhwBOL/qxIXCExyOTgbKCS058CVAOAZX9cG7EBzfrCHR17qCVYEER8tJzDKFQHJSmJrAfw0oP4fLAHGqAn3fzuG/RAvYKfUawFnAre8I4J9/2tnjwngIiPAjgD+qb65+xYEZZx3VWklAzx9jvMeiqhswhqPnYNvgF9iTCrNKc2u00E6QI3GIIfQZB+lEojeO1pVcopvfe4An44gX6Dzipc+sAUfrOL520mSvlBL+7RWcJYCbvTg5I8OzgF7kxbUZyeGeYNpe7NN1HIk3w9zzqgJ0SeXcO0fkA20jA1cmLmhqTfwVMCa437u2cfZBr+kMGTyjUgknso/muetunS2wP/Kp8rfGJ3ONEhe/SxLYUrzuwXO4CJ5dXUvS1wIczMveeWaMz0aJi8/Ryz5bXQ+QX8mRE1Uywpbhm2SnTUyAufSn75KSFq8kLq3to590g7NHD/gE0A6e8PJ8JCP+zpVTz62fPpXERV+Bqv0plBi/BcbQR/Rng9GsIhdLYD50Mg97mG8mCt/2gHH2gjZOtSUxcG7p0wOZeu2JC/rwbwqOZO9qwHOnjWRe/FSRxzOBDtm/4uoeGxr8+dm9NwSOAHzZF/ZHceVKsDY7gG5OsNFhZLNeMsD31SYuNswZCGIkC04c9iggQyrY9/L33jvF5iDAMnDGlFDvcTLWFhAJNNaMf8A6HLzrKoJe1wtcq0gVZW08ZRdgqhy3Tj5gLMcmMBpVWu1Z4ET4AgIMR9+quMAc8PDSv89y570FCijIEewE9HE9Q9W1v/8LD843d8Yzn8/1l4z43drGCeRdoevxR2tJhZMOOPR4+ZtTYRydKtnHCKyjr72r/sIL/dFVJTffSLcF+sBdFWnWEAUH/GIQBG4CzX5Pa2AONLJPSaFgKe+8zEIcC73MyaM9XZm4wBvf8F8y62/02Lu2sYJUp4kCUDpz1GHCRaKL1hL+JRkbAV6RMcUAeCztS79R4mI/7J1THklAD/bHLnoJVdLXy5J56TObh9fBQb8+cQF+mtPJl0SnB+PokD35kg/f2pPCRa+7/s77ZP3eR4kL3qtsC9ZHCYA52Bn6Smb6RAmQ4XxzXKsTlcQFmJNvYKPwucV5BJ7DS4DuNAqf2MQjYE57N4/km12Ef8/bWUAPsuQbIdHffEtz6vuaExc8gHuuQ/r9aiBX/Iy44Sjv9wBdoqN84554K3IsFkTDq089AL65GeOmw5Yunw2RIfqbU6c3CfD8VSYuDJ9gk9C6lqLq1jqTCiAYgedEndZQoopDaUFfY1QPOBZBO6fdO9MKGOO0xQuso0BiBAyAaihnr9pvHzmJ2DJI1kBHDntkPBgGFTROmbPv5zOGs5aQJBDgBJ3+UKgAHPVxbc0eW/C3o2IBQ6t8aGecAFWFsg3oPLOehEMQFL7DTxDnSofn+pnf17LaY4+/v923F4gKvLKHgPH26ATC/f6typDxghPzSV5805uvUsWPinOCqwAR/nsTaDigG2fOMMzohb1lf/hIllUEg8va3kdgzxIf8uzUgwyRt6dOXMytjwAK/mQUbfF777rGoqtASTB9RpXLeEkLfgtm0auXwSWwH4Uato8er43zrE9c7Ie+WZfzbfXLM7hIWtgWNmkkv+RK8OW02wmfdcxv/ChxAdZhb1X8zdnTz9+CasmS66M56W37+d3akhbJiPVaGCUu5oCTz13J63XLnHDjA1x7cgLl73Zda5Il9rCdu5q4WFMCrRDiVBUPt8De9GtPSUZFllmACxzoB1vNbq3J0BagkznZcvqOB61MtYCOrzlxQWcFV/KbhP9KsD558hX2/N9zAH+swIqHR2SZb2LX7KPV1SuAvFtf4pD3ea8EfGQLFJbZyTcJ0O7VJS6MlQBdgCxZ4Fj3GF2CJph2P5sDZFAp0Qwh9ecIBal5SXcvcICU0DsqVSUQGHjh/Z133nl8vfDai/g92Dsn2jvoAHq4ukK4OOq+DxwpruAnQb3ggEB6uTggeObYR8aFwzS/xKavcuKpaxL47Vk71jPVXve/nRr523OOQAIHN3/76fqfvv3a/rZHyY/Ec4nm5uTgBeGZdwnIQ/b0wQcfPL4WGk8rPAk+Eh1BSjV57QEOZMnpj9MGAcoaziPQn1yo9AogHcczNrPz2I9gUGKKj/T1qRMX+1eNj14ngNq7pnH4LoF1dYnTPQrmpA9kTwCXwLGKo/65HorGa+PM2ycu9uNETuJDd/UJoBe60WO2aGl+wRe9zh11TT8/lxIX65JHdsfJRbtuwPpkhU1gd8zXgjEctRNmAViP2yhxCaA1e2VPoB+LrmjlNIJd6W0CPUYzzzK2mrjoL1BTBCFLvU1bAvuHB1oKklx5GyWSs2Btvst1l3xdfwWfNcAbCa85+YbRfGj/mhMXvJTww3sk/08N6KbY4KSADF4NZALvFcZim/cCO5NCQ28nnhqsR/f5M/pT1eezwFrskRs65OnKtY/Cq0tcKB2B9/89CKyq45bjHgHCCajcsfSTAJhjZh5Kp4LmnqgAQkW2d3QzIAAQyASXCjgR8T9OfA+7IN/YLQX2XFAs8IH/Es4UkXGzN316nMwjqMcPeOgjkFBxS7CMX4wU3NCrn4PiOy0SbPR46Cu5SmDXgmfmcxojSAnNBGFe+OMQNMGhe6AMXL82sAdBhyBqiQ7Wcd2JgRglcC14Zh54UMqvf/3rn//xj3987LMCxpNp9/zhtMXLEZjD3uHg9Mdckvul/S2BedDVdUBXZFyrm01e9EU/SR++eO8FTjNz9GC+pcTFM8GYkwJ6vcc2tGCsOQTyihMSXGscmRNv6IkAmg763Wczc5JrSRRZMXYNPB8lLt4dcXrkd59r+ItXEnX2iN4t4SZYEHyl2pc+fi4lLuSZTfC11Jyt33tAX6fqEpeRLfQZupEB4/vna4kLPfAuhnH21euXv83vVIi8ojOaBOCGbwK/rDuTuJhfMUjysmVLWjAOvk6jOHxFidH+ZoFss9Wu+0mm7K+nyQwYCy+BHP3s3z8CrzlxQR94+/cFbMkRWu8FOsmWkaVZn3AUIsMScInt0fXJK9s8GzedAdbSXAtXTEjx9kqgS26Z5EYI+r4J8CoTF45DMMgJtU6lCoRdEPa9733v4Xwpz4wBQWjrMviSFgaf81py8FtgbQLoyoigq6rM1hKc+ceN7mVXHSHcKRvHL4FZ2jtDIOi1xkghrGW8AEhwi66Mkv+hQ4nMy/EzktYc0ccJlXvR6DfCPe/QjAJ//QVIAjjBivXMJ3FSDYY/pVY9lVQu0UYQhH5LdDevufBaorZlHKxjLkEfg/Lzn/98lc49kHHBfQK76rge4GBdjt3JCxrO6ou94KtADl9H12wqYA768t3vfvdJEpfQ3JG9o3Ny0ye7s2BOeyVbeGjuyPBe/I3HUyejqtKSlj08ob/ktnLCa802cSFPaOiENu8FaujnZFLSpz/6Za/tfv0OZ32cnNhPC54vJS4BBQfvCY1OTNhCsi+wHQUjxtBF8gzv/vla4sIOsAfkwzoj3IB1JRgSdnuJzts3m+K9wqzvZyVxAdaDP11q562Asfrbl/GCdnuAwxEwhxNK8uhkNXRZos0WwMccil6Ki/BtbatnrzVxoXduIziBXvNJTwHWwhtFRIlD9PtKwHt6pSjBNx1dnz6YUzFCAeWoLuwBNs4NoEoR6WywHputkMH+oMWbAOzoq0tcVLtsmpDObFpfDkkQ5tthXPlgSGYJh+gCOA7QexYMEAGaxScAB440d8UrymcdtFCZdJ2H86wqDYfJqSQhWcIZLhyP6yJLezMeDk5lOCjK6+4qo0SJOHRXB5bWkdh4BwUNRqD6SylHAZC/GV/VPacheIsXrllxxHAwFn08W6KrO95OjdYCSGMFapx7xTjAzRj4uB7FWfQB3hIYxxhZa89JSQAO6I4O+KiKbr4RH5Ygc+CPYBtv8STPZsAc8Dj6MqN5+sQF71x98h/x8ZI+HFkDD9DdqYPriJIufPe5effOTR7Im2Q+X3hhzhkgR4o3TgErMmWNNnFBKwGqO/ZkI3uli/jLpvl7CS+fw11SPvpGH2tsJS4KHKr8rly1NNXQOYnLKPkU+KhwsjcZ28Ja4gJXdh/94TjCDZgXbdFCQSnVabT0OxmWwJDFmcQF2BN7RZ9mg0d94YH++dKR0T5nIHMqwAmonQS0sr4HjGVzVcI1NMtc5n6tiQt9Y9fJYK83Tw14Qt6csrrJgH57+bsXxBQKLuzRGWvbE3vG5ism0M+rAR0lYnv0+ShYCw3oQa7WvgmAZ68ucVHh5yhmAHGStOSbYzidWUE3hmGXtLju4GQBE8yfNgtOB1Shq/90DugnScB4QVVw2IIELYL7NccNnC5wMJK0tX6CDErLKOXY1tzWEaiuVZYEGTmRGYEqN1z97OfwNzoIxhNECSIENpKVBP9O1TjLNRwokT5rwOmo5ucKUgWCn5eNBYZrdGyBXDJEnEyCiD1gPbTFA3xxgiZYrOIPzGF9tKV79AfNqjIXQAvy5AXjmXE99ImLeckeGaAP9MlnRwB9yBO5cDoQh4QOfs7ij5+ciiSbTKoOWmPPPOyPk45q8GNMm7jAg/3CQziYg36xqd7TW9NXoL+xZGnkqP29lbgYl+QLPpFvfeGEv+yrfgF9/M0e5As79O/nh9tS4qKvOXyLm34j3ALo5nQEnhINv9s7/PJtRnRiNnExB/57d2x04rQF6GDvrtqwr/kWtNBwL/CP9oV2ueI1i1sL6Ic2TtbIK1kI/V5r4sIXKSCRzSO03QPsNZkny2KHWft9FMinddke9uYsMC9fz8f6ScauBHKuaCaO4eOP6uEecFU/3zr4HOvPAtm7E5cCIBQD74VdTo+wYXCVaPrp750Q1wecjuQ6lGdtmwHjGTOV4hknxnFJGBjuFoc18JwjEeDlpds18I4FOjM2a3NLBlUmGQ2VSEmM3zknwRX8lsBJjYom/owAjR1rm2cJB7y0puRP0KWvIJ1xFBwIovRZGg9XVy+26OE5mqhU41UVrK1C7PRnjRYtwFXi6J69IMIcR8C6OYHiOM1ZxQXAR8MncgevUbV9DfQ9M3EROGdfjCC5TmX3yPzmFFg6EZAEszmxF9rs/PoKLMk6fcr7YHvA3iUhgo9q4AH3JC7wZ8O8V5Z9CSgFl/gCz+x1aW7rSqjxYATGbSUu1uDsfZFG9BPoS89GiYt1nfap2CrWLNFwLXEB5ndSYc9r+wzAE98UEtDMGGugp0CFzZlJXADc2RGVb/PNQPCFiyKKq7JOmO1ray9rYCwaK/r4AoIzgjB8VUjAMzLLnpPh15i4oK8kGM5ocDVITPlp/CV/Fdk/C6xjz071FB7gchaYmyzY29Ip7VMDHPggvhFtr6JrgA2iB7kudvX6s8DO3InLCiAKIklafP2fd1sojc9nCEbpnDy4NiJQN0ec7V5gOARdlM1VMWtUcDLO+t7l4FyqYJwkScBXwd81GcevAvs1vASwnJJEQTVeJdD1MEEyhVpzfipxqnx4NALBBxwEW2s4uDrhbr6AU1N9ECDil2Rtjd+q9RKc0H+pn33Ah4EQ0K7h04Jx+OQaDtyqQSsDJDCRaBw19HDFb0GiBE/ggGZVXALmQQP6JHmZ+QY8/c5MXPCYPRCA5pRvdj894JXgiixJhiO/azK8BvYJX6ep+Ej/qno+AnIg0Vd8qc6B7xIXjgJ9EjzhI9mXVCs4CDDt07xLc5sLDl4uliSOwNitxMVn5BsPc5W0/XyUuOA1WyGB8PkST7YSFzRwCuq0Itf11sBzhQr2lq3GPzKicswn2Ots4mLO/B8avy/Rew3sHy7stDv+CkxotGeugLHoJsmGW3vFaw8Yy7ajM7spsCNn+POaEpfInKCdL9uSubMBH/hxvp8N8nfaFUBW6Qm7mhsiZwJ6kjEJN5tzNaCjffmGVzp5NX/RU9zlNF+8s9dfXQXwvROXFdDfXeSPP/74obAINkMofTlS1zNUB1XqJBtnEJsxczrAeafSWZlXXwZbhaFaXSDIaOH/i1S+zQQeXgB2EiR4W8NLMM55cOK+NEGQTiA58rUgzef2LjhfMmT2lxOiNZztzSmYoMxVKI4R/hKpJC5L4LkKNMO3Jh8+tx9Bu0B5KykLGGcf+ZpjuFaAPFgPbva/tocqmJP84hcacSZLtF8CeAheXXtyPK4KXpmDvJ+VuAiAGGnBD1sgAD9qrOGHNuhNjqxzZE57RBcBJTl0jbLK+xHgnYA5pxRVME7iImgRgEv0JFL2SmecLPm8slc0YQPZhaXEwL63EhdgPYUkNilVQm2UuPgcjvROkWdpTrCVuFjXM35EIlTlifV9QyE9Fny75sbOsRtOX2YTFzQia+SD7O0FNMLPJC9k7ojcwo1+04EUBHx2BPBUYGe/7LIkHu2OAL69KYkLvVHsYvu2/NHZYC0yIf6RbKcI5vOr8CA/ZPRIor4F6Ook2PukR+R/L1jfe2fiTfJ+JaCnNX1L7nMkxrNAHu/EZQEQx+lIKsP+niUShyIgF2Dn/vcZQgEPxkxAo/JWVWZ9VG6cJDBAVQVFt7x/wtFtrYVWqm4CbU5+rb+gkSNC408++eQR2Od/2izhZz5rMKRObJYUHd6CC0ZfnyU8rIMeFNc+ndLA3TWzGOolsD+nEJUXFq0jgHGaRLbwsALm5LjJkcSqAsaY3/G6QHELtyrYA95IItCfPM2COeCWayWM5ZZ+nZW4oIOA2zd9Bf8zdDJVYaeHZOaog0UPgaSTNkme/S/pwxbAA06CeS+iVuUO2IfEBa04dfS3V/M4RVCJreImcWJP104O4VpNXCRPEhc6Efmh56PERR92ZquiupW4APNJNJxe2VMF0JHuk3f8zBVTJ7uc8Ezikn2ya+RtDdctQEc6wV7bd2i5F+CGt+jiihfbiE9HAZ58KX4rcJn3CNjzm5K4oJ8CHFu+1wbsBeuJW8i6pP+IbOwFsq7IpUj5VPuPr+T3YzeuBDrDD7JR/OuVEJ1lT6x/hj98SiCDd+LSAaZx8py0+78CaePADJEomxMRlV2BOGcc53oUzE2JVergWpkzwsnRUdClwGEEnKygOacKW5CKpMB0ywjoKyiSYKhIunay9k1igPFyDYHTcQK0hJPPVWXzjRlrRs96AgqBBePlBVwB7hb+1hB0cCxbSY79WEeSIykiE1VQkRE0CsrIY4XneOxdFwHE2snULJiXM3VNiAER7JDJWeAQyYlKqgQUfks8suZZiYvTxt/97neP0x54H5kPXoJhRhSdOR2f7Z3TODRAUxU4V87I7hEczQdHL6DSG/hVgbxKXNgxuilhYc/YRglI9LSCHz3BazxY4rN5KomLzxNsqJSmoIKfbeLiM+uxRXCPLV+CSuICclJMhuxlCc8WyLf+5FiQ4Cd7vCdx0bxrFrt8BPCQ3KKb4pS9reniGgQ3Mub6LxqRF/Zrz3wt4Dc74Z8Ck8UjQA7ehMQFzdCRvh3l8x4gG+IY7/eewcNZiM7wy1u3N44AeVUo4vvP+B8xs2BfdJA+5zrelYCvig3inqOnuE8NZOJOXDrQR5Dnu/5n3h0J6EsIVIe8iO9aCkfs87SjYD6ZMUPGsFSAIKraO00wvjoOvgJ6wd6W0wf2LuDgkN2fJmRrINgXxPsuczQXNKwFLMCcTmgEims0gIuki3Pa2rO+rnAwkIK0X/ziF4/gYmvP8Mw4hrUC9od/1WtSIAbcVZ2ZUwL9BJ4M8pmVHPPatwDiv/43wOZcfTYj3/qir+Ac7wWNS/Qgv2clLgI0DuIowIMzFyQ71XLNscqXEZjPPr3Q7R0Ktgq+ZHPvno2Dk4q6AsGsQ9LfWJVuMuvKmJ/5n0QVvPSxB4UWJ5prSa6+lcQFeEa/2Q7yZ40+cYEjhyzw0neLP9XEBd/ZH4kTH1HlkfUVU3zDoMDZKTOauiIyC+iUr6etrL0F6OQ02JxJwO1rDxhHlyVB7KmrY0eLBMCc5Nh8R4BevfTEBa1iHxXftgpjTwFkgN4qLG7pzlMAPWT3JfZ4f4acjyDy6vpwpWD5FIDXCixweMq9jsBa7JirvPT/OWStCmhzJy7/BEqJIBSEM1HZIbxVhxTAfAEmR+8+LsWj/OZIOwLwkUgwZDNJFUF0KuCuqL1WxumnQitYdIxZCXqMEVg7Wua0thyfPTDM/nv/D3/4w0eys+Xg8BENVFAlC2trwMeVOkHLGv7Ww3/rw8U/GHXyA78tgK9qtkCiQlf9JcUCJAHCFo2APsYxKpwIXCsAHwG1YNO1FPQ4A0IvwQ6cJAJbNO7BHPAhm+jhGtFS8mLeMxIX/BTYChaPABzgLXB1ioDG0fM9EFqgoX2SbcHxkTmBsegpcPetdrP8tz5d++yzzz7/yU9+8nhBlhyZs4qXOZwOCIDYBH8vgTmriQtgX10b8u4IHcFf+sH544/9KkBIBCu0rCYu9s9HkH1XwGboyn6xM/4HFbo6/WNrZiF7bU+XjoA95FqQhgaz8tICfqATPirswPVoQGQ++nE0aMGDl564oB/7+re//e1RJKr4iTMB763rJgQeXr0+cPKsMAGPo/K9Bean0+TLevnsKmCfnPb40qHnOPXAX0UzV4Cf43SvCmzAnbj8L9g8w6/q1V8hSdsCfYxxrCqQERwJSil/O09lriWIIVE9F4RgYAUIpMQjX/laBfO7MsfhJInbAsrmtEmyUAn67QluX/nKVx53l/Foi05wIbRecIXjWl/Pcq2lYggEoIKm99577xHE4OkWmNd9dUpfAf3tUzDp5GjGQKksO51wVasaVFhLFQnN/H6WA0Jb9BfsMCSSFw6uKpfhs30IaJxO0p3RSRQanZm4mGsvwBcdBZvei8L3ipwsgf3Yn4DdCZagMUlw2l4wL32kA2Rmlvf4gF5f+tKXPv/www//78RkZp7In3kEw2tj7XUmcYGfEwc2UaJnLXIkmLcWfkuIXb+o4FxNXMiAuZ0mzJycAnigoermu++++/nbb7/9KHLNAt5KItl1AV7VHmyBYNmL0E41IjN7ZTB7ZX/ptm+0O2KD0DmJy945ABxeeuKCn4p6ku4jPNgLaOSan5iJfb5yfWuRbzc96PLRhLcKfJnYRRxHv6+ku3XENrnOj/5Xg/1bn216DpmrABvw6hMXG2dYBehOSXLXntHAuKpxpGT5li+KxomeDfBXPeSYzQ/HLcZFGRhAbUYZODDvMXDmVUA7NPRtQxWhQl/O8fvf//7jfSJ03AJBBWftCkuFP6qiTpoqc6OVIMvLgK7WVcboQ6asUQF0MUbV1bc82U+FVgD/BO+CCrhWAI0k0aqegqyzAhwAbw0PBQGucQiiqmAs/ODEUXBUwbOlvd9fSuKCX75cwXWfyMhenLJ/DkOlzUlQ9RSuAnB1FVNg3NKzCvSZzfz2t7/9uHJI/uA2s18JCNkg71v7Mu9M4mI+19acuigCsIvsoxMXNBXYKxBUk7Zq4hK+kSXvhaDTDBhPFuH6wQcfPAKVPYCnbDS/cCR5bgFu9p/3M/0e2m3xYwTGwA1/6J0Cob3vgTZoqfBzCcjxS05c0AyOCmIKe0f2uhdU3fO11levzx/QfwUJV/fJyx7ZmwXypTiTE8fnoLtYVMzCZl0N6My3uTJq/TNjhbOgtQFPKRMvNnGJceDUBdptNciztC3AbA5SICPwyv3gs4EiC24kBuYPnmugn2NH97E5+Ape5uRo3MWWfc9UOwiVhILiVYBiuB7jONz+KooioRKMuV5UASczrtZV9g5/FWqV75nEJRXlKuAdugp6XMWpOnJroZNKP/wqgJ/oKjjiiCLjZwK6MXTkRXDiSxOqgVT0DE6CTSd8gk3yF3mw7+dOXOBnPLmTYDjWlzwewcfY0I0xpqNn8kalWzGFDdgzLx7Ci/2wV/yY2a++6MTmVk4k9Wc///CHPzzsQgVnPJGokBtJj5/0nZ54p49dt48K3tXEBZhPUPfXv/71sW7FdrVgb3jPVsKfjPtshr6APOIROsyOXQK4ON0VwKGnhNDce+e3r+gOeXRjYA++CVoUbo7slQ18yYkL2ri2bq9kZI/u7gV0tZ4AOieZVwN9lUAoSMxexTwC9m2/bjUowF1J9wCbomhQvSVyJqAzO60wnCL+S4PYgFeZuNgw5VAl936CKhAhmSGEvhidF/EFyJzu2cTMOpyq6lrWSFsCz+xRMiVYqyqhfhy36vnsXUtCJcjh7CoAvxjoaqWZYglIXP+ogOq9e5uVfVgfHgIf9K6M0YccVZO1AJ4yjpKJmTulaMbpelHfHGsyELAvCZ93FGavp1UADuYUwEn6fCPWzAvcLdifCjKdCg9irJ4rcYkuCcIlLYLho9cn8AQ+ZE2wLNCuyH8VzCX4oI/4AtcZfPGOHXCFk86Zb2YO/cJLAUg1GZhNXAD9S3LkBVc0Val2P57cV2EmcQGSOcEdfcTLWUAfJ83s+t4AzfVhlfkUfmZ4vAZorwDBdgoi6WD4v3cNOmMu/7TXu5B0e0bm0cu7Uv/pL+dLVryXSB7J1QyNjoK1yD+dVexC86tBAcv6bolcuXdAh8QvYgzyeZY+VYFNcdKpVW9VnAno7daQ9yyfY/0teLWJC8NHGQXM77///sPBYRCGzRDCHE5aXDHjnBnlpxB0eDFkHLNA17rWSFsC4+AEvxnnTXHd3/dNSbP7IVTuMlevTaG7aqMTi+pJABoIPKv/Q0RA8Oc///mxly2wV/0EyJJZ+9kC/Tl4AW1kaKm1oG/ulDp16Z8vAf54F0dimbu4W2M9txe0czL4FFU0a8BN4KlSC7+9lWhyIaBzssR5kw08P2qs9iQu1sPj/B8OyQAdrMjqGtgTxyxQTnXtyN5agBsec34zJ3otGJPT0z1JGr6rHjpVVNyp6t+exMUYvKEPbLzERcGGHleTEDCbuNgT2+q/XtPlWUAj4wVpORmaBXwSaEmg4H2WDAH788UKTsRnv5RhBPZL7um2kyryMbNnfflv/D0CcHjJiYtiW77whA6g+Zl8XQM8Vm13uyPfFnklWE8SrqBn/av2HUBvxQD/lJX/qtitM8H+2RLXZa/4UoIRsKMKFnxI5O+lAPl8lYmLF1VdD2OMOVTGkLBUiaCfMZirKqLCFwH37Eximsu8gmiJi3VbQ7a2luCP8RFo2XsFrCUIENw6BagGDgH4SRJU2ytAMQS4M/9IU9WbUc83f2yB/fhmlEqAH1DRc63BfrYA3pIo/JlZA5hfAu1otvp1uubHWy/QCVYYucqa5hbYwFPVs7LWHiBDEm3GBW/JUYWOAXshd3AVXHhZ0rUxPD/rxCXfMrXVGEm4c6AMuYDJ3kb9qs18EgF8d0WV7OOnz9t+/Rozjb67jqP4wE75u32O92nt522jX/4BpmQXfi1ulWaM0z10y/5G62gZo48E8Te/+c3j9GRtjJY90AEVQvrAoblKSs7d1yZHLV5rjZw5LWTP289Ha2uesWF8Sv7/VPtsq9kf3ijcOKlkq9r5K41Mo9mvfvWrhw3ZotlMM1f46FvQnHKnQDfbQhtz2qdkC63pls962vTjNWsrOvynnriwbfZOlslECgZpVwB+4w1bh2dXrQusRRYk8QoeZyfiFbAeuueLCaqx05mA7op/1vf71YDuictmY5qnBvrx6hIXxuDXv/7146VopwJ7hMIY1SdJCwOaqjIipp0FAjhOWQChqt+vs7aWqo2rQQSvmjUzWmjkpVG/V8YE9OXEfTObKmJlbCqlnFcFzMmJCmKtVQH0SyJW5bf9+4pVRnQL0NbcqVBxsPActR58xkiouEpE8KoKKqFOeSTfo7lHYH4BlkQJj2YT0yrAB38YX3pCdmfAeLihP3l0aqgC5qXB6l5HYP94S+YE9lsNbQWz7jzTQRV5J7Q+b5vPqk3w50V3FWenbZyDz+mrpNxPf/drzDQVU1fEVC3N6bPsp28tbm1DJ9f1vFTv7+BXbfbF/ghAjLdWT9seB2uoxn/00UeP4CW0GLV2vPXI9ccff/xIlBRP7N1JOJ6ZJ200V5pAAa+9EFzpr4XWTprbvVQb3CXTZFxBod1XpZmDTJEndsR8/Rp7W+a3R4GsKrAr0T0OldbyHF0VhsiHAImM2UNL83Zsmj5suaDlCLzUxIV/4kvYBfFJgsa0K4BPllDSm6uBzZeE58txBKlX7bsFa/Ll3jVRDLgaB3LAZ/qHkElerwQxDP7TT4W65+DBEry6xEUQxDG99dZbj+CawUQAR8+aCl2lGfPpp58+rpkRbMEjA9j28fcZzVwCpp/+9KcPY9KukXUEh6P2pz/96fH+jgpVP+9SU4n+5S9/+XD+fg9tqk3l0f9AkViM5u+bPfn/ECrPo+d9s4b73KqL8Bv1aRsa6ac/h4d3o359E7A5pdF/RNu2WcO+vQtgDTiO5hw1Y/UXdJBJ11TC261GfiXhAq3R81EjC4yRirafPhvhdbSZF+3w1VdLCyTts8VlqWV85kJbgSy9PXriwug5vVS46NddamQZb+FhTz4Lbmn9mLVmDkG160X4nTnRx++a3480fLZHAUC7tmfV+QXiviSBfWvnqDbrsD/o1uvMiH5Zl77+5S9/edCmxWer2TM5+/vf//6gbXAPTSv7drrn65PhkH2M+rXNuvwAPez3VWnmsFfvfVTtZtuCI9sj0PF3++xIa+dHS/y0Tt+v0oKTlvnYILY2vniLR56TaSdpR0BCIHH57W9/+294PXezR0UVCSJZtN+efi1Nn6LxyWIbtnv0/Cmb/XsvzbeM0it/j/pd0RTc/E+/vfJ+pOE7neD3xGLocAXvNetYnw38zne+85CH5+RD3+Amfjn6BR1b8GISF1mkaw8quJorXhIZPxkxx6Oa37caonk3ox3vZ1rff28zvyPLHB2P+gTvvuWoU+XU36Oxfct6fvq73dNWC0292OVnZU3r6J+xlYYWcPR79mquvl/bQkPrjZ73Ld8f7/ce51GDf3jUz9U26/ctY/Ouz6jPUsuafh+tN2r62ttePm+14Ob38Cq0zLO1lnl6+ppj9uSmBxU9FSSnVebaauyFlr9du2o/b1v6VJp5tHZsPssaZ7XRGqPnbWv7Ol3W8vdMa+dv12zn17KG1o5Za/18ae3a7d/tZ1utupbWzr3Vd9RC2/yd+WZbi2v7+6hvtWV8cNvb4KKCHrzaOfNZtdHd6vXYJVDA8MUzfGNrZ567xWbyP/2z+LreNp7dzF/xRbHTZzd7RwO/93hd3eIjKzHGUzS0SKxz1fqhtz1n/1fwvdqCG9vxKhIXm5S8OHlxVNw2FZhqOzJ2th1Z68iYjGv/nm393KN2pP9VY0bP+9b2r47R+nFH2mj+vu0dN9tG61TaaK620d8jwAZIXhzFC14qzZqjz9rWP6+2M+bYaltztzikjfrtaaO500b900b9+zYa17dqv77tWUvrx221fmz792zr5z46X9v6+do19rR2nvxeaRl/9IprGwuM7MxztRlb+FStxWGtjcYebU89/0x7blza9V8KDs+Fx6jR36eEF5O43HDDDTfccMMNN9xwww03LMGduNxwww033HDDDTfccMMNLx7uxOWGG2644YYbbrjhhhtuePFwJy433HDDDTfccMMNN9xww4uHO3G54YYbbrjhhhtuuOGGG1483InLDTfccMMNN9xwww033PDi4U5cbrjhhhtuuOGGG2644YYXDp9//j9qb4q5NgmC4AAAAABJRU5ErkJggg==\" /></p>\n</div>\n</section>\n\n\n\n<!-- code folding -->\n\n\n<!-- dynamically load mathjax for compatibility with self-contained -->\n<script>\n  (function () {\n    var script = document.createElement(\"script\");\n    script.type = \"text/javascript\";\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\n  })();\n</script>\n\n</body>\n</html>\n"
  },
  {
    "path": "2020年/2020.12.24Mandalas/Mandalas/Mandalas.rmd",
    "content": "---\ntitle: \"Mandalas\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n\n## 参考文献\n\n[Mandalas](https://github.com/aschinchon/mandalas)\n\n[R-blogger-Mandalas](https://www.r-bloggers.com/2018/02/mandalas/)\n\n[Mandalas](https://fronkonstin.com/2018/02/14/mandalas/)\n\n[Inspired by your work I created this](https://hrafnkelle.github.io/p5mandalas/)\n\n\n## 简介\n\n- 以（0,0）为中心的单位圆中获得n个等距点\n\n- 对所有这些点重复该过程，再次获得每个点周围的n个点； 半径按比例缩放\n\n- 舍弃了以前的（父）n分\n\n\n我反复地重复这些步骤。如果我从n个点开始，迭代k次，最后我得到nk个点。然后，我计算它们的Voronoi镶嵌，并用ggplot绘制。\n\n```{r message=FALSE, warning=FALSE}\n# Load in libraries\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(deldir)\n\n# Parameters to change as you like\niter=5 # Number of iterations (depth)\npoints=8 # Number of points\nradius=4.8 # Factor of expansion/compression\n\n# Angles of points from center\nangles=seq(0, 2*pi*(1-1/points), length.out = points)+pi/2\n\n# Initial center\ndf=data.frame(x=0, y=0)\n\n# Iterate over centers again and again\nfor (k in 1:iter)\n{\n  temp=data.frame()\n  for (i in 1:nrow(df))\n  {\n    data.frame(x=df[i,\"x\"]+radius^(k-1)*cos(angles),\n               y=df[i,\"y\"]+radius^(k-1)*sin(angles)) %>% rbind(temp) -> temp\n  }\n  df=temp\n}\n\n# Obtain Voronoi regions\ndf %>%\n  select(x,y) %>%\n  deldir(sort=TRUE) %>%\n  .$dirsgs -> data\n\n# Plot regions with geom_segmen\ndata %>%\n  ggplot() +\n  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), color=\"black\") +\n  scale_x_continuous(expand=c(0,0))+\n  scale_y_continuous(expand=c(0,0))+\n  coord_fixed() +\n  theme(legend.position  = \"none\",\n        panel.background = element_rect(fill=\"white\"),\n        panel.border     = element_rect(colour = \"black\", fill=NA),\n        axis.ticks       = element_blank(),\n        panel.grid       = element_blank(),\n        axis.title       = element_blank(),\n        axis.text        = element_blank())->plot\n\nplot\n\n# Do you like the result? Save it (change the name if you want)\nggsave(\"mandala4.png\", height=5, width=5, units='in', dpi=600)\n\n```\n\n![](1.jpg)\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2020年/2020.12.24圣诞节/1.json",
    "content": 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\n"
  },
  {
    "path": "2020年/2020.12.24圣诞节/marry.R",
    "content": "# https://www.data-imaginist.com/2016/Data-driven-x-mas-card/\n\nlibrary(jsonlite)\nlibrary(ggplot2)\n\ngreet <- fromJSON(readLines('F:/我的学习/R/R_example/2020.12.24圣诞节/1.json'))\nlayer_names <- greet$layers$name\nheart <- greet$layers$paths[[which(layer_names == 'Heart')]]$points\nmerry <- greet$layers$paths[[which(layer_names == 'Merry')]]$points\nx_mas <- greet$layers$paths[[which(layer_names == 'X-MAS')]]$points\nrange <- greet$layers$paths[[which(layer_names == 'back')]]$points[[1]]\n\nggplot(as.data.frame(heart[[1]])) +\n  geom_polygon(aes(V1, V2))\n#============\nlibrary(mgcv)\nheart_points <- data.frame(\n  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),\n  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),\n  colour = 'green',\n  stringsAsFactors = FALSE\n)\nheart_points$colour[\n  in.out(\n    do.call(rbind, lapply(heart, rbind, c(NA, NA))),\n    cbind(heart_points$x, heart_points$y)\n  )\n] <- 'red'\n\nmerry_points <- data.frame(\n  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),\n  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),\n  colour = 'red',\n  stringsAsFactors = FALSE\n)\nmerry_points$colour[\n  in.out(\n    do.call(rbind, lapply(merry, rbind, c(NA, NA))),\n    cbind(merry_points$x, merry_points$y)\n  )\n] <- 'green'\n\nxmas_points <- data.frame(\n  x = runif(25000, min = min(range[,1]), max = max(range[, 1])),\n  y = runif(25000, min = min(range[,2]), max = max(range[, 2])),\n  colour = 'red',\n  stringsAsFactors = FALSE\n)\nxmas_points$colour[\n  in.out(\n    do.call(rbind, lapply(x_mas, rbind, c(NA, NA))),\n    cbind(xmas_points$x, xmas_points$y)\n  )\n] <- 'green'\n\nggplot(merry_points) +\n  geom_point(aes(x, y, colour = colour))\n\n#===================\n\nlibrary(deldir)\nlibrary(ggforce)\n\nheart_vor <- deldir(heart_points$x, heart_points$y)\nheart_con <- split(c(heart_points$colour[heart_vor$delsgs$ind2],\n                     heart_points$colour[heart_vor$delsgs$ind1]),\n                   c(heart_vor$delsgs$ind1, heart_vor$delsgs$ind2))\nheart_boring <- lengths(lapply(heart_con, unique)) == 1\nheart_remove <- c(\n  sample(which(heart_boring & heart_points$colour == 'red'),\n         sum(heart_boring & heart_points$colour == 'red') -\n           (1000 - sum(!heart_boring & heart_points$colour == 'red'))),\n  sample(which(heart_boring & heart_points$colour == 'green'),\n         sum(heart_boring & heart_points$colour == 'green') -\n           (1000 - sum(!heart_boring & heart_points$colour == 'green')))\n)\nheart_points <- heart_points[-heart_remove, ]\n\nmerry_vor <- deldir(merry_points$x, merry_points$y)\nmerry_con <- split(c(merry_points$colour[merry_vor$delsgs$ind2],\n                     merry_points$colour[merry_vor$delsgs$ind1]),\n                   c(merry_vor$delsgs$ind1, merry_vor$delsgs$ind2))\nmerry_boring <- lengths(lapply(merry_con, unique)) == 1\nmerry_remove <- c(\n  sample(which(merry_boring & merry_points$colour == 'red'),\n         sum(merry_boring & merry_points$colour == 'red') -\n           (1000 - sum(!merry_boring & merry_points$colour == 'red'))),\n  sample(which(merry_boring & merry_points$colour == 'green'),\n         sum(merry_boring & merry_points$colour == 'green') -\n           (1000 - sum(!merry_boring & merry_points$colour == 'green')))\n)\nmerry_points <- merry_points[-merry_remove, ]\n\nxmas_vor <- deldir(xmas_points$x, xmas_points$y)\nxmas_con <- split(c(xmas_points$colour[xmas_vor$delsgs$ind2],\n                    xmas_points$colour[xmas_vor$delsgs$ind1]),\n                  c(xmas_vor$delsgs$ind1, xmas_vor$delsgs$ind2))\nxmas_boring <- lengths(lapply(xmas_con, unique)) == 1\nxmas_remove <- c(\n  sample(which(xmas_boring & xmas_points$colour == 'red'),\n         sum(xmas_boring & xmas_points$colour == 'red') -\n           (1000 - sum(!xmas_boring & xmas_points$colour == 'red'))),\n  sample(which(xmas_boring & xmas_points$colour == 'green'),\n         sum(xmas_boring & xmas_points$colour == 'green') -\n           (1000 - sum(!xmas_boring & xmas_points$colour == 'green')))\n)\nxmas_points <- xmas_points[-xmas_remove, ]\n\nggplot(xmas_points) +\n  geom_voronoi_tile(aes(x, y, fill = colour), colour = 'black')\n\n\n#=====================\nheart_points <- heart_points[order(heart_points$colour), ]\nheart_points$id <- seq_len(nrow(heart_points))\nmerry_points <- merry_points[order(merry_points$colour), ]\nmerry_points$id <- seq_len(nrow(merry_points))\nxmas_points <- xmas_points[order(xmas_points$colour), ]\n\neu_dist <- function(x, y) {\n  ifelse(\n    merry_points$colour[x] != xmas_points$colour[y],\n    1e5,\n    sqrt((merry_points$x[x] - xmas_points$x[y])^2 +\n           (merry_points$y[x] - xmas_points$y[y])^2)\n  )\n}\ndistance <- outer(seq_len(nrow(merry_points)),\n                  seq_len(nrow(merry_points)),\n                  eu_dist\n)\n\npair <- Reduce(function(l, r) {\n  pair_order <- order(distance[r, ])\n  c(l, pair_order[which(!pair_order %in% l)[1]])\n}, seq_len(nrow(merry_points)), init = integer())\n\nxmas_points$id[pair] <- seq_len(nrow(xmas_points))\nxmas_points <- xmas_points[order(xmas_points$id), ]\n\n\n#=======================\nlibrary(tweenr)\ntween_points <- function(from, to, length, stagger) {\n  leave <- sample(seq_len(stagger), nrow(from), replace = TRUE)\n  arive <- sample(seq_len(stagger), nrow(from), replace = TRUE)\n  x <- tween_t(Map(function(.f, .t) c(.f, .t), .f = from$x, .t = to$x),\n               length - leave - arive)\n  y <- tween_t(Map(function(.f, .t) c(.f, .t), .f = from$y, .t = to$y),\n               length - leave - arive)\n  points <- Map(function(x, y, l, a) {\n    data.frame(x = x, y = y)[c(rep(1, l), seq_along(x), rep(length(x), a)), ]\n  }, x = x, y = y, l = leave, a = arive)\n  points <- do.call(rbind, points)\n  points$frame <- rep(seq_len(length), nrow(from))\n  cbind(\n    points,\n    from[rep(seq_len(nrow(from)), each = length), !names(from) %in% c('x', 'y')])\n}\n\n\n#================\nn_frames_still <- 36\nn_frames_trans <- 60\nn_frames_stagger <- 20\nn_frames_stage <- 96\n\nheart_to_merry <- tween_points(heart_points,\n                               merry_points,\n                               n_frames_trans,\n                               n_frames_stagger)\nmerry_to_xmas <- tween_points(merry_points,\n                              xmas_points,\n                              n_frames_trans,\n                              n_frames_stagger)\nxmas_to_heart <- tween_points(xmas_points,\n                              heart_points,\n                              n_frames_trans,\n                              n_frames_stagger)\n\n\n#================\nheart_still <- heart_points[rep(seq_len(nrow(heart_points)), n_frames_still), ]\nheart_still$frame <- rep(seq_len(n_frames_still), each = nrow(heart_points))\nheart_to_merry$frame <- heart_to_merry$frame + n_frames_still\n\nmerry_still <- merry_points[rep(seq_len(nrow(merry_points)), n_frames_still), ]\nmerry_still$frame <- rep(seq_len(n_frames_still), each = nrow(merry_points)) + n_frames_stage\nmerry_to_xmas$frame <- merry_to_xmas$frame + n_frames_stage + n_frames_still\n\nxmas_still <- xmas_points[rep(seq_len(nrow(xmas_points)), n_frames_still), ]\nxmas_still$frame <- rep(seq_len(n_frames_still), each = nrow(xmas_points)) + 2*n_frames_stage\nxmas_to_heart$frame <- xmas_to_heart$frame + 2*n_frames_stage + n_frames_still\n\nframes <- rbind(\n  heart_still,\n  heart_to_merry,\n  merry_still,\n  merry_to_xmas,\n  xmas_still,\n  xmas_to_heart\n)\n\n\nchristmas_card <- function(data) {\n  bound <- c(range(range[,1]), range(-range[,2]))\n  ggplot(data, aes(x = x, y = y)) +\n    geom_voronoi_tile(aes(fill = colour), bound = bound) +\n    geom_voronoi_segment(alpha = 0.3, bound = bound) +\n    scale_y_reverse() +\n    scale_fill_manual(values = c('forestgreen', 'firebrick')) +\n    labs(caption = 'www.data-imaginist.com') +\n    coord_fixed(expand = FALSE) +\n    theme_void() +\n    theme(legend.position = 'none')\n}\nplot(christmas_card(heart_points))\n\nfor (i in seq_len(max(frames$frame))) {\n  data <- frames[frames$frame == i, ]\n  plot(christmas_card(data))\n}\n\n"
  },
  {
    "path": "2020年/2020.12.25峰峦图/峰峦图.rmd",
    "content": "---\ntitle: \"峰峦图\"\nauthor:\n  - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n  - 中文\n  - R Markdown\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\neditor_options: \n  chunk_output_type: console\n---\n\n\n\n# 峰峦图\n\n上次可视化系列说了[瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=1431845296&lang=zh_CN#rd)。它可以用于展示拥有相同的X轴变量数据（如相同的时间序列）、不同的Y轴离散型变量（如不同的类别变量）和Z轴数值变量。\n\n本节使用的**峰峦图**也可以很好地展示瀑布图的数据信息。它们对于可视化随时间或空间分布的变化非常有用。本节主要使用`ggridges`包中的`geom_density_ridges()`进行绘制峰峦图。详细介绍如下：\n\n## 数据结构\n\n这里使用`base`包中的`diamonds`数据集做例子。\n```{r message=FALSE, warning=FALSE}\n# library\nlibrary(ggridges) # Ridgeline Plots in 'ggplot2', CRAN v0.5.2\nlibrary(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics, CRAN v3.3.2 \nhead(diamonds)\n```\n\n## 绘图教程\n\n### 基础版本\n\n使用`price`作为x轴, `cut`为y轴，`fill`参数也是设定为`cut`。`geom_density_ridges()`内部全部使用默认参数。使用了ggridges包中的主题`theme_ridges()`。\n```{r message=FALSE, warning=FALSE}\nggplot(diamonds, aes(x = price, y = cut, fill = cut)) +\n\tgeom_density_ridges() +\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 形状变化\n\n如果不想绘制密度图，则可以使用`stat=\"binline\", bins=20`绘制柱形图，其中`bins=20`表示每格格子大小。为了防止上下图片重叠，这里使用了透明度参数：`alpha=0.6`。\n```{r message=FALSE, warning=FALSE}\nggplot(diamonds, aes(x = price, y = cut, fill = cut)) +\n\tgeom_density_ridges(alpha=0.7, stat=\"binline\", bins=20) +\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 根据第三变量进行分面\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7) +\n\tfacet_wrap(~color) + \n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n### 加入统计量\n\n通过设置选项`quantile_lines = TRUE`，我们可以使`stat_density_ridges`计算指示分位数的线的位置。 默认情况下，绘制了三行，分别对应于第一，第二和第三四分位数：\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7,quantile_lines = TRUE) +\n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n> **注意**：`quantiles=2`意味着在两个分位数之间的边界上有一条线即，(中位数)。\n\n我们还可以通过切点而不是数字来指定分位数。例如，我们可以指出2.5％和97.5％的尾巴。\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = cut)) +\n\tgeom_density_ridges(alpha=0.7,quantile_lines = TRUE,quantiles = c(0.025, 0.975)) +\n  theme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n\n使用`stat_density_ridges`，计算`stat(quantile)`，通过分位数进行着色，。 注意，仅当`calc_ecdf = TRUE`时才能计算。\n\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +\n\tstat_density_ridges(\n\t  geom = \"density_ridges_gradient\", \n\t  calc_ecdf = TRUE,\n    quantiles = 4, quantile_lines = TRUE) +\n  theme_ridges() +\n  scale_fill_viridis_d(name = \"Quartiles\")\n```\n\n我们可以使用相同的方法来突出分布的尾部。\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = factor(stat(quantile)))) +\n\tstat_density_ridges(\n\t  geom = \"density_ridges_gradient\", \n\t  calc_ecdf = TRUE,\n    quantiles = c(0.025, 0.975)) +\n  theme_ridges() +\n    scale_fill_manual(\n    name = \"Probability\", values = c(\"#FF0000A0\", \"#A0A0A0A0\", \"#0000FFA0\"),\n    labels = c(\"(0, 0.025]\", \"(0.025, 0.975]\", \"(0.975, 1]\")\n  )\n```\n\n最后，当`calc_ecdf = TRUE`时，我们还可以计算`stat(ecdf)`，它表示该分布的经验累积密度函数。 我们将其概率直接映射到颜色上。\n```{r}\nggplot(diamonds, aes(x = price, y = cut,fill = 0.5 - abs(0.5 - stat(ecdf)))) +\n\tstat_density_ridges(geom = \"density_ridges_gradient\", calc_ecdf = TRUE) +\n  scale_fill_viridis_c(name = \"Tail probability\", direction = -1)\n```\n\n### 加入抖动点\n\n`stat_density_ridges()`还提供了可视化生成分布的原始数据点的选项。可以通过设置`jittered_points = TRUE`实现。为了只管我们这里使用`iris`包。\n\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(jittered_points = TRUE)+\n\ttheme_ridges() + \n\ttheme(legend.position = \"none\")\n```\n当然可以将其放在密度函数的下方，通过使用`position = \"raincloud\"`参数。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE, position = \"raincloud\",\n    alpha = 0.7, scale = 0.9\n  )\n```\n\n我们还可以模拟地毯形式：\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE,\n    position = position_points_jitter(width = 0.05, height = 0),\n    point_shape = '|', point_size = 3, point_alpha = 1, alpha = 0.7,\n  )\n```\n\n\n可以使用ggridges提供的特殊比例来设置抖动点的样式。 首先，`scale_discrete_manual()`可用于制作具有任意形状和比例的图形。 `scale_point_color_hue()`。 \n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +\n  geom_density_ridges(\n    aes(point_color = Species, point_fill = Species, point_shape = Species),\n    alpha = .2, point_alpha = 1, jittered_points = TRUE\n  ) +\n  scale_point_color_hue(l = 40) +\n  scale_discrete_manual(aesthetics = \"point_shape\", values = c(21, 22, 23))\n```\n\n如果你还想再加入一个变量进行可视化，可以在`geom_density_ridges()`加入，例如：`point_shape = Species, point_fill = Species, point_size = Petal.Length`。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species, fill = Species)) +\n  geom_density_ridges(\n    aes(point_shape = Species, point_fill = Species, point_size = Petal.Length), \n    alpha = .2, point_alpha = 1, jittered_points = TRUE\n  ) +\n  scale_point_color_hue(l = 40) + scale_point_size_continuous(range = c(0.5, 4)) +\n  scale_discrete_manual(aesthetics = \"point_shape\", values = c(21, 22, 23))\n```\n\n另外一种有趣的可视化是通过`vline_xxx`构造以下图形。\n```{r}\nggplot(iris, aes(x = Sepal.Length, y = Species)) +\n  geom_density_ridges(\n    jittered_points = TRUE, quantile_lines = TRUE, scale = 0.9, alpha = 0.7,\n    vline_size = 1, vline_color = \"red\",\n    point_size = 0.4, point_alpha = 1,\n    position = position_raincloud(adjust_vlines = TRUE)\n  )\n```\n\n## 其他资料\n\n对于该包的其他有趣函数与可视化可参考以下资料：\n- [Introduction to ggridges](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html \"Introduction to ggridges\")\n\n- [RDocumentation-ggridges](https://www.rdocumentation.org/packages/ggridges/versions/0.5.2 \"RDocumentation-ggridges\")\n\n- [Basic ridgeline plot](https://www.r-graph-gallery.com/294-basic-ridgeline-plot \"Basic ridgeline plot\")\n\n\n\n\n\n\n\n\n\n"
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    "path": "2020年/2020.12.30ggvis/ggvis.html",
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)}.ui-widget-header 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t(e,t,i){return[parseFloat(e[0])*(p.test(e[0])?t/100:1),parseFloat(e[1])*(p.test(e[1])?i/100:1)]}function i(t,i){return parseInt(e.css(t,i),10)||0}function s(t){var i=t[0];return 9===i.nodeType?{width:t.width(),height:t.height(),offset:{top:0,left:0}}:e.isWindow(i)?{width:t.width(),height:t.height(),offset:{top:t.scrollTop(),left:t.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:t.outerWidth(),height:t.outerHeight(),offset:t.offset()}}e.ui=e.ui||{};var n,a,o=Math.max,r=Math.abs,h=Math.round,l=/left|center|right/,u=/top|center|bottom/,d=/[\\+\\-]\\d+(\\.[\\d]+)?%?/,c=/^\\w+/,p=/%$/,f=e.fn.position;e.position={scrollbarWidth:function(){if(void 0!==n)return n;var t,i,s=e(\"<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>\"),a=s.children()[0];return 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p,m,g,v,y,b,_=e(n.of),x=e.position.getWithinInfo(n.within),w=e.position.getScrollInfo(x),k=(n.collision||\"flip\").split(\" \"),T={};return b=s(_),_[0].preventDefault&&(n.at=\"left top\"),m=b.width,g=b.height,v=b.offset,y=e.extend({},v),e.each([\"my\",\"at\"],function(){var e,t,i=(n[this]||\"\").split(\" \");1===i.length&&(i=l.test(i[0])?i.concat([\"center\"]):u.test(i[0])?[\"center\"].concat(i):[\"center\",\"center\"]),i[0]=l.test(i[0])?i[0]:\"center\",i[1]=u.test(i[1])?i[1]:\"center\",e=d.exec(i[0]),t=d.exec(i[1]),T[this]=[e?e[0]:0,t?t[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===k.length&&(k[1]=k[0]),\"right\"===n.at[0]?y.left+=m:\"center\"===n.at[0]&&(y.left+=m/2),\"bottom\"===n.at[1]?y.top+=g:\"center\"===n.at[1]&&(y.top+=g/2),p=t(T.at,m,g),y.left+=p[0],y.top+=p[1],this.each(function(){var s,l,u=e(this),d=u.outerWidth(),c=u.outerHeight(),f=i(this,\"marginLeft\"),b=i(this,\"marginTop\"),D=d+f+i(this,\"marginRight\")+w.width,S=c+b+i(this,\"marginBottom\")+w.height,M=e.extend({},y),C=t(T.my,u.outerWidth(),u.outerHeight());\"right\"===n.my[0]?M.left-=d:\"center\"===n.my[0]&&(M.left-=d/2),\"bottom\"===n.my[1]?M.top-=c:\"center\"===n.my[1]&&(M.top-=c/2),M.left+=C[0],M.top+=C[1],a||(M.left=h(M.left),M.top=h(M.top)),s={marginLeft:f,marginTop:b},e.each([\"left\",\"top\"],function(t,i){e.ui.position[k[t]]&&e.ui.position[k[t]][i](M,{targetWidth:m,targetHeight:g,elemWidth:d,elemHeight:c,collisionPosition:s,collisionWidth:D,collisionHeight:S,offset:[p[0]+C[0],p[1]+C[1]],my:n.my,at:n.at,within:x,elem:u})}),n.using&&(l=function(e){var t=v.left-M.left,i=t+m-d,s=v.top-M.top,a=s+g-c,h={target:{element:_,left:v.left,top:v.top,width:m,height:g},element:{element:u,left:M.left,top:M.top,width:d,height:c},horizontal:0>i?\"left\":t>0?\"right\":\"center\",vertical:0>a?\"top\":s>0?\"bottom\":\"middle\"};d>m&&m>r(t+i)&&(h.horizontal=\"center\"),c>g&&g>r(s+a)&&(h.vertical=\"middle\"),h.important=o(r(t),r(i))>o(r(s),r(a))?\"horizontal\":\"vertical\",n.using.call(this,e,h)}),u.offset(e.extend(M,{using:l}))})},e.ui.position={fit:{left:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=e.left-t.collisionPosition.marginLeft,h=n-r,l=r+t.collisionWidth-a-n;t.collisionWidth>a?h>0&&0>=l?(i=e.left+h+t.collisionWidth-a-n,e.left+=h-i):e.left=l>0&&0>=h?n:h>l?n+a-t.collisionWidth:n:h>0?e.left+=h:l>0?e.left-=l:e.left=o(e.left-r,e.left)},top:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollTop:s.offset.top,a=t.within.height,r=e.top-t.collisionPosition.marginTop,h=n-r,l=r+t.collisionHeight-a-n;t.collisionHeight>a?h>0&&0>=l?(i=e.top+h+t.collisionHeight-a-n,e.top+=h-i):e.top=l>0&&0>=h?n:h>l?n+a-t.collisionHeight:n:h>0?e.top+=h:l>0?e.top-=l:e.top=o(e.top-r,e.top)}},flip:{left:function(e,t){var i,s,n=t.within,a=n.offset.left+n.scrollLeft,o=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=e.left-t.collisionPosition.marginLeft,u=l-h,d=l+t.collisionWidth-o-h,c=\"left\"===t.my[0]?-t.elemWidth:\"right\"===t.my[0]?t.elemWidth:0,p=\"left\"===t.at[0]?t.targetWidth:\"right\"===t.at[0]?-t.targetWidth:0,f=-2*t.offset[0];0>u?(i=e.left+c+p+f+t.collisionWidth-o-a,(0>i||r(u)>i)&&(e.left+=c+p+f)):d>0&&(s=e.left-t.collisionPosition.marginLeft+c+p+f-h,(s>0||d>r(s))&&(e.left+=c+p+f))},top:function(e,t){var i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c=\"top\"===t.my[1],p=c?-t.elemHeight:\"bottom\"===t.my[1]?t.elemHeight:0,f=\"top\"===t.at[1]?t.targetHeight:\"bottom\"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName(\"body\")[0],h=document.createElement(\"div\");t=document.createElement(r?\"div\":\"body\"),s={visibility:\"hidden\",width:0,height:0,border:0,margin:0,background:\"none\"},r&&e.extend(s,{position:\"absolute\",left:\"-1000px\",top:\"-1000px\"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText=\"position: absolute; left: 10.7432222px;\",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML=\"\",i.removeChild(t)}()}(),e.ui.position,e.widget(\"ui.accordion\",{version:\"1.11.4\",options:{active:0,animate:{},collapsible:!1,event:\"click\",header:\"> li > :first-child,> 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i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({\"aria-hidden\":\"true\"}),s.prev().attr({\"aria-selected\":\"false\",\"aria-expanded\":\"false\"}),i.length&&s.length?s.prev().attr({tabIndex:-1,\"aria-expanded\":\"false\"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr(\"tabIndex\"),10)}).attr(\"tabIndex\",-1),i.attr(\"aria-hidden\",\"false\").prev().attr({\"aria-selected\":\"true\",\"aria-expanded\":\"true\",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css(\"box-sizing\"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return\"number\"==typeof d&&(a=d),\"string\"==typeof 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i=e(t.currentTarget);\ni.siblings(\".ui-state-active\").removeClass(\"ui-state-active\"),this.focus(t,i)}},mouseleave:\"collapseAll\",\"mouseleave .ui-menu\":\"collapseAll\",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:\"_keydown\"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr(\"aria-activedescendant\").find(\".ui-menu\").addBack().removeClass(\"ui-menu ui-widget ui-widget-content ui-menu-icons ui-front\").removeAttr(\"role\").removeAttr(\"tabIndex\").removeAttr(\"aria-labelledby\").removeAttr(\"aria-expanded\").removeAttr(\"aria-hidden\").removeAttr(\"aria-disabled\").removeUniqueId().show(),this.element.find(\".ui-menu-item\").removeClass(\"ui-menu-item\").removeAttr(\"role\").removeAttr(\"aria-disabled\").removeUniqueId().removeClass(\"ui-state-hover\").removeAttr(\"tabIndex\").removeAttr(\"role\").removeAttr(\"aria-haspopup\").children().each(function(){var t=e(this);t.data(\"ui-menu-submenu-carat\")&&t.remove()}),this.element.find(\".ui-menu-divider\").removeClass(\"ui-menu-divider ui-widget-content\")},_keydown:function(t){var i,s,n,a,o=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move(\"first\",\"first\",t);break;case e.ui.keyCode.END:this._move(\"last\",\"last\",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(\".ui-state-disabled\")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||\"\",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(\".ui-menu-item\"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(\".ui-state-disabled\")||(this.active.is(\"[aria-haspopup='true']\")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass(\"ui-menu-icons\",!!this.element.find(\".ui-icon\").length),a.filter(\":not(.ui-menu)\").addClass(\"ui-menu ui-widget ui-widget-content ui-front\").hide().attr({role:this.options.role,\"aria-hidden\":\"true\",\"aria-expanded\":\"false\"}).each(function(){var t=e(this),i=t.parent(),s=e(\"<span>\").addClass(\"ui-menu-icon ui-icon \"+n).data(\"ui-menu-submenu-carat\",!0);i.attr(\"aria-haspopup\",\"true\").prepend(s),t.attr(\"aria-labelledby\",i.attr(\"id\"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(\".ui-menu-item\").each(function(){var t=e(this);s._isDivider(t)&&t.addClass(\"ui-widget-content ui-menu-divider\")}),i.not(\".ui-menu-item, .ui-menu-divider\").addClass(\"ui-menu-item\").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(\".ui-state-disabled\").attr(\"aria-disabled\",\"true\"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:\"menuitem\",listbox:\"option\"}[this.options.role]},_setOption:function(e,t){\"icons\"===e&&this.element.find(\".ui-menu-icon\").removeClass(this.options.icons.submenu).addClass(t.submenu),\"disabled\"===e&&this.element.toggleClass(\"ui-state-disabled\",!!t).attr(\"aria-disabled\",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&\"focus\"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass(\"ui-state-focus\").removeClass(\"ui-state-active\"),this.options.role&&this.element.attr(\"aria-activedescendant\",s.attr(\"id\")),this.active.parent().closest(\".ui-menu-item\").addClass(\"ui-state-active\"),e&&\"keydown\"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(\".ui-menu\"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger(\"focus\",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],\"borderTopWidth\"))||0,s=parseFloat(e.css(this.activeMenu[0],\"paddingTop\"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass(\"ui-state-focus\"),this.active=null,this._trigger(\"blur\",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),\"true\"===e.attr(\"aria-hidden\")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(\".ui-menu\").not(t.parents(\".ui-menu\")).hide().attr(\"aria-hidden\",\"true\"),t.show().removeAttr(\"aria-hidden\").attr(\"aria-expanded\",\"true\").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(\".ui-menu\"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(\".ui-menu\").hide().attr(\"aria-hidden\",\"true\").attr(\"aria-expanded\",\"false\").end().find(\".ui-state-active\").not(\".ui-state-focus\").removeClass(\"ui-state-active\")},_closeOnDocumentClick:function(t){return!e(t.target).closest(\".ui-menu\").length},_isDivider:function(e){return!/[^\\-\\u2014\\u2013\\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(\".ui-menu-item\",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(\".ui-menu \").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move(\"next\",\"first\",e)},previous:function(e){this._move(\"prev\",\"last\",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(\".ui-menu-item\").length},isLastItem:function(){return this.active&&!this.active.nextAll(\".ui-menu-item\").length},_move:function(e,t,i){var s;this.active&&(s=\"first\"===e||\"last\"===e?this.active[\"first\"===e?\"prevAll\":\"nextAll\"](\".ui-menu-item\").eq(-1):this.active[e+\"All\"](\".ui-menu-item\").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(\".ui-menu-item\").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?\"last\":\"first\"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(\".ui-menu-item\").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop(\"scrollHeight\")},select:function(t){this.active=this.active||e(t.target).closest(\".ui-menu-item\");var i={item:this.active};this.active.has(\".ui-menu\").length||this.collapseAll(t,!0),this._trigger(\"select\",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\\-\\[\\]{}()*+?.,\\\\\\^$|#\\s]/g,\"\\\\$&\"),s=RegExp(\"^\"+i,\"i\");return this.activeMenu.find(this.options.items).filter(\".ui-menu-item\").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget(\"ui.autocomplete\",{version:\"1.11.4\",defaultElement:\"<input>\",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:\"left top\",at:\"left bottom\",collision:\"none\"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a=\"textarea\"===n,o=\"input\"===n;this.isMultiLine=a?!0:o?!1:this.element.prop(\"isContentEditable\"),this.valueMethod=this.element[a||o?\"val\":\"text\"],this.isNewMenu=!0,this.element.addClass(\"ui-autocomplete-input\").attr(\"autocomplete\",\"off\"),this._on(this.element,{keydown:function(n){if(this.element.prop(\"readOnly\"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move(\"previousPage\",n);break;case a.PAGE_DOWN:t=!0,this._move(\"nextPage\",n);break;case a.UP:t=!0,this._keyEvent(\"previous\",n);break;case a.DOWN:t=!0,this._keyEvent(\"next\",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(\":visible\")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(\":visible\"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move(\"previousPage\",s);break;case n.PAGE_DOWN:this._move(\"nextPage\",s);break;case n.UP:this._keyEvent(\"previous\",s);break;case n.DOWN:this._keyEvent(\"next\",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e(\"<ul>\").addClass(\"ui-autocomplete ui-front\").appendTo(this._appendTo()).menu({role:null}).hide().menu(\"instance\"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(\".ui-menu-item\").length||this._delay(function(){var t=this;this.document.one(\"mousedown\",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one(\"mousemove\",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data(\"ui-autocomplete-item\"),!1!==this._trigger(\"focus\",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr(\"aria-label\")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e(\"<div>\").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data(\"ui-autocomplete-item\"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger(\"select\",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e(\"<span>\",{role:\"status\",\"aria-live\":\"assertive\",\"aria-relevant\":\"additions\"}).addClass(\"ui-helper-hidden-accessible\").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr(\"autocomplete\")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass(\"ui-autocomplete-input\").removeAttr(\"autocomplete\"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),\"source\"===e&&this._initSource(),\"appendTo\"===e&&this.menu.element.appendTo(this._appendTo()),\"disabled\"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(\".ui-front\")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):\"string\"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:\"json\",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(\":visible\"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger(\"search\",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass(\"ui-autocomplete-loading\"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass(\"ui-autocomplete-loading\")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger(\"response\",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger(\"open\")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(\":visible\")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger(\"close\",e))},_change:function(e){this.previous!==this._value()&&this._trigger(\"change\",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return\"string\"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width(\"\").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data(\"ui-autocomplete-item\",t)},_renderItem:function(t,i){return e(\"<li>\").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(\":visible\")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(\":visible\"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\\-\\[\\]{}()*+?.,\\\\\\^$|#\\s]/g,\"\\\\$&\")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),\"i\");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget(\"ui.autocomplete\",e.ui.autocomplete,{options:{messages:{noResults:\"No search results.\",results:function(e){return e+(e>1?\" results are\":\" result is\")+\" available, use up and down arrow keys to navigate.\"}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e(\"<div>\").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var c,p=\"ui-button ui-widget ui-state-default ui-corner-all\",f=\"ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only\",m=function(){var t=e(this);setTimeout(function(){t.find(\":ui-button\").button(\"refresh\")},1)},g=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,\"\\\\'\"),n=s?e(s).find(\"[name='\"+i+\"'][type=radio]\"):e(\"[name='\"+i+\"'][type=radio]\",t.ownerDocument).filter(function(){return!this.form})),n};e.widget(\"ui.button\",{version:\"1.11.4\",defaultElement:\"<button>\",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest(\"form\").unbind(\"reset\"+this.eventNamespace).bind(\"reset\"+this.eventNamespace,m),\"boolean\"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop(\"disabled\"):this.element.prop(\"disabled\",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr(\"title\");var t=this,i=this.options,s=\"checkbox\"===this.type||\"radio\"===this.type,n=s?\"\":\"ui-state-active\";null===i.label&&(i.label=\"input\"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(p).attr(\"role\",\"button\").bind(\"mouseenter\"+this.eventNamespace,function(){i.disabled||this===c&&e(this).addClass(\"ui-state-active\")}).bind(\"mouseleave\"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind(\"click\"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass(\"ui-state-focus\")},blur:function(){this.buttonElement.removeClass(\"ui-state-focus\")}}),s&&this.element.bind(\"change\"+this.eventNamespace,function(){t.refresh()}),\"checkbox\"===this.type?this.buttonElement.bind(\"click\"+this.eventNamespace,function(){return i.disabled?!1:void 0}):\"radio\"===this.type?this.buttonElement.bind(\"click\"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass(\"ui-state-active\"),t.buttonElement.attr(\"aria-pressed\",\"true\");var s=t.element[0];g(s).not(s).map(function(){return e(this).button(\"widget\")[0]}).removeClass(\"ui-state-active\").attr(\"aria-pressed\",\"false\")}):(this.buttonElement.bind(\"mousedown\"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass(\"ui-state-active\"),c=this,t.document.one(\"mouseup\",function(){c=null}),void 0)}).bind(\"mouseup\"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass(\"ui-state-active\"),void 0)}).bind(\"keydown\"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass(\"ui-state-active\"),void 0)}).bind(\"keyup\"+this.eventNamespace+\" blur\"+this.eventNamespace,function(){e(this).removeClass(\"ui-state-active\")}),this.buttonElement.is(\"a\")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption(\"disabled\",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is(\"[type=checkbox]\")?\"checkbox\":this.element.is(\"[type=radio]\")?\"radio\":this.element.is(\"input\")?\"input\":\"button\",\"checkbox\"===this.type||\"radio\"===this.type?(e=this.element.parents().last(),t=\"label[for='\"+this.element.attr(\"id\")+\"']\",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass(\"ui-helper-hidden-accessible\"),i=this.element.is(\":checked\"),i&&this.buttonElement.addClass(\"ui-state-active\"),this.buttonElement.prop(\"aria-pressed\",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass(\"ui-helper-hidden-accessible\"),this.buttonElement.removeClass(p+\" ui-state-active \"+f).removeAttr(\"role\").removeAttr(\"aria-pressed\").html(this.buttonElement.find(\".ui-button-text\").html()),this.hasTitle||this.buttonElement.removeAttr(\"title\")},_setOption:function(e,t){return this._super(e,t),\"disabled\"===e?(this.widget().toggleClass(\"ui-state-disabled\",!!t),this.element.prop(\"disabled\",!!t),t&&(\"checkbox\"===this.type||\"radio\"===this.type?this.buttonElement.removeClass(\"ui-state-focus\"):this.buttonElement.removeClass(\"ui-state-focus ui-state-active\")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is(\"input, button\")?this.element.is(\":disabled\"):this.element.hasClass(\"ui-button-disabled\");t!==this.options.disabled&&this._setOption(\"disabled\",t),\"radio\"===this.type?g(this.element[0]).each(function(){e(this).is(\":checked\")?e(this).button(\"widget\").addClass(\"ui-state-active\").attr(\"aria-pressed\",\"true\"):e(this).button(\"widget\").removeClass(\"ui-state-active\").attr(\"aria-pressed\",\"false\")}):\"checkbox\"===this.type&&(this.element.is(\":checked\")?this.buttonElement.addClass(\"ui-state-active\").attr(\"aria-pressed\",\"true\"):this.buttonElement.removeClass(\"ui-state-active\").attr(\"aria-pressed\",\"false\"))},_resetButton:function(){if(\"input\"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(f),i=e(\"<span></span>\",this.document[0]).addClass(\"ui-button-text\").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push(\"ui-button-text-icon\"+(n?\"s\":s.primary?\"-primary\":\"-secondary\")),s.primary&&t.prepend(\"<span class='ui-button-icon-primary ui-icon \"+s.primary+\"'></span>\"),s.secondary&&t.append(\"<span class='ui-button-icon-secondary ui-icon \"+s.secondary+\"'></span>\"),this.options.text||(a.push(n?\"ui-button-icons-only\":\"ui-button-icon-only\"),this.hasTitle||t.attr(\"title\",e.trim(i)))):a.push(\"ui-button-text-only\"),t.addClass(a.join(\" \"))}}),e.widget(\"ui.buttonset\",{version:\"1.11.4\",options:{items:\"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)\"},_create:function(){this.element.addClass(\"ui-buttonset\")},_init:function(){this.refresh()},_setOption:function(e,t){\"disabled\"===e&&this.buttons.button(\"option\",e,t),this._super(e,t)},refresh:function(){var t=\"rtl\"===this.element.css(\"direction\"),i=this.element.find(this.options.items),s=i.filter(\":ui-button\");i.not(\":ui-button\").button(),s.button(\"refresh\"),this.buttons=i.map(function(){return e(this).button(\"widget\")[0]}).removeClass(\"ui-corner-all ui-corner-left ui-corner-right\").filter(\":first\").addClass(t?\"ui-corner-right\":\"ui-corner-left\").end().filter(\":last\").addClass(t?\"ui-corner-left\":\"ui-corner-right\").end().end()},_destroy:function(){this.element.removeClass(\"ui-buttonset\"),this.buttons.map(function(){return e(this).button(\"widget\")[0]}).removeClass(\"ui-corner-left ui-corner-right\").end().button(\"destroy\")}}),e.ui.button,e.extend(e.ui,{datepicker:{version:\"1.11.4\"}});var v;e.extend(n.prototype,{markerClassName:\"hasDatepicker\",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(e){return r(this._defaults,e||{}),this},_attachDatepicker:function(t,i){var s,n,a;s=t.nodeName.toLowerCase(),n=\"div\"===s||\"span\"===s,t.id||(this.uuid+=1,t.id=\"dp\"+this.uuid),a=this._newInst(e(t),n),a.settings=e.extend({},i||{}),\"input\"===s?this._connectDatepicker(t,a):n&&this._inlineDatepicker(t,a)},_newInst:function(t,i){var s=t[0].id.replace(/([^A-Za-z0-9_\\-])/g,\"\\\\\\\\$1\");return{id:s,input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:i,dpDiv:i?a(e(\"<div class='\"+this._inlineClass+\" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>\")):this.dpDiv}},_connectDatepicker:function(t,i){var s=e(t);i.append=e([]),i.trigger=e([]),s.hasClass(this.markerClassName)||(this._attachments(s,i),s.addClass(this.markerClassName).keydown(this._doKeyDown).keypress(this._doKeyPress).keyup(this._doKeyUp),this._autoSize(i),e.data(t,\"datepicker\",i),i.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,i){var s,n,a,o=this._get(i,\"appendText\"),r=this._get(i,\"isRTL\");i.append&&i.append.remove(),o&&(i.append=e(\"<span class='\"+this._appendClass+\"'>\"+o+\"</span>\"),t[r?\"before\":\"after\"](i.append)),t.unbind(\"focus\",this._showDatepicker),i.trigger&&i.trigger.remove(),s=this._get(i,\"showOn\"),(\"focus\"===s||\"both\"===s)&&t.focus(this._showDatepicker),(\"button\"===s||\"both\"===s)&&(n=this._get(i,\"buttonText\"),a=this._get(i,\"buttonImage\"),i.trigger=e(this._get(i,\"buttonImageOnly\")?e(\"<img/>\").addClass(this._triggerClass).attr({src:a,alt:n,title:n}):e(\"<button type='button'></button>\").addClass(this._triggerClass).html(a?e(\"<img/>\").attr({src:a,alt:n,title:n}):n)),t[r?\"before\":\"after\"](i.trigger),i.trigger.click(function(){return e.datepicker._datepickerShowing&&e.datepicker._lastInput===t[0]?e.datepicker._hideDatepicker():e.datepicker._datepickerShowing&&e.datepicker._lastInput!==t[0]?(e.datepicker._hideDatepicker(),e.datepicker._showDatepicker(t[0])):e.datepicker._showDatepicker(t[0]),!1}))},_autoSize:function(e){if(this._get(e,\"autoSize\")&&!e.inline){var t,i,s,n,a=new Date(2009,11,20),o=this._get(e,\"dateFormat\");o.match(/[DM]/)&&(t=function(e){for(i=0,s=0,n=0;e.length>n;n++)e[n].length>i&&(i=e[n].length,s=n);return s},a.setMonth(t(this._get(e,o.match(/MM/)?\"monthNames\":\"monthNamesShort\"))),a.setDate(t(this._get(e,o.match(/DD/)?\"dayNames\":\"dayNamesShort\"))+20-a.getDay())),e.input.attr(\"size\",this._formatDate(e,a).length)}},_inlineDatepicker:function(t,i){var s=e(t);s.hasClass(this.markerClassName)||(s.addClass(this.markerClassName).append(i.dpDiv),e.data(t,\"datepicker\",i),this._setDate(i,this._getDefaultDate(i),!0),this._updateDatepicker(i),this._updateAlternate(i),i.settings.disabled&&this._disableDatepicker(t),i.dpDiv.css(\"display\",\"block\"))},_dialogDatepicker:function(t,i,s,n,a){var o,h,l,u,d,c=this._dialogInst;return c||(this.uuid+=1,o=\"dp\"+this.uuid,this._dialogInput=e(\"<input type='text' id='\"+o+\"' style='position: absolute; top: -100px; width: 0px;'/>\"),this._dialogInput.keydown(this._doKeyDown),e(\"body\").append(this._dialogInput),c=this._dialogInst=this._newInst(this._dialogInput,!1),c.settings={},e.data(this._dialogInput[0],\"datepicker\",c)),r(c.settings,n||{}),i=i&&i.constructor===Date?this._formatDate(c,i):i,this._dialogInput.val(i),this._pos=a?a.length?a:[a.pageX,a.pageY]:null,this._pos||(h=document.documentElement.clientWidth,l=document.documentElement.clientHeight,u=document.documentElement.scrollLeft||document.body.scrollLeft,d=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[h/2-100+u,l/2-150+d]),this._dialogInput.css(\"left\",this._pos[0]+20+\"px\").css(\"top\",this._pos[1]+\"px\"),c.settings.onSelect=s,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),e.blockUI&&e.blockUI(this.dpDiv),e.data(this._dialogInput[0],\"datepicker\",c),this},_destroyDatepicker:function(t){var i,s=e(t),n=e.data(t,\"datepicker\");s.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),e.removeData(t,\"datepicker\"),\"input\"===i?(n.append.remove(),n.trigger.remove(),s.removeClass(this.markerClassName).unbind(\"focus\",this._showDatepicker).unbind(\"keydown\",this._doKeyDown).unbind(\"keypress\",this._doKeyPress).unbind(\"keyup\",this._doKeyUp)):(\"div\"===i||\"span\"===i)&&s.removeClass(this.markerClassName).empty(),v===n&&(v=null))},_enableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,\"datepicker\");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),\"input\"===i?(t.disabled=!1,a.trigger.filter(\"button\").each(function(){this.disabled=!1}).end().filter(\"img\").css({opacity:\"1.0\",cursor:\"\"})):(\"div\"===i||\"span\"===i)&&(s=n.children(\".\"+this._inlineClass),s.children().removeClass(\"ui-state-disabled\"),s.find(\"select.ui-datepicker-month, select.ui-datepicker-year\").prop(\"disabled\",!1)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}))},_disableDatepicker:function(t){var i,s,n=e(t),a=e.data(t,\"datepicker\");n.hasClass(this.markerClassName)&&(i=t.nodeName.toLowerCase(),\"input\"===i?(t.disabled=!0,a.trigger.filter(\"button\").each(function(){this.disabled=!0}).end().filter(\"img\").css({opacity:\"0.5\",cursor:\"default\"})):(\"div\"===i||\"span\"===i)&&(s=n.children(\".\"+this._inlineClass),s.children().addClass(\"ui-state-disabled\"),s.find(\"select.ui-datepicker-month, select.ui-datepicker-year\").prop(\"disabled\",!0)),this._disabledInputs=e.map(this._disabledInputs,function(e){return e===t?null:e}),this._disabledInputs[this._disabledInputs.length]=t)},_isDisabledDatepicker:function(e){if(!e)return!1;for(var t=0;this._disabledInputs.length>t;t++)if(this._disabledInputs[t]===e)return!0;return!1},_getInst:function(t){try{return e.data(t,\"datepicker\")}catch(i){throw\"Missing instance data for this datepicker\"}},_optionDatepicker:function(t,i,s){var n,a,o,h,l=this._getInst(t);return 2===arguments.length&&\"string\"==typeof i?\"defaults\"===i?e.extend({},e.datepicker._defaults):l?\"all\"===i?e.extend({},l.settings):this._get(l,i):null:(n=i||{},\"string\"==typeof i&&(n={},n[i]=s),l&&(this._curInst===l&&this._hideDatepicker(),a=this._getDateDatepicker(t,!0),o=this._getMinMaxDate(l,\"min\"),h=this._getMinMaxDate(l,\"max\"),r(l.settings,n),null!==o&&void 0!==n.dateFormat&&void 0===n.minDate&&(l.settings.minDate=this._formatDate(l,o)),null!==h&&void 0!==n.dateFormat&&void 0===n.maxDate&&(l.settings.maxDate=this._formatDate(l,h)),\"disabled\"in n&&(n.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(e(t),l),this._autoSize(l),this._setDate(l,a),this._updateAlternate(l),this._updateDatepicker(l)),void 0)},_changeDatepicker:function(e,t,i){this._optionDatepicker(e,t,i)},_refreshDatepicker:function(e){var t=this._getInst(e);t&&this._updateDatepicker(t)},_setDateDatepicker:function(e,t){var i=this._getInst(e);i&&(this._setDate(i,t),this._updateDatepicker(i),this._updateAlternate(i))},_getDateDatepicker:function(e,t){var i=this._getInst(e);return i&&!i.inline&&this._setDateFromField(i,t),i?this._getDate(i):null},_doKeyDown:function(t){var i,s,n,a=e.datepicker._getInst(t.target),o=!0,r=a.dpDiv.is(\".ui-datepicker-rtl\");if(a._keyEvent=!0,e.datepicker._datepickerShowing)switch(t.keyCode){case 9:e.datepicker._hideDatepicker(),o=!1;break;case 13:return n=e(\"td.\"+e.datepicker._dayOverClass+\":not(.\"+e.datepicker._currentClass+\")\",a.dpDiv),n[0]&&e.datepicker._selectDay(t.target,a.selectedMonth,a.selectedYear,n[0]),i=e.datepicker._get(a,\"onSelect\"),i?(s=e.datepicker._formatDate(a),i.apply(a.input?a.input[0]:null,[s,a])):e.datepicker._hideDatepicker(),!1;case 27:e.datepicker._hideDatepicker();break;case 33:e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,\"stepBigMonths\"):-e.datepicker._get(a,\"stepMonths\"),\"M\");break;case 34:e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,\"stepBigMonths\"):+e.datepicker._get(a,\"stepMonths\"),\"M\");break;case 35:(t.ctrlKey||t.metaKey)&&e.datepicker._clearDate(t.target),o=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&e.datepicker._gotoToday(t.target),o=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?1:-1,\"D\"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?-e.datepicker._get(a,\"stepBigMonths\"):-e.datepicker._get(a,\"stepMonths\"),\"M\");break;case 38:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,-7,\"D\"),o=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,r?-1:1,\"D\"),o=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&e.datepicker._adjustDate(t.target,t.ctrlKey?+e.datepicker._get(a,\"stepBigMonths\"):+e.datepicker._get(a,\"stepMonths\"),\"M\");break;case 40:(t.ctrlKey||t.metaKey)&&e.datepicker._adjustDate(t.target,7,\"D\"),o=t.ctrlKey||t.metaKey;break;default:o=!1}else 36===t.keyCode&&t.ctrlKey?e.datepicker._showDatepicker(this):o=!1;o&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var i,s,n=e.datepicker._getInst(t.target);\nreturn e.datepicker._get(n,\"constrainInput\")?(i=e.datepicker._possibleChars(e.datepicker._get(n,\"dateFormat\")),s=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||\" \">s||!i||i.indexOf(s)>-1):void 0},_doKeyUp:function(t){var i,s=e.datepicker._getInst(t.target);if(s.input.val()!==s.lastVal)try{i=e.datepicker.parseDate(e.datepicker._get(s,\"dateFormat\"),s.input?s.input.val():null,e.datepicker._getFormatConfig(s)),i&&(e.datepicker._setDateFromField(s),e.datepicker._updateAlternate(s),e.datepicker._updateDatepicker(s))}catch(n){}return!0},_showDatepicker:function(t){if(t=t.target||t,\"input\"!==t.nodeName.toLowerCase()&&(t=e(\"input\",t.parentNode)[0]),!e.datepicker._isDisabledDatepicker(t)&&e.datepicker._lastInput!==t){var i,n,a,o,h,l,u;i=e.datepicker._getInst(t),e.datepicker._curInst&&e.datepicker._curInst!==i&&(e.datepicker._curInst.dpDiv.stop(!0,!0),i&&e.datepicker._datepickerShowing&&e.datepicker._hideDatepicker(e.datepicker._curInst.input[0])),n=e.datepicker._get(i,\"beforeShow\"),a=n?n.apply(t,[t,i]):{},a!==!1&&(r(i.settings,a),i.lastVal=null,e.datepicker._lastInput=t,e.datepicker._setDateFromField(i),e.datepicker._inDialog&&(t.value=\"\"),e.datepicker._pos||(e.datepicker._pos=e.datepicker._findPos(t),e.datepicker._pos[1]+=t.offsetHeight),o=!1,e(t).parents().each(function(){return o|=\"fixed\"===e(this).css(\"position\"),!o}),h={left:e.datepicker._pos[0],top:e.datepicker._pos[1]},e.datepicker._pos=null,i.dpDiv.empty(),i.dpDiv.css({position:\"absolute\",display:\"block\",top:\"-1000px\"}),e.datepicker._updateDatepicker(i),h=e.datepicker._checkOffset(i,h,o),i.dpDiv.css({position:e.datepicker._inDialog&&e.blockUI?\"static\":o?\"fixed\":\"absolute\",display:\"none\",left:h.left+\"px\",top:h.top+\"px\"}),i.inline||(l=e.datepicker._get(i,\"showAnim\"),u=e.datepicker._get(i,\"duration\"),i.dpDiv.css(\"z-index\",s(e(t))+1),e.datepicker._datepickerShowing=!0,e.effects&&e.effects.effect[l]?i.dpDiv.show(l,e.datepicker._get(i,\"showOptions\"),u):i.dpDiv[l||\"show\"](l?u:null),e.datepicker._shouldFocusInput(i)&&i.input.focus(),e.datepicker._curInst=i))}},_updateDatepicker:function(t){this.maxRows=4,v=t,t.dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var i,s=this._getNumberOfMonths(t),n=s[1],a=17,r=t.dpDiv.find(\".\"+this._dayOverClass+\" a\");r.length>0&&o.apply(r.get(0)),t.dpDiv.removeClass(\"ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4\").width(\"\"),n>1&&t.dpDiv.addClass(\"ui-datepicker-multi-\"+n).css(\"width\",a*n+\"em\"),t.dpDiv[(1!==s[0]||1!==s[1]?\"add\":\"remove\")+\"Class\"](\"ui-datepicker-multi\"),t.dpDiv[(this._get(t,\"isRTL\")?\"add\":\"remove\")+\"Class\"](\"ui-datepicker-rtl\"),t===e.datepicker._curInst&&e.datepicker._datepickerShowing&&e.datepicker._shouldFocusInput(t)&&t.input.focus(),t.yearshtml&&(i=t.yearshtml,setTimeout(function(){i===t.yearshtml&&t.yearshtml&&t.dpDiv.find(\"select.ui-datepicker-year:first\").replaceWith(t.yearshtml),i=t.yearshtml=null},0))},_shouldFocusInput:function(e){return e.input&&e.input.is(\":visible\")&&!e.input.is(\":disabled\")&&!e.input.is(\":focus\")},_checkOffset:function(t,i,s){var n=t.dpDiv.outerWidth(),a=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,r=t.input?t.input.outerHeight():0,h=document.documentElement.clientWidth+(s?0:e(document).scrollLeft()),l=document.documentElement.clientHeight+(s?0:e(document).scrollTop());return i.left-=this._get(t,\"isRTL\")?n-o:0,i.left-=s&&i.left===t.input.offset().left?e(document).scrollLeft():0,i.top-=s&&i.top===t.input.offset().top+r?e(document).scrollTop():0,i.left-=Math.min(i.left,i.left+n>h&&h>n?Math.abs(i.left+n-h):0),i.top-=Math.min(i.top,i.top+a>l&&l>a?Math.abs(a+r):0),i},_findPos:function(t){for(var i,s=this._getInst(t),n=this._get(s,\"isRTL\");t&&(\"hidden\"===t.type||1!==t.nodeType||e.expr.filters.hidden(t));)t=t[n?\"previousSibling\":\"nextSibling\"];return i=e(t).offset(),[i.left,i.top]},_hideDatepicker:function(t){var i,s,n,a,o=this._curInst;!o||t&&o!==e.data(t,\"datepicker\")||this._datepickerShowing&&(i=this._get(o,\"showAnim\"),s=this._get(o,\"duration\"),n=function(){e.datepicker._tidyDialog(o)},e.effects&&(e.effects.effect[i]||e.effects[i])?o.dpDiv.hide(i,e.datepicker._get(o,\"showOptions\"),s,n):o.dpDiv[\"slideDown\"===i?\"slideUp\":\"fadeIn\"===i?\"fadeOut\":\"hide\"](i?s:null,n),i||n(),this._datepickerShowing=!1,a=this._get(o,\"onClose\"),a&&a.apply(o.input?o.input[0]:null,[o.input?o.input.val():\"\",o]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:\"absolute\",left:\"0\",top:\"-100px\"}),e.blockUI&&(e.unblockUI(),e(\"body\").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(e){e.dpDiv.removeClass(this._dialogClass).unbind(\".ui-datepicker-calendar\")},_checkExternalClick:function(t){if(e.datepicker._curInst){var i=e(t.target),s=e.datepicker._getInst(i[0]);(i[0].id!==e.datepicker._mainDivId&&0===i.parents(\"#\"+e.datepicker._mainDivId).length&&!i.hasClass(e.datepicker.markerClassName)&&!i.closest(\".\"+e.datepicker._triggerClass).length&&e.datepicker._datepickerShowing&&(!e.datepicker._inDialog||!e.blockUI)||i.hasClass(e.datepicker.markerClassName)&&e.datepicker._curInst!==s)&&e.datepicker._hideDatepicker()}},_adjustDate:function(t,i,s){var n=e(t),a=this._getInst(n[0]);this._isDisabledDatepicker(n[0])||(this._adjustInstDate(a,i+(\"M\"===s?this._get(a,\"showCurrentAtPos\"):0),s),this._updateDatepicker(a))},_gotoToday:function(t){var i,s=e(t),n=this._getInst(s[0]);this._get(n,\"gotoCurrent\")&&n.currentDay?(n.selectedDay=n.currentDay,n.drawMonth=n.selectedMonth=n.currentMonth,n.drawYear=n.selectedYear=n.currentYear):(i=new Date,n.selectedDay=i.getDate(),n.drawMonth=n.selectedMonth=i.getMonth(),n.drawYear=n.selectedYear=i.getFullYear()),this._notifyChange(n),this._adjustDate(s)},_selectMonthYear:function(t,i,s){var n=e(t),a=this._getInst(n[0]);a[\"selected\"+(\"M\"===s?\"Month\":\"Year\")]=a[\"draw\"+(\"M\"===s?\"Month\":\"Year\")]=parseInt(i.options[i.selectedIndex].value,10),this._notifyChange(a),this._adjustDate(n)},_selectDay:function(t,i,s,n){var a,o=e(t);e(n).hasClass(this._unselectableClass)||this._isDisabledDatepicker(o[0])||(a=this._getInst(o[0]),a.selectedDay=a.currentDay=e(\"a\",n).html(),a.selectedMonth=a.currentMonth=i,a.selectedYear=a.currentYear=s,this._selectDate(t,this._formatDate(a,a.currentDay,a.currentMonth,a.currentYear)))},_clearDate:function(t){var i=e(t);this._selectDate(i,\"\")},_selectDate:function(t,i){var s,n=e(t),a=this._getInst(n[0]);i=null!=i?i:this._formatDate(a),a.input&&a.input.val(i),this._updateAlternate(a),s=this._get(a,\"onSelect\"),s?s.apply(a.input?a.input[0]:null,[i,a]):a.input&&a.input.trigger(\"change\"),a.inline?this._updateDatepicker(a):(this._hideDatepicker(),this._lastInput=a.input[0],\"object\"!=typeof a.input[0]&&a.input.focus(),this._lastInput=null)},_updateAlternate:function(t){var i,s,n,a=this._get(t,\"altField\");a&&(i=this._get(t,\"altFormat\")||this._get(t,\"dateFormat\"),s=this._getDate(t),n=this.formatDate(i,s,this._getFormatConfig(t)),e(a).each(function(){e(this).val(n)}))},noWeekends:function(e){var t=e.getDay();return[t>0&&6>t,\"\"]},iso8601Week:function(e){var t,i=new Date(e.getTime());return i.setDate(i.getDate()+4-(i.getDay()||7)),t=i.getTime(),i.setMonth(0),i.setDate(1),Math.floor(Math.round((t-i)/864e5)/7)+1},parseDate:function(t,i,s){if(null==t||null==i)throw\"Invalid arguments\";if(i=\"object\"==typeof i?\"\"+i:i+\"\",\"\"===i)return null;var n,a,o,r,h=0,l=(s?s.shortYearCutoff:null)||this._defaults.shortYearCutoff,u=\"string\"!=typeof l?l:(new Date).getFullYear()%100+parseInt(l,10),d=(s?s.dayNamesShort:null)||this._defaults.dayNamesShort,c=(s?s.dayNames:null)||this._defaults.dayNames,p=(s?s.monthNamesShort:null)||this._defaults.monthNamesShort,f=(s?s.monthNames:null)||this._defaults.monthNames,m=-1,g=-1,v=-1,y=-1,b=!1,_=function(e){var i=t.length>n+1&&t.charAt(n+1)===e;return i&&n++,i},x=function(e){var t=_(e),s=\"@\"===e?14:\"!\"===e?20:\"y\"===e&&t?4:\"o\"===e?3:2,n=\"y\"===e?s:1,a=RegExp(\"^\\\\d{\"+n+\",\"+s+\"}\"),o=i.substring(h).match(a);if(!o)throw\"Missing number at position \"+h;return h+=o[0].length,parseInt(o[0],10)},w=function(t,s,n){var a=-1,o=e.map(_(t)?n:s,function(e,t){return[[t,e]]}).sort(function(e,t){return-(e[1].length-t[1].length)});if(e.each(o,function(e,t){var s=t[1];return i.substr(h,s.length).toLowerCase()===s.toLowerCase()?(a=t[0],h+=s.length,!1):void 0}),-1!==a)return a+1;throw\"Unknown name at position \"+h},k=function(){if(i.charAt(h)!==t.charAt(n))throw\"Unexpected literal at position \"+h;h++};for(n=0;t.length>n;n++)if(b)\"'\"!==t.charAt(n)||_(\"'\")?k():b=!1;else switch(t.charAt(n)){case\"d\":v=x(\"d\");break;case\"D\":w(\"D\",d,c);break;case\"o\":y=x(\"o\");break;case\"m\":g=x(\"m\");break;case\"M\":g=w(\"M\",p,f);break;case\"y\":m=x(\"y\");break;case\"@\":r=new Date(x(\"@\")),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case\"!\":r=new Date((x(\"!\")-this._ticksTo1970)/1e4),m=r.getFullYear(),g=r.getMonth()+1,v=r.getDate();break;case\"'\":_(\"'\")?k():b=!0;break;default:k()}if(i.length>h&&(o=i.substr(h),!/^\\s+/.test(o)))throw\"Extra/unparsed characters found in date: \"+o;if(-1===m?m=(new Date).getFullYear():100>m&&(m+=(new Date).getFullYear()-(new Date).getFullYear()%100+(u>=m?0:-100)),y>-1)for(g=1,v=y;;){if(a=this._getDaysInMonth(m,g-1),a>=v)break;g++,v-=a}if(r=this._daylightSavingAdjust(new Date(m,g-1,v)),r.getFullYear()!==m||r.getMonth()+1!==g||r.getDate()!==v)throw\"Invalid date\";return r},ATOM:\"yy-mm-dd\",COOKIE:\"D, dd M yy\",ISO_8601:\"yy-mm-dd\",RFC_822:\"D, d M y\",RFC_850:\"DD, dd-M-y\",RFC_1036:\"D, d M y\",RFC_1123:\"D, d M yy\",RFC_2822:\"D, d M yy\",RSS:\"D, d M y\",TICKS:\"!\",TIMESTAMP:\"@\",W3C:\"yy-mm-dd\",_ticksTo1970:1e7*60*60*24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925)),formatDate:function(e,t,i){if(!t)return\"\";var s,n=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,a=(i?i.dayNames:null)||this._defaults.dayNames,o=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,r=(i?i.monthNames:null)||this._defaults.monthNames,h=function(t){var i=e.length>s+1&&e.charAt(s+1)===t;return i&&s++,i},l=function(e,t,i){var s=\"\"+t;if(h(e))for(;i>s.length;)s=\"0\"+s;return s},u=function(e,t,i,s){return h(e)?s[t]:i[t]},d=\"\",c=!1;if(t)for(s=0;e.length>s;s++)if(c)\"'\"!==e.charAt(s)||h(\"'\")?d+=e.charAt(s):c=!1;else switch(e.charAt(s)){case\"d\":d+=l(\"d\",t.getDate(),2);break;case\"D\":d+=u(\"D\",t.getDay(),n,a);break;case\"o\":d+=l(\"o\",Math.round((new Date(t.getFullYear(),t.getMonth(),t.getDate()).getTime()-new Date(t.getFullYear(),0,0).getTime())/864e5),3);break;case\"m\":d+=l(\"m\",t.getMonth()+1,2);break;case\"M\":d+=u(\"M\",t.getMonth(),o,r);break;case\"y\":d+=h(\"y\")?t.getFullYear():(10>t.getYear()%100?\"0\":\"\")+t.getYear()%100;break;case\"@\":d+=t.getTime();break;case\"!\":d+=1e4*t.getTime()+this._ticksTo1970;break;case\"'\":h(\"'\")?d+=\"'\":c=!0;break;default:d+=e.charAt(s)}return d},_possibleChars:function(e){var t,i=\"\",s=!1,n=function(i){var s=e.length>t+1&&e.charAt(t+1)===i;return s&&t++,s};for(t=0;e.length>t;t++)if(s)\"'\"!==e.charAt(t)||n(\"'\")?i+=e.charAt(t):s=!1;else switch(e.charAt(t)){case\"d\":case\"m\":case\"y\":case\"@\":i+=\"0123456789\";break;case\"D\":case\"M\":return null;case\"'\":n(\"'\")?i+=\"'\":s=!0;break;default:i+=e.charAt(t)}return i},_get:function(e,t){return void 0!==e.settings[t]?e.settings[t]:this._defaults[t]},_setDateFromField:function(e,t){if(e.input.val()!==e.lastVal){var i=this._get(e,\"dateFormat\"),s=e.lastVal=e.input?e.input.val():null,n=this._getDefaultDate(e),a=n,o=this._getFormatConfig(e);try{a=this.parseDate(i,s,o)||n}catch(r){s=t?\"\":s}e.selectedDay=a.getDate(),e.drawMonth=e.selectedMonth=a.getMonth(),e.drawYear=e.selectedYear=a.getFullYear(),e.currentDay=s?a.getDate():0,e.currentMonth=s?a.getMonth():0,e.currentYear=s?a.getFullYear():0,this._adjustInstDate(e)}},_getDefaultDate:function(e){return this._restrictMinMax(e,this._determineDate(e,this._get(e,\"defaultDate\"),new Date))},_determineDate:function(t,i,s){var n=function(e){var t=new Date;return t.setDate(t.getDate()+e),t},a=function(i){try{return e.datepicker.parseDate(e.datepicker._get(t,\"dateFormat\"),i,e.datepicker._getFormatConfig(t))}catch(s){}for(var n=(i.toLowerCase().match(/^c/)?e.datepicker._getDate(t):null)||new Date,a=n.getFullYear(),o=n.getMonth(),r=n.getDate(),h=/([+\\-]?[0-9]+)\\s*(d|D|w|W|m|M|y|Y)?/g,l=h.exec(i);l;){switch(l[2]||\"d\"){case\"d\":case\"D\":r+=parseInt(l[1],10);break;case\"w\":case\"W\":r+=7*parseInt(l[1],10);break;case\"m\":case\"M\":o+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o));break;case\"y\":case\"Y\":a+=parseInt(l[1],10),r=Math.min(r,e.datepicker._getDaysInMonth(a,o))}l=h.exec(i)}return new Date(a,o,r)},o=null==i||\"\"===i?s:\"string\"==typeof i?a(i):\"number\"==typeof i?isNaN(i)?s:n(i):new Date(i.getTime());return o=o&&\"Invalid Date\"==\"\"+o?s:o,o&&(o.setHours(0),o.setMinutes(0),o.setSeconds(0),o.setMilliseconds(0)),this._daylightSavingAdjust(o)},_daylightSavingAdjust:function(e){return e?(e.setHours(e.getHours()>12?e.getHours()+2:0),e):null},_setDate:function(e,t,i){var s=!t,n=e.selectedMonth,a=e.selectedYear,o=this._restrictMinMax(e,this._determineDate(e,t,new Date));e.selectedDay=e.currentDay=o.getDate(),e.drawMonth=e.selectedMonth=e.currentMonth=o.getMonth(),e.drawYear=e.selectedYear=e.currentYear=o.getFullYear(),n===e.selectedMonth&&a===e.selectedYear||i||this._notifyChange(e),this._adjustInstDate(e),e.input&&e.input.val(s?\"\":this._formatDate(e))},_getDate:function(e){var t=!e.currentYear||e.input&&\"\"===e.input.val()?null:this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return t},_attachHandlers:function(t){var i=this._get(t,\"stepMonths\"),s=\"#\"+t.id.replace(/\\\\\\\\/g,\"\\\\\");t.dpDiv.find(\"[data-handler]\").map(function(){var t={prev:function(){e.datepicker._adjustDate(s,-i,\"M\")},next:function(){e.datepicker._adjustDate(s,+i,\"M\")},hide:function(){e.datepicker._hideDatepicker()},today:function(){e.datepicker._gotoToday(s)},selectDay:function(){return e.datepicker._selectDay(s,+this.getAttribute(\"data-month\"),+this.getAttribute(\"data-year\"),this),!1},selectMonth:function(){return e.datepicker._selectMonthYear(s,this,\"M\"),!1},selectYear:function(){return e.datepicker._selectMonthYear(s,this,\"Y\"),!1}};e(this).bind(this.getAttribute(\"data-event\"),t[this.getAttribute(\"data-handler\")])})},_generateHTML:function(e){var t,i,s,n,a,o,r,h,l,u,d,c,p,f,m,g,v,y,b,_,x,w,k,T,D,S,M,C,N,A,P,I,H,z,F,E,O,j,W,L=new Date,R=this._daylightSavingAdjust(new Date(L.getFullYear(),L.getMonth(),L.getDate())),Y=this._get(e,\"isRTL\"),B=this._get(e,\"showButtonPanel\"),J=this._get(e,\"hideIfNoPrevNext\"),q=this._get(e,\"navigationAsDateFormat\"),K=this._getNumberOfMonths(e),V=this._get(e,\"showCurrentAtPos\"),U=this._get(e,\"stepMonths\"),Q=1!==K[0]||1!==K[1],G=this._daylightSavingAdjust(e.currentDay?new Date(e.currentYear,e.currentMonth,e.currentDay):new Date(9999,9,9)),X=this._getMinMaxDate(e,\"min\"),$=this._getMinMaxDate(e,\"max\"),Z=e.drawMonth-V,et=e.drawYear;if(0>Z&&(Z+=12,et--),$)for(t=this._daylightSavingAdjust(new Date($.getFullYear(),$.getMonth()-K[0]*K[1]+1,$.getDate())),t=X&&X>t?X:t;this._daylightSavingAdjust(new Date(et,Z,1))>t;)Z--,0>Z&&(Z=11,et--);for(e.drawMonth=Z,e.drawYear=et,i=this._get(e,\"prevText\"),i=q?this.formatDate(i,this._daylightSavingAdjust(new Date(et,Z-U,1)),this._getFormatConfig(e)):i,s=this._canAdjustMonth(e,-1,et,Z)?\"<a class='ui-datepicker-prev ui-corner-all' data-handler='prev' data-event='click' title='\"+i+\"'><span class='ui-icon ui-icon-circle-triangle-\"+(Y?\"e\":\"w\")+\"'>\"+i+\"</span></a>\":J?\"\":\"<a class='ui-datepicker-prev ui-corner-all ui-state-disabled' title='\"+i+\"'><span class='ui-icon ui-icon-circle-triangle-\"+(Y?\"e\":\"w\")+\"'>\"+i+\"</span></a>\",n=this._get(e,\"nextText\"),n=q?this.formatDate(n,this._daylightSavingAdjust(new Date(et,Z+U,1)),this._getFormatConfig(e)):n,a=this._canAdjustMonth(e,1,et,Z)?\"<a class='ui-datepicker-next ui-corner-all' data-handler='next' data-event='click' title='\"+n+\"'><span class='ui-icon ui-icon-circle-triangle-\"+(Y?\"w\":\"e\")+\"'>\"+n+\"</span></a>\":J?\"\":\"<a class='ui-datepicker-next ui-corner-all ui-state-disabled' title='\"+n+\"'><span class='ui-icon ui-icon-circle-triangle-\"+(Y?\"w\":\"e\")+\"'>\"+n+\"</span></a>\",o=this._get(e,\"currentText\"),r=this._get(e,\"gotoCurrent\")&&e.currentDay?G:R,o=q?this.formatDate(o,r,this._getFormatConfig(e)):o,h=e.inline?\"\":\"<button type='button' class='ui-datepicker-close ui-state-default ui-priority-primary ui-corner-all' data-handler='hide' data-event='click'>\"+this._get(e,\"closeText\")+\"</button>\",l=B?\"<div class='ui-datepicker-buttonpane ui-widget-content'>\"+(Y?h:\"\")+(this._isInRange(e,r)?\"<button type='button' class='ui-datepicker-current ui-state-default ui-priority-secondary ui-corner-all' data-handler='today' data-event='click'>\"+o+\"</button>\":\"\")+(Y?\"\":h)+\"</div>\":\"\",u=parseInt(this._get(e,\"firstDay\"),10),u=isNaN(u)?0:u,d=this._get(e,\"showWeek\"),c=this._get(e,\"dayNames\"),p=this._get(e,\"dayNamesMin\"),f=this._get(e,\"monthNames\"),m=this._get(e,\"monthNamesShort\"),g=this._get(e,\"beforeShowDay\"),v=this._get(e,\"showOtherMonths\"),y=this._get(e,\"selectOtherMonths\"),b=this._getDefaultDate(e),_=\"\",w=0;K[0]>w;w++){for(k=\"\",this.maxRows=4,T=0;K[1]>T;T++){if(D=this._daylightSavingAdjust(new Date(et,Z,e.selectedDay)),S=\" ui-corner-all\",M=\"\",Q){if(M+=\"<div class='ui-datepicker-group\",K[1]>1)switch(T){case 0:M+=\" ui-datepicker-group-first\",S=\" ui-corner-\"+(Y?\"right\":\"left\");break;case K[1]-1:M+=\" ui-datepicker-group-last\",S=\" ui-corner-\"+(Y?\"left\":\"right\");break;default:M+=\" ui-datepicker-group-middle\",S=\"\"}M+=\"'>\"}for(M+=\"<div class='ui-datepicker-header ui-widget-header ui-helper-clearfix\"+S+\"'>\"+(/all|left/.test(S)&&0===w?Y?a:s:\"\")+(/all|right/.test(S)&&0===w?Y?s:a:\"\")+this._generateMonthYearHeader(e,Z,et,X,$,w>0||T>0,f,m)+\"</div><table class='ui-datepicker-calendar'><thead>\"+\"<tr>\",C=d?\"<th class='ui-datepicker-week-col'>\"+this._get(e,\"weekHeader\")+\"</th>\":\"\",x=0;7>x;x++)N=(x+u)%7,C+=\"<th scope='col'\"+((x+u+6)%7>=5?\" class='ui-datepicker-week-end'\":\"\")+\">\"+\"<span title='\"+c[N]+\"'>\"+p[N]+\"</span></th>\";for(M+=C+\"</tr></thead><tbody>\",A=this._getDaysInMonth(et,Z),et===e.selectedYear&&Z===e.selectedMonth&&(e.selectedDay=Math.min(e.selectedDay,A)),P=(this._getFirstDayOfMonth(et,Z)-u+7)%7,I=Math.ceil((P+A)/7),H=Q?this.maxRows>I?this.maxRows:I:I,this.maxRows=H,z=this._daylightSavingAdjust(new Date(et,Z,1-P)),F=0;H>F;F++){for(M+=\"<tr>\",E=d?\"<td class='ui-datepicker-week-col'>\"+this._get(e,\"calculateWeek\")(z)+\"</td>\":\"\",x=0;7>x;x++)O=g?g.apply(e.input?e.input[0]:null,[z]):[!0,\"\"],j=z.getMonth()!==Z,W=j&&!y||!O[0]||X&&X>z||$&&z>$,E+=\"<td class='\"+((x+u+6)%7>=5?\" ui-datepicker-week-end\":\"\")+(j?\" ui-datepicker-other-month\":\"\")+(z.getTime()===D.getTime()&&Z===e.selectedMonth&&e._keyEvent||b.getTime()===z.getTime()&&b.getTime()===D.getTime()?\" \"+this._dayOverClass:\"\")+(W?\" \"+this._unselectableClass+\" ui-state-disabled\":\"\")+(j&&!v?\"\":\" \"+O[1]+(z.getTime()===G.getTime()?\" \"+this._currentClass:\"\")+(z.getTime()===R.getTime()?\" ui-datepicker-today\":\"\"))+\"'\"+(j&&!v||!O[2]?\"\":\" title='\"+O[2].replace(/'/g,\"&#39;\")+\"'\")+(W?\"\":\" data-handler='selectDay' data-event='click' data-month='\"+z.getMonth()+\"' data-year='\"+z.getFullYear()+\"'\")+\">\"+(j&&!v?\"&#xa0;\":W?\"<span class='ui-state-default'>\"+z.getDate()+\"</span>\":\"<a class='ui-state-default\"+(z.getTime()===R.getTime()?\" ui-state-highlight\":\"\")+(z.getTime()===G.getTime()?\" ui-state-active\":\"\")+(j?\" ui-priority-secondary\":\"\")+\"' href='#'>\"+z.getDate()+\"</a>\")+\"</td>\",z.setDate(z.getDate()+1),z=this._daylightSavingAdjust(z);M+=E+\"</tr>\"}Z++,Z>11&&(Z=0,et++),M+=\"</tbody></table>\"+(Q?\"</div>\"+(K[0]>0&&T===K[1]-1?\"<div class='ui-datepicker-row-break'></div>\":\"\"):\"\"),k+=M}_+=k}return _+=l,e._keyEvent=!1,_},_generateMonthYearHeader:function(e,t,i,s,n,a,o,r){var h,l,u,d,c,p,f,m,g=this._get(e,\"changeMonth\"),v=this._get(e,\"changeYear\"),y=this._get(e,\"showMonthAfterYear\"),b=\"<div class='ui-datepicker-title'>\",_=\"\";if(a||!g)_+=\"<span class='ui-datepicker-month'>\"+o[t]+\"</span>\";else{for(h=s&&s.getFullYear()===i,l=n&&n.getFullYear()===i,_+=\"<select class='ui-datepicker-month' data-handler='selectMonth' data-event='change'>\",u=0;12>u;u++)(!h||u>=s.getMonth())&&(!l||n.getMonth()>=u)&&(_+=\"<option value='\"+u+\"'\"+(u===t?\" selected='selected'\":\"\")+\">\"+r[u]+\"</option>\");_+=\"</select>\"}if(y||(b+=_+(!a&&g&&v?\"\":\"&#xa0;\")),!e.yearshtml)if(e.yearshtml=\"\",a||!v)b+=\"<span class='ui-datepicker-year'>\"+i+\"</span>\";else{for(d=this._get(e,\"yearRange\").split(\":\"),c=(new Date).getFullYear(),p=function(e){var t=e.match(/c[+\\-].*/)?i+parseInt(e.substring(1),10):e.match(/[+\\-].*/)?c+parseInt(e,10):parseInt(e,10);return isNaN(t)?c:t},f=p(d[0]),m=Math.max(f,p(d[1]||\"\")),f=s?Math.max(f,s.getFullYear()):f,m=n?Math.min(m,n.getFullYear()):m,e.yearshtml+=\"<select class='ui-datepicker-year' data-handler='selectYear' data-event='change'>\";m>=f;f++)e.yearshtml+=\"<option value='\"+f+\"'\"+(f===i?\" selected='selected'\":\"\")+\">\"+f+\"</option>\";e.yearshtml+=\"</select>\",b+=e.yearshtml,e.yearshtml=null}return b+=this._get(e,\"yearSuffix\"),y&&(b+=(!a&&g&&v?\"\":\"&#xa0;\")+_),b+=\"</div>\"},_adjustInstDate:function(e,t,i){var s=e.drawYear+(\"Y\"===i?t:0),n=e.drawMonth+(\"M\"===i?t:0),a=Math.min(e.selectedDay,this._getDaysInMonth(s,n))+(\"D\"===i?t:0),o=this._restrictMinMax(e,this._daylightSavingAdjust(new Date(s,n,a)));e.selectedDay=o.getDate(),e.drawMonth=e.selectedMonth=o.getMonth(),e.drawYear=e.selectedYear=o.getFullYear(),(\"M\"===i||\"Y\"===i)&&this._notifyChange(e)},_restrictMinMax:function(e,t){var i=this._getMinMaxDate(e,\"min\"),s=this._getMinMaxDate(e,\"max\"),n=i&&i>t?i:t;return s&&n>s?s:n},_notifyChange:function(e){var t=this._get(e,\"onChangeMonthYear\");t&&t.apply(e.input?e.input[0]:null,[e.selectedYear,e.selectedMonth+1,e])},_getNumberOfMonths:function(e){var t=this._get(e,\"numberOfMonths\");return null==t?[1,1]:\"number\"==typeof t?[1,t]:t},_getMinMaxDate:function(e,t){return this._determineDate(e,this._get(e,t+\"Date\"),null)},_getDaysInMonth:function(e,t){return 32-this._daylightSavingAdjust(new Date(e,t,32)).getDate()},_getFirstDayOfMonth:function(e,t){return new Date(e,t,1).getDay()},_canAdjustMonth:function(e,t,i,s){var n=this._getNumberOfMonths(e),a=this._daylightSavingAdjust(new Date(i,s+(0>t?t:n[0]*n[1]),1));return 0>t&&a.setDate(this._getDaysInMonth(a.getFullYear(),a.getMonth())),this._isInRange(e,a)},_isInRange:function(e,t){var i,s,n=this._getMinMaxDate(e,\"min\"),a=this._getMinMaxDate(e,\"max\"),o=null,r=null,h=this._get(e,\"yearRange\");return h&&(i=h.split(\":\"),s=(new Date).getFullYear(),o=parseInt(i[0],10),r=parseInt(i[1],10),i[0].match(/[+\\-].*/)&&(o+=s),i[1].match(/[+\\-].*/)&&(r+=s)),(!n||t.getTime()>=n.getTime())&&(!a||t.getTime()<=a.getTime())&&(!o||t.getFullYear()>=o)&&(!r||r>=t.getFullYear())},_getFormatConfig:function(e){var t=this._get(e,\"shortYearCutoff\");return t=\"string\"!=typeof t?t:(new Date).getFullYear()%100+parseInt(t,10),{shortYearCutoff:t,dayNamesShort:this._get(e,\"dayNamesShort\"),dayNames:this._get(e,\"dayNames\"),monthNamesShort:this._get(e,\"monthNamesShort\"),monthNames:this._get(e,\"monthNames\")}},_formatDate:function(e,t,i,s){t||(e.currentDay=e.selectedDay,e.currentMonth=e.selectedMonth,e.currentYear=e.selectedYear);var n=t?\"object\"==typeof t?t:this._daylightSavingAdjust(new Date(s,i,t)):this._daylightSavingAdjust(new Date(e.currentYear,e.currentMonth,e.currentDay));return this.formatDate(this._get(e,\"dateFormat\"),n,this._getFormatConfig(e))}}),e.fn.datepicker=function(t){if(!this.length)return this;e.datepicker.initialized||(e(document).mousedown(e.datepicker._checkExternalClick),e.datepicker.initialized=!0),0===e(\"#\"+e.datepicker._mainDivId).length&&e(\"body\").append(e.datepicker.dpDiv);var i=Array.prototype.slice.call(arguments,1);return\"string\"!=typeof t||\"isDisabled\"!==t&&\"getDate\"!==t&&\"widget\"!==t?\"option\"===t&&2===arguments.length&&\"string\"==typeof arguments[1]?e.datepicker[\"_\"+t+\"Datepicker\"].apply(e.datepicker,[this[0]].concat(i)):this.each(function(){\"string\"==typeof t?e.datepicker[\"_\"+t+\"Datepicker\"].apply(e.datepicker,[this].concat(i)):e.datepicker._attachDatepicker(this,t)}):e.datepicker[\"_\"+t+\"Datepicker\"].apply(e.datepicker,[this[0]].concat(i))},e.datepicker=new n,e.datepicker.initialized=!1,e.datepicker.uuid=(new Date).getTime(),e.datepicker.version=\"1.11.4\",e.datepicker,e.widget(\"ui.draggable\",e.ui.mouse,{version:\"1.11.4\",widgetEventPrefix:\"drag\",options:{addClasses:!0,appendTo:\"parent\",axis:!1,connectToSortable:!1,containment:!1,cursor:\"auto\",cursorAt:!1,grid:!1,handle:!1,helper:\"original\",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:\"default\",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:\"both\",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){\"original\"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass(\"ui-draggable\"),this.options.disabled&&this.element.addClass(\"ui-draggable-disabled\"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),\"handle\"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(\".ui-draggable-dragging\")?(this.destroyOnClear=!0,void 0):(this.element.removeClass(\"ui-draggable ui-draggable-dragging ui-draggable-disabled\"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(\".ui-resizable-handle\").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?\"iframe\":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e(\"<div>\").css(\"position\",\"absolute\").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&\"body\"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass(\"ui-draggable-dragging\"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css(\"position\"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return\"fixed\"===e(this).css(\"position\")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger(\"start\",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo(\"absolute\"),!i){var s=this._uiHash();if(this._trigger(\"drag\",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+\"px\",this.helper[0].style.top=this.position.top+\"px\",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),\"invalid\"===this.options.revert&&!s||\"valid\"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger(\"stop\",t)!==!1&&i._clear()}):this._trigger(\"stop\",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(\".ui-draggable-dragging\")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass(\"ui-draggable-handle\")},_removeHandleClassName:function(){this.handleElement.removeClass(\"ui-draggable-handle\")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):\"clone\"===i.helper?this.element.clone().removeAttr(\"id\"):this.element;return n.parents(\"body\").length||n.appendTo(\"parent\"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css(\"position\"))||n.css(\"position\",\"absolute\"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css(\"position\"))||(this.element[0].style.position=\"relative\")},_adjustOffsetFromHelper:function(t){\"string\"==typeof t&&(t=t.split(\" \")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),\"left\"in t&&(this.offset.click.left=t.left+this.margins.left),\"right\"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),\"top\"in t&&(this.offset.click.top=t.top+this.margins.top),\"bottom\"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return\"absolute\"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css(\"borderTopWidth\"),10)||0),left:t.left+(parseInt(this.offsetParent.css(\"borderLeftWidth\"),10)||0)}},_getRelativeOffset:function(){if(\"relative\"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css(\"top\"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css(\"left\"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css(\"marginLeft\"),10)||0,top:parseInt(this.element.css(\"marginTop\"),10)||0,right:parseInt(this.element.css(\"marginRight\"),10)||0,bottom:parseInt(this.element.css(\"marginBottom\"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?\"window\"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):\"document\"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):(\"parent\"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css(\"overflow\")),this.containment=[(parseInt(i.css(\"borderLeftWidth\"),10)||0)+(parseInt(i.css(\"paddingLeft\"),10)||0),(parseInt(i.css(\"borderTopWidth\"),10)||0)+(parseInt(i.css(\"paddingTop\"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css(\"borderRightWidth\"),10)||0)-(parseInt(i.css(\"paddingRight\"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css(\"borderBottomWidth\"),10)||0)-(parseInt(i.css(\"paddingBottom\"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)\n},_convertPositionTo:function(e,t){t||(t=this.position);var i=\"absolute\"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-(\"fixed\"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-(\"fixed\"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),\"y\"===o.axis&&(h=this.originalPageX),\"x\"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+(\"fixed\"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+(\"fixed\"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass(\"ui-draggable-dragging\"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){\"y\"!==this.options.axis&&\"auto\"!==this.helper.css(\"right\")&&(this.helper.width(this.helper.width()),this.helper.css(\"right\",\"auto\")),\"x\"!==this.options.axis&&\"auto\"!==this.helper.css(\"bottom\")&&(this.helper.height(this.helper.height()),this.helper.css(\"bottom\",\"auto\"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo(\"absolute\"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add(\"draggable\",\"connectToSortable\",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable(\"instance\");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger(\"activate\",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css(\"position\"),top:e.placeholder.css(\"top\"),left:e.placeholder.css(\"left\")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger(\"deactivate\",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data(\"ui-sortable-item\",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger(\"toSortable\",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger(\"out\",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger(\"fromSortable\",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add(\"draggable\",\"cursor\",{start:function(t,i,s){var n=e(\"body\"),a=s.options;n.css(\"cursor\")&&(a._cursor=n.css(\"cursor\")),n.css(\"cursor\",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e(\"body\").css(\"cursor\",n._cursor)}}),e.ui.plugin.add(\"draggable\",\"opacity\",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css(\"opacity\")&&(a._opacity=n.css(\"opacity\")),n.css(\"opacity\",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css(\"opacity\",n._opacity)}}),e.ui.plugin.add(\"draggable\",\"scroll\",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&\"HTML\"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&\"HTML\"!==o.tagName?(n.axis&&\"x\"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&\"y\"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&\"x\"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&\"y\"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add(\"draggable\",\"snap\",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||\":data(ui-draggable)\":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var 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n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css(\"zIndex\"),10)||0)-(parseInt(e(i).css(\"zIndex\"),10)||0)});o.length&&(n=parseInt(e(o[0]).css(\"zIndex\"),10)||0,e(o).each(function(t){e(this).css(\"zIndex\",n+t)}),this.css(\"zIndex\",n+o.length))}}),e.ui.plugin.add(\"draggable\",\"zIndex\",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css(\"zIndex\")&&(a._zIndex=n.css(\"zIndex\")),n.css(\"zIndex\",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css(\"zIndex\",n._zIndex)}}),e.ui.draggable,e.widget(\"ui.resizable\",e.ui.mouse,{version:\"1.11.4\",widgetEventPrefix:\"resize\",options:{alsoResize:!1,animate:!1,animateDuration:\"slow\",animateEasing:\"swing\",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:\"e,s,se\",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if(\"hidden\"===e(t).css(\"overflow\"))return!1;var s=i&&\"left\"===i?\"scrollLeft\":\"scrollTop\",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass(\"ui-resizable\"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||\"ui-resizable-helper\":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e(\"<div class='ui-wrapper' style='overflow: hidden;'></div>\").css({position:this.element.css(\"position\"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css(\"top\"),left:this.element.css(\"left\")})),this.element=this.element.parent().data(\"ui-resizable\",this.element.resizable(\"instance\")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css(\"marginLeft\"),marginTop:this.originalElement.css(\"marginTop\"),marginRight:this.originalElement.css(\"marginRight\"),marginBottom:this.originalElement.css(\"marginBottom\")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css(\"resize\"),this.originalElement.css(\"resize\",\"none\"),this._proportionallyResizeElements.push(this.originalElement.css({position:\"static\",zoom:1,display:\"block\"})),this.originalElement.css({margin:this.originalElement.css(\"margin\")}),this._proportionallyResize()),this.handles=r.handles||(e(\".ui-resizable-handle\",this.element).length?{n:\".ui-resizable-n\",e:\".ui-resizable-e\",s:\".ui-resizable-s\",w:\".ui-resizable-w\",se:\".ui-resizable-se\",sw:\".ui-resizable-sw\",ne:\".ui-resizable-ne\",nw:\".ui-resizable-nw\"}:\"e,s,se\"),this._handles=e(),this.handles.constructor===String)for(\"all\"===this.handles&&(this.handles=\"n,e,s,w,se,sw,ne,nw\"),t=this.handles.split(\",\"),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a=\"ui-resizable-\"+s,n=e(\"<div class='ui-resizable-handle \"+a+\"'></div>\"),n.css({zIndex:r.zIndex}),\"se\"===s&&n.addClass(\"ui-icon ui-icon-gripsmall-diagonal-se\"),this.handles[s]=\".ui-resizable-\"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=[\"padding\",/ne|nw|n/.test(i)?\"Top\":/se|sw|s/.test(i)?\"Bottom\":/^e$/.test(i)?\"Right\":\"Left\"].join(\"\"),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(\".ui-resizable-handle\")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:\"se\")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass(\"ui-resizable-autohide\").mouseenter(function(){r.disabled||(e(this).removeClass(\"ui-resizable-autohide\"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass(\"ui-resizable-autohide\"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass(\"ui-resizable ui-resizable-disabled ui-resizable-resizing\").removeData(\"resizable\").removeData(\"ui-resizable\").unbind(\".resizable\").find(\".ui-resizable-handle\").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css(\"position\"),width:t.outerWidth(),height:t.outerHeight(),top:t.css(\"top\"),left:t.css(\"left\")}).insertAfter(t),t.remove()),this.originalElement.css(\"resize\",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css(\"left\")),s=this._num(this.helper.css(\"top\")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio=\"number\"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(\".ui-resizable-\"+this.axis).css(\"cursor\"),e(\"body\").css(\"cursor\",\"auto\"===n?this.axis+\"-resize\":n),o.addClass(\"ui-resizable-resizing\"),this._propagate(\"start\",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate(\"resize\",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger(\"resize\",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],\"left\")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css(\"left\"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css(\"top\"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e(\"body\").css(\"cursor\",\"auto\"),this.element.removeClass(\"ui-resizable-resizing\"),this._propagate(\"stop\",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+\"px\"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+\"px\"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+\"px\"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+\"px\"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),\"sw\"===s&&(e.left=t.left+(i.width-e.width),e.top=null),\"nw\"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css(\"borderTopWidth\"),e.css(\"borderRightWidth\"),e.css(\"borderBottomWidth\"),e.css(\"borderLeftWidth\")],n=[e.css(\"paddingTop\"),e.css(\"paddingRight\"),e.css(\"paddingBottom\"),e.css(\"paddingLeft\")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e(\"<div style='overflow:hidden;'></div>\"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:\"absolute\",left:this.elementOffset.left+\"px\",top:this.elementOffset.top+\"px\",zIndex:++i.zIndex}),this.helper.appendTo(\"body\").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),\"resize\"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add(\"resizable\",\"animate\",{stop:function(t){var i=e(this).resizable(\"instance\"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],\"left\")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css(\"left\"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css(\"top\"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css(\"width\"),10),height:parseInt(i.element.css(\"height\"),10),top:parseInt(i.element.css(\"top\"),10),left:parseInt(i.element.css(\"left\"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate(\"resize\",t)}})}}),e.ui.plugin.add(\"resizable\",\"containment\",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable(\"instance\"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e([\"Top\",\"Right\",\"Left\",\"Bottom\"]).each(function(e,s){i[e]=h._num(t.css(\"padding\"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,\"left\")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable(\"instance\"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css(\"position\"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css(\"position\")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable(\"instance\"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css(\"position\"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css(\"position\"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add(\"resizable\",\"alsoResize\",{start:function(){var t=e(this).resizable(\"instance\"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data(\"ui-resizable-alsoresize\",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css(\"left\"),10),top:parseInt(t.css(\"top\"),10)})})},resize:function(t,i){var s=e(this).resizable(\"instance\"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data(\"ui-resizable-alsoresize\"),n={},a=t.parents(i.originalElement[0]).length?[\"width\",\"height\"]:[\"width\",\"height\",\"top\",\"left\"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData(\"resizable-alsoresize\")}}),e.ui.plugin.add(\"resizable\",\"ghost\",{start:function(){var t=e(this).resizable(\"instance\"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:\"block\",position:\"relative\",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass(\"ui-resizable-ghost\").addClass(\"string\"==typeof i.ghost?i.ghost:\"\"),t.ghost.appendTo(t.helper)},resize:function(){var 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i=e(this).css(t).offset().top;0>i&&e(this).css(\"top\",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr(\"title\"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr(\"title\").addClass(\"ui-dialog-content ui-widget-content\").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||\"body\").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass(\"ui-dialog-content ui-widget-content\").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr(\"title\",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger(\"beforeClose\",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(\":focusable\").focus().length)try{i=this.document[0].activeElement,i&&\"body\"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger(\"close\",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(\".ui-front:visible\").map(function(){return+e(this).css(\"z-index\")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css(\"z-index\")&&(this.uiDialog.css(\"z-index\",a+1),s=!0),s&&!i&&this._trigger(\"focus\",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css(\"z-index\",this.uiDialog.css(\"z-index\")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger(\"focus\")}),this._makeFocusTarget(),this._trigger(\"open\"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find(\"[autofocus]\")),e.length||(e=this.element.find(\":tabbable\")),e.length||(e=this.uiDialogButtonPane.find(\":tabbable\")),e.length||(e=this.uiDialogTitlebarClose.filter(\":tabbable\")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e(\"<div>\").addClass(\"ui-dialog ui-widget ui-widget-content ui-corner-all ui-front \"+this.options.dialogClass).hide().attr({tabIndex:-1,role:\"dialog\"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;\nif(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(\":tabbable\"),s=i.filter(\":first\"),n=i.filter(\":last\");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find(\"[aria-describedby]\").length||this.uiDialog.attr({\"aria-describedby\":this.element.uniqueId().attr(\"id\")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e(\"<div>\").addClass(\"ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix\").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(\".ui-dialog-titlebar-close\")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e(\"<button type='button'></button>\").button({label:this.options.closeText,icons:{primary:\"ui-icon-closethick\"},text:!1}).addClass(\"ui-dialog-titlebar-close\").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e(\"<span>\").uniqueId().addClass(\"ui-dialog-title\").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({\"aria-labelledby\":t.attr(\"id\")})},_title:function(e){this.options.title||e.html(\"&#160;\"),e.text(this.options.title)},_createButtonPane:function(){this.uiDialogButtonPane=e(\"<div>\").addClass(\"ui-dialog-buttonpane ui-widget-content ui-helper-clearfix\"),this.uiButtonSet=e(\"<div>\").addClass(\"ui-dialog-buttonset\").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass(\"ui-dialog-buttons\"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:\"button\"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e(\"<button></button>\",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass(\"ui-dialog-buttons\"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:\".ui-dialog-content, 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n?n:\"n,e,s,w,se,sw,ne,nw\";this.uiDialog.resizable({cancel:\".ui-dialog-content\",containment:\"document\",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass(\"ui-dialog-resizing\"),i._blockFrames(),i._trigger(\"resizeStart\",s,t(n))},resize:function(e,s){i._trigger(\"resize\",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:\"left top\",at:\"left\"+(r>=0?\"+\":\"\")+r+\" \"+\"top\"+(h>=0?\"+\":\"\")+h,of:i.window},e(this).removeClass(\"ui-dialog-resizing\"),i._unblockFrames(),i._trigger(\"resizeStop\",n,t(a))}}).css(\"position\",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data(\"ui-dialog-instances\");return e||(e=[],this.document.data(\"ui-dialog-instances\",e)),e},_minHeight:function(){var e=this.options;return\"auto\"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(\":visible\");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(\":data(ui-resizable)\")&&this.uiDialog.resizable(\"option\",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;\"dialogClass\"===e&&n.removeClass(this.options.dialogClass).addClass(t),\"disabled\"!==e&&(this._super(e,t),\"appendTo\"===e&&this.uiDialog.appendTo(this._appendTo()),\"buttons\"===e&&this._createButtons(),\"closeText\"===e&&this.uiDialogTitlebarClose.button({label:\"\"+t}),\"draggable\"===e&&(i=n.is(\":data(ui-draggable)\"),i&&!t&&n.draggable(\"destroy\"),!i&&t&&this._makeDraggable()),\"position\"===e&&this._position(),\"resizable\"===e&&(s=n.is(\":data(ui-resizable)\"),s&&!t&&n.resizable(\"destroy\"),s&&\"string\"==typeof t&&n.resizable(\"option\",\"handles\",t),s||t===!1||this._makeResizable()),\"title\"===e&&this._title(this.uiDialogTitlebar.find(\".ui-dialog-title\")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:\"auto\",minHeight:0,maxHeight:\"none\",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:\"auto\",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i=\"number\"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):\"none\",\"auto\"===s.height?this.element.css({minHeight:t,maxHeight:i,height:\"auto\"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(\":data(ui-resizable)\")&&this.uiDialog.resizable(\"option\",\"minHeight\",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find(\"iframe\").map(function(){var t=e(this);return e(\"<div>\").css({position:\"absolute\",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(\".ui-dialog\").length?!0:!!e(t.target).closest(\".ui-datepicker\").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data(\"ui-dialog-overlays\")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e(\"<div>\").addClass(\"ui-widget-overlay ui-front\").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:\"_keepFocus\"}),this.document.data(\"ui-dialog-overlays\",(this.document.data(\"ui-dialog-overlays\")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data(\"ui-dialog-overlays\")-1;e?this.document.data(\"ui-dialog-overlays\",e):this.document.unbind(\"focusin\").removeData(\"ui-dialog-overlays\"),this.overlay.remove(),this.overlay=null}}}),e.widget(\"ui.droppable\",{version:\"1.11.4\",widgetEventPrefix:\"drop\",options:{accept:\"*\",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:\"default\",tolerance:\"intersect\",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass(\"ui-droppable\")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var 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i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger(\"start\",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(\".ui-selected\").each(function(){var s=e.data(this,\"selectable-item\");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass(\"ui-selected\"),s.selected=!1,s.$element.addClass(\"ui-unselecting\"),s.unselecting=!0,i._trigger(\"unselecting\",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,\"selectable-item\");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass(\"ui-selected\"),n.$element.removeClass(s?\"ui-unselecting\":\"ui-selected\").addClass(s?\"ui-selecting\":\"ui-unselecting\"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger(\"selecting\",t,{selecting:n.element}):i._trigger(\"unselecting\",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,\"selectable-item\"),l=!1;i&&i.element!==s.element[0]&&(\"touch\"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):\"fit\"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass(\"ui-selected\"),i.selected=!1),i.unselecting&&(i.$element.removeClass(\"ui-unselecting\"),i.unselecting=!1),i.selecting||(i.$element.addClass(\"ui-selecting\"),i.selecting=!0,s._trigger(\"selecting\",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass(\"ui-selecting\"),i.selecting=!1,i.$element.addClass(\"ui-selected\"),i.selected=!0):(i.$element.removeClass(\"ui-selecting\"),i.selecting=!1,i.startselected&&(i.$element.addClass(\"ui-unselecting\"),i.unselecting=!0),s._trigger(\"unselecting\",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass(\"ui-selected\"),i.selected=!1,i.$element.addClass(\"ui-unselecting\"),i.unselecting=!0,s._trigger(\"unselecting\",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(\".ui-unselecting\",this.element[0]).each(function(){var s=e.data(this,\"selectable-item\");s.$element.removeClass(\"ui-unselecting\"),s.unselecting=!1,s.startselected=!1,i._trigger(\"unselected\",t,{unselected:s.element})}),e(\".ui-selecting\",this.element[0]).each(function(){var s=e.data(this,\"selectable-item\");s.$element.removeClass(\"ui-selecting\").addClass(\"ui-selected\"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger(\"selected\",t,{selected:s.element})}),this._trigger(\"stop\",t),this.helper.remove(),!1}}),e.widget(\"ui.selectmenu\",{version:\"1.11.4\",defaultElement:\"<select>\",options:{appendTo:null,disabled:null,icons:{button:\"ui-icon-triangle-1-s\"},position:{my:\"left top\",at:\"left bottom\",collision:\"none\"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr(\"id\");this.ids={element:e,button:e+\"-button\",menu:e+\"-menu\"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e(\"label[for='\"+this.ids.element+\"']\").attr(\"for\",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e(\"<span>\",{\"class\":\"ui-selectmenu-button ui-widget ui-state-default ui-corner-all\",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:\"combobox\",\"aria-expanded\":\"false\",\"aria-autocomplete\":\"list\",\"aria-owns\":this.ids.menu,\"aria-haspopup\":\"true\"}).insertAfter(this.element),e(\"<span>\",{\"class\":\"ui-icon \"+this.options.icons.button}).prependTo(this.button),this.buttonText=e(\"<span>\",{\"class\":\"ui-selectmenu-text\"}).appendTo(this.button),this._setText(this.buttonText,this.element.find(\"option:selected\").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one(\"focusin\",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e(\"<ul>\",{\"aria-hidden\":\"true\",\"aria-labelledby\":this.ids.button,id:this.ids.menu}),this.menuWrap=e(\"<div>\",{\"class\":\"ui-selectmenu-menu ui-front\"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:\"listbox\",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data(\"ui-selectmenu-item\"),e)},focus:function(e,i){var s=i.item.data(\"ui-selectmenu-item\");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger(\"focus\",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr(\"aria-activedescendant\",t.menuItems.eq(s.index).attr(\"id\"))}}).menu(\"instance\"),this.menu.addClass(\"ui-corner-bottom\").removeClass(\"ui-corner-all\"),this.menuInstance._off(this.menu,\"mouseleave\"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find(\"option\");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find(\"li\").not(\".ui-selectmenu-optgroup\"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data(\"ui-selectmenu-item\")),this._setOption(\"disabled\",this.element.prop(\"disabled\")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(\".ui-state-focus\").removeClass(\"ui-state-focus\"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger(\"open\",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger(\"close\",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n=\"\";e.each(i,function(i,a){a.optgroup!==n&&(e(\"<li>\",{\"class\":\"ui-selectmenu-optgroup ui-menu-divider\"+(a.element.parent(\"optgroup\").prop(\"disabled\")?\" ui-state-disabled\":\"\"),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data(\"ui-selectmenu-item\",t)},_renderItem:function(t,i){var s=e(\"<li>\");return i.disabled&&s.addClass(\"ui-state-disabled\"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html(\"&#160;\")},_move:function(e,t){var i,s,n=\".ui-menu-item\";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=\":not(.ui-state-disabled)\"),s=\"first\"===e||\"last\"===e?i[\"first\"===e?\"prevAll\":\"nextAll\"](n).eq(-1):i[e+\"All\"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?\"close\":\"open\"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(\".ui-selectmenu-menu, #\"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move(\"prev\",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move(\"next\",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move(\"prev\",t);break;case e.ui.keyCode.RIGHT:this._move(\"next\",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move(\"first\",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move(\"last\",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass(\"ui-state-disabled\")||this._select(t.data(\"ui-selectmenu-item\"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger(\"select\",t,{item:e}),e.index!==i&&this._trigger(\"change\",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr(\"id\");this.button.attr({\"aria-labelledby\":t,\"aria-activedescendant\":t}),this.menu.attr(\"aria-activedescendant\",t)},_setOption:function(e,t){\"icons\"===e&&this.button.find(\"span.ui-icon\").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),\"appendTo\"===e&&this.menuWrap.appendTo(this._appendTo()),\"disabled\"===e&&(this.menuInstance.option(\"disabled\",t),this.button.toggleClass(\"ui-state-disabled\",t).attr(\"aria-disabled\",t),this.element.prop(\"disabled\",t),t?(this.button.attr(\"tabindex\",-1),this.close()):this.button.attr(\"tabindex\",0)),\"width\"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(\".ui-front\")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass(\"ui-corner-top\",this.isOpen).toggleClass(\"ui-corner-all\",!this.isOpen).attr(\"aria-expanded\",this.isOpen),this.menuWrap.toggleClass(\"ui-selectmenu-open\",this.isOpen),this.menu.attr(\"aria-hidden\",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width(\"\").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop(\"disabled\")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent(\"optgroup\");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr(\"label\")||\"\",disabled:a.prop(\"disabled\")||n.prop(\"disabled\")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr(\"for\",this.ids.element)}}),e.widget(\"ui.slider\",e.ui.mouse,{version:\"1.11.4\",widgetEventPrefix:\"slide\",options:{animate:!1,distance:0,max:100,min:0,orientation:\"horizontal\",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass(\"ui-slider ui-slider-\"+this.orientation+\" ui-widget\"+\" ui-widget-content\"+\" ui-corner-all\"),this._refresh(),this._setOption(\"disabled\",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(\".ui-slider-handle\").addClass(\"ui-state-default ui-corner-all\"),a=\"<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>\",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join(\"\")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data(\"ui-slider-handle-index\",t)})},_createRange:function(){var t=this.options,i=\"\";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass(\"ui-slider-range-min ui-slider-range-max\").css({left:\"\",bottom:\"\"}):(this.range=e(\"<div></div>\").appendTo(this.element),i=\"ui-slider-range ui-widget-header ui-corner-all\"),this.range.addClass(i+(\"min\"===t.range||\"max\"===t.range?\" ui-slider-range-\"+t.range:\"\"))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass(\"ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all\"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass(\"ui-state-active\").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(\".ui-slider-handle\"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css(\"borderTopWidth\"),10)||0)-(parseInt(a.css(\"borderBottomWidth\"),10)||0)+(parseInt(a.css(\"marginTop\"),10)||0)},this.handles.hasClass(\"ui-state-hover\")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass(\"ui-state-active\"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation=\"vertical\"===this.options.orientation?\"vertical\":\"horizontal\"},_normValueFromMouse:function(e){var t,i,s,n,a;return\"horizontal\"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),\"vertical\"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger(\"start\",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger(\"slide\",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger(\"slide\",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger(\"stop\",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger(\"change\",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch(\"range\"===t&&this.options.range===!0&&(\"min\"===i?(this.options.value=this._values(0),this.options.values=null):\"max\"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),\"disabled\"===t&&this.element.toggleClass(\"ui-state-disabled\",!!i),this._super(t,i),t){case\"orientation\":this._detectOrientation(),this.element.removeClass(\"ui-slider-horizontal ui-slider-vertical\").addClass(\"ui-slider-\"+this.orientation),this._refreshValue(),this.handles.css(\"horizontal\"===i?\"bottom\":\"left\",\"\");break;case\"value\":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case\"values\":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case\"step\":case\"min\":case\"max\":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case\"range\":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=\"\"+e,i=t.indexOf(\".\");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u[\"horizontal\"===h.orientation?\"left\":\"bottom\"]=i+\"%\",e(this).stop(1,1)[l?\"animate\":\"css\"](u,r.animate),h.options.range===!0&&(\"horizontal\"===h.orientation?(0===s&&h.range.stop(1,1)[l?\"animate\":\"css\"]({left:i+\"%\"},r.animate),1===s&&h.range[l?\"animate\":\"css\"]({width:i-t+\"%\"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?\"animate\":\"css\"]({bottom:i+\"%\"},r.animate),1===s&&h.range[l?\"animate\":\"css\"]({height:i-t+\"%\"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u[\"horizontal\"===this.orientation?\"left\":\"bottom\"]=i+\"%\",this.handle.stop(1,1)[l?\"animate\":\"css\"](u,r.animate),\"min\"===o&&\"horizontal\"===this.orientation&&this.range.stop(1,1)[l?\"animate\":\"css\"]({width:i+\"%\"},r.animate),\"max\"===o&&\"horizontal\"===this.orientation&&this.range[l?\"animate\":\"css\"]({width:100-i+\"%\"},{queue:!1,duration:r.animate}),\"min\"===o&&\"vertical\"===this.orientation&&this.range.stop(1,1)[l?\"animate\":\"css\"]({height:i+\"%\"},r.animate),\"max\"===o&&\"vertical\"===this.orientation&&this.range[l?\"animate\":\"css\"]({height:100-i+\"%\"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data(\"ui-slider-handle-index\");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass(\"ui-state-active\"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data(\"ui-slider-handle-index\");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass(\"ui-state-active\"))}}}),e.widget(\"ui.sortable\",e.ui.mouse,{version:\"1.11.4\",widgetEventPrefix:\"sort\",ready:!1,options:{appendTo:\"parent\",axis:!1,connectWith:!1,containment:!1,cursor:\"auto\",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:\"original\",items:\"> *\",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:\"default\",tolerance:\"intersect\",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css(\"float\"))||/inline|table-cell/.test(e.css(\"display\"))},_create:function(){this.containerCache={},this.element.addClass(\"ui-sortable\"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),\"handle\"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(\".ui-sortable-handle\").removeClass(\"ui-sortable-handle\"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass(\"ui-sortable-handle\")})},_destroy:function(){this.element.removeClass(\"ui-sortable ui-sortable-disabled\").find(\".ui-sortable-handle\").removeClass(\"ui-sortable-handle\"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+\"-item\");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||\"static\"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+\"-item\")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+\"-item\")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find(\"*\").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css(\"position\",\"absolute\"),this.cssPosition=this.helper.css(\"position\"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&\"auto\"!==o.cursor&&(a=this.document.find(\"body\"),this.storedCursor=a.css(\"cursor\"),a.css(\"cursor\",o.cursor),this.storedStylesheet=e(\"<style>*{ cursor: \"+o.cursor+\" !important; }</style>\").appendTo(a)),o.opacity&&(this.helper.css(\"opacity\")&&(this._storedOpacity=this.helper.css(\"opacity\")),this.helper.css(\"opacity\",o.opacity)),o.zIndex&&(this.helper.css(\"zIndex\")&&(this._storedZIndex=this.helper.css(\"zIndex\")),this.helper.css(\"zIndex\",o.zIndex)),this.scrollParent[0]!==this.document[0]&&\"HTML\"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger(\"start\",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger(\"activate\",t,this._uiHash(this));\nreturn e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass(\"ui-sortable-helper\"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo(\"absolute\"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&\"HTML\"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo(\"absolute\"),this.options.axis&&\"y\"===this.options.axis||(this.helper[0].style.left=this.position.left+\"px\"),this.options.axis&&\"x\"===this.options.axis||(this.helper[0].style.top=this.position.top+\"px\"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?\"next\":\"prev\"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&(\"semi-dynamic\"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?\"down\":\"up\",\"pointer\"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger(\"change\",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger(\"sort\",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&\"x\"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&\"y\"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),\"original\"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass(\"ui-sortable-helper\"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger(\"deactivate\",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger(\"out\",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),\"original\"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||\"id\")||\"\").match(t.expression||/(.+)[\\-=_](.+)/);i&&s.push((t.key||i[1]+\"[]\")+\"=\"+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+\"=\"),s.join(\"&\")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||\"id\")||\"\")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d=\"x\"===this.options.axis||s+l>r&&h>s+l,c=\"y\"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return\"pointer\"===this.options.tolerance||this.options.forcePointerForContainers||\"pointer\"!==this.options.tolerance&&this.helperProportions[this.floating?\"width\":\"height\"]>e[this.floating?\"width\":\"height\"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t=\"x\"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i=\"y\"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&\"right\"===a||\"down\"===n?2:1:n&&(\"down\"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?\"right\"===n&&i||\"left\"===n&&!i:s&&(\"down\"===s&&t||\"up\"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?\"down\":\"up\")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?\"right\":\"left\")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(\".ui-sortable-helper\").not(\".ui-sortable-placeholder\"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(\".ui-sortable-helper\").not(\".ui-sortable-placeholder\"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(\":data(\"+this.widgetName+\"-item)\");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+\"-item\",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?\"x\"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e(\"<\"+s+\">\",t.document[0]).addClass(i||t.currentItem[0].className+\" ui-sortable-placeholder\").removeClass(\"ui-sortable-helper\");return\"tbody\"===s?t._createTrPlaceholder(t.currentItem.find(\"tr\").eq(0),e(\"<tr>\",t.document[0]).appendTo(n)):\"tr\"===s?t._createTrPlaceholder(t.currentItem,n):\"img\"===s&&n.attr(\"src\",t.currentItem.attr(\"src\")),i||n.css(\"visibility\",\"hidden\"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css(\"paddingTop\")||0,10)-parseInt(t.currentItem.css(\"paddingBottom\")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css(\"paddingLeft\")||0,10)-parseInt(t.currentItem.css(\"paddingRight\")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e(\"<td>&#160;</td>\",s.document[0]).attr(\"colspan\",e(this).attr(\"colspan\")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger(\"out\",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger(\"over\",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?\"left\":\"top\",r=u?\"width\":\"height\",d=u?\"clientX\":\"clientY\",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?\"up\":\"down\"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger(\"over\",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger(\"change\",t,this._uiHash()),this.containers[p]._trigger(\"change\",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger(\"over\",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):\"clone\"===i.helper?this.currentItem.clone():this.currentItem;return s.parents(\"body\").length||e(\"parent\"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css(\"position\"),top:this.currentItem.css(\"top\"),left:this.currentItem.css(\"left\")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){\"string\"==typeof t&&(t=t.split(\" \")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),\"left\"in t&&(this.offset.click.left=t.left+this.margins.left),\"right\"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),\"top\"in t&&(this.offset.click.top=t.top+this.margins.top),\"bottom\"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return\"absolute\"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&\"html\"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css(\"borderTopWidth\"),10)||0),left:t.left+(parseInt(this.offsetParent.css(\"borderLeftWidth\"),10)||0)}},_getRelativeOffset:function(){if(\"relative\"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css(\"top\"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css(\"left\"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css(\"marginLeft\"),10)||0,top:parseInt(this.currentItem.css(\"marginTop\"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;\"parent\"===n.containment&&(n.containment=this.helper[0].parentNode),(\"document\"===n.containment||\"window\"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,\"document\"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,(\"document\"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s=\"hidden\"!==e(t).css(\"overflow\"),this.containment=[i.left+(parseInt(e(t).css(\"borderLeftWidth\"),10)||0)+(parseInt(e(t).css(\"paddingLeft\"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css(\"borderTopWidth\"),10)||0)+(parseInt(e(t).css(\"paddingTop\"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css(\"borderLeftWidth\"),10)||0)-(parseInt(e(t).css(\"paddingRight\"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css(\"borderTopWidth\"),10)||0)-(parseInt(e(t).css(\"paddingBottom\"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s=\"absolute\"===t?1:-1,n=\"absolute\"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-(\"fixed\"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-(\"fixed\"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r=\"absolute\"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return\"relative\"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+(\"fixed\"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+(\"fixed\"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],\"down\"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)(\"auto\"===this._storedCSS[s]||\"static\"===this._storedCSS[s])&&(this._storedCSS[s]=\"\");this.currentItem.css(this._storedCSS).removeClass(\"ui-sortable-helper\")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger(\"receive\",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(\".ui-sortable-helper\")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger(\"update\",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger(\"remove\",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger(\"receive\",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger(\"update\",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i(\"deactivate\",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i(\"out\",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find(\"body\").css(\"cursor\",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css(\"opacity\",this._storedOpacity),this._storedZIndex&&this.helper.css(\"zIndex\",\"auto\"===this._storedZIndex?\"\":this._storedZIndex),this.dragging=!1,t||this._trigger(\"beforeStop\",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger(\"stop\",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget(\"ui.spinner\",{version:\"1.11.4\",defaultElement:\"<input>\",widgetEventPrefix:\"spin\",options:{culture:null,icons:{down:\"ui-icon-triangle-1-s\",up:\"ui-icon-triangle-1-n\"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption(\"max\",this.options.max),this._setOption(\"min\",this.options.min),this._setOption(\"step\",this.options.step),\"\"!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr(\"autocomplete\")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each([\"min\",\"max\",\"step\"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:\"_stop\",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger(\"change\",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},\"mousedown .ui-spinner-button\":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass(\"ui-spinner-up\")?1:-1,t)},\"mouseup .ui-spinner-button\":\"_stop\",\"mouseenter .ui-spinner-button\":function(t){return e(t.currentTarget).hasClass(\"ui-state-active\")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass(\"ui-spinner-up\")?1:-1,t),void 0):void 0},\"mouseleave .ui-spinner-button\":\"_stop\"},_draw:function(){var e=this.uiSpinner=this.element.addClass(\"ui-spinner-input\").attr(\"autocomplete\",\"off\").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr(\"role\",\"spinbutton\"),this.buttons=e.find(\".ui-spinner-button\").attr(\"tabIndex\",-1).button().removeClass(\"ui-corner-all\"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return\"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>\"},_buttonHtml:function(){return\"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon \"+this.options.icons.up+\"'>&#9650;</span>\"+\"</a>\"+\"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>\"+\"<span class='ui-icon \"+this.options.icons.down+\"'>&#9660;</span>\"+\"</a>\"},_start:function(e){return this.spinning||this._trigger(\"start\",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger(\"spin\",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=\"\"+e,i=t.indexOf(\".\");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger(\"stop\",e))},_setOption:function(e,t){if(\"culture\"===e||\"numberFormat\"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}(\"max\"===e||\"min\"===e||\"step\"===e)&&\"string\"==typeof t&&(t=this._parse(t)),\"icons\"===e&&(this.buttons.first().find(\".ui-icon\").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(\".ui-icon\").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),\"disabled\"===e&&(this.widget().toggleClass(\"ui-state-disabled\",!!t),this.element.prop(\"disabled\",!!t),this.buttons.button(t?\"disable\":\"enable\"))},_setOptions:h(function(e){this._super(e)}),_parse:function(e){return\"string\"==typeof e&&\"\"!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),\"\"===e||isNaN(e)?null:e},_format:function(e){return\"\"===e?\"\":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({\"aria-valuemin\":this.options.min,\"aria-valuemax\":this.options.max,\"aria-valuenow\":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;\"\"!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass(\"ui-spinner-input\").prop(\"disabled\",!1).removeAttr(\"autocomplete\").removeAttr(\"role\").removeAttr(\"aria-valuemin\").removeAttr(\"aria-valuemax\").removeAttr(\"aria-valuenow\"),this.uiSpinner.replaceWith(this.element)},stepUp:h(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:h(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:h(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:h(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(h(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget(\"ui.tabs\",{version:\"1.11.4\",delay:300,options:{active:null,collapsible:!1,event:\"click\",heightStyle:\"content\",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,\"\"),s=location.href.replace(e,\"\");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass(\"ui-tabs ui-widget ui-widget-content ui-corner-all\").toggleClass(\"ui-tabs-collapsible\",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(\".ui-state-disabled\"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr(\"aria-controls\")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(\".ui-tabs-active\"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest(\"li\"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr(\"aria-selected\",\"false\"),this.tabs.eq(s).attr(\"aria-selected\",\"true\"),this.activating=this._delay(function(){this.option(\"active\",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,i){function s(){return t>n&&(t=0),0>t&&(t=n),t}for(var n=this.tabs.length-1;-1!==e.inArray(s(),this.options.disabled);)t=i?t+1:t-1;return t},_focusNextTab:function(e,t){return e=this._findNextTab(e,t),this.tabs.eq(e).focus(),e},_setOption:function(e,t){return\"active\"===e?(this._activate(t),void 0):\"disabled\"===e?(this._setupDisabled(t),void 0):(this._super(e,t),\"collapsible\"===e&&(this.element.toggleClass(\"ui-tabs-collapsible\",t),t||this.options.active!==!1||this._activate(0)),\"event\"===e&&this._setupEvents(t),\"heightStyle\"===e&&this._setupHeightStyle(t),void 0)},_sanitizeSelector:function(e){return e?e.replace(/[!\"$%&'()*+,.\\/:;<=>?@\\[\\]\\^`{|}~]/g,\"\\\\$&\"):\"\"},refresh:function(){var t=this.options,i=this.tablist.children(\":has(a[href])\");t.disabled=e.map(i.filter(\".ui-state-disabled\"),function(e){return i.index(e)}),this._processTabs(),t.active!==!1&&this.anchors.length?this.active.length&&!e.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=e()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=e()),this._refresh()},_refresh:function(){this._setupDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({\"aria-selected\":\"false\",\"aria-expanded\":\"false\",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({\"aria-hidden\":\"true\"}),this.active.length?(this.active.addClass(\"ui-tabs-active 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this.clear().add(cos*x1-sin*y1+cx,sin*x1+cos*y1+cy).add(cos*x1-sin*y2+cx,sin*x1+cos*y2+cy).add(cos*x2-sin*y1+cx,sin*x2+cos*y1+cy).add(cos*x2-sin*y2+cx,sin*x2+cos*y2+cy)};prototype.union=function(b){if(b.x1<this.x1)this.x1=b.x1;if(b.y1<this.y1)this.y1=b.y1;if(b.x2>this.x2)this.x2=b.x2;if(b.y2>this.y2)this.y2=b.y2;return this};prototype.encloses=function(b){return b&&(this.x1<=b.x1&&this.x2>=b.x2&&this.y1<=b.y1&&this.y2>=b.y2)};prototype.intersects=function(b){return b&&!(this.x2<b.x1||this.x1>b.x2||this.y2<b.y1||this.y1>b.y2)};prototype.contains=function(x,y){return!(x<this.x1||x>this.x2||y<this.y1||y>this.y2)};prototype.width=function(){return this.x2-this.x1};prototype.height=function(){return this.y2-this.y1};return bounds}();vg.Gradient=function(){function gradient(type){this.id=\"grad_\"+vg_gradient_id++;this.type=type||\"linear\";this.stops=[];this.x1=0;this.x2=1;this.y1=0;this.y2=0}var prototype=gradient.prototype;prototype.stop=function(offset,color){this.stops.push({offset:offset,color:color});return this};return gradient}();var vg_gradient_id=0;vg.canvas={};vg.canvas.path=function(){var cmdLength={m:2,l:2,h:1,v:1,c:6,s:4,q:4,t:2,a:7},re=[/([MLHVCSQTAZmlhvcsqtaz])/g,/###/,/(\\d)-/g,/\\s|,|###/];function parse(path){var result=[],currentPath,chunks,parsed;path=path.slice().replace(re[0],\"###$1\").split(re[1]).slice(1);for(var i=0,j,chunksParsed,len=path.length;i<len;i++){currentPath=path[i];chunks=currentPath.slice(1).trim().replace(re[2],\"$1###-\").split(re[3]);chunksParsed=[currentPath.charAt(0)];for(var j=0,jlen=chunks.length;j<jlen;j++){parsed=parseFloat(chunks[j]);if(!isNaN(parsed)){chunksParsed.push(parsed)}}var command=chunksParsed[0].toLowerCase(),commandLength=cmdLength[command];if(chunksParsed.length-1>commandLength){for(var k=1,klen=chunksParsed.length;k<klen;k+=commandLength){result.push([chunksParsed[0]].concat(chunksParsed.slice(k,k+commandLength)))}}else{result.push(chunksParsed)}}return result}function drawArc(g,x,y,coords,bounds,l,t){var rx=coords[0];var ry=coords[1];var rot=coords[2];var large=coords[3];var sweep=coords[4];var ex=coords[5];var ey=coords[6];var segs=arcToSegments(ex,ey,rx,ry,large,sweep,rot,x,y);for(var i=0;i<segs.length;i++){var bez=segmentToBezier.apply(null,segs[i]);g.bezierCurveTo.apply(g,bez);bounds.add(bez[0]-l,bez[1]-t);bounds.add(bez[2]-l,bez[3]-t);bounds.add(bez[4]-l,bez[5]-t)}}function boundArc(x,y,coords,bounds){var rx=coords[0];var ry=coords[1];var rot=coords[2];var large=coords[3];var sweep=coords[4];var ex=coords[5];var ey=coords[6];var segs=arcToSegments(ex,ey,rx,ry,large,sweep,rot,x,y);for(var i=0;i<segs.length;i++){var bez=segmentToBezier.apply(null,segs[i]);bounds.add(bez[0],bez[1]);bounds.add(bez[2],bez[3]);bounds.add(bez[4],bez[5])}}var arcToSegmentsCache={},segmentToBezierCache={},join=Array.prototype.join,argsStr;function arcToSegments(x,y,rx,ry,large,sweep,rotateX,ox,oy){argsStr=join.call(arguments);if(arcToSegmentsCache[argsStr]){return arcToSegmentsCache[argsStr]}var th=rotateX*(Math.PI/180);var sin_th=Math.sin(th);var cos_th=Math.cos(th);rx=Math.abs(rx);ry=Math.abs(ry);var px=cos_th*(ox-x)*.5+sin_th*(oy-y)*.5;var py=cos_th*(oy-y)*.5-sin_th*(ox-x)*.5;var pl=px*px/(rx*rx)+py*py/(ry*ry);if(pl>1){pl=Math.sqrt(pl);rx*=pl;ry*=pl}var a00=cos_th/rx;var a01=sin_th/rx;var a10=-sin_th/ry;var a11=cos_th/ry;var x0=a00*ox+a01*oy;var y0=a10*ox+a11*oy;var x1=a00*x+a01*y;var y1=a10*x+a11*y;var d=(x1-x0)*(x1-x0)+(y1-y0)*(y1-y0);var sfactor_sq=1/d-.25;if(sfactor_sq<0)sfactor_sq=0;var sfactor=Math.sqrt(sfactor_sq);if(sweep==large)sfactor=-sfactor;var xc=.5*(x0+x1)-sfactor*(y1-y0);var yc=.5*(y0+y1)+sfactor*(x1-x0);var th0=Math.atan2(y0-yc,x0-xc);var th1=Math.atan2(y1-yc,x1-xc);var th_arc=th1-th0;if(th_arc<0&&sweep==1){th_arc+=2*Math.PI}else if(th_arc>0&&sweep==0){th_arc-=2*Math.PI}var segments=Math.ceil(Math.abs(th_arc/(Math.PI*.5+.001)));var result=[];for(var i=0;i<segments;i++){var th2=th0+i*th_arc/segments;var th3=th0+(i+1)*th_arc/segments;result[i]=[xc,yc,th2,th3,rx,ry,sin_th,cos_th]}return arcToSegmentsCache[argsStr]=result}function segmentToBezier(cx,cy,th0,th1,rx,ry,sin_th,cos_th){argsStr=join.call(arguments);if(segmentToBezierCache[argsStr]){return segmentToBezierCache[argsStr]}var a00=cos_th*rx;var a01=-sin_th*ry;var a10=sin_th*rx;var a11=cos_th*ry;var cos_th0=Math.cos(th0);var sin_th0=Math.sin(th0);var cos_th1=Math.cos(th1);var sin_th1=Math.sin(th1);var th_half=.5*(th1-th0);var sin_th_h2=Math.sin(th_half*.5);var t=8/3*sin_th_h2*sin_th_h2/Math.sin(th_half);var x1=cx+cos_th0-t*sin_th0;var y1=cy+sin_th0+t*cos_th0;var x3=cx+cos_th1;var y3=cy+sin_th1;var x2=x3+t*sin_th1;var y2=y3-t*cos_th1;return segmentToBezierCache[argsStr]=[a00*x1+a01*y1,a10*x1+a11*y1,a00*x2+a01*y2,a10*x2+a11*y2,a00*x3+a01*y3,a10*x3+a11*y3]}function render(g,path,l,t){var current,previous=null,x=0,y=0,controlX=0,controlY=0,tempX,tempY,tempControlX,tempControlY,bounds=new vg.Bounds;if(l==undefined)l=0;if(t==undefined)t=0;g.beginPath();for(var i=0,len=path.length;i<len;++i){current=path[i];switch(current[0]){case\"l\":x+=current[1];y+=current[2];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"L\":x=current[1];y=current[2];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"h\":x+=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"H\":x=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"v\":y+=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"V\":y=current[1];g.lineTo(x+l,y+t);bounds.add(x,y);break;case\"m\":x+=current[1];y+=current[2];g.moveTo(x+l,y+t);bounds.add(x,y);break;case\"M\":x=current[1];y=current[2];g.moveTo(x+l,y+t);bounds.add(x,y);break;case\"c\":tempX=x+current[5];tempY=y+current[6];controlX=x+current[3];controlY=y+current[4];g.bezierCurveTo(x+current[1]+l,y+current[2]+t,controlX+l,controlY+t,tempX+l,tempY+t);bounds.add(x+current[1],y+current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);x=tempX;y=tempY;break;case\"C\":x=current[5];y=current[6];controlX=current[3];controlY=current[4];g.bezierCurveTo(current[1]+l,current[2]+t,controlX+l,controlY+t,x+l,y+t);bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(x,y);break;case\"s\":tempX=x+current[3];tempY=y+current[4];controlX=2*x-controlX;controlY=2*y-controlY;g.bezierCurveTo(controlX+l,controlY+t,x+current[1]+l,y+current[2]+t,tempX+l,tempY+t);bounds.add(controlX,controlY);bounds.add(x+current[1],y+current[2]);bounds.add(tempX,tempY);controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;break;case\"S\":tempX=current[3];tempY=current[4];controlX=2*x-controlX;controlY=2*y-controlY;g.bezierCurveTo(controlX+l,controlY+t,current[1]+l,current[2]+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);controlX=current[1];controlY=current[2];break;case\"q\":tempX=x+current[3];tempY=y+current[4];controlX=x+current[1];controlY=y+current[2];g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"Q\":tempX=current[3];tempY=current[4];g.quadraticCurveTo(current[1]+l,current[2]+t,tempX+l,tempY+t);x=tempX;y=tempY;controlX=current[1];controlY=current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"t\":tempX=x+current[1];tempY=y+current[2];if(previous[0].match(/[QqTt]/)===null){controlX=x;controlY=y}else if(previous[0]===\"t\"){controlX=2*x-tempControlX;controlY=2*y-tempControlY}else if(previous[0]===\"q\"){controlX=2*x-controlX;controlY=2*y-controlY}tempControlX=controlX;tempControlY=controlY;g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;controlX=x+current[1];controlY=y+current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"T\":tempX=current[1];tempY=current[2];controlX=2*x-controlX;controlY=2*y-controlY;g.quadraticCurveTo(controlX+l,controlY+t,tempX+l,tempY+t);x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"a\":drawArc(g,x+l,y+t,[current[1],current[2],current[3],current[4],current[5],current[6]+x+l,current[7]+y+t],bounds,l,t);x+=current[6];y+=current[7];break;case\"A\":drawArc(g,x+l,y+t,[current[1],current[2],current[3],current[4],current[5],current[6]+l,current[7]+t],bounds,l,t);x=current[6];y=current[7];break;case\"z\":case\"Z\":g.closePath();break}previous=current}return bounds.translate(l,t)}function bounds(path,bounds){var current,previous=null,x=0,y=0,controlX=0,controlY=0,tempX,tempY,tempControlX,tempControlY;for(var i=0,len=path.length;i<len;++i){current=path[i];switch(current[0]){case\"l\":x+=current[1];y+=current[2];bounds.add(x,y);break;case\"L\":x=current[1];y=current[2];bounds.add(x,y);break;case\"h\":x+=current[1];bounds.add(x,y);break;case\"H\":x=current[1];bounds.add(x,y);break;case\"v\":y+=current[1];bounds.add(x,y);break;case\"V\":y=current[1];bounds.add(x,y);break;case\"m\":x+=current[1];y+=current[2];bounds.add(x,y);break;case\"M\":x=current[1];y=current[2];bounds.add(x,y);break;case\"c\":tempX=x+current[5];tempY=y+current[6];controlX=x+current[3];controlY=y+current[4];bounds.add(x+current[1],y+current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);x=tempX;y=tempY;break;case\"C\":x=current[5];y=current[6];controlX=current[3];controlY=current[4];bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(x,y);break;case\"s\":tempX=x+current[3];tempY=y+current[4];controlX=2*x-controlX;controlY=2*y-controlY;bounds.add(controlX,controlY);bounds.add(x+current[1],y+current[2]);bounds.add(tempX,tempY);controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;break;case\"S\":tempX=current[3];tempY=current[4];controlX=2*x-controlX;controlY=2*y-controlY;x=tempX;y=tempY;bounds.add(current[1],current[2]);bounds.add(controlX,controlY);bounds.add(tempX,tempY);controlX=current[1];controlY=current[2];break;case\"q\":tempX=x+current[3];tempY=y+current[4];controlX=x+current[1];controlY=y+current[2];x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"Q\":tempX=current[3];tempY=current[4];x=tempX;y=tempY;controlX=current[1];controlY=current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"t\":tempX=x+current[1];tempY=y+current[2];if(previous[0].match(/[QqTt]/)===null){controlX=x;controlY=y}else if(previous[0]===\"t\"){controlX=2*x-tempControlX;controlY=2*y-tempControlY}else if(previous[0]===\"q\"){controlX=2*x-controlX;controlY=2*y-controlY}tempControlX=controlX;tempControlY=controlY;x=tempX;y=tempY;controlX=x+current[1];controlY=y+current[2];bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"T\":tempX=current[1];tempY=current[2];controlX=2*x-controlX;controlY=2*y-controlY;x=tempX;y=tempY;bounds.add(controlX,controlY);bounds.add(tempX,tempY);break;case\"a\":boundArc(x,y,[current[1],current[2],current[3],current[4],current[5],current[6]+x,current[7]+y],bounds);x+=current[6];y+=current[7];break;case\"A\":boundArc(x,y,[current[1],current[2],current[3],current[4],current[5],current[6],current[7]],bounds);x=current[6];y=current[7];break;case\"z\":case\"Z\":break}previous=current}return bounds}function area(items){var o=items[0];var area;if(o.orient===\"horizontal\"){area=d3.svg.area().y(function(d){return d.y}).x0(function(d){return d.x}).x1(function(d){return d.x+d.width})}else{area=d3.svg.area().x(function(d){return d.x}).y1(function(d){return d.y}).y0(function(d){return d.y+d.height})}if(o.interpolate)area.interpolate(o.interpolate);if(o.tension!=null)area.tension(o.tension);return area(items)}function line(items){var o=items[0];var line=d3.svg.line().x(function(d){return d.x}).y(function(d){return d.y});if(o.interpolate)line.interpolate(o.interpolate);if(o.tension!=null)line.tension(o.tension);return line(items)}return{parse:parse,render:render,bounds:bounds,area:area,line:line}}();vg.canvas.marks=function(){var parsePath=vg.canvas.path.parse,renderPath=vg.canvas.path.render,halfpi=Math.PI/2,sqrt3=Math.sqrt(3),tan30=Math.tan(30*Math.PI/180),tmpBounds=new vg.Bounds;function arcPath(g,o){var x=o.x||0,y=o.y||0,ir=o.innerRadius||0,or=o.outerRadius||0,sa=(o.startAngle||0)-Math.PI/2,ea=(o.endAngle||0)-Math.PI/2;g.beginPath();if(ir===0)g.moveTo(x,y);else g.arc(x,y,ir,sa,ea,0);g.arc(x,y,or,ea,sa,1);g.closePath()}function areaPath(g,items){var o=items[0],m=o.mark,p=m.pathCache||(m.pathCache=parsePath(vg.canvas.path.area(items)));renderPath(g,p)}function linePath(g,items){var o=items[0],m=o.mark,p=m.pathCache||(m.pathCache=parsePath(vg.canvas.path.line(items)));renderPath(g,p)}function pathPath(g,o){if(o.path==null)return;var p=o.pathCache||(o.pathCache=parsePath(o.path));return renderPath(g,p,o.x,o.y)}function symbolPath(g,o){g.beginPath();var size=o.size!=null?o.size:100,x=o.x,y=o.y,r,t,rx,ry;if(o.shape==null||o.shape===\"circle\"){r=Math.sqrt(size/Math.PI);g.arc(x,y,r,0,2*Math.PI,0);g.closePath();return}switch(o.shape){case\"cross\":r=Math.sqrt(size/5)/2;t=3*r;g.moveTo(x-t,y-r);g.lineTo(x-r,y-r);g.lineTo(x-r,y-t);g.lineTo(x+r,y-t);g.lineTo(x+r,y-r);g.lineTo(x+t,y-r);g.lineTo(x+t,y+r);g.lineTo(x+r,y+r);g.lineTo(x+r,y+t);g.lineTo(x-r,y+t);g.lineTo(x-r,y+r);g.lineTo(x-t,y+r);break;case\"diamond\":ry=Math.sqrt(size/(2*tan30));rx=ry*tan30;g.moveTo(x,y-ry);g.lineTo(x+rx,y);g.lineTo(x,y+ry);g.lineTo(x-rx,y);break;case\"square\":t=Math.sqrt(size);r=t/2;g.rect(x-r,y-r,t,t);break;case\"triangle-down\":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;g.moveTo(x,y+ry);g.lineTo(x+rx,y-ry);g.lineTo(x-rx,y-ry);break;case\"triangle-up\":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;g.moveTo(x,y-ry);g.lineTo(x+rx,y+ry);g.lineTo(x-rx,y+ry)}g.closePath()}function lineStroke(g,items){var o=items[0],lw=o.strokeWidth,lc=o.strokeCap;g.lineWidth=lw!=null?lw:vg.config.render.lineWidth;g.lineCap=lc!=null?lc:vg.config.render.lineCap;linePath(g,items)}function ruleStroke(g,o){var x1=o.x||0,y1=o.y||0,x2=o.x2!=null?o.x2:x1,y2=o.y2!=null?o.y2:y1,lw=o.strokeWidth,lc=o.strokeCap;g.lineWidth=lw!=null?lw:vg.config.render.lineWidth;g.lineCap=lc!=null?lc:vg.config.render.lineCap;g.beginPath();g.moveTo(x1,y1);g.lineTo(x2,y2)}function drawPathOne(path,g,o,items){var fill=o.fill,stroke=o.stroke,opac,lc,lw;path(g,items);opac=o.opacity==null?1:o.opacity;if(opac==0||!fill&&!stroke)return;if(fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fill()}if(stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.stroke()}}}function drawPathAll(path,g,scene,bounds){var i,len,item;for(i=0,len=scene.items.length;i<len;++i){item=scene.items[i];if(bounds&&!bounds.intersects(item.bounds))continue;drawPathOne(path,g,item,item)}}function drawRect(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,fill,stroke,opac,lc,lw,x,y,w,h;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;x=o.x||0;y=o.y||0;w=o.width||0;h=o.height||0;opac=o.opacity==null?1:o.opacity;if(opac==0)continue;if(fill=o.fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fillRect(x,y,w,h)}if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.strokeRect(x,y,w,h)}}}}function drawRule(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,stroke,opac,lc,lw,x1,y1,x2,y2;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;x1=o.x||0;y1=o.y||0;x2=o.x2!=null?o.x2:x1;y2=o.y2!=null?o.y2:y1;opac=o.opacity==null?1:o.opacity;if(opac==0)continue;if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:vg.config.render.lineWidth;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(g,o,stroke);g.lineWidth=lw;g.lineCap=(lc=o.strokeCap)!=null?lc:vg.config.render.lineCap;g.vgLineDash(o.strokeDash||null);g.vgLineDashOffset(o.strokeDashOffset||0);g.beginPath();g.moveTo(x1,y1);g.lineTo(x2,y2);g.stroke()}}}}function drawImage(g,scene,bounds){if(!scene.items.length)return;var renderer=this,items=scene.items,o;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;if(!(o.image&&o.image.url===o.url)){o.image=renderer.loadImage(o.url);o.image.url=o.url}var x,y,w,h,opac;w=o.width||o.image&&o.image.width||0;h=o.height||o.image&&o.image.height||0;x=(o.x||0)-(o.align===\"center\"?w/2:o.align===\"right\"?w:0);y=(o.y||0)-(o.baseline===\"middle\"?h/2:o.baseline===\"bottom\"?h:0);if(o.image.loaded){g.globalAlpha=(opac=o.opacity)!=null?opac:1;g.drawImage(o.image,x,y,w,h)}}}function drawText(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,o,fill,stroke,opac,lw,x,y,r,t;for(var i=0,len=items.length;i<len;++i){o=items[i];if(bounds&&!bounds.intersects(o.bounds))continue;g.font=vg.scene.fontString(o);g.textAlign=o.align||\"left\";g.textBaseline=o.baseline||\"alphabetic\";opac=o.opacity==null?1:o.opacity;if(opac==0)continue;x=o.x||0;y=o.y||0;if(r=o.radius){t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}if(o.angle){g.save();g.translate(x,y);g.rotate(o.angle*Math.PI/180);x=o.dx||0;y=o.dy||0}else{x+=o.dx||0;y+=o.dy||0}if(fill=o.fill){g.globalAlpha=opac*(o.fillOpacity==null?1:o.fillOpacity);g.fillStyle=color(g,o,fill);g.fillText(o.text,x,y)}if(stroke=o.stroke){lw=(lw=o.strokeWidth)!=null?lw:1;if(lw>0){g.globalAlpha=opac*(o.strokeOpacity==null?1:o.strokeOpacity);g.strokeStyle=color(o,stroke);g.lineWidth=lw;g.strokeText(o.text,x,y)}}if(o.angle)g.restore()}}function drawAll(pathFunc){return function(g,scene,bounds){drawPathAll(pathFunc,g,scene,bounds)}}function drawOne(pathFunc){return function(g,scene,bounds){if(!scene.items.length)return;if(bounds&&!bounds.intersects(scene.items[0].bounds))return;drawPathOne(pathFunc,g,scene.items[0],scene.items)}}function drawGroup(g,scene,bounds){if(!scene.items.length)return;var items=scene.items,group,axes,legends,renderer=this,gx,gy,gb,i,n,j,m;drawRect(g,scene,bounds);for(i=0,n=items.length;i<n;++i){group=items[i];axes=group.axisItems||[];legends=group.legendItems||[];gx=group.x||0;gy=group.y||0;g.save();g.translate(gx,gy);if(group.clip){g.beginPath();g.rect(0,0,group.width||0,group.height||0);g.clip()}if(bounds)bounds.translate(-gx,-gy);for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer===\"back\"){renderer.draw(g,axes[j],bounds)}}for(j=0,m=group.items.length;j<m;++j){renderer.draw(g,group.items[j],bounds)}for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer!==\"back\"){renderer.draw(g,axes[j],bounds)}}for(j=0,m=legends.length;j<m;++j){renderer.draw(g,legends[j],bounds)}if(bounds)bounds.translate(gx,gy);g.restore()}}function color(g,o,value){return value.id?gradient(g,value,o.bounds):value}function gradient(g,p,b){var w=b.width(),h=b.height(),x1=b.x1+p.x1*w,y1=b.y1+p.y1*h,x2=b.x1+p.x2*w,y2=b.y1+p.y2*h,grad=g.createLinearGradient(x1,y1,x2,y2),stop=p.stops,i,n;for(i=0,n=stop.length;i<n;++i){grad.addColorStop(stop[i].offset,stop[i].color)}return grad}function pickGroup(g,scene,x,y,gx,gy){if(scene.items.length===0||scene.bounds&&!scene.bounds.contains(gx,gy)){return false}var items=scene.items,subscene,group,hit,dx,dy,handler=this,i,j;for(i=items.length;--i>=0;){group=items[i];dx=group.x||0;dy=group.y||0;g.save();g.translate(dx,dy);for(j=group.items.length;--j>=0;){subscene=group.items[j];if(subscene.interactive===false)continue;hit=handler.pick(subscene,x,y,gx-dx,gy-dy);if(hit){g.restore();return hit}}g.restore()}return scene.interactive?pickAll(hitTests.group,g,scene,x,y,gx,gy):false}function pickAll(test,g,scene,x,y,gx,gy){if(!scene.items.length)return false;var o,b,i;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}for(i=scene.items.length;--i>=0;){o=scene.items[i];b=o.bounds;if(b&&!b.contains(gx,gy)||!b)continue;if(test(g,o,x,y,gx,gy))return o}return false}function pickArea(g,scene,x,y,gx,gy){if(!scene.items.length)return false;var items=scene.items,o,b,i,di,dd,od,dx,dy;b=items[0].bounds;if(b&&!b.contains(gx,gy))return false;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}if(!hitTests.area(g,items,x,y))return false;return items[0]}function pickLine(g,scene,x,y,gx,gy){if(!scene.items.length)return false;var items=scene.items,o,b,i,di,dd,od,dx,dy;b=items[0].bounds;if(b&&!b.contains(gx,gy))return false;if(g._ratio!==1){x*=g._ratio;y*=g._ratio}if(!hitTests.line(g,items,x,y))return false;return items[0]}function pick(test){return function(g,scene,x,y,gx,gy){return pickAll(test,g,scene,x,y,gx,gy)}}function textHit(g,o,x,y,gx,gy){if(!o.fontSize)return false;if(!o.angle)return true;var b=vg.scene.bounds.text(o,tmpBounds,true),a=-o.angle*Math.PI/180,cos=Math.cos(a),sin=Math.sin(a),x=o.x,y=o.y,px=cos*gx-sin*gy+(x-x*cos+y*sin),py=sin*gx+cos*gy+(y-x*sin-y*cos);return b.contains(px,py)}var hitTests={text:textHit,rect:function(g,o,x,y){return true},image:function(g,o,x,y){return true},group:function(g,o,x,y){return o.fill||o.stroke},rule:function(g,o,x,y){if(!g.isPointInStroke)return false;ruleStroke(g,o);return g.isPointInStroke(x,y)},line:function(g,s,x,y){if(!g.isPointInStroke)return false;lineStroke(g,s);return g.isPointInStroke(x,y)},arc:function(g,o,x,y){arcPath(g,o);return g.isPointInPath(x,y)},area:function(g,s,x,y){areaPath(g,s);return g.isPointInPath(x,y)},path:function(g,o,x,y){pathPath(g,o);return g.isPointInPath(x,y)},symbol:function(g,o,x,y){symbolPath(g,o);return g.isPointInPath(x,y)}};return{draw:{group:drawGroup,area:drawOne(areaPath),line:drawOne(linePath),arc:drawAll(arcPath),path:drawAll(pathPath),symbol:drawAll(symbolPath),rect:drawRect,rule:drawRule,text:drawText,image:drawImage,drawOne:drawOne,drawAll:drawAll},pick:{group:pickGroup,area:pickArea,line:pickLine,arc:pick(hitTests.arc),path:pick(hitTests.path),symbol:pick(hitTests.symbol),rect:pick(hitTests.rect),rule:pick(hitTests.rule),text:pick(hitTests.text),image:pick(hitTests.image),pickAll:pickAll}}}();vg.canvas.Renderer=function(){var renderer=function(){this._ctx=null;this._el=null;this._imgload=0};var prototype=renderer.prototype;prototype.initialize=function(el,width,height,pad){this._el=el;if(!el)return this;var canvas=d3.select(el).selectAll(\"canvas.marks\").data([1]);canvas.enter().append(\"canvas\").attr(\"class\",\"marks\");canvas.exit().remove();return this.resize(width,height,pad)};prototype.resize=function(width,height,pad){this._width=width;this._height=height;this._padding=pad;if(this._el){var canvas=d3.select(this._el).select(\"canvas.marks\");canvas.attr(\"width\",width+pad.left+pad.right).attr(\"height\",height+pad.top+pad.bottom);var s;this._ctx=canvas.node().getContext(\"2d\");this._ctx._ratio=s=scaleCanvas(canvas.node(),this._ctx)||1;this._ctx.setTransform(s,0,0,s,s*pad.left,s*pad.top)}initializeLineDash(this._ctx);return this};function scaleCanvas(canvas,ctx){var devicePixelRatio=window.devicePixelRatio||1,backingStoreRatio=ctx.webkitBackingStorePixelRatio||ctx.mozBackingStorePixelRatio||ctx.msBackingStorePixelRatio||ctx.oBackingStorePixelRatio||ctx.backingStorePixelRatio||1,ratio=devicePixelRatio/backingStoreRatio;if(devicePixelRatio!==backingStoreRatio){var w=canvas.width,h=canvas.height;canvas.setAttribute(\"width\",w*ratio);canvas.setAttribute(\"height\",h*ratio);canvas.style.width=w+\"px\";canvas.style.height=h+\"px\"}return ratio}function initializeLineDash(ctx){if(ctx.vgLineDash)return;var NODASH=[];if(ctx.setLineDash){ctx.vgLineDash=function(dash){this.setLineDash(dash||NODASH)};ctx.vgLineDashOffset=function(off){this.lineDashOffset=off}}else if(ctx.webkitLineDash!==undefined){ctx.vgLineDash=function(dash){this.webkitLineDash=dash||NODASH};ctx.vgLineDashOffset=function(off){this.webkitLineDashOffset=off}}else if(ctx.mozDash!==undefined){ctx.vgLineDash=function(dash){this.mozDash=dash};ctx.vgLineDashOffset=function(off){}}else{ctx.vgLineDash=function(dash){};ctx.vgLineDashOffset=function(off){}}}prototype.context=function(ctx){if(ctx){this._ctx=ctx;return this}else return this._ctx};prototype.element=function(){return this._el};prototype.pendingImages=function(){return this._imgload};function translatedBounds(item,bounds){var b=new vg.Bounds(bounds);while((item=item.mark.group)!=null){b.translate(item.x||0,item.y||0)}return b}function getBounds(items){return!items?null:vg.array(items).reduce(function(b,item){return b.union(translatedBounds(item,item.bounds)).union(translatedBounds(item,item[\"bounds:prev\"]))},new vg.Bounds)}function setBounds(g,bounds){var bbox=null;if(bounds){bbox=new vg.Bounds(bounds).round();g.beginPath();g.rect(bbox.x1,bbox.y1,bbox.width(),bbox.height());g.clip()}return bbox}prototype.render=function(scene,items){var g=this._ctx,pad=this._padding,w=this._width+pad.left+pad.right,h=this._height+pad.top+pad.bottom,bb=null,bb2;this._scene=scene;g.save();bb=setBounds(g,getBounds(items));g.clearRect(-pad.left,-pad.top,w,h);\nthis.draw(g,scene,bb);if(items){g.restore();g.save();bb2=setBounds(g,getBounds(items));if(!bb.encloses(bb2)){g.clearRect(-pad.left,-pad.top,w,h);this.draw(g,scene,bb2)}}g.restore();this._scene=null};prototype.draw=function(ctx,scene,bounds){var marktype=scene.marktype,renderer=vg.canvas.marks.draw[marktype];renderer.call(this,ctx,scene,bounds)};prototype.renderAsync=function(scene){var renderer=this;if(renderer._async_id){clearTimeout(renderer._async_id)}renderer._async_id=setTimeout(function(){renderer.render(scene);delete renderer._async_id},50)};prototype.loadImage=function(uri){var renderer=this,scene=renderer._scene,image=null,url;renderer._imgload+=1;if(vg.config.isNode){image=new(require(\"canvas\").Image);vg.data.load(uri,function(err,data){if(err){vg.error(err);return}image.src=data;image.loaded=true;renderer._imgload-=1})}else{image=new Image;url=vg.config.baseURL+uri;image.onload=function(){vg.log(\"LOAD IMAGE: \"+url);image.loaded=true;renderer._imgload-=1;renderer.renderAsync(scene)};image.src=url}return image};return renderer}();vg.canvas.Handler=function(){var handler=function(el,model){this._active=null;this._handlers={};if(el)this.initialize(el);if(model)this.model(model)};var prototype=handler.prototype;prototype.initialize=function(el,pad,obj){this._el=d3.select(el).node();this._canvas=d3.select(el).select(\"canvas.marks\").node();this._padding=pad;this._obj=obj||null;var canvas=this._canvas,that=this;events.forEach(function(type){canvas.addEventListener(type,function(evt){prototype[type].call(that,evt)})});return this};prototype.padding=function(pad){this._padding=pad;return this};prototype.model=function(model){if(!arguments.length)return this._model;this._model=model;return this};prototype.handlers=function(){var h=this._handlers;return vg.keys(h).reduce(function(a,k){return h[k].reduce(function(a,x){return a.push(x),a},a)},[])};var events=[\"mousedown\",\"mouseup\",\"click\",\"dblclick\",\"wheel\",\"keydown\",\"keypress\",\"keyup\",\"mousewheel\"];events.forEach(function(type){prototype[type]=function(evt){this.fire(type,evt)}});events.push(\"mousemove\");events.push(\"mouseout\");function eventName(name){var i=name.indexOf(\".\");return i<0?name:name.slice(0,i)}prototype.mousemove=function(evt){var pad=this._padding,b=evt.target.getBoundingClientRect(),x=evt.clientX-b.left,y=evt.clientY-b.top,a=this._active,p=this.pick(this._model.scene(),x,y,x-pad.left,y-pad.top);if(p===a){this.fire(\"mousemove\",evt);return}else if(a){this.fire(\"mouseout\",evt)}this._active=p;if(p){this.fire(\"mouseover\",evt)}};prototype.mouseout=function(evt){if(this._active){this.fire(\"mouseout\",evt)}this._active=null};prototype.DOMMouseScroll=function(evt){this.fire(\"mousewheel\",evt)};prototype.fire=function(type,evt){var a=this._active,h=this._handlers[type];if(a&&h){for(var i=0,len=h.length;i<len;++i){h[i].handler.call(this._obj,evt,a)}}};prototype.on=function(type,handler){var name=eventName(type),h=this._handlers;h=h[name]||(h[name]=[]);h.push({type:type,handler:handler});return this};prototype.off=function(type,handler){var name=eventName(type),h=this._handlers[name];if(!h)return;for(var i=h.length;--i>=0;){if(h[i].type!==type)continue;if(!handler||h[i].handler===handler)h.splice(i,1)}return this};prototype.context=function(){return this._canvas.getContext(\"2d\")};prototype.pick=function(scene,x,y,gx,gy){var g=this.context(),marktype=scene.marktype,picker=vg.canvas.marks.pick[marktype];return picker.call(this,g,scene,x,y,gx,gy)};return handler}();vg.svg={};vg.svg.marks=function(){function x(o){return o.x||0}function y(o){return o.y||0}function xw(o){return o.x+o.width||0}function yh(o){return o.y+o.height||0}function key(o){return o.key}function size(o){return o.size==null?100:o.size}function shape(o){return o.shape||\"circle\"}var arc_path=d3.svg.arc(),area_path_v=d3.svg.area().x(x).y1(y).y0(yh),area_path_h=d3.svg.area().y(y).x0(xw).x1(x),line_path=d3.svg.line().x(x).y(y),symbol_path=d3.svg.symbol().type(shape).size(size);var mark_id=0,clip_id=0;var textAlign={left:\"start\",center:\"middle\",right:\"end\"};var styles={fill:\"fill\",fillOpacity:\"fill-opacity\",stroke:\"stroke\",strokeWidth:\"stroke-width\",strokeOpacity:\"stroke-opacity\",strokeCap:\"stroke-linecap\",strokeDash:\"stroke-dasharray\",strokeDashOffset:\"stroke-dashoffset\",opacity:\"opacity\"};var styleProps=vg.keys(styles);function style(d){var i,n,prop,name,value,o=d.mark?d:d.length?d[0]:null;if(o===null)return;for(i=0,n=styleProps.length;i<n;++i){prop=styleProps[i];name=styles[prop];value=o[prop];if(value==null){if(name===\"fill\")this.style.setProperty(name,\"none\",null);else this.style.removeProperty(name)}else{if(value.id){vg.svg._cur._defs.gradient[value.id]=value;value=\"url(\"+window.location.href+\"#\"+value.id+\")\"}this.style.setProperty(name,value+\"\",null)}}}function arc(o){var x=o.x||0,y=o.y||0;this.setAttribute(\"transform\",\"translate(\"+x+\",\"+y+\")\");this.setAttribute(\"d\",arc_path(o))}function area(items){if(!items.length)return;var o=items[0],path=o.orient===\"horizontal\"?area_path_h:area_path_v;path.interpolate(o.interpolate||\"linear\").tension(o.tension==null?.7:o.tension);this.setAttribute(\"d\",path(items))}function line(items){if(!items.length)return;var o=items[0];line_path.interpolate(o.interpolate||\"linear\").tension(o.tension==null?.7:o.tension);this.setAttribute(\"d\",line_path(items))}function path(o){var x=o.x||0,y=o.y||0;this.setAttribute(\"transform\",\"translate(\"+x+\",\"+y+\")\");if(o.path!=null)this.setAttribute(\"d\",o.path)}function rect(o){this.setAttribute(\"x\",o.x||0);this.setAttribute(\"y\",o.y||0);this.setAttribute(\"width\",o.width||0);this.setAttribute(\"height\",o.height||0)}function rule(o){var x1=o.x||0,y1=o.y||0;this.setAttribute(\"x1\",x1);this.setAttribute(\"y1\",y1);this.setAttribute(\"x2\",o.x2!=null?o.x2:x1);this.setAttribute(\"y2\",o.y2!=null?o.y2:y1)}function symbol(o){var x=o.x||0,y=o.y||0;this.setAttribute(\"transform\",\"translate(\"+x+\",\"+y+\")\");this.setAttribute(\"d\",symbol_path(o))}function image(o){var w=o.width||o.image&&o.image.width||0,h=o.height||o.image&&o.image.height||0,x=o.x-(o.align===\"center\"?w/2:o.align===\"right\"?w:0),y=o.y-(o.baseline===\"middle\"?h/2:o.baseline===\"bottom\"?h:0),url=vg.config.baseURL+o.url;this.setAttributeNS(\"http://www.w3.org/1999/xlink\",\"href\",url);this.setAttribute(\"x\",x);this.setAttribute(\"y\",y);this.setAttribute(\"width\",w);this.setAttribute(\"height\",h)}function fontString(o){return(o.fontStyle?o.fontStyle+\" \":\"\")+(o.fontVariant?o.fontVariant+\" \":\"\")+(o.fontWeight?o.fontWeight+\" \":\"\")+(o.fontSize!=null?o.fontSize:vg.config.render.fontSize)+\"px \"+(o.font||vg.config.render.font)}function text(o){var x=o.x||0,y=o.y||0,dx=o.dx||0,dy=o.dy||0,a=o.angle||0,r=o.radius||0,align=textAlign[o.align||\"left\"],base=o.baseline===\"top\"?\".9em\":o.baseline===\"middle\"?\".35em\":0;if(r){var t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}this.setAttribute(\"x\",x+dx);this.setAttribute(\"y\",y+dy);this.setAttribute(\"text-anchor\",align);if(a)this.setAttribute(\"transform\",\"rotate(\"+a+\" \"+x+\",\"+y+\")\");else this.removeAttribute(\"transform\");if(base)this.setAttribute(\"dy\",base);else this.removeAttribute(\"dy\");this.textContent=o.text;this.style.setProperty(\"font\",fontString(o),null)}function group(o){var x=o.x||0,y=o.y||0;this.setAttribute(\"transform\",\"translate(\"+x+\",\"+y+\")\");if(o.clip){var c={width:o.width||0,height:o.height||0},id=o.clip_id||(o.clip_id=\"clip\"+clip_id++);vg.svg._cur._defs.clipping[id]=c;this.setAttribute(\"clip-path\",\"url(#\"+id+\")\")}}function group_bg(o){var w=o.width||0,h=o.height||0;this.setAttribute(\"width\",w);this.setAttribute(\"height\",h)}function cssClass(def){var cls=\"type-\"+def.type;if(def.name)cls+=\" \"+def.name;return cls}function draw(tag,attr,nest){return function(g,scene,index){drawMark(g,scene,index,\"mark_\",tag,attr,nest)}}function drawMark(g,scene,index,prefix,tag,attr,nest){var data=nest?[scene.items]:scene.items,evts=scene.interactive===false?\"none\":null,grps=g.node().childNodes,notG=tag!==\"g\",p=(p=grps[index+1])?d3.select(p):g.append(\"g\").attr(\"id\",\"g\"+ ++mark_id).attr(\"class\",cssClass(scene.def));var id=p.attr(\"id\"),s=\"#\"+id+\" > \"+tag,m=p.selectAll(s).data(data),e=m.enter().append(tag);if(notG){p.style(\"pointer-events\",evts);e.each(function(d){if(d.mark)d._svg=this;else if(d.length)d[0]._svg=this})}else{e.append(\"rect\").attr(\"class\",\"background\").style(\"pointer-events\",evts)}m.exit().remove();m.each(attr);if(notG)m.each(style);else p.selectAll(s+\" > rect.background\").each(group_bg).each(style);return p}function drawGroup(g,scene,index,prefix){var p=drawMark(g,scene,index,prefix||\"group_\",\"g\",group),c=p.node().childNodes,n=c.length,i,j,m;for(i=0;i<n;++i){var items=c[i].__data__.items,legends=c[i].__data__.legendItems||[],axes=c[i].__data__.axisItems||[],sel=d3.select(c[i]),idx=0;for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer===\"back\"){drawGroup.call(this,sel,axes[j],idx++,\"axis_\")}}for(j=0,m=items.length;j<m;++j){this.draw(sel,items[j],idx++)}for(j=0,m=axes.length;j<m;++j){if(axes[j].def.layer!==\"back\"){drawGroup.call(this,sel,axes[j],idx++,\"axis_\")}}for(j=0,m=legends.length;j<m;++j){drawGroup.call(this,sel,legends[j],idx++,\"legend_\")}}}return{update:{group:rect,area:area,line:line,arc:arc,path:path,symbol:symbol,rect:rect,rule:rule,text:text,image:image},nested:{area:true,line:true},style:style,draw:{group:drawGroup,area:draw(\"path\",area,true),line:draw(\"path\",line,true),arc:draw(\"path\",arc),path:draw(\"path\",path),symbol:draw(\"path\",symbol),rect:draw(\"rect\",rect),rule:draw(\"line\",rule),text:draw(\"text\",text),image:draw(\"image\",image),draw:draw}}}();vg.svg.Renderer=function(){var renderer=function(){this._svg=null;this._ctx=null;this._el=null;this._defs={gradient:{},clipping:{}}};var prototype=renderer.prototype;prototype.initialize=function(el,width,height,pad){this._el=el;d3.select(el).select(\"svg.marks\").remove();this._svg=d3.select(el).append(\"svg\").attr(\"class\",\"marks\");this._ctx=this._svg.append(\"g\");return this.resize(width,height,pad)};prototype.resize=function(width,height,pad){this._width=width;this._height=height;this._padding=pad;this._svg.attr(\"width\",width+pad.left+pad.right).attr(\"height\",height+pad.top+pad.bottom);this._ctx.attr(\"transform\",\"translate(\"+pad.left+\",\"+pad.top+\")\");return this};prototype.context=function(){return this._ctx};prototype.element=function(){return this._el};prototype.updateDefs=function(){var svg=this._svg,all=this._defs,dgrad=vg.keys(all.gradient),dclip=vg.keys(all.clipping),defs=svg.select(\"defs\"),grad,clip;if(dgrad.length===0&&dclip.length==0){defs.remove();return}if(defs.empty())defs=svg.insert(\"defs\",\":first-child\");grad=defs.selectAll(\"linearGradient\").data(dgrad,vg.identity);grad.enter().append(\"linearGradient\").attr(\"id\",vg.identity);grad.exit().remove();grad.each(function(id){var def=all.gradient[id],grd=d3.select(this);grd.attr({x1:def.x1,x2:def.x2,y1:def.y1,y2:def.y2});stop=grd.selectAll(\"stop\").data(def.stops);stop.enter().append(\"stop\");stop.exit().remove();stop.attr(\"offset\",function(d){return d.offset}).attr(\"stop-color\",function(d){return d.color})});clip=defs.selectAll(\"clipPath\").data(dclip,vg.identity);clip.enter().append(\"clipPath\").attr(\"id\",vg.identity);clip.exit().remove();clip.each(function(id){var def=all.clipping[id],cr=d3.select(this).selectAll(\"rect\").data([1]);cr.enter().append(\"rect\");cr.attr(\"x\",0).attr(\"y\",0).attr(\"width\",def.width).attr(\"height\",def.height)})};prototype.render=function(scene,items){vg.svg._cur=this;if(items){this.renderItems(vg.array(items))}else{this.draw(this._ctx,scene,-1)}this.updateDefs();delete vg.svg._cur};prototype.renderItems=function(items){var item,node,type,nest,i,n,marks=vg.svg.marks;for(i=0,n=items.length;i<n;++i){item=items[i];node=item._svg;type=item.mark.marktype;item=marks.nested[type]?item.mark.items:item;marks.update[type].call(node,item);marks.style.call(node,item)}};prototype.draw=function(ctx,scene,index){var marktype=scene.marktype,renderer=vg.svg.marks.draw[marktype];renderer.call(this,ctx,scene,index)};return renderer}();vg.svg.Handler=function(){var handler=function(el,model){this._active=null;this._handlers={};if(el)this.initialize(el);if(model)this.model(model)};function svgHandler(handler){var that=this;return function(evt){var target=evt.target,item=target.__data__;if(item){item=item.mark?item:item[0];handler.call(that._obj,evt,item)}}}function eventName(name){var i=name.indexOf(\".\");return i<0?name:name.slice(0,i)}var prototype=handler.prototype;prototype.initialize=function(el,pad,obj){this._el=d3.select(el).node();this._svg=d3.select(el).select(\"svg.marks\").node();this._padding=pad;this._obj=obj||null;return this};prototype.padding=function(pad){this._padding=pad;return this};prototype.model=function(model){if(!arguments.length)return this._model;this._model=model;return this};prototype.handlers=function(){var h=this._handlers;return vg.keys(h).reduce(function(a,k){return h[k].reduce(function(a,x){return a.push(x),a},a)},[])};prototype.on=function(type,handler){var name=eventName(type),h=this._handlers,dom=d3.select(this._svg).node();var x={type:type,handler:handler,svg:svgHandler.call(this,handler)};h=h[name]||(h[name]=[]);h.push(x);dom.addEventListener(name,x.svg);return this};prototype.off=function(type,handler){var name=eventName(type),h=this._handlers[name],dom=d3.select(this._svg).node();if(!h)return;for(var i=h.length;--i>=0;){if(h[i].type!==type)continue;if(!handler||h[i].handler===handler){dom.removeEventListener(name,h[i].svg);h.splice(i,1)}}return this};return handler}();vg.data={};vg.data.ingestAll=function(data){return vg.isTree(data)?vg_make_tree(vg.data.ingestTree(data[0],data.children)):data.map(vg.data.ingest)};vg.data.ingest=function(datum,index){return{data:datum,index:index}};vg.data.ingestTree=function(node,children,index){var d=vg.data.ingest(node,index||0),c=node[children],n,i;if(c&&(n=c.length)){d.values=Array(n);for(i=0;i<n;++i){d.values[i]=vg.data.ingestTree(c[i],children,i)}}return d};function vg_make_tree(d){d.__vgtree__=true;d.nodes=function(){return vg_tree_nodes(this,[])};return d}function vg_tree_nodes(root,nodes){var c=root.values,n=c?c.length:0,i;nodes.push(root);for(i=0;i<n;++i){vg_tree_nodes(c[i],nodes)}return nodes}function vg_data_duplicate(d){var x=d,i,n;if(vg.isArray(d)){x=[];for(i=0,n=d.length;i<n;++i){x.push(vg_data_duplicate(d[i]))}}else if(vg.isObject(d)){x={};for(i in d){x[i]=vg_data_duplicate(d[i])}}return x}vg.data.mapper=function(func){return function(data){data.forEach(func);return data}};vg.data.size=function(size,group){size=vg.isArray(size)?size:[0,size];size=size.map(function(d){return typeof d===\"string\"?group[d]:d});return size};vg.data.load=function(uri,callback){var url=vg_load_hasProtocol(uri)?uri:vg.config.baseURL+uri;if(vg.config.isNode){var get=vg_load_isFile(url)?vg_load_file:vg_load_http;get(url,callback)}else{vg_load_xhr(url,callback)}};var vg_load_protocolRE=/^[A-Za-z]+\\:\\/\\//;var vg_load_fileProtocol=\"file://\";function vg_load_hasProtocol(url){return vg_load_protocolRE.test(url)}function vg_load_isFile(url){return url.indexOf(vg_load_fileProtocol)===0}function vg_load_xhr(url,callback){vg.log(\"LOAD: \"+url);if(!vg_url_check(url)){vg.error(\"URL is not whitelisted: \"+url);return}d3.xhr(url,function(err,resp){if(resp)resp=resp.responseText;callback(err,resp)})}function vg_url_check(url){if(!vg.config.domainWhiteList)return true;var a=document.createElement(\"a\");a.href=url;var domain=a.hostname.toLowerCase();return window.location.hostname===domain||vg.config.domainWhiteList.some(function(d){var ind=domain.length-d.length;return d===domain||ind>1&&domain[ind-1]===\".\"&&domain.lastIndexOf(d)===ind})}function vg_load_file(file,callback){vg.log(\"LOAD FILE: \"+file);var idx=file.indexOf(vg_load_fileProtocol);if(idx>=0)file=file.slice(vg_load_fileProtocol.length);require(\"fs\").readFile(file,callback)}function vg_load_http(url,callback){vg.log(\"LOAD HTTP: \"+url);var req=require(\"http\").request(url,function(res){var pos=0,data=new Buffer(parseInt(res.headers[\"content-length\"],10));res.on(\"error\",function(err){callback(err,null)});res.on(\"data\",function(x){x.copy(data,pos);pos+=x.length});res.on(\"end\",function(){callback(null,data)})});req.on(\"error\",function(err){callback(err)});req.end()}vg.data.read=function(){var formats={},parsers={number:vg.number,\"boolean\":vg.boolean,date:Date.parse};function read(data,format){var type=format&&format.type||\"json\";data=formats[type](data,format);if(format&&format.parse)parseValues(data,format.parse);return data}formats.json=function(data,format){var d=vg.isObject(data)?data:JSON.parse(data);if(format&&format.property){d=vg.accessor(format.property)(d)}return d};formats.csv=function(data,format){var d=d3.csv.parse(data);return d};formats.tsv=function(data,format){var d=d3.tsv.parse(data);return d};formats.topojson=function(data,format){if(topojson==null){vg.error(\"TopoJSON library not loaded.\");return[]}var t=vg.isObject(data)?data:JSON.parse(data),obj=[];if(format&&format.feature){obj=(obj=t.objects[format.feature])?topojson.feature(t,obj).features:(vg.error(\"Invalid TopoJSON object: \"+format.feature),[])}else if(format&&format.mesh){obj=(obj=t.objects[format.mesh])?[topojson.mesh(t,t.objects[format.mesh])]:(vg.error(\"Invalid TopoJSON object: \"+format.mesh),[])}else{vg.error(\"Missing TopoJSON feature or mesh parameter.\")}return obj};formats.treejson=function(data,format){data=vg.isObject(data)?data:JSON.parse(data);return vg.tree(data,format.children)};function parseValues(data,types){var cols=vg.keys(types),p=cols.map(function(col){return parsers[types[col]]}),tree=vg.isTree(data);vg_parseArray(tree?[data]:data,cols,p,tree)}function vg_parseArray(data,cols,p,tree){var d,i,j,len,clen;for(i=0,len=data.length;i<len;++i){d=data[i];for(j=0,clen=cols.length;j<clen;++j){d[cols[j]]=p[j](d[cols[j]])}if(tree&&d.values)parseValues(d,cols,p,true)}}read.formats=formats;read.parse=parseValues;return read}();vg.data.aggregate=function(){var groupby=[],fields=[],gaccess,faccess;var OPS={count:function(){},sum:function(c,s,x){return s+x},avg:function(c,s,x){return s+(x-s)/c.count},min:function(c,s,x){return x<s?x:s},max:function(c,s,x){return x>s?x:s}};OPS.min.init=function(){return+Infinity};OPS.max.init=function(){return-Infinity};function fkey(x){return x.op+\"_\"+x.field}var cells={};function cell(x){var k=gaccess.reduce(function(v,f){return v.push(f(x)),v},[]).join(\"|\");return cells[k]||(cells[k]=new_cell(x))}function new_cell(x){var o={};for(var i=0,f;i<groupby.length;++i){o[groupby[i]]=gaccess[i](x)}o.count=0;for(i=0;i<fields.length;++i){if(fields[i].op===\"count\")continue;var op=OPS[fields[i].op];o[fkey(fields[i])]=op.init?op.init():0}return o}function aggregate(input){var output=[],k;var keys=fields.map(fkey);var ops=fields.map(function(x){return OPS[x.op]});input.forEach(function(x){var c=cell(x);c.count+=1;for(var i=0;i<fields.length;++i){c[keys[i]]=ops[i](c,c[keys[i]],faccess[i](x))}});var index=0;for(k in cells){output.push({index:index++,data:cells[k]})}cells={};return output}aggregate.fields=function(f){fields=vg.array(f);faccess=fields.map(function(x,i){var xf=x.field;if(xf.indexOf(\"data.\")===0){fields[i]={op:x.op,field:xf.slice(5)}}return vg.accessor(xf)});return aggregate};aggregate.groupby=function(f){groupby=vg.array(f);gaccess=groupby.map(function(x,i){if(x.indexOf(\"data.\")===0){groupby[i]=x.slice(5)}return vg.accessor(x)});return aggregate};return aggregate};vg.data.array=function(){var fields=[];function array(data){return data.map(function(d){var list=[];for(var i=0,len=fields.length;i<len;++i){list.push(fields[i](d))}return list})}array.fields=function(fieldList){fields=vg.array(fieldList).map(vg.accessor);return array};return array};vg.data.bin=function(){var field,accessor,setter,min=undefined,max=undefined,step=undefined,maxbins=20,output=\"bin\";function compare(a,b){return a<b?-1:a>b?1:a>=b?0:NaN}function bisectLeft(a,x,lo,hi){if(arguments.length<3){lo=0}if(arguments.length<4){hi=a.length}while(lo<hi){var mid=lo+hi>>>1;if(compare(a[mid],x)<0){lo=mid+1}else{hi=mid}}return lo}function bins(opt){opt=opt||{};var maxb=opt.maxbins||1024,base=opt.base||10,div=opt.div||[5,2],mins=opt.minstep||0,logb=Math.log(base),level=Math.ceil(Math.log(maxb)/logb),min=opt.min,max=opt.max,span=max-min,step=Math.max(mins,Math.pow(base,Math.round(Math.log(span)/logb)-level)),nbins=Math.ceil(span/step),precision,v,i,eps;if(opt.step!=null){step=opt.step}else if(opt.steps){step=opt.steps[Math.min(opt.steps.length-1,bisectLeft(opt.steps,span/maxb))]}else{do{step*=base;nbins=Math.ceil(span/step)}while(nbins>maxb);for(i=0;i<div.length;++i){v=step/div[i];if(v>=mins&&span/v<=maxb){step=v;nbins=Math.ceil(span/step)}}}v=Math.log(step);precision=v>=0?0:~~(-v/logb)+1;eps=Math.pow(base,-precision-1);min=Math.min(min,Math.floor(min/step+eps)*step);max=Math.ceil(max/step)*step;return{start:min,stop:max,step:step,unit:precision}}function bin(input){var opt={min:min!=null?min:+Infinity,max:max!=null?max:-Infinity,step:step!=null?step:null,maxbins:maxbins};if(min==null||max==null){input.forEach(function(d){var v=accessor(d);if(min==null&&v>opt.max)opt.max=v;if(max==null&&v<opt.min)opt.min=v})}var b=bins(opt);input.forEach(function(d){var v=accessor(d);setter(d,b.start+b.step*~~((v-b.start)/b.step))});return input}bin.min=function(x){min=x;return bin};bin.max=function(x){max=x;return bin};bin.step=function(x){step=x;return bin};bin.maxbins=function(x){maxbins=x;return bin};bin.field=function(f){field=f;accessor=vg.accessor(f);return bin};bin.output=function(f){output=f;setter=vg.mutator(f);return bin};return bin};vg.data.copy=function(){var from=vg.accessor(\"data\"),fields=[],as=null;var copy=vg.data.mapper(function(d){var src=from(d),i,len,source=fields,target=as||fields;for(i=0,len=fields.length;i<len;++i){d[target[i]]=src[fields[i]]}return d});copy.from=function(field){from=vg.accessor(field);return copy};copy.fields=function(fieldList){fields=vg.array(fieldList);return copy};copy.as=function(fieldList){as=vg.array(fieldList);return copy};return copy};vg.data.cross=function(){var other=null,nodiag=false,output={left:\"a\",right:\"b\"};function cross(data){var result=[],data2=other||data,o,i,j,n=data.length;for(i=0;i<n;++i){for(j=0;j<n;++j){if(nodiag&&i===j)continue;o={};o[output.left]=data[i];o[output.right]=data2[j];result.push(o)}}return result}cross[\"with\"]=function(d){other=d;return cross};cross.diagonal=function(x){nodiag=!x;return cross};cross.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return cross};return cross};vg.data.facet=function(){var keys=[],sort=null;function facet(data){var result={key:\"\",keys:[],values:[]},map={},vals=result.values,obj,klist,kstr,len,i,j,k,kv,cmp;if(keys.length===0){vals.push(obj={key:\"\",keys:[],index:0,values:sort?data.slice():data});if(sort)sort(obj.values);return result}for(i=0,len=data.length;i<len;++i){for(k=0,klist=[],kstr=\"\";k<keys.length;++k){kv=keys[k](data[i]);klist.push(kv);kstr+=(k>0?\"|\":\"\")+String(kv)}obj=map[kstr];if(obj===undefined){vals.push(obj=map[kstr]={key:kstr,keys:klist,index:vals.length,values:[]})}obj.values.push(data[i])}if(sort){for(i=0,len=vals.length;i<len;++i){sort(vals[i].values)}}return result}facet.keys=function(k){keys=vg.array(k).map(vg.accessor);return facet};facet.sort=function(s){sort=vg.data.sort().by(s);return facet};return facet};vg.data.filter=function(){var test=null;function filter(data){return test?data.filter(test):data}filter.test=function(func){test=vg.isFunction(func)?func:vg.parse.expr(func);return filter};return filter};vg.data.flatten=function(){function flatten(data){return flat(data,[])}function flat(data,list){if(data.values){for(var i=0,n=data.values.length;i<n;++i){flat(data.values[i],list)}}else{list.push(data)}return list}return flatten};vg.data.fold=function(){var fields=[],accessors=[],output={key:\"key\",value:\"value\"};function fold(data){var values=[],item,i,j,n,m=fields.length;for(i=0,n=data.length;i<n;++i){item=data[i];for(j=0;j<m;++j){var o={index:values.length,data:item.data};o[output.key]=fields[j];o[output.value]=accessors[j](item);values.push(o)}}return values}fold.fields=function(f){fields=vg.array(f);accessors=fields.map(vg.accessor);return fold};fold.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return fold};return fold};vg.data.force=function(){var layout=d3.layout.force(),links=null,linkDistance=20,linkStrength=1,charge=-30,iterations=500,size=[\"width\",\"height\"],params=[\"friction\",\"theta\",\"gravity\",\"alpha\"];function force(data,db,group){layout.size(vg.data.size(size,group)).nodes(data);if(links&&db[links]){layout.links(db[links])}layout.start();for(var i=0;i<iterations;++i){layout.tick()}layout.stop();return data}force.links=function(dataSetName){links=dataSetName;return force};force.size=function(sz){size=sz;return force};force.linkDistance=function(field){linkDistance=typeof field===\"number\"?field:vg.accessor(field);layout.linkDistance(linkDistance);return force};force.linkStrength=function(field){linkStrength=typeof field===\"number\"?field:vg.accessor(field);layout.linkStrength(linkStrength);return force};force.charge=function(field){charge=typeof field===\"number\"?field:vg.accessor(field);layout.charge(charge);return force};force.iterations=function(iter){iterations=iter;return force};params.forEach(function(name){force[name]=function(x){layout[name](x);return force}});return force};vg.data.force.dependencies=[\"links\"];vg.data.formula=function(){return function(){var field=null,expr=vg.identity;var formula=vg.data.mapper(function(d,i,list){if(field)d[field]=expr.call(null,d,i,list);return d});formula.field=function(name){field=name;return formula};formula.expr=function(func){expr=vg.isFunction(func)?func:vg.parse.expr(func);return formula};return formula}}();vg.data.geo=function(){var params=[\"center\",\"scale\",\"translate\",\"rotate\",\"precision\",\"clipAngle\"];function geo(){var opt={},projection=\"mercator\",func=d3.geo[projection](),lat=vg.identity,lon=vg.identity,output={x:\"x\",y:\"y\"};var map=vg.data.mapper(function(d){var ll=[lon(d),lat(d)],xy=func(ll);d[output.x]=xy[0];d[output.y]=xy[1];return d});map.func=function(){return func};map.projection=function(p){if(projection!==p){projection=p;func=d3.geo[projection]();for(var name in opt){func[name](opt[name])}}return map};params.forEach(function(name){map[name]=function(x){opt[name]=x;func[name](x);return map}});map.lon=function(field){lon=vg.accessor(field);return map};map.lat=function(field){lat=vg.accessor(field);return map};map.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return map};return map}geo.params=params;return geo}();vg.data.geopath=function(){var geopath=d3.geo.path().projection(d3.geo.mercator()),projection=\"mercator\",geojson=vg.identity,opt={},output={path:\"path\"};var map=vg.data.mapper(function(d){d[output.path]=geopath(geojson(d));return d});map.projection=function(proj){if(projection!==proj){projection=proj;var p=d3.geo[projection]();for(var name in opt){p[name](opt[name])}geopath.projection(p)}return map};vg.data.geo.params.forEach(function(name){map[name]=function(x){opt[name]=x;geopath.projection()[name](x);return map}});map.value=function(field){geojson=vg.accessor(field);return map};map.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return map};return map};vg.data.link=function(){var shape=\"line\",source=vg.accessor(\"source\"),target=vg.accessor(\"target\"),tension=.2,output={path:\"path\"};function line(d){var s=source(d),t=target(d);return\"M\"+s.x+\",\"+s.y+\"L\"+t.x+\",\"+t.y}function curve(d){var s=source(d),t=target(d),dx=t.x-s.x,dy=t.y-s.y,ix=tension*(dx+dy),iy=tension*(dy-dx);return\"M\"+s.x+\",\"+s.y+\"C\"+(s.x+ix)+\",\"+(s.y+iy)+\" \"+(t.x+iy)+\",\"+(t.y-ix)+\" \"+t.x+\",\"+t.y}function diagonalX(d){var s=source(d),t=target(d),m=(s.x+t.x)/2;return\"M\"+s.x+\",\"+s.y+\"C\"+m+\",\"+s.y+\" \"+m+\",\"+t.y+\" \"+t.x+\",\"+t.y}function diagonalY(d){var s=source(d),t=target(d),m=(s.y+t.y)/2;return\"M\"+s.x+\",\"+s.y+\"C\"+s.x+\",\"+m+\" \"+t.x+\",\"+m+\" \"+t.x+\",\"+t.y}var shapes={line:line,curve:curve,diagonal:diagonalX,diagonalX:diagonalX,diagonalY:diagonalY};function link(data){var path=shapes[shape];data.forEach(function(d){d[output.path]=path(d)});return data}link.shape=function(val){shape=val;return link};link.tension=function(val){tension=val;return link};link.source=function(field){source=vg.accessor(field);return link};link.target=function(field){target=vg.accessor(field);return link};link.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return link};return link};vg.data.pie=function(){var one=function(){return 1},value=one,start=0,end=2*Math.PI,sort=false,output={startAngle:\"startAngle\",endAngle:\"endAngle\",midAngle:\"midAngle\"};function pie(data){var values=data.map(function(d,i){return+value(d)}),a=start,k=(end-start)/d3.sum(values),index=d3.range(data.length);if(sort){index.sort(function(a,b){return values[a]-values[b]})}index.forEach(function(i){var d;data[i].value=d=values[i];data[i][output.startAngle]=a;data[i][output.midAngle]=a+.5*d*k;data[i][output.endAngle]=a+=d*k});return data}pie.sort=function(b){sort=b;return pie};pie.value=function(field){value=field?vg.accessor(field):one;return pie};pie.startAngle=function(startAngle){start=Math.PI*startAngle/180;return pie};pie.endAngle=function(endAngle){end=Math.PI*endAngle/180;return pie};pie.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return pie};return pie};vg.data.slice=function(){var by=null,field=vg.accessor(\"data\");function slice(data){data=vg.values(data);if(by===\"min\"){data=[data[vg.minIndex(data,field)]]}else if(by===\"max\"){data=[data[vg.maxIndex(data,field)]]}else if(by===\"median\"){var list=data.slice().sort(function(a,b){a=field(a);b=field(b);return a<b?-1:a>b?1:0});data=[data[~~(list.length/2)]]}else{var idx=vg.array(by);data=data.slice(idx[0],idx[1])}return data}slice.by=function(x){by=x;return slice};slice.field=function(f){field=vg.accessor(f);return slice};return slice};vg.data.sort=function(){var by=null;function sort(data){data=vg.isArray(data)?data:data.values||[];data.sort(by);for(var i=0,n=data.length;i<n;++i)data[i].index=i;return data}sort.by=function(s){by=vg.comparator(s);return sort};return sort};vg.data.stack=function(){var layout=d3.layout.stack(),point=vg.accessor(\"index\"),height=vg.accessor(\"data\"),params=[\"offset\",\"order\"],output={y0:\"y2\",y1:\"y\",cy:\"cy\"};function stack(data){var out_y0=output[\"y0\"],out_y1=output[\"y1\"],out_cy=output[\"cy\"];var series=stacks(data);if(series.length===0)return data;layout.out(function(d,y0,y){if(d.datum){d.datum[out_y0]=y0;d.datum[out_y1]=y+y0;d.datum[out_cy]=y0+y/2}})(series);return data}function stacks(data){var values=vg.values(data),points=[],series=[],a,i,n,j,m,k,p,v,x;if(values.length===0)return series;for(i=0,n=values.length;i<n;++i){a=vg.values(values[i]);for(j=0,m=a.length;j<m;++j){points.push({x:point(a[j]),y:height(a[j]),z:i,datum:a[j]})}series.push([])}points.sort(function(a,b){return a.x<b.x?-1:a.x>b.x?1:a.z<b.z?-1:a.z>b.z?1:0});for(x=points[0].x,i=0,j=0,k=0,n=points.length;k<n;++k){p=points[k];if(p.x!==x){while(i<series.length)series[i++].push({x:j,y:0});x=p.x;i=0;j+=1}while(p.z>i)series[i++].push({x:j,y:0});p.x=j;series[i++].push(p)}while(i<series.length)series[i++].push({x:j,y:0});return series}stack.point=function(field){point=vg.accessor(field);return stack};stack.height=function(field){height=vg.accessor(field);return stack};params.forEach(function(name){stack[name]=function(x){layout[name](x);return stack}});stack.output=function(map){d3.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return stack};return stack};vg.data.stats=function(){var value=vg.accessor(\"data\"),assign=false,median=false,output={count:\"count\",min:\"min\",max:\"max\",sum:\"sum\",mean:\"mean\",variance:\"variance\",stdev:\"stdev\",median:\"median\"};function reduce(data){var min=+Infinity,max=-Infinity,sum=0,mean=0,M2=0,i,len,v,delta;var list=(vg.isArray(data)?data:data.values||[]).map(value);\nfor(i=0,len=list.length;i<len;++i){v=list[i];if(v<min)min=v;if(v>max)max=v;sum+=v;delta=v-mean;mean=mean+delta/(i+1);M2=M2+delta*(v-mean)}M2=M2/(len-1);var o=vg.isArray(data)?{}:data;if(median){list.sort(vg.numcmp);i=list.length>>1;o[output.median]=list.length%2?list[i]:(list[i-1]+list[i])/2}o[output.count]=len;o[output.min]=min;o[output.max]=max;o[output.sum]=sum;o[output.mean]=mean;o[output.variance]=M2;o[output.stdev]=Math.sqrt(M2);if(assign){list=vg.isArray(data)?data:data.values;v={};v[output.count]=len;v[output.min]=min;v[output.max]=max;v[output.sum]=sum;v[output.mean]=mean;v[output.variance]=M2;v[output.stdev]=Math.sqrt(M2);if(median)v[output.median]=o[output.median];for(i=0,len=list.length;i<len;++i){list[i].stats=v}if(vg.isArray(data))o=list}return o}function stats(data){if(vg.isArray(data)){return reduce(data)}else{return(data.values||[]).map(reduce)}}stats.median=function(bool){median=bool||false;return stats};stats.value=function(field){value=vg.accessor(field);return stats};stats.assign=function(b){assign=b;return stats};stats.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return stats};return stats};vg.data.treemap=function(){var layout=d3.layout.treemap().children(function(d){return d.values}),value=vg.accessor(\"data\"),size=[\"width\",\"height\"],params=[\"round\",\"sticky\",\"ratio\",\"padding\"],output={x:\"x\",y:\"y\",dx:\"width\",dy:\"height\"};function treemap(data,db,group){data=layout.size(vg.data.size(size,group)).value(value).nodes(vg.isTree(data)?data:{values:data});var keys=vg.keys(output),len=keys.length;data.forEach(function(d){var key,val;for(var i=0;i<len;++i){key=keys[i];if(key!==output[key]){val=d[key];delete d[key];d[output[key]]=val}}});return data}treemap.size=function(sz){size=sz;return treemap};treemap.value=function(field){value=vg.accessor(field);return treemap};params.forEach(function(name){treemap[name]=function(x){layout[name](x);return treemap}});treemap.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return treemap};return treemap};vg.data.truncate=function(){var value=vg.accessor(\"data\"),as=\"truncate\",position=\"right\",ellipsis=\"...\",wordBreak=true,limit=100;var truncate=vg.data.mapper(function(d){var text=vg.truncate(value(d),limit,position,wordBreak,ellipsis);return d[as]=text,d});truncate.value=function(field){value=vg.accessor(field);return truncate};truncate.output=function(field){as=field;return truncate};truncate.limit=function(len){limit=+len;return truncate};truncate.position=function(pos){position=pos;return truncate};truncate.ellipsis=function(str){ellipsis=str+\"\";return truncate};truncate.wordbreak=function(b){wordBreak=!!b;return truncate};return truncate};vg.data.unique=function(){var field=null,as=\"field\";function unique(data){return vg.unique(data,field).map(function(x){var o={};o[as]=x;return o})}unique.field=function(f){field=vg.accessor(f);return unique};unique.as=function(x){as=x;return unique};return unique};vg.data.window=function(){var size=2,step=1;function win(data){data=vg.isArray(data)?data:data.values||[];var runs=[],i,j,n=data.length-size,curr;for(i=0;i<=n;i+=step){for(j=0,curr=[];j<size;++j)curr.push(data[i+j]);runs.push({key:i,values:curr})}return{values:runs}}win.size=function(n){size=n;return win};win.step=function(n){step=n;return win};return win};vg.data.wordcloud=function(){var layout=d3.layout.cloud().size([900,500]),text=vg.accessor(\"data\"),size=[\"width\",\"height\"],fontSize=function(){return 14},rotate=function(){return 0},params=[\"font\",\"fontStyle\",\"fontWeight\",\"padding\"];var output={x:\"x\",y:\"y\",size:\"fontSize\",font:\"font\",rotate:\"angle\"};function cloud(data,db,group){function finish(tags,bounds){var size=layout.size(),dx=size[0]/2,dy=size[1]/2,keys=vg.keys(output),key,d,i,n,k,m=keys.length;data.sort(function(a,b){return fontSize(b)-fontSize(a)});for(i=0,n=tags.length;i<n;++i){d=data[i];for(k=0;k<m;++k){key=keys[k];d[output[key]]=tags[i][key];if(key===\"x\")d[output.x]+=dx;if(key===\"y\")d[output.y]+=dy}}}layout.size(vg.data.size(size,group)).text(text).fontSize(fontSize).rotate(rotate).words(data).on(\"end\",finish).start();return data}cloud.text=function(field){text=vg.accessor(field);return cloud};cloud.size=function(sz){size=sz;return cloud};cloud.fontSize=function(field){fontSize=vg.accessor(field);return cloud};cloud.rotate=function(x){var v;if(vg.isObject(x)&&!Array.isArray(x)){if(x.random!==undefined){v=(v=x.random)?vg.array(v):[0];rotate=function(){return v[~~(Math.random()*v.length-1e-5)]}}else if(x.alternate!==undefined){v=(v=x.alternate)?vg.array(v):[0];rotate=function(d,i){return v[i%v.length]}}}else{rotate=vg.accessor(field)}return cloud};params.forEach(function(name){cloud[name]=function(x){layout[name](x);return cloud}});cloud.output=function(map){vg.keys(output).forEach(function(k){if(map[k]!==undefined){output[k]=map[k]}});return cloud};return cloud};vg.data.zip=function(){var z=null,as=\"zip\",key=vg.accessor(\"data\"),defaultValue=undefined,withKey=null;function zip(data,db){var zdata=db[z],zlen=zdata.length,v,d,i,len,map;if(withKey){map={};zdata.forEach(function(s){map[withKey(s)]=s})}for(i=0,len=data.length;i<len;++i){d=data[i];d[as]=map?(v=map[key(d)])!=null?v:defaultValue:zdata[i%zlen]}return data}zip[\"with\"]=function(d){z=d;return zip};zip[\"default\"]=function(d){defaultValue=d;return zip};zip.as=function(name){as=name;return zip};zip.key=function(k){key=vg.accessor(k);return zip};zip.withKey=function(k){withKey=vg.accessor(k);return zip};return zip};vg.data.zip.dependencies=[\"with\"];vg.parse={};vg.parse.axes=function(){var ORIENT={x:\"bottom\",y:\"left\",top:\"top\",bottom:\"bottom\",left:\"left\",right:\"right\"};function axes(spec,axes,scales){(spec||[]).forEach(function(def,index){axes[index]=axes[index]||vg.scene.axis();axis(def,index,axes[index],scales)})}function axis(def,index,axis,scales){if(def.scale!==undefined){axis.scale(scales[def.scale])}axis.orient(def.orient||ORIENT[def.type]);axis.offset(def.offset||0);axis.layer(def.layer||\"front\");axis.grid(def.grid||false);axis.title(def.title||null);axis.titleOffset(def.titleOffset!=null?def.titleOffset:vg.config.axis.titleOffset);axis.tickValues(def.values||null);axis.tickFormat(def.format||null);axis.tickSubdivide(def.subdivide||0);axis.tickPadding(def.tickPadding||vg.config.axis.padding);var size=[];if(def.tickSize!==undefined){for(var i=0;i<3;++i)size.push(def.tickSize)}else{var ts=vg.config.axis.tickSize;size=[ts,ts,ts]}if(def.tickSizeMajor!=null)size[0]=def.tickSizeMajor;if(def.tickSizeMinor!=null)size[1]=def.tickSizeMinor;if(def.tickSizeEnd!=null)size[2]=def.tickSizeEnd;if(size.length){axis.tickSize.apply(axis,size)}if(def.ticks!=null){var ticks=vg.isArray(def.ticks)?def.ticks:[def.ticks];axis.ticks.apply(axis,ticks)}else{axis.ticks(vg.config.axis.ticks)}var p=def.properties;if(p&&p.ticks){axis.majorTickProperties(p.majorTicks?vg.extend({},p.ticks,p.majorTicks):p.ticks);axis.minorTickProperties(p.minorTicks?vg.extend({},p.ticks,p.minorTicks):p.ticks)}else{axis.majorTickProperties(p&&p.majorTicks||{});axis.minorTickProperties(p&&p.minorTicks||{})}axis.tickLabelProperties(p&&p.labels||{});axis.titleProperties(p&&p.title||{});axis.gridLineProperties(p&&p.grid||{});axis.domainProperties(p&&p.axis||{})}return axes}();vg.parse.data=function(spec,callback){var model={defs:spec,load:{},flow:{},deps:{},source:{},sorted:null};var count=0;function load(d){return function(error,data){if(error){vg.error(\"LOADING FAILED: \"+d.url)}else{model.load[d.name]=vg.data.read(data.toString(),d.format)}if(--count===0)callback()}}(spec||[]).forEach(function(d){if(d.url){count+=1;vg.data.load(d.url,load(d))}else if(d.values){model.load[d.name]=vg.data.read(d.values,d.format)}else if(d.source){(model.source[d.source]||(model.source[d.source]=[])).push(d.name)}if(d.transform){var flow=vg.parse.dataflow(d);model.flow[d.name]=flow;flow.dependencies.forEach(function(dep){(model.deps[dep]||(model.deps[dep]=[])).push(d.name)})}});var names=(spec||[]).map(vg.accessor(\"name\")),order=[],v={},n;function visit(n){if(v[n]===1)return;if(!v[n]){v[n]=1;(model.source[n]||[]).forEach(visit);(model.deps[n]||[]).forEach(visit);v[n]=2;order.push(n)}}while(names.length){if(v[n=names.pop()]!==2)visit(n)}model.sorted=order.reverse();if(count===0)setTimeout(callback,1);return model};vg.parse.dataflow=function(def){var tx=(def.transform||[]).map(vg.parse.transform),df=tx.length?function(data,db,group){return tx.reduce(function(d,t){return t(d,db,group)},data)}:vg.identity;df.transforms=tx;df.dependencies=vg.keys((def.transform||[]).reduce(function(map,tdef){var deps=vg.data[tdef.type].dependencies;if(deps)deps.forEach(function(d){if(tdef[d])map[tdef[d]]=1});return map},{}));return df};vg.parse.expr=function(){var CONSTANT={E:\"Math.E\",LN2:\"Math.LN2\",LN10:\"Math.LN10\",LOG2E:\"Math.LOG2E\",LOG10E:\"Math.LOG10E\",PI:\"Math.PI\",SQRT1_2:\"Math.SQRT1_2\",SQRT2:\"Math.SQRT2\"};var FUNCTION={abs:\"Math.abs\",acos:\"Math.acos\",asin:\"Math.asin\",atan:\"Math.atan\",atan2:\"Math.atan2\",ceil:\"Math.ceil\",cos:\"Math.cos\",exp:\"Math.exp\",floor:\"Math.floor\",log:\"Math.log\",max:\"Math.max\",min:\"Math.min\",pow:\"Math.pow\",random:\"Math.random\",round:\"Math.round\",sin:\"Math.sin\",sqrt:\"Math.sqrt\",tan:\"Math.tan\"};var lexer=/([\\\"\\']|[\\=\\<\\>\\~\\&\\|\\?\\:\\+\\-\\/\\*\\%\\!\\^\\,\\;\\[\\]\\{\\}\\(\\) ]+)/;return function(x){if(vg.config.safeMode){vg.error(\"Safe mode: Expression parsing disabled.\");return vg.true}var tokens=x.split(lexer),t,v,i,n,sq,dq;for(sq=0,dq=0,i=0,n=tokens.length;i<n;++i){var t=tokens[i];if(t===\"'\"){if(!dq)sq=!sq;continue}if(t==='\"'){if(!sq)dq=!dq;continue}if(dq||sq)continue;if(CONSTANT[t]){tokens[i]=CONSTANT[t]}if(FUNCTION[t]&&(v=tokens[i+1])&&v[0]===\"(\"){tokens[i]=FUNCTION[t]}}return Function(\"d\",\"index\",\"data\",\"return (\"+tokens.join(\"\")+\");\")}}();vg.parse.legends=function(){function legends(spec,legends,scales){(spec||[]).forEach(function(def,index){legends[index]=legends[index]||vg.scene.legend();legend(def,index,legends[index],scales)})}function legend(def,index,legend,scales){legend.size(def.size?scales[def.size]:null);legend.shape(def.shape?scales[def.shape]:null);legend.fill(def.fill?scales[def.fill]:null);legend.stroke(def.stroke?scales[def.stroke]:null);if(def.orient)legend.orient(def.orient);if(def.offset!=null)legend.offset(def.offset);legend.title(def.title||null);legend.values(def.values||null);legend.format(def.format!==undefined?def.format:null);var p=def.properties;legend.titleProperties(p&&p.title||{});legend.labelProperties(p&&p.labels||{});legend.legendProperties(p&&p.legend||{});legend.symbolProperties(p&&p.symbols||{});legend.gradientProperties(p&&p.gradient||{})}return legends}();vg.parse.mark=function(mark){var props=mark.properties,group=mark.marks;vg.keys(props).forEach(function(k){props[k]=vg.parse.properties(mark.type,props[k])});if(mark.delay){mark.delay=vg.parse.properties(mark.type,{delay:mark.delay})}if(mark.from){var name=mark.from.data,tx=vg.parse.dataflow(mark.from);mark.from=function(db,group,parentData){var data=vg.scene.data(name?db[name]:null,parentData);return tx(data,db,group)}}if(group){mark.marks=group.map(vg.parse.mark)}return mark};vg.parse.marks=function(spec,width,height){return{type:\"group\",width:width,height:height,scales:spec.scales||[],axes:spec.axes||[],legends:spec.legends||[],marks:(spec.marks||[]).map(vg.parse.mark)}};vg.parse.padding=function(pad){if(pad==null)return\"auto\";else if(vg.isString(pad))return pad===\"strict\"?\"strict\":\"auto\";else if(vg.isObject(pad))return pad;var p=vg.isNumber(pad)?pad:20;return{top:p,left:p,right:p,bottom:p}};vg.parse.properties=function(){function compile(mark,spec){var code=\"\",names=vg.keys(spec),i,len,name,ref,vars={};code+=\"var o = trans ? {} : item;\\n\";for(i=0,len=names.length;i<len;++i){ref=spec[name=names[i]];code+=i>0?\"\\n  \":\"  \";code+=\"o.\"+name+\" = \"+valueRef(name,ref)+\";\";vars[name]=true}if(vars.x2){if(vars.x){code+=\"\\n  if (o.x > o.x2) { \"+\"var t = o.x; o.x = o.x2; o.x2 = t; };\";code+=\"\\n  o.width = (o.x2 - o.x);\"}else if(vars.width){code+=\"\\n  o.x = (o.x2 - o.width);\"}else{code+=\"\\n  o.x = o.x2;\"}}if(vars.xc){if(vars.width){code+=\"\\n  o.x = (o.xc - o.width/2);\"}else{code+=\"\\n  o.x = o.xc;\"}}if(vars.y2){if(vars.y){code+=\"\\n  if (o.y > o.y2) { \"+\"var t = o.y; o.y = o.y2; o.y2 = t; };\";code+=\"\\n  o.height = (o.y2 - o.y);\"}else if(vars.height){code+=\"\\n  o.y = (o.y2 - o.height);\"}else{code+=\"\\n  o.y = o.y2;\"}}if(vars.yc){if(vars.height){code+=\"\\n  o.y = (o.yc - o.height/2);\"}else{code+=\"\\n  o.y = o.yc;\"}}if(hasPath(mark,vars))code+=\"\\n  item.touch();\";code+=\"\\n  if (trans) trans.interpolate(item, o);\";try{return Function(\"item\",\"group\",\"trans\",code)}catch(e){vg.error(e);vg.log(code)}}function hasPath(mark,vars){return vars.path||(mark===\"area\"||mark===\"line\")&&(vars.x||vars.x2||vars.width||vars.y||vars.y2||vars.height||vars.tension||vars.interpolate)}var GROUP_VARS={width:1,height:1,\"mark.group.width\":1,\"mark.group.height\":1};function valueRef(name,ref){if(ref==null)return null;var isColor=name===\"fill\"||name===\"stroke\";if(isColor){if(ref.c){return colorRef(\"hcl\",ref.h,ref.c,ref.l)}else if(ref.h||ref.s){return colorRef(\"hsl\",ref.h,ref.s,ref.l)}else if(ref.l||ref.a){return colorRef(\"lab\",ref.l,ref.a,ref.b)}else if(ref.r||ref.g||ref.b){return colorRef(\"rgb\",ref.r,ref.g,ref.b)}}var val=\"item.datum.data\";if(ref.value!==undefined){val=vg.str(ref.value)}if(ref.group!=null){var grp=\"group.datum\";if(vg.isString(ref.group)){grp=GROUP_VARS[ref.group]?\"group.\"+ref.group:\"group.datum[\"+vg.field(ref.group).map(vg.str).join(\"][\")+\"]\"}}if(ref.field!=null){if(vg.isString(ref.field)){val=\"item.datum[\"+vg.field(ref.field).map(vg.str).join(\"][\")+\"]\";if(ref.group!=null){val=\"this.accessor(\"+val+\")(\"+grp+\")\"}}else{val=\"this.accessor(group.datum[\"+vg.field(ref.field.group).map(vg.str).join(\"][\")+\"])(item.datum.data)\"}}else if(ref.group!=null){val=grp}if(ref.scale!=null){var scale=vg.isString(ref.scale)?vg.str(ref.scale):(ref.scale.group?\"group\":\"item\")+\".datum[\"+vg.str(ref.scale.group||ref.scale.field)+\"]\";scale=\"group.scales[\"+scale+\"]\";val=scale+(ref.band?\".rangeBand()\":\"(\"+val+\")\")}val=\"(\"+(ref.mult?vg.number(ref.mult)+\" * \":\"\")+val+\")\"+(ref.offset?\" + \"+vg.number(ref.offset):\"\");return val}function colorRef(type,x,y,z){var xx=x?valueRef(\"\",x):vg.config.color[type][0],yy=y?valueRef(\"\",y):vg.config.color[type][1],zz=z?valueRef(\"\",z):vg.config.color[type][2];return\"(this.d3.\"+type+\"(\"+[xx,yy,zz].join(\",\")+') + \"\")'}return compile}();vg.parse.scales=function(){var LINEAR=\"linear\",ORDINAL=\"ordinal\",LOG=\"log\",POWER=\"pow\",TIME=\"time\",QUANTILE=\"quantile\",GROUP_PROPERTY={width:1,height:1};function scales(spec,scales,db,group){return(spec||[]).reduce(function(o,def){var name=def.name,prev=name+\":prev\";o[name]=scale(def,o[name],db,group);o[prev]=o[prev]||o[name];return o},scales||{})}function scale(def,scale,db,group){var s=instance(def,scale),m=s.type===ORDINAL?ordinal:quantitative,rng=range(def,group),data=vg.values(group.datum);m(def,s,rng,db,data);return s}function instance(def,scale){var type=def.type||LINEAR;if(!scale||type!==scale.type){var ctor=vg.config.scale[type]||d3.scale[type];if(!ctor)vg.error(\"Unrecognized scale type: \"+type);(scale=ctor()).type=scale.type||type;scale.scaleName=def.name}return scale}function ordinal(def,scale,rng,db,data){var dataDrivenRange=false,pad=def.padding||0,outer=def.outerPadding||0,domain,sort,str,refs;if(vg.isObject(def.range)&&!vg.isArray(def.range)){dataDrivenRange=true;refs=def.range.fields||vg.array(def.range);rng=extract(refs,db,data)}sort=def.sort&&!dataDrivenRange;domain=domainValues(def,db,data,sort);if(domain)scale.domain(domain);if(def.bandWidth){var bw=def.bandWidth,len=domain.length,start=rng[0]||0,space=def.points?pad*bw:pad*bw*(len-1)+2*outer;rng=[start,start+(bw*len+space)]}str=typeof rng[0]===\"string\";if(str||rng.length>2||rng.length===1||dataDrivenRange){scale.range(rng)}else if(def.points){scale.rangePoints(rng,pad)}else if(def.round||def.round===undefined){scale.rangeRoundBands(rng,pad,outer)}else{scale.rangeBands(rng,pad,outer)}}function quantitative(def,scale,rng,db,data){var domain,interval;domain=def.type===QUANTILE?domainValues(def,db,data,false):domainMinMax(def,db,data);scale.domain(domain);if(def.range===\"height\")rng=rng.reverse();scale[def.round&&scale.rangeRound?\"rangeRound\":\"range\"](rng);if(def.exponent&&def.type===POWER)scale.exponent(def.exponent);if(def.clamp)scale.clamp(true);if(def.nice){if(def.type===TIME){interval=d3.time[def.nice];if(!interval)vg.error(\"Unrecognized interval: \"+interval);scale.nice(interval)}else{scale.nice()}}}function extract(refs,db,data){return refs.reduce(function(values,r){var dat=vg.values(db[r.data]||data),get=vg.accessor(vg.isString(r.field)?r.field:\"data.\"+vg.accessor(r.field.group)(data));return vg.unique(dat,get,values)},[])}function domainValues(def,db,data,sort){var domain=def.domain,values,refs;if(vg.isArray(domain)){values=sort?domain.slice():domain}else if(vg.isObject(domain)){refs=domain.fields||vg.array(domain);values=extract(refs,db,data)}if(values&&sort)values.sort(vg.cmp);return values}function domainMinMax(def,db,data){var domain=[null,null],refs,z;function extract(ref,min,max,z){var dat=vg.values(db[ref.data]||data);var fields=vg.array(ref.field).map(function(f){return vg.isString(f)?f:\"data.\"+vg.accessor(f.group)(data)});fields.forEach(function(f,i){f=vg.accessor(f);if(min)domain[0]=d3.min([domain[0],d3.min(dat,f)]);if(max)domain[z]=d3.max([domain[z],d3.max(dat,f)])})}if(def.domain!==undefined){if(vg.isArray(def.domain)){domain=def.domain.slice()}else if(vg.isObject(def.domain)){refs=def.domain.fields||vg.array(def.domain);refs.forEach(function(r){extract(r,1,1,1)})}else{domain=def.domain}}z=domain.length-1;if(def.domainMin!==undefined){if(vg.isObject(def.domainMin)){domain[0]=null;refs=def.domainMin.fields||vg.array(def.domainMin);refs.forEach(function(r){extract(r,1,0,z)})}else{domain[0]=def.domainMin}}if(def.domainMax!==undefined){if(vg.isObject(def.domainMax)){domain[z]=null;refs=def.domainMax.fields||vg.array(def.domainMax);refs.forEach(function(r){extract(r,0,1,z)})}else{domain[z]=def.domainMax}}if(def.type!==LOG&&def.type!==TIME&&(def.zero||def.zero===undefined)){domain[0]=Math.min(0,domain[0]);domain[z]=Math.max(0,domain[z])}return domain}function range(def,group){var rng=[null,null];if(def.range!==undefined){if(typeof def.range===\"string\"){if(GROUP_PROPERTY[def.range]){rng=[0,group[def.range]]}else if(vg.config.range[def.range]){rng=vg.config.range[def.range]}else{vg.error(\"Unrecogized range: \"+def.range);return rng}}else if(vg.isArray(def.range)){rng=def.range}else if(vg.isObject(def.range)){return null}else{rng=[0,def.range]}}if(def.rangeMin!==undefined){rng[0]=def.rangeMin}if(def.rangeMax!==undefined){rng[rng.length-1]=def.rangeMax}if(def.reverse!==undefined){var rev=def.reverse;if(vg.isObject(rev)){rev=vg.accessor(rev.field)(group.datum)}if(rev)rng=rng.reverse()}return rng}return scales}();vg.parse.spec=function(spec,callback,viewFactory){viewFactory=viewFactory||vg.ViewFactory;function parse(spec){spec=vg.duplicate(spec);var width=spec.width||500,height=spec.height||500,viewport=spec.viewport||null;var defs={width:width,height:height,viewport:viewport,padding:vg.parse.padding(spec.padding),marks:vg.parse.marks(spec,width,height),data:vg.parse.data(spec.data,function(){callback(viewConstructor)})};var viewConstructor=viewFactory(defs)}vg.isObject(spec)?parse(spec):d3.json(spec,function(error,json){error?vg.error(error):parse(json)})};vg.parse.transform=function(def){var tx=vg.data[def.type]();vg.keys(def).forEach(function(k){if(k===\"type\")return;tx[k](def[k])});return tx};vg.scene={};vg.scene.GROUP=\"group\",vg.scene.ENTER=0,vg.scene.UPDATE=1,vg.scene.EXIT=2;vg.scene.DEFAULT_DATA={sentinel:1};vg.scene.data=function(data,parentData){var DEFAULT=vg.scene.DEFAULT_DATA;data=vg.values(data||parentData||[DEFAULT]);if(data===DEFAULT)data=[DEFAULT];return data};vg.scene.fontString=function(o){return(o.fontStyle?o.fontStyle+\" \":\"\")+(o.fontVariant?o.fontVariant+\" \":\"\")+(o.fontWeight?o.fontWeight+\" \":\"\")+(o.fontSize!=null?o.fontSize:vg.config.render.fontSize)+\"px \"+(o.font||vg.config.render.font)};vg.scene.Item=function(){function item(mark){this.mark=mark}var prototype=item.prototype;prototype.hasPropertySet=function(name){var props=this.mark.def.properties;return props&&props[name]!=null};prototype.cousin=function(offset,index){if(offset===0)return this;offset=offset||-1;var mark=this.mark,group=mark.group,iidx=index==null?mark.items.indexOf(this):index,midx=group.items.indexOf(mark)+offset;return group.items[midx].items[iidx]};prototype.sibling=function(offset){if(offset===0)return this;offset=offset||-1;var mark=this.mark,iidx=mark.items.indexOf(this)+offset;return mark.items[iidx]};prototype.remove=function(){var item=this,list=item.mark.items,i=list.indexOf(item);if(i>=0)i===list.length-1?list.pop():list.splice(i,1);return item};prototype.touch=function(){if(this.pathCache)this.pathCache=null;if(this.mark.pathCache)this.mark.pathCache=null};return item}();vg.scene.item=function(mark){return new vg.scene.Item(mark)};vg.scene.visit=function(node,func){var i,n,s,m,items;if(func(node))return true;var sets=[\"items\",\"axisItems\",\"legendItems\"];for(s=0,m=sets.length;s<m;++s){if(items=node[sets[s]]){for(i=0,n=items.length;i<n;++i){if(vg.scene.visit(items[i],func))return true}}}};vg.scene.build=function(){var GROUP=vg.scene.GROUP,ENTER=vg.scene.ENTER,UPDATE=vg.scene.UPDATE,EXIT=vg.scene.EXIT,DEFAULT={sentinel:1};function build(def,db,node,parentData,reentrant){var data=vg.scene.data(def.from?def.from(db,node,parentData):null,parentData);node=buildNode(def,node);node.items=buildItems(def,data,node);buildTrans(def,node);if(def.type===GROUP){buildGroup(def,db,node,reentrant)}return node}function buildNode(def,node){node=node||{};node.def=def;node.marktype=def.type;node.interactive=!(def.interactive===false);return node}function buildItems(def,data,node){var keyf=keyFunction(def.key),prev=node.items||[],next=[],map={},i,key,len,item,datum,enter;for(i=0,len=prev.length;i<len;++i){item=prev[i];item.status=EXIT;if(keyf)map[item.key]=item}for(i=0,len=data.length;i<len;++i){datum=data[i];key=i;item=keyf?map[key=keyf(datum)]:prev[i];enter=item?false:(item=vg.scene.item(node),true);item.status=enter?ENTER:UPDATE;item.datum=datum;item.key=key;next.push(item)}for(i=0,len=prev.length;i<len;++i){item=prev[i];if(item.status===EXIT){item.key=keyf?item.key:next.length;next.splice(item.index,0,item)}}return next}function buildGroup(def,db,node,reentrant){var groups=node.items,marks=def.marks,i,len,m,mlen,name,group;for(i=0,len=groups.length;i<len;++i){group=groups[i];if(!reentrant&&group.scales)for(name in group.scales){if(name.indexOf(\":prev\")<0){group.scales[name+\":prev\"]=group.scales[name].copy()}}group.items=group.items||[];for(m=0,mlen=marks.length;m<mlen;++m){group.items[m]=build(marks[m],db,group.items[m],group.datum);group.items[m].group=group}}}function buildTrans(def,node){if(def.duration)node.duration=def.duration;if(def.ease)node.ease=d3.ease(def.ease);if(def.delay){var items=node.items,group=node.group,n=items.length,i;for(i=0;i<n;++i)def.delay.call(this,items[i],group)}}function keyFunction(key){if(key==null)return null;var f=vg.array(key).map(vg.accessor);return function(d){for(var s=\"\",i=0,n=f.length;i<n;++i){if(i>0)s+=\"|\";s+=String(f[i](d))}return s}}return build}();vg.scene.bounds=function(){var parse=vg.canvas.path.parse,boundPath=vg.canvas.path.bounds,areaPath=vg.canvas.path.area,linePath=vg.canvas.path.line,halfpi=Math.PI/2,sqrt3=Math.sqrt(3),tan30=Math.tan(30*Math.PI/180),gfx=null;function context(){return gfx||(gfx=(vg.config.isNode?new(require(\"canvas\"))(1,1):d3.select(\"body\").append(\"canvas\").attr(\"class\",\"vega_hidden\").attr(\"width\",1).attr(\"height\",1).style(\"display\",\"none\").node()).getContext(\"2d\"))}function pathBounds(o,path,bounds){if(path==null){bounds.set(0,0,0,0)}else{boundPath(path,bounds);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}}return bounds}function path(o,bounds){var p=o.path?o.pathCache||(o.pathCache=parse(o.path)):null;return pathBounds(o,p,bounds)}function area(o,bounds){var items=o.mark.items,o=items[0];var p=o.pathCache||(o.pathCache=parse(areaPath(items)));return pathBounds(items[0],p,bounds)}function line(o,bounds){var items=o.mark.items,o=items[0];var p=o.pathCache||(o.pathCache=parse(linePath(items)));return pathBounds(items[0],p,bounds)}function rect(o,bounds){var x=o.x||0,y=o.y||0,w=x+o.width||0,h=y+o.height||0;bounds.set(x,y,w,h);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function image(o,bounds){var w=o.width||0,h=o.height||0,x=(o.x||0)-(o.align===\"center\"?w/2:o.align===\"right\"?w:0),y=(o.y||0)-(o.baseline===\"middle\"?h/2:o.baseline===\"bottom\"?h:0);return bounds.set(x,y,x+w,y+h)}function rule(o,bounds){var x1,y1;bounds.set(x1=o.x||0,y1=o.y||0,o.x2!=null?o.x2:x1,o.y2!=null?o.y2:y1);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function arc(o,bounds){var cx=o.x||0,cy=o.y||0,ir=o.innerRadius||0,or=o.outerRadius||0,sa=(o.startAngle||0)-halfpi,ea=(o.endAngle||0)-halfpi,xmin=Infinity,xmax=-Infinity,ymin=Infinity,ymax=-Infinity,a,i,n,x,y,ix,iy,ox,oy;var angles=[sa,ea],s=sa-sa%halfpi;for(i=0;i<4&&s<ea;++i,s+=halfpi){angles.push(s)}for(i=0,n=angles.length;i<n;++i){a=angles[i];x=Math.cos(a);ix=ir*x;ox=or*x;y=Math.sin(a);iy=ir*y;oy=or*y;xmin=Math.min(xmin,ix,ox);xmax=Math.max(xmax,ix,ox);ymin=Math.min(ymin,iy,oy);ymax=Math.max(ymax,iy,oy)}bounds.set(cx+xmin,cy+ymin,cx+xmax,cy+ymax);if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function symbol(o,bounds){var size=o.size!=null?o.size:100,x=o.x||0,y=o.y||0,r,t,rx,ry;switch(o.shape){case\"cross\":r=Math.sqrt(size/5)/2;t=3*r;bounds.set(x-t,y-r,x+t,y+r);break;case\"diamond\":ry=Math.sqrt(size/(2*tan30));rx=ry*tan30;bounds.set(x-rx,y-ry,x+rx,y+ry);break;case\"square\":t=Math.sqrt(size);r=t/2;bounds.set(x-r,y-r,x+r,y+r);break;case\"triangle-down\":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;bounds.set(x-rx,y-ry,x+rx,y+ry);break;case\"triangle-up\":rx=Math.sqrt(size/sqrt3);ry=rx*sqrt3/2;bounds.set(x-rx,y-ry,x+rx,y+ry);break;default:r=Math.sqrt(size/Math.PI);bounds.set(x-r,y-r,x+r,y+r)}if(o.stroke&&o.opacity!==0&&o.strokeWidth>0){bounds.expand(o.strokeWidth)}return bounds}function text(o,bounds,noRotate){var x=(o.x||0)+(o.dx||0),y=(o.y||0)+(o.dy||0),h=o.fontSize||vg.config.render.fontSize,a=o.align,b=o.baseline,r=o.radius||0,g=context(),w,t;g.font=vg.scene.fontString(o);g.textAlign=a||\"left\";g.textBaseline=b||\"alphabetic\";w=g.measureText(o.text||\"\").width;if(r){t=(o.theta||0)-Math.PI/2;x+=r*Math.cos(t);y+=r*Math.sin(t)}if(a===\"center\"){x=x-w/2}else if(a===\"right\"){x=x-w}else{}if(b===\"top\"){y=y+h/5}else if(b===\"bottom\"){y=y-h}else if(b===\"middle\"){y=y-h/2+h/10}else{y=y-4*h/5}bounds.set(x,y,x+w,y+h);if(o.angle&&!noRotate){bounds.rotate(o.angle*Math.PI/180,o.x||0,o.y||0)}return bounds.expand(noRotate?0:1)}function group(g,bounds,includeLegends){var axes=g.axisItems||[],legends=g.legendItems||[],j,m;for(j=0,m=axes.length;j<m;++j){bounds.union(axes[j].bounds)}for(j=0,m=g.items.length;j<m;++j){bounds.union(g.items[j].bounds)}if(includeLegends){for(j=0,m=legends.length;j<m;++j){bounds.union(legends[j].bounds)}if(g.width!=null&&g.height!=null){bounds.add(g.width,g.height)}if(g.x!=null&&g.y!=null){bounds.add(0,0)}}bounds.translate(g.x||0,g.y||0);return bounds}var methods={group:group,symbol:symbol,image:image,rect:rect,rule:rule,arc:arc,text:text,path:path,area:area,line:line};function itemBounds(item,func,opt){func=func||methods[item.mark.marktype];if(!item.bounds_prev)item[\"bounds:prev\"]=new vg.Bounds;var b=item.bounds,pb=item[\"bounds:prev\"];if(b)pb.clear().union(b);item.bounds=func(item,b?b.clear():new vg.Bounds,opt);if(!b)pb.clear().union(item.bounds);return item.bounds}function markBounds(mark,bounds,opt){bounds=bounds||mark.bounds&&mark.bounds.clear()||new vg.Bounds;var type=mark.marktype,func=methods[type],items=mark.items,item,i,len;if(type===\"area\"||type===\"line\"){if(items.length){items[0].bounds=func(items[0],bounds)}}else{for(i=0,len=items.length;i<len;++i){bounds.union(itemBounds(items[i],func,opt))}}mark.bounds=bounds}return{mark:markBounds,item:itemBounds,text:text,group:group}}();vg.scene.encode=function(){var GROUP=vg.scene.GROUP,ENTER=vg.scene.ENTER,UPDATE=vg.scene.UPDATE,EXIT=vg.scene.EXIT,EMPTY={};function main(scene,def,trans,request,items){request&&items?update.call(this,scene,def,trans,request,items):encode.call(this,scene,scene,def,trans,request);return scene}function update(scene,def,trans,request,items){items=vg.array(items);var i,len,item,group,props,prop;for(i=0,len=items.length;i<len;++i){item=items[i];group=item.mark.group||null;props=item.mark.def.properties;prop=props&&props[request];if(prop){prop.call(vg,item,group,trans);vg.scene.bounds.item(item)}}}function encode(group,scene,def,trans,request){encodeItems.call(this,group,scene.items,def,trans,request);if(scene.marktype===GROUP){encodeGroup.call(this,scene,def,group,trans,request)}else{vg.scene.bounds.mark(scene)}}function encodeLegend(group,scene,def,trans,request){encodeGroup.call(this,scene,def,group,trans,request);encodeItems.call(this,group,scene.items,def,trans,request);vg.scene.bounds.mark(scene,null,true)}function encodeGroup(scene,def,parent,trans,request){var i,len,m,mlen,group,scales,axes,axisItems,axisDef,leg,legItems,legDef;for(i=0,len=scene.items.length;i<len;++i){group=scene.items[i];scales=group.scales||(group.scales=def.scales?vg.extend({},parent.scales):parent.scales);if(def.scales){vg.parse.scales(def.scales,scales,this._data,group)}if(def.axes){axes=group.axes||(group.axes=[]);axisItems=group.axisItems||(group.axisItems=[]);vg.parse.axes(def.axes,axes,group.scales);axes.forEach(function(a,i){axisDef=a.def();axisItems[i]=vg.scene.build(axisDef,this._data,axisItems[i],null,1);axisItems[i].group=group;encode.call(this,group,group.axisItems[i],axisDef,trans)})}for(m=0,mlen=group.items.length;m<mlen;++m){encode.call(this,group,group.items[m],def.marks[m],trans,request)}}vg.scene.bounds.mark(scene,null,!def.legends);if(def.legends){for(i=0,len=scene.items.length;i<len;++i){group=scene.items[i];leg=group.legends||(group.legends=[]);legItems=group.legendItems||(group.legendItems=[]);vg.parse.legends(def.legends,leg,group.scales);leg.forEach(function(l,i){legDef=l.def();legItems[i]=vg.scene.build(legDef,this._data,legItems[i],null,1);legItems[i].group=group;encodeLegend.call(this,group,group.legendItems[i],legDef,trans)})}vg.scene.bounds.mark(scene,null,true)}}function encodeItems(group,items,def,trans,request){var props=def.properties||EMPTY,enter=props.enter,update=props.update,exit=props.exit,i,len,item,prop;if(request){if(prop=props[request]){for(i=0,len=items.length;i<len;++i){prop.call(vg,items[i],group,trans)}}return}for(i=0;i<items.length;++i){item=items[i];if(item.status===ENTER){if(enter)enter.call(vg,item,group);item.status=UPDATE}if(item.status!==EXIT&&update){update.call(vg,item,group,trans)}if(item.status===EXIT){if(exit)exit.call(vg,item,group,trans);if(trans&&!exit)trans.interpolate(item,EMPTY);else if(!trans)items[i--].remove()}}}return main}();vg.scene.Transition=function(){function trans(duration,ease){this.duration=duration||500;this.ease=ease&&d3.ease(ease)||d3.ease(\"cubic-in-out\");this.updates={next:null}}var prototype=trans.prototype;var skip={text:1,url:1};prototype.interpolate=function(item,values){var key,curr,next,interp,list=null;for(key in values){curr=item[key];next=values[key];if(curr!==next){if(skip[key]||curr===undefined){item[key]=next}else if(typeof curr===\"number\"&&!isFinite(curr)){item[key]=next}else{interp=d3.interpolate(curr,next);interp.property=key;(list||(list=[])).push(interp)}}}if(list===null&&item.status===vg.scene.EXIT){list=[]}if(list!=null){list.item=item;list.ease=item.mark.ease||this.ease;list.next=this.updates.next;this.updates.next=list}return this};prototype.start=function(callback){var t=this,prev=t.updates,curr=prev.next;for(;curr!=null;prev=curr,curr=prev.next){if(curr.item.status===vg.scene.EXIT)curr.remove=true\n}t.callback=callback;d3.timer(function(elapsed){return step.call(t,elapsed)})};function step(elapsed){var list=this.updates,prev=list,curr=prev.next,duration=this.duration,item,delay,f,e,i,n,stop=true;for(;curr!=null;prev=curr,curr=prev.next){item=curr.item;delay=item.delay||0;f=(elapsed-delay)/duration;if(f<0){stop=false;continue}if(f>1)f=1;e=curr.ease(f);for(i=0,n=curr.length;i<n;++i){item[curr[i].property]=curr[i](e)}item.touch();vg.scene.bounds.item(item);if(f===1){if(curr.remove)item.remove();prev.next=curr.next;curr=prev}else{stop=false}}this.callback();return stop}return trans}();vg.scene.transition=function(dur,ease){return new vg.scene.Transition(dur,ease)};vg.scene.axis=function(){var scale,orient=vg.config.axis.orient,offset=0,titleOffset=vg.config.axis.titleOffset,axisDef=null,layer=\"front\",grid=false,title=null,tickMajorSize=vg.config.axis.tickSize,tickMinorSize=vg.config.axis.tickSize,tickEndSize=vg.config.axis.tickSize,tickPadding=vg.config.axis.padding,tickValues=null,tickFormatString=null,tickFormat=null,tickSubdivide=0,tickArguments=[vg.config.axis.ticks],gridLineStyle={},tickLabelStyle={},majorTickStyle={},minorTickStyle={},titleStyle={},domainStyle={};var axis={};function reset(){axisDef=null}axis.def=function(){var def=axisDef?axisDef:axisDef=axis_def(scale);tickFormat=!tickFormatString?null:scale.type===\"time\"?d3.time.format(tickFormatString):d3.format(tickFormatString);var major=tickValues==null?scale.ticks?scale.ticks.apply(scale,tickArguments):scale.domain():tickValues;var minor=vg_axisSubdivide(scale,major,tickSubdivide).map(vg.data.ingest);major=major.map(vg.data.ingest);var fmt=tickFormat==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:tickFormat;major.forEach(function(d){d.label=fmt(d.data)});var tdata=title?[title].map(vg.data.ingest):[];def.marks[0].from=function(){return grid?major:[]};def.marks[1].from=function(){return major};def.marks[2].from=function(){return minor};def.marks[3].from=def.marks[1].from;def.marks[4].from=function(){return[1]};def.marks[5].from=function(){return tdata};def.offset=offset;def.orient=orient;def.layer=layer;return def};function axis_def(scale){var newScale,oldScale,range;if(scale.type===\"ordinal\"){newScale={scale:scale.scaleName,offset:.5+scale.rangeBand()/2};oldScale=newScale}else{newScale={scale:scale.scaleName,offset:.5};oldScale={scale:scale.scaleName+\":prev\",offset:.5}}range=vg_axisScaleRange(scale);var gridLines=vg_axisTicks();var majorTicks=vg_axisTicks();var minorTicks=vg_axisTicks();var tickLabels=vg_axisTickLabels();var domain=vg_axisDomain();var title=vg_axisTitle();gridLines.properties.enter.stroke={value:vg.config.axis.gridColor};vg_axisTicksExtend(orient,gridLines,oldScale,newScale,Infinity);vg_axisTicksExtend(orient,majorTicks,oldScale,newScale,tickMajorSize);vg_axisTicksExtend(orient,minorTicks,oldScale,newScale,tickMinorSize);vg_axisLabelExtend(orient,tickLabels,oldScale,newScale,tickMajorSize,tickPadding);vg_axisDomainExtend(orient,domain,range,tickEndSize);vg_axisTitleExtend(orient,title,range,titleOffset);vg.extend(gridLines.properties.update,gridLineStyle);vg.extend(majorTicks.properties.update,majorTickStyle);vg.extend(minorTicks.properties.update,minorTickStyle);vg.extend(tickLabels.properties.update,tickLabelStyle);vg.extend(domain.properties.update,domainStyle);vg.extend(title.properties.update,titleStyle);var marks=[gridLines,majorTicks,minorTicks,tickLabels,domain,title];return{type:\"group\",interactive:false,properties:{enter:vg_axisUpdate,update:vg_axisUpdate},marks:marks.map(vg.parse.mark)}}axis.scale=function(x){if(!arguments.length)return scale;if(scale!==x){scale=x;reset()}return axis};axis.orient=function(x){if(!arguments.length)return orient;if(orient!==x){orient=x in vg_axisOrients?x+\"\":vg.config.axis.orient;reset()}return axis};axis.title=function(x){if(!arguments.length)return title;if(title!==x){title=x;reset()}return axis};axis.ticks=function(){if(!arguments.length)return tickArguments;tickArguments=arguments;return axis};axis.tickValues=function(x){if(!arguments.length)return tickValues;tickValues=x;return axis};axis.tickFormat=function(x){if(!arguments.length)return tickFormatString;if(tickFormatString!==x){tickFormatString=x;reset()}return axis};axis.tickSize=function(x,y){if(!arguments.length)return tickMajorSize;var n=arguments.length-1,major=+x,minor=n>1?+y:tickMajorSize,end=n>0?+arguments[n]:tickMajorSize;if(tickMajorSize!==major||tickMinorSize!==minor||tickEndSize!==end){reset()}tickMajorSize=major;tickMinorSize=minor;tickEndSize=end;return axis};axis.tickSubdivide=function(x){if(!arguments.length)return tickSubdivide;tickSubdivide=+x;return axis};axis.offset=function(x){if(!arguments.length)return offset;offset=vg.isObject(x)?x:+x;return axis};axis.tickPadding=function(x){if(!arguments.length)return tickPadding;if(tickPadding!==+x){tickPadding=+x;reset()}return axis};axis.titleOffset=function(x){if(!arguments.length)return titleOffset;if(titleOffset!==+x){titleOffset=+x;reset()}return axis};axis.layer=function(x){if(!arguments.length)return layer;if(layer!==x){layer=x;reset()}return axis};axis.grid=function(x){if(!arguments.length)return grid;if(grid!==x){grid=x;reset()}return axis};axis.gridLineProperties=function(x){if(!arguments.length)return gridLineStyle;if(gridLineStyle!==x){gridLineStyle=x}return axis};axis.majorTickProperties=function(x){if(!arguments.length)return majorTickStyle;if(majorTickStyle!==x){majorTickStyle=x}return axis};axis.minorTickProperties=function(x){if(!arguments.length)return minorTickStyle;if(minorTickStyle!==x){minorTickStyle=x}return axis};axis.tickLabelProperties=function(x){if(!arguments.length)return tickLabelStyle;if(tickLabelStyle!==x){tickLabelStyle=x}return axis};axis.titleProperties=function(x){if(!arguments.length)return titleStyle;if(titleStyle!==x){titleStyle=x}return axis};axis.domainProperties=function(x){if(!arguments.length)return domainStyle;if(domainStyle!==x){domainStyle=x}return axis};axis.reset=function(){reset()};return axis};var vg_axisOrients={top:1,right:1,bottom:1,left:1};function vg_axisSubdivide(scale,ticks,m){subticks=[];if(m&&ticks.length>1){var extent=vg_axisScaleExtent(scale.domain()),subticks,i=-1,n=ticks.length,d=(ticks[1]-ticks[0])/++m,j,v;while(++i<n){for(j=m;--j>0;){if((v=+ticks[i]-j*d)>=extent[0]){subticks.push(v)}}}for(--i,j=0;++j<m&&(v=+ticks[i]+j*d)<extent[1];){subticks.push(v)}}return subticks}function vg_axisScaleExtent(domain){var start=domain[0],stop=domain[domain.length-1];return start<stop?[start,stop]:[stop,start]}function vg_axisScaleRange(scale){return scale.rangeExtent?scale.rangeExtent():vg_axisScaleExtent(scale.range())}var vg_axisAlign={bottom:\"center\",top:\"center\",left:\"right\",right:\"left\"};var vg_axisBaseline={bottom:\"top\",top:\"bottom\",left:\"middle\",right:\"middle\"};function vg_axisLabelExtend(orient,labels,oldScale,newScale,size,pad){size=Math.max(size,0)+pad;if(orient===\"left\"||orient===\"top\"){size*=-1}if(orient===\"top\"||orient===\"bottom\"){vg.extend(labels.properties.enter,{x:oldScale,y:{value:size}});vg.extend(labels.properties.update,{x:newScale,y:{value:size},align:{value:\"center\"},baseline:{value:vg_axisBaseline[orient]}})}else{vg.extend(labels.properties.enter,{x:{value:size},y:oldScale});vg.extend(labels.properties.update,{x:{value:size},y:newScale,align:{value:vg_axisAlign[orient]},baseline:{value:\"middle\"}})}}function vg_axisTicksExtend(orient,ticks,oldScale,newScale,size){var sign=orient===\"left\"||orient===\"top\"?-1:1;if(size===Infinity){size=orient===\"top\"||orient===\"bottom\"?{group:\"mark.group.height\",mult:-sign}:{group:\"mark.group.width\",mult:-sign}}else{size={value:sign*size}}if(orient===\"top\"||orient===\"bottom\"){vg.extend(ticks.properties.enter,{x:oldScale,y:{value:0},y2:size});vg.extend(ticks.properties.update,{x:newScale,y:{value:0},y2:size});vg.extend(ticks.properties.exit,{x:newScale})}else{vg.extend(ticks.properties.enter,{x:{value:0},x2:size,y:oldScale});vg.extend(ticks.properties.update,{x:{value:0},x2:size,y:newScale});vg.extend(ticks.properties.exit,{y:newScale})}}function vg_axisTitleExtend(orient,title,range,offset){var mid=~~((range[0]+range[1])/2),sign=orient===\"top\"||orient===\"left\"?-1:1;if(orient===\"bottom\"||orient===\"top\"){vg.extend(title.properties.update,{x:{value:mid},y:{value:sign*offset},angle:{value:0}})}else{vg.extend(title.properties.update,{x:{value:sign*offset},y:{value:mid},angle:{value:-90}})}}function vg_axisDomainExtend(orient,domain,range,size){var path;if(orient===\"top\"||orient===\"left\"){size=-1*size}if(orient===\"bottom\"||orient===\"top\"){path=\"M\"+range[0]+\",\"+size+\"V0H\"+range[1]+\"V\"+size}else{path=\"M\"+size+\",\"+range[0]+\"H0V\"+range[1]+\"H\"+size}domain.properties.update.path={value:path}}function vg_axisUpdate(item,group,trans){var o=trans?{}:item,offset=item.mark.def.offset,orient=item.mark.def.orient,width=group.width,height=group.height;if(vg.isArray(offset)){var ofx=offset[0],ofy=offset[1];switch(orient){case\"left\":{o.x=-ofx;o.y=ofy;break}case\"right\":{o.x=width+ofx;o.y=ofy;break}case\"bottom\":{o.x=ofx;o.y=height+ofy;break}case\"top\":{o.x=ofx;o.y=-ofy;break}default:{o.x=ofx;o.y=ofy}}}else{if(vg.isObject(offset)){offset=-group.scales[offset.scale](offset.value)}switch(orient){case\"left\":{o.x=-offset;o.y=0;break}case\"right\":{o.x=width+offset;o.y=0;break}case\"bottom\":{o.x=0;o.y=height+offset;break}case\"top\":{o.x=0;o.y=-offset;break}default:{o.x=0;o.y=0}}}if(trans)trans.interpolate(item,o)}function vg_axisTicks(){return{type:\"rule\",interactive:false,key:\"data\",properties:{enter:{stroke:{value:vg.config.axis.tickColor},strokeWidth:{value:vg.config.axis.tickWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_axisTickLabels(){return{type:\"text\",interactive:true,key:\"data\",properties:{enter:{fill:{value:vg.config.axis.tickLabelColor},font:{value:vg.config.axis.tickLabelFont},fontSize:{value:vg.config.axis.tickLabelFontSize},opacity:{value:1e-6},text:{field:\"label\"}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_axisTitle(){return{type:\"text\",interactive:true,properties:{enter:{font:{value:vg.config.axis.titleFont},fontSize:{value:vg.config.axis.titleFontSize},fontWeight:{value:vg.config.axis.titleFontWeight},fill:{value:vg.config.axis.titleColor},align:{value:\"center\"},baseline:{value:\"middle\"},text:{field:\"data\"}},update:{}}}}function vg_axisDomain(){return{type:\"path\",interactive:false,properties:{enter:{x:{value:.5},y:{value:.5},stroke:{value:vg.config.axis.axisColor},strokeWidth:{value:vg.config.axis.axisWidth}},update:{}}}}vg.scene.legend=function(){var size=null,shape=null,fill=null,stroke=null,spacing=null,values=null,format=null,formatString=null,title=undefined,orient=\"right\",offset=vg.config.legend.offset,padding=vg.config.legend.padding,legendDef,tickArguments=[5],legendStyle={},symbolStyle={},gradientStyle={},titleStyle={},labelStyle={};var legend={},legendDef=null;function reset(){legendDef=null}legend.def=function(){var scale=size||shape||fill||stroke;format=!formatString?null:scale.type===\"time\"?d3.time.format(formatString):d3.format(formatString);if(!legendDef){legendDef=(scale===fill||scale===stroke)&&!discrete(scale.type)?quantDef(scale):ordinalDef(scale)}legendDef.orient=orient;legendDef.offset=offset;legendDef.padding=padding;return legendDef};function discrete(type){return type===\"ordinal\"||type===\"quantize\"||type===\"quantile\"||type===\"threshold\"}function ordinalDef(scale){var def=o_legend_def(size,shape,fill,stroke);var data=(values==null?scale.ticks?scale.ticks.apply(scale,tickArguments):scale.domain():values).map(vg.data.ingest);var fmt=format==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:format;var fs,range,offset,pad=5,domain=d3.range(data.length);if(size){range=data.map(function(x){return Math.sqrt(size(x.data))});offset=d3.max(range);range=range.reduce(function(a,b,i,z){if(i>0)a[i]=a[i-1]+z[i-1]/2+pad;return a[i]+=b/2,a},[0]).map(Math.round)}else{offset=Math.round(Math.sqrt(vg.config.legend.symbolSize));range=spacing||(fs=labelStyle.fontSize)&&fs.value+pad||vg.config.legend.labelFontSize+pad;range=domain.map(function(d,i){return Math.round(offset/2+i*range)})}var sz=padding,ts;if(title){ts=titleStyle.fontSize;sz+=5+(ts&&ts.value||vg.config.legend.titleFontSize)}for(var i=0,n=range.length;i<n;++i)range[i]+=sz;var scale={name:\"legend\",type:\"ordinal\",points:true,domain:domain,range:range};var tdata=(title?[title]:[]).map(vg.data.ingest);data.forEach(function(d){d.label=fmt(d.data);d.offset=offset});def.scales=[scale];def.marks[0].from=function(){return tdata};def.marks[1].from=function(){return data};def.marks[2].from=def.marks[1].from;return def}function o_legend_def(size,shape,fill,stroke){var titles=vg_legendTitle(),symbols=vg_legendSymbols(),labels=vg_vLegendLabels();vg_legendSymbolExtend(symbols,size,shape,fill,stroke);vg.extend(titles.properties.update,titleStyle);vg.extend(symbols.properties.update,symbolStyle);vg.extend(labels.properties.update,labelStyle);titles.properties.enter.x.value+=padding;titles.properties.enter.y.value+=padding;labels.properties.enter.x.offset+=padding+1;symbols.properties.enter.x.offset=padding+1;labels.properties.update.x.offset+=padding+1;symbols.properties.update.x.offset=padding+1;return{type:\"group\",interactive:false,properties:{enter:vg.parse.properties(\"group\",legendStyle),update:vg_legendUpdate},marks:[titles,symbols,labels].map(vg.parse.mark)}}function quantDef(scale){var def=q_legend_def(scale),dom=scale.domain(),data=dom.map(vg.data.ingest),width=gradientStyle.width&&gradientStyle.width.value||vg.config.legend.gradientWidth,fmt=format==null?scale.tickFormat?scale.tickFormat.apply(scale,tickArguments):String:format;var layout={name:\"legend\",type:scale.type,round:true,zero:false,domain:[dom[0],dom[dom.length-1]],range:[padding,width+padding]};if(scale.type===\"pow\")layout.exponent=scale.exponent();var tdata=(title?[title]:[]).map(vg.data.ingest);data.forEach(function(d,i){d.label=fmt(d.data);d.align=i==data.length-1?\"right\":i==0?\"left\":\"center\"});def.scales=[layout];def.marks[0].from=function(){return tdata};def.marks[1].from=function(){return[1]};def.marks[2].from=function(){return data};return def}function q_legend_def(scale){var titles=vg_legendTitle(),gradient=vg_legendGradient(),labels=vg_hLegendLabels(),grad=new vg.Gradient;var dom=scale.domain(),min=dom[0],max=dom[dom.length-1],f=scale.copy().domain([min,max]).range([0,1]);var stops=scale.type!==\"linear\"&&scale.ticks?scale.ticks.call(scale,15):dom;if(min!==stops[0])stops.unshift(min);if(max!==stops[stops.length-1])stops.push(max);for(var i=0,n=stops.length;i<n;++i){grad.stop(f(stops[i]),scale(stops[i]))}gradient.properties.enter.fill={value:grad};vg.extend(titles.properties.update,titleStyle);vg.extend(gradient.properties.update,gradientStyle);vg.extend(labels.properties.update,labelStyle);var gp=gradient.properties,gh=gradientStyle.height,hh=gh&&gh.value||gp.enter.height.value;labels.properties.enter.y.value=hh;labels.properties.update.y.value=hh;if(title){var tp=titles.properties,fs=titleStyle.fontSize,sz=4+(fs&&fs.value||tp.enter.fontSize.value);gradient.properties.enter.y.value+=sz;labels.properties.enter.y.value+=sz;gradient.properties.update.y.value+=sz;labels.properties.update.y.value+=sz}titles.properties.enter.x.value+=padding;titles.properties.enter.y.value+=padding;gradient.properties.enter.x.value+=padding;gradient.properties.enter.y.value+=padding;labels.properties.enter.y.value+=padding;gradient.properties.update.x.value+=padding;gradient.properties.update.y.value+=padding;labels.properties.update.y.value+=padding;return{type:\"group\",interactive:false,properties:{enter:vg.parse.properties(\"group\",legendStyle),update:vg_legendUpdate},marks:[titles,gradient,labels].map(vg.parse.mark)}}legend.size=function(x){if(!arguments.length)return size;if(size!==x){size=x;reset()}return legend};legend.shape=function(x){if(!arguments.length)return shape;if(shape!==x){shape=x;reset()}return legend};legend.fill=function(x){if(!arguments.length)return fill;if(fill!==x){fill=x;reset()}return legend};legend.stroke=function(x){if(!arguments.length)return stroke;if(stroke!==x){stroke=x;reset()}return legend};legend.title=function(x){if(!arguments.length)return title;if(title!==x){title=x;reset()}return legend};legend.format=function(x){if(!arguments.length)return formatString;if(formatString!==x){formatString=x;reset()}return legend};legend.spacing=function(x){if(!arguments.length)return spacing;if(spacing!==+x){spacing=+x;reset()}return legend};legend.orient=function(x){if(!arguments.length)return orient;orient=x in vg_legendOrients?x+\"\":vg.config.legend.orient;return legend};legend.offset=function(x){if(!arguments.length)return offset;offset=+x;return legend};legend.values=function(x){if(!arguments.length)return values;values=x;return legend};legend.legendProperties=function(x){if(!arguments.length)return legendStyle;legendStyle=x;return legend};legend.symbolProperties=function(x){if(!arguments.length)return symbolStyle;symbolStyle=x;return legend};legend.gradientProperties=function(x){if(!arguments.length)return gradientStyle;gradientStyle=x;return legend};legend.labelProperties=function(x){if(!arguments.length)return labelStyle;labelStyle=x;return legend};legend.titleProperties=function(x){if(!arguments.length)return titleStyle;titleStyle=x;return legend};legend.reset=function(){reset()};return legend};var vg_legendOrients={right:1,left:1};function vg_legendUpdate(item,group,trans){var o=trans?{}:item,gx,offset=item.mark.def.offset,orient=item.mark.def.orient,pad=item.mark.def.padding*2,lw=~~item.bounds.width()+(item.width?0:pad),lh=~~item.bounds.height()+(item.height?0:pad);o.x=.5;o.y=.5;o.width=lw;o.height=lh;if(!trans&&group.bounds){group.bounds.delta=group.bounds.x2-group.width}switch(orient){case\"left\":{gx=group.bounds?group.bounds.x1:0;o.x+=gx-offset-lw;break};case\"right\":{gx=group.width;if(group.bounds)gx=trans?group.width+group.bounds.delta:group.bounds.x2;o.x+=gx+offset;break}}if(trans)trans.interpolate(item,o);item.mark.def.properties.enter(item,group,trans)}function vg_legendSymbolExtend(mark,size,shape,fill,stroke){var e=mark.properties.enter,u=mark.properties.update;if(size)e.size=u.size={scale:size.scaleName,field:\"data\"};if(shape)e.shape=u.shape={scale:shape.scaleName,field:\"data\"};if(fill)e.fill=u.fill={scale:fill.scaleName,field:\"data\"};if(stroke)e.stroke=u.stroke={scale:stroke.scaleName,field:\"data\"}}function vg_legendTitle(){var cfg=vg.config.legend;return{type:\"text\",interactive:false,key:\"data\",properties:{enter:{x:{value:0},y:{value:0},fill:{value:cfg.titleColor},font:{value:cfg.titleFont},fontSize:{value:cfg.titleFontSize},fontWeight:{value:cfg.titleFontWeight},baseline:{value:\"top\"},text:{field:\"data\"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1}}}}}function vg_legendSymbols(){var cfg=vg.config.legend;return{type:\"symbol\",interactive:false,key:\"data\",properties:{enter:{x:{field:\"offset\",mult:.5},y:{scale:\"legend\",field:\"index\"},shape:{value:cfg.symbolShape},size:{value:cfg.symbolSize},stroke:{value:cfg.symbolColor},strokeWidth:{value:cfg.symbolStrokeWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{field:\"offset\",mult:.5},y:{scale:\"legend\",field:\"index\"},opacity:{value:1}}}}}function vg_vLegendLabels(){var cfg=vg.config.legend;return{type:\"text\",interactive:false,key:\"data\",properties:{enter:{x:{field:\"offset\",offset:5},y:{scale:\"legend\",field:\"index\"},fill:{value:cfg.labelColor},font:{value:cfg.labelFont},fontSize:{value:cfg.labelFontSize},align:{value:cfg.labelAlign},baseline:{value:cfg.labelBaseline},text:{field:\"label\"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{opacity:{value:1},x:{field:\"offset\",offset:5},y:{scale:\"legend\",field:\"index\"}}}}}function vg_legendGradient(){var cfg=vg.config.legend;return{type:\"rect\",interactive:false,properties:{enter:{x:{value:0},y:{value:0},width:{value:cfg.gradientWidth},height:{value:cfg.gradientHeight},stroke:{value:cfg.gradientStrokeColor},strokeWidth:{value:cfg.gradientStrokeWidth},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{value:0},y:{value:0},opacity:{value:1}}}}}function vg_hLegendLabels(){var cfg=vg.config.legend;return{type:\"text\",interactive:false,key:\"data\",properties:{enter:{x:{scale:\"legend\",field:\"data\"},y:{value:20},dy:{value:2},fill:{value:cfg.labelColor},font:{value:cfg.labelFont},fontSize:{value:cfg.labelFontSize},align:{field:\"align\"},baseline:{value:\"top\"},text:{field:\"label\"},opacity:{value:1e-6}},exit:{opacity:{value:1e-6}},update:{x:{scale:\"legend\",field:\"data\"},y:{value:20},opacity:{value:1}}}}}vg.Model=function(){function model(){this._defs=null;this._data={};this._scene=null;this._reset={axes:false,legends:false}}var prototype=model.prototype;prototype.defs=function(defs){if(!arguments.length)return this._defs;this._defs=defs;return this};prototype.data=function(data){if(!arguments.length)return this._data;var deps={},defs=this._defs,src=defs.data.source,tx=defs.data.flow||{},keys=defs.data.sorted,len=keys.length,i,k,x;function sources(k){(src[k]||[]).forEach(function(s){deps[s]=k;sources(s)})}vg.keys(data).forEach(sources);for(i=0;i<len;++i){if(data[k=keys[i]]){x=data[k]}else if(deps[k]){x=vg_data_duplicate(this._data[deps[k]]);if(vg.isTree(data))vg_make_tree(x)}else continue;this._data[k]=tx[k]?tx[k](x,this._data,defs.marks):x}this._reset.legends=true;return this};prototype.width=function(width){if(this._defs)this._defs.width=width;if(this._defs&&this._defs.marks)this._defs.marks.width=width;if(this._scene)this._scene.items[0].width=width;this._reset.axes=true;return this};prototype.height=function(height){if(this._defs)this._defs.height=height;if(this._defs&&this._defs.marks)this._defs.marks.height=height;if(this._scene)this._scene.items[0].height=height;this._reset.axes=true;return this};prototype.scene=function(node){if(!arguments.length)return this._scene;this._scene=node;return this};prototype.build=function(){var m=this,data=m._data,marks=m._defs.marks;m._scene=vg.scene.build.call(m,marks,data,m._scene);m._scene.items[0].width=marks.width;m._scene.items[0].height=marks.height;m._scene.interactive=false;return this};prototype.encode=function(trans,request,item){this.reset();var m=this,scene=m._scene,defs=m._defs;vg.scene.encode.call(m,scene,defs.marks,trans,request,item);return this};prototype.reset=function(){if(this._scene&&this._reset.axes){vg.scene.visit(this._scene,function(item){if(item.axes)item.axes.forEach(function(axis){axis.reset()})});this._reset.axes=false}if(this._scene&&this._reset.legends){vg.scene.visit(this._scene,function(item){if(item.legends)item.legends.forEach(function(l){l.reset()})});this._reset.legends=false}return this};return model}();vg.View=function(){var view=function(el,width,height){this._el=null;this._build=false;this._model=new vg.Model;this._width=this.__width=width||500;this._height=this.__height=height||500;this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._viewport=null;this._renderer=null;this._handler=null;this._io=vg.canvas;if(el)this.initialize(el)};var prototype=view.prototype;prototype.width=function(width){if(!arguments.length)return this.__width;if(this.__width!==width){this._width=this.__width=width;if(this._el)this.initialize(this._el.parentNode);this._model.width(width);if(this._strict)this._autopad=1}return this};prototype.height=function(height){if(!arguments.length)return this.__height;if(this.__height!==height){this._height=this.__height=height;if(this._el)this.initialize(this._el.parentNode);this._model.height(this._height);if(this._strict)this._autopad=1}return this};prototype.padding=function(pad){if(!arguments.length)return this._padding;if(this._padding!==pad){if(vg.isString(pad)){this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._strict=pad===\"strict\"}else{this._autopad=0;this._padding=pad;this._strict=false}if(this._el){this._renderer.resize(this._width,this._height,pad);this._handler.padding(pad)}}return this};prototype.autopad=function(opt){if(this._autopad<1)return this;else this._autopad=0;var pad=this._padding,b=this.model().scene().bounds,inset=vg.config.autopadInset,l=b.x1<0?Math.ceil(-b.x1)+inset:0,t=b.y1<0?Math.ceil(-b.y1)+inset:0,r=b.x2>this._width?Math.ceil(+b.x2-this._width)+inset:0,b=b.y2>this._height?Math.ceil(+b.y2-this._height)+inset:0;pad={left:l,top:t,right:r,bottom:b};if(this._strict){this._autopad=0;this._padding=pad;this._width=Math.max(0,this.__width-(l+r));this._height=Math.max(0,this.__height-(t+b));this._model.width(this._width);this._model.height(this._height);if(this._el)this.initialize(this._el.parentNode);this.update({props:\"enter\"}).update({props:\"update\"})}else{this.padding(pad).update(opt)}return this};prototype.viewport=function(size){if(!arguments.length)return this._viewport;if(this._viewport!==size){this._viewport=size;if(this._el)this.initialize(this._el.parentNode)}return this};prototype.renderer=function(type){if(!arguments.length)return this._io;if(type===\"canvas\")type=vg.canvas;if(type===\"svg\")type=vg.svg;if(this._io!==type){this._io=type;this._renderer=null;if(this._el)this.initialize(this._el.parentNode);if(this._build)this.render()}return this};prototype.defs=function(defs){if(!arguments.length)return this._model.defs();this._model.defs(defs);return this};prototype.data=function(data){if(!arguments.length)return this._model.data();var ingest=vg.keys(data).reduce(function(d,k){return d[k]=vg.data.ingestAll(data[k]),d},{});this._model.data(ingest);this._build=false;return this};prototype.model=function(model){if(!arguments.length)return this._model;if(this._model!==model){this._model=model;if(this._handler)this._handler.model(model)}return this};prototype.initialize=function(el){var v=this,prevHandler,w=v._width,h=v._height,pad=v._padding;d3.select(el).select(\"div.vega\").remove();this._el=el=d3.select(el).append(\"div\").attr(\"class\",\"vega\").style(\"position\",\"relative\").node();if(v._viewport){d3.select(el).style(\"width\",(v._viewport[0]||w)+\"px\").style(\"height\",(v._viewport[1]||h)+\"px\").style(\"overflow\",\"auto\")}v._renderer=(v._renderer||new this._io.Renderer).initialize(el,w,h,pad);prevHandler=v._handler;v._handler=(new this._io.Handler).initialize(el,pad,v).model(v._model);if(prevHandler){prevHandler.handlers().forEach(function(h){v._handler.on(h.type,h.handler)})}return this};prototype.render=function(items){this._renderer.render(this._model.scene(),items);return this};prototype.on=function(){this._handler.on.apply(this._handler,arguments);return this};prototype.off=function(){this._handler.off.apply(this._handler,arguments);return this};prototype.update=function(opt){opt=opt||{};var view=this,trans=opt.duration?vg.scene.transition(opt.duration,opt.ease):null;view._build=view._build||(view._model.build(),true);view._model.encode(trans,opt.props,opt.items);if(trans){trans.start(function(items){view._renderer.render(view._model.scene(),items)})}else view.render(opt.items);return view.autopad(opt)};return view}();vg.ViewFactory=function(defs){return function(opt){opt=opt||{};var v=(new vg.View).width(defs.width).height(defs.height).padding(defs.padding).viewport(defs.viewport).renderer(opt.renderer||\"canvas\").defs(defs);if(defs.data.load)v.data(defs.data.load);if(opt.data)v.data(opt.data);if(opt.el)v.initialize(opt.el);if(opt.hover!==false){v.on(\"mouseover\",function(evt,item){if(item.hasPropertySet(\"hover\")){this.update({props:\"hover\",items:item})}}).on(\"mouseout\",function(evt,item){if(item.hasPropertySet(\"hover\")){this.update({props:\"update\",items:item})}})}return v}};vg.Spec=function(){var spec=function(s){this.spec={width:500,height:500,padding:0,data:[],scales:[],axes:[],marks:[]};if(s)vg.extend(this.spec,s)};var prototype=spec.prototype;prototype.width=function(w){this.spec.width=w;return this};prototype.height=function(h){this.spec.height=h;return this};prototype.padding=function(p){this.spec.padding=p;return this};prototype.viewport=function(v){this.spec.viewport=v;return this};prototype.data=function(name,params){if(!params)params=vg.isString(name)?{name:name}:name;else params.name=name;this.spec.data.push(params);return this};prototype.scale=function(name,params){if(!params)params=vg.isString(name)?{name:name}:name;else params.name=name;this.spec.scales.push(params);return this};prototype.axis=function(params){this.spec.axes.push(params);return this};prototype.mark=function(type,mark){if(!mark)mark={type:type};else mark.type=type;mark.properties={};this.spec.marks.push(mark);var that=this;return{from:function(name,obj){mark.from=obj?(obj.data=name,obj):vg.isString(name)?{data:name}:name;return this},prop:function(name,obj){mark.properties[name]=vg.keys(obj).reduce(function(o,k){var v=obj[k];return o[k]=vg.isObject(v)?v:{value:v},o},{});return this},done:function(){return that}}};prototype.parse=function(callback){vg.parse.spec(this.spec,callback)};prototype.json=function(){return this.spec};return spec}();vg.spec=function(s){return new vg.Spec(s)};vg.headless={};vg.headless.View=function(){var view=function(width,height,pad,type,vp){this._canvas=null;this._type=type;this._el=\"body\";this._build=false;this._model=new vg.Model;this._width=this.__width=width||500;this._height=this.__height=height||500;this._padding=pad||{top:0,left:0,bottom:0,right:0};this._autopad=vg.isString(this._padding)?1:0;this._renderer=new vg[type].Renderer;this._viewport=vp||null;this.initialize()};var prototype=view.prototype;prototype.el=function(el){if(!arguments.length)return this._el;if(this._el!==el){this._el=el;this.initialize()}return this};prototype.width=function(width){if(!arguments.length)return this._width;if(this._width!==width){this._width=width;this.initialize();this._model.width(width)}return this};prototype.height=function(height){if(!arguments.length)return this._height;if(this._height!==height){this._height=height;this.initialize();this._model.height(this._height)}return this};prototype.padding=function(pad){if(!arguments.length)return this._padding;if(this._padding!==pad){if(vg.isString(pad)){this._autopad=1;this._padding={top:0,left:0,bottom:0,right:0};this._strict=pad===\"strict\"}else{this._autopad=0;this._padding=pad;this._strict=false}this.initialize()}return this};prototype.autopad=function(opt){if(this._autopad<1)return this;else this._autopad=0;var pad=this._padding,b=this._model.scene().bounds,inset=vg.config.autopadInset,l=b.x1<0?Math.ceil(-b.x1)+inset:0,t=b.y1<0?Math.ceil(-b.y1)+inset:0,r=b.x2>this._width?Math.ceil(+b.x2-this._width)+inset:0,b=b.y2>this._height?Math.ceil(+b.y2-this._height)+inset:0;pad={left:l,top:t,right:r,bottom:b};if(this._strict){this._autopad=0;this._padding=pad;this._width=Math.max(0,this.__width-(l+r));this._height=Math.max(0,this.__height-(t+b));this._model.width(this._width);this._model.height(this._height);if(this._el)this.initialize();this.update({props:\"enter\"}).update({props:\"update\"})}else{this.padding(pad).update(opt)}return this};prototype.viewport=function(vp){if(!arguments.length)return _viewport;this._viewport=vp;this.initialize();return this};prototype.defs=function(defs){if(!arguments.length)return this._model.defs();this._model.defs(defs);return this};prototype.data=function(data){if(!arguments.length)return this._model.data();var ingest=vg.keys(data).reduce(function(d,k){return d[k]=vg.data.ingestAll(data[k]),d},{});this._model.data(ingest);this._build=false;return this};prototype.renderer=function(){return this._renderer};prototype.canvas=function(){return this._canvas};prototype.canvasAsync=function(callback){var r=this._renderer,view=this;function wait(){if(r.pendingImages()===0){view.render();callback(view._canvas)}else{setTimeout(wait,10)}}r.pendingImages()>0?wait():callback(this._canvas)};prototype.svg=function(){if(this._type!==\"svg\")return null;var p=this._padding,w=this._width+(p?p.left+p.right:0),h=this._height+(p?p.top+p.bottom:0);if(this._viewport){w=this._viewport[0]-(p?p.left+p.right:0);h=this._viewport[1]-(p?p.top+p.bottom:0)}var svg=d3.select(this._el).select(\"svg\").node().innerHTML.replace(/ href=/g,\" xlink:href=\");\nreturn\"<svg \"+'width=\"'+w+'\" '+'height=\"'+h+'\" '+vg.config.svgNamespace+\">\"+svg+\"</svg>\"};prototype.initialize=function(){var w=this._width,h=this._height,pad=this._padding;if(this._viewport){w=this._viewport[0]-(pad?pad.left+pad.right:0);h=this._viewport[1]-(pad?pad.top+pad.bottom:0)}if(this._type===\"svg\"){this.initSVG(w,h,pad)}else{this.initCanvas(w,h,pad)}return this};prototype.initCanvas=function(w,h,pad){var Canvas=require(\"canvas\"),tw=w+pad.left+pad.right,th=h+pad.top+pad.bottom,canvas=this._canvas=new Canvas(tw,th),ctx=canvas.getContext(\"2d\");ctx.setTransform(1,0,0,1,pad.left,pad.top);this._renderer.context(ctx);this._renderer.resize(w,h,pad)};prototype.initSVG=function(w,h,pad){var tw=w+pad.left+pad.right,th=h+pad.top+pad.bottom;this._renderer.initialize(this._el,w,h,pad)};prototype.render=function(items){this._renderer.render(this._model.scene(),items);return this};prototype.update=function(opt){opt=opt||{};var view=this;view._build=view._build||(view._model.build(),true);view._model.encode(null,opt.props,opt.items);view.render(opt.items);return view.autopad(opt)};return 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#999;\npadding: 4px;\nopacity: 0.85;\nbackground-color: #fff;\nbox-shadow: 2px 2px 6px #888888;\nwidth: 200px;\nmargin-left: -200px;\n}\nul.ggvis-dropdown li {\nlist-style-type: none;\npadding: 5px 2px;\n}\nul.ggvis-dropdown li a {\ncolor: #00f;\ntext-decoration: none;\npadding: 2px 2px;\ncursor: pointer;\n}\nul.ggvis-dropdown li a.inactive {\ncolor: black;\ncursor: initial;\n}\n</style>\n<script>/*jshint forin:true, noarg:true, noempty:true, eqeqeq:true, bitwise:true,\n    strict:false, undef:true, unused:true, browser:true, jquery:true, maxerr:50,\n    curly:false, multistr:true */\n/*global vg, ggvis:true, lodash*/\n\nggvis = (function(_) {\n  var ggvis = {\n    // Keep track of information about all plots: contains ggvis.Plot objects\n    plots: {}\n  };\n\n  // Get a ggvis.Plot object with a particular name, creating it if needed\n  ggvis.getPlot = function(plotId) {\n    if (!this.plots[plotId]) {\n      this.plots[plotId] = new ggvis.Plot(plotId);\n    }\n    return this.plots[plotId];\n  };\n\n  // Internal functions --------------------------------------------------\n\n  // Returns the value of a GET variable\n  function queryVar (name) {\n    return decodeURI(window.location.search.replace(\n      new RegExp(\"^(?:.*[&\\\\?]\" +\n                 encodeURI(name).replace(/[\\.\\+\\*]/g, \"\\\\$&\") +\n                 \"(?:\\\\=([^&]*))?)?.*$\", \"i\"),\n      \"$1\"));\n  }\n\n  function pixelRatio() {\n    if (window.devicePixelRatio) {\n      return window.devicePixelRatio;\n    } else {\n      return 1;\n    }\n  }\n\n  // Are we in a panel - an iframe or RStudio viewer pane?\n  function inPanel() {\n    return (queryVar(\"viewer_pane\") === \"1\" || queryVar(\"__subapp__\") === \"1\");\n  }\n\n  // Are we in a window for exporting the image?\n  function inExportPanel() {\n    return queryVar(\"viewer_export\") === \"1\";\n  }\n\n  // ggvis.CallbackRegistry class ----------------------------------------------\n  ggvis.CallbackRegistry = (function() {\n    // obj is an object to use as the context for the callbacks\n    var callbackregistry = function(obj) {\n      this._registry = {};\n      this._obj = obj || null;\n    };\n    // this._registry has a structure like:\n    // {\n    //   \"resize\": [\n    //     { \"type\": \"resize\", \"fn\": fn1 },\n    //     { \"type\": \"resize.foo\", \"fn\": fn2 },\n    //     { \"type\": \"resize.foo\", \"fn\": fn3 }\n    //   ]\n    //   \"click\": [\n    //     { \"type\": \"click\", \"fn\": fn1 }\n    //   ]\n    // }\n\n    var prototype = callbackregistry.prototype;\n\n    // Register a callback for an event.\n    // type can be a string like \"resize\" or \"resize.foo\". If there is a period,\n    // the part after the period is an id which can be used for for targeted\n    // removal.\n    prototype.on = function(type, fn) {\n      var event = eventName(type);\n      var reg = this._registry;\n\n      reg[event] = reg[event] || [];\n      reg[event].push({\n        type: type,\n        fn: fn\n      });\n    };\n\n    // Clear callbacks for a given event and (optional) id\n    prototype.off = function(type) {\n      var event = eventName(type);\n      // If there's no . in the type, remove all callbacks for this event\n      if (event === type) {\n        delete this._registry[event];\n        return;\n      }\n\n      var callbacks = this._registry[event];\n      if (!callbacks) return;\n\n      // Remove individual callbacks that match type\n      for (var i = callbacks.length-1; i >= 0; i--) {\n        if (callbacks[i].type === type) callbacks.splice(i, 1);\n      }\n    };\n\n    // Trigger callbacks for a named event. Any extra arguments are passed to\n    // the function.\n    prototype.trigger = function(event) {\n      var args = Array.prototype.slice.call(arguments, 1);\n      var callbacks = this._registry[event];\n      if (!callbacks) return;\n\n      for (var i = 0; i < callbacks.length; i++) {\n        callbacks[i].fn.apply(this._obj, args);\n      }\n    };\n\n    // Given a string, return the part before \".\"\n    function eventName(name) {\n      var i = name.indexOf(\".\");\n      if (i < 0) return name;\n      return name.substring(0, i);\n    }\n\n    return callbackregistry;\n  })(); // ggvis.CallbackRegistry\n\n\n  // ggvis.Plot class ----------------------------------------------------------\n  ggvis.Plot = (function() {\n\n    var Plot = function(plotId) {\n      this.plotId = plotId;\n      this.pendingData = {}; // Data objects that have been received but not yet used\n      this.chart = null;     // Vega chart object on the page\n      this.spec = null;      // Vega spec for this plot\n      this.initialized = false; // Has update() or enter() been run?\n      this.opts = {};\n      this.brush = new Plot.Brush(this);\n      this.handlers = [];    // Interaction input handlers\n      this._callbacks = new ggvis.CallbackRegistry(this);\n    };\n\n    var prototype = Plot.prototype;\n\n    // Wrappers for CallbackRegistry methods\n    prototype.on = function(type, fn) { this._callbacks.on(type, fn); };\n    prototype.off = function(type) { this._callbacks.off(type); };\n    prototype.trigger = function() {\n      this._callbacks.trigger.apply(this._callbacks, arguments);\n    };\n\n    // opts is an optional object which can have any entries that are in spec.opts\n    // (they get merged on top of spec.opts), and additionally:\n    // * handlers: An object where the keys are event names and the values are\n    //     functions. They are passed to Vega's view.on(key, value), which\n    //     registers the handler functions for the named event.\n    prototype.parseSpec = function(spec, opts) {\n      var self = this;\n      self.spec = spec;\n      // Merge options passed to this function into options from the spec\n      self.opts = $.extend(true, self.spec.ggvis_opts, opts);\n\n      vg.parse.spec(spec, function(chart) {\n        var opts = self.opts;\n\n        // If hover_duration is supplied, use that later in a custom callback,\n        // instead of the default hover behavior.\n        var default_hover = true;\n        if (opts.hover_duration && opts.hover_duration !== 0) {\n          default_hover = false;\n        }\n\n        self.initialized = false;\n\n        chart = chart({\n          el: \"#\" + self.plotId,\n          renderer: opts.renderer || \"svg\",\n          hover: default_hover\n        });\n        // Save the chart object\n        self.chart = chart;\n\n        // Set the renderer (update buttons and download link)\n        self.setRenderer(opts.renderer, false);\n\n        // If hover_duration is specified, set callbacks for hover behavior\n        if (opts.hover_duration && opts.hover_duration !== 0) {\n          chart.on(\"mouseover\", function(event, item) {\n            this.update({ props:\"hover\", items:item, duration:opts.hover_duration });\n          });\n          chart.on(\"mouseout\", function(event, item) {\n            this.update({ props:\"update\", items:item, duration:opts.hover_duration });\n          });\n        }\n\n        // If handlers are specified (typically for mouseover and out), add them.\n        if (opts.handlers) {\n          $.each(opts.handlers, function(eventname, fn) {\n            chart.on(eventname, fn);\n          });\n        }\n\n        self.removeHandlers();\n        self.addHandlers(self.spec.handlers);\n\n        self.brush.enable();\n\n        if (inExportPanel()) {\n          $('.plot-gear-icon').hide();\n        }\n\n        self.removeResizeListeners();\n\n        if (inPanel()) {\n          self.getWrapper().width(\"auto\");\n          self.enableAutoResizeToWindow();\n\n        } else {\n\n          var $wrap = self.getWrapper();\n          // If auto-width, simply set CSS width to auto. This doesn't work for\n          // height.\n          if (opts.width)  $wrap.width(opts.width);\n          if (opts.height) $wrap.height(opts.height);\n\n          if (opts.width === \"auto\" || opts.height === \"auto\") {\n            self.enableAutoResizeToWrapperParent();\n          } else if (opts.resizable) {\n            self.enableResizable();\n          }\n        }\n\n        // If the data arrived earlier, use it.\n        if (this.pendingData) self.loadPendingData();\n\n        if (self.dataReady()) self.initialUpdate();\n      });\n    };\n\n    // Get the div which wraps the svg or canvas object (created by vega).\n    prototype.getVegaDiv = function() {\n      // This is also known is this.getDiv().children(\".vega\")\n      return $(this.chart._el);\n    };\n\n    // Get the div which wraps the .vega div\n    prototype.getDiv = function() {\n      return $(\"#\" + this.plotId);\n    };\n\n    // Wrapper div, which includes sizing handle and gear\n    prototype.getWrapper = function() {\n      return this.getDiv().parent();\n    };\n\n    // Get the marks object (the Canvas or SVG object, which is rendered too)\n    prototype.getMarks = function() {\n      // Can't do this.getVegaDiv().children(\".marks\") because it doesn't work\n      // for SVG DOM objects. So we'll just grab any svg or canvas object.\n      return this.getVegaDiv().children(\"svg, canvas\");\n    };\n\n    // Get the width of the marks object\n    prototype.marksWidth = function() {\n      var $marks = this.getMarks();\n      // Can't use width() because it returns 0 for hidden DOM elements, and\n      // sometimes the ggvis objects can start hidden.\n      // getAttribute does work for hidden DOM elements, but for canvas, it\n      // needs to be scaled down by the pixel ratio.\n      var width = parseFloat($marks[0].getAttribute('width'));\n      if (this.renderer === 'canvas') {\n        width = width / pixelRatio();\n      }\n      return width;\n    };\n\n    prototype.marksHeight = function() {\n      var $marks = this.getMarks();\n      var height = parseFloat($marks[0].getAttribute('height'));\n      if (this.renderer === 'canvas') {\n        height = height / pixelRatio();\n      }\n      return height;\n    };\n\n    prototype.resizeEventNamespace = function() {\n      return this.plotId + '_ggvis_resize';\n    };\n\n    // Remove any existing resize event listeners\n    prototype.removeResizeListeners = function() {\n      this.disableResizable();\n      this.disableAutoResizeToWindow();\n      this.disableAutoResizeToWrapperParent();\n    };\n\n    // Set the width of the chart to the wrapper div. If keep_aspect is true,\n    // also set the height to maintain the aspect ratio.\n    prototype.resizeToWrapper = function(duration, keep_aspect) {\n      if (this.chart === null) return;\n      if (duration === undefined) duration = this.opts.duration;\n      if (duration === undefined) duration = 0;\n      if (keep_aspect === undefined) keep_aspect = this.opts.keep_aspect;\n      if (keep_aspect === undefined) keep_aspect = false;\n\n      var $wrap = this.getWrapper(),\n          $gear = $wrap.find(\".plot-gear-icon\"),\n          chart = this.chart,\n          padding = chart.padding(),\n          ratio = this.opts.width/this.opts.height;\n\n      var newWidth = $wrap.width() - $gear.width() - padding.left - padding.right,\n          newHeight = $wrap.height() - padding.top - padding.bottom;\n\n      if (keep_aspect) {\n        if (newHeight > newWidth / ratio) {\n          newHeight = Math.floor(newWidth / ratio);\n        } else if (newHeight < newWidth / ratio) {\n          newWidth = Math.floor(newHeight * ratio);\n        }\n      }\n      // Chart height ends up 5 pixels too large, so compensate for it\n      newHeight -= 5;\n\n      chart.width(newWidth);\n      chart.height(newHeight);\n      chart.update({ duration: duration });\n    };\n\n    // Set height to fill window. Don't need to set width, because the wrapper\n    // div should already have width=\"auto\".\n    prototype.resizeWrapperToWindow = function() {\n      var $body = $('body');\n      var $wrap = this.getWrapper();\n\n      // Use $body.height() here because dynamic plots may have controls that\n      // take some vertical space, which we need to take into account.\n      var extra_height = $body.outerHeight(true) - $body.height();\n\n      // Resize the wrapper div to the window, inside of scrollbars if present\n      // The wrapper has overflow:hidden so that objects inside of it won't\n      // scrollbars to appear while it's being resized.\n      var docEl = document.documentElement;\n      $wrap.height(docEl.clientHeight - extra_height);\n\n      // Now if there are any other elements in the body that cause the page to\n      // be larger than the window (like controls), we need to shrink the\n      // plot so that they end up inside the window.\n      $wrap.height(2 * (docEl.clientHeight - extra_height) - $body.height());\n    };\n\n    // Resize wrapper to fit its parent\n    prototype.resizeWrapperToParent = function() {\n      // We don't need to set the width, because when this.opts.width===\"auto\",\n      // the div gets width=\"auto\", and the browser automatically sizes it.\n      // But that strategy doesn't work for height.\n      if (this.opts.height === 'auto') {\n        var $wrap = this.getWrapper();\n        $wrap.height($wrap.parent().height());\n      }\n    };\n\n    // Run an update on the chart for the first time\n    prototype.initialUpdate = function() {\n      // If chart hasn't been run yet, we need to run it once so that\n      // resizeToWrapper will work properly (it needs the spec to have been run\n      // before it can figure out what the padding will be).\n      if (!this.initialized) this.chart.update({ duration: 0 });\n\n      this.initialized = true;\n\n      // Resizing to fit has to happen after the initial update\n      if (inPanel()) {\n        this.resizeWrapperToWindow();\n      } else {\n        this.resizeWrapperToParent();\n      }\n      this.resizeToWrapper(0);\n    };\n\n    // Make manually resizable (by dragging corner)\n    prototype.enableResizable = function() {\n      var self = this;\n\n      // When done resizing, update chart with new width and height\n      this.getWrapper().resizable({\n        helper: \"ui-resizable-helper\",\n        grid: [10, 10],\n        handles: \"se\",\n        stop: function() { self.resizeToWrapper(); }\n      });\n    };\n\n    prototype.disableResizable = function() {\n      var $wrap = this.getWrapper();\n      if ($wrap.resizable('instance') !== undefined)\n        $wrap.resizable('destroy');\n    };\n\n    // Make the plot auto-resize to fit window, if in viewer panel\n    prototype.enableAutoResizeToWindow = function() {\n      var self = this;\n      var debounce_id = null;\n\n      $(window).on('resize.' + this.resizeEventNamespace(), function() {\n        clearTimeout(debounce_id);\n        // Debounce to 100ms\n        debounce_id = setTimeout(function() {\n          self.resizeWrapperToWindow();\n          self.resizeToWrapper(0);\n        }, 100);\n      });\n    };\n\n    prototype.disableAutoResizeToWindow = function() {\n      $(window).off('.' + this.resizeEventNamespace());\n    };\n\n    // Make the wrapper auto-resize to its parent\n    prototype.enableAutoResizeToWrapperParent = function() {\n      var self = this;\n      var debounce_id = null;\n\n      this.getWrapper().parent().resize(function() {\n        clearTimeout(debounce_id);\n        // Debounce to 100ms\n        debounce_id = setTimeout(function() {\n          self.resizeWrapperToParent();\n          self.resizeToWrapper(0);\n        }, 100);\n      });\n    };\n\n    prototype.disableAutoResizeToWrapperParent = function() {\n      // Unfortunately, with the javascript resize listener, there's no way to\n      // test if a resize listener has already been added, and attempting to\n      // remove when none is present results in an error.\n      try { this.getWrapper().parent().removeResize(); }\n      catch(e) {}\n    };\n\n    // This is called when control outputs for a plot are updated\n    prototype.onControlOutput = function() {\n      if (inPanel()) {\n        this.resizeWrapperToWindow();\n        this.resizeToWrapper(0);\n      }\n    };\n\n    prototype.loadPendingData = function() {\n      this.chart.data(this.pendingData);\n      delete this.pendingData;\n    };\n\n    // Returns true if all data objects for a spec have been registered, using\n    // this.chart.data(dataset)\n    prototype.dataReady = function() {\n      var existing_data = Object.keys(this.chart.data());\n      var expected_data = this.spec.data.map(function (x) {\n        return x.name ;\n      });\n\n      return arraysEqual(existing_data, expected_data);\n    };\n\n    // Set the renderer, and update the renderer button and download link text if\n    // present. Also update the chart (unless update is false).\n    // renderer is either \"canvas\" or \"svg\".\n    prototype.setRenderer = function(renderer, update) {\n      // Don't set renderer if no change\n      if (renderer === this.renderer) return;\n      if (update === undefined) update = true;\n\n      this.renderer = renderer;\n      if (update) {\n        this.chart.renderer(renderer).update();\n      }\n      this.setRendererButton(renderer);\n      this.updateDownloadButtonText(renderer);\n    };\n\n    // Set the value of the renderer button, if present\n    prototype.setRendererButton = function(renderer) {\n      var $el = $(\"#\" + this.plotId + \"_renderer_\" + renderer);\n\n      // Toggle the renderer buttons when clicked\n      $el.addClass('inactive');\n      $el.siblings().removeClass('inactive');\n    };\n\n    // Given an <a> element, set the href of that element to the canvas content\n    // of the plot converted to SVG or PNG. This will set the href when the link\n    // is clicked; the download happens when it is released.\n    prototype.updateDownloadLink = function(el) {\n      var plot = $(\"#\" + this.plotId + \".ggvis-output .marks\")[0];\n      var imageUrl;\n\n      if (this.renderer === \"svg\") {\n        // Extract the svg code and add needed xmlns attribute\n        var svg = $(plot).clone().attr(\"xmlns\", \"http://www.w3.org/2000/svg\");\n        // Convert to string\n        svg = $('<div>').append(svg).html();\n        imageUrl = \"data:image/octet-stream;base64,\\n\" + btoa(svg);\n\n      } else if (this.renderer === \"canvas\") {\n        imageUrl = plot.toDataURL(\"image/png\").replace(\"image/png\", \"image/octet-stream\");\n      }\n\n      // Set download filename and data URL\n      var ext = \"\";\n      if      (this.renderer === \"svg\")    ext = \".svg\";\n      else if (this.renderer === \"canvas\") ext = \".png\";\n      el.setAttribute(\"download\", this.plotId + ext);\n      el.setAttribute(\"href\", imageUrl);\n    };\n\n    prototype.updateDownloadButtonText = function(renderer) {\n      var $el = $(\"#\" + this.plotId + \"_download\");\n      if ($el[0]) {\n        var filetype = \"\";\n        if      (renderer === \"svg\")    filetype = \"SVG\";\n        else if (renderer === \"canvas\") filetype = \"PNG\";\n\n        $el.text(\"Download \" + filetype);\n      }\n    };\n\n    prototype.removeHandlers = function() {\n      for (var i=0; i<this.handlers.length; i++) {\n        this.handlers[i].remove();\n      }\n\n      this.handlers = [];\n    };\n\n    // h_spec is an array of handler specifications (typically taken from spec)\n    prototype.addHandlers = function(h_spec) {\n      if (h_spec === [] || h_spec === undefined || h_spec === null)\n        return;\n\n      var h, HandlerClass;\n\n      // Call the appropriate handlers\n      for (var i=0; i<h_spec.length; i++) {\n        h = h_spec[i];\n        // Grab the appropriate handler class and instantiate it\n        HandlerClass = ggvis.handlers[h.type];\n        this.handlers[i] = new HandlerClass(this, h);\n      }\n    };\n\n\n    // Private methods ------------------------------------------------\n\n    // Returns all top-level mark definitions in the scene graph.\n    // These are available as soon as the spec is parsed.\n    prototype._allMarkDefs = function() {\n      return this.chart.model().defs().marks.marks;\n    };\n\n    // Returns all top-level marks in the scene graph.\n    // These are available after the first update.\n    prototype._allMarks = function() {\n      return this.chart.model().scene().items[0].items;\n    };\n\n    prototype._getSceneBounds = function() {\n      return this.chart.model().scene().items[0].bounds;\n    };\n\n\n    // Internal functions----------------------------------------------\n\n    // Returns true if arrays have same contents (in any order), false otherwise.\n    function arraysEqual(a, b) {\n      return $(a).not(b).length === 0 && $(b).not(a).length === 0;\n    }\n\n    // Given a mark definition, property name, return an object with the\n    // properties. If key is provided, then pull out that key.\n    function getMarkProp(markdef, propname) {\n      if (propname === undefined || propname === null ||\n          markdef.properties === undefined) {\n        return {};\n      }\n      var property = markdef.properties[propname];\n\n      if (property === undefined || property === null) {\n        return {};\n      }\n\n      // Call the property function on a dummy object\n      var temp = {};\n      property(temp);\n\n      return temp;\n    }\n\n\n    // ggvis.Plot.Brush class --------------------------------------------------\n    Plot.Brush = (function() {\n      // Constructor\n      // plot: The ggvis.Plot object which uses this brush.\n      var brush = function(plot) {\n        this.plot = plot;\n\n        this._enabled = false;\n        this._brushBounds = new vg.Bounds();\n        this._clickPoint = null;      // Coordinates where mouse was clicked\n        this._lastPoint = null;       // Previous mouse coordinate\n        this._lastMatchingItems = [];\n        this._callbacks = new ggvis.CallbackRegistry(this);\n\n        this._brushing = false;\n        this._dragging = false;\n      };\n\n      var prototype = brush.prototype;\n\n      // Wrappers for CallbackRegistry methods\n      prototype.on = function(type, fn) { this._callbacks.on(type, fn); };\n      prototype.off = function(type) { this._callbacks.off(type); };\n      prototype.trigger = function() {\n        this._callbacks.trigger.apply(this._callbacks, arguments);\n      };\n\n      // Returns true if the plot has a brush mark object, false otherwise.\n      prototype.hasBrushMark = function() {\n        if (this._getBrushMarkDef()) return true;\n        else return false;\n      };\n\n      // Enable the brush, if a brush mark is present.\n      prototype.enable = function() {\n        if (!this.hasBrushMark()) return;\n\n        var self = this;\n        var $div = this.plot.getDiv();\n\n        if (self._enabled) {\n          // Clear any existing mouse event handlers from the div\n          $div.off(\"mousedown.ggvis_brush\");\n          $div.off(\"mouseup.ggvis_brush\");\n          $div.off(\"mousemove.ggvis_brush\");\n\n          // Clear any existing brush event handlers\n          self.off(\"brushMove\");\n          self.off(\"updateItems\");\n        }\n\n\n        // Hook up mouse event handlers\n        $div.on(\"mousedown.ggvis_brush\", \"div.vega\", function (event) {\n          var point = self._removePadding(mouseOffset(event));\n\n          if (self._brushBounds.contains(point.x, point.y)) {\n            self._startDragging(point);\n          } else {\n            self._startBrushing(point);\n          }\n        });\n        $div.on(\"mouseup.ggvis_brush\", \"div.vega\", function (event) {\n          /* jshint unused: false */\n          if (self._dragging) self._stopDragging();\n          if (self._brushing) self._stopBrushing();\n        });\n        $div.on(\"mousemove.ggvis_brush\", \"div.vega\", function (event) {\n          var point = self._removePadding(mouseOffset(event));\n          if (self._dragging) self._dragTo(point);\n          if (self._brushing) self._brushTo(point);\n        });\n\n        // Register functions to be called each time brush is dragged or resized.\n        self.on(\"brushMove\", self._updateBrush);\n\n        // It's not uncommong for mouse events to occur at up to 120 Hz, but\n        // throttling brush updates to 20 Hz still gives a responsive feel, while\n        // allowing the CPU to spend more time doing other stuff.\n        var updateThrottled = _.throttle(self._updateBrushedItems, 50);\n        self.on(\"brushMove\", updateThrottled);\n\n        self._enabled = true;\n      };\n\n      // Dragging functions\n      prototype._startDragging = function(point) {\n        this._dragging = true;\n        this._lastPoint = point;\n        this._clickPoint = point;\n        this.trigger(\"brushMove\");\n      };\n      prototype._dragTo = function(point) {\n        if (!this._dragging) return;\n\n        var dx = point.x - this._lastPoint.x;\n        var dy = point.y - this._lastPoint.y;\n\n        this._brushBounds.translate(dx, dy);\n        this._lastPoint = point;\n        this.trigger(\"brushMove\");\n      };\n      prototype._stopDragging = function() {\n        this._dragging = false;\n        this._clickPoint = null;\n        this.trigger(\"brushMove\");\n      };\n\n      // Brushing functions\n      prototype._startBrushing = function(point) {\n        // Reset brush\n        this._brushBounds.set(0, 0, 0, 0);\n        this._brushing = true;\n        this._clickPoint = point;\n        this.trigger(\"brushMove\");\n      };\n      prototype._brushTo = function(point) {\n        if (!this._brushing) return; // We're not brushing right now\n\n        var limits = this.plot._getSceneBounds();\n\n        // Calculate the bounds based on start and end points\n        var end = point;\n        var maxX = Math.min(Math.max(this._clickPoint.x, end.x), limits.x2);\n        var minX = Math.max(Math.min(this._clickPoint.x, end.x), limits.x1);\n        var maxY = Math.min(Math.max(this._clickPoint.y, end.y), limits.y2);\n        var minY = Math.max(Math.min(this._clickPoint.y, end.y), limits.y1);\n\n        this._brushBounds.set(minX, minY, maxX, maxY);\n        this.trigger(\"brushMove\");\n      };\n      prototype._stopBrushing = function() {\n        this._brushing = false;\n        this._clickPoint = null;\n        this.trigger(\"brushMove\");\n      };\n\n      // Update the brush with new coordinates stored in brushBounds variable\n      // and call update on plot.\n      prototype._updateBrush = function() {\n        this.plot.chart.data({\n          ggvis_brush: [{\n            x:      this._brushBounds.x1,\n            y:      this._brushBounds.y1,\n            width:  this._brushBounds.width(),\n            height: this._brushBounds.height()\n          }]\n        });\n\n        this.plot.chart.update({\n          props: \"update\",\n          items: this._getBrushItem()\n        });\n      };\n\n      // Find items that are and aren't under the brush, then call update on\n      // each set, with the \"brush\" or \"update\" property set, as appropriate.\n      prototype._updateBrushedItems = function() {\n        // TODO: This function is a performance bottleneck.\n        //   Could use a faster method for finding array differences, but it'll\n        //   probably be even better to track brushed and unbrushed items from\n        //   the previous run.\n\n        // Find the items in the current scene that match\n        var items = this._getBrushableItems();\n        var matchingItems = [];\n        for (var i = 0; i < items.length; i++) {\n          if (this._brushBounds.intersects(items[i].bounds)) {\n            matchingItems.push(items[i]);\n          }\n        }\n\n        var newBrushItems = _.difference(matchingItems, this._lastMatchingItems);\n        var unBrushItems  = _.difference(this._lastMatchingItems, matchingItems);\n\n        this._lastMatchingItems = matchingItems;\n\n        this.plot.chart.update({ props: \"brush\", items: newBrushItems });\n        this.plot.chart.update({ props: \"update\", items: unBrushItems });\n\n        // Collect information run updateItems callbacks\n        var bounds = this._brushBounds;\n        var info = {\n          plot_id: this.plot.plotId,\n          x1: bounds.x1,\n          x2: bounds.x2,\n          y1: bounds.y1,\n          y2: bounds.y2,\n          items: matchingItems\n        };\n        this.trigger(\"updateItems\", info);\n      };\n\n\n      // Return the definition of the brush mark\n      prototype._getBrushMarkDef = function() {\n        var def = _.find(this.plot._allMarkDefs(), function(markdef) {\n          var data = getMarkProp(markdef, \"ggvis\").data || null;\n          return data === \"ggvis_brush\";\n        });\n\n        if (def === undefined) return null;\n\n        return def;\n      };\n\n      // Return the brush mark; if not present, return null.\n      prototype._getBrushMark = function() {\n        // We can identify the brush mark because it draws data from ggvis_brush.\n        var brushMark = _.find(this.plot._allMarks(), function(mark) {\n          var data = getMarkProp(mark.def, \"ggvis\").data || null;\n          return data === \"ggvis_brush\";\n        });\n\n        if (brushMark === undefined) return null;\n\n        return brushMark;\n      };\n\n      prototype._getBrushItem = function() {\n        var brushMark = this._getBrushMark();\n        if (brushMark === null || brushMark.items === null) return null;\n\n        return brushMark.items[0];\n      };\n\n      // Return all brushable items\n      prototype._getBrushableItems = function() {\n        // Get the brush mark, so we can make sure not to include it as a\n        // brushable item.\n        var brushMark = this._getBrushMark();\n\n        var brushableMarks = this.plot._allMarks().filter(function(mark) {\n          if (_.isEmpty(getMarkProp(mark.def, \"brush\")) || mark === brushMark)\n            return false;\n          else\n            return true;\n        });\n\n        var items = _.pluck(brushableMarks, \"items\");\n        return _.flatten(items);\n      };\n\n      // x/y coords are relative to the containing div. We need to account for the\n      // padding that surrounds the data area by removing the padding before we\n      // compare it to any scene item bounds.\n      prototype._removePadding = function(point) {\n        return {\n          x: point.x - this.plot.chart.padding().left,\n          y: point.y - this.plot.chart.padding().top\n        };\n      };\n\n      // Internal functions --------------------------------------------\n      function mouseOffset(e) {\n        // A workaround for Firefox, which doesn't provide offsetX/Y\n        // for mouse event.\n        if (typeof(e.offsetX) === \"undefined\") {\n          var offset = $(e.currentTarget).offset();\n          return {\n            x: e.pageX - offset.left,\n            y: e.pageY - offset.top\n          };\n        }\n        else {\n          return {\n            x: e.offsetX,\n            y: e.offsetY\n          };\n        }\n      }\n\n      return brush;\n    })(); // ggvis.Plot.Brush\n\n    return Plot;\n  })(); // 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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">ggvis包</h1>\n<h4 class=\"author project-author\">庄闪闪</h4>\n<h4 class=\"date project-date\">2020-12-30</h4>\n</section>\n\n\n\n<section class=\"main-content\">\n<div id=\"简介\" class=\"section level2\">\n<h2>简介</h2>\n<p><a href=\"http://ggvis.rstudio.com\">ggvis</a>是R的一个数据可视化包，它可以：</p>\n<ul>\n<li><p>使用与ggplot2类似的语法描述数据图形；</p></li>\n<li><p>创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；</p></li>\n<li><p>利用shiny的基础结构发布交互式图形。</p></li>\n</ul>\n<p><strong>ggvis 与 ggplot2主要区别</strong>：</p>\n<ul>\n<li>基本命名转换：</li>\n</ul>\n<p>ggplot→ggvis</p>\n<p>geom→layer function</p>\n<p>stat→compute function</p>\n<p>aes→props</p>\n<p>+→%&gt;%</p>\n<ul>\n<li><p>ggvis目前不支持分面；</p></li>\n<li><p>使用ggvis而不添加任何层类似于qplot</p></li>\n</ul>\n<blockquote>\n<p>更详细的区别可见：<a href=\"http://ggvis.rstudio.com/ggplot2.html\">ggvis vs ggplot2</a></p>\n</blockquote>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\"></a><span class=\"kw\">library</span>(ggvis)</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\"></a><span class=\"kw\">library</span>(dplyr)</span></code></pre></div>\n</div>\n<div id=\"静态图\" class=\"section level2\">\n<h2>静态图</h2>\n<div id=\"散点图\" class=\"section level3\">\n<h3>散点图</h3>\n<p>使用<code>layer_points()</code>绘制，其中内部参数都用默认值。注意这里<code>ggvis(~wt, ~mpg)</code>比ggplot多了一个波浪线。</p>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb2-2\"><a href=\"#cb2-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb2-3\"><a href=\"#cb2-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>()</span></code></pre></div>\n<p><div id=\"plot_id503856967-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id503856967\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id503856967_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id503856967\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id503856967_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id503856967\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id503856967_download\" class=\"ggvis-download\" data-plot-id=\"plot_id503856967\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id503856967_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": \"number\",\n          \"mpg\": \"number\"\n        }\n      },\n      \"values\": \"\\\"wt\\\",\\\"mpg\\\"\\n2.62,21\\n2.875,21\\n2.32,22.8\\n3.215,21.4\\n3.44,18.7\\n3.46,18.1\\n3.57,14.3\\n3.19,24.4\\n3.15,22.8\\n3.44,19.2\\n3.44,17.8\\n4.07,16.4\\n3.73,17.3\\n3.78,15.2\\n5.25,10.4\\n5.424,10.4\\n5.345,14.7\\n2.2,32.4\\n1.615,30.4\\n1.835,33.9\\n2.465,21.5\\n3.52,15.5\\n3.435,15.2\\n3.84,13.3\\n3.845,19.2\\n1.935,27.3\\n2.14,26\\n1.513,30.4\\n3.17,15.8\\n2.77,19.7\\n3.57,15\\n2.78,21.4\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"fill\": {\n            \"value\": \"#000000\"\n          },\n          \"size\": {\n            \"value\": 50\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.wt\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id503856967\").parseSpec(plot_id503856967_spec);\n</script> 如果要加拟合线，和ggplot语法很类似，再加一层<code>layer_smooths()</code>。</p>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb3-2\"><a href=\"#cb3-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb3-3\"><a href=\"#cb3-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>() <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb3-4\"><a href=\"#cb3-4\"></a><span class=\"st\">  </span><span class=\"kw\">layer_smooths</span>()</span></code></pre></div>\n<p><div id=\"plot_id177415128-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id177415128\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id177415128_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id177415128\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id177415128_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id177415128\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id177415128_download\" class=\"ggvis-download\" data-plot-id=\"plot_id177415128\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id177415128_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": \"number\",\n          \"mpg\": \"number\"\n        }\n      },\n      \"values\": \"\\\"wt\\\",\\\"mpg\\\"\\n2.62,21\\n2.875,21\\n2.32,22.8\\n3.215,21.4\\n3.44,18.7\\n3.46,18.1\\n3.57,14.3\\n3.19,24.4\\n3.15,22.8\\n3.44,19.2\\n3.44,17.8\\n4.07,16.4\\n3.73,17.3\\n3.78,15.2\\n5.25,10.4\\n5.424,10.4\\n5.345,14.7\\n2.2,32.4\\n1.615,30.4\\n1.835,33.9\\n2.465,21.5\\n3.52,15.5\\n3.435,15.2\\n3.84,13.3\\n3.845,19.2\\n1.935,27.3\\n2.14,26\\n1.513,30.4\\n3.17,15.8\\n2.77,19.7\\n3.57,15\\n2.78,21.4\"\n    },\n    {\n      \"name\": \".0/model_prediction1\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"pred_\": \"number\",\n          \"resp_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"pred_\\\",\\\"resp_\\\"\\n1.513,32.08897233857\\n1.56250632911392,31.6878645869701\\n1.61201265822785,31.2816303797919\\n1.66151898734177,30.8703709543688\\n1.7110253164557,30.4541875480347\\n1.76053164556962,30.0331813981232\\n1.81003797468354,29.6074537419678\\n1.85954430379747,29.1771058169022\\n1.90905063291139,28.7422388602601\\n1.95855696202532,28.3001719301537\\n2.00806329113924,27.834621969428\\n2.05756962025316,27.3476575600419\\n2.10707594936709,26.84497968394\\n2.15658227848101,26.3322893230667\\n2.20608860759494,25.8152874593666\\n2.25559493670886,25.2996750747841\\n2.30510126582278,24.7911531512637\\n2.35460759493671,24.29542267075\\n2.40411392405063,23.8181846151875\\n2.45362025316456,23.3651399665205\\n2.50312658227848,22.955253039598\\n2.55263291139241,22.6138488714952\\n2.60213924050633,22.3275852300224\\n2.65164556962025,22.0817586181852\\n2.70115189873418,21.8616655389892\\n2.7506582278481,21.65260249544\\n2.80016455696203,21.4398659905432\\n2.84967088607595,21.2087525273044\\n2.89917721518987,20.953335722037\\n2.9486835443038,20.7158424594628\\n2.99818987341772,20.4957065225374\\n3.04769620253165,20.2829337645837\\n3.09720253164557,20.0675300389245\\n3.14670886075949,19.8395011988825\\n3.19621518987342,19.5888530977805\\n3.24572151898734,19.2971559094315\\n3.29522784810127,18.9444093670088\\n3.34473417721519,18.5670026794964\\n3.39424050632911,18.2056968860288\\n3.44374683544304,17.9009022641924\\n3.49325316455696,17.620602502374\\n3.54275949367089,17.3400153015964\\n3.59226582278481,17.079077805285\\n3.64177215189873,16.8175887231322\\n3.69127848101266,16.5575726926136\\n3.74078481012658,16.3083303048321\\n3.79029113924051,16.0791621508901\\n3.83979746835443,15.8793688218903\\n3.88930379746835,15.7018119854881\\n3.93881012658228,15.5259429561214\\n3.9883164556962,15.3517253848296\\n4.03782278481013,15.1793328075288\\n4.08732911392405,15.0089387601353\\n4.13683544303798,14.8407167785652\\n4.1863417721519,14.6748403987346\\n4.23584810126582,14.5114831565596\\n4.28535443037975,14.3508185879563\\n4.33486075949367,14.193020228841\\n4.3843670886076,14.0382616151298\\n4.43387341772152,13.8867162827388\\n4.48337974683544,13.7385577675841\\n4.53288607594937,13.5939596055819\\n4.58239240506329,13.4530953326483\\n4.63189873417722,13.3161384846995\\n4.68140506329114,13.1832625976516\\n4.73091139240506,13.0546412074207\\n4.78041772151899,12.930447849923\\n4.82992405063291,12.8108560610747\\n4.87943037974684,12.6960393767918\\n4.92893670886076,12.5861713329905\\n4.97844303797468,12.4814254655869\\n5.02794936708861,12.3819753104973\\n5.07745569620253,12.2879944036376\\n5.12696202531646,12.1996562809241\\n5.17646835443038,12.117134478273\\n5.2259746835443,12.0406025316002\\n5.27548101265823,11.9702339768221\\n5.32498734177215,11.9062023498547\\n5.37449367088608,11.8486811866141\\n5.424,11.7978440230166\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"fill\": {\n            \"value\": \"#000000\"\n          },\n          \"size\": {\n            \"value\": 50\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.wt\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    },\n    {\n      \"type\": \"line\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"strokeWidth\": {\n            \"value\": 2\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.pred_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.resp_\"\n          },\n          \"fill\": {\n            \"value\": \"transparent\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/model_prediction1\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/model_prediction1\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id177415128\").parseSpec(plot_id177415128_spec);\n</script> 内部参数也很类似（<code>se = TRUE</code>加入拟合区间），拟合方式使用“lm”方法。</p>\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb4-1\"><a href=\"#cb4-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb4-2\"><a href=\"#cb4-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb4-3\"><a href=\"#cb4-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>() <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb4-4\"><a href=\"#cb4-4\"></a><span class=\"st\">  </span><span class=\"kw\">layer_model_predictions</span>(<span class=\"dt\">model =</span> <span class=\"st\">&quot;lm&quot;</span>, <span class=\"dt\">se =</span> <span class=\"ot\">TRUE</span>)</span></code></pre></div>\n<pre><code>## Guessing formula = mpg ~ wt</code></pre>\n<div id=\"plot_id880992869-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id880992869\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id880992869_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id880992869\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id880992869_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id880992869\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id880992869_download\" class=\"ggvis-download\" data-plot-id=\"plot_id880992869\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id880992869_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": \"number\",\n          \"mpg\": \"number\"\n        }\n      },\n      \"values\": \"\\\"wt\\\",\\\"mpg\\\"\\n2.62,21\\n2.875,21\\n2.32,22.8\\n3.215,21.4\\n3.44,18.7\\n3.46,18.1\\n3.57,14.3\\n3.19,24.4\\n3.15,22.8\\n3.44,19.2\\n3.44,17.8\\n4.07,16.4\\n3.73,17.3\\n3.78,15.2\\n5.25,10.4\\n5.424,10.4\\n5.345,14.7\\n2.2,32.4\\n1.615,30.4\\n1.835,33.9\\n2.465,21.5\\n3.52,15.5\\n3.435,15.2\\n3.84,13.3\\n3.845,19.2\\n1.935,27.3\\n2.14,26\\n1.513,30.4\\n3.17,15.8\\n2.77,19.7\\n3.57,15\\n2.78,21.4\"\n    },\n    {\n      \"name\": \".0/model_prediction1\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"resp_upr_\": \"number\",\n          \"pred_\": \"number\",\n          \"resp_lwr_\": \"number\",\n          \"resp_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"resp_upr_\\\",\\\"pred_\\\",\\\"resp_lwr_\\\",\\\"resp_\\\"\\n31.4341217313206,1.513,26.9637596243046,29.1989406778126\\n31.1204993874088,1.56250632911392,26.748211630978,28.9343555091934\\n30.8072470380303,1.61201265822785,26.532293643118,28.6697703405742\\n30.4943905399572,1.66151898734177,26.3159798039527,28.4051851719549\\n30.1819579750846,1.7110253164557,26.0992420315869,28.1406000033357\\n29.8699798618842,1.76053164556962,25.8820498075489,27.8760148347165\\n29.5584893857579,1.81003797468354,25.6643699464367,27.6114296660973\\n29.2475226490581,1.85954430379747,25.4461663458981,27.3468444974781\\n28.9371189411946,1.90905063291139,25.2273997165231,27.0822593288588\\n28.6273210287383,1.95855696202532,25.0080272917409,26.8176741602396\\n28.3181754647066,2.00806329113924,24.7880025185342,26.5530889916204\\n28.0097329152165,2.05756962025316,24.5672747307859,26.2885038230012\\n27.7020485003464,2.10707594936709,24.3457888084175,26.023918654382\\n27.3951821442752,2.15658227848101,24.1234848272503,25.7593334857627\\n27.0891989274822,2.20608860759494,23.9002977068049,25.4947483171435\\n26.7841694309033,2.25559493670886,23.6761568661453,25.2301631485243\\n26.4801700583702,2.30510126582278,23.4509859014399,24.9655779799051\\n26.1772833193625,2.35460759493671,23.2247023032092,24.7009928112859\\n25.8755980490886,2.40411392405063,22.9972172362447,24.4364076426666\\n25.5752095372801,2.45362025316456,22.7684354108148,24.1718224740474\\n25.276219531088,2.50312658227848,22.5382550797684,23.9072373054282\\n24.9787360715631,2.55263291139241,22.3065682020549,23.642652136809\\n24.6828731181045,2.60213924050633,22.073260818275,23.3780669681898\\n24.3887499120139,2.65164556962025,21.8382136871272,23.1134817995705\\n24.096490030255,2.70115189873418,21.6013032316477,22.8488966309513\\n23.8062200853351,2.7506582278481,21.3624028393291,22.5843114623321\\n23.5180680386105,2.80016455696203,21.1213845488153,22.3197262937129\\n23.2321611137817,2.84967088607595,20.8781211364057,22.0551411250937\\n22.9486233256676,2.89917721518987,20.6324885872813,21.7905559564744\\n22.6675726760022,2.9486835443038,20.3843688997083,21.5259707878552\\n22.3891181105935,2.99818987341772,20.1336531278785,21.261385619236\\n22.1133563760938,3.04769620253165,19.8802445251398,20.9968004506168\\n21.8403689531932,3.09720253164557,19.624061610802,20.7322152819976\\n21.5702192684414,3.14670886075949,19.3650409583153,20.4676301133783\\n21.3029503917649,3.19621518987342,19.1031394977534,20.2030449447591\\n21.0385834062175,3.24572151898734,18.8383361460623,19.9384597761399\\n20.7771165901197,3.29522784810127,18.5706326249216,19.6738746075207\\n20.5185254843377,3.34473417721519,18.3000533934652,19.4092894389015\\n20.2627638386563,3.39424050632911,18.0266447019082,19.1447042702822\\n20.0097653533643,3.44374683544304,17.7504728499618,18.880119101663\\n19.7594460674074,3.49325316455696,17.4716217986802,18.6155339330438\\n19.5117072017912,3.54275949367089,17.190190327058,18.3509487644246\\n19.266438250352,3.59226582278481,16.9062889412588,18.0863635958054\\n19.023520118358,3.64177215189873,16.6200367360143,17.8217784271861\\n18.7828281372767,3.69127848101266,16.3315583798572,17.5571932585669\\n18.5442348238948,3.74078481012658,16.0409813560006,17.2926080899477\\n18.307612296075,3.79029113924051,15.748433546582,17.0280229213285\\n18.0728342993708,3.83979746835443,15.4540412060478,16.7634377527093\\n17.8397778342311,3.88930379746835,15.157927333949,16.49885258409\\n17.6083244005099,3.93881012658228,14.8602104304317,16.2342674154708\\n17.3783608941716,3.9883164556962,14.5610035995316,15.9696822468516\\n17.1497802013801,4.03782278481013,14.2604139550846,15.7050970782324\\n16.9224815391307,4.08732911392405,13.9585422800956,15.4405119096132\\n16.6963705909696,4.13683544303798,13.6554828910183,15.1759267409939\\n16.4713594827389,4.1863417721519,13.3513236620105,14.9113415723747\\n16.2473666380091,4.23584810126582,13.0461461695019,14.6467564037555\\n16.0243165469005,4.28535443037975,12.740025923372,14.3821712351363\\n15.8021394760335,4.33486075949367,12.4330326570007,14.1175860665171\\n15.5807711417929,4.3843670886076,12.1252306540028,13.8530008978978\\n15.3601523641871,4.43387341772152,11.8166790943701,13.5884157292786\\n15.1402287143902,4.48337974683544,11.5074324069286,13.3238305606594\\n14.9209501655981,4.53288607594937,11.1975406184823,13.0592453920402\\n14.7022707540356,4.58239240506329,10.8870496928064,12.794660223421\\n14.4841482547477,4.63189873417722,10.5760018548558,12.5300750548017\\n14.266543875108,4.68140506329114,10.2644358972571,12.2654898861825\\n14.0494219676868,4.73091139240506,9.95238746743981,12.0009047175633\\n13.8327497631713,4.78041772151899,9.6398893347169,11.7363195489441\\n13.6164971233358,4.82992405063291,9.32697163731397,11.4717343803249\\n13.400636313583,4.87943037974684,9.01366210982825,11.2071492117056\\n13.185141794249,4.92893670886076,8.69998629192387,10.9425640430864\\n12.96999002966,4.97844303797468,8.38596771927446,10.6779788744672\\n12.7551593138153,5.02794936708861,8.07162809788066,10.413393705848\\n12.5406296115153,5.07745569620253,7.75698746294225,10.1488085372288\\n12.3263824137488,5.12696202531646,7.44206432347029,9.88422336860955\\n12.1124006061819,5.17646835443038,7.1268757937988,9.61963819999033\\n11.8986683496337,5.2259746835443,6.81143771310854,9.35505303137111\\n11.685170971489,5.27548101265823,6.49576475401476,9.09046786275189\\n11.4718948670624,5.32498734177215,6.17987052120298,8.82588269413267\\n11.2588274100015,5.37449367088608,5.86376764102542,8.56129752551345\\n11.0459568708893,5.424,5.5474678428992,8.29671235689423\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n4.12984123504415\\n35.317626607855\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"fill\": {\n            \"value\": \"#000000\"\n          },\n          \"size\": {\n            \"value\": 50\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.wt\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    },\n    {\n      \"type\": \"area\",\n      \"properties\": {\n        \"update\": {\n          \"fill\": {\n            \"value\": \"#333333\"\n          },\n          \"y2\": {\n            \"scale\": \"y\",\n            \"field\": \"data.resp_upr_\"\n          },\n          \"fillOpacity\": {\n            \"value\": 0.2\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.pred_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.resp_lwr_\"\n          },\n          \"stroke\": {\n            \"value\": \"transparent\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/model_prediction1\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/model_prediction1\"\n      }\n    },\n    {\n      \"type\": \"line\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"strokeWidth\": {\n            \"value\": 2\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.pred_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.resp_\"\n          },\n          \"fill\": {\n            \"value\": \"transparent\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/model_prediction1\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/model_prediction1\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id880992869\").parseSpec(plot_id880992869_spec);\n</script>\n</div>\n<div id=\"分组的散点图\" class=\"section level3\">\n<h3>分组的散点图</h3>\n<p>如果想要使用分组说明散点图，可以加入<code>fill = ~factor(cyl)</code>或者<code>group_by(cyl)</code>进行分布。</p>\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb6-1\"><a href=\"#cb6-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb6-2\"><a href=\"#cb6-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb6-3\"><a href=\"#cb6-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>(<span class=\"dt\">fill =</span> <span class=\"op\">~</span><span class=\"kw\">factor</span>(cyl))</span></code></pre></div>\n<p><div id=\"plot_id350862318-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id350862318\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id350862318_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id350862318\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id350862318_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id350862318\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id350862318_download\" class=\"ggvis-download\" data-plot-id=\"plot_id350862318\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id350862318_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": \"number\",\n          \"mpg\": \"number\"\n        }\n      },\n      \"values\": \"\\\"wt\\\",\\\"mpg\\\",\\\"factor(cyl)\\\"\\n2.62,21,\\\"6\\\"\\n2.875,21,\\\"6\\\"\\n2.32,22.8,\\\"4\\\"\\n3.215,21.4,\\\"6\\\"\\n3.44,18.7,\\\"8\\\"\\n3.46,18.1,\\\"6\\\"\\n3.57,14.3,\\\"8\\\"\\n3.19,24.4,\\\"4\\\"\\n3.15,22.8,\\\"4\\\"\\n3.44,19.2,\\\"6\\\"\\n3.44,17.8,\\\"6\\\"\\n4.07,16.4,\\\"8\\\"\\n3.73,17.3,\\\"8\\\"\\n3.78,15.2,\\\"8\\\"\\n5.25,10.4,\\\"8\\\"\\n5.424,10.4,\\\"8\\\"\\n5.345,14.7,\\\"8\\\"\\n2.2,32.4,\\\"4\\\"\\n1.615,30.4,\\\"4\\\"\\n1.835,33.9,\\\"4\\\"\\n2.465,21.5,\\\"4\\\"\\n3.52,15.5,\\\"8\\\"\\n3.435,15.2,\\\"8\\\"\\n3.84,13.3,\\\"8\\\"\\n3.845,19.2,\\\"8\\\"\\n1.935,27.3,\\\"4\\\"\\n2.14,26,\\\"4\\\"\\n1.513,30.4,\\\"4\\\"\\n3.17,15.8,\\\"8\\\"\\n2.77,19.7,\\\"6\\\"\\n3.57,15,\\\"8\\\"\\n2.78,21.4,\\\"4\\\"\"\n    },\n    {\n      \"name\": \"scale/fill\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {}\n      },\n      \"values\": \"\\\"domain\\\"\\n\\\"4\\\"\\n\\\"6\\\"\\n\\\"8\\\"\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"fill\",\n      \"type\": \"ordinal\",\n      \"domain\": {\n        \"data\": \"scale/fill\",\n        \"field\": \"data.domain\"\n      },\n      \"points\": true,\n      \"sort\": false,\n      \"range\": \"category10\"\n    },\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"size\": {\n            \"value\": 50\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.wt\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          },\n          \"fill\": {\n            \"scale\": \"fill\",\n            \"field\": \"data.factor(cyl)\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    }\n  ],\n  \"legends\": [\n    {\n      \"orient\": \"right\",\n      \"fill\": \"fill\",\n      \"title\": \"factor(cyl)\"\n    }\n  ],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id350862318\").parseSpec(plot_id350862318_spec);\n</script> 如果想要预测每组数据拟合情况，可以使用<code>ayer_model_predictions()</code>。</p>\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb7-1\"><a href=\"#cb7-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb7-2\"><a href=\"#cb7-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg, <span class=\"dt\">fill =</span> <span class=\"op\">~</span><span class=\"kw\">factor</span>(cyl)) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb7-3\"><a href=\"#cb7-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>() <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb7-4\"><a href=\"#cb7-4\"></a><span class=\"st\">  </span><span class=\"kw\">group_by</span>(cyl) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb7-5\"><a href=\"#cb7-5\"></a><span class=\"st\">  </span><span class=\"kw\">layer_model_predictions</span>(<span class=\"dt\">model =</span> <span class=\"st\">&quot;lm&quot;</span>)</span></code></pre></div>\n<pre><code>## Guessing formula = mpg ~ wt</code></pre>\n<div id=\"plot_id248413961-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id248413961\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id248413961_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id248413961\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id248413961_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id248413961\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id248413961_download\" class=\"ggvis-download\" data-plot-id=\"plot_id248413961\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id248413961_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": 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3.17291139240506,19.5878147067567\\n6,3.18354430379747,19.5582540866442\\n6,3.19417721518987,19.5286934665316\\n6,3.20481012658228,19.4991328464191\\n6,3.21544303797468,19.4695722263065\\n6,3.22607594936709,19.440011606194\\n6,3.23670886075949,19.4104509860815\\n6,3.2473417721519,19.3808903659689\\n6,3.2579746835443,19.3513297458564\\n6,3.26860759493671,19.3217691257439\\n6,3.27924050632911,19.2922085056313\\n6,3.28987341772152,19.2626478855188\\n6,3.30050632911392,19.2330872654062\\n6,3.31113924050633,19.2035266452937\\n6,3.32177215189873,19.1739660251812\\n6,3.33240506329114,19.1444054050686\\n6,3.34303797468354,19.1148447849561\\n6,3.35367088607595,19.0852841648436\\n6,3.36430379746835,19.055723544731\\n6,3.37493670886076,19.0261629246185\\n6,3.38556962025316,18.9966023045059\\n6,3.39620253164557,18.9670416843934\\n6,3.40683544303797,18.9374810642809\\n6,3.41746835443038,18.9079204441683\\n6,3.42810126582278,18.8783598240558\\n6,3.43873417721519,18.8487992039433\\n6,3.44936708860759,18.8192385838307\\n6,3.46,18.7896779637182\\n8,3.17,16.9180008491539\\n8,3.19853164556962,16.8554469873031\\n8,3.22706329113924,16.7928931254522\\n8,3.25559493670886,16.7303392636013\\n8,3.28412658227848,16.6677854017505\\n8,3.3126582278481,16.6052315398996\\n8,3.34118987341772,16.5426776780488\\n8,3.36972151898734,16.4801238161979\\n8,3.39825316455696,16.4175699543471\\n8,3.42678481012658,16.3550160924962\\n8,3.4553164556962,16.2924622306453\\n8,3.48384810126582,16.2299083687945\\n8,3.51237974683544,16.1673545069436\\n8,3.54091139240506,16.1048006450928\\n8,3.56944303797468,16.0422467832419\\n8,3.5979746835443,15.9796929213911\\n8,3.62650632911392,15.9171390595402\\n8,3.65503797468354,15.8545851976894\\n8,3.68356962025316,15.7920313358385\\n8,3.71210126582278,15.7294774739876\\n8,3.7406329113924,15.6669236121368\\n8,3.76916455696203,15.6043697502859\\n8,3.79769620253165,15.5418158884351\\n8,3.82622784810127,15.4792620265842\\n8,3.85475949367089,15.4167081647334\\n8,3.88329113924051,15.3541543028825\\n8,3.91182278481013,15.2916004410316\\n8,3.94035443037975,15.2290465791808\\n8,3.96888607594937,15.1664927173299\\n8,3.99741772151899,15.1039388554791\\n8,4.02594936708861,15.0413849936282\\n8,4.05448101265823,14.9788311317774\\n8,4.08301265822785,14.9162772699265\\n8,4.11154430379747,14.8537234080756\\n8,4.14007594936709,14.7911695462248\\n8,4.16860759493671,14.7286156843739\\n8,4.19713924050633,14.6660618225231\\n8,4.22567088607595,14.6035079606722\\n8,4.25420253164557,14.5409540988214\\n8,4.28273417721519,14.4784002369705\\n8,4.31126582278481,14.4158463751197\\n8,4.33979746835443,14.3532925132688\\n8,4.36832911392405,14.2907386514179\\n8,4.39686075949367,14.2281847895671\\n8,4.42539240506329,14.1656309277162\\n8,4.45392405063291,14.1030770658654\\n8,4.48245569620253,14.0405232040145\\n8,4.51098734177215,13.9779693421637\\n8,4.53951898734177,13.9154154803128\\n8,4.56805063291139,13.8528616184619\\n8,4.59658227848101,13.7903077566111\\n8,4.62511392405063,13.7277538947602\\n8,4.65364556962025,13.6652000329094\\n8,4.68217721518987,13.6026461710585\\n8,4.71070886075949,13.5400923092077\\n8,4.73924050632911,13.4775384473568\\n8,4.76777215189873,13.4149845855059\\n8,4.79630379746835,13.3524307236551\\n8,4.82483544303797,13.2898768618042\\n8,4.8533670886076,13.2273229999534\\n8,4.88189873417722,13.1647691381025\\n8,4.91043037974684,13.1022152762517\\n8,4.93896202531646,13.0396614144008\\n8,4.96749367088608,12.97710755255\\n8,4.9960253164557,12.9145536906991\\n8,5.02455696202532,12.8519998288482\\n8,5.05308860759494,12.7894459669974\\n8,5.08162025316456,12.7268921051465\\n8,5.11015189873418,12.6643382432957\\n8,5.1386835443038,12.6017843814448\\n8,5.16721518987342,12.539230519594\\n8,5.19574683544304,12.4766766577431\\n8,5.22427848101266,12.4141227958922\\n8,5.25281012658228,12.3515689340414\\n8,5.2813417721519,12.2890150721905\\n8,5.30987341772152,12.2264612103397\\n8,5.33840506329114,12.1639073484888\\n8,5.36693670886076,12.101353486638\\n8,5.39546835443038,12.0387996247871\\n8,5.424,11.9762457629362\"\n    },\n    {\n      \"name\": \".0/group_by1/model_prediction2\",\n      \"source\": \".0/group_by1/model_prediction2_flat\",\n      \"transform\": [\n        {\n          \"type\": \"treefacet\",\n          \"keys\": [\n            \"data.cyl\"\n          ]\n        }\n      ]\n    },\n    {\n      \"name\": \"scale/fill\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {}\n      },\n      \"values\": \"\\\"domain\\\"\\n\\\"4\\\"\\n\\\"6\\\"\\n\\\"8\\\"\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"fill\",\n      \"type\": \"ordinal\",\n      \"domain\": {\n        \"data\": \"scale/fill\",\n        \"field\": \"data.domain\"\n      },\n      \"points\": true,\n      \"sort\": false,\n      \"range\": \"category10\"\n    },\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"size\": {\n            \"value\": 50\n          },\n          \"fill\": {\n            \"scale\": \"fill\",\n            \"field\": \"data.factor(cyl)\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.wt\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    },\n    {\n      \"type\": \"group\",\n      \"from\": {\n        \"data\": \".0/group_by1/model_prediction2\"\n      },\n      \"marks\": [\n        {\n          \"type\": \"line\",\n          \"properties\": {\n            \"update\": {\n              \"stroke\": {\n                \"value\": \"#000000\"\n              },\n              \"strokeWidth\": {\n                \"value\": 2\n              },\n              \"x\": {\n                \"scale\": \"x\",\n                \"field\": \"data.pred_\"\n              },\n              \"y\": {\n                \"scale\": \"y\",\n                \"field\": \"data.resp_\"\n              },\n              \"fill\": {\n                \"value\": \"transparent\"\n              }\n            },\n            \"ggvis\": {\n              \"data\": {\n                \"value\": \".0/group_by1/model_prediction2\"\n              }\n            }\n          }\n        }\n      ]\n    }\n  ],\n  \"legends\": [\n    {\n      \"orient\": \"right\",\n      \"fill\": \"fill\",\n      \"title\": \"factor(cyl)\"\n    }\n  ],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id248413961\").parseSpec(plot_id248413961_spec);\n</script>\n</div>\n<div id=\"柱状图\" class=\"section level3\">\n<h3>柱状图</h3>\n<p>柱状图是使用<code>layer_bars()</code>函数，内部参数包括width（设置柱子宽度）等。</p>\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb9-1\"><a href=\"#cb9-1\"></a><span class=\"kw\">head</span>(pressure)</span></code></pre></div>\n<pre><code>##   temperature pressure\n## 1           0   0.0002\n## 2          20   0.0012\n## 3          40   0.0060\n## 4          60   0.0300\n## 5          80   0.0900\n## 6         100   0.2700</code></pre>\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb11-1\"><a href=\"#cb11-1\"></a>pressure <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb11-2\"><a href=\"#cb11-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>temperature, <span class=\"op\">~</span>pressure) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb11-3\"><a href=\"#cb11-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_bars</span>(<span class=\"dt\">fill :=</span> <span class=\"st\">&quot;#ff8080&quot;</span>)</span></code></pre></div>\n<div id=\"plot_id648800652-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id648800652\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id648800652_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id648800652\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id648800652_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id648800652\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id648800652_download\" class=\"ggvis-download\" data-plot-id=\"plot_id648800652\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id648800652_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/count1/align2/stack3\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"xmin_\": \"number\",\n          \"xmax_\": \"number\",\n          \"stack_upr_\": \"number\",\n          \"stack_lwr_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"xmin_\\\",\\\"xmax_\\\",\\\"stack_upr_\\\",\\\"stack_lwr_\\\"\\n-10,10,2e-04,0\\n10,30,0.0012,0\\n30,50,0.006,0\\n50,70,0.03,0\\n70,90,0.09,0\\n90,110,0.27,0\\n110,130,0.75,0\\n130,150,1.85,0\\n150,170,4.2,0\\n170,190,8.8,0\\n190,210,17.3,0\\n210,230,32.1,0\\n230,250,57,0\\n250,270,96,0\\n270,290,157,0\\n290,310,247,0\\n310,330,376,0\\n330,350,558,0\\n350,370,806,0\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-29\\n389\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n0\\n846.3\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"rect\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"fill\": {\n            \"value\": \"#ff8080\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmin_\"\n          },\n          \"x2\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmax_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_upr_\"\n          },\n          \"y2\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_lwr_\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/count1/align2/stack3\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/count1/align2/stack3\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"temperature\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"pressure\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id648800652\").parseSpec(plot_id648800652_spec);\n</script>\n<div class=\"sourceCode\" id=\"cb12\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb12-1\"><a href=\"#cb12-1\"></a>pressure <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb12-2\"><a href=\"#cb12-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>temperature, <span class=\"op\">~</span>pressure) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb12-3\"><a href=\"#cb12-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_bars</span>(<span class=\"dt\">width =</span> <span class=\"dv\">15</span>,<span class=\"dt\">fill :=</span> <span class=\"st\">&quot;#ff8080&quot;</span>)</span></code></pre></div>\n<div id=\"plot_id816802935-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id816802935\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id816802935_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id816802935\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id816802935_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id816802935\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id816802935_download\" class=\"ggvis-download\" data-plot-id=\"plot_id816802935\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id816802935_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/count1/align2/stack3\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"xmin_\": \"number\",\n          \"xmax_\": \"number\",\n          \"stack_upr_\": \"number\",\n          \"stack_lwr_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"xmin_\\\",\\\"xmax_\\\",\\\"stack_upr_\\\",\\\"stack_lwr_\\\"\\n-7.5,7.5,2e-04,0\\n12.5,27.5,0.0012,0\\n32.5,47.5,0.006,0\\n52.5,67.5,0.03,0\\n72.5,87.5,0.09,0\\n92.5,107.5,0.27,0\\n112.5,127.5,0.75,0\\n132.5,147.5,1.85,0\\n152.5,167.5,4.2,0\\n172.5,187.5,8.8,0\\n192.5,207.5,17.3,0\\n212.5,227.5,32.1,0\\n232.5,247.5,57,0\\n252.5,267.5,96,0\\n272.5,287.5,157,0\\n292.5,307.5,247,0\\n312.5,327.5,376,0\\n332.5,347.5,558,0\\n352.5,367.5,806,0\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-26.25\\n386.25\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n0\\n846.3\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"rect\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"fill\": {\n            \"value\": \"#ff8080\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmin_\"\n          },\n          \"x2\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmax_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_upr_\"\n          },\n          \"y2\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_lwr_\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/count1/align2/stack3\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/count1/align2/stack3\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"temperature\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"pressure\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id816802935\").parseSpec(plot_id816802935_spec);\n</script>\n</div>\n<div id=\"曲线图\" class=\"section level3\">\n<h3>曲线图</h3>\n<p>使用<code>layer_lines()</code>绘制曲线图，当然你可以和散点图合并，效果更好。</p>\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb13-1\"><a href=\"#cb13-1\"></a>pressure <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>temperature, <span class=\"op\">~</span>pressure) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">layer_lines</span>()</span></code></pre></div>\n<div id=\"plot_id909476656-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id909476656\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id909476656_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id909476656\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id909476656_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id909476656\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id909476656_download\" class=\"ggvis-download\" data-plot-id=\"plot_id909476656\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id909476656_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/arrange1\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"temperature\": \"number\",\n          \"pressure\": \"number\"\n        }\n      },\n      \"values\": \"\\\"temperature\\\",\\\"pressure\\\"\\n0,2e-04\\n20,0.0012\\n40,0.006\\n60,0.03\\n80,0.09\\n100,0.27\\n120,0.75\\n140,1.85\\n160,4.2\\n180,8.8\\n200,17.3\\n220,32.1\\n240,57\\n260,96\\n280,157\\n300,247\\n320,376\\n340,558\\n360,806\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-18\\n378\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-40.29979\\n846.29999\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"line\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.temperature\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.pressure\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/arrange1\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/arrange1\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"temperature\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"pressure\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id909476656\").parseSpec(plot_id909476656_spec);\n</script>\n<div class=\"sourceCode\" id=\"cb14\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb14-1\"><a href=\"#cb14-1\"></a>pressure <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>temperature, <span class=\"op\">~</span>pressure) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb14-2\"><a href=\"#cb14-2\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>(<span class=\"dt\">size :=</span> <span class=\"dv\">50</span>) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb14-3\"><a href=\"#cb14-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_lines</span>()</span></code></pre></div>\n<div id=\"plot_id489038140-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id489038140\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id489038140_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id489038140\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id489038140_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id489038140\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id489038140_download\" class=\"ggvis-download\" data-plot-id=\"plot_id489038140\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id489038140_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"temperature\": \"number\",\n          \"pressure\": \"number\"\n        }\n      },\n      \"values\": \"\\\"temperature\\\",\\\"pressure\\\"\\n0,2e-04\\n20,0.0012\\n40,0.006\\n60,0.03\\n80,0.09\\n100,0.27\\n120,0.75\\n140,1.85\\n160,4.2\\n180,8.8\\n200,17.3\\n220,32.1\\n240,57\\n260,96\\n280,157\\n300,247\\n320,376\\n340,558\\n360,806\"\n    },\n    {\n      \"name\": \".0/arrange1\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"temperature\": \"number\",\n          \"pressure\": \"number\"\n        }\n      },\n      \"values\": \"\\\"temperature\\\",\\\"pressure\\\"\\n0,2e-04\\n20,0.0012\\n40,0.006\\n60,0.03\\n80,0.09\\n100,0.27\\n120,0.75\\n140,1.85\\n160,4.2\\n180,8.8\\n200,17.3\\n220,32.1\\n240,57\\n260,96\\n280,157\\n300,247\\n320,376\\n340,558\\n360,806\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-18\\n378\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n-40.29979\\n846.29999\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"symbol\",\n      \"properties\": {\n        \"update\": {\n          \"fill\": {\n            \"value\": \"#000000\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.temperature\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.pressure\"\n          },\n          \"size\": {\n            \"value\": 50\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    },\n    {\n      \"type\": \"line\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.temperature\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.pressure\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/arrange1\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/arrange1\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"temperature\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"pressure\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id489038140\").parseSpec(plot_id489038140_spec);\n</script>\n</div>\n<div id=\"直方图\" class=\"section level3\">\n<h3>直方图</h3>\n<p>使用<code>layer_histograms()</code>绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。</p>\n<div class=\"sourceCode\" id=\"cb15\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb15-1\"><a href=\"#cb15-1\"></a><span class=\"kw\">head</span>(faithful)</span></code></pre></div>\n<pre><code>##   eruptions waiting\n## 1     3.600      79\n## 2     1.800      54\n## 3     3.333      74\n## 4     2.283      62\n## 5     4.533      85\n## 6     2.883      55</code></pre>\n<div class=\"sourceCode\" id=\"cb17\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb17-1\"><a href=\"#cb17-1\"></a>faithful <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>eruptions, <span class=\"dt\">fill :=</span> <span class=\"st\">&quot;#ff8080&quot;</span>) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb17-2\"><a href=\"#cb17-2\"></a><span class=\"st\">  </span><span class=\"kw\">layer_histograms</span>(<span class=\"dt\">width=</span><span class=\"fl\">0.25</span>, <span class=\"dt\">boundary=</span><span class=\"dv\">0</span>) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb17-3\"><a href=\"#cb17-3\"></a><span class=\"st\">  </span><span class=\"kw\">add_axis</span>(<span class=\"st\">&quot;x&quot;</span>, <span class=\"dt\">title =</span> <span class=\"st\">&quot;month&quot;</span>) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb17-4\"><a href=\"#cb17-4\"></a><span class=\"st\">  </span><span class=\"kw\">add_axis</span>(<span class=\"st\">&quot;y&quot;</span>, <span class=\"dt\">title =</span> <span class=\"st\">&quot;count&quot;</span>)</span></code></pre></div>\n<div id=\"plot_id722304771-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id722304771\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id722304771_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id722304771\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id722304771_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id722304771\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id722304771_download\" class=\"ggvis-download\" data-plot-id=\"plot_id722304771\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id722304771_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/bin1/stack2\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"xmin_\": \"number\",\n          \"xmax_\": \"number\",\n          \"stack_upr_\": \"number\",\n          \"stack_lwr_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"xmin_\\\",\\\"xmax_\\\",\\\"stack_upr_\\\",\\\"stack_lwr_\\\"\\n1.5,1.75,10,0\\n1.75,2,45,0\\n2,2.25,24,0\\n2.25,2.5,13,0\\n2.5,2.75,2,0\\n2.75,3,3,0\\n3,3.25,1,0\\n3.25,3.5,8,0\\n3.5,3.75,10,0\\n3.75,4,24,0\\n4,4.25,33,0\\n4.25,4.5,42,0\\n4.5,4.75,34,0\\n4.75,5,20,0\\n5,5.25,3,0\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.3125\\n5.4375\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n0\\n47.25\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"rect\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"fill\": {\n            \"value\": \"#ff8080\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmin_\"\n          },\n          \"x2\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmax_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_upr_\"\n          },\n          \"y2\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_lwr_\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/bin1/stack2\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/bin1/stack2\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"title\": \"month\",\n      \"layer\": \"back\",\n      \"grid\": true\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"title\": \"count\",\n      \"layer\": \"back\",\n      \"grid\": true\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id722304771\").parseSpec(plot_id722304771_spec);\n</script>\n<div class=\"sourceCode\" id=\"cb18\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb18-1\"><a href=\"#cb18-1\"></a>faithful <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>eruptions, <span class=\"dt\">fill :=</span> <span class=\"st\">&quot;#90bff9&quot;</span>) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb18-2\"><a href=\"#cb18-2\"></a><span class=\"st\">  </span><span class=\"kw\">layer_histograms</span>(<span class=\"dt\">width=</span><span class=\"fl\">0.25</span>, <span class=\"dt\">center=</span><span class=\"dv\">0</span>) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span></span>\n<span id=\"cb18-3\"><a href=\"#cb18-3\"></a><span class=\"st\">  </span><span class=\"kw\">add_axis</span>(<span class=\"st\">&quot;x&quot;</span>, <span class=\"dt\">title =</span> <span class=\"st\">&quot;month&quot;</span>) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb18-4\"><a href=\"#cb18-4\"></a><span class=\"st\">  </span><span class=\"kw\">add_axis</span>(<span class=\"st\">&quot;y&quot;</span>, <span class=\"dt\">title =</span> <span class=\"st\">&quot;count&quot;</span>)</span></code></pre></div>\n<div id=\"plot_id785180486-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id785180486\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id785180486_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id785180486\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id785180486_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id785180486\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id785180486_download\" class=\"ggvis-download\" data-plot-id=\"plot_id785180486\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id785180486_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/bin1/stack2\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"xmin_\": \"number\",\n          \"xmax_\": \"number\",\n          \"stack_upr_\": \"number\",\n          \"stack_lwr_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"xmin_\\\",\\\"xmax_\\\",\\\"stack_upr_\\\",\\\"stack_lwr_\\\"\\n1.375,1.625,1,0\\n1.625,1.875,35,0\\n1.875,2.125,30,0\\n2.125,2.375,20,0\\n2.375,2.625,7,0\\n2.625,2.875,2,0\\n2.875,3.125,3,0\\n3.125,3.375,4,0\\n3.375,3.625,10,0\\n3.625,3.875,14,0\\n3.875,4.125,28,0\\n4.125,4.375,39,0\\n4.375,4.625,41,0\\n4.625,4.875,28,0\\n4.875,5.125,10,0\"\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.1875\\n5.3125\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n0\\n43.05\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"rect\",\n      \"properties\": {\n        \"update\": {\n          \"stroke\": {\n            \"value\": \"#000000\"\n          },\n          \"fill\": {\n            \"value\": \"#90bff9\"\n          },\n          \"x\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmin_\"\n          },\n          \"x2\": {\n            \"scale\": \"x\",\n            \"field\": \"data.xmax_\"\n          },\n          \"y\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_upr_\"\n          },\n          \"y2\": {\n            \"scale\": \"y\",\n            \"field\": \"data.stack_lwr_\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0/bin1/stack2\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0/bin1/stack2\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"title\": \"month\",\n      \"layer\": \"back\",\n      \"grid\": true\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"title\": \"count\",\n      \"layer\": \"back\",\n      \"grid\": true\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id785180486\").parseSpec(plot_id785180486_spec);\n</script>\n</div>\n<div id=\"箱型图\" class=\"section level3\">\n<h3>箱型图</h3>\n<p>使用<code>layer_boxplots()</code>绘制箱型图，具体内部参数再次不做具体陈述。</p>\n<div class=\"sourceCode\" id=\"cb19\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb19-1\"><a href=\"#cb19-1\"></a>mtcars <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span><span class=\"kw\">factor</span>(cyl), <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span><span class=\"st\"> </span><span class=\"kw\">layer_boxplots</span>(<span class=\"dt\">fill :=</span> <span class=\"st\">&quot;#90bff9&quot;</span>) </span></code></pre></div>\n<div id=\"plot_id857762827-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id857762827\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id857762827_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id857762827\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id857762827_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id857762827\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id857762827_download\" class=\"ggvis-download\" data-plot-id=\"plot_id857762827\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id857762827_spec = {\n  \"data\": [\n    {\n      \"name\": \".0/group_by1/boxplot2_flat\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"min_\": \"number\",\n          \"max_\": \"number\",\n          \"lower_\": \"number\",\n          \"upper_\": \"number\",\n          \"median_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"min_\\\",\\\"max_\\\",\\\"factor(cyl)\\\",\\\"lower_\\\",\\\"upper_\\\",\\\"median_\\\"\\n21.4,33.9,\\\"4\\\",22.8,30.4,26\\n17.8,21.4,\\\"6\\\",18.65,21,19.7\\n13.3,18.7,\\\"8\\\",14.4,16.25,15.2\"\n    },\n    {\n      \"name\": \".0/group_by1/boxplot2\",\n      \"source\": \".0/group_by1/boxplot2_flat\",\n      \"transform\": [\n        {\n          \"type\": \"treefacet\",\n          \"keys\": [\n            \"data.factor(cyl)\"\n          ]\n        }\n      ]\n    },\n    {\n      \"name\": \".0/group_by1/boxplot2/boxplot_outliers3_flat\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"value_\": \"number\"\n        }\n      },\n      \"values\": \"\\\"value_\\\",\\\"factor(cyl)\\\"\\n10.4,\\\"8\\\"\\n10.4,\\\"8\\\"\\n19.2,\\\"8\\\"\"\n    },\n    {\n      \"name\": \".0/group_by1/boxplot2/boxplot_outliers3\",\n      \"source\": \".0/group_by1/boxplot2/boxplot_outliers3_flat\",\n      \"transform\": [\n        {\n          \"type\": \"treefacet\",\n          \"keys\": [\n            \"data.factor(cyl)\"\n          ]\n        }\n      ]\n    },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {}\n      },\n      \"values\": \"\\\"domain\\\"\\n\\\"4\\\"\\n\\\"6\\\"\\n\\\"8\\\"\"\n    },\n    {\n      \"name\": \"scale/xcenter\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {}\n      },\n      \"values\": \"\\\"domain\\\"\\n\\\"4\\\"\\n\\\"6\\\"\\n\\\"8\\\"\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"padding\": 0.1,\n      \"type\": \"ordinal\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"name\": \"x\",\n      \"points\": false,\n      \"sort\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"points\": true,\n      \"padding\": 1.1,\n      \"name\": \"xcenter\",\n      \"type\": \"ordinal\",\n      \"domain\": {\n        \"data\": \"scale/xcenter\",\n        \"field\": \"data.domain\"\n      },\n      \"sort\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      \"type\": \"group\",\n      \"from\": {\n        \"data\": \".0/group_by1/boxplot2\"\n      },\n      \"marks\": [\n        {\n          \"type\": \"rect\",\n          \"properties\": {\n            \"update\": {\n              \"stroke\": {\n                \"value\": \"#000000\"\n              },\n              \"fill\": {\n                \"value\": \"#90bff9\"\n              },\n              \"y\": {\n                \"scale\": \"y\",\n                \"field\": \"data.min_\"\n              },\n              \"y2\": {\n                \"scale\": \"y\",\n                \"field\": \"data.max_\"\n              },\n              \"x\": {\n                \"scale\": \"xcenter\",\n                \"field\": \"data.factor(cyl)\"\n              },\n              \"width\": {\n                \"value\": 0.5\n              }\n            },\n            \"ggvis\": {\n              \"data\": {\n                \"value\": \".0/group_by1/boxplot2\"\n              }\n            }\n          }\n        }\n      ]\n    },\n    {\n      \"type\": \"group\",\n      \"from\": {\n        \"data\": \".0/group_by1/boxplot2\"\n      },\n      \"marks\": [\n        {\n          \"type\": \"rect\",\n          \"properties\": {\n            \"update\": {\n              \"stroke\": {\n                \"value\": \"#000000\"\n              },\n              \"fill\": {\n                \"value\": \"#90bff9\"\n              },\n              \"x\": {\n                \"scale\": \"x\",\n                \"field\": \"data.factor(cyl)\"\n              },\n              \"y\": {\n                \"scale\": \"y\",\n                \"field\": \"data.lower_\"\n              },\n              \"y2\": {\n                \"scale\": \"y\",\n                \"field\": \"data.upper_\"\n              },\n              \"width\": {\n                \"scale\": \"x\",\n                \"band\": true\n              }\n            },\n            \"ggvis\": {\n              \"data\": {\n                \"value\": \".0/group_by1/boxplot2\"\n              }\n            }\n          }\n        }\n      ]\n    },\n    {\n      \"type\": \"group\",\n      \"from\": {\n        \"data\": \".0/group_by1/boxplot2\"\n      },\n      \"marks\": [\n        {\n          \"type\": \"rect\",\n          \"properties\": {\n            \"update\": {\n              \"stroke\": {\n                \"value\": \"#000000\"\n              },\n              \"fill\": {\n                \"value\": \"#90bff9\"\n              },\n              \"x\": {\n                \"scale\": \"x\",\n                \"field\": \"data.factor(cyl)\"\n              },\n              \"y\": {\n                \"scale\": \"y\",\n                \"field\": \"data.median_\"\n              },\n              \"width\": {\n                \"scale\": \"x\",\n                \"band\": true\n              },\n              \"height\": {\n                \"value\": 1\n              }\n            },\n            \"ggvis\": {\n              \"data\": {\n                \"value\": \".0/group_by1/boxplot2\"\n              }\n            }\n          }\n        }\n      ]\n    },\n    {\n      \"type\": \"group\",\n      \"from\": {\n        \"data\": \".0/group_by1/boxplot2/boxplot_outliers3\"\n      },\n      \"marks\": [\n        {\n          \"type\": \"symbol\",\n          \"properties\": {\n            \"update\": {\n              \"size\": {\n                \"value\": 50\n              },\n              \"fill\": {\n                \"value\": \"black\"\n              },\n              \"y\": {\n                \"scale\": \"y\",\n                \"field\": \"data.value_\"\n              },\n              \"x\": {\n                \"scale\": \"xcenter\",\n                \"field\": \"data.factor(cyl)\"\n              }\n            },\n            \"ggvis\": {\n              \"data\": {\n                \"value\": \".0/group_by1/boxplot2/boxplot_outliers3\"\n              }\n            }\n          }\n        }\n      ]\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"factor(cyl)\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id857762827\").parseSpec(plot_id857762827_spec);\n</script>\n</div>\n</div>\n<div id=\"小编有话说\" class=\"section level2\">\n<h2>小编有话说</h2>\n<p>本篇推送参考<a href=\"http://ggvis.rstudio.com/cookbook.html\">ggvis cookbook</a>，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。</p>\n</div>\n<div id=\"交互性\" class=\"section level2\">\n<h2>交互性</h2>\n<p><a href=\"https://ggvis.rstudio.com/interactivity.html\">Interactivity</a></p>\n</div>\n<div id=\"基本的交互式控件\" class=\"section level2\">\n<h2>基本的交互式控件</h2>\n<div class=\"sourceCode\" id=\"cb20\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb20-1\"><a href=\"#cb20-1\"></a>mtcars <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb20-2\"><a href=\"#cb20-2\"></a><span class=\"st\">  </span><span class=\"kw\">ggvis</span>(<span class=\"op\">~</span>wt, <span class=\"op\">~</span>mpg) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb20-3\"><a href=\"#cb20-3\"></a><span class=\"st\">  </span><span class=\"kw\">layer_smooths</span>(<span class=\"dt\">span =</span> <span class=\"kw\">input_slider</span>(<span class=\"fl\">0.5</span>, <span class=\"dv\">1</span>, <span class=\"dt\">value =</span> <span class=\"dv\">1</span>)) <span class=\"op\">%&gt;%</span></span>\n<span id=\"cb20-4\"><a href=\"#cb20-4\"></a><span class=\"st\">  </span><span class=\"kw\">layer_points</span>(<span class=\"dt\">size :=</span> <span class=\"kw\">input_slider</span>(<span class=\"dv\">100</span>, <span class=\"dv\">1000</span>, <span class=\"dt\">value =</span> <span class=\"dv\">100</span>))</span></code></pre></div>\n<pre><code>## Warning: Can&#39;t output dynamic/interactive ggvis plots in a knitr document.\n## Generating a static (non-dynamic, non-interactive) version of the plot.</code></pre>\n<div id=\"plot_id236241828-container\" class=\"ggvis-output-container\">\n<div id=\"plot_id236241828\" class=\"ggvis-output\"></div>\n<div class=\"plot-gear-icon\">\n<nav class=\"ggvis-control\">\n<a class=\"ggvis-dropdown-toggle\" title=\"Controls\" onclick=\"return false;\"></a>\n<ul class=\"ggvis-dropdown\">\n<li>\nRenderer: \n<a id=\"plot_id236241828_renderer_svg\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id236241828\" data-renderer=\"svg\">SVG</a>\n | \n<a id=\"plot_id236241828_renderer_canvas\" class=\"ggvis-renderer-button\" onclick=\"return false;\" data-plot-id=\"plot_id236241828\" data-renderer=\"canvas\">Canvas</a>\n</li>\n<li>\n<a id=\"plot_id236241828_download\" class=\"ggvis-download\" data-plot-id=\"plot_id236241828\">Download</a>\n</li>\n</ul>\n</nav>\n</div>\n</div>\n<script type=\"text/javascript\">\nvar plot_id236241828_spec = {\n  \"data\": [\n    {\n      \"name\": \".0\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"wt\": \"number\",\n          \"mpg\": \"number\",\n          \"reactive_229839864\": \"number\"\n        }\n      },\n      \"values\": 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   },\n    {\n      \"name\": \"scale/x\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n1.31745\\n5.61955\"\n    },\n    {\n      \"name\": \"scale/y\",\n      \"format\": {\n        \"type\": \"csv\",\n        \"parse\": {\n          \"domain\": \"number\"\n        }\n      },\n      \"values\": \"\\\"domain\\\"\\n9.225\\n35.075\"\n    }\n  ],\n  \"scales\": [\n    {\n      \"name\": \"x\",\n      \"domain\": {\n        \"data\": \"scale/x\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"width\"\n    },\n    {\n      \"name\": \"y\",\n      \"domain\": {\n        \"data\": \"scale/y\",\n        \"field\": \"data.domain\"\n      },\n      \"zero\": false,\n      \"nice\": false,\n      \"clamp\": false,\n      \"range\": \"height\"\n    }\n  ],\n  \"marks\": [\n    {\n      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       \"scale\": \"y\",\n            \"field\": \"data.mpg\"\n          },\n          \"size\": {\n            \"field\": \"data.reactive_229839864\"\n          }\n        },\n        \"ggvis\": {\n          \"data\": {\n            \"value\": \".0\"\n          }\n        }\n      },\n      \"from\": {\n        \"data\": \".0\"\n      }\n    }\n  ],\n  \"legends\": [],\n  \"axes\": [\n    {\n      \"type\": \"x\",\n      \"scale\": \"x\",\n      \"orient\": \"bottom\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"wt\"\n    },\n    {\n      \"type\": \"y\",\n      \"scale\": \"y\",\n      \"orient\": \"left\",\n      \"layer\": \"back\",\n      \"grid\": true,\n      \"title\": \"mpg\"\n    }\n  ],\n  \"padding\": null,\n  \"ggvis_opts\": {\n    \"keep_aspect\": false,\n    \"resizable\": true,\n    \"padding\": {},\n    \"duration\": 250,\n    \"renderer\": \"svg\",\n    \"hover_duration\": 0,\n    \"width\": 672,\n    \"height\": 480\n  },\n  \"handlers\": null\n};\nggvis.getPlot(\"plot_id236241828\").parseSpec(plot_id236241828_spec);\n</script>\n</div>\n</section>\n\n\n\n<!-- code folding -->\n\n\n<!-- dynamically load mathjax for compatibility with self-contained -->\n<script>\n  (function () {\n    var script = document.createElement(\"script\");\n    script.type = \"text/javascript\";\n    script.src  = \"https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\";\n    document.getElementsByTagName(\"head\")[0].appendChild(script);\n  })();\n</script>\n\n</body>\n</html>\n"
  },
  {
    "path": "2020年/2020.12.30ggvis/ggvis.md",
    "content": "## 简介\n[ggvis](http://ggvis.rstudio.com \"ggvis github\")是R的一个数据可视化包，它可以：\n\n- 使用与ggplot2类似的语法描述数据图形；\n\n- 创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；\n\n- 利用shiny的基础结构发布交互式图形。\n\n**ggvis 与 ggplot2主要区别**：\n\n- 基本命名转换：\n\n|ggplot|ggvis|\n|-|-|\n|geom|layer function|\n|stat|compute function|\n|aes|props|\n|+|%>%|\n\n- ggvis目前不支持分面；\n\n- 使用ggvis而不添加任何层类似于qplot\n\n> 更详细的区别可见：<http://ggvis.rstudio.com/ggplot2.html>\n\n这里先对包进行加载（可以直接使用instll.packages(\"\")下载）\n```{r message=FALSE, warning=FALSE}\nlibrary(ggvis)\nlibrary(dplyr)\n```\n\n## 静态图\n\n### 1 散点图\n\n使用`layer_points()`绘制，其中内部参数都用默认值。注意这里`ggvis(~wt, ~mpg) `比ggplot多了一个波浪线。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>% \n  layer_points()\n```\n![](https://imgkr2.cn-bj.ufileos.com/e05e6ea6-945d-49eb-bf21-eb530501cb53.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=LXs5vhSIWoeH%252BPk02VPMmv3o4us%253D&Expires=1609416269)\n\n\n\n如果要加拟合线，和ggplot语法很类似，再加一层`layer_smooths()`。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>%\n  layer_points() %>%\n  layer_smooths()\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/a63e61ca-6a9c-4799-9d42-79dfc8eed04b.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=7HcrOMNKMPjazM9ggUWg0x7w8hI%253D&Expires=1609416281)\n\n\n\n\n内部参数也很类似（`se = TRUE`加入拟合区间），拟合方式使用\"lm\"方法。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>%\n  layer_points() %>%\n  layer_model_predictions(model = \"lm\", se = TRUE)\n```\n![](https://imgkr2.cn-bj.ufileos.com/2c4cf0e9-942d-40d2-badc-62badaa1cfe3.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=RTssV26FflgDOGONU21qxGqIk%252BM%253D&Expires=1609416292)\n\n\n### 2 分组的散点图\n\n如果想要使用分组说明散点图，可以加入`fill = ~factor(cyl)`或者`group_by(cyl)`进行分布。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>% \n  layer_points(fill = ~factor(cyl))\n```\n![](https://imgkr2.cn-bj.ufileos.com/6c6425a9-1eab-4807-8c42-252c0e230820.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=fs0d49JC85yxV8PF3rfmpE0ghmw%253D&Expires=1609415742)\n\n如果想要预测每组数据拟合情况，可以使用`ayer_model_predictions()`。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>% \n  layer_points() %>% \n  group_by(cyl) %>% \n  layer_model_predictions(model = \"lm\")\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/736edb8b-4921-40e7-a29b-81e9f7b738bf.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=pLrTlICigwIqryUu%252F8g4rT7sqI8%253D&Expires=1609415748)\n\n### 3 柱状图\n\n柱状图是使用`layer_bars()`函数，内部参数包括width（设置柱子宽度）等。\n\n```{r}\nhead(pressure)\n\npressure %>% \n  ggvis(~temperature, ~pressure) %>%\n  layer_bars(fill := \"#ff8080\")\n```\n![](https://imgkr2.cn-bj.ufileos.com/9be6d335-8709-416c-8f15-bfd60895602e.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=sOXRDgS2IAgJRChrA%252FQunzIBaNg%253D&Expires=1609415753)\n```{r}\npressure %>% \n  ggvis(~temperature, ~pressure) %>%\n  layer_bars(width = 15,fill := \"#ff8080\")\n```\n![](https://imgkr2.cn-bj.ufileos.com/ea13652f-f47f-420b-9290-b26f6acfc6d9.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=zs%252BCjmsAMfs6y8J6SiXRrQvdNHg%253D&Expires=1609415758)\n### 4 曲线图\n\n使用`layer_lines()`绘制曲线图，当然你可以和散点图合并，效果更好。\n```{r}\npressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()\n```\n![](https://imgkr2.cn-bj.ufileos.com/618bc22c-12d7-4d3a-be90-3369f086c589.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gC7M9I7zFB5mlE2DtswbIWI1pFM%253D&Expires=1609415765)\n\n\n```{r}\npressure %>% ggvis(~temperature, ~pressure) %>%\n  layer_points(size := 50) %>% \n  layer_lines()\n```\n![](https://imgkr2.cn-bj.ufileos.com/07d3bb9d-8610-45e7-a1e4-33cb85ec3349.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=wWoV7E%252F8mLO%252F6MP0bynE3nTRglY%253D&Expires=1609415771)\n\n\n\n### 5 直方图\n使用`layer_histograms()`绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。\n\n```{r}\nhead(faithful)\n\nfaithful %>% ggvis(~eruptions, fill := \"#ff8080\") %>%\n  layer_histograms(width=0.25, boundary=0) %>% \n  add_axis(\"x\", title = \"month\") %>%\n  add_axis(\"y\", title = \"count\")\n\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/9d792a91-74c5-4b68-9488-a5ed9821a5b6.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=LIfmPNVBe0FVYDGuU%252FvyK%252BmUiUA%253D&Expires=1609415818)\n\n\n\n```{r}\nfaithful %>% ggvis(~eruptions, fill := \"#90bff9\") %>%\n  layer_histograms(width=0.25, center=0) %>% \n  add_axis(\"x\", title = \"month\") %>%\n  add_axis(\"y\", title = \"count\")\n```\n\n![](https://imgkr2.cn-bj.ufileos.com/09cb5684-f255-43fc-91ac-c5171157e4a2.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=HkPrRKYfaZWYtRUnisPymCIEsFQ%253D&Expires=1609415823)\n\n\n> add_axis()可以设置坐标轴的名称等其他参数。\n\n### 6 箱型图\n\n使用`layer_boxplots()`绘制箱型图，具体内部参数再次不做具体陈述。\n\n```{r}\nmtcars %>% \n  ggvis(~factor(cyl), ~mpg) %>% \n  layer_boxplots(fill := \"#90bff9\") \n```\n\n![](https://imgkr2.cn-bj.ufileos.com/35f114a1-6a53-4b5d-a8d8-8086b891d524.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=v9oowcqbxPwcZ3i%252FKA%252BnY0cXj%252FE%253D&Expires=1609415827)\n\n\n## 小编有话说\n\n本篇推送参考[ggvis cookbook](http://ggvis.rstudio.com/cookbook.html \"ggvis cookbook\")，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。\n\n\n"
  },
  {
    "path": "2020年/2020.12.30ggvis/ggvis.rmd",
    "content": "---\ntitle: \"ggvis包\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n  prettydoc::html_pretty:\n    theme: cayman\n    highlight: github\nvignette: >\n  %\\VignetteIndexEntry{Vignette Title}\n  %\\VignetteEncoding{UTF-8}\n  %\\VignetteEngine{knitr::rmarkdown}\neditor_options: \n  chunk_output_type: console\n---\n\n\n\n## 简介\n[ggvis](http://ggvis.rstudio.com)是R的一个数据可视化包，它可以：\n\n- 使用与ggplot2类似的语法描述数据图形；\n\n- 创建丰富的交互式图形，在本地Rstudio或浏览器中使用这些图形；\n\n- 利用shiny的基础结构发布交互式图形。\n\n**ggvis 与 ggplot2主要区别**：\n\n- 基本命名转换：\n\nggplot→ggvis\n\ngeom→layer function\n\nstat→compute function\n\naes→props\n\n+→%>%\n\n- ggvis目前不支持分面；\n\n- 使用ggvis而不添加任何层类似于qplot\n\n> 更详细的区别可见：[ggvis vs ggplot2](http://ggvis.rstudio.com/ggplot2.html)\n\n```{r message=FALSE, warning=FALSE}\nlibrary(ggvis)\nlibrary(dplyr)\n```\n\n## 静态图\n\n### 散点图\n\n使用`layer_points()`绘制，其中内部参数都用默认值。注意这里`ggvis(~wt, ~mpg) `比ggplot多了一个波浪线。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>% \n  layer_points()\n```\n如果要加拟合线，和ggplot语法很类似，再加一层`layer_smooths()`。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>%\n  layer_points() %>%\n  layer_smooths()\n```\n内部参数也很类似（`se = TRUE`加入拟合区间），拟合方式使用\"lm\"方法。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>%\n  layer_points() %>%\n  layer_model_predictions(model = \"lm\", se = TRUE)\n```\n\n### 分组的散点图\n\n如果想要使用分组说明散点图，可以加入`fill = ~factor(cyl)`或者`group_by(cyl)`进行分布。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg) %>% \n  layer_points(fill = ~factor(cyl))\n```\n如果想要预测每组数据拟合情况，可以使用`ayer_model_predictions()`。\n```{r}\nmtcars %>% \n  ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>% \n  layer_points() %>% \n  group_by(cyl) %>% \n  layer_model_predictions(model = \"lm\")\n```\n\n### 柱状图\n\n柱状图是使用`layer_bars()`函数，内部参数包括width（设置柱子宽度）等。\n\n```{r}\nhead(pressure)\n\npressure %>% \n  ggvis(~temperature, ~pressure) %>%\n  layer_bars(fill := \"#ff8080\")\n```\n\n```{r}\npressure %>% \n  ggvis(~temperature, ~pressure) %>%\n  layer_bars(width = 15,fill := \"#ff8080\")\n```\n\n### 曲线图\n\n使用`layer_lines()`绘制曲线图，当然你可以和散点图合并，效果更好。\n```{r}\npressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()\n```\n\n```{r}\npressure %>% ggvis(~temperature, ~pressure) %>%\n  layer_points(size := 50) %>% \n  layer_lines()\n```\n\n\n### 直方图\n使用`layer_histograms()`绘制直方图，内部参数包括width（柱子宽度），boundary（两个箱子之间的边界），center（柱子中央为中心）等。\n```{r}\nhead(faithful)\n\nfaithful %>% ggvis(~eruptions, fill := \"#ff8080\") %>%\n  layer_histograms(width=0.25, boundary=0) %>% \n  add_axis(\"x\", title = \"month\") %>%\n  add_axis(\"y\", title = \"count\")\n\n```\n\n```{r}\nfaithful %>% ggvis(~eruptions, fill := \"#90bff9\") %>%\n  layer_histograms(width=0.25, center=0) %>% \n  add_axis(\"x\", title = \"month\") %>%\n  add_axis(\"y\", title = \"count\")\n```\n\n\n### 箱型图\n\n使用`layer_boxplots()`绘制箱型图，具体内部参数再次不做具体陈述。\n\n```{r}\nmtcars %>% ggvis(~factor(cyl), ~mpg) %>% layer_boxplots(fill := \"#90bff9\") \n```\n\n\n## 小编有话说\n\n本篇推送参考[ggvis cookbook](http://ggvis.rstudio.com/cookbook.html)，小编也只是一个搬运工。这篇主要是对该包中的常见图形进行静态展示，但是其实这个包更强大的功能在于交互式。鉴于本文内容较多，将在下次对这个包的交互使用进行详细解释。\n\n## 交互性\n\n[Interactivity](https://ggvis.rstudio.com/interactivity.html)\n\n## 基本的交互式控件\n```{r}\nmtcars %>%\n  ggvis(~wt, ~mpg) %>%\n  layer_smooths(span = input_slider(0.5, 1, value = 1)) %>%\n  layer_points(size := input_slider(100, 1000, value = 100))\n```\n\n\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/2020.08.23几何对象/几何对象.md",
    "content": "## 前言\n\n本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。第一章目录如下：![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenWvrnBtdFRLwcPLeN3Peia1xnHicVnaCa1YeoVGaBu7F1WtRTS3ic1YYUg/640?wx_fmt=png)前面几节的内容比较少，第1.5节我对其做了补充，可见[R可视乎|分面一页多图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&scene=21#wechat_redirect)，课后练习题也可在该篇文章中找到。1.6节主要讲几何对象：表示数据的几何图形对象，比如条形图，折线图，箱线图等。要想改变图中的几何对象，需要修改添加在ggplot\\(\\) 函数中的几何对象函数。1.6节的内容不是很多，我们主要通过写本节的练习来回顾知识点。\n\n## 练习\n\n### \\(1\\) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？\n\n**答**：geom\\_line\\(\\),   geom\\_boxplot\\(\\),  geom\\_histogram\\(\\),\n\n### \\(2\\) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。\n\n`ggplot(data = mpg,  \n  mapping = aes(x = displ, y = hwy, color = drv)) +  \n  geom_point() +  \n  geom_smooth(se = FALSE)  \n`\n\n想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes\\(x = displ, y = hwy, color = drv\\)\\)。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenC3nb0Q0tBfeLsWqKLM9xMybnHTY9laoezVgBtJhAQtCuNSWbAbWhibg/640?wx_fmt=png)\n\n### \\(3\\)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？\n\n**答**：是把图例隐藏了，默认参数是show.legend = TRUE.\n\n### \\(4\\)  geom\\_smooth\\(\\) 函数中的 se 参数的作用是什么？\n\n**答**：可以绘制出区间，当se = TRUE（默认）则会出现区间。\n\n### \\(5\\) 以下代码生成的两张图有什么区别吗？为什么？\n\n`#第一幅图  \nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +  \ngeom_point() +  \ngeom_smooth()  \n#第二幅图  \nggplot() +  \ngeom_point(data = mpg,  \nmapping = aes(x = displ, y = hwy)  \n) +  \ngeom_smooth(data = mpg,  \nmapping = aes(x = displ, y = hwy)  \n)  \n`\n\n没有什么区别，第一个图在原始上就设定了x，y。后面两个集合对象就可以默认使用前面的设置了。而第二个图则是原始没有设置，而是在集合对象中一一设置了。  \n第一种方法：简便；第二种方法：灵活，可以设置不同的x，y。\n\n### \\(6\\) 自己编写 R 代码来生成以下各图\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenGuoerDicLZuE7qdOZE4oeX5Q98ZtLiaplXQp8bAOBPmp5oGtAnpHiahew/640?wx_fmt=png)\n\n**第一个图：** x为displ，y为hwy。画了散点图（geom\\_point）并绘制了拟合曲线（geom\\_smooth），没加置信区间（se =FALSE）\n\n`ggplot(data = mpg,aes(x = displ,y = hwy))+  \n\tgeom_point(size=3)+  \n\tgeom_smooth(se=F,size=2)  \n`\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenPBfZBjDqY9sr4IOVM5wKbRemDGhX1pGxImKQZzzpYKqb60E1tJlMibw/640?wx_fmt=png)\n\n**第二个图：** 在第一个基础上根据drv变量绘制了三条拟合曲线，并且没有绘制区间。并且把图例删除了（show.legend = FALSE）\n\n```\nggplot(data = mpg,aes(x = displ,y = hwy))+\tgeom_point(size=3)+\tgeom_smooth(aes(fill=drv),se=F,size=2,show.legend = FALSE)\n```\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenWCtTf9TjquM9ubJF9ibibC6CM0CWfiaicjPfich5RfRAMUEa0tyYnWABqCA/640?wx_fmt=png)\n\n**第三个图：** 散点图颜色的颜色根据drv变量进行变化，并且拟合曲线也是和散点图相同颜色（所以可以在最原始图层中加入color=drv），没有拟合曲线的区间，但是有图例（默认就是有的）。\n\n`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  \n\tgeom_point(size=3)+  \n\tgeom_smooth(se=F,size=2)  \n`\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenN9BC3yu7cB9cRyvbwROMia6zYxSuRyQBS07Zea58dOYtTm9pyZ2c7Mg/640?wx_fmt=png)\n\n**第四个图：** 根据frv变量给散点图填充，但是只绘制了一条拟合线。所以这里不可以直接放在原始图层里，得放在geom\\_point\\(\\)中。\n\n`ggplot(data = mpg,aes(x = displ,y = hwy))+  \n\tgeom_point(aes(color=drv),size=3)+  \n\tgeom_smooth(se=F,size=2)  \n`\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjenaWcJd6T5EDU7bvZ3TVpibbPb2SJBP9ibbgiaOZG8Iicloya46yL3icsTIdQ/640?wx_fmt=png)\n\n**第五幅图：**在第三幅图基础上改变了拟合曲线的线的类型\\(linetype\\)。\n\n`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  \n\tgeom_point(size=3)+  \n\tgeom_smooth(aes(linetype=drv),se=F,size=2)  \n`\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjen6AoK8icH7MTiaQrnW8xicQbrzkVpNmHCEzWFWdwgTghLf7z3WoqhhwxLg/640?wx_fmt=png)\n\n**第六幅图：** 没有拟合曲线，直接将散点图的颜色区分开了，有点像第三幅图的简化版。\n\n`ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+  \n\tgeom_point(size=3)  \n`\n\n![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjOOibYl3m2UdPdjen59Lak7t1buKRSiacyTLTDWciaD0ThruzghrU0fF9KNNwAU2V3dqsyQlw/640?wx_fmt=png)\n\n## 参考\n\n1.  R for Data Science \\[https://r4ds.had.co.nz/\\]\n\n  \n\n![](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgfkvXafZE9scXp4icvdcNFyic0z7THajQBAyLNRiau3CKnZ3L9Y9K2YXObhaiblBm0Jbnicaq9lW3pz4g/640?wx_fmt=jpeg)  \n欢迎**关注**我的**公众号**，**点赞，在看，收藏\\~\\~\\~**"
  },
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{color:white}\n.CtxtMenu_MenuArrow.CtxtMenu_RTL {left:.5em; right:auto}\n.CtxtMenu_MenuCheck {  position:absolute; left:.7em;  font-family: null}\n.CtxtMenu_MenuCheck.CtxtMenu_RTL { right:.7em; left:auto }\n.CtxtMenu_MenuRadioCheck {  position:absolute; left: .7em;}\n.CtxtMenu_MenuRadioCheck.CtxtMenu_RTL {  right: .7em; left:auto}\n.CtxtMenu_MenuInputBox {  padding-left: 1em; right:.5em; color:#666666;  font-family: null;}\n.CtxtMenu_MenuInputBox.CtxtMenu_RTL {  left: .1em;}\n.CtxtMenu_MenuComboBox {  left:.1em; padding-bottom:.5em;}\n.CtxtMenu_MenuLabel {  padding: 1px 2em 3px 1.33em;  font-style:italic}\n.CtxtMenu_MenuRule {  border-top: 1px solid #DDDDDD;  margin: 4px 3px;}\n.CtxtMenu_MenuDisabled {  color:GrayText}\n.CtxtMenu_MenuActive {  background-color: #606872;  color: white;}\n.CtxtMenu_MenuDisabled:focus {  background-color: #E8E8E8}\n.CtxtMenu_MenuLabel:focus {  background-color: #E8E8E8}\n.CtxtMenu_ContextMenu:focus {  outline:none}\n.CtxtMenu_ContextMenu .CtxtMenu_MenuItem:focus {  outline:none}\n.CtxtMenu_Menu .CtxtMenu_MenuClose {  top:-10px; left:-10px}\n</style><style id=\"MJX-SVG-styles\">\nmjx-container[jax=\"SVG\"] {\n  direction: ltr;\n}\n\nmjx-container[jax=\"SVG\"] > svg {\n  overflow: visible;\n}\n\nmjx-container[jax=\"SVG\"] > svg a {\n  fill: blue;\n  stroke: blue;\n}\n\nmjx-assistive-mml {\n  position: absolute !important;\n  top: 0px;\n  left: 0px;\n  clip: rect(1px, 1px, 1px, 1px);\n  padding: 1px 0px 0px 0px !important;\n  border: 0px !important;\n  display: block !important;\n  width: auto !important;\n  overflow: hidden !important;\n  -webkit-touch-callout: none;\n  -webkit-user-select: none;\n  -khtml-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n\nmjx-assistive-mml[display=\"block\"] {\n  width: 100% !important;\n}\n\nmjx-container[jax=\"SVG\"][display=\"true\"] {\n  display: block;\n  text-align: center;\n  margin: 1em 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.nice-themeselect-theme-item-name.nice-menu-subscribe-more.cc_pointer, .nice-menu-shortcut, .nice-menu-shortcut.cc_pointer, .nice-footer-message, .nice-footer-engine.ant-dropdown-trigger, .nice-footer-engine.ant-dropdown-trigger.cc_pointer, .nice-footer-message.cc_pointer, .ant-btn.nice-btn-login.ant-btn-circle, .ant-btn.nice-btn-login.ant-btn-circle.cc_pointer, .cc_pointer,[type=\"search\"]::-webkit-search-cancel-button, [type=\"search\"]::-webkit-search-decoration, .paper-button, .ytp-progress-bar-container, input[type=submit], :link, :visited, a > *, img, button, ::-webkit-scrollbar-button, .ogdlpmhglpejoiomcodnpjnfgcpmgale_pointer, ::-webkit-file-upload-button, button, .ytp-volume-panel, #myogdlpmhglpejoiomcodnpjnfgcpmgale .icon { cursor: 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22 3, auto !important; } </style><style id=\"markdown-theme\">/*自定义样式，实时生效*/\n#nice {\n}\n\n#nice p {\n\tmargin: 0 0 20px;\n\tpadding: 0;\n\tline-height: 1.8em;\n\tcolor: #3a3a3a;\n}\n\n/* 一级标题 */\n#nice h1 {\n  font-size: 2.1em;\n\tline-height: 1.1em;\n\tpadding-top: 16px;\n  padding-bottom: 10px;\n  margin-bottom: 4px;\n  border-bottom: 1px solid #c99833;\n}\n/* 一级标题内容 */\n#nice h1 .content {\n  color: #515151;\n  font-weight: 700;\n}\n\n#nice h2, h3, h4, h5, h6 {\n line-height: 1.5em;\n margin-top: 2.2em;\n margin-bottom: 4px;\n}\n\n/* 一级标题修饰 请参考有实例的主题 */\n#nice h1:after {}\n\n/* 二级标题 */\n#nice h2 {\n margin-bottom: 35px;\n}\n\n/* 二级标题内容 */\n\n#nice h2 .content {\n  display: inline-block;\n  font-weight: bold;\n  background: linear-gradient(#fff 60%, #ffb11b 40%);\n  color: #515151;\n  padding: 2px 13px 2px;\n  margin-right: 3px;\n  height: 50%;\n}\n\n/* 二级标题修饰 请参考有实例的主题 */\n#nice h2:after {}\n\n/* 三级标题 */\n#nice h3 {\n  line-height: 1.4;\n  padding-top: 10px;\n  margin: 10px 0 5px;\n}\n\n/* 三级标题内容 */\n#nice h3 .content {\n  color: #515151;\n  font-weight: 700;\n font-size: 1.0em;\n  padding-left: 20px;\n  border-left: 3px solid #f9bf45;\n}\n\n/* 三级标题修饰 请参考有实例的主题 */\n#nice h3:after {}\n\n/* 引用\n* 左边缘颜色 border-left-color: black;\n* 背景色 background: gray;\n*/\n#nice blockquote {\n  border-left-color: #ffb11b;\n background: #fff5e3;\n}\n\n/* 引用文字 */\n#nice blockquote p {\n  color: #595959;\n}\n\n/* 链接 */\n#nice a {\n  border: none;\n  text-decoration: none;\n  color: #dda52d;\n}\n\n#nice a:hover {\n  color: #f9bf45;\n  text-decoration: underline;\n}\n\n/* 无序列表整体样式\n * list-style-type: square|circle|disc;\n */\n#nice ul {\n}\n\n/* 有序列表整体样式\n * list-style-type: upper-roman|lower-greek|lower-alpha;\n */\n#nice ol {\n}\n\n/* 列表内容，不要设置li\n */\n#nice li section {\n}\n\n/* 加粗 */\n#nice strong {}\n\n/* 斜体 */\n#nice em {}\n\n/* 加粗斜体 */\n#nice em strong {}\n\n/* 删除线 */\n#nice del {\n  color: #d19826;\n}\n\n/* 分隔线\n* 粗细、样式和颜色\n* border-top: 1px solid #3e3e3e;\n*/\n#nice hr {\n  border-top: 1px solid #f9bf45;\n  margin: 20px 0px;\n}\n\n/* 图片\n* 宽度 width: 80%;\n* 居中 margin: 0 auto;\n* 居左 margin: 0 0;\n*/\n#nice img {\n  width: 100%;\n border-radius: 5px;\n display: block;\n margin-bottom: 15px;\n height: auto;\n}\n\n/* 图片描述文字 */\n#nice figcaption {\n  color: #dda52d;\n  font-size: 14px;\n}\n\n/* 行内代码 */\n#nice p code, #nice li code {\n  color: #9b6e23;\n  background-color: #fff5e3;\n  padding: 3px;\n  margin: 3px;\n}\n\n/* 非微信代码块\n * 代码块不换行 display: -webkit-box !important;\n * 代码块换行 display: block;\n */\n#nice pre code {}\n\n/*\n * 表格内的单元格\n * 字体大小 font-size: 16px;\n * 边框 border: 1px solid #ccc;\n * 内边距 padding: 5px 10px;\n */\n#nice table tr th,\n#nice table tr td {\n  text-align: center;\n}\n\n/* 脚注文字 */\n#nice .footnote-word {\n  color: #ffb11b;\n  padding: 3px;\n}\n\n/* 脚注上标 */\n#nice .footnote-ref {\n  color: #dda52d;\n  margin: 2px;\n  padding: 3px;\n}\n\n/* \"参考资料\"四个字 \n * 内容 content: \"参考资料\";\n */\n#nice .footnotes-sep:before {\n  margin: 30px 0px 15px 0px;\n  font-weight: 800;\n}\n\n\n/* 参考资料编号 */\n#nice .footnote-num {\n}\n\n/* 参考资料文字 */\n#nice .footnote-item p { \n}\n\n/* 参考资料解释 */\n#nice .footnote-item p em {\n}\n\n/* 行间公式\n * 最大宽度 max-width: 300% !important;\n */\n#nice .block-equation svg {\n}\n\n/* 行内公式\n */\n#nice .inline-equation svg {  \n}\n\n/* 滑动图片\n */\n#nice .imageflow-img {\n  display: inline-block;\n  width:100%;\n  margin-bottom: 0;\n}</style><style id=\"code-theme\">/*\n\nAtom One Light by Daniel Gamage\nOriginal One Light Syntax theme from https://github.com/atom/one-light-syntax\n\nbase:    #fafafa\nmono-1:  #383a42\nmono-2:  #686b77\nmono-3:  #a0a1a7\nhue-1:   #0184bb\nhue-2:   #4078f2\nhue-3:   #a626a4\nhue-4:   #50a14f\nhue-5:   #e45649\nhue-5-2: #c91243\nhue-6:   #986801\nhue-6-2: #c18401\n\n*/\n\n.hljs {\n  display: block;\n  overflow-x: auto;\n  padding: 16px;\n  color: #383a42;\n  background: #fafafa;\n}\n\n.hljs-comment,\n.hljs-quote {\n  color: #a0a1a7;\n  font-style: italic;\n}\n\n.hljs-doctag,\n.hljs-keyword,\n.hljs-formula {\n  color: #a626a4;\n}\n\n.hljs-section,\n.hljs-name,\n.hljs-selector-tag,\n.hljs-deletion,\n.hljs-subst {\n  color: #e45649;\n}\n\n.hljs-literal {\n  color: #0184bb;\n}\n\n.hljs-string,\n.hljs-regexp,\n.hljs-addition,\n.hljs-attribute,\n.hljs-meta-string {\n  color: #50a14f;\n}\n\n.hljs-built_in,\n.hljs-class .hljs-title {\n  color: #c18401;\n}\n\n.hljs-attr,\n.hljs-variable,\n.hljs-template-variable,\n.hljs-type,\n.hljs-selector-class,\n.hljs-selector-attr,\n.hljs-selector-pseudo,\n.hljs-number {\n  color: #986801;\n}\n\n.hljs-symbol,\n.hljs-bullet,\n.hljs-link,\n.hljs-meta,\n.hljs-selector-id,\n.hljs-title {\n  color: #4078f2;\n}\n\n.hljs-emphasis {\n  font-style: italic;\n}\n\n.hljs-strong {\n  font-weight: bold;\n}\n\n.hljs-link {\n  text-decoration: underline;\n}\n\n#nice .custom code {\n  padding-top: 15px;\n  background: #fafafa;\n  border-radius: 5px;\n}\n\n#nice .custom:before {\n  content: '';\n  display:block;\n  background: url(https://my-wechat.mdnice.com/point.png);\n  height: 30px;\n  width: 100%;\n  background-size:40px;\n  background-repeat: no-repeat;\n  background-color: #fafafa;\n  margin-bottom: -7px;\n  border-radius: 5px;\n  background-position: 10px 10px;\n}\n\n#nice .custom {\n  border-radius: 5px;\n  box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px;\n}</style></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id=\"root\"><div style=\"display: flex;\"><div class=\"nice-app\"><div class=\"nice-navbar\"><div class=\"nice-left-nav\"><section id=\"nice-title\" class=\"nice-title\">Markdown Nice</section><a id=\"nice-menu-file\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">文件</a><a id=\"nice-menu-pattern\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">格式</a><a id=\"nice-menu-function\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">功能</a><a id=\"nice-menu-view\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">查看</a><a id=\"nice-menu-theme\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">主题</a><a id=\"nice-menu-codetheme\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">代码主题</a><a id=\"nice-menu-setting\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">设置</a><a id=\"nice-menu-help\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">帮助</a></div><div class=\"nice-right-nav\"><button type=\"button\" class=\"ant-btn nice-btn-login ant-btn-circle cc_pointer\"><svg viewBox=\"0 0 1024 1024\" class=\"nice-btn-login-icon\" fill=\"rgba(0,0,0,0.65)\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><path d=\"M512 16C238 16 16 238 16 512s222 496 496 496 496-222 496-496S786 16 512 16z m256 843.2c-71.8 53-160.2 84.8-256 84.8s-184.2-31.8-256-84.8V832c0-70.6 57.4-128 128-128 22.2 0 55 22.8 128 22.8 73.2 0 105.6-22.8 128-22.8 70.6 0 128 57.4 128 128v27.2z m61.2-55c-13.6-92.8-92.6-164.2-189.2-164.2-41 0-60.8 22.8-128 22.8S425.2 640 384 640c-96.6 0-175.6 71.4-189.2 164.2C123.8 727.2 80 624.8 80 512c0-238.2 193.8-432 432-432s432 193.8 432 432c0 112.8-43.8 215.2-114.8 292.2zM512 240c-97.2 0-176 78.8-176 176s78.8 176 176 176 176-78.8 176-176-78.8-176-176-176z m0 288c-61.8 0-112-50.2-112-112s50.2-112 112-112 112 50.2 112 112-50.2 112-112 112z\" p-id=\"5538\"></path></svg></button></div></div><div class=\"nice-text-container\"><div id=\"nice-md-editor\" class=\"nice-md-editing\"><textarea style=\"display: none;\"></textarea><div class=\"CodeMirror cm-s-md-mirror CodeMirror-wrap CodeMirror-focused\" style=\"width: 100%; height: 100%;\"><div style=\"overflow: hidden; position: relative; width: 3px; height: 0px; top: 98.5px; left: 72px;\"><textarea autocorrect=\"off\" autocapitalize=\"off\" spellcheck=\"false\" 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role=\"presentation\"><div role=\"presentation\" style=\"position: relative; outline: none;\"><div class=\"CodeMirror-measure\"><pre class=\"CodeMirror-line-like\"><span>xxxxxxxxxx</span></pre></div><div class=\"CodeMirror-measure\"></div><div style=\"position: relative; z-index: 1;\"></div><div class=\"CodeMirror-cursors\" style=\"visibility: hidden;\"><div class=\"CodeMirror-cursor\" style=\"left: 52px; top: 74.5px; height: 25.5px;\">&nbsp;</div></div><div class=\"CodeMirror-code\" role=\"presentation\" style=\"\"><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-link\">[R数据科学]</span> 1.6几何对象</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。前面几节的内容比较少，第1.5节</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/68657890-31e3-4050-a1b0-e3d9dd369b08.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=xxhAwDgNHHoS6%252B5pdJ2mlIkVK6c%253D&amp;Expires=1598255421)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">1.6节的内容不是很多，我们通过写本节的练习来回顾知识点：</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-2\">## (1) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-strong\">**答**</span>： geom_line(), &nbsp; geom_boxplot(),  geom_histogram(),</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-2\">## (2) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-comment\">```{r}</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-comment\">ggplot(data = mpg,</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"> &nbsp;<span class=\"cm-comment\">mapping = aes(x = displ, y = hwy, color = drv)) +</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"> &nbsp;<span class=\"cm-comment\">geom_point() +</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"> &nbsp;<span class=\"cm-comment\">geom_smooth(se = FALSE)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-comment\">```</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes(x = displ, y = hwy, color = drv))。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/548a3c56-9776-459c-adc4-5014f0d8e5f0.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=MzTb0eRJfMrwlO9rxcvr3%252FLvqjM%253D&amp;Expires=1598254653)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-2\">## (3)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-strong\">**答**</span>：是把图例隐藏了，默认参数是show.legend = TRUE.</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-2\">## (4)  geom_smooth() 函数中的 se 参数的作用是什么？</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-strong\">**答**</span>：可以绘制出区间，当se = TRUE（默认）则会出现区间。</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre></div></div></div></div></div><div style=\"position: absolute; height: 24px; width: 1px; border-bottom: 0px solid transparent; top: 3883px;\"></div><div class=\"CodeMirror-gutters\" style=\"display: none; height: 3907px;\"></div></div></div></div><div id=\"nice-rich-text\" class=\"nice-marked-text nice-marked-text-pc\"><div class=\"nice-sidebar\"><a 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xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><path d=\"M736 0l-448 0c-52.8 0-96 43.2-96 96l0 832c0 52.8 43.2 96 96 96l448 0c52.8 0 96-43.2 96-96l0-832c0-52.8-43.2-96-96-96zM384 48l256 0 0 32-256 0 0-32zM512 960c-35.346 0-64-28.654-64-64s28.654-64 64-64 64 28.654 64 64-28.654 64-64 64zM768 768l-512 0 0-640 512 0 0 640z\"></path></svg></a></div><div id=\"nice-rich-text-box\" class=\"nice-wx-box nice-wx-box-pc\"><section id=\"nice\" data-tool=\"mdnice编辑器\" data-website=\"https://www.mdnice.com\"><p>[R数据科学] 1.6几何对象</p>\n<p>本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。前面几节的内容比较少，第1.5节\n<img src=\"./让微信排版变 Nice_files/68657890-31e3-4050-a1b0-e3d9dd369b08.png\" alt=\"\">\n1.6节的内容不是很多，我们通过写本节的练习来回顾知识点：</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">(1) 在绘制折线图、箱线图、直方图和分区图时，应该分别使用哪种几何对象？</span><span class=\"suffix\"></span></h2>\n<p><strong>答</strong>： geom_line(),   geom_boxplot(),  geom_histogram(),</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">(2) 在脑海中运行以下代码，并预测会有何种输出。接着在 R 中运行代码，并检查你的预测是否正确。</span><span class=\"suffix\"></span></h2>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,\n  mapping = aes(x = displ, y = hwy, color = drv)) +\n  geom_point() +\n  geom_smooth(se = FALSE)\n</code></pre>\n<p>想象下：x轴是displ，y轴是hwy，颜色使用drv填充，然后加了一个散点图（中间没有参数，用默认），再加上一个拟合曲线，没有绘制出区间。由于两个集合对象都没有对mapping进行设置，所以会使用原始涂层的aes(x = displ, y = hwy, color = drv))。所以出来的结果，散点图和拟合曲线都是三种颜色。下面是出来的颜色：</p>\n<figure><img src=\"./让微信排版变 Nice_files/548a3c56-9776-459c-adc4-5014f0d8e5f0.png\" alt=\"\"></figure>\n<h2><span class=\"prefix\"></span><span class=\"content\">(3)  show.legend = FALSE 的作用是什么？删除它会发生什么情况？</span><span class=\"suffix\"></span></h2>\n<p><strong>答</strong>：是把图例隐藏了，默认参数是show.legend = TRUE.</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">(4)  geom_smooth() 函数中的 se 参数的作用是什么？</span><span class=\"suffix\"></span></h2>\n<p><strong>答</strong>：可以绘制出区间，当se = TRUE（默认）则会出现区间。</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">(5) 以下代码生成的两张图有什么区别吗？为什么？</span><span class=\"suffix\"></span></h2>\n<pre class=\"custom\"><code class=\"hljs\">#第一幅图\nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +\ngeom_point() +\ngeom_smooth()\n#第二幅图\nggplot() +\ngeom_point(data = mpg,\nmapping = aes(x = displ, y = hwy)\n) +\ngeom_smooth(data = mpg,\nmapping = aes(x = displ, y = hwy)\n)\n</code></pre>\n<p>没有什么区别，第一个图在原始上就设定了x，y。后面两个集合对象就可以默认使用前面的设置了。而第二个图则是原始没有设置，而是在集合对象中一一设置了。</p>\n<p>第一种方法：简便；是第二种方法灵活，可以设置不同的x，y。</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">(6) 自己编写 R 代码来生成以下各图。</span><span class=\"suffix\"></span></h2>\n<figure><img src=\"./让微信排版变 Nice_files/895d147f-bbd7-4e9c-b315-92ef71266684.png\" alt=\"\"></figure>\n<p>第一个图，x为displ，y为hwy。画了散点图（geom_point）并绘制了拟合曲线（geom_smooth），没加置信区间（se =FALSE）</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy))+\n\tgeom_point(size=3)+\n\tgeom_smooth(se=F,size=2)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/1688792f-a05a-46c7-bd43-8b3072842fe2.png\" alt=\"\"></figure>\n<p>第二个图在第一个基础上根据drv变量绘制了三条拟合曲线，并且没有绘制区间。并且把图例删除了（show.legend = FALSE）</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy))+\n\tgeom_point(size=3)+\n\tgeom_smooth(aes(fill=drv),se=F,size=2,show.legend = FALSE)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/cdcfe212-6ff5-4f56-8b9d-a0667cf189bc.png\" alt=\"\"></figure>\n<p>第三个图散点图颜色的颜色根据drv变量进行变化，并且拟合曲线也是和散点图相同颜色（所以可以在最原始图层中加入color=drv），没有拟合曲线的区间，但是有图例（默认就是有的）。</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+\n\tgeom_point(size=3)+\n\tgeom_smooth(se=F,size=2)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/feb40f9d-6846-4c9f-93ec-7acfbd62b13b.png\" alt=\"\"></figure>\n<p>第四个图根据frv变量给散点图填充，但是只绘制了一条拟合线。所以这里不可以直接放在原始图层里，得放在geom_point()中。</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy))+\n\tgeom_point(aes(color=drv),size=3)+\n\tgeom_smooth(se=F,size=2)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/e0193630-2caf-4269-931a-d54231c820f0.png\" alt=\"\"></figure>\n<p>第五幅图在第三幅图基础上改变了拟合曲线的线的类型(linetype)。</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+\n\tgeom_point(size=3)+\n\tgeom_smooth(aes(linetype=drv),se=F,size=2)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/d859c9de-89a1-420c-ab6d-1a2ab492956b.png\" alt=\"\"></figure>\n<p>最后一幅没有拟合曲线，直接将散点图的颜色区分开了，有点像第三幅图的简化版</p>\n<pre class=\"custom\"><code class=\"hljs\">ggplot(data = mpg,aes(x = displ,y = hwy,color=drv))+\n\tgeom_point(size=3)\n</code></pre>\n<figure><img src=\"./让微信排版变 Nice_files/1c33c089-f147-4913-a2b0-3e9b967ae5d2.png\" alt=\"\"></figure>\n<h2><span class=\"prefix\"></span><span class=\"content\">参考</span><span class=\"suffix\"></span></h2>\n<ol>\n<li><section><a href=\"https://r4ds.had.co.nz/data-visualisation.html\">R for Data Science</a></section></li></ol>\n</section></div></div><div></div><ul id=\"nice-editor-menu\" class=\"ant-menu nice-editor-menu ant-menu-light ant-menu-root ant-menu-vertical\" role=\"menu\"><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link-to-foot\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">微信外链转脚注</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+L</span></div></li><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-format\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">格式化文档</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+F</span></div></li><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-image\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">图片</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+I</span></div></li></ul></div><div class=\"nice-footer-container\"><p>行数：129</p><p>字数：1133</p><p>主题：山吹</p></div></div></div></div><script>!function(b){function e(e){for(var r,t,f=e[0],c=e[1],a=e[2],n=0,d=[];n<f.length;n++)t=f[n],Object.prototype.hasOwnProperty.call(u,t)&&u[t]&&d.push(u[t][0]),u[t]=0;for(r in c)Object.prototype.hasOwnProperty.call(c,r)&&(b[r]=c[r]);for(s&&s(e);d.length;)d.shift()();return i.push.apply(i,a||[]),o()}function o(){for(var e,r=0;r<i.length;r++){for(var t=i[r],f=!0,c=1;c<t.length;c++){var a=t[c];0!==u[a]&&(f=!1)}f&&(i.splice(r--,1),e=l(l.s=t[0]))}return e}var t={},u={6:0},i=[];function l(e){if(t[e])return t[e].exports;var r=t[e]={i:e,l:!1,exports:{}};return b[e].call(r.exports,r,r.exports,l),r.l=!0,r.exports}l.e=function(c){var e=[],t=u[c];if(0!==t)if(t)e.push(t[2]);else{var r=new Promise(function(e,r){t=u[c]=[e,r]});e.push(t[2]=r);var 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class=\"nice-themeselect-menu-item\"><div id=\"nice-menu-view-css\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">查看主题 CSS</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 290px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-full-screen\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">全屏</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-edit-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">编辑区域</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-preview-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">预览区域</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">主题CSS区域</span></span></div></li></ul></div></div></div><input id=\"copy-input\" style=\"position: absolute; left: -1000px; z-index: -1000;\"><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 386px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-wechat\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">微信代码主题</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-atomOneDark\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">atom-one-dark</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-atomOneLight\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"><span>✔️</span></span><span class=\"nice-codetheme-item-name\">atom-one-light</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-monokai\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">monokai</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-github\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">github</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-vs2015\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">vs2015</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-xcode\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">xcode</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-apple\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"><span>✔️</span></span><span class=\"nice-codetheme-item-name\">Mac 风格</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 462px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-sync-scroll\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">同步滚动</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-contain-img-name\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">上传图片时包含名称</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 194px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-del\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">删除线</span></span><span class=\"nice-menu-shortcut\">Ctrl+U</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-bold\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">加粗</span></span><span class=\"nice-menu-shortcut\">Ctrl+B</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-italic\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">倾斜</span></span><span class=\"nice-menu-shortcut\">Ctrl+I</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-code\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">代码</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+C</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-inline-code\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">行内代码</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+V</span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">链接</span></span><span class=\"nice-menu-shortcut\">Ctrl+K</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-form\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">表格</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+T</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-image\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">图片</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+I</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-font\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">衬线字体</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link-to-foot\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">微信外链转脚注</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+L</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-format\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">格式化文档</span></span><span class=\"nice-menu-shortcut cc_pointer\">Ctrl+Alt+F</span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 146px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-export-markdown\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导出 Markdown</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-export-pdf\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导出 PDF</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><label id=\"nice-menu-import-file\" class=\"nice-menu-item\" for=\"importFile\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导入</span><input type=\"file\" id=\"importFile\" accept=\".txt,.md\" style=\"display: none;\"></span></label></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-tooltip  ant-tooltip-placement-left  ant-tooltip-hidden\" style=\"left: 1763px; top: 50px; transform-origin: 112px 50%;\"><div class=\"ant-tooltip-content\"><div class=\"ant-tooltip-arrow\"></div><div class=\"ant-tooltip-inner\" role=\"tooltip\">复制到公众号</div></div></div></div></div><div><div class=\"ant-message\"><span></span></div></div><div class=\"xl-chrome-ext-bar\" id=\"xl_chrome_ext_{4DB361DE-01F7-4376-B494-639E489D19ED}\" style=\"display: none;\">\n      <div class=\"xl-chrome-ext-bar__logo\"></div>\n\n      <a id=\"xl_chrome_ext_download\" href=\"javascript:;\" class=\"xl-chrome-ext-bar__option\">下载视频</a>\n      <a id=\"xl_chrome_ext_close\" href=\"javascript:;\" class=\"xl-chrome-ext-bar__close\"></a>\n    </div></body></html>"
  },
  {
    "path": "2020年/2020年R数据科学系列/2020.08.23速查表/速查表.html",
    "content": "<!DOCTYPE html>\n<!-- saved from url=(0023)https://www.mdnice.com/ -->\n<html lang=\"en\"><head><meta http-equiv=\"Content-Type\" content=\"text/html; charset=UTF-8\"><link rel=\"icon\" href=\"https://www.mdnice.com/favicon.svg\"><meta name=\"viewport\" content=\"width=device-width,initial-scale=1\"><meta name=\"theme-color\" content=\"#000000\"><link rel=\"manifest\" href=\"https://www.mdnice.com/manifest.json\"><script src=\"./速查表_files/hm.js.下载\"></script><script>var _hmt=_hmt||[];!function(){var e=document.createElement(\"script\");e.src=\"https://hm.baidu.com/hm.js?f857e607ecc7cde02ed32a561f10efb2\";var t=document.getElementsByTagName(\"script\")[0];t.parentNode.insertBefore(e,t)}()</script><title>让微信排版变 Nice</title><meta name=\"keywords\" content=\"mdnice 编辑器,Markdown 编辑器,Markdown 微信文章排版,Markdown 微信图文美化,Markdown 公众号内容编辑,Markdown 新媒体编辑,Markdown 微信编辑器, Markdown 公众号在线编辑, Markdown 微信图文排版\"><meta name=\"description\" content=\"mdnice 微信 Markdown 编辑器是一款 Markdown 微信编辑器,拥有良好的兼容性、海量主题样式、免费的图床、强大的技术团队，提供文章一键排版，同时支持知乎、掘金、微信订阅号等多个平台。\"><link href=\"./速查表_files/7.bd9d6b76.chunk.css\" rel=\"stylesheet\"><link href=\"./速查表_files/main.9fc69ba1.chunk.css\" rel=\"stylesheet\"><style id=\"font-theme\"></style><style id=\"basic-theme\">/*默认样式，最佳实践*/\n\n/*全局属性*/\n#nice {\n  font-size: 16px;\n  color: black;\n  padding: 0 10px;\n  line-height: 1.6;\n  word-spacing: 0px;\n  letter-spacing: 0px;\n  word-break: break-word;\n  word-wrap: break-word;\n  text-align: left;\n  font-family: Optima-Regular, Optima, PingFangSC-light, PingFangTC-light, 'PingFang SC', Cambria, Cochin, Georgia, Times, 'Times New Roman', serif;\n  /* margin-top: -10px; 解决开头空隙过大问题*/\n}\n\n/*段落*/\n#nice p {\n  font-size: 16px;\n  padding-top: 8px;\n  padding-bottom: 8px;\n  margin: 0;\n  line-height: 26px;\n  color: black;\n}\n\n/*标题*/\n#nice h1,\n#nice h2,\n#nice h3,\n#nice h4,\n#nice h5,\n#nice h6 {\n  margin-top: 30px;\n  margin-bottom: 15px;\n  padding: 0px;\n  font-weight: bold;\n  color: black;\n}\n#nice h1 {\n  font-size: 24px;\n}\n#nice h2 {\n  font-size: 22px;\n}\n#nice h3 {\n  font-size: 20px;\n}\n#nice h4 {\n  font-size: 18px;\n}\n#nice h5 {\n  font-size: 16px;\n}\n#nice h6 {\n  font-size: 16px;\n}\n\n#nice h1 .prefix,\n#nice h2 .prefix,\n#nice h3 .prefix,\n#nice h4 .prefix,\n#nice h5 .prefix,\n#nice h6 .prefix {\n  display: none;\n}\n\n#nice h1 .suffix\n#nice h2 .suffix,\n#nice h3 .suffix,\n#nice h4 .suffix,\n#nice h5 .suffix,\n#nice h6 .suffix {\n  display: none;\n}\n\n/*列表*/\n#nice ul,\n#nice ol {\n  margin-top: 8px;\n  margin-bottom: 8px;\n  padding-left: 25px;\n  color: black;\n}\n#nice ul {\n  list-style-type: disc;\n}\n#nice ul ul {\n  list-style-type: square;\n}\n\n#nice ol {\n  list-style-type: decimal;\n}\n\n#nice li section {\n  margin-top: 5px;\n  margin-bottom: 5px;\n  line-height: 26px;\n  text-align: left;\n  color: rgb(1,1,1); /* 只要是纯黑色微信编辑器就会把color这个属性吞掉。。。*/\n  font-weight: 500;\n}\n\n/*引用*/\n#nice blockquote {\n  display: block;\n  font-size: 0.9em;\n  overflow: auto;\n  overflow-scrolling: touch;\n  border-left: 3px solid rgba(0, 0, 0, 0.4);\n  background: rgba(0, 0, 0, 0.05);\n  color: #6a737d;\n  padding-top: 10px;\n  padding-bottom: 10px;\n  padding-left: 20px;\n  padding-right: 10px;\n  margin-bottom: 20px;\n  margin-top: 20px;\n}\n\n#nice blockquote p {\n  margin: 0px;\n  color: black;\n  line-height: 26px;\n}\n\n#nice .table-of-contents a {\n  border: none;\n  color: black;\n  font-weight: normal;\n}\n\n/*链接*/\n#nice a {\n  text-decoration: none;\n  color: #1e6bb8;\n  word-wrap: break-word;\n  font-weight: bold;\n  border-bottom: 1px solid #1e6bb8;\n}\n\n/*加粗*/\n#nice strong {\n  font-weight: bold;\n  color: black;\n}\n\n/*斜体*/\n#nice em {\n  font-style: italic;\n  color: black;\n}\n\n/*加粗斜体*/\n#nice em strong {\n  font-weight: bold;\n  color: black;\n}\n\n/*删除线*/\n#nice del {\n  font-style: italic;\n  color: black;\n}\n\n/*分隔线*/\n#nice hr {\n  height: 1px;\n  margin: 0;\n  margin-top: 10px;\n  margin-bottom: 10px;\n  border: none;\n  border-top: 1px solid black;\n}\n\n/*代码块*/\n#nice pre {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n#nice pre code {\n  display: -webkit-box;\n  font-family: Operator Mono, Consolas, Monaco, Menlo, monospace;\n  border-radius: 0px;\n  font-size: 12px;\n  -webkit-overflow-scrolling: touch;\n}\n#nice pre code span {\n  line-height: 26px;\n}\n\n/*行内代码*/\n#nice p code,\n#nice li code {\n  font-size: 14px;\n  word-wrap: break-word;\n  padding: 2px 4px;\n  border-radius: 4px;\n  margin: 0 2px;\n  color: #1e6bb8;\n  background-color: rgba(27,31,35,.05);\n  font-family: Operator Mono, Consolas, Monaco, Menlo, monospace;\n  word-break: break-all;\n}\n\n/*图片*/\n#nice img {\n  display: block;\n  margin: 0 auto;\n  max-width: 100%;\n}\n\n/*图片*/\n#nice figure {\n  margin: 0;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n\n/*图片描述文字*/\n#nice figcaption {\n  margin-top: 5px;\n  text-align: center;\n  color: #888;\n  font-size: 14px;\n}\n\n\n/*表格容器 */\n#nice .table-container{\n  overflow-x: auto;\n}\n\n/*表格*/\n#nice table {\n  display: table;\n  text-align: left;\n}\n#nice tbody {\n  border: 0;\n}\n\n#nice table tr {\n  border: 0;\n  border-top: 1px solid #ccc;\n  background-color: white;\n}\n\n#nice table tr:nth-child(2n) {\n  background-color: #F8F8F8;\n}\n\n#nice table tr th,\n#nice table tr td {\n  font-size: 16px;\n  border: 1px solid #ccc;\n  padding: 5px 10px;\n  text-align: left;\n}\n\n#nice table tr th {\n  font-weight: bold;\n  background-color: #f0f0f0;\n}\n\n/* 表格最小列宽4个汉字 */\n#nice table tr th:nth-of-type(n),\n#nice table tr td:nth-of-type(n){\n  min-width:85px;\n}\n\n/* 微信代码块 */\n#nice .code-snippet__fix {\n  word-wrap: break-word !important;\n  font-size: 14px;\n  margin: 10px 0;\n  display: block;\n  color: #333;\n  position: relative;\n  background-color: rgba(0,0,0,0.03);\n  border: 1px solid #f0f0f0;\n  border-radius: 2px;\n  display: flex;\n  line-height: 20px;\n}\n#nice .code-snippet__fix pre {\n  margin-bottom: 10px;\n  margin-top: 0px;\n}\n#nice .code-snippet__fix .code-snippet__line-index {\n  counter-reset: line;\n  flex-shrink: 0;\n  height: 100%;\n  padding: 1em;\n  list-style-type: none;\n  padding: 16px;\n  margin: 0;\n}\n#nice .code-snippet__fix .code-snippet__line-index li {\n  list-style-type: none;\n  text-align: right;\n  line-height: 26px;\n  color: black;\n  margin: 0;\n}\n#nice .code-snippet__fix .code-snippet__line-index li::before {\n  min-width: 1.5em;\n  text-align: right;\n  left: -2.5em;\n  counter-increment: line;\n  content: counter(line);\n  display: inline;\n  color: rgba(0,0,0,0.3);\n}\n#nice .code-snippet__fix pre {\n  overflow-x: auto;\n  padding: 16px;\n  padding-left: 0;\n  white-space: normal;\n  flex: 1;\n  -webkit-overflow-scrolling: touch;\n}\n#nice .code-snippet__fix code {\n  text-align: left;\n  font-size: 14px;\n  display: block;\n  white-space: pre;\n  display: flex;\n  position: relative;\n  font-family: Consolas,\"Liberation Mono\",Menlo,Courier,monospace;\n  padding: 0px;\n}\n\n#nice .footnote-word {\n  color: #1e6bb8;\n  font-weight: bold;\n}\n\n#nice .footnote-ref {\n  color: #1e6bb8;\n  font-weight: bold;\n}\n\n#nice .footnote-item {\n  display: flex;\n}\n\n#nice .footnote-num {\n  display: inline;\n  width: 10%; /*神奇，50px就不可以*/\n  background: none;\n  font-size: 80%;\n  opacity: 0.6;\n  line-height: 26px;\n  font-family: ptima-Regular, Optima, PingFangSC-light, PingFangTC-light, 'PingFang SC', Cambria, Cochin, Georgia, Times, 'Times New Roman', serif;\n}\n\n#nice .footnote-item p {\n  display: inline;\n  font-size: 14px;\n  width: 90%;\n  padding: 0px;\n  margin: 0;\n  line-height: 26px;\n  color: black;\n  word-break:break-all;\n  width: calc(100%-50)\n}\n\n#nice sub, sup {\n  line-height: 0;\n}\n\n#nice .footnotes-sep:before {\n  content: \"参考资料\";\n  display: block;\n}\n\n/* 解决公式问题 */\n#nice .block-equation {\n  display:block;\n  text-align: center;\n  overflow: auto;\n  display: block;\n  -webkit-overflow-scrolling: touch;\n}\n\n#nice .block-equation svg {\n  max-width: 300% !important;\n  -webkit-overflow-scrolling: touch;\n}\n\n#nice .inline-equation {\n}\n\n#nice .inline-equation svg {\n}\n\n#nice .imageflow-layer1 {\n  margin-top: 1em;\n  margin-bottom: 0.5em;\n  white-space: normal;\n  border: 0px none;\n  padding: 0px;\n  overflow: hidden;\n}\n\n#nice .imageflow-layer2 {\n  white-space: nowrap;\n  width: 100%;\n  overflow-x: scroll;\n}\n\n#nice .imageflow-layer3 {\n  display: inline-block;\n  word-wrap: break-word;\n  white-space: normal;\n  vertical-align: middle;\n  width: 100%;\n}\n\n#nice .imageflow-img {\n  display: inline-block;\n}\n\n#nice .imageflow-caption {\n  text-align: center;\n  margin-top: 0px;\n  padding-top: 0px;\n  color: #888;\n}\n\n#nice .nice-suffix-juejin-container {\n  margin-top: 20px !important;\n}\n\n#nice figure a {\n  border: none;\n}\n\n#nice figure a img {\n  margin: 0px;\n}\n\n#nice figure {\n  display:flex;\n  flex-direction: column;\n  justify-content: center;\n  align-items: center;\n}\n\n/* 图片链接嵌套 */\n#nice figure a {\n  display: flex;\n  justify-content: center;\n  align-items: center;\n}\n\n/* 图片链接嵌套，图片解释 */\n#nice figure a + figcaption {\n  display: flex;\n  justify-content: center;\n  align-items: center;\n  width: 100%;\n  margin-top: -35px;\n  background: rgba(0,0,0,0.7);\n  color: white;\n  line-height: 35px;\n  z-index: 20;\n}\n</style><script charset=\"utf-8\" src=\"./速查表_files/3.e00a7e4b.chunk.js.下载\"></script><style type=\"text/css\">.CtxtMenu_InfoClose {  top:.2em; right:.2em;}\n.CtxtMenu_InfoContent {  overflow:auto; text-align:left; font-size:80%;  padding:.4em .6em; border:1px inset; margin:1em 0px;  max-height:20em; max-width:30em; background-color:#EEEEEE;  white-space:normal;}\n.CtxtMenu_Info.CtxtMenu_MousePost {outline:none;}\n.CtxtMenu_Info {  position:fixed; left:50%; width:auto; text-align:center;  border:3px outset; padding:1em 2em; background-color:#DDDDDD;  color:black;  cursor:default; font-family:message-box; font-size:120%;  font-style:normal; text-indent:0; text-transform:none;  line-height:normal; letter-spacing:normal; word-spacing:normal;  word-wrap:normal; white-space:nowrap; float:none; z-index:201;  border-radius: 15px;                     /* Opera 10.5 and IE9 */  -webkit-border-radius:15px;               /* Safari and Chrome */  -moz-border-radius:15px;                  /* Firefox */  -khtml-border-radius:15px;                /* Konqueror */  box-shadow:0px 10px 20px #808080;         /* Opera 10.5 and IE9 */  -webkit-box-shadow:0px 10px 20px #808080; /* Safari 3 & Chrome */  -moz-box-shadow:0px 10px 20px #808080;    /* Forefox 3.5 */  -khtml-box-shadow:0px 10px 20px #808080;  /* Konqueror */  filter:progid:DXImageTransform.Microsoft.dropshadow(OffX=2, OffY=2, Color=\"gray\", Positive=\"true\"); /* IE */}\n</style><style type=\"text/css\">.CtxtMenu_MenuClose {  position:absolute;  cursor:pointer;  display:inline-block;  border:2px solid #AAA;  border-radius:18px;  -webkit-border-radius: 18px;             /* Safari and Chrome */  -moz-border-radius: 18px;                /* Firefox */  -khtml-border-radius: 18px;              /* Konqueror */  font-family: \"Courier New\", Courier;  font-size:24px;  color:#F0F0F0}\n.CtxtMenu_MenuClose span {  display:block; background-color:#AAA; border:1.5px solid;  border-radius:18px;  -webkit-border-radius: 18px;             /* Safari and Chrome */  -moz-border-radius: 18px;                /* Firefox */  -khtml-border-radius: 18px;              /* Konqueror */  line-height:0;  padding:8px 0 6px     /* may need to be browser-specific */}\n.CtxtMenu_MenuClose:hover {  color:white!important;  border:2px solid #CCC!important}\n.CtxtMenu_MenuClose:hover span {  background-color:#CCC!important}\n.CtxtMenu_MenuClose:hover:focus {  outline:none}\n</style><style type=\"text/css\">.CtxtMenu_Menu {  position:absolute;  background-color:white;  color:black;  width:auto; padding:5px 0px;  border:1px solid #CCCCCC; margin:0; cursor:default;  font: menu; text-align:left; text-indent:0; text-transform:none;  line-height:normal; letter-spacing:normal; word-spacing:normal;  word-wrap:normal; white-space:nowrap; float:none; z-index:201;  border-radius: 5px;                     /* Opera 10.5 and IE9 */  -webkit-border-radius: 5px;             /* Safari and Chrome */  -moz-border-radius: 5px;                /* Firefox */  -khtml-border-radius: 5px;              /* Konqueror */  box-shadow:0px 10px 20px #808080;         /* Opera 10.5 and IE9 */  -webkit-box-shadow:0px 10px 20px #808080; /* Safari 3 & Chrome */  -moz-box-shadow:0px 10px 20px #808080;    /* Forefox 3.5 */  -khtml-box-shadow:0px 10px 20px #808080;  /* Konqueror */}\n.CtxtMenu_MenuItem {  padding: 1px 2em;  background:transparent;}\n.CtxtMenu_MenuArrow {  position:absolute; right:.5em; padding-top:.25em; color:#666666;  font-family: null; font-size: .75em}\n.CtxtMenu_MenuActive .CtxtMenu_MenuArrow {color:white}\n.CtxtMenu_MenuArrow.CtxtMenu_RTL {left:.5em; right:auto}\n.CtxtMenu_MenuCheck {  position:absolute; left:.7em;  font-family: null}\n.CtxtMenu_MenuCheck.CtxtMenu_RTL { right:.7em; left:auto }\n.CtxtMenu_MenuRadioCheck {  position:absolute; left: .7em;}\n.CtxtMenu_MenuRadioCheck.CtxtMenu_RTL {  right: .7em; left:auto}\n.CtxtMenu_MenuInputBox {  padding-left: 1em; right:.5em; color:#666666;  font-family: null;}\n.CtxtMenu_MenuInputBox.CtxtMenu_RTL {  left: .1em;}\n.CtxtMenu_MenuComboBox {  left:.1em; padding-bottom:.5em;}\n.CtxtMenu_MenuLabel {  padding: 1px 2em 3px 1.33em;  font-style:italic}\n.CtxtMenu_MenuRule {  border-top: 1px solid #DDDDDD;  margin: 4px 3px;}\n.CtxtMenu_MenuDisabled {  color:GrayText}\n.CtxtMenu_MenuActive {  background-color: #606872;  color: white;}\n.CtxtMenu_MenuDisabled:focus {  background-color: #E8E8E8}\n.CtxtMenu_MenuLabel:focus {  background-color: #E8E8E8}\n.CtxtMenu_ContextMenu:focus {  outline:none}\n.CtxtMenu_ContextMenu .CtxtMenu_MenuItem:focus {  outline:none}\n.CtxtMenu_Menu .CtxtMenu_MenuClose {  top:-10px; left:-10px}\n</style><style id=\"MJX-SVG-styles\">\nmjx-container[jax=\"SVG\"] {\n  direction: ltr;\n}\n\nmjx-container[jax=\"SVG\"] > svg {\n  overflow: visible;\n}\n\nmjx-container[jax=\"SVG\"] > svg a {\n  fill: blue;\n  stroke: blue;\n}\n\nmjx-assistive-mml {\n  position: absolute !important;\n  top: 0px;\n  left: 0px;\n  clip: rect(1px, 1px, 1px, 1px);\n  padding: 1px 0px 0px 0px !important;\n  border: 0px !important;\n  display: block !important;\n  width: auto !important;\n  overflow: hidden !important;\n  -webkit-touch-callout: none;\n  -webkit-user-select: none;\n  -khtml-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n\nmjx-assistive-mml[display=\"block\"] {\n  width: 100% !important;\n}\n\nmjx-container[jax=\"SVG\"][display=\"true\"] {\n  display: block;\n  text-align: center;\n  margin: 1em 0;\n}\n\nmjx-container[jax=\"SVG\"][justify=\"left\"] {\n  text-align: left;\n}\n\nmjx-container[jax=\"SVG\"][justify=\"right\"] {\n  text-align: right;\n}\n\ng[data-mml-node=\"merror\"] > g {\n  fill: red;\n  stroke: red;\n}\n\ng[data-mml-node=\"merror\"] > rect[data-background] {\n  fill: yellow;\n  stroke: none;\n}\n\ng[data-mml-node=\"mtable\"] > line[data-line] {\n  stroke-width: 70px;\n  fill: none;\n}\n\ng[data-mml-node=\"mtable\"] > rect[data-frame] {\n  stroke-width: 70px;\n  fill: none;\n}\n\ng[data-mml-node=\"mtable\"] > .mjx-dashed {\n  stroke-dasharray: 140;\n}\n\ng[data-mml-node=\"mtable\"] > .mjx-dotted {\n  stroke-linecap: round;\n  stroke-dasharray: 0,140;\n}\n\ng[data-mml-node=\"mtable\"] > svg {\n  overflow: visible;\n}\n\n[jax=\"SVG\"] mjx-tool {\n  display: inline-block;\n  position: relative;\n  width: 0;\n  height: 0;\n}\n\n[jax=\"SVG\"] mjx-tool > mjx-tip {\n  position: absolute;\n  top: 0;\n  left: 0;\n}\n\nmjx-tool > mjx-tip {\n  display: inline-block;\n  padding: .2em;\n  border: 1px solid #888;\n  font-size: 70%;\n  background-color: #F8F8F8;\n  color: black;\n  box-shadow: 2px 2px 5px #AAAAAA;\n}\n\ng[data-mml-node=\"maction\"][data-toggle] {\n  cursor: pointer;\n}\n\nmjx-status {\n  display: block;\n  position: fixed;\n  left: 1em;\n  bottom: 1em;\n  min-width: 25%;\n  padding: .2em .4em;\n  border: 1px solid #888;\n  font-size: 90%;\n  background-color: #F8F8F8;\n  color: black;\n}\n\nforeignObject[data-mjx-xml] {\n  font-family: initial;\n  line-height: normal;\n  overflow: visible;\n}\n\n.MathJax path {\n  stroke-width: 3;\n}\n</style><style type=\"text/css\"></style><style type=\"text/css\">.CodeMirror-line, .cm-header.cm-header-2, .cm-strong, .cm-image.cm-image-marker, .cm-image.cm-image-alt-text.cm-link, .cm-string.cm-url, .cm-image.cm-image-alt-text.cm-link.CodeMirror-matchingbracket, .CodeMirror-lines, .ant-input, .cc_cursor, body, .ogdlpmhglpejoiomcodnpjnfgcpmgale_default, body, html, input[type=\"date\"], input[type=\"time\"], input[type=\"datetime-local\"], input[type=\"month\"], input::-webkit-contacts-auto-fill-button, input:read-only {cursor: 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1 1 , auto !important; }.nice-themeselect-theme-item-name, .nice-themeselect-theme-item, .nice-codetheme-item, .nice-codetheme-item-name, .nice-menu-item, .nice-menu-name, .ant-select-selection-selected-value, .ant-select-selection__rendered, .ant-select-selection\n...........ant-select-selection--single, .ant-btn.ant-btn-primary, .ant-btn.ant-btn-primary.cc_pointer, .ant-btn, .ant-btn.cc_pointer, .ant-select-selection__rendered.cc_pointer, .ant-select-selection-selected-value.cc_pointer, .nice-menu-link.ant-dropdown-trigger, .nice-menu-link.ant-dropdown-trigger.cc_pointer, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open, .nice-menu-link.ant-dropdown-trigger.ant-dropdown-open.cc_pointer, .nice-btn-zhihu, .ant-modal-close-x, .ant-modal-close-x.cc_pointer, .nice-btn-previewtype.ant-tooltip-open, .nice-themeselect-theme-item-author, .nice-themeselect-theme-item-author.cc_pointer, .nice-themeselect-theme-item-name.nice-menu-subscribe-more, .nice-themeselect-theme-item-name.nice-menu-subscribe-more.cc_pointer, .nice-menu-shortcut, .nice-menu-shortcut.cc_pointer, .nice-footer-message, .nice-footer-engine.ant-dropdown-trigger, .nice-footer-engine.ant-dropdown-trigger.cc_pointer, .nice-footer-message.cc_pointer, .ant-btn.nice-btn-login.ant-btn-circle, .ant-btn.nice-btn-login.ant-btn-circle.cc_pointer, .cc_pointer,[type=\"search\"]::-webkit-search-cancel-button, [type=\"search\"]::-webkit-search-decoration, .paper-button, .ytp-progress-bar-container, input[type=submit], :link, :visited, a > *, img, button, ::-webkit-scrollbar-button, .ogdlpmhglpejoiomcodnpjnfgcpmgale_pointer, ::-webkit-file-upload-button, button, .ytp-volume-panel, #myogdlpmhglpejoiomcodnpjnfgcpmgale .icon { cursor: 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22 3, auto !important; } </style><style id=\"markdown-theme\">/*自定义样式，实时生效*/\n#nice {\n}\n\n#nice p {\n\tmargin: 0 0 20px;\n\tpadding: 0;\n\tline-height: 1.8em;\n\tcolor: #3a3a3a;\n}\n\n/* 一级标题 */\n#nice h1 {\n  font-size: 2.1em;\n\tline-height: 1.1em;\n\tpadding-top: 16px;\n  padding-bottom: 10px;\n  margin-bottom: 4px;\n  border-bottom: 1px solid #c99833;\n}\n/* 一级标题内容 */\n#nice h1 .content {\n  color: #515151;\n  font-weight: 700;\n}\n\n#nice h2, h3, h4, h5, h6 {\n line-height: 1.5em;\n margin-top: 2.2em;\n margin-bottom: 4px;\n}\n\n/* 一级标题修饰 请参考有实例的主题 */\n#nice h1:after {}\n\n/* 二级标题 */\n#nice h2 {\n margin-bottom: 35px;\n}\n\n/* 二级标题内容 */\n\n#nice h2 .content {\n  display: inline-block;\n  font-weight: bold;\n  background: linear-gradient(#fff 60%, #ffb11b 40%);\n  color: #515151;\n  padding: 2px 13px 2px;\n  margin-right: 3px;\n  height: 50%;\n}\n\n/* 二级标题修饰 请参考有实例的主题 */\n#nice h2:after {}\n\n/* 三级标题 */\n#nice h3 {\n  line-height: 1.4;\n  padding-top: 10px;\n  margin: 10px 0 5px;\n}\n\n/* 三级标题内容 */\n#nice h3 .content {\n  color: #515151;\n  font-weight: 700;\n font-size: 1.0em;\n  padding-left: 20px;\n  border-left: 3px solid #f9bf45;\n}\n\n/* 三级标题修饰 请参考有实例的主题 */\n#nice h3:after {}\n\n/* 引用\n* 左边缘颜色 border-left-color: black;\n* 背景色 background: gray;\n*/\n#nice blockquote {\n  border-left-color: #ffb11b;\n background: #fff5e3;\n}\n\n/* 引用文字 */\n#nice blockquote p {\n  color: #595959;\n}\n\n/* 链接 */\n#nice a {\n  border: none;\n  text-decoration: none;\n  color: #dda52d;\n}\n\n#nice a:hover {\n  color: #f9bf45;\n  text-decoration: underline;\n}\n\n/* 无序列表整体样式\n * list-style-type: square|circle|disc;\n */\n#nice ul {\n}\n\n/* 有序列表整体样式\n * list-style-type: upper-roman|lower-greek|lower-alpha;\n */\n#nice ol {\n}\n\n/* 列表内容，不要设置li\n */\n#nice li section {\n}\n\n/* 加粗 */\n#nice strong {}\n\n/* 斜体 */\n#nice em {}\n\n/* 加粗斜体 */\n#nice em strong {}\n\n/* 删除线 */\n#nice del {\n  color: #d19826;\n}\n\n/* 分隔线\n* 粗细、样式和颜色\n* border-top: 1px solid #3e3e3e;\n*/\n#nice hr {\n  border-top: 1px solid #f9bf45;\n  margin: 20px 0px;\n}\n\n/* 图片\n* 宽度 width: 80%;\n* 居中 margin: 0 auto;\n* 居左 margin: 0 0;\n*/\n#nice img {\n  width: 100%;\n border-radius: 5px;\n display: block;\n margin-bottom: 15px;\n height: auto;\n}\n\n/* 图片描述文字 */\n#nice figcaption {\n  color: #dda52d;\n  font-size: 14px;\n}\n\n/* 行内代码 */\n#nice p code, #nice li code {\n  color: #9b6e23;\n  background-color: #fff5e3;\n  padding: 3px;\n  margin: 3px;\n}\n\n/* 非微信代码块\n * 代码块不换行 display: -webkit-box !important;\n * 代码块换行 display: block;\n */\n#nice pre code {}\n\n/*\n * 表格内的单元格\n * 字体大小 font-size: 16px;\n * 边框 border: 1px solid #ccc;\n * 内边距 padding: 5px 10px;\n */\n#nice table tr th,\n#nice table tr td {\n  text-align: center;\n}\n\n/* 脚注文字 */\n#nice .footnote-word {\n  color: #ffb11b;\n  padding: 3px;\n}\n\n/* 脚注上标 */\n#nice .footnote-ref {\n  color: #dda52d;\n  margin: 2px;\n  padding: 3px;\n}\n\n/* \"参考资料\"四个字 \n * 内容 content: \"参考资料\";\n */\n#nice .footnotes-sep:before {\n  margin: 30px 0px 15px 0px;\n  font-weight: 800;\n}\n\n\n/* 参考资料编号 */\n#nice .footnote-num {\n}\n\n/* 参考资料文字 */\n#nice .footnote-item p { \n}\n\n/* 参考资料解释 */\n#nice .footnote-item p em {\n}\n\n/* 行间公式\n * 最大宽度 max-width: 300% !important;\n */\n#nice .block-equation svg {\n}\n\n/* 行内公式\n */\n#nice .inline-equation svg {  \n}\n\n/* 滑动图片\n */\n#nice .imageflow-img {\n  display: inline-block;\n  width:100%;\n  margin-bottom: 0;\n}</style><style id=\"code-theme\">/*\n\nAtom One Light by Daniel Gamage\nOriginal One Light Syntax theme from https://github.com/atom/one-light-syntax\n\nbase:    #fafafa\nmono-1:  #383a42\nmono-2:  #686b77\nmono-3:  #a0a1a7\nhue-1:   #0184bb\nhue-2:   #4078f2\nhue-3:   #a626a4\nhue-4:   #50a14f\nhue-5:   #e45649\nhue-5-2: #c91243\nhue-6:   #986801\nhue-6-2: #c18401\n\n*/\n\n.hljs {\n  display: block;\n  overflow-x: auto;\n  padding: 16px;\n  color: #383a42;\n  background: #fafafa;\n}\n\n.hljs-comment,\n.hljs-quote {\n  color: #a0a1a7;\n  font-style: italic;\n}\n\n.hljs-doctag,\n.hljs-keyword,\n.hljs-formula {\n  color: #a626a4;\n}\n\n.hljs-section,\n.hljs-name,\n.hljs-selector-tag,\n.hljs-deletion,\n.hljs-subst {\n  color: #e45649;\n}\n\n.hljs-literal {\n  color: #0184bb;\n}\n\n.hljs-string,\n.hljs-regexp,\n.hljs-addition,\n.hljs-attribute,\n.hljs-meta-string {\n  color: #50a14f;\n}\n\n.hljs-built_in,\n.hljs-class .hljs-title {\n  color: #c18401;\n}\n\n.hljs-attr,\n.hljs-variable,\n.hljs-template-variable,\n.hljs-type,\n.hljs-selector-class,\n.hljs-selector-attr,\n.hljs-selector-pseudo,\n.hljs-number {\n  color: #986801;\n}\n\n.hljs-symbol,\n.hljs-bullet,\n.hljs-link,\n.hljs-meta,\n.hljs-selector-id,\n.hljs-title {\n  color: #4078f2;\n}\n\n.hljs-emphasis {\n  font-style: italic;\n}\n\n.hljs-strong {\n  font-weight: bold;\n}\n\n.hljs-link {\n  text-decoration: underline;\n}\n\n#nice .custom code {\n  padding-top: 15px;\n  background: #fafafa;\n  border-radius: 5px;\n}\n\n#nice .custom:before {\n  content: '';\n  display:block;\n  background: url(https://my-wechat.mdnice.com/point.png);\n  height: 30px;\n  width: 100%;\n  background-size:40px;\n  background-repeat: no-repeat;\n  background-color: #fafafa;\n  margin-bottom: -7px;\n  border-radius: 5px;\n  background-position: 10px 10px;\n}\n\n#nice .custom {\n  border-radius: 5px;\n  box-shadow: rgba(0, 0, 0, 0.55) 0px 2px 10px;\n}</style></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id=\"root\"><div style=\"display: flex;\"><div class=\"nice-app\"><div class=\"nice-navbar\"><div class=\"nice-left-nav\"><section id=\"nice-title\" class=\"nice-title\">Markdown Nice</section><a id=\"nice-menu-file\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">文件</a><a id=\"nice-menu-pattern\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">格式</a><a id=\"nice-menu-function\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">功能</a><a id=\"nice-menu-view\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">查看</a><a id=\"nice-menu-theme\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">主题</a><a id=\"nice-menu-codetheme\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">代码主题</a><a id=\"nice-menu-setting\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">设置</a><a id=\"nice-menu-help\" class=\"nice-menu-link ant-dropdown-trigger\" href=\"https://www.mdnice.com/#\">帮助</a></div><div class=\"nice-right-nav\"><button type=\"button\" class=\"ant-btn nice-btn-login ant-btn-circle cc_pointer\"><svg viewBox=\"0 0 1024 1024\" class=\"nice-btn-login-icon\" fill=\"rgba(0,0,0,0.65)\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><path d=\"M512 16C238 16 16 238 16 512s222 496 496 496 496-222 496-496S786 16 512 16z m256 843.2c-71.8 53-160.2 84.8-256 84.8s-184.2-31.8-256-84.8V832c0-70.6 57.4-128 128-128 22.2 0 55 22.8 128 22.8 73.2 0 105.6-22.8 128-22.8 70.6 0 128 57.4 128 128v27.2z m61.2-55c-13.6-92.8-92.6-164.2-189.2-164.2-41 0-60.8 22.8-128 22.8S425.2 640 384 640c-96.6 0-175.6 71.4-189.2 164.2C123.8 727.2 80 624.8 80 512c0-238.2 193.8-432 432-432s432 193.8 432 432c0 112.8-43.8 215.2-114.8 292.2zM512 240c-97.2 0-176 78.8-176 176s78.8 176 176 176 176-78.8 176-176-78.8-176-176-176z m0 288c-61.8 0-112-50.2-112-112s50.2-112 112-112 112 50.2 112 112-50.2 112-112 112z\" p-id=\"5538\"></path></svg></button></div></div><div class=\"nice-text-container\"><div id=\"nice-md-editor\" class=\"nice-md-editing\"><textarea style=\"display: none;\"></textarea><div class=\"CodeMirror cm-s-md-mirror CodeMirror-wrap CodeMirror-focused\" style=\"width: 100%; height: 100%;\"><div style=\"overflow: hidden; position: relative; width: 3px; height: 0px; top: 272px; left: 187.225px;\"><textarea autocorrect=\"off\" autocapitalize=\"off\" spellcheck=\"false\" style=\"position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;\" tabindex=\"0\"></textarea></div><div class=\"CodeMirror-vscrollbar\" tabindex=\"-1\" cm-not-content=\"true\" style=\"bottom: 0px; display: block;\"><div style=\"min-width: 1px; height: 2033px;\"></div></div><div class=\"CodeMirror-hscrollbar\" tabindex=\"-1\" cm-not-content=\"true\" style=\"right: 6px; left: 0px;\"><div style=\"height: 100%; min-height: 1px; width: 0px;\"></div></div><div class=\"CodeMirror-scrollbar-filler\" cm-not-content=\"true\" style=\"height: 6px; width: 6px;\"></div><div class=\"CodeMirror-gutter-filler\" cm-not-content=\"true\"></div><div class=\"CodeMirror-scroll\" tabindex=\"-1\" draggable=\"false\"><div class=\"CodeMirror-sizer\" style=\"margin-left: 0px; margin-bottom: -5px; border-right-width: 25px; min-height: 1992px; padding-right: 5px; padding-bottom: 0px;\"><div style=\"position: relative; top: 0px;\"><div class=\"CodeMirror-lines\" role=\"presentation\"><div role=\"presentation\" style=\"position: relative; outline: none;\"><div class=\"CodeMirror-measure\"></div><div class=\"CodeMirror-measure\"></div><div style=\"position: relative; z-index: 1;\"></div><div class=\"CodeMirror-cursors\" style=\"\"><div class=\"CodeMirror-cursor\" style=\"left: 167.225px; top: 248px; height: 24.8px;\">&nbsp;</div></div><div class=\"CodeMirror-code\" role=\"presentation\" style=\"\"><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-link\">[R数据科学]</span> R速查表</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-2\">## 前言</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\">R相关包的速查表如下：</span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-3\">### 基础</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/31cff8bc-c3f7-41d9-8d4a-b791c947cc26.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=l9R5bULa866eIzEw%252Feq7uRtLs4c%253D&amp;Expires=1598259449)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-3\">### ggplot2（版本一）</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/3097a1b4-5470-4f8e-a958-bb09c83b9bf8.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=VfqQ%252B7KMM8kwu9%252BP4HfshShmxwY%253D&amp;Expires=1598259307)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/b94499a4-0d73-411e-a950-ac8658f7dcb2.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=8tZbJIdpBOnhp5cRXHbq6BTcosE%253D&amp;Expires=1598259317)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-3\">### ggplot2（版本二）</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/0bca34e0-f859-44f4-b3f4-2b668a47f598.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=DFBtf9o6b1wqv7KB5rRvatMgFlk%253D&amp;Expires=1598259405)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/f80dd042-071f-4054-aef8-d9becf5c9618.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=kKZOVm2SflLAPB8pFVDuUVIlFF4%253D&amp;Expires=1598259417)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-header cm-header-3\">### Rmarkdown</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span class=\"cm-image cm-image-marker\">!</span><span class=\"cm-image cm-image-alt-text cm-link\">[]</span><span class=\"cm-string cm-url\">(https://imgkr2.cn-bj.ufileos.com/a7632e1e-1be2-4a65-94dd-bd4098fc6e01.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&amp;Signature=B2fwf%252Fsq27ifq1WEHBjN%252BSKrdJQ%253D&amp;Expires=1598259187)</span></span></pre><pre class=\" CodeMirror-line \" role=\"presentation\"><span role=\"presentation\" style=\"padding-right: 0.1px;\"><span cm-text=\"\">​</span></span></pre></div></div></div></div></div><div style=\"position: absolute; height: 25px; width: 1px; border-bottom: 0px solid transparent; top: 1992px;\"></div><div class=\"CodeMirror-gutters\" style=\"display: none; height: 2017px;\"></div></div></div></div><div id=\"nice-rich-text\" class=\"nice-marked-text nice-marked-text-pc\"><div class=\"nice-sidebar\"><a id=\"nice-sidebar-wechat\" class=\"nice-btn-wechat\"><svg viewBox=\"0 0 40 40\" class=\"nice-btn-wechat-icon\" 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d=\"M20.6,30.9c-1.3,0-2.6-0.2-3.8-0.4c-0.1,0-0.3,0-0.5,0c-0.4,0-0.7,0.1-1,0.3l-4,2.6 c-0.1,0.1-0.2,0.1-0.4,0.1c-0.3,0-0.6-0.3-0.7-0.6c0-0.2,0-0.3,0.1-0.5c0-0.1,0.4-2,0.7-3.2c0-0.1,0.1-0.3,0-0.4 c0-0.4-0.2-0.8-0.6-1c-4.3-2.9-7.2-7.5-7.8-12.2c-1.1,1.7-1.6,3-2.2,5c-2.1,7.3,2.5,16,9.9,18.4c8.6,2.8,16.7-0.3,19.5-7.6 c0.3-0.9,0.7-2.4,0.8-3.6C27.7,29.9,24.6,30.9,20.6,30.9z\"></path></g></g></g></svg></a><a id=\"nice-sidebar-zhihu\" class=\"nice-btn-zhihu\"><svg viewBox=\"0 0 1024 1024\" class=\"nice-btn-zhihu-icon\" fill=\"rgba(0,0,0,0.65)\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><path d=\"M564.7 230.1V803h60l25.2 71.4L756.3 803h131.5V230.1H564.7z m247.7 497h-59.9l-75.1 50.4-17.8-50.4h-18V308.3h170.7v418.8zM526.1 486.9H393.3c2.1-44.9 4.3-104.3 6.6-172.9h130.9l-0.1-8.1c0-0.6-0.2-14.7-2.3-29.1-2.1-15-6.6-34.9-21-34.9H287.8c4.4-20.6 15.7-69.7 29.4-93.8l6.4-11.2-12.9-0.7c-0.8 0-19.6-0.9-41.4 10.6-35.7 19-51.7 56.4-58.7 84.4-18.4 73.1-44.6 123.9-55.7 145.6-3.3 6.4-5.3 10.2-6.2 12.8-1.8 4.9-0.8 9.8 2.8 13 10.5 9.5 38.2-2.9 38.5-3 0.6-0.3 1.3-0.6 2.2-1 13.9-6.3 55.1-25 69.8-84.5h56.7c0.7 32.2 3.1 138.4 2.9 172.9h-141l-2.1 1.5c-23.1 16.9-30.5 63.2-30.8 65.2l-1.4 9.2h167c-12.3 78.3-26.5 113.4-34 127.4-3.7 7-7.3 14-10.7 20.8-21.3 42.2-43.4 85.8-126.3 153.6-3.6 2.8-7 8-4.8 13.7 2.4 6.3 9.3 9.1 24.6 9.1 5.4 0 11.8-0.3 19.4-1 49.9-4.4 100.8-18 135.1-87.6 17-35.1 31.7-71.7 43.9-108.9L497 850l5-12c0.8-1.9 19-46.3 5.1-95.9l-0.5-1.8-108.1-123-22 16.6c6.4-26.1 10.6-49.9 12.5-71.1h158.7v-8c0-40.1-18.5-63.9-19.2-64.9l-2.4-3z\" fill=\"#1296db\" p-id=\"8715\"></path></svg></a><a id=\"nice-sidebar-preview-type\" class=\"nice-btn-previewtype\"><svg viewBox=\"0 0 1024 1024\" class=\"nice-btn-previewtype-icon\" fill=\"rgba(0,0,0,0.65)\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><path d=\"M736 0l-448 0c-52.8 0-96 43.2-96 96l0 832c0 52.8 43.2 96 96 96l448 0c52.8 0 96-43.2 96-96l0-832c0-52.8-43.2-96-96-96zM384 48l256 0 0 32-256 0 0-32zM512 960c-35.346 0-64-28.654-64-64s28.654-64 64-64 64 28.654 64 64-28.654 64-64 64zM768 768l-512 0 0-640 512 0 0 640z\"></path></svg></a></div><div id=\"nice-rich-text-box\" class=\"nice-wx-box nice-wx-box-pc\"><section id=\"nice\" data-tool=\"mdnice编辑器\" data-website=\"https://www.mdnice.com\"><p>[R数据科学] R速查表</p>\n<h2><span class=\"prefix\"></span><span class=\"content\">前言</span><span class=\"suffix\"></span></h2>\n<p>R相关包的速查表如下：</p>\n<h3><span class=\"prefix\"></span><span class=\"content\">基础</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/31cff8bc-c3f7-41d9-8d4a-b791c947cc26.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">ggplot2（版本一）</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/3097a1b4-5470-4f8e-a958-bb09c83b9bf8.png\" alt=\"\"></figure>\n<figure><img src=\"./速查表_files/b94499a4-0d73-411e-a950-ac8658f7dcb2.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">ggplot2（版本二）</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/0bca34e0-f859-44f4-b3f4-2b668a47f598.png\" alt=\"\"></figure>\n<figure><img src=\"./速查表_files/f80dd042-071f-4054-aef8-d9becf5c9618.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">Rmarkdown</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/a7632e1e-1be2-4a65-94dd-bd4098fc6e01.png\" alt=\"\"></figure>\n<figure><img src=\"./速查表_files/ff197626-b340-43e2-8a6b-608d77aaca1b.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">dplyr</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/ef71736f-0b28-45c0-9f69-2ea54677f267.png\" alt=\"\"></figure>\n<figure><img src=\"./速查表_files/76caf006-e560-436b-b5bf-95181ad4ee83.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">stringr</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/2cf04768-102e-4482-a772-49c2009bbb8f.png\" alt=\"\"></figure>\n<figure><img src=\"./速查表_files/7d82200e-78d0-44e4-8955-c525055a03f4.png\" alt=\"\"></figure>\n<h3><span class=\"prefix\"></span><span class=\"content\">forcats</span><span class=\"suffix\"></span></h3>\n<figure><img src=\"./速查表_files/88fc1f2f-dba2-4d11-b200-9ac94c6d172f.png\" alt=\"\"></figure>\n</section></div></div><div></div><ul id=\"nice-editor-menu\" class=\"ant-menu nice-editor-menu ant-menu-light ant-menu-root ant-menu-vertical\" role=\"menu\"><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link-to-foot\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">微信外链转脚注</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+L</span></div></li><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-format\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">格式化文档</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+F</span></div></li><li class=\"ant-menu-item ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-image\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">图片</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+I</span></div></li></ul></div><div class=\"nice-footer-container\"><p>行数：56</p><p>字数：318</p><p>主题：山吹</p></div></div></div></div><script>!function(b){function e(e){for(var r,t,f=e[0],c=e[1],a=e[2],n=0,d=[];n<f.length;n++)t=f[n],Object.prototype.hasOwnProperty.call(u,t)&&u[t]&&d.push(u[t][0]),u[t]=0;for(r in c)Object.prototype.hasOwnProperty.call(c,r)&&(b[r]=c[r]);for(s&&s(e);d.length;)d.shift()();return i.push.apply(i,a||[]),o()}function o(){for(var e,r=0;r<i.length;r++){for(var t=i[r],f=!0,c=1;c<t.length;c++){var 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id=\"nice-menu-theme-normal\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">默认主题</span></span><span class=\"nice-themeselect-theme-item-author\"></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-1\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">橙心</span></span><span class=\"nice-themeselect-theme-item-author\">zhning12</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-3\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">姹紫</span></span><span class=\"nice-themeselect-theme-item-author\">djmaxwow</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-4\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">嫩青</span></span><span class=\"nice-themeselect-theme-item-author\">画手</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-5\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">绿意</span></span><span class=\"nice-themeselect-theme-item-author\">夜尽天明</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-6\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">红绯</span></span><span class=\"nice-themeselect-theme-item-author\">HeyRain</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-8\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">蓝莹</span></span><span class=\"nice-themeselect-theme-item-author\">谭淞宸</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-10\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">兰青</span></span><span class=\"nice-themeselect-theme-item-author\">Krahets</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-11\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"><span>✔️</span></span><span class=\"nice-themeselect-theme-item-name\">山吹</span></span><span class=\"nice-themeselect-theme-item-author\">ElyhG</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-12\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">前端之巅同款</span></span><span class=\"nice-themeselect-theme-item-author cc_pointer\">HeyRain</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-13\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">极客黑\n\n</span></span><span class=\"nice-themeselect-theme-item-author\">hyper-xx</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-15\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">蔷薇紫</span></span><span class=\"nice-themeselect-theme-item-author\">HeyRain</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-16\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">萌绿</span></span><span class=\"nice-themeselect-theme-item-author\">koala</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-17\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">全栈蓝</span></span><span class=\"nice-themeselect-theme-item-author\">Nealyang</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-18\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">极简黑</span></span><span class=\"nice-themeselect-theme-item-author\">小鱼</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-19\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">橙蓝风</span></span><span class=\"nice-themeselect-theme-item-author\">axuebin</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-custom\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">自定义</span></span><span class=\"nice-themeselect-theme-item-author\"></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"nice-themeselect-menu-item\"><div id=\"nice-menu-subscribe-more\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name nice-menu-subscribe-more cc_pointer\">订阅更多主题</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"nice-themeselect-menu-item\"><div id=\"nice-menu-view-css\" class=\"nice-themeselect-theme-item\"><span><span class=\"nice-themeselect-theme-item-flag\"></span><span class=\"nice-themeselect-theme-item-name\">查看主题 CSS</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 290px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-full-screen\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">全屏</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-edit-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">编辑区域</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-preview-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">预览区域</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-theme-area\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">主题CSS区域</span></span></div></li></ul></div></div></div><input id=\"copy-input\" style=\"position: absolute; left: -1000px; z-index: -1000;\"><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 386px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-wechat\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">微信代码主题</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-atomOneDark\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">atom-one-dark</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-atomOneLight\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"><span>✔️</span></span><span class=\"nice-codetheme-item-name\">atom-one-light</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-monokai\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">monokai</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-github\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">github</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-vs2015\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">vs2015</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-xcode\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"></span><span class=\"nice-codetheme-item-name\">xcode</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-codetheme-apple\" class=\"nice-codetheme-item\"><span><span class=\"nice-codetheme-item-flag\"><span>✔️</span></span><span class=\"nice-codetheme-item-name\">Mac 风格</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 462px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-sync-scroll\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">同步滚动</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-contain-img-name\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">上传图片时包含名称</span></span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 194px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-del\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">删除线</span></span><span class=\"nice-menu-shortcut\">Ctrl+U</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-bold\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">加粗</span></span><span class=\"nice-menu-shortcut\">Ctrl+B</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-italic\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">倾斜</span></span><span class=\"nice-menu-shortcut\">Ctrl+I</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-code\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">代码</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+C</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-inline-code\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">行内代码</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+V</span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">链接</span></span><span class=\"nice-menu-shortcut\">Ctrl+K</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-form\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">表格</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+T</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-image\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">图片</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+I</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-font\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"><span>✔️</span></span><span class=\"nice-menu-name\">衬线字体</span></span></div></li><li class=\" ant-dropdown-menu-item-divider\"></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-link-to-foot\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">微信外链转脚注</span></span><span class=\"nice-menu-shortcut\">Ctrl+Alt+L</span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-format\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">格式化文档</span></span><span class=\"nice-menu-shortcut cc_pointer\">Ctrl+Alt+F</span></div></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-dropdown nice-overlay ant-dropdown-placement-bottomLeft  ant-dropdown-hidden\" style=\"left: 146px; top: -995px;\"><ul class=\"ant-dropdown-menu ant-dropdown-menu-light ant-dropdown-menu-root ant-dropdown-menu-vertical\" role=\"menu\" tabindex=\"0\"><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-export-markdown\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导出 Markdown</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><div id=\"nice-menu-export-pdf\" class=\"nice-menu-item\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导出 PDF</span></span></div></li><li class=\"ant-dropdown-menu-item\" role=\"menuitem\"><label id=\"nice-menu-import-file\" class=\"nice-menu-item\" for=\"importFile\"><span><span class=\"nice-menu-flag\"></span><span class=\"nice-menu-name\">导入</span><input type=\"file\" id=\"importFile\" accept=\".txt,.md\" style=\"display: none;\"></span></label></li></ul></div></div></div><div style=\"position: absolute; top: 0px; left: 0px; width: 100%;\"><div><div class=\"ant-tooltip  ant-tooltip-placement-left  ant-tooltip-hidden\" style=\"left: 1763px; top: 50px; transform-origin: 112px 50%;\"><div class=\"ant-tooltip-content\"><div class=\"ant-tooltip-arrow\"></div><div class=\"ant-tooltip-inner\" role=\"tooltip\">复制到公众号</div></div></div></div></div><div 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  },
  {
    "path": "2020年/2020年R数据科学系列/2020.09.27tidy data/Tidy_data.rmd",
    "content": "---\ntitle: \"[R数据科学]Tidy data案例\"\nauthor:\n  - 庄亮亮\ndocumentclass: ctexart\nalways_allow_html: true\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\n    toc_depth: 3\nclassoption: \"hyperref,\"\neditor_options: \n  chunk_output_type: console\n---\n\n# Tidy data介绍\n\n在本章中，你将学习在R中组织数据的一种一致的方法，这种组织被称为tidy data。将数据转换为这种格式需要一些前期工作，但这些工作从长远来看是值得的。一旦你有了整洁的数据和一些包提供的整洁工具，您将花费很少时间将数据从一种表示转换到另一种，从而可以将更多的时间花在分析问题上。\n\n本文将为您提供整理数据的实用介绍以及tidyr包中附带的工具。如果你想了解更多的基本理论，你可能会喜欢发表在《统计软件杂志》上的整洁数据论文<http://www.jstatsoft.org/v59/i10/paper>\n\n\n# 数据清洗案例\n\n我们主要通过一个案例,来了解如何整洁数据,并将案例中的各个有用函数进行详细解读.该案例数据来自`tidyr::who`,其包含按年份，国家，年龄，性别和诊断方法细分的结核病（TB）病例。数据来自2014年世界卫生组织《全球结核病报告》，网址为<http://www.who.int/tb/country/data/download/en/>。\n\n```{r}\nlibrary(tidyverse) \nwho\n```\n\n\n这是一个非常典型的现实示例数据集。它包含**冗余列，奇数变量代码和许多缺失值**。我们需要采取多个步骤来对其进行整理。\n\n## 不是变量的列汇集在一起\n\n首先将不是变量的列聚集在一起。所包含的列包括：\n\n- country，iso2和iso3是三个指定国家/地区的变量。\n\n- year是一个变量。\n\n- 变量名中给出的结构（例如new_sp_m014，new_ep_m014，new_ep_f014）可能是值，而不是变量。\n\n因此，我们需要将从`new_sp_m014`到`newrel_f65`的所有列汇总在一起。我们用通用名称\"key\"来表示他们。我们知道单元格代表案件数，因此我们将变量数存储在`cases`中,并用`na.rm`去除含有缺失值的行。\n\n这里使用`pivot_longer()`将数据变长,具体见后面函数详情.\n\n\n```{r}\nwho1 <- who %>% \n\tpivot_longer(\n\t\tcols = new_sp_m014:newrel_f65,\n\t\tnames_to = 'key',\n\t\tvalues_to = 'cases',\n\t\tvalues_drop_na = T\n)\n\nwho1\n```\n\n对key进行计数，我们可以得到一些有关值结构的提示：\n\n```{r}\nwho1 %>% count(key)\n```\n\n其中key的具体含义，查阅可得：\n\n每列的前三个字母：新、旧病例。\n\n之后两个字母：结核的类型。\n\n- rel 代表复发病例\n\n- ep 代表肺外结核病例\n\n- sn 代表无法通过肺部涂片诊断（涂片阴性）的肺结核病例\n\n- sp 代表可被诊断为肺部涂片（涂片阳性）的肺结核病例\n\n第六字母：结核病患者的性别。男性（m）和女性（f）\n\n其余数字给出了年龄段。数据集将案例分为七个年龄组：\n\n- 014 = 0 – 14岁\n\n- 1524 = 15 – 24岁\n\n- 2534 = 25 – 34岁\n\n- 3544 = 35 – 44岁\n\n- 4554 = 45 – 54岁\n\n- 5564 = 55 – 64岁\n\n- 65 = 65岁或以上\n\n## 替换数据\n\n我们需要对列名称的格式进行较小的修正：将new_rel替换为newrel（很难在这里找到它，但是如果您不修正它，我们将在后续步骤中出错）。这里使用了stringr包中的str_replace(),将`newrel`替换`new_rel`.\n\n```{r}\nwho2 <- who1 %>% \n\tmutate( names_from = stringr::str_replace(key,'newrel','new_rel')\n\t)\nwho2\n```\n\n## 字符分割\n\n接下来就是将`key`中的字符进行分割,我们使用`separate()`对字符进行两次分割.\n\n1.将在每个下划线处拆分代码。\n\n```{r}\nwho3 <- who2 %>% \n\tseparate(key,c('new','type','sexage'),sep = '_')\nwho3\n```\n\n利用select()删除没用的列:new,iso2，iso3.\n\n```{r}\nwho3 %>% count(new)\nwho4 <- who3 %>% select(-new,-iso2,-iso3)\nwho4\n```\n\n2. 将分离sexage到sex和age通过的第一个字符后拆分：\n\n```{r}\nwho5 <- who4 %>% \n\tseparate(sexage,c('sex','age'),sep=1)\nwho5\n```\n\n\n这时,who数据集是目前整洁！\n\n## 可视化\n\n数据清洗完毕,就可以做一些初步的可视化,探索性分析.这里简单绘制了\n前几个国家不同年份,不同性别的结核病病例总数。\n\n```{r}\nwho5 %>% group_by(country,year,sex)  %>% filter(year<2003) %>% \n\tcount() %>% \n\thead(100) %>%\n\tggplot(aes(x=as.factor(year),y=n,fill=country))+geom_col() +facet_wrap(~sex,nrow = 1)+\n\t  scale_fill_brewer(palette = \"Paired\")\n\n```\n\n## 复杂的管道函数\n\n事实上你可以直接只用管道函数构建一个复杂的函数：\n\n```{r eval=FALSE, include=T}\nwho %>%\n  pivot_longer(\n    cols = new_sp_m014:newrel_f65, \n    names_to = \"key\", \n    values_to = \"cases\", \n    values_drop_na = TRUE\n  ) %>% \n  mutate(\n    key = stringr::str_replace(key, \"newrel\", \"new_rel\")\n  ) %>%\n  separate(key, c(\"new\", \"var\", \"sexage\")) %>% \n  select(-new, -iso2, -iso3) %>% \n  separate(sexage, c(\"sex\", \"age\"), sep = 1)\n```\n\n# 所用函数详细解释\n\n## pivot_longer()、poivot_wider()\n\n`pivot_longer()` 将在列中列名（数值）转换到一列上。具体可见下图,将列变量转化为数据存在year列名中,相当于把数据变长(longer).\n\n\n![pivot_longer，从左到右](36.jpg)\n\n函数主要参数:\n\n> cols选取的列\n> names_to 字符串，指定要从数据的列名中存储的数据创建的列的名称。\n> values_to 字符串，指定要从存储在单元格值中的数据创建的列的名称。\n> values_drop_na 如果为真，将删除value_to列中只包含NAs的行。这有效地将显式缺失值转换为隐式缺失值，通常只在由其结构创建的数据中缺失值时使用.\n\n例子如上面例子:将new_sp_m014到newrel_f65之间的列选取,汇总到`key`列名中,值存在`cases`列名中,并将含有缺失值的行进行删除.\n\n```{r}\nwho1 <- who %>% \n\tpivot_longer(\n\t\tcols = new_sp_m014:newrel_f65,\n\t\tnames_to = 'key',\n\t\tvalues_to = 'cases',\n\t\tvalues_drop_na = T\n)\n```\n\n\n当然还有一个和他相反功能的函数`poivot_wider()`。具体见下图,相当于把key中的值变为列名,对应的values数据转化到population中.下面是简单的例子.\n\n\n![pivot_wider，从右到左](37.jpg)\n\n```{r}\nlibrary(tidyverse)\nstocks <- tibble(\n  year   = c(2015, 2015, 2016, 2016),\n  half  = c(   1,    2,     1,    2),\n  return = c(1.88, 0.59, 0.92, 0.17)\n)\nstocks\n```\n\n我们将数据变宽,将year变为列名,对应在return中的数据进行填充.\n\n```{r}\nstocks %>%\n\tpivot_wider(names_from = year,values_from = return) \n```\n\n\n## separate()\n\n该函数可将字符进行分割,具体案例如上.\n\n默认情况下，当separate()看到非字母数字字符(即不是数字或字母的字符)时，它将分割值。可以用里面的参数`sep`。比如：`sep='_'`。他还有一个功能,当`sep=2`时,可通过第二个位置进行分割,使用在省份市级,等数据上.例如以下函数,其中into = c(\"century\", \"year\")将原始分割后的数据导入两个新列上,分别叫century和year.\n\n\n```\ntable3 %>% \n  separate(year, into = c(\"century\", \"year\"), sep = 2)\n```\n\n**注意**:默认情况下，会转化成字符形式，你可以用参数`convert=T`，将数据转化最佳结构.\n\n![](38.jpg)\n\n## unite\n\n是`separate()`的反函数,这里做个补充\n\n![unite](39.jpg)\n\n默认情况下，`sep='_'`如果我们不需要任何分隔符可以使用`sep=''`.\n\n\n# 缺失值处理\n\n两种情况会出现缺失值: \n\n\n1. **显式**: 标记为`NA`.\n\n2. **隐式**: 没出在书中的.\n\n以下为例子\n\n```{r}\nstocks <- tibble(\n  year   = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),\n  qtr    = c(   1,    2,    3,    4,    2,    3,    4),\n  return = c(1.88, 0.59, 0.35,   NA, 0.92, 0.17, 2.66)\n)\n```\n\n在这个数据集中有两个缺失的值:\n\n1. 2015年第四季度出现显式丢失，因为它的值应该在的单元格中包含NA。\n\n2. 2016年第一季度的收益是隐式缺失的，因为它根本没有出现在数据集中。\n\n对于隐式缺失值,我们可以将数据进行变化,显示隐性缺失.\n\n```{r}\nstocks %>% \n\tpivot_wider(names_from = year,values_from = return)\n```\n这样就可以看到原来隐型的缺失值了.\n\n这些显式缺失的值在数据的其他表示中可能并不重要，你可以在pivot_longer()中设置values_drop_na = TRUE来将显式缺失的值变成隐式缺失值:\n\n\n```{r}\nstocks %>% \n  pivot_wider(names_from = year, values_from = return) %>% \n  pivot_longer(\n    cols = c(`2015`, `2016`), \n    names_to = \"year\", \n    values_to = \"return\", \n    values_drop_na = TRUE\n  )\n```\n\n#### complete()\n\n在整洁数据中显式显示缺失值的另一个重要工具是complete().\n\ncomplete()获取一组列，并查找所有唯一的组合。然后，它确保原始数据集包含所有这些值，并在必要时显式填充NAs。\n\n```{r}\nstocks %>% \n\tcomplete(year,qtr)\n```\n\n有时，当一个数据源主要用于数据输入时，丢失的值表明以前的值应该结转:\n\n```{r}\ntreatment <- tribble(\n\t~person,~treatment,~response,\n\t'A',1,7,\n\tNA,2,10,\n\tNA,3,9,\n\t'B',1,4\n)\n```\n\n\n#### fill()\n\n您可以使用fill()填充这些缺失的值。它采用一组列，在这些列中，您希望用最近的非缺失值(有时称为最后一次观察结转)替换缺失值。\n\n```{r}\ntreatment %>% fill(person)\n```\n直接将缺失值填写为前面的字符('A').\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/DESCRIPTION",
    "content": "Package: r4ds\nTitle: R for data science.\nVersion: 0.1\nAuthors@R: c(\n  person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", c(\"aut\", \"cre\")),\n  person(\"Garrett\", \"Grolemund\", , \"garrett@rstudio.com\", \"aut\")\n  )\nDepends: R (>= 3.1.0)\nURL: https://github.com/hadley/r4ds\nImports:\n  bookdown,\n  condvis,\n  gapminder,\n  ggrepel,\n  hexbin,\n  htmltools,\n  htmlwidgets,\n  jpeg,\n  knitr,\n  Lahman,\n  leaflet,\n  maps,\n  microbenchmark,\n  nycflights13,\n  png,\n  pryr,\n  tidyverse,\n  viridis\nRemotes:\n  hadley/ggplot2,\n  slowkow/ggrepel,\n  rstudio/bookdown,\n  rstudio/rmarkdown\n"
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    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/EDA.Rmd",
    "content": "# Exploratory Data Analysis\n\n## Introduction\n\nThis chapter will show you how to use visualisation and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:\n\n1. Generate questions about your data.\n\n1. Search for answers by visualising, transforming, and modelling your data.\n\n1. Use what you learn to refine your questions and/or generate new questions.\n\nEDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will home in on a few particularly productive areas that you'll eventually write up and communicate to others.\n\nEDA is an important part of any data analysis, even if the questions are handed to you on a platter, because you always need to investigate the quality of your data. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. To do data cleaning, you'll need to deploy all the tools of EDA: visualisation, transformation, and modelling.\n\n### Prerequisites\n\nIn this chapter we'll combine what you've learned about dplyr and ggplot2 to interactively ask questions, answer them with data, and then ask new questions.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n```\n\n## Questions\n\n> \"There are no routine statistical questions, only questionable statistical\n> routines.\" --- Sir David Cox\n\n> \"Far better an approximate answer to the right question, which is often\n> vague, than an exact answer to the wrong question, which can always be made\n> precise.\" --- John Tukey\n\nYour goal during EDA is to develop an understanding of your data. The easiest way to do this is to use questions as tools to guide your investigation. When you ask a question, the question focuses your attention on a specific part of your dataset and helps you decide which graphs, models, or transformations to make.\n\nEDA is fundamentally a creative process. And like most creative processes, the key to asking _quality_ questions is to generate a large _quantity_ of questions. It is difficult to ask revealing questions at the start of your analysis because you do not know what insights are contained in your dataset. On the other hand, each new question that you ask will expose you to a new aspect of your data and increase your chance of making a discovery. You can quickly drill down into the most interesting parts of your data---and develop a set of thought-provoking questions---if you follow up each question with a new question based on what you find.\n\nThere is no rule about which questions you should ask to guide your research. However, two types of questions will always be useful for making discoveries within your data. You can loosely word these questions as:\n\n1. What type of variation occurs within my variables?\n\n1. What type of covariation occurs between my variables?\n\nThe rest of this chapter will look at these two questions. I'll explain what variation and covariation are, and I'll show you several ways to answer each question. To make the discussion easier, let's define some terms: \n\n*   A __variable__ is a quantity, quality, or property that you can measure. \n\n*   A __value__ is the state of a variable when you measure it. The value of a\n    variable may change from measurement to measurement.\n  \n*   An __observation__ is a set of measurements made under similar conditions\n    (you usually make all of the measurements in an observation at the same \n    time and on the same object). An observation will contain several values, \n    each associated with a different variable. I'll sometimes refer to \n    an observation as a data point.\n\n*   __Tabular data__ is a set of values, each associated with a variable and an\n    observation. Tabular data is _tidy_ if each value is placed in its own\n    \"cell\", each variable in its own column, and each observation in its own \n    row. \n\nSo far, all of the data that you've seen has been tidy. In real-life, most data isn't tidy, so we'll come back to these ideas again in [tidy data].\n\n## Variation\n\n**Variation** is the tendency of the values of a variable to change from measurement to measurement. You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light. Each of your measurements will include a small amount of error that varies from measurement to measurement. Categorical variables can also vary if you measure across different subjects (e.g. the eye colors of different people), or different times (e.g. the energy levels of an electron at different moments). \nEvery variable has its own pattern of variation, which can reveal interesting information. The best way to understand that pattern is to visualise the distribution of the variable's values.\n\n### Visualising distributions\n\nHow you visualise the distribution of a variable will depend on whether the variable is categorical or continuous. A variable is **categorical** if it can only take one of a small set of values. In R, categorical variables are usually saved as factors or character vectors. To examine the distribution of a categorical variable, use a bar chart:\n\n```{r}\nggplot(data = diamonds) +\n  geom_bar(mapping = aes(x = cut))\n```\n\nThe height of the bars displays how many observations occurred with each x value. You can compute these values manually with `dplyr::count()`:\n\n```{r}\ndiamonds %>% \n  count(cut)\n```\n\nA variable is **continuous** if it can take any of an infinite set of ordered values. Numbers and date-times are two examples of continuous variables. To examine the distribution of a continuous variable, use a histogram:\n\n```{r}\nggplot(data = diamonds) +\n  geom_histogram(mapping = aes(x = carat), binwidth = 0.5)\n```\n\nYou can compute this by hand by combining `dplyr::count()` and `ggplot2::cut_width()`:\n\n```{r}\ndiamonds %>% \n  count(cut_width(carat, 0.5))\n```\n\nA histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin. In the graph above, the tallest bar shows that almost 30,000 observations have a `carat` value between 0.25 and 0.75, which are the left and right edges of the bar. \n\nYou can set the width of the intervals in a histogram with the `binwidth` argument, which is measured in the units of the `x` variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. For example, here is how the graph above looks when we zoom into just the diamonds with a size of less than three carats and choose a smaller binwidth.\n\n```{r}\nsmaller <- diamonds %>% \n  filter(carat < 3)\n  \nggplot(data = smaller, mapping = aes(x = carat)) +\n  geom_histogram(binwidth = 0.1)\n```\n\nIf you wish to overlay multiple histograms in the same plot, I recommend using `geom_freqpoly()` instead of `geom_histogram()`. `geom_freqpoly()` performs the same calculation as `geom_histogram()`, but instead of displaying the counts with bars, uses lines instead. It's much easier to understand overlapping lines than bars.\n\n```{r}\nggplot(data = smaller, mapping = aes(x = carat, colour = cut)) +\n  geom_freqpoly(binwidth = 0.1)\n```\n\nThere are a few challenges with this type of plot, which we will come back to in [visualising a categorical and a continuous variable](#cat-cont).\n\nNow that you can visualise variation, what should you look for in your plots? And what type of follow-up questions should you ask? I've put together a list below of the most useful types of information that you will find in your graphs, along with some follow-up questions for each type of information. The key to asking good follow-up questions will be to rely on your curiosity (What do you want to learn more about?) as well as your skepticism (How could this be misleading?).\n\n### Typical values\n\nIn both bar charts and histograms, tall bars show the common values of a variable, and shorter bars show less-common values. Places that do not have bars reveal values that were not seen in your data. To turn this information into useful questions, look for anything unexpected:\n\n* Which values are the most common? Why?\n\n* Which values are rare? Why? Does that match your expectations?\n\n* Can you see any unusual patterns? What might explain them?\n\nAs an example, the histogram below suggests several interesting questions: \n\n* Why are there more diamonds at whole carats and common fractions of carats?\n\n* Why are there more diamonds slightly to the right of each peak than there \n  are slightly to the left of each peak?\n  \n* Why are there no diamonds bigger than 3 carats?\n\n```{r}\nggplot(data = smaller, mapping = aes(x = carat)) +\n  geom_histogram(binwidth = 0.01)\n```\n\nClusters of similar values suggest that subgroups exist in your data. To understand the subgroups, ask:\n\n* How are the observations within each cluster similar to each other?\n\n* How are the observations in separate clusters different from each other?\n\n* How can you explain or describe the clusters?\n\n* Why might the appearance of clusters be misleading?\n\nThe histogram below shows the length (in minutes) of 272 eruptions of the Old Faithful Geyser in Yellowstone National Park. Eruption times appear to be clustered into two groups: there are short eruptions (of around 2 minutes) and long eruptions (4-5 minutes), but little in between.\n\n```{r}\nggplot(data = faithful, mapping = aes(x = eruptions)) + \n  geom_histogram(binwidth = 0.25)\n```  \n\nMany of the questions above will prompt you to explore a relationship *between* variables, for example, to see if the values of one variable can explain the behavior of another variable. We'll get to that shortly.\n\n### Unusual values\n\nOutliers are observations that are unusual; data points that don't seem to fit the pattern. Sometimes outliers are data entry errors; other times outliers suggest important new science. When you have a lot of data, outliers are sometimes difficult to see in a histogram.  For example, take the distribution of the `y` variable from the diamonds dataset. The only evidence of outliers is the unusually wide limits on the x-axis.\n\n```{r}\nggplot(diamonds) + \n  geom_histogram(mapping = aes(x = y), binwidth = 0.5)\n```   \n\nThere are so many observations in the common bins that the rare bins are so short that you can't see them (although maybe if you stare intently at 0 you'll spot something). To make it easy to see the unusual values, we need to zoom to small values of the y-axis with `coord_cartesian()`:\n\n```{r}\nggplot(diamonds) + \n  geom_histogram(mapping = aes(x = y), binwidth = 0.5) +\n  coord_cartesian(ylim = c(0, 50))\n```   \n\n(`coord_cartesian()` also has an `xlim()` argument for when you need to zoom into the x-axis. ggplot2 also has `xlim()` and `ylim()` functions that work slightly differently: they throw away the data outside the limits.)\n\nThis allows us to see that there are three unusual values: 0, ~30, and ~60. We pluck them out with dplyr: \n\n```{r, include = FALSE}\nold <- options(tibble.print_max = 10, tibble.print_min = 10)\n```\n\n```{r}\nunusual <- diamonds %>% \n  filter(y < 3 | y > 20) %>% \n  select(price, x, y, z) %>%\n  arrange(y)\nunusual\n```\n\n```{r, include = FALSE}\noptions(old)\n```\n\nThe `y` variable measures one of the three dimensions of these diamonds, in mm. We know that diamonds can't have a width of 0mm, so these values must be incorrect. We might also suspect that measurements of 32mm and 59mm are implausible: those diamonds are over an inch long, but don't cost hundreds of thousands of dollars!\n\nIt's good practice to repeat your analysis with and without the outliers. If they have minimal effect on the results, and you can't figure out why they're there, it's reasonable to replace them with missing values, and move on. However, if they have a substantial effect on your results, you shouldn't drop them without justification. You'll need to figure out what caused them (e.g. a data entry error) and disclose that you removed them in your write-up.\n\n\n### Exercises\n\n1.  Explore the distribution of each of the `x`, `y`, and `z` variables \n    in `diamonds`. What do you learn? Think about a diamond and how you\n    might decide which dimension is the length, width, and depth.\n\n1.  Explore the distribution of `price`. Do you discover anything unusual\n    or surprising? (Hint: Carefully think about the `binwidth` and make sure\n    you try a wide range of values.)\n\n1.  How many diamonds are 0.99 carat? How many are 1 carat? What\n    do you think is the cause of the difference?\n    \n1.  Compare and contrast `coord_cartesian()` vs `xlim()` or `ylim()` when\n    zooming in on a histogram. What happens if you leave `binwidth` unset?\n    What happens if you try and zoom so only half a bar shows?\n    \n## Missing values\n\nIf you've encountered unusual values in your dataset, and simply want to move on to the rest of your analysis, you have two options.\n\n1.  Drop the entire row with the strange values:\n\n    ```{r, eval = FALSE}\n    diamonds2 <- diamonds %>% \n      filter(between(y, 3, 20))\n    ```\n    \n    I don't recommend this option because just because one measurement\n    is invalid, doesn't mean all the measurements are. Additionally, if you\n    have low quality data, by time that you've applied this approach to every\n    variable you might find that you don't have any data left!\n\n1.  Instead, I recommend replacing the unusual values with missing values.\n    The easiest way to do this is to use `mutate()` to replace the variable\n    with a modified copy. You can use the `ifelse()` function to replace\n    unusual values with `NA`:\n\n    ```{r}\n    diamonds2 <- diamonds %>% \n      mutate(y = ifelse(y < 3 | y > 20, NA, y))\n    ```\n\n`ifelse()` has three arguments. The first argument `test` should be a logical vector. The result will contain the value of the second argument, `yes`, when `test` is `TRUE`, and the value of the third argument, `no`, when it is false. Alternatively to ifelse, use `dplyr::case_when()`. `case_when()` is particularly useful inside mutate when you want to create a new variable that relies on a complex combination of existing variables.\n\nLike R, ggplot2 subscribes to the philosophy that missing values should never silently go missing. It's not obvious where you should plot missing values, so ggplot2 doesn't include them in the plot, but it does warn that they've been removed:\n\n```{r, dev = \"png\"}\nggplot(data = diamonds2, mapping = aes(x = x, y = y)) + \n  geom_point()\n```\n\nTo suppress that warning, set `na.rm = TRUE`:\n\n```{r, eval = FALSE}\nggplot(data = diamonds2, mapping = aes(x = x, y = y)) + \n  geom_point(na.rm = TRUE)\n```\n\nOther times you want to understand what makes observations with missing values different to observations with recorded values. For example, in `nycflights13::flights`, missing values in the `dep_time` variable indicate that the flight was cancelled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times. You can do this by making a new variable with `is.na()`.\n\n```{r}\nnycflights13::flights %>% \n  mutate(\n    cancelled = is.na(dep_time),\n    sched_hour = sched_dep_time %/% 100,\n    sched_min = sched_dep_time %% 100,\n    sched_dep_time = sched_hour + sched_min / 60\n  ) %>% \n  ggplot(mapping = aes(sched_dep_time)) + \n    geom_freqpoly(mapping = aes(colour = cancelled), binwidth = 1/4)\n```\n\nHowever this plot isn't great because there are many more non-cancelled flights than cancelled flights. In the next section we'll explore some techniques for improving this comparison.\n\n### Exercises\n\n1.  What happens to missing values in a histogram?  What happens to missing\n    values in a bar chart? Why is there a difference?\n\n1.  What does `na.rm = TRUE` do in `mean()` and `sum()`?\n\n## Covariation\n\nIf variation describes the behavior _within_ a variable, covariation describes the behavior _between_ variables. **Covariation** is the tendency for the values of two or more variables to vary together in a related way. The best way to spot covariation is to visualise the relationship between two or more variables. How you do that should again depend on the type of variables involved.\n\n### A categorical and continuous variable {#cat-cont}\n\nIt's common to want to explore the distribution of a continuous variable broken down by a categorical variable, as in the previous frequency polygon. The default appearance of `geom_freqpoly()` is not that useful for that sort of comparison because the height is given by the count. That means if one of the groups is much smaller than the others, it's hard to see the differences in shape. For example, let's explore how the price of a diamond varies with its quality:\n\n```{r}\nggplot(data = diamonds, mapping = aes(x = price)) + \n  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)\n```\n\nIt's hard to see the difference in distribution because the overall counts differ so much:\n\n```{r, fig.width = \"50%\", fig.width = 4}\nggplot(diamonds) + \n  geom_bar(mapping = aes(x = cut))\n```\n\nTo make the comparison easier we need to swap what is displayed on the y-axis. Instead of displaying count, we'll display __density__, which is the count standardised so that the area under each frequency polygon is one.\n\n```{r}\nggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) + \n  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)\n```\n\nThere's something rather surprising about this plot - it appears that fair diamonds (the lowest quality) have the highest average price!  But maybe that's because frequency polygons are a little hard to interpret - there's a lot going on in this plot.\n\nAnother alternative to display the distribution of a continuous variable broken down by a categorical variable is the boxplot. A **boxplot** is a type of visual shorthand for a distribution of values that is popular among statisticians. Each boxplot consists of:\n\n* A box that stretches from the 25th percentile of the distribution to the \n  75th percentile, a distance known as the interquartile range (IQR). In the\n  middle of the box is a line that displays the median, i.e. 50th percentile,\n  of the distribution. These three lines give you a sense of the spread of the\n  distribution and whether or not the distribution is symmetric about the\n  median or skewed to one side. \n\n* Visual points that display observations that fall more than 1.5 times the \n  IQR from either edge of the box. These outlying points are unusual\n  so are plotted individually.\n\n* A line (or whisker) that extends from each end of the box and goes to the   \n  farthest non-outlier point in the distribution.\n\n```{r, echo = FALSE, out.width = \"100%\"}\nknitr::include_graphics(\"images/EDA-boxplot.png\")\n```\n\nLet's take a look at the distribution of price by cut using `geom_boxplot()`:\n\n```{r fig.height = 3}\nggplot(data = diamonds, mapping = aes(x = cut, y = price)) +\n  geom_boxplot()\n```\n\nWe see much less information about the distribution, but the boxplots are much more compact so we can more easily compare them (and fit more on one plot). It supports the counterintuitive finding that better quality diamonds are cheaper on average! In the exercises, you'll be challenged to figure out why.\n\n`cut` is an ordered factor: fair is worse than good, which is worse than very good and so on. Many categorical variables don't have such an intrinsic order, so you might want to reorder them to make a more informative display. One way to do that is with the `reorder()` function.\n\nFor example, take the `class` variable in the `mpg` dataset. You might be interested to know how highway mileage varies across classes:\n\n```{r}\nggplot(data = mpg, mapping = aes(x = class, y = hwy)) +\n  geom_boxplot()\n```\n\nTo make the trend easier to see, we can reorder `class` based on the median value of `hwy`:\n\n```{r fig.height = 3}\nggplot(data = mpg) +\n  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy))\n```\n\nIf you have long variable names, `geom_boxplot()` will work better if you flip it 90°. You can do that with `coord_flip()`.\n\n```{r}\nggplot(data = mpg) +\n  geom_boxplot(mapping = aes(x = reorder(class, hwy, FUN = median), y = hwy)) +\n  coord_flip()\n```\n\n#### Exercises\n\n1.  Use what you've learned to improve the visualisation of the departure times\n    of cancelled vs. non-cancelled flights.\n\n1.  What variable in the diamonds dataset is most important for predicting\n    the price of a diamond? How is that variable correlated with cut?\n    Why does the combination of those two relationships lead to lower quality\n    diamonds being more expensive?\n\n1.  Install the ggstance package, and create a horizontal boxplot.\n    How does this compare to using `coord_flip()`?\n\n1.  One problem with boxplots is that they were developed in an era of \n    much smaller datasets and tend to display a prohibitively large\n    number of \"outlying values\". One approach to remedy this problem is\n    the letter value plot. Install the lvplot package, and try using\n    `geom_lv()` to display the distribution of price vs cut. What\n    do you learn? How do you interpret the plots?\n\n1.  Compare and contrast `geom_violin()` with a facetted `geom_histogram()`,\n    or a coloured `geom_freqpoly()`. What are the pros and cons of each \n    method?\n\n1.  If you have a small dataset, it's sometimes useful to use `geom_jitter()`\n    to see the relationship between a continuous and categorical variable.\n    The ggbeeswarm package provides a number of methods similar to \n    `geom_jitter()`. List them and briefly describe what each one does.\n\n### Two categorical variables\n\nTo visualise the covariation between categorical variables, you'll need to count the number of observations for each combination. One way to do that is to rely on the built-in `geom_count()`:\n\n```{r}\nggplot(data = diamonds) +\n  geom_count(mapping = aes(x = cut, y = color))\n```\n\nThe size of each circle in the plot displays how many observations occurred at each combination of values. Covariation will appear as a strong correlation between specific x values and specific y values. \n\nAnother approach is to compute the count with dplyr:\n\n```{r}\ndiamonds %>% \n  count(color, cut)\n```\n\nThen visualise with `geom_tile()` and the fill aesthetic:\n\n```{r}\ndiamonds %>% \n  count(color, cut) %>%  \n  ggplot(mapping = aes(x = color, y = cut)) +\n    geom_tile(mapping = aes(fill = n))\n```\n\nIf the categorical variables are unordered, you might want to use the seriation package to simultaneously reorder the rows and columns in order to more clearly reveal interesting patterns. For larger plots, you might want to try the d3heatmap or heatmaply packages, which create interactive plots.\n\n#### Exercises\n\n1.  How could you rescale the count dataset above to more clearly show\n    the distribution of cut within colour, or colour within cut?\n\n1.  Use `geom_tile()` together with dplyr to explore how average flight\n    delays vary by destination and month of year.  What makes the \n    plot difficult to read? How could you improve it?\n\n1.  Why is it slightly better to use `aes(x = color, y = cut)` rather\n    than `aes(x = cut, y = color)` in the example above?\n\n### Two continuous variables\n\nYou've already seen one great way to visualise the covariation between two continuous variables: draw a scatterplot with `geom_point()`. You can see covariation as a pattern in the points. For example, you can see an exponential relationship between the carat size and price of a diamond.\n\n```{r, dev = \"png\"}\nggplot(data = diamonds) +\n  geom_point(mapping = aes(x = carat, y = price))\n```\n\nScatterplots become less useful as the size of your dataset grows, because points begin to overplot, and pile up into areas of uniform black (as above).\nYou've already seen one way to fix the problem: using the `alpha` aesthetic to add transparency.\n\n```{r, dev = \"png\"}\nggplot(data = diamonds) + \n  geom_point(mapping = aes(x = carat, y = price), alpha = 1 / 100)\n```\n\nBut using transparency can be challenging for very large datasets. Another solution is to use bin. Previously you used `geom_histogram()` and `geom_freqpoly()` to bin in one dimension. Now you'll learn how to use `geom_bin2d()` and `geom_hex()` to bin in two dimensions.\n\n`geom_bin2d()` and `geom_hex()` divide the coordinate plane into 2d bins and then use a fill color to display how many points fall into each bin. `geom_bin2d()` creates rectangular bins. `geom_hex()` creates hexagonal bins. You will need to install the hexbin package to use `geom_hex()`.\n\n```{r, fig.asp = 1, out.width = \"50%\", fig.align = \"default\", message = FALSE}\nggplot(data = smaller) +\n  geom_bin2d(mapping = aes(x = carat, y = price))\n\n# install.packages(\"hexbin\")\nggplot(data = smaller) +\n  geom_hex(mapping = aes(x = carat, y = price))\n```\n\nAnother option is to bin one continuous variable so it acts like a categorical variable. Then you can use one of the techniques for visualising the combination of a categorical and a continuous variable that you learned about. For example, you could bin `carat` and then for each group, display a boxplot:\n\n```{r}\nggplot(data = smaller, mapping = aes(x = carat, y = price)) + \n  geom_boxplot(mapping = aes(group = cut_width(carat, 0.1)))\n```\n\n`cut_width(x, width)`, as used above, divides `x` into bins of width `width`. By default, boxplots look roughly the same (apart from number of outliers) regardless of how many observations there are, so it's difficult to tell that each boxplot summarises a different number of points. One way to show that is to make the width of the boxplot proportional to the number of points with `varwidth = TRUE`.\n\nAnother approach is to display approximately the same number of points in each bin. That's the job of `cut_number()`:\n\n```{r}\nggplot(data = smaller, mapping = aes(x = carat, y = price)) + \n  geom_boxplot(mapping = aes(group = cut_number(carat, 20)))\n```\n\n#### Exercises\n\n1.  Instead of summarising the conditional distribution with a boxplot, you\n    could use a frequency polygon. What do you need to consider when using\n    `cut_width()` vs `cut_number()`? How does that impact a visualisation of\n    the 2d distribution of `carat` and `price`?\n\n1.  Visualise the distribution of carat, partitioned by price.\n\n1.  How does the price distribution of very large diamonds compare to small \n    diamonds? Is it as you expect, or does it surprise you?\n    \n1.  Combine two of the techniques you've learned to visualise the \n    combined distribution of cut, carat, and price.\n\n1. Two dimensional plots reveal outliers that are not visible in one \n   dimensional plots. For example, some points in the plot below have an \n   unusual combination of `x` and `y` values, which makes the points outliers \n   even though their `x` and `y` values appear normal when examined separately.\n  \n    ```{r, dev = \"png\"}\n    ggplot(data = diamonds) +\n      geom_point(mapping = aes(x = x, y = y)) +\n      coord_cartesian(xlim = c(4, 11), ylim = c(4, 11))\n    ```\n    \n    Why is a scatterplot a better display than a binned plot for this case?\n\n## Patterns and models\n\nPatterns in your data provide clues about relationships. If a systematic relationship exists between two variables it will appear as a pattern in the data. If you spot a pattern, ask yourself:\n\n+ Could this pattern be due to coincidence (i.e. random chance)?\n\n+ How can you describe the relationship implied by the pattern?\n\n+ How strong is the relationship implied by the pattern?\n\n+ What other variables might affect the relationship?\n\n+ Does the relationship change if you look at individual subgroups of the data?\n\nA scatterplot of Old Faithful eruption lengths versus the wait time between eruptions shows a pattern: longer wait times are associated with longer eruptions. The scatterplot also displays the two clusters that we noticed above.\n\n```{r fig.height = 2}\nggplot(data = faithful) + \n  geom_point(mapping = aes(x = eruptions, y = waiting))\n``` \n\nPatterns provide one of the most useful tools for data scientists because they reveal covariation. If you think of variation as a phenomenon that creates uncertainty, covariation is a phenomenon that reduces it. If two variables covary, you can use the values of one variable to make better predictions about the values of the second. If the covariation is due to a causal relationship (a special case), then you can use the value of one variable to control the value of the second.\n\nModels are a tool for extracting patterns out of data. For example, consider the diamonds data. It's hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It's possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain. The following code fits a model that predicts `price` from `carat` and then computes the residuals (the difference between the predicted value and the actual value). The residuals give us a view of the price of the diamond, once the effect of carat has been removed. \n\n```{r, dev = \"png\"}\nlibrary(modelr)\n\nmod <- lm(log(price) ~ log(carat), data = diamonds)\n\ndiamonds2 <- diamonds %>% \n  add_residuals(mod) %>% \n  mutate(resid = exp(resid))\n\nggplot(data = diamonds2) + \n  geom_point(mapping = aes(x = carat, y = resid))\n```\n\nOnce you've removed the strong relationship between carat and price, you can see what you expect in the relationship between cut and price: relative to their size, better quality diamonds are more expensive. \n\n```{r}\nggplot(data = diamonds2) + \n  geom_boxplot(mapping = aes(x = cut, y = resid))\n```\n\nYou'll learn how models, and the modelr package, work in the final part of the book, [model](#model-intro). We're saving modelling for later because understanding what models are and how they work is easiest once you have tools of data wrangling and programming in hand.\n\n## ggplot2 calls\n\nAs we move on from these introductory chapters, we'll transition to a more concise expression of ggplot2 code. So far we've been very explicit, which is helpful when you are learning:\n\n```{r, eval = FALSE}\nggplot(data = faithful, mapping = aes(x = eruptions)) + \n  geom_freqpoly(binwidth = 0.25)\n```\n\nTypically, the first one or two arguments to a function are so important that you should know them by heart. The first two arguments to `ggplot()` are `data` and `mapping`, and the first two arguments to `aes()` are `x` and `y`. In the remainder of the book, we won't supply those names. That saves typing, and, by reducing the amount of boilerplate, makes it easier to see what's different between plots. That's a really important programming concern that we'll come back in [functions].\n\nRewriting the previous plot more concisely yields:\n\n```{r, eval = FALSE}\nggplot(faithful, aes(eruptions)) + \n  geom_freqpoly(binwidth = 0.25)\n```\n\nSometimes we'll turn the end of a pipeline of data transformation into a plot. Watch for the transition from `%>%` to `+`. I wish this transition wasn't necessary but unfortunately ggplot2 was created before the pipe was discovered.\n\n```{r, eval = FALSE}\ndiamonds %>% \n  count(cut, clarity) %>% \n  ggplot(aes(clarity, cut, fill = n)) + \n    geom_tile()\n```\n\n## Learning more\n\nIf you want to learn more about the mechanics of ggplot2, I'd highly recommend grabbing a copy of the ggplot2 book: <https://amzn.com/331924275X>. It's been recently updated, so it includes dplyr and tidyr code, and has much more space to explore all the facets of visualisation. Unfortunately the book isn't generally available for free, but if you have a connection to a university you can probably get an electronic version for free through SpringerLink.\n\nAnother useful resource is the [_R Graphics Cookbook_](https://amzn.com/1449316956) by Winston Chang. Much of the contents are available online at <http://www.cookbook-r.com/Graphs/>.\n\nI also recommend [_Graphical Data Analysis with R_](https://amzn.com/1498715230), by Antony Unwin. This is a book-length treatment similar to the material covered in this chapter, but has the space to go into much greater depth. \n"
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    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/LICENSE",
    "content": "This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/us/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.\n"
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    "content": "# R for Data Science\n\nThis is code and text behind the [R for Data Science](http://r4ds.had.co.nz)\nbook. \n\nThe R packages used in this book can be installed via\n\n```{r}\ndevtools::install_github(\"hadley/r4ds\")\n```\nThe site is built using [bookdown package](https://github.com/rstudio/bookdown).\nTo create the site, you also need:\n\n* [pandoc](http://johnmacfarlane.net/pandoc/)\n"
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    "content": "set.seed(1014)\noptions(digits = 3)\n\nknitr::opts_chunk$set(\n  comment = \"#>\",\n  collapse = TRUE,\n  cache = TRUE,\n  out.width = \"70%\",\n  fig.align = 'center',\n  fig.width = 6,\n  fig.asp = 0.618,  # 1 / phi\n  fig.show = \"hold\"\n)\n\noptions(dplyr.print_min = 6, dplyr.print_max = 6)\n"
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    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/communicate-plots.Rmd",
    "content": "# Graphics for communication\n\n## Introduction\n\nIn [exploratory data analysis], you learned how to use plots as tools for _exploration_. When you make exploratory plots, you know---even before looking---which variables the plot will display. You made each plot for a purpose, could quickly look at it, and then move on to the next plot. In the course of most analyses, you'll produce tens or hundreds of plots, most of which are immediately thrown away.\n\nNow that you understand your data, you need to _communicate_ your understanding to others. Your audience will likely not share your background knowledge and will not be deeply invested in the data. To help others quickly build up a good mental model of the data, you will need to invest considerable effort in making your plots as self-explanatory as possible. In this chapter, you'll learn some of the tools that ggplot2 provides to do so.\n\nThis chapter focuses on the tools you need to create good graphics. I assume that you know what you want, and just need to know how to do it. For that reason, I highly recommend pairing this chapter with a good general visualisation book. I particularly like [_The Truthful Art_](https://amzn.com/0321934075), by Albert Cairo. It doesn't teach the mechanics of creating visualisations, but instead focuses on what you need to think about in order to create effective graphics.\n\n### Prerequisites\n\nIn this chapter, we'll focus once again on ggplot2. We'll also use a little dplyr for data manipulation, and a few ggplot2 extension packages, including __ggrepel__ and __viridis__. Rather than loading those extensions here, we'll refer to their functions explicitly, using the `::` notation. This will help make it clear which functions are built into ggplot2, and which come from other packages. Don't forget you'll need to install those packages with `install.packages()` if you don't already have them.\n\n```{r, message = FALSE}\nlibrary(tidyverse)\n```\n\n## Label\n\nThe easiest place to start when turning an exploratory graphic into an expository graphic is with good labels. You add labels with the `labs()` function. This example adds a plot title:\n\n```{r, message = FALSE}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = class)) +\n  geom_smooth(se = FALSE) +\n  labs(title = \"Fuel efficiency generally decreases with engine size\")\n```\n\nThe purpose of a plot title is to summarise the main finding. Avoid titles that just describe what the plot is, e.g. \"A scatterplot of engine displacement vs. fuel economy\". \n\nIf you need to add more text, there are two other useful labels that you can use in ggplot2 2.2.0 and above (which should be available by the time you're reading this book):\n\n*   `subtitle` adds additional detail in a smaller font beneath the title.\n\n*   `caption` adds text at the bottom right of the plot, often used to describe\n    the source of the data.\n\n```{r, message = FALSE}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = class)) +\n  geom_smooth(se = FALSE) +\n  labs(\n    title = \"Fuel efficiency generally decreases with engine size\",\n    subtitle = \"Two seaters (sports cars) are an exception because of their light weight\",\n    caption = \"Data from fueleconomy.gov\"\n  )\n```\n\nYou can also use `labs()` to replace the axis and legend titles. It's usually a good idea to replace short variable names with more detailed descriptions, and to include the units.\n\n```{r, message = FALSE}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  geom_smooth(se = FALSE) +\n  labs(\n    x = \"Engine displacement (L)\",\n    y = \"Highway fuel economy (mpg)\",\n    colour = \"Car type\"\n  )\n```\n\nIt's possible to use mathematical equations instead of text strings. Just switch `\"\"` out for `quote()` and read about the available options in `?plotmath`:\n\n```{r, fig.asp = 1, out.width = \"50%\", fig.width = 3}\ndf <- tibble(\n  x = runif(10),\n  y = runif(10)\n)\nggplot(df, aes(x, y)) +\n  geom_point() +\n  labs(\n    x = quote(sum(x[i] ^ 2, i == 1, n)),\n    y = quote(alpha + beta + frac(delta, theta))\n  )\n```\n\n### Exercises\n\n1.  Create one plot on the fuel economy data with customised `title`,\n    `subtitle`, `caption`, `x`, `y`, and `colour` labels.\n\n1.  The `geom_smooth()` is somewhat misleading because the `hwy` for\n    large engines is skewed upwards due to the inclusion of lightweight\n    sports cars with big engines. Use your modelling tools to fit and display\n    a better model.\n\n1.  Take an exploratory graphic that you've created in the last month, and add\n    informative titles to make it easier for others to understand.\n\n## Annotations\n\nIn addition to labelling major components of your plot, it's often useful to label individual observations or groups of observations. The first tool you have at your disposal is `geom_text()`. `geom_text()` is similar to `geom_point()`, but it has an additional aesthetic: `label`. This makes it possible to add textual labels to your plots.\n\nThere are two possible sources of labels. First, you might have a tibble that provides labels. The plot below isn't terribly useful, but it illustrates a useful approach: pull out the most efficient car in each class with dplyr, and then label it on the plot:\n\n```{r}\nbest_in_class <- mpg %>%\n  group_by(class) %>%\n  filter(row_number(desc(hwy)) == 1)\n\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  geom_text(aes(label = model), data = best_in_class)\n```\n\nThis is hard to read because the labels overlap with each other, and with the points. We can make things a little better by switching to `geom_label()` which draws a rectangle behind the text. We also use the `nudge_y` parameter to move the labels slightly above the corresponding points:\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  geom_label(aes(label = model), data = best_in_class, nudge_y = 2, alpha = 0.5)\n```\n\nThat helps a bit, but if you look closely in the top-left hand corner, you'll notice that there are two labels practically on top of each other. This happens because the highway mileage and displacement for the best cars in the compact and subcompact categories are exactly the same. There's no way that we can fix these by applying the same transformation for every label. Instead, we can use the __ggrepel__ package by Kamil Slowikowski. This useful package will automatically adjust labels so that they don't overlap:\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  geom_point(size = 3, shape = 1, data = best_in_class) +\n  ggrepel::geom_label_repel(aes(label = model), data = best_in_class)\n```\n\nNote another handy technique used here: I added a second layer of large, hollow points to highlight the points that I've labelled.\n\nYou can sometimes use the same idea to replace the legend with labels placed directly on the plot. It's not wonderful for this plot, but it isn't too bad. (`theme(legend.position = \"none\"`) turns the legend off --- we'll talk about it more shortly.)\n\n```{r}\nclass_avg <- mpg %>%\n  group_by(class) %>%\n  summarise(\n    displ = median(displ),\n    hwy = median(hwy)\n  )\n\nggplot(mpg, aes(displ, hwy, colour = class)) +\n  ggrepel::geom_label_repel(aes(label = class),\n    data = class_avg,\n    size = 6,\n    label.size = 0,\n    segment.color = NA\n  ) +\n  geom_point() +\n  theme(legend.position = \"none\")\n```\n\nAlternatively, you might just want to add a single label to the plot, but you'll still need to create a data frame. Often, you want the label in the corner of the plot, so it's convenient to create a new data frame using `summarise()` to compute the maximum values of x and y.\n\n```{r}\nlabel <- mpg %>%\n  summarise(\n    displ = max(displ),\n    hwy = max(hwy),\n    label = \"Increasing engine size is \\nrelated to decreasing fuel economy.\"\n  )\n\nggplot(mpg, aes(displ, hwy)) +\n  geom_point() +\n  geom_text(aes(label = label), data = label, vjust = \"top\", hjust = \"right\")\n```\n\nIf you want to place the text exactly on the borders of the plot, you can use `+Inf` and `-Inf`. Since we're no longer computing the positions from `mpg`, we can use `tibble()` to create the data frame:\n\n```{r}\nlabel <- tibble(\n  displ = Inf,\n  hwy = Inf,\n  label = \"Increasing engine size is \\nrelated to decreasing fuel economy.\"\n)\n\nggplot(mpg, aes(displ, hwy)) +\n  geom_point() +\n  geom_text(aes(label = label), data = label, vjust = \"top\", hjust = \"right\")\n```\n\nIn these examples, I manually broke the label up into lines using `\"\\n\"`. Another approach is to use `stringr::str_wrap()` to automatically add line breaks, given the number of characters you want per line:\n\n```{r}\n\"Increasing engine size is related to decreasing fuel economy.\" %>%\n  stringr::str_wrap(width = 40) %>%\n  writeLines()\n```\n\nNote the use of `hjust` and `vjust` to control the alignment of the label. Figure \\@ref(fig:just) shows all nine possible combinations.\n\n```{r just, echo = FALSE, fig.cap = \"All nine combinations of `hjust` and `vjust`.\", fig.asp = 0.5, fig.width = 4.5, out.width = \"60%\"}\nvjust <- c(bottom = 0, center = 0.5, top = 1)\nhjust <- c(left = 0, center = 0.5, right = 1)\n\ndf <- tidyr::crossing(hj = names(hjust), vj = names(vjust)) %>%\n  mutate(\n    y = vjust[vj],\n    x = hjust[hj],\n    label = paste0(\"hjust = '\", hj, \"'\\n\", \"vjust = '\", vj, \"'\")\n  )\n\nggplot(df, aes(x, y)) +\n  geom_point(colour = \"grey70\", size = 5) +\n  geom_point(size = 0.5, colour = \"red\") +\n  geom_text(aes(label = label, hjust = hj, vjust = vj), size = 4) +\n  labs(x = NULL, y = NULL) \n```\n\nRemember, in addition to `geom_text()`, you have many other geoms in ggplot2 available to help annotate your plot. A few ideas:\n\n*   Use `geom_hline()` and `geom_vline()` to add reference lines. I often make\n    them thick (`size = 2`) and white (`colour = white`), and draw them\n    underneath the primary data layer. That makes them easy to see, without\n    drawing attention away from the data.\n\n*   Use `geom_rect()` to draw a rectangle around points of interest. The\n    boundaries of the rectangle are defined by aesthetics `xmin`, `xmax`,\n    `ymin`, `ymax`.\n\n*   Use `geom_segment()` with the `arrow` argument to draw attention\n    to a point with an arrow. Use aesthetics `x` and `y` to define the\n    starting location, and `xend` and `yend` to define the end location.\n\nThe only limit is your imagination (and your patience with positioning annotations to be aesthetically pleasing)!\n\n### Exercises\n\n1.  Use `geom_text()` with infinite positions to place text at the\n    four corners of the plot.\n\n1.  Read the documentation for `annotate()`. How can you use it to add a text\n    label to a plot without having to create a tibble?\n\n1.  How do labels with `geom_text()` interact with faceting? How can you\n    add a label to a single facet? How can you put a different label in\n    each facet? (Hint: think about the underlying data.)\n\n1.  What arguments to `geom_label()` control the appearance of the background\n    box?\n\n1.  What are the four arguments to `arrow()`? How do they work? Create a series\n    of plots that demonstrate the most important options.\n\n## Scales\n\nThe third way you can make your plot better for communication is to adjust the scales. Scales control the mapping from data values to things that you can perceive. Normally, ggplot2 automatically adds scales for you. For example, when you type:\n\n```{r default-scales, fig.show = \"hide\"}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class))\n```\n\nggplot2 automatically adds default scales behind the scenes:\n\n```{r, fig.show = \"hide\"}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  scale_x_continuous() +\n  scale_y_continuous() +\n  scale_colour_discrete()\n```\n\nNote the naming scheme for scales: `scale_` followed by the name of the aesthetic, then `_`, then the name of the scale. The default scales are named according to the type of variable they align with: continuous, discrete, datetime, or date. There are lots of non-default scales which you'll learn about below.\n\nThe default scales have been carefully chosen to do a good job for a wide range of inputs. Nevertheless, you might want to override the defaults for two reasons:\n\n*   You might want to tweak some of the parameters of the default scale.\n    This allows you to do things like change the breaks on the axes, or the\n    key labels on the legend.\n\n*   You might want to replace the scale altogether, and use a completely\n    different algorithm. Often you can do better than the default because\n    you know more about the data.\n\n### Axis ticks and legend keys\n\nThere are two primary arguments that affect the appearance of the ticks on the axes and the keys on the legend: `breaks` and `labels`. Breaks controls the position of the ticks, or the values associated with the keys. Labels controls the text label associated with each tick/key. The most common use of `breaks` is to override the default choice:\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point() +\n  scale_y_continuous(breaks = seq(15, 40, by = 5))\n```\n\nYou can use `labels` in the same way (a character vector the same length as `breaks`), but you can also set it to `NULL` to suppress the labels altogether. This is useful for maps, or for publishing plots where you can't share the absolute numbers.\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point() +\n  scale_x_continuous(labels = NULL) +\n  scale_y_continuous(labels = NULL)\n```\n\nYou can also use `breaks` and `labels` to control the appearance of legends. Collectively axes and legends are called __guides__. Axes are used for x and y aesthetics; legends are used for everything else.\n\nAnother use of `breaks` is when you have relatively few data points and want to highlight exactly where the observations occur. For example, take this plot that shows when each US president started and ended their term.\n\n```{r}\npresidential %>%\n  mutate(id = 33 + row_number()) %>%\n  ggplot(aes(start, id)) +\n    geom_point() +\n    geom_segment(aes(xend = end, yend = id)) +\n    scale_x_date(NULL, breaks = presidential$start, date_labels = \"'%y\")\n```\n\nNote that the specification of breaks and labels for date and datetime scales is a little different:\n\n* `date_labels` takes a format specification, in the same form as\n  `parse_datetime()`.\n\n* `date_breaks` (not shown here), takes a string like \"2 days\" or \"1 month\".\n\n### Legend layout\n\nYou will most often use `breaks` and `labels` to tweak the axes. While they both also work for legends, there are a few other techniques you are more likely to use.\n\nTo control the overall position of the legend, you need to use a `theme()` setting. We'll come back to themes at the end of the chapter, but in brief, they control the non-data parts of the plot. The theme setting `legend.position` controls where the legend is drawn:\n\n```{r fig.asp = 1, fig.align = \"default\", out.width = \"50%\", fig.width = 4}\nbase <- ggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class))\n\nbase + theme(legend.position = \"left\")\nbase + theme(legend.position = \"top\")\nbase + theme(legend.position = \"bottom\")\nbase + theme(legend.position = \"right\") # the default\n```\n\nYou can also use `legend.position = \"none\"` to suppress the display of the legend altogether.\n\nTo control the display of individual legends, use `guides()` along with `guide_legend()` or `guide_colourbar()`. The following example shows two important settings: controlling the number of rows the legend uses with `nrow`, and overriding one of the aesthetics to make the points bigger. This is particularly useful if you have used a low `alpha` to display many points on a plot.\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(colour = class)) +\n  geom_smooth(se = FALSE) +\n  theme(legend.position = \"bottom\") +\n  guides(colour = guide_legend(nrow = 1, override.aes = list(size = 4)))\n```\n\n### Replacing a scale\n\nInstead of just tweaking the details a little, you can instead replace the scale altogether. There are two types of scales you're mostly likely to want to switch out: continuous position scales and colour scales. Fortunately, the same principles apply to all the other aesthetics, so once you've mastered position and colour, you'll be able to quickly pick up other scale replacements.\n\nIt's very useful to plot transformations of your variable. For example, as we've seen in [diamond prices](diamond-prices) it's easier to see the precise relationship between `carat` and `price` if we log transform them:\n\n```{r, fig.align = \"default\", out.width = \"50%\"}\nggplot(diamonds, aes(carat, price)) +\n  geom_bin2d()\n\nggplot(diamonds, aes(log10(carat), log10(price))) +\n  geom_bin2d()\n```\n\nHowever, the disadvantage of this transformation is that the axes are now labelled with the transformed values, making it hard to interpret the plot. Instead of doing the transformation in the aesthetic mapping, we can instead do it with the scale. This is visually identical, except the axes are labelled on the original data scale.\n\n```{r}\nggplot(diamonds, aes(carat, price)) +\n  geom_bin2d() + \n  scale_x_log10() + \n  scale_y_log10()\n```\n\nAnother scale that is frequently customised is colour.The default categorical scale picks colours that are evenly spaced around the colour wheel. Useful alternatives are the ColorBrewer scales which have been hand tuned to work better for people with common types of colour blindness. The two plots below look similar, but there is enough difference in the shades of red and green that the dots on the right can be distinguished even by people with red-green colour blindness.\n\n```{r, fig.align = \"default\", out.width = \"50%\"}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = drv))\n\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = drv)) +\n  scale_colour_brewer(palette = \"Set1\")\n```\n\nDon't forget simpler techniques. If there are just a few colours, you can add a redundant shape mapping. This will also help ensure your plot is interpretable in black and white.\n\n```{r}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = drv, shape = drv)) +\n  scale_colour_brewer(palette = \"Set1\")\n```\n\nThe ColorBrewer scales are documented online at <http://colorbrewer2.org/> and made available in R via the __RColorBrewer__ package, by Erich Neuwirth. Figure \\@ref(fig:brewer) shows the complete list of all palettes. The sequential (top) and diverging (bottom) palettes are particularly useful if your categorical values are ordered, or have a \"middle\". This often arises if you've used `cut()` to make a continuous variable into a categorical variable.\n\n```{r brewer, fig.asp = 2.5, echo = FALSE, fig.cap = \"All ColourBrewer scales.\"}\npar(mar = c(0, 3, 0, 0))\nRColorBrewer::display.brewer.all()\n```\n\nWhen you have a predefined mapping between values and colours, use `scale_colour_manual()`. For example, if we map presidential party to colour, we want to use the standard mapping of red for Republicans and blue for Democrats:\n\n```{r}\npresidential %>%\n  mutate(id = 33 + row_number()) %>%\n  ggplot(aes(start, id, colour = party)) +\n    geom_point() +\n    geom_segment(aes(xend = end, yend = id)) +\n    scale_colour_manual(values = c(Republican = \"red\", Democratic = \"blue\"))\n```\n\nFor continuous colour, you can use the built-in `scale_colour_gradient()` or `scale_fill_gradient()`. If you have a diverging scale, you can use `scale_colour_gradient2()`. That allows you to give, for example, positive and negative values different colours. That's sometimes also useful if you want to distinguish points above or below the mean.\n\nAnother option is `scale_colour_viridis()` provided by the __viridis__ package. It's a continuous analog of the categorical ColorBrewer scales. The designers, Nathaniel Smith and Stéfan van der Walt, carefully tailored a continuous colour scheme that has good perceptual properties. Here's an example from the viridis vignette.\n\n```{r, fig.align = \"default\", fig.asp = 1, out.width = \"50%\", fig.width = 4}\ndf <- tibble(\n  x = rnorm(10000),\n  y = rnorm(10000)\n)\nggplot(df, aes(x, y)) +\n  geom_hex() +\n  coord_fixed()\n\nggplot(df, aes(x, y)) +\n  geom_hex() +\n  viridis::scale_fill_viridis() +\n  coord_fixed()\n```\n\nNote that all colour scales come in two variety: `scale_colour_x()` and `scale_fill_x()` for the `colour` and `fill` aesthetics respectively (the colour scales are available in both UK and US spellings).\n\n### Exercises\n\n1.  Why doesn't the following code override the default scale?\n\n    ```{r fig.show = \"hide\"}\n    ggplot(df, aes(x, y)) +\n      geom_hex() +\n      scale_colour_gradient(low = \"white\", high = \"red\") +\n      coord_fixed()\n    ```\n\n1.  What is the first argument to every scale? How does it compare to `labs()`?\n\n1.  Change the display of the presidential terms by:\n\n    1. Combining the two variants shown above.\n    1. Improving the display of the y axis.\n    1. Labelling each term with the name of the president.\n    1. Adding informative plot labels.\n    1. Placing breaks every 4 years (this is trickier than it seems!).\n\n1.  Use `override.aes` to make the legend on the following plot easier to see.\n\n    ```{r, dev = \"png\", out.width = \"50%\"}\n    ggplot(diamonds, aes(carat, price)) +\n      geom_point(aes(colour = cut), alpha = 1/20)\n    ```\n\n## Zooming\n\nThere are three ways to control the plot limits:\n\n1. Adjusting what data are plotted\n1. Setting the limits in each scale\n1. Setting `xlim` and `ylim` in `coord_cartesian()`\n\nTo zoom in on a region of the plot, it's generally best to use `coord_cartesian()`. Compare the following two plots:\n\n```{r out.width = \"50%\", fig.align = \"default\", message = FALSE}\nggplot(mpg, mapping = aes(displ, hwy)) +\n  geom_point(aes(color = class)) +\n  geom_smooth() +\n  coord_cartesian(xlim = c(5, 7), ylim = c(10, 30))\n\nmpg %>%\n  filter(displ >= 5, displ <= 7, hwy >= 10, hwy <= 30) %>%\n  ggplot(aes(displ, hwy)) +\n  geom_point(aes(color = class)) +\n  geom_smooth()\n```\n\nYou can also set the `limits` on individual scales. Reducing the limits is basically equivalent to subsetting the data. It is generally more useful if you want _expand_ the limits, for example, to match scales across different plots. For example, if we extract two classes of cars and plot them separately, it's difficult to compare the plots because all three scales (the x-axis, the y-axis, and the colour aesthetic) have different ranges.\n\n```{r out.width = \"50%\", fig.align = \"default\", fig.width = 4}\nsuv <- mpg %>% filter(class == \"suv\")\ncompact <- mpg %>% filter(class == \"compact\")\n\nggplot(suv, aes(displ, hwy, colour = drv)) +\n  geom_point()\n\nggplot(compact, aes(displ, hwy, colour = drv)) +\n  geom_point()\n```\n\nOne way to overcome this problem is to share scales across multiple plots, training the scales with the `limits` of the full data.\n\n```{r out.width = \"50%\", fig.align = \"default\", fig.width = 4}\nx_scale <- scale_x_continuous(limits = range(mpg$displ))\ny_scale <- scale_y_continuous(limits = range(mpg$hwy))\ncol_scale <- scale_colour_discrete(limits = unique(mpg$drv))\n\nggplot(suv, aes(displ, hwy, colour = drv)) +\n  geom_point() +\n  x_scale +\n  y_scale +\n  col_scale\n\nggplot(compact, aes(displ, hwy, colour = drv)) +\n  geom_point() +\n  x_scale +\n  y_scale +\n  col_scale\n```\n\nIn this particular case, you could have simply used faceting, but this technique is useful more generally, if for instance, you want spread plots over multiple pages of a report.\n\n## Themes\n\nFinally, you can customise the non-data elements of your plot with a theme:\n\n```{r, message = FALSE}\nggplot(mpg, aes(displ, hwy)) +\n  geom_point(aes(color = class)) +\n  geom_smooth(se = FALSE) +\n  theme_bw()\n```\n\nggplot2 includes eight themes by default, as shown in Figure \\@ref(fig:themes). Many more are included in add-on packages like __ggthemes__ (<https://github.com/jrnold/ggthemes>), by Jeffrey Arnold.\n\n```{r themes, echo = FALSE, fig.cap = \"The eight themes built-in to ggplot2.\"}\nknitr::include_graphics(\"images/visualization-themes.png\")\n```\n\nMany people wonder why the default theme has a grey background. This was a deliberate choice because it puts the data forward while still making the grid lines visible. The white grid lines are visible (which is important because they significantly aid position judgements), but they have little visual impact and we can easily tune them out. The grey background gives the plot a similar typographic colour to the text, ensuring that the graphics fit in with the flow of a document without jumping out with a bright white background. Finally, the grey background creates a continuous field of colour which ensures that the plot is perceived as a single visual entity.\n\nIt's also possible to control individual components of each theme, like the size and colour of the font used for the y axis. Unfortunately, this level of detail is outside the scope of this book, so you'll need to read the [ggplot2 book](https://amzn.com/331924275X) for the full details. You can also create your own themes, if you are trying to match a particular corporate or journal style.\n\n## Saving your plots\n\nThere are two main ways to get your plots out of R and into your final write-up: `ggsave()` and knitr. `ggsave()` will save the most recent plot to disk:\n\n```{r, fig.show = \"none\"}\nggplot(mpg, aes(displ, hwy)) + geom_point()\nggsave(\"my-plot.pdf\")\n```\n```{r, include = FALSE}\nfile.remove(\"my-plot.pdf\")\n```\n\nIf you don't specify the `width` and `height` they will be taken from the dimensions of the current plotting device. For reproducible code, you'll want to specify them.\n\nGenerally, however, I think you should be assembling your final reports using R Markdown, so I want to focus on the important code chunk options that you should know about for graphics. You can learn more about `ggsave()` in the documentation.\n\n### Figure sizing\n\nThe biggest challenge of graphics in R Markdown is getting your figures the right size and shape. There are five main options that control figure sizing: `fig.width`, `fig.height`, `fig.asp`, `out.width` and `out.height`. Image sizing is challenging because there are two sizes (the size of the figure created by R and the size at which it is inserted in the output document), and multiple ways of specifying the size (i.e., height, width, and aspect ratio: pick two of three).\n\nI only ever use three of the five options:\n\n* I find it most aesthetically pleasing for plots to have a consistent\n  width. To enforce this, I set `fig.width = 6` (6\") and `fig.asp = 0.618`\n  (the golden ratio) in the defaults. Then in individual chunks, I only\n  adjust `fig.asp`.\n\n* I control the output size with `out.width` and set it to a percentage\n  of the line width). I default to `out.width = \"70%\"`\n  and `fig.align = \"center\"`. That give plots room to breathe, without taking\n  up too much space.\n\n* To put multiple plots in a single row I set the `out.width` to\n  `50%` for two plots, `33%` for 3 plots, or `25%` to 4 plots, and set\n  `fig.align = \"default\"`. Depending on what I'm trying to illustrate (e.g.\n  show data or show plot variations), I'll also tweak `fig.width`, as\n  discussed below.\n\nIf you find that you're having to squint to read the text in your plot, you need to tweak `fig.width`. If `fig.width` is larger than the size the figure is rendered in the final doc, the text will be too small; if `fig.width` is smaller, the text will be too big. You'll often need to do a little experimentation to figure out the right ratio between the `fig.width` and the eventual width in your document. To illustrate the principle, the following three plots have `fig.width` of 4, 6, and 8 respectively:\n\n```{r, include = FALSE}\nplot <- ggplot(mpg, aes(displ, hwy)) + geom_point()\n```\n```{r, fig.width = 4, echo = FALSE}\nplot\n```\n```{r, fig.width = 6, echo = FALSE}\nplot\n```\n```{r, fig.width = 8, echo = FALSE}\nplot\n```\n\nIf you want to make sure the font size is consistent across all your figures, whenever you set `out.width`, you'll also need to adjust `fig.width` to maintain the same ratio with your default `out.width`. For example, if your default `fig.width` is 6 and `out.width` is 0.7, when you set `out.width = \"50%\"` you'll need to set `fig.width` to 4.3 (6 * 0.5 / 0.7).\n\n### Other important options\n\nWhen mingling code and text, like I do in this book, I recommend setting `fig.show = \"hold\"` so that plots are shown after the code. This has the pleasant side effect of forcing you to break up large blocks of code with their explanations.\n\nTo add a caption to the plot, use `fig.cap`. In R Markdown this will change the figure from inline to \"floating\".\n\nIf you're producing PDF output, the default graphics type is PDF. This is a good default because PDFs are high quality vector graphics. However, they can produce very large and slow plots if you are displaying thousands of points. In that case, set `dev = \"png\"` to force the use of PNGs. They are slightly lower quality, but will be much more compact.\n\nIt's a good idea to name code chunks that produce figures, even if you don't routinely label other chunks. The chunk label is used to generate the file name of the graphic on disk, so naming your chunks makes it much easier to pick out plots and reuse in other circumstances (i.e. if you want to quickly drop a single plot into an email or a tweet).\n\n## Learning more\n\nThe absolute best place to learn more is the ggplot2 book: [_ggplot2: Elegant graphics for data analysis_](https://amzn.com/331924275X). It goes into much more depth about the underlying theory, and has many more examples of how to combine the individual pieces to solve practical problems. Unfortunately, the book is not available online for free, although you can find the source code at <https://github.com/hadley/ggplot2-book>.\n\nAnother great resource is the ggplot2 extensions guide <http://www.ggplot2-exts.org/>. This site lists many of the packages that extend ggplot2 with new geoms and scales. It's a great place to start if you're trying to do something that seems hard with ggplot2.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/communicate.Rmd",
    "content": "# (PART) Communicate {-}\n\n# Introduction {#communicate-intro}\n\nSo far, you've learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn't matter how great your analysis is unless you can explain it to others: you need to __communicate__ your results.\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science-communicate.png\")\n```\n\nCommunication is the theme of the following four chapters:\n\n* In [R Markdown], you will learn about R Markdown, a tool for integrating\n  prose, code, and results. You can use R Markdown in notebook mode for \n  analyst-to-analyst communication, and in report mode for \n  analyst-to-decision-maker communication. Thanks to the power of R Markdown\n  formats, you can even use the same document for both purposes.\n  \n* In [Graphics for communication], you will learn how to take your exploratory\n  graphics and turn them into expository graphics, graphics that help the\n  newcomer to your analysis understand what's going on as quickly and \n  easily as possible.\n  \n* In [R Markdown formats], you'll learn a little about the many other varieties\n  of outputs you can produce using R Markdown, including dashboards, websites,\n  and books.\n  \n* We'll finish up with [R Markdown workflow], where you'll learn about the\n  \"analysis notebook\" and how to systematically record your successes and \n  failures so that you can learn from them.\n\nUnfortunately, these chapters focus mostly on the technical mechanics of communication, not the really hard problems of communicating your thoughts to other humans. However, there are lot of other great books about communication, which we'll point you to at the end of each chapter.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/contribs.txt",
    "content": "   625\thadley\n    93\tGarrett\n    77\tHadley Wickham\n    50\tS'busiso Mkhondwane\n    21\tbehrman\n    11\tBrett Klamer\n    10\tRadu Grosu\n     9\tBrandon Greenwell\n     8\tBill Behrman\n     7\tGarrett Grolemund\n     7\tRademeyer Vermaak\n     7\tColin Gillespie\n     7\tharrismcgehee\n     6\tjjchern\n     6\tJakub Nowosad\n     6\tOaCantona\n     5\tkdpsingh\n     5\tJulian During\n     4\tThomas Klebel\n     4\tMine Cetinkaya-Rundel\n     4\tJennifer (Jenny) Bryan\n     4\tTerence Teo\n     4\tPatrick Kennedy\n     3\tJonathan Page\n     3\tJose Roberto Ayala Solares\n     3\tyahwes\n     3\tseamus-mckinsey\n     3\tIan Lyttle\n     3\tIan Sealy\n     3\tYihui Xie\n     2\tCooper Morris\n     2\tChristian G. Warden\n     2\tDaniel Gromer\n     2\tDevin Pastoor\n     2\tEtienne B. Racine\n     2\tJim Hester\n     2\tJoanne Jang\n     2\tKirill Sevastyanenko\n     2\tMJMarshall\n     2\tNirmal Patel\n     2\tPaul\n     2\tRobert Schuessler\n     2\tWill Beasley\n     2\trlzijdeman\n     2\trobinlovelace\n     2\tsibusiso16\n     2\tspirgel\n     1\trobinsones\n     1\tJeroen Janssens\n     1\tMustafa Ascha\n     1\tNelson Areal\n     1\tNick Clark\n     1\tAlex\n     1\tHengni Cai\n     1\tGregory Jefferis\n     1\tseanpwilliams\n     1\tPeter Hurford\n     1\tFlemming Villalona\n     1\tEric Watt\n     1\tshoili\n     1\tEarl Brown\n     1\tShannon Ellis\n     1\tSteve Mortimer\n     1\tTJ Mahr\n     1\tDylan Cashman\n     1\tDerwin McGeary\n     1\tTom Prior\n     1\tAjay Deonarine\n     1\tDavid Clark\n     1\tadi pradhan\n     1\tbahadir cankardes\n     1\tbatpigandme\n     1\tCurtis Alexander\n     1\tAhmed ElGabbas\n     1\tChristian Mongeau\n     1\tjennybc\n     1\tBen Marwick\n     1\tjonathanflint\n     1\tAndrew Landgraf\n     1\tkoalabearski\n     1\tnate-d-olson\n     1\tnickelas\n     1\tnwaff\n     1\tzeal626\n     1\tJon Calder\n     1\tJulia Stewart Lowndes\n     1\tJohn Sears\n     1\tJustinas Petuchovas\n     1\tKara Woo\n     1\tKenny Darrell\n     1\tsvenski\n     1\tKyleHumphrey\n     1\tLawrence Wu\n     1\tMatthew Sedaghatfar\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/contribute.rmd",
    "content": "# Contributing\n\nThis book has been developed in the open, and it wouldn't be nearly as good \nwithout your contributions. There are a number of ways you can help make the\nbook even better:\n\n* If you don't understand something, please \n  [let me know](mailto:h.wickham@gmail.com). Your feedback on what is confusing\n  or hard to understand is valuable.\n\n* If you spot a typo, feel free to edit the underlying page and send a pull \n  request. If you've never done this before, the process is very easy: \n  \n    * Click the edit this page on the sidebar.\n  \n    * Make the changes using github's in-page editor and save.\n    \n    * Submit a pull request and include a brief description of your changes.\n      \"Fixing typos\" is perfectly adequate.\n      \n    * If you make significant changes, include the phrase \"I assign the \n      copyright of this contribution to Hadley Wickham\" - I need this so I can \n      publish the printed book.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/datetimes.Rmd",
    "content": "# Dates and times\n\n## Introduction\n\nThis chapter will show you how to work with dates and times in R. At first glance, dates and times seem simple. You use them all the time in your regular life, and they don't seem to cause much confusion. However, the more you learn about dates and times, the more complicated they seem to get. To warm up, try these three seemingly simple questions:\n\n* Does every year have 365 days?\n* Does every day have 24 hours?\n* Does every minute have 60 seconds?\n\nI'm sure you know that not every year has 365 days, but do you know the full rule for determining if a year is a leap year? (It has three parts.) You might have remembered that many parts of the world use daylight savings time (DST), so that some days have 23 hours, and others have 25. You might not have known that some minutes have 61 seconds because every now and then leap seconds are added because the Earth's rotation is gradually slowing down.\n\nDates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and DST. This chapter won't teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.\n\n### Prerequisites\n\nThis chapter will focus on the __lubridate__ package, which makes it easier to work with dates and times in R. lubridate is not part of core tidyverse because you only need it when you're working with dates/times. We will also need nycflights13 for practice data.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n\nlibrary(lubridate)\nlibrary(nycflights13)\n```\n\n## Creating date/times\n\nThere are three types of date/time data that refer to an instant in time:\n\n* A __date__. Tibbles print this as `<date>`.\n\n* A __time__ within a day. Tibbles print this as `<time>`.\n\n* A __date-time__ is a date plus a time: it uniquely identifies an\n  instant in time (typically to the nearest second). Tibbles print this\n  as `<dttm>`. Elsewhere in R these are called POSIXct, but I don't think\n  that's a very useful name.\n  \nIn this chapter we are only going to focus on dates and date-times as R doesn't have a native class for storing times. If you need one, you can use the __hms__ package.\n\nYou should always use the simplest possible data type that works for your needs. That means if you can use a date instead of a date-time, you should. Date-times are substantially more complicated because of the need to handle time zones, which we'll come back to at the end of the chapter.\n\nTo get the current date or date-time you can use `today()` or `now()`:\n\n```{r}\ntoday()\nnow()\n```\n\nOtherwise, there are three ways you're likely to create a date/time:\n\n* From a string.\n* From individual date-time components.\n* From an existing date/time object.\n\nThey work as follows.\n\n### From strings\n\nDate/time data often comes as strings. You've seen one approach to parsing strings into date-times in [date-times](#readr-datetimes). Another approach is to use the helpers provided by lubridate. They automatically work out the format once you specify the order of the component. To use them, identify the order in which year, month, and day appear in your dates, then arrange \"y\", \"m\", and \"d\" in the same order. That gives you the name of the lubridate function that will parse your date. For example:\n\n```{r}\nymd(\"2017-01-31\")\nmdy(\"January 31st, 2017\")\ndmy(\"31-Jan-2017\")\n```\n\nThese functions also take unquoted numbers. This is the most concise way to create a single date/time object, as you might need when filtering date/time data. `ymd()` is short and unambiguous:\n\n```{r}\nymd(20170131)\n```\n\n`ymd()` and friends create dates. To create a date-time, add an underscore and one or more of \"h\", \"m\", and \"s\" to the name of the parsing function:\n\n```{r}\nymd_hms(\"2017-01-31 20:11:59\")\nmdy_hm(\"01/31/2017 08:01\")\n```\n\nYou can also force the creation of a date-time from a date by supplying a timezone:\n\n```{r}\nymd(20170131, tz = \"UTC\")\n```\n\n### From individual components\n\nInstead of a single string, sometimes you'll have the individual components of the date-time spread across multiple columns. This is what we have in the flights data:\n\n```{r}\nflights %>% \n  select(year, month, day, hour, minute)\n```\n\nTo create a date/time from this sort of input, use `make_date()` for dates, or `make_datetime()` for date-times:\n\n```{r}\nflights %>% \n  select(year, month, day, hour, minute) %>% \n  mutate(departure = make_datetime(year, month, day, hour, minute))\n```\n\nLet's do the same thing for each of the four time columns in `flights`. The times are represented in a slightly odd format, so we use modulus arithmetic to pull out the hour and minute components. Once I've created the date-time variables, I focus in on the variables we'll explore in the rest of the chapter.\n\n```{r}\nmake_datetime_100 <- function(year, month, day, time) {\n  make_datetime(year, month, day, time %/% 100, time %% 100)\n}\n\nflights_dt <- flights %>% \n  filter(!is.na(dep_time), !is.na(arr_time)) %>% \n  mutate(\n    dep_time = make_datetime_100(year, month, day, dep_time),\n    arr_time = make_datetime_100(year, month, day, arr_time),\n    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),\n    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)\n  ) %>% \n  select(origin, dest, ends_with(\"delay\"), ends_with(\"time\"))\n\nflights_dt\n```\n\nWith this data, I can visualise the distribution of departure times across the year:\n\n```{r}\nflights_dt %>% \n  ggplot(aes(dep_time)) + \n  geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day\n```\n\nOr within a single day:\n\n```{r}\nflights_dt %>% \n  filter(dep_time < ymd(20130102)) %>% \n  ggplot(aes(dep_time)) + \n  geom_freqpoly(binwidth = 600) # 600 s = 10 minutes\n```\n\nNote that when you use date-times in a numeric context (like in a histogram), 1 means 1 second, so a binwidth of 86400 means one day. For dates, 1 means 1 day.\n\n### From other types\n\nYou may want to switch between a date-time and a date. That's the job of `as_datetime()` and `as_date()`:\n\n```{r}\nas_datetime(today())\nas_date(now())\n```\n\nSometimes you'll get date/times as numeric offsets from the \"Unix Epoch\", 1970-01-01. If the offset is in seconds, use `as_datetime()`; if it's in days, use `as_date()`.\n\n```{r}\nas_datetime(60 * 60 * 10)\nas_date(365 * 10 + 2)\n```\n\n### Exercises\n\n1.  What happens if you parse a string that contains invalid dates?\n\n    ```{r, eval = FALSE}\n    ymd(c(\"2010-10-10\", \"bananas\"))\n    ```\n\n1.  What does the `tzone` argument to `today()` do? Why is it important?\n\n1.  Use the appropriate lubridate function to parse each of the following dates:\n\n    ```{r}\n    d1 <- \"January 1, 2010\"\n    d2 <- \"2015-Mar-07\"\n    d3 <- \"06-Jun-2017\"\n    d4 <- c(\"August 19 (2015)\", \"July 1 (2015)\")\n    d5 <- \"12/30/14\" # Dec 30, 2014\n    ```\n\n## Date-time components\n\nNow that you know how to get date-time data into R's date-time data structures, let's explore what you can do with them. This section will focus on the accessor functions that let you get and set individual components. The next section will look at how arithmetic works with date-times.\n\n### Getting components\n\nYou can pull out individual parts of the date with the accessor functions `year()`, `month()`, `mday()` (day of the month), `yday()` (day of the year), `wday()` (day of the week), `hour()`, `minute()`, and `second()`. \n\n```{r}\ndatetime <- ymd_hms(\"2016-07-08 12:34:56\")\n\nyear(datetime)\nmonth(datetime)\nmday(datetime)\n\nyday(datetime)\nwday(datetime)\n```\n\nFor `month()` and `wday()` you can set `label = TRUE` to return the abbreviated name of the month or day of the week. Set `abbr = FALSE` to return the full name.\n\n```{r}\nmonth(datetime, label = TRUE)\nwday(datetime, label = TRUE, abbr = FALSE)\n```\n\nWe can use `wday()` to see that more flights depart during the week than on the weekend:\n\n```{r}\nflights_dt %>% \n  mutate(wday = wday(dep_time, label = TRUE)) %>% \n  ggplot(aes(x = wday)) +\n    geom_bar()\n```\n\nThere's an interesting pattern if we look at the average departure delay by minute within the hour. It looks like flights leaving in minutes 20-30 and 50-60 have much lower delays than the rest of the hour!\n\n```{r}\nflights_dt %>% \n  mutate(minute = minute(dep_time)) %>% \n  group_by(minute) %>% \n  summarise(\n    avg_delay = mean(arr_delay, na.rm = TRUE),\n    n = n()) %>% \n  ggplot(aes(minute, avg_delay)) +\n    geom_line()\n```\n\nInterestingly, if we look at the _scheduled_ departure time we don't see such a strong pattern:\n\n```{r}\nsched_dep <- flights_dt %>% \n  mutate(minute = minute(sched_dep_time)) %>% \n  group_by(minute) %>% \n  summarise(\n    avg_delay = mean(arr_delay, na.rm = TRUE),\n    n = n())\n\nggplot(sched_dep, aes(minute, avg_delay)) +\n  geom_line()\n```\n\nSo why do we see that pattern with the actual departure times? Well, like much data collected by humans, there's a strong bias towards flights leaving at \"nice\" departure times. Always be alert for this sort of pattern whenever you work with data that involves human judgement!\n\n```{r}\nggplot(sched_dep, aes(minute, n)) +\n  geom_line()\n```\n\n### Rounding\n\nAn alternative approach to plotting individual components is to round the date to a nearby unit of time, with `floor_date()`, `round_date()`, and `ceiling_date()`. Each function takes a vector of dates to adjust and then the name of the unit round down (floor), round up (ceiling), or round to. This, for example, allows us to plot the number of flights per week:\n\n```{r}\nflights_dt %>% \n  count(week = floor_date(dep_time, \"week\")) %>% \n  ggplot(aes(week, n)) +\n    geom_line()\n```\n\nComputing the difference between a rounded and unrounded date can be particularly useful.\n\n### Setting components\n\nYou can also use each accessor function to set the components of a date/time: \n\n```{r}\n(datetime <- ymd_hms(\"2016-07-08 12:34:56\"))\n\nyear(datetime) <- 2020\ndatetime\nmonth(datetime) <- 01\ndatetime\nhour(datetime) <- hour(datetime) + 1\ndatetime\n```\n\nAlternatively, rather than modifying in place, you can create a new date-time with `update()`. This also allows you to set multiple values at once.\n\n```{r}\nupdate(datetime, year = 2020, month = 2, mday = 2, hour = 2)\n```\n\nIf values are too big, they will roll-over:\n\n```{r}\nymd(\"2015-02-01\") %>% \n  update(mday = 30)\nymd(\"2015-02-01\") %>% \n  update(hour = 400)\n```\n\nYou can use `update()` to show the distribution of flights across the course of the day for every day of the year: \n\n```{r}\nflights_dt %>% \n  mutate(dep_hour = update(dep_time, yday = 1)) %>% \n  ggplot(aes(dep_hour)) +\n    geom_freqpoly(binwidth = 300)\n```\n\nSetting larger components of a date to a constant is a powerful technique that allows you to explore patterns in the smaller components.\n\n### Exercises\n\n1.  How does the distribution of flight times within a day change over the \n    course of the year?\n    \n1.  Compare `dep_time`, `sched_dep_time` and `dep_delay`. Are they consistent?\n    Explain your findings.\n\n1.  Compare `air_time` with the duration between the departure and arrival.\n    Explain your findings. (Hint: consider the location of the airport.)\n    \n1.  How does the average delay time change over the course of a day?\n    Should you use `dep_time` or `sched_dep_time`? Why?\n\n1.  On what day of the week should you leave if you want to minimise the\n    chance of a delay?\n\n1.  What makes the distribution of `diamonds$carat` and \n    `flights$sched_dep_time` similar?\n\n1.  Confirm my hypothesis that the early departures of flights in minutes\n    20-30 and 50-60 are caused by scheduled flights that leave early. \n    Hint: create a binary variable that tells you whether or not a flight \n    was delayed.\n\n## Time spans\n\nNext you'll learn about how arithmetic with dates works, including subtraction, addition, and division. Along the way, you'll learn about three important classes that represent time spans:\n\n* __durations__, which represent an exact number of seconds.\n* __periods__, which represent human units like weeks and months.\n* __intervals__, which represent a starting and ending point.\n\n### Durations\n\nIn R, when you subtract two dates, you get a difftime object:\n\n```{r}\n# How old is Hadley?\nh_age <- today() - ymd(19791014)\nh_age\n```\n\nA difftime class object records a time span of seconds, minutes, hours, days, or weeks. This ambiguity can make difftimes a little painful to work with, so lubridate provides an alternative which always uses seconds: the __duration__.\n\n```{r}\nas.duration(h_age)\n```\n\nDurations come with a bunch of convenient constructors:\n\n```{r}\ndseconds(15)\ndminutes(10)\ndhours(c(12, 24))\nddays(0:5)\ndweeks(3)\ndyears(1)\n```\n\nDurations always record the time span in seconds. Larger units are created by converting minutes, hours, days, weeks, and years to seconds at the standard rate (60 seconds in a minute, 60 minutes in an hour, 24 hours in day, 7 days in a week, 365 days in a year).\n\nYou can add and multiply durations:\n\n```{r}\n2 * dyears(1)\ndyears(1) + dweeks(12) + dhours(15)\n```\n\nYou can add and subtract durations to and from days:\n\n```{r}\ntomorrow <- today() + ddays(1)\nlast_year <- today() - dyears(1)\n```\n\nHowever, because durations represent an exact number of seconds, sometimes you might get an unexpected result:\n\n```{r}\none_pm <- ymd_hms(\"2016-03-12 13:00:00\", tz = \"America/New_York\")\n\none_pm\none_pm + ddays(1)\n```\n\nWhy is one day after 1pm on March 12, 2pm on March 13?! If you look carefully at the date you might also notice that the time zones have changed. Because of DST, March 12 only has 23 hours, so if we add a full days worth of seconds we end up with a different time.\n\n### Periods\n\nTo solve this problem, lubridate provides __periods__. Periods are time spans but don't have a fixed length in seconds, instead they work with \"human\" times, like days and months. That allows them work in a more intuitive way:\n\n```{r}\none_pm\none_pm + days(1)\n```\n\nLike durations, periods can be created with a number of friendly constructor functions. \n\n```{r}\nseconds(15)\nminutes(10)\nhours(c(12, 24))\ndays(7)\nmonths(1:6)\nweeks(3)\nyears(1)\n```\n\nYou can add and multiply periods:\n\n```{r}\n10 * (months(6) + days(1))\ndays(50) + hours(25) + minutes(2)\n```\n\nAnd of course, add them to dates. Compared to durations, periods are more likely to do what you expect:\n\n```{r}\n# A leap year\nymd(\"2016-01-01\") + dyears(1)\nymd(\"2016-01-01\") + years(1)\n\n# Daylight Savings Time\none_pm + ddays(1)\none_pm + days(1)\n```\n\nLet's use periods to fix an oddity related to our flight dates. Some planes appear to have arrived at their destination _before_ they departed from New York City.\n\n```{r}\nflights_dt %>% \n  filter(arr_time < dep_time) \n```\n\nThese are overnight flights. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day. We can fix this by adding `days(1)` to the arrival time of each overnight flight.\n\n```{r}\nflights_dt <- flights_dt %>% \n  mutate(\n    overnight = arr_time < dep_time,\n    arr_time = arr_time + days(overnight * 1),\n    sched_arr_time = sched_arr_time + days(overnight * 1)\n  )\n```\n\nNow all of our flights obey the laws of physics.\n\n```{r}\nflights_dt %>% \n  filter(overnight, arr_time < dep_time) \n```\n\n### Intervals\n\nIt's obvious what `dyears(1) / ddays(365)` should return: one, because durations are always represented by a number of seconds, and a duration of a year is defined as 365 days worth of seconds.\n\nWhat should `years(1) / days(1)` return? Well, if the year was 2015 it should return 365, but if it was 2016, it should return 366! There's not quite enough information for lubridate to give a single clear answer. What it does instead is give an estimate, with a warning:\n\n```{r}\nyears(1) / days(1)\n```\n\nIf you want a more accurate measurement, you'll have to use an __interval__. An interval is a duration with a starting point: that makes it precise so you can determine exactly how long it is:\n\n```{r}\nnext_year <- today() + years(1)\n(today() %--% next_year) / ddays(1)\n```\n\nTo find out how many periods fall into an interval, you need to use integer division:\n\n```{r}\n(today() %--% next_year) %/% days(1)\n```\n\n### Summary\n\nHow do you pick between duration, periods, and intervals? As always, pick the simplest data structure that solves your problem. If you only care about physical time, use a duration; if you need to add human times, use a period; if you need to figure out how long a span is in human units, use an interval.\n\nFigure \\@ref(fig:dt-algebra) summarises permitted arithmetic operations between the different data types.\n\n```{r dt-algebra, echo = FALSE, fig.cap = \"The allowed arithmetic operations between pairs of date/time classes.\"}\nknitr::include_graphics(\"diagrams/datetimes-arithmetic.png\")\n```\n\n### Exercises\n\n1.  Why is there `months()` but no `dmonths()`?\n\n1.  Explain `days(overnight * 1)` to someone who has just started \n    learning R. How does it work?\n\n1.  Create a vector of dates giving the first day of every month in 2015.\n    Create a vector of dates giving the first day of every month\n    in the _current_ year.\n\n1.  Write a function that given your birthday (as a date), returns \n    how old you are in years.\n\n1.  Why can't `(today() %--% (today() + years(1)) / months(1)` work?\n\n## Time zones \n\nTime zones are an enormously complicated topic because of their interaction with geopolitical entities. Fortunately we don't need to dig into all the details as they're not all important for data analysis, but there are a few challenges we'll need to tackle head on.\n\nThe first challenge is that everyday names of time zones tend to be ambiguous. For example, if you're American you're probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme \"<area>/<location>\", typically in the form \"\\<continent\\>/\\<city\\>\" (there are a few exceptions because not every country lies on a continent). Examples include \"America/New_York\", \"Europe/Paris\", and \"Pacific/Auckland\".\n\nYou might wonder why the time zone uses a city, when typically you think of time zones as associated with a country or region within a country. This is because the IANA database has to record decades worth of time zone rules. In the course of decades, countries change names (or break apart) fairly frequently, but city names tend to stay the same. Another problem is that name needs to reflect not only to the current behaviour, but also the complete history. For example, there are time zones for both \"America/New_York\" and \"America/Detroit\". These cities both currently use Eastern Standard Time but in 1969-1972 Michigan (the state in which Detroit is located), did not follow DST, so it needs a different name. It's worth reading the raw time zone database (available at <http://www.iana.org/time-zones>) just to read some of these stories!\n\nYou can find out what R thinks your current time zone is with `Sys.timezone()`:\n\n```{r}\nSys.timezone()\n```\n\n(If R doesn't know, you'll get an `NA`.)\n\nAnd see the complete list of all time zone names with `OlsonNames()`:\n\n```{r}\nlength(OlsonNames())\nhead(OlsonNames())\n```\n\nIn R, the time zone is an attribute of the date-time that only controls printing. For example, these three objects represent the same instant in time:\n\n```{r}\n(x1 <- ymd_hms(\"2015-06-01 12:00:00\", tz = \"America/New_York\"))\n(x2 <- ymd_hms(\"2015-06-01 18:00:00\", tz = \"Europe/Copenhagen\"))\n(x3 <- ymd_hms(\"2015-06-02 04:00:00\", tz = \"Pacific/Auckland\"))\n```\n\nYou can verify that they're the same time using subtraction:\n\n```{r}\nx1 - x2\nx1 - x3\n```\n\nUnless otherwise specified, lubridate always uses UTC. UTC (Coordinated Universal Time) is the standard time zone used by the scientific community and roughly equivalent to its predecessor GMT (Greenwich Mean Time). It does not have DST, which makes a convenient representation for computation. Operations that combine date-times, like `c()`, will often drop the time zone. In that case, the date-times will display in your local time zone:\n\n```{r}\nx4 <- c(x1, x2, x3)\nx4\n```\n\nYou can change the time zone in two ways:\n\n*   Keep the instant in time the same, and change how it's displayed.\n    Use this when the instant is correct, but you want a more natural\n    display.\n  \n    ```{r}\n    x4a <- with_tz(x4, tzone = \"Australia/Lord_Howe\")\n    x4a\n    x4a - x4\n    ```\n    \n    (This also illustrates another challenge of times zones: they're not\n    all integer hour offsets!)\n\n*   Change the underlying instant in time. Use this when you have an\n    instant that has been labelled with the incorrect time zone, and you\n    need to fix it.\n\n    ```{r}\n    x4b <- force_tz(x4, tzone = \"Australia/Lord_Howe\")\n    x4b\n    x4b - x4\n    ```\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/explore.Rmd",
    "content": "# (PART) Explore {-}\n\n# Introduction {#explore-intro}\n\nThe goal of the first part of this book is to get you up to speed with the basic tools of __data exploration__ as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science-explore.png\")\n```\n\nIn this part of the book you will learn some useful tools that have an immediate payoff: \n\n*   Visualisation is a great place to start with R programming, because the \n    payoff is so clear: you get to make elegant and informative plots that help \n    you understand data. In [data visualisation] you'll dive into visualisation, \n    learning the basic structure of a ggplot2 plot, and powerful techniques for \n    turning data into plots. \n\n*   Visualisation alone is typically not enough, so in [data transformation] \n    you'll learn the key verbs that allow you to select important variables, \n    filter out key observations, create new variables, and compute summaries.\n  \n*   Finally, in [exploratory data analysis], you'll combine visualisation and\n    transformation with your curiosity and scepticism to ask and answer \n    interesting questions about data.\n\nModelling is an important part of the exploratory process, but you don't have the skills to effectively learn or apply it yet. We'll come back to it in [modelling](#model-intro), once you're better equipped with more data wrangling and programming tools.\n\nNestled among these three chapters that teach you the tools of exploration are three chapters that focus on your R workflow. In [workflow: basics], [workflow: scripts], and [workflow: projects] you'll learn good practices for writing and organising your R code. These will set you up for success in the long run, as they'll give you the tools to stay organised when you tackle real projects.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/factors.Rmd",
    "content": "# Factors\n\n## Introduction\n\nIn R, factors are used to work with categorical variables, variables that have a fixed and known set of possible values. They are also useful when you want to display character vectors in a non-alphabetical order.\n\nHistorically, factors were much easier to work with than characters. As a result, many of the functions in base R automatically convert characters to factors. This means that factors often crop up in places where they're not actually helpful. Fortunately, you don't need to worry about that in the tidyverse, and can focus on situations where factors are genuinely useful.\n\n### Prerequisites\n\nTo work with factors, we'll use the __forcats__ package, which provides tools for dealing with **cat**egorical variables (and it's an anagram of factors!). It provides a wide range of helpers for working with factors. forcats is not part of the core tidyverse, so we need to load it explicitly.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\nlibrary(forcats)\n```\n\n### Learning more\n\nIf you want to learn more about factors, I recommend reading Amelia McNamara and Nicholas Horton’s paper, [_Wrangling categorical data in R_](https://peerj.com/preprints/3163/). This paper lays out some of the history discussed in [_stringsAsFactors: An unauthorized biography_](http://simplystatistics.org/2015/07/24/stringsasfactors-an-unauthorized-biography/) and [_stringsAsFactors = \\<sigh\\>_](http://notstatschat.tumblr.com/post/124987394001/stringsasfactors-sigh), and compares the tidy approaches to categorical data outlined in this book with base R methods. A early version of the paper help motivate and scope the forcats package; thanks Amelia & Nick!\n\n## Creating factors\n\nImagine that you have a variable that records month:\n\n```{r}\nx1 <- c(\"Dec\", \"Apr\", \"Jan\", \"Mar\")\n```\n\nUsing a string to record this variable has two problems:\n\n1.  There are only twelve possible months, and there's nothing saving you\n    from typos:\n     \n    ```{r}\n    x2 <- c(\"Dec\", \"Apr\", \"Jam\", \"Mar\")\n    ```\n    \n1.  It doesn't sort in a useful way:\n\n    ```{r}\n    sort(x1)\n    ```\n\nYou can fix both of these problems with a factor. To create a factor you must start by creating a list of the valid __levels__:\n\n```{r}\nmonth_levels <- c(\n  \"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \n  \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"\n)\n```\n\nNow you can create a factor:\n\n```{r}\ny1 <- factor(x1, levels = month_levels)\ny1\nsort(y1)\n```\n\nAnd any values not in the set will be silently converted to NA:\n\n```{r}\ny2 <- factor(x2, levels = month_levels)\ny2\n```\n\nIf you want a warning, you can use `readr::parse_factor()`:\n\n```{r}\ny2 <- parse_factor(x2, levels = month_levels)\n```\n\nIf you omit the levels, they'll be taken from the data in alphabetical order:\n\n```{r}\nfactor(x1)\n```\n\nSometimes you'd prefer that the order of the levels match the order of the first appearance in the data. You can do that when creating the factor by setting levels to `unique(x)`, or after the fact, with `fct_inorder()`:\n\n```{r}\nf1 <- factor(x1, levels = unique(x1))\nf1\n\nf2 <- x1 %>% factor() %>% fct_inorder()\nf2\n```\n\nIf you ever need to access the set of valid levels directly, you can do so with `levels()`:\n\n```{r}\nlevels(f2)\n```\n\n## General Social Survey\n\nFor the rest of this chapter, we're going to focus on `forcats::gss_cat`. It's a sample of data from the [General Social Survey](http://gss.norc.org), which is a long-running US survey conducted by the independent research organization NORC at the University of Chicago. The survey has thousands of questions, so in `gss_cat` I've selected a handful that will illustrate some common challenges you'll encounter when working with factors.\n\n```{r}\ngss_cat\n```\n\n(Remember, since this dataset is provided by a package, you can get more information about the variables with `?gss_cat`.)\n\nWhen factors are stored in a tibble, you can't see their levels so easily. One way to see them is with `count()`:\n\n```{r}\ngss_cat %>%\n  count(race)\n```\n\nOr with a bar chart:\n\n```{r}\nggplot(gss_cat, aes(race)) +\n  geom_bar()\n```\n\nBy default, ggplot2 will drop levels that don't have any values. You can force them to display with:\n\n```{r}\nggplot(gss_cat, aes(race)) +\n  geom_bar() +\n  scale_x_discrete(drop = FALSE)\n```\n\nThese levels represent valid values that simply did not occur in this dataset. Unfortunately, dplyr doesn't yet have a `drop` option, but it will in the future.\n\nWhen working with factors, the two most common operations are changing the order of the levels, and changing the values of the levels. Those operations are described in the sections below.\n\n\n## Modifying factor levels\n\nMore powerful than changing the orders of the levels is changing their values. This allows you to clarify labels for publication, and collapse levels for high-level displays. The most general and powerful tool is `fct_recode()`. It allows you to recode, or change, the value of each level. For example, take the `gss_cat$partyid`:\n\n```{r}\ngss_cat %>% count(partyid)\n```\n\nThe levels are terse and inconsistent. Let's tweak them to be longer and use a parallel construction.\n\n```{r}\ngss_cat %>%\n  mutate(partyid = fct_recode(partyid,\n    \"Republican, strong\"    = \"Strong republican\",\n    \"Republican, weak\"      = \"Not str republican\",\n    \"Independent, near rep\" = \"Ind,near rep\",\n    \"Independent, near dem\" = \"Ind,near dem\",\n    \"Democrat, weak\"        = \"Not str democrat\",\n    \"Democrat, strong\"      = \"Strong democrat\"\n  )) %>%\n  count(partyid)\n```\n\n`fct_recode()` will leave levels that aren't explicitly mentioned as is, and will warn you if you accidentally refer to a level that doesn't exist.\n\nTo combine groups, you can assign multiple old levels to the same new level:\n\n```{r}\ngss_cat %>%\n  mutate(partyid = fct_recode(partyid,\n    \"Republican, strong\"    = \"Strong republican\",\n    \"Republican, weak\"      = \"Not str republican\",\n    \"Independent, near rep\" = \"Ind,near rep\",\n    \"Independent, near dem\" = \"Ind,near dem\",\n    \"Democrat, weak\"        = \"Not str democrat\",\n    \"Democrat, strong\"      = \"Strong democrat\",\n    \"Other\"                 = \"No answer\",\n    \"Other\"                 = \"Don't know\",\n    \"Other\"                 = \"Other party\"\n  )) %>%\n  count(partyid)\n```\n\nYou must use this technique with care: if you group together categories that are truly different you will end up with misleading results.\n\nIf you want to collapse a lot of levels, `fct_collapse()` is a useful variant of `fct_recode()`. For each new variable, you can provide a vector of old levels:\n\n```{r}\ngss_cat %>%\n  mutate(partyid = fct_collapse(partyid,\n    other = c(\"No answer\", \"Don't know\", \"Other party\"),\n    rep = c(\"Strong republican\", \"Not str republican\"),\n    ind = c(\"Ind,near rep\", \"Independent\", \"Ind,near dem\"),\n    dem = c(\"Not str democrat\", \"Strong democrat\")\n  )) %>%\n  count(partyid)\n```\n\n### Exercises\n\n1.  How have the proportions of people identifying as Democrat, Republican, and\n    Independent changed over time?\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/figures.R",
    "content": "library(stringr)\nlibrary(purrr)\n\nchapters <- dir(\"_bookdown_files\", full.names = TRUE, pattern = \"_files$\")\n\nfigures <- dir(chapters, full.names = TRUE, pattern = \"-latex\")\n\nname <- figures %>%\n  dirname() %>%\n  basename() %>%\n  str_replace(\"_files\", \"\")\n\nout_path <- file.path(\"figures\", name)\n\ndir.create(\"figures/\")\nout_path %>% walk(dir.create)\n\nmap2(figures, out_path, ~ file.copy(dir(.x, full.names = TRUE), .y))\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/functions.Rmd",
    "content": "# Functions\n\n## Introduction \n\nOne of the best ways to improve your reach as a data scientist is to write functions. Functions allow you to automate common tasks in a more powerful and general way than copy-and-pasting. Writing a function has three big advantages over using copy-and-paste:\n\n1.  You can give a function an evocative name that makes your code easier to \n    understand.\n\n1.  As requirements change, you only need to update code in one place, instead\n    of many.\n\n1.  You eliminate the chance of making incidental mistakes when you copy and \n    paste (i.e. updating a variable name in one place, but not in another).\n\nWriting good functions is a lifetime journey. Even after using R for many years I still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to teach you every esoteric detail of functions but to get you started with some pragmatic advice that you can apply immediately.\n\nAs well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good code style is like correct punctuation. Youcanmanagewithoutit, but it sure makes things easier to read! As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.\n\n### Prerequisites\n\nThe focus of this chapter is on writing functions in base R, so you won't need any extra packages.\n\n## When should you write a function?\n\nYou should consider writing a function whenever you've copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?\n\n```{r}\ndf <- tibble::tibble(\n  a = rnorm(10),\n  b = rnorm(10),\n  c = rnorm(10),\n  d = rnorm(10)\n)\n\ndf$a <- (df$a - min(df$a, na.rm = TRUE)) / \n  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))\ndf$b <- (df$b - min(df$b, na.rm = TRUE)) / \n  (max(df$b, na.rm = TRUE) - min(df$a, na.rm = TRUE))\ndf$c <- (df$c - min(df$c, na.rm = TRUE)) / \n  (max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))\ndf$d <- (df$d - min(df$d, na.rm = TRUE)) / \n  (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))\n```\n\nYou might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.\n\nTo write a function you need to first analyse the code. How many inputs does it have?\n\n```{r, eval = FALSE}\n(df$a - min(df$a, na.rm = TRUE)) /\n  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))\n```\n\nThis code only has one input: `df$a`. (If you're surprised that `TRUE` is not an input, you can explore why in the exercise below.) To make the inputs more clear, it's a good idea to rewrite the code using temporary variables with general names. Here this code only requires a single numeric vector, so I'll call it `x`:\n\n```{r}\nx <- df$a\n(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))\n```\n\nThere is some duplication in this code. We're computing the range of the data three times, so it makes sense to do it in one step:\n\n```{r}\nrng <- range(x, na.rm = TRUE)\n(x - rng[1]) / (rng[2] - rng[1])\n```\n\nPulling out intermediate calculations into named variables is a good practice because it makes it more clear what the code is doing. Now that I've simplified the code, and checked that it still works, I can turn it into a function:\n\n```{r}\nrescale01 <- function(x) {\n  rng <- range(x, na.rm = TRUE)\n  (x - rng[1]) / (rng[2] - rng[1])\n}\nrescale01(c(0, 5, 10))\n```\n\nThere are three key steps to creating a new function:\n\n1.  You need to pick a __name__ for the function. Here I've used `rescale01` \n    because this function rescales a vector to lie between 0 and 1.\n\n1.  You list the inputs, or __arguments__, to the function inside `function`.\n    Here we have just one argument. If we had more the call would look like\n    `function(x, y, z)`.\n\n1.  You place the code you have developed in __body__ of the function, a \n    `{` block that immediately follows `function(...)`.\n\nNote the overall process: I only made the function after I'd figured out how to make it work with a simple input. It's easier to start with working code and turn it into a function; it's harder to create a function and then try to make it work.\n\nAt this point it's a good idea to check your function with a few different inputs:\n\n```{r}\nrescale01(c(-10, 0, 10))\nrescale01(c(1, 2, 3, NA, 5))\n```\n\nAs you write more and more functions you'll eventually want to convert these informal, interactive tests into formal, automated tests. That process is called unit testing. Unfortunately, it's beyond the scope of this book, but you can learn about it in <http://r-pkgs.had.co.nz/tests.html>.\n\nWe can simplify the original example now that we have a function:\n\n```{r}\ndf$a <- rescale01(df$a)\ndf$b <- rescale01(df$b)\ndf$c <- rescale01(df$c)\ndf$d <- rescale01(df$d)\n```\n\nCompared to the original, this code is easier to understand and we've eliminated one class of copy-and-paste errors. There is still quite a bit of duplication since we're doing the same thing to multiple columns. We'll learn how to eliminate that duplication in [iteration], once you've learned more about R's data structures in [vectors].\n\nAnother advantage of functions is that if our requirements change, we only need to make the change in one place. For example, we might discover that some of our variables include infinite values, and `rescale01()` fails:\n\n```{r}\nx <- c(1:10, Inf)\nrescale01(x)\n```\n\nBecause we've extracted the code into a function, we only need to make the fix in one place:\n\n```{r}\nrescale01 <- function(x) {\n  rng <- range(x, na.rm = TRUE, finite = TRUE)\n  (x - rng[1]) / (rng[2] - rng[1])\n}\nrescale01(x)\n```\n\nThis is an important part of the \"do not repeat yourself\" (or DRY) principle. The more repetition you have in your code, the more places you need to remember to update when things change (and they always do!), and the more likely you are to create bugs over time.\n\n### Practice\n\n1.  Why is `TRUE` not a parameter to `rescale01()`? What would happen if\n    `x` contained a single missing value, and `na.rm` was `FALSE`?\n\n1.  In the second variant of `rescale01()`, infinite values are left\n    unchanged. Rewrite `rescale01()` so that `-Inf` is mapped to 0, and \n    `Inf` is mapped to 1.\n\n1.  Practice turning the following code snippets into functions. Think about \n    what each function does. What would you call it? How many arguments does it\n    need? Can you rewrite it to be more expressive or less duplicative?\n\n    ```{r, eval = FALSE}\n    mean(is.na(x))\n    \n    x / sum(x, na.rm = TRUE)\n    \n    sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)\n    ```\n\n1.  Follow <http://nicercode.github.io/intro/writing-functions.html> to \n    write your own functions to compute the variance and skew of a numeric \n    vector.\n\n1.  Write `both_na()`, a function that takes two vectors of the same length \n    and returns the number of positions that have an `NA` in both vectors.\n\n1.  What do the following functions do? Why are they useful even though they\n    are so short?\n    \n    ```{r}\n    is_directory <- function(x) file.info(x)$isdir\n    is_readable <- function(x) file.access(x, 4) == 0\n    ```\n\n1.  Read the [complete lyrics](https://en.wikipedia.org/wiki/Little_Bunny_Foo_Foo) \n    to \"Little Bunny Foo Foo\". There's a lot of duplication in this song. \n    Extend the initial piping example to recreate the complete song, and use \n    functions to reduce the duplication.\n\n## Functions are for humans and computers\n\nIt's important to remember that functions are not just for the computer, but are also for humans. R doesn't care what your function is called, or what comments it contains, but these are important for human readers. This section discusses some things that you should bear in mind when writing functions that humans can understand.\n\nThe name of a function is important. Ideally, the name of your function will be short, but clearly evoke what the function does. That's hard! But it's better to be clear than short, as RStudio's autocomplete makes it easy to type long names.\n\nGenerally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. `mean()` is better than `compute_mean()`), or accessing some property of an object (i.e. `coef()` is better than `get_coefficients()`). A good sign that a noun might be a better choice is if you're using a very broad verb like \"get\", \"compute\", \"calculate\", or \"determine\". Use your best judgement and don't be afraid to rename a function if you figure out a better name later.\n\n```{r, eval = FALSE}\n# Too short\nf()\n\n# Not a verb, or descriptive\nmy_awesome_function()\n\n# Long, but clear\nimpute_missing()\ncollapse_years()\n```\n\nIf your function name is composed of multiple words, I recommend using \"snake\\_case\", where each lowercase word is separated by an underscore. camelCase is a popular alternative. It doesn't really matter which one you pick, the important thing is to be consistent: pick one or the other and stick with it. R itself is not very consistent, but there's nothing you can do about that. Make sure you don't fall into the same trap by making your code as consistent as possible.\n\n```{r, eval = FALSE}\n# Never do this!\ncol_mins <- function(x, y) {}\nrowMaxes <- function(y, x) {}\n```\n\nIf you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That's better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family. \n\n```{r, eval = FALSE}\n# Good\ninput_select()\ninput_checkbox()\ninput_text()\n\n# Not so good\nselect_input()\ncheckbox_input()\ntext_input()\n```\n\nA good example of this design is the stringr package: if you don't remember exactly which function you need, you can type `str_` and jog your memory.\n\nWhere possible, avoid overriding existing functions and variables. It's impossible to do in general because so many good names are already taken by other packages, but avoiding the most common names from base R will avoid confusion.\n\n```{r, eval = FALSE}\n# Don't do this!\nT <- FALSE\nc <- 10\nmean <- function(x) sum(x)\n```\n\nUse comments, lines starting with `#`, to explain the \"why\" of your code. You generally should avoid comments that explain the \"what\" or the \"how\". If you can't understand what the code does from reading it, you should think about how to rewrite it to be more clear. Do you need to add some intermediate variables with useful names? Do you need to break out a subcomponent of a large function so you can name it? However, your code can never capture the reasoning behind your decisions: why did you choose this approach instead of an alternative? What else did you try that didn't work? It's a great idea to capture that sort of thinking in a comment.\n\nAnother important use of comments is to break up your file into easily readable chunks. Use long lines of `-` and `=` to make it easy to spot the breaks.\n\n```{r, eval = FALSE}\n# Load data --------------------------------------\n\n# Plot data --------------------------------------\n```\n\nRStudio provides a keyboard shortcut to create these headers (Cmd/Ctrl + Shift + R), and will display them in the code navigation drop-down at the bottom-left of the editor:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rstudio-nav.png\")\n```\n\n### Exercises\n\n1.  Read the source code for each of the following three functions, puzzle out\n    what they do, and then brainstorm better names.\n    \n    ```{r}\n    f1 <- function(string, prefix) {\n      substr(string, 1, nchar(prefix)) == prefix\n    }\n    f2 <- function(x) {\n      if (length(x) <= 1) return(NULL)\n      x[-length(x)]\n    }\n    f3 <- function(x, y) {\n      rep(y, length.out = length(x))\n    }\n    ```\n    \n1.  Take a function that you've written recently and spend 5 minutes \n    brainstorming a better name for it and its arguments.\n\n1.  Compare and contrast `rnorm()` and `MASS::mvrnorm()`. How could you make\n    them more consistent? \n    \n1.  Make a case for why `norm_r()`, `norm_d()` etc would be better than\n    `rnorm()`, `dnorm()`. Make a case for the opposite.\n\n## Conditional execution\n\nAn `if` statement allows you to conditionally execute code. It looks like this:\n\n```{r, eval = FALSE}\nif (condition) {\n  # code executed when condition is TRUE\n} else {\n  # code executed when condition is FALSE\n}\n```\n\nTo get help on `if` you need to surround it in backticks: `` ?`if` ``. The help isn't particularly helpful if you're not already an experienced programmer, but at least you know how to get to it!\n\nHere's a simple function that uses an `if` statement. The goal of this function is to return a logical vector describing whether or not each element of a vector is named.\n\n```{r}\nhas_name <- function(x) {\n  nms <- names(x)\n  if (is.null(nms)) {\n    rep(FALSE, length(x))\n  } else {\n    !is.na(nms) & nms != \"\"\n  }\n}\n```\n\nThis function takes advantage of the standard return rule: a function returns the last value that it computed. Here that is either one of the two branches of the `if` statement.\n\n### Conditions\n\nThe `condition` must evaluate to either `TRUE` or `FALSE`. If it's a vector, you'll get a warning message; if it's an `NA`, you'll get an error. Watch out for these messages in your own code:\n\n```{r, error = TRUE}\nif (c(TRUE, FALSE)) {}\n\nif (NA) {}\n```\n\nYou can use `||` (or) and `&&` (and) to combine multiple logical expressions. These operators are \"short-circuiting\": as soon as `||` sees the first `TRUE` it returns `TRUE` without computing anything else. As soon as `&&` sees the first `FALSE` it returns `FALSE`. You should never use `|` or `&` in an `if` statement: these are vectorised operations that apply to multiple values (that's why you use them in `filter()`). If you do have a logical vector, you can use `any()` or `all()` to collapse it to a single value.\n\nBe careful when testing for equality. `==` is vectorised, which means that it's easy to get more than one output.  Either check the length is already 1, collapse with `all()` or `any()`, or use the non-vectorised `identical()`. `identical()` is very strict: it always returns either a single `TRUE` or a single `FALSE`, and doesn't coerce types. This means that you need to be careful when comparing integers and doubles:\n\n```{r}\nidentical(0L, 0)\n```\n\nYou also need to be wary of floating point numbers:\n\n```{r}\nx <- sqrt(2) ^ 2\nx\nx == 2\nx - 2\n```\n\nInstead use `dplyr::near()` for comparisons, as described in [comparisons].\n\nAnd remember, `x == NA` doesn't do anything useful!\n\n### Multiple conditions\n\nYou can chain multiple if statements together:\n\n```{r, eval = FALSE}\nif (this) {\n  # do that\n} else if (that) {\n  # do something else\n} else {\n  # \n}\n```\n\nBut if you end up with a very long series of chained `if` statements, you should consider rewriting. One useful technique is the `switch()` function. It allows you to evaluate selected code based on position or name.\n\n```{r, echo = FALSE}\nfunction(x, y, op) {\n  switch(op,\n    plus = x + y,\n    minus = x - y,\n    times = x * y,\n    divide = x / y,\n    stop(\"Unknown op!\")\n  )\n}\n```\n\nAnother useful function that can often eliminate long chains of `if` statements is `cut()`. It's used to discretise continuous variables.\n\n### Code style\n\nBoth `if` and `function` should (almost) always be followed by squiggly brackets (`{}`), and the contents should be indented by two spaces. This makes it easier to see the hierarchy in your code by skimming the left-hand margin.\n\nAn opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it's followed by `else`. Always indent the code inside curly braces.\n\n```{r, eval = FALSE}\n# Good\nif (y < 0 && debug) {\n  message(\"Y is negative\")\n}\n\nif (y == 0) {\n  log(x)\n} else {\n  y ^ x\n}\n\n# Bad\nif (y < 0 && debug)\nmessage(\"Y is negative\")\n\nif (y == 0) {\n  log(x)\n} \nelse {\n  y ^ x\n}\n```\n\nIt's ok to drop the curly braces if you have a very short `if` statement that can fit on one line:\n\n```{r}\ny <- 10\nx <- if (y < 20) \"Too low\" else \"Too high\"\n```\n\nI recommend this only for very brief `if` statements. Otherwise, the full form is easier to read:\n\n```{r}\nif (y < 20) {\n  x <- \"Too low\" \n} else {\n  x <- \"Too high\"\n}\n```\n\n### Exercises\n\n1.  What's the difference between `if` and `ifelse()`? Carefully read the help\n    and construct three examples that illustrate the key differences.\n\n1.  Write a greeting function that says \"good morning\", \"good afternoon\",\n    or \"good evening\", depending on the time of day. (Hint: use a time\n    argument that defaults to `lubridate::now()`. That will make it \n    easier to test your function.)\n\n1.  Implement a `fizzbuzz` function. It takes a single number as input. If\n    the number is divisible by three, it returns \"fizz\". If it's divisible by\n    five it returns \"buzz\". If it's divisible by three and five, it returns\n    \"fizzbuzz\". Otherwise, it returns the number. Make sure you first write \n    working code before you create the function.\n    \n1.  How could you use `cut()` to simplify this set of nested if-else statements?\n\n    ```{r, eval = FALSE}\n    if (temp <= 0) {\n      \"freezing\"\n    } else if (temp <= 10) {\n      \"cold\"\n    } else if (temp <= 20) {\n      \"cool\"\n    } else if (temp <= 30) {\n      \"warm\"\n    } else {\n      \"hot\"\n    }\n    ```\n    \n    How would you change the call to `cut()` if I'd used `<` instead of `<=`?\n    What is the other chief advantage of `cut()` for this problem? (Hint:\n    what happens if you have many values in `temp`?)\n\n1.  What happens if you use `switch()` with numeric values?\n\n1.  What does this `switch()` call do? What happens if `x` is \"e\"?\n\n    ```{r, eval = FALSE}\n    switch(x, \n      a = ,\n      b = \"ab\",\n      c = ,\n      d = \"cd\"\n    )\n    ```\n    \n    Experiment, then carefully read the documentation. \n\n## Function arguments\n\nThe arguments to a function typically fall into two broad sets: one set supplies the __data__ to compute on, and the other supplies arguments that control the __details__ of the computation. For example:\n\n* In `log()`, the data is `x`, and the detail is the `base` of the logarithm.\n\n* In `mean()`, the data is `x`, and the details are how much data to trim\n  from the ends (`trim`) and how to handle missing values (`na.rm`).\n\n* In `t.test()`, the data are `x` and `y`, and the details of the test are\n  `alternative`, `mu`, `paired`, `var.equal`, and `conf.level`.\n  \n* In `str_c()` you can supply any number of strings to `...`, and the details\n  of the concatenation are controlled by `sep` and `collapse`.\n  \nGenerally, data arguments should come first. Detail arguments should go on the end, and usually should have default values. You specify a default value in the same way you call a function with a named argument:\n\n```{r}\n# Compute confidence interval around mean using normal approximation\nmean_ci <- function(x, conf = 0.95) {\n  se <- sd(x) / sqrt(length(x))\n  alpha <- 1 - conf\n  mean(x) + se * qnorm(c(alpha / 2, 1 - alpha / 2))\n}\n\nx <- runif(100)\nmean_ci(x)\nmean_ci(x, conf = 0.99)\n```\n\nThe default value should almost always be the most common value. The few exceptions to this rule are to do with safety. For example, it makes sense for `na.rm` to default to `FALSE` because missing values are important. Even though `na.rm = TRUE` is what you usually put in your code, it's a bad idea to silently ignore missing values by default.\n\nWhen you call a function, you typically omit the names of the data arguments, because they are used so commonly. If you override the default value of a detail argument, you should use the full name:\n\n```{r, eval = FALSE}\n# Good\nmean(1:10, na.rm = TRUE)\n\n# Bad\nmean(x = 1:10, , FALSE)\nmean(, TRUE, x = c(1:10, NA))\n```\n\nYou can refer to an argument by its unique prefix (e.g. `mean(x, n = TRUE)`), but this is generally best avoided given the possibilities for confusion.\n\nNotice that when you call a function, you should place a space around `=` in function calls, and always put a space after a comma, not before (just like in regular English). Using whitespace makes it easier to skim the function for the important components.\n\n```{r, eval = FALSE}\n# Good\naverage <- mean(feet / 12 + inches, na.rm = TRUE)\n\n# Bad\naverage<-mean(feet/12+inches,na.rm=TRUE)\n```\n\n### Choosing names\n\nThe names of the arguments are also important. R doesn't care, but the readers of your code (including future-you!) will. Generally you should prefer longer, more descriptive names, but there are a handful of very common, very short names. It's worth memorising these:\n\n* `x`, `y`, `z`: vectors.\n* `w`: a vector of weights.\n* `df`: a data frame.\n* `i`, `j`: numeric indices (typically rows and columns).\n* `n`: length, or number of rows.\n* `p`: number of columns.\n\nOtherwise, consider matching names of arguments in existing R functions. For example, use `na.rm` to determine if missing values should be removed.\n\n### Checking values\n\nAs you start to write more functions, you'll eventually get to the point where you don't remember exactly how your function works. At this point it's easy to call your function with invalid inputs. To avoid this problem, it's often useful to make constraints explicit. For example, imagine you've written some functions for computing weighted summary statistics:\n\n```{r}\nwt_mean <- function(x, w) {\n  sum(x * w) / sum(w)\n}\nwt_var <- function(x, w) {\n  mu <- wt_mean(x, w)\n  sum(w * (x - mu) ^ 2) / sum(w)\n}\nwt_sd <- function(x, w) {\n  sqrt(wt_var(x, w))\n}\n```\n\nWhat happens if `x` and `w` are not the same length?\n\n```{r}\nwt_mean(1:6, 1:3)\n```\n\nIn this case, because of R's vector recycling rules, we don't get an error. \n\nIt's good practice to check important preconditions, and throw an error (with `stop()`), if they are not true:\n\n```{r}\nwt_mean <- function(x, w) {\n  if (length(x) != length(w)) {\n    stop(\"`x` and `w` must be the same length\", call. = FALSE)\n  }\n  sum(w * x) / sum(w)\n}\n```\n\nBe careful not to take this too far. There's a tradeoff between how much time you spend making your function robust, versus how long you spend writing it. For example, if you also added a `na.rm` argument, I probably wouldn't check it carefully:\n\n```{r}\nwt_mean <- function(x, w, na.rm = FALSE) {\n  if (!is.logical(na.rm)) {\n    stop(\"`na.rm` must be logical\")\n  }\n  if (length(na.rm) != 1) {\n    stop(\"`na.rm` must be length 1\")\n  }\n  if (length(x) != length(w)) {\n    stop(\"`x` and `w` must be the same length\", call. = FALSE)\n  }\n  \n  if (na.rm) {\n    miss <- is.na(x) | is.na(w)\n    x <- x[!miss]\n    w <- w[!miss]\n  }\n  sum(w * x) / sum(w)\n}\n```\n\nThis is a lot of extra work for little additional gain. A useful compromise is the built-in `stopifnot()`: it checks that each argument is `TRUE`, and produces a generic error message if not.\n\n```{r, error = TRUE}\nwt_mean <- function(x, w, na.rm = FALSE) {\n  stopifnot(is.logical(na.rm), length(na.rm) == 1)\n  stopifnot(length(x) == length(w))\n  \n  if (na.rm) {\n    miss <- is.na(x) | is.na(w)\n    x <- x[!miss]\n    w <- w[!miss]\n  }\n  sum(w * x) / sum(w)\n}\nwt_mean(1:6, 6:1, na.rm = \"foo\")\n```\n\nNote that when using `stopifnot()` you assert what should be true rather than checking for what might be wrong.\n\n### Dot-dot-dot (...)\n\nMany functions in R take an arbitrary number of inputs:\n\n```{r}\nsum(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)\nstringr::str_c(\"a\", \"b\", \"c\", \"d\", \"e\", \"f\")\n```\n\nHow do these functions work? They rely on a special argument: `...` (pronounced dot-dot-dot). This special argument captures any number of arguments that aren't otherwise matched. \n\nIt's useful because you can then send those `...` on to another function. This is a useful catch-all if your function primarily wraps another function. For example, I commonly create these helper functions that wrap around `str_c()`:\n\n```{r}\ncommas <- function(...) stringr::str_c(..., collapse = \", \")\ncommas(letters[1:10])\n\nrule <- function(..., pad = \"-\") {\n  title <- paste0(...)\n  width <- getOption(\"width\") - nchar(title) - 5\n  cat(title, \" \", stringr::str_dup(pad, width), \"\\n\", sep = \"\")\n}\nrule(\"Important output\")\n```\n\nHere `...` lets me forward on any arguments that I don't want to deal with to `str_c()`. It's a very convenient technique. But it does come at a price: any misspelled arguments will not raise an error. This makes it easy for typos to go unnoticed:\n\n```{r}\nx <- c(1, 2)\nsum(x, na.mr = TRUE)\n```\n\nIf you just want to capture the values of the `...`, use `list(...)`.\n\n### Lazy evaluation\n\nArguments in R are lazily evaluated: they're not computed until they're needed. That means if they're never used, they're never called. This is an important property of R as a programming language, but is generally not important when you're writing your own functions for data analysis. You can read more about lazy evaluation at <http://adv-r.had.co.nz/Functions.html#lazy-evaluation>.\n\n### Exercises\n\n1.  What does `commas(letters, collapse = \"-\")` do? Why?\n\n1.  It'd be nice if you could supply multiple characters to the `pad` argument, \n    e.g. `rule(\"Title\", pad = \"-+\")`. Why doesn't this currently work? How \n    could you fix it?\n    \n1.  What does the `trim` argument to `mean()` do? When might you use it?\n\n1.  The default value for the `method` argument to `cor()` is \n    `c(\"pearson\", \"kendall\", \"spearman\")`. What does that mean? What \n    value is used by default?\n\n## Return values\n\nFiguring out what your function should return is usually straightforward: it's why you created the function in the first place! There are two things you should consider when returning a value: \n\n1. Does returning early make your function easier to read? \n\n2. Can you make your function pipeable?\n\n### Explicit return statements\n\nThe value returned by the function is usually the last statement it evaluates, but you can choose to return early by using `return()`. I think it's best to save the use of `return()` to signal that you can return early with a simpler solution. A common reason to do this is because the inputs are empty:\n\n```{r}\ncomplicated_function <- function(x, y, z) {\n  if (length(x) == 0 || length(y) == 0) {\n    return(0)\n  }\n    \n  # Complicated code here\n}\n\n```\n\nAnother reason is because you have a `if` statement with one complex block and one simple block. For example, you might write an if statement like this:\n\n```{r, eval = FALSE}\nf <- function() {\n  if (x) {\n    # Do \n    # something\n    # that\n    # takes\n    # many\n    # lines\n    # to\n    # express\n  } else {\n    # return something short\n  }\n}\n```\n\nBut if the first block is very long, by the time you get to the `else`, you've forgotten the `condition`. One way to rewrite it is to use an early return for the simple case:\n\n```{r, eval = FALSE}\n\nf <- function() {\n  if (!x) {\n    return(something_short)\n  }\n\n  # Do \n  # something\n  # that\n  # takes\n  # many\n  # lines\n  # to\n  # express\n}\n```\n\nThis tends to make the code easier to understand, because you don't need quite so much context to understand it.\n\n### Writing pipeable functions\n\nIf you want to write your own pipeable functions, it's important to think about the return value. Knowing the return value's object type will mean that your pipeline will \"just work\". For example, with dplyr and tidyr the object type is the data frame. \n\nThere are two basic types of pipeable functions: transformations and side-effects. With __transformations__, an object is passed to the function's first argument and a modified object is returned. With __side-effects__, the passed object is not transformed. Instead, the function performs an action on the object, like drawing a plot or saving a file. Side-effects functions should \"invisibly\" return the first argument, so that while they're not printed they can still be used in a pipeline. For example, this simple function prints the number of missing values in a data frame:\n\n```{r}\nshow_missings <- function(df) {\n  n <- sum(is.na(df))\n  cat(\"Missing values: \", n, \"\\n\", sep = \"\")\n  \n  invisible(df)\n}\n```\n\nIf we call it interactively, the `invisible()` means that the input `df` doesn't get printed out:\n\n```{r}\nshow_missings(mtcars)\n```\n\nBut it's still there, it's just not printed by default:\n\n```{r}\nx <- show_missings(mtcars) \nclass(x)\ndim(x)\n```\n\nAnd we can still use it in a pipe:\n\n```{r, include = FALSE}\nlibrary(dplyr)\n```\n```{r}\nmtcars %>% \n  show_missings() %>% \n  mutate(mpg = ifelse(mpg < 20, NA, mpg)) %>% \n  show_missings() \n```\n\n## Environment\n\nThe last component of a function is its environment. This is not something you need to understand deeply when you first start writing functions. However, it's important to know a little bit about environments because they are crucial to how functions work. The environment of a function controls how R finds the value associated with a name. For example, take this function:\n\n```{r}\nf <- function(x) {\n  x + y\n} \n```\n\nIn many programming languages, this would be an error, because `y` is not defined inside the function. In R, this is valid code because R uses rules called __lexical scoping__ to find the value associated with a name. Since `y` is not defined inside the function, R will look in the __environment__ where the function was defined:\n\n```{r}\ny <- 100\nf(10)\n\ny <- 1000\nf(10)\n```\n\nThis behaviour seems like a recipe for bugs, and indeed you should avoid creating functions like this deliberately, but by and large it doesn't cause too many problems (especially if you regularly restart R to get to a clean slate). \n\nThe advantage of this behaviour is that from a language standpoint it allows R to be very consistent. Every name is looked up using the same set of rules. For `f()` that includes the behaviour of two things that you might not expect: `{` and `+`. This allows you to do devious things like:\n\n```{r}\n`+` <- function(x, y) {\n  if (runif(1) < 0.1) {\n    sum(x, y)\n  } else {\n    sum(x, y) * 1.1\n  }\n}\ntable(replicate(1000, 1 + 2))\nrm(`+`)\n```\n\nThis is a common phenomenon in R. R places few limits on your power. You can do many things that you can't do in other programming languages. You can do many things that 99% of the time are extremely ill-advised (like overriding how addition works!). But this power and flexibility is what makes tools like ggplot2 and dplyr possible. Learning how to make best use of this flexibility is beyond the scope of this book, but you can read about in [_Advanced R_](http://adv-r.had.co.nz).\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/hierarchy.Rmd",
    "content": "# Hierarchical data {#hierarchy}\n\n## Introduction\n\nThis chapter belongs in [wrangle](#wrangle-intro): it will give you a set of tools for working with hierarchical data, such as the deeply nested lists you often get when working with JSON. However, you can only learn it now because working with hierarchical structures requires some programming skills, particularly an understanding of data structures, functions, and iteration. Now you have those tools under your belt, you can learn how to work with hierarchical data.\n\nThe \n\nAs well as tools to simplify iteration, purrr provides tools for handling deeply nested lists. There are three common sources of such data:\n\n* JSON and XML\n* \n\nThe map functions apply a function to every element in a list. They are the most commonly used part of purrr, but not the only part. Since lists are often used to represent complex hierarchies, purrr also provides tools to work with hierarchy:\n\n* You can extract deeply nested elements in a single call by supplying\n  a character vector to the map functions.\n\n* You can remove a level of the hierarchy with the flatten functions.\n\n* You can flip levels of the hierarchy with the transpose function.\n\n### Prerequisites\n\nThis chapter focusses mostly on purrr. As well as the tools for iteration that you've already learned about, purrr also provides a number of tools specifically designed to manipulate hierarchical data.\n\n```{r setup}\nlibrary(purrr)\n```\n\n## Initial exploration\n\nSometimes you get data structures that are very deeply nested. A common source of such data is JSON from a web API. I've previously downloaded a list of GitHub issues related to this book and saved it as `issues.json`. Now I'm going to load it into a list with jsonlite. By default `fromJSON()` tries to be helpful and simplifies the structure a little for you. Here I'm going to show you how to do it with purrr, so I set `simplifyVector = FALSE`:\n\n```{r}\n# From https://api.github.com/repos/hadley/r4ds/issues\nissues <- jsonlite::fromJSON(\"issues.json\", simplifyVector = FALSE)\n```\n\nYou might be tempted to use `str()` on this data. Unfortunately, however, `str()` is not designed for lists that are both deep and wide, and you'll tend to get overwhelmed by the output. A better strategy is to pull the list apart piece by piece.\n\nFirst, figure out how many elements are in the list, take a look at one, and then check they're all the same structure. In this case there are eight elements, and the first element is another list.\n\n```{r}\nlength(issues)\nstr(issues[[1]])\n```\n\n(In this case we got lucky and the structure is (just) simple enough to print out with `str()`. If you're unlucky, you may need to repeat this procedure.)\n\n```{r}\ntibble::tibble(\n  i = seq_along(issues),\n  names = issues %>% map(names) \n) %>% \n  tidyr::unnest(names) %>% \n  table() %>% \n  t()\n```\n\nAnother alternative is the __listviewer__ package, <https://github.com/timelyportfolio/listviewer>. \n\n## Extracting deeply nested elements\n\nTo work with this sort of data, you typically want to turn it into a data frame by extracting the related vectors that you're most interested in:\n\n```{r}\n\nissues %>% map_int(\"id\")\nissues %>% map_lgl(\"locked\")\nissues %>% map_chr(\"state\")\n```\n\nYou can use the same technique to extract more deeply nested structure. For example, imagine you want to extract the name and id of the user. You could do that in two steps:\n\n```{r}\nusers <- issues %>% map(\"user\")\nusers %>% map_chr(\"login\")\nusers %>% map_int(\"id\")\n```\n\nBut by supplying a character _vector_ to `map_*`, you can do it in one:\n\n```{r}\nissues %>% map_chr(c(\"user\", \"login\"))\nissues %>% map_int(c(\"user\", \"id\"))\n```\n\nWhat happens if that path is missing in some of the elements? For example, lets try and extract the HTML url to the pull request:\n\n```{r, error = TRUE}\nissues %>% map_chr(c(\"pull_request\", \"html_url\"))\n```\n\nUnfortunately that doesn't work. Whenever you see an error from purrr complaining about the \"type\" of the result, it's because it's trying to shove it into a simple vector (here a character). You can diagnose the problem more easily if you use `map()`:\n\n```{r}\nissues %>% map(c(\"pull_request\", \"html_url\"))\n```\n\nTo get the results into a character vector, we need to tell purrr what it should change `NULL` to. You can do that with the `.null` argument. The most common value to use is `NA`:\n\n```{r}\nissues %>% map_chr(c(\"pull_request\", \"html_url\"), .null = NA)\n```\n\n(You might wonder why that isn't the default value since it's so useful. Well, if it was the default, you'd never get an error message if you had a typo in the names. You'd just get a vector of missing values. That would be annoying to debug because it's a silent failure.)\n\nIt's possible to mix position and named indexing by using a list\n\n```{r}\nissues %>% map_chr(list(\"pull_request\", 1), .null = NA)\n```\n\n\n## Removing a level of hierarchy\n\nAs well as indexing deeply into hierarchy, it's sometimes useful to flatten it. That's the job of the flatten family of functions: `flatten()`, `flatten_lgl()`, `flatten_int()`, `flatten_dbl()`, and `flatten_chr()`. In the code below we take a list of lists of double vectors, then flatten it to a list of double vectors, then to a double vector.\n\n```{r}\nx <- list(list(a = 1, b = 2), list(c = 3, d = 4))\nstr(x)\n\ny <- flatten(x) \nstr(y)\nflatten_dbl(y)\n```\n\nGraphically, that sequence of operations looks like:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/lists-flatten.png\")\n```\n\nWhenever I get confused about a sequence of flattening operations, I'll often draw a diagram like this to help me understand what's going on.\n\nBase R has `unlist()`, but I recommend avoiding it for the same reason I recommend avoiding `sapply()`: it always succeeds. Even if your data structure accidentally changes, `unlist()` will continue to work silently the wrong type of output. This tends to create problems that are frustrating to debug.\n\n## Switching levels in the hierarchy {#transpose}\n\nOther times the hierarchy feels \"inside out\". You can use `transpose()` to flip the first and second levels of a list: \n\n```{r}\nx <- list(\n  x = list(a = 1, b = 3, c = 5),\n  y = list(a = 2, b = 4, c = 6)\n)\nx %>% str()\nx %>% transpose() %>% str()\n```\n\nGraphically, this looks like:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/lists-transpose.png\")\n```\n\nYou'll see an example of this in the next section, as `transpose()` is particularly useful in conjunction with adverbs like `safely()` and `quietly()`.\n\nIt's called transpose by analogy to matrices. When you subset a transposed matrix, you switch indices: `x[i, j]` is the same as `t(x)[j, i]`. It's the same idea when transposing a list, but the subsetting looks a little different: `x[[i]][[j]]` is equivalent to `transpose(x)[[j]][[i]]`. Similarly, a transpose is its own inverse so `transpose(transpose(x))` is equal to `x`.\n\nTranspose is also useful when working with JSON APIs. Many JSON APIs represent data frames in a row-based format, rather than R's column-based format. `transpose()` makes it easy to switch between the two:\n\n```{r}\ndf <- tibble::tibble(x = 1:3, y = c(\"a\", \"b\", \"c\"))\ndf %>% transpose() %>% str()\n```\n\n## Turning lists into data frames\n\n* Have a deeply nested list with missing pieces\n* Need a tidy data frame so you can visualise, transform, model etc.\n* What do you do?\n* By hand with purrr, talk about `fromJSON` and `tidyJSON`\n* tidyjson\n\n### Exercises\n\n1.  Challenge: read all the CSV files in a directory. Which ones failed\n    and why? \n\n    ```{r, eval = FALSE}\n    files <- dir(\"data\", pattern = \"\\\\.csv$\")\n    files %>%\n      set_names(., basename(.)) %>%\n      map_df(safely(readr::read_csv), .id = \"filename\") %>%\n    ```\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/import.Rmd",
    "content": "# Data import\n\n## Introduction\n\nWorking with data provided by R packages is a great way to learn the tools of data science, but at some point you want to stop learning and start working with your own data. In this chapter, you'll learn how to read plain-text rectangular files into R. Here, we'll only scratch the surface of data import, but many of the principles will translate to other forms of data. We'll finish with a few pointers to packages that are useful for other types of data.\n\n### Prerequisites\n\nIn this chapter, you'll learn how to load flat files in R with the __readr__ package, which is part of the core tidyverse.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n```\n\n## Getting started\n\nMost of readr's functions are concerned with turning flat files into data frames:\n\n* `read_csv()` reads comma delimited files, `read_csv2()` reads semicolon\n  separated files (common in countries where `,` is used as the decimal place),\n  `read_tsv()` reads tab delimited files, and `read_delim()` reads in files\n  with any delimiter.\n\n* `read_fwf()` reads fixed width files. You can specify fields either by their\n  widths with `fwf_widths()` or their position with `fwf_positions()`.\n  `read_table()` reads a common variation of fixed width files where columns\n  are separated by white space.\n\n* `read_log()` reads Apache style log files. (But also check out\n  [webreadr](https://github.com/Ironholds/webreadr) which is built on top\n  of `read_log()` and provides many more helpful tools.)\n\nThese functions all have similar syntax: once you've mastered one, you can use the others with ease. For the rest of this chapter we'll focus on `read_csv()`. Not only are csv files one of the most common forms of data storage, but once you understand `read_csv()`, you can easily apply your knowledge to all the other functions in readr.\n\nThe first argument to `read_csv()` is the most important: it's the path to the file to read.\n\n```{r, message = TRUE}\nheights <- read_csv(\"data/heights.csv\")\n```\n\nWhen you run `read_csv()` it prints out a column specification that gives the name and type of each column. That's an important part of readr, which we'll come back to in [parsing a file].\n\nYou can also supply an inline csv file. This is useful for experimenting with readr and for creating reproducible examples to share with others:\n\n```{r}\nread_csv(\"a,b,c\n1,2,3\n4,5,6\")\n```\n\nIn both cases `read_csv()` uses the first line of the data for the column names, which is a very common convention. There are two cases where you might want to tweak this behaviour:\n\n1.  Sometimes there are a few lines of metadata at the top of the file. You can\n    use `skip = n` to skip the first `n` lines; or use `comment = \"#\"` to drop\n    all lines that start with (e.g.) `#`.\n    \n    ```{r}\n    read_csv(\"The first line of metadata\n      The second line of metadata\n      x,y,z\n      1,2,3\", skip = 2)\n    \n    read_csv(\"# A comment I want to skip\n      x,y,z\n      1,2,3\", comment = \"#\")\n    ```\n    \n1.  The data might not have column names. You can use `col_names = FALSE` to\n    tell `read_csv()` not to treat the first row as headings, and instead\n    label them sequentially from `X1` to `Xn`:\n    \n    ```{r}\n    read_csv(\"1,2,3\\n4,5,6\", col_names = FALSE)\n    ```\n    \n    (`\"\\n\"` is a convenient shortcut for adding a new line. You'll learn more\n    about it and other types of string escape in [string basics].)\n    \n    Alternatively you can pass `col_names` a character vector which will be\n    used as the column names:\n    \n    ```{r}\n    read_csv(\"1,2,3\\n4,5,6\", col_names = c(\"x\", \"y\", \"z\"))\n    ```\n\nAnother option that commonly needs tweaking is `na`: this specifies the value (or values) that are used to represent missing values in your file:\n\n```{r}\nread_csv(\"a,b,c\\n1,2,.\", na = \".\")\n```\n\nThis is all you need to know to read ~75% of CSV files that you'll encounter in practice. You can also easily adapt what you've learned to read tab separated files with `read_tsv()` and fixed width files with `read_fwf()`. To read in more challenging files, you'll need to learn more about how readr parses each column, turning them into R vectors.\n\n### Compared to base R\n\nIf you've used R before, you might wonder why we're not using `read.csv()`. There are a few good reasons to favour readr functions over the base equivalents:\n\n* They are typically much faster (~10x) than their base equivalents.\n  Long running jobs have a progress bar, so you can see what's happening. \n  If you're looking for raw speed, try `data.table::fread()`. It doesn't fit \n  quite so well into the tidyverse, but it can be quite a bit faster.\n\n* They produce tibbles, they don't convert character vectors to factors,\n  use row names, or munge the column names. These are common sources of\n  frustration with the base R functions.\n\n* They are more reproducible. Base R functions inherit some behaviour from\n  your operating system and environment variables, so import code that works \n  on your computer might not work on someone else's.\n\n### Exercises\n\n1.  What function would you use to read a file where fields were separated with  \n    \"|\"?\n    \n1.  Apart from `file`, `skip`, and `comment`, what other arguments do\n    `read_csv()` and `read_tsv()` have in common?\n    \n1.  What are the most important arguments to `read_fwf()`?\n   \n1.  Sometimes strings in a CSV file contain commas. To prevent them from\n    causing problems they need to be surrounded by a quoting character, like\n    `\"` or `'`. By convention, `read_csv()` assumes that the quoting\n    character will be `\"`, and if you want to change it you'll need to\n    use `read_delim()` instead. What arguments do you need to specify\n    to read the following text into a data frame?\n    \n    ```{r, eval = FALSE}\n    \"x,y\\n1,'a,b'\"\n    ```\n    \n1.  Identify what is wrong with each of the following inline CSV files. \n    What happens when you run the code?\n    \n    ```{r, eval = FALSE}\n    read_csv(\"a,b\\n1,2,3\\n4,5,6\")\n    read_csv(\"a,b,c\\n1,2\\n1,2,3,4\")\n    read_csv(\"a,b\\n\\\"1\")\n    read_csv(\"a,b\\n1,2\\na,b\")\n    read_csv(\"a;b\\n1;3\")\n    ```\n\n## Parsing a vector\n\nBefore we get into the details of how readr reads files from disk, we need to take a little detour to talk about the `parse_*()` functions. These functions take a character vector and return a more specialised vector like a logical, integer, or date:\n\n```{r}\nstr(parse_logical(c(\"TRUE\", \"FALSE\", \"NA\")))\nstr(parse_integer(c(\"1\", \"2\", \"3\")))\nstr(parse_date(c(\"2010-01-01\", \"1979-10-14\")))\n```\n\nThese functions are useful in their own right, but are also an important building block for readr. Once you've learned how the individual parsers work in this section, we'll circle back and see how they fit together to parse a complete file in the next section.\n\nLike all functions in the tidyverse, the `parse_*()` functions are uniform: the first argument is a character vector to parse, and the `na` argument specifies which strings should be treated as missing:\n\n```{r}\nparse_integer(c(\"1\", \"231\", \".\", \"456\"), na = \".\")\n```\n\nIf parsing fails, you'll get a warning:\n\n```{r}\nx <- parse_integer(c(\"123\", \"345\", \"abc\", \"123.45\"))\n```\n\nAnd the failures will be missing in the output:\n\n```{r}\nx\n```\n\nIf there are many parsing failures, you'll need to use `problems()` to get the complete set. This returns a tibble, which you can then manipulate with dplyr.\n\n```{r}\nproblems(x)\n```\n\nUsing parsers is mostly a matter of understanding what's available and how they deal with different types of input. There are eight particularly important parsers:\n\n1.  `parse_logical()` and `parse_integer()` parse logicals and integers\n    respectively. There's basically nothing that can go wrong with these\n    parsers so I won't describe them here further.\n    \n1.  `parse_double()` is a strict numeric parser, and `parse_number()` \n    is a flexible numeric parser. These are more complicated than you might\n    expect because different parts of the world write numbers in different\n    ways.\n    \n1.  `parse_character()` seems so simple that it shouldn't be necessary. But\n    one complication makes it quite important: character encodings.\n\n1.  `parse_factor()` create factors, the data structure that R uses to represent\n    categorical variables with fixed and known values.\n\n1.  `parse_datetime()`, `parse_date()`, and `parse_time()` allow you to\n    parse various date & time specifications. These are the most complicated\n    because there are so many different ways of writing dates.\n\nThe following sections describe these parsers in more detail.\n\n### Numbers\n\nIt seems like it should be straightforward to parse a number, but three problems make it tricky:\n\n1. People write numbers differently in different parts of the world.\n   For example, some countries use `.` in between the integer and fractional \n   parts of a real number, while others use `,`.\n   \n1. Numbers are often surrounded by other characters that provide some\n   context, like \"$1000\" or \"10%\".\n\n1. Numbers often contain \"grouping\" characters to make them easier to read, \n   like \"1,000,000\", and these grouping characters vary around the world.\n\nTo address the first problem, readr has the notion of a \"locale\", an object that specifies parsing options that differ from place to place. When parsing numbers, the most important option is the character you use for the decimal mark. You can override the default value of `.` by creating a new locale and setting the `decimal_mark` argument:\n\n```{r}\nparse_double(\"1.23\")\nparse_double(\"1,23\", locale = locale(decimal_mark = \",\"))\n```\n\nreadr's default locale is US-centric, because generally R is US-centric (i.e. the documentation of base R is written in American English). An alternative approach would be to try and guess the defaults from your operating system. This is hard to do well, and, more importantly, makes your code fragile: even if it works on your computer, it might fail when you email it to a colleague in another country.\n\n`parse_number()` addresses the second problem: it ignores non-numeric characters before and after the number. This is particularly useful for currencies and percentages, but also works to extract numbers embedded in text.\n\n```{r}\nparse_number(\"$100\")\nparse_number(\"20%\")\nparse_number(\"It cost $123.45\")\n```\n\nThe final problem is addressed by the combination of `parse_number()` and the locale as `parse_number()` will ignore the \"grouping mark\":\n\n```{r}\n# Used in America\nparse_number(\"$123,456,789\")\n\n# Used in many parts of Europe\nparse_number(\"123.456.789\", locale = locale(grouping_mark = \".\"))\n\n# Used in Switzerland\nparse_number(\"123'456'789\", locale = locale(grouping_mark = \"'\"))\n```\n\n### Strings {#readr-strings}\n\nIt seems like `parse_character()` should be really simple --- it could just return its input. Unfortunately life isn't so simple, as there are multiple ways to represent the same string. To understand what's going on, we need to dive into the details of how computers represent strings. In R, we can get at the underlying representation of a string using `charToRaw()`:\n\n```{r}\ncharToRaw(\"Hadley\")\n```\n\nEach hexadecimal number represents a byte of information: `48` is H, `61` is a, and so on. The mapping from hexadecimal number to character is called the encoding, and in this case the encoding is called ASCII. ASCII does a great job of representing English characters, because it's the __American__ Standard Code for Information Interchange.\n\nThings get more complicated for languages other than English. In the early days of computing there were many competing standards for encoding non-English characters, and to correctly interpret a string you needed to know both the values and the encoding. For example, two common encodings are Latin1 (aka ISO-8859-1, used for Western European languages) and Latin2 (aka ISO-8859-2, used for Eastern European languages). In Latin1, the byte `b1` is \"±\", but in Latin2, it's \"ą\"! Fortunately, today there is one standard that is supported almost everywhere: UTF-8. UTF-8 can encode just about every character used by humans today, as well as many extra symbols (like emoji!).\n\nreadr uses UTF-8 everywhere: it assumes your data is UTF-8 encoded when you read it, and always uses it when writing. This is a good default, but will fail for data produced by older systems that don't understand UTF-8. If this happens to you, your strings will look weird when you print them. Sometimes just one or two characters might be messed up; other times you'll get complete gibberish. For example:\n\n```{r}\nx1 <- \"El Ni\\xf1o was particularly bad this year\"\nx2 <- \"\\x82\\xb1\\x82\\xf1\\x82\\xc9\\x82\\xbf\\x82\\xcd\"\n\nx1\nx2\n```\n\nTo fix the problem you need to specify the encoding in `parse_character()`:\n\n```{r}\nparse_character(x1, locale = locale(encoding = \"Latin1\"))\nparse_character(x2, locale = locale(encoding = \"Shift-JIS\"))\n```\n\nHow do you find the correct encoding? If you're lucky, it'll be included somewhere in the data documentation. Unfortunately, that's rarely the case, so readr provides  `guess_encoding()` to help you figure it out. It's not foolproof, and it works better when you have lots of text (unlike here), but it's a reasonable place to start. Expect to try a few different encodings before you find the right one.\n\n```{r}\nguess_encoding(charToRaw(x1))\nguess_encoding(charToRaw(x2))\n```\n\nThe first argument to `guess_encoding()` can either be a path to a file, or, as in this case, a raw vector (useful if the strings are already in R).\n\nEncodings are a rich and complex topic, and I've only scratched the surface here. If you'd like to learn more I'd recommend reading the detailed explanation at <http://kunststube.net/encoding/>.\n\n### Factors {#readr-factors}\n\nR uses factors to represent categorical variables that have a known set of possible values. Give `parse_factor()` a vector of known `levels` to generate a warning whenever an unexpected value is present:\n\n```{r}\nfruit <- c(\"apple\", \"banana\")\nparse_factor(c(\"apple\", \"banana\", \"bananana\"), levels = fruit)\n```\n\nBut if you have many problematic entries, it's often easier to leave as character vectors and then use the tools you'll learn about in [strings] and [factors] to clean them up.\n\n### Dates, date-times, and times {#readr-datetimes}\n\nYou pick between three parsers depending on whether you want a date (the number of days since 1970-01-01), a date-time (the number of seconds since midnight 1970-01-01), or a time (the number of seconds since midnight). When called without any additional arguments:\n\n*   `parse_datetime()` expects an ISO8601 date-time. ISO8601 is an\n    international standard in which the components of a date are\n    organised from biggest to smallest: year, month, day, hour, minute, \n    second.\n    \n    ```{r}\n    parse_datetime(\"2010-10-01T2010\")\n    # If time is omitted, it will be set to midnight\n    parse_datetime(\"20101010\")\n    ```\n    \n    This is the most important date/time standard, and if you work with\n    dates and times frequently, I recommend reading\n    <https://en.wikipedia.org/wiki/ISO_8601>\n    \n*   `parse_date()` expects a four digit year, a `-` or `/`, the month, a `-` \n    or `/`, then the day:\n    \n    ```{r}\n    parse_date(\"2010-10-01\")\n    ```\n\n*   `parse_time()` expects the hour, `:`, minutes, optionally `:` and seconds, \n    and an optional am/pm specifier:\n  \n    ```{r}\n    library(hms)\n    parse_time(\"01:10 am\")\n    parse_time(\"20:10:01\")\n    ```\n    \n    Base R doesn't have a great built in class for time data, so we use \n    the one provided in the hms package.\n\nIf these defaults don't work for your data you can supply your own date-time `format`, built up of the following pieces:\n\nYear\n: `%Y` (4 digits). \n: `%y` (2 digits); 00-69 -> 2000-2069, 70-99 -> 1970-1999.\n\nMonth\n: `%m` (2 digits).\n: `%b` (abbreviated name, like \"Jan\").\n: `%B` (full name, \"January\").\n\nDay\n: `%d` (2 digits).\n: `%e` (optional leading space).\n\nTime\n: `%H` 0-23 hour.\n: `%I` 0-12, must be used with `%p`.\n: `%p` AM/PM indicator.\n: `%M` minutes.\n: `%S` integer seconds.\n: `%OS` real seconds. \n: `%Z` Time zone (as name, e.g. `America/Chicago`). Beware of abbreviations:\n  if you're American, note that \"EST\" is a Canadian time zone that does not\n  have daylight savings time. It is _not_ Eastern Standard Time! We'll\n  come back to this [time zones].\n: `%z` (as offset from UTC, e.g. `+0800`). \n\nNon-digits\n: `%.` skips one non-digit character.\n: `%*` skips any number of non-digits.\n\nThe best way to figure out the correct format is to create a few examples in a character vector, and test with one of the parsing functions. For example:\n\n```{r}\nparse_date(\"01/02/15\", \"%m/%d/%y\")\nparse_date(\"01/02/15\", \"%d/%m/%y\")\nparse_date(\"01/02/15\", \"%y/%m/%d\")\n```\n\nIf you're using `%b` or `%B` with non-English month names, you'll need to set the  `lang` argument to `locale()`. See the list of built-in languages in `date_names_langs()`, or if your language is not already included, create your own with `date_names()`.\n\n```{r}\nparse_date(\"1 janvier 2015\", \"%d %B %Y\", locale = locale(\"fr\"))\n```\n\n### Exercises\n\n1.  What are the most important arguments to `locale()`? \n\n1.  What happens if you try and set `decimal_mark` and `grouping_mark` \n    to the same character? What happens to the default value of \n    `grouping_mark` when you set `decimal_mark` to \",\"? What happens\n    to the default value of `decimal_mark` when you set the `grouping_mark`\n    to \".\"?\n\n1.  I didn't discuss the `date_format` and `time_format` options to\n    `locale()`. What do they do? Construct an example that shows when \n    they might be useful.\n\n1.  If you live outside the US, create a new locale object that encapsulates \n    the settings for the types of file you read most commonly.\n    \n1.  What's the difference between `read_csv()` and `read_csv2()`?\n    \n1.  What are the most common encodings used in Europe? What are the\n    most common encodings used in Asia? Do some googling to find out.\n\n1.  Generate the correct format string to parse each of the following \n    dates and times:\n    \n    ```{r}\n    d1 <- \"January 1, 2010\"\n    d2 <- \"2015-Mar-07\"\n    d3 <- \"06-Jun-2017\"\n    d4 <- c(\"August 19 (2015)\", \"July 1 (2015)\")\n    d5 <- \"12/30/14\" # Dec 30, 2014\n    t1 <- \"1705\"\n    t2 <- \"11:15:10.12 PM\"\n    ```\n\n## Parsing a file\n\nNow that you've learned how to parse an individual vector, it's time to return to the beginning and explore how readr parses a file. There are two new things that you'll learn about in this section:\n\n1. How readr automatically guesses the type of each column.\n1. How to override the default specification.\n\n### Strategy\n\nreadr uses a heuristic to figure out the type of each column: it reads the first 1000 rows and uses some (moderately conservative) heuristics to figure out the type of each column. You can emulate this process with a character vector using `guess_parser()`, which returns readr's best guess, and `parse_guess()` which uses that guess to parse the column:\n\n```{r}\nguess_parser(\"2010-10-01\")\nguess_parser(\"15:01\")\nguess_parser(c(\"TRUE\", \"FALSE\"))\nguess_parser(c(\"1\", \"5\", \"9\"))\nguess_parser(c(\"12,352,561\"))\n\nstr(parse_guess(\"2010-10-10\"))\n```\n\nThe heuristic tries each of the following types, stopping when it finds a match:\n\n* logical: contains only \"F\", \"T\", \"FALSE\", or \"TRUE\".\n* integer: contains only numeric characters (and `-`).\n* double: contains only valid doubles (including numbers like `4.5e-5`).\n* number: contains valid doubles with the grouping mark inside.\n* time: matches the default `time_format`.\n* date: matches the default `date_format`.\n* date-time: any ISO8601 date.\n\nIf none of these rules apply, then the column will stay as a vector of strings.\n\n### Problems\n\nThese defaults don't always work for larger files. There are two basic problems:\n\n1.  The first thousand rows might be a special case, and readr guesses\n    a type that is not sufficiently general. For example, you might have \n    a column of doubles that only contains integers in the first 1000 rows. \n\n1.  The column might contain a lot of missing values. If the first 1000\n    rows contain only `NA`s, readr will guess that it's a character \n    vector, whereas you probably want to parse it as something more\n    specific.\n\nreadr contains a challenging CSV that illustrates both of these problems:\n\n```{r}\nchallenge <- read_csv(readr_example(\"challenge.csv\"))\n```\n\n(Note the use of `readr_example()` which finds the path to one of the files included with the package)\n\nThere are two printed outputs: the column specification generated by looking at the first 1000 rows, and the first five parsing failures. It's always a good idea to explicitly pull out the `problems()`, so you can explore them in more depth:\n\n```{r}\nproblems(challenge)\n```\n\nA good strategy is to work column by column until there are no problems remaining. Here we can see that there are a lot of parsing problems with the `x` column - there are trailing characters after the integer value. That suggests we need to use a double parser instead.\n\nTo fix the call, start by copying and pasting the column specification into your original call:\n\n```{r, eval = FALSE}\nchallenge <- read_csv(\n  readr_example(\"challenge.csv\"), \n  col_types = cols(\n    x = col_integer(),\n    y = col_character()\n  )\n)\n```\n\nThen you can tweak the type of the `x` column:\n\n```{r}\nchallenge <- read_csv(\n  readr_example(\"challenge.csv\"), \n  col_types = cols(\n    x = col_double(),\n    y = col_character()\n  )\n)\n```\n\nThat fixes the first problem, but if we look at the last few rows, you'll see that they're dates stored in a character vector:\n\n```{r}\ntail(challenge)\n```\n\nYou can fix that by specifying that `y` is a date column:\n\n```{r}\nchallenge <- read_csv(\n  readr_example(\"challenge.csv\"), \n  col_types = cols(\n    x = col_double(),\n    y = col_date()\n  )\n)\ntail(challenge)\n```\n\nEvery `parse_xyz()` function has a corresponding `col_xyz()` function. You use `parse_xyz()` when the data is in a character vector in R already; you use `col_xyz()` when you want to tell readr how to load the data.\n\nI highly recommend always supplying `col_types`, building up from the print-out provided by readr. This ensures that you have a consistent and reproducible data import script. If you rely on the default guesses and your data changes, readr will continue to read it in. If you want to be really strict, use `stop_for_problems()`: that will throw an error and stop your script if there are any parsing problems.\n\n### Other strategies\n\nThere are a few other general strategies to help you parse files:\n\n*   In the previous example, we just got unlucky: if we look at just\n    one more row than the default, we can correctly parse in one shot:\n   \n    ```{r}\n    challenge2 <- read_csv(readr_example(\"challenge.csv\"), guess_max = 1001)\n    challenge2\n    ```\n\n*   Sometimes it's easier to diagnose problems if you just read in all\n    the columns as character vectors:\n   \n    ```{r}\n    challenge2 <- read_csv(readr_example(\"challenge.csv\"), \n      col_types = cols(.default = col_character())\n    )\n    ```\n    \n    This is particularly useful in conjunction with `type_convert()`,\n    which applies the parsing heuristics to the character columns in a data\n    frame.\n\n    ```{r}\n    df <- tribble(\n      ~x,  ~y,\n      \"1\", \"1.21\",\n      \"2\", \"2.32\",\n      \"3\", \"4.56\"\n    )\n    df\n    \n    # Note the column types\n    type_convert(df)\n    ```\n    \n*   If you're reading a very large file, you might want to set `n_max` to\n    a smallish number like 10,000 or 100,000. That will accelerate your \n    iterations while you eliminate common problems.\n\n*   If you're having major parsing problems, sometimes it's easier\n    to just read into a character vector of lines with `read_lines()`,\n    or even a character vector of length 1 with `read_file()`. Then you\n    can use the string parsing skills you'll learn later to parse\n    more exotic formats.\n\n## Writing to a file\n\nreadr also comes with two useful functions for writing data back to disk: `write_csv()` and `write_tsv()`. Both functions increase the chances of the output file being read back in correctly by:\n\n* Always encoding strings in UTF-8.\n  \n* Saving dates and date-times in ISO8601 format so they are easily\n  parsed elsewhere.\n\nIf you want to export a csv file to Excel, use `write_excel_csv()` --- this writes a special character (a \"byte order mark\") at the start of the file which tells Excel that you're using the UTF-8 encoding.\n\nThe most important arguments are `x` (the data frame to save), and `path` (the location to save it). You can also specify how missing values are written with `na`, and if you want to `append` to an existing file.\n\n```{r, eval = FALSE}\nwrite_csv(challenge, \"challenge.csv\")\n```\n\nNote that the type information is lost when you save to csv:\n\n```{r, warning = FALSE}\nchallenge\nwrite_csv(challenge, \"challenge-2.csv\")\nread_csv(\"challenge-2.csv\")\n```\n\nThis makes CSVs a little unreliable for caching interim results---you need to recreate the column specification every time you load in. There are two alternatives:\n\n1.  `write_rds()` and `read_rds()` are uniform wrappers around the base \n    functions `readRDS()` and `saveRDS()`. These store data in R's custom \n    binary format called RDS:\n    \n    ```{r}\n    write_rds(challenge, \"challenge.rds\")\n    read_rds(\"challenge.rds\")\n    ```\n  \n1.  The feather package implements a fast binary file format that can\n    be shared across programming languages:\n    \n    ```{r, eval = FALSE}\n    library(feather)\n    write_feather(challenge, \"challenge.feather\")\n    read_feather(\"challenge.feather\")\n    #> # A tibble: 2,000 x 2\n    #>       x      y\n    #>   <dbl> <date>\n    #> 1   404   <NA>\n    #> 2  4172   <NA>\n    #> 3  3004   <NA>\n    #> 4   787   <NA>\n    #> 5    37   <NA>\n    #> 6  2332   <NA>\n    #> # ... with 1,994 more rows\n    ```\n\nFeather tends to be faster than RDS and is usable outside of R. RDS supports list-columns (which you'll learn about in [many models]); feather currently does not.\n\n```{r, include = FALSE}\nfile.remove(\"challenge-2.csv\")\nfile.remove(\"challenge.rds\")\n```\n\n## Other types of data\n\nTo get other types of data into R, we recommend starting with the tidyverse packages listed below. They're certainly not perfect, but they are a good place to start. For rectangular data:\n\n* __haven__ reads SPSS, Stata, and SAS files.\n\n* __readxl__ reads excel files (both `.xls` and `.xlsx`).\n\n* __DBI__, along with a database specific backend (e.g. __RMySQL__, \n  __RSQLite__, __RPostgreSQL__ etc) allows you to run SQL queries against a \n  database and return a data frame.\n\nFor hierarchical data: use __jsonlite__ (by Jeroen Ooms) for json, and __xml2__ for XML. Jenny Bryan has some excellent worked examples at <https://jennybc.github.io/purrr-tutorial/>.\n\nFor other file types, try the [R data import/export manual](https://cran.r-project.org/doc/manuals/r-release/R-data.html) and the [__rio__](https://github.com/leeper/rio) package.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/index.rmd",
    "content": "---\nknit: \"bookdown::render_book\"\ntitle: \"R for Data Science\"\nauthor: [\"Garrett Grolemund\", \"Hadley Wickham\"]\ndescription: \"This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.\"\nurl: 'http\\://r4ds.had.co.nz/'\ngithub-repo: hadley/r4ds\ntwitter-handle: hadley\ncover-image: cover.png\nsite: bookdown::bookdown_site\ndocumentclass: book\n---\n\n# Welcome {-}\n\nThis is the website for __\"R for Data Science\"__. This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data. \n\nWant a physical copy of this material?\n\nPublished by O'Reilly January 2017 First Edition. Order from [amazon](http://amzn.to/2aHLAQ1).\n\n<img src=\"cover.png\" width=\"250\" height=\"375\" alt=\"Cover image\" />\n\n(R for Data Science was formerly called Data Science with R in Hands-On Programming with R)\n\nThis work is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivs 3.0](http://creativecommons.org/licenses/by-nc-nd/3.0/us/) United States License. \n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/intro.Rmd",
    "content": "# Introduction\n\nData science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of \"R for Data Science\" is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R. \n\n## What you will learn\n\nData science is a huge field, and there's no way you can master it by reading a single book. The goal of this book is to give you a solid foundation in the most important tools. Our model of the tools needed in a typical data science project looks something like this:\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science.png\")\n```\n\nFirst you must __import__ your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it!\n\nOnce you've imported your data, it is a good idea to __tidy__ it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions.\n\nOnce you have tidy data, a common first step is to __transform__ it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing velocity from speed and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called __wrangling__, because getting your data in a form that's natural to work with often feels like a fight!\n\nOnce you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.\n\n__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you're asking the wrong question, or you need to collect different data. Visualisations can surprise you, but don't scale particularly well because they require a human to interpret them.\n\n__Models__ are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well. Even when they don't, it's usually cheaper to buy more computers than it is to buy more brains! But every model makes assumptions, and by its very nature a model cannot question its own assumptions. That means a model cannot fundamentally surprise you.\n\nThe last step of data science is __communication__, an absolutely critical part of any data analysis project. It doesn't matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.\n\nSurrounding all these tools is __programming__. Programming is a cross-cutting tool that you use in every part of the project. You don't need to be an expert programmer to be a data scientist, but learning more about programming pays off because becoming a better programmer allows you to automate common tasks, and solve new problems with greater ease.\n\nYou'll use these tools in every data science project, but for most projects they're not enough. There's a rough 80-20 rule at play; you can tackle about 80% of every project using the tools that you'll learn in this book, but you'll need other tools to tackle the remaining 20%. Throughout this book we'll point you to resources where you can learn more.\n\n## How this book is organised\n\nThe previous description of the tools of data science is organised roughly according to the order in which you use them in an analysis (although of course you'll iterate through them multiple times). In our experience, however, this is not the best way to learn them:\n\n* Starting with data ingest and tidying is sub-optimal because 80% of the time \n  it's routine and boring, and the other 20% of the time it's weird and\n  frustrating. That's a bad place to start learning a new subject! Instead, \n  we'll start with visualisation and transformation of data that's already been\n  imported and tidied. That way, when you ingest and tidy your own data, your\n  motivation will stay high because you know the pain is worth it.\n  \n* Some topics are best explained with other tools. For example, we believe that\n  it's easier to understand how models work if you already know about \n  visualisation, tidy data, and programming.\n  \n* Programming tools are not necessarily interesting in their own right, \n  but do allow you to tackle considerably more challenging problems. We'll\n  give you a selection of programming tools in the middle of the book, and \n  then you'll see how they can combine with the data science tools to tackle \n  interesting modelling problems.\n\nWithin each chapter, we try and stick to a similar pattern: start with some motivating examples so you can see the bigger picture, and then dive into the details. Each section of the book is paired with exercises to help you practice what you've learned. While it's tempting to skip the exercises, there's no better way to learn than practicing on real problems.\n\n## What you won't learn\n\nThere are some important topics that this book doesn't cover. We believe it's important to stay ruthlessly focused on the essentials so you can get up and running as quickly as possible. That means this book can't cover every important topic.\n\n### Big data\n\nThis book proudly focuses on small, in-memory datasets. This is the right place to start because you can't tackle big data unless you have experience with small data. The tools you learn in this book will easily handle hundreds of megabytes of data, and with a little care you can typically use them to work with 1-2 Gb of data. If you're routinely working with larger data (10-100 Gb, say), you should learn more about [data.table](https://github.com/Rdatatable/data.table). This book doesn't teach data.table because it has a very concise interface which makes it harder to learn since it offers fewer linguistic cues. But if you're working with large data, the performance payoff is worth the extra effort required to learn it.\n\nIf your data is bigger than this, carefully consider if your big data problem might actually be a small data problem in disguise. While the complete data might be big, often the data needed to answer a specific question is small. You might be able to find a subset, subsample, or summary that fits in memory and still allows you to answer the question that you're interested in. The challenge here is finding the right small data, which often requires a lot of iteration.\n\nAnother possibility is that your big data problem is actually a large number of small data problems. Each individual problem might fit in memory, but you have millions of them. For example, you might want to fit a model to each person in your dataset. That would be trivial if you had just 10 or 100 people, but instead you have a million. Fortunately each problem is independent of the others (a setup that is sometimes called embarrassingly parallel), so you just need a system (like Hadoop or Spark) that allows you to send different datasets to different computers for processing. Once you've figured out how to answer the question for a single subset using the tools described in this book, you learn new tools like sparklyr, rhipe, and ddr to solve it for the full dataset.\n\n### Python, Julia, and friends\n\nIn this book, you won't learn anything about Python, Julia, or any other programming language useful for data science. This isn't because we think these tools are bad. They're not! And in practice, most data science teams use a mix of languages, often at least R and Python.\n\nHowever, we strongly believe that it's best to master one tool at a time. You will get better faster if you dive deep, rather than spreading yourself thinly over many topics. This doesn't mean you should only know one thing, just that you'll generally learn faster if you stick to one thing at a time. You should strive to learn new things throughout your career, but make sure your understanding is solid before you move on to the next interesting thing.\n\nWe think R is a great place to start your data science journey because it is an environment designed from the ground up to support data science. R is not just a programming language, but it is also an interactive environment for doing data science. To support interaction, R is a much more flexible language than many of its peers. This flexibility comes with its downsides, but the big upside is how easy it is to evolve tailored grammars for specific parts of the data science process. These mini languages help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.\n\n### Non-rectangular data\n\nThis book focuses exclusively on rectangular data: collections of values that are each associated with a variable and an observation. There are lots of datasets that do not naturally fit in this paradigm: including images, sounds, trees, and text. But rectangular data frames are extremely common in science and industry, and we believe that they are a great place to start your data science journey.\n\n### Hypothesis confirmation\n\nIt's possible to divide data analysis into two camps: hypothesis generation and hypothesis confirmation (sometimes called confirmatory analysis). The focus of this book is unabashedly on hypothesis generation, or data exploration. Here you'll look deeply at the data and, in combination with your subject knowledge, generate many interesting hypotheses to help explain why the data behaves the way it does. You evaluate the hypotheses informally, using your scepticism to challenge the data in multiple ways.\n\nThe complement of hypothesis generation is hypothesis confirmation. Hypothesis confirmation is hard for two reasons:\n\n1.  You need a precise mathematical model in order to generate falsifiable\n    predictions. This often requires considerable statistical sophistication.\n\n1.  You can only use an observation once to confirm a hypothesis. As soon as\n    you use it more than once you're back to doing exploratory analysis. \n    This means to do hypothesis confirmation you need to \"preregister\" \n    (write out in advance) your analysis plan, and not deviate from it\n    even when you have seen the data. We'll talk a little about some \n    strategies you can use to make this easier in [modelling](#model-intro).\n\nIt's common to think about modelling as a tool for hypothesis confirmation, and visualisation as a tool for hypothesis generation. But that's a false dichotomy: models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it's confirmation; if you look more than once, it's exploration.\n\n## Prerequisites\n\nWe've made a few assumptions about what you already know in order to get the most out of this book. You should be generally numerically literate, and it's helpful if you have some programming experience already. If you've never programmed before, you might find [Hands on Programming with R](http://amzn.com/1449359019) by Garrett to be a useful adjunct to this book.\n\nThere are four things you need to run the code in this book: R, RStudio, a collection of R packages called the __tidyverse__, and a handful of other packages. Packages are the fundamental units of reproducible R code. They include reusable functions, the documentation that describes how to use them, and sample data. \n\n### R \n\nTo download R, go to CRAN, the **c**omprehensive **R** **a**rchive **n**etwork. CRAN is composed of a set of mirror servers distributed around the world and is used to distribute R and R packages. Don't try and pick a mirror that's close to you: instead use the cloud mirror, <https://cloud.r-project.org>, which automatically figures it out for you.\n\nA new major version of R comes out once a year, and there are 2-3 minor releases each year. It's a good idea to update regularly. Upgrading can be a bit of a hassle, especially for major versions, which require you to reinstall all your packages, but putting it off only makes it worse.\n\n### RStudio\n\nRStudio is an integrated development environment, or IDE, for R programming. Download and install it from <http://www.rstudio.com/download>. RStudio is updated a couple of times a year. When a new version is available, RStudio will let you know. It's a good idea to upgrade regularly so you can take advantage of the latest and greatest features. For this book, make sure you have RStudio 1.0.0.\n\nWhen you start RStudio, you'll see two key regions in the interface:\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/rstudio-console.png\")\n```\n\nFor now, all you need to know is that you type R code in the console pane, and press enter to run it. You'll learn more as we go along!\n\n### The tidyverse\n\nYou'll also need to install some R packages. An R __package__ is a collection of functions, data, and documentation that extends the capabilities of base R. Using packages is key to the successful use of R. The majority of the packages that you will learn in this book are part of the so-called tidyverse. The packages in the tidyverse share a common philosophy of data and R programming, and are designed to work together naturally. \n\nYou can install the complete tidyverse with a single line of code:\n\n```{r, eval = FALSE}\ninstall.packages(\"tidyverse\")\n```\n\nOn your own computer, type that line of code in the console, and then press enter to run it. R will download the packages from CRAN and install them on to your computer. If you have problems installing, make sure that you are connected to the internet, and that <https://cloud.r-project.org/> isn't blocked by your firewall or proxy. \n\nYou will not be able to use the functions, objects, and help files in a package until you load it with `library()`. Once you have installed a package, you can load it with the `library()` function:\n\n```{r}\nlibrary(tidyverse)\n```\n\nThis tells you that tidyverse is loading the ggplot2, tibble, readr, purrr, and dplyr packages. These are considered to be the __core__ of the tidyverse because you'll use them in almost every analysis. \n\nPackages in the tidyverse change fairly frequently. You can see if updates are available, and optionally install them, by running `tidyverse_update()`.\n\n### Other packages\n\nThere are many other excellent packages that are not part of the tidyverse, because they solve problems in a different domain, or are designed with a different set of underlying principles. This doesn't make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R, you'll learn new packages and new ways of thinking about data. \n\n\n## Running R code\n\nThe previous section showed you a couple of examples of running R code. Code in the book looks like this:\n\n```{r, eval = TRUE}\n1 + 2\n#> [1] 3\n```\n\nIf you run the same code in your local console, it will look like this:\n\n```\n> 1 + 2\n[1] 3\n```\n\nThere are two main differences. In your console, you type after the `>`, called the __prompt__; we don't show the prompt in the book. In the book, output is commented out with `#>`; in your console it appears directly after your code. These two differences mean that if you're working with an electronic version of the book, you can easily copy code out of the book and into the console.\n\nThroughout the book we use a consistent set of conventions to refer to code:\n\n* Functions are in a code font and followed by parentheses, like `sum()`, \n  or `mean()`.\n\n* Other R objects (like data or function arguments) are in a code font,\n  without parentheses, like `flights` or `x`.\n  \n* If we want to make it clear what package an object comes from, we'll use\n  the package name followed by two colons, like `dplyr::mutate()`, or   \n  `nycflights13::flights`. This is also valid R code.\n\n## Getting help and learning more\n\nThis book is not an island; there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that I do not answer. This section describes a few tips on how to get help, and to help you keep learning.\n\nIf you get stuck, start with Google. Typically adding \"R\" to a query is enough to restrict it to relevant results: if the search isn't useful, it often means that there aren't any R-specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn't in English, run `Sys.setenv(LANGUAGE = \"en\")` and re-run the code; you're more likely to find help for English error messages.)\n\nIf Google doesn't help, try [stackoverflow](http://stackoverflow.com). Start by spending a little time searching for an existing answer, including `[R]` to restrict your search to questions and answers that use R. If you don't find anything useful, prepare a minimal reproducible example or __reprex__.  A good reprex makes it easier for other people to help you, and often you'll figure out the problem yourself in the course of making it.\n\nThere are three things you need to include to make your example reproducible: required packages, data, and code.\n\n1.  **Packages** should be loaded at the top of the script, so it's easy to\n    see which ones the example needs. This is a good time to check that you're\n    using the latest version of each package; it's possible you've discovered\n    a bug that's been fixed since you installed the package. For packages\n    in the tidyverse, the easiest way to check is to run `tidyverse_update()`.\n\n1.  The easiest way to include **data** in a question is to use `dput()` to \n    generate the R code to recreate it. For example, to recreate the `mtcars` \n    dataset in R, I'd perform the following steps:\n  \n    1. Run `dput(mtcars)` in R\n    2. Copy the output\n    3. In my reproducible script, type `mtcars <- ` then paste.\n    \n    Try and find the smallest subset of your data that still reveals\n    the problem.\n\n1.  Spend a little bit of time ensuring that your **code** is easy for others to\n    read:\n\n    * Make sure you've used spaces and your variable names are concise, yet\n      informative.\n    \n    * Use comments to indicate where your problem lies.\n    \n    * Do your best to remove everything that is not related to the problem.  \n      The shorter your code is, the easier it is to understand, and the \n      easier it is to fix.\n\nFinish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in. \n\nYou should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way is to follow what Hadley, Garrett, and everyone else at RStudio are doing on the [RStudio blog](https://blog.rstudio.org). This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley ([\\@hadleywickham](https://twitter.com/hadleywickham)) or Garrett ([\\@statgarrett](https://twitter.com/statgarrett)) on Twitter, or follow [\\@rstudiotips](https://twitter.com/rstudiotips) to keep up with new features in the IDE.\n\nTo keep up with the R community more broadly, we recommend reading <http://www.r-bloggers.com>: it aggregates over 500 blogs about R from around the world. If you're an active Twitter user, follow the `#rstats` hashtag. Twitter is one of the key tools that Hadley uses to keep up with new developments in the community.\n\n## Acknowledgements\n\nThis book isn't just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we've had with the many people in the R community. There are a few people we'd like to thank in particular, because they have spent many hours answering our dumb questions and helping us to better think about data science:\n\n* Jenny Bryan and Lionel Henry for many helpful discussions around working\n  with lists and list-columns.\n  \n* The three chapters on workflow were adapted (with permission), from\n  <http://stat545.com/block002_hello-r-workspace-wd-project.html> by \n  Jenny Bryan.\n\n* Genevera Allen for discussions about models, modelling, the statistical\n  learning perspective, and the difference between hypothesis generation and \n  hypothesis confirmation.\n\n* Yihui Xie for his work on the [bookdown](https://github.com/rstudio/bookdown) \n  package, and for tirelessly responding to my feature requests.\n\n* Bill Behrman for his thoughtful reading of the entire book, and for trying \n  it out with his data science class at Stanford.\n\n* The \\#rstats twitter community who reviewed all of the draft chapters\n  and provided tons of useful feedback.\n\n* Tal Galili for augmenting his dendextend package to support a section on clustering that did not make it into the final draft.\n\nThis book was written in the open, and many people contributed pull requests to fix minor problems. Special thanks goes to everyone who contributed via GitHub: \n\n```{r, results = \"asis\", echo = FALSE, message = FALSE}\nlibrary(dplyr)\n# git --no-pager shortlog -ns > contribs.txt\ncontribs <- readr::read_tsv(\"contribs.txt\", col_names = c(\"n\", \"name\"))\n\ncontribs <- contribs %>% \n  filter(!name %in% c(\"hadley\", \"Garrett\", \"Hadley Wickham\",\n                      \"Garrett Grolemund\")) %>% \n  arrange(name) %>% \n  mutate(uname = ifelse(!grepl(\" \", name), paste0(\"@\", name), name))\n\ncat(\"Thanks go to all contributers in alphabetical order: \")\ncat(paste0(contribs$uname, collapse = \", \"))\ncat(\".\\n\")\n```\n\n## Colophon\n\nThis book was built with:\n\n```{r}\ndevtools::session_info(c(\"tidyverse\"))\n```\n"
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Not sure if that was just an error that crept in while rewriting and fiddling around with the sentence or a conscious decision from you.\\r\\nJust corrected a couple of obvious typos apart from that. \"\n  },\n  {\n    \"url\": \"https://api.github.com/repos/hadley/r4ds/issues/7\",\n    \"labels_url\": \"https://api.github.com/repos/hadley/r4ds/issues/7/labels{/name}\",\n    \"comments_url\": \"https://api.github.com/repos/hadley/r4ds/issues/7/comments\",\n    \"events_url\": \"https://api.github.com/repos/hadley/r4ds/issues/7/events\",\n    \"html_url\": \"https://github.com/hadley/r4ds/pull/7\",\n    \"id\": 110795521,\n    \"number\": 7,\n    \"title\": \"howver -> however\",\n    \"user\": {\n      \"login\": \"benmarwick\",\n      \"id\": 1262179,\n      \"avatar_url\": \"https://avatars.githubusercontent.com/u/1262179?v=3\",\n      \"gravatar_id\": \"\",\n      \"url\": \"https://api.github.com/users/benmarwick\",\n      \"html_url\": \"https://github.com/benmarwick\",\n      \"followers_url\": \"https://api.github.com/users/benmarwick/followers\",\n      \"following_url\": \"https://api.github.com/users/benmarwick/following{/other_user}\",\n      \"gists_url\": \"https://api.github.com/users/benmarwick/gists{/gist_id}\",\n      \"starred_url\": \"https://api.github.com/users/benmarwick/starred{/owner}{/repo}\",\n      \"subscriptions_url\": \"https://api.github.com/users/benmarwick/subscriptions\",\n      \"organizations_url\": \"https://api.github.com/users/benmarwick/orgs\",\n      \"repos_url\": \"https://api.github.com/users/benmarwick/repos\",\n      \"events_url\": \"https://api.github.com/users/benmarwick/events{/privacy}\",\n      \"received_events_url\": \"https://api.github.com/users/benmarwick/received_events\",\n      \"type\": \"User\",\n      \"site_admin\": false\n    },\n    \"labels\": [\n\n    ],\n    \"state\": \"open\",\n    \"locked\": false,\n    \"assignee\": null,\n    \"milestone\": null,\n    \"comments\": 0,\n    \"created_at\": \"2015-10-10T13:46:39Z\",\n    \"updated_at\": \"2015-10-10T13:46:39Z\",\n    \"closed_at\": null,\n    \"pull_request\": {\n      \"url\": \"https://api.github.com/repos/hadley/r4ds/pulls/7\",\n      \"html_url\": \"https://github.com/hadley/r4ds/pull/7\",\n      \"diff_url\": \"https://github.com/hadley/r4ds/pull/7.diff\",\n      \"patch_url\": \"https://github.com/hadley/r4ds/pull/7.patch\"\n    },\n    \"body\": \"Thanks for making this open access. I look forward to seeing the rest of the chapters!\"\n  },\n  {\n    \"url\": \"https://api.github.com/repos/hadley/r4ds/issues/6\",\n    \"labels_url\": \"https://api.github.com/repos/hadley/r4ds/issues/6/labels{/name}\",\n    \"comments_url\": \"https://api.github.com/repos/hadley/r4ds/issues/6/comments\",\n    \"events_url\": \"https://api.github.com/repos/hadley/r4ds/issues/6/events\",\n    \"html_url\": \"https://github.com/hadley/r4ds/pull/6\",\n    \"id\": 109680972,\n    \"number\": 6,\n    \"title\": \"typos, wording for import.Rmd\",\n    \"user\": {\n      \"login\": \"datalove\",\n      \"id\": 222907,\n      \"avatar_url\": \"https://avatars.githubusercontent.com/u/222907?v=3\",\n      \"gravatar_id\": \"\",\n      \"url\": \"https://api.github.com/users/datalove\",\n      \"html_url\": \"https://github.com/datalove\",\n      \"followers_url\": \"https://api.github.com/users/datalove/followers\",\n      \"following_url\": \"https://api.github.com/users/datalove/following{/other_user}\",\n      \"gists_url\": \"https://api.github.com/users/datalove/gists{/gist_id}\",\n      \"starred_url\": \"https://api.github.com/users/datalove/starred{/owner}{/repo}\",\n      \"subscriptions_url\": \"https://api.github.com/users/datalove/subscriptions\",\n      \"organizations_url\": \"https://api.github.com/users/datalove/orgs\",\n      \"repos_url\": \"https://api.github.com/users/datalove/repos\",\n      \"events_url\": \"https://api.github.com/users/datalove/events{/privacy}\",\n      \"received_events_url\": \"https://api.github.com/users/datalove/received_events\",\n      \"type\": \"User\",\n      \"site_admin\": false\n    },\n    \"labels\": [\n\n    ],\n    \"state\": \"open\",\n    \"locked\": false,\n    \"assignee\": null,\n    \"milestone\": null,\n    \"comments\": 0,\n    \"created_at\": \"2015-10-04T13:23:06Z\",\n    \"updated_at\": \"2015-10-04T13:43:31Z\",\n    \"closed_at\": null,\n    \"pull_request\": {\n      \"url\": \"https://api.github.com/repos/hadley/r4ds/pulls/6\",\n      \"html_url\": \"https://github.com/hadley/r4ds/pull/6\",\n      \"diff_url\": \"https://github.com/hadley/r4ds/pull/6.diff\",\n      \"patch_url\": \"https://github.com/hadley/r4ds/pull/6.patch\"\n    },\n    \"body\": \"Hi Hadley, I made a few changes here, fixing some typos that I found. I've also proposed a few minor changes where I thought the wording could be improved for clarity.\"\n  },\n  {\n    \"url\": \"https://api.github.com/repos/hadley/r4ds/issues/5\",\n    \"labels_url\": \"https://api.github.com/repos/hadley/r4ds/issues/5/labels{/name}\",\n    \"comments_url\": \"https://api.github.com/repos/hadley/r4ds/issues/5/comments\",\n    \"events_url\": \"https://api.github.com/repos/hadley/r4ds/issues/5/events\",\n    \"html_url\": \"https://github.com/hadley/r4ds/issues/5\",\n    \"id\": 107925580,\n    \"number\": 5,\n    \"title\": \"Do we also need \\\"export\\\" chapter?\",\n    \"user\": {\n      \"login\": \"hadley\",\n      \"id\": 4196,\n      \"avatar_url\": \"https://avatars.githubusercontent.com/u/4196?v=3\",\n      \"gravatar_id\": \"\",\n      \"url\": \"https://api.github.com/users/hadley\",\n      \"html_url\": \"https://github.com/hadley\",\n      \"followers_url\": \"https://api.github.com/users/hadley/followers\",\n      \"following_url\": \"https://api.github.com/users/hadley/following{/other_user}\",\n      \"gists_url\": \"https://api.github.com/users/hadley/gists{/gist_id}\",\n      \"starred_url\": \"https://api.github.com/users/hadley/starred{/owner}{/repo}\",\n      \"subscriptions_url\": \"https://api.github.com/users/hadley/subscriptions\",\n      \"organizations_url\": \"https://api.github.com/users/hadley/orgs\",\n      \"repos_url\": \"https://api.github.com/users/hadley/repos\",\n      \"events_url\": \"https://api.github.com/users/hadley/events{/privacy}\",\n      \"received_events_url\": \"https://api.github.com/users/hadley/received_events\",\n      \"type\": \"User\",\n      \"site_admin\": false\n    },\n    \"labels\": [\n\n    ],\n    \"state\": \"open\",\n    \"locked\": false,\n    \"assignee\": null,\n    \"milestone\": null,\n    \"comments\": 1,\n    \"created_at\": \"2015-09-23T13:57:33Z\",\n    \"updated_at\": \"2015-09-23T14:23:32Z\",\n    \"closed_at\": null,\n    \"body\": \"Rmarkdown and shiny chapters will talk about communicating with other humans, but it's probably worthwhile to think about what a chapter about communicating with other programs might look like.  By parallel to the import section, it might contain:\\r\\n\\r\\n* saving csv files\\r\\n* loading data into a database\\r\\n* exporting to excel, spss, sas, etc.\\r\\n* uploading data to a web api\\r\\n\\r\\n(To be comprehensive, it would probably need a decent amount of software engineering, since, e.g., readxl currently doesn't do exports)\"\n  },\n  {\n    \"url\": \"https://api.github.com/repos/hadley/r4ds/issues/4\",\n    \"labels_url\": \"https://api.github.com/repos/hadley/r4ds/issues/4/labels{/name}\",\n    \"comments_url\": \"https://api.github.com/repos/hadley/r4ds/issues/4/comments\",\n    \"events_url\": \"https://api.github.com/repos/hadley/r4ds/issues/4/events\",\n    \"html_url\": \"https://github.com/hadley/r4ds/issues/4\",\n    \"id\": 107506216,\n    \"number\": 4,\n    \"title\": \"Make r4ds package\",\n    \"user\": {\n      \"login\": \"hadley\",\n      \"id\": 4196,\n      \"avatar_url\": \"https://avatars.githubusercontent.com/u/4196?v=3\",\n      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  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/iteration.Rmd",
    "content": "# Iteration\n\n## Introduction\n\nIn [functions], we talked about how important it is to reduce duplication in your code by creating functions instead of copying-and-pasting. Reducing code duplication has three main benefits:\n\n1.  It's easier to see the intent of your code, because your eyes are\n    drawn to what's different, not what stays the same.\n    \n1.  It's easier to respond to changes in requirements. As your needs \n    change, you only need to make changes in one place, rather than\n    remembering to change every place that you copied-and-pasted the \n    code.\n    \n1.  You're likely to have fewer bugs because each line of code is \n    used in more places.\n\nOne tool for reducing duplication is functions, which reduce duplication by identifying repeated patterns of code and extract them out into independent pieces that can be easily reused and updated. Another tool for reducing duplication is __iteration__, which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. \nIn this chapter you'll learn about two important iteration paradigms: imperative programming and functional programming. On the imperative side you have tools like for loops and while loops, which are a great place to start because they make iteration very explicit, so it's obvious what's happening. However, for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop. Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors.\n\n### Prerequisites\n\nOnce you've mastered the for loops provided by base R, you'll learn some of the powerful programming tools provided by purrr, one of the tidyverse core packages.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n```\n\n## For loops\n\nImagine we have this simple tibble:\n\n```{r}\ndf <- tibble(\n  a = rnorm(10),\n  b = rnorm(10),\n  c = rnorm(10),\n  d = rnorm(10)\n)\n```\n\nWe want to compute the median of each column. You _could_ do with copy-and-paste:\n\n```{r}\nmedian(df$a)\nmedian(df$b)\nmedian(df$c)\nmedian(df$d)\n```\n\nBut that breaks our rule of thumb: never copy and paste more than twice. Instead, we could use a for loop:\n\n```{r}\noutput <- vector(\"double\", ncol(df))  # 1. output\nfor (i in seq_along(df)) {            # 2. sequence\n  output[[i]] <- median(df[[i]])      # 3. body\n}\noutput\n```\n\nEvery for loop has three components:\n\n1.  The __output__: `output <- vector(\"double\", length(x))`. \n    Before you start the loop, you must always allocate sufficient space \n    for the output. This is very important for efficiency: if you grow\n    the for loop at each iteration using `c()` (for example), your for loop \n    will be very slow. \n    \n    A general way of creating an empty vector of given length is the `vector()`\n    function. It has two arguments: the type of the vector (\"logical\", \n    \"integer\", \"double\", \"character\", etc) and the length of the vector. \n\n1.  The __sequence__: `i in seq_along(df)`. This determines what to loop over:\n    each run of the for loop will assign `i` to a different value from \n    `seq_along(df)`. It's useful to think of `i` as a pronoun, like \"it\".\n    \n    You might not have seen `seq_along()` before. It's a safe version of the \n    familiar `1:length(l)`, with an important difference: if you have a\n    zero-length vector, `seq_along()` does the right thing:\n\n    ```{r}\n    y <- vector(\"double\", 0)\n    seq_along(y)\n    1:length(y)\n    ```\n    \n    You probably won't create a zero-length vector deliberately, but\n    it's easy to create them accidentally. If you use `1:length(x)` instead\n    of `seq_along(x)`, you're likely to get a confusing error message.\n    \n1.  The __body__: `output[[i]] <- median(df[[i]])`. This is the code that does\n    the work. It's run repeatedly, each time with a different value for `i`.\n    The first iteration will run `output[[1]] <- median(df[[1]])`, \n    the second will run `output[[2]] <- median(df[[2]])`, and so on.\n\nThat's all there is to the for loop! Now is a good time to practice creating some basic (and not so basic) for loops using the exercises below. Then we'll move on some variations of the for loop that help you solve other problems that will crop up in practice. \n\n### Exercises\n\n1.  Write for loops to:\n\n    1. Compute the mean of every column in `mtcars`.\n    1. Determine the type of each column in `nycflights13::flights`.\n    1. Compute the number of unique values in each column of `iris`.\n    1. Generate 10 random normals for each of $\\mu = -10$, $0$, $10$, and $100$.\n    \n    Think about the output, sequence, and body __before__ you start writing\n    the loop.\n\n1.  Eliminate the for loop in each of the following examples by taking \n    advantage of an existing function that works with vectors:\n    \n    ```{r, eval = FALSE}\n    out <- \"\"\n    for (x in letters) {\n      out <- stringr::str_c(out, x)\n    }\n    \n    x <- sample(100)\n    sd <- 0\n    for (i in seq_along(x)) {\n      sd <- sd + (x[i] - mean(x)) ^ 2\n    }\n    sd <- sqrt(sd / (length(x) - 1))\n    \n    x <- runif(100)\n    out <- vector(\"numeric\", length(x))\n    out[1] <- x[1]\n    for (i in 2:length(x)) {\n      out[i] <- out[i - 1] + x[i]\n    }\n    ```\n\n1.  Combine your function writing and for loop skills:\n\n    1. Write a for loop that `prints()` the lyrics to the children's song \n       \"Alice the camel\".\n\n    1. Convert the nursery rhyme \"ten in the bed\" to a function. Generalise \n       it to any number of people in any sleeping structure.\n\n    1. Convert the song \"99 bottles of beer on the wall\" to a function.\n       Generalise to any number of any vessel containing any liquid on \n       any surface.\n\n1.  It's common to see for loops that don't preallocate the output and instead\n    increase the length of a vector at each step:\n    \n    ```{r, eval = FALSE}\n    output <- vector(\"integer\", 0)\n    for (i in seq_along(x)) {\n      output <- c(output, lengths(x[[i]]))\n    }\n    output\n    ```\n    \n    How does this affect performance? Design and execute an experiment.\n\n## For loop variations\n\nOnce you have the basic for loop under your belt, there are some variations that you should be aware of. These variations are important regardless of how you do iteration, so don't forget about them once you've mastered the FP techniques you'll learn about in the next section.\n\nThere are four variations on the basic theme of the for loop:\n\n1.  Modifying an existing object, instead of creating a new object.\n1.  Looping over names or values, instead of indices.\n1.  Handling outputs of unknown length.\n1.  Handling sequences of unknown length.\n\n### Modifying an existing object\n\nSometimes you want to use a for loop to modify an existing object. For example, remember our challenge from [functions]. We wanted to rescale every column in a data frame:\n\n```{r}\ndf <- tibble(\n  a = rnorm(10),\n  b = rnorm(10),\n  c = rnorm(10),\n  d = rnorm(10)\n)\nrescale01 <- function(x) {\n  rng <- range(x, na.rm = TRUE)\n  (x - rng[1]) / (rng[2] - rng[1])\n}\n\ndf$a <- rescale01(df$a)\ndf$b <- rescale01(df$b)\ndf$c <- rescale01(df$c)\ndf$d <- rescale01(df$d)\n```\n\nTo solve this with a for loop we again think about the three components:\n\n1.  __Output__: we already have the output --- it's the same as the input!\n\n1.  __Sequence__: we can think about a data frame as a list of columns, so \n    we can iterate over each column with `seq_along(df)`.\n\n1.  __Body__: apply `rescale01()`.\n\nThis gives us:\n\n```{r}\nfor (i in seq_along(df)) {\n  df[[i]] <- rescale01(df[[i]])\n}\n```\n\nTypically you'll be modifying a list or data frame with this sort of loop, so remember to use `[[`, not `[`. You might have spotted that I used `[[` in all my for loops: I think it's better to use `[[` even for atomic vectors because it makes it clear that I want to work with a single element.\n\n### Looping patterns\n\nThere are three basic ways to loop over a vector. So far I've shown you the most general: looping over the numeric indices with `for (i in seq_along(xs))`, and extracting the value with `x[[i]]`. There are two other forms:\n\n1.  Loop over the elements: `for (x in xs)`. This is most useful if you only\n    care about side-effects, like plotting or saving a file, because it's\n    difficult to save the output efficiently.\n\n1.  Loop over the names: `for (nm in names(xs))`. This gives you name, which\n    you can use to access the value with `x[[nm]]`. This is useful if you want \n    to use the name in a plot title or a file name. If you're creating\n    named output, make sure to name the results vector like so:\n    \n    ```{r, eval = FALSE}\n    results <- vector(\"list\", length(x))\n    names(results) <- names(x)\n    ```\n\nIteration over the numeric indices is the most general form, because given the position you can extract both the name and the value:\n\n```{r, eval = FALSE}\nfor (i in seq_along(x)) {\n  name <- names(x)[[i]]\n  value <- x[[i]]\n}\n```\n\n### Unknown output length\n\nSometimes you might not know how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:\n\n```{r}\nmeans <- c(0, 1, 2)\n\noutput <- double()\nfor (i in seq_along(means)) {\n  n <- sample(100, 1)\n  output <- c(output, rnorm(n, means[[i]]))\n}\nstr(output)\n```\n\nBut this is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get \"quadratic\" ($O(n^2)$) behaviour which means that a loop with three times as many elements would take nine ($3^2$) times as long to run.\n\nA better solution to save the results in a list, and then combine into a single vector after the loop is done:\n\n```{r}\nout <- vector(\"list\", length(means))\nfor (i in seq_along(means)) {\n  n <- sample(100, 1)\n  out[[i]] <- rnorm(n, means[[i]])\n}\nstr(out)\nstr(unlist(out))\n```\n\nHere I've used `unlist()` to flatten a list of vectors into a single vector. A stricter option is to use `purrr::flatten_dbl()` --- it will throw an error if the input isn't a list of doubles.\n\nThis pattern occurs in other places too:\n\n1.  You might be generating a long string. Instead of `paste()`ing together \n    each iteration with the previous, save the output in a character vector and\n    then combine that vector into a single string with \n    `paste(output, collapse = \"\")`.\n   \n1.  You might be generating a big data frame. Instead of sequentially\n    `rbind()`ing in each iteration, save the output in a list, then use \n    `dplyr::bind_rows(output)` to combine the output into a single\n    data frame.\n\nWatch out for this pattern. Whenever you see it, switch to a more complex result object, and then combine in one step at the end.\n\n### Unknown sequence length\n\nSometimes you don't even know how long the input sequence should run for. This is common when doing simulations. For example, you might want to loop until you get three heads in a row. You can't do that sort of iteration with the for loop. Instead, you can use a while loop. A while loop is simpler than for loop because it only has two components, a condition and a body:\n\n```{r, eval = FALSE}\nwhile (condition) {\n  # body\n}\n```\n\nA while loop is also more general than a for loop, because you can rewrite any for loop as a while loop, but you can't rewrite every while loop as a for loop:\n\n```{r, eval = FALSE}\nfor (i in seq_along(x)) {\n  # body\n}\n\n# Equivalent to\ni <- 1\nwhile (i <= length(x)) {\n  # body\n  i <- i + 1 \n}\n```\n\nHere's how we could use a while loop to find how many tries it takes to get three heads in a row:\n\n```{r}\nflip <- function() sample(c(\"T\", \"H\"), 1)\n\nflips <- 0\nnheads <- 0\n\nwhile (nheads < 3) {\n  if (flip() == \"H\") {\n    nheads <- nheads + 1\n  } else {\n    nheads <- 0\n  }\n  flips <- flips + 1\n}\nflips\n```\n\nI mention while loops only briefly, because I hardly ever use them. They're most often used for simulation, which is outside the scope of this book. However, it is good to know they exist so that you're prepared for problems where the number of iterations is not known in advance.\n\n### Exercises\n\n1.  Imagine you have a directory full of CSV files that you want to read in.\n    You have their paths in a vector, \n    `files <- dir(\"data/\", pattern = \"\\\\.csv$\", full.names = TRUE)`, and now\n    want to read each one with `read_csv()`. Write the for loop that will \n    load them into a single data frame. \n\n1.  What happens if you use `for (nm in names(x))` and `x` has no names?\n    What if only some of the elements are named? What if the names are\n    not unique?\n\n1.  Write a function that prints the mean of each numeric column in a data \n    frame, along with its name. For example, `show_mean(iris)` would print:\n    \n    ```{r, eval = FALSE}\n    show_mean(iris)\n    #> Sepal.Length: 5.84\n    #> Sepal.Width:  3.06\n    #> Petal.Length: 3.76\n    #> Petal.Width:  1.20\n    ```\n    \n    (Extra challenge: what function did I use to make sure that the numbers\n    lined up nicely, even though the variable names had different lengths?)\n\n1.  What does this code do? How does it work?\n\n    ```{r, eval = FALSE}\n    trans <- list( \n      disp = function(x) x * 0.0163871,\n      am = function(x) {\n        factor(x, labels = c(\"auto\", \"manual\"))\n      }\n    )\n    for (var in names(trans)) {\n      mtcars[[var]] <- trans[[var]](mtcars[[var]])\n    }\n    ```\n\n## For loops vs. functionals\n\nFor loops are not as important in R as they are in other languages because R is a functional programming language. This means that it's possible to wrap up for loops in a function, and call that function instead of using the for loop directly.\n\nTo see why this is important, consider (again) this simple data frame:\n\n```{r}\ndf <- tibble(\n  a = rnorm(10),\n  b = rnorm(10),\n  c = rnorm(10),\n  d = rnorm(10)\n)\n```\n\nImagine you want to compute the mean of every column. You could do that with a for loop:\n\n```{r}\noutput <- vector(\"double\", length(df))\nfor (i in seq_along(df)) {\n  output[[i]] <- mean(df[[i]])\n}\noutput\n```\n\nYou realise that you're going to want to compute the means of every column pretty frequently, so you extract it out into a function:\n\n```{r}\ncol_mean <- function(df) {\n  output <- vector(\"double\", length(df))\n  for (i in seq_along(df)) {\n    output[i] <- mean(df[[i]])\n  }\n  output\n}\n```\n\nBut then you think it'd also be helpful to be able to compute the median, and the standard deviation, so you copy and paste your `col_mean()` function and replace the `mean()` with `median()` and `sd()`:\n\n```{r}\ncol_median <- function(df) {\n  output <- vector(\"double\", length(df))\n  for (i in seq_along(df)) {\n    output[i] <- median(df[[i]])\n  }\n  output\n}\ncol_sd <- function(df) {\n  output <- vector(\"double\", length(df))\n  for (i in seq_along(df)) {\n    output[i] <- sd(df[[i]])\n  }\n  output\n}\n```\n\nUh oh! You've copied-and-pasted this code twice, so it's time to think about how to generalise it. Notice that most of this code is for-loop boilerplate and it's hard to see the one thing (`mean()`, `median()`, `sd()`) that is different between the functions.\n\nWhat would you do if you saw a set of functions like this:\n\n```{r}\nf1 <- function(x) abs(x - mean(x)) ^ 1\nf2 <- function(x) abs(x - mean(x)) ^ 2\nf3 <- function(x) abs(x - mean(x)) ^ 3\n```\n\nHopefully, you'd notice that there's a lot of duplication, and extract it out into an additional argument:\n\n```{r}\nf <- function(x, i) abs(x - mean(x)) ^ i\n```\n\nYou've reduced the chance of bugs (because you now have 1/3 less code), and made it easy to generalise to new situations. \n\nWe can do exactly the same thing with `col_mean()`, `col_median()` and `col_sd()` by adding an argument that supplies the function to apply to each column:\n\n```{r}\ncol_summary <- function(df, fun) {\n  out <- vector(\"double\", length(df))\n  for (i in seq_along(df)) {\n    out[i] <- fun(df[[i]])\n  }\n  out\n}\ncol_summary(df, median)\ncol_summary(df, mean)\n```\n\nThe idea of passing a function to another function is extremely powerful idea, and it's one of the behaviours that makes R a functional programming language. It might take you a while to wrap your head around the idea, but it's worth the investment. In the rest of the chapter, you'll learn about and use the __purrr__ package, which provides functions that eliminate the need for many common for loops. The apply family of functions in base R (`apply()`, `lapply()`, `tapply()`, etc) solve a similar problem, but purrr is more consistent and thus is easier to learn.\n\nThe goal of using purrr functions instead of for loops is to allow you break common list manipulation challenges into independent pieces: \n\n1. How can you solve the problem for a single element of the list? Once\n   you've solved that problem, purrr takes care of generalising your\n   solution to every element in the list.\n\n1. If you're solving a complex problem, how can you break it down into\n   bite-sized pieces that allow you to advance one small step towards a \n   solution? With purrr, you get lots of small pieces that you can\n   compose together with the pipe.\n\nThis structure makes it easier to solve new problems. It also makes it easier to understand your solutions to old problems when you re-read your old code.\n\n### Exercises\n\n1.  Read the documentation for `apply()`. In the 2d case, what two for loops\n    does it generalise?\n\n1.  Adapt `col_summary()` so that it only applies to numeric columns\n    You might want to start with an `is_numeric()` function that returns\n    a logical vector that has a TRUE corresponding to each numeric column.\n\n## The map functions\n\nThe pattern of looping over a vector, doing something to each element and saving the results is so common that the purrr package provides a family of functions to do it for you. There is one function for each type of output:\n\n* `map()`     makes a list.\n* `map_lgl()` makes a logical vector.\n* `map_int()` makes an integer vector.\n* `map_dbl()` makes a double vector.\n* `map_chr()` makes a character vector.\n\nEach function takes a vector as input, applies a function to each piece, and then returns a new vector that's the same length (and has the same names) as the input. The type of the vector is determined by the suffix to the map function. \n\nOnce you master these functions, you'll find it takes much less time to solve iteration problems. But you should never feel bad about using a for loop instead of a map function. The map functions are a step up a tower of abstraction, and it can take a long time to get your head around how they work. The important thing is that you solve the problem that you're working on, not write the most concise and elegant code (although that's definitely something you want to strive towards!).\n\nSome people will tell you to avoid for loops because they are slow. They're wrong! (Well at least they're rather out of date, as for loops haven't been slow for many years). The chief benefits of using functions like `map()` is not speed, but clarity: they make your code easier to write and to read.\n\nWe can use these functions to perform the same computations as the last for loop. Those summary functions returned doubles, so we need to use `map_dbl()`:\n\n```{r}\nmap_dbl(df, mean)\nmap_dbl(df, median)\nmap_dbl(df, sd)\n```\n\nCompared to using a for loop, focus is on the operation being performed (i.e. `mean()`, `median()`, `sd()`), not the bookkeeping required to loop over every element and store the output. This is even more apparent if we use the pipe:\n\n```{r}\ndf %>% map_dbl(mean)\ndf %>% map_dbl(median)\ndf %>% map_dbl(sd)\n```\n\nThere are a few differences between `map_*()` and `col_summary()`:\n\n*   All purrr functions are implemented in C. This makes them a little faster\n    at the expense of readability.\n    \n*   The second argument, `.f`, the function to apply, can be a formula, a \n    character vector, or an integer vector. You'll learn about those handy \n    shortcuts in the next section.\n    \n*   `map_*()` uses ... ([dot dot dot]) to pass along additional arguments \n    to `.f` each time it's called:\n\n    ```{r}\n    map_dbl(df, mean, trim = 0.5)\n    ```\n\n*   The map functions also preserve names:\n\n    ```{r}\n    z <- list(x = 1:3, y = 4:5)\n    map_int(z, length)\n    ```\n\n### Shortcuts\n\nThere are a few shortcuts that you can use with `.f` in order to save a little typing. Imagine you want to fit a linear model to each group in a dataset. The following toy example splits the up the `mtcars` dataset in to three pieces (one for each value of cylinder) and fits the same linear model to each piece:  \n\n```{r}\nmodels <- mtcars %>% \n  split(.$cyl) %>% \n  map(function(df) lm(mpg ~ wt, data = df))\n```\n\nThe syntax for creating an anonymous function in R is quite verbose so purrr provides a convenient shortcut: a one-sided formula.\n\n```{r}\nmodels <- mtcars %>% \n  split(.$cyl) %>% \n  map(~lm(mpg ~ wt, data = .))\n```\n\nHere I've used `.` as a pronoun: it refers to the current list element (in the same way that `i` referred to the current index in the for loop). \n\nWhen you're looking at many models, you might want to extract a summary statistic like the $R^2$. To do that we need to first run `summary()` and then extract the component called `r.squared`. We could do that using the shorthand for anonymous functions:\n\n```{r}\nmodels %>% \n  map(summary) %>% \n  map_dbl(~.$r.squared)\n```\n\nBut extracting named components is a common operation, so purrr provides an even shorter shortcut: you can use a string.\n\n```{r}\nmodels %>% \n  map(summary) %>% \n  map_dbl(\"r.squared\")\n```\n\nYou can also use an integer to select elements by position: \n\n```{r}\nx <- list(list(1, 2, 3), list(4, 5, 6), list(7, 8, 9))\nx %>% map_dbl(2)\n```\n\n### Base R\n  \nIf you're familiar with the apply family of functions in base R, you might have noticed some similarities with the purrr functions:\n\n*   `lapply()` is basically identical to `map()`, except that `map()` is \n    consistent with all the other functions in purrr, and you can use the \n    shortcuts for `.f`.\n\n*   Base `sapply()` is a wrapper around `lapply()` that automatically\n    simplifies the output. This is useful for interactive work but is \n    problematic in a function because you never know what sort of output\n    you'll get:\n    \n    ```{r}\n    x1 <- list(\n      c(0.27, 0.37, 0.57, 0.91, 0.20),\n      c(0.90, 0.94, 0.66, 0.63, 0.06), \n      c(0.21, 0.18, 0.69, 0.38, 0.77)\n    )\n    x2 <- list(\n      c(0.50, 0.72, 0.99, 0.38, 0.78), \n      c(0.93, 0.21, 0.65, 0.13, 0.27), \n      c(0.39, 0.01, 0.38, 0.87, 0.34)\n    )\n    \n    threshold <- function(x, cutoff = 0.8) x[x > cutoff]\n    x1 %>% sapply(threshold) %>% str()\n    x2 %>% sapply(threshold) %>% str()\n    ```\n\n*   `vapply()` is a safe alternative to `sapply()` because you supply an\n    additional argument that defines the type. The only problem with \n    `vapply()` is that it's a lot of typing: \n    `vapply(df, is.numeric, logical(1))` is equivalent to\n    `map_lgl(df, is.numeric)`. One advantage of `vapply()` over purrr's map\n    functions is that it can also produce matrices --- the map functions only \n    ever produce vectors.\n\nI focus on purrr functions here because they have more consistent names and arguments, helpful shortcuts, and in the future will provide easy parallelism and progress bars.\n\n### Exercises\n\n1.  Write code that uses one of the map functions to:\n\n    1. Compute the mean of every column in `mtcars`.\n    1. Determine the type of each column in `nycflights13::flights`.\n    1. Compute the number of unique values in each column of `iris`.\n    1. Generate 10 random normals for each of $\\mu = -10$, $0$, $10$, and $100$.\n\n1.  How can you create a single vector that for each column in a data frame\n    indicates whether or not it's a factor?\n\n1.  What happens when you use the map functions on vectors that aren't lists?\n    What does `map(1:5, runif)` do? Why?\n    \n1.  What does `map(-2:2, rnorm, n = 5)` do? Why?\n    What does `map_dbl(-2:2, rnorm, n = 5)` do? Why?\n\n1.  Rewrite `map(x, function(df) lm(mpg ~ wt, data = df))` to eliminate the \n    anonymous function. \n\n## Dealing with failure\n\nWhen you use the map functions to repeat many operations, the chances are much higher that one of those operations will fail. When this happens, you'll get an error message, and no output. This is annoying: why does one failure prevent you from accessing all the other successes? How do you ensure that one bad apple doesn't ruin the whole barrel?\n\nIn this section you'll learn how to deal this situation with a new function: `safely()`. `safely()` is an adverb: it takes a function (a verb) and returns a modified version. In this case, the modified function will never throw an error. Instead, it always returns a list with two elements:\n\n1. `result` is the original result. If there was an error, this will be `NULL`.\n\n1. `error` is an error object. If the operation was successful, this will be \n   `NULL`.\n\n(You might be familiar with the `try()` function in base R. It's similar, but because it sometimes returns the original result and it sometimes returns an error object it's more difficult to work with.)\n\nLet's illustrate this with a simple example: `log()`:\n\n```{r}\nsafe_log <- safely(log)\nstr(safe_log(10))\nstr(safe_log(\"a\"))\n```\n\nWhen the function succeeds, the `result` element contains the result and the `error` element is `NULL`. When the function fails, the `result` element is `NULL` and the `error` element contains an error object.\n\n`safely()` is designed to work with map:\n\n```{r}\nx <- list(1, 10, \"a\")\ny <- x %>% map(safely(log))\nstr(y)\n```\n\nThis would be easier to work with if we had two lists: one of all the errors and one of all the output. That's easy to get with `purrr::transpose()`:\n\n```{r}\ny <- y %>% transpose()\nstr(y)\n```\n\nIt's up to you how to deal with the errors, but typically you'll either look at the values of `x` where `y` is an error, or work with the values of `y` that are ok:\n\n```{r}\nis_ok <- y$error %>% map_lgl(is_null)\nx[!is_ok]\ny$result[is_ok] %>% flatten_dbl()\n```\n\nPurrr provides two other useful adverbs:\n\n*   Like `safely()`, `possibly()` always succeeds. It's simpler than `safely()`, \n    because you give it a default value to return when there is an error. \n    \n    ```{r}\n    x <- list(1, 10, \"a\")\n    x %>% map_dbl(possibly(log, NA_real_))\n    ```\n    \n*   `quietly()` performs a similar role to `safely()`, but instead of capturing\n    errors, it captures printed output, messages, and warnings:\n    \n    ```{r}\n    x <- list(1, -1)\n    x %>% map(quietly(log)) %>% str()\n    ```\n\n## Mapping over multiple arguments\n\nSo far we've mapped along a single input. But often you have multiple related inputs that you need iterate along in parallel. That's the job of the `map2()` and `pmap()` functions. For example, imagine you want to simulate some random normals with different means. You know how to do that with `map()`:\n\n```{r}\nmu <- list(5, 10, -3)\nmu %>% \n  map(rnorm, n = 5) %>% \n  str()\n```\n\nWhat if you also want to vary the standard deviation? One way to do that would be to iterate over the indices and index into vectors of means and sds:\n\n```{r}\nsigma <- list(1, 5, 10)\nseq_along(mu) %>% \n  map(~rnorm(5, mu[[.]], sigma[[.]])) %>% \n  str()\n```\n\nBut that obfuscates the intent of the code. Instead we could use `map2()` which iterates over two vectors in parallel:\n\n```{r}\nmap2(mu, sigma, rnorm, n = 5) %>% str()\n```\n\n`map2()` generates this series of function calls:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/lists-map2.png\")\n```\n\nNote that the arguments that vary for each call come _before_ the function; arguments that are the same for every call come _after_.\n\nLike `map()`, `map2()` is just a wrapper around a for loop:\n\n```{r}\nmap2 <- function(x, y, f, ...) {\n  out <- vector(\"list\", length(x))\n  for (i in seq_along(x)) {\n    out[[i]] <- f(x[[i]], y[[i]], ...)\n  }\n  out\n}\n```\n\nYou could also imagine `map3()`, `map4()`, `map5()`, `map6()` etc, but that would get tedious quickly. Instead, purrr provides `pmap()` which takes a list of arguments. You might use that if you wanted to vary the mean, standard deviation, and number of samples:\n\n```{r}\nn <- list(1, 3, 5)\nargs1 <- list(n, mu, sigma)\nargs1 %>%\n  pmap(rnorm) %>% \n  str()\n```\n\nThat looks like:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/lists-pmap-unnamed.png\")\n```\n\nIf you don't name the elements of list, `pmap()` will use positional matching when calling the function. That's a little fragile, and makes the code harder to read, so it's better to name the arguments:\n\n```{r, eval = FALSE}\nargs2 <- list(mean = mu, sd = sigma, n = n)\nargs2 %>% \n  pmap(rnorm) %>% \n  str()\n```\n\nThat generates longer, but safer, calls:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/lists-pmap-named.png\")\n```\n\nSince the arguments are all the same length, it makes sense to store them in a data frame:\n\n```{r}\nparams <- tribble(\n  ~mean, ~sd, ~n,\n    5,     1,  1,\n   10,     5,  3,\n   -3,    10,  5\n)\nparams %>% \n  pmap(rnorm)\n```\n\nAs soon as your code gets complicated, I think a data frame is a good approach because it ensures that each column has a name and is the same length as all the other columns.\n\n### Invoking different functions\n\nThere's one more step up in complexity - as well as varying the arguments to the function you might also vary the function itself:\n\n```{r}\nf <- c(\"runif\", \"rnorm\", \"rpois\")\nparam <- list(\n  list(min = -1, max = 1), \n  list(sd = 5), \n  list(lambda = 10)\n)\n```\n\nTo handle this case, you can use `invoke_map()`:\n\n```{r}\ninvoke_map(f, param, n = 5) %>% str()\n```\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/lists-invoke.png\")\n```\n\nThe first argument is a list of functions or character vector of function names. The second argument is a list of lists giving the arguments that vary for each function. The subsequent arguments are passed on to every function.\n\nAnd again, you can use `tribble()` to make creating these matching pairs a little easier:\n\n```{r, eval = FALSE}\nsim <- tribble(\n  ~f,      ~params,\n  \"runif\", list(min = -1, max = 1),\n  \"rnorm\", list(sd = 5),\n  \"rpois\", list(lambda = 10)\n)\nsim %>% \n  mutate(sim = invoke_map(f, params, n = 10))\n```\n\n## Walk {#walk}\n\nWalk is an alternative to map that you use when you want to call a function for its side effects, rather than for its return value. You typically do this because you want to render output to the screen or save files to disk - the important thing is the action, not the return value. Here's a very simple example:\n\n```{r}\nx <- list(1, \"a\", 3)\n\nx %>% \n  walk(print)\n```\n\n`walk()` is generally not that useful compared to `walk2()` or `pwalk()`. For example, if you had a list of plots and a vector of file names, you could use `pwalk()` to save each file to the corresponding location on disk:\n\n```{r, eval = FALSE}\nlibrary(ggplot2)\nplots <- mtcars %>% \n  split(.$cyl) %>% \n  map(~ggplot(., aes(mpg, wt)) + geom_point())\npaths <- stringr::str_c(names(plots), \".pdf\")\n\npwalk(list(paths, plots), ggsave, path = tempdir())\n```\n\n`walk()`, `walk2()` and `pwalk()` all invisibly return `.x`, the first argument. This makes them suitable for use in the middle of pipelines. \n\n## Other patterns of for loops\n\nPurrr provides a number of other functions that abstract over other types of for loops. You'll use them less frequently than the map functions, but they're useful to know about. The goal here is to briefly illustrate each function, so hopefully it will come to mind if you see a similar problem in the future. Then you can go look up the documentation for more details.\n\n### Predicate functions\n\nA number of functions work with __predicate__ functions that return either a single `TRUE` or `FALSE`.\n\n`keep()` and `discard()` keep elements of the input where the predicate is `TRUE` or `FALSE` respectively:\n\n```{r}\niris %>% \n  keep(is.factor) %>% \n  str()\n\niris %>% \n  discard(is.factor) %>% \n  str()\n```\n\n`some()` and `every()` determine if the predicate is true for any or for all of\nthe elements.\n\n```{r}\nx <- list(1:5, letters, list(10))\n\nx %>% \n  some(is_character)\n\nx %>% \n  every(is_vector)\n```\n\n`detect()` finds the first element where the predicate is true; `detect_index()` returns its position.\n\n```{r}\nx <- sample(10)\nx\n\nx %>% \n  detect(~ . > 5)\n\nx %>% \n  detect_index(~ . > 5)\n```\n\n`head_while()` and `tail_while()` take elements from the start or end of a vector while a predicate is true:\n\n```{r}\nx %>% \n  head_while(~ . > 5)\n\nx %>% \n  tail_while(~ . > 5)\n```\n\n### Reduce and accumulate\n\nSometimes you have a complex list that you want to reduce to a simple list by repeatedly applying a function that reduces a pair to a singleton. This is useful if you want to apply a two-table dplyr verb to multiple tables. For example, you might have a list of data frames, and you want to reduce to a single data frame by joining the elements together:\n\n```{r}\ndfs <- list(\n  age = tibble(name = \"John\", age = 30),\n  sex = tibble(name = c(\"John\", \"Mary\"), sex = c(\"M\", \"F\")),\n  trt = tibble(name = \"Mary\", treatment = \"A\")\n)\n\ndfs %>% reduce(full_join)\n```\n\nOr maybe you have a list of vectors, and want to find the intersection:\n\n```{r}\nvs <- list(\n  c(1, 3, 5, 6, 10),\n  c(1, 2, 3, 7, 8, 10),\n  c(1, 2, 3, 4, 8, 9, 10)\n)\n\nvs %>% reduce(intersect)\n```\n\nThe reduce function takes a \"binary\" function (i.e. a function with two primary inputs), and applies it repeatedly to a list until there is only a single element left.\n\nAccumulate is similar but it keeps all the interim results. You could use it to implement a cumulative sum:\n\n```{r}\nx <- sample(10)\nx\nx %>% accumulate(`+`)\n```\n\n### Exercises\n\n1.  Implement your own version of `every()` using a for loop. Compare it with\n    `purrr::every()`. What does purrr's version do that your version doesn't?\n\n1.  Create an enhanced `col_sum()` that applies a summary function to every\n    numeric column in a data frame.\n\n1.  A possible base R equivalent of `col_sum()` is:\n\n    ```{r}\n    col_sum3 <- function(df, f) {\n      is_num <- sapply(df, is.numeric)\n      df_num <- df[, is_num]\n\n      sapply(df_num, f)\n    }\n    ```\n    \n    But it has a number of bugs as illustrated with the following inputs:\n    \n    ```{r, eval = FALSE}\n    df <- tibble(\n      x = 1:3, \n      y = 3:1,\n      z = c(\"a\", \"b\", \"c\")\n    )\n    # OK\n    col_sum3(df, mean)\n    # Has problems: don't always return numeric vector\n    col_sum3(df[1:2], mean)\n    col_sum3(df[1], mean)\n    col_sum3(df[0], mean)\n    ```\n    \n    What causes the bugs?\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-assess.Rmd",
    "content": "# Model assessment\n\nIn this chapter, you'll turn the tools of multiple models towards model assessment: learning how the model performs when given new data. So far we've focussed on models as tools for description, using models to help us understand the patterns in the data we have collected so far. But ideally a model will do more than just describe what we have seen so far - it will also help predict what will come next.  \n\nIn other words, we want a model that doesn't just perform well on the sample, but also accurately summarises the underlying population.\n\nIn some industries this is primarily the use of models: you spend relatively little time fitting the model compared to how many times you use it.\n\nThere are two basic ways that a model can fail with new data:\n\n* You can under- or over-fit the model.  Underfitting is where you fail\n  to model and important trend: you leave too much in the residuals, and not \n  enough in the model. Overfitting is the opposite: you fit a trend to\n  what is actually random noise: you've too put much model and not left\n  enough in the residuals. Generally overfitting tends to be more of a \n  problem than underfitting.\n\n* The process that generates the data might change. There's nothing the \n  model can do about this. You can protect yourself against this to some\n  extent by creating models that you understand and applying your knowledge\n  to the problem. Are these fundamentals likely to change? If you have \n  a model that you are going to use again and again for a long time, you\n  need to plan to maintain the model, regularly checking that it still \n  makes sense. i.e. is the population the same?\n  \n    <http://research.google.com/pubs/pub43146.html>\n    <http://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/>\n \nThe most common problem with a model that causes it to do poorly with new data is overfitting. \n\n\nObviously, there's a bit of a problem here: we don't have new data with which to check the model, and even if we did, we'd presumably use it to make the model better in the first place. One powerful technique of approaches can help us get around this problem: resampling.\n\nThere are two main resampling techniques that we're going to cover.\n\n* We will use __cross-validation__ to assess model quality. In \n  cross-validation, you split the data into test and training sets. You fit \n  the data to the training set, and evaluate it on the test set. This avoids\n  intrinsic bias of using the same data to both fit the model and assess it's\n  quality. However it introduces a new bias: you're not using all the data to\n  fit the model so it's not going to be quite as good as it could be.\n  \n* We will use __boostrapping__ to understand how stable (or how variable)\n  the model is. If you sample data from the same population multiple times, \n  how much does your model vary? Instead of going back to collect new data, \n  you can use the best estimate of the population data: the data you've\n  collected so far. The amazing idea of the bootstrap is that you can resample\n  from the data you already have.\n\nThere are lots of high-level helpers to do these resampling methods in R. We're going to use the tools provided by the modelr package because they are explicit - you'll see exactly what's going on at each step. \n\n<http://topepo.github.io/caret>. [Applied Predictive Modeling](https://amzn.com/1461468485), by Max Kuhn and Kjell Johnson.\n\nIf you're competing in competitions, like Kaggle, that are predominantly about creating good predictions, developing a good strategy for avoiding overfitting is very important. Otherwise you risk tricking yourself into thinking that you have a good model, when in reality you just have a model that does a good job of fitting your data.\n\nThere is a closely related family that uses a similar idea: model ensembles. However, instead of trying to find the best models, ensembles make use of all the models, acknowledging that even models that don't fit all the data particularly well can still model some subsets well. In general, you can think of model ensemble techniques as functions that take a list of models, and a return a single model that attempts to take the best part of each.\n\n\n### Prerequisites\n\n```{r setup, message = FALSE}\n# Standard data manipulation and visulisation\nlibrary(dplyr)\nlibrary(ggplot2)\n\n# Tools for working with models\nlibrary(broom)\nlibrary(modelr)\nlibrary(splines)\n\n# Tools for working with lots of models\nlibrary(purrr)\nlibrary(tidyr)\n```\n\n```{r}\n# Options that make your life easier\noptions(\n  contrasts = c(\"contr.treatment\", \"contr.treatment\"),\n  na.option = na.exclude\n)\n```\n\n\n## Overfitting\n\nBoth bootstrapping and cross-validation help us to spot and remedy the problem of __over fitting__, where the model fits the data we've seen so far extremely well, but does a bad job of generalising to new data.\n\nA classic example of over-fitting is to using a polynomial with too many degrees of freedom.\n\nBias - variance tradeoff.  Simpler = more biased. Complex = more variable.  Occam's razor.\n\n```{r}\ntrue_model <- function(x) {\n  1 + 2 * x + rnorm(length(x), sd = 0.25)\n}\n\ndf <- tibble(\n  x = seq(0, 1, length = 20),\n  y = true_model(x)\n)\n\ndf %>% \n  ggplot(aes(x, y)) +\n  geom_point()\n```\n\nWe can create a model that fits this data very well:\n\n```{r, message = FALSE}\nlibrary(splines)\nmy_model <- function(df) {\n  lm(y ~ poly(x, 7), data = df)\n}\n\nmod <- my_model(df)\nrmse(mod, df)\n\ngrid <- df %>% \n  expand(x = seq_range(x, 50))\npreds <- grid %>% \n  add_predictions(mod, var = \"y\")\n\ndf %>% \n  ggplot(aes(x, y)) +\n  geom_line(data = preds) + \n  geom_point()\n```\n\nAs we fit progressively more and more complicated models, the model error decreases:\n\n```{r}\nfs <- list(\n  y ~ x,\n  y ~ poly(x, 2),\n  y ~ poly(x, 3),\n  y ~ poly(x, 4),\n  y ~ poly(x, 5),\n  y ~ poly(x, 6),\n  y ~ poly(x, 7)\n)\n\nmodels <- tibble(\n  n = 1:7, \n  f = fs,\n  mod = map(f, lm, data = df),\n  rmse = map2_dbl(mod, list(df), rmse)\n)\n\nmodels %>% \n  ggplot(aes(n, rmse)) + \n  geom_line(colour = \"grey70\") + \n  geom_point(size = 3)\n```\n\nBut do you think this model will do well if we apply it to new data from the same population? \n\nIn real-life you can't easily go out and recollect your data. There are two approaches to help you get around this problem. I'll introduce them briefly here, and then we'll go into more depth in the following sections.\n\n```{r}\nboot <- bootstrap(df, 100) %>% \n  mutate(\n    mod = map(strap, my_model),\n    pred = map2(list(grid), mod, add_predictions)\n  )\n\nboot %>% \n  unnest(pred) %>% \n  ggplot(aes(x, pred, group = .id)) +\n  geom_line(alpha = 1/3)\n```\n\nIt's a little easier to see what's going on if we zoom on the y axis:\n\n```{r}\nlast_plot() + \n  coord_cartesian(ylim = c(0, 5))\n```\n\n(You might notice that while each individual model varies a lot, the average of all the models seems like it might not be that bad. That gives rise to a model ensemble technique called model averaging.)\n\nBootstrapping is a useful tool to help us understand how the model might vary if we'd collected a different sample from the population. A related technique is cross-validation which allows us to explore the quality of the model. It works by repeatedly splitting the data into two pieces. One piece, the training set, is used to fit, and the other piece, the test set, is used to measure the model quality.\n\nThe following code generates 100 test-training splits, holding out 20% of the data for testing each time. We then fit a model to the training set, and evaluate the error on the test set:\n\n```{r}\ncv <- crossv_mc(df, 100) %>% \n  mutate(\n    mod = map(train, my_model),\n    rmse = map2_dbl(mod, test, rmse)\n  )\ncv\n```\n\nObviously, a plot is going to help us see distribution more easily. I've added our original estimate of the model error as a white vertical line (where the same dataset is used for both training and testing), and you can see it's very optimistic.\n\n```{r}\ncv %>% \n  ggplot(aes(rmse)) +\n  geom_ref_line(v = rmse(mod, df)) +\n  geom_freqpoly(binwidth = 0.2) +\n  geom_rug()\n```\n\nThe distribution of errors is highly skewed: there are a few cases which have very high errors. These represent samples where we ended up with a few cases on all with low values or high values of x.  Let's take a look:\n\n```{r}\nfilter(cv, rmse > 1.5) %>% \n  unnest(map(train, as.data.frame)) %>% \n  ggplot(aes(x, .id)) + \n    geom_point() + \n    xlim(0, 1)\n```\n\nAll of the models that fit particularly poorly were fit to samples that either missed the first one or two or the last one or two observation. Because polynomials shoot off to positive and negative, they give very bad predictions for those values.\n\nNow that we've given you a quick overview and intuition for these techniques, let's dive in more detail.\n\n## Resamples\n\n### Building blocks\n\nBoth the boostrap and cross-validation are built on top of a \"resample\" object. In modelr, you can access these low-level tools directly with the `resample_*` functions. \n\nThese functions return an object of class \"resample\", which represents the resample in a memory efficient way. Instead of storing the resampled dataset itself, it instead stores the integer indices, and a \"pointer\" to the original dataset. This makes resamples take up much less memory.\n\n```{r}\nx <- resample_bootstrap(as_tibble(mtcars))\nclass(x)\n\nx\n```\n\nMost modelling functions call `as.data.frame()` on the `data` argument. This generates a resampled data frame. Because it's called automatically you can just pass the object.\n\n```{r}\nlm(mpg ~ wt, data = x)\n```\n\nIf you get a strange error, it's probably because the modelling function doesn't do this, and you need to do it yourself. You'll also need to do it yourself if you want to `unnest()` the data so you can visualise it.  If you want to just get the rows selected, you can use `as.integer()`.\n\n### Dataframe API\n\n`bootstrap()` and `crossv_mc()` are built on top of these simpler primitives. They are designed to work naturally in a model exploration environment by returning data frames. Each row of the data frame represents a single sample. They return slightly different columns:\n\n*   `boostrap()` returns a data frame with two columns:\n\n    ```{r}\n    bootstrap(df, 3)\n    ```\n    \n    `strap` gives the bootstrap sample dataset, and `.id` assigns a \n    unique identifier to each model (this is often useful for plotting)\n    \n*   `crossv_mc()` return a data frame with three columns:\n\n    ```{r}\n    crossv_mc(df, 3)\n    ```\n    `train` contains the data that you should use to fit (train) the model,\n    and `test` contains the data you should use to validate the model. Together,\n    the test and train columns form an exclusive partition of the full dataset.\n\n## Numeric summaries of model quality\n\nWhen you start dealing with many models, it's helpful to have some rough way of comparing them so you can spend your time looking at the models that do the best job of capturing important features in the data. \n\nOne way to capture the quality of the model is to summarise the distribution of the residuals. For example, you could look at the quantiles of the absolute residuals. For this dataset, 25% of predictions are less than \\$7,400 away, and 75% are less than \\$25,800 away. That seems like quite a bit of error when predicting someone's income!\n\n```{r}\nheights <- tibble(readRDS(\"data/heights.RDS\"))\nh <- lm(income ~ height, data = heights)\nh \n\nqae(h, heights)\nrange(heights$income)\n```\n\nYou might be familiar with the $R^2$. That's a single number summary that rescales the variance of the residuals to between 0 (very bad) and 1 (very good):\n\n```{r}\nrsquare(h, heights)\n```\n\n$R^2$ can be interpreted as the amount of variation in the data explained by the model. Here we're explaining 3% of the total variation - not a lot! But I don't think worrying about the relative amount of variation explained is that useful; instead I think you need to consider whether the absolute amount of variation explained is useful for your project.\n\nIt's called the $R^2$ because for simple models like this, it's just the square of the correlation between the variables:\n\n```{r}\ncor(heights$income, heights$height) ^ 2\n```\n\nThe $R^2$ is an ok single number summary, but I prefer to think about the unscaled residuals because it's easier to interpret in the context of the original data. As you'll also learn later, it's also a rather optimistic interpretation of the model. Because you're assessing the model using the same data that was used to fit it, it really gives more of an upper bound on the quality of the model, not a fair assessment. \n\n\n\n## Bootstrapping\n\n\n## Cross-validation\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-basics.Rmd",
    "content": "# Model basics\n\n## Introduction\n\nThe goal of a model is to provide a simple low-dimensional summary of a dataset. In the context of this book we're going to use models to partition data into patterns and residuals. Strong patterns will hide subtler trends, so we'll use models to help peel back layers of structure as we explore a dataset.\n\nHowever, before we can start using models on interesting, real, datasets, you need to understand the basics of how models work. For that reason, this chapter of the book is unique because it uses only simulated datasets. These datasets are very simple, and not at all interesting, but they will help you understand the essence of modelling before you apply the same techniques to real data in the next chapter.\n\nThere are two parts to a model:\n\n1.  First, you define a __family of models__ that express a precise, but \n    generic, pattern that you want to capture. For example, the pattern \n    might be a straight line, or a quadratic curve. You will express\n    the model family as an equation like `y = a_1 * x + a_2` or \n    `y = a_1 * x ^ a_2`. Here, `x` and `y` are known variables from your\n    data, and `a_1` and `a_2` are parameters that can vary to capture \n    different patterns.\n\n1.  Next, you generate a __fitted model__ by finding the model from the \n    family that is the closest to your data. This takes the generic model \n    family and makes it specific, like `y = 3 * x + 7` or `y = 9 * x ^ 2`.\n\nIt's important to understand that a fitted model is just the closest model from a family of models. That implies that you have the \"best\" model (according to some criteria); it doesn't imply that you have a good model and it certainly doesn't imply that the model is \"true\". George Box puts this well in his famous aphorism:\n\n> All models are wrong, but some are useful.\n\nIt's worth reading the fuller context of the quote:\n\n> Now it would be very remarkable if any system existing in the real world \n> could be exactly represented by any simple model. However, cunningly chosen \n> parsimonious models often do provide remarkably useful approximations. For \n> example, the law PV = RT relating pressure P, volume V and temperature T of \n> an \"ideal\" gas via a constant R is not exactly true for any real gas, but it \n> frequently provides a useful approximation and furthermore its structure is \n> informative since it springs from a physical view of the behavior of gas \n> molecules.\n> \n> For such a model there is no need to ask the question \"Is the model true?\". \n> If \"truth\" is to be the \"whole truth\" the answer must be \"No\". The only \n> question of interest is \"Is the model illuminating and useful?\".\n\nThe goal of a model is not to uncover truth, but to discover a simple approximation that is still useful. \n\n### Prerequisites\n\nIn this chapter we'll use the modelr package which wraps around base R's modelling functions to make them work naturally in a pipe.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n\nlibrary(modelr)\noptions(na.action = na.warn)\n```\n\n## A simple model\n\nLets take a look at the simulated dataset `sim1`, included with the modelr package. It contains two continuous variables, `x` and `y`. Let's plot them to see how they're related:\n\n```{r}\nggplot(sim1, aes(x, y)) + \n  geom_point()\n```\n\nYou can see a strong pattern in the data. Let's use a model to capture that pattern and make it explicit. It's our job to supply the basic form of the model. In this case, the relationship looks linear, i.e. `y = a_0 + a_1 * x`.  Let's start by getting a feel for what models from that family look like by randomly generating a few and overlaying them on the data. For this simple case, we can use `geom_abline()` which takes a slope and intercept as parameters. Later on we'll learn more general techniques that work with any model.\n\n```{r}\nmodels <- tibble(\n  a1 = runif(250, -20, 40),\n  a2 = runif(250, -5, 5)\n)\n\nggplot(sim1, aes(x, y)) + \n  geom_abline(aes(intercept = a1, slope = a2), data = models, alpha = 1/4) +\n  geom_point() \n```\n\nThere are 250 models on this plot, but a lot are really bad! We need to find the good models by making precise our intuition that a good model is \"close\" to the data. We need a way to quantify the distance between the data and a model. Then we can fit the model by finding the value of `a_0` and `a_1` that generate the model with the smallest distance from this data.\n\nOne easy place to start is to find the vertical distance between each point and the model, as in the following diagram. (Note that I've shifted the x values slightly so you can see the individual distances.)\n\n```{r, echo = FALSE}\ndist1 <- sim1 %>% \n  mutate(\n    dodge = rep(c(-1, 0, 1) / 20, 10),\n    x1 = x + dodge,\n    pred = 7 + x1 * 1.5\n  )\n\nggplot(dist1, aes(x1, y)) + \n  geom_abline(intercept = 7, slope = 1.5, colour = \"grey40\") +\n  geom_point(colour = \"grey40\") +\n  geom_linerange(aes(ymin = y, ymax = pred), colour = \"#3366FF\") \n```\n\nThis distance is just the difference between the y value given by the model (the __prediction__), and the actual y value in the data (the __response__).\n\nTo compute this distance, we first turn our model family into an R function. This takes the model parameters and the data as inputs, and gives values predicted by the model as output:\n\n```{r}\nmodel1 <- function(a, data) {\n  a[1] + data$x * a[2]\n}\nmodel1(c(7, 1.5), sim1)\n```\n\nNext, we need some way to compute an overall distance between the predicted and actual values. In other words, the plot above shows 30 distances: how do we collapse that into a single number?\n\nOne common way to do this in statistics to use the \"root-mean-squared deviation\". We compute the difference between actual and predicted, square them, average them, and the take the square root. This distance has lots of appealing mathematical properties, which we're not going to talk about here. You'll just have to take my word for it!\n\n```{r}\nmeasure_distance <- function(mod, data) {\n  diff <- data$y - model1(mod, data)\n  sqrt(mean(diff ^ 2))\n}\nmeasure_distance(c(7, 1.5), sim1)\n```\n\nNow we can use purrr to compute the distance for all the models defined above. We need a helper function because our distance function expects the model as a numeric vector of length 2.\n\n```{r}\nsim1_dist <- function(a1, a2) {\n  measure_distance(c(a1, a2), sim1)\n}\n\nmodels <- models %>% \n  mutate(dist = purrr::map2_dbl(a1, a2, sim1_dist))\nmodels\n```\n\nNext, let's overlay the 10 best models on to the data. I've coloured the models by `-dist`: this is an easy way to make sure that the best models (i.e. the ones with the smallest distance) get the brighest colours.\n\n```{r}\nggplot(sim1, aes(x, y)) + \n  geom_point(size = 2, colour = \"grey30\") + \n  geom_abline(\n    aes(intercept = a1, slope = a2, colour = -dist), \n    data = filter(models, rank(dist) <= 10)\n  )\n```\n\nWe can also think about these models as observations, and visualising with a scatterplot of `a1` vs  `a2`, again coloured by `-dist`. We can no longer directly see how the model compares to the data, but we can see many models at once. Again, I've highlighted the 10 best models, this time by drawing red circles underneath them.\n\n```{r}\nggplot(models, aes(a1, a2)) +\n  geom_point(data = filter(models, rank(dist) <= 10), size = 4, colour = \"red\") +\n  geom_point(aes(colour = -dist))\n```\n\nInstead of trying lots of random models, we could be more systematic and generate an evenly spaced grid of points (this is called a grid search). I picked the parameters of the grid roughly by looking at where the best models were in the plot above.\n\n```{r}\ngrid <- expand.grid(\n  a1 = seq(-5, 20, length = 25),\n  a2 = seq(1, 3, length = 25)\n  ) %>% \n  mutate(dist = purrr::map2_dbl(a1, a2, sim1_dist))\n\ngrid %>% \n  ggplot(aes(a1, a2)) +\n  geom_point(data = filter(grid, rank(dist) <= 10), size = 4, colour = \"red\") +\n  geom_point(aes(colour = -dist)) \n```\n\nWhen you overlay the best 10 models back on the original data, they all look pretty good:\n\n```{r}\nggplot(sim1, aes(x, y)) + \n  geom_point(size = 2, colour = \"grey30\") + \n  geom_abline(\n    aes(intercept = a1, slope = a2, colour = -dist), \n    data = filter(grid, rank(dist) <= 10)\n  )\n```\n\nYou could imagine iteratively making the grid finer and finer until you narrowed in on the best model. But there's a better way to tackle that problem: a numerical minimisation tool called Newton-Raphson search. The intuition of Newton-Raphson is pretty simple: you pick a starting point and look around for the steepest slope. You then ski down that slope a little way, and then repeat again and again, until you can't go any lower. In R, we can do that with `optim()`:\n\n```{r}\nbest <- optim(c(0, 0), measure_distance, data = sim1)\nbest$par\n\nggplot(sim1, aes(x, y)) + \n  geom_point(size = 2, colour = \"grey30\") + \n  geom_abline(intercept = best$par[1], slope = best$par[2])\n```\n\nDon't worry too much about the details of how `optim()` works. It's the intuition that's important here. If you have a function that defines the distance between a model and a dataset, an algorithm that can minimise that distance by modifying the parameters of the model, you can find the best model. The neat thing about this approach is that it will work for any family of models that you can write an equation for. \n\nThere's one more approach that we can use for this model, because it's a special case of a broader family: linear models. A linear model has the general form `y = a_1 + a_2 * x_1 + a_3 * x_2 + ... + a_n * x_(n - 1)`. So this simple model is equivalent to a general linear model where n is 2 and `x_1` is `x`. R has a tool specifically designed for fitting linear models called `lm()`. `lm()` has a special way to specify the model family: formulas. Formulas look like `y ~ x`, which `lm()` will translate to a function like `y = a_1 + a_2 * x`. We can fit the model and look at the output:\n\n```{r}\nsim1_mod <- lm(y ~ x, data = sim1)\ncoef(sim1_mod)\n```\n\nThese are exactly the same values we got with `optim()`! Behind the scenes `lm()` doesn't use `optim()` but instead takes advantage of the mathematical structure of linear models. Using some connections between geometry, calculus, and linear algebra, `lm()` actually finds the closest model in a single step, using a sophisticated algorithm. This approach is both faster, and guarantees that there is a global minimum.\n\n### Exercises\n\n1.  One downside of the linear model is that it is sensitive to unusual values\n    because the distance incorporates a squared term. Fit a linear model to \n    the simulated data below, and visualise the results. Rerun a few times to\n    generate different simulated datasets. What do you notice about the model? \n    \n    ```{r}\n    sim1a <- tibble(\n      x = rep(1:10, each = 3),\n      y = x * 1.5 + 6 + rt(length(x), df = 2)\n    )\n    ```\n\n1.  One way to make linear models more robust is to use a different distance\n    measure. For example, instead of root-mean-squared distance, you could use\n    mean-absolute distance:\n    \n    ```{r}\n    measure_distance <- function(mod, data) {\n      diff <- data$y - model1(mod, data)\n      mean(abs(diff))\n    }\n    ```\n    \n    Use `optim()` to fit this model to the simulated data above and compare it \n    to the linear model.\n\n1.  One challenge with performing numerical optimisation is that it's only\n    guaranteed to find one local optimum. What's the problem with optimising\n    a three parameter model like this?\n    \n    ```{r}\n    model1 <- function(a, data) {\n      a[1] + data$x * a[2] + a[3]\n    }\n    ```\n\n## Visualising models\n\nFor simple models, like the one above, you can figure out what pattern the model captures by carefully studying the model family and the fitted coefficients. And if you ever take a statistics course on modelling, you're likely to spend a lot of time doing just that. Here, however, we're going to take a different tack. We're going to focus on understanding a model by looking at its predictions. This has a big advantage: every type of predictive model makes predictions (otherwise what use would it be?) so we can use the same set of techniques to understand any type of predictive model.\n\nIt's also useful to see what the model doesn't capture, the so-called residuals which are left after subtracting the predictions from the data. Residuals are powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain.\n\n### Predictions\n\nTo visualise the predictions from a model, we start by generating an evenly spaced grid of values that covers the region where our data lies. The easiest way to do that is to use `modelr::data_grid()`. Its first argument is a data frame, and for each subsequent argument it finds the unique variables and then generates all combinations:\n\n```{r}\ngrid <- sim1 %>% \n  data_grid(x) \ngrid\n```\n\n(This will get more interesting when we start to add more variables to our model.)\n\nNext we add predictions. We'll use `modelr::add_predictions()` which takes a data frame and a model. It adds the predictions from the model to a new column in the data frame:\n\n```{r}\ngrid <- grid %>% \n  add_predictions(sim1_mod) \ngrid\n```\n\n(You can also use this function to add predictions to your original dataset.)\n\nNext, we plot the predictions. You might wonder about all this extra work compared to just using `geom_abline()`. But the advantage of this approach is that it will work with _any_ model in R, from the simplest to the most complex. You're only limited by your visualisation skills. For more ideas about how to visualise more complex model types, you might try <http://vita.had.co.nz/papers/model-vis.html>.\n\n```{r}\nggplot(sim1, aes(x)) +\n  geom_point(aes(y = y)) +\n  geom_line(aes(y = pred), data = grid, colour = \"red\", size = 1)\n```\n\n### Residuals\n\nThe flip-side of predictions are __residuals__. The predictions tells you the pattern that the model has captured, and the residuals tell you what the model has missed. The residuals are just the distances between the observed and predicted values that we computed above. \n\nWe add residuals to the data with `add_residuals()`, which works much like `add_predictions()`. Note, however, that we use the original dataset, not a manufactured grid. This is because to compute residuals we need actual y values.\n\n```{r}\nsim1 <- sim1 %>% \n  add_residuals(sim1_mod)\nsim1\n```\n\nThere are a few different ways to understand what the residuals tell us about the model. One way is to simply draw a frequency polygon to help us understand the spread of the residuals:\n\n```{r}\nggplot(sim1, aes(resid)) + \n  geom_freqpoly(binwidth = 0.5)\n```\n\nThis helps you calibrate the quality of the model: how far away are the predictions from the observed values?  Note that the average of the residual will always be 0.\n\nYou'll often want to recreate plots using the residuals instead of the original predictor. You'll see a lot of that in the next chapter.\n\n```{r}\nggplot(sim1, aes(x, resid)) + \n  geom_ref_line(h = 0) +\n  geom_point() \n```\n\nThis looks like random noise, suggesting that our model has done a good job of capturing the patterns in the dataset.\n\n### Exercises\n\n1.  Instead of using `lm()` to fit a straight line, you can use `loess()`\n    to fit a smooth curve. Repeat the process of model fitting, \n    grid generation, predictions, and visualisation on `sim1` using \n    `loess()` instead of `lm()`. How does the result compare to \n    `geom_smooth()`?\n    \n1.  `add_predictions()` is paired with `gather_predictions()` and \n    `spread_predictions()`. How do these three functions differ?\n    \n1.  What does `geom_ref_line()` do? What package does it come from?\n    Why is displaying a reference line in plots showing residuals\n    useful and important?\n    \n1.  Why might you want to look at a frequency polygon of absolute residuals?\n    What are the pros and cons compared to looking at the raw residuals?\n\n## Formulas and model families\n\nYou've seen formulas before when using `facet_wrap()` and `facet_grid()`. In R, formulas provide a general way of getting \"special behaviour\". Rather than evaluating the values of the variables right away, they capture them so they can be interpreted by the function.\n\nThe majority of modelling functions in R use a standard conversion from formulas to functions. You've seen one simple conversion already: `y ~ x` is translated to `y = a_1 + a_2 * x`.  If you want to see what R actually does, you can use the `model_matrix()` function. It takes a data frame and a formula and returns a tibble that defines the model equation: each column in the output is associated with one coefficient in the model, the function is always `y = a_1 * out1 + a_2 * out_2`. For the simplest case of `y ~ x1` this shows us something interesting:\n\n```{r}\ndf <- tribble(\n  ~y, ~x1, ~x2,\n  4, 2, 5,\n  5, 1, 6\n)\nmodel_matrix(df, y ~ x1)\n```\n\nThe way that R adds the intercept to the model is just by having a column that is full of ones.  By default, R will always add this column. If you don't want, you need to explicitly drop it with `-1`:\n\n```{r}\nmodel_matrix(df, y ~ x1 - 1)\n```\n\nThe model matrix grows in an unsurprising way when you add more variables to the the model:\n\n```{r}\nmodel_matrix(df, y ~ x1 + x2)\n```\n\nThis formula notation is sometimes called \"Wilkinson-Rogers notation\", and was initially described in _Symbolic Description of Factorial Models for Analysis of Variance_, by G. N. Wilkinson and C. E. Rogers <https://www.jstor.org/stable/2346786>. It's worth digging up and reading the original paper if you'd like to understand the full details of the modelling algebra.\n\nThe following sections expand on how this formula notation works for categorical variables, interactions, and transformation.\n\n### Categorical variables\n\nGenerating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and zero otherwise:\n\n```{r}\ndf <- tribble(\n  ~ sex, ~ response,\n  \"male\", 1,\n  \"female\", 2,\n  \"male\", 1\n)\nmodel_matrix(df, response ~ sex)\n```\n\nYou might wonder why R also doesn't create a `sexfemale` column. The problem is that would create a column that is perfectly predictable based on the other columns (i.e. `sexfemale = 1 - sexmale`). Unfortunately the exact details of why this is a problem is beyond the scope of this book, but basically it creates a model family that is too flexible, and will have infinitely many models that are equally close to the data.\n\nFortunately, however, if you focus on visualising predictions you don't need to worry about the exact parameterisation. Let's look at some data and models to make that concrete. Here's the `sim2` dataset from modelr:\n\n```{r}\nggplot(sim2) + \n  geom_point(aes(x, y))\n```\n\nWe can fit a model to it, and generate predictions:\n\n```{r}\nmod2 <- lm(y ~ x, data = sim2)\n\ngrid <- sim2 %>% \n  data_grid(x) %>% \n  add_predictions(mod2)\ngrid\n```\n\nEffectively, a model with a categorical `x` will predict the mean value for each category. (Why? Because the mean minimises the root-mean-squared distance.) That's easy to see if we overlay the predictions on top of the original data:\n\n```{r}\nggplot(sim2, aes(x)) + \n  geom_point(aes(y = y)) +\n  geom_point(data = grid, aes(y = pred), colour = \"red\", size = 4)\n```\n\nYou can't make predictions about levels that you didn't observe. Sometimes you'll do this by accident so it's good to recognise this error message:\n\n```{r, error = TRUE}\ntibble(x = \"e\") %>% \n  add_predictions(mod2)\n```\n\n### Interactions (continuous and categorical)\n\nWhat happens when you combine a continuous and a categorical variable?  `sim3` contains a categorical predictor and a continuous predictor. We can visualise it with a simple plot:\n\n```{r}\nggplot(sim3, aes(x1, y)) + \n  geom_point(aes(colour = x2))\n```\n\nThere are two possible models you could fit to this data:\n\n```{r}\nmod1 <- lm(y ~ x1 + x2, data = sim3)\nmod2 <- lm(y ~ x1 * x2, data = sim3)\n```\n\nWhen you add variables with `+`, the model will estimate each effect independent of all the others. It's possible to fit the so-called interaction by using `*`. For example, `y ~ x1 * x2` is translated to `y = a_0 + a_1 * x1 + a_2 * x2 + a_12 * x1 * x2`. Note that whenever you use `*`, both the interaction and the individual components are included in the model.\n\nTo visualise these models we need two new tricks:\n\n1.  We have two predictors, so we need to give `data_grid()` both variables. \n    It finds all the unique values of `x1` and `x2` and then generates all\n    combinations. \n   \n1.  To generate predictions from both models simultaneously, we can use \n    `gather_predictions()` which adds each prediction as a row. The\n    complement of `gather_predictions()` is `spread_predictions()` which adds \n    each prediction to a new column.\n    \nTogether this gives us:\n\n```{r}\ngrid <- sim3 %>% \n  data_grid(x1, x2) %>% \n  gather_predictions(mod1, mod2)\ngrid\n```\n\nWe can visualise the results for both models on one plot using facetting:\n\n```{r}\nggplot(sim3, aes(x1, y, colour = x2)) + \n  geom_point() + \n  geom_line(data = grid, aes(y = pred)) + \n  facet_wrap(~ model)\n```\n\nNote that the model that uses `+` has the same slope for each line, but different intercepts. The model that uses `*` has a different slope and intercept for each line.\n\nWhich model is better for this data? We can take look at the residuals. Here I've facetted by both model and `x2` because it makes it easier to see the pattern within each group.\n\n```{r}\nsim3 <- sim3 %>% \n  gather_residuals(mod1, mod2)\n\nggplot(sim3, aes(x1, resid, colour = x2)) + \n  geom_point() + \n  facet_grid(model ~ x2)\n```\n\nThere is little obvious pattern in the residuals for `mod2`. The residuals for `mod1` show that the model has clearly missed some pattern in `b`, and less so, but still present is pattern in `c`, and `d`. You might wonder if there's a precise way to tell which of `mod1` or `mod2` is better. There is, but it requires a lot of mathematical background, and we don't really care. Here, we're interested in a qualitative assessment of whether or not the model has captured the pattern that we're interested in. \n\n### Interactions (two continuous)\n\nLet's take a look at the equivalent model for two continuous variables. Initially things proceed almost identically to the previous example:\n\n```{r}\nmod1 <- lm(y ~ x1 + x2, data = sim4)\nmod2 <- lm(y ~ x1 * x2, data = sim4)\n\ngrid <- sim4 %>% \n  data_grid(\n    x1 = seq_range(x1, 5), \n    x2 = seq_range(x2, 5) \n  ) %>% \n  gather_predictions(mod1, mod2)\ngrid\n```\n\nNote my use of `seq_range()` inside `data_grid()`. Instead of using every unique value of `x`, I'm going to use a regularly spaced grid of five values between the minimum and maximum numbers. It's probably not super important here, but it's a useful technique in general. There are two other useful arguments to `seq_range()`:\n\n*  `pretty = TRUE` will generate a \"pretty\" sequence, i.e. something that looks\n    nice to the human eye. This is useful if you want to produce tables of \n    output:\n    \n    ```{r}\n    seq_range(c(0.0123, 0.923423), n = 5)\n    seq_range(c(0.0123, 0.923423), n = 5, pretty = TRUE)\n    ```\n    \n*   `trim = 0.1` will trim off 10% of the tail values. This is useful if the \n    variables have a long tailed distribution and you want to focus on generating\n    values near the center:\n    \n    ```{r}\n    x1 <- rcauchy(100)\n    seq_range(x1, n = 5)\n    seq_range(x1, n = 5, trim = 0.10)\n    seq_range(x1, n = 5, trim = 0.25)\n    seq_range(x1, n = 5, trim = 0.50)\n    ```\n    \n*   `expand = 0.1` is in some sense the opposite of `trim()` it expands the \n    range by 10%.\n    \n    ```{r}\n    x2 <- c(0, 1)\n    seq_range(x2, n = 5)\n    seq_range(x2, n = 5, expand = 0.10)\n    seq_range(x2, n = 5, expand = 0.25)\n    seq_range(x2, n = 5, expand = 0.50)\n    ```\n\nNext let's try and visualise that model. We have two continuous predictors, so you can imagine the model like a 3d surface. We could display that using `geom_tile()`:\n\n```{r}\nggplot(grid, aes(x1, x2)) + \n  geom_tile(aes(fill = pred)) + \n  facet_wrap(~ model)\n```\n\nThat doesn't suggest that the models are very different! But that's partly an illusion: our eyes and brains are not very good at accurately comparing shades of colour. Instead of looking at the surface from the top, we could look at it from either side, showing multiple slices:\n\n```{r, asp = 1/2}\nggplot(grid, aes(x1, pred, colour = x2, group = x2)) + \n  geom_line() +\n  facet_wrap(~ model)\nggplot(grid, aes(x2, pred, colour = x1, group = x1)) + \n  geom_line() +\n  facet_wrap(~ model)\n```\n\nThis shows you that interaction between two continuous variables works basically the same way as for a categorical and continuous variable. An interaction says that there's not a fixed offset: you need to consider both values of `x1` and `x2` simultaneously in order to predict `y`.\n\nYou can see that even with just two continuous variables, coming up with good visualisations are hard. But that's reasonable: you shouldn't expect it will be easy to understand how three or more variables simultaneously interact! But again, we're saved a little because we're using models for exploration, and you can gradually build up your model over time. The model doesn't have to be perfect, it just has to help you reveal a little more about your data.\n\nI spent some time looking at the residuals to see if I could figure if `mod2` did better than `mod1`. I think it does, but it's pretty subtle. You'll have a chance to work on it in the exercises.\n\n### Transformations\n\nYou can also perform transformations inside the model formula. For example, `log(y) ~ sqrt(x1) + x2` is transformed to `log(y) = a_1 + a_2 * sqrt(x1) + a_3 * x2`. If your transformation involves `+`, `*`, `^`, or `-`, you'll need to wrap it in `I()` so R doesn't treat it like part of the model specification. For example, `y ~ x + I(x ^ 2)` is translated to `y = a_1 + a_2 * x + a_3 * x^2`. If you forget the `I()` and specify `y ~ x ^ 2 + x`, R will compute `y ~ x * x + x`. `x * x` means the interaction of `x` with itself, which is the same as `x`. R automatically drops redundant variables so `x + x` become `x`, meaning that `y ~ x ^ 2 + x` specifies the function `y = a_1 + a_2 * x`. That's probably not what you intended!\n\nAgain, if you get confused about what your model is doing, you can always use `model_matrix()` to see exactly what equation `lm()` is fitting:\n\n```{r}\ndf <- tribble(\n  ~y, ~x,\n   1,  1,\n   2,  2, \n   3,  3\n)\nmodel_matrix(df, y ~ x^2 + x)\nmodel_matrix(df, y ~ I(x^2) + x)\n```\n\nTransformations are useful because you can use them to approximate non-linear functions. If you've taken a calculus class, you may have heard of Taylor's theorem which says you can approximate any smooth function with an infinite sum of polynomials. That means you can use a polynomial function to get arbitrarily close to a smooth function by fitting an equation like `y = a_1 + a_2 * x + a_3 * x^2 + a_4 * x ^ 3`. Typing that sequence by hand is tedious, so R provides a helper function: `poly()`:\n\n```{r}\nmodel_matrix(df, y ~ poly(x, 2))\n```\n\nHowever there's one major problem with using `poly()`: outside the range of the data, polynomials rapidly shoot off to positive or negative infinity. One safer alternative is to use the natural spline, `splines::ns()`.\n\n```{r}\nlibrary(splines)\nmodel_matrix(df, y ~ ns(x, 2))\n```\n\nLet's see what that looks like when we try and approximate a non-linear function:\n\n```{r}\nsim5 <- tibble(\n  x = seq(0, 3.5 * pi, length = 50),\n  y = 4 * sin(x) + rnorm(length(x))\n)\n\nggplot(sim5, aes(x, y)) +\n  geom_point()\n```\n\nI'm going to fit five models to this data.\n\n```{r}\nmod1 <- lm(y ~ ns(x, 1), data = sim5)\nmod2 <- lm(y ~ ns(x, 2), data = sim5)\nmod3 <- lm(y ~ ns(x, 3), data = sim5)\nmod4 <- lm(y ~ ns(x, 4), data = sim5)\nmod5 <- lm(y ~ ns(x, 5), data = sim5)\n\ngrid <- sim5 %>% \n  data_grid(x = seq_range(x, n = 50, expand = 0.1)) %>% \n  gather_predictions(mod1, mod2, mod3, mod4, mod5, .pred = \"y\")\n\nggplot(sim5, aes(x, y)) + \n  geom_point() +\n  geom_line(data = grid, colour = \"red\") +\n  facet_wrap(~ model)\n```\n\nNotice that the extrapolation outside the range of the data is clearly bad. This is the downside to approximating a function with a polynomial. But this is a very real problem with every model: the model can never tell you if the behaviour is true when you start extrapolating outside the range of the data that you have seen. You must rely on theory and science.\n\n### Exercises\n\n1.  What happens if you repeat the analysis of `sim2` using a model without\n    an intercept. What happens to the model equation? What happens to the\n    predictions?\n    \n1.  Use `model_matrix()` to explore the equations generated for the models\n    I fit to `sim3` and `sim4`. Why is `*` a good shorthand for interaction?\n\n1.  Using the basic principles, convert the formulas in the following two\n    models into functions. (Hint: start by converting the categorical variable\n    into 0-1 variables.)\n    \n    ```{r, eval = FALSE}\n    mod1 <- lm(y ~ x1 + x2, data = sim3)\n    mod2 <- lm(y ~ x1 * x2, data = sim3)\n    ```\n\n1.   For `sim4`,  which of `mod1` and `mod2` is better? I think `mod2` does a \n     slightly better job at removing patterns, but it's pretty subtle. Can you \n     come up with a plot to support my claim? \n\n## Missing values\n\nMissing values obviously can not convey any information about the relationship between the variables, so modelling functions will drop any rows that contain missing values. R's default behaviour is to silently drop them, but `options(na.action = na.warn)` (run in the prerequisites), makes sure you get a warning.\n\n```{r}\ndf <- tribble(\n  ~x, ~y,\n  1, 2.2,\n  2, NA,\n  3, 3.5,\n  4, 8.3,\n  NA, 10\n)\n\nmod <- lm(y ~ x, data = df)\n```\n\nTo suppress the warning, set `na.action = na.exclude`:\n\n```{r}\nmod <- lm(y ~ x, data = df, na.action = na.exclude)\n```\n\nYou can always see exactly how many observations were used with `nobs()`:\n\n```{r}\nnobs(mod)\n```\n\n## Other model families\n\nThis chapter has focussed exclusively on the class of linear models, which assume a relationship of the form `y = a_1 * x1 + a_2 * x2 + ... + a_n * xn`. Linear models additionally assume that the residuals have a normal distribution, which we haven't talked about. There are a large set of model classes that extend the linear model in various interesting ways. Some of them are:\n\n* __Generalised linear models__, e.g. `stats::glm()`. Linear models assume that\n  the response is continuous and the error has a normal distribution. \n  Generalised linear models extend linear models to include non-continuous\n  responses (e.g. binary data or counts). They work by defining a distance\n  metric based on the statistical idea of likelihood.\n  \n* __Generalised additive models__, e.g. `mgcv::gam()`, extend generalised\n  linear models to incorporate arbitrary smooth functions. That means you can\n  write a formula like `y ~ s(x)` which becomes an equation like \n  `y = f(x)` and let `gam()` estimate what that function is (subject to some\n  smoothness constraints to make the problem tractable).\n  \n* __Penalised linear models__, e.g. `glmnet::glmnet()`, add a penalty term to\n  the distance that penalises complex models (as defined by the distance \n  between the parameter vector and the origin). This tends to make\n  models that generalise better to new datasets from the same population.\n\n* __Robust linear models__, e.g. `MASS:rlm()`, tweak the distance to downweight \n  points that are very far away. This makes them less sensitive to the presence\n  of outliers, at the cost of being not quite as good when there are no \n  outliers.\n  \n* __Trees__, e.g. `rpart::rpart()`, attack the problem in a completely different\n  way than linear models. They fit a piece-wise constant model, splitting the\n  data into progressively smaller and smaller pieces. Trees aren't terribly\n  effective by themselves, but they are very powerful when used in aggregate\n  by models like __random forests__ (e.g. `randomForest::randomForest()`) or \n  __gradient boosting machines__ (e.g. `xgboost::xgboost`.)\n\nThese models all work similarly from a programming perspective. Once you've mastered linear models, you should find it easy to master the mechanics of these other model classes. Being a skilled modeller is a mixture of some good general principles and having a big toolbox of techniques. Now that you've learned some general tools and one useful class of models, you can go on and learn more classes from other sources.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-building.Rmd",
    "content": "# Model building\n\n## Introduction\n\nIn the previous chapter you learned how linear models work, and learned some basic tools for understanding what a model is telling you about your data. The previous chapter focussed on simulated datasets. This chapter will focus on real data, showing you how you can progressively build up a model to aid your understanding of the data.\n\nWe will take advantage of the fact that you can think about a model partitioning your data into pattern and residuals. We'll find patterns with visualisation, then make them concrete and precise with a model. We'll then repeat the process, but replace the old response variable with the residuals from the model. The goal is to transition from implicit knowledge in the data and your head to explicit knowledge in a quantitative model. This makes it easier to apply to new domains, and easier for others to use. \n\nFor very large and complex datasets this will be a lot of work. There are certainly alternative approaches - a more machine learning approach is simply to focus on the predictive ability of the model. These approaches tend to produce black boxes: the model does a really good job at generating predictions, but you don't know why. This is a totally reasonable approach, but it does make it hard to apply your real world knowledge to the model. That, in turn, makes it difficult to assess whether or not the model will continue to work in the long-term, as fundamentals change. For most real models, I'd expect you to use some combination of this approach and a more classic automated approach.\n\nIt's a challenge to know when to stop. You need to figure out when your model is good enough, and when additional investment is unlikely to pay off. I particularly like this quote from reddit user Broseidon241: \n\n> A long time ago in art class, my teacher told me \"An artist needs to know \n> when a piece is done. You can't tweak something into perfection - wrap it up. \n> If you don't like it, do it over again. Otherwise begin something new\". Later\n> in life, I heard \"A poor seamstress makes many mistakes. A good seamstress \n> works hard to correct those mistakes. A great seamstress isn't afraid to \n> throw out the garment and start over.\"\n\n-- Broseidon241, <https://www.reddit.com/r/datascience/comments/4irajq>\n\n### Prerequisites\n\nWe'll use the same tools as in the previous chapter, but add in some real datasets: `diamonds` from ggplot2, and `flights` from nycflights13.  We'll also need lubridate in order to work with the date/times in `flights`.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\nlibrary(modelr)\noptions(na.action = na.warn)\n\nlibrary(nycflights13)\nlibrary(lubridate)\n```\n\n## Why are low quality diamonds more expensive? {#diamond-prices}\n\nIn previous chapters we've seen a surprising relationship between the quality of diamonds and their price: low quality diamonds (poor cuts, bad colours, and inferior clarity) have higher prices.\n\n```{r dev = \"png\"}\nggplot(diamonds, aes(cut, price)) + geom_boxplot()\nggplot(diamonds, aes(color, price)) + geom_boxplot()\nggplot(diamonds, aes(clarity, price)) + geom_boxplot()\n```\n\nNote that the worst diamond color is J (slightly yellow), and the worst clarity is I1 (inclusions visible to the naked eye).\n\n### Price and carat\n\nIt looks like lower quality diamonds have higher prices because there is an important confounding variable: the weight (`carat`) of the diamond. The weight of the diamond is the single most important factor for determining the price of the diamond, and lower quality diamonds tend to be larger.\n\n```{r}\nggplot(diamonds, aes(carat, price)) + \n  geom_hex(bins = 50)\n```\n\nWe can make it easier to see how the other attributes of a diamond affect its relative `price` by fitting a model to separate out the effect of `carat`. But first, lets make a couple of tweaks to the diamonds dataset to make it easier to work with:\n\n1. Focus on diamonds smaller than 2.5 carats (99.7% of the data)\n1. Log-transform the carat and price variables.\n\n```{r}\ndiamonds2 <- diamonds %>% \n  filter(carat <= 2.5) %>% \n  mutate(lprice = log2(price), lcarat = log2(carat))\n```\n\nTogether, these changes make it easier to see the relationship between `carat` and `price`:\n\n```{r}\nggplot(diamonds2, aes(lcarat, lprice)) + \n  geom_hex(bins = 50)\n```\n\nThe log-transformation is particularly useful here because it makes the pattern linear, and linear patterns are the easiest to work with. Let's take the next step and remove that strong linear pattern. We first make the pattern explicit by fitting a model:\n\n```{r}\nmod_diamond <- lm(lprice ~ lcarat, data = diamonds2)\n```\n\nThen we look at what the model tells us about the data. Note that I back transform the predictions, undoing the log transformation, so I can overlay the predictions on the raw data:\n\n```{r}\ngrid <- diamonds2 %>% \n  data_grid(carat = seq_range(carat, 20)) %>% \n  mutate(lcarat = log2(carat)) %>% \n  add_predictions(mod_diamond, \"lprice\") %>% \n  mutate(price = 2 ^ lprice)\n\nggplot(diamonds2, aes(carat, price)) + \n  geom_hex(bins = 50) + \n  geom_line(data = grid, colour = \"red\", size = 1)\n```\n\nThat tells us something interesting about our data. If we believe our model, then the large diamonds are much cheaper than expected. This is probably because no diamond in this dataset costs more than $19,000.\n\nNow we can look at the residuals, which verifies that we've successfully removed the strong linear pattern:\n\n```{r}\ndiamonds2 <- diamonds2 %>% \n  add_residuals(mod_diamond, \"lresid\")\n\nggplot(diamonds2, aes(lcarat, lresid)) + \n  geom_hex(bins = 50)\n```\n\nImportantly, we can now re-do our motivating plots using those residuals instead of `price`. \n\n```{r dev = \"png\"}\nggplot(diamonds2, aes(cut, lresid)) + geom_boxplot()\nggplot(diamonds2, aes(color, lresid)) + geom_boxplot()\nggplot(diamonds2, aes(clarity, lresid)) + geom_boxplot()\n```\n\nNow we see the relationship we expect: as the quality of the diamond increases, so too does its relative price. To interpret the `y` axis, we need to think about what the residuals are telling us, and what scale they are on. A residual of -1 indicates that `lprice` was 1 unit lower than a prediction based solely on its weight. $2^{-1}$ is 1/2, points with a value of -1 are half the expected price, and residuals with value 1 are twice the predicted price.\n\n### A more complicated model\n\nIf we wanted to, we could continue to build up our model, moving the effects we've observed into the model to make them explicit. For example, we could include `color`, `cut`, and `clarity` into the model so that we also make explicit the effect of these three categorical variables:\n\n```{r}\nmod_diamond2 <- lm(lprice ~ lcarat + color + cut + clarity, data = diamonds2)\n```\n\nThis model now includes four predictors, so it's getting harder to visualise. Fortunately, they're currently all independent which means that we can plot them individually in four plots. To make the process a little easier, we're going to use the `.model` argument to `data_grid`:\n\n```{r}\ngrid <- diamonds2 %>% \n  data_grid(cut, .model = mod_diamond2) %>% \n  add_predictions(mod_diamond2)\ngrid\n\nggplot(grid, aes(cut, pred)) + \n  geom_point()\n```\n\nIf the model needs variables that you haven't explicitly supplied, `data_grid()` will automatically fill them in with \"typical\" value. For continuous variables, it uses the median, and categorical variables it uses the most common value (or values, if there's a tie).\n\n```{r}\ndiamonds2 <- diamonds2 %>% \n  add_residuals(mod_diamond2, \"lresid2\")\n\nggplot(diamonds2, aes(lcarat, lresid2)) + \n  geom_hex(bins = 50)\n```\n\nThis plot indicates that there are some diamonds with quite large residuals - remember a residual of 2 indicates that the diamond is 4x the price that we expected. It's often useful to look at unusual values individually:\n\n```{r}\ndiamonds2 %>% \n  filter(abs(lresid2) > 1) %>% \n  add_predictions(mod_diamond2) %>% \n  mutate(pred = round(2 ^ pred)) %>% \n  select(price, pred, carat:table, x:z) %>% \n  arrange(price)\n```\n\nNothing really jumps out at me here, but it's probably worth spending time considering if this indicates a problem with our model, or if there are errors in the data. If there are mistakes in the data, this could be an opportunity to buy diamonds that have been priced low incorrectly.\n\n### Exercises\n\n1.  In the plot of `lcarat` vs. `lprice`, there are some bright vertical\n    strips. What do they represent?\n\n1.  If `log(price) = a_0 + a_1 * log(carat)`, what does that say about \n    the relationship between `price` and `carat`?\n    \n1.  Extract the diamonds that have very high and very low residuals. \n    Is there anything unusual about these diamonds? Are they particularly bad\n    or good, or do you think these are pricing errors?\n\n1.  Does the final model, `mod_diamond2`, do a good job of predicting\n    diamond prices? Would you trust it to tell you how much to spend\n    if you were buying a diamond?\n\n## What affects the number of daily flights?\n\nLet's work through a similar process for a dataset that seems even simpler at first glance: the number of flights that leave NYC per day. This is a really small dataset --- only 365 rows and 2 columns --- and we're not going to end up with a fully realised model, but as you'll see, the steps along the way will help us better understand the data. Let's get started by counting the number of flights per day and visualising it with ggplot2.\n\n```{r}\ndaily <- flights %>% \n  mutate(date = make_date(year, month, day)) %>% \n  group_by(date) %>% \n  summarise(n = n())\ndaily\n\nggplot(daily, aes(date, n)) + \n  geom_line()\n```\n\n### Day of week\n\nUnderstanding the long-term trend is challenging because there's a very strong day-of-week effect that dominates the subtler patterns. Let's start by looking at the distribution of flight numbers by day-of-week:\n\n```{r}\ndaily <- daily %>% \n  mutate(wday = wday(date, label = TRUE))\nggplot(daily, aes(wday, n)) + \n  geom_boxplot()\n```\n\nThere are fewer flights on weekends because most travel is for business. The effect is particularly pronounced on Saturday: you might sometimes leave on Sunday for a Monday morning meeting, but it's very rare that you'd leave on Saturday as you'd much rather be at home with your family.\n\nOne way to remove this strong pattern is to use a model. First, we fit the model, and display its predictions overlaid on the original data:\n\n```{r}\nmod <- lm(n ~ wday, data = daily)\n\ngrid <- daily %>% \n  data_grid(wday) %>% \n  add_predictions(mod, \"n\")\n\nggplot(daily, aes(wday, n)) + \n  geom_boxplot() +\n  geom_point(data = grid, colour = \"red\", size = 4)\n```\n\nNext we compute and visualise the residuals:\n\n```{r}\ndaily <- daily %>% \n  add_residuals(mod)\ndaily %>% \n  ggplot(aes(date, resid)) + \n  geom_ref_line(h = 0) + \n  geom_line()\n```\n\nNote the change in the y-axis: now we are seeing the deviation from the expected number of flights, given the day of week. This plot is useful because now that we've removed much of the large day-of-week effect, we can see some of the subtler patterns that remain:\n\n1.  Our model seems to fail starting in June: you can still see a strong \n    regular pattern that our model hasn't captured. Drawing a plot with one \n    line for each day of the week makes the cause easier to see:\n\n    ```{r}\n    ggplot(daily, aes(date, resid, colour = wday)) + \n      geom_ref_line(h = 0) + \n      geom_line()\n    ```\n\n    Our model fails to accurately predict the number of flights on Saturday:\n    during summer there are more flights than we expect, and during Fall there \n    are fewer. We'll see how we can do better to capture this pattern in the\n    next section.\n\n1.  There are some days with far fewer flights than expected:\n\n    ```{r}\n    daily %>% \n      filter(resid < -100)\n    ```\n\n    If you're familiar with American public holidays, you might spot New Year's \n    day, July 4th, Thanksgiving and Christmas. There are some others that don't \n    seem to correspond to public holidays. You'll work on those in one \n    of the exercises.\n    \n1.  There seems to be some smoother long term trend over the course of a year.\n    We can highlight that trend with `geom_smooth()`:\n\n    ```{r}\n    daily %>% \n      ggplot(aes(date, resid)) + \n      geom_ref_line(h = 0) + \n      geom_line(colour = \"grey50\") + \n      geom_smooth(se = FALSE, span = 0.20)\n    ```\n\n    There are fewer flights in January (and December), and more in summer \n    (May-Sep). We can't do much with this pattern quantitatively, because we \n    only have a single year of data. But we can use our domain knowledge to \n    brainstorm potential explanations.\n\n### Seasonal Saturday effect\n\nLet's first tackle our failure to accurately predict the number of flights on Saturday. A good place to start is to go back to the raw numbers, focussing on Saturdays:\n\n```{r}\ndaily %>% \n  filter(wday == \"Sat\") %>% \n  ggplot(aes(date, n)) + \n    geom_point() + \n    geom_line() +\n    scale_x_date(NULL, date_breaks = \"1 month\", date_labels = \"%b\")\n```\n\n(I've used both points and lines to make it more clear what is data and what is interpolation.)\n\nI suspect this pattern is caused by summer holidays: many people go on holiday in the summer, and people don't mind travelling on Saturdays for vacation. Looking at this plot, we might guess that summer holidays are from early June to late August. That seems to line up fairly well with the [state's school terms](http://schools.nyc.gov/Calendar/2013-2014+School+Year+Calendars.htm): summer break in 2013 was Jun 26--Sep 9. \n\nWhy are there more Saturday flights in the Spring than the Fall? I asked some American friends and they suggested that it's less common to plan family vacations during the Fall because of the big Thanksgiving and Christmas holidays. We don't have the data to know for sure, but it seems like a plausible working hypothesis.\n\nLets create a \"term\" variable that roughly captures the three school terms, and check our work with a plot:\n\n```{r}\nterm <- function(date) {\n  cut(date, \n    breaks = ymd(20130101, 20130605, 20130825, 20140101),\n    labels = c(\"spring\", \"summer\", \"fall\") \n  )\n}\n\ndaily <- daily %>% \n  mutate(term = term(date)) \n\ndaily %>% \n  filter(wday == \"Sat\") %>% \n  ggplot(aes(date, n, colour = term)) +\n  geom_point(alpha = 1/3) + \n  geom_line() +\n  scale_x_date(NULL, date_breaks = \"1 month\", date_labels = \"%b\")\n```\n\n(I manually tweaked the dates to get nice breaks in the plot. Using a visualisation to help you understand what your function is doing is a really powerful and general technique.)\n\nIt's useful to see how this new variable affects the other days of the week:\n\n```{r}\ndaily %>% \n  ggplot(aes(wday, n, colour = term)) +\n    geom_boxplot()\n```\n\nIt looks like there is significant variation across the terms, so fitting a separate day of week effect for each term is reasonable. This improves our model, but not as much as we might hope:\n\n```{r}\nmod1 <- lm(n ~ wday, data = daily)\nmod2 <- lm(n ~ wday * term, data = daily)\n\ndaily %>% \n  gather_residuals(without_term = mod1, with_term = mod2) %>% \n  ggplot(aes(date, resid, colour = model)) +\n    geom_line(alpha = 0.75)\n```\n\nWe can see the problem by overlaying the predictions from the model on to the raw data:\n\n```{r}\ngrid <- daily %>% \n  data_grid(wday, term) %>% \n  add_predictions(mod2, \"n\")\n\nggplot(daily, aes(wday, n)) +\n  geom_boxplot() + \n  geom_point(data = grid, colour = \"red\") + \n  facet_wrap(~ term)\n```\n\nOur model is finding the _mean_ effect, but we have a lot of big outliers, so mean tends to be far away from the typical value. We can alleviate this problem by using a model that is robust to the effect of outliers: `MASS::rlm()`. This greatly reduces the impact of the outliers on our estimates, and gives a model that does a good job of removing the day of week pattern:\n\n```{r, warn = FALSE}\nmod3 <- MASS::rlm(n ~ wday * term, data = daily)\n\ndaily %>% \n  add_residuals(mod3, \"resid\") %>% \n  ggplot(aes(date, resid)) + \n  geom_hline(yintercept = 0, size = 2, colour = \"white\") + \n  geom_line()\n```\n\nIt's now much easier to see the long-term trend, and the positive and negative outliers.\n\n\n### Computed variables\n\nIf you're experimenting with many models and many visualisations, it's a good idea to bundle the creation of variables up into a function so there's no chance of accidentally applying a different transformation in different places. For example, we could write:\n\n```{r}\ncompute_vars <- function(data) {\n  data %>% \n    mutate(\n      term = term(date), \n      wday = wday(date, label = TRUE)\n    )\n}\n```\n\nAnother option is to put the transformations directly in the model formula:\n\n```{r}\nwday2 <- function(x) wday(x, label = TRUE)\nmod3 <- lm(n ~ wday2(date) * term(date), data = daily)\n```\n\nEither approach is reasonable. Making the transformed variable explicit is useful if you want to check your work, or use them in a visualisation. But you can't easily use transformations (like splines) that return multiple columns. Including the transformations in the model function makes life a little easier when you're working with many different datasets because the model is self contained.\n\n### Time of year: an alternative approach\n\nIn the previous section we used our domain knowledge (how the US school term affects travel) to improve the model. An alternative to using our knowledge explicitly in the model is to give the data more room to speak. We could use a more flexible model and allow that to capture the pattern we're interested in. A simple linear trend isn't adequate, so we could try using a natural spline to fit a smooth curve across the year:\n\n```{r}\nlibrary(splines)\nmod <- MASS::rlm(n ~ wday * ns(date, 5), data = daily)\n\ndaily %>% \n  data_grid(wday, date = seq_range(date, n = 13)) %>% \n  add_predictions(mod) %>% \n  ggplot(aes(date, pred, colour = wday)) + \n    geom_line() +\n    geom_point()\n```\n\nWe see a strong pattern in the numbers of Saturday flights. This is reassuring, because we also saw that pattern in the raw data. It's a good sign when you get the same signal from different approaches.\n\n\n### Exercises\n\n1.  Use your Google sleuthing skills to brainstorm why there were fewer than\n    expected flights on Jan 20, May 26, and Sep 1. (Hint: they all have the\n    same explanation.) How would these days generalise to another year?\n\n1.  What do the three days with high positive residuals represent?\n    How would these days generalise to another year?\n\n    ```{r}\n    daily %>% \n      top_n(3, resid)\n    ```\n\n1.  Create a new variable that splits the `wday` variable into terms, but only\n    for Saturdays, i.e. it should have `Thurs`, `Fri`, but `Sat-summer`, \n    `Sat-spring`, `Sat-fall`. How does this model compare with the model with \n    every combination of `wday` and `term`?\n    \n1.  Create a new `wday` variable that combines the day of week, term \n    (for Saturdays), and public holidays. What do the residuals of \n    that model look like?\n\n1.  What happens if you fit a day of week effect that varies by month \n    (i.e. `n ~ wday * month`)? Why is this not very helpful? \n\n1.  What would you expect the model `n ~ wday + ns(date, 5)` to look like?\n    Knowing what you know about the data, why would you expect it to be\n    not particularly effective?\n\n1.  We hypothesised that people leaving on Sundays are more likely to be \n    business travellers who need to be somewhere on Monday. Explore that \n    hypothesis by seeing how it breaks down based on distance and time: if \n    it's true, you'd expect to see more Sunday evening flights to places that \n    are far away.\n\n1.  It's a little frustrating that Sunday and Saturday are on separate ends\n    of the plot. Write a small function to set the levels of the \n    factor so that the week starts on Monday.\n\n## Learning more about models\n\nWe have only scratched the absolute surface of modelling, but you have hopefully gained some simple, but general-purpose tools that you can use to improve your own data analyses. It's OK to start simple! As you've seen, even very simple models can make a dramatic difference in your ability to tease out interactions between variables.\n\nThese modelling chapters are even more opinionated than the rest of the book. I approach modelling from a somewhat different perspective to most others, and there is relatively little space devoted to it. Modelling really deserves a book on its own, so I'd highly recommend that you read at least one of these three books:\n\n* *Statistical Modeling: A Fresh Approach* by Danny Kaplan,\n  <http://www.mosaic-web.org/go/StatisticalModeling/>. This book provides \n  a gentle introduction to modelling, where you build your intuition,\n  mathematical tools, and R skills in parallel. The book replaces a traditional\n  \"introduction to statistics\" course, providing a curriculum that is up-to-date \n  and relevant to data science.\n\n* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten, \n  Trevor Hastie, and Robert Tibshirani, <http://www-bcf.usc.edu/~gareth/ISL/> \n  (available online for free). This book presents a family of modern modelling\n  techniques collectively known as statistical learning.  For an even deeper\n  understanding of the math behind the models, read the classic \n  *Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and\n  Jerome Friedman, <http://statweb.stanford.edu/~tibs/ElemStatLearn/> (also\n  available online for free).\n\n* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson, \n  <http://appliedpredictivemodeling.com>. This book is a companion to the \n  __caret__ package and provides practical tools for dealing with real-life\n  predictive modelling challenges.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-many.Rmd",
    "content": "# Many models\n\n## Introduction\n\nIn this chapter you're going to learn three powerful ideas that help you to work with large numbers of models with ease:\n\n1.  Using many simple models to better understand complex datasets.\n\n1.  Using list-columns to store arbitrary data structures in a data frame.\n    For example, this will allow you to have a column that contains linear \n    models.\n   \n1.  Using the __broom__ package, by David Robinson, to turn models into tidy \n    data. This is a powerful technique for working with large numbers of models\n    because once you have tidy data, you can apply all of the techniques that \n    you've learned about earlier in the book.\n\nWe'll start by diving into a motivating example using data about life expectancy around the world. It's a small dataset but it illustrates how important modelling can be for improving your visualisations. We'll use a large number of simple models to partition out some of the strongest signals so we can see the subtler signals that remain. We'll also see how model summaries can help us pick out outliers and unusual trends.\n\nThe following sections will dive into more detail about the individual techniques:\n\n1. In [list-columns], you'll learn more about the list-column data structure,\n   and why it's valid to put lists in data frames.\n   \n1. In [creating list-columns], you'll learn the three main ways in which you'll\n   create list-columns.\n   \n1. In [simplifying list-columns] you'll learn how to convert list-columns back\n   to regular atomic vectors (or sets of atomic vectors) so you can work\n   with them more easily.\n   \n1. In [making tidy data with broom], you'll learn about the full set of tools\n   provided by broom, and see how they can be applied to other types of \n   data structure.\n\nThis chapter is somewhat aspirational: if this book is your first introduction to R, this chapter is likely to be a struggle. It requires you have to deeply internalised ideas about modelling, data structures, and iteration. So don't worry if you don't get it --- just put this chapter aside for a few months, and come back when you want to stretch your brain. \n\n### Prerequisites\n\nWorking with many models requires many of the packages of the tidyverse (for data exploration, wrangling, and programming) and modelr to facilitate modelling.\n\n```{r setup, message = FALSE}\nlibrary(modelr)\nlibrary(tidyverse)\n```\n\n## List-columns\n\nNow that you've seen a basic workflow for managing many models, let's dive back into some of the details. In this section, we'll explore the list-column data structure in a little more detail. It's only recently that I've really appreciated the idea of the list-column. List-columns are implicit in the definition of the data frame: a data frame is a named list of equal length vectors. A list is a vector, so it's always been legitimate to use a list as a column of a data frame. However, base R doesn't make it easy to create list-columns, and `data.frame()` treats a list as a list of columns:.\n\n```{r}\ndata.frame(x = list(1:3, 3:5))\n```\n\nYou can prevent `data.frame()` from doing this with `I()`, but the result doesn't print particularly well:\n\n```{r}\ndata.frame(\n  x = I(list(1:3, 3:5)), \n  y = c(\"1, 2\", \"3, 4, 5\")\n)\n```\n\nTibble alleviates this problem by being lazier (`tibble()` doesn't modify its inputs) and by providing a better print method:\n\n```{r}\ntibble(\n  x = list(1:3, 3:5), \n  y = c(\"1, 2\", \"3, 4, 5\")\n)\n```\n\nIt's even easier with `tribble()` as it can automatically work out that you need a list:\n\n```{r}\ntribble(\n   ~x, ~y,\n  1:3, \"1, 2\",\n  3:5, \"3, 4, 5\"\n)\n```\n\nList-columns are often most useful as intermediate data structure. They're hard to work with directly, because most R functions work with atomic vectors or data frames, but the advantage of keeping related items together in a data frame is worth a little hassle.\n\nGenerally there are three parts of an effective list-column pipeline:\n\n1.  You create the list-column using one of `nest()`, `summarise()` + `list()`,\n    or `mutate()` + a map function, as described in [Creating list-columns].\n\n1.  You create other intermediate list-columns by transforming existing\n    list columns with `map()`, `map2()` or `pmap()`. For example, \n    in the case study above, we created a list-column of models by transforming\n    a list-column of data frames.\n    \n1.  You simplify the list-column back down to a data frame or atomic vector,\n    as described in [Simplifying list-columns].\n\n## Creating list-columns\n\nTypically, you won't create list-columns with `tibble()`. Instead, you'll create them from regular columns, using one of three methods: \n\n1.  With `tidyr::nest()` to convert a grouped data frame into a nested data \n    frame where you have list-column of data frames.\n    \n1.  With `mutate()` and vectorised functions that return a list.\n\n1.  With `summarise()` and summary functions that return multiple results. \n\nAlternatively, you might create them from a named list, using `tibble::enframe()`.\n\nGenerally, when creating list-columns, you should make sure they're homogeneous: each element should contain the same type of thing. There are no checks to make sure this is true, but if you use purrr and remember what you've learned about type-stable functions, you should find it happens naturally.\n\n### With nesting\n\n`nest()` creates a nested data frame, which is a data frame with a list-column of data frames. In a nested data frame each row is a meta-observation: the other columns give variables that define the observation, and the list-column of data frames gives the individual observations that make up the meta-observation.\n\nThere are two ways to use `nest()`. When applied to a grouped data frame, `nest()` keeps the grouping columns as is, and bundles everything else into the list-column.\n\nYou can also use it on an ungrouped data frame, specifying which columns you want to nest.\n\n### From vectorised functions\n\nSome useful functions take an atomic vector and return a list. For example, in [strings] you learned about `stringr::str_split()` which takes a character vector and returns a list of character vectors. If you use that inside mutate, you'll get a list-column:\n\n```{r}\ndf <- tribble(\n  ~x1,\n  \"a,b,c\", \n  \"d,e,f,g\"\n) \n\ndf %>% \n  mutate(x2 = stringr::str_split(x1, \",\"))\n```\n\n`unnest()` knows how to handle these lists of vectors:\n\n```{r}\ndf %>% \n  mutate(x2 = stringr::str_split(x1, \",\")) %>% \n  unnest()\n```\n\n(If you find yourself using this pattern a lot, make sure to check out `tidyr::separate_rows()` which is a wrapper around this common pattern).\n\nAnother example of this pattern is using the `map()`, `map2()`, `pmap()` from purrr. For example, we could take the final example from [Invoking different functions] and rewrite it to use `mutate()`:\n\n```{r}\nsim <- tribble(\n  ~f,      ~params,\n  \"runif\", list(min = -1, max = 1),\n  \"rnorm\", list(sd = 5),\n  \"rpois\", list(lambda = 10)\n)\n\nsim %>%\n  mutate(sims = invoke_map(f, params, n = 10))\n```\n\nNote that technically `sim` isn't homogeneous because it contains both double and integer vectors. However, this is unlikely to cause many problems since integers and doubles are both numeric vectors.\n\n### From multivalued summaries\n\nOne restriction of `summarise()` is that it only works with summary functions that return a single value. That means that you can't use it with functions like `quantile()` that return a vector of arbitrary length:\n\n```{r, error = TRUE}\nmtcars %>% \n  group_by(cyl) %>% \n  summarise(q = quantile(mpg))\n```\n\nYou can however, wrap the result in a list! This obeys the contract of `summarise()`, because each summary is now a list (a vector) of length 1.\n\n```{r}\nmtcars %>% \n  group_by(cyl) %>% \n  summarise(q = list(quantile(mpg)))\n```\n\nTo make useful results with unnest, you'll also need to capture the probabilities:\n\n```{r}\nprobs <- c(0.01, 0.25, 0.5, 0.75, 0.99)\nmtcars %>% \n  group_by(cyl) %>% \n  summarise(p = list(probs), q = list(quantile(mpg, probs))) %>% \n  unnest()\n```\n\n### From a named list\n\nData frames with list-columns provide a solution to a common problem: what do you do if you want to iterate over both the contents of a list and its elements? Instead of trying to jam everything into one object, it's often easier to make a data frame: one column can contain the elements, and one column can contain the list.  An easy way to create such a data frame from a list is `tibble::enframe()`.  \n\n```{r}\nx <- list(\n  a = 1:5,\n  b = 3:4, \n  c = 5:6\n) \n\ndf <- enframe(x)\ndf\n```\n\nThe advantage of this structure is that it generalises in a straightforward way - names are useful if you have character vector of metadata, but don't help if you have other types of data, or multiple vectors.\n\nNow if you want to iterate over names and values in parallel, you can use `map2()`:\n\n```{r}\ndf %>% \n  mutate(\n    smry = map2_chr(name, value, ~ stringr::str_c(.x, \": \", .y[1]))\n  )\n```\n\n### Exercises\n\n1.  List all the functions that you can think of that take a atomic vector and \n    return a list.\n    \n1.  Brainstorm useful summary functions that, like `quantile()`, return\n    multiple values.\n    \n1.  What's missing in the following data frame? How does `quantile()` return\n    that missing piece? Why isn't that helpful here?\n\n    ```{r}\n    mtcars %>% \n      group_by(cyl) %>% \n      summarise(q = list(quantile(mpg))) %>% \n      unnest()\n    ```\n\n1.  What does this code do? Why might might it be useful?\n\n    ```{r, eval = FALSE}\n    mtcars %>% \n      group_by(cyl) %>% \n      summarise_each(funs(list))\n    ```\n\n## Simplifying list-columns\n\nTo apply the techniques of data manipulation and visualisation you've learned in this book, you'll need to simplify the list-column back to a regular column (an atomic vector), or set of columns. The technique you'll use to collapse back down to a simpler structure depends on whether you want a single value per element, or multiple values:\n\n1.  If you want a single value, use `mutate()` with `map_lgl()`, \n    `map_int()`, `map_dbl()`, and `map_chr()` to create an atomic vector.\n    \n1.  If you want many values, use `unnest()` to convert list-columns back\n    to regular columns, repeating the rows as many times as necessary.\n\nThese are described in more detail below.\n\n### List to vector\n\nIf you can reduce your list column to an atomic vector then it will be a regular column. For example, you can always summarise an object with its type and length, so this code will work regardless of what sort of list-column you have:\n\n```{r}\ndf <- tribble(\n  ~x,\n  letters[1:5],\n  1:3,\n  runif(5)\n)\n  \ndf %>% mutate(\n  type = map_chr(x, typeof),\n  length = map_int(x, length)\n)\n```\n\nThis is the same basic information that you get from the default tbl print method, but now you can use it for filtering. This is a useful technique if you have a heterogeneous list, and want to filter out the parts aren't working for you.\n\nDon't forget about the `map_*()` shortcuts - you can use `map_chr(x, \"apple\")` to extract the string stored in `apple` for each element of `x`. This is useful for pulling apart nested lists into regular columns. Use the `.null` argument to provide a value to use if the element is missing (instead of returning `NULL`):\n\n```{r}\ndf <- tribble(\n  ~x,\n  list(a = 1, b = 2),\n  list(a = 2, c = 4)\n)\ndf %>% mutate(\n  a = map_dbl(x, \"a\"),\n  b = map_dbl(x, \"b\", .null = NA_real_)\n)\n```\n\n### Unnesting\n\n`unnest()` works by repeating the regular columns once for each element of the list-column. For example, in the following very simple example we repeat the first row 4 times (because there the first element of `y` has length four), and the second row once:\n\n```{r}\ntibble(x = 1:2, y = list(1:4, 1)) %>% unnest(y)\n```\n\nThis means that you can't simultaneously unnest two columns that contain different number of elements:\n\n```{r, error = TRUE}\n# Ok, because y and z have the same number of elements in\n# every row\ndf1 <- tribble(\n  ~x, ~y,           ~z,\n   1, c(\"a\", \"b\"), 1:2,\n   2, \"c\",           3\n)\ndf1\ndf1 %>% unnest(y, z)\n\n# Doesn't work because y and z have different number of elements\ndf2 <- tribble(\n  ~x, ~y,           ~z,\n   1, \"a\",         1:2,  \n   2, c(\"b\", \"c\"),   3\n)\ndf2\ndf2 %>% unnest(y, z)\n```\n\nThe same principle applies when unnesting list-columns of data frames. You can unnest multiple list-cols as long as all the data frames in each row have the same number of rows.\n\n### Exercises\n\n1.  Why might the `lengths()` function be useful for creating atomic\n    vector columns from list-columns?\n    \n1.  List the most common types of vector found in a data frame. What makes\n    lists different?\n\n## Making tidy data with broom\n\nThe broom package provides three general tools for turning models into tidy data frames:\n\n1.  `broom::glance(model)` returns a row for each model. Each column gives a \n    model summary: either a measure of model quality, or complexity, or a \n    combination of the two.\n   \n1.  `broom::tidy(model)` returns a row for each coefficient in the model. Each \n    column gives information about the estimate or its variability.\n    \n1.  `broom::augment(model, data)` returns a row for each row in `data`, adding\n    extra values like residuals, and influence statistics.\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model.Rmd",
    "content": "# (PART) Model {-}\n\n# Introduction {#model-intro}\n\nNow that you are equipped with powerful programming tools we can finally return to modelling. You'll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you'll learn a few basic tools that help you understand the variation within your models.\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science-model.png\")\n```\n\nThe goal of a model is to provide a simple low-dimensional summary of a dataset. Ideally, the model will capture true \"signals\" (i.e. patterns generated by the phenomenon of interest), and ignore \"noise\" (i.e. random variation that you're not interested in). Here we only cover \"predictive\" models, which, as the name suggests, generate predictions. There is another type of model that we're not going to discuss: \"data discovery\" models. These models don't make predictions, but instead help you discover interesting relationships within your data. (These two categories of models are sometimes called supervised and unsupervised, but I don't think that terminology is particularly illuminating.)\n\nThis book is not going to give you a deep understanding of the mathematical theory that underlies models. It will, however, build your intuition about how statistical models work, and give you a family of useful tools that allow you to use models to better understand your data:\n\n* In [model basics], you'll learn how models work mechanistically, focussing on\n  the important family of linear models. You'll learn general tools for gaining\n  insight into what a predictive model tells you about your data, focussing on\n  simple simulated datasets.\n\n* In [model building], you'll learn how to use models to pull out known\n  patterns in real data. Once you have recognised an important pattern\n  it's useful to make it explicit in a model, because then you can\n  more easily see the subtler signals that remain.\n\n* In [many models], you'll learn how to use many simple models to help \n  understand complex datasets. This is a powerful technique, but to access\n  it you'll need to combine modelling and programming tools.\n\nThese topics are notable because of what they don't include: any tools for quantitatively assessing models. That is deliberate: precisely quantifying a model requires a couple of big ideas that we just don't have the space to cover here. For now, you'll rely on qualitative assessment and your natural scepticism. In [Learning more about models], we'll point you to other resources where you can learn more.\n\n## Hypothesis generation vs. hypothesis confirmation\n\nIn this book, we are going to use models as a tool for exploration, completing the trifecta of the tools for EDA that were introduced in Part 1. This is not how models are usually taught, but as you will see, models are an important tool for exploration. Traditionally, the focus of modelling is on inference, or for confirming that an hypothesis is true. Doing this correctly is not complicated, but it is hard. There is a pair of ideas that you must understand in order to do inference correctly:\n\n1. Each observation can either be used for exploration or confirmation, \n   not both.\n\n1. You can use an observation as many times as you like for exploration,\n   but you can only use it once for confirmation. As soon as you use an \n   observation twice, you've switched from confirmation to exploration.\n   \nThis is necessary because to confirm a hypothesis you must use data independent of the data that you used to generate the hypothesis. Otherwise you will be over optimistic. There is absolutely nothing wrong with exploration, but you should never sell an exploratory analysis as a confirmatory analysis because it is fundamentally misleading. \n\nIf you are serious about doing an confirmatory analysis, one approach is to split your data into three pieces before you begin the analysis:\n\n1.  60% of your data goes into a __training__ (or exploration) set. You're \n    allowed to do anything you like with this data: visualise it and fit tons \n    of models to it.\n  \n1.  20% goes into a __query__ set. You can use this data to compare models \n    or visualisations by hand, but you're not allowed to use it as part of\n    an automated process.\n\n1.  20% is held back for a __test__ set. You can only use this data ONCE, to \n    test your final model. \n    \nThis partitioning allows you to explore the training data, occasionally generating candidate hypotheses that you check with the query set. When you are confident you have the right model, you can check it once with the test data.\n\n(Note that even when doing confirmatory modelling, you will still need to do EDA. If you don't do any EDA you will remain blind to the quality problems with your data.)\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/pipes.Rmd",
    "content": "# Pipes\n\n## Introduction\n\nPipes are a powerful tool for clearly expressing a sequence of multiple operations. So far, you've been using them without knowing how they work, or what the alternatives are. Now, in this chapter, it's time to explore the pipe in more detail. You'll learn the alternatives to the pipe, when you shouldn't use the pipe, and some useful related tools.\n\n### Prerequisites\n\nThe pipe, `%>%`, comes from the __magrittr__ package by Stefan Milton Bache. Packages in the tidyverse load `%>%` for you automatically, so you don't usually load magrittr explicitly.  Here, however, we're focussing on piping, and we aren't loading any other packages, so we will load it explicitly.\n\n```{r setup, message = FALSE}\nlibrary(magrittr)\n```\n\n## Piping alternatives\n\nThe point of the pipe is to help you write code in a way that is easier to read and understand. To see why the pipe is so useful, we're going to explore a number of ways of writing the same code. Let's use code to tell a story about a little bunny named Foo Foo:\n\n> Little bunny Foo Foo  \n> Went hopping through the forest  \n> Scooping up the field mice  \n> And bopping them on the head  \n\nThis is a popular Children's poem that is accompanied by hand actions.\n\nWe'll start by defining an object to represent little bunny Foo Foo:\n\n```{r, eval = FALSE}\nfoo_foo <- little_bunny()\n```\n\nAnd we'll use a function for each key verb: `hop()`, `scoop()`, and `bop()`. Using this object and these verbs, there are (at least) four ways we could retell the story in code:\n\n1. Save each intermediate step as a new object.\n1. Overwrite the original object many times.\n1. Compose functions.\n1. Use the pipe.\n\nWe'll work through each approach, showing you the code and talking about the advantages and disadvantages.\n\n### Intermediate steps\n\nThe simplest approach is to save each step as a new object:\n\n```{r, eval = FALSE}\nfoo_foo_1 <- hop(foo_foo, through = forest)\nfoo_foo_2 <- scoop(foo_foo_1, up = field_mice)\nfoo_foo_3 <- bop(foo_foo_2, on = head)\n```\n\nThe main downside of this form is that it forces you to name each intermediate element. If there are natural names, this is a good idea, and you should do it. But many times, like this in this example, there aren't natural names, and you add numeric suffixes to make the names unique. That leads to two problems:\n\n1. The code is cluttered with unimportant names\n\n1. You have to carefully increment the suffix on each line. \n\nWhenever I write code like this, I invariably use the wrong number on one line and then spend 10 minutes scratching my head and trying to figure out what went wrong with my code.\n\nYou may also worry that this form creates many copies of your data and takes up a lot of memory. Surprisingly, that's not the case. First, note that proactively worrying about memory is not a useful way to spend your time: worry about it when it becomes a problem (i.e. you run out of memory), not before. Second, R isn't stupid, and it will share columns across data frames, where possible. Let's take a look at an actual data manipulation pipeline where we add a new column to `ggplot2::diamonds`:\n\n```{r}\ndiamonds <- ggplot2::diamonds\ndiamonds2 <- diamonds %>% \n  dplyr::mutate(price_per_carat = price / carat)\n\npryr::object_size(diamonds)\npryr::object_size(diamonds2)\npryr::object_size(diamonds, diamonds2)\n```\n\n`pryr::object_size()` gives the memory occupied by all of its arguments. The results seem counterintuitive at first:\n\n* `diamonds` takes up 3.46 MB,\n* `diamonds2` takes up 3.89 MB,\n* `diamonds` and `diamonds2` together take up 3.89 MB!\n\nHow can that work? Well, `diamonds2` has 10 columns in common with `diamonds`: there's no need to duplicate all that data, so the two data frames have variables in common. These variables will only get copied if you modify one of them. In the following example, we modify a single value in `diamonds$carat`. That means the `carat` variable can no longer be shared between the two data frames, and a copy must be made. The size of each data frame is unchanged, but the collective size increases:\n\n```{r}\ndiamonds$carat[1] <- NA\npryr::object_size(diamonds)\npryr::object_size(diamonds2)\npryr::object_size(diamonds, diamonds2)\n```\n\n(Note that we use `pryr::object_size()` here, not the built-in `object.size()`. `object.size()` only takes a single object so it can't compute how data is shared across multiple objects.)\n\n### Overwrite the original\n\nInstead of creating intermediate objects at each step, we could overwrite the original object:\n\n```{r, eval = FALSE}\nfoo_foo <- hop(foo_foo, through = forest)\nfoo_foo <- scoop(foo_foo, up = field_mice)\nfoo_foo <- bop(foo_foo, on = head)\n```\n\nThis is less typing (and less thinking), so you're less likely to make mistakes. However, there are two problems:\n\n1.  Debugging is painful: if you make a mistake you'll need to re-run the \n    complete pipeline from the beginning.\n    \n1.  The repetition of the object being transformed (we've written `foo_foo` six \n    times!) obscures what's changing on each line. \n\n### Function composition\n\nAnother approach is to abandon assignment and just string the function calls together:\n\n```{r, eval = FALSE}\nbop(\n  scoop(\n    hop(foo_foo, through = forest),\n    up = field_mice\n  ), \n  on = head\n)\n```\n\nHere the disadvantage is that you have to read from inside-out, from right-to-left, and that the arguments end up spread far apart (evocatively called the \n[dagwood sandwhich](https://en.wikipedia.org/wiki/Dagwood_sandwich) problem). In short, this code is hard for a human to consume.\n\n### Use the pipe \n\nFinally, we can use the pipe:\n\n```{r, eval = FALSE}\nfoo_foo %>%\n  hop(through = forest) %>%\n  scoop(up = field_mice) %>%\n  bop(on = head)\n```\n\nThis is my favourite form, because it focusses on verbs, not nouns. You can read this series of function compositions like it's a set of imperative actions. Foo Foo hops, then scoops, then bops. The downside, of course, is that you need to be familiar with the pipe. If you've never seen `%>%` before, you'll have no idea what this code does. Fortunately, most people pick up the idea very quickly, so when you share your code with others who aren't familiar with the pipe, you can easily teach them.\n\nThe pipe works by performing a \"lexical transformation\": behind the scenes, magrittr reassembles the code in the pipe to a form that works by overwriting an intermediate object. When you run a pipe like the one above, magrittr does something like this:\n\n```{r, eval = FALSE}\nmy_pipe <- function(.) {\n  . <- hop(., through = forest)\n  . <- scoop(., up = field_mice)\n  bop(., on = head)\n}\nmy_pipe(foo_foo)\n```\n\nThis means that the pipe won't work for two classes of functions:\n\n1.  Functions that use the current environment. For example, `assign()`\n    will create a new variable with the given name in the current environment:\n     \n    ```{r}\n    assign(\"x\", 10)\n    x\n    \n    \"x\" %>% assign(100)\n    x\n    ```\n    \n    The use of assign with the pipe does not work because it assigns it to \n    a temporary environment used by `%>%`. If you do want to use assign with the\n    pipe, you must be explicit about the environment:\n    \n    ```{r}\n    env <- environment()\n    \"x\" %>% assign(100, envir = env)\n    x\n    ```\n    \n    Other functions with this problem include `get()` and `load()`.\n\n1.  Functions that use lazy evaluation. In R, function arguments\n    are only computed when the function uses them, not prior to calling the \n    function. The pipe computes each element in turn, so you can't \n    rely on this behaviour.\n    \n    One place that this is a problem is `tryCatch()`, which lets you capture\n    and handle errors:\n    \n    ```{r, error = TRUE}\n    tryCatch(stop(\"!\"), error = function(e) \"An error\")\n    \n    stop(\"!\") %>% \n      tryCatch(error = function(e) \"An error\")\n    ```\n    \n    There are a relatively wide class of functions with this behaviour,\n    including `try()`, `suppressMessages()`, and `suppressWarnings()`\n    in base R.\n  \n## When not to use the pipe\n\nThe pipe is a powerful tool, but it's not the only tool at your disposal, and it doesn't solve every problem! Pipes are most useful for rewriting a fairly short linear sequence of operations. I think you should reach for another tool when:\n\n* Your pipes are longer than (say) ten steps. In that case, create \n  intermediate objects with meaningful names. That will make debugging easier,\n  because you can more easily check the intermediate results, and it makes\n  it easier to understand your code, because the variable names can help \n  communicate intent.\n  \n* You have multiple inputs or outputs. If there isn't one primary object\n  being transformed, but two or more objects being combined together,\n  don't use the pipe.\n\n* You are starting to think about a directed graph with a complex\n  dependency structure. Pipes are fundamentally linear and expressing \n  complex relationships with them will typically yield confusing code.\n\n## Other tools from magrittr\n\nAll packages in the tidyverse automatically make `%>%` available for you, so you don't normally load magrittr explicitly. However, there are some other useful tools inside magrittr that you might want to try out:\n\n*   When working with more complex pipes, it's sometimes useful to call a \n    function for its side-effects. Maybe you want to print out the current \n    object, or plot it, or save it to disk. Many times, such functions don't \n    return anything, effectively terminating the pipe.\n    \n    To work around this problem, you can use the \"tee\" pipe. `%T>%` works like \n    `%>%` except that it returns the left-hand side instead of the right-hand \n    side. It's called \"tee\" because it's like a literal T-shaped pipe.\n\n    ```{r}\n    rnorm(100) %>%\n      matrix(ncol = 2) %>%\n      plot() %>%\n      str()\n    \n    rnorm(100) %>%\n      matrix(ncol = 2) %T>%\n      plot() %>%\n      str()\n    ```\n\n*   If you're working with functions that don't have a data frame based API  \n    (i.e. you pass them individual vectors, not a data frame and expressions \n    to be evaluated in the context of that data frame), you might find `%$%` \n    useful. It \"explodes\" out the variables in a data frame so that you can \n    refer to them explicitly. This is useful when working with many functions \n    in base R:\n    \n    ```{r}\n    mtcars %$%\n      cor(disp, mpg)\n    ```\n\n*   For assignment magrittr provides the `%<>%` operator which allows you to\n    replace code like:\n  \n    ```{r, eval = FALSE}\n    mtcars <- mtcars %>% \n      transform(cyl = cyl * 2)\n    ```\n    \n    with\n     \n    ```{r, eval = FALSE}\n    mtcars %<>% transform(cyl = cyl * 2)\n    ```\n    \n    I'm not a fan of this operator because I think assignment is such a \n    special operation that it should always be clear when it's occurring.\n    In my opinion, a little bit of duplication (i.e. repeating the \n    name of the object twice) is fine in return for making assignment\n    more explicit.\n"
  },
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    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/program.Rmd",
    "content": "# (PART) Program {-}\n\n# Introduction {#program-intro}\n\nIn this part of the book, you'll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper. \n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science-program.png\")\n```\n\nProgramming produces code, and code is a tool of communication. Obviously code tells the computer what you want it to do. But it also communicates meaning to other humans. Thinking about code as a vehicle for communication is important because every project you do is fundamentally collaborative. Even if you're not working with other people, you'll definitely be working with future-you! Writing clear code is important so that others (like future-you) can understand why you tackled an analysis in the way you did. That means getting better at programming also involves getting better at communicating. Over time, you want your code to become not just easier to write, but easier for others to read. \n\nWriting code is similar in many ways to writing prose. One parallel which I find particularly useful is that in both cases rewriting is the key to clarity. The first expression of your ideas is unlikely to be particularly clear, and you may need to rewrite multiple times. After solving a data analysis challenge, it's often worth looking at your code and thinking about whether or not it's obvious what you've done. If you spend a little time rewriting your code while the ideas are fresh, you can save a lot of time later trying to recreate what your code did. But this doesn't mean you should rewrite every function: you need to balance what you need to achieve now with saving time in the long run. (But the more you rewrite your functions the more likely your first attempt will be clear.)\n\nIn the following four chapters, you'll learn skills that will allow you to both tackle new programs and to solve existing problems with greater clarity and ease: \n\n1.  In [pipes], you will dive deep into the __pipe__, `%>%`, and learn more \n    about how it works, what the alternatives are, and when not to use it.\n\n1.  Copy-and-paste is a powerful tool, but you should avoid doing it more than\n    twice. Repeating yourself in code is dangerous because it can easily lead \n    to errors and inconsistencies. Instead, in [functions], you'll learn\n    how to write __functions__ which let you extract out repeated code so that \n    it can be easily reused.\n\n1.  As you start to write more powerful functions, you'll need a solid\n    grounding in R's __data structures__, provided by [vectors]. You must master \n    the four common atomic vectors, the three important S3 classes built on \n    top of them, and understand the mysteries of the list and data frame. \n\n1.  Functions extract out repeated code, but you often need to repeat the\n    same actions on different inputs. You need tools for __iteration__ that\n    let you do similar things again and again. These tools include for loops \n    and functional programming, which you'll learn about in [iteration].\n\n## Learning more\n\nThe goal of these chapters is to teach you the minimum about programming that you need to practice data science, which turns out to be a reasonable amount. Once you have mastered the material in this book, I strongly believe you should invest further in your programming skills. Learning more about programming is a long-term investment: it won't pay off immediately, but in the long term it will allow you to solve new problems more quickly, and let you reuse your insights from previous problems in new scenarios.\n\nTo learn more you need to study R as a programming language, not just an interactive environment for data science. We have written two books that will help you do so:\n\n* [_Hands on Programming with R_](https://amzn.com/1449359019),\n  by Garrett Grolemund. This is an introduction to R as a programming language \n  and is a great place to start if R is your first programming language. It \n  covers similar material to these chapters, but with a different style and\n  different motivation examples (based in the casino). It's a useful complement \n  if you find that these four chapters go by too quickly.\n  \n* [_Advanced R_](https://amzn.com/1466586966) by Hadley Wickham. This dives into the\n  details of R the programming language. This is a great place to start if you\n  have existing programming experience. It's also a great next step once you've \n  internalised the ideas in these chapters. You can read it online at\n  <http://adv-r.had.co.nz>.\n"
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    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/relational-data.Rmd",
    "content": "# Relational data\n\n## Introduction\n\nIt's rare that a data analysis involves only a single table of data. Typically you have many tables of data, and you must combine them to answer the questions that you're interested in. Collectively, multiple tables of data are called __relational data__ because it is the relations, not just the individual datasets, that are important.\n\nRelations are always defined between a pair of tables. All other relations are built up from this simple idea: the relations of three or more tables are always a property of the relations between each pair. Sometimes both elements of a pair can be the same table! This is needed if, for example, you have a table of people, and each person has a reference to their parents.\n\nTo work with relational data you need verbs that work with pairs of tables. There are three families of verbs designed to work with relational data:\n\n* __Mutating joins__, which add new variables to one data frame from matching\n  observations in another.\n\n* __Filtering joins__, which filter observations from one data frame based on\n  whether or not they match an observation in the other table.\n\n* __Set operations__, which treat observations as if they were set elements.\n\nThe most common place to find relational data is in a _relational_ database management system (or RDBMS), a term that encompasses almost all modern databases. If you've used a database before, you've almost certainly used SQL. If so, you should find the concepts in this chapter familiar, although their expression in dplyr is a little different. Generally, dplyr is a little easier to use than SQL because dplyr is specialised to do data analysis: it makes common data analysis operations easier, at the expense of making it more difficult to do other things that aren't commonly needed for data analysis.\n\n### Prerequisites\n\nWe will explore relational data from `nycflights13` using the two-table verbs from dplyr.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\nlibrary(nycflights13)\n```\n\n## nycflights13 {#nycflights13-relational}\n\nWe will use the nycflights13 package to learn about relational data. nycflights13 contains four tibbles that are related to the `flights` table that you used in [data transformation]:\n\n*   `airlines` lets you look up the full carrier name from its abbreviated\n    code:\n\n    ```{r}\n    airlines\n    ```\n\n*   `airports` gives information about each airport, identified by the `faa`\n    airport code:\n\n    ```{r}\n    airports\n    ```\n\n*   `planes` gives information about each plane, identified by its `tailnum`:\n\n    ```{r}\n    planes\n    ```\n\n*   `weather` gives the weather at each NYC airport for each hour:\n\n    ```{r}\n    weather\n    ```\n\nOne way to show the relationships between the different tables is with a drawing:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"diagrams/relational-nycflights.png\")\n```\n\nThis diagram is a little overwhelming, but it's simple compared to some you'll see in the wild! The key to understanding diagrams like this is to remember each relation always concerns a pair of tables. You don't need to understand the whole thing; you just need to understand the chain of relations between the tables that you are interested in.\n\nFor nycflights13:\n\n* `flights` connects to `planes` via a single variable, `tailnum`. \n\n* `flights` connects to `airlines` through the `carrier` variable.\n\n* `flights` connects to `airports` in two ways: via the `origin` and\n  `dest` variables.\n\n* `flights` connects to `weather` via `origin` (the location), and\n  `year`, `month`, `day` and `hour` (the time).\n\n### Exercises\n\n1.  Imagine you wanted to draw (approximately) the route each plane flies from\n    its origin to its destination. What variables would you need? What tables\n    would you need to combine?\n\n1.  I forgot to draw the relationship between `weather` and `airports`.\n    What is the relationship and how should it appear in the diagram?\n\n1.  `weather` only contains information for the origin (NYC) airports. If\n    it contained weather records for all airports in the USA, what additional\n    relation would it define with `flights`?\n\n1.  We know that some days of the year are \"special\", and fewer people than\n    usual fly on them. How might you represent that data as a data frame?\n    What would be the primary keys of that table? How would it connect to the\n    existing tables?\n\n## Keys\n\nThe variables used to connect each pair of tables are called __keys__. A key is a variable (or set of variables) that uniquely identifies an observation. In simple cases, a single variable is sufficient to identify an observation. For example, each plane is uniquely identified by its `tailnum`. In other cases, multiple variables may be needed. For example, to identify an observation in `weather` you need five variables: `year`, `month`, `day`, `hour`, and `origin`.\n\nThere are two types of keys:\n\n* A __primary key__ uniquely identifies an observation in its own table.\n  For example, `planes$tailnum` is a primary key because it uniquely identifies\n  each plane in the `planes` table.\n\n* A __foreign key__ uniquely identifies an observation in another table.\n  For example, the `flights$tailnum` is a foreign key because it appears in the \n  `flights` table where it matches each flight to a unique plane.\n\nA variable can be both a primary key _and_ a foreign key. For example, `origin` is part of the `weather` primary key, and is also a foreign key for the `airport` table.\n\nOnce you've identified the primary keys in your tables, it's good practice to verify that they do indeed uniquely identify each observation. One way to do that is to `count()` the primary keys and look for entries where `n` is greater than one:\n\n```{r}\nplanes %>% \n  count(tailnum) %>% \n  filter(n > 1)\n\nweather %>% \n  count(year, month, day, hour, origin) %>% \n  filter(n > 1)\n```\n\nSometimes a table doesn't have an explicit primary key: each row is an observation, but no combination of variables reliably identifies it. For example, what's the primary key in the `flights` table? You might think it would be the date plus the flight or tail number, but neither of those are unique:\n\n```{r}\nflights %>% \n  count(year, month, day, flight) %>% \n  filter(n > 1)\n\nflights %>% \n  count(year, month, day, tailnum) %>% \n  filter(n > 1)\n```\n\nWhen starting to work with this data, I had naively assumed that each flight number would be only used once per day: that would make it much easier to communicate problems with a specific flight. Unfortunately that is not the case! If a table lacks a primary key, it's sometimes useful to add one with `mutate()` and `row_number()`. That makes it easier to match observations if you've done some filtering and want to check back in with the original data. This is called a __surrogate key__.\n\nA primary key and the corresponding foreign key in another table form a __relation__. Relations are typically one-to-many. For example, each flight has one plane, but each plane has many flights. In other data, you'll occasionally see a 1-to-1 relationship. You can think of this as a special case of 1-to-many. You can model many-to-many relations with a many-to-1 relation plus a 1-to-many relation. For example, in this data there's a many-to-many relationship between airlines and airports: each airline flies to many airports; each airport hosts many airlines.\n\n### Exercises\n\n1.  Add a surrogate key to `flights`.\n\n1.  Identify the keys in the following datasets\n\n    1.  `Lahman::Batting`,\n    1.  `babynames::babynames`\n    1.  `nasaweather::atmos`\n    1.  `fueleconomy::vehicles`\n    1.  `ggplot2::diamonds`\n    \n    (You might need to install some packages and read some documentation.)\n\n1.  Draw a diagram illustrating the connections between the `Batting`,\n    `Master`, and `Salaries` tables in the Lahman package. Draw another diagram\n    that shows the relationship between `Master`, `Managers`, `AwardsManagers`.\n\n    How would you characterise the relationship between the `Batting`,\n    `Pitching`, and `Fielding` tables?\n\n## Mutating joins {#mutating-joins}\n\nThe first tool we'll look at for combining a pair of tables is the __mutating join__. A mutating join allows you to combine variables from two tables. It first matches observations by their keys, then copies across variables from one table to the other.\n\nLike `mutate()`, the join functions add variables to the right, so if you have a lot of variables already, the new variables won't get printed out. For these examples, we'll make it easier to see what's going on in the examples by creating a narrower dataset:\n\n```{r}\nflights2 <- flights %>% \n  select(year:day, hour, origin, dest, tailnum, carrier)\nflights2\n```\n\n(Remember, when you're in RStudio, you can also use `View()` to avoid this problem.)\n\nImagine you want to add the full airline name to the `flights2` data. You can combine the `airlines` and `flights2` data frames with `left_join()`:\n\n```{r}\nflights2 %>%\n  select(-origin, -dest) %>% \n  left_join(airlines, by = \"carrier\")\n```\n\nThe result of joining airlines to flights2 is an additional variable: `name`. This is why I call this type of join a mutating join. In this case, you could have got to the same place using `mutate()` and R's base subsetting:\n\n```{r}\nflights2 %>%\n  select(-origin, -dest) %>% \n  mutate(name = airlines$name[match(carrier, airlines$carrier)])\n```\n\nBut this is hard to generalise when you need to match multiple variables, and takes close reading to figure out the overall intent.\n\nThe following sections explain, in detail, how mutating joins work. You'll start by learning a useful visual representation of joins. We'll then use that to explain the four mutating join functions: the inner join, and the three outer joins. When working with real data, keys don't always uniquely identify observations, so next we'll talk about what happens when there isn't a unique match. Finally, you'll learn how to tell dplyr which variables are the keys for a given join.\n\n### Understanding joins\n\nTo help you learn how joins work, I'm going to use a visual representation:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-setup.png\")\n```\n```{r}\nx <- tribble(\n  ~key, ~val_x,\n     1, \"x1\",\n     2, \"x2\",\n     3, \"x3\"\n)\ny <- tribble(\n  ~key, ~val_y,\n     1, \"y1\",\n     2, \"y2\",\n     4, \"y3\"\n)\n```\n\nThe coloured column represents the \"key\" variable: these are used to match the rows between the tables. The grey column represents the \"value\" column that is carried along for the ride. In these examples I'll show a single key variable, but the idea generalises in a straightforward way to multiple keys and multiple values.\n\nA join is a way of connecting each row in `x` to zero, one, or more rows in `y`. The following diagram shows each potential match as an intersection of a pair of lines.\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-setup2.png\")\n```\n\n(If you look closely, you might notice that we've switched the order of the key and value columns in `x`. This is to emphasise that joins match based on the key; the value is just carried along for the ride.)\n\nIn an actual join, matches will be indicated with dots. The number of dots = the number of matches = the number of rows in the output.\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-inner.png\")\n```\n\n### Inner join {#inner-join}\n\nThe simplest type of join is the __inner join__. An inner join matches pairs of observations whenever their keys are equal:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-inner.png\")\n```\n\n(To be precise, this is an inner __equijoin__ because the keys are matched using the equality operator. Since most joins are equijoins we usually drop that specification.)\n\nThe output of an inner join is a new data frame that contains the key, the x values, and the y values. We use `by` to tell dplyr which variable is the key:\n\n```{r}\nx %>% \n  inner_join(y, by = \"key\")\n```\n\nThe most important property of an inner join is that unmatched rows are not included in the result. This means that generally inner joins are usually not appropriate for use in analysis because it's too easy to lose observations.\n\n### Outer joins {#outer-join}\n\nAn inner join keeps observations that appear in both tables. An __outer join__ keeps observations that appear in at least one of the tables. There are three types of outer joins:\n\n* A __left join__ keeps all observations in `x`.\n* A __right join__ keeps all observations in `y`.\n* A __full join__ keeps all observations in `x` and `y`.\n\nThese joins work by adding an additional \"virtual\" observation to each table. This observation has a key that always matches (if no other key matches), and a value filled with `NA`.\n\nGraphically, that looks like:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-outer.png\")\n```\n\nThe most commonly used join is the left join: you use this whenever you look up additional data from another table, because it preserves the original observations even when there isn't a match. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.\n\nAnother way to depict the different types of joins is with a Venn diagram:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-venn.png\")\n```\n\nHowever, this is not a great representation. It might jog your memory about which join preserves the observations in which table, but it suffers from a major limitation: a Venn diagram can't show what happens when keys don't uniquely identify an observation.\n\n### Duplicate keys {#join-matches}\n\nSo far all the diagrams have assumed that the keys are unique. But that's not always the case. This section explains what happens when the keys are not unique. There are two possibilities:\n\n1.  One table has duplicate keys. This is useful when you want to\n    add in additional information as there is typically a one-to-many\n    relationship.\n\n    ```{r, echo = FALSE, out.width = NULL}\n    knitr::include_graphics(\"diagrams/join-one-to-many.png\")\n    ```\n\n    Note that I've put the key column in a slightly different position\n    in the output. This reflects that the key is a primary key in `y`\n    and a foreign key in `x`.\n\n    ```{r}\n    x <- tribble(\n      ~key, ~val_x,\n         1, \"x1\",\n         2, \"x2\",\n         2, \"x3\",\n         1, \"x4\"\n    )\n    y <- tribble(\n      ~key, ~val_y,\n         1, \"y1\",\n         2, \"y2\"\n    )\n    left_join(x, y, by = \"key\")\n    ```\n\n1.  Both tables have duplicate keys. This is usually an error because in\n    neither table do the keys uniquely identify an observation. When you join\n    duplicated keys, you get all possible combinations, the Cartesian product:\n\n    ```{r, echo = FALSE, out.width = NULL}\n    knitr::include_graphics(\"diagrams/join-many-to-many.png\")\n    ```\n\n    ```{r}\n    x <- tribble(\n      ~key, ~val_x,\n         1, \"x1\",\n         2, \"x2\",\n         2, \"x3\",\n         3, \"x4\"\n    )\n    y <- tribble(\n      ~key, ~val_y,\n         1, \"y1\",\n         2, \"y2\",\n         2, \"y3\",\n         3, \"y4\"\n    )\n    left_join(x, y, by = \"key\")\n    ```\n\n### Defining the key columns {#join-by}\n\nSo far, the pairs of tables have always been joined by a single variable, and that variable has the same name in both tables. That constraint was encoded by `by = \"key\"`. You can use other values for `by` to connect the tables in other ways:\n\n  * The default, `by = NULL`, uses all variables that appear in both tables,\n    the so called __natural__ join. For example, the flights and weather tables\n    match on their common variables: `year`, `month`, `day`, `hour` and\n    `origin`.\n\n    ```{r}\n    flights2 %>% \n      left_join(weather)\n    ```\n\n  * A character vector, `by = \"x\"`. This is like a natural join, but uses only\n    some of the common variables. For example, `flights` and `planes` have\n    `year` variables, but they mean different things so we only want to join by\n    `tailnum`.\n\n    ```{r}\n    flights2 %>% \n      left_join(planes, by = \"tailnum\")\n    ```\n\n    Note that the `year` variables (which appear in both input data frames,\n    but are not constrained to be equal) are disambiguated in the output with\n    a suffix.\n\n  * A named character vector: `by = c(\"a\" = \"b\")`. This will\n    match variable `a` in table `x` to variable `b` in table `y`. The\n    variables from `x` will be used in the output.\n\n    For example, if we want to draw a map we need to combine the flights data\n    with the airports data which contains the location (`lat` and `lon`) of\n    each airport. Each flight has an origin and destination `airport`, so we\n    need to specify which one we want to join to:\n\n    ```{r}\n    flights2 %>% \n      left_join(airports, c(\"dest\" = \"faa\"))\n    \n    flights2 %>% \n      left_join(airports, c(\"origin\" = \"faa\"))\n    ```\n\n### Exercises\n\n1.  Compute the average delay by destination, then join on the `airports`\n    data frame so you can show the spatial distribution of delays. Here's an\n    easy way to draw a map of the United States:\n\n    ```{r, eval = FALSE}\n    airports %>%\n      semi_join(flights, c(\"faa\" = \"dest\")) %>%\n      ggplot(aes(lon, lat)) +\n        borders(\"state\") +\n        geom_point() +\n        coord_quickmap()\n    ```\n\n    (Don't worry if you don't understand what `semi_join()` does --- you'll\n    learn about it next.)\n\n    You might want to use the `size` or `colour` of the points to display\n    the average delay for each airport.\n\n1.  Add the location of the origin _and_ destination (i.e. the `lat` and `lon`)\n    to `flights`.\n\n1.  Is there a relationship between the age of a plane and its delays?\n\n1.  What weather conditions make it more likely to see a delay?\n\n1.  What happened on June 13 2013? Display the spatial pattern of delays,\n    and then use Google to cross-reference with the weather.\n\n    ```{r, eval = FALSE, include = FALSE}\n    worst <- filter(flights, !is.na(dep_time), month == 6, day == 13)\n    worst %>%\n      group_by(dest) %>%\n      summarise(delay = mean(arr_delay), n = n()) %>%\n      filter(n > 5) %>%\n      inner_join(airports, by = c(\"dest\" = \"faa\")) %>%\n      ggplot(aes(lon, lat)) +\n        borders(\"state\") +\n        geom_point(aes(size = n, colour = delay)) +\n        coord_quickmap()\n    ```\n\n### Other implementations\n\n`base::merge()` can perform all four types of mutating join:\n\ndplyr              | merge\n-------------------|-------------------------------------------\n`inner_join(x, y)` | `merge(x, y)`\n`left_join(x, y)`  | `merge(x, y, all.x = TRUE)`\n`right_join(x, y)` | `merge(x, y, all.y = TRUE)`,\n`full_join(x, y)`  | `merge(x, y, all.x = TRUE, all.y = TRUE)`\n\nThe advantages of the specific dplyr verbs is that they more clearly convey the intent of your code: the difference between the joins is really important but concealed in the arguments of `merge()`. dplyr's joins are considerably faster and don't mess with the order of the rows.\n\nSQL is the inspiration for dplyr's conventions, so the translation is straightforward:\n\ndplyr                        | SQL\n-----------------------------|-------------------------------------------\n`inner_join(x, y, by = \"z\")` | `SELECT * FROM x INNER JOIN y USING (z)`\n`left_join(x, y, by = \"z\")`  | `SELECT * FROM x LEFT OUTER JOIN y USING (z)`\n`right_join(x, y, by = \"z\")` | `SELECT * FROM x RIGHT OUTER JOIN y USING (z)`\n`full_join(x, y, by = \"z\")`  | `SELECT * FROM x FULL OUTER JOIN y USING (z)`\n\nNote that \"INNER\" and \"OUTER\" are optional, and often omitted.\n\nJoining different variables between the tables, e.g. `inner_join(x, y, by = c(\"a\" = \"b\"))` uses a slightly different syntax in SQL: `SELECT * FROM x INNER JOIN y ON x.a = y.b`. As this syntax suggests, SQL supports a wider  range of join types than dplyr because you can connect the tables using constraints other than equality (sometimes called non-equijoins).\n\n## Filtering joins {#filtering-joins}\n\nFiltering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:\n\n* `semi_join(x, y)` __keeps__ all observations in `x` that have a match in `y`.\n* `anti_join(x, y)` __drops__ all observations in `x` that have a match in `y`.\n\nSemi-joins are useful for matching filtered summary tables back to the original rows. For example, imagine you've found the top ten most popular destinations:\n\n```{r}\ntop_dest <- flights %>%\n  count(dest, sort = TRUE) %>%\n  head(10)\ntop_dest\n```\n\nNow you want to find each flight that went to one of those destinations. You could construct a filter yourself:\n\n```{r}\nflights %>% \n  filter(dest %in% top_dest$dest)\n```\n\nBut it's difficult to extend that approach to multiple variables. For example, imagine that you'd found the 10 days with highest average delays. How would you construct the filter statement that used `year`, `month`, and `day` to match it back to `flights`?\n\nInstead you can use a semi-join, which connects the two tables like a mutating join, but instead of adding new columns, only keeps the rows in `x` that have a match in `y`:\n\n```{r}\nflights %>% \n  semi_join(top_dest)\n```\n\nGraphically, a semi-join looks like this:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-semi.png\")\n```\n\nOnly the existence of a match is important; it doesn't matter which observation is matched. This means that filtering joins never duplicate rows like mutating joins do:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-semi-many.png\")\n```\n\nThe inverse of a semi-join is an anti-join. An anti-join keeps the rows that _don't_ have a match:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"diagrams/join-anti.png\")\n```\n\nAnti-joins are useful for diagnosing join mismatches. For example, when connecting `flights` and `planes`, you might be interested to know that there are many `flights` that don't have a match in `planes`:\n\n```{r}\nflights %>%\n  anti_join(planes, by = \"tailnum\") %>%\n  count(tailnum, sort = TRUE)\n```\n\n### Exercises\n\n1.  What does it mean for a flight to have a missing `tailnum`? What do the\n    tail numbers that don't have a matching record in `planes` have in common?\n    (Hint: one variable explains ~90% of the problems.)\n\n1.  Filter flights to only show flights with planes that have flown at least 100\n    flights.\n\n1.  Combine `fueleconomy::vehicles` and `fueleconomy::common` to find only the\n    records for the most common models.\n\n1.  Find the 48 hours (over the course of the whole year) that have the worst\n    delays. Cross-reference it with the `weather` data. Can you see any\n    patterns?\n\n1.  What does `anti_join(flights, airports, by = c(\"dest\" = \"faa\"))` tell you?\n    What does `anti_join(airports, flights, by = c(\"faa\" = \"dest\"))` tell you?\n\n1.  You might expect that there's an implicit relationship between plane\n    and airline, because each plane is flown by a single airline. Confirm\n    or reject this hypothesis using the tools you've learned above.\n\n## Join problems\n\nThe data you've been working with in this chapter has been cleaned up so that you'll have as few problems as possible. Your own data is unlikely to be so nice, so there are a few things that you should do with your own data to make your joins go smoothly.\n\n1.  Start by identifying the variables that form the primary key in each table.\n    You should usually do this based on your understanding of the data, not\n    empirically by looking for a combination of variables that give a\n    unique identifier. If you just look for variables without thinking about\n    what they mean, you might get (un)lucky and find a combination that's\n    unique in your current data but the relationship might not be true in\n    general.\n\n    For example, the altitude and longitude uniquely identify each airport,\n    but they are not good identifiers!\n\n    ```{r}\n    airports %>% count(alt, lon) %>% filter(n > 1)\n    ```\n\n1.  Check that none of the variables in the primary key are missing. If\n    a value is missing then it can't identify an observation!\n\n1.  Check that your foreign keys match primary keys in another table. The\n    best way to do this is with an `anti_join()`. It's common for keys\n    not to match because of data entry errors. Fixing these is often a lot of\n    work.\n\n    If you do have missing keys, you'll need to be thoughtful about your\n    use of inner vs. outer joins, carefully considering whether or not you\n    want to drop rows that don't have a match.\n\nBe aware that simply checking the number of rows before and after the join is not sufficient to ensure that your join has gone smoothly. If you have an inner join with duplicate keys in both tables, you might get unlucky as the number of dropped rows might exactly equal the number of duplicated rows!\n\n## Set operations {#set-operations}\n\nThe final type of two-table verb are the set operations. Generally, I use these the least frequently, but they are occasionally useful when you want to break a single complex filter into simpler pieces. All these operations work with a complete row, comparing the values of every variable. These expect the `x` and `y` inputs to have the same variables, and treat the observations like sets:\n\n* `intersect(x, y)`: return only observations in both `x` and `y`.\n* `union(x, y)`: return unique observations in `x` and `y`.\n* `setdiff(x, y)`: return observations in `x`, but not in `y`.\n\nGiven this simple data:\n\n```{r}\ndf1 <- tribble(\n  ~x, ~y,\n   1,  1,\n   2,  1\n)\ndf2 <- tribble(\n  ~x, ~y,\n   1,  1,\n   1,  2\n)\n```\n\nThe four possibilities are:\n\n```{r}\nintersect(df1, df2)\n\n# Note that we get 3 rows, not 4\nunion(df1, df2)\n\nsetdiff(df1, df2)\n\nsetdiff(df2, df1)\n```\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-formats.Rmd",
    "content": "# R Markdown formats\n\n## Introduction\n\nSo far you've seen R Markdown used to produce HTML documents. This chapter gives a brief overview of some of the many other types of output you can produce with R Markdown. There are two ways to set the output of a document:\n\n1.  Permanently, by modifying the YAML header: \n    \n    ```yaml\n    title: \"Viridis Demo\"\n    output: html_document\n    ```\n    \n1.  Transiently, by calling `rmarkdown::render()` by hand:\n    \n    ```{r eval = FALSE}\n    rmarkdown::render(\"diamond-sizes.Rmd\", output_format = \"word_document\")\n    ```\n    \n    This is useful if you want to programmatically produce multiple types of\n    output.\n\nRStudio's knit button renders a file to the first format listed in its `output` field. You can render to additional formats by clicking the dropdown menu beside the knit button.\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rmarkdown-knit.png\")\n```\n\n## Output options\n\nEach output format is associated with an R function. You can either write `foo` or `pkg::foo`. If you omit `pkg`, the default is assumed to be rmarkdown. It's important to know the name of the function that makes the output because that's where you get help. For example, to figure out what parameters you can set with `html_document`, look at `?rmarkdown::html_document`.\n\nTo override the default parameter values, you need to use an expanded `output` field. For example, if you wanted to render an `html_document` with a floating table of contents, you'd use:\n\n```yaml\noutput:\n  html_document:\n    toc: true\n    toc_float: true\n```\n\nYou can even render to multiple outputs by supplying a list of formats:\n\n```yaml\noutput:\n  html_document:\n    toc: true\n    toc_float: true\n  pdf_document: default\n```\n\nNote the special syntax if you don't want to override any of the default options.\n\n## Documents\n\nThe previous chapter focused on the default `html_document` output. There are a number of basic variations on that theme, generating different types of documents:\n\n*   `pdf_document` makes a PDF with LaTeX (an open source document layout \n    system), which you'll need to install. RStudio will prompt you if you \n    don't already have it.\n  \n*   `word_document` for Microsoft Word documents (`.docx`).\n  \n*   `odt_document` for OpenDocument Text documents (`.odt`).\n  \n*   `rtf_document` for Rich Text Format (`.rtf`) documents.\n  \n*   `md_document` for a Markdown document. This isn't typically useful by \n    itself, but you might use it if, for example, your corporate CMS or\n    lab wiki uses markdown.\n    \n*   `github_document`: this is a tailored version of `md_document` \n    designed for sharing on GitHub. \n\nRemember, when generating a document to share with decision makers, you can turn off the default display of code by setting global options in the setup chunk:\n\n```{r, eval = FALSE}\nknitr::opts_chunk$set(echo = FALSE)\n```\n\nFor `html_document`s another option is to make the code chunks hidden by default, but visible with a click:\n\n```yaml\noutput:\n  html_document:\n    code_folding: hide\n```\n\n## Notebooks\n\nA notebook, `html_notebook`, is a variation on a `html_document`. The rendered outputs are very similar, but the purpose is different. A `html_document` is focused on communicating with decision makers, while a notebook is focused on collaborating with other data scientists. These different purposes lead to using the HTML output in different ways. Both HTML outputs will contain the fully rendered output, but the notebook also contains the full source code. That means you can use the `.nb.html` generated by the notebook in two ways:\n\n1. You can view it in a web browser, and see the rendered output. Unlike\n   `html_document`, this rendering always includes an embedded copy of \n   the source code that generated it.\n\n1. You can edit it in RStudio. When you open an `.nb.html` file, RStudio will\n   automatically recreate the `.Rmd` file that generated it. In the future, you \n   will also be able to include supporting files (e.g. `.csv` data files), which \n   will be automatically extracted when needed. \n\nEmailing `.nb.html` files is a simple way to share analyses with your colleagues. But things will get painful as soon as they want to make changes. If this starts to happen, it's a good time to learn Git and GitHub. Learning Git and GitHub is definitely painful at first, but the collaboration payoff is huge. As mentioned earlier, Git and GitHub are outside the scope of the book, but there's one tip that's useful if you're already using them: use both `html_notebook` and `github_document` outputs:\n\n```yaml\noutput:\n  html_notebook: default\n  github_document: default\n```\n\n`html_notebook` gives you a local preview, and a file that you can share via email. `github_document` creates a minimal md file that you can check into git. You can easily see how the results of your analysis (not just the code) change over time, and GitHub will render it for you nicely online.\n\n## Presentations\n\nYou can also use R Markdown to produce presentations. You get less visual control than with a tool like Keynote or PowerPoint, but automatically inserting the results of your R code into a presentation can save a huge amount of time. Presentations work by dividing your content into slides, with a new slide beginning at each first (`#`) or second (`##`) level header. You can also insert a horizontal rule (`***`) to create a new slide without a header. \n\nR Markdown comes with three presentation formats built-in:\n\n1.  `ioslides_presentation` - HTML presentation with ioslides\n\n1.  `slidy_presentation` - HTML presentation with W3C Slidy\n\n1.  `beamer_presentation` - PDF presentation with LaTeX Beamer.\n\nTwo other popular formats are provided by packages:\n\n1.  `revealjs::revealjs_presentation` - HTML presentation with reveal.js. \n    Requires the __revealjs__ package.\n\n1.  __rmdshower__, <https://github.com/MangoTheCat/rmdshower>, provides a \n    wrapper around the __shower__, <https://github.com/shower/shower>, \n    presentation engine\n\n## Dashboards\n\nDashboards are a useful way to communicate large amounts of information visually and quickly. Flexdashboard makes it particularly easy to create dashboards using R Markdown and a convention for how the headers affect the layout:\n\n* Each level 1 header (`#`) begins a new page in the dashboard.\n* Each level 2 header (`##`) begins a new column.\n* Each level 3 header (`###`) begins a new row.\n\nFor example, you can produce this dashboard:\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"screenshots/rmarkdown-flexdashboard.png\")\n```\n\nUsing this code:\n\n```{r comment = \"\", echo = FALSE}\ncat(readr::read_file(\"rmarkdown/dashboard.Rmd\"))\n```\n\nFlexdashboard also provides simple tools for creating sidebars, tabsets, value boxes, and gauges. To learn more about flexdashboard visit <http://rmarkdown.rstudio.com/flexdashboard/>.\n\n## Interactivity\n\nAny HTML format (document, notebook, presentation, or dashboard) can contain interactive components.\n\n### htmlwidgets\n\nHTML is an interactive format, and you can take advantage of that interactivity with __htmlwidgets__, R functions that produce interactive HTML visualisations. For example, take the __leaflet__ map below. If you're viewing this page on the web, you can drag the map around, zoom in and out, etc. You obviously can't do that in a book, so rmarkdown automatically inserts a static screenshot for you.\n\n```{r}\nlibrary(leaflet)\nleaflet() %>%\n  setView(174.764, -36.877, zoom = 16) %>% \n  addTiles() %>%\n  addMarkers(174.764, -36.877, popup = \"Maungawhau\") \n```\n\nThe great thing about htmlwidgets is that you don't need to know anything about HTML or JavaScript to use them. All the details are wrapped inside the package, so you don't need to worry about it.  \n\nThere are many packages that provide htmlwidgets, including:\n\n* __dygraphs__, <http://rstudio.github.io/dygraphs/>, for interactive time \n  series visualisations.\n\n* __DT__, <http://rstudio.github.io/DT/>, for interactive tables.\n\n* __threejs__, <https://github.com/bwlewis/rthreejs> for interactive 3d plots.\n\n* __DiagrammeR__, <http://rich-iannone.github.io/DiagrammeR/> for diagrams\n  (like flow charts and simple node-link diagrams).\n\nTo learn more about htmlwidgets and see a more complete list of packages that provide them visit <http://www.htmlwidgets.org/>.\n\n### Shiny\n\nhtmlwidgets provide __client-side__ interactivity --- all the interactivity happens in the browser, independently of R. On one hand, that's great because you can distribute the HTML file without any connection to R. However, that fundamentally limits what you can do to things that have been implemented in HTML and JavaScript.  An alternative approach is to use __shiny__, a package that allows you to create interactivity using R code, not JavaScript.\n\nTo call Shiny code from an R Markdown document, add `runtime: shiny` to the header:\n\n```yaml\ntitle: \"Shiny Web App\"\noutput: html_document\nruntime: shiny\n```\n\nThen you can use the \"input\" functions to add interactive components to the document:\n\n```{r, eval = FALSE}\nlibrary(shiny)\n\ntextInput(\"name\", \"What is your name?\")\nnumericInput(\"age\", \"How old are you?\", NA, min = 0, max = 150)\n```\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rmarkdown-shiny.png\")\n```\nYou can then refer to the values with `input$name` and `input$age`, and the code that uses them will be automatically re-run whenever they change. \n\nI can't show you a live shiny app here because shiny interactions occur on the __server-side__. This means that you can write interactive apps without knowing JavaScript, but you need a server to run them on. This introduces a logistical issue: Shiny apps need a Shiny server to be run online. When you run shiny apps on your own computer, shiny automatically sets up a shiny server for you, but you need a public facing shiny server if you want to publish this sort of interactivity online. That's the fundamental trade-off of shiny: you can do anything in a shiny document that you can do in R, but it requires someone to be running R.\n\nLearn more about Shiny at <http://shiny.rstudio.com/>.\n\n## Websites\n\nWith a little additional infrastructure you can use R Markdown to generate a complete website:\n\n*   Put your `.Rmd` files in a single directory. `index.Rmd` will become \n    the home page.\n\n*   Add a YAML file named `_site.yml` provides the navigation for the site.\n    For example:\n\n    ```{r echo = FALSE, comment = \"\"}\n    cat(readr::read_file(\"rmarkdown/example-site.yml\"))\n    ```\n\nExecute `rmarkdown::render_site()` to build `_site`, a directory of files ready to deploy as a standalone static website, or if you use an RStudio Project for your website directory. RStudio will add a Build tab to the IDE that you can use to build and preview your site. \n\nRead more at <http://rmarkdown.rstudio.com/rmarkdown_websites.html>.\n\n## Other formats\n\nOther packages provide even more output formats:\n\n*   The __bookdown__ package, <https://github.com/rstudio/bookdown>, \n    makes it easy to write books, like this one. To learn more, read \n    [_Authoring Books with R Markdown_](https://bookdown.org/yihui/bookdown/),\n    by Yihui Xie, which is, of course, written in bookdown. Visit\n    <http://www.bookdown.org> to see other bookdown books written by the \n    wider R community.\n\n*   The __prettydoc__ package, <https://github.com/yixuan/prettydoc/>, \n    provides lightweight document formats with a range of attractive\n    themes.\n\n*   The __rticles__ package, <https://github.com/rstudio/rticles>, compiles a\n    selection of formats tailored for specific scientific journals.\n\nSee <http://rmarkdown.rstudio.com/formats.html> for a list of even more formats.  You can also create your own by following the instructions at <http://rmarkdown.rstudio.com/developer_custom_formats.html>.\n\n## Learning more\n\nTo learn more about effective communication in these different formats I recommend the following resources:\n\n* To improve your presentation skills, I recommend \n  [_Presentation Patterns_](https://amzn.com/0321820800), by Neal Ford,\n  Matthew McCollough, and Nathaniel Schutta. It provides a set of effective\n  patterns (both low- and high-level) that you can apply to improve your \n  presentations.\n  \n* If you give academic talks, I recommend reading the [_Leek group guide\n  to giving talks_](https://github.com/jtleek/talkguide).\n  \n* I haven't taken it myself, but I've heard good things about Matt \n  McGarrity's online course on public speaking: \n  <https://www.coursera.org/learn/public-speaking>.\n\n* If you are creating a lot of dashboards, make sure to read Stephen Few's\n  [Information Dashboard Design: The Effective Visual Communication \n  of Data](https://amzn.com/0596100167). It will help you create dashboards\n  that are truly useful, not just pretty to look at.\n\n* Effectively communicating your ideas often benefits from some\n  knowledge of graphic design. [_The Non-Designer's Design\n  Book_](http://amzn.com/0133966151) is a great place to start.\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-workflow.Rmd",
    "content": "# R Markdown workflow\n\nEarlier, we discussed a basic workflow for capturing your R code where you work  interactively in the _console_, then capture what works in the _script editor_. R Markdown brings together the console and the script editor, blurring the lines between interactive exploration and long-term code capture. You can rapidly iterate within a chunk, editing and re-executing with Cmd/Ctrl + Shift + Enter. When you're happy, you move on and start a new chunk.\n\nR Markdown is also important because it so tightly integrates prose and code. This makes it a great __analysis notebook__ because it lets you develop code and record your thoughts. An analysis notebook shares many of the same goals as a classic lab notebook in the physical sciences. It:\n\n*   Records what you did and why you did it. Regardless of how great your\n    memory is, if you don't record what you do, there will come a time when\n    you have forgotten important details. Write them down so you don't forget!\n\n*   Supports rigorous thinking. You are more likely to come up with a strong\n    analysis if you record your thoughts as you go, and continue to reflect\n    on them. This also saves you time when you eventually write up your\n    analysis to share with others.\n\n*   Helps others understand your work. It is rare to do data analysis by\n    yourself, and you'll often be working as part of a team. A lab notebook\n    helps you share not only what you've done, but why you did it with your\n    colleagues or lab mates.\n\nMuch of the good advice about using lab notebooks effectively can also be translated to analysis notebooks. I've drawn on my own experiences and Colin Purrington's advice on lab notebooks  (<http://colinpurrington.com/tips/lab-notebooks>) to come up with the following tips:\n\n*   Ensure each notebook has a descriptive title, an evocative filename, and a\n    first paragraph that briefly describes the aims of the analysis.\n\n*   Use the YAML header date field to record the date you started working on the\n    notebook:\n\n    ```yaml\n    date: 2016-08-23\n    ```\n\n    Use ISO8601 YYYY-MM-DD format so that's there no ambiguity. Use it\n    even if you don't normally write dates that way!\n\n*   If you spend a lot of time on an analysis idea and it turns out to be a\n    dead end, don't delete it! Write up a brief note about why it failed and\n    leave it in the notebook. That will help you avoid going down the same\n    dead end when you come back to the analysis in the future.\n\n*   Generally, you're better off doing data entry outside of R. But if you \n    do need to record a small snippet of data, clearly lay it out using\n    `tibble::tribble()`.\n\n*   If you discover an error in a data file, never modify it directly, but\n    instead write code to correct the value. Explain why you made the fix.\n\n*   Before you finish for the day, make sure you can knit the notebook\n    (if you're using caching, make sure to clear the caches). That will\n    let you fix any problems while the code is still fresh in your mind.\n\n*   If you want your code to be reproducible in the long-run (i.e. so you can\n    come back to run it next month or next year), you'll need to track the\n    versions of the packages that your code uses. A rigorous approach is to use\n    __packrat__, <http://rstudio.github.io/packrat/>, which stores packages \n    in your project directory, or __checkpoint__,\n    <https://github.com/RevolutionAnalytics/checkpoint>, which will reinstall\n    packages available on a specified date. A quick and dirty hack is to include\n    a chunk that runs `sessionInfo()` --- that won't let you easily recreate \n    your packages as they are today, but at least you'll know what they were.\n\n*   You are going to create many, many, many analysis notebooks over the course\n    of your career. How are you going to organise them so you can find them\n    again in the future? I recommend storing them in individual projects,\n    and coming up with a good naming scheme.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown.Rmd",
    "content": "# R Markdown\n\n## Introduction\n\nR Markdown provides an unified authoring framework for data science, combining your code, its results, and your prose commentary. R Markdown documents are fully reproducible and support dozens of output formats, like PDFs, Word files, slideshows, and more. \n\nR Markdown files are designed to be used in three ways:\n\n1.  For communicating to decision makers, who want to focus on the conclusions,\n    not the code behind the analysis.\n\n1.  For collaborating with other data scientists (including future you!), who\n    are interested in both your conclusions, and how you reached them (i.e.\n    the code).\n    \n1.  As an environment in which to _do_ data science, as a modern day lab \n    notebook where you can capture not only what you did, but also what you\n    were thinking.\n\nR Markdown integrates a number of R packages and external tools. This means that help is, by-and-large, not available through `?`. Instead, as you work through this chapter, and use R Markdown in the future, keep these resources close to hand:\n\n*   R Markdown Cheat Sheet: _Help > Cheatsheets > R Markdown Cheat Sheet_,\n\n*   R Markdown Reference Guide: _Help > Cheatsheets > R Markdown Reference \n    Guide_.\n\nBoth cheatsheets are also available at <http://rstudio.com/cheatsheets>.\n\n### Prerequisites\n\nYou need the __rmarkdown__ package, but you don't need to explicitly install it or load it, as RStudio automatically does both when needed.\n\n```{r setup, include = FALSE}\nchunk <- \"```\"\ninline <- function(x = \"\") paste0(\"`` `r \", x, \"` ``\")\nlibrary(tidyverse)\n```\n\n## R Markdown basics\n\nThis is an R Markdown file, a plain text file that has the extension `.Rmd`:\n\n```{r echo = FALSE, comment = \"\"}\ncat(htmltools::includeText(\"rmarkdown/diamond-sizes.Rmd\"))\n```\n\nIt contains three important types of content:\n\n1.  An (optional) __YAML header__ surrounded by `---`s.\n1.  __Chunks__ of R code surrounded by ```` ``` ````.\n1.  Text mixed with simple text formatting like `# heading` and `_italics_`.\n\nWhen you open an `.Rmd`, you get a notebook interface where code and output are interleaved. You can run each code chunk by clicking the Run icon (it looks like a play button at the top of the chunk), or by pressing Cmd/Ctrl + Shift + Enter. RStudio executes the code and displays the results inline with the code:\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"rmarkdown/diamond-sizes-notebook.png\")\n```\n\nTo produce a complete report containing all text, code, and results, click \"Knit\" or press Cmd/Ctrl + Shift + K.  You can also do this programmatically with `rmarkdown::render(\"1-example.Rmd\")`. This will display the report in the viewer pane, and create a self-contained HTML file that you can share with others.\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"rmarkdown/diamond-sizes-report.png\")\n```\n\nWhen you __knit__ the document, R Markdown sends the .Rmd file to __knitr__, http://yihui.name/knitr/, which executes all of the code chunks and creates a new markdown (.md) document which includes the code and its output. The markdown file generated by knitr is then processed by __pandoc__, <http://pandoc.org/>, which is responsible for creating the finished file. The advantage of this two step workflow is that you can create a very wide range of output formats, as you'll learn about in [R markdown formats].\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"images/RMarkdownFlow.png\")\n```\n\nTo get started with your own `.Rmd` file, select *File > New File > R Markdown...* in the menubar. RStudio will launch a wizard that you can use to pre-populate your file with useful content that reminds you how the key features of R Markdown work. \n\nThe following sections dive into the three components of an R Markdown document in more details: the markdown text, the code chunks, and the YAML header.\n\n### Exercises\n\n1.  Create a new notebook using _File > New File > R Notebook_. Read the \n    instructions. Practice running the chunks. Verify that you can modify\n    the code, re-run it, and see modified output.\n    \n1.  Create a new R Markdown document with _File > New File > R Markdown..._\n    Knit it by clicking the appropriate button. Knit it by using the \n    appropriate keyboard short cut. Verify that you can modify the\n    input and see the output update.\n    \n1.  Compare and contrast the R notebook and R markdown files you created\n    above. How are the outputs similar? How are they different? How are\n    the inputs similar? How are they different? What happens if you\n    copy the YAML header from one to the other?\n\n1.  Create one new R Markdown document for each of the three built-in\n    formats: HTML, PDF and Word. Knit each of the three documents.\n    How does the output differ? How does the input differ? (You may need\n    to install LaTeX in order to build the PDF output --- RStudio will\n    prompt you if this is necessary.)\n\n## Text formatting with Markdown\n\nProse in `.Rmd` files is written in Markdown, a lightweight set of conventions for formatting plain text files. Markdown is designed to be easy to read and easy to write. It is also very easy to learn. The guide below shows how to use Pandoc's Markdown, a slightly extended version of Markdown that R Markdown understands.\n\n```{r, echo = FALSE, comment = \"\"}\ncat(readr::read_file(\"rmarkdown/markdown.Rmd\"))\n```\n\nThe best way to learn these is simply to try them out. It will take a few days, but soon they will become second nature, and you won't need to think about them. If you forget, you can get to a handy reference sheet with *Help > Markdown Quick Reference*.\n\n### Exercises\n\n1.  Practice what you've learned by creating a brief CV. The title should be\n    your name, and you should include headings for (at least) education or\n    employment. Each of the sections should include a bulleted list of\n    jobs/degrees. Highlight the year in bold.\n    \n1.  Using the R Markdown quick reference, figure out how to:\n\n    1.  Add a footnote.\n    1.  Add a horizontal rule.\n    1.  Add a block quote.\n    \n1.  Copy and paste the contents of `diamond-sizes.Rmd` from\n    <https://github.com/hadley/r4ds/tree/master/rmarkdown> in to a local\n    R markdown document. Check that you can run it, then add text after the \n    frequency polygon that describes its most striking features.\n\n## Code chunks\n\nTo run code inside an R Markdown document, you need to insert a chunk. There are three ways to do so:\n\n1. The keyboard shortcut Cmd/Ctrl + Alt + I\n\n1. The \"Insert\" button icon in the editor toolbar.\n\n1. By manually typing the chunk delimiters ` ```{r} ` and ` ``` `.\n\nObviously, I'd recommend you learn the keyboard shortcut. It will save you a lot of time in the long run!\n\nYou can continue to run the code using the keyboard shortcut that by now (I hope!) you know and love: Cmd/Ctrl + Enter. However, chunks get a new keyboard shortcut: Cmd/Ctrl + Shift + Enter, which runs all the code in the chunk. Think of a chunk like a function. A chunk should be relatively self-contained, and focussed around a single task. \n\nThe following sections describe the chunk header which consists of ```` ```{r ````, followed by an optional chunk name, followed by comma separated options, followed by `}`. Next comes your R code and the chunk end is indicated by a final ```` ``` ````.\n\n### Chunk name\n\nChunks can be given an optional name: ```` ```{r by-name} ````. This has three advantages:\n\n1.  You can more easily navigate to specific chunks using the drop-down\n    code navigator in the bottom-left of the script editor:\n\n    ```{r, echo = FALSE, out.width = \"30%\"}\n    knitr::include_graphics(\"screenshots/rmarkdown-chunk-nav.png\")\n    ```\n\n1.  Graphics produced by the chunks will have useful names that make\n    them easier to use elsewhere. More on that in [other important options].\n    \n1.  You can set up networks of cached chunks to avoid re-performing expensive\n    computations on every run. More on that below.\n\nThere is one chunk name that imbues special behaviour: `setup`. When you're in a notebook mode, the chunk named setup will be run automatically once, before any other code is run.\n\n### Chunk options\n\nChunk output can be customised with __options__, arguments supplied to chunk header. Knitr provides almost 60 options that you can use to customize your code chunks. Here we'll cover the most important chunk options that you'll use frequently. You can see the full list at <http://yihui.name/knitr/options/>. \n\nThe most important set of options controls if your code block is executed and what results are inserted in the finished report:\n  \n*   `eval = FALSE` prevents code from being evaluated. (And obviously if the\n    code is not run, no results will be generated). This is useful for \n    displaying example code, or for disabling a large block of code without \n    commenting each line.\n\n*   `include = FALSE` runs the code, but doesn't show the code or results \n    in the final document. Use this for setup code that you don't want\n    cluttering your report.\n\n*   `echo = FALSE` prevents code, but not the results from appearing in the \n    finished file. Use this when writing reports aimed at people who don't\n    want to see the underlying R code.\n    \n*   `message = FALSE` or `warning = FALSE` prevents messages or warnings \n    from appearing in the finished file.\n\n*   `results = 'hide'` hides printed output; `fig.show = 'hide'` hides\n    plots.\n\n*   `error = TRUE` causes the render to continue even if code returns an error.\n    This is rarely something you'll want to include in the final version\n    of your report, but can be very useful if you need to debug exactly\n    what is going on inside your `.Rmd`. It's also useful if you're teaching R\n    and want to deliberately include an error. The default, `error = FALSE` causes \n    knitting to fail if there is a single error in the document.\n    \nThe following table summarises which types of output each option supressess:\n\nOption             | Run code | Show code | Output | Plots | Messages | Warnings \n-------------------|----------|-----------|--------|-------|----------|---------\n`eval = FALSE`     | -        |           | -      | -     | -        | -\n`include = FALSE`  |          | -         | -      | -     | -        | -\n`echo = FALSE`     |          | -         |        |       |          |\n`results = \"hide\"` |          |           | -      |       |          | \n`fig.show = \"hide\"`|          |           |        | -     |          |\n`message = FALSE`  |          |           |        |       | -        |\n`warning = FALSE`  |          |           |        |       |          | -\n\n### Table\n\nBy default, R Markdown prints data frames and matrices as you'd see them in the console:\n\n```{r}\nmtcars[1:5, ]\n```\n\nIf you prefer that data be displayed with additional formatting you can use the `knitr::kable` function. The code below generates Table \\@ref(tab:kable).\n\n```{r kable}\nknitr::kable(\n  mtcars[1:5, ], \n  caption = \"A knitr kable.\"\n)\n```\n\nRead the documentation for `?knitr::kable` to see the other ways in which you can customise the table. For even deeper customisation, consider the __xtable__, __stargazer__, __pander__, __tables__, and __ascii__ packages. Each provides a set of tools for returning formatted tables from R code.\n\nThere is also a rich set of options for controlling how figures are embedded. You'll learn about these in [saving your plots].\n\n### Caching\n\nNormally, each knit of a document starts from a completely clean slate. This is great for reproducibility, because it ensures that you've captured every important computation in code. However, it can be painful if you have some computations that take a long time. The solution is `cache = TRUE`. When set, this will save the output of the chunk to a specially named file on disk. On subsequent runs, knitr will check to see if the code has changed, and if it hasn't, it will reuse the cached results.\n\nThe caching system must be used with care, because by default it is based on the code only, not its dependencies. For example, here the `processed_data` chunk depends on the `raw_data` chunk:\n\n    `r chunk`{r raw_data}\n    rawdata <- readr::read_csv(\"a_very_large_file.csv\")\n    `r chunk`\n    \n    `r chunk`{r processed_data, cache = TRUE}\n    processed_data <- rawdata %>% \n      filter(!is.na(import_var)) %>% \n      mutate(new_variable = complicated_transformation(x, y, z))\n    `r chunk`\n\nCaching the `processed_data` chunk means that it will get re-run if the dplyr pipeline is changed, but it won't get rerun if the `read_csv()` call changes. You can avoid that problem with the `dependson` chunk option:\n\n    `r chunk`{r processed_data, cache = TRUE, dependson = \"raw_data\"}\n    processed_data <- rawdata %>% \n      filter(!is.na(import_var)) %>% \n      mutate(new_variable = complicated_transformation(x, y, z))\n    `r chunk`\n\n`dependson` should contain a character vector of *every* chunk that the cached chunk depends on. Knitr will update the results for the cached chunk whenever it detects that one of its dependencies have changed.\n\nNote that the chunks won't update if `a_very_large_file.csv` changes, because knitr caching only tracks changes within the `.Rmd` file. If you want to also track changes to that file you can use the `cache.extra` option. This is an arbitrary R expression that will invalidate the cache whenever it changes. A good function to use is `file.info()`: it returns a bunch of information about the file including when it was last modified. Then you can write:\n\n    `r chunk`{r raw_data, cache.extra = file.info(\"a_very_large_file.csv\")}\n    rawdata <- readr::read_csv(\"a_very_large_file.csv\")\n    `r chunk`\n\nAs your caching strategies get progressively more complicated, it's a good idea to regularly clear out all your caches with `knitr::clean_cache()`.\n\nI've used the advice of [David Robinson](https://twitter.com/drob/status/738786604731490304) to name these chunks: each chunk is named after the primary object that it creates. This makes it easier to understand the `dependson` specification.\n\n### Global options\n\nAs you work more with knitr, you will discover that some of the default chunk options don't fit your needs and you want to change them. You can do this by calling `knitr::opts_chunk$set()` in a code chunk. For example, when writing books and tutorials I set:\n\n```{r, eval = FALSE}\nknitr::opts_chunk$set(\n  comment = \"#>\",\n  collapse = TRUE\n)\n```\n\nThis uses my preferred comment formatting, and ensures that the code and output are kept closely entwined. On the other hand, if you were preparing a report, you might set:\n\n```{r eval = FALSE}\nknitr::opts_chunk$set(\n  echo = FALSE\n)\n```\n\nThat will hide the code by default, so only showing the chunks you deliberately choose to show (with `echo = TRUE`). You might consider setting `message = FALSE` and `warning = FALSE`, but that would make it harder to debug problems because you wouldn't see any messages in the final document.\n\n### Inline code\n\nThere is one other way to embed R code into an R Markdown document: directly into the text, with:  `r inline()`. This can be very useful if you mention properties of your data in the text. For example, in the example document I used at the start of the chapter I had:\n\n> We have data about `r inline('nrow(diamonds)')` diamonds. \n> Only `r inline('nrow(diamonds) - nrow(smaller)')` are larger \n> than 2.5 carats. The distribution of the remainder is shown below:\n\nWhen the report is knit, the results of these computations are inserted into the text:\n\n> We have data about 53940 diamonds. Only 126 are larger than \n> 2.5 carats. The distribution of the remainder is shown below:\n\nWhen inserting numbers into text, `format()` is your friend. It allows you to set the number of `digits` so you don't print to a ridiculous degree of accuracy, and a `big.mark` to make numbers easier to read. I'll often combine these into a helper function:\n\n```{r}\ncomma <- function(x) format(x, digits = 2, big.mark = \",\")\ncomma(3452345)\ncomma(.12358124331)\n```\n\n### Exercises\n\n1.  Add a section that explores how diamond sizes vary by cut, colour,\n    and clarity. Assume you're writing a report for someone who doesn't know\n    R, and instead of setting `echo = FALSE` on each chunk, set a global \n    option.\n\n1.  Download `diamond-sizes.Rmd` from\n    <https://github.com/hadley/r4ds/tree/master/rmarkdown>. Add a section\n    that describes the largest 20 diamonds, including a table that displays\n    their most important attributes.\n\n1.  Modify `diamonds-sizes.Rmd` to use `comma()` to produce nicely\n    formatted output. Also include the percentage of diamonds that are\n    larger than 2.5 carats.\n\n1.  Set up a network of chunks where `d` depends on `c` and `b`, and\n    both `b` and `c` depend on `a`. Have each chunk print `lubridate::now()`,\n    set `cache = TRUE`, then verify your understanding of caching.\n\n## Troubleshooting\n\nTroubleshooting R Markdown documents can be challenging because you are no longer in an interactive R environment, and you will need to learn some new tricks. The first thing you should always try is to recreate the problem in an interactive session. Restart R, then \"Run all chunks\" (either from Code menu, under Run region), or with the keyboard shortcut Ctrl + Alt + R. If you're lucky, that will recreate the problem, and you can figure out what's going on interactively.\n\nIf that doesn't help, there must be something different between your interactive environment and the R markdown environment. You're going to need to systematically explore the options. The most common difference is the working directory: the working directory of an R Markdown is the directory in which it lives. Check the working directory is what you expect by including `getwd()` in a chunk.\n\nNext, brainstorm all the things that might cause the bug. You'll need to systematically check that they're the same in your R session and your R markdown session. The easiest way to do that is to set `error = TRUE` on the chunk causing the problem, then use `print()` and `str()` to check that settings are as you expect.\n\n## YAML header\n\nYou can control many other \"whole document\" settings by tweaking the parameters of the YAML header.  You might wonder what YAML stands for: it's \"yet another markup language\", which is designed for representing hierarchical data in a way that's easy for humans to read and write. R Markdown uses it to control many details of the output. Here we'll discuss two: document parameters and bibliographies.\n\n### Parameters\n\nR Markdown documents can include one or more parameters whose values can be set when you render the report. Parameters are useful when you want to re-render the same report with distinct values for various key inputs. For example, you might be producing sales reports per branch, exam results by student, or demographic summaries by country. To declare one or more parameters, use the `params` field. \n\nThis example uses a `my_class` parameter to determine which class of cars to display:\n\n```{r, echo = FALSE, out.width = \"100%\", comment = \"\"}\ncat(readr::read_file(\"rmarkdown/fuel-economy.Rmd\"))\n```\n\nAs you can see, parameters are available within the code chunks as a read-only list named `params`.\n\nYou can write atomic vectors directly into the YAML header. You can also run arbitrary R expressions by prefacing the parameter value with `!r`. This is a good way to specify date/time parameters.\n\n```yaml\nparams:\n  start: !r lubridate::ymd(\"2015-01-01\")\n  snapshot: !r lubridate::ymd_hms(\"2015-01-01 12:30:00\")\n```\n\nIn RStudio, you can click the \"Knit with Parameters\" option in the Knit dropdown menu to set parameters, render, and preview the report in a single user friendly step. You can customise the dialog by setting other options in the header. See <http://rmarkdown.rstudio.com/developer_parameterized_reports.html#parameter_user_interfaces> for more details.\n\nAlternatively, if you need to produce many such paramterised reports, you can call `rmarkdown::render()` with a list of `params`:\n\n```{r eval = FALSE}\nrmarkdown::render(\"fuel-economy.Rmd\", params = list(my_class = \"suv\"))\n```\n\nThis is particularly powerful in conjunction with `purrr:pwalk()`. The following example creates a report for each value of `class` found in `mpg`. First we create a data frame that has one row for each class, giving the `filename` of the report and the `params`:\n\n```{r}\nreports <- tibble(\n  class = unique(mpg$class),\n  filename = stringr::str_c(\"fuel-economy-\", class, \".html\"),\n  params = purrr::map(class, ~ list(my_class = .))\n)\nreports\n```\n\nThen we match the column names to the argument names of `render()`, and use purrr's **parallel** walk to call `render()` once for each row:\n\n```{r, eval = FALSE}\nreports %>% \n  select(output_file = filename, params) %>% \n  purrr::pwalk(rmarkdown::render, input = \"fuel-economy.Rmd\")\n```\n\n### Bibliographies and Citations\n\nPandoc can automatically generate citations and a bibliography in a number of styles. To use this feature, specify a bibliography file using the `bibliography` field in your file's header. The field should contain a path from the directory that contains your .Rmd file to the file that contains the bibliography file:\n\n```yaml\nbibliography: rmarkdown.bib\n```\n\nYou can use many common bibliography formats including BibLaTeX, BibTeX, endnote, medline.\n\nTo create a citation within your .Rmd file, use a key composed of ‘@’ + the citation identifier from the bibliography file. Then place the citation in square brackets. Here are some examples:\n\n```markdown\nSeparate multiple citations with a `;`: Blah blah [@smith04; @doe99].\n\nYou can add arbitrary comments inside the square brackets: \nBlah blah [see @doe99, pp. 33-35; also @smith04, ch. 1].\n\nRemove the square brackets to create an in-text citation: @smith04 \nsays blah, or @smith04 [p. 33] says blah.\n\nAdd a `-` before the citation to suppress the author's name: \nSmith says blah [-@smith04].\n```\n\nWhen R Markdown renders your file, it will build and append a bibliography to the end of your document. The bibliography will contain each of the cited references from your bibliography file, but it will not contain a section heading. As a result it is common practice to end your file with a section header for the bibliography, such as `# References` or `# Bibliography`.\n\nYou can change the style of your citations and bibliography by referencing a CSL (citation style language) file in the `csl` field:\n\n```yaml\nbibliography: rmarkdown.bib\ncsl: apa.csl\n```\n\nAs with the bibliography field, your csl file should contain a path to the file. Here I assume that the csl file is in the same directory as the .Rmd file. A good place to find CSL style files for common bibliography styles is  <http://github.com/citation-style-language/styles>.\n\n## Learning more\n\nR Markdown is still relatively young, and is still growing rapidly. The best place to stay on top of innovations is the official R Markdown website: <http://rmarkdown.rstudio.com>.\n\nThere are two important topics that we haven't covered here: collaboration, and the details of accurately communicating your ideas to other humans. Collaboration is a vital part of modern data science, and you can make your life much easier by using version control tools, like Git and GitHub. We recommend two free resources that will teach you about Git:\n\n1.  \"Happy Git with R\": a user friendly introduction to Git and GitHub from \n    R users, by Jenny Bryan. The book is freely available online:\n    <http://happygitwithr.com>\n    \n1.  The \"Git and GitHub\" chapter of _R Packages_, by Hadley. You can also \n    read it for free online: <http://r-pkgs.had.co.nz/git.html>.\n\nI have also not touched on what you should actually write in order to clearly communicate the results of your analysis. To improve your writing, I highly recommend reading either [_Style: Lessons in Clarity and Grace_](https://amzn.com/0134080416) by Joseph M. Williams & Joseph Bizup, or [_The Sense of Structure: Writing from the Reader's Perspective_](https://amzn.com/0205296327) by George Gopen. Both books will help you understand the structure of sentences and paragraphs, and give you the tools to make your writing more clear. (These books are rather expensive if purchased new, but they're used by many English classes so there are plenty of cheap second-hand copies). George Gopen also has a number of short articles on writing at <https://www.georgegopen.com/the-litigation-articles.html>. They are aimed at lawyers, but almost everything applies to data scientists too. \n  \n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/strings.Rmd",
    "content": "# Strings\n\n## Introduction\n\nThis chapter introduces you to string manipulation in R. You'll learn the basics of how strings work and how to create them by hand, but the focus of this chapter will be on regular expressions, or regexps for short. Regular expressions are useful because strings usually contain unstructured or semi-structured data, and regexps are a concise language for describing patterns in strings. When you first look at a regexp, you'll think a cat walked across your keyboard, but as your understanding improves they will soon start to make sense.\n\n### Prerequisites\n\nThis chapter will focus on the __stringr__ package for string manipulation. stringr is not part of the core tidyverse because you don't always have textual data, so we need to load it explicitly.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\nlibrary(stringr)\n```\n\n## String basics\n\nYou can create strings with either single quotes or double quotes. Unlike other languages, there is no difference in behaviour. I recommend always using `\"`, unless you want to create a string that contains multiple `\"`.\n\n```{r}\nstring1 <- \"This is a string\"\nstring2 <- 'If I want to include a \"quote\" inside a string, I use single quotes'\n```\n\nIf you forget to close a quote, you'll see `+`, the continuation character:\n\n```\n> \"This is a string without a closing quote\n+ \n+ \n+ HELP I'M STUCK\n```\n\nIf this happen to you, press Escape and try again!\n\nTo include a literal single or double quote in a string you can use `\\` to \"escape\" it:\n\n```{r}\ndouble_quote <- \"\\\"\" # or '\"'\nsingle_quote <- '\\'' # or \"'\"\n```\n\nThat means if you want to include a literal backslash, you'll need to double it up: `\"\\\\\"`.\n\nBeware that the printed representation of a string is not the same as string itself, because the printed representation shows the escapes. To see the raw contents of the string, use `writeLines()`:\n\n```{r}\nx <- c(\"\\\"\", \"\\\\\")\nx\nwriteLines(x)\n```\n\nThere are a handful of other special characters. The most common are `\"\\n\"`, newline, and `\"\\t\"`, tab, but you can see the complete list by requesting help on `\"`: `?'\"'`, or `?\"'\"`. You'll also sometimes see strings like `\"\\u00b5\"`, this is a way of writing non-English characters that works on all platforms:\n\n```{r}\nx <- \"\\u00b5\"\nx\n```\n\nMultiple strings are often stored in a character vector, which you can create with `c()`:\n\n```{r}\nc(\"one\", \"two\", \"three\")\n```\n\n### String length\n\nBase R contains many functions to work with strings but we'll avoid them because they can be inconsistent, which makes them hard to remember. Instead we'll use functions from stringr. These have more intuitive names, and all start with `str_`. For example, `str_length()` tells you the number of characters in a string:\n\n```{r}\nstr_length(c(\"a\", \"R for data science\", NA))\n```\n\nThe common `str_` prefix is particularly useful if you use RStudio, because typing `str_` will trigger autocomplete, allowing you to see all stringr functions:\n\n```{r, echo = FALSE}\nknitr::include_graphics(\"screenshots/stringr-autocomplete.png\")\n```\n\n### Combining strings\n\nTo combine two or more strings, use `str_c()`:\n\n```{r}\nstr_c(\"x\", \"y\")\nstr_c(\"x\", \"y\", \"z\")\n```\n\nUse the `sep` argument to control how they're separated:\n\n```{r}\nstr_c(\"x\", \"y\", sep = \", \")\n```\n\nLike most other functions in R, missing values are contagious. If you want them to print as `\"NA\"`, use `str_replace_na()`:\n\n```{r}\nx <- c(\"abc\", NA)\nstr_c(\"|-\", x, \"-|\")\nstr_c(\"|-\", str_replace_na(x), \"-|\")\n```\n\nAs shown above, `str_c()` is vectorised, and it automatically recycles shorter vectors to the same length as the longest:\n\n```{r}\nstr_c(\"prefix-\", c(\"a\", \"b\", \"c\"), \"-suffix\")\n```\n\nObjects of length 0 are silently dropped. This is particularly useful in conjunction with `if`:\n\n```{r}\nname <- \"Hadley\"\ntime_of_day <- \"morning\"\nbirthday <- FALSE\n\nstr_c(\n  \"Good \", time_of_day, \" \", name,\n  if (birthday) \" and HAPPY BIRTHDAY\",\n  \".\"\n)\n```\n\nTo collapse a vector of strings into a single string, use `collapse`:\n\n```{r}\nstr_c(c(\"x\", \"y\", \"z\"), collapse = \", \")\n```\n\n### Subsetting strings\n\nYou can extract parts of a string using `str_sub()`. As well as the string, `str_sub()` takes `start` and `end` arguments which give the (inclusive) position of the substring:\n\n```{r}\nx <- c(\"Apple\", \"Banana\", \"Pear\")\nstr_sub(x, 1, 3)\n# negative numbers count backwards from end\nstr_sub(x, -3, -1)\n```\n\nNote that `str_sub()` won't fail if the string is too short: it will just return as much as possible:\n\n```{r}\nstr_sub(\"a\", 1, 5)\n```\n\nYou can also use the assignment form of `str_sub()` to modify strings:\n\n```{r}\nstr_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1))\nx\n```\n\n### Locales\n\nAbove I used `str_to_lower()` to change the text to lower case. You can also use `str_to_upper()` or `str_to_title()`. However, changing case is more complicated than it might at first appear because different languages have different rules for changing case. You can pick which set of rules to use by specifying a locale:\n\n```{r}\n# Turkish has two i's: with and without a dot, and it\n# has a different rule for capitalising them:\nstr_to_upper(c(\"i\", \"ı\"))\nstr_to_upper(c(\"i\", \"ı\"), locale = \"tr\")\n```\n\nThe locale is specified as a ISO 639 language code, which is a two or three letter abbreviation. If you don't already know the code for your language, [Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) has a good list. If you leave the locale blank, it will use the current locale, as provided by your operating system.\n\nAnother important operation that's affected by the locale is sorting. The base R `order()` and `sort()` functions sort strings using the current locale. If you want robust behaviour across different computers, you may want to use `str_sort()` and `str_order()` which take an additional `locale` argument:\n\n```{r}\nx <- c(\"apple\", \"eggplant\", \"banana\")\n\nstr_sort(x, locale = \"en\")  # English\n\nstr_sort(x, locale = \"haw\") # Hawaiian\n```\n\n### Exercises\n\n1.  In code that doesn't use stringr, you'll often see `paste()` and `paste0()`.\n    What's the difference between the two functions? What stringr function are\n    they equivalent to? How do the functions differ in their handling of \n    `NA`?\n    \n1.  In your own words, describe the difference between the `sep` and `collapse`\n    arguments to `str_c()`.\n\n1.  Use `str_length()` and `str_sub()` to extract the middle character from \n    a string. What will you do if the string has an even number of characters?\n\n1.  What does `str_wrap()` do? When might you want to use it?\n\n1.  What does `str_trim()` do? What's the opposite of `str_trim()`?\n\n1.  Write a function that turns (e.g.) a vector `c(\"a\", \"b\", \"c\")` into \n    the string `a, b, and c`. Think carefully about what it should do if\n    given a vector of length 0, 1, or 2.\n\n## Matching patterns with regular expressions\n\nRegexps are a very terse language that allow you to describe patterns in strings. They take a little while to get your head around, but once you understand them, you'll find them extremely useful. \n\nTo learn regular expressions, we'll use `str_view()` and `str_view_all()`. These functions take a character vector and a regular expression, and show you how they match. We'll start with very simple regular expressions and then gradually get more and more complicated. Once you've mastered pattern matching, you'll learn how to apply those ideas with various stringr functions.\n\n### Basic matches\n\nThe simplest patterns match exact strings:\n\n```{r}\nx <- c(\"apple\", \"banana\", \"pear\")\nstr_view(x, \"an\")\n```\n\nThe next step up in complexity is `.`, which matches any character (except a newline):\n\n```{r}\nstr_view(x, \".a.\")\n```\n\nBut if \"`.`\" matches any character, how do you match the character \"`.`\"? You need to use an \"escape\" to tell the regular expression you want to match it exactly, not use its special behaviour. Like strings, regexps use the backslash, `\\`, to escape special behaviour. So to match an `.`, you need the regexp `\\.`. Unfortunately this creates a problem. We use strings to represent regular expressions, and `\\` is also used as an escape symbol in strings. So to create the regular expression `\\.` we need the string `\"\\\\.\"`. \n\n```{r}\n# To create the regular expression, we need \\\\\ndot <- \"\\\\.\"\n\n# But the expression itself only contains one:\nwriteLines(dot)\n\n# And this tells R to look for an explicit .\nstr_view(c(\"abc\", \"a.c\", \"bef\"), \"a\\\\.c\")\n```\n\nIf `\\` is used as an escape character in regular expressions, how do you match a literal `\\`? Well you need to escape it, creating the regular expression `\\\\`. To create that regular expression, you need to use a string, which also needs to escape `\\`. That means to match a literal `\\` you need to write `\"\\\\\\\\\"` --- you need four backslashes to match one!\n\n```{r}\nx <- \"a\\\\b\"\nwriteLines(x)\n\nstr_view(x, \"\\\\\\\\\")\n```\n\nIn this book, I'll write regular expression as `\\.` and strings that represent the regular expression as `\"\\\\.\"`.\n\n#### Exercises\n\n1.  Explain why each of these strings don't match a `\\`: `\"\\\"`, `\"\\\\\"`, `\"\\\\\\\"`.\n\n1.  How would you match the sequence `\"'\\`?\n\n1.  What patterns will the regular expression `\\..\\..\\..` match? \n    How would you represent it as a string?\n\n### Anchors\n\nBy default, regular expressions will match any part of a string. It's often useful to _anchor_ the regular expression so that it matches from the start or end of the string. You can use:\n\n* `^` to match the start of the string.\n* `$` to match the end of the string.\n\n```{r}\nx <- c(\"apple\", \"banana\", \"pear\")\nstr_view(x, \"^a\")\nstr_view(x, \"a$\")\n```\n\nTo remember which is which, try this mnemonic which I learned from [Evan Misshula](https://twitter.com/emisshula/status/323863393167613953): if you begin with power (`^`), you end up with money (`$`).\n\nTo force a regular expression to only match a complete string, anchor it with both `^` and `$`:\n\n```{r}\nx <- c(\"apple pie\", \"apple\", \"apple cake\")\nstr_view(x, \"apple\")\nstr_view(x, \"^apple$\")\n```\n\nYou can also match the boundary between words with `\\b`. I don't often use this in R, but I will sometimes use it when I'm doing a search in RStudio when I want to find the name of a function that's a component of other functions. For example, I'll search for `\\bsum\\b` to avoid matching `summarise`, `summary`, `rowsum` and so on.\n\n#### Exercises\n\n1.  How would you match the literal string `\"$^$\"`?\n\n1.  Given the corpus of common words in `stringr::words`, create regular\n    expressions that find all words that:\n    \n    1. Start with \"y\".\n    1. End with \"x\"\n    1. Are exactly three letters long. (Don't cheat by using `str_length()`!)\n    1. Have seven letters or more.\n\n    Since this list is long, you might want to use the `match` argument to\n    `str_view()` to show only the matching or non-matching words.\n\n### Character classes and alternatives\n\nThere are a number of special patterns that match more than one character. You've already seen `.`, which matches any character apart from a newline. There are four other useful tools:\n\n* `\\d`: matches any digit.\n* `\\s`: matches any whitespace (e.g. space, tab, newline).\n* `[abc]`: matches a, b, or c.\n* `[^abc]`: matches anything except a, b, or c.\n\nRemember, to create a regular expression containing `\\d` or `\\s`, you'll need to escape the `\\` for the string, so you'll type `\"\\\\d\"` or `\"\\\\s\"`.\n\nA character class containing a single character is a nice alternative to backslash escapes when you want to include a single metacharacter in a regex. Many people find this more readable.\n\n```{r}\n# Look for a literal character that normally has special meaning in a regex\nstr_view(c(\"abc\", \"a.c\", \"a*c\", \"a c\"), \"a[.]c\")\nstr_view(c(\"abc\", \"a.c\", \"a*c\", \"a c\"), \".[*]c\")\nstr_view(c(\"abc\", \"a.c\", \"a*c\", \"a c\"), \"a[ ]\")\n```\n\nThis works for most (but not all) regex metacharacters: `$` `.` `|` `?` `*` `+` `(` `)` `[` `{`. Unfortunately, a few characters have special meaning even inside a character class and must be handled with backslash escapes: `]` `\\` `^` and `-`.\n\nYou can use _alternation_ to pick between one or more alternative patterns. For example, `abc|d..f` will match either '\"abc\"', or `\"deaf\"`. Note that the precedence for `|` is low, so that `abc|xyz` matches `abc` or `xyz` not `abcyz` or `abxyz`. Like with mathematical expressions, if precedence ever gets confusing, use parentheses to make it clear what you want:\n\n```{r}\nstr_view(c(\"grey\", \"gray\"), \"gr(e|a)y\")\n```\n\n#### Exercises\n\n1.  Create regular expressions to find all words that:\n\n    1. Start with a vowel.\n\n    1. That only contain consonants. (Hint: thinking about matching \n       \"not\"-vowels.)\n\n    1. End with `ed`, but not with `eed`.\n    \n    1. End with `ing` or `ise`.\n    \n1.  Empirically verify the rule \"i before e except after c\".\n\n1.  Is \"q\" always followed by a \"u\"?\n\n1.  Write a regular expression that matches a word if it's probably written\n    in British English, not American English.\n\n1.  Create a regular expression that will match telephone numbers as commonly\n    written in your country.\n\n### Repetition\n\nThe next step up in power involves controlling how many times a pattern matches:\n\n* `?`: 0 or 1\n* `+`: 1 or more\n* `*`: 0 or more\n\n```{r}\nx <- \"1888 is the longest year in Roman numerals: MDCCCLXXXVIII\"\nstr_view(x, \"CC?\")\nstr_view(x, \"CC+\")\nstr_view(x, 'C[LX]+')\n```\n\nNote that the precedence of these operators is high, so you can write: `colou?r` to match either American or British spellings. That means most uses will need parentheses, like `bana(na)+`.\n\nYou can also specify the number of matches precisely:\n\n* `{n}`: exactly n\n* `{n,}`: n or more\n* `{,m}`: at most m\n* `{n,m}`: between n and m\n\n```{r}\nstr_view(x, \"C{2}\")\nstr_view(x, \"C{2,}\")\nstr_view(x, \"C{2,3}\")\n```\n\nBy default these matches are \"greedy\": they will match the longest string possible. You can make them \"lazy\", matching the shortest string possible by putting a `?` after them. This is an advanced feature of regular expressions, but it's useful to know that it exists:\n\n```{r}\nstr_view(x, 'C{2,3}?')\nstr_view(x, 'C[LX]+?')\n```\n\n#### Exercises\n\n1.  Describe the equivalents of `?`, `+`, `*` in `{m,n}` form.\n\n1.  Describe in words what these regular expressions match:\n    (read carefully to see if I'm using a regular expression or a string\n    that defines a regular expression.)\n\n    1. `^.*$`\n    1. `\"\\\\{.+\\\\}\"`\n    1. `\\d{4}-\\d{2}-\\d{2}`\n    1. `\"\\\\\\\\{4}\"`\n\n1.  Create regular expressions to find all words that:\n\n    1. Start with three consonants.\n    1. Have three or more vowels in a row.\n    1. Have two or more vowel-consonant pairs in a row.\n\n1.  Solve the beginner regexp crosswords at\n    <https://regexcrossword.com/challenges/beginner>.\n\n### Grouping and backreferences\n\nEarlier, you learned about parentheses as a way to disambiguate complex expressions. Parentheses also create a _numbered_ capturing group (number 1, 2 etc.). A capturing group stores _the part of the string_ matched by the part of the regular expression inside the parentheses. You can refer to the same text as previously matched by a capturing group with _backreferences_, like `\\1`, `\\2` etc. For example, the following regular expression finds all fruits that have a repeated pair of letters.\n\n```{r}\nstr_view(fruit, \"(..)\\\\1\", match = TRUE)\n```\n\n(Shortly, you'll also see how they're useful in conjunction with `str_match()`.)\n\n#### Exercises\n\n1.  Describe, in words, what these expressions will match:\n\n    1. `(.)\\1\\1`\n    1. `\"(.)(.)\\\\2\\\\1\"`\n    1. `(..)\\1`\n    1. `\"(.).\\\\1.\\\\1\"`\n    1. `\"(.)(.)(.).*\\\\3\\\\2\\\\1\"`\n\n1.  Construct regular expressions to match words that:\n\n    1. Start and end with the same character.\n    \n    1. Contain a repeated pair of letters\n       (e.g. \"church\" contains \"ch\" repeated twice.)\n    \n    1. Contain one letter repeated in at least three places\n       (e.g. \"eleven\" contains three \"e\"s.)\n\n## Tools\n\nNow that you've learned the basics of regular expressions, it's time to learn how to apply them to real problems. In this section you'll learn a wide array of stringr functions that let you:\n\n* Determine which strings match a pattern.\n* Find the positions of matches.\n* Extract the content of matches.\n* Replace matches with new values.\n* Split a string based on a match.\n\nA word of caution before we continue: because regular expressions are so powerful, it's easy to try and solve every problem with a single regular expression. In the words of Jamie Zawinski:\n\n> Some people, when confronted with a problem, think “I know, I’ll use regular\n> expressions.” Now they have two problems. \n\nAs a cautionary tale, check out this regular expression that checks if a email address is valid:\n\n```\n(?:(?:\\r\\n)?[ \\t])*(?:(?:(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t]\n)+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\n\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(\n?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \n\\t]))*\"(?:(?:\\r\\n)?[ \\t])*))*@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\0\n31]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\\n](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+\n(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:\n(?:\\r\\n)?[ \\t])*))*|(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z\n|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)\n?[ \\t])*)*\\<(?:(?:\\r\\n)?[ \\t])*(?:@(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\\nr\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[\n \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)\n?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t]\n)*))*(?:,@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[\n \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*\n)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t]\n)+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*)\n*:(?:(?:\\r\\n)?[ \\t])*)?(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+\n|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\n\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\n\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t\n]))*\"(?:(?:\\r\\n)?[ \\t])*))*@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031\n]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](\n?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?\n:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?\n:\\r\\n)?[ \\t])*))*\\>(?:(?:\\r\\n)?[ \\t])*)|(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?\n:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?\n[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)*:(?:(?:\\r\\n)?[ \\t])*(?:(?:(?:[^()<>@,;:\\\\\".\\[\\] \n\\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\n\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>\n@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"\n(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*))*@(?:(?:\\r\\n)?[ \\t]\n)*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\n\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?\n:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\n\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*|(?:[^()<>@,;:\\\\\".\\[\\] \\000-\n\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(\n?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)*\\<(?:(?:\\r\\n)?[ \\t])*(?:@(?:[^()<>@,;\n:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([\n^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\"\n.\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\\n]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*(?:,@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\\n[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\\nr\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \n\\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]\n|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*)*:(?:(?:\\r\\n)?[ \\t])*)?(?:[^()<>@,;:\\\\\".\\[\\] \\0\n00-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\\n.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,\n;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?\n:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*))*@(?:(?:\\r\\n)?[ \\t])*\n(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\n\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t])*(?:[\n^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]\n]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*\\>(?:(?:\\r\\n)?[ \\t])*)(?:,\\s*(\n?:(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\n\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(\n?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\n\\[\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t\n])*))*@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t\n])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?\n:\\.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\n\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*|(?:\n[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\\n]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)*\\<(?:(?:\\r\\n)\n?[ \\t])*(?:@(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"\n()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)\n?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>\n@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*(?:,@(?:(?:\\r\\n)?[\n \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,\n;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\\r\\n)?[ \\t]\n)*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\n\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*)*:(?:(?:\\r\\n)?[ \\t])*)?\n(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\n\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])*)(?:\\.(?:(?:\n\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z|(?=[\\[\n\"()<>@,;:\\\\\".\\[\\]]))|\"(?:[^\\\"\\r\\\\]|\\\\.|(?:(?:\\r\\n)?[ \\t]))*\"(?:(?:\\r\\n)?[ \\t])\n*))*@(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])\n+|\\Z|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*)(?:\\\n.(?:(?:\\r\\n)?[ \\t])*(?:[^()<>@,;:\\\\\".\\[\\] \\000-\\031]+(?:(?:(?:\\r\\n)?[ \\t])+|\\Z\n|(?=[\\[\"()<>@,;:\\\\\".\\[\\]]))|\\[([^\\[\\]\\r\\\\]|\\\\.)*\\](?:(?:\\r\\n)?[ \\t])*))*\\>(?:(\n?:\\r\\n)?[ \\t])*))*)?;\\s*)\n```\n\nThis is a somewhat pathological example (because email addresses are actually surprisingly complex), but is used in real code. See the stackoverflow discussion at <http://stackoverflow.com/a/201378> for more details. \n\nDon't forget that you're in a programming language and you have other tools at your disposal. Instead of creating one complex regular expression, it's often easier to write a series of simpler regexps. If you get stuck trying to create a single regexp that solves your problem, take a step back and think if you could break the problem down into smaller pieces, solving each challenge before moving onto the next one.\n\n### Detect matches\n\nTo determine if a character vector matches a pattern, use `str_detect()`. It returns a logical vector the same length as the input:\n\n```{r}\nx <- c(\"apple\", \"banana\", \"pear\")\nstr_detect(x, \"e\")\n```\n\nRemember that when you use a logical vector in a numeric context, `FALSE` becomes 0 and `TRUE` becomes 1. That makes `sum()` and `mean()` useful if you want to answer questions about matches across a larger vector:\n\n```{r}\n# How many common words start with t?\nsum(str_detect(words, \"^t\"))\n# What proportion of common words end with a vowel?\nmean(str_detect(words, \"[aeiou]$\"))\n```\n\nWhen you have complex logical conditions (e.g. match a or b but not c unless d) it's often easier to combine multiple `str_detect()` calls with logical operators, rather than trying to create a single regular expression. For example, here are two ways to find all words that don't contain any vowels:\n\n```{r}\n# Find all words containing at least one vowel, and negate\nno_vowels_1 <- !str_detect(words, \"[aeiou]\")\n# Find all words consisting only of consonants (non-vowels)\nno_vowels_2 <- str_detect(words, \"^[^aeiou]+$\")\nidentical(no_vowels_1, no_vowels_2)\n```\n\nThe results are identical, but I think the first approach is significantly easier to understand. If your regular expression gets overly complicated, try breaking it up into smaller pieces, giving each piece a name, and then combining the pieces with logical operations.\n\nA common use of `str_detect()` is to select the elements that match a pattern. You can do this with logical subsetting, or the convenient `str_subset()` wrapper:\n\n```{r}\nwords[str_detect(words, \"x$\")]\nstr_subset(words, \"x$\")\n```\n\nTypically, however, your strings will be one column of a data frame, and you'll want to use filter instead:\n\n```{r}\ndf <- tibble(\n  word = words, \n  i = seq_along(word)\n)\ndf %>% \n  filter(str_detect(words, \"x$\"))\n```\n\n\nA variation on `str_detect()` is `str_count()`: rather than a simple yes or no, it tells you how many matches there are in a string:\n\n```{r}\nx <- c(\"apple\", \"banana\", \"pear\")\nstr_count(x, \"a\")\n\n# On average, how many vowels per word?\nmean(str_count(words, \"[aeiou]\"))\n```\n\nIt's natural to use `str_count()` with `mutate()`:\n\n```{r}\ndf %>% \n  mutate(\n    vowels = str_count(word, \"[aeiou]\"),\n    consonants = str_count(word, \"[^aeiou]\")\n  )\n```\n\nNote that matches never overlap. For example, in `\"abababa\"`, how many times will the pattern `\"aba\"` match? Regular expressions say two, not three:\n\n```{r}\nstr_count(\"abababa\", \"aba\")\nstr_view_all(\"abababa\", \"aba\")\n```\n\nNote the use of `str_view_all()`. As you'll shortly learn, many stringr functions come in pairs: one function works with a single match, and the other works with all matches. The second function will have the suffix `_all`.\n\n### Exercises\n\n1.  For each of the following challenges, try solving it by using both a single\n    regular expression, and a combination of multiple `str_detect()` calls.\n    \n    1.  Find all words that start or end with `x`.\n    \n    1.  Find all words that start with a vowel and end with a consonant.\n    \n    1.  Are there any words that contain at least one of each different\n        vowel?\n\n1.  What word has the highest number of vowels? What word has the highest\n    proportion of vowels? (Hint: what is the denominator?)\n\n### Extract matches\n\nTo extract the actual text of a match, use `str_extract()`. To show that off, we're going to need a more complicated example. I'm going to use the [Harvard sentences](https://en.wikipedia.org/wiki/Harvard_sentences), which were designed to test VOIP systems, but are also useful for practicing regexps. These are provided in `stringr::sentences`:\n\n```{r}\nlength(sentences)\nhead(sentences)\n```\n\nImagine we want to find all sentences that contain a colour. We first create a vector of colour names, and then turn it into a single regular expression:\n\n```{r}\ncolours <- c(\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\")\ncolour_match <- str_c(colours, collapse = \"|\")\ncolour_match\n```\n\nNow we can select the sentences that contain a colour, and then extract the colour to figure out which one it is:\n\n```{r}\nhas_colour <- str_subset(sentences, colour_match)\nmatches <- str_extract(has_colour, colour_match)\nhead(matches)\n```\n\nNote that `str_extract()` only extracts the first match. We can see that most easily by first selecting all the sentences that have more than 1 match:\n\n```{r}\nmore <- sentences[str_count(sentences, colour_match) > 1]\nstr_view_all(more, colour_match)\n\nstr_extract(more, colour_match)\n```\n\nThis is a common pattern for stringr functions, because working with a single match allows you to use much simpler data structures. To get all matches, use `str_extract_all()`. It returns a list:\n\n```{r}\nstr_extract_all(more, colour_match)\n```\n\nYou'll learn more about lists in [lists](#lists) and [iteration].\n\nIf you use `simplify = TRUE`, `str_extract_all()` will return a matrix with short matches expanded to the same length as the longest:\n\n```{r}\nstr_extract_all(more, colour_match, simplify = TRUE)\n\nx <- c(\"a\", \"a b\", \"a b c\")\nstr_extract_all(x, \"[a-z]\", simplify = TRUE)\n```\n\n#### Exercises\n\n1.  In the previous example, you might have noticed that the regular\n    expression matched \"flickered\", which is not a colour. Modify the \n    regex to fix the problem.\n\n1.  From the Harvard sentences data, extract:\n\n    1. The first word from each sentence.\n    1. All words ending in `ing`.\n    1. All plurals.\n\n### Grouped matches\n\nEarlier in this chapter we talked about the use of parentheses for clarifying precedence and for backreferences when matching. You can also use parentheses to extract parts of a complex match. For example, imagine we want to extract nouns from the sentences. As a heuristic, we'll look for any word that comes after \"a\" or \"the\". Defining a \"word\" in a regular expression is a little tricky, so here I use a simple approximation: a sequence of at least one character that isn't a space.\n\n```{r}\nnoun <- \"(a|the) ([^ ]+)\"\n\nhas_noun <- sentences %>%\n  str_subset(noun) %>%\n  head(10)\nhas_noun %>% \n  str_extract(noun)\n```\n\n`str_extract()` gives us the complete match; `str_match()` gives each individual component. Instead of a character vector, it returns a matrix, with one column for the complete match followed by one column for each group:\n\n```{r}\nhas_noun %>% \n  str_match(noun)\n```\n\n(Unsurprisingly, our heuristic for detecting nouns is poor, and also picks up adjectives like smooth and parked.)\n\nIf your data is in a tibble, it's often easier to use `tidyr::extract()`. It works like `str_match()` but requires you to name the matches, which are then placed in new columns:\n\n```{r}\ntibble(sentence = sentences) %>% \n  tidyr::extract(\n    sentence, c(\"article\", \"noun\"), \"(a|the) ([^ ]+)\", \n    remove = FALSE\n  )\n```\n\nLike `str_extract()`, if you want all matches for each string, you'll need `str_match_all()`.\n\n#### Exercises\n\n1. Find all words that come after a \"number\" like \"one\", \"two\", \"three\" etc.\n   Pull out both the number and the word.\n\n1. Find all contractions. Separate out the pieces before and after the \n   apostrophe.\n\n### Replacing matches\n\n`str_replace()` and `str_replace_all()` allow you to replace matches with new strings. The simplest use is to replace a pattern with a fixed string:\n\n```{r}\nx <- c(\"apple\", \"pear\", \"banana\")\nstr_replace(x, \"[aeiou]\", \"-\")\nstr_replace_all(x, \"[aeiou]\", \"-\")\n```\n\nWith `str_replace_all()` you can perform multiple replacements by supplying a named vector:\n\n```{r}\nx <- c(\"1 house\", \"2 cars\", \"3 people\")\nstr_replace_all(x, c(\"1\" = \"one\", \"2\" = \"two\", \"3\" = \"three\"))\n```\n\nInstead of replacing with a fixed string you can use backreferences to insert components of the match. In the following code, I flip the order of the second and third words.\n\n```{r}\nsentences %>% \n  str_replace(\"([^ ]+) ([^ ]+) ([^ ]+)\", \"\\\\1 \\\\3 \\\\2\") %>% \n  head(5)\n```\n\n#### Exercises\n\n1.   Replace all forward slashes in a string with backslashes.\n\n1.   Implement a simple version of `str_to_lower()` using `replace_all()`.\n\n1.   Switch the first and last letters in `words`. Which of those strings\n     are still words?\n\n### Splitting\n\nUse `str_split()` to split a string up into pieces. For example, we could split sentences into words:\n\n```{r}\nsentences %>%\n  head(5) %>% \n  str_split(\" \")\n```\n\nBecause each component might contain a different number of pieces, this returns a list. If you're working with a length-1 vector, the easiest thing is to just extract the first element of the list:\n\n```{r}\n\"a|b|c|d\" %>% \n  str_split(\"\\\\|\") %>% \n  .[[1]]\n```\n\nOtherwise, like the other stringr functions that return a list, you can use `simplify = TRUE` to return a matrix:\n\n```{r}\nsentences %>%\n  head(5) %>% \n  str_split(\" \", simplify = TRUE)\n```\n\nYou can also request a maximum number of pieces:\n\n```{r}\nfields <- c(\"Name: Hadley\", \"Country: NZ\", \"Age: 35\")\nfields %>% str_split(\": \", n = 2, simplify = TRUE)\n```\n\nInstead of splitting up strings by patterns, you can also split up by character, line, sentence and word `boundary()`s:\n\n```{r}\nx <- \"This is a sentence.  This is another sentence.\"\nstr_view_all(x, boundary(\"word\"))\n\nstr_split(x, \" \")[[1]]\nstr_split(x, boundary(\"word\"))[[1]]\n```\n\n#### Exercises\n\n1.  Split up a string like `\"apples, pears, and bananas\"` into individual\n    components.\n    \n1.  Why is it better to split up by `boundary(\"word\")` than `\" \"`?\n\n1.  What does splitting with an empty string (`\"\"`) do? Experiment, and\n    then read the documentation.\n\n### Find matches\n\n`str_locate()` and `str_locate_all()` give you the starting and ending positions of each match. These are particularly useful when none of the other functions does exactly what you want. You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them.\n\n## Other types of pattern\n\nWhen you use a pattern that's a string, it's automatically wrapped into a call to `regex()`:\n\n```{r, eval = FALSE}\n# The regular call:\nstr_view(fruit, \"nana\")\n# Is shorthand for\nstr_view(fruit, regex(\"nana\"))\n```\n\nYou can use the other arguments of `regex()` to control details of the match:\n\n*   `ignore_case = TRUE` allows characters to match either their uppercase or \n    lowercase forms. This always uses the current locale.\n    \n    ```{r}\n    bananas <- c(\"banana\", \"Banana\", \"BANANA\")\n    str_view(bananas, \"banana\")\n    str_view(bananas, regex(\"banana\", ignore_case = TRUE))\n    ```\n    \n*   `multiline = TRUE` allows `^` and `$` to match the start and end of each\n    line rather than the start and end of the complete string.\n    \n    ```{r}\n    x <- \"Line 1\\nLine 2\\nLine 3\"\n    str_extract_all(x, \"^Line\")[[1]]\n    str_extract_all(x, regex(\"^Line\", multiline = TRUE))[[1]]\n    ```\n    \n*   `comments = TRUE` allows you to use comments and white space to make \n    complex regular expressions more understandable. Spaces are ignored, as is \n    everything after `#`. To match a literal space, you'll need to escape it: \n    `\"\\\\ \"`.\n    \n    ```{r}\n    phone <- regex(\"\n      \\\\(?     # optional opening parens\n      (\\\\d{3}) # area code\n      [) -]?   # optional closing parens, space, or dash\n      (\\\\d{3}) # another three numbers\n      [ -]?    # optional space or dash\n      (\\\\d{3}) # three more numbers\n      \", comments = TRUE)\n    \n    str_match(\"514-791-8141\", phone)\n    ```\n\n*   `dotall = TRUE` allows `.` to match everything, including `\\n`.\n\nThere are three other functions you can use instead of `regex()`:\n\n*   `fixed()`: matches exactly the specified sequence of bytes. It ignores\n    all special regular expressions and operates at a very low level. \n    This allows you to avoid complex escaping and can be much faster than \n    regular expressions. The following microbenchmark shows that it's about\n    3x faster for a simple example.\n  \n    ```{r}\n    microbenchmark::microbenchmark(\n      fixed = str_detect(sentences, fixed(\"the\")),\n      regex = str_detect(sentences, \"the\"),\n      times = 20\n    )\n    ```\n    \n    Beware using `fixed()` with non-English data. It is problematic because \n    there are often multiple ways of representing the same character. For \n    example, there are two ways to define \"á\": either as a single character or \n    as an \"a\" plus an accent:\n    \n    ```{r}\n    a1 <- \"\\u00e1\"\n    a2 <- \"a\\u0301\"\n    c(a1, a2)\n    a1 == a2\n    ```\n\n    They render identically, but because they're defined differently, \n    `fixed()` doesn't find a match. Instead, you can use `coll()`, defined\n    next, to respect human character comparison rules:\n\n    ```{r}\n    str_detect(a1, fixed(a2))\n    str_detect(a1, coll(a2))\n    ```\n    \n*   `coll()`: compare strings using standard **coll**ation rules. This is \n    useful for doing case insensitive matching. Note that `coll()` takes a\n    `locale` parameter that controls which rules are used for comparing\n    characters. Unfortunately different parts of the world use different rules!\n\n    ```{r}\n    # That means you also need to be aware of the difference\n    # when doing case insensitive matches:\n    i <- c(\"I\", \"İ\", \"i\", \"ı\")\n    i\n    \n    str_subset(i, coll(\"i\", ignore_case = TRUE))\n    str_subset(i, coll(\"i\", ignore_case = TRUE, locale = \"tr\"))\n    ```\n    \n    Both `fixed()` and `regex()` have `ignore_case` arguments, but they\n    do not allow you to pick the locale: they always use the default locale.\n    You can see what that is with the following code; more on stringi\n    later.\n    \n    ```{r}\n    stringi::stri_locale_info()\n    ```\n    \n    The downside of `coll()` is speed; because the rules for recognising which\n    characters are the same are complicated, `coll()` is relatively slow\n    compared to `regex()` and `fixed()`.\n\n*   As you saw with `str_split()` you can use `boundary()` to match boundaries.\n    You can also use it with the other functions: \n    \n    ```{r}\n    x <- \"This is a sentence.\"\n    str_view_all(x, boundary(\"word\"))\n    str_extract_all(x, boundary(\"word\"))\n    ```\n\n### Exercises\n\n1.  How would you find all strings containing `\\` with `regex()` vs.\n    with `fixed()`?\n\n1.  What are the five most common words in `sentences`?\n\n## Other uses of regular expressions\n\nThere are two useful function in base R that also use regular expressions:\n\n*   `apropos()` searches all objects available from the global environment. This\n    is useful if you can't quite remember the name of the function.\n    \n    ```{r}\n    apropos(\"replace\")\n    ```\n    \n*   `dir()` lists all the files in a directory. The `pattern` argument takes\n    a regular expression and only returns file names that match the pattern.\n    For example, you can find all the R Markdown files in the current\n    directory with:\n    \n    ```{r}\n    head(dir(pattern = \"\\\\.Rmd$\"))\n    ```\n    \n    (If you're more comfortable with \"globs\" like `*.Rmd`, you can convert\n    them to regular expressions with `glob2rx()`):\n\n## stringi\n\nstringr is built on top of the __stringi__ package. stringr is useful when you're learning because it exposes a minimal set of functions, which have been carefully picked to handle the most common string manipulation functions. stringi, on the other hand, is designed to be comprehensive. It contains almost every function you might ever need: stringi has `r length(getNamespaceExports(\"stringi\"))` functions to stringr's `r length(getNamespaceExports(\"stringr\"))`.\n\nIf you find yourself struggling to do something in stringr, it's worth taking a look at stringi. The packages work very similarly, so you should be able to translate your stringr knowledge in a natural way. The main difference is the prefix: `str_` vs. `stri_`.\n\n### Exercises\n\n1.  Find the stringi functions that:\n\n    1. Count the number of words.\n    1. Find duplicated strings.\n    1. Generate random text.\n\n1.  How do you control the language that `stri_sort()` uses for \n    sorting?\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/tibble.Rmd",
    "content": "# Tibbles\n\n## Introduction\n\nThroughout this book we work with \"tibbles\" instead of R's traditional `data.frame`. Tibbles _are_ data frames, but they tweak some older behaviours to make life a little easier. R is an old language, and some things that were useful 10 or 20 years ago now get in your way. It's difficult to change base R without breaking existing code, so most innovation occurs in packages. Here we will describe the __tibble__ package, which provides opinionated data frames that make working in the tidyverse a little easier. In most places, I'll use the term tibble and data frame interchangeably; when I want to draw particular attention to R's built-in data frame, I'll call them `data.frame`s. \n\nIf this chapter leaves you wanting to learn more about tibbles, you might enjoy `vignette(\"tibble\")`.\n\n### Prerequisites\n\nIn this chapter we'll explore the __tibble__ package, part of the core tidyverse.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n```\n\n## Creating tibbles {#tibbles}\n\nAlmost all of the functions that you'll use in this book produce tibbles, as tibbles are one of the unifying features of the tidyverse. Most other R packages use regular data frames, so you might want to coerce a data frame to a tibble. You can do that with `as_tibble()`:\n\n```{r}\nas_tibble(iris)\n```\n\nYou can create a new tibble from individual vectors with `tibble()`. `tibble()` will automatically recycle inputs of length 1, and allows you to refer to variables that you just created, as shown below.\n\n```{r}\ntibble(\n  x = 1:5, \n  y = 1, \n  z = x ^ 2 + y\n)\n```\n\nIf you're already familiar with `data.frame()`, note that `tibble()` does much less: it never changes the type of the inputs (e.g. it never converts strings to factors!), it never changes the names of variables, and it never creates row names.\n\nIt's possible for a tibble to have column names that are not valid R variable names, aka __non-syntactic__ names. For example, they might not start with a letter, or they might contain unusual characters like a space. To refer to these variables, you need to surround them with backticks, `` ` ``:\n\n```{r}\ntb <- tibble(\n  `:)` = \"smile\", \n  ` ` = \"space\",\n  `2000` = \"number\"\n)\ntb\n```\n\nYou'll also need the backticks when working with these variables in other packages, like ggplot2, dplyr, and tidyr.\n\nAnother way to create a tibble is with `tribble()`, short for **tr**ansposed tibble.  `tribble()` is customised for data entry in code: column headings are defined by formulas (i.e. they start with `~`), and entries are separated by commas. This makes it possible to lay out small amounts of data in easy to read form.\n\n```{r}\ntribble(\n  ~x, ~y, ~z,\n  #--|--|----\n  \"a\", 2, 3.6,\n  \"b\", 1, 8.5\n)\n```\n\nI often add a comment (the line starting with `#`), to make it really clear where the header is.\n\n## Tibbles vs. data.frame\n\nThere are two main differences in the usage of a tibble vs. a classic `data.frame`: printing and subsetting.\n\n### Printing\n\nTibbles have a refined print method that shows only the first 10 rows, and all the columns that fit on screen. This makes it much easier to work with large data. In addition to its name, each column reports its type, a nice feature borrowed from `str()`:\n\n```{r}\ntibble(\n  a = lubridate::now() + runif(1e3) * 86400,\n  b = lubridate::today() + runif(1e3) * 30,\n  c = 1:1e3,\n  d = runif(1e3),\n  e = sample(letters, 1e3, replace = TRUE)\n)\n```\n\nTibbles are designed so that you don't accidentally overwhelm your console when you print large data frames. But sometimes you need more output than the default display. There are a few options that can help.\n\nFirst, you can explicitly `print()` the data frame and control the number of rows (`n`) and the `width` of the display. `width = Inf` will display all columns:\n\n```{r, eval = FALSE}\nnycflights13::flights %>% \n  print(n = 10, width = Inf)\n```\n\nYou can also control the default print behaviour by setting options:\n\n* `options(tibble.print_max = n, tibble.print_min = m)`: if more than `n`\n  rows, print only `m` rows. Use `options(tibble.print_min = Inf)` to always\n  show all rows.\n\n* Use `options(tibble.width = Inf)` to always print all columns, regardless\n  of the width of the screen.\n\nYou can see a complete list of options by looking at the package help with `package?tibble`.\n\nA final option is to use RStudio's built-in data viewer to get a scrollable view of the complete dataset. This is also often useful at the end of a long chain of manipulations.\n\n```{r, eval = FALSE}\nnycflights13::flights %>% \n  View()\n```\n\n### Subsetting\n\nSo far all the tools you've learned have worked with complete data frames. If you want to pull out a single variable, you need some new tools, `$` and `[[`. `[[` can extract by name or position; `$` only extracts by name but is a little less typing.\n\n```{r}\ndf <- tibble(\n  x = runif(5),\n  y = rnorm(5)\n)\n\n# Extract by name\ndf$x\ndf[[\"x\"]]\n\n# Extract by position\ndf[[1]]\n```\n\nTo use these in a pipe, you'll need to use the special placeholder `.`:\n\n```{r}\ndf %>% .$x\ndf %>% .[[\"x\"]]\n```\n\nCompared to a `data.frame`, tibbles are more strict: they never do partial matching, and they will generate a warning if the column you are trying to access does not exist.\n\n## Interacting with older code\n\nSome older functions don't work with tibbles. If you encounter one of these functions, use `as.data.frame()` to turn a tibble back to a `data.frame`:\n\n```{r}\nclass(as.data.frame(tb))\n```\n\nThe main reason that some older functions don't work with tibble is the `[` function.  We don't use `[` much in this book because `dplyr::filter()` and `dplyr::select()` allow you to solve the same problems with clearer code (but you will learn a little about it in [vector subsetting](#vector-subsetting)). With base R data frames, `[` sometimes returns a data frame, and sometimes returns a vector. With tibbles, `[` always returns another tibble.\n\n## Exercises\n\n1.  How can you tell if an object is a tibble? (Hint: try printing `mtcars`,\n    which is a regular data frame). \n\n1.  Compare and contrast the following operations on a `data.frame` and \n    equivalent tibble. What is different? Why might the default data frame\n    behaviours cause you frustration?\n    \n    ```{r, eval = FALSE}\n    df <- data.frame(abc = 1, xyz = \"a\")\n    df$x\n    df[, \"xyz\"]\n    df[, c(\"abc\", \"xyz\")]\n    ```\n\n1.  If you have the name of a variable stored in an object, e.g. `var <- \"mpg\"`,\n    how can you extract the reference variable from a tibble?\n\n1.  Practice referring to non-syntactic names in the following data frame by:\n\n    1.  Extracting the variable called `1`.\n\n    1.  Plotting a scatterplot of `1` vs `2`.\n\n    1.  Creating a new column called `3` which is `2` divided by `1`.\n        \n    1.  Renaming the columns to `one`, `two` and `three`. \n    \n    ```{r}\n    annoying <- tibble(\n      `1` = 1:10,\n      `2` = `1` * 2 + rnorm(length(`1`))\n    )\n    ```\n\n1.  What does `tibble::enframe()` do? When might you use it?\n\n1.  What option controls how many additional column names are printed\n    at the footer of a tibble?\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/transform.Rmd",
    "content": "# Data transformation {#transform}\n\n## Introduction\n\nVisualisation is an important tool for insight generation, but it is rare that you get the data in exactly the right form you need. Often you'll need to create some new variables or summaries, or maybe you just want to rename the variables or reorder the observations in order to make the data a little easier to work with. You'll learn how to do all that (and more!) in this chapter, which will teach you how to transform your data using the dplyr package and a new dataset on flights departing New York City in 2013.\n\n### Prerequisites\n\nIn this chapter we're going to focus on how to use the dplyr package, another core member of the tidyverse. We'll illustrate the key ideas using data from the nycflights13 package, and use ggplot2 to help us understand the data. \n\n```{r setup, message = FALSE}\nlibrary(nycflights13)\nlibrary(tidyverse)\n```\n\nTake careful note of the conflicts message that's printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you'll need to use their full names: `stats::filter()` and `stats::lag()`.\n\n### nycflights13\n\nTo explore the basic data manipulation verbs of dplyr, we'll use `nycflights13::flights`. This data frame contains all `r format(nrow(nycflights13::flights), big.mark = \",\")` flights that departed from New York City in 2013. The data comes from the US [Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0), and is documented in `?flights`.\n\n```{r}\nflights\n```\n\nYou might notice that this data frame prints a little differently from other data frames you might have used in the past: it only shows the first few rows and all the columns that fit on one screen. (To see the whole dataset, you can run `View(flights)` which will open the dataset in the RStudio viewer). It prints differently because it's a __tibble__. Tibbles are data frames, but slightly tweaked to work better in the tidyverse. For now, you don't need to worry about the differences; we'll come back to tibbles in more detail in [wrangle](#wrangle-intro).\n \nYou might also have noticed the row of three (or four) letter abbreviations under the column names. These describe the type of each variable:\n\n* `int` stands for integers.\n\n* `dbl` stands for doubles, or real numbers.\n\n* `chr` stands for character vectors, or strings.\n\n* `dttm` stands for date-times (a date + a time).\n\nThere are three other common types of variables that aren't used in this dataset but you'll encounter later in the book:\n\n* `lgl` stands for logical, vectors that contain only `TRUE` or `FALSE`.\n\n* `fctr` stands for factors, which R uses to represent categorical variables\n  with fixed possible values.\n\n* `date` stands for dates.\n\n### dplyr basics\n\nIn this chapter you are going to learn the five key dplyr functions that allow you to solve the vast majority of your data manipulation challenges:\n\n* Pick observations by their values (`filter()`).\n* Reorder the rows (`arrange()`).\n* Pick variables by their names (`select()`).\n* Create new variables with functions of existing variables (`mutate()`).\n* Collapse many values down to a single summary (`summarise()`).\n\nThese can all be used in conjunction with `group_by()` which changes the scope of each function from operating on the entire dataset to operating on it group-by-group. These six functions provide the verbs for a language of data manipulation.\n\nAll verbs work similarly: \n\n1.  The first argument is a data frame.\n\n1.  The subsequent arguments describe what to do with the data frame,\n    using the variable names (without quotes).\n    \n1.  The result is a new data frame.\n\nTogether these properties make it easy to chain together multiple simple steps to achieve a complex result. Let's dive in and see how these verbs work.\n\n## Filter rows with `filter()`\n\n`filter()` allows you to subset observations based on their values. The first argument is the name of the data frame. The second and subsequent arguments are the expressions that filter the data frame. For example, we can select all flights on January 1st with:\n\n```{r}\nfilter(flights, month == 1, day == 1)\n```\n\nWhen you run that line of code, dplyr executes the filtering operation and returns a new data frame. dplyr functions never modify their inputs, so if you want to save the result, you'll need to use the assignment operator, `<-`:\n\n```{r}\njan1 <- filter(flights, month == 1, day == 1)\n```\n\nR either prints out the results, or saves them to a variable. If you want to do both, you can wrap the assignment in parentheses:\n\n```{r}\n(dec25 <- filter(flights, month == 12, day == 25))\n```\n\n### Comparisons\n\nTo use filtering effectively, you have to know how to select the observations that you want using the comparison operators. R provides the standard suite: `>`, `>=`, `<`, `<=`, `!=` (not equal), and `==` (equal). \n\nWhen you're starting out with R, the easiest mistake to make is to use `=` instead of `==` when testing for equality. When this happens you'll get an informative error:\n\n```{r, error = TRUE}\nfilter(flights, month = 1)\n```\n\nThere's another common problem you might encounter when using `==`: floating point numbers. These results might surprise you!\n\n```{r}\nsqrt(2) ^ 2 == 2\n1 / 49 * 49 == 1\n```\n\nComputers use finite precision arithmetic (they obviously can't store an infinite number of digits!) so remember that every number you see is an approximation. Instead of relying on `==`, use `near()`:\n\n```{r}\nnear(sqrt(2) ^ 2,  2)\nnear(1 / 49 * 49, 1)\n```\n\n### Logical operators\n\nMultiple arguments to `filter()` are combined with \"and\": every expression must be true in order for a row to be included in the output. For other types of combinations, you'll need to use Boolean operators yourself: `&` is \"and\", `|` is \"or\", and `!` is \"not\". Figure \\@ref(fig:bool-ops) shows the complete set of Boolean operations.\n\n```{r bool-ops, echo = FALSE, fig.cap = \"Complete set of boolean operations. `x` is the left-hand circle, `y` is the right-hand circle, and the shaded region show which parts each operator selects.\"}\nknitr::include_graphics(\"diagrams/transform-logical.png\")\n```\n\nThe following code finds all flights that departed in November or December:\n\n```{r, eval = FALSE}\nfilter(flights, month == 11 | month == 12)\n```\n\nThe order of operations doesn't work like English. You can't write `filter(flights, month == 11 | 12)`, which you might literally translate into  \"finds all flights that departed in November or December\". Instead it finds all months that equal `11 | 12`, an expression that evaluates to `TRUE`. In a numeric context (like here), `TRUE` becomes one, so this finds all flights in January, not November or December. This is quite confusing!\n\nA useful short-hand for this problem is `x %in% y`. This will select every row where `x` is one of the values in `y`. We could use it to rewrite the code above:\n\n```{r, eval = FALSE}\nnov_dec <- filter(flights, month %in% c(11, 12))\n```\n\nSometimes you can simplify complicated subsetting by remembering De Morgan's law: `!(x & y)` is the same as `!x | !y`, and `!(x | y)` is the same as `!x & !y`. For example, if you wanted to find flights that weren't delayed (on arrival or departure) by more than two hours, you could use either of the following two filters:\n\n```{r, eval = FALSE}\nfilter(flights, !(arr_delay > 120 | dep_delay > 120))\nfilter(flights, arr_delay <= 120, dep_delay <= 120)\n```\n\nAs well as `&` and `|`, R also has `&&` and `||`. Don't use them here! You'll learn when you should use them in [conditional execution].\n\nWhenever you start using complicated, multipart expressions in `filter()`, consider making them explicit variables instead. That makes it much easier to check your work. You'll learn how to create new variables shortly.\n\n### Missing values\n\nOne important feature of R that can make comparison tricky are missing values, or `NA`s (\"not availables\"). `NA` represents an unknown value so missing values are \"contagious\": almost any operation involving an unknown value will also be unknown.\n\n```{r}\nNA > 5\n10 == NA\nNA + 10\nNA / 2\n```\n\nThe most confusing result is this one:\n\n```{r}\nNA == NA\n```\n\nIt's easiest to understand why this is true with a bit more context:\n\n```{r}\n# Let x be Mary's age. We don't know how old she is.\nx <- NA\n\n# Let y be John's age. We don't know how old he is.\ny <- NA\n\n# Are John and Mary the same age?\nx == y\n# We don't know!\n```\n\nIf you want to determine if a value is missing, use `is.na()`:\n\n```{r}\nis.na(x)\n```\n\n`filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values. If you want to preserve missing values, ask for them explicitly:\n\n```{r}\ndf <- tibble(x = c(1, NA, 3))\nfilter(df, x > 1)\nfilter(df, is.na(x) | x > 1)\n```\n\n### Exercises\n\n1.  Find all flights that\n\n    1. Had an arrival delay of two or more hours\n    1. Flew to Houston (`IAH` or `HOU`)\n    1. Were operated by United, American, or Delta\n    1. Departed in summer (July, August, and September)\n    1. Arrived more than two hours late, but didn't leave late\n    1. Were delayed by at least an hour, but made up over 30 minutes in flight\n    1. Departed between midnight and 6am (inclusive)\n\n1.  Another useful dplyr filtering helper is `between()`. What does it do?\n    Can you use it to simplify the code needed to answer the previous \n    challenges?\n\n1.  How many flights have a missing `dep_time`? What other variables are \n    missing? What might these rows represent?\n\n1.  Why is `NA ^ 0` not missing? Why is `NA | TRUE` not missing?\n    Why is `FALSE & NA` not missing? Can you figure out the general\n    rule?  (`NA * 0` is a tricky counterexample!)\n\n## Arrange rows with `arrange()`\n\n`arrange()` works similarly to `filter()` except that instead of selecting rows, it changes their order. It takes a data frame and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:\n\n```{r}\narrange(flights, year, month, day)\n```\n\nUse `desc()` to re-order by a column in descending order:\n\n```{r}\narrange(flights, desc(dep_delay))\n```\n\nMissing values are always sorted at the end:\n\n```{r}\ndf <- tibble(x = c(5, 2, NA))\narrange(df, x)\narrange(df, desc(x))\n```\n\n### Exercises\n\n1.  How could you use `arrange()` to sort all missing values to the start?\n    (Hint: use `is.na()`).\n    \n1.  Sort `flights` to find the most delayed flights. Find the flights that\n    left earliest.\n\n1.  Sort `flights` to find the fastest flights.\n\n1.  Which flights travelled the longest? Which travelled the shortest?\n\n## Select columns with `select()` {#select}\n\nIt's not uncommon to get datasets with hundreds or even thousands of variables. In this case, the first challenge is often narrowing in on the variables you're actually interested in. `select()` allows you to rapidly zoom in on a useful subset using operations based on the names of the variables.\n\n`select()` is not terribly useful with the flights data because we only have 19 variables, but you can still get the general idea:\n\n```{r}\n# Select columns by name\nselect(flights, year, month, day)\n# Select all columns between year and day (inclusive)\nselect(flights, year:day)\n# Select all columns except those from year to day (inclusive)\nselect(flights, -(year:day))\n```\n\nThere are a number of helper functions you can use within `select()`:\n\n* `starts_with(\"abc\")`: matches names that begin with \"abc\".\n\n* `ends_with(\"xyz\")`: matches names that end with \"xyz\".\n\n* `contains(\"ijk\")`: matches names that contain \"ijk\".\n\n* `matches(\"(.)\\\\1\")`: selects variables that match a regular expression.\n   This one matches any variables that contain repeated characters. You'll \n   learn more about regular expressions in [strings].\n   \n*  `num_range(\"x\", 1:3)`: matches `x1`, `x2` and `x3`.\n   \nSee `?select` for more details.\n\n`select()` can be used to rename variables, but it's rarely useful because it drops all of the variables not explicitly mentioned. Instead, use `rename()`, which is a variant of `select()` that keeps all the variables that aren't explicitly mentioned:\n\n```{r}\nrename(flights, tail_num = tailnum)\n```\n\nAnother option is to use `select()` in conjunction with the `everything()` helper. This is useful if you have a handful of variables you'd like to move to the start of the data frame.\n\n```{r}\nselect(flights, time_hour, air_time, everything())\n```\n\n### Exercises\n\n1.  Brainstorm as many ways as possible to select `dep_time`, `dep_delay`,\n    `arr_time`, and `arr_delay` from `flights`.\n    \n1.  What happens if you include the name of a variable multiple times in\n    a `select()` call?\n  \n1.  What does the `one_of()` function do? Why might it be helpful in conjunction\n    with this vector?\n    \n    ```{r}\n    vars <- c(\"year\", \"month\", \"day\", \"dep_delay\", \"arr_delay\")\n    ```\n    \n1.  Does the result of running the following code surprise you?  How do the\n    select helpers deal with case by default? How can you change that default?\n\n    ```{r, eval = FALSE}\n    select(flights, contains(\"TIME\"))\n    ```\n\n## Add new variables with `mutate()`\n\nBesides selecting sets of existing columns, it's often useful to add new columns that are functions of existing columns. That's the job of `mutate()`. \n\n`mutate()` always adds new columns at the end of your dataset so we'll start by creating a narrower dataset so we can see the new variables. Remember that when you're in RStudio, the easiest way to see all the columns is `View()`.\n\n```{r}\nflights_sml <- select(flights, \n  year:day, \n  ends_with(\"delay\"), \n  distance, \n  air_time\n)\nmutate(flights_sml,\n  gain = dep_delay - arr_delay,\n  speed = distance / air_time * 60\n)\n```\n\nNote that you can refer to columns that you've just created:\n\n```{r}\nmutate(flights_sml,\n  gain = dep_delay - arr_delay,\n  hours = air_time / 60,\n  gain_per_hour = gain / hours\n)\n```\n\nIf you only want to keep the new variables, use `transmute()`:\n\n```{r}\ntransmute(flights,\n  gain = dep_delay - arr_delay,\n  hours = air_time / 60,\n  gain_per_hour = gain / hours\n)\n```\n\n### Useful creation functions {#mutate-funs}\n\nThere are many functions for creating new variables that you can use with `mutate()`. The key property is that the function must be vectorised: it must take a vector of values as input, return a vector with the same number of values as output. There's no way to list every possible function that you might use, but here's a selection of functions that are frequently useful:\n\n*   Arithmetic operators: `+`, `-`, `*`, `/`, `^`. These are all vectorised,\n    using the so called \"recycling rules\". If one parameter is shorter than \n    the other, it will be automatically extended to be the same length. This \n    is most useful when one of the arguments is a single number: `air_time / 60`,\n    `hours * 60 + minute`, etc.\n    \n    Arithmetic operators are also useful in conjunction with the aggregate\n    functions you'll learn about later. For example, `x / sum(x)` calculates \n    the proportion of a total, and `y - mean(y)` computes the difference from \n    the mean.\n    \n*   Modular arithmetic: `%/%` (integer division) and `%%` (remainder), where\n    `x == y * (x %/% y) + (x %% y)`. Modular arithmetic is a handy tool because \n    it allows you to break integers up into pieces. For example, in the \n    flights dataset, you can compute `hour` and `minute` from `dep_time` with:\n    \n    ```{r}\n    transmute(flights,\n      dep_time,\n      hour = dep_time %/% 100,\n      minute = dep_time %% 100\n    )\n    ```\n  \n*   Logs: `log()`, `log2()`, `log10()`. Logarithms are an incredibly useful\n    transformation for dealing with data that ranges across multiple orders of\n    magnitude. They also convert multiplicative relationships to additive, a\n    feature we'll come back to in modelling.\n    \n    All else being equal, I recommend using `log2()` because it's easy to\n    interpret: a difference of 1 on the log scale corresponds to doubling on\n    the original scale and a difference of -1 corresponds to halving.\n\n*   Offsets: `lead()` and `lag()` allow you to refer to leading or lagging \n    values. This allows you to compute running differences (e.g. `x - lag(x)`) \n    or find when values change (`x != lag(x)`). They are most useful in \n    conjunction with `group_by()`, which you'll learn about shortly.\n    \n    ```{r}\n    (x <- 1:10)\n    lag(x)\n    lead(x)\n    ```\n  \n*   Cumulative and rolling aggregates: R provides functions for running sums,\n    products, mins and maxes: `cumsum()`, `cumprod()`, `cummin()`, `cummax()`; \n    and dplyr provides `cummean()` for cumulative means. If you need rolling\n    aggregates (i.e. a sum computed over a rolling window), try the RcppRoll\n    package.\n    \n    ```{r}\n    x\n    cumsum(x)\n    cummean(x)\n    ```\n\n*   Logical comparisons, `<`, `<=`, `>`, `>=`, `!=`, which you learned about\n    earlier. If you're doing a complex sequence of logical operations it's \n    often a good idea to store the interim values in new variables so you can\n    check that each step is working as expected.\n\n*   Ranking: there are a number of ranking functions, but you should \n    start with `min_rank()`. It does the most usual type of ranking \n    (e.g. 1st, 2nd, 2nd, 4th). The default gives smallest values the small\n    ranks; use `desc(x)` to give the largest values the smallest ranks. \n    \n    ```{r}\n    y <- c(1, 2, 2, NA, 3, 4)\n    min_rank(y)\n    min_rank(desc(y))\n    ```\n    \n    If `min_rank()` doesn't do what you need, look at the variants\n    `row_number()`, `dense_rank()`, `percent_rank()`, `cume_dist()`,\n    `ntile()`.  See their help pages for more details.\n    \n    ```{r}\n    row_number(y)\n    dense_rank(y)\n    percent_rank(y)\n    cume_dist(y)\n    ```\n\n### Exercises\n\n```{r, eval = FALSE, echo = FALSE}\nflights <- flights %>% mutate(\n  dep_time = hour * 60 + minute,\n  arr_time = (arr_time %/% 100) * 60 + (arr_time %% 100),\n  airtime2 = arr_time - dep_time,\n  dep_sched = dep_time + dep_delay\n)\n\nggplot(flights, aes(dep_sched)) + geom_histogram(binwidth = 60)\nggplot(flights, aes(dep_sched %% 60)) + geom_histogram(binwidth = 1)\nggplot(flights, aes(air_time - airtime2)) + geom_histogram()\n```\n\n1.  Currently `dep_time` and `sched_dep_time` are convenient to look at, but\n    hard to compute with because they're not really continuous numbers. \n    Convert them to a more convenient representation of number of minutes\n    since midnight.\n    \n1.  Compare `air_time` with `arr_time - dep_time`. What do you expect to see?\n    What do you see? What do you need to do to fix it?\n    \n1.  Compare `dep_time`, `sched_dep_time`, and `dep_delay`. How would you\n    expect those three numbers to be related?\n\n1.  Find the 10 most delayed flights using a ranking function. How do you want \n    to handle ties? Carefully read the documentation for `min_rank()`.\n\n1.  What does `1:3 + 1:10` return? Why?\n\n1.  What trigonometric functions does R provide?\n\n## Grouped summaries with `summarise()`\n\nThe last key verb is `summarise()`. It collapses a data frame to a single row:\n\n```{r}\nsummarise(flights, delay = mean(dep_delay, na.rm = TRUE))\n```\n\n(We'll come back to what that `na.rm = TRUE` means very shortly.)\n\n`summarise()` is not terribly useful unless we pair it with `group_by()`. This changes the unit of analysis from the complete dataset to individual groups. Then, when you use the dplyr verbs on a grouped data frame they'll be automatically applied \"by group\". For example, if we applied exactly the same code to a data frame grouped by date, we get the average delay per date:\n\n```{r}\nby_day <- group_by(flights, year, month, day)\nsummarise(by_day, delay = mean(dep_delay, na.rm = TRUE))\n```\n\nTogether `group_by()` and `summarise()` provide one of the tools that you'll use most commonly when working with dplyr: grouped summaries. But before we go any further with this, we need to introduce a powerful new idea: the pipe.\n\n### Combining multiple operations with the pipe\n\nImagine that we want to explore the relationship between the distance and average delay for each location. Using what you know about dplyr, you might write code like this:\n\n```{r, fig.width = 6}\nby_dest <- group_by(flights, dest)\ndelay <- summarise(by_dest,\n  count = n(),\n  dist = mean(distance, na.rm = TRUE),\n  delay = mean(arr_delay, na.rm = TRUE)\n)\ndelay <- filter(delay, count > 20, dest != \"HNL\")\n\n# It looks like delays increase with distance up to ~750 miles \n# and then decrease. Maybe as flights get longer there's more \n# ability to make up delays in the air?\nggplot(data = delay, mapping = aes(x = dist, y = delay)) +\n  geom_point(aes(size = count), alpha = 1/3) +\n  geom_smooth(se = FALSE)\n```\n\nThere are three steps to prepare this data:\n\n1.  Group flights by destination.\n\n1.  Summarise to compute distance, average delay, and number of flights.\n\n1.  Filter to remove noisy points and Honolulu airport, which is almost\n    twice as far away as the next closest airport.\n\nThis code is a little frustrating to write because we have to give each intermediate data frame a name, even though we don't care about it. Naming things is hard, so this slows down our analysis. \n\nThere's another way to tackle the same problem with the pipe, `%>%`:\n\n```{r}\ndelays <- flights %>% \n  group_by(dest) %>% \n  summarise(\n    count = n(),\n    dist = mean(distance, na.rm = TRUE),\n    delay = mean(arr_delay, na.rm = TRUE)\n  ) %>% \n  filter(count > 20, dest != \"HNL\")\n```\n\nThis focuses on the transformations, not what's being transformed, which makes the code easier to read. You can read it as a series of imperative statements: group, then summarise, then filter. As suggested by this reading, a good way to pronounce `%>%` when reading code is \"then\".\n\nBehind the scenes, `x %>% f(y)` turns into `f(x, y)`, and `x %>% f(y) %>% g(z)` turns into `g(f(x, y), z)` and so on. You can use the pipe to rewrite multiple operations in a way that you can read left-to-right, top-to-bottom. We'll use piping frequently from now on because it considerably improves the readability of code, and we'll come back to it in more detail in [pipes].\n\nWorking with the pipe is one of the key criteria for belonging to the tidyverse. The only exception is ggplot2: it was written before the pipe was discovered. Unfortunately, the next iteration of ggplot2, ggvis, which does use the pipe, isn't quite ready for prime time yet. \n\n### Missing values\n\nYou may have wondered about the `na.rm` argument we used above. What happens if we don't set it?\n\n```{r}\nflights %>% \n  group_by(year, month, day) %>% \n  summarise(mean = mean(dep_delay))\n```\n\nWe get a lot of missing values! That's because aggregation functions obey the usual rule of missing values: if there's any missing value in the input, the output will be a missing value. Fortunately, all aggregation functions have an `na.rm` argument which removes the missing values prior to computation:\n\n```{r}\nflights %>% \n  group_by(year, month, day) %>% \n  summarise(mean = mean(dep_delay, na.rm = TRUE))\n```\n\nIn this case, where missing values represent cancelled flights, we could also tackle the problem by first removing the cancelled flights. We'll save this dataset so we can reuse in the next few examples.\n\n```{r}\nnot_cancelled <- flights %>% \n  filter(!is.na(dep_delay), !is.na(arr_delay))\n\nnot_cancelled %>% \n  group_by(year, month, day) %>% \n  summarise(mean = mean(dep_delay))\n```\n\n### Counts\n\nWhenever you do any aggregation, it's always a good idea to include either a count (`n()`), or a count of non-missing values (`sum(!is.na(x))`). That way you can check that you're not drawing conclusions based on very small amounts of data. For example, let's look at the planes (identified by their tail number) that have the highest average delays:\n\n```{r}\ndelays <- not_cancelled %>% \n  group_by(tailnum) %>% \n  summarise(\n    delay = mean(arr_delay)\n  )\n\nggplot(data = delays, mapping = aes(x = delay)) + \n  geom_freqpoly(binwidth = 10)\n```\n\nWow, there are some planes that have an _average_ delay of 5 hours (300 minutes)!\n\nThe story is actually a little more nuanced. We can get more insight if we draw a scatterplot of number of flights vs. average delay:\n\n```{r}\ndelays <- not_cancelled %>% \n  group_by(tailnum) %>% \n  summarise(\n    delay = mean(arr_delay, na.rm = TRUE),\n    n = n()\n  )\n\nggplot(data = delays, mapping = aes(x = n, y = delay)) + \n  geom_point(alpha = 1/10)\n```\n\nNot surprisingly, there is much greater variation in the average delay when there are few flights. The shape of this plot is very characteristic: whenever you plot a mean (or other summary) vs. group size, you'll see that the variation decreases as the sample size increases.\n\nWhen looking at this sort of plot, it's often useful to filter out the groups with the smallest numbers of observations, so you can see more of the pattern and less of the extreme variation in the smallest groups. This is what the following code does, as well as showing you a handy pattern for integrating ggplot2 into dplyr flows. It's a bit painful that you have to switch from `%>%` to `+`, but once you get the hang of it, it's quite convenient.\n\n```{r}\ndelays %>% \n  filter(n > 25) %>% \n  ggplot(mapping = aes(x = n, y = delay)) + \n    geom_point(alpha = 1/10)\n```\n\n--------------------------------------------------------------------------------\n\nRStudio tip: a useful keyboard shortcut is Cmd/Ctrl + Shift + P. This resends the previously sent chunk from the editor to the console. This is very convenient when you're (e.g.) exploring the value of `n` in the example above. You send the whole block once with Cmd/Ctrl + Enter, then you modify the value of `n` and press Cmd/Ctrl + Shift + P to resend the complete block.\n\n--------------------------------------------------------------------------------\n\nThere's another common variation of this type of pattern. Let's look at how the average performance of batters in baseball is related to the number of times they're at bat. Here I use data from the __Lahman__ package to compute the batting average (number of hits / number of attempts) of every major league baseball player.  \n\nWhen I plot the skill of the batter (measured by the batting average, `ba`) against the number of opportunities to hit the ball (measured by at bat, `ab`), you see two patterns:\n\n1.  As above, the variation in our aggregate decreases as we get more \n    data points.\n    \n2.  There's a positive correlation between skill (`ba`) and opportunities to \n    hit the ball (`ab`). This is because teams control who gets to play, \n    and obviously they'll pick their best players.\n\n```{r}\n# Convert to a tibble so it prints nicely\nbatting <- as_tibble(Lahman::Batting)\n\nbatters <- batting %>% \n  group_by(playerID) %>% \n  summarise(\n    ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),\n    ab = sum(AB, na.rm = TRUE)\n  )\n\nbatters %>% \n  filter(ab > 100) %>% \n  ggplot(mapping = aes(x = ab, y = ba)) +\n    geom_point() + \n    geom_smooth(se = FALSE)\n```\n\nThis also has important implications for ranking. If you naively sort on `desc(ba)`, the people with the best batting averages are clearly lucky, not skilled:\n\n```{r}\nbatters %>% \n  arrange(desc(ba))\n```\n\nYou can find a good explanation of this problem at <http://varianceexplained.org/r/empirical_bayes_baseball/> and <http://www.evanmiller.org/how-not-to-sort-by-average-rating.html>.\n\n### Useful summary functions {#summarise-funs}\n\nJust using means, counts, and sum can get you a long way, but R provides many other useful summary functions:\n\n*   Measures of location: we've used `mean(x)`, but `median(x)` is also\n    useful. The mean is the sum divided by the length; the median is a value \n    where 50% of `x` is above it, and 50% is below it.\n    \n    It's sometimes useful to combine aggregation with logical subsetting. \n    We haven't talked about this sort of subsetting yet, but you'll learn more\n    about it in [subsetting].\n    \n    ```{r}\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      summarise(\n        avg_delay1 = mean(arr_delay),\n        avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay\n      )\n    ```\n\n*   Measures of spread: `sd(x)`, `IQR(x)`, `mad(x)`. The root mean squared deviation,\n    or standard deviation or sd for short, is the standard measure of spread.\n    The interquartile range `IQR()` and median absolute deviation `mad(x)`\n    are robust equivalents that may be more useful if you have outliers.\n    \n    ```{r}\n    # Why is distance to some destinations more variable than to others?\n    not_cancelled %>% \n      group_by(dest) %>% \n      summarise(distance_sd = sd(distance)) %>% \n      arrange(desc(distance_sd))\n    ```\n  \n*   Measures of rank: `min(x)`, `quantile(x, 0.25)`, `max(x)`. Quantiles\n    are a generalisation of the median. For example, `quantile(x, 0.25)`\n    will find a value of `x` that is greater than 25% of the values,\n    and less than the remaining 75%.\n\n    ```{r}\n    # When do the first and last flights leave each day?\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      summarise(\n        first = min(dep_time),\n        last = max(dep_time)\n      )\n    ```\n  \n*   Measures of position: `first(x)`, `nth(x, 2)`, `last(x)`. These work \n    similarly to `x[1]`, `x[2]`, and `x[length(x)]` but let you set a default \n    value if that position does not exist (i.e. you're trying to get the 3rd\n    element from a group that only has two elements). For example, we can\n    find the first and last departure for each day:\n    \n    ```{r}\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      summarise(\n        first_dep = first(dep_time), \n        last_dep = last(dep_time)\n      )\n    ```\n    \n    These functions are complementary to filtering on ranks. Filtering gives\n    you all variables, with each observation in a separate row:\n    \n    ```{r}\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      mutate(r = min_rank(desc(dep_time))) %>% \n      filter(r %in% range(r))\n    ```\n\n*   Counts: You've seen `n()`, which takes no arguments, and returns the \n    size of the current group. To count the number of non-missing values, use\n    `sum(!is.na(x))`. To count the number of distinct (unique) values, use\n    `n_distinct(x)`.\n    \n    ```{r}\n    # Which destinations have the most carriers?\n    not_cancelled %>% \n      group_by(dest) %>% \n      summarise(carriers = n_distinct(carrier)) %>% \n      arrange(desc(carriers))\n    ```\n    \n    Counts are so useful that dplyr provides a simple helper if all you want is \n    a count:\n    \n    ```{r}\n    not_cancelled %>% \n      count(dest)\n    ```\n    \n    You can optionally provide a weight variable. For example, you could use \n    this to \"count\" (sum) the total number of miles a plane flew:\n    \n    ```{r}\n    not_cancelled %>% \n      count(tailnum, wt = distance)\n    ```\n  \n*   Counts and proportions of logical values: `sum(x > 10)`, `mean(y == 0)`.\n    When used with numeric functions, `TRUE` is converted to 1 and `FALSE` to 0. \n    This makes `sum()` and `mean()` very useful: `sum(x)` gives the number of \n    `TRUE`s in `x`, and `mean(x)` gives the proportion.\n    \n    ```{r}\n    # How many flights left before 5am? (these usually indicate delayed\n    # flights from the previous day)\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      summarise(n_early = sum(dep_time < 500))\n    \n    # What proportion of flights are delayed by more than an hour?\n    not_cancelled %>% \n      group_by(year, month, day) %>% \n      summarise(hour_perc = mean(arr_delay > 60))\n    ```\n\n### Grouping by multiple variables\n\nWhen you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll up a dataset:\n\n```{r}\ndaily <- group_by(flights, year, month, day)\n(per_day   <- summarise(daily, flights = n()))\n(per_month <- summarise(per_day, flights = sum(flights)))\n(per_year  <- summarise(per_month, flights = sum(flights)))\n```\n\nBe careful when progressively rolling up summaries: it's OK for sums and counts, but you need to think about weighting means and variances, and it's not possible to do it exactly for rank-based statistics like the median. In other words, the sum of groupwise sums is the overall sum, but the median of groupwise medians is not the overall median.\n\n### Ungrouping\n\nIf you need to remove grouping, and return to operations on ungrouped data, use `ungroup()`. \n\n```{r}\ndaily %>% \n  ungroup() %>%             # no longer grouped by date\n  summarise(flights = n())  # all flights\n```\n\n### Exercises\n\n1.  Brainstorm at least 5 different ways to assess the typical delay \n    characteristics of a group of flights. Consider the following scenarios:\n    \n    * A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of \n      the time.\n      \n    * A flight is always 10 minutes late.\n\n    * A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of \n      the time.\n      \n    * 99% of the time a flight is on time. 1% of the time it's 2 hours late.\n    \n    Which is more important: arrival delay or departure delay?\n\n1.  Come up with another approach that will give you the same output as \n    `not_cancelled %>% count(dest)` and \n    `not_cancelled %>% count(tailnum, wt = distance)` (without using \n    `count()`).\n\n1.  Our definition of cancelled flights (`is.na(dep_delay) | is.na(arr_delay)`\n    ) is slightly suboptimal. Why? Which is the most important column?\n\n1.  Look at the number of cancelled flights per day. Is there a pattern?\n    Is the proportion of cancelled flights related to the average delay?\n\n1.  Which carrier has the worst delays? Challenge: can you disentangle the\n    effects of bad airports vs. bad carriers? Why/why not? (Hint: think about\n    `flights %>% group_by(carrier, dest) %>% summarise(n())`)\n\n1.  What does the `sort` argument to `count()` do. When might you use it?\n\n## Grouped mutates (and filters)\n\nGrouping is most useful in conjunction with `summarise()`, but you can also do convenient operations with `mutate()` and `filter()`:\n\n*   Find the worst members of each group:\n\n    ```{r}\n    flights_sml %>% \n      group_by(year, month, day) %>%\n      filter(rank(desc(arr_delay)) < 10)\n    ```\n\n*   Find all groups bigger than a threshold:\n\n    ```{r}\n    popular_dests <- flights %>% \n      group_by(dest) %>% \n      filter(n() > 365)\n    popular_dests\n    ```\n\n*   Standardise to compute per group metrics:\n\n    ```{r}\n    popular_dests %>% \n      filter(arr_delay > 0) %>% \n      mutate(prop_delay = arr_delay / sum(arr_delay)) %>% \n      select(year:day, dest, arr_delay, prop_delay)\n    ```\n\nA grouped filter is a grouped mutate followed by an ungrouped filter. I generally avoid them except for quick and dirty manipulations: otherwise it's hard to check that you've done the manipulation correctly.\n\nFunctions that work most naturally in grouped mutates and filters are known as  window functions (vs. the summary functions used for summaries). You can learn more about useful window functions in the corresponding vignette: `vignette(\"window-functions\")`.\n\n### Exercises\n\n1.  Refer back to the lists of useful mutate and filtering functions. \n    Describe how each operation changes when you combine it with grouping.\n\n1.  Which plane (`tailnum`) has the worst on-time record?\n\n1.  What time of day should you fly if you want to avoid delays as much\n    as possible?\n    \n1.  For each destination, compute the total minutes of delay. For each \n    flight, compute the proportion of the total delay for its destination.\n    \n1.  Delays are typically temporally correlated: even once the problem that\n    caused the initial delay has been resolved, later flights are delayed \n    to allow earlier flights to leave. Using `lag()`, explore how the delay\n    of a flight is related to the delay of the immediately preceding flight.\n    \n1.  Look at each destination. Can you find flights that are suspiciously\n    fast? (i.e. flights that represent a potential data entry error). Compute\n    the air time a flight relative to the shortest flight to that destination.\n    Which flights were most delayed in the air?\n    \n1.  Find all destinations that are flown by at least two carriers. Use that\n    information to rank the carriers.\n\n1.  For each plane, count the number of flights before the first delay \n    of greater than 1 hour.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/vectors.Rmd",
    "content": "# Vectors\n\n## Introduction\n\nSo far this book has focussed on tibbles and packages that work with them. But as you start to write your own functions, and dig deeper into R, you need to learn about vectors, the objects that underlie tibbles. If you've learned R in a more traditional way, you're probably already familiar with vectors, as most R resources start with vectors and work their way up to tibbles. I think it's better to start with tibbles because they're immediately useful, and then work your way down to the underlying components.\n\nVectors are particularly important as most of the functions you will write will work with vectors. It is possible to write functions that work with tibbles (like ggplot2, dplyr, and tidyr), but the tools you need to write such functions are currently idiosyncratic and immature. I am working on a better approach, <https://github.com/hadley/lazyeval>, but it will not be ready in time for the publication of the book. Even when complete, you'll still need to understand vectors, it'll just make it easier to write a user-friendly layer on top.\n\n### Prerequisites\n\nThe focus of this chapter is on base R data structures, so it isn't essential to load any packages. We will, however, use a handful of functions from the __purrr__ package to avoid some inconsistencies in base R.\n\n```{r setup, message = FALSE}\nlibrary(tidyverse)\n```\n\n## Vector basics\n\nThere are two types of vectors:\n\n1. __Atomic__ vectors, of which there are six types:\n  __logical__, __integer__, __double__,  __character__, __complex__, and \n  __raw__. Integer and double vectors are collectively known as\n  __numeric__ vectors. \n\n1. __Lists__,  which are sometimes called recursive vectors because lists can \n  contain other lists. \n\nThe chief difference between atomic vectors and lists is that atomic vectors are __homogeneous__, while lists can be __heterogeneous__. There's one other related object: `NULL`. `NULL` is often used to represent the absence of a vector (as opposed to `NA` which is used to represent the absence of a value in a vector). `NULL` typically behaves like a vector of length 0. Figure \\@ref(fig:datatypes) summarises the interrelationships. \n\n```{r datatypes, echo = FALSE, out.width = \"50%\", fig.cap = \"The hierarchy of R's vector types\"}\nknitr::include_graphics(\"diagrams/data-structures-overview.png\")\n```\n\nEvery vector has two key properties: \n\n1.  Its __type__, which you can determine with `typeof()`.\n\n    ```{r}\n    typeof(letters)\n    typeof(1:10)\n    ```\n\n1. Its __length__, which you can determine with `length()`.\n\n    ```{r}\n    x <- list(\"a\", \"b\", 1:10)\n    length(x)\n    ```\n\nVectors can also contain arbitrary additional metadata in the form of attributes. These attributes are used to create __augmented vectors__ which build on additional behaviour. There are three important types of augmented vector:\n\n* Factors are built on top of integer vectors.\n* Dates and date-times are built on top of numeric vectors.\n* Data frames and tibbles are built on top of lists.\n\nThis chapter will introduce you to these important vectors from simplest to most complicated. You'll start with atomic vectors, then build up to lists, and finish off with augmented vectors.\n\n## Important types of atomic vector\n\nThe four most important types of atomic vector are logical, integer, double, and character. Raw and complex are rarely used during a data analysis, so I won't discuss them here.\n\n### Logical\n\nLogical vectors are the simplest type of atomic vector because they can take only three possible values: `FALSE`, `TRUE`, and `NA`. Logical vectors are usually constructed with comparison operators, as described in [comparisons]. You can also create them by hand with `c()`:\n\n```{r}\n1:10 %% 3 == 0\n\nc(TRUE, TRUE, FALSE, NA)\n```\n\n### Numeric\n\nInteger and double vectors are known collectively as numeric vectors. In R, numbers are doubles by default. To make an integer, place an `L` after the number:\n\n```{r}\ntypeof(1)\ntypeof(1L)\n1.5L\n```\n\nThe distinction between integers and doubles is not usually important, but there are two important differences that you should be aware of:\n\n1.   Doubles are approximations. Doubles represent floating point numbers that \n     can not always be precisely represented with a fixed amount of memory. \n     This means that you should consider all doubles to be approximations. \n     For example, what is square of the square root of two?\n\n    ```{r}\n    x <- sqrt(2) ^ 2\n    x\n    x - 2\n    ```\n\n    This behaviour is common when working with floating point numbers: most\n    calculations include some approximation error. Instead of comparing floating\n    point numbers using `==`, you should use `dplyr::near()` which allows for \n    some numerical tolerance.\n\n1.  Integers have one special value: `NA`, while doubles have four:\n    `NA`, `NaN`, `Inf` and `-Inf`. All three special values `NaN`, `Inf` and `-Inf` can arise during division:\n   \n    ```{r}\n    c(-1, 0, 1) / 0\n    ```\n\n    Avoid using `==` to check for these other special values. Instead use the \n    helper functions `is.finite()`, `is.infinite()`, and `is.nan()`:\n    \n    |                  |  0  | Inf | NA  | NaN |\n    |------------------|-----|-----|-----|-----|\n    | `is.finite()`    |  x  |     |     |     |\n    | `is.infinite()`  |     |  x  |     |     |\n    | `is.na()`        |     |     |  x  |  x  |\n    | `is.nan()`       |     |     |     |  x  |\n\n### Character\n\nCharacter vectors are the most complex type of atomic vector, because each element of a character vector is a string, and a string can contain an arbitrary amount of data. \n\nYou've already learned a lot about working with strings in [strings]. Here I wanted to mention one important feature of the underlying string implementation: R uses a global string pool. This means that each unique string is only stored in memory once, and every use of the string points to that representation. This reduces the amount of memory needed by duplicated strings. You can see this behaviour in practice with `pryr::object_size()`:\n\n```{r}\nx <- \"This is a reasonably long string.\"\npryr::object_size(x)\n\ny <- rep(x, 1000)\npryr::object_size(y)\n```\n\n`y` doesn't take up 1,000x as much memory as `x`, because each element of `y` is just a pointer to that same string. A pointer is 8 bytes, so 1000 pointers to a 136 B string is 8 * 1000 + 136 = 8.13 kB.\n\n### Missing values\n\nNote that each type of atomic vector has its own missing value:\n\n```{r}\nNA            # logical\nNA_integer_   # integer\nNA_real_      # double\nNA_character_ # character\n```\n\nNormally you don't need to know about these different types because you can always use `NA` and it will be converted to the correct type using the implicit coercion rules described next. However, there are some functions that are strict about their inputs, so it's useful to have this knowledge sitting in your back pocket so you can be specific when needed.\n\n### Exercises\n\n1.  Describe the difference between `is.finite(x)` and  `!is.infinite(x)`.\n\n1.  Read the source code for `dplyr::near()` (Hint: to see the source code,\n    drop the `()`). How does it work? \n\n1.  A logical vector can take 3 possible values. How many possible\n    values can an integer vector take? How many possible values can\n    a double take? Use google to do some research.\n\n1.  Brainstorm at least four functions that allow you to convert a double to an\n    integer. How do they differ? Be precise.\n    \n1.  What functions from the readr package allow you to turn a string\n    into logical, integer, and double vector?\n\n## Using atomic vectors\n\nNow that you understand the different types of atomic vector, it's useful to review some of the important tools for working with them. These include:\n\n1.  How to convert from one type to another, and when that happens\n    automatically.\n\n1.  How to tell if an object is a specific type of vector.\n\n1.  What happens when you work with vectors of different lengths.\n\n1.  How to name the elements of a vector.\n\n1.  How to pull out elements of interest.\n\n### Coercion\n\nThere are two ways to convert, or coerce, one type of vector to another:\n\n1.  Explicit coercion happens when you call a function like `as.logical()`,\n    `as.integer()`, `as.double()`, or `as.character()`. Whenever you find\n    yourself using explicit coercion, you should always check whether you can\n    make the fix upstream, so that the vector never had the wrong type in \n    the first place. For example, you may need to tweak your readr \n    `col_types` specification.\n\n1.  Implicit coercion happens when you use a vector in a specific context\n    that expects a certain type of vector. For example, when you use a logical\n    vector with a numeric summary function, or when you use a double vector\n    where an integer vector is expected.\n    \nBecause explicit coercion is used relatively rarely, and is largely easy to understand, I'll focus on implicit coercion here. \n\nYou've already seen the most important type of implicit coercion: using a logical vector in a numeric context. In this case `TRUE` is converted to `1` and `FALSE` converted to `0`. That means the sum of a logical vector is the number of trues, and the mean of a logical vector is the proportion of trues:\n\n```{r}\nx <- sample(20, 100, replace = TRUE)\ny <- x > 10\nsum(y)  # how many are greater than 10?\nmean(y) # what proportion are greater than 10?\n```\n\nYou may see some code (typically older) that relies on implicit coercion in the opposite direction, from integer to logical:\n\n```{r, eval = FALSE}\nif (length(x)) {\n  # do something\n}\n```\n\nIn this case, 0 is converted to `FALSE` and everything else is converted to `TRUE`. I think this makes it harder to understand your code, and I don't recommend it. Instead be explicit: `length(x) > 0`.\n\nIt's also important to understand what happens when you try and create a vector containing multiple types with `c()`: the most complex type always wins.\n\n```{r}\ntypeof(c(TRUE, 1L))\ntypeof(c(1L, 1.5))\ntypeof(c(1.5, \"a\"))\n```\n\nAn atomic vector can not have a mix of different types because the type is a property of the complete vector, not the individual elements. If you need to mix multiple types in the same vector, you should use a list, which you'll learn about shortly.\n\n### Test functions\n\nSometimes you want to do different things based on the type of vector. One option is to use `typeof()`. Another is to use a test function which returns a `TRUE` or `FALSE`. Base R provides many functions like `is.vector()` and `is.atomic()`, but they often returns surprising results. Instead, it's safer to use the `is_*` functions provided by purrr, which are summarised in the table below.\n\n|                  | lgl | int | dbl | chr | list |\n|------------------|-----|-----|-----|-----|------|\n| `is_logical()`   |  x  |     |     |     |      |\n| `is_integer()`   |     |  x  |     |     |      |\n| `is_double()`    |     |     |  x  |     |      |\n| `is_numeric()`   |     |  x  |  x  |     |      |\n| `is_character()` |     |     |     |  x  |      |\n| `is_atomic()`    |  x  |  x  |  x  |  x  |      |\n| `is_list()`      |     |     |     |     |  x   |\n| `is_vector()`    |  x  |  x  |  x  |  x  |  x   |\n\nEach predicate also comes with a \"scalar\" version, like `is_scalar_atomic()`, which checks that the length is 1. This is useful, for example, if you want to check that an argument to your function is a single logical value.\n\n### Scalars and recycling rules\n\nAs well as implicitly coercing the types of vectors to be compatible, R will also implicitly coerce the length of vectors. This is called vector __recycling__, because the shorter vector is repeated, or recycled, to the same length as the longer vector. \n\nThis is generally most useful when you are mixing vectors and \"scalars\". I put scalars in quotes because R doesn't actually have scalars: instead, a single number is a vector of length 1. Because there are no scalars, most built-in functions are __vectorised__, meaning that they will operate on a vector of numbers. That's why, for example, this code works:\n\n```{r}\nsample(10) + 100\nrunif(10) > 0.5\n```\n\nIn R, basic mathematical operations work with vectors. That means that you should never need to perform explicit iteration when performing simple mathematical computations.\n\nIt's intuitive what should happen if you add two vectors of the same length, or a vector and a \"scalar\", but what happens if you add two vectors of different lengths?\n\n```{r}\n1:10 + 1:2\n```\n\nHere, R will expand the shortest vector to the same length as the longest, so called recycling. This is silent except when the length of the longer is not an integer multiple of the length of the shorter:\n\n```{r}\n1:10 + 1:3\n```\n\nWhile vector recycling can be used to create very succinct, clever code, it can also silently conceal problems. For this reason, the vectorised functions in tidyverse will throw errors when you recycle anything other than a scalar. If you do want to recycle, you'll need to do it yourself with `rep()`:\n\n```{r, error = TRUE}\ntibble(x = 1:4, y = 1:2)\n\ntibble(x = 1:4, y = rep(1:2, 2))\n\ntibble(x = 1:4, y = rep(1:2, each = 2))\n```\n\n### Naming vectors\n\nAll types of vectors can be named. You can name them during creation with `c()`:\n\n```{r}\nc(x = 1, y = 2, z = 4)\n```\n\nOr after the fact with `purrr::set_names()`:\n\n```{r}\nset_names(1:3, c(\"a\", \"b\", \"c\"))\n```\n\nNamed vectors are most useful for subsetting, described next.\n\n### Subsetting {#vector-subsetting}\n\nSo far we've used `dplyr::filter()` to filter the rows in a tibble. `filter()` only works with tibble, so we'll need new tool for vectors: `[`. `[` is the subsetting function, and is called like `x[a]`. There are four types of things that you can subset a vector with:\n\n1.  A numeric vector containing only integers. The integers must either be all \n    positive, all negative, or zero.\n    \n    Subsetting with positive integers keeps the elements at those positions:\n    \n    ```{r}\n    x <- c(\"one\", \"two\", \"three\", \"four\", \"five\")\n    x[c(3, 2, 5)]\n    ```\n    \n    By repeating a position, you can actually make a longer output than \n    input:\n    \n    ```{r}\n    x[c(1, 1, 5, 5, 5, 2)]\n    ```\n    \n    Negative values drop the elements at the specified positions:\n    \n    ```{r}\n    x[c(-1, -3, -5)]\n    ```\n    \n    It's an error to mix positive and negative values:\n    \n    ```{r, error = TRUE}\n    x[c(1, -1)]\n    ```\n\n    The error message mentions subsetting with zero, which returns no values:\n    \n    ```{r}\n    x[0]\n    ```\n    \n    This is not useful very often, but it can be helpful if you want to create \n    unusual data structures to test your functions with.\n  \n1.  Subsetting with a logical vector keeps all values corresponding to a\n    `TRUE` value. This is most often useful in conjunction with the \n    comparison functions.\n    \n    ```{r}\n    x <- c(10, 3, NA, 5, 8, 1, NA)\n    \n    # All non-missing values of x\n    x[!is.na(x)]\n    \n    # All even (or missing!) values of x\n    x[x %% 2 == 0]\n    ```\n\n1.  If you have a named vector, you can subset it with a character vector:\n    \n    ```{r}\n    x <- c(abc = 1, def = 2, xyz = 5)\n    x[c(\"xyz\", \"def\")]\n    ```\n    \n    Like with positive integers, you can also use a character vector to \n    duplicate individual entries.\n\n1.  The simplest type of subsetting is nothing, `x[]`, which returns the \n    complete `x`. This is not useful for subsetting vectors, but it is useful\n    when subsetting matrices (and other high dimensional structures) because\n    it lets you select all the rows or all the columns, by leaving that\n    index blank. For example, if `x` is 2d, `x[1, ]` selects the first row and \n    all the columns, and `x[, -1]` selects all rows and all columns except\n    the first.\n    \nTo learn more about the applications of subsetting, reading the \"Subsetting\" chapter of _Advanced R_: <http://adv-r.had.co.nz/Subsetting.html#applications>.\n\nThere is an important variation of `[` called `[[`. `[[` only ever extracts a single element, and always drops names. It's a good idea to use it whenever you want to make it clear that you're extracting a single item, as in a for loop. The distinction between `[` and `[[` is most important for lists, as we'll see shortly.\n\n### Exercises\n\n1.  What does `mean(is.na(x))` tell you about a vector `x`? What about\n    `sum(!is.finite(x))`?\n\n1.  Carefully read the documentation of `is.vector()`. What does it actually\n    test for? Why does `is.atomic()` not agree with the definition of \n    atomic vectors above?\n    \n1.  Compare and contrast `setNames()` with `purrr::set_names()`.\n\n1.  Create functions that take a vector as input and returns:\n    \n    1. The last value.  Should you use `[` or `[[`?\n\n    1. The elements at even numbered positions.\n    \n    1. Every element except the last value.\n    \n    1. Only even numbers (and no missing values).\n\n1.  Why is `x[-which(x > 0)]` not the same as `x[x <= 0]`? \n\n1.  What happens when you subset with a positive integer that's bigger\n    than the length of the vector? What happens when you subset with a \n    name that doesn't exist?\n\n## Recursive vectors (lists) {#lists}\n\nLists are a step up in complexity from atomic vectors, because lists can contain other lists. This makes them suitable for representing hierarchical or tree-like structures. You create a list with `list()`:\n\n```{r}\nx <- list(1, 2, 3)\nx\n```\n\nA very useful tool for working with lists is `str()` because it focusses on the **str**ucture, not the contents.\n\n```{r}\nstr(x)\n\nx_named <- list(a = 1, b = 2, c = 3)\nstr(x_named)\n```\n\nUnlike atomic vectors, `list()` can contain a mix of objects:\n\n```{r}\ny <- list(\"a\", 1L, 1.5, TRUE)\nstr(y)\n```\n\nLists can even contain other lists!\n\n```{r}\nz <- list(list(1, 2), list(3, 4))\nstr(z)\n```\n\n### Visualising lists\n\nTo explain more complicated list manipulation functions, it's helpful to have a visual representation of lists. For example, take these three lists:\n\n```{r}\nx1 <- list(c(1, 2), c(3, 4))\nx2 <- list(list(1, 2), list(3, 4))\nx3 <- list(1, list(2, list(3)))\n```\n\nI'll draw them as follows:\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/lists-structure.png\")\n```\n\nThere are three principles:\n\n1.  Lists have rounded corners. Atomic vectors have square corners.\n  \n1.  Children are drawn inside their parent, and have a slightly darker\n    background to make it easier to see  the hierarchy.\n  \n1.  The orientation of the children (i.e. rows or columns) isn't important, \n    so I'll pick a row or column orientation to either save space or illustrate \n    an important property in the example.\n\n### Subsetting\n\nThere are three ways to subset a list, which I'll illustrate with a list named `a`:\n\n```{r}\na <- list(a = 1:3, b = \"a string\", c = pi, d = list(-1, -5))\n```\n\n*   `[` extracts a sub-list. The result will always be a list.\n\n    ```{r}\n    str(a[1:2])\n    str(a[4])\n    ```\n    \n    Like with vectors, you can subset with a logical, integer, or character\n    vector.\n    \n*   `[[` extracts a single component from a list. It removes a level of \n    hierarchy from the list.\n\n    ```{r}\n    str(a[[1]])\n    str(a[[4]])\n    ```\n\n*   `$` is a shorthand for extracting named elements of a list. It works\n    similarly to `[[` except that you don't need to use quotes.\n    \n    ```{r}\n    a$a\n    a[[\"a\"]]\n    ```\n\nThe distinction between `[` and `[[` is really important for lists, because `[[` drills down into the list while `[` returns a new, smaller list. Compare the code and output above with the visual representation in Figure \\@ref(fig:lists-subsetting).\n\n```{r lists-subsetting, echo = FALSE, out.width = \"75%\", fig.cap = \"Subsetting a list, visually.\"}\nknitr::include_graphics(\"diagrams/lists-subsetting.png\")\n```\n\n### Lists of condiments\n\nThe difference between `[` and `[[` is very important, but it's easy to get confused. To help you remember, let me show you an unusual pepper shaker.\n\n```{r, echo = FALSE, out.width = \"25%\"} \nknitr::include_graphics(\"images/pepper.jpg\")\n```\n\nIf this pepper shaker is your list `x`, then, `x[1]` is a pepper shaker containing a single pepper packet:\n\n```{r, echo = FALSE, out.width = \"25%\"} \nknitr::include_graphics(\"images/pepper-1.jpg\")\n```\n\n`x[2]` would look the same, but would contain the second packet. `x[1:2]` would be a pepper shaker containing two pepper packets. \n\n`x[[1]]` is:\n\n```{r, echo = FALSE, out.width = \"25%\"} \nknitr::include_graphics(\"images/pepper-2.jpg\")\n```\n\nIf you wanted to get the content of the pepper package, you'd need `x[[1]][[1]]`:\n\n```{r, echo = FALSE, out.width = \"25%\"} \nknitr::include_graphics(\"images/pepper-3.jpg\")\n```\n\n### Exercises\n\n1.  Draw the following lists as nested sets:\n\n    1.  `list(a, b, list(c, d), list(e, f))`\n    1.  `list(list(list(list(list(list(a))))))`\n\n1.  What happens if you subset a tibble as if you're subsetting a list?\n    What are the key differences between a list and a tibble?\n\n## Attributes\n\nAny vector can contain arbitrary additional metadata through its __attributes__. You can think of attributes as named list of vectors that can be attached to any object. \nYou can get and set individual attribute values with `attr()` or see them all at once with `attributes()`.\n\n```{r}\nx <- 1:10\nattr(x, \"greeting\")\nattr(x, \"greeting\") <- \"Hi!\"\nattr(x, \"farewell\") <- \"Bye!\"\nattributes(x)\n```\n\nThere are three very important attributes that are used to implement fundamental parts of R:\n\n1. __Names__ are used to name the elements of a vector.\n1. __Dimensions__ (dims, for short) make a vector behave like a matrix or array.\n1. __Class__ is used to implement the S3 object oriented system.\n\nYou've seen names above, and we won't cover dimensions because we don't use matrices in this book. It remains to describe the class, which controls how __generic functions__ work. Generic functions are key to object oriented programming in R, because they make functions behave differently for different classes of input. A detailed discussion of object oriented programming is beyond the scope of this book, but you can read more about it in _Advanced R_ at <http://adv-r.had.co.nz/OO-essentials.html#s3>.\n\nHere's what a typical generic function looks like:\n\n```{r}\nas.Date\n```\n\nThe call to \"UseMethod\" means that this is a generic function, and it will call a specific __method__, a function, based on the class of the first argument. (All methods are functions; not all functions are methods). You can list all the methods for a generic with `methods()`:\n\n```{r}\nmethods(\"as.Date\")\n```\n\nFor example, if `x` is a character vector, `as.Date()` will call `as.Date.character()`; if it's a factor, it'll call `as.Date.factor()`.\n\nYou can see the specific implementation of a method with `getS3method()`:\n\n```{r}\ngetS3method(\"as.Date\", \"default\")\ngetS3method(\"as.Date\", \"numeric\")\n```\n\nThe most important S3 generic is `print()`: it controls how the object is printed when you type its name at the console. Other important generics are the subsetting functions `[`, `[[`, and `$`. \n\n## Augmented vectors\n\nAtomic vectors and lists are the building blocks for other important vector types like factors and dates. I call these __augmented vectors__, because they are vectors with additional __attributes__, including class. Because augmented vectors have a class, they behave differently to the atomic vector on which they are built. In this book, we make use of four important augmented vectors:\n\n* Factors\n* Dates \n* Date-times\n* Tibbles\n\nThese are described below.\n\n### Factors\n\nFactors are designed to represent categorical data that can take a fixed set of possible values. Factors are built on top of integers, and have a levels attribute:\n\n```{r}\nx <- factor(c(\"ab\", \"cd\", \"ab\"), levels = c(\"ab\", \"cd\", \"ef\"))\ntypeof(x)\nattributes(x)\n```\n\n### Dates and date-times\n\nDates in R are numeric vectors that represent the number of days since 1 January 1970.\n\n```{r}\nx <- as.Date(\"1971-01-01\")\nunclass(x)\n\ntypeof(x)\nattributes(x)\n```\n\nDate-times are numeric vectors with class `POSIXct` that represent the number of seconds since 1 January 1970. (In case you were wondering, \"POSIXct\" stands for \"Portable Operating System Interface\", calendar time.)\n\n```{r}\nx <- lubridate::ymd_hm(\"1970-01-01 01:00\")\nunclass(x)\n\ntypeof(x)\nattributes(x)\n```\n\nThe `tzone` attribute is optional. It controls how the time is printed, not what absolute time it refers to.\n\n```{r}\nattr(x, \"tzone\") <- \"US/Pacific\"\nx\n\nattr(x, \"tzone\") <- \"US/Eastern\"\nx\n```\n\nThere is another type of date-times called POSIXlt. These are built on top of named lists:\n\n```{r}\ny <- as.POSIXlt(x)\ntypeof(y)\nattributes(y)\n```\n\nPOSIXlts are rare inside the tidyverse. They do crop up in base R, because they are needed to extract specific components of a date, like the year or month. Since lubridate provides helpers for you to do this instead, you don't need them. POSIXct's are always easier to work with, so if you find you have a POSIXlt, you should always convert it to a regular data time `lubridate::as_date_time()`.\n\n### Tibbles\n\nTibbles are augmented lists: they have class \"tbl_df\" + \"tbl\" + \"data.frame\", and `names` (column) and `row.names` attributes:\n\n```{r}\ntb <- tibble::tibble(x = 1:5, y = 5:1)\ntypeof(tb)\nattributes(tb)\n```\n\nThe difference between a tibble and a list is that all the elements of a data frame must be vectors with the same length. All functions that work with tibbles enforce this constraint.\n\nTraditional data.frames have a very similar structure:\n\n```{r}\ndf <- data.frame(x = 1:5, y = 5:1)\ntypeof(df)\nattributes(df)\n```\n\nThe main difference is the class. The class of tibble includes \"data.frame\" which means tibbles inherit the regular data frame behaviour by default.\n\n### Exercises\n\n1.  What does `hms::hms(3600)` return? How does it print? What primitive\n    type is the augmented vector built on top of? What attributes does it \n    use?\n    \n1.  Try and make a tibble that has columns with different lengths. What\n    happens?\n\n1.  Based on the definition above, is it ok to have a list as a\n    column of a tibble?\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/visualize.Rmd",
    "content": "# Data visualisation\n\n## Introduction\n\n> \"The simple graph has brought more information to the data analyst’s mind \n> than any other device.\" --- John Tukey\n\nThis chapter will teach you how to visualise your data using ggplot2. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. ggplot2 implements the __grammar of graphics__, a coherent system for describing and building graphs. With ggplot2, you can do more faster by learning one system and applying it in many places.\n\nIf you'd like to learn more about the theoretical underpinnings of ggplot2 before you start, I'd recommend reading \"The Layered Grammar of Graphics\", <http://vita.had.co.nz/papers/layered-grammar.pdf>.\n\n### Prerequisites\n\nThis chapter focusses on ggplot2, one of the core members of the tidyverse. To access the datasets, help pages, and functions that we will use in this chapter, load the tidyverse by running this code:\n\n```{r setup}\nlibrary(tidyverse)\n```\n\nThat one line of code loads the core tidyverse; packages which you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded). \n\nIf you run this code and get the error message \"there is no package called ‘tidyverse’\", you'll need to first install it, then run `library()` once again.\n\n```{r eval = FALSE}\ninstall.packages(\"tidyverse\")\nlibrary(tidyverse)\n```\n\nYou only need to install a package once, but you need to reload it every time you start a new session.\n\nIf we need to be explicit about where a function (or dataset) comes from, we'll use the special form `package::function()`. For example, `ggplot2::ggplot()` tells you explicitly that we're using the `ggplot()` function from the ggplot2 package.\n\n## First steps\n\nLet's use our first graph to answer a question: Do cars with big engines use more fuel than cars with small engines? You probably already have an answer, but try to make your answer precise. What does the relationship between engine size and fuel efficiency look like? Is it positive? Negative? Linear? Nonlinear?\n\n### The `mpg` data frame\n\nYou can test your answer with the `mpg` __data frame__ found in ggplot2 (aka  `ggplot2::mpg`). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). `mpg` contains observations collected by the US Environment Protection Agency on 38 models of car. \n\n```{r}\nmpg\n```\n\nAmong the variables in `mpg` are:\n\n1. `displ`, a car's engine size, in litres.\n\n1. `hwy`, a car's fuel efficiency on the highway, in miles per gallon (mpg). \n  A car with a low fuel efficiency consumes more fuel than a car with a high \n  fuel efficiency when they travel the same distance. \n\nTo learn more about `mpg`, open its help page by running `?mpg`.\n\n### Creating a ggplot\n\nTo plot `mpg`, run this code to put `displ` on the x-axis and `hwy` on the y-axis:\n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy))\n```\n\nThe plot shows a negative relationship between engine size (`displ`) and fuel efficiency (`hwy`). In other words, cars with big engines use more fuel. Does this confirm or refute your hypothesis about fuel efficiency and engine size?\n\nWith ggplot2, you begin a plot with the function `ggplot()`. `ggplot()` creates a coordinate system that you can add layers to. The first argument of `ggplot()` is the dataset to use in the graph. So `ggplot(data = mpg)` creates an empty graph, but it's not very interesting so I'm not going to show it here.\n\nYou complete your graph by adding one or more layers to `ggplot()`. The function `geom_point()` adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each add a different type of layer to a plot. You'll learn a whole bunch of them throughout this chapter.\n\nEach geom function in ggplot2 takes a `mapping` argument. This defines how variables in your dataset are mapped to visual properties. The `mapping` argument is always paired with `aes()`, and the `x` and `y` arguments of `aes()` specify which variables to map to the x and y axes. ggplot2 looks for the mapped variable in the `data` argument, in this case, `mpg`.\n\n### A graphing template\n\nLet's turn this code into a reusable template for making graphs with ggplot2. To make a graph, replace the bracketed sections in the code below with a dataset, a geom function, or a collection of mappings.\n\n```{r eval = FALSE}\nggplot(data = <DATA>) + \n  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))\n```\n\nThe rest of this chapter will show you how to complete and extend this template to make different types of graphs. We will begin with the `<MAPPINGS>` component.\n\n### Exercises\n\n1.  Run `ggplot(data = mpg)`. What do you see?\n\n1.  How many rows are in `mpg`? How many columns?\n\n1.  What does the `drv` variable describe?  Read the help for `?mpg` to find\n    out.\n     \n1.  Make a scatterplot of `hwy` vs `cyl`.\n\n1.  What happens if you make a scatterplot of `class` vs `drv`? Why is\n    the plot not useful?\n\n## Aesthetic mappings\n\n> \"The greatest value of a picture is when it forces us to notice what we\n> never expected to see.\" --- John Tukey\n\nIn the plot below, one group of points (highlighted in red) seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? \n\n```{r, echo = FALSE}\nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +\n  geom_point() + \n  geom_point(data = dplyr::filter(mpg, displ > 5, hwy > 20), colour = \"red\", size = 2.2)\n```\n\nLet's hypothesize that the cars are hybrids. One way to test this hypothesis is to look at the `class` value for each car. The `class` variable of the `mpg` dataset classifies cars into groups such as compact, midsize, and SUV. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular).\n\nYou can add a third variable, like `class`, to a two dimensional scatterplot by mapping it to an __aesthetic__. An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word \"value\" to describe data, let's use the word \"level\" to describe aesthetic properties. Here we change the levels of a point's size, shape, and color to make the point small, triangular, or blue:\n\n```{r, echo = FALSE, asp = 1/4}\nggplot() +\n  geom_point(aes(1, 1), size = 20) +\n  geom_point(aes(2, 1), size = 10) + \n  geom_point(aes(3, 1), size = 20, shape = 17) + \n  geom_point(aes(4, 1), size = 20, colour = \"blue\") + \n  scale_x_continuous(NULL, limits = c(0.5, 4.5), labels = NULL) + \n  scale_y_continuous(NULL, limits = c(0.9, 1.1), labels = NULL) + \n  theme(aspect.ratio = 1/3)\n```\n\nYou can convey information about your data by mapping the aesthetics in your plot to the variables in your dataset. For example, you can map the colors of your points to the `class` variable to reveal the class of each car.\n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy, color = class))\n```\n\n(If you prefer British English, like Hadley, you can use `colour` instead of `color`.)\n\nTo map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside `aes()`. ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as __scaling__. ggplot2 will also add a legend that explains which levels correspond to which values.\n\nThe colors reveal that many of the unusual points are two-seater cars. These cars don't seem like hybrids, and are, in fact, sports cars! Sports cars have large engines like SUVs and pickup trucks, but small bodies like midsize and compact cars, which improves their gas mileage. In hindsight, these cars were unlikely to be hybrids since they have large engines.\n\nIn the above example, we mapped `class` to the color aesthetic, but we could have mapped `class` to the size aesthetic in the same way. In this case, the exact size of each point would reveal its class affiliation. We get a _warning_ here, because mapping an unordered variable (`class`) to an ordered aesthetic (`size`) is not a good idea.\n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy, size = class))\n```\n\nOr we could have mapped `class` to the _alpha_ aesthetic, which controls the transparency of the points, or the shape of the points.\n\n```{r out.width = \"50%\", fig.align = 'default', warning = FALSE, fig.asp = 1/2, fig.cap =\"\"}\n# Left\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))\n\n# Right\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy, shape = class))\n```\n\nWhat happened to the SUVs? ggplot2 will only use six shapes at a time. By default, additional groups will go unplotted when you use the shape aesthetic.\n\nFor each aesthetic, you use `aes()` to associate the name of the aesthetic with a variable to display. The `aes()` function gathers together each of the aesthetic mappings used by a layer and passes them to the layer's mapping argument. The syntax highlights a useful insight about `x` and `y`: the x and y locations of a point are themselves aesthetics, visual properties that you can map to variables to display information about the data. \n\nOnce you map an aesthetic, ggplot2 takes care of the rest. It selects a reasonable scale to use with the aesthetic, and it constructs a legend that explains the mapping between levels and values. For x and y aesthetics, ggplot2 does not create a legend, but it creates an axis line with tick marks and a label. The axis line acts as a legend; it explains the mapping between locations and values.\n\nYou can also _set_ the aesthetic properties of your geom manually. For example, we can make all of the points in our plot blue:\n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy), color = \"blue\")\n```\n\nHere, the color doesn't convey information about a variable, but only changes the appearance of the plot. To set an aesthetic manually, set the aesthetic by name as an argument of your geom function; i.e. it goes _outside_ of `aes()`. You'll need to pick a level that makes sense for that aesthetic:\n\n* The name of a color as a character string.\n\n* The size of a point in mm.\n\n* The shape of a point as a number, as shown in Figure \\@ref(fig:shapes).\n\n```{r shapes, echo = FALSE, out.width = \"75%\", fig.asp = 1/3, fig.cap=\"R has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the `colour` and `fill` aesthetics. The hollow shapes (0--14) have a border determined by `colour`; the solid shapes (15--18) are filled with `colour`; the filled shapes (21--24) have a border of `colour` and are filled with `fill`.\", warning = FALSE}\nshapes <- tibble(\n  shape = c(0, 1, 2, 5, 3, 4, 6:19, 22, 21, 24, 23, 20),\n  x = (0:24 %/% 5) / 2,\n  y = (-(0:24 %% 5)) / 4\n)\nggplot(shapes, aes(x, y)) + \n  geom_point(aes(shape = shape), size = 5, fill = \"red\") +\n  geom_text(aes(label = shape), hjust = 0, nudge_x = 0.15) +\n  scale_shape_identity() +\n  expand_limits(x = 4.1) +\n  scale_x_continuous(NULL, breaks = NULL) + \n  scale_y_continuous(NULL, breaks = NULL, limits = c(-1.2, 0.2)) + \n  theme_minimal() +\n  theme(aspect.ratio = 1/2.75)\n```\n\n### Exercises\n\n1.  What's gone wrong with this code? Why are the points not blue?\n\n    ```{r}\n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = displ, y = hwy, color = \"blue\"))\n    ```\n    \n1.  Which variables in `mpg` are categorical? Which variables are continuous? \n    (Hint: type `?mpg` to read the documentation for the dataset). How\n    can you see this information when you run `mpg`?\n\n1.  Map a continuous variable to `color`, `size`, and `shape`. How do\n    these aesthetics behave differently for categorical vs. continuous\n    variables? \n    \n1.  What happens if you map the same variable to multiple aesthetics? \n\n1.  What does the `stroke` aesthetic do? What shapes does it work with?\n    (Hint: use `?geom_point`)\n    \n1.  What happens if you map an aesthetic to something other than a variable \n    name, like `aes(colour = displ < 5)`?  \n\n## Common problems\n\nAs you start to run R code, you're likely to run into problems. Don't worry --- it happens to everyone. I have been writing R code for years, and every day I still write code that doesn't work! \n\nStart by carefully comparing the code that you're running to the code in the book. R is extremely picky, and a misplaced character can make all the difference. Make sure that every `(` is matched with a `)` and every `\"` is paired with another `\"`. Sometimes you'll run the code and nothing happens. Check the left-hand of your console: if it's a `+`, it means that R doesn't think you've typed a complete expression and it's waiting for you to finish it. In this case, it's usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.\n\nOne common problem when creating ggplot2 graphics is to put the `+` in the wrong place: it has to come at the end of the line, not the start. In other words, make sure you haven't accidentally written code like this:\n\n```R\nggplot(data = mpg) \n+ geom_point(mapping = aes(x = displ, y = hwy))\n```\n\nIf you're still stuck, try the help. You can get help about any R function by running `?function_name` in the console, or selecting the function name and pressing F1 in RStudio. Don't worry if the help doesn't seem that helpful - instead skip down to the examples and look for code that matches what you're trying to do.\n\nIf that doesn't help, carefully read the error message. Sometimes the answer will be buried there! But when you're new to R, the answer might be in the error message but you don't yet know how to understand it. Another great tool is Google: try googling the error message, as it's likely someone else has had the same problem, and has gotten help online.\n\n## Facets\n\nOne way to add additional variables is with aesthetics. Another way, particularly useful for categorical variables, is to split your plot into __facets__, subplots that each display one subset of the data. \n\nTo facet your plot by a single variable, use `facet_wrap()`. The first argument of `facet_wrap()` should be a formula, which you create with `~` followed by a variable name (here \"formula\" is the name of a data structure in R, not a synonym for \"equation\"). The variable that you pass to `facet_wrap()` should be discrete. \n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy)) + \n  facet_wrap(~ class, nrow = 2)\n```\n\nTo facet your plot on the combination of two variables, add `facet_grid()` to your plot call. The first argument of `facet_grid()` is also a formula. This time the formula should contain two variable names separated by a `~`. \n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy)) + \n  facet_grid(drv ~ cyl)\n```\n\nIf you prefer to not facet in the rows or columns dimension, use a `.` instead of a variable name, e.g. `+ facet_grid(. ~ cyl)`.\n\n### Exercises\n\n1.  What happens if you facet on a continuous variable?\n\n1.  What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean?\n    How do they relate to this plot?\n    \n    ```{r, eval = FALSE}\n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = drv, y = cyl))\n    ```\n\n1.  What plots does the following code make? What does `.` do?\n\n    ```{r eval = FALSE}\n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = displ, y = hwy)) +\n      facet_grid(drv ~ .)\n    \n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = displ, y = hwy)) +\n      facet_grid(. ~ cyl)\n    ```\n\n1.  Take the first faceted plot in this section:\n\n    ```{r, eval = FALSE}\n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = displ, y = hwy)) + \n      facet_wrap(~ class, nrow = 2)\n    ```\n    \n    What are the advantages to using faceting instead of the colour aesthetic?\n    What are the disadvantages? How might the balance change if you had a \n    larger dataset?\n    \n1.  Read `?facet_wrap`. What does `nrow` do? What does `ncol` do? What other\n    options control the layout of the individual panels? Why doesn't\n    `facet_grid()` have `nrow` and `ncol` arguments?\n\n1.  When using `facet_grid()` you should usually put the variable with more\n    unique levels in the columns. Why?\n\n## Geometric objects\n\nHow are these two plots similar? \n\n```{r echo = FALSE, out.width = \"50%\", fig.align=\"default\", message = FALSE}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy))\n\nggplot(data = mpg) + \n  geom_smooth(mapping = aes(x = displ, y = hwy))\n```\n\nBoth plots contain the same x variable, the same y variable, and both describe the same data. But the plots are not identical. Each plot uses a different visual object to represent the data. In ggplot2 syntax, we say that they use different __geoms__.\n\nA __geom__ is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. As we see above, you can use different geoms to plot the same data. The plot on the left uses the point geom, and the plot on the right uses the smooth geom, a smooth line fitted to the data. \n\nTo change the geom in your plot, change the geom function that you add to `ggplot()`. For instance, to make the plots above, you can use this code:\n\n```{r eval = FALSE}\n# left\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy))\n\n# right\nggplot(data = mpg) + \n  geom_smooth(mapping = aes(x = displ, y = hwy))\n```\n\nEvery geom function in ggplot2 takes a `mapping` argument. However, not every aesthetic works with every geom. You could set the shape of a point, but you couldn't set the \"shape\" of a line. On the other hand, you _could_ set the linetype of a line. `geom_smooth()` will draw a different line, with a different linetype, for each unique value of the variable that you map to linetype.\n\n```{r message = FALSE}\nggplot(data = mpg) + \n  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))\n```\n\nHere `geom_smooth()` separates the cars into three lines based on their `drv` value, which describes a car's drivetrain. One line describes all of the points with a `4` value, one line describes all of the points with an `f` value, and one line describes all of the points with an `r` value. Here, `4` stands for four-wheel drive, `f` for front-wheel drive, and `r` for rear-wheel drive.\n\nIf this sounds strange, we can make it more clear by overlaying the lines on top of the raw data and then coloring everything according to `drv`. \n\n```{r echo = FALSE, message = FALSE}\nggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + \n  geom_point() +\n  geom_smooth(mapping = aes(linetype = drv))\n```\n\nNotice that this plot contains two geoms in the same graph! If this makes you excited, buckle up. In the next section, we will learn how to place multiple geoms in the same plot.\n\nggplot2 provides over 30 geoms, and extension packages provide even more (see <https://www.ggplot2-exts.org> for a sampling). The best way to get a comprehensive overview is the ggplot2 cheatsheet, which you can find at <http://rstudio.com/cheatsheets>. To learn more about any single geom, use help: `?geom_smooth`.\n\nMany geoms, like `geom_smooth()`, use a single geometric object to display multiple rows of data. For these geoms, you can set the `group` aesthetic to a categorical variable to draw multiple objects. ggplot2 will draw a separate object for each unique value of the grouping variable. In practice, ggplot2 will automatically group the data for these geoms whenever you map an aesthetic to a discrete variable (as in the `linetype` example). It is convenient to rely on this feature because the group aesthetic by itself does not add a legend or distinguishing features to the geoms.\n\n```{r, fig.width = 3, fig.align = 'default', out.width = \"33%\", message = FALSE}\nggplot(data = mpg) +\n  geom_smooth(mapping = aes(x = displ, y = hwy))\n              \nggplot(data = mpg) +\n  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))\n    \nggplot(data = mpg) +\n  geom_smooth(\n    mapping = aes(x = displ, y = hwy, color = drv),\n    show.legend = FALSE\n  )\n```\n\nTo display multiple geoms in the same plot, add multiple geom functions to `ggplot()`:\n\n```{r, message = FALSE}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy)) +\n  geom_smooth(mapping = aes(x = displ, y = hwy))\n```\n\nThis, however, introduces some duplication in our code. Imagine if you wanted to change the y-axis to display `cty` instead of `hwy`. You'd need to change the variable in two places, and you might forget to update one. You can avoid this type of repetition by passing a set of mappings to `ggplot()`. ggplot2 will treat these mappings as global mappings that apply to each geom in the graph.  In other words, this code will produce the same plot as the previous code:\n\n```{r, eval = FALSE}\nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n  geom_point() + \n  geom_smooth()\n```\n\nIf you place mappings in a geom function, ggplot2 will treat them as local mappings for the layer. It will use these mappings to extend or overwrite the global mappings _for that layer only_. This makes it possible to display different aesthetics in different layers.\n\n```{r, message = FALSE}\nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n  geom_point(mapping = aes(color = class)) + \n  geom_smooth()\n```\n\nYou can use the same idea to specify different `data` for each layer. Here, our smooth line displays just a subset of the `mpg` dataset, the subcompact cars. The local data argument in `geom_smooth()` overrides the global data argument in `ggplot()` for that layer only.\n\n```{r, message = FALSE}\nggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n  geom_point(mapping = aes(color = class)) + \n  geom_smooth(data = filter(mpg, class == \"subcompact\"), se = FALSE)\n```\n\n(You'll learn how `filter()` works in the next chapter: for now, just know that this command selects only the subcompact cars.)\n\n### Exercises\n\n1.  What geom would you use to draw a line chart? A boxplot? \n    A histogram? An area chart?\n\n1.  Run this code in your head and predict what the output will look like.\n    Then, run the code in R and check your predictions.\n    \n    ```{r, eval = FALSE}\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + \n      geom_point() + \n      geom_smooth(se = FALSE)\n    ```\n\n1.  What does `show.legend = FALSE` do?  What happens if you remove it?  \n    Why do you think I used it earlier in the chapter?\n\n1.  What does the `se` argument to `geom_smooth()` do?\n\n\n1.  Will these two graphs look different? Why/why not?\n\n    ```{r, eval = FALSE}\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_point() + \n      geom_smooth()\n    \n    ggplot() + \n      geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))\n    ```\n\n1.  Recreate the R code necessary to generate the following graphs.\n    \n    ```{r echo = FALSE, fig.width = 3, out.width = \"50%\", fig.align = \"default\", message = FALSE}\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_point() + \n      geom_smooth(se = FALSE)\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_smooth(aes(group = drv), se = FALSE) +\n      geom_point()\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + \n      geom_point() + \n      geom_smooth(se = FALSE)\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_point(aes(color = drv)) + \n      geom_smooth(se = FALSE)\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_point(aes(color = drv)) +\n      geom_smooth(aes(linetype = drv), se = FALSE)\n    ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + \n      geom_point(size = 4, colour = \"white\") + \n      geom_point(aes(colour = drv))\n    ```\n\n## Statistical transformations\n\nNext, let's take a look at a bar chart. Bar charts seem simple, but they are interesting because they reveal something subtle about plots. Consider a basic bar chart, as drawn with `geom_bar()`. The following chart displays the total number of diamonds in the `diamonds` dataset, grouped by `cut`. The `diamonds` dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the `price`, `carat`, `color`, `clarity`, and `cut` of each diamond. The chart shows that more diamonds are available with high quality cuts than with low quality cuts. \n\n```{r}\nggplot(data = diamonds) + \n  geom_bar(mapping = aes(x = cut))\n```\n\nOn the x-axis, the chart displays `cut`, a variable from `diamonds`. On the y-axis, it displays count, but count is not a variable in `diamonds`! Where does count come from? Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot:\n\n* bar charts, histograms, and frequency polygons bin your data \n  and then plot bin counts, the number of points that fall in each bin.\n\n* smoothers fit a model to your data and then plot predictions from the\n  model.\n\n* boxplots compute a robust summary of the distribution and then display a \n  specially formatted box.\n\nThe algorithm used to calculate new values for a graph is called a __stat__, short for statistical transformation. The figure below describes how this process works with `geom_bar()`.\n\n```{r, echo = FALSE, out.width = \"100%\"}\nknitr::include_graphics(\"images/visualization-stat-bar.png\")\n```\n\nYou can learn which stat a geom uses by inspecting the default value for the `stat` argument. For example, `?geom_bar` shows that the default value for `stat` is \"count\", which means that `geom_bar()` uses `stat_count()`. `stat_count()` is documented on the same page as `geom_bar()`, and if you scroll down you can find a section called \"Computed variables\". That describes how it computes two new variables: `count` and `prop`.\n\nYou can generally use geoms and stats interchangeably. For example, you can recreate the previous plot using `stat_count()` instead of `geom_bar()`:\n\n```{r}\nggplot(data = diamonds) + \n  stat_count(mapping = aes(x = cut))\n```\n\nThis works because every geom has a default stat; and every stat has a default geom. This means that you can typically use geoms without worrying about the underlying statistical transformation. There are three reasons you might need to use a stat explicitly:\n\n1.  You might want to override the default stat. In the code below, I change \n    the stat of `geom_bar()` from count (the default) to identity. This lets \n    me map the height of the bars to the raw values of a $y$ variable. \n    Unfortunately when people talk about bar charts casually, they might be\n    referring to this type of bar chart, where the height of the bar is already\n    present in the data, or the previous bar chart where the height of the bar\n    is generated by counting rows.\n    \n    ```{r, warning = FALSE}\n    demo <- tribble(\n      ~cut,         ~freq,\n      \"Fair\",       1610,\n      \"Good\",       4906,\n      \"Very Good\",  12082,\n      \"Premium\",    13791,\n      \"Ideal\",      21551\n    )\n    \n    ggplot(data = demo) +\n      geom_bar(mapping = aes(x = cut, y = freq), stat = \"identity\")\n    ```\n    \n    (Don't worry that you haven't seen `<-` or `tribble()` before. You might be\n    able to guess at their meaning from the context, and you'll learn exactly\n    what they do soon!)\n\n1.  You might want to override the default mapping from transformed variables\n    to aesthetics. For example, you might want to display a bar chart of\n    proportion, rather than count:\n    \n    ```{r}\n    ggplot(data = diamonds) + \n      geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))\n    ```\n\n    To find the variables computed by the stat, look for the help section\n    titled \"computed variables\".\n    \n1.  You might want to draw greater attention to the statistical transformation\n    in your code. For example, you might use `stat_summary()`, which\n    summarises the y values for each unique x value, to draw \n    attention to the summary that you're computing:\n    \n    ```{r}\n    ggplot(data = diamonds) + \n      stat_summary(\n        mapping = aes(x = cut, y = depth),\n        fun.ymin = min,\n        fun.ymax = max,\n        fun.y = median\n      )\n    ```\n    \nggplot2 provides over 20 stats for you to use. Each stat is a function, so you can get help in the usual way, e.g. `?stat_bin`. To see a complete list of stats, try the ggplot2 cheatsheet.\n\n### Exercises\n\n1.  What is the default geom associated with `stat_summary()`? How could\n    you rewrite the previous plot to use that geom function instead of the \n    stat function?\n\n1.  What does `geom_col()` do? How is it different to `geom_bar()`?\n\n1.  Most geoms and stats come in pairs that are almost always used in \n    concert. Read through the documentation and make a list of all the \n    pairs. What do they have in common?\n\n1.  What variables does `stat_smooth()` compute? What parameters control\n    its behaviour?\n\n1.  In our proportion bar chart, we need to set `group = 1`. Why? In other\n    words what is the problem with these two graphs?\n    \n    ```{r, eval = FALSE}\n    ggplot(data = diamonds) + \n      geom_bar(mapping = aes(x = cut, y = ..prop..))\n    ggplot(data = diamonds) + \n      geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))\n    ```\n  \n\n## Position adjustments\n\nThere's one more piece of magic associated with bar charts. You can colour a bar chart using either the `colour` aesthetic, or, more usefully, `fill`:\n\n```{r out.width = \"50%\", fig.align = \"default\"}\nggplot(data = diamonds) + \n  geom_bar(mapping = aes(x = cut, colour = cut))\nggplot(data = diamonds) + \n  geom_bar(mapping = aes(x = cut, fill = cut))\n```\n\nNote what happens if you map the fill aesthetic to another variable, like `clarity`: the bars are automatically stacked. Each colored rectangle represents a combination of `cut` and `clarity`.\n\n```{r}\nggplot(data = diamonds) + \n  geom_bar(mapping = aes(x = cut, fill = clarity))\n```\n\nThe stacking is performed automatically by the __position adjustment__ specified by the `position` argument. If you don't want a stacked bar chart, you can use one of three other options: `\"identity\"`, `\"dodge\"` or `\"fill\"`.\n\n*   `position = \"identity\"` will place each object exactly where it falls in \n    the context of the graph. This is not very useful for bars, because it\n    overlaps them. To see that overlapping we either need to make the bars\n    slightly transparent by setting `alpha` to a small value, or completely\n    transparent by setting `fill = NA`.\n    \n    ```{r out.width = \"50%\", fig.align = \"default\"}\n    ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + \n      geom_bar(alpha = 1/5, position = \"identity\")\n    ggplot(data = diamonds, mapping = aes(x = cut, colour = clarity)) + \n      geom_bar(fill = NA, position = \"identity\")\n    ```\n    \n    The identity position adjustment is more useful for 2d geoms, like points,\n    where it is the default.\n    \n*   `position = \"fill\"` works like stacking, but makes each set of stacked bars\n    the same height. This makes it easier to compare proportions across \n    groups.\n\n    ```{r}\n    ggplot(data = diamonds) + \n      geom_bar(mapping = aes(x = cut, fill = clarity), position = \"fill\")\n    ```\n\n*   `position = \"dodge\"` places overlapping objects directly _beside_ one \n    another. This makes it easier to compare individual values.\n\n    ```{r}\n    ggplot(data = diamonds) + \n      geom_bar(mapping = aes(x = cut, fill = clarity), position = \"dodge\")\n    ```\n\nThere's one other type of adjustment that's not useful for bar charts, but it can be very useful for scatterplots. Recall our first scatterplot. Did you notice that the plot displays only 126 points, even though there are 234 observations in the dataset?\n\n```{r echo = FALSE}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy))\n```\n\nThe values of `hwy` and `displ` are rounded so the points appear on a grid and many points overlap each other. This problem is known as __overplotting__. This arrangement makes it hard to see where the mass of the data is. Are the data points spread equally throughout the graph, or is there one special combination of `hwy` and `displ` that contains 109 values? \n\nYou can avoid this gridding by setting the position adjustment to \"jitter\".  `position = \"jitter\"` adds a small amount of random noise to each point. This spreads the points out because no two points are likely to receive the same amount of random noise.\n\n```{r}\nggplot(data = mpg) + \n  geom_point(mapping = aes(x = displ, y = hwy), position = \"jitter\")\n```\n\nAdding randomness seems like a strange way to improve your plot, but while it makes your graph less accurate at small scales, it makes your graph _more_ revealing at large scales. Because this is such a useful operation, ggplot2 comes with a shorthand for `geom_point(position = \"jitter\")`: `geom_jitter()`.\n\nTo learn more about a position adjustment, look up the help page associated with each adjustment: `?position_dodge`, `?position_fill`, `?position_identity`, `?position_jitter`, and `?position_stack`.\n\n### Exercises\n\n1.  What is the problem with this plot? How could you improve it?\n\n    ```{r}\n    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + \n      geom_point()\n    ```\n\n1.  What parameters to `geom_jitter()` control the amount of jittering?\n\n1.  Compare and contrast `geom_jitter()` with `geom_count()`.\n\n1.  What's the default position adjustment for `geom_boxplot()`? Create\n    a visualisation of the `mpg` dataset that demonstrates it.\n\n## Coordinate systems\n\nCoordinate systems are probably the most complicated part of ggplot2. The default coordinate system is the Cartesian coordinate system where the x and y positions act independently to determine the location of each point. There are a number of other coordinate systems that are occasionally helpful.\n\n*   `coord_flip()` switches the x and y axes. This is useful (for example),\n    if you want horizontal boxplots. It's also useful for long labels: it's\n    hard to get them to fit without overlapping on the x-axis.\n    \n    ```{r fig.width = 3, out.width = \"50%\", fig.align = \"default\"}\n    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + \n      geom_boxplot()\n    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + \n      geom_boxplot() +\n      coord_flip()\n    ```\n\n*   `coord_quickmap()` sets the aspect ratio correctly for maps. This is very\n    important if you're plotting spatial data with ggplot2 (which unfortunately\n    we don't have the space to cover in this book).\n\n    ```{r fig.width = 3, out.width = \"50%\", fig.align = \"default\", message = FALSE}\n    nz <- map_data(\"nz\")\n\n    ggplot(nz, aes(long, lat, group = group)) +\n      geom_polygon(fill = \"white\", colour = \"black\")\n\n    ggplot(nz, aes(long, lat, group = group)) +\n      geom_polygon(fill = \"white\", colour = \"black\") +\n      coord_quickmap()\n    ```\n\n*   `coord_polar()` uses polar coordinates. Polar coordinates reveal an \n    interesting connection between a bar chart and a Coxcomb chart.\n    \n    ```{r fig.width = 3, out.width = \"50%\", fig.align = \"default\", fig.asp = 1}\n    bar <- ggplot(data = diamonds) + \n      geom_bar(\n        mapping = aes(x = cut, fill = cut), \n        show.legend = FALSE,\n        width = 1\n      ) + \n      theme(aspect.ratio = 1) +\n      labs(x = NULL, y = NULL)\n    \n    bar + coord_flip()\n    bar + coord_polar()\n    ```\n\n### Exercises\n\n1.  Turn a stacked bar chart into a pie chart using `coord_polar()`.\n\n1.  What does `labs()` do? Read the documentation.\n\n1.  What's the difference between `coord_quickmap()` and `coord_map()`?\n\n1.  What does the plot below tell you about the relationship between city\n    and highway mpg? Why is `coord_fixed()` important? What does \n    `geom_abline()` do?\n    \n    ```{r, fig.asp = 1, out.width = \"50%\"}\n    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +\n      geom_point() + \n      geom_abline() +\n      coord_fixed()\n    ```\n\n## The layered grammar of graphics\n\nIn the previous sections, you learned much more than how to make scatterplots, bar charts, and boxplots. You learned a foundation that you can use to make _any_ type of plot with ggplot2. To see this, let's add position adjustments, stats, coordinate systems, and faceting to our code template:\n\n```\nggplot(data = <DATA>) + \n  <GEOM_FUNCTION>(\n     mapping = aes(<MAPPINGS>),\n     stat = <STAT>, \n     position = <POSITION>\n  ) +\n  <COORDINATE_FUNCTION> +\n  <FACET_FUNCTION>\n```\n\nOur new template takes seven parameters, the bracketed words that appear in the template. In practice, you rarely need to supply all seven parameters to make a graph because ggplot2 will provide useful defaults for everything except the data, the mappings, and the geom function.\n\nThe seven parameters in the template compose the grammar of graphics, a formal system for building plots. The grammar of graphics is based on the insight that you can uniquely describe _any_ plot as a combination of a dataset, a geom, a set of mappings, a stat, a position adjustment, a coordinate system, and a faceting scheme. \n\nTo see how this works, consider how you could build a basic plot from scratch: you could start with a dataset and then transform it into the information that you want to display (with a stat).\n\n```{r, echo = FALSE, out.width = \"100%\"}\nknitr::include_graphics(\"images/visualization-grammar-1.png\")\n```\n\nNext, you could choose a geometric object to represent each observation in the transformed data. You could then use the aesthetic properties of the geoms to represent variables in the data. You would map the values of each variable to the levels of an aesthetic.\n\n```{r, echo = FALSE, out.width = \"100%\"}\nknitr::include_graphics(\"images/visualization-grammar-2.png\")\n```\n\nYou'd then select a coordinate system to place the geoms into. You'd use the location of the objects (which is itself an aesthetic property) to display the values of the x and y variables. At that point, you would have a complete graph, but you could further adjust the positions of the geoms within the coordinate system (a position adjustment) or split the graph into subplots (faceting). You could also extend the plot by adding one or more additional layers, where each additional layer uses a dataset, a geom, a set of mappings, a stat, and a position adjustment.\n\n```{r, echo = FALSE, out.width = \"100%\"}\nknitr::include_graphics(\"images/visualization-grammar-3.png\")\n```\n\nYou could use this method to build _any_ plot that you imagine. In other words, you can use the code template that you've learned in this chapter to build hundreds of thousands of unique plots.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-basics.Rmd",
    "content": "# Workflow: basics\n\nYou now have some experience running R code. I didn't give you many details, but you've obviously figured out the basics, or you would've thrown this book away in frustration! Frustration is natural when you start programming in R, because it is such a stickler for punctuation, and even one character out of place will cause it to complain. But while you should expect to be a little frustrated, take comfort in that it's both typical and temporary: it happens to everyone, and the only way to get over it is to keep trying.\n\nBefore we go any further, let's make sure you've got a solid foundation in running R code, and that you know about some of the most helpful RStudio features.\n\n## Coding basics\n\nLet's review some basics we've so far omitted in the interests of getting you plotting as quickly as possible. You can use R as a calculator:\n\n```{r}\n1 / 200 * 30\n(59 + 73 + 2) / 3\nsin(pi / 2)\n```\n\nYou can create new objects with `<-`:\n\n```{r}\nx <- 3 * 4\n```\n\nAll R statements where you create objects, __assignment__ statements, have the same form:\n\n```{r eval = FALSE}\nobject_name <- value\n```\n\nWhen reading that code say \"object name gets value\" in your head.\n\nYou will make lots of assignments and `<-` is a pain to type. Don't be lazy and use `=`: it will work, but it will cause confusion later. Instead, use RStudio's keyboard shortcut: Alt + - (the minus sign). Notice that RStudio automagically surrounds `<-` with spaces, which is a good code formatting practice. Code is miserable to read on a good day, so giveyoureyesabreak and use spaces.\n\n## What's in a name?\n\nObject names must start with a letter, and can only contain letters, numbers, `_` and `.`. You want your object names to be descriptive, so you'll need a convention for multiple words. I recommend __snake_case__ where you separate lowercase words with `_`. \n\n```{r, eval = FALSE}\ni_use_snake_case\notherPeopleUseCamelCase\nsome.people.use.periods\nAnd_aFew.People_RENOUNCEconvention\n```\n\nWe'll come back to code style later, in [functions].\n\nYou can inspect an object by typing its name:\n\n```{r}\nx\n```\n\nMake another assignment:\n\n```{r}\nthis_is_a_really_long_name <- 2.5\n```\n\nTo inspect this object, try out RStudio's completion facility: type \"this\", press TAB, add characters until you have a unique prefix, then press return.\n\nOoops, you made a mistake! `this_is_a_really_long_name` should have value 3.5 not 2.5. Use another keyboard shortcut to help you fix it.  Type \"this\" then press Cmd/Ctrl + ↑. That will list all the commands you've typed that start those letters. Use the arrow keys to navigate, then press enter to retype the command. Change 2.5 to 3.5 and rerun.\n\nMake yet another assignment:\n\n```{r}\nr_rocks <- 2 ^ 3\n```\n\nLet's try to inspect it:\n\n```{r, eval = FALSE}\nr_rock\n#> Error: object 'r_rock' not found\nR_rocks\n#> Error: object 'R_rocks' not found\n```\n\nThere's an implied contract between you and R: it will do the tedious computation for you, but in return, you must be completely precise in your instructions. Typos matter. Case matters.\n\n## Calling functions\n\nR has a large collection of built-in functions that are called like this:\n\n```{r eval = FALSE}\nfunction_name(arg1 = val1, arg2 = val2, ...)\n```\n\nLet's try using `seq()` which makes regular **seq**uences of numbers and, while we're at it, learn more helpful features of RStudio. Type `se` and hit TAB. A popup shows you possible completions. Specify `seq()` by typing more (a \"q\") to disambiguate, or by using ↑/↓ arrows to select. Notice the floating tooltip that pops up, reminding you of the function's arguments and purpose. If you want more help, press F1 to get all the details in the help tab in the lower right pane. \n\nPress TAB once more when you've selected the function you want. RStudio will add matching opening (`(`) and closing (`)`) parentheses for you. Type the arguments `1, 10` and hit return.\n\n```{r}\nseq(1, 10)\n```\n\nType this code and notice you get similar assistance with the paired quotation marks:\n\n```{r}\nx <- \"hello world\"\n```\n\nQuotation marks and parentheses must always come in a pair. RStudio does its best to help you, but it's still possible to mess up and end up with a mismatch. If this happens, R will show you the continuation character \"+\":\n\n```\n> x <- \"hello\n+\n```\n\nThe `+` tells you that R is waiting for more input; it doesn't think you're done yet. Usually that means you've forgotten either a `\"` or a `)`. Either add the missing pair, or press ESCAPE to abort the expression and try again.\n\nIf you make an assignment, you don't get to see the value. You're then tempted to immediately double-check the result:\n\n```{r}\ny <- seq(1, 10, length.out = 5)\ny\n```\n\nThis common action can be shortened by surrounding the assignment with parentheses, which causes assignment and \"print to screen\" to happen.\n\n```{r}\n(y <- seq(1, 10, length.out = 5))\n```\n\nNow look at your environment in the upper right pane:\n\n```{r, echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rstudio-env.png\")\n```\n\nHere you can see all of the objects that you've created.\n\n## Practice\n\n1.  Why does this code not work?\n\n    ```{r, error = TRUE}\n    my_variable <- 10\n    my_varıable\n    ```\n    \n    Look carefully! (This may seem like an exercise in pointlessness, but\n    training your brain to notice even the tiniest difference will pay off\n    when programming.)\n    \n1.  Tweak each of the following R commands so that they run correctly:\n\n    ```{r, eval = FALSE}\n    library(tidyverse)\n\n    ggplot(data = mpg) + \n      geom_point(mapping = aes(x = displ, y = hwy))\n    \n    fliter(mpg, cyl = 8)\n    filter(diamond, carat > 3)\n    ```\n    \n1.  Press Alt + Shift + K. What happens? How can you get to the same place\n    using the menus?\n\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-projects.Rmd",
    "content": "# Workflow: projects\n\nOne day you will need to quit R, go do something else and return to your analysis the next day. One day you will be working on multiple analyses simultaneously that all use R and you want to keep them separate. One day you will need to bring data from the outside world into R and send numerical results and figures from R back out into the world. To handle these real life situations, you need to make two decisions:\n\n1.  What about your analysis is \"real\", i.e. what will you save as your \n    lasting record of what happened?\n\n1.  Where does your analysis \"live\"?\n\n## What is real?\n\nAs a beginning R user, it's OK to consider your environment (i.e. the objects listed in the environment pane) \"real\". However, in the long run, you'll be much better off if you consider your R scripts as \"real\". \n\nWith your R scripts (and your data files), you can recreate the environment. It's much harder to recreate your R scripts from your environment! You'll either have to retype a lot of code from memory (making mistakes all the way) or you'll have to carefully mine your R history.\n\nTo foster this behaviour, I highly recommend that you instruct RStudio not to preserve your workspace between sessions:\n\n```{r, echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"screenshots/rstudio-workspace.png\")\n```\n\nThis will cause you some short-term pain, because now when you restart RStudio it will not remember the results of the code that you ran last time. But this short-term pain will save you long-term agony because it forces you to capture all important interactions in your code. There's nothing worse than discovering three months after the fact that you've only stored the results of an important calculation in your workspace, not the calculation itself in your code. \n\nThere is a great pair of keyboard shortcuts that will work together to make sure you've captured the important parts of your code in the editor:\n\n1. Press Cmd/Ctrl + Shift + F10 to restart RStudio.\n2. Press Cmd/Ctrl + Shift + S to rerun the current script.\n\nI use this pattern hundreds of times a week.\n\n## Where does your analysis live?\n\nR has a powerful notion of the __working directory__. This is where R looks for files that you ask it to load, and where it will put any files that you ask it to save. RStudio shows your current working directory at the top of the console:\n\n```{r, echo = FALSE, out.width = \"50%\"}\nknitr::include_graphics(\"screenshots/rstudio-wd.png\")\n```\n\nAnd you can print this out in R code by running `getwd()`:\n\n```{r eval = FALSE}\ngetwd()\n#> [1] \"/Users/hadley/Documents/r4ds/r4ds\"\n```\n\nAs a beginning R user, it's OK to let your home directory, documents directory, or any other weird directory on your computer be R's working directory. But you're six chapters into this book, and you're no longer a rank beginner. Very soon now you should evolve to organising your analytical projects into directories and, when working on a project, setting R's working directory to the associated directory.\n\n__I do not recommend it__, but you can also set the working directory from within R:\n\n```{r eval = FALSE}\nsetwd(\"/path/to/my/CoolProject\")\n```\n\nBut you should never do this because there's a better way; a way that also puts you on the path to managing your R work like an expert.\n\n## Paths and directories\n\nPaths and directories are a little complicated because there are two basic styles of paths: Mac/Linux and Windows. There are three chief ways in which they differ:\n\n1.  The most important difference is how you separate the components of the\n    path. Mac and Linux uses slashes (e.g. `plots/diamonds.pdf`) and Windows\n    uses backslashes (e.g. `plots\\diamonds.pdf`). R can work with either type\n    (no matter what platform you're currently using), but unfortunately, \n    backslashes mean something special to R, and to get a single backslash \n    in the path, you need to type two backslashes! That makes life frustrating, \n    so I recommend always using the Linux/Mac style with forward slashes.\n\n1.  Absolute paths (i.e. paths that point to the same place regardless of \n    your working directory) look different. In Windows they start with a drive\n    letter (e.g. `C:`) or two backslashes (e.g. `\\\\servername`) and in\n    Mac/Linux they start with a slash \"/\" (e.g. `/users/hadley`). You should\n    __never__ use absolute paths in your scripts, because they hinder sharing: \n    no one else will have exactly the same directory configuration as you.\n\n1.  The last minor difference is the place that `~` points to. `~` is a\n    convenient shortcut to your home directory. Windows doesn't really have \n    the notion of a home directory, so it instead points to your documents\n    directory.\n\n## RStudio projects\n\nR experts keep all the files associated with a project together --- input data, R scripts, analytical results, figures. This is such a wise and common practice that RStudio has built-in support for this via __projects__.\n\nLet's make a project for you to use while you're working through the rest of this book. Click File > New Project, then:\n\n```{r, echo = FALSE, out.width = \"50%\"}\nknitr::include_graphics(\"screenshots/rstudio-project-1.png\")\nknitr::include_graphics(\"screenshots/rstudio-project-2.png\")\nknitr::include_graphics(\"screenshots/rstudio-project-3.png\")\n```\n\nCall your project `r4ds` and think carefully about which _subdirectory_ you put the project in. If you don't store it somewhere sensible, it will be hard to find it in the future!\n\nOnce this process is complete, you'll get a new RStudio project just for this book. Check that the \"home\" directory of your project is the current working directory:\n\n```{r eval = FALSE}\ngetwd()\n#> [1] /Users/hadley/Documents/r4ds/r4ds\n```\n\nWhenever you refer to a file with a relative path it will look for it here. \n\nNow enter the following commands in the script editor, and save the file, calling it \"diamonds.R\". Next, run the complete script which will save a PDF and CSV file into your project directory. Don't worry about the details, you'll learn them later in the book.\n\n```{r toy-line, eval = FALSE}\nlibrary(tidyverse)\n\nggplot(diamonds, aes(carat, price)) + \n  geom_hex()\nggsave(\"diamonds.pdf\")\n\nwrite_csv(diamonds, \"diamonds.csv\")\n```\n\nQuit RStudio. Inspect the folder associated with your project --- notice the `.Rproj` file. Double-click that file to re-open the project. Notice you get back to where you left off: it's the same working directory and command history, and all the files you were working on are still open. Because you followed my instructions above, you will, however, have a completely fresh environment, guaranteeing that you're starting with a clean slate.\n\nIn your favorite OS-specific way, search your computer for `diamonds.pdf` and you will find the PDF (no surprise) but _also the script that created it_ (`diamonds.R`). This is huge win! One day you will want to remake a figure or just understand where it came from. If you rigorously save figures to files __with R code__ and never with the mouse or the clipboard, you will be able to reproduce old work with ease!\n\n## Summary\n\nIn summary, RStudio projects give you a solid workflow that will serve you well in the future:\n\n* Create an RStudio project for each data analysis project. \n\n* Keep data files there; we'll talk about loading them into R in \n  [data import].\n\n* Keep scripts there; edit them, run them in bits or as a whole.\n\n* Save your outputs (plots and cleaned data) there.\n\n* Only ever use relative paths, not absolute paths.\n\nEverything you need is in one place, and cleanly separated from all the other projects that you are working on.\n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-scripts.Rmd",
    "content": "# Workflow: scripts\n\nSo far you've been using the console to run code. That's a great place to start, but you'll find it gets cramped pretty quickly as you create more complex ggplot2 graphics and dplyr pipes. To give yourself more room to work, it's a great idea to use the script editor. Open it up either by clicking the File menu, and selecting New File, then R script, or using the keyboard shortcut Cmd/Ctrl + Shift + N. Now you'll see four panes:\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/rstudio-editor.png\")\n```\n\nThe script editor is a great place to put code you care about. Keep experimenting in the console, but once you have written code that works and does what you want, put it in the script editor. RStudio will automatically save the contents of the editor when you quit RStudio, and will automatically load it when you re-open. Nevertheless, it's a good idea to save your scripts regularly and to back them up.\n\n## Running code\n\nThe script editor is also a great place to build up complex ggplot2 plots or long sequences of dplyr manipulations. The key to using the script editor effectively is to memorise one of the most important keyboard shortcuts: Cmd/Ctrl + Enter. This executes the current R expression in the console. For example, take the code below. If your cursor is at █, pressing Cmd/Ctrl + Enter will run the complete command that generates `not_cancelled`. It will also move the cursor to the next statement (beginning with `not_cancelled %>%`). That makes it easy to run your complete script by repeatedly pressing Cmd/Ctrl + Enter.\n\n```{r, eval = FALSE}\nlibrary(dplyr)\nlibrary(nycflights13)\n\nnot_cancelled <- flights %>% \n  filter(!is.na(dep_delay)█, !is.na(arr_delay))\n\nnot_cancelled %>% \n  group_by(year, month, day) %>% \n  summarise(mean = mean(dep_delay))\n```\n\nInstead of running expression-by-expression, you can also execute the complete script in one step: Cmd/Ctrl + Shift + S. Doing this regularly is a great way to check that you've captured all the important parts of your code in the script. \n\nI recommend that you always start your script with the packages that you need. That way, if you share your code with others, they can easily see what packages they need to install. Note, however, that you should never include `install.packages()` or `setwd()` in a script that you share. It's very antisocial to change settings on someone else's computer!\n\nWhen working through future chapters, I highly recommend starting in the editor and practicing your keyboard shortcuts. Over time, sending code to the console in this way will become so natural that you won't even think about it.\n\n## RStudio diagnostics\n\nThe script editor will also highlight syntax errors with a red squiggly line and a cross in the sidebar:\n\n```{r echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rstudio-diagnostic.png\")\n```\n\nHover over the cross to see what the problem is:\n\n```{r echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rstudio-diagnostic-tip.png\")\n```\n\nRStudio will also let you know about potential problems:\n\n```{r echo = FALSE, out.width = NULL}\nknitr::include_graphics(\"screenshots/rstudio-diagnostic-warn.png\")\n```\n\n## Practice\n\n1.  Go to the RStudio Tips twitter account, <https://twitter.com/rstudiotips>\n    and find one tip that looks interesting. Practice using it!\n\n1.  What other common mistakes will RStudio diagnostics report?  Read\n    <https://support.rstudio.com/hc/en-us/articles/205753617-Code-Diagnostics> to \n    find out.\n    \n"
  },
  {
    "path": "2020年/2020年R数据科学系列/《R数据科学》源代码/wrangle.Rmd",
    "content": "# (PART) Wrangle {-}\n\n# Introduction {#wrangle-intro}\n\nIn this part of the book, you'll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modelling. Data wrangling is very important: without it you can't work with your own data! There are three main parts to data wrangling:\n\n```{r echo = FALSE, out.width = \"75%\"}\nknitr::include_graphics(\"diagrams/data-science-wrangle.png\")\n```\n\nThis part of the book proceeds as follows:\n\n*   In [tibbles], you'll learn about the variant of the data frame that we use\n    in this book: the __tibble__.  You'll learn what makes them different\n    from regular data frames, and how you can construct them \"by hand\".\n\n*   In [data import], you'll learn how to get your data from disk and into R.\n    We'll focus on plain-text rectangular formats, but will give you pointers \n    to packages that help with other types of data.\n\n\t\nData wrangling also encompasses data transformation, which you've already learned a little about. Now we'll focus on new skills for three specific types of data you will frequently encounter in practice:\n\n*   [Relational data] will give you tools for working with multiple\n    interrelated datasets.\n    \n*   [Strings] will introduce regular expressions, a powerful tool for\n    manipulating strings.\n\n*   [Factors] are how R stores categorical data. They are used when a variable\n    has a fixed set of possible values, or when you want to use a non-alphabetical\n    ordering of a string.\n    \n*   [Dates and times] will give you the key tools for working with \n    dates and date-times.\n"
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    "path": "2021年/2021.01.31主成分结果可视化/pac_visual.rmd",
    "content": "---\ntitle: \"主成分分析结果可视化\"\nauthor:\n  - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n  - 中文\n  - R Markdown\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\neditor_options: \n  chunk_output_type: console\n---\n\n```{r message=FALSE, warning=FALSE, include=FALSE}\nshowtext::showtext_auto()\n```\n\n# 简介\n\n主成分分析法，也被称为主分量分析法，是很常用的一种[数据降维方法](https://github.com/EasyChart/Beautiful-Visualization-with-R)。采用一个线性变换将数据变换到一个新的坐标系统，使得任何数据点投影到第一个坐标（成为第一主成\n分）的方差最大，在第二个坐标（第二主成分）的方差为第二大，以此类推。因此，主成分分析可以减少数据的维数，并保持对方差贡献最大的特征，相当于保留低阶主成分，忽略高阶主成分。\n\n关于主成分的理论介绍和R语言代码实现可见前段时间赵西西写的推文：\n\n但是后面留了一个小尾巴，如果想对主成分结果进行可视化，那得怎么实现？有没有简便的方法呢？\n\n正好这几天有读者问起，那今天就来说说这个问题吧。\n\n\n# 方法一\n\n使用ggbiplot包中的ggbiplot()函数，该函数 使用ggplot2对主成分进行可视化。函数内部参数如下\n```\n  ggbiplot(pcobj, choices = 1:2, scale = 1, pc.biplot =\n  TRUE, obs.scale = 1 - scale, var.scale = scale, groups =\n  NULL, ellipse = FALSE, ellipse.prob = 0.68, labels =\n  NULL, labels.size = 3, alpha = 1, var.axes = TRUE, circle\n  = FALSE, circle.prob = 0.69, varname.size = 3,\n  varname.adjust = 1.5, varname.abbrev = FALSE, ...)\n```\n内部参数过多，就不做详细解释。如果对内部参数有兴趣可以通过帮助文档进行查询（?ggbiplot）。\n\n这里使用鸢尾花数据，给出一个简单的例子。大家可以将自己的数据进行导入（如何导入？可见推文：xxx），替换鸢尾花数据。\n\n> 注意：检查自己数据集的数据结构是否和鸢尾花数据结构一致\n\n这个包在github中，官方说可以使用以下参数进行下载（但是小编下载不了，只能通过强暴的方法进行，具体可见推文：。该压缩包已经处理成tar.gz放到公众号内了，如有需要，后台回复[ggbiplot]即可获得）。\n\n之后使用`prcomp()`进行主成分分析，然后将结果保存到res.pca变量中。之后使用`ggbiplot()`进行可视化。其中观测的尺度因子为1（`obs.scale = 1`），变量的尺度因子为1（`var.scale = 1`），每组绘制一个椭圆（`ellipse = TRUE`）并添加相关系数的圆。\n\n```{r message=FALSE, warning=FALSE}\n# install_github(\"vqv/ggbiplot\")\nlibrary(ggbiplot)\nres.pca <- prcomp(iris[, -5],  scale = TRUE)\nggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE, circle = TRUE)\n```\n\n如果想给不同组别添加分别显示不同颜色，则可以使用参数groups，然后设定为原始数据对应的组别向量（如果原始数据没有，可以自行构造一个向量。）\n\n```{r}\n# 添加组别颜色\nggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE,groups = iris$Species, circle = TRUE)\n```\n\n当然你可以在此基础上加入ggplot内部的参数，比如更改主题，更改颜色，添加标题等一系列操作。\n```{r}\n# 更改主题\nggbiplot(res.pca, obs.scale = 1, var.scale = 1, ellipse = TRUE,groups = iris$Species, circle = TRUE) +\n  theme_bw() +\n  theme(panel.grid = element_blank()) +\n  scale_color_brewer(palette = \"Set2\") +\n  labs(title = \"庄闪闪的R语言手册\",subtitle = \"快来关注这个宝藏公众号呀！\",caption =\"绘于：洞头岛\")\n```\n\n\n小编最近有幸上了两节线上的R语言数据可视化公益课，把R语言base包以及ggplot语法系统的过了一遍，如果有需要补补可视化基础的朋友，可移步我的b站[账号名：庄闪闪]，视频回放已等候多时了。\n\n\n# 方法二\n\n使用`FactoMineR`包的`PCA()`函数或者使用基础包的\n`prcomp()`函数进行数据降维处理，然后使用`factoextra`包的`fviz_pca_ind()`函数对结果进行可视化。\n\n这里还是以鸢尾花的数据作为例子，沿用方法一的主成分分析结果res.pca。\n\n这个包内部有四个主要绘制主成分结果的函数。\n\n- fviz_pca_ind(): 各个样本图\n\n- fviz_pca_var(): 变量图\n\n- fviz_pca_biplot(): 各个样本和变量的联合图\n\n- fviz_pca(): fviz_pca_biplot()的别名\n\n内部参数不做过多介绍，有兴趣的读者请看帮助文档。这里只对下面的代码中出现的参数进行解释。\n\n使用散点图进行绘制（`geom = \"point\"`），颜色使用\"cos2\"（`col.ind=\"cos2\"`），使用3阶梯度颜色（`gradient.cols = c(\"white\", \"#2E9FDF\", \"#FC4E07\" )`）。\n\n```{r message=FALSE, warning=FALSE}\nlibrary(ggplot2)\nlibrary(factoextra) #可以直接通过install.packages()进行下载\n# 各个样本图\nfviz_pca_ind(res.pca, col.ind=\"cos2\", geom = \"point\",\n             gradient.cols = c(\"white\", \"#2E9FDF\", \"#FC4E07\" ))\n```\n\n如果想展示分组变量信息，可以通过habillage参数设定，和第一种方法类似，这里还加入了一些细节：各组添加椭圆（`addEllipses=TRUE`），图的版式使用\"Dark2\"（`palette = \"Dark2\"`）。\n\n```{r}\nfviz_pca_ind(res.pca, label=\"none\", habillage=iris$Species,\n             addEllipses=TRUE, ellipse.level=0.95, palette = \"Dark2\")\n```\n当然可以使用`palette = c(\"#999999\", \"#E69F00\", \"#56B4E9\")`，根据论文全文配色，进行手动调整。\n```{r}\nfviz_pca_ind(res.pca, label=\"none\", habillage=iris$Species,\n             addEllipses=TRUE, ellipse.level=0.95,\n             palette = c(\"#999999\", \"#E69F00\", \"#56B4E9\"))\n```\n\n如果想绘制个体和变量的双图，可以使用`fviz_pca_biplot()`，内部其他参数构造相同，然后可以添加各种其他ggplot的函数。\n\n```{r}\n# 个体和变量的双图\n# 只保留变量的标签 #按组改变颜色，添加省略号\nfviz_pca_biplot(res.pca, label = \"var\", habillage=iris$Species,\n                addEllipses=TRUE, ellipse.level=0.95,\n                ggtheme = theme_bw()) +\n  theme(panel.grid = element_blank()) +\n  scale_color_brewer(palette = \"Set1\") +\n  labs(title = \"庄闪闪的R语言手册\",subtitle = \"快来关注这个宝藏公众号呀！\",caption =\"绘于：洞头岛\")\n```\n\n\n\n\n"
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    "path": "2021年/2021.02.21克利夫兰点图系列/DotPlots_Data.csv",
    "content": "City,Female,Male\nAcapulco,2565.51,2595.8\nBellingham,453.36,539.27\nBeverly Hills,5050.46,5269.71\nBremerton,5269.89,5705\nCamacho,3643.3,2154.15\nGuadalajara,290.99,232.33\nHidalgo,7361.04,3951.73\nLos Angeles,6014.64,6281.53\nMerida,4770.14,3970.31\nMexico City,1255.17,1233.14\nOrizaba,3531.85,2713.37\nPortland,5612.43,6314.17\nSalem,9416.67,8795.17\nSan Andres,3458.54,4747.51\nSan Diego,5800.65,5569.12\nSan Francisco,392.54,351.76\nSeattle,6561.9,5590.49\nSpokane,5835.68,5398.36\nTacoma,8467.8,8363.35\nVancouver,3973.95,4691.05\nVictoria,1151.91,1250.12\nWalla Walla,488.42,676.18\nYakima,2158.89,2911.08\n"
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    "path": "2021年/2021.02.21克利夫兰点图系列/cleveland's .Rmd",
    "content": "---\ntitle: \"R可视乎｜克利夫兰点图系列\"\nauthor:\n  - 庄亮亮\ndocumentclass: ctexart\nkeywords:\n  - 中文\n  - R Markdown\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\neditor_options: \n  chunk_output_type: console\n---\n\n# 克利夫兰点图系列\n\n\n## 数据结构\n\n为了方便起见，我这里直接模拟产生数据进行实验。大家根据自己数据进行变化即可。test_data包含两列，产品名称（因子类型），产品失效时间。\n\n> 注：随机种子的设定，方便大家可以运行出和我一样的结果。\n\n```{r}\nset.seed(1) #设置随机种子\ntest_data = data.frame(\"Id\" = LETTERS[1:20], \"Time\" = rnorm(20,10,10)+20)\ntest_data$Id = as.factor(test_data$Id) #将Id转换为因子型数据\n```\n```{r}\nhead(test_data)\n```\n\n\n## 棒棒糖图\n\n**棒棒糖图（lollipop chart）**：棒棒糖图传达了与柱形图或者条形图相同的信息，只是将矩形转变成线条，这样可减少展示空间，重点放在数据点上，从而看起来更加简洁、美观。相对柱形图与条形图，棒棒糖图更加适合数据量比较多的情况。图3-4-1(a)为横向棒棒糖图，对应条形图；而如果是纵向棒棒糖图则对应柱形图。\n\n\n使用 ggplot2 包中的散点绘制函数`geom_point()`及连接线函数 `geom_segment()`来绘制棒棒图。其中`geom_segment()`函数根据起点坐标`（x,y）`和终点坐标`（xend,yend）`绘制两者之间的连接线，棒棒糖图的连接线为平行于X轴水平绘制，其长度（length）对应 X 轴变量的数值。\n\n\n```{r}\nlibrary(ggplot2)\nggplot(test_data,aes(y = Id,x = Time))+\n  geom_segment(aes(x=0,xend=Time,y=Id,yend=reorder(Id,Time)),col=\"gray60\") + #添加连接线\n  geom_point(shape=21,size=4,colour=\"gray60\",fill=\"skyblue\")\n```\n\n如果想按照产品失效时间进行绘制，则可以使用`y = reorder(Id,Time)`对`y = Id`进行替换。\n\n```{r}\nggplot(test_data,aes(y = reorder(Id,Time),x = Time))+\n  geom_segment(aes(x=0,xend=Time,y=reorder(Id,Time),yend=reorder(Id,Time)),col=\"gray60\") + #添加连接线\n  geom_point(shape=21,size=4,colour=\"gray60\",fill=\"skyblue\") + ylab(\"Product\")\n```\n\n\n当然也可以实际使用中，尤其是在生存分析，可靠性分析中。我们可以数据包含产品测试开始时间和终点时间。这时只需将segement中的x参数进行变化即可。\n\n```{r}\ntest_data$start_Time = rnorm(20,5,3) #模拟产生开始时间数据\nggplot(test_data,aes(y = reorder(Id,Time),x = Time))+\n  geom_segment(aes(x=start_Time,xend=Time,y=reorder(Id,Time),yend=reorder(Id,Time)),col=\"gray60\") + #添加连接线\n  geom_point(shape=21,size=4,colour=\"gray60\",fill=\"skyblue\") + ylab(\"Product\")\n```\n\n\n## 克利夫兰点图\n\n**克利夫兰点图（Cleveland's dot plot）**：也就是我们常用的滑珠散点图，非常类似棒棒糖图，只是没有连接的线条，重点强调数据的排序展示及互相之间的差距。克利夫兰点图一般都横向展示，所以 Y 轴变量一般为类别型变量。\n\n只需使用`geom_point()`即可绘制克利夫兰点图。\n```{r}\nggplot(test_data,aes(y = reorder(Id,Time),x = Time))+\n  geom_point(shape=21,size=4,colour=\"gray60\",fill=\"skyblue\") + ylab(\"Product\")\n```\n\n\n## 哑铃图\n\n**哑铃图（dumbbell plot）**：可以看成多数据系列的克利夫兰点图，只是使用直线连接了两个数据系列的数据点。哑铃图主要用于：\n\n①展示在同一时间段两个数据点的相对位置（增加或者减少）；\n\n②比较两个类别之间的数据值差别。\n\n这里，我们的模拟数据就不大适合了，为了绘制该图我将数据进行变化。实际背景如下：两个工厂对不同产品类型进行相同实验测试，得到数据如下：\n\n```{r}\nset.seed(2)\ntest_data1 = data.frame(\"Id\" = LETTERS[1:20], \"Time\" = rnorm(20,10,10)+20)\ntest_data1$Id = as.factor(test_data1$Id) \ntest_data1$start_Time = rnorm(20,5,3) #模拟产生开始时间数据\ntest_data_dum = rbind(test_data,test_data1)\n\ntest_data_dum$Group = c(rep(1,20),rep(2,20))\ntest_data_dum$Group = as.factor(test_data_dum$Group)\n```\n\n此时前6行数据如下：\n```{r}\nhead(test_data_dum)\n```\n\n```{r}\nggplot(test_data_dum,aes(y = reorder(Id,Time),x = Time,fill=Group)) +\n  geom_line(aes(group = reorder(Id,Time))) +\n  geom_point(shape=21,size=4,colour=\"gray60\") + ylab(\"Product\") +\n  scale_fill_manual(values=c( \"#FC4E07\",\"#36BED9\"))+\n  theme(\n    legend.background = element_blank(),\n    legend.position = c(0.85,0.12)\n  )\n```\n\n\n"
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  },
  {
    "path": "2021年/2021.02.28常用主题风格/R可视乎｜ggplot常用主题风格汇总.md",
    "content": "\n\n借助`theme()`函数，可以自定义ggplot2图表的任何部分。 幸运的是，可以使用大量的预构建主题，仅用一行代码即可获得良好的样式。小编汇总了常用几个包的主题风格以供参考，以后可以根据论文的风格选择内置的一些主题。\n\n## 1.具体操作\n\n这里使用iris数据集，给出绘制散点图的例子，这里没有对主题风格进行设置，使用了默认主题。\n```r\nlibrary(ggplot2)\nggplot(iris,aes(x = Sepal.Length,y = Sepal.Width,col = Species,shape = Species)) +\n  geom_point()\n```\n\n如果你想更换主题，可以直接在之后加入对应参数即可，例如\n\n```r\nlibrary(ggplot2)\nggplot(iris,aes(x = Sepal.Length,y = Sepal.Width,col = Species,shape = Species)) +\n  geom_point() +\n  theme_bw()\n```\n\n接下来，我们对常用的主题风格进行汇总。\n\n## 2.ggplot2包\n\nggplot2包内部就有一些内置主题样式。\n\n### default\n\n![](https://static01.imgkr.com/temp/12bc9ce4738f4eb58f4f3b92063247fd.png)\n\n### theme_bw()\n\n![](https://static01.imgkr.com/temp/5e6e4b3ea8f74a4c9d1d9f3c48b176ad.png)\n\n### theme_minimal()\n\n![](https://imgkr2.cn-bj.ufileos.com/d60b293c-7cb7-4870-a561-9518455d60ed.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=f66%252FptGu2ii4wKaNV5VZpUzZZQg%253D&Expires=1614591717)\n\n### theme_classic()\n\n![](https://imgkr2.cn-bj.ufileos.com/875f9127-458a-42b1-891e-f0785391ec9a.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=z3ioiQ2RqTsicIp6SsyfbF7KapY%253D&Expires=1614591721)\n\n### theme_gray()\n\n![](https://static01.imgkr.com/temp/6f0dc53f3d7c461f8fd2b2de356d6b60.png)\n\n## ggthemes包\n\n> **注**： 使用该包内部的函数，记得提前安装和加载该包\n\n>该包的github网站为：https://github.com/jrnold/ggthemes。jornld给出了很多主题风格的例子可见：https://github.com/BTJ01/ggthemes/tree/master/inst/examples\n\n这个包算是ggplot拓展包最热门的包之一了，这里罗列一些常用的主题风格函数。想继续研究的伙伴可以看上面的两个网站。\n\n### theme_excel()\n\n\n![](https://imgkr2.cn-bj.ufileos.com/5941f66a-2826-4907-bce2-0010cf8f9860.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=XFmfeDCgWixVtXlPqWGMrzkdnAE%253D&Expires=1614591989)\n\n\n### theme_economist()\n\n\n![](https://imgkr2.cn-bj.ufileos.com/6be40e75-3fe1-45b7-8e93-3c04ca7f3be5.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Ry4tY9ZvkGm3PHzqlIGYRPAEEDk%253D&Expires=1614592030)\n\n\n### theme_fivethirtyeight()\n\n![](https://static01.imgkr.com/temp/3b6e7b6132594662a98c1a84512efc54.png)\n\n\n\n\n### theme_tufte()\n\n![](https://static01.imgkr.com/temp/67078048f3564990a5272b8302ede352.png)\n\n### theme_geocs()\n![](https://static01.imgkr.com/temp/5092ca26a5794278adc171720929baee.png)\n\n### theme_wsj()\n![](https://imgkr2.cn-bj.ufileos.com/cdc7ed4a-7e76-4558-8e79-b87c245a1362.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=gRzvU1c1NwNWIzRWLp%252FamMUsgys%253D&Expires=1614592045)\n\n### theme_calc()\n![](https://imgkr2.cn-bj.ufileos.com/032dbaa9-594d-4e51-8dbb-2be49b3ed8d8.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=Uq7K0LZE1I3DDXxPtVo%252BDSsgp1o%253D&Expires=1614592045)\n\n### theme_hc()\n\n![](https://imgkr2.cn-bj.ufileos.com/6eb78ba2-414a-431f-8be1-7550ad33a7be.png?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=pf8zkArhfXq0RdKZ00C4PIhSAPM%253D&Expires=1614592067)\n\n## 其他包\n\n### egg包中的theme_article()\n\n![](https://static01.imgkr.com/temp/f6256958de4c4e97806944ec73815898.png)\n\n### ggpubr包中的theme_pubr() \n\n\n![](https://static01.imgkr.com/temp/7076cb88f033425cb96d406ae0bc97f6.png)\n\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2022年/2022.01.14 如何使用 ggplot2 绘制双轴分离图？/双轴分离.R",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/QiMHA10X8nGtK5iOH4armQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(ggthemes)\nlibrary(viridis)\n# Build the demo data set.\ndf1 <- data.frame(\n  term = paste0('term', 1:4),\n  p.val = runif(4, 1, 5)\n)\ndf2 <- data.frame(x = c(0,2,4,6), y = df1$term)\n# （初级版本）============\nggplot(df1, aes(p.val, term)) +\n  geom_col(width = 0.6) +\n  labs(x = '-log(BH p value)', \n       y = 'Terms')\n# （高级版本）============\nggplot(df1, aes(p.val, term)) +\n  geom_col(aes(fill = term), width = 0.6) +\n  geom_rangeframe(data = df2, aes(x = x, y = y), sides = 'bl') +\n  scale_fill_viridis(discrete = T)+\n  theme_tufte() +\n  theme(\n    legend.position = 'none',\n    panel.grid = element_blank(),\n    axis.text = element_text(size = rel(1.1))\n  ) +\n  labs(x = '-log(BH p value)', \n       y = 'Terms')\n#================\n\ndf1 <- data.frame(\n  term = paste0('term', 1:8),\n  p.val = runif(8, 1, 5),\n  score = rnorm(8, 0, 1)\n)\ndf2 <- data.frame(x = c(0:6,6), y = df1$term)\n\nggplot(df1, aes(p.val, term)) +\n  geom_col(aes(fill = score), width = 0.6) +\n  geom_rangeframe(data = df2, aes(x = x, y = y), sides = 'bl') +\n  scale_fill_gradient2(\n    low = 'cyan',\n    mid = '#fbffff',\n    high = 'chocolate1',\n    midpoint = 0)+\n  theme_tufte() +\n  theme(\n    legend.position = c(0.9,0.75),\n    panel.grid = element_blank(),\n    axis.text = element_text(size = rel(1.1))\n  ) +\n  labs(x = '-log(BH p value)', \n       y = 'Terms')\n"
  },
  {
    "path": "2022年/2022.01.28 如何绘制省市级地图？/各区县经营效率平均值.csv",
    "content": ",2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\r\n龙湾区,0.726381633,0.721543426,0.753039566,0.728987862,0.717086546,0.701303469,0.697723125,0.709898114,0.709184237,0.650927818,0.728344797,0.70534892\r\n鹿城区,0.620662164,0.592147605,0.644306726,0.580623772,0.550632408,0.528825852,0.544839502,0.565875985,0.563623198,0.391130443,0.422182473,0.386727617\r\n龙港市,0.353384772,0.371474523,0.401951999,0.397120003,0.420499522,0.392652937,0.375298437,0.358311457,0.390752188,0.345212808,0.329515816,0.277278401\r\n苍南县,0.174382638,0.18029899,0.194481119,0.190436193,0.210348171,0.193244431,0.194091057,0.1762433,0.187351086,0.1590393,0.156389091,0.164463369\r\n瓯海区,0.502000673,0.467288971,0.574963688,0.544259758,0.486134852,0.499343913,0.508989627,0.55334276,0.539849982,0.394010518,0.430174909,0.392200534\r\n瑞安市,0.41951156,0.422906122,0.424430702,0.402121258,0.406506067,0.431667346,0.412837307,0.402695101,0.430972464,0.518576519,0.545342852,0.54953396\r\n洞头县,0.32535264,0.319407835,0.332293072,0.316885715,0.323690662,0.339532771,0.33917024,0.297769657,0.311690382,0.349087931,0.378338345,0.407340944\r\n泰顺县,0.115587064,0.114197982,0.126368864,0.124187238,0.121185888,0.116080305,0.10429468,0.092062868,0.095106192,0.072609564,0.069480051,0.079565197\r\n永嘉县,0.134189854,0.13104519,0.153101248,0.142955503,0.138648165,0.142197666,0.13018194,0.140898589,0.142483923,0.111985898,0.112345301,0.12088453\r\n文成县,0.170379994,0.171080432,0.20234863,0.184824087,0.203160858,0.198642651,0.180153264,0.162254801,0.170313981,0.122547619,0.123558338,0.130797695\r\n平阳县,0.220193661,0.209816842,0.217141214,0.213597191,0.221699962,0.213311602,0.210978055,0.210844849,0.234571794,0.183145765,0.187311718,0.196576114\r\n乐清市,0.274827744,0.292215059,0.317271895,0.296091351,0.278735652,0.289949025,0.269916085,0.286228804,0.305679949,0.213311931,0.206464669,0.201076792\r\n"
  },
  {
    "path": "2022年/2022.01.28 如何绘制省市级地图？/温州地图绘制.r",
    "content": "#===============================================================\n#======================== 温州市地图绘制 =======================\n#===============================================================\n\n\nlibrary(leafletCN)\nlibrary(dplyr)\nlibrary(leaflet)\n#demomap(\"温州\")\n\n\n#主函数——geojsonmap：地图标色函数  \ndata = read.csv(\"各区县经营效率平均值.csv\",header=T)\ndata$mean = apply(data[,2:13],1,mean)\ncolnames(data) = c('region',as.character(2009:2020),\"mean\")\ndim(data)\nregion = regionNames(\"温州市\")\ndat = data.frame(region,runif(length(region)))\ndata1 = full_join(dat,data[,c(1,8)])\ndata2 = data1[,-2]\nmap = leafletGeo(\"温州市\", data2)\n\n#涂色环节heat.colors(6,rev = T)\n# c(\"#FFFF00FF\",\"#FF0000FF\",\"#00FF00FF\",\"#00FFFFFF\",\"#FF00FFFF\",\"#0000FFFF\")\npal <- colorNumeric(\npalette = c(\"purple\",\"blue\",\"lightblue\",\"green\",\"yellow\",\"orange\",'red'),\ndomain = map$value)\n\n#载入高德地图amap\nleaflet(map) %>% amap() %>%\n#加入框边界及颜色\n    addPolygons(stroke = TRUE,\n                smoothFactor = 1,\n                fillOpacity = 1,\n                weight = 1,\n                color = ~pal(value),\n                popup = ~htmltools::htmlEscape(popup)\n    ) %>%\n    #加入右下角边框\n    addLegend(\"bottomright\", pal = pal, values = ~value,\n              title = \"效率值\",\n              labFormat = leaflet::labelFormat(prefix = \"\"),\n              opacity = 2) \n\n\n\n\n"
  },
  {
    "path": "2022年/2022.01.28 如何绘制省市级地图？/温州市.json",
    "content": 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  },
  {
    "path": "2022年/2022.01.28 如何绘制省市级地图？/绘制浙江省地图.R",
    "content": "# 代码解释见推文:https://mp.weixin.qq.com/s/qCNTvFCgaNBH_HJQ_U7uPg\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 绘制浙江省地图 ===========\nlibrary(leaflet)\nlibrary(leafletCN)\nregion = regionNames(\"浙江\")\ndem_data = c(30,52,20,27,16,37,38,42,39,76,45) # 这里转换成自己的数据\ndat = data.frame(region,dem_data)\nmap = leafletGeo(\"浙江\", dat)\n\n#涂色环节\npal <- colorNumeric(\n  palette = \"Greens\",\n  domain = map$value)\n\n#载入高德地图amap\nleaflet(map) %>% amap() %>%\n  #加入框边界及颜色\n  addPolygons(stroke = TRUE,\n              smoothFactor = 2,\n              fillOpacity = 1.7,\n              weight = 4,\n              color = ~pal(value),\n              popup = ~htmltools::htmlEscape(popup)\n  ) %>%\n  #加入右下角边框\n  addLegend(\"bottomright\", pal = pal, values = ~value,\n            title = \"legendTitle\",\n            labFormat = leaflet::labelFormat(prefix = \"\"),\n            opacity = 1)\n"
  },
  {
    "path": "2022年/2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/acchist.r",
    "content": "# 本篇推文由西南财经大学经济学院杜云晗投稿，由闪闪排版整理。以下是正文内容：\n# 代码解释见推文：https://mp.weixin.qq.com/s/f47_ujT2diu5CO0yUxKTbw\n# 公众号：《庄闪闪的R语言手册》\n\n# 2.1 安装说明\nrm(list = ls())\nif (is.element(\"chip\", installed.packages()[, 1]) == FALSE) {\n  if (is.element(\"devtools\", installed.packages()[, 1]) == FALSE) {\n    install.packages(\"devtools\")\n    library(devtools)\n  } else if (is.element(\"devtools\", installed.packages()[, 1]) == TRUE) {\n    library(devtools)\n  }\n  install_github(\"youngyaoguai/Rdraw/chip0.1/chip\")\n  library(chip)\n} else if (is.element(\"chip\", installed.packages()[, 1]) == TRUE) {\n  library(chip)\n}\n\n## 2.2 堆积柱状图黑白印刷风格绘制\nhelp(acchist)  #查看函数说明\ndata(\"acchist_test_data\",package = \"chip\")  #加载包内置数据集\nView(acchist_test_data)  #查看内置数据集结构\naxis.x.title <- c(\"region\")   #x轴标题\naxis.y.title <- c(\"ratio\")    #y轴标题\nindividual.name <- c(\"B\",\"C\",\"D\",\"E\",\"F\")  #区域名称\nfill.name <- c(\"academic\",\"vocational\",\"college\",\"high\",\"further\",\"primary\")  #要填充的名称\nbar_width <- 0.3   #柱子宽度\naxis.title.size <- 30  #坐标轴标题大小\naxis.text.x.size <- 30  #x轴的文本显示大小\naxis.text.y.size <- 30  #y轴的文本显示大小\nlegend.text.size <- 30  #图例的文本显示大小\nlegend.title.size <- 30  #图例标题大小\n#调用绘图函数\nacchist(acchist_test_data,acchist_test_data$region,acchist_test_data$edu,acchist_test_data$ratio)\n\n\n# 2.3 棒棒图黑白印刷风格绘制\nhelp(bonbon)  #查看函数说明\ndata(\"bonbon_test_data\",package = \"chip\")  #load integration sample data  #载入内置数据集\nView(bonbon_test_data) #查看数据结构\n#要着重显示的个体（如果没有要着重显示的个体，请设置为outstanding <- c(“ ”)）\noutstanding <- c(\"wholesaleretail\",\"Leasing and business services\",\"realestate\") \noutstanding.label <- c(\"24.75%\",\"28.51%\",\"5.16%\") #要着重显示的个体的数据标签，需要和outstanding一并调整\noutstanding.label.size <- 7  #要着重显示的个体的数据标签大小\noutstanding.size <- 4    #要着重显示的个体的棒状物大小\nnot.outstanding.size <- 2.3  #不需要着重显示的个体的棒状物大小\noutstanding.head.size <- 7  #要着重显示的个体的棒状物圆头大小\nnot.outstanding.head.size <- 5   #不需要重显示的个体的棒状物圆头大小\naxis.title.x.size <- 30     #x轴标题大小\naxis.text.y.size <- 30     #y轴的文本大小\nvalue.max <- 100   #样本最大值\nvalue.break <- c(0,value.max%/%3,78,100)   #要在x轴上显示的样本数值点\nxlab.name <- c(\"ratio of service industry(%)\")  #x轴标题\n#所有个体名称排序，会按照从左往右的顺序绘制，绘制结果为从下到上\nindividual.name <- c(\"Leasing and business services\",\"wholesaleretail\",\"scientificresearch\",\"Accommodation and Catering\",\"Informationtransmission\",\"culture\",\"socialwork\",\"publicfacilities\",\"residentsservice\",\"financial\",\"education\",\"transportation\",\"publicmanagement\",\"realestate\")\nbonbon(bonbon_test_data,bonbon_test_data$industry,bonbon_test_data$index)\n\n# 2.4 箱线图黑白印刷风格绘制\nhelp(boxeasy)  #查看绘图函数说明\ndata(\"box_test_data\",package = \"chip\")  #载入内置数据集\nView((box_test_data)) #查看数据结构\nylab.name <- c(\"index\")       #y轴标题\nxlab.name <- c(\"year\")  #x轴标题\naxis.title.x.size <- 15  #x轴标题大小\naxis.title.y.size <- 15  #y轴标题大小\naxis.text.x.size <- 15  #x轴文本大小\naxis.text.y.size <- 15  #y轴文本大小\n#调用函数\nboxeasy(box_test_data,box_test_data$year,box_test_data$index)\n\n\n# 2.5 简单折线图黑白印刷风格绘制\nhelp(easyline)  #查看函数说明\ndata(\"easyline_test_data\",package = \"chip\")  #载入内置数据集\nView(easyline_test_data) #查看数据结构\nylab.name <- \"population\" #y轴标题\nxlab.name <- \"age\" #x轴标题\naxis.title.x.size <- 25 #x轴标题大小\naxis.title.y.size <- 25#y轴标题大小\naxis.line.x.thickness <- 0.5 #x轴粗细\naxis.line.y.thickness <- 0.5 #y轴粗细\naxis.text.x.size <- 20  #x轴文本大小\naxis.text.y.size <- 20 #y轴文本大小\nx.start <- 0  #x轴开始数值\nx.end <- 100 #x轴结束数值\nx.interval <- 10 #x轴数值间隔\ny.start <- 0 #y轴开始数值\ny.end <- 21571 #y轴结束数值\ny.interval <- 5000 #y轴数值间隔\n#调用函数\neasyline(easyline_test_data,easyline_test_data$population,easyline_test_data$age)\n\n# 2.6 核密度图黑白印刷风格绘制\n\nhelp(densline)  #查看说明\ndata(\"densline_test_data\",package = \"chip\")  #载入内置数据集\nView(densline_test_data)  #查看数据结构\nyscope <- c()  #第一次使用无法知道具体的范围，填空\nxscope <- c() #第一次使用无法知道具体的范围，填空\n#根据数据集从左往右填写\nlegend.name <- c(\"line1\",\"line2\",\"line3\",\"line4\",\"line5\")\n#调用函数\ndensline(test_data)\n#根据图示大概知道数值范围，进行微调\nyscope <- c(0.0004,0.00155) \nxscope <- c(0,1000)  \nlegend.name <- c(\"line1\",\"line2\",\"line3\",\"line4\",\"line5\")\ndensline(densline_test_data)\n\n\n# 2.7 柱状图黑白印刷风格绘制\nhelp(histeasy)  # 查看帮助\ndata(\"hist_test_data \",package = \"chip\")  #载入内置数据集\nView(hist_test_data)   #查看数据结构\nx.size <- 2  #x轴文本大小\ny.size <- 2  #y轴文本大小\nlab.size <- 2  #坐标轴标题大小\nlegend.x <- 30   #图例水平位置\nlegend.y <- 0.3   #图例垂直位置\nlegend.x.distance <- 0.1  #图例中图片和文本的距离\naxisname.x <- c(\"region\")  #x轴标题\naxisname.y <- c(\"ratio\")  #y轴标题\ntype.name = c(\"0-14\",\"15-64\",\"65+\",\"immortality\")  #不同类别\nindividual.name=c(\"A\",\"B\",\"C\",\"D\",\"E\")   #不同个体\nhisteasy(hist_test_data,hist_test_data$region,hist_test_data$age,hist_test_data$ratio)\n\n\n# 2.8 加点折线图黑白印刷风格绘制\nhelp(linepoint)\ndata(\"line_test_data\",package = \"chip\")  \nView(line_test_data)  #查看内置数据集数据结构\nstart <- 2009  #x轴开始数值\nend <- 2019  #x轴结束数值\ninterval <- 2  #x轴数值间隔\naxis.text.x.size <- 30  #x轴文本大小\naxis.text.y.size <- 30  #y轴文本大小\nlegend.title.size <- 30  #图例标题大小\nlegend.text.size <- 30   #图例文本大小\naxis.title.x.size <- 30  #x轴标题大小\naxis.title.y.size <- 30  #y轴标题大小\naxis.line.x.size <- 0.5  #x轴粗细\naxis.line.y.size <- 0.5  #y轴粗细\ngeom.point.size <- 7  #折线点大小\nylab.name <- c(\"index\")  #y轴标题\nxlab.name <- c(\"year\")  #x轴标题\nindividual.name <- c(\"A\",\"B\",\"C\",\"D\",\"E\")  #个体名称\nlegend.name <- c(\"region\")  #图例标题\nlinepoint(line_test_data,line_test_data$region,line_test_data$index,line_test_data$year)\n\n# 2.9 金字塔图黑白印刷风格绘制\n\nhelp(pyramid)\nView(pyramid_test_data)  #查看内置数据集数据结构\ndata(\"pyramid_test_data\",package = \"chip\")  #load integration sample data\nv_max <- 4400  #样本中的最大数值\nmiddle.size <- 6.6  #中间文本大小\nleft.axis.title.x.size <- 30  #左边x轴标题大小\nleft.axis.text.x.size <- 30  #左边x轴文本大小\nright.axis.title.x.size <- 30  #右边x轴标题大小\nright.axis.text.x.size <- 30  #右边x轴文本大小\nright.title <- c(\"女\")  #右边x轴标题\nleft.title <- c(\"male\")  #左边x轴标题\npyramid(pyramid_test_data,pyramid_test_data$male,pyramid_test_data$female,pyramid_test_data$group,pyramid_test_data$group_name)\n\n\n"
  },
  {
    "path": "2022年/2022.03.14绘制混合密度函数图以及添加分位数线/mix-quantile.r",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/bQ_aefTxk32x30Avfzv5kw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n\n# =====绘制混合密度函数图以及添加分位数线=====\n\n# 加载包\nlibrary(ggplot2)\nlibrary(ggridges)\n\n# 产生数据集\nitem <- 10000\ninds <- rbinom(1, item, 0.5)\nx <- c(rnorm(inds, 1, 1), rnorm(item - inds, 8, 1))\ndata <- data.frame(\"value\" = x, \"class\" = rep(1, length(x)))\n\n# 绘制密度函数图并添加分位数线\n\n# 绘图\np1 <- ggplot(data, aes(x = value, y = class, fill = factor(stat(quantile)))) +\n  stat_density_ridges(\n    geom = \"density_ridges_gradient\",\n    calc_ecdf = TRUE,\n    quantiles = c(0.025, 0.975) #添加分为数线\n  ) +\n  scale_fill_manual(\n    name = \"Probability\", values = c(\"#E2EAF6\", \"#436FB0\", \"#E2EAF6\")\n  ) +\n  theme_bw() +\n  theme(legend.position = \"none\", panel.grid = element_blank()) +\n  labs(x = \"x\", y = \"Density\")\np1\n\n\np2 <- ggplot(data, aes(x = value, y = class, fill = factor(stat(quantile)))) +\n  stat_density_ridges(\n    geom = \"density_ridges_gradient\",\n    calc_ecdf = TRUE,\n    quantiles = c(0.005, 0.495, 0.51, 0.99)\n  ) +\n  scale_fill_manual(\n    name = \"Probability\", values = c(\"#E2EAF6\", \"#436FB0\", \"#E2EAF6\", \"#436FB0\", \"#E2EAF6\"),\n  ) +\n  theme_bw() +\n  theme(legend.position = \"none\", panel.grid = element_blank()) +\n  labs(x = \"x\", y = \"Density\")\n\np2\n\n\n# 合并两图\nlibrary(cowplot)\n# pdf(\"plot_cow.pdf\", width = 8, height = 4)\nplot_grid(p1, p2, ncol = 1, nrow = 2)\n# dev.off()\n"
  },
  {
    "path": "2022年/2022.04.08R 案例｜绘制不同分布的 QQ 图/qqplot.r",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/ncorK41sntA4iBJWBCdVkA\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n# 参考博客：An Introduction to qqplotr: https://cran.r-project.org/web/packages/qqplotr/vignettes/introduction.html\n\n# 1. 加载包\nlibrary(qqplotr)\nlibrary(ggplot2)\n\n# 2. 随机产生数据\nset.seed(0)\nsmp <- data.frame(norm = rnorm(100))\n\n# 3. 绘制正态分布的 QQ 图\ngg <- ggplot(data = smp, mapping = aes(sample = norm)) +\n  stat_qq_band() +\n  stat_qq_line() +\n  stat_qq_point() +\n  labs(x = \"Theoretical Quantiles\", y = \"Sample Quantiles\")\ngg\n\n# 4.拓展\nlibrary(viridis)\ngg <- ggplot(data = smp, mapping = aes(sample = norm)) +\n  geom_qq_band(bandType = \"ks\", mapping = aes(fill = \"KS\"), alpha = 0.9) +\n  geom_qq_band(bandType = \"ts\", mapping = aes(fill = \"TS\"), alpha = 0.9) +\n  geom_qq_band(bandType = \"pointwise\", mapping = aes(fill = \"Normal\"), alpha = 0.9) +\n  geom_qq_band(bandType = \"boot\", mapping = aes(fill = \"Bootstrap\"), alpha = 0.9) +\n  stat_qq_line() +\n  stat_qq_point() +\n  labs(x = \"Theoretical Quantiles\", y = \"Sample Quantiles\") +\n  scale_fill_viridis(discrete = T,direction = -1)\n\ngg\n\n# 进阶版本\n\ndata = data.frame('y' = c(1.339, 1.434, 1.549, 1.574 ,1.589, 1.613, 1.746 ,1.753, 1.764 ,1.807, 1.812, 1.84, 1.852, 1.852, 1.862, 1.864, 1.931, 1.952, 1.974, 2.019, 2.051, 2.055, 2.058 ,2.088, 2.125, 2.162, 2.171, 2.172 ,2.18 ,2.194 ,2.211 ,2.27, 2.272, 2.28, 2.299, 2.308, 2.335 ,2.349 ,2.356 ,2.386, 2.39, 2.41, 2.43, 2.431, 2.458, 2.471, 2.497, 2.514 ,2.558, 2.577, 2.593, 2.601, 2.604, 2.62 ,2.633, 2.67, 2.682, 2.699, 2.705, 2.735, 2.785, 2.785,3.02, 3.042, 3.116, 3.174))\n\n## 绘制指数分布的 QQ 图\ndp <- list(rate = 2.2867)\ndi <- \"exp\"\np1 = ggplot(data = data, mapping = aes(sample = y)) +\n  stat_qq_band(distribution = di, dparams = dp) +\n  stat_qq_line(distribution = di, dparams = dp) +\n  stat_qq_point(distribution = di, dparams = dp) +\n  labs(x = \"Theoretical Quantiles\", y = \"Sample Quantiles\") +\n  theme_bw() + \n  theme(panel.grid = element_blank())\np1\n\n## 绘制威布尔分布的 QQ 图\n# weibull distribution\ndi <- \"weibull\" # exponential distribution\ndp <- list(shape=5.4766,scale=2.4113)\np2 = ggplot(data = data, mapping = aes(sample = y)) +\n  stat_qq_band(distribution = di, dparams = dp) +\n  stat_qq_line(distribution = di, dparams = dp) +\n  stat_qq_point(distribution = di, dparams = dp) +\n  labs(x = \"Theoretical Quantiles\", y = \"Sample Quantiles\") +\n  theme_bw() + \n  theme(panel.grid = element_blank())\np2\n\n## 合并两幅 QQ 图\nlibrary(cowplot)\nplot_grid(p1, p2, ncol = 2, nrow = 1)\n\n\n\n\n\n"
  },
  {
    "path": "2022年/2022.05.15老板让你复现一个图片，你会使用什么软件？/example.r",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/V8H-U719PjCgsn1lVY_PFg\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n# 数据产生\nset.seed(1) #确保不同机子产生的随机数相同\nmu = c(2,5)\nstd = c(1,1)\nnum = 1000\nr1 = rnorm(num,mu[1],std[1]) #正态分布\nr2 = rnorm(num,mu[2],std[2]) #正态分布\ndata = data.frame('value' = c(r1,rep(NA,num),r2,rep(NA,5*num)),\n                  'class' = factor(rep(c(1:8),each= num)))\nknitr::kable(head(data))\n\n# 定义主题\ntheme_manual = function(){ \n  theme(panel.grid = element_blank(),\n        panel.border = element_blank(),\n        axis.text = element_blank(),\n        axis.ticks = element_blank(),\n        legend.position=\"none\") \n}\n\n# 画图\n\nggplot(data, aes(x = value, y = class,fill = factor(stat(quantile)))) + \n  # 添加密度函数图\n  stat_density_ridges(\n    geom = \"density_ridges_gradient\",\n    calc_ecdf = TRUE,rel_min_height = 0.02,\n    quantiles = c(0.025, 0.975),\n    alpha = 1,scale = 0.6,bandwidth = 1\n  ) +\n  scale_fill_manual(\n    name = \"Probability\", values = c(\"#FF0000A0\", \"white\", \"#FF0000A0\"),\n    labels = c(\"(0, 0.025]\", \"(0.025, 0.975]\", \"(0.975, 1]\")\n  ) + \n  # 手动一条条添加各种线段，文字\n  annotate(\"segment\", x = mu[1], xend = mu[1], y = 1, yend = 7,colour = \"black\") +\n  annotate(\"segment\", x = mu[2]-1, xend = mu[2]-1, y = 1, yend = 7,colour = \"#0000FFA0\",lty = \"dashed\") +\n  annotate(\"segment\", x = mu[1]-2.15, xend = mu[1]-2, y = 1, yend = 7,colour = \"#0000FFA0\",lty = \"dashed\") +\n  annotate(\"segment\", x = mu[2], xend = mu[2], y = 3-0.02, yend = 4.18,colour = \"black\") +\n  annotate(\"segment\", x = 0, xend = 10, y = 3, yend = 3,colour = \"black\") +\n  annotate(\"segment\", x = -3, xend = 7, y = 1, yend = 1,colour = \"black\") +\n  annotate(\"text\", x = mu[1], y = 0.7, label = expression(mu[T])) +\n  annotate(\"text\", x = mu[1]-2.15, y = 0.7, label = expression(alpha[1])) + \n  annotate(\"text\", x = mu[1]+2, y = 0.7, label = expression(alpha[2])) +\n  annotate(\"text\", x = mu[2], y = 2.8, label = expression(mu[T])) +\n  annotate(\"text\", x = 0.5, y = 3.9, label = expression(beta)) + \n  annotate(geom = \"line\",x = c(0.8, 3.1),\n           y = c(3.8, 3.2),\n           arrow = arrow(angle = 20, length = unit(4, \"mm\"))) + # 添加线段并且包含箭头\n  annotate(\"text\", x = mu[1], y = 7.3, label = \"CL\") +\n  annotate(\"text\", x = mu[2]-1, y = 7.4, label = \"LCL\") +\n  annotate(\"text\", x = mu[1]-2, y = 7.4, label = \"UCL\") +\n  annotate(\"text\", x = -1.3, y = 2.3, label = expression(alpha[1] + alpha[2] == alpha)) +\n  coord_flip() + # 转换横纵坐标\n  theme_bw() + # 主题设置\n  theme_manual() + xlab('') + ylab('')\n\n"
  },
  {
    "path": "2022年/2022.08.08 ggplot 分面的细节调整汇总/ggplot_facet.r",
    "content": "# 代码解释见推文:https://mp.weixin.qq.com/s/fzcGJugs0VxJpM6nb25hLA\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 生成数据\n\n# 导入包\nlibrary(ggplot2)\n\nset.seed(1)\ndat = data.frame(\"time\" = rep(2013:2019,6),\n                 \"method\" = rep(LETTERS[1:6],each = 7),\n                 \"value\" = rnorm(7*6,5,1),\n                 \"upper\" = rnorm(7*6,5,1) + abs(rnorm(7*6,2,0.1)),\n                 \"lower\" = rnorm(7*6,5,1) - abs(rnorm(7*6,2,0.1))\n)\nhead(dat)\n\n# 基础版本绘图\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\") #设置颜色\n\np = ggplot(dat,aes(x = time, y = value,fill = method)) +                                                       \n  geom_line(aes(color = method)) + #添加线\n  geom_point(aes(color = method)) + #添加散点\n  geom_ribbon(aes(ymin=lower, ymax=upper), alpha=0.3) + #添加区间\n  scale_x_continuous(breaks = 2013:2019) +\n  facet_wrap(vars(method),nrow = 4) + \n  theme_bw() + ylab(\"Value\") + xlab(\"Time\") + #主题设置\n  theme(panel.grid = element_blank())\np\n\n# 刻度尺修改\n## x\np + facet_wrap(vars(method),nrow = 4,scales = \"free_x\")\n## y \np + facet_wrap(vars(method),nrow = 4,scales = \"free_y\")\n## 双轴\np + facet_wrap(vars(method),nrow = 4,scales = \"free\")\n\n## 标题框调整\np + facet_wrap(vars(method),nrow = 3,strip.position = \"left\")\n\n## 去除标题框背景\np + theme(\n  strip.background = element_blank() #去除标题框背景\n)\n\n## 修改标题框背景颜色\np + theme(strip.background=element_rect(colour=\"black\",\n                                        fill=\"#2072A8\"))\n\n## 修改标题框文字颜色\np +  theme(strip.text.x=element_text(colour=\"white\"))\n\n\n## 删除标题框\np +  theme(strip.text.x = element_blank())\n\n\n"
  },
  {
    "path": "2022年/2022.09.09 如何在分面中添加数学表达式标签？ /add_math_label.r",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/Dbx9J_flkTtyi7LtRAdRPQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 构建模拟数据\nlibrary(ggplot2)\nlibrary(grid)\nmydf <- data.frame(letter = factor(rep(c(\"A\", \"B\", \"C\", \"D\"), each = 20)), x = rnorm(80), y = rnorm(80))\nhead(mydf)\n\n# 基本绘图\nggplot(mydf, aes(x = x, y = y)) + \n  geom_smooth(method = \"lm\") + \n  geom_point() + \n  facet_wrap(~ letter)\n\n# 设置表达式标签\nf_names <- list('A' = expression(paste(alpha[2])), 'B' = expression(Gamma(3,4)), 'C' = expression(paste(y = beta[0] + beta[1]*x[1])), 'D' = expression(delta))\nf_labeller <- function(variable, value){return(f_names[value])}\nggplot(mydf, aes(x = x, y = y)) + \n  geom_smooth(method = \"lm\") + \n  geom_point() + \n  facet_wrap(~ letter, labeller = f_labeller)\n\n\n# 修改主题\nggplot(mydf, aes(x = x, y = y)) + \n  geom_smooth(method = \"lm\",color = \"#e99e9c\",fill = \"#98c0d7\") + \n  geom_point(color = \"gray60\") + \n  facet_wrap(~ letter, labeller = f_labeller,scales = \"free\") +\n  theme_bw() + ylab(\"Value\") + xlab(\"Time\") + #主题设置\n  theme(panel.grid = element_blank(), \n        strip.background = element_blank()) \n\n\n# 添加中文标签\n\nf_names <- list('A' = \"庄闪闪\", 'B' = \"庄亮亮\", \n                'C' = \"庄晶晶\", 'D' = \"庄暗暗\") # 设置标签\n\nlibrary(showtext)\nshowtext.auto()\n\nf_labeller <- function(variable, value){return(f_names[value])}\nggplot(mydf, aes(x = x, y = y)) + \n  geom_smooth(method = \"lm\",color = \"#e99e9c\",fill = \"#98c0d7\") + \n  geom_point(color = \"gray60\") + \n  facet_wrap(~ letter, labeller = f_labeller,scales = \"free\") +\n  theme_bw() + ylab(\"Value\") + xlab(\"Time\") + #主题设置\n  theme(panel.grid = element_blank(), \n        strip.background = element_blank()) \n"
  },
  {
    "path": "2022年/2022.09.24 中国地图绘制/.Rhistory",
    "content": "\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'White',parse = T,size=2) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 122) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.7) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.8) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.9) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 0.96) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"none\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nshow_data <- read_xlsx(\"河北省各市GDP增长率.xlsx\",\"hubei\")\nshp_data <- st_read(\"CHN_adm/CHN_adm2.shp\")\nindex <- which(shp_data$NAME_1==\"Shanghai\")\nshp_data$NL_NAME_2[index] <- \"上海\"\nshp_data$NL_NAME_2[index]\nshp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)\nmy_data <- dplyr::left_join(show_data, shp_data,by = c(\"city\" = \"NL_NAME_2\"))\nhead(my_data)\nlabel_city <- data.frame(x = 121.48,y = 31.22)\nlabel_city['city'] <- \"Wuhan\"\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = '#116E13',parse = T,nudge_x = 1) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"none\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nlabel_city <- data.frame(x = 114.31,y = 30.52)\nlabel_city['city'] <- \"Wuhan\"\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"none\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nggplot(data = my_data) + geom_sf(aes(fill= as.factor(Spatial), geometry = `geometry`)) +\ngeom_text(data = label_city, aes(x = x, y = y, label = city), color = 'white',parse = T) +\n#ggtitle(\"河北省GDP\") +\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\nscale_fill_manual(\n\"\",\nvalues = c(\"#116E13\", \"#9CE69B\", \"#DCEEE2\"),\nbreaks = c(\"Target site\", \"Spatial correlated site\", \"Other site\"),\nlabels = c(\"Target site\", \"Spatial correlated site\", \"Other site\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nlibrary(rgdal)\nlibrary(ggplot2)\nlibrary(maptools)\nlibrary(mapproj)\nlibrary(ggsn)\nlibrary(readxl)\nlibrary(sf)\nshow_data <- read_xlsx(\"测试数据.xlsx\",\"hubei\")\nshp_data <- st_read(\"china_shp/CHN_adm2.shp\")\nshp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)\nmy_data <- dplyr::left_join(show_data, shp_data,by = c(\"city\" = \"NL_NAME_2\"))\nhead(my_data)\nggplot(data = my_data) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) +\ngeom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\n##更改图形颜色，不加这个语句使用随机配色\nscale_fill_manual(\n\"value\",\nvalues = c(\"#DFD3F2\", \"#00C2F9\", \"#FFF7D0\"),\nbreaks = c(\"A\", \"B\", \"C\"),\nlabels = c(\"A\", \"B\", \"C\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nhubei <- read_xlsx(\"测试数据.xlsx\",\"hubei\")\njiangxi <- read_xlsx(\"测试数据.xlsx\",\"jiangxi\")\nall_province <- rbind(hubei,jiangxi)\nshp_data <- st_read(\"china_shp/CHN_adm2.shp\")\nshp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)\ndata2 <- dplyr::left_join(all_province, shp_data,by = c(\"city\" = \"NL_NAME_2\"))\nggplot(data = data2) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) +\ngeom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+\nxlab(\"Long (°E)\") + ylab(\"Lat (°N)\") +\n##更改图形颜色，不加这个语句使用随机配色\nscale_fill_manual(\n\"value\",\nvalues = c(\"#DFD3F2\", \"#00C2F9\", \"#FFF7D0\"),\nbreaks = c(\"A\", \"B\", \"C\"),\nlabels = c(\"A\", \"B\", \"C\")\n)+\ntheme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\npanel.grid = element_line(color = \"grey\"))\nlibrary(geojsonsf)\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nAPI_pre = \"http://xzqh.mca.gov.cn/data/\"\nChina = st_read(dsn = paste0(API_pre, \"quanguo.json\"),\nstringsAsFactors=FALSE)\nst_crs(China) = 4326\n# 2.国境线\nChina_line = st_read(dsn = paste0(API_pre, \"quanguo_Line.geojson\"),\nstringsAsFactors=FALSE)\nst_crs(China_line) = 4326\ngjx <- China_line[China_line$QUHUADAIMA == \"guojiexian\",]\nprovince <- read.csv(\"province.csv\")\n# 4.着色数据+全国地图\ncolour <- read.csv(\"colour.csv\")\ncolour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)\nCHINA <- dplyr::left_join(China,colour,by= \"QUHUADAIMA\")\nhead(colour)\n###----全国地图完整（无右下角小地图）----------###\nfig1 <-  ggplot() +\n# 绘制主图\ngeom_sf(\ndata = CHINA,\naes(fill = factor(new_colour))) +\nscale_fill_manual(\n\"class\",\nvalues = c(\"#FFFFFF\", \"#79CBC2\", \"#5EA9AC\", \"#4B8793\", \"#3C6777\"),\nbreaks = c(\"0\", \"1\", \"2\", \"3\", \"4\"),\nlabels = c(\"0\", \"1\", \"2\", \"3\", \"4\")\n) +\ngeom_sf(data = gjx) +\ngeom_text(\ndata = province,\naes(x = dili_Jd, y = dili_Wd, label = 省市),\nposition = \"identity\",\nsize = 3,\ncheck_overlap = TRUE\n) +\nlabs(title = \"中国地图\") +\ntheme(\nplot.title = element_text(\ncolor = \"black\",\nsize = 16,\nface = \"bold\",\nvjust = 0.1,\nhjust = 0.5\n),\nlegend.title = element_blank(),\nlegend.position = c(0.2, 0.2),\npanel.grid = element_blank(),\npanel.background = element_blank(),\naxis.text = element_blank(),\naxis.ticks = element_blank(),\naxis.title = element_blank()\n) +\nannotation_north_arrow(\nlocation = 'tl',\nwhich_north = 'false',\nstyle = north_arrow_orienteering()\n)\nfig1 <-  ggplot()+\n# 绘制主图\ngeom_sf(data = CHINA,fill='NA') +\n# 绘制国界线及十段线\ngeom_sf(data = gjx)+\n##添加省份名称\ngeom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = \"identity\",size=3,check_overlap = TRUE) +\nlabs(title=\"中国地图\")+\ntheme(plot.title = element_text(color=\"black\", size=16, face=\"bold\",vjust = 0.1,hjust = 0.5),\nlegend.title=element_blank(),\nlegend.position = c(0.2,0.2),\npanel.grid=element_blank(),\npanel.background=element_blank(),\naxis.text=element_blank(),\naxis.ticks=element_blank(),\naxis.title=element_blank()\n)+\n##添加指北针，“style”参数可以更改样式\nannotation_north_arrow(location='tl', which_north='false',\nstyle=north_arrow_orienteering())\nAPI_pre = \"http://xzqh.mca.gov.cn/data/\"\nChina = st_read(dsn = paste0(API_pre, \"quanguo.json\"),\nstringsAsFactors=FALSE)\nst_crs(China) = 4326\n# 2.国境线\nChina_line = st_read(dsn = paste0(API_pre, \"quanguo_Line.geojson\"),\nstringsAsFactors=FALSE)\nst_crs(China_line) = 4326\ngjx <- China_line[China_line$QUHUADAIMA == \"guojiexian\",]\nprovince <- read.csv(\"province.csv\")\nfig1 <-  ggplot()+\n# 绘制主图\ngeom_sf(data = CHINA,fill='NA') +\n# 绘制国界线及十段线\ngeom_sf(data = gjx)+\n##添加省份名称\ngeom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = \"identity\",size=3,check_overlap = TRUE) +\nlabs(title=\"中国地图\")+\ntheme(plot.title = element_text(color=\"black\", size=16, face=\"bold\",vjust = 0.1,hjust = 0.5),\nlegend.title=element_blank(),\nlegend.position = c(0.2,0.2),\npanel.grid=element_blank(),\npanel.background=element_blank(),\naxis.text=element_blank(),\naxis.ticks=element_blank(),\naxis.title=element_blank()\n)+\n##添加指北针，“style”参数可以更改样式\nannotation_north_arrow(location='tl', which_north='false',\nstyle=north_arrow_orienteering())\nlibrary(geojsonsf)\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\n??annotation_north_arrow\nlibrary(ggspatial)\nfig1 <-  ggplot()+\n# 绘制主图\ngeom_sf(data = CHINA,fill='NA') +\n# 绘制国界线及十段线\ngeom_sf(data = gjx)+\n##添加省份名称\ngeom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = \"identity\",size=3,check_overlap = TRUE) +\nlabs(title=\"中国地图\")+\ntheme(plot.title = element_text(color=\"black\", size=16, face=\"bold\",vjust = 0.1,hjust = 0.5),\nlegend.title=element_blank(),\nlegend.position = c(0.2,0.2),\npanel.grid=element_blank(),\npanel.background=element_blank(),\naxis.text=element_blank(),\naxis.ticks=element_blank(),\naxis.title=element_blank()\n)+\n##添加指北针，“style”参数可以更改样式\nannotation_north_arrow(location='tl', which_north='false',\nstyle=north_arrow_orienteering())\nfig1\nnine_lines = read_sf('南海.geojson')\n# 绘制九段线小图\nfig2 = ggplot() +\ngeom_sf(data = CHINA,fill='NA', size=0.5) +\ngeom_sf(data = nine_lines,color='black',size=0.5)+\n##去掉主图的部分区域\ncoord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs=\"+proj=laea +lat_0=40 +lon_0=104\")+\ntheme(\naspect.ratio = 1.25, #调节长宽比\naxis.text = element_blank(),\naxis.ticks = element_blank(),\naxis.title = element_blank(),\npanel.grid = element_blank(),\npanel.background = element_blank(),\npanel.border = element_rect(fill=NA,color=\"grey10\",linetype=1,size=0.5),\nplot.margin=unit(c(0,0,0,0),\"mm\"))\nfig2\nfig = ggdraw() +\ndraw_plot(fig1) +\ndraw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)\nfig\n??ggdraw\nlibrary(cowplot)\nfig = ggdraw() +\ndraw_plot(fig1) +\ndraw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)\nfig\ncolour <- read.csv(\"colour.csv\")\nhead(colour)\ncolour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)\nCHINA <- dplyr::left_join(China,colour,by= \"QUHUADAIMA\")\nfig1 <-  ggplot() +\ngeom_sf(\ndata = CHINA,\naes(fill = factor(colour))) +\n## 填色\nscale_fill_manual(\n\"class\",\nvalues = c(\"#9CEED3\", \"#79CBC2\", \"#5EA9AC\", \"#4B8793\", \"#3C6777\"),\nbreaks = c(\"0~200\", \"200~400\", \"400~600\", \"600~1000\", \"1000+\"),\nlabels = c(\"0~200\", \"200~400\", \"400~600\", \"600~1000\", \"1000+\")\n) +\ngeom_sf(data = gjx) +\ngeom_text(\ndata = province,\naes(x = dili_Jd, y = dili_Wd, label = 省市),\nposition = \"identity\",\nsize = 3,\ncheck_overlap = TRUE\n) +\nlabs(title = \"中国地图\") +\ntheme(\nplot.title = element_text(\ncolor = \"black\",\nsize = 16,\nface = \"bold\",\nvjust = 0.1,\nhjust = 0.5\n),\nlegend.title = element_blank(),\nlegend.position = c(0.2, 0.2),\npanel.grid = element_blank(),\npanel.background = element_blank(),\naxis.text = element_blank(),\naxis.ticks = element_blank(),\naxis.title = element_blank()\n) +\nannotation_north_arrow(\nlocation = 'tl',\nwhich_north = 'false',\nstyle = north_arrow_orienteering()\n)\n# 读入九段线数据\nnine_lines = read_sf('南海.geojson')\n# 绘制九段线小图\nnine_map = ggplot() +\ngeom_sf(data = CHINA,fill='NA', size=0.5) +\ngeom_sf(data = nine_lines,color='black',size=0.5)+\ncoord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs=\"+proj=laea +lat_0=40 +lon_0=104\")+\ntheme(\naspect.ratio = 1.25, #调节长宽比\naxis.text = element_blank(),\naxis.ticks = element_blank(),\naxis.title = element_blank(),\npanel.grid = element_blank(),\npanel.background = element_blank(),\npanel.border = element_rect(fill=NA,color=\"grey10\",linetype=1,size=0.5),\nplot.margin=unit(c(0,0,0,0),\"mm\"))\n# 使用cowplot包将大图小图拼在一起\nfig = ggdraw() +\ndraw_plot(fig1) +\ndraw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)\nfig\ncolour$new_colour <- rep(0,nrow(colour))\n##给目标省份赋予不同的数值\ncolour$new_colour[which(colour$shengfen==\"重庆\")] <- 1\ncolour$new_colour[which(colour$shengfen==\"山西\")] <- 2\ncolour$new_colour[which(colour$shengfen==\"新疆\")] <- 3\ncolour$new_colour[which(colour$shengfen==\"内蒙古\")] <- 4\nCHINA <- dplyr::left_join(China,colour,by= \"QUHUADAIMA\")\nfig1 <-  ggplot() +\ngeom_sf(\ndata = CHINA,\naes(fill = factor(new_colour))) +\nscale_fill_manual(\n\"class\",\nvalues = c(\"#FFFFFF\", \"#79CBC2\", \"#5EA9AC\", \"#4B8793\", \"#3C6777\"),\nbreaks = c(\"0\", \"1\", \"2\", \"3\", \"4\"),\nlabels = c(\"0\", \"1\", \"2\", \"3\", \"4\")\n)+\ngeom_sf(data = gjx) +\ngeom_text(\ndata = province,\naes(x = dili_Jd, y = dili_Wd, label = 省市),\nposition = \"identity\",\nsize = 3,\ncheck_overlap = TRUE\n) +\nlabs(title = \"中国地图\") +\ntheme(\nplot.title = element_text(\ncolor = \"black\",\nsize = 16,\nface = \"bold\",\nvjust = 0.1,\nhjust = 0.5\n),\nlegend.title = element_blank(),\nlegend.position = c(0.2, 0.2),\npanel.grid = element_blank(),\npanel.background = element_blank(),\naxis.text = element_blank(),\naxis.ticks = element_blank(),\naxis.title = element_blank()\n) +\nannotation_north_arrow(\nlocation = 'tl',\nwhich_north = 'false',\nstyle = north_arrow_orienteering()\n)\nfig1\nfig = ggdraw() +\ndraw_plot(fig1) +\ndraw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)\nfig\n"
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    "content": "\"OBJECTID\",\"ID_0\",\"ISO\",\"NAME_0\",\"OBJECTID_1\",\"ISO3\",\"NAME_ENGLISH\",\"NAME_ISO\",\"NAME_FAO\",\"NAME_LOCAL\",\"NAME_OBSOLETE\",\"NAME_VARIANTS\",\"NAME_NONLATIN\",\"NAME_FRENCH\",\"NAME_SPANISH\",\"NAME_RUSSIAN\",\"NAME_ARABIC\",\"NAME_CHINESE\",\"WASPARTOF\",\"CONTAINS\",\"SOVEREIGN\",\"ISO2\",\"WWW\",\"FIPS\",\"ISON\",\"VALIDFR\",\"VALIDTO\",\"POP2000\",\"SQKM\",\"POPSQKM\",\"UNREGION1\",\"UNREGION2\",\"DEVELOPING\",\"CIS\",\"Transition\",\"OECD\",\"WBREGION\",\"WBINCOME\",\"WBDEBT\",\"WBOTHER\",\"CEEAC\",\"CEMAC\",\"CEPLG\",\"COMESA\",\"EAC\",\"ECOWAS\",\"IGAD\",\"IOC\",\"MRU\",\"SACU\",\"UEMOA\",\"UMA\",\"PALOP\",\"PARTA\",\"CACM\",\"EurAsEC\",\"Agadir\",\"SAARC\",\"ASEAN\",\"NAFTA\",\"GCC\",\"CSN\",\"CARICOM\",\"EU\",\"CAN\",\"ACP\",\"Landlocked\",\"AOSIS\",\"SIDS\",\"Islands\",\"LDC\"\r\n1,49,\"CHN\",\"China\",45,\"CHN\",\"China\",\"CHINA\",\"China\",\"Zhong Guo\",NA,NA,\"<U+4E2D><U+534E><U+4EBA><U+6C11><U+5171><U+548C><U+56FD>|<U+4E2D><U+83EF><U+4EBA><U+6C11><U+5171><U+548C><U+570B>\",\"Chine \",\"China \",\"<U+041A><U+0438><U+0442><U+0430><U+0439>\",\"<U+062C><U+0645><U+0647><U+0648><U+0631><U+064A><U+0629> <U+0627><U+0644><U+0635><U+064A><U+0646> <U+0627><U+0644><U+0634><U+0639><U+0628><U+064A><U+0629>\",\"<U+4E2D><U+56FD>, <U+4E2D><U+534E> \",NA,\"Tibet\",\"China\",\"CN\",NA,\"CH\",156,\"Unknown\",\"Present\",1275132866,9338902,136.539912936232,\"Eastern Asia\",\"Asia\",1,NA,NA,NA,\"East Asia & Pacific\",\"Lower middle income\",\"Less indebted\",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA\r\n"
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    "content": "\"OBJECTID\",\"ID_0\",\"ISO\",\"NAME_0\",\"ID_1\",\"NAME_1\",\"TYPE_1\",\"ENGTYPE_1\",\"NL_NAME_1\",\"VARNAME_1\"\r\n1,49,\"CHN\",\"China\",1,\"Anhui\",\"Sheng\",\"Province\",\"<U+5B89><U+5FBD>|<U+5B89><U+5FBD>\",\"Anhui\"\r\n2,49,\"CHN\",\"China\",2,\"Beijing\",\"Zhíxiáshì\",\"Municipality\",\"<U+5317><U+4EAC>|<U+5317><U+4EAC>\",\"Beijing\"\r\n3,49,\"CHN\",\"China\",3,\"Chongqing\",\"Zhíxiáshì\",\"Municipality\",\"<U+91CD><U+6176>|<U+91CD><U+5E86>\",\"Chóngqìng\"\r\n4,49,\"CHN\",\"China\",4,\"Fujian\",\"Sheng\",\"Province\",\"<U+798F><U+5EFA>\",\"Fújiàn\"\r\n5,49,\"CHN\",\"China\",5,\"Gansu\",\"Sheng\",\"Province\",\"<U+7518><U+8085>|<U+7518><U+8083>\",\"Gansù\"\r\n6,49,\"CHN\",\"China\",6,\"Guangdong\",\"Sheng\",\"Province\",\"<U+5EE3><U+6771>|<U+5E7F><U+4E1C>\",\"Guangdong\"\r\n7,49,\"CHN\",\"China\",7,\"Guangxi\",\"Zìzhìqu\",\"Autonomous Region\",\"<U+5EE3><U+897F><U+58EF><U+65CF><U+81EA><U+6CBB><U+5340>|<U+5E7F><U+897F><U+58EE><U+65CF><U+81EA><U+6CBB><U+533A>\",\"Guangxi Zhuàngzú\"\r\n8,49,\"CHN\",\"China\",8,\"Guizhou\",\"Sheng\",\"Province\",\"<U+8CB4><U+5DDE>|<U+8D35><U+5DDE>\",\"Gùizhou\"\r\n9,49,\"CHN\",\"China\",9,\"Hainan\",\"Sheng\",\"Province\",\"<U+6D77><U+5357>\",\"Hainán\"\r\n10,49,\"CHN\",\"China\",10,\"Hebei\",\"Sheng\",\"Province\",\"<U+6CB3><U+5317>\",\"Hébei\"\r\n11,49,\"CHN\",\"China\",11,\"Heilongjiang\",\"Sheng\",\"Province\",\"<U+9ED1><U+9F99><U+6C5F><U+7701>|<U+9ED1><U+9F8D><U+6C5F><U+7701>\",\"Heilóngjiang\"\r\n12,49,\"CHN\",\"China\",12,\"Henan\",\"Sheng\",\"Province\",\"<U+6CB3><U+5357>\",\"Hénán\"\r\n13,49,\"CHN\",\"China\",13,\"Hubei\",\"Sheng\",\"Province\",\"<U+6E56><U+5317>\",\"Húbei\"\r\n14,49,\"CHN\",\"China\",14,\"Hunan\",\"Sheng\",\"Province\",\"<U+6E56><U+5357>\",\"Húnán\"\r\n15,49,\"CHN\",\"China\",15,\"Jiangsu\",\"Sheng\",\"Province\",\"<U+6C5F><U+8607>|<U+6C5F><U+82CF>\",\"Jiangsu\"\r\n16,49,\"CHN\",\"China\",16,\"Jiangxi\",\"Sheng\",\"Province\",\"<U+6C5F><U+897F>\",\"Jiangxi\"\r\n17,49,\"CHN\",\"China\",17,\"Jilin\",\"Sheng\",\"Province\",\"<U+5409><U+6797>\",\"Jílín\"\r\n18,49,\"CHN\",\"China\",18,\"Liaoning\",\"Sheng\",\"Province\",\"<U+907C><U+5BE7>|<U+8FBD><U+5B81>\",\"Liáoníng\"\r\n19,49,\"CHN\",\"China\",19,\"Nei Mongol\",\"Zìzhìqu\",\"Autonomous Region\",\"<U+5167><U+8499><U+53E4><U+81EA><U+6CBB><U+5340>|<U+5185><U+8499><U+53E4><U+81EA><U+6CBB><U+533A>\",\"Inner Mongol|Nèimenggu\"\r\n20,49,\"CHN\",\"China\",20,\"Ningxia Hui\",\"Zìzhìqu\",\"Autonomous Region\",\"<U+5BE7><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+5340>|<U+5B81><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+533A>\",\"Níngxià Húizú\"\r\n21,49,\"CHN\",\"China\",21,\"Qinghai\",\"Sheng\",\"Province\",\"<U+9752><U+6D77>\",\"Qinghai\"\r\n22,49,\"CHN\",\"China\",22,\"Shaanxi\",\"Sheng\",\"Province\",\"<U+965D><U+897F>|<U+9655><U+897F>\",\"Shanxi\"\r\n23,49,\"CHN\",\"China\",23,\"Shandong\",\"Sheng\",\"Province\",\"<U+5C71><U+6771>|<U+5C71><U+4E1C>\",\"Shandong\"\r\n24,49,\"CHN\",\"China\",24,\"Shanghai\",\"Zhíxiáshì\",\"Municipality\",\"<U+4E0A><U+6D77>|<U+4E0A><U+6D77>\",\"Shànghai\"\r\n25,49,\"CHN\",\"China\",25,\"Shanxi\",\"Sheng\",\"Province\",\"<U+5C71><U+897F>\",\"Shanxi\"\r\n26,49,\"CHN\",\"China\",26,\"Sichuan\",\"Sheng\",\"Province\",\"<U+56DB><U+5DDD>\",\"Sìchuan\"\r\n27,49,\"CHN\",\"China\",27,\"Tianjin\",\"Zhíxiáshì\",\"Municipality\",\"<U+5929><U+6D25>|<U+5929><U+6D25>\",\"Tianjin\"\r\n28,49,\"CHN\",\"China\",28,\"Xinjiang Uygur\",\"Zìzhìqu\",\"Autonomous Region\",\"<U+65B0><U+7586><U+7DAD><U+543E><U+723E><U+81EA><U+6CBB><U+5340>|<U+65B0><U+7586><U+7EF4><U+543E><U+5C14><U+81EA><U+6CBB><U+533A>\",\"Xinjiang Wéiwúer\"\r\n29,49,\"CHN\",\"China\",29,\"Xizang\",\"Zìzhìqu\",\"Autonomous Region\",\"<U+897F><U+85CF><U+81EA><U+6CBB><U+5340>|<U+897F><U+85CF><U+81EA><U+6CBB><U+533A>\",\"Tibet|Xizàng\"\r\n30,49,\"CHN\",\"China\",30,\"Yunnan\",\"Sheng\",\"Province\",\"<U+96F2><U+5357>|<U+4E91><U+5357>\",\"Yúnnán\"\r\n31,49,\"CHN\",\"China\",31,\"Zhejiang\",\"Sheng\",\"Province\",\"<U+6D59><U+6C5F>\",\"Zhèjiang\"\r\n"
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    "content": "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137.0,298.257223563]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]]"
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    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.csv",
    "content": "\"OBJECTID\",\"ID_0\",\"ISO\",\"NAME_0\",\"ID_1\",\"NAME_1\",\"ID_2\",\"NAME_2\",\"TYPE_2\",\"ENGTYPE_2\",\"NL_NAME_2\",\"VARNAME_2\"\r\n1,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",\"Dìjíshì\",\"Prefecture City\",\"<U+5B89><U+5E86><U+5E02>\",\"Anqìng\"\r\n2,49,\"CHN\",\"China\",1,\"Anhui\",2,\"Bengbu\",\"Dìjíshì\",\"Prefecture City\",\"<U+868C><U+57E0><U+5E02>\",\"Bèngbù\"\r\n3,49,\"CHN\",\"China\",1,\"Anhui\",3,\"Bozhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+4EB3><U+5DDE><U+5E02>\",\"Bózhou\"\r\n4,49,\"CHN\",\"China\",1,\"Anhui\",4,\"Chaohu\",\"Dìjíshì\",\"Prefecture City\",\"<U+5DE2><U+6E56><U+5E02>\",\"Cháohú\"\r\n5,49,\"CHN\",\"China\",1,\"Anhui\",5,\"Chizhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6C60><U+5DDE><U+5E02>\",\"Chízhou\"\r\n6,49,\"CHN\",\"China\",1,\"Anhui\",6,\"Chuzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6EC1><U+5DDE><U+5E02>\",\"Chúzhou\"\r\n7,49,\"CHN\",\"China\",1,\"Anhui\",7,\"Fuyang\",\"Dìjíshì\",\"Prefecture 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City\",\"<U+9F99><U+5CA9><U+5E02>\",\"Lóngyán\"\r\n22,49,\"CHN\",\"China\",4,\"Fujian\",22,\"Nanping\",\"Dìjíshì\",\"Prefecture City\",\"<U+5357><U+5E73><U+5E02>\",\"Nánpíng\"\r\n23,49,\"CHN\",\"China\",4,\"Fujian\",23,\"Ningde\",\"Dìjíshì\",\"Prefecture City\",\"<U+5B81><U+5FB7><U+5E02>\",\"Níngdé\"\r\n24,49,\"CHN\",\"China\",4,\"Fujian\",24,\"Putian\",\"Dìjíshì\",\"Prefecture City\",\"<U+8386><U+7530><U+5E02>\",\"Pútián\"\r\n25,49,\"CHN\",\"China\",4,\"Fujian\",25,\"Quanzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6CC9><U+5DDE><U+5E02>\",\"Quánzhou\"\r\n26,49,\"CHN\",\"China\",4,\"Fujian\",26,\"Sanming\",\"Dìjíshì\",\"Prefecture City\",\"<U+4E09><U+660E><U+5E02>\",\"Sanmíng\"\r\n27,49,\"CHN\",\"China\",4,\"Fujian\",27,\"Xiamen\",\"Dìjíshì\",\"Prefecture City\",\"<U+53A6><U+95E8><U+5E02>\",\"Xiàmén\"\r\n28,49,\"CHN\",\"China\",4,\"Fujian\",28,\"Zhangzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6F33><U+5DDE><U+5E02>\",\"Zhangzhou\"\r\n29,49,\"CHN\",\"China\",5,\"Gansu\",29,\"Baiyin\",\"Dìjíshì\",\"Prefecture City\",\"<U+767D><U+94F6><U+5E02>\",\"Báiyín\"\r\n30,49,\"CHN\",\"China\",5,\"Gansu\",30,\"Dingxi\",\"Dìjíshì\",\"Prefecture City\",\"<U+5B9A><U+897F><U+5E02>\",\"Dìngxi\"\r\n31,49,\"CHN\",\"China\",5,\"Gansu\",31,\"Gannan Tibetan\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+7518><U+5357><U+85CF><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Gannán Zàngzú\"\r\n32,49,\"CHN\",\"China\",5,\"Gansu\",32,\"Jiayuguan\",\"Dìjíshì\",\"Prefecture City\",\"<U+5609><U+5CEA><U+5173><U+5E02>\",\"Jiayùguan\"\r\n33,49,\"CHN\",\"China\",5,\"Gansu\",33,\"Jinchang\",\"Dìjíshì\",\"Prefecture City\",\"<U+91D1><U+660C><U+5E02>\",\"Jinchang\"\r\n34,49,\"CHN\",\"China\",5,\"Gansu\",34,\"Jiuquan\",\"Dìjíshì\",\"Prefecture City\",\"<U+9152><U+6CC9><U+5E02>\",\"Jiuquán\"\r\n35,49,\"CHN\",\"China\",5,\"Gansu\",35,\"Lanzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+5170><U+5DDE><U+5E02>\",\"Lánzhou\"\r\n36,49,\"CHN\",\"China\",5,\"Gansu\",36,\"Linxia Hui\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+4E34><U+590F><U+56DE><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Línxià Huizù\"\r\n37,49,\"CHN\",\"China\",5,\"Gansu\",37,\"Longnan\",\"Dìjíshì\",\"Prefecture City\",\"<U+9647><U+5357><U+5E02>\",\"Longnán\"\r\n38,49,\"CHN\",\"China\",5,\"Gansu\",38,\"Pingliang\",\"Dìjíshì\",\"Prefecture City\",\"<U+5E73><U+51C9><U+5E02>\",\"Píngliáng\"\r\n39,49,\"CHN\",\"China\",5,\"Gansu\",39,\"Qingyang\",\"Dìjíshì\",\"Prefecture City\",\"<U+5E86><U+9633><U+5E02>\",\"Qìngyáng\"\r\n40,49,\"CHN\",\"China\",5,\"Gansu\",40,\"Tianshui\",\"Dìjíshì\",\"Prefecture City\",\"<U+5929><U+6C34><U+5E02>\",\"Tianshui\"\r\n41,49,\"CHN\",\"China\",5,\"Gansu\",41,\"Wuwei\",\"Dìjíshì\",\"Prefecture City\",\"<U+6B66><U+5A01><U+5E02>\",\"Wuwei\"\r\n42,49,\"CHN\",\"China\",5,\"Gansu\",42,\"Zhangye\",\"Dìjíshì\",\"Prefecture 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City\",\"<U+97F6><U+5173><U+5E02>\",\"Sháoguan\"\r\n57,49,\"CHN\",\"China\",6,\"Guangdong\",57,\"Shenzhen\",\"Dìjíshì\",\"Prefecture City\",\"<U+6DF1><U+5733><U+5E02>\",\"Shenzhèn\"\r\n58,49,\"CHN\",\"China\",6,\"Guangdong\",58,\"Yangjiang\",\"Dìjíshì\",\"Prefecture City\",\"<U+9633><U+6C5F><U+5E02>\",\"Yángjiang\"\r\n59,49,\"CHN\",\"China\",6,\"Guangdong\",59,\"Yunfu\",\"Dìjíshì\",\"Prefecture City\",\"<U+4E91><U+6D6E><U+5E02>\",\"Yúnfú\"\r\n60,49,\"CHN\",\"China\",6,\"Guangdong\",60,\"Zhanjiang\",\"Dìjíshì\",\"Prefecture City\",\"<U+6E5B><U+6C5F><U+5E02>\",\"Zhànjiang\"\r\n61,49,\"CHN\",\"China\",6,\"Guangdong\",61,\"Zhaoqing\",\"Dìjíshì\",\"Prefecture City\",\"<U+8087><U+5E86><U+5E02>\",\"Zhàoqìng\"\r\n62,49,\"CHN\",\"China\",6,\"Guangdong\",62,\"Zhongshan\",\"Dìjíshì\",\"Prefecture City\",\"<U+4E2D><U+5C71><U+5E02>\",\"Zhongshan\"\r\n63,49,\"CHN\",\"China\",6,\"Guangdong\",63,\"Zhuhai\",\"Dìjíshì\",\"Prefecture 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City\",\"<U+6CB3><U+6C60><U+5E02>\",\"Héchí\"\r\n71,49,\"CHN\",\"China\",7,\"Guangxi\",71,\"Hezhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+8D3A><U+5DDE><U+5E02>\",\"Hèzhou\"\r\n72,49,\"CHN\",\"China\",7,\"Guangxi\",72,\"Laibin\",\"Dìjíshì\",\"Prefecture City\",\"<U+6765><U+5BBE><U+5E02>\",\"Láibin\"\r\n73,49,\"CHN\",\"China\",7,\"Guangxi\",73,\"Liuzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+67F3><U+5DDE><U+5E02>\",\"Liuzhou\"\r\n74,49,\"CHN\",\"China\",7,\"Guangxi\",74,\"Nanning\",\"Dìjíshì\",\"Prefecture City\",\"<U+5357><U+5B81><U+5E02>\",\"Nánníng\"\r\n75,49,\"CHN\",\"China\",7,\"Guangxi\",75,\"Qinzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+94A6><U+5DDE><U+5E02>\",\"Qinzhou\"\r\n76,49,\"CHN\",\"China\",7,\"Guangxi\",76,\"Wuzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+68A7><U+5DDE><U+5E02>\",\"Wúzhou\"\r\n77,49,\"CHN\",\"China\",7,\"Guangxi\",77,\"Yulin\",\"Dìjíshì\",\"Prefecture City\",\"<U+7389><U+6797><U+5E02>\",\"Yùlín\"\r\n78,49,\"CHN\",\"China\",8,\"Guizhou\",78,\"Anshun\",\"Dìjíshì\",\"Prefecture City\",\"<U+5B89><U+987A><U+5E02>\",\"Anshùn\"\r\n79,49,\"CHN\",\"China\",8,\"Guizhou\",79,\"Bijie\",\"Dìqu\",\"Prefecture\",\"<U+6BD5><U+8282><U+5730><U+533A>\",\"Bìjié\"\r\n80,49,\"CHN\",\"China\",8,\"Guizhou\",80,\"Guiyang\",\"Dìjíshì\",\"Prefecture City\",\"<U+8D35><U+9633><U+5E02>\",\"Guìyáng\"\r\n81,49,\"CHN\",\"China\",8,\"Guizhou\",81,\"Liupanshui\",\"Dìjíshì\",\"Prefecture City\",\"<U+516D><U+76D8><U+6C34><U+5E02>\",\"Liùpánshui\"\r\n82,49,\"CHN\",\"China\",8,\"Guizhou\",82,\"Qiandongnan Miao and Dong\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+9ED4><U+4E1C><U+5357><U+82D7><U+65CF><U+4F97><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Qiándongnán Miáozú|Dòngzú\"\r\n83,49,\"CHN\",\"China\",8,\"Guizhou\",83,\"Qiannan Buyei and Miao\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+9ED4><U+5357><U+5E03><U+4F9D><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Qiánnán Bùyizú|Miáozú\"\r\n84,49,\"CHN\",\"China\",8,\"Guizhou\",84,\"Qianxinan Buyei and Miao\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+9ED4><U+897F><U+5357><U+5E03><U+4F9D><U+65CF><U+82D7><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Qiánxinán Bùyizú|Miáozú\"\r\n85,49,\"CHN\",\"China\",8,\"Guizhou\",85,\"Tongren\",\"Dìqu\",\"Prefecture\",\"<U+94DC><U+4EC1><U+5730><U+533A>\",\"Tóngrén\"\r\n86,49,\"CHN\",\"China\",8,\"Guizhou\",86,\"Zunyi\",\"Dìjíshì\",\"Prefecture City\",\"<U+9075><U+4E49><U+5E02>\",\"Zunyì\"\r\n87,49,\"CHN\",\"China\",9,\"Hainan\",87,\"Haikou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6D77><U+53E3><U+5E02>\",\"Haikou\"\r\n88,49,\"CHN\",\"China\",9,\"Hainan\",88,\"Hainan\",\"Dìqu\",\"Prefecture\",\"<U+6D77><U+5357>\",\"Hainán\"\r\n89,49,\"CHN\",\"China\",9,\"Hainan\",89,\"Sanya\",\"Dìjíshì\",\"Prefecture City\",\"<U+4E09><U+4E9A><U+5E02>\",\"Sanyà\"\r\n90,49,\"CHN\",\"China\",10,\"Hebei\",90,\"Baoding\",\"Dìjíshì\",\"Prefecture City\",\"<U+4FDD><U+5B9A><U+5E02>\",\"Baodìng\"\r\n91,49,\"CHN\",\"China\",10,\"Hebei\",91,\"Cangzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6CA7><U+5DDE><U+5E02>\",\"Cangzhou\"\r\n92,49,\"CHN\",\"China\",10,\"Hebei\",92,\"Chengde\",\"Dìjíshì\",\"Prefecture City\",\"<U+627F><U+5FB7><U+5E02>\",\"Chéngdé\"\r\n93,49,\"CHN\",\"China\",10,\"Hebei\",93,\"Handan\",\"Dìjíshì\",\"Prefecture City\",\"<U+90AF><U+90F8><U+5E02>\",\"Hándan\"\r\n94,49,\"CHN\",\"China\",10,\"Hebei\",94,\"Hengshui\",\"Dìjíshì\",\"Prefecture City\",\"<U+8861><U+6C34><U+5E02>\",\"Héngshui\"\r\n95,49,\"CHN\",\"China\",10,\"Hebei\",95,\"Langfang\",\"Dìjíshì\",\"Prefecture City\",\"<U+5ECA><U+574A><U+5E02>\",\"Lángfáng\"\r\n96,49,\"CHN\",\"China\",10,\"Hebei\",96,\"Qinhuangdao\",\"Dìjíshì\",\"Prefecture City\",\"<U+79E6><U+7687><U+5C9B><U+5E02>\",\"Qínhuángdao\"\r\n97,49,\"CHN\",\"China\",10,\"Hebei\",97,\"Shijiazhuang\",\"Dìjíshì\",\"Prefecture City\",\"<U+77F3><U+5BB6><U+5E84><U+5E02>\",\"Shíjiazhuang\"\r\n98,49,\"CHN\",\"China\",10,\"Hebei\",98,\"Tangshan\",\"Dìjíshì\",\"Prefecture City\",\"<U+5510><U+5C71><U+5E02>\",\"Tángshan\"\r\n99,49,\"CHN\",\"China\",10,\"Hebei\",99,\"Xingtai\",\"Dìjíshì\",\"Prefecture City\",\"<U+90A2><U+53F0><U+5E02>\",\"Xíngtái\"\r\n100,49,\"CHN\",\"China\",10,\"Hebei\",100,\"Zhangjiakou\",\"Dìjíshì\",\"Prefecture City\",\"<U+5F20><U+5BB6><U+53E3><U+5E02>\",\"Zhangjiakou\"\r\n101,49,\"CHN\",\"China\",11,\"Heilongjiang\",101,\"Daqing\",\"Dìjíshì\",\"Prefecture City\",\"<U+5927><U+5E86><U+5E02>\",\"Dàqìng\"\r\n102,49,\"CHN\",\"China\",11,\"Heilongjiang\",102,\"Daxing'anling\",\"Dìqu\",\"Prefecture\",\"<U+5927><U+5174><U+5B89><U+5CAD><U+5730><U+533A>\",\"Dàxing'anling\"\r\n103,49,\"CHN\",\"China\",11,\"Heilongjiang\",103,\"Harbin\",\"Dìjíshì\",\"Prefecture 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Zhuàngzú|Miáozú\"\r\n331,49,\"CHN\",\"China\",30,\"Yunnan\",331,\"Xishuangbanna Dai\",\"Zìzhìzhou\",\"Autonomous Prefecture\",\"<U+897F><U+53CC><U+7248><U+7EB3><U+50A3><U+65CF><U+81EA><U+6CBB><U+5DDE>\",\"Xishuangbannà|Daizú\"\r\n332,49,\"CHN\",\"China\",30,\"Yunnan\",332,\"Yuxi\",\"Dìjíshì\",\"Prefecture City\",\"<U+7389><U+6EAA><U+5E02>\",\"Yùxi\"\r\n333,49,\"CHN\",\"China\",30,\"Yunnan\",333,\"Zhaotong\",\"Dìjíshì\",\"Prefecture City\",\"<U+662D><U+901A><U+5E02>\",\"Zhaotong\"\r\n334,49,\"CHN\",\"China\",31,\"Zhejiang\",334,\"Hangzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+676D><U+5DDE><U+5E02>\",\"Hángzhou\"\r\n335,49,\"CHN\",\"China\",31,\"Zhejiang\",335,\"Huzhou\",\"Dìjíshì\",\"Prefecture City\",\"<U+6E56><U+5DDE><U+5E02>\",\"Húzhou\"\r\n336,49,\"CHN\",\"China\",31,\"Zhejiang\",336,\"Jiaxing\",\"Dìjíshì\",\"Prefecture City\",\"<U+5609><U+5174><U+5E02>\",\"Jiaxing\"\r\n337,49,\"CHN\",\"China\",31,\"Zhejiang\",337,\"Jinhua\",\"Dìjíshì\",\"Prefecture 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  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm2.prj",
    "content": "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137.0,298.257223563]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]]"
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  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.cpg",
    "content": "UTF-8"
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  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.csv",
    "content": "\"OBJECTID\",\"ID_0\",\"ISO\",\"NAME_0\",\"ID_1\",\"NAME_1\",\"ID_2\",\"NAME_2\",\"ID_3\",\"NAME_3\",\"TYPE_3\",\"ENGTYPE_3\",\"NL_NAME_3\",\"VARNAME_3\"\r\n1,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",1,\"Anqing\",\"Xiànjíshì\",\"County City\",NA,NA\r\n2,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",2,\"Huaining\",\"Xiàn\",\"County\",\"<U+6000><U+5B81><U+53BF>\",\"Huáiníng\"\r\n3,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",3,\"Qianshan\",\"Xiàn\",\"County\",\"<U+6F5C><U+5C71><U+53BF>\",\"Qiánshan\"\r\n4,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",4,\"Susong\",\"Xiàn\",\"County\",\"<U+5BBF><U+677E><U+53BF>\",\"Sùsong\"\r\n5,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",5,\"Taihu\",\"Xiàn\",\"County\",\"<U+592A><U+6E56><U+53BF>\",\"Tàihú\"\r\n6,49,\"CHN\",\"China\",1,\"Anhui\",1,\"Anqing\",6,\"Tongcheng\",\"Xiànjíshì\",\"County 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Yi\",1943,\"Dechang\",\"Xiàn\",\"County\",\"<U+5FB7><U+660C><U+53BF>\",\"Déchang\"\r\n1944,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1944,\"Ganluo\",\"Xiàn\",\"County\",\"<U+7518><U+6D1B><U+53BF>\",\"Ganluò\"\r\n1945,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1945,\"Huidong\",\"Xiàn\",\"County\",\"<U+4F1A><U+4E1C><U+53BF>\",\"Huìdong\"\r\n1946,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1946,\"Huili\",\"Xiàn\",\"County\",\"<U+4F1A><U+7406><U+53BF>\",\"Huìli\"\r\n1947,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1947,\"Jingyang\",\"Shìxiáqu\",\"District\",\"<U+65CC><U+9633><U+533A>\",\"Jingyáng\"\r\n1948,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1948,\"Leibo\",\"Xiàn\",\"County\",\"<U+96F7><U+6CE2><U+53BF>\",\"Léibo\"\r\n1949,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1949,\"Meigu\",\"Xiàn\",\"County\",\"<U+7F8E><U+59D1><U+53BF>\",\"Meigu\"\r\n1950,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1950,\"Mianning\",\"Xiàn\",\"County\",\"<U+5195><U+5B81><U+53BF>\",\"Mianníng\"\r\n1951,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1951,\"Muli Tibetan\",\"Zìzhìxiàn\",\"Autonomous County\",\"<U+6728><U+91CC><U+85CF><U+65CF><U+81EA><U+6CBB><U+53BF>\",\"Mùli Zàngzú\"\r\n1952,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1952,\"Ningnan\",\"Xiàn\",\"County\",\"<U+5B81><U+5357><U+53BF>\",\"Níngnán\"\r\n1953,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1953,\"Puge\",\"Xiàn\",\"County\",\"<U+666E><U+683C><U+53BF>\",\"Pugé\"\r\n1954,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1954,\"Xichang\",\"Xiànjíshì\",\"County City\",\"<U+897F><U+660C><U+5E02>\",\"Xichang\"\r\n1955,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1955,\"Xide\",\"Xiàn\",\"County\",\"<U+559C><U+5FB7><U+53BF>\",\"Xidé\"\r\n1956,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1956,\"Yanyuan\",\"Xiàn\",\"County\",\"<U+76D0><U+6E90><U+53BF>\",\"Yányuán\"\r\n1957,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1957,\"Yuexi\",\"Xiàn\",\"County\",\"<U+8D8A><U+897F><U+53BF>\",\"Yuèxi\"\r\n1958,49,\"CHN\",\"China\",26,\"Sichuan\",281,\"Liangshan Yi\",1958,\"Zhaojue\",\"Xiàn\",\"County\",\"<U+662D><U+89C9><U+53BF>\",\"Zhaojué\"\r\n1959,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1959,\"Gulin\",\"Xiàn\",\"County\",\"<U+53E4><U+853A><U+53BF>\",\"Gulìn\"\r\n1960,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1960,\"Hejiang\",\"Xiàn\",\"County\",\"<U+5408><U+6C5F><U+53BF>\",\"Héjiang\"\r\n1961,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1961,\"Lu\",\"Xiàn\",\"County\",\"<U+6CF8><U+53BF>\",\"Lú\"\r\n1962,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1962,\"Luzhou\",\"Xiànjíshì\",\"County City\",\"<U+6CF8><U+5DDE><U+5E02>\",\"Lúzhou\"\r\n1963,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1963,\"Nàxi\",\"Shìxiáqu\",\"District\",\"<U+7EB3><U+6EAA><U+533A>\",NA\r\n1964,49,\"CHN\",\"China\",26,\"Sichuan\",282,\"Luzhou\",1964,\"Xuyong\",\"Xiàn\",\"County\",\"<U+53D9><U+6C38><U+53BF>\",\"Xùyong\"\r\n1965,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1965,\"Danleng\",\"Xiàn\",\"County\",\"<U+4E39><U+68F1><U+53BF>\",\"Danléng\"\r\n1966,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1966,\"Hongya\",\"Xiàn\",\"County\",\"<U+6D2A><U+96C5><U+53BF>\",\"Hóngya\"\r\n1967,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1967,\"Jingyan\",\"Xiàn\",\"County\",\"<U+4E95><U+7814><U+53BF>\",\"Jingyán\"\r\n1968,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1968,\"Meishan\",\"Xiàn\",\"County\",NA,NA\r\n1969,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1969,\"Pengshan\",\"Xiàn\",\"County\",\"<U+5F6D><U+5C71><U+53BF>\",\"Péngshan\"\r\n1970,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1970,\"Qingshen\",\"Xiàn\",\"County\",\"<U+9752><U+795E><U+53BF>\",\"Qingshén\"\r\n1971,49,\"CHN\",\"China\",26,\"Sichuan\",283,\"Meishan\",1971,\"Renshou\",\"Xiàn\",\"County\",\"<U+4EC1><U+5BFF><U+53BF>\",\"Rénshòu\"\r\n1972,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1972,\"An\",\"Xiàn\",\"County\",\"<U+5B89><U+53BF>\",\"An\"\r\n1973,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1973,\"Beichuan Qiang\",\"Zìzhìxiàn\",\"Autonomous County\",\"<U+5317><U+5DDD><U+7F8C><U+65CF><U+81EA><U+6CBB><U+53BF>\",\"Beichuan Qiangzú\"\r\n1974,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1974,\"Jiangyou\",\"Xiànjíshì\",\"County City\",\"<U+6C5F><U+6CB9><U+5E02>\",\"Jiangyóu\"\r\n1975,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1975,\"Mianyang\",\"Xiànjíshì\",\"County City\",NA,NA\r\n1976,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1976,\"Pingwu\",\"Xiàn\",\"County\",\"<U+5E73><U+6B66><U+53BF>\",\"Píngwu\"\r\n1977,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1977,\"Santai\",\"Xiàn\",\"County\",\"<U+4E09><U+53F0><U+53BF>\",\"Santái\"\r\n1978,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1978,\"Yanting\",\"Xiàn\",\"County\",\"<U+76D0><U+4EAD><U+53BF>\",\"Yántíng\"\r\n1979,49,\"CHN\",\"China\",26,\"Sichuan\",284,\"Mianyang\",1979,\"Zitong\",\"Xiàn\",\"County\",\"<U+6893><U+6F7C><U+53BF>\",\"Zitóng\"\r\n1980,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1980,\"Langzhong\",\"Xiànjíshì\",\"County City\",\"<U+9606><U+4E2D><U+5E02>\",\"Lángzhong\"\r\n1981,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1981,\"Nanbu\",\"Xiàn\",\"County\",\"<U+5357><U+90E8><U+53BF>\",\"Nánbù\"\r\n1982,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1982,\"Nanchong Shì\",\"Xiànjíshì\",\"County City\",\"<U+5357><U+5145><U+5E02>\",\"Nánchong\"\r\n1983,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1983,\"Nanchong Xiàn\",\"Xiàn\",\"County\",\"<U+5357><U+5145><U+5E02>\",\"Nánchong\"\r\n1984,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1984,\"Peng'an\",\"Xiàn\",\"County\",\"<U+84EC><U+5B89><U+53BF>\",\"Péng'an\"\r\n1985,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1985,\"Xichong\",\"Xiàn\",\"County\",\"<U+897F><U+5145><U+53BF>\",\"Xichong\"\r\n1986,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1986,\"Yilong\",\"Xiàn\",\"County\",\"<U+4EEA><U+9647><U+53BF>\",\"Yílong\"\r\n1987,49,\"CHN\",\"China\",26,\"Sichuan\",285,\"Nanchong\",1987,\"Yingshan\",\"Xiàn\",\"County\",\"<U+8425><U+5C71><U+53BF>\",\"Yíngshan\"\r\n1988,49,\"CHN\",\"China\",26,\"Sichuan\",286,\"Neijiang]]\",1988,\"Longchang\",\"Xiàn\",\"County\",\"<U+9686><U+660C><U+53BF>\",\"Lóngchang\"\r\n1989,49,\"CHN\",\"China\",26,\"Sichuan\",286,\"Neijiang]]\",1989,\"Zizhong\",\"Xiàn\",\"County\",\"<U+8D44><U+4E2D><U+53BF>\",\"Zizhong\"\r\n1990,49,\"CHN\",\"China\",26,\"Sichuan\",287,\"Neijiang\",1990,\"Dongxing\",\"Shìxiáqu\",\"District\",\"<U+4E1C><U+5174><U+533A>\",\"Dongxing\"\r\n1991,49,\"CHN\",\"China\",26,\"Sichuan\",287,\"Neijiang\",1991,\"Shizhong\",\"Shìxiáqu\",\"District\",\"<U+5E02><U+4E2D><U+533A>\",\"Shìzhong\"\r\n1992,49,\"CHN\",\"China\",26,\"Sichuan\",287,\"Neijiang\",1992,\"Weiyuan\",\"Xiàn\",\"County\",\"<U+5A01><U+8FDC><U+53BF>\",\"Weiyuan\"\r\n1993,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1993,\"Aba\",\"Xiàn\",\"County\",NA,NA\r\n1994,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1994,\"Barkam\",\"Xiàn\",\"County\",\"<U+9A6C><U+5C14><U+5EB7><U+53BF>\",\"Ma'erkang\"\r\n1995,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1995,\"Guan\",\"Xiàn\",\"County\",NA,NA\r\n1996,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1996,\"Heishui\",\"Xiàn\",\"County\",\"<U+9ED1><U+6C34><U+53BF>\",\"Heishui\"\r\n1997,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1997,\"Hongyuan\",\"Xiàn\",\"County\",\"<U+7EA2><U+539F><U+53BF>\",\"Hóngyuán\"\r\n1998,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1998,\"Jinchuan\",\"Xiàn\",\"County\",\"<U+91D1><U+5DDD><U+53BF>\",\"Jinchuan\"\r\n1999,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",1999,\"Li\",\"Xiàn\",\"County\",\"<U+7406><U+53BF>\",\"Li\"\r\n2000,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2000,\"Mao\",\"Xiàn\",\"County\",\"<U+8302><U+53BF>\",\"Mào\"\r\n2001,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2001,\"Nanping\",\"Xiàn\",\"County\",NA,NA\r\n2002,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2002,\"Songpan\",\"Xiàn\",\"County\",\"<U+677E><U+6F58><U+53BF>\",\"Songpan\"\r\n2003,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2003,\"Wenchuan\",\"Xiàn\",\"County\",\"<U+6C76><U+5DDD><U+53BF>\",\"Wènchuan\"\r\n2004,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2004,\"Xiaojin\",\"Xiàn\",\"County\",\"<U+5C0F><U+91D1><U+53BF>\",\"Xiaojin\"\r\n2005,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2005,\"Zamtang\",\"Xiàn\",\"County\",\"<U+58E4><U+5858><U+53BF>\",\"Rangtáng\"\r\n2006,49,\"CHN\",\"China\",26,\"Sichuan\",288,\"Ngawa Tibetan and Qiang\",2006,\"Zoigê\",\"Xiàn\",\"County\",\"<U+82E5><U+5C14><U+76D6><U+53BF>\",\"Ruò'ergài\"\r\n2007,49,\"CHN\",\"China\",26,\"Sichuan\",289,\"Panzhihua\",2007,\"East\",\"Shìxiáqu\",\"District\",\"<U+4E1C><U+533A>\",\"Dong\"\r\n2008,49,\"CHN\",\"China\",26,\"Sichuan\",289,\"Panzhihua\",2008,\"Miyi\",\"Xiàn\",\"County\",\"<U+7C73><U+6613><U+53BF>\",\"Miyì\"\r\n2009,49,\"CHN\",\"China\",26,\"Sichuan\",289,\"Panzhihua\",2009,\"Yanbian\",\"Xiàn\",\"County\",\"<U+76D0><U+8FB9><U+53BF>\",\"Yánbian\"\r\n2010,49,\"CHN\",\"China\",26,\"Sichuan\",290,\"Suining\",2010,\"Pengxi\",\"Xiàn\",\"County\",\"<U+84EC><U+6EAA><U+53BF>\",\"Péngxi\"\r\n2011,49,\"CHN\",\"China\",26,\"Sichuan\",290,\"Suining\",2011,\"Shehong\",\"Xiàn\",\"County\",\"<U+5C04><U+6D2A><U+53BF>\",\"Shèhóng\"\r\n2012,49,\"CHN\",\"China\",26,\"Sichuan\",290,\"Suining\",2012,\"Suining\",\"Xiàn\",\"County\",NA,NA\r\n2013,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2013,\"Baoxing\",\"Xiàn\",\"County\",\"<U+5B9D><U+5174><U+53BF>\",\"Baoxing\"\r\n2014,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2014,\"Hanyuan\",\"Xiàn\",\"County\",\"<U+6C49><U+6E90><U+53BF>\",\"Hànyuán\"\r\n2015,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2015,\"Lushan\",\"Xiàn\",\"County\",\"<U+82A6><U+5C71><U+53BF>\",\"Lúshan\"\r\n2016,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2016,\"Mingshan\",\"Xiàn\",\"County\",\"<U+540D><U+5C71><U+53BF>\",\"Míngshan\"\r\n2017,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2017,\"Shimian\",\"Xiàn\",\"County\",\"<U+77F3><U+68C9><U+53BF>\",\"Shímián\"\r\n2018,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2018,\"Tianquan\",\"Xiàn\",\"County\",\"<U+5929><U+5168><U+53BF>\",\"Tianquán\"\r\n2019,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2019,\"Ya 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City\",\"<U+96C5><U+5B89><U+5E02>\",\"Ya'an\"\r\n2020,49,\"CHN\",\"China\",26,\"Sichuan\",291,\"Ya'an\",2020,\"Yingjing\",\"Xiàn\",\"County\",\"<U+8365><U+7ECF><U+53BF>\",\"Yíngjing\"\r\n2021,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2021,\"Changning\",\"Xiàn\",\"County\",\"<U+957F><U+5B81><U+53BF>\",\"Chángníng\"\r\n2022,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2022,\"Gao\",\"Xiàn\",\"County\",\"<U+9AD8><U+53BF>\",\"Gao\"\r\n2023,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2023,\"Gong\",\"Xiàn\",\"County\",\"<U+73D9><U+53BF>\",\"Gong\"\r\n2024,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2024,\"Jiang'an\",\"Xiàn\",\"County\",\"<U+6C5F><U+5B89><U+53BF>\",\"Jiang'an\"\r\n2025,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2025,\"Junlian\",\"Xiàn\",\"County\",\"<U+7B60><U+8FDE><U+53BF>\",\"Junlián\"\r\n2026,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2026,\"Nanxi\",\"Xiàn\",\"County\",\"<U+5357><U+6EAA><U+53BF>\",\"Nánxi\"\r\n2027,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2027,\"Pingshan\",\"Xiàn\",\"County\",\"<U+5C4F><U+5C71><U+53BF>\",\"Píngshan\"\r\n2028,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2028,\"Xingwen\",\"Xiàn\",\"County\",\"<U+5174><U+6587><U+53BF>\",\"Xingwén\"\r\n2029,49,\"CHN\",\"China\",26,\"Sichuan\",292,\"Yibin\",2029,\"Yibin 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City\",\"<U+745E><U+5B89><U+5E02>\",\"Ruì'an\"\r\n2402,49,\"CHN\",\"China\",31,\"Zhejiang\",343,\"Wenzhou\",2402,\"Taishun\",\"Xiàn\",\"County\",\"<U+6CF0><U+987A><U+53BF>\",\"Tàishùn\"\r\n2403,49,\"CHN\",\"China\",31,\"Zhejiang\",343,\"Wenzhou\",2403,\"Wencheng\",\"Xiàn\",\"County\",\"<U+6587><U+6210><U+53BF>\",\"Wénchéng\"\r\n2404,49,\"CHN\",\"China\",31,\"Zhejiang\",343,\"Wenzhou\",2404,\"Wenzhou\",\"Xiànjíshì\",\"County City\",\"<U+6E29><U+5DDE><U+5E02>\",\"Wenzhou\"\r\n2405,49,\"CHN\",\"China\",31,\"Zhejiang\",343,\"Wenzhou\",2405,\"Yongjia\",\"Xiàn\",\"County\",\"<U+6C38><U+5609><U+53BF>\",\"Yongjia\"\r\n2406,49,\"CHN\",\"China\",31,\"Zhejiang\",343,\"Wenzhou\",2406,\"Yueqing\",\"Xiànjíshì\",\"County City\",\"<U+4E50><U+6E05><U+5E02>\",\"\tYuèqing\"\r\n2407,49,\"CHN\",\"China\",31,\"Zhejiang\",344,\"Zhoushan\",2407,\"Daishan\",\"Xiàn\",\"County\",\"<U+5CB1><U+5C71><U+53BF>\",\"Dàishan\"\r\n2408,49,\"CHN\",\"China\",31,\"Zhejiang\",344,\"Zhoushan\",2408,\"Dinghai\",\"Shìxiáqu\",\"District\",\"<U+5B9A><U+6D77><U+533A>\",\"Dìnghai\"\r\n2409,49,\"CHN\",\"China\",31,\"Zhejiang\",344,\"Zhoushan\",2409,\"Putuo\",\"Shìxiáqu\",\"District\",\"<U+666E><U+9640><U+533A>\",\"Putuó\"\r\n"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/CHN_adm3.prj",
    "content": "GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137.0,298.257223563]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]]"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/license.txt",
    "content": "These data were extracted from the GADM database (www.gadm.org), version 2.5, July 2015. They can be used for non-commercial purposes only.  It is not allowed to redistribute these data, or use them for commercial purposes, without prior consent."
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/china_shp/my_file.txt",
    "content": ""
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/colour.csv",
    "content": "shengfen,colour,QUHUADAIMA\r\n½,0~200,650000\r\n,0~200,540000\r\nຣ,600~1000,630000\r\n,0~200,620000\r\n,200~400,640000\r\nɹ,400~600,150000\r\nɽ,0~200,140000\r\n,0~200,230000\r\n,0~200,220000\r\n,200~400,530000\r\n,400~600,520000\r\n,0~200,450000\r\n,0~200,500000\r\n,0~200,360000\r\n,0~200,460000\r\nĴ,200~400,510000\r\n,0~200,610000\r\n,0~200,410000\r\nӱ,0~200,130000\r\n,200~400,340000\r\n,200~400,350000\r\n,200~400,430000\r\n,400~600,420000\r\n,400~600,210000\r\n,400~600,120000\r\n㽭,600~1000,330000\r\nϺ,600~1000,310000\r\n,1000+,110000\r\nɽ,1000+,370000\r\n,1000+,320000\r\n㶫,600~1000,440000\r\n̨,600~1000,710000\r\n,600~1000,810000\r\n,600~1000,820000\r\n"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/map.Rproj",
    "content": "Version: 1.0\r\n\r\nRestoreWorkspace: Default\r\nSaveWorkspace: Default\r\nAlwaysSaveHistory: Default\r\n\r\nEnableCodeIndexing: Yes\r\nUseSpacesForTab: Yes\r\nNumSpacesForTab: 2\r\nEncoding: UTF-8\r\n\r\nRnwWeave: knitr\r\nLaTeX: pdfLaTeX\r\n"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/province.csv",
    "content": "ʡ,ʡ,shenghui_mid,sf_Jd,sf_Wd,dili_mid,dili_Jd,dili_Wd,beizhu\r\n,,\"c(116.4666667,39.9)\",116.4666667,39.9,\"c(116.533982575088, 40.315106188478)\",116.5339826,40.31510619,\r\nϺ,Ϻ,\"c(121.4833333,31.23333333)\",121.4833333,31.23333333,\"c(121.540428873312, 31.2095383685209)\",121.5404289,31.20953837,\r\n,,\"c(117.1833333,39.15)\",117.1833333,39.15,\"c(117.458023010432, 39.4371653956601)\",117.458023,39.4371654,\r\n,,\"c(106.5333333,29.53333333)\",106.5333333,29.53333333,\"c(108.01001812709, 30.2218546606162)\",108.0100181,30.22185466,\r\n,,\"c(126.6833333,45.75)\",126.6833333,45.75,\"c(127.893527270599, 47.9970969498844)\",127.8935273,47.99709695,\r\n,,\"c(125.3166667,43.86666667)\",125.3166667,43.86666667,\"c(126.320389141051, 43.7944047371614)\",126.3203891,43.79440474,\r\n,,\"c(123.4,41.83333333)\",123.4,41.83333333,\"c(122.743364828602, 41.427633679966)\",122.7433648,41.42763368,\r\nɹ,ͺ,\"c(111.8,40.81666667)\",111.8,40.81666667,\"c(114.058881423766, 44.2194391897076)\",114.0588814,43.21943919,γ-1\r\nӱ,ʯׯ,\"c(114.4666667,38.03333333)\",114.4666667,38.03333333,,115.4666667,39.03333333,ʡ꾭+1γ+1\r\nɽ,̫ԭ,\"c(112.5666667,37.86666667)\",112.5666667,37.86666667,\"c(112.425479766607, 37.6953486118982)\",112.4254798,37.69534861,\r\nɽ,,\"c(117,36.63333333)\",117,36.63333333,\"c(118.281712436475, 36.4808315698521)\",118.2817124,36.48083157,\r\n,֣,\"c(113.7,34.8)\",113.7,34.8,\"c(113.732611968596, 34.0262791911931)\",113.732612,34.02627919,\r\n,,\"c(108.9,34.26666667)\",108.9,34.26666667,\"c(109.000236165247, 35.3344425983614)\",109.0002362,35.3344426,\r\n,,\"c(103.8166667,36.05)\",103.8166667,36.05,\"c(100.730361465946, 38.0176991236257)\",100.7303615,38.51769912,γ+0.5\r\n,,\"c(106.2666667,38.33333333)\",106.2666667,38.33333333,\"c(106.295906065515, 37.4065563460928)\",106.2959061,37.40655635,\r\nຣ,,\"c(101.75,36.63333333)\",101.75,36.63333333,\"c(96.1677035508218, 35.7983962533039)\",96.16770355,35.79839625,\r\n½,³ľ,\"c(87.6,43.8)\",87.6,43.8,\"c(85.3249856320736, 41.2501570887227)\",85.32498563,41.25015709,\r\n,Ϸ,\"c(117.3,31.85)\",117.3,31.85,\"c(117.351643586328, 31.9537017702415)\",117.3516436,31.95370177,\r\n,Ͼ,\"c(118.8333333,32.03333333)\",118.8333333,32.03333333,\"c(119.582729973854, 33.1019082410552)\",119.58273,33.10190824,\r\n㽭,,\"c(120.15,30.23333333)\",120.15,30.23333333,\"c(120.184224978466, 29.2972448333294)\",120.184225,29.29724483,\r\n,ɳ,\"c(113,28.18333333)\",113,28.18333333,\"c(111.842280603681, 27.7447628512016)\",111.8422806,27.74476285,\r\n,ϲ,\"c(115.8666667,28.68333333)\",115.8666667,28.68333333,\"c(115.857549380971, 27.734680323998)\",115.8575494,27.73468032,\r\n,人,\"c(114.35,30.61666667)\",114.35,30.61666667,\"c(112.405873792758, 31.1119538140425)\",112.4058738,31.11195381,\r\nĴ,ɶ,\"c(104.0833333,30.65)\",104.0833333,30.65,\"c(102.820301877824, 30.7598119258279)\",102.8203019,30.75981193,\r\n,,\"c(106.7,26.58333333)\",106.7,26.58333333,\"c(107.015315250832, 26.9522197194248)\",107.0153153,26.95221972,\r\n,,\"c(119.3,26.08333333)\",119.3,26.08333333,\"c(118.113118831087, 26.2094758350264)\",118.1131188,26.20947584,\r\n̨,̨,\"c(121.5166667,25.05)\",121.5166667,25.05,,121.0166667,24.05,ʡ꾭-0.5γ-1\r\n㶫,,\"c(113.25,23.13333333)\",113.25,23.13333333,\"c(113.542568068397, 23.4656883053524)\",113.5425681,23.46568831,\r\n,,\"c(110.3333333,20.03333333)\",110.3333333,20.03333333,\"c(109.877177580232, 19.3269620231367)\",109.8771776,19.32696202,\r\n,,\"c(108.3333333,22.8)\",108.3333333,22.8,\"c(108.90840775646, 23.9644856706756)\",108.9084078,23.96448567,\r\n,,\"c(102.6833333,25)\",102.6833333,25,\"c(101.619355216508, 25.1023431677425)\",101.6193552,25.10234317,\r\n,,\"c(91.16666667,29.66666667)\",91.16666667,29.66666667,\"c(88.5701164584765, 31.6231104944092)\",88.57011646,31.62311049,\r\n,,\"c(114.1666667,22.3)\",114.1666667,22.3,,114.1666667,22.3,ʡ\r\n,,\"c(113.5,22.2)\",113.5,22.2,,113.5,22.2,ʡ\r\n"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/南海.geojson",
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  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/地图十段线.R",
    "content": "library(geojsonsf)\r\nlibrary(sf)\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)\r\nlibrary(ggspatial)\r\nlibrary(cowplot)\r\n\r\nAPI_pre = \"http://xzqh.mca.gov.cn/data/\"\r\n\r\nChina = st_read(dsn = paste0(API_pre, \"quanguo.json\"), \r\n                stringsAsFactors=FALSE) \r\nst_crs(China) = 4326\r\n\r\n# 2.国境线\r\nChina_line = st_read(dsn = paste0(API_pre, \"quanguo_Line.geojson\"), \r\n                     stringsAsFactors=FALSE) \r\nst_crs(China_line) = 4326\r\n\r\ngjx <- China_line[China_line$QUHUADAIMA == \"guojiexian\",]\r\n\r\n\r\nprovince <- read.csv(\"province.csv\")\r\n\r\nfig1 <-  ggplot()+\r\n  # 绘制主图\r\n  geom_sf(data = CHINA,fill='NA') +\r\n  # 绘制国界线及十段线\r\n  geom_sf(data = gjx)+\r\n  ##添加省份名称\r\n  geom_text(data = province,aes(x=dili_Jd,y=dili_Wd,label=省市),position = \"identity\",size=3,check_overlap = TRUE) +\r\n  labs(title=\"中国地图\")+\r\n  theme(plot.title = element_text(color=\"black\", size=16, face=\"bold\",vjust = 0.1,hjust = 0.5),\r\n        legend.title=element_blank(),\r\n        legend.position = c(0.2,0.2),\r\n        panel.grid=element_blank(),\r\n        panel.background=element_blank(),\r\n        axis.text=element_blank(),\r\n        axis.ticks=element_blank(),\r\n        axis.title=element_blank()\r\n  )+\r\n  ##添加指北针，“style”参数可以更改样式\r\n  annotation_north_arrow(location='tl', which_north='false',\r\n                         style=north_arrow_orienteering())\r\nfig1\r\n\r\n\r\n# 读入数据\r\nnine_lines = read_sf('南海.geojson') \r\n\r\n# 绘制九段线小图\r\nfig2 = ggplot() +\r\n  geom_sf(data = CHINA,fill='NA', size=0.5) + \r\n  geom_sf(data = nine_lines,color='black',size=0.5)+\r\n  ##去掉主图的部分区域\r\n  coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs=\"+proj=laea +lat_0=40 +lon_0=104\")+\r\n  theme(\r\n    aspect.ratio = 1.25, #调节长宽比\r\n    axis.text = element_blank(),\r\n    axis.ticks = element_blank(),\r\n    axis.title = element_blank(),\r\n    panel.grid = element_blank(),\r\n    panel.background = element_blank(),\r\n    panel.border = element_rect(fill=NA,color=\"grey10\",linetype=1,size=0.5),\r\n    plot.margin=unit(c(0,0,0,0),\"mm\"))\r\nfig2\r\n\r\nfig = ggdraw() +\r\n  draw_plot(fig1) +\r\n  draw_plot(fig2, x = 0.8, y = 0, width = 0.13, height = 0.39)\r\nfig\r\n\r\n\r\ncolour <- read.csv(\"colour.csv\")\r\nhead(colour)\r\n\r\ncolour$QUHUADAIMA <- as.character(colour$QUHUADAIMA)\r\nCHINA <- dplyr::left_join(China,colour,by= \"QUHUADAIMA\")\r\n\r\nfig1 <-  ggplot() +\r\n  geom_sf(\r\n    data = CHINA,\r\n    aes(fill = factor(colour))) +\r\n  ## 填色\r\n  scale_fill_manual(\r\n    \"class\",\r\n    values = c(\"#9CEED3\", \"#79CBC2\", \"#5EA9AC\", \"#4B8793\", \"#3C6777\"),\r\n    breaks = c(\"0~200\", \"200~400\", \"400~600\", \"600~1000\", \"1000+\"),\r\n    labels = c(\"0~200\", \"200~400\", \"400~600\", \"600~1000\", \"1000+\")\r\n  ) +\r\n  geom_sf(data = gjx) +\r\n  geom_text(\r\n    data = province,\r\n    aes(x = dili_Jd, y = dili_Wd, label = 省市),\r\n    position = \"identity\",\r\n    size = 3,\r\n    check_overlap = TRUE\r\n  ) +\r\n  labs(title = \"中国地图\") +\r\n  theme(\r\n    plot.title = element_text(\r\n      color = \"black\",\r\n      size = 16,\r\n      face = \"bold\",\r\n      vjust = 0.1,\r\n      hjust = 0.5\r\n    ),\r\n    legend.title = element_blank(),\r\n    legend.position = c(0.2, 0.2),\r\n    panel.grid = element_blank(),\r\n    panel.background = element_blank(),\r\n    axis.text = element_blank(),\r\n    axis.ticks = element_blank(),\r\n    axis.title = element_blank()\r\n  ) +\r\n  annotation_north_arrow(\r\n    location = 'tl',\r\n    which_north = 'false',\r\n    style = north_arrow_orienteering()\r\n  )\r\n\r\n\r\n# 读入九段线数据\r\nnine_lines = read_sf('南海.geojson') \r\n\r\n# 绘制九段线小图\r\nnine_map = ggplot() +\r\n  geom_sf(data = CHINA,fill='NA', size=0.5) + \r\n  geom_sf(data = nine_lines,color='black',size=0.5)+\r\n  coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs=\"+proj=laea +lat_0=40 +lon_0=104\")+\r\n  theme(\r\n    aspect.ratio = 1.25, #调节长宽比\r\n    axis.text = element_blank(),\r\n    axis.ticks = element_blank(),\r\n    axis.title = element_blank(),\r\n    panel.grid = element_blank(),\r\n    panel.background = element_blank(),\r\n    panel.border = element_rect(fill=NA,color=\"grey10\",linetype=1,size=0.5),\r\n    plot.margin=unit(c(0,0,0,0),\"mm\"))\r\n\r\n# 使用cowplot包将大图小图拼在一起\r\nfig = ggdraw() +\r\n  draw_plot(fig1) +\r\n  draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)\r\nfig\r\n\r\n\r\ncolour$new_colour <- rep(0,nrow(colour))\r\n##给目标省份赋予不同的数值\r\ncolour$new_colour[which(colour$shengfen==\"重庆\")] <- 1\r\ncolour$new_colour[which(colour$shengfen==\"山西\")] <- 2\r\ncolour$new_colour[which(colour$shengfen==\"新疆\")] <- 3\r\ncolour$new_colour[which(colour$shengfen==\"内蒙古\")] <- 4\r\nCHINA <- dplyr::left_join(China,colour,by= \"QUHUADAIMA\")\r\n\r\nfig1 <-  ggplot() +\r\n  geom_sf(\r\n    data = CHINA,\r\n    aes(fill = factor(new_colour))) +\r\n  scale_fill_manual(\r\n    \"class\",\r\n    values = c(\"#FFFFFF\", \"#79CBC2\", \"#5EA9AC\", \"#4B8793\", \"#3C6777\"),\r\n    breaks = c(\"0\", \"1\", \"2\", \"3\", \"4\"),\r\n    labels = c(\"0\", \"1\", \"2\", \"3\", \"4\")\r\n  )+\r\n  geom_sf(data = gjx) +\r\n  geom_text(\r\n    data = province,\r\n    aes(x = dili_Jd, y = dili_Wd, label = 省市),\r\n    position = \"identity\",\r\n    size = 3,\r\n    check_overlap = TRUE\r\n  ) +\r\n  labs(title = \"中国地图\") +\r\n  theme(\r\n    plot.title = element_text(\r\n      color = \"black\",\r\n      size = 16,\r\n      face = \"bold\",\r\n      vjust = 0.1,\r\n      hjust = 0.5\r\n    ),\r\n    legend.title = element_blank(),\r\n    legend.position = c(0.2, 0.2),\r\n    panel.grid = element_blank(),\r\n    panel.background = element_blank(),\r\n    axis.text = element_blank(),\r\n    axis.ticks = element_blank(),\r\n    axis.title = element_blank()\r\n  ) +\r\n  annotation_north_arrow(\r\n    location = 'tl',\r\n    which_north = 'false',\r\n    style = north_arrow_orienteering()\r\n  )\r\n\r\n\r\nfig = ggdraw() +\r\n  draw_plot(fig1) +\r\n  draw_plot(nine_map, x = 0.8, y = 0, width = 0.13, height = 0.39)\r\nfig\r\n\r\n"
  },
  {
    "path": "2022年/2022.10.12使用 ggplot2 绘制单个和多个省份地图/省份地图.R",
    "content": "\r\nlibrary(rgdal)\r\nlibrary(ggplot2)\r\nlibrary(maptools)\r\nlibrary(mapproj)\r\nlibrary(ggsn)\r\nlibrary(readxl)\r\nlibrary(sf)\r\nshow_data <- read_xlsx(\"测试数据.xlsx\",\"hubei\")\r\nshp_data <- st_read(\"china_shp/CHN_adm2.shp\")\r\n\r\n\r\nshp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)\r\nmy_data <- dplyr::left_join(show_data, shp_data,by = c(\"city\" = \"NL_NAME_2\"))\r\nhead(my_data)\r\n\r\nggplot(data = my_data) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) + \r\n  geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+\r\n  xlab(\"Long (°E)\") + ylab(\"Lat (°N)\") + \r\n  ##更改图形颜色，不加这个语句使用随机配色\r\n  scale_fill_manual(\r\n    \"value\",\r\n    values = c(\"#DFD3F2\", \"#00C2F9\", \"#FFF7D0\"),\r\n    breaks = c(\"A\", \"B\", \"C\"),\r\n    labels = c(\"A\", \"B\", \"C\")\r\n  )+\r\n  theme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\r\n        panel.grid = element_line(color = \"grey\"))\r\n\r\n## 绘制多个省份\r\nhubei <- read_xlsx(\"测试数据.xlsx\",\"hubei\")\r\njiangxi <- read_xlsx(\"测试数据.xlsx\",\"jiangxi\")\r\nall_province <- rbind(hubei,jiangxi)\r\n\r\nshp_data <- st_read(\"china_shp/CHN_adm2.shp\")\r\nshp_data$NL_NAME_2 <- as.character(shp_data$NL_NAME_2)\r\ndata2 <- dplyr::left_join(all_province, shp_data,by = c(\"city\" = \"NL_NAME_2\"))\r\n\r\nggplot(data = data2) + geom_sf(aes(fill = as.factor(value), geometry = `geometry`)) + \r\n  geom_sf_text(aes(label = `city`,geometry = `geometry`), color = 'Black',size=2)+\r\n  xlab(\"Long (°E)\") + ylab(\"Lat (°N)\") + \r\n  ##更改图形颜色，不加这个语句使用随机配色\r\n  scale_fill_manual(\r\n    \"value\",\r\n    values = c(\"#DFD3F2\", \"#00C2F9\", \"#FFF7D0\"),\r\n    breaks = c(\"A\", \"B\", \"C\"),\r\n    labels = c(\"A\", \"B\", \"C\")\r\n  )+\r\n  theme(legend.position = \"top\",panel.background = element_rect(fill = \"white\",color = \"black\"),\r\n        panel.grid = element_line(color = \"grey\"))\r\n"
  },
  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rhistory",
    "content": "data(mtcars)\ncorr <- round(cor(mtcars), 1)\n# Plot\nggcorrplot(corr, hc.order = TRUE,\ntype = \"lower\",\nlab = TRUE,\nlab_size = 3,\nmethod=\"circle\",\ncolors = c(\"tomato2\", \"white\", \"springgreen3\"),\ntitle=\"Correlogram of mtcars\",\nggtheme=theme_bw)\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\nscale_fill_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\nlabs(subtitle=\"Normalised mileage from 'mtcars'\",\ntitle= \"Diverging Bars\") +\ncoord_flip()\nggplot(cty_mpg, aes(x=make, y=mileage)) +\ngeom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") +\nlabs(title=\"Ordered Bar Chart\",\nsubtitle=\"Make Vs Avg. Mileage\",\ncaption=\"source: mpg\") +\ntheme(axis.text.x = element_text(angle=65, vjust=0.6))\n# Chunk 1: setup\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n# Chunk 2\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # 关掉像1e+48这样的科学符号\ndata(\"midwest\", package = \"ggplot2\") #加载数据集\n# Chunk 3\n# 颜色设置（灰色系列）\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n\"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n# 颜色设置（黑色系列）\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n\"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\nggplot <- function(...) ggplot2::ggplot(...) +\nscale_color_manual(values = cbp1) +\nscale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写\ntheme_bw()\n# Chunk 4\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) +\ngeom_point(aes(col=state, size=popdensity)) +\ngeom_smooth(method=\"loess\", se=F) +\nxlim(c(0, 0.1)) +\nylim(c(0, 500000)) +\nlabs(subtitle=\"Area Vs Population\",\ny=\"Population\",\nx=\"Area\",\ntitle=\"Scatterplot\",\ncaption = \"Source: midwest\")\nplot(gg)\n# Chunk 5\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 &\nmidwest$poptotal <= 500000 &\nmidwest$area > 0.01 &\nmidwest$area < 0.1, ]\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) +\ngeom_point(aes(col=state, size=popdensity)) +   # draw points\ngeom_smooth(method=\"loess\", se=F) +\nxlim(c(0, 0.1)) +\nylim(c(0, 500000)) +   # draw smoothing line\ngeom_encircle(aes(x=area, y=poptotal),\ndata=midwest_select,\ncolor=\"red\",\nsize=2,\nexpand=0.08) +   # encircle\nlabs(subtitle=\"Area Vs Population\",\ny=\"Population\",\nx=\"Area\",\ntitle=\"Scatterplot + Encircle\",\ncaption=\"Source: midwest\")\n# Chunk 6\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\nlabs(subtitle=\"mpg: city vs highway mileage\",\ny=\"hwy\",\nx=\"cty\",\ntitle=\"Jittered Points\")\n# Chunk 7\ng + geom_count(col=\"tomato3\", show.legend=F) +\nlabs(subtitle=\"mpg: city vs highway mileage\",\ny=\"hwy\",\nx=\"cty\",\ntitle=\"Counts Plot\")\n# Chunk 8\ndata(mpg, package=\"ggplot2\")\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) +\nlabs(subtitle=\"mpg: Displacement vs City Mileage\",\ntitle=\"Bubble chart\")\ng + geom_jitter(aes(col=manufacturer, size=hwy)) +\ngeom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n# Chunk 9\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) +\ngeom_count() +\ngeom_smooth(method=\"lm\", se=F)\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n# Chunk 10\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n# Chunk 11\nggMarginal(g, type = \"density\", fill=\"transparent\")\n# Chunk 12\nlibrary(ggcorrplot)\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n# Plot\nggcorrplot(corr, hc.order = TRUE,\ntype = \"lower\",\nlab = TRUE,\nlab_size = 3,\nmethod=\"circle\",\ncolors = c(\"tomato2\", \"white\", \"springgreen3\"),\ntitle=\"Correlogram of mtcars\",\nggtheme=theme_bw)\n# Chunk 13\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\nscale_fill_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\nlabs(subtitle=\"Normalised mileage from 'mtcars'\",\ntitle= \"Diverging Bars\") +\ncoord_flip()\n# Chunk 14\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', fill=\"black\", size=6)  +\ngeom_segment(aes(y = 0,\nx = `car name`,\nyend = mpg_z,\nxend = `car name`),\ncolor = \"black\") +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Lollipop Chart\",\nsubtitle=\"Normalized mileage from 'mtcars': Lollipop\") +\nylim(-2.5, 2.5) +\ncoord_flip()\n# Chunk 15\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', aes(col=mpg_type), size=6)  +\nscale_color_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Dot Plot\",\nsubtitle=\"Normalized mileage from 'mtcars': Dotplot\") +\nylim(-2.5, 2.5) +\ncoord_flip()\n# Chunk 16\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) +\ngeom_area() +\nscale_x_date(breaks=brks, labels=lbls) +\ntheme(axis.text.x = element_text(angle=90)) +\nlabs(title=\"Area Chart\",\nsubtitle = \"Perc Returns for Personal Savings\",\ny=\"% Returns for Personal savings\",\ncaption=\"Source: economics\")\n# Chunk 17\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\nggplot(cty_mpg, aes(x=make, y=mileage)) +\ngeom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") +\nlabs(title=\"Ordered Bar Chart\",\nsubtitle=\"Make Vs Avg. Mileage\",\ncaption=\"source: mpg\") +\ntheme(axis.text.x = element_text(angle=65, vjust=0.6))\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', aes(col=mpg_type), size=6)  +\nscale_color_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Dot Plot\",\nsubtitle=\"Normalized mileage from 'mtcars': Dotplot\") +\nylim(-2.5, 2.5) +\ncoord_flip()\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', fill=\"black\", size=6)  +\ngeom_segment(aes(y = 0,\nx = `car name`,\nyend = mpg_z,\nxend = `car name`),\ncolor = \"black\") +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Lollipop Chart\",\nsubtitle=\"Normalized mileage from 'mtcars': Lollipop\") +\nylim(-2.5, 2.5) +\ncoord_flip()\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', aes(col=mpg_type), size=6)  +\nscale_color_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Dot Plot\",\nsubtitle=\"Normalized mileage from 'mtcars': Dotplot\") +\nylim(-2.5, 2.5) +\ncoord_flip()\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) +\ngeom_area() +\nscale_x_date(breaks=brks, labels=lbls) +\ntheme(axis.text.x = element_text(angle=90)) +\nlabs(title=\"Area Chart\",\nsubtitle = \"Perc Returns for Personal Savings\",\ny=\"% Returns for Personal savings\",\ncaption=\"Source: economics\")\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\nhead(df)\nlibrary(scales)\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\nhead(df)\ndf\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\nhead(df)\n# Chunk 1: setup\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n# Chunk 2\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # 关掉像1e+48这样的科学符号\ndata(\"midwest\", package = \"ggplot2\") #加载数据集\n# Chunk 3\n# 颜色设置（灰色系列）\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n\"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n# 颜色设置（黑色系列）\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n\"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\nggplot <- function(...) ggplot2::ggplot(...) +\nscale_color_manual(values = cbp1) +\nscale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写\ntheme_bw()\n# Chunk 4\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) +\ngeom_point(aes(col=state, size=popdensity)) +\ngeom_smooth(method=\"loess\", se=F) +\nxlim(c(0, 0.1)) +\nylim(c(0, 500000)) +\nlabs(subtitle=\"Area Vs Population\",\ny=\"Population\",\nx=\"Area\",\ntitle=\"Scatterplot\",\ncaption = \"Source: midwest\")\nplot(gg)\n# Chunk 5\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 &\nmidwest$poptotal <= 500000 &\nmidwest$area > 0.01 &\nmidwest$area < 0.1, ]\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) +\ngeom_point(aes(col=state, size=popdensity)) +   # draw points\ngeom_smooth(method=\"loess\", se=F) +\nxlim(c(0, 0.1)) +\nylim(c(0, 500000)) +   # draw smoothing line\ngeom_encircle(aes(x=area, y=poptotal),\ndata=midwest_select,\ncolor=\"red\",\nsize=2,\nexpand=0.08) +   # encircle\nlabs(subtitle=\"Area Vs Population\",\ny=\"Population\",\nx=\"Area\",\ntitle=\"Scatterplot + Encircle\",\ncaption=\"Source: midwest\")\n# Chunk 6\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\nlabs(subtitle=\"mpg: city vs highway mileage\",\ny=\"hwy\",\nx=\"cty\",\ntitle=\"Jittered Points\")\n# Chunk 7\ng + geom_count(col=\"tomato3\", show.legend=F) +\nlabs(subtitle=\"mpg: city vs highway mileage\",\ny=\"hwy\",\nx=\"cty\",\ntitle=\"Counts Plot\")\n# Chunk 8\ndata(mpg, package=\"ggplot2\")\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) +\nlabs(subtitle=\"mpg: Displacement vs City Mileage\",\ntitle=\"Bubble chart\")\ng + geom_jitter(aes(col=manufacturer, size=hwy)) +\ngeom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n# Chunk 9\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) +\ngeom_count() +\ngeom_smooth(method=\"lm\", se=F)\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n# Chunk 10\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n# Chunk 11\nggMarginal(g, type = \"density\", fill=\"transparent\")\n# Chunk 12\nlibrary(ggcorrplot)\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n# Plot\nggcorrplot(corr, hc.order = TRUE,\ntype = \"lower\",\nlab = TRUE,\nlab_size = 3,\nmethod=\"circle\",\ncolors = c(\"tomato2\", \"white\", \"springgreen3\"),\ntitle=\"Correlogram of mtcars\",\nggtheme=theme_bw)\n# Chunk 13\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\nscale_fill_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\nlabs(subtitle=\"Normalised mileage from 'mtcars'\",\ntitle= \"Diverging Bars\") +\ncoord_flip()\n# Chunk 14\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', fill=\"black\", size=6)  +\ngeom_segment(aes(y = 0,\nx = `car name`,\nyend = mpg_z,\nxend = `car name`),\ncolor = \"black\") +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Lollipop Chart\",\nsubtitle=\"Normalized mileage from 'mtcars': Lollipop\") +\nylim(-2.5, 2.5) +\ncoord_flip()\n# Chunk 15\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +\ngeom_point(stat='identity', aes(col=mpg_type), size=6)  +\nscale_color_manual(name=\"Mileage\",\nlabels = c(\"Above Average\", \"Below Average\"),\nvalues = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) +\ngeom_text(color=\"white\", size=2) +\nlabs(title=\"Diverging Dot Plot\",\nsubtitle=\"Normalized mileage from 'mtcars': Dotplot\") +\nylim(-2.5, 2.5) +\ncoord_flip()\n# Chunk 16\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) +\ngeom_area() +\nscale_x_date(breaks=brks, labels=lbls) +\ntheme(axis.text.x = element_text(angle=90)) +\nlabs(title=\"Area Chart\",\nsubtitle = \"Perc Returns for Personal Savings\",\ny=\"% Returns for Personal savings\",\ncaption=\"Source: economics\")\n# Chunk 17\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n# Chunk 18\nggplot(cty_mpg, aes(x=make, y=mileage)) +\ngeom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") +\nlabs(title=\"Ordered Bar Chart\",\nsubtitle=\"Make Vs Avg. Mileage\",\ncaption=\"source: mpg\") +\ntheme(axis.text.x = element_text(angle=65, vjust=0.6))\n# Chunk 19\nggplot(cty_mpg, aes(x=make, y=mileage)) +\ngeom_point(size=3) +\ngeom_segment(aes(x=make,\nxend=make,\ny=0,\nyend=mileage)) +\nlabs(title=\"Lollipop Chart\",\nsubtitle=\"Make Vs Avg. Mileage\",\ncaption=\"source: mpg\") +\ntheme(axis.text.x = element_text(angle=65, vjust=0.6))\n# Chunk 20\nggplot(cty_mpg, aes(x=make, y=mileage)) +\ngeom_point(col=\"tomato2\", size=3) +   # Draw points\ngeom_segment(aes(x=make,\nxend=make,\ny=min(mileage),\nyend=max(mileage)),\nlinetype=\"dashed\",\nsize=0.1) +   # Draw dashed lines\nlabs(title=\"Dot Plot\",\nsubtitle=\"Make Vs Avg. Mileage\",\ncaption=\"source: mpg\") +\ncoord_flip()\n# Chunk 21\nlibrary(scales)\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n"
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    "content": "---\ntitle: \"ggplot绘图手册\"\nauthor: \"赵子茜\"\ndate: \"`r Sys.Date()`\"\noutput: \n  prettydoc::html_pretty:\n    toc: true\n    toc_depth: 4\n    theme: cayman\n    highlight: github\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n```\n\n## 全局主题设置\n```{r}\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # turn-off scientific notation like 1e+48\n# theme_set(theme_bw())  # pre-set the bw theme.\ndata(\"midwest\", package = \"ggplot2\")\nmidwest <- read.csv(\"http://goo.gl/G1K41K\")  # bkup data source\n# write.csv(midwest,\"midwest.csv\")\n\n```\n\n\n\n\n\n```{r}\n# The palette with grey:\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# The palette with black:\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales\n  theme_bw()\n\n```\n\n\n\n\n\n## 1. 散点图\n### 1.1 两个变量\n展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。\n\n\n```{r}\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\nplot(gg)\n\n```\n\n\n### 1.2 环绕式散点图\n使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 & \n                            midwest$poptotal <= 500000 & \n                            midwest$area > 0.01 & \n                            midwest$area < 0.1, ]\n\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) +   # draw points\n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) +   # draw smoothing line\n  geom_encircle(aes(x=area, y=poptotal), \n                data=midwest_select, \n                color=\"red\", \n                size=2, \n                expand=0.08) +   # encircle\n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot + Encircle\", \n       caption=\"Source: midwest\")\n```\n\n\n\n\n### 1.3 抖动图\n\n抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。\n```{r}\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Jittered Points\")\n\n```\n\n### 1.4 数值图\n\n克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。\n\n```{r}\ng + geom_count(col=\"tomato3\", show.legend=F) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Counts Plot\")\n```\n\n### 1.5 气泡图\n气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。\n```{r}\ndata(mpg, package=\"ggplot2\")\n\n\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) + \n  labs(subtitle=\"mpg: Displacement vs City Mileage\",\n       title=\"Bubble chart\")\n\ng + geom_jitter(aes(col=manufacturer, size=hwy)) + \n  geom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n```\n\n\n<!-- ### 1.6 动态气泡图 -->\n\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year),  -->\n<!--               method = \"lm\",  -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n\n\n### 1.6 边际直方图/箱线图\n\n如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。\n\n```{r}\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) + \n  geom_count() + \n  geom_smooth(method=\"lm\", se=F)\n\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n\n```\n```{r}\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n\n```\n\n```{r}\nggMarginal(g, type = \"density\", fill=\"transparent\")\n```\n\n### 1.7 相关系数图\n\n相关系数图可以查看同一组数据中多个连续变量的相关性。\n```{r}\nlibrary(ggcorrplot)\n\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n\n# Plot\nggcorrplot(corr, hc.order = TRUE, \n           type = \"lower\", \n           lab = TRUE, \n           lab_size = 3, \n           method=\"circle\", \n           colors = c(\"tomato2\", \"white\", \"springgreen3\"), \n           title=\"Correlogram of mtcars\", \n           ggtheme=theme_bw)\n```\n\n## 2 偏差\n### 2.1 发散条形图\n发散条形图是一种可以同时处理负值和正值的条形图。这可以通过`geom_bar()`来实现。但是`geom_bar()`的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()`中`stat`参数的默认值为`count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：\n\n- 设置`stat=identity`;\n- 在`aes()`中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。\n\n为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars`数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。\n```{r}\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)  # convert to factor to retain sorted order in plot.\n\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\n  scale_fill_manual(name=\"Mileage\", \n                    labels = c(\"Above Average\", \"Below Average\"), \n                    values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  labs(subtitle=\"Normalised mileage from 'mtcars'\", \n       title= \"Diverging Bars\") + \n  coord_flip()\n```\n\n\n### 2.2 带标记的发散型棒棒糖图\n这个图形是发散条形图的一个变体。\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', fill=\"black\", size=6)  +\n  geom_segment(aes(y = 0, \n                   x = `car name`, \n                   yend = mpg_z, \n                   xend = `car name`), \n               color = \"black\") +\n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Lollipop Chart\", \n       subtitle=\"Normalized mileage from 'mtcars': Lollipop\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.3 发散性点图\n\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', aes(col=mpg_type), size=6)  +\n  scale_color_manual(name=\"Mileage\", \n                     labels = c(\"Above Average\", \"Below Average\"), \n                     values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Dot Plot\", \n       subtitle=\"Normalized mileage from 'mtcars': Dotplot\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.4 面积图\n面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。\n```{r}\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) + \n  geom_area() + \n  scale_x_date(breaks=brks, labels=lbls) + \n  theme(axis.text.x = element_text(angle=90)) + \n  labs(title=\"Area Chart\", \n       subtitle = \"Perc Returns for Personal Savings\", \n       y=\"% Returns for Personal savings\", \n       caption=\"Source: economics\")\n\n```\n\n## 3 排序\n### 3.1 有序条形图\n有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。\n```{r}\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n\n```\n\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") + \n  labs(title=\"Ordered Bar Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.2 棒棒糖图\n棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(size=3) + \n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=0, \n                   yend=mileage)) + \n  labs(title=\"Lollipop Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.3 点图\n点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(col=\"tomato2\", size=3) +   # Draw points\n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=min(mileage), \n                   yend=max(mileage)), \n               linetype=\"dashed\", \n               size=0.1) +   # Draw dashed lines\n  labs(title=\"Dot Plot\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") +  \n  coord_flip()\n```\n\n### 3.4 坡度图\n坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。\n```{r}\nlibrary(scales)\n\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\n```\n\n```{r}\nlibrary(ggplot2)\np <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + \n                  geom_vline(xintercept=1, linetype=\"dashed\", size=.1) + \n                  geom_vline(xintercept=2, linetype=\"dashed\", size=.1) +\n                  scale_color_manual(labels = c(\"Up\", \"Down\"), \n                                     values = c(\"green\"=\"#00ba38\", \"red\"=\"#f8766d\")) +  # color of lines\n                  labs(x=\"\", y=\"Mean GdpPerCap\") +  # Axis labels\n                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits\n\n# Add texts\np <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)\np <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)\np <- p + geom_text(label=\"Time 1\", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title\np <- p + geom_text(label=\"Time 2\", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title\n\n\n#设置主题\np + theme(panel.background = element_blank(),\n           panel.grid = element_blank(),\n           axis.ticks = element_blank(),\n           axis.text.x = element_blank(),\n           panel.border = element_blank(),\n           plot.margin = unit(c(1,2,1,2), \"cm\"))\n\n\n```\n\n### 3.5 哑铃图\n哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。\n```{r}\nhealth <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/health.csv\")\nhealth$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells\n\n# health$Area <- factor(health$Area)\ngg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + \n        geom_dumbbell(color=\"#a3c4dc\", \n                      size=0.75, \n                      point.colour.l=\"#0e668b\") + \n        scale_x_continuous(label=percent) + \n        labs(x=NULL, \n             y=NULL, \n             title=\"Dumbbell Chart\", \n             subtitle=\"Pct Change: 2013 vs 2014\", \n             caption=\"Source: https://github.com/hrbrmstr/ggalt\") +\n        theme(plot.title = element_text(hjust=0.5, face=\"bold\"),\n              plot.background=element_rect(fill=\"#f7f7f7\"),\n              panel.background=element_rect(fill=\"#f7f7f7\"),\n              panel.grid.minor=element_blank(),\n              panel.grid.major.y=element_blank(),\n              panel.grid.major.x=element_line(),\n              axis.ticks=element_blank(),\n              legend.position=\"top\",\n              panel.border=element_blank())\n\nplot(gg)\n\n```\n\n## 4 分布\n### 4.1 直方图\n#### 4.4.1 连续变量的直方图\n连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。\n```{r}\n# 连续（数值）变量的直方图\ng <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = \"Spectral\")\n\ng + geom_histogram(aes(fill=class), \n                   binwidth = .1, \n                   col=\"black\", \n                   size=.1) +  # change binwidth\n  labs(title=\"Histogram with Auto Binning\", \n       subtitle=\"Engine Displacement across Vehicle Classes\")\n\n \n```\n\n\n```{r}\n\ng + geom_histogram(aes(fill=class), \n                   bins=5, \n                   col=\"black\", \n                   size=.1) +   # change number of bins\n  labs(title=\"Histogram with Fixed Bins\", \n       subtitle=\"Engine Displacement across Vehicle Classes\") \n\n \n```\n\n#### 4.1.2分类变量的直方图\n分类变量的直方图实际上是根据每个类别的频率绘制的条形图。\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Histogram on a Categorical variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Histogram on Categorical Variable\", \n       subtitle=\"Manufacturer across Vehicle Classes\")\n\n \n```\n\n### 4.2 密度图\n\n```{r}\ng <- ggplot(mpg, aes(cty))\ng + geom_density(aes(fill=factor(cyl)), alpha=0.8) + \n    labs(title=\"Density plot\", \n         subtitle=\"City Mileage Grouped by Number of cylinders\",\n         caption=\"Source: mpg\",\n         x=\"City Mileage\",\n         fill=\"# Cylinders\")\n \n```\n\n### 4.3 箱线图\n箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(varwidth=T, fill=\"plum\") + \n    labs(title=\"Box plot\", \n         subtitle=\"City Mileage grouped by Class of vehicle\",\n         caption=\"Source: mpg\",\n         x=\"Class of Vehicle\",\n         y=\"City Mileage\")\n \n```\n\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(aes(fill=factor(cyl))) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot\", \n       subtitle=\"City Mileage grouped by Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.4 点图+箱线图\n在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。\n```{r}\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_boxplot() + \n  geom_dotplot(binaxis='y', \n               stackdir='center', \n               dotsize = .5, \n               fill=\"red\") +\n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot + Dot plot\", \n       subtitle=\"City Mileage vs Class: Each dot represents 1 row in source data\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.5 塔夫特箱线图\n这是一个简化版的箱线图。\n```{r}\n\nlibrary(ggthemes)\nlibrary(ggplot2)\ntheme_set(theme_tufte())  # from ggthemes\n\n# plot\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_tufteboxplot() + \n      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n      labs(title=\"Tufte Styled Boxplot\", \n           subtitle=\"City Mileage grouped by Class of vehicle\",\n           caption=\"Source: mpg\",\n           x=\"Class of Vehicle\",\n           y=\"City Mileage\")\n\n\n```\n\n### 4.6 小提琴图\n小提琴图类似于箱线图，但显示了组内的密度。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_violin() + \n  labs(title=\"Violin plot\", \n       subtitle=\"City Mileage vs Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.7 金字塔图\n金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。\n```{r}\nlibrary(ggplot2)\nlibrary(ggthemes)\noptions(scipen = 999)  # turns of scientific notations like 1e+40\n\n# Read data\nemail_campaign_funnel <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n\n# X Axis Breaks and Labels \nbrks <- seq(-15000000, 15000000, 5000000)\nlbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), \"m\")\n\n# Plot\nggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column\n                              geom_bar(stat = \"identity\", width = .6) +   # draw the bars\n                              scale_y_continuous(breaks = brks,   # Breaks\n                                                 labels = lbls) + # Labels\n                              coord_flip() +  # Flip axes\n                              labs(title=\"Email Campaign Funnel\") +\n                              theme_tufte() +  # Tufte theme from ggfortify\n                              theme(plot.title = element_text(hjust = .5), \n                                    axis.ticks = element_blank()) +   # Centre plot title\n                              scale_fill_brewer(palette = \"Dark2\")  # Color palette\n```\n\n## 5 组成\n### 5.1 华夫饼图\n华夫图可以显示总体的组成成分。\n```{r}\nvar <- mpg$class  # the categorical data \n\n## Prep data (nothing to change here)\nnrows <- 10\ndf <- expand.grid(y = 1:nrows, x = 1:nrows)\ncateg_table <- round(table(var) * ((nrows*nrows)/(length(var))))\n\n\ndf$category <- factor(rep(names(categ_table), categ_table))  \n# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.\n\n## Plot\nggplot(df, aes(x = x, y = y, fill = category)) + \n        geom_tile(color = \"black\", size = 0.5) +\n        scale_x_continuous(expand = c(0, 0)) +\n        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +\n        scale_fill_brewer(palette = \"Set3\") +\n        labs(title=\"Waffle Chart\", subtitle=\"'Class' of vehicles\",\n             caption=\"Source: mpg\") + \n        theme(panel.border = element_rect(size = 2),\n              plot.title = element_text(size = rel(1.2)),\n              axis.text = element_blank(),\n              axis.title = element_blank(),\n              axis.ticks = element_blank(),\n              legend.title = element_blank(),\n              legend.position = \"right\")\n\n```\n\n### 5.2 饼图\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Source: Frequency table\ndf <- as.data.frame(table(mpg$class))\ncolnames(df) <- c(\"class\", \"freq\")\npie <- ggplot(df, aes(x = \"\", y=freq, fill = factor(class))) + \n  geom_bar(width = 1, stat = \"identity\") +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n\npie + coord_polar(theta = \"y\", start=0)\n\n# Source: Categorical variable.\n# mpg$class\npie <- ggplot(mpg, aes(x = \"\", fill = factor(class))) + \n  geom_bar(width = 1) +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n  \npie + coord_polar(theta = \"y\", start=0)\n```\n\n\n\n### 5.3 条形图\n```{r}\n #prep frequency table\nfreqtable <- table(mpg$manufacturer)\ndf <- as.data.frame.table(freqtable)\n\n```\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Plot\ng <- ggplot(df, aes(Var1, Freq))\ng + geom_bar(stat=\"identity\", width = 0.5, fill=\"tomato2\") + \n      labs(title=\"Bar Chart\", \n           subtitle=\"Manufacturer of vehicles\", \n           caption=\"Source: Frequency of Manufacturers from 'mpg' dataset\") +\n      theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n```{r}\n# From on a categorical column variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +\n  labs(title=\"Categorywise Bar Chart\", \n       subtitle=\"Manufacturer of vehicles\", \n       caption=\"Source: Manufacturers from 'mpg' dataset\")\n```\n\n\n\n## 6 变化趋势\n### 6.1 时间序列图：基于时间序列对象（ts）\n`ggfortify`包可以对时间序列直接绘图。\n```{r}\n## From Timeseries object (ts)\nlibrary(ggplot2)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Plot \nautoplot(AirPassengers) + \n  labs(title=\"AirPassengers\") + \n  theme(plot.title = element_text(hjust=0.5))\n```\n\n### 6.2 时间序列图：基于数据框\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# 使用默认的时间跨度\nggplot(economics, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Time Series Chart\", \n       subtitle=\"Returns Percentage from 'Economics' Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\")\n\n```\n\n如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_m <- economics[1:24, ]\n\n# 设定时间跨度为一个月\nlbls <- paste0(month.abb[month(economics_m$date)], \" \", lubridate::year(economics_m$date))\nbrks <- economics_m$date\n\n# plot\nggplot(economics_m, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Monthly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n设置时间跨度为 1 年：\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_y <- economics[1:90, ]\n\n# labels and breaks for X axis text\nbrks <- economics_y$date[seq(1, length(economics_y$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(economics_y, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Yearly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n### 6.3 多个时间序列\n在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。\n\n```{r}\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\n```\n\n在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics_long[economics_long$variable %in% c(\"psavert\", \"uempmed\"), ]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=value, col=variable)) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn from Long Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\", \n       color=NULL) +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(labels = c(\"psavert\", \"uempmed\"), \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=psavert, col=\"psavert\")) + \n  geom_line(aes(y=uempmed, col=\"uempmed\")) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn From Wide Data format\", \n       caption=\"Source: Economics\", y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(name=\"\", \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n\n### 6.4 堆叠面积图\n堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：\n\n- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；\n- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。\n- 希望展示各个类别的贡献。\n\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_area(aes(y=psavert+uempmed, fill=\"psavert\")) + \n  geom_area(aes(y=uempmed, fill=\"uempmed\")) + \n  labs(title=\"Area Chart of Returns Percentage\", \n       subtitle=\"From Wide Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_fill_manual(name=\"\", \n                    values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n### 6.5 日历热力图\n\n当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。\n```{r}\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\n\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n```\n\n```{r}\nggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + \n  geom_tile(colour = \"white\") + \n  facet_grid(year~monthf) + \n  scale_fill_gradient(low=\"red\", high=\"green\") +\n  labs(x=\"Week of Month\",\n       y=\"\",\n       title = \"Time-Series Calendar Heatmap\", \n       subtitle=\"Yahoo Closing Price\", \n       fill=\"Close\")\n```\n\n### 6.6 坡度图\n坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。\n```{r}\nlibrary(dplyr)\ntheme_set(theme_classic())\nsource_df <- read.csv(\"https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv\")\n\n# Define functions. Source: https://github.com/jkeirstead/r-slopegraph\ntufte_sort <- function(df, x=\"year\", y=\"value\", group=\"group\", method=\"tufte\", min.space=0.05) {\n    ## First rename the columns for consistency\n    ids <- match(c(x, y, group), names(df))\n    df <- df[,ids]\n    names(df) <- c(\"x\", \"y\", \"group\")\n\n    ## Expand grid to ensure every combination has a defined value\n    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))\n    tmp <- merge(df, tmp, all.y=TRUE)\n    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))\n  \n    ## Cast into a matrix shape and arrange by first column\n    require(reshape2)\n    tmp <- dcast(df, group ~ x, value.var=\"y\")\n    ord <- order(tmp[,2])\n    tmp <- tmp[ord,]\n    \n    min.space <- min.space*diff(range(tmp[,-1]))\n    yshift <- numeric(nrow(tmp))\n    ## Start at \"bottom\" row\n    ## Repeat for rest of the rows until you hit the top\n    for (i in 2:nrow(tmp)) {\n        ## Shift subsequent row up by equal space so gap between\n        ## two entries is >= minimum\n        mat <- as.matrix(tmp[(i-1):i, -1])\n        d.min <- min(diff(mat))\n        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)\n    }\n\n    \n    tmp <- cbind(tmp, yshift=cumsum(yshift))\n\n    scale <- 1\n    tmp <- melt(tmp, id=c(\"group\", \"yshift\"), variable.name=\"x\", value.name=\"y\")\n    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y\n\n    tmp <- transform(tmp, ypos=y + scale*yshift)\n    return(tmp)\n   \n}\n\nplot_slopegraph <- function(df) {\n    ylabs <- subset(df, x==head(x,1))$group\n    yvals <- subset(df, x==head(x,1))$ypos\n    fontSize <- 3\n    gg <- ggplot(df,aes(x=x,y=ypos)) +\n        geom_line(aes(group=group),colour=\"grey80\") +\n        geom_point(colour=\"white\",size=8) +\n        geom_text(aes(label=y), size=fontSize, family=\"American Typewriter\") +\n        scale_y_continuous(name=\"\", breaks=yvals, labels=ylabs)\n    return(gg)\n}    \n\n## Prepare data    \ndf <- tufte_sort(source_df, \n                 x=\"year\", \n                 y=\"value\", \n                 group=\"group\", \n                 method=\"tufte\", \n                 min.space=0.05)\n\ndf <- transform(df, \n                x=factor(x, levels=c(5,10,15,20), \n                            labels=c(\"5 years\",\"10 years\",\"15 years\",\"20 years\")), \n                y=round(y))\n\n## Plot\nplot_slopegraph(df) + labs(title=\"Estimates of % survival rates\") + \n                      theme(axis.title=element_blank(),\n                            axis.ticks = element_blank(),\n                            plot.title = element_text(hjust=0.5,\n                                                      family = \"American Typewriter\",\n                                                      face=\"bold\"),\n                            axis.text = element_text(family = \"American Typewriter\",\n                                                     face=\"bold\"))\n```\n\n### 6.7 季节图\n如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。\n```{r}\nlibrary(ggplot2)\nlibrary(forecast)\ntheme_set(theme_classic())\n\n# Subset data\nnottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow\n\n# Plot\nggseasonplot(AirPassengers) + labs(title=\"Seasonal plot: International Airline Passengers\")\n\n```\n\n```{r}\nggseasonplot(nottem_small) + labs(title=\"Seasonal plot: Air temperatures at Nottingham Castle\")\n```\n\n## 7 群体\n### 7.1 谱系图\n```{r}\n\nlibrary(ggplot2)\nlibrary(ggdendro)\ntheme_set(theme_bw())\n\nhc <- hclust(dist(USArrests), \"ave\")  # hierarchical clustering\n\n# plot\nggdendrogram(hc, rotate = TRUE, size = 2)\n```\n\n### 7.2 聚类图\n可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。\n```{r}\n# devtools::install_github(\"hrbrmstr/ggalt\")\nlibrary(ggplot2)\nlibrary(ggalt)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Compute data with principal components ------------------\ndf <- iris[c(1, 2, 3, 4)]\npca_mod <- prcomp(df)  # compute principal components\n\n# Data frame of principal components ----------------------\ndf_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components\ndf_pc_vir <- df_pc[df_pc$Species == \"virginica\", ]  # df for 'virginica'\ndf_pc_set <- df_pc[df_pc$Species == \"setosa\", ]  # df for 'setosa'\ndf_pc_ver <- df_pc[df_pc$Species == \"versicolor\", ]  # df for 'versicolor'\n \n# Plot ----------------------------------------------------\nggplot(df_pc, aes(PC1, PC2, col=Species)) + \n  geom_point(aes(shape=Species), size=2) +   # draw points\n  labs(title=\"Iris Clustering\", \n       subtitle=\"With principal components PC1 and PC2 as X and Y axis\",\n       caption=\"Source: Iris\") + \n  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), \n                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits\n  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles\n  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + \n  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))\n```\n\n"
  },
  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/161F88A0/sources/per/t/CD5422DD-contents 2",
    "content": "---\ntitle: \"ggplot绘图手册\"\nauthor: \"赵子茜\"\ndate: \"`r Sys.Date()`\"\noutput: \n  prettydoc::html_pretty:\n    toc: true\n    toc_depth: 4\n    theme: cayman\n    highlight: github\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n```\n\n## 全局主题设置\n```{r}\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # turn-off scientific notation like 1e+48\n# theme_set(theme_bw())  # pre-set the bw theme.\ndata(\"midwest\", package = \"ggplot2\")\nmidwest <- read.csv(\"http://goo.gl/G1K41K\")  # bkup data source\n# write.csv(midwest,\"midwest.csv\")\n\n```\n\n\n\n\n\n```{r}\n# The palette with grey:\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# The palette with black:\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales\n  theme_bw()\n\n```\n\n\n\n\n\n## 1. 散点图\n### 1.1 两个变量\n展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。\n\n\n```{r}\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\nplot(gg)\n\n```\n\n\n### 1.2 环绕式散点图\n使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 & \n                            midwest$poptotal <= 500000 & \n                            midwest$area > 0.01 & \n                            midwest$area < 0.1, ]\n\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) +   # draw points\n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) +   # draw smoothing line\n  geom_encircle(aes(x=area, y=poptotal), \n                data=midwest_select, \n                color=\"red\", \n                size=2, \n                expand=0.08) +   # encircle\n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot + Encircle\", \n       caption=\"Source: midwest\")\n```\n\n\n\n\n### 1.3 抖动图\n\n抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。\n```{r}\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Jittered Points\")\n\n```\n\n### 1.4 数值图\n\n克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。\n\n```{r}\ng + geom_count(col=\"tomato3\", show.legend=F) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Counts Plot\")\n```\n\n### 1.5 气泡图\n气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。\n```{r}\ndata(mpg, package=\"ggplot2\")\n\n\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) + \n  labs(subtitle=\"mpg: Displacement vs City Mileage\",\n       title=\"Bubble chart\")\n\ng + geom_jitter(aes(col=manufacturer, size=hwy)) + \n  geom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n```\n\n\n<!-- ### 1.6 动态气泡图 -->\n\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year),  -->\n<!--               method = \"lm\",  -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n\n\n### 1.6 边际直方图/箱线图\n\n如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。\n\n```{r}\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) + \n  geom_count() + \n  geom_smooth(method=\"lm\", se=F)\n\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n\n```\n```{r}\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n\n```\n\n```{r}\nggMarginal(g, type = \"density\", fill=\"transparent\")\n```\n\n### 1.7 相关系数图\n\n相关系数图可以查看同一组数据中多个连续变量的相关性。\n```{r}\nlibrary(ggcorrplot)\n\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n\n# Plot\nggcorrplot(corr, hc.order = TRUE, \n           type = \"lower\", \n           lab = TRUE, \n           lab_size = 3, \n           method=\"circle\", \n           colors = c(\"tomato2\", \"white\", \"springgreen3\"), \n           title=\"Correlogram of mtcars\", \n           ggtheme=theme_bw)\n```\n\n## 2 偏差\n### 2.1 发散条形图\n发散条形图是一种可以同时处理负值和正值的条形图。这可以通过`geom_bar()`来实现。但是`geom_bar()`的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()`中`stat`参数的默认值为`count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：\n\n- 设置`stat=identity`;\n- 在`aes()`中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。\n\n为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars`数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。\n```{r}\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`)  # convert to factor to retain sorted order in plot.\n\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\n  scale_fill_manual(name=\"Mileage\", \n                    labels = c(\"Above Average\", \"Below Average\"), \n                    values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  labs(subtitle=\"Normalised mileage from 'mtcars'\", \n       title= \"Diverging Bars\") + \n  coord_flip()\n```\n\n\n### 2.2 带标记的发散型棒棒糖图\n这个图形是发散条形图的一个变体。\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', fill=\"black\", size=6)  +\n  geom_segment(aes(y = 0, \n                   x = `car name`, \n                   yend = mpg_z, \n                   xend = `car name`), \n               color = \"black\") +\n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Lollipop Chart\", \n       subtitle=\"Normalized mileage from 'mtcars': Lollipop\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.3 发散性点图\n\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', aes(col=mpg_type), size=6)  +\n  scale_color_manual(name=\"Mileage\", \n                     labels = c(\"Above Average\", \"Below Average\"), \n                     values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Dot Plot\", \n       subtitle=\"Normalized mileage from 'mtcars': Dotplot\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.4 面积图\n面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。\n```{r}\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) + \n  geom_area() + \n  scale_x_date(breaks=brks, labels=lbls) + \n  theme(axis.text.x = element_text(angle=90)) + \n  labs(title=\"Area Chart\", \n       subtitle = \"Perc Returns for Personal Savings\", \n       y=\"% Returns for Personal savings\", \n       caption=\"Source: economics\")\n\n```\n\n## 3 排序\n### 3.1 有序条形图\n有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。\n```{r}\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n\n```\n\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") + \n  labs(title=\"Ordered Bar Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.2 棒棒糖图\n棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(size=3) + \n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=0, \n                   yend=mileage)) + \n  labs(title=\"Lollipop Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.3 点图\n点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(col=\"tomato2\", size=3) +   # Draw points\n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=min(mileage), \n                   yend=max(mileage)), \n               linetype=\"dashed\", \n               size=0.1) +   # Draw dashed lines\n  labs(title=\"Dot Plot\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") +  \n  coord_flip()\n```\n\n### 3.4 坡度图\n坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。\n```{r}\nlibrary(scales)\n\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\n```\n\n```{r}\nlibrary(ggplot2)\np <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + \n                  geom_vline(xintercept=1, linetype=\"dashed\", size=.1) + \n                  geom_vline(xintercept=2, linetype=\"dashed\", size=.1) +\n                  scale_color_manual(labels = c(\"Up\", \"Down\"), \n                                     values = c(\"green\"=\"#00ba38\", \"red\"=\"#f8766d\")) +  # color of lines\n                  labs(x=\"\", y=\"Mean GdpPerCap\") +  # Axis labels\n                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits\n\n# Add texts\np <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)\np <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)\np <- p + geom_text(label=\"Time 1\", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title\np <- p + geom_text(label=\"Time 2\", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title\n\n\n#设置主题\np + theme(panel.background = element_blank(),\n           panel.grid = element_blank(),\n           axis.ticks = element_blank(),\n           axis.text.x = element_blank(),\n           panel.border = element_blank(),\n           plot.margin = unit(c(1,2,1,2), \"cm\"))\n\n\n```\n\n### 3.5 哑铃图\n哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。\n```{r}\nhealth <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/health.csv\")\nhealth$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells\n\n# health$Area <- factor(health$Area)\ngg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + \n        geom_dumbbell(color=\"#a3c4dc\", \n                      size=0.75, \n                      point.colour.l=\"#0e668b\") + \n        scale_x_continuous(label=percent) + \n        labs(x=NULL, \n             y=NULL, \n             title=\"Dumbbell Chart\", \n             subtitle=\"Pct Change: 2013 vs 2014\", \n             caption=\"Source: https://github.com/hrbrmstr/ggalt\") +\n        theme(plot.title = element_text(hjust=0.5, face=\"bold\"),\n              plot.background=element_rect(fill=\"#f7f7f7\"),\n              panel.background=element_rect(fill=\"#f7f7f7\"),\n              panel.grid.minor=element_blank(),\n              panel.grid.major.y=element_blank(),\n              panel.grid.major.x=element_line(),\n              axis.ticks=element_blank(),\n              legend.position=\"top\",\n              panel.border=element_blank())\n\nplot(gg)\n\n```\n\n## 4 分布\n### 4.1 直方图\n#### 4.4.1 连续变量的直方图\n连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。\n```{r}\n# 连续（数值）变量的直方图\ng <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = \"Spectral\")\n\ng + geom_histogram(aes(fill=class), \n                   binwidth = .1, \n                   col=\"black\", \n                   size=.1) +  # change binwidth\n  labs(title=\"Histogram with Auto Binning\", \n       subtitle=\"Engine Displacement across Vehicle Classes\")\n\n \n```\n\n\n```{r}\n\ng + geom_histogram(aes(fill=class), \n                   bins=5, \n                   col=\"black\", \n                   size=.1) +   # change number of bins\n  labs(title=\"Histogram with Fixed Bins\", \n       subtitle=\"Engine Displacement across Vehicle Classes\") \n\n \n```\n\n#### 4.1.2分类变量的直方图\n分类变量的直方图实际上是根据每个类别的频率绘制的条形图。\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Histogram on a Categorical variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Histogram on Categorical Variable\", \n       subtitle=\"Manufacturer across Vehicle Classes\")\n\n \n```\n\n### 4.2 密度图\n\n```{r}\ng <- ggplot(mpg, aes(cty))\ng + geom_density(aes(fill=factor(cyl)), alpha=0.8) + \n    labs(title=\"Density plot\", \n         subtitle=\"City Mileage Grouped by Number of cylinders\",\n         caption=\"Source: mpg\",\n         x=\"City Mileage\",\n         fill=\"# Cylinders\")\n \n```\n\n### 4.3 箱线图\n箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(varwidth=T, fill=\"plum\") + \n    labs(title=\"Box plot\", \n         subtitle=\"City Mileage grouped by Class of vehicle\",\n         caption=\"Source: mpg\",\n         x=\"Class of Vehicle\",\n         y=\"City Mileage\")\n \n```\n\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(aes(fill=factor(cyl))) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot\", \n       subtitle=\"City Mileage grouped by Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.4 点图+箱线图\n在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。\n```{r}\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_boxplot() + \n  geom_dotplot(binaxis='y', \n               stackdir='center', \n               dotsize = .5, \n               fill=\"red\") +\n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot + Dot plot\", \n       subtitle=\"City Mileage vs Class: Each dot represents 1 row in source data\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.5 塔夫特箱线图\n这是一个简化版的箱线图。\n```{r}\n\nlibrary(ggthemes)\nlibrary(ggplot2)\ntheme_set(theme_tufte())  # from ggthemes\n\n# plot\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_tufteboxplot() + \n      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n      labs(title=\"Tufte Styled Boxplot\", \n           subtitle=\"City Mileage grouped by Class of vehicle\",\n           caption=\"Source: mpg\",\n           x=\"Class of Vehicle\",\n           y=\"City Mileage\")\n\n\n```\n\n### 4.6 小提琴图\n小提琴图类似于箱线图，但显示了组内的密度。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_violin() + \n  labs(title=\"Violin plot\", \n       subtitle=\"City Mileage vs Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.7 金字塔图\n金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。\n```{r}\nlibrary(ggplot2)\nlibrary(ggthemes)\noptions(scipen = 999)  # turns of scientific notations like 1e+40\n\n# Read data\nemail_campaign_funnel <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n\n# X Axis Breaks and Labels \nbrks <- seq(-15000000, 15000000, 5000000)\nlbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), \"m\")\n\n# Plot\nggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column\n                              geom_bar(stat = \"identity\", width = .6) +   # draw the bars\n                              scale_y_continuous(breaks = brks,   # Breaks\n                                                 labels = lbls) + # Labels\n                              coord_flip() +  # Flip axes\n                              labs(title=\"Email Campaign Funnel\") +\n                              theme_tufte() +  # Tufte theme from ggfortify\n                              theme(plot.title = element_text(hjust = .5), \n                                    axis.ticks = element_blank()) +   # Centre plot title\n                              scale_fill_brewer(palette = \"Dark2\")  # Color palette\n```\n\n## 5 组成\n### 5.1 华夫饼图\n华夫图可以显示总体的组成成分。\n```{r}\nvar <- mpg$class  # the categorical data \n\n## Prep data (nothing to change here)\nnrows <- 10\ndf <- expand.grid(y = 1:nrows, x = 1:nrows)\ncateg_table <- round(table(var) * ((nrows*nrows)/(length(var))))\n\n\ndf$category <- factor(rep(names(categ_table), categ_table))  \n# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.\n\n## Plot\nggplot(df, aes(x = x, y = y, fill = category)) + \n        geom_tile(color = \"black\", size = 0.5) +\n        scale_x_continuous(expand = c(0, 0)) +\n        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +\n        scale_fill_brewer(palette = \"Set3\") +\n        labs(title=\"Waffle Chart\", subtitle=\"'Class' of vehicles\",\n             caption=\"Source: mpg\") + \n        theme(panel.border = element_rect(size = 2),\n              plot.title = element_text(size = rel(1.2)),\n              axis.text = element_blank(),\n              axis.title = element_blank(),\n              axis.ticks = element_blank(),\n              legend.title = element_blank(),\n              legend.position = \"right\")\n\n```\n\n### 5.2 饼图\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Source: Frequency table\ndf <- as.data.frame(table(mpg$class))\ncolnames(df) <- c(\"class\", \"freq\")\npie <- ggplot(df, aes(x = \"\", y=freq, fill = factor(class))) + \n  geom_bar(width = 1, stat = \"identity\") +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n\npie + coord_polar(theta = \"y\", start=0)\n\n# Source: Categorical variable.\n# mpg$class\npie <- ggplot(mpg, aes(x = \"\", fill = factor(class))) + \n  geom_bar(width = 1) +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n  \npie + coord_polar(theta = \"y\", start=0)\n```\n\n\n\n### 5.3 条形图\n```{r}\n #prep frequency table\nfreqtable <- table(mpg$manufacturer)\ndf <- as.data.frame.table(freqtable)\n\n```\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Plot\ng <- ggplot(df, aes(Var1, Freq))\ng + geom_bar(stat=\"identity\", width = 0.5, fill=\"tomato2\") + \n      labs(title=\"Bar Chart\", \n           subtitle=\"Manufacturer of vehicles\", \n           caption=\"Source: Frequency of Manufacturers from 'mpg' dataset\") +\n      theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n```{r}\n# From on a categorical column variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +\n  labs(title=\"Categorywise Bar Chart\", \n       subtitle=\"Manufacturer of vehicles\", \n       caption=\"Source: Manufacturers from 'mpg' dataset\")\n```\n\n\n\n## 6 变化趋势\n### 6.1 时间序列图：基于时间序列对象（ts）\n`ggfortify`包可以对时间序列直接绘图。\n```{r}\n## From Timeseries object (ts)\nlibrary(ggplot2)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Plot \nautoplot(AirPassengers) + \n  labs(title=\"AirPassengers\") + \n  theme(plot.title = element_text(hjust=0.5))\n```\n\n### 6.2 时间序列图：基于数据框\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# 使用默认的时间跨度\nggplot(economics, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Time Series Chart\", \n       subtitle=\"Returns Percentage from 'Economics' Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\")\n\n```\n\n如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_m <- economics[1:24, ]\n\n# 设定时间跨度为一个月\nlbls <- paste0(month.abb[month(economics_m$date)], \" \", lubridate::year(economics_m$date))\nbrks <- economics_m$date\n\n# plot\nggplot(economics_m, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Monthly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n设置时间跨度为 1 年：\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_y <- economics[1:90, ]\n\n# labels and breaks for X axis text\nbrks <- economics_y$date[seq(1, length(economics_y$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(economics_y, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Yearly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n### 6.3 多个时间序列\n在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。\n\n```{r}\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\n```\n\n在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics_long[economics_long$variable %in% c(\"psavert\", \"uempmed\"), ]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=value, col=variable)) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn from Long Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\", \n       color=NULL) +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(labels = c(\"psavert\", \"uempmed\"), \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=psavert, col=\"psavert\")) + \n  geom_line(aes(y=uempmed, col=\"uempmed\")) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn From Wide Data format\", \n       caption=\"Source: Economics\", y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(name=\"\", \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n\n### 6.4 堆叠面积图\n堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：\n\n- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；\n- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。\n- 希望展示各个类别的贡献。\n\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_area(aes(y=psavert+uempmed, fill=\"psavert\")) + \n  geom_area(aes(y=uempmed, fill=\"uempmed\")) + \n  labs(title=\"Area Chart of Returns Percentage\", \n       subtitle=\"From Wide Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_fill_manual(name=\"\", \n                    values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n### 6.5 日历热力图\n\n当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。\n```{r}\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\n\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n```\n\n```{r}\nggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + \n  geom_tile(colour = \"white\") + \n  facet_grid(year~monthf) + \n  scale_fill_gradient(low=\"red\", high=\"green\") +\n  labs(x=\"Week of Month\",\n       y=\"\",\n       title = \"Time-Series Calendar Heatmap\", \n       subtitle=\"Yahoo Closing Price\", \n       fill=\"Close\")\n```\n\n### 6.6 坡度图\n坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。\n```{r}\nlibrary(dplyr)\ntheme_set(theme_classic())\nsource_df <- read.csv(\"https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv\")\n\n# Define functions. Source: https://github.com/jkeirstead/r-slopegraph\ntufte_sort <- function(df, x=\"year\", y=\"value\", group=\"group\", method=\"tufte\", min.space=0.05) {\n    ## First rename the columns for consistency\n    ids <- match(c(x, y, group), names(df))\n    df <- df[,ids]\n    names(df) <- c(\"x\", \"y\", \"group\")\n\n    ## Expand grid to ensure every combination has a defined value\n    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))\n    tmp <- merge(df, tmp, all.y=TRUE)\n    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))\n  \n    ## Cast into a matrix shape and arrange by first column\n    require(reshape2)\n    tmp <- dcast(df, group ~ x, value.var=\"y\")\n    ord <- order(tmp[,2])\n    tmp <- tmp[ord,]\n    \n    min.space <- min.space*diff(range(tmp[,-1]))\n    yshift <- numeric(nrow(tmp))\n    ## Start at \"bottom\" row\n    ## Repeat for rest of the rows until you hit the top\n    for (i in 2:nrow(tmp)) {\n        ## Shift subsequent row up by equal space so gap between\n        ## two entries is >= minimum\n        mat <- as.matrix(tmp[(i-1):i, -1])\n        d.min <- min(diff(mat))\n        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)\n    }\n\n    \n    tmp <- cbind(tmp, yshift=cumsum(yshift))\n\n    scale <- 1\n    tmp <- melt(tmp, id=c(\"group\", \"yshift\"), variable.name=\"x\", value.name=\"y\")\n    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y\n\n    tmp <- transform(tmp, ypos=y + scale*yshift)\n    return(tmp)\n   \n}\n\nplot_slopegraph <- function(df) {\n    ylabs <- subset(df, x==head(x,1))$group\n    yvals <- subset(df, x==head(x,1))$ypos\n    fontSize <- 3\n    gg <- ggplot(df,aes(x=x,y=ypos)) +\n        geom_line(aes(group=group),colour=\"grey80\") +\n        geom_point(colour=\"white\",size=8) +\n        geom_text(aes(label=y), size=fontSize, family=\"American Typewriter\") +\n        scale_y_continuous(name=\"\", breaks=yvals, labels=ylabs)\n    return(gg)\n}    \n\n## Prepare data    \ndf <- tufte_sort(source_df, \n                 x=\"year\", \n                 y=\"value\", \n                 group=\"group\", \n                 method=\"tufte\", \n                 min.space=0.05)\n\ndf <- transform(df, \n                x=factor(x, levels=c(5,10,15,20), \n                            labels=c(\"5 years\",\"10 years\",\"15 years\",\"20 years\")), \n                y=round(y))\n\n## Plot\nplot_slopegraph(df) + labs(title=\"Estimates of % survival rates\") + \n                      theme(axis.title=element_blank(),\n                            axis.ticks = element_blank(),\n                            plot.title = element_text(hjust=0.5,\n                                                      family = \"American Typewriter\",\n                                                      face=\"bold\"),\n                            axis.text = element_text(family = \"American Typewriter\",\n                                                     face=\"bold\"))\n```\n\n### 6.7 季节图\n如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。\n```{r}\nlibrary(ggplot2)\nlibrary(forecast)\ntheme_set(theme_classic())\n\n# Subset data\nnottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow\n\n# Plot\nggseasonplot(AirPassengers) + labs(title=\"Seasonal plot: International Airline Passengers\")\n\n```\n\n```{r}\nggseasonplot(nottem_small) + labs(title=\"Seasonal plot: Air temperatures at Nottingham Castle\")\n```\n\n## 7 群体\n### 7.1 谱系图\n```{r}\n\nlibrary(ggplot2)\nlibrary(ggdendro)\ntheme_set(theme_bw())\n\nhc <- hclust(dist(USArrests), \"ave\")  # hierarchical clustering\n\n# plot\nggdendrogram(hc, rotate = TRUE, size = 2)\n```\n\n### 7.2 聚类图\n可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。\n```{r}\n# devtools::install_github(\"hrbrmstr/ggalt\")\nlibrary(ggplot2)\nlibrary(ggalt)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Compute data with principal components ------------------\ndf <- iris[c(1, 2, 3, 4)]\npca_mod <- prcomp(df)  # compute principal components\n\n# Data frame of principal components ----------------------\ndf_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components\ndf_pc_vir <- df_pc[df_pc$Species == \"virginica\", ]  # df for 'virginica'\ndf_pc_set <- df_pc[df_pc$Species == \"setosa\", ]  # df for 'setosa'\ndf_pc_ver <- df_pc[df_pc$Species == \"versicolor\", ]  # df for 'versicolor'\n \n# Plot ----------------------------------------------------\nggplot(df_pc, aes(PC1, PC2, col=Species)) + \n  geom_point(aes(shape=Species), size=2) +   # draw points\n  labs(title=\"Iris Clustering\", \n       subtitle=\"With principal components PC1 and PC2 as X and Y axis\",\n       caption=\"Source: Iris\") + \n  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), \n                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits\n  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles\n  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + \n  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))\n```\n\n"
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    "content": "# install.packages(\"ggplot2\")\nlibrary(ggplot2)\noptions(scipen=999)  # turn-off scientific notation like 1e+48\n# theme_set(theme_bw())  # pre-set the bw theme.\ndata(\"midwest\", package = \"ggplot2\")\nmidwest <- read.csv(\"http://goo.gl/G1K41K\")  # bkup data source\nwrite.csv(midwest,\"midwest.csv\")\n\n\n# The palette with grey:\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# The palette with black:\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\nlibrary(plotrix)\nsliceValues <- rep(10, 8) # each slice value=10 for proportionate slices\n(\n  p <- pie3D(sliceValues, \n             explode=0, \n             theta = 1.2, \n             col = cbp1, \n             labels = cbp1, \n             labelcex = 0.9,\n             shade = 0.6,\n             main = \"Colorblind\\nfriendly palette\")\n)\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # note: needs to be overridden when using continuous color scales\n  theme_bw()\n\n\n\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\n\n"
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    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/C6560784/sources/per/t/C36CFFC0-contents",
    "content": "---\ntitle: \"ggplot绘图手册\"\nauthor: \"赵子茜\"\ndate: \"`r Sys.Date()`\"\noutput: \n  prettydoc::html_pretty:\n    toc: true\n    toc_depth: 4\n    theme: cayman\n    highlight: github\neditor_options: \n  chunk_output_type: console\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n```\n\n## 加载数据集\n\n```{r}\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # 关掉像1e+48这样的科学符号\ndata(\"midwest\", package = \"ggplot2\") #加载数据集\n```\n\n## 全局主题设置\n\n```{r}\n# 颜色设置（灰色系列）\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# 颜色设置（黑色系列）\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写\n  theme_bw()\n\n```\n\n\n\n\n## 1. 相关关系\n### 1.1 两个变量\n展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。\n\n\n```{r}\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\nplot(gg)\n\n```\n\n\n### 1.2 环绕式散点图\n使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 & \n                            midwest$poptotal <= 500000 & \n                            midwest$area > 0.01 & \n                            midwest$area < 0.1, ]\n\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) +   # draw points\n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) +   # draw smoothing line\n  geom_encircle(aes(x=area, y=poptotal), \n                data=midwest_select, \n                color=\"red\", \n                size=2, \n                expand=0.08) +   # encircle\n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot + Encircle\", \n       caption=\"Source: midwest\")\n```\n\n\n\n\n### 1.3 抖动图\n\n抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。\n```{r}\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Jittered Points\")\n\n```\n\n### 1.4 计数图\n\n克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。\n\n```{r}\ng + geom_count(col=\"tomato3\", show.legend=F) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Counts Plot\")\n```\n\n### 1.5 气泡图\n气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。\n```{r}\ndata(mpg, package=\"ggplot2\")\n\n\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) + \n  labs(subtitle=\"mpg: Displacement vs City Mileage\",\n       title=\"Bubble chart\")\n\ng + geom_jitter(aes(col=manufacturer, size=hwy)) + \n  geom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n```\n\n\n<!-- ### 1.6 动态气泡图 -->\n\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year), -->\n<!--               method = \"lm\", -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n\n\n### 1.6 边际直方图/箱线图\n\n如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。\n\n```{r}\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) + \n  geom_count() + \n  geom_smooth(method=\"lm\", se=F)\n\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n\n```\n```{r}\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n\n```\n\n```{r}\nggMarginal(g, type = \"density\", fill=\"transparent\")\n```\n\n### 1.7 相关系数图\n\n相关系数图可以查看同一组数据中多个连续变量的相关性。\n```{r}\nlibrary(ggcorrplot)\n\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n\n# Plot\nggcorrplot(corr, hc.order = TRUE, \n           type = \"lower\", \n           lab = TRUE, \n           lab_size = 3, \n           method=\"circle\", \n           colors = c(\"tomato2\", \"white\", \"springgreen3\"), \n           title=\"Correlogram of mtcars\", \n           ggtheme=theme_bw)\n```\n\n## 2 偏差\n### 2.1 发散条形图\n发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：\n\n- 设置 `stat=identity`;\n- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。\n\n为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。\n\n```{r}\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) \n\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\n  scale_fill_manual(name=\"Mileage\", \n                    labels = c(\"Above Average\", \"Below Average\"), \n                    values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  labs(subtitle=\"Normalised mileage from 'mtcars'\", \n       title= \"Diverging Bars\") + \n  coord_flip()\n```\n\n\n### 2.2 带标记的发散型棒棒糖图\n这个图形是发散条形图的一个变体。\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', fill=\"black\", size=6)  +\n  geom_segment(aes(y = 0, \n                   x = `car name`, \n                   yend = mpg_z, \n                   xend = `car name`), \n               color = \"black\") +\n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Lollipop Chart\", \n       subtitle=\"Normalized mileage from 'mtcars': Lollipop\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.3 发散性点图\n\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', aes(col=mpg_type), size=6)  +\n  scale_color_manual(name=\"Mileage\", \n                     labels = c(\"Above Average\", \"Below Average\"), \n                     values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Dot Plot\", \n       subtitle=\"Normalized mileage from 'mtcars': Dotplot\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.4 面积图\n面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。\n```{r}\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) + \n  geom_area() + \n  scale_x_date(breaks=brks, labels=lbls) + \n  theme(axis.text.x = element_text(angle=90)) + \n  labs(title=\"Area Chart\", \n       subtitle = \"Perc Returns for Personal Savings\", \n       y=\"% Returns for Personal savings\", \n       caption=\"Source: economics\")\n\n```\n\n## 3 排序\n### 3.1 有序条形图\n有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。\n```{r}\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n\n```\n\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") + \n  labs(title=\"Ordered Bar Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.2 棒棒糖图\n棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(size=3) + \n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=0, \n                   yend=mileage)) + \n  labs(title=\"Lollipop Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.3 点图\n点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(col=\"tomato2\", size=3) +   # Draw points\n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=min(mileage), \n                   yend=max(mileage)), \n               linetype=\"dashed\", \n               size=0.1) +   # Draw dashed lines\n  labs(title=\"Dot Plot\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") +  \n  coord_flip()\n```\n\n### 3.4 坡度图\n坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。\n```{r}\nlibrary(scales)\n\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\nhead(df)\n```\n\n```{r}\nlibrary(ggplot2)\np <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + \n                  geom_vline(xintercept=1, linetype=\"dashed\", size=.1) + \n                  geom_vline(xintercept=2, linetype=\"dashed\", size=.1) +\n                  scale_color_manual(labels = c(\"Up\", \"Down\"), \n                                     values = c(\"green\"=\"#00ba38\", \"red\"=\"#f8766d\")) +  # color of lines\n                  labs(x=\"\", y=\"Mean GdpPerCap\") +  # Axis labels\n                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits\n\n# Add texts\np <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)\np <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)\np <- p + geom_text(label=\"Time 1\", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title\np <- p + geom_text(label=\"Time 2\", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title\n\n\n#设置主题\np + theme(panel.background = element_blank(),\n           panel.grid = element_blank(),\n           axis.ticks = element_blank(),\n           axis.text.x = element_blank(),\n           panel.border = element_blank(),\n           plot.margin = unit(c(1,2,1,2), \"cm\"))\n\n\n```\n\n### 3.5 哑铃图\n哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。\n```{r}\nhealth <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/health.csv\")\nhealth$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells\n\n# health$Area <- factor(health$Area)\ngg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + \n        geom_dumbbell(color=\"#a3c4dc\", \n                      size=0.75, \n                      point.colour.l=\"#0e668b\") + \n        scale_x_continuous(label=percent) + \n        labs(x=NULL, \n             y=NULL, \n             title=\"Dumbbell Chart\", \n             subtitle=\"Pct Change: 2013 vs 2014\", \n             caption=\"Source: https://github.com/hrbrmstr/ggalt\") +\n        theme(plot.title = element_text(hjust=0.5, face=\"bold\"),\n              plot.background=element_rect(fill=\"#f7f7f7\"),\n              panel.background=element_rect(fill=\"#f7f7f7\"),\n              panel.grid.minor=element_blank(),\n              panel.grid.major.y=element_blank(),\n              panel.grid.major.x=element_line(),\n              axis.ticks=element_blank(),\n              legend.position=\"top\",\n              panel.border=element_blank())\n\nplot(gg)\n\n```\n\n## 4 分布\n### 4.1 直方图\n#### 4.4.1 连续变量的直方图\n连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。\n```{r}\n# 连续（数值）变量的直方图\ng <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = \"Spectral\")\n\ng + geom_histogram(aes(fill=class), \n                   binwidth = .1, \n                   col=\"black\", \n                   size=.1) +  # change binwidth\n  labs(title=\"Histogram with Auto Binning\", \n       subtitle=\"Engine Displacement across Vehicle Classes\")\n\n \n```\n\n\n```{r}\n\ng + geom_histogram(aes(fill=class), \n                   bins=5, \n                   col=\"black\", \n                   size=.1) +   # change number of bins\n  labs(title=\"Histogram with Fixed Bins\", \n       subtitle=\"Engine Displacement across Vehicle Classes\") \n\n \n```\n\n#### 4.1.2分类变量的直方图\n分类变量的直方图实际上是根据每个类别的频率绘制的条形图。\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Histogram on a Categorical variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Histogram on Categorical Variable\", \n       subtitle=\"Manufacturer across Vehicle Classes\")\n\n \n```\n\n### 4.2 密度图\n\n```{r}\ng <- ggplot(mpg, aes(cty))\ng + geom_density(aes(fill=factor(cyl)), alpha=0.8) + \n    labs(title=\"Density plot\", \n         subtitle=\"City Mileage Grouped by Number of cylinders\",\n         caption=\"Source: mpg\",\n         x=\"City Mileage\",\n         fill=\"# Cylinders\")\n \n```\n\n### 4.3 箱线图\n箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(varwidth=T, fill=\"plum\") + \n    labs(title=\"Box plot\", \n         subtitle=\"City Mileage grouped by Class of vehicle\",\n         caption=\"Source: mpg\",\n         x=\"Class of Vehicle\",\n         y=\"City Mileage\")\n \n```\n\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(aes(fill=factor(cyl))) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot\", \n       subtitle=\"City Mileage grouped by Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.4 点图+箱线图\n在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。\n```{r}\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_boxplot() + \n  geom_dotplot(binaxis='y', \n               stackdir='center', \n               dotsize = .5, \n               fill=\"red\") +\n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot + Dot plot\", \n       subtitle=\"City Mileage vs Class: Each dot represents 1 row in source data\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.5 塔夫特箱线图\n这是一个简化版的箱线图。\n```{r}\n\nlibrary(ggthemes)\nlibrary(ggplot2)\ntheme_set(theme_tufte())  # from ggthemes\n\n# plot\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_tufteboxplot() + \n      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n      labs(title=\"Tufte Styled Boxplot\", \n           subtitle=\"City Mileage grouped by Class of vehicle\",\n           caption=\"Source: mpg\",\n           x=\"Class of Vehicle\",\n           y=\"City Mileage\")\n\n\n```\n\n### 4.6 小提琴图\n小提琴图类似于箱线图，但显示了组内的密度。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_violin() + \n  labs(title=\"Violin plot\", \n       subtitle=\"City Mileage vs Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.7 金字塔图\n金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。\n```{r}library(ggplot2)\nlibrary(ggthemes)\noptions(scipen = 999)  # turns of scientific notations like 1e+40\n\n# Read data\nemail_campaign_funnel <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n\n# X Axis Breaks and Labels \nbrks <- seq(-15000000, 15000000, 5000000)\nlbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), \"m\")\n\n# Plot\nggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column\n                              geom_bar(stat = \"identity\", width = .6) +   # draw the bars\n                              scale_y_continuous(breaks = brks,   # Breaks\n                                                 labels = lbls) + # Labels\n                              coord_flip() +  # Flip axes\n                              labs(title=\"Email Campaign Funnel\") +\n                              theme_tufte() +  # Tufte theme from ggfortify\n                              theme(plot.title = element_text(hjust = .5), \n                                    axis.ticks = element_blank()) +   # Centre plot title\n                              scale_fill_brewer(palette = \"Dark2\")  # Color palette\n```\n\n## 5 组成\n### 5.1 华夫饼图\n华夫图可以显示总体的组成成分。\n```{r}\nvar <- mpg$class  # the categorical data \n\n## Prep data (nothing to change here)\nnrows <- 10\ndf <- expand.grid(y = 1:nrows, x = 1:nrows)\ncateg_table <- round(table(var) * ((nrows*nrows)/(length(var))))\n\n\ndf$category <- factor(rep(names(categ_table), categ_table))  \n# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.\n\n## Plot\nggplot(df, aes(x = x, y = y, fill = category)) + \n        geom_tile(color = \"black\", size = 0.5) +\n        scale_x_continuous(expand = c(0, 0)) +\n        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +\n        scale_fill_brewer(palette = \"Set3\") +\n        labs(title=\"Waffle Chart\", subtitle=\"'Class' of vehicles\",\n             caption=\"Source: mpg\") + \n        theme(panel.border = element_rect(size = 2),\n              plot.title = element_text(size = rel(1.2)),\n              axis.text = element_blank(),\n              axis.title = element_blank(),\n              axis.ticks = element_blank(),\n              legend.title = element_blank(),\n              legend.position = \"right\")\n\n```\n\n### 5.2 饼图\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Source: Frequency table\ndf <- as.data.frame(table(mpg$class))\ncolnames(df) <- c(\"class\", \"freq\")\npie <- ggplot(df, aes(x = \"\", y=freq, fill = factor(class))) + \n  geom_bar(width = 1, stat = \"identity\") +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n\npie + coord_polar(theta = \"y\", start=0)\n\n# Source: Categorical variable.\n# mpg$class\npie <- ggplot(mpg, aes(x = \"\", fill = factor(class))) + \n  geom_bar(width = 1) +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n  \npie + coord_polar(theta = \"y\", start=0)\n```\n\n\n\n### 5.3 条形图\n```{r}\n #prep frequency table\nfreqtable <- table(mpg$manufacturer)\ndf <- as.data.frame.table(freqtable)\n\n```\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Plot\ng <- ggplot(df, aes(Var1, Freq))\ng + geom_bar(stat=\"identity\", width = 0.5, fill=\"tomato2\") + \n      labs(title=\"Bar Chart\", \n           subtitle=\"Manufacturer of vehicles\", \n           caption=\"Source: Frequency of Manufacturers from 'mpg' dataset\") +\n      theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n```{r}\n# From on a categorical column variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +\n  labs(title=\"Categorywise Bar Chart\", \n       subtitle=\"Manufacturer of vehicles\", \n       caption=\"Source: Manufacturers from 'mpg' dataset\")\n```\n\n\n\n## 6 变化趋势\n### 6.1 时间序列图：基于时间序列对象（ts）\n`ggfortify`包可以对时间序列直接绘图。\n```{r}\n## From Timeseries object (ts)\nlibrary(ggplot2)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Plot \nautoplot(AirPassengers) + \n  labs(title=\"AirPassengers\") + \n  theme(plot.title = element_text(hjust=0.5))\n```\n\n### 6.2 时间序列图：基于数据框\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# 使用默认的时间跨度\nggplot(economics, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Time Series Chart\", \n       subtitle=\"Returns Percentage from 'Economics' Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\")\n\n```\n\n如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_m <- economics[1:24, ]\n\n# 设定时间跨度为一个月\nlbls <- paste0(month.abb[month(economics_m$date)], \" \", lubridate::year(economics_m$date))\nbrks <- economics_m$date\n\n# plot\nggplot(economics_m, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Monthly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n设置时间跨度为 1 年：\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_y <- economics[1:90, ]\n\n# labels and breaks for X axis text\nbrks <- economics_y$date[seq(1, length(economics_y$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(economics_y, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Yearly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n### 6.3 多个时间序列\n在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。\n\n```{r}\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\n```\n\n在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics_long[economics_long$variable %in% c(\"psavert\", \"uempmed\"), ]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=value, col=variable)) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn from Long Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\", \n       color=NULL) +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(labels = c(\"psavert\", \"uempmed\"), \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=psavert, col=\"psavert\")) + \n  geom_line(aes(y=uempmed, col=\"uempmed\")) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn From Wide Data format\", \n       caption=\"Source: Economics\", y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(name=\"\", \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n\n### 6.4 堆叠面积图\n堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：\n\n- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；\n- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。\n- 希望展示各个类别的贡献。\n\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_area(aes(y=psavert+uempmed, fill=\"psavert\")) + \n  geom_area(aes(y=uempmed, fill=\"uempmed\")) + \n  labs(title=\"Area Chart of Returns Percentage\", \n       subtitle=\"From Wide Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_fill_manual(name=\"\", \n                    values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n### 6.5 日历热力图\n\n当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。\n```{r}\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\n\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n```\n\n```{r}\nggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + \n  geom_tile(colour = \"white\") + \n  facet_grid(year~monthf) + \n  scale_fill_gradient(low=\"red\", high=\"green\") +\n  labs(x=\"Week of Month\",\n       y=\"\",\n       title = \"Time-Series Calendar Heatmap\", \n       subtitle=\"Yahoo Closing Price\", \n       fill=\"Close\")\n```\n\n### 6.6 坡度图\n坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。\n```{r}\nlibrary(dplyr)\ntheme_set(theme_classic())\nsource_df <- read.csv(\"https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv\")\n\n# Define functions. Source: https://github.com/jkeirstead/r-slopegraph\ntufte_sort <- function(df, x=\"year\", y=\"value\", group=\"group\", method=\"tufte\", min.space=0.05) {\n    ## First rename the columns for consistency\n    ids <- match(c(x, y, group), names(df))\n    df <- df[,ids]\n    names(df) <- c(\"x\", \"y\", \"group\")\n\n    ## Expand grid to ensure every combination has a defined value\n    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))\n    tmp <- merge(df, tmp, all.y=TRUE)\n    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))\n  \n    ## Cast into a matrix shape and arrange by first column\n    require(reshape2)\n    tmp <- dcast(df, group ~ x, value.var=\"y\")\n    ord <- order(tmp[,2])\n    tmp <- tmp[ord,]\n    \n    min.space <- min.space*diff(range(tmp[,-1]))\n    yshift <- numeric(nrow(tmp))\n    ## Start at \"bottom\" row\n    ## Repeat for rest of the rows until you hit the top\n    for (i in 2:nrow(tmp)) {\n        ## Shift subsequent row up by equal space so gap between\n        ## two entries is >= minimum\n        mat <- as.matrix(tmp[(i-1):i, -1])\n        d.min <- min(diff(mat))\n        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)\n    }\n\n    \n    tmp <- cbind(tmp, yshift=cumsum(yshift))\n\n    scale <- 1\n    tmp <- melt(tmp, id=c(\"group\", \"yshift\"), variable.name=\"x\", value.name=\"y\")\n    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y\n\n    tmp <- transform(tmp, ypos=y + scale*yshift)\n    return(tmp)\n   \n}\n\nplot_slopegraph <- function(df) {\n    ylabs <- subset(df, x==head(x,1))$group\n    yvals <- subset(df, x==head(x,1))$ypos\n    fontSize <- 3\n    gg <- ggplot(df,aes(x=x,y=ypos)) +\n        geom_line(aes(group=group),colour=\"grey80\") +\n        geom_point(colour=\"white\",size=8) +\n        geom_text(aes(label=y), size=fontSize, family=\"American Typewriter\") +\n        scale_y_continuous(name=\"\", breaks=yvals, labels=ylabs)\n    return(gg)\n}    \n\n## Prepare data    \ndf <- tufte_sort(source_df, \n                 x=\"year\", \n                 y=\"value\", \n                 group=\"group\", \n                 method=\"tufte\", \n                 min.space=0.05)\n\ndf <- transform(df, \n                x=factor(x, levels=c(5,10,15,20), \n                            labels=c(\"5 years\",\"10 years\",\"15 years\",\"20 years\")), \n                y=round(y))\n\n## Plot\nplot_slopegraph(df) + labs(title=\"Estimates of % survival rates\") + \n                      theme(axis.title=element_blank(),\n                            axis.ticks = element_blank(),\n                            plot.title = element_text(hjust=0.5,\n                                                      family = \"American Typewriter\",\n                                                      face=\"bold\"),\n                            axis.text = element_text(family = \"American Typewriter\",\n                                                     face=\"bold\"))\n```\n\n### 6.7 季节图\n如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。\n```{r}\nlibrary(ggplot2)\nlibrary(forecast)\ntheme_set(theme_classic())\n\n# Subset data\nnottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow\n\n# Plot\nggseasonplot(AirPassengers) + labs(title=\"Seasonal plot: International Airline Passengers\")\n\n```\n\n```{r}\nggseasonplot(nottem_small) + labs(title=\"Seasonal plot: Air temperatures at Nottingham Castle\")\n```\n\n## 7 群体\n### 7.1 谱系图\n```{r}\n\nlibrary(ggplot2)\nlibrary(ggdendro)\ntheme_set(theme_bw())\n\nhc <- hclust(dist(USArrests), \"ave\")  # hierarchical clustering\n\n# plot\nggdendrogram(hc, rotate = TRUE, size = 2)\n```\n\n### 7.2 聚类图\n可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。\n```{r}\n# devtools::install_github(\"hrbrmstr/ggalt\")\nlibrary(ggplot2)\nlibrary(ggalt)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Compute data with principal components ------------------\ndf <- iris[c(1, 2, 3, 4)]\npca_mod <- prcomp(df)  # compute principal components\n\n# Data frame of principal components ----------------------\ndf_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components\ndf_pc_vir <- df_pc[df_pc$Species == \"virginica\", ]  # df for 'virginica'\ndf_pc_set <- df_pc[df_pc$Species == \"setosa\", ]  # df for 'setosa'\ndf_pc_ver <- df_pc[df_pc$Species == \"versicolor\", ]  # df for 'versicolor'\n \n# Plot ----------------------------------------------------\nggplot(df_pc, aes(PC1, PC2, col=Species)) + \n  geom_point(aes(shape=Species), size=2) +   # draw points\n  labs(title=\"Iris Clustering\", \n       subtitle=\"With principal components PC1 and PC2 as X and Y axis\",\n       caption=\"Source: Iris\") + \n  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), \n                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits\n  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles\n  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + \n  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))\n```\n\n"
  },
  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/.Rproj.user/C6560784/sources/per/t/C36CFFC0-contents 2",
    "content": "---\ntitle: \"ggplot绘图手册\"\nauthor: \"赵子茜\"\ndate: \"`r Sys.Date()`\"\noutput: \n  prettydoc::html_pretty:\n    toc: true\n    toc_depth: 4\n    theme: cayman\n    highlight: github\neditor_options: \n  chunk_output_type: console\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n```\n\n## 加载数据集\n\n```{r}\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # 关掉像1e+48这样的科学符号\ndata(\"midwest\", package = \"ggplot2\") #加载数据集\n```\n\n## 全局主题设置\n\n```{r}\n# 颜色设置（灰色系列）\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# 颜色设置（黑色系列）\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写\n  theme_bw()\n\n```\n\n\n\n\n## 1. 相关关系\n### 1.1 两个变量\n展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。\n\n\n```{r}\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\nplot(gg)\n\n```\n\n\n### 1.2 环绕式散点图\n使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 & \n                            midwest$poptotal <= 500000 & \n                            midwest$area > 0.01 & \n                            midwest$area < 0.1, ]\n\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) +   # draw points\n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) +   # draw smoothing line\n  geom_encircle(aes(x=area, y=poptotal), \n                data=midwest_select, \n                color=\"red\", \n                size=2, \n                expand=0.08) +   # encircle\n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot + Encircle\", \n       caption=\"Source: midwest\")\n```\n\n\n\n\n### 1.3 抖动图\n\n抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。\n```{r}\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Jittered Points\")\n\n```\n\n### 1.4 计数图\n\n克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。\n\n```{r}\ng + geom_count(col=\"tomato3\", show.legend=F) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Counts Plot\")\n```\n\n### 1.5 气泡图\n气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。\n```{r}\ndata(mpg, package=\"ggplot2\")\n\n\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) + \n  labs(subtitle=\"mpg: Displacement vs City Mileage\",\n       title=\"Bubble chart\")\n\ng + geom_jitter(aes(col=manufacturer, size=hwy)) + \n  geom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n```\n\n\n<!-- ### 1.6 动态气泡图 -->\n\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year), -->\n<!--               method = \"lm\", -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n\n\n### 1.6 边际直方图/箱线图\n\n如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。\n\n```{r}\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) + \n  geom_count() + \n  geom_smooth(method=\"lm\", se=F)\n\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n\n```\n```{r}\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n\n```\n\n```{r}\nggMarginal(g, type = \"density\", fill=\"transparent\")\n```\n\n### 1.7 相关系数图\n\n相关系数图可以查看同一组数据中多个连续变量的相关性。\n```{r}\nlibrary(ggcorrplot)\n\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n\n# Plot\nggcorrplot(corr, hc.order = TRUE, \n           type = \"lower\", \n           lab = TRUE, \n           lab_size = 3, \n           method=\"circle\", \n           colors = c(\"tomato2\", \"white\", \"springgreen3\"), \n           title=\"Correlogram of mtcars\", \n           ggtheme=theme_bw)\n```\n\n## 2 偏差\n### 2.1 发散条形图\n发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：\n\n- 设置 `stat=identity`;\n- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。\n\n为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。\n\n```{r}\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) \n\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\n  scale_fill_manual(name=\"Mileage\", \n                    labels = c(\"Above Average\", \"Below Average\"), \n                    values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  labs(subtitle=\"Normalised mileage from 'mtcars'\", \n       title= \"Diverging Bars\") + \n  coord_flip()\n```\n\n\n### 2.2 带标记的发散型棒棒糖图\n这个图形是发散条形图的一个变体。\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', fill=\"black\", size=6)  +\n  geom_segment(aes(y = 0, \n                   x = `car name`, \n                   yend = mpg_z, \n                   xend = `car name`), \n               color = \"black\") +\n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Lollipop Chart\", \n       subtitle=\"Normalized mileage from 'mtcars': Lollipop\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.3 发散性点图\n\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', aes(col=mpg_type), size=6)  +\n  scale_color_manual(name=\"Mileage\", \n                     labels = c(\"Above Average\", \"Below Average\"), \n                     values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Dot Plot\", \n       subtitle=\"Normalized mileage from 'mtcars': Dotplot\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.4 面积图\n面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。\n```{r}\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) + \n  geom_area() + \n  scale_x_date(breaks=brks, labels=lbls) + \n  theme(axis.text.x = element_text(angle=90)) + \n  labs(title=\"Area Chart\", \n       subtitle = \"Perc Returns for Personal Savings\", \n       y=\"% Returns for Personal savings\", \n       caption=\"Source: economics\")\n\n```\n\n## 3 排序\n### 3.1 有序条形图\n有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。\n```{r}\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n\n```\n\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") + \n  labs(title=\"Ordered Bar Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.2 棒棒糖图\n棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(size=3) + \n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=0, \n                   yend=mileage)) + \n  labs(title=\"Lollipop Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.3 点图\n点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(col=\"tomato2\", size=3) +   # Draw points\n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=min(mileage), \n                   yend=max(mileage)), \n               linetype=\"dashed\", \n               size=0.1) +   # Draw dashed lines\n  labs(title=\"Dot Plot\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") +  \n  coord_flip()\n```\n\n### 3.4 坡度图\n坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。\n```{r}\nlibrary(scales)\n\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\nhead(df)\n```\n\n```{r}\nlibrary(ggplot2)\np <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + \n                  geom_vline(xintercept=1, linetype=\"dashed\", size=.1) + \n                  geom_vline(xintercept=2, linetype=\"dashed\", size=.1) +\n                  scale_color_manual(labels = c(\"Up\", \"Down\"), \n                                     values = c(\"green\"=\"#00ba38\", \"red\"=\"#f8766d\")) +  # color of lines\n                  labs(x=\"\", y=\"Mean GdpPerCap\") +  # Axis labels\n                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits\n\n# Add texts\np <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)\np <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)\np <- p + geom_text(label=\"Time 1\", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title\np <- p + geom_text(label=\"Time 2\", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title\n\n\n#设置主题\np + theme(panel.background = element_blank(),\n           panel.grid = element_blank(),\n           axis.ticks = element_blank(),\n           axis.text.x = element_blank(),\n           panel.border = element_blank(),\n           plot.margin = unit(c(1,2,1,2), \"cm\"))\n\n\n```\n\n### 3.5 哑铃图\n哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。\n```{r}\nhealth <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/health.csv\")\nhealth$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells\n\n# health$Area <- factor(health$Area)\ngg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + \n        geom_dumbbell(color=\"#a3c4dc\", \n                      size=0.75, \n                      point.colour.l=\"#0e668b\") + \n        scale_x_continuous(label=percent) + \n        labs(x=NULL, \n             y=NULL, \n             title=\"Dumbbell Chart\", \n             subtitle=\"Pct Change: 2013 vs 2014\", \n             caption=\"Source: https://github.com/hrbrmstr/ggalt\") +\n        theme(plot.title = element_text(hjust=0.5, face=\"bold\"),\n              plot.background=element_rect(fill=\"#f7f7f7\"),\n              panel.background=element_rect(fill=\"#f7f7f7\"),\n              panel.grid.minor=element_blank(),\n              panel.grid.major.y=element_blank(),\n              panel.grid.major.x=element_line(),\n              axis.ticks=element_blank(),\n              legend.position=\"top\",\n              panel.border=element_blank())\n\nplot(gg)\n\n```\n\n## 4 分布\n### 4.1 直方图\n#### 4.4.1 连续变量的直方图\n连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。\n```{r}\n# 连续（数值）变量的直方图\ng <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = \"Spectral\")\n\ng + geom_histogram(aes(fill=class), \n                   binwidth = .1, \n                   col=\"black\", \n                   size=.1) +  # change binwidth\n  labs(title=\"Histogram with Auto Binning\", \n       subtitle=\"Engine Displacement across Vehicle Classes\")\n\n \n```\n\n\n```{r}\n\ng + geom_histogram(aes(fill=class), \n                   bins=5, \n                   col=\"black\", \n                   size=.1) +   # change number of bins\n  labs(title=\"Histogram with Fixed Bins\", \n       subtitle=\"Engine Displacement across Vehicle Classes\") \n\n \n```\n\n#### 4.1.2分类变量的直方图\n分类变量的直方图实际上是根据每个类别的频率绘制的条形图。\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Histogram on a Categorical variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Histogram on Categorical Variable\", \n       subtitle=\"Manufacturer across Vehicle Classes\")\n\n \n```\n\n### 4.2 密度图\n\n```{r}\ng <- ggplot(mpg, aes(cty))\ng + geom_density(aes(fill=factor(cyl)), alpha=0.8) + \n    labs(title=\"Density plot\", \n         subtitle=\"City Mileage Grouped by Number of cylinders\",\n         caption=\"Source: mpg\",\n         x=\"City Mileage\",\n         fill=\"# Cylinders\")\n \n```\n\n### 4.3 箱线图\n箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(varwidth=T, fill=\"plum\") + \n    labs(title=\"Box plot\", \n         subtitle=\"City Mileage grouped by Class of vehicle\",\n         caption=\"Source: mpg\",\n         x=\"Class of Vehicle\",\n         y=\"City Mileage\")\n \n```\n\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(aes(fill=factor(cyl))) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot\", \n       subtitle=\"City Mileage grouped by Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.4 点图+箱线图\n在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。\n```{r}\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_boxplot() + \n  geom_dotplot(binaxis='y', \n               stackdir='center', \n               dotsize = .5, \n               fill=\"red\") +\n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot + Dot plot\", \n       subtitle=\"City Mileage vs Class: Each dot represents 1 row in source data\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.5 塔夫特箱线图\n这是一个简化版的箱线图。\n```{r}\n\nlibrary(ggthemes)\nlibrary(ggplot2)\ntheme_set(theme_tufte())  # from ggthemes\n\n# plot\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_tufteboxplot() + \n      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n      labs(title=\"Tufte Styled Boxplot\", \n           subtitle=\"City Mileage grouped by Class of vehicle\",\n           caption=\"Source: mpg\",\n           x=\"Class of Vehicle\",\n           y=\"City Mileage\")\n\n\n```\n\n### 4.6 小提琴图\n小提琴图类似于箱线图，但显示了组内的密度。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_violin() + \n  labs(title=\"Violin plot\", \n       subtitle=\"City Mileage vs Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.7 金字塔图\n金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。\n```{r}library(ggplot2)\nlibrary(ggthemes)\noptions(scipen = 999)  # turns of scientific notations like 1e+40\n\n# Read data\nemail_campaign_funnel <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n\n# X Axis Breaks and Labels \nbrks <- seq(-15000000, 15000000, 5000000)\nlbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), \"m\")\n\n# Plot\nggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column\n                              geom_bar(stat = \"identity\", width = .6) +   # draw the bars\n                              scale_y_continuous(breaks = brks,   # Breaks\n                                                 labels = lbls) + # Labels\n                              coord_flip() +  # Flip axes\n                              labs(title=\"Email Campaign Funnel\") +\n                              theme_tufte() +  # Tufte theme from ggfortify\n                              theme(plot.title = element_text(hjust = .5), \n                                    axis.ticks = element_blank()) +   # Centre plot title\n                              scale_fill_brewer(palette = \"Dark2\")  # Color palette\n```\n\n## 5 组成\n### 5.1 华夫饼图\n华夫图可以显示总体的组成成分。\n```{r}\nvar <- mpg$class  # the categorical data \n\n## Prep data (nothing to change here)\nnrows <- 10\ndf <- expand.grid(y = 1:nrows, x = 1:nrows)\ncateg_table <- round(table(var) * ((nrows*nrows)/(length(var))))\n\n\ndf$category <- factor(rep(names(categ_table), categ_table))  \n# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.\n\n## Plot\nggplot(df, aes(x = x, y = y, fill = category)) + \n        geom_tile(color = \"black\", size = 0.5) +\n        scale_x_continuous(expand = c(0, 0)) +\n        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +\n        scale_fill_brewer(palette = \"Set3\") +\n        labs(title=\"Waffle Chart\", subtitle=\"'Class' of vehicles\",\n             caption=\"Source: mpg\") + \n        theme(panel.border = element_rect(size = 2),\n              plot.title = element_text(size = rel(1.2)),\n              axis.text = element_blank(),\n              axis.title = element_blank(),\n              axis.ticks = element_blank(),\n              legend.title = element_blank(),\n              legend.position = \"right\")\n\n```\n\n### 5.2 饼图\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Source: Frequency table\ndf <- as.data.frame(table(mpg$class))\ncolnames(df) <- c(\"class\", \"freq\")\npie <- ggplot(df, aes(x = \"\", y=freq, fill = factor(class))) + \n  geom_bar(width = 1, stat = \"identity\") +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n\npie + coord_polar(theta = \"y\", start=0)\n\n# Source: Categorical variable.\n# mpg$class\npie <- ggplot(mpg, aes(x = \"\", fill = factor(class))) + \n  geom_bar(width = 1) +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n  \npie + coord_polar(theta = \"y\", start=0)\n```\n\n\n\n### 5.3 条形图\n```{r}\n #prep frequency table\nfreqtable <- table(mpg$manufacturer)\ndf <- as.data.frame.table(freqtable)\n\n```\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Plot\ng <- ggplot(df, aes(Var1, Freq))\ng + geom_bar(stat=\"identity\", width = 0.5, fill=\"tomato2\") + \n      labs(title=\"Bar Chart\", \n           subtitle=\"Manufacturer of vehicles\", \n           caption=\"Source: Frequency of Manufacturers from 'mpg' dataset\") +\n      theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n```{r}\n# From on a categorical column variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +\n  labs(title=\"Categorywise Bar Chart\", \n       subtitle=\"Manufacturer of vehicles\", \n       caption=\"Source: Manufacturers from 'mpg' dataset\")\n```\n\n\n\n## 6 变化趋势\n### 6.1 时间序列图：基于时间序列对象（ts）\n`ggfortify`包可以对时间序列直接绘图。\n```{r}\n## From Timeseries object (ts)\nlibrary(ggplot2)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Plot \nautoplot(AirPassengers) + \n  labs(title=\"AirPassengers\") + \n  theme(plot.title = element_text(hjust=0.5))\n```\n\n### 6.2 时间序列图：基于数据框\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# 使用默认的时间跨度\nggplot(economics, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Time Series Chart\", \n       subtitle=\"Returns Percentage from 'Economics' Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\")\n\n```\n\n如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_m <- economics[1:24, ]\n\n# 设定时间跨度为一个月\nlbls <- paste0(month.abb[month(economics_m$date)], \" \", lubridate::year(economics_m$date))\nbrks <- economics_m$date\n\n# plot\nggplot(economics_m, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Monthly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n设置时间跨度为 1 年：\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_y <- economics[1:90, ]\n\n# labels and breaks for X axis text\nbrks <- economics_y$date[seq(1, length(economics_y$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(economics_y, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Yearly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n### 6.3 多个时间序列\n在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。\n\n```{r}\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\n```\n\n在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics_long[economics_long$variable %in% c(\"psavert\", \"uempmed\"), ]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=value, col=variable)) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn from Long Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\", \n       color=NULL) +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(labels = c(\"psavert\", \"uempmed\"), \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=psavert, col=\"psavert\")) + \n  geom_line(aes(y=uempmed, col=\"uempmed\")) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn From Wide Data format\", \n       caption=\"Source: Economics\", y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(name=\"\", \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n\n### 6.4 堆叠面积图\n堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：\n\n- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；\n- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。\n- 希望展示各个类别的贡献。\n\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_area(aes(y=psavert+uempmed, fill=\"psavert\")) + \n  geom_area(aes(y=uempmed, fill=\"uempmed\")) + \n  labs(title=\"Area Chart of Returns Percentage\", \n       subtitle=\"From Wide Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_fill_manual(name=\"\", \n                    values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n### 6.5 日历热力图\n\n当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。\n```{r}\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\n\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n```\n\n```{r}\nggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + \n  geom_tile(colour = \"white\") + \n  facet_grid(year~monthf) + \n  scale_fill_gradient(low=\"red\", high=\"green\") +\n  labs(x=\"Week of Month\",\n       y=\"\",\n       title = \"Time-Series Calendar Heatmap\", \n       subtitle=\"Yahoo Closing Price\", \n       fill=\"Close\")\n```\n\n### 6.6 坡度图\n坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。\n```{r}\nlibrary(dplyr)\ntheme_set(theme_classic())\nsource_df <- read.csv(\"https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv\")\n\n# Define functions. Source: https://github.com/jkeirstead/r-slopegraph\ntufte_sort <- function(df, x=\"year\", y=\"value\", group=\"group\", method=\"tufte\", min.space=0.05) {\n    ## First rename the columns for consistency\n    ids <- match(c(x, y, group), names(df))\n    df <- df[,ids]\n    names(df) <- c(\"x\", \"y\", \"group\")\n\n    ## Expand grid to ensure every combination has a defined value\n    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))\n    tmp <- merge(df, tmp, all.y=TRUE)\n    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))\n  \n    ## Cast into a matrix shape and arrange by first column\n    require(reshape2)\n    tmp <- dcast(df, group ~ x, value.var=\"y\")\n    ord <- order(tmp[,2])\n    tmp <- tmp[ord,]\n    \n    min.space <- min.space*diff(range(tmp[,-1]))\n    yshift <- numeric(nrow(tmp))\n    ## Start at \"bottom\" row\n    ## Repeat for rest of the rows until you hit the top\n    for (i in 2:nrow(tmp)) {\n        ## Shift subsequent row up by equal space so gap between\n        ## two entries is >= minimum\n        mat <- as.matrix(tmp[(i-1):i, -1])\n        d.min <- min(diff(mat))\n        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)\n    }\n\n    \n    tmp <- cbind(tmp, yshift=cumsum(yshift))\n\n    scale <- 1\n    tmp <- melt(tmp, id=c(\"group\", \"yshift\"), variable.name=\"x\", value.name=\"y\")\n    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y\n\n    tmp <- transform(tmp, ypos=y + scale*yshift)\n    return(tmp)\n   \n}\n\nplot_slopegraph <- function(df) {\n    ylabs <- subset(df, x==head(x,1))$group\n    yvals <- subset(df, x==head(x,1))$ypos\n    fontSize <- 3\n    gg <- ggplot(df,aes(x=x,y=ypos)) +\n        geom_line(aes(group=group),colour=\"grey80\") +\n        geom_point(colour=\"white\",size=8) +\n        geom_text(aes(label=y), size=fontSize, family=\"American Typewriter\") +\n        scale_y_continuous(name=\"\", breaks=yvals, labels=ylabs)\n    return(gg)\n}    \n\n## Prepare data    \ndf <- tufte_sort(source_df, \n                 x=\"year\", \n                 y=\"value\", \n                 group=\"group\", \n                 method=\"tufte\", \n                 min.space=0.05)\n\ndf <- transform(df, \n                x=factor(x, levels=c(5,10,15,20), \n                            labels=c(\"5 years\",\"10 years\",\"15 years\",\"20 years\")), \n                y=round(y))\n\n## Plot\nplot_slopegraph(df) + labs(title=\"Estimates of % survival rates\") + \n                      theme(axis.title=element_blank(),\n                            axis.ticks = element_blank(),\n                            plot.title = element_text(hjust=0.5,\n                                                      family = \"American Typewriter\",\n                                                      face=\"bold\"),\n                            axis.text = element_text(family = \"American Typewriter\",\n                                                     face=\"bold\"))\n```\n\n### 6.7 季节图\n如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。\n```{r}\nlibrary(ggplot2)\nlibrary(forecast)\ntheme_set(theme_classic())\n\n# Subset data\nnottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow\n\n# Plot\nggseasonplot(AirPassengers) + labs(title=\"Seasonal plot: International Airline Passengers\")\n\n```\n\n```{r}\nggseasonplot(nottem_small) + labs(title=\"Seasonal plot: Air temperatures at Nottingham Castle\")\n```\n\n## 7 群体\n### 7.1 谱系图\n```{r}\n\nlibrary(ggplot2)\nlibrary(ggdendro)\ntheme_set(theme_bw())\n\nhc <- hclust(dist(USArrests), \"ave\")  # hierarchical clustering\n\n# plot\nggdendrogram(hc, rotate = TRUE, size = 2)\n```\n\n### 7.2 聚类图\n可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。\n```{r}\n# devtools::install_github(\"hrbrmstr/ggalt\")\nlibrary(ggplot2)\nlibrary(ggalt)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Compute data with principal components ------------------\ndf <- iris[c(1, 2, 3, 4)]\npca_mod <- prcomp(df)  # compute principal components\n\n# Data frame of principal components ----------------------\ndf_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components\ndf_pc_vir <- df_pc[df_pc$Species == \"virginica\", ]  # df for 'virginica'\ndf_pc_set <- df_pc[df_pc$Species == \"setosa\", ]  # df for 'setosa'\ndf_pc_ver <- df_pc[df_pc$Species == \"versicolor\", ]  # df for 'versicolor'\n \n# Plot ----------------------------------------------------\nggplot(df_pc, aes(PC1, PC2, col=Species)) + \n  geom_point(aes(shape=Species), size=2) +   # draw points\n  labs(title=\"Iris Clustering\", \n       subtitle=\"With principal components PC1 and PC2 as X and Y axis\",\n       caption=\"Source: Iris\") + \n  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), \n                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits\n  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles\n  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + \n  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))\n```\n\n"
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blockquote{padding:0 1rem;margin-left:0;color:#819198;border-left:.3rem solid #dce6f0;font-size:1.2rem}.main-content blockquote>:first-child{margin-top:0}.main-content blockquote>:last-child{margin-bottom:0}@media screen and (max-width:42em){.main-content blockquote{font-size:1.1rem}}.main-content table{width:100%;overflow:auto;word-break:normal;word-break:keep-all;-webkit-overflow-scrolling:touch;border-collapse:collapse;border-spacing:0;margin:1rem 0}.main-content table th{font-weight:700;background-color:#159957;color:#fff}.main-content table td,.main-content table th{padding:.5rem 1rem;border-bottom:1px solid #e9ebec;text-align:left}.main-content table tr:nth-child(odd){background-color:#f2f2f2}.main-content dl{padding:0}.main-content dl dt{padding:0;margin-top:1rem;font-size:1rem;font-weight:700}.main-content dl dd{padding:0;margin-bottom:1rem}.main-content hr{height:2px;padding:0;margin:1rem 0;background-color:#eff0f1;border:0}code span.kw { color: #a71d5d; font-weight: normal; } \ncode span.dt { color: #795da3; } \ncode span.dv { color: #0086b3; } \ncode span.bn { color: #0086b3; } \ncode span.fl { color: #0086b3; } \ncode span.ch { color: #4070a0; } \ncode span.st { color: #183691; } \ncode span.co { color: #969896; font-style: italic; } \ncode span.ot { color: #007020; } \n</style>\n\n\n\n\n\n</head>\n\n<body>\n\n\n\n\n<section class=\"page-header\">\n<h1 class=\"title toc-ignore project-name\">ggplot绘图手册</h1>\n<h4 class=\"author project-author\">赵子茜</h4>\n<h4 class=\"date project-date\">2022-08-09</h4>\n</section>\n\n\n<div id=\"TOC\" class=\"toc\">\n<div class=\"toc-box\">\n<ul>\n<li><a href=\"#全局主题设置\" id=\"toc-全局主题设置\">全局主题设置</a></li>\n<li><a href=\"#散点图\" id=\"toc-散点图\">1. 散点图</a>\n<ul>\n<li><a href=\"#两个变量\" id=\"toc-两个变量\">1.1 两个变量</a></li>\n<li><a href=\"#环绕式散点图\" id=\"toc-环绕式散点图\">1.2\n环绕式散点图</a></li>\n<li><a href=\"#抖动图\" id=\"toc-抖动图\">1.3 抖动图</a></li>\n<li><a href=\"#数值图\" id=\"toc-数值图\">1.4 数值图</a></li>\n<li><a href=\"#气泡图\" id=\"toc-气泡图\">1.5 气泡图</a></li>\n<li><a href=\"#边际直方图箱线图\" id=\"toc-边际直方图箱线图\">1.6\n边际直方图/箱线图</a></li>\n<li><a href=\"#相关系数图\" id=\"toc-相关系数图\">1.7 相关系数图</a></li>\n</ul></li>\n<li><a href=\"#偏差\" id=\"toc-偏差\">2 偏差</a>\n<ul>\n<li><a href=\"#发散条形图\" id=\"toc-发散条形图\">2.1 发散条形图</a></li>\n<li><a href=\"#带标记的发散型棒棒糖图\" id=\"toc-带标记的发散型棒棒糖图\">2.2 带标记的发散型棒棒糖图</a></li>\n<li><a href=\"#发散性点图\" id=\"toc-发散性点图\">2.3 发散性点图</a></li>\n<li><a href=\"#面积图\" id=\"toc-面积图\">2.4 面积图</a></li>\n</ul></li>\n<li><a href=\"#排序\" id=\"toc-排序\">3 排序</a>\n<ul>\n<li><a href=\"#有序条形图\" id=\"toc-有序条形图\">3.1 有序条形图</a></li>\n<li><a href=\"#棒棒糖图\" id=\"toc-棒棒糖图\">3.2 棒棒糖图</a></li>\n<li><a href=\"#点图\" id=\"toc-点图\">3.3 点图</a></li>\n<li><a href=\"#坡度图\" id=\"toc-坡度图\">3.4 坡度图</a></li>\n<li><a href=\"#哑铃图\" id=\"toc-哑铃图\">3.5 哑铃图</a></li>\n</ul></li>\n<li><a href=\"#分布\" id=\"toc-分布\">4 分布</a>\n<ul>\n<li><a href=\"#直方图\" id=\"toc-直方图\">4.1 直方图</a>\n<ul>\n<li><a href=\"#连续变量的直方图\" id=\"toc-连续变量的直方图\">4.4.1\n连续变量的直方图</a></li>\n<li><a href=\"#分类变量的直方图\" id=\"toc-分类变量的直方图\">4.1.2分类变量的直方图</a></li>\n</ul></li>\n<li><a href=\"#密度图\" id=\"toc-密度图\">4.2 密度图</a></li>\n<li><a href=\"#箱线图\" id=\"toc-箱线图\">4.3 箱线图</a></li>\n<li><a href=\"#点图箱线图\" id=\"toc-点图箱线图\">4.4 点图+箱线图</a></li>\n<li><a href=\"#塔夫特箱线图\" id=\"toc-塔夫特箱线图\">4.5\n塔夫特箱线图</a></li>\n<li><a href=\"#小提琴图\" id=\"toc-小提琴图\">4.6 小提琴图</a></li>\n<li><a href=\"#金字塔图\" id=\"toc-金字塔图\">4.7 金字塔图</a></li>\n</ul></li>\n<li><a href=\"#组成\" id=\"toc-组成\">5 组成</a>\n<ul>\n<li><a href=\"#华夫饼图\" id=\"toc-华夫饼图\">5.1 华夫饼图</a></li>\n<li><a href=\"#饼图\" id=\"toc-饼图\">5.2 饼图</a></li>\n<li><a href=\"#条形图\" id=\"toc-条形图\">5.3 条形图</a></li>\n</ul></li>\n<li><a href=\"#变化趋势\" id=\"toc-变化趋势\">6 变化趋势</a>\n<ul>\n<li><a href=\"#时间序列图基于时间序列对象ts\" id=\"toc-时间序列图基于时间序列对象ts\">6.1\n时间序列图：基于时间序列对象（ts）</a></li>\n<li><a href=\"#时间序列图基于数据框\" id=\"toc-时间序列图基于数据框\">6.2\n时间序列图：基于数据框</a></li>\n<li><a href=\"#多个时间序列\" id=\"toc-多个时间序列\">6.3\n多个时间序列</a></li>\n<li><a href=\"#堆叠面积图\" id=\"toc-堆叠面积图\">6.4 堆叠面积图</a></li>\n<li><a href=\"#日历热力图\" id=\"toc-日历热力图\">6.5 日历热力图</a></li>\n<li><a href=\"#坡度图-1\" id=\"toc-坡度图-1\">6.6 坡度图</a></li>\n<li><a href=\"#季节图\" id=\"toc-季节图\">6.7 季节图</a></li>\n</ul></li>\n<li><a href=\"#群体\" id=\"toc-群体\">7 群体</a>\n<ul>\n<li><a href=\"#谱系图\" id=\"toc-谱系图\">7.1 谱系图</a></li>\n<li><a href=\"#聚类图\" id=\"toc-聚类图\">7.2 聚类图</a></li>\n</ul></li>\n</ul>\n</div>\n</div>\n\n<section class=\"main-content\">\n<div id=\"全局主题设置\" class=\"section level2\">\n<h2>全局主题设置</h2>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(plotrix)</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">options</span>(<span class=\"at\">scipen=</span><span class=\"dv\">999</span>)  <span class=\"co\"># turn-off scientific notation like 1e+48</span></span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># theme_set(theme_bw())  # pre-set the bw theme.</span></span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(<span class=\"st\">&quot;midwest&quot;</span>, <span class=\"at\">package =</span> <span class=\"st\">&quot;ggplot2&quot;</span>)</span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>midwest <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;midwest.csv&quot;</span>)</span></code></pre></div>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># The palette with grey:</span></span>\n<span id=\"cb2-2\"><a href=\"#cb2-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>cbp1 <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;#999999&quot;</span>, <span class=\"st\">&quot;#E69F00&quot;</span>, <span class=\"st\">&quot;#56B4E9&quot;</span>, <span class=\"st\">&quot;#009E73&quot;</span>,</span>\n<span id=\"cb2-3\"><a href=\"#cb2-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>          <span class=\"st\">&quot;#F0E442&quot;</span>, <span class=\"st\">&quot;#0072B2&quot;</span>, <span class=\"st\">&quot;#D55E00&quot;</span>, <span class=\"st\">&quot;#CC79A7&quot;</span>)</span>\n<span id=\"cb2-4\"><a href=\"#cb2-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb2-5\"><a href=\"#cb2-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># The palette with black:</span></span>\n<span id=\"cb2-6\"><a href=\"#cb2-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>cbp2 <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;#000000&quot;</span>, <span class=\"st\">&quot;#E69F00&quot;</span>, <span class=\"st\">&quot;#56B4E9&quot;</span>, <span class=\"st\">&quot;#009E73&quot;</span>,</span>\n<span id=\"cb2-7\"><a href=\"#cb2-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>          <span class=\"st\">&quot;#F0E442&quot;</span>, <span class=\"st\">&quot;#0072B2&quot;</span>, <span class=\"st\">&quot;#D55E00&quot;</span>, <span class=\"st\">&quot;#CC79A7&quot;</span>)</span>\n<span id=\"cb2-8\"><a href=\"#cb2-8\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb2-9\"><a href=\"#cb2-9\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb2-10\"><a href=\"#cb2-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>ggplot <span class=\"ot\">&lt;-</span> <span class=\"cf\">function</span>(...) ggplot2<span class=\"sc\">::</span><span class=\"fu\">ggplot</span>(...) <span class=\"sc\">+</span> </span>\n<span id=\"cb2-11\"><a href=\"#cb2-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">values =</span> cbp1) <span class=\"sc\">+</span></span>\n<span id=\"cb2-12\"><a href=\"#cb2-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_fill_manual</span>(<span class=\"at\">values =</span> cbp1) <span class=\"sc\">+</span> <span class=\"co\"># note: needs to be overridden when using continuous color scales</span></span>\n<span id=\"cb2-13\"><a href=\"#cb2-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme_bw</span>()</span></code></pre></div>\n</div>\n<div id=\"散点图\" class=\"section level2\">\n<h2>1. 散点图</h2>\n<div id=\"两个变量\" class=\"section level3\">\n<h3>1.1 两个变量</h3>\n<p>展示两个变量之间的相关关系，最常使用的是<strong>散点图</strong>。在\nggplot\n中，使用<code>geom_point()</code>绘制。此外，默认情况下，<code>geom_smooth</code>会绘制一条平滑线（基于\nlosses\n），可以通过设置<code>method=&#39;lm&#39;</code>来调整以绘制最佳拟合的线。</p>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Scatterplot</span></span>\n<span id=\"cb3-2\"><a href=\"#cb3-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>gg <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(midwest, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>area, <span class=\"at\">y=</span>poptotal)) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-3\"><a href=\"#cb3-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">col=</span>state, <span class=\"at\">size=</span>popdensity)) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-4\"><a href=\"#cb3-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_smooth</span>(<span class=\"at\">method=</span><span class=\"st\">&quot;loess&quot;</span>, <span class=\"at\">se=</span>F) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-5\"><a href=\"#cb3-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">xlim</span>(<span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"fl\">0.1</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-6\"><a href=\"#cb3-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">ylim</span>(<span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"dv\">500000</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-7\"><a href=\"#cb3-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;Area Vs Population&quot;</span>, </span>\n<span id=\"cb3-8\"><a href=\"#cb3-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Population&quot;</span>, </span>\n<span id=\"cb3-9\"><a href=\"#cb3-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;Area&quot;</span>, </span>\n<span id=\"cb3-10\"><a href=\"#cb3-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Scatterplot&quot;</span>, </span>\n<span id=\"cb3-11\"><a href=\"#cb3-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption =</span> <span class=\"st\">&quot;Source: midwest&quot;</span>)</span>\n<span id=\"cb3-12\"><a href=\"#cb3-12\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb3-13\"><a href=\"#cb3-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">plot</span>(gg)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0HfFNV/8Z/3YNuRpmCyhIREFkOBByIMpygIKIICOIWRUHlfVVw8aqIG3z1dYCiCAqiTAcqgoAIoizZmwIFWrrb+z/P4X9j0iZp2iZpcvscPiH3nn2+J7158jsrxFBO6EiABEiABEiABEiABEjATwRC/VQOiyEBEiABEiABEiABEiABTYAClB8EEiABEiABEiABEiABvxKgAPUrbhZGAiRAAiRAAiRAAiRAAcrPAAmQAAmQAAmQAAmQgF8JUID6FTcLIwESIAESIAESIAESoADlZ4AESIAESIAESIAESMCvBChAPcA9bdo0GTlypFx88cXSt29fGT9+vGzevNmDlBWLcvjwYYcMit87BPro5pdffpHhw4fLvn37ylVCZdS5XBVlIhIgARIgARIgAb8RoAB1gzorK0sLzoEDB8rSpUslOTlZfv/9d3niiSekffv2MnfuXDepyx+Un58vPXr0kLffftuWyZw5c+Sss86y3fvrAkJ7ypQpcvTo0TIV6awNZcqAkUmABEiABEiABCxLgALUTdd++umnMnPmTJk4caKsX79evvzyS9myZYts3bpV4uPjpX///pKTk+Mmh/IFZWdny4IFCxwSf//993Ls2DEHv0C+cdaGQK4v60YCJEACJEACJOA/AhSgblgvWrRIoqKi5O6773aIdcYZZ8jo0aPl5MmT8t133zmE4SYvL09++ukn2b59e4kweOTm5mpBO2/ePG1RhaXVdEi7a9cufZueni47duyQgwcPyokTJ7Qf7uFv72CdRD2WL18u9nmZcfbu3avTZGZm6ngZGRk6CP5Ii8Ow/vzzT13ngoICM5nbd9RzzZo18s033+g62kd21gYeuGVPiNckQAIkQAIkUMUJ4ChOOucEnnrqKRxTarz77rslIhQWFhpKSJbwf+ihh4yYmBgjLCxMpz3nnHOMhQsX2uK9//77RlJSkg4LDQ3V7zVq1DC+/vprHUeJOu2Hcs1Xr169bNfwU+JXx1VCz3j00UcN5INXSEiIkZCQYHzwwQe28nDRuHFj46677jLUEL7Op1atWoYaItf3gwYNMi666CIjIiLCiI2NNRITE43Jkyfb0v/vf//Taf744w+bH9rTqFEj7W+287LLLjN2796t4zhrgxLGtvS8IAESIAESIAESqNoEYP2ic0Fgz549RvPmzbXQatmypQFxqSx+hrIgOk3xzDPPaCH4+uuvGxBcmzZtMjp37mwoi6mhhuqNdevW6bzU0L2hhvENZUE11LC+Ub16deP000/XeSoLpKEspzre2LFjDbWIx1DWT2PEiBFGeHi4vkc6uCeffFLHGzNmjLFz505DWUYNZa3VfmrOqo6D/yBAIyMjjT59+hgff/yx8eGHH+owU5D269dPt8kUtBCVpmguLkBRDkTuhRdeqNsHITt//nwDorZdu3YGhLmzNtgqwwsSIAESIAESIIEqT4ACtJSPgBqi1qIuNTVVCztYICHmbrjhBkMNjTukhmVzwIABDn5r1641rrrqKmPlypXGkiVLjMGDBxtpaWkOcYYMGaLzhpiDO378uL5Xq+1t8R544AEtQE0PxIGl9fzzzze99DvygEW1W7duNn8IUIhGU7iaARCgiGsvqCEg4X/55ZfraMUFKISwmpZgs3aaeUHUgo2aM6u9nLXBjMt3EiABEiABEiCBqk0gXIkGOjcEsPL91VdfFTUsLcqCKd9++6189dVXenES5lwuXrxYmjVrpudBYpHQpZde6pBbq1atBHM9TXfJJZcI5mAi7caNG/VcUOQJhwVNcXFxZlS378q6KljooyyPMmnSJIe49erV03W190Qd1RC7vZe+VhZahzLVUL7Ab9asWSXiwkMNxQvaVL9+fYfwnj176nsluOX66693COMNCZAACZAACZAACdgToAC1p2F3XVRUJPv379eiDSJUza+U1q1b65eyRmqBBqH1zjvv6FXyWB0PpyyKdrk4XmLx0Z133inKWihY7KOsqqKGreW0007TC5bUbyHHBG7u1PQAHYptocyy7aPXrl1bC9ro6GjtXadOHftg2zXqUNylpKTIkSNHtMAtHoZy1XSE4t56iyo1RUCnKxFIDxIgARIgARIgARKwI8BV8HYw7C9hpVTzMkXNw7T3tl1fd911ohbiyOrVq7UfrI5wWLFe3GFLJbVARx577DFRQ9ry3HPP6fsDBw5oa6qaZ6qTlEWAmhZINXyvV7BjFbv9C9tGmeITmUNAO3OHDh0q4Y1N588880xRQ/wlwho0aOB0U3q0G6JaLboqkYYeJEACJEACJEACJGBPgALUnobdtVoNrk8+wl6g2G6ouNuwYYPeLqlLly46CIINaeyH2xGg5n3qTeVhqcRWSbB2jho1yjaEreZsyrJly3Qeav6lfscwOBwEnenUwiCBVRYvOIhWDNerFe9mFP2OYXlskn/rrbc6+Lu6wf6iENumwzZOalGRtsyafvbvyBtTETB9wN7NmDFD35577rn63Vkb7OPzmgRIgARIgARIoOoSoAB10/dqGyZtObzgggv00DmG26dPny5q6yPp2LGjFpO33XabzgH7heKEJJxYhHdsVv/FF1/Igw8+qOP27t1bmjZtKmoVuT7hCEdUYh4ojvb866+/dB6mEMRcTbXQSdTWTLo87KsJcQvx+Z///EdWrVqlN8JHOX///bcgb7VqXecH4fnbb7/po0PdNM0WhH1AUQfM7cRLLa4SCGEcN+rMqRX3uuxrrrlGz39Fe6ZOnaqtu7AKY0oBnLM2OMuPfiRAAiRAAiRAAlWQQNVeg1V667FdUvfu3fVWSerjoVd6Y69MJcAMNXztkIESiIYSiIayTOp4iK8sgnr7JUTEtk5YJY/0CMNq8nvvvddQG97re3N7JMRVw/V6tT3iYSX9tm3bDGVl1fGuvfZaRDFQ3iuvvGLbVxTbJ2Hbp/fee0+Hm/9hFfzVV19t3tresdpdnW9vKOGo9xDFPqLKwmmoTfRtcYqvgkeAGurXq+9NHjVr1tTbRGEbJ3tXvA32YbwmARIgARIgARKougRC0HQlJOg8IIBFSTj9CMPtruZUIhtYKtVentoK6GzxDyyasBxijikW7rhyiIfTi7AoyHRYHKS2VBK1cbzppd+RH44HtY/rEMHJTYsWLQSnOmFVP05XQnnOFiU5Saq9cDoT5pC64+GsDa7yoz8JkAAJkAAJkEDVIEABWjX62Wkr7QWo0wj0JAESIAESIAESIAEfEOAcUB9AZZYkQAIkQAIkQAIkQAKuCbge/3WdhiEWIaCO7dT7d1qkOWwGCZAACZAACZBAkBDgEHyQdBSrSQIkQAIkQAIkQAJWIcAheKv0JNtBAiRAAiRAAiRAAkFCgAI0SDqK1SQBEiABEiABEiABqxCgALVKT7IdJEACJEACJEACJBAkBChAg6SjWE0SIAESIAESIAESsAoBClCr9CTbQQIkQAIkQAIkQAJBQoACNEg6itUkARIgARIgARIgAasQ4D6gHvQkjt9UZ7xL27ZtpXfv3h6k8G6UN954Qx+Ref311zvN+PXXX9fHYfbo0cNpuCvPxYsXy88//2wLDg0NFXVOvXTs2FG/bAE+vvjtt9/k+++/lwcffNDjknAMKI4kLSwslPHjx8ugQYP00aYeZ8CIJEACJEACJEAClUaAFlAP0M+cOVML0BEjRkhBQYEHKbwbZdu2bTJy5EgttornvGPHDrnnnnsE4rGsDgL0+eefl6VLl+rXt99+K//973+lU6dOcvPNN5c1u3LHX716tbz00ksep8cG+pMnT9bxTQG6fft2j9MzIgmQAAmQAAmQQOUSKLtqqdz6VkrpEGWjRo0SWEK//PJLv9dhyJAhcujQIVm0aFGJsj/44AM57bTT5LLLLisR5olH/fr1ZcmSJfr13Xffydq1a+Wdd96R6dOna1HqSR7+jrNixQpbkZGRkZKfny+XXHKJzY8XJEACJEACJEACgU2AArSU/tmyZYv8+OOPcu211+rXm2++6ZAiNzdXhg0bJitXrhQMkY8ePVqKiooEQ8SPP/64YFj8lltu0QLPPiHSvfDCC9K3b1+54oor5N5775WdO3faR7Fdn3XWWXLBBRfIRx99ZPMzLyBAb7/9dpsFdM2aNTJ48GC59NJLdb1+/fVXM6rH7zfddJOEhIRoMYpEsDK+9dZbuv19+vTR1kqIPtO99tprMm/ePO3fs2dPeeSRR2TDhg1msEyaNEk+/fRT2z0unnjiiRJMzAju2Lz44osCqy9+CDz77LPaIg3+GzduNJPLsmXL5NZbb5XLL79c7rvvPtm9e7ctbPny5TJhwgRZtWqVQNhfddVVgjzRRjoSIAESIAESIAH/EKAALYXze++9Jy1btpTWrVvreYYYpt68ebMtFYQYLIYDBgyQqKgoOXbsmGRnZ8t5550n33zzjVx99dUSHh4uEGYffvihLR1E5yeffKKF4pVXXinIF1Y8iFdnDmLpiy++0FZYM/ynn34SDD1DcMIdPHhQunbtKtHR0Vp8QkReeOGFDuLMTOvufcGCBWIYhjRq1EhHg8B99NFHpUmTJtKuXTt57rnnBHVGHDjEv+2222TGjBkC8bp+/Xrp1q2b7N27V4d//fXX8ssvv+hr8z8IUsRz5tyxad68uVSrVk3q1asnLVq00LzA3yxr7ty50rlzZzl+/Lj+QYA5ruecc45gGgMcflBAEONHQYMGDfR0g7Fjx2pR6qwu9CMBEiABEiABEvABASUi6FwQUPM9jbp16xpqAZKOocShoUSZoRbL2FJkZGRAhRlKoNn8nnnmGUOJJEOJUQe/1NRUA3kcPnzYUJZP46+//rKFK5Gm8zlw4IDNz/4C5cTFxRnKCmrzHjp0qKEseLZ7NafTUGLXUEJU+6EsNbfSoRxbZHWhLJVGSkqKMW7cOP3CfZcuXQw1rG2oeaAG2q8sqLpeyuJoS2r6zZ49W/v16tXLUAuCDCXGbXHOPPNMQ81b1ffKEmncf//9tjBcNG3a1FBCUPtNmTLFUIJSX3vCRolg4+mnn9bxlbVU1w9th0P/DBw4UF+b/8Gvf//++lZZjHX8devWmcHGHXfcYVx00UW2e16QAAmQAAmQAAn4lgBXwbsR9fPnz5d9+/aJEn62uZ+wpv3vf//TFjNYGk2HleOmw7B37dq19RCx6QcLHSyUe/bs0ZY3WAB///13ndemTZts8y1hPXXmUIcbb7xRpk2bphcIIR7yQF1Mhzoo4actld27d9dWSqwOr169uhmlxHtOTo4eskZAWFiYTt+vXz9R4lbfY0gfll0M6ZsOVlC0D9MOrrnmGu2NOaiw9JoO5WNxUVkd6lpWNmYZ6enpenhe/QAwvfS7EsjaSmt6xsTEaKuoeY85tOWZqmCm5zsJkAAJkAAJkEDZCPyjGMqWrkrEfvfdd7V4wzZH9g5CB8PNmGdoOnuRh/DY2FjbvEzEwXDvmDFjtB9EH4Qb5ipibieGybHqHPfuHIbhL774Yr0gCSvYIaQgrkwHkQohhbmimJOJlfMPPPCAHrrHkLgzh6FsZ4ubzLiYUpCUlKSHvU0/DO3XqlXLYd6ksjKawfpdWVYdpguo31EO4Xl5eQ735k152SA96gqHNtk7ZXl2qCv6xt5hB4Hi9bMP5zUJkAAJkAAJkIB3CVCAuuCZlpYmmE8IEaqGdB1iwRqIRTn2AtQ+QuPGjWXhwoV6f0pze6StW7fqPTdr1qwps2bN0gtwMC8RwhQO8zvhXM0BRdj555+vrZvYFgoCE+VHREQgSDvMTYVVEsITLyyEgiUSi4RcCVAzrat3tAWWW1hr27Rpo6Pt379f1BC2YO6k6TCH1d5B1GLfVDhYUDMzM23B2MoKlmBnDhywKr+sbJAXLJlYFQ/LNYS66TBH1ay76cd3EiABEiABEiCByiPARUgu2MOKCDGD1e/FHRblYDU1RJkzN3z4cC2wnnzySYE1FIINFs6vvvpK54nha6y6hrCDw+r3xx57TF/DAujOwQqKYXeINFwXd1gMBIGK/CFAUT6G5cvrsNioYcOGouaJ6gU8EI5Y5Q4LqL3Iw/ZNWLCFqQF4BxtToKv5nrrtf//9t64TtrRC/ZxZHT1hA2szVtmDq73DFAI1n1NbgLHwCXWZOnWq7ivsNkBHAiRAAiRAAiQQIAR8O8U0eHNXK98NtVLaaQOUsDHUsLRevGIuQlKbuTvEVftoGkqkGcr6pxfoXHfddYb9AiMlHrW/ElyGsoIaakW8ztN+kZFDhv9/o/YDNZTV01Diz1mwXtjTrFkzvQgKC5LU1lCGWhHuNC4WHamV7U7D7D2xYEdZMw1lzdXtUSv8jT/++MMWBYuQlIXVwMIjLGDCwi21d6otXM1/NTp06KAX/6h5s8add95pYGGSs0VISFQaG7UVli5H7WFqFF+EhP5QPxAMJUZ1XREH8U2HRUhKwJq3+h2LxtQuBw5+vCEBEiABEiABEvAdgRBkHSBa2JLVwOIjDLvDmlrcYR6kWvUtSrAVD6rwvRK7eu6m/UKpimZ69OhRvT9ocnKyQ1Y4nhRTCXBkKNqL9mCeaHEHi298fLyeH1s8rPh9aWwQjqH84vM5zXywlyimUWCjfToSIAESIAESIIHAIkABGlj9EZS1sRegQdkAVpoESIAESIAESMCvBDgH1K+4rVkYVsnDsklHAiRAAiRAAiRAAp4QoAXUE0qMQwIkQAIkQAIkQAIk4DUCtIB6DSUzIgESIAESIAESIAES8IQABagnlBiHBEiABEiABEiABEjAawQoQL2GkhmRAAmQAAmQAAmQAAl4QoAC1BNKjEMCJEACJEACJEACJOA1AhSgXkPJjEiABEiABEiABEiABDwhwLPgXVDChuonT550EVp+b3Pff2cbtZc/18BJyfYFTl+Upybsv/JQC6w06EOrPl9Amp/RwPq8lbU2vuy/GjVqSEpKSlmrxPiVRIDbMLkAj3PW1XGcLkLL752ZmalPRXJ2MlL5cw2clDihCGey42VFl5OTo78AY2JirNg83Tb0oTpC1pLtw+lZ6MNq1apZVqSpo4LFqp9PfCj5DA3uP01fPUOPHDkiOKVv1KhRwQ2oCtWeFlAXna3OPZcHHnjARWj5vffv3683bY+Liyt/JgGcEl8O6qx6ywqY9PR0KSwsFPzStqJD29CHiYmJVmyeFp/ow1q1aln2RxKOzLWyFYjP0OD+0/TVM3Tjxo0yb9684IZTxWrPOaBVrMPZXBIgARIgARIgARKobAIUoJXdAyyfBEiABEiABEiABKoYAQrQKtbhbC4JkAAJkAAJkAAJVDYBCtDK7gGWTwIkQAIkQAIkQAJVjAAFaBXrcDaXBEiABEiABEiABCqbAAVoZfcAyycBEiABEiABEiCBKkaAArSKdTibSwIkQAIkQAIkQAKVTYACtLJ7gOWTAAmQAAmQAAmQQBUjQAFaxTqczSUBEiABEiABEiCByiZAAVrZPcDySYAESIAESIAESKCKEfDLUZw42m/9+vUOaDt16mS737Rpk+zcuVPatm3rcMTh4cOHZfXq1dKoUSNp1qyZLT4uvJnGIWPekAAJkAAJkAAJkAAJ+JSAXyygK1eulIkTJ8rs2bNtL7NVL7/8sg5bs2aNDBkyRHbt2qWDcD948GDZvHmzjB49WqfzRRozT76TAAmQAAmQAAmQAAn4h4BfLKBbtmyRPn36yK233urQqh07dsiPP/4oM2fOlNDQUPnkk09k2rRpMmbMGJk0aZKMHz9eWrduLf369ZOhQ4dKz549Zd++fV5N41Ah3pAACZAACZAACZAACficgN8EKIbcp0+fLk2aNJF27dpJSEiIbNu2TVq1aqXFJ1qKIfh58+ZJQUGB7NmzR4fBPzU1VWJjY2Xv3r2yfft2r6VB3qY7fvy4HDhwwLzVdcjPz7fde/OisLBQfJW3N+tZnrzQNvQtflBY0RmGIXhZtf+KiooEL6u2D88WOLQP7bSis3L/mf1l5TZa/RmKvvPFM9T82zY/I3wPfAJ+E6BAARH6wQcfyIwZM+Q///mP7N+/XxITE22UEhIS5MiRI3Lo0CGpVq2aFjJmIOIdPXrUq2nMvPG+ePFiGTt2rM2rRYsWgjmovnBZWVmCF13wEvDVZyNQiOTk5ARKVXxSj/T0dJ/kGyiZWv3zefLkScGLLngJePszeuzYMYF4pwseAn4RoO+++64kJSVpq1jv3r3l6quv1hbOsLAwhw8MfsHExMRIcX/gRFh0dHSJsIqkse+mLl26yEcffWTzeu211yQlJcV2760LiGi0ES8ruuzsbAkPD5eIiAgrNk+woA4POfsfTlZqKKwT6EP8ALSiy8vL031oPo+s2MaMjAyJj4+3YtN0m/AMxYgYvg+s6KrCMxTPGRicvOkgaKEd6IKHgM8FKB74WOFuirnIyEg9pI7h7po1a8q6detstPBgqVOnjlSvXl3/us3NzZWoqCgdjrC6devqYXJvpbEVrC5q1KjhsAIf9TTLto/njWsINF/l7Y36VSQPDG1CfFq1fbBcY/jIqu2DuMbfrFXbh76Dw9+3Vb+sYBm0av+Zzyb0nVXbyGeo2ctle7eq0aNsFIIrts8n6uFD8dJLL8ny5cs1mQ0bNuhh9jZt2kj79u319ky7d+/WFs65c+dKhw4dtAWtY8eOMmfOHJ1m6dKlkpycrF/eTBNcXcXakgAJkAAJkAAJkIA1CPjcAooFKffff79MmTJFv9LS0uTRRx/VIhMm+DvuuEOvcIeFtGHDhjJgwABNduTIkbbtl7CgZdy4cdrf22ms0Y1sBQmQAAmQAAmQAAkED4EQNSR1akzKD3XGSnMISIhSe4chBwy3x8XF2Xvra0wsxnyt4s7baYrnP2zYMJk6dWpx7wrfY+EV5mc5a2uFMw+ADDBH0spD8Fi8gmFqTNmwokPb0IdWneOKxVXow1q1all2CB7TlcwpT1b8jPIZGty96qtn6MaNG/UuOqNGjQpuQFWo9j63gNqzdPWlBsHiav6GM/GJPL2dxr6evCYBEiABEiABEiABEvAdAZ/PAfVd1ZkzCZAACZAACZAACZBAMBKgAA3GXmOdSYAESIAESIAESCCICVCABnHnseokQAIkQAIkQAIkEIwEKECDsddYZxIgARIgARIgARIIYgJ+XYQUxJxYdRLwGYH8Y9ske+ciKcg8IGGxNSXmtG4SWf0sn5XHjEmABEiABEigsglQgFZ2D7D8KksAO6AdXnyXZG2eJUZRvhgFWSJhMRISGiZxzW+UGpe9VmXZsOEkQAIkQALWJkABau3+ZesCmMDB2X2U5XOJqqHdVryF2WIUimT89aHAMlr7+q/UvrmcKRPA3ciqkQAJkAAJlIMAv9nKAY1JSKCiBE5u/Uqyd32rsrETn/aZFuZJ7sHVkrnhY3tfXpMACZAACZCAJQhQgFqiG9mIYCOQsf59pT2L3FbbyDshGeu8fxqX20IZSAIkQAIkQAJ+IEAB6gfILIIEihPIP/JXcS+n9/nHtjr1pycJkAAJkAAJBDMBCtBg7j3WPWgJhIR6Nv06JDQiaNvIipMACZAACZCAKwIUoK7I0J8EfEggqu75KveQUkuITD231DiMQAIkQAIkQALBRoACNNh6jPUtM4H8wgLZfGSf/Jm2WzJys8uc3hcJEtqMkNDIhFKzTmx7b6lxGIEESIAESIAEgo2AZ+OAwdYq1pcEFIFDJ4/LhGWfy5wtqyRMbWUUGhIi+UWF0q72mTKh6wBpmlKn0jhF1WojKZe8JIfnD3FZh+TOz0hMgy4uwxlAAiRAAiRAAsFKgAI0WHuO9XZL4ERullz80RNyXL1DdNq773etlws+GCs/DnxazqpR3z7Ir9fxZw2QsKgkOfLdg1KkVryLYEDCkJDwaEm+8CmJb3GzX+vDwkiABEiABEjAXwQoQP1FmuX4jUBWfq6c8eZdLsszd968eubzsrD/E9IosZbLuL4OiD3jKok5vYfkp2+RwpMH1UFINSQipZk+DcnXZTN/EiABEiABEqgsApwDWlnkWa7PCMzcuFziIqJLzT8jL1teWjG31Hi+joCTjiKV6IxpcLFE1mhB8elr4MyfBEiABEig0glQgFZ6F7AC3ibwxeYVkpmfU2q2GJpfuH1tqfEYgQRIgARIgARIwLsEKEC9y5O5BQCB3SeOeFwLDNfnqVXydCRAAiRAAiRAAv4jQAHqP9YsyU8E4iJLH343qxKqhr/DQ/lnYPLgOwmQAAmQAAn4gwC/ef1BmWX4lcCF9ZvrbZc8KbRZ9Xpqeyb+GXjCinFIgARIgARIwFsE+M3rLZLMJ2AIDGx5sVSLiCq1PiHqJKKRba8oNR4jkAAJkAAJkAAJeJcABah3eTK3ACDQXFk1H+zQWyJDw1zWJkKF3XHuZXJ10/Yu4zCABEiABEiABEjANwQoQH3DlblWMoG7210pky8fIsnR1SQmPNJWm8jQcEmMihVYSSd0GWDz5wUJkAAJkAAJlJdAXl6e5OSUvvuKff4nTuAAkqrruBF91e17y7f8hrPOl64Nz5Z5f6+WVQe26dXuZ9doIFc1biuNk2tbvv1sIAmQAAmQgO8JpKeny/nnny9z5syRpk2belTg3XffLbVr15bHH3/co/hWjEQBasVeZZtsBGrEJsitrbrpl82TFyRAAiRAAiTgJQLHjh2TTZs2lSm3FStWyNVXX12mNFaLTAHqokeLiookKyvLRWjFvPPz832Wd8VqVvHUaBvYFRY6nr9e8ZwDIwe0y5efjcpuJdpWUFBg6c8nGGdnZ0uoRbffwmfUV8+uyv58muXzGWqSCL53Xz1DMfxtGOZBy97nApE5ceJEWblypSQnJ8vll18uQ4YM0X9rY8aM0QXCmjls2DAdtm3bNpk8ebJs3LhRYmNjtYX0vvvuk8jISHnxxRdlx44d8uWXX0pYWJiY6T/44AP56quv9FB+t27d5J577pHwcOvKNOu2zAufv5CQEC/k4jwLX+btvET/+rJ9/uXtrdLMfjPfvZVvoORjtgvv5nWg1M2b9bBy20xOVm8j22f2tGfvvuY1cOBAOXr0qNxxxx1y+PBhefjhhyUtLU0efPBBad26tcyYMUPOOeccPay+fft2adWqlVx33XVyyy23yLp16+Tpp5/W8V944QVp3ry5VKtWTerVqyctWrTQDYQ4nT59us4/Pj5eEO+HH36QL774wjMAQRiLAtRFp8E6EhMT4yK0/N74FRUREeGTvMtfK++lxK9btC8qqvRtkLxXqv9yMieZ++Kz4b9WuC4J/QcLqFXbhy+pkydPSnR0tLY8uCYRvCGw7lq1/9ArfIYG72cTNffVMxTfOb4UocuWLZNnn31WbrvtNt0BEJF4XqLcm266ScaOHSs33nijngM6f/58fT116lQ90nLzzTdr8bl8+XKdtmfPnvLvf/9b2rVrp4fhN2/eLK+99pp89NFH0r9/fx3nhhtukCZNmmgR2qVLF+1ntf8oQK3Wo2wPCZAACZAACZCAVwnceuutMnLkSPnwww/lqquu0sLx7LPPdlpGjx49pGvXrvLtt9/Khg0b5K+//pLFixdr66izBKtWrdLTBzC8v3btWluUuLg4QZhVBSi3YbJ1NS9IgARIgARIgARIoCSBl19+Wc/ZbNasmZ7b2bJlS3n00UdLRlQ+GHI//fTT9RxRLDbCcLw7EQmrPuZ6wpqK0VfzhTmgrkSu04KDzJMW0CDrMFaXBEiABEiABEjAfwSwqA9zPHv16qVfWKyJOZ0TJkzQQ+nm0L+5CArD6xiih9UTi4zgfvrpJ5eLcxs3bixYWNe7d2+54IILdHwM77///vseb+ukEwXZf7SABlmHsbokQAIkQAIkQAL+I4A51W+88Ya2eMJaiXnWWICERUSYT56SkqIrs3r1ajl+/LgeaseCJcSDKMVq95kzZ0pubq6t0tWrV9fD8/v37xeseIdlddy4cfLnn3/qebIQsY888ogkJCTY0ljtggLUaj3K9pAACZAACZAACXiNACycWCS0detWLTohOGHd/Pzzz3UZEImY94nFRk899ZReGZ+YmCipqalSo0YNefXVV/UWTkhvnn50zTXXaFHaoUMHvXAXIhWWVqykR5olS5bo+aa4tqrjELxVe5btIgESIAESIAES8AqBjh07yvfffy+ZmZnaslmzZk2HfL/55hvJyMjQe35i2H3p0qVy5MgRve8ntlWCu/fee21pRowYIbfffrvedQSesIBipT0sqNiJBBZSqzsKUKv3MNtHAm4IrD+UK7M2ZMi29Hw1VCTSOCVcejQMk/aJbhIxiARIgASqKAGsTMfLmTOFphlWmojEpvR42TtYTquKowCtKj3NdpJAMQLfbj8pk1cck9zCf04POZRVKMv2iDwq0dKlUWyxFLwlARIgARIgAe8Q4BxQ73BkLiQQVATWHMiRicvSHcSnfQP+88tR+ePgPxPm7cN4TQIkQAIkQAIVJUABWlGCTE8CQUhgxvoMt7UuKBKZ8ecJt3EYSAIkQAIkQALlJUABWl5yTEcCQUwAcz5Lc38fLT1OaXkwnARIgARIgAScEaAAdUaFfiRgcQKG/DPv01VTS4/hKiX9SYAESIAESMA9AQpQ93wYSgKWJFAvvvT1hw0SSo9jSThsFAmQAAmQgM8JUID6HDELIIHAI3Bt83iJjQhxW7Hrzjq1d53bSAwkARIgARIggXIQoAAtBzQmIYFgJ4Atlq5sXM1pM8KULu3fMl4uaBDjNJyeJEACJEACJFBRAhxjqyhBpieBICUwtG2SnJ4UITPVRvQHMwulSE36rBsfJr1OD5erWlSdzZCDtPtYbRIgARIIagIUoEHdfaw8CVSMwKVnVBO8DByDpFxRUZE+aq5iuTI1CZAACZAACbgnQAHqng9DSaBKEAgJcT8ftEpAYCNJgARIgAT8RoBzQP2GmgWRAAmQAAmQAAmQAAmAAAUoPwckQAIkQAIkQAIkQAJ+JUAB6lfcLIwESIAESIAESIAESIAClJ8BEiABEiABEiABEvACgcJCtaOIWszpLXfgwAH5+OOPdXYnTpyQ//73v97KutLzoQCt9C5gBUiABEiABEiABIKZAITnl19+KZMmTZKJEyfKjBkzvCJEt2zZImPHjtVo0tLS5K677gpmTA51pwB1wMEbEiABEiABEiABEigbAVgmN2zYIPn5+Xpbu127dsn8+fPLlkkVi00BWsU6nM0lARIgARIgARLwHoGTJ0/KsWPHHDKERRSCtKCgwMGfN/8QoAD9hwWvSIAESIAESIAESKBMBHCQR0RERIk0EKHmIR8lAunBbZj4GSABEiABEiABEiCB8hKIi4uTWrVqOSQPDQ2VhIQEp8LUIWIVvvG7BfS7776TjIwMB+SbNm2ShQsXyuHDhx38cb9gwQJBeHHnzTTF8+Y9CZAACZAACZAACXhKoG/fvlpshoeHS1RUlDRq1EiGDRvmafIqGc+vAnTp0qUybtw4OXr0qA32yy+/rFeMrVmzRoYMGSKYuAuH+8GDB8vmzZtl9OjRMnv2bJ+ksWXKCxIgARIgARIgARIoB4HIyEgZNWqUDB8+XAvPfv36SVhYWDlyqjpJ/HYWPKyZ7777riQlJdno7tixQ3788UeZOXOmwFz9ySefyLRp02TMmDF6K4Px48dL69atBR05dOhQ6dmzp+zbt8+raWyV4QUJkAAJkAAJkAAJVIBAfHx8BVKXnjQ3N7eEsIXhrl69eqUnDrAYfhGgmIT73HPPyd133y3PP/+8DcG2bdukVatWWnzCs23btjJv3jy9amzPnj06DP6pqakSGxsre/fule3bt3stDfI23aFDh+Tvv/82bwWdjJcvHFbF+SpvX9S3LHlafcUfNhjGy6r9h7Zh4rxV24ctUuDy8vJsz52yfL6DIa6VP58mfyt/RqvCMxSawNvPGPNv2/yMWOW9c+fOWvegPWeeeaalFjX5RYDCwtmgQQNp166dw2di//79kpiYaPPDhN0jR44IxGC1atUkJCTEFoZ4GLr3Zhpb5uoCllhzs1f4t2jRwmGqgH3cil5nZ2cLXnTBS8B+GknwtsJ1zb395eC6pMoJKb5lSuXUwnelWv3zmZWVJXjRBS8Bb39Gjx8/rn88By+RqldznwtQWCy/+eYbefPNN0vQxfwI/JI1HX75xcTEaPOyvT/CERYdHV0irCJpzHLx3r17d2nTpo3NC8P/NWrUsN176wJTEWDNxcuKDl8K2I7C2ZYUVmgvFtDhs2k/lcQK7TLbAOsZ+hCrOq3oIKzRh8nJySWGsazSXnwR2/+wt0q7zHbgGQoDBb4rrOis/gzFcZJ4znj7GQrjFedcBtdfhM8F6JIlS2Tnzp3Su3dvTQaWP8znnDBhgtSsWVPWrVtnI4ZfRHXq1JHq1asLNnbFlwVWk8EhrG7duoJzUb2VxlawusC8Dfu5G1jJ5isRhT8SX+Vt36bKuEbbrNw+WOXxsmr/QVxjPraV24e/C7TPql9WVu4/85lm5TZa/RmKvnO1b6bZv+V5x3c2XXAR8PkqeIhNiFBss4RX7dq15Z133pEOHTpI+/btZf369bJ7925t4Zw7d672xwepY8eOMmfOHE0Tq+dhscDLm2mCq6tYWxIgARIgARIgARKwBoFK/cmAOZ933HGHtoimpKRIw4YNZcCAAZrsyJEjbdsv4RcTtm+C83YanSn/IwESIAESIAESIAES8BsBvwvQzz77zKFxvXr1kiuuuEIPt9vPO4MYnTFjhj5ftfhcEW+mcagMb0iABEiABEiABEiABHxOwO8C1FmLMB/L1Zyz4uLTTO/tNGa+fCcBEiABEiABEiABEvAtAZ/PAfVt9Zk7CZAACZAACZAACZBAsBGgAA22HmN9SYAESIAESIAESCDICVCABnkHsvokQAIkQAIkQAIkEGwEKECDrcdYXxIgARIgARIgARIIcgIUoEHegaw+CZAACZAACZAACQQbAQrQYOsx1pcESIAESIAESIAEgpwABWiQdyCrTwIkQAIkQAIkYE0COH78448/1o3LysqSKVOmSHp6ukNjly9fLqtWrXLwC4YbCtBg6CXWkQRIgARIgARIIGAJGEWFkrboTtnxWg3Z/kqCHPr6Vn3mfUUrvGXLFhk7dqzOBsJz+PDhct999zlkiwN+zKPLHQIC/IYCNMA7iNUjARIgARIgARIIbAL7PrlYMte/L0b+SZGifDn59xw58u39Xq80DuFZvHhxUArO4jAoQIsT4T0JkAAJkAAJkAAJeEig8ORByTu4RsU2/klRmCOZf30oRQU5//h54QoC9K233pIRI0bI0aNHvZBj5WUREEdxVl7zWTIJWIfA4u3rZNbmFbL7xGGpF58i1zfrJJef3to6DWRLSIAESCAACRhGgYRExomRl+FQO6OoQGnSIgc/b9z06dNHMOx+7733ykcffeSNLCslDwrQSsHOQknAuwRu++o1+XH3Bjmem2XLePamX6VD3cby5Q2PSGgIBztsYHgRcASKijIlL2uRFORulJCwJImMuVAioloFXD1ZIRJwRiA8rp5EVj9bcvevUMH/bwUNCZewmOoSGhHrLEmF/SZPnixnn322fPnllxXOq7Iy4LdSZZFnuSTgJQJ3zp8ii3escxCfyLpQ/fJevneLPP/LF14qidmQgPcJGEXZcmxfXzmZPklyT86SnBP/kxMHh0j2ieC17HifEnMMdAK1r4UQNCQkIk5Co5Mluu75Uv/2DT6rdnJysrz99tt6KP7IkSM+K8eXGVOA+pIu8yYBHxM4cPKYzPt7teQU5DstyVAPxKm/L5Z9GcE9V8hp4+gZ9AQwdHl0z8VqlFJ9gRqm9f7UkGXWsVeUVfSHoG8jG1A1CIRGJcjpD2RLvYHLpW7/n6ROv4USGh7t08b37t1bunfvHrTD8BSgPv14MHMS8C2BdYd2SmRYhNtCQkJCZM3B7W7jMJAEKoNAYd4mCQmppoq2W7xhV5HsjBl2d7wkgcAnEJF0pkQkneG3ik6aNElSU1P9Vp43C+IcUG/SZF4k4GcC+YUFUuTiy9usimEYkqfi0ZFAoBEoKkxTVQpzWa1T4S6DGUAClifQuXNn2b79lAGhXr16cvKk2ubJzmEofu/evXY+wXNJC2jw9BVrSgIlCDRNqSshJXyLeSgLaPPq9Yp58pYEKp9AaHhtcfcBDkM4HQmQgCUJUIBaslvZqKpC4IykVGmuRGioEpnOHPxrxsQLhGpVd7m5udqSsHXrVtmzZ48UFNAqXNmfibCIxmqeHD6bzj+/0fH9K7uKLJ8ESMBHBDgE7yOwzJYE/EEgLDRUPrr6Pmny1j0Srq4Liv7Zcy4yNFziIqPlu5ufFMSrym737t0ya9YsfTQehOfKlSsFghRH2sXExFRlNJXa9hC1VU1irbfUQqSuSoOqBRsGNu2OUPNCIyU2+WG1HdMFlVo/Fk4CJOA7AlX7W8l3XJkzCfiNQHJ0nOy66y0Z1LKr1FDWzpjwSKkZmyA3t+wsv9z6jMRGRPmtLoFYUFpamkybNk2ys7MlJydHWz4hPuGwl17xOVWB2AYr1ykktJqkNFgmcSlPSEzCEIlJvFMSUqdKdFxPKzebbSOBKk+AFtAq/xEgACsQgMh84ZJb9MsK7fFmGxYuXOgyu1BlGf7111+lW7duLuMwwPcEQkIiJKpad98XxBJIgAQChgAtoAHTFawICZCALwgcPHjQZbaFhYWyc+dOl+EMIAESIAES8A0BClDfcGWuJEACAUIA+6DSkQAJkAAJBBYBCtDA6g/WhgRIwMsEatas6TJHDMGfdtppLsMZQAIkQAIk4BsCFKC+4cpcSYAEAoRAly5dXNakSO0a0KlTJ5fhDCABEiABEvANAQpQ33BlriRAAgFCoEGDBjJo0CBdm6ioKAkPD5fo6GhJSkqSO++8U2JjYwOkpqwGCZAACVQdAlwF76KvYRnJzMx0EVox77y8PJ/lXbGaVTw12oZ9FvPz8yueWQDmgLb58rNR2U1G29B3vvrsV1b7EhISZNiwYbJr1y45ceKEYFi+bt26EhYWZrm24jNqtf4r/rnhM7Q4keC599UzFNus4dhhuuAhQAHqpq8iIiLchJY/CPPOfJV3+WvlnZR4uMDCZNX2Yf9IPOSs2j6sCkcfWrF9aFPjxo21OIP1E3+HVnRWfr6Y/YUfDlb8jKJ9VeEZinZ6u//wvcMFhyAbPI4C1EVf4SGO4TpfOPyh+CpvX9S3LHnCeoYHi1Xbl5WVpQWoVdsHAYo+tGr7TAtJZGSktn6W5bMdLHGxsb5V+8/sAwhQq7aRz1Czl8v27m1BW7bSGbs8BKxpAigPCaYhARIgARIgARIggQAisHbtWpkyZYrs2bPHoVaYLvXOO+/I8uXLtf+yZctkxYoVDnEC/YYCNNB7iPUjARIgARIgARIIaAJFamrW26uOSd/P9so1n+yV135N90p9FyxYICNGjNAi1D7DpUuX6nntn332mfb+5JNPZNasWfZRAv6aAjTgu4gVJAESIAESIAESCGQCY5Ycli82ZUpmniG5hYYs2nZSpqw+5pUqd+zYUT799FOHvCA4W7du7eAXbDcUoMHWY6wvCZAACZAACZBAwBA4llMo6w7mOtQnr1Bk3pZMyS2o+Mr8li1b6sW9f/zxhy4DC9W+++476d69u0OZwXZDARpsPcb6kgAJkAAJkAAJBAyB/CJDYiNKHvlbWCSCoXlvuBtvvNFmBV28eLHggA0spgxmRwEazL3HupMACZAACZAACVQqgRoxYVIv3nFToVClRyPDQiQmwjsyCwLUfr5n//79K7XN3ijcO2S8URPmQQIkQAIkQAIkQAJBRgD7j064pKauNSyhCVGh0jg5QqZfX8drLWnatKnExMTIypUr9Wp3d0cMe61QH2fkKNl9XBizJwESIAESIAESIAGrEYhXonPegHqyPT1fwpX587RE72+MDyvoyJEjpUePHpY4SIMWUKv9FbA9JEACJEACJEACficQqiyhZ6ZESsOkCJ+cytSvXz9ZtWqVWGH4HZ1DC6jfP6IskARIgARIgARIgARKJzB69GhbpDPOOMPhvPvx48fbwiZPnmy7DpYLWkCDpadYTxIgARIgARIgARKwCAEKUIt0JJtBAiRAAiRAAiRAAsFCgAI0WHqK9SQBEiABEiABEiABixCgALVIR7IZJEACJEACJEACJBAsBLgIKVh6ivW0DIHCwkI5evSoXiWZmJgoERERlmkbG0ICJEACJEACnhCgAPWEEuOQgJcILF26VH777TfbFh2GOqbtggsukA4dOnipBGZDAiRAAiRAAoFPgAI08PuINbQIgVmzZsmWLVscttFA07799lvZsGGDDBo0yCZMLdJkNoMESIAESIAEnBLgHFCnWOhJAt4lsHHjRtm8eXMJ8WmWcvjwYVmzZo15y3cSIAESIAESsDQBClBLdy8bFygE1q1b57Yq+fn5snbtWrdxGEgCJEACJEACViFAAWqVnmQ7ApoAFh2V5o4fP15aFIaTAAmQAAmQgCUIUIBaohvZiEAnEBUVVWoVIyMjS43DCCRAAiRAAiRgBQIUoFboRbYh4Ak0bdpUwsNdr/kLDQ2Vxo0bB3w7WEESIAESIAES8AYBClBvUGQeJFAKgfPOO0/cWTiLioqkc+fOpeTCYBIgARIgARKwBgEKUGv0I1sR4ASio6NlxIgRupb2QhRD89iMfuTIkRITE1OhVhQVZUpe1g+Sk/GZev9eiooyKpQfE5MACZAACQQGAWzh99Zbb8nzzz8vK1asEBxoYjrssvLNN9+Yt7b3Dz/8UI4cOWK7D7QL12OCgVZT1ocEgpwAhOdDDz0k27dvlwMHDujW1KxZU84888wKn4aUl/2LZB55QuVZJEZRntpPNFJt+ZQh8bWmSmR0myAnx+qTAAmQQGATwKEiE5Z9LtPW/ygFRqHc1qqbPHbB9V6p9BtvvCFPPvmk3HzzzZKcnCwPPPCAYNrWokWLtOHip59+ks8//1yuvPJKh/JGjRolLVu2lOrVqzv4B8oNBWig9ATrUSUIYB5okyZN9MtbDS7I2yQZafc6ZGcYufo+49Awia/5mkTGdHQI5w0JkAAJkID3CNwyd7LM3/a7LcM3f1sgRUqUPnHhDTa/8lz88ccfMnbsWL1P9Omnn66zePzxx+Wmm26S22+/XT7++OPyZBsQaTgEHxDdwEqQQPkJZB4Z5zbxyfSJbsO9EVhUkC0Zf30kaYtGyuEl90rmppnKElvgjayZBwmQAAkENIFDJ487iE9UNqcgX976baFk5Z8yBpS3Af/73/9kwIABYopP5BMSEiLPPfecfPnll1JQELzPWb9ZQA8dOqTPwG7WrJkDSMDctGmT7Ny5U9q2bSs1atSAl3Y4HWb16tXSqFEjQTp758009vnymgSCiUBR4TEpKjjotspG4REVJ01Cw2u6jVfewKL8LNk7rZMUnjwoRt4JnU0mxOg3t0rDuw+rcis2t7W89WI6EiABEvAHgdzCfImPjJaMvByH4oqMIilUr4o4nKB32WWXlcgCgjQiIkLWr1+vwzAcHx8f7xDv5MmTDveBduMXC+hXX30lDz/8sOzZs0dgOp43b56Nw8svvywTJ07U5uUhQ4bIrl27dBiOJRw8eLA+vnD06NEye/Zsn6SxZcoLEghCAoaBB16Y+5qHhIohFfsV7qoAWDl3vlZdCtL/tolPxDWURRTu4Oyr1VzUij2AdUb8jwRIgAQClEC9+BQ5LeEf4xmqGaqslIZ6j4+s2A/wlJQUyclxFLbIHzun5Obm2ox2l1xyiRw8eNDhFahzP1F/OJ8LUEzMXbhwoZ5AO3ToULn//vtlxowZuvAdO3bIjz/+KFOmTJFHHnlE+vfvL9OmTdNhkyZNkvHjx8s999yjw999913Jy8sTb6fRhfE/EghSAqFhyqqpHnSludCw2qVFKVd4zp4fJSQSv7rxqC3mlPDMS/tDvdwfQ1osFW9JgARIIKgIhKof+bOuH63rnKAEZ0p0nDSvXk/+HvFahdtx4YUXypIlS0rk8/PPP0utWrWkfv36OiwsLExiY2MdXhiqD2Tn8yF4AJg8ebJmgPOuAa2RGlKH27Ztm7Rq1Uqv5sI9huBhHcWcBlhLEQaXmpqqoe7du1evIPZWGp35//+3e/dusT+vG784srNPWXHs43njGhx8lbc36leRPNA2/DLDy4oOW1+gbYHUf2FRV0hB9iyF2/lcoLDIK9Qv6HwVjpd7h7bh78/T9mUd/FNZO11bV42iQjl54A8pinecQuO+Fr4LxecTDn/fWEVqRYfPqKf9F6zt5zM0WHtO9PZBvniGwhoIg1dlueox8XLg3ndkzYHtEqaeLa1rNdLvFa0PVr6/8847MmHCBBkzZox+bmEKIrb1g5EumF2ZBCg6GCZevNu7uLg4qVOnjr1XiesTJ07oLQTwYJw6daoO379/v94D0YyckJCg96zCfNFq1arpibZmGPZKxHna3kxj5o33X3/9Va80M/1atGghx44dM2+9+o4vP2cmda8Wwsx8SsBXn41yVdoYIOHym4TI3+r1jzMkWtkl60tW4S2SVcbPMkYbPHEFuUrQharHiNr6yZkzVI2ycgokt4zlO8vLm354HlnZBdTn0weg+Qz1AVQ/Z+ntz2hGRkalGz7CQ8Okfd3GXiUJLQTD3N133y1nnHGGwNIJzYVpjYMGDfJqWf7OzGMBij2mMITu7EPTt29f+fTTT93WHeJyzpw5snTpUhk+fLhevQWQ9pupwvKCzbiL+yNjhGEz7+JhFUljX+HevXsL5lCYDvtnwfLqbQcBjw8PPlRWdJj0jK2GPDn7PBjbf/z4cf2ZxbwcT9yi7Wvls42/yM4Th6VOXLLc0KyT9Gp8nidJyxhnmuRkzlYb0H8tRmGahITVUFsv9ZCouOvUDznPLX34e0Qf4u/VE5cf1Vv2r3teWUFdxM4/IaktrpKwWN8sgHJRqktvCBf0IRY74lliRZeenq73CrRi29AmPkODu2fL+gz1tLXQJlb9m8Z+0Zi6CMsx+GEvUHsHbYZXcQdjXiA7jwUoTmq55ppr5M4775TiX74QVK4cLCm//fabdOrUSVs0u3TpIq+//rr8+eefAqj2w96wcMKSiomz+BKEpdUUMgirW7eu3sDbW2ns64xNwu1PqMEH2VdDdJiW4Ku87dtUGddm26zaPpOpJ+0b9vWb8u3O9XI8N0snW3NwuyxUgrRJcm35/uanvDI8Y9YH77EJ1+uXvV9ZrzGEZfahJ2mjUhpLwjlD5fiqF51GT7lkskTEef+HnNPCPPA0+w3v5rUHyYIqSln6L6gaZldZK7fRbJtVP59mN3q7feBmdQdmxcVnMLfZI9MIhqugpHEEVIcOHaRx48YOr9q1XS9wwDYBOD4KQ9xwmLuAo6EaNmwo7du311sIYP4lLJlz587V+cOC1rFjR20xRRpYTQEdL2+mQd50JOALAv/6cYZ8vfU3m/g0yyhQcyI3Hd0n/1kxx/QK+veUzuMl6cInJSQ8VkKjq6tXin5P6fqSJLYeFvTtYwNIgARIgAS8T8AjCyiG47Dn1KpVq+Sqq64qUy3wqwQr3yFCsdodFk0cKQXrJ9wdd9yhTcewqkKUYsNVOFhcze2XoPrHjRun/VEXb6bRmfI/EvAigYzcbPlw/VLJLXQ+Lo3TMd5es1AGnH2RNCi2dYcXq+HXrJI7jJbEc++W/PQtqlxDIlPOUoI0yq91YGEkQAIkQALBQ8AjAYrmYLUVzh/F6nSIUVgpTYetAM4++2zztsR7mzZttADNzMzU8x/tI/Tq1UuuuOIKPdxuP5QPMYo5D5jXkZSUZJ9EvJnGIWPekIAXCGw8slfCS5l3iR9m6w7ttIwABbbQiFiJqtXaCwSZBQmQAAmQgNUJ/KMiS2kpLJKY/IoFRMVs/WANAABAAElEQVSdJ4uQkMZeYNrngWF6vJy54uLTjOPtNGa+fCeBihIwnO2JWSzTU3GsP2epWLN5SwIkQAIkQAKagMcCFCsPXe2xZdWVZ/yMkEB5CDROriP5aq6nO1dUZEjTFPdbl7lL7yqsUOV7PLdIEqNC1SIn/wnc/EJDft2bLRsO5+mtoFrWipIO9aIdtlJzVWf6kwAJkAAJVD0CHgtQzN3EcPj06dP18ZhYeHTuuedKt27dXFovqx5OtpgERFJi4qTHGW1k9uYVUuBkQ37sFdcoqZY0VqvhveUy84rk9V/T5dd9aoN1lanSodJOCcBR56dIZJhvhSjE5wMLDsnBzALJzD+1EfQXmzIlQYng96+pI+F+FMLe4sl8SIAESIAEfEvAYwGKrY+6d++u92DDSURpaWl6U/gePXroc9qxRycdCZDAKQKTL79dftm7WfZnpkuh3VnosRGRUrtasizp/y+vWQchAPt+tk+gM9Wlzf20K1uW7twrn95QV+KVGPSFw4Kqmz7fJ9lKeNoVrYS3yLGcIhm/9Ij8u6vjGcm+qAfzJAESIAESCC4CHn8rYeX5+eefL9gyae3atYJjMbG/58aNG+WVV14JrlaztiTgYwIRYepsottfkKcvvkkfyZYamygtatSXUR36yHc3P+nVPUAnrUjXVk978YnmwQoKUfrmqnSftXbLUXX0qirHXnyahcH/j0O5si/D+W4AZjy+kwAJkAAJVD0CHllAs7Ky9BZMsIKaB99jFS+G4B988EH56quv5JFHHql69NhiEnBDIFSthL/j3Mv1y020CgVhXvave7JFGRydOojSFXtzlEg0JNQHGzXvOZ6v53w6LVx5YvR9z4l8qRvv0aPGVTb0JwESIAESsBgBjyyg2IcTX3QQosUdTizCJvJ0JEAC/ieQU2A4DLs7qwFmgJ7Mc2ajdBa7bH6R4SHKmus6DUSvr+egui6dISRAAiRAAoFKwM1Xxz9VxvzOrl27aivnypUrtRjFmcrffPONTJ48WS6//PJ/IvOKBEjAbwSilQCM8GCRT1wkZKj33Tlqtbv6berSnVAr8pvXiHQZzgASIAESIIGqScAjAQo0OMkIWzHhKM7U1FR9HjxORWrXrp2MGjWqatJjq0mgkglgKkznhjFqpbnzikB2XnRajNcWPBUvJSk6TAa2StBzTYuH4X58t+oS7apyzhLQjwRIgARIoEoQ8HhiVpMmTWT16tWyePFivfAIVlGccHThhRdWCVBsJAl4g8APu/6S536ZJX8d3quyM+TMpNryUKc+ctWZbcud/bC2SbJo28lT2y/Z5QLdB+vkPR2S7Xy9f3lN83ipVS1MXl95TPL+fyVUbESoDD8vUc6rG+P9ApkjCZAACVQBAhkZGfpEyCFDhtiMCFj8jYXggwcPthFYvny5vm7UqJF899130r9/f1tYIF+4FaArVqzQpxfhmM0ffvhBz/U0hScahWH4JUuWaItoy5YtA7mdrBsJVDqB+dvWyMA5kx3qsS5tpwya+6q8orZtuvnszg5hnt5EqWH4Wf3qyVurjsmPauulXDUvFH4XKsvnsLaJftmQ/oIGsdKxXowczTm1AX/1mDCfLHrylAnjkQAJkIC/CWQdf09yM2epYoskOn6AxCTcXKEqxMbGykMPPSQXXXSRNG/eXOf15JNPyqJFi+SCCy6QZs2aab9x48bJoEGDJD8/X8aOHWsNAQrVjT0/sfn81VdfrY/idEbT06M4naWlHwlUBQJb0w+UEJ/27X7ih4/lrOr1pG3tM+y9Pb7GqUd3KUsnXtgXNMLHm887qxjqUDPW7W9aZ8noRwIkQAJBTyDj8DjJy/rG1o6sY2+KUZQtsUlDbX5lvcApkzjs5+eff9YCNC8vT2AYhPVz/vz5WoBiEfgvv/wiH374oT4kqKxlVGZ8FzPHTlVpzZo18sEHH+ibQ4cOSW5urtMXBCodCZCAawJztqxUi4XCXEbIyMuRGX/97DK8LAGVIT7LUj/GJQESIAErESgqPOogPk+1LVeyM/6nRWhF2nrZZZfJTz/9pLPAO9bd9OrVSy8ChyemRp555pl6JLoi5VRGWrcCNCIiQsLDT1k0evbsKVDfkZGRDi+IT5h+6UjAKgQylRgc//NMafPfh6TBa8PlrCn3yagl78vxnJLbkHna5vVpu92eD49zhP46vMfT7BiPBEiABEggQAgYRo6ao1mtZG2MQvVkPzUtqWSgZz4QoLCAwsHqidMnu3Tpoi2h2dnqtLulS/UplZ7lFlixTqlLF3WCqRdzDeCgvJ977jmxP3KzsLBQvvjiC8ECJToSsAKBInVsZpePxsnBk8ckpzBfNym7IE+mrf9R3v/je/m577+lepSTB00pja8eE19KDJGkmLLnW2qmPoxQpM65x97A1apVs02Q92FxzJoESIAEApJAaFiqhIbVksKC7Xb1wx4kBRIaGmfnV/ZLzPOE0MQoNATo559/LpgbCksotsXE+pz777+/7BkHQAq3ArRx48b6pCMMvWNy67x58wRzEkwHCylWXWHSKx0JWIFA39kvyb7MoyWslQXqlyweJ7cufFNmXVX2P/YeZ7SRWZuWy7Fc51bUyNBw6dOkfVAgxLMAD8ItW7aoh+upQyowIb5jx45BUX9WkgRIgAS8SSAkJEwSUt+Q9L1Xqh/jSnCGhEtIaLwk1j41hbGiZV166aUye/ZswcE/psHviiuukGXLlgmmSnbuXL4FrBWtV0XTuxWg1atXt5l++/TpoxcjxcVVTM1XtMJMTwK+IrA/M11WH9haQnya5WG/9bTsE/Jb2g6pm1rb9Pbo/ZJG50iHuk1k4fa1JeKHqb08E6Nj5YZmnUqEBZoHrJ6TJk3Sh1Hg2nTY+mPVqlUyYsQIhx+pZjjfSYAESMDKBELDakhKg5+lIPd31cwwCY9qpcRohFeajGH48ePHOwy1d+/eXfr166cXikdFRXmlHH9n4nYOqH1l5syZo79Y9uzZIzt27NCvrVu3CvakWrhwoX1UXpNAUBLYfHS/hKtfsu5clhqO33b8kLsoLsOm9blPupzWQqLDIiQhMsb2urRRK1k75MWgGMbGXCQcy2svPs0GY1u29evXm7d8JwESIIEqRSAkJFIiojuo13leE58ACAvopk2b9PxPEyh2KDpx4oSDKDXDguXdrQXUvhEfffSRtm7ABFzcDRs2LKghFG8P76suAWWMdOvCQkLVg8VtFJeBOLXo8+selp3H02TdoZ06Xosa9eXM5LJZU10W4IcADLs7E58oGkPzEKCtW7f2Q01YBAmQAAlUDQJ16tTRP/yLt3bfvn0OXhiK377dfh6qQ3DA3XhsAX344Yflhhtu0CchxcfHC3bexznwAPPss88GXMNYIRIoK4GWNRtIgd2wsrP0EKAtqzdwFuSxX8PEmtK7STv9CibxiQZCZLpzpYW7S8swEiABEiCBqkPAIwF6/PhxOXDggJ6DAFNwzZo1JSkpSe655x69SOmZZ56pOsTYUssSwEr165qp4RO1IMiZC1HLkBKjYuXslHrOgquEH/72XTlYeFNTU10F058ESIAESIAEbAQ8EqBY8o8V7+YWTDgSytyXCitfsV0THQlYgcDESwZJm9SGSmo6jrPHR0brk4qWXDdWHTHp0Z+Nxzgwr3rBggXyySef6M2FMcc6UF2nTq4XSmFuaPv2wbGSP1D5sl4kQAIkUFUIePRNCvGJeV3/+te/JCMjQ9q0aSOfffaZHo77+uuv5fTTT68qvNhOixOAuPzmxsfl7SuHy2Vq5XrTlDpyYf1m8u/ON8qi/uMk3M1pRuVBgyPU8LeErTQgPNeuXauFKLY5CkRXt25dGTBggK6aeUgFng8xMTFy2223SY0aNQKx2qwTCZAACZBAgBFwPtbopJJvvPGG9O7dW7p27SpYdHTeeecJtmTCZvRYIU9HAlYicF2zjmo4vuS+ls538Sxfy9etW6c3EXaW+vfff5fk5OSA3FvztNNOkzvvvFO2bdumV2EmJibqo+AwN5yOBEiABEiABDwh4LEAxdDarl279HGcEJ6w2GD7pcsvv1waNmzoSVmMQwIkYEcAC/ncuV9//VUPaWOz90BzEJ3nnnuu16uFERZYhfGsQbtxGAY2uTetrV4vkBmSAAmQAAlUCgGPBShqZ54Dj2tYQYYOHYpLOhIgARcEMjd8LBl/figFGbskLDZV4s7qLwmthuotNSC23LmCggJ9BBuOuqwKDvuIvv7661p4mls9HT58WJ/2gaPmzDnoVYEF20gCJEACVifgVoBiGBBnPZfmMO+radOmpUVjOAlUKQKHv71PMv+aJkb+qb1zC45tldx9y+XE729KnQG/lMoCi3oC0fpZasXLEQGC86233tIpTfGJG1xjdf3cuXOlb9++5ciZSUiABEiABAKRgFsBOnDgQPnzzz9LrTe+GD799NNS4wVTBHzx4ZQBX7jc3FyXm3n7ojx/5ol9IPFCG63o0DYIw9I+G1m/T5LstVOcICiS/KOb5fCKSVK9ei29vZmTSNoLlk+Tp6s43vbH5x5lltY+b5eLrd7shad9/uCNIfn09PQKH/MJqzIcrM9WFfdoo7/7z76//HFt5WdoXl4en6Hl+BDhkBw8K+iCh4BbAYq5WK6+FOybiFWwVnOwumBlr7cd/kgwn80XeXu7ruXJDw8AtA/TNazo8OWOhXfu+s8wiiR943uum2+oofW1L0nXHj/KJ59+7jLelVde6bYclwkrEIC2oQ/dta8C2btMCgEaFub6GFSIRbwqWi8IF7wwnO+uPJcVDYIACJiKcgrkZuIZau68EMj1LG/drP4MxQ9c6Apvf0ZxHjq+t+mCh4BbAWq/qnX//v0uf13gg4S9Qq3k8EH2lbDGF5+v8q7sPkDbIECt2j58Lkr7bBTlHrcNu7vqj5CQcKmTVCSDBw+W9957T/DwhLgFO+Tfs2dPPc/aVXpf+UPkQaD5u/8wjQfi15UDEzyPKmq1NMtA+6wqQMHI3/3nqt985W/lNlr9GYq+g8j29mcUz0664CLgcY+dddZZAiuFM2fFIXhn7aQfCXhEICxKRFlB3TqjUELCoyU1JVWwwAZDzBg2xQ4T2FXC29YBt3UJgEC0F/PIMeUHX072Dl/IHTp0qLD4tM+T1yRAAiRAApVLwGMB+tNPPzlYKDCH6rffftOrVp9//vnKbQVLJ4EAIhCqhGVkjZaSu9/1CWGh0ckSHn+arjWGg7mIT7TVNy0tTQ4dOqStwLB6wqqBH7/YiomOBEiABEjAOgQ8FqAtW7Ys0eqLLrpIr5LHWfBTp04tEU4PEqiqBKp3nSj7Pr7YZfNrXfUB5ysVowPBiSkJu3fv1ouzMFTXoEEDqVWrVrGYvCUBEiABEgh2Ah4LUFcNrVOnjnzxxReugulPAlWSQFTt9lLvltWy98PzJDQqWYoKcyU0XA3NqzPmq1/2ukTVblcluXjSaIhOvOhIgARIgASsS8BjAYpTWcwtTIAD1wcOHJAJEyZIV3U8Jx0JkIAjgcgaLaThXWmSs2+ZFJzYrTairyXR9TtLWHSSY0TekQAJkAAJkEAVI+CxAO3evbvTRUgYhn/66aerGDY2lwQ8IxAaGSexjbp7FpmxSIAESIAESKCKEPBYgGKVbvHVqdgom1sfVJFPCptJAiRAAiRAAiRAAl4i4LEATUhIkGPHjsm3334ra9euVae4VJd27dpxdaqXOoLZkAAJkAAJkAAJkEBVIeCxAMW58Ndee63s2LFDmjdvrrdKOXr0qAwYMEDeffddvZF2VYHGdpKAVQgUqhNJFmz/XZbv3aKb1LFOY7moVmOrNI/tIAESIAESCFACHgvQkSNHSpcuXQT7gdarV08fpbV69Wq56aabZNKkSfLII48EaBNZLRIgAWcEitRm+b0/e1Y2Hd0nx3OzdJSpoWFSIzpefhsyUSLCPH48OMuefiRAAiRAAiTgkkCoyxC7AJy9i03nJ06cqMUngrBHX/v27bXwnD9/vl1sXpIACQQDgUunPym/7v/bJj5R5/yiQjmYfVwmrZwXDE1gHUmABEiABIKUgEcCFIuNEhMTBRbP4m7z5s2Smppa3Jv3JEACPiRwMr9IZv51Qh5bkiZj1euzP09ItvLz1O3PTJctR/c7jV6kjsJ847cFcvCk86N3nSaiJwmQAAmQAAmUgYDHY2yPPfaYDB06VAYOHCg9evQQWEUXLFgg7733nrz44ovy3Xff6WIxPM9jBcvQA4xKAmUkkFtgyG1f7Jf8QkNyC08lXn8oV979/YTMuKGOJESFlZrjtmMHJTYiSnIK853GjVRD8buOp0lqtUSn4fQkARIgARIggYoQ8FiAjhs3Tu8DinPfi5/9Pnz4cFsd7rrrLnnttdds97wgARLwHoHCIkMGzNonWfmGQ6am8fPRxWky+cpUCQ8NcQgvfpMcXU0M9c+VK1TzQxNVHDoSIAESIAES8AUBjwXowYMHS+wD6qxC3BfUGRX6kYB3COw87txiaeZ+OKtQtqbnS7PqkaaX0/emKXUlPjJG0nNOOg0vUKvjz0jiGexO4dCTBEiABEigwgQ8FqBRUVF6H9Dp06cL5n3Wrl1bzj33XOnWrZtERrr/sqtwLZlB0BDIVUO6P+7bJH+l71VWujwlhOrK5ae3kuTouKBpQyBX9EBmobgzbioDqRzIKChVgIarIfZPrnlQLvhgrDqdPsRmDQ0NCRHMAV1x23PKilr6UH4gs2LdSIAESIAEApeAxwJ03bp1guM4YQlt1aqVpKWlyf79+/V80NmzZ0t0dHTgtpI18wuBv9MPyE2zX5JjuSf1ymoM8MZFRAuGc9/qcYf0bHyeX+ph5UKSokOVAMXwuvPhc4QlqDieuKYpdWT9sJdlzHfTZNX+rVKk/nWq21TubdWdcz89Acg4JEACJEAC5SbgsQC944475Pzzz5dXX31V6tevr4fjsTn9ddddJ6+88gr3AS13F1gjYVrWCen0/pgSjcnMz9F+Q75+U17odosMOqdLiTj08JxA45RIiXCjLzPyiuSsGp6PSNSuliTv9brLVoHCwkLJzMy03fOCBEiABEiABHxBwM1X2T/FZWVlyapVq2TChAlafCIkRFlaMAT/4IMP6uM5/4nNq6pGwFBDtrd+9arbZheo/SWf+ukzbu3jllLpgZFhIfLvrjVcRny7V6pEh3v0Z+0yDwaQAAmQAAmQgK8JePRNhU3nITIgRIs7bMdUUFBQ3Jv3VYgA9pTceHhvqS3OU/ND529bU2o8RnBPAFbQ966uLe3rRqstl0L167w6UfLqlbXktMQI94ktGrr5SJ68sTJd/vX9YXnnt2NyMJPPJIt2NZtFAiRgEQIeDcFjfmfXrl31MPtzzz0n7dq1k9zcXL335+TJk+Xee++1CA42ozwENqujHPEjpTSXVZAn6w7tLC0awz0gUDsuXJ7q5toS6kEWlomyYk+2PPnDEd0ezIxdvU/k8w2ZAmtwVRXklulcNoQESMCyBEpXDf/f9LfeeksvQOrQoYM++SglJUWuuuoqLUZHjRplWUBsWOkEwkPD1Tpqz1wUzxf3DBRjeURgt9qW6t9KfEJ4msuy1P782o1elCaZak4sHQmQAAmQQOAR8MgCimo3adJEH8W5ePFi2bhxo1713qZNG7nwwgsDr1WskV8JNFdbLeEM8dIc9p08t/YZpUVjOAl4TGDF3my1XZRIgROdiS2p/krLlQ71YjzOjxFJgARIgAT8Q8AjAZqfny/p6elSq1Yt6dmzp375p3osJRgI1IhNkK6nna3nd2IDc1fupFoR371Ra1fB9CeBMhM4mWc4FZ/IqFDNW0c4HQmQAAmQQOARcDsED+GJozUTEhL0sHuzZs3k559/DrxWsEaVTmDSZYOVECj6/z0qS1YHm5r/OPBpdbxjbMlA+pBAOQk0TIqQahHOJ4DkFhhqDqhHv7HLWTqTkQAJkAAJlJeAWwH69ttvy5QpUwRzPN98802JiYmRm266yelq+PJWgOmsQSBJnRu+756p0qX+WeqIx2hJjIqVBDXknhRVTZok15Gv+o5Rp/PUs0Zj2YqAIXB+/Ri9C0BxCYrTourFh8uZascAOhIgARIIRgLffvutvPfee36p+h9//CEvvPCCray8vDzJyTm1j7fN08sXbs0Dc+bMkeHDh8v48eN1sRdddJGcc845ei5o586dvVwVZhfsBCLVAqN3u4+QtLxM2Xhsv7KIFqrzxFPl7BoN9L6xwd4+1j/wCESFh8hbvWrLgM/36aF43OerVUht1bZUYy6qHngVZo1IgARIwEMCEKA//PCDDB482MMU5Y8GAfqf//xHRo8eradc4uAhaMCmTZuWP9NSUroVoIcPH5ZevXrZsmjZsqXUrFlTdu7cKWUVoJhDunr1ajn77LOlTp06tjxxsWnTJp1n27ZtpUaNf7aWQflI06hRI8Hwv73zZhr7fHldMQI4oKBeXIqcUd2xjyuWK1OTgGsC2Jx/xg111SEHhXI0u1BS48KkZqzbR5vrzBhCAiRAAlWQwIABAwQvuGPHjmld5msMbofgYYKNjHQcwqpevXqZh+C//PJLvVfo9u3b5cknn5RJkybZ2vXyyy/LxIkTZc2aNTJkyBDZtWuXDsM9VP/mzZu1Isd586bzZhozT76TAAkEL4EwNeZeVw25t6wVRfEZvN3ImpNAwBJ47bXXZN68efLSSy/phdiPPPKIbNiwwaG+y5Ytk1tvvVUuv/xyue+++2T37t228OXLl8vTTz+tT47EVMZBgwYJtJG927Ztmzz22GM6fwyH42jk4u6DDz6Qfv36SZ8+fQRayDwICGt2hg0bJsjj0UcflSuuuELuvvtu2b9/vy0LCEvk3717d7nxxhvlnXfe0YcMIcKKFSvk/vvvFxwuNGbMqWO1H3/8cVm0aJGOh7LsHbQajmg/ceKEvXfZrtUJRy6dslYaau6nQ3jz5s0NNTfUwc/djYJjXH/99YaCoqOpxhlqJb1x9OhRQwlS49prrzUUZB328ccfG88884y+Vp1jqLPm9fWBAwcMZYk11Ob3Xk+jC3Dy39ChQ534Vtxr3759RkZGRsUzCtAc0DY1byRAa1fxauFzm5aWVvGMAjQH/L2qh1SA1q7i1crOzjbwN4h2WtUdOXLEqk3T7eIzNLi711fPUCUGDTWE7DM40CBqhNZQe6EbSgQaah90IzU11dizZ48uUw1XG+pAFuPqq6/Wuum8884zEhMTja1bt+pwpFELuo169eoZL774ovHwww8bsbGxxvTp03U4/m5PP/10o1OnTsYbb7yh3xGupj7a2qQO/dF1GDt2rPHss88atWvX1uUhAp5tSv0ZarTYuO222wwlmA3oNTVybUsP7aWG1o333nvPUIY/Iykpyaa5PvroI0Pt766/v6HDkNdTTz1lrFu3zoA2UwcSGcePH7flhbD27dvb7stzUeo4FRS6vYpXX77yxRdf6CFzU+piXigUvTMXFhYm77//vlSrVk0HY1JrZmamVvZQ6q1atbKdooMhePzCgKJXnarDkEh1sqiOkL179wqsqN5KY19f9SFxWOGvOlP/ErCP461rWJbxK8OKDm3Drzb0oRUd2qX+0Pzef4aRL0bBFjGKDklIaE0JCW+q5tV6/9jNIrWTAX5JW/XzibbB4VhhT07vCsbPMD6jVu0/sz/4DDVJBN+7r56h+M7Gs9mXDp877AQUHh4ut9xyizRu3FiUWJPXX39dj/JiCPvDDz/UVRgxYoQoQSmwIiqRqf1gLVRiTh/iAw/oI5wk2b9/f23NjIuLE1hRMZXtzjvvFCVGdTr8h9FgWGGVUNTx4XfDDTfoPdoxT7Rjx47w0tZRJQ71NaYuwhoLKyimPiJvJVxFCVQdrgRqCStrVFSU1nNK5GorKeaAYh941Ofzzz+3zUdFO2ExrYhzK0Dr16+v5wFgvqXpsCUTNqLHy3RKFbsUoIhjik98ub3yyivSo0cPPdcTUNQvBDMbvd2T+hUghw4d0mnQCaZDPPXLSYP0Vhozb7yvXbtWf5BMv7POOqtipmUzIyfvOMYUL7rgJVChYYeyNttIl/CCx9VpU8dUSuyzGqL+ZUpe+EfqMqGsuXkU3xRqHkUOwkj4EWxl59fPZyWA5DO0EqB7uUhvf0bxowsaw5fusssu0+LTLAND2VingjUuO3bscNAQiIM1NAsWLDCjC8QdjjU3HdLjeHMY+dSIrw6z1z3QSkuWLNHRV61apQX2ypUrtV4x84BoRZgpQHFapelOO+00fWn+IMX0gJEjR2qRjJMslbVWr8sx47t6x3HsEMkQv5ga+csvv+jpkq4Mj67yKe7vVoDOnz+/ePxy3+OBAVWOXyiYgwAH9W8/xwG/jLDVU3F/xEUYIBQPq0ga5Gu66667TvAyHeZSFF8sZYZV5B2iOz4+XvChsaLDF3tERIT+Q7Ni+/CgwWfWfrGcL9tZVJQp6Xv6OC0ismCgJNf7RkLD/lm45zRiGTzRNvSh/Y+8MiQP+KgYgTEP1cCzxIoOP9RxVLJVHZ+hwd2zvnqGwhDm679pLIi2d/g7g7jD3Eo4NbxuH6xHb+01DhZxYzTXdObfKZ65yMM+LuLgu9R0CIflFSLWXqTec889DiLSNPghnTnKY1qGMY/z0ksvFaypmTx5stZimMsKEVyag/CERRYj0bB+9u7du8LPGbeLkEqrkKfhGO566KGHtPDCJFxzYRM6Aw9L0+Eaog8LndCp9lZChNWtW1evwvdWGrNcvpNAoBLIyfhMVe2fh5BjPSMk+8Qnjl68IwESIAES8AkBbItk77BAB1MHYWmErilutIP1E0eWmw5TC7ds2WLeCo42h2DEUDnyWbhwoS0MF6b1E9cY7sfIFIQfhv3xgp6CvydbJUGHqbmf2lL63//+V9Rcavn3v/+tF1UV3+/TFLimcEX5ar6nYGR45syZMmvWLNswPsLK6/wiQP/1r39pwFiZZf8LBQ1av369Nj/Dkjl37lyB+RgqH+Zk7EEFt3TpUklOTtYvb6YpLzSmIwF/ESjIXa2KOjVvsWSZ+ZKvw0uG0IcESIAESMC7BDBVDyIO803xjmFzDGtD12BFOIaov/76ax0+depUwcr3vn37OlQCI8GYZohV5xCCt6n5mLBUYogbK8sxTRH5f/rppw7rUrp166Z11Lhx4+TPP//Um8RDQMKCiamRpTmMLqvFTXqFPKypKANremC1xeiyvTMts5heAMuy6WAFhbUUAhWr7Cvq3A7BVzRzpMc2BegEvADUdJhMi8VE6DS14lybchs2bGjbhwrzFLAhKkzF6BxAhwNob6Yx68N3EghMAo7boBWv4071bHhpw2E5t3aU9GoaJxFqT0w6EiABEiAB7xPA/M0JEyYIFhhhGhZOiLzkkkt0QVjcAysjtkeCEQ0jvK+++qrD+hhMbcLILiym0DVYRGRuS4nhbYhaCEpoHyy+xlZN5nobDMdjUThEIBZ+YygfGgrD4ahLcStm8dZDNEJ3IX+IThj9sEgKC4uKO+gszD+9+eab5cEHHxS1al9HGThwoBawWHyENlbUhSgTq2+XjXlQQ5iV0SnO5kVCqautAkrk4u00xQvAHFD8gvG24/wlbxP1b36+mr/kqhU5mV/KyfSX1IYYWSWiFBSFyfTtt8rCfb0lXI1lFKj595/3qyuxEeUf2OAc0BKYg84DU5RMC0bQVd6DCvMZ6gGkAI7iq2cohBp20cHR4b5wGPpu0KCBtiJiHiSmBJpD1fblQcvAsohF3PYOQvGBBx4QHLCDxdYQkLBKOnPu8kd8WCUhIDFdsTwOc05hAYVIdufUtoq6nubINSy3YABLMFbQV9SV/5uqoiXbpYeydyY+EcWZ+IS/t9MgTzoSCDQCUdWulP9j7zvgo6i2/7+72d30XkhCgITem4B0qQIWRFFRFMQu1vdQ9Fn+tif6rKg/efrs2AsigihSFOm9iPReAum9bN//OXeZzdY0NpByD59hZm6bO9/dzH7n3FMCNGzY7moHarHyn64KK86ME1Nm8qkm5ecLq3LrPBRJfcNIzkciIBGQCJxPBFiD6I188hzYScidfLrPjYmjL/LJbSsbn+tZk1pb8sn9mW9VRT65HTtMM/nkiAWbNm0S5H7o0KF+IZ88/rnrUHkUKRIBiUCdIKBS6RCV9BWKs2fCqN9E19CijFYM/s7vjvcO/AMWW8WfsJXWMg7kGlFosCIqyLuHN785s1kM7/khxkbl7h7vvCjCWjR+6HAd219LkQhIBCQCTRkBVoYxIautsJ1ldUhfbcevy36stWUTAXZ4cneUOpfrVvx6ncsosq9EQCJQpwiEx79Kmk0D0gvO4PHfbcjVu2pElYtrSA2aU2bxSkB5WYiXgXiZnTd+s125cqWwwVbCSnGgZbYzYgLKNkps6sJv9GyTrSzDKNeSe4mAREAi0FQQ4GfnuQg7I7k7JJ3LeOezb+vWrYV9q7uz0rnOoV4swZ/rTcj+EoGmgIBKFYiYsJYoM/t2TNKbbZQL3VP7yRpP9rhUMlUxXkrMOX6wcr2SKIJt7NiOiW2E2M6IQ6Jx/mGlfVPAWt6jREAiIBGQCFQg4G/yySNLAlqBrzySCNR7BNjBqG/zIGHv6T5Z9n9PjdIg0svyOy+7+9JgMrHkes545suzkQkqp85tjML3z+S7HvhjNkZ45T1JBCQCEgGvCMgleK+wyEKJQP1F4OEBMdiRcQalRivZgNrnGaJVIZqI5xtjErxOnAmkLw0maznZ3lPReHobgEN8sAck5wRuLJKZmSli9rFHKgsb9XN+Z18kvLHct7wPiYBEQCJQHxCQBLQ+fApyDn5DwGI6DkPZclhMRyhFZTPoQoZBG9jdb+PXh4F0FOvzm4lJ+PN4Obad0Ysp9UwMwpCWwaQZ9R4HlJ2JmFgx2XQX1oyygT0TVLb79JZPmduwLWhjEQ5DwjH3nIUJ6WuvvQaOcVcXy03O15LHEgGJgESgqSMgCWhT/wY0ovs3lC5Dad5LtJTKpIyzB6mgL/4cgaFXISz2qUZ0p3RnRDSHpYaIrTo3xt7u69at89qUiSfHdGMN6Pr164WdqHtDbpOamupe3GDPOUWeN2GSvn37dgwYMMBbtSyTCEgEJAISAT8hIG1A/QSkHObCImAs34CS3CeIfBbTRJTUlfb1aUPpTygr+N+FneAFvjprQDmrBotiC8pki7WanHSBM19w5g1O9+ZNrrjiCpFtw1tdQyzLyMjwOm3WEHO+5vMhfK3jx4+LTCcc5kSKREAiIBFoSghIDWhT+rQb8b2WFf630rvTl3yH4IhboFK75ryttFMjq+RQSxxOiTOGsE0ok1LWfDrnEebUbkxQ2SmJs2BwXb9+/RqV9pM/Vl5i57R53iQ0NNRbsV/LOMoARxbgCAMsbGPbvn17XH311T4DXPt1AnIwiYBEQCJwgRGQBPQCfwDy8v5BwGI6VsVAaljMp6DRta2iXeOuVghlZXfZqlUrkWO4sjYNva5Lly7C3MCbTWznzp3r9PbYnGH27Nke1zhw4AA2b94sCL9HpSyQCEgEJAKNDAG5BN/IPlB5O74Q4OX4sy7jvprI8iaDAGf1cLZpZecrNkkYN26cS3ldAMJxVjmVsDdZu3atDAflDRhZJhGQCDQ6BKQGtNF9pBf+hsyFR2HK3Q6b1QxdbEfo4ntCFeD9B9dfs9VoW8Ns3F3JcDYEaFtWUi+rmhoC1157LY4dO4aTJ09Cp9OBNb+JiYl1DgMv/bOZA2eZchclHqmvPNPu7eW5REAiIBFoqAhIAtpQP7l6OG+b2YDCpVNhOrOWZkfaRgrurQrQQaUNQfOb1iEgxHuMSn/cSnDkdMqXfr+PoXRk/zmNbOsaTxghHzcqi2uIAGtBnTWhNexeq+acD9pX0Ht2CpPks1awyk4SgQuOAEfQYFMalm7duoGjj8i/Z98fiySgvrGRNTVAwGa14MSHbWHV5xP3tDh62szkZGHIx4kP2qH5lM3QxbR31PnzQBd8McLiXkdJzsM0LKeqNNIWQH/8wdCFXk4EdIo/LyfHkgjUGoHo6GgR0P/vv//2GEM6IXlAIgskAvUegdWrV+Oee+4Bm9coduW8ysGrKvPnzwfnUpfiiYAkoJ6YyJJaIFC0fQ6sRgqB5EQ+XYaxGpGzbDqSJ61wKfbnSWDIUGiSf4SxbCUFoj9GgejjKRA9lek6+fMyciyJwDkjwGGtQkJCsGPHDqENDQ8Px4gRI9C8efNzHlsOIBGQCJw/BLZs2YJRo0Z5jZ9cUFCANm3aYNOmTejbt2+tJ2U0GrFixQqEhYVh8ODBLlrVbdu2iagl/PxISkpyXIMjayxbtkwkFxk9erQwM1IqmSjzeOxw2bt3b6VYROPw1cfRyI8HkoD6EcymPFTJni8Ai6FSCIw5u2EuzYAmtO7s7AI0KaTtvLnSeTSGyuxSMz7ZUUgpOe2Yd0sIxP39ohEeKP0KG8rnyz8YvEmRCEgEGiYC6enp1SKWV155JXbv3i3S/db0TjlW8JAhQzB+/HiRMpnjNvNSf3BwMB544AFs2LABPXv2xCOPPIKVK1eiQ4cOIrxbr169REKNw4cP480338TSpUsFceU2kyZNwk033YSZM2fi6aefxvTp0yvtU9M5V7e9/LWqLlKyXaUImEvSK63nSlUApYIsOlFlu8bUoMxkxdqT5ViwrxhrT5Sh2GA959srpzGnLsjAn8fKka+3im31iXJcP+80MkvM5zy+HEAiIBGQCEgEqkaA0/lWJ20vx/v9/vvvqx7QS4u33noLt99+O9555x0RO7hHjx746quvhNZzwYIF2LhxIz744ANBJl955RUxwhtvvIGxY8di7ty5WLNmDTj1sJL97cEHH8S8efPAbbjvM888A45LXFkfL9PyS5EkoH6BUQ6i1oZVDYLNBnVgVNXtGkmL3DILpv+ciTc35OGj7YWYvSFfkMTTxedGEp9flUtJRsnHywknDjDFZS+vzfPp4OLUXB5KBCQCEgGJwDkiwKSOl7qrEiaAS5YsqaqZ1/qXXnoJjz/+uKOOs6bxeLt27RLL8RxCjoWz2LE2lGXnzp0uWe2UOrZPPXjwoOjH7Vq2bCmSjRw6dMhnH25XVyIJaF0h28TGDU4dDagrD7Wk0gRDG9Wm0SLDns1lhxcj948ZOLrsUdz84xlkEQktMdpgJrZYarLHIb19YQZqS0ItVhv25xi9RjTl0Y8WmMS1Gi3I8sYkAhIBiUA9QYBtPKsrtU23y5ExOEwcy3fffQcmi9OmTRMh5Di7nSIxMTFQUgxzeLnY2FilCkodh5zjDHjOnvncLjMzU4znrY9jkDo4kAS0DkBtikNG9XuU0lwGVHrrMcNeq7JNpQPU88qMHy5D9m93oGjHu/j9cCG0Nu9vxgGkqvzlYEmt74Y4aKViISIsRSIgEZAISATqFoGUlJRqXyAtLa3abb015GX2f/3rX2IpnUkkJ85QPO65PccVZiclFk504a3OvQ+35X6cfthXH25TVyIJaF0h28TG1US0RMq0Xfa7DnDKt64KoGX3SMSMeAth7SY0WlSyfrkF+pN/wmqwvxGf0HSFSeWEg9OdW4gfHsjlMFE1lwC1Csnhvn0HY4LUCNLIP+uaIyt7SAQkAhKBmiFw/fXXiyXsqnoxYeQQa7UVXobn9L3sQNSxY0cxDEfMUDSeXMDHSkzj5ORkjzomwJxoo6ioyMVsgPtxna8+tZ1zdfrJX6rqoCTbVAsBTXgKYm/eg/B+TyI49VIEpQxFRO/7kXTdMkT2uKtaYzTERlaznpbef6apV2gew2z5UPkKSUUtIwIr1xZXhsPdF0X6rH58SMWyi89GskIiIBGQCEgEzhmBG264Ac2aNXNZ0nYflOOBsrAnfG3k008/xbfffgtO08s2m4pwaKX169cLm07WYn744YcYM2aMqJ4wYYJwQOKsa6dPn8bixYuFTShrOdk56f333xft2ImJ55+QkABffZTr1cXetyqlLq4mx2z0CKiD4xHe6wEE9n+k0d/rmZJ8LDiwCYczDyECHXAJDqIl7Evr3Q0rsD7oWpSrIrziMLhlsNfy6hT2SAzCO+MS8MiybLCyk52PAunggX5RaBtjtxWqzjiyjURAIiARkAjUHgEOhbRu3TpwdjMmd+7pdbmeCd6+fftEfW2u9Oyzz+LEiRNwtve8//77wd7xs2bNQp8+fcQ1OOsSh1ViYWK8cOFCtG/fXqT9nTFjhoj5yXXsKc9xiOfMmSPqvvzySy6utI9oUAf/SQJaB6DKIRs/AgfzzuDy715EudkoNi3a4m1VB/yf7Q/0RybamzZhWPnn+DX0Phcw2P5zQscwDEsNcSmv6UkbIpo/XJ9MYZfsWacSQgPAy/P1WTgE1f+2FuCvTAMYh15JQRS7NApqVf2ed33GVM5NIiARuLAIMDFkZySOp8nhkdj20mKxiEQTU6dOxVNPPQV2JKqtsEORL+HwTFOmTEF5eblwLlLaMRnmsE88L7YLZdtPRXgJnx2Z2CnKmdRW1kfp6+99xaz8PbIcTyLQgBFgzeb725fhZHEuEkIicWPnQbij5yhxR6dL8zHixxdd7s5EaT9ZHsAwfIpl6II8XFn6JuIsx/F78K3IUqci0KpHs5JDGJ8yyKVvbU+YuCVVYg9a23Hrop+RDF85TikTT7aBZVlyqJRimZbhq4nJpMGVJNSOivxfIiARaGgIsI0nayQ54Ht2drZYkmet6PkQ9pBXvOTdrxcV5TvsoTP5dO5XWR/ndv44lgTUHyjKMRoVAnN3rcTM3z+D9aw3OS+178w6hhXHduG/l0zDT0e2QqfWwGj1jOfJyrzvbO3wHDYS0QrAAP2POF3YgfSjWx0YffrpQdxxxx0iNIajsAEecEB8DoQfGxxQJYF8nzSfrKBVyCffLvNQM2HMEQGu7hTeABGQU5YISAQkAhUIcHgjtqeUUj0EJAH1gROr0GsS48vHMF6LWV3uHCLBa6MGWsg2MJxVge+xIcqGM4fw8Iq5Xqe+6uQefL5nNQ4VZHoln9zJRhaZJ7RJMGrb40hJAvYZ+8MMz+WXzZs34+KLL/Z6nQtZaLVaxXezsu8+xzv9YJcB606boSUb1FITcEmKBnf38O71z/ezNb2MCL3nnRnJgmDNsWIMT7KbEni28G8J/12zsCeocyw8/17lwo7Gf4OVfX4Xdnb+uToH/5bPUP9geb5H4e8nP0P8/R0tLi4GP7+kNBwEJAH18VlxdoHwcP9rZZiYcequkJBzswH0Me0LXsxed2xv4mtJ4IJPsIoJrNi6x2cLg8WMxcd3oFdcK1pKVpM2z/vDrmXLATgedCt2btvmdSx+SPLDUvl+ma0W7M1Nx8miXCSGRqJbfEtoKW3phRAmaJw2Tpmbtzk8ujwHe3PMQoNpOMsbV54yIyLEirt6e/fQD9ZRTNQyT40xjx8SqKn0et7mUNsyfjkyGo0i7p3inVrbseprP/4MK/v86uu8qzsvfoayTV1jfYby3x/b4zXUZ2hVn2NhYaGwkfT3d5RjWSpZgaqag6yvHwhcmF+5+nHvlc6CtSN19QNVl2NXelPnoZLvjR8CdYVdXd9CeklepZfIKS/GmFbd8fXB9aT5M3htO4nsRaPyLAIDRePm3pBthhgjHuOaH17B4fwMcmdXURYjzpykx/H73kOo1lNz6j5OXZxX9vmdKDRhTw6pPL3I8qPluLZLBOJDPB8rF6cEE8Eu9pqlaUCLkPP2fVF+oBj7hvod9QK9S1Fjfr4oN9qY75G/o5X9DSoYNNQ9f3Z18fkpf9sNFZemOG8ZB7Qpfurynn0i0DkuhZaVfcfoTI2IQ7fYFnhx2E0eY+hIa/nUwGtxeduL0LZtWxfPQ/fGXbp0EWSz1Zx7sD3jKAoMZSjQlwryye44A+c+4ZPguo/l69xGGlp9yUIU5zxB25N0/JNHU5vNhLLCj1GYeZfYDCXzK80lf7LITMSYZ+gpGjLyTKd6b3JztwiPAPq8fH9Fu1Bc1s6evcNbP1kmEZAISAQkAo0TAU9VReO8T3lXEoFqITCp0yC8u20ptfVuk3hr52FinJu6DEFyWDQ+2LEcxwuziVzFgMsmtO8n6jn37sSJE0VYDn7bZ5snFn5LHzdunMg68duRHQjR6Ghl2jUrErcsNpZj8aGtuL7TQNGvNv8VZt4Ki+kIGabaU4Iay5ZCX/wdIhM/Iw1EgJhTfvoVsFk5dql9DmbDDtLEfgJb5E/URutx2QgdabfpfpyD7iuNzORhFE71ipQXfYXyorl0nTIaKwxvD38Yy9L7E+HWQ0Nj9GkehNGtQ5Xmci8RkAhIBCQCTQgBSUCb0Ictb7VqBNrFJGHx9U9g1NfPOTzd2d4zmIjifb3HYlfOCWzLOoKhrbtheKuuYvM1KmetuPXWW7F9+3bk5+eDw1t07doVLVq0EF0O5p8B25V6kyIioLuzTwKdvNVWXVZa8F9YjHupoavnDxNSQ8kCBIVPRFn+bCKfnDrU2ZaV2tsKSFs6H8Hhkzwu1D5WhxDSgBa5cmbRrsxsQ6soO2ktL/kRZQWzHf1tRIJLch/HyMQZuKL9jY5yeSARkAhIBCQCTRMBSUCb5ucu77oSBHo2S8Wmaf/B/H0bcJBsM1vSsvtuIp7/27FMaCY5PNN/dy1HSngcNtzyImUj8r1kz1kwOPWZN4kKDKW87VqvS+1sBhAXUnsnOGPZ73RJV/Jpn4MZ5cVfCQJq1K+nImfyqcySIhkQSfVGQDle5+yxCbjxhzMIoqCeRnJtD6ayEFpP/98VzQgLFayWHJTlucZJVUYuL/wAQWFXQaVunE54yn3KvURAIiARkAhUjoAkoJXjI2ubKAKto5rhkf5Xibt/Zf0C/Hlij4u20kzE60RRNmZv/hkzL7a3qylUw1p28UleTeSMdGlaj0qH5Dlklti90ZPCNC6ZkGy2Up99bdazdTYvasyzvWyV1EUFBWAeZWHacLIcueUW4XTUPyUIwWzUSWI2HoRNFQmVrfDsaBU7Kyh6gPkENLqOFYXySCIgEZAISASaHAKSgDa5j1zecE0QsFDIpA93rnAhn0p/oQndugS3dR9Bwdhrrq1MiYjFF+MfxJXf/4eih5Kd6FmNJVtYfnLF/egQ21y5lMd+b7YBs1bnguNoshQbrZhzWQJaR9tzwWt0nWEqX0017lpQFTSBXUQfbVBfGEp/pmN3e1c1tEGVxygNJbI50of9pskWSs5UVoR7mpDCaDaQzlU6HYkPQP4nEZAINAoEOLTbokWLRP71I0eOCC//du3aYcKECWIFjMNqSfFEQBJQT0xkiUTAgUCRsUx4qzsK3A60RPD2bZ+LXskdEZTcH2pdzcjVgOYdsPKm5/DB5kU4lH8KrSITcWvvy9E3ua3blSpOTxWZMGNpdkXB2aP7fsnC65fGo3M8xUiMug+F5as82jAhDY1+TJSHRN1LBJQ9452diliLaUVo1INe+lav6ERpGnHaUIRpiulBXNHHauPwK1bk6uOQZOfJFZXySCIgEZAINEAEtm7dihtuuAG5ubnC1l+5hdWrV+PHH39EUlISlixZ4rD9V+rlnpxyJQgSgfqCAC8Nm42HYDVnO7zGL/TcwnXBRNnctYgVszIYS6De/CIyF0/G8TnxsJRlVVRW48hG6Tzj1z2Mfx55Fv8t/gKP0r7ZL+PIOci7cxIPyWktfcncnUWiSqNtjajkxWRrGUP8MoSIHwVp1qSSB/xXCNDYU8WpA2IQk7IauuDh1C5WbNqgYQgI/4GOg3xdospyo0WL1/e8LMin3hJIGZBUZOcagkJjFB7Z8jmMlKJUikRAIiARaOgI7NmzB4MGDcKhQ4dcyKdyX+x8ym3YIXX37t1KsdyfRUBqQOVX4YIjYLOWozj3WZj1m4gs0VfSZiaylIyIhDlQB0Rd0Pmxg9FossVceGAz5S13ddhRkTNSCEyIJ69xYlVinic+7IiWd+xDQIid5FU1+ZMfd4Kl+DQ1s8Jm4nBIpDwsTsfpby5B8qSVUAV4Lt0cLfBNTo8XVASJZ6IZ3XwJkfliMa5KFU6k0EklSaVMNMPjXxb1/B8Hzi8psc/DUejlgIPlLz26E5mlBRSOKgbj2vQCx0FlaROjQ74hEneu+wL94tYjSpePHEM8tuT0h8Gqo/byseMF0gZRlJGRAdbscMaz1NRUDB061OM71SBuRE5SInCOCJw4cQIcz7m6MmrUKPz111+Ij4+vbhePdtvOZtfr3bu3o47L9u7dixEjRghtq1LB6WqXLVsmQv+NHj3aJbPWmTNnsGLFCnTu3BnOY1XWRxnXn3upAfUnmnKsGiNgI7KZd2oY2Sv+SUSpRIQF4r3FdAD56WOFRrTGg/q5w5ujboWGMuc4e7vrbBZytFHhBywGRdSsuKKlHEV/fVxx7uVIbzZhe+ZRrN+1EGUlvJTuSmx5mdxceBTlx5d76Q2fgeC5cahTHE4+Z8KpVkeIzZ18cn1thMnnoM+fxMzfP8Pza77HfUs/RPL/3SkiBPB4YTSH+/pFoZyW4f/MHIWfTl6HtVnDiHwG4Q0yEdCS97yUhocA/+B+/vnnOHz4MPgHbOPGjXjllVfES0vDuxs5Y4nAuSHwwgsvuJC6qkYrKirCm2++WVUzn/U5OTm4/PLLsXjxYkebBx54AHfffTdWrlwpiOT+/ftFHadz7d69O+bNm4eXXnpJ9FNiUXPbnj17gokrj/fuu+9W2cdxQT8fSALqZ0DlcDVDoLzwQ+rAX0N3Rxgeh7Rxef/mgwsqIZQS89i97+KZwdehX7M26Blow1TsxXIbxcr0Mu+yo7/4nO+fJ3aj+4czMOnHNzB11SIMxtVYjSSP9lZDEYx5+zzKuWBU6xAE+ljFHplWt+GNTBS3NPW/05FenIdCyt7E3vp6CqSvJqLL98ROWywj0kLx4sg4dIrToVloAHo0C8TLo+LRiexTpTQ8BEwmk0iq4Jxa1kqfNacz3bBhQ8O7ITljicA5IMDf/fnz54Odj6orvGrAL3C1lTvvvBOJiYmO7qz1XLBggXgR/OCDDzBz5kzxQsgN3njjDeH8NHfuXKxZs0asav3222+i74MPPiiIKbfhl8hnnnkGBoOh0j6Oi/r5QBJQPwMqh6sZAvZ4lb6XlK2m4xRX0rfNY82uVvvWrP2c3nsMvhx7Hz6OL8Td+BuRZ7MHuY9qJbtQb7Ir6wQmzn8NefoSseWb7Pc9Q3UJ/oSrx7sqIAgBgd7ND67tHEGEztVGk2Nx9koMxGRKeVkXUlhYiH379mHZtvVec9RzRID9eaeRUZrvuHyvRNJ4jknApxOS8B8in92JhEppmAiwRiUw0PPzM5vNOHXqVMO8KTlriUAtETh58iSYhNZUCgoKwFtN5cMPPwTHlOaldEV27dqFwYMHiyV2Lhs+fLjjZXDnzp3iXGmr1PHf68GDB0U/rmPb1IiICGHD6quPMkZd7KUxVl2gKsesNgJWq90+0WcHsgm1Wcmx5gLbgpaaDHh782IsObSdPMfj0Bc9BQkNhSd51sV29no7T/75lddyLvwvuuESpDvqbeZSBLca6Th3P3hueBzWURzOnZmUZpMsALoT2RvUIti9mV/O+U37l19+EQ+6EyDzCLJ7FY7zbqOrKWNUPuWzbx4e61YjTxs6Akw+vf3gsllHSEjdat0bOnZy/o0PAV4OZ+1/TUWn04H7cla86go7OM2ZM0doMp977jlHt2PHjiEuLs5xzumf2UabhetiYyuew1zH4zBxjoykGM30d6sIt8vMzPTZR2lXF3tJQOsCVTlmtREI0DQnO88c3+2FQ1LFsoPvhudeY7MYyQ7VQg5QrkSOl5Xbv3e/iNWpt9idfI6q2uNrdMQS24+IhcHl4uFdpricKyeHKPWmLzlDXurClJRIHDFuJF7/OzQRLX01F+UDiXDyVpdy/Phx/PQTh2qyC1mTVRJokAAAQABJREFUwuqpCBOV+aTZ5QD+UhofAsHBwcLGjEPOsBZFEbYrY+2KFIlAU0IgISGhVrbPvNTNmszqCv+t3XLLLcJOMzSUfiOcRKPRuPwtsplMWJg9DCDHHXX+O1Xq3PvwcFzHY/vq43RJvx9KAup3SOWANUEgKPw6svM8RKTrbHYet87aEAoRpNK5lfr31Ji7DzlL74Yx52/22oEmvCUSLv8cuji7h+NDyz6mgO/kdCQYov3aRFMp048NM8mK8yOssCsEiTxG9X8SIWljvE4wjEI6ZZXZwyS5NwgK0CG45eXQRqYhvMtU6OK7uTe5IOdMOJwlmFyuLjJHY70217lYHL89+jZKyemDnXq0lgUNDQEmmmzztn37dvFjxT92HGhb+dFraPcj5ysRqC0CLVq0EJp/DrNUE+El7/Dw6ict4RBOmzZtwqWXXiouwwSWtZenT5/GsGHDREQK5fqs/UxNTRWnycnJDm0oF3Bd+/bthQ0pO0Oxt3tQkN2Mi+vS0tLgq48YsI7+kzagdQSsHLZ6CASGjhG5wT1ac+zKgDiExTztUeXPAktZNtI/6wVDxiaK/lRGoZBKYcrbi/TP+6Ls2FJxqeXH/nIhn8r12Qv+CMXZLA9OJsLYHbHD30R0/yeUao/9xA79wTnevcn4zpcg8ap5iB32ql/JJ2uomDTwpnhBeru+rzI2nHeXQZY4XGpqhkh1IOWB16FlRBzeufR2TO4yxL2pPG9kCIwZMwYPP/ww7r33XrBTRE20OY0MCnk7TRyBe+65p0bmJ6xlvP/++2uEGnuys4aSSSNvDz30EJ588kmhEWV70PXr1wubTm7DdqL898nCL4bsgMTPbyar7DnPL5Cs5Rw7dizef/990Y6dmPhvmDW6vvqIhnX0n9SA1hGwctjqIxAa/U9KD9kd+uIfKAj9KYpNGQFdyGgER0x2sVWp/ojVb5n1i/flcl4Pz1l2H1rcsR/l5OXtS7S6CMTcvAVJYdG+mjjKH6Xc8pxTftOZg46yMG0Q4kLC8frIWxxl53JgofzwJtq0ahWKiwqFp6byls52fLfffjt4ObW6wl6X/ABzt//raYvB9F6XY8gQSTqri2Vjacc/YrxJkQg0ZQTYm/zjjz/G0aNHq4RBrbbr+m699dYq21a3Adt1zpo1C3369BEkslOnTsITnvtzZqaFCxcKrSfbqs6YMUPE/OQ6Dp12xRVXCLtSrvvyyy+5uNI+okEd/CcJaB2AKoesOQKBISPB2/kUG4UQMmTYl5jZnzED7FKkQjJKQa5PQiNqLjiMNlGJ+Cv7uNepWcheMz6kep7nvHTyy6QnMG/fevxx/G8RsmhEajdc0+Fir2PXtPC73UWYt6eEUofaKPuQDUm529GqOMsxDL8lv/XWW5g+fbowRHdUVHLQr18/bNmyxaMFE1KukyIRkAg0bAQ4FrPZsJ1WSIykCOhBzoZ2O8KGfVd1P3v2HmcNJL+kM5FzDlHmfHV2POLg8wcOHHAsezvX1+SYyaOzsEJhypQpKC8vd3mm8wvi999/Lzzu2USGbT8V6dixo3BIYmcoZyemyvooff29r5iVv0eW40kEGggCu8iN6CkMQDG0wpbTQHaO/8FaDD3rXPTEwGtww0+zvd7Nv4fe4BKg3msjt8JrOw4Ab/6UXw6W4JMdrvalx6J6wazWoU3hdselmARz7DfFpogr0otzsTXjCP0AAb2bpYL0z472/JD9xz/+gY8++kjYDXEFP7QmTpzoNSyPo6M8kAhIBOo9Apz+uDDjNlrhYJtueummiCNRSfMRoG1R7+deHybIy9d5eXmYNm0a/vzzT7FcrpgtMfFjYspL3hzsva6iRTDB5c2bVOZt70w+nftW1se5nT+OJQH1B4pyjAaJgIrsMbOieuK2nBSP+f+TgiLNMmzCXRGpGBWtxfxrHsE1FMMzKtAecoa4Gu7ueRlCAnrhrY15FGxdg0tahSAp/Pz/SRXoLfi/Td5jy50Ja4cWxXuhs1K4JhK2A1VCdfD5L4e34Z4l7ztsUzm4/Eej7sJVkRUEmY3V2eZPecPnh6pzGA8eR4pEQCLQ8BDIO6VEMOAnml0KM+9AVPIPUhOqAFLFPjo6WkQK4QxhS5cuFeGMeMm9devWIm6n4hhUxTBNsvr8/1o2SZjlTddXBJ5T8TLyaa/Te0M3BM0p3znnOu/fvAOO3/ce1h3+G5wNqHPzzvjHkiKsO14IgwWkBQXm7izCu5c3Q2rU+bWPO1VkRgSlvywyegZGVpGJQKk2EjqDnYDyjSpv4ptOH8TURf/nce+3L38fzWMT0CepjaOOCafzMo6jQh5IBCQCDRIBkeCDI4zYXMPIgQyRzIZd0AVXvIQ2yBs8z5Nu06aNMG86z5dt0JeTBLRBf3xy8ueKwO7CPJ9D5FLO8/t/foMWptRkFaoV6Tj7NGuNEoMZdy0udOlnPsv9Hl2WjTmXJ5Bd6Ln9aXHs0Zxye5D+eHJS4iDvvoRzrzuHiHJuZ6N+WqurE1WvXr1Ek4//+sO5qcvxF7tXuRBQl0p5IhGQCDR8BCjJhz0NstutUCxklUqGU3NDRZ7WAQLn9itZBxOSQ0oEzicCvsIi2eegorw/Zx/EtHR90f/uwbLrX8KRQjWlo1Sh1FSxbKXMmT3Qt542YGzb2v9pHc7PEDanuWV2Amokjeu6qS+iZWRF1gvlerxPidCQJ30Aio0VAcLt9TZYyAY0EmVQk1E6azFHjhyJ1MQw4XyVXZztPIzLcWaJ9yV9l0byRCIgEWiwCLCzETt+GkqX0j0oL6kqMtMhJ8zAng32vuTEGw4Ctf+VbDj3KGcqEfCJwKCUjviV7CAt7IFTmRB501M4pkWbPkRKq7tI4+hdDGYb2CaztpJNgeovnvu4R/fen8zE2ikvoEOsa854bqihkEsvj0rA9fNOIzBARUHzbQgmghxEdgEvD9IhL9MeXYADDdu2PIaTqxdCpQlCm/JErFN15cSaLqIhranz8rtLpTxp0ghkZWVh5cqVIr4g27hxGC5pD9xwvxKhMU/BYj4jvOBBWk+NtgPCE2bTZ+p7xaXh3q2ceX1DQBLQ+vaJyPmcVwReGT4FPx/aynmNYFVoJZNRIpzuUkr2UvtP70TfzipaEnevtZ/riAA2J41kbeWVDT8hgB7+HN7JWVQ0v7c2/4j/GzWBnANiKVaqayzP8EA1fpyUTPnh9cgpMyM2OAADKE1niFaNlGZ2zWnG/KtQfnwZDUsL9qYS3EB+/x+jq/D8Vwg142Cma9970Vjny8tjiYBwXvvss88cMWE5fzRnyuIoCZKENswviEoVgMhm75H3ezk9Fcz0bKl+lp6Gecdy1vUJgdr/Utanu5BzkQjUEoGE0EicIOciTre55uQ+lOkLoScCZqFQTO6iJduoWEsB0iJUSCNHo7+zKLuQUyPmpAbSPvZv7koOnZqg2FCOR//4HKtO7hGxOgc0b485Y+4UGYW43V9ZxzzIJ5c/0vU0JrfeQyFTFtCPRSECwyZRlqhHuMohrPEckWb30ncUnj2wlOcQ+eSltgqJpjBTq23f43bVpUjXJhD1VKFHs1Z4a8hUx3wqWsujpowAR0D44osvHOSTseCIChwPllNz9u7duynD0+DvnV9ofbxTN/h7kzdQfxGQBLT+fjZyZucJAc5f/sFl08XVjiyfgTG7ziDfCwE1kbbgkhCr0Pa8NDIOty3MQF65hcia3QkoMUyD/4yKh5a0oN6EbTnT3r2XlszVFCzeruH8+eBWLDy4BXvveksEtPcW1H5W71O4NtVuk8nkk8VQ8i05KZViadZAMR9eMu/VLM3bZUWZpTQL6uBYWMs53l+FBBHV/jpwE5KuXwZdbCcRaqmkpKSigTySCBACTDa9aTk5uQEvy0uRCEgEJAI1ReC8ElD+Ydu5cycGDRrkMs/9+/fj+PHj4i3aOTgqR+rnJZ7U1FR06NChzvq4DCxPmjQCCSn98P6uh3AdLoOGNJ5mIp3qs8vhs7EK7ZpPFPgE0Br83AlJOFVkQmaJBTG05N0yUgMu9yWzN/0sltcV8snteNlfQ/FI/7N+PqXjnIZp3YZjyZEdjiEitWYH+XQUKgf6X/DelqPI1OtI82rC80Mm+Vw6DwhLosxO7uFW7ANZ9bnQhCUro9Z6X2Yy4GD+GTJnUKNdTBLZoJ7fcFS1nrjsWC0EOP4rE05n4TIOuC1FItCUEeBg9D/++KPYzpw5I6Bo0aIFrrvuOlx++eU4n8HdG9LncN4sjfV6PZ599lkRsNUZoNmzZ+PVV18VyzicVurEiROimpd1OG8qp6969NFHxQer9PNnH2VMuZcIMAKBiX2RRqHxFtkW4mbsw3DbSdyIA/gMSzEQmdAmDnQBKiVCi4uSg5BGwep9kU8rZRcxGw9ia/pWr8vrZkoJupJyxLOMSuuO2aNuFcehpJltS1k+8w3e3xNNVhUSgsoE+eQOT6/+Fkspbqk3CQiKRliXKd6qENZ1GtSBkV7rqlu4LzcdPT56GNfNfx0TfngZKe/chYxS6UlfXfzqezuOAcvZs5hwKsLBtnlpfsAAGS9SwUTumxYCvDLwzDPPgGOAPvTQQ1i8eDG2bdsmtp9++knEBeU69xSaTQsl33fr/ZfNd/ta1Rw5cgRPPPGEyFUaGVnxQ3fs2DGsXr0a8+bNI+NnNb755ht8+eWXePzxx/Hmm2/ihRdeQI8ePXD99dfjjjvuEG8Sp0+f9mufWt2Q7NRoEdBGtUFYp8lI3Pke7sOuivsMCIQ2qjO0KcMdGYEqKn0fleS+BEPZElq+1GFO3wIsSozEY1tSSO/pqillsqnIlK5DcXFyO2ymQPFaMgaIDHydqtxDLAGhGity3cjpd3vX4tK0HspQLvu44W/QMHoU//0J1EExsFkMCO92O2IvedmlXU1PThXlYvDnT3l06/3RTGy//TXKElXxN+/RSBY0GAQ6d+6M8PBw8SNrNBrBGp5x48a5kNIGczNyohIBPyDAaTY5H3xxsT1knvuQSvljjz0m2rGWVEoFAueFgHJu1CeffBK5ubn45ZdfHFdnYtq9e3dBPrmQDdn5DcJsNuPUqVOijss53ypnb0lPT8fRo0f91ofHVmT37t0ijZZyXlpa6vNLpbSp7d5gMAibqtr2r8/9+IeJl+l431AlsO/zMAdEQL/rv+QNT4sEtASva3MNQvrdAlP5V/TZGVGYdxHF1rQHdPd1n6aSp8nbfC1Vk9e5jWJxEucc36IQUToL7lqX6tJtREpnl+9bkjYM41vZxzcVL6Pl801iHKUThRtFuUWNoyWkrnWSzOICl3GcqsRhYP+XobvoabIFzSab0HiotKEu7dmphD875cHJnVjLtWbNGpw8eVLYAXbt2lW8GCpjf7J9uUjlaSJNrrOwrev3f6/BLZ2HOhdf0GN+trCwORC/9DZG4Xt0/vz8eY+8lHjTTTc5huS/dfdleUdlHR7IZ2gdglvHQ/P3k58z/v6OMs9gjeT5Ev474JBk1f2t++233zBjxgy88QYpAqQIBM4LAeUfLBb+sJyFbSWcNaIRERGCpLJRe2hoqIvRO7djOwt/9nGeC2tjWROrCJNe/kLXhfADW/khrIvxL+SYygOgun+UF3KulV1b1fFeBLS9A3nkvBMdmgANFsJc+ijpLc20WWEu+QpW9QBYtU96H8aWD41pjUcdR3fqFVOG9hF6HCgKIi1mIJqHRmN6pxG+v2/qx8kl6hYai52DginOpwl7CgIwZVUqnVdoUjl8U9/4NN/jUGu7UB9NAkQAUJPnd5x/HJTvPn+e3377LdiERvlsmYzu2bMH48ePF3+jZ4rz4U4++TrlFDc1p6TQMdbZi1/QnXIP5eXlLs+XCzopP1/c+fPz89D1Zjj5DK03H0WNJ8LfTxblGVPjAXx04GeUMraPJn4rZi7DyrKa/M7xM+fjjz/GlClToGSjq+6EuO+yZcvESzObwuh0FYoHXvLfu3cvRowYgaSkJMeQjIfSZ/To0S59mEetWLECvKrhHMGisj6Ogf14cF4IqK/5sj0Ra1cUYVIWHBwslnScy7me64KCgjzqzqWPcl3es6Ewb4rceeedQvOqnPtrzx88G+03VsN91ixpKetOYGDFkrK/sDuf47y8fgH+t32pSIE5MD4Ts/sd87i82roRoUG/IiRymkedyZCB4uwoUp562kEGEem8vn1L7ChogWEtu2BK10vIfrRybZzNtgwW0z4KGp0Bky0C035+V8TrZO0qCweP5/idSZFj8L99ViSHazCxUziiyTmqJsJ/d/wZKi+Gf//9twv5VMbiNkVFRWjfvj2Gtu6Kn4/vADshOQvPbEBa5zr5O3K+Tk2O+QGbn5+P+Pj4Rrt0zC/qMTExNYGlQbWVz9AG9XF5TJb//vg54+xw7NGoFgU8rrONci2GqHYXXtEtLCysdnulIT8zn376aSxatEgpqnLPz1p23O7bt68goBx3l5/LzIceeOABbNiwAT179sQjjzwilHzssM0ruExy2T778OHDwqRx6dKl4qWbyfOkSZPESsbMmTPFfKZPn15pnyonWcsGlf/q1XLQ6nbjHwF+WCrCx8zgY2NjBRi8zKII13EmF3/2UcaWe4mAMwLvbVuKVzf+hCJjOQoMpbiqRY5ztdOxldLYeX+QqAMo+LvN026TO+uIE97XdzI+veJ+TOs+vEryyX04BI5G14lS5w1HWOhFOH7f+5jYoT8SQ6PENrZ1H/SOfRIL95dh/Sk9FuwrweT5Z3C62PsceMzqCJu9KFpD5/b85s/22Cw8jzBtkHM1ZWTSonNcCsa07ulSvim9HB9sLcDcHYUUR7Xi79ulkTyRCEgEJAL1FAEmnkwAayP8LGW/l5qYrbz//vvo06cPPvzwQ/Dx1KlTsWULhe4jreeCBQuwceNGfPDBB2AyqTg78TI/26fOnTtXmE8xiWUTAJYHH3xQrPZyG+7LTlTMtSrrU5t7rU6fC0pAmdHzB8m2ZazJ5LeCfv36gT0uL774YixcuFDcw6pVqxAdHS02f/apDkCyTdNCgD23n1r1tctNxwf5JnE28nD3JuqAZAToOlKV+58YLX/byolMdvHWrdplrDF9b9xd+PvO2dh5+xvILr6SiGwQaUHtQ1A8fCFP/p4NCxuM1lLYFMabrSRrGriORRugwW6ax1TS5KZFJqBtdCJuJWL9x+TnXK761a4iPPdnLn4kcvzt7mLMXJaNNcdOoqzgfRTnPIPyoi+I7PrHdpjv+aNtBbj1pzOYvjgDSw+XusxFnkgEJAISgdogwL4rzFFqK6xMYM5TXWHieMUVV2DJkiX4+eef8dRTT2Hw4MHYtWuX2CvP5+HDhwttKI/L4S75XBGljnnWwYMHRT+ua9myJdj08dChQz77KGPUxb72KPphNnzjd911l/Bw5yWjVq1aYfLkyWLke++91xF+iQFmtTWLv/uIQeV/DQKBssOLUbD5NZgKjyCAHGjCu0xF5EUP1njuVlMpyg4vgqngGAJC4hDahshbaDMxzn4KJxQTFIY8fUUw9j0FQegQqad4nZ6XCtCmehZSCT9kIhLeQt7JYXRGf2YqLZVRTFF1HCITP6Vj//3plRitIgd8ucI+nWZUbLCikDaOU1ob6dKli3iDdu/LS2idOnVyFPP9vjFqmuPc/WBnhh6f/+VK1kMCStFJfTPKiggbMkg1lmmIjL6F6JQVhFOE+xA1Op/2UwYK9BTH1W5uhnc25eNwngm3dnPV1HoblJd42eZKeen11kaWSQQkAk0TgezsbPF8r+3dM3nlMVq3bl2tIXgV6rXXXkNKSorQVD788MMizBP7rTibMTCHysjIEGNyHa8kK8J1TDKZ+LJ5FT+vFeF2nFbXVx+lXV3s/fcrWI3ZDRs2DLw5CzP7MWPGCGCd7SKZjLLzQ0FBgUcQV3/2cZ6LPK6/CJTs/QbZS251TNBaloW8VY/BkLEJCZd/4Siv6oDJ56m5vWE1FMJmLCReGILcFQ8g5ba90EamIlznmUbzgwPxuC7N05aTrxUa/ZjPS3LopZgWa2A27ITVcpoc6uOgDepFf/wVBuQ+O9egwulZ4tGLNaEVjxqP6ioLmISxPTQv8bDNkfLg4mUg57/Xqgbi5XaOAuCsjH2lz/2k8WSyrgQ3Z02zGiW5zyMi/rWqhvRZv/p4mQv55IYmIqK/Hy3FqFYahPnoyctjvKTFD2J+6WUSyrZXQ4YM8dFDFksEJAJNDQF2UPZmllRdHFgLmZiYWN3mYnWYbTznzJkj+lxzzTX44YcfhBaWx1KEl/WVZzL7YXirY/LrXM59uR+vZvnqo4xfF/vzSkB93QDfOG/exFcGAX/38XZtWVY/EDAVHXchn86zKju6BKUH5iO0/TXOxV6PbeQ9fvwdss0UlMy+LG076wV++uuhSJm6BV3iWoBjcjprQI+XBmLorx2wcux+6hpCvSnXT0AwwmKfoaX0tl6vpRSqyDmISSf5vitFft+HatVoFhaAYwUVDyPlIkZioBGBXlS3SgMvextlgDLpNwuHpwBNMr1JXyTCh3BmMha2w/b19+plOFHEeep5FmcVkqIsVFNC5NO9B0UYMPzlXlij8wzKTOVMdJXOTJ6zyqwIC1RKXPe8vMVaAmcHyLVr14ofi3bt2rk2lmcSAYlAk0SAHS/dSVxNgGDzJY6hW11hzWf//v0dzdm5aNOmTeLFmO1JFWHtZ2pqqjhlfxlFG8oFfMzzZuLLjlDsjMkKBaUuLS1N+Nh46yMa1dF/NftlqqNJyGElApUhoD+1BipduNcmNl5OP/qr1zr3QmPubtJ4sv7Li00kBWUvO7YMgZQ+ctF1j4uuOrJtZNFSqsxsSneZEfQdLdW/BwTRMnHyIuiCLhb1F/o/zsD00sh46C15OF78E/YVfIwTJT/CZCnCd9eSLSqrHqspNvKkt5T+kzz4/4XS/NdRlHUP8tOvIMKpFg8ofrDVlHzypfs0r7BPVaZisHhfDlepfekolZ6V7yOD1JQG1POei8lUIYbqfAl7izqTT6UdZ2WTIhGQCEgEGAGO1MOEUFkNqgkqTD45jJJit1mdvqzx/Prrr0WIKXYW4gxLbAPKoZU4CD7bdLIWk52UeDWZZcKECcIBiUNdsbMoh4xiO1B+drNzEjszsfCKD2t0ExISfPYRDevoP99P4zq6oBxWIuALASY/VmMxLW8468nYmbycVGcV4brc+1sNrraF7vXKuaWMbHcCvC9/W81lIjg7t02JiMX+u9/Gv/pPwKROg3DfRWOxbuqL6NYsjZbRaelEnUwPn9rZVCpz8fdebynGrrzZyDFsQ7HpKLLKt2NH3qtIL7bbBFX3eiU591Dk+T30GRCmNrvjjtWSLRyEqhrDYDZhe+ZR7Mw6RuTXVRvbKlKLV0ax9pnMFrQqhOlU2JY3kF4FPB9BgaETqrpUpfUj00IQHxLgYbPbKzEQaVG+F318Laudi7aj0onKSomARKBBIvDvf/9b+KPUdPK8RM5ORDURzgLJq05sc89aTHbQvuGGG0SotVmzZgkPebbV5zBU7AnPwvXsL8PtORQTh2vimJ8s7Cn/9ttvg8M1cYZKjk3KUlkf0aAO/vP9NK6Di8khJQK+ECjY8iaKtr1FBJRID9nfhba9CnGj3xNvmYHNehNxDCQi6hk0HVQe1OISX8O6lGuj21FcTrMIJb8ZzXCSrAFjoUd/ZCBMrYUmqmI5fdPpQ/h+33qkl+QhKjCUiIvdu9tlwHpyYiXCPugze0B8Pmah3EtiP2XR2/ju6hkIIMLMKTHZY91qoTh85kwcPpWM7fsicPIMkEXR0LJygTOZzyCnMA7lhmDCnhOG0kZ7tloIpqXr+GggjrcY+3Ez4pTtWpJSOCYHd638j4gFytfO15di711vERGscCbq1iwIn01IxL5cI2mVVegQ+yxUBSdEfFOVmtJ10tK/LmQ0xVWdKuZe2/9Y4zvnsmZ4njzu9+YYRdirsW1DcVO3CGFr7mtcflhzVA53Iqok0vDVT5ZLBCQCTQsBjtbDsTOZyNUkoD4TV4UIVhcxDjrP4ZQ4lBJrTjkrpCK33367CGzP9upK7GauY03n999/L3xomPQ6e+137NhRmBqxSZWzE1NlfZTr+XsvCai/EZXj1RiBor8+Qv5q+7K30pmdjgwZW9F88lowAQ1KGUKe6wuVavue02SKfOa3uZb7OGMno4AWw3Hz4XKcRigMlF8ogMhSGXmoLzX/iFZpY0XP5Uf/AhM3RYoM5Xho2Sc4UZiDezoNV4rrzd5IHumcdcibHCnIxPAvn6XlGxv0Ocl4vNUg7NgXjL8Pd0VxmTezBmKTZ8VmY/pJYueyKCH+z9vRdKWF8z4O1oB/wxJ2GpbwkwiMPISRH8zBhntnIMQpz318qAa8OST4c5hJY2u1ZCFA05K2JEfVuRwwCX1uuF3jWt1xePlq//79YqmLl9f4Yc9aAk4XLEUiIBGQCDgj8NJLL4GzNn711VfCptK5zv2YfVmYsLImsraiOBi592eC6pwZybnelw8Nt3Emn9Xt49zOH8dOvwT+GE6OIRHwRICXYzeSRvFoYaYIcTQwpQOiKdQRiyn/IHmh3+/ZyWqEuegYivd+hYhut6LZ+G+RsWAiyo9TOjJdhNBk6uK7Ufn3UGu82xK6D8qarRGn41CgKiFO5WojeGv4bVhDS8iBtLJ+w0+z3buK8/d3LMdVLXshIcgbcfPa5bwUcs51tlc1ui1788UDClrDktkXuox+CDLE4F1OKV+JqFRWRIXlIzS4lDSBhJEgoUxEtSg3JqCAVuZ9idoSDHVhG2hpCzo1DMbdwNC1Jgwg/nYRhT0dSH5Y7VM9e2u0afTKTtsFFtYScK7mAwcOiB8UDl3CDgBSJAISAYmANwQ++ugjDBw4EP/v//0/ETWDg9QrKyj8EssEMDw8HK+++iquv/56b0M06TJJQJv0x1/3N19i1OPy717EaVrKZoKkIYeeQkMZVt38PGXKaQHhYKQJpeV1Wnp3E+FgdGSxIKBclTjhB1j0+URMT1D8TkqlGJpIy8OeNoRuwzhOTxXnotxk9CCf3KCUNIhrT+1Dq4h4IsehYgnZ0fHsQQA9UHbmHMfolK7uVX4/txJZVmJYcvxRNV3blzCmI1p1xeJD22ChJXh1aTMEpQ9BYMbFCNB71wLqtHp0SduNHm23o0Ork2jT/jkkUNi48OAs6HOvpOD1KnJeojmYyZZSY0F44u/0lk0pcckUN5ciUuXk27cTtHz/45YT+OsQhVkqaQ61uWJ5iOdbUqLFsnUQG583o2sMvggY0ocIKSVJijg3fyMe0u/CS/FVibF8NWltiygKQhvaOlbVXNZLBCQCjRQBXga/+eabsXLlSvzyyy84ceKEMB1jz3JO781h3GrjuNlI4XK5LUlAXeCQJ/5EgDWfqf+dTrpG1qCdXcc9e4GhXzyN1Tf/GylMrNjG0Je4OSQFBEWDt9pIZmmhWA7WUzgmd+G55pQVizBMTP58iXvKSV/tzqWcg8c/szKHwiqZiITaEEwM9JPxFJ9UvwhW8wnS+CYjKPx6WiKuYG9vj7wbq9Z/DtvRAdDmexIixr9L2i4M67VSkM6OrfZCE3DWsUsViNgWz4kpz537G3Kyx6NNpzP05l6GwqIwnDjYDKlpK4SXpIY0xEwieVOkU18Trpn/urD/VOujoClKg6agHc2jHYJL2hBprSDPmWRn+sNS+xZAxLpPN2D0AGDUQHrB8M6VlcvUiz1rN4qy7obFeIgQJXtbctQKCr+R4sHOqBfzk5OQCEgEzj8CgYGBwgNd8UI//zNomFeUBLRhfm4NYtbLjv0lCF+ZyeAx33EpRRQe4jEkx4RBlxYNwyH2dHcjfuQYFJw62qNvbQvYkcjkw5veRHaUnEIyKSwaCdZiFNqIHblpHQtIc9sjtvrx22ozT47bef08ClpPnR1oUBSA8syrqIS8gMhyldarUV74LqKb/4rjZ+Lw3RLSQi7XEPG7lepcxRRxBMbETTAkbsY/L9uEzlF61wZ0plZXEPrc3FzsiByBlbnk7Z9HGk8Kmh8VkQH1ifUe/ZSCPkltML3XpXh9ExHkoAIYg7bDmLAdL1xyI27t0ha7DgDrdwCrt0IcK/zeQvxt40779sJ7QFcKtTmaiOiYwUBqc2X06u2ZGBrLlpJpRiER9JbQBfevXscatirJfYbilJJtAYyOnvrib6AN7EMOVEMdZfJAIiARkAhIBCpHQBLQyvGRteeAwIE8SmlIS97u8nSPdExoVYBQjX2ZWdclBoHdolH00x5iPMRKWM6mqozofqf93A//xwaH45oO/TB3158uo/HydrBWh4uTyUue4oq+Y1yIMaqrobOZYaR58N5C4YJ+UC1BcOkdQGQHl/7+PPn9aBl5iNsz9yjjvthrBnFzcopR2Ym8mZjbqh2jsODNTGzd56k2ZE/1ay614ZvS13DYSJieled3tMQ3w4gNOsROcyOave8o2RdxEXIDm8Omrng0FAQm4pCl8nt+fOA1Ar+Fh7ZAQx7313bsj/7N7UvZfchigbcHbgYKioG124iMbgH+3AzkO9mU/n0Q4G32XKBTa+CyS4BxQygsFnHhyoTJZ2HGFNIOp5P9FWu3DdBQjNbIhHcq61arOpN+I/Vz/07bYCDyKwlorSCVnSQCEoEmikDFr0wTBUDedt0hEK4LooDgpK1z8tC+rV02bmpDBoROoiJbQ1b3hQxMg35jLmmxLAhuNRLxYz+iuJv+/Yq+PnIaSo0GzNu/QThEGSks04Dk9ph75QPCbsdYfAoxRGD+tM3DQqThuC0cCSjHOBxHoq0Mluzt0NQhAc0pM8NAJgJHiuej2HhELPNGB5IGkswUTpMX+6I1V2HR2quQX+y0Bk5YsrKWbSsnkSP/sIsp+3yACv/ETDz6++f44ziFFqI2PZp3QViz/8BQROGuiKypNc0p5NHdFOIqEaeLzeITyQpuSW3dMKfBz4S1FfWV/TcitRt4q0yiwoHLiVjyZqV3ja2kTGQb0RWkYE3Pqui59wjA2+uf2DWj3P6yobT078m3KXXn/6NQTkepcwUxNOu3orz4BwSHT6wY1A9HKnUIfT9JNewiZGTiZA7hUiVPJAISAYmARMArAm6/NF7byEKJQK0QGNayi4fzzETSfHoVIlCahDgkTZkHbTAFfNcEem3mj8L3xt2Nl4dPwcniHIpTGSniYzrGPWtzGkLRQm8AqePcRNj9uZX58zQ+RI1tObOIBNpoWGJoVg2WbhqN5RvGY+v+vlTGWssKiYu24dpLVbiOiGfzZhXlytErI6Yoh459oFOe9fxyC55clkn2r/b0lQZa3vcmmkCyQa1C/jyxG/P3byQNrgaTuwxG70RSY1Yi5LyPvsRXeXvibmDPYeC3NcCvqwB2blJE0Yy+8hFpUsmb/vJh9mX66Ah7C04b6kw+7aWkvS5b5ncCGhR+M8ry33C7Htnpht9kv6z8XyLQQBBgUxj+m1uz1f73NusfDWTicpqNBgFJQBvNR1n/bqQN2VS+PvIW3LOkYok3SmfXtHmbrYricaoDtXVKPpXrRgaFIDKopXLq2KurcHBS6yIdbb0dcDYgNj0wU1D19jHJIq+8t3a+yopN+6jKhoBicjRKH4rA0wPwsrnC2cjez4aLOmzGNcNXYfxlj0BbyV+xsXwtDKW/UjcVAkPHkW3kQMel2d508nwnpueocT1gkwDOIlSZvL5xIV5a/6Ojyae7/sB7Y++ipXjyMKqmdG4D8PbPW4Ddh4BfiIguoU3RjPIP5ua/7du//0te9L3tWtG+aXFkQuGulaQ7VpO61c/CGlUrBfHXF39C4zMD1iA8bhYCtC38fCU5nETA/whw9Ip1ZI/NpJO3vMKKa/DfHSeZkFJzBDi/+qpVq4QnPHvBcwzhVq1aYcSIEcIL3jl4fM1Hb7w9Kvnparw3Le/MOwKWMgrBk76OlhgpU01MR+jiuollae+tq1fKBIRtL9/c9DOOFGQh1xiGuCCnp57TMDZrCQUir8Lgz6l9XRyKmKJqHWkeK5ZzHdchpyh1mG+icaooF2O/fUGEm2JP+gJDKdZNmYX2scmOISo7KCU/rK9/1SBi05PQFrX2aBodnovLBvyK8YOXoGVyCaKS5rn7Sbn0KS2YA30RGVQKbSrpCMvIhjXyLlp2t9vVrjtZTh72KjKRIGbnJhz6iZ2EgiltZjMKHD9zYIxbi4rTPTknXcinUvPYH1+gP5k3cGrTmgr5LoG3mbcBO4mTL/7TrhnNph9QFp4b25HyptV8hv5d1mJUn98wqPsqytikF21CIu8Q+5r+d/LkSZHdhAM1x8Z6zj00+l4ER0wme1POTBJHRDSoppeQ7SUC5wUBPZmN80vbuu122+sDx3xflonp+OG+62WNJwKcgei1117D7NmzRfzPgoKKFT6OA8o51zn/++uvv47JkyfLcExuEEoC6gZIUz0t2DIbBRtfFoRT5GIn20h1cAxa3LaXfmDP7WsynGJU8sbC8ROLs72HrNGFjKJrhZ6Xj8Bq1sNSko6A4DjSukY6rslB7rUx7VGSswd/IgWnKF1nNKXrvBQnEGo1ISC6Ay+Me0hueTF6fvyIR/nAz58UeeTbxyR51CkF7CX+v/kWrN6oplSRvVwWwXnJ3xS3C4bmqzHlou2YFvw7rEf0FAP1VaiSfX8uZuM+Ip+fKpdw7MuLviQt6GCKXdmJwiZZ6aHpqHIcBFGopUEtQ4g4ahBLOdU5tzo/TH3Jjsxj4PBUJSY78VPacfitv7KP14qAKmPwvgdFleLtX8SbN/0F/ExkdOlaoKjE3spEsUpX7xwqNp3GiP5dN+PSwdG4NKYDIuhdoiYyf/58HD9+XNyvXq/H0KFDRaBp9zHUAVFUxJsUiUD9QcBEC0x/7Qc27KSNCOUOennjMm/CYdB6dqKYvGQ7znF5efVBSvUR4Nzrw4YNw+HDh1Fa6iWONSshzhLS++67D08//bRo65wWs/pXa5wtff+CNc77lXflBYGS/fMoFeYTosaZj1iKKWXltyOQPOkPIobESvwguuAhCIudRY4jT9IaKS8tW4mmBEAb1J+WMv/thytUPUQ+Ee2irbPp+gGw6vMQ3vU2xI56R5AOvs/4q37EmI9moJRMAkpt5AVP2YFeRD/suv4fZB5AgdYpZJO7/HRgM2kTdS4OV9yGg8R/vXs1nhnimgXDQApWtnX88md7aCLKWeQypCUoB/rmqwTx5NBGLJEZZTCaMsRxbvqdInxqeOebxbn7f2YDeb+ryG7TRmpVF7GIMEJMQDvE6uieXSrFiZ5u77rO4WgV5d0e1L1HZGCouE/3cnamiqI6fwnbjPbvad+euc+u0eFlenZgYu0xi9GsowgBg2gDnn3X3nZkf3LM6gckxdvb+Pr/zz//xJEjRyj4fsUvNi+rJScnIzU11Vc3WS4RuGAIMLlkG2l+Mdu8y+7UV05aT1/SIpFeLnvbtwH0txTmmjfCVzdZ7oYAE07OlMbaTYuX3wO35oKgGgwGdOnSBdu2bUNoqP+ei+7XakjnkoA2pE+rDubKmsCcZff4GJnCJOUfQvnRJQhpc7mPNjUvDgy9FJrA7kSEdpAGjkLm6DrT1q7mA9WiR/Hfn6Fg3bMuPYv//hjqoCjEDJklyq9Y/BEyVWcfEETQKMePKL9m2bf46YoZZ8+A/bnpeJVsH/fRXk+e/s7e/soFzOTRn1VWpJziTDbwxSIKxP6bPSSRo4IPKBqAJukM8hK/hTGGflGcyKGa9KKlpr0uzfPXPgNfBJQ1ySqyT3R+obB3JtJtC0JxcTFahgfhxq7h+HhHxfy4zbCUXNzx68c4U5KPxNAofHnVP1wdtVxmAYxt3ZPip0aR1365yMTE1RzaKpDSg3La1boQtntlUskbk3kO6fTrKj3tdSjT2z8vztqk2Lo9N8eeBnRQLw16ddBiGK2s02+Hi+zbt8+FfCqVe/fulQRUAUPuLygCvKS+kzScHD2CCed2eiRwmS/hqBP9ewADehHppK2qkGa+xpHlFQhw2Lfx48dThjiN1+dFRUvXI36xZfOeV155Bc8995xrZRM9kwS0iX7wym2b8g+QIjBQxL9Uypz3VkM+ytPX+JWA8vhs6xmgGet8qTo/tpFZQc7y+7xep2jn+4joQSGJwlKwJ+eU1zYni3JwprQAzUOiwFmVBn3+FJE8zyxPzp01lCq0LwVqP34aeP874KcV9nSWLm1CzQhtm4/AtNMoxN8wlbkSTdGWiHqxy+I8afv0ReDlb5HliNKacj54RXQhw1BGNqA2C69TO9FQytzz4Sf7iYQeEvnOr7zySrw4sg1WHy8TrbrG63HDwrfEmDxWBt1vtw9nkBnBLBGoXxnfeR9AqsmlNz6NPh8/igJ9qSCfw1t2xfuXT3duVmfHgbTMPrjLe7io5Vd47IYwbNzdEWt2P4I/tyY7lun54geO8abBJ4ijXPc2XNzd/sPMmqC25I/mLV0emx7odDVcx6+zO5UDNzUEOO3tDnocMOHkjZ3z+MXKl4TSogfH3BWkk77XHdLoPdbpRdZXP1lefQR4VWTLli01Ip/K6Gwz+uabb+Lqq69Gz570AVVTmLwuXboUbJferx+9dTsJa1T5JZkdnpKSkhw1bEK0bNky4RA1evRol+fYmTNnsGLFCnTu3Bm9e5NK/KxU1kdp4899xS+WP0eVYzUYBJhAuajavMxcRUvVjUGsxhKotBTH0eiq8eN747BPIsd8aDLF4axYgnW+b15SLtSXIDEwHFf/8Iaock8x6tyel98Dipvjr1+G4ZU1ZGzgYjxqQ0KqEZq0YuRF/o5j5WTmUK4m7/ky+kQ88bYSke1iy3MMn4Mg3GMdgeIfXxdkMZ+I36473hCZnLiRitJrRiZ9gfxTI+mYTB2ov8Uag28WdEdZuYla8Eae5pS7eOLEiXjw4tbi/NKv/+0gn6KA/mObYI4nOn/iTKXIY59zJhO3FCTDSvFHWftp3VuG4oEFCIqP92jr7wJD6XKK+fkFTZSCSAWUY3D3bNpW4/n7P8WOg12wcpNdQ3osveLKpeUq/L4RYuNSDt7fKW0iLKUbkBB+DJEh+aIxazv69u1b0VEeSQTqCAEmlvyStGmnBrsOaoQt50m7xY3PK/IS+kVd7KHM+nWzO+65a/Z9dpYVtULgjTfeAHu911Z49entt9/Gxx9/XK0hSkpK0K1bN7CyIDOTQubl5AjyyJ0feOABbNiwQZDZRx55RHjhd+jQQSz59+rVCwMGDBB2p0x6mcDyC/XKlSsxadIk3HTTTZg5c6awTZ0+fXqlfao10Vo0kgS0FqA1pi6aaF76dtKQud2cShOMwCTXNy63Jg3mVK0LO6ux9JyytTwXAeEthC2jL60mo7R40c/YYNHhqC7TcxAq0ZHDVkJoBMwFSYg+PBHZh1PxiyD59uYayv4UklaKiE6kzwy3IF+/F+nFy+gTqCC97rFGA+ihcaN1D/rD/mvEJgHjVBPsHxsRYkWGffkcesY8hDKzlmJxqvCfUfFo12ITrBZSv5J88ukvKC3LVZqLPdsv8QOpdWs7Ac0p93yw8n1nlNoJmUvnsydlZWX48ssv7WdEshWe/c033+Duu+92efP21v9cyxTy6T6OxbAIA3p2oQ14/C4ILfTydSas3GjFrkM6WrqsUA2xd312fiQNMUYME6IrQfPYM7hsRDwlAIhAGHF4tkGVIhHwFwKn6M+ZHYb+IidEXlbfQ9pNNicBvVz6ktgoO+Fk0smaTs4YJr+XvtDyfzlrIvl5eS7CL7U//0zG/9UUfo5efPHFgrRyl/bt2ws70uDgYCxYsEA4TXLYJybGvLz/0UcfieOxY8c6+jAR/e2338BlDz74IObNmyfCQ/3jH/9Anz59cNttt1Xap5pTrXEzSUBrDFnj6sBhh6L6PYa8VY95uTE1hWQyIaT15V7qGl4Re/NH9Pmnhw0oOyOFpI2DNrKVuCnO4FRE9ozeJJAUh1b6p6XUmKYK/uJoailshsFFz2P5OjWyHaWU5Yl+U8aMsGBLUCY0wQpFA05SjE5n8mnvYhNe5QMolaWFtI+jU3tgWut2yFl6J0yFx7BT2wYRJToUuaU5LdAbcaz4AGICu1BGUxseWpKF1y+NQ+f45mJYg/3XzWlW9kNeFlKkHcVu5XBS5B+vFAmNJsc09SXp6ekICgoSS/rObfhh/dlnn8FoNCIyMhLXXHMN+KHpdzmbPMBjXIrF6iyt6BZuusKCywblIyo6gbRMAVi/3R6iZtdBVw11GYULO3imHd4iXs0bL22yJ37vznbP4W703hZJ9nVSJALVQYDJ5t9EMHfT94yX0XlfWPHu6HMINg1hT3X+3jHp5O+wlAuHQFZWltAinusM2ImJX9yrEx80NTUV27dvF/ajeXl5yM7ORkJCAtatW4fBgweLJXaez/DhwwX55OOdO3cKDScfs3Ada0pHjRqFgwcPin5c3rJlS0RERODQoUM++zBprSuRBLSukG1A40Ze9CARTTMKNrxAK7U6WnK10J60aLEdkTiR9He0lNxYJPrix4S9a9F28nonj3abRS8Idvy4Txy3eGeXAXh9+++Ocz5Q0VtrJyKdIeTYw9LdHIlNmjxYz5LQgJJkhBwej8DMfljupPEMDwWmjAemksJyY5Ye+7fYyFlJDCH+M1sryF9FKdl30ufx9YR/Ohch6bql4nzzvg20dvyZSx2f8FK5xeoaCumFVXn4aqL9V4sDI+/Zs0eMbSGCGUDzDAzQioDJymD/GXYz+s79F+lYaRmd2/DSPY37zqV3KE089t5sJ7kRe33yxsLLTu+88w54qSeM1Yl+lKCwCSjN3+sxYmAYAe9DdFqAlyx5e2gqUFIGbCMbu03k2MHOHexZzLFGFWEve46lyJsiTAa6k48Vb93ak71dKsVNDVJq5b4pImA0AYdOUBrZw8C+I/Z0srzn71dVEkl/Ft3ou9QpzYheRDj7dtOBnx9S6g8CvATu63lXk1nyi3hGRoZj5amyviNHjhQa0Hbt2omXedZypqSk4NixY8ImVOnLXvk8JgvXOccw5jommewExcoA57B63I7vy1cfZfy62EsCWheoNsAxo/rOQFinGykQ/WrYzAboiHzqml1EX9TGt+4YM/h5RBERNVPe94DgWBEL1Pkjm3rqvzhC4Zd+VLVFlE1PGcYD0BeZeBY7aTm9HdEyNQZb4pGvMuFwSTwiT1xFa9QUtN+JeLLG7BYindOuBiLO8q3wAjU49p6LMIGtUDY6qniZxpf48iy3kr9+uC7NpVuBvoJFXXrppViwZwOWUm55M13USOGlpqrb4eHLLnP0aRkRh+2TX8QTG76nVKW5aBPVDLOGTUYYaYV9ifIWzWTTed5WwulkeGeUa8IQZspHi9IDYvnqiiuu8DVUrcq35I/FmTNbMCJpGWmuI4g0WzD38B24MawD2gZWb0i2pRtKpp68sZQRj9++h0jp2Y2XSpVQT/YW9iX946eBRX/YS3gptHUKEYg2dls83rdPBZSUoUo/uW/4CHDkHU4Xy2Tz4DG77eZ+2vP3wdXW2/u9BtH3kpfP+cWFN36JUbSbJSWUCESrRSC1kVK/EGjWrBlMJnrLOEfhVafERIqJVQ155plnwDFHeaWJ44qyLSiTUXcvfJ6X8nLP3x9egVJEqXPvw/Vcx2GhfPVRxqiLvSSgdYFqAx1TE5aEsA7XN9DZ12zaam0odBSo3JuYCg7jCfI5v8e2SwSij6VA9M1RSkQ0CIEoo7MwnM5Phe7kZEQVtHQZgoknazxvvcZzibZ3UiDFClWj1Mj6R7uoVTrSMLpqLbkmiGKK+pKksGi8P+4e3PDTbLL1DBBjmelXr3X4tQgKiHHpdlZBK8oOF2VhgS7dpf5rHMENGYfRL5nWlM9KRGAIPh//oHJa5Z7tj26++Wbh3ckPOH67thEZX97sOqhJk2sl04cs0qIei+yBFsWbqhyvpg0WHSjBX5n34udTVyNCW4QsfTMUm6KQdKwMbWN848jXYRtYfvvneH7846IIm0wo8RLt7ewkg8PesL3eX/uAo65QCuLBhIQ3hZRy3wT6SNrTe0GHVDshbd2CiCpt/F2pC7HZjCjKfgwm/Rp6uTEiNPoJBIXTm5CUGiPAmksmlUdP2bfD9NkePkkaJvrsfQV4d78If87sja5k9+pCf2r8oiKdhdyRqv/n/Ixw1h7Wdsbh4eHVWn7n8dlb/cknnxQaTdZWsvMQ23NecsklWL16tWMKrP3k5XoWjl2saEP5nI/ZdpRJLztQsbc7m00pdWlpaT77iEZ19J8koHUErBy24SKg1obDYixGDGkUeVMkgCjovqze+OvUQOSVuL69spbzpitpqZ2Uob40XkFEPj+4MhETvk2nkEkU9occhSK0McilcEpOylNxuUhd5exkVFp3rJ/6IpYc2U4hlWxYfiiZ7D7jlak69mGBFRT0kd8/c5QrByaKU/r8mu/x8/VPKEW12nOookcffRSKPemzi4mlkfaVySeLjTTpNhtpRCNobdFJrNZiChf1LuVXT6dYsB0pVSiFwqqh1p3NBViy9Eli42MuqbhzLvEU9i794osvhJkAawGYhN5///1el9iYLLBGk7fJZxW4xaX2JAKsHWW7PnYiUfLWO18tK4/mRhvHJHUWDozf5iwZZe1Xq+ZAKu2TE87RsaR0EsU44Jcau/a7NP81AiMIQWHjnC8vj88iwBm1WJt5kjbei41IJ5NMJfVrdcFqTp9d+1SgY2v7xt8XDv5O72RSGgEC/IwYNmyYcP6p7e0wgWUtZnWF2/7xxx+4jFaq+Dm1fPlyPPzwwyJD20MPPSRsOpl4fvjhhxgzZowYdsKECZg7dy54z1rTxYsXC4dQ1nKyTSenCGVnJHZiYlLNNqW++lR3nrVpJwlobVCTfRodAtkULH5j+gHoKdRSu053IWLLs4571FtC8Fv6rZh34mHklCc5yvmAw/dMI23nJPptr05WkUDKvf7zjc1RZLC7+Xy5IBXPZR5zGZOp0xRNe7cyz9PU8DhcEdtJVKQXhGBXlufSUHK41tExz8lj3lFIBwV6UvP4SRQno7IQ+tU1VCwB8fBMRsspzJUiNpuJwkSNoFN+DJlJY7cR5UWfIKbFWvrBrpi30t7X/qqOYRQPteJFgduxhnlcO3or8CGs+WSbVGfhskWLFglnKedyX8dsnzewl31T2hQW2+3/dh+22wAeOAYcOeldW8ZJCXhbs03pbd9zkP0UUsam0FeNCQ0HD+eNj5m0sie0L0JjNh6gQRj3CtMLsigmXD+sEwLK9uJlBf8HY9lSuqYa4fFv0ItE1d9d+53W/f+8VJ6TTxqgHPvGeJ/OAk5l2vfptOcXiZpKQqz95YGdhNq1smu4+bg6z4CaXku2r18IcLij33//vdahmHiZnMlfdeXOO+8EX3PIkCEoLCwEx/RkMsorT7NmzRJe7EwiO3XqJMIq8bg33HADFi5cKLSeTJpnzJghYn5yHduQshnUnDlzxEu3EsGksj7cry5EEtC6QFWO2aAQOFaYhZFfPSc0ZhbSJnJGn7e0HdCxpAALT07H4pN3ocRMTNNJbCFZeHxqFCaP04EdWmoiAaT5jA4OEF1OlyeQJjQcJtIEKsK2pEeNpBqrRNiphz3M2c6H7S4zQi6iNd00jx7xlMtdkTFpPXG8IFs4ISllrJgZQ5mM/C1xIRoczmdL0wqhEKGID62YT2neq1TJ5wpRJbZARg6crz4kchodV0/6pwTjzt6R+GBbIdnA2uOQPjYoBsnhro839mDdtP5tYqd5KCdtqU4XKoz6laswjpzX+Vzk/7f3HeBRldn7Z1omyaQXEhKq9Kr0ZgEL1gULoK5iQ0VlXct/bauuq+667vrbta+oa1u7Ioq6goqISFE6CEivIYX0nkkyc//v+SZ3cmeYSSaFQOCc57lz29fu+xAy3LsAAEAASURBVN07973nO+d87BmvTxeql8PxHXkId9seDM/v8wzh8jDufmjZAgUV56FdHt73H+LXy2OCygSIyWhqEjDFEH8Sbk9eJ0TBTMTVh+KjD1CMowQ2x3VENICZh15eS9bF2dPJVcOYeeopzr6GYlL+Qzb7wJYU22heHhrPLyEqKMZSBJLJC4hmLjTNrG3m7RyQTtZghmKTGahCfq5ZM92NNdNYunfykM4eQjQDwXXCHBs3bpyKr7l48WKvk2WoF88ayAsvvFDF9Qw1Dw+7v/HGG8prnkea2MxJlxkzZtD06dPVyBM7F+nC9Xz88cdK+8mE15inb9++yiGJ44lyYHtdGsqjp2ntdf2VtHbJ7bw81oZwyIMjIRx+gUPTHI/ChIi/zDjMREuEwz9VrrifajMQIxPThVriepNj4ocIJO9oSbGH5WWt4OiPHvE5bintRA/um0GOrJGwzwzzOVcTu4squ82nmg5r6f+yY2hi4QOw12wiAzWU+LVzpw/55FMcB/TbmvW4/0YbUtZv8r3Jsd6MUhkbY9z1bu8tqPLex7f2m0Bv/7IYeGqo00UOq50cNjvN6n+mNw2TMO5DrqMlckUveJSDRLEwCTVjcWHjsm7u+roqd/JRLEZxUkHuPMrL/i9oOIe6uoLiki4DyQbD0MA4TInQ/h1+D4yHxnDYWRFUUaNRLMwOwq0VqKdes8vPXMmhGQhlw2Vo+Ghw0frNvWnVhkHGytV1H4nnPhHEdOxgz6JXyD4CGTlm2p9toQNYMrJhosDbORYQKf404OVwYYLKmjteDhewI3q97rBG0ZElIKPFCBmFEGRR1RTlwDFeIhHqCwvPCBUR7lkcdWu7TSOeXcoexjghti32mfTisfYRzbUXqt0dPse4P0ty/0mmyH/6HGeijdFD2E2iT9H+qmqTisPKkcEqEY+VY7JWVJng6OVZKurWpRUmzGZlgpYS63KzWheXpoK4B8bGp9IQdqIj3SDybkpPwdLBpdZpyS5onN2UkgA3Or9r5iKrq0B6sRwpaa3/0CPVvpaWy8PI/D/T2s8Z2za6m/u10cSL+vDDDxWJ5BmFuL9CESZ4TARZcdAcCRayiUlpsNna4uLiglZlJJ/GRA3lMaZrjW0hoEFQZLV1fLyv1itI0iYdZmNgvpHY6+x4FCae/JDZW+DCyX9OGf/pRa4KvGHrYjm68tZTyXu9qdNNu8jq8B0GbwmOP+3aS9EgYaUYLg7LG0ThB86isHyP9kanRjzbUU3SeqrovoBq47d7q+NYod/m/ErXDDzDe6ypGwerfwmYpQBapWD3X6C4m2GuSmAFhuc3NpvoCPMpZ+etL9J7m39U87ynRyfSFf3H+dTPxJNtI41f0z4JQtzhR+eDKW564scCmC24qGuslX43Io5iww0aUK0POcvh1ePVgHouIdyWQfDNUlJb+x8qy19FjvBtuDQbwoUVkyPpOQqLOJycN/S07tjyd0y36ftBOajvTjqYnUqZOVAlQvi+5WnpguGuErXyD08UNSSAspCJWeYhPAfZHrtSXvPQcRa0etm5Hk2fS48BFrRNIG4VsWpBEIcWiwnE3YruUwveHCaKxS33HfrFragyn3cjVJnbbcMnVKTSPPJ3jMdZp3UIY1MuwoZJH1Kg4GEtcQo0xrxOBd5sypCODxZeOyK5XXxP8tL8D8mmtKuxtPz8MVlpyX9oY3UczfNsk8j/M639nHE8S1Z+tIXw/yOHNWLHnvz8fPWf2VC9HAZpwIABtGDBgoA25g3lPZ7PCQFtoHdbw9stWPFHsuxgdbbVcb62llxf5d6vQT7xxvUJMA5yhdikJWuepcQz/t4ql8Ix+9avx1fp2t9SQu5wMtdG+pSrmWvImbaMKruiPQ60x08qahH4/cBqKnX9SuaIZEQQmEJbVvyLPtvwFTSMbpp4EqZCO89XE+RXBEIGeTSD/seZRwbDkD+O/M/1LFpNK1PT6oqpf9nfi6Fo/7RXDTzdvzrvPqfVF+/BZm5E2y30N8zGFEwc8b8DAZ2D0/w3xFoEG+pGpxjEanWDGK7DveAhp3yqLO/3mGb0I7LauhtSNrwZH7Pz8AQgTJ3TS0DoUtRLgafc5MDOx4JwmB7dWz5Qe1jRw8PNOfmetZrJCfs89JyZU01lVWEYmkZs3xIzAp63zktZ48kX0E2KUCqTWyZtMYGad8SOcXSCaEctJcSaKCnBQokYdUzgBYoejjagmyLwOj6m/jk4Yg06AgXrz5//c3sEqjqqRbb362Ot47Zt2+jZZ59VdpVMqtlGkxUoLEyGmajyxwR7sc+aNUvZXB5V0I+xyoWAHmMdciI3Jw+OQHuKDpHt4AboVgK8PDAsX52ztkUQse0YeyN/vxKx3H9iB4RBSvdhLNSNUD6VXRZRVedFpIWVGk8dtp2380vK37kBxzX6bv49NIPGY5YkzImOI89vLaCn86bS9Ks/9ubjP6d3fymh+TvL4bUOLUxUT9pTwlpAXxnSoZvvAcMeG5yzBl33OOdTNWYwlgDe4/NXbKTibT+poamTTz75mCFY3GaT2QGHoxVwYnkRXvBZGIaNgCPSNxj6hfqvQUE8Asz/bo27qcFUxpPhEegT936fIVUmVAMGjKYJE2cYkx62zQH+3bX70WArWaydDjt/NA6wooe1e7z4S0FBGbHGxUPs8YEDLSTPusMOUuxww9ulWEqwzbFNK7Doa45/WgVyWYUuYCshXrM2lkknl8OjjTVY8zaTYDYX0VwlWCP0FvC04CPRgul7Od4tt5HXbLLGQ/k8jO8Z0sfwPm7XCCxMtJlU6mt24jEu0VEIZ8ZLtCeeLpeRBU8iDmOjxzz0v37/fVfNfkxU8H/4ni0lu2MyQlJd7J9E9gWBZiHAWmqO/sFzsi9btoyWLFmigr1zYd26daPx48cre9FgQ+TNqvQ4yiQE9DjqzPZ8KY8vm0NvbFhENmj3CirLKIUuok/pc+jGPF+T+rXllrpp4dtvKwLGXoGJseHkzF5N7ppysqcMJWtUmp7Uu2YHkCWrQTp/Jlq9KbDjh2aCF3biJnKmrMSyCm9NvGkbETOISVdXHlJVEQbNaIZpvMpRg6k9dXkwL5zyFr5ApeEpxPE7MwoH08pMvNHrpEP4FBDQx/Vdteb55N8d3IMy3z8dL3UXxQy+maIHXkv5laWUWVpIKY5Yb9xNd0Q8XvQmyokZ4lOGvvO/3U4ahK9yFp6KjW0hOSD9sSImkDpH/B2qOTaEo8rd951P0wJYFeA8Pk6QrynS6aRHKP/AhSBNmEbArIFAmTCU7KaEtOsbLIbDRJXk3KIIsqaVkiVsAMWmvA5NLZhVA1INg9f38aGRV+GiU1LtdNZJR9bkhj9GeAlkOoFHyqMlhKaw9YXDa8WQ25UJTCLJbGlbjWhj1+N2FVNR1mVIxh+0sG+u2Umu2gO4525vLOth56srlyuPf7crn2zhIygqETPH8VCFyAmPAEf/4GkueREJHYGm/YuHXq6kFARCRuD1Dd/Rs6v+55M+B0HfEXiCnqElPsd/LhhI+a6D6tj+7WtpcuzbcHABWWQtlbOIUqd8TTnm02nlRs+0irxmr9hAwtqU0YOrKcH1GL2egETWcqXJwVuFmFy6jSQjABPi88M1j3HdNoqnGM1JJSaodAxSCf3q/21ZR06oi5hYVrvfpWFJf8L86h57MzecnM7o+Ch1jPgfZRbvppPiU+j2qGIq/WaGt5S8RXfSe9tW0d9hE2iDWikfBP3BsVOpaPgNtCO/Wjn3OKGRCiQ8a5MubCz/yy+/0MiRI6ktDc31+htbh4djFiPH/0G1Ngtatzp83GY4yfDHgPECaxFSaHJjxfmcN1uSKKnL93Qo4w9whskme8QQSki9hw6Vu2lFRjk0dSaYTDiIw2Tt3btX2XSxptlceYFPOa7qLSAhz4DA3O1z3LjjAsmd/MFB9BW0hdASLtxTQauzqui+cTBEPAKyZs0aFSeQh/zYufG3v4VJidKAHoHKAhTJZLzClaa0nYagCwFStv2hsoK/oFIP+VS1IxpAVen7FBFzDchy6IzcVbOXSnM9H0pcTnXFD1Ru+gtI6MOqWPkRBASBpiMgBLTpmEmOVkbgaT/yycXzkN46SqZDthRKtdRQjbOSfnReSvmaxwHJigDxk+3/pKryCNpRMoy2Fo+krUUj6dcfelNhvYLxsJbGYSiPp1ucMJLo1GE8dF1NvV/2EFoMGnrTM7m0QPvogjZTkVH1EvOe9myApHIbC0GW4Xrlp6utSwIFLpNPFp7fHdSW9pfNp27RkzwJ8GtyOWli1iIa7FpKGuLJeFJ7T9NGl4MePIDxUoP8dfnH1CvGTnH2PnVHWVN8uDYmtXyHIReGRKEO0+dn9zlxjOykdRqJ0CGfUtH+LzFEb6Mu3S8GkduJl//vcHkRIA2JFJ30FNY8xNw0MZkjKbbDv9S0dnEIvLy90EV3LshWkwIwcv9eVUS/ta+hQ/t2Ks1WmK2QLp9kw8eC0S5Vo6qyL6m2ehu0otnQhI0CCfmjT0P+uwHhj1Agk09dfsqool8Qq3RQiu8Hin6+ues9e/bQt99+65P9k08+UfZmbTHsV46LfGhRHu3GQ4cJvmjGkFia0h8P2bEiKvyU7ygK/7vwDFFNkcqSt/ySO0FCvyMt4UHcK/X/G36JZFcQEAQaQEAIaAPgyKm2QaCiRnk0HFZZOIaXLeNfoRQYgr05dznlQRFWWJ5M+ZiFqKIskr4ru4T2lfeH523w25ht0Hjqu9EnE40H8TylHxRsFZmU993tVPTBZlpp7kxxYb2oCB7tcWG1IDsa5VZZKQzkMwKa1SplUK6RM2BgdBP9nYZhYk44e2CmmSStgiqRr9Y7BI8Xnd8QHdvMFatg4fWXW65FUFrVOihxEWoogIBqBjjKM/+sMhBQhK+pyaTtxYgNqlWC5lqof9wt5KjxDL/rBfAUbGw7x56oHI2hLUiKXneo67i4TtDQ3mJInghb0aUgDYwnT/UZvL8NmQ7bZPOD975cRNklTurVMZbeLOqr0jBx8ohGaxEm6Naz58MOEX1ZZcdQvYuguPYVDMXXOteqY87yT/FxtJri0+ZiGJrNMayUXYYPFz/Ow4Q0H9EAmiq10KZ+trUMkwW4aEhqOA1Lg8GkQTZsYPvjwyUzM5O6det2+IlWPjLlo0yfEl9bV0zpCPk0pnOEz/GjtRMedRnsin8yVI+OAAE1W5MNx0LZ5Hy+omn8Uch9KgTUFxnZEwRCQ8D/rzW0XJJKEGhFBPolptNPmQZNHZwZzJXJVHYonZaunkDvZtho5bp+VFCKsC+N/NlboK3qk5pFY8d1oVGDiYYO8J1z211dRgde7QEiA8M4kEUEYMLQYSI9P+oQjelQrkp32Nw0/It+IIqIBdSQdgPkshaEowgLSx5s4OJMmBkGRMkGD+tqkMAaRWB9wapxl9PO4g9wLS7qEDGKbq56mxLduhbWN22oezWuUtqCKS11gQEBbcIMNQlwuhhbDs8rCA/Bn3nmmWrKNnaGYjI6ZcoU6tmzp56tVdcc+H3evHlqxhAeHp4xYwatXbuWmDQxsRs9erSaTi6USk1s2nA4Bwglq0rDsQfvfmMRZcQMQIgnFy0rCoeGG/FODWQ2ylpKD4153FumFXbALrcFHzjQgvtoQb1J1IYbNoWFBy+FfWk+7qkKuiT9HFqecZtyMtNTliM+ac8Em74b0pr76LKPDqrLZhOLT34to2tOjqErB8Z487MTBNsh6p63fII13Ox5e6Qlp4w/2Hw1vVznV3CwO1YIaFjkeCqy/I7iXC9AI22j3Oq+NKDHC02GJjx6KiI2fIV89R8RYRFnA/sjj3OTGysZBIF2goAQ0HbSUcdbM9mLlkPIcGzDs80308btP5OlIsW7mNyeQJCz1+tXHq9v+KyT7BnUN3alWvrF/Uw9o9dR50vfo8iTuvik03fyFv3eSz752AAqoC/O3YOZiRB0uo7guDB0OnfCLrpgYU+fYVS9jIbWqaYq+nLmG2p+9onvP0Z7S3IPSw5XCJgJbFbHi6q30kHNo007LGHdgYtpFy2gboedTg0f4z22vfht6hRZTU+NOEDdEHC8ymWmK3/oThtqltODEy5X6XhGjQ8++MCbhzfmzJlD119/vZoP2OdEC3c4Huzrr7/uU8pLL73kQ5SWL1+uyNOYMfXX4ZOhFXfe/HYt7Ys7pa5ED2lw4QPBKJM7fwzNJTy58fHAYrFwUH58ZLgHk922V3nta+4yaKoLjNnUttt1wHss2fotXdOzE72+fRJFwqa0GlrM20fGU6eYppGVDzZB04p7Edm98tHmUjqtS4S3LA4bxXa9LExCmeizfW96ero3z5HaCGO1rp/wkQhc87EiWaW19LvFZ5HdPA7PN2umHXRGfhXdPQbu9k0Qa1g/ik19k0rz/ogPAoyQRJ5FkXEzm1CCJBUEBAF/BISA+iPSjvfvecozD3XHDpimLwkL1hx8WcXKi6v3hOWQJ0dKOGQLh3mpxDoP72mOT8ixCXlhZyB9LmaeJo+JnkeSELb6Qn0n6DoxFsPHtv2UEpeHKQezaVz33TS87OF6Qomx0vgxD4N81juO8Eu5cNkjVLLxNcwAVAESgVsemk9dzFEI1G7H7E0GTScP28dgppzTUspoUVaMnjSktQbNaly4Q6UNs4RGOF6hgTSR9lMSvOl1CUsZTvYOp4DsVNO5va+kiR8voG9sORShmZUOZkxtB6qw9yCL26lsVCMtxfTdedsVWWEi7cY1fjNxB138bQQNGwZjV4i/raBe16ZNm1qFgHIA7R07digixMGZ/TVzRi0d180aWfbMbwsCurXcd+ia6zdBA6pBg8V0ibmUFdNWwqebT3nFjBPxKVPJDsLBUpr3MGz/FnjPB9s4O30hpnG8ngqr3NQ3MYy6xoV2LxjLy4aG0Ug++ZwNnZtbjpl66sgshyPieaXnzp2rvOB79eqlAl4by2nJNnvWZ2RkKFONrl27+hTF08le2DuK5u8oJydsDhhHRo/JtlE04FyW/7gaCtdgHhKT/BzAx2xpBU/CbOEQWWy9KKbDC7hffD8IjGU0d/uL7fgzgjjd9f2//EAl3TUa7YXmuCliDeurTC2akkfSCgKCQHAEhIAGx6bdndm1H3NN1y0NNZ4JqB5rzwFTLV4isbBXOC8ct0+t8T7gF6CK94c1E0bM4KhiA1bCbFPFC8S6ErxJjy/orI5qqOqQznEQ6bhIhDrR9lCcI4/iI3MpMaaAUpIdysO3uDiaIiL6Q9NzKTkPnU0VO79AGKZSCu90Ojl6XORTR87nU6hyNxMGD9s1cE9POquZSmptFGepJ6V8wgwtmANB0L0CIos3lnc34AbSmCxg/XXSOyoZji6+NnJgZ4eVw/PG5ICCGwmoJSKBks5+XpW0aNEiOkVLoJ5VUfC8raVI2LxGI8/e6jWUZcWMUXDW+V2/UtU/TJ5ZmIRaYc/6h4ElngN8DNqxQNLUF3GgMphwvvrqq96hXx7yDkVCTRdKWQ2l6ZQUR79mgHAaDDo1DL9f0z+MyhGJwAaieU73aURFX/kUY7NWU1j4aO+xqMRHqKDia+yjHxsUN41Mb5kd5GA4LGVgFqhZfZ/CbF0l0N5Z6JYV/6XuiJRglPDwcPVc6Mdaa4pDvU+5fDbXYA36jTfe6EPcZg6Lo06w+fxmdzlFh5npthHxFG33vc+Ks6/CfPF70DwPZiWHbtKbqta1zg1KsxiT/Hef462xw08sL8beYnMI1izbWp/vtkaTpQxB4IRBQAjocdTVHDQ6FNGJI2slj4bwVH4cQJunwkvD0qUjUVeMGHbD0jUNNmXOQnr55VcPa1pJiUtNfzZ48GDvOdYS8qKLBk/z4rXPITboGjKHRYN8+hIKPZ2+PliCObIRExIKYh+JCXPTqjzDMF1j5JNzI83eanZM8EhFEYbf/QlngHLYgSlVOTToOaGBRexPXdh5huc4jsTQH5NPfp2uSbuQysxxCBXl0az1jo1UYXD0PLxmrd6ItHpv8WBT+3EMu5YI23My+WRpCqFkj/xOnTq1pOqQ8848tTN9ww4ziFzA2jcTvkQSwmGmMAQ3oFdiyWl9BjMt3Yk07HHfAZq559TQu56EHaASOv9E5UX/hhf8PpDTU/FRdpAqEdqHlMc1p7TAU/8fepZmr8d3KaJTrA8Z8rvopXEPYq571HWExdinTD5Z2HFt5cqVNGrUKJ/aWQvKSyDR3FU+5DNQGtw1VFu1Eo+KE49Q6w7PnN/LQfO2lfk4hU3sEak+OAK3RY4KAoJAWyEgBLStkG6Der562aOV5GHubAxxs30l21kWFhMVYMkv8qyLSjHzSYVn9pPWala4pZKiw8soBkQoNi6C4mPNaujfODVeMrgQmwTwlHlBlHGqOfvzSqHhjPCZ6YdPcIzD0lI0PojwEO/+2Z0wwl6N4fZKkIgQVBwgdrN+TKe55+6jshoOksSaEY1mrehC2ZUeO9Qg1QU+bCCYJucB1QaHFc5GiGWZ77RSSc3hbbpHW02JCCtlDk9UQ+5xI+6hyB71JgnsJLRlyxZFQrlStmUrM8eDfNY/vhq0Y4GkpLpeG8X4BRKdYAQ6F8oxJsfs9OJPPplgMpFhDRpvT548mT799FPVj6yN7dGjB02aNKnRKkqdbnp+ZSEdKKlVjjx3j4730cI1WgASRMJb5osr0+n/lmRRRpGThnaKouuG4Wb0E3vkOArr9CMoPkJm4cOACWdlyfuIHfkhjjlBLJ8km/1kisI0okYxIc6os3QOOieCHHF3kjWst/F0s7arSj9Fvnr9HWvl7eZ8ELodKL9vs8oMNRPfKxwhwXjPcF9mZ2eHWkRdOh7qdoBceobCPQfrr0kvTJ1XHwf6kdZZs6nCa5NS6cmlBSChmrKfnTqgaWY1rdMSKUUQEAT8Eah/g/mfkf12iQAPr3eHUomXxoSVczz1nj4NH0+3x1Pt1WI0mkdQeZiKh3J5YRNJXoNHYKpEzzR6YZZyyn2zG4J3O9nf21td/PnvU1zfi737Td1guzZ+2fkLkxg+F0yK1zxN7OXutfE8bLz98JwdqYIsZVU08vO+dGYaiC+G3ZdkR1NGRQjk01+7ieLHwJ7h4UW5SuPSK7YzPOF/padGZqohwAS7i/64Jo32HOxBGZbOypazvzWeLiv7mDpe8YNqtzUqnawxXXwa2rdvX6X5ZVtNT9gki8Ibs6R702VUdqEh7lXQgtYPNrrcJtpTaqOb37wfTkk1NCX1ZFyfVdleejNio6VBy3kIn7Wr/gSUPwjuvvtu5ZXNJJRJ6q03j4T28G2FR2zSlKBmAXr7amBbOG1OptLmcmijgyU1tLughp6/oAPuR9yQdcLTLZbm3YdZbuA1jmgE8Wmf+GguOZkVN/CdoxNUHNAO+Aoy5tfL4bXJDOJZd6C88FmQz3e8p0tyZip7RVv4cO8x3oiInqYWn4Mt3VF2ydzH9c+W5maTivp+b0oV67OrEM7JTf2Tw6iDo+Ey+D7jPjMSUK4rNTW1KVUCywiyR01C7NS5dRpiRpbvUa6fn3HP/RoWea5KiwOtLilRVnr6PKOmu9WrkAIFAUGgGQg0/C/UjAIlS/tBgN/fug1oc1qd+/Ud8PKFKrXOvlIvo3jx7RTb+yK8UJp3e8XHxxPPW75u3TovWWKNGS/G4XeuT4NRalXmMmg84fmd+TMOHE5c9XYFWz+tLaGzai+jb/c5QOw0Ksa0ml4GEiwTH2cA/Uhohf0OzHoDw1hIasQk+s/wb9S2/vPEsEx6wnojWUqGqEM1GHZ8M/ItmgVHqZr8rWSJ6kgdL/8OcQp9h8UvuugiGjJkiCJPPN2iecN2WlTUlWqgpWNZcPA3dF46grdjGJg/FNh2l8no//u5FOGkmLQQvVyxlMaHd6SBZZ6yrSCjMTExCmuVoJk/XM7FF19M77zzDiAxeZcbbrhBEWY91mhV+ddUnv8oavEQqqKsaRSNIe4wBHMPJjx8agz1w8Hdc8pr6WcEdtdD/bhByjzTLXpK4aHcoqzfUlzax2hLCB8SwSrHcSP59CRDmK2i2fCI/k8DuVrnVET0b9WsPZ57GheOu9MS1gfaz55NruBP3+fSpkMIDIYPybJqjf41MZn6JQcf7uaPvWnTpqmQXUxE+WOC48f6D7+H0hBH/F0wZ0iC89ZirJMpEtNgsna5NO9BNeweFjmBIjEzkYggIAicWAg0jyGcWBjJ1RoQ0GqdVHVwKbmqCqkyYynO8IvRV9gOs7bkANniuvueaMLeWWedpTRzq1atUloY9u7lY0bh+d8zP5hArrKDigi6YTtKcMiBR5IxWcDtDEsf5cAT48ql3jU/03OaHUHkM+HIU0OPWEHsAuXyI5sqiUELx/sFNfsQHN4z/Dom+UeUh5Dw8K42yqkpP9KWOgLqgs3bJvdAerz0VsqJPAle7bV0779Pps5X/0SLDloVYTiru4MSMcdhcmoaZt1JpXA4Tt0B28maBStpaX4i6HI1RcJb/s6fX6YHBj+CSAGllFPZkW5dUQ3y6dEwcf2sBf0RnvR/OO8WYkcVdiphot8awract956q4rxyeVxuf7zkpfnP+RXlRuE9G+0YuddlJOTQ126dFGxQY2JOBA7k2l/cbpwj1XvhaYcnuK1q3GaiaZuYuAGsSmHLfG6Bsmtf5kB92ELSvDcNooW+O4wJmmVbbMljhLSF1BJ7t340CqmsIjxFBF7c5PL/mFvBa3K9HwU6Zkf/SGf3rzYcy/px/zXSUlJdMcdd9CBAwfUh0S3bt38k4S8HxEzHdNfTvdJH5sy22dfdgQBQeDEQkAI6InV3y26WldlPgjfGeSugjEpNI0893og0XDcHB4X6FSTjrHGj5dg4rH3BFU0aj1B4PQg88HyfR1xM813zII+qZYqzTHUx7mMphQ8QctrpyKLidJMB2kfbFoblQCENA8zGukElL2WObAP/+riRpzJWr+Zm5xmB+2xnYKqoZ5CmY8mfEU0H9hCe8fc6431JXTv2HjiWWaqajXMeOTRYN1z/ii6p67gx+b9SCtKu9GD657WqwIJZW0j8DBIOWadOuUU1HUEhAnn6aef3kDJ7DDlS+0rKw+pUEysYdu9ezdt27aNrr32Wm8ZpyLm5VuY2jLGVkRJ9lzY5aZhykcrdXfOpJx9hSBG1SChDkRugObVmwswIjyVz31hONeUTbtjEjnLPkMWncDZEP/xtqYU0aK0JtwbsSkvt6iMLIRzYoc04+xMTOxLYFvLHzMNCdti9+7t+aBqKJ2cEwQEAUGgqQgIAW0qYidoeh7qZsLXuJjJ3m0iWcLjG0/aghRuZ4kaFvQnGSZ7HIX36UwmRxVpNWFUuWYTiEh9RdttI2le9D0IWL+VukbtoUJnAm3IxzzyjlF0SeWTIIsu2kOjaR91q8+kb/lpO/XDxrXZMDPKkpyzaHLnT3C6ngSyI8m8A0x0dQE55fYpez9eM0HloVEwBkO7/7Ec2l2D3P1NLv19rJX2b16thrzHJNlBQFVB3lTx9v64vo3eYtg8tKPFQe+++67SgLJJAw+TW2g9HG3eRH3VFB57F/J39ZbRmhthEadTdeUiFOm5MLdmo/2ZqZ5+rKuInVx4fvPu3burI+xE8uyEjRRZ9SRmlbKBiJaQswbxAMyVKlA8J9LM5dA082xFcCIDvi7ESrVgQgCXdWRdqaGv3Ag0X1H4DBx99pIV+Dni/x/IbCmGj79BF0WDfP6+2VpV9giv5QkIcI/Y7PWRHEJvXfNS9oi3ee8BvQT+iIkLD+y4pqeRtSAgCAgCRxIBIaBHEt3jqOzqvF/IDHJ3uNYTRAmvN+XB7aoiWycMnZ/3VrOu3FWRS9UFsIOM7EBhCX0aLgNB3jlwtYfK1CeNPC2ZLPDA9wzJWsnWdSCVfIaXfp36Z1vYGJrW9b90bvr/kNdE4RYn7SrtSe/+fCmdhSkxWeKoTAV3d+ukUB0N8MOElBvAENRJJwdmXcFUm3yiqDqByqruIbsDIXlQlhlhkyIT/kodY1KpIBdkD1q7RHMJ5TjhwIOZ55siXOXL81dT5xIQbEhhWApiW/Wl4YkrqGNEJuVUpWLo+lLMWb6DyjCEbIMmNt0dQRc70+lAxQGVh3/+9/nDNGHMMmx5SHJZ7s1kjvwz9s/D0nIpKYF9JsL38HB/VNJjVJS5EWGLQKZBwvILe9Li5b18KmFNKA/Hd68joLXV2ymx9hHls4K4CCptGOyOjd8CbNe4t6wT4rZWkBVxUreX9KMP9v2e7hpRTrbiA4qg9+vXr1GHJw6YXpgxAXUwMXOBLG6FU1M2AqdzSKVHVd3N/XG7iqkw80K0BfaUGE632HrAjvR91bbGynTCdpbnmbeFD2uW9/sIxCO9tF8UzdlShhBOZszupdEL56dQoJmMGmtLebUbGuliyqtw0fhukXR618jGssh5QUAQEAQCIiAENCAscvAwBNiuMiAh0yj65FsQt3IGWSKSYUfpCOml6l9+GYLJ534FGzGuB+FYIntPoZQLPYTQPy3vs5NOVL8rqXTLO+AKnuFRayc456gZYnSNIwKPI3p+9uCz6KkDT1I1vKOHRy2hq7q84FNkF8deGpu+FFM4eQ6PoSx6lQb4pAm0YwZZgpUmiJ6G+JwW+hd9S30KPqe19vNBP800sHox2Re7Kf6mnWivx1mIy7kufTN9v3s5vMZradCwkfTPPXG+xRvZle8Z7x4TtSp4a+uSFdWTfoeA5UMTVykSxprAMcmjaM76y+jUMHiEI+GhzI6gVbxVL6eP/BE79SYCTLzclRwAv3kE1FWbg7waWayptHnzZlqwYIHyfudYppdffjmI5VcgU4gNhjS7snZjzRpRX2HbQ11qq7fgvnMgOZN6j2gwYzDh+nRhb/+S6nh6aN0z+iG1/vyTjyga2ksOE/XFF18oj3yfBH471ZU/oC5g6o3n6VTxKZmE8vXowtg7yz9V5NRmHwrbzNH6qaDroqxLUS7Cg8FBioW99qsQOzQi5rdB8/CJoqzpKuySp4/gCIT4onY47TRVZgyJo/N7RlExJpXvFmujCPbsaqLwsP2Uj+sjEqzJqiKerWmahDVqIpKSXBAQBBgBIaByHzSIQNm2j+B09JPSfpqsrO0oOCy9o/elFJY00HMc0zH6C8eZZAKQmZmptFBMRDp06OBN5szbQrlfTPPu80bF9jlUmNif4kc/4HPcuJN41nNUW5pBlXu/htLKTmGpPUFM2MawnlCZoB6rikuhyoOxKmtSXDE0QNCMmnWSirif2B4Xs9BrnTiI8ukebQ09ZRpOMSAMTNp4tqIiwtC4gRy6sc0kFGHNqQRl7HFH08laDo2t4mF3j9Q6bbRv727KLSgj9u7nUEVfffWVN8zU+uXf0/kde9F8GgbNaRm82N1UZeFQU75EUS/Pu0bddleFd7dv/BYanbzcu88bg+PX0cDT16N9PM+9BiK4it7//GIqK2MNn0dqXfCsttTjpY5qwWOt6vn81xrscEsOzQJZ2q6GrDVTd/ryyyEgXCZveKZPPvmEbrnlFnhTJ6rsAwZEEM/y5C/dunXzHjJboNk19CefMNeFmqqphQYc5fPy6o57FWJMS9neMcGZTfaqfK9bEpsbLFmyhHju9IaE5/mup7bgjIow+vZFcdbl6L9MRSir6A0My98NjWY32Ip+rop2JNwHb2+/jwpv6/TaEXwd2t2GxFnxA/Dc6pOkDFOBhnX6Ft+CTdOYcyFpmLGIl+bKj/sqoTWFq1ddoAlef7a1rM0IKGuoS/MeIlvNCnIWVFC47S1lJtHc65F8goAgcHQRaP6/0dFtt9TeBghkzTmfnFmYoQRzqDPB0zWNJhsCS8Oj2mRGXM4R99HiX8po35ezVfD4c889VxEtvXkcz/OZZ55RWlHWHLG88cYbytFEjynIc7UHkuI1zzRIQKFKI1d5Fnx1YkEaNKrO3E7Wruww4dEycZnshb63/CRv8cU18Yhv6ktANWh2zNDQGWWaaSeN1bJhCxqNKTIr6VYa70M+9bRVShPnua6/modTL3c+DTB57DX5cjHgSXPmgpjAXICx0IOz6/l5PvTaA7/SabSNauHBb8FLdmn65QHr0vPoazfK3JR4OgLSW+gC8xyqqI1AWCzPMDWnsZlrQIR4y9M+JrWnj1xJXy0awweV1HWJvutZm5gANyxMzMoLnyYXhqnN1m5KG+iq3ohMdU5G7n00YmgUvbbvZiq3xZENWupxZUsoLy9PhfPh0h0OB9188830yiuvKC0pxyOdOnUqHIrq/5bCIsbAXnIE5hFfghxsG1sLreH1VF59NmXs/kDdV916Tqd3p3SiDzeX0oHiGhVeaN+388hIo1kLWlJ8kKqKHgCBgVlCXlfPLEfAThdb+Mi6TSacjBk+LeAEZIajWnXlCtTFhNeNa92jZ1HriqIXsWZWxh81Jqo+uBghoD710ZpabX3hlb8S5/W+QP+EN2wHWlvF3v3+4oTGHfavTTTZ8C+lOfv87WXhH8M1cFzRQLLpkJN4AoHBqXZyNEPbGqjM4uzrQci3AWEPhsU51wLnz4BzeqDkQY8pIpt7D8xT1qE/y2EK8XazTBuCVnAETvB9V174FNVULkWba1SIMTNskkUEgfaMQP0/fXu+Cml7qyNQ9itmfzmwuL5cHubGEHx4+mkU1RcaIHi6h6efSs+8hZiV5l/UMCfb+r355ps0ffp0Sk/3vBQ4licTCiZaujARXbhwIV199dWeQ5i5KKDgT9df2KaQbQQ5IL37+2lUnf8rFGQe0lMLpZQzr4rsSR6NldMVRnZLNX28t64eFLb00AS6tMsHsP2s9JBiDNErLenGHLzYfKUc2rB9FEV2kMIKg3ORNxWzN/VC9hyxoL3VfRAqqWcHkGKE5s8opvJVWSDBfN6jNgoUYJ/PspYyjD23IYmVGZQf2UVt6z8RsBethAc8C2uhrJWFtC9mkOoTPvY9XULnWn7gTa+w1tNXNOqU5vJgB0LGdpnh4Rju1rJ8kwW6Vt8UVHDgDBxhxNCv1Tuw5ro8/YANwOImR0cnFeSkqTZWWyJoWdTpdA3ijhqFSef9999vPHTYdkyHf6oYkhxknmcAYltI1sUnd3jQJ+0VA+vL/nJ7V2UCoH/02ECCzhjxAggz66vdIB+FVJr7/0BC64ft+YXOxLE45wZcTi20a0MRs/L3sN28WO1r0AybYMdLuCcQTdNQd/0HjwcHmEeUfYoZkW71polKftJjX8phndDbYeFjKDwKw/INiMVW/+FkTHa0iMeINMxohS7X48xymy7py1j4yj+XF9CP+yvhKIZJLuDs9DpmIurYAs2rXroLU5968PUcMeFDqda5vskEtCjrSjWNql5WcfZ09Pv/UE4Hvapjbl1y6BZ1rZ42m6jo4GSK77QAz1nYMddWaZAgECoCQkBDReoES1eZ8ePhVwyC5Ty0ljpOXaDOrV279jCNHp/47rvv6JprrlFpnE5obEB2/MU4/WM07Ecr933jn4QcvS/zObZz50764bMXKNlykIrhCT3GvhlxM+tJTw1iQd7262c0NnkxvNy3U351kgrQzoPkRrl71Wx6NO46SkkuoMpyGy1ZP4bOMW1SSXRO+abWl140nawolsZv3ABk2Eg+OfO07gU0rh9e0FabKiusK6bLrET71qpd9cOB2ll0YsT7+rY6gZ+B+Utod9yldKA6UtU/oEMY/ePsZPoBQ6B7i2qoc4yVXlxS4iWfnC+zqgvN3nYH3db3GdhDxoB4Oymzsit1j9qFsx7yyxY3FmsSzZo1i7MoKcvfDXvGL7Gtp8ELzexLfuuSelc1zg3YZs2h/uHAa96vF1iBYmpTkJM6u2EN2kNXRDxVWKMpoT5ZyFthkeNDTssJzz//fNq3D9OrwiSEZ2AaNQKUFTbApOma7mr1QnfDRtRI6HjoPD5trrcuJiuauwj7nntYg5mEkWijUOwfTvRNfniYzVGYQ34lNHi7cNvYMWTf2VuHceOXHCct3F2u4r/OHAY7XNauanXGyUhoi8CMQSF8IBjLbK1tR5iZ3rqkI/1taT7xDFVjO0fQpD6+WriN3P49HoxhbqrkSaR/Fk5PLRWLtYvSgOrl8AcBB7Zvqrhdh5Clvs9M5lh8mGw5pgloLaJZ1LcZbefnCfeSNQx/OCKCQDtFQAhoO+24I91sS0SSz7C7Xh/Pja1LZWVlQHLJDie6cIDyQAQ0LQ2asTpx9JxEUYNmUNkvr+mH4FWfRMkTZ3v3i4uLacPcP9B46zcYgrSS1QYbOsSDVO//ulTbw0ZTOAjF8tzxauHDJhUjlP+wPcQPbA+axgpatOkcb9m8MZfupT4WDLOCaGzV4unFMI9mx/uaYiKls1OfnJ4dG+qZ2r1IxaM0ntY6p/sQUH+y6b+v531oVDgCs3fSd9WavY51ee4ndszR9zzr5bmn0zZ4gHcIz6HcqhTSTLH07IgrcEV2JA0jG8hnTMorPpnYXrG6CjNJuQoAUSRm2hlImv1RnzSBdphEaXCqqRcPvlWucPQPe1rb6D8776s/ja1KmEMEm/7SJ2Er7LCpAxNt/tBhjK2mbdB48nzu9cLDr/nl1fRjRqmapvPCXpgJiz82fISJp/EDCp85jslUXf4FuHUUCMAgOCGdieHRJ5FO/xhCfE1Mzekv/LHR0CxGmzFs/cB3nmlcuRmL91bSm5O/BP+8C89QIdkjzoa96Y3+xbbpfiQ0yY9PCE76iqpcFBVmUrMt6Q3LKIWWvBUkGlrkosxL0IdwdITm3e44D9rwkSGVzCYBHEN3cIod/x0nwf7W88HJmTkqgdnaMaRyjlYisxX/o0oD7GkBfxSZYBoiIgi0ZwTq2UR7vgppe6sjEDv8TipZPxvExDC8CM1L1aC/0osvvqjm905JSVEaUOPwOr/4B0ZuoL3PJyrbUS2mH17qU6ljSg6lpRyi8opI2ry9pyIFxkYnn/0CRcOr3ZmzjiyODhTVx/MCZ6cdfnFn7fyZhtq+VlnYrpJFtwVTO/jhoO9MII1igmYvybWPcq0ngXiWUffaDXRj2UP0Jc1Q5bJ5AF9HRkYGbXKdqbKutmA4XisEgTOQkTryqY9qw++F3rUsoWW1sbSBkukkuCj1QmxSz/BsfQvCIthukdS82owTT2fIpgpGYXOC0lKQILSFydKgQYPUrEDGNP7bHaLCKKNU11rqZ02U7+ygFj7Cet+7186jZNs2EEKiHSUn0aeX28hueOpZm5aQPh92jawVQiJTstIacv5gYg0bAAVMAvq3Ckm4LyxUDcI566d3aFD8GuxrtKlwONlt4TAXgFkCmslDt70SwlrkBBOsPQ0d52kkPXIK2sz3JGsw6yT8UrrhS5hiwMaXielLq4uAT5pPcHYbhspdNXtwtp66muxn0StbbqVdhTV0MgjN7aPiQWA6IJbqWyAFsRh6vyWAE5JeafD1C6sK9WhhIPGeGvdm/ofSrevRLwhCVfIyyO9ZIFDdgxdylM8M7GD3IZ/cnIENTPnZlOaaLen0YdZcyi1aQ7nOGHpgPMyBQiiATQKW7K9Q9qtsxvLexfhYyMdUwaYo9REVGTcLHwZ9Qijp6CWJTnqSirOvRJtj0OYKioy9pcmmB0ev9VKzIBAYAcOrKHACOXrsI+Aqz6HiNc+SG3N+R0KbGIlA8M0VnnaPiRJrLjnMUcnGeo2ZCcbvX6zIghOPZ0g7KytL2RMWFhZiGNOsCFQX23ZKL4KmCTFBlZT8ShcNmkPJ/T15+NgpA7bSqs29aONGxITE8DzPtBIZGQmb0nFq8WSEvebSpbRy5UrlRd3dkUWDtQiym+qdbMwgl3vMp8F7PF9pOvPL0ykyCoHKoclz8zAlyJwbNlL3FU4FfWBqCqcprQAvczPde++9qlwmzGynygRUl64cLkff0dcgo+fAiajCzUOJGvV3x+KltZ+up+UgC1tRtpUKt3WilFQmrXpuK8V2eIxmzhxIubm5FBcXRz/++ONhBJTnSmd7WE7DNpFdu3bVaw265hii/oIQjyDNHi9ltr9j0ldY6aZD5b1UUt5/c30xzRzOdoy+otu/BbNRNaZmLXhcxw/g9X4HtEcFSgv4z/XXUiWGZVfmjfUmHdHJrgjnHpgN9E8Koyn9fYdrvQmbseGCwa+z7Ev0pVVN8RjKsHR82hwqyX+Dqip2UHTcWPrLqlG4l+s/sOwA6JtdFRhWrqc1HHi+tvpXDNevVRqniJib6NJ5naHJhfYf3XwQ2j0r1JUzh4+i7IqhsC02IVi+xwSjqZcVa2czhnptYVJYBsjnW55i6iITlGH+9LiO7zW16DZLnxBhoZcvSqGZX+Yg0L2ZxnQKp9+NjG+V+p8Ckfx+L25iGq7Ku/GLHPp4KmyuYRoQTLbm1ZsE8HPJKV9Zb8XMYstUFAI2ubBAu3isC2vO49O/wX24AR87qXjm+h7rTZb2CQKNIiAEtFGIju0EtWVZlPHGABABvLhgD1mKYey40Q9S/JiHmtzwDz74QIVKYo1jePV+Oi/sFdC2euGpJfuZvqdf6Cx1kIkq29hNmjRJkSqeti9x4/vkKqgjn0hlgaauwwDfF0SYrRZhjebDEWmfIqAcK5Jn5DGGZtq6dSstW4ahYdY8QnLKYDdnr3858zFWUObUpNK+2jN5V8mAnOW0OXksbA1jKKY2j6YX34sYndBM2iwqvRo1hv0Uk+eDBw8qj/29e/fW5fasumuIF0p7aDWBbNbVDz9oOg/Dy1nuRG/aVc5z6KC5M8WZcqgcw/bZeSPocvPpFG2foxpnj5qsZs2xY+ScQzCxDBs2jHbt2uU1S2ACzNNXJifsoHjHJrxYeiJV4wS0xDDHuyoYP+EYHj2vp4N2F1ZTT2gbdxXU0MrM+r4AP6SMEn2YWM/VvDWT0NgU9v72yMj0UlqXXW+ryGSOp9E8tUu92YCetqVrDupelHmpt5jK4pcoodNiEESYJTQiYY4r4UGPKTwx2YHN7Du7lAt9zbEujWLCxwpPhcme0+yYtL/EAvvaHNLtGzntD/sq6BcM8eZW4HlAv5zfM5J+PyrBWExI29MHx9CGb3PVM8fB9cPMMGWBGYXRBtRVeyCkso5moi6IMzr/qk6t3gS+n43CQfXZJpq1rsGEh92jQVBLEUCfhX+zyxBYDR+mNvtAday9/Jgt8bhvx7eX5ko7BYFGERAC2ihEx3YCnptdq63XCnJrWRvq6Anykwwv6RDl+++/V1pAfTg9wVxMTj+No8XkohhTnk+JrMFkbWn//v3V8QNbfF8GpnArZk+qRRzR+lvNYnFRZEQlVVfX2xBynNAZM2Z4y16+fLmXfPLBcoqn1TUX0Wjbp5iSMQwvaY0y3b1APj316hkjUc8ZtZuopKBEDXf36zMMKRETsnciJt+B4X5RJZX9kEtvv43QK3Xe+Rz4nDW4uq1qmZZID5jKaImWSet4eB3bM22Z9GkNO+94XmRM0lky3X0pkzzaCBuwcIO0Rif/TZ0L9NOtWzf67W9/S99++y3mMQ+joUOHUueUxQgL9B9vcp6BxxF/l3c/0EbXWCs0m75D8J1x7LpTPPFOOc/n20p9CCiTwqEdwwMV1+JjF/eNpgMltfTVjnKKATGYDC3ikSCf3NCK4tn45X6oI4sYnnZWfN2oVznnNQq3cTUIOhNz7s1aFHnOSYFJrLJ9Zm0rPp58KSqeN4QbqoCNA5s5sHy7u4LYXndwStOwHgAi9ebkVPofMORIBxf3HUPVOS7Ux63z1BoWebaq40T86Y+hfLYn1b8RGPf0Rrzr+2GKWnyXqdEA7mceGbjmZLGdPBHvH7nmYw+BelZw7LWt1Vu0bds25RnLL33jbCutXlEbFqjVlB9Wm8lspZqiXU0ioOwxrJNPLrDEnYQBa1+NAx/P13w1G6zBizGE1nEM/yMVL7gc/AAaI4irGE4geHEbhblbZk4H4yFiJyOjGNuiH9/nHkyF1amUaDpIFVos5Zl7QZPh9iGq3J7zL7+O2Bu3KzQxCeELqbxolV4EZkoKpwM9TiPaCMIBDS4L183kkwkpE0vWup5y/Rc0NGMeOTN/JhumBY0eMovS3ntfkXQmjsnJyYq0cnB9HrbmfGyvanSu8lbqt8GE/frrr1dHeX7wgoxrfFI4y+apWJeHBzOvT3YHNGxXf5rlPcBOKw+fXq+d5RPsobwbdopfY1jZYTPR2SBXl/RrvWFwb+V1G7djqJWXIy3+HuasmTyMFYbQiCEg4/83MZleX1dM8Rg6vvbkWGivwVAakA4OK101KIbeWI8PnDpnmzHp4bTiYL2mmae4ZHLUHEmJstINQ+o/IrT0L6ko+xrcXxbl7BQJ+9ITVW4bEUerMispH2YlMcD+z+OTVL81hIcdpiqvT+5IT8ATH92iRgiGpDbtw6Ch8uWcICAINB8BX2bQ/HKO+ZxPP/20igvYq1cv5UTz/PPPN+roccxfFBpoiUwmdxXsGg3C87VbotIMRxrfjI2NVfE19SHvcgTLWVN7AY2yfU61mk1pYSo1kJde0FJieJyJXpcuXejSS+uHQrmWsPTTKXHS51S4YDqcPsIoetANFJZyMVUVXk9VmBXICqeUQ0UT6WC279As24AahTWqbANqFCZ5JVoHtajjIH5sV8lOPexswlrMc6ZeTzfBNkyPm/mPsbsojd88dQzFBKaWnIKhTT/hsFFsg8nSo0cPT7D02GsoekA9OWQ7zf379yvC2b17d0Va2WyBY5Oy+cAFF1yg2uBXdMO7GOL1OEPUO8ewd3ZjkhhpobnT0uiL7Z58Z3Z3QPMItZmf3Dk6gW4b4SGFzZn726+4Y2I3HNNXVpV9iLbw9fJSTfaoSc1qW19oyP5xju/HUGMF8dST7E2dg6HcvrBtZU20TkD5TqtA7MvhraRpZrOC+LRPGmvSCXHehuf4nUvTaPeBTIqF415inO9oSzAQmIQ+CrIqIggIAscWAicEAWUbP3b+mDNnjiIITBreffddeuCBB46t3mhGa5LPf5My3x2DnHUkC9rGyB4XUnjHkU0qjWcwYg2xcSi6x4Q/0NeLOlJqWCZVuSOo+6m30cWjRilNHxfO8RUDiT1tLHW9NcPnVHjkd5jNZrvyRE45qTtl5MynDRs2qGFonhFp2rRpPunHjRtHW7ZsoYKCAkV2uV0zZ86kl19mezxNEU8mv+y0k52drY4xCbz+80OqHD1o+1e7O9KNHh8cT/kIH+SmenKuay47duwYkvaSPdZ1Rx1uEw+nt0TYFs0e9RsVuNwzB7kZoWVGheRFzfN5hzIP9/FCPHWcLdY0BOH+HnOpfwjybsPQ+2VYt+1fGRPXvnWchrWWHKd19poiSnFY6FY4eTVnrnX9+mTdMAL8ccmkUkQQEATaNwImvMz9TZra9xUFaD3PN83zQP/5z39WZ7dv306PP/64sgPUk3NQ9blz5+q7xEHPZ89mW7PWFY6dyUO9wchbc2pzV+RQxU8PkVZTQrbukym8d/NIEQ9JMw48lNyzZ08Vnoi3WcPIDkYcQqgx4TKYmPHSmOgxGrnsYMKzHvHwOA95M26NyeMrKmlLga9t5DtnvoloQfOQFfZ9Vjge2B+l+fPnq+tiU4wJEyaQ3R6aNoXtVrk99eF9GmtRaOfd1QswkrwRTt29yRx2cWiZjkAq/jvgPmczg+NR+OOB+5D7jz8+jkfhyR9CvZ/b4/Xzfyj/f4byf9Aer68p/6Ht8fr4+eP/mda+R3fs2EGLFy+me+65pz3CckK2ufE3+nEAC3s88xCzLmyzmJ+fr++qNWvaWCOnCz8c/CI+EsIEplXLtiWQ/bR/e5vakrKHDBlyWDk8zM0SSrl8bbyE8nLnYfzGymUNJ4tOjNROAz/T+5rpgeUuDMEj8DlMA28ZaCG3dSZGaq/w5GKvYrSPNb5GCeXaOD1fW6htMZbf6LbpLKiUsUBcR+i+a7QNSMDX1ur3ZygVt1EavjYW7u9Q7tEPDZFvAAAX8UlEQVQ2alarVnNE7s9WbWHLC+MPCb7O41H4HuXleL0/+dqOxD2q2/Qfj/fE8XpNJwQBZaKjD5tyR/KN6q91O/vss4kXXW666SalddP3W2vNZNjhcISkTWytOtuyHH3qw9b+ug31GqAopfc7umhDtpPSMWUlhyPyCE60gnDMU76XjhcnNn9I+Nq4D40fbP5p2vM+a925DxMTE5VpR3u+lmBt549p/cMtWJr2fFz+Q9tz7yE28RH6D2WlUigjb+0bveOr9Y2Pkx4H18vDt/ynrAtvs82fyPGJQFy4hc5AGJx68nl8XqdclSAgCAgCgoAg0F4ROCEI6IgRI2jTpk2kz/LDMSdHjmyak0577WBptyAgCAgCgoAgIAgIAscaAifEEDzbfN5888104403eqc6bKn38rHWkdIeQUAQEAQEAUFAEBAE2gsCJwQB5c646KKLlOMJe4iG4s3dXjpQ2ikICAKCgCAgCAgCgkB7Q+CEIaDcMRy6ozXDH7W3zpb2CgKCgCAgCAgCgoAgcCwgcELYgB4LQEsbBAFBQBAQBAQBQUAQEAQ8CAgBlTtBEBAEBAFBQBAQBAQBQaBNERAC2qZwS2WCgCAgCAgCgoAgIAgIAkJA5R4QBAQBQUAQEAQEAUFAEGhTBISAtincUpkgIAgIAoKAICAICAKCgBBQuQcEAUFAEBAEBAFBQBAQBNoUASGgbQq3VCYICAKCgCAgCAgCgoAgIARU7gFBQBAQBAQBQUAQEAQEgTZFQAhom8ItlQkCgoAgIAgIAoKAICAInFAzITWlu1NSUui6665rSpaQ0paWlpLdbqewsLCQ0re3RG63m0wmk1raW9tDaW9lZSVpmkaRkZGhJG+XaVwuF1kslnbZ9sYaXVtbS9yHPB0v36fHoxzP/cf9Jf+h7fuuPVL/ofxsn3322e0bnBOs9Sa8TLUT7JqP6uUOHDiQ7r77brrhhhuOajuk8uYh8Ic//IH2799PH330UfMKkFxHFYGFCxfSrFmz6IcffqDU1NSj2hapvHkIDBgwgPg5vP7665tXgOQ6qgjw++/gwYP04YcfHtV2SOVHHwEZgj/6fSAtEAQEAUFAEBAEBAFB4IRCQAjoCdXdcrGCgCAgCAgCgoAgIAgcfQQsf4Yc/WacOC1g27oRI0bI8F877XK2G+zVqxf179+/nV7Bid1s7r+EhAQaOXLkcWuHfbz3MP+Hcv+JCUX77Gmz2Uw9e/aU/9D22X2t2mqxAW1VOKUwQUAQEAQEAUFAEBAEBIHGEJAh+MYQkvOCgCAgCAgCgoAgIAgIAq2KgIRhaiGc27Zto3379tHQoUMpKSkpYGlOp5NWr16twr7w8LvNZvOmy8vLozVr1lC3bt2oT58+3uMN5fEmko0WIxAMf/+Cg/VzTU2N6r+IiAgaPHiwN7QPe8pnZmZ6i0lMTFRD994DstEqCIT6nATr54b6KVieVmm4FOJFINiz5U2AjUD9zMfWrl1rTKa2e/fuTfy8rVu3TuXTE/BxNr8QaV0EmvKcbN26lcLDw9X7Tm9FoL7Vz4Vyb+hpZd3+EBAb0Bb02dNPP02fffYZVVdX04svvkjjxo2j2NhYnxI55hmHCykvL6dly5bR999/T+eee64iKvwHeeedd1JcXBy9/PLL6sHs16+filMYLI9P4bLTIgSC4e9faLB+zs7OVuG02K5w8+bNqg8vuugislqt9M9//pOWLl1Ke/fuJf7T5Rh1TFBFWg+Bhp4tYy0N9XOwfmooj7Fs2W4ZAsGeLWOpwfqZ/1NfffVV9XzxM7Zx40Z666231P8wKwOuvfZaKi4u9p7v3r07cXxnkdZDoCnPyZ49e9T77qSTTvJ+jAfrW/5PDeXeaL0rkZKOCgIcB1Sk6QjgYdIuueQSDUGfVeb3339fe+KJJw4r6I033tDwIHmP33zzzdqKFSvU/jXXXKOtX79ebYPMaCAvGr4GtYbyeAuSjRYjEAx/Y8EN9fNzzz2nvfbaa97kDz/8sPbFF1+o/SuuuEKDZtx7TjZaH4FQn5OG+jlYPzWUp/Wv5MQssaFny4hIqP3873//W3v88cdV1h07dmggoMZiZPsIIBDqcwJFjTZ58mTtyiuv1L766itvS4L1baj3hrcg2WiXCIgNaDNp/+7du5VGiz36WHgIfsuWLYeVtnPnTnVOP6GnY41YRkaGVyvGX+Y8uw4H6A2WRy9D1i1HoCH8jaU31M8zZ86k6dOne5OztoW/6CsqKqigoIByc3PpnXfeUf3sTSQbrYZAKM9JQ/0crJ8aytNqjZeCqKFnywhPKP3MIxA8unTXXXeprCCg1KlTJ1qwYAHNmzdPPZPGMmW75Qg05TlhEyV8rCvNJ2s3dQnWt6HeG3o5sm6fCAgBbWa/ZWVl+Qy3x8TEUH5+/mGl8TAtn9NFT3fo0CFyOBxem0E+z8P3TFyC5dHLkHXLEWgIf2PpDfUzT6eq2/MuWrRIEc3zzz+fdu3apWzPVq1aRTwtIptZ/O9//zMWK9utgEAoz0lD/RysnxrK0wrNliLqEGjo2TKCFEo/Q5NGU6dOVf+pnHf79u3E9oM8bSevp02bRmyrKNJ6CDTlOZk4caKyy+XaoarzNiJY34Z6b3gLko12iYA4ITWz2zgWHZMLXfhrkL/y/CVYOv/jnI/LYANt/3PByvavS/ZDR8AfY86p428sxT9doL74/PPPlabzX//6l5pjnO14P/30U4qPj1dFccy7119/nS688EJj0bLdQgRC6Rv/NFyl3s/s9Beon4YPH+7zbBvztLDJkt2AgH/fBHq2OHlj6XJycpT9pzGk9U033US88KgSCzu6sDb06quvVvvy03IE/PuFS9SfrVBL9y9DvweCHQ+1XEnXPhAQDWgz+yk5OVlpK/XsrLns2LGjvutdszE8n9OFt9PS0tTXIBvR8x+jLvq5YHn0dLJuOQLsJRsMf2PpjfXz22+/reaFf/7556lr164qa1FRERUWFnqL6dKlC/FL0u12e4/JRssRCOU5aaifg/UTfziEcm+0/ApO7BIae7Z0dBrrZyaWZ555pvr40/OwKZPxv5WfQdaqibQeAg09W6HWEqxvQ703Qq1H0h2bCAgBbWa/cDilTZs20YEDB9RXH5xP1OwcXBwP9ejDPaeddhrNnz+fqqqq1LHly5fTkCFDlKf0qFGjiLVnLEuWLFEaM375BcujEspPqyDAnurB8Gc7Tg6txdJQP8OYnr777jt66aWXfLxr2RaUbdG4HB5u+vLLL+mMM84g3V64VS5ACmnwOeHoA/zMNdTPwfqJTSuC3RsCe+sh0NCzFcp/qN6SX3/9lQYNGqTvqjX/n86ePVtts60vm8hMmDDBJ43stAyBhp4t439oQ7UEe9c1dG80VJ6ca18IyExILegvJhas+eLYcqz9+stf/qJeeE899ZSyDWTbPx5SePTRRxVZZQICr1tlq8TVMsm599571RATn/vTn/5EHKuuoTwtaK5k9UMgGP4cs/Wxxx7zfhwE6+cpU6YozabRqP6yyy6jO+64Q4WDWbhwoepLtvuFdy516NDBrwWy2xIEGnpO2Bb3ySefpJNPPjnoc8Z1c9ieQP0U7N5oSXsl7+EIBHu2Qv0P5RLhWa3+O9n0RZeSkhL6+9//rmLxsjPgOeecQ7fffrt8BOoAtdI62HPi/x+qV/fII4/Q6NGjiZ9Ploae4WD3hl6WrNs/AkJAW9iHHIich3qioqIaLImN4dlGlL8a/YWHAjkWqL80lMc/rew3H4Fg+BtLDLWfjXl4yL2srMzHCc14XrZbB4FQn5Ng/dxQPwXL0zotl1IYgVCfrVD72Ygqaz/ZntButxsPy3YrI9DS5yRY34Z6b7Ty5UhxbYSAENA2AlqqEQQEAUFAEBAEBAFBQBDwICA2oHInCAKCgCAgCAgCgoAgIAi0KQJCQNsUbqlMEBAEBAFBQBAQBAQBQUAIqNwDgoAgIAgIAoKAICAICAJtioAQ0DaFWyoTBAQBQUAQEAQEAUFAEBACKveAICAItGsEeMpTDnXGU2uKCAKCgCAgCLQPBA6PCdQ+2i2tFAQEAUFAIcBTMHLgcZ6b+sUXXxRUBAFBQBAQBNoBAqIBbQedJE0UBASBwAhkZmbS119/rQL9v/POOyruauCUclQQEAQEAUHgWEJACOix1BvSFkFAEGgSAjyTUY8ePei2225TAcffe+89n/zLli1TMyJ99NFHNGnSJOI1y/r16+mmm26iiRMnEs9YxkTWKLt371bHzzvvPLr00kuJZ+aprq42JpFtQUAQEAQEgRYgIAS0BeBJVkFAEDi6CLz++ut01VVXEc/ffvnll9NLL73k06Dt27fT008/TQ8++KCabYznqOZ5wceMGaO0pVOnTqWff/6ZBg8e7CWhe/bsUfsFBQU0ffp06tWrl9KwPvTQQz5ly44gIAgIAoJA8xGQmZCaj53kFAQEgaOIANt9jh8/nlhb2a1bN1q5ciWNGjWKVqxYoeab5qa98cYbdMMNN9CaNWto6NChqrW85vRz5871tp6PjR07ll544QVasGABffzxx/Tqq6965w7nMnbu3KlsTb2ZZEMQEAQEAUGg2QiIE1KzoZOMgoAgcDQRYO1n9+7dacOGDWrhtiQkJNDs2bO9BJSP8Tzgp5xyCm+S0+lUaTt27Ej333+/OsY/PF/46tWr1T4PuzOxZU3pr7/+Slu2bKGFCxdSamqqN71sCAKCgCAgCLQMASGgLcNPcgsCgsBRQKC0tFRpKZkUPvzww94WxMTE0Icffkj/+te/FBnlE3FxcV5NZklJCbndboqKivIe4zTnnHMOxcfH8yZt3LiRzj33XDWsf9ppp9G4cePUcP22bdvUefkRBAQBQUAQaDkCQkBbjqGUIAgIAm2MAJNMJpJr166l2NhYb+05OTnUqVMnevPNN+nuu+/2Htc3kpOTiUlqWloaPfHEE/ph+uabb8hms6l9DuvUt29fpfVkzSjL0qVLyeVyqW35EQQEAUFAEGg5AuKE1HIMpQRBQBBoYwRee+01+s1vfuNDPrkJKSkpdOGFF9LLL79MmqYFbNWtt95Kb7/9Nn3++eeKVLIt6eTJkykvL0+lZ60qOyCxwxKXMW/ePJozZ44avg9YoBwUBAQBQUAQaDICogFtMmSSQRAQBI4mAmyX+dNPP9Ef//jHgM1ghyEmlGzDGUgeeeQRNaQ+ZcoUslqtirTec889xB7xLKw53bRpkzoeHh5OQ4YMUWGY7rvvPuIhfNagiggCgoAgIAi0DAHxgm8ZfpJbEBAE2ikCNTU1pA/ZB7qE/Px8ZQcaHR0d6LQcEwQEAUFAEGgBAkJAWwCeZBUEBAFBQBAQBAQBQUAQaDoCYgPadMwkhyAgCAgCgoAgIAgIAoJACxAQAtoC8CSrICAICAKCgCAgCAgCgkDTERAC2nTMJIcgIAgIAoKAICAICAKCQAsQEALaAvAkqyAgCAgCgoAgIAgIAoJA0xEQAtp0zCSHICAICAKCgCAgCAgCgkALEBAC2gLwJKsgIAgIAoKAICAICAKCQNMREALadMwkhyAgCAgCgoAgIAgIAoJACxAQAtoC8CSrICAICAKCgCAgCAgCgkDTERAC2nTMJIcgIAgIAoKAINAiBIqLi+mdd96hxx57jN577z0qKipqUXlHO/OhQ4do7ty5AZvx7bff0q5duwKea42DlZWVrVGMlNHGCAgBbWPApTpBQBAQBASBExuB3NxcGjlyJH388cfkdrsVER0+fDgdOHCg3QLDBPTTTz8N2P6XXnqJVq1aFfBcSw/ylLoDBw5saTGS/yggIAT0KIAuVQoCgoAgIAicuAh8/vnn1KVLF5o3bx79+c9/pq+++or69euniKgRlR07dlBJSYn3EJMto6bU6XR6z5eVlRHv7969mzgdi8vlou3bt6u1txBsVFVV0aZNm7x5+Rznyc/PNybzbuvH9+3bRwUFBeo4p9+5c6c3Td++fem5557z7vMGt7+iosJ7rLS0lIzayry8PEXAOUF1dTWxVphF0zSlMQ1EyPfs2UPbtm2j2tpalZbL2Lt3LzGpF2lfCAgBbV/9Ja0VBAQBQUAQaOcIdO3alTZs2EDz589XxIsv57PPPqMHHnhAXRkTVNbq3XzzzdS5c2diDSLL8uXL6bzzzlPb/MND3tddd53a/+Mf/0i/+c1vlGb1nnvuoaVLl1JqairNmjWLBgwYoOrihIsWLaJu3brRnXfeSdyO119/XeVfvHgxDRo0SG37/5x88sl0xRVX0DXXXKPy/OMf/6ChQ4fStddeS4MHD1YEd/Xq1XTmmWeqrDk5OdSnTx+aPn069e/fnzZv3qyOv/zyy3TXXXepbSaXycnJ9MMPP6j9Z555hv72t79RYWEhjR49mqZOnUpnnHEGXXzxxYqQcqLLL79cHf/973+vrokJ8V//+ldFYrltOilVBcrPsY8AvjREBAFBQBAQBAQBQaANEXjllVe0nj17apGRkdrEiRO1Dz/8UNUOTaYWGxurff/992ofJEvr0KGDBg2hBpKojRo1yttK2I5ql1xyidq//fbbtQkTJmgY0teg+dRAPjVoVtW5lStXaiCDavuss87SFi5cqLazs7O13r17qzzQnmrQIqrj/j/p6ena888/rw7/+9//1qxWqzftmDFjNJBIbcWKFdopp5yi0nBdIMFqGxpKLTw8XHv//fc1aGO17t27q+OvvfaaBnKtPfTQQ2r/9NNPV2XMnj1bmzlzpjoGQqldcMEF2rJlyzRoXjWHw6GVl5erc6+++qoGQq5Bw6pZLBZ1TH7aFwKiAT32vxGkhYKAICAICALHGQI33XSTGqJm20jWarJW76mnnqJff/2VQNiU9o8vmYfqQRIJpLFRBE477TQymUx08OBB4uFukE2VZ8SIEfTf//5XDVOzZpS3WXN63333KY3jTz/9RGFhYZSUlBS0Di6b5aSTTlLaTz1tWlqaqs+YEYSXLrzwQnUoMTGRxo4dq7Z79epFdrtdXfc333yjNL6skWWtJw+tg1wru1ge2uf2zZgxg9i2dM6cORQfH69wYu3t9ddfTyDlBPJrrFa22xkC1nbWXmmuICAICAKCgCDQrhGA1k+RJyZpPETNCw9Z/+lPf6KrrrpKDWmzcxI0e+o62Y5SH15mu05d2O7TKFFRUWo3Ojpa5YU+zHt6y5YtimAy0bzxxhvJZrOpc7fccgtBE+tNF2xDL5vPR0REBEumjnMdbGeqi14X70+aNIkWLFignJLefvtt+stf/qJIJzSdijxzmnPPPZd0wsv7TD5ZmIgyQWdnpzvuuIPWr19PbG4g0j4REA1o++w3abUgIAgIAoJAO0WAbTPvv/9+pQnkS2CHHnZIGjJkCLFGkR16vvjiC3V17CzEy7Bhw4i1iexwoxNPtiENJHFxcUrrqJfBWkbWJrLWkLWMbDvJdpasXWVtIpNbLnPr1q2BimvysfHjx9MHH3ygbDOzsrK8dp5c0OTJk5WzEpNuJqZMNB999FFl68nn2c7z559/VtfLbWStMGuJ2a6U83Cb2d71tttuU85ITHZZ2IlJpH0hIBrQ9tVf0lpBQBAQBASBdo4AOwax1zYPTZvNZqUtPOecc7wOQY8//rjShD788MNqeBr2k4qYMjmFnacakmetIDvpwI4zIBqsZWUyxxpGHtJ/9tlnVTome1deeSWxIxFrWdkpiAkxx+pkp6LMzMyA5TXlIDsTwTZVaXW5DibWujCpZG933Tzg7LPPVg5SugPTtGnT6JNPPlGOUjzMzw5U3F7WBl999dWKmMbExBBsQemjjz5Sx0899VTq2LEjsZY3JSVFr0rWxzgCJjZZPcbbKM0TBAQBQUAQEASOSwSYiMLpSNlg+l8gn2NPcX/hUExwyPEOo/ufN+5zCCXWnPpLsLL907Vkn9vJZJFJdlOFw0+xdpPJs1GYsnAoKP9rYjMFOHQZk8r2MY6AENBjvIOkeYKAICAICAKCgCAgCBxvCDT9s+R4Q0CuRxAQBAQBQUAQEAQEAUGgTREQAtqmcEtlgoAgIAgIAoKAICAICAJCQOUeEAQEAUFAEBAEBAFBQBBoUwSEgLYp3FKZICAICAKCgCAgCAgCgoAQULkHBAFBQBAQBAQBQUAQEATaFAEhoG0Kt1QmCAgCgoAgIAgIAoKAICAEVO4BQUAQEAQEAUFAEBAEBIE2ReD/A8n4HLPp6IMkAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n<div id=\"环绕式散点图\" class=\"section level3\">\n<h3>1.2 环绕式散点图</h3>\n<p>使用 ggalt\n包中的<code>geom_encircle()</code>函数可实现在散点图中圈选特定区域：</p>\n<div class=\"sourceCode\" id=\"cb4\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb4-1\"><a href=\"#cb4-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># install.packages(&quot;ggalt&quot;)</span></span>\n<span id=\"cb4-2\"><a href=\"#cb4-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggalt)</span>\n<span id=\"cb4-3\"><a href=\"#cb4-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">options</span>(<span class=\"at\">scipen =</span> <span class=\"dv\">999</span>)</span>\n<span id=\"cb4-4\"><a href=\"#cb4-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb4-5\"><a href=\"#cb4-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggalt)</span>\n<span id=\"cb4-6\"><a href=\"#cb4-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb4-7\"><a href=\"#cb4-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## 设定筛选条件</span></span>\n<span id=\"cb4-8\"><a href=\"#cb4-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>midwest_select <span class=\"ot\">&lt;-</span> midwest[midwest<span class=\"sc\">$</span>poptotal <span class=\"sc\">&gt;</span> <span class=\"dv\">350000</span> <span class=\"sc\">&amp;</span> </span>\n<span id=\"cb4-9\"><a href=\"#cb4-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            midwest<span class=\"sc\">$</span>poptotal <span class=\"sc\">&lt;=</span> <span class=\"dv\">500000</span> <span class=\"sc\">&amp;</span> </span>\n<span id=\"cb4-10\"><a href=\"#cb4-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            midwest<span class=\"sc\">$</span>area <span class=\"sc\">&gt;</span> <span class=\"fl\">0.01</span> <span class=\"sc\">&amp;</span> </span>\n<span id=\"cb4-11\"><a href=\"#cb4-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            midwest<span class=\"sc\">$</span>area <span class=\"sc\">&lt;</span> <span class=\"fl\">0.1</span>, ]</span>\n<span id=\"cb4-12\"><a href=\"#cb4-12\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb4-13\"><a href=\"#cb4-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb4-14\"><a href=\"#cb4-14\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(midwest, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>area, <span class=\"at\">y=</span>poptotal)) <span class=\"sc\">+</span> </span>\n<span id=\"cb4-15\"><a href=\"#cb4-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">col=</span>state, <span class=\"at\">size=</span>popdensity)) <span class=\"sc\">+</span>   <span class=\"co\"># draw points</span></span>\n<span id=\"cb4-16\"><a href=\"#cb4-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_smooth</span>(<span class=\"at\">method=</span><span class=\"st\">&quot;loess&quot;</span>, <span class=\"at\">se=</span>F) <span class=\"sc\">+</span> </span>\n<span id=\"cb4-17\"><a href=\"#cb4-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">xlim</span>(<span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"fl\">0.1</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb4-18\"><a href=\"#cb4-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">ylim</span>(<span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"dv\">500000</span>)) <span class=\"sc\">+</span>   <span class=\"co\"># draw smoothing line</span></span>\n<span id=\"cb4-19\"><a href=\"#cb4-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_encircle</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x=</span>area, <span class=\"at\">y=</span>poptotal), </span>\n<span id=\"cb4-20\"><a href=\"#cb4-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">data=</span>midwest_select, </span>\n<span id=\"cb4-21\"><a href=\"#cb4-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">color=</span><span class=\"st\">&quot;red&quot;</span>, </span>\n<span id=\"cb4-22\"><a href=\"#cb4-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">size=</span><span class=\"dv\">2</span>, </span>\n<span id=\"cb4-23\"><a href=\"#cb4-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">expand=</span><span class=\"fl\">0.08</span>) <span class=\"sc\">+</span>   <span class=\"co\"># encircle</span></span>\n<span id=\"cb4-24\"><a href=\"#cb4-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;Area Vs Population&quot;</span>, </span>\n<span id=\"cb4-25\"><a href=\"#cb4-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Population&quot;</span>, </span>\n<span id=\"cb4-26\"><a href=\"#cb4-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;Area&quot;</span>, </span>\n<span id=\"cb4-27\"><a href=\"#cb4-27\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Scatterplot + Encircle&quot;</span>, </span>\n<span id=\"cb4-28\"><a href=\"#cb4-28\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: midwest&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"抖动图\" class=\"section level3\">\n<h3>1.3 抖动图</h3>\n<p>抖动图可以解决数据点重叠的问题。通过<code>geom_jitter</code>函数中的<code>width</code>参数设置抖动范围，重叠点在其原始位置周围随机抖动。</p>\n<div class=\"sourceCode\" id=\"cb5\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb5-1\"><a href=\"#cb5-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(cty, hwy))</span>\n<span id=\"cb5-2\"><a href=\"#cb5-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_jitter</span>(<span class=\"at\">width =</span> .<span class=\"dv\">5</span>, <span class=\"at\">size=</span><span class=\"dv\">1</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb5-3\"><a href=\"#cb5-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;mpg: city vs highway mileage&quot;</span>, </span>\n<span id=\"cb5-4\"><a href=\"#cb5-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;hwy&quot;</span>, </span>\n<span id=\"cb5-5\"><a href=\"#cb5-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;cty&quot;</span>, </span>\n<span id=\"cb5-6\"><a href=\"#cb5-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Jittered Points&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"数值图\" class=\"section level3\">\n<h3>1.4 数值图</h3>\n<p>克服数据点重叠问题的第二个选择是使用计数图。重叠点越多，圆就越大。</p>\n<div class=\"sourceCode\" id=\"cb6\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb6-1\"><a href=\"#cb6-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_count</span>(<span class=\"at\">col=</span><span class=\"st\">&quot;tomato3&quot;</span>, <span class=\"at\">show.legend=</span>F) <span class=\"sc\">+</span></span>\n<span id=\"cb6-2\"><a href=\"#cb6-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;mpg: city vs highway mileage&quot;</span>, </span>\n<span id=\"cb6-3\"><a href=\"#cb6-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;hwy&quot;</span>, </span>\n<span id=\"cb6-4\"><a href=\"#cb6-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;cty&quot;</span>, </span>\n<span id=\"cb6-5\"><a href=\"#cb6-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Counts Plot&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"气泡图\" class=\"section level3\">\n<h3>1.5 气泡图</h3>\n<p>气泡图适合 4 维数据，其中两个是数值型（分别是 X 和\nY），另一个是分类变量（用<code>color</code>表示）和另一个数值变量（用<code>size</code>表示）。</p>\n<div class=\"sourceCode\" id=\"cb7\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb7-1\"><a href=\"#cb7-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(mpg, <span class=\"at\">package=</span><span class=\"st\">&quot;ggplot2&quot;</span>)</span>\n<span id=\"cb7-2\"><a href=\"#cb7-2\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb7-3\"><a href=\"#cb7-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb7-4\"><a href=\"#cb7-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>mpg_select <span class=\"ot\">&lt;-</span> mpg[mpg<span class=\"sc\">$</span>manufacturer <span class=\"sc\">%in%</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;audi&quot;</span>, <span class=\"st\">&quot;ford&quot;</span>, <span class=\"st\">&quot;honda&quot;</span>, <span class=\"st\">&quot;hyundai&quot;</span>), ]</span>\n<span id=\"cb7-5\"><a href=\"#cb7-5\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb7-6\"><a href=\"#cb7-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Scatterplot</span></span>\n<span id=\"cb7-7\"><a href=\"#cb7-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())  <span class=\"co\"># pre-set the bw theme.</span></span>\n<span id=\"cb7-8\"><a href=\"#cb7-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg_select, <span class=\"fu\">aes</span>(displ, cty)) <span class=\"sc\">+</span> </span>\n<span id=\"cb7-9\"><a href=\"#cb7-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;mpg: Displacement vs City Mileage&quot;</span>,</span>\n<span id=\"cb7-10\"><a href=\"#cb7-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Bubble chart&quot;</span>)</span>\n<span id=\"cb7-11\"><a href=\"#cb7-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb7-12\"><a href=\"#cb7-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_jitter</span>(<span class=\"fu\">aes</span>(<span class=\"at\">col=</span>manufacturer, <span class=\"at\">size=</span>hwy)) <span class=\"sc\">+</span> </span>\n<span id=\"cb7-13\"><a href=\"#cb7-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_smooth</span>(<span class=\"fu\">aes</span>(<span class=\"at\">col=</span>manufacturer), <span class=\"at\">method=</span><span class=\"st\">&quot;lm&quot;</span>, <span class=\"at\">se=</span>F)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<!-- ### 1.6 动态气泡图 -->\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year),  -->\n<!--               method = \"lm\",  -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n</div>\n<div id=\"边际直方图箱线图\" class=\"section level3\">\n<h3>1.6 边际直方图/箱线图</h3>\n<p>如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改<code>ggMarginal()</code>函数中的<code>type</code>参数可以将边际直方图换成箱线图或者密度图。</p>\n<div class=\"sourceCode\" id=\"cb8\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb8-1\"><a href=\"#cb8-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggExtra)</span>\n<span id=\"cb8-2\"><a href=\"#cb8-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(mpg, <span class=\"at\">package=</span><span class=\"st\">&quot;ggplot2&quot;</span>)</span>\n<span id=\"cb8-3\"><a href=\"#cb8-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb8-4\"><a href=\"#cb8-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb8-5\"><a href=\"#cb8-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Scatterplot</span></span>\n<span id=\"cb8-6\"><a href=\"#cb8-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())  <span class=\"co\"># pre-set the bw theme.</span></span>\n<span id=\"cb8-7\"><a href=\"#cb8-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>mpg_select <span class=\"ot\">&lt;-</span> mpg[mpg<span class=\"sc\">$</span>hwy <span class=\"sc\">&gt;=</span> <span class=\"dv\">35</span> <span class=\"sc\">&amp;</span> mpg<span class=\"sc\">$</span>cty <span class=\"sc\">&gt;</span> <span class=\"dv\">27</span>, ]</span>\n<span id=\"cb8-8\"><a href=\"#cb8-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(cty, hwy)) <span class=\"sc\">+</span> </span>\n<span id=\"cb8-9\"><a href=\"#cb8-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_count</span>() <span class=\"sc\">+</span> </span>\n<span id=\"cb8-10\"><a href=\"#cb8-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_smooth</span>(<span class=\"at\">method=</span><span class=\"st\">&quot;lm&quot;</span>, <span class=\"at\">se=</span>F)</span>\n<span id=\"cb8-11\"><a href=\"#cb8-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb8-12\"><a href=\"#cb8-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggMarginal</span>(g, <span class=\"at\">type =</span> <span class=\"st\">&quot;histogram&quot;</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;transparent&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb9\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb9-1\"><a href=\"#cb9-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggMarginal</span>(g, <span class=\"at\">type =</span> <span class=\"st\">&quot;boxplot&quot;</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;transparent&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb10\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb10-1\"><a href=\"#cb10-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggMarginal</span>(g, <span class=\"at\">type =</span> <span class=\"st\">&quot;density&quot;</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;transparent&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n<div id=\"相关系数图\" class=\"section level3\">\n<h3>1.7 相关系数图</h3>\n<p>相关系数图可以查看同一组数据中多个连续变量的相关性。</p>\n<div class=\"sourceCode\" id=\"cb11\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb11-1\"><a href=\"#cb11-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggcorrplot)</span>\n<span id=\"cb11-2\"><a href=\"#cb11-2\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb11-3\"><a href=\"#cb11-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Correlation matrix</span></span>\n<span id=\"cb11-4\"><a href=\"#cb11-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(mtcars)</span>\n<span id=\"cb11-5\"><a href=\"#cb11-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>corr <span class=\"ot\">&lt;-</span> <span class=\"fu\">round</span>(<span class=\"fu\">cor</span>(mtcars), <span class=\"dv\">1</span>)</span>\n<span id=\"cb11-6\"><a href=\"#cb11-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb11-7\"><a href=\"#cb11-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb11-8\"><a href=\"#cb11-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggcorrplot</span>(corr, <span class=\"at\">hc.order =</span> <span class=\"cn\">TRUE</span>, </span>\n<span id=\"cb11-9\"><a href=\"#cb11-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">type =</span> <span class=\"st\">&quot;lower&quot;</span>, </span>\n<span id=\"cb11-10\"><a href=\"#cb11-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">lab =</span> <span class=\"cn\">TRUE</span>, </span>\n<span id=\"cb11-11\"><a href=\"#cb11-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">lab_size =</span> <span class=\"dv\">3</span>, </span>\n<span id=\"cb11-12\"><a href=\"#cb11-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">method=</span><span class=\"st\">&quot;circle&quot;</span>, </span>\n<span id=\"cb11-13\"><a href=\"#cb11-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">colors =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;tomato2&quot;</span>, <span class=\"st\">&quot;white&quot;</span>, <span class=\"st\">&quot;springgreen3&quot;</span>), </span>\n<span id=\"cb11-14\"><a href=\"#cb11-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">title=</span><span class=\"st\">&quot;Correlogram of mtcars&quot;</span>, </span>\n<span id=\"cb11-15\"><a href=\"#cb11-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">ggtheme=</span>theme_bw)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"偏差\" class=\"section level2\">\n<h2>2 偏差</h2>\n<div id=\"发散条形图\" class=\"section level3\">\n<h3>2.1 发散条形图</h3>\n<p>发散条形图是一种可以同时处理负值和正值的条形图。这可以通过<code>geom_bar()</code>来实现。但是<code>geom_bar()</code>的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，<code>geom_bar()</code>中<code>stat</code>参数的默认值为<code>count</code>。这意味着，当您只提供一个连续的\nx 变量（而不提供 y\n变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：</p>\n<ul>\n<li>设置<code>stat=identity</code>;</li>\n<li>在<code>aes()</code>中同时输入 x 和 y，其中 x 是字符或因子型变量，y\n是数值型。</li>\n</ul>\n<p>为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，<code>mtcars</code>数据集的\nmpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于\n0 的车辆被标记为红色。</p>\n<div class=\"sourceCode\" id=\"cb12\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb12-1\"><a href=\"#cb12-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(<span class=\"st\">&quot;mtcars&quot;</span>)  <span class=\"co\"># load data</span></span>\n<span id=\"cb12-2\"><a href=\"#cb12-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>mtcars<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span> <span class=\"ot\">&lt;-</span> <span class=\"fu\">rownames</span>(mtcars)  <span class=\"co\"># create new column for car names</span></span>\n<span id=\"cb12-3\"><a href=\"#cb12-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>mtcars<span class=\"sc\">$</span>mpg_z <span class=\"ot\">&lt;-</span> <span class=\"fu\">round</span>((mtcars<span class=\"sc\">$</span>mpg <span class=\"sc\">-</span> <span class=\"fu\">mean</span>(mtcars<span class=\"sc\">$</span>mpg))<span class=\"sc\">/</span><span class=\"fu\">sd</span>(mtcars<span class=\"sc\">$</span>mpg), <span class=\"dv\">2</span>)  <span class=\"co\"># compute normalized mpg</span></span>\n<span id=\"cb12-4\"><a href=\"#cb12-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>mtcars<span class=\"sc\">$</span>mpg_type <span class=\"ot\">&lt;-</span> <span class=\"fu\">ifelse</span>(mtcars<span class=\"sc\">$</span>mpg_z <span class=\"sc\">&lt;</span> <span class=\"dv\">0</span>, <span class=\"st\">&quot;below&quot;</span>, <span class=\"st\">&quot;above&quot;</span>)  <span class=\"co\"># above / below avg flag</span></span>\n<span id=\"cb12-5\"><a href=\"#cb12-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>mtcars <span class=\"ot\">&lt;-</span> mtcars[<span class=\"fu\">order</span>(mtcars<span class=\"sc\">$</span>mpg_z), ]  <span class=\"co\"># sort</span></span>\n<span id=\"cb12-6\"><a href=\"#cb12-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>mtcars<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span> <span class=\"ot\">&lt;-</span> <span class=\"fu\">factor</span>(mtcars<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>, <span class=\"at\">levels =</span> mtcars<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>)  <span class=\"co\"># convert to factor to retain sorted order in plot.</span></span>\n<span id=\"cb12-7\"><a href=\"#cb12-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb12-8\"><a href=\"#cb12-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Diverging Barcharts</span></span>\n<span id=\"cb12-9\"><a href=\"#cb12-9\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(mtcars, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>, <span class=\"at\">y=</span>mpg_z, <span class=\"at\">label=</span>mpg_z)) <span class=\"sc\">+</span> </span>\n<span id=\"cb12-10\"><a href=\"#cb12-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_bar</span>(<span class=\"at\">stat=</span><span class=\"st\">&#39;identity&#39;</span>, <span class=\"fu\">aes</span>(<span class=\"at\">fill=</span>mpg_type), <span class=\"at\">width=</span>.<span class=\"dv\">5</span>)  <span class=\"sc\">+</span></span>\n<span id=\"cb12-11\"><a href=\"#cb12-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_fill_manual</span>(<span class=\"at\">name=</span><span class=\"st\">&quot;Mileage&quot;</span>, </span>\n<span id=\"cb12-12\"><a href=\"#cb12-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>                    <span class=\"at\">labels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;Above Average&quot;</span>, <span class=\"st\">&quot;Below Average&quot;</span>), </span>\n<span id=\"cb12-13\"><a href=\"#cb12-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>                    <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;above&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;below&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb12-14\"><a href=\"#cb12-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">subtitle=</span><span class=\"st\">&quot;Normalised mileage from &#39;mtcars&#39;&quot;</span>, </span>\n<span id=\"cb12-15\"><a href=\"#cb12-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span> <span class=\"st\">&quot;Diverging Bars&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb12-16\"><a href=\"#cb12-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">coord_flip</span>()</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"带标记的发散型棒棒糖图\" class=\"section level3\">\n<h3>2.2 带标记的发散型棒棒糖图</h3>\n<p>这个图形是发散条形图的一个变体。</p>\n<div class=\"sourceCode\" id=\"cb13\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb13-1\"><a href=\"#cb13-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(mtcars, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>, <span class=\"at\">y=</span>mpg_z, <span class=\"at\">label=</span>mpg_z)) <span class=\"sc\">+</span> </span>\n<span id=\"cb13-2\"><a href=\"#cb13-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"at\">stat=</span><span class=\"st\">&#39;identity&#39;</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;black&quot;</span>, <span class=\"at\">size=</span><span class=\"dv\">6</span>)  <span class=\"sc\">+</span></span>\n<span id=\"cb13-3\"><a href=\"#cb13-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_segment</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y =</span> <span class=\"dv\">0</span>, </span>\n<span id=\"cb13-4\"><a href=\"#cb13-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">x =</span> <span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>, </span>\n<span id=\"cb13-5\"><a href=\"#cb13-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">yend =</span> mpg_z, </span>\n<span id=\"cb13-6\"><a href=\"#cb13-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">xend =</span> <span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>), </span>\n<span id=\"cb13-7\"><a href=\"#cb13-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb13-8\"><a href=\"#cb13-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_text</span>(<span class=\"at\">color=</span><span class=\"st\">&quot;white&quot;</span>, <span class=\"at\">size=</span><span class=\"dv\">2</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb13-9\"><a href=\"#cb13-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Diverging Lollipop Chart&quot;</span>, </span>\n<span id=\"cb13-10\"><a href=\"#cb13-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Normalized mileage from &#39;mtcars&#39;: Lollipop&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb13-11\"><a href=\"#cb13-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">ylim</span>(<span class=\"sc\">-</span><span class=\"fl\">2.5</span>, <span class=\"fl\">2.5</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb13-12\"><a href=\"#cb13-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">coord_flip</span>()</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0H3BTV9fevqSamaFTECiKKBQTBrigW7NgQUWyAooCoKCpYQcAWEBUbEVCagGAHRVERFRQ7ioq9oaZpoib5p7xJ9p3viWe9O89sfXZ2d/Y55/N5npmduXPvnd+duXPuqaulAnJGhoAhYAgYAoaAIWAIGAKGQIUQ+F6F2rFmDAFDwBAwBAwBQ8AQMAQMAUHAGFB7EAwBQ8AQMAQMAUPAEDAEKoqAMaAVhdsaMwQMAUPAEDAEDAFDwBAwBtSeAUPAEDAEDAFDwBAwBAyBiiJgDGhF4bbGDAFDwBAwBAwBQ8AQMASMAbVnwBAwBAwBQ8AQMAQMAUOgoggYA1pRuGu7sT/+8Y/utNNOy/g788wz3ciRI92sWbPc//t//6/BDbz88stS/sMPP2xwrpoHqtGvG2+80YFXOejZZ58VXD///HOpLvy7GvdX7H199tln7o477nD9+vVzu+++u+vVq5ebOXOm++9//5tR1YgRIxzYVYK++OKLSjRjbRgChoAhYAjkQ4A4oEaGAAi8++67xIRN/fznP09ttNFG8te8efPU6quvLsdbtGiRevzxxzPAmjt3rpxbtmxZxvFq/6hGvw466KDUGmusUZZbnzJliuC6YsUKqS/8uxr3V8yNPfTQQ6kf/ehHcg9bbbVV6pBDDkn97Gc/k98HHHBARlWcP/jggzOOlfvHv/71r9T++++fGj16dLmrtvoMAUPAEDAESkDAJKD5OPQmeH7QoEFu1apV8vfb3/7W/fWvf3V33nmn+/3vfy9SLCSlSl27dnWvvvqq23bbbfVQTWxrtV/lAqeW72/RokXuyCOPdF26dHFIxt988003b9489+WXX7rhw4e7hx9+2N16663lgqKgev7+97+7Rx55pKCyVsgQMAQMAUMgfgSMAY0f48S38P3vf98dffTR8gH/wx/+4M4666z0Pf3whz90v/jFLxxl/vGPf7iPPvpIGNZ0gW93vvrqKznnq1//9Kc/uSeeeMIF0lP3f//3f+FLHCrcP//5z1If5f7yl79klIHxRTX9z3/+U66n7f/85z9Sxu8XB/72t79J+8EiTco/88wzjr+odin/9ddfu8cee0yYcH6jCoeBKhcFEjn3yiuvuAULFki/iq03fH9gBZ7c3xtvvOGWLFni/v3vf2etloXFo48+6p577jnBJlyw2Pr0etrv0aOHW2uttdz06dNdy5Yt9ZQLJKLukksucR07dnRDhw51Uerw999/3y1cuFAWO+kLQzu/+93v3OLFi10gjU+Pj18k/Nwwbp988okU4XniOaGfRoaAIWAIGAJVRCCYiI0MAUFAVfAXXHBBVkT22muv1DrrrJM+76uCAwYo9eMf/zh1+umnp8/rzn777ZfafPPN5Sfq0GHDhqW+973vyd9qq62WCpjY1LRp07S4bFu3bi11oaINXpFUs2bNUoEdaiqQoKU22GADOUYd7NNnygSSW7nW7xcHAgmunL/nnnvExICy/K277rqpJ598Uq7hH30LpHepH/zgB6mAqZYyAwYMSG299dapk08+OV0uaqdQFXzAYKUCxkzq1jb23XffdN+pO6xyD/8O3x8YnXjiianA1jIVMKepn/70p6lf/vKXqfHjx2d0NWC4U3379k2BubaNyUUgkcwoV2h9GRcFPwJpp9zXNddcEz6V/h1I0lMBg5/+TVv0+/DDD5drdWxOOeWUVLBgSZcLFgEpMOY8467ljjjiiFSw+EmXCz83ATOcLqvXgIORIWAIGAKGQPUQMAlo8EUyKhwBpFdIrpCEhgmpV8BEiLred1hC2oa0qnfv3nLJlVde6a666iqRgqGiRXIXME/y9/TTT2dUO3HiRBcwruIEFTA1Ir065phjRIpGvd98843DZIA6CyGcrK644gqRaCJpQxIZMMzpS88++2yR9M6ePVskukhZAwZV1MjpQo3YQRJ31FFHuQ033NC9/fbb0gYq6ddee80FjFQDB51imgoYeBcw44InEueAcXbcD5JOpYBRF8nkLbfcIhJlxrF79+7u1FNPdQ888IAWk20h9WVcEPxAmg21bdtWtlH/goWESM39cyqxff31190777zjGONJkyZl9OmMM85wqPfBC2n4ypUrXcBMu3vvvVeeOb8+/7m57rrrxBSA8xdeeKE8vz/5yU/84rZvCBgChoAhUGkEqsf7Wsu1hkAhEtCAeRNpUsAwSPfDkjikk8EznAps/tK3N3bsWJFYIZ1E8hV8/FO77LJL+jw7SDaRrCJhVUKShWQ0UJ/roVSg/pdjSFt9UslYPgnor3/9a/+yVP/+/aW/gRo/RZ30/eKLL84o8/zzz8vxckhAaQ8psfZTGwrU1dLGXXfdJYfCEs/w7zDuSBHBL2DMtMpUYI6Q4nhgLyrHkCAi+QxLqANVfapNmzapgGlMX1tIfenC3k4QBUDu47333vOO5t6lLaS1vlQyMCOQetRpiHs599xzUxMmTMioDBwZs4suuih9POq54bmjnNaXLmw7hoAhYAgYAlVB4AfBpGxkCBSMAPZ30Nprrx15Dc4xgQe9hN8JPJ+lDJI0Pf7CCy84HEKQgiGZ8gmpIJJAnwLGyAXq5PQhwg9tt912YmOYPhjsBCp+F3he+4ci9zt06JBxnL5COFotX75c9vfZZx/Z6r8ddtjBrbnmmvqzUdvAq10ctrRdrSzwApddJK5IJEuhzp07u8DTPH1poKZ2HAvMDuQYtqHBLOMCZj1dhh3sdwMPcXfTTTeJRBhbTShffVIo9C+IlCBHsMPcbLPNQmez/9xyyy2dL5UMTB5k3KkH4l7GjBkj/f/ggw9EIo0EWSWu2B/7FH5u/HO2bwgYAoaAIVB9BIwBrf4YJKoHOIkEUjQX2DBG9htG4aSTTnLjxo0TNSkqdphKVJ/Qp59+KluYvUDiKvv+vyDsk6ilg9BPcnj99df3T4sqFTOAMP3qV78KH4r8jZmATzBfEIwZfYUCaZxs/X+F1u9fE7XP/Uepp+lXYHfaKEen9dZbr0GT9BsnHJh+xR7mP0yBLaw4cGHSEEhS5XS++nyGUevTsYHZ3WOPPfRwxhamkrHfeeednY6ztukX5FliXJQwJRg4cKALpKvi0BRITtNt+OUoH35utA7bGgKGgCFgCNQGAmYDWhvjkIheIP3EHnKnnXZKMw5RHe/Tp48wPPfdd5+bMWOGSCsPO+wwKaqSv0CdLd7aMCr+HzaAypRwAcyuT5tsskmakfKPB6pY/2dJ+9QNKaOmleC5rwHh9Vip24033jiyLkJc4bXerl27UquOtMul30giYRZpG4q6F5hCGE6fEYyy8/Xri+oo0mnGDDvNbDRkyBAXmFrkLBO+lr5gX8ziYOnSpbK4YRGjC5swAxp+bsL12W9DwBAwBAyB6iJgDGh18U9M66g4cQJBVX3++efn7DcMD9IvVL+BraI79thj00wlqlbUxKjlfUJCh6ob6WkuCoKYS/giX1VP6CUY3cYSjDWSyMDeMqMqdUjKOFjiD+6Rvr/11lsZNRBnFYKBK5UITeSHqiLEFIzg9ttvL1XC3Ab2p27OnDkZTTC2OCCF285XX0Yl3/6AQQxsNaW+wPa3QRFMMGgfZrhbt24Nzmc7QLgo7geHs1133VUkoJRlQQRp+C35EfEPaSqUKzRVxGV2yBAwBAwBQyAmBEwFHxOwSa4WCVPgrCG3AGOI5/ZTTz0lW1SgSKLyEVJQpJxID9X7nWuCkD8SC5I4kDAgMLXEEUVlj31nvpSMgwcPdpMnTxabz8DxxKFixuMZ0wCoMZIv+kFaSOKckjaS+4RRhJGi3kLqxqtesZMOef8CBySHF/rtt98udXOvePjjjc+9ELxdmUXvsoJ3iSZADE5sJSEwhjHT/qB6hzkkCgDSXjzIiXdK27r1G8tXn1/W3yfCAUx2EGpLbDRZNGAbGjhzOSIZIOGGCVXzB//abPvgBP6Mdfv27UVaC24wpNTjM95RdWBHjG0rdsLURSQCtXWNKm/HDAFDwBAwBGJGIFBdGRkCgoB6wQePHIZ38kc8zEBalSKOZ8A0NEAq7I2tBQJJqaRe9D2r9RyxHa+//vpU4NgjbQQMRCpweEkFjJkWkS3ezIHqPuMYP4gj2bNnT4n/SQzQgAlJBQHOpS71jg/3S+OABhK4jPqC8E1yXRBaKn0cj/OAEZR4oYFUNBVIAiWtJB7euUg98RW78JYYmVBgciBRAPQ8sUjxjicGqVLY6z38O3x/eJIHUmeJYRowauLtHkhbUxqtQOvF451IBpoWE+wpFwT61yKyLbS+jIu8H0HA91SQA17ikep90q/AwSv10ksveSVT4qkflYqTPoKLUsB8pgIprYwXdRKbldSwBx54YCqQumuxVLbnBk/5gOmU6wNnr3R52zEEDAFDwBCoPAKr0WQwmRsZAlVB4OOPPxapaKFOPkhjg3zrDbzwkewFQdfF9rQYyZp/00gvyZLTqlUrcQjSc0jXkI6OHDlSpLd6vLFbHH6wbcRkoRDpaq728Bqn3/Pnz5fsUdxLlBOR1oFkGqcrnI+4tzAVW1/4ev1NPFjsRsGQ/vkRDbRMsVtsdLFpzRaJIVd94IIZSaHPW6667JwhYAgYAoZA6QiYDWjp2NmVZUAA1WwxzAD2maiSCUKuBIODWh6701KZT+rCThA7SYLi+zRq1Cj5ieNMOQnGL5DWNZr5DPcJO9ZczCfluVcY3yjms5T6wtfob1KGMsZ4/peD+aReHNlKYT65FrV7Mc8b1xgZAoaAIWAIlB8BswEtP6ZWY4wIYFtKvEqcefbcc08J00P2JJhSmNPGEGGQzjnnHMnShD0q9piBuljsQLGbDNJFNqZ6u9YQMAQMAUPAEDAEvkXAVPD2KCQOAbyhA7tMSZmJijfIquRwdEGdXA4iFeQjjzzi8LxGFU1Adv5qmW6++Wbx4CfiQDmo3PWVo09WhyFgCBgChkD9IGAMaP2Mpd2JIWAIGAKGgCFgCBgCiUDAbEATMUzWSUPAEDAEDAFDwBAwBOoHAWNA62cs7U4MAUPAEDAEDAFDwBBIBALGgCZimKyThoAhYAgYAoaAIWAI1A8CxoDWz1janRgChoAhYAgYAoaAIZAIBIwBTcQwWScNAUPAEDAEDAFDwBCoHwSMAa2fsbQ7MQQMAUPAEDAEDAFDIBEIGAOaiGGqTCc/++wzd9lll7nly5c3aPD999+Xc//85z8bnKvWAYLFjxs3Tpr/z3/+I/0jvWQctGjRIhfkqi9b1bNnz5a0mVQ4ffp0iTtatspjqOjee+91p5xyigvyqcdQe/4qV6xYIeNL+tJSqTHXltom1915552SqauQOnh+eQe/+uqrQoo3KMP7yfWrVq2Sc/6z5T9zDS60A4aAIWAIVBgBY0ArDHgtN0eO7REjRjiCmf/jH//I6CoMKOdqiQElS5HPgI4ePVrym2d0vEw/YEBvu+22MtXm3Jw5c9xDDz0k9cEkPPzww2Wru9wVgXP37t0dDNwGG2xQ7uoLqg8GlOevVCZy0KBBbvz48QW1Ve5Cs2bNcpMmTSqoWhhQ7vPPf/5zQeXDhXhvud5nQPXZ8p+58HX22xAwBAyBSiPwg0o3aO3VPgIwm5dccokbM2ZM7Xf22x6S45usSEmhe+65Jyldda+//rr7+c9/7pCgkUO+GrTGGmtIsz/72c9Kap6sVocddlhJ1zb2IvpM9q5qU5KeuWpjZe0bAoZA/AgYAxo/xolrATXryJEj3ZFHHilpLrPdwDPPPON+85vfuM8//1xSVp577rlu4403luJLly515Ghv1aqVmzFjhjv++OPdH/7wB7fFFluIlPLBBx90G264oTvvvPOEqYHZ5XzPnj3dUUcdlW7ygw8+EMnVW2+95X76059Kf8466ywHw+nTv//9bzdgwAA3ZMgQkdKxjaKrr77a/epXvxJJ2q9//Wv34osvSgrP3r17u3322Sd9Ce1OnjxZzBHIOY+KPxvB+A4cONCdf/75buLEicKw7brrru6CCy4QyebUqVPd+uuv704++WS37bbbSjXks//lL38puITrRcqXq29Ioa+//nr3wgsvyH20adNG7rtFixZSFefp+8KFC93aa6/tGBckxUjGwBzCzII+fPzxxzJ29D1KugnTQl3c/2mnneb69u3r/vvf/zYY26OPPtrleh6WLVvmnnjiCdelSxd5Zv7617+6k046yR144IGy0OFazvXp00fGRzrp/SMlKv1bc801XbF1XXPNNe6jjz5y999/v/v+978v40LVSJ7BaLXVVhPmFAb1Bz/435Q4b948h9kB+IAZWgH6CiFJ5Tnm2t///vfu0ksvdeutt57cB2Oy1lprua5du8p4Uzd91zSxPG+8M1dddZWMjVRY5D/aHDt2rHv11VelXd6t/fffP28t/jMHho8++qjbbbfd3K233irvE1Jun0lnzHmeSUvLPuNzxhlnuB/+8Iey2OOZP/PMM+V+wIn3BzONUhcJeW/AChgChkBdIVAdcUZdQVh/NwMTwAcNpuzvf/975A3ygSY/+tdffy3qWRjOdu3aORg3iHzq1157rdgMwjRQDx8ymDCu3Xfffd2SJUtct27d3OGHH+5+8YtfuC233FIYUFS+EOpIGLY//elP7oQTTnCbb765GzVqlLv44ovlvP8Ppgg1J3asMBHbbLNN+o86YBZo7yc/+Yn729/+5jp16uQWLFggH1zKH3zwwcKQUCft0T/U7occcogwIrnUt3ycaRvMYHB22mknYSDJTw9zAjP63nvvyX1qn7U/+lu3+fpGOdpBGskHH6aIfu69997CGHIexoAFxM477yyMBYwD/VO1LuV32WUXBxPYo0cPyXkPRiwkwsSComXLllLPjjvu6NZZZ53Isc33PLz77rsORvDUU091HTp0ECYGhgeMYIa5JzBmgRBFm222mYwh54qti+cKCSqMJMwgNHz4cGGeWBhsv/32wljBoEFse/XqJYsnmGSklwcddJAw/JxHpc19wEyCIQwXTCAMNtftsMMOsrCCyYT69esnixP2YR4pFzZx4VwhxBh27NhRzDcOPfRQGXPeoVtuuSXv5f4zB4YwsSeeeKJjXJs3by59x1xAicXGsGHD5L0DI+6H5y2VSglDyjMFo827TV9uvPFGWVRw3sgQMAQMgbwIBJOFkSEgCARSEb4cqU8++SQV2IOmAgld6pxzzpFzAfMo5wKGU34HTEkq+OhmIMexQFIkxwJ7SSkfMJPpMgEzlwqkdalAYijHAumalAmkc+kyAVObCtT/8jtgEFPBRzAVMHjp8wFznKIMFEhuUgFTIfuB1E/qeuyxx+S3/y+Q/KUCxikVmBbI4SuuuCIVMCSpwNEjXYxjgRQrFTCyqYDBTQXMtOxrgYCpTO2+++76M2MbfICl7eBjnT4ODmAZ2OLJsd/97nfy+7XXXpPfwQc7FUgUZT/4iKcGDx4s+/n69sUXX6QCpjH15ptvSnn+BbakUjdtgDftBkxO+jz3zzFte7vttksdccQR6fPscOz000/POKY/AilYaqONNtKfqaixzfc8TJs2Tfqgz8O//vWvVLAYSAWMcLregAFNBcxi+ne2nVLqChioVLB4kSoDRlv68uSTT6abmDt3bipgHmXMg0VDKpBSps/xvAaLqBT9gwImLBVIQFOB1D1dJpB6piZMmJD+HTDkqfvuuy/9u9Cdxx9/XPoWLOQiLwk0BqnAHCLF867EsWABlwoYZXmmGetgQSin/WfLf+YUw0ATodWkeH55T6Dnn39e+hFIjdPn9VggGU7pMx8w6OnzUc9e+qTtGAKGgCEQQsBU8MFsbdQQAaRF1113nUgsUcX7hBQGlWbALPmHRVqIlFPpxz/+sUi79Dfb9u3bp9WcSLUgpGBKqCoDRkp+chzpHRK7lStXuoDpcgGDKdIaLZ9vi/obyU3wYReJFuWDD6nUceWVV6YvR3KKdApHLCRytIv6VIm+UEcuQkqkxL21bdvWBYybHEJyCHFvSIqzUb6+IZHEmYQ+Tpkyxb399tvuqaeekuqQRCGVQ7UfMMvpJpDuBkyo/EY9j+oWyR/SLSUkt1xbKPljW+jzgKSZ8YdQ43Iv4bH/7W9/W1AXGlPXK6+84ug/EnwlzD7U9AMvcvqB+QGmH+AFtr7UEgk6mCkhKUXyjFofaSmqbKTw5SYiPyCd901QkIBiwsKzsOmmmxbcJBjwnCvtt99+8q7gwKQY+WYpPN9ISjEz0HFTswTqCBYxYmpAH/16tX7bGgKGgCHgI/A9/4ftGwI+Aqjg+dCgkvedKDREDEyqT9jBoY5WQvUedlrBJjFMqN+jKJDYyQcVtT1OJKiJsccslGAYUZXivY4aXAmGCXtS+qZ/MEPYbPKb+/Pvg+tgmPIRtqU+4bij5DOzeixqm69vMEGMyR577CFqeO7juOOOS1eF+QDjAIOmxDgoYV+KuQJqY713tqhSUYkXSv7YFvo8wBj7TBuY+GNfKEb0sTF1sdDAFCNbeyy8YORYoGCXDGParFmzDGjCzzHmJtiYYo+LKQGLD5/Bz7i4ET8weYl676gy/Mzma4bFHs+Pkj6/mBUwpoyxOn9RBrzAwW9H7Y71PNdwvZEhYAgYAvkQ+O4rla+knW+SCOCgwMdUJWiAsMkmm4gEBls4GCElpJ/Y95WLaBP7PaSeyrhgx+l/ALO1hec2DBUOVTiQ+NS6dWuxJyRsE8wXhOc/dqx8lNXGzr8mn/TTL9uY/Xx9QypHX7C1hWmGAlWvbGEskbrhEAIjqgyFL5Xm/mD6cOjxJdjYBxbCZEtDoX+Veh5CzZb8E+k0DBbMpTKWSPywCyXW69ChQ8U+EocbiOcNG2TwjSIWZ8T6xF6YP8phq3z55ZfLe7P66qtHXVbSMZ4PDaukFfCbBQfvqS+l1fPZtkj7sQXFthriPYPhhIlG4wCjjqRd32mkwiwKL7zwwnSVaCewNYYC0x2pj/fHyBAwBAyBfAiYBDQfQk38vKriUUMqwQwiWcS7nViWqCfxlsWzFqeWchHqPhgp6g9MR0TCdNddd+WNRcqHEjUoKkUcQPjQ6h914c3Nb1StSBwpjxRx/vz5wljDsPIxxdOc8qi8YU4rQfn6BiYwRDAHEMymBoeH+eC+iTyA2URg4+hwKtFYqdp/ogWgKn7ggQekLlT4qIwD+1ItUtS2Us9DUZ0KFUZiCVPFWCMNxxmpf//+wsj/8Y9/lLBjypxTFlMJGEmYSzy9MV3IxtwhTb355ptF4gljyzNDnbw7YeYTlT6OVkgycxFqbqJI+H+8C/QZhzZU7n/5y1/E/AKvepyAUKkXSzirwYijYSDaAVoPFmWo1pFu4kQHk8r7AmMOw+4vOgNbUjFp4XkkdBsMsn++2P5YeUPAEGg6CBgD2nTGuuQ7xb4NO0KfUE+ituXDR9gZPmQ33HCDO+aYY/xijdoPHKBE1YpKGRtK6ufDi7QyV0ByJELYsQWOJSLpQ1Kof0gL8VK+4447XOA0IraQSFlhFqgfQqKDJAwvYVSKhDHCW7gSlK9vXbp0kVBI2OZhx4kdI0wC/USKBzOIJAv1P9EFkMQRhB2CUYKQ9DFOqJYpx/gSDqsxi4dKPA/S+RL/gQWLFzy+kRbefffd8owQTgkJIDjwDCMFJgQWUQZgtmD4MVcAL/CNIlTTeIDzXPIcIXlmDGgjTCzkUM2z8MlFhCODkfP/CFWFCQqMInjDMLPgQELJ81wsYcYAY40Ee6+99pJoAJgfQDwrRDbANpr3A8YSG2yk7zx3SjyvPJMwq2+88YYjvJpvVqHlbGsIGAKGQBiB1XBKCh+034ZAoQjwAUPao842hV5XTLkvv/xSJJO+TWUx1+cqyweWD7nv1OGX5zzq6mz2gn7Zcu/n6lvgRS4Sy3DsTsYC8wOYAu0zUjQYF5gemA4l4pciuSrn2FXiedD+F7sFM+LF+naPPFtIDqNiVyL1gwH17WnztYn9IxJQnqk4iWlb+1eK6QQS8LPPPlueITAAE12ghPuN5JVniYWmEtJgyqOCJ5YoEt2471nbtq0hYAjUBwLGgNbHONpdGAKCALE8kWghIcP8AFMC1PpISIl7amQIgIDPgJaCiM+AIj01MgQMAUOgWARMBV8sYlbeEKhhBJCIzpw5UxxVkN4RsgePbswRjAwBRQDb1MZILJGI5tIcaDu2NQQMAUMgGwImAc2GjB03BBKOAOrwUhxTEn7b1n1DwBAwBAyBBCBgDGgCBsm6aAgYAoaAIWAIGAKGQD0hYCr4ehpNuxdDwBAwBAwBQ8AQMAQSgIAxoAkYJOuiIWAIGAKGgCFgCBgC9YSAMaD1NJp2L4aAIWAIGAKGgCFgCCQAAWNAEzBI1kVDwBAwBAwBQ8AQMATqCQHLBV9Po9mIeyGwNSn34iLNd6DB0eNqJ856k34PSe8/Y1sv92DvQZxvav66eY5sDPLjFGcJG4NodMGF+M2kDSZcWjidr15FlBNSBZMpkFB7pTzPcY8BCT7CyVK0/2zNC95Hownvk1e6V69ebu+9944FBfJqk3knyWGByHJD/0vJPBMLqEVWWg9j8Le//U3wz5a5qkhIKl6cCZ8PR7aPSsU7VEKDSR8DbplsVdkyP5UAScUvgfEg7W6S59N6GIPvfe97ZX2XmR+uvfZa+VaSta0Qog/M7UOGDCm6L3GOAX1677333C233JL1NkwCmhWapndiq622kvR8cdw5zCcPe5LzRLMipf9rrLFGHBDFXicTGsyDn44z9kbL3MDvfvc7SZsZlTqzzE3FUh2T8jfffCOZqWJpoAKVkr6V1J1xpMatQPdFik5a2l/96leVaC6WNv7whz8I85nkd5kUr0keA9IeI4wgy1y5qHv37o4UzDCixRDpgh988EH35JNPysKk0GvjHAO+N2eccUbOrpgNaE547KQhYAgYAoaAIWAIGALxIvD444+7xx57rGjmk17B7L322mtu1qxZ8XayzLUnXgL67LPPOlYiYWJlcuCBB4YPN+p3qZllUJesWLHCvfPOO26bbbZx2267rWPFUi56+OGH3Z577inSrZUrV7rOnTuXq2qrxxAwBAwBQ8AQMARiRuD8888X7UipzWBGd+GFF4opHWr5JFAyepkDScTVMHb8jRs3zr388suy//777+e4qvhTrDBOPPHEoi9EZdinTx93zz33iJ3G7Nmz5QGBKS0X3XTTTfLgrlq1ys2YMaNc1Vo9hoAhYAgYAoaAIRAzAgjR4GGgtdZaS3iEdu3aZbSK3fjhhx/uunTp4pTB3HLLLaWsmiRh3vPmm29mXFfLPxLPgB511FHu3HPPlT+kin379pX9QYMGpXHH/vDjjz8WMTUHscPCBsgnGEIknEowc9jLKX311VfileZfh00jjO4XX3yhxRpszznnHHfEEUe4q6++2p100knu17/+tUhBf/Ob32SUDben9npff/21o22l8L3o8ajtf/7zH/fpp5+KRx31GRkChoAhYAgYAoZAbSHw3HPPpZ1bb775ZolIAw/jM6GXX365eL23b99ehGH4bFx88cXC24wfP15uCP4ArXBSqHx64Bq9Y6SDTz/9tBgKs8oYM2aM22STTVzv3r1FYrrZZptJzwcOHOgQgWOUC3OIYTErkv79+wsDOW3aNLHN4CG46qqr3EMPPeSmT5/uWrVqJSuOPfbYw5133nkZKMBUwjz27Nkz4/iwYcPSK5glS5ZEtscq5vrrr5frYayRnPJghu+F9qPoww8/dJdccolr1qyZ+/LLL4W5vvXWWzOcgPC209USHqGEceD+4yCYfv6SzgiHFypxYBVXnRi2gz8Lk6QS98AY/Otf/0rkLegYxPWeVQIU5hY+fKWEfalE/wppg3FIev+5z6TfQ9L7X64x+Pzzzx3CJgiBGppdJJ3wKpjvQWh4Oc4cjpkdURCw+Vy6dKl86/nN3PjBBx8U/B2HYY1rLirkW1PXDOhTTz3lnnjiCVFLI76eM2eOmzt3rhs6dKjbb7/93MKFC92AAQMkVABgEUsLieWVV17ptttuO4e35ymnnOK6devmTj/9dHf//fe7sWPHCiP6wgsvCINIjCu8o2EyCYOgonEemLfeesttvPHGDSYJDZ2BBHX06NGR7XE9DxJ9XnvttYXxzHYvlA3TK6+84o455hh3yCGHyCnu46WXXnJ77bVXuiiTsFLSJ2S9D9saAoZA/Aj4c0f8rZW/Bet/+TEttkYbg+8Q87GAyeS7vfXWWwu/oaU4zjF4FEImIjSaOHGi+H/AZ8BXwIAmiamvawb0jTfecLvuums6NhYxLrHjhFE86KCDZHvaaac5nHhwWEJNT3zBDh06yJgT4JWBffHFF4Uh1QeBAUZaSsgDGEQCuPMAsZpQ5pKy2HLoqkav9be52iPMCcztuuuuK5fkuhe/Tt3HVmTZsmXygBKLC1W8b2JAOR5kJQyYR40aFVtYDLCphzBMjIuFYdKnpvJbbKoZA7V5qnwPGtdiPYRhYqF+3333WRimxj0KjbrawjA1Cr6yXFzOMEzz5s0TEz1M/Hr06OF22203d/LJJ2d4xMOXXHDBBcJ8YuuJUA3BGN/u5s2bC/PJ3Mj7WWh4K6SfhZYtFjSEekhlc1FdM6Awb74zEmo7/pBSMkjrrLOOW758uVu0aJFDPQ2pqliBg2kLqyz/8Y9/yMPBQ7LTTjvJA0H8rjBtscUWabtRmFElwi3AvJ555pk52/MDJee6F63X36K+R4J68MEHC7ONut3IEDAEDAFDwBAwBMqDALwBPARqctToLIwxe0PYheayUNp5551FgAWfgNkg2lnM/qZMmeJatGgh2kt+I+zCFA/+4a677hItLFmTHnnkEWmK6D+77LJLoc1WvVxdM6DYUMycOVMcjhhYJJ0Y7qqaHCkojCd2lDCjEDYX2FRg0wkDx1+bNm3SYZOQ5LHyQbLJ6gP7TB38MKNK0HIY00svvVRU7QQNxn4KG1OYT9rM1h4Ps0/57sUvyz4xwVC7wySzWsYcwFe/h8vbb0PAEDAEDAFDwBDIjwAOyvAON9xwgwi14AtQf8MAIoXkN85COB/zDc5HCJgQWBHFBwY2igjfGKZjjz1WtLZqEw/PgZo+KVTXDCgMHhJAbCFZjbA6wYlIaZ999pEHiNhZSv369XOXXXaZmzRpkoi2R4wYkWZOeQAOO+wwsSkl7iYicups2bKlqMtRc/MQ+QSTijSSskhXUd9Sx+677y7FsrUXZkDz3YvfJvvYiLCSwuYVk4GOHTuKGj5czn4bAoaAIWAIGAKGQGEIoFU94IADRLvpR8rhajSmauqGIAv+o3fg8HzdddflrZwIOUceeWTRsUCV+SQz2RVXXJEWsOVtsAYKNIlc8KxGUJuHU8dhO8HDgSdZOLc0thi+2lzHino0jzMrHlT1vt2nlovaYreRLRVltvbC9WS7l3A5fsPwkr88W5v+NWoDyksQB9HverABBUuzAY3jCSmsTkvFWRhOcZayVJxxoltY3WYDWhhO5S5FRJkNN9xQpJ6+41C+dvbff3/RwOYrR8hGIuwoU5mvvJ5Hq4vqffHixWltrZ7LtY3bBpRUnLlywSc+DmgucPUcYvEw8zl//nzxhsdZJ8x8cl0U88lxZT7ZR9ReKPNJ+VyMYLb2uM6nqHvxz/v7PJS52vTL2r4hYAgYAoaAIWAIRCOAkIjA70g4i2E+qY3wiYRFzEcIwxBwFOPJDk+AsAlflnJmWMzX13KcbxIMaBRQrGKwzzz++OOjTtsxQ8AQMAQMAUPAEDAEBAHCM6JRLIXQlhIsHh+QXISAi8Q2qO7xGclHCJh23HFHieUZJUjLd321z9e1DWgucInzWU4i6DyxN8OElzwPBk5B2I3mIlZW+SSqjz76qDgT+SsdwizRPp50vuc8YRCIV8qDnCTD5FwY2TlDwBAwBAwBQ6CSCKB6x+kIE7xSCSYUh2QS2OQiNJeEZcK5mZCReLlzjOuRvKJ5hXBcwuYTQZpG7clVby2ea7IMaLkHgzidt99+ewOPt7Zt20pKT4Li52JAYRaJUXrnnXdm7drdd98txsx46CsDSixPHj4yOk2YMEEeWFZEPKyk/sQDDzsPvPZxhjIyBAwBQ8AQMAQMgcIRIAmMqt2JXNMliLDDMQQ/Sgh6Dj30UAnJRGgmJYLKY/LHN37BggVSTyEqduxG+cO5mYyJ2P3SB7zkd9hhB/nmF1KP9qMWt8aAlnFUeDBJoxUmHrzBgwenDxOuiexJ2G0QbB5m0s81H7YH5XpWTqzCfHr99dclJJSuqAgXRcpOGFBijSL11HYJ1/D2229LSCm/Dts3BAwBQ8AQMAQMgewI4NyDEzHSR2Jqk8EQlfpxxx0nwh6u5PjUqVMlWDyJakg0g7c85WBAIRhIYnmGo+XIySz/NtpoI4nkk+V0og8bA1qB4YPxI4UnEtJsOdrDueZVwkn3YFhRrxO31I/lCYOpAfQph5RTVQTrr7++POh4rOLhDoMbZmyvueYat3LlSi4V5yokqXHlhYXZ5g9mOsmEZFnDbCTtPsCfZykcrzZJ98EEzhgU6yVaK/dI/3kH4nrPKnGfxEa+6KKLinKUqES/immDcUiy9Ij+Q0m/h6T0HxM6iLmT7InYaRLVhW8q8xH3gakdAeJxIuI7DdOJBJNMikqMG2GcNO64Hq/Wlnk0rrmIeS7ft8YY0DKOPMxc37590zUSe3PQoEHp3+xky9Hu55rPuCD4gV0oov0wYReiNp+I55GEnnfeeVKsU6dOIgFF8kkIpIEDBzYIcMv1/EFJtSGRztu/ghBIymRf0M1YoaohwHPEfJHk5ynpDCiLSfC3MajMa6A4I3zgb9iwYfItJz87RJQdXRQTG5T44MQQx/OdxDNKPHe6eNBj1dzqfVWrD8aAlhH5lkFAekTvSlHxIgvJ0a7XF7pFqjp06FDXp08fkZRy3eTJk0Xyee+994rnHucR5Xfu3Dld7dlnn53e1zigceWFrZc4oKhgosY1DWQN77AiZXIsxLuyVm/DcsFXf2RIY3zPPfc0CG1X/Z4V1gMYAELqxDXXFdaLxpWyOKCNw6/YqxEkkdgFGj58uEOziM+FEup5DfXIdxYJKU5CY8aMcdtvv7079dRTRVuJwKd169Y18+wh/YzrPeB7k0+wZQyoPkFl2CKN5OHKReXO0Y7U9YILLnAwk76T00svvSROTTAb/HXt2tU9//zzGQxorn7aOUPAEDAEDAFDoJYRQMWLVhEnYPwqNBc7WQ6LycWe7x5Rqc+YMUO0iqS4RtVONkOknDCXMKX4XZDxaL311pNvL/4Y0Jw5c9KmckgcN99883zNNZnzxoBWeKiz5WhXm08khQSWLYRYZSFxHTlypAuHlcL7nbyyZEdgJYIdCilJjQwBQ8AQMAQMgSQjgMYOCSR+DKi++cMmk+8oWiq+eR06dHBk9uMb2FjqEni9wzw+++yzbuONN86oTrWe9IdvN99wn44++mj5Sd+I71lttbfft2rvGwNa4RHIlqMdUbWfa74QsTgrK5yLzjrrrPRdcN19993nunXrJioDbD9RuxK2oRwvYroh2zEEDAFDwBAwBCqMwFtvveUOPPBAiQATzsWuzChdInQRjrsnn3yyOAE3pptIU5F03nDDDcLoZqsrzHz65WCMkZgafYdAk8gF/93t1sYeBuTZcrT7uebL0VvqY+WlEtZsdaoNqOWCz4aQExWP5YLPjk8lzlgu+EqgnLsNywWfG59KnG2qNqC8/y1atBApYzHOPORYx265MYTdMI7FhFcqpm1tE4cktJW1RHHbgFou+Foa7W/7giFythztfq75cnSd+vIxn+Vox+owBAwBQ8AQMATiQoA42NhPIuUslgEkq1BjmT9CLmHnSdvFqNGRfMKINbb9uHCtZr3/i8FTzR5Y24aAIWAIGAKGgCFgCORA4LLLLmtgX5mjeMYpYnViL+pnLsooUOCPVq1aiYc7zsaFREPBARjVPcHojRoiUNc2oHiqkR2oefPmDe88x5FCcrLnuDx96plnnpGHNX3g2x3CMpABycgQMAQMAUPAEDAEciOA2QfJWhqTBAQnJaSQEydOzN1YnrPYg5JqE6aSqDaYueH05DtBYQuKYzAmbSSRMYpGoK4ZUNJiYYBcDAPKA4X3nB/SKBq6/EfJ6464nhSdPsEUGxkChoAhYAgYAoZAfgRQfeM7obTvvvtKLnbNtIMTL0lXlDQ0E+Zne+yxh1u0aJFIT+fNm6dFGrVFrQ5vgXSTYPQrVqwQHwGkouRqJ2xTOPNgoxqs04vrmgHNN2asUj7//HO34YYbip0kq6uPPvoobWeigVTJRAThcUeYBVJuQVxLGc3nLgdD//DW4y+KSJ1J8FrNRoSRs6b24sUhzBIPs9pwch77F+KMQbTN6ov+YJxNAFyti/O8nOGc8xw3MgQMAUPAEDAEkoIADCiOslC/fv3clVdeKd9dZUAR9Pzxj3+U88TE5jtOukvicvKdhAGF+H5yjqQx5SC+t+3atZO/ctTX1OposgwoWQ2efvppt+aaa8qDS8YCvNvee+89yamO1HTx4sXykGv8zMsvv1xWNgR1v/TSS4UBxZkI5m/cuHFFSVp50I4//ng3adIkYSh5kUi3+cQTT0jQWvryzjvvuP32288dd9xx7oorrpB+EXYJO5SrrrrKEY7i6quvTjPB5JjlhePlypZz3nd+wibmzTfflGdenZUw9I6DmCBYweYKUxFHu+Wuk0UITH8SiTHgOWNCTipxD9hzNUYVV+17B/+43rNK3Bu54C+88MKiHDEq0a9i2uA5KsaRpJi6K1GW/kNJv4dC+//qq6+mYUWosmDBgvRvdvi24GiEeRuxtgnB1LZtW/luapxOvYBvvGYt0mOlbJlLaTfJ7zLf47j6zzynC4Rs+DZJBvSpp54SRo/MBjBexNOcO3eupLPkwSZYLOEWYECjiJUYAWkR53M9W6SVUap+8sDOnDkzo5rbb789LdXMOOH9YODmz58vKvxRo0aJdBMml0EdPHiwI9MRagBWc+SAh+kk/ieMNMx1tpzzqAaUUFuodFW3hU4IWkeh23qYMLlX8IkLo0KxbGy5pPdfx6GxOFTj+np4D3h+0AQl+TlKOgMK4wP+TWUM/Pvku9izZ8/I1/fMM8+U7zgnX3/9dflGRhX064s6X8yxctZVTLvlKEvf4+p/IfU2SQaUtF277rqrMI8M4t577y2ps4YMGVLQmLJ62mmnnVyPHj0kuDs2Jqy2oogyfv51yiizF1Vej7Vv3z49wdBf0m1CXIt96mOPPSYSU7IytPxWncA9ESgX5rWQnPMwskoaB7SQAPh6TTFbVloYafsS2GKur4WyrLxh+gvxfqyF/ob7wOIFCa7lgg8jU7nfMA7kjUbzklQiF/zdd99dFilSNTCA+bRc8NVAPrPNYmJQkv5y8uTJmRWEfjE3IxBins5FhHIqx3cOlT8LsSS/y8WMQS5Mo87xvcmXC75JhmFad911M1TBmj3Bt59UQH0RMgyU0ujRo4XZw+4SxyWkmlHEg44Tkv/nl9P6USv6RF55JWxZfNU1/dXrtAxb+oc9KINOn2bNmiVpw1gVZmOQ/ett3xAwBAwBQ8AQqDUEyO2eTXiB0AXaaqutxBkoV9/x5yCQvVFtINAkGVDyuj733HOyCmYYHn74YXl4YUBh4NS+DEkRtpQQdhLYZEKsGrDfxBnoxBNPdGRZ+OSTT+RcMf94obT+ZcuWZb0UCe3ChQtF/Q6jimnANttsI+WJa4ZNC4RUVHPCYweD7eoBBxwgqzTsRaOYVrnQ/hkChoAhYAgYAjWKAN9AX6V7wgkniEMR3SXIO4RZGuZqPvG97N27txxCWnnYYYf5p22/ygjUvQp+2LBhGQ8uDytqcew8YdCI6fWzn/3M4WAEofoeO3asMKF4r+NRB7OJRFKliEg1cQ4iDAPqWJxSsmU5wHkIjz2fcCo67bTTHC8RklQY2S222CKrahSnJ+xWsXuBiUSlj4odByKkoyNGjBBbUe4D5yQoW855vx+2bwgYAoaAIWAI1DoCfCNhJPGpKNUJlG846TCNageBJp0LHrU2D3PYIw4JKKslVclreKTwsGFLhD1XY2zq6APtwTzmI+w0sXGhbxBG1tdee63YxnAufB/Ym2XLOR9uS21ALRd8GJnvfmNbhNTabEC/w6TSe5YLvtKIN2zPcsE3xKTSR5piLng0jwiIPv3005LgRhikvhQlVRC6yGxAQ4CEfmIDmi8XfN1LQEOYZPyEkVNmzj+hcT/1WLaAsqgEGsN8Un+2Pmjb/jbMYOY7BwOdzW7Gv9b2DQFDwBAwBAyBWkYAgQrCoFII5118J4xqC4EmaQNaW0NQem/wgB8wYEDpFdiVhoAhYAgYAoZAAhAgFzxStVKI6whjWIqvRint2TWFIdAkGVDU3g888ECDhxnHnZUrVxaGXJ5SqOdpg22xhHoFxyhSeYZfmEcffVRC6VAn0lcC7/pEyKZ3333XP2T7hoAhYAgYAoZAYhHA9IZ41+ogXMqNECUm7KRUSj12TfkQaJIMKA8xAdvDInm8yJ988smyoIu6gDaKZUDpA6nGyPyAfSkZR8hYpMR5YjlmIxyTjAHNho4dNwQMAUPAEEgaAlG54P0Yk+zjLKx/GiUG1Tse9JAKnpJ27/Xc3ybJgBYzoIQ58hk+RPk49uCNThYi/xz1wtQitYxiPDGiJmRSOOan9uftt98WD/zx48dLNgcY0ZtvvlmY4uXLl0sxGFI892FOfXUE7dKvQw45JP3CcQH9pD8WgklRtq0hYAgYAoZAkhAglzuOshDfxdmzZ2cEOed7i1MQfzCh+G0QIWbKlCkSKlHvFSaU77ZRbSDQpJ2Qcg0BuWQJ+UCWA+J/9u/fX+J9Ek8TWxIknDgFkX+dEEyowgmVRBgkMoX4DB+MIuEf8B5FbY6an9+77bZbRheITcpqzQ+UixMRqTzVS75Pnz4OBnXq1Klus802k1BSVDJx4kTxkNdsQ8QnxaQAhpXMD7RNQPqdd9453SbhplDZQ4SowCEqrrywTBBgwgSQZGLBUWoYkGrft46Bv3Cpdp+KbZ97YAHXGFVcsW2Wuzz4x/WelbuvUfWRCz4c3i6qXC0f4zny40rWcl+j+kb/oaTfQ6H951umZLngFYnGb/kexzUXMc/5fFBUb5s0A0osTZ+QIhIbFCaO+JzE7ySwO8wbqcC6desmxVFxT5s2TRjNO+64Q2w9O3ToINeQgYiMDA899FCaufv4448ldM9tt90m18NQPvLIIw0YUJjbbbfd1u+S7Cvz6Z846KCDJBMT/YUZRjVPDvh77rknXQwm9+KLLxamE6aX9Hk+A0rQfcI6QezHPSkz2RQ64aRvosZ2kn4PSe+/Pg718BzpvSRtC/YsWJM8BsyZGmYvafjTX/qf9He5mDHwx8pywZfviY3zGSpkfmjSDChifPLHKmlKLxhGGDKYSogguHicv/jiiyKJbN68uTCfnOP4K6+8ImJ9wjdtueWWHJZc87IT/ENSSfB52oN5JX5ny2/zt2sZtqgNvv76a/9Q1n36hjSOTEpffPGFIyXoBhtskC4P04ykqFOnTnIMphhm1CckokoaB7QcOXK1Tn/LSkuls/7xJO1bLvjqjxbOCLyzUYuy6vcufw/46NZDLvi5c+c2iDuc/+5rowQLbcsFX/2xKCYPueWCj2e8ihmDYnuABNS30426vknbgCL98/8ULGxH+FDwp4TKT8XJUbE1UV9zXq+hLl21vfzyy5JRibq6d+8uzKjW62/btGmTlpr6x5HGLliwwD8kq1+koEg+kaay7xMfadpXVQ3nYFb933552zcEDAFDwBBoOgigeiXSCiZdeIdPnjzZPfvssxm+BbWCxr777pt1waOCo0JywSNY8k3cauX+mmo/mjQDmm3QYUA32WQTt3TpUimCzRN/MIjZiPI83JrT/Yknnkgzo0g8sRFFXY6ENNtLzkvGimTGjBliK8kKgrBLzz//vNt9990bNE2ed9ohB26XLl0yzmOf2q5du/Q9UAb71ELE4hkV2Q9DwBAwBAyBukGAb9Qee+whWjh8G84991w3fPhw8RHAiRVNGkKPr776qmbuGd8IFejQqVJzwYfN7mrmBptoR5q0Cj7XmONpR+DbSZMmifcd+dZhTFEBRhGMHfnkeZEnTJgganvNqLT//vuLM9Dpp58ujCV2pVHhnpBaYneKHSkqLqh169ZSZ1QWJEwD+GvWrJnYZIX7hSMS/cFhib74KvdwWfttCBgChoAhUN8I4DzG9ynK1AuTLY3QwncIJ9xnnnlGzMyqjQrfOBxw6XupTqB8Xy0XfLVHMrP9Jp0LPhOK6F/Z8sBHl/7fUWy8otT0vPQcL0QKifQTtX858o7Tbr6UoWoDarngs4+s5YLPjk2lzlgu+Eohnb0d7Mv5mEctirNfVTtnmqoN6JFHHinmWspkFjoixKSOco4t9Pps5Yq1P6Q8Pg0flRhGCQ3g0KFDs3Wn6OOWCz43ZPAw+XLBmwo+N4biGJSnSIPTUcwnhWACC2E+KUsA3XIwn9ouWyNDwBAwBAyBpocAzNe9996blnAWgwCmXiy+q004yOLzABX6HaUsi6Xzzz+/rMwn9Ro1HgFjQBuPodVgCBgChoAhYAjUJAIwj6jUSyWclcIRVEqtq7HXEVGGVNVs80XCgElF6DN48GB39dVXN7Zpuz4GBMwGtARQyY5E6KUw7bTTThISCWckgr/nItTraiOq5TiGRzsOR344JDIkYQrgx/Ak6xGqkXAwe62LLV75BLdHIrv11ltnGHH75WzfEDAEDAFDoD4RgPkkBF6pRHzsu+66y+EHQdjBatO6664r3z689mEsMSlA3YttKJpDYtTSZ76XSH41FGG1+23tN0TAGNCGmOQ9Qvag22+/vQHz17ZtW0cudlTnuRhQXhYchAiB4RMMKVmXFi9eLPnfWcGx2sNuxV/B8qIxGeAVmI0B/fTTT92QIUMklqmmMMNJqhjVhd832zcEDAFDwBBIHgIkJwlnoINRwxOeFJc+tW/f3vEde/DBBzO84BFmLFy40J188sl+8arto1bHvhDHXr7H/H3++edil4xj7p577pnX76FqnbeG0wgYA5qGorgdwi4RviJM66+/fgaTx8qTFwNVAF70EOEtUItEOTjxUvXt21dWnEcccYS79NJLXa9evSS7EtcSDor0mtTHXzYibigv4cCBA6XIoEGDhLHda6+9sl1ixw0BQ8AQMATqCAGc9khY4hPfoeuuu06khj4Dus0228j3grTOt956qzv66KPTl1HH448/XjMMqHYMIQzhBvkzSh4CxoCWecxQC6DyRsL5wAMPuOnTp7tWrVqJZJQV53nnnSdpPPHERCKJioDVqBJSUEInnXXWWe69996TXPT+RIC64aKLLpL8raT7zEbkzu3Zs2f69BZbbCF5630GdMyYMenA96gtMB2IKy8s98sqOrwST3cwITuWC766A8VzxDuAuUpSCQ1IXO9ZJTBhEYxWJsnaFJ6jpPefsc53D7wrmGv5RCa/K664Qhxz/OOqNeMa1Nxhwvu8nM9tnHnIw32P47cmniknJnH0M1edcY4B8xwY5aLvOJ9cpexcAwTIrY6kUqljx44OKaMSE9wLL7wgMT0J7IvEE4YQtThqg/vvv9+NHTtWi2dsYRbJbITdDfnbfUI9AqGmz0Wo5vEYxA4GlT3x3Fjh+gSziyoDYp8sTvkmNP/6YvepO876i+1PKeWTfg9J77+OWT08R3ovSdsidcLMKMljwFznBzZP2hjoXJ1vDDQzn39/JEbRed8/Trg+hCEIUbKF48vXnl9fvn3qKmd9+dqL63yS7yHOMSgEF2NAS3wqWwa53AntoBQOmQT4nCfg/Jw5cyQHPExpIRJAcrvDPCI5nTZtmjvnnHO0mazbW265JZ1CDYkpoTO40h+HAABAAElEQVSwR2WfNKEEvw/30Q9Mr3FAfeenrI2VcIL7tlzwJQBXxktYkSLBzWW6UcbmYqnKcsHHAmtRlTL3MadZHNCiYCtrYXwDEBrke5d5X0hRWUhWI74TJF6ZMmWKZNgLd3jTTTfNcI4Nny/2d7FxQIutP+7yFgc0N8J8bzS9ebaSxoBmQybPcVTWZCnKRkgdMdhGEol3PHac5IHPR6xYycB08MEHu8MOO8z17t1brs/mbKT1MREx4BCDvuaaa0o9qF8IV0FWC7JJGBkChoAhYAgkBwHUmAgTSKeMPwGmUquvvrrr3Lmzw2koF6Fu59uQiwFF3T5gwABJA92hQwf5bvHtOumkk9IqVL4hXbt2zdWUnTMEikbAGNCiISvsAlZHqDRQt6PWILwSxGTCyhVCKsiq0ydWn4SQQL3PdUg/sRPl+Nprr+0XzdiHwfVp1qxZYq+DWQD9eOqppxw2n0aGgCFgCBgCtY8A34EZM2aITwCCBL4X2GdiOoAABNpwww0lYgoCi2xE/nNSWPp209SDcAPiWzVy5EjZv/HGG2Ub/kebxoCGUbHfjUXAAtE3FsEs1xMvDS90VpIwoTgFYQuKoTcSSlKbIeFEDaH08ssvSw54nJDUMYmXfocddhCjcVT4hdKhhx4qan9y2sPEwtAyWRkZAoaAIWAI1DYCpDvdZZddJIg63wwkmJjP8A1AiAFDyh8xoo877jjHfB9l78ldEjVFGdZS7hqBCW3Y96MU9OyaXAhYLvhc6JThHCtNGE6VevpVoqZHlRInqQo+XxtqA5rN+Dzf9fnO14sNKBEOwra0+e69Vs7Xiw0o6sB8WVBqBfNwP2ASvvnmGzGRCZ9Lym/LBR/vSPHNYI7hu5HPi1h7gs8BGjKY1ahvzfXXXy8h/Xj2iiUclj4KPOCjPOOLrcsvbzagPhrV2Y9zDPjeWC746oxrulVe3qgJgQJxM5+0kdQPNX03MgQMAUOgKSGAUAJ7f1TehTKf4INkFCECH/woIqyfH5Yvqkz4GEwt3y7CAZab+Qy3Zb+bJgKmgm+a4253bQgYAoaAIVBjCNx2223C8GVTp+fqLjaeZNfD3j+KCC6PrSdOSflCUCG4wOsd0zGSqxgZAnEgkHgGlJeOgO/6Rwag5cuXy4owDsC0TkTXhFhqLKESIeQScUKj6NFHH017t4fPP/HEE7LqDR/X36i9ly1bJnlzi7Ef1ettawgYAoaAIVAZBJiviYCC2VSpxPfEDw8YrueSSy6R7xZh+mAyCbuHlg5nWMJqrbXWWsJwjho1Sr4bxKQ2MgTiQiDxXvC8rHh3Y4SNyoCXmNiZOPxwPN9Kr1RgCYdBOzgalUowzXfccYfUMXXqVDH0ZmJQIgg9KdPIoKROSXqOVS5pOvGSjIrHR/w3nJ9Q52BTdPXVV0v++mzmAFqvbQ0BQ8AQMAQqj8Arr7wiEVBoeaONNnL77befCCc++eSTdGf4DuDxTvadJUuWyHG+D0g1Eb5gd0de9Kg0z1oJoZvI9c63k4Qm2PTCuPLNJGYoyU7i+m5qH2xrCIBA4hlQHcazzz47zaTBcHXr1k28wNu0aSNFeCEJa7HeeuvpJRKeCGNvgvry8vHyfvzxx/Iya952LUydvKTEVfOJa1atWiWrRt+mE8nsZ599Ju1lc1qZPXu2GzFihLz02OfwR6pMjM9hLrOl+CJQPaoaYn1mo7lz58pEpdmaqA9p6iGHHJLtEjtuCBgChoAhUCUEnnvuOREWICRA8MDfTTfd5I444oi0FgwnUYQPfCeY//n+7Ljjju6tt96SaCecJ07oiy++mDdsEhJQ+x5UabCtWUGgbhjQ8HjCxMH4wayR95ZYmISyILsQcTU5T772zTbbTF5echsTrB1GFIYU+xfUIRC2M/PmzZOyrBqZGCDU8EgZeeEJh4GUkYxDBAzmWtriOLE4w3HaYIhpR1UceDAiyYRpJdwFKTRJx+nnbqdNVOn0nzppLxuddtppGWnOuH8yEfnEZMVqGYJ55i8b0+tfV8q+hg9BQp1kslzw1R09niMWg35Mw+r2qLjW9T2I6z0rrjellX7//fcTnwseG8tak/J9+OGHIiQhRB9pnJFwMv8jlVyxYoUMFlmoCKvHvD148GAJ2wcjimBF53feDeqq9WeMb0Gt9zHXG4KTGM9Rku8hzjFgcZTPka5uGFDU2TCVMIXPPvusQ/KJGgNGkO24ceNktchLC4PIqhHafffdJZAvDCaZjTDSZlBgRvnQ4QHIOSSKMGioysmrDqMKQ0sKTKSit99+u3vooYeEAZ0+fbpIMLfffnvpD8wizCQmAkrY2sD8ogLZZ599HOoXmGVsQWFcMSmIIvLDE2OUunMRTLHSokWLJDzHgQceqIdkS2w49ZLn3nhg/D5mFC7DD+qOs/4ydDFvFUm/h6T3Xwcoqc8R/WZSTmr/wZ95lnBkSb6HWmRA1TwKYYTagbLg9TVoDz/8sEhFYZ6x3WTe5jqy3KFK96nWx4f+1XoffTyz7Sf5HuIcg0JwqRsG9IMPPpAVLS8kaoX9999fnhdWihdccIHsYz+DzSZOP8qAknoMoAj2DnNJ+jHsJpFY8oKTAo1z1AtxHnr99ddFUqkq+c0331w8BpFs4jnIRKHZjwiPQT+wrfEJKSWZJ5Cw4mm49dZbi/rfL+Pvs6rFzgemN0zhXPBqagBjjp0oDLgym3qtH7JD44BaLnhFp+GWxQHPhP9BaFiqdo+wwLBc8NUdHxgfTHlymc9Ut4f5W28ZSOEwH4qyPc9/dfVLIIVmno5rriv1DvkWnHfeeWLShf0nhPDEtwF9/vnn3Zw5c0TiecwxxziyHPF9437uu+8++Z7wrUKIUWv3F8YlzhiU4bbi+G254HOjyveGxWouqhsGFMlm2FGHG8eW01f7Ygfqi4U1QwQMICEs8KDHux3m8OabbxaGECCVYNSI1QbpipV9n9unH9igan+YJKKySKCux5aTDxISBWxAo8pRP/T444+LjSp1Q6hcTjnlFHf55Zdn9FMHHUksTPANN9yQYfsqF9s/Q8AQMAQMgUYhwPcEjdg777wjWiaYPhb/MJC5UidHNUrmI5hHtG58T8aPHy8qeRxeMZciQx7q+eOPP17qhlnFVAytHWp3GFOIfYQmRoZArSNQNwxoNqD33ntvt3DhQtexY0d5mVF5h1XRXItkETsbQlig3saoG4kXKnrU8dhQ4mnIPqvSsDRT20e1vs022zi80JHColYnCDCTSZhOPfVUseOh/NKlS4VZ5vpsBLPJn1KPHj3c2LFjXYsWLdISXT2HOQAMK5LRpEoq9F5sawgYAoZALSGA9Ouaa65xEydOlG5hroVgAqEDmiaEFnxHsNNXbVu+/uOdjoYF065hw4ZJaCQVnmhoJSKvYF4F4wvxfcHhCKGDCkoQbPCtMjIEah2BumdAyaWO1yDSRSSfnTt3FrVFeGBgVFHN9+7dW15u7EGZOJByHnnkkQ51B1JSVO6ort99991wFenfeJ6PGDHCzZw5U4yUaTtqNYyUlVBRrHqZQPBULxchWcUmyHd+6t69uzDD5WrD6jEEDAFDoKkhgLc6Wq0ox06YQBhICE0aklAitCC9zEcwr8TfRJuHpk2Zz/B1ynzqcUwKlPlEk4ZQwsgQSAICTSYXPC80jB4Bd3MRq1heaFXNa1kmA/5YoRZK2OXkkmhqPRich+0z9VyltmoDiqonDgI7TAaYIJNKSMTpv9mAVm8E0SzwrlT7fSkVgXqwAW3KueBXrlzpkFRmYw6zPRcIGyZMmJDtdPo4jCRxPbH19E3F0gXy7CBAwSE2CWQ2oNUfpTjHgGfZcsF/O8aoofMxnxSFSQ0znxzn2mKYT64phPmkXFI/pvTdyBAwBAyBpoAATqA4ihbLfIIN2rAoM6wwbkhBCRIP86m2/OEyUb/5ZqG1Q/NlZAgkBYHEp+JMCtDWT0PAEDAEDIHkIoDTTzFMoX+naJgwyyokZiSCC7RFOCUVojHC3hMzMaSzvjOs377tGwK1iIAxoN6o4H1I2CICxPuE2oxYoBq03T9Xrn3aJre7BhMO10tAe/58wgAdL3f/OKt07oG/+fPni/0rk5+RIWAIGAKGQGkI4JSKU2cpanFtEUclbP4LITRxOMxOnjxZkpWgwYPRxCcBaSf7mALh6MS3iVB7GnWlkPqtjCFQCwgYA+qNAsblTBAwbz69+uqrEgYDBjEOIrMF3vUwktjwYP/jE0bteEESYF+JwPV9+vSR8B+cu/fee+UUQfaZjAgLQqxSYsMdffTR4mWv19rWEDAEDAFDoHAEiJKi4ffatWsnc68vnUQySngk/SOyCUwkzkpdunSRGNWER7rnnnsKbpQ6jzrqKInIQoQWmNFrr71WMvFdf/31Em+ajEk41hoZAklEoO694IsdFGx8yBzkhzvCO56sRT7hqER8NladGvQdo1smGbac12DTrHyj8shTH0wiIT2I2QmRwYkgz37oDhyDwkGFSQc6evRoMYiHwaS/6vGOLdC5554r9fEPxpn0nTCmFp4jDYvtGAKGgCFQEAJIP2FAibs5ZMgQmUux6URgADHfM49DeL1/9NFHEp8ZDRVOSwTvnzJlioT2K9bpFLU6ofb4U0JL58eh1uO2NQSShIAxoKHRIoMETCH5jmE6YSaRNhIPFFU8hEqbSYhzTCaUI988WZNYmSKxhDGFkSROHCoSylAWxtF3OoLhJROSEqE9dKXNMa7FJojYo0q0++mnn0pQYo7BAOMgRR75KCKfsK6Y/dUyMerUrAC1Div2QmyUotrId4wJGvVVKQb8+equ5HnLBV9JtBu2xXPEgo6FXhJJ34O43rNKYMLciNYlyfaGzOXF5ILnGwAhzWQhj1CCOM5K1AeziUqcTHjkccccijmZ+VrnXb4LaLpgSBtDzKV8J6g7qcS3IMnvAWPAuCf5HuIcA57NfCYrxoBGvL3kZkcKCtNIkF/SdeIFrx89zpF6E6N0ACZoMEwrREpQMlIQ9xO1CQxkOI+8plmjPJOget2zqkUSSr0QTCYqGzIy+aE1KIf9j/8BQLJJSIVstOWWW2akdKMcdagaidU0D0wxk3K2tqKO86LS37jqj2ozjmNJvgcbgzieiOLqVAY0ye8BqmHmmyTfA+9CMf1X5yPmdZx9ID7eflB4jp155plu6NCh7ArziYABE6tevXrJMcafdotpWy4M/WMeSvJcxO0kvf/1cA9xjkEhz7gxoKEXm58EpYep7NevnxiekzkJ20oltQUlBSZEvE8Mxskzv8EGG7h1111XjmfLIy8nQ/9YLTNxYde58847CzNI/ajSlUHVS5gMwysLmEckmNkI5jlcz6BBg9LFNQ5ooaGj0hcWuMNkXQ9xQJE0WxzQAgc9hmLEAeU59rUIMTQTW5UwPpYLPjZ4C6oYJrDYXPCHHHKIhEdatWpVOl0y87AfFJ65gTmYeMEQggvytMN8MuYQ8/YWW2zR6DlEVfBJNqlCYBLX90bAjvlfveSCj2sM4El04ZZtKIwBjUBmk002EfsaVrowkUwiPgPKJdhoYtujhJciqnefyWNyYBCUYPJQmyiDqsdphzawHYKJhbAh4jjHIBg4Vit4vpMpA1UwTKXaAfEyw/zq6lwu+vYfjB+Zm2BujQwBQ8AQqHcEMHdCTQ7DCEPI3LjxxhtLZqJcC/VsuBxwwAGiFcNB9YYbbnBk2EPoAPGbgNtbbbWVaL3kYPCPtJnMu2iwyIqEORbflqQuYPW+bGsIlAsBY0CzIIkanly/O+20UwN1CRJSPNVx/sGm56KLLpLsFaTq9ClbHnnSeirBUGJPNXLkSEcOXyUciXSC4xiTGCvs3t8avdMvJkPywZNqlFVMeCXDaptVGjamTL7bbrutVm9bQ8AQMATqDgEW4JdddplIKzGbghFVVTlScwQChx56qKQkDjt25gIDLdgll1wiQgYcPqmbeiGYTwghhS+oCM+3CCeYr40MAUPgfwgYA5rlSYDJJDQS9jth4hwrWkJk4OmOZ+S+++6bdujR8khFo/LI63m22IsiOT3rrLPSh5kYCZ+UiwYOHCiMK+GXsLXw88g//fTTwhAjMaV/SGvxgjcyBAwBQ6BeEcA5CBtMNE2+5on7RVWuNvI4hxIjme3+++9fEBz4A6CGv/POO4XxVOazoIu/LQQDqhqtYq6zsoZAvSLQZHLBxzGAqMGRgKoaPFsbTFb8IcEsN8G8arinxtStNqCWCz47ipYLPjs2lTpjueArhXT2dmoxFzzqbZhPP4JI9jv47gyhkU466aTvDuTYI+xep06dHM9gKTR16lR34oknlnJpg2vqxQa0GCl0AxCqfKBebEDjGgMWgZYLPsaHFFuefMwnzZeSR77QbpeD+Sy0LStnCBgChkCtIbBw4UIJiVQs88l9YE+PxqgQwo700UcfLaRoRhkEDzi1lov5zKjcfhgCCUbAMiElePCs64aAIWAINGUEsHPv379/yRCgQTrttNMkkHwhlbRt21bC2RXiTIR2DAEB5k9XXnllIdVbGUOgSSFgDGiRw43Dz0MPPdTgKgIO4xREeI84CBX+smXLHGlBCSPiE8HkydTBZOoTXvJLly51zzzzTNpg3j9v+4aAIWAIJBkB7DgbO+eiUs9nc+9jhEMncZ9HjBgh3vWoMImnTExQtGLECsX+H2cn0ierk5Jfh+0bAoaAc+aEVORTQGgNMlmQMhPDdCWM03EI4ljYG13LlLplgjz99NPdbrvtJllgyGBEYHrU/8QJJawI2ZDwdsdzn6xJhF7q27evIycxtksEwx83bpyEciq1H3adIWAIGAK1hMBdd92VXniT8a158+biKES8VZ9gCom/CUPInEiMTohQTdi0kvDjiCOO8C/JuQ+DydyLkyqZjwi3hI04Ek/m4j322CMjJF/OyuykIdBEETAGtISB13zxyoCiBiJjUrNmzTJqQyLJ6pwVM+oYiDSC7BN+ifLscz0S1A033DAycCvMI3neYSghPN6xRWIiRTI6duxYOU7dTKiEaoIhJlQTNk4QaqbnnntOgtzLAftnCBgChkDCESAcHoSHOmkw33vvPQmLN2rUqIw7u/baayUmqEokH3zwQfGMZ/6E0BSVQkQggZlVhraUOuwaQ6CpImAMaAkjT4xQVDZkSoKI/dauXTtZCWt1EyZMEFU90ki85ZBMYjeElPLjjz9277zzjgRFZtV+4YUXSmpPVuJ4cpIJySeYR0IqKZEvHgkn4Z/I8Q7B7KKe1z4xEfspPzt27ChB9f26sU3SFKIYyhMmJK68tkgk+NMJX+8laVvLBV/dEcP8xHLBV3cMyAWP9M+fk6rVI/VIJ1A8cTpZ8LNg94kEHJghMRdDbInR+dZbb6XV98x7cc19fl/KtY/QwnLBlwvN0uphDPimJem5Cd8p3+O4+o8XPBjlImNAc6GT5VzLli3Fsx0mj4Dx2F8SBxRVDMQkiIQS9RB2QcScg2GFuYQYFI7xMUXtc/HFFwvTSRDlu+++uwEDSh1K5KEnRzyBkZXwAkUtT7531PQQE7Pmeec3+1znE2okDcGAOp9YeYXkb/XrKGaf+42z/mL6UmpZPrpJvQcmyyT3X8csyffAO8D7n9RniDFAa4NKu5bugf4QdD5MaKlatWolWYk03BK2+jCpeKYzVzOnJu2Zor9J63N4bJLef+4n6fcQZ/8LmR+MAQ2/FQX+Jhg9OeFbtGgh6dfIZqREWA8m6TFjxsghVqqk9FRjdFJ4MvDEckOaQ2w5iFRuMKPZiImTYMvYcvq5sJF0knVp/PjxbvTo0ZJVKZwvntWInyaUNrArVdI4oOW2X9X6WWlZLnhFozpbngEkuEnOH83CiufYf/6rg2ZprbIISHoueOa8mTNniqNNaSiU7yrMlrBx1xztLLJxvlQiCQcmUFdccYVomaZPny4aKRhP1O4wpzCgMLBxzX3al3Ju6yUOaJIwD49fvcQBjWsM+N5YLvjwU1Om3zCgQ4cOddiD7rDDDhlAI+VAzXP44YdHtqaMIGpvVgmUV/rwww9dy0DCCoPqExPnI488InmHMXKHUNmjeschirqQpmqmjXXWWSed+YOyZAFhIjYyBAwBQ6BeEMDZB094AtGjgkdbNGnSJLfuuuu6AQMGyGJ81qxZwnwiAcWB9LjjjhP7eLQ+2Ioy12IKZWQIGAKVRcDCMJWIN8wc3ud4o6N+92nPPfeUiY7c8DgKsTpnEgwzlajAsR1VA3hsSbHLDJcj7BNq/ltuuUU8LLUtpKeokTQAMxJZTAKgzp07uwULFsg5HJ6wgfJzzWsdtjUEDAFDIKkIkA4ZCQ5e6CeffLI4ai5evFiknCNHjkzfFudVu4QTJ8wqi3UW5lzfvXv3dFnbMQQMgcogYCr4RuCMMxKhlwhO7BOhOHr27OmOPfZYkToihoZRjCKyYwwfPtyRpo0VudqJ+mVvu+02kXbiCa/EhEn+eLannnqqSGBxStJ2YIphbOkDUtZjjjlGnJb0etsaAoaAIZB0BJj/YChRqUOFOjn65Zg3fZv6pGNi/TcEkoKA5YKPcaSwgcDuEUlnPsKzvVTbPOzKkIKihg8Ttp2o/LFJzUVqA2q54LOjZLngs2NTqTPYgGL/aTaglUK8YTu1lgse7Q7CANUENexx7iOE0FM7/Nwla+dsvdiAqhNs7SBbeE/qxQY0rjGA/8mXCz43V1L4WFjJCARg+gphPrm0VOaTa5FwRjGfnCu0fcoaGQKGgCGQNATwfi+V+WTejPKeTxoG1l9DIIkImA1oEkfN+mwIGAKGgCEgIa2Ik1wqYUdPLnnfEbTUuuw6Q8AQKA4Bk4AGeGErqcFY8aLEgxKHnULiWBHyA9vNOIlg8ajIfcLLffPNN08fIqgyTlF40CvRN9RLODXhqf/DH/5QT9nWEDAEDIHEI4AHPJFAGkOYFBCnuZhUnI1pz641BAyB/yFgDGiAAzHtYM4InYTNJgHmsau88cYbc6rGsXHAiYi0l3HSk08+6T755JN0EytWrHBdu3ZNh1widNOQIUPcoEGD0gwo92G54NOQ2Y4hYAjUIQLlygVPPcaA1uEDYrdU0wiYCv7b4cHDnPRyhOfA65wMGgR8VyJ7CUGOYQRhPCFW3jimqAcmxwiK7JfhGA5GMLRKWh71D9mH2MJEar1aTrcEjCfTEX8wvDgVEXIEuv/++4URDduQai54Qo/cfPPN0ga54I0MAUPAEKgXBHROIxc84e+IOHLRRRc1uD1ywVMGwjGCZACEp1NveHLEGxkChkBlETAJaATeTGKE9xg8eLAwjuRuhzFt1qyZqOpRbZPTfdq0aWI7dPnll0v8TuLOwYCS9hJvXRjY5s2bu+OPP16CIxNAHkb20EMPlSxKBEyGgYX5xKsXqSX1rrHGGhG9cpLhg4weBMDXNJswo5MnT5YsSH780EJzwSNNhTDG509NESI70IiDMOD86YTfiKqqeqnlgq8q/PK+sWDzs91Ut0fFta6pOON6z4rrTWmlmVtqJRc86nPIcsGXNpbVvIpvQZLfA77lfNOSfA9xjgECNTDKRcaAZkFniy22EOkkjOQrr7wicTR1BX3KKac4gsYjmUQCOXbsWLHRZBU9b948scVkywobBjQXIVXVIPX9+vVzzz//fNasHI899piYBJBeTok0nEq+IT39ViaV81G54DmGLSmEHSsf9ULsXuWCEv7RvzjrL6FLRV8Ck5/UewB/Jsyk9l8Hy8ZAkajOFnMl7ORr4TnSRbflgq/Os9CYVpP8Hut9J/0e4ux/IfODMaD6JIW2qMYhJIyk1Fy2bJmke2P1H843TDnCHe20006uR48ebpdddnGkiAsHqKdcmCirkyh5jZHuZCNywZP5oxBCiuuvPliNcC8+DRw4MP1T44DGlReWlRYSXp8pTjeekB2k1UiJs0moa/02eAYsF3x1R4kFQD3kgr/jjjtqIsQbdvufffaZ5YKv7mNdUuuahaqki2vgonqJAxrXN5/vDXxILjIGNAs6qKZZVTM42A998MEHDjvRgw46SH5HXTZ69Gj30Ucfuaeeespdf/31kh6uT58+UlSZwTCD6TNkyohG1Y1dKRNtly5dok43OGa54BtAYgcMAUOgBhFgbiWNMOp0FqmkOW7fvr0Eh8+XQIOFPhqkxuaC33vvvWsQGeuSIVDfCBgDGhpf1NBIOW+44QbXq1cvOfvaa6851O677babIwMFIY/22muvdHYhpHtIEEmjyUSIoxATJ/mHIZhM7Dw32GADkaTKwSL/0eaWW25ZcCglzQXPlkDLZAshz7yRIWAIGAK1gABmSuedd57YwSMZRjqPmQjh5NDWoOq/4IILJNUwmocoQiP08MMPp3PBcw3zMZQrF7xfjkwwRx55ZFT1dswQMARiRMAY0G/BZbIaNWqUMI7YbaJ2P/roo+UsjOhNN93k5s6dK+ryjh07ihoe8fK2227rDjvsMDdjxgyHPSbe6ahoycyhE+AJJ5zgkI7ihIRtadhjvZDxRbLaqlWrQopKGcsFXzBUVtAQMAQqiADmTccee6x7/PHHJUJIuGnmTs1shPPnhAkTZOG+5pprhosK4zh8+PB0JBJlPhsUDB3wyxHxxHLBhwCyn4ZABRCwXPAFgswKHUmirzLXS5ksWbVDrOCx8QozmUx4SFcrncMaySzShHyqLLUBtVzwOqoNt9iAMv5mA9oQm0odsVzwlUI6ezuNyQXPPIqtOyHslMnM3tL/zmCaxLyKFqmll2hDr8P5Ey945t1SCIdShApJIssFX/3Rqhcb0Grmgrc4oAU+x3h0RTGfXK7MJ/tMlmHmk+OofCrNfNIuzlH5mE/KGRkChoAhEDcCxx13nEQHKZT5pD8wn8yraJii8rbjyElIvGKJyB+LFy9OHPNZ7H1aeUOgVhEwBrRWR8b6ZQgYAoZAHSFAiLmHHnooa8KNXLcKE0qMZZw7owgzKJhJnC99gUBUWc5Tjr4QvN7IEDAEqoNAYm1AcfFnAomiFi1aiBdl1LlSj6E+LyXnO6qSl19+WWydWKlvsskmpXbBrjMEDAFDILEIkECjVDU5N42T0jXXXCOZ36KckmAmly9fLnGZkYgiNcXelL8f/ehH8sex3r17S5rirbbaKrFYWscNgXpAILEMKGGN3nnnHRkDbJLeeOMNp6E0wvEuGztQ2AjhaRmV4i1X3QSOx5t+1113ddhZXHjhhW677baTvO25rrNzhoAhYAjUEwIwjy+++KLcEiZKZIMj1B0MoxJmToRVwtlowYIFYjPfqVMniad89913i/odSSgRPXCyjCLsSwmbB6OKbSgxmxEC4ADK4n/nnXcWlb6mQ46qw44ZAoZAZRBILAOKNJJ0cBATDQbB+tuHjomGFTATEIQkE+mp70iCQTyTIqtjnIVQ9TCRYTuJ0Twe6Eyg/Ol1BNHlj9BKUavxt99+W1biv/nNbxwSWahnz56SlnOfffZxHTp0kGP8W7VqlTCoWjexQmmbTEqk/1QbzvC9cB/YUsFwUwfZSbQO6oVJx3GGe6CfWg/njAwBQ8AQqBQCr776qmiQsOGEQZw6daqEWDr//PMdqY4h7EOJvYww4eqrr5b0xUQVIej9jTfeKJJLJKio8rMxoHo/MLOEzTMyBAyB2kUgsQxoPkhh3sib/vXXX4vHJSGMiIPJ5MbqmEkNIsA7jOudd94poZaefvppWYHD0I4ZM0b2yUBEPZQhxiehQZC6wrSuXLlSfocnu+eee04kssp80hZOTDNnzkw7Iy1ZssTBoLLiR5rbv39/d8QRR0g+eCZljmF4z8QcdS/EBiXfPAwmzkbvv/++hH7afvvtxWM0Kn+970hFna+//jpdEyYW5hXc4iAkF/STxUCSiQ8owbKTSDoGfgiapN0H98BCsBgnllq7RxaGcb1nlbhX4iSfc845smAvtD0W6yz0WeSjDn/yySdlsUw85SlTpkg1CxcuFFxgQk899VSZm84++2zXrVs3uZZCzCEs7huLH4v3xtYhna7SP54h3oEkv8tJHwP6n/R3Oc4xUHxyvSJ1y4ASt5M0bTBoADF48GDJ377DDjsIE6SB3R955BG3//77OxhPsnEQzxMj9Tlz5kjcT+yWiFlHKs6+ffsKkwejdttttwmuMJTUEWZAqZ8YoWFST3iYGGKDXnnllaKWh6El2D2TLcSDPX/+fFEXEZ806l6QvBLsHnunTTfdVJhqmGUY0Gz565nwlcjypJJhPgpMaPlSZ+m1xW75cMA8xFV/sf0ppTyTPZKVpN4D+NsYlDLy5bsG/HkXkvoMgQQRPdZff/2icsHDeBJGiYWyLkJZSOh8SL3MgZgqTZ48Oa3NQtPVMgi95GtvwK6x+DG/NrYO+lwt4psGpkm+h3oYgyR/D3h24xwD5rp8VLcMKJJOsmhATF4YqGOTueOOO0rQYVbbbdq0cWzxrLz//vvFVlM9KLEnRdo5ZMiQDAwJWoxEcvbs2cL8IUFkggwTzB1S02yEhBOmT1XxMIKkoFM7KVLRMcHwl+1esKMiaD7MJ8T1MJ5QIfnrBwwYIGX5p3FA48oLC/NmueDTcFdlh48WH/2oMGFV6VAJjRIHFJMTn3EpoZqqXQLziRo5Kqh61TpVZMNodaZPn15ULnjMpJivkDrChEIsqlX9zm/MjQg6j7AAbU67du1Ewjd27FiZn5nH+aiRzKMx8xR1YM7UmDrobzWpXuKAJnkM6iUOaFxjwPcm3wKpbsMwEWbDV0+w6obbh8h6sWjRIjGAh/HDPhL7yXB5rmGF4xMe7aiFoO7duwsz6p/XfZhbGMcwIfXEwJ7+8THiTwn7VO2j70iV6158lbrWwxammhzJMKWkCG3btq1/2vYNAUPAEKgYAgR6V8knGZCuu+46d8ghh4hzJ3aeLMSx++RjeNlll4nJEbb4mBFx/KmnnhJNFvPd7rvvXrF+W0OGgCEQHwJ1KwFFgol0Uye+xUGMOE23hvqIVTz2l6ry7tKli9hn6soYr3fCdMCAIqmEOYSQeKLiPuaYY4R5RBIApx8mjORR46PSx/kISSYqfgzozzrrLJEC4JW5dOlS8fz84IMPHH8wri+88EJGdbnuJaOg9yNb/nqviO0aAoaAIVARBFCl4+H+4IMPipTTz8WOyh3q06dPg76gbWL+VeYVKShaLCNDwBBIPgJ1y4B27dpVVs0wf0gVO3fuLGppHbKDDjpIvNTVJhIp48EHHyyMJUbwqPguv/xyKd66dWspO2zYMJF+Ek7p9NNPF4kpYZUwqA8T9pnYdyKJJIc8RD3kLVYVVL9+/WS1P2nSJFGBjxgxQiSj4bqy3cubb74ZLpr+nS1/fbqA7RgChoAhUEEESPPLXIm5j69tytcFZT6ZN5kjYUiNDAFDIPkI1H0ueCY77DpZcfuEdJT4nmonqueYGHHGUSZRj6Mq55wGo8e+E3UQks18hIQUCaofIsm/RqWu/rGo/Wz3ElWWY/Q5W/768DVqA2q54MPIfPfbcsF/h0W19iwXfLWQ/65dnIVYYIfnyO9KRO9he4n9MXNNscQ8izYLjZHOwcXWoeXVBjSuHNjaTpzberEBTfIY1IsNaFxjAN9zxhlnuFtuuSXrq1C3ElC94/AkiTR0/PjxEswY26IwwaiGmVXKoIr3J75iHDlQG/GXjQo1Ag7fS7b69Dh9zmYjqmVsawgYAoZAJRBAxV5qCDOYRjLf+XNwJfpsbRgChkB8CGR62MTXTs3UjFcWnuw4AxEb1MgQMAQMAUMgXgSIznHPPfdE2ssX0jJaLLLKGRkChkD9IJBdLFc/99jgTggHooQdJZmHcCzyiXA1OA1hF5pNzY6jD2p1GNpy0aOPPuqwS42SmNIf+qmSUEwCMCPAY56Yo9pPUtVFBVnmWjz+jQwBQ8AQqCQCpDHGHKhUwiyKbEhkTtL5r9S67DpDwBCoDQSanAQ0DDvMJ9JQPxwSZQgVsjjwnFemLnwdv4krGvZYjypX6DHyHY8cOTIdism/jjAkl156qaT/5Di2cHjiw2zOmzdPwkGppz4Zm+gbmZT8v1Jsr/w+2L4hYAgYAsUiAPOI7abagJ5wwgnp+MdaF+ZCXYJIJMQvVjU7sZDxgvdjpkY5fGodtjUEDIFkIdDkGdBOnTqJzSfxPX0iVqeGaOI40kaCJkeFXCKwtH8cr01/tZ/rWurmWjzrcYyKIqSZZF7yJ2I865HOkhLv4osvFg97pKdKhJwixaj/R4gnI0PAEDAEKokAWia1qycPPOmPcf700xTDaBILFM0TtvnbbLONGzhwoKTdvPXWW6W7pPIkxbGRIWAI1AcCTVIF7w8dEk4YOdJpqhp+1apV7rPPPkun1yStZzhHvG8/im0SangkktDEiRPF854Ay/mupTyOUTvvvLMjNJSGheI4hNSAHPaDBg2Sifl/R5077bTTMqSzeOUXa+BPLvgVK1ZIlXi1Wi54RTf71nLBZ8emEmd4H2BSkKollXjfo0xkknI/5IInGUcu7ZB/LzCOxeaB//TTTyULHRoqkoQokeK4HNix6C9HPdqvSm95hngHEG4klZI+BvQ/6e9ynGOg+OR6Pps8Awo4SAt79+4toZJQ/xCE/oADDhA7TFTf2XLEK7AwjjChGpwe9TeMZyHXUgdt+napWi/bu+66S7IZKXOs5/xYeGR1YsLWQPuUIcg+eep9uv322zNsSwm/QCpPiD7AwOZLneXXV8w+Jg4wD3HVX0xfSi3LZI+qMKn3AP42BqWOfnmuA3/ehaQ+Q6CANHPDDTdskCUuG0KUJw88C1yN6ckiwk+nGs4Dz4IaO3iC1Puh4ThWDuxgHMpRT7Z7jvs4H3cWAEm+h3oYgyR/D3hG4xwD5rp8ZAxogBDpOFH5LFmyxJF1CGnouHHjBDvSae66664i0eQA58M54lV1xCTLqpocxzj75MovH+VkJA16/6gPU4BccbQeeOABybZEf/0JvUePHhJ836sug/nkeP/+/dOnNQ5ooSGh0hcWuAPzZrngCwQrpmJ8tPjwFxNCLKaulFyt5YIvGbqyXYjqfNq0aQU7A+EBTzKNL7/8Mn1NvjzwMK0k6JgyZYoIAOg8DFdj88BTDx/GQmMvU74WqV7igMb1vanEmNVLHNC4xoDvTb4FkjGg3z6pqOGRfGJnCUNKmkwI9c/777//bSknK3hW8ax8lJgYkYIi+WQlzz5UyLVaR9QWRyjsTtUWFQbulFNOkQxNpKMjDSjMMtJX+uwT0k29B/+47RsChoAhUEkE2rVrl3as1DzwzFcs5DFTIpIH6Yn5EJIHHhtR8sCzsOc8fyeddJLENCadp5EhYAjUBwLGgH47jrvvvrsEqMejXBk+TuGZiSpbV8x+jnj/EUBlj10UTOKQIUPkVKHX+vX4+zCb/Ckh1Rw7dqwY7xOUmckc6aiFJVGEbGsIGAK1hgDanv32288R5WPChAmiwlfbxVx54Am75BPSFLRRRoaAIVAfCBgD+u04ovJBvQ5j56fnzJUj3n8EWNHz16xZM4nLyblCr/XrKXQfr3ikrUhulbp37y6SBH7jYEQuep/wNMV5ycgQMAQMgUoiwHyEtgbbTmU+i2kf8yIihRDz2MgQMATqA4G6zwVfrmFi0sTrsBRpY2OuLVf/89WjNqC+wX++a4o5Dwb1YANKalOcKZJI9WIDCjPi2zsnaSxwQCJsmx9SLUn9p6+l5oJnYU+IpUKcE8KYtG7d2pH4oxwMqNqAxpUDO9z3OH7Xiw1oksegXmxA4xoDvjdNPhd8uV5+JKT8lUKNubaU9uwaQ8AQMARqDQHsOkslFrAk2igHA1pqH+w6Q8AQKC8C33nSlLdeq80QMAQMAUPAEBAESPRx3333lST9pAIkfmPGjDE0DQFDoI4QqAsGFE/xZcuWNRgWHIawOaoEaRrMbG2h4iaeaBQRbolwSvzNnz9f4ofmS5tJ1iNC6hgZAoaAIVDrCJALHtODUgnznZtvvrlRdZTatl1nCBgC8SBQFwwotkEEbA8TweCxV4qbrr/++kgG2G+XOJ1Tgph2UUQYkhkzZkje9tdff10kBUcffbTkT44qzzFCPhkDmg0dO24IGAK1ggC2888884x0hxi0jckFT3IPI0PAEKgPBOqCAS10KLAjCudzJ9UbcT2xT8JoViWmMK5RzCsp5ZBYUhZC8vnRRx8JM6hZPsL9Icc77eYijOzJ2z5s2DAJgn/++edLCk7tD1vaVDsqPEJxZNDzWjdMqUpjMbYnpWjUfWh52xoChoAhECcCJPNQ+3nLBR8n0la3IZAsBJpMGKZsOdlvvfVWYQ7feecdiVW3ePFiR+BkZUDJzU6QZIgYdoRpIosHHnDXXHONBKknNzKMIGktO3bsmPEEUA9xRMnlTsD4Qol2kawi3e3cubMEbSbfPLmQyQ0/YsQIaZ96CclECCgYTlKK4mlKEHzikcK0kr+cawmF4udvHj16dEYueDz848qPTN/wAM7GpBeKS7XLWS746o4AzxGLLMsFX71xePfdd93gwYMz5pJcvWFutFzwuRAq/hwpFC0XfPG4lfMKvq2MQ1zfzHL2NVtd3ENc/Vd8srXN8bphQF944QVhIP2bxW4IypeTnYcI20s+bjCgLVu2dCNHjnS//e1v3bHHHiuhBJhEsbtE1U8edspjVH/mmWdKukzicYaZTxiuyy+/XCZrcq0XS1tuuaVkBdHrCJYP06tMJFKFffbZR/p1/PHHC7OKiqtVq1aSBnTzzTd35513nrwkSFZR78NcK8GkwkxD3BPS4Hyps/TaYrdgy19c9Rfbn1LKI0FPcu5fG4NSRr381yQ9FzxzGek4/WxwuVAifSpaIssFnwul4s7xcec7kOT5NM485MWhWVppxiDJ3wPuOs4x4HuTj+qGAYX5CwdeP+qoo+T+8+Vzb9++vbzMythpto31119fmCZWmk8//bTkUldPTI69+eabwpxmA3nWrFkOJpKUcitXrsxWLOvxcNgR6tE+6kUwvkgyYUAJ9KxpQNXhCQYYIpMTzLXPgPpB6TUOaFx5YeslDuhPf/pTiwOqD18VtpYLvgqgh5okxS/27IXGRLZc8CEAy/CzXuKAxvW9KQPEeauolzigcY0BDHq+BVLdMKDc6Oqrr57x0Cizli8nezi2nD+xah1w80gLDz/88Iw2cv1AXc9DquFHYCgPO+wwkU7muo5zSG9RdfXp0yddNNxPTrRp00ZWYTDDS5YscQMGDEiXJ188zLWSf196zLaGgCFgCMSJAKZJqo2yXPBxIm11GwLJQqBuGNBcsDc2Jzt177nnnm7ixIkOqSiZAwjxBMOH9BH1tTr++P2444470j+RgGK7OXXq1PSxqB1E4jCt2KZuvPHGbtttt40qlnEMKShe9khIlckkrejzzz/v8KYnFzNhUPbYYw+H9MLIEDAEDIG4EXj22WfFqZIYoGr73Zhc8Kg7d9ttt7i7bfUbAoZAhRBoEgxoOXKy43Hes2dPsQmFMUTiil0lhJRx7NixwoR269atpKFDxQ+DiMSVtpBewrAWQl27dpUYeaecckq6OAzok08+6TBDoL5NN93U7bvvvunztmMIGAKGQFwIYEOPxzu282FbMMxxiiVSr15yySUNtFzF1mPlDQFDoHYQaFK54Jn4sN1UKWEpw4BdA+qkcB1IQHEKKtQwv5S2S7kGj2EkoPmcoNQG1HLBZ0cZpzTLBZ8dn0qcwQbUcsFXAunsbRDZA1vo8ByoV1x33XWOMHGqdtfjpW5Z7G+11Vai0YkyQyq2XhhibOLjyoFdbH9KKV8vNqBJHoN6sQGNawzglSwXvPd2lyMnO8xc1MSbj8HzulHRXTxPjQwBQ8AQqAQCt9xyizv77LPL1pTOty+++GLeRXTZGrWKDAFDoCIINKlA9BVB1BoxBAwBQ6AJIkCsz4svvrhsd462gegmb7/9tjGfZUPVKjIEageBmmNAidmJ93iYyOhDrnRUJ+Ug4oZSZ6GEEb3ma2c7b948Sb+p/UEFz/E//elPGVUyeUblqSdMUlS+d+w2NdsRFVGGev1sRrRJTFIjQ8AQMARqBYEbb7yx0Wp3bOBR76NynzRpkqTwJIqJkSFgCNQfAjXHgE6bNk3ieb7//vsZaN95552OGJw+c5ZRoMgf9957b1GxObFnov1Vq1ZJH9iSv/2kk06SIMuo4MmmhFe8Gt1jpzN06FBHcHifYLIvvfTSBswqZciZTOpOJX6TEQmveyW878kFb2QIGAKGQK0gMHfuXGFAMXXq3r27wzkyinCG1PiAZHk77rjj5I/rYEBxpiSsXI8ePdLlouqxY4aAIZBsBGqOAQXOrbfe2i1atCiNLKGJsAFq1qxZ+hg7MIUwqn4qqW+++UakpEgJ9Y/MI0owjjgihSmqrnAZfjM59u/f3w0cONCx4m/btq0EgOccBrdIKsmWhAEuTGavXr1kNc95iL6SOhPP9CjafvvtJaORnnvuuefEk92Xoi5fvly85LUMUlfSgZLJKEzcL8e5v6hQUeHy9tsQMAQMgWIRYI5WocEFF1wgjCSMJiHifOrXr5+bPXt2mrEkzTFzIn8s3JmrCdtkZAgYAvWPQE2GYSK9JMHbmaygl156STL4kBddCbX09OnTJe0kq2VCGJF2klSVqgYnBAhqdurCA5686TgQffnllxK7M19dej7blkkX5o5AyxBS0OHDh0vueBhCmEzicCoxwRJaiX6Qrz2KOnXqJPfARIw0gMkYqTCpP1HH038Y0N5BzneYXEKTwPQiZSXWKL+JlQcGer9466299tryMfAD6Y8aNSrN7KL2og6fmY/qX6nH9OOi8QBLrafa11ku+OqOAM9RknPB03/mjbjes0qMDgkyYBx90ggjHCM5BmGYCC2HTeiDDz6YLkokiQULFshvHIxQr5N5Dq0P8xnEnB03PrQVdxtyMzH94xlCkFJKSKuYulR0tUkfA/qf9Hc5zjFQfHI9GDXJgLYMcrGjjoGJa926tSN7BqtpZUCZxLHhRDW9wQYbSM52YnQOGTLEXXbZZXK/MGtkBWIChGlDNU4udSSX/5+9+4y2par2BF4f/dKj7RZBsBUkRxWQLOG1PCSnh0i6EiUIDUiWS5BwCZIEFC5JcnyABIkSrmQQCQIt0IiA4hg9Wt54443u0f2x72/51qZO3R3P2XXOrn3mHOOcvXeFVav+VbVqrpn+FNOdd945bdetrXYllVg1KYbAFQ6AarPsalpxxRUTHSYr6F133TUBe8vUEGXl7CRKIiyxxBLJmuDm/uIXv5hK/ygy75zFRumXdlgcZLmzqJJbbrklWWMpoJdeemlSyllrvbDz+ZaPy6KM05kopm+77BorbzeM73D2V1f7w+hjrzYM9rBv6jnENeh1hadnfdOfAxNtdYWNg1lMLDPdcK4IYjxRMqssJtLGamLSe80116TJtXhPIUwmy56vup8xY2vdxyif97C/e//Av8nnMA7XoMnvA/dkndfAONdLRlIB1WmF1CXqUJDeeOON4rjjjmudiwfPbwk7d9xxR6KsdLIUBIOfwZAb6Nvf/nbxD//wD2k/VlIWUsLSx3VOerWVNir9Q6XpppME5HjaLNemM6sWn7nssssmy+VRRx2V9v7Tn/6UZv7KlPQSCqpzpkQrSE/WW2+9ZPl0bnnZcsstl2KnuLRYJd58881imYXKO3nrrbdSDJXvlNS8j99Zggs+I9HfJ+uNl2ZTS1t5aVEKqjHJ/Z39aGwVXPAzfx2wqV177bUTytF5kbmvstXDGIm6+MMPP+zYYZNe63lrjHe2p4AyKtTFT60z3hXCs+o8RseTHtKKcakD2uRrMC51QOu6BsaCXhOkkYwB9YxmBRSd5DrrrDPhRLge9t9//2QBpJhxe2cxuJx99tnJcjhnzpy8OFlUy+5f7h/Sra3WzqUvXOTrr79+cnVTdllWs3Cbs8CKe+Jqp6Q+++yzaTUrrsEWU9IWW2xReJGKJ3V+VaGAUiatcyxCgaREv/766wkPy7jnc809Qf+C+bNwuYuHzcJ1HBIIBAKBQB0IeNFghCOsmjwwxmH0xdzsPEdVEQr1ve99L21nPH711VeTAWHrrbeubhq/A4FAYAwRGFkFlIv5M5/5TJppVykkzTy40Q899NBigw02SAOXa2MWjkPdjDpbO/M1W3PNNZMrn5LISimOknRrK+/b6VOGOyUwZ6hfd911yfq63377JTc66ydF1EBL2aSEinXyJ26U26mdZdJAzp3F4salT2xPgdZvSjChpFJWd9tttxQKgHvZrIOIiVXOSowq1xbFNSQQCAQCgboQ4FoX7mR8EyMq7Ec8pzFWTGgWhgFjGWMBQwLldO7cuWk1b9K2226bN43PQCAQGGMERtYFD3PJSMolZXd5vg6U00033TQNXmKMuJ25bbidlUYy4zYYGuCIxBzuZvRwstItX2GFFdK6Tm395S9/aSl/acM2/1gZxZSa7Rs4lSEx+8/WVbGhlMKzzjorccWXY6baNNdaxM0rFlSMZnkfRZnffvvtlutLiIFzoogLB6BkC0sg22+/fVJiDfaUXLGquV+tA8WXQCAQCASGhIB4c1S+QofyRLifpo1dhBXVmLnaaqv1s1tsEwgEAg1HoNFc8Kx7Bq0c9N7PtTA4iuETo1SWybRV3n8mv7MGYw0pK6vc92K1coY+BXzfffdtufSr/YWLrPjggq8i8+lvFunggv8Uj5n4FlzwM4H6xGN244KX8S4caLIl3z766KOUYDnxiMP9xQAhBrQuDuzh9rZ9a+MSA9rkazAuMaB1XQOT0LHmgmcpHFS4iNrJZNpq185MLGuXVOIFcMIJJ6R4U9UEKOospCGBQCAQCNSFgDj3ySqfJncUQ16pkEAgEBh/BEbaBT/+8Nd3hhhGhCYI7Jf0RPkcxFJcX8+i5UAgEBhHBHDBC3earPDCiB1V/SQkEAgExh+BWa2AcrsrmcRVrc5mWdTcNKCWa3yW1w/yXSC+mqGy+fsV2fOSl7Kw3FIo1d8j1pfLSVlmAF+wYEGx5ZZbpqx/lgR/+Og/+OCDVCDadiGBQCAQCAwbgalywXONq/Usbl5yaUggEAiMNwITAyHH+1wXOTsKJn53f2WRTS8ecv78+eXFk/6ufBI2pkHk9ttvT0omxdUfq4CEp7vvvjs1w5qpFqqYOGLwVhLKb0X8szhH2xnUQwKBQCAQqAuBYXDBKx2nWH1IIBAIjD8Cs1oBdXnFfkreyTzGlnFbVwNzKaUy4wXJ5wxPn5lvPn+yQmZRDqod3Vu7tvI+5U9F9GWW+lPGRFknhfeJ8kvqjVKUlZa69dZbC7U+lYAqi6Si6rmU18f3QCAQCASmioAxKI+hU+GCN6Y+9dRTU+1O7B8IBAINQGBWu+Dz9VH0/oknnigwCxH1Ornes7URi5HYJmWRuMUF2as3Suk8//zz0z6UWNspIaL2pz9ufAquv8UWWyxt16ktAfi9hHKbs9pte+CBB6byUvrw3HPPpaLPko2y3H///YntA8NIO6HUZnpTlQG49NspzO32HXQZC62XVJkMYNA2RmH74IKf2avgPsLmhECiiaL/JqB1PWfTgQnWtcMPP3zCoSiO+ZoEF/wEaGr54R6Cdy5hVctBam7UPdPk50D/m/4s13kNMj7dbqOBFND/+3//b3HvvfemwuYKq3MNq01ZLWnU7YCjuE69UbP273//+8m6qWj7jjvu2FJAWUQVe88Fkp377373u0TzqQYoefnll1MJo2OOOSa5zil2KDIphNrO0q2tvE3+ZO0U0+mlxZoK5/LAz9XOKorz/sgjj0z1T/O+rLUU6MsuuywV88/Ly59LLrlksppapi0v9rpqhVI+SV3tp8Zr/mewdz2beg6ugXupqf13eV0Dz0FTzwH+Tb8Gwn+WX375CeO+SfkwueDrvL7wpzjUeYyah6L0nmL0aPI5jMM1aPJY5B6d6WvQtwJqcNlqq61SFq8qqgAAQABJREFUjKEXGUpJVkE1KCk6Zctc3Q/fsNvHN4+XWLki9dUwDZUtiZTRF154IVkYbUO5K5caef/994t58+YlBZSlU/F8LvIciymgnjWU9GqrfG6SlvRFaRPKrIL2rAtlueWWW9Iyyqrrw9pq5qE/lOEyT315P98p3FlYV7nzP/vZz+ZFQ/2kOJjA9GPpHeqBh9iY/sMzuOCHCOqATbH6uAYIKJooxk5xjnU9Z9OBiaRNhBvlknZeZMPkgq8THwoo71Wdx6j7OoxLHdAmX4NxqQNa1zWgh5T1qHbPRN8xoCjTKFJu/FynDfWkGYD4w6ZL5p7nfq9Sf1588cXpHJ03C2SZmYkLASUnZS+zK1E8y67m8iy1W1tVDGW8YzH6zne+k6g80Yu66bPceeedieru5z//ebomF110UVol492EAU+8EkySA2644YZWuEDePz4DgUAgECgj4KVhssyzg2VNLWHfeXys6yReNMEF3wmdWB4IBALtEOjLAqpcEWad66+/foIFa4kllkh128RDUnaaLNzwFDwD6Ve/+tVWQL1z4k7ndt9oo42SAo4OU4IQXOyD9tO6LEo6nXbaacnSwSLJjZ5rcHZqK+/b6ZN1UzviPc8999xUWokVgotd29z8e++9dyorRYG2bRbb6Mc+++yTF8VnIBAIBAItBHh0GBROPfXU5N1h7c9ens985jOFP2OIuHEUwO1EmNIbb7yRuODF1LOKsjaSKhd83p9hw4Q9xzKybudQp7xNfAYCgcB4ItCXAsqCx9IpRrAq+Neta7p88YtfTAMs5bFMaem88MezMrIkWifulRv+oYceKrjfLecKJwbQm2++udhpp52K733ve0k5zLU7re/UlnW95Oijjy722muvdFyTAUlIuW289Cyx3PQstE0OieiFQ6wPBAKB4SHAq2KC++6776ZawtWWhT34U9KNoWHFFVcsHn744QkuePug+zU5Fs7TzVpabT8rn94zPDbBBV9FKH4HAuOJQN9c8GalFFBlfcx0uXTFJhqQuGmabgHtdXnFbsmAHiSGkRueFYBSWpbJtFXev47vOQY0uOA7oxtc8J2xma41wQU/XKR5ccQ08/wYq/oRBgdjmMTI//Sf/tOEXXDB77777m0V2QkbdvgRXPAdgKksHpcY0CaXCByXGNC6roFJaC8u+L5Nl9zsBisxiUoJcUHvueeehdjJcmZ25TkZm58G3UGUTycusamqfFo+mbbsFxIIBAKBwLAQMEHedNNN03jUr/Lp2JRP1kqJqFVLp9rE4twHFS5+3qScXzDo/rF9IBAINA+BvlzwTmuppZZKmeBPP/10IQaScsVdXaWwbB4E0eNAIBAIBGYfAsKG0PRSKAcViqd4TxU/JEmWZd99903hTJjbeI3KCZnl7XxXzUD4EMtpDieqbhO/A4FAYDwR6FsBdfoGEjGQeaBggv71r39dSEaSuNM0efHFF4v/+T//54Ruc0WZxU+HwG7DDTecUNZH4P8jjzzSOjz32LLLLtvC3ApxtyYAOeu+tXF8CQQCgUCgDwQonT/+8Y8n7Sp3COWkZMrvsssui8TNS8xcf/31EyEHWmFeH7GeLK2SjrxHlHESuiXBs6lltfqAOjYJBAKBDgj0rYByq6j7KVawKmbAmSKyum6Uf991110prjUr1Pqaa3dOR78fe+yxVLqkXFeSxQA3/fbbb5+6IPAf9ptttlkqOm8hbnn7hAI6HVcpjhEIjB8CvFjGlizGPWMOpdLEuCzCrSQ1UiQprqpu7LDDDomy2AReSJZJclXUV7788stTAufzzz+fakiLG0WAYftVVlllEcW12kb8DgQCgfFFoC8F1KBDKTr77LNTZndZYQKN2WxTBeWm4vDthDXy448/ThbefM5cT5b7VGLEzL38W1FXs3wJK3ATupDrgCrarx0B5JazHnSa+bMM5P0kXhx66KEp+54iKiEM5vqQC3P/+c9/TkxIuZ/5fBRcZrlmpQ4JBAKBQAAC6nqW4z6VcTOxVcKNd4VLnBhrkGog4Jg7d256B3DdK6vEIKH+MRa4dgpoamDhP9bPcpm6vDw+A4FAYHYj0JcCSoFRcJ2iVma/GGfoDNBqeRpYxUkddthhyTVvkGaRZD2gIFIiL7nkktZvSvopp5yyCG+8BCZlmfDNsz7giucCs28nrvaML+uDZIGnnnqqoICq/6k95aAuvPDCpOi6Ln/84x/Ti8ELw/VSkonSq6/OwzGFGGTxEkE7StT4k9FaFzcvZZ1C3i0eLPdrlD+DC35mr477KLjgp34NJPxIKs3Sib99yy23TIqmiaxyc2uuuWYaZySfGk8sp5zWNW7k/g3z0z1E+W5Sn6vnr/+MD7mEVXV9E34zoDT5Guh/0++jOq9BxqfbvdiXAipDUcY7ph1KC2VlXOTGG28s7rvvvtbpqK0pdslyiiRljtuIArr11lun7QzeQg4Ez7/55pspezP/vueeezryxtv5m9/8ZnHBBRcMbDX2gvACqMr/+B//I5XEEkag/qhz0Wd1Sym2FFQ3Aq54SrUqBlnKXPCsHpSrbHXN2wzrk/JJ6mp/WP3s1o7BngLf1HNwDbx8m9p/18Y1YFFr6jnAfxSugeddvc1lllkmTYgzUQblvuyVMcYZF7KY+BofJRhRQHOt4yZdD/h7FprU54x//jSm84I1+Rwob03uv2vQ5LHIvTTT16AvBVRHKWMGLKw6Eo4An8Vs+Mwzz8w/G/Vphi/GKQtXtVk9xiLFlnNCkNhXyT+E+/zzn/983mXC715c7+0K3bca6vKFm79dSSfW0RzDqoTJq6++mlrRV2414iFnQRVzWlZAgws+wdP3v+CC7xuq2jbMISdlJam2g9XQMMVnFLjgxWeq/8frYVzxIjKmm7Sq75xFaA+SDsQbxiA0nb/5zW/SxPu1114r1lhjjaTE1sUnnfsxzE8KqDG+SX2unv+41AFt8jUYlzqgdV0DCnrZ41q9h/3uWwEVoM5ihoatWny4zI3e7iCjvIwiufzyy0/oosGJ0qbOXZ6hUSwNxAolVxXB8m/ueRZSmfQsppmfPR+gvG1e1s+nkifVftqvU23SxRZbbIJ7huvbSyYkEAgEZi8CxiZJRkKJnnvuuaRMQkNYD853k2veHp4a491VV12VXPAspldffXVSno0lDA4mvEJ4bBsSCAQCgcCgCPSlgHLBsAgatGQujrtQsFl7Jf9QuMWpHHHEESles9e5T5brvVO7rCVqr7I6/OIXv+i02SLLWaUfffTRFCfqhbFgITc8ur2QQCAQmH0I8OZIGGLRNBGt0iobK8r87ZIvs1T52s8444yUqGRcEYceCY4ZqfgMBAKBQRDoSwHl7uKy4YKcLbLffvulJCEZn9xm6tqJhyoPzO2wmArXe7k9CiRXkax25ZbOP//8nslK5f1l90ta0m8vnI033rhjtn95v/geCAQC44OAsUudTQXjy2WX2p0hl1knqSa7UD7/43/8jymevdM+sTwQCAQCgW4I9M0Ff/311yeFzCxazGHZlbz44osni2G3AzV1HXd8NeSg17kY9FmNO7nHe+0/zPViVyWR9apvGlzwvVEPLvjeGNW9RXDBD4awmG+JknUYD3bbbbfi1ltvHaxDI7B1jgGtiwN7Ok5xXGJAm3wNxiUGtK5rYELbiwu+LwuoB4oLWkkf8UFVaWoh+up5tPs9qPKpDcH8o6B86stsKZvlXEMCgUDgUwTUElajk8I1TJExz+3OOxQSCAQCgcBkEehbAcV40Wkg65XpNNnOxX6BQCAQCAQCgyMg2UjMeKcxe/AW/76HibXwHslJTSYgmez5x36BQCAwPAT6VkDNerlxPvnkk1RXUhfEFrKKStLZYosthterNi0pAZJLIlnNpSwr/2tf+9qUBkLtOrdcekm5oqlKN475yfC4c6Fxo1ez4Afps/IpMlxltoYEAoHAeCMgUaiaaDSZM1bz2dgj5tNYKxZdCFauDjKZNmOfQCAQCAQg0LcCetNNNxUHH3xw20FNPcm6FVAxlZkj3cxbUPwNN9yQlCrLy3VJ+720YhQUUsZxTJmTtDMMBbQbx7xKAjmxqN9+Pv7444WySu0U0H77LAlBYlMooP2iHtsFAs1FAJVmuexalc+9fGZqeTIs/PWvf02x/GoVE2OLKiASGZFb5HqBvGEhgUAgEAhMFYG+FdBjjz222GWXXYo5c+YUO+20U6ol99JLLyVuYPST0yVljnRUcmp1YgPCFEQopgZSilaepbPSytjMA2cuGyIrVGIJ5fPLX/5yYgvK52Hwbsfnbr11suEdo1P4QSeO+czjrh39KnPDW6ZP+qPGXu6/5cRy55yVyHKfLbc9a7SksLxv5oj/ewvxPxAIBMYdAc98OaO9yufOOprlW9/6VjF//vxi7733TuOmpAHKK4tnpjzFDx8SCAQCgcCwEehLAaUoyT5VfBhThuLtZsMGKy5snOPoJWdCKICUOIJ+Us1MfZOhxjKKA32fffZJjB0UUH+sAZKqWFDFSM2bNy9ZQhWNv/baa4s//elPqfgyRY5lwDleeeWVKbFInU/870ojaevwww9P1HT9nnvmcWd5rXLDKwz94IMPJoz1H6aUTPLkk08mmk3nqxyUc8P9ziWmz/qHweTdd99N1mgTBQljkpCcg0LSVcF1X+aClw1XFzcvnFUH8GJrsrDE15FRPB2Y5GtQLakzHcce1jGcQ1aMhtXmdLaj/yawdT1nzuWdd96ZcEpVPvfyShNVY4d+EeM7pru33347TXh5TrJFNO+33HLLJUY842ITZTquQd24uIeCC75ulLu3b5JX97PcvQdTX+sc6hqLMj7detmXAioOSMylWCCy8sorF88++2yyOq633notysduBxrWOlznlDD87M8//3zqg0GTu4iSJlRAP3Gz404//vjj06FxHuOxZ9Xcfffdk/J86KGHFvfee29S4jLNpo3RWSoxwnJA1NHDo05xPfnkk4uTTjopKZ1/+MMfCu523MhV6cQxX90uc8OzfGIoufPOO5Oy+Ktf/arAK0/BJZQ3XO9CDSy7//77i1VXXXVCcx4G+xlgTQq0jbPZBGLnnXeesK0fcMvKFAVV0ftsOV1k4yku0H9SV/tT7F5fu1Pc3HtNPQfXwL3R1P67SK6BZ6Cp5wD/uq+BmHaTcs+/57vK516+2cXVIxfJCUXGV+PmCSeckBRQ42J1nBF73+Rr4Pw9C029h/Tfyz244CExc+IaNP05oDPM5HPQlwJK+RSAfuqppxbnnHNOmhEbpFjZWOwyF/l03AqyO110SiYFUYwSMVBuuOGGLSVZvCML49FHH53WW0cMnl4AZo+UiXbSic+dxZOre+211067Gbgpo+2kHcd8u+0yNzzLrRuBZZPon3hRVmbiZZJvlA022CCtq74YckKWgcm+wiaI8IN2dKkYTrLkOqA5zisvH9YnxcHLcFTKU03mvPRf/dtscZ9MGzO5jwGT9dD90FQJLvjeV47XxxiphBxltMrn3qkF4wbPi1AfBgbtmLjz2pTFOGj8bWqJN+O/c6xrrCtjVdf3cakD2uRrMC51QOu6Bt43nXSs/Fz0pYDa+LLLLkvxlptttlkh6YgShiGJBm3WPF1y5JFHthSx8jGFBXBJZ+Hq9WcgJuXBMs/287bVz0587izB2jOAZeGuN0hX22zHMZ/3KX/mgv7aZJGk/LaTfB7WcX3Ztiq5LctNGsru7qy8VveJ34FAIDBeCIgRN14IMSLCbHhuMp+7semQQw5JHqHymRuD1lxzzYJXi+L605/+tPjJT35S3iS+BwKBQCAwNAT+rp310dw666xTfPTRR4lPnMLFTS3mktK39dZb99FCvZtQjJU/MrMluI9ZKMuKW7UHWSmrxsSJ8+SCZ8WkyImHomhTYmWMsg4QbnkW4aryWT1OP79l34vdYqHFQ89qgWUkty0+lJueFe6ZZ54pNtlkk67NepHInudqEuPx2muvdd0+VgYCgcD4ICBhlBJJJGnydqAXXrBgQbJyCkfKQtE0vpBTTjklKauSPY03PEkhgUAgEAjUgUDfFlAHN4POySySY8RGjoooU7TNNtskxVHME+us5KJuwjws4H6HHXZIg27ethufO7e+UATUpAb4HKOZ953sJzO4cifiU2XA65s4rCzcpvssTKaiUG600UYpCaoct5q3y58HHXRQSpZyLiwbkqZCAoFAYHYgYOxQiL6cKFSdaHdCwna8KaykxtWQQCAQCATqQKBvLngHN5iJ/ykPapZT9ljuRkEMnuLEyi73Xv2yfU6wyttS9LrxuefSTnn7YX2Km2DlbNd/LnXnN0gMothO23ezBOt7jgGty+Wm302PAQ0u+GHd5ZNvJ7jg+8cOW5HazcayQcX4wwvTLl7YO0A4UrsxatDjzMT2JuQ8ZXVxYE/HOY1LDGiTr8G4xIDWdQ3oMkPjgpftzeLJLVOVUeKC5zL3N4hUlU/7Uti6Jcy0G5gHOWanbYUFdBrYyxboTvtXl3dqq7pd/A4EAoHxQkCVjskon1Aw1qiKUdc4N15Ix9kEAoHAZBDo2wWvnI8EGW6ZqsbMAhoSCAQCgUAgMBoIqBZSzV4fpGeUzx/96EeprN0g+8W2gUAgEAj0i0BfCqjBiMn/3HPPTSw7/TY+jtupN8o1X028wowkKWnjjTdO5U+Gfe5c2NoXmyVuNScnOY6XzV/+8pdW9mo+tlCJl19+OW0riWxQy3BuJz4DgUCgWQhIMpoKF7zxRk3hDz74IFX5aNbZR28DgUCgCQj0lQXPFa3WJ2VmtovSJqhHyyWfYIJPXg1PNKDDFnFvsvJlqipAv+eee7bicPXl0ksvLd58881CwlE+vnhLSUu5OP9xxx03oXzUsPsY7QUCgcDoINCOC16CY7tYcJU9llpqqdR5FTiML/54upBjhAQCgUAgUAcCfSmgDoyGU2kOtG0GJYpN/uuWjV1Hp2e6TQXgn3jiiVY3lGiinKPuLIt4WXVCBeNmUcheMhFFMS+3vxJXPtuJov8y/I866qhU+H755ZdP10D7v/nNb1IpKCESivLnmqwUYvX8FMpXw9VxlakKCQQCgfFGQPJQeSxB2KHMm8oac+fOnXDyuODvvvvulpVT0gCPl9JtEi15fEICgUAgEKgDgb5c8A6cKR2V96nKKCUhVftWx2+DNppMBfkJ1zgrgvqhWebPn9+W173K2Y7esxe3PMzLLncvBhZOsbfc6pRfTEesoOqhkvfeey9xwqcfC/+ttdZaiR2pTBuqnFTmgpcpr3yVzL46ROapv2oFhTqOVWebwi8o800VikmZoKBp5+Ee4lp2/zdVXIO6njOYSD4qy1S54I1tZTEBvvzyy1O1kfLyJn2v+xrUjYX+ewaa/Cw3/RowIDX9HOrsf8an27PQtwKq9IbBv530oltqt0+TlynET/Gj5BmMFXzffPPNWwqoEh/deN1d9MzZvtNOO/Xkls+1V2HG8irec6uttkqMUFzrav7ZZsUVVyy23377BC23fTmL33f7lWXppZdOZZ0scz4U27riRGXjOu+62i+fV13fPVDu9UxgUNdx6mo3TwKafg24kZt6DtNxDcSJm1AOgwsepS+K37Iouec5aOo1cC7Goyb331ja5OcgrkH5iZq573U+B2WjWacz7FsBzawanRqabcsxhAhBoMS98cYbBUUwSy9e98zZLrGrX255bXOv33TTTcWFF16YrJ8YTtTtvPbaa1MMF2YqMaFo97wgDFJZKE9lqk7LMaNkyXVA6+KFHYc6oCwO+eWecWvSp3sguOBn9ooZ8Lm463rOnN1yyy2XlJPggm9/rU0Cggu+PTbTufRf/uVfan0O6j6XcakDWtdYlA023a5D3wpot0Zm4zoK6PHHH1+IB5VhXrYCG+C68bpnRXAQbvkbb7yxeOSRR1LC0RJLLJEgR6+5wQYbJCusBZiUhEoQDCYe8Cy+Y1gKCQQCgfFFwEvx0UcfTUpucMGP73WOMwsExgGBvpOQzNxDPkWAMqeAPesj93tZevG6520Vie+HW/7BBx9Mbn5xV1n51IZ4TvFeOR5OklGO8VQO6qGHHkpxWhIKZNDjhw8JBAKB8UNAHLrScMsuu2xxxBFHTCAMmSwXvJAc8e4hgUAgEAjUgUDfFlBJLuecc04rxrCOzjStTYPzL3/5y5QAVO57L1738rb9cMvjdBaDKxM+yz/90z+lF41lRx99dApGl4WPKIBQip999tnELS9WSBknpbRCAoFAYLwQEHqj2gXXfjdDgTCYfsR2vDTGEomJIYFAIBAI1IFAX1zwLGyUKqV9sCGF9IeAGAjY9UOHydowWdo7Ln/H4dKvithOL5NeiTM5BjS44KsIfvo7uOA/xWKmvgUX/ETkjcn7LKz3+//+3/+buGIKvyQPiB9VQq6cyJibDC74jMTMfQYX/Mxhn488LjGgVWbLfH5T/aT/DIULngLD+inRBuPPCiusMCGhRbzhKqusMtX+jt3+3Xjdqyc7WeVTO14Y7ZRP6/pRfm0XEggEAs1C4N57702ejWH2Wiy7DPpOyucwjxVtBQKBwOxGoG8X/GmnnZbK9Bx22GGLIDbb6oAuAkAsCAQCgUBgGhHgJm83Fk+lC5nxDotSO8vnVNqOfQOBQCAQqCLQtwLK8snV2056uXer+zz//PPFF7/4xeLLX/5ya5VEGUXRpyvoXUH0dqWlFBoXO4mpSCa7pJ5hWxE7HbsFxsIvMlnV8etk2Sxv28/3fo7ZTzuxTSAQCMw8ArfeemshbGaqwtrJ6mk8Fke6yy67pJrCU2039g8EAoFAoBcCfWfBG6gw77T7kw0+iNxxxx2pdmZ5H/RxWIKmQy6++OLihRdeWORQasMdeOCBqb4nZVvZowMOOCAlAC2y8SQXyFY9//zze+6NPhPV5jBELIZkp5BAIBAYDwTQ84obz6Kaxr777ruI5VIs5x577JGqbdjWWM1jlRnTMKShUlZw3nZl0ovcdnwGAoFAIFAHAn0roA4u0QWbzgcffJD+/vjHPxavvPJKstbV0TkKoYD3shh0KVQslCQPwrarbkuJZLktL2cJ1H8Fuas0ZiyfyhyJd1WknaKI9Yg1kigab3/HLLeZVi78xy324Ycftjje8z6Z+91nPrbjE8XiYYoL3nlVhWKez7G8rt2xbFfOgoUfochKoMm/y+3E90AgEGgeAq+++mqr06pbqIRhrLjkkktay30xkVWGibueknrmmWem+sDbbbddUkLtw9MTEggEAoHAdCPQtwseA8/BBx+cFLdqJ3Gib7HFFtXFk/7NHX/WWWclxYvypLYdpZCriCUP08fbb7+dluEzN7BmBRS3ujp43FMGZUodt7p9tKkeJgpNytoXvvCFxJGeO+q3dayja6+9dqJqs08uMn/11VcXlG7Zb9z3SpTolxAEpVAwIKkWYP15552X+l3mft9kk02SpcGxZa/qK9Yi5ZM++eSTpNzaPsdfibtVQklCwJw5c4o999wzdbXTsfbaa69CHynRFFu0nNiaFKSmjM+bN6/VXw3BTjF7wrIdXPAJiq7/mswF7x4wQalOvLqe8IitdA7BBV9MmEyyZhqfH3vsseTBKV+yY445Jk3CbSPkCW2vpNHdd989jYsmvsbPQcRYOn/+/KFm3g9y/GFsa3w0TjdV9J9BqMnPctOvAd2i6edQZ/8zPt2esb4V0GOPPTbFB1GE8JfjOn/ppZcS9SP6x0HlggsuKLjCswDCwEgoWGblKCedxJFHHllwXa+77rppvdhI+2euUVbK008/PVn5DKxS/3Gmy9bXb20beN98882k9CnQrn4m91NZvvGNbxSU6Z/+9KcFJfirX/1qse222xZYj7KotUeho5T+8Ic/LGSifv7zn0+KnpcAF5cQAy4yTEnE8TP3O2sqBZeF9e67705ZrI5BuPudJ8WUiD/lVjNQ7rrrrqm25zvvvNP1WGnHyr9DDz009bPq+kcjCl+CF5myXxc/MsUHDnW1XznlWn7CynUfNOa5ls5MolHKG8t5069BkzmwXQN/U70Gxr6tttoq3YsmsAgpiOvLjZ4VEx4g4wvGNs+/YyPR8Dwab93PX//61we6m0zU7TfVcxjooEPe2Pk3uf/G0iY/By5nXIMh39STaK7Oa5D1s27d6ksBZbFTf4/7hmJI4WLpo+hxSbMSUggHkcMPP7zYcsstW7tIQKJwEjFJP/rRj9J3L3vMQmb3WQE1YJZPbsMNN0zbLrnkkmmAVROP5Y+w+hHu5wULFvSc7bMa+mN1pGBTRp27+CjiWFkBoSDqKzwsz7GwFFaWWhZYkrnfy322XE1VyuhVV12VLK9c8fDMwmpBtM/qQPl0vG7HSjv0+S+44PsE6t83Y3FQkkw8dBOFAh1c8DN75Qz4w+CCV7tPfKexiJIpiYhQDLPy6bd4z4022qjYf//909hoImuCzONiYs89j+Z3EOFtMtYNOzlzkD5MZVtKuPeBd1hTZVzqgDb5GjAMmcQ0+RxQdNfV/2yw6faM9aWAysQGdFawVl555ZQpvtJKKxXrrbdeS1nsdqDqunJ71pWD383MzeSzGFDN+LJQAspSHgjLSh6FlfKXpbxdXlb+pAiuttpqScETV+WP2+qaa65pKaDl7SkkMKEgcs1n0V9/Zqik2t+8HQvw+++/n6yxaPQuuuiivCp95v39oJhS/g083Y6VcRKvGhIIBALjgYDQj6eeeiolb5ocqxt8yy23pJMzvlx66aXFP/7jP6ZJtoV+n3LKKcmbxOvCa3PdddelcdY4Yyw0/lRphMcDrTiLQCAQaAICfSmglEWKnJhBMY8skFzM3PF4yilqwxQWRIMmFzlFjuWSu2kQ0QYLphk/K8HcuXMLMZgUSspu2dKY22VVENvEReWPBv/MM88kV37eRgyp8yaSlr773e8mjnUvA7NqVomHH344FeYvK5B5//Kxf//73ye3OwsFxVJca3a/2z5n4VM49UUsrPjQTseyzstpqaWWmpDlny22TXe/ZgzjMxCYLQgYF4y56HiNJ9VEQziYCAvfMU7niTvvFOGer4r4c9sZq1D6hgQCgUAgMBMI9KWA6phsypw5KU5Sko7EFRa3++67b6h9N5M326fcaX/jjTcemAKUAvqb3/wmxa0yMVOS82yfMi0eihLqnLLsvPPOKQ4SBzJLKuV3nXXWmWDhpUDmZCChAfpKxJTiW5fIA5fs+s9t58/ll18+HVtMKre+eFfKvONRuLnhs8iotw2XXY4nZR3udCyKsTAJSUgrrrhii9qTW0486w477JCSFeqi3sr9js9AIBCYOgK//e1vUziQ2Ox+qDaz8tnryHk7nqw8fvXaJ9YHAoFAIDBsBPrigs8HpZBR2rhvZE/ef//9KcBdMksdIpOdi9vMfrIi5o0FsFp03nlot52V0rFYMymS5WMrcUJ5Y1UVy5VDEnLfDOxeFL1c/fa1rT75zr2WM99zW/kTBlxs2YqZl3c6luXOTd+rom/VPudtHOeMM84oggs+I7LoZ3DBL4rJdC+ZLVzwPBmSKHNITR04v/HGG8Xqq68+cNNiQIVl9RrnBm54mnbIMaBNnoiPSwxok6/BuMSA1nUNeG17ccH/PUixjwffy/eggw4q7rrrrrQ1S5/Mc5ncDlSHGODKCuBkjiFhpKp8aseyTsqn9dxTnY7NCtpOkbN9P4Oy4+Y++d5J+dQP7VWVT8s7Hcvydsqnfdr12fKQQCAQGB0E1DjmsahL+TQOvPzyy5NSPkcHpehJIBAINB2BvhVQJYc+WFjAPWecs8rJEGcVHLYLflRBlSgkjjQkEAgEAoG6EJA8xIsxbGG1VClkwcKYeiFUIYFAIBAIzCQCfcWAcr1LiFECSIILYblTc5PrVlKM+MlxF/GbgwqKOxnpaoyWRWiAUlHiOcuZ++VtJCmx4CrBpO4q5X+yJYC80LLVtXyM+B4IBAKjgwBP05133tlKJppsz3hCPO+8J8Zq4waaYTWBeXdCAoFAIBCYaQT6soBSkMQWqjdXFQkyakmFtEeA8ikxSKxnWR5//PFkieikfNpW7VOJCPk7pXUyIkQiuOAng1zsEwhMLwKe+bLrXbklyYXVYvHc6GVOd71k1dx7771TCA7lk4eKcYDFU8z+SSedFMrn9F7OOFogEAh0QaAvBdRsWla5zG3cwVnEEakpVy4on9fF598RyJSer7zyygRIsDGVM/Ap8WhAO9XvPPHEE1OGfW6k3fZKtJBMS5q3DS74jER8BgKjjYAyb5ISsxhfKY+IOcrJnlVOd8lEis0bQ372s5+lUnPChZSvG5RqMx87PgOBQCAQqBOBvlzwOnDdwiLGFCZ1NCXNcMvLqlYm6Kijjqqzj41um4WTm10IQ3bDU+JZk9X/ZJ3EB09pZO1Aqee3dWVBySneFg1ep+332Wef9LLJCqiaokcccURHLnjtYKAiXHQK6tfFjyzzlBW4jti2Mk51fw8u+LoR7t6++4gnQO3LpgoLZ6fnDDNRFmOHhEfl5DyfnmfjMKlyuqMZFqdvjDbhNBndYostUrZ62mGI/4QiXXHFFX2VhhriYYfaVLdrMNQD1dSY/nsGvIebKk2/Bt7dTT+HOvuf8el2f/atgErVf/rppxMd5GuvvZbiisyscQyHdEeAFYJymOMwFapnNRafpci8l4tC04TLjLJaVUDzEdQG7bb9Msssk6oTiCUTo6sMQicueLGlXujEi45VlbW7DqF8utnrar+OPlfb9ECpqdquKkF121H87VoLpWn6NRDT2ORzcB069d9zKNZblREx91nBoHSXq1u4F8uc7u43bnfPv/vTfWpsVjd42BJc8MNGdPD2jKVNfw68Ezo9B4MjMv17xDXojnm38MK8Z98KqB3c8Kusskr6yw3EZ28EFIZH8YlVSSgDBTPz3lMCFba/7bbbEi8zS4aXSCfptX2uUiDb1YuOldrLqJ1QirPkOqB18cJSfMzYu5Wcyn0Z1U/9V/3BBKCJQmmhyLC0N1Xcz65BWRlr0rl46XbjgmddfPfdd1OspglrLuuGhtfkM0uV051HxTOGYOPee+9N15jHxHgxbOFhEYOa+zbs9utuz7gYXPB1o9y7fQaPut43vY8+9S3GpQ5oXdcgG2y6IT2QAtqtoVjXHQFueJZPF5tCKpSBiA1VTxWLElo8LE0oPjtJr+3LL4V+ZiCdjhPLA4FAYPoRwEyEtz3Hc0tWlExkzJBIKM7zd7/7XfJYlDnd1Q6lcGJSwyLHslSH8jn9iMQRA4FAYFwRCAV0mq7sN7/5zfSCwB5VTj5i8RQbSgFlHbnxxhu7FvYfdHunl13GTXe/TtOlisMEAjOCgPhiyiULaZb58+cnZdKzS6655pr0KQwqc7qnBQv/8aRw4Zt4oksOCQQCgUBglBHoKwt+lE+gKX1jkeB+f/HFF1MyQe73t7/97QLtnjjNgw8+OFlG25W7muz29itzwXN7hAQCgcBoIYDkY6211koudC7ismTls7zM93bLxYwqOK/kUkggEAgEAqOMwEBc8KN8Ik3vG5eb+Mh+3eaDbg+f4IKf2l0iscs1ihjQqeE4lb3HkQte1rri8J79qvI5Gawuu+yy4pBDDpnMrn3tIwY0uOD7gqq2jYILvjZo+254XGJAG8EF3/dViQ0nhYDEkH6VTwcYdHv7BBc8FEICgdFBQN3f9dZbbyjKp4nROeecU6vyOTrIRU8CgUCg6QhEDGjTr2D0PxAIBBqLwOWXX55qAk/F8qk6Ccs8Dnm1QEMCgUAgEGgCAqGAlq4SFhHc7euvv36x+OKLt9ZIDnrggQeKZZddNpVTaq0Y4hfHVqDesZWZyaKUATpOFs9qzdW//e1vKWlB2aaVVlop75I+uWhkzHPVb7DBBq2s+wkbxY9AIBCYMQTEcLJYTpZiN3dc5QusdJH1nhGJz0AgEGgCApGEVLpKEoTOO++84r777istLRJb0E9+8pPiySefnLB8WD8wSWEWeeeddwq1OV966aXUNPecYvL6pU4oVqMsr776aoEdSc3A4447rvjlL3+ZVyUOeVmwWI5k1KLxvOCCC1rr40sgEAjMPAImliaYWSQqKsWmFFNZhOZgNcL9nsN01BVWOYPyqSB2U+OSy+cZ3wOBQGB2IRAKaOV6szI+8cQTE5Y+9thji1gXuMxkqwvIz5ILfbM6SizIQpGUPNFOlFUSzEzxlQXvj7JJ1ADUnyOPPDLVCmXVpKQStQHxQWM6uvLKKxOTkgxY6xWjVhPw+OOPT+VYJCWg81O6JSQQCARGAwHPY5lSFN87BXPzzTdP9L25l3vttVex4oorJiXTZHWppZZK3PCqZ1x88cWp9JIJaUggEAgEAk1CIFzwlavFzU4pRJHJpUWpNLir48kVT7AGHX300Wmd2n22O+uss5L73guB8qn2JkXyqquuKtT+tI1tKY5lFhcKJgUyC+VVtjrBZoQbmpLrmDlbVp8UnP7qV7+atlOkWlYqhZi1VLmnpZdeOq3zT3wYis/ycS1XqqXMBS/soBNHte2nIhR2+AUX/FRQnNq++RpkeseptTYzezuHceGC/8Mf/jDheRBGg5QC5bFnU9gPufnmm9Oze/jhh6fn3jJxnxRRzxNWHwXq62I0cbyyYGsyZuVxqryuKd9Zjesa66YDA/0PLvjpQLrzMbyHx+E+qus5yPh0RnBhjfJuK2frum9961vJCkppFFv19a9/PRWDzsoTC+kKK6xQHHvssekGPOGEE5LSCq/333+/uOOOOxKX8xtvvJGUz3/+539OGejXX3998dxzzyV3WsbWiyTHfLJwKkSvXbL22msnCyg3vHixH/zgByk2FesJl1t2x9lWjKgan2+//XZLMbU8S1X5tNyLJLfB/Wd/hazrEMqnG7Ku9uvoc7VN/VdTFVZNlLgGM3/VKND+PAeeYa50k17PLS53UuV9d91wuqtioVyTZ9m96PmlhLgnfTdeTYeY8DpmU5/lPBFrav9dY4pPk6+BczCBafo18P5u8jnUeQ1g00tCAW2DEAsipVIcJTf4VlttlZJ98qY5FnTevHlpEQvEggULik033TRZJT7/+c+n5RKa1llnnVb5o7333js3scgndxqXubhOiUgE6wnLp/hO1lPrcUJzxxmAyuKFlF9QrKj9iHjTLJkLniJbh1CgvSxZY5sqQimCC35mr557yH3ebkI1sz3r7+iUSXHZnjPJg6yWlEr3lWfaoF3lfUdW8fzzzxcnn3xyik83FqDrFBqUJ7vWVWNH++vR4FuNCxd8XWPd4IgOvgdjCMWnyefgfm9y/3mSGCOafA51XoNssOl2d4cC2gYdPO2sEVxklEixWSjyyrLuuusm3va8TDIAF3m2ZlruxnQRslDyzDiygpqXO45jKKFCic3imPiftePPC0aCkqx2VhKDULaasF5yyXHjeTFVRbwoiyplOiQQCARmDgFKqLAe7EcSkZ599tnUGRPOSy+9ND3Hhx12WAr7+eIXv5i8KnjfKZ6/+MUvkov+7LPPTqE24kiNBcaEkEAgEAgEmoRAKKAdrhY3vMxxRaKrpmQWUorgrrvummI9586dW2yyySYpZrPcnLhR2e0skhRI31k3ZK9mUUpJFrvYrzXXXDMvTp8bbbRRKqXk5UKRFQ5gX/Gl+iVbX2bsU089lawoLCkSGFhFbrrppuK73/1ucrGz2OpvOYt+woHiRyAQCNSOANevWGyTTcokxbFMp/noo4+m0B9WCduWKXlNRo1DeULLO8P64vc//MM/NNYiXDvocYBAIBAYWQRCAe1waSiZFEZZp1WxTlb5LrvsklxoX/nKV5Li99Zbb03YlFV05513TkqjhKIvfOELKWu9vBFlkeW0rByixrrnnnuK7bbbrvj5z3+eYj+9rLjzs6VDPGguv+TFpAg1kYzEOiIZSuwpER926qmnppItaUH8CwQCgWlFgOdjp512SjHgXPCdJCuY1fWspv7KQnkV0qKCRkggEAgEAk1DILjgp3DFKIWskdkN3qkpLwp/lMPJiJeX4/irCuW1U/arlxk3fT81AnMMqHqndYjzb3oMaHDB13FnDNZmE7ngWTQ/97nPpfCbnMg42Fl33lpmvMnmdEpwwU8n2u2PFVzw7XGZzqWyx3khOr1/p7Mvkz2W0L2Z5IJfVKOZ7JnMwv36UezA4ib1N1npxuHe7ebvpLROth+xXyAQCAyOgLAZCWwmYcMSY89aa6017crnsPof7QQCgUAg0DtPPjAKBAKBQCAQmBQCYq/Fdg5L+VQ2TTz5AQcckMKAJtWp2CkQCAQCgRFAYFZaQFkjZIrLdlfjsyyyUrm16y5pIjHoG9/4RisuU1Zs2T2n1FI2jXfifJfIIINWXVDJTco3iTslCthzq7OShAQCgcDMIKCgfLeYz357ZSwQUiMxUZjM6quv3u+usV0gEAgEAiOJwKy0gFIwBe5Xg/fFap1xxhnF/Pnza71YstYlDYm/IF4sis+r95n/cgYsxbQd57vaowceeGDip5cx+8gjjySrSKYGfeGFF1pMKrWeTDQeCAQCbREQ8+w5LIuMdcQS1coatllllVVaxeTFlauygfBC7LhkSPGvDz74YCifZUDjeyAQCDQWgVmpgLpaBnXuLJSbWSh72eqYl+VyKFmxy8uVVqI4sj5m6cb5nrdhzVTLrxy7qR4gC+a5557b+ltjjTXSLp0431k+MZKcc845xX777Zf43xW25u4LCQQCgZlHALtROfZ72223TXV+Mdgo3VYW9YOxoKHXFbutXNM777yTis/LfuftKNcYLu8b3wOBQCAQaCICs9IFny+UckpoNTOFHdYjrve77747bcKF3Y7zneKqQLz9vGQogSwd3TjfNUiZta0i05TNLNzlFNCHH344ueH1gYJMwe3E+a6k03vvvZeOq8C8Fx0+ei+3foV7kNJNMMtQaOvihXXuXqTlMIN++zlK21EETDSaKPkaBBf89Fw9YwOvSpYtt9wyKZS8F7lEWl6HyUhJNqIesImx8cl4oQqGcmy8I6MgyrpdffXVqV+j0J/J9MF1qWusm0x/Bt1H/1nYm/wsN/0aeD83/Rzq7H/Gp9u9PasVUMXmFYVW1BlYr7/+erHjjju2FNBOnO/ZOqnQvGL1/XC+uwh33nln8aUvfSnFfpYvyrvvvpusHV/72tcKlJxXXXVVcd1116XBRbZr5mu3jwQErnvxo/rNQsqqynLCwuKl1a+IM80KK6vLJ598UhuvLeUTxk3mzdV/eJWtWv1iPQrbxTWY3qtgEomdDEkF97lSTCYwVdl6660LNYS9DIjanuK51fqlgHLZmxxuuOGG1V1n5Hdwwc8I7BMO6l4xFjV5PDWxanL/XQOhNE0+hzqvQbswowk38cIfs1oBXXrppdPNw5KorhpLYlbIANWJ8z0roBKYKIf9cL5TLB966KHi8ssvr16DpEhSJr2wCCshayhLaH4p5Z0oQbks0/bbb1/407ZsW8qoF90ee+yRN+/6yYqbJdcBpeDWIeNQB5Tlkxu03/JbdeA4lTbdO2rX1nWNp9K3fvdtEhc8ggr3C4Yyiuef//znArUmr0bZEyBRUPgMj4rnncKJ6OLNN98sUG2i1zW5PO200/qFqdbtggu+Vnj7atz9Q/Fp8rPsXm9y/4MLvvutmg023baa1QooYFgMKZoUN8pcVdpxvudtckyWhwjYWShzZhZlznfu/Q8//DCxG9nOi1QplXnz5qUX1GKLLdZSQGXnK3rOYkJhMNjkYveZ852VdLXVVktWES86f/bDJ92vApr7G5+BQCAwOAJcuJL/eDDEcWM78wyyaHoeZap75l955ZXUOMWTq53iwIVtfDjkkEMSDa8N1Av1rGvXy+3MM89MHhNtSF4KCQQCgUBgnBCY9QooN7wMdJZPbuxyUhLltB3nu5dMWfrhfKds+suCw/38888vWGEpjSywwgFY2bj+/9t/+28pGaET57tkKdn6XPr+KMDPPPNMyprNx4jPQCAQGD4CPB4UyV//+tfJA1Ius8SL4TkW3qJc0jbbbFPceuutqQ6oWO/9998/hXDkuqCnn356q4O33XZb67tqHBRViihFdVTc760OxpdAIBAIBKaIwKxXQLnEvDSyO72MJwW0Hed7eRvf++F8r+5T/k0ZlZSk3BLrB9d7rt/ZifMdx7xyUiwowgC8qHDFe/llkRHvJZmFReayyy7LP+MzEAgEBkTg9ttvT88cpbMaHqOpnKD2u9/9rvinf/qnYq+99kohG57VLFn5zL87fXqmjS1KtjU5zqzT+cXyQCAQmN0IBBd8H9d/ujjfvbxYYrO7vdw1L7By6abyOlm1stinkhyTY0CDC76M7MTvwQU/EY+Z+CVUxr3ub7rlrrvuSuEtFMNBRGw5N71nbBCRGMgr8/zzz4+UAhpc8INcxXq2DS74enAdpFXGIu/cTu/lQdqaqW1nmgt+1tYBHeSCSzpppxRW23Az5kSi6rp+ftu303G63eSSHKaifPbTt9gmEJjNCFACd9lll+RpGBQHSY6rrrrqQLuxeIoLf/HFF0dK+RzoJGLjQCAQCAS6IBAKaBdwYlUgEAgEAhA49NBDJw0E0grxn+JGJSyWK220a1QZJnHlf/jDH1IceLttYlkgEAgEAk1HYNbFgLJGSCJQZ2/xxRdvXT81Eh944IFi2WWXTdnlrRVD/sIN9/LLL7eyWpVnUW6lLPqWBRuKLFgxoSwiWezXiQf+ueeeS7VB87b5U+1QdQlDAoFAoH8EJAV6BqciuXyUpMYTTjghVd7gYpc8aOzxnWt/5ZVXTnTAW2yxRVu6zqn0IfYNBAKBQGCUEJh1CiiXluzxvffee0JWuiL04h+/+93v1qqAXnjhhcX777/fUkB/+9vfFj/72c8K7CJZsgJ60UUXpQLV+KAxoVx66aWpzIuYTxYZ2fiybZWCUdblkksuSQWrJUpgvalm66snGBIIBAKDIXDvvfemhL+8F9YyCuJjjz1WfPTRR3lx+pQIaGKr5i/Fknz7299Ozyj2I8onKl5hPRKVxPIp2WZiyE1fpQJODcS/QCAQCATGEIFZp4C6hgZ6Vo1yWSQvk0zJma+zmnwff/xxUupy8XEWC8t9UvJybKbAfNt0S46QlV61pHDNqT9KIS6LuoJPP/10Yk/CKKBEy80335yy3Ms88Hmf448/PvHAz5kzJy3aaqutCn8hgUAgMDUExH9mZVKMNsIHfyaFO+20U6sGsHJuan1ys6tScd9996Xi8ZtvvnmrXmjuiZjtPNHMy+IzEAgEAoHZhMCsVEC52bm91fykdFImcaKLu8ovGtYJzCO25QbH366mH/f9xRdfnCwi3GZqeOKLp5RSFNFpHnHEEYvcQxTUW265JbXDkpmFAupFZB1LJze5skqspDJgM50VF7wQATIMHnjtzJ07dwIXvHZl9tUhmYccTk0WoQ+51E7TziNfg0GzuEfpPJ2DqhRc2tMlJqFZeBF4LdTcVRx+lVVWSVS81rN+YjtCaXvPPfcka+bdd9/dmtiydJbpb3ObTfvkrTHuOZ8mSn4O6hrrpgMTJcA8A01+lp1Dk68BvaHp51Bn/zM+3Z6HWamAAkQBelZQCqiYTHVAWSWygnTjjTem+nsUQqUKKKAYTgjl8I477khMRdzeaPTU3wQ4SyRu9nK8JqUW49GRRx65SJY7BZRQQm+44YaC+1yBeiV/yjRlEhO82Eg/PPBXXHFFUmrTDv/+79prr52Q1OBlmrPnKdP6XVe9QRjAp672y+dZ13f9l0CSMavrOHW1G9dgcsi6Z3kTPCOULpMQQhHOnhG/UfRyr5u07rfffsWVV16ZXOzWEZNJBeV99kpE+vseo/lfiIH+N/VZpoAa55vaf3cFxaHJ18A5eJaafg08y00+hzqvAWwY07rJrFVAFZkXj4WDHU2mFwyrJxFj+fvf/z7xsYuvJJKH3nrrrfRdvFam2ZSpqtg08YK64IIL0vfyP0wokgsoubYvixcWN76Ltd122xU77LBDcuMZXAwyWSg/mfrTsl488Irbb7zxxnn39Kl/ZQku+DIavb+zfAYXfG+c6twiJ/N0C3UZ9vFRbCp15vkxWTV2ELGg5RhQITbGC+PDSSedtEg3lFnzvFNgc+jOIhs1YEFwwc/8RcoKdNlIMfO9GqwH3m9N7j/rM2NEk8+hzmtAZ6HXdJOJGkm3LcdsnQQd8VwUQm51FsysgDpVLxsKYVbadtxxxwJrkhdOWRF08wE6i0LZrJXleqAPPvhgcjVwy+XZN0VTUoKY0Jx4YCa1xBJLJF56Ci4lOAsrrBch6YcHXpvVJKTcVnwGAoFAZwRYNrnYKZt/+tOfUlgOz0SeEJrV83x4Af31r39NyYunnnpq8ctf/rI466yzUkLhOeecs8gB1l133Z4D8iI7xYJAIBAIBMYUgVmrgLqe3PAslvjWy5o6a8dqq62WFEEZrFzT4jq9dKqy6aabFk899VSxySabpBcUDncxomUFVPJQFgqvl9P111+flFFZ8TLaueCt42ZnKWVt046YMorn/fffX3iBkeCBz2jGZyAwPATU6/Q8itE2UfTbZ1V4Tlg+MqXmcccdlzZZsGBBShz0Iyurvu+6667JSrLnnnv6GRIIBAKBQCCwEIFZrYBypYmVPOqooxa5GcRw/fjHP05xlGLnlGf63Oc+l7LiyxtTQPGt77bbbskyiv+531qbLCniQsWK+ROQ7eXG6sqKeuCBB6ZMfQrn0ksvnWgAHbsfHniWmLPPPrvc1cIL8KCDDpqwLH4EAoFAkagyTTaVReonySwrn1XsyopneZ2xI4fqlJfH90AgEAgEZisCwQXf48qLB2UR7SViRFk9J5tcwNpC6awG7XrRiffpFPM2DB5456b/Z5xxRnIn9jrXyax3HuL3nGNTJbjgZ/7K1cEFz+sgadCz187iOYyz/s1vfpO8JCaz//Zv/9b4GFBj3X/4D/9hGNBMexuusXEzhz5NeweGcMDggh8CiFNsgsGIJ6TJ8dwzzQU/qy2g/dx//Sif2pnqYNwpkNkN7q+T9Nu/TvvH8kBgNiNAGcylz+pQPsWLS0IUohMSCAQCgUAg8CkCoYB+ikV8CwQCgVmGgLhu1qRhK58UT16Lm266KbEmzTJY43QDgUAgEOiJQGMVULFWuTB7+Sy/9KUvFWuuuWZ50UDfFZkWwynjvSzYh3IdTglLXMksJ+Vko/L2/X7nXpeNP2xRX5RbXQH7kEAgEFgUATXw0N32E/O56N6fLhF2Q9k0Lvjjsdh3331TfLeamSGBQCAQCAQCiyLQWAVU6aPzzjsv1e8sF4Lt5q5e9PQXXaKUiuSkqgKKqUjbav+xlii/cu6556YkJssmI7LcZbxLZBq2vPDCC8UHC+k8QwEdNrLR3rgg8Nxzz7WYz5yTMBj1dd94443itddea53mRhttVHzlK19Jv9XwVE6NoNh88skn07hgLLANNjEMZiGBQCAQCAQC3RForAKaTwtDUafEFokvFEXKZK7nydrhu9JKrBN5uXJHubh8brv6iYrzH//xH1uLUVn+6le/Kg4++OC0TECvPxbUbBnNx6MwK+DMQuuYLJ8URPSb6glSoi3rl3u+3bFaHYsvgUAg0BMBdXYzq5GNWUOVR1MTWGklNXqJOFEJB2g3KZ0UUAQWqkzkZx09pbJuIYFAIBAIBAL9IdB4BbTTaf785z9PNflkqHl5sJaiyFPuyIvl3XffTbFZc+bMSTSbkoi42MvW1E5t5+WyZrneKJcnn3xyUjBZUdTz9Jvl5Oqrr060mgpa21YmuD689NJLxXvvvZdqDbKasKr2wz0vpuyUU05pe6zcr34/TzzxxBYXPEpBL1PxcHWJDGBuzyaLsAaFypsqroGJTlPFc0JpnKrb3Pm/+eabLRIJz7JnX7a6ZwHP+3XXXZdgYhG1rRJm++yzT1qmIsJDDz2UvsuoVuJsqh33tmwAAEAASURBVImIqbEG/DNpxuhkLGuqeA7qHOvqxkUIGvyb/CyPwzVwHZp8H9V5DehFncrS5eej8Qqowu/l0kdYgsRrco1R1j7zmc8k3nasQ3jaCVBYLr3M1Mv85je/WfzgBz9IyqAam50EM4oXH4ull9KLL76YCtRTaL20DMqEux4lHwWU/OUvf0mZsF5yLCeUTy84LzBWVW5y9UAplr245ymy3Y6VDtjnv1VXXTXhY3NWWQ9SHfGo2nejuyEHUfDtN0qSrdlTDfOYqXOKazAReRNChBMmpjwgnmtigmFdWYTJYEpjDSXGD7WBiWdn9dVXb7np08Iu/4w/5TGry6YjuarpXPBANRGua6ybjotG+XQPNXk8HYdrIOa7yfdRndfA/Unn6SaNV0B/+MMfTnhZOGmc7RtuuGFLuRLTiff86KOPTlh87WtfS8AABw3nsccem5azXnqRdJKsoDnGSiutlArFc9v7YwG57bbbCsk/rCXLLLNMq5kNNtigdSGEA1StNywo/XLPL7fccl2P1TpoH1/KhbFzHdBO5aD6aK7rJuNQB9R1Cy74rpe59pVevCaVVQVxMgf2LEk6VMrMJDJbMMV0+10Wz0o7fnfbeGZMHvsptWQSMA51QF2DjFcZpyZ8Z3gwCahrrJsODFg+KZ9NPoemXwMTVsaIuAbt73gGJwp6N2m8Asp6UY0BpRD+8Y9/bJ23G8VfBqPM5e4GypYPO+SY0NbOpS9bb731hBjQvOqVV14pTj/99MSGhAmJgssKm6Xcv04zAsfth3u+17HyMeMzEAgEuiPAqilURjISefzxx4uf/vSnxRJLLJEmrDwjJox44BWqVwS/nRg/Mk1uu/WxLBAIBAKBQGBRBLqrp4tu34glm222WXKPsyyShx9+OCUQZAW0fBJKNnnxsExITCpnv5a36/adxZPrHB3nyiuvXDz//POt2LJu+5nBmsmywGTueZ9egEIDzBCrMtljVduJ34HAbEaA6+mpp55KpcoyDvPnz0/ekN133z3xvD/zzDNJ+bR+xx13zJu1PsWPm+VTZFkEQwKBQCAQCAT6R6DxFtB2p8paIbaSQoiDmbtu3rx57TZNiQWScfbYY48UEyrAflDBIa2NQw89NL24KLWSGXoJS+n555+flNB+uecHOdajjz6aeOpzP4QXXHbZZflnfAYCsxIBE03PkUREE8+yCBUZRLihZcOHBAKBQCAQCAyGwFhzwXuZsHT0E6skBlJyTzsrab+QduJz77Y/C6gwgHxcVtt+6DUnc6xu/cgxoD/5yU+6bTbpdeMQAxpc8JO+/EPbcapc8OJ4PedCXlgvpypKNvFW9CvjEgMaXPD9XvF6tsv5CE2OP6yTh7we1Ce2GlzwE/Go/jK+ShK//PLLq6tav8fSAprPjmLnrx/pR0nt1c5kBoNqBl0/yqd+TOZYvfof6wOBcUZASIuEREmEU1U+KbESGwdRPscZ2zi3QCAQCAQGRWAsY0AHBSG2DwQCgfFH4O677041etvFVvd79pIITf6OOOKICGfpF7TYLhAIBAKBNgjMmAVUPUu1NAn3s0xxsZOTsUR24m9vc759LeImU05JQtJ//s//uVh//fVb/eI20O9B6DPFnClOv/HGG6dyS6wnSsCEBAKBwPQgoPSOUkm5judkj8qjosKFZMGQQCAQCAQCgckjMGMWUEWdFYqXCCCzG73drrvuOqF8Ub+nhb+dgjcMoXxiJJJY4GVFCZUVS8klqD1vuOGGgQ6lyLVzJY899lirrYEaiY0DgUBg0gh4BtHcZqFIKplWpta1ThULNT/zn0oVwmSMTRIUTR7L9J25vfgMBAKBQCAQGAyBGVNAdRN/8jHHHFOccMIJxYUXXpj4l88555zESJRPQ/KKotDtYra8VCQZVUUiD951+0iuycIK4iVkXSe55pprEnWnT6xFitTjhZacU6Y90zYrbvX4CmWrQcrq2Y9o8/33359A7yhRQm1Bn45RPfeMCVciJdl5kU7nJ2FJG5TnkEBgNiKA6EHsZxbJQ9zpuN1VzMji+ZVc4LnEjmYbzGbvvPNOqhnqGXv99dfz5vEZCAQCgUAgMEkEZswF366/6Ckvvvji5Kbmru7E584NfthhhyW3eJW/neUSZSZLBR54LxBtUkQxIVHEWDC4wCUQWF8WRamVUyq/rDCcCA/ICUOObxvWES+mc889N62/7777ihtvvDFR+2FYsl9mWSofI39nBW7H/96JP945qV3oeArwowyUFYyByXl0Oj/JEs737bffLij4X/3qV1MXvIRfffXV9F3bim7XyWvLulxV2DMWTfl0HwUX/MxdLQrgZLjg3fvlew+TGfKIp59+OjEcPfDAA+mkTHb9XXrppWkC6lkxoZS85Nn513/91zRhrqtaxMwh2/+RV1xxxeCC7x+uWrZkfDBZKhtFajlQjY16H9T5vqmx66lp18Bfk8+hzmtA14JPNxkpBVRHucA++uijpGh14nNXy7IdfzvlDA0nhUyZlVNPPbVlTX3iiSeSC41CCBRWV67/NdZYo4UPqyNr5Je//OXWsvylHJvqJaS0wBe+8IXi2muvLR588MHi61//enKtU3aXWmqpQskeXNGZ/jO3U/6krHbif2/HH49b/swzz0wvRy9Q+Nif9Do/eF1wwQUTFG5xbJkVikuSZTgr2eV+DuO7G90NSWlvqrBIu6/6rawwauc5m69BnmCtuuqqadKW7/N2vO/qCPszlnzrW99KMeCsoRRQ4Tg8N573yYrxpzzBnWw7M7VfcMHPFPKfHpfy6R5q8nhqQpifw0/PrDnfXAP5K00+hzqvgfuzauCrXt2RU0DN6ChF3fjcO/G32wfvelYQMCLde++96ZwpayQXpOemX7BgwQQF1MPsRUXBNMh2EpZCyicRF8a9B2iuegXo77jjjpTExFrDXd5OHL8b/3s7/njKsWQtyifBd58HoF7n54VZvRmCC77dlem8jAIaXPCd8ZmONQb9yXDBL7PMMsljoswZCyol0MujHe/7d77zneTJcD5ivz3TJqviwT1HSCP8TUZMAoILfjLIDW8f47Lr3+RSdt6Txv4mn0PTrwGDFV0jrkH7Z5PBKdc3b7/FQurzTitmYrmXi+zzfffdNzGUdOJzd9Fd/CysUgTrUXmfarIAvmbsQ1nKVs28jHuJguszixhKZVey260848lKnZnE/vvvX7BSrrfeeolZSZJDN9Hvdvzv9mnHHy8jnxKULSgGobKC2+38sqWzW39iXSAwrgh4Lt97770W1a4xhJudt0I4T5n3nZJ51113JSgoi8YanocvfelLaWJsYhsSCAQCgUAgMDUEZjQJKXedQiWW8bzzzkuDvLgrg/yLL75YsBSSMp97J/52MZf24b6mNP7617/Oh0gxXBRL1kOuZ4kF7TLn99lnn+Kqq65KLxo7a8eLSsxlN6uoxAXbig1lvcyxlc6tnQzC/573d3zWVxSbZhey/83miRi1fs4vtxWfgcBsQcAzIj67PGn1DJlU7rzzzikxscz7ftBBB02I6zrjjDNSvOjee+9dLLnkkmksmC3YxXkGAoFAIFAXAjNqAZUAQGlkRZQwxIInzop043P3gmjH384tLkbLi0Uc51e+8pVW6RUKGlfaLrvsko5lnQzYqrB+2F9WPuWXAqkOqBiwbsI6ojYoKyjueS4/1hXKcCfpxP/eaXvLxZTC6Oabb25l77Kk9nt+3dqOdYHAuCHAM0HJlFxYVkCdp0lcv2JfrjYJgCGBQCAQCAQCU0dg5LnguZi9RNq5y2Uki9nMcQZKLLGkrr322gkZyUjiJimrWSQdUNjKbvS8rvppW/FmgyQMcJHbvp/28/Eouv1QcLLkSHjaaqut0jlLGpozZ07xyCOPtOI7Bzm/fHyfsGTpyWEG5XXD+O46CrEohxYMo93pbCO44KcT7fbHGoQL3uSR18AzMYxsYZPXf/7nf27fsT6XjksMaHDB93nBa9osuOBrAnaAZoMLvjtYJviN54IXq+WvnVSVUjGShx9+eLHlllumzbnZctJR3p/C2q8Msm1u08A8qPSjfGqTpVjpppdeeimFEjz++OMFt2COQ7XNZPpsv5BAYNwQkGQnDrxq+Rz0PD3T4sslF4YEAoFAIBAIDAeBGXXBD+cUPm1Fos11112X6oCyuCnX9PnPf/7TDcbgG2uubP8PPvggZd3njPgxOLU4hUBgaAi88sorhdqeU1U+Wey33377VPeyPNEbWkejoUAgEAgEZikCY6WAuoasogradxPJOlwY5WxWrnM0mWJHxYGWRSkWpZnKtH22xwmPSlRSk8SpnI1f3rf83bbcgZKoJiuOIZO/nM0vQUv8qVAFpZ0G4amfbD9iv0BglBFQH1dYyVREKM3tt9/e8qhMpa3YNxAIBAKBQGAiAiORBT+xS/X/UpBdklGZkpOCKQvfX1nEkYmNnD9/fmuxODSlou6+++5ULkqs6R577JFKJLU2avNFIsRDDz3UZs3UFmGMUi5GLKli/CGBwGxGgNVTgmNZTEolKOZ48bxOVQlx1Lmur+W8JiaJXO+Y1kICgUAgEAgEho/ArFNA1RnlnpfIozRLWbxwuNnKtUSVUxJbWpajjjqq2GmnnVJGrBhMiTusoFdccUV5s57fKbey5DE/5YxcimT+rgEv03I9U4pyO35427LeHnnkkb4mZdi+LLWdtk8bxr9AYMwQ8PyW48a33Xbb5BVg0Zw7d27rbCmjP/vZz1KReeE6nn1MbCaW6oKyoIq3DgkEAoFAIBAYPgJj54LvBZG4MC5qf0oasWSWXefKGaG1xJ1OJPpwvXspkT//+c/JHY9msyyoPavWlfL66ndK4cknn5yydFlZZOleeeWVqeaoY++2225pFzVJZeIr78QKKwsee4sMPJbcMm0oXvrzzz8/0YN245PPfdFnsXJE6EKdXPAy+P0JE2iyUEqEUTRVZGEPIyN8ps7fPWRCZmLVSYTYOM8skhI9azwE5Sx2bdlOuTTfifJvJpTLLCyjRjxH999/f/oe//6OAJIOsfaqWjRVXPcmc3gzXsC/yc/yOFwD16HJ91Gd14AhDT7dZFYpoCyCitNT9ChbLJtcdeWYUdzPP/rRj4rvf//7yRL5+uuvFzvuuGNLAX377bdTsfxqQsIgZZdcEJZVSibrDDnggANShvvWW2+dlFDr3BziUrnYvTz1/c4770wUbL/61a+Ke+65J2X9pwba/GvHJ18+1zXWWCPVLLUrJVxowaDn0eawbRc5Fzdkpg5tu9GIL6T0wKlsXRvxLk/oHiWL9X/cr4E6vArGs2K6p2Wwl70IGRRJi17iFCrX1kRPQfqNN944b5Jc8+XY79aKKXwxKA9S2m0Kh6plVyEK+t/k+4jiVtdYVwvolUbdt3ENKqBM80/XgNGpyfdRnc+B+7OqJ1Uv0axSQL1c3DA5Pkzxe2xCZaVs6aWXTgMr2j4zGzVFyy8LJZMwHk1VKLUvvPBCYl1yLMqim0ECFAsbC+nf/va3ZO1koREuQPnJMaosiSw96mx1knZ88uVt99xzz9bPXAe0Ll7bcagDSkkJLvjWLTMjXwz6vbjgsZZRjjyrFE9eCxPO/IzljnO343i/6KKLCklLfmcGM9sYPNHpDrM2rolYcMHnKzAznyZiJvR1jXXTcVbeFe7xJp/Dv/zLvzS6/wxajBFxDdrf8QxOvbzCs0oBZTXE057dbd/4xjeKa665pvjwww8nWLW44Z988slkPVGCpSysJQqSG8DK9Tu56jEtnX766eXNk8WSldNL0wXJs6WLL744FcnfZpttClZPL0Hipec3y6ckKd+JPnO9U1z7lXLR914zkX7bjO0CgVFHYIUVVkjP6GuvvZa6SvHkgvfC5lJnwTvkkEPSs4qNTJIhb0hZ+bSjF8uwrZ+jjl30LxAIBAKB6UJg1iQhUea409Fsyob1xwLITXfvvfdOwJsbngKqbJLySmWh1LGKsJhkSyhrpQSkLbbYorxp+v7ss88Wzz//fFIgs/veCuWSuNnFp5lFWZfjJSxzfEXnN9tss9SOmFUxnlyLEp5YdW699daeJu60c/wLBGYZAp7R7CKWeCiGmrK5YMGCFD+dJ4qHHXZYUkQPPfTQFkI8JJdeemnyOGy00Uat5fElEAgEAoFAYHgIzBoLqPJHON2r7EksjF5GZUsnd52MeO7wdpZDLysWTC81LjXsQzvssENSZquXZtddd00vM65z7v1s0VS2SWynpAjHWGuttVq88UsssUThD40gly8RLiDxieKMd15YgCSikEAgEFgUARbPW265ZUIhemEg7aTdcnGkni/jQEggEAgEAoHA8BEYeS744Z/ycFsUz1V2dXdqXYxlVfmlvIpR62f/3C43vji4alt5/WQ/cwzoMOPdyn0ZhxjQ4IIvX9GZ+T4IF7zSSmXL5iA9NlnkAclW1EH27bbtuMSAUsyHPQZ1w22Y63IMaLW83jCPUXdb8hOEczU9/rDJ1yC44Lvf5XSVXlzws8YF3x2qya/tV3lsN1gL0O13/9xDiUjt2srr4zMQCAT+jgBShl5B8J2wknDmBRMSCAQCgUAgUA8CoYDWg2u0GggEAjOIgPhpNXNZHCcj4rvFeYcEAoFAIBAI1IPAWMaAsnxwbed4ywzdxx9/nBJ71PkrZ7Dn9cP6lCxEVlpppfSpL8q9lEU8KlFO4+WXX05xoOuss86EbHzZ+dZxucvslcGfRca99ssUgnmdTy4aRea9SJVjKhesL28X3wOBcUTg1FNPnRIXvBIrau6eeeaZKfFvHDGKcwoEAoFAYCYRGEsL6A033FCcffbZEyg1gXz77benOpp//etfa8McVeZxxx2XMt/zQX7729+m46o5mv+so1jus88+KeP9pptuSvvlElGy52Xsy+ClwKIMPO2003KTxfXXX7/I+eWVSjgppC/rX4zqiSeemFiT8vr4DATGGQHKY671m89zMlzwnkXl1UICgUAgEAgEho/AWCqgYFp11VUTpWaGTIkj1kSZ5WVhgXz//fcn0CuK//ISo6gKpM2ilBNlsJtI4qkGVlMiZdmfe+65rT9tUIhZNU866aRCwoTjvvjii6l51J/KxsjEPfjgg1MmvUL6ahp2k0zHeckllxTHH398UkS1zWKa6yJ22z/WBQJNRyC44Jt+BaP/gUAgMBsQGEsXvAunlieqSpZAIiYM9aT6m1ksY1XEnEJxUxNQYXhUndzfsmDV9kSTedRRRyV3uaSGr33ta8k6mdvJnzijufYVjC8LBZTLXVkYrnQF8JVewoBUrh2qFBN2I9tyratZqM9f+cpXUhmmhx9+eAIrU/kY+TsFViF9WbxZJDo5ttIyZaGglrng9bsuXlvWJH/BBV++AtP7PV8Dk66minMwCTRZ6yRvvfXWhNjP4ILvhNTklgv9aToXPINEXWPd5FAdbC/950Fr8rM8DtdgHM6hrueA8Q4+3WRsFdBlllkmxVNS8pZffvnkStt8880nKKA33nhjSjSgEKIFo4DmuFHAYU7ywmNN1B6OeKBS3NBkLrbYYi1sWSZZLVkbr7322tZyXyighGIpPIDl8/zzz09MS+UseN+zhVNfFMPG2IJFSXyowvn60U0UtK8Wz7d9Vfm0jCKdjy+7XpmhzNRk/TBFMgjshl3WZph97NUWpSe44HuhVO/6fq6BKhHD4oJHgztsNiRjS5net17Eht96cMEPH9NBW6R8uoeaPJ5Snut63wyK52S2dw0YpJp8DnVeA/dnuzrqZazHVgF1kplSkzXwjTfeSDGW+eRRabKGsio+8sgjabFamKwnhHIGPH9/+MMfir322istp4BccMEF6Xv+R7GaN29eccwxx7QKx+d1Pn/xi18kC6abdbvttktF6ymaLlB5hqCdXHjep1jSH/7whymBCTMSVzwFl8W2k7DAZoamTtvk5YrhZ8l1QOuqKzcOdUApP8EFn++Ymfk06E8nF/zOO+8cXPCVSy0UyTVoajk4RoXggq9c1Bn4GVzwMwB65ZB1XgP6TK8yeGOvgLJWigdlQaxaHSiTFEKfBM86FiSSFUHfKWXAzKIYNsthZkn54IMPkpJKWSSULYorK+nhhx+e3Pk5LtSMFcuRNlhQ3QBZfMdyZFYi9vOss85K/VhzzTULf9zXaD27KaDcY5KQqiKbd+211y622mqr6qr4HQiMFQLCXDxfOebZZC+44MfqEsfJBAKBwBggMLZJSK4NZc5MnUuc+70sLIU41b2ofFIKKXxli2TeHg+70k7WSU5S3V+2exYufvGajz76aPr7zne+U3zve99LFlE87xdeeGHxwgsvpM1ZUz/55JNE86kcFIpQiiVl9bnnnkuKJpM+JVbme46ZFKehlJOXazdxnhRZWfXaoDj/+te/Ll566aW2VKHd2op1gUBTEcAFnyeWQmAmywX/zW9+s6kQRL8DgUAgEBhpBMbaAgp5yUhKH62++uqLXAhZ5j/+8Y9Tgo4YRVzrn/vc5xbZjgJKidttt92SRdLLTWxYP8ISeuSRR6bEJslN2FVYN70cKYvKLeF3Z6rWvoQjcsYZZyS3PgstrnmK5Jw5c4p11123dVjtaD8LxZjyqwQVrno884SCrC5iU11m+fziMxDoBwETRVbPstfCfiZk7aTdcjHTYr7LnpB2+8ayQCAQCAQCgckhEFzwC3ETD9RPYXpxktzuVVd+v9CLzeS6LyuN9tWuF1222JTb8zJlbW2nGJe3a/fdC5g7nwLbS3IMaHDBd0YquOA7YzNda3gsKIftkupyH0w2lWLK3oO8vN9Pz+GKK65YvPrqq0NP8ggu+H6vQn3b5RjQHBZV35Hqa5lHjKesrpj9+nr+acs8dU2+BsEF/+m1bPeN/tGLC37sLaDtgKku60f5tM9ULYidBotu7VJ2J6N86q8XaTul1rqQQGAcEUDeoBJEu1Cafs5XyIzYbPGjvocEAoFAIBAI1IPAWMeA1gNZtBoIBAKjiABCieuuu27SyqdzMhlUizeUz1G8wtGnQCAQGCcExsoCqkyODHC851//+tcnXCd0mFzZw67pN+EgffzIdbeUe5IRX00qwliUyyhZv8zCup8rr7xyHy3HJoHA7EZA7PP/+T//Z0ogsJxiQPvsZz87pXZi50AgEAgEAoHuCIyVBZSCed5556W/8ml7qUjqmT9/fnnxtH+XGJSz4VlZcoH6ckdwvMu4x8JEaZY8JIs+JBAIBLojgIms7HofhP9dqIsSZeJGH3jgge4HirWBQCAQCAQCU0ZgrBRQaEgSkuQjCSGLZIJ2wc6CoDElsZyWhQXko48+WiSLlvWyyhsv0JbVhdUyl2byEpSFW27DvuqF2lYpp2233TYVyi8fN3/faaedUgkntQspzffdd98iffzzn/88wdrjHKr89e36kY8Rn4HAOCGgtFk56cjzpXoFxXLu3LmtU1VtQnkzJc2QOuSEQJUqfvCDH6SkPbV2QwKBQCAQCATqRWCsXPAZKgxITzzxRLHccsulRY8//nhyvaPKJJRGyh1GD4lBanP6vdFGGyVqTgqobHUZt6yPeNk78cazZLJsUj4l/HADnnLKKcXiiy+e6n1SPJVfsj9ll6KqPTU/HUO90G5CiZWElMvBPPPMM8UVV1yRXISspNiRKKxV/vrtt98+nVO1H46ZBdOSfhGxb0IX6uKFlXnqr6wk5H406VO1gKm6eWfqfPM1cE82VZxDOy54IS1l1o1B+N9R8RoTsCwRFlChL3WJc8iKb13HqLNdFQJQCme86jxWXW2bnNc11tXV53K7+g//Jj/L43ANxuEc6noOjKnw6SZjqYCq/amG3/e///30Ynn99dcTy1FWQD/88MNUmghFJrnlllsSHScOddYPrjwF7H0qEE9h7MYbzyp6xx13JEXxnnvuSfU8WWDIAQcckJQ87kBF57fZZptirbXWSgpo2qDNv3PPPTcd3+CiRJTSSF5YBhyMRpRczEgUaO2rFUpc7Mxf360f+ZDayBUAKM8Ub+ddh+ibG7LJvLkUT8kpTU1QUQJIzcumXwP3apUDW8wm74fC8Z4TkzaKalVM5KynRPEauP+NE4ccckiagNrevp7TusSzMNlSbnX1aZB2ccG3uwaDtDGT25oAGFvrGuum49zcu+6hJj/LjBFNvgbGEZPeuAbt73jv+14T7bFUQHG/e0GxONLuUVCWB3yW0T333LO47bbbUhwmdxyLByvgeuutl4q5b7DBBsUmm2ySCtj34o1XlN6gTNB5ivO86qqr0vG54gedpR544IEFqwyX+jvvvJMK0rOy+u28coIV9iZsTy+//HI6dpm/vp9+KICfJdcBLVtI87phfFJ8PLB1tT+MPvZqgwJqwOynrmqvtmZivQHBOTT5GnjxugbVOqCZwIFC6YUgRAWtbvX5k9Dneb/ooouSp0Lcp7Z4EDxzW2yxRQrX+fnPf17LJRqXOqAw7lY+rhbwhtQoBdSY3uTngPLmXdDkczAeNbn/3uuMEU0+hzqvgbbLXql2j+9YKqBOlBv+ySefTG507uiyvPLKK8Xpp5+eLJVYjShuGIkIC6NYTYlAlD6JQpQ5M/52vPFc5Nk9bn/7sIiyoGy99dbpRWf5IOIlyupKuMWdB8vst7/97cILzF9WqD0E2cw97H4M0ufYNhCYSQRYQD03XMOE16Mf/nfjwM0335z24cXwnM10smLqTPwLBAKBQGDMERi7JKR8vbjhKW7iJLnWy8ICwtqB+pJFxEuHti4paa+99kq88GIzxVZSML3Y+uWN//3vf5/aFYNmdlQuim3GOog11EydMiu+bdlll00FsimkWVm2zt9KK61UPr30vVs/Ftk4FgQCDUcAVa5EpCyD8r/bb9ddd00z9pku1ZbPIT4DgUAgEBhnBMbWAsr9JiaMu7oah8CSeOKJJxaHHnpoiokTC6n+pkx5Lrj9998/uVm5OVhISCfe+I8//njC/bHHHnsU3Hd42B1XvCc3IGFpPf/883sqoZnjnfmaa3+XXXYphAQQ8WqnnXZacfXVVycKzx//+MdJMU0rS/+69aO0WXwNBBqPgOdFYl6uQlE+oXY879a3W86DIPwlh9OU24nvgUAgEAgEAsNFYFZzwXfiZmd5/Ld/+7e2PLv98MZzkUuAaBcbkuNGesVG9LrMU+1Htf0cAxpc8FVkPv0dXPCfYjFT36pc8Pvuu29x++23p/jiqfTJ8ygW1GSxztjGcYkBNbmvE6epXMte++YY0Hal+XrtOyrr5TaIw+1E7zwq/ezWj+CC74bO9Kyr8xrwKgcXfJfr2OnhZbnstI47vpd4mbVTPu03rIy5qfaj1znE+kBg1BFQxxNxA4ViKiK+2/Mu9rua3DSVdmPfQCAQCAQCgc4IjG0MaOdTjjWBQCDQdAR4KNTbnaryaaIoTEacdSifTb8rov+BQCDQJARGVgHlYnj44YeTe00iUBYmY/Gawxb1Pp9++ukpNfvGG2+kF9mUGpnEzhIwehVHl5RUZoeaxGFil0BgZBC45pprpkxqoJKE+p9IIZQ0CwkEAoFAIBCYPgRGUgF97LHHUrKNUiosHRKGLrjggoSKYum51MowYVI38KabbppSkxiXlG+aboFXLwXUNrjlQwKBcUBAkl/5nl9jjTUK8aDV0Be/JeSpdkG423fYYYdUsF75MnG9uaTZOOAS5xAIBAKBQFMQGDkFVOF1meKXXHJJcfzxxydFVKwXq+drr73WwlWA65/+9KcJVpAyH7qXU/kFZUdZstll14nvPR/AdjLcsQ2VReKSY9u/m+S+6EM5U95+5VJM2nMsyQ/2qYo+O0/HzJLb1pblFHTsLVlYidtx3Of18RkINB0B9LlZFKE/+uijU/F540ZZ/FaSKdPjZlYxzGTYyjIVbXmf+B4IBAKBQCBQPwIjV4bpxRdfTEXksRllYcVAlylGSw1PCpYSSupqUlhRVyqlVOZDV35JUkEuMs2Nf8wxxySXvpcR5U27me89H8unjHAvNMqdbHbMSWeddVYqq6Q+qN/qe55zzjmL1BjN7SiTRPFUm5ASqY4oi4u+s7oopaQ+6T777JNYmGTHogidM2dOoTg+URD7wQcfLP7Lf/kvxf/6X/8rWYHVAS2fp7JRlHMvWsXrO3Hc536VP4899tjWC1hGK8zr4oWlZPsLLvjyFZje7/kalCdA09uDqR/NObhHsYJl2WyzzZL3gpVfGaWyuN+UZJNYyNK5zELGs6OOOiqFyhx55JEpS9MzNZ3iHKql4abz+FM9FhpT3iLMZk0VY3FdY910YKL/8G/yszwO12AczqGu54D+BJ9uMnIKKMWuWjjeCZQTBCh0l19+eVK4rr322qSkUUCJE8aHTqFTaF573G+PPPJIYhKiULbje087//u/J554olhhhRUKCpr21Bmk+HLzEXzTQgJ6vURYKlFy6i82Ju35VDaGuz6fp0/uQ6WVKJ8oQLkKxXbeeeedSdF2TvjdDz/88NSHfJ5eZjkmthPH/UYbbZT2qf5DUZotp17OlPK6uHn11w05rCoA1XOZjt+s2cEFPx1Idz6Ga4AKVc1OZBNewu7hbBFV39PENCuoXtDLL798uveUDhJXrk6vShV+a6fKlNb56MNZ41losts/uOCHcx9MpRXvFvdQk8dTk8O63jdTwbbffY09xpG4Bu0R877vpSONnAKqvBC3dDdh0chUlRRFCTZZMh+6hxPP86OPPpqYgnyiyWTpa8f3/uqrr+YmEoOSH/PmzUvLKIYLFixoKaDtitu3di59oVi6AM7Jy85xyWKLLZaoAvOmFE5iO+dDaXZMSuh5552X1nlY//t//+/JYmNBPs/yBe7EcZ8aaPOPgp4l1wGtxtDl9VP9DC74qSI49f0NCBS4uq7x1HvYuwUvXgqoskmeF4N/5n23t+c+K58UUcrpEUcckahxud1feumlAuWmpCP3v+ftZz/7We8DD2mLcakDCvem1wFt8nPgfeD+bvI5GI+a3H+T2+CC7zwwur4U9G4ycgooWkmutKrgaGex+9KXvjRhxlFWwOxT5kOngMpy3XjjjdMLZ6mllkrNtuN7p1SWZd11101KXl5WHmzLx8jr231WtzNg9BKzKrNClk2udzz07aTatm26cdy3ayOWBQJNRIClnvKZkxE9C5deemmBQnPBwoki8VsRZJM24S7c7EJwttlmm0S3SzH90Y9+lGLM0w7xLxAIBAKBQGBaERg5BXTzzTdPFgoxRt/97neTBRGnO8sFSwY3c7+y5JJLprhGNH3bbbdd2k0MJjc217h4TlZGSQplBfS//tf/mo6HG9r6uXPnJrd4XbFiXP6sl3jdxaQKGWCZ0UfngLGD6/CZZ54pKM+dpMxxz9Jy4403Jtdjp+1jeSDQJARUxaBEihPPVk79N2k74IADkjUiU2xSPonYbVaKvJzSml30lFgZ8SGBQCAQCAQC04/AyCmgXNVnn312cpcrtULEcJ166qmTcvlsvfXWKaue+4104nsXp5mFAiquEgf7Zz/72UKWLcW4LhFCsOeee6YXKWtNtrZSwHffffdk9eVaFIvaTTpx3HfbJ9YFAk1AQDIhpVJ4jslVO8lKZnVddTnl1fPEM8KrEhIIBAKBQCAw/QiMNBe8GAJxFmK+JitiP5Va4W4rCxd3J773vJ14ORbQOoOMd9pppxSDJhZGolU1pAAGLDxZKc196/bpJa29alvd9skxoMEF3xml4ILvjE2da+66665Uy7Ns9RzG8VTGENIznTIuMaAMBYOMSdOJca9jGfvF2AcXfC+k6l3PG9nka6AyDe8KI1VTpc5rQHdpNBc85c/fZESmqdJEWE6UaaoK5awT33vediqKb26j389Og7nz77SuU9u9zqvTfrE8EBg1BDy/PBHDEs+951q4ynQrn8M6h2gnEAgEAoFxQGBy2l0DzpyLTVwlF/yyyy47sj1WmqnJs8CRBTY6NhYIiPselpjIqZ6hpFm5zvCw2o92AoFAIBAIBPpHYGwVUBAMWt8P4xA+d6J8ADe2+qKDWiBTA6V/wgg6ufE33HDD0padvyq7IfmC9UbCVNky/NZbbyUmJZn7EiuyOO7LL7+cXPHrrLNOchfkdfEZCIw6Ak8//XRKzJtqPz17kvoos3vttVejaw9OFYvYPxAIBAKBUUGge5GmUenlNPVDrKjs+3fffTe56BR+lwn/7LPPTroH2kQtOhURayJJidtQYlY5nhVbklAD8WwygTO1oLjRfRayLKkg4JyOO+64Fg3pVPoS+wYC04XAfffdN6EmsLJk++23XyqpVO6DyVjmd8/LJQ5K4hOrqG6wiaXno8mFr/O5xWcgEAgEAuOAwFhbQCdzgWTcK/WShQKHcpMSJ7ZSgg+LaC6wKpg9Wx0l8uCOV2/Ui0/CwQcL6UAlM2UGF+3KuLefGLRsydQu6yZarFyvNPcBaxKGpD322CMxM7HsKkclANo6bFASmGT0qnvoE9uSwvfoBslBBx2ULKjrr79+bjY+A4GRRoDFXyw3YcX86U9/mv4wGUneE+ROJM499dRThUoXEgIkK2H/Usni+OOPTzHgTWcfSica/wKBQCAQGCMEQgHtcTG91DAoKZWkbAsXHp53TCpeapRBSqo/dTszT/wPfvCDpAhmKw6FkPWmE797N475MmMRxibHRYdH+UQPykKqRqiX7lprrZXO6L333ivwxGexnHJaVkAp2tliSqleZiFPdl28sDJP/QklaLKYZJhMNFHyNRCa0QTBcJQFQcVvf/vbdJ97JldZZZVWuIz7tszvrs7n/fffn8q3YRLjDTDZExceMnUEXAsTcrg2UTwHjAN1jXXTgYl3APyb8iy3w8Q5NP0ajMM51HUNGAjg001CAe2Gzr+vUxiei7ubPPDAA4l1ycvxnXfeKcRlSi5Sx/OFF15IyierZzd+914c82qhKlqP3YmFleLJwspCu+KKKxZXXnllcrVTMhW0L9Oc+f6Xv/xlwimIGaXIEi/njz/+uDYXpQHfDdkPG9SETo7QD4onq7O/JoproCZmp3jkUTsn7nJsZrwEJi7/+3//79TFsjfBgiq/u/PjyRD/zDqaqTjbsYfNxDkblJusDKMSdk2a+ixTQCluTQ7HCC74mXhyJx7TBCC44CdiUv7lfd+rFGQooGXEOnw3WPV6eXlRYl1heaFIogWsiqQKA3cnfvdeHPOK1B944IHF0UcfnRiSKBTqeKmT6IWGe/6Xv/xlsnL6XZ59uBmq5yC+NUuuA1pWWvO6YXxSfDywdbU/jD72aoPi46U1neW5evVpkPXuAefQlGsgdlN4i2eGMokggogFLU8Iq/zu6vuK35ZUyMpPIeV5GAWlzzOrf02uHSjMCKZTTc4c5N4d5rYUUMaApjwH7c49uODboTK9y+gFjBFNvo+8E+rqv7ZzqGKnKxMKaCdk/n05pQlV57777tvaMit2ZqFZsBBtsMEGKWHpkUceSS/AzFWdtzHwDcrvbl/WVOVj8Fd7KW+22WbphczqyR2WX6x+S0girBSU0yy+R93DjEZ8zhQCBu3nn38+hY+IY/Z8CVsRt0zBdI9n2XHHHZNF3zbEbNr9LcYTHzzrJq+AiZfQGPsqayYeGt0uBemOO+4ovvGNb7Sekdx2fAYCgUAgEAjMLAKhgHbAn5LJxc2tTXHzkiNmCywrEoW41rOceOKJiW+eJZQlU0ynGQA3lZcu2XTTTQfmd7ef2DdWBwkVZr5e4F7OFN5rrrkmrROTmmNC7SNe9aGHHkqfXJcKenPVhwQCM4EAC7hniRXfc1Gl1Myz8Dlz5hQnn3xyirHeZpttipNOOqkVa4iKlsUhU2uq7EDK/O5+/+1vf0sxoZ49FusLLrjA4pBAIBAIBAKBEUIgFNDKxeAm32STTZK1hZtMnGRZcfOCPPPMM9MLksUxsw5JFLroootSRjrFFQUV16FYNGWYvDy1Myi/u+5hgsHmJImJYky5lPzE8inZyR+rj+NlKk3c9cpHiUFlBtc/pWlCAoHpRoDLecstt0xlxIR6tBPbEAl+EvZeeeWVdL+69yW85ElcVj6rbbSj6bTMRNFkMCQQCAQCgUBgtBAYaS740YLq0954CXohUvqq4kVqeTn2oZr8wQLErThoDBWXP4tOuW3Hp5SycmZluNwnL3yxn5TTbpJjQLMC223byayDmXPOlq7JtDHT+wQX/OBXwHPini1bLvtpxT2uDBMWM2QQ5ZjPfvbP2yjPZMI2KjIuMaDBBT+zd5TMZXG47cb8me1Z/0evk4e8/15MfkuGJuNak+O567wG9JxeXPBRiH4S95+brp3yqSkKVlVB9NtgkYUyOKjyaV+DfrVty1lCOw1EjtNL+dRGSCAwbATEPLPUu/86WS47HZOitt1226X9lBgj7e79Tvt7VlBujpLy2amvsTwQCAQCgdmIQCigs/GqxzkHAtOAgJhkcdJmwpMRmcoqS4jBZm1YffXVO078cvsUT/HQ9957byGGNCQQCAQCgUBgNBHo7pcdzT5PuVdc2aj5UG6uttpqKcFoVK2EXC3lZKd88rLcM4+8ovOKdqv/WS615MUvgYl1dNVVV827xmcgMC0IyFDPsZ2TOSD3/fXXX58Sl9zvSjFh/br88ssTR7xnlpWVyJBn7d9nIf2sDHg1eEMCgUAgEAgERheBWaeAKtCuMDX2FErZbbfdlpKDrrvuuuTiHrVLpW4jRbksEqVk5VNAMcBwwStlo9ahGqESpyjZe++9d2JHEuch6xijU0ggMB0IuN+wh1VFuTJlyqoiaQ6bmHhmyXIIHWwntnnBggWp6oPQFy59lLTiiVWjUI5J+SVWT0mBIYFAIBAIBALNQGDWKaAUNjzSZXpLpWGuuOKK4oc//GG6al5uXmwshywvhDVRCSQWRoxC6nGywEjekW1brl/oJSphRRybLNxsXe3E9856yWLTrsC5l3GZmx7jkpg4SvSbb76ZXJM33nhj6qOaoBRqCij3JwU7c8HLhsfQZJuQQKBuBN5+++0U95zZi5RE8pxRNKsK6Pe///3i7LPPTs+K50si3Lx581LZJm503gplx8qCwSuzeJWXx/dAIBAIBAKBZiAwqxRQit6//uu/plJI5cujRFJOcMDdTqGTfYs7XUkmxa29UNUTZIXh7uMaVyLGy1SChSLaXqSsMuoYLr744sUnn3ySsuXVP5ScVOV7556k+MqiY+U8+OCDk3Jc7lv5u4L0CnFfeOGFSTGmtGo7CwWXkkyWXHLJVEBf/VBKsvPGKlMW1lJuTcJ9Sdm1fV3CXUq5b7LAMitVTTwP1yDfI3X2371aTjxyj999993JUl89rsmamrUEtiZL66yzTprYWUY5Zd0PGQ0ETGJvvvnm5GUZjR4N3gvGgTrHusF7NNge+s/LNR3P8mA963/rcbgGDFO5RFz/Zz46W9Z5DWDDGNdNZpUCSomU0FDlJ80Z6l7OYia5qlkuvRjV7aSokQ8W8q6zMHL3iTOjvCmCTRk94IADkgL66quvJuvqtttum/ax/He/+11yKVqQ+d4NHCyxXq7KzBgMbSvzN1tMUwP//o8yO3fu3HRccauE0pxjPvWB4kxZJphlWEC9zCkCaoVSisuy3nrrJYYly7jxKei5vfJ2w/juRndDNpU/GgbCIUxA/DVRXAP3Qr7f6zwH3gNeAuQLwl48V+7RdiJb/f+3dx6wUlRvGz8WxIKIFSIoHwQbSBFUDCDcf+gQBUFiUCMdIxI0KhAk4qVEQRTEIEQRFFFUSuSqXAEpUkWDioohQSWfiJqIKBpbLPHjd/7fWeeus/XO7uzsPm+y987unDnlOVOeec9buM6ccJ7gcMSNHacizEt4wUPoP+c952tUhZtylPvPqhDXQJSvZe6/hAeLqkA+OYfycS3nCqOozwHKFO5FmgP/M4T7eDzXii9ZUgQUDSBawkQCWGRX2bx5s03hRwpOSKnT5KBVhHwiEFTsSBFuyHjsAjhLhTgNLViwwOAcxHK99w3J5XsnDSE3cL4j1As5htTiTOQV6kWrysPcz7MXrStZkkgX6o4lQxLaOnLDo1ViP2lAvWFplAvei3LqbeWCT42RK4E2nZcZrrlMNMZcS82bN7fXD0kcLrjgAvtCxyoFApklBFqiMGiu/UL9z0sAKx9Rjh3IyzIP3WxCyRXCvHBP554a5ZjEkDeeH1EeQ9TngOc6L2KaA/+rmvl1K8v+JYwpKQKKkwJaTciidzkae0lIJ+k0hw0bZh130A7i7NC/f/8YdvHaQacB8LJ8tKf79++3RLFXr142O1KsgqMbrg4etDyM+DhtCCe0n8r60UcftWUI6hove/fuNRMmTLD2qxBUJ2hdWfJHE8Wna9eu5p133qlCQF1Z/Y8eArx0YC6CXS+aa16O0BSiQS+EiAe8WLFiQEYjP8F+87bbbjNTpkypshtNP1p7nJUgObyoOe1nlYL6IgSEgBAQApFGoKQIKG8qEMpJkybZdJoQMx7k2GGypE6sQTSkt99+u10Gd84SfqQw0azj+ctSevv27e2SI8v+ePTGCwT0/PPPt+kysTOFtPKJdxJieZLlS/oYvzRPzms0tjzEWcb3Cu3z8CdfPG8iaFa9jlfestqODgI7duywaV05z3B+89rUQtgw6WjYsKFN3Ur6y7CEN1+09ZisuBUE+uK07lxrXvJJilsnI0eOtNodxscyfuvWrd0u/RcCQkAICIEiQaCkCChzBrlES4mmE+0jnud9+vSxtpnsR4vIPpb4WGJnqZ1l9HQFrenjjz9uli9fbu0feHgmOh6nJTyDyX/Ncnl5eXnM6961V1lZaT3yCT/jFTRe9BXnIjzineCYtGrVKqsJox/YfrJ0jFMHZFQSXQRmz55tSRsvSSwjxgsadD6QU2wqcWqbMWNGfLG8fYdgvvrqq/YczbRRyCcviHjDx794ZVqXygsBISAEhEDhIVDSueCxxfKz36iugTfEFrs3v7r9ToF4kwC/Mtn+hq0QD/BUD3EI8NSpU20InGzbSnYcWjC0delikqyusPZhvkH//cJl5bpPaBKx8WU+0xU0orwQLVq0yB6CJpyXEYhdvgTSzIpDJnag9A3bKl6wWIXw2hHJBjRfM5e4HWxAcQ6Lsg0o99woJyvAoY/rO5/XcuIzIrs9ucxDnl2PMjtKueCT48XzRrngk2CUiAxxc+XizlZ4YCaq269Orz2q3/7q/IanZyryWZ36dWzuEUCLiF1kJuSTXqENJfQRmvCwhNi6JETIRLCTZvVh3bp1VchnJnWorBAQAkJACBQ2AsoFX9jzo96VOALYH7tkAtlAwXJ9dVNiZtOu95i5c+caHOnw/HaOe9793m1e3Hr27Gk++OCDlCE8vMdpWwgIASEgBKKFQF4IKB7mLJ3FC8sg7Ata1qxZU8U5I936Wdbg2JdeeskcOHAg3cOSlkMLhRCYm7BOuRDXRrK6sQv87LPPkhXRvgJEoKKiwkZtqE7X0JwSlitMwU6Zc5DIDDjgkTkMzT/LoGyznNu5c2er9Vy5cmUsWkSYfVbbQkAICAEhkDsE8kJAFy9e7Et+IKBbtmwJfHQsOWLfmYmsX7/eBpJH88KxhGQi81F1BGcnYoIiZFXKBQEl3NLDDz+cspuMD296SbQQgIxxnXiFOJk4x3mFUGCE/XJxYNmH6QUhjLD7xCkubCHOLfFxedF782h+9xUrVpjnnnvOpo3FrpBzlPBnEiEgBISAECh+BEL1gicMkVtexPGHByaGqzyMeFh5bRfZDzGsV69elVnBuYW87S43u3cndaH9waaMWInEHvRzICGWIiSOUEeEsEHwIr755putVsYFi+d36kFr4+pJ1G+0kv97NA4igbTx6CUzkosXyrIoxuOME3HB7dnGu5nxMHbv737H4OxEGxAMPq5PGHfzgaRgzyqJLgLxcTTREpKWErtKzhMnnLu86BFqi1iakFaWvbkGNm7caFPJurJh/+c6INORRAgIASEgBEoXgVAJqCN+Tz/9tA1FhJcxcTkJgYTHNHnOIVX8xxGDQNt40/JgpQyazq1bt1rbMjzSZs6caXO4u+kkBid50yFqLPGxBE1omMsvv9wVsf/ffvttqyly5JMfsUVbunRpLOPKtm3bfPO2E0LJr98EfScTEsQR0kz8RupkCXLw4ME22wsElA9xQlmixBOdtJ+QBsbJeB944AFLXP2OIXYiwchpA7MB6iZjEnVCcAlSz3digvrJXXfdVSUXfOPGjWOk2K98dX+DXHvjVla3vjCOZ44y9eiuTj/j01fyYsL14A3DhMc4y9m8KJH5Ck07BI9zhzixCOG6/NLQVqdvOrY0ESBWMfdGXr6jKjwTnAIgimOg/+CfqWNiIY21GOaAZ3U6JnCFhLu3L7mcA7BJFUM9VALqBYJt4mW+8MILlnARIxMSh30Y5JMlRDy60fJA5tjetGmTXcJje9myZbYMKSe9wsOYfO2kBnz++ectYYsnoBDVFi1aeA+z2y7dH6Rp2rRpvnnbKejXb0jl66+/boNxEwuUPnsFL1/IMOQVD2fCFaCpQmNKPncmjvSDe/bssWSVY/2O4ViW+YcOHWoJNoTdhd3hIUEYm0QElLigbimXbEzYvbpMTd6+BrHNic4JmcoJJYi2clUHWmbIHp98CXjhlAPxZC75XHLJJTFtOv3gPGUuIZxcNxBkTDO82m8iMxAEHuLqMm/lawxBtsOKB2OJ8hi4tqPcf2x4uQaifC1D3HhuRFUgn5xD1YnWEvbYoz4H8ALuRZoD/zOJ571b9fUvUWCpOHmIug6zpM5FxlsqQdTdzcKFdJk/f75p165d7Hds3dAAokH0CtpHyCeCBuj999/37rbbaI/QIiaSZHnbOcav34nqcr/Td4SA8pACLkYINULwbYRlVGzlsPlD/I6xO/7/DxrTm266yWafgXhDXiGtiWTAgAGxXS4OaCbho2IHp7FRDHFAIaCch87UIY1hV7sIZhicnxBQbnZ+wnlCuCXObQgqWbEgoF7hRQ6bZsYQ5diBigPqndVwtrkn89CNchxQHo65utflY1Z4XvACEOUxRH0O0HzyIqY58D/jmd9Ezyx3REFpQL0T6YgoD0sG4gSixMWHPafXqxs7Sz7xA/bW6eqI/8+SEg4Q8YLWs02bNpYAo8Hj4zQXnHxOvextw/U7vq74796bt/eYK6+80rRs2TJW3FvOu+09xhXGXhCtKnaApBylnu3bt7vd+h9BBMiANX369CrpLL3DeOihh2yYJchneXm51eSTASlesB2VCAEhIASEgBAoFAT8VSqF0ruj/ejQoYMN3+I0lDhbbNiwwZSVlRlsN52HMOGTWJqMJ6DpDKVLly7WaQePXDR1EN433njDmgDQvjdvO/Ulytse3xZvqJnYh6DFxVseQtysWTNrZ4UdZzLxtoHGE/MCCOjFF19s3nrrrSrkPVk92leYCPTt2zdmh+x6iA20M+nAxpOlIOyoWYLv16+f9TKnLCsI2A6jQcVUQyIEhIAQEAJCoFAQyJsGFHtGr9YOm8emTZumxAGtHw9VSBXL1SypcyxLQL1797a/s7yIHZxbuk5ZaVwBbOUefPBBmyPehatp0qSJ1Sw5rWM6edvjqrUaSLzr0yWhEFDiol5//fXWsQrTAchxMqGftAG+ZJ0hfBT57iHSLMXmIs5qsv5oX7AIYD989dVXm8rKypQvE6wA+AlmGFwraPAlQkAICAEhIAQKAYHI5IKHUPHxOlYAIL+xJO+IYnVBRfsJYUxk54fGFY1SukJd2IlkopnFTg+bv3SNmyEW4ODKoy3GLMBL+FP119mAsqSbC6F/aOq85gq5aCeXdYaVCx4nNzTimca2dVgQAB47Ys5t2YA6VML5z7XKPJIVKqqCDSj34aDuufnGAZt77uPKBZ9v5Ku2p1zwVfEI41su54DnTdHkgofExZNPJozfg7wRQvwSkU/ay4R8Uh5SmAn55Bjad2T7dhOfAAARPUlEQVSS76mE+r3lsZvNhHymql/7w0UA295sySfnUi6SPYSLiFoXAkJACAiBqCNQ8DagUQdY/RcC1UEARzeXrCGbetB4hp0LPpt+6xghIASEgBAobgTyZgMKjDhFfPTRR2bfvn12SZHYm2gcM5Evv/zSZoAhNBP51XHCIXYmTkidOnWyQbjR+hCSqDrCg3/16tX/qoJQTthWuvayjZvJ0rxXa/mvhgL+gTScxPwkvJUkOgisWrXKBpGvTo8xUSE27OjRo6tTjY4VAkJACAgBIRAYAnnTgBK/b8iQITZeIXZQL774ornxxhstKc1kNHiJ81BGvPnVXf73oHKeY79AZiU8yyHM7sM4ENee/ZLhH+omZmk+hQw5qTzq89kftZUeAsT3dJEe3BHZ5IInU5ZECAgBISAEhEChIJCZ+rEavSbt43XXXWe91l01kydPtukt8d5G0Dri6AFBRVvn1Y5iLMvvXvHmV/f+7t3G8YWc2dhFEk7JK2hk/fLLe8ugNUrlOINRu1/+dupB04nWloDizraUtIiME2KBrSb1Y8cZ71BF/8AALS9CeTydvTni+Z3jaB/tphcz9pG7npipkmgiUIy54KM5E+q1EBACQkAIBIlAXggoJAjSdcMNN1TpO6GDnIMOOeDJW37OOeeYw4cPW+JGzmvIGYG4WULGCYmPI5ILFy60+xNpE8mTvmTJEpsfHm1px44dbZpLOpEov3yVDqbxBe/xRPnbyUYDySbHOnnvIbOEwyE1KKSVsFF4rBM4nID3K1eujKULhZiOGTPG9pcwU8R4pCw4Uh+YEBQfTezWrVutV+2hQ4es1pb9EHbaw0ELPB2J9Q4J4r9r1y77E+UwW8hlfmTGzAtBlIX5jmIueM4dXlDknBbls68w+k68ZeWCD3cuUMagoMC8JqrCGHL5vMk1LvSf1UyUTFGVXM4B2LhkPYnwyQsBJdc6tpPxDz+vDSSZXIj1iVYTGT58uE0nCHEijAxL9hCuCRMmJBpLld8hO5DWOXPmWG0qGkcIMGSRZXW//PLdunWrUgdfCCPgsh/xfcGCBVW+J8vfDvmdNGmSDQ7vCGGvXr1snM6Kigobv5Pc9uS8h4BCBhkvGZ7wtodsEpQeEtugQQMza9Yse8LjlAK55eZD+k4C6JMictmyZYY4puPHjzfz5s2zQfxHjRpliSuxVOOFHPHUizBGUo5ma9MaX3f8d3ex+hHh+LKF+h2HHqIu8MmXgFdQueAxeeG68J7P+RpHUO2g7eelNapjAH+uhaj2n3nkhZhrIMrXMvdOl945qHMzn/VAPjmHvM/QfLYfRFtRnwOUKfERaILAJZ915HIOIKDxnC9+bHkhoJApNDDJhIwvO3futATv008/NcQ+5M2CbbL7uIc+edchlqmEgZMlhkDsEDNyo3Pz5wGGNtQvv7xfnWgJCXLvJP7BkSh/O8QO4oyzEvm5EbRnOE7hNOWEIONTp041w4YNs0v13bt3Nyy7ouklCxPCMY54s8SOsxW2ruBKfnh3IyWQPdpgSDZjHDt2rD0e84NLL73Ubnv/EPDeiYsDmsrcwJXP9D+4Rz0OKAQ0yrngsWlWHNBMz9xgy0M+iyEOKMQnyPB3waKcvDaeAzwcc3WvS956MHshDrwARHkMUZ8D+Am8RHPgf04zv26F27+EMXkhoBdeeGHM5tEbR5OUmhBE8pejqSTFJUvUaAlnz55t+8wEezO8xNs4JhoYFyikDi1f27ZtrcMT+dGRZPnl4+tjOTvVCZYofzt9veaaa2J2mZDseC906udtFhwINg7ZxmEEouv6i8kBBM4JeKDaxrYTbakTfufDpGeLm6tL/wsDAeWCL4x5UC+EgBAQAkIgWATy4gUPgYNMsRztNKHYfJLX3S17oy1kCb5Hjx6WPLFsD8lq1aqVzfmO1gBG/eabb6aFAPaQtEVaSrSmLPEj1Jkov3xaFccVSpS/HaINocRrnv84DmHHSfuORDtSiRYTe1bIJ5pKxo7NaMuWLW1rtLFu3To7fsgqGFBnWVmZxcZ5SaNtxT4LAkqoKAg+Gpdvv/3W7N69O67n+hoFBHhpcc5rrr/Z5IJn+V0iBISAEBACQqBQEMiLBpTBQgTRcqKVhBTxUO3Tp09smZkHJA412DCyfE4ObJbh0YjiPc/SMss+5EdPR7A5Zama9lhC/5+j+bDxrKdONLJ++eXTqTe+TLL87UOHDjXl5eXWYJ8xY4OK/RRCDFTGj/0my/ArVqywdqAsq+AMRJo4R1S7du1qs9lwPASW8hATiCb4QNypl3Hi2ITceuutNi+8s/vzLvvbAvoTCQS4DnCeIyYtc59MvCsF3nKc+6wqcA5KhIAQEAJCQAgUAgKh5IJHm+m3rM0DEg9jv31u2TlTJ5lkxtpoIPn4pfjMdHKwrYMw+hmFo6H0mh64ujM1AMZOExtEZw/r6mEM1OVnk8UxkP1UthjOBlS54B2q//6PIxvnZrxG8t8lg/2FMF44qWXrMUryBzTrrCDIBjTYucm0Nu5xxWADyj3T736TKR5hlMcGlHuycsGHgf4/beYyD/k/reRui1VWnsV16tTJXSM5rjmXc8DzJlUu+FAIaI4xVfVZIAABRZvatGnTLI5OfQg3fa/5QeojCq8EJJ8bTrwjWj56CnEk4kImQj+xgcbmGSmGOcDwn3G51YFM8CiEss4BJv4lshD6lm4foj4HjJOXds1BujOem3JRnwOUYqzWRvk8yuUc8LLNvXr+/PkJTyAR0ITQlN4OHo6SxAhAzolVO3DgwMSFcryHOUpnnrgx8ik2wU6auLmEaZOEgwAmIdj033HHHeF0QK3a0GyYYt17771CIyQEiCJDmERn9hZSNwq62VTPoLzZgBY0SuqcRSDVyVLqMPFGxydMnMJsuxDmvxDmoBBwCLMPzAEvQaV+LmoOwkQg/LZ1HVR/DvLiBV/9bqoGISAEhIAQEAJCQAgIgWJBQBrQYplJjSPnCBAirGHDhjlvRw0kRoCIEMTOlYSHwH/+8x8bSSS8HqhlzCByZa8vdNNDAPt6IutIskdANqDZY6cjhYAQEAJCQAgIASEgBLJAQEvwWYCmQ4SAEBACQkAICAEhIASyR0AENHvsdGQJIkD8wPXr19vUsiU4/IIZMvGCt2/fXjD9KZWOkFVt7dq1NlNbqYy5EMep8z/cWdFzIBj8RUCDwVG1lAACFRUVZsyYMYY0spMnTzakxJTkHwHisZJhjPmQ5A8B0hkPGTLE7Nu3z4wbN868/PLL+WtcLcUQ0PkfgyKUDT0HgoNdBDQ4LFVTESNAEP0lS5aYKVOmmBEjRphZs2ZZTShvwpL8IbB//34zePBgQ+IESX4R4IVr2rRpNrvJk08+aRYtWmQSpX/Nb89KpzWd/+HOtZ4DweIvL/hg8VRtRYoAGR0WL14cS8OJFoJlsFT52YsUjtCGRWrdiRMnmsOHD5vKysrQ+lFqDZNW7+DBg6ZFixZ26HXr1rUpjEkT26hRo1KDI7Tx6vwPDXrbsJ4DweIvDWiweKq2IkbA5YAnAPGcOXMMYZnOOuusIh5x4Q2NnPbNmzcvvI4VeY+++eYb+/LlDT5PildySUvyh4DO//xhnaglPQcSIZP579KAZo6ZjigBBLDzfO211+xIa9eubQYNGmS3yYPNMjyZYNDESXKHQKI5yF2LqjkRAmh+4rX9aEVPPPHERIfodyFQtAjoORDM1IqABoOjaikyBGrUqGHQ8CC1atWy/1n+Gj9+vKlfv74ZO3as4aEsyR0CfnOQu9ZUczIEzjzzTPPzzz8bHrw1a9a0RdF+KhB3MtS0rxgR0HMguFkVAQ0OS9VURAg0aNDA3HLLLVVGdP/995uLLrrIjB49usrv+pIbBPzmIDctqdZUCBx//PGmbdu25pVXXjEDBgwwW7ZsMaeffrr9pDpW+4VAMSGg50BwsykCGhyWqqmIEdi7d6/ZuXOn/Sxbtiw20rlz58YcM2I/akMIFCECo0aNioVfOvbYY82kSZOKcJQakhBIjICeA4mxyWaPUnFmg5qOEQJCQAiUKAJHjhwxderUKdHRa9hCQAgEhYAIaFBIqh4hIASEgBAQAkJACAiBtBBQGKa0YFIhISAEhIAQEAJCQAgIgaAQEAENCknVIwSEgBAQAkJACAgBIZAWAiKgacGkQkJACAgBISAEhIAQEAJBISACGhSSqkcICAEhIASEgBAQAkIgLQREQNOCSYWEgBAQAkJACAgBISAEgkJABDQoJFWPEBACQkAICAEhIASEQFoIiICmBZMKCQEhIASEgBAQAkJACASFgDIhBYWk6hECQkAIFBgCZO/atGmTKSsrM0888YT56aefzKBBg0zPnj3NzJkzzY4dO+y+IUOGmDPOOMP88ccfhoxHY8aMseU///xz07lzZzN8+HBTq1YtOzrywS9cuNCsW7fOkCP+nnvuMbNmzTLl5eWmfv36SRH48ccfzd133+1bZsaMGbYPvjv1oxAQAkWHgDSgRTelGpAQEAJC4L8IfPLJJ+aRRx4xI0eONK1atTI1atQw/fv3Nz169DC7d+823bt3N4899piB/CF//fWXeeqpp0zXrl3Nr7/+aq699lpDullI699//23LQFCnTJlirrrqKnPCCSdYAssx33//vd2f7A855Zs1axb7tGjRwhLZbdu2mZNOOinZodonBIRAkSEgDWiRTaiGIwSEgBDwInD48GFL8lq3bm01nBUVFeaXX34x69evt8UglvPmzYuRUH6EoKLlRNq0aWM/mzdvNrVr1zaLFi2KaVXZX69ePav9dASV3xLJySefbO68887Y7smTJ9u+oKUVAY3Bog0hUBIISANaEtOsQQoBIVCqCKB1bNmypR0+GtDzzjvPEkyHx9lnn22+/vpr99X+Z4neyWWXXWYo895775ldu3aZ0047zXTo0MHtNr17945tZ7KxePFiM336dAMhbty4cSaHqqwQEAJFgIA0oEUwiRqCEBACQiARAhDG4447Lrb7mGOOsZpM9wPf46Vhw4axn9hfp04daz/6+++/m7p16xpIrRP2ZSobNmywZgHPPPOMadeuXaaHq7wQEAJFgIA0oEUwiRqCEBACQiBIBDZu3Bir7sCBAwZbUpbwsd/EMem7776L7V+7dm1sO52NPXv2WDvUiRMnmoEDB6ZziMoIASFQhAj88xpbhIPTkISAEBACQiBzBJ599lnTpUsXgyb0vvvuM02aNDEdO3Y0p5xyil0u79evn8F+86uvvrIe8Om2wFJ/r169TLdu3cyIESPMwYMHY4fiUS870Bgc2hACRY+ANKBFP8UaoBAQAkIgMwSuuOIK690OAf3444/N6tWr7bI9S/k4L5166qmmb9++ZurUqWb06NG28nTI45o1a8wXX3xhli9fbs4991xrj4pNKp9Vq1Zl1kmVFgJCINIIHHPUc/G/sTUiPQx1XggIASEgBKqLwG+//Wa1kCzBt2/f3vzwww/WAcnVe+jQIcMSellZmXG2o1u3bjWdOnWyYZiwN5UIASEgBNJBQBrQdFBSGSEgBIRAiSFAjE+8371CoHpihD788MPmyJEj5sMPPzTjxo2z8UQhn3/++WfSj/QdXjS1LQRKGwER0NKef41eCAgBIRBDAK0mpBPy6Scsmy9dutSwlE78T+xEGzVqZJfU3333XbusTplEn8rKSr9q9ZsQEAIliICW4Etw0jVkISAEhEB1ESAlZ82aNatbjY4XAkKgRBEQAS3RidewhYAQEAJCQAgIASEQFgJagg8LebUrBISAEBACQkAICIESRUAEtEQnXsMWAkJACAgBISAEhEBYCIiAhoW82hUCQkAICAEhIASEQIkiIAJaohOvYQsBISAEhIAQEAJCICwEREDDQl7tCgEhIASEgBAQAkKgRBH4P1nkbKZxuB2jAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n<div id=\"发散性点图\" class=\"section level3\">\n<h3>2.3 发散性点图</h3>\n<div class=\"sourceCode\" id=\"cb14\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb14-1\"><a href=\"#cb14-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(mtcars, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span><span class=\"st\">`</span><span class=\"at\">car name</span><span class=\"st\">`</span>, <span class=\"at\">y=</span>mpg_z, <span class=\"at\">label=</span>mpg_z)) <span class=\"sc\">+</span> </span>\n<span id=\"cb14-2\"><a href=\"#cb14-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"at\">stat=</span><span class=\"st\">&#39;identity&#39;</span>, <span class=\"fu\">aes</span>(<span class=\"at\">col=</span>mpg_type), <span class=\"at\">size=</span><span class=\"dv\">6</span>)  <span class=\"sc\">+</span></span>\n<span id=\"cb14-3\"><a href=\"#cb14-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">name=</span><span class=\"st\">&quot;Mileage&quot;</span>, </span>\n<span id=\"cb14-4\"><a href=\"#cb14-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">labels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;Above Average&quot;</span>, <span class=\"st\">&quot;Below Average&quot;</span>), </span>\n<span id=\"cb14-5\"><a href=\"#cb14-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;above&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;below&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb14-6\"><a href=\"#cb14-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_text</span>(<span class=\"at\">color=</span><span class=\"st\">&quot;white&quot;</span>, <span class=\"at\">size=</span><span class=\"dv\">2</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb14-7\"><a href=\"#cb14-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Diverging Dot Plot&quot;</span>, </span>\n<span id=\"cb14-8\"><a href=\"#cb14-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Normalized mileage from &#39;mtcars&#39;: Dotplot&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb14-9\"><a href=\"#cb14-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">ylim</span>(<span class=\"sc\">-</span><span class=\"fl\">2.5</span>, <span class=\"fl\">2.5</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb14-10\"><a href=\"#cb14-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">coord_flip</span>()</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7F0FnBXVFz7A0s3S3d0pCEgI/BVQERQUG5GQDglBpFFCLAQBlZBUVFAkRLq7u7u7lnj/+x28w7y3896+2t1h9xx+y8zcuTXfzHvvm3NPxHEoIRFBQBAQBAQBQUAQEAQEAUEgihCIG0XjyDCCgCAgCAgCgoAgIAgIAoIAIyAEVB4EQUAQEAQEAUFAEBAEBIEoRUAIaJTCLYMJAoKAICAICAKCgCAgCAgBlWdAEBAEBAFBQBAQBAQBQSBKERACGqVwy2CCgCAgCAgCgoAgIAgIAkJA5RkQBAQBQUAQEAQEAUFAEIhSBEKidDQZzNYInD9/nnr16uU0x4QJE1LatGkpX7581KhRI4ofP77T+U2bNtGYMWOoe/fulCtXLqdz0XkQHfP64YcfaO3atcZlx4kTh1KlSkXp0qWjevXqUYECBYxz/uxcuHCB74W7tq7jox7uH+ZQvnx5+t///kchIY8/8h9//DFlz56dWrRo4a5Lt+URzcVtQzkhCAgCgoAgIAgoBOJIHFB5DjQCBw4cYKKZPHlySpkyJRffv3+frly5Qnfu3KEcOXIQSE6NGjV0E/rll1/olVdeoTVr1lCFChWM8ujeiY55vfHGG/Tzzz9TlixZCOQTcuvWLbp06RLFjRuXmjZtSj/++CPFixfPJ3ju3btH9evXpypVqhBIozuxGh9tMT62hQsXplWrVhn3Fi8MZcqU4Xvork/Xcm/n4tpOjgUBQUAQEAQEATMCsgRvRkP2GYE2bdrQ8ePH+e/06dN048YNmj59Op09e5Zef/11gqZUS61atWjr1q1UvHhxXWSLbXTO69ChQwZ+Fy9eZNxq1qxJkyZNokGDBvmMz+3bt2n+/PletzOPf+bMGUJ7aLZ37dpFH330kdf9WFX0dS5WfUiZICAICAKCgCAgBFSegQgRgMbu1VdfZRJ07tw5at++vdEGS/IpUqRgrR60pEeOHGHCalT4bwdaVJx7+PChcQqaucWLF7P2FJpCVzl58iRdvnyZ+0O969evO1UB8V29ejXdvXuXNY3o/8GDB1zHPC8U3Lx5k8dH4i/UhyYQf1bjov7Vq1fpn3/+YSKJ41OnThHIpD+SPn16+uuvv3gJvF+/frRz506nbsLCwmjz5s30999/8xzNJ3Hu2LFjXAQscI2+Ji/D/evbty9hHsAxIsFLx8KFC9mcALhpCcZcdF+yFQQEAUFAEIjlCGAJXkQQAAL79+9HWlZHjx493AJSvXp1h7IJNc7PnDmT26gleIcilA5lc+j48MMPjfN6p3bt2g5lR8qHisg4lM2oQy1L859arnYoEuuYOHGirs7bvHnzcl+FChXiMRSBcqglYMe8efMcmTNn5jL0gX3MGXNXmltua54XCpQGl8/PmjXLoUwMeB/1lX2mY+nSpdwG/2FuL7/8skPZSjoUceN6rVq1cqjla0ezZs2MelY7aomd6yuCa3Wax8GY33zzjXF+wYIFjpw5c3I7Pd6zzz5rXIcipsZc0RZ/ijQb7c07nsZXphSME7DSgnEbNmyoD7nf9957z4H7oecCrL7//nuu48tcjE5lRxAQBAQBQUAQsEBANKDqF13EewRKly5NcECBJtRVUqdOTS+99BIv18NWUAs0aosWLaJ33nmHiwYPHkxDhgyhbt260eHDh9lG8a233iL8LV++XDfj7dixY9kuderUqTR8+HDWADZp0oQwD/R77do1gskA+vRG4HCDZXBoNBX5I2j1FGE2mnbs2JE1vdOmTWO7V2hZFUHl5Wujkp87mDNEa0Ch2YRjF2xG9+7dy+Mpck3btm2jBg0asLa4WLFijBHa9ezZk7FPnDgxDn0S2MTimitWrOi2nSLxbCbw3XffsbYZ91gRVPrggw9o9uzZFKy5uJ2AnBAEBAFBQBCIPQhYkFIpiqUIeKMBVeSNtXArVqxglFw1jdBOqk+PY86cOQaKw4YNY00ntJNqaduhCJRDESHjPHag2YRmFRpWLdCAQjOqloF1kUMt/3MZtK1mef7553nciDSgn3/+ubmZo2XLltwOWkv0ibkre0mnOuvWrePyQDWg6DRBggQOaDghGBsaYz1nLlT/KVtRHk+RRi4CZpjXgAEDdBXLrdaAvvvuu47333+f/958801H5cqVWaupHMsc+/btM9qaNaCKnHIdV+01NKfKe99RtGhRn+ZiDCI7goAgIAgIAoKABQKPY7LEHs4tVxoAAnBqgYSGhlr2AuefrFmzsjc4Qg9B1NI66fL169ezUwzsEUeOHOnUBzSB0P6ZBaGLkiRJYhQhvFKpUqUI2lazqCV+mjt3rrnIcr9kyZJO5ZgrBI5WW7Zs4X04DJmlXLlyHMrIXObPPmw4oXHV2G3fvp2dt/QcdJ9169blXWhfoYH0VaBt1l74iRIl4ugFnTp1Ygck4G4l0Mqq7wdSRN7pNOxH69SpQ99++y3P3emkHAgCgoAgIAgIAn4iIATUT+Bia7ODBw8yuVHaM0sIEG7o7bffphEjRvAyLpbYQSqxfAw5ceIEb0H2lMaV983/ZcyYkZeiQZwgmTJlMp/m5Wi9lG0+kSZNGvOh231X4gqCBQH5wlwhOgQVH/z3n7f9m9u47gM7SO7cuXkLLJRmkffN/2GOiNfpr9MTcFWaVnOXEe7r+2JFUBHHFM5dMHfwtd8IB5YKgoAgIAgIArESASGgsfK2+3fR0H7CHhLxPjVBtOpJLQHTwIED6ffffydo+UCoXnzxRa6qtX1qOZt69+5t1dypTGvydCECp2uypMuwVcvY5kO/9tE3BP1Dy6oFnvuwnwxUEMoK8swzz/A2W7Zslv0i3BXir8LmMqoEc4FYXSeiEWTIkIGD4IOEiggCgoAgIAgIAoEiIE5IgSIYS9ojxFLbtm15qTqiWJJ58uShqlWrkvI4J2UjSq+99ppBWAsWLEjJkiXjZXkzdIgviaVuaE89CbL5IGSReake2rnJkyd7aubVORBrkOWffvrJqb52SHIq9PEAIZ++/PJLKlGiBC9pozmuF9exZ88ep940UdUkGFplCEhpZAnILrImzZgxw2kI3Hc4IEXlXJwmIAeCgCAgCAgCMRIB0YDGyNsa2EWtXLmSlMMLdwJiCG/tZcuW8bZ169bs6R7RCNCCQssJ7aH2fkcbZFmC5hMe8MjuA1KLOKJYsod9pwpR5LHrDh060Pjx4wk2n8gKhKVxeMrr5W1XjanHzlxOYh6ffvopxzlFwH149IMcKicqNjvwtm945GNpH8v60GZiSRxxNZHS1EyU4XWOzEgYB9eNdKfwzMd1qVBQVLZsWZ4hbGCx9A0bV50SNdhL4Vh679KlC0cIgCZYhWPiWKiYC2KiYguJirnwQPKfICAICAKCQMxGwMIxSYpiKQLaC1498ex1jS3iYarlWQfieCrtWDhkXL3gdQXl1ONQmk7De1qXY6tIqUNpAx0qRzmPg5iTKs2kQ5ExczUHvODV0r1TGQ4UqXM0btzYiGupwjA5FKnlvrR3vOu8dBxQ5QTl1J8ii9xOhZYyypUG1KHIH8cLVVpRx5IlS9h7vV27dkYdqx3thW7GD9dYpEgRhyJ3DkXkwzVTzj8cEUC3QVxSeMcjHqlZFAHkOaCeck4ynzL29fju4pAaFf/bMXvBowge74hygPuGcXBflJbWoYLX/9fi0cabuTg1kANBQBAQBAQBQcAFAckFr35pRaIPgaNHj7JW1FsnH2hjkyZNaniS65lDe/fVV1+xh712LNLnvN3CQx2ZhuAkBCcgLcjABO0oshh5Y7eq2/myhW0l4m7CfMGdphXzg7e+t1j5Mr65LrTWcMiC8xGu20qiai5WY0uZICAICAKCwJOPgNiAPvn38Im+ghw5cvhEqGCfieXi3bt3G9cNxxksy8Pu1F/yic5gawlbSATEN0v//v35UMUoNRcHdR9ET2l83ZJPDIZl98gmnxgHOIAIuyOfUTkXjCUiCAgCgoAgEPMQEA1ozLunMfqK4O0O20jkKIc3udLoc/YkkFJ46Gsve39BgF0msjQh/ijG2bhxI9uBInsSzokIAoKAICAICAKCQOAICAENHEPpIYoRULnQSdllcspMpPxEekl4x2PJOBiisgVx32vXriWVA56UfSr/BaNv6UMQEAQEAUFAEBAEiISAylMgCAgCgoAgIAgIAoKAIBClCIgNaJTCLYMJAoKAICAICAKCgCAgCAgBlWdAEBAEBAFBQBAQBAQBQSBKERACGqVwy2CCgCAgCAgCgoAgIAgIAkJA5RkQBAQBQUAQEAQEAUFAEIhSBISARincMpggIAgIAoKAICAICAKCgBBQeQYEAUFAEBAEBAFBQBAQBKIUASGgUQq3vQc7efIk9e3bl7Zs2RJuogcPHuRzKs94uHPRVbBp0yYaMWIED//gwQOeH1JIRob8+++/pHLVB63radOm0Z9//sn9TZo0ieOOBq3zSOjot99+o/fff59UHvhI6D3iLrdv3873FylL/ZVA2vo7JtpNnz6dM3V508fp06f5OvE5xN+AAQPou+++o9WrV3vTPFwdX67Z/HkK15GbAl/6d9OFFAsCgkAsRUAIaCy98VaXfeLECfr000/ptddeozt37jhVAQHFOTsRUGQpMhNQ/FhHJgH94YcfnDAJ5GDGjBk0d+5c7gIEdN68eYF0F6ltgXPDhg0JZCNz5syROpa7zkFA8fz5S3jatGlDX331lbvuI7V86tSpNG7cOK/GQFpZXCeejWXLlvGLyeeff86JEGrWrElIwuCtoA8kaPBWzJ8nb9pEJ6bezE/qCAKCgL0REAJq7/sTLbMD2ezdu3e0jO3voMiTjqxINWrU8LeLKG03a9YsGjVqVJSO6e9gO3bsoOTJkxO0th9++KG/3QTULmnSpNw+WbJkfvWDrFbRJZgz8PNFvv/+e1q0aBGnmcVL1YIFC2jDhg3UtWtXr7vBfUPK2siS6MQ0sq5J+hUEBIGoQyAk6oaSkZ4UBLDM2q9fP3r55Zc5zaW7ea9atYrGjBlD0NogZWWXLl0oW7ZsXH3lypX845k7d26aPHkyvfHGG3Tu3DnKnz8/ayn/+usvypIlC/+gxo0bl4YOHcrnGzduTI0aNTKGPHToEGuu9uzZQ0mSJOH5tG/fnkA4zXL//n1q1aoVde7cmbV02FrJZ599RmnSpGFNGjRL+FFHCs933nmHoGHSgnHHjx/P5gjIOY8lfncC4tu6dWv66KOPaOzYsYQf/kqVKnHueGg2J0yYQJkyZaJmzZpR8eLFuZtvv/2WUqZMybi49gstn6e5QQv95Zdf0vr16/k6kLce15sjRw7uCucxd5CW0NBQvi/QFEOzBswhMLPAHI4ePcr3DnO30m6CKKMvXH+LFi3ovffeo4cPH4a7t6+++ip5eh7WrFlDixcvpmrVqvEzc+PGDXr77bfpueee43uPtjj37rvv8v3hSZr+w/OF+aVKlYp87Wv48OF05MgR+uOPPyhevHh8X9A1NM/AKE6cOPTiiy/yX0jIo6/EOXPmEMwOgA8ww6oA5gqBJhXPMdqePXuWPvnkE8qQIQNfB+5J6tSpqVatWny/0TfmrtPE4nnDZ2bIkCF8b7hDL/7DixU0/O3ataOePXsa99Ed5kuXLqVff/2VsKrRvHlzfp5+/vlnypUrF+3du5fJbdGiRfm5L1SokOUMcM/xPM+fP5/vP+5P27ZtKX78+OQOU8uOpFAQEAQEAQsE4lqUSVEsRwAkoE6dOvzjdPv2bUs08AONHOlXr17l5VkQzmLFihGIGwT51L/44gu2GQRpQD/4IQMJQ9tnn32WVqxYQfXr16eXXnqJUqRIQQULFiQQUCwFQqD5AWG7dOkSvfnmm5QvXz7q378/9erVi8+b/wMpwjIn7FhBIooUKWL8oQ+QBYyXOHFi1gqVKVOG/v77b4N01K1blwkJ+sR4mB/sPuvVq8dExNPyLX6oMTYwA8GpUKEC/+Bj+RPkBGT0wIEDfJ16zno++lhvobHyNDfUwzjQRoIwgxRhniAowAACMowXiKeeeoqJOogD5nf58mU+j/oVK1YkkMBXXnmFoMkCRniRcBW8UOTMmZP7KV++PKVNm9by3kb0POzfv59JywcffEAlS5ZkEoNlfWAEMoxrAsZ4QbCSPHny8D3EOV/7wnMFDSqIJMggpE+fPkzm8GJQtmxZJlYg5BBsX3/9dcLLE0gylr2ff/55Jvw4j5cKXAfIJDCEhhMvWCDYaFeuXDl+sQLJhIAAguBDQFhRz9XEhU9G8B/uGQQvOBBPmKdPn56yZs3KL224b3hhw+fvHfWiBZvUJk2acD/Vq1fnzwx36PIfXja6d+/OnztghOvB8+ZwOPiz6oqpS3M5FAQEAUHAMwLqy0REEGAElGbJoZ4Wx7FjxxxKc+JQGjpHp06d+Jz68eJzinDysSIlDvWj64QcypSmiMuUvSTXV2TSqKPInENp6xxKY8hlSrvGdZR2zqijSK1DLf/zsSKIDvUj6FAEzzivyLEDdSBqmdKhSAXvK60f9/XPP//wsfk/pflzKOLkUKYFXDxo0CCH+vF0XLlyxaiGMqXFcigS51AE16HINO/rCopUOipXrqwPnbaKXPPY6sfaKAcOwPL48eNcdubMGT7etm0bH7/wwgsOpVHkfaUtc3To0IH3I5rbhQsXHIo0Onbt2sX18Z+y9eO+MQbwxriK5Bjncf0o02OXKlXK0aBBA+M8dlCmltedyvSB0oI5FJnRhw6rexvR8zBx4kSeg34ewsLCHOplwKFIldGvIqAORRaNY3c7/vSlCJRDvbxwl4po81yUltAYYubMmQ5FHvmeq5cGh9JSGufwvKqXKAfmB1EkzKE0oA6ldTfqKK2nY/To0caxIoeO33//3Tj2dkeRWp6bIuXhmgAz9YLjUNpvPhcR5orMO9SLhdEPPn/qRc/4/OGEIvYO9cLCdcyfp3Xr1vE8lNbYaK/LlGaYy8yYGpVkRxAQBAQBLxGQJXj1yywSHgFoi0aOHMkaSyzFmwWaNCxpKrJkLmZtIbQsWhImTMjaLn2MbYkSJVhDiX1otSBmRwksVSoiZZRDeweN3e7du0mRLlIEkzJmzMjnvfkPy9/Q3MCeDhotiPoh5T4GDx5sdAHNKbRTWLKERg7jYvlUC+aIPjwJtERacG1Y4oQWCgLNIQTXBk2xO4lobtBIwoEJc/zpp594ORXOKhBomaGVw9K+IsvGENDuKhLKx1ie37p1K5sEQLulBZpbtPVWzPfW2+cBmmncfwiWcXEtrvceXuDeSCB9bd68mTB/aPC1wOxDm37A+xzzgPkBTD+AF7A1ay2hpQZmWqApheYZy/rQlmJJH1r4YAq0rdC24/56i7nr+NDsazMDnKtdu7ax4mCuqzEym6Xg+cZnD2YGWLUQEQQEAUEgEATiBtJY2sZsBLBcB4KAJXmz963SHPKFg6SaBXZw+IHUgqV32HeaBTaJroLldytRGju2WcOyvV4mhj2mtwLCiKVSeK9jGVwLfrxhT4q56T+QoR49evAxrs98HWgHwhSRwLbULGbHEzOZNddx3Y9obiBBuCdVq1blZXhcR9OmTY1uYD6A+2AmGbgPWmBfiqV6LBvra8cWNotYEvdWzPfW2+cBxMlM2oCJ+d57ixHmGEhfeNGAKYa78fDiBVtJvKDAbhnEFEvaZnF9jmFuAhtT2OPClAAvH2aCb27r7z7IMCRv3rzkLeauYymtqVMRnlkrRyX0j3usnb/QCHgBB9fPhlOHciAICAKCgJcIiAbUS6BiazV44+LHVGvQgEP27NnZpgy2cCBCWqD9hH1fsARjwn4PWk9NXGDH6c0PIOzkQKjgUAUHErPgBxw2mHDqAPmCwPMfdqzQwJYuXdoIkaTbRaT91PUC3UY0N2jlMBfY2oI0Q9RSL29BLKF1g+MMiKgmxGatNK4PpA8OPWYNNvDwhmTzQC7/RdXz4DKs34fQToNggVxqYgmNH+xCEeu1W7duNGzYMLYLxSB43mCDrG1sXQfGyxnsKmEvjD/Ug63ywIED+XOTKFEi1yZ+HYMQQ4sP214I7Do9fQatCDZWE8yycOFCft7NZdjHcwiiDk27/kxDK4yXQjhBiQgCgoAgECgCzuqpQHuT9jEOAb0Uj2VILSCD0CzCux2xBrE8CW9ZeCfDqSVYguU+ECn0r0xKWMP0yy+/RBiLFD+UWAbF8iIcQLCsrv/QF7y5cYylVmgcUR9aRASGx486CKuyg2VPc9THkjfIaVRIRHMDJiBEIAcQkE0dHB7aUVw3SArMJuAJjRiUOlaqnj+iBWCpePbs2dwXlvCxZKzsS3UVn7ZR9Tz4NCmXytBYwowD9xracDgjtWzZkon8+fPnOeyYJueoC1MJEEmQS3iew3TBvARv7h7aVITUgsYTxBbPDPrEZ8eVfEKLCUcrOO95EgSFX758OZuf4F5hyRtOc9C0Am9vMMcLCK4XDoGIEgHB5xgkG3PEFgQT5gOuAmcjRFWAEx2cvvB5ATEHYdcvnWZMXdvLsSAgCAgCESLgpa2oVIsFCJidkFwvV9kRslOCdkK6fv06OwipH0KHsqdjJxWVscVoBkcVOPWYBU4QivwYRerHkPtUP25GmSJODkUa+Vj98LHDkVpmdqgfU4eyR2MHDPWj7sA8zE4TZick7SSjHn7u37ydMmUK942t+jHlucMxA+PCiUeLClnD16QIqUNpGh0qC1CETkhKu6SbsyOT2cFGkUaei9I0ch13Tkg4GdHclEkCO5MoMspzUx7x7CSjXgi4b2XP6gDWagnVoULsOODkBQyAJ0SRKnY4UhpPdgRSy7KG4xdXcPnPygnJ9d5G9DzAcUgRFqee4ZCmCJVRhutQS+vGsbsdf/rCs4l7qZ2pFBl1wIkGzy/GVF7h7HiHMRXhcygSz/NVZhQO5cHO5xVJ5ynBCUkFYXeaHj47yjzEgWcV4+DatMOVuSKuEfdCRXgwFxv72glJP7NKQ+/AfcaY5ucLDSLCXBFPbou+VCYlfiaU1zs7HmGOSgvuUCG2jLHNnycUwmlNrQY4MAd8xpXdq0MlBDDqu2JqnJAdQUAQEAS8QCAO6qgvKBFBwC8EoBmCtkc72/jVSQSNLl68yJpJs01lBE28Pg3nI2i+1A+yZRucx3K11XKmZYMgFnqam/KIZo0l5mYW3AuYH1Sr9tiJCpo02M5C2wvbSS2IXwpNajDvXVQ8D3r+vm6BGTSBsJvVgmcLDklWAe6h9YPG2WxPq9u528JRCNpFPFNRJRFhru05EfIMZhvQ1vryXGMVAs8/4pu6ihWmrnXkWBAQBAQBKwSEgFqhImWCwBOKAGJ5wiYT9oIwP4ApAZb14VCCJVyR2IuAmYDGXhTkygUBQcAuCIgNqF3uhMxDEAgCAtCIqiV8dk6B9g5hd+DRreJcBqF36eJJRgAvIZGxivAkYyJzFwQEgehDQDSg0Ye9jCwIRCoCWJrF8rKIICAICAKCgCBgNwSEgNrtjsh8BAFBQBAQBAQBQUAQiOEIyBJ8DL/BcnmCgCAgCAgCgoAgIAjYDQEhoHa7IzIfQUAQEAQEAUFAEBAEYjgCQkBj+A2WyxMEBAFBQBAQBAQBQcBuCAgBtdsdkfkIAoKAICAICAKCgCAQwxGQXPAx/AZ7e3nIR4CUe4GKzmsQHYHbreZux/kINlZ3SqXr+S8nhuDjHh+7YYOZ2mlOdpqLv9g8dDyk47dO0vm7FylxvESUIJ51kow7D+5S2MO7lDFhesqSxDkhhesTFKzPlsqexmHdXPuXY0HAHwTEC94f1GJgG5XWj15//XWqUaNGQFeH0D/4klLp+wLqJxiN8aV78+ZNzsftSzabYIztrg9kyUHucDsIMiHhflllAYqO+SHHPf7cZaWK6jkhDzyeY9d87lE9Dz0ectEjrJYdSNbDhw8J+OBZRl56O4idPlv4XCHrVtKkSX2C5oEin18fGEdxKQ495EzCETePo6okjJuImuduSvHiWN8LfNbx3AT6Pbh48WKaPXt2xJOSGoKAFwiIBtQLkGJLFZU3nDp27BjQ5V67do1/lEBCo1vwI4lUkwjAbRfSh7SGKq99dEPD44Oc435lypTJFvPBjzZ+KO1CiJHWFM8xnh87CFJqpkiRwhYvd7hPFy5c4GfZLrFm7fTZwucKhDhDhgxePzr3H96nl1a+Tffyxlfk86HX7VDxoWKhp7JdoxEl+1m2w8sCCGig34Pbtm2z7F8KBQF/EIh+NZU/s5Y2goAgIAgIAoJADEJg1om5tOf6AZ/JJyAIc9yjOacW0IoLa2MQInIpMR2BJ14Dunr1aoKmwlWguXjuuedciwM6hobGn7d9vH1u376d9u3bR0WKFKHixYsHvBRivpB58+bRM888w8vNu3fvpipVqphPy74gIAgIAoKAjRF44HhAQ/Z8STfu3/R7ltfv36D+u0bQ/KrT/e5DGgoCUYnAE68BPXnyJBM7kLsRI0bQpk2b+PjgwYNBxRH2PG+99ZbPfZ45c4beffddmjVrFmFJeNq0aWxrCVIaLPn22295KfX48eM0efLkYHUr/QgCgoAgIAhEAQJ7rx1Q5PPRb0KWxBnplawvUOZEGZ1GThg3Af0vYw2qFFpOWYjC8pOofJpSXKaPD1w/TJfCrji1kwNBwK4IPPEa0EaNGhnYQhP43nvvUfbs2Y0y7MBe6dSpU5QlSxbWPIIIXr16lVKnTm3UAyGEMb3WcILMwVZPG5HD/ur06dN0+fJlox1sfNBvypQpKW3atEZf5p1OnTpRgwYNqEmTJkZx3759acyYMU72lq7jgfBqQ3Y402g7NNdrMTq12IFDB+aM682cOXNQta4Ww0mRICAICAKCgB8IbLy8je49vMde7/2KdKMv9o2hgcV6UOtN3ej2gzvcY49C7WnxuZVUOEUByqa83u8rrWnWxJlp3/WD1DF/CxqxbzTFjxufNlzaQrUzVvNjFtJEEIhaBJ54AhoRXNAOLl++nAkcluqHDh3KBPWdd95hjWmePHm4i9atW9NHH31EMGQHOQThg1a1ZcuWTCAnTpzIYWIGDhxIQ4YMoblz59KkSZMod+7ctGvXLqpatSp17drVaToglSCujRs3dirv3r274UiwYsUKy/HQ55dffsnt4bkIzemoUaPCXQvGt5LDhw9T7969KX369HTx4kUms99//z07Mej6X3zxBc8dxzBOh5E6rj8QAXEOCwszri+QvgJtq0OP3Lhxgx0CAu0vGO3xAhEoxsGYB/rAvYLYZT54UcIfnh87CF7gMB+74INnBy/AdvGCxz1C9Aw4s9lB7PbZ8uXZOXL5GN1+eIcSKALZdVtf1mIizFLK+CkMAjr64AQ6c+ccPXDcpwppylC6hKE0fN93HK7p7Zyv8i1Am2OXT9ClBM7f43iWIVCaBCL6OyOQPqStIKARiNEEdNmyZYSwEViWRiiVGTNm0MyZM6lbt25Uu3ZtWrBgAbVq1YoOHDjAP8a5cuUiaCwHDx5MpUqVYg/q999/n+rXr08ffvgh/fHHHzRs2DAmouvXr2eCCM0itIwgmZ07d3YiXnv27KFs2bKF+8HQWlZ8GQwYMMByPNygQ4cO8ZxDQ0OZeLq7Fn0zzdvNmzez1rVevXpcjOvYuHEjVa9e3aimCRoKsG+HHzZjcrIjCAgCgkAsQUB/94YpLSiW0NvmbUbbr+5mwqkhAPnMnyw3tcjzNrXa+JHShOanfkW70QUVLxSaTxFB4ElDIEYT0J07d1KlSpWMOH6IcQk7ThDF559/nrctWrQgLN3DYeno0aMcg7BkyZJ8HxFCAwRyw4YNTEj1zcWXBbSlS5cuZYKIAO4gcHgD1+QSdbHEj6V+d+JpvCRJkvCyebp06bi5p2ux6v+ll16iNWvW0NixY5lgnzhxgrWg5rog21qgyejfv3/AIYLsGIYJYX0CDT+icQp0C22a3cIw2WU+EobJ89NlxzBMyZMnd/rO83wFkXvWTp8tHYbJ289WjmvZKPGJRKwF7Zy/FWs1Jx6d4QRYEbX03i5fcyafcFa6cPcSfbx9EN19EEYD1HI9JFG8hJQjTbZw3zHBCsMUaBxRpwuSg1iPwBPvhOTpDoK8gRRqwdKeXh6GthN2m1u2bKF///2X6tSpw8d6GVC3wY+iXr7QZQgI3axZM4KjU4UKFahPnz76lNM2f/78ht2o+cSiRYvok08+iXA8M2nydC3mvvU+lu+nTp3KBLpdu3ZUtGhRfUq2goAgIAgIApGMwOm7Z+k3FVpp7KFJ/DfrxF905OYxVla4Dl0mdXHWYpZJXYKa5mhIFUJL07elh1DOpNmod+FOlChuQvqm9GAKUYHmPyvem5pmb0gp4ienjwt1pD5FutBPh6dxl7AjLZ2quGv3ciwI2BKBGK0BrVatGk2ZMsVwHIKmE8HWdZYeaEFhFwk7Su1EBAemlStXsk0nlsDxV6BAAcOBB4QWtqTQbGJZHm+E8+fP55vrSlQRNLphw4ZMNrHUDmcl2GbCxhSkEGO6Gw/OTWaJ6FrMdbGPgMFYdn/66afp3LlzBHMA8/K7a305FgQEAUFAEAgcgYVnl9Kn24fS6TtnKV7ceMqG85HdZSKVVhOSNkEa6lm4A9XN9KyRuahAiryUNCQJbby8lUovfNZpEgitBKm+5GWnchxgmR6pOx3/ZU3KkywnhSZ87FwbroEUCAI2QiBGE1AQvLp167ItJOwosRQLJyItNWvWpK+//pp69uypi6h58+YEL/Vx48axgf2nn35qkFPE73zxxRfZphRxN6EFRZ85c+bk5XIsc0PraRaQVGgjURfaVXjVo4/KlStzNXfjuRLQiK7FPCb2kVYTDliweYXJQOnSpQnzExEEBAFBQBAIPgLwVm++vhOtvbSJEJOTxZTQSMf4xLbTlk/ox8NTaXrFsex4hBSaPQq2p547BvoUCxTxQ7UkD0lGnxTprA9lKwjYHoFYkQseWkssm8NeySywe4Q3PJaqXfNPm8MtmdugH50b2jV0k7me1T7sgqAVtRJ347nWdXctrvVwDMILD3B3Y5rbaBvQzz//3Fzs874dbUAlFaf1bZRUnNa46FJJxamRCL/F95Ck4nyMC4hg3rkVVCil+yo80qPoEo/PWu/FVRnfkXJz73Or2Ntdp+LccmUHh1iybmVditzxzyuN6rhyX1hWCJYNKGJa//jjj5ZjSKEg4CsCMdoGVIOBrEiu5PPPP/9kb3g467iST7QzxwjV/WCrySf24ShkdjpCmSfxRATdjefan9W1uNbRxzA18DSmridbQUAQEAQEAf8ReGddO0IIJG/JJ0YC+YTm823VFgQ2JG4ITa4wisknyKm3ggD1yZU96Pdlh3vbROoJArZAwPun3BbTDd4kEJQe9plvvPFG8DqVngQBQUAQEARiFQKLz62gNRc3GnaYvlw8iOcOZcc55+QCbpYqQUo6WncTlVZOSclCkkbYFZbdn0lXibbVXkJx48Tan/MIcZIK9kQgRtuAeoIccT6DKQg6j9ibrgIveWhY4RQEu1FP4k2u+YULF7IzkTkcBuKYYvynnnrKKdwQggYjXimcnwoXLuxpaDknCAgCgoAg4AcC/XYOf2zz6Ud72IQO3P0FvZClDpPIhPES0B+VJzApHazywyMuKJyM7nBGpDiURDkzwe0oa+JM7AFfPf0jfwI/hpYmgkC0IhBrCWiwUUecTtjGwOvcLAh/BFs7BMX3REBBFhGjdPr06ebmTvu//vorjRw5kj30NQFFLE+kEEVGp9GjR3Ns0/LlyxNsft5++22eD+LjwWsfzlAigoAgIAgIAsFB4NydC3Ts1knuDFmLaigyuPvaftpzfb8xQEicEKqTsTqT1GXnVxvlFUPL0rYru+jmg1t05d41OnDjMOVP/igzH5bmX8r6HJPSIzeP09YrO+lC2EVl6RmXvdwrpClNmVXOeBFB4ElGQAhoEO8eQip16dIlXI8glx06dDDKEa7JNUe7Va553QDtETcUKTXNsmPHDg4JhZSgEISLQspOEFDEGoXWU4/72muv0d69e7mOuQ/ZFwQEAUFAEPAPAYRNgu0mKWf0IcV70ZiDE6lZrtdp/OEpBgltl+99ztdeNe1TlCBOfPrn3DLKniQrjSkzjJ5b3oRu3r7FoZSQD14TUD0bLKvnTpaD/3SZbAWBmIKAENAouJMgfkjhCQ2puxztrrnmtYYT0wNhxfI64paaY3mCYCKOqRZoOeGlD8mUKRMhQ9PZs2c5nBQIrquj0/Dhw2n37t1cH85V0KQGmvcaZFkH++eOo/E/nWrUTrnggU2gGAcLUtwriF3mo5NAACM7CD53mJNd8IHnueSCd/9kRMdnC3nXb99/9J3bd+cwTp1ZKbQcL49rLWjeZLloxL7RtEaFZ+qYrwX9q2xG2+R9j+acemT3iSu6pWKFHrt8nC4lc87h7v5qfTujY1RLLnjfcJPakYuAENAg4gsy99577xk9IvZmmzZtjGPsuMvRbs4179RAHcDT/oUXXnAt5oD6OlsSgs1DE9q1a1euV6ZMGdaAQvOJH67WrVtT+vTpnfqAl7wOyg/yKRL5COicz5E/kowgCMQuBKLjs6UDwANp5GqvleEZ1mKOOvg4VFGC//K037p/i4PNt8r7Dv10ZDq9krV+7LpBcrWCgAsCQkBdAAnkEAHpkSNeC4LOu4o3Odpd20R0DK1qt27dCDHaoCmFjB8/njWfv/32G8cCxfmsWbNSlSpVjO46duxo7Os4oN7mLjYauuzYMQ6o5IJ3uUn/Heo4oIHec+vefS+VXPCeMZNc8J7xgaY6qp/lHHdVDveQRBR2L4zqZ65N5dOUoo5bejtN9IHKVBRH/YPN5onbpyh3UrWkniuH8nQvxsv3vXcMocTxElP21OFzuDt1FMBBsOKAmlfmApiONBUEGAEhoEF8EKCNzJs3r8ce4QiE9J7I0IQl9S++sA4c7LET00loXXv06EEgk2Ynp40bN7JTEzzg8VerVi1at26dEwE1dSO7goAgIAjEKgRuKo3k8vNr6NzdC8rT/DKlS6hSIyvbzIqhZR7ZdXqBBnK3I/YnHJAGF+tFS8+v4pzt0479TtmSZGbnoSnHZtGgYj0pY6L01GP7QNaUous+hbvQuMOTeZR4ytazrOpLRBCITQgIAY3iu+0uR7t+s8RyOYLNeyPIRAKNa79+/cg1rBS88Tdt2kQVK1Yk2Ppt2LCBU5J606/UEQQEAUEgpiKw7/pB+mzPN7Tk3EqKr5bHkav97sMwQq72xHETcoijJtleog+VnWb6RGk9woDzORRp3XVtHxWdX9Vt3RUX1nKweXOFvruGGYep4qck5HEXEQRiEwJCQKP4brvL0Q4bTHOueW+WkmbMmEFYlmvfvr1xFWj3+++/U/369TkXPGw/sdRarlw5JqNGRdkRBAQBQSCWITBB2V4O2j2S861jaRze61oQZ/NRrE1SNprT6NcTf9K0it9T0ZQFdRXL7SeFO1PzDZ09xgI152x37QQB53sV7iCB5F2BkeMYj0CsyAVvt7voKUe7Odd8MOaN/qBd1RpWd31qG1DJBe8OoeCUR4edmruZaxtQREywg4gNqOe7YEcbULzw+pKO2PMVBnY2os/Wr8f/pM5b+9Cdh3d9GmhmxXFUJd0j23p3Dd9a24YWK43qPcc9d1Usy5Fys4Ja8v+l0jhOy2lZKQiFwbIBlVzwQbgZ0oWBgOTuMqCIuh1POdrNueaDMSP0FxH5DMY40ocgIAgIAnZF4M9TC+nDzd19Jp+4Hmg396ple08ypuwwQgYjOBt5KwhQj3zwM5SWFYHnRQSB2IaAENDYdsflegUBQUAQiEUI3FZL6z22D/D7iq/cu0o9tw302D6xsh/d99xqqpm+CiE/e0SCZfdyaUpyG9ihiggCsRGBGG0DunTpUs78kzFjRp/urTc52b3pcNWqVQRHIVcpW7YsZc6c2bVYjgUBQUAQEASCjMDUo7Po1v3bAfW689oeWn9psyKNpdz2g6xFk58aRYvOLqc+2z+nU3fOKG/6eCrI/KNA9SCpiBuaXnnb9yzUnp7P9KzYfbpFU07EBgRiNAGdMGECNWvWjHwhoAiTVLJkSaeQRv4+CMjrjmw8SNFpFqTMFBEEBAFBQBCIfAR+O/k351vXI1VL9zQtOb9SHxIyFZVIVcQ4nn1ynrLlvE8Fk+ejy2FX6Ozd88rB6CbNP7PEIwHVHdTMUIXKJS5BR64co0PxjtPFu5f5VJqEqah0qmIc6ik6gubr+clWELALAjGagEYEMkIenTp1irJkycJ2ktB8HjlyhPLly8fpJBG+CN7p2sgeThsIkZQgQQLuGm1RB9pMd3aWzz33HOHPSpA6M3ny5EY2IqTZQ7pMGIyjP2hPkb1I943zSDeXIUMG7g5jw8kI8zlz5gwHmteZjVDBKue81TykTBAQBASBmIgAFAC7r+/jS4uvbC67FmxDVZVDkZmAwvMdJDF5/KTUIvdb9LsirJXTVqChxftQ2809mYDCi33FhTU+QZQhYToqnqGoT22ksiAQmxCItQT022+/peXLl1OqVKno/PnzNHToUDp69CgdOHCAQAyhNV2yZAmTyyZNmvAzMXDgQM7FjqDun3zyCZPXFClSMPkbMWKET5pWdPjGG2/QuHHjmFCCLCLd5uLFizm/O+ayb98+ql27NjVt2pQGDRrE84InbO7cuWnIkCG0Z88e+uyzzwwSfPDgQRo5ciQhI5O7nPOYrxbkgt+1axcfamelixcv6tN+bXEdIMl2eMPXueDh4Q9SbwfBS0+gGAfrOnR+aLvMB/cLESLwImgHAT6Yj13wwQsnXkLtIMAFgsxn5pfe6Jyb1WfrhtJc6u8BBIufe/ofypk0m9M0T9w+rTIUnaaBRXuylzzIJhyEJh2dyUvmuvK52xe9fhZwr+z07Oj7Fej3IK5LRBAIFgKxkoAuW7aMid7kyZMJxAvxNGfOnMnpLP/++2/OUoQ87iCgVgJCs3r1apozZw63xxbaSqul/jFjxtCUKVOcuvnxxx8NrabTCdMBfvz+/PNP/vLs378/azdBcvEF0KFDB0KmoyRJkrDGFjngQToR/xNEGuTaXc756tWrG6NAu6u1q3obLOIYrH6MyQawg7nYZT52mouG1C7YaKJgl/nYDR87zUffIzs9z5ZzUU7p+rm6EHaJ8GclaRKkIiyRa293aEjzJc8dzqtdX7dVH+YyXU9vzeeic99u84lOLGTs6EcgVhLQnTt3UqVKlZg84hbUqFGD01Z27tzZqzuCZfMKFSrQK6+8wsHdq1atSkWLWi+1oI45/zoG0GTP02AlSpRg0oQvDMwX6TZ1W6Tc/Oeff1hjmi1bNiafOIdr+vrrr3np3Zuc8yCyWnQcUG8C4Os2VlvJBW+FyuOyiGIVPq4Z+Xs6Dmig9zxYM5U4oJ6RtGMcUHwXahMlz7OP/LNWn61UjlSPNLSPFLZuJ1E/cx365fgct+dxIl2iUK9zzeN78Pbt217X9zhwEE4GKw6oN79dQZiudBFLEIiVYZjSpUtHWK7RgiVj/FktJellStTFF4qWAQMGMNnLmjUrwXEJWk0rwY87nJDMf+Z6un/XpRHkldeSNm3acPPV7XQdbDE/2INCs4k5TZ06lUBQ27Vr55Ygm9vLviAgCAgCMQkBeKYXSWGdySg0QWrqlL8lX25RVWfrlZ1uLz1Exel8Jl0lt+flhCAgCPiOQKwkoNWqVaO1a9ca9lTz5s2jQoUKMQEFgdM2aClTpmRbSsAKOzDYZELwpg37TTgDvfXWW9SgQQM6duwYn/PlP9hjwlYTsmaNewN3aGgXLFjAy+8gqjANKFLkkdfm8ePH2W4VfUArqnPCI+c8bFf/97//seMU7EWtSCvaiQgCgoAgEFMReFFpN5OFJDEu7wMVWB5yMewyjdg3mve7busbbnl+7KFJtOHyFj6PuJ11MlbjfflPEBAEgoNAjF+C7969u5P9X9u2bXnpvG7dukzQQkNDKVmyZAQHIwiWvocNG8YkFN7rHTt2ZLIJjaReZodWE85BCPGUNGlS9kTv16+f5R2B89DgwYOdzsGpqEWLFvTmm28SNKkgsvnz5ycQXiuB0xPsVhs3bswkEkv6WGKHAxG0o59++inbOeE64JwEcZdz3qp/KRMEBAFBIKYi0DRHIxq5/3uV/90/R0RkNyqesjCVSVMipkIk1yUIRAsCsToXPJbhEcYIdkxmgQYU4Zb0krwOj2Sug30Yt8PWxx1xdK1vdYw5YDyQx4gEdppwmsLcIDt27KAvvviCxo8fTzjneh3wfLxx4waZPd/djaFtQCUXvDuEglNuZacWnJ5970XbgEoueGvsEB0DnzVEyrCD2NEGFC/jdrYB1fdt3ul/qeXGrn6l4kQfK2rM4Xihur+IttoGVIfMi6h+ZJ8Plg2o5IKP7DsVu/qP8RpQT7cTPy6azJnruX6hIjanlcBBKBDyiT7dzcFqPFeCaa5jdQ4E2hvyae5H9gUBQUAQiGkIhD0M85t8Yvn9gSMCL6aYBphcjyAQBQjEShvQKMA1SoaAg1GrVq2iZCwZRBAQBASBJxEB5IL/eLuzGZQv14FYoj22DfClidQVBAQBLxCIlQQUy96zZ89mpx4zRnDc2b17t7nI730sz2MMHYPOl47OnTtHcIxCKk9X56aFCxcSlk4h0L4ir7xZELJp//795iLZFwQEAUEg1iIw5eivKh/74wgm/gCx69peWndxkz9NpY0gIAi4QSBWElDYXCJgO0IvmQVe5EuXLjUX+b0P+0uM4SsBxRyaN29OW7duZfvSnj17EjIWacF5TUB1mXkLxyQhoGZEZF8QEARiMwK/q9zuNx88dkBCLnizIBd8w6z1jD+k7IQgFzzSaUKQC37B2eD8NnCH8p8gIAhQrCSgvtx3hDkyEz5kIoJjD0IaIW+8+Rz6BamF1tKKeMIBBak+XWN+6vns3buXPfC/+uorzsoEIjpq1CgmxVu2PAoHAkIKz30YuZvTomFczKtevXocWF/3iXliPhKCSSMiW0FAEIgtCOB72JwLvmehDtS9UFuny9e54GEn+m7OJvRQJeBELvgfy31J2ZJk4bpIz7n8vPtQeU4dyoEgIAh4hUCsdkLyhNCKFSsIaTThAYv4ny1btuR4n4iniZSY0HDC8Qf51xGCCUvhCJWEMEi5cuVyInwgir1796azZ8/ysjmW+XH89NPOb+KITYqYnzly5DCmBicipPLUXvLwQgRBnTBhAuXJk4dDSaHy2LFj2UMewejRBvFJYVIAwpovXz4eGwHpn3rqKaNvhJvCkj0EYabgEBVo3mtcK8iwHVK+6ZcAePi7I/0GGFG0A/OPQDEO1lT1S4ld5oPPFO6ZjsMbrOv0tx/ggznZBR98tiQXvPu7afXZCmYu+PN3JBe8Wenh/k7IGUHAOwRiNQFFLE2zgDgheDtIHOJzIn4nAruDOL7//vtUv359ro4l7okTJzLR/Pnnn9nWs2TJktwGGYgQ1H7u3LkGuTt69CjHC/3hhx+4PQjl/PnzwxFQkNvixYubp8T7mnyaTzz//POciQnzxY8kluaRA37WrFlGNZDcXr16MekE6f3111+dCCiC7iOsEwT7+PEPlDiivf4zJhLNO3aaj53mom8L5mQHwTyC8QwG+1rsgg+uyy5z0fPAVu8HG3df+7OaC8oequ81iOSCt8/z4+u9lfoxE4FYTUCnTZtGSZI8zpCBPOoQEEYQMpBKCGK5weN8w4YNrInMmDEjk0+cQ/nmzZt5OR7hmwoWLIhizsvOO+o/aCoRfB7jgbwifmfOnDn1aWOLcE9Xr141jj3tYG5Y/kcmpQsXLhBSgmbOnNloAtIMrV+ZMmW4DKQYZNQs0Ihq0XFAA80LDtMArU3VfUfXFsQcOIDAm1ObRtd8MK4d44AGes+DhSc0n9BiWb1wBWsMX/qROKDu0cJ9wvcOVoFcw9a5bxW5Z6w+W6kdqSmeCken1tU9ije54DMkTut1bncdB9Qun61gxQGVXPAeHyM56SMCsdoGFNo/8x9yqEOQXQjkBX9a8OOolyytYmti+Vov2aEN+tKB7Ddt2sQZlVDesGFDJqPYd5UCBQoYWlPzOWhj//77b3MRax2gBYXmE9pU7JsFxBrj62VonANZNR+b68u+ICAICAKRhQBsKHdc3UMzjv9BX+8fR2MOTqRfjs+h07fPRtaQ3C80oMHKBV81XcVInat0LgjENgRiNQF1d7NBQLNnz04rV67kKocOHSL8gSC6E9SH1lTndF+8eLFBYKHxhI0olsuhIV29erWTA5Hu89lnn2UN2eTJk1kTBHsbhF1at24dVa5cWVcztsjzjnE2btxI1apVM8qxA81EsWLFjGtAHdin2mW5zGmyciAICAIxEoE7D+7ShCPTqczCWvTyynepu4qnOWj3l9Rv5zDqsX0gVf63PtVc0pCWnlsVadf/UpbnKGm8xytd/uaCr52hWqTNUToWBGIjArF6Cd7TDYcHet++fWncuHGc5hL51kFMz5w5Y9kMxA755Pv06UOjR4/mZXu9NFWnTh12Bvrwww+ZWMKu1CrcE7SWsDuFHenMmTN5nLx583KfVpmOYBqAv/Tp01suMcMRCfOBwxLmYl5yt7wIKRQEBAFBIEgInLx9ml5f05JO3DrtFAYJ3T9QnubX79/gkXaqGJvNN3Smmhmq0HdlPg/S6I+7eT37yzRy35hwc3hcw/OesnKVXPCeIZKzgoBfCMTqXPDeIOYuD7yntrD/sVqmh30nyr3RQkL7iWX/pEmTehrKq3MYN6KUodoGVHLBewWp35Ws7NT87izAhrAhxrMqueCtgRQbUGtcUKptQN3lgr95/xYVmVeFwh7eU+aXj02Z3Pf46EzupDloWfU/KCSu77oRT5+tBWeW0IebuhukN6J5uJ5fWeNPypMsp2ux22NtAyq54N1CJCcEAYkDGtEz4C4PvKd2VuQT9UECvSGfqAtj72CQTz0utiKCgCAgCEQ2AiCfdZY1prsqrqYv5BPzgta009Y+QZ9i7YzVqHtB5/if3gySIG58+ueZX3win970K3UEAUGAhIDKQyAICAKCgCAQPAR+ODyVTt0+oxbZH4U/8qVnkNa5p/+hbVd2+dLMq7rNcjelb0oNppTxk1NInEcOp+4aJoqbkNIlDKVfK/1ARVM+imzirq6UCwKCgH8I+L7O4d84MaoVsiMh9JKrVKhQgUOTwBkJwd89CZbXtY2orocyeLTD4cgcvgMZkmAKYA4ij6xHSNfpGsxe94UtvPIR3B4a2cKFCxte+eY6si8ICAKCQLAQuP3gDn174IeAcq8jePzA3V/Q9IpjgzUto59G2epTmdQl6PO939BClVoTaTfvPLzLpgKJ4iagBOovTpy49EaORtQ89xtMQo3GsiMICAJBRUAIqB9wInvQjz/+GI78FS1alJCLHUvnnggo7DvhIDR9+nSn0UFIkXVpyZIlnP8dy/Xnzp3jtJxwTtJy584dglMUwiy5I6AnTpygzp07cyxT2HdC4CTlrQmAHku2goAgIAh4i8CGS1tUqLfwNp/Iq3457AqdvXve6CqhInvV01ema/eu0+qLG5w0phsvbyOQ2cTxHiXKMBoFYSdXsuzs7AQP/VUX1vOcroRdZbKZXaXeLJumJMVVJFREEBAEIhcBIaB+4ouwS126dAnXGg4dZpKHrEqnTp1i+0940UOuXLlCp0+fZq2mq41p27Zt6b333qNffvmFU39+8skn9Prrr3N2JbRFOCik14Q9qSfHIsQNfeaZZ6h169ZoRm3atGFiW716dT6W/wQBQUAQCDYCGy5vpWv/ebfrvpFXfWjxPtR2c08nAtqjUHtafG4lFU5RQOVcz0zTVYxQLQnixKedKm4oyGBkSaJ4CalGhvDh7SJrPOlXEBAEnBEQAuqMR8BH48ePN3Kxz56Z4Ni9AABAAElEQVQ9myZNmkS5c+dmzWjVqlWpa9eunMYTAeGhkURsTnN2CWhBETqpffv2dODAAc5F/+qrrxrzQkaLjz/+mPNTI92nO0Ee+MaNGxun8+fPz3nrzQR06NChRuB7ZAqC6UCgea+h3ZVc8Abs4Xas8lWHqxRFBTqxQqD3PFjTReIHfC5gimIH0Ykl7IIPPlt2zwV/6PIRJ00m7mOIWuaedHRmuPLRByfQmTvn6IHjPlVI8yhjm77v9x7ep8MXjlIuRzZdFOHWTp8t3Cs8z3Z5dnRSFfx+BCK4LhFBIFgICAH1E0nkVoemUkvp0qVZy6iP8UO6fv16jumJFJnQeIIQYlkc8UD/+OMPGjZsmK7utAVZRGYjaEGRv90sWOaHYJnek2BpHlmSYDeKJftVq1ZRkSJFnJqA7OpUpNjHl5RZe+tU2csDtNd/XjaJ9Gp2mo+d5qKBx5zsIHoeemuHOWEOdpqPXeai54Gt3nd3v5acX0n5kudW0TSdnzOQz/zJclOLPG9Tq40fhWvuTd/mRr7WN7cN9r7GRG+D3b+//dltPv5eh7SLGQgIAfXzPuZUudw/+ujxl6ZryCR80HEeAednzJjBOeBBSvGWHpEgxzLIIzSnEydOpE6dOkXUhL777jsjuxI0psiSBHtU7CNNKILfu87RHJhexwE1Oz9FOKhFBcS/k1zwFsD8V+QpVqH7VpFzRscBDfSeB2t20Hzi8yG54K0RhekOHAp1il/rWlFTivtklQs+b+rcFPdsXK/CLxVRS+/t8jVn8gnHI7PEjxdCuUJzODljms9b7dvps6XjgNrlsyW54K2eGCmLbgSEgPp5B0CykKXInUDr2KxZM3YSgnc87DiRBz4igRYSGZjq1q1LL774Ir3zzjuE9u6cjXR/sAfVyyPIQ58qVSruB97y+EEfM2YMZ0zS9WUrCAgCgoAvCBy/dUo5C62nC3cv0Z17dyjp/cRUPqQMFU9bmOL9F9aonLLZTB4/KV1VjkXuBJmJtl7ZSd+UHkyHbhylz4r3Zmegn489Xu1BAPsiEv7IHYRSLgjECASEgEbSbUQWFWQgwnI7bDwRXgkCuzIsd0OgRYB20iw//fQT21BieR/toP2EnSjKQ0NDzVWd9kFwzTJ16lS2P4LzEeaxbNkygs2niCAgCAgCviCw6Oxy6qtyt5+68ygN8e0Ht+mhWs1BrMz4x0MoSUgS6py/Fb2a7UUOcRTXIrz02EOTjCGnHJvF+9WXvGyUmXewVA+bUDgJiQgCgkDMRUBiTUTSvc2WLRt7oUMLChIKpyDYgiI8EjSUxYsXZw0nlo20bNq0iXPAwwlJOybVqlWLypUrR4MGDWIHDV03ou0LL7zAy/7IaQ8SC0KbJUuWiJrJeUFAEBAEGAFoIZut70AtN3alfTcOEpbJ8fdAhVlCkPnbD++wxztsOfvuHErPLW9C95VDUYf8LShJvCR+o5hUEdpehTv43V4aCgKCwJOBgOSCj+T7BNsbEE6t9TQPh2X6RImCH+fOPIZegjeXWe1rG1DJBW+FTvDK7GSnpm1AJRe89f2NzbngHyqSWWpBTbpy7yqn1LRGyLkUmksQ03U159O769vR7mv7lC2o79mQmuVqSgOL9XDu3IsjO322tA2o5IL34sZJlViLgGhAI/nWw8vcinxi2MgmnxjDLg4dmIuIICAIPBkItN7Ujc7fveg1+cRVgXyChL65rjX9VP5rJp9Wy/HuEMCSfvGUhf0in+76lHJBQBCwLwJCQO17b2RmgoAgIAhEOQIbL22lf84u88qT3XVyIKHHb52mv08vooPPr1OORAUoWbykrtXCHScPSUYNs9aneVWnhTsnBYKAIBAzEXjiCShCtyDgu/5DBqAtW5AOzvelH19uMZZ7EGIpUMFSDUIuIU6olSxcuNDwbnc9v3jxYtJpNl3P4RhOTmvWrOGc8ZGNh9X4UiYICAJPHgL9d41gW09/Z37rwS0auf97ih83Ps2tMoWGFO9FSHEJkok/5F9HGs6U8VNQUmUrWjpVcfq5wigaXlKlF47zxP8k+QubtBMEYh0CT7wXPGwc4d0NpxvE3gTpQuxMOPygPLJi5iG9JsZBukt/BaT5559/5j4mTJhATZs25fiduj8EoR85ciQhg5J2StLn4NWONJ2TJ0+m5MmT62Jje+bMGXZ+Qvgm2KF+9tlnnL/enTmA0VB2BAFBINYicP3eDdp+dTdfP8hinYzV2ZZz57W9TpiUT1OK0iRITfPPLOal9wLJ81Ch5Plp4dmldFMR0AeOB7RJ5XN/KrQMNcpWX2k369Hhm8dol+rnzJ3zTEDTJkxDFdT5NAlSOfUtB4KAIBA7EHjiCai+TR07djRIGghX/fr12Qu8QIECXAUp7JAi0mwUjvBECM5+7tw5JqyIo3n06FGnvO26f/QJbWXGjBl1EW/R5vjx4wRHDrNNJzSzJ0+e5PFcA8DrDqZNm0affvop53lHliT8IVUmnJZALt2lcUMA6B9++IFjfeq+XLczZ87kWKI6WxP6gza1Xr16rlXlWBAQBAQBRgDkM4HSXN58QNSvaDeaofKzI1h8/13D6cTtR6s0IJNZE2emfdcPUkfl8f7z0V+5zvhDP9MA5TzUcUtvuqm85Tdf3s4EFB1DOZA7WQ7+E6gFAUFAEAACMYaAut5OkDgQP5A1hDAC2UQmEWQXQlxNnH/rrbcoT548tGfPHurWrRsHa4fmFIQ0V65cHMgd/X7//fc0Z84crguNK7SSECzDI8QScqjv3buXtYzIOLRx40Zui7FQjlicCCxvFhBijIO0mxDE+IQmE6QV4ZKQQhPpOM2521EPS+mYP/qEVtOdtGjRwilFHq7/9u3bTtXh8b5z504uA3nGnzvS69TQwwEIueSCdw8QNPSBYuy+d9/OSC54z3jZMRc8vnMiM53ikYtHCXnYISCiqy9uUCGVEtPTacvTdEVGIeVSl6Th+75jJ6W3c77KBBTORhkSpaewh2Fc577SgB5UeeGj8lm302cL34N2ygWPZxnPDRQpgQiuS0QQCBYCMYaAYjkbpBJf0KtXryZoPrNmzcpEENsRI0awLWWHDh2YIJYvX54xrFy5Mg0fPpwJJjIb9evXj5fxkTkIH9YDBw7wOWgUQdCwVI686iCqILRIgQmt6I8//khz587llJeTJk1iDWbZsmV5PiCLIJPmH47UqVMzoUVO95o1a9LmzZuZLMMWFMQVJgVWgvzwiDGKvj0JSLGWf//9l+OPPvfcc7qIt8jmpL3kcW34cjHP0amylwdor/+8bBLp1ew0HzvNRQOPOdlB9Dz01g5zwhzsNJ/IMinSWMdRNphwJEoWklSRyUdpg2+pwPOIzall1sm/WDt6QXnJw84T5xD/M1fS7HTnwV1dTeEWtdjhPtnlXul56K0BSjTtaGzsMp9ogkGGtRkCMYaAHjp0iO09QaSwzFynTh2GGhq+Hj0exZSDHSVsNuH0owloyZIl+UsLwd5BLt9++21OewmNJUIoIZ86zqFfCM5DduzYwZpKvSSfL18+DjYPzSaCzs+bN8/IfgRHIcyjaNGi3Fb/By3lN998wxpWLOEXLlyYl//1edft4cOHCU5WIL2u4poLXpsagJjDThQEXJNN3bZt27Z6l52Z+vfv71PuZaOxaQdmCpIL3gSIyy5ekOySH1rHAbXLfCQXvMvD4nIYFbngczzMxvaZl+5dYRKKKWRKlIFOKM92LUjF+fH2QXT3QRgvuVcMLauW6mfT8gtr6PuywylEORnhlSZP6lxR+qzb6bOl44Da5bMFZQrIJ76bAxFXX4RA+pK2gkCMIaDQbFp9ONKmTcsaTX2rsTyslx5Rpj+QIIDTp09nD3p4t4Mcjho1igmhedkBZBIB5CFmhx7zmyXmARtUPZ+XXnrJMgsRluthy4kvqxQpUrANqKdsRYsWLWIbVfQNwZL6+++/TwMHDnSaJzTBEGhikQL066+/drJ95ZPynyAgCMR4BG7ev0XLz69RaTTP0lmVsSijWibPnDgjVU1XkRLHC58Eo0SqIkbsT7Trr+xA0yYMpbabepLO4Q6t54d532NHo58OT6Pjt0/Sx4U6UOW0FWjH1T2sDYWHO5bqRQQBQUAQcIdAjCGg7i6wRo0atGDBAipdujTbJmLJ23UpGm2hWdy+fTt99NFHvLwNu1Ash2OJHsvxsKFMmTIl72NJ31WbqcfH0nqRIkUIXujQwsIGtX379vTVV1/pKsb2gw8+IBBn1F+5ciWBLKO9OwHZxJ+WV155hYYNG0Y5cuQwNLr6HMwBQFihGbXyktf1ZCsICAIxD4EDNw7T8L3f0YIzS3iZHET0nuMeJYgTX+VuT8zpNP+nPNw7F2hFOdXSuRYsp5dKXYxWXFhLE4/OYG0mltchOoc79uGshGxJWK6HdN76qVNd2I8WT1WYz8l/goAgIAhYIRDjCShyqSNkETzMofmsUqUKQSPpKiCqWJp/5513mKjCHhTL9NByvvzyy9SkSRP2dMeSO5au9+/f79qFcQzPc3i3T5kyhQ3RMTacjFwFWlaEisLyPrSW8FQPlkCzevbsWSfnp4YNGzIZDtYY0o8gIAjYD4FZJ/6i7tsGGOGQSHm0awlTJDRMOcJBfjv5N4dNGl1mKFVL/7SuQp8U7kwvr3qXY4Fq8mmc/G8HYZZcRddF4PkuBVoz8XWtI8eCgCAgCGgEYk0ueCydg+jFjx9fX7vlFsvr8DTXS/O6Ejws8Qe7UG8F9qCeNJq6H2/ztev6kbEFPrABlVzwkYHu4z7tZKembUAlF/zj+2PeexJzwU85Oot6bB9gLKObr8fT/lelBtKr2V40qnTZ8inNODHb8Go3TkSwE1dZf2IZ/4/Kk9iLPoLqQT1tp8+WtgHVtvhBvVA/OguWDei7777LDrd+TEGaCALhEIg1aSewDB0R+QQ6IKmu5BPlaOsL+UQbb8gn6rk6B6FMRBAQBAQBXxBYdn41ddr6ic/kE2N8vH0wrbm40RjusxK9Kbdpad444WEnJE48zv/++9MTo5x8epiWnBIEBAGbIhBrCKhN8ZdpCQKCgCAQMAJYEu+6ta/f/Vy/f4Pb65S98RSZXFL9d9aKIiNSRIKwTSVSFaVd/1tBCeM9DgEXUTs5LwgIArEXgRhvA+rLrUXMT4RdQhD49OnTG00RUPivv/7i+JxwGIoMwdjIqISxrTSwCGgP0ZmdsA8HJwS9z5kzp1GOUE1wpoIgZiC86+FtL45IDIn8JwjESAT+ODmPLoddDejazt29QH+fWUTPZ3rW6AdL8+tzNKI+Oz6nvSrzUUjcEBV+6S47ICVQ+dxVagxKlygtdS/YjuqqdjgvIggIAoKANwjIt4UJpbVr19Lo0aM51qfZ23zr1q1sGwlnosggoJ06dWInJGRlwvidO3d28mpH/D945zdo0MAgmghcD6el2rVrc7goOE/hPAjpjBkzuD3CRyHb0uDBg6lXr14c39R0ubIrCAgCMQSBP08toGv3r/PVPB1aXpHCUAIp1V7qaROkoWfSVzKuFiGW7qqsRTXSV1a53vfTnuv76eq9a/TXqX+cCCgalFN53+dWnUqXwq7Quoub6ELYJbp97w4lCUtI5TOVobypctkmALxxgbIjCAgCtkdACKjLLUIweGQOMhNQeMeDHJoFS1WnTp3iWJ/a0ByED8G0scX5VKlScRMYgMMoXQetN/eDgPZwdkDMTgg0nMgRrwPlowyOQa4BjZEOdMCAAVSiRAl69dVXeb463Sc8+Lt06YKmLIsXL+b0nQhIj1BSIoKAIBCzENh05dGqR60Mz7AT0OGbx6hD/g/oi31j+ELvqVBKF+9eVkSRWFsJe9EhxXvRmIMTqVmu12n84SlMQtdd3uwWmDQJUtH/MtV41J9yyMQKTJokaYR8ukVMTggCgoAnBISAuqCDNJgghQcPHmTSCTIJbSPigWIpHgKPcWgpcQ4e7CCnyDeP5fsvv/ySU3QiCD2I5NixY8PlkTc7HYHwIte8FsQb1YHuUYYc9HBmQuxRLRj3xIkTVLx4cS4CAYaDFPLIWwnyyWNeyNCEMFRakEte54LHsj8csALN3Yy5SS54jXD4rZ3yVeuEDIHe8/BX6V8JPl94ccNLnB0E+Ngpnzc+W/D0Nie90DhdDbvGu9WVRvOzPV+zNnNs2RH6NB8vOb+Sg8mPPjiB87j33TmMzqjg9JVCy1HWxJmYgF65e9Wr7wD9XYgX68hOD2pcRAQ7dvps4V7Z6dnBs4znBsqQQATXJSIIBAsBIaAWSCI3O7SgIJYbNmwgpOuEF7z+YcQ5pN7s2rUrxxbt3r07k1Z0hZSgWAJH3E/YYoJAuuaRx7K5Fnx5a5tPLJdDE4p+ISCZs2bN4iV25JrXgnpJkyZ1+iGCZhM/Tu6kYMGCdOzYMafT6AM2ohDEO8WXS6A/JviSw1+g/ThN1M8D7VCBudhhPrgMu2CDuWgSYRdszPcL87OD2Ol+AQ/cK8zJVfRSe+oEKQlB591J/cy16fU1rfg0yCc0pvmT56FRBx9/v/jyPKCuL/XdzSsY5Xa6V/oe2QUbfNbthE8w7rf08eQjIATU4h4iKD1IZfPmzTmbEDInwbZSC5a0IUiBCUG8T2RYQp75zJkzU7p06bjcXR55PunyH5yHunXrRoizBkckkEH0j6V0TVB1EwSt19orXYb60GC6E5Bn137atGljVNdxQL0NHWU0dNmBRgTjeBPyyqVp0A/xpYtg/CDartce9MG87BAvCYFi7OVQEVbTcUDtMh88o9BimVcIIryISKzwJMUBTaWIJwjlqdtnVO729Jx6M0zZeJqllPJSX39pi2EXCjJaXtl3dtzS26iWOmFKr55P3CcsweNemVMSGx1Fw46dPls6DqhdPlvBigOq00tHw+2VIWMgAkJALW5q9uzZ+Ut19+7dvKzeo0cPJwKKJrDRhP2lFniZw1nITHSglTQvWYDkYXldE1TdFuNgjI4dOzKJRfmRI0cI5SiD4Asfb7D40kf6TpAH/GDrL398+YL8oo2rIGc8MjeB3IoIAoKAvRE4fusUrbywji7cvagche6q/O0ZqHCK/JSNMrmd+FNpytDvp/6myUd/oY4FWnLKzZ+P/kqhCVLT2zkb04h9o6lIyoK09cpO7gO52gcX60VLz6+ib0oPpmnHfqflF9YQHJhEBAFBQBCICgSEgLpBGcvww4cPpwoVKoRbYoKGdN26dez8gzfCjz/+mKpWrcqpOs3ducsjj7SeWkAo4eHer18/Dpeky+FIBK2qllGjRrGdJ7zdIZjX7NmzCfngkWoUb9qub9vQkkKLAxvTbNmyGTaj3IH8JwgIArZCYNHZ5dRv1zA6qbSYSLF+68FtFdj9ISWOm4gSqNiaIRSP2udrTm/kfIXzuZsnXy9zLVqsbDzhfNRpyydOedlBPiEgp1rg8V50flV9yNtUipTWVf2ICAKCgCAQFQgIAXWDMkjmmDFjCCGSXAXnli5dSo0aNWJP91y5ctGzzz5rOPTo+tCKWuWR1+exhb0oNKft27c3iuHx/vvvvxvHVjutW7dm4vrbb78xQTbnkV++fDkTYmhM4YkPbe2QIUOsupEyQUAQiGYE7j28Rx9u6k5Lzq1UoZRuhJvN7Yd3CH+Qz/Z+QxOOzaC5VaYQtJha6maqpZyPvmFnI5TpvOz6vDfbbEmy0LMZnEmpN+2kjiAgCAgC/iAQa3LB+wNORG2wDA4NqF4Gd1cfy+f48zWVp7v+zOUgrzrck7nc131tAyq54H1Fzrf6MJVwDanlWw/Bq61tQCUXvDWmUWEDCserCov+R2fvnPc6haZy82M7znXPzqfsijRq2aDsO19b05KQ1cgfmV91Oodw8qattgHFsxzR9583/QWjjp0+W9oGVIfoC8b1BdJHsGxAJRd8IHdB2roiIKk4XRHx4RjOLd58+fqTR97baQSDfHo7ltQTBASB4CLQfnMvdhxCUHhvBR7vIKGNVzd38ngvm6YkjSzZ39tujHoJVUajmRXHeU0+jYayIwgIAoJAAAgIAQ0APGkqCAgCgoC/CMAhaO6Zf9Ry+QOfuwAJPaO0pmMOTXRqCxvOmRXHU+r4KZUjElJluhcQTzgpTarwLVVJ95T7inJGEBAEBIFIQEAIqI+gwuFn7ty54VohCDycghCSKTIES15r1qwhpAXV8RL1OAgmv2jRIrYl1WXYwkt+5cqVtGrVKjYBMJ+TfUFAEIheBAbtHkk37t/0exK3lZMSMhndun/bqY8q6SrQomq/0lvKWQl2oqnjp6Kk8ZJQ/DjxeYvjVIqgwjv+32qzqGq6ik7t5UAQEAQEgahAQJyQfER54sSJtHfvXk6ZaU7POX36dIJDEMpcvdF9HCJc9TNnztCHH37Iudxhy4MMRghMj+V/xAlF/E/YGsHbHZ77yJqE0Evvvfce565HylAEwx8xYoRlEOtwA0qBICAIRCoCtx/coY2Xt/EYyUOSUZ2M1VVO9n2089recONWDC1L267sopsPbnE9kEkQ1wVnl3DddZc2UbX0Tzu1y5w4Iw0o1oP6Fv2I1l3aTKdvn+WwTukSpqUs6hzyu+tg6U4N5UAQEAQEgShCQDSgfgCt88Xrpgh3hIxJ6dOn10W8hYMQAsybY4GCQCJVJUihLkd7ZClyDS6vOwN5RJ53eOT36tWLEKJp4cKF3Dc0o8gJ37ZtWypdujQhbz0EhBihmlAfIZww7tq1a3WXshUEBIFoRGD3tf0UP258nkG/ot1U6KXT1E6FWEJKTLNkT5KVxpQZRqkSpFAazBB6I0cjuhR2xfB2v3bvOm26/CgPvLmd3o8XJx6BwL6ctS59kOctapD1eSofWlrIpwZItoKAIBBtCIgG1A/oESMUYZKQKQmCLEnFihXjXOu6u9GjR/NSPbSR8KaFZhIB7qGlPHr0KO3bt4+QkhN52nv27MmpPZG1p127dpwJSfeDbYsWLZx+MJAvHhpOhH9CjncIyC6W5/WcDhw4wP3zSfUfyCkyMyHLkhaEZkLeewg89BFEP9C84CDVWPq3Qwo6baoAD38QcDsIXhgCxThY16FfeOwyH2Suwj3D82MHAT6YU2Tgc/jSEbr/4FFe7QSKiK6+uIGSxEtMT6ctT9OP/8GXr5JcUpu879GcUwv4GCkz7zy4S2XTlKBZJ/7iMtiCHrx8OFLm6OkeABeI5IK3Rgnfg5H17FiP6LlUf9YD/R7UShPPo8lZQcA7BISAeoeTU62cOXNyqkmQPGgjYX+JOKDbtj1aUoMdKDSUv/zyCyVIkID+/PNPJqwglxB8GaAMP7YNGjRgLSWIIbIY/frrr04kEfXRhxbkoUeOeKQH1bJgwQJelke+96effrQUh2V7necd9bCPdmZBnFIdEgjL+dDMBkocdb7hQPsxz9PffU1AMRc7zAfXofHx95qC2U6TCLtgY75fwbzOQPqKrPulMU8WkpTCVBxQCALPJw1JYky3Vd536Kcj0+mVrPW5DMv20479xsv0w0v0paZrH+V0j6w5GhPxsCOfLWtwcE8g+j5b14q6UnzWo/M5iborlZGeJASEgPp5txCMHjnhc+TIQdu3b+eg8LorBIJHfNChQ4dyEdJvQvuIZXIIUnjiy+DcuXOsmStTpgyXFypUiMkoH1j8ByenyZMnsy2nOV82NKnIuvTVV1/xcjyyKrnmi8ebqzlNKLqHXakWHQc0UPtVyQWvEbXe2ilftY4DGug9t75S30uh+YSG2Pxs+95L8FpEZhzQnJSdQuKG0OV7VwgkFJJJpdw8ceu0cQG5k+ag3LlyUOnUxbju5KMz6cjN45zzHfag8GK/9/A+5UmdK+h258Yk3OzgPkkueDfgqGIdB9Quny1oPvGb4/ob4P4KrM9ILnhrXKTUPwSEgPqHG4GAduvWjWAPWq5cOSZ8uitocrD0/tJLL+kip63+EsCyN96QteYHlWAzmlNpWPUbtG44adIkmj9/Pn399dfscIRyLNlj6b1AgQK8hA5tqs4dnzZtWgLZ0YJ9pOMUEQQEgehHAHnZdbai5efXUH9lB5o2YSi13dSTXs/+Muds77y1D0+0T+EuNO7wZIIGdGCxnnTs5gladWE9B66Hl3t55VAkIggIAoLAk4aAOCH5ecdA5uB9Dm90LL+b5ZlnnmFPeWSYKVKkCB0/fpymTp0ajlRiCRy2owiVBIEtKewyXcknwj5hmf+7774zyCfq4622e/fuBA0rBBpZmARAqlSpQn///Tefg6YCoZhKlZIfKgZH/hMEohkB2H1WCi3HAeUnHp1BfXcOp1YbP2JSOuXYLCdv+L7/5YeH8xHqDN07Si3NT+MrgJNR2dQlo/lqZHhBQBAQBHxHQDSgvmNmtIAzEkIvFS1a1CjDDrITNW7cmF577TXWOmI5HETRSt566y3q06cPTZgwgcMqaTtRc90ffviBtZ3whNfSsGFDzh+P7QcffMAaWDgl6XFAikFsMQdoWZs0acJOS7q9bAUBQSB6Efi4cAdaeXEdh1TS2lBvZqTrJlHhmDrmb0EJ4z22EfemvdQRBAQBQcAOCEgu+Ei8C7C7hLc6NJ0RCTzbU6ZMGVE1y/MwMIcW1CrXPGw7seQfke2OtgGVXPCWEAet0E75qrUNqOSCt769kWkDqkfsvX0IL687dIEP23zJctOCZ2ZQ4niJfGgVnKraBlRywVvjqW1AJRe8NT5SKggAAdGARuJzANLnDfnEFPwln2gLDacV+cQ5b8dHXRFBQBCIWgSwrO4P+cQswx6E0QM/0nhG7RXKaIKAICAIWCMgNqDWuEipICAICAKRigBywc8/u9jvMc6FXaDvD03yu700FAQEAUEgOhEQDahCH7aSOtg0Ym6mS5eOHXa8ieGG0DGIoRmZgmDxWCI3C7zc8+XLZxTt2bOHnaLgQa8Fc0OGJjg1wVM/fvxHmVf0edkKAoJA9CEwcFegueDvcC74lrnfpiQhiaPvQmRkQUAQEAT8QEAIqAJtypQpTM50DnUEmIdd5TfffONxaRw2nnAiQtrLyJSlS5dyqk49BuKO1qpVywi5hNBNnTt3pjZt2nAIJ9STXPAaLdkKAvZD4Nb927TpSuTlgrffFcuMBAFBQBBwRkCW4P/DAx7mXbp0od69exO8zvPkycMB3zVcyF6ETELI2a7TkSEG5+nTpwmZj7Qgx7u5DsrhYKSzzuBY10cYJWQfwtY1ZzzqaUHA+M8++4z/QHjhVNSsWTM+/ccffzARdbUhlVzwGj3ZCgL2Q2D3dZULPs6jFYnIzAVvvyuXGQkCgoAg8AgB0YBaPAkIm4TwRh06dGDiiNztIKbp06fnpXosbSOn+8SJEzmI/MCBAzl+JzIQgYAi7SVSYY4YMYIyZsxIb7zxBo0bN45jeILIvvDCCxyzE2UgsCCfyP4CrSX6TZr0UWYU16lh3EGDBnEAfJ1mE2R0/PjxnAXJHD/U21zw0KZC4MSEP22K4Dq2t8cg55inN+YL3vbpbz0d4B/mCyD5dhDJBe/+LuAlDfcMz48dBJ9VzCnQz4TVtRy5dJTuqyxGEMkFb4WQ72V2+mzhezCynh3fkXmU/hntAv0e1MoXf+YgbQQBVwSEgLoi8t9x/vz5WTsJIrl582aOo1mvXj0++/7773PQeGgmoYEcNmwY22iuXr2a5syZw7aY2CIAPAioJ4FWVQepb968Oa1bt46qV69u2eSff/5hk4Dy5csb55GGU4smXDj2Jhc8SCxsSSGwYw0GcQQJBvm0EwG1y3yAs8YH+9EtWitvh3ulscCc7DSfyLpf+holF7y+84FvI+te+TMzzAWi77M/fQSzjf5tsMt8gnlt0teTi4AQUDf3DkvjEGgYkVJzzZo1NHbsWIJmEaTRVUuDcEcVKlSgV155hSpWrEhVq1YNF6DeaijU1V9WWbJk8fiGilzwjRo1suomXJk3ueBbt25ttNNxQAPNXSy54A1ILXckF7wlLFyIzxS0WLEhF3wOlQs+vsoFf0lywbt/IHw8Y6fPlo4DGuj3qY8QuK0OzSd+Z/B7FohEFE86kL6lbexDQAiom3uOpenQ0FDCF8gXX3xBhw4dItiJPv/883xs1WzAgAF05MgRWrZsGX355Ze0f/9+evfdd7kqlvMgrksgeikd5zQRxb6rwK705MmTVK1aNddTlseSC94SFikUBCIdgcthV2nJuZV05s45unD3EmVKnIFyJM1KldNWMILGF1W54O85Hi3BSy74SL8lMoAgIAjYEAEhoC43BVoYaDm//vprev311/nstm3bCMvuTz/9NJ07d44Q8gjL5PptEFobaBCRRhNaUjgK4RwIKAQkE3aemTNnZk0qF/r4H8YsWLCg16GUdC54bG/cuMG54JFnXkQQEAQiB4EtV3bQ4N1f0oZLWwk52m8+uKkCxT9UzkYhlDQkCT1U9q0vZXmO2udvTlkSZ+Jc8AvOLiHkgg9RdXSKTeSCNwtywWtBLnhzXckFr5GRrSAgCDxpCAgB/e+OwYGof//+TBxht4ll91dffZXPgoh+++23NHPmTNZSli5dmpfhscxdvHhxevHFF2ny5MkEe0x4p8OJCKkx0SfkzTffJGhHkZYNtqWuHutcKYL/oFnNnTt3BLUen5Zc8I+xkD1BILIR+Gb/D/TVgbF0/d4NldnIObcRNJ1X7l3jKUw99hvNOb2Apj01hnoV7kirLq6n6/dvGOTTm3lqoppU5YLvnL+l5IL3BjSpIwgIArZDQHLBe3lL4BwBTaJ5yVw3BdlMlOhRPmYYe8P+x5VkQksK7WpU27dBMwu7H62t1XN23WobUMkF74pMcI8lF7x7PJ9UG9CJR6bTJzs+pzsPffPen1T+W1pxYS1NOjqTbj247R4YizPKzY8Kp8hPf1b5mRLFi9xEGBbDs60unCwlF7wVOsS/AYhqIrngrfGRUkEACEgcUC+fA3gPWpFPNNfkE/uw43QlnyhHFqKoJp8YF85REZFP1BMRBAQB3xGYfux3+mhbf5/JJ0Zqu7kHNc72IpVMVdSngUPU8j685/+qMiVayKdPk5XKgoAgIAi4QUAIqBtgpFgQEAQEAU8I3Lh/k/ruGu6pisdzWJbvuX0QzXr6R2qWqymlCEnusT5OgngWS1mIVtX8S5beI0RLKggCgoCdEXhibUAREHfu3LmW2ObIkYNKlChhec7fQiwP+pPzHU5LmzZt4mxICLmUPXt2f6cg7QQBQcBGCExQS++3fVw6d53+HpURad3FTTSwWA9qlLUe9VOEdtuVXSpEU3y6++AuqdD8lDBuArV9QGnjh1L3Qu2oXuZaFKJCOIkIAoKAIPAkI/DEfoshrNG+ffsY+7Nnz9LOnTupRo0afBxorDPXG7px40aaN28effzxx66nPB4jcDy86StVqsS2Uj179qRSpUpx3naPDeWkICAI2B6B2SfnKwJ6h73S62Sszs5Ey86vdpp34RQFqGDyvPTP2WV07f51yp4kCz0dWp4Wn38Upum60qIuPLuUyoeWplKpi9FvT/9E1+5dp7WXNtHFu5fprrIrzZgovbL3LEDJ7iZm8x4JJu4EsRwIAoLAE4rAE0tAoY1E7nYIMhCdP3/eODbfC+RdR1B5bQwOTSa0p+Z0l8jpDrtN2G/CWQjpNBEUHraTcD6CB/rNmzf5T7eDMwn+EFoJKSxdZe/evZwhacyYMQSNLKRx48aclrNmzZpUsmRJo8nx48eZoOq+ESsUY8PIH+k/tQ2n67XgOuAABcKNPtKlS+d0XSDpSPWJa8A8dT/GwLIjCAgCfiEAZ8P9Nw5x23b53qd91w9S1bRPUQKV3/2fc8u4vEDyPPRuziY0+egvNKzkp9Rhcy/qXbgzjdw3hoaV+JTeWNtahWl6QCuVJ7xZUsRPTrUyPGMu4v0rYVfClUmBICAICAJPKgJPLAGNCHCQN+RNv3r1KoFgIoQR4mBCUzp8+HD6+eefuQsEeAeRnT59OodaWr58OaVKlYoJ7dChQ3kfGYjQD+ogxifywkPrCtK6e/duPkaMULOsXbuWNbKafOIcnJimTJliOCOtWLGCQFAxHrS5LVu2pAYNGnA+eOSfRxlCOzVt2tTyWhAbFPnmQTDhbHTw4EEO/VS2bFmOO2qVv97sSAV8duzYwdMGiQUBBm6BCEgvSL6noPqB9O9LW51+Dh7+eIGwg+ClIVCMg3UdOjmCXeaD+4U/vFTZQXCvgJEVPleV/SY80SF5k+WiEftG0xqlteyYr4VBQE/dPkt9dn7OWtLQBKnZ073lxq7sdHTv4T3jEs/eOm85hlHhvx3MBS+9dhD92cL3ol00snb7bOF72erZiY77h7lAAv0eBMYigkCwEIixBBRxO7NmzcoEDR+aDh06cP72cuXKsUZUB3afP38+1alTh0A8Fy9ezPE84dU+Y8YMjvvZrVs3eu3/7J0FnBVV+8efbcAuUJBOsQsTO1HEQAxQsAO7EzFQecVuEQMDVMTArhcUEAkLDFBBESnF//v6Utv/8z3Lucxe5t6de+fuMrv7PPu5O/fOnDlz5jf1mydPOskmkD/99NMtyYOoPfXUU/YYQCjpI56A0j85QuPFRcKTooPcoHfccYc1y0NoSXbfvXt3uwoPm7feess+kMlP6rcvaF5Jdj98+HBp3bq1JdWQZQhoovr13jrzVHlymuH8/Hz74Ce3aRjhRscDKQoPJR6SHHvGEna/wmDiXZfjGpWxgA/HKyrjYSxRGk+ycyenzFhHzB+Sb/w1keUly23CefvD/CO/J4ni793hVnn454r7BRpPTPKFZUW2KhIm/GxjeQlyDNy1FYWXO8aCMO4oXOuMJUrXlhcfxhYVCXKeRWWsOo66j0CdJaBoOq+99lp7BDE977vvvoJPZpcuXeTwww+XDz74QDp27GinlM184403rK+mS6mEPynazssvv7zSWdC2bVurkRw5cqQlf2gQW7VqVakNPyB3aAcSCRpOSJ8zxUMEmzdvLlOnTrWrEETFg4ZPon056qijhKT5kE+E9SGeSJD69eedd55tyz+XBzRs7WKtBR+D1PcLGqywGPt2nMZMtCEcr6iMpzblAd2gfANDHCs0oFQ7MleqNG24ucxbMT92JKhYdM8ON8tLc98wZvbJsnH+hrLVeh3khbmvSut1WkjLRlvKj//7WZo03CzQMcCSgwUjCoQPVyW0e7xQpxOcGQMpg1+idG1pLfgMHljtqs4iUHEHrYO7Ry10bpJO8APlDRmBgH7yySfy9ddfWw0g/pH4T8a3Z534mz0R7Zdeeqnt57jjjrNk1P6I+we5hTjGC1rPd999Vxgfb8nuTZl2PIDdGL2BVMn2xWtS924LUj1ixAhLSikRus02qeUa9Pal3xUBRaAyApDPrdfvZGdSOvP2ba+T67e6xPp7Ym6/zFQoOrXV8bKNaXNyy2Pl4Z3ulP8U/SPHbNnNtLvUplCCfEJSD2jctXLn+ksRUAQUgXqAQJ0loGgw0XJiRiOoZ+zYsbL11lvbQ7rFFlvYwCD8LyGjyH777Sf4bRLogxD1vtVWW1kCiqYScoig8cTEfeKJJ9ra7ARAsY14oRQmb+SU6ITY0ubDDz+UyZMny957720JKCmZJkyYYFedPXu28IG4xkuyfYlv635Tv54xHnbYYTYJPi4Bjty6NjpVBBSB9BHo0fQwoRzmuD8nyg0z7pTTplwsC1culiVF/2d9Qp+aM0IOGHec9P/yGvvBZH/FNwPlXz8+KDfOGGw3TI34QzbfL/1B6JqKgCKgCNRSBOqsCf7ggw+WTz/91EaeQ7y6du1qzdLuOHXr1s1GqTufSLSMRxxxhCVtm2yyiTUtDRo0yDZv166dbXvNNddY7SfplPr372+JJWmVxo0b57qNTfHPxL8TTSQ15BH6uemmm2zAEL/POussufnmm+XJJ5+0JvCBAwdaYsoyryTal++//97brNL3RPXrKzXSH4qAIpA2An1a9ZQHf35SlpUut9HsQTuiNjxCENPuG+8k22xQoUkNur62UwQUAUWgLiBQ52vB49uIXyelML2CdpT8ns5P1C1DW0kULlHlXsFUzjLn74R/J+bvIAEBaD/RoLo0S95++Y7WNYgfXqJ9ie/P/WbMierXuzZu6nxAtRa8Q6R6pmjFqZ8dBXE+oFgEoiC1yQfU4fXk7Oet9tP9TnX6xYHvSst1mgdaLYo+oFoL3v/QOR9QF+Tp36rm5mIF5Fnlde1KZ+unnXaaPP300+msqusoAmsgUGc1oG5P44kk2tAHHnhAJk6cKIMHV5jBXFumENV4ssp8fEEd+eS3X7135vsJQVDJcnAGIZ/0G78vftvyzmPMiXxEve30uyKgCKSOwOylv4Uinxe3Pysw+Ux9dLqGIqAIKALRRqDO+oAmgp00FESyEwxEblAVRUARUATSQeBG4/dJ9Hu68syvI+X/ihJnyki3X11PEVAEFIHagECd14D6HQTSFznBjxLzBIFFXsE8SV5Q/EITmdkJ9MGsDqHNlBCohF+qn8aU8TBOpwnFJQA3Aswq5Bx140S765cAmXWJ+FdRBBSBcAh8998fZer/fWMqtZen3dFKU+v98V+etfXd0+5EV1QEFAFFoJYiUO80oPHHCfKJNtSbDok2H3/8sY2cd6Qufj1+k1d0ypTKZfT82gWd9+qrr9pKRn7R6gRUDRgwIFYJZeHChTZgCrI5ZswYmw7KRepTsYmxUUnJ+8HPU0URUATCI/DRos9M4vkVtqNO67WXE5sfLevmrlOp4w3y1pdjmnUziefb2/kF2fly2OYHyJ6b7Go1pySjf2vBh5XW0R+KgCKgCNQXBOqlBtR7cHfeeWfr80l+T68WlFydpDFygrbRWyPezWeKwzlR705rSf5QPq7qUbJ1WZ8gJcjlkiVL+LmGoM2k8hIlO50QWY92lupMCOujPT3yyCPtb9JLuRRTdob+UwQUgYwhMOGvyVJcXiwtGjWT89r2k1Hzxsigba6Ti7++PraNO7e7wWg4h8sZrU+WYXNelBNbHC3/XjxBOq/fUZo3aiov/f6GzF+xUNCENsgpiK2nXxQBRUARqA8I1HsCioYTIkc5TUdAf//9d/njjz9i5TUp6xlfI97rP/rggw9aM7wjrEOHDrWR92eccYZvfXnvupxkaDx33313ITWUSwvlTj7KJVLD/oILLqgUNHXOOefETO60JSqf8p6pCLXgp0+fbleBQGst+FTQS68tLxt+7hHp9RZuLadpj8p4ONf51IZa8POXLbTgo82EfH721yQ5xSSe98rN3w2xeUFps2XDLeQxY24nT2ipScO028Y726Ykop+5YJY0a1B1JgKOF1kUoiAcJ0RrwfsfDY4VVrWoXFvOwqe14P2Pl85dOwjUewIK7GgK+/XrZ1MlEelOEnoSuKPRxPSdqEa8O2QQR0goBJQLHfM3pDXIuvTBNr1+qa5fpqNGjbLVjBw5dstIju+Eqk7z5s2rpPEkyT516r1C+gynpWU+KVQo5YkwBghs2FrB7D/R9/EVpOxGavgfD0kIH2MJu1+ZGjoPpqiMBXw4XlEZD2OJ0niSnTs55pxCNjLlNWctnW2/l5SVSJ4hlC7PJ2Tz4Cb7Sof12sojv1Skrumwbhs5p21fOW/aVXYdjkFebl6gY+CurWRuQbbTGvjHWBDOnShc64wlSteWFx/GFhUJe61H4dyLCpY6jvAIKAE1GJKrjSpJ48ePF6oOoQ295557LLqU09xzzz2tRpMZLI+vEU89d94s58yZY994t9xySxvsk6y+vJcI2g35/KM/XAEeffRRn6UVs958801bbYnxOpM/S44//nibfN+7Yvw2zz333Nhilwc0aEqo2IpxX3BHICjKL5VVXNNq/8lDYNGiRVazGzb/XaYGG6V61S4PaNhjnilsalMe0KaNNpeZy36xJvQtGjSxEFCe05FPZnRveoh02XhHufTrG+3yrY3p/SKTegnyubRkmZ1XKqXSdrM2tjSnnZHkXxTzgGoteP8D5vKARuXaylQe0LAE1h8tnVtfEaj3QUjuwGOGJzk9vqAQUspkIkFqxPNWiBYUzSfkle9B17UNE/wjEOq3336T7t27yyGHHCIEHp155pm2nCerPPfcc/Lyyy9b7WvLli0r9YJ2k33wfio10B+KgCKQNgIHNtlHGmQXyPsL/21que8t/9pugEz8qyIgEV9QApDu2PYG2djUhX9opzuk66a722luVo4M3u5G6d3iOLvtNuu0DEQ+0x6orqgIKAKKQEQRUA3oqgNDfXYS1BNRDuFzst9++1lTtqtW5K0R79owxWR/6aWXWjP25ZdfbhcFXdfbj/c7ZJOPE7SaQ4YMsXXs33nnHRupj3bUpWVy7XSqCCgC1YuAJZ0/PiT/lPxPLv/mJsGXs2RVic3rZ9xuN77N+/tUGsT+Y4+t9LtRTkM5qumhlebpD0VAEVAE6gsCSkBXHWlMxpjXIXbe8pzJasR7TxK0pnwaN24cK3cWdF1vP0G/ExWPeRnNrZPjjjtOLr74YvuTACNq0Xuld+/eQvCSiiKgCIRDoM26LeWQzfeV1/94zxJPRz5T6RUCemabPqmsom0VAUVAEagzCNT5WvCZOlKkUvKrER+k/zDrBuk/E22cD6jWgs8Emon70FrwibGpTT6g7AVBRvv/+xj5v+L0qhk9vvMQ6dHssMSAxC2Jog+o1oKPO0irfjofUJQSUZBM+YBqLfgoHM26MwbVgAY8lolqxAdZPcy6QfrXNoqAIlDzCCwrWZ42+czPypMFKxbV/KB1i4qAIqAIRAQBDUKKyIHQYSgCikDtQuDGGYPTrgVfZJLY3/vT4/J30X9q107raBUBRUARyBACdYKAEik+adKkNSAhYIhEyTUhrgxmom1h4iafqJ+Qbol0Snzeeustmz+0qrKZVD0Km1TYbyw6TxFQBKpGgFrw00LWgl9RslKeMJWSVBQBRUARqI8I1AkC+u2339qE7fEHkGTwBOpUt9x///2+BNi7XfJ0PvPMM95Zse/Tpk2zuTyp2z5jxgx5/fXXpVevXjJhwoRYm/gvpHxSAhqPiv5WBGoGgUzUgi8qL5Ix8z+omQHrVhQBRUARiBgCdYKABsWUYCC0pVQ4cYJzNnXbqfPOfKcxhbj6kVcCAdBYuj7QfP7666+WDNKPn5BflO0mk3bt2skVV1wh11xzjU2Cf9VVV9kSnG48TNkm40Suu+46WxveLXd9Q0qdNpYqK5QU9dsP116nioAikDoCE/76olIt+D9MTXfyf3qFWvBzls21teA7rddert3qYllRutLWgu/V/CjbdMHKRbYWvHc9/a4IKAKKQH1AoN4EISWq5/7EE09Ycoj2kWTvY8eOlW233daSNogbtdldaqPHHnvMpmmi0tGff/4pd999t/zyyy/y888/W+JKWcuddtqp0nlDH5TEpJY75TqDCttFs4p2t2vXrrb6Utu2beXHH3+0xHTgwIF2+/RLSiaiLSGclBQdPHiwTaBPPlJI69KlS22telIzeUup3XbbbZVqwZNPNGztYsrhQYC92wm6z5luBx4I7gxR0RZzPMJinCmcOFZIVMbD8aJ6Ve2oBV9hWQldC15yZdaCn6Rpg4qSuMmOLcdLa8EnRihq1xbnclSuLVcaNOx9EIxVFIFMIVBnCOiUKVMsgfQCQ21zpKqa7NzY8b3kAQgBbdWqldxyyy2yYMECOemkk+TCCy+0BBO/S2qzU4ed9pjKL7roIlsuk3yc8eSTi37QoEFyySWX2Frr3rEF+d6pUyeZO3durCnJ8iG9jtwRXX/ggQcK4+rTp48lqxtssIG0adNGKAPavn17ufLKK22NZDSrmPch106o8gSZRtgntMFhS62xz/Thxui2tTamHE9umFoL3h99R/jCHnP/3lOf6x6SURlPsnMnY7XgpVwokRtkn921lTqymV/De6y0Fvya+HrxWXPp2psT5DxLNroo3NeTjU+X1S4E6gwBhfzFJ17v2bOnPRpV1XPffvvtLWFyFxe135EtttjCklI0Mp999pl9UNx11112GfO+//57S07tDJ9/I0aMEEgkteJ/+OEHnxbJZ6FJ9NYwpx83RrcmxBdNJgTUWwbUBTxBgBEqOUGuvQTUm5Te5QENW7tYa8G7I+M/1Vrw/rgwl/MdNxnqi0dBsHLwkrfhhhuuMZym62yRsVrw7Rq3lfzsvDW2ET8jinlAtRZ8/FGq+O3ygIa9n/r3nvrcTOUBDUtgUx+5rlGXEagzBJQLo0GDBpWOlSNraPowlTvBV5OPe3P3kjzaeEtbuj7QFqEtPProo103VU6pqsRDDE0p6/OA7dGjh9VOVrUy2tuffvpJSPzrJH6czO/YsaPdD8jw+PHj5bzzznPNpUuXLgK5duLdLzdPp4qAIpA6Agc27mpqv0+2teAHbXud7LvZHpVqwf9r5kO2Fvy4PyfaGvAj574ut2xzlcxe+putBU/d+BfmvirUgg9CPlMfoa6hCCgCikC0EagzBDQZzGFrstP3vvvuK0OHDrVaUap/kOIJwof2EfM15DJeXnjhhdgsNKB33nmnPPvss7F5fl9wB4C04pvavHlz2W677fyaVZqHFpQoezSkjmRSVnTy5Mk2mh4T3/XXXy/77LOPtGjRotK6+kMRUARSQ2C+CTia+vfXUlhWEXQYphZ8KpWQUhultlYEFAFFINoI1AsCmoma7JjhTjjhBOsTCjFE44pfJYKWcciQIZaEdu/ePa0jjokfgojGlW2hvYSwBpGDDz5YHnnkETnzzDNjzSGg48aNE9wQ6K9169Zy0EEHxZbrF0VAEUgdgc/+nCTnTLtC/le8rNLK6dSCXye3kYmQ712pH/2hCCgCikB9QaBe1YLPRE12AhMwjztNoztR0IDiL+bM+m7+2p4S9YgGtKCgIOlQnA+o1oJPClPohVoLPjGEUfcBnfb3N3L0hL4m/VJmIoGf2PluOarZoYkBiVsSRR9QrQUfd5BW/XQ+oFoL3h8fnasIgEC90IC6Q52JmuyQuXjySf9VETw3hpqerrPOOjW9Sd2eIlDnEJj1v1/kiPGZ0VY2yG4gV3Y6PyXyWecA1R1SBBSBeo9AvSKg9f5oKwCKgCKQFgI3zrjT1n0vN2mT0pXcrBxZJ3cduXnrK+XEFsek242upwgoAopAnUAgcpWQyNlJ9Hi8UNGHWumkE8qEkDeUPoMKUfOuXjvTMWPG2PKbbjyYD5kfnyh65syZvmU6SZPkV+8dv01X7Yix0YZ+vdWM2Ca5P1UUAUWg+hH45j/fydfmE4Z8MsqO67WT9/cZqeSz+g+ZbkERUARqAQKRI6DDhw+3+Ty9aZPA8aWXXhJycHrJWRh8X3vttZRyc+L3yfZ///13Owamzz//vPTt29eW4sQETzUlouJJuYQsXrxYrr76aiE5vFcg2QMGDFiDrNJm4sSJQulOJ/ymIhJR906IvqcWvIoioAhUPwIfL/pMlpUstxuipOaJzY+WdY0mM15Y1qRgMzu7ccGmcmyzI+ynw7pt7LzismJptY5moYjHTX8rAopA/UQgcgSUw9C5c2f55JNPYkeE1ERTp06Vxo0bx+bxBVIIUfWWO8P5Gw2h9+OqUrAOxNGv1J9fX7SPFyLNzz33XDn//PPloYcekm222cYmgKcdFZPQVFItiWAlSObJJ58sW221VawbxkrpTL/k1jTaZZddbEUjt8IXX3xhI9knTZrkZsnXX39to+TdDLSulAMl2XC8sL/MZ//8UkXFt9ffioAiUBmBCSbfJ1HuLRo1k/Pa9hO/uu97b7qbPL3r/dLctEEO3Xx/2aRgY/m76D+ysqwiRdu8FQsEEqqiCCgCioAiENEgJMpLkrz9rLPOssdo2rRptoIPddGdYJZ+7rnnbNlJkrCTwoiyk5SqdGbw//73v9bMTl9EwFM3nQCiJUuW2NydVfXllieaQowhd9SAR9CC3nTTTbZ2PIQQLiVYHAAAQABJREFUktmrV6/Y6mhGSa3EOKjX7ic777yz3QdIMymZvvzyS0ErTOlPzPGMHwLaz9R8h+TeeOONlvSiZSXXKL/32msvi4HbX/KKbrLJJkK+UG8i/VtvvTVGdhs1amQ1tV4y7ze+quaBCUTXJfCvqn11LneaaHALWwM5U+PkmIXFOFNj4VghURkP5zzHzO8FMVP7nEo/HCswmr98oV0tWd333Kxcee63V2Jm+m026GSJKqR10pKpdn1TcFNmmrrvmxdUfpEOOibGw30sStcW99ioZP6I2rXF+Ryla4vzLOx9EIxVFIFMIRDJICRqsROxDolr166dfPzxxzaHpSOgPKTw4cQ03bRpU1uznRydl19+udx8880WG0gHVYFuuOEGS9owjVNLHc0lN81jjz3WtkvWl9+NFa0mDwAuRNwBKLVJHk4nHTp0kG7dulkt6Kuvvupm2ymaUXKIouVMJKQ1IXUHml0efs2aNZP111/fJplnn9GmMi76oQ1R7mhUkRdffNFqYyGgDz74oCXlaGu56bj99W4XjXLLli3tLJLp0y5sqTVuuvQRlYckxwm8wu6XF7cw3zmmURkL5747XmH2KVPrcs5EaTyxc8cEDyEb5W8os5bOtt9Lykokz5BOl5Jp7J8TpP16bWygEg2odPTF31/KHpvsIme06S0P//yUGHotebl5aR9/sCELRxSEsSCcy373ybUxxihdW1581gYWibYZ9t4Thft6on3T+bUPgWjczXxwI5E6gToQpOnTp8tVV10Va8VFwG8Cdl5++WVbspKHKdoBtJAEDF177bVy6KGHyv7772/XQ0uKhhRBW4jpHKmqL9vI849SmtxwCQJie/TpLZHJGy/+mW3atLGay8suu8yuPWfOHHn33Xfl0Ucf9fTm/xWCyj5DoklIj+y2225W88m+uXlt27aV3r17y8iRIy0GM2bMkFaGvCPfffedHH/88fY7JNWtY2es+qe14L1o1Mx3tPNRqQ/NCwcuK1EZT1TzgG7RqIn8vHyOUAFpiwZN7ImSnZUdI59+Z05JeaksXLlYPjfaz0s7nGOblBkK2mYz83IdoO67X59RzAOqteD9jpTY6wq3p6hcW1oL3v846dy1i0AkfUCBxBFQyknuuuuulbQGmOjOOOMMqwGEmGH2dgIRveOOO6zm8JRTTnGzrUYV8ubEaRKS9eXaeqeYyHfffXdr6obsoll1wlsvGlhM3ZjaIakTJkywi9Hi/vbbb0KlpEMOOUQWLlxoKxexf/ECAYVMsoxtIRBISPQ333xj8WAe5vlLL72Ur3LcccdZMmp/mH+Y3CEXTpYuXeq+6lQRUARSQOCgJvtIQXa+rft+QOO95V/bDahU992vq0Y5DeXWba6W/u1Ok6fnjLBNOhgNabrk028bOk8RUAQUgdqMQGQJKCbmBg0ayNNPP71GCUl8GjGj9+/fX/bYYw/56quv7DHABEMNdbRMTtvpDs6OO+5oTfmQRLSU+FEiyfpy6yaaEuEOCXQR6s8884zVvp5++unWjI72EyKKzynBS5BQItz54Df65JNP+momKe2JP+eCBQsEkz5Cewg044YEI5BUyOqJJ55oXQE+//xz6xrAMnxiSWfFmy/R+RBXFUVAEUgdgQObdDUEtMAGE1H3/brpt8vw3162HV0/4/ZYh0NnPydT/6/ivvLKvDfl5u/ulhtnDJYf//ezNMppJEc36xZrq18UAUVAEajvCETWBM+BIRiJdEnOXO4OFuR03333tVpQTECYnfEFxexMaqTNNtvM1m1HG4oQmIO5+brrrrNR6cxv3769XZaor3nz5sXIn23o8w8tIz6l+Ftihn/llVdk2LBhMT8tfEMhhbfffrutFR/Uf4aAIHxB8dH0rrPTTjvJjz/+GKvEhIsB+wQRxx0Ako1bAnLUUUdZEosWGO0pvqpO6+uzKzpLEVAEEiDQbt3WAgkdM/99Ew1faiPiEzStNNtbH35dU/e9X6sTKy3XH4qAIqAI1GcEanUteLR7OFVjCg8q+FXiExnvOJ9OX0G3Wd3t0AYTqOQlq5jvW7RoEYvQh4CfdtppMZN+/JjAhah4rQUfj0xmf6Od5+UiCuJ8QLfYYosoDMdmT+BFipfKKAjWEYIhyWaxYMUi2X/sMfKf4tVuLamM8ald7pNuTQ9KZZU12kbRB1Rrwa9xmOwMrQXvj4vOVQS8CERaA+odqN93NIWpil8dd/pIp69Ut11d7eMT3bMdAjquueYa629KNgGIOhpSFUVAEUgdgX9KlqZNPguy8uSPlRWpnFLfsq6hCCgCikDdRKBWE9C6eUgys1ddu3a1rgn4xxL0BPlMRVOcmVFoL4pA3UDgxul3pF0LvrC8WIbMfER6btndpHKqXBWtbqCje6EIKAKKQOoIRDYIKfVdSX0NzO4ktHcBSd4eyLmZqXrr1Jynv1SE6HnG5j6kpCKVkxOWE4TkFczo1KjHjIng34ovKKlAfv31VztP/ykCikBqCHz9nxny7X+/jyWZT23titYrS1fKE788l86quo4ioAgoAnUSgXpNQPGpor47H68QTY8/5GOPPeadnfZ30idRjSkVeemll2wKJ5Ld84GAEvA0evRo2w3aTHKhks4JIbCKlFD8xm/NCftIO4KhVBQBRSB1BD5a9KksDVkLvrCsSN5e8EHqG9c1FAFFQBGoowjUawLKMcX3k+Adqgo5wWwdHygCKSUyfu7cubFUR1RK8dac5ztaSCcEnPiVYvPry63jnZJEn0pGfG655RYhrROJ9xHSL5FvFKJMaqkRI0YIuT5JAeUVgori98W7XL8rAopAcgSoaqS14JNjpEsVgbAIkCebPNqJAmEXLVpkl7vKfzxHaY9lkJgHvv/+++9hh6Hr1yAC6gNqwCbp/SeffCJUFkLI10kKJadt5AQnlRNpkcjpyclOvlEI55AhQ+w6kFjabb311jb3J/k/MbtDcPlsuummtl2ivohir0ogt67uPG3PPvtsm16KMUycOFGGDh1aKWE/5njM71tuuaVv15BaV96UzAAEM/kRZt+VE8zkpgA+3oj8BE2rfbZLwwVuRHxHQXhpCYtxpvaDY4VEZTy8SHHMolYLfsHyRRYnrQVvYbD/3LWlteBXY+L9xrXlck5756+t74wFCXsf5P5VXQIBHThwoO3+sMMOk+22267SplCysJzc3yhawBirH/EOKFlYxnMb1zOV2oFASgSU0mJvvPGGTWxOYnVMw+SmjE9pVDt2ffUoyTdK6c6zzjrLajdJ2n700UfHCCgaUZK9H3nkkXYl9n3atGm2zCc5QJGpU6faN7crrrjCms4hdpTIJPqcvp0k68u1cVO0nWPHjrUPZbSp4HzRRRe5xdbUjlaUmveXXHKJzX/qFqKthUA/8sgjNpm/m++dkn7HVUjCbM/NKWyuUB5M7HMUzgnG4up5h90vL25hvnPTjMpY2A8eTFEZj3toR2U8sXMng7Xg83Pz08bbHasovNwxFvDhWucTBYnStcW9h0+UzmWOUdjx1MS5R/EVcmrHE1Cep82aNYudavn5+bF4B16EVGofAoEJKJV5Dj/8cOtjyM2HkpJoBTnwEB2vZq62wUC9eU5m0hUtXrzYVhry3lQho5MmTbIaRtpA7tDyOZk9e7YMGjTIElA0nSTPx0TufDF5Y3NBSFX15fpkSglSqh7xZsjFR0L7jh07epvIiy++aOdBVjk+aFt5MDAeyLC3Tn2lFc0PCLcTtISY88l5GEbIf8c23b6H6SvsupynaNPQ7ibDIex2UlmfF4mwGKeyvWRteeEgYC0q4+GaimIe0M1NLfifMlQLvvVmLdMuxxm1PKAcr6jVgo/KuezygEZlPATcQh7D3ge9z8Vk95Ywy0444QQhBoLnkRMsh8RSoAhywbc851C+XH755eKXy5jg4ocfftg+Pzt37mxjIShY44Tn9gMPPGCLu/Dc5Dl98cUXWy5AG5Y/Y6obolziWXzQQQfZwGQsh06GDx8ub731ln3O4DJ34YUXhib5ru/6MA3sA0rtdQ4QBM2puDk4aLpQjdd2cbXnMb9zonnl/vvvt/vIfqOB9FZmwnxJSU7InquuBPnyqztPn8n68m6T761bt7ZVjI4//nhbypPyoiTHdjJq1Cghwp6LjLHde++9dhER77wwUCeeFEy8TXKhOHcBt75OFYH6jkC5seqUTJ0ihe+Yh8jwZ6Twzdel+LNxUubRqGSuFnzbtMlnfT9Ouv/1B4HjjjvOxmQ49zD2HEJKRheIohMUDJSz5hkYL7jUwVew8PH8/OKLL6xGFastAqFFw4pCgGqBPLshvDfccINdjqsdllEy4bBdKgyidONZ6gSyCvll3T333NMqoHr27OkW6zQAAoE0oLw9UVnn2WeftRV3XL9NmjSxbwz4Q0J2arNwskHweMPjxPQGJXEhYHbfa6+9LAGnHCZvO+DCOryxsczJDjvsYB2ieQvmgsGM7nJwJurLrZtoinaTfiCRgwcPlpkzZ9qyn5jY6Rszf9++feWjjz6yBJq2TmjDOPr16+dm6VQRqNcIlJmAhqI3RkuJcaWR7CwRQ0SNzbQCkwYNzXS45DfdUsp7HC0Htusqd896VP4p/p9QCz43KzdWjjO+FrwDlVrwr/3xTqzdOqYWfI+mh7nFOlUEFIEECMAr9ttvPxtw68zwWACx6gVNjYhCiGemU45h7cNdECviQw89ZJ+fPLeJm0CJ1rt3b6vcwdKJ8Jzl+U5sBZpjAoGJ74D0IrNmzbL9UPr7pJNOsvMgn5BRyCqlwlWqRiAQAcVvhIPk58BM/fUo+PtVvavJW+BbAkmDPMb7uZx88slWy8jbD8s4kTHDv/vuu1ZNz3xM4QgmjhdeeEGOOeYYOfXUUy05RJPpJFFfbnmyKW9bffr0sdvlZYAgJNc3denRxHKBoaGtzS4RyTDQZYpAWARKvv5SVj5hUqwZ9wwTybBmdysNGTWSPftnkceMdaFbdzmo2T7y5vz3DKFMrxb8OqYW/GmtKx5Ua25Q5ygCioAXAcghJJAgIxQ+PG+x5gUhoLiGEMeBWZ5qgE5QLmFORwhyguSiKcVaiHkf5Y17btIOAuvlAsSAkJcbYTk+vrjWsS0nuKSwTAmoQyT5NBABxT+Sg4+W06VI4O0A0vXoo49WOsjJNxetpfiDvP/++7FBoc530q5dO3n11VftTyLr0JCizo+PVsdc4CeQT/xVcIz3+t0k68vbD74p8UIU/gcfVOQS5OKIF6IBIcXxQv5QFUVAERApmf6trHzgfkM8q47mNXpRqxktfvVluebQveSzTTaUPwuXpAXjv7YbIA1yCtJaV1dSBOobAscee6zNew25I6aC52zQuAIsj2gqIYNe5RjPXrLCIFgiDz30UOvvyXMTCybPdyyLCCb43Xff3X53/7z+vPhjo5jD+uglqfiAoilVCYZAIAJKV5jZ0ep16dLFAo4JmoABSJY3MjvYZmtfK07kePJZ1V5A3P0knb78+tF5ioAiEByBUuP3tXLI4OAreFpu/OlXMvL4c+TAwts9c6v+2iC7gdzY+VI5bIsDqm6sLRQBRcAigEUPpQ9xDiiCcCMLKptttpl9VqNgwiLoBOWNI7GkbOrUqZPVerrAqvHjx1uFEe0hkV9++aVb1U69yioUVPAf/ELx/0RQNmGZJIpfJRgCgYOQOJj4R+DfQIUgCCkphfCxcAcw2Ca1lSKgCCgCNY9A4fCn09/oiuXScvQ4mdZ1jDRv2Ezw6UwmecZPdIO89eWeHW6WM9r0TtZUlykCioAPApjh4Rpk2tlnn318WiSeRXT8c889Z03mEMNPP/1UevToEct5jKmdACRSS2JKJ70kZNdlt8HaC+HE7Q1t6U033WTTK7otooAjI82AAQMEN0SyrUBqcYNLVVHl+qyP08AaUMAhsht1s/M7JCIbnwychp2zcG0Ckcg4qit4BTJNhaGaELDj7Yk0QU64ALxvWixr06ZNDHPaccKjXXVR925dnSoCioA/AiU/fC9liyrK1vq3CDDX3P82nThDxh76moyYO1oe/2W4/FOy1D7AisuLbXBSbnaulJWXyZFbHCwXdzhbWjRanbcwwBa0iSKgCKxCAIsrwT/9+/evZEoPAhCEEZM6gUGYyuEoBAwTEY+QP5t0Tsxv0KCB7LjjjrYkNwQSEz5xHvh7Xn/99TZQiec0gcguSAlNKqT1tNNOk2233dbGj8CBIL2u6EyQcdb3NoEJKOmDyPvpLTXpwOOguhKRbl5tmKLaJ7DKEWrG7FT0NTF+nJ633377SgSUi4ba9KR+QPA1AXscprloEBymIaZKQC0c+k8RqBKB0q+/EhPWGmuXtdHGkrPNtlL63Qwp/9vj12leQHN23Mn6frIMydp0M8lp01YIXiqd/IWsc0R3ObNNHzmjdW/5eekcmbNsriwu/MtqPBsXbCq7bLy95GRFIzl7bIf1iyIQcQTQcqKNdILPpdNIunn33Xef+2qVMN723u/EXRDtTmpClEzx1QAxoaMVxdcTZc56661n+3XuhPieovjByuuEZcRhOEEDSpQ8GlpykuI2oJIaAoEIKA69kKI77rjDRnZ7NXZszuuEm9rm135rHJNJDu8nnPzkGOMtye0zJxrzmXLC4+js/c1Fg8p/wYIF1hEa1wVXfYITlX7Ipcr86667zq7vt21MAG69hQsX2rdA3sogokTjgTljQPXPxUYNXHxf3Dhdn5QLRXPNPqgoAvUVgdKZP65Os2Q0IgW9T5Gi99+Vgr79ZOX9Jn/uqvQqeUceJWXz/5CcrbehbIyNks9p30FKf/xB8g3xLHrnLXvdc/3xab9eG/upr7jqfisCUUYAhVI8+fSONxFp/Oyzz6zCjSTzKInGjh1rc45CauOFEtYq6SEQiIBCYEi4DlFzbwrpba72rEWpzZtvvtm+BREZd8EFF1jTPNpHNJIuCg4SScS6+w1Jxy8kvm48fiFExlNvnrQS1IofOHCgXTfZBQJi+KuQ1oE3NgjosGHDrJ8JhPSee+6xRJfjQu5SqjRQhYnjhQM2pJex8TbHNr3+urR1KSRIQUWEYNi64JBvCHkUXkrcG7HWgve/7jhWSNhj7t976nN50eWYVUct+HxzDTiH92yTnqV0zmwp+2mWlJprJrtpMymb97sdMMuK33hNygxhzTvqGMkyDzA0n1lNNq8gn4Z0Lpk7V8ybXuo7GHINXjgJfIjStcX9xRtpHHIXQ60OPlE5l7m2OJ+jMh7GgvilUkwFdDCuD4LpH6UOqQ55XhNYRB5SatCrZA6BQAQUHwkStaLOhrRAVuqKOEdltz+ccKRfYD5EEjKHszIEtFu3brYZJbpwOeDtCT8S7+/XX389Yd14Vt57773l7rvvTvkhgrrfW4XBjfenn36yVY5wIyD/KH4rjJnqSBBbCCo3DWrFQ6rJYuDEWwseMwTmf6d1dW1SnUIgILlReCgxFvadsYTdr1RxSNSeB1NUxsIYeTBFZTzuoV0d48kyyeZzttveJPc0NNSQOJsDFADMy5LJpcI3K1m5eXZazktUAzPfXBfZjZtI6a9zpOCEk2XlyBcl18wzoK1ao+Ym7lhFgYAyFq4trnXvS23NobHmlqJ0bXHv4VMd5/Kae171HEccw44nCude1XsbvgU4UfCFDwoVV0gmfM/agxeBwHdRyBipCUiHgLOtl2BQxpKEsbVRSEhLRJsTTNWYrYl8e++992IBQWjRCP5BMJ9j7nbi/V1VrXe/RPeun2RTLgJvPlHXFu2o82GlHKfzWWGsVEdCuJjQoOJz6iWgWgveoVhzU60FnxhrzvHqqgW/fMONJKuR0VrmZFsymdN5azuQrI03lvIlf8UGVY6mCPM6840VwZbqnGaSTv/1p2QdfoRkGQK7gblHMK1pwZKBJcV7763pMbjtcZw4XloL3iFSeaq14CvjUZt/KfmsvqMXmIASFIPGjOStLpmrG5a3NrqbV1umEEkckr0CAYW0kePLvTFCLKmWNNeY3+KJoPc35nk0okTSozF19dld/962bl6Q6fTp09cYJ+slSvlAJB4PCSe4UaAhUFEE6hsCaKKyjQtKyUcfrvYDlSzJ73OqyUxvfLkNscs/4SQpGj1KSj41ZXNP6StZhrAWPveMIaw5knfwoUYb2lBKJk+S7Fat1gr5rG/HTPdXEVAE6j4CgQgoplk0gvg/brXVVnUeFQg22l6CfyDc+PFcfPHF1l+zqp0HJ7+68VWtl2g5b9I4RJN/9amnnkrUbI35aKVJvIufKORzrHGi9quetMaKOkMRqEMIlEyZLIUvjZDyf/7rIZ8mpdwrIy25NG9ldm+LTBukdMZ0Kf3eWDpW+cwxr2jECxVt8/Kl4KSTmaWiCCgCioAiEBKBQAQUM0vLli1t0taQ26s1q+NsPHDgQFtuFH8nkuLi80lUfDIJU+vd2y8EEs0NUe2kW6IublXBSt71ie4naIlxo/mk3FiiaH/vevpdEagLCJQbzebKh+430esm+t0kkfeVRBYBD/mMrWfaZm3YSHK77hubpV8UAUVAEVAE0kcgy5Cc1Ym3kvRDiSkI2RVXXGF9Dr2mZCK+62r9U8zx8S4HSWCyiyCsaI0TmcerWj+Ty/FdJYisqvymtLv11lvlX//6V6jNo7Hl3Khqe6E2EnBljgM54EiN5T1fA65eLc3wAd3Y+BdGQYiI5XjhWhMFyZQPKH6cyy+5QMrN/mFiz5Q0uOpaySU901qSqPmAYhniXI6Kj1yUri3nAxqV9HfLTQ5cAojC3gdJvP700yEqiq2la0c3G00EAmlAGTomaFJuEA0eL7U1EX38fvj9TpV80gdBAlEgn4ylvqTNYl9VFAEQWPnEo1JuXgCdeT00KpjeTztjrZLP0PugHSgCioAiEDEEAhNQNEmJlKVRScMRMWx1OIqAIlDDCJTOmSOlVC9JZF5PYTzlOeb2WJAvDc44W/J22TWFNbWpIqAIZBIBuMf3/8yUjxd9JnOX/yF/F/1HmjdqKh3XbycHNu4qTRqszkqTye1qX9WLQGACipllxYoVtnSVyymGbyFaUUwxhxxySLWOFPOct0Y6Jl5Mh1QpCJObjH7ZN5d6iXRFYSVZjfl06riTaxQzul+0PkFPQcY8ZcoUmz6KSH4VRaCuIlA06iWjAl0RfveM73XJDjtK1iGHSV6r1uH70x4UAUUgLQTemv+BDPrhPllS+H+yonSFFJevdqtZJ6eRDMgaLHtvuptc0+ki6WQIqUrtQSBwMrvnn3/e5r4k1yR5J/lAiHbeeWcZNWpUte+xq5FOVaJZs2bJ119/bZPE4pOKr186ApGmOhECASVoJxNCjXnIMuN0n59//tl2TSYBksenIh9//LFAauMllTG/9tpr8sMPP8R3ob8VgTqDQDmpxn6ufG3lbNVZcnffw+b2jN/RnM7Gn9P4xSHZHTpK7h572o+ptCEFZ5wlJcf0FDHpmFQUAUWg5hEoKy+Ti768Ti77+iaZs2yu/FPyv0rkkxEtK10uS0uWyQcLx0r38X3k87+m1vxAdYtpIxBYA3rllVdKz5495ZRTTpFjjjlGPvzwQ5k8ebKtD0/5yZoSb410HKvJ1Qmho1IQQu7L+fPn25ydLocnWlrqteJGgDjHcJz6qdkOkWvRooWtFmQbmH9od1kGufXWc2c5y4iGR5uYyP0gUY15V8edfhiXtzY88xgT44Hou/EzH2E+++y0mN4xM5/2aKMJCnPruhrxFT3of0Wg7iJQtmB+pQpFOdvvKDnmRbls8SLJ695Dit98PbbzufvuLwW9TpBlF/W35vp8o+ksHvtJxfKVK6XMmPKlRatYe/2iCCgCNYtA9/GnyIz//iCFZUVVbrhMyuR/JUul5+dnyP073CY9m3evch1tsPYRCERAIUrkxKTaEamASN5OZPGFF15oq2FQc5zykmtDIICQOITyk+TMZGx//vmn3HXXXbYGer9+/WTbbbe1BBQSSuUjgqqGDx9u/Vqp8YomlKTxRPjNMQ+fG2+80beeOyZv6r+TGom+LrroIlu6M+i+uzrubC++NvzEiRPlnXfesRgzfjCFZCL//ve/bZlN9pd0UOwbtd9Jz8SYn3jiCfntt9+sxhV3CF4UCBgjCGnJkiWmomD+GkOk1r23FjwRrRDYMAI5x60hjFtEmO1713U+y1oL3ovK6u8cKyTsMV/dY7hvvOxxzNKtBZ9tikTkmX2q0Gma1J2mYlvRq8Y6s3yZNOh/kRR7hkfy+ZJvvqmYY4IGs8x1ktO+g5SYPKDkAF0573dbahKMooIPFhtesKN0bfFsiEJlJg4k+ETlWHHecD5HZTzOSlhbasHfbkzuX/9nhpSWp1Y8hfbXz7hD2q3XWnbY0Fg4VCKNQCACSu13fC7xQ0Q6deokEyZMsFrH3XbbLVbysSb2lFrnkDBSbnz++ed2DJBizOeQNFwFGCe12qmdfvXVV9thtTIVTKhjj1bzpJNOsuS5f//+8sYbb1gS58ps0phylieeeKKgrURILE8ddYgrxPSGG26wpBOTNuZ2asfHS6Ia8/HtXG14NJ9olXFngCy+9dZbQl15CC7CDYRa79zsmTdmzBjp3Llzpe646bEeD3FeCuj7/PPPt5rWY489tlJbfoAbfr0I2yR1iNOc2plp/GPbHJ8oPJQYCw8lxhJ2v9KAwncVjlFUxsIAOa+iMh730E53PFnmHM4yL585HTtJuSFGWeuuZ2q9r/Q/Dt98Jbm7rbpu8wuk+NOxUvL1V9Lg9LNk5cIFkmXudxC9KJ077lhFgYAyFq4trnU+UZAoXVvce/ikey5nGk+OFedN2GNVE+fe/BUL5ek5I1Mmnw6z/xb/IwNmDJY3937OzdJpRBEIREAhnwT73HTTTXLnnXcK9cwhd2jZ0NjhD1pTQplLHgqQTAgilYoQCOSee+4ZI8kkckfDePnll9vlLEMIXOLGgJYl0cWYqJ47Gk9M3fi9IlSFgoz6iV+Neb92rjY8mltuVmg2EcaHvyhaZgQy6W5me+yxh10WT0BdQBY3CdbFbQLB/cCvXOoZZ5xhl/PP5QFFexxGopYHFBzRkIfNfxcGE++6Wgvei0bl72HzgJZuvrmshIRiETFa+PK/l0iWqWpWbl5WSUyfUHJzpAzNv8kbWjbvd8neaGPJ26KpFBpixb0v7DWRcLspLohaHlCOl9aC9z+ILg9oVM4dnls8F8LeBxM9M/1RSG/uIz8/Y4KN/F8cg/Y483+/yLS/v5GdN94+6Cq23ciRI61Sa8cdd4yth6ILJRzP15oWNOijR4+2BWV4jtc1CURA2elHHnnE+lvut99+ctZZZ1kSxs2Ht060kjUll1xySYyIebeJWwAmaSeUn+TjNHHefJhVvcUlqufOSUh/EFgnmOtbGe1qfJ9+NebdOt6puyHQJxrJRNWK3H6wLjd+2saL64v5PDjZfyeOvLrfOlUE6hoC2c1bSPmff0mJcUVBygwBze9xjLHF50rJuH+TFFfyDjhIit94rfKum5yheXt3lfLtzAOmrNyQ0T8lv1PdLzlcGQT9pQhEA4H3Fn4iJZ5I93RGtaxkuXy0+LOUCCiBwrjmtW3bVnCHc/LSSy/FMu64eTU1ffLJJ+Wee+6xii+4T12TwFHwu+66q8w1PlbUE4dwYabG5xLS161bt7WOC8SYSHECdZD33nvPaii9xC1+kI6U4VflFfw8McGjxYTI/WjK+UG0IbH4kuJ+gGCWRyMcTz69fQX9TiolIvzR0FJViuChESNGxPrmgkADgsl8/Pjxss8++yTtmjc4oucxlfEWRdYAFUWgLiOQZV4Oc9BSmClSbiwWhcOGms8TpiSnyQBhqn15yWehSVhvLmzblnZFo0cZn9GXzfrGrNyuvZ2v/xQBRaDmEEDz+XdRxTM8zFYhsNP+Tu2ZRywFLmvEu7jYCO8YiMvwKrm8ywiERuvtBEuXl1egNMJfGkHZRD8846uSp556ysaCPPbYY7GmaLPJCuQVxuYEqx+pG73joT1jwILsxgWngXSTqQcXDa+gvHK8h31xSrdUxu7tL9H3wASUDvATROuJEByDbyQ14qMgm266qRxxxBGWOFKPfdKkSTJgwICkQ8OcsJ0JVOjRo0elg+XqufPGQYT/TjvtJPPmzbN9YdZ/4IEHpF+/fjbwx/loJt1QgIWYaqjbjn8quJI2yWsiR/3ONmlDxD9BUMnknHPOsYFY7Au+rlW1T9aXLlMEagsCBcceb5LHF1Qe7iqSWXmmzy/aFTSQ/KN6CP6kKoqAIlCzCCxauVjyszNz7S0wfQUVyBjlxo877jjhmeklfPSByyGub3AMYkEggQjWX9zbzj77bJu55tFHzUutkcsuu0wefPBB+51/uOqhyURBRswI1SNROmHxdOQu1njVF+eWh6sjbT75pCJLB7EuZNlxQo5vlwucNigI4S5wMwgsQuA0GYO6dOliXfNwWUSZBjcguxHuhBBNBO5B5p9zzz1XUDzCHRh3KmO3HQX4F7gWPH3BoPGDZOoVSGlU6knD7nkD8JrcvWP1+057F2DllqM5TFbPnbeZ6vDJ4E0ELaff+HkrYf9c1L8ba7Ipvp20T6YJZn3nA6q14JOhGX4ZF7nWgvfHkfsK57d7yfVvVfXcwtdeleLXR1fd0KdFVpPNpdEgY9Uwlg+0CuoD6gOSmcVxwrKiteD98XE+oC7ln3+rmpubKR/Q6q4Fv9CQxq6fHGVTKoVFZ5v1O8lH+40K1A2xLCispk6datM6EudBqkWew5A5gpyxsKK0goAS1IuiiJSIBAtjgcVCDGFDWYXFkpSRX375pbVCorCjDwKNsR5DcCG9Rx11lFx//fU2fiV+oGBNwDeB1CjC6AsizHr0N27cOJuLnTgR0jZeddVVctBBB9mg8AMPPNByNSylaDJxLUArilUUMgtRhvM4JRfjZn3GgzKPvlG8ERQNWSaTDtsOOvb4fUn0O7APKNHeAI4ZOF6iVAueBwafVCSefLIuhC1ZPffqIJ9sF7cAP/LJMjTQfFKRRH2l0oe2VQRqEwJlJtNFulJeZF6uecFO8R6S7vZ0PUVAEViNwMb5G6Ud/b66l4pvmzdoHD8r4W80hbxMoaVE4ARk1DnvvPPsb4iZ4xW4HEJG99prL9vOaR8hhR06dJCPPvrIuu/xEkIwMESWoj1t2rSxJI4OsWYiixcvtiTPBUnbmeYfCiEIH9tnTBBAMvbgHrC5CbZEK0pWHDSbkETIKS/MuOeRXpIsPAhaS6zBSNeuXa1LHy6D9Pvuu+/arD645+FCgCJu+vTpNs0j5BNB4+v4EeNBqhq7bRTwX2ACim8E6mIGHq/BCauxCDhWbaYIKAKKQFIESufMltJvU/P9qtSh8ZUqeutNKTjx5Eqz9YcioAhUPwL52XnSab328uV/vg21sUY5DeWwLQ4I1AeafMgYGlAsnwgZdtBSOgLqjf6HqGGWxkyNNpJ13HI0zS7lFUTtxRdftJpRtJlOyNwDGXSykcnUES+kkWQbaNDpH9KJuyBBSZjz6Q9SjKaWDxZoCCgKKhSFjixjRnclvL08DY0oZPP000+3hBItKppRtgNxZr/YJ6yx3mDmIGOP35dkvwMRUAYEUx88eLBNzp6sw7q+DDU6pvn4wCvecghK4sTyO6HC4oLJi/6JdOdE9AY+4ViM2p+crNS1d4JJE5MCbVGxu5PSLdepIlDXECgaZYKIVuW2TWvfzHVWPPbfkt+9R1qr60qKgCIQDoHjTRUj0igtK12Wdke5WblyQOO9A62PphOzussZzko84yF1mNIR8mtDRnn+oolEYwoBxUROTm6Uc5i4+bg0jX379rW+mvTlfEOJ4aBMN2Z9nsf4nFJZ0lVytBsz/yhYQxv6cIJPJ2klr732WtuegjQDBw6M5WEn8w4cgII0ffr0sRUhMcXjMxovaErJi45fKNwBP1J8QcmuA2EFk969e9txO1IedOzx20r2O1AQEqZocn1CZuq7oN7GHyM+Go5UDeTwpAxopgW1O1H5XAyc7JwYzg+XseDszImP87TbPm8uvIG55Pz4dyRyds70eLU/RWBtIFBRC/7nSptOqxa8cb8pnWmi5lUUAUWgxhE4scUxsn5eRbBzOhsniOnYLY+Qpg03D7Q6ZBLC5hW0hb169YoRR/wiyQNKiiYIJvm7kVtvvdUG8hDQQ9YcMtdATBH8MjHLozV0cRv0CSElUIj+UBgReOwVfDaJwo8vHkOwNBrWt99+2zZHe/nrr79aEunWp7ohPqUoqaiIiB8qWs14IUf4FVdcYX1G0aZCwImIR0i5SVVI+mB8KLAgy0HGHr+dqn4H0oDSCWU42RnYMmTUpTBiGbXHSR1UX4QE8ESbcTIiqKsh5+DgFfxl8cHw1nXnBAI71P6uZjvrJ6stj+8FvhiccAimApyZSU6PszBvZJwo9EtUHmp3CDFvQ7xFIUTF47fiV7XJNtB/ikAtR6Bs/h/GiTonthdp14JfsVxKySncPBoZPmI7pF8UgXqAQMOcBvLAjoPktMkXydLSimjzVHa7pKxErt2qooJgkPVIu+gnaCG9gtKHjzc2hOAjnt2Yv9FAxssHH3xQaRaxI6SIxKqMudz5V3oboVX1K5lKe3iDE0ztfLyCLyka0PjxkLnHKyirUGrBUbwulSipwAOfUuJgSBUFqYWQQ0SrGrt3G0G+ByagrqQjRCZeohSEFD+26viNWpvINxLyI5jGeQPynsj4j/jVdY+v2c6bR1W15cHca3InAh8NJycFbyaQX1JBoAXlgkB4m+ENyAlOxThEewkola1cvjPe0FDpc+KGEdT1+Mh4xxumvzDrOo0vFztvnVEQXjbCYpyp/XCmlaiMh+PFh3M7HaEWfL7BN2vVymFqwa8wL9r4ckXpeDEWXHGiIO7a4l4UhWsdTKJ2rMAoKtcW1zrHKex9MD5fZHWdi103213u3+l2OWNKhQIlyHZysrJlvdx1ZdKB78oGeesHWSWlNih5+PiJH/n0a+fmeUmsm5fJaZDxQDC95JPtc46gXIM4o+mlMhQ+p95rPJNjD0xASb/kbjrxQDkH3Pj5dfV3K6M+h/hB8vCXILUB6Q8cAUXrmayuOzdKV7Md/w8OMMQwUW15b+Q7JwdaaAoCoPHEtH7NNdfYtyki8HBMRjDbe08UvrOeV/ApcQ809oeHCdMwQn+cD1WlfQqzjaDrcr6CNePxauyDrl8d7XgQhMU4U+PiYRKl8XCsOGbpHqsszEUZqgWfnb+6FnxUjpfDxvswyNS5kGo/nDfu2orK/T9K5zJ4gk9Uzh0XGBP2WNXkuXfEFgfJi7s9Jhd+da0sL12RtDwnxLP1Oi1k+G4PyYb5G6R6Omt7DwLPPPOMUH4UhRUKM+fT6mmSsa+BCWgi5p+xkdSyjqg1j38lJI5oMoigE5dANlFdd1ezncAuTPLuACerLU/fmNdxDiYtA9pPUieQt5MKDvidUJkKn9Abb7zRki5ugE64AXlLdTLfmfT57vKAhq1djLaR7UThxssDCW0s2t34fWef14aQBzQsxpkaN2YeXhiiMh7MW4yHczsdyVgt+I030VrwVRwAjhPHi2MVlWdDlK4t7oNo8qNybfGcgTyGvQ+GJbBVnFZrLD6gyd7y6f5vyhOzn5NX5r0py0uwjhhLifmfZf5KTcWjNuu0kgvbnyGHbr6/5JkoepVwCKAAIJjaG6kfrsfEawcmoIm7qJ9LIKBEzeEPSoS598JEU5Gsrru7CaRSW568XkTPEXDkkhuTv4vqDC7NAlFquEogOE27ygb85ju+qCqKQF1FoKIW/J/ha8H/aaqxaC34unqa6H7VMgQ2KdjI+nTi1/nL0l9lceFf8p+i/0oTk+ez1TrNZeP8DWvZHulwHQKBouBpjDZJZTUCkDkciNE+Yn73SlV13V1bksQHqS2PLylmflI5OPJJH85s73zmvEFGvL2Q2wwNII7LRNBTH15FEaiLCJT+/JOsuHOQmBM+tntaCz4GhX5RBOoEAm3XbSV7bLKLHL7FgbLTRtsq+azlRzWwBpQglzvvvDPmY1jL9zsjwycYibqpYOMVzC5oI0mvAFFFO4qfpp9QW55gIOrQYsryqy1Pmgh8cImEd0L+MJLJMo/cYCSLJareJc6FFE+YMMGOAX9MIt7IXqCiCNQ1BEgcX/T2GDH+LP675nFF8W+wai7tzEtlfo9jtBZ8UqB0oSKgCCgC4REIVAve+bKQ2oeEqyrBEMDvEuyClMMMU1sekz/bwaQfL/h2YvKvKrDD+YBqLfh4BDP7G1eI+MjDzG4heG/4gOKrRsLlKEg6PqDFn3wshS8+R3Hy8LtgXtSym20pDW+6RWvBV4EmPqBYVjiXo+QDGpVry/mAei1WVUBarYsz5QNKzkisfiqKQCYQCKQBhcCg/STQhpxXlIhyfowMAn9DAmhUKiMA6QtCPlkrTG15nMv9yCf9Bt0+bVUUgdqEQPH4z6Tw2acyM2RjpZBGJlht4K2SZa5bFUVAEYgWAmUms0vJ9G+k3Lz4lP9j0n81biLZm28huSYFYta660VrsDqaQAgEvtOSjBQt3QUXXLBGx/UtD+gaAOgMRUARqFEEyk0EdtFLL2Zmm+YFO3vL5tLwksuVfGYGUe1FEcgYAiXffC1FL4+UsiUmCbuxKlaydphrt9CExOeY+IaCY46T7CZrVv3J2EC0o4wjEJiAovnE1OsnVZl349chx1SzZs1smSq3DHMOSdHxq6wJwdznZzoiUS++k5S0JJKdQJ9MaxETbdu73ySCpdJRIs2mt22Q70G2GaQfbaMIRAGB4k/HSXlhUeihZJnqJQUnnyI5O+woWcYEr6IIKALRQAC+UTj8aSmZ9Hli/+5VRStKv5gky00Fn4ZXXiM5GusQjQMYYBSB77jkUiTnm9/Hr5xUsm2//PLLNnemtw0ln0h6WhNy//33y6RJk9bYFAnkzz77bJvfk5OftEeUuiIAKFNC1aQhQ4ZU2R31WCmTlQnBF5VgJxVFoK4gUDLZXL+FqyPe0WDmdt3HBBE1rLyLxqyeu/ueVsNpF5hCC7m77iY5nSpchnI6by25O+2s5LMyavpLEVjrCKy8604p+ezTxOTTO0Ky9CxbKisG3iDFk7/wLtHvEUYgMAFlHwh0oZrOr7/+aj+/mHrJ1AyNr3eaqf2FEMaTP9wAIFRoKBF+I7SLbwuJRHPrnY8mkPETgEHkuFfQfOI0jr8rSdohilQ9cvuHIzfrs01vn64PHPOpw8r4nLAO22G8TN22Xa1XksWD6VxTRtC7nlsfYu720c1j6rct2nnTZYEfApFdsGCBrUtvZ+g/RaCWI1BmrmsnWZtuJnmHd5PyJX9LwSmVX7QK+p4mZYsWSt5Bh1gSmn/c8VJuHlQ5O+5kSSjpm1QUAUUgWggUvjZaSr//rrK5PeAQC58eJqXmeaoSfQQCm+CpwHPuueda4ha/W9RE99Ydj1+e6m/M8bfffrslXpCnNm3aWFJIOiM0eW3btpUff/zRziOFEbk0HQGltjrpiYjqJj0RpA6zOuvQJ/kwKaEJWdt8882FGulO+M0ytKNUJ6KaD+u4JPNPPvmkQLqp74v5ntrpkFVcEKhCRAUkUjCxnCpIjNtb+32fffax5a3YNhkFGCtVi0iftGTJEktuae9KaOJ3SwqlOXPmyCmnnCK9e/e2Q020rT59+ghjhERDbCnLSbWm4cOHW/eJQYMGxcZLR2BHMnsEzbbWgrdQVOs/jkuU6kOzs1EZDy+MfHjRTSrmRa9BcZGpg1IhOSYAssS8PJZ+P0Py9j+g0qpFI1+QcvMiVmY0nlnmesWXLMvk383dfQ8pNzlDy8y1mGj/uXdE6XgxFl48oyAcJ4R7WU2WZ0y271E7VmCU6NxKth/VsQzFBMepNtSCLzPP/OL33zEFj/xd/qrEZ/kymxmj0TXXV9lUG6xdBAIT0CuvvFJ69uxpiRD1y6l1PnnyZFv6kfKPqcrdd98tmMKdcPMgmh6BYOF/SclJHgKXXHKJYLru0qWLXY5vJOu7Gx9ayltuucVq+ci9eeGFFwo104nWZ9z0TR7OGTNmWNJHgnbyZ3rJJx3vsssuApm+7777bIqR7bbbTo488kih6pET0mtA6CCll156qbzxxhuymfEjg+hB0nFHwMXglVdesZWSWI/tu9rvaFMhuGhYR48ebfNzsg0Ecz/7CTFF8D8l7QU3sV69etmE9zNnzky6Lbti3L/+/fvbccab/ikj6rSukG3IftgSmjwgwUZrwccdhFU/eRCExdi/59TncuyjNB6uEx7aVfqU46uZly+Yz6Wo2EbAlq2yiJSXGusDEe2mLwTymbP9jia9UjMpfvdtOy/LlNrk4QYRNSXCEh4PxsN5HJXj5bBx9z27M2vpH+cN+HCtuxf0tTSU2GajdC4zKPCJyrnDtc55E/ZY1cS5V/yeIZ9x1snYQQ74pcxYIkvnzDb+oG2qXINn+siRI2PteJ6jgGrRokVsXqIvVCg89thjbbnnRG3SnQ+/wgJKzu+6KoEIKG+5CxculNtuu80SQw4Qmj6IHiZptIQQwlSEhOuHHXZYbBUCkCCcyHfffSfXXnut/c7DiMpCH330UYyA7rDDDjHySaM999zTtiWfITdpqv9ACBG0fgjm6LFjx1ptqZ2R4B9aQz5oHTkBIKPs+8knn2zXYFvuAQlBZKzgwXznCwthRVOLBhZxtd/jL15yqkJGhw4dajWvmOLB08l+++1nv9I/GlzIJ9tLti23bpCp1oIPglJm20SpXnVtrgW/1ES/Zhm/9PK8ImN6XyJZG21UcaAgp6vIJzPw98xu30EKhw21y3O77CYlxkesyFhF8g49TMrNS9d65l7mJ7z4QSCiUs+bF0SsI1F4ueNFk3uV1oL3O3PE5tdFkx+VcydTeUDDElh/tCrPLZk6pdI1XHlpwF/GP5zo+SAElOucEtYon+APWGDPOOMMGTFihBx66KFJN8gznuc0MTKZForK4EJIqe8gZDjT26+J/szdumohEpsbsSNYnTp1spHirLnbbrsJJSBTFdcfffLJz8+PdYEm1GtqwneSt0kn3hykzPNGqXtJHhpTSB4ftJU9evRwXfhOIYKY6BGqBlHNCCKMNtVPuMEwdghi/HgZs3tQxI/X9YUGmJOcakkQ8viKSm592nOzRytc1bYcTtxwVBSBuogA+QCzzHVXMmG8lE6ZLCVfTZPc7baXgr6nS+kPP9hdzu/T1+b1LDi1n9V0Fpx9ntGYbmMYaa7kn9xH8rsfJSWfjpUcY+VQUQQUgWggUG6em+T4DC2GL5TNmhm4G5RKlLp+7LHHZNSoUXLrrbfKQw89FFsfYor7HRrJZMJzH/dAxwdwBfS6FEFu0dQjtEW55ye4xrG8b9++lYKzUWC4vlkPXuDtg/6///77mGWTNrj3sc7s2bP5aQXlw/Tp02OxNG4+Uwg5+4nWnJdeJyj2sCKjMc6UBCKgkEW0ePgMAigaSEzM7BR1yjNd4hENIqZqAIBIobncemtjbktB6IMD0bFjR7vuiy++KD+4h5Mhu15No+uWKhqcgO4kY/vjx4+3pnzXBoLKgeBD0BL11XkDgoS7oJ/33nvPJub3Eki3PkTbbftbkzaCEplogsGYE9cRSNoThc/J+tNPP1ks8ClNti20I2huEW+Uv9PYek9c20j/KQK1CIGSr7+SZddeJctNpGv5gvmrR4427qknpfD5Z6Xk3x/b+UXmuxhfsGUXnieFjz9iP/iIlkycYPKHjrDty/5aIrk777q6H/2mCCgCaxUBLBK8JGZCylYF4abT16xZs6RDhw52VZ7rWDvJd441FoUWhDRecPeDp2BZbNeunSVrjz/+uFV+0RZegQJp3LhxdlWsq4ncFym/jWkfy+uwYcNipPOyyy6TBx98MLbpG264IWY5RlmGEuucc86xnAWLKcI83PzQpMJniDNh3vXXX2/5C+2doPnFFbFbt242rseZ/1HCtTKujrhD4rrH+DIhgY80aYG6d+9uCRCqanwkML9AmN58881MjCXWx8EHHyyffvqp1UDSf9euXVMuAQoB5UDjt4oZBJJMfXQEMo0/JESQfXLCAYfxo/pGk8obCAfNuQPQDgLpgoE4GRkrgk8pZJJAHnBxpn+70POPE5Nt45PKyYW/K2Se7eGTihneCRH1tOGN4+qrr7az0Q4n2haBSrhJEITExeOqK2E2wZ8VDTB+qlEpV+f2U6eKQDIEys1LWOFTQ6Vk2tTkKVnMvSKQ0M5cbznGrSWnbbtAq2gjRUARqAEEIJ+rNIRht5ZKRTOe9Sh4IJYQTqyqaPsQYjpQNKGcgo/gokcuc+f6R5vXX3/dPsdRemExhlxi4aR6pDPjEzeDtRMyR5zH22+/LYMHD2b1SgIvQWGGUgvXO4Kj6R8CTEwI1lyIKMopLKhwJbSWL7zwgs3CQ4A0pJXx3nvvvbZvnv0oxhDG9fHHH9v9JSsP20D7C49jv1B4oRCDz6BRRXCzpH/ytBPwTUA1Y/FanG3DFP8FqgXv+uQgAQ4Hh7RBY8aMscwaRlwdgrYVEzdgpCuomtEAxiedZz/o109LybY4CSGS3m0/8MADlrwREMTBZ2xeQcOIZtTrEuBd7r6zLm0ZE9+JTHSR766Nm4IBJnynxXTzE22L+ewbY48XxhY/ZteG7WB20FrwDpHqmWot+MS4ct5y/safu8tvHiBlv89NKyVL4q0ZC/2/7jGVU5okbKI+oAmhsceJh5PWgvfHCKUBpletBe+PT6K55cbquOz8s7EtJ2oSeH6OcctpePlVVbbHtN65c2dB64nw7Ce4iDgS0kw6xRUucAhxGCjFiFkhgw2Bw5jreY6jUEJIu4iWEbKG8gdyR8YblFYogQhKRhGGkimexEF4ia9xSieCpiG+EFcIMsHV9Id/KIquscZCjFaTZzcB2gi8BwJL/02bNrVB44wHQcn26quv2sI/mPrJ3sO5ioUbPkIGH4RxoMElQAvijEuiGyuWbwKw99hjD9s23X/mdSOYkEfyuuuuswD269fPavqIPIeUAlY8QQrWa/JWVRG55GtXLE3kHBxPSOP72sgFNcQvML+9/qrexZBVL2H1LvN+h/S67fM9EflknUQYJNpWovn0lYh8skxFEYgiAoUjXpCyOcZ3ycfklfZ4zXXa6JZBScln2n3rioqAIpA2Amgts5s2q7jm0+7FrGgsleT6DSoQK6dIY7qVSe2GJhMNIYIWE9LpJJ4fQE5xqXOCsocXaqyPaEzRPk6ZMsUSW6yUWD0xcztC59ZjivYStzzIIIKVdcCAAdZFj/gb+BcaUpSAaCERiCnWVVJl+ol7qcelEUsrll+2gYYWcsn6BHHjF+rE61sK5yFLj+M3bIfthZVAPqBsBLUvB8OpndHKoWZGK5hpE3zYnaqu9TlhUD2rKAKKQPUjUPbnYike9+/Mkc+CAskyft4NbxxoH3LVvwe6BUVAEUgVgdw99xIpqGxdTLUPw/wkd/sdUl6NFbDCPPvss1YxhPYQzR/aRNwO8QVFQwiZ9Aq+kpBMrCYIqRoJgkbBhPkbnoSWFQIHkSXHN76k8YKfKBpJuNUVV1xhP5BESCzaSITAJAKl0IjiYoiwfTSxuBoyRszouPrFE1y0pgQlsQw+g/YUId6F9JrsA7nQaYPJHcFvlWBztKn0TUQ+xJd1wkogDShaTgJiUD1zQBCAJecmplvYOP6TdV3SYfz4hPDWgWOvV1CRo+LHnzP+JHHteKNCg4uPBv4jkP9EGl23TqIpb2NO65qojc5XBKKEQNGbb2TEFCcNG0kWmTwOPkTy9ttfssxvFUVAEYgmAnn7HSDFb4+RcpNKKS3JyZXcLrtL9iabBl7daStZAc0nGtDXXnvNWjtxucNk3coE4RCDQaAR3Mcr8CIXfIQmkfgL8nwjkDa0ifhPIpj0MasTpxIvzzzzjMNOnpoAAEAASURBVBx++OESr2Glb+JB8MVEYwkJZOr4AOPCL5TYDz5YpClKEy+Y7+FqkGliYyDF+L5COvFzhfQyLoK3MdkzH4Ews8+Y+dHMopDENzWsBCKgECTeCmDPjoC6DePrgl+bij8CkE9U7pyMkHYnOAHjPOyS0Lv53im5T8EbAsp3fEbcCedtV9V33lTIS0r1JRVFoLYgUGqi3s3dLjbcnK06S5a5sZd8MamSVpRSnCwrnfGtTTwv66wruSZTR5mx2JQtXGDKcB4sBT17xfrRL4qAIhBdBLKMubfg9LNk5SMm2tuYslOW0hIp6HVC4NV4vmKCTiSQSTSDcB1M0V5XtsWLF8dWg6QRWc4z35v/lee+tx2mbD5+gp+on2Au9/IsVx7c25ZgaSLxUQp6CSzuk155+umnrb8qJNW7L/jCwjEw7SP4uKIoQ1B+oQFFw4tGNFOymhEl6RG1MayYyG2XoojmU6dOtVFWgKPijwBvGuCHM7NXeAPyRuBzcvG2wcnrJ/jfEmHvxK+989lwZUldW5yOtRa8Q0OntQEBSmSWl6wuO0k1o5yOnTC9SF53Tz5fY1YvOOEkKfvtVyk4reKmXtC3n5SZbBL5xxrzlNFmlJlAABVFQBGoPQhgPi84pV9qAzaKMsOoZJ0HH7XV0VJbuerWxGp4CZvfGhBUL/n0a1Od8yCVXvKZaFv4hMbvCxpN/EtJ7cSHdE/kJ/dKJskn/QbSgNIQ1TCECdUvBwKzPI62qIVR/ar4I4D2GDM7LgzODA+JR5u81157WT8K3nogjbxpkauU3yzzCj4X+JFwkiRqz8mz7bbb2r7oj1QPF198ccJa8PRDBSoEzSonl/Nh8W47le9E63FeJHIrSKWvsG3dWy1vrmFrIIcdi1sffMJi7PoKO3VO7lEZD8eLDxGZWX/ME+Oxaf4qhITxRa+Osrk9G/S/SGLU1LiWrHz0Iclu3QZHJtu48LFHJKuxiW7H4mDwLlq0UJau8s1KBTMsB1E6XowFS1QUxF1bvPRG4VoHk6gdKzCKyrXlrvWw98FM+P0FPX/z9jZBPw0KpPDpYUYTWmiu7yTnviGemNwbXHqFZBl+opI6AnAA+ADWVvgdOUObmRLG1SmBCSjpNnCOJbkpofuolSE7+BCoJEcAnw7IofPDRJ2P1pi3FdTeHHiX2BV/WshqPAF1W0ANnqw9fipkJ0Djic8GGQoS1YL3mh54c0Or6qLc3PbSmRL553U3SKePTKzDA4CHEuMB6ygID4JMYJyJfeFhEqXxMBY+9lgZzaYUmEhW48/k6r1LIp8ws072liaSkweUOY/N3dNGuJcbcpq13vqSZSwQ6WDOucN5nM66mTg+8X1wPkdlLBwnd21xfUVBonQugwf4ROV4ca3zohD2WNX0y0beLl1Mrt72UvzOGCk2OSql3LjklBlzOVNeMM15mLXxJpJ/xJHW75NrXSV9BNCekm+0piSlpzI3Y5xz+agER4BccDgu80aBKwME09W9hwSS2J5cW0SukfwWEplIqmrvshTgCM0DC21kopsOpNiJywMa1nyAtpEMCVG48fJAYv8h7InKobr9r6kpJD8sxpkaK/49aNSiMh5e0BgP5qGy7CxZLtmr673/XVHvvdzgR67AmJi2Oc1bSImJlienZ/ZmjSXL+E1TojMrv0By2rWXHPOwapCg3nusH58vaK84j6OCD640WJ+i8HLHceJ4cayiEtwYpWuL+yCa/KicO7h2QR7D3gcTPUt8Lp+Mzco2pKig96n2U2pcbcpxzzH3rixzTWc32VyyjXJMpXYikBIBrZ27GI1RY4ZH88kNCUKKKwOCbygaS6oOkEoBJ2BKfCaSqtp784bW9NtqojHrfEUgVQSy19/AEMhcW++ddcvm/ib5PY4RMRGukE2TIFfyDjhIit94TXJ330NyttnOOBTlSZkx3eftarQmrdqYB9QGUvjyS1Jw0smpbl7bKwKKQAQRyGnZKoKj0iGli4AS0HSRS3E9KhTgw0n1KG/wERpPfEMhoGjsqMCQzM8m1fYM05mf0VpEQTOZInTavJ4ikLPNtlJClKnR5Jcbn+bCYUMNATXmXmPaRCCfiPUR885/ffTqdkYbn7N1RQUQ21j/KQKKgCKgCEQCgUBR8JEYaS0fBMQP8zsJbQkOckKFhTlz5lg/TaoLoBklQCmRpNqefjCbuFrwmKlUFIGoI1AyY7qUTMLnKy49yiryucb44+fz21xz5BREm6qiCCgCioAiEC0EUqoFH62h163REE2Kf1dQs3mq7UELf8j41AsORecDqrXgHSLVM+UFgIC+KAg+oPiq4S8cBXE+oA0XL5IVt90c03SmPTaj/VznngdsEvp0+lAf0MSoYU3RWvCJ8XE+oFoLPjFGukQRUBN8RM4BUjClIqm2p+9E5DOV7WpbRaBaETCJ41fcOjDcJkywhTnZQ5HPcAPQtRUBRUARUASqQkAJaFUI6XJFQBGoOQRefUWMGWBN03sKI8jaoqk0vPKatDWfKWxKmyoCioAioAikiYD6gHqAoxLRm2++WalsFosJDiJ46LvvvvO0zuxXtk1teFJ3eIWApM9N/jNqyscLJjBSOpGbNV4o/UXUPeU3XWmt+Db6WxGIEgLl834XmTM7FPmEvFKWU1OzROnI6lgUAUVAEVgTASWgHkwIELrrrrssCfXMttUB8I2EIFaHUEnq8ccft0Syn8nNOXnyZLsZcreRTJ5xkSeUqkZOvvrqK6E60qxZs+Sqq66S116riAhmOZUMzjrrLDtufJEo43n33Xe7VXWqCEQSgbLp39qk825wWRttLLld97WJpt08OzXBRbm77iY5nVbnI842ZTpzdtjRak9Lv/mqUnP9oQgoAoqAIhA9BNQEH3dMqOz0ySefyJlnnhlbAqEjAbxXSPI+f/58m+LIOZqjrSSQginLXRJiiCREkDKa8UJaJYIdSL+EdOzY0ZLNLl26yMcff2wrTV1yySV2GWQUbSdt7rvvPrntttts3tBevXrZ8ZJrlIj6IUOGWELbsmVLu94JJ5wgffr0kQMPPFB22GEHO0//KQJRQ6D8p1km8GhVknlTuaqg9ylS9P67Qm33lfffa6ueMOb8446X0m+/kZwdd6rYBUNIs9u0lfIF8yXvsG5S/MlHUk45TxOEpKIIKAKKgCIQTQSUgMYdlzZt2thqRJTIhHRCJtE2kscTUzxCxPjll19ul1Fbl3a33367NZPff//9QsUScm+itRw6dKg139OGthBHqoc4gfA+8cQT7qcQ3U60OkJ0MtWRqOvONumXUlmMad68eTa1Eu0gwI0aNbLpm9CWku7JkU+WE11PiU/vdpl/ww03VKoF37hx49C1iyk/x/iDRvMzjuoSXgIQyH/YGsiZGiP4RK0+dFTGk79kieSsAjrbnPulxhxfZkhpqbkWs5s2kzJM9EaKXh5pSmyuZxPQl3OumXlZ5vwX0i2ZMpzlpmLbktm/SDn14EMI11mUjhdjIfo8CuKuLa0F7380OFZgFJVryz27wt4HuSZUFIFMIaAE1AdJNIVoQSGNU6dOtVpD8nii3URY1r59e7nyyivtA+qaa66xpJVls2fPlpdfflk22WQTmT59uiWfr7zyio1Af/bZZ2XixIlyyCGH0NQKZfVceTT8NtGE0i+y8847Ww0omk8ePOeff75AEtG8Ul7SS/KIiifFz48//hgjpraTVf/iySez27VrF+uD/WN9asKHkSJDACDfUSgXyAMAn1rG45Lxh9m3TKwLOQ+LcSbGQR+cUzyYojKeLM6bdu0lixe0bENFV72ImQvP1IU3teGdmDFT/5kcoRBRxNaANi9a5f/8187PQ/sZ8lyGRHAeRwUf7j9cp97r3kFS01POG/Dhulob5Rn99jdK1xb3QfCJyrnjiGPY+2AUzj2/Y6/zaicCSkB9jhsaREglfpSYwQ8//HCZNm1arKXzBR00aJCd93//938yduxY2XfffaWpqUO92Wab2fkEDu26666x9Ed9+/aN9RH/BdP51Vdfbf06d999d7t42LBhVvOJfydvrizfcsstpUOHDvbm5u2DGwxpltCQopUIIvibOnF5QNNJ7+T6YIq2MUq14CGgaIcdyfeOdW1856EUFuNMjdvVgo/MeEx+1CyDT5Z5uSozuUCzzAsgkmXmly/5K7bbuV12k5LJX0iRuSbyDj1Mskx5zsJXXhJZvkwaXHypZH32qazfrJlkFTSIrZPOF0gEhC8q+EStFjyEjxfhqNSCj9K1xX2Qe09Uzp3aXAs+nWtX16kdCCgB9TlOVCPipvrDDz9Ys/q1115biYCyCj6a1G13Qg12HhBeosPNx7150g6Sx03bEVS3LtthG5deeqklsW4+pPfUU0+1NzH6Ovjgg22A0h577CGQBzQi7uaP9hLyi38oPqvxgr8oGlXItIoiEEUEsrbeVkpfHiHmoqkY3g47S36fU+3vcnNt5Z9wkhSNHkVtWck/uY9kmReu4g/etxWP8o/tadoVS4lxQck2LilhyWcU8dExKQKKgCJQlxDQKPgERxMzPJHju+222xrmZDSkaDche1tvvbX1r4RExgt+o99++21MI0mkOxpVr5BKiSj2m266qRL5pM1ee+0lX375pW0OkcUdAO0oZhTGRcoo5NNPP7WaT7SfBx10kDWlP//889bEynoffvihJa6MR0URiBoC5YUrpejtMVL2miGXjnyaQRa9MlKKRrwgRSNftEMuesmQU+M2UDJxgvC98KknrV9omfEVLXr+WesbWvLVNMnpskfUdlHHowgoAoqAIhCHgGpA4wBxPyGZEEZSJMULy8aNGyc9e/a0ke6tW7e2xC8+Tyha0WOPPVZOPPFEG1BEFPyFF15YqTv8RdGcelMsUarx9ddfl+7du8vDDz9sfT/ReGLOR/uJ4A/q0i/hpzZgwAA7H3PzHXfcIQRD4XuK4OsJwWU8KopAlBAoM4FHK+66Q8qNBt/6esYPzpjkfcVvPr6hxnKRf/BqH2vfdXWmIqAIKAKKwFpHQGvBhzgEkEK0kc4Mnqgrgj34QA7TEcz2bMfPgRzy6tI9xfeN9hMzPX5aVYnzAdVa8FUhFW651oJfjV/5iuWy7NyzjJNnuMpHq3s0sUoXXiJ5u+zqnZX2dyKY8QFNdH2l3XGaK0bNB1RrwSc+kM4H1KXoS9yyZpZkygeU3NNPP/10zQxat1LnEVANaIhDHITY0T0PMT7pSrIa7skejolIa7rj0PUUgUwhUG4CfJbfclPmyKeJeM8/4cSMkc9M7af2owgoAoqAIuCPgBJQf1x0riKgCFQjAsXjxprIdmN2X5WrNe1NGcuAmGj3glP6St4ee6bdja6oCCgCioAiULMI1EsCijmCSHGi3eMrA02ZMsX6ZBJxXp1CKqdddtkl5pdJsnuXZ5TtkmoJX1AEUxcR8a1atbKBT3am+UdqpgkTJti8oKRnIkDJ+XmSwB6z+k47raoW41bSqSKwlhEoN/6bRW++Znw+KwoupD0cY7rP7bqP5Hc/WrJN3l0VRUARUAQUgdqDQL2MgseXiprvfLxCHrlbb71VHnvsMe/sjH8nap2gIfwBEXw1ST5Pvk/3+eOPP+yyRDXfyT169tln2/r0JFx///33bTlOqiYhkyZNkrffftt+13+KQJQQKJs7t1K0u8lkLjnGbzNn6218h0mdd5PktmKZ8WfONZrOrPXWt4nm8488SsmnL2o6UxFQBBSBaCNQLzWgHBICgqjq4EpuMg+yh9YRzaETyF18zXeWkewdH1CqF5F/E0lW8902MP/QZj711FOVAht+/fVXm2B+8ODBrllsmqjmO5pPHNzvvPPOWFsS1X/wwQdyyimnxObpF0UgagiUzf3VpFNaXdIvz5DIsvl/VBBQY1Iv/ebr2JCzNmssDc6/UFbcbLI8NGwk+T17mdyf75np8VI4coSUzf1NsjetKPwQW0m/KAKKgCKgCEQegXpLQDkypFNyJTf5TY5OTO+jR4/mpyWifjXfIa4kiKdUJ6UvIYFoHMeMGWPnYRqPr/lOf5BZ2l5wwQXiJZuYyzGhv/fee9YMzxggyGhGE9V8J6XTzz//bLdLgnmCnKhHn0pZPGrBQ7oRSnVCaMPWLkaLTNR+FEq2gTeiteAtDGv8c/Whwx7zNTquYkbuvHmSW1xka7jTlLrvxW+8JmUzf5S8o45ZTUDNdZbf7UgpmTLZ9piz446mLvzPkmPy7xaNflXKTR///D5PSpu3rGKL6S3m+uN8rml8Eo2WsVCdKUrXltaC9z9aHCvuP1E5d9y1rrXg/Y+Xzl07CNRrAkqyeSoQUXKTh80333wjRx99dIyAJqr5vu2229qjRWJ3ktUHqfnOCqNGjZLmzZtb30/v4Z41a5bMnDnTVlaiJOfQoUPlmWeesQ8btKzeBw4VkTDd4z/KuCG6aFW32247OfLIIy2p9vad7Dt+po6wEjG/xORkDFu7WGvBJ0Oc8uZaC54qRdnNtpRsU7K2bMF8ycrNs6CVm5RhWQ0KYgDmHX6EFH/ykeTuVVFAIctoQLMbN5Hijz6QglP7yYrHHpFck/czJ2TN99gG475AIqJWC57r03s/iBtyjf3UWvDJodZa8Mnx0aWKAAjUawLasmVLS7jQJGJKR5PoCBngJKr57ggoAUw8DILUfIdYvvvuu/Loo4/SdSWBSPJxeUIJRkIbiiaUh6BXIMouLdNRRx0lfOh78uTJlowuXLhQTj75ZO8qCb+jxXXi8oCGrV2steAdov5TjmdYjP17Tn3u2qoFX9y4sRQbjTs137MMoSo3CeTNhVRR8928TDnJNlp++2ljasIbP9GSqVNsjfiy340PKe0NWW1otPa55qWsOkRrwSdGlbzGvEzxglxVHuTEvWR2SZSuLZcHNCrXeqbygHqfj5k9etpbfUSgXhNQDjhmeIgmxA0yFy9+Nd9dG1f3nZsMxNAJZI6bs7fmO+b93377zVY3ot2KFSts0NCgQYNsGc1NN900RkCJzl+wYIFsYiJ7E9V8R0tKGdA999xTWptKTHxYb9iwYYEJqBuvThWB6kIAcllmTO6l388waZeWCMnnpdG6UjZ7tjW5s92ST8faNEpZG24khc89I9ntO0j2RhvbUpsszz/xZFvzvfy/FfXgc9q1tyU4pahYcjp0pImKIqAIKAKKQC1DoN4TUMzwRKDzZocZm6AkJ5BTNIu9evWyVYiuv/562WeffSzRc22YYoqnbCf+UJBRvuPTSQlOJ2eeeaYlnO738ccfL0OGDBG0sJBGNLC4A/Cmiumfkp2YxV3Nd9p7a74TLEW0PiZ9PhDg8ePHS/v27d0mdKoIrFUEir+YZOqzj5Byc05LYZFI6eqXNDSYTkpnTDcE9TsRNKFGyk2Gh4pvFS1cLXh+Fb34vNWGGtOAZBvyiRZVRRFQBBQBRaD2IVDvCWizZs2s5tGZ072HEALqV/Pd24bvQWq+x6/j/Q25JCiJMmc4rWN6d/k7E9V8p8Y86aTOO+886waAuZBa8ZBYJ0TEf/jhh+6nbLPNNvLII4/EfusXRaA6ECg3L0MrH35ASn/4HlW//yZWBYjFFq4in7Hfyb7gltKwoRSccFKyVrpMEVAEFAFFIMIIaC34AAenpmq+o/1EE+vnU5Ws5js5QYliD1Pu0/mAai34ACdEiCZ1vRY8kb/LL7tIyv/5p3KuzxCY+a2au/+B0qDf6X6LMjaPl0GuqWTlbjO2sQAdaS345CBF6dpyPqBaCz75MdOl9RuBeq8BDXL4a6rmuwtC8htTsofgRhtt5LeKzlMEahyBwmFPVC/5NEFL2ca9pbrJZ40DpxtUBBQBRaCeIaAEtJ4dcN1dRaC6ECg10ek2Z6cnIC+j2zJm99w995aCPquzN2S0f+1MEVAEFAFFoMYQqHcElJRLpE2ibnpjkw7GCXntKF3Zpk0bG13u5md6iql76tSpsv/++9uuSQw8Y8aMSpthbE7ID0r0PD6hRMo7SVYHfuLEiTY3qGvrpuQOdVWb3DydKgKZQqBo9CgSnYbvjsAi5xNqXFJMhJ3ktO8o+SecKDnNW4TvX3tQBBQBRUARWOsI1DsC+sUXX9jo8b59+1aKSicJPf6PJ5xwQrUS0HvuuUdmmxQ0joBOmTJFHnroIWnXrl3sZHAE9N5775XvvvvORrY//PDD8uCDD9oIfHw++/fvb7+TTJ468E8++aQ88MADtprRSy+9ZKtwkJbJKx1NBRkVRaA6ECg3gUGlP/5QqeucrTpLFinKTDS8OSFXLzPZHXJ32llKf/tVyhctqpi/3no29VLZwgWSd8BBktOqlZlvcn2uv75kt2pt84Wu7kC/KQKKgCKgCNR2BOodAeWAde7c2aY6IjWSk48++siW0XS/mZIQ/o8//rCkzvmBku6I+UwJuHC+mYvMg5Q2BAMlEqLS0WZ6hTKc5B+FEHuF+vCfffaZrZ5ENZaRI0fKCy+8YKPcg9SBP/zww4WPiiJQEwiUm4Adb2qlnO13lByTm7Zs8SLJ695Dit98vWIY5lxucF5/KXr9NcnvcYwUPj1Msoxmv8HpphqZIarUdi9ftlRyd+lSE8PWbSgCioAioAisJQTqJQHFzI7Zm5yf1HOHTFITnXyermbutGnT5Oabb7Ymeczg1G8/4ogjrPn+/vvvtymQyNNJDk/qxUNKIYrbb7+9XHzxxWscTgjqiy++aPtBk+kEAorGk2Xk8MRMTnUltKTkJaVPBBM8LgJIJurA0w95Tb214Ok3bO1iqpFoLXjQ9RfwCYuxf8+pz3XneibGk/3rHMk3L2Quu2eOOXeLXjUm+eXLpEH/i6R41fCyW7YypHSx5HTeWoreGiNiKupkrbueFL09RrI33cy2KvxjvvwPQruWhftClI4XYyHdWhRKcfLyjWgteP+TlGOlteD9sdG5ioBDoF4SUHaeBPQkfIeA4pNJHlBSrvx/e+cBJkW1tOGajZgDgoqogGAAFUEFs+gVFDBhwIAYUQyIGQyPmNMF8ZowIoqKCgYQE4iggoiKOWcFRb1X/PVeFTbOf96znKFntmd2Znp2t3e26nl2p6fD6dNfh/m6TlV9EEnswQcflFGjRllCSHkPCGi/fv3sMsjh5MmTrVIRw97tzHAh9Tf5wRo5cqSNv/TGa/JDj+LR2WefXavEEgQUg4ROnDhRGD6nQD1KSF4ZtzXNUCRa7Vg6OvAUw4fUem3ChAm2uL2bx5C8K90EmUZTXrXgHTr185m3WvAtVjHD7WtbZaKoEWSAVEpZ7XjQiEkkKtyso5Q9MEFKBx4py8fdJtVffmGLyjvEI6Ulga9D11aQT0iEasH7I8gzDXx4boRFnjFM95ZqwftfNzpXEfAi0GwJKEXmL7zwQqvBjkwmw9V4PTFiLD/44AOrx058JUbyEPGYGIk8Tmbz008/lWOOOcbO52F844032mnvv0ceeUS23HJLS3JZ32v33XefHcbnh+6AAw6Qgw46SH4w0oU81HnAO4PcOulP5tWlA09x+913391tbj/pn9dUC96LRsNMc069LxYNs1f/veRSC756ww1lWXFRjTKReYmL/rZUIqY8WNS8vFGY3lmVeXkjVrT6xx/scDta7wy7e610/Q2k1MSONrapFnzyM6Ba8MmxYYmrAxqWe1214FOfL13aOAjEM5LG6UOj7JUEHQq+QwjJiseD6QgoHYKsQQgdaTv44IMF1aRFixbFEUEeMJBDZ2jK46301vR87rnn7LDr1KlT7bAMXlaI5pQpU2xMKLKaGN5HChfTBgQXEuwML+yG5kceS0cHnjYTk5BcW/qpCOQSAQhm9Pf/k6gZWq809wdWbQgoMZ5SWCSVr8xBLswmF1VMe8oS0pJDD5fIui2l2pRuijM8pFtvEzdLvygCioAioAjkHwI1AYb5d1xpHRHD8Hgs0Vt3sZZsSGH3Ll26WCLIJ6Tw2muvjfNIuh3sueeeVqMdzxYeEzTcUSzxGslDJCDxx5B9e5OcMW3aNDv8TVb8ggUmS9gYZJhhdsIBkNUkTnXx4sWW4E6fPl169KhJzHA68CzDIMCqA2+h0H8NiEDUxG+WvzRL/jp7mCz719i4Ekxkt5eNv8f83V2THW9GECCfGAlJ5VOflLJ77oxlx1d/8blUzJppE5kKt9yqAY9Cd6UIKAKKgCLQGAg0Ww8oYDMMT6zkueeeWwv7E088US6//HIbR0m8E+WZWrZsabPivStDQNFbP/LII61n9NBDD0271ibJBMSF3n333faPZBDCAvC64kU95ZRTbKkoCOemm24qRx99tN11OjrwEObrrrvO21UZNGiQDB06NG6eflEEskEg+uf/ZNk/r5dq4633i/WMtekJI4nNY8JvfkmpyD59JNKiRdyq+kURUAQUAUUg/xBQLfg6zinxoOlIXRIjyrB7tgH5ZJNCOhMzXIm1Ysg+WXmnXOjAA4FqwddxIeRocZj0qokBJVbNhXake4hRcz3+dcqJYoYNVhaMT3fjZOuZlzFpaYQWLhklq68ISUm2akPN54VQteD90ea5RNIiL8eEMoXBwnRvuRhQ1YIPw5WhfQgrAs3aA5rOSUmHfNLOGibGLYglC1bnB9Blqvu1n27//LbVeYpApghEzWjAshuuran5aaZzYqgdmZe3oiuulpXR1DlpWRtRBBQBRUARCCkCSkBDemK0W4pAGBGoWviWVC/5MRa7GbiPJumooG1bWeXs86TcvGxRF1RNEVAEFAFFIP8RaLIElKQfV5jde5o23nhj6datm3dWRtNIY1JmiYx3r6E+5OpwkrDEcDn1OL3Z7t71051meL0+hrCoL8qwOgXs1RSBXCBAYe2yJ6eILFsWvDkT51lgyi2VDDhUCrtuJxGG8829oKYIKAKKgCLQPBBosgSUzO/Ro0fb+p3e4umphqvTOaVPPfWUTU5KJKAUdafttsZbww/xkiVL5IYbbrBJTMzLxlBUIuOdRKZcG5n1yHkqAc01ss23PWp7Rk1MdMzM0HlhN/OCYwhp1ccfxWZHTLmwwvYdYt+tFrx5YSxou7GQvBQ167c482wp0nJLMYx0QhFQBBSB5oZAkyWg7kShUIQ30s8IlIcoQiZdPU8K8jJNAH3r1q1j8ylp5IrL+7XFPKQ4e/fuHVuMlOUzzzwjp556qp1HEDx/eFCdZ9TtD8KMHCceWvaP5xOCiPwm5Zsg0cxLV3veb1+xjumEIlAPCNianXgqV1jx/gfa4fjCLltTOFeq3n+vZkl5hURNclNklVWleL++UrngdSu9WXr8ibL87jsl+v13YrXjXUP6qQgoAoqAItDsEGjyBDTZGbv99ttl7ty5VmWIbFa8pWjAU/Lo+++/ly+++EL69OkjgwcPtjKbJBExxO71piZr280nY53sdMjlpZdeagkmyUTU8+T7rrvuKvfee6+V1fz222/tusuM94c+vPnmm/LVV19ZLWU02PGqpqM9/9BDD1mJUMhs4r5cv9L9vPjii2Na8Kuttpolzv82xcSDGCWrkMQLg4EpRoUBwhHCYOATFONcHQd9wdLtT5EpMl9syKXTey8wnk5qe1Z//pkUHzggRkCjS3+VKvNXcsxxUnbfPTWZ8oa4VsyZbWNHqR/6vx9MfduEa82dL17awmCE+fCXLj713WfOFy+rYTB3rqh5nFi5o7H6F7Z7C4zCcu248xX0OchvnZoikCsEmjwBpfC7t/QRKkHEa86ZM0cgay1MrBm67agOodOO8aOC55KbknqZu+22m5x++umWqFBjM5mhGc+PIz8CH374obzxxhty1llnWUILgUNWE2O4HglPCCiGtCZynDyoTz75ZEs+99prL3n++eetV5VhcuqBpqM9D5FNtS+7wzT/de7c2eLD6nhleVgGjUcFG9ryFvZPszs5X43zC+EndII+hcEg50ExztVxMELAX7r9iaD3bkQaCs2IgtV7LzJJQ8YoyxRpkVCKx7yYRcxLXfWPJmHJWNVHH0pBGxNXTbkl81dk7svC0vhtuC8hEUHDaOwOc/CPa4frOF18crDLlE0wQsILchgIH+eJa5lz5X3+pjyAel4YpnuL5yDXc1iuHUccgz4Hw3Dt1fNlpM03IALh+FUOcMDnnHNOXI1MHoZotu+yyy4xckXBeXTPzzvvPLunrl272oc4NxMynBdccIGdj0dx663NcGIScwSNfWyxxRa2UDzD9vxR5P3RRx8Vkn9QMGrXrl2slZ133jn2o0E4QKKHJxPt+c022yzlvmI7TWPCadizqqsDmqwcVBrN2VWof4dmfRhIBD+SkAjCIehTGIwfpaAY5+o4MtWCrzSKYGUGx4h52SJhiJJMkMmIqQUZNSEtXivq0VMqX5vrnRWbjpiC8y1M+Etxgt47BAtCnKzmbayBBpqARHAdh+V84W0k3CgML3ecJwgfL8NhIVlhurdcHdCwXDv85vB7F/Q5GJaXjQZ6BOhu6hmBJk9AGVZPjAGFEH799dcx6Pgh4c89uL03IT8wLHOW6g2xX79+cTGgbpt33nlHrrzySquGhBISBBcvrDNv/5K9QbLfdLTn69qX26d+KgK5RqCw0+YS/eVno/f+fU3T5kWsdPBxEll7HSl78H4pMMsL1llXKt9cIIWbtJNyU1HC14wTtHCrLr6LdKYioAgoAopA80BgZUZBHh1vr1697PA4nkXshRdekK222ipGQL2HSsmml156yQ79kZj03nsrEim8K9UxjceTkkzIcW655Zby+uuv27jQOjaLJR5loj2f7b7q6osuVwTqQiBiPMmUTnLGsHrZQxNl+S032ez46i+/sOST5WX3j5fo//7rVrWfFTNfkOqvvjTD+OtKgfGAqikCioAioAg0XwSavAfU79Stt956NrYSQoh+O0N611xzjd+qVhudZBx01okZJCs9U9t3332FNs444ww7hAipfeWVV+psBk/pmDFjbPZ7utrzmexr5syZVqfedYTwgnHjxrmv+qkIZIRA2dPTpNp4QOOMYfhMzAzhlx55VCZb6LqKgCKgCCgCeYhAXmvBuzildGQyiYEknskN02dzrpPpuadqi7g3wgDcftPVns9mX6n64WJA//nPf6Zarc5lYYsBpVrA2muvHTj2qc4DT3OFMOlVZ6IFX/HyHOPtfCCwUlHhDjvKKqYGqJ+FLQZUteD9zlLNPJ6tqgWfHB8XA6pa8Mkx0iWKQF56QN1phdjxl46lQ1LraiebgPPEAP50td2z2Vdd/dflioAfAhWvzZOyCff6LUp/nrkPI6uvIS2GnZX+NrqmIqAIKAKKQN4ikNcENG/Pmh6YItBACERJ4HtsUrC9mezbwu47SItTTo1VgwjWoG6tCCgCioAi0NQRaDQCSj1LamliDD+TKU7sZDaeyGT67dmeHMr3UE6JhKR1TYmZnXbaKdYvhlDpdybymQxVUZx+9913lw8++MAO9VNOSU0RCDsClfPn2Tqfgfppyi4Vm6H3SEhqsQY6Ft1YEVAEFAFFICcINFoW/Ntvv20LxaNIRGb31KlTZeDAgXHli9I9QvTbIXi5MMgnikTXXXedEMcDCT3qqKMEkosh7Tlx4sSMdoXMJ0XxsVmzZsXayqgRXVkRaAQEKt5YIKbgY2zP6LkX7b6HiClKHzPj4SzaZdfYX4GpdYsVGD34wq7biZSb+p5vvRFbXScUAUVAEVAEFIFG84ACfceOHeX888+PnQXUi66//npL1lyMI8HuiXruboNk+u0k8lDbkwx4CpE7rypZ7rRFzc1kweHjx48Xkg/4dEV3e/bsKSTnOBLJ/lGWYP8bGjlC1JacsT/2Qf/Jxq/LSLxI1H+naDB9ZB9e/XjXlsOkbdu2QiIJx0d90WTHR8ISCVYU0kenXk0RSBeB6A+LY6tG1mslxX37mQLzr5n6n8dK2T13xZah/Y4V79dPys39U9y3vynN9JuI0YMn8ajajHioKQKKgCKgCCgCDoFGJaCuE+4Tecqbb77ZDlMzXJ1Mz51h8GHDhlnilajfjucSyUwIF9nPEDPaJMsbJSRI3Z9//ikMgSPDmVgYfv78+backiOf9G2PPfaw4QEuYYj9U3IJWbzPP/9cbrjhBrv86aeflgcffNBqzqOwxHZOZckdo/cTL7Cf/nsy/XiO6dVXX7X7owA/5PPnn3+2CkwcR7LjQwWK4/3ss88swd92221tNy666KI4LXhUmoJqFzuJPu9xNtY0hBxTLXj/M8C5wpKec7N8FfNCZerGWys0tXQhn1WffCTFe+29Yq75MDhbqc127aV68SKhHmhJn/2kcuFSMTebVL29UKLmXkm6nxUtcb74S1QKW7mjhp1CWYe/uvrdUL3ifHlFMxpqv377cfeWasH7oSO2rjQYheXaceeL38Eg5iQ9g7Sh2yoCDoFQEVA6RSH3RYsWWaKVTM+dWpZ++u2QM2Q4kcTEg3jZZZdZ8kG7s2fPtjU+IYT8qFx44YV26H+bbbZhsTUe7t98841ssskmblbs03lRmcFD94477pANNthAJkyYIM8995xst912dmgdsouX8aeffpIjjjgiJv8Za8gzAVlNpv/upx+PtvzVV18tt956q5UCBR+2x+o6PvC68cYb4wh3ly5dYuWJqBaAt9WRbE83M5oEQ7B3ZaUy2jjHK/PQxSPNsdGnMFiY9KrxpPNX1zkv3LYrgdpG231NqTbefSxaVWnkjAqNyHtVDNbiPvuahKVH7XeG6qOvviyRVq2laI9edgi+rv1wX0Ky0q1cEdtxPU1w7XAd19Xvetp9rWYZLVEt+FqwxGaE6d7iOcj1HJZrxxHHoM/BRIdNDHydUASyQCAcv8qejvOQRSozlZ57Mv12tkF33f2AoYg0bdo02zpkDXMF6Rmmf/nll8VLQHm442WEYLZOodSCpxDyiVG4nsQibswRI0bYAvSTJ0+2SUwQIH7g/Yz9sx0qTTNmzLCr8HbKMWB++vGQY5K10KHH0Lunz1hdxwdBTnx4qBa8ha5B//Gj5MJLGnTHPjtLRwv+LyPiEFnVaL8XFkj09/8zKkbr1LRkiJmXfJoLUSLFJRL943e7vPKjD6TqQ3NfmPVLDjxYqj77RNZI0H5P7FLY6oBCIniWhOV8qRZ84hUT/z1M95arAxqWa0e14OOvFf0WDgRCRUDxOJB9fsIJJ1hPSDI9d34U+HFw5t7qiPn0bsNQu9d69OhhddrdPK9X083bfPPNBYLLpzOGcM866ywbB8o871utI3W8fZ900kmCl5KYUZSV0IVPZfTbT/+dbfz048nI50HCg5YQAfeD7faR6vgg9WqKQKYIFG7VWSrnv1azmbnv0H4v6rKNVK1I+is55jgpf+gBKWizkVSZ4XdnDLuXHH6EkdxcX8qfmy5F25pkJDVFQBFQBBQBRWAFAsaN0fgGoSKWcfTo0bLxxhsLMYp4L9944w3BU4h59dyT6bcTc8k2DF9DGl988cXYwe29996WWOI9ZOh50qRJvpnzxx9/vNxzzz0xTyTtMORNzGUqryiJS6xLbCjey3fffdfum2Pzs0z039327B/vKxKbDKmQ/e9ie9I9PteWfioC6SBgh98N8bRmvPll991rFZEq57xkZ0E+servvpWKp6faaf5VffaplD8+WZaPu1WqTfJb0Y49Yst0QhFQBBQBRUARaFQP6Ny5c22iDl5EEobw4JEFj6XScx86dKjVXk/Ub2dYnJJJeCuJ42zfvr3NMKc9CBr67IcddpjdF8v22WcfFsUZQ9VsP3bsWEt+IZDUAT399NPj1kv8AnGmNiheULTn27VrZ2NBIcPJLJn+e7L1mU+iERg9/PDDVu+eeXhS0z0+1ldTBNJBoMpkwJc99oiR3zTxnl5L8lLlXcVOmxAUrHDTTaVw85qwETtD/ykCioAioAg0ewRCrwVPDCXD237D5cRMErPpEl4oZ4Qndfvtt7cnlmQk4iYvvvji2Ikm7g3C5h1Gjy1MmGBdSix5M+ITVqn1lSFy1k+nfbcxXt50JDjxdpLw1LdvX3vMJA0NHjzYxpC6UIBMjs/tn0+wvOqqq2JhBt5lmUyrFnxqtJqKFnz1f/+Qv89M/dKV+khXLl31n2OlYP31V85IMuVCSniBC4OpFnzys8BzWbXgk+PjYkCTlftLvmX9LMlVDCjhcSTeqikCuUCgUT2g6RwA8Z4uqShx/URSSozk8OHDZb/99rOrMlTtko7cthDWdC2TdV2bq666qptM+zMd8kljkExKN7355ps2Eemll16S4447Li65KJs+p91RXbFZIBA1sdPLLr80+LGaxKRVr7g6LfIZfGfagiKgCCgCikBTQiD0BDQTMEm0uf/++20dUN7QKdfUqlWrTJoI/bp4c8mU/+6772zWvcuID33HtYNNBoHyGc9LNGC9QJNFJ6tccKFNTmoyB64dVQQUAUVAEWgwBPKKgIIaXlEK2qcystwpEEyikzOGKJDJJHaUOFCvIcNJCZTevXvHZrM+mvBIiZLUROKUy8aPrZQwwboMkZNEla2xj65du8Zl85OgRfwpoQqUdspEpz7bfuh2+YlA1JRBq5g108hnrqwykc2RRsxoROEmm2azqW6jCCgCioAi0AwQCEUWfEPjTEF2koyIoXQGwSQLnz+vkYREbOSdd94Zm02cKbEwTz75pC0XRawpCVGQ0lSGytLzzz+fapWslqEYRcwRsaQU41dTBLJFoOqLz+M3NaMKRbvsJgU+ZLJgiy2lcDvzMlWw8jFS2HlrYkUkaoQYouZlS00RUAQUAUVAEfBDYOUvh9/SPJxHnVGG50nkQTrTa8RvEmfprSVKOSViS7127rnnyoABA6wkJjGY6MTjBb3rrpXa2N71k01DbsmSR/nJKVVAJN0021Hv1FvPFKL8rdHV9q7j2sd7e/bZZ9uvkGG25TPZ+m47/VQEHAK2lqepx+us9OjBUr30Vyne/0CJtFzPzZbCbbaVwi23koiJ80T/HSvacy9pccawGkJqYreR5VRTBBQBRUARUAT8EMi7IXi/g/TOe/bZZ+0QNcPUlDTCk+kdOqecEbKWaKdjJPow9I63E1u8eLEdjkdm02tIe7psfO/8ZNOQwksvvdTWFkXPngzgu+++29YcZd9HHnmk3ZSapGTiU94JLyxZ8G3bthUydPHkemVD0aUfM2aMzVJMpSfv+kSf33nnHfuV0IVcacEjKOCy8t2+GuPT1UilPmtQDeRc9R+pybDoQ/tpwRd//70UryifZI/ZhHxUf/6ZVJaWii1KP2+Fh928xEVIuFtzrdhwfdS8HFW+/77dLGpKN/2+6HupWrdl2tBxvvirayQh7QYDrsgLIn9hOl9eAY6Ahxdoc3dv8UIchnudgwnbvQVGYbl23PkK+hz0c3wEupB042aNQLMioDy8KU4P0YNs4dmkFqk3ZvQf//iHXHTRRXLyySdbL+P75gf14IMPjhHQzz77zBbLT3zoZlJ2iSsOzyokc//997cX4JAhQ2yGe79+/SwJZRkPVOJSGWJneJ2+P/7441Z+85lnnpGpU6farP9kV7Cfnrz3WJEhdSVvIOGEFmR6HIn7BmPayoSMJ7aRq+88dMOmBc+LRlCMc4UPIwH8efsTMS87BebeQMO9+relYm4CuztiQwtalK7ctfFwRswfyUZRU7IJq3r/XSnquVPNOmYkoYjhe0Nc0zXIHtd8sqoX6baTq/VUCz45kpwnYs45V5mUqUveYvAlYbq3eA5yPXvvreBHmH0Ljjh6nS3ZtJb4u5dNG7qNIuAQaFYEdN68eZYYQToxit+jJuQlZZuaotnoq3/11Vf27ZWaot4HLCWT8KgFNUjtggULrOoS+4Is8gAlAYpEJTyk1NnD29mmTRsbLsDDw8Wo8vAnmerMM89M2hU/PXnvyoMGDYp9dXVAg2oXh60OKCSC0IqwSJFSBzQoxrGTFnCC6wwC6u1PubnWKs3QecSUK4uYoXcIKVZgXtaqzfXorKj7DlI25TGRv02t3LPOMYlLK1XHWCdSVCirb9hGCuvQf3ft8cn1T3/cS5F3WWNMQyIgWF58GqMfbp94G5HoDcPLHeeJZxBl38JCssJ0b7k6oGG5dhhVgDwGfQ56fwvddamfikC2CDQrAorXEJ12Nxyxww47yPjx4+V7hh3x5qwwhuHnzJljPYIHHnigm20/0Yj/ySRYJBaPZ6gepaUrr7wybn08lng5GUbnLdQ9rG+++WZbJL9///6C1/Omm26y2/GQ4DueT5KkmMboM2QU4pqu+enJp7utrtc8ESCus3yqSa77+isLQOUnH0uJiQONGCJZdvcdUrRHrxrZzVdflpJDDjMe0gqpNPK3tcx4fwrata81W2coAoqAIqAIKAIg0GySkCBzDKcjs4lcJ394AHfbbTeZNm1a3NXAMDwElLJJlFfyGqTu0EMPlVGjRsU8oXgrSUDq06ePd1U7/dprr8nrr79uCaQbvmcB5ZIYZqdoPuSXZQzZYMxj/xSdd6WiiFklxnPDDTe0CU/Eoj7yyCOhib+yHdd/TR6Bgo03kUhpjdeTg0HzvfyxSVJ2x21mjL1KKg3xrDaxndXffiPowJdPflQqF8yPHTck1YyjS2GXrY0XtFm938Yw0AlFQBFQBBSBuhFoNr8QlD9C0z1RPQkPI15Lr6eT+FCGbRkO94t5OeOMMwQPJolBxEIxDHXQQQdZMpsI+cCBA21MJ0PnDO87jyZlm4jtnDJlit1H9+7d7TA82yPfxl/r1q1jQyaEC5D4BHFGd56hEJKI1BSBXCIQMSWVSg4fKGUPPiAmgLam6RUvRr77Mdd/LWuxipQeNrDWbJ2hCCgCioAioAg4BEKvBe86GtZPYn28Q93J+kmMZSL5hbxSYimd7V27DOMT15jYllue7aeLAaWkVBALWwwonm/Ie9DYpyCYeLdtClrwUXNdLrvhGqk2XvlsjJJNpYfHV4lIp52wxYCqFnzys0YMqGrBJ8fHxYCqFnxyjHSJItBshuDr61SnSx79CCPJBOlu7/pPIpJfW265fioCgREwLzjVhrhnZSaGufqbr2Nx1lm1oRspAoqAIqAI5D0CSkDz/hTrASoCmSFQ/vyzYlzzmW3k1jbJclXffCNVH33o5uinIqAIKAKKgCJQC4G8jAFFjpKhbRdv6Y76xx9/tIk9u+++u1BOqb6MZCFsiy22sJ/05aOPPrLT7h/xqBjDjgsXLrRxoDvuuGNcNj7Z+SxjyL1Tp042g99tT8Y97W+wwQZuVtwnBZApMk/JKMoxeQvWx62oXxQBDwLRsuVS8dIsMfWQPHMznFy+TMqfmCJFRi1JTRFQBBQBRUAR8EMgLz2gEydOlOuuuy5OUpODf+yxx2wdzSVLlvhhkZN51OobMWKEzXx3Db711lt2v9QcdX8sg1gef/zxNuP9oYcestu5ElFkz5Oxj3QoBPa2226TK664wjUpDzzwQK3jcwsp4UQhfbL+iUW6+OKLrWqSW66fikAyBKpM5QdT9Gvl4iy14Kt/WiLRbL2oK/euU4qAIqAIKAJ5ikBeekA5V507d46T1KTEEd5EMsu9hgcSzyjB4mSzYxTtJdaSIHvWd+oRJLSwTqpC2STxJGrHQyLJskc33msQYuqSOv32oUOHyhumpiLeUaQ/TzzxxFh2PiWbDj/8cJspTz3QZObkOCkLRdY9Rvb8McccI5SXIrNfTRFIhoCfFnzFvFetFnz5Y49IdGlNMXq04As6bCZRQzTRgq947hmrBV868Aj5a/gZIoVGwvOHxVYvPtm+dL4ioAgoAopA80UgbwkoZAupSjyBGDU1kZ6k/qYz5uFV7NChg62xOWzYMKEwPFKdDH9TB5TanshknnvuuXa4nMShrl27Wu+ka8d9Tp8+3Q7tJxJECCikctKkSXYonQL4lHdCAclbO5RSTKgbsS5D6y+//LLtc/v27W0m9wsvvBCnyuT26/2EwFJI35FPlpHoxL4TifPIkSPjtODpd1DtYjL78ez6la/y9rMhpp03OUxa8LwIBcU4V9hxrjBvf3KmBW+qNfxu7iHVgs/V2arROkedKQzm7q0wacGH7d4CI++91ZjnzZ2voFrwVD9QUwRyhUDeEtB27drZeEpIXseOHQWlon322SeOgD744IO2oDyEkPI4EFAXN8rDDOUkbtxbbrlFaA+NeMogQdzwjq633nqx84CUJl7LcePGyYQJE2LzmYCAYhBLwgPwfI4ZM8YqLXmz4JmmHYy+3HrrrXLaaadZFSXiQymcTz9SGQXtE4vns34i+WQeRNrtHy8vCk9OqYnl2ZhqwadGDY97UIxT7yH9pfyY8OftT0614FdVLfj0z0bda4bp2uHlRbXgk5+zfNWCD4MMbHLUdUlTQyBvCSgnwklq4g388MMPbYylO0FIaeINxas4Y8YMO5u3w48//thOQ87w4vH36aef2iFsFkDUbrzxRruO+wcpveaaa+T888/3rTd53333WQ8mN+8BBxxgi9ZDNCkmD9F1RjuuXiWfxJKec845NoEJZaRTTz3VElw8tsksE616iuE7c3VAg2oXh60OKN5Y1YJ3Zzn+s9614DfYULXg4yEP9A1vIyXYwkACeHFRLfjkp9PVAQ36PE2+h8yWqBZ8Znjp2g2DQN4TULyVxIPiQYTweQ0yCSF0MZ7orKOChDkiyDQPEcihs59//tl6DiE22HfffWdJKmQR4+EMccVLOnz4cDuc7+JCS0pKbLwpbeBBxfPqjGlUjvB0oHJ07bXX2n5069ZN+OOBj6xnKgJKZjxJSIl29dVXy/bbby99+/ZNXKTfFYEYAjnVgm+f/EUptkOdUAQUAUVAEWiWCORlFrw7k5C5Fi1a2CFxht+9hqewS5cudhicT5KQIHxej6RbHx12SjuxjKGVM888U/BGOGOIn3jNmTNn2j+ShY499ljrEUXnfezYsbJgwQK7Ot7UpUuX2mQgykEhEQqxhKzOnz/fEk2GRCGxZL6zDCOWiFJOlGNKZRwnRJasetqAOL/44ovy5ptv+kqFpmpLlzU/BKwWvLlnnGWvBb+NasE7EPVTEVAEFAFFoBYCee0B5WhJRqL00dZbb13r4Mkyv/zyy22CDjFNZIu3bNmy1noQUEgcmeh4Rg899FBp06ZNrfX8ZuAJJcudxCb+kPfDu4nXFbJIuSX03RlWo30SjrCrrrrKDuvjoSXzHiI5ePBg6dGjR2w3tEP7ziDGkF9KUKFVj848BkG+7LLLVEHJAaWfSRFAC75wp12k0mS1x8wTJhKb5yZWJDK5r/bTasEfHjdLvygCioAioAgoAl4EVAveoEE8aDqF6YmTZNg9cSjfC2iqabKxSfrxkkbWp12IrQsF8LaB1xVvqx8x9q7nNw1pZTjflZfyW8fNczGgqgXvEKmfT7zTLhyjfvaQfqvEgBKrtuGGG8Y2qvruW1l2+aWmFKinFmhsaXoTxYccJqUHDUhvZc9aXKt47f0S5jyrNdikasEnh5rzxKgN17I3iS35FvW/JEz3losBVS34+j/vuoemi0Dee0DTOTXpkE/aCarBniwgPVW7kN1syCf9hdD6kVqWqSkCiQhQPH7ZlZdlTz7NtVrce9+syGdiX/S7IqAIKAKKQH4jkNcxoPl96vToFIHcIlA2+VGKTQZqtHivvQNtrxsrAoqAIqAINA8E8soDSqkJMsDRPU9U/EEOk6Hs3r17N+qZdbX8KPdERnxiUhEa7wzVYyxvZ+p+brnllo3aZ915/iNQbaoyVH32afbeTyAy5LV82lRpMfS0/AdMj1ARUAQUAUUgEAJ55QGFYI4ePdr+eVEhjpKknjvvvNM7u8GnSQxy2fAoHrkC9d6OoPFOxj0qTJBmkofIoldTBOoTgcoP3zf1w1aWGjNByVK0y25SsEmNnGts32aYvbDb9lLQafPYLCYKtjAvSaZ6Q+UH71nxhriF+kURUAQUAUVAEUhAIK8IKMdGkhBJPl9//XXsUN99912kCDhsAAA7RUlEQVTfxA+C1lFKwnPqtSVLlsiiRYvian+yHO/lN998IyRvOCPRh+94LV1pJggvhea9bbDtd6ZeKOtSymn//fe3hfJdO97PAQMG2BJOl156qSXNTz/9dK0+Ll68OK4fHAPt0nf6hPn1w7sfnVYEHALVXxm1roqVMo+lRw+WaqP7Xrz/gRJpuVLxq7jf/oZtRqTIaMEXdu5iN4+0ai0tTj9TIquuZrygUYl6SpS59vVTEVAEFAFFQBHwIpBXQ/DuwFBAmj17tmy22WZ2FjKcDL0jlYlB0CB3v/zyiy0yT21Ovu+6665WmhMSR7Y6xeLxPqLLnkw3Hk8mnk3IJwk/lEAaNWqUtG7d2tb7hHhSfontIbsQVdqj5if7oF5oKoPEkoTkCuPPmzdP7rrrLqushJcUdSQIa6J+/YEHHmiPKbEfTnqTfaK0RL8wEqEIXQiqXaxa8BbOpP94KQiKcdLGM1zg1YIvNTKscTIN5lqu/vwzqTRezcKtOkvlvFdrWjd1aQtMDV1TYFei5ljM256UGFJa+dabdjm5879987VUb7xJhr1h9N+QV/OX+EKYcUM52oBzFbbzxUtmGMxpi/PcS6zq0Vj9C9u5AqOw3OvufFHtJIhR/UBNEcgVAnlJQKn9iW77ySefbMnm+++/L6gcOQL6/fff29JESGRikyZNsnKcaKijNDR9+nRbwJ5PSo1AGFPpxuMVnTx5siWKU6dOtfU88XBiQ4YMsSRvr732skXn+/fvL927d7cE1K7g8++GG26w+4e8UiKK0kg85JGVRNEIkosyEgSa9qkVivEAdvr1qfrhdkkbrgIA5BniTeH+IEafaSvbUlVB9p24rSMzxNKGpRoAwgJBMU48zmy/82MCoaE/EUM0Czp2ksjqq0v1jz/ylmabjZrzWdCidOUuzHUYWW0NiRSXSKSwSIr79peK2bOkaNfdVqwTlRJTt9Y0unKbNKd4MYQUc77CYNxv1OcNS5khrh36EgbC5140OVdhuNe5XsJ0b3FfcT2H6V7nugn6HAyDDGwYng3ah9wgkJcEFO13Hox4HHkDRYLS+5DEMzpo0CB59NFHbRwmCkPtTLIPXsCePXvaYu4777yz7LHHHraAfV268RSlb9WqlT0jEF3iPO+55x67f4biIWWZ2CmnnCI77LCDJQeff/65LUiPl5WHGsflEqyoMYfa08KFC23zXv36dPpBAXxnrg6o10PqlmXyGTYteLxpeI+dBzmTY6mPdflRCopxrvrlwkHoz3JzLUXNy04E8lhppGRXEMgCU+ex2ryEOSs0sZ/Lb79FIkaetuTgQ8TcWFJgXtAKOpjRBjNd/uQTspq5HwpMm5ka90mY6oDSH5TMwnK+IH08o8JAAjhPEHRCnsJC0MN0b/EcxCEQlmuH5yAENOhz0Ps7mun9resrAokI5CUB5SAZhp8zZ44dRmc42mvvvPOOXHnlldZTiaoRxA1FIgwP43cmVpNEIEgfiUKQOd4c/XTjGSL33tRsg0cUT2e/fv3kpptu8u46rWm8knhdMYbFOQ48s/vuu6/1EPFD5B4E/EjyoMNy3Q/bqP5rFggUbbudLL/vHjGswh5v5SemSoOJA4Volt19hxTt0UuqTZH6yrcXSsnhRxiiurpUvDJHqr/9xq5fcuTRUjFzhll/TUM+12oWmOlBKgKKgCKgCGSPQN4lITkoGIaHuBEnydC61/B44mFE+pISR5A73p5JSjrmmGOsLjyxmcRWQjAhhOnqxn/wwQe23f322896Tz777LMYQcR7mYk3lCFkyCwlmzp06CDrrbeeJaSOLLOMvy222MJ7eHY6VT9qrawzmj0CNot9xYsMYCTTgK9cMF/Kn5giZfePj5FP1i9/dJJE//yfFPXcma9qioAioAgoAopASgTy1gO60UYb2eEhhqsTY6bwJF588cVyxhln2CE/YiGpv4msXJ8+feSkk06yMaLEFOEpxZLpxv9IvJzHjj76aLn99tutDjv7Jd6TYXgMT+uYMWPqJKFO452hNob2DzvsMCEkACOu9YorrpB7773XSnhefvnllpjahZ5/qfrhWU0nFQGJmuucYfVaReg9hDQOJuOB9zUTn1iyb1/fRTpTEVAEFAFFQBHwItCsteCTabPjeSSGx086Mx3deIbI//zzT9/4HxdXFjSOK2g/vBcB0y4GVLXgE5HJ7fcw6VUTA/pfE/e5xiUjbfymcdUHOtjSM86U4h47Zd0G90aYYkBVCz75qeQ8qRZ8cnxUCz45NrpEEXAI5K0H1B1gqk8/gsn6eC6TLXNZ46nahVwmCz7PVcB+0H6k6r8uaz4ItJg4wdT1NJE4QcinCS0pOWxgIPLZfBDXI1UEFAFFQBEAgWZNQPUSUASaMwLRL7+QIpNYVGvoPRNQTHJe6UmnSPFOGvuZCWy6riKgCCgCzR2B0CYhUT7phRdekMcee8wmArkTxRAm8Zq5NoaT5s6dG6jZDz/80CYMBWoki41ffPHFOFUkvyZISvKqQ/mto/OaGQLTp0nEDHsHMkNAUUVSUwQUAUVAEVAEMkEglAR01qxZNtmGAvLE0pAwdOONN9rjolj6xIkTMznGtNZF2vKhhx5Ka91kK6G4RPmmhjbwIp4vlbEO2vJqigAIkHgkixfFgYHiURGeTBOC4rUCk9BX1NPEdq6oDUppJgrPR9Za265bZUo2qSkCioAioAgoApkgEDoCSuF1MsVvueUWGTlypCWi48aNs17P9957L3ZslE369ttvrfqFm+nVQ4eQJZIyZOOcJBlE1qvV7tpwn6xHhjtqQ14jcYl9s30qc32hD95MebbzlmKiPfZFpjzbJBp95jjZpzPXNm0xH4KOXKezZBr3brl+KgLVP5nr13gvnRV27SaFW2xp40GLDzjIzZbI2msbPfiDpPo//5HSQcfabUqHDBW2Lxk02ComVS36Pra+TigCioAioAgoAukgsPIXKJ21G2CdN954wxaRR83IGQk9yGWubmQCqeEJwaKEEnU1IaxIV1JKyauHTvklCso//PDDthnI5vnnn2+H9NFqh7zRrtN7d/vik4zw8847z5I7stlRTrr22mttchL1QflOfc/rr7++Vo1R1w5lkiCeS5cutTrx1BGlYDx9/8noblNKifqkxx9/vLQzKkxkziMROnjwYKE4PnbnnXfKc889J23bthUycvECU5jee5yUjSIkAcJO8fpkGveuX97PCy64IE4LHsyDahdzHCikJJa+8u63oabdywYkP6gGcq76zDUQFONc9KXQ3BvF5ly5N9BCcy2WP/G4yN9/SYszhkuc4rPxiEbQfzcKSQVtNpKqr76UalN/Nrr0V6OCtKEsM9ftHyZkJqhxvvjzexEL2nY223OuwnK+6D/3Fi+vYbq3VAve/8riXHEth+Fed9cO103Q5yDVD9QUgVwhEDoCCrFLLBzPwUI+nfHQu+OOOyzhmjBhgiVpEFCMHwz00HkAUGie9ig2P2PGDKskBKH003t3bfM5e/Zs6dSpk0DQaI+6nBDfbbbZxq622267WTJY1w8BP6RIctJf1Jhoj0/iWhmud8fJ5wknnGB13yGfSICivERs5+OPP26JNseEvvvw4cNtH9xx8pBzMbHJNO533XVXu03iPyRKnecUZSVIeVDtYn4g6btTakrcZ0N+d2SGF5WgGsi56ndo9KqN5GaB8W7i9Ywagh5ZfQ2RMjMsn2ilRtO9ukoKWhupTiMFW228nSXGQxoZeKQUbtZRKt95WwpXXSXwdcNuuab543yFwcKmBc+9BTZ1PXcaAjv3okl/wnCvc8yhubdMX1QLviGuQt1HU0cgdASU8kJ4rFIZReadVCVEkQQbZ04PnYdi3759ZebMmVYpiE9kMpPpvb/77ruuCaugxJdrrrnGzqPm5ssvvxwjoH7F7WMbeyYglvxYcExoJqMzj6FoRHyrMwgnxnocD6SZfUKaRo8ebZfxcP3kk0/kzDPPtN/dcXp/jJJp3NsNfP5B0J25OqDJyke59er6JGYXSVA0tBvb+JHkJYD+eGVKG7NfhEwExTgX/a8y3vLl5hxZ7XdDbKK/LbVezqjx0Ec94R6FW24llfPmSdUnH1nPqBhCWvbkFIn+939S0n9/iRrPfIlJQirNQvs98TggWGGqA0p/wqQFz4usasEnXjUrv4fl3qJHPAd5mQrDvU5/eA7yWxH0ORiWlw2OSa3pIxA6AoqsJAkziYZGOx67jTfeWLy1NL0EjG28NxgE9LTTTpPdd9/dymu2adPGNktbDM979d4hlV7r0aOHVS5y83jwO/Puw83z+0xcLx3PDl4XvJB47xh6R4fezxLbZp1UGvd+bei85otAwcabSPTXpVJpQlOwakNASw4aYArSF0ml0Xg3TEeK995HKl56UUoPN97Ozp2latF31kta0re/RA0ZIpEJr2hR5y62Df2nCCgCioAioAiki4ALAUt3/Xpfb5999rFxkmSk4w3hrZah6DfffFMY+s7ENtxwQyGu8a677rLeULYlBtNP793b7t577229jZBhYjeJP/3000+9q+R0miF/DF13YlIJGdhzzz1tfCvHQB/I0n/kkUdSDr8l07jPaWe1sSaPQNR4Zipmv2TGCY3nc8XRRE2yXdn4e8zf3VL1mbnWTRx0xbSnxEh6SdmEe6X8qSekYvrTdu2y+8x3s6zi6akSKS6SgvYdmjwmegCKgCKgCCgCDYtA6DygDFVfd911drh8ypQpFo2OHTvKZZddZoefMoWnX79+Nqt+r732spsm03tneMsZBJS4SjTY1zZxcu3btxeIcX0ZIQSDBg2yyTskJzlv6xFHHCFHHXWU9foy9EEsaipLpnGfahtd1rwQiC77W5aN+adUm6oL5u1O4gsuGSwMOfW1xPl8J9RiwGES8WTT+26rMxUBRUARUAQUgQQEQq0Fj/eTOKzVTMJEtkbs59tvvy0XXXRRXBMMcROnk0xyk5UpoUQcpnfIP66RHHwZMGCA3HbbbTZWiESrxJACMGBY3pHSdHZJDC2xR4ltpdrWxYCqFnwqlIIvwwPPS1BjWNSMKPw15HhTaqnQJhYF6oN5ISrosJmscvGlEkHKMwcWthhQ1YJPflJVCz45Nizht4Xn9vrrr596xQZamqsYUJJlSfxVUwRygUDoPKDeg4L8ZZu9TAA4pYnmz59vyzR522UacpaKfLJOEOLL9plYMoLJ8Sdblqz9uo4r2XY6P38R4IVr+c1jbZ1PstoDWZFJXlp7rZySz0D90Y0VAUVAEVAEmhwCoSagQdBkyJqscIbgO3QIb4wapZkayyMWBF/dtmkhgFoR9TsD6b5zyGbYvXDzLU1G/LCceT6bFpLaW0VAEVAEFIFcIJC3BBRwqLmZiaE4hJ47VmCGFRnGpr5oph7IxH0ytJhsGH+XXXZJXN33O2WYKNKPV5aMfa9n+OOPP7ZKSmTuU8rJGftduHCh9fbuuOOOoSiN5Pqmnw2LQPmTpsi8GRIMZOaeaHHaMCnqGl8xIlCburEioAgoAopAs0QgN8FbeQIdsaJk33/xxRe28DyF3wcOHCivvfZa1kdIm0iLBjFi0UhSIsudxCxvPCtqSYQaoPQ0ZMiQmLIR8UfHG5WlOXPm2GMaMWKELe0UpB+6bdNEIGpqAFanqfseMVUXinYyL0WlpTUHSxhMj54SIZaNovQmJllNEVAEFAFFQBEIikBee0CzAYeMeyQ7nUHgkNyEmBJbSYIPHlE8pBgF453XkUQetOOpN0o2P4XQqTfqdOldTCkZ92xHTVPnyaRdliPd5uqVuj6gmoRC0tFHH22LG+PZRfWIItksIyicBCbqpFKsnk/Ulih8f/bZZ9tmhg4daj2oO+20k2tWP5sJAtVG+lVM3KbJ6LNHbHXfTWWH6n//Iui+U04Ji2zYRkoOPNiUaJolpcccZ8svtTjtDCmf+pStEVo2YbxUGSJbtP0Odn39pwgoAoqAIqAIZIuAEtA6kKN8EwpKlEqioD01RNF5J7uRRCfIICSVP2Q3nU786aefbong008/bUkrhPDEE09Mqu+eSmPeq1iEYhP7bdWqlSWfyIPiIZ1n1GqQ3Ozevbs9oq+++krQiXfGfMipl4BCtPHQYpDqdkaTPqh2sZPoyyQD3/Ux158k3mCQ+6AayLnqG+cuKMaZ9qXwu2+lxOzXlVxKpvte2GlzqVwwX6q//EIipih9QTtI6r9NEfouUv7MdDGFeXOm++53DJwv/sjYDYNxrhrjfCU7du4twmrCdG/xMh2G/oBZ2M4V13JD3+uprh2WBX0OqhZ8MoR1fjYIKAFNAzUKwzPEncqeffZZq7oEYf3888+FuEySi6jjuWDBAks+8Xqm0nevS2OeWqgUrUfdCQ8rxBMPKx7azTffXO6++25hqB2SSUF7rwwc0z9Q+9FjxIxCZDGStn788cfAmt5oIOPVdR5iz+4afNKRGRSonKe5wTuRsMNG0as2nvWIiQ0uXD+17nvVe+9I6YknC/KbBebajRivOnrvZQ9MkFKj/b583G1SaJKQUOqqD3OELx3FsPrYf2KbhLFwHSeL305cv76/Qz7BJgyEz71o0h+eHWGwRrm3khy4asEnAUZnKwIeBJSAesBINsmD30/60rs+sp/XXnutTJ8+3So29e7d27vYTs+dO9cSoWT67nVpzFOk/pRTTpHzzjtPUEjiR4C6kk888YT9EUB7/qmnnrIElB8FftCdUU808RiIb3Xm6oB6SatblsmnasGnRovzEBTj1HuovbRqfaP7XlK37juySGUTJ1iJzYgJN6kysdCoIlX/+INUL/peCkxoSaH5y4Xue+1eEiGgWvB+uLh5qgXvkPD/bIx7y78nqgWfDBedrwh4EVAC6kXDZxovyJdffikU4HXmiJ13qBAVop133tkmLM2YMUNIYJo4caLbxH7ikctU350N8aZusMEG0rJlS9loo42kV69eNrsdrydyoc4DwXcSkrD11lvPklP7xfyDqBJzqpa/CKByVPXZZ1JlhtCjS381BxqRiLkOCrYwns///CqVJm4YS6r7/uJME/t5rN226sMPRJYvk6ghzCWHHi6RdVtK9X/+LaVbqe67BVH/KQKKgCKgCARCQAloEvggmQxxM6wNccO7iOG9olwTiUIMrTu7+OKL5YADDrCa83gyienkjZwhKjw7GPruxInivWR4/oUXXrCxm1dffbVrxvfzrbfesslNI0eOFIaZXn/9dTn44IMt4R0/frxdRkwqCUnEhGLEqz7//PP280+j501Bfobq1fIPAbLcy59/Vipmzaw5uMQYytmzahKQjPiCCbIUp/tu3lxi0ptW991sXXbHbcRjrJxPgtKK7wzjF7Rrl38A6hEpAoqAIqAINDgCSkATIGeYfI899rBxVujAEyfpJW6DBw8WCCOED4+jUx0iUeimm26yGekQ1zPPPNMOt5NVTxkmdNxpJ1N9d7qHJv0NN9xg40ghxpBLkp/wfJLsxB9Z8MQ5OilNtOspH0UMKnFs9A9Ne7X8QoAkoWU3XCvRP363SUK+R5dISN1KnhANN8t+Js7nO7rvhxhPKGRUTRFQBBQBRUARCIhAqLXgAx5bvW1OJiBeTUhfohEDyXxvEg6xmmzjkhnwjDK0n2mBe4b8SQDxts3+IaV4OR0Z9vaJ2E5iP+tKwnExoI7AetvIZDpsMaCUxeJFIjH+NZNjyuW6udSCj5pz/tcZQ3PZPf+2DOksNMP4LS64sF7Vj8IWA8qLJKXOuH7CYMSAMgKTeP83Rt9UCz416qoFnxofXaoIgIB6QLO4DvhR4s/P/BJM+MFw5JNtIIOZkk+2I/Pdz/CE+pFP1s1mP3770HnhQiBatlz+vmJU/XfKXOcREy5S3+Sz/g9E96AIKAKKgCIQJgSUgIbpbGhfFIE0EaiYM7tm2D3N9X1XWxET6ruMmcbbTg3QFqeeUa+ez6T71wWKgCKgCCgCeYtAsySgDGWj+Y7kZpcuXWyCUV1D1I11BVDI2Jvs5PpBlrvTkafo/OLFi235Je9QM0P9JDDhHe3cubPbVD+bOAJRE3JhC8OvSG7L+nBMgly18Z7bId1qU4PJ5CjZf1WVUtC+g5QeNlAKO3bKunndUBFQBBQBRUARSIZAsyOgFGg/66yzpJ3J5oWUPfroozY56P777086xJ0MvIaYj4wnRNlrJEqRlQ8BPffcc20yEgpM6MJTI5TEKUj2cccdZ9WRiDtEDQhFJ7Wmj0D199+ZwN8ETXYT5kGcZtWnn9Q6wMKtt5Gqjz6081E3KtiorVQufNNwzYj8fdqZ0mqdtSVqYpfReY+YeMeCDTaUSGlprXZ0hiKgCCgCioAikCsEmh0BhbANGDDAZoU7ECnwftddd8k555xjZ5EgtGTJEus5xNOI4U2kBBIeRhSFqMeJ15TkHVQvqNHpjKSgn4z+NslHlGty3tVkeu94LynL5LTiXTt8krnu1aZHcQnZTUj0Rx99ZEtFPfjgg3YTaoJCqCGglGSCYDsteLLhUWhiHbWmjUA1ilYVHgJqkt5KBx1rCnwa6UgvATXezZJDDpPCLlvLMkNAIZ7Fe/SSitdfk9Jjj5eyhyZK4S8/C9KcaoqAIqAIKAKKQEMi0KwIKESPTFJKIXmNEkkusxTtdghdhw4drHY6JZkuuOAC+cwU+L7xxhtt8hFSeAyNUx6JovNkhO69995y8skn2xqhl156qbRu3VqWLl1qs+WpJUpyUqLeO5mSEF+ybPFynnrqqZYce/vmnaYgPYXmx44dawvNQ1pp2xkEF5KMUWuUAvpkgUOSOe51TB1Hr+EtXbhwoZ1FshJkl/WDGMX2IfBhMPqCgQtYh8F4KQmKcbFRJSquKK8ZMTcHVbDW2lI+fZqU9N8/7hAjq65mPJ1vSUHr9e18FI3KJj0ohd26S/SvvyVqXpwiBpeg/YnbacAvnDO8/mEwzhUvnq6Ob2P3CWzC1BfwQF44DNKg9CUX9xbt5MLoCxaWe8s9C4M+B/mtU1MEcoVAsyKgkEiKyic+MF2GOjcpMZMMVeO5xIsJWYWoYeiu42GkBujw4cMteUPtCDI6ZMgQS0Dfffdd613df/8aMsD8t99+W9CIx5zeO0QRT+x1110n3bp1sw8q1qWYvfOY2g1W/IPMXnLJJXa/xK1ikGYX80kfIM6QZWz77be3HlA8nzw0qBUKKfZaz549rcIS88ikh6C79rzrZTKtWvCp0eK8B9ZSN9rueDMLWrWS6p+WWKlMo3hQa8fR//1X+IuzomIpWK+VOeEFXEBi3qgCn/O49gN8YeSAH+5kFSYCNJ3VprxIJVawyKqhHG0E+QybFjzPTqfElqPDzLqZnNxbWe89fkOnBR/0eRrfavbf+A3gd8/vtyWTVp2jJpNtdF1FIBkCzYqA4gHEG5bMuEFHjBghr7zyikyePNl6ECGl7q0PryLkE4OgtjNxpBjD9HgC8JagUETSEIpHJAcxXO/1Wji99++NLCI/JnzHaBdyjEdyp512svPcP9rFq4qSUv/+/d3s2CfKTKgkIRfqtkUhCc8n2vDUCGU5MqAUsXemWvAOiYb75Fz6lerKpAcVxqNZYYbdI4aIRsw1VOPnrbuFgrYbS9TEjlYY1aQWZ51r4jxbSLVpI2h/6t5zemtwn3Cv+dXXTa+F3K5FfyDDYcFHteBTn99c3Fup95D+UjyNvFCF5dohJ4Dft6CEOCwvG+mfCV0zzAgYF0jzMZSL8GpCFr1GvOSoUaPs8PVJJ50kX3/9teAdvOyyy7yr1bp5IZCY16OK9/SRRx6xZBIv6dZbb+3bBqQVb48bqmElfvB4aCXav/71L+tlQF0p0T799FMbu3rGGWeI87qyDl5XyDAZ8MSr9u7dW9580ySeqOUcATTSy42O+vKJE2TZTWNk+X33StkzT0vVD4tzvi8aLNyqs5CIVPnaPKk2XutEK+i0uRT1iH+JYZ1qc92XHDRASgYeKVWff2biSMulyoSaqCkCioAioAgoAg2NQLPygPI2euihh1qyiZwm5AzvIXGYkEWUT/CQQuYYqiC+E/MjhclO1AcffGCH43fddVc7NM+wvxt+924DAd1kk02sXCZxpt988439S0wSeuaZZ2xYAH1MHD759ddfrcf2yiuvtMP43vbZ/zvvvGP14vEM4FlFjlMtdwhUff2VlE1+1JJBqTQvDobQxcxcPxXPTLdF3EuPOEqKutZ4umPLA0wUmNjfglatDflctLIVE89Zds9d9nv1l19ITQRazeLlt99SM/HXn1J25zjiLWq03juZhLQWq6xsQ6cUAUVAEVAEFIEGQqBZEVAwhVzipcTTifeRzPODDjrIxmaynGFuljEMyBA7Q+0Mo6drRx99tNx+++0yZcoU6xnt3r170u1JWiID/95777XD5Zdffrkdzvfu67nnnrMZ+Wi/e41wAPrKsBwZ8c5ITJo6daqNJaUfxH6S1LHjjjtaMurW089gCJTPminlT0wRU+/KvyFD+ilrFP3xb1l+x+1SvNvuUnqMyVTPkZUYUrv89ltFliXZf6r94GU31RzkkENTraXLFAFFQBFQBBSBekOgWWvBE6fjF6NDvAyxLi45KVP0IbbEXfq17dcWIQGJGep+62Uzj8B8PKeJ3tPEtlQLPhGR5N8r3n5LysbdZglm8rUSlphzULhjD1k28ChbcithaVZfl991h1S+9YbxvGaYmVpcIsV7/0MqzXA89wAvM2GwsMWAqhZ88quCWF1GYHjhzfY5mbz17JZQ75j+hMG4r0hiczkDjd2nXMWAkmcwYcKExj4c3X+eINCsYkATz1kygojmepCHKpmCydpO7APf64t80jYZ13WRT9ZTSw+Byg/el7Jb/pUZ+aRp4w2teu9dKXh5dno7SmOt0pOHxkospbF6zSomqUZKS6T06GPS3kRXVAQUAUVAEVAEco1AsyaguQZT28tvBKLGs1328MTsD9J4RApfnGFqcOamzmWEMlzXXC9FxpuJbntKMxmwxHsW99lPVr+9JlY05fq6UBFQBBQBRUARqEcEGoSAUtYICcxEY+iZZbm2F154Iati6NTSZNvHHntMFi1alJNuMayIUUSewvD1YW4fqdomOYrsfrXsEah6/z1TV/N/2TfAlmbosjyHXlAqMLQ47kRZ5dIrTIH57WtiO038sgluFjGF6E0ws/lcVQp79JRVr7xGSk0GvJoioAgoAoqAItDYCDQIAX3ggQd8yQ8E9NVXX805BiTfEIOTic2aNcsWkn///fftthdffLFVPsqkjcR1SXaiJij2ySef1AsBpdzSmDFjEndd6zvHR5F9tewRqHx7oZiMrrgGqK2JfnqiFW7bVQo26xg3G032iCGgVW+auM0cW6Gp8brK2efKarfdKauMvERanH6mtBg23ExfLKsZj+cq5nvBihq2Od61NqcIKAKKgCKgCGSMQIMQ0GS9ogyR0yonSBr1CD4pjUTpIK8x38+LSjA8Rd0T12db5pEMRBklVIySSfyhkQ6JQ+aSgu1kp48bN856Z9977z1vN6xakLedZP3GK+n2yXFRoxO5TswVw0emLVGqjcL3P/74Y635ftuQ7OT24e0TwfgUwadvarlDoPq7b+MaK+zcRVoMP1siKAt5rPSEkyRqhtuLeu4sEUifSWgrOfwIKTlsoF2r+uefPGvndjJCspO5r4oM2S0yGvCFm2wqDNWrKQKKgCKgCCgCYUKgUcswOeJHVh2liCgSD/mkBBIZhOicUyaJz+nTp8tmm21mCSWF2VkHT+fcuXOtljoZq6NHj7Ya7g5ganCimw5RQ+ucIWhqZu6www5uFfv5xhtvWHK46aabxuaTRDRp0iS7H2bOmzfPV7c9Wb8p+g4JhDhusMEGMn/+fJuYhB788ccfL9tss40lmRBQ6oRSSolMdGQ/HXHmeK+99lpbzslvm8GDBwva9eyDsAHaRjGJNqlxSpF6vlMT1M/OPffcOC34DqYoeSIh9tsu1TwIdL5qwa9iPPZxVM4Qu4o5JqnIHHPMDNlEgx3iV2my5aPmXETWWDNOkx2MfjEvRLYUUmzDhp+gH1jQc56rnrv+eF+mctV2Nu3w3OBeTCfEJZv2M92G/oSlL+5cMYrlFeLI9JhyuT74hOVapi9YWPrjzlemI4OJ58epAibO1++KQDYINCoBTeww9TZREeKBhhcSEteyZUtLPqmrSUY3w/mQOabnzJkjDz30kJ1GOpN18GB6jbhL9Nrbt28vDz/8sCVsiQQUorrtttt6N7PTThIQQkXhej/ddlb06zek8vnnn7fSmdQCpc9eo8YoZBjSjV47KkezZ8+WTp06WT13vLYXXnihfPTRR5assq3fNmzLMP+JJ55oCTaE/b777rO7gkBTTD8ZAd15551tnVNWpuwUca9BpdryWQs+YjLIGVpHQ73qww+k6qMPpaDNRkhhWbztPyNvWdCxk5RPnybFvfaWCuMJx3Map8lufpxWMS8IeEYb0/gx4XwFPee5OgaueX64VQveH1HIp2rB+2PDXNWCT46NGyEMWhFFteCTY6xLMkcgVAQUQuTeppGPZAiZN0iKqEM4seOOO85+3nHHHbLLLrvE5jO8jQcQD6LX8D5CPjG01t99913vYjtdl0Z8Kt12GvDrd62dJMyg7xg1GHk75eEJocauueYa+4l34eWXX44RUL9t7Ior/uExHTRokDz66KM23hTyCmlNZocffnhskasDmkn5qNjGngnesCE0YSARkBmuIfqTC5L1t4n1xLsphcYPyrA2Bd0T7e+/rOezepEJCzEktcB41ROH7iOlpbI6bXmJa2I7DfAdTyMENOg5z1VXIViQYvfil6t2s22H/qgWvD96nCdezIOWrPNvPbu5kKywXMs8B3mhCkt/eA7yvAn6HFQt+OyuTd3KH4FQEVDvzep+nBlKdm9vHAJECbLWqlWruMQmfkj5S3xD87bpD4EI8pck6SQaXs/tt9/eEmDIDH/uBuTHiQcM5t2H63diW4nfCQlw5t2mR48e0rWr8bKtMO963mnvNm5dpDfxqiK5ieQo7bz22mtusX4GRKCwW3epmPaUP/E0baNOVP7k41aas+TgQ6Rg402k7P4ab7R314VbbNno5NPbH51WBBQBRUARUAQaGoG4kLaG3nk6+9ttt92EEkIuCQdN9Jdeekl69eolxG7iJcQon7TVVlvVIqDp7GOfffYREncYzufNHsL74osv2hAA9u/Vbae9ZLrtiftiuAyimq7hxSVbHkLcpUsXG4NKHGcq8+4DjyfhBRDQLbfcUl5//fU48p6qHV1WNwJF3U2ZI+O99FrFzBek+qua8lrljz1iyyxVzn3VDsEvv/VfcUPvaLJH8Q7v7B+T621XpxUBRUARUAQUgXxGoME8oMQzer12xDx27ty5Tmzx+h1yyCGWVDFczZA626JU1L9/fzufOFGG7dzQdZ2NJqzAMBLxnZRNIo4U69ixo1x22WU2eYnv6ei2s57X8ECSXZ8uCYWAUhf1sMMOs4lVhA5AjlMZ/WQf4HvOOecI5aPQu4dId+vWrV7qrKbqTz4vKzQezcLOW0vVO6Yck/GGpzS/4XmzQXSttaVw+/gkuJTt6EJFQBFQBBQBRSAPEWgyWvAQKv4gi15jHkPy3uFp7/JMp/F+QhhJ5vEzPK6ZSGfSFnFkiaEBfm27ecTmESyerhwooQHg4NbHW0xYgJfwu7aTfboY0H/+85/JVklrfthiQIkhXtvEWwaNfXIHX/37/8nfF40QE1zqZmX0WXHeCFmHRKYQGNcZ50u14P1PhmrB++PCXJ43qgWfHB/uK2JkVQs+OUa6RBEI/RC8O0WQuETyyTLm54p80h7ELxn5ZHkm5JP1IYWZkE+2Yf+OTPK9LqN97/rEzWZCPutqX5evRKDa1JzNlnyaFGaJrBiuX9miTikCioAioAgoAs0PgSZDQJvfqdEjDhsCNVrwD2bfLZMkVzgzd1rw2XdEt1QEFAFFQBFQBBoXgQaLAeUwKQXx4YcfyhdffGGTbKi9mWldMlSClixZYjPT0VcnCYfamSQh7bnnnrb8EB5EShIFMTLcn3322VpNUMqJ2Eq3v2yHdhma93ota+0oxzOQ4WzTpo1Q3kotOwSsFvyfAbXgK2u04Ev7H5BdJ3QrRUARUAQUAUUgDxBoMA8oMponnHCCPPnkk7acEbUqjz76aEtKM8GRLPGpU6faTbz66k7/PVea58SCoqxEZjmE2f05OVC3v0z67talbWqWNqQ99dRTVhmpIfeZb/vKmRb8GwvyDRo9HkVAEVAEFAFFICMEGswDiuzjgAEDbNa66+EVV1xh5S3J3sbwOqIMRFIN3jqvd5QyScz3GvrqdcU6EgiOx5S4SMopeQ2PLMHiZNYns2HDhsXV+fRbj0Ly7IP+Jgad4+nEa8t8F1v6+++/2+N0MnYkDBHHmZhQRf9oEy8vxvrUOk3cB9uxf7ybXszYZvHixbZmKtNqwRCo/vabuAbQgi89/kRZfvedEjXn1Bla8BXz5lot+ArjMY3++quUHHKYFBpt9mVGPan6l5/dqvqpCCgCioAioAg0SwQahIBCgiBdRxxxRBzIlA5yCTpowKNb3rp1a1m6dKnNREcDHnJ2/fXXC0PIJCHx54jk+PHj7fJk3kR00h988EGrD4+3dI899rAyl3Qimb58XAfT+JJKv/3tt98WSDYa6+jeQ2YpHYU0KKSVslFkrJ966qm24P0TTzwRkwuFWA8fPtz2lzJTaMKzLjjSHphQFB9P7Ny5c22mN1m7eG1ZDmFnfyRogacjsd5DgvgvXGhKChljPcIWgmoXc1yQ/jAYfcHAjReNoLaKyYCPGzLgpSErLXijEa1a8LVOhztfqgVfCxo7gxdwXmjDYO5cuZfoMPQJfII+v3J1HM5ZEpb+uPMV9DmIs0NNEcgVAg1CQNFaJ3Yy0VvpjYFEIpMC6ng1sSFDhggEDuJEIXqG7CFcF110UVrHzg0HaaW2J95UPKsQYKQ6GVafPn26rfnp1Zfv06dPrbapOerUj1h4zz33xH1Ppd8O+R01apQtDu8IYb9+/WydzmnTptn6nWjbo3mP4hJkkOP9+uuvbbY9ZJOi9JDYtm3bytixY21h+bPPPtti4+Q7KaDPcUyePNke08iRI2XcuHFCEf3TTz/dEjBqqSYaGvG0i3GMSI5mG9Pq2sZDixfWvVi4+Y3xyTWAFxlMEz3D2fQnUlySIy34KtWC9zkBjIDww01lizAYL1KJFSYas1+QT67lxOdoY/SJ8wQ+PMO9z8fG6IvbJ89DJ9ns5jXWJ89BQq2CPk9z1X/6ggV9DobhuZ4rTLSdxkegQQhoXVrrwHDwwQfLggULLMH76quv5IcffrBv+0yj7uN+lNBdh1jWZTykR4wYYQuxQ8y+/PJL63XkDQ5vqJ++vF+beAm92tSJD9tk+u0QO4gzyUozZsywTeMtJXGKpClnu+++u1x11VVy0kkn2aH6fffdV5DUxNMLgcTYxhFvHiAkWxHrCq7ow7uHLoXs8QZDsjnGCy64wG5P+MHWW29tp73/KHjvzNUB9cqKumWZfPKGzUPXna9Mts31uvxIQkDpTy5+CFQLPtdnKL49CBb3p/d+i1+jYb/RH67joPdErnrNCykjFWEgAZwnCCjPKa8jIVfHmk07kKywnCueg7xQhaU/PAf5TQz6HEz8/cvmPOk2ioBDoEEI6Oabbx6LefTW0URSE+Uf9MvxVCJxyRA1XsKbbrrJ9pEfAN4mnaX7BsfbMKQOL1/Pnj1twhP66FgqfXm3H/fJcHZdD5Fk+u309YADDoi9dUKyE7PQaZ+HAzggvwnZfuyxx6xXwfWXkAPv0Ad48HBr1aqV9Za6vjKfP36gssXNtaWftREo7L69VEx9Mgda8FuFwotV+wh1jiKgCCgCioAi0DAIxIW01dcuIXCQKYajicfDiPlE190Ne+MtZAh+v/32s+SJYXtI1nbbbWc133mj5A335ZdfttvX9Y94SPaFLCVeU4b4MdpMpi9fV5t+y5Ppt0O0IZRkzfNJ4hBxnOzfkWhHKvFiEs8K+cRTybETM4qUJ8Y+Zs6caY8fsgoGtNmrVy+LDXFYGN7WrbbayhJQSkVB8PEColjy3nvv2XX0X/YIFHXrnhsteHO+1RQBRUARUAQUgeaMQIN4QAEYIoiXE68kpIiM8IMOOig2zExJJhJq0GJnqKB79+52GB6PKNnzDC0z1IM+ejpGzClD1eyPIb127drZWFCG9vHI+unLp9Nu4jqp9NtPPPFEufzyy2XSpEn2mIlBJaEIowYqx0/8JsPwjz/+uI0DJcaLZKB11103RlR79+4tr776qo1hhcCyPt5UPJ3gA3GnXY6TxCZs6NChVhceXImF9A772xX0X8YI5EwLvrtqwWcMvm6gCCgCioAikFcINIoWPN5Mv2FtiOmff/7pu8wNO2caw4LHkLgVvzglPJD8EccU1FLpt+Oh9IYeuH1lGjRPnCbxnonxlRwDbflJkrINZL+uuDEXA6pa8O7s+H9Wmzi8ZaMukegfK8su+a/pP7fi/JGyzjbb+i9s4LmqBZ8acNWCT44PzxzVgk+OD79xxMgmlsxLvkX9LslVDCi1vCdMmFC/ndXWmw0CjUJAmw26TehAIaB4Uzt37hyo14RJQPjDkKmL59clkuQyeD5qklOqPvlYzEGKcS+nh5fxVhd27CSVxsOd+AKRXgO5XwtvOkTCJbHlfg+ZtcgLKH8uRCWzrXO/Ni+9XMdhOV+cK7AJ073FiE1dL7e5PzP+LYJPWM4Vz0HuLz/Hh3/v63cufcGCPge/M+XjCPVSUwRygYAS0FygmCdtQNjyySDVJKDh1XXlvXJ5fODFn6GhSQ1EIQxhIA2JnaRMGNggj6tWGwGqRFAGzYW11F6j+c6hqgghRJSRo6KIWjwCY8aMkeeff97G4ccvadrfwvgca9qINu/eN1gMaPOGuWkcfT4+XPCoWZKItzLH1tTxAhc8I039OHJ8WmPN1ee1E9tJE54AH0yvn9onkXsLfBSb2tjoHEXAIdAgWfBuZ/qpCCgCioAioAgoAoqAIqAIqAdUr4G8RYB4MMp6oYSlVhuBdqYyBPio+SNADWG9dvyxIeGRa4dqHWq1ESB0g8QfNUVAEUiOgMaAJsdGlygCioAioAgoAoqAIqAI1AMCOgRfD6Bqk4qAIqAIKAKKgCKgCCgCyRFQApocG12SJwhQh3XWrFlWDjZPDinnh/Hiiy9apa2cN9xEG6TG5YwZM6wiWRM9hHrvNoptlOVRi0eAclALFiyQ999/3yZAxi/Vb4qAIuAQUALqkNDPvERg2rRpMnz4cCv9esUVV8i//vWvvDzOIAf1xBNPyJVXXmkz4oO0ky/bIttLwe0vvvhCRowYIU899VS+HFrOjgMp5fPOO08+/fTTnLWZDw0hvYwy3fz582X69OkyaNAgW4s4H45Nj0ERyDUCmoSUa0S1vdAgQIkhal2OHj3aSrjyYzBw4EA57rjjfJWpQtPxBuoIxbJHjRolS5cubaA9No3d8JJy9dVXS9euXe31MmTIECvSQNF1NRFe6lDDWWuttRSOBASQkkbQAxlmjPuL0YX6qEOcsGv9qgg0OQTUA9rkTpl2OF0EUP2gUHb79u3tJsiVIvXqVEHSbSdf1wOHnXbaSW6//fZ8PcSMjwtS/sMPP8i229bIpSKliFTvjz/+mHFb+boBcsjjx4+XTp06aZ3LhJM8dOhQGTx4cGzuH3/8YSU5YzN0QhFQBGIIKAGNQaET+YjAaqutZg+LotA333yzLR2z3nrr5eOhZnxMyAQeeOCBoZG+zPgA6mGDf//738I14y0gjqfvt99+q4e9Nc0m+/TpIy1btrSdp+C62koE8JI7OdDZs2fbl5m+ffuuXEGnFAFFIIaADsHHoNCJpo4AcWnPPPOMPYw111zTDrXzBT14Yhz5sbzkkkua+mFm3X+GAkkcwbp37y7UuVSLRwCveaKHHK9oixYt4lfUb4pACgSefvppeeihh2Ts2LGy+uqrp1hTFykCzRcBJaDN99zn3ZHjeXBxae6hTzHokSNHykYbbSQXXHCBQDCaqzGU7PBRQuV/FeDZ++uvv+xLCx5iDO+nFqT3x0vn1kaAuHMqKNx6661CCIeaIqAI+COgBNQfF53bBBFo27atHHvssXE9v+yyywRVkmHDhsXNb45f8Hiq1zP1mS8qKpKePXsKHqzDDz9cXn31VZuwts4666TeUJcqAgaB5557Tl566SW54447BLUoNUVAEUiOgBLQ5NjokiaOACViqMfH3+TJk2NHc9ttt8WSTGIzdUIRWIHA6aefHiu/VFBQYDOZFRxFIB0E7rvvPvnll19sJrxb/9BDD5WzzjrLfdVPRUARWIGASnHqpaAIKAKKgA8Cv//+u6y99to+S3SWIqAIKAKKQFAElIAGRVC3VwQUAUVAEVAEFAFFQBHICAEtw5QRXLqyIqAIKAKKgCKgCCgCikBQBJSABkVQt1cEFAFFQBFQBBQBRUARyAgBJaAZwaUrKwKKgCKgCCgCioAioAgERUAJaFAEdXtFQBFQBBQBRUARUAQUgYwQUAKaEVy6siKgCCgCioAioAgoAopAUASUgAZFULdXBBQBRUARUAQUAUVAEcgIASWgGcGlKysCioAioAgoAoqAIqAIBEVAlZCCIqjbKwKKQKMjgNrVnDlzpFevXnLXXXfJn3/+Kccdd5z07dtXRo8eLfPnz7fLTjjhBFl33XWloqJCUDwaPny4Xf/777+Xf/zjHzJkyBBZffXV7fGUlZXJ+PHjZebMmYJG/Pnnny9jx46Vyy+/XDbaaKOUx/zf//5XzjvvPN91brjhBtsH34U6UxFQBBSBZoKAekCbyYnWw1QE8hmBL7/8Um688UY55ZRTZLvttpPi4mJBAnG//faT9957T/bdd1+55ZZbBPKHVVVVyb333iu9e/eWZcuWyYEHHihItEJao9GoXQeCeuWVV8pOO+0kJSUllsCyzf/93//Z5an+oSnfpUuX2N+2225riey8efNklVVWSbWpLlMEFAFFoFkgoB7QZnGa9SAVgfxHYOnSpZbkde/e3Xo4p02bJn///bfMmjXLHjzEcty4cTESykwIKl5ObPvtt7d/r7zyiqy55pqCrrfzqrJ8gw02sN5PR1CZl8xWXXVVOfvss2OLr7jiCtsX2lMCGoNFJxQBRaAZI6Ae0GZ88vXQFYF8QgCvY9euXe0h4QHdeOONLcF0x9iqVSv56aef3Ff7yRC9s27dugnrvPPOO7Jw4UJZa621ZLfddnOLpX///rHpTCYeeOABuf766wVC3KFDh0w21XUVAUVAEchbBNQDmrenVg9MEWheCEAYCwsLYwcdiUSsJ9PN4HuibbrpprFZLF977bVt/Gh5ebmsv/76Aql1xrJM7aWXXrJhAffff7/ssssumW6u6ysCioAikLcIqAc0b0+tHpgioAjUhcDs2bNjqyxatEiIJWUIn/hNEpN+++232PIZM2bEptOZ+Oijj2wc6iWXXCJHHXVUOpvoOoqAIqAINBsEVr7eN5tD1gNVBBQBRaAGgYkTJ8o+++wjeEIvvfRS6dixo+yxxx6y2mqr2eHyQw45RIjfXLJkic2ATxc3hvr79esnffr0kZNPPll++OGH2KZk1GscaAwOnVAEFIFmioB6QJvpidfDVgQUAZEdd9zRZrdDQD/++GN59tln7bA9Q/kkL62xxhpy8MEHy1VXXSXDhg2zkKVDHl944QVZvHixTJkyRdq0aWPjUYlJ5W/q1KkKvSKgCCgCzR6BiMnorKk50uyhUAAUAUWguSCwfPly64VkCH7XXXeVP/74wyYgueP/z3/+Iwyh9+rVS1zs6Ny5c2XPPfe0ZZiIN1VTBBQBRUARyB4B9YBmj51uqQgoAnmAADU+yX73GoXqqRE6ZswY+f333+WDDz6QESNG2HqikE/qiFZWVib9q66u9jan04qAIqAIKAIJCCgBTQBEvyoCikD+I4BXE9IJ+fQzhs0nTZokDKVT/5M40fbt29shddanZijrJPs77bTT/JrVeYqAIqAIKAIrENAheL0UFAFFQBFIgQCSnKWlpSnW0EWKgCKgCCgCmSKgBDRTxHR9RUARUAQUAUVAEVAEFIFACOgQfCD4dGNFQBFQBBQBRUARUAQUgUwRUAKaKWK6viKgCCgCioAioAgoAopAIASUgAaCTzdWBBQBRUARUAQUAUVAEcgUASWgmSKm6ysCioAioAgoAoqAIqAIBEJACWgg+HRjRUARUAQUAUVAEVAEFIFMEfh/EbTzEv1OaEAAAAAASUVORK5CYII=\" /><!-- --></p>\n</div>\n<div id=\"面积图\" class=\"section level3\">\n<h3>2.4 面积图</h3>\n<p>面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。</p>\n<div class=\"sourceCode\" id=\"cb15\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb15-1\"><a href=\"#cb15-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(quantmod)</span>\n<span id=\"cb15-2\"><a href=\"#cb15-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(<span class=\"st\">&quot;economics&quot;</span>, <span class=\"at\">package =</span> <span class=\"st\">&quot;ggplot2&quot;</span>)</span>\n<span id=\"cb15-3\"><a href=\"#cb15-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb15-4\"><a href=\"#cb15-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 计算利润</span></span>\n<span id=\"cb15-5\"><a href=\"#cb15-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>economics<span class=\"sc\">$</span>returns_perc <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"fu\">diff</span>(economics<span class=\"sc\">$</span>psavert)<span class=\"sc\">/</span>economics<span class=\"sc\">$</span>psavert[<span class=\"sc\">-</span><span class=\"fu\">length</span>(economics<span class=\"sc\">$</span>psavert)])</span>\n<span id=\"cb15-6\"><a href=\"#cb15-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb15-7\"><a href=\"#cb15-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 为轴刻度创建断点和标签</span></span>\n<span id=\"cb15-8\"><a href=\"#cb15-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> economics<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(economics<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)]</span>\n<span id=\"cb15-9\"><a href=\"#cb15-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(economics<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(economics<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)])</span>\n<span id=\"cb15-10\"><a href=\"#cb15-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb15-11\"><a href=\"#cb15-11\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb15-12\"><a href=\"#cb15-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(economics[<span class=\"dv\">1</span><span class=\"sc\">:</span><span class=\"dv\">100</span>, ], <span class=\"fu\">aes</span>(date, returns_perc)) <span class=\"sc\">+</span> </span>\n<span id=\"cb15-13\"><a href=\"#cb15-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_area</span>() <span class=\"sc\">+</span> </span>\n<span id=\"cb15-14\"><a href=\"#cb15-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">breaks=</span>brks, <span class=\"at\">labels=</span>lbls) <span class=\"sc\">+</span> </span>\n<span id=\"cb15-15\"><a href=\"#cb15-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">90</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb15-16\"><a href=\"#cb15-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Area Chart&quot;</span>, </span>\n<span id=\"cb15-17\"><a href=\"#cb15-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle =</span> <span class=\"st\">&quot;Perc Returns for Personal Savings&quot;</span>, </span>\n<span id=\"cb15-18\"><a href=\"#cb15-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;% Returns for Personal savings&quot;</span>, </span>\n<span id=\"cb15-19\"><a href=\"#cb15-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: economics&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"排序\" class=\"section level2\">\n<h2>3 排序</h2>\n<div id=\"有序条形图\" class=\"section level3\">\n<h3>3.1 有序条形图</h3>\n<p>有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。</p>\n<div class=\"sourceCode\" id=\"cb16\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb16-1\"><a href=\"#cb16-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 准备数据：按厂商分组计算平均城市里程。</span></span>\n<span id=\"cb16-2\"><a href=\"#cb16-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>cty_mpg <span class=\"ot\">&lt;-</span> <span class=\"fu\">aggregate</span>(mpg<span class=\"sc\">$</span>cty, <span class=\"at\">by=</span><span class=\"fu\">list</span>(mpg<span class=\"sc\">$</span>manufacturer), <span class=\"at\">FUN=</span>mean)  <span class=\"co\"># aggregate</span></span>\n<span id=\"cb16-3\"><a href=\"#cb16-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">colnames</span>(cty_mpg) <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;make&quot;</span>, <span class=\"st\">&quot;mileage&quot;</span>)  <span class=\"co\"># change column names</span></span>\n<span id=\"cb16-4\"><a href=\"#cb16-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>cty_mpg <span class=\"ot\">&lt;-</span> cty_mpg[<span class=\"fu\">order</span>(cty_mpg<span class=\"sc\">$</span>mileage), ]  <span class=\"co\"># sort</span></span>\n<span id=\"cb16-5\"><a href=\"#cb16-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>cty_mpg<span class=\"sc\">$</span>make <span class=\"ot\">&lt;-</span> <span class=\"fu\">factor</span>(cty_mpg<span class=\"sc\">$</span>make, <span class=\"at\">levels =</span> cty_mpg<span class=\"sc\">$</span>make)  <span class=\"co\"># to retain the order in plot.</span></span>\n<span id=\"cb16-6\"><a href=\"#cb16-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(cty_mpg, <span class=\"dv\">4</span>)</span></code></pre></div>\n<pre><code>##          make  mileage\n## 9     lincoln 11.33333\n## 8  land rover 11.50000\n## 3       dodge 13.13514\n## 10    mercury 13.25000</code></pre>\n<div class=\"sourceCode\" id=\"cb18\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb18-1\"><a href=\"#cb18-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(cty_mpg, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>make, <span class=\"at\">y=</span>mileage)) <span class=\"sc\">+</span> </span>\n<span id=\"cb18-2\"><a href=\"#cb18-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_bar</span>(<span class=\"at\">stat=</span><span class=\"st\">&quot;identity&quot;</span>, <span class=\"at\">width=</span>.<span class=\"dv\">5</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;tomato3&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb18-3\"><a href=\"#cb18-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Ordered Bar Chart&quot;</span>, </span>\n<span id=\"cb18-4\"><a href=\"#cb18-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Make Vs Avg. Mileage&quot;</span>, </span>\n<span id=\"cb18-5\"><a href=\"#cb18-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;source: mpg&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb18-6\"><a href=\"#cb18-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"棒棒糖图\" class=\"section level3\">\n<h3>3.2 棒棒糖图</h3>\n<p>棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。</p>\n<div class=\"sourceCode\" id=\"cb19\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb19-1\"><a href=\"#cb19-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(cty_mpg, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>make, <span class=\"at\">y=</span>mileage)) <span class=\"sc\">+</span> </span>\n<span id=\"cb19-2\"><a href=\"#cb19-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"at\">size=</span><span class=\"dv\">3</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb19-3\"><a href=\"#cb19-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_segment</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x=</span>make, </span>\n<span id=\"cb19-4\"><a href=\"#cb19-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">xend=</span>make, </span>\n<span id=\"cb19-5\"><a href=\"#cb19-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">y=</span><span class=\"dv\">0</span>, </span>\n<span id=\"cb19-6\"><a href=\"#cb19-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">yend=</span>mileage)) <span class=\"sc\">+</span> </span>\n<span id=\"cb19-7\"><a href=\"#cb19-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Lollipop Chart&quot;</span>, </span>\n<span id=\"cb19-8\"><a href=\"#cb19-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Make Vs Avg. Mileage&quot;</span>, </span>\n<span id=\"cb19-9\"><a href=\"#cb19-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;source: mpg&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb19-10\"><a href=\"#cb19-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"点图\" class=\"section level3\">\n<h3>3.3 点图</h3>\n<p>点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。</p>\n<div class=\"sourceCode\" id=\"cb20\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb20-1\"><a href=\"#cb20-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(cty_mpg, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>make, <span class=\"at\">y=</span>mileage)) <span class=\"sc\">+</span> </span>\n<span id=\"cb20-2\"><a href=\"#cb20-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"at\">col=</span><span class=\"st\">&quot;tomato2&quot;</span>, <span class=\"at\">size=</span><span class=\"dv\">3</span>) <span class=\"sc\">+</span>   <span class=\"co\"># Draw points</span></span>\n<span id=\"cb20-3\"><a href=\"#cb20-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_segment</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x=</span>make, </span>\n<span id=\"cb20-4\"><a href=\"#cb20-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">xend=</span>make, </span>\n<span id=\"cb20-5\"><a href=\"#cb20-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">y=</span><span class=\"fu\">min</span>(mileage), </span>\n<span id=\"cb20-6\"><a href=\"#cb20-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">yend=</span><span class=\"fu\">max</span>(mileage)), </span>\n<span id=\"cb20-7\"><a href=\"#cb20-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">linetype=</span><span class=\"st\">&quot;dashed&quot;</span>, </span>\n<span id=\"cb20-8\"><a href=\"#cb20-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">size=</span><span class=\"fl\">0.1</span>) <span class=\"sc\">+</span>   <span class=\"co\"># Draw dashed lines</span></span>\n<span id=\"cb20-9\"><a href=\"#cb20-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Dot Plot&quot;</span>, </span>\n<span id=\"cb20-10\"><a href=\"#cb20-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Make Vs Avg. Mileage&quot;</span>, </span>\n<span id=\"cb20-11\"><a href=\"#cb20-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;source: mpg&quot;</span>) <span class=\"sc\">+</span>  </span>\n<span id=\"cb20-12\"><a href=\"#cb20-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">coord_flip</span>()</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0HYBTV2oa/TSOFJCT0qjTLBQuIHa6KIl1BvYqoYEFREMFerihFUX8potJVFBULFkTlSlGKgBQRQXqXXkJ6b/vPe3CW2c3uZpNs3/domJkzZ055zszsO9/5zozJrAVhIAESIAESIAESIAESIAEvEQjzUjkshgRIgARIgARIgARIgAQUAQpQnggkQAIkQAIkQAIkQAJeJUAB6lXcLIwESIAESIAESIAESIAClOcACZAACZAACZAACZCAVwlQgHoVNwsjARIgARIgARIgARKgAOU5QAIkQAIkQAIkQAIk4FUCEV4tjYUpAh988IGsWbPGQsNkMkmNGjWkdu3a0qNHDzn33HMt+yqzkpKSIrVq1XJ46MmTJ+XFF1+02h8WFibx8fFSv359ufXWW6VJkyaW/f/973/V9sCBAy1xrq6UVxdX82E6EiABEiABEiCB4CFg4ntAvd+Zd999t3z66afSsGFDgfhEyM3NldTUVIEQvOuuu2TmzJkSHh5eocoVFRVJz549pUOHDgLR6Cjs3r1bWrZsqQRnYmKiJVlmZqbgD3WYPHmy6IKzadOmcskll8hXX31lSevKyrx58+SBBx4QCF4GEiABEiABEiABEtAJcAheJ+GD5d69e+XgwYPq79SpU3L8+HG5/vrr5eOPP5YxY8ZUuEZ5eXmyYMECl4979NFHLeWjHhkZGbJ582Zp3ry5YN+BAwdczstewqVLl0p6erq9XYwjARIgARIgARIIYQIUoH7U+XXq1JEff/xRunTpIqNGjZItW7aUqd3Ro0dl0aJFagg/JyfHsr+wsNAiGNPS0mT//v1SmY9ctWrVSoYOHSrFxcWyYsUKS/72VhzVBWlPnDihrKlYR11QJwYSIAESIAESIAESAAEKUD87DyIjI+X5559XAhAWRD3AuonhbAzbd+3aVa644grlrzljxgyVZOvWrXLBBReo9XHjxgmGzfPz8/XDK7TUh/6NAteYQXl1QdqHHnpI3n//fdUO1OX11183ZsF1EiABEiABEiCBECZAAeqHnd+2bVtVK6MFFKIUQ/NTpkyRrKwsZWHEZCEIPfhaQnzu27dPHffCCy8IJv/ExMRUuHUFBQUydepU5ZsKkWsvlFcXHIO6PvzwwxIREaHq8vLLL9vLinEkQAIkQAIkQAIhSICz4P2w06tXry5RUVGya9cuVTsMdb/99tsyaNAgy8QgiMv33ntPfvvtNzXh6KabbpLk5GSVPjY2VmrWrFluy+bPn2+ZIIThevihYnY+ykNZukXVmJGrdcGMel0Au1IXYxlcJwESIAESIAESCG4CFKB+2L/wl4RPpy7cYAmFQOzWrZtVbTFU3rlzZ5k0aZJKb7XThQ1YTPVJQpiNDx9UzKDv06eP9O7d224OrtYFApqBBEiABEiABEiABOwRoAC1R8XHcXv27FE1aNasmVoeOnRILSEQbQPeHVpSUqIm/FRU9A0ePLjCs+1drYuz95DatoHbJEACJEACJEACoUWAPqB+2N9ffPGFqtU111yjlo0bN1bLI0eOlKnt4cOHpW7duk5fPF/moCpE+FNdqtAMHkoCJEACJEACJOBDAhSgPoRvr+hVq1bJxIkT5aKLLlLD60gDX8xq1arJl19+aXUIZrljAlKbNm1UPF4gj4BXKHkquFoXlA8XgdLSUvXnqfowXxIgARIgARIggcAjwCF4H/bZa6+9pkQa/DvxEnpMOsI7PjF8/cknn1hqhqH3p556Sg2X4xOZ999/v3ppPL52hJfH6189wuQjDMNjchG+dHTbbbepbUtGblhxtS4oCl9ZggAdO3asdOzYUdq1a+eGGjALEiABEiABEiCBgCegiR8GLxPQPrVp1k4cqz/tW/Bm7SXwZk1omrUvEJWpkWbVNGtfRzJrM+TVcZp10XzppZealyxZYpVWE6NmTYSqNBs3brTap29oQlft116npEc5XZ599tlm7ZVPljSu1kX70pNZ+6qSKkub1GQ5niskQAIkQAIkQAKhTYDfgg+wRwhYFDF7HZOPEhIS7NYeM+izs7Mtr2Wym8gNka7UBcXg9U6oK16yz0ACJEACJEACJEACFKA8B0iABEiABEiABEiABLxKgJOQvIqbhZEACZAACZAACZAACVCA8hwgARIgARIgARIgARLwKgEKUK/iZmEkQAIkQAIkQAIkQAIUoDwHSIAESIAESIAESIAEvEqAAtSruFkYCZAACZAACZAACZAABSjPARIgARIgARIgARIgAa8SoAD1Km4WRgIkQAIkQAIkQAIkQAHKc4AESIAESIAESIAESMCrBChAvYqbhZEACZAACZAACZAACVCA+uAc+N///icjR46UH374wW7p2vfd1f7Vq1fb3W8b+ddff8n//d//2UZXanvp0qXyyiuvCD6zaS+gbuPGjbO3y+W4Dz/8UN544w2X07sr4d9//624OmJ1/Phxtf+DDz5QRZaUlKhtfPoU4eOPP5YFCxaodf5DAiRAAiRAAiRQBQJmBq8TePTRR81al5nPPfdcu2VfddVVav+ECRPs7reN/OSTT8zJycm20ZXa1kSvKvvnn3+2e/xll11mfuCBB+zucyUyNTXVHB0dbY6LizNrYteVQ9yWZtmyZaptYL9x48Yy+YI39l155ZVqX0FBgTkiIsKss+jUqZN52LBhZY5jBAmQAAmQAAmQQMUI0AJaBfFelUObN28uu3btkk2bNlllAysd4mrUqGEV762Nyy+/XFq3bi2ffvppmSK3bdsma9eulQcffLDMPlcjkO/5558vt9xyi0yZMsXVw9ya7pxzzpE5c+aUyfPzzz+Xhg0bWuKjoqKkqKhIOnbsaInjCgmQAAmQAAmQQNUJUIBWnWGlcmjQoIF06NChjBD68ssvpUePHhIbG2uV7/fffy/333+/XH/99dKvXz/BML6jsGHDBiUSf//9d0uSWbNmye233y433XSTaJY+KS4utuyzXdEsnPL1119Lfn6+1a6PPvpIiVOIVASUc99996k6QZRCnJYXMLzdrVs36dOnj3zzzTeCYW89wOVg8ODBgqFvY9CsjrJq1SoVtXfvXnnppZdUHi+//LL8+uuvatuYvrz1O+64Q8DZGDDMvnXrVpWvHg9GaNf27dv1KKtlZmamvPjii9KlSxe55557RLOUWu1HXVF37IfgfvPNN6WwsNCSRrOwyuTJk6VXr14C5hD4KO/w4cOWNBXpN8tBXCEBEiABEiABPydAAerDDrInhGCFu/POO61qNWnSJOnbt680a9ZM+vfvL7m5uUoorVu3ziodNrShZdGGiqV+/frSrl07tX/o0KHy5JNPSsuWLUUb3lf+orfddluZY/WIu+++W/Ly8gSiVw/wCdWG+mXAgAEqCsLx2muvFW04XYkmk8kkV199tUOxhoMgWPF31113yY033iia24C8//77ehECq/CMGTOshNzKlSsF7ce+U6dOKbG7aNEiJaS1IXXp2bNnGRFvydDByq233ip79uyxsj5/8cUXKk+j8Eeb33vvPStBqGeZk5Mjl1xyiXoQuPnmm0Ubqpfu3bsrP1GkgaC98MILRXM5UOIU7EePHq0Eq57HoEGDZNSoUXLFFVcIrK3gifLS0tJUkor2m54vlyRAAiRAAiTg9wQqNmLP1O4gAB9QzfppPnHihDk8PNz8559/qmx37Nhh1obezfA91CykZt0HVLP4madNm2YpWhsWVunefvttFaf7gGqTkcy1atUyv/rqq5a0yDMsLMw8e/ZsS5w29K98HZ35YP7nP/8xa8LKcow2+cZcrVo1syYCVdzixYuVf6QmRNW2JtbM48ePN2tWRMsxtitoN3xI9aCJYvNZZ51l1iyeepRZsxSaNWuiZXvgwIFmTWSq7eeee85cp04dM8rSw7/+9S/zeeedp286Xeo+oEePHjVrlmTzf//7X0v6iy66yKxNCjNros/KB1S7gM1oK4LRB3TMmDHKjzU9Pd2SB+Lq1q2r6qdZqM2axdqqbZq1WPU7Dli/fr3qA21Sl+X4ESNGqDjNBcNc2X6zZMYVEiABEiABEvBjAhF+r5CDuIK1a9eW6667TlnwNAEksH7COgdrmDFgxrwmmtSQNYaDYeWEhdI4RA6rqO6rOGTIEMvhGIbXzj+BtRTH6aF69eqCfddcc40eZbXEkDCG62HBg6USw++9e/dW60iIYXhYJWHZgzWza9euyjWgZs2aVvnoGxhuhv8n3Au+++47Fa2JZYHPK9wJYD1EwJA+LMBojybO1VC5biVFfVEOrK16QH7z5s3TN11ewvo8duxYNeMfTA8dOqTaAeuqKwHuBvXq1ZPXXnvNkhxD57AMIy8Mu8Oi+csvv6ihdQzva0JWHYMD0JbExERp37695Xgw0ESo2q5sv1ky4woJkAAJkAAJ+DEBDsH7uHMghPQJMRCg8I20DW+99ZY0bdpUiR3NaioYPtcsgVbJIEafeOIJiY+Pl6eeesqyT7PQqeFhzXopmiXU8geR2qpVK0s62xUM40NgoW7wdfz222/VULueDgIWIgwCDGIRw8lwEcBrmuwFHJ+VlSWatVeGDx+u/jSrrJpsZZyMBOGGvCFS8ZoqiFCITAQMwTdu3Ngq+8pO1oJPJnw0Icp14R8ZGWmVt7MNDJNjuN7IFHV7/vnnVRwmkqHPIOTXrFmjhuONYh/CXrOWqr7RyzG2pbL9pufFJQmQAAmQAAn4MwFaQH3cOxBCEG8QYxAlsIgaAyydzz77rLLW6ZZNTNLBpBf4KOoBVkptiFr5fcIiCZEKEdmiRQs1kxu+kvD/RMDxsGhiNrijAGEFayTEGfw84VNqrNvOnTuVVRV1xx9EKsp99913rdLp+euTj3Trpx4/depUNfEIllBtOF4JMkyygk8m/Crhj6oLQwjmP/74Qz9ULSv7Xk5YajGh66uvvlITrjAZqCIBXBcuXKgsqGCFAL9S+KzCso2+0lwDlNUTIhphxYoVlglWaAvarFuYsd/Ylsr2G/JhIAESIAESIAF/J0ALqI97CMLxhhtuEEw4wSx1Xazo1YIIg1g6duyYEpywNj722GOCIW3jELyeHnlBOGKyEEQhRKP2vlE1U3zLli3qGAzzQtQmJCToh9ldIh8IKohEWPKMQ9844N5771UCDoIWZcEqiGF523DgwAE1sQii2TZguB3W2enTp1t2oVyIu/nz56sy9B2PP/64EmmYUAULI2bBo37GgBfcw+LoSoD1GW3LyMiQf//7364cYkmj+aaqoXa4R6DdcJHA5CpYbeFCAesxxCUeIOACAeENsYt+Q8CbAGAxxgMIJlN99tlnovnQWvKvSr9ZMuEKCZAACZAACfgpAQpQP+gYCKGUlJQys99RNVj/8OUeWCIx7A5hgyFqDNVjRrm9AN9GvO4HQg3HQ/xAuF5wwQUCv0u8Lghf9cG6swCL5LXXXqssnRCbxgDrKcrBa4jgy4jhZuSPbdswc+ZMJXZhhbUNOBZ+r/DzxDs3EWA5hE+svtSPadu2rfL3hF8lfCchNCG0YaHVA6zA2uQqfdPpEj6tEM7gr1sxnR5g2HnppZcqn1YIWFiHUVe8Q/Sdd95RqeAOgbZhmB2cEY/XMMFKijLxoAGfULhM4DVMmCGvTdJSx8bExFSp3wzV5CoJkAAJkAAJ+CUBEyZI+WXNWKkyBDC5BQIUVtHKBFj68G5LRxOFKpMnjoF1Fv6LRiFY2bycHQd/TVhLIfb0AGuwNmPcavha3+etJSYfYdjddvIYyoffKuIhNI3h5MmTsnnzZiXwdcsy3mkKP1FYVCFe9eCpftPz55IESIAESIAEvE2AFlBvE69CeY0aNaq0+ESxEDXuFp/IF6LY0+IT5UCgaZ/JVMPu2dnZargb782EBdOXAZZPe+ITdQJvW/GJeFh74aMLKzImHMGl4JlnnpHOnTtbiU+k9VS/IW8GEiABEiABEvAFAVpAfUGdZVaKAKy32rs7lfDEq5PgBvDQQw8JfEMDMeBrTNr7XZWghj8u/HfhCwsXCwYSIAESIAESCGYCFKDB3LtB3DZM5sFwfDCEYGpLMPQH20ACJEACJOB5AhSgnmfMEkiABEiABEiABEiABAwE6ANqgMFVEiABEiABEiABEiABzxOgAPU8Y5ZAAiRAAiRAAiRAAiRgIEABaoDBVRIgARIgARIgARIgAc8ToAD1PGOWQAIkQAIkQAIkQAIkYCBQuTeaGzLgasUI4AXkeNE4g30C+C6C/mJ2+ykY6y0C+jcq2B/eIu64HPaFYzbe3sO+8DZx5+XxN8M5H2/utb028ErBBg0aOKwCBahDNJ7ZgU83lpaWlvsdds+U7v+54vv2eL0SRY/v+wrfsUc/eOMjA75vrX/XoKSkRPDn6IMH/l374KodPiKBV6fxfb3+0a+4T+HzxQy+J4APxOD3G58Ah87ZvXu3TJkyxWHFKEAdovHcjvvuu0+aNGniuQICOGd8FQhfDsK30hl8SyAlJUX1Q1JSkm8rwtKlsLBQiR57X9UiHu8SyM3NFXweF1+A44Oyd9nbKy01NVWSk5Pt7WKclwkcPXpUGdfi4uLUZ7+HDBnitAb0AXWKhztJgARIgARIgARIgATcTYAC1N1EmR8JkAAJkAAJkAAJkIBTAhSgTvFwJwmQAAmQAAmQAAmQgLsJUIC6myjzIwESIAESIAESIAEScEqAAtQpHu4kARIgARIgARIgARJwNwEKUHcTZX4kQAIkQAIkQAIkQAJOCVCAOsXDnSRAAiRAAiRAAiRAAu4mwPeAupso8yMBEiABEiABEiABPyJg1j6gkLtgnuT99J2UnjwupphYqdbuSom97W6JqOf4a0WebEJQClC8mPavv/6Sa665xiPs1q1bpz4v1bBhQ4/kz0xJgARIgARIgARIwB0EzNpX1E4Ne0BKDv0t5tycf7I8JbnHjkjud19KrQ+/lYiGjd1RVIXyCMoh+CNHjsisWbMqBKIiib/99lvZtm1bRQ5hWhIgARIgARIgARLwKgF8nz3loTukeO9Og/j8pwqaMEU4NfgeKT56+J9I7y2CUoDq+IqLi2Xfvn2C74vbhrS0NDl+/LglGmnxHVN8b3n//v2Sk6M/JViSyMGDB+3mBYsrvnmKT7QxkAAJkAAJkAAJkIA/ECjasUUbcj8h2rcxHVbHrGmknDkfO9zvqR1BOQQPWBCFgwcPlqioKNmxY4e88cYb0qZNG8H3rceMGaO+5Yvvjjdr1kxef/112b59u4wfP15KS0vVt8j37Nkjo0aNknbt2qm8Hn30URV/6tQplSfKgGgdPny4ErKJiYnKKortq6++GrtVQD2WLl36z5YIyszLy6NYtRCxXgFT8AkLC+pnI+tG++kWrgUEPlj5voNwXeCPfeH7vigsLFSVQF/wW/C+7w8YjXhdOO6Hgt9Xi/mfc9ZhqpJiyV+7UiLdYETD9YHrAvcr/TfEUblBK0Ah9KZMmSL16tWTmTNnyvz585UAnTRpkjRq1EiJTQAaNmyYrF+/XmJjY2XXrl1q6L5p06by6aefyrx585QAnTx5srRv314GDRqkhOstt9yieP79998SFxcnH3zwgdqePXu2LFiwwEqAIs3zzz9v4d+2bVvJyspS+VgiPbBy4MABOXr0qFXO8fHx8q9//csqDht//vmnFBQUWMWfddZZip0xEhbiLVu2GKPUOoQ9hL4xsPzg4I+be0ZGhupa9j/Pf17/vP8Z7/NY94f7P37LjYG/P4bfn3W/i+lUusSHh8m/4mONmKzWSzXfUP1eb7WjghsYccafKwLUpPkHmCuYv98n37x5s7JqfvLJJ6quK1askK+//lomTJggt99+uxKEuGgQ5syZo4TnTTfdJCNHjlTbiF++fLkSoGPHjpW+ffvK008/rS407Bs6dKj07NlTbrjhBjXEv2bNGpUHyj377LOVtRXpEPAEYHQBePPNN6V///7SpEmT0wn4rxUBXAAQyrSAWmHxyQas9+Hh4QLrPoNvCRRpM1jxkFi9enXfVoSlqxGazMxMqVOnDi2gfnA+wJ0uKSnJD2rin1XIm/+tZE17SyQ/z2kFI5qfK8mTqzYMD7dG3KNgmIMAhVaCIdBRCFoLaLVq1SxtNg6T1KpVS3Az1wPMxbDyICQkJOjRVsvIyEjRh12wIyLiNLY//vhDDdP36dNHbr31Vrnoootk5cqVVsdCSMG6qgds6396HJdnCKCv8EcBeoaJr9fYF77uAVHXBK8L3/cDaqD/nuC60Nf9o2ahWQteF877PfrqayX7/XfFqaUxMkpiut7klt9dXd+48rsRco52HTt2lIULF1r8qeCf2apVK6c9CGvpzz//rKyZ8CHFkDUCLJ7wEYUAPe+88+S3335T+TrNjDtJgARIgARIgARIwAsEwpNqSlyfe0U0Q5rdgPkW2vB8bPdb7e72ZGTICdBOnTrJiRMn5I477lBD6xCOvXr1csp44MCBcvLkSZUeE5tatmyp0nfu3FkNwSPu4YcfVsPqhw97/1UGTivPnSRAAiRAAiRAAiFLoPod/SX+oWFiitVceKpFn+YQFi6m+ASJbN1G6n61WEz/jOx6E1JQ+oC6AhATgaKjo7WHAgdPBXYywTHwbbA1LcNvEcP3rgzHjB49mj6gdtjqUZg8Bh9Q+B4y+JYArP3oB/pX+bYfUDpcgOADimuDwbcEMOMa93xMcHXlnu/b2gZ/6fBVT05ODv6GuqGFJakpUrBqmRQfOiBhCYkSdWFbifzXhWKCFdQNAROfoYV0H9AhQ4aEpg9oeSwrcyN3dAwnaZRHm/tJgARIgARIgAR8SSA8uZbE9vD+ULujNrtH9jrKnfEkQAIkQAIkQAIkQAIkYEOAAtQGCDdJgARIgARIgARIgAQ8S4AC1LN8mTsJkAAJkAAJkAAJkIANAQpQGyDcJAESIAESIAESIAES8CwBClDP8mXuJEACJEACJEACJEACNgQoQG2AcJMESIAESIAESIAESMCzBChAPcuXuZMACZAACZAACZAACdgQoAC1AcJNEiABEiABEiABEiABzxKgAPUsX+ZOAiRAAiRAAiRAAiRgQ4AC1AYIN0mABEiABEiABEiABDxLgALUs3yZOwmQAAmQAAmQAAmQgA0BClAbINwkARIgARIgARIgARLwLIEIz2bP3EmABEiABEiABDxBoCTtlOQvWyyFm/8UKS2RyPNaSfS/O0lEvQaeKI55koBbCfilAN20aZPExcVJ8+bNZdGiRXLVVVepbbe2nJmRAAmQAAmQQIASKNqxRVL/O0zMBXki+fmqFQWrV0j2jHekxugJEn1FhwBtGasdKgT8cgh+8eLFsm7dOtUHU6ZMkbS0tFDpD7aTBEiABEiABJwSKD64X0492l/MGdpv4z/iUx1QVKgW6cMfl/zfljvNgztJwNcEPC5A87WLIzc316qdtoLy4MGDkpOTY5XG3kZBQYFkZmZadh05ckQOHDggxcXFKi49PV1KS0vVelFRkWRlZVnSog6FhacvzpKSEjl06JDVsXpC1O348eMqT+PxZrNZDh8+rPbpaVFudna2IL/9+/e71Ab9WC5JgARIgARIoDIEMsa/Uu5hme+8IWbtt4mBBPyVgMeH4P/44w/56KOPZNq0aYrBtm3b5NVXX5VPPvlEVqxYoeJr1KghO3fulIcfflh69+5tlxVE4bBhw+TBBx+Ujh07yksvvSQQoAkJCXLs2DEZP368ihs4cKBccskl8vXXX8unn34q8+bNE5PJJI899pg8/fTTEhUVJcOHD5c6derIqVOnBKJ2+vTpKp/XXntNfv/9dzXcjzrhuIkTJyoh++STTypRCsEJ14AxY8bI9u3bVbkQvfHx8bJnzx4ZNWqUtGvXztIGCHCIXT1gG+IYfwxlCYAlhL3+IFE2BWO8RQAPXfjjueot4o7L0a8J9oVjRt7aA4MDAvoCvxHeDqU52VK0d2e5xZq135r8Xdslovk55aYN5AT4reB14T89iOsD/YF7Fn4/nAWPC9DLL79c/u///k+JsEaNGslPP/0kXbt2lby8PHnllVcEoq9NmzbKsjhgwADp2bNnmfqeOHFCxo0bJ4MGDZIOHTooQfjbb7/J999/L9HR0WqZkpIiV199taxdu1YJUAhJiE2IwqSkJIF19Nxzz5VvvvlG+vTpIz169FDloMz169dLeHi4bNmyRT7//HOJiIiQl19+WTIyMlSaX375RVq2bKkELOA+99xzsnnzZnXz2bVrl8yaNUuaNm1qEbxGAQrBjfL00LZtW+VSEBsbq0d5ZIl2G4UvCklMTJSLL764THmrV69WQty4AyIb/WUMsD5v2LDBGKXWr7jiCqlWrZpVPMsPDv64ieAhDYH9z/Of178f3P9SU0RSMsSk/YYiXJFUXaqFWQ9m7snJl4O5mpHjm69Fmp0WoMF8/7cVoPz9cf33R51EbvwHo9n4w2+H/rDmKHuTplCdS1RHR1YgfurUqUoM9uvXT2699VZ5//33BYLxmWeeke+++87yFAmBiTSrVq2SBg0aKOF2yy23KDWdnJysjoM4RHjxxRdl48aNcuWVV8q///1vad++vezdu1dGjx6trKr9+/eX6667TmDJhNjbvXu3sqDiaQmCC2ITccgDltV9+/Ypq9vgwYNV/hCdqBssoNiPULt2bbVE2osuukjlP3LkSJkzZ46KX758ubK4jh07Vm3jH1hMIVb1MHfuXIHobdy4sR7FpYEAeKG/wmxuqIYkXPUSATyA4cGsevXqXiqRxTgigJs5XIg8/eDqqHzGnyGABzL8wMKw4RMLaGaGZAz4j0iuc7c1U0KiVB8xTiLOOf9M5YNwDa5yGIFk8D2B1NRUdY+CYRD3LIwcQ/85Ch63gKLgbt26KashLJD4q1WrlqoPxCD+8COHgAvbnmJGIyAGMaQOYYkA6yn8LiH6IBJhibzvvvuUv+myZcukVatWaij8iy++UPlD+CIgLYRq9+7dVb0mTJig4mvWrKmspWpD+wdCyBguu+wyJTr1OJzwsAjBBcBZwI83LIR6wKx+WGZtLYb6/lBfwjIOPvo5Eeo8fNl+PATgj+eqL3vhdNkQOrhXsi983xf6bxT6whcCVGrXkZwGjaR49w6nMEzhERJ7fmsx/WO0cZo4gHfiYYDXhf90IIyE6A/8hpd3fVjb7T3UhiZNmiihNnPmTCX6UAxEKOJXrlypSoUoxB8Eqm1o0aKFGv6GmIRpHSr77rvvlrp16yqLKfxGMRkJAa9sgoUVw+CtW7dWfpo7duywiEe84glD4l26dJHIyEi1HzcUWFHXrFmjhq1h+YFQ1AN8Trdu3arqBmE7e/ZswdA6AwmQAAmQAAl4m0DCw0+UW2T8oCeDXnyWC4EJ/JqAVyygIACLI0yx8NPUAyYUYQj7vffeU36dI0aMsFhH9TT6EkPy9957r5r8gwlNN954ozzwwANqwhAm9mDyDwJ8RL/66ivlBwpLGnwZMXyvD9337dtXJk2apIbNoc7hkwlfSdTvzjvvlKFDhyphDJ9OzHpHgACFVfW2225TQ/rYd8MNN1CEKjr8hwRIgARIwJsEoi66RJL+T3tF4TOPiPbjJtp45+niTWFi0lyYqvcbKDHX3ujNKrEsEqgwAa/4gJZXK7z6CP40FQ1wX8UwOJyrKxIwlIUhduPwOcQmZtNjBj0CJiPBIvvCCy9YsoapXzcvWyIruAIfVbgRwPrLUJYAJovBvYFD8GXZeDsGftroh8pcm96ua7CXB/9PuCjR1833PY1X+mGUrF69euUOMXq6tsWHDkjeT99J4ZaN/3wJqbXEdOohkS3KjiR6ui6+yh8jojAyMfiewNGjR5WuwoeE4AM6ZMgQwbvcHQWvWUAdVQDxlf2BgwWzouIT5cGvzSg+EYcTGK9qwtA8wsKFC9XrotTGP/8AKgMJkAAJkAAJ+AOBiEZNJH7AEH+oCutAAhUm4BcCtMK19sABMTEx8uGHH6r3gOKVDpMnT7bMevdAccySBEiABEiABEiABEKWAAWooesxvIVXNzGQAAmQAAmQAAmQAAl4joBXZsF7rvrMmQRIgARIgARIgARIINAIUIAGWo+xviRAAiRAAiRAAiQQ4AQoQAO8A1l9EiABEiABEiABEgg0AhSggdZjrC8JkAAJkAAJkAAJBDgBCtAA70BWnwRIgARIgARIgAQCjQAFaKD1GOtLAiRAAiRAAiRAAgFOgAI0wDuQ1ScBEiABEiABEiCBQCNAARpoPcb6kgAJkAAJkAAJkECAE6AADfAOZPVJgARIgARIgARIINAIUIAGWo+xviRAAiRAAiRAAiQQ4AT4Kc4A70BWnwRIgARCkYC5uFiKtm6S4oP7RUpLJeLs5hJ5/gViiuDPWiieD2xz4BEI6St14cKF0r59e4mNjQ28nmONSYAESCBECRQfOSRpw4dJaVqqmPPzRUza/1HRYoqJkVrTP5ew6vEhSobNJoHAIRDSQ/CTJ0+W9PT0wOkt1pQESIAEQpxAaUa6pPTvJSUH9os5K1OkqFCksFDM2ZlSevK4nOjTVYqPHQlxSmw+Cfg/gaASoFlZWbJ7927Jzc21kMd6QUGBZTsnJ0e7V2k3LEM4ePCgZGRkGGJESkpK5NChQ3LgwAEp1oZ69IB02D5y5Ihaokw9IF/kz0ACJEACJOAZAmmjnnWesXYfzpz4mvM03EsCJOBzAkEzBL9o0SKZMWOGNG/eXLZv3y6DBg2STp06ydSpU6VBgwbSp08fBfvVV1+V6667Tu1DxMiRIyUsLEz27dsn99xzj9x1111qffjw4VKnTh05deqUErDTp0+XhIQE6devn6WMBx54QDCMP23aNJX3smXLZMmSJTJmzBi1zX9IgARIgATcR6BEG3Iv3r3deYbmUuUbWqpZR8PiE5yn5V4SIAGfEQgaAfrjjz/KI488osTljh07ZMuWLS5BveKKK+S+++6TkydPyu233y433HCDbNiwQQnWHj16qDwGDBgg69evV3kjAn6j48aNkz///LPcMjZt2iT9+/e3pGvVqpUqKyoqyhLniRVYb9EmY4iLi5NzzjnHGKXWN2/eLEVFRVbxjRo1ktq1a1vFwbq7c+dOqzhstG7dWiIjI63iK1u+2WwWk8kkvipfbwTLbyS1atVS58WxY8cUFm/0P/mfJmB7/uG6AP8VK1boiCxLd15/eqa25SPeL/p/8QKRDG2UKffMSFPr+FiJDNOcQA3hYH6R/PHl5yINGlti3XX/Q1/oAfeqkOLv4fu/zrUi5x9+U21HNSv7+1OZ8gO9//U2u2uZmZkpGBnGSLFx9Nhe/ibtYjpzNdlLESBxCxYskLFjx8oFF1ygBCKsn/Hx8TJ+/HgrC+gLL7xgsYD26tVLJkyYIE2bNlWthNCEhRMCc/Xq1UrEYkh/48aNMmzYMOnSpYvcfPPN6phmzZopoQoLq24BhRXW1gJ6/PhxmTt3roXi1q1b5eGHH5bGjc/cGC07uSJ5eXlSrVo1ZZUmDt8SgODA6ECMNrGDwbcE4BKEmzmujVAOJfv3SN5Lj4vA99NZ0Cyfsa++I2GNz3KWqlL78LCer018ql69unpYrlQmPMhtBOBmx4nEbsNZpYwgPHGPgoEN96tnn33Woo/sZRw0FtDOnTvLlVdeKStXrhSIUYi+WbNmqTbj5q0HCBxjwA+sHuAriieviRMnyt69e6V79+7SrVs3JTj1NFgaf5Cd5Y20devWlYEDB2JVhdGjRwuexHHzYihLACct+ISHh5fdyRivEsCPLPqB56pXsdstDBYe3J9CvS/M5/5L8rV7dnlWkzDtVUzx55wrpnD3/8RB8FCA2j1NfRKJayPUrwufgLdTqC5A8RuO33KjvrKTXM6oL3t7AygOlk0Mu3ft2lWp7qNHjyoAiYmJyqcTTYE/p+0QMsRqqfYOuV27dqn0sGxi2Bw+o7B4YmgZPqVGoaljQd4YntQnPcFqykACJEACJOAZAibtfhx3611iinZulY+7o79HxKdnWsVcSSA0Cbj/8dBHHCEYMZw+c+ZM5fs4ZMgQidCegiFIH3/8cbn77ruV5RL+Gsbw999/S9++fQV+CzAXI2B70qRJMmfOHDXE0rZtWzUj3ngc1iFWsQ/pMdx/8cUXK5Frm47bJEACJEAC7iFQ/c77pHjvLslfurBMhngPaEzP/yiRWmYnI0iABPyKQND4gOpUISRhjrc1/aalpUlSUpKezGoJszGG1SFY9QCraHZ2tpr5rsc5Wto73lFaDMFjUlKTJk0cJQnpeLyXFWKeQ/C+Pw1SUlJUPzi6bnxfw9CpgT4Ej2uD4TSBvMXzJWfu51Jy+ODpLyE1OVtib+krMdd19igijHjhdXz16tWjD6hHSbuWeWpqqiQnJ7uWmKk8SgAjz3hbkD4ED0PglClTHJZ5RnE5TBJYO9B4e8HZj6i9mzoErKO8bPO3d7xtGm6TAAmQAAm4j0DMDd0EfwwkQAKBSSBofEADEz9rTQIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SoAD1KX4WTgIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SoAD1KX4WTgIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SoAD1KX4WTgIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SoAD1KX4WTgIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SoAD1KX4WTgIkQAIkQAIkQAKhR4ACNPT6nC0mARIgARIgARIgAZ8SiPBp6SycBEiABEgg6AiYS0ul5ODfUpJ+SsKTa0l4o7PEZDIFXTvZIBIggcoToACtPDseSQIkQAIkYEMgf8USyZw6Xsy5OSImbZDNbJaw+HhJfHqERLW+2CY1N0mABEKVQEAOwW/evFl27drl1T47fPiwrFu3zmmZaWlpsmzZMqdpuJMESIAEgpVA3rJFkj7yaSk9flTMWZlizkzXlhlScuSQpD4+QPJ+/SVYm852kQAJVJBAQArQX375RdasWVPBplYt+datW2Xu3LlOM4EAXb58udM03EkCJEACwUig+MB+yXjleadNyxw/WoqPHnKahjtJgARCg0BAClC9a7Kzs+Xvv/+WUs3fCCErK0uKior03WodccXFxWqfvqOwsFBycrThIS3k5uYKtrHct2+fSqunwzI1NVVSUlKMUWq9pKREDh06JAcOHLAc06RJExk2bFiZtIwgARIggWAnkLfoB5HwcKfNNBfkS/6ShU7TcCcJkEBoEAhYH9Bff/1VYAmFEDRrPkaTJ0+W6dOnS2JiogwYMED13uzZswUi9aqrrpKpU6fKtGnTVDyGyZcsWSJjxoyR9957T44eParEZ/Xq1SUvL0/lExcXJ6+//roado+NjRX81apVSx0PoTp8+HCpU6eOnDp1SgoKCtQxBw8elLFjx8rMmTMtZ8/u3btl9OjRlu3IyEiBpRT5ezJANKenp1sVERMTIw0bNrSKw8b+/fstIlrfWbt2bcVS38YyPz9fiW5jHNabNm2q/e5Y//BUtnz0Z1hYmGKLvjQGb5Svl+er9vtT+Tjn0R84xxHI3/Pnv6P+x0M27k0YibEN7rz+9Lwrc/4X//WnaCeMyiKlsEgyik6v63lGh4cJ7j45mzZIQefT5xT2+dP9R6+rs/ajL3BdwPiBiVX+wl+vO5aVvf/qeThrv55GX/q6/Y0aNbLco/Q6hVL7q8pfZ+auJYx7+K2A4Q/XibMQsAI0KipKpkyZosRKv379ZNOmTdKtWzcZMWKERYAuWLBAXn311TJCzBYILJmfffaZupk8+OCDsnbtWkH+yPPzzz9X4ur5588MLW3YsEH69OkjPXr0UFlB8K5fv16JJtu8IcwgbPUACy1uWp6eEYpyIXaNAXH2ykU623iIQHtxtnkif6SzTVvZ8pEPjvVV+Tovln9mcETvWzDxdP+T/2kCtuefvh0o/LW7h0Ro17IxhBs29XMK+/3p/qPXV+etb2Opn//4UUX99Xpj3dgepK3s/Q/HIjgr/3SKM/+yfPKvyvl35kxy35q9c9Je7gErQC+++GJ1kaJRLVu2VE9AsHRCOP71119qHyw4eDqAYHQWrrzySssNBBZCDMfDctmuXTvLDy7S6JOQevXqJatXr5YZM2aodBCwsILaCyh/0qRJll2whtaoUUOSk5MtcZ5YqUj+FUlbv359l6pbkTyNaWG1jddmzOIGbi94unx7ZRrjQql8WBHQD0lJSRYEodR+S6MNK75qP9yEcG9r3LixoTaOV43XlONUp/dUJK2z9mdd1FZydmwWzfQhyVER6q9M2ZFREtemnVQ33P/cVb6xrIrkWZG0aD9+HzIyMqRevXqW3w1j2VivSJ4VSeuMv7EOFcmzImn9sXy4ydm2wXbbyMZ2vSJp/bH9tu3BdkXaZO/4ysZhNBmju/iDBdTR77ie/xkzhx4TIMtq1apZagq1jWF4BFhBFy1apP66du1qSWM0BWMoyxgSEhIsm/qTBJ5ucdPXQ0TEGa0+ceJEZTHFj8Fjjz0mrVu31pNxSQIkQAIhSSDmxp5iio513vaiQom5rrPzNNxLAiQQEgQCVoA66p3OnTvLqlWrlIXyhhtuUMngS3js2DH15IoIWC/LC7CwYqZ9ZmamUvJLly61HIKheQzBd+nSRVlIt2/fXq6vg+VgrpAACZBAEBKIaNhYEp8b5bRlSRPek/A69Zym4U4SIIHQIHDGrBck7YXpuXnz5koY6pNYmjVrJm3btpW+ffuq4V2IS31ihaNmI03v3r0F/qWwtmIoXQ/IB8Pqc+bMUUMwyBvD8GeffbaehEsSIAESCDkC0Ze3l2RNZGa8NUZKU06IYOQIQ3H1GkjCo8/wRfQhd0awwSTgmIBJG7o+PXbtOE3A7XnuuecEfppXXHGFVd3xSibMBDcOp1slsLOBYXgM3+M4Y8BMSMywNw7fG/c7WocPaP/+/QWvbGIoS6A8H9CyRzDGUwTs+YB6qizm65wA7kPwM4d/dKCEkpPHpTQ9TcKSakp4rdqBUu1y6+mKD2i5mTCB2wjY8wF1W+bMqEIE4AMKTaT7gA4ZMkRNFneUSVBZQLdt2ybffPONegXF5ZdfXqbNlbl5w/HfXsAsxYqKT3v5MI4ESIAEgpFAeO26gj8GEiABErBHIKgEKN7TiQlBgwYNcjg70R4ExpEACZAACZAACZAACXiPQFAJULw89+abb/YePZZEAiRAAiRAAiRAAiRQYQJBNwu+wgR4AAmQAAmQAAmQAAmQgFcJUIB6FTcLIwESIAESIAESIAESoADlOUACJEACJEACJEACJOBVAhSgXsXNwkiABEiABEiABEiABChAeQ6QAAmQAAmQAAmQAAl4lQAFqFdxszASIAESIAESIAESIAEKUJ4DJEACJEACJEACJEACXiVAAepV3CyMBEiABEiABEiABEiAApTnAAmQAAmQAAmQAAmQgFcJUIB6FTcLIwESIIHgJGA2mwV/DCRAAiTgCoGg+hSnKw1mGhIgARIgAfcRyPn2c8n9/ispPXlcE6ClEl6vocT27iNx3W9xXyHMiQRIIOgIBLUALSgokGrVqrm90zyVr9srygxJgARIwIMEUp95RAq3/SWSn28ppeTvvZL11hgpXPebJI140xLPFRIgARIwEgjaIfj169fL2LFjjW11y3pxcbH069fPLXkxExIgARIIVAJZH0ySwg3rrMSnsS0Fa36V7NkfGKO4TgIkQAIWAkEpQEtLS2X//v2Sk5Oj/vTWFhUVyd9//y0QkQhIl5aWpu9Wy9zcXIGFUw8HDx60yiM9PV2OHj1qdVxqaqrs3r1bcCwDCZAACQQ7AXNhgeT+8LXzZmr32ZxvPxNzyen7rfPE3EsCJBBqBIJyCB4icd68eZKRkSFffPGF3H///TJp0iT59ddfpUaNGnLy5El58803pUmTJnLvvffK+PHjpXnz5qrvBw0aJM8884xAVE6bNk2l37lzpzz88MPSu3dvmTVrlnK0f/XVVwV/I0aMkOPHj0tiYqJs27ZNhg8fLldffbXlPDpy5Ih89tlnlm2kzc7OlszMTEscV84QwEMC+JhMpjORXPMJgZKSEvWQxnPVJ/itCkVf4M9fJvmU7t0lrkw3MheXSMaO7RLWqIlVewJ5A/coBFwXvE/5vidhUOI9yvf9oNcgX3PHwb0K/QIjn7MQlAI0OTlZ7rzzTlm9erUSn8uXL5clS5bIJ598ItHR0fLll1/KnDlz5Nlnn5Ubb7xRFi5cKI888oiyYgJa06ZN5YknnpDXXntN2rRpowTmgAEDpGfPnjJ48GD57rvv1PD+nj17JC4uTj744PQw0+zZs2XBggVWAhRCdu7cuZY+QN7ooLy8PEucJ1ZgpU1JSbHKOjY21iK0jTsgnHWrsB5fv359qVWrlr6plrAo79271yoOG+edd55ERkZaxVe2fJywYWFh4qvy9Uaw/PqC6whBP1e90f/kf5qA7fkH4YkRFk9ff67yNx/YJ5KmPURrdTqveoxEhlk/MB7NL5SUQs3yWazF/7FeTEeOSSDcf1xpvz7bH+ITf+68/7lSvp5GX4Z6+eecc06ZB7PK/v7oTG2vP8QH6/1Pb7O7lnhAg57AX3kPzCYtgSsPsu6qm9fy+emnn5QAhYVyypQp6kcUohIBwgx+nLCSYoj9ySeflK+++komT54sSUlJcskllygrKISm/oQLyyiOgSCFaF22bJnKa9++fbJmzRrZtWuXbN68Wc4++2x544031D57/4wePVr69++vrK/29od6HKzX8fHxEh4eHuoofN5+XCfoB1wTDL4lUFhYqFyDcG34QyjNSJeT/W4Wc26O0+qYtIfe2p//JGExsU7TBdJOPAhgdK1evXqW34dAqn+w1RVGHv1hOdjaFmjtgfBPSEhQhjkI0CFDhij95agdQekDatvY2rVriz5sgn24meMPljZYJGHp+/PPP+WXX36Rzp07q21Y4ozmY/iFwqxsDH/88Yc8/vjjKurWW2+Vu+66y7ib6yRAAiQQlATCEmtI5PmtRUxOfkK0fVFtrwgq8RmUnclGkYCPCDi5e/ioRm4qNioqyjKZ6Nprr1VWSn3CEayj559/vhKgKK5bt24yffp0adasmRKfEKTwD125cqWqDYa98HfuuedKRMRprwUIWlg827VrJ3369FHDML/99luZoWw3NYfZkAAJkIBfEUh8/EXRfG80EWo9/K4qiTjtnaCJT7/sV3VmZUiABPyHQNAK0BYtWsjGjRvlueeeU6Kye/fuSij27dtXDc2/9NJLll64/vrr1RB6165dLXEPPvigmpyEYXcM0WMoH8IUQ5IXXnih3HzzzXL55ZcLhuDhF4pJShCthw8ftuTBFRIgARIIVgLhdetLnTkLJeKsZmKqrrkGaMPsJvxVT5DI81pJ7S8XSFhsXLA2n+0iARKoIoGgnIQEJhCDP/zwg2Xo/YEHHlA+nJgAZM+PCrPjO3ToYMEJX09MHoLV1NYH7p133lETiTCh6f3331f+QPB7gL8oJjMxkAAJkEAoEIDArDl1thTv3yPFB/fj3XYScXbz06KUftyhcAqwjSRQaQJBK0BBBD6exi8hYaa27WxtiNT58+dLr169BMP2tsFWfOr7IT71gFcwMZAACZBAKBIwaUIzsvk56i8U2882kwAJVI5AUAtQV5A0bNhQMIGoY8eOriRnGhIgARIgARIgARIggSoSCHkBiqF2BhIgARIgARIgARIgAe8RCNpJSN5DyJJIgARIgARIgARIgAQqQoACtCK0mJYESIAESIAESIAESKDKBChAq4yQGZAACZAACZAACZAACVSEAAVoRWgxLQmQAAmQAAmQAAmQQJUJUIBWGSEzIAESIAESIAESIAESqAgBCtCK0GJaEiABEiABEiABEiCBKhOgAK0yQmZAAiRAAiRAAiRAAiRQEQIUoBWhxbQkQAIkQAIkQAIkQAJVJkABWmWEzIAESIAESIAESIAESKAiBChAK0KLaUmABEiABEiABEiABKpMIOQ/xVllgsyABEiABAKQQNHObZI7b44U7dgiYjJJRMvzJLbHbRJ1fusAbA2rTAIkEGgEQkqALlq0SK666iqJi4sLtH5ifUmABEjAbQRyF8yTrCnjxZybI2I2q3yL9+2W/IU/SPzDT0jcrX3dVhYzIgESIAF7BEJqCH7x4sWSk6PdcBlIgARIIEQJ5K9aKpljR4k5J9siPo0osqaOl9xFPxijuE4CJEACbicQdAI0NzdXCgsLBct9+/ZJcXGxBdoLL7wgNWvWVNuI37Nnj6SkpFj2YyUrK0t2796tjjfuKCkpkUOHDsmBAwcseSKP7Oxswb79+/dT3BqBcZ0ESMDvCJg1a2fW1Anl1iv7/Uli1u5rDCRAAiTgKQJBNwT/3nvvydGjR5X4rF69uuTl5cn06dPVsPt9990nb7/9tkRFRcmwYcOkQYMGcuLECWnatKmMHDlSMEQ/Y8YMad68uWzfvl0GDRoknTp1UnkNHz5c6tSpI6dOnZKCggKVJ8To+PHjpbS0VOLj45WgHTVqlLRr187SXxkZGbJ27VrLdmZmpjo+Pz/fEseVMwQg5sE3LCzono3ONDJA1nBemzTfQJ6rvu8wPOzir6p9UXLssJRmZZbbIHNhgeTu3iHhZzUrN22oJSgqKlJNRl/g+mDwLQH8ZlT1uvBtC4KrdFwf6A/cr/DA6ywEnQBFY2Gp/Oyzz9TN4cEHH1QC8LrrrrNwWL16tbRo0UIgFgFr2rRpyuL5448/yiOPPCJIu2PHDtmyRXPO18KGDRukT58+0qNHD7U9YMAAWb9+vdSuXVt27dols2bNUiL2008/lXnz5lkJ0L1798qjjz6qjsM/bdu2FYhSiGNPBlh/jxw5YlUERPKFF15oFYeN33//XYk+4w6Icgh0Y4B1eNOmTcYotX7ppZcqUW/cwfKDgz9u7hhRQGD/B8H5nxCr9aRJ++9M+D09WwpKrX8ozo6IkgZHDoskJFkSsv+DoP+D9P4PsWMM/P1x/ffHyM0d6zD64Q99gt8PZ8GkKVTrO4+z1AGwDxbOiIgIZb1EdUeMGCEQSN27d5dbbrlFWUDDw8OVKIQIvPrqq5WVE4JrwYIFMnbsWLngggukffv2Kh6iDZYgiFYIUgzPb9y4UVlQGzVqpCync+bMUWSWL1+uBCjy0AN+vNPS0vRNmTx5stx7773SpEkTSxxXzhCAhRiTxNBHDL4lkJqaqvohMTHRtxVh6eohAPeSqj64lqadktQBt5/2/3TC1VQ9XpLe/UjC6zV0kio0d+HHFWIcBghaQH1/DqSnp0uNGjV8XxHWQI0o4x4VGxurBOjQoUNl6tSpDskEpQU0ISHB0mB7N4j69evLF198IX/++acsW7ZMBg4cqIRh586d5corr5SVK1cqMTp37lxl3Zw4caLAkgkR261bN5kw4YwPlbEsS6GGFQz3161b1xIDcQxxRYFlQWK1gv4iHyskPttAX+j94bNKsGBFANcE3FKqet8Ir1VHIhqfLUXbNzslG167rkQ15EOyPUi6exD6wt7vi71jGOc5ArxHeY5tZXLW71OwbZZ3fYSko93//vc/5bsJX80nn3xSzjrrLOU3iklKsHJ27dpVnn32WRUHMzKGnTEE36VLF4mMjFT+oeWZlivTcTyGBEiABDxNIGHoc+UWUeO/Y8pNwwQkQAIkUBUCQWkBLQ9Ix44dBa9kulcbCsewFvxBL7vsMsFQI6ybM2fOlJMnT8qQIUPUcH7fvn1l0qRJgqF2KHr4ccLPFMKVgQRIgAQCiUBki/Ok1vtfScoDt4kpJlbMebmq+liXqGqS9NIbEsHJR4HUpawrCQQkgaDzAa1IL2CmFszEMTExVofBDxF+DPpQC3bCDxSvXCpvyN1PH2dLAABAAElEQVQqIzsbo0ePlv79+9MH1A4bRMGfB363VR1qdJA9oytAAK8oQz8kJZ2ZiFKBw5nUjQTwoIy3Q+DacFcozcyQ/N+WS9G2v0S72QmEafRV10hYDfa3M8Z4xR8mktarV6/cIUZn+XCfewjAVz05Odk9mTGXKhHAG4igkTCPA6PHMOJNmTLFYZ4haQHVaURHR+urVkt7IhNi1F681YHcIAESIIEAIRCWkCixnXuK4I+BBEiABLxMICR9QL3MmMWRAAmQAAmQAAmQAAkYCFCAGmBwlQRIgARIgARIgARIwPMEKEA9z5glkAAJkAAJkAAJkAAJGAhQgBpgcJUESIAESIAESIAESMDzBChAPc+YJZAACZAACZAACZAACRgIUIAaYHCVBEiABEiABEiABEjA8wQoQD3PmCWQAAmQAAmQAAmQAAkYCFCAGmBwlQRIgARIgARIgARIwPMEKEA9z5glkAAJkAAJkAAJkAAJGAhQgBpgcJUESIAESIAESIAESMDzBChAPc+YJZAACZAACZAACZAACRgIUIAaYHCVBEiABEiABEiABEjA8wQiPF8ESyABEiABEnAXgaIdWyV/9XIpOXZEwmvVlmqXd5Co1he7K3vmQwIkQAJeIUAB6gbM69atkwYNGkhsbKxs2rRJrrnmGjfkyixIgARI4AwBs9ks6a+9KAVrV4o5N1ektEQkLExy5n4hEfUbSc2ps8WkbTOQAAmQQCAQ4N3KDb307bffyrZt2yQtLU2WL1/uhhyZBQmQAAlYE8gf+bTk//KTmLOzTotP7C4tFcnPl+J9uyXt+UcFIpWBBEiABAKBAAWo1kslJSVy6NAhOXDggBQXF6t+wzIrS7vR/xMKCwslJydH31TLgwcPavf+fEtckyZNZNiwYZZtrpAACZCAOwgUbdkopVs3Os2qaOc2KVj9q9M03EkCJEAC/kIg5Ifg9+3bJ8OHD5c6derIqVOnpKCgQKZPny579uyRqVOnyrRp01RfLVu2TJYsWSJjxoyR1NRUefTRRyU+Pl4dExUVpdLs2LFDxo4dKzNnzrT0b1FRkUqvR0DYQvDij6EsAVhwyKYsF1/EoC/YH74gX7bMgl9/xpNy2R2GGFhGcxf9IJGXXW2I5aq7CZTC6qwF3KdMJpO7s2d+FSTAe1QFgXk4Oa4PXeOgb5yFkBegGzZskD59+kiPHj0UpwEDBsj69eulRo0aDrlNnjxZ2rdvL4MGDZKMjAy55ZZbHKbdvHmzyl9P0LZtWyVao6Oj9SiPLCGsjxw5YpU3BPOFF15oFYeN33//XQlv446mTZsqv1ZjHCzC8HG1DZdeeqnoIlzfx/KDgz8emE6cOKG6lf3vw/N//17JLiqWTZma76dNuLRGnET94/tZcPCA6i9ef8Fx/Rm7mtef4+sPI5TGwPPf9fPfyM0d69nZ2YI/3djmLE+TplCdS1RnRwfBPqj11atXy5YtW2T37t2yceNGNYxet25dKwvookWLLBbQvn37ytNPPy1t2rRRBIYOHSo9e/aU+vXrl7GApqenCyYp6WHhwoXy0EMPSePGjfUoLg0E4OYQExOjza2gd4gBi09WMzMzJTw8XOLi4nxSPgs9QyBr3GgpWbrgTISDtYi2l0v1keMc7GW0OwhglCxXmwQGIwUtoO4gWrU8IMxhXGHwPQHMg8HvNwxsEKBPPfWU0lGOahbyFtCJEyfK3r17pXv37tKtWzeZMGGChZVxKDgvL88SHxkZKcYnrogIxxhxk+rUqZPlWIjdatWqqQ6yRHLFQgA+teAD4cPgWwJ4isWDgKet9b5tZWCUXnzVvyV73UqRnGzHFdZu+rEdOrK/HBNyyx59CB7XBQWoW5BWKRM8DPAeVSWEbj0Y+kgXoOVdHyFvZsKQMobgu3TpIgC3fft25b+QmJgox44dU0+66B0IRz3A8vnzzz9rE1BLJSUlRf788099F5ckQAIk4HYCkdq7Pk2164qmeBznrT28xdzQ3fF+7iEBEiABPyLg2HTnR5X0ZFUwnD5p0iSZM2eOepqFjyZmxMMiinXsh3n/4osvVr6bqMvAgQPlhRdeUPvgwdCyZUtPVpF5kwAJhDgBkzbKEjPmHcnt200kqppIYcEZItqQV3hi0un3gHrYt/xMoVwjARIggaoRCHkBiuHx66+/XjnNJiQkWNEcNWqUehUTfBqMw+zYxlA9fE/gH2f0VzTOgLfKjBskQAIkUAUCpugYqTv/N8md/63kL10opadSJCwpSap1uF5iu98iYTGxVcidh5IACZCAdwmEvAAFbghIW/Gpd4Mz52Zn+/TjuSQBEiABdxEwaW5CcTffrv7clSfzIQESIAFfEAh5H1BfQGeZJEACJEACJEACJBDKBCokQDET/PPPPxcMTeMdk3h/pD4jMJQhsu0kQAIkQAIkQAIkQAKuE3B5CB7fOu/atauaGQ7Rifde4gtCeBH7N998I/Xq1XO9VKYkARIgARIgARIgARIIWQIuW0AfeOABufLKK9VXNvSXqH/44YfKf/Kzzz4LWYBsOAmQAAmQAAmQAAmQQMUIuCRA8aLXtWvXqqF342QdfC0IXwGaP39+xUplahIgARIgARIgARIggZAl4JIAxSuIMFMcn0m0DfiEpfE1RLb7uU0CJEACJEACJEACJEACRgIuCdCoqCi58cYb5fHHH7d81xxW0dmzZ8uUKVPUV4SMmXKdBEiABEiABEiABEiABBwRcHkS0vTp06V3795y2WWXqS8GXXfddVJUVKQ+Y/nYY485yp/xJEACJEACJEACJEACJGBFwGUB2qBBA/U99F9//VV9Lx1WUXyeEn+ZmZkOX+RuVRo3SIAESIAESIAESIAEQp6AywL0jTfekGeffVb+/e9/qz+QKy4uVhOTduzYIZ9++mnIwyQAEiABEiABEiABEiCB8gm4LEDfffddyc/Pl5dfflnlumnTJrn33ntl165dMm3atPJLYgoSIAESIAESIAESIAES0Ai4LEC///57ueGGGwQz4s1ms4wePVquv/56mTt3rjRp0oQwSYAESIAESIAESIAESMAlAi4LUPh6/vLLL0p0Ygb8+++/L3fffbdLhTARCZAACYQKgZKUE1K0ZZOU5uVIeN0GEnVBGzFpD+4MJEACJEACZwg4vSuuWbNGsrOzz6TW1l588UV58skn5e+//5aff/5Z7cML6Vu3bm2VjhskQAIkEEoEzNonijMnvCL5v/4i2qtCxFxSLKbIKE18RkrSqxMlssW5oYSDbSUBEiABpwScClB8fhMvmrcXIET18J///Ee+/PJLfTPglwUFBVKtWjXVdsz2b9myZcC3iQ0gARLwLIFTg/tJ8d5dIqUlloLMeXli1rZODbpHaowaJ9FXdLDs4woJkAAJhDIBpwJ0w4YNyt+zPEDB9CWkiRMnqldLXXPNNbJ161aJi4ujAC3vBOB+EghxArk/faeJz52a+Cy1T8KsWUfHj5aomd9IWFx1+2kYSwIkQAIhRMCpAI2MjLRCkac9zZ86dUq9fgk7SkpKJCMjQ1JSUtSXkqwSe2kDr4LC7HxYKo8dOyaNGjUq82nQgwcPSnJyshKTqJZ+TExMjGBf7dq11T5YPvfv368EZ2FhofTo0UO9dF9vCtp/5MgRSUxMlFq1aunRann8+HGVR/Xq/HGxAsMNEggBAnk/fuNYfP7TfrN2Tyn8Y61Ed+gYAkTYRBIgARJwTsCpADUe+sknn8jDDz9s93vwDz74oM8E6Pbt2wXvKIWoxMvy9+zZI2+99ZacffbZsmLFCvWKqBo1asjOnTtV/fE1Jxwzfvx4zVhRKvHx8eqYUaNGSVZWluzevVuJ6nr16smqVavUC/b79esn8+bNk48//liaNWumLKN4H+rTTz+teDzxxBMC8QpL8EUXXSRDhw61oIMLwyOPPGLZRr0g2DHE78lw9OhRVY6xDFhzUX/bAB74qpUx1K9fv4zIzsnJkb179xqTqfXzzjtPbB9WKls++gQcfVW+3jiWX189tOG8wMMVgjf6P1D5Rxw6qFddLY/mF0pKYbFVXFxugTTb+peYzmllFV+R6w9++bbBndefnjfPf8f3P7wFBn+bN29WBgryd9/9vzLn3znnnFPm96uyvz+VKT/Q+19vs7uW0FGYOwRNBiOls2DSLiS4KJUbcEPq3Lmz3HPPPeqTnIsWLZK1a9fKa6+9Jn/99ZfUrFmz3Dw8kQA3AQg8iEOIO7wWCnUbO3asqifq16ZNG/UjOmDAAPn222+VAMUxs2bNkqZNm6qX6ONl+hChzzzzjHTv3l0wBI93nyYkJKg2v/TSS6ociFyc3HfccYcsXbpU3n77bYFl9Pnnn1fA8bJ+rOsWUlhMZ8+ebWk6LKyDBg1SllpLJFcsBCDkYc02mUyWOK74hgDOa/RDdHS0byoQQKXmDrhNJC3VeY3DIyTy7gcl8qb/OE9nZy9u5PjDtcHgWwJ4KMMIWWxsLO9Tvu0KVTpGQHmP8oOO0KoAIwXuUTBIQYBCCzl7T7xLFlAMs2N4+5VXXlHCCUPWsCoOGTJEWf7GjBkj48aN8xmBxo0bK/GJClx11VXyzjvvKEsdQOD1UQiYqY90v//+u2CYHBZOiE8ExMPf1VHAjzCE6bJly9RkK7x8H7odN6Jt27ZZXkeFd6TacoBgfeqppyxZ4/2pKB/ClqEsgfT0dMUnPDy87E7GeJUAfmTRDzxXy8de0uoiKVixxGlCkybk4y++RKIqce2jL/BwhhEbBt8SwGsI0R+4Lvig7Nu+QOkQOrxH+b4fUAMIUDwMYLQV/VLe/KAwV6qNJz0oWv0pAybnlStXqkMvv/xysTcs5Eq+nkgDqw2EJwQnhnPxpwfcwHWTcEVOWDxh4Y0AGN5He/WvQSFf+IMCtB4g1HGDYiABEggdAnG39xdTdIzjBmtuJabYOIk8n6+rcwyJe0iABEKJgEsCFOITvo0QXhjfh1Vxzpw5ygI4f/58iyXRV+AwkQi+mwiLFy9WQ+4YAscXmnShDN9F/J17rvN38UG8Qqgaw8mTJ5Vf6ODBg+XKK6+0WEshZjFUv3z5ciVs8VQMqzCseAwkQAKhQyBKE5aJz42232DtXaBhSclS+8NvxaQNwzOQAAmQAAlU4FOckydPlp49e8q1114rmHR0ySWXqKFSiDBM0PFlgNgcMWKEGhbH8Pbrr7+uqoN6jhw5Ut577z0lnJEGaWGldBQgtOE/ahShGKKH0IQVFPmfrfmaYmj90KFDKh4+p3369BHMqr/11lvVPkf5M54ESCA4CURffa0kjZ0q2R9OleI92iuZELTJhjEdu0h1zffTpD3cMpAACZAACZwm4PIkJCSHhQ9/EGEHDhyQhQsXSqdOneSss87yGU9MQpowYYL6NCiss/Z8pNLS0iQpKcnlOkJ8wupr67+AoXX4xNmbwY6y4apQnu8ifED79++vrLMuVyiEEsJ6jD4sj2MIIfFZU/G2BvRDRa4dn1XWzwo2aw/meO1SmPZQ6o6A+y7uS/bub+7In3m4TgC/A5gXgXkE9AF1nZunUqampqo3dngqf+brOgFM0IZ7o+4DihHhKVOmOMygQuNBmHiDyTrwiUSA8MTrjXAxXnjhhQ4L8dYORzfniv6A2hOYaAMEpqPgqGxH6RlPAiQQvARMmnA3uUl8Bi8ltowESCCUCbgsQPGFoOHDh6uhbFtgvvwUJ4bHje/ZtK0bt0mABEiABEiABEiABPyLgEsCFDPJ33zzTfXOT7yUHeZVY/DlMARmobdr185YHa6TAAmQAAmQAAmQAAn4MQGXBCj8j+AP1qtXL/og+XFnsmokQAIkQAIkQAIkEAgEXHoNE97/edddd6nJPnzHZSB0K+tIAiRAAiRAAiRAAv5LwCULKKqPT1G2atVK8DomTDgyzhDv2LGj+kqS/zaTNSMBEiABEiABEiABEvAXAi4L0Jtuukn078Hbzipv3Zpf9/CXDmU9SIAESIAESIAESMDfCbgkQLOzs2XTpk2ydetWOf/88/29TawfCZAACZAACZAACZCAHxNwyQcUL57HOz/xnXUGEiABEiABEiABEiABEqgKAZcsoCgAn7TEZyafeuop9e13fHZSD3Xq1FH+ofo2lyRAAiRAAiRAAiRAAiTgiIDLAnTo0KHqi0ePPvpombx8+SL6MpVhBAmQAAmQAAmQAAmQgF8TcFmAHj9+XPApTnuB3+22R4VxJEACJEACJEACJEAC9gi4LEAdfR/dXqaMIwESCGwCZu3jE+YjB6U0PELM8fFiinD5VhHYDWftSYAESIAEvEKAvypewcxCSCAwCJTm5UrW5LGSv2KJYLyjRPs7ERYm8fc+IrE9bwuMRrCWJEACJEACfk/ApVnw/tSK1NRUWbZsmder9NNPP7n0FoCCggKv140FkoA7CJhLiuXEzddI3uL/iTk7S+SfP3NmhmS+/bqkPNRHzCWQpAwkQAIkQAIkUDUCASdAjxw5IrNmzapaqytx9KRJkyQzM9PpkRMnTpTVq1c7TcOdJOCvBNLHvKhVzSRSXGS3isWHDkjeT9/Z3cdIEiABEiABEqgIAb8XoPj2/LFjx8q0qbi4WPbt2yf5+flW+2CB3Lt3r+Tk5FjiIRyRXg+Fmn8bXq6POKTLyMiQ9PR0fbekpaUJJl05C0iP8vV8Ue7+/ftVfsifgQQCiUBpRroU/v6biLnUcbWLCiVr5hQtiZM0jo/mHhIgARIgARKwEPBrH9Dp06fL999/L82bN1eC8a233lIVxzD84MGDJSoqSnbs2CFvvPGGtGnTRtavX6/eV9qsWTMVj1dGde/eXd555x2VR58+fdTxM2bMkOjoaLn00ksFVkuIyQhtksW7774rr732mkWQIp/XX39dbGf5T506VebPny+NGjWSkydPyrhx42TPnj2ye/dudWy9evWkbdu2qiyIVOzXQ1FRkUoDkevJAEa2FltMJMPnVG3DoUOHLEJa35ecnCwJCQn6plpCZB89etQqDhuNGzcuw6iy5YMP+qJmzZo+KV9vnK/a76vyC7dtluI8zX0k97QLSXJUhMRHhOvVUcsCTXgey8iUw0t+EVNyTcs+d/a/nmmo8dcfZB21v1Rjj4dx3GNsA/m77/6js3V2/qGvSjRXFNwLTSaTW+9/rpSvp9GXod7/devWVUYjnQeWlf390fNw1v96Gn0Z6Pz1drhrifsUjHD6deIsX78VoPj0J8TnnDlzlFj86KOPZNWqVdKgQQMlGKdMmSIQejNnzlRiEAL0448/lpdeeknatWunTkAI0G7duqk/iFAIUNzIFy9eLBhST0lJUdbSL7/8Ugme0aNHK1E5fvx4BW/YsGFK1F522WUWhhCOixYtkq+++koJ4B9++EHmzp0rjz32mPzvf/9TglcXnzgINyqjEIRoRh3w58mAzre1xEJI2ytXP1mM9UG9bdMizjZPHIN0uBEbQ2XL18vVl8Y8vVG+Xl6olV+k9W0JXrP2z6vWSu28cg1RGEcwa0P0JoOV3539H6r8cb0Yg+35B8Z4DZ6nrz+9DrblI57X3+n7H/oCLBBw3+P57777v4Kq/VOR8w/XBfrAGCr7+6PnUZHyA73/9Ta7a6n3h22f2MvfbwUovjsPCyUslQj9+/dXy82bN0vDhg2V+EREy5Yt1XfqIQwhWjFZaMGCBSptVlaWbNmyRS6++GI1NA5rJEQnLJcQsljHsnbt2io90j7//PNqHVa4a665RolVowD99ddflYXuzTffVOngAoC6DhkyRG3b/tOiRQsrn1WI3KSkJCV4bdO6cxsWRFdDRdKCvSuhInka08IaHa+99sfW6qyX6eny9XIcLYO1/JJmTSXl8xnazHfHk4yiw8OkRUJ1qXPDjWLSHmacBWOfOkuHfRVJG6z8y2ME4Yl7YZMmTcpLqvZXhGlF0oYqfx062g8LD9y2YACxffDW01WEaUXSkn/Z3x9YO2GxNIaKMK1I2mDkb+RW1XWMCsTFxak/PAQ4+h3Xy/FbAZqYmGg1LAwxqft7Gt9JarwBQDT27NlTCUQ0sFevXkqsIg0sobB8wrcT63owflK0Vq1agiFgPeCmrz/p6nFQ9xCwyJuBBIKFQHhSskRe2FYK167STNoORKj2OqaYTj3KFZ/BwoTtIAESIAES8BwBv52E1L59e2XRxJMmwrRp0+Tnn392SAJWxVatWqkJS1jCL2TMmDEWAdmlSxdZsmSJGlK/9tpr7ebTsWNHWbhwoRK+eMpdunRpmW/cwyoKv1P4UqKcgwcPymeffaaehDG8ztcw2UXLyAAgUOPpEafFpyY0y4SoahKWkCjxDw0ts4sRJEACJEACJFBRAn5rAcUw7C233KL8NiH2MNyBYe5du3Y5bOP9998vI0aMkNmzZyufkDvuuMMyvAdBir86deqI0eppzKxTp06yfPlywXGwfHbo0KGMpbNGjRpq/5133mlxPn/uuedUNhdddJGMHTtWiVBYYhlIIJAIQGDW/XGlpL/6Xyn8c52YlRA1iUmbGR/d4XpJeOw5MdkTp4HUSNaVBEiABEjALwiYtCFlfPDEbwOGxPEXGxvrch3hDwqLaGUDhvvhbxUZGekwC/g35OXlKX9FYyJYQHFcmIMfaviAwp/VVV8uY96hsF6eD2goMPCHNpZqL6E/9ddGCdNmwie1ulDCYuP8oVohWwe4A+HeggdzBt8ScMUH1Lc1DK3S7fmAhhYB/2ktfEDx9hz4gUIjwWiICeOOgt9aQPUKQ8w5E4J6OuOyKuIT+bhyk4e/qb10Rv9UY524TgKBRCCsuvb995bnSZg22YjiM5B6jnUlARIggcAgYMfZKzAqzlqSAAmQAAmQAAmQAAkEJgEK0MDsN9aaBEiABEiABEiABAKWAAVowHYdK04CJEACJEACJEACgUmAAjQw+421JgESIAESIAESIIGAJUABGrBdx4qTAAmQAAmQAAmQQGASoAANzH5jrUmABEiABEiABEggYAlQgAZs17HiJEACJEACJEACJBCYBChAA7PfWGsSIAESIAESIAESCFgCFKAB23WsOAmQAAmQAAmQAAkEJgEK0MDsN9aaBEiABEiABEiABAKWAAVowHYdK04CJEACJEACJEACgUmAAjQw+421JgESIAESIAESIIGAJRARsDVnxUkgQAkUbtss2Z++J0XaUooKJKxmbYntfafEdu8tpnBekgHaraw2CZAACZBABQjQAvoPrIKCAvnmm2/k448/rgA+66SHDx+WdevWWUdyiwQMBPLXrZTUpwZK4ZoVYs5MF3NenpQcOiBZU8bJiV7Xibkg35CaqyRAAiRAAiQQnAQoQP/pVwjPBQsWSHJycqV7euvWrTJ37txKH88Dg5tA4fbNkv7CUJHCgrINLS4WsxafNuLpsvsYQwIkQAIkQAJBRoACVOvQ3Nxc2b9/v1x33XXqT+/jtLQ0OX78uL6plhkZGVKsiYUjR45Y4lNTUyUlJcWyzRUSsEcg+9P37UWfiSstlSJNpBbt3XUmjmskQAIkQAIkEIQE6HCmderixYtl8+bNcuDAAYmJiZGrr75axowZIxCb6enp0qxZM3n99dclPDxc+vXrJ82bN5ft27eruPnz56th99jYWMFfrVq1rE4TCNh58+ZZ4k6dOiU5OTmSnZ1tiePKGQJFRUWKT1hY8D0bFW7ddKahDtbMhYWS/cdaiaxT30EK70WXlJSI2Wzmueo95A5LQl/gwZf3DYeIvLYD9ygE9IXJZPJauSzIPgFeF/a5+CoW7oz43UC/lGpGFWeBAlSjc9NNN8mGDRvk0ksvlW7dusnIkSOlUaNGMn78eAVx2LBhsn79ernssssUy/bt28u4ceNk1apVsmnTJvn888+VOH3++efLsIYAfffddy3xrVu3VgIrKyvLEueJlUOHDsnJkyetsq5evbq0bNnSKg4bEN/6TVXfifbXrl1b31RLCOedO3daxWEDbYqMjLSKZ/l2+KekyTlRZYX15qxcKSo1K37msDyRDRul0TkX+AV/3ED0c5X9z/Of1z/vf1Y3ej+5/+MBzRj4+2Pn98fB77+RmzvWIUDx54oANWlK9fQvnztKDuA8IDp1AXr77bcLxGSbNm1Ui+bMmSO7du2SF154QW6++WaZMGGCsorOmDFD/Tg/8cQTKh38PzEJ6dVXX3VIYvTo0dK/f39p0qSJwzShvAMW5/j4eCXog43Dyftvk5KD+502y1Q9XhKfflmir7rWaTpv7IRbCaz+SUlJ3iiOZTghUKhZxnFTx7XB4FsCcNnC6Fi9evVoAfVtV6jS4QJXlbkbftCEoKnC0aNHJSEhQeLi4pQAHTJkiEyZMsVh+8qaYxwmDZ0dGEY3WgRx8zc+YWGYHgFWP+zTQ0QEDco6Cy7LEoi5sYeYok+fO2X3/hOjPclHtTltaXeYhjtIgARIgARIIMAJUIDa6cCOHTvKwoULlYLH0+7SpUulVatWZVJefPHFsmbNGsnMzFRpkY6BBBwRiLulr4TXbyji6EFFszbWeHWihMXEOsqC8SRAAiRAAiQQFARosrPTjZ06dZLly5fLHXfcoSyfHTp0kF69epVJCQHau3dvNTGpWrVq0rRp0zJpGEECOgFTVJTUnPaZpA69T4r/3idm+C2VFCurqEkTpfFDX5BqF5x2+9CP4ZIESIAESIAEgpEABeg/vfryyy9b+jcxMVHefvtt5d8ZHR1tNcHmu+++s6TDCmbF9+nTRwlVfWjeKgE3SMBAALNma779oRTt2y1F2qx4c36eZhVtJNXaXq4J0WhDSq6SAAmQAAmQQPASoAB10reuOvxHaZYtBhKoCIHIpi0EfwwkQAIkQAIkEIoE6AMair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkQAEair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkQAEair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkQAEair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkQAEair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkQAEair3ONpMACZAACZAACZCADwlQgPoQPosmARIgARIgARIggVAkEBGKjWabSaAiBMz5+ZL383zJX7lUStNTJbxOfYm5oZtUu/paMZlMFcmKaUmABEiABEiABDQCIS9AN2/eLNHR0dKiRQueECRQhkBpTracGtxPStNOiTk3R+0v3rVdCtatlPCkWlLro2/EFB7yl1EZbowgARIgARIgAWcEQn4IHgJ0165dzhhxX4gSMBfky4le10rJ4QMW8WlBUVgoJSeOStp/h4rZbLZEc4UESIAESIAESOD/2zsPOCmK9P2/mxMbYHclI1FElCT6I4ikE1AUETiBU0A9OfEUUcEjmFCQUxFMJ6CiHArCAXciIn8kyAGnJImSc4Zll2Vz3p3/PKXd9szOzO7CpJ156vOZ6e6q6grf6vD0W1XdZRPwewHap08f6dq1q04qLS1NTpw4IUVFRbofViAyzp07J0lJSbo/4mRlZUmBWYycPn1aSkpK9DCuVH4COSuWioSF2a+I+ZgoPHRACnZvtx+HISRAAiRAAiRAAqUI+H3f4aeffioJCQny0EMPyaxZs2TFihVSp04dSU5OlmnTpkm9evUkMzNTRo8erUQpBGejRo1kypQpcvDgQXnrrbeUf61ateTYsWPy3nvvSf369XXQiL9v3z59Ozs7WwnW/Px83Y8rvxMoLi5WfAIDPf9slLN+tUgZ7WTKzpQc89hQaXbL75XwkTU8UGGMK49VzzcoHnbxY1t4R1ugFGgLjgH3fHvgOsXzwvPtoJVAu05hWVbvoN8LUA3alStXZPXq1bJkyRIJDQ2V5cuXy9KlS+WZZ56RH374QZo0aSIvvPCCQCCNGzdO0HWPi8/Jkyflyy+/VKIT8adOnSofffSRlqzq3h86dKi+3aZNG4GVNSoqSvdzxQrE8NmzZy2Sjo2NlVatWln4YWPz5s2lTmCIbAhxo8vIyJCdO3cavdR6u3btzIZCS0uhT+SfelmMU4wyioplZ/qv40CNEP7v9AnJS001eqmHEV/gj+M99be6+V37+/vxz/pbnNM8/r33+l9YWGjRVj5x/3HT+WcBzgkbOTk5gh8EKO4fjlyAWaH69QC2999/X1lAo6OjZf78+dKiRQvFK88883n//v1KkD733HPKLzExUS3RRd+yZUvVdQ9L6FdffaX8L126JIMHD5ZVq1ZJUFCQns6ZM2fUOv7++c9/ymOPPaYsq7onV3QCsDZDnHuDBTR9/NNSVFb3utlSG97/TxL16FN6HXxlBQ9KOI5xbtB5lgBusBjq4+oHV8/WsnLkjnsDerbi4+NpAfWCJktPTxcYV+g8TyAlJUVdoyIiIpQAhXZCz7I9Rwvob2Sgw2Hx69u3r01Wt99+uxKdWiBuyngiNrrc3FxlPdXEJ8Iwwx7WU81hOyQkRP00Py5/JwDhGRwcrAv430PcvxbZ/W7JNM9412a/2yyBuayRd3T3yfaEhR8/HK90niWA6xOsCWwLz7YDctesbWgLdsF7vj1wz+B54fl20EoA/aOdG2WdH54faKeV2sPLzp07y6FDh6RmzZrSvHlzgdVywYIF6gLTrVs3ZQ1t2rSpCoPF88CBA6rEiHf06FG1vmbNGmndurWHa8LsnUUgovs9ElSztpgPArtJhpvfBRp6k++N/7RbYQaQAAmQAAmQgBMI0AL6G8S4uDgZOHCg6kKvW7eussBhrCccBOj69etlwIABgngNGjSQP/zhD0qEYgLTxIkT1WDbKlWqyJtvvvlbilxUdgIB5rHA8R/8U5Lu7ywBZmuHKTfn1yrBMhhVRUJbtZXY8W9U9mqy/CRAAiRAAiTgdgJ+L0Axrgrd4nBDhgxRAhRd6cZxbxCWmBGPGezoHjZOuKlWrZp89tlnaqa8cR+3tyQzdAkBiNDqyzdK/k/rJW/Lj1KSmiLBtetKeKfuEtqijUvyZKIkQAIkQAIk4OsE/FqAYtb71q1bpWfPnno7Q2DaE5KOJgDY20dPmCuVlgC+dATBiR8dCZAACZAACZDAtRPwawEKfK+//ro0a9bsqkiiq/7JJ5+8qn25EwmQAAmQAAmQAAn4KwG/FqB33XXXNbU7Xv3Qtm3ba0qDO5MACZAACZAACZCAvxHgLHh/a3HWlwRIgARIgARIgAQ8TIAC1MMNwOxJgARIgARIgARIwN8IUID6W4uzviRAAiRAAiRAAiTgYQIUoB5uAGZPAiRAAiRAAiRAAv5GgALU31qc9SUBEiABEiABEiABDxOgAPVwAzB7EiABEiABEiABEvA3AhSg/tbirC8JkAAJkAAJkAAJeJgABaiHG4DZkwAJkAAJkAAJkIC/EaAA9bcWZ31JgARIgARIgARIwMMEKEA93ADMngRIgARIgARIgAT8jYBff4rT3xqb9S1NoOjUccn/eZMUJ1+SoMTrJKxtBwm+vkHpiPQhARIgARIgARJwGgEKUKehZEKViYDJZJL0aZMk/8d1YsrPEyksFAkJlczPZ0j4nd0lbuzrlak6LCsJkAAJkAAJVCoCFKCVqrlYWGcRuPLiKCnY9pNlcoUFajtv/RpJKymRuPGTLcO5RQIkQAIkQAIk4BQClW4MaHp6uqr4xYsXJSMjQ60XFRXJ2bNnSwFJS0uTEydOCMI1l5OTIwUFBXL+/HkL/6SkJMnKytKiCfIpMYsQzV25ckWtIq3s7GwVnpqaKpq/Fg/p5+fna5tceiGBgt3bpWDHFvslMwvR/K0/SsG+3fbjMIQESIAESIAESOCqCVQ6C+gjjzwiLVq0kMuXL8vhw4dl2LBh8v3330tUVJRA/H3++ecSFBQks2bNkhUrVkidOnUkOTlZpk2bJvXq1ZNPPvlETp06pfbt0aOHPP744/L8888r0RgYGCgtW7aUUaNGycMPPyyzZ8+W6tWrS3FxsfTp00fWrVsn+/fvl/fff18gbpEPxOb06dOlUaNGqhH++te/yt/+9je56aab1DZELASv5rCN7l/86EoT0Ni4kk/u2v8n5kYtnbnBx5SdJbmrv5OQm1oYfP1z1ZVt4Z9EK15rd5wXFS+Vf+6hnQ/a0j8peE+ttXPDe0rk3yXR2qM850elE6BoWgjQ/v37y9dff63E4NKlSyUuLk5GjBghv/zyi1x//fWyevVqWbJkiYSGhsry5csFcZ555hl1ZEBQwg+APvjgA6lfv76MHz9eWUTHjh0rKSkpDo+g48ePy6JFiyQ+Pl5mzpwpq1atkieffFKOHj2q0tDEJxLZvXu3DBo0SE+vTZs2ShCjXK50p0+flgsXLlhkER0drQtjY8CuXbtKWW3BsEaNGsZoykK8b98+Cz9stG7dWnE2Bnh1/scOS4ChsDvTs6XA6oHg+ohQqX7siOSYLe2ag4XcJ+qvVei3paP2LzSPjUVvA5w/1v83RF5T/x9++MFYJLVe6c4/qxo4Ov6MUXn8+d/1p7ztbzTyYB+vvv8YK2Ve9/Txb1Wca97MzMwU/NBbbOx9tpVwgFmEVSpT3AMPPCBTp06Vxo0by5YtW+Szzz5TVk1U7qWXXpLOnTtLbm6uzJ8/XwlV+Ofl5SnLJQQprJcQYn/+858RJE888YSydnbq1Elta3/33XefhQW0S5cuugX0rbfeUukjLrr4R48ercTujBkzpGrVqvLQQw9pyQi66Y03jU2bNslf/vIXqVu3rh6HK78TgBU7PDxcYI12lct+Y7wUm7vYy3LB7e+UyHH+Ow4UN3y0Q2RkZFmoGO5iAtrFHOcGnWcJQOzgHhMTEyMBAcZHWc+Wy19zx5A49IDSeZ4Ahi7iGhUWFqbE5wsvvCAff/yx3YJVSgtoRESEXiFU1NpBU6PrvW/fvtZBatu4f2xsrIVKh7UHFxY4WErhIIqMzrh/gwYNJCEhQT3FQGiii9/oqlWrJgMGDNC9Dhw4INifN3UdicUKLu7gg+ENLnMdukjm3p1ismpXi/zQRh06+3U74bhHO/BYtTgyPLKB8wLDfdgWHsFfKlMIULQFBWgpNG73gIGJ54XbsdvMEAIUvbtoDzw0l2VIcp2ZyWbx3OMJK+ihQ4ekZs2a0rx5czlz5owsWLDA5sUCcTds2KDEJi7yI0eOVOM7IUJh3YTbvHmzw4Lfc889Sng2bNhQiVGHkRnocQLh3XpKYHSc43KYbzARXXo6jsNQEiABEiABEiCBqyJQKS2gZdUU40EHDhwogwcPVl3dsOKMGzfO5m4QoBgvinGasLxhbGmtWrVkyJAhMnnyZDUJ6YYbbhBYSu257t27y4cffigTJkywF4X+XkQgMCJS4j/6Qi4N+IN696f89volVcQwc/d/dIwkzF4kAezu9KJWY1FIgARIgAR8iUClGwNaEfgwAaOrBGM+y3IYNAuzsbHrFxMw0O1VpUoVh7tj30fMs/NhZS1rctGkSZPUzH3MyKcrTQBvF0B7GduhdCzn+JSYZ7pnL/5S8v+3TkrSr0hgbFUJM7+EPqrfnySwStnHjHNK4b2pYDIe2gHjmuk8S0Drgi/PtcyzJfX93DE0BV2NmKTJLnjPtzfmWWCoG53nCWDiM3qPMSYX+gs9ypiobc/5pAVUq2xwcHC5xCfi27qwh4SECH6OHGbT43VPGG9alvh0lA7D3E8gMKqKRD/ypPq5P3fmSAIkQAIkQAL+S8CnBag7mrV27dqq275bt27uyI55kAAJkAAJkAAJkEClJ0ABeo1NiHfw0ZEACZAACZAACZAACZSfgE/Ogi9/9RmTBEiABEiABEiABEjA3QQoQN1NnPmRAAmQAAmQAAmQgJ8ToAD18wOA1ScBEiABEiABEiABdxOgAHU3ceZHAiRAAiRAAiRAAn5OgALUzw8AVp8ESIAESIAESIAE3E2AAtTdxJkfCZAACZAACZAACfg5AQpQPz8AWH0SIAESIAESIAEScDcBClB3E2d+JEACJEACJEACJODnBChA/fwAYPVJgARIgARIgARIwN0EKEDdTZz5kQAJkAAJkAAJkICfE+CnOP38APCn6ptKSqQkOUkKTx6TAHPFgxvdIEEJ1/kTAtaVBEiABEiABLyCAAWoVzQDC+FqAsUplyRtyotSdPyISIBZfuJXXCyhLW+VuFffloAgngqubgOmTwIkQAIkQAIaAb/ogj937pxs27ZNq3O5llezT7kSZiS3EyjJyZbkwfdI4d7dYsrOElNWppgyM8Rk9s/fvFGSerUTxKEjARIgARIgARJwDwG/EKD79++XpUuXVojo1exToQwY2S0ETCaTpL065leLp6mkdJ7mcAkJkcyP3ysdRh8SIAESIAESIAGXEPBpAZqamiopKSk2wV25ckWSkpJKhTna58yZM5KTkyO5ubmSn5+v75uWliYnTpyQoqIi3Y8r3kGgJC1VCg/vF4HQtOcKCyVv/Woxmbvk6UiABEiABEiABFxPwGcHvr355puq2z0yMlLwS0hIUDQhSKdMmSLp6ekC4diwYUNB3KCgILVEV731PhClTz/9tERHR0tycrLEx8dL7969pW/fvjJr1ixZsWKF1KlTR4VNmzZN6tWrp7fcwYMHZcwYswXuN4dyIL2IiAjNyyVLiGvkY3TIs379+kYvtX706FEpNIswo6tevbpUq1bN6KWE98mTJy38sNGkSRMJDrY8lK42/xLzRKEA8/jMGjVqOCV/07HD5i73HJGcXIkIDJTrI8NKlf9odp4UlQTIwWVLJSA+UYV7qv5a4bwh/5iYGPVQhWMeDg9erm5/b6q/J45/e/WHJR8Pvzt37tSi6Etnnn9aot5w/HkTf3DRjn9co9Aehw8fVtcq8nfe9f9qjj/cw4utjAdXe/+5mvwre/trdXbWMisrS12rYJCzbhfrPCxVg3VoJd3+8ccfZc+ePbJw4UIlLMePH6/X5KOPPlJicfr06erm+uyzz8r27duVALO3z4cffih33nmnjBgxQrKzs6Vfv34qPVhRV69eLUuWLJHQ0FBZvny56up/5pln9PyioqKkTZs2+jYEcIi5yxfxXekgHiCqjQ552soXgtr6QIHYthU3MfFXgWZMNzw8XALN4s7orjb/goICJWadlX9xWooUgnVBvoQEYu57aRcTHCTFZgEdmnidBMb/+qDirPyNuVUG/lp5UX+0KR4GjMeBq9vfmL8xX83fX/PH+Ynrhr/W35vaH22B6xSuezg/nHn90+rJ60/57z+4z1lfK672/uOP/LU6O2MJ0Yn2wLXKWhPYSt8nBSjGb7Zt21ZBQKXbt2+vT0Lat2+faIIUVrvOnTvLmjVr1IXd0T5//OMfFT8Iyttvv12tb9y4UYmlqVOnqu28vDxB3iNHjlQXJnjWrVtXXn/9dRWOv0mTJilLamxsrO7nipWKpF+RuLZugLbKX5E0jXFhlcbF11o8a3lUNP+SumbL9GcfiCncvuC/Lsx8ssTFSmL7DubZ8JaiXctXW1Y0f20/R0tj/R3FQ5g788fDEtrBWD535m+Lhb/mD8GDi3rt2rVtYSnlZ2yzUoFWHhWJ66/8NWSoPyzR6EFDLw0EqC1XEaYViUv+pQ0g6OmzZmi9bauNNL+KxPVF/hoHZyxxbqCnFTpJE6OO0vVJAYoLNS7YmjN2D6ML3NjdjHiadcHePrAQZmRkaMkJTMxw6IZB1zu64um8k0BglWgJ79RNcteuEPMZYbeQkQ8MKlN82t2ZASRAAiRAAiRAAhUiYNlvWqFdvTdyq1atZMuWLUo0QoX/97//1QvbrVs3WbVqlVLnUOsIa968uTjaB93vGOeJ+Bj3s3v3bpUerKeHDh2SmjVrqjQwSWnBggV2n4r1QnDFrQRiRo2XwNiqYu4TKJ1vaJiE3tZBqgx6pHQYfUiABEiABEiABFxCwCctoBCTDzzwgAwdOlTCwsKkQYMGOry77rpLNmzYIAMHDlSWz06dOikLJsYr2NunT58+cuDAARkyZIjqfr/xxhtV13tcXJxKZ/DgwaqrHd2V48aN0/PiincQCMDYuQUrJGvODMn9/lsx5eepWfEBUdES2W+QVBnwsHcUlKUgARIgARIgAT8hEGDuRnbwfprKTUHrXrc14zwzM1MNHkd3vdHZ2mfr1q1qZjvG/MA98cQT8uijj0q7du3UNqysmCGJsYtlOYwBHTZsmMVM+bL28afwssaAOoNFSUa6ei9ogLl73t4YLmfkU9nT0MaAVq1qth7TeZQArkt49Vt5rjEeLagfZF6eMaB+gMFrqogxoNZvTPCawvlZQS5cuCCYAKaNAcV8mJkzZ9ql4JMWUK221jPjNH8s7V3Ibe2DCz8smz169BC8sgiWztatW+vJYYypvfT0SFzxGgKBMa6dAOY1FWVBSIAESIAESMBLCfi0AHUWc3TT1ze/PxPv4IMIhfhE1z4dCZAACZAACZAACZBAxQlQgJaTGV6nhB8dCZAACZAACZAACZDAtRGwMS342hLk3iRAAiRAAiRAAiRAAiTgiAAFqCM6DCMBEiABEiABEiABEnA6AQpQpyNlgiRAAiRAAiRAAiRAAo4IUIA6osMwEiABEiABEiABEiABpxOgAHU6UiZIAiRAAiRAAiRAAiTgiAAFqCM6DCMBEiABEiABEiABEnA6AQpQpyNlgiRAAiRAAiRAAiRAAo4IUIA6osMwEiABEiABEiABEiABpxOgAHU6UiZIAiRAAiRAAiRAAiTgiAC/hOSIjo+FmUpKxJSRLiZTiQTGVZOAgAAfqyGrQwIkQAIkQAIkUBkIUIBWhla6xjKaTCbJXvhPyflmkZjycsWsPCUgJESqPDxcIu7tLwGBNIRfI2LuTgIkQAIkQAIkUAECXqM89uzZI8eOHatA0e1HheBatmyZ2dJnsh/Jj0JSxzwhWfM+lZLLyWLKzhJTVqaUXEmVjH9MleTB94ipsNCPaLCqJEACJEACJEACnibgNQJ0zZo1sm3bNqfwKDF3NU+dOpUC1Ewzc+7HUrhnh0hBQWm25q74EnOXfPbXC0qH0YcESIAESIAESIAEXETAawSodf1SU1Pl6NGjkpOTowdhvcAspLA8ceKEFBUV6WFYQdjp06ftCk/Ez87OlvT0dElLS9P3LTRbAE+dOqWnl5eXZ5EvIl65ckWPn5+fL8ePH1dpaZ5a2c6fP6+no4V5amkqLpac5UscZ19UKNnzP5MSs2WUjgRIgARIgARIgATcQcDrxoBCJL788suSlJQksbGxcuDAAbXdsWNHmT17tly4cEGJzypVqkhubq588sknEhUVJRs2bJA333xTGjRoIMVm4WXL7d+/X95//30lPoODg2XhwoUyY8YM2bhxo8TFxUlycrKynF68eFHmzp0rH3/8sUoGZXjjjTdk3rx5sn37dnnttdekYcOGcujQIXn66aeld+/eqhwQsYcPH5YePXrIqFGj1L4QxLNmzdKLAyGbkZFhIYD1QCeuQGRnnDgmBRlmYZn3q/UzLDBAEkJDSuVysbBILq1dJQENb9DDwAOMjQ4C/9KlS0YvtV6rVi0JtBpHivwzMzMt4oaFhUliYqKFHzbAW3uYwMMA2qZq1aoeyV8rnKfq7035ox3Qm6A9rLmj/b2p/p44/u3VH+2A6x2uMdbOmeefljaPf/vXP9xfcJ1KSUlREznJ33nX/6s5/uLj4/VrlLb/1d5/tP396fjX6uysJa5TOD9wT7enxbS8vE6A4gILQfn555+rMn711Vfy/fffCwQo3NmzZ2XBggXqxB8+fLhs3bpVOnXqJJMnT1bislmzZrJixQrZt2+fim/9B8vlokWLBActhOe6deuUsAwPD1f+ixcvljFjxsjbb7+t8qpTp46sXLlS7r77bpXUl19+Ka+88oq0bdtWYKWFAL3nnntUGGAvX77cwgILy6ixLNWqVVMNgwZypYP4g9g1FZnFOH5mVxxk2+CdZRagxVfSJMBg5Q0NDRUIRqODZdhoCdbCwBJixei0/I1+ERERSugb/bAOgaPxwI0WYhZ5eyJ/rWyeqr835R8dHa0EqNY27mh/b6q/Nx1/GM+OdnD1+Uf+vxJwdP7jGoX2wLUdbxJx5vWP/MvmrzHSlhCL2jVK87va+4+2v6P21+Joy8re/lo9nLWEDsL5oRmVHKVrqRocxXRTWKNGjeShhx5S1skjR47I3r17pX79+nru7du3Vyc9PGrXrq0uAidPnlRi5cYbb1TxOnTooMe3XsHTqmaFgzBEXIhPuG7dusnQoUNl9OjR0qtXL1m1apXa/u9//yufffaZuvhjshQEKUQxHA50TWC2bNlSlc34eiOU6ZtvvlFx8Tdp0iSBCNXKoAc4eQXpm8xi/NIX/xBTYInD1BtHhkti334SGBvnMB4C69WrV2YcRKhI/YxxIUYhfIKCgmzm4+r8bWZq8PSn/GHhQTvAGq05f6q/Vmfj0lP1h/UZD3DGa6GxXNbrxnPKOsx6uyJxPVV/rczekD+EJyxsNWrU0O9FWvm0ZUWYViSuN9Rfq6OjZUXqVJG4tuoPQxDuqUZXkTQrEtdW/sZ8tfWKpFmRuK7IXyuzM5booUbPEYyIEKD27uNaXrZNYlqoB5Y7duyQ5557TuXcv39/JUaNxYiJidE3NaEXYn6lEFQ3nkzhUGnrLmFtJ1zENYeGNz454SKPH/aFVRMTo7Zs2SJNmzaVhIQEtRssfffdd5/07dtX/dDtDyspnDFt5eHhvwAzh/BO3USCS3e760ULCJCQ5i0kICZW9+IKCZAACZAACZAACbiSgNcJUFg80b09aNAggfVw06ZNZZpy8VQAk/nmzZsVK3Sra2LUEbwuXboogal1a8GyiS58CFCkCbE7Z84cvYsdlqDmzZurMYtYVq9eXaZMmVLmOAdHZXB1WPSI581d8Obu/gAbTW2up9lWLnEvv233Sd7V5WP6JEACJEACJEAC/kfA67rge/bsKRMmTJCnnnpKWSdbt24t69evd9gysIRiktCrr76qJvxAGFqP37KVAKyamEAEsYtxHDAdIx3NIQwTiLTxp/B/7LHHZOLEiYKxqRC5AwcOVPtq+3jbMjAySqqv3Cxpr4+Tgl3m11yhaxuvRzW/gimkSTOJnTBZAg1WYW8rP8tDAiRAAiRAAiTgewQCzINFvfJt7RhjAwuk1s1eXvSYYW7spi/PfuiGxwQLjD0sr4PV1Dg2rrz7YQzosGHDyj2Wsrzplide8eUUKTp5VEUNrtdAAhOuqzDf8uRzLXHKGgN6LWlz34oRsDUGtGIpMLazCGBoEF7/VpFrlLPyZjqWBMozBtRyD265koCtMaCuzI9p2yeAMaDQX9oY0JEjR8rMmTPt7uB1FlCtpHgF09W4iopP5IExpPhVxF2N+KxI+q6IGxSfIPjRkQAJkAAJkAAJkIAnCdgYGOjJ4jBvEiABEiABEiABEiABXydAAerrLcz6kQAJkAAJkAAJkICXEaAA9bIGYXFIgARIgARIgARIwNcJUID6eguzfiRAAiRAAiRAAiTgZQQoQL2sQVgcEiABEiABEiABEvB1AhSgvt7CrB8JkAAJkAAJkAAJeBkBClAvaxAWhwRIgARIgARIgAR8nQAFqK+3MOtHAiRAAiRAAiRAAl5GgALUyxqExSEBEiABEiABEiABXydAAerrLcz6kQAJkAAJkAAJkICXEaAA9bIGYXFIgARIgARIgARIwNcJUID6eguzfiRAAiRAAiRAAiTgZQSCvaw8LM41EChOTZHsRV9K/qb1UpKZKYFVq0lE154SNeBhCQgPv4aUuSsJkAAJkAAJkAAJOI+AT1pAV65cKbm5uXLlyhVZv36982j9ltKePXvk2LFjTk/3WhIsvnJZUh5/UHKWLZLi82fFlJkuxadPSNZXn0vSfXeYBWnGtSTPWu6+GwAAFsBJREFUfUmABEiABEiABEjAaQR8UoB+9NFHkpGRoQTohg0bnAZLS2jNmjWybds2bdPjy5KMNEl+sKdZdJpFZmGhZXkKC8zbAXL52cfEVIB1OhIgARIgARIgARLwLAGfFKAa0nr16smzzz6rNnNycqTALMCwPHHihBQVFWnR9PCLFy9a+GGj0CzoTp06VSo+wpBGVlaWFBcXy8mTJyU7Oxvebnc5K5eJhIY5yNckJamXJe8n51uDHWTKIBIgARIgARIgARKwScCnx4AeOnRI3nnnHZkzZ47Mnj1bLly4oMRnlSpVVBf9J598IlFRUYLlt99+K40aNVKC8r333hPEgSV148aNEhcXJ8nJyTJ16lRp2LChDvLgwYMyffp0KSkpkejoaNUt//rrr0vbtm31OJcvXxZYTDWHYQEYHgAh7CyXC2FZkO8wOVNWpuRs2Sim2zs6jOfpQIh68AkM9OlnI09jLlf+OK7hnHmslitjRipFAOcFfmyLUmjc7gFDBhzaIiAgwO35M0NLAjAA8bywZOLJLZwfOC9wvdLuIfbK49MC1LrSZ8+elQULFig4w4cPl61bt0p8fLwSn4sXL5Zw80SduXPnyk8//aTW161bJ/PmzVPrixYtEsQZO3asRbJHjhyRL774Qho0aCDz58+XZcuWWQjQ06dPyyuvvKLv06ZNG8k0TxBKT0/X/a55xdz1bn0ZPJObLxfzLbvjow4fkWY28t29e7eyDhvLAetx9erVjV5KnB84cMDCDxutWrWSkJAQC/8zZ86ItUUZor5Zs2YW8bDB/L2XPy7u2rEKaz/bn8e/9QnM85/XP09f/yF2jI73n/Lff43cnLGel5cn+FGAWtFs3769/sRau3Zt9dSUlJQkt912mxKZiD5s2DC118yZM6VDhw66f7du3WTo0KEyevRoi1Rr1KihxCc869atKzt37rQIx8V57969ut/f//53SUxMFOznLJdWv5HkmyccGV3diDDBT3eBQRLZoaNE28i3ImVp3LixnqSjlYqkaYyblpamrMlBQUE2k3d1/jYzNXj6U/6w3qMd0AOgOX+qv1Zn49JT9YdVAb01nspfY8D8G6v7BuYY4AHdngXUeE3T2NlbViQu+Ze+/6Smpkq1atUs8FaEaUXi+iJ/C3DXuAGjE3qDca2CAA0OdmzjdBx6jYXxtt1jYmL0ImkXjtjYWAVKC4B1EuodItE40x03APysu4aNaWppGJfIx/h0iG3tZ4x3LesRPe6Vgh1bBN3sdp2pRL2SSau33XgeDtDYeHs5PYzJrdmzLdyK22ZmPC9sYvGIp3Y+aG3ikUIwU50A20FH4RUrWnto54mjQvn9QLs77rhD8FolrZvx448/lrVr10qXLl1ky5YtaiY9AOLVTug+thagjuC6Kyz8/+6QkJtbiYSE2s0y4r4BEtKwid1wBpAACZAACZAACZCAuwj4lQXUFlSYi/v16yeDBg2SmjVrqq7xkSNHSlhYmPTu3Vv5Y5woxi++8cYbtpLwCr9qk96V1PEjpeCXHSIlJvP0ffNA+bBwCTCbwCPu7isxT/z6NgCvKCwLQQIkQAIkQAIk4NcEAkxm59cEfqs8XreEX2RkpAUO+KFLHkLVGW7SpElqnCkm+bjCFZ48Zu6O3yol5hfTB11XQ0Jv6yDBNWq5IiuXpFnWGFCXZMpEbRJISUlRY0CrVq1qM5ye7iOA4T/5+flOuw65r+S+lxNmXKPHDGMHy9PN6HsEvKtGtsaAelcJ/ac0eNMQhiVqY0BhzMN8GnvO7y2gGhiM0zSO1SzLXwv3tmWIeUISfnQkQAIkQAIkQAIk4K0E/H4MqLc2DMtFAiRAAiRAAiRAAr5KgALUV1uW9SIBEiABEiABEiABLyVAAeqlDcNikQAJkAAJkAAJkICvEqAA9dWWZb1IgARIgARIgARIwEsJUIB6acOwWCRAAiRAAiRAAiTgqwQoQH21ZVkvEiABEiABEiABEvBSAhSgXtowLBYJkAAJkAAJkAAJ+CoBvgfUzS2Lb8y/+OKL6gXfbs66UmRXXFxMNl7SUnjhNl60HRER4SUl8t9i4Hsh+Hnjp4D9rVW0j5Pg63h8Eb3nW5/3DM+3gVaCzMxM9RXJ0NBfPwvesGFDLcjmkl9CsomFniRAAg8++KDgi13vvPMOYZAACfxG4N///rdMmDBBdu3axYczHhUkYCDQrFkzGTdunPrao8Hb7iq74O2iYQAJkAAJkAAJkAAJkIArCFCAuoIq0yQBEiABEiABEiABErBLIGii2dkNZQAJkIDfEsB4w+bNm0ujRo38lgErTgLWBHBeXHfddXL77bdzTK41HG77NYGgoCB1XlSvXr1cHDgGtFyYGIkESIAESIAESIAESMBZBNgF7yySTIcESIAESIAESIAESKBcBPgapnJhYiQS8A8CWVlZsnv3bunYsaNe4Z07d0p+fr6+fcMNN0i1atX0ba6QgC8TwGuXtm/frma8t2jRwuLVS4cOHZJTp05JmzZtJCEhwZcxsG4kUIrA0aNH5cyZM9KuXTuLN0KU957BLvhSSOlBAv5JIC8vT1566SU1ru3tt99WEIqKiqRXr15y66236lAefvhhueWWW/RtrpCArxK4ePGiPPXUU+qBDO/F3b9/v8yZM0e96/Ddd9+Vffv2SZMmTeSnn36SDz/8UL22zFdZsF4kYCTw/PPPq3d2Y47AunXrZPTo0Wr8Z0XuGbSAGolynQT8lMDx48fVuw1jY2MFP82dPHlS6tSpI2+99ZbmxSUJ+A2BxYsXS+/eveWxxx5TdX7llVdk9erVcvPNN8vGjRtlyZIl6oFt4cKFMn/+fBk/frzfsGFF/ZfA3r17JTk5Wb788ksFoWnTpoJzABPzKnLPoAD132OINScBnQCsO/hC1+XLl2XFihW6/5EjR5QAXblypeqGv+uuuyQyMlIP5woJ+DKBJ554wqLLPT09XXJzcwUPbOiO175MhS747777zpdRsG4koBO46aab5JNPPtG3cV6gBw2uIvcMTkLSEXKFBPyXACw6trrVDx8+LBjnhk+sYYmvI6WkpPgvKNbcrwjgk4IhISGqzj/88IOcPXtW7r77brlw4YJFT0FMTIx6ePMrOKys3xLAg5f2ieZLly4pS+jQoUMVj4rcM2gB9dtDiBUngbIJDB8+XPDTrJ6YjARrKMaB0pGAvxBYtmyZzJs3T6ZPny74Bjzed4hvkGsO4960G7LmxyUJ+DqBEydOyNixY+XRRx9VE5FQ34rcM2gB9fUjhPUjgWsgcO7cOYsZ8Pg2PKw/dCTgLwQwzm3RokVqktH111+vqp2YmCipqak6AqzXrFlT3+YKCfg6gQMHDshzzz2nJunde++9enUrcs+gANWxcYUESMCawIYNG2TWrFnKG+NE0Q3ZtWtX62jcJgGfJIDx0GvXrpWZM2eK8esut912m2AiBl5BA+vnt99+qyZg+CQEVooErAhgGNbf/vY3efXVV6Vz584WoRW5Z7AL3gIdN0iABIwE/vjHP6oZ8OhiwaxHTELChAs6EvAHAp9//rkkJSWpmfBaffv37y+jRo2Sv/zlL/L444+rd+LCMvqnP/1Ji8IlCfg0AfQIpKWlqfNAqyjeDb106VKpyD2D7wHV6HFJAiRglwCsnxj3FhYWZjcOA0jA3wjgJfUYF41xoXQkQAK/EyjPPYMC9HdeXCMBEiABEiABEiABEnADAY4BdQNkZkECJEACJEACJEACJPA7AQrQ31lwjQRIgARIgARIgARIwA0EKEDdAJlZkAAJkAAJkAAJkAAJ/E6AAvR3FlwjARIgARIgARIgARJwAwEKUDdAZhYkQAIk4CwC+ALPa6+9JvgKCRxelP799987K3mmQwIkQAJuIUAB6hbMzIQESIAEnEMAAnTy5MkWAhSfR6UjARIggcpEgC+ir0ytxbKSAAn4PYHQ0FDB+yfpSIAESKAyE6AFtDK3HstOAiRQ6Qls3rxZ/v73v8umTZvkkUcekQEDBqhPO+ITj/C/7777ZNq0afq3x+E/fPhwOXjwoM2679q1S4X36NFDnn32WTl//rxFvOPHjyv/Xr16Sb9+/WTq1KlSUFCgx0H4K6+8Ivfcc4/61N7GjRvVth7BvPLFF1/Igw8+KH369JF3331XfY7SGM51EiABEiiLAAVoWYQYTgIkQAIuJHDkyBElMPFpx1atWklISIjgc48QiBCTPXv2lA8++EB9EhXFKCkpkdmzZ8u5c+dKleqHH36Q9u3bS1ZWlvok3pYtW6RFixa6CMW4UWynpqbKkCFDpEmTJjJp0iR56aWXVFqXL1+W7t27y+rVq5W4XL9+vRLAixcv1vPCZyhHjx6t9u3QoYO8/fbbSjTrEbhCAiRAAuUgwC74ckBiFBIgARJwJQEIv1WrVkmbNm1U9/o333wj+JTdmjVrVLYmk0lmzJihi1B7ZRkzZozcfffdsmDBAhUFllKkOWXKFPnHP/4hhw4dkoEDB8qnn34qgYGB8tBDD0lycrLACgv3zjvvqHx/+uknCQgIkBEjRkjz5s2V6EX44cOHVTrz5s2TwYMHw0uJTwhZiNXOnTsrP/6RAAmQQFkEKEDLIsRwEiABEnAxgeDgYGnZsqXKBRbQunXrKguolm1iYqJcuHBB27S5xDfJd+/eLTVr1pRx48bpcYKCguTnn39W27CqdunSRWApPXDggOzfv1+J3Bo1aqhwxIOAhfjU3L333ivLli3TwyGGt23bpvLS4uBb6NiXAlQjwiUJkEBZBChAyyLEcBIgARJwMYHY2FiBUNQcBGBMTIy2aSEIdU+rlYyMDGWphBiEdVNzd911l1StWlVt7tmzR3XpYyJTp06dpGPHjqq7HpZROFhi27Vrp9a1v7i4OG1V0tLSBGI5LCzMokwjR45UllI9IldIgARIoAwCFKBlAGIwCZAACVQGArCSQrTWqlVLdblrZUbXPqyqcBMnTpQbb7xRWT01wfu///1P8GonOHS379ixQ61rf8Z3jDZu3FgNEcDEKIz/hMO+c+fOlRtuuEHbhUsSIAESKJPA74/JZUZlBBIgARIgAW8m8OSTT6oX06PLHMJww4YNcv/990tKSooqNrraMQEpNzdX0JWOsaZLliwRdN/DPffcc+ql9phkBGvpq6++Kj/++KMKw1/Xrl2ladOmalb8vn37JC8vT4nasWPHWlhs9R24QgIkQAJ2CFCA2gFDbxIgARKobAQgGAcNGqQmBkVHR8uwYcPkhRdeUDPiUZfnn39e0N1fvXp1SUhIkA8//FC9hunYsWOCLnxMWIJ4xRjRO+64QzCL/vHHH5fw8HCFApZUbYLULbfcotJYu3atEr1Ij44ESIAEyksgwPwUbCpvZMYjARIgARLwfgJ4UX1SUpLUqVPHZmEx1hPjQCFSjQ6TmDC+E930mnvmmWfU7HljVzzC0tPT1fs/4+PjtahckgAJkEC5CdACWm5UjEgCJEAClYMALJX2xCdqANFoLT7hj5fO4z2i6HbHu0SXL1+u3jmKVzdZO1hSKT6tqXCbBEigvARoAS0vKcYjARIgAR8ngK8svfjii0p44ktLmFiEF+RjbCgdCZAACTiTAAWoM2kyLRIgARLwEQKYmITueDoSIAEScAUBClBXUGWaJEACJEACJEACJEACdglwDKhdNAwgARIgARIgARIgARJwBQEKUFdQZZokQAIkQAIkQAIkQAJ2CVCA2kXDABIgARIgARIgARIgAVcQoAB1BVWmSQIkQAIkQAIkQAIkYJcABahdNAwgARIgARIgARIgARJwBQEKUFdQZZokQAIkQAIkQAIkQAJ2CVCA2kXDABIgARIgARIgARIgAVcQoAB1BVWmSQIkQAIkQAIk4PcECgsL5ZdffpHz58+XYnHp0iU5c+aMhf/ly5elpKRE90tOTlbrSCcjI0MQnpKSooefPn1a0tPT9W2smEwmOXbsWKm0MzMz1Sd2LSKbN/DZXXx4AuljP82dOHFC8vLytE2VN9I+evSozXRQVtQHX1RLS0vT97O3whfR2yNDfxIgARIgARIgARK4SgJnz56V7t27S6NGjZQwa968uSxcuFCJ0UcffVQJSYi2W265RZYtWyZBQUFy3XXXyfbt26Vu3bpSXFwswcHBAvG5efNmGTVqlCB+aGioitOrVy/JyclR+915553y3nvvyZUrVwT+2AcisEWLFvL1119LQECADB8+XCIjI+X999+3qBE+tQtReeHCBVWmdu3aKRGZlJQkEKH/+te/pGPHjlKnTh256aabVLkOHDggEyZMkKefflql9ec//1nWrFkjMTExkpiYqPJbu3atRT7WG7SAWhPhNgmQAAmQAAmQAAlcI4EVK1ZIy5YtBUuISgg4WBvHjBkjTZo0UX7Hjx9XfmWJNRRl7969smnTJjl8+LC8/PLLSgzu3r1btmzZIgcPHlTCdtGiRdK6dWvZsWOHHDlyRAlR7AM3ffp0mTx5slq3/oN1dNu2beqHNHr27CkbN26U0aNHK9Gsxb/jjjsEZd21a5eqx7lz52Tp0qWqXMhvz549SoBq8R0tKUAd0WEYCZAACZAACZAACVwFgR49eihxCCvka6+9JrB6VqlSRVkzBwwYoFKEhbN///6yYMGCMnNo2LCh1K5dWwIDA2Xr1q3Sp08ftU9ISIisXLlSatWqJYsXL1bWzEceeURglUQ3/5IlS1S86Ohowc+Wg7CElRTWS8SBAIVDmrDkau6BBx5Qq7DUQuj+/PPPSnz27t1bWWaRBupTHhdcnkiMQwIkQAIkQAIkQAIkUH4C9evXV2Mq169fL//+97+lffv2yqoIEVlQUKAnlJubq7q84QEBhzGUcLBKGh3Eq+YSEhKUdVPbPnXqlMTHx6tNiMdOnTppQVK1alV93d6KMW3ECQ8PtxdV94c1F136NWvWVONctQDrMamav/WSFlBrItwmARIgARIgARIggWskMHfuXPnrX/+qxoHOmDFDbrzxRjl58qQ8+OCDMn/+fCU0IeI0cYrsqlWrJvv27VM5o+venoOVEWM7MU4UE4i6dOmirJ0DBw5UVtdbb71VMJZz6tSpqlsd6aC7HOM8r8VhPCgchgNA9LZt21ZgFYUFFuNIMUkKdSuPowW0PJQYhwRIgARIgARIgAQqQABCE13rGAeK2eStWrVSXdvZ2dlKPGJyEqyd999/v4wYMUKljIk9Q4YMkXr16kmbNm0Elk5brl+/fkroNW7cWHXrjxw5UtBFDysoBC2sr9gXE58GDx6skpg4caLNSUi20rfn9+OPP0qzZs3UuFVMqIJ1FT+Ma+3WrZs0bdpUbr75ZiVG7aWh+XMWvEaCSxIgARIgARIgARJwMgHMVMfri6KioixSxix1dGFjVrvRoXse3fKxsbFGb5vrSANjNjGD3ujwSiWkW56udON+jtYxiQrDCSByUTYMF4DDq5tgDYUAhcNkJ7x6as6cOWrb3h8toPbI0J8ESIAESIAESIAErpEARKYtFxcXZ8tbCUdrUWozotnTXhp4HZKrnHWeNWrUkK5du8rQoUNVluiC/89//lNm9rSAlomIEUiABEiABEiABEjAvwl89913aqyptSUXVPD+UbwHFNZbjEfFRKuyHAVoWYQYTgIkQAIkQAIkQAIk4FQCnAXvVJxMjARIgARIgARIgARIoCwCFKBlEWI4CZAACZAACZAACZCAUwlQgDoVJxMjARIgARIgARIgARIoiwAFaFmEGE4CJEACJEACJEACJOBUAhSgTsXJxEiABEiABEiABEiABMoi8P8Bc8HMMBMWrOsAAAAASUVORK5CYII=\" /><!-- --></p>\n</div>\n<div id=\"坡度图\" class=\"section level3\">\n<h3>3.4 坡度图</h3>\n<p>坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。</p>\n<div class=\"sourceCode\" id=\"cb21\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb21-1\"><a href=\"#cb21-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(scales)</span>\n<span id=\"cb21-2\"><a href=\"#cb21-2\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb21-3\"><a href=\"#cb21-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 数据准备</span></span>\n<span id=\"cb21-4\"><a href=\"#cb21-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv&quot;</span>)</span>\n<span id=\"cb21-5\"><a href=\"#cb21-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">colnames</span>(df) <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;continent&quot;</span>, <span class=\"st\">&quot;1952&quot;</span>, <span class=\"st\">&quot;1957&quot;</span>)</span>\n<span id=\"cb21-6\"><a href=\"#cb21-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>left_label <span class=\"ot\">&lt;-</span> <span class=\"fu\">paste</span>(df<span class=\"sc\">$</span>continent, <span class=\"fu\">round</span>(df<span class=\"sc\">$</span><span class=\"st\">&#39;1952&#39;</span>),<span class=\"at\">sep=</span><span class=\"st\">&quot;, &quot;</span>)</span>\n<span id=\"cb21-7\"><a href=\"#cb21-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>right_label <span class=\"ot\">&lt;-</span> <span class=\"fu\">paste</span>(df<span class=\"sc\">$</span>continent, <span class=\"fu\">round</span>(df<span class=\"sc\">$</span><span class=\"st\">&#39;1957&#39;</span>),<span class=\"at\">sep=</span><span class=\"st\">&quot;, &quot;</span>)</span>\n<span id=\"cb21-8\"><a href=\"#cb21-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"sc\">$</span>class <span class=\"ot\">&lt;-</span> <span class=\"fu\">ifelse</span>((df<span class=\"sc\">$</span><span class=\"st\">&#39;1957&#39;</span> <span class=\"sc\">-</span> df<span class=\"sc\">$</span><span class=\"st\">&#39;1952&#39;</span>) <span class=\"sc\">&lt;</span> <span class=\"dv\">0</span>, <span class=\"st\">&quot;red&quot;</span>, <span class=\"st\">&quot;green&quot;</span>)</span></code></pre></div>\n<div class=\"sourceCode\" id=\"cb22\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb22-1\"><a href=\"#cb22-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb22-2\"><a href=\"#cb22-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(df) <span class=\"sc\">+</span> <span class=\"fu\">geom_segment</span>(<span class=\"fu\">aes</span>(<span class=\"at\">x=</span><span class=\"dv\">1</span>, <span class=\"at\">xend=</span><span class=\"dv\">2</span>, <span class=\"at\">y=</span><span class=\"st\">`</span><span class=\"at\">1952</span><span class=\"st\">`</span>, <span class=\"at\">yend=</span><span class=\"st\">`</span><span class=\"at\">1957</span><span class=\"st\">`</span>, <span class=\"at\">col=</span>class), <span class=\"at\">size=</span>.<span class=\"dv\">75</span>, <span class=\"at\">show.legend=</span>F) <span class=\"sc\">+</span> </span>\n<span id=\"cb22-3\"><a href=\"#cb22-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"fu\">geom_vline</span>(<span class=\"at\">xintercept=</span><span class=\"dv\">1</span>, <span class=\"at\">linetype=</span><span class=\"st\">&quot;dashed&quot;</span>, <span class=\"at\">size=</span>.<span class=\"dv\">1</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb22-4\"><a href=\"#cb22-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"fu\">geom_vline</span>(<span class=\"at\">xintercept=</span><span class=\"dv\">2</span>, <span class=\"at\">linetype=</span><span class=\"st\">&quot;dashed&quot;</span>, <span class=\"at\">size=</span>.<span class=\"dv\">1</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb22-5\"><a href=\"#cb22-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">labels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;Up&quot;</span>, <span class=\"st\">&quot;Down&quot;</span>), </span>\n<span id=\"cb22-6\"><a href=\"#cb22-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                     <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;green&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;red&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span>  <span class=\"co\"># color of lines</span></span>\n<span id=\"cb22-7\"><a href=\"#cb22-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"fu\">labs</span>(<span class=\"at\">x=</span><span class=\"st\">&quot;&quot;</span>, <span class=\"at\">y=</span><span class=\"st\">&quot;Mean GdpPerCap&quot;</span>) <span class=\"sc\">+</span>  <span class=\"co\"># Axis labels</span></span>\n<span id=\"cb22-8\"><a href=\"#cb22-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"fu\">xlim</span>(.<span class=\"dv\">5</span>, <span class=\"fl\">2.5</span>) <span class=\"sc\">+</span> <span class=\"fu\">ylim</span>(<span class=\"dv\">0</span>,(<span class=\"fl\">1.1</span><span class=\"sc\">*</span>(<span class=\"fu\">max</span>(df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1952</span><span class=\"st\">`</span>, df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1957</span><span class=\"st\">`</span>))))  <span class=\"co\"># X and Y axis limits</span></span>\n<span id=\"cb22-9\"><a href=\"#cb22-9\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb22-10\"><a href=\"#cb22-10\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Add texts</span></span>\n<span id=\"cb22-11\"><a href=\"#cb22-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">&lt;-</span> p <span class=\"sc\">+</span> <span class=\"fu\">geom_text</span>(<span class=\"at\">label=</span>left_label, <span class=\"at\">y=</span>df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1952</span><span class=\"st\">`</span>, <span class=\"at\">x=</span><span class=\"fu\">rep</span>(<span class=\"dv\">1</span>, <span class=\"fu\">NROW</span>(df)), <span class=\"at\">hjust=</span><span class=\"fl\">1.1</span>, <span class=\"at\">size=</span><span class=\"fl\">3.5</span>)</span>\n<span id=\"cb22-12\"><a href=\"#cb22-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">&lt;-</span> p <span class=\"sc\">+</span> <span class=\"fu\">geom_text</span>(<span class=\"at\">label=</span>right_label, <span class=\"at\">y=</span>df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1957</span><span class=\"st\">`</span>, <span class=\"at\">x=</span><span class=\"fu\">rep</span>(<span class=\"dv\">2</span>, <span class=\"fu\">NROW</span>(df)), <span class=\"at\">hjust=</span><span class=\"sc\">-</span><span class=\"fl\">0.1</span>, <span class=\"at\">size=</span><span class=\"fl\">3.5</span>)</span>\n<span id=\"cb22-13\"><a href=\"#cb22-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">&lt;-</span> p <span class=\"sc\">+</span> <span class=\"fu\">geom_text</span>(<span class=\"at\">label=</span><span class=\"st\">&quot;Time 1&quot;</span>, <span class=\"at\">x=</span><span class=\"dv\">1</span>, <span class=\"at\">y=</span><span class=\"fl\">1.1</span><span class=\"sc\">*</span>(<span class=\"fu\">max</span>(df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1952</span><span class=\"st\">`</span>, df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1957</span><span class=\"st\">`</span>)), <span class=\"at\">hjust=</span><span class=\"fl\">1.2</span>, <span class=\"at\">size=</span><span class=\"dv\">5</span>)  <span class=\"co\"># title</span></span>\n<span id=\"cb22-14\"><a href=\"#cb22-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">&lt;-</span> p <span class=\"sc\">+</span> <span class=\"fu\">geom_text</span>(<span class=\"at\">label=</span><span class=\"st\">&quot;Time 2&quot;</span>, <span class=\"at\">x=</span><span class=\"dv\">2</span>, <span class=\"at\">y=</span><span class=\"fl\">1.1</span><span class=\"sc\">*</span>(<span class=\"fu\">max</span>(df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1952</span><span class=\"st\">`</span>, df<span class=\"sc\">$</span><span class=\"st\">`</span><span class=\"at\">1957</span><span class=\"st\">`</span>)), <span class=\"at\">hjust=</span><span class=\"sc\">-</span><span class=\"fl\">0.1</span>, <span class=\"at\">size=</span><span class=\"dv\">5</span>)  <span class=\"co\"># title</span></span>\n<span id=\"cb22-15\"><a href=\"#cb22-15\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb22-16\"><a href=\"#cb22-16\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb22-17\"><a href=\"#cb22-17\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\">#设置主题</span></span>\n<span id=\"cb22-18\"><a href=\"#cb22-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"sc\">+</span> <span class=\"fu\">theme</span>(<span class=\"at\">panel.background =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb22-19\"><a href=\"#cb22-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">panel.grid =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb22-20\"><a href=\"#cb22-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">axis.ticks =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb22-21\"><a href=\"#cb22-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb22-22\"><a href=\"#cb22-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">panel.border =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb22-23\"><a href=\"#cb22-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">plot.margin =</span> <span class=\"fu\">unit</span>(<span class=\"fu\">c</span>(<span class=\"dv\">1</span>,<span class=\"dv\">2</span>,<span class=\"dv\">1</span>,<span class=\"dv\">2</span>), <span class=\"st\">&quot;cm&quot;</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"哑铃图\" class=\"section level3\">\n<h3>3.5 哑铃图</h3>\n<p>哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2)\n比较两类之间的距离。为了得到哑铃的正确顺序，Y\n变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。</p>\n<div class=\"sourceCode\" id=\"cb23\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb23-1\"><a href=\"#cb23-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>health <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;https://raw.githubusercontent.com/selva86/datasets/master/health.csv&quot;</span>)</span>\n<span id=\"cb23-2\"><a href=\"#cb23-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>health<span class=\"sc\">$</span>Area <span class=\"ot\">&lt;-</span> <span class=\"fu\">factor</span>(health<span class=\"sc\">$</span>Area, <span class=\"at\">levels=</span><span class=\"fu\">as.character</span>(health<span class=\"sc\">$</span>Area))  <span class=\"co\"># for right ordering of the dumbells</span></span>\n<span id=\"cb23-3\"><a href=\"#cb23-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb23-4\"><a href=\"#cb23-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># health$Area &lt;- factor(health$Area)</span></span>\n<span id=\"cb23-5\"><a href=\"#cb23-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>gg <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(health, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>pct_2013, <span class=\"at\">xend=</span>pct_2014, <span class=\"at\">y=</span>Area, <span class=\"at\">group=</span>Area)) <span class=\"sc\">+</span> </span>\n<span id=\"cb23-6\"><a href=\"#cb23-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">geom_dumbbell</span>(<span class=\"at\">color=</span><span class=\"st\">&quot;#a3c4dc&quot;</span>, </span>\n<span id=\"cb23-7\"><a href=\"#cb23-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                      <span class=\"at\">size=</span><span class=\"fl\">0.75</span>, </span>\n<span id=\"cb23-8\"><a href=\"#cb23-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>                      <span class=\"at\">point.colour.l=</span><span class=\"st\">&quot;#0e668b&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb23-9\"><a href=\"#cb23-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">scale_x_continuous</span>(<span class=\"at\">label=</span>percent) <span class=\"sc\">+</span> </span>\n<span id=\"cb23-10\"><a href=\"#cb23-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">labs</span>(<span class=\"at\">x=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb23-11\"><a href=\"#cb23-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>             <span class=\"at\">y=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb23-12\"><a href=\"#cb23-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>             <span class=\"at\">title=</span><span class=\"st\">&quot;Dumbbell Chart&quot;</span>, </span>\n<span id=\"cb23-13\"><a href=\"#cb23-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>             <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Pct Change: 2013 vs 2014&quot;</span>, </span>\n<span id=\"cb23-14\"><a href=\"#cb23-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>             <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: https://github.com/hrbrmstr/ggalt&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb23-15\"><a href=\"#cb23-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">theme</span>(<span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust=</span><span class=\"fl\">0.5</span>, <span class=\"at\">face=</span><span class=\"st\">&quot;bold&quot;</span>),</span>\n<span id=\"cb23-16\"><a href=\"#cb23-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">plot.background=</span><span class=\"fu\">element_rect</span>(<span class=\"at\">fill=</span><span class=\"st\">&quot;#f7f7f7&quot;</span>),</span>\n<span id=\"cb23-17\"><a href=\"#cb23-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">panel.background=</span><span class=\"fu\">element_rect</span>(<span class=\"at\">fill=</span><span class=\"st\">&quot;#f7f7f7&quot;</span>),</span>\n<span id=\"cb23-18\"><a href=\"#cb23-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">panel.grid.minor=</span><span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb23-19\"><a href=\"#cb23-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">panel.grid.major.y=</span><span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb23-20\"><a href=\"#cb23-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">panel.grid.major.x=</span><span class=\"fu\">element_line</span>(),</span>\n<span id=\"cb23-21\"><a href=\"#cb23-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">axis.ticks=</span><span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb23-22\"><a href=\"#cb23-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">legend.position=</span><span class=\"st\">&quot;top&quot;</span>,</span>\n<span id=\"cb23-23\"><a href=\"#cb23-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">panel.border=</span><span class=\"fu\">element_blank</span>())</span>\n<span id=\"cb23-24\"><a href=\"#cb23-24\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb23-25\"><a href=\"#cb23-25\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">plot</span>(gg)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0HfJTF1sZPekILJQUIJYB0pDdBujQF9QpYQRBFuoB44SpKE0SKIoigoCLKd0VAsKKiAtIEqSK995CEFkgIkAS+fY73XTeb3TSyyZbn+Ev2LfPOO/OfYJ6cmTnH69atW7dNX0IjARIgARIgARIgARIgAUcT8Pb2Fl+Izxs3bjj6XayfBEiABEiABEiABEiABCQgIEC8yYEESIAESIAESIAESIAEcpMABWhu0ua7SIAESIAESIAESIAE6AHlzwAJkAAJkAAJkAAJkEDuEqAHNHd5820kQAIkQAIkQAIk4PEEKEA9/keAAEiABEiABEiABEggdwn45u7r+DYSIAEScB4Cx44dk7fffjtVg3x9fSUsLEwiIiLkkUcekQIFCqS6n1sn27dvl/nz5+vr3nzzTcmfP7/dV2elrN1KrG7MmjVLDhw4ILVr15Znn31W79q6ZvVYqtN9+/bJsmXL5K+//pILFy5IpUqVpHXr1tKlS5dU5Q4fPiwzZszQa6+88oqUKFEi1X1HnNy8eVOuXLkiISEhjqiedZIACWRAwCs5Ofk2wzBlQIm3SYAE3JLApk2bpE2bNnb7BiH0zjvvSKdOneyWcdSNL7/8Up5++mmt/uTJk1KsWDG7r8pKWbuVWN144IEHZM2aNfLggw/K559/rndtXbN6TE9v374tM2fOlLFjxwqEnrU1b95clixZYhb369evl/bt22uxLVu2SLVq1awfydHzFStWyMiRI2Xy5Mly//3352jdrIwESCBjAgjDRA9oxpxYggRIwAMIdO7cWe666y4VTCdOnBCIlKioKOnevbv88ccf6r3zAAw50sWFCxcKPJmwqlWryqOPPiomZ4csX75c9u7dK2vXrpXp06fLa6+9liPvy0olEPPdunXLyiMsSwIk4AACFKAOgMoqSYAEXI/AU089JRChhu3cuVNatmwpSUlJ6i2DeIJBlMKrhylxy+nb06dPS0pKigQHB0vhwoW1rHENU/qBgYGyZ88euXz5stSrV0+CgoK0TExMjE5Rw+uX3tQznsNUO+qCqPPx8dHnbX3LTNno6GjZvXu3elZRHzwSOWFXr16VMWPGaFUNGjSQH374wdxXeB2bNGmi73333XdlwIABNj27mJXbsWOHIFtKzZo1lZ1l2+BhxbT+mTNntEy5cuXS/IFw9uxZHbuiRYuq+MV4Vq9eXTAmhoH9qVOnpHTp0sYlfpIACeQSAW5CyiXQfA0JkIBrEcDaR3g/Yb/88ot5Kvmxxx7TKWLDw2f0qkWLFnodU8+GNW3aVK9hLWejRo30C1PNFSpUUIEFoQbxhGlueF9ffPFF49FUn5988omWg0BGPaj30KFDqcoYJxmVTUxMlBdeeEHKly+v70VdOP7666+NKu7oE6wgbmFgZAhtnEM0f/TRRzr9jvWltpYVYDoea0WxNKJVq1YqGrdu3YrH1S5evCgQtvfcc4907dpV1+nWqVNHjxMSEoxiumwCov6tt95SwY9lFBD+bdu2NZcZOHCg1K9f33zOAxIggdwjQAGae6z5JhIgARcjAM8gDCmLMS2fXRsxYoT4+/vLc889p+se4+LiBIL1vffekyeffFIqVqyoVX/wwQe67tL6PaNHj1ZPoCGe4P2DOEa7rC2jsq+++qqKQC8vL13/CDEHjynasW3bNuvqsnyODUWG1a1b1zg0f9aoUUPfW6RIEfM1y4Nhw4apwOzZs6d6Zc+dOye4Zth//vMfweYmbBLr06ePee0oPK3wqlobBOj58+fVMw2PNryghkF416pVyzjlJwmQQC4SoADNRdh8FQmQgGsRKFmypLnB2DGfXatSpYqsWrVKd3pj2hmG6fopU6bIvHnzBF4/TDfD4Bm0tueff15+++03+eqrr2Tu3Ll6G9PnP/30k3VRSa8spqyN519//XX1RGKjkbEBaNKkSWnqy+qFo0eP6iPwdlouUchsPf3795elS5fK7NmzzR7hP//8Ux8HM2xgGjRokJbBBjHssr/77rv1/pEjR9K8BlzhQcXaTwhU1G0YNiGtXLnSOOUnCZBALhKgAM1F2HwVCZCAaxFAmB7DMgrHhHWJ9gzT5vCAwizXeSIkEQx1G+tGY2Nj9ZrlN0zRG4YlAPBewmyJ1fTK7tq1y+w1hfcUO9HxFRoaqvVZTnXrhWx8K1iwoD4FsRgfH5/lGgwvLx7E8gSYURdELTy/b7zxhly7dk0F/L/+9S8zBywvsDYIVkzpI7yWwdi6DM9JgARynwA3IeU+c76RBEjARQhYbljBuk1Ls57+xmYlexYeHm6+BSFkWPHixY1DFUjmE6sDTBUbhuexAQriDrE1rS29svCAGoapemuD+EU//Pz8rG9l+txYToAH4HW0DqmEzUWbN2/WDV+YRrc2S6+zZTuwix6GzWBYw4q1oDBM6WOjEabqDS+y3vjfN8v6LK/zmARIIG8J0AOat/z5dhIgASclAI/md999p62DiDFEpCFyrl+/bm45xGh63j57O9YtBZa5MhsHEG2GYSe98S5bu7fTK2speDEVjWl86y97bTXen9Gn5aYeI5C+5TPYmDR8+HD1StpaV5ve+xGBoEePHio++/btK1hvCjFrrNU1xsbyfTm1u9+yTh6TAAncOQEK0DtnyBpIgATcgAC8f/DYQdRs3LhRevfuLZiyhlmujYyMjNRrEG6GVw7rEG0FXNeCOfANcTUxDY0vHBtma5NPemWxC9zwwCIWJ6a48YUpbaxNhXfRlogz3peZT+xIR9xPGDZVYZ0mvJXHjx8XeF3xXhg2YZUtW1aPM/sNSwSMpQ4YHyxnuHTpkopo1GErqYrRX+MdlgIX0/g0EiCBvCHwz1xQ3ryfbyUBEiABpyAwePBgm+1A9h+E+zEMog/rJhEGqXHjxrrRBhmVsL4Qu8lz2rB2dPXq1bp7G+IJQg7WsWPHNCGEMlMWm5SwwQfholAv1mxCcEN4Yod8ThgELTyxYDRq1Cj9sqwXm5Ns7Vi3LGPrGJu5sP4VIhS79hGm6ccffxRj3awxLW/rWeMapuvhecZSgyFDhggiFKCdlsLUKMtPEiABxxGgB9RxbFkzCZCACxKAEMOUO+JMIqbm4sWLU/UCsSMhfuBZQzggiJdPP/1UbHkjUz2YzROEK0JWJggviE98YuPNxx9/nKbGzJTFznt4IlEW4Zx+//13jY+JkFCICZoTBs8kRG2/fv1SxfpE27FJ6ueff9ZYqFl9F9aXwquK0EnY8Y6Yopjyx252GDykCISfniEuqRFvFX8wYKOZrbW06dXBeyRAAndOgLng75whayABEvBAAgh6jviSWZ1GvhNUWDMJ4VioUKEMq8lMWWyyypcvn27iybDCOyiArERYtwpWObUmExmMwCKj6AT2mo2pewhPbNq602UH9t7B6yRAArYJ4P8DFKC22fAqCZAACZAACZAACZCAAwhAgHIK3gFgWSUJkAAJkAAJkAAJkIB9AhSg9tnwDgmQAAmQAAmQAAmQgAMIUIA6ACqrJAESIAESIAESIAESsE+AAtQ+G94hARIgARIgARIgARJwAAEKUAdAZZUkQAIkQAIkQAIkQAL2CVCA2mfDOyRAAiRAAiRAAiRAAg4gQAHqAKiskgRIgARIgARIgARIwD4BClD7bHiHBEiABEiABEiABEjAAQQoQB0AlVWSAAmQAAmQAAmQAAnYJ+Br/xbvOBOBqKioNLmfkYIuMjJS2rdvL4GBgZlq7s2bN+XWrVsZlkfO6bVr18qmTZskf/780qZNG+nQoYP5Hcgh/csvv8iwYcPM11zxACyQC3vdunVSpkwZeeSRR9KwOXjwoPzwww8SHh4uHTt2lODg4DRdTUlJkalTp8rzzz+fJq0h0jV+8803cvv2bbnvvvtyNXUj8mL/+OOPcuzYMWnUqJG0aNEiVduRC/v7778XfLZt21YqVaqU6r5xcvToUa1nwIABxqU0n7Nnz5ZmzZrJ3XffneYeL5AACZAACZCAJQF6QC1pOPHxuXPn5I033pCffvpJNmzYoF/Lli2TPn36SM2aNQUCNSND7uOGDRsKciinZ5988olUr15dpk+fLr6+vrJjxw7p1q2bvPjii+bHIEDffvtt87krHoBZxYoVpW/fvirQXnnlFeVz8eJFc3cgKuvVqydbt26VWbNmqRCPiYkx3zcO8Ozrr78uV65cMS7p55dffilVqlSRlStXyvLly1UErl69OlUZR53897//1T9QMJ7btm2TBx98UF544QXz6/bu3SvlypWTOXPmyObNm6VJkybaTnOB/x3ExcXJo48+KqjPns2fP1/+/e9/C34uaCRAAiRAAiSQEQEK0IwIOdl9iKAVK1bo12+//abC6Nq1a+p9y6ipEBKHDh1Kt9jSpUtl0KBBAtEC4Tlz5kz1dH7xxRfywQcfCN7pLgbhBQG2e/du7duePXsE3sp3331XuwhWEP3wfn722WcC4QhPs3EfhU6fPq1e03nz5qXBAm/za6+9JqNHj5ZFixbJd999Jw8//LCMGzcuTdmcvgDP7ptvvinjx49XDyfGD3346KOPZNeuXfq6fv36Se/evdX7++mnn6qAxB8Z8NQa9vPPP6sohwfUnh05ckTGjh0r/v7+9orwOgmQAAmQAAmkIkABmgqH651g2hhTq5aeJ3i7Ro4cKV27dlXvVnR0tCQkJKgQQg8hgH799VebnX3nnXfk8ccfV4+nZYH7779f4OVDXZYGkQovLN4FcWwpXjDtC6/YQw89pHXCowpRBktKSpKBAweq5xEiDd45TOdbenJv3Lghc+fOVe9b//79Zf/+/frM2bNnzU34v//7P+nevbu+H8IwOTnZfG/79u1a/sKFC+ZrlgdYwjBixAjzJSw1qFu3rmD5AQxLDCBQ7733Xj338/OTp556ShYvXqzn+IZ2QexBuFsb2jJ58mR55plnzLfCwsLSMDRuvv/++2mWWezbt08GDx4s169f12lyCL3OnTtLjx49BF5HS95GPfjEOGHZxGOPPWa+jOlx2IkTJwQedfycPPvss+Ll5aXXe/bsqeMBby8M0/L4WQBfe0st0EeI2P/85z8CnkZdWgG/kQAJkAAJkIAdAhSgdsC4ymUIS0zJly1bVpu8ZcsWgViE+IOo+/rrr+XJJ5/UqXRjbR6m17Ge0dogciBksRbQlo0aNUrFoHEPHlWIFtRXuXJlGTNmjLz11lt6GyIO0/2Y9n/iiSfkrrvuUo+c4f3Dmkl4Wf/1r38JprQfeOABWbNmjbbZqH/o0KEyadIkrQfiD2tQ8QzqhL300kvy8ssva92NGzfWJQPoq2EQYfDYQsjaMojPdu3amW+hPNa9NmjQQK+hD+XLlzffxwHW3EIAQ3TCsO7xq6++koiICD23/JYvXz4ViwULFlTBt3DhQhXUEN62LCAgQBlCnBv28ccfy+HDh9XzCrGI/mA6HMsCMB7Tpk0ziqb6LFGihPIICQkxX8dyAB8fH6lTp46cPHlSr1v2r3jx4voeeHVhEOTwCuMPBCzFsGXwshYqVEjgTaWRAAmQAAmQQGYJ2P6tktmnWS7XCWCqF+IRni9sjoEggRfKWJ8JQQYRBm8jDGIU3jKIKazjhAcN3kqsfbQ2iA3UBbGYGYOIXLBggQoalIeowWYeCEO0De957733xNv7779zML39xx9/pKq6S5cuKnBwEW2Cdw9eUHjoMC2M6e/mzZvrM+g3psTRd0yPGx5DCDIYxCzWw6IN8PZhwxC+MmMQqb169dL1ms8995w+gv4ULVo01eNFihRR8Ym+wJtZunTpVPftncBLuHHjRvWooo+2DLzgMca0N8YNY7FkyRLtM8pjQxim1DGeMGwYMoSwXkjnG5YZYCnA8OHDpVSpUrrxKigoKM2Gq8KFC+sfBKgKoh+i1J5h3Sh+HrGJi55Pe5R4nQRIgARIwBYBClBbVJz4GtbvwaMGK1asmE5/YgocHjgIM9yH59AweMAg4mAQoekZvGYw62l2e8/AY1erVi3z7dq1a+taR1yAZxHCEV7NAwcOCKaSV61alcbzCk+eYYaYw5pWTO3Ds4aNMYZBTEKAwjC9jv5iGtly+QG8drhnTDcbz6b3iU1HmKrGJ9ZpGmsZ8WnpjUQdxhICYwzSq9fyHtbtQrTCA4w+gwnGz9JQJ0Q01otCgEKIwiuNdaMwTP8PGTJENwMh8kGnTp2kWrVqllXYPIbwhUi3FPu2+oaHIXoxlZ6RxcfH688ePLAlS5bMqDjvkwAJkAAJkEAqApyCT4XD+U+wzhEiCV/wPsKjaUz/YjoeX5j6zY5BSMCrh93Rtgy7oLHO0zCIPcO7iWuWxxCFEEdYI4llAZj+tyUKUYdhxvMQlhCDaIvl1K9l+CNM/+MeRDCeM77wvqpVqxpVZvgJbytCI0FYYqe6IcLxII6N6X6jIpyjXfAeZtXwxwDGCyIP0QxsGbybCIuE8Emff/65ikZjPKdMmaLrT+H5xAYqLBXA9Hh6hrogVDF9j58dgzH6hnbgPZYG7lhmkJFhaQDYYXMTPLf4QgQAvANrhWkkQAIkQAIkkB4BCtD06LjYPXiuQkNDU+10xxQtpuQxLW1Mk0Lg2TOIsQ8//FC9dZZl4L3DND+8aZmxiRMn6hQxxCzECry0aEtmp4whXjEFDkFkGDYFGYa1ixBQ8BTCq4gvTDHjuq3lBcZzlp9Y64iNOlhygFiZ1h5JtAHeVLzHMCwhwMakzBg2TWFtLNbjGgbvLuqzxwEiHdPemHqHeDSm2/EcliRAdEJ8Yuc5hB6iFMBLassQpgsbiBBKylh7a5SrUKGCTrFbLomANxntykz/0A6soa1fv775C38QQLwi7BSNBEiABEiABNIjQAGaHh0XvIcd19jJDrEGrx7ECtboYdoX6xdhmN6GB9GWYRMRvHtYL4ppY2wQgpDBRiLYq6++auuxNNewXhPewsTERJ0qh8cWcTDtbQiyrgBTzBBCeC/EM3aeQ2wZhoDqEJqIvQmRCxEG0QuPoDE9jmlu9MdeXzGdjXWs2BQEoWnEVzU8wGAAQx0QZlgji1BGWKeZGYMQw7ICtAk78SF4sXEIQhf9s2X4IwGiERu6sFYTm6tgGBNEBIDIxu50cMUfBfBa20pCgDW0CBqPKX20w+gbPrHEAm3ADnds8gIfCFysL8U0f2am1O+55x7d+Y7d78aXsenq6aefttU1XiMBEiABEiABMwEKUDMK9ziAVwpeTAgPeNKw8xmbdSAOsKYSO9yxIQbCw5ahDEIKwZMIgQYRCC8cvHbwyGVmzSHqRcBz1AWPGEQYhDDWbyKepHWwdlvtwG5tvA9iEuszsdva2GkNMYYNMhClEE7wxuEdiNMJ762x8xvLAAzBZv0OeCXh9YQohBcVa1aNL2MKGe8xdq5jyhqbhxC0PrMbm/BOiFcIV3gcsTQAa2EhxOGptmcQoBDvhvcT5SBMsbEM/OCxxbIL9NdecHi0G9PrmCI3+mV8ot8wiHcsYUAEBcMjCs40EiABEiABEnA0AS+TsLidWa+UoxvD+nOOAMYUnjJb4ZYgTCBIIfIyMgg0CEl8Zcfg+cOGF8Mrmdk6YmNj1bOJjUzG0gF47yCiEAbJej0oBLL1FHpm35WZcuAAz6CxhjIzz1iWOXPmjK5ZtTUeluUyc4wNQPCApidiM1OPUQbLHDB9nt0xNurhJwmQAAmQAAlkhgCcHxSgmSHFMrlOABtcsNkG08JYVoD0oQjIjjBBiLtJIwESIAESIAEScE0CuoHYNZvOVrs7AUx5I+g8QhFhGQB2cmM6H1PLNBIgARIgARIgAdcmQA+oa4+fR7Qeywnw1xKNBEiABEiABEjA9QnQA+r6Y+gRPaD49IhhZidJgARIgAQ8iAB3wXvQYLOrJEACJEACJEACJOAMBChAnWEU2AYSIAESIAESIAES8CACFKAeNNjsKgmQAAmQAAmQAAk4AwEKUGcYBbaBBEiABEiABEiABDyIAAWoBw22M3QVeejTy0XvDG1kGxxDgGPvGK6uUCvH3hVGyTFt5Ng7hqsr1JrR73oKUFcYRTdpI/KpIw1nUlKSm/SI3cgsAfyPiGOfWVruVY5j717jmZXecOyzQsu9ymLskSo7PaMATY8O75EACZAACZAACZAACeQ4AQrQHEfKCkmABEiABEiABEiABNIjQAGaHh3eIwESIAESIAESIAESyHECvjleowtVeOTIETlw4IC22NvbW8LDw6VKlSoSFBSU7V4wbWS20fFBEiABEiABEiABDyHg0R7QLVu2yJIlS+Tw4cOyd+9eWbhwoQwePFg3S2Rn/Hfu3CkzZ87MzqN8hgRIgARIgARIgAQ8hoBHe0AxyvB4vvDCC+YBf+mll2Tz5s3Stm1b87UzZ85I4cKFJX/+/OZrycnJcurUKSlUqJAUK1ZMsMP7xIkTuusrISHBXBY7vqOioqRkyZLi6/s37sTERPHx8RHUERsbK6VKldJzc+W5fJCUckt+3BUlVxKT5d5KIVI25J9+5nJT+DoSIAESIAESIAEPIODxAtRyjCEcL1++LBEREXr5999/l/nz50twcLB6SXv37i2dO3dW0Thy5EgpUaKEHkdGRkr//v1lxYoV6j1dtmyZ9OjRQ+bNmycbN27U58+fPy8TJkwQlF2wYIGcO3dOBStE7fXr12XGjBlm0WrZptw4/mzDCfNrft0bIy2rhkr50ALmazwgARIgARIgARIggZwk4PECFALx6aefVg8mxGerVq2kWrVqKgqnTJkiY8eOlVq1aklMTIwMGjRIOnbsKJi6L1++vLz66qsa0xIiNTAwULp27ar3ID43bNgga9euVRGKexCly5cvl2HDhun4nT17Vj7++GPx8vLSaf/t27dLs2bNcnJsM1XXjaSUNOUOnL1CAZqGCi+QAAmQAAmQAAnkFAGPXgMKiHXr1pWpU6fKtGnT5M0331Sv5KJFi+TkyZPi7+8vNWvWVNZhYWE6Vb5jxw59Zv/+/dK3b19dN9quXbs0G5f27dsnDRs2VGGKClq0aCHr16/XaXecN2jQQMUnjuFJzShgK8o5wnx9vMXLquKw4OxvwrKqiqckQAIkQAIkQAIkkIaAxwtQeCex+7148eJSo0YNefjhh1UoGus6sbbTMOxwT0lJ0bKYRu/Xr5/Ex8fL0KFDBTvqLS0kJMQsNnH95s2b6i3FbntYwYIF9RPfcA1ZA/LCfLy95F/1/l5ygPdHFAmUepFF8qIpfCcJkAAJkAAJkICHEPD4KXjLcUaqwHXr1uk6TQjQ0qVLy6ZNm6Rp06Zy/Phx/apYsaL8/PPPsmfPHhWederUkYMHD0p0dLR6TCE0YZhOxw57TOtjA9Mvv/wilStXVrFp+U5nOC6c3196Ny/nDE1hG0iABEiABEiABDyAgMcL0FWrVsnq1at1qOGVxJR7nz599Lxnz546LQ9vJzydL7/8su54b968uT4DDygEZ4UKFaR+/fq6ThRhmMaMGSPjxo2T9u3byzPPPCNFixbVDUajR4/2gB8pdpEESIAESIAESIAE0ifgZQoFdBtTyzT7BAwvpnUJ7F6HYRrfMEzZI7wS1o/CEIYJfAsU4K5ysLl69aqulzX4GNz46d4EsMQEMwxI8sCxd++xtu4dx96aiOecc+w9Z6yte4qxx94Wy/CVlmUCAgLE4z2glkDsHWMK3ZZZCk/jPtZzWv6C9fPzE3zRSIAESIAESIAESIAE/ibg8ZuQ+INAAiRAAiRAAiRAAiSQuwQoQHOXN99GAiRAAiRAAiRAAh5PgALU438ECIAESIAESIAESIAEcpcABWju8ubbSIAESIAESIAESMDjCTilAEUay5UrV6YZHKSvRL517EqHIYQS8rc7s124cEHzwaONyI5kHbDemdvOtpEACZAACZAACZCAIwg4pQD9/PPP5a233pJjx46l6vOXX34pM2bMkKioKL2+Zs2aPEthmaph6ZycOXNGkNoThvSdFKDpwOItEiABEiABEiABjyDgtGGYqlSpIr/99puUK/d3hh6kwEQe9tDQUPPAvPTSSxpjCvcQkxNhkSD4kMXIiD2F2IOFChXSIPF4EDndLS0uLk49qqVKlRIfHx/zLdQHoYtnUR/MeA/CKiHzUURERJrMRng/wjYZ7zdXaDro2LGj5amcO3dO44SWLFky1btTFeJJlgicvXRN1h44L9dupkjzyiFyV/g/KU+zVJETFD4YdVW2nbgkiaa+PNqwtBQIdNp/rk5Ai00gARIgARJwJQJO+xutRYsW8v3330uvXr2U586dOzVX+19//WXm279/f5kyZYpARM6aNUsQ6BzZjI4ePSqjRo2SunXrar726tWrq2CMjY0VZDHCc7CPP/5YfvrpJxWS58+fl4kTJ2r6TUzzw2sZGRmpXkuk4hwyZIgcOHBApk+froHmS5QooR7ayZMnS5kyZeT333+X+fPnS3BwsBw+fFh69+4tnTt3NrcVB8ioBEH7xBNP6LsgcNFeiNlJkyZpTvpUD/AkSwSuXk+SH/+KNj8DIbrvzBXx83VKR7+5nbYObibfkvPxf6d1xf3l207LE43LiK+P6/XFVv94jQRIgARIwLMJOK0ALVu2rPj6+qqYLF++vGC6vWXLlmIpQC2HDlPbH3zwgYrGxYsXyw8//KACFGUgECFI4XFEasy+fftqVhasIf3ss880cPyPP/6oghf3tm3bJhCWEJl4BiJ48ODB+rqTJ0/KvHnztM7vvvtOlwRAuEIIjx07VmrVqqXe1kGDBqXxeBrtRTagzZs3C9oJry3airWi4eHhRhF+ZoNAwnVTBiqT2IR4MyzWQsQZ11zxMynltiTfum0SoK7YeraZBEiABEiABFITcFoBimbCC7p27Vr1Su7du1eGDh2auvUWZxBv8FjCMDX+559/6jG+NWrUSI+LFy+un0iNuXHjRhW4WFMKw5Q7PJwQoMOGDZP169fL8uXLdc0mUkohvSYMU/UQtDDUC9GLtarIfoQ88jBM86Mclgwg3ZS1wevZoEEDefrpp6Vhw4YCD2u1atWsi/E8iwRCCgakEp94vFGFouLj7ZXFmvK+OET01mOXtCFofpC/jwT6UX3m/ciwBSRAAiRAAjlBwOkF6OjRowXrQTGdbrlG07rzEHX2zDIPu5eXl0BQ4gtrLzt16pTqMQjRAQMGSOPGjaV+/frSrVs36d69u5ZPVdB0kpiYqGk2ITgx/Y8vo40QuVgzas9ee+01gTcVO/7nzJmjQhfvoWWfAKann25aVtYdPC/+puM6kYUln79T/4in29nKxQvKH0cvSsEgX6lZ2nY62HQr4E0SIAESIAEScFICTr2gDJ5MeBAXLlyo0+85yfDee++VQ4cOCbyiVatW1c1LS5culYsXLwqmyJ9//nn1Tu7atUtfa4jJ06dPm3eyY1kAptyxSal06dKyadMmLXv8+HHBV8WKFW02+dKlS/Lcc8+ppxTrQbFWFPXS7pwARGirqmHStFKIS4tPkAgweTybVQ6V2mWKiLfpDycaCZAACZAACbgLAad3D2EaHmstc3qKGpuFunTpopuFIHThucTUO44xJT5w4ECB5xTT7RCpiEEKg9jEhiFYvnz5ZPz48Xrcs2dPefPNN3WjUXx8vLz88staFrvira1IkSLSpk0bfQfqgNcVa1RpJEACJEACJEACJOAJBLxMaxtvY7rYUw2ezWvXruludEsGmF6HKMXaTsOwDvW9997TL3hJbU37I0g+wjBlxrAMAPVgZ7wnGJYooL9BQUGpuHpC3z29j/hZR0g0jr3n/SRw7D1vzI0ec+wNEp73ibGHtrIVkhI0MLvt9B5QRw8bRKYtIYlflOmZrWdQPrPiE2WxHtVTxCf6SyMBEiABEiABEiABEHDqNaDONkTY2f7ss886W7PYHhIgARIgARIgARJwKQIUoFkYLngrsRufRgIkQAIkQAIkQAIkkH0CFKDZZ8cnSYAESIAESIAESIAEskGAAjQb0PgICZAACZAACZAACZBA9gm49CYkxN1E3E6Yt7e3biaqU6eOhkfCbvTdu3cL4n3CsNPfyEpkeaw3rb4hTid2vCMckyMMdaMtFSpUcET1rJMESIAESIAESIAEnJqAS3tAlyxZIr/99pscPnxY02h+++23mt4SsTchQJFlCLZz506ZOXOmHiOlJoLMp2eI+fn555+nV+SO7iG96JYtW+6oDj5sn0Ds1RsSc+W6/QI5cAepMs+b3pNiys9OIwESIAESIAESyBoBl/aAoqsdOnSQVq1amXuNwPA//vij9OrVS1NqIvbkiRMnNB5VQkKCBn2Pjo5WgWqETDp37pwkJSVpak4jlSYqvHnzpkRFRUmJEiXMcSsRHxTeVsObijr9/Pz0mpF+E/GvEOgeBiGMehDAHs9apgXFsxcuXNC88aiTducE1uyLkRPnr0mKaQzCCwVIh5ol7rxSqxriryfLl1tPi68pSXuySYA+0biM5mq3KsZTEiABEiABEiABOwRcXoBa9wuxNRH49ODBg+r1fOONN2TFihUaBHvZsmUSFxened2nTp2qWYyQvQgiE3E9IUyNLEcQhn379pWiRYtqmkwI28qVK8uHH36oQhVZlGDTpk2T5s2bS2hoqOZ0R/2+vr4yf/58mT59umzfvl2XBBhid8qUKfrcxo0bBZ5QBMKHYEVZewFb9QF+y5AAPJJHYxPM5aKv3JAF64+bz3P6AOITtvnoBWlZJSynq2d9JEACJEACJOC2BFxegO7YsUO9m/AyYm0lprb79+8vsbGxOmhIe9m1a1e93qNHD10L+v3338vEiRM1K8/mzZtl8eLFEhgYKD/88IN6JPEg1pbOmzdPU3F+9dVX8v7776tITO8nAfnfFyxYoKIVAhPtgRCFIIUQhjg1DBmW3nnnHfWcYkkA1qs2atTIuM3PbBC4ZRLyhlcyG49n+5GriUnZfpYPkgAJkAAJkIAnEnB5AQqhiSlwTJ1XrFhRp95DQkLMAjS9QYXXs0GDBrputGHDhrrpCDnn9+zZo8ITeeBhjRs3VjGK6fz0DFP1eDds3759KijRNhg2Q0H4GlazZk0VnzjHZiRjM5Vxn59ZJxBWKFBCCgbIubjrKkQD/XykU+2cn4K/lHBTftodLf4+3nIz5ZbcV7141hvLJ0iABEiABEjAgwm4vABt165dqjWgWR3L1157TU6ePKkblubMmSNHjhwR7KS3XJOJtZ0Ql7iGKX5Mmxt2/fo/m13gRTUMU/fwiBqG9Z6WZpljHnViGp525wTur1VCTl+8JqBZqkiQjted15q6hnwBvrru80L8DQk1Cd4Ak9ClkQAJkAAJkAAJZJ6AR+x8gdjDFD0M0+EwbDpCuKXnnntOwsLC5IknnpDOnTvrek/chyjdv38/DmX16tXmkEzIhoRNTTB4LQ8dOqTH1t8QwgnLAbAj/8qVK7Jq1SrrIjx3EIFSRfNJadMXhL2jLMjfR/Aeik9HEWa9JEACJEAC7kzA5T2gmRkcTHEjDNOYMWNk3LhxUr16dRWcWOPZpk0bGThwoG4Ugjdz1KhRAm9l6dKldYMRvJ3wbOI5WNu2beXll19W4RoUFCSYsrdl4eHh0q1bNxk5cqTuco+MjBSEd6KRAAmQAAmQAAmQgKcT8DLFxbyNKWZ3N6zfRAxQY+obYtOYMsf099WrVwXeTWvDxiEjpJLlPYRXMna2W143jiE2Y2JipHbt2nrpyy+/lGPHjslLL71kFPG4T4wBOEO4G+PgcRA8tMP4N4aZAI695/0AcOw9b8yNHnPsDRKe94mxv3btmt3oPghl6REeUAw91m9aih5DfOIepmptiU/csyU+cT098Yn7WAM6YsQIue+++3CqU/CjR4/WY34jARIgARIgARIgAU8m4DEe0LwYZHj7ECYK601r1apl3iGfF21xhnfSA+oMo5A3baAnJG+4O8NbOfbOMAp50waOfd5wd4a30gOax6OAME8IUk8jARIgARIgARIgARL4h4BH7IL/p7s8IgESIAESIAESIAESyGsCbrMGFPE7Dxw4oDyx3hO70KtUqaKbHtKDjJSbeK5JkyZaDBuysDgWgeSxZhQ76GkkQAIkQAIkQAIkQAI5R8BtPKCIublkyRI5fPiwpsBcuHChDB48WHfepocLcToXLVqkRRCIHvXAEAMUopZGAiRAAiRAAiRAAiSQswTcxgMKLPB4vvDCC2ZCCHmEXO+I3QlD6KWoqCjd8V6sWDFzORzA84ng8/B4Imh9x44dzfcRPga75KOjo9WjimOEdMJ5RESEuRwOsOEI7yhZsqQ56H1iYqKmCoW3NTQ0VK9jgS7KITA+AuHTcoZAsik15l+n4yQ55bbUjSwiPt6OC0afMy3O+1oOnbsqCTdTpFrJQuLv6zZ/k+Y9WLaABEiABEjALgG3EqCWvUQwecTqNATiihUr1NOJgPDwbiJT0ZAhQ8yP/PHHH+rxRNxPCEIIVwhNZEjq16+f1KhRQyAg4WF96qmn5JdfftH4VohzNXv2bBWYCGy/ceNGDd10/vx5mTBhguB98+fPl1OnTmnWJAS+7969uwa8h4iNj4+X8uXLa5B8R2buMXfUjQ8g6j/dcEKgOU2Hsu/sFWlSsZj4mnK202wT2G0S6zFXbphCkYlsP35JHm1YWgoEuu3/FmxD4FUSIAESIIFcJ+BWv2kg/p5++mlBuB+Iz1atWmmmIgiTbdu2yeTJkzWn+7lz56RXr146RW8Qb9asmaxcuVLat2+vweMhQC0N2ZMeeugh+fbbb1VwYtoeMUKHDh2qU/7wkq5du1YgQhFjdNmyZbJ8+XIZNmyYVoOMSlgigLb88MMP6mmFAMZ1ZGjau3evZmiyfCePs0Yg2iSk4PFMuYVM8CLJps+1B85nrRIPLQ3BDuF+JCZeapUp7KEU2G0SIAESIIHcIuBWArRu3brqrYTIgwdy7ty56vV8/PHHVQiuX79eRSHWdqIMPJCZNXhAYSVKlJCKFSuaA9RjKh/vQp0NGzY0Z1dq0aKFPP/882aRi+fh4cQXhCps6tSp+gmxvG7dOgpQpZH9b0F+3mbxadRSyOTNowfUoJH28/K1m/I/va6f9H6mZcQrJEACJEACOU/ArQQoPI/Y/Q4rXry4PPzww+qJxOeAAQOkcePGUr9+fc3RjmlwiNDMGlIIGmaZUcm4FhISoqk2jXOsI8V6UOzIh1k+j3O0wxC1OC9QoAA+aHdAIDifvzSrFCLrDp4XPx8vqV+uqFQ1rWuk2SeQcCNZvth8SnxN7s9KxQtKhTD+HNqnxTskQAIkQAI5RcCtBKglFEyJw6sYaVqDibWbyEoEjyQ2/fz6669aFNPflgZhic1I2TFM4WOK3cgRjzWilStXNgtQyzrhHcWSgH/961/anvHjx+ua1NKlS1sW43E2CFQ0iajI0Pz6pB/XfmZIMH+Ar/RuXi7DcixAAiRAAiRAAjlJwK0E6KpVq2T16tXKB1mIatasKX369NHpcmw6GjhwoHoay5Qpox7Ss2fPpmJ59913y7vvvqu74FPdyMQJpuKxfvSZZ57RPPD58+cXe7nfIUCxHKBHjx7atrJly0rLli0z8RYWyQwBCs/MUGIZEiABEiABEsg7Ah6VC94Ih2RrCt0YAkydw0tqTJ0b1zP7iWl3eFEzM6WOnfp+fn4a8D6z9btyOeaCd+XRu7O2Y7kLZiWwFCW9f3939hY+7YwEOPbOOCq50yaOfe5wdsa3YOwRJQjOOFuGhD9u5QG11UnLa9brMC3vGcd3+ssRghJfmTF7A5OZZ1mGBEiABEiABEiABFyVAAMkuurIsd0kQAIkQAIkQAIk4KIEKEBddODYbBIgARIgARIgARJwVQIUoK46cmw3CZAACZAACZAACbgoAQpQFx04NpsESIAESIAESIAEXJVArm1C2rp1q+ZCx+7wWrVqCUIh5ZSh7piYmFTVYRd7hw4dUl3L6ROEfWrUqJHdXV45/T7WRwIkQAIkQAIkQALuQCBXBOg777wj+/bt01SViL25cOFCzUyEeJg5YV9//bVu90c8TcMQSsnRtmbNGkHsUO5mdzRp1k8CJEACJEACJOBOBByu0uLj4+Xnn3+W//73v+b86Tj/4osvxFKAXrp0SfCFFJr58uVTxogbWKhQIbN3MywszC77Vq1aSadOndLcRx2oLzY2VvO4o8D169clKipK60YAeZgRIxT54VG2VKlS4uPjo/fwDVmT8AxywRvXX3rpJbP4RKYlPGfZfuNheGchUq2FKrImIe5oev0y6uCn5xE4GhMv245fkqvXk+XxxqUln7/D/7m6NOSr15Nk3YHzciH+hjxQu6QUze/v0v1h40mABEjAnQk4/DcaYm9CAH7//fdy//33S+HCheW+++6TNm3aKFcIu9dff11FJsTmgQMHZOTIkZq3vV+/flK9enWJjo5Wcde8eXPp379/lsYD6TfLlSun0/9jx46VkydPyqJFizRF5/79+zUF5pAhQ2TBggVy7tw5OXHihApFiNQZM2bo8e7du2XcuHFSoUIFbQfa1aBBA23LlClTtM2ffPKJlC9fXg4ePCjPPfectG7dWhBo/pVXXtHA9FgSAG8p2o/UoNOmTdPA3HFxcdoW1G8I2yx1kIXdkkDs1RuyZn+suW9fbDol5cPyi7eXl/maKx3cNjU2yfTHlo/vdfEx/VvIabtlCnp8JCbBXO1X287Iow1LS4FAh/8vzvxOHpAACZAACWSegMP/7wxRNXnyZJkzZ45OvWOavHHjxvLYY49JYGCgCkJ4BmfPnq2tRj515FFHGRjWio4aNUrFIdJc9u3b12aWIojKFStW6DP4hrIQibAmTZrIG2+8ocfLly/X9sCTCcHZq1cvGTx4sN7D8oCPP/5YvEy/5HFt+/btghzvEyZMkBEjRmh9EMiY8jfqxoM//fSTpvxE2UOHDulyA1yHqEV/X3zxRYFnFak5IT7nzp0rERERMmnSJPWsou6dO3dKvXr18BiNBCTmCoSayfN+628YEHCWAst1Ed3MlaYjHeulazcpQHOFNl9CAiRAAlkn4HABiibBMzh16lSBt2/Hjh3y7bffCjYOIe86vJPdunWTpUuXypEjR1S8WW5QwiYfGKa2YUhzaSujEbyqEICGhYeHG4fqeYSohA0bNkzzsEOI4n1IFwVxCIOoNMpBoCKNFKbP8Vm7dm0tU7lyZRWjevK/b+3atdP+QQBD7ML7CYOH9fHHH9djrEk1RDDWww4fPlyvQ6Cj3chhTwGqSPjNRKB4cKBZfBpAapYONv98Gtdc5tP07wz/dn1NWcIc4emHB/SvU3FmHEkm5R5eKNB8zgMSIAESIAHnIuBwAQrBCbHZp08fXQPasmVLFVxdu3bVNZWYXoeHFOcPPfSQisVNmzaZKVnmVIc4hGC0ZSEhITpFbusePK0wTKsPGDBAvav169dX4du9e3dznQULFjQ/jilzvAvLB/AL0/K9mKa3FMlYTgDxunnzZvXeQmDDyxkcHKweTqNS9BXLDNBWQ/TiHn4xYykCjQQMAsUKBEin2iVkzb5YKVbAX+6tFCIBfv+sSTbKucon/v04Ohd8rdKF5ce/zkl+fx9pWKGo+Pvm/FS/q/BmO0mABEjA2Qk4/P/Q2Mzz3Xffybp16+TWrb/nE7dt26aiDptv4A2sU6eOdOnSRSpVqiR//PFHKnGWkwAx/Y3NQlgX2rBhQ9m1a5dWn574gwCuVq2aiksUxlT522+/ncoThfWb8Ha2bdtWPayY2ofAvPfee2XDhg0qLrHZ6N///rdg4xHWsiKEE96LzU/r16/Xd+RkX1mX6xMIM3nwHm1UWtpUD3dp8ZlbIwHB+WCdksqrYKBfbr2W7yEBEiABEsgGAYd7QENDQ2X8+PG6oeett94STEVjIxKmo+FZhPcQAg5T0klJSVKzZk0VbdnoS4aPYN1l06ZNZeDAgQJhCS8mpvax9jM9e/LJJ2XixIm6k9/PNIUIL6qlwXs7a9YsXeN6/vx5wSYl9BMCFEIT61HhhYWHF1P7mKLfuHGj9OzZU0Uopu0feOAByyp5TAIkQAIkQAIkQAJuS8DL5Km7jSng3DBMgcMzaDmtbrwX03OYAjfWYBrXHfFphFzy989amBYjLJS9NuE++obpe0tDKCqsW7Ve+4brAQEBAlHrCQYPODzQYJFV9p7Ax537mBtT8O7Mz5X7xrF35dG7s7Zz7O+Mnys/jbHH/hnr8JNGn6B9HO4BNV6GT2MtpuU14xhrI3PLbG1iysy7M2qjvfu2BDfeZ+96ZtrCMiRAAiRAAiRAAiTgqgRSu+pctRdsNwmQAAmQAAmQAAmQgMsQoAB1maFiQ0mABEiABEiABEjAPQhQgLrHOLIXJEACJEACJEACJOAyBChAXWao2FASIAESIAESIAEScA8CuboJyRmRIV87At9HRUVJyZIlNT7onW4OQlQB7PCCGceIQYo0ngi5RCMBEiABEiABEiABTybg0R5QBIVHzve1a9dqpqNff/1VBg0apOk3s/tDgZz3W7Zs0ccRtH7mzJl6fObMGUG+ehoJZIXA1cQkSbzJLFlZYebosrdumcKL3ExOlR3N0e9k/SRAAiTgbgQ82gMKzyeyMSEQvmGjR4/W4PFGDndkMEKgegTUt45ndenSJcEXgtkjZSe8nSdPntSUoIh5ipSdiIMFL6u1IUYWvK4IWI820EjAmsBPf0VJdNwNSTYJnpZVQqV8WAHrIjzPZQI3klPkyy2n5UbSLUFS4B5Ny4qfj0f/HZ/LI8DXkQAJuAsBjxagEH5Hjx5Vj2Xt2rU1IPyYMWPMAePhwZw0aZJERkbKoUOHNIVnhw4dNHvR66+/rp5SxP7E1PrIkSM1k9ORI0ckLi5Og62vWLFC818vW7ZMatWqZf6ZQTD2UaNGaVB+BKMvX7684L25EYTf3AgeODWBw9FX5cyl6+Y2rtkfK78fPu/SPyP4o0vksqkP5m653MF1k/A0DN3YdPiCNKscalziJwmQAAmQQCYJeLQArVu3rvTq1Utmz54tWKNZvXp1gcBs0aKF4vv888/lP//5j+aqh6cT6ULbt2+vXk54Q/EcbMmSJfLLL7/Iq6++KitXrtQySMMJLyim43v06GHOO4/ymPKvUKGCDBkyRMUsxOfevXv1/bhPI4GbybfE19tLvZ8GjRvJEHD4cmVz9fb/wx49ib2aO1nk/nkrj0iABEjAPQh4tADFEN5///36henybdu2qaiMiYmRtm3byp49e1RYQlzC4K3ct2+fVKtWTbp16yZLly4VeDxxDXnlM2sQoLCpU6fqJ9airlu3jgJUafAbCFQILyCbjlwUkwZVyVkkn780qVjMZeHA+5mQcO1/qWdd9387502CE+Ni/HHQuhqXz7jsDyUbTgIkkKcEXPc3QQ5g++STT6Rq1arSqFEjKVu2rH6VKlVKPv30UxWgWJ8Jjyg+YZ06ddKd8pianzx5snTt2lUeeughufvuu3UnfVaaVL9+falRo4b5kTvdeW+uiAduQSDA10d63hspB6Kuir+vt9xlEqSubBCgV+SmaWmKv/j7+7tsV8IKBUpEkSCJvnJDwk3Hwfn8XLYvbDgJkAAJ5CUBj149X6RIEfnoo48EO9RhycnJ8vvvv+v0eOHChaVKlSq6zhMiFetF33rrLZ0yh8ezTp060qVLF6lUqZL88ccf+izqwC9XbEYyjrGJydowxb9//36pWLGiCmBM4R88eNC6GM89nICPyf1ZLaKQy4tPdxvGYJM3ulLxghSf7jaw7A8JkECuEvBoD+iDDz6oG4aGDh2qmzuSkpIE60JffPFFHQSs3cQmpMWLF2vIlUceeUSKFi0qbdq00Z3zWBOKZ2rWrCkbNmzQZ+ANfffddwXCE8cIw4Q1nhCrhkGArl+/XteGBgcHq+e1ZcuWxm1+kgAJkAAJkAAJkIBbE/Ayef1uGx47t+5pBp3DOkxsLPLzSzulhnvwiFrblStXpGDBgml2JkN8Ytre29tbbt26pd5RW9OOCM+E99m6Z/0udzgHC0QACAoK8pg+u8O45UQfdAre9O+FY58TNF2rDo69a41XTraWY5+TNF2rLow9wlBah680eoFkPR7tATVA4NOWwDTu27uHEEy2zFJQQoRanluWtzcwlmV4TAIkQAIkQAIkQALuRsCj14C622CyPyRAAiRAAiRAAiTgCgQoQF1hlNhGEiABEiABEiABEnAjAhSgbjSY7AoJkAAJkAAJkAAJuAIBClBXGCW2kQRIgARIgARIgATciIBTCVDsxkf+dOPr559/lr/++ktDIIE50mEifJEtQ2xOZCVCSs2NGzfaKmL3GnZr4Z34TM+QEQnpNdOzX3/91ZTxJSFNEeykt9f2NIV5gQRIgARIgARIgATcmIBTCVAItxkzZsihQ4fk8OHDKj7ffvttGTVqlIYzioqKEuRnt2UI7A4BiqDyixYtslXE7jWEB8J7MxKgc+fOFYReSs8Q2D4uLi5NEQhQI1Zompu8QAI2CCAf/N4zV+RITLyNu6516drNZDlx8YZciE+bmMG1esLWkgAJkAAJ5AQBpwzDNGjQIPHx8dH+JSYmyqOPPqri0ugwMhZBaIaHh0tgYKBe7tixo35aZxRKSUmRc+fOqbgsUaKEuV4URrxO5H0vXry4Pmt8gxCF2EUsT2RAsjbUCU8oYnhGR0dLRESExvy0LId34n6xYn/n7y5durQMGDDAXCS9dpkL8cBjCaTcui0LN54w5xzff/aKNKzgmrngbySnyMq/ok19MWUbO5UgLaqYct2HuXZqUY/9wWTHSYAESCCHCDilALXsG8QgYmnmy5dPPYuYhkemIsTWhJd03LhxUqtWLVmwYIEgLmf16tXNjx8/flwmTJggoaGhcvHiRRWc8HSiHLyR06dP1yxEEIOGIVA6PK4QufHx8VK+fHnNZOTl5WUUkQMHDuizKANRe+zYMc0NX6ZMGS2Dd+DeiRMnNKd83759NdUmsiLNmTNH0muX+SU88GgCyAFvysQpySYhCkPu8W93nHVpJiaHrtqWoxcpQF16JNl4EiABErhzAk4pQLEeE6ITYhN51pFvHV5GTG3jC8IR3s+FCxfKypUrVYDaQrFr1y7p2rWrdOjQQW/Ds7pz505p0qSJTJ06VUVj5cqVtQ6sIYWtXbtWc8EPGTJE874jjebevXtTCVuUO3nypMybN08gOr/77judwkeueNg999wjDz/8sJbBO59//nm9bnyz167mzZsbRfjp4QT8fP/5g8dA4e/jlSbrlnHPmT+TU25JisXy6ms3//mDz5nbzbaRAAmQAAk4joBTClB4FOFxxPQ6xCNyrxsGjyPEJ6xChQqyZ88e41aaz06dOsmWLVvkk08+kaNHj+q0PabdIR7hQYWwhTVq1Mj8LAQoDAIVhrWb69atSyNAS5UqpeITZfD8Bx98oIIV57Vr18aH3sf6UqSjsjR77bIsw2PPJlAxvKDsP3tVYq/eMP1bEGleOdRlvYZYTvD5ppOCNa2+JrfuI/VLefbgsvckQAIkQALOmYpz4MCBqdZqWo4T8ocaZjktblyz/MR0N8Rs+/btpV27dvLee+/pWlCs7cS0O8Qh1prC24ovw+rXry81atQwTqVAgfTXq2GdKtZ7GutW7aXeNCq01y7jPj9JAAQ61ykpcYlJ4u/jLUH+f6+JdkUyPibR+dQ9ZST6wmUpVCCf5At0yr97XREt20wCJEACLkvgH9Xlsl2w3/Ddu3frFHzbtm1VIGKDEoQnNgRBJMI7CoOHE2IU1qJFC8GO+ooVK0rVqlVlyZIlun5Tb1p8O336tHlj1Jo1a+wuA7B4xHxor13mAjwggf8RCA7yc2nxaTmQQX7e6gG1vMZjEiABEiABzyTg1q6Ibt26CUInLV++XKf0sVnp7Nmzeoy1nRMnThSETcJOd8OzCgGKeJ2MQtsjAABAAElEQVQ9evSQ4OBg3aTUsmXLND8d2N0+adIkvY4NUuPHj09Txt4Fe+2yV57XSYAESIAESIAESMCdCHiZdmvfRgB4dzV4NrGbHTvfbRnietq6h5ikmFa3NZ2OTUmYzscXds0XLFjQVtXpXsuoXek+7KI30WfwCgoKssnVRbvFZmeCAKJZ4N8axz4TsNysCMfezQY0C93h2GcBlpsVxdhj/0v+/Plt9gxOP7f2gKLXWNtpS2AaROzdswfNeM74zI74xLMZtcuon58kQAIkQAIkQAIk4G4E3HoNqKMGCzvgn332WUdVz3pJgARIgARIgARIwK0JUIBmY3jhNa1bt242nuQjJEACJEACJEACJEACFKD8GSABEiABEiABEiABEshVAm6/BjQzNJEaExuLYFibiXWdNWvWzPTmImziMnbRZ+Z9KLNq1SoNYI+1ptl5PrPvYTkSIAESIAESIAEScDYC9ICaRgTpORctWqS55SFEkVqzZ8+esmnTpgzHC88ix3tWDbFDsUMsu89n9X0sTwIkQAIkQAIkQALOQoAe0P+NBNJ6vvDCC+ZxQUrOt99+Wz788EPzLnrkoUdqTmxCQtYjhBU6ceKECkmEbYL3FNcvXLggoaGhgoxLMDyDFKCIN2rYSy+9pCFpNm7caH4+szvvjTr4SQJ3SgB52jceviCxV25I4wpFJaJovjutMs+ev5GUIpuPXDRlj7opbaqFS74A/u8tzwaDLyYBEiCBDAjw/9B2ADVv3lyQMhNZi5o0aSIff/yx/PTTTxIRESHnz5/XIPZI0blixQqNb7hs2TKNcXnq1Ck5dOiQ5q9/9NFHZdq0aXof4jUyMlLGjRunIrV///4yatSoVM8j+D2NBHKTwKcbTogp1bzcNn39tDvalG8+vxQM9HNAE27rUhNf3yRzytqcfAlizv15Ks5c5aLNpzT9Z4Cf66YwNXeGByRAAiTghgQoQNMZ1EqVKglSbsKDiTWbn332mQZQ//HHH+X777+Xfv36aapPpPSEeJw9e7am+kT6TvxCnDx5sgpWZExCCtARI0bolHu9evX0rci01LVrV00JSvGZzkDwlkMI3Ez+O/0sxKdhR2ISjEMHfV53UL2pq/X39ZaYqzektAt7dFP3iGckQAIk4F4EKEDTGU9sDkLmFkyTYzp9xowZWvr69ety4MAB6du3b5qna9Sooak+vby8ZN++fTJ8+HAtg6n5Zs2ayerVq8UQoGke5gUSyEUCvj5eJm+nr1y9nqxvhSe0eHCg5HfA1PVtk481Kelv76ePd857JVNu3ZZj5/8RzxDXxfL75yJNvooESIAESCArBChA7dCCyDxy5Ih0795d13mWLFlSOnXqZKf0P5chWA0LCQkRU6pT41SnIOEJpZGAMxDwNv2R9GDdkvJ/G0+aRKePVI8Ilhqlgh3StNxIydfAJKSXbT0thfP7SZO7QrgG1CEjyUpJgARIIGcIcBe8FUcIxOjoaPV2YrMRPJr33nuvrussXry4VK1aVc6cOSNLly5VTydyxWODkS3DOlJM3aPOxMREWb9+vVSrVi1V0fSeT1WQJyTgAAIBvj7Su3k5eaxRGYeJTwc022aVBUze3KfvjZQH60RISMEAm2V4kQRIgARIwDkI0AP6v3HANHuHDh1UVGJtJqbJsWEIhvMuXbpI7969dU0nptOHDRum97B7HmGYxowZI+Hh4XrN+Na6dWudvkdIJ4hQbGZ64IEHjNtpnjfel6oAT0iABEiABEiABEjAzQh4maaIb2OtIy1jAhCRiN2JQPWWhnBMmGqHN9OWxcfHa6B6Pz/bu4szet5Wna54Df28evWqrqu1x8oV+8U2Z0wgN6bgM24FS+QFAY59XlB3jndy7J1jHPKiFRh76CV74SWRvIce0CyMDDyf1uITjyP+Z3qCCuGa0rOMnk/vWd4jARIgARIgARIgAVcjwDWgrjZibC8JkAAJkAAJkAAJuDgBClAXH0A2nwRIgARIgARIgARcjQAFqKuNGNtLAiRAAiRAAiRAAi5OgALUxQeQzScBEiABEiABEiABVyPg1gL05MmT8vXXX8uiRYtk69atacYmo93/CQkJgrSbOWXIjITg9jQSIAESIAESIAES8GQCbitAN23aJCNHjlTBByH5wQcfyJtvvmke6zlz5mgOdvMFGwdxcXEya9YsG3eyd2n//v0UoNlDx6ccSCAZYcRS/s4L78DXsGoSIAESIAESMBNw2zBM33zzjfTo0UPuv/9+7WzXrl31HFmMkCIT3lEEkUcWo/RCKJlJ2Tm4fPmy1hEWFqYlEA8UaTyN0EuoHzmwEQurY8eOqWo5d+6c3kOaT4R4opFAbhO4kpgkK/6Mkms3U6RU0SBpV6O4Q5qw9kCsHItNEFPKdnnqnjIS4Mefd4eAZqUkQAIk4CIE3FaAIivRunXrNPVlZGSkZjNavny5Cj1cx1Q4PJwQjrVr187ycF24cEGmTZsmV65c0XrwDmQy2rt3r3z44YeaHQmVbtiwQdauXauZkhYsWCCFChWSJ554QiZOnChRUVEaVxSpPydNmpQmk1KWG8UHSCALBOD1XLrltPmJ0xcT5YvNJyUwh8Vhwg3TH2VJ/3hYV+6Ols51SprfywMSIAESIAHPI+C2ArRv377y/vvva8rMwMBAqVu3rjz22GNSpkwZadasmaxcuVLat2+fLfGJH5O5c+dqWk4IR2RIGjFihOzcuVPsZTuy/NFCNqDNmzfL4sWLBW374YcfBILWOpWn5TM8JoGcJmByRoqvj5dp+h1Hf1vCjRTBlyMt9iozrzmSL+smARIgAVcg4LYCFMJu6NChMnDgQPVKwgs5ZMgQmT59usBbeaeGDUXDhw/XajB9DlG7evVqadeuXYZVI5tSgwYN5Omnn5aGDRtK06ZN1VOb4YMsQAI5SMDPx1siCgfJ6UuJkoK5cZM1rRgi/r45uzT8qmmaf+vxS+Jlqh9vqVUmGK+ikQAJkAAJeDABtxSgWHc5evRoGTt2rHoYa9WqJfjCrnd4HnNCgGIdKdZ7Goa64QmFIee5YYmJicZhqs/XXntN16Fiih4borAkoHv37qnK8IQEHE2gTfVw+fPkZbmRfEsqhheQIvn9HfLKCmH55a+TF6RogUCpVLKwQ97BSkmABEiABFyHQM66Opyk39hUhI0/2PmODUGw2NhYgdfyrrvu0nOUMcIwoQw2JWXFmjdvLqtWrVLRCZG5fv169WJijSfWdBrCc8uWLWmqvXTpkjz33HO6/hTrQTt37iynT59OU44XSCA3CNQqU1gali/qMPGJPuQL8JWq4UESGZIvN7rEd5AACZAACTg5Abf0gII5PIxTp06VRx99VHegw1sJsVevXj0dkrvvvlveffdd3cGOtZeTJ0+WL774Is1wQch26NAh1fWFCxdK69atZePGjdKzZ08VoU2aNJEHHnhAvL291dvau3dv3Qlfs2ZNuXjxYqrnixQpIm3atNHlAfny5VORPGrUqFRleEICJEACJEACJEAC7krAyyTMbhueQHfsJKbFsdu9aNGiabqHqXpfX18VjWluZvJCfHy8BAQEpNl8hOtYh4r67dnt27cFG5LgNfUEw9IE9DcoKOiOQl95Ait36yN+1hExgmPvbiObcX849hkzctcSHHt3HdmM+4Wxv3btmjoAbZWGbrKvjmw94YLXsEHIlvhEV+4k/qeBwoj3aZwbn/auG/fx6eXl5THi07LfPCYBEiABEiABEvBsAm65BtSzh5S9JwESIAESIAESIAHnJkAB6tzjw9aRAAmQAAmQAAmQgNsRoAB1uyFlh0iABEiABEiABEjAuQlQgDr3+LB1JEACJEACJEACJOB2BNx+E9KmTZvMYZAQIglZiOrUqSMIf5RVQ7QA7NxC2swDBw4IQi/lhG3btk1KlCghJUsyP3ZO8GQdJEACJEACJEACzk3A7T2gS5Yskd9++00OHz6sovHbb7/VFJhnzpzJ0sggz/vMmTP1GTy7aNGiLD2fXuHvvvtODh48mF4R3iMBhxGIvXJDTl5IMGXw+icnvMNexoozTSDqcqJciL+R6fIsSAIkQAKuRMDtPaAYDASSb9WqlXlcxo8fLz/++KM8++yz5msQlYULFzbHrEImI4RwgrezWLFicuLECY1plZCQYH7GOECs0XPnzgniXsGTiedwDRmWEAsUdaOO/PnzG4/op3E91UWekEAuEvjr1GXZcuyS+Hp7SbJJgHapXyrHc8GjO/i3cT3JlKLWJ0XwHy19At/sOKPpUZNTbku9yCKCbFU0EiABEnAnAh4hQK0HDPE3DTH4+++/y/z58yU4OFi9pMhghNSYuHbq1Ck5dOiQNG3aVPbv36+BtJctW6aZjow6jx8/LhMmTJDQ0FCd6kdw+xkzZmhqzVmzZmleeEz7Hz16VJDtqG7duoJUnMOHD9dMSTj28/MzquMnCeQagaSUWyo+8UKIT9iXW0/rJ785D4GdJy5L5RIFJdDPx3kaxZaQAAmQwB0S8AgBumPHDvVeQhzu3btXkJ+9f//+6qGcMmWKjB07VkVlTEyMDBo0SDp27KhY4cXEFD68N7/++qs+16NHD9m1a5cZO467du1qTteJ5zFdHxISIkeOHNF89JGRkbJ48WL54YcfVIDOmzdP7rnnHunTp4+K2ieffNJcHw9IIDcJ+Pt4yU2Tl43mvARSTP//oZEACZCAuxHwCAEaGxurXkZMjVesWFF69eqlAhHrLpENCfnaYWFhYVKqVCmBYIXVqFFDsxXBY2rPOnXqpML0k08+US8nptUhdGHIMQ/xCYuIiJA///xTj7GBaciQIXqMNJzVqlXTY34jgdwk4OfjLY3vCpG1B2LF33TsaxKjj9SPEO90ft6z2z78EXcFaVhNS1JyIgNZdtvhKs99s+OsXL6WpM1tXKEovZ+uMnBsJwmQQKYJeIQAbdeuXao1oAYdrMtEfnJ8QZzCsNMdnk8Y8lZnZHPmzJFjx45J+/btBe9577331GOK5zD1bsuQH94QqbifXr54W8/zGgnkFIG7wgtIsQL+pvWZKRJWKFB8TGtBHWEQoFhn6qtC1+33Pt4xwkdMa3GxOczfz1uCg7hE546BsgISIAGnI+DRvwkgQEuXLi0I1QTDek58wUtqbfDaWIpG4/7u3bt1Cr5t27bqZYVX1RCwRhnrz1q1asmaNWtU+GKTk+WUvnVZnpOAowkUye8vJQoHOUx8Orr97lp/aKEAik93HVz2iwRIQDzCA5reOPfs2VPefPNNWbBggcTHx8vLL7+sO9atn6lQoYKGYRozZox06dLFfLtbt24yd+5cWb58uU7XQ1yePXtWypQpYy5jfYCNTuPGjdNd+PC+3nXXXdZFeE4CJEACJEACJEACbkvAKzk5+TamnT3dLl++rGGY0uMAsWjilWYNG65DvGI9Z1YMzyAgPgLke4KB01WsAzQtbeA6QE8Y8X/6qGtAr1zh2P+DxGOOOPYeM9RpOsqxT4PEYy5g7K9du2aOOGTdcST18XgPqAEFMUAzMghFW8IJ17MqPvGuAgUKZPRK3icBEiABEiABEiABtyPgGa43txs2dogESIAESIAESIAEXJcABajrjh1bTgIkQAIkQAIkQAIuSYAC1CWHjY0mARIgARIgARIgAdclkO4a0PXr1+sO7eLFi2sPsWlm7dq1Ur9+fQ3ajovYvLN9+3Zp3bp1likgKxEWomKHuaWhToQ3uvfeey0vZ+kYG6tQd04bwjSh3TCs/USsTwSytxfzE+Ww+QZZkf766y/d6NSgQYN0y+MZGgmQAAmQAAmQAAm4K4F0PaCbN2/WFJRG53GOwOu//PKLcUmQSx0xLbNjELNIi2ltEKAbNmywvpzpc6TCnDlzZqbLZ6Ug6l60aJHmjYcQ/e677wShnIxYotZ1QXwizNPbb7+taTchQhGGadu2bdZFeU4CJEACJEACJEACHkEgXQ9onTp15OeffzaD2Lp1qzz00EMqGo385QiiXq9ePXOZ69evS1RUlO4KR6B3wxB+Bykx4U1F6CFLS0hIEARkRxpMeBURHH7AgAFaJDExUbMUIfwRnkcZI2sRCiQlJen7kOoS9eTPn19OnDih2/+Nc8tyJUuWNGceyqhubYCNb/DYvvDCC+Y7ENIQmB9++GGa3fCffvqptnvWrFnmdsODPGPGDC1va1e9uWIekICDCdwyhcr48+RluZRwU+pFFpHgfP4OfqPjqk9OuSV/nY7TrE4NyxdjYH3HoWbNJEACJHDHBDIUoO+++65OISMfOrx/H3zwgXrwICgx7QyPXvfu3bUhK1asUO8g8p/v379fmjZtqjnPV61aJciVXr58eUGmoOeee848Zb9x40ad1kf2IMSNmj59upw8eVI9mPC2IkD8uXPnVFRCXELgQrzhGF5SlMf7EG8qJiZGhSDaccUUc3DZsmXSo0cPmTdvnuA9wcHBcv78eZkwYYI+k17dWSHbvHlz9Qxj2UCTJk1SPQqvcZ8+fcziEzfBBQHrKT5ToeJJHhBYvvWMXLmeZPq3Z8oEdv6a1C5TWPIHpPu/hWy28rYkJppSS/oj7W3Oxx3G/zs2Hr4gRiLRfWevSo+mZQX57mkkQAIkQALORyDd3zRFihTRtZ7IdQ6BCO8h4l1izSPWfVauXFk9lvA+4hcAppUnT54sJUqUUNHYq1cvGTx4sPz0008qwpo1ayaHDh2Sffv2mUlAhL3zzjtaz/PPP69rP61jaiKz0Mcff6yZhlAf3t2oUSOZMmWKTJs2TVNnrlu3ToUl4nl27dpVvbQQnxCp8FBChAYGBqooRdaiYcOGaRts1Y12ZtUqVaokp0+fTvUYUndizSg8utbGGKDWRHie2wSSkm9JXGJSqtfuNHlDXdlMOlrN39dbouOuS6miqWdbXLlvbDsJkAAJuBOBdAUoOopp+D179mimH2OqHVPI8HxCYBnX4CGFqMPGJQg8bLqBKMXUebt27WTq1KkCzyQ8hJYbliBmMe0Ow9T2xYsX00xjY9MO6odB3MLbCWEHoWrkbW/YsKHmYtdCFt8gdnEP4hPWokULgdCFkIXZqltvZPEbNj0hw4+lQVxjuUFcXJyEhoZa3uIxCeQ5AW9vLwny95HEmynmthQK8pV8/hn+b8FcPisHKab/F3ibPJJeXjnvlbx167bEXP3Hs3rTJK4LBjqmH1npM8uSAAmQAAnYJpDh/6Hr1q0rq1ev1untfv36aS0QnRCTWH8JMQrD1DjWbTZu3FivIUc6puYhQtu0aaNCD9PR2MD07bffav50PGc5DQ2RifLWZrnDHGIVZeCdhRCFZxZrQiEAIXatLSQkRODBNQyiGe02RK+tuo2ymf1E3yG4jaUIls8hzzuWI1jme8fygBEjRsjrr79OYWoJi8e5SsDHJEAfrFNSvth8yjRV7SV3hReQe+4KcUgb8G8WP/eOTMN63iRAv9lx1rSEwEeaVAxx6fWsDhkEVkoCJEACTkQgQ1fE3XffrQIK6zANERUeHq7eT2xAgocUhk1EWBcK7yI8jrgHg0AcN26c1tG2bVv1kqIuW2JRH8jkN3gUsSQA60vxDuxGN8QrRC2EJgzT6dg8hZ31MAhgY+mAXrDzDRuZIGrTM7w3Ojpa16Ric1SNGjW0ONawQpTCIEqx/hUiFIZfwlhHW65cORWfKIfyNBLICwJY79m7eTnTeslIh4nP3OpXSMEA7ctjjcpIaU695xZ2vocESIAEskUgQwEKj0XRokUFQtSYBsebateurZt6jLWMWAeKzTUDBw6U4cOH61pO7HjHGkusyZw/f77uHH/xxRcFnlRf3wydrxl2CNPo2GjUt29f8/Q76sVUPpYIjBkzRrATv3379vLMM8/Is88+q2tDR44cmam6LT2nlg9gQ1OHDh2kU6dOuskK3lSIbMOGDh2qa11xDm79+/cXbOZ66qmnVKDDYwuhDkMop3//+996zG8kQAIkQAIkQAIk4AkEvEyeyNsZefqyAsIIbWQ5tW48D+8fBKsx/W1cz84nvJ3Y3IT1pagPO+Cx2/yrr75SoYz4m/CyGu3AtDv6aQjmjN752WefSceOHQVT+DllCAuFtaiWYaRyqm5XqAdjAi+5I6dhXYGDJ7YxN6bgPZGrK/SZY+8Ko+SYNnLsHcPVFWrF2GOZJCIW2TIkCrpzN6RVzdYbcSxvW+9ut7yX1WN4Y//880/dEY+NSGtMwfARm9Tw0kKUGuITdfv5+Zm9pJl5Fzy6OSk+8U57A5GZ9rAMCZAACZAACZAACbgLgRz3gOYmGKzBxBQ21lAiDJKxIz4328B3ZZ4APaCZZ+VuJekJcbcRzXx/OPaZZ+VuJTn27jaime9PnnhAM9+8Oy+JqWysscQXjQRIgARIgARIgARIwDUIZLgJyTW6wVaSAAmQAAmQAAmQAAm4CgEKUFcZKbaTBEiABEiABEiABNyEQI5vQnIVLps2bdKsS2gvNighrqhlViZX6QfbSQIkQAIkQAIkQAKuRsBjBeiSJUs0Fil2uyN01NGjRwWbZN566600qUBdbVDZXhIgARIgARIgARJwZgIeK0AxKAgm36pVKx0f7KifPHmyzJo1S1555RW9hl1cUVFRKlTDwsL0Gr4hnilCSiH2KAz38DzifFqGmsI5vKsIB5VeXcgXHxsbq3nutUJ+IwE3I/D74QtyJDpekm5dlO5Nyoq/L1f/5PUQ30hOkV/3xEh03HVpUL6o1CgVnNdN4vtJgAQ8iIBHC1DLccaO+oceekiQJQmeUIjHUaNGaTD7+Ph4KV++vGZWQpxRZHKqXr26puGEcGzevLlmWerdu7fMnj1bBSkEJzIgjR8/XrMx2asLGZGQlvPQoUMyduxYczpPy7bxmARcmcDWYxdlf9RVcxcW/3FKSgQHms95kDcETly4Zn7xH0cvSsFAXykbYjtotLkgD0iABEgghwhQgFqARBxRZEyCZ3Pbtm2a0nPIkCHq3URaT8QchfCElSlTRgUq8tojzSfSgbZs2VJz0z/++OOaihTe0MjISPn+++/TratJkybyxhtvmIPoWzSJhyTg8gTiriWl6sPN5FtiKX5S3eRJnhAw/V0tl0zjVDZP3s6XkgAJeCIBClCLUTdSkiJd5tq1a/XO1KlT9fPy5cuybt06swBt1KiRXke+exieRc55lIcA/fXXX/Uc9zKqC3FMjQxOKE8jAXciUCG8QCrBmT/AR6pFcLo3r8f4cPRVuZTw9x8HpgkbuSusQF43ie8nARLwIAIUoBaDDQ9n0aJFpXDhwnq1fv36qabELfPIWx5DPGLKHR5UHO/fv19+//13nZY3qk+vLgheGgm4K4FI07Ru66qhsuvkJQk3Tb03rBDCP7icYLBrRBQSrM2FR7qa6biAaQqeRgIkQAK5RYA7AUyk4b3ct2+fvP/++9KtWzdl36JFCxWSEJVVq1YV7Jo/ePBghuMCLyg2MiGkU8GCBe+orgxfxgIk4CIEsLawxV2FpE6ZYIpPJxkz/LHcpGKItKwaJmGF+EewkwwLm0ECHkPAo//kffPNN3Xnu6+vr4SHh0vnzp3lkUce0cGHAF2/fr306NFDgoODpWzZsrrGM6OfjNatW8uHH34oPXv2NBfNbl3mCnhAAiRAAiRAAiRAAm5EwCs5Ofm2sfbRjfqVY12xDKV0p5XmZF132pa8eB7RBa5evSpBQUEamiov2sB35g0BLFFB+DKOfd7wz8u3cuzzkn7evptjn7f88/LtGPtr165J/vy2I2sEBASIR3tAMzM49uBl5lnrMjlZl3XdPCcBEiABEiABEiABVyHANaCuMlJsJwmQAAmQAAmQAAm4CQEKUDcZSHaDBEiABEiABEiABFyFAAWoq4wU20kCJEACJEACJEACbkKAAtRNBpLdIAESIAESIAESIAFXIeAWAhRB35FL3dKQuei3336zvHTHx8iEZCsWKNJ27t69O0v1r1q1SvPNZ+khFiYBEiABEiABEiABNyDgFgJ08eLFMnr0aA3xY4xJVFSUfPrpp8ZpjnwiXJX1e44dOyYTJ040Z0/K7Is++ugjgUimkYAnEEi5dduUazxZrieluHx3k1NuyeVrN+UW8lfSSIAESIAEskXALQQoeu7j4yOzZ8+2CwExqc6ePSsxMTHmMohRZRkDNS4uThCrEpaUlJTGQ3nfffdJpUqVNGMSyqSkpMhbb70lvXr1klKlSuGSGoSl5XtwETEQTTFXBcLY2tAG3KeRgDsSSDb9m/q/jSdk3dGrsnTbOTl/9YbLdjPxZoosNPXl6+1n5ZN1x+WGGwhqlx0MNpwESMClCbhNHNBnnnlGMxBhOv6ee+5JNSgIfj5q1CgVgPHx8VK+fHkZM2aMfP/99ypKhwwZooIRWY8mT54stWvXluXLl6tH9dlnn01VF8r27dtXduzYIUeOHNF0m8igBLtw4YJMmzZNxSTEbGRkpIwbN07F8fPPPy/lypXTpQJjx47V8vgGoTpy5EgVsciYRCMBdyOwZl+syVuIP9j+9hh+s+OsKR2na/bS7PT838GmIxekRZUw1+wMW00CJEACeUjAbTygyLsOcThjxow03sS1a9dKhQoV1EM6f/58FaJ79+6VJk2aCNZvwrZv3y6hoaGyc+dOPd+8ebPe1xOLb8WKFZP+/fvLzJkz5auvvpKXXnrJnNt67ty5EhERIe+9954sWLBAEhMTzfWhCrwPOeVr1KihNcbGxsorr7wiffr0EYpPC8g8dCsCfj5eYj1ZDf3mil/WA3Mx4ab1JZ6TAAmQAAlkgoDbeEDR18aNG0vdunVlzpw58uCDD5q7DwEKmzp1qn5iihwbivr166cpIc+cOaMC9LHHHhNsDoLHNDo6WqpUqaLlrb+1adNGPaQtW7YUCFLD9u3bJ8OHD9dTLAlo1qyZrF69WurVq6fX7r77brNYxYU33nhDihQpIo0aNdL7/EYC7kigTtkiciQmQXxMf+6alk9K3bKFpVyo7fRszt7/SybBucrk0YWoTjJ5dO+rHu7sTWb7SIAESMApCbiVAAVheCcx3R0SEpIKeP369c2eR9woUKCA3odo3bJlixw4cEBGjBgh//3vf1WcNmjQIJVYTFWZ6QQeV3xZGt6JdZ6GYW0n1okaFhgYaBzq5+DBg3UZwBdffCFPPvlkqns8IQF3IVAoyE+evKe0HDpzUYoWDJKIYqn/3bhSP4Pz+cujBQPlfPwNCSsYIPkC3O5/oa40HGwrCZCACxNwmyl4YwyMqfilS5cal3R6e//+/VKxYkWpWrWqToMb4ZSwXvTrr7+WsmXLiq+vr8BL+dlnn9mcfjdXaOegefPm6kGF6MT0+/r166VatWp2SouuRR06dKh8+eWXgt30NBJwVwIBvj5SpkiAhJpEm6tbgUBfiQzJT/Hp6gPJ9pMACeQpAbcToKAJr2br1q3NYLG+EoIQm4ww7e7n5yeYPodBkCYkJOjGI5zXqVNHrl+/bj7Htcwa3ol1nT179pTevXvrjvkHHngg3ceLFy8uTz31lG5esvSWpvsQb5IACZAACZAACZCACxPwMk0Z37YMReTCfcmw6RCaEJ/+/v4Zlr2TAthpHxAQoO+6k3rc7VmEuML62qCgIIePgbuxc/X+IAwaQo1x7F19JLPefo591pm5yxMce3cZyaz3A2OPUJf589te7w+N5FELmOyByDra9J8w1pemX4p3SYAESIAESIAESMAzCbjlFLxnDiV7TQIkQAIkQAIkQAKuQYAC1DXGia0kARIgARIgARIgAbchQAHqNkPJjpAACZAACZAACZCAaxDwGAGKXfBbt26VRYsWaXYiyx3nyIqEtJrWhoD1CKVEIwESIAESIAESIAESyDkCHiFAkdUIAeq/+eYbwU5sxN1E7njs0IIhUxKC0VsbBOiGDRusL/OcBEiABEiABEiABEjgDgh4xC74l19+WTp37ixdunQxo5o0aZIgL/zAgQPN1xCm6cKFC1KqVCnx9vaW0qVLy4ABA8z3cRATE6NhBSx31MObeu7cOVNu69tSokQJQRpOwyBib968qSk74YW13CFv3AsLCzOK85MESCCbBFJu3ZZ9Z69o3vnqEYXE28srmzV5zmPHzydIwo1kqRReUPx8PcIf4TmDy56SgJMTcHsBijzvcXFx8sgjj6QaihdffFFFpnFx48aN6gmFmISQnD59upw8eVJmzpypueUhTl955RVBzFSIU2RMglf1+PHjMmHCBAkNDZWLFy+q2JwxY4YUKlRI3n77bc0xny9fPilcuLC+asqUKSpyp02bpnER0bbIyEgZN25cKuFqtIufJEACmSOwcOMJgfq8Zfr3u+XoRbmvWpgp/zxFqD16+6OuyIkLiSahLrL5yEV5vFFpZneyB4vXSYAEcpyA2wtQpNyER9PLyhuCIKiWhuD077zzjopL5JLfvXu3ikijzIIFCzRdJ4Qr8r2PHj1aheSuXbuka9eu0qFDBy06aNAgXWMKLyjWlsLLihSfb7zxhgphFJo7d65ERETI/7d3HnAyX+v/f5atLMtiV129d9F7ECS48hOSECJEdBGRG7nXFXG1SHOJkESukuQvgnBTtEQQLUGU6CV6L7ssdhdr+c/nke/47pjZZsZO+Twva771fM95n9nZzzznnOeBFxaCFznot2/fLo888ojxOL6SAAmkg8C52OsqPC3a02or9py3bnPDMQGL41hF6IGzV6Va0dyOL+QZEiABEnAiAa8XoPA8IgNLalalShWrR7RkyZLqzYQX0zDkkn/22Wd11xCU2Gnbtq3OH501a5YcPnxY4HHFkDs8o3Xq1LFmQ2rYsKEsXrxY79+7d68MHTpUtyFUGzVqJKtWraIAVSL8jwTSTyAoIItl5CL5fSGBWcWfHtDkUEx7167f0ukKOITpCiGBXv/nwNR6bpIACWQ2Aa//xIGYxPxMzLc0hsEBffXq1brC/V//+pf2gTk9J7ylGIY3W1hYmHorjWNY2ASBOmPGDDly5Ii0atVKWrZsKR999JHeGx4eriLUuB5D+IblzZtXvajGPob1zavyjeN8JQESSBuBXNkCpV6pPPLrn9EqOisVDpMaxejNS4nelYREWbD5pE5TiMqTTcoWyJHS5TxHAiRAAk4l4PWzziES27dvr/M0DU8ovJMQjs2bN08zTHgwsSIeQhEezr///e8qajFUjyH4xx57TL2dGPLHNQ0aNFDPKDyieO7KlSutz2rcuLHu4zosTEKopwoVKljPc4MESCD9BMoXzCnP1Y+SZyxzGSk+U+eXMyRAujcsJl3qRUnT8lwImToxXkECJOBMAl7vAQWsl156SaZOnaor3hGGCSvYMXRer169NLOEAIWIRPim4OBgFbVY8d6pUyed07lo0SKdZ1q1alU5ffq0zgnFuWHDhukc1GKWhUY4DmvWrJlg0VP37t1VrNavX1/atGmT5rrwQhIgAfsEgvzvRaCwfwWPmglgkVZW4UItMxNukwAJPBwCfpYFNXcwBOwrBm+keW5nett97do1CQkJSbZiHaIWx83lQmwiZFO1atX0EYg9iqH61157zfpI3IPFUAEBAdZj3rwBTlevXlV+5ikP3txmtu0uAUxpwe8efnfY9771rmDf+1Z/m1vLvjfT8K1t9D1irZtDVpoJQPv4hAfU3GizSDQfT+u2OY6ncQ/CMtmWizmgWN3eokULvQzeU6ycN5u9ssznuU0CJEACJEACJEAC3kjA5zygD7MT4e3btm2bJCYmCobmsfjIl40eUN/tfXpC2Pf0fvvee4C/977X50aL6QE1SGTSa44cOQQLjmgkQAIkQAIkQAIkQAL3CHj9Kvh7TeUWCZAACZAACZAACZCAOxCgAHWHXmAdSIAESIAESIAESMCHCLhMgCLQ+59//pkM5e7du3UlOA6uWLFCrl+/nuw8dhBr88cff7zvOFaVL1myRGNv3nfS5oCzVvUj+9HatWttSucuCZAACZAACZAACZDAgxBwiQBF8HXE3ZwyZUqyumEl+NatW/UY8qEbgeHNF3311Vfy/vvvW4WqcQ5hjCZNmiRnzpwxDtl9nTZtmgaAt3synQc//PBDbQPCKdFIgARIgARIgARIgAScQ8AlAhQezHbt2mk+9UOHDqW7puXKlZNffvnFeh8yBmE1eb58+azHsBEbGyvHjh2zprGE5/P48eMaewrZipBlCK8QrZZ4p9Z7kZYzNVGJemP1OjIcwfNqNghnlGcWw0b+d3PKTdyDukOQnzx50lpPoyykCD1x4sR9x43zfCUBEkg7AaSWXPLHGflywzGJvuY7sY3TTij5lbcscXnX7Lsg8zadkOPR91IFJ7+KeyRAAiTgGgJOjwMKwfXTTz/JuHHjVFgtXrxYXn755XTVvkmTJoL7XnjhBb1v+/btUqlSJdm5c6e1HKTSXL58uRQqVEguXrwoY8eO1dzrEI4QphEREZptCALv4MGDmnbz6aeflvfee089r7gG2YlGjRqVLKi88QCUjexHSKk5fPhw6dq1q/j738XVu3dvKV68uJb71ltvqRgdP368lodn4Xzr1q21PmPGjFHhHBMTo2IYXlzEDEV9IWCxUh555XF/ZGSk8Xi+kgAJpIPAzVu3Na+5ccu3W09LqYhQCfR3yXds4zEe/br3zBWxxIpWW7H7vDxeJb8UyBXi0W1i5UmABDyHgNMF6JYtWwRB2CHu4D0cMGCA9OrVS7Jly5ZmKkWLFlWxd/jwYSlRooRgPmnTpk2tAhQeTAznf/HFF5pVZdmyZSpY+/btq/NHW7VqpRmIkO4Sgnj+/PmWD9o7MmHCBBWsEHs4jkDxELePPPJIsrrBm4nyJ0+eLAULFpTcuXOrmDWHVEL6TIhsPz8/Tbf5xhtvSPXq1eXSpUsydOhQQR127NiheeIhRmEDBw7U5+G6jRs3yrx58zSt59KlSyU6OpoCNFkvcIcE0k4gxuLxhNiEEDXsz/PXjE2+pkLA8jEmZ2OvU4CmwomnSYAEnEfA6QIUnkN49TBnE4a0ez///LMOyaen2vCCrlmzRooUKSJ79uyRV155xXo7hCW8kfAmwrCYaf/+/dKnTx/rNcYGPKcQifjZu3evikOcy5o1qzRq1EhWrVp1nwD99ddfBdmN8AoLCwuTH374IVlMz8qVK2uZEMNYXIVFVfiBIcUmnoV885s3b5ZZs2YJxDSG4iFuwadWrVry/PPPS+3atdXLWqFCBb2X/5EACaSfQO7sgcnEJ0ooHRkqAVnpAXVE8+jFOIm/mWQ9XSQ87U4C603cIAESIIEMEnCqAMWwNgTXc889J8h6A6tTp456JzEnND0GAYrUlZgPWqNGjWTD5PBmwjMJgZeaIfuGYchEZJ4Lijmj8ITaGjyqEIhGG/D8zz//XOeXRkVF6eXBwcHW2yCG4eU0huhRL9QPC6KQ/x3e0JYtW8pHH32knljcOGLECC0Pq/5xHaYOYJifRgIkkH4CQQFZ5enahQVD7zlDAqReqTySN0dQ+gvyoTtqlQiXH7aftnyRFqlcOIy8fKjv2VQScAcCTnUPwNOJlJPPPPOMdOrUSX/69++vcx3hJUyPYW4nktV/+eWXOvxuvhdzMzHXMn/+/FK+fHn1LC5YsEA9kvC4OgrDhCF0DK1DdGKB0rp168TW84jFSbt27RIM5xttwNzRunXr3rcYCXXKlSuXimTch7pg7ilW8eMZKKdjx446FSEgIEAOHDigxzFMj2kJuLZz587qHcYiJRoJkEDGCYQGB8hz9YtKu+oFKabSgDFrFj9pX6OQ/K16ISmeLzQNd/ASEiABEnAeAad6QDH8DkFlNngg4c3EEHZ65oGiDOM+W5GIIfGnnnpKevbsqXM6MZw+ZMgQfSyGxhE+CUPdttasWTOdy9m9e3cVgpjH2aZNm2SXYQEVvJ8YJjcbvJhvv/229OjRw3xYt7t166aLiDCnE97ZDh066DxYCFiEm1q0aJGKY4hzxDPFnNLmzZvr/FgwwRQCLHSikQAJkAAJkAAJkIAvEPCzDEnfceQxdHcA8DLGx8ffJxYhPjEcjnmc9gxzNOFdhVfSmYb5oPCImg3D+HgeVr7bGsTq1atX7Z6zvdYb9sEC7cWXEniqab5DAO91hC9j3/tOnxstZd8bJHzvlX3ve31utBh9D32WPXt241CyV2gwp3pAk5X+EHbg+bT1VOKxqYmb0FDXDDfZik/UBSLYnvjEOSyMcnQO52kkQAIkQAIkQAIk4I0E7LsIvbGlbBMJkAAJkAAJkAAJkIBbEKAAdYtuYCVIgARIgARIgARIwHcIUID6Tl+zpSRAAiRAAiRAAiTgFgQoQN2iG1gJEiABEiABEiABEvAdAh67COn48eOybds2jedZqlQpqVmzptN67bfffhPkbodhpXy+fPmkSpUq1lX1WO2OGJ+IR0ojARIgARIgARIgARJIHwGPFKAQiEjDiXidiAn6ySefaBpM5GN3hiF3PMI4IRg+AtYjjSZCCCHAPFatQ4AigxEFqDNoswwS8CwCSbfvyI3EJAkJzKqRLDyr9t5b28Sku9n3mH7Ve/uYLfMuAh4pQL/77jtB8PcnnnhCewPZhrCPXOsQjTBkG8IPsiUZAfAhJhG6Cek4L1y4IIULF06W4lNv/Os/pNZ89NFHdQ/xRidMmCBTpkyRf/7zn5qfHhmeDEO8qzNnzqhoRXYjs0GsIi5pnjx5VMyaQ0AZ52zvMd/PbRIgAfchAOH5ze8nNe+8RYdKV0vmpUB/zmTK7B46ERMvv+y9IDctIrRm8dxSpUjyeMyZXT8+nwRI4H4CHilAIyMjZe3atZpGs1ixYuoFRbYhiEuIxdGjRwtSY8JbuX//fhk2bJim0pw9e7acPXtWjh07psFRkYEInlRHgVINXCi3ffv2Wg48oUipOXnyZM3hjsDqyGIEUYuA8yVKlJCRI0eqZ+SDDz6QrVu3qgA2YoS+8847Eh0dLe+9954G5o6NjRW0YdSoUQ7FsFEPvpIACWQugTm/HbdkO7tXh7mWfXhCaY4JANcdy+emn1+sSzzGty0dEncjyVqB349ckgK5QiRfjiDrMW6QAAm4HwGPFKB9+vSRjz/+WNNvBgcHS40aNTT/fFRUlGBuKATl1KlTlTaG01esWKECFAeQCnPGjBn6QTho0CAViI0aNUq1Z0qXLi2JiYkqbM0Xr1mzRkqWLCmDBw9W8QvxuWfPHh2mx+vMmTPVMzpu3DiB2IQhPSc8tePHj9d7Xn/9ddm+fbs88sgj5qK5TQIk4GYEQoP85er1W9Za3bK4Qc371hPcyDQClhT3knDzniDNtIrwwSRAAikS8EgBCtH5yiuvaC51iDyIQAjAiRMnSvHixQU52BcsWCCHDh2SvXv3CoSpYZg3igxEsAIFCmiqKONcSq9GulI822x4Nuzdd9/VVwyrwzuLOaR16tSxpvvEfNHFixfrNajT0KFDdRveVQjgVatWUYAqEf5HAu5LoEpUmKw/EG35DLF49SyuPQz35gmlpy2lHjNS8gVZ0u/6Ozn9sfHc9QcvyjXLF4OsFvWJObpFwkOMU3wlARJwUwIeJ0Axn/LNN9+Ut956SyAGq1atqj8QiBs3blTPI+ZrYl4ohs0rV64sWLRkmDl1J9Jk4sMxLQahGx4errneMd/TbFiBX6lSJeshzPPcvHmzHD161HosLi7Oup03b14dsjcOoO6YOkAjARJwbwJl8+eUsJBAORt7XfKHBeuPe9c482uHz9grWRMlJCQ41TTJGa3t07WLyMGzVwUe6TL5c1idDBktj/eRAAm4noDHzZ5HnncMhWPlO+ZwwrCgCF5FhGPCa/Xq1eWpp56SMmXKyKZNm5KJvfQihThEmRjyh2fV1po0aSL79u0TDNGXL19eMOSPOaINGjRQEYqFUVeuXJGVK1dab23cuLHuQ3RiYdS6det0Pqv1Am6QAAm4LQEIz2pRuSg+3ayHSluEZ/mCOdUL6mZVY3VIgATsEPA4DyjaMGLECB3yfvrpp3W+JxYAde7cWYewixQpogt6MMQNoYr4nQiZlF57++23deU7htKx6Kldu3bSoUOH+4qBAIWAxCp8hIQqWrSoNG3aVBcUQbBiARRW22OhEeafwpo1ayYbNmyQ7t27q+ezfv360qZNm/vK5gESIAESIAESIAES8EYCfhbxdseY3+hpDYQHEQt7MDRua/A6YrjdmO9pe97Z+xhiR9B6eGhhEJtYiV+tWjXd/+abb+TIkSPy2muv6T7+w6r5oKAg6zxR6wkv3UAEAUQNCAkJsXLy0qayWTYEdBjW8jvJvrcB4wO77Hsf6GQHTWTfOwDjA4fR9/Hx8Q6jDEH7eKQH1Og7LOCxJz5xHiGYHqbZhnJCvbC6vUWLFloNDMFj7qrZzDFBzce5TQIkQAIkQAIkQALeTMCjPaDu3jHw9iFdKKYCYLEUFh/5stED6ru9T08I+57eb997D/D33vf63Gix13tAjYa66yumAGDBEY0ESIAESIAESIAESOAeAY9bBX+v6twiARIgARIgARIgARLwRAIUoJ7Ya6wzCZAACZAACZAACXgwAa8UoJhrePDgQVm4cKHG28RcTMMuXbqkYZOM/Yy8Ipc7wiilZrbRBWz3U7uf50mABEiABEiABEjAGwl4nQCF+EQMzw8++EADwO/cuVN69uwpW7Zs0f5DFqOvvvrqgfoSweXnzp2bYhmITdq7d2/rNdOmTdPA9NYD3CABEiABBwRuJVm+RJ+7Ksej72VQc3Cp2x++kZgkp2JvyuX4RLevKytIAiTw8Ah4dBgme5g+//xzzYw0ZcoUDQaPa5Aqc9KkSfLZZ59Zb4FAhJBEkHlzfndkV4JIRRinPHny6PW4Ft5LxB3Fyi57hhzwSBMaERGhpxGf9Ny5c5oaFKs/jx8/LiVLltRrECsU5eA5CHRv3GOvXB4jARLwLQK3LZ8Nn68/JgFZ/SQx6Y4Uzh0itUveH+vYE6hASH+3zfI5Z3F13Lp9TZpXiJCiebN7QtVZRxIgARcT8DoBinzwL730klV8gh/SYiIMkhEkHsPwr776qu7/+eefmjkJ55csWaKeTWQtQnpN3Dd48GDdhgcTohKCcciQIdZuwXD8e++9p95WnMe9o0aNUi8rROa7774rLVu2lEOHDun9EJsQosOHD9cUoQhGX6JECRk5cuRDC5pvrTw3SIAE3I7AgTNXJYufqPhE5U5eSpCTv59yu3qmp0K3bt+9+rdD0RSg6QHHa0nAiwl4lQCFB/Lo0aOCdJy2Zg76DqE4ceJE9X5++eWX8uOPP2rKTgzTT5gwQQoUKCBnz56VF154QQYNGqRFodzZs2dr4Ptdu3ZZi//000+lUKFCMn78ePWQIvj89u3bVQQvXrxYxo4dq9euWLFCWrVqpZmRcBwiFOIWXlWIzz179kjFihWt5XKDBEjANwn4WzyfFv2ZzLDvZ3sw2RXuuXPbZsAo7kaSe1aUtSIBEnjoBLxKgMLDmS1bNvU05suXzyFMCEwMvcMgBHfv3q3eR3g2kdd90aJF6rGEBxPD7zDcYy+Q/N69ewV552HIzNSoUSNZtWqVVKpUSY/Z+2/NmjV6GN5RGIbv165dSwGqNPgfCfg2gZIRobLjRKzOmYTmrF0iXCoWDvNIKLcsc/L/34bjctuiRKFFO9Qs5JHtYKVJgAScT8CrBCjwlCpVSofM8WoY8sLDMzl69Gg9hBykhhm54jH3s3///lK3bl2dM9qpUyfp2rWrdc6neZ6ocS9eIUoNkYp9Y64otlMyzEs1i1Szhzal+3iOBEjAuwngM6lDzcISfe2GZR5oFskZEuCxDfbPkkWeb1BUTl24JLlCs0lotkCPbQsrTgIk4FwCXrcKHqJx1qxZKkKBCuLzk08+keLFi0tKXlHM5US4Jqxcr127tuzYsUNJY4g8JUOmI+R5x3UJCQnqQa1QoYLOFcV9SMMJg3fWCMPUpEkTrV/p0qWlfPnyMn/+fDlw4IBex/9IgARIAATyhAZ5tPg092KOoKwSiJVINBIgARL4i4DXeUArV64s/fr1kw8//FCHtiEMISh79eqVYqdjHicWHQ0YMEDgjYyKipL8+fPL6dOnU7yvWbNmGhO0e/fuKkLr168vbdq0kSyWb/6Y09m5c2eZPn26oF6oE+apQoBiqL9bt24SFhYmRYsWlaZNm6b4HJ4kARIgARIgARIgAW8h4GcZPr5jeOa8pVFGO+Li4jTEEuZmptXgxcT1xor5tN6H1ewY2g8ISD5chqF9Y/ge4hOr6CFOYagfrk/vs9JaJ3e7DjFa4WVGWCpfabO79UFm1QfzqTEawb7PrB7IvOey7zOPfWY/mX2f2T2Qec9H38fHx0v27PbDrkEveZ0H1IzbUcPN19hu4w9kRszRHE5DfKJMW9GVkfplpG68hwRIgARIgARIgATciQAn5bhTb7AuJEACJEACJEACJOADBChAfaCT2UQSIAESIAESIAEScCcCFKDu1BusCwmQAAmQAAmQAAn4AAGvngNq9B+yGCHTkGGY51m2bFkpWLCgHsI5TIhFUHpXGoLWYx6oq5/jyjawbBIgARIgARIgARJ4UAI+4QFFasy5c+cK8r7jB/ni+/btK0iJCUNmos2bNz8oy1TvR3555ISnkQAJkAAJkAAJkIAvE/AJDyg6GF7Hl19+2drXq1evlpkzZ2rMTuMgwiIhIH3hwoWtoZJwDsHkz5w5ox5ThFEyDGEGcBzHIiIi9DDijhqhl06dOiV58uSxhiF4/PHH9RqEY8I1OXPm1H2Uj/BPxr5RPl9JgAR8kwCyIG09ekluWVJYPlYpUpBRiJb5BPafuSInohOkRER2y09o5leINSABDyZwT015cCMyUnWkuzOHQdqwYYN6QiEgISwnTpyo5xFEHucQMP7ixYsyZswYKVasmMazHD58uKbhRAzQEiVKyMiRI2X//v0yZcoUS+7j25IjRw45fPiw4LoaNWrI7NmzVWRCiPbp00f+8Y9/SLVq1bRMBKPv2bNnRprCe0iABLyIQPyNW/Lt1nsJMOZYcqlXiwoTI22wpzUVOeBvWL5wBwTc0hjLnlZ/o76nLyfIqUvXdfd4TLx+OSiTP4dxmq8kQALpJOAzAvTs2bM65A5xeezYMc1E1KVLFysuzM38z3/+o55PpOPctWuXiksMz0OEIp7nwoULZdGiRTJkyBAVq/CqDh48WDMgQXxiLin+SGCYHek/IVTnzZsnS5cuVQFqPCxXrlxaBkRu+/btJTY2VpBJiUYCJEACRy/GS9YsfpJk8X7C4AX9/ehlLwCT4AVtuNeE/WeuCgXoPR7cIoH0EvAZAYrhdXgjYRCAr776qtSqVcvKq0qVKtZhdwjLmJgYwRA60ngaweSRQhPidNCgQSpAcfO7776rZVy+fFnWrl0ryA0fGRmp4hMnkOLzjz/+0GvM/9WtW1c9qzNmzJD//ve/Hu0ZMLeL2yRAAg9GIFe2AKv4NEoqEh4iWSxfbj3RIKNvWaYZIcOckQXOE9uBaRHXbiRZq54zxGf+fFrbzA0ScCYBn/kNgqiEcHRk5ixF8GLCU5o3b145cuSI9RbM3cR8TeNDtGbNmlKpUiXreWRDQqpJDL2nZpYUqOopxbxPeFshWmkkQAIkUDB3iNQvlUc2/BktEKPNK0ZKWEjyFL+eRAmfpd6QhvW2pR3fbzstVxNuSbF82aRB6bye1A2sKwm4HQHObE+hSxo1aiS///67wLsJW7FihYZvggCFNxSr2kuXLi3ly5eX+fPny4EDB1IoLfmpzz//XAoUKCDjxo3T4frz588nv4B7JEACPkugXMGc0rNxcelQs7BHi09v6kB4oNvXKCRdGxSVhmXyeeycXG/qE7bFswlQgKbQf1jB3qpVK+nRo4e8+OKLGqpp2LBhegcEKFaud+vWTUM6BQQESNOmTVMo7d6pnTt3yvLly2Xgdo6owwAAQABJREFUwIE6VP/kk0/KO++8owuX7l3FLRIgARIgARIgARLwTgJ+lqHgOzdu3PDO1jmpVRh2ByMMsdsa5pZCfJqH8G2v4f5dAogMgCkKSARAXr71rvCWYVjf6jXntJZ97xyOnlgK+94Te805dUbfx8fHJ4s2ZC4ZyX98Zg6oueHp3YbAxI89M4dysneex0iABEiABEiABEiABJIT4BB8ch7cIwESIAESIAESIAEScDEBClAXA2bxJEACJEACJEACJEACyQlQgCbnwT0SIAESIAESIAESIAEXE6AAdTFgFk8CJEACJEACJEACJJCcgFMWISFAO2JkGoZA7gjijhiZyDoEW7lypdSpU8fhiijj3gd5dfSMLVu2aMzNggULZrh4rOBGik2EUEKbkEUpLQHnwaVhw4Zy3ZILGQHnsQ3bvHmz7N27Vx577DGtW4YrxhtJgARIgARIgARIwMMIOMUDCnE1adIkTV2JnOsnT56UuXPnSp8+feT48eOKZPXq1bok35V8HD3jhx9+SFeQeNs6Qny+/fbb8sEHH2hGD4jQnj17CoRtavbpp5/qPQhmv379er0cKUHHjx+vKT4Zjig1gjxPAiRAAiRAAiTgbQSc4gE1oHTv3j1ZfMdRo0apZxRi7bXXXlPvJ1KyZcuWTfz97z7aSG9phDOKjY3VzEOFCxe25kc37rlw4YLVWwihi/ic8GoixzDMeIZRH+RyRzB5W8MzT58+Lfny5UuTRxZZi/DsKVOmWJ+FNJwQ3Z999pm2OSkpSVAnxL5ChiOjTsazixQpIv379xdcBwFaokQJad26tSAVJ40ESIAEHjaBa9dvybKdZ+SKJbVk8XzZ5dHyEQ+7CnxeJhG4npgky3aclZi4m1LBknWrriX1K40EHjYBpwpQc+UhtBCE1Mhx3q9fP832M2fOHBVfTz31lF4+e/ZsQUDS559/XmbMmKEZggoVKiQXL16UsWPHCoRb7969pXjx4nLw4EF566235Ntvv5UzZ87oEPi5c+fUm4jnGM+AwB06dKgGjr906VKyGJ7bt2/X64sVK6bloWwIwZRs48aN8tJLLyUTlQ0aNJCqVauq+Dx69KiMGTNGBW1MTIxA4EKcmsUl0nROnjxZ/vGPf8jChQtV0E6bNk2MzEopPZ/nSIAESMCZBG7fviPzNp2wFnnkQpxEXz0h2YJc8yfBkvDE8vl5zZK+0imDbtZ6cyNjBM7GXrfeuOf0FcmbI0hKRd6faMV6ETdIwAUEnPppAxGG+Z/4sIE3sEyZMvLoo48mqzZSW3788ccCAYqh7VWrVsn777+vXk/M4fziiy9U1C1btkwWL16saS5RQP369TVv+rVr1wSCcN68eTqEvXTpUomOjrYKXVw7ffp0qVevnopGeE+7dOmCw2pfffWVvPHGG1K9enWBOIVQRZ1Qb3sGMQmBCSFsa0ZmpB07dkjHjh2tQhYpNiF0GzdubHuLREVFyXPPPSdLliyh+LyPDg+QAAk8DAK3LSM1/lksn9UWIWrYFYtHFD803yKAv3xXExJ9q9FsrVsQcKoAbdOmjWTJkkUXHGF4fPDgwSoSzS2tXLmyekaPHTumXk4MoWPIGoIMw/LwHMIwr3T//v06jxT7uA8iEQt/sAAIHtPatWsLPJEVKlTAJVbDfXg2DF5I4zzmYe7evVunBRiLpiBosRjIuMZayF8bmKMJjyqmBmDI3p61bdtWFxXNmjVLh9cx9A/hSiMBEiABdyTgnzWLFMwVLMdjEqzVq1k8twQH3J3OZD3ohA1MS7qecF0CAgOsU6+cUCyLeAACB89elXNX7qbgxleQcpZheBoJPGwCThWg8CpCsEEgjhgxQofc33zzzWRtgohs2bKlej7Pnz+v3kdcgA8piFGIOXsWHBxsPYyysbgJi3owjI3V6V27drWeh5A1C0BjvikuwDaG3I1jeF5qq+NLlSol+/btE7waBs/q66+/LqNHj1Zv7JEjR7QtaNtHH32k7TGu5SsJkAAJuBuB5hUjZePhGElMui1l8+eQiJz3PmOdWVd8tl+5ckdCQkL074Mzy2ZZGSNQxtLfO0/GSvyNW1Lash0S6PwvHhmrGe/yJQIum5AzZMgQHYY2PI1mqC1atJC1a9fq+UaNGukphCfCHM/8+fNL+fLldUX9ggUL7hsax7B5r169JCIiQjp37izt2rXTVffm8jE3EyviMcSP4XkMkcMQPqlcuXIC4YtnoAwM/2O+Kgxe2Rs37n4r1AN//QdxC+8mRCgM4vOTTz7ReanwiiK8EobgEVIJOeMx39Mo868iHL7A02tECnB4EU+QAAmQgJMJwBlQt2QeaVQmn8vEp5OrzOKcSKBy4TCpY+n/8OyBTiyVRZFA2gk41QNqfmx4eLjOwcR8T6wYNxuEH34g3gzPZlhYmM4LxYp5LELCKnKIWFvLnTu3NG/eXAYMGKBD4xBww4cPT3YZysAK/BdffFFFqNlz2a1bN12EhDmk+GbeoUMHQV1hgwYNUq8tRKrZMPyPBU4ffvihzlWFuMTwP4QwrFOnToJwS4sWLVLBDAGMVfZpsT179siECRPk66+/TsvlvIYESIAESIAESIAEPJ6An2XB0B17Xr/Mapmxej61IO8Qj1evXk220ty2zpjfifmbmJdqa5gPagTJN85hAdTjjz+uQfSNY7avcXFxKpptwyzB24rnmVe+297r6/tghD7jUJzvvRPuDsNeYd/7XtfrF32MGvH33vc6n7/3vtfnRovR94iEZITYNI4br4h+5DIPqPGQ9L5C2KUmPlEmho9SE3vGKnV7dbAVn7gGnldkcErJHMGEyE2tPimVy3MkQAIkQAIkQAIk4CsE7ncN+krL7bSzWbNmdo7yEAmQAAmQAAmQAAmQgDMJUIA6kybLIgESIAESIAESIAESSJUABWiqiHgBCZAACZAACZAACZCAMwlQgDqTJssiARIgARIgARIgARJIlYDbLUJKtcZOugDpQn/88Ue7pSHtJkIv0UiABEiABEiABEiABJxPwGcFKEIC/fnnn0oUgekRZN7I3W7EJnU+bpZIAiRAAu5D4FLcTbmemCSRYcGSxRJZhJYygRu3kiQ2PlHCQwPF3054v5Tv5lkSIAEzAZ8VoEgZ+vLLLyuLTZs2aV56Y98AhJikZ8+e1Th2yFdvxP5ETDuEXDp37pzGtsM2PKrYRygnGO5FkHxkRjKOm+OROirbeDZfSYAESMCVBA5Y8oGvO3BRsmbxk6Tbd+TZukUkwJIj3tmGeIC3kvBzW/wsP55qcZa0lQt/P2Vh5GdJX2rhVaeIZAvy2T+hntqNrLcbEeBvj4POOHr0qIwZM0azNcXExGhu+UmTJqnw7Nu3r1SqVEnTfMKL+txzzwlSjiJGKAKvTp06Vfbv3y8TJ05UYQrxilzxyHgUFRUlKZXtoDo8TAIkQAJOI4D87xCfMIhP2NzfTugr/0uZAMQn7LdD0dKsQmTKF/MsCZCAQwIUoA7QIH888ru3bt1arxg4cKDmrjeG6StWrCjt27eX77//XgXn3LlzBelEX3nlFUF6TXhLkeN9+vTpKjp/+OEHgYBF7vnUynZQJR4mARIgAacRCPTPIjdvea5H0mkgMlgQhuJpJEACGSdAAeqAXdu2bWXz5s0ya9YsOXz4sJw6dUq9oMbl8IDC4N0sXbq0ik/s58mTR4fzIyMjpXDhwio+cbxOnTryySef6NB8amXjehoJkAAJuIoAhtprFc8t6w9GS6BlSNmSWk6erFFIh+Od/UwMwSMFL+bWY+qTp9qVhERZ/McZ6xB8q8r5PbUprDcJuAUBClAH3TBt2jQdNm/VqpW0bNlSPvroI50LalyOvMaGpeVDNSEhQeeDwjOKsjAk76hso1y+kgAJkICrCJQtkFPyhAZJws0kyZ8r2CXzP1F3CNDEgCwSEpjVIkCzuqo5Li8X9X+uXpREX7tp4RYoQQGe2xaXw+IDSCANBJw/4zwND/WES3bt2qVD8I899pgKxwMHDqj3Mj11P3nypBw6dEhvWb16tVStWlW3nVF2eurBa0mABEjAHoG8OYKkSJ5sLhOf9p7pyccgOgvmDqH49OROZN3dhgA9oA66olOnTvLpp5/KokWLLKNTfioeT58+7eBq+4cxHD9+/Hg9mS1bNvn3v/+t284o2/4TeZQESIAESIAESIAE3J+AnyV80J0bN264f00zoYaIFXrt2jVd+Z7ex2MhEoba8YP5Tzly5EhWxIOUnawgD9pBm8EC0xfSMm3Bg5rGqqZCAMOwCF/Gvk8FlBeeZt97YaemsUns+zSC8sLL0PeICoToQPYsKChIOARvj8xfxxC3EzE+H9RsxSfKc1bZD1o33k8CJEACJEACJEACD5sABaiLiGMF/Isvvuii0lksCZAACZAACZAACXguAQpQF/UdPKc1atRwUekslgRIgARIgARIgAQ8lwAFqOf2HWtOAiRAAiRAAiRAAh5JwK0EKEIWbd++3WUgY2NjZcmSJRIXF+eyZ0RHR8uGDRtcVj4LJgESIAESIAESIAFPJ+BWAhSZh5YvX+4ypkuXLtXMRq58BjImIS0njQRIgARIgARIgARIwD4BtxKg9qt472hiYqLmV7eEjrp30LKF0D5Il4kl/ykZhGfv3r1l8eLFyS5DlqKbN2/q/ceOHbsv4Pzly5fl/PnzehxhmQxDmAHEBsU5R5bSNWfPnpUTJ07c9zxHZfE4CZAACTiLAD6bdp+KlV//jJa4G8k/U531DJbj2wQux9+UvecS5Gh0yn+bfZuS77beYwLRT58+XYe2w8LCNNf6mDFjpFixYrJy5Ur1apYoUUKQrahXr17SrFmz+3p0586dgjSYLVq0kDlz5uhQf7Vq1fS62bNnC8QgxCdiVl2/fl0mTZqk2x988IFs3bpVEEg+V65cev0777yjonf48OECMQxRiuePHDky2XMhjO1dg8D2Y8eOlTNnzmh80HPnzmnAeuSPp5EACZDAwyCwZMcZOR97Q+5YHrb39BWpackNHxrsgj8JlgckJNyQgPg74u9/82E0jc9wAwLXE5Pktz9jtCb+56/Lleu3Le+xcDeoGavgLgRc8Gnj/KatX79e1qxZIxChwcHBsnDhQs1QNGTIEB2yf+mll6RRo0Zy8OBB2bt3r90KwPuJtJow5Hb//vvvxRCgOAZP5owZMzTr0aBBg1R0IlYnAsrPnDnT8sHpL+PGjRPMI4WhPiVLlpTBgwerBxPiE9eazdE1UVFRsnHjRpk3b562B1MDMHeUAtRMj9skQAKuInDLkhTinEV8mu33I5fMuy7Ydt3cexdUlkU6kcCt23dk3+mrFKBOZOoNRXmEAIWorF27too1QG/SpIkOpUMoQky+++67uriofv36dr2fGJqHGGzXrp188803mpHl119/lZiYGAkPv/uNrFatWio+UX6BAgV0OB7D43Xq1NFc8DjesGFD6/A9yoPh2TAM069du1ZQB8McXdO3b1/B855//nltV4MGDaRChQrGbXwlARIgAZcTyBaYVeJvJlmfg/0Qy48rLCkpSZNvYPSH5hsEEizvLfP762bSbd9oOFuZZgIeIUDz5s0rR44csTYK8zUxHxQeyubNm6uYg0dxxYoV6tlEDnezQQgWKlRIh9CRDjJ37tw6ZL5s2TLp0qWLXmrOVoRyMT8K4vTo0aPWomxXz9esWVMqVapkPR8aGiqXLiX3Iti7BjeMGDFC57PCuztt2jRBBICuXbtay+IGCZAACbiKgL/lM+6JqgVkweaT4p/FT4rmzSZNykW45HH4LGUaVpegdftCf9plWecQkyC5QvylTbWCbl9fVvDhEvCIRUgYXv/999/Vywg8EJply5ZVATpq1CjZt2+fDq9jSB5zOW0XKUFodujQQTp16mT9efrpp9Vrim/mjgyeSazMx8p2fIBivqlh8MLiuaVLl5by5cvL/PnzdQ6qcR6vjq6BSMVc1YiICOncubN6Zk+ePGm+ldskQAIk4FICOUMCpHvDYvJc/aIuE58ubQALd3sCLSpGylNVw6Vt1UgJCnCNd93tIbCCDgm4nQcUIm/VqlXWCkPcTZw4UVq1aiU9evRQryQWCr355pt6TceOHWXKlCny5Zdf6uIkDG9jvqZhx48f1xXyEJNmq1evnkyePFk2bdpkPpxsG3MyIVqHDRsmSK2JRU+YKwqDuFy3bp1069ZNsDCqaNGi0rRpU9m9e7e1DEfXYDEUPLcDBgzQxU1Y9ITFSjQSIAESeJgEslq8nzQSIAESyAwCfhZv4Z0bN5JPRs+MiqTlmRh2R10x1G1r8FDiOIbPnWVGiCVjsRLmj2IqwGuvvWZ9BIblAwICJDAw0HrMdsPRNRiawkp5pO30BcP0B7Q3JCQkRV6+wMLX2shhWF/r8XvtZd/fY+FrW+x7X+vxe+1F32P9DRyG9iwoKEjuuQrtXeFmxyD08GPPXCHiMAf09ddf19BNeCa8s4bn1aiDI7jGebw6ugYT8l1Rb/OzuU0CJEACJEACJEAC7kbAozygmQEPHrtt27bpoqeqVasKFkTRMkaAHtCMcfOGu+gJ8YZezFgb2PcZ4+YNd7HvvaEXM9YGr/OAZgzDg92F1fGNGzd+sEJ4NwmQAAmQAAmQAAmQgJWA8yZMWovkBgmQAAmQAAmQAAmQAAk4JkAB6pgNz5AACZAACZAACZAACbiAAAWoBSrSay5ZskRsA82DtxEhAOGVzMHwM9IXRlkZuZf3kAAJkAAJkAAJkIC3EKAAtfQkcrHPmjVL88qbOxYZihCIHoYV8Fu3bjWfTte2uax03ciLSYAESIAESIAESMDLCFCAWjp0+fLlmlt+8eLF1u6FtxJB7BHHCqk/bQ3B4+ERjY6Otp5KSEjQa3HPsWPHxMiyZK8snEOGJWRAMq6zFsQNEiABEvASAluPXZIfdl+SrzadlkTmA0+1V89cTpBFv5+U2euOyvVEx5n6Ui2IF5CAmxPwqDigrmC5c+dOQWaiFi1ayJw5c2T79u2CwPPIkIT87BieR8pMs2G4fu7cuZoZCek4kWVp8ODBMnv2bE0FCvGJ2J8QqZMmTVLPqbmsXLlyyZgxYyRfvnwSExOjohXXMSaomTK3SYAEPJ3AzhOX5Y/jsdZmzNt4QgqHZ7PucyM5gZu3bltyp8dbD87fdEI614sSfycmWLEWzg0SyGQCPi9A4f187LHHtBtatmwp33//vQpQ5J//8ccfNQUoBOnatWv1GsS22rJli0yYMEEKFCiggvOFF16QQYMG6XlkT5oxY4YgyDyOYdjetqzvvvtOkEK0devWes/AgQNV+DLck+LgfyRAAl5C4MLV5Fn2blgE1qHz17ykda5vRtLtO5JwM0lyBHOw0vW0+YSHTcCnBSiGytesWSPt2rUTpNlEOs9ff/1VvZLIgmTPICyHDBmieeAXLVqkXlKIUktKU728Vq1aKj6xA4GKZ9ha27ZtdW4p5p0ePnxYh+LtDfPb3sd9EiABEvAkAsXyZZejF+99BgZm9ZMKhcI8qQkPta7wgO45fUWf6Wf536I/JTTIp/9MP1T+fNjDJeDT72yIz0KFCgmGxJGlJ3fu3FKiRAlZtmyZdOnSxW5PYFi9f//+UrduXalZs6Z06tRJunbtKhChMASuNwx56Y3jxjG8YkES5o+2atVK4HX96KOP7F5nvofbJEACJOBpBErkC5XbSXdk+/EYicwZLA3KREiWLJBWNEcEKhTKKb/sOy+5swdJnZLhVoeGo+t5nAQ8lYBPC1AIzQ4dOliH4NGJkZGR8umnn8ozzzwjgYGB1jBMRgdj0RHSc/bu3Vv8/f3l559/1lOpLSQyl7Vr1y7p3r27itgLFy7IgQMHdJjeeAZfSYAESMBbCJSMDJV8IbclJCSE4jMNnZozJEDaVS+Uhit5CQl4NgGfFaBY4Y7hbywgMlu9evVk8uTJugipcuXK8uGHHyZbBQ+PKe4ZMGCAhIaGSlRUlOTPn18w9zMlM5cFrylELobwMaSPHPOp3Z9S2TxHAiRAAiRAAiRAAp5EwM8yd/EOA6Q77jLMzYSnE8PpZkPIJayeh2czrWYuC0P+165d86mV72gzvMfwhKSHW1r58jr3JYCpKJhjzb533z5yVc3Y964i6/7lsu/dv49cVUP0PdbAICKQPQsKChKf9YDaA2LvmCOhhD+k6TVzWRC0DLuUXoK8ngRIgARIgARIwBsIJHfreUOL2AYSIAESIAESIAESIAG3JkAB6tbdw8qRAAmQAAmQAAmQgPcRoAD1vj5li0iABEiABEiABEjArQlQgLp197ByJEACJEACJEACJOB9BNxSgP7+++/y1VdfaVpMhEtyhSGHu5FeM63lP0i0gMuXL2v2pLQ+i9eRAAmQAAmQAAmQgLcScDsB+p///EemT5+uy/cRoP3vf/+7/PLLL07nj/ieU6ZMkfPnz6ep7O3bt2t80DRdbOciCND169fbOcNDJEACJOC9BBCO5er1JEGaSZr7EEhMui3xN+6mkHafWrEmvkTArcIwIS7mTz/9JHPmzJGwsLv5grH/9ddfS5MmTaz9cunSJcEPAsBny5ZNjxtxOZGTHdmFChcurHE6rTeZNg4dOiSJiYmaAWnJkiXywgsv6FlkM0KqzeDgYM3PnidPHo1hhfiVx44dU1EcFxdnjWuFMs6cOSMFCxbUWKEoBLEOEV7JELYRERFadpEiRTSFp+789R9EKWKDGteYz3GbBEiABDydwG2L+Jy36aQk3kqSxNux8uQjhSQ8e9pjJ3t6+921/pfibsp3205LkiXZfJgl81KHmoWY8tNdO8uL6+VWAhSxNSEoFy9eLE888YTmaG/RooU0b95cuwACcfTo0SruIPL2798vw4YN05SWs2fPlrNnz6pQROBTCMlJkyZZxaK5D5cvXy4NGzbUjEbDhw/XXO4INo/y4BWF4EROd2RKwvnixYsLhCrE5cKFC6Vbt27qpd2wYYMK5YsXL8qYMWOkWLFi0rdvX6lYsaKcO3dOhXDjxo2lX79+mm4TGZaQBx7pPN977z0tLzY2Vu8bNWqUQ8Fsrju3SYAESMBTCKzdf0HibyZZq/u/Lack0N/tBt6s9fOVDbM3+ur1RNl7+opUKHTX6eMrDNjOzCfgVgIUmYUmTJigIu3LL7+UokWLqrhEXnZ4JTEfFOJy6tSpSm7+/PmyYsUKvQYHkM5yxowZ+k1u0KBBsnXr1vtyrMPjuHLlSh1Oh+cyd+7cAiEJoQiDd/STTz5RUThv3jxZunSpitCOHTvK5s2bVXxiKH3NmjUqQlEviFKk1RwyZIiWgfScEK4QxD169JA+ffroceM/pOFESs/x48cLRPXrr78uGOJ/5JFHjEv4SgIkQAJeScAsfryygR7YqITEe18SPLD6rLKHEnArAQqGJUqUkHfffVfgGdy2bZsuRMKiJMzZhCcSedQXLFigQnHv3r2ai91gX6tWLeswQoECBXTI3DhnvP7666+aVhOvMAz1//DDD1YBGhkZqeIT5yAS//jjD2wmMzy3du3aKopxAtMDevfuLRC9sDp16ugrpgjAbBcv4f6hQ4fqOYjuRo0ayapVqyhAlQj/IwES8BYC1aJyy6HzcZLFT8Qy2ivlCuSQMvlzeEvzPLYdRy7Eyc6TsRKQ1U/7pWpULo9tCyvuuQTcSoBCcEJsvvTSSyoMmzZtquIM3kfMtcSwNjyk2G/fvr1UrlxZfvvtNyt9DJsbhlSXmPxua8uWLRMIVQyzw2rUqCGff/65elexby4D+/Ysb968cuTIEespeFUxH9TIFx8aGmo95+fnd189cD/mqhoGgQpPKI0ESIAEvIlAWLYA6VK3iOw5ES3hOUKkWEROb2qex7Ylb44gKZInm1y7fkuKhIeIv+XvJY0EHjYBtxKgWDj01ltvSbly5XR+JgTdli1bdG4kFuqsXr1aqlevLk899ZQKyLlz5yYTcqnBw8KgXbt26SIns9DEanvM8TSG4e2VgzzuEJoweCwx/I9FRLly5dJpAGXLlrUKUHv3m4/hOZgGUK1aNS1z3bp10rJlS/Ml3CYBEiABryAQFJBVSuYNlpCQYK9oj7c0In+YpT847dNbutMj2+FWX3vy5csn//73v2XmzJnSoUMH9XRivuS4ceNUhGIxElajY/j6lVde0ZXu8Iym1bCiHt5Ps/jEva1atdLV9/BiOrKSJUvKzp07ZeTIkYLV8bgH8ztffPFFnRuKxVBptWbNmukCpe7du0vPnj2lTJky0qZNm7TezutIgARIgARIgARIwKMJ+FmGgu/YzlF0hxZhFTuGqc3D2Ua9sBodIhLD2w/TMGyPOsEbCoNgBTt7dUxLvRB2KigoSAICAtJyucdfA35Xr161eEJCrAw9vlFsQJoIYDoMfm/Z92nC5VUXse+9qjvT1Rj2fbpwedXF6Pv4+Hi7kYjQUGgftxqCN9PH6nJHhhBMmWGYEmCITzwfwvFBxGNGhWtmtJ3PJAESIAESIAESIAFnEXCrIXhnNYrlkAAJkAAJkAAJkAAJuC8BClD37RvWjARIgARIgARIgAS8kgAFqFd2KxtFAiRAAiRAAiRAAu5LgALUffuGNSMBEiABEiABEiABryTgtouQMoM2gtrHxMToo7G4CGGhqlSpkqb4nkgJivzycXFxmlO+fv36mdEEPpMESIAESIAESIAE3J4ABaipixBc3t/fX1NwJiQkyOHDhzXg/fvvvy+prbxHvFKIVeR/R4B8ClATWG6SAAmQAAmkicDNW7flz3NXLWkys0hppi1NEzOkFr2VdFtKRYY+9PCMaaogL7JLgALUBkvr1q3l0Ucf1aNIj4nUn1OmTJF//vOf1isvXbok+EGu92zZslmP29tAGRCliImF/PTI/W4YjiOWaMGCBZMdN87zlQRIgARIwHcI3L59R77ccEyyZvGTJMv2rlOx0qB0Xo8FgL97cXGJEnQriyVk4d30185uzIaDF+VyfKLlb6zIhj+jpVuDopLlIccId3abfKU8CtAUehpiETnnkeUIQdTxyzR69GhBSk94RPfv36/n6tata7eUo0ePypgxY3QoH0P7SOU5adIkvXfs2LGa3x4B9ZHjfvz48RIZGWm3HB4kARIgARLwfgIHLZ7PrJb8KhCfsEsW8fbD9rRn+3NfQlcfStUsul1ORMdL0bzZH8rz+JAHI0ABmgq/0qVLq5cSohPD8tmzZ5epU6fqXRiyx9xPRwJ0x44dmk4UXlXYwIEDZfv27ZrPfuPGjTJv3jxBwP2lS5dKdHQ0BahS4n8kQAIk4JsEAv2zyl3pea/9Qf5+Hj2sDMeNCNpwr03O2kLZN27dI5aYdEeCAu6NMjrrOSzHNQQoQFPhaqQphVDEkHunTp1kwYIFcujQIdm7d69ERUU5LKFt27aaJ37WrFk6n/TUqVPqBYXXEznpn3/+ealdu7Y0aNBAKlSo4LAcniABEiABEvB+AsXzZZcDZ0Pk1KUEFWwNy+SV0pE5PLbhEIiuTsF76Pw1+WXfBZ22UKlwTskf5jiLoseC9NKKU4Cm0rF79uyR8PBwyZUrl3ovMSe0Y8eOOjRfuXJlwcp5RzZt2jQ5cuSItGrVSlq2bCkfffSRDuPj+hEjRsjx48dl/fr1gusgaLt27eqoKB4nARIgARLwAQKtKueXq9cTxd+S+jkkkN681Lq8ZESoFA4P0TmgwfR+pobLrc4zDqiD7oDnEx7Ojz/+WL2euAz71atXl6eeekrKlCkjmzZtklu3bjkoQWTXrl0qVh977DHNGX/gwAHBoiQsYOrVq5dERERI586dpV27dnLy5EmH5fAECZAACZCA7xDIERxA8ZmO7g6yTF2g+EwHMDe5lB5Qm454++23deU7wjFhURDEYYcOHfSq5s2by6hRo2To0KE6LxRhl+DBdGQYrkd4pkWLFukcnqpVq8rp06cld+7cgrIGDBigq+ivX78uw4cPd1QMj5MACZAACZAACZCAVxHws3jw7hjzHL2qZS5sDOa0YB6nXxpmVWP1/LVr1+zGEcX8mKtXr9o958LqZ1rRYIH2hoSESGBgYKbVgw9++AQexlywh98qPjEtBNj3aaHkndew772zX9PSKvR9fHy8Lty2d31QUJDQA2qPTCrHUgtKb749i2Uej6PrIWAdnTOXwW0SIAESIAESIAES8CYCnAPqTb3JtpAACZAACZAACZCABxCgAPWATmIVSYAESIAESIAESMCbCFCAelNvsi0kQAIkQAIkQAIk4AEEKEBtOgnxOJcsWSIXLlxIdgYLaJCxCKGYYHjFtc6wlStXWvLlxjmjKJZBAiRAAiRAAiRAAm5PgALUpou2bNmi+dohQs22c+dO+c9//iNr1qzRw/v27XOaAF29erWuFjM/j9skQAIkQAIkQAIk4K0EKEDt9Gy5cuWsQtM4DZFYokQJY1cef/xxady4sXUfsTyR9Qg53Q1D7vibN2+qdxPxPw07e/asHjf2X3vtNc22ZOzzlQRIgARIgARcReBW0m1ZY0lfuWDzCTly4ZqrHsNy3ZDAbUt4pI2HouW7rafkzOWETK0hwzDZwV+sWDEdYoegLF68uGYv+uOPP6RevXqCoXjY7NmzNYQSMhnBWzp37lzBffCMIrf74MGD9RoIz5iYGImNjZXy5ctbMyFBhCL4fMWKFaVfv37yzjvvSKFChezUhodIgARIgARIwHkEvthwTOSO/pNVey/I0QtxkjMkwHkP+KskyyPkpiWroH9AoiVXO/1dTgecgQL3nrkqN2/d1TFLd5yVx6vklwK5QjJQ0oPfQgHqgGGTJk3kl19+UQG6detWQdajgIAAsQ3aj2CrGLZHjvgCBQoIhOULL7wggwYN0pIRiPXDDz9UAfrMM8+oMH3iiSdk4cKFAq8qBCiNBEiABEiABB4GgVsWJ4rlz1YyO3IxPtm+83euO79IlugUAqdiEihAnULSiYVAgI4cOVLFJIQi8rlv3779vicgmPyQIUNk3bp1mnITC5MgSo0c8ZUqVdKMSbly5dK0mzVr1tQywsPDBfNKaSRAAiRAAiTwsAhktfzNyp09QC7FJeoj/Sz/R4YFCfLPu8JuJt4U/6z+gqQstMwncDImXhIS73pA8V4okDtzvJ8gQQ+og/dD4cKFNV3k/v37BT/I/25PgGLuZ//+/aVu3boCcYn87127dlURiqKDg4OTPYEpKJPh4A4JkAAJkMBDJACnSduqBeX//XpMsgX6S/mCOaRykVwuqQFTcboE6wMVmnT7jnyz+aTlS4GfVCiUUwpRgD4QT5fdDC8ohs8hLB19e8OiI+Q37927t/j7+8vPP/+s9UlKSnJZvVgwCZAACZAACWSUQIB/FnmhUfGM3s77PJhA1ix+8nSdIm7RAnpAU+gGCNCZM2fKwIEDHV6FhUNYdDRgwAAJDQ2VqKgoyZ8/v5hXvTu8mSdIgARIgARIgARIwAcJ+FnmKt6xXVjjgxweuMkIuZQ1a1Ydtn/gwry0AEQQgLc4JCSEnLy0jx01i0Nxjsh4/3H2vff3saMWsu8dkfH+4+h7LMLOnj273cYGBQVxDqhdMhk4CFFFIwESIAESIAESIAESSJ0Al6WlzohXkAAJkAAJkAAJkAAJOJEABagTYbIoEiABEiABEiABEiCB1AlQgKbOiFeQAAmQAAmQAAmQAAk4kQAFqBNhsigSIAESIAESIAESIIHUCXi9AN20aZOcOHEiGQnE7kR2I1cYIwq4girLJAESIAESIAES8CYCXi9AFy1aJLt3707WZ6dOnZJZs2YlO+aMHWRKmjx5sjOKYhkkQAIkQAIk4DICyAl/K+luSkaXPYQFk0AKBBiI/i84iYmJcubMGSlYsKBmNMLhK1euaHB5IwvS5cuXBTndYcj1Ds9qzpw5JU+ePIIYl8eOHdO4V3FxcdbYV/bKNWKGoowLFy4I0n4ihiiNBEiABEiABFxN4EpCoiz+44wk3EySiBxB0qZaAUGKTpp9AkhfuWzHGTl35YamsOxSL0r8mdvePqx0HKUAtcCaPn26bNiwQcLCwuTixYsyZswYKVasmPTq1UumTJkiERERgtSazzzzjCxZskRiYmJk2LBhUqBAARWQuLZfv356DqJ14cKF0q1bN4flzp49W86ePauCFUFakU9+0qRJVtGajv7jpSRAAiRAAiSQZgIQUwssucANO3/1hszdeEJCApzvBLkjd+S2xcvql+WKZPHz3AHXy/E3xYJNLSnpjvx6MFoalc1nIORrBgn4hACFiJw2bZoVEbyV8FrC1q9fL2vWrFGxGBwcrOIRw/ZDhgyxXm+7sXnzZilRooT861//Eng4ka4T93bs2FFwDuIztXKRqnPGjBn6rXPQoEGydetWadSoke2juE8CJEACJEACTiXgn9XPMvz+l6KylAxPKH5oqRMAtbOx11O/kFekSsAnBGjfvn2lRYsWVhi7du1SzyYO7N27V2rXrq0CEvvI/967d2+BKHRkNWrUkDlz5kifPn2kbt260rJlS00vab4+tXJr1aplHfKAJxUpq2gkQAIkQAIk4EoCWbP4SdE82eToxXiBNxRWv3QeCXaBB1SQjtGSpjowMNA6tc2VbXNV2ecsgnP3qSuWv9mCJkmD0nld9SifKtcnBKi/v79VYKJ38ctgWN68eeXIkSPGrty8eVO9msa8T3hLYZi3aVj+/PkFw+g7duyQdevWySuvvCLvv/++cVpfUys3R44c1uvxLORNpZEACZAACZCAqwk0KRcheU/FyvXEJCmeL1TCs9/7m+jMZ9/NBZ+kDhrz311nPuNhlFUsb3YpGREqpy4lSGRYsOS3/NAenIBPCNCUMGHYe/78+WIsMFqxYoWULVtWIAqxwOjo0aMCwYlwTob99NNPurIewrN69epy4MABOXfunApbCFhYSuUa5fCVBEiABEiABDKDQMVCYZnxWI99Zl7LYi380JxHwOcFKOaCtmrVSnr06CHh4eG6EOjNN99Uws8++6y88847ugipVKlSKkhxonHjxrJq1SrB0D4EZ8mSJaVmzZpy/vx5DcM0cuRIGTVqlMNyndd9LIkESIAESIAESIAEPI+AnyUU0B0GTxcddgeH0NDQZL2IRUYQmVitbmtYvQ7DAiTDMGSP8ErGcAPut1eucb0vvYLN1atXPX44xpf6zFltvTsUd4V97yygHlQO+96DOsvJVWXfOxmoBxWHvsfaFnvaCc0ICgoSn/eAGv0ZEBAg+LE1R8dxnVl4Gvdh6N4QnziW0v3GPXwlARIgARIgARIgAV8i4LmBuXypl9hWEiABEiABEiABEvAiAhSgXtSZbAoJkAAJkAAJkAAJeAIBzgH1hF7yojpiXghTvnlRh6ajKez7dMDyskvZ917WoeloDvs+HbB86FLMAaUH1Ic63B2aSvHpDr2QOXVg32cOd3d4KvveHXohc+rAvs8c7p7wVApQT+gl1pEESIAESIAESIAEvIgABagXdSabQgIkQAIkQAIkQAKeQIAC1BN6iXUkARIgARIgARIgAS8iQAHqRZ3JppAACZAACZAACZCAJxCgAPWEXmIdSYAESIAESIAESMCLCDATkhd1prs05cSJE3L27FlrdcLDw6VkyZK6v2PHDjl37pw0adLEmjFq3759mr60UqVK1nu44XkEVq1aJY0bN5asWbNaKx8dHS3bt2+XqKgoKV26tPU43wdWFF6xYdv3cXFxsmfPnmRtq1Wrlu6z75Nh8dgdpJnG7zYyAuKz27zanb/3Htutaaq4o75P6ff+woULsn79emnYsKHkzZtXn0MPaJpw86L0EJg9e7Z88cUX8v333+vPli1b9Ha8+XAMb9LPPvvMWuR///tfKVy4sHWfG55H4Ntvv5W3335bv0gYtf/jjz+kX79+cvDgQRkxYoT2Pc7xfWAQ8o5Xe32/detWmTRpkvUzAL/3MPa9d/Q5nAg9evSQjRs3ytKlS6VXr15y48YNbRx/772jjx21IqW+d/R7f/PmTfnnP/8p+fPnlzfffFMQGxZGD6gjyjyeYQKHDh2S0aNH3ycq8cH06KOPCjwhffv21fLxB6lChQqSK1euDD+PN2YegaSkJBkzZozExMTcV4mpU6eq8KxcubJ06NBBBg4cKK1atRK+D+5D5ZEHUur7P//8U5544gnp0qVLsrax75Ph8NidRYsW6e9yt27dtA1jx44VeMFbt24t/L332G5NU8VT6ntHv/f79++XUqVKSd26dWX58uVy+PBhKVq0KAPRp4k4L0ozgYSEBLl8+bJcvHhR5s6dK6dOnbLeW6JECR2aP3PmjL75bt++LQsWLJCOHTtar+GGZxG4deuWfqH44IMPklUc4gR9b0yriIiIkGzZssnp06eF74NkqDx2x1Hfo0H4Epo9e3aZP3++YATE8Hiw7z22u5NVvGfPnvLss89aj8XGxgo++/l7b0XitRuO+h4NdvR7D7F5/vx5wd98TM8oVKiQfP311xSgXvsuyaSG4ZvN9evX9Y8O3mxvvPGGLFu2TGvToEEDOXbsmGCI/rnnntNvzPCG5siRwzp8k0nV5mMzSADp1ODpMs/7RFGY7wMBYp4XljNnTv1ywvdBBmG72W2O+h7VxB+izZs36zzvr776Sv71r39p7dn3btaJGaxOYGCgBAQE6N2//PKLfrFs2bIlf+8zyNOTbnPU92iDo997fPY3atRIR8Tw9wJD8tu2beMQvCd1vCfUtVy5coI/OMaQOjwemA+KoRkIzSFDhmgz8E158uTJMn78ePn444/VM4oPtOHDh3tCM1nHVAhkyZJFvSHmy+Axw4IFvg/MVLxzG8OwYWFhgvfB448/rt4yeMTh+eBngPf0+ZIlS3Ska9y4cfqFE/P78dluNv7em2l4z7Zt36NlKf3eP/nkk4If2PTp06Vz5870gCoN/uc0Ahh+x49hRYoUsbrejWN4/fHHH3U1HD6cdu3aJW+99ZZ60TCHhOb5BBD5ID4+Xr/pGq25dOmSTkI39vHK94GZhndsw7uBSBgQnzB4TCIjI/VzwNxC9r2ZhudtY4rVwoUL5b333tMoF2gBf+89rx8zUmN7fZ/W33sMwR84cEDq169PAZoR+LzHMYErV67osDuG4THvCyskEXbB+GOEO/FGxfG//e1vOicEQ3kw/KHCPCKa5xPw9/eXmjVrCr4lw7DYDF5xwzOOY3wfgIL3GUYyPvzwQx2CR+uwAAF/dKpUqWJtLPveisIjN/DlAUPvEydOFMzvNoy/9wYJ73111Pdp+b0HFYyQGvOHuQree98nmdKy4sWLq7B8+eWXBbHCMPfDmP9lVAiipEWLFgLhiZ/Q0FBBKCZ4TbAinuYdBBCaBSE3vvvuO/0CgvnAZuP7wEzDe7Yx73fAgAEyc+ZM/cGCxFdffTXZPGH2vWf3N6ZVYVFJp06drA2BQ6F///4akom/91YsXreRUt+n9nuPEE6YivPII48oFz/LEOgdI36X15FigzKNABYgXbt2TQWobSV27twpmCtqTGKHpxRD75gvaruYxfZe7nseAayQxXxAW+P7wJaI9+1jRARzfs2L0dBK9r339bVti/h7b0vEd/Yd/d4jAg4+CxAPFM4nClDfeU+wpSRAAiRAAiRAAiSQ6QQgQJkJKdO7gRUgARIgARIgARIgAd8iQAHqW/3N1pIACZAACZAACZBAphOgAM30LmAFSIAESIAESIAESMC3CFCA+lZ/s7UkQAIkQAIkQAIkkOkEKEAzvQtYARIgARIgARIgARLwLQIUoL7V32wtCZAACZAACZAACWQ6AQrQTO8CVoAESIAESIAESIAEfIsABahv9TdbSwIkQAIkkAoBBFFHysBx48bJ119/LZcvX07ljsw7jbzcCPxtz8ypjc3b9q5N77F58+YJAovD9u7dKz/99JPDIpA16dtvv7WeN+qCzDjm49YLUtjAPciultmG9iOhQlrrv3HjRtmxY8dDqbbB197DkPgFqXKdbb/++qvs2rXLWmxKdTAuogA1SPCVBEiABEjA5wlcuHBBGjduLAsXLhT8sYbAa9iwoZw8edIt2SDVMdKd2trq1atl8ODBehhpkWvWrGl7yQPtT5o0SfLmzatlgBGe4cjA1BCN5nodPXpU3nnnHUe32T1++PDhdN9jt6AHPIj2Q/intf4LFixIUaQ/YHWst5v5Wg+aNiCC//jjD9MR52zii9rKlSu1sL///e+CnPGpGQVoaoR4ngRIgARIwGcILF68WIoUKSLz58+X4cOHy6JFizR1MDyiZkP6YLPnEQLM7ClFimvjfHR0tAq0I0eOWItISkqSgwcPCl7Ndv36ddm9e7f1XpxD2SgjJTt27JjVI4lUyPv27RN4clEHCFSchxCEoSyI60OHDmnKZHO5eBY8WYZ3E+dsnw8RWKhQIWs65V9++UWaNGmixdy8eVP279+v7YqJidHnlC1bVt577z2xrZfxXJQPL2p8fLxxSOuI6w0z6m7sgy/uSS2V+IkTJ5SDcZ/xCq+s+UuFJS25Xof+QP1RJ9ilS5fk7Nmzxm36arTf399f9+3VH6moUTf0uVEWLkY7cL9hOIc+Qp+gn9JTD9QNXtirV69qcbZ8bcvGRfBUP/bYY8bjtT7ggOea37+4AO9xlB0XFyd4XxoGL7T5ucZxvMLzifce2pRa39ylZ76b2yRAAiRAAiTgowSioqL0j+vy5cvl0UcflcDAQB2Gz5o1qxKBQB05cqR6/7Zv3y6jR4+Wl156SX777TeBNxJiDIahWXhR4R2sVauWVKpUSbZu3SoYuoU9++yzUqVKFRVBEyZMkFatWgm8Vz169JAKFSrItm3bZPz48dK9e3dZs2aN9O7dWwWj3mzz36BBg1TkQJA999xzMmTIEJkxY4aKGgy3QthAcKKe8MTVqVNHypcvryIRYgseq759+8qpU6fkiSeekBIlSmi9cM3nn39+3/MhYlq2bKm1gBjJnj27/sDLOWDAAK0/hMvx48d12BlCBt7Y//3vf8nq1aJFC8H9eA0ODhbwhPCHB7pGjRqyfv16KVy4sNazWLFiViEJcQymkZGRKpLQpkceeSQZFQigv/3tbyqI0HcNGjSQd999V4V1nz59lA3EIPoFz9yyZYu88sorKqqRrxws+vfvr1MxIKQ6duwob775pj7Dtv326o/3yIEDB7RN6GsYvszMnDlTIFyRDx3vD7QZ/FEXvNc+++wz7b+AgADNm+6oHvA4jho1yvq+Gjt2rDRt2jQZX7x/zWVDNP7888/ap6hPv3791GuZM2dOfT+j3UuWLNE+Qf/mypVLTp8+rXXF+/CFF16QLl266HsjPDxcmf33v//V9wzKgy1btkz7HIIav0vGF5O7Z5P/Tw9och7cIwESIAES8GECzZo1U4H52muvScGCBVXEQDjB4Anq1auXfPDBB/qHdvPmzYI//GYPlyN0bdu2FXjj6tevL127dpXp06fLDz/8IPgDDgEEg0CCcITIhQDFcyAcGzVqJJhD6MhQNoY8IYxQXkREhAo+TB2AFxdzWbNkyaIC0PDa1atXT8UG5u794x//UKEB0V25cmUVShB/8HLCk2f7fDwHogsGQdO8eXP1doENRCjKgfCBh85sEIwQoka9cA7i+IsvvtB7cO7LL78032J3Gx5JCHnUA895/fXX77sO4gwCGtzwpQBiEMIVbS1ZsqSK2z179mj7Vq1apfdDwM+ZM0evL126tOA4+njp0qUqHI2HmNufUv3hVYQIxxcMGDyU6Fd8EcE2xCgMHm98+cDwOERgWuqBLwZ474ED3j/wXtrjay4bfQlPZr58+eT7779XNhClmzZtsk6nQH3eeOMNad++vXKAMIenHgbPZlhYmGzYsEHfu+AOXmb7v//7P53uMWzYsBTFJ+6hADWT4zYJkAAJkIDPE+jZs6d6QdeuXavDlRCjEydO1KFZeOogyGAYqi9VqpR17ltK4OCBg7jAcCe8g/BOweC5g9cLHjD8YccfdHg7R4wYoaIC4gCeMWO+pb1nwGMIK1eunA6lwvuXmsE7CINYrVq1qooiCEkIrtq1a6to7datm4SGhiZ7PobYIeSKWTySMIgxDOlC6MArVr16dT3eunVrCQoK0u2U/oO3FZ4yGOphHvp3dB/aiR8YPLYQSebhehz//fffpU2bNthUryY80gUKFFCxBZEEgxh/8sknrV8AihYtqh5XnCtevLhVZOM+DNlDUNq2P6X6G30O8Q9DfeDZxD483mANw7PwZce4Li31wJcYvE/QjyjnmWee0bJs/zOXDSHe1OIlhUGYo4/w3sL7EhwMwznj/QHvKN4XMHiL4V3H/NcXX3xRv+yYh+aN+9P6SgGaVlK8jgRIgARIwOsJwHMGjxcMHjQMb0+bNk2++eYbFTCYI2gWO5jzZszjNF5xL7xNZoOQg+EVQ8LwbBoGjxf2IQYwzAlPIn7g2YK3LjULCQmxXgIxkV6DZxdlQPhASMJjB+8lRLLtym2IZHhPYagzvIjwmsL7BmFtMAAXiLXUzFHd0Q4IPhjKNZsxHQLH8ByILEO8GddBsJs90/BEok8g9Mz1wv3Gc4w+MsrAlw2Ymam5/TjnqP44h6kJZjPXEcINX15gttelpR6dO3fWL0RPP/20Tvcwz+s0P9NcNrzkxnWYAmD2UGO+sGE4h/m7hhlfaCBg27VrpzwwfP/qq68al2TolQI0Q9h4EwmQAAmQgDcSgJDCXD/MW4RBxGBIHN45eMLKlCmjQ9c4B7GGH3j94P0zRA7OOVoFjHl1devWtZaBoVf8MYcnEvMaUQY8kPCuYq4ixBGEE+ZqpscgniCuYBC2MLPwgqCGof54JuZcYvgb8yAhPOHlQluNNhnPNw8/Yzi5WrVqWg6G6+ENxGIt1BlTDMwiWy+y/Geul3HM3mvu3LlV3OIchvTNhqFgw3uIIWgMF8PgPTU8qDiG6QAQxJjDCY8jvMxPPfWUzuk1uGJ6BebEptXM7U/rPcZ1+GKDIXsIfrynIOYyavB4wjsOT+jUqVMFEQXwXk2Jr+HdxjPh4cR7FAvRMF8Tc0oNwzlMi8D7DnNU161bp6fgGcV74+WXX9b3C+Z7mkW+cT8838Z7zzhm75UC1B4VHiMBEiABEvBJAhB9EC+YC1rMMswMIYg/0FgQBIM4hUiDWMR1s2fPVmFasWJFXTwDoYphdQhZR4b5cZi7CCGKxSqY+wnDIqa33npLBSjECbyv8EbhDz+GmtNjWOCEeZydOnVSjyvmnkIgYtEPDHM/IZwx9Io2QPBBnGGeKgQZ2oH2Y66n+flYEGUM+UPAGHNBUSZE65QpU7T+xvA7hpzNZq6X+bjtNvjAC4y6ILxPnjx5rJdgVT2Gn1EWRB2ug2GuK1bbw9A3GDbHsDHmnELkw1MKjyGmQaC/0EYIb+N+vTGV/8ztT+XS+07DK4v2oE5gj+dn1PAehLcefYEhcnitwdoRX8yBRX8ac4Ax7QFlPP7447rQDQvfjL7CFBR8eQAbfJHA+xn3YQESvPXwomIoH19QzCv6jbaAN+bzGnNcjeO2r36WbwF3Ulsqb3sT90mABEiABEjA2wnAY4ZFF4YH0dxenMNiDlvDYhAMexp/zG3Pm/chbM3CyjjnqGzjfFpfMVUAXk9jKBlhjrJly6ZDvxCP8NqifeYhZpSN6yBAzMO3xjMhUCHKYRCzEK7gg+uxMAbzRjHUDJEHAYNrbMu3rZdRtu0r6g5PGupoz+BNTGluLO5Bf+TIkUNFuLkMHAcLe31rvs5229x+23Np2Yfewg/mVjrDMFQOr7p5eN8eX3hd8Vz0OQzCEd5tCEnY5MmT1Rv+ySefyIoVK1RcGnNzcQ0Wb8GLDMP7FuXY9que/Os/TDEAW3O9zOfxBYUC1EyE2yRAAiRAAiTg5QQw9xACFB5RZxo8ZxjahncPoZE6dOggQ4cOdeYjWJaTCECQosE3a78AAAD0SURBVJ8QtguGcGGYPoEpFYjOgPBiCB+FVfKIIYspA8YXGWdUgQLUGRRZBgmQAAmQAAl4EAEMW2Po1p6H80GaAfGJ4XrM0YT3EwKH5r4EsAgJoabgacb7AQu0DMMcaEw3QBxWRH0wL7YyrnmQVwrQB6HHe0mABEiABEiABEiABNJNAAI0S7rv4g0kQAIkQAIkQAIkQAIk8AAEKEAfAB5vJQESIAESIAESIAESSD8BCtD0M+MdJEACJEACJEACJEACD0CAAvQB4PFWEiABEiABEiABEiCB9BPws8SLuoOYUTQSIAESIAESIAESIAEScDUBxAf9/+4llUL2GMrwAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n</div>\n<div id=\"分布\" class=\"section level2\">\n<h2>4 分布</h2>\n<div id=\"直方图\" class=\"section level3\">\n<h3>4.1 直方图</h3>\n<div id=\"连续变量的直方图\" class=\"section level4\">\n<h4>4.4.1 连续变量的直方图</h4>\n<p>连续变量的直方图可以使用<code>geom_bar()</code>或<code>geom_histogram()</code>来完成。当使用<code>geom_histogram()</code>时，可以使用<code>bins</code>参数来控制分箱的数量。也可以使用<code>binwidth</code>设置每个分箱覆盖的范围。<code>binwidth</code>的值与建立直方图的连续变量在同一个尺度上。</p>\n<div class=\"sourceCode\" id=\"cb24\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb24-1\"><a href=\"#cb24-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 连续（数值）变量的直方图</span></span>\n<span id=\"cb24-2\"><a href=\"#cb24-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(displ)) <span class=\"sc\">+</span> <span class=\"fu\">scale_fill_brewer</span>(<span class=\"at\">palette =</span> <span class=\"st\">&quot;Spectral&quot;</span>)</span>\n<span id=\"cb24-3\"><a href=\"#cb24-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb24-4\"><a href=\"#cb24-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_histogram</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span>class), </span>\n<span id=\"cb24-5\"><a href=\"#cb24-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">binwidth =</span> .<span class=\"dv\">1</span>, </span>\n<span id=\"cb24-6\"><a href=\"#cb24-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>, </span>\n<span id=\"cb24-7\"><a href=\"#cb24-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">size=</span>.<span class=\"dv\">1</span>) <span class=\"sc\">+</span>  <span class=\"co\"># change binwidth</span></span>\n<span id=\"cb24-8\"><a href=\"#cb24-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Histogram with Auto Binning&quot;</span>, </span>\n<span id=\"cb24-9\"><a href=\"#cb24-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Engine Displacement across Vehicle Classes&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb25\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb25-1\"><a href=\"#cb25-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_histogram</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span>class), </span>\n<span id=\"cb25-2\"><a href=\"#cb25-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">bins=</span><span class=\"dv\">5</span>, </span>\n<span id=\"cb25-3\"><a href=\"#cb25-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">col=</span><span class=\"st\">&quot;black&quot;</span>, </span>\n<span id=\"cb25-4\"><a href=\"#cb25-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>                   <span class=\"at\">size=</span>.<span class=\"dv\">1</span>) <span class=\"sc\">+</span>   <span class=\"co\"># change number of bins</span></span>\n<span id=\"cb25-5\"><a href=\"#cb25-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Histogram with Fixed Bins&quot;</span>, </span>\n<span id=\"cb25-6\"><a href=\"#cb25-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Engine Displacement across Vehicle Classes&quot;</span>) </span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n<div id=\"分类变量的直方图\" class=\"section level4\">\n<h4>4.1.2分类变量的直方图</h4>\n<p>分类变量的直方图实际上是根据每个类别的频率绘制的条形图。</p>\n<div class=\"sourceCode\" id=\"cb26\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb26-1\"><a href=\"#cb26-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb26-2\"><a href=\"#cb26-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb26-3\"><a href=\"#cb26-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb26-4\"><a href=\"#cb26-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Histogram on a Categorical variable</span></span>\n<span id=\"cb26-5\"><a href=\"#cb26-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(manufacturer))</span>\n<span id=\"cb26-6\"><a href=\"#cb26-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_bar</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span>class), <span class=\"at\">width =</span> <span class=\"fl\">0.5</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb26-7\"><a href=\"#cb26-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb26-8\"><a href=\"#cb26-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Histogram on Categorical Variable&quot;</span>, </span>\n<span id=\"cb26-9\"><a href=\"#cb26-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Manufacturer across Vehicle Classes&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"密度图\" class=\"section level3\">\n<h3>4.2 密度图</h3>\n<div class=\"sourceCode\" id=\"cb27\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb27-1\"><a href=\"#cb27-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(cty))</span>\n<span id=\"cb27-2\"><a href=\"#cb27-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_density</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span><span class=\"fu\">factor</span>(cyl)), <span class=\"at\">alpha=</span><span class=\"fl\">0.8</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb27-3\"><a href=\"#cb27-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Density plot&quot;</span>, </span>\n<span id=\"cb27-4\"><a href=\"#cb27-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage Grouped by Number of cylinders&quot;</span>,</span>\n<span id=\"cb27-5\"><a href=\"#cb27-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb27-6\"><a href=\"#cb27-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">x=</span><span class=\"st\">&quot;City Mileage&quot;</span>,</span>\n<span id=\"cb27-7\"><a href=\"#cb27-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">fill=</span><span class=\"st\">&quot;# Cylinders&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"箱线图\" class=\"section level3\">\n<h3>4.3 箱线图</h3>\n<p>箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是\n75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中\nIQR （四分位差）是 25% 到 75%\n分位数之间距离。须外的点通常被认为是极值点。设置<code>varwidth=T</code>将调整盒子的宽度，使其与观察的数量成比例。</p>\n<div class=\"sourceCode\" id=\"cb28\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb28-1\"><a href=\"#cb28-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(class, cty))</span>\n<span id=\"cb28-2\"><a href=\"#cb28-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_boxplot</span>(<span class=\"at\">varwidth=</span>T, <span class=\"at\">fill=</span><span class=\"st\">&quot;plum&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb28-3\"><a href=\"#cb28-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Box plot&quot;</span>, </span>\n<span id=\"cb28-4\"><a href=\"#cb28-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>\n<span id=\"cb28-5\"><a href=\"#cb28-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb28-6\"><a href=\"#cb28-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">x=</span><span class=\"st\">&quot;Class of Vehicle&quot;</span>,</span>\n<span id=\"cb28-7\"><a href=\"#cb28-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">y=</span><span class=\"st\">&quot;City Mileage&quot;</span>)</span></code></pre></div>\n<p><img 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7jj3w6xv375pLW9hYaFt3rzZBgwYkNb1sjIEEEAAgfoXyMoAVC2Lp556qp100kllBI866ii79tprbdGiRW68Ak6dmle64447XGCg04x/+tOf7JFHHrG77rrLTQv+mzt3rgtsqzpNv/3227tgY8KECcEi7nX8+PGmdTdt2jQ2Pn7dsZFxA0888YT96le/cq22KkdpaWncVLOXXnrJzjjjDBfg/OY3vzFdAhCfJk+ebGeddZYLHlQeBeEvvvhibBbVQS1xhx9+uDu9+q9//Ss2rbKB2bNn2zXXXGO/+MUvnKECer1XKi4uduuaOnWqnXDCCXbZZZfZ1q1bbfHixXbppZfawIED3Tpee+21WNYbN250y3z33XexcQrYVOZVq1a5wEPDaqVWy/TRRx9td999d6zVMFhIrZaaT+vQtlQrY3ySw5lnnmm//OUvLX798fOUH3777bedvZYJtqVa48477zx75513ysz+3HPPVXka+emnn7aSkhIbM2aM5efnx5bTDxHtX82bN3fTYxP+b0CearnX+gcNGmR/+MMfTD9igiSfkSNHujr/+te/dnl5nhdMtscff9yOPfZYt62uuOIKW7FiRWxa+QHV66GHHnL7ic4QqCVeQZ+S1iFfbVcZqy7xSS3JMlEe8Unb4YMPPnCjktnP1HqpFuJgGwd10T6v9c6cOTOWvcoyYsQI97lQa7b2scpSMuutbDnGIYAAAghkhkDWBaC6Zk2n9hRYVZYUgJ588slu0rfffmtPPvmkG+7evbs1a9bMfvKTn9juu+/ugoMrr7zS4oNNfbHrC7lly5aVZe3GKSBQwBmfxo0bF1tnMD5+3cG44PWCCy6wiy++2HbeeWc76KCDXDBy4oknBpNdK65aUHfccUf3xb1hwwYXbOjLWUmXAyiY0Bf4wQcf7PJSa9snn3zipuv6w3322ccFrYMHD7aCggI78sgjYxZuprj/li9f7gLd119/3eWrAE3BQhCcKWBRQKUyNWrUyAWQq1evtr333jtWFgWkWkaBsJKCGS0TH0BoPRqn+iio0fBhhx1mClZVH107qUAlCFD+/e9/24EHHuiCUgVrCup79uwZC0JfffVVt5zm33fffV3AHh/IxVUxNrhy5UrTNtQp8nbt2rnAWYGOAkj9cCl/qYQuu6gqffjhh658rVq1qjCLvHVqW6185ZOCTu0zaj094ogjTPVUK6AMlU477TTXii/vPn36uCBfeSnph8tFF13kfgipHlr25z//uZtW2X/6EaN9Q/uajJSP1ikzWars7du3t/32269MEK281OKv4Dr+x8v777/v9k9NS2Y/036iz+Oee+5pu+22m+kzF5yWV321D/z444+u6Nq+MtGPIbXKKvjXD57yKZn1ll+G9wgggAACGSbgfxFlVfI7mKgpyNNrdWns2LFeUVFRbDb/C9i78cYb3Xs/6PH8lirv0UcfjU33v6Q9/xR+7H38gN9K5/mnWj3/lL7nByueH6i6ybNmzXL5+K1ant9C6vmtmW58/LpHjx7t+YFvbP4GDRp4/unaWPb+aX1Xp7feesuN81sevYcffjg23Q8A3Tr861fdOP/L3/vd734Xmz5jxgy3/NVXX+3G3XLLLZ4fbHt+S1psHo3bbrvtPP9LPzYuGPADFG/bbbctM80P0j0/aHez+Nc1uvw1X5D8lk+vRYsWnuodJI3zg3fPDzDdurWd/IAlmOzMNM4PODz5a9gPOGPT/ZYyN86/hMKN22uvvTw/EIlN14DG+a1ybpx/7aXndwyLTfcDF88Ptr177rknNi5+4LHHHnP5+63JsdH+DwGvTZs2nh/Me36rs+cHjJ4fKLvp06ZNc9vab3WNzR8/0LlzZ091ri7Fb/9ly5Z5fjDtffXVV7HF/B8Urlx+AOzG+ddien6rZWy6yuVfXuLe+5eeeH7QHttWfguz5wfNnv/DLDZ/MBB8VvxW/2CU+9zI/fnnn3fj/JZlzw9oY9PLDxx//PGe30EpNvrss8/2/B8a7n11+5kfLLt6absGyW/19/wA3L3VvqOyvPHGG+79T3/609i21YilS5d6hx56qOdfZuPFG1a3XpcZ/yGAAAIIZLRAgf8FkFVJp8GVgtPstS1848aNXcuMHyja0KFDTa1ZOgVf/rR++fzVauZ/KbrWQbWiqSVLrTSVtXSVX1bv1Urp7xHutKdacoOk07Wa1q9fP9czeOHCha4FSKcnNZ9aCdX6qxY8ndbWqfIgqUW3S5cuwVvXC1ytWrrUIEhqZVJrpE6D77DDDsFo96r1qlUsLy8vNl6XFMSf0teE/fffPzZdvZLV8hZfb7WAqtezH5Rb165dY/MmGtB6g+QHl65VUnmr5VP17tChg2vBC+ZRS6XKq1YwXY8bf/2g1rnLLrsEs1b6qssk+vfvH5umFlhdnqGWU7Wq+z9YXAv3Oeec41ob1VqpMlSW1Jpe0/3QD3Zd/mpp9wNiZxWc9tc2VlIrsP8Dw7VYazurFXuPPfZw07R/yqxbt25uH9B2Usc7tXKXT34A7Vqs1aoYJLWCat9Qa7pO41eX9NlQC6ZarWWv1n91AFTS3QYS7WeaR5+z3r17a9AltZrrM1c+6TMhE91JIEht27Z1Lbx6r0tCglTdesvv38FyvCKAAAIIZI5A1p2C15e+35JnfqtfpYo65a5rCZNJ+nL1Wx3dKUAtpwBKAUh1Sac+g9PTCkCrC1rj89P1fQoWdCrbbwmN/SmICIIMlV/BlAJIdaLS6Xm/hdJlE1zvV/5LNr4Hs4JUBVrx+Wt+XS+oceWTTo2Xzy++Q1Uwv4KnIOnUqrZFfNJ2UYq/ZlCBRZCCaw+D93r1WxFjbxUAa73qPa5LI3SKVoF5fD0UMCrgV29zTS9/7azuiJAoKahRUBSkwFXXZWq76HZECpBUB3Uq+u1vfxvMWuFVwVRV+6F+OOj62OD0crCwfkQo0FWHH+072k66njk+6ZpgXaus3vW6a0OPHj1iQbh+/ChQ0z733nvvubz0w0D7VfmkcfLUpSdBkrHqHL+NgmmVvaqs2gYqj241pSBUQa9SMvuZ1h2/z8UPx69PPyi03ePLGj89fjiZ9cbPzzACCCCAQOYJVGw2ybwyViiRWqXUsUL3/1RAEST/9Kbr0KIgJeh8FEyr7FXX1+m6tGeffda1NgYtO5XNGz/OPy3pWqgUoCggVFCQbFLLlQIxBbu6/lNJwYCuP1XrnVrBLr/8cnf7qOCWUpquwEgBlwI2fUmrlVDX8CmpZVNBSRAYaB3qnKNb3ARf+Go11fV7asEtnxT4Kr/4VF2HHq1D12DGJ71XEKeASWVVCm5FpOE5fu/w8knXMB5wwAFutFqg1aqpwE7l1LW4avH2T7nGFlO9FGSq5U1/eh+0aPqnbCvcoSC24P8NaB0KYIKAXde9KqgKWmwVcKqTm1r6dB2rrk2tKmk//Mtf/uICs8A+mFfbUNdO+pdFBKPcq+7UoPG6zjEI+jVOSWZqaVRPeuWnP43zLxuxm2++2a677jrT9bn+pQ/uvcaplVPXb2p76YdRfNI2CvaNoBVSLevq+a5rMZNJ2p7qBKcyaVjXpwZBfk33s0TrU5CrwFit58E1raq7OuoFn4Ng+TDXG+TJKwIIIIBAegUqNoeld/21Wps6jTRp0sQFB2qVUSuhWiQVGCrphvGVJbXg6bZN+hIOklpB1TFDLUMKKJJJaiXVl6Q6E+kLUgFMsknBqlq21MNcrWdqEVNgoYBFAZe+5FVOndoNAhL1klYLnebVdK1XAYQ6iKinsgLx+JZG/zo9d6pdDgq2VF+1sskq/pR5UGZ1alEAo45RCk7UkUvBaqKkU9TqaKVT7mqN1Glk/7pVt03Uuqvt07FjR3e6VtMVcCkgLp/UqUanVBUoKVhTcBHcDujcc891p6F1KYCCcK1Dp6P1Q0NJwaJ+BKgVW/VUueMdyq8reK+ATj8CdNmFfnTIL2gV1eUMCujUE1utjKpLVUkBoraNbLUtFNxqeyhgUpl1j83yHdoUNKsuqq+STv2rN7qStq/cFNSq45BaMPWDRIG1WptVRl2WoB8jCtRVV+0nagVWp6DySafq9YNF+5rm1+UX2s8U6AXG5Zep7L0+Iwr01flN5kGq6X4WLFfVq3rEK/jXuhT863OuVl79UIxPYa83Pm+GEUAAAQTSJOB/iWVl8r9QPb81xvODHNeRwefy/OsGy3ROiu8IpEr6PbRdJxMtEyT/C9x1XPGvPQtGVfoadEIKJvrX77n1+rejCUYl1QlJM/unZ11Z/aDXdRZSueM7xviXA3h+D3jXOUYdffzbHnl+MOT5wZdblzrM+KfTvU6dOrnOQ5rutzp6fotYrCzq5OQHGp4fQLmOQepMorpWlf75z396fiuZ61jkB+KeH2C695o/6ITkB4BlFveDN08dZrQOv1XW83ttu85FwUzqPKNOYH6A7uZTvbSd4jshaRv6QZfLw++576lTV5DUmUkdjvwWNzdPly5dvKCjlebxAznPDx49/zS2W16dY/zW04SdkNRxyb+m0S0jf3WK8U/3B6t0r+oApHL6vbLLjK/sTbAttF6/tdktp85efhAZmz2+A41G+reNctvED0Y9vxXU80/Fu05m2l+V/CDW868FdmVUpyiVOejI4wekbl/wf6Q4c+0fVXW6Ul7+DwpnorJpO8nYv/WVJrlUXSekYD6/ldp1AAveB6+J9jN1QlI545N/+ym3r2pc+U5I2t7qZKX9xQ+2Pf8MgacOWkrlDROt1y3AfwgggAACGS2Qp9KlKdZN2WrmzZvnbidTvrWpshWqZUUtRrr2TkmtpzoVqpYl3aopnUnXUaosavGsLKnFSi1mavWMT36vYVOHnWA5tZSqDmpx0z0x45OuQdTp7MpaPoP5VHe19MXXXy17Oh1a3al47T5BOYNTs0G+elXZVAa14AWXA2h80NqnU/C6lZQsKrs8QPOqtVIthmpRrSwpL53qj78co7L54sfp0gm1XFd2CyW1Zqr1Ta3BNUlqsdQp9KCjXKJltR+qJTfRvKqTWkArcwlcZaLW++qS6qv5gksPqpu/ptOT2c+SzVOt/bIMrilOtFyY6020HqYhgAACCIQrEIkAtDYk6uSijiIKNHQaU9cCZktST3kF0Hrqk17VacVvIXIBU3BdYU3qovtv6vS3TtGrZ79OaevSAo3XfSRTkeID0JpcQ5uKsgR56pGQOo2uOutUtZ62RUIAAQQQQACB8AXKNq2Fn3/G5qjWJ3V+CTo0ZGxBKymYgk1d36nez2ol003n1VJZm+BT2et6TrUiK+BSUK7OUOrgkqrgU+tUa1x1LbOaL51J1xHrTgHqzKPrQkkIIIAAAgggkBqBnG0BFada4YLOJ6nhTW2uOg2rDi2Vnfqu7Zp1+lOn43M1Zfs+kavbjXojgAACCGSXQE4HoNm1qSgtAggggAACCCAQDYGsvA1TNOipBQIIIIAAAgggkJsCBKC5ud2pNQIIIIAAAgggUG8CBKD1Rs+KEUAAAQQQQACB3BQgAM3N7U6tEUAAAQQQQACBehPIutsw6dGF6qkdhRQ8AyCZG4lHob7pqgOuqZHGNbWuyp1jQbjG7LPhega54RpIhPsaNdfmzZsnfNhK1vWC1w3S/UdXhrvV6yk3PTVHT+PJ5dsepYJet1LSB1nPVSeFJ6CnN+lPBxVSeAK6lZqeeKWHSsQ/LSy8NeRmTjoGrF+/3t1qr/zT5HJTJLxay1W3/0v0hL3w1pY7Oek4oBSF7y7dJvLbb781/xHoVW7ArGsB9Z85bhdddFGVFcqmCUuXLnUf4MoeB5lN9ci0sq5cudLdH7Umj+bMtDpkYnn00IO1a9dahw4dMrF4WVsmndHRo1L1YAYCpfA2owJ7PWq5devWkfhCD0+m7jnp0cj6wdSiRYu6Z0YOMYHly5e7syBFRUWxcdk6oMeMn3/++QmLzzWgCXmYiAACCCCAAAIIIBC2AAFo2KLkhwACCCCAAAIIIJBQgAA0IQ8TEUAAAQQQQAABBMIWIAANW5T8EEAAAQQQQAABBBIKEIAm5GEiAggggAACCCCAQNgCBKBhi5IfAggggAACCCCAQEIBAtCEPExEAAEEEEAAAQQQCFuAADRsUfJDAAEEEEAAAQQQSChAAJqQh4kIIIAAAggggAACYQsQgIYtSn4IIIAAAggggAACCQXS9ihOPRLtP//5j+26667WtWvXWKHmzp1rCxYsiL1v06aN7bzzzrH3DCCAAAIIIIAAAghESyAtAejLL79sEyZMsEMOOcSefPJJO+WUU+zII490ko888ojpubJ6Xq9Sz549CUCdBP8hgAACCCCQWQIffvihvfjii+47+7zzzrPmzZtnVgEpTdYIpDwA9TzPJk+ebNdff7116dLFevXqZffdd18sAP3mm2/s1ltvtU6dOmUNGgVFAAEEEEAg1wQeeughu+uuu1y1CwoK7NFHH7W3337b2rdvn2sU1DcEgZQHoHl5eXbvvfe6om7evNnef/99F4hqxIYNG2zFihW2dOlSe+edd6x///7WsWPHMtUKpgcjt2zZYsonCknB+datWyNTn0zZJnLVX1T2k0xx1WdPCddwt0hpaanLUK/ab0nhCOjYqhSl74xwZGqXy5w5c2LBp3LQ/qrv94svvtgee+yx2mXKUmUEgs9/FI6xyRzPUh6ABrpr1qyxU0891TZu3Ghjxoxxo7/77jsrLi62qVOnWpMmTezCCy+0oUOHxlpHNdMzzzxjd999d5CN9ejRw5YtWxZ7n+0DOjhu2rQp26uRkeWP0n6SScC4pmZrrFy5MjUZ53iua9euNf2R6ibw1VdfWcuWLU3f5UFSwDR79uxIfScHdavP1ygcYxWABo0WVVmmLQDVjqvrRtTSefbZZ9vEiRNtt912s+eff9622WYbV75u3bq5Jv3g+lCNPPbYY23fffeNlV+tqUVFRbH32TywevVq02mMZs2aZXM1Mq7s69atcy3L2udI4Qnox6P+ovL5C0+mbjmVlJSY9tlWrVpZfn5+3TJj6ZiAWkBXrVrlrlEsLCyMjWegdgL6fq4soFDjEceE2pmWX0rBvVqVW7RoUX5S1r1XAFrd8SzlAagOrur9fsABBzjYfv362QMPPGAzZsxwp+IFHgSgug5UHZJ04GjQ4H93iOrQoYPpL0gK2Bo1ahS8zepX7WjaQFGpT6ZsDF3aoV/muIa7RYLTQriG6xrkpiBJxzdSOAJBsBSl74xwZGqXyx577GGnnXaaPfzwwy4DfUfr++upp57iWFs70gpLyVRxQRSOsdo3VJdEKeX3AW3YsKHpwuUpU6a4csyaNcuWL19unTt3NrUAXnTRRa5VRQGDessrQA2Cz0QFZxoCCCCAAAIIpE9gxIgRrk+H+msMHjzYXn/99TINROkrCWuKgkDKf24rAta1nQpCR48e7SJ79Yhv166d+zv++ONt+PDh7oJmnTK98cYbo+BKHRBAAAEEEIicwKBBg6x3797WtGnTSJwqjtwGyqIKpTwAlYV2VgWgus6p/D3DTj/9dBsyZIibxjV7WbTnUFQEEEAAAQQQQKCWAik/BR9frvLBZzBNp9wJPgMNXhFAAAEEEEAAgWgLpDUAjTYltUMAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMkIAAQQQQAABBBBIRoAANBkl5kEAAQQQQAABBBAITYAANDRKMsoEgUmTJtmQIUPs17/+tf31r3/NhCJRBgQQQAABBBAoJ1BQ7j1vEchagaeeespGjRplJSUlrg733nuvLV682K688sqsrRMFRwABBBBAIIoCtIBGcavmYJ2Ki4vthhtuiAWfIti0aZONHz/eFi1alIMiVBkBBBBAAIHMFSAAzdxtQ8lqKFBYWFhhiYYNG9q6desqjGcEAggggAACCNSfAAFo/dmz5hAFFHx27969Qo5r1qyxHXbYocJ4RiCAAAIIIIBA/QkQgNafPWsOUSAvL88efvhhl2OzZs2sRYsW1qhRI5s8ebJ7DXFVZIUAAggggAACdRSgE1IdAVk8cwSKiops+vTp9sorr7hrQQcOHGitWrXKnAJSEgQQQAABBBBwAgSg7AiREtCp+L59+9qWLVsIPiO1ZakMAggggECUBDgFH6WtSV0QQAABBBBAAIEsEMi6FlC1bK1cuTILaKsvouqi2wdFpT7V1zg9c+g+oJ7n4Royd2lpqcuR/TVcWB0HlFavXm0NGtAmEJaujgFK69evd7dkCytf8jHbunWrbdy40YJjAibhCASeUTjGqi7Bsa0qnawLQPPz8yNzanXZsmWmU8YtW7asavswvhYC+iLXjs/1n7XAS7CIvsh1SytcEyDVYpJ+hK5atcp1nCsoyLpDci1qnJ5Fgh/4TZs2tcaNG6dnpTmyFu2zMm3evHmO1Dg91VTgqQ61UTjGKgBVvJYoZeXRLiqtBNrR9BeV+iTa0epjGq7hqmtfVcI1XNfAU6/BcLhryM3cghZQjrHhb3++u8I3jc8xCseBZOrA+Z74rc4wAggggAACCCCAQMoFCEBTTswKEEAAAQQQQAABBOIFCEDjNRhGAAEEEEAAAQQQSLkAAWjKiVkBAggggAACCCCAQLwAAWi8BsMIIIAAAggggAACKRcgAE05MStAAAEEEEAAAQQQiBcgAI3XYBgBBBBAAAEEEEAg5QIEoCknZgUIIIAAAggggAAC8QIEoPEaDCOAAAIIIIAAAgikXIAANOXErAABBBBAAAEEEEAgXoAANF6DYQQQQAABBBBAAIGUCxCAppyYFSCAAAIIIIAAAgjECxCAxmswjAACCCCAAAIIIJByAQLQlBOzAgQQQAABBBBAAIF4AQLQeA2GEUAAAQQQQAABBFIuQACacmJWgAACCCCAAAIIIBAvQAAar8EwAggggAACCCCAQMoFCEBTTswKEEAAAQQQQAABBOIFCEDjNRhGAAEEEEAAAQQQSLkAAWjKiVkBAggggAACCCCAQLwAAWi8BsMIIIAAAggggAACKRcgAE05MStAAAEEEEAAAQQQiBcgAI3XYBgBBBBAAAEEEEAg5QIEoCknZgUIIIAAAggggAAC8QIEoPEaDCOAAAIIIIAAAgikXIAANOXErAABBBBAAAEEEEAgXiBtAeiSJUvs1Vdfte+//z5+/W541qxZNnnyZFu2bFmFaYxAAAEEEEAAAQQQiJZAWgLQl19+2S699FKbP3++XXXVVfbPf/4zpnjXXXfZ7bffbtOmTbMzzzzT5s6dG5vGAAIIIIAAAggggED0BApSXSXP81zr5vXXX29dunSxXr162X333WdHHnmkzZkzx95991179tlnrUGDBjZu3Dh76qmn7Iorrkh1scgfAQQQQAABBBBAoJ4EUh6A5uXl2b333uuqt3nzZnv//fddIKoRs2fPtp49e7rgU+/33nvvMq2jGvfMM8/Y448/rkGXmjVrZkuXLg3eZvVraWmpbdmyxUpKSrK6HplWeJnqh09U9pNM8d26dasrCq7hbpHAdcWKFabjJSkcAR0DlNasWWPr1q0LJ1NycQLaZ9evX2+bNm1CJEQBfXcpReEYG8Q3iXhSHoAGK9dB4NRTT7WNGzfamDFj3OiFCxdaq1atglmsZcuWtnz58th7DXTo0MH222+/2DhdL1pYWBh7n80D2tny8/MjU59M2RbFxcUuAI3KfpIprvoBqS8eXMPdIjoOaJ9t2LBh7Md4uGvIzdwUgOr7pqCgwP3lpkJqaq3ggu+u8G0V0OtHaBSOsTqrXd0P6rQFoAouX3zxRXvnnXfs7LPPtokTJ7odOIj4tSm1Uzdp0qTMVu3bt6/pL0hDhw4tE7QG47PxVS2f2tHig/BsrEemlXnlypWuZRnXcLeMWpEUhOIarquCT/21aNGCQClEWn23KABt2rRphe+VEFeTk1kpUGrcuLHbZ3MSIEWVVgykoC0Kx1jVRUFoopR4aqIlk5ymIOujjz5ycwu2X79+1rp1a5sxY4a1a9fOdNopSBpWiycJAQQQQAABBBBAILoCKQ9AdVrpoYcesilTpjhFnULXafbOnTtbnz597Msvv7R58+a51s+XXnqpzOn26LJTMwQQQAABBBBAIHcFUn4KXq2eF154oQtCR48ebY0aNTL1iFfrp9Lw4cNt2LBhVlRU5ILSU045JXe3BjVHAAEEEEAAAQRyQCDlAagMe/fu7QJQXUPWvHnzMqxHHXWUDRo0yF0DVX5amRl5gwACCCCAAAIIIBAJgbQEoIFUVQGmTtPrj4QAAggggAACCCAQfYGUXwMafUJqiAACCCCAAAIIIFATAQLQmmgxLwIIIIAAAggggECdBQhA60xIBggggAACCCCAAAI1ESAArYkW8yKAAAIIIIAAAgjUWYAAtM6EZIAAAggggAACCCBQEwEC0JpoMS8CCCCAAAI5LjB9+nT78ccfc1yB6tdVgAC0roIsjwACCCCAQA4JjBw50vTkQhICdREgAK2LHssigAACCCCAAAII1FiAALTGZCyAAAIIIIAAAgggUBcBAtC66LEsAggggAACCCCAQI0FCEBrTMYCCCCAAAIIIIAAAnURIACtix7LIoAAAggggAACCNRYgAC0xmQsgAACCCCAAAIIIFAXAQLQuuixLAIIIIAAAggggECNBQhAa0zGAggggAACCCCAAAJ1ESioy8Isi0CmCaxYscLGjx9vmzdvthNPPNE6dOiQaUWkPAgggAACCOS8AAFozu8C0QGYN2+eHXPMMbZlyxbzPM/uv/9+e+aZZ6x3797RqSQ1QQABBBBAIAICnIKPwEakCuaCzp///Oe2YcMGKy4utpKSEsdy7rnn2vr16yFCAAEEEEAAgQwSIADNoI1BUWovUFpaak2aNKmQgVpDFy1aVGE8IxBAAAEEEECg/gQIQOvPnjWHKJCXl2eFhYUVcly9erW1bt26wnhGIIAAAggggED9CRCA1p89aw5RQMHnzTffXCHHiy++2Nq0aVNhPCMQQAABBBBAoP4E6IRUf/asOWSBww47zCZOnGh33HGHuwZ0yJAhputCSQgggAACCCCQWQIEoJm1PShNHQW6d+9ut912m+uU1LZt2zrmxuIIIIAAAgggkAoBTsGnQpU8EUAAAQQQQAABBKoUyLoWUN3fcdOmTVVWKJsmbN261dR7Oyr1yRR7ueoP13C3iPZVJVzDddVDE5R0+7DAONw15GZuOgYoyVedFEnhCnCMDddTuclU+2oUjrE6lileS5SyMgANDiyJKpZN06JWn/q2D3Z6XMPdEriG6xnkFuyneiVQClTq/hq4ar8NhuueKzkEArgGEuG+RsVVn7ngO6MqoawLQBs0aGBNmzatqj5ZNV43SC8oKIhMfTIFXy1JSlHZTzLFVQcU/TLHNdwtov1VD1DQfWx1PCCFI6B7AK9du9bdnq2yewSHs5bczSU/P59jQcibf+PGje5HaBSOsWoBVbyWKCWemmhJpiGAAAIIIIAAAgggUAsBAtBaoLEIAggggAACCCCAQO0FCEBrb8eSCCCAAAIIIIAAArUQIACtBRqLIIAAAggggAACCNRegAC09nYsiQACCCCAAAIIIFALAQLQWqCxCAIIIIAAAggggEDtBQhAa2/HkggggAACCCCAAAK1ECAArQUaiyCAAAIIIIAAAgjUXoAAtPZ2LIkAAggggAACCCBQCwEC0FqgsQgCCCCAAAIIIIBA7QUIQGtvx5IIIIAAAggggAACtRAgAK0FGosggAACCCCAAAII1F6AALT2diyJAAIIIIAAAgggUAsBAtBaoLEIAggggAACCCCAQO0FCEBrb8eSGSgwZswYGzhwoPu7/fbbM7CEFAkBBBBAAAEECiBAICoCt9xyiz3++OOx6jzyyCP25ZdflhkXm8gAAggggAACCNSbAC2g9UbPisMUWLNmTaWB5kcffWQzZ84Mc1XkhQACCCCAAAJ1FCAArSMgi2eGwNatW6ssyPz586ucxgQEEEAAAQQQSL8AAWj6zVljCgSaN29uhYWFlea89957VzqekQgggAACCCBQPwIEoPXjzlpDFigoKLDHHnusQq633XabFRUVVRjPCAQQQAABBBCoPwE6IdWfPWsOWWCfffaxd955x0aNGmUlJSV27rnnWo8ePUJeC9khgAACCCCAQF0FCEDrKsjyGSWw3Xbb2TXXXGNbtmyxtm3bZlTZKAwCCCCAAAII/E+AU/DsCQgggAACCCCAAAJpFSAATSs3K0MAAQQQQAABBBAgAGUfQAABBBBAAAEEEEirAAFoWrlZGQIIIIAAAggggEDaOiGtXLnSPv30U9tjjz2sQ4cOMfm5c+faggULYu/btGljO++8c+w9AwgggAACCCCAAALREkhLADpx4kR79tlnrW/fvu61e/fuduGFFzpJPa978eLF1rp1a/e+Z8+eBKDR2seoDQIIIIAAAgggUEYg5QGobofz5JNP2u23325du3a1U0891X71q1/Z6aefbttss4198803duutt1qnTp3KFIw3CCCAAAIIIIAAAtEUqFEAunHjRlNr5n//+18bNmyYO3Wuxxw2aFD1paT5+fn2+OOPW7NmzZzgpk2bbN26de4+jRs2bLAVK1bY0qVL3Q3E+/fvbx07diwj/cknn9hHH30UG1dcXGxr166Nvc/mAT2/XDdMj0p9MmVblJaWmmxxDXeLaF9VwjVcV+2vSuvXr094LA13rdHPTccAJX3nBMbRr3X6aihTjgXheqvBTikKrsH3cCKhpAPQr7/+2o444ghbtGiR+3I/+uij7eqrr7bVq1fbc889Z+3bt69yPUHwqQPCPffcY4cffri7Sfj06dNNAeXUqVOtSZMm7rT80KFD7cgjj4zl9cUXX9gTTzwRe6+WUh2oo5A8z3OWUalPpmwTuSrhGu4WwTVczyC3wFU/yPPy8oLRvNZRIHBVAIprHTErWVzf5xxjK4Gpw6hgn42Ca6gB6JlnnmkHHnigPfzww7bXXns5Yj17+4QTTrCnn37aLrroooTsCjRvuOEGE/DIkSPdvLvttps9//zz7lS8RnTr1s0effTRMgHoGWecYfoLkgLURMFuMF82vKrlt7Cw0Fq1apUNxc2aMqrDG09CCn9z6cyFfplH5fMXvlDtctSxUWeC2rVrZwUFSbcJ1G5lObSUjgFLlixx/QvUwEEKV0DfXRwLwjVdvny5+7FUVFQUbsb1kJsC0OqOZ1WfO48rsH6ZT5kyxQWQLVu2jE3RYw8vuOACmzRpUmxcZQNa/pJLLrEWLVrYjTfe6IIuzbdq1SpTsBAktW6qQ1Jw6iQYzysCCCCAAAIIIIBAdASSCkAVxeo6z8qahWfMmFHtdUvXXnut7brrrvbHP/7RdE1okHT6Xi2nurZULaMvv/yy9evXr9r8guV5RQABBBBAAAEEEMg+gaTO96ipfeDAgS5YvO2221wt1ar597//3R588EEXWFZVdV07qk5E+hs/fnxstvvvv990y6Xjjz/ehg8f7i4SV+uqWkhJCCCAAAIIIIAAAtEVSCoAVfVHjx5txx13nO23337uGoVDDz3UNm/ebCeddJL94Q9/qFJI13m+++67VU7X7ZiGDBniesbHn96vcgEmIIAAAggggAACCGS1QNIB6Pbbb+9aMRVMzpw5013H2bt3b9NfXZNO7xN81lWR5RFAAAEEEEAAgewQSDoA/eqrr1zPYt08Xr3hg6RrQJs3b+56wzVq1CgYzSsCCCCAAAIIIIAAApUKJB2AHnTQQe6en5Xm4o/UbS7U2ejyyy+vahbGI4AAAggggAACCCBgSfWCl9P1119vu+++u7sP6Jw5c+y9995z9/Ns06aNG37ooYfc4zZ1X08SAggggAACCCCAAAJVCSTVAqr7cqp1U088GjBggMurc+fOdvDBB5tOwU+ePNkFqHqu+xtvvOE6K1W1QsYjgAACCCCAAAII5LZAUi2gepSZ7tWpG8WXT+qctHDhQjda0/XkCRICCCCAAAIIIIAAAlUJJBWANm3a1HQNqJ5mNGvWrFhe//nPf0yP4/zpT3/qrg995pln7Gc/+1lsOgMIIIAAAggggAACCJQXSOoUvBYaM2aMDR482Lp37246/a7T8vPnz3fPadd9PM877zwXnB5zzDHl18F7BNIqcOutt9qiRYvcj6O0rpiVIYAAAggggEBSAkkHoN26dbOpU6faBx98YNOmTbPGjRu7ls+99trLrWjEiBGuE1KzZs2SWjEzIZAqAT0ydu3atanKnnwRQAABBBBAoI4CSQegWk9eXp5rAVUwqrRlyxbTafhly5a5R3W6kfyHAAIIIIAAAggggEACgaQD0LFjx9o555xjal0qn8466ywC0PIovEcAAQQQQAABBBCoVCCpTkha8tJLL7UTTzzR3WapRYsW7rGc9957r3Xo0MH+9Kc/VZo5IxFAAAEEEEAAAQQQKC+QVAvo6tWrXaeOm266yTp27Gjt2rWz1q1b2/nnn2/FxcV2yy232B133FE+b94jgAACCCCAQJoFvv/+e3frxFSttrS01F16p0d0pyqpcUuP/iZFVyCpAFS3YWrYsKHreCQK9YR///33bdddd7X999/frrjiiugKUTMEEEAAAQSyRGD27Nl2xBFHpLy0L7zwgukvVanPvn1s7FNjU5U9+WaAQFIBqILPXr16uachjRo1ynr37m0TJkww3X5p0qRJ1rVr1wyoCkVAAAEEEEAgtwX04Bil4QefbZ226ZyVGP/47FnbsGFDVpadQicvkFQAquz+8pe/2NFHH239+/c3dTraZ599rHnz5q4n/Isvvpj8GpkTAQQQQAABBFIq0Lmoi3XfrntK15GqzFs1aWVLjacqpso3U/JNOgDt06ePzZ0710pKSlzgqXuB6hnwhx12mLsxfaZUiHIggAACCCCAAAIIZLZA0gGoqlFYWOj+NKznvg8bNkyDJAQQQAABBBBAAAEEkhZI+jZMynHjxo02btw4u+GGG2zBggX2ySefuEdyJr02ZkQAAQQQQAABBBDIeYGkW0C//vpr17NOz9jWc+B1PejVV19tukXTc889Z+3bt895TAAQiLLA5s2bLejgEOV6UjcEEEAAgdQLJN0CeuaZZ9qBBx5oS5YssR122MGV7LHHHrMGDRrY008/nfqSsgYEEKhXgb///e82ePDgei0DK0cAAQQQiIZAUgGobocwZcoUd+q9ZcuWsZpvt912dsEFF7hbMcVGMoAAAggggAACCCCAQAKBpALQgoIC19JZ2XPgZ8yY4aYlWAeTEEAAAQQQQAABBBCICSQVgKr3+8CBA+2iiy6yqVOnuoXVKqpTcg8++KAdfvjhsQwZQAABBBBAAAEEEEAgkUDSnZBGjx5txx13nO23336Wl5dnhx56qKlTwkknnWR/+MMfEq2DaQgggAACCCCAAAIIxASSDkC33357++ijj+zdd9+1mTNnuvuB6pGc+iMhgAACCCCAAAIIIJCsQMIA9LPPPqvwPFZdD9qjRw+Xv07Df/DBB9a2bVvbZZddkl1nnebbsmWLrVixok55ZMrCqotua6NXUngCuk2Y53mR2U/Ck6lbTnoKmlJUPn910whvae2vSrqlnc4ukcIR0DFAad26de4e1uHkmvm5aD+KQiotjc53fbLbQ2eVdQyIwjG2tLS02tgmYQB62mmnmToZVZd++ctf2vjx46ubLZTp+fn5VlRUFEpe9Z3J0qVLXUtyq1at6rsokVq/bg2mD3FU9pNM2Ti6FlwJ13C3SHFxsfvC0XFAP/BJ4Qjoh71uG9i8eXNr0qRJOJlmQS5R+T4pKIjOd32yu83y5csj892lAFTxWqKU8Gj34YcfJvWko4YNGyZaB9MQQAABBBBAAAEEEIgJJAxAv/rqK/frcY899rC3337bFNFWlnQ/0OC0fGXTGYcAAggggAACCCCAQCCQMADV04969uzpbrekJ6BUdW1JOk/BBwXnFQEEEEAAAQQQQCA7BRIGoNOmTYtdFK9raapKuuaOhAACCCCAAAIIIIBAMgIJA9CVK1e6e31Wl5Eu8KZjQnVKTEcAAQQQQAABBBCQQMIAdMCAARnXC57NhgACCCCAAAIIIJDdAgkD0Hbt2rna7b///nbyySfbvvvuW2ltdR9QEgIIIIAAAggggAACyQgkDEDffPNN9/SjcePG2a233mrNmjVzgagev7n77rsnkz/zIIAAAggggAACCCBQRqDa3kMHHHCA3X333TZ//nwbM2aMu7Fvv379rFevXjZq1CibM2dOmQx5gwACCCCAAAIIIIBAIoFqA9BgYfV079+/vz300EO2cOFC1yKqpyR169bNRowYEczGKwIIIIAAAggggAACCQUSnoKvbEk9D/r11193j958+eWXXe/3Ll26VDYr4xBAAAEEEEAAAQQQqCCQVAAaBJ0TJkywiRMnunuDHn/88S4IPfTQQ3l+cQVWRiCAAAIIIIAAAghUJZAwAP3Xv/5lY8eOtRdeeME9E/7YY4+1p556yg477DDj+e9VkTIeAQQQQAABBBBAIJFAwgD0ggsusJkzZ7prPw8//HBr1KiRfffdd+4vPtOdd97ZNJ2EAAIIIIAAAggggEB1AgkDUD3dqE2bNjZ9+nT3V1VmxxxzDAFoVTiMRwABBBBAAAEEECgjkDAAfeedd8rMzBsEEEAAAQQQQAABBOoqkDAArWvmLI9AILB58+ZgMOWvW7duNc/zLJ3rLCgocJ3zUl65KlaQjrpu2bIlba667Vt+fn4VtWU0AggggEC2CxCAZvsWzILyn/e78+yNf72R9pL26NEjbevsvmt3m/jixLStL35Fl1xyib300kvxo1I6nA7XgvwCe/GlF22nnXZKaV3IHAEEEECgfgQIQOvHPafWunjxYtu53c42YNefR7LeU3+YYt8vml1vdZNv1zZdbeBu0egIuHz9Mnt22gRbuXJlvZmyYgQQQACB1AoQgKbWl9z/T6BDq+1tYPeBkfRYsX65fb+m/gJQoW7bYrvI+M5ZPscFoJHcWagUAggggIATSPpRnGvWrIEMAQQQQAABBBBAAIE6CyQdgO6yyy72q1/9yiZNmmTqjEBCAAEEEEAAAQQQQKA2AkkHoHoMZ8uWLe3kk0+2jh072qWXXmozZsyozTpZBgEEEEAAAQQQQCCHBZIOQA855BB75JFHbNGiRXbXXXfZ119/bfvss4/16dPHHnjgAVu9enUOM1J1BBBAAAEEEEAAgWQFkg5AgwybNGliJ510kl1zzTU2bNgw++yzz+yyyy6zn/zkJ6bbwaTjfoRBWXhFAAEEEEAAAQQQyD6BGgWg3377rV133XWmZ78fdNBBNmfOHBs3bpy7Xcrbb79tL7zwgpuefQyUGAEEEEAAAQQQQCBdAknfhmnAgAH25ptv2m677WZnnXWWDRkyxDp06BArp07HH3fccfbll1/GxsUP6J5+n376qe2xxx5lltM8s2bNsh9++MH23ntva9u2bfxiDCOAAAIIIIAAAghETCDpAFTB4Z/+9Cfbf//9qyT4wx/+YE2bNq0wfeLEifbss89a37593Wv37t3twgsvdPPpelJ1ZlKrqq4lve+++6xTp04V8mAEAggggAACCCAQRYEPPvjA7r//ftNjiHVZY8+ePaNYzTJ1SvoU/Oeff+5aL8ss7b957LHH7JRTTnGjd9hhB2vTpk2ZWXTLpieffNJuuOEG13J655132htvvOFO2+sU/rvvvmujR4+2yy+/3PWwf+qpp8oszxsEEEAAAQQQQCCqArqUcejQoe4s8dSpU+2Xv/ylPffcc1GtbqxeCVtAP/74Y3v99dfdzO+9956NGjXKGjduHFtYwaWu+1TrZVUpPz/fHn/8cWvWrJmbZdOmTbZu3Tp3L9HZs2e7KF8Rv5JaWf/5z3+64eC/VatW2YoVK4K3brnS0tLY+2we8DzPtm7dalGpT1XbQvWbMudj+90z51Q1S1aPX1e8zhoUNqi37aj9KC+rBSsvvI4vUf9sqObBfZWD18o1GFtTAR1blXLhGBtvE5X9SMe1XPj8K8a59tpr4zehGx45cqTp0sfmzZtXmJYNI7TttA0TpYQBaLdu3WzEiBFWXFzsercrOFRAGaSGDRtaly5d7MorrwxGVfoaBJ86ENxzzz12+OGHu2s9Fy5caK1atYoto/uMLl++PPZeA08//bTdfffz+v3bAAA6zUlEQVTdsXE9evSwpUuXxt5n+4AOFgrKo5y0I24q3WSb1ka3ns0aNqu3/VJ3nmhk//9zGZV9SQfmKH3Wq9su8T+0q5uX6ckL5NpT/NTfIgpJ3xu58PmfOXNmpZtL8ZL6x2TrJYnaftX9GEoYgOp0+vvvv+9wjjnmGPv73/9e62hcQaxOwysiVmSvpGA2voAqsG7zFJ+OPvpo69WrV2zUgw8+aEVFRbH32Tyge6cWFBTEWoezuS6Jyt4g/38t3A3ykr7iI1F2GTfNtUDm5dXbftmwoGHGmYRRoBYtWtSbaRjlTzaPkpISd1ZIP8bjf+AnuzzzVS6gL3D9iFELUmFhYeUzRXCsGnKikAryC3Li87/rrrtWubk6d+5s22yzTZXTM3mC4rnqjmcJA1CdgteHVz3XL774YtP7ytJ2221napmsKm3YsMFd46l7heoJSkGh2rVrZ1988UVsMbUAxPes1wQ9dUl/QRozZow1atQoeJvVr3l+0CKLqNSnqo1R2LDQ+nbrZxf0/1/Hs6rmy9bx4z592t6Y/Xq9bce8BlE8AW8uaIj6ZyN+n1eQpB+kpHAEgsYNmebafhSOYP3mouNaLmy37bff3t1VSH1l4tO5555r7du3jx+VVcOKbRTjJEoJj3Znnnmmu0ZTLZ+DBw+u8mlHumB2/PjxVa5H1zcoyv/9739fZh49RUmn5OfNm+cCz5deesn222+/MvPwBgEEEEAAAQQQiKrAVVdd5WItXW6oPjHnnHOOnXjiiVGtbqxeCQPQadOmxSLYJUuWxBYqPxB0Iio/Xu/1yM6PPvrI/cUHqbrdgG4zMHz4cPdEJZ1WV3Nz0KO+srwYhwACCCCAAAIIRE1AlzkefPDBLuaKymWG1W2jhAGoOhkFqbbX0OjG9brVUlXpqKOOskGDBrmOTtna26uqujEeAQQQQAABBBBAoKJAtb1CdB2Nbr80d+7cMkv/4he/sD/+8Y+m6zvrmhToEnzWVZHlEUAAAQQQQACB7BBIGIDOnz/fDjzwQLvxxhtt8eLFZWqknlm33XabHXrooZG/jVCZivMGAQQQQAABBBBAoE4CCQPQq6++2jZu3Oie764OQ/FJTyzStZ3Tp0833RqJhAACCCCAAAIIIIBAMgIJA9DXXnvNPT6za9eulealHuu6hlPzkRBAAAEEEEAAAQQQSEYgYQC6fv1623PPPRPm069fP3ez34QzMREBBBBAAAEEEECgUoFPPvnErrvuOvenuwflQkoYgB500EH25ptvJnTQzel16wASAggggAACCCCAQM0EJk6caKeeeqpNnjzZnVE+9thjc+LMcsLbMA0YMMBd3/mb3/zG9Fz48untt982wY0dO7b8JN4jgAACCCCAQD0JjHzpinpaczir3XWXqh9RGc4aMiOXdevW2WWXXVahMHpked++fSs8nrzCjFk8ImEAesEFF9gHH3xge++9t3uE5l577WV67OZ///tfU3OxbiZ/2mmnuft4ZrEBRUcAAQQQQAABBNIuoAC0ZcuWtmbNmjLr9jzPdQJv0qRJmfFRepMwANXN5ydMmGCXXHKJPfbYY3bNNde4uuvJR7vssovdeeeddv7550fJg7oggAACCCCQ9QJtmrW1wvzCrKzHqo0rY09hzMoK1KDQrVu3rvSZ9wpMo35/9IQBqAwLCgpMzyfVnx7HOWfOHNt9990jD1OD/YdZEUAAAQQQyCiBEQMutu7bdc+oMiVbmPvevteW5lf9+O9k88mG+Ro3bmwPPPCA/epXv7L8/HwXeCvuevrpp622T6DMhnqrjNUGoPEV2XbbbU1/JAQQQAABBBBAAIG6C/Tq1cveeustGz9+vOkM83HHHWcdO3ase8YZnkONAtAMrwvFQwABBBBAAAEEsk6gQ4cOrk9NXl6eFRUVZV35a1PghLdhqk2GLIMAAggggAACCCCAQCIBAtBEOkxDAAEEEEAAAQQQCF2AADR0UjJEAAEEEEAAAQQQSCRAAJpIh2kIIIAAAggggAACoQsQgIZOSoYIIIAAAggggAACiQQIQBPpMA0BBBBAAAEEEEAgdAFuwxQ6KRkikF4BPbKtZPMmW7I2GjduXrF+RXoBWRsCCCCAQNoFCEDTTp6bK1yweoG9+tUrkaz8t0u/rdd6KQD97MfP7Nxnzq7XcrByBBBAAAEEkhUgAE1WivlqLaAb7E7+crJ9u/SbWueR6Qvq8bT1ldq3b19fq07ZevU0kFy5GfNXX33lHnU8atQo02eFhAACuSkwevRo9/jNiy++OCcACEBzYjPXbyXvu/8+UytdutL5559vCxYssH/84x/pWqV7fm/aVlZuRQpcVq1aVW5s+G/nzp1r8+bNs4MPPjj8zMvleOSRR9qOO+5Ybmw0365cudI++ugj27BhQzQrSK0QQCApgRkzZlijRo2SmjcKMxGARmErZkEd9HixdCWtK/hL1zrrcz0NGza0v/71rykvwgMPPGAPP/xwWtaV8sqwAgQQQACBehWgF3y98rNyBBBAAAEEEEAg9wQIQHNvm1NjBBBAAAEEEECgXgUIQOuVn5UjgAACCCCAAAK5J5B114Bu2bIlLR0u0rErqC7FxcWRqU86zJJZx9atW12np3R0zEmmPFGZZ/Pmza4quIa7RTdu3OgyXLduHceCEGmDjo/q3KXjbK6ktWvXRqKqUfquT3aDaJ/VXxSOsaWlpaZtmChlXQCan59vLVq0SFSnrJlWUlJi6kASlfpkCrxu4aOEa7hbpKDgf4cLXMN1DXq9Nm3alH02RFp9+W3atMkaN27s/kLMOqOz0n4UhaTjeK4da4LOs1GotwJQxWuJUtYFoKpMdZVKVOFMmqadTR+yqNQn02xxDXeLaH9VwjVc1+AHE8eCcF2D3HLNNSqfTx1volKXYF9M9jUK9Q7OQCSqM9eAJtJhGgIIIIAAAggggEDoAgSgoZOSIQIIIIAAAggggEAiAQLQRDpMQwABBBBAAAEEEAhdICuvAQ1dgQwRQACBEASef/55mzVrVo1y0uNNlR555BFr1apVjZbdf//97dBDD63RMsycGwKvzJhkH8/5MCsr++3Sb6zV9jX7LGRlRXO80ASgOb4DUH0EEAhP4K477rK1q9da88bNa5Rpuxbt7L033qvRMqs3rLbpX0wnAK1CbeXKlfab3/zGrrjiChswYEAVc0Vv9Lbbbms77biTzd7wnf+XmvqtWLHCGjVuZM2aNkvNChqb9d6rd2ryJteMESAAzZhNQUEQQCDbBdTzc9Buh9tpfYakvCp//tftttlKUr6ebF2BbgMzd+5cW79+fbZWoVblbtu2rU16ZVKtlk12IbW8n3jiiXbppZcmuwjzIVBBgGtAK5AwAgEEEEAAAQQQQCCVAgSgqdQlbwQQQAABBBBAAIEKAgSgFUgYgQACCCCAAAIIIJBKAQLQVOqSNwIIIIAAAggggEAFAQLQCiSMQAABBBBAAAEEEEilAAFoKnXJGwEEEEAAAQQQQKCCAAFoBRJGIIAAAggggAACCKRSgAA0lbrkjQACCCCAAAIIIFBBgAC0AgkjEEAAAQQQQAABBFIpwJOQUqlL3gggkFMCW7dutTnLv7c3Zr6e8novXrvYttmmdcrXwwoQQACBVAgQgKZC1c/zpptusoULFybMvaSkxBo0aGAFBYk3wyGHHGInnXRSwryYiAAC9S+gAHTa/GnuLx2l2avjXulYDetAAAEEQhdIHPmEvrrcyFBfQk8++aR1aLW9tWlWVKdKz1sxz5YtXUYAWidFFkYAAQQQQACBTBIgAE3h1jhqj6Ps8N2PqNMa7vz3Hbbe1tUpDxZGAIH0COiMRo8OPWzvHfZJ+Qrf/uYty2uQl/L1sAIEEEAgFQIEoKlQJU8EEMhJAQWgO2+7iw3ueWzK6//N0m9sc15JytfDChBAAIFUCNALPhWq5IkAAggggAACCCBQpQABaJU0TEAAAQQQQAABBBBIhQCn4FOhSp4IIJCzAsvWLbUZC2ekvP5rNq22Jv4/EgIIIJCNAgSg2bjVKDMCCGSkQJOmTezd7951f+ko4CHdDknHalgHAgggELoAAWjopGSIAAK5KjDumXG2dOnSGlV/ypQp7r7BDzzwgO2www41Wnb77bev0fzMjAACCGSKAAFopmwJyoEAAlkvUFRUZPqrSfrxxx/d7F27drWddtqpJosyLwIIIJC1AmkNQNetW2eff/65HXzwwTGwuXPn2oIFC2Lv27RpYzvvvHPsPQMIIIAAAggggAAC0RJIWwC6adMmu+6669yjJ+MD0EceecQWL15srVv/75nGPXv2JACN1j5GbRBAAAEEEEAAgTICaQlAZ8+ebVdeeaW1atXK/cWX4JtvvrFbb73VOnXqFD86EsNTf5hiy9cvr1Ndflgxx4rq+DjPOhWAhRFAAAEEEEAAgZAF0hKAbtiwwUaOHGnLly+3SZMmxaqg8StWrHAX7b/zzjvWv39/69ixY2y6BiZMmGBjx46NjSssLKzxRf6xhdM0oGfBK33242fur66rbdGhRcbXua51DHP5pk2bWosWmIVpqrzy8/NNl8jUtJNN2OWIWn4lJSXuulFdooRteFt35cqVLjN9z+AanmuQk/ZbXAONcF49zzP9RcG1tLTUtmzZkhAmLQFojx49XCHeeuutMoX57rvvrLi42KZOnWpNmjSxCy+80IYOHWpHHnlkbL527dpZsLxGapmGDRvGpmfqwMCBA23y5MmhFO+4447LijqHUtkQMrn44ovdhzgb9pMQqpu2LE444QQbPHgw+2LI4n369LHnnnvOGjVq5C5RCjn7nM0u+Pzrh1MwnLMYKai4HjuLawpg/Syj4JqXl1ctTloC0KpKsdtuu9nzzz9v22yzjZulW7du9uijj5YJQNUqqr8gKUANrhcNxmXi6zXXXGNHH310wqKtXr3a7WhqsUuUBgwYYAUF9bqpEhUv46ap5UO/vLJhP8k4vAQFUgvd2rVrcU1gVJtJ+hGuM0EtW7bkc14bwCqWUb8DJQX2HAuqQKrDaH0n4VoHwEoWVdCmvyi4qgVUP/4SpXqNalatWmVr1qyJBaC6DlQdknQKW7+usjmp5VatoImSmtl1SYGujSUhgAACCCCAQP0J3HvvvbZs2bJ6K8CcOXNc0KYGrPpK7du3t9/97ndpWX29BqBqARwxYoSNGzfOGjdubC+//LL169cv64PPtGw5VoIAAggggAACoQjoWmE9DEKdfps3bh5KnjXNpLHX2My/bPLDNz6s6aKhzL9201pbuX6lnX322dW2XoaxwnoNQHXT5eOPP96GDx9uaq7VKagbb7wxjHqRBwIIIIAAAgggUCOBk/c51QbsMqBGy0Rl5te+ftVGv/9w2qqT1gC0/PWcquXpp59uQ4YMMV1fpgCUhAACCCCAAAIIIBBtgYy40FLXexJ8RntHo3YIIIAAAggggEAgkBEBaFAYXhFAAAEEEEAAAQSiL0AAGv1tTA0RQAABBBBAAIGMEiAAzajNQWEQQAABBBBAAIHoCxCARn8bU0MEEEAAAQQQQCCjBAhAM2pzUBgEEEAAAQQQQCD6Amm9DVP0OakhAggggECyAnfddZc9+cSTyc5eo/k8z7OC/AK7auRVdu0119Zo2WRn3nPPPe3xJx5PdvbIzKfHaOuJOSQE6iJAAFoXPZZFAAEEEKi1wOzZs61ZfjP7+a6H1TqP+lrw8x8/s2+++aa+Vl+v67311lutadOm9VoGVp79AgSg2b8NqQECCCCQtQLtWmxrJ/Q+MevKX1xabIvmLsq6clNgBDJFgGtAM2VLUA4EEEAAAQQQQCBHBAhAc2RDU00EEEAAAQQQQCBTBAhAM2VLUA4EEEAAAQQQQCBHBAhAc2RDU00EEEAAAQQQQCBTBAhAM2VLUA4EEEAAAQQQQCBHBAhAc2RDU00EEEAAAQQQQCBTBAhAM2VLUA4EEEAAAQQQQCBHBAhAc2RDU00EEEAAAQQQQCBTBAhAM2VLUA4EEEAAAQQQQCBHBHgSUo5saKqJAAIIZKLA98tm2+UTL8vEoiUs0/L1y8xr6CWch4nZJzBh2nh77etXs6/gIZR49cZVIeSSfBYEoMlbMScCCCCAQMgCG0s22rdLs/OZ6q1atgpZg+zqW2DJ2sWmP1LqBTgFn3pj1oAAAggggAACCCAQJ0ALaBwGgwgggAAC6RXYtX13u3LgyPSuNIS1PTttgr03/90QciKLTBIYdtBw69utbyYVKW1l+fesN+yxjx9L2/oIQNNGzYoQQAABBMoL5DfIt2aFzcqPzvj3DfMbWl5eXsaXkwLWTKBRQaOs3B9rVsvK5y70657OxCn4dGqzLgQQQAABBBBAAAEjAGUnQAABBBBAAAEEEEirQNadgvc8zzZt2pRWpFStbOvWrVZaWhqZ+qTKqab5ylV/UdlPalr/VM2vfVUJ13CFN2/e7DIsLi52x4Nwc8/s3PQ5tWy+k5Ff9lz8POh7OGrfXbm4Has6OsgiPz+/qslJjdf+of0kUcrKAHTLli2J6pRV07SBolSfTMB3X2p+QXANd2vgGq5nkFvgGrwG43PhtbovqEw38PzoOVePM9pfo1T3XPz8VfX5CmO7Ko/qPt9ZF4A2aNDAmjXLvgvWK9vQGzZssIYNG0amPpXVsT7GlZSUuANjVPaT+jCsbJ06mKiVDtfKdGo/TqY6FjRp0sQKCrLukFz7ivtLulaWLO7Ho05Iufh5WLdunRUWFkaq7nQo+/8fZe3TYbSAKl5LlBJPTbQk0xBAAAEEEEAAAQQQqIUAAWgt0FgEAQQQQAABBBBAoPYCBKC1t2NJBBBAAAEEEEAAgVoI5NYFR7UAYhEEEEAAAQQQyA0BPQf+26Xf5kZly9Vy6bql5cak9i0BaGp9yR0BBBBAAAEEMlxAHQDV8WbCtPHuL8OLm7LiqWN0dZ2Hwlo5AWhYkuSDAAIIIIAAAlkpoF79r7zyiq1atareyn/ttde6uwuMHDmy3spQVFSUtkfMEoDW22ZmxQgggAACCCCQKQKdO3c2/dVXat68uTVq1Mh69epVX0VI63rphJRWblaGAAIIIIAAAgggQADKPoAAAggggAACCCCQVgEC0LRyszIEEEAAAQQQQAABAlD2AQQQQAABBBBAAIG0ChCAppWblSGAAAIIIIAAAggQgLIPIIAAAggggAACCKRVgAA0rdysDAEEEEAAAQQQQID7gLIPIIAAAvUkoJte33zzzTZnzhzba6+97I9//GPankJST1VmtQgggIATIABlR0AAAQTqQaC4uNj2339/0yMAS0tL7csvv7SxY8fa559/bnocHgkBBBCIsgABaJS3LnVDAIGMFRg1apR79rSCT6WtW7dakyZN7KmnnrLf/va3blwu/Ld64yp7f/Z7WVfVeSvnZV2ZKTACmSRAAJpJW4OyIIBAzggsWLDAtmzZUqa+GzdutGXLlpUZF+U3rVu3tvkr59ud/74jK6u5007dsrLcFBqBTBAgAM2ErUAZEEAg5wT2228/++CDD6ykpKRM3Xv27FnmfZTfXHfddXbRRRelpIpLliyxwYMHm9YxaNCglKyjWbNmKcmXTBHIBQEC0FzYytQRAQQyTuCMM86wcePG2bx589ypeF33eeihh9rAgQMzrqypKlB+fr4VFRWlJPsgsG/evHnK1pGSgpMpAjkiQACaIxuaaiKAQGYJ5OXl2euvv27PP/+8/fDDD6aWzwEDBmRWISkNAgggkCIBAtAUwZItAgggkIzAL37xC1uxYoW1a9cumdmZBwEEEIiEADeij8RmpBIIIIAAAggggED2CBCAZs+2oqQIIIAAAggggEAkBAhAI7EZqQQCCCCAAAIIIJA9AgSg2bOtKCkCCCCAAAIIIBAJgbQGoOvWrbP333+/AtysWbNs8uTJOXUD5goIjEAAAQQQQAABBHJEIG0B6KZNm9wNgSdOnFiG9q677rLbb7/dpk2bZmeeeabNnTu3zHTeIIAAAggggAACCERLIC0B6OzZs92zjdeuXVtGb86cOfbuu+/a6NGj7fLLL7eTTz7ZPQe5zEy8QQABBBBAAAEEEIiUQFruA7phwwYbOXKkLV++3CZNmhQDVGCqmy83aPC/OHjvvfe2f/7zn7HpGlDL6JQpU2LjiouLTafyo5C2bt1qmzdvjkx9MmWblJaWmmyjsp9kiqs+e0q4hrtFtL8q6TgZHAvDXUNu5iZPJT0RiX023H3A8zxcwyV1uclVf1HYX4Pv4URMaQlAe/To4crw1ltvlSnLwoULrVWrVrFxLVu2dEFqbIQ/8Omnn9pDDz0UG7XTTjtFYuOoQtrRFCgFX0CxSjJQJwG5KkXhQ1wniJAXxjVk0P/LLnBdv3696elIpHAEggCUH/nheMbnon1Wgb1sSeEJyFV/UfjuypgAtKrNo+cAb9myJTZZBW7SpEnsvQaGDRvm/oKRQ4cOtfbt2wdvs/p16dKlVlhYWCYIz+oKZUjhV65c6fartm3bZkiJolEMHRR1GU1UPn+ZslXUshw8CamgIC1tAplS9bSUo1mzZuyzIUsvXrzYmjZtai1atAg559zOTmdA9BeFY6ziueqOZ2m5BrSqXUqPntOBN0ga7tChQ/CWVwQQQAABBBBAAIEICtRrANqnTx/78ssvbd68ee409EsvvWT77bdfBJmpEgIIIIAAAggggEAgUK/ne3TN5/Dhw90p9qKiIuvcubOdcsopQdl4RQABBBBAAAEEEIigQFoD0P79+5v+4tNRRx1lgwYNMl0H1bx58/hJDCOAAAIIIIAAAghEUCCtAWhVfg0bNjT9kRBAAAEEEEAAAQSiL1Cv14BGn5caIoAAAggggAACCJQXIAAtL8J7BBBAAAEEEEAAgZQKEICmlJfMEUAAAQQQQAABBMoLEICWF+E9AggggAACCCCAQEoFCEBTykvmCCCAAAIIIIAAAuUFCEDLi/AeAQQQQAABBBBAIKUCGXEbppTWkMwRQAABBHJOoE2bNjZhwgTr1KlTztWdCmenwA033GB5eXnZWfhalJoAtBZoLIIAAgggkNkCDRo0sHbt2lmjRo0yu6CUDoH/E2jVqlVOBaCcgmfXRwABBBBAAAEEEEirAAFoWrlZGQIIIIAAAggggAABKPsAAggggAACCCCAQFoFCEDTys3KEEAAAQQQQAABBAhA2QcQQAABBBBAAAEE0ipAAJpWblaGAAIIIIAAAgggQADKPoAAAggggAACCCCQVgEC0LRyszIEEEAAAQQQQAABAlD2AQQQQAABBBBAAIG0ChCAppWblSGAAAIIIIAAAggQgLIPIIAAAggggAACCKRVgAA0rdysDAEEEEAAAQQQQIAAlH0AAQQQQAABBBBAIK0CBKBp5WZlCCCAAAIIIIAAAgXZRtC0aVP77W9/m23FrrS869ats/z8fGvSpEml0xlZO4GNGzea53mmfYUUnkBJSYkVFxdbixYtwsuUnKy0tNS0zzZr1swaNKBNIKxdQscAHWN1fC0oyLqvurAYUpKPXBs2bGiNGjVKSf65mumGDRssLy8vMjHBjjvumHBT5vkfUi/hHExMmcCRRx5p++67r11//fUpW0cuZnzxxRfb/Pnz7ZlnnsnF6qeszmPGjLH777/fPv/885StIxczfvfdd23YsGE2efJk69y5cy4SpKTOS5cutZ/+9Kd277332qBBg1KyjlzN9MADD7STTjrJLrjgglwlSEm9hw4dao0bN7YHH3wwJflnWqb83M60LUJ5EEAAAQQQQACBiAsQgEZ8A1M9BBBAAAEEEEAg0wTyr/NTphUqV8qj6z979uxpXbp0yZUqp6Weuo6uW7dutvvuu6dlfbmyErluv/327rKRXKlzOuop19atW9t+++3nTr+lY525sA5dS6drFOVaVFSUC1VOWx21z+6zzz72k5/8JG3rzIUVyXW33XaznXfeOReqa1wDmhObmUoigAACCCCAAAKZI8Ap+MzZFpQEAQQQQAABBBDICQEC0Fpu5m+//dbefPNNd/uUWmZR7WK65Q0pMwSivi1Wrlxpb7/9dqXYU6dOtR9//LHSaRqZaNkqF8qRCTNmzLBvvvkmYW3xS8hT6cRkzF5//XVbv359pcvn6kjdaUG3+snWFPXjcLZul9qWmwC0FnIjRoywhx9+2GbNmuXuSTplypRa5JJ4kU8//dT+/Oc/J56JqWkRyIVtoS/0d955p1LP559/3r7++utKp2lkomWrXChHJnz11VdJBaBV2ecIU42rmcw+p1vZaD7S/xf4y1/+YqtWrfr/I7JoSPfL/c1vfpNFJaao1QkQgFYnVG76l19+abq/3O23327nnHOO+xs3blxsrrVr15paR8v/ytTtVtWKtHjx4ti8wcCKFSvKLLN161abM2eO+/Ue/wteB47vv//e3bg6WHb16tXu/YIFC4JRWfkqF93cuHzSF0i8mQ5Cmm/Lli32ww8/xCzkvnz58tjiwXy6efrcuXNNpvFJN/7+7rvvbNmyZfGj3bC23aJFi9xwVduiwkIZPEL7iJLqtGbNGjcsH90rNUidOnWyCy+8MHjrXufNm2ebNm0qM07LlXeLX1b5a5vF/wX2ar2YPXt2JFqlgv2ruv3wqKOOsgEDBjjDYDtof47fpwM/5RXME6Dr8x+0+lS2z8aXIzhmBMtG4VUeOnZqX40/pgZm8XWs6hiieWQY7PvKM9gnNS0IUgPLqo4ZmjfTUmWfR43T8TBIqk/894jG67Ndfl/T+Phjn94HqfxxWOOD5RMdV6oz1T6vbatjtOaNT+XLou+/hQsXxrZX/LzZNFxZjKC6Bp9z1UXbS9tN+2y8i8ZV9j2ZTfWPLyuPh4jXSGJYPatHjx4dm1MfwuBLWqd8dLPunXbayWbOnGm/+93v7LDDDnMHA90cPfgwavott9zigqirr77afRm1atXKtTLpvXrBvfjii+4Drpupn3HGGfbQQw/ZpEmTrGPHji4AvuOOO0wHYf0iDNY3atQo16s+VrgsGNAHTS3K+vCpB2CvXr3czY0VGMpIvjrw6IkKqp9cVXc9hUO9XJcsWWInnniivfbaa7Z582b3ZX/WWWe5+W699VZnrp7bCpruvvtud8cB2T755JMuT7VQ9e3b1y699FKnpW370ksvOVN90K+99toK2yILWMsUUU8O090WFKD/97//tdNPP9156ck7OvA9+uijrjVfLe5/+9vfTD+Ifv/737snHmmZwsJCl5+sFaTKU8Ndu3Z1D1HQmYBgWW0bLa+kbacfXS+88IJbrx64oO2o+ZW/HsSQrSnZ/fCvf/2rtWzZ0n1OtR323HNP93lXsHTooYe6fT3w01mVU045xW2P7bbbzgVeWkb7sX74VrbPqhx33nmnC6j0hCrt5zfccENk7lSg+nfx7xKigFE/OIcMGWInnHBCmf21qmNIsG/JWvutjgv6MXDaaafZI488YjJWAHTMMce4y6lkWdUxI8grk16r+jxOnz7dfV9of1LSpTW6XEzHUyV9DnWsVWOGPE899VQ3vvyxT8dLfbdVdhzWHVy0bao7riQy1fr1fbftttu6Y5O+A1QGfV4qK8sTTzzhPhM333yz+y7IxqdbVRUj6Ptdx1Xd3F9JddTxQWdX9f0ejFd8oRvVn3nmmW6+rP9PT0Ii1U7AP7B5xx9/vPfhhx+6DPynQnj//ve/3bD/wfP+8Y9/uGH/C9i77bbb3LAfhHqXXHKJ98UXX3h+S6l34403uvH676mnnvL8D6R7/8orr3h+8OOG/S90tx7/A+re+wGSd88997hh/+Dp1uMfoN37bPvvrrvu8vwDnCu2H0B6fjDq+S3Mnn93MM8PZmLjzzvvPO/jjz/2/IOr179/f8//1e2mnX/++Z7/5eKGtT2OPvpoN6z5/KegeP5Bzr33TyN7/g8CT05XXXWV5wdGbrzfcuwdcsghnv9F5PlP+PH8FivPb2ly0x577DHPD2y9+G3hJmTZf8cee6z37LPPulI/99xzXr9+/Ty/RcO9P/vss71p06Z5foDj+V8obpz2yQceeMAN+8G/539pe/6B05s4cWJs//R/iXv33Xef53/5l1nWLeT/5/9y9/wvttjnQZ8N/1pSN9kPar2TTz7ZbYtg/mx7TXY/lNHjjz/uqqft4Aekblj7nbaD9rt4e+3zfqDp5vnss888/4sm4T4b7Od+y7JbZuzYsbFt5EZk+X8y838guVroOOh/KXt+4FXGrKpjyHHHHef5l894flDv+Zc4xCT0GQ+OHzoe6zihY09gWf6YEVswwwaq+jz+5z//8YYPHx4rrX/dp3fFFVe494MHD455yjE4llZ17KvqOKzMkjmuJDLV96O+y4KkfV3fn1WVxQ+GPb+xIJg9K1+rihH0uX/66adjddL20nbTtvSfjuTG61gh8+C7KzZzFg9wCr6WPyH8g5RrxdGjsw444ACXyxFHHOF+LapFT50P1PqppF+fagkKfrnpdMZbb73lftno16dO4ftf+uYfUMo0w7uF/f/0qD792tNpf+XhB2LuV62/37lZevfu7VoDg/mz6VXXFupxeUqqo1rQ2rZt6/z0CzAY739Z2xtvvOHed+jQwbVe6I1+Ne6///5uvJaTbXDKYocddnCtJ5p40EEHuVZRtaRcdtll5n+wXYvoTTfd5H5Vq/VUraF9+vSJ3YtRLYUDBw50eWf7f2pZVpLXLrvs4u47qfcy0yUl8UkOetSeklrme/To4YZlo/1aLmopVQtm06ZN3bT4/3SayD+Auscfahtqm/g/uOzVV1/9f+2dCYwURRuGi18xHiCXqwiICJiVUxbwXEVRIxs1GhXBIxKIcmg8IIoK0bCJGhJNCCiJRE1QExE5vBCiBmKIrCIoQfGKeBIOLxRF8bb/er7fmr+3nZ6ZxV2YmX2/ZHd6uquru57urnn7q6+q7P4lNo9mKPIqZSv0PoyXkfsQY1+e39B6EtLAFG8+xuc555xjz3baPUu6jh07mjeaZe55vNrlZLRQYO3atbPxEfGqxS2tDiGNf4m3eiXcz/H9si1nqzPwkhajFfo8Js/di05bVVFRYb9BeODT6j6e0bR6mEwKqVfSmHoxZeOz4tW79dZbrSkeL2jaudhJl/i/NI2QVix+2/Hwozfoi0ALKHV4uZia4HfjSlLh8QM7adIkhzAKxnzDVHR1dXX240HTI80GGIMhh4eV7zSXIYJoLsO9TrMS29k3afxQcePxwGazAw44INvqkliHwAmCkRMmnogmGIQRojAYoib8ECTLG5qIaZJPM+LnSEeeNF9UV1ebcKXJE/ZY8lwQSUmBkJZ/sa+PM2Nw7lxGeAO8g4WmLkQTISHeM2cvQN576ujUEDfu1enTp9sA1TTvBSMP7502McA67uVSH8Q6zpQyFXIf8twHy3a/VlZWWvMoP8KrVq1y11xzjd2DafcsefG8NBfjOaYJMm7J5zbUIaQh9IkXe9+6ZC9OYb9Ql+QS66HOoLm5GC3X8xjKx3lTjrjR/B4MwcdvCyInXg+Hui9XPUwe8WcgX71C+jhT77m2mHBeunjR4juWvJ7hXMrhPs+lEbJdM+oI2OB8IZSE5XKy/9+J5VSqJiwLsYl4I3izjotPDjl16lTz6vCWwxsdAdM81MQd8YPCj0ufPn3cvHnzLN6TuK7BgwebAD3mmGOcb8rPVAL8mFE5YByHt1QqHPYngNy760vW62mF+vsfZaMHMA8fosc3qVvMJ8wYMgR+/EjgMabsDTE40SEM4wGuqqoybx+xib5J314WfPOzbef4eGLx1LEdI4ZqxYoVJizCtbANZf4PTpQbbzH3O4IT86EIFm/IPcsP+5FHHmn3eBwHsVvEgIaYWrbhueLaIQz4JPaOuLJ4hRvPo7kv84NMXCfeDwQrHuq0e7Y5sPLNslZMOrBxD1FXxi2tDiENM6JxL/LiRHwshpDBo4StXr3aPsO/bHVG2FZsn2nPIwIOTkFcJ8uIZ51nm+HBqF+Jy06r+5qqHoYldS3Ol5qaGovpx7Odqx4OL8Jxx0SxXZN855OmEbhm4Z4MsfohL/jQiooHNHivw7ZS/5QHtIFXcMGCBSaQfCxHZk+mecPbycPEWxzNk/xoIKZ4aHiICQSnswxT7tF546yzzrIfd25IxBAPFT/8YSxGKk46dtx2220WcD1y5Ejn4+asiY03ctaXg/HjQWA27HibxhtJEwPhCwhTyk2l5OM0zWuGkC/UeHuvra21ps5WrVoZR8QQx8SjxLpuvoMDx6MnJk3TPqbXzgWxT9Mm15A3z/i1KPT4pZoOzyb3Jd5hPJphWjjuY4S8jxW1lwXuUTz7QeQjPH0MoqNpj+vGvhgdDehIx7Xg5YsfP7Z36NChVBE16Xlz7+NZvvrqq+04NGGm3bNNeiJFkjlChVAlvGd0oIl7kTnFtDoknD7PN/csLz28VOKZJ/SGFyGeeX78g2WrM8K2YvtMex7xRA4cONCeX1jxIoOoCUZnLp5teljjKMFIl63uowUoWz0c8irkM40p5+Bjzd3ChQvNmcI5Uw+nnUuYutrHsVo9U4rTq6ZpBJxWtKjSQY7fwRD2BF/uU/7orBX3OBfCvtjTaCrOJrhCPNiIm3hTB4ehmQNBmmyqwLvBW3mySY4faoRpSM/bKpVwsgJugiLs8SxpZiGeMNncxXqa3GgWbojhXeZlgF7I5JFkhneAYwW28bxhzl88vjF5LeLpy3UZbvSUT97H/CghLnenMiQelJcAWcMJ5LpnG55baezhOxK52bNnW/1InZqsI+OlSKtD4mnCMs83rRrkGSxfnRHSFdtn2vMID57R4DmMn3fatmx1H/uRvinqYepVRhvJ1ryedi6UNxmGES9bKSynaYTmVj/KA9oEd2u2h4nD8GOezeJv4PHt/PDHBRIVSVJIxdOX8nJaudLWN6Ss2fKIi8tkXojdpOBNXovkPuX4PRs3yvlvKn+Jz92/U3Lds7ufa2nsmXYvxs++kDQhfbZnPGzjsyF5xffbG8tpz2OuMqRtS+OSlr4h5c2WB/Vq2u9l2rmklbch57K306aVubnVj/KA7u07UcdvEgJ4lYlxIl5RJgIiUJoEXn31VQtN2h1ve0NLrDqjocTypxfT/IyacwoJ0OZ89VV2ERABERABERABEdgLBNQLfi9A1yFFQAREQAREQAREoDkTkABtzldfZRcBERABERABERCBvUBAAnQvQNchRUAEGocAQ3QxMxhzeDNDE0M+hWGhwhGYQz3MLhTWFcsnk1r4qXltmCDGoAzGMG4MOcT2bMY84GzftGlTts311jGMEbOopRkMySuMQ5iWjvXFzDLXeWubCIhA8RGQAC2+a6IzEgERKIAAw7QwTA9jxDLNJx1VmKCBgcoZuDkYoontxWjMDsU4uIzxF+8Zy9iJjzzyiLvvvvuynjYzrDGGImPV5jM/t7YJ9LR0CFDGxSxUgBYry7Tyab0IiEBxEtAwTMV5XXRWIiACeQgwaPOaNWvcZ599Vm9+5FGjRtlEAxs2bEgd+ixP1ntkM2P6MjvPsmXLHANRx43xLseMGeNmzZplf2Gaz5Dm0UcftQHVk+vD9oZ8kgdiXiYCIiACe5KAPKB7kraOJQIi0CgEEJ3MSvbQQw/VE59kzgQEzCZEmmzGlI4TJ060KQCZ/YXmaaaBDYa4Y6565l2eMmWKTS0atjF1K8LwzDPPdGPHjjUBHLZl+2QYIUIDOB9mTwvN7DSdM887NmfOHHf//ff/Y3eOs2PHjn94b9etW+cYND3MlMSODGx9++23W5mY6YepVJPGVH6cC17XmTNnZmaqYoILysJUiMHwGpMPYn7x4sWZKYLD9vBZyHFDWn2KgAiIQJyABGichpZFQARKggDzWzOTFcIuaUzxyZSLzDufNJqZ+/fvb6ISgcU0o3feeaeJN9LStM2UeDTrM10o85AzbS7GlKzMxcxA2Ag2vJTV1dX1hJsl/PvfkiVLLB/GQmSK2bq6OtevXz+HAGZQeaYexHr16mVTQv69W+aDKTgpH9Obxg2BzHHZD2OGtUGDBjnmBmeaQiasYD55RGQwzoGpfDk++zHVKnGzGLPRPPzww27Lli32fdq0ae6GG25wTEfLOLpMR0tzf9IKOW5yH30XAREQgQwBP6WeTAREQARKisDkyZOjbt26FXTOXsRF3uNpab1Ii/y89JGPe8zs6z2NkRec9n3cuHER6b0os+++iTyaMWNG5Kf/i5YvXx55cRd5IWrbSMO29957L5NXfIHz82EC8VV2zl4I2jovCiNfEUfeS1ovTfyL9/JGXvBGpMW8pzby8aHR3Llz7Tv//BznkZ9lLfLe0nrr/PzRVg4vqu043gOa2T5ixIho2LBh9t1PSWnbKd/WrVtteeXKlZm0fq7uyM/bbXnFWeY7biYDLYiACIhAFgKKAc1IcS2IgAiUCoFOnTo5eoLjvUvOVZ+rDDU1NebFxLNJD3MvHp0XXpnOPJdeeqnFY/bs2dOa4M877zzzAOJVPOGEE1yPHj3Ma3r22WdbOpqo8bgmjTmdCQHwIq3eJvJrSI98PJrMV04zOE3yS5cutXABLyAz+RIHS2ek6dOnZ9bhzcRju3nzZluH13bAgAGZ7Xhfk55VNhJiwPS/eICDDR8+3PGXtHzHxYMrEwEREIE0AmqCTyOj9SIgAkVLAAG1a9cuE3nJk/Qv2u6WW26x5vPkNoYkOuqoo6yTEsM30Rx/2mmnZZINHTrUrV+/3iFEV61aZTGVCE9iMRGCiC6EHse+9tprXffu3ev1uA8ZkR7r3LlzWGWf3ivp6HVeqNFBiFCBIBZpfqcpPT4vPGKX7wjx8If4I341iHPvIc0sc+ywPnkeiFZGEyC8IJ8Vctx8eWi7CIhA8yUgD2jzvfYquQiULAEEaEVFhcVv+uboeuWYN2+edSwiLjJptbW1NkwTXk9iSDGEZhCFeCdbt27t7r77bvvDI3j88ceb17KqqsqtXbvWhCfikw44eEJnz57tEK5x69q1q0M8MmTRkCFDMpvIP+6JzGzIseBDBtyxxx5rsaZ4QOnYFDe8tS+99JINpRSEJb3riTmFUUMMDy/iGe8yQ0NhMCAudP78+fWyaszj1stYX0RABJoFAXlAm8VlViFFoLwI4I30sYnmGaSTDMKQnuU+JtMGdqcJmR7uSaOp+ttvv3UMgYSn9Nlnn3WLFi1yPg7SkjJmJh7HjRs32vYvvvjCeoAjzLDRo0dbegQrAhQvYNhmCf7+h7j18aR2fgyzxPHosU/nqUsuuSSeNO9y37593XHHHWe93ulYRceguI0fP96a2hlMnvPZtm2bu+KKK9zzzz9vIjieNt/yySef7Hr37u0mTJhgnaUYEJ/B/RGyca8r+TTmcfOdl7aLgAiUIYEscaFaJQIiIAIlQYAOMnSm8YLUOs+0bNky8j3UIy8yM+cf7zjjhaV1OPJiKmrfvn3kh1OK/FibmY4+XihGvvk98nGd1rHHe0Nte8jMD18UVVZW2jY6JPne7ZkOQiFN+Ny5c6d1ePJiNPJxlVGXLl2iBx54IGy2/fxPSs5OSCHxgw8+aOXz3tawqt6n9/pG3mNpx/ED2kdefEdePFsaOiFRnrjdc889kRe2tireCYkVPjY28iI34rzbtGljPHwsqaWNs2RFruPaDvonAiIgAikEWrC+DHW1iiQCItCMCDCWJWNsepHnvAjNW/Lt27ebd5Dm9mxG5yY68pBftnhIPKNt27a1IZmy7R9fh3cVTyJ5NbVxzngrG2OAehjRIQlvcz5rzOPmO5a2i4AIlAcBCdDyuI4qhQiIgAiIgAiIgAiUDAHFgJbMpdKJioAIiIAIiIAIiEB5EJAALY/rqFKIgAiIgAiIgAiIQMkQkAAtmUulExUBERABERABERCB8iAgAVoe11GlEAEREAEREAEREIGSISABWjKXSicqAiIgAiIgAiIgAuVBQAK0PK6jSiECIiACIiACIiACJUNAArRkLpVOVAREQAREQAREQATKg4AEaHlcR5VCBERABERABERABEqGwL4lc6Y6UREQAREQAREQAREoEQIvvviie/PNN23WtKFDh7pevXqVyJnvmdOUB3TPcNZRREAEREAEREAEmgmBcePGucmTJ7sffvjBvfHGGw4BumDBgmZS+sKKqak4C+OkVCIgAiIgAiIgAiKQl8COHTvcYYcd5rZs2eIOOeQQS//YY4+5mTNnunXr1mX2/+qrr9yvv/7qjjjiiMy67du3u3bt2rn//Od//sGvv/7aVVRUuN9//939/PPP9hlFUSbfTZs2uTZt2thfyITtn3zyidtvv/3q5b1z507XokUL16pVq5DUPn/88UfXsmVLOxeO16NHD1v/6aefusMPP9ztv//+9p1za9++vfv4449dx44d/5EP+/7yyy+2D3m2bdu23nGSXyRAk0T0XQREQAREQAREQAR2k8Cff/5pAm3ixIkOTygCElHIH8Jy69atbsyYMe6bb75xiLZ+/fq55557zu2zzz7u0EMPtWZ7RCn57LvvviY6V69e7W688UZLj7Ckab+mpsbt2rXL9hsyZIgJ3O+++87WI1gRwv3793dPP/20Cc+xY8e6Aw880M2aNateySZNmuQ++ugjt23bNjunE0880f3xxx/uyy+/dIjQJ5980lVXV7suXbq43r1723m9//77burUqe66666zvK666iq3fPlyd/DBB1t5EborVqyod5zkFzXBJ4nouwiIgAiIgAiIgAjsJgGEJOILQda5c2c3YMAAN23aNPMOkuXNN9/sjj76aBOReCrxFuYTa+z3zjvvuNdee819+OGH7o477jAx+NZbb7nXX3/dffDBByZsaeavqqoyT+vGjRtNvLIPNmPGDHfXXXfZcvIf3tG1a9faH3kMGzbMvfLKK+6mm25y8+fPzyQ/5ZRT7FzXr19v5cDL+8wzz9h5cby3337bBGhmhxwLEqA54GiTCIiACIiACIiACDSUAJ7Hl19+2UThlClT3MqVK93pp59u2eDNHD58uC3j4bz44ovdE088kfcQ3bt3N0GLF3XNmjXu/PPPt31oPn/hhRdcp06d3MKFC82bOXr0aIdXkmb+RYsWWbrWrVs7/rIZwhKvJd5a0iBAMfLcvHlzZpcLL7zQlvHUInSJb0XgnnvuudbkTx6UpxBTL/hCKCmNCIiACIiACIiACBRAAG8mgvDee++1WM2RI0ea4CR+Eo8nXtHffvstkxOxnTR5Ywi4sIxXMm7x2E1iS2lmD/b555+7Dh062FfE46mnnho2WUxp5kvKQjxvkoS4z5TkthrPLU36xIlu2LAhk/T777/PLOdakAc0Fx1tEwEREAEREAEREIEGEKisrHRz5sxxixcvdn/99ZftyZBMeCq7du3qRowY4R5//HETmog40p100kmWDpH67rvv2vKyZctSj4qXkdhO4kTpyIR3FW8nYpcm+UGDBjliORHBNK1jNJcT5/lvjHhQjHAARO/gwYMdXlEEN3GkdFSibIWYPKCFUFIaERABERABERABESiAAJ11lixZ4saPH2+djeg0RJM1Io0m98svv9zEI73N8XZecMEFbsKECZYzHXuuvPJKE6oDBw7M9HZPHvaiiy4yodezZ0/rjX799dc7mujxgiJou3XrZvv26dPHXXbZZbZ7bW1t1k5Iybxzfa+rq7PxTBHOxIbSY58/4lrPOOMMh/ju27evidFc+bBNveDzEdJ2ERABERABERABEdgNAj/99JM1lWcbkohe6jRhI1DjRvM8zfIMr5TPyIOYTTo+xY3xR8m3kKb0+H65lhHWxLIicjk3wgUwhmXCG4oAxejsRJP83Llz7XvaP3lA08hovQiIgAiIgAiIgAj8CwIHHXRQ6t7ZRCmJEY5JUZqWSVoeDIfUVJY8JmOCMtD+qFGj7JA0wT/11FN5Dy8PaF5ESiACIiACIiACIiACzZvA0qVLLdY0m6hm/FGGncJ7SzwqHa3ymQRoPkLaLgIiIAIiIAIiIAIi0KgE1Au+UXEqMxEQAREQAREQAREQgXwEJEDzEdJ2ERABERABERABERCBRiUgAdqoOJWZCIiACIiACIiACIhAPgISoPkIabsIiIAIiIAIiIAIiECjEpAAbVScykwEREAEREAEREAERCAfgf8CmqPcWIaKDncAAAAASUVORK5CYII=\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb29\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb29-1\"><a href=\"#cb29-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(class, cty))</span>\n<span id=\"cb29-2\"><a href=\"#cb29-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_boxplot</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span><span class=\"fu\">factor</span>(cyl))) <span class=\"sc\">+</span> </span>\n<span id=\"cb29-3\"><a href=\"#cb29-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb29-4\"><a href=\"#cb29-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Box plot&quot;</span>, </span>\n<span id=\"cb29-5\"><a href=\"#cb29-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>\n<span id=\"cb29-6\"><a href=\"#cb29-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb29-7\"><a href=\"#cb29-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;Class of Vehicle&quot;</span>,</span>\n<span id=\"cb29-8\"><a href=\"#cb29-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;City Mileage&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n<div id=\"点图箱线图\" class=\"section level3\">\n<h3>4.4 点图+箱线图</h3>\n<p>在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。</p>\n<div class=\"sourceCode\" id=\"cb30\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb30-1\"><a href=\"#cb30-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(manufacturer, cty))</span>\n<span id=\"cb30-2\"><a href=\"#cb30-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_boxplot</span>() <span class=\"sc\">+</span> </span>\n<span id=\"cb30-3\"><a href=\"#cb30-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_dotplot</span>(<span class=\"at\">binaxis=</span><span class=\"st\">&#39;y&#39;</span>, </span>\n<span id=\"cb30-4\"><a href=\"#cb30-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">stackdir=</span><span class=\"st\">&#39;center&#39;</span>, </span>\n<span id=\"cb30-5\"><a href=\"#cb30-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">dotsize =</span> .<span class=\"dv\">5</span>, </span>\n<span id=\"cb30-6\"><a href=\"#cb30-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">fill=</span><span class=\"st\">&quot;red&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb30-7\"><a href=\"#cb30-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb30-8\"><a href=\"#cb30-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Box plot + Dot plot&quot;</span>, </span>\n<span id=\"cb30-9\"><a href=\"#cb30-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage vs Class: Each dot represents 1 row in source data&quot;</span>,</span>\n<span id=\"cb30-10\"><a href=\"#cb30-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb30-11\"><a href=\"#cb30-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;Class of Vehicle&quot;</span>,</span>\n<span id=\"cb30-12\"><a href=\"#cb30-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;City Mileage&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"塔夫特箱线图\" class=\"section level3\">\n<h3>4.5 塔夫特箱线图</h3>\n<p>这是一个简化版的箱线图。</p>\n<div class=\"sourceCode\" id=\"cb31\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb31-1\"><a href=\"#cb31-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggthemes)</span>\n<span id=\"cb31-2\"><a href=\"#cb31-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb31-3\"><a href=\"#cb31-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_tufte</span>())  <span class=\"co\"># from ggthemes</span></span>\n<span id=\"cb31-4\"><a href=\"#cb31-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb31-5\"><a href=\"#cb31-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb31-6\"><a href=\"#cb31-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(manufacturer, cty))</span>\n<span id=\"cb31-7\"><a href=\"#cb31-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_tufteboxplot</span>() <span class=\"sc\">+</span> </span>\n<span id=\"cb31-8\"><a href=\"#cb31-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>      <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb31-9\"><a href=\"#cb31-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>      <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Tufte Styled Boxplot&quot;</span>, </span>\n<span id=\"cb31-10\"><a href=\"#cb31-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage grouped by Class of vehicle&quot;</span>,</span>\n<span id=\"cb31-11\"><a href=\"#cb31-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb31-12\"><a href=\"#cb31-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">x=</span><span class=\"st\">&quot;Class of Vehicle&quot;</span>,</span>\n<span id=\"cb31-13\"><a href=\"#cb31-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">y=</span><span class=\"st\">&quot;City Mileage&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"小提琴图\" class=\"section level3\">\n<h3>4.6 小提琴图</h3>\n<p>小提琴图类似于箱线图，但显示了组内的密度。</p>\n<div class=\"sourceCode\" id=\"cb32\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb32-1\"><a href=\"#cb32-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(class, cty))</span>\n<span id=\"cb32-2\"><a href=\"#cb32-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_violin</span>() <span class=\"sc\">+</span> </span>\n<span id=\"cb32-3\"><a href=\"#cb32-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Violin plot&quot;</span>, </span>\n<span id=\"cb32-4\"><a href=\"#cb32-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;City Mileage vs Class of vehicle&quot;</span>,</span>\n<span id=\"cb32-5\"><a href=\"#cb32-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>,</span>\n<span id=\"cb32-6\"><a href=\"#cb32-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"st\">&quot;Class of Vehicle&quot;</span>,</span>\n<span id=\"cb32-7\"><a href=\"#cb32-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;City Mileage&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0H/BNF+sZfeu+goHRBEBUUFRUUsKOo2I8TPUWsfyv2Xs+Cnl1PBU/FBmI7FLHhqRRFxQaiIr0I0juIAvnnGX3jJr/NZpNski3PfD4/spmdmZ35zrJ59p2Zd8rF4kEYSIAESIAESIAESIAESKBIBMoX6Tq8DAmQAAmQAAmQAAmQAAkYAhSgvBFIgARIgARIgARIgASKSoACtKi4eTESIAESIAESIAESIAEKUN4DJEACJEACJEACJEACRSVAAVpU3LwYCZAACZAACZAACZAABSjvARIgARIgARIgARIggaISCJwAvf322+WCCy6QLVu2pAU1Z84cOeecc2T06NHy1VdfmePZs2enTW934tNPPzX5Fi5caE7nWo5d2W7iUq/vJo81zbJly6xfeUwCJEACJEACJEACviEQOAEK4fnoo4/KBx98kBbiU089JYMHD5YWLVrIrFmzzPGSJUvSprc78dNPP5l8K1asMKdzLceubDdxqdd3kwdpfv/9d+nVq5c88cQTbrMwHQmQAAmQAAmQAAkUlUDgBOjpp58u5cuXl+eff94WFPzqP/vss7LPPvvIzjvvLIcccoh8++230rFjR9v0biO9Ksft9XJNt3HjRnn33Xdzzc58JEACJEACJEACJFBwAoEToM2bNzei8vXXX5cNGzaUAfTxxx/L3Llz5cwzzzTnKlWqJLVr15YKFSokpf3tt9/k66+/lrffflswZJ8ppJbz888/y8qVK022efPmGdHnphxkQF5YViGWp06dKuPHj5fNmzdnqoI571RvnENdEFA31IcbXRkc/IcESIAESIAESMBHBAInQMFuwIABsm7dOhk5cmQZlEOHDpVatWrJ3/72N3MO80BbtWplxKYmfv/996Vdu3bSuXNnOeqoo8x5WDgXLFigScp8ppaD9Ndff70cd9xxZqgfw964zimnnJJRTCLvwIEDpXv37rL77rvLYYcdJg0bNpSHH364zHWtEZnq/f3338uuu+5qstx7772mPr/++qu1CB6TAAmQAAmQAAmQQMkJBFKA9unTxwi2F154IQng+vXr5ZVXXpG+fftKzZo1k87pF1gITzjhBNl+++1l2rRpAoH2zjvvyOTJk+XYY4+VrVu3atKMn5hnivRjx4414vW0004T1MlOGKcWhmkC2223nbGErlq1Ss477zwjSiEy7YKbekN86mKra6+9VrAQqVq1anbFMY4ESIAESIAESIAESkYgkAK0cuXKxtKIuY5Lly5NwHv11VeNZVSH3xMnLAd33nmnbNq0SYYPHy477rijVKxY0VggYTGcNGmSYGjfbahTp44MGzZM9t9/fyNoBw0aZLJ++eWXGYuAxfM///mPEcoY3sfqftTnnnvusc3rpt6YZlC/fn2Tv3r16tKgQQPbshhJAiRAAiRAAiRAAqUkEEgBCmAYhse8yREjRiT4YfgdVsAuXbok4lIPpkyZYhYkNW3aNOlU7969zXcsWHIbMIxvtTBuu+22AjGJ6QGZAkSr1UqLhVWIw7xUu+Blve3KZxwJkAAJkAAJkAAJFItAYAXoLrvsYoSmrobHEPWHH36YWHyUDiDmeW6zzTZlTterV89YQ5cvX17mXLoI5EkNEJJuFv5ArKYGWC9xfaxkTw1e1ju1bH4nARIgARIgARIggWISCKwABSQMtU+cONH4+oQQrVKlihmadwLYrFkzUefy1nSLFy82FlVdxGM9V4hjO7+kqNcOO+yQZFXVa/ul3loffpIACZAACZAACZBArgQCLUCx0h1zHTH3E0PxWJGucyDTAdlrr73MgqMff/wxKclLL71kvmNVejHCRx99JGvXrk1cCi6lsBhqzz33TMRZD9zWGxZYBLdunazX4DEJkAAJkAAJkAAJFINAoAUo/HuedNJJZmckzN10WnykMK+55hrjpumYY46RMWPGGJ+hQ4YMkeuuu84I2HQCUPN79Qk/oCeeeKJgbif+sDIfuzz985//tL2E23pDkGORFtxGvfjiiwLfoAwkQAIkQAIkQAIk4CcCgRagAInFSHA8j6Hrnj17ZmTbqFEjmTBhgrGUwh9ny5YtjfiE/06sjE91WJ+xwBwTwAdojRo1pFOnTuYPLpPgvgntsAtu6w0L6BVXXCHffPON9OvXT1ItvXZlM44ESIAESIAESIAEikmgXHzBTKyYF/TTtdasWSOYiwnRV65cuaJVrUOHDtK6dWsZNWqU2bEIVkq7RUnpKuSm3igTq/EzTUlIdw3GkwAJkAAJkAAJkEChCFQsVMFBKBdD+PgrZbBbSZ+pPm7qjWF4is9MJHmeBEiABEiABEigFAQCPwRfCmi8JgmQAAmQAAmQAAmQQO4EIm0BzR1bfjkvuOACycXymd9VmZsESIAESIAESIAE/EEg0nNA/dEFrAUJkAAJkAAJkAAJRIsAh+Cj1d9sLQmQAAmQAAmQAAmUnAAFaMm7gBUgARIgARIgARIggWgRoACNVn+ztSRAAiRAAiRAAiRQcgIUoCXvAlaABEiABEiABEiABKJFgAI0Wv3N1pIACZAACZAACZBAyQlQgJa8C1gBEiABEiABEiABEogWgUAL0NmzZ8vTTz8tZ555plxyySUyevTopN6bMmWK3H333Ulx2MYyl/D222/LLbfcYrbPtMv/4YcfmvMTJ040p63X/uqrr+S+++6zyxbauC1btshnn30mgwYNktNOO01uuOEGmTFjRqK9mzZtMrzmz5+fiPPTweuvv27uq+uuu87Tam3evNm0e86cOWnLHT58eNr7zJopiveVtf08JgESIAESCC6BwArQ//znP2YPdwjMSpUqyZdffilHH320XHjhhYnegAj817/+lfgOB/APPfRQ4ns2BxC3N998s1x++eW22a6//npz3ipA9dqoW5QE6O+//y7HHnus7L///vLOO+9ItWrVZNiwYdK+fXuBUEf49ddfDS8/ClD01/HHHy94Wdluu+1s+zvXSAhQ3Ed4eUoXRowYUeZlyi5t1O4rOwaMIwESIAESCCaBQO6E9NJLL8nZZ58tL774ovTt2zdBftSoUUaEHnfccXLAAQfIySefbP40ASxyffr00a9Zf+6www4yffp0mTx5snTs2DGRf+7cuSaubt26ibjUaydORODglFNOkc8//1xg5bMKuH/84x8yYMAAwYuBn8N3330ntWrVElgiy5cv/jvaa6+95mc8rBsJkAAJkAAJ5E2g+L+ueVdZjFWzX79+SeITxR555JFy0003yS+//GKuAsGJoXmEe++91wiikSNHyp133ilPPvmk3H///eac/jNv3jwjbNMN00NMwar38ssvaxbzCYsVrl29evVEvPXaiUjLwbPPPisnnXSSEcyoByxj1vDmm2/KGWecIQcddJBAuGEKgDW89957ctZZZxlLI+rz2GOPyRtvvJFIgjbAKturVy859dRT5YMPPkicsx5gKPzcc8+Vr7/+2hotqJ9ai3Guf//+pi64JsRlugDRCR5DhgxJEp9Ij3Yecsghph/s8mdq89ChQ+WYY46RI444Qq655hpZsWJFophs6ohMn3zyiZkagPpcfPHFopZYiD9Y1zGF4JxzzpFPP/00cQ0cuOGFdJn6d/Xq1XLllVfKYYcdJpdeemninkXeRx99VJ5//nkcmvDFF1+YNLDwP/zww7J48WI9VeYz03XLZGAECZAACZAACZSAQOAEKIZuv/32WyOs7HhBgP797383pzDn8LnnnjPHGP6tUaOGbL/99tKhQwepWbOmXHvttWaYVcuBwPnmm2+kdu3aGlXm829/+5sRWNYTsJTpNTXeem2N008Inssuu0zatm0rXbt2NfNUTzjhBD1tBAgsqK1btzYiacOGDUZ0QYggYDoAxAhEa7du3UxZV199tUyaNMmcX79+veyxxx5GtMLiW7FiRendu3eChUn05z9VqlSRWbNmmWtq/NatWw2bypUrG7HTs2dPqVq1qhG85cqVM9f88ccfNXnSJ6YgVKhQwQjNpBPxLw0aNJAnnnhCdt5559RTGdsMYTVw4EDzAoA++N///icHH3ywKQeCLJs6QujiRQIiEEPtEyZMkF133dVwaNasmbRs2VLQ9i5dukjDhg2T6pqJFxJn6l+kwbxYTFXAC8Jbb71l+gfxCHi5GD9+vDnGiwxeQtBHmNYAgYw62wU317XLxzgSIAESIAESKDqBWMBC3PoWi0OK4TNTiFuRYvXr108k23PPPWO33Xab+b5x48ZYfMg89tRTTyXOxwVhLG59Sny3HsTnj8bioiW2ZMmSWFxgxeJC1ZyeNm2aKSduGYvFLaSxuJXPxFuvPXjw4Fhc+CbSx4d1Y/HpA4ni48P6pk0fffSRibvxxhtjcaGWOB8XKuYacYukiYtPBYj93//9X+L81KlTTf74Qh8Td8cdd8TiYju2atWqRBrEbbvttrG4uEzE6UF8fmasTp06sbi4N1FjxoyJxQVnbOXKlTEcxwVsLC7yzDnkj89njX3//feaPenziiuuiMUFXFKc3RfUDf0YF3/mdKY2x6dcxOLWykT9Z86caeqBOmdbR9QvPk0gqVqIi79EmLi49TbWtGnTpPPWL068cD849S/uO7Q7/sKQKDJuwTZxcUFs4uIvF7G49dUc77fffrHzzz8/kXbp0qWx+PSS2A8//BDL9r5KFMIDEiABEiABEigxgcDNAdU5hTrMnqtih0UPVksMdWJ4GUOtGIK3zim1K7tRo0ZmfimGvTt16mTmCcIiBYuZmwArZbzPBdZMWHI1wCKLcz169DCrpBctWmSsXbA0Il1cuJiFO3FRKHHxZSyimhcW3biA0q9miLxx48ZmqoFG/vzzz8aauWDBAoGVzxowrH3eeeeZlddoC6zGiMOc1r333tss9oK19tBDD5XDDz/cTAmANdMuoH/iIl1gRc1m/iQ8DKRrM66DfsG127RpY9qOKQ9YcAbrbjZ1BD9ME4gL8qTqo7x33303KS7dFydesE479S/qirDPPvskioe1GmHhwoVJ1neUA4u8deEbLLKw/iKMGzfOfOIfN/dVIjEPSIAESIAESKDEBAI3BI8h9LglT+JWP1t0EE8PPPCA7bnUSAjPuNVRIM6Q76ijjpK4xTQ1WZnvGALWeaAYfs8kWq0FxC1/RjRhKBcCTf8gpnRoGvVv1aqVEZAQcxie32abbUwxOu8xVUTWq1cvcRmILMxH1bLxifSYN4nj1AAxjiF/iHEM97/66qtGlCMdhDHmfGLeLM7FLa9maoCuZk8tq3PnziYdRF5qgKDCvEcVUNbzTm1GOiwqgxgDawxPY+gaYg48s6kj0iPgPrIG3FOY9+kmOPFy07+4hnWaB6Y1IICPNWAqxbp168zUEWu83bHb69rlZRwJkAAJkAAJFJ1A/EcvcCG+KCfWokWLGIYjrQHf4z/ssbgVz0Rbh8ERYR2C13xx0ReLix8zPB1fRa/RZT51CB4nli9fHou7foq98MILJl98LqZJ72YIPm5lSxp6Rkbkjy98iWFYOS7yYnFrakyH2/V8XLDG7rrrrhiG4zG8Hvd/ilMmxK3BsbiIiekQfHyleSwuOGNxQaVJYvE5qbH4HNcYpgrYhbhFNoZrYPjZmhdDyuCoAcPEceEXi3sa0Kikz7Vr18biVuLY6aefnhSPLygnfoPH4qLdTA/AMYbgM7UZeePunBLD9fge94FppgagrGzqCNbgGxfjKCYRMNStbco0BI9M6Xhl6l8dgo+L8MS14y6ZDBed1mAdgo+/eMQeeeSRRFr0Ke5vTNewDsFnum6iAB6QAAmQAAmQgA8IlDWHFV0CZ39BrASGb0ksxIHrJVgJYZGE+yUEDOfaBQwbx+fOmaFePQ8raFzYCaxQWJHsJsBKigUwWPSBlexYdOM2wJLXrl07ic95NFZc9Yd51VVXGasYhpRRT0wxwDA2rI4XXXSRWX2NtDiP62IBFVaaY9EPHPHH76VEFbB6G0Pt4ABrKIa24TUArNJNFYiLc7MoCouZsOreaimNi0l55ZVXjIUQq+tRJlxS2QVYI9EXsKbCqoupBpjaAD+oGErG4h/tJ82fqc1Ih2kIWM0PN1hoK/hgEZbWw20d0Vdw4YX6YbgcUxuU44knnqhVyviZjlem/s1YcEoCeB2AP1ksTPrtt9/MKnhYgPfaa6+klF5fN6lwfiEBEiABEiABrwn4QATnVAUs3MFCEiwWiTMxf/vuu2/S4iRY3KyLkOKuioz1y7rABNbDuACKxcWRYz2sFlAkfOaZZ8w14+58EvncWECROD6vM4a6wmoJayaO426WEuXEpwPE4ivgY3EhGov7o4zFh61j8aHnWHxFu0kDKx4seM2bN4/BQobzu+yyS+z2229PlIFFTjgHqyaswrDuoa1OAYuLwBJsrQEW4rhoNnUFK1jgdMGMNZ31OC5CY3FBH4sLUlMmLMZxMRWLTyEwyeJDxiZeFyFlajMsh2AAJmAGLg8++GDiktnUEVbauIsrs5gMfHA/4N7Q4MYCirTpeDn1b7YWUFiHsQALC9+wMCzuNSEWF86mqlYLKCKcrmsy8B8SIAESIAES8AmBcqiH16K22OXBh2N8FXfSvLp0dYAVCZYz9dkJ6ynmR8LCBldNxQxwA4S6wOJpF2DFxGIiWAitIb7qW3bfffdEPlhK0YZbb73VOHq3psX8ViycSmf5tKbNdAyrIxYmYQ6k24D2oX/iIs/sWJUpX7o2az60FW1CeTp3Us/hM5s6wqdnfNqGKctahlfHmfo3m+ugrpjnibmqmYKX1810LZ4nARIgARIggVwIhEKA5tJwDCVjhTmG8yFa3n///VyKKUkerJSHgIbDcnzCwXvcGmZ2Y4IQZSABEiABEiABEiABPxMI5BxQL4AuW7bMuMKBo2/MAQxSgNjEqnesAsdqeThShwshis8g9SLrSgIkQAIkQALRJRBZCyi6HIt6shlO9tttguFouA6Kz6/0W9VYHxIgARIgARIgARJISyDSAjQtFZ4gARIgARIgARIgARIoGIHIDsEXjCgLJgESIAESIAESIAEScCRAAeqIhydJgARIgARIgARIgAS8JkAB6jVRlkcCJEACJEACJEACJOBIgALUEQ9PkgAJkAAJkAAJkAAJeE0g2cO516UXoLy5c+eabSkLUHTRi9Q9AOwcqhe9MiG6ILkWpjPJtbBcUTqfBd4y5j3rLU8tjVyVhLefYeOKrbnjO0SmhRS4VfDYez2+dWXaBgXpBPZ5x97k8e0gg1Rt39cV7rXwH7latWq+r2uQKohdxPCHhwqDdwTgSi2+RavZVKJ8eQ5KeUUWz4D169cbV3upu8l5dY2olgOucP/nxQ57UWVo1248BxDC8NsFN5EzZsyQ+DbXdk01cYGzgMb3AZeBAwembVCQTmAbSPwHxjaiDN4RWLlypfGP2rBhQ+8KZUmybt06Wbt2rTRp0oQ0PCSAbVZXrFhhtsylUPIOLIQ9tlrG9sFh+EH3jkz+JS1evNi8MNWqVSv/wlhCgsDy5cvNKEj9+vUTcUE9wDbcF154oWP1+brtiIcnSYAESIAESIAESIAEvCZAAeo1UZZHAiRAAiRAAiRAAiTgSIAC1BEPT5IACZAACZAACZAACXhNgALUa6IsjwRIgARIgARIgARIwJEABagjHp4kARIgARIgARIgARLwmgAFqNdEWR4JkAAJkAAJkAAJkIAjAQpQRzw8SQIkQAIkQAIkQAIk4DUBClCvibI8EiABEiABEiABEiABRwIUoI54eJIESIAESIAESIAESMBrAhSgXhNleSRAAiRAAiRAAiRAAo4EirYVJ7ZE++qrr6Rdu3bSqlWrRKXmzZsnCxcuTHxv0KCBtG3bNvGdByRAAiRAAiRAAiRAAuEiUBQBOmrUKHn55Zdl//33l+eee05OPvlk6d27tyH55JNPCvaVxX69CB07dqQANST4DwmQAAmQAAmQAAmEk0DBBWgsFpP33ntPbrnlFmnZsqV06tRJHn744YQAnT59ugwaNEiaN28eTsJsFQmQAAmQAAmQAAmQQBKBggvQcuXKyUMPPWQu+vvvv8uECROMEEXEhg0bZMWKFbJ06VIZO3as9OzZU5o2bZpUQT2vkVu2bBGUE4YAcb5169bQtMcvfQKu+AvLfeIXrvi/h0Cu3vbI5s2bTYH4xH3L4A0BPFsRwvSb4Q2Z/EvB1LltttlGqlatmn9hLCFBQP//h+EZ6+Z5VnABqmTXrFkj/fr1k40bN8qQIUNM9MyZM2XTpk3yxRdfSLVq1eSSSy6R/v37J6yjSPTSSy/JAw88oMXILrvsIsuWLUt8D/oBHo6//vpr0Jvhy/qH6T7xE2ByLUxvrFy5sjAFR7zUtWvXCv4YvCGAUcuzzjpL2rdvL48//rg3hbKUJAJheMZCgKrRIqlxli9FE6C1a9eWN954w1g6zznnHBk5cqTstNNO8vrrr0u9evVMldq0aSNPPfVUkgA95phjZM8990xUGdbU+vXrJ74H+WD16tVSsWJFqVGjRpCb4bu6r1u3zliWcc8xeEcAL4/4C8v/P+/I5FfSb7/9Jrhn69SpIxUqVMivMOZOEIAFdNWqVVKzZk2pXLlyIp4H+RGoVKmSKQCGEz4L8mOZmhuGOowa16pVK/VU4L5DgGZ6nhVcgOLhitXv++yzjwHbo0cPefTRR2Xq1KlmKB7AVYBiHigWJOHBUb78Hx6imjRpIvjTAMFWpUoV/RroT9xo6KCwtMcvnYGpHRjKIFdve0SHhcjVW65aGkQSnm8M3hBQ60uYfjO8IZNfKSpAUQqfBfmxTM0N3QNdEAau0DZoi1MouB9Q3Kww03/++eemHtOmTZPly5dLixYtBBbAgQMHGqsKBANWy0Ogqvh0qjjPkQAJkAAJkAAJlIZAJnFRmlrxqkEiUPDXbdykmNsJETp48GCj7LEivlGjRubvuOOOk7PPPltgrsWQ6W233RYkfqwrCZAACZAACZAACZBAlgQKLkBRn912280IUMxzwnwcazjttNPk1FNPNXOgOGfPSobHJEACJEACJEACJBBOAgUfgrdiSxWfeg5D7hSfSoOfJEACJEACJOBPAhx692e/BLFWRRWgQQTEOpMACZAACZAACSQToBBN5sFv2ROgAM2eGXOQAAmQAAmQQCQJUHhGstsL0mgK0IJgZaEkQAIkQAIkEF4CFKLh7dtitYwCtFikeR0SIAESIAESCDgBFZ76GfDmsPolJEABWkL4vDQJkAAJkAAJBIkAhWeQesvfdaUA9Xf/sHYkQAIkQAIk4BsCFKC+6YrAV4QCNPBdyAaQAAmQAAmQQHEJUIgWl3cYr0YBGsZeZZtIgARIgARIoAAEVHjqZwEuwSIjQoACNCIdzWaSAAmQAAmQgFcEKEC9IhndcihAo9v3bDkJkAAJkAAJZEVAhad+ZpWZiUnAQoAC1AKDhyRAAiRAAiRAAukJqPDUz/QpeYYEnAlQgDrz4VkSIAESIAESIIE/CVB48lbwigAFqFckWQ4JkAAJkAAJhJyAClD9DHlz2bwCEqAALSBcFk0CJEACJEACYSJA4Rmm3ixtWyhAS8ufVycBEiABEiCBwBBQAaqfgak4K+o7AhSgvusSVogESIAESIAE/E2AAtTf/ROE2lGABqGXWEcSIAESIAESIAESCBEBCtAQdSabQgIkQAIkQAKFJKCWT/0s5LVYdrgJUICGu38j17qtW7fKyJEj5dVXX5WNGzdGrv1sMAmQAAkUkgCFZyHpRqvsitFqLlsbdgKzZ8+WO++80zSzffv20qNHj7A3me0jARIggaIToBAtOvLQXZAW0NB1abQbFIvFog2ArScBEiCBAhJQ4amfBbwUiw45AQrQkHdw1JrHh2LUepztJQESIAESCCIBCtAg9hrrnJZAhQoVEuesx4lIHpAACZAACZAACZScAAVoybuAFfCSgFV0li/P29tLtiyLBEiABDjKxHvAKwL8hfaKJMvxBQGrAK1YkWvsfNEprAQJkEDoCFCIhq5Li96gwP1Cb9myRVauXFl0UIW4INqyadOm0LSnEIyyLXPdunWJLHDDFJZ7JdGoEh5s3rzZXJ1Mve0EPAcQVq9eLbTae8dWFySuX79efv31V+8KjnhJa9asMQTg8o7PAm9vhjA9Y9EWfbaloxQ4AQoLV506ddK1J1Dxy5Ytk8qVK0vt2rUDVW8/VxYPRQ1169YNzb2ibSrlJ37IIfDD8v+vlCyt18ZL6KpVq6RWrVpCq72VTH7H+oJfvXp1qVq1an6FMXeCQM2aNRPHfBYkUHhyAEEPy3IYuEKAWkck7QAFToCiEWGxEuBGw19Y2mN3gxU7DoJeQ5UqVchWYXjwqUNuvF89gGkpQnniU48tp3mYIwG1gPIZmyPANNn0OUCuaQB5EB2G54CbNnAOqAc3C4vwDwGrAK1UqZJ/KsaakAAJkAAJkAAJJAhQgCZQ8CAMBKyi03ochraxDSRAAiRQagJqWdbPUteH1w8uAQrQ4PYda25DAGZ/nXditYbaJGUUCZAACZBAlgRUeOpnltmZnAQSBChAEyh4EBYCavmkAA1Lj7IdJEACfiNAAeq3HglefShAg9dnrHEGAipA9TNDcp4mARIgARJwSYDC0yUoJstIgAI0IyImCBoBtXzqZ9Dqz/qSAAmQgF8JqADVT7/Wk/XyPwEKUP/3EWuYJQH1pUgLaJbgmJwESIAEMhBQ4amfGZLzNAmkJUABmhYNTwSVAIQnFiO58UMW1Day3iRAAiRQCgK62QcFaCnoh+uaFKDh6k+2Jk4AApTWT94KJEACJOA9ARWeKkS9vwJLjAoBCtCo9HSE2okheB2Gj1Cz2VQSIAESKDgBCtCCI47MBShAI9PV0WkoBWh0+potJQESKC4BtXyqEC3u1Xm1MBGgAA1Tb7IthgAc0aszeiIhARIgARLwjsCWLVtMYSpEvSuZJUWNAAVo1Ho8Au2F+OQQfAQ6mk0kARIoOgG1fFKAFh196C5IARq6LmWDaAHlPUACJEAChSFAC2hhuEaxVArQKPZ6yNsM6yeH4EPeyWweCZBASQio5VM/S1IJXjQUBChAQ9GNbISVAH2AWmnwmARIgAS8I6DCUy2h3pXMkqJGgAI0aj0egfZSgEagk9lEEiCBkhBQ4alCtCSV4EVDQYACNBTdyEZYCZQrV467IFmB8JgESIAEPCJAAeoRSBYjFKC8CUJHABZQiFAGEiABEiABbwmoANVPb0tnaVEiQAEapd6OSFshPilAI9LZbCYJkEBRCajw1M+iXpwXCxUBCtBQdScbAwIUoLwPSIAESKAwBFR46mdhrsJSo0CAAjQKvRyxNlKARqzD2VwSIIGiEVDhuXnz5qJdkxcKJwEK0HD2K1tFAiRAAiRAAp4TUAGqn55fgAVGhgAFaGS6mg0lARIgARIggfwIqOWTAjQ/jswtXAXPm4AESIAESIAESMAdARWg9APqjhdTpSdAC2h6NjxDAiRAAiRAAiRgIaACFFHWY0sSHpKAKwJFE6BLliyRd955R2bPnl2mYtOmTZP33ntPli1bVuYcI0ggWwKxWEzwx0ACJEACJOAtAavotB57exWWFgUCRRGgo0aNkiuuuEIWLFgg119/vbz11lsJtvfff7/cc8898vXXX8uAAQNk3rx5iXM8IIFcCGBoiAI0F3LMQwIkQALOBKxzPylAnVnxrDOBis6n8z8LIQDr5i233CItW7aUTp06ycMPPyy9e/eWOXPmyLhx4+SVV14xWycOHz5cXnjhBbnmmmvyvzBLiDQBzk+KdPez8SRAAgUiYBWd1uMCXY7FhphAwQUofDI+9NBDBuHvv/8uEyZMMEIUEbNmzZKOHTsm9u3u3LlzknUUaV566SUZOnQoDk2oUaOGLF26VL8G+hP/efE2+dtvvwW6HX6rPLhCgIblPvELXxX15OptjyjXFStWcAcvD9HqKMiaNWtk3bp1HpYc7aJWr16dALB48WLB7zqDNwTUuhyGZ6zqGycyBRegenE8BPr16ycbN26UIUOGmOhFixZJnTp1NInUrl1bli9fnviOgyZNmkiXLl0ScZgvWrly5cT3IB/gZqtQoUJo2uOXvsAPD/7Ccp/4hSt+aCCWyNXbHsFzYNOmTVKpUqXEy7i3V4hmaXgG4PemYsWK5i+aFLxvtQp7lAwDE58H3jH+9ddfQ8O0fPnyGV+oiyZAIS7feOMNGTt2rJxzzjkycuRII75U8aMLoZirVauW1Jvdu3cX/Gno379/kmjV+CB+wvKJ/7xWER7EdvitznhAQiiRq7c9AysSRCi5essV4hN/tWrVolDyEC1+WyBAq1evXuZ3xcPLRK4oGE004PeazwOlkf8nNBBEfRiYoi0QoU7B+axTTpfnILImTpxoUgNsjx49pG7dujJ16lRp1KiRYNhJA45h8WQggXwIQHzqsGY+5TAvCZAACZBAMgEICw3WY43jJwm4JVBwAYphpccff1w+//xzUycMoWOYvUWLFrLXXnvJd999J/PnzzfWzzfffDNpuN1tI5iOBKwEYPmgALUS4TEJkAAJeEPAOueTAtQbplEtpeBD8LB6XnLJJUaEDh48WKpUqWJWxMP6iXD22WfLmWeeKfXr1zei9OSTT45qX7DdHhGAAOWD0SOYLIYESIAELASsz1arGLUk4SEJuCJQcAGKWuy2225GgGIOWc2aNZMqduSRR8phhx1m5kClnktKyC8k4JIABCj+GEiABEiABLwlYBWdVjHq7VVYWhQIFEWAKsh0AhPD9PhjIAEvCOChyAejFyRZBgmQAAkkE8CzFZ4F8GkVo8mp+I0EMhMo+BzQzFVgChLwlgAejLSAesuUpZEACZAACEB0qrcaClDeE/kQoADNhx7z+pIA38x92S2sFAmQQAgIWAUonrUMJJArAQrQXMkxn28J4AHJN3Pfdg8rRgIkEGACeLbCtyoCn7MB7kgfVJ0C1AedwCp4SwAPRTij5zC8t1xZGgmQAAnA6kkByvvACwIUoF5QZBm+IqBv5dgEgYEESIAESMA7Ani+cg6odzyjXBIFaJR7P6RtV+GpnyFtJptFAiRAAkUnAAFao0YNc1192S96JXjBUBCgAA1FN7IRVgL6UKQAtVLhMQmQAAnkT4AW0PwZsoQ/CFCA8k4IHQEVnvoZugayQSRAAiRQIgJ4rnIOaIngh+yyFKAh69CoN8cqOjdt2hR1HGw/CZAACXhKABbQqlWrSvny5bkK3lOy0SuMAjR6fR7qFlsFqPU41I1m40iABEigSAQgQHX3Qp3uVKRL8zIhI0ABGrIOjXpzrKLTehx1Lmw/CZAACXhBgALUC4osAwQoQHkfhIqA9Y2cAjRUXcvGkAAJ+IAAnrGVK1c2VlDr89YHVWMVAkaAAjRgHcbqOhOwik7rsXMuniUBEiABEnBDwGoB5TPWDTGmSUeAAjQdGcYHkoD1gci380B2IStNAiTgYwJ4xmIOKKygfMb6uKMCUDUK0AB0EqvonoD1gWgVo+5LYEoSIAESIIF0BPCM5RB8OjqMz4YABWg2tJjW9wSsotMqRn1fcVaQBEiABHxOYOvWrbJly5bEKnjr89bnVWf1fEiAAtSHncIq5U7AKjo3b96ce0HMSQIkQAIkkERAn6+wgOKPAjQJD79kSYACNEtgTO5vAlbRaT32d61ZOxIgARLwPwEVnJwD6v++CkINKUCD0Euso2sCVtFpPXZdABOSAAmQAAnYErBaQCFCVZDaJmYkCWQgQAGaARBPB4uAVXTqwzJYLWBtSYAESMCfBFRwYvi9SpUqFKD+7KbA1IoCNDBdxYq6IWAVoNZjN3mZhgRIgARIID0BqwDlHND0nHjGHQEKUHecmCogBLBCU4P1WOP4SQIkQAIkkBuBVAHKUabcODLXHwQoQHknhIqA1eppPQ5VI9kYEiABEigBAasAxRzQTZs2laAWvGRYCFCAhqUn2Q5DwCo6rcfEQwIkQAIkkB8BqwDlEHx+LJlbpGLQIMRiMfn111+DVm3b+sKpL0RSWNpj28giR27cuNFcUd/Oyda7DlBBT6beMUVJOowJa5Iy9vYK0SwNz1cE8C1Xrlw0IXjc6rVr15oSIT7xh3uWzwPvIOOexb0aBqZ4lkGvOYVAClB9sDg1LEjnwtaeUrLXH/Nq1aqZHx6y9a439GFCpt4xRUnKE58USt6xVa64b/XYu9KjWZIOuWMFvK6CJ1tv74Ww3K+4L/Q3Ix2hwAnQ8uXLS/Xq1dO1J1Dx69evl4oVK4amPX6Aj/sDoWrVquYzLPeKaUyJ/8EDBW/mZOptR+BHfcOGDYKXJjwPGLwhgEWIsNjBUge2DN4RUAsohuT5PPCOK0bw8BIaBqawgOrvcTpCnAOajgzjA0lAh9vwds7hzEB2IStNAiTgUwI6B1QtoGoR9Wl1WS2fE6AA9XkHsXrZEYDoxPxP/OlwfHYlMDUJkAAJkIAdARWgsIDqELxdOsaRgBsCFKBuKDFNYAhAdFKABqa7WFESIIEAEVCLp3UIPkDVZ1V9RoAC1GcdwurkRwBv6CpA9W09vxKZmwRIgARIAAT0maoWUGscCZFAtgQoQLMlxvS+JoAHJN/Ofd1FrBwJkEBACcACiqF3BDxnEdQqar7wHxLIggAFaBawmNT/BCBA8YDEw1Hf1v1fa9aQBEiABPxPwCpAVYhSgPq/3/xaQwpQv/YM65UTATwMdXiIAjQnhMxEAiRAArYEKEBtsTAyRwIUoDmCYzZ/ErBaQPlm7s8+Yq1IgASCSYACNJj95tdaU4D6tWdYr5wIWC2gFKA5IWQmEiABErAloM9XnNQheI402aJipAsCFKAuIDFJcAioBRQPRz4Yg9NvrCkJkID/CdAC6v8+ClINKUCD1Fusa0YCeEDCDRPmgdICmhEXE5AACZCAawJ4puo2x2oBxfa8DCSQCwEK0FyoMY9vCcARPR6MXAXv2y5ixUiABAJKgBbQgHacT6tNAerTjmG1ciOAYXeIT27FmRs/5iIBEiCBdAQgQPF8RVBLKEea0tFifCYCFKCZCPF8oAhwK85AdRcrSwIkECAC1iF4FaIUoAHqQJ9VlQLUZx3C6uRHYPPmzVKxYkVjAcUxAwmQAAmQgDcEMN9T537SAuoN0yiXQgEa5d4PYdshOitUqGBE6JYtW0LYQjaJBEiABEpDANZOFaD6SQtoafoiDFelAA1DL7INCQIQnbCAli9fXmgBTWDhAQmQAAnkTUDd3KEgPGPxrOUq+LyxRrYACtDIdn04G75161bzYIQVFMcMJEACJEAC3hCwDsGjRFhB6W/ZG7ZRLIUCNIq9HuI2Q3SWK1fO/MVisRC3lE0jARIggeISsA7B48oQoByCL24fhOlqFKBh6k22RSA6VYDSAsobggRIgAS8IwCxqYuPUCoEKIfgveMbtZIoQKPW4xForwrQCDSVTSQBEiCBohFQP8t6QVpAlQQ/cyFAAZoLNebxNQFYQTn87usuYuVIgAQCRgAjSrrTnFadc0CVBD9zIUABmgs15vEtAVg/NViPNY6fJEACJEAC2RPQxUYQnRrgjJ5zQJUGP7MlQAGaLTGm9zUBiE61gFKA+rqrWDkSIIEAEVChaRWgHIIPUAf6sKoUoD7sFFYpdwIUoLmzY04SIAESSEeAAjQdGcbnSoACNFdyzOdLAlYB6ssKslIkQAIkEEACKkB1D3g0gXNAA9iRPqpyxWLVZeXKlfLll1/KzjvvLE2aNElcdt68ebJw4cLE9wYNGkjbtm0T33lAAtkS4BB8tsSYngRIgAScCegcUKsAxfHq1audM/IsCaQhUBQBOnLkSHnllVeke/fu5rN9+/ZyySWXmCo9+eSTsnjxYqlbt6753rFjRwrQNJ3F6MwErBZQzgHNzIspSIAESMANARWgqXNANd5NGUxDAlYCBReg2Jv7ueeek3vuuUdatWol/fr1k5NOOklOO+00qVevnkyfPl0GDRokzZs3t9aLxySQEwGr6LQe51QYM5EACZAACRgCdkPwsIBSgPIGyZVAVgJ048aNAmvmTz/9JGeeeaYZOu/cubPZeztdBbAn99ChQ6VGjRomCXZNWLdunUCYbtiwQVasWCFLly6VsWPHSs+ePaVp06ZJRU2aNEkmTpyYiMN/grVr1ya+B/kAftXwnzcs7fFDX6TufkS23vWK/tCQqXdMUdLmzZtNgevXr3d8lnp71fCXps8C/OYo4/C3unAtXLVqlSk8dQgefPlM8IY7dBFCGHji/5z+H0xHx7UA/eGHH+Twww+XX375xRR61FFHyQ033GDmf7z22mvSuHHjdNdIiE9U5sEHH5RevXpJw4YNZcqUKcaH2BdffCHVqlUzw/L9+/eX3r17J8qaPHmyPPvss4nvsJTiQR2GgLmKYBKW9vihT6w3PPiSrXe9Ap4IZOodU5SkXPFCTqu9d2yVKwQSuebPVUVRqgCFUYjPhPz5ogS9Z8PA01MBOmDAANl3333liSeekN13393QfuaZZ+T444+XYcOGycCBAx17ADfprbfeagBfd911Ju1OO+0kr7/+uhmKR0SbNm3kqaeeShKgZ5xxhuBPAwSqk9jVdEH4hOUX/5nr1KkThOoGoo7ly5dP7AWPCoflXvEDfIxc4EeITL3tDTwbMRLUqFEjqVjRtU3A20qEsDRYk5YsWWLWF8DAwZAfAR3FtM4Bxe8XOPOZkB9bzb18+XLz+1W/fn2NCuwnBGim55krN0x4M//888+NgKxdu3YCyLbbbisXX3yxjB49OhFnd4D8l19+udSqVUtuu+02I7qQDiZ9rI7XAOsmFiRZrVh6jp8k4JYArB34433klhjTkQAJkIAzAZ2CU6lSpURCCFCNT0TygARcEnAlQKFiYVmyMwtPnTo147ylm266Sdq1aydXX321YE6oBrhvgOUUc0theh41apT06NEjY3man58kkEoAolMFqA5npKbhdxIgARIggewIqNBMHYLX+OxKY2oSEHE13oMb7tBDDzVi8e677zbcYNV88cUX5bHHHjPCMh1MzB3FIiL8jRgxIpHskUceEbhcOu644+Tss882k8RhXYWFlIEEciUAAYqXHPzRAporReYjARIggWQCKjStAhTWUI1PTs1vJJCZgCsBimIGDx4sxx57rHTp0sVYmA444AD5/fffpW/fvnLRRRelvRLmeY4bNy7tebhjOvXUU83KeOvwftoMPEECDgQgOmGtxx8FqAMoniIBEiCBLAjg9x7BKkBxjDmg+tzNojgmJQF3FlBw2m677YwVE2Lyxx9/NDfhbrvtJvjLN0AsUHzmS5H5QQAPQ1g/cU8h8MFoMPAfEiABEsiLACydmN5knUanYhTi1Lo4Ka8LMXNkCLi2gH7//ffmxx3O47EaXgPmgNasWdOsguMNqFT4WSoCKkB19R2+qxgtVZ14XRIgARIIOgGITBWc2hZdkARxyt9/pcJPtwRcC9CuXbs67vkKNxdYbHTVVVe5vTbTkYDnBFSAqujEd31Ien4xFkgCJEACESEAAZr6LNXvOjwfERRspkcEXK2Cx7VuueUW6dChg/EDOmfOHBk/frzAn2eDBg3M8eOPP26224RfTwYSKBUBFaBqAeUOKKXqCV6XBEggTARg5VTBqe1SiygFqBLhZzYEXFlAMY8O1k3seHTggQea8lu0aCHdunUTDMG/9957RqBiX/cxY8aYxUrZVIJpScArAur8VucpQZAykAAJkAAJ5EfAbgheBShXwufHNqq5XVlAsZUZfHXCUXxqwOKkRYsWmWicx84TDCRQKgKpFlAK0FL1BK9LAiQQJgJOQ/AUoGHq6eK1xZUArV69umAOKHYzmjZtWqJ2X331lWA7zv3228/MD33ppZfkoIMOSpznAQkUm4BaQHUInkNDxe4BXo8ESCCMBCAy1eKp7dPvfM4qEX5mQ8DVEDwKHDJkiPTp00fat28vGH7HsPyCBQvMPu3w43n++ecbcXr00Udnc32mJQFPCeBBCPGpApRzQD3Fy8JIgAQiSsDJAkoBGtGbIs9muxagbdq0kS+++EI++eQT+frrr6Vq1arG8rn77rubKlx66aVmEVKNGjXyrBKzk0BuBHS4HRPlKUBzY8hcJEACJGBHwM4CqouSOARvR4xxmQi4FqAoCE5oYQGFGEXADz6G4ZctW2a26jSR/IcESkRA38LxUNQHo8aVqEq8LAmQAAmEgoCdBZRD8KHo2pI1wrUAff755+Xcc8+V9evXl6nsWWedRQFahgojik1A38LxUNQHo8YVuy68HgmQAAmEiQAtoGHqTX+0xdUiJFT1iiuukBNOOMG4WapVq5bZlvOhhx6SJk2ayJ133umP1rAWkSYAbw0I2JEDU0QQNm3aZD75DwmQAAmQQO4EIEB1ZElL0Rd9jjQpEX5mQ8CVBXT16tXyyy+/yD//+U9p2rSpNGrUSOrWrSsXXnih+YG/44475N57783mukxLAp4TULEJ8akCFO7DGEiABEiABPIjAJEJjzjWoIKUI01WKjx2S8CVBRQ3HW40/VHHPNAJEyaYa+y9997y2Wefub0e05FAwQjo9BDcr/qg3LBhQ8Gux4JJgARIICoE7Ibg1QJKARqVu8DbdroSoBCfnTp1MrshrV27VnbbbTd5+eWXBW9Eo0ePllatWnlbK5ZGAjkQWLdunclVs2ZNwR+Cxpkv/IcESIAESCAnAhSgOWFjJgcCrobgkf/f//63HHXUUdKzZ0/BoqM99tjD/MhjJfwbb7zhcAmeIoHiEFizZo25UO3atc29Ca8NGlecGvAqJEACJBBOAhSg4ezXUrbKtQDda6+9ZN68eYKbENYl+ALFHvCHHHKIcUxfykbw2iQAAitXrjQg6tWrZ1yGYZ6yxpEQCZAACZBA7gTsBGj58uWNz2Wdf5976cwZRQKuBSjgWN3bYN/3M888M4rM2GafEoA/2mrVqiXmfzZs2FCWLl3q09qyWiRAAiQQHAIQmTrn01preB3hKngrER67JeBqDqgWhhXFw4cPl1tvvVUWLlwokyZNMlty6nl+kkApCcBTw7bbbpuoAo4XL16c+M4DEiABEiCB3AhAgEJspgaIUlpAU6nwuxsCri2gP/zwgxx++OHGHRP2gcd80BtuuEHgoum1116Txo0bu7ke05BAwQgsWLBAtttuu0T5OMZLEkN+BCZPnix33323bN682ex+hkWJ8IRx44035lcwc5MACQSGAESmesKxVhqiVH0wW+N5TAKZCLi2gA4YMED23XdfWbJkiTRr1syU+8wzzwjmgAwbNizTdXieBApOYO7cuUnzkVu0aCHz58+nlT5P8uPHj5dvvvnGWJch6uHaasSIERKLxfIsmdlJgASCQiCdBRSilBbQoPSiv+rpSoDiB+fzzz83Q+9YYawBQ5wXX3yxccWkcfwkgVIQwAR5iM0ddtghcfnWrVubuUmIZ8idAKYx4KXzwQcfNH/wgoE5XytWrMi9UOYkARIIDAGMeuL/vN0QPOIoQAPTlb6qqCsBWrFiRWPpVEff1hZMnTrVnLPG8ZgEik1g+vTpxtK54447Ji7drl07c/zjjz8m4niQPYFFixaVmVuLUjDnloEESCD8BHSI3W4IHnF6Pvwk2EIvCbgSoJhkfOihh8rAgQPliy++MNeHVfTFF1+Uxx57THr16uVlnVgWCWRN4LvvvjN5OnTokMi7/fbbmy1j9VziBA+yIgChmTq3FgVgISIDCZBA+AmowEwnQLnlcfjvgUK00JUAxYUHDx5s5n516dJFZs+eLQcccID069dPDjzwQLnooosKUTeWSQKuCXz11VeCIfdatWol5enYsaPxWZsUyS9ZEcDiLoh5DZh6U6FCBfn55581ip8kQAIhJqACFG7uUgMtoKlE+N0tAder4GEBmThxoowbN04wpAmrKLbkxB8DCZSaAFa7d+3atUw19txzT3nkkUfMBgp2PuzKZGBEEgHM88TUm6ZNmybiIT6bNGli5twmInlAAiQQWgJq4bSzgEKU0t9yaLu+oA1zFKBY+YqhdmvAfNBddtnFROHcJ598InD4bZ17Z03v9TG2/gzL4ge0BW+W+GTInQAscbDS7bPPPmUKgeeG++67Tz766COBGGXIjoBOX4BHAWvA9xkzZoTm/6K1bcU+xgIPBLi0w/axDN4QUC8N69atExVQ3pQcvVLUn7KdBRRxYByW3+VS9i4WeuEZEAaW6rbPiaejAD3llFMEi4wyhRNPPNG4ZcmUzovzsL7Ur1/fi6JKXgbeGmGVq1OnTsnrEuQKvP322+Y/rZ0FFC9L4IutYzGPmSE7AthdCqFVq1ZJGfEdoj4s/xeTGlfkL1hBjB8c3Kd4wWfwhgBe7OE2EFtH2wknb64SjVLg+xfBjmP16tXNCBOfBfnfC8uXLze/ZWFgCQEKveYUHJ92n376qSsfinpzOl2I50igUAQghDp16iTYAz41wE/t/vvvb8TSVVddlXqa3zMQgHeBRo0alXlJatOmjTz//PNmhAQ/QAwkQALhJaAjoTVq1CjTSIhSPV/mJCNIwIGAowD9/vvvzdvjzjvvLB9//LHZCcWuLCxK0GF5u/OMI4FCEcDQD16UnBbCHXTQQTJq1CiZNWuWWahUqLqEsdxp06bZTq/RKTcQqBD/DCRAAuEloALT7mUTcXo+vATYskIQcBSg2P0Iq4jhbqlPnz5mjpJdJYo5BG93fcZFl8CHH35oHCQ7Da/36NHDTHV499135bzzzosurBxajpfQ448/vkxObMWJuUqYokMBWgYPI0ggVARUYFKAhqpbS94YRwGKeXM6KR5zadIFDHMykEApCGD+JxzOt2zZMu3lMWzUvXt3QVoK0LSYypzA4q6VK1fajm6AKeaB6iKlMpkZQQIkEBoC8IQBLWA3BxTPAsy3xW509DQSmi4vSkMcBSh+fLAqK1PATRmGSbOZ2snz/iKwdu1aGTt2rFx44YUZK3bEEUfIpZdeKjNnzkzarjNjxggn+Pbbb03rMQpiFxCvaezOM44ESCAcBCBA7ayfaJ3OC8V0KOqAcPR3sVrhaLqEk3n4/8v0d+655xarvrwOCSQIvP/+++YFCeIyU8DGCXhReuuttzIl5fk/CUBcYgGS1Qm9FQ58AEPQ40WAgQRIILwEIEBVaKa2UuORhoEEsiHgaAHFjw/C3nvvLX//+9/T+lGEH1AGEig2AYhJWOGaNWuW8dJ4e4cIRR6nBUsZC4pQgi+//FJ23333tC3GOfhahL9geBpgIAESCCcBiEu4s7ILGk8BakeHcU4EHC2gWOCBFcZw8D1o0CA5/fTTBQs54O6mW7duiT/MwWMggWISgN9E3Ju9e/d2fVmknTNnjvzwww+u80Q1ITZIACcnAYqV8LB+YBtUBhIggfASwPC6WjpTW6nxSMNAAtkQcBSgKAji84EHHjA7zQwZMsQ49sWqYqx8veuuu8wPejYXZFoS8ILAmDFjzMT3ww8/3HVxWIiEh+Xo0aNd54lqQiwugiPhzp07p0WAxYcYhsdiRQYSIIHwEoC4VEtnais1nhbQVDL8nolARgGqBeDHpmfPnvL444/LokWLjEUULljgkBqLOxhIoJgE3nvvPSN+4IPWbcAKTdzDmDvK4EwAw+rYYKJDhw6OCSFAJ0+e7GrDCseCeJIESMC3BJwEqFpAORfct93n24q5FqDaArhawDD8sGHDjHNvrHpzcoGj+fhJAl4RwJv2xIkT5eCDD866yEMOOURmz55t/rLOHKEMU6ZMEfj6zORWBSMh6A8wZSABEggnAYjLWrVq2TZO4zkEb4uHkQ4EXAlQiE4s3sAcUFicTj31VGMdGTFihCxcuJCLOhwA85T3BDD3E+7BYM3MNmCxDPanxc5eDOkJYHTDze5maiGFw3oGEiCBcBKAuFShmdpCjJRUqVKF3jBSwfB7RgJ5yTrXAABAAElEQVSOAvSDDz6Q/v37G9F58sknmxWvL7zwgixevFiefPJJgTWpYkXHhfQZK8AEJJAtgU8++US22WYbadu2bbZZzTwmWO1QBoM9gY0bN8r8+fNtt+BMzQFPGViU+NNPP6We4ncSIIGQEHAagkcTIU45BB+Szi5iMxzV48UXXyw//vijsTT16tXLvOXA7x/+rAFCAOcZSKAYBCZNmiRdunTJ+VLIixcpuBDSnb5yLiyEGefOnWta1bp1a1etQzoOwbtCxUQkEEgCEJe62MiuARCgHIK3I8M4JwKOAhTzOxs0aCCYD4a/dOHoo4+mAE0Hh/GeEoB1bvr06XLSSSflXC5cC2Ex3axZs7grkg1FbMGJgA0o3ASkmzFjhpukTEMCJBAwAnDJBo8Y6Ybg0RyIU1pAA9axPqiuowDFNocMJOAnAhCfW7dulZ122innamleWPd32GGHnMsJa8Zly5aZpmGag5uAdJiXy0ACJBA+Aiosa9eunbZxHIJPi4YnHAg4zgF1yMdTJFASArBaIsD9V64BC+nwxq5l5VpOWPOtXr1aqlatmnEFvLa/Tp06gjwMJEAC4SOwZs0a0ygnCygFaPj6vRgtogAtBmVewzMCGB6GeIToySdgf3N4cGAoSwBDbtWqVSt7Ik0M0m7atCnNWUaTAAkEmYDO7cw0B1QtpUFuK+teXAIUoMXlzavlSWD58uWCldf5Bgwb61BzvmWFLf+WLVsEG0+4DbqQC1MjGEiABMJFQIVlJguoCtVwtZ6tKSQB978yhawFyyYBlwQwHOT0IHRZjCmDD0x7WnCtBhHqNmjabESr27KZjgRIoLQE3AhQLkIqbR8F9equBajOAwlqQ1nvcBDApghwepxvwA4/HDa2p4j5n/A24DYgLfIwkAAJhI+AGwEKowBf6MPX94VukWsBuuOOOxrXN6NHj87KOlLoBrD86BGA/06GwhGANQPiHLtNuQn44XGaH+amDKYhARLwJwH8/8aoiNOLPwQopuBgW14GEnBLwLUAffnllwVuGP7+978b/4BXXHGFYLs+BhIoJgFYLmEFzTegjEz7nOd7jaDmV3cravnI1A6MjmieTGl5ngRIIFgEIEAzTXvSF1BaQYPVt6WurWsBij20sf3mL7/8Ivfff7/88MMPsscee8hee+0ljz76KN2wlLonI3J9POi8eMihDH1oRgSd62bWrVvXpF25cqWrPHDBhO04GUiABMJHAM/KGjVqODZMn6VePJsdL8SToSLgWoBqq+FypW/fvnLjjTfKmWeeKd98841ceeWVArc2l19+uethOy2PnySQDQGII7fCyKlclKFCyyldFM+piyu3vj1XrVqVt1usKHJmm0kgCATcvKyrAOUQfBB61D91zEqAYru9m2++WbD3e9euXWXOnDkyfPhwIwg+/vhj+e9//2vO+6d5rEnYCDRs2FAgeNzOT0zX/qVLlwrKYihLQIfT3Q7BI12mIbqyV2EMCZBAEAhAVGaygOp5WkCD0KP+qaPjVpzWah544IHy4Ycfmi0QzzrrLDn11FOlSZMmiSQYjj/22GPlu+++S8RZD2Bx+vLLL2XnnXdOyoc006ZNk7lz50rnzp0pCqzQeFyGQOPGjQWLkJYsWWKs7mUSuIiA2yAIUOv96yJbZJJUr17dtHXDhg2u2ox0msdVBiYiARIIDIFsBCgtoIHpVl9U1LUAhTi88847Ze+9905b8Ysuusj2h2jkyJHyyiuvSPfu3c1n+/bt5ZJLLjHlYD4pFjPBqoq5pA8//LA0b9487TV4ItoEmjZtagBgRyRM+8glYB4zROh2222XS/bQ56lUqZJp4+bNm121FdZozeMqAxORAAkEhgBEJV78nYJaQClAnSjxXCoB10Pw3377rbFephbwzDPPyMknn2yimzVrJg0aNEhKgh/65557Tm699VaB5fS+++6TMWPGmGF7DOGPGzdOBg8eLFdddZVZYf/CCy8k5ecXErASUAE6b948a3RWx/Pnzzfpcb8ylCWgOxrpDkdlUyTHwAG95kk+w28kQAJBJwBRmWmEQ8+7HTUJOhPW3xsCjhbQzz77TN5//31zpfHjx8tdd92V5HAa4hLzPmG9TBcqVKggQ4cOTcwhwT7TmCeCvLNmzZKOHTsmtv2DlfWtt95KKgrz/VasWJGIQz63lplEJp8eYCgZP9xhaU8xMMPhef369c2UjVyvp+IVFlCyL0tR5366dS6PdPjhIcuyLN3E4JmGoJ9u8jBNZgL6UsRnbGZWTincTLHB7zzc2uG3nc8BJ5rpz0HrYCoiXvyhDXQxaPoc/j6D+yCTz25HAdqmTRu59NJLE06pIQ5xo2nAsFvLli3l2muv1SjbTzXP40Hw4IMPSq9evcxcz0WLFiVBxuIH7PVtDcOGDZMHHnggEbXLLruY+XuJiIAf4EcHopzBPQHM3VQR6T7XXynxnxyWerzZc8joLy56hBdDBLeuleBNANMaMK+WIXcC1hft3EthzlQC3MUvlUh23/GMhPebTAFpli1bxudAJlBpzsPHOjQRQosWLYzhLk3SQERDgGZ6qXYUoPiRnjBhgmns0UcfLS+++GLOvhOxswqG4aGIr7vuOlMmxKy1gqhw6o1+1FFHSadOnRLAH3vsMWMBS0QE+ABubrDDhAr0ADelqFVv3bq1wCNDrgECFP/BYUllKEtARbnbObJIh8WH5FmWpZsYbIoAyxEsHtYXfDd5mSY9ARg8YFWCiyBuOpGeU6Yz+O3WIXantJqGzwEnSunP4V494YQTzEYrmJoYdI7Qc5meZ44CFEPw+M+LleuXXXaZ4Ltd2HbbbQWWyXQBJnzM8cSiEeygpJVq1KiRTJ48OZENFoDUlcmY86fz/pBwyJAhjluCJQoLwAFM7WDhtMVZAJpR9CrC6v6///0v5+vCetqhQwdyT0Nw9uzZ5gcn08IDzb7DDjvIqFGjzL2MFyqG3AhAJJFfbuzscqlxI9M2knZ5GfcXAYzQuZmOA+MRFiTy9+wvdtkc4UUU0xkh+LFOJugcoW0yrSNw/LUYMGCAmaMJy2efPn3S7nZ04oknyogRI9Kyvummm6Rdu3ZywQUXJKXBLkoYkseiEAjPN998U7p06ZKUhl9IIJUAFg/BSoc3xlycyS9YsEAOO+yw1GL5/U8CWHAIgZ7p4aHA8PKJH54ff/zR8UVU0/OTBEggGATw/xpC3o0AhWDidLLc+lWHq8EQo8QQoVEIjgL066+/TvwIwe9iuoBVsOkCtuycOHGi+bOK1EceecSI27PPPtvsqARzM4ZFdUV9uvIYTwLqfgmumLIVoJj2APGqZZBmMgH84MBf72mnnZZ8wuHbbrvtZtwwffrppxSgDpx4igSCRkCFkBsBijQUoLn1MKyfCBgFwdQRiH58Ommr3K7kr1yOAtTq2y/XOTQ77bSTcbWUrtlHHnmksUbhRtftvNKlZTwJgIBO08CEbUwPySboJG8tI5u8UUj7xRdfGIEOn71uA4be9txzT7NRBVytMZAACYSDgFUYZWoRrHeaPlNank8mgBd/BBWgOEZc0Ifh0Q6nkN50+WcuKHG4X0pddXzEEUfI1VdfbdyvOF3AzTkIXYpPN6SYBgS22WYbA8LJKp+OlObBvGWGsgRGjx5t+MKqmU3AlIavvvrKrIbPJh/TkgAJ+JeAWkDdGKCQhgI0t75UAQotpIY/jcutxGDkchSgmCu37777ym233SaLFy9OahFctNx9991ywAEH0OyeRIZfCk0A/0GxYjjVZZeb68JNCAL3gS9La+PGjQIBilEJt/M/tRS4VsNiD+x6xkACJBAOAiqCKEAL25/KGc9QFaBREPOOAvSGG24Q/CjBxQoWDFkDdizC3M4pU6YIXCMxkEAxCWDOcC5+E1euXGn+g9PiXra3ID4xPxauQLINeCE9+OCDzWJEzF1iIAESCD4BFUYqipxahDSa3ikdz5UloNzAUFlrXNnU4YlxFKDvvvuu2T6zVatWti3GinVYS5COgQSKSQAWUCwoyjYgT9B3mMi2zW7TP//887L33nsL3CrlEuBIGaMmH3/8cS7ZmYcESMBnBFQEqShyqh6sd5reKR3PlSWg3ChALWxgDdl1110tMWUPe/ToYdzhlD3DGBIoHAFYMOG8O9uAPLR+lqWGxUfff/+9nHrqqWVPuoyBeIW7NWy9y0ACJBB8AnAPhABxmSkgjfpezZSW55MJqADFVAcV+xqXnDJc3xwtoF27djUrW52aDOf03bp1c0rCcyTgOQHsuoHpIdkGN/saZ1tmGNI/88wzAv+qBx10UF7NOf300wXumOATlIEESCDYBFRQunEHBAGqgjXYrS5+7XW+JwSozrfVuOLXpnhXdBSgBx54oGBYLt22hxhqw6KDnj17Fq/GvBIJxAnk6nMOqzrd+LSLEmRsTfrBBx8Y359ufmic2GBKDhZ4Pf30007JeI4ESCAABFSAurGA4tnB+d+5daqKTQpQC7+LL77Y7MPeuXNnsxIe2+1hqA4LkAYOHGgWHRx33HHcVcbCjIfFIYBhCv1Pm80VkUffMLPJF+a0zz77rJmWcPzxx+fdTLDt16+fvPXWW6IeB/IulAWQAAmUhIAKSjcvpkijgrUklQ3wRfW3DL9r+vukcQFuVsaqO1pAAeLll1+WM844QzBEd9RRR5mtMv/xj3/IO++8I/fdd5+xdCiwjFdjAhLwiECuwz0YIsIetQx/EMCc2Ndff92sfMe0Bi9C3759TTHDhw/3ojiWQQIkUCIC2BYSwY1bNjcitUTN8P1l1d8qHM+r83mN833l86igowBFufihf+CBB2TmzJnGFyjmfGIlMbbYvPDCC/O4NLOSQO4EICL17TybUpCHAvQvYhjVwGJDWC29CnCRhY0qsPVuLn3kVT1YDgmQQH4EVIC6LSXb9G7LDXs63cIU08MoQNP0NnaggeslriJOA4jRRSOQ63wjCCK+qf/VTa+++qrZbAILkLwMJ510knlhnTBhgpfFsiwSIIESEHBjAS1BtUJzSbV2QoDqGgUVpaFppE1DMlpAbfIwigRKTgAPxFysa8jDh+kf3Td//nyZPHmy9OnTx/P+3GOPPWT77bcXWFgZSIAEgk2Als3C9p8KUFg/VYBqXGGvXNrSKUBLy59Xz5EArJi5PBSRhxbQP6CPGTPGTEfI1/WSXRdC5B966KHy0Ucf5fSiYFcm40iABIpLQJ+Vbp61SMOX+9z6By4FwRqLkHQInhbQ3FgyFwkUnAAedG4eiqkV4UPyLyKffPKJ7LbbblK7du2/Ij086t69u9mkAg7uGUiABIJLwM2zlqNLufcvrJ0qPCFCIUZpAc2dJ3OSQEEJQIDmOgSvb/UFrWAACv/2228FQ+WFChC3YP3NN98U6hIslwRIoIAEdMGmm2ct0mj6AlYplEVDbOrQOxoIMUoLaCi7mo0KAwF4Z8jF5xzy8CEpsmTJEuPNon379gW7HeDWqUWLFvLTTz8V7BosmARIoHAE9Fnp5lkLAcqX+9z6Aj4/YfnUANeWkfcDqjD4SQJ+I4AHYy7bviGPm109/NZer+uzcOFCU6TXq99T69m0aVPRa6We43cSIAF/E8hGgPLZmntfYt93qz91HEd+L/jccTInCRSWQK7/QVPfNAtbS/+WDl++CHXr1i1oJevUqWMsrQW9CAsnARIoCAEVoG5e9jm6lHsXQGxaLaAwklCA5s6TOUmgoARynSNjnexd0Ar6vHCd01XoITP8gOm1fI6E1SMBEkghoKLIjRiiBTQFXhZfId6tz+KoPDcrZsGISUnANwQwYRvWzGznHWFid7Vq1XzTjlJVRCe8w/1HIcOGDRvI+0/AK1askHfffbeMIMcPNzhhgw/rjxCyQQAcffTRSQsUCtlfLJsErARUgLqxgKZa8azl8NiZAH7H1NqMlHgOuJl361yq/89SgPq/j1hDGwK6G5f+cNsksY3CtpNe7Xlue4GARDZq1MjUFIuR2rZtW7Bao3w4pGcQufHGG+X999/PGsXcuXPliiuuyDofM5BAvgRUgLqxgMIgYJ3HmO+1o5Q/1c1VVPypUoBG6S4PUVtVgK5ZsyarrWHXrl1bML+XQcKLxUd4y541a5Z069atYFWfPXu2dO3atWDlB6Xgjz/+2IjP22+/XY4//njX1b7vvvvkqaeekmOOOaagLwquK8SEkSKgAtTNimyIVL7ce3N7pApSb0r1XyncCcl/fcIauSCgi2cgQN0GiE8MdWBhTNQD5tDusMMOMmXKlIKhmDdvnqB/dt5554JdIwgFw+p+8803G5+rEJ+wbrj9O//886Vx48Zy/fXXlxm6D0LbWcdgE1CLphsBSgto7n2N4XfrkHvqkHzuJfs7JwWov/uHtUtDoF69euYM5tW5DStXrjRJNa/bfGFNt+eee8pnn31WsOZNnDjRlN25c+eCXSMIBf/rX/+SpUuXym233Zb1VoWYq4t8cOY/dOjQIDSXdQwRAd2dx82uPBSguXd86qKj1EVJuZfs75wUoP7uH9YuDYH69eubM8uXL0+Tomy0ptW8ZVNEK2a//faTX375RaZNm1aQhmPYuUOHDtKgQYOClB+EQrHd6YsvvigXX3yxsTjnUmdMYTjppJMEw/EzZ87MpQjmIYGcCGRjAaWHkZwQm0ypbpcgQKPgr5oCNPd7hjlLSABD8JifBMuS26Bpt9lmG7dZQp0Ocz/hEQArs70OGHYeN26cHHzwwV4XHZjy4Gv16quvFliABwwYkFe9UQ7uWyxGcrMgJK+LMTMJ/EkAU0XwnHVjAYWHEbWYEmB2BCA2rZ4GouJRIHCLkPBmsGrVqux616ep0Rb8xw5Le4qNGZa1xYsXu74s0uKBiuEOMv8DG6ygb775plx00UWuObpJiNXeuLf333//yLK+7rrrBPOO77777jLuldwwtKapUaOGKeeUU04RDOmfd9551tM8tiGgCzngKcONgLIpglFxAm59Luve5Xy2Zn/bQAtYXywxnSHoWgeCGm1wCoEToBAPtWrVcmpTYM7hJsPbZVjaU2zwTZo0MUPIbq+7aNEigfshzgH9ixgWxZx77rkyadIkwZxQr8Jrr70mHTt2jOwCJIj6MWPGyB133CFebXe6xx57yFlnnSVDhgwxluWoz63NdK/ixw+iCPNo1e9tpjw8X5YARklUXJY9+1cMRH7t2rX5e/YXEtdH0ABWAYpjvHQGWRtAgEKvOYXACVA0JlOjnBrsp3OwxsEVTljaU2y28C8JH4luAwTodtttR94WYN27dzdDuy+//LJnAhSr37G4CYtnonhv4z5D2zH9IBuXS5ZuSXt44YUXyvjx483Q/htvvGF+pNIm5glDgM/Y/G4EiHc3AhSbWsANUxT/z+dH+I8NJ1IFKCzPQWapIxBObDgH1IkOz/maAMTkwoULXddxwYIFRoC6zhCBhHjAnXDCCfL2228bl0leNHnEiBHmh+iII47worjAlXHttdeaYUuIUK8DRkzuuecegYN/DO0zkEChCcACmmnHNLgNwogeLc259Qb+X+scULCEeOMipNxYMhcJFIUAhjaxsj3Tw1Er8/PPP0vTpk31Kz//JIAV1nj7/u9//5s3E5Tz6quvypFHHpnVBgF5X9gnBbzyyiuCle+33HKLFMrbAvy3XnLJJTJ8+PCCutHyCVJWo8QE3AzBq4UUaRmyJ2BdhKRCFKI07IEW0LD3cIjbp2ISls1MAQsRIFa9mo+X6XpBOo+5tFgs9NJLL+VdbSw+gm/Wvn375l1W0ApAuwcNGiS9evWSQw45pKDVP/3002XXXXc123vC8sRAAoUi4MYCqkYACtDcegHTRHTIGhZQBEzRC3ugAA17D4e4fSomMecwU5g/f75JonkypY/a+b/97W8yY8YM+frrr/NqOobfsfNRFHc/gp9OLHzB6vdCB0yduPXWW80c6KeffrrQlwtc+c8884yZovDvf/9b7r33XnnggQdk3bp1gWuHHyoMUYkXeKeg57kVpxOlzOcgQlWIRkGABnIRUuZuZIooEMAcUAxdqLh0arOK1ObNmzsli+y5nj17Gg8BGD7ffffdc+KAKQ6ffvqp3HTTTTnlD3ImOPPH8Ptll11mFnUVoy1w8n/iiSfKE088YT4LNeRfjLZ4eQ1srnDnnXeaDRDULyXmirdt21Z69+7t5aUiURYEaCbXSrSAenMr6MJklKaWUG9K9mcptID6s19YKxcEYAWCCFVx6ZQFIhVzajDczFCWAFj26dNHRo8e7WrFa9kSREaOHCnYOQXzP6MWHnzwQdl2223ltNNOK2rT4b8VVtfBgwcX9bp+vtisWbNM9WAZ/vDDD80fXNrMnj3bz9X2bd3ATi2c6Sqp52kBTUfIOR5z5/EMRtDPTD40nUsMxlkK0GD0E2uZhgAsmm4EKNLAbRPm2jDYE4AAxQ5G+NHOJcD35QEHHGB8AeaSP6h5YP384IMPjHN43bqwWG2BX9uTTz5Zhg0bJitXrizWZX19HQhQ/D9v1apVop6tW7fmNqYJGtkdQFTiueAUKECd6GQ+h3nc+uzAqB4soVGY281f48z3BlP4mEA2ApTD784dueOOO0qbNm2MFdQ5ZdmzEGH44Y+i9XPo0KFmuPe4444rC6YIMf379zdWUC8WkRWhugW/xPTp081iQ/1BxwVxX2OOM0P2BCBAVWCmy60CFdZShuwJ6IYJmhPTHtSzgMaF8ZMCNIy9GqE2YVER5h5mGq6ABZQLkDLfGFjBPXbs2Kwffu+9957ZVx6O7aMUsNXmqFGjBIu4rIKnmAywR/yhhx5qvBjoAoZiXt9v1/rpp58EL1PWgO94QbI6+7ae53F6Am4EqApUDsGn5+h0BgvkatasmUiC4ygsmqMATXQ5D4JIAFZN+E3DwoN0AeIUixBoAU1H6K94uA/Cmzd8WWYTMATdrVu3yDmihvDGFoSlsn5qH2EzAdzj2FI1ygECHNb4du3aJWFo3769eU7MnDkzKZ5fMhOAVRPPBKeXfFpAM3N0SrF69WqpU6dOIgm2NM208CuROMAHFKAB7jxWXRJWTaeV8PhhxsOTFtDMdwx+qLGY5uOPP86c+M8U2JXnhx9+MPM/XWcKSUL4PYU/zlLfW3vvvbfUq1fP7D8fErQ5NQMjHRBDO+20U1J+3NcIuE8ZsiOgw+pq5bTLjXNB3zrSrl3FioMPYasXCxwjLuyBAjTsPRzy9qkzeicBqo7qSy0SgtIVcEqP/cbdBrWWIl+UAl5qPv/8c+PEv9TtxspZWKC1L0pdn1Jdf+rUqebSqX5o8YPeuHFj0fOlql8Qr6sCVK2cdm3AcLGmszvPOGcCeInHgkINmFaDuLAHCtCw93DI24eHHiw/KjLtmqvnVKzapWHcXwS6du1qeCq3v87YH0H0YJUxLKdRCnPmzDHWtlz9pnrNCvXAApwoLF5Ix27KlClmQZidu7VddtlFcJ4hOwIqLJ3mJEKcarrsSmdqEMAonfWexfGiRYtCD4cCNPRdHP4GQlg6iSWcg0jlBHl390KXLl1Mwi+++MJVBlgBMQQctTB37lzTZKu7n1IywEsA5kA6/V8oZf2Kce1vv/3WTImwu1bHjh3l+++/50IkOzgOcbo4JpMFVNM5FMVTNgQw/xOLGa0jdPhNW7p0qaiDf5tsoYiiAA1FN0a7EfjPipXw6QLO0fqZjk7ZeAwFtWzZUtwIULy54019r732KltQyGPww4GAlxs/hLp165pqaL38UKdi1gGLETHEvttuu9leFvHwrch5oLZ40kaqsHSygOKcpktbEE/YEtANEvDM1aDH+pKr8WH7pAANW49GsD1wMJ9JgCINg3sCe+yxh3z11VcZM2gapI9a0E0N/LJlnq5S1npFrT8gLDH9IN2UCCwWw1zZr7/+Ompo8mqvCksK0Lwwps2MaTMIO+ywQyIN/NYi6LnEiZAdcC/4lA6Fn7hbb721KCvQ8DaOHwvsfFDogIfymWeeWejLlKR8bMeJCdvW3SSsFcGQZLofJWs6Hv9FALywLzxcgahl7a+zfx1BgGJxB/6iFho2bGiavHjxYl/s/oR6IGi9zJcI/fPll18agYmhdruAKThYHY90xd4y1a4+QYlTAYph4nQB4rRBgwbpTjPegcCPP/5otpSuVatWIhX+D2PhHM4dddRRifiwHRRe+QSMGHzIjRgxQtq2bZvkFiFgzUiqLhZLfPrpp6EVoGrdxFBwixYtktqOFwrMpYFIZXBPQIcxMaeuR48eaTPifFTFvTo7/+6778zzIi2kIp3A8DMWguj/hyJd1jeXgQ/UDh06OM713nPPPQVbxjK4JwDhDstxJguoVUC5L50p8f8W921qgCeHsHttoABN6XWYwfHmgQf5Y489FviVfXC83K9fv1DP0VNxiWH4VAEKB/VYmBHVH+WU29v1V/w/gOXjm2++SStA4YAdb+hR3H4TIDFXFkNlH330kRx77LGu2RYq4YcffmgWg0VxCB7/xyFAM/UDBOgzzzxjdkXCoi0GdwTwLHCygOKcWkrdlchUIAADCUTmBRdcUAYILPm4VzHFJ6z/pzkHNKXbsQfrgw8+aH5YTznlFFm2bFlKiuB8xVynk08+2QwP3nbbbcGpeJY1VQFq57ZC54ZaXVxkWXwkk+OBhzlzsHCmC3hwYuFHp06d0iUJfXzv3r0Fu0CV2mk0VndjDmRUXwYwV27lypUZvTHoYjl4bmBwTwDWTScLKAQoLaDueWpK/L/F1LHOnTtrVOITI0vwPICtZcMaKEBtehZuaJ577jmzuvfUU091/I9nk90XURAH/fv3N6u/hw0bJnBsG9aAN29sXYYV2alBRSktoKlkMn+HsITfRFiX7ALEKeYvpzr9tksb1riTTjpJypUrZywVpWzj4MGDzf9xbKUaxfDZZ58ZKxEsnE4B85mxTSfSM7gn4GQBxUsoFn9RgLrnqSnxIlSpUiWxm7cMAQpDgBtvJFpe0D4pQNP0GG6IoUOHCnbYef7559Ok8m/0PffcYxzbog1RmBwOC6edAIUFFA9PPhyzv1fxf2DNmjWCOcR2YfLkyYJ5kNiCL6oBiwX69u0rzz77rOgioGKzwEvC22+/LWeffbZUrly52Jf3xfUmTpxoXoTcDAPvu+++gvTpXqx80SCfVQIv+HgW2AWNRxqG7AjgPsR8e7tnKO5lvNwjTVgDBahDz+IBBQtPEJ3B4o0Ub1Z4O41CgIXTToDCAqpD9FHg4GUb9a0cQtMuIF7T2J2PStz//d//mf9rd911V9GbjPlht9xyi8AZPoRwFAMYwKIJYekm7LPPPmbKRNhd3Lhh4TYNxGW6OaA6NO9G/Lu9XhTSYegd85ax81y6gHMQoOpiLV26oMZTgNr0HCw+cMV0wgknGCtiEF12XHnllWZHlF69esmTTz5p3OnYNDU0URCZOt/T2ijEUYBaibg/xiIbuFey274Q8+3g3ooC9A9H9JdffrmMHj1asBComAGWV/TPjTfeaERwMa/tl2thuhHEkVsBinmgWNUNzyAM7ghAXKqlMzWHxtMCmkrG+TvEJwxF++23X9qE+++/v5kCGFbftRSgf3Y9hs8w1I5FO4cddpj897//lQEDBhhfiFgVH7SASc1vvPGGdOvWTe69915zk2Ol3VtvvRXIOa2Z+ENk6op3a1pYRSlArUSyO8ZCJDsLqIpSCtA/eGIuKCxrN9xwg1kMkx3l3FLPmjVL7rvvPjnxxBMdrSi5lR6cXBCSGO2xW8hh1wqIKdzXFKB2dOzj6tSpQwFqjybn2I8//tjsorbLLrukLQPzQHG/jh07Nm2aIJ+ItACFC5lHH33UWDq7d+8u//znP82bMT7HjRsnAwcOdPQp5/eOx/aTEJ+wypx//vnG9cill15qfijhlP7FF180C6383g439YPIVJ+fmh5TKDgErzRy+8QPNVZXp07lgACFxwjr7h25XSEcubAQ6Y477pANGzbI9ddfX/BG4V6H1RVzUK+++uqCX8/PF4CQhPisWrWq62rCWooFIGEd2nQNwmVCzKFPNwRPC6hLiCnJ8LsM3eHkYglTAGEhhau3MIaiClDMFZkwYUISx3nz5pk5DpjngL9Cz8vBzi233367HHDAAdKnTx954oknzEMcbopQN6x+h0UBfkDDEjCMet5555khQgwTXnjhheZhgmkGPXv2NL7z4PM0yPvO6ip36zzQ5cuXGxcXtIDmfidDgGKu0owZM5IKgfN1OE92engmZYjAF9yDN998s4wZM0aGDx9e0Bbff//95sUAiw2jPPcO9yZ2NnI7/K6dgvRwcYP7mCEzAbWA2i3cogDNzC81Bfxz4/f2wAMPTD1V5ju0CjbIsZtiViZxwCKK5ogecx3wcMYPFoaFNWB+Ioa/dbs/DOlhFyIvAxxmwxXRCy+8IBC89erVMx1/8MEHm6GrbN6cvaxXKcqCxQp/55xzjkCg4c0KP5j//ve/5YEHHhDs6Q33TUFz56IiE/9JdRcfFaMqTkvBO+jXVBdL+KFu3759ojmYd4f5xQzJBI4++mgZP368sYZi+Awuf7wOGLr7z3/+IxdddJH5/+p1+UEqD67A8HzH9IdsAvoGHgNg9IiyH1u3zDC/E+ITRqRUjyIQoGBpt5LbbflRS/e///3PTBtxmv+pTLATHXQT/A3/4x//0OhQfBZFgGKu0rXXXit4i8KfNcDiOWjQIGnevLk12rNjCBIIKgjPgw46yAyPQQAXY/91zxpRoILgnun44483f3iwvPvuu0aoY64oLKMPP/xwYNy6oC14CKroBDJ9Y1RxWiCMoS4W/18h4OEwWQOcruOl0W77OE0T5U+8aGPe7CWXXGLmkGMrQ68CuF911VVGcGFUI+oBAhJ8YanPJuBZgWF75MfLOIMzAV1gtHr16jICFHGpv+vOpfEsjD54aXIzegGDGQxDFKA53jeYF3XdddcZixuGgDUgHj9m2Ksbk2whejBv0RpefvnlJD+ceHAgvdsAEQVTN/6DYNUuJu3jj8GegLqe+ihuGR05cqTpE/uU/ovddttt5amnnpJRo0aZymGlNhYnIGRzz5gM/CdBABZzLMrDUCeCuiXD1A5yTWBKOoAIhV9OrE7/17/+lXQu1y+YhwtRi3DNNdeY52muZYUlH6ZNQUjmYlDAhiOYgoV54rnkDwtDN+3QoXcdbrfmgQDFlDU+C6xU0h9j5BFbHMN9mtuA0dq7775bMHSvLwNu85YqHZ5XmeZYF8UCqqu8IGqsATAxfAJP/1jQgIcrrJXY3k4DXMFofsQhj4oKTeP0ecQRR2SE4JS/kOfQQVi8AJcgfgoYNsQDGUPZ2bAudRuwsAoiSW96cIV4wksLQ+4EsBsYpsjA3yL+cG9gXhLuE9y/DGUJYLrCZZddZobisTuPFz468eIMdyx4qYb4j3pw2kfbDRsI0IceesjMb87Wguqm/DClgRUOAWIzNUCUwsATpN+K1DYU87t6X3Az/1PrBQF65513Gn+30DRBCG5+G4oiQNPB2mmnneT11183czKRpk2bNsaCZRWgsIriTwMEqs4X1TinT6wyw58fA94YIY44fOFN7+BHHn+wfEKEYoUwQ/4E9P8QpmlgJSx2nWLITAD+gzF1AV41MLc9nykLGH7TeZ9Bm5+dmVRuKfCyiUVImbbfTFc6+gSiCd5Q4G+RIT2BZs2amZN2AnTVqlUCV4XZ/C6nv1L4z3zyySdm3nE222NjZBgv/MgLV5FBCDCwZTKulS9lQ3DjQixowDxQzHGClYWBBEiABIJOAEPxLVu2THieyKU92A4Y8z6xYAG7LjH8QQACFAIyV+slFs0gLzyjMDgTUCOJnQBVC6hzCTwLAvC8AAso1qNkG2AFxQJHvHSFJZRUgOJmhq9NzCnDHBPM3dMVX2EBzHaQAAlElwCmFmGYF3PdMW8z24AfG0xNwkIbuFxyM6yV7TWCmh7TESAg85lig/mjYd1lxst+BWN4i4HRKDXgd5zWz1Qq9t9hwcTUkVwEKIbsoZV0CN/+CsGKLakAxfy84447zkzWh1kZE3ODuO1lsLqctSUBEigmgdatWwv8DL///vtJCyrd1AGiExsBwEVaEHdkc9PGXNPABVO+LpQwzx2jcPCSwuBMAFZQOwsoRKlaSJ1L4Fk4n8dwOqYbZhvgEg/D9h+lrKXJthw/pS/qHNDU+ZwAAcGJRQ6YXxaU1V1+6kDWhQRIwP8EjjzySOPy56677jJzFq0+VdPVHv4+sdc7di+DpY7hLwJwt4bVxPkKUN1KFm6zCuUK8K9aB/sIVs5UAYp5fvjtpgB117fw9nPooYe6S5ySCqMfmI8fpm05S2oBVb5wskrxqTT4SQIkEEYCcEUH68cVV1yRcR6XDtnDVyDcOTEkE9AdjKweUpJTuPsG123wtKLlucsVzVQQoKlD8OqWiUPwme8JLHbDwuN8FkVjsRzcSc6ePTvzBQOQwhcCNACcWEUSIAESyIsA5oOqL79HH33UsSwM2cNFHSymnPdZFhW8C2BHHl2dXTaF+xh4Y7FutOA+Z7RS2g3BqyClAM18L2D+J1zYwf1XrgFbyOJ5gLLCEChAw9CLbAMJkEAgCGDI94wzzhBsQTxjxgzbOmPoHRt2YOU7XV7ZIjJ7Y7uZxmCfOzkWAhR7bTM4E4AAVcGpKXVInkPwSiT952effWamjOSzMxo4w50bygpDoAANQy+yDSRAAoEhgK1usZjg9ttvL1NnzKm74447BHuVn3jiiWXOM+IPAj/99JPsuOOOnuBAORBW3MnHGSec0avg1JQqSGkBVSL2n/DyA7dhufqstZa61157yaRJk6xRgT2mAA1s17HiJEACQSQAdzaXX365GUZLHUp79dVXZc6cOXLttddy6D1N52Jqws8//yzwLuBFgDcWhFmzZnlRXGjLgPXN6rcbDVVBSgHq3O24t7CJB/Z0zzdgQSIW4ME/cNADBWjQe5D1JwESCBwBbKfXtm1befzxxxN1x+5dgwcPFvj709XZiZM8SBCAyyRYlFq1apWIy+egRYsWJvvcuXPzKSb0eSEy4cMSvig1QJBit5uaNWtqFD9tCEyZMsXE5rppgrVIfTaEYeFcUd0wWSHymARIgASiSgALCQYMGCBXX321TJ8+3YhR+PfDCtdBgwZFFYurdoMRghcLkFAO5uQ1aNAgFBYltKdQQa2c3bp1S1wCGyVofCKSB2UIwJcvpt144csX88LBHGUefvjhZa4VpAgK0CD1FutKAiQQGgKwgmKf+Ndff12uvPJK84nhYC/miYUGkk1D4AMUoXHjxjZnc4vabrvtZNGiRblljkgurMDGfQorKHx/YhtUbGeqUxgigiGnZmLBoVdzllEBOLKfOXNmTnXxUyYKUD/1ButCAiQQGQL48caWfO+9955cdNFFxsH0OeecE5n259pQLBbCgph8tuBMvTasU0uWLEmN5ncLgRo1ahirPaIWL15sLMdwhcWQmQDmdWMjHq8Cpp+EYQtZzgH16o5gOSRAAiSQJYEePXqYod+RI0cav5/4zuBMAE76vRjKtF4F5aUusLGe5zEJ5EoA85V/+eUX2X777XMtokw+lKUjAWVOBiiCAjRAncWqkgAJhIuADrc//fTTxqIEn5QMzgSwmtjrnfNQnu7q43x1niWB7AjgxQbu1bDrllcBFvsNGzbI+vXrvSqyJOVQgJYEOy9KAiRAAmJ+lLAABlvrtWvXzqwoJhdnAliFjV2lvAwoz7q628uyWVa0CahlHdNGvApalvph9arcYpdDAVps4rweCZAACVgItGzZ0nzTT8spHtoQgDUJWxp6GbCgBuUykIDXBNRKiTm0XgUtS8v2qtxil0MBWmzivB4JkAAJWAhgBTaCflpO8ZAESCDgBOA1AAEvOV4FXYCnZXtVbrHLoQAtNnFejwRIgAQsBBo2bGi+6aflFA9tCOCH3OsfXpTnpUCwqTajSIAEUghQgKYA4VcSIAESKCYBbHGIoJ/FvHYQrwXH8ViA4WXAUCbKZSABrwnoi42XL03YAABBy/a6zsUqjwK0WKR5HRIgARKwIaDCR+d12SRhlIUAhLruQW6JzusQ5Xm9sj6vCjFzaAjo/2sv52vqC5g+O4IKiwI0qD3HepMACYSCQNWqVU07vF7ZHQo4No2A14Bly5bZnMk9CuVxCkTu/JgzPQEd2fByxbqWFfRtUClA0983PEMCJEACBSegw2j6WfALBvwC2IITW0Hiz6sAR+Febu3pVb1YTvAJwGVSuXLlZPny5Z41Bi9MWIhUs2ZNz8osRUEUoKWgzmuSAAmQwJ8EKlSoYI7Kl+fj2M1N0bRpU5NswYIFbpK7SjN//nzRcl1lYCIScEkA/78bNWokixYtcpkjczKU5aVj+8xXLEwKPvEKw5WlkgAJkIArArCOIOinq0wRTqT+UmfNmuUJBexrDif0Wq4nhbIQErAQwNaZP//8syUmv0OUFYYXJgrQ/O4D5iYBEiCBvAhgr2gG9wRg+cHQ44wZM9xnckg5ffp0c7ZNmzYOqXiKBHIn0Lx5c5k7d27uBaTknDdvnjRr1iwlNnhfKUCD12esMQmQQIgIbNmyxbRGP0PUtII1BduWTps2zZPyUQ7m39IC6glOFmJDAPeWVwIUzwlMGWnVqpXNlYIVRQEarP5ibUmABEJGQLeA1M+QNa8gzWnfvr388MMPnpSNcmD95CIwT3CyEBsCEItwnYTpHvkGzH2GT1EK0HxJMj8JkAAJRJyAOqhW59IRx+Gq+R06dDBz6tQdjatMaRJNnTpVUB4DCRSKQOvWrU3RM2fOzPsSOvdZy8y7wBIWULGE187p0pgv9euvv+aU12+Ztm7dKrB6hKU9fuELrvgjV297RC105Oot17Vr15oC16xZw3vWJdq2bdualN9//7107drVZa6yyeAcfPbs2dK3b1+yL4snbQx+h/nblRZPmRNw8YVFhhCP+dyvKBhlVKxY0fit9fOzGPdHpvntgRSgYZorhQ4KU3vK/M8rQQTEJwK5egufXL3lqaXpDinwa8l7Vqk4f2JOHfwgTpkyJa8fdAy/4xmMIX2yd2aeehbPAzJLpWL/HYIRInTOnDn2CbKIxQuTLkDyM3/ULXQCFL7ydGurLPrMl0kxJwTzjsLSHr9AxlAmbn5y9bZH8DD5//bOA1yq4vzDg4ICinRUQKQpUqQoYEFRNIKJvcQaDURFY4kYWzT6SKLGRBJ7IlETS+xdUaIGCyoWjAiCFStSlCIiCljw/Of9/s7m3HX3brmFPbu/73nu3VOmvjNnzne+aV999ZW41i5WFxRQ2gPV2fzhojTSfV4TmTVrlmOdxv79+7t11123JkFVlF8+lvgAUH3Nv9gZs8ns9ZoKE5D4ACt19lhAc61trElINa0N8i8CIiACNSAQdkj59NNPaxBK5Xnt3bt3jRVQuvCZgCTls/LqT33nmHU7a2PzBMIIFtD6zkNtxycFtLaJKjwREAERKIDAwoULzXVtzJAtINrEO2XiEC/jmmzJSRd8z549E89CGSh9Au3bt6/xbkj0QrFt7MYbb1z6Gc4jhVJA84AkJyIgAiJQVwTCDim1YR2pqzSWYrisBYoUux4oqw8woYOufIkI1DWBdu3a2VJMYchNMfEtXbrUJn+Vwzac5F8KaDG1QH5EQAREoBYIhLUBmzdvXisTFGohSYkJIuxcVOyOSEzmYJza5ptvnpg8K6HJJdC6dWtLfE2G2gS/rVq1Si6IWMqlgMZg6FAEREAE6pNA2AZy+PDhbvHixQ4LhyQ/AkzCoCuyWAU0rMnYrVu3/CKUKxGoAQE+MpFly5YVHUpY97ZFixZFh1FKHqWAllJpKC0iIAIVRYBZ3KwPuN9++1m+azqru6Lg+cwys7jYpW3w16RJE1sep9K4Kb/1T2D99de3SGsyZjl034ew6j8XtRujFNDa5anQREAERCBvAtOnT3dY4FgGCGVoxowZefuVQ+c23XTTovfYZm/uTp06CaMI1AsBnm9k5cqVRcfHkB0khFV0QCXiUQpoiRSEkiECIlB5BKZNm+YGDBhga1H27dvXvfzyy5UHoQY5RoGcP3++7XxWaDCsp1guy9kUmne5r38CjRs3tkhrsnsR6zAjISw7SfA/KaAJLjwlXQREILkEWHYJJWjQoEGWCX5feeUVmxiT3FzVb8pZ2obZ7IsWLSo4YhRX1maUiEB9EAhrzQYlspg4g182ASgHkQJaDqWoPIiACCSOwIsvvmhp3mabbex38ODBtkwLu/NI8iOAAoqgTBYi7JTGB0C5rKdYSN7lds0QCAooO/UVK1JAiyUnfyIgAiIgAikCU6dOtS5g9ohGGAfK1rxcl+RHICiQCxYsyM/D966wmKKEBv8FeZZjESiCAFZLJhzWpAsev+XS/Q5CWUCLqEjyIgIiIAI1JcB4z4EDB6aCwUKicaApHHkdtGnTxpR2docpRILCKgW0EGpyW1MCTZs2tV6OYsNhEhJhlItIAS2XklQ+REAEEkNg+fLltgsPE5DiwrlmwseJVH+MRYldYYJCWb3r/90N7oP1+X93dCQCdUeA5ZM+//zzoiPAb7NmzYr2X2oepYCWWokoPSIgAmVPgD3Ikd69e1fJK+csRq994atgqfYEK2ahY0CxmDLcoW3bttWGrZsiUJsE2MEo7GZUTLi0DeWyCxL5lwJaTC2QHxEQARGoAQG2gUTSd+EJ5+xRLsmPABORClVA586dawvQY0GViEB9EcBaX+hwkXja8Fsu+8CTLymg8dLVsQiIgAjUAwG6gFu2bPmDBaU7dOhgsdfkJVUPyS+pKFhKCYWyEJk3b56WYCoEmNzWCgHqKkuvFStz5swpq3rbsFgQ8icCIiACIlAcAcZyrb322m7ixIlVAoiiyM5rsl90lQAr4ITF5OEJs7Dfdq5s8yKPTwDL5V73RaA2CHTv3t3dfPPNNhGp0MlE7AO/ePHiH/Sa1Ea61lQYUkDXFHnFKwIiULEEmIzAy+SUU07JyKBc9nrOmLlavsh2nAhba7KKQC757rvvzAp1wAEH5HKq+yJQqwR69epl4b3++usFfwCF9YFDGLWasDUUmBTQNQRe0YqACFQugTFjxriRI0caABaXxrrRunVr17BhQ1srkO55SX4EunTpYg4ZN5uPAkp3PbsnBX/5xSJXIlBzAj179nSsB5q+BFs+IeNnvfXWc5tttlk+zhPhRmNAE1FMSqQIiEA5EVhrrbVsNiszWvlr0aJF6lzKZ2ElDS/+3n333bw8BndhwldenuRIBGqBAMrnVltt5Z577rmCQ3v++efNasrQnXIRKaDlUpLKhwiIgAhUKIHNN9/cvf3223nlHncs+t+pU6e83MuRCNQmgaFDh5oF9Isvvsg7WJZumj59usNvOYkU0HIqTeVFBERABCqQQCEK6FtvveWYDIIVWiIC9U1g1113tSEgTz75ZN5RP/HEE44JivgtJ9ETWE6lqbyIgAiIQAUS6NGjh60Fms8uM2+++abbYostKpCSslwKBDp37uyor+krYFSXtocfftj169fPldvWsVJAqyt13RMBERABESh5AkzuQFAuqxMmfLEJgBTQ6ijpXl0T2Guvvdwzzzxjkw9zxbVo0SL3wgsvuD333DOX08TdlwKauCJTgkVABERABOIE6IJnckbY4jR+L37M+E+WYQoKa/yejkWgvgiggH777bd5WUEnTJhgK2Psscce9ZW8eotHCmi9oVZEIiACIiACdUGA2cVdu3bNqYAGBVUW0LooBYWZL4GNNtrIbbvttu7+++/P6QU3O+64oy3TltNxwhzUqwLKrK8pU6b8ABGDwh977DFbmPkHN3VBBERABERABHIQQKnM1QXPfbY7bdasWY7QdFsE6pbAvvvu62bMmGFDQrLFRH1FP8JtOUq9KaCrVq1yY8eOdQ888EAVjpdeeqkbN26ce+WVV9xRRx3l2CJNIgIiIAIiIAKFEGBixzvvvONWr16d1Rtd8LiTiMCaJrDbbru5xo0bO7rYs8mDDz7o2BVtl112yeYk0dfrRQFlhwp2/Vi+fHkVWB988IENxL3mmmvcmWee6Q499FB3yy23VHGjExEQAREQARHIRYBxoOxwxJac2WT27NlltZNMtnzqeukTYFcjFMuHHnooY2JZdomZ8sOHD7d1azM6SvjFetmKc8WKFe63v/2tW7JkSZVBt2HrtLAeGzsEsNxAXLCMTp06NXWJWYyFLOCa8liCBwyGp8Esl/yUCmIGd8NWXGu3RHj2EHGtXa7UV4R2MrSFtRtDZYTGuDqEnY4YD5ouLObNlqcdO3ZUHU6HU+A5ytHXX38tjgVyS3eOAoqSSVd7+rjkV1991S1YsMANGzYskZzDezg9z/HzelFA+/TpY3E+9dRT8bgNbvPmzVPXNthgA1NSUxf8Afufjh8/PnWJ7dPK5QXIQ4yiFF5AqUzqoEYE4IqUSz2pEYxa9CyutQgzFlTg+uWXX9ps19gtHRZAgHGdTEaiZy2TYPBA2rVrp7YhE6ACrlFnUUAxoEiKJ9C3b1+zbj7++OM/UEAnTZpke7/36tUrkfW1ZBTQbMXDshnx8TokuEmTJlWcH3300Y6/IKNGjXLhSzdcS+ov63vRYMaV8KTmpZTSvXTpUqtXbdq0KaVkJT4tKPQMoymX569UCgTLMta5tm3buoYN68UmUCpZr/V0bLLJJlm74D/66COLj542jB2S4gl88sknrmnTpprMVTzClM/tttvOTZ482Z1wwgmpaxw8/fTTNvsdi30SBX0uV3tWL2NAs8GjwaXhDcJxua30H/KmXxEQAREQgbolwMt67ty5GSPhOoqnlM+MeHRxDREYMmSImzlzZhUrJ7oQ3fLcK2dZowrooEGD3KxZsxxfpmjLzAYbPHhwOfNW3kRABERABOqIAEssMW4uk8yfP18GjkxgdG2NEkDnYSje9OnTU+mYNm2aHaMjlbOsUQWUL9HRo0dbF/sRRxxh3XuHHXZYOfNW3kRABERABOqIwIYbbujoHs4kXNfwkUxkdG1NEmD1BoYeMukoCMcMzevSpUu4VJa/9TrgaOedd3b8xYX9TUeMGOEYB8V6VxIREAEREAERKIYAE4xWrlxp3Znp7xPG3Pfr16+YYOVHBOqMACtfMAM+7NJFRBwz+ajcZY1aQAPcRo0aSfkMMPQrAiIgAiJQFIHWrVubP5b8S5fFixeX5XaG6fnUefIIdO/e3TZRCClnKTGulbuUhAJa7pCVPxEQAREQgbon0KpVK4skPrmVCywbxBqg4X7dp0QxiED+BOhqZy4MY0FZ3orxyp07d84/gIS6rNcu+IQyUrJFQAREQAQSQKBFixaWSpTNuCxbtsxe7i1btoxf1rEIlAQBJs+xpirDRBiOyAdTUpdfKgSoFNBCaMmtCIiACIhAyRIICmYmBZREBwW1ZDOghFUkgTA5jolyq1atMgbhWjkDkQJazqWrvImACIhABRFg4hGLX7MZRVyCQioFNE5Fx6VCgDXRESygQQEN10oljXWRDimgdUFVYYqACIiACKwRAixfQ5d7XKSAxmnouNQIhLHJjF2mCx6phI8lKaClVhOVHhEQAREQgaIJ8OJmH+34TPiwDWclvNSLBiePa4wA64Cuu+667q677rKxyqyRzlbl5S5SQMu9hJU/ERABEaggArvuuqt76qmnbGeZ1atXO9ZZ5G/o0KHahrOC6kHSssp66K+99pole/jw4UlLflHplQJaFDZ5EgEREAERKEUCp556quMP5XPhwoXWlYmFSSICpUxg3LhxZrVv0KBBxSwXpnVAS7lGKm0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygSkgJZy6ShtIiACIiACIiACIlCGBKSAlmGhKksiIAIiIAIiIAIiUMoEpICWcukobSIgAiIgAiIgAiJQhgSkgJZhC7RkiwAAH4VJREFUoSpLIiACIiACIiACIlDKBKSAlnLpKG0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygSkgJZy6ShtIiACIiACIiACIlCGBKSAlmGhKksiIAIiIAIiIAIiUMoEpICWcukobSIgAiIgAiIgAiJQhgSkgJZhoSpLIiACIiACIiACIlDKBKSAlnLpKG0iIAIiIAIiIAIiUIYEpICWYaEqSyIgAiIgAiIgAiJQygQalnLiMqWtadOmbuTIkZluJe7aF1984dZee23XpEmTxKW9lBO8cuVKF0WRo65Iao/A119/7b766ivXrFmz2gtUIblvv/3WUWfXW289t9ZasgnUVpWgDaCNpX1t2DBxr7rawlAn4cC1UaNGbt11162T8Cs10BUrVrgGDRqUjU7QtWvXaouygX9Io2pd6GadEdhjjz3cwIED3e9+97s6i6MSAz711FPd3Llz3R133FGJ2a+zPF977bXuqquucjNmzKizOCox4GeeecYdffTR7rHHHnObbrppJSKokzwvWrTI7bDDDu6KK65wI0aMqJM4KjXQ7bbbzh1yyCHu5JNPrlQEdZLvUaNGucaNG7urr766TsIvtUD1uV1qJaL0iIAIiIAIiIAIiECZE5ACWuYFrOyJgAiIgAiIgAiIQKkRWHusl1JLVKWkh/Gfffv2dZ07d66ULNdLPhlH1717d9erV696ia9SIoFr+/btbdhIpeS5PvIJ1xYtWrjBgwdb91t9xFkJcTCWjjGKcG3VqlUlZLne8kid3XrrrV2HDh3qLc5KiAiuPXv2dJtttlklZNdpDGhFFLMyKQIiIAIiIAIiIAKlQ0Bd8KVTFkqJCIiACIiACIiACFQEASmgRRbzO++845588klbPqXIIHJ6Y8kbSWkQKPeyWLp0qZs8eXJG2C+99JKbN29exntcrM5vVk8VcuO1115zs2fPrja34lctnow382H2n//8x3355ZcZ/VfqRVZaYKmfpEq5t8NJLZdi0y0FtAhyv/71r93f//5399Zbb9mapFOnTi0ilOq9vPzyy+7Pf/5z9Y50t14IVEJZ8EJ/+umnM/K877773BtvvJHxHher85vVU4XceP311/NSQLOxrxBMBWcznzrHUja4k/yPwN/+9jf32Wef/e9Cgo5YL/fII49MUIqV1FwEpIDmIpR2f9asWY715caNG+eOO+44+7v99ttTrpYvX+6wjqZ/ZbLcKlakTz75JOU2HHz66adV/Hz33Xfugw8+sK/3+Bc8Dcf7779vC1cHv8uWLbPz+fPnh0uJ/IULixunCy+QODMaIdytXr3affjhhykWcF+yZEnKe3DH4ulz5sxxMI0LC3+/++67bvHixfHLdkzZffzxx3acrSx+4KmEL1BHEPL0+eef2zF8WCs1SKdOndyYMWPCqf1+9NFHbtWqVVWu4S+dW9wv4VNm8b/AHuvFe++9VxZWqVC/ctXDPffc0+2yyy7GMJQD9TlepwM/wgpuAnSe/2D1yVRn4+kIbUbwWw6/8KDtpK7G29TALJ7HbG0IbmAY6j5hhjrJvaCkBpbZ2gzclppkeh65RnsYhPzE3yNc59lOr2tcj7d9nAdJb4e5HvxX167kYkqdp2xpo3Ebl/S08P5bsGBBqrzibpN0nElHIK/hOScvlBflRp2Nc+FapvdkkvIfT6u2h4jTyOOYmdXXXHNNyiUPYXhJ0+XDYt3dunVzb775pjv++OPdbrvtZo0Bi6OHh5H7f/jDH0yJOvfcc+1l1Lx5c7Mycc4suAcffNAecBZT/8UvfuHGjx/vJk6c6Dp27GgK8F/+8hdHI8wXYYjvj3/8o82qTyUuAQc8aFiUefiYAdivXz9b3BjFEEbwpeFhRwXyB1fyzi4czHJduHChO/DAA92jjz7qvvnmG3vZH3PMMebuT3/6kzFn5jZK02WXXWYrDsD2X//6l4WJhWro0KHu9NNPN1qU7YQJE4wpD/p55533g7JIANYqSWTnMFZbQEF/++233c9//nPjxc47NHz//Oc/zZqPxf366693fBCdeOKJtuMRftZZZx0LD9YoqfDkuEuXLraJAj0BwS9lg3+EsuOj6/7777d42XCBcsQ94bMRQ1Il33r4j3/8w22wwQb2nFIOW265pT3vKEvDhg2zuh740aty2GGHWXlsuOGGpnjhh3rMh2+mOks6LrnkElOo2KGKev773/++bFYqIP+d/SohKIx8cB5xxBHugAMOqFJfs7UhoW7BmnpLu8DHwM9+9jN33XXXORijAO299942nAqW2dqMEFYp/WZ7HmfOnGnvC+oTwtAahovRniI8h7S1GDPgefjhh9v19LaP9pJ3W6Z2mBVcKJtc7Up1TImf9127du2sbeIdQBp4XjKl5aabbrJn4sILL7R3QRJ3t8qmI/B+p11lcX+EPNI+0LvK+z1cR79gofqjjjrK3CX+HzshSYoj4Bu2aP/994+ef/55C8DvChE98cQTduwfvOiee+6xY/8Cji6++GI79kpodNppp0Wvvvpq5C2l0fnnn2/X+XfLLbdE/oG083//+9+RV37s2L/QLR7/gNq5V5Ciyy+/3I5942nx+AbazpP279JLL418A2fJ9gpk5JXRyFuYI786WOSVmdT1E044IXrxxRcj37hGO++8c+S/uu3eSSedFPmXix1THnvttZcd487vghL5Rs7OfTdy5D8IIjidc845kVeM7Lq3HEc77rhj5F9Ekd/hJ/IWq8hbmuzeDTfcEHnFNoqXhd1I2L999903uvvuuy3V9957b7TTTjtF3qJh58cee2z0yiuvRF7BifwLxa5RJ//617/asVf+I//SjnzDGT3wwAOp+um/xKMrr7wy8i//Kn7Nk//nv9wj/2JLPQ88G34sqd32Sm106KGHWlkE90n7zbcewujGG2+07FEOXiG1Y+od5UC9i7OnzntF09xMnz498i+aautsqOfesmx+br755lQZ2YWE/4OZ/0CyXNAO+pdy5BWvKsyytSH77bdf5IfPRF6pj/wQhxQJnvHQftAe007Q9gSW6W1GymOJHWR7HqdNmxaNHj06lVo/7jM666yz7HyfffZJ8YRjaEuztX3Z2mECy6ddqY4p70feZUGo67w/s6XFK8ORNxYE54n8zaYj8NzfdtttqTxRXpQbZel3R7LrtBUwD++ulOMEH6gLvshPCN9ImRWHrbO23XZbC+XHP/6xfS1i0WPyAdZPhK9PLEHhy43ujKeeesq+bPj6pAvfv/Sdb1CqmOHNs//HVn187dHtTxheEbOvWl/vzEn//v3NGhjcJ+mXsYVsl4eQRyxobdq0MX58AYbr/mXtJk2aZOcbb7yxWS844atxm222sev4g23osthkk03MesLN7bff3qyiWFLOOOMM5x9ss4hecMEF9lWN9RRr6KBBg1JrMWIpHD58uIWd9H9YlhF4bb755rbuJOcwY0hJXODAVnsIlvk+ffrYMWyo13DBUooFs2nTpnYv/o9uIt+A2vaHlCFl4j+43COPPGL1l7F5dEMRVpIl33oYzyP1EMEvz2/oPQluYIo1H+H3Jz/5iT3b2eos7jbaaCOzRnNMnceqXU5CDwXSsmVLWx8Rq1pcsrUhuPEf8dauhPoc95fpOFObgZW0FCXf5zE97V7ptEtt27a1dxAW+GxtH89otnaYQPJpV7Ix9cqUrc+KVe/MM8+0rnisoNnSYolO+L9sOkK2bPFux8KPvsFcBHpAacPLRdQFX0RJ0uDxgj3llFMcilEQ9humoZsyZYq9POh6pNsAYTHk8LByTncZShDdZZjX6VbiPn7ThRcVFY8HNpM0adIk0+VEXEPBCQojCWY8EV0wKEYohUFQasKLID2/oYuYLvlswvg53BEm3RdDhgwxxZUuT9gj6WlBSUpXELKFX+rX48xYnLs6YXgDvIOEri6UJoaEeMucfQB566ljUkNcqKsXXXSRLVBN914QwvDWaVMGuEZdTvoi1nGm5CmfeshzHyRTfe3Ro4d1j/ISfvbZZ90vf/lLq4PZ6ixh8bxUivAc0wUZl/TnNrQhuGHoEx/2vnfJPpyCv9CWVKeshzaD7uZSlOqex5A/0k0+4kL3exAUPt4tKDnxdji0fdW1w4QRfwZytSu4jzP1lmsbE85HFx9anCPp5RnSUg71vDodIVOZ0UbABuMLQ0k4Lif5X00sp1zVYV4Ym4g1gi/ruPJJlGeffbZZdfjK4YuOAdM81Iw74oXCy6V3797u1ltvtfGejOsaOHCgKaBbbLGF8135qUaAlxmNA0I8fKXS4OCfAeTeXJ9Yq6dl6vt/5I0ZwDx8KD2+S93GfMKMJUPgx0sCizF5L0TgxIQwhAd4wIABZu1jbKLv0rePBd/9bPeJH0ssljruI4yhevzxx02xCGVhN8r8H5zIN9Zi6jsKJ+KHIth4Q+osL/ZNN93U6ngcB2O3GAMaxtRyD8sVZYdiwC9j7xhXFm9w42FU+jEvZMZ1Yv1AYcVCna3OVgIr3y1r2WQCG3WItjIu2doQ3LAjGnWRDyfGxyIoMliUkBdeeMF+w79MbUa4V2q/2Z5HFDg4BeU6PY9Y1nm2WR6M9pVx2dnavrpqh2FJW4vxZffdd7cx/Vi2q2uHw4dw3DBRamWSKz3ZdATKLNTJMFY/hAUfelGxgAbrdbiX9F9ZQAsswTvvvNMUJD+WI+WTbd6wdvIw8RVH9yQvDZQpHhoeYgaCM1mGLfeYvPGjH/3IXu5USJQhHipe/GEtRhpOJnb85je/sQHXBx98sPPj5qyLjS9yrpeD8PJgYDbs+JrGGkkXA8MXUEzJN42SH6dpVjMU+XyFr/exY8daV+f6669vHFGGiBOLEtc6+wkOxMdMTLqm/ZheSwvKPl2blCFfnvGyyDf+pLrDskm9xDqMRTNsC0c9RpH3Y0XtY4E6imU/KPkonn4MoqNrj3LDL8JEAybSURZ8fPHy437r1q2TiqhO003dx7J89NFHWzx0YWars3WakBIJHEWFoUpYz5hAE7cik8RsbUhIPs83dZaPHj4qscwz9IYPIZ55Xv5BMrUZ4V6p/WZ7HrFEbrXVVvb8wooPGZSaIEzm4tlmhjWGEgR3mdo+eoAytcMhrHx+szElDX6subvrrrvMmEKaaYezpSVsXe3HsVo7k8TtVbPpCBit6FFlghzvwTDsCb7UU/6YrBW3OOfDvtTdaCvOOighHmyUm3hXB9HQzYFCmt5VgXWDr/L0Ljle1CimwT1fqzTC6Q1wHWSh3oOkm4XxhOndXVyny41u4UIE6zIfA8xCJox0ZlgHiCuwjYcNc/7i4xvTyyLuvlyP4cZM+fR6zEsJ5bKYxpDxoHwESAonUF2dLTy0ZPjwE4ncVVddZe0jbWp6GxnPRbY2JO4mHPN806tBmEFytRnBXan9Znse4cEzGiyH8XRnu5ep7cMf7uuiHaZdZbWRTN3r2dJCftOHYcTzloTjbDpCpbWPsoDWQW3N9DARDS/zTBL/Ao/f58UfV5BoSNIVqbj7JB9ny1e264XkNVMYceUyPSyU3XSFN70s0v2U43kmbuSzJo2/lM/ia0p1dbb4UJPhM1tdjKc+HzfBfaZnPNzjt5Cw4v7WxHG257G6PGS7l41LNveF5DdTGLSr2d6X2dKSLb+FpGVNu82W50prH2UBXdM1UfHXCQGsyoxxYryiRAREIJkEnnvuORuaVIy1vdAcq80olFhu92Kam1Elu5ACWsmlr7yLgAiIgAiIgAiIwBogoFnwawC6ohQBERABERABERCBSiYgBbSSS195FwEREAEREAEREIE1QEAK6BqArihFQARqhwBLdLEzGHt4s0MTSz6FZaFCDOyhHnYXCtdK5ZdNLfzWvLZMEGtQBmEZN5Yc4n4mYR9w7s+ZMyfT7SrXWMaIXdSyCQwJK6xDmM0d10uZZXXp1j0REIHSIyAFtPTKRCkSARHIgwDLtLBMD2vEss0nE1XYoIGFylm4OQhKE/dLUdgdinVwWeMvPjOWtRNvuOEGd8UVV2RMNjussYYia9XmEr+3tino2dyhgLIuZr4KaKmyzJY/XRcBEShNAlqGqTTLRakSARHIQYBFm6dOneo++OCDKvsjH3nkkbbRwMyZM7MufZYj6Hq5zZq+7M4zceJEx0LUcWG9y1GjRrnLL7/c/sI2n8HNjTfeaAuqp18P9wv5JQyUeYkIiIAI1CcBWUDrk7biEgERqBUCKJ3sSnbttddWUT4JnA0I2E0IN5mELR3HjBljWwCy+wvd02wDGwTljr3q2Xf5rLPOsq1Fwz22bkUx3HXXXd0xxxxjCnC4l+mXZYQYGkB62D0tdLPTdc4+78j48ePdlVde+QPvxPPZZ5/9wHo7bdo0x6LpYackPLKw9TnnnGN5YqcftlJNF7byIy1YXS+77LLUTlVscEFe2AoxCFZjwkGZv+eee1JbBIf74TefeINb/YqACIhAnIAU0DgNHYuACCSCAPtbs5MVil26sMUnWy6y73y60M3ct29fUypRsNhm9PzzzzflDbd0bbMlHt36bBfKPuRsm4uwJSt7MbMQNgobVsohQ4ZUUdzM4ff/JkyYYOGwFiJbzE6ZMsVtueWWDgWYReXZehDp2bOnbQn5vbfUD1twkj+2N40LCjLx4g9hh7Wtt97asTc42xSyYQX7yaNEBiENbOVL/Phjq1XGzSLsRnPddde5efPm2fl5553nfvWrXzm2o2UdXbajpbs/XfKJN92PzkVABEQgRcBvqScRAREQgUQROP3006POnTvnlWavxEXe4mluvZIW+X3pIz/uMeXXWxojr3Da+ejRoyPce6XMzn0XeXTJJZdEfvu/aNKkSZFX7iKviNo93HDv9ddfT4UVPyB9fphA/JKl2SuCds0rhZFviCNvJa3iJn7irbyRV3gj3CLeUhv58aHR9ddfb+f883ucR36XtchbS6tc8/tHWz68Um3xeAto6v5BBx0UjRgxws79lpR2n/zNnz/fjidPnpxy6/fqjvy+3RZWnGWueFMB6EAEREAEMhDQGNCUKq4DERCBpBBo3769YyY41rv0veqry8Puu+9uVkwsm8ww98qj84pXajLPIYccYuMxu3fvbl3we+65p1kAsSpus802rlu3bmY1HT58uLmjixqLa7qwpzNDALySVuUW4RUyIx+LJvuV0w1Ol/zDDz9swwW8ApkKl3GwTEa66KKLUtewZmKxnTt3rl3Datu/f//Ufayv6ZZVbjLEgO1/sQAHOfDAAx1/6ZIrXiy4EhEQARHIRkBd8NnI6LoIiEDJEkCBWrFihSl56Yn0H9rujDPOsO7z9HssSdSlSxebpMTyTXTH77TTTilnw4YNc9OnT3coos8++6yNqUTxZCwmiiBKF4oecR9//PGua9euVWbch4Bwj3To0CFcsl9vlXTMOs9XmCDEUIGgLNL9Tld6fF94lF3OUcTDH8of41eDcu4tpKlj4g7X09OB0spqAgwvyCX5xJsrDN0XARGoXAKygFZu2SvnIpBYAiigbdu2tfGbvju6Sj5uvfVWm1jEuMh0GTt2rC3ThNWTMaQIimZQCrFONmvWzF144YX2h0Vw8ODBZrUcMGCAe+mll0zxRPlkAg6W0KuuusqhuMalU6dODuWRJYuGDh2aukX4cUtk6kY1B37IgOvXr5+NNcUCysSmuGCtfeyxx2wppaBYMrueMacwKkSw8KI8Y11maSgEBowLvf3226sEVZvxVglYJyIgAhVBQBbQiihmZVIEyosA1kg/NtEsg0ySQTFkZrkfk2kLu9OFzAz3dKGr+tNPP3UsgYSl9IEHHnB333238+MgzSlrZmJxnD17tt3/+OOPbQY4ihkycuRIc4/CigKKFTDcMwff/0O59eNJLX0ss0R8zNhn8tRPf/rTuNOcx3369HGDBg2yWe9MrGJiUFyOPfZY62pnMXnSs2DBAnf44Ye7hx56yJTguNtcx9tvv73r1auXO+6442yyFAvis7g/imzc6ko4tRlvrnTpvgiIQBkSyDAuVJdEQAREIBEEmCDDZBqvkNrkmUaNGkV+hnrklcxU+uMTZ7xiaROOvDIVtWrVKvLLKUV+rc3URB+vKEa++z3y4zptYo+3htr9EJhfvijq0aOH3WNCkp/dnpogFNyE3+XLl9uEJ6+MRn5cZdSxY8fo6quvDrfNn3+lVDsJKTi+5pprLH/e2houVfn1Vt/IWywtHr+gfeSV78grz+aGSUjkJy4XX3xx5BVbuxSfhMQFPzY28kpuRLqbN29uPPxYUnMbZ8mF6uI1D/onAiIgAlkINOB6GerVypIIiEAFEWAtS9bY9Eqe80pozpwvWbLErIN0t2cSJjcxkYfwMo2HxDLaokULW5Ipk//4NayrWBIJq66FNGOtrI0F6mHEhCSszbmkNuPNFZfui4AIlAcBKaDlUY7KhQiIgAiIgAiIgAgkhoDGgCamqJRQERABERABERABESgPAlJAy6MclQsREAEREAEREAERSAwBKaCJKSolVAREQAREQAREQATKg4AU0PIoR+VCBERABERABERABBJDQApoYopKCRUBERABERABERCB8iAgBbQ8ylG5EAEREAEREAEREIHEEJACmpiiUkJFQAREQAREQAREoDwISAEtj3JULkRABERABERABEQgMQQaJialSqgIiIAIiIAIiIAIJITAo48+6l5++WXbNW3YsGGuZ8+eCUl5/SRTFtD64axYREAEREAEREAEKoTA6NGj3emnn+4+//xz99///tehgN55550Vkvv8sqmtOPPjJFciIAIiIAIiIAIikJPAZ5995jbccEM3b94816ZNG3N/0003ucsuu8xNmzYt5X/hwoXuq6++cptssknq2pIlS1zLli3dWmv9v31w0aJFrm3btu6bb75xK1eutN8oilLhzpkzxzVv3tz+QiDcf++999w666xTJezly5e7Bg0auPXXXz84td8vvvjCNWrUyNJCfN26dbPr77//vtt4441d48aN7Zy0tWrVyr377rtuo402+kE4+F21apX5IcwWLVpUiSf9RApoOhGdi4AIiIAIiIAIiECRBFavXm0K2pgxYxyWUBRIlEL+UCznz5/vRo0a5RYvXuxQ2rbcckv34IMPurXXXtu1a9fOuu1RSgmnYcOGpnS+8MIL7uSTTzb3KJZ07e++++5uxYoV5m/o0KGm4C5dutSuo7CiCPft29fdd999pngec8wxrmnTpu7yyy+vkrNTTjnFvfPOO27BggWWpm233dZ9++237pNPPnEooXfccYcbMmSI69ixo+vVq5el64033nBnn322O/HEEy2so446yk2aNMltsMEGll8U3ccff7xKPOkn6oJPJ6JzERABERABERABESiSAIokyhcKWYcOHVz//v3deeedZ9ZBgjzttNPcZpttZkoklkqshbmUNfzNmjXLPf/88+7tt9925557rimDM2bMcC+++KJ78803TbGlm3/AgAFmaZ09e7Ypr/hBLrnkEnfBBRfYcfo/rKMvvfSS/RHGiBEj3DPPPONOPfVUd/vtt6ec77DDDpbW6dOnWz6w8t5///2WLuJ79dVXTQFNeajmQApoNXB0SwREQAREQAREQAQKJYDl8cknnzSl8KyzznKTJ092O++8swWDNfPAAw+0YyycBxxwgLvttttyRtG1a1dTaLGiTp061e29997mh+7zRx55xLVv397dddddZs0cOXKkwypJN//dd99t7po1a+b4yyQollgtsdbiBgUUIcy5c+emvOy33352jKUWRZfxrSi4e+yxh3X5Ewb5yUc0Cz4fSnIjAiIgAiIgAiIgAnkQwJqJQjhu3Dgbq3nwwQebwsn4SSyeWEW//vrrVEiM7aTLG0GBC8dYJeMSH7vJ2FK62YN8+OGHrnXr1naK8rjjjjuGWzamNHWS5SAeNk7CuM8szu0yllu69BknOnPmzJTTZcuWpY6rO5AFtDo6uicCIiACIiACIiACBRDo0aOHGz9+vLvnnnvcd999Zz5ZkglLZadOndxBBx3kbrnlFlM0UeJwt91225k7lNTXXnvNjidOnJg1VqyMjO1knCgTmbCuYu1E2aVLfuutt3aM5UQJpmsdobuccZ41EcaDIgwHQOkdOHCgwyqKws04UiYqkbd8RBbQfCjJjQiIgAiIgAiIgAjkQYDJOhMmTHDHHnusTTZi0hBd1ihpdLkfdthhpjwy2xxr5z777OOOO+44C5mJPUcccYQpqltttVVqtnt6tPvvv78pet27d7fZ6CeddJKjix4rKApt586dzW/v3r3doYceat7Hjh2bcRJSetjVnU+ZMsXWM0VxZmwoM/b5Y1zrLrvs4lC++/TpY8podeFwT7PgcxHSfREQAREQAREQAREogsCXX35pXeWZliRiljpd2CiocaF7nm55llfKJYTBmE0mPsWF9UcJN5+u9Li/6o5RrBnLipJL2hgugLAsE9ZQFFCEyU50yV9//fV2nu2fLKDZyOi6CIiACIiACIiACNSAwHrrrZfVdyalFMcojulKabZAsoXBckh1JelxsiYoC+0feeSRFiVd8Pfee2/O6GUBzYlIDkRABERABERABESgsgk8/PDDNtY0k1LN+qMsO4X1lvGoTLTKJVJAcxHSfREQAREQAREQAREQgVoloFnwtYpTgYmACIiACIiACIiACOQiIAU0FyHdFwEREAEREAEREAERqFUCUkBrFacCEwEREAEREAEREAERyEVACmguQrovAiIgAiIgAiIgAiJQqwSkgNYqTgUmAiIgAiIgAiIgAiKQi8D/AS2KnXWPV80eAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n<div id=\"金字塔图\" class=\"section level3\">\n<h3>4.7 金字塔图</h3>\n<p>金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。</p>\n<div class=\"sourceCode\" id=\"cb33\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb33-1\"><a href=\"#cb33-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb33-2\"><a href=\"#cb33-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggthemes)</span>\n<span id=\"cb33-3\"><a href=\"#cb33-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">options</span>(<span class=\"at\">scipen =</span> <span class=\"dv\">999</span>)  <span class=\"co\"># turns of scientific notations like 1e+40</span></span>\n<span id=\"cb33-4\"><a href=\"#cb33-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb33-5\"><a href=\"#cb33-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Read data</span></span>\n<span id=\"cb33-6\"><a href=\"#cb33-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>email_campaign_funnel <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv&quot;</span>)</span>\n<span id=\"cb33-7\"><a href=\"#cb33-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb33-8\"><a href=\"#cb33-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># X Axis Breaks and Labels </span></span>\n<span id=\"cb33-9\"><a href=\"#cb33-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> <span class=\"fu\">seq</span>(<span class=\"sc\">-</span><span class=\"dv\">15000000</span>, <span class=\"dv\">15000000</span>, <span class=\"dv\">5000000</span>)</span>\n<span id=\"cb33-10\"><a href=\"#cb33-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">=</span> <span class=\"fu\">paste0</span>(<span class=\"fu\">as.character</span>(<span class=\"fu\">c</span>(<span class=\"fu\">seq</span>(<span class=\"dv\">15</span>, <span class=\"dv\">0</span>, <span class=\"sc\">-</span><span class=\"dv\">5</span>), <span class=\"fu\">seq</span>(<span class=\"dv\">5</span>, <span class=\"dv\">15</span>, <span class=\"dv\">5</span>))), <span class=\"st\">&quot;m&quot;</span>)</span>\n<span id=\"cb33-11\"><a href=\"#cb33-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb33-12\"><a href=\"#cb33-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb33-13\"><a href=\"#cb33-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(email_campaign_funnel, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> Stage, <span class=\"at\">y =</span> Users, <span class=\"at\">fill =</span> Gender)) <span class=\"sc\">+</span>   <span class=\"co\"># Fill column</span></span>\n<span id=\"cb33-14\"><a href=\"#cb33-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">geom_bar</span>(<span class=\"at\">stat =</span> <span class=\"st\">&quot;identity&quot;</span>, <span class=\"at\">width =</span> .<span class=\"dv\">6</span>) <span class=\"sc\">+</span>   <span class=\"co\"># draw the bars</span></span>\n<span id=\"cb33-15\"><a href=\"#cb33-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">scale_y_continuous</span>(<span class=\"at\">breaks =</span> brks,   <span class=\"co\"># Breaks</span></span>\n<span id=\"cb33-16\"><a href=\"#cb33-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                 <span class=\"at\">labels =</span> lbls) <span class=\"sc\">+</span> <span class=\"co\"># Labels</span></span>\n<span id=\"cb33-17\"><a href=\"#cb33-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">coord_flip</span>() <span class=\"sc\">+</span>  <span class=\"co\"># Flip axes</span></span>\n<span id=\"cb33-18\"><a href=\"#cb33-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Email Campaign Funnel&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb33-19\"><a href=\"#cb33-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">theme_tufte</span>() <span class=\"sc\">+</span>  <span class=\"co\"># Tufte theme from ggfortify</span></span>\n<span id=\"cb33-20\"><a href=\"#cb33-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">theme</span>(<span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust =</span> .<span class=\"dv\">5</span>), </span>\n<span id=\"cb33-21\"><a href=\"#cb33-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                    <span class=\"at\">axis.ticks =</span> <span class=\"fu\">element_blank</span>()) <span class=\"sc\">+</span>   <span class=\"co\"># Centre plot title</span></span>\n<span id=\"cb33-22\"><a href=\"#cb33-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>                              <span class=\"fu\">scale_fill_brewer</span>(<span class=\"at\">palette =</span> <span class=\"st\">&quot;Dark2&quot;</span>)  <span class=\"co\"># Color palette</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"组成\" class=\"section level2\">\n<h2>5 组成</h2>\n<div id=\"华夫饼图\" class=\"section level3\">\n<h3>5.1 华夫饼图</h3>\n<p>华夫图可以显示总体的组成成分。</p>\n<div class=\"sourceCode\" id=\"cb34\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb34-1\"><a href=\"#cb34-1\" aria-hidden=\"true\" tabindex=\"-1\"></a>var <span class=\"ot\">&lt;-</span> mpg<span class=\"sc\">$</span>class  <span class=\"co\"># the categorical data </span></span>\n<span id=\"cb34-2\"><a href=\"#cb34-2\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb34-3\"><a href=\"#cb34-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## Prep data (nothing to change here)</span></span>\n<span id=\"cb34-4\"><a href=\"#cb34-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>nrows <span class=\"ot\">&lt;-</span> <span class=\"dv\">10</span></span>\n<span id=\"cb34-5\"><a href=\"#cb34-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">expand.grid</span>(<span class=\"at\">y =</span> <span class=\"dv\">1</span><span class=\"sc\">:</span>nrows, <span class=\"at\">x =</span> <span class=\"dv\">1</span><span class=\"sc\">:</span>nrows)</span>\n<span id=\"cb34-6\"><a href=\"#cb34-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>categ_table <span class=\"ot\">&lt;-</span> <span class=\"fu\">round</span>(<span class=\"fu\">table</span>(var) <span class=\"sc\">*</span> ((nrows<span class=\"sc\">*</span>nrows)<span class=\"sc\">/</span>(<span class=\"fu\">length</span>(var))))</span>\n<span id=\"cb34-7\"><a href=\"#cb34-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb34-8\"><a href=\"#cb34-8\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb34-9\"><a href=\"#cb34-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"sc\">$</span>category <span class=\"ot\">&lt;-</span> <span class=\"fu\">factor</span>(<span class=\"fu\">rep</span>(<span class=\"fu\">names</span>(categ_table), categ_table))  </span>\n<span id=\"cb34-10\"><a href=\"#cb34-10\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># </span><span class=\"al\">NOTE</span><span class=\"co\">: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.</span></span>\n<span id=\"cb34-11\"><a href=\"#cb34-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb34-12\"><a href=\"#cb34-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## Plot</span></span>\n<span id=\"cb34-13\"><a href=\"#cb34-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> x, <span class=\"at\">y =</span> y, <span class=\"at\">fill =</span> category)) <span class=\"sc\">+</span> </span>\n<span id=\"cb34-14\"><a href=\"#cb34-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">geom_tile</span>(<span class=\"at\">color =</span> <span class=\"st\">&quot;black&quot;</span>, <span class=\"at\">size =</span> <span class=\"fl\">0.5</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb34-15\"><a href=\"#cb34-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">scale_x_continuous</span>(<span class=\"at\">expand =</span> <span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"dv\">0</span>)) <span class=\"sc\">+</span></span>\n<span id=\"cb34-16\"><a href=\"#cb34-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">scale_y_continuous</span>(<span class=\"at\">expand =</span> <span class=\"fu\">c</span>(<span class=\"dv\">0</span>, <span class=\"dv\">0</span>), <span class=\"at\">trans =</span> <span class=\"st\">&#39;reverse&#39;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb34-17\"><a href=\"#cb34-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">scale_fill_brewer</span>(<span class=\"at\">palette =</span> <span class=\"st\">&quot;Set3&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb34-18\"><a href=\"#cb34-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Waffle Chart&quot;</span>, <span class=\"at\">subtitle=</span><span class=\"st\">&quot;&#39;Class&#39; of vehicles&quot;</span>,</span>\n<span id=\"cb34-19\"><a href=\"#cb34-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>             <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb34-20\"><a href=\"#cb34-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">theme</span>(<span class=\"at\">panel.border =</span> <span class=\"fu\">element_rect</span>(<span class=\"at\">size =</span> <span class=\"dv\">2</span>),</span>\n<span id=\"cb34-21\"><a href=\"#cb34-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">size =</span> <span class=\"fu\">rel</span>(<span class=\"fl\">1.2</span>)),</span>\n<span id=\"cb34-22\"><a href=\"#cb34-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">axis.text =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb34-23\"><a href=\"#cb34-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">axis.title =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb34-24\"><a href=\"#cb34-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">axis.ticks =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb34-25\"><a href=\"#cb34-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">legend.title =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb34-26\"><a href=\"#cb34-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>              <span class=\"at\">legend.position =</span> <span class=\"st\">&quot;right&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"饼图\" class=\"section level3\">\n<h3>5.2 饼图</h3>\n<div class=\"sourceCode\" id=\"cb35\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb35-1\"><a href=\"#cb35-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb35-2\"><a href=\"#cb35-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb35-3\"><a href=\"#cb35-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb35-4\"><a href=\"#cb35-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Source: Frequency table</span></span>\n<span id=\"cb35-5\"><a href=\"#cb35-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">as.data.frame</span>(<span class=\"fu\">table</span>(mpg<span class=\"sc\">$</span>class))</span>\n<span id=\"cb35-6\"><a href=\"#cb35-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">colnames</span>(df) <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;class&quot;</span>, <span class=\"st\">&quot;freq&quot;</span>)</span>\n<span id=\"cb35-7\"><a href=\"#cb35-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>pie <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"st\">&quot;&quot;</span>, <span class=\"at\">y=</span>freq, <span class=\"at\">fill =</span> <span class=\"fu\">factor</span>(class))) <span class=\"sc\">+</span> </span>\n<span id=\"cb35-8\"><a href=\"#cb35-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_bar</span>(<span class=\"at\">width =</span> <span class=\"dv\">1</span>, <span class=\"at\">stat =</span> <span class=\"st\">&quot;identity&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb35-9\"><a href=\"#cb35-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.line =</span> <span class=\"fu\">element_blank</span>(), </span>\n<span id=\"cb35-10\"><a href=\"#cb35-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust=</span><span class=\"fl\">0.5</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb35-11\"><a href=\"#cb35-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">fill=</span><span class=\"st\">&quot;class&quot;</span>, </span>\n<span id=\"cb35-12\"><a href=\"#cb35-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb35-13\"><a href=\"#cb35-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb35-14\"><a href=\"#cb35-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Pie Chart of class&quot;</span>, </span>\n<span id=\"cb35-15\"><a href=\"#cb35-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>)</span>\n<span id=\"cb35-16\"><a href=\"#cb35-16\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb35-17\"><a href=\"#cb35-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>pie <span class=\"sc\">+</span> <span class=\"fu\">coord_polar</span>(<span class=\"at\">theta =</span> <span class=\"st\">&quot;y&quot;</span>, <span class=\"at\">start=</span><span class=\"dv\">0</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0HmBvF+ca/69137h1woRcDCc2A6b0ECL23JEDoxQEC4Q8xkNAhIQSTQMCAMRhMt6kGbIONwTa44N7P9vXedHfSf94xe+jO0p2km12tpHeeR5a0OzvlN/Leu983802STyVhIgESIAESIAESIAESIAGHCCQ7VA+rIQESIAESIAESIAESIAFNgAKUPwQSIAESIAESIAESIAFHCVCAOoqblZEACZAACZAACZAACVCA8jdAAiRAAiRAAiRAAiTgKAEKUEdxszISIAESIAESIAESIAEKUP4GSIAESIAESIAESIAEHCWQ6mhtrIwE4pBASUmJ3HXXXe16lpycLHl5eTJw4ED57W9/K9ttt13b+Xnz5smzzz4rt99+uwwbNqzteHc+NDc3y5w5c+TLL7+Ur776SpKSkmSPPfaQa6+9VnbYYYe2or/77jt57rnn5M4775Ttt9++7bgdH0pLS6VPnz5Gii4uLpYHHnhA0P4BAwbI5MmTIyr3m2++kf/9739yzz33yKBBgyIqgxeRAAmQAAl0nwAtoN1nyBISnEBVVZWMHz9eJk6cKB9++KF+vf/++1pk3nzzzVpkQnBaafXq1To/RJWJ1NTUJCeeeKIceuih8uCDD0pqaqoUFhbKo48+KjvttJMWpFY9Vt0QzXamd999V3bddVdjVVx++eXy9NNPa1HfHdG+fPlyzb68vNxY21gQCZAACZBA+AQoQMNnxitIICABWBs3bNjQ9oIwXbRokYwYMUJbItevX6+vO+aYY+SHH36QvfbaK2A54RyE5fPMM8+U6dOny2uvvSZlZWXywQcfyMKFC2XVqlXSr18/ueKKK6S+vj6cYrud94svvpDKyspul2MVAOvucccdJ1OnTpWHH37YOsx3EiABEiCBGCVAARqjA8dmxwaB3XffXW644QZpaWmRmTNn6kanpaVJjx49JCUlpV0nWltbZcmSJTJt2jRZu3Ztu3PBvjzzzDMCa+v//d//yTnnnCMZGRltWYcPHy7//Oc/ZeXKldoy2nbi5w91dXUyY8YM7dZG+wIlWFchoiFqFyxYsI2QRRloKzZUQ74ff/xRYNmtrq7WxeFcRUVFoKLbHdu8ebN88sknehoByrQShDPKQHl9+/bVn/3PW/k6vkPsQwSj/aGkLVu26PyfffaZfoAIdE1NTY3mhXZu3LgxUBYJJU/AC3mQBEiABBKNALbiZCIBEoicwIoVK7Cdre+OO+4IWIgSifq8ctPr82+88Yb+Pnv27Lb833//vW+XXXbRx5Uw1e/HH3+8TwmjtjyBPpx99tm+goICnxKvgU7rY0qA+rxer/48adIkXfbdd9+tr0O78erdu7dv/vz57cp48cUX2/KoOa06n5rT6VPTDNryvfzyy/q4co/rd5S15557tn3G97Fjx7bl7/hBCUyfcq/71JxVn9VvNXfWZ7Gy2mu1E+9qqkPHYtq+//TTTz417UDXj/JQ1sUXX9zGR83/1OeUhVhfs2nTJp+avqCPWX1EHaeffrqvsbGxrVyMmXpoaCsXeU466SSfsjiHlactMz+QAAmQQIITgOWCiQRIoBsEOhOgEDGjRo3SAktZB3UtHQUoRAwEoHLJ+9555x2fx+PxqYVEvsGDB/uOOuqoTlumFjf5DjrooE7z+J+0BF1mZqbvlVde8am5oD61oEcLtb333rstK9oKkXXeeef5lCvfp6yOum1op5qD2ZbPEqAQpv/61798Tz75pG/p0qW+q666yqfmovrUQiR9bdsFHT4o67BPWYR9//73v30Qo8p66rv00kt13WChLJi6DGXZ9f3+97/Xn3EsUFJWVJ9aWOXbf//9fcuWLfM1NDT4XnjhBV2WWnilL+koQNUCMR9YKKuzbicELAQx+g4BjoRysrKyfGeccYZv3bp1up1WOWoxU8h5dEb+QwIkQAIkoAlwFbz6S8NEAiYIYAGStbhH/e/S8zExdxHu5WuuuUaUZTBgNY8//rjO++qrr8qxxx6r82BB0W233SY33nijXkQ0ZsyYba6Faxuu5kDntsnc4cBf/vIXOf/88/VRrNJXwlSwcEhZSgUr+NGPyy67TB566KG2leynnnqqnHbaafLf//5XTynAYicroZ1XX3219VWUYNOflWBtO9bxA7g89dRTms0f/vAHfRrX/ec//xGsVv/zn/8sqNMqQwnFts8dy8J3zIHFHNyPPvpIL77CMSVm9XxY9ZCAr+0S+ooFTU888YSeX4qTygot9957rzz//POCBUtIRUVFokSonrNrRTO45JJLJDs7uy3CQCh5dGH8hwRIgARIQBP45S8IgZAACXSLwJo1a9oW3iAMEhYAQUiee+65oly6QctGWCaIq8WLF+s5oFZGzEtEwrzKQCJTud71XFKseA837bPPPu0u2W+//URZZgWhk9DuI488Ur8wp1FNFRBl1dRzPD///HN9nbLsSm5ublsZHctrO9HJB/QXQh0r+P0T5sZiwRFWvStrsKSnp/ufDvoZc1SV1Vh23nnndnkQDSBQgtDGgia0AdEBMP9WWU51f5EffURSVlU5+OCD9TxbiPQTTjhBvyDcUUaoeXRG/kMCJEACJKAJUIDyh0AChgj88Y9/1LEqwy0OC1ogWGH565h22223oAtpcA2EH4RcZ+nTTz+VkSNHtlnrkLdjfE5LSEGMIWHxDiyaEyZM0NbO/v37y69//WsdzxRC28qnM6t/EO803GQt5IHg7Ziw4AiLsrD4qGNbO+a1vsPKGWpe6xosKIJ1Ggu1IHQROsoS+/59/Pjjj+X++++X119/Xb/jM4QprNajR4/WxYWSx6qX7yRAAiSQ6AS4Cj7RfwHsf9QJDBkyRMfunDt3rhaTEJT+r1tuuSVoG3/1q1/pVedqAVHAPChHLWbq1AIb6EK4v9U8R/nb3/6m3dqwxmK1PVzUSP7iDN8hhsNNQ4cO1ZeohUDbXAqrLkRvOIIS1k+4wjsmuNK//vprLaT9z2G1PqYU5Ofny6xZs/QKdlhREaQfyb+PcLdDdELkIryVmuuqrd2IPGClUPJYeflOAiRAAolOgAI00X8B7H/UCcD9DVf3lClT2rVFLaDRMUS//fbbdsf9v1x//fVapKkFMtp97n8On//0pz9pS6L//MyOeQJ9R1xRzHeE+IVARkLMUQg5JFgnO0two2OOJV7BEubEImwUrIr+Ca5vuLrDdevvu+++AjHbUYwjDio4+c9ZRX2Yn4swT4jfCium5erHblJIVh+x+xLmiqJNSAhvhfIgPjGPtba2Voey6iqPvpj/kAAJkAAJaAIUoPwhkECUCWC3JGwvCbE3btw4Pd9SrcDWC5DgElaruoO2EG5gbEsJd7ZaDa+398QiHCwqguUT8TsxV/HKK68MWkagE9hBSa341rs5YV4o5oGeddZZbXNUIZg7S7AqQnw+8sgjWpwFygvX+6233qrd2LA6wg2uwlFpqySC+MMKG06CkMRUACyugoBWK9p1GYi/Cq4d04477qgtt9iaFKIV4hVWXwhWCGirj7AyY0oAFoVhoRMsoCqCgOZ+xBFH6LmwoeTpWD+/kwAJkEBCE1BuJiYSIIFuEOgsDFOgYjuGYUIexOpUYkaHa1I3JJ8SpD4lhHxKWAYqYptjynqqwz3hWuvVq1cvn9qas108SysMk3L3tytDCUV9nRV3FPUqIedTQlIfRxgkZfXzqTmT+ruaG6qvt8IwdYwhqhb1+NQOUDovYmoGSyoAvk/t8e5TC5p0XsTuVBZhnxKQ7S6x6m93MMAX5W73HXLIIboscMB1d911V1tOJTD1OSsOKMIzKUtrW34159angtH71EIj3X7rQmUF9eGcxVZZS3WsUBWJwMriCyVPW2Z+IAESIIEEJ5CE/qubKhMJkIALCMAljPmPWDQUybxKhGbC9VghjzmRkZThjwGr0GEJhXu5owvbP1+wz9gaFLs+YfenzhKspVjcBEsj8nc3YfESOMBdDjd/VwkWZISAskI+BcuPubDYRx7jY7nsO+YNJU/Ha/idBEiABBKNAAVooo04+0sCJEACJEACJEACUSbAOaBRHgBWTwIkQAIkQAIkQAKJRoACNNFGnP0lARIgARIgARIggSgToACN8gCwehIgARIgARIgARJINAIUoIk24uwvCZAACZAACZAACUSZAAVolAeA1ZMACZAACZAACZBAohGgAE20EWd/SYAESIAESIAESCDKBChAozwArJ4ESIAESIAESIAEEo0ABWiijTj7SwIkQAIkQAIkQAJRJpAa5fpdW31zc7M0NDS4tn1sGAmQAAmQQPwRyMvL6/YOZvFHhT2KRwIUoEFGdfLkyTJt2jRRe3IHycHDJEACJEACJGCOwMKFC2X8+PEyZMgQc4WyJBJwKQEK0E4G5pJLLpEjjzyykxw8RQIkQAIkQAJmCDzwwANmCmIpJBADBDgHNAYGiU0kARIgARIgARIggXgiQAEaT6PJvpAACZAACZAACZBADBCgAI2BQWITSYAESIAESIAESCCeCFCAxtNosi8kQAIkQAIkQAIkEAMEKEBjYJDYRBIgARIgARIgARKIJwIUoPE0muwLCZAACZAACZAACcQAAQrQGBgkNpEESIAESIAESIAE4okABWg8jSb7QgIkQAIkQAIkQAIxQIACNAYGiU0kARIgARIgARIggXgiQAEaT6PJvpAACZAACZAACZBADBCgAI2BQWITSYAESIAESIAESCCeCFCAxtNosi8kQAIkQAIkQAIkEAMEKEBjYJDYRBIgARIgARIgARKIJwIUoPE0muwLCZAACZAACZAACcQAAQrQGBgkNpEEYoVAaWmpfPTRR7Js2bJYaTLbSQIkQAIkEAUCFKBRgM4qSSAeCcyfP18uu+wyWb58uYwdO1amTJkSj91kn0iABEiABAwQSDVQBosgARIgAXniiSdk3LhxMmrUKDn77LPlyiuvlJNOOknS09NJhwRIgARIgATaEaAFtB0OfiEBEoiEQEtLi2zcuFH23HNP8Xq90rdvX8nOzpbCwsJIiuM1JEACJEACcU6AFtA4H2B2jwRCJQDh2NraqgWk/2cc8/l87V4o0/9YUVGRZGVlCd6tBAG6cuVKfRzHkpKS2r38jyUnJwteKSkp7d6t41aZfCcBEiABEogPAhSg8TGO7AUJdEoAYhFCEpZKvPDZEpvWe6AC/EUhBCS++wtJXIPvPXr00MI1Pz9fqqurtZBEnb169ZLc3Nx2YhXX+ItXfG5ubm4Tvjjvn6x6/duSmpoq1guilYkESIAESCC2CFCAxtZ4sbUk0CkBiDlLZHZ8978Q4s0SdJijaX3Gu/9n/2s6+wzrZ319vRaeEIwQhZWVlTJ8+HAtQDu71v8c2g/rq78F1v8zxDLEKuqyEuqzxKj/O9qAc0wkQAIkQALuI0AB6r4xYYtIICQCEGsej0cLMrxblk3rYkuYpaWlaTe4Jc7sEGYo+4ADDpB3331XxowZI7Nnz5aePXvql9WeUN4t8Yo2ot3BUiChDWHa0NDQ7hK0C+XgBaGN7xSl7RDxCwmQAAlEhQAFaFSws1ISCJ8ArH8QmpbohOCyEgRWRkZGO0sgRJyT6ZprrtHhl9544w1tAb333nttqx4i0hKW/pVAmIKTv/XXX5ha10GMWqIUFl8mEiABEiABZwlQgDrLm7WRQEgEIKQgnCyxiXe4opEgmCCg4Pa2RJgbrHrbb7+9TJo0SVasWCG9e/fW8z9D6qzBTOBgWXr9iwU7iydY1tXV6XmoyGNZScHUspL6X8vPJEACJEAC5glQgJpnyhJJICICsNw1NTW1vSBCkSA4MzMztTiCQHLashluZ7AgyW0JDGEhxgvJcuH7i1LLfW/xtvK7Qdy7jSfbQwIkQALdJUAB2l2CvJ4EIiRgWTkt0QkxhASLXE5OTpvgxGIerCR3u/CMEENULrNc8bAgI1wUEqyksI5iPBobG9sWOllCFO8YGyYSIAESIIHuE+DdtPsMWQIJhEwAIscSnHjHd4ghWDYRwggip6PQhEjCnMaOx0OulBlDImBZPmFtxljggcASowgthQQBaglSjBmtoyGhZSYSIAES2IYABeg2SHiABMwSgMiERQ0uXljYkCAmIXQs13pnQgaiBwIUwofJOQLW/FpYn60HB8syijmkGDOMCebi4r2zMXSu1ayJBEiABGKDAAVobIwTWxljBOBeh/UMohOiBQmCJi8vT4vOcFy5uM4qI8YwxE1zYR2F0MTLmjqBMbFe/ucxXkwkQAIkQAKdE6AA7ZwPz5JAWATgtoXoxAtWM1g6YUGDcAlHdPpXijIgelAehA5TdAnA0gn3O15YcAWrNsYbwfFhGcU4W2KV0yaiO1asnQRIwL0EKEDdOzZsWYwQwOp1S3TCVQ6BYgkQiBQTCaIG4pZueBM0zZbhL0ZhEcVvoaamRr9wDr8FTLXgw4NZ7iyNBEggtglQgMb2+LH1USJgudhh8bLmdUIcwsVux3xAuHU5DzRKgx1itf4PHrBWWw8lVVVVgheEKFbcm3ooCbFZzEYCJEACriRAAerKYWGj3ErAEhYQnrB8QhjCDQtxYaeFCxZQzCllig0C+C0glBZeeHCwXPR4x28Gx2EV5cKl2BhPtpIESMA8AQpQ80xZYhwSgIiA6ISAgPUT4qGgoMAxaxYEKMQv54HG3o8LYwfLOOYCw0WP3xFiu2J+KCyieNn58BJ7xNhiEiCBRCBAAZoIo8w+RkwAVkcIBrzDWmUJBogKpxPqhBCmC9dp8mbq83fRY9oGfleYK1pbW6st6LCKRuN3ZaZ3LIUESIAEwiPg/F/R8NrH3CTgOAFYOGHphECA4IOlygk3e1cdhesWC5EoQLsi5f7zGEO8MI0DvzOsoMcL84chRLnYzP1jyBaSAAl0jwAFaPf48eo4IgDhCREAixRc3RAIPXv2tGVRUSTYYB3jPNBIyLn3GuvhBi56K4xTeXm5toRaMWPd23q2jARIgAQiJ0ABGjk7XhknBCyLJ9yhEJ6Y34n5erA4uilBrHAeqJtGxFxb4J6H5RNTPPCQgYegioqKts0LaBE1x5olkQAJuIMABag7xoGtiAIBS3jijz1cofgjD6uT24SnhQYihfNALRrx+Y4xxgMQXliwhIciWERhjcdvE+9MJEACJBAPBChA42EU2YewCEB4Wn/cLeEJV7tbhad/5yBAnZoHCk6wxoFVoBfY4QWrbLvPNeUyfNU0qcrOlSRltU1KSZWk1DT1nibJWbmSkpWnXuo9O099z5PUnAJJK+grqQX9JCUzx7+7Cf0ZIhQPRWCPh6SysjIK0YT+RbDzJBBfBChA42s82ZsuCFjC01pN7mQopS6aFtJpiGQsWjGRGmtKpLSqSVvZqqurBS9Y3Kz3SOvpleyRvl+9JjURNDI5Q21Zmt9PC9L0PkMkve9Qyei/vaT3204y+qrXgB0kNa9XBCXH5iXWynmIUSyMs4So2631sUmbrSYBEnCSAAWok7RZV9QIwJIHcQXrIURcr169YnKlcbjzQH3eFmkuXyae0kXSXLFCfV6u3vFaIb78XWXSppOjNiaBKvY2NYineJ1+1S3/LlAWSe3RWzKH7CSZg3eWrKE768/ZO+yhxOmwgPnj4SCEKOaHYsMDCFH8lktLS7WrHq55hm+Kh1FmH0ggsQhQgCbWeCdcb+EahkUPlk/8kYarHdakWE2dzQP1tjSKp2ieeEp+kKbiH9T7j+IpWyzS6gnY3eSGjQGPu/1gS3WZ1C75Rr/825qS3UOyh4+S7BF47S05I/aRrO13lyS1K1G8JH8hakVsKCkp0YvmsHAO55lIgARIIBYIUIDGwiixjWETwPxFuJDhskRCHE9YkOLhD7S1ECm5cYs0bp4jTZtm63cIT1EWz1CTt75I0lN94mmJD9HSWq+mECyaoV8WA8w5zd15f8nd9UD1Okh/Ts3raZ2O2Xf8jrFqHhZR/MbxgmUUv/NYfsCK2QFhw0mABMImQAEaNjJe4HYCcLfD6ol5nvgDjT/K8bDVYUvtJmnc8IXUrZsujeuni7eusNtDMSCvWdZXxO/Kam9DrVQv+Fy/LFhZw/aSHqMOl/y9j5S8PQ7Vi6Gsc7H2jt+1tUkCfvMI3YT5oThGt3ysjSbbSwKJRYACNLHGO6576+9uxzzP3r17x3TYGrjUGzd8KfVrpmrBibmbplPf7Ma4FqCBeDWs+VHwKnr7KZHkFMnd6dfSY5+jpWD/EyVHfY5FK7n1e4cVFEKUbvlAI89jJEACbiJAAeqm0WBbIiLQ0d2en5+vLZ+xKCRaagulfvU0JZCmSsP6z8XX0hARk1AvKkjDWvUeoWaPv3zeVqldOke/Nk28X4eCKtjvBC1G8/c9RoeLiqVOw+IPCyjd8rE0amwrCSQmAQrQxBz3uOk13O1VVVU6DiXmeGJFcKy521uq10vdirekbvlb0rRlrqNjk5NUoeob7Gidbq6spbJYSj95Ub8QuxQitNehZ0rBgaeoeKX5bm56W9volm9DwQ8kQAIuJkAB6uLBYdOCE4DVE65GrASG+zHW4nm21GxQgvPNqIhOf6qZLcX+X/nZj4CvpVkqv/1Qv5JS07eK0TFnSs8DT42JeaMd3fII24S5oXhQYyIBEiCBaBOgAI32CLD+sAl4PB5t9cQiI1g8sRo4Ftzt3uZ6qV/5ttQsnqDndor4wu676QtSGjeZLjIuy/O1eJQQ/UC/ECy/50GnSZ9jLlaLmY5wfZgnyy0PTwFeCEmGaSqIKctEAiRAAtEiQAEaLfKsN2wCsHpac9uwwrdPnz4xsX1mw8YZUqtEJ9zsvmYzuxiFDS/IBd7aDeoMhHB8hGIK0k2jhxEsv+yLifqV1nuw9Dn6Qul79MUqMP6ORusxWRjc8oiBi0VKEKFYpGTNlTZZD8siARIggVAJUICGSor5okoA1s7Kykq9kxEsnrB8utnq6W2qkpolr0jNj+P1TkRRhddZ5V61bWauV0pqaQ3rDFOwc81lhbJ50t/1C9bQfidfpVz0p6h97915a4U1ND09XYtQ/H/CHOp4CVMWbIx4nARIwJ0E3HmXdCcrtioKBGD1xDxPzPeEy9DtoZWaihdI9Q/PSt3S19UK9vooEAu/yn45TUqAcl5g+OTaX1H9w3Q19tMlrddA6XfCldL3+Cskvfeg9plc8A3/j7AVLTZqwJaeEKGYQ43V80wkQAIk4BQBClCnSLOesAkgriesNJjz6eaA8hDJDas/lKrvn5TGwhlh9zPaF/TKxLQAClBT49BcvlkKX/mrbHrtQek15mwZcMaNalvQvU0Vb6wceBIgOvF/rLy8XC9OgjXUzZ4FY51nQSRAAlEnQAEa9SFgAwIRsOaq4Y+hW/dvR6D4WuVmr573pDRXrAjUjZg4lpdcpdrZNybaGkuN9LW2SNn0V/UL7vkBv71ZCn59nKu6gLnU8Cr4W0Px/w0r6JlIgARIwE4CFKB20mXZYROANRFuQfxBhHUGrkG3xfX0emrU1o7PSNW8f4i3oTTsPrrtgixviWrSSLc1K67aY7nns7bfXQad8ydtGU1SC4PckPCQl5ub22YNLSsr4wIlNwwM20ACcU7AHXfAOIfM7oVGAC53uAIhPrHICJYYN4lPb5Paa3vOg7LhPztLxax74kJ8YmTSPFtCGyDm6jaBhnWLZdVDF8vCP+wlpZ+/Ij71m3dLsuKGWm55rJbHAyETCZAACdhBgALUDqosM2wCmOeJQNnNzc16gQQsMm6Zi4YV7RXfjJMN/91JKr++T7xN2D0ojlIdQjExOUmgsXC5rH7kMvnxD3tKyacvuUaIWuGa8ACIxX94IMSDIRMJkAAJmCZAAWqaKMsLmwD+0MHthz9+iO3pltW4vha1zadaWLTh+d2kcvb9SnhirmT8JZ8S1DkZtHRFY2SbNq2UNY9dKQuv2Uc95LwbjSYErBMPgFgpjwdCPBjiAZGJBEiABEwSoAA1SZNlhUUA7j2swIWrD6vcIT6xKCLayefz6t2KNvxvTyn/6nbxNpZHu0m21z8gt9n2OlhBcAKNG5bKir+eKUtuPlRqFs0MntHBM3gQxAIlJDwgYmoMEwmQAAmYIkABaookywmLAALL448aVrsj9AsWG7nB5V6/9mMpnPBrKf3499Kq9mtPlNQ7KzZilsb7eNQunSM/jT1Slv/fadKwcVlUu4sHRHgn4I7HAyJi8eKBkfNCozosrJwE4oZA9M1NcYOSHQmVAAJfV1RUaMHplsDyzZWr1NaKt0nDmqmhdiOu8uWnVqv+FMRVn2K5M5XfIq7sx9L/1D/KoPPvktScfMe7A/GJoPXZ2dn6Ze2gBLc8Fgi6wVvhOBRWSAIkYIwALaDGULKgUAhYCxuw4hYud/xRi2ZCSKXyGX+WjS/uk7DiE/xzk+J/mkE0f2eR1I04olumPCk/XrmblHz0vPi83kiKieiaxsZGgZcCweqtBCGKB0avage8FxCiTCRAAiQQKQEK0EjJ8bqwCdTW1ur5npmZmXqBA6wr0Uy1y16XjS/sKVXfPSbiTew/punNxdEcCtbdCYGWqhJZ8+RVsuSm0VK3Yl4nOc2cgvDE1JhAkSjwwIgHRywYhAiFN4OJBEiABCIhQAEaCTVeExYBzBnDQiMEmIdFJdrzPZur1sqWt06Vkg8vkdb6orD6Eq+Zkxs2xmvX4qZfEJ+LlQhdN/5WaW2otaVfsG7iQRH/T4M9IOI4LKHwYiBME8QqEwmQAAmES4ACNFxizB8WAWulO1zvWGwUzb2mfd4WqZz7qBS+tK80rPskrH7Ee2ZvXaEkJznn4o13nrb1TwnEoref0oHsK+a8b7Qa/F+F+MTq966mxsACijBN8GZgYRKuYyIBEiCBcAhQgIZDi3nDIgBrCiwkmE8Gq6f/fLKwCjKQuan4B9n0ymipmHmX+FposdkGqQo91T+PAnQbLi494CndKCvuPUNWPnCeNFeZ2Q4WD4mIRIEV76Ek5MX/a8wNhXcDq+S5Qj4UcsxDAiQAAhSg/B3YQgC7p1gLFWApCfWPmunGwOpZMfsB2TTxEPGULjRdfFyV1y+nMa76kwidKZ/5piy8apSUf/12t7qLuZyY+4l5n+EkiND8/HwdqglxQhmmKRx6zEsCiU2AAjSxx9+W3lu7p8ACirli0drZyFO6RAnPMVL5zV/VIqMWW/oaT4UWpNONGovjiUVKK8edrfaYv0RaasLfJhbCE9bPQIuOQuWBa2ENhbcDXg/832ciARIggc4IUIB2RofnwiaALftg+YRlxFqoEHYh3bxAL3r6/gkpfPUg8RTP72ZpiXN5XnJl4nQ2Dnta9sVEWXj13lI1L/T5zaEsOgoVFbwc1vaduAdwD/lQyTEfCSQmAQrQxBx3W3oN8QnrBwJUR2tbzda6IimacqraQvMOkVbuXx3OQGe1loSTnXldSKC5fLMsu+skWf9ftYVsS+ehxaxFR1hw1NWio1C7am3fac3/pggNlRzzkUDiEaAATbwxt6XH/uITVhCsknU61a/9RDa+vL9a4f6p01XHRX0pTZvjoh/shMiWNx+Tn245VBo3rwqKA+GTwll0FLSgDicQngn3AEuE0h3fARC/kgAJaALOqwSCjzsC0RafvtZmbfGE5dNbz4DqEf/A6jZEfCkvdB8BxA1ddO1+Ujr91W0ah0VH+H+LyBQQoaaTJUKtxYgUoaYJszwSiH0CFKCxP4ZR7QEWHMHtjuDU0bB8ttRuks2Tj1P7Zj8RVQ7xULmvuVbyM1vjoSvsw88EvCpg/eqHL5W1/7xWbfa1dUqK/6IjOz0VEKGYB04Ryp8jCZBAIAIUoIGo8FhIBCA+sdjA2hnFzj9mgRrUsHGGFL5ykDRt+ibQaR6LgMCAvM7nDUZQJC9xAYHiD8fLT2OPkMbi9W07HWGutt3JX4RydbzdtFk+CcQWAQrQ2Bov17Q22uKz6vsnZcvkE+hyN/yL6JVZb7hEFucWAnXL5sri6w4Qz7JvjC06CqVvljselleK0FCIMQ8JJAYBCtDEGGejvYym293b0ijFH1ys5nzeLuKju9jowKrC8lOrTBfJ8lxEoLWmTNb+9XTZMuVJR1uFVfaYokMR6ih2VkYCriZAAerq4XFf46w/INZe0HC/O5Va6rbI5tePkbrlbzhVZcLVk+0rS7g+J1yHVZD49c/dJmuevKrLUE0m2VCEmqTJskgg9glQgMb+GDrWA2sxAVbNwprhpPhsKl4gm15V22kWfedYfxOxorTmokTsdkL2ueSj52XZn0+Q5mrnHjogQnv27CnwolRUVHDv+IT85bHTJLCVAAUofwkhEbBi+iEzVrY6KT7rVr4jmycdJa21hSG1lZkiJ5Bcz1BMkdOLvStrFn4lS248WBo2LHWs8QhWjwdYhIGqquKUD8fAsyIScBkBClCXDYgbm4MdU2CtgAXUactn1fx/SfF754mvhYtjnPhteOuLJC2F+3g7wdotdTRtWS1LbjlMahZ/7ViTIELz8/MFwfBramocq5cVkQAJuIcABah7xsK1LYGVAtaKgoICwYpWp1L5zLul/ItbVHU+p6pkPYrAwB4t5JBgBFprK2TpncdJxdfvONbz7OxsHQi/trZWC1HHKmZFJEACriBAAeqKYXBvI6w/Dj169JDMzExHGurztkjJtCulau4jjtTHStoT6JPV2P4AvyUEAV9zk6x44Bwp+mC8Y/3Ny8vT95XKykrB7kxMJEACiUOAAjRxxjrsnlruMWzXh5cTydtcL0VvnyG1P73iRHWsIwCBnum1AY7yUEIQUCvk1z19rWyccK8j3cWCRsuzgmk+iLLBRAIkkBgEKEATY5zD7iVc7rBKwOoJK4UTyeupkS1vnSIN6z5xojrWEYRATlJFkDM8nCgENk28X9aNv9WR7lpRNRDaDYHqMdeciQRIIP4JUIDG/xiH3UMr1ifme8I6gT8QdqfWxgq1p/uJaltN5xZC2N2nWC0/s6U4VpvOdhskUPT2U7LmH9c4EirJiiuMaBsMz2RwEFkUCbiYAAWoiwcnGk2zwi1ZfxAcEZ/1JbLljeMY4zMaAx6gzpRGhrsKgCUhD5VM/Y+sfuwKtemY/VZJ7E2PKBuIEQrvC6JvMJEACcQvAQrQ+B3bdj2DVeHTTz+VzZs3tzuOif+zZs2Sr7/+Wq90Rz6IUPwhgDvso48+kmXLlrW7xuQXvbvRG8eIp3ShyWJZVjcIeOs2qqv5x78bCOPq0rLPXpZVD13kiAhFoHp4XRobGwULIJlIgATilwAFaPyObVvP3nnnHbn++utlzZo1cu+998oTTzyhz2GR0aWXXirTp0+Xl19+WW655Ra9EhU7lSxcuFAuu+wyWb58uYwdO1amTJnSVp6pD62wfE5WO7GU2ydwTbU1ocppbZK+uYwFmlBj3kVny2dMllWPXCo+9XBqd8rKypLc3FwtQLky3m7aLJ8EokeAAjR67B2pGRP6J0yYIPfdd5/87ne/k8cee0xbQmHpnDRpkhxwwAFy11136eN1dXXa2okg0RCp48aNk+uuu07Gjx8vzz//vLaQmmp0a2O5bH7zRCU+nduBxVTbE6GcfjmeROgm+xgGgfIvJ8max3/niGscAhTWUNynuCgpjEFiVhKIIQIUoDE0WJE0FVtmvvjiizJs2DB9ueXawk195cqVsu++++obPOZc7bXXXrJ69WodCmXjxo36Oy7q37+/IGh0YaGZuYGtjZWy5c2Tpbl0USRd4jUOEOiZQfenA5hjrorSzybIWgcWJmHuubUAEiKU80Fj7qfCBpNAlwQoQLtEFPsZrBiemNv55JNPyvHHHy99+vSRLVu26BBL1g0eIrOsrEyKi4t13E//BUjYNg9zQrubvJ5aKZpyqniK53e3KF5vI4EeKdyj20a8MV10ybT/yrp/32h7H/DwDBGKRUncrtN23KyABBwnQAHqOPLoVIi5VHfffbe+md988826EbjB48aOGzxifSL8EiwN+N7R7YVz3d0JydfaLEXvnSNNW+ZGBwJrDZlAlrc05LzMmHgEit97RgpfHWd7xzEdCPcmTA+C94aJBEggfghQgMbPWAbtSX19vdx66636Rv7Xv/5Vz61CZiw2Kioq0scx5woidcCAAYIQTB1v+LB+Dho0KGgdXZ2AsC2ZdoU0rv+8q6w87wICaZ720RJc0CQ2wWUECl++T4o/fM72VsGDAyGKaULcKcl23KyABBwjQAHqGOroVXTPPffIzjvvLLfffrvA6omEG/k+++wjX331lSD+XmlpqcyePVvGjBmjV6DuvffeMnHiRB2SCXkgVvGKNJV/cavULX8j0st5ndME6hGKiYkEOiewVm3bWT7zrc4zdfOsNR8UD8bWdKFuFsnLSYAEXEAg1QVtYBNsJPDTTz9pYQlx+frrr7fVhBXuhx56qA63dN5552mr57nnntu2WOmqq67Sq+OnTp0q2BEJIjbSVPntQ1K94F+RXs7rokDAp6IUZKd7pd7DZ9Qo4I+dKpVnY9VDF0tqj97SY6/DbGs3xCfmg2KOenV1tWBOOhMJkEBsE6AAje3x67L1u+66q8yYMaNdPriyEAMUC5EgRDEPFLH3YAm10i677KItoGvXrtUW0Uj3g69d9rpUzIpcvFrt4bvzBAbkNcvqsgznK2aNMUXA1+KRFePOkt0emyFZQ3a2re0Iy9SjRw8tQPEZ9ywmEiCB2CVA80bsjl1ELcdEfohP3Mhh2USCuPQXn1bBOD9ixAidDyIV1odwwqE0bpotpR/93iqO7zFGoE9WQ4y1mM2NFoHW2kpZfs9p0lxdZmsTrPmgVVVV2yyUtLViFk4CJGCcAAWocaTuLRBhmHDjhvUAIZdCSXB9wVKKRUoej0eHaMIq+a5Sc9U6KXr3bLV9X1NXWXnepQTyU6td2jI2y40EmjavkhV/PVO8zfZuYmC533EvYyIBEohdAhSgsTt2Ybccc6dgwcQN3D/GZygFwUqK/eFxPRYsYWV9sORtqpaid04Xb0NJsCw8HgMEcqT7cV9joJtsokECtYtnyZqnrjJY4rZFYSEl7mGI2tHZfWjbK3mEBEjATQQoQN00Gja2xXK9B3O3h1I1QqH069dPu+thfcBc0o4ueXwvnnqJNJf9FEqRzONiAhktxS5uHZvmVgJln72sttl9zNbmYf4n7kd4qO4Ys9jWilk4CZCAMQIUoMZQuregSFzvwXpjueTxBwBzSUtKStr9Aaj8Zpw0rJkW7HIejyECyQ1mtl6NoS6zqYYIbHjhThX5Yrqh0gIXQ1d8YC48SgKxQoACNFZGqhvthJUAIjQS13ugaq24fCgP1gds3andYas+kMo5DwS6hMdikIC3rlCSk7wx2HI2OeoE1P1m5d/Ol6bi9bY1xd8Vj4dhJhIggdgiQAEaW+MVdmshDK1V74FWuoddoN8FWMiEBUr4Q1C67nvler/M7yw/xjwBX6v0z6MAjflxjFIHWtSK+BXjzhavx74tNC1XPFfFR2mQWS0JdIMABWg34Ln9Ulg9MU8znFXv4fYJoZp6F+RIy6yrxddcE+7lzO9yAn1z7BMPLu86m2eAQP3KebL26esNlBS8CLrig7PhGRJwMwEKUDePTjfbZtr1Hqw55dNvEm/l8mCneTyGCfRMr43h1rPpbiBQ+sn/pHT6q7Y1ha5429CyYBKwlQAFqK14o1e4na53/17VLn1NapdM8D/Ez3FEIC+5Mo56w65Ei8Daf/xRGgtX2Fa9vysenh8mEiAB9xOgAHX/GIXdQoRCgvUT7vFQA86HXYm6oLlylZR+el0kl/KaGCGQ1cpYrjEyVK5uprexTlY+eIEKUm/fxhSWKx67tjGRAAm4nwAFqPvHKOwWIjhzS0uL3m4z3IDzoVbma1W7In1woZr3SRdtqMxiMV9K05ZYbDbb7EIC9asXyPr/jLWtZXDFY8c23P9C2a3NtoawYBIggZAIUICGhCl2MsH9BAsAXFJYfGRXqph1j3iKF9hVPMt1C4G69W5pCdsRBwSK33tGKufaFycYe8VDiMIDxEQCJOBuAhSg7h6fsFtnuZ+w45FdqXHjTKn6/km7ime5LiIAC3d+JufUuWhIYr4pa574vbTU2LPNKzw+PXr0EI/Ho8PPxTwsdoAE4pgABWgcDS7cTnA/WVYAO7rm9dRKyUdXqqJ9dhTPMl1IoH+ex4WtYpNilUBzxRZZ+89rbWt+Zmam9v7gYbzjVsG2VcqCSYAEwiZAARo2MvdeALeTNQ/KrlaWfzlWWqrX2VU8y3Uhgd6Z9S5sFZsUywTKZ0yWsi9es60LsIJil7a6ujrb6mDBJEAC3SNAAdo9fq65urGxUbud4Hq3a+FR/ZqPpGbRC67pMxviDIH8VM6nc4Z0YtWy9l/Xi6d8sy2dtiKA1NbWaiFqSyUslARIoFsEKEC7hc8dF1thl7DoCIuP7EheT40KufRHO4pmmS4nkO0rdXkL2bxYJNBaWynr/nWDbU235sFb8+Jtq4gFkwAJRESAAjQibO66CG4muJvgdrIrlc/8i7TWFtpVPMt1MYG05iIXt45Ni2UCFV+/LeXqZUdKTk4WiNCGhgbtHbKjDpZJAiQQOQEK0MjZueJKCE+4mRBwHm4nO1Lj5jlS88OzdhTNMmOAQHL9xhhoJZsYqwRgBW2pq7Kl+bgvpqamMixTCHQnTJggH330UQg5mYUEzBCgADXDMWqlQHwiIQCzHcnX2iyln1yjiuaqdzv4xkKZ3votkpbCUEyxMFax2MZmNQ90w/N32NJ0zIeHFRQRQjBPnik4AQjQadPsi9EavGaeSVQCFKAxPPKwfiLsEp7ysfrdjlT13WPSXLbEjqJZZgwRGNCjNYZay6bGGoGSqf+RmiXf2NJshGWCd8h6WLelEhZKAiQQNgEK0LCRuecCzP3EE75d1s+Wmg1S+e3f3dNhtiRqBPpmNUStblacGATgivepndzsSLhH0gq6lSwsnRdddJFcfPHF8uabb+ptmwMxf++99+Tyyy+Xo446SuedOnVqu2wvvviinHbaaXLiiSfKHXfcIeXlv2wuMH/+fLnsssv0tb/73e/k22+/bXctv5AACFCAxujvwIpxB+snJtvbkcq/vF18LRQedrCNtTIL0rdO9Yi1drO9sUMAe8UXK0uoHYlW0K1U77nnHrn++utl4MCB8utf/1quu+46efrpp7dBjmPnn3++DB8+XC655BLtaYPQnDt3rs770ksvyU033SSHHnqonHPOOfL555/L0Ucfrc8VFRXJ4YcfLmAO8QkjycEHHyxLly7dph4eSGwCqYnd/V96j63bJk2a1HZgw4YN0r9//7bvbvtgBVjGrkd2pIYNX0rdirfsKJplxiCB3KQK1epBMdhyNjmWCGx88W7pdehvJa1Hb+PNhhW0oqJCzwWFOEq0tHnzZrnvvvvkyy+/lDFjxujuDxo0SKZMmbLNjlHFxcXy6KOPyu9//3udD2K0b9++Mnv2bNlvv/1k1qxZWsDefPPNWmBCiL7zzjvS1NQkixYt0oL13nvvlX79+mmBuuuuu25TR6LxZ3+3JUAB+jMTCNBx48a1EcLT4YEHHtj23U0frLmfdm256fO2SNn0m93UZbYlygRSmxAwfPeQWsHtD0PCxEwBCLTWVkjhS3+RHa7d1ioXIHtYh/ytoIkoQOEWz8jI0FZLC9yZZ54peHVMEI8QrG+99Za2XP7www86nJW1kOvcc8+VE044QUaOHKld8CeffLK2piLiwAEHHCAjRoyQHXfcUY499lidD+7+3r3NP1R0bDe/xxYBe3y3scVAtxZPx8uWLWt74T+UWxOsn/gjb5f1s/qH8Vx45NbBj1K7Ups2hVyzXTtxhdwAZoxpAnDD169ZaEsfrLmgsNQlWoJrHBuVhPL/84knnpBhw4bJgw8+KLCGQqTCmmmlI444QhYsWCAQojNnzpTjjz9eC8/Kykq9JgFzPnEtFslec8012pU/ffp063K+k4AmQAEaYz8Er5qkj//Udlk/vU3VUjn7gRijwubaTSCpAQKUobjs5szy8TPz2RaWCZZPWOkScXckWCUhECEorQSr6Kmnnqr/pljHELj/T3/6kzz88MN6zifE6BlnnKGvw98fJMQLxaKj+++/X1DGvHnz5Mcff9THly9fLljABOH5wQcf6Ovggv/nP/9pVcF3EtAEKEBj7IeAUCJ2Wj8rv3tUvI1lMUaFzbWdQGuT9M6xZ4Wy7W1nBTFHoOr7j6Vqwee2tNuKC5poVtDRo0fLbrvtJldddZWsXr1aSkpK5O6779ZzO7GY1UoQ6HCXb9myRSyDBxYugZflgodLHivpV6xYof8eIW9LS4t2vaOcSy+9VCZPnqx36KuurtZzbyGAmUjAnwAFqD8Nl3+2bgZ2xf1sqd0k1fP+4XIKbF60CAzI9USratabgAQ2/PcOLW5Mdx3zIBPRCoo+I+wSFtjutNNOeo4mxDgWJvknxEx96KGH5LXXXtNu9wEDBmi3OtztsHYiQZDuv//+ctBBB+lA/1gJ/+STT+qFSSj7kUcekbvuukvy8/O1K3/PPffU3/3r4WcSSFLWNPrVAvwOJk6cqFfBH3nkkQHORucQ3EawgGIujh2B50s+uVpqF/0vOp1jra4nUNj/jzJzfZ8u29kr2SN7f/HLgr4uL2AGEghCYPht/5M+R5wf5Gzkh+Fmhju6V69eemFO5CWZvfKBBx7QMTeHDBlituAOpZWVlel+Y05sZ2njxo0CAQrxGijBKFJYWChob6C5pbCMFhQU6JBMga7nscQmQAtojIw/nhMw9xOTyO0Qn57yZVK7+KUYocFmRoNAj5TKaFTLOhOYwMaX/k98rS3GCWAuKO6juKcmYoKLvSvxCS4QlsHEJ84jBvXQoUMDik+ch3hNxIgD6DtT1wQoQLtm5IocmHuDp027Vr7rhUc+zvFzxWC7tBFZXs4NdunQxG2zPEVrpfRT8w/GsNZhKhPuqwhrx0QCJOA8AQpQ55lHVCNCL2FuDl6mk6dsqdQte8N0sSwvzgikeRALlIkEnCVQOPFB8bY0G6/UWniTqFZQ40BZIAmESYACNExg0ciOPYzxss36OedB1S1OBY7G2MZUnfUbYqq5bGx8EPAUr5PST1403hm4jzGlCQKUSyGM42WBJNAlAQrQLhFFPwOsn7hZ2jGXhtbP6I9vrLTA11gu2el8UImV8Yqndm6a9DdbrKB4qMfUJiu8UDwxY19IwO0EKEBdPkK4OWLFJtxFgVYZdrf5lXP+poqgqOgux0S5fkAeQzElyli7qZ+e4vVS9tkE402ypjXhIZ+JBEjAWQIUoM7yDrs2a36SNV8p7AI6uaC5ap3ULefcz04Q8VQHAr2zGjoc4VcScIbA5smP2eIqhxXUmubkTE9YCwmQAAhQgLr4d2CFXkLgZDtCL1XPe0oZP7ny3cU/Adc1rSC1xnVtYoMSg0Bj4XKp/OZd453F1CZMcaIV1DhaFkgCnRIIHF2200t40ikCHo9HhwjBbhKmU6uaz1fDoPOmscZ9eTlJ5aqPQ+O+n+ygOwlsnvyI9Bz9G6ONs0IyYZOPHj16aDFqtAIXFfa3v2HKldl0++23my2QpSUMAVpAXTzUeCJHEGBYQE2n6h+eFV9LYgZhNs0ykcrLaC5KpO6yry4jULt0jtQsnmW8VdYUJ2vKk/EKWCAJkMA2BChAt0HijgMIjtzU1KQXH5luka+lSarnP2O6WJaXAASSGwoToJfsopsJbH7rcePNwxQnPOhTgBpHywJJICgBCtCgaKJ7AivfkRCnznSqVQuPvA0lpotleQlAwFtXKMlJnDecAEPt2i5WznlfmorWGW8frKB48MeCJCYSIAH7CVCA2s84ohoQlw5P5JgcbzpVL3jWdJEsL1EI+Fqlfx4FaKIMtyv7qULTFX843njTcL/FfFDr4d94BSyQBEigHQHz6qZd8fwSCYGWlhb9FG6H9bNpy/fiKfoukmbxGhLQBPrmNJEECUSVQMlHz4u32ezvEOITK+IhQLkzUlSHl5UnCAEKUBcOtPUEbs/io3+7sMdsUiwR6JleG0vNZVvjkEBLdZmUfTnJeM/w0I/NP+iGN45WSkpK5LXXXpO1a9eaL/znEq2/nbZVwIKNEqAANYrTTGFwv1ux6cyUuLWU1oYyqVvGwPMmmSZiWXnJFYnYbfbZZQSK3zf/MJ2enq6nPVHImB3sZ599Vg4//HBZtGiRnH/++XL99debrUCV9vnnn8vVV19tvFwWaB8BClD72EZUMp684YK3w/1e+9NE8bWadVtF1EleFNMEMltLY7r9bHx8EKhb/p3Ur11ktDOWGx5GALrhzaDFwq4HH3xQ3njjDRk3bpx8/PHHMnHiRG0RRQ0VFRXy448/Sk1N+00uwH/VqlWyYcOGbRqCEIULFy6UTZs26XOwWi9ZskSqqqqkurq6LX9paak+jr+pViorK9MW7tWrV1uH+B4lAhSgUQIfrFo8eeMmaIf7vXbxS8Gq5XESCJlAatPmkPMyIwnYSaDk4/8ZL95yw2MjEKbuE0CIKwjM3XbbTRcG8QihCGPLq6++Kr/61a/kL3/5i+y66676OzJBlB544IFy1llnyWGHHSannXZa2wPB+PHjZY899pA///nPss8++8gf/vAHLWZxfM6cOfL441vDdN1xxx06H87vuOOOsmzZMl0/rj355JNlv/32k5kzZ+pj/Cc6BChAo8M9aK2W+x0i1GRqKl4gntKFJotkWYlKoG5bi0SiomC/o0ug7PNXxdtiNmxSWloa3fCGhxU7TCHBUnnDDTfIxRdfLIMGDZLnn39eHnroIXn77bflnXfe0cIT+V5//XUtLufNmycrVqzQYvWbb77RIvSTTz6Rzz77TN59910tOP/zn/9I37595dZbb9Vi9Z577tGC9JVXXpF169bJjBkz5K677pJ///uXKRu/+c1vBNbRQw45BNUxRYkAt+KMEvhA1Vpbb2L+p+lE66dpoolbnq+5RvIzvVLVyOfXxP0VuKPnLdWlUvntB9Jr9GnGGoSHf1hBEZQebmDTxgBjDY2xgmBcueCCCzTTp59+Wrf+0ksvFbyee+45gSjEeSS465FwDqm4uFgmT54so0eP1nnffPNNeeKJJ2TBggVa1Ha0Vk+ZMkUwnxfWTyRYXWEdfeyxx/T3MWPGcFw1iej+QwEaXf7tarfL/e5r9UjtUvMrRts1nl8SikD/PI8SoOYflBIKIjtrhEDpxy8aFaBoFAQoRAt2o7PDIGCk4zFUCOZ3nnLKKTJy5EjBgiS45ZEuvPBCOfHEE+W9996TCRMmyDPPPKPnduLccccdJ4ceeig+6tSzZ0/9ULDvvvvqso4//ngZO3asDB06tM09b+XFgwPquuqqq6xD7d5zc3PbfeeX6BCgCSM63LepFf9h8ISIG5/pJ+76NdPE21i+TZ08QAKREuidWR/ppbyOBIwSqPr+I2mpMXt/gxseIomr4c0M1bnnnqvnesJdbolPlHz66afL7Nmz5ZJLLhGcW7NmjXa3n3POOdpiifmhmAv68MMPy9y5c6WwsFCwiOiRRx7RwvWLL77QDcQiIyuGKw789re/le+//16GDRumr4cbH9eY/tuqK+c/EROgAI0YndkL8R8I82PseNquWzbZbGNZWsITyE/9ZaVpwsMggKgS8LW2SPmsKcbbgHsxLKAwDjBFTgDC8cMPP9SLgyA+rRcWAGHeJhYTHXDAAXo+JhYQQfyfffbZUltbKzvssIOMGjVKL8o977zz9GKiM844Q4tZuNFRxvDhw2XlypWy9957y1dffaVd+X369JGbb75ZdtppJ73Y6F//+pfce++9kXeCV9pCgC54W7CGXyhudEiYt2IyeZvrpX71ByaLZFkkINm+MkVhB5IgAVcQKP/ydel3/BVG24JIJHDDY7W26fuy0Ya6vDCsNu9MxM+fP1/Ky8u+RpsSAABAAElEQVSloKCgbevp/Px8mTZtmg6pBPb+hpkXXnhBi9PU1NR2x4EBC4us+aBYBX/bbbfp8E5w31tp82ZG8bBYRPudFtBoj8DP9UOA4j+aaRdBw5qp4muhu9Qlwxw3zUhr3hI3fWFHYp9A9cIvpbmiyGhHrPuxZRwwWjgLa0egV69ebeLT/wRWz/uLT+sc5nAGOp6cnNzuOESqv/i0rue7OwhQgLpgHOB6x1ObLbE/6X53wQjHXxOS6wvjr1PsUewSUPfQ8hlvGm0/jAEQoRSgRrGyMBJoI0AB2oYieh8sl4FpAeptrpMGtQCJiQRME/DWb5a0FK/pYlkeCURMoHzWWxFfG+xC3JPhgoeRgIkESMAsAQpQszwjKg1P2HAdYPK1ydSw7jO19WajySJZFgm0Eeif19r2mR9IINoEahbPNL4a3jIK0Aoa7dFl/fFIgALUBaOKm5t1ozPZnPpV75ssjmWRQDsC/bL5cNMOCL9El4CyUlbOnWq0DZhDiFXblpfKaOEsjAQSnABXwUf5B4DwS62trcYFqM/nFcT/ZCIBuwgUpNeoovPsKp7lkkDYBCpnvy99jty6m07YFwe5AMaBeLGA3n777UF6ycMk4DwBWkCdZ96uRuvGZtoC2rT5W/E2lLSri19IwCSB3KQKk8WxLBLoNoFKFZTe2+zpdjn+BWAhEowEMBYwkQAJmCNAC6g5lhGVBAGKuZ+YA2oy1a/+0GRxLIsEtiGQ2VKsju2+zXEeIIFoEfA21ErNwq8kf9+ju9WE9evXy6ZNm3QZVgxLhASCSx6xJrHLDoKk77zzzt2qx+mL1zyeZbzKYTc1GC+TBSYGAQrQKI4zbmyYW5STk2O8FQ1rPzZeJgskAX8CKU0M6OzPg5/dQaBq3ifdFqDYFrKoqEgHR0evsK84xOby5cvlL3/5ixx77LGC3XUuvfRSvZ2kO3rOVpBAbBGgAI3ieCG8B0QoXDwmU2uD2g2i5AeTRbIsEtiGgLd2vTqGbQqTtjnHAyQQLQJV8z/rdtXYO/zvf/+7bLfddrqsmpoavSvSE088IePGjdPbQ2K7yCuvvFJOOukk4/fwbneABZBADBAw6/eNgQ67qYkQoEjGwy+t/0KXy39IwFYCrU3SO4fxEW1lzMLDJtCw5kdprsT0kMhSfX293hqypKREJkyYIKtXr9axQDEHdMOGDbLXXnvpgvv37y/Z2dlSWMhNGSIjzasSnQAFaBR/ARCgmFNkev5n4/rPo9grVp1IBPrnNCVSd9nXGCFQvSCyeyAWGy1evFgaGxvliy++0HuR33LLLTJ16lQpKyvT06X8t0vGnuXYx5yJBEggfAJ0wYfPzNgVEKCmrZ9oXAMFqLExYkGdE+iVWa8yZHeeiWdJwGECcMP3PvzcTmvF9CcITohNLAa1pkQNHDhQxo8fL9ifHNOjdt99d20J3WeffXR+/0JhFQ20J7l/Hn4mARIITIACNDAX249iazfcvODCMZmaq9ZJS/U6k0WyLBIISqBHSpU61yfoeZ4ggWgQwEr4jsla9AnBicWf/mGVYNWEMQDh8Gpra/Xnvn376iJGjBihFyT17t1b4J6HWLXC5sH6OWjQoI5V8XuMEmhoaJCsLPORAmIUh+3NpgvedsSBK7Dmf5pegNS06evAFfIoCdhAIMtbakOpLJIEukegacsaaSwtFAiKiooKKS4uli1btmh3OUQkxCgsl3ChYy7ngAEDBAIzNzdXLza66aab9LXI9/7778thhx2m3e977723vPvuu7pxX331lfTs2VO/utdaXu0GAjfeeKOeauGGtiRKGyhAozTSlgDFHFCTqXHTNyaLY1kk0CmBNM+WTs/zJAlEi0DhN1OlsrJSu9hh4YS3CW51iM1+/fpp4YhjHefgw+J5xhlnyO9//3s5//zzZcGCBXLJJZdoq+iFF14ob7zxhj4ON/0dd9wRre7FXL2Y7oAwVnj3T7BGL126VE+BsI7DCg1Lc3V1taxatco6LGvWqAcLZcG2Eubl4iFh5cqV2nJtHbfeEUrrxx9/FEQx8E8d24IHlSVLluj6UC+TMwTMqh9n2hwXtVjzP/0ntJvoGAWoCYosI2QC9RtCzsqMJOAkAd+6H6T3cRdHFCIJgvOiiy7SogYB6JEwbWrw4MHywgsvaLFUUFDgZHdiuq6ZM2fqeKmwICOSwOOPPy4nnHCC3HbbbfL2228LpjsgmsAHH3wge+yxh9x9991aVG7evFkH/j/wwAP1lAkISojQSZMmycEHH6zDYe22225a1P70009y5513yrXXXqvznnnmmYINBWDZnjt3rrz88sty8sknS6C21NXV6QcNbDKw/fbbyxFHHBHTvGOl8RSgURopPPWZnrzubaqW5tLFUeoRq01EAr7GMslO90m9h7FAE3H83dznphVzIxKfVp9gGbXEJ47hOzxWMB5QfFqUQns/66yz5KWXXtKiE2LwH//4h7ZwwpoMyyMs0Yix+uSTT8pzzz2nC4XVEnkhCjFN4tlnn5Xf/e53Wry+9tprWoAi4yGHHKI3B8A0C8RtPf300/VUC0yvmDdvni7r4Ycf1vVDgAZqC9r2v//9Ty6//HKKT03MmX/ogneGc7taYP7H07TpFfCNav/3rYHB21XHLyRgK4H+eVvj2dpaCQsngTAJ1K36QbxNZreJxD3bmj4VZnMSNjssnhCTRx11lGaw3377aTH4zTff6CD+1kJcBPZ/88032/hCWMJDCOtoXl6eHHfccfp6LPrauHFjG08ITiRMq0Ckgu+++0723HNPGTt2rDz66KPakg3xCjd7sLa0FcYPjhKgAHUU99bKrBuY6QVInqLvotAbVpnoBPpkIRQTEwm4jIC3VepVUHqTCQIUq+dhQGAKjQDEY0pKip6raV0BqyemM8ATaCUrHBbyImFBmH8KxWOIuaMQtNOnT5djjjlGC9jrrrtOi1GUFawtmEfK5DwBClDnmesnPDzZWf/RTDWhqXiBqaJYDgmETKAgtf0E/5AvZEYSsJlA3YqtLlhT1VhGA8uIYKrceC4H0xVGjx4t7733nu7m559/LldccYVgjua0adMEO04hwQ2+//77b7MoTJ/s5B/MB0VatGiRrFu3Tn79618LrKtHH3203Hzzzfr7hx9+2DZ1IlBb8PcYAhdWUibnCHAOqHOs22rCEzTmEplegOQpnt9WBz+QgFMEcpKwE8xQp6pjPSQQMoG6lWbviVbUEtzDrVigITcmgTPeddddcs4558i4ceO00MNcT7jSMedy5MiRguD/mLP51ltvhU1p1qxZsuuuu+oFY5gbitBYWEB22mmnyZgxY/Rc08MPP7yt7EBtQaXIe/XVV2sReuWVV4bdDl4QPoEkZXqm7TkAt4kTJ+qJz0ceeWSAs907hCc+uHJMTmRvbayQ9c8wIHL3RoZXR0Sg72iZtPGYtkt7JXtk7y/GtX3nBxKIFoGsYXvJnk+bnZqEldgIVu6/QMlU/x544AG5+OKLZciQIaaKbFfOmsfNB1kfdlPoVkOETcKqdP8ENzxis0by9xCcvvzyS10mBGxHow7qQ+itjsdRf6C2YBoArNwdQ3P5t5efzRGgBdQcy5BLwiKkUOazhFygyugp/iGc7MxLAsYIJDcWGiuLBZGASQKN65eIt7lJktMyjBULK6j/LkrGCk6AgjqKT3QZgs+a2hApgmDiNVB9Vh2Bzpn+u2zVxffABDgHNDAX245CfMLobLlyTFW0sH6gfL3jc7Jm8B+kLnukqWJZDgl0ScBbu1GSk+hI6RIUMzhOwNfaIo0blhqtlwLUKM5uFYbV7dhYgCk2CdAC6vC4QYAimRagX1f2lY+qDlclq1furVKgvBzbZdXJ0KRCGdS8RPrXzpa+ZZ9KWiv27mYiAYMEfK3SL7dVttTwdmKQKosyRKBh/U+SPXyUodJELx61DAmBXLvGKmJBXRI46aSTuszDDO4lwL8YDo+N5boxvQJ+Q3VLu55Uqt3EKpty5EfZSR3H6zRJUqJ0YI5XhqZXyhDfWhnU+IP0r/pKCqq+lmRhWJF2APklLAL9cpsoQMMixsxOEWiwwQKKtkOEmjYkOMWE9ZCAGwhQgDo8ChCgmOBsepLzhqr2AjRQt+Ak3VSXrF69ZI70Ut/2Fcm4TDIGqjXM2c0yNK1EBrWukIH1c6V/xWeSU78yUDE8RgLbEOiZXqeO5WxznAdIINoEYAE1mSzjAQWoSaosKxEJUIA6POq4aVk3MFNVVza2So0ncgtmk5oVsLImTVYKVtHjdVg7N/6QpE3ajT9Au/E/oRvf1MDFUTl5yRWqN/3iqEfsSrwQaFhvfg4o2MRiKKZwVqzHy/izH+4lQAHq8NjgpmV6C85QrJ+RdPMXN/6O6nK8fqPc+A/+4saXdTKw8Uflxv9SelbOohs/Eshxck2mt1T1ZOc46Q27EU8EmjatEJ968E/6eYed7vYN8z5hRLCmU3W3PF5PAolKgALU4ZGHBRQx5EymTbVdu99N1betG38fFUfjEu3GH6Lc+Nu1ufG/k34Vn0tu/XJTVbMcFxNIbdrk4taxaYlMACvhPaUbJaP/9sYwQIDiXh5r6YRXNhpv8tQLhhgvkwUmBgEKUAfHGTcshGAy7YIvclCABsMFN/4q5cZf1c6Nf4vkq6mm22XXy5DkTTLYs1i0G7/8M0lrgcuWKW4I1G6Im66wI/FHoKl4nVEBisVHTU1qpScTCZBAxAQoQCNGF/6F1hOz6ZWTxXXufRKv8ogs9GTLQhmpgOGl3PhKlA7I9sp2GVUy2Kfc+E0/yoCqL+jGD/8n5ZorfM01kpeh5iI3pbimTWwICVgEmrasEdlzjPW12++WBRQGBYZi6jZOFpCgBChAHRx4S4CaXgFfVOecC94ELrjxN9cnq1dP9QmvvZUb/2JJ91uNP7h1pQyo/176V34muXXLTFTLMmwmMLBHs9SUUIDajJnFR0CgacvaCK4KfonlxaIADc6IZ0igKwIUoF0RMngeNysk4wK01r0W0HDwebZx4yuLRc5N0kNp1O1/duMP8vyk3PjfSL9yFVSfbvxw8Nqet1cm9oTOtL0eVkAC4RKAC95ksu7hXq/X+P3cZDtZFgm4mQAFqIOjAwso3DUmXTatXp+UNcSHAA02FNXbuPFPUW78B5Qb36fc+JV+bvyvlBt/BlfjBwNp8/H8lCpVAyzaTCTgLgKeErNzlP0FqLt6ytaQQOwQoAB1cKzseFquavKK0qAJl7a68ZM6ceOXymAVVJ9ufOd+GtlSpirbwbkKWRMJhEiguaI4xJyhZaMADY1TOLmKi4tl5syZcsYZZ2xz2SeffCLDhw+XESNGbHMOBzq7NuAFPOgKAhSgDg4DXPDWjctUtRVxbv0Ml9Mvbnw1oVTw8nPjZ2E1/mYVVP8n6a/c+P3LVVB9uvHDRRw0f3pzUdBzPEEC0STQXLHFaPXWfRxGBSYzBCAip0yZElCAPvPMM3L22Wd3KkCDXWumdSzFDgIUoHZQDVImXPDWjStIlrAPVyoLKFPXBH5x4+MJGq+TlRv/fu3GH6rc+ENkvfSrn6+C6n8lfWu+kWRfbC3s6pqA/TmS6zfaXwlrIIEICLTWVoi32SPJaekRXL3tJdY0KgrQbdkEOlJWVia9e/eWdevWSV5envTq1Uuam5v195EjR+pLdtllF3nqqafaXb5ixQoZPHhwu2O4bunSpbq8QYOwc5+I/7Xl5eXbxGhF3fjb29jYKCtXrpTttttOevTo0a5cfnGeAAWog8xhAbVWT5qqtpIW0IhR+rvxv9VzF0epNTSXSlq2il2a0yxD00plkFqNP1CvxldB9evM7ikdccNdeqG3frOkpSiqCTglxKVDwmb5EWiuLJKMvkP9jnTvIwQNBWhoDEeNGiWHHHKIbN68WebNmyd33323TJgwQYvAmpoamT9/vnz33Xdy9dVX689FRUUyZswY6dmzp2zZskUyMjJ0RRs3bpSjjjpKW0I3bNggu+++u7z22mvtrr3mmmv0NXhIKC0tlVWrVsmmTZt0veeff77sscce8v3338ujjz4ql19+eWgdYC5bCFCA2oI1cKGwgKanm3kCt2qoaKQF1GJh6r1ZId0aVN9y4x+qVuPfqFfjb5fVIENVUP2tbvzZejV+egvmPjKBQP+8FqmvJgsScB8BzAOlAI3euECAXnvttQJ3+vXXX6/FaJ8+fWT06NEya9asdn8bb7vtNvnNb34jDz30kMB6OmTI1t2WPvzwQ4GYff3118Xj8cidd94ptbW17ToFQYpUUVEhBx98sLz44otSUFAgDzzwgLzyyitawFoC97LLLjO6KLhdQ/ilSwIUoF0iMpfBnkVI8b0C3hz97pcEN/4iT5Ys0i78rW586TXuZzd+lXLjr5NBOqg+VuPPTEg3ft/sRllXvdVa0X3iLIEEzBGAG95kogU0PJqHHqoe5FXCYqJ9991XID6R4EYvLCyUYcOG6e/459tvv5Vnn31Wf4f7HCIV6dhjj5X7779f9tprLznllFMEAjI3N1ef8/8Hu1SddtppctFFF8lZZ50lJSUleoHTSy+9pC2vyAuBOnv2bDnooIP8L+VnBwlQgDoE264YoPXN9Hc6NIRBq9lSnyRb6gtkrhSoPMqNn3aRpPUXGQo3fqpaje9dJQMbVFD9CrjxlwQtJx5O9EyvUTKcAjQexjLe+tBajzBh5hIEqLW5iLlS47ckf6GYlZXVaUfhKcR8TSulpaXpjzvssIN2qX/55Zfy5ptvavE4Y8YMK5t+x99aCFOsmL/jjjvazqHMK6+8UqyyrrrqKrHmn7Zl4gdHCVCAOoTbmiuEm5bJVA9/MZPrCGBYVtekyWq9Eh+u/EOUG/+GNjf+kBS48ZfKwNrZ0rfsE4kXN35uEqxMWy0brhsUNiihCbTWmRegWBDDZJ7A4Ycfrud2HnPMMQJ3OQTnpZdeqt3pcNePHz9eu9Ixb3Tt2rXSv7964v85/fnPf9bXTJs2zTokffv2lQMOOEAverrwwgv1nFDMJZ0+fXpbHn5wngAFqEPM7ROgtIA6NIRGqtnWjX+SSK+/Sn8dVN9y4y9Uq/FnSK/Kr2LOjZ/egniLOxphxUJIwCSBljqzk5Ppgjc5Ou3LevDBB+X000+XnXfeWS/02meffXQGhGKaOHGingcKC+nee+8txx13nCxYsECfh1jFtZgzCguo5Xl8+eWX5d5775XzzjtPzyvF3+ObbrpJBgwY0L5ifnOUAAWoo7jNV0YLqHmm0SixSLnxi9q58S/UbvwhOS3ajT/Eq1bjw41fOV1yaxdHo4kh1ZnSuCmkfMxEAk4TMG0BtUIxOd2PWKwPq9etBMGIl5UmT55sfdQr4PElJydHPv74Y6msrNQr5f09h7Bs1tfXa3GJfEgHHnhg27WW6NQnOvyDMFCYDwqLKFP0CVCAOjQGnf2n6E4TOAe0O/TcfS3c+GtqUmWN4CkdL+XGz75B8tRUU6zGR1D9wS1w438jfco/lYzm0uh3qE79oVFzsJhIwG0EWhtqjDfJrvu68YbGaIFYvR4oZWerWHkRJorPCMHZcBkFqA1QOyvS9FNzUwv/2HfGOx7P1ajV+IvVavzFMlx1D68T1Rbsv7jxB6ug+oOaFsqAauXGr/jSWTd+a6P0yOS8uHj83cV6n3zNTbHeBbafBOKKAAWoQ8Np15NySyJuBO/QmMVaNe3d+HuJpF4gqXo1vnLjq6D6g1uxGn+ecuN/Lnm1i2zrXs+MRtvKZsEkECkBX4vZByPTxoRI+8XrSCBWCVCAOjxypm9arVwE7/AIxlZ1Ldu48Q9WbvzrJDdfufGzEVR/qxt/QO0c6Vv+sRE3fl6qeVdnbFFna91IwNuiXAc2JBgXTN/XbWgmiyQB1xGgAHXdkITXoBbOtwsPGHNrArXKGLSkKkuWtHPj3yv9s3wyNLP656D6W934PStmSIov9D/e2VIloefmgJCAMwRMu+ApOp0ZN9YSvwQoQB0aW7rgHQLNarpFoKhBrcZvyJfvRLnw8bLc+Nlw45f9ElS/Ezd+hrecArRbo8CL7SDga+Zj0dQLtm5paQdflkkC4RKgAA2XWDfzm35qpgu+mwPCy7skoN34tViNj2DPeI3+xY2fVa9W42+RQWo1fn8VVL+/Wo2f2oxYoEwk4C4CPrFnwSZd8O4aZ7YmdghQgDo0VnZZQJOSHOoAqyGBDgS0G785u70bv9d98pv+X8nqzesk21MnA1JqpW9SreS3VEl2U7mk1ZdKUnWxeGvLGa6pA09+tZdAUpLZXehMGxPs7f3W0vs8cZnxakpvfMF4mSwwMQhQgDo8zt25aXUUsfieTAHq8Aiyus4I5KaVyK8GfyWpBw6WG1/fOXBWtVV8WqZXRuR4ZISKZzo0vU4GpdRJX6mWnq1VkttcIVkNZZJSVyo+JVZ9jXWBy+FREgiDQFJyShi5Q8/a8b4c+pXMSQKJTYAC1OHxr66ublsx2fHG1dV3q6n+IjbZJreSVRffSSBUAslJzVLS9KYMy1kqw/f6Xl6de6t8uybwvLtmX7Isrc3ULxXENHgVuepsQavsmNsgwzJU8P20ehmQXC19fNWS31qprKoVklFfJkm1JeKtUq5/b2vwsngmsQkkm7WAJjZM9p4Euk+AArT7DMMqATs4pKRs+yTuLypRYFffrUpTUxrUR8ZisnjwPXoEMtI/liP7NkqSbLVY3nvSNDn5X0dJazdj1Va0pMi3lbnyrSg1Gizhv1RPn+yQ3SwjVXip7dPrZVBarfSTGunprZIezZWShSkASqhKjRKrdZXBSuLxeCVg2AUfr5jYLxJwigAFqEOkLUEJ8RlIgEbajBROAo0UHa8zSKBf7mL5YM1s+du+vxS624BFctGBY+R/Xzt0m1H/F9Y2pOuXiAp0GixliWTleGWn3EYZlqmmACir6kA1V7UPpgC0VEqOmgKQqaYAJCux6lNWVdPhe4I1i8ftJZCUYvZ3aHmsrHu7va1n6SQQfwTM/o+MPz7GemTdpLxer1EBmkqvkrExYkGREcjPLJfPN7wlw/IypHfqgnaF3HzkK/LBwiulpMbsLjTtKongS4M3WX6oztYvkd7BS1A6dkBGs+yY3Sg7ZNTL4LQ66Z9cI7291dJDidXsRmVVbVALq2BVrS7lwqrgJKN+JjlDPXkYTBSgBmGyqIQkQAHq0LBbAtS6aZmqNlMrUM57M8WT5YRHICW5WdbUTJIGtcvMLXtDyLX/LeZnVsnY49bKbZMHh1ewi3JvaUoTvGZIXvBWpamQqb19amFVkwxXUwC2wxQARAD4eQpAnmerVTVVLawSRAForA1eFs/YQiAlM8doubiX475u3duNFp6AhRUXF8vMmTPljDPOCNr7UPIEvZgnXEeAAtShIbFuUqYFaFYal8E7NISsJgABzPtcVbRJnzm078YAOUTOHPWmvPb9WPl+TXzvEd+iZr8uq8vUr64WVhUUtMiOSqzuoKcAqHBVyqqKKQD5mAKgFlalYwpAjRKq1WrOamtLQK48GB6BZJsEaHitYO5gBCAup0yZ0qUA7SpPsPJ53H0EKEAdGpPkn1dgmhagmakUoA4NIavpQKBf3hL5YPVsfXTMwDzJlEUdcmz9ilBh9574sZzyrzHCnWO3MqlsSZW5VXjBKtcnIDfBwqoCn2yX5ZGROVunACBcVb+kGumlFlblWVMALKtqXUXgcnhUEzAtQDGdyjIsEHHnBMrKyqR3796ybt06ycvLk169eklzc7P+PnLkSH3xLrvsIk899ZT+XFtbK2lpaeLxeGTDhg2y0047SWpqqlh5WltbpbKyUpdp1YwIM+np6ZKZmSk4v2bNGsEYDR8+XF/b0tIidXV1kpubK8uXL5fBgwdLjx49rMv5HgUCFKAOQbduVKYFaDYngTo0gqzGnwDmfX62/s22Q9fusW1kh7aT6sOeAxfIRaMPk5dm2bMbjX9dcfVZuXjXN2boVygLq0ZiCgCsqmoKwMCUrVbVnipcVa6aApDRUC4pWFiF2Kqe+LZGB/oNpGTY44IPVBePtScwatQoOeSQQ2Tz5s0yb948ufvuu2XChAlaANbU1Mj8+fPlu+++k6uvvlp/xnkIyMWLF0tBQYFAkM6ZM0eWLFmi88yePVt23nlnXdZ2222nHmx9gjree+89/VBw1llnydChQ3V9DQ0N+tply5bJH//4Ry1OIYB/+OEHef311+Xoo49u31h+c4wABahDqC0BiicykymTLniTOFlWCARS1bzP1TWvSaOa97k1+WTPHsvVApzOL775sAny/g+XS3mtdV3n+Xk2PAJYWLWwJku/RHoFv1gZffqltyiraoNaWIXYqmoKgLKq9vapcFWwqqpwVemIrYqFVTVqzqrhe1bwhtl7JiW7kzm8EVQN0WPd1yO4POEugQC99tpr5ZlnnpHrr79ei8M+ffrI6NGjZdasWdp66Q9l5cqV2lIJxvvvv798/PHHMmTI1r3sMzIy5Nxzz5VXX31Vbr/9dj13FGXtscce8vTTT8stt9wiV1xxhS5uv/32k88++0xfC6G7aNEi2X333eWhhx6S5557jgLUH7rDnylAHQJu3ahMW0Bz07gM3qEhZDU/E0hL+0hWF21u43HJLr0l2be47XuwDz2zy+X24zfJ2MlBXM7BLuRx4wSKPalS7MmTr7tYWJXcS+1Yla12rFJRANoWViWpcFWYAuBRsVVVFIDUOjVPFQurGmqMt9Nkgak9Ool2EEFFuJdbU6siuDzhLjn00EN1n+ES33fffQWCEWnQoEFSWFgow4YN09+tf0488cQ2gT9ixAiBpdQ/XXbZZXLJJZdoAQprKr4jwYo6depUbWVdsGCBrFixQhobt1r8d9hhBy0+kW/HHXeU6dOn4yNTlAhQgDoIHiLUtADNz6QAdXAIE76qrfE+57TjcOHI0N25Z+41Ue2Q9CdZsK6+XRn84k4CXkmWFfWZ+qUmpAZvpPJu98hXO1ZlN8mwrHptVR2YXKusqtVSoKYA5HjK9Y5VybU/b6/a4nxYrtQeZh98KECD/xwCncHcSytlZXUdEgtucitB6Hf82/mrX/1KPwB8++238u6778rf//53nf2GG26QhQsXyuWXXy6XXnqpXHfddW3X+pdpGYWsOvjuPAEKUAeZB/pP1N3qCzI7n3vX3fJ5PQlYBAoyy+QzFe/TP+UpC/x2Gcr62YX73bomJdkn9508XU59+gDrEN/jhEC12rHqexVbFa+gC6vwvKx07NDMrVbVHdRc1a2xVavVwqpqyVObAGQ1VkhaPWKrKqtqTbkxOqn5Zi2gpmM6G+toAhUEkXnNNdfIYYcdJj17bt3SF6Gc7rvvPjnllFNk48aNMnfuXDnzzDMTiErsdJUC1MGxssUCmkELqINDmLBVYd7nyupJfvM+t6K4YZSyUvh+DIvLqIFz5ILRh8grXztvBQurocxsG4ENjemCl7KbBq8jQyQjC1ZVjwxDbNWfd6zqq8JVFbRW6YVVmWoTgBQVBUDvWOXBtsTBkx0WUFrRgvN24swFF1wgY8eOlXHjxrVVd9ttt8mtt94qTz75pHbhH3HEEYL5pLvuumtbHn5wB4EkZdYO0XbhjgY71YqJEydK//795cgjjzRWJUJR4Ibl7wbobuE/lTTJzR+rOVhMJGAjgcyM92TGxm+3qWHOaclSkByeAEUh5fX95YgnLpTKeorQbaDyQEQE+qY3y0g1BQCxVbGwqr9aWNVHLazSsVWbK2X0Yx9FVG6wi7Zs2SI5OTk6rFCwPOEef+CBB+Tiiy9uW2wT7vVd5e/zxNZ5kl3lC+d86Y0vhJPdkbywTiNMk8m/tY40PMEqoQXUwQHHHvCIRWYy5dMFbxInywpAoF/eIhXvc1vxOSI/U4nP+QGu6PpQr+wiGXtCqdz5Zid7tnddDHOQQBuBEk+a4PWNYK5h37bj+DCkd7YsbXeke18gcGC7wT2dyX0EMN2N4tN949KxRfTfdiRi43f8p0CAXJOpVxaH0CRPltWegJ73uX5K+4M/f7tlL8RVjPz3fO6ol2XPoZgvyEQC9hIY3BO/VXPJCqdHAWqOKUtKPAJULw6OOW5W1pOzqWqxF3wPzgM1hZPl+BFITcK8T/94n34n1cdD+m1sfyDMbynJrfLXU2aGeRWzk0D4BGABNZksQwIFqEmqLCvRCFCAOjji1s3KunmZqrpfDt1ApliynF8IpKZPlTVVW3454PfpsEF5kuFb5Xckso/7DJopZx+AxShMJGAfgcG9zFpArXs4vFpMJEACkRHg/57IuEV0lSVALfdNRIUEuKhfNgVoACw81A0C/XIXqkVHc4OWcO3u5n5zY4+cKHmZnI4eFDZPdJvA4F7mLaBYUEoB2u2hYQEJTIB3fQcH3xKg1tOzqar75nAYTbFkOSpMY2ap2uc98LzPrXx8skcIW2+GyrJvTqHcenyl3PP2L4GqQ72W+UggFAJDe5u3gFr381Dqd0seN65YdwsbtsN5ArSAOsjcelo2LUD755qzRjmIg1W5kADmfa5AvM/W4OGRLtNbbxYbbf2F+7wkuwzqencUo5WysIQhMGKA2X3g4cWKRQGaMAPOjsYEAZrOHB4m3LRMC9DBeRxGh4cxbqtLUfM+1xYHnvdpdfqCMLbetK7p6j01pUX+euq3cta/9+wqK8+TQNgERvTrJOB92KWp2A8qmklaWloEV0b3krL1+xlvQO/tgk/VMV4ZC4wrArSAOjycdgjQIT1i70boMHZWFwKBfnk/ysxO5n2iCGy9ORRbb9qQ9h8yXU77ldr+hokEDBLACvjMdLNeIghQWkANDhKLSkgCFKAODztuWqYXIfVXq+BVNCYmEoiYQM8sNe9z3dtdXn+j3nqzrst8kWa485hJkpNhVixE2hZeFx8Edhxg1vpphdKjAI2P3wd7ET0ClC0Os7cjGH1KcpIMohve4ZGMn+rSkj2yrFLF++xk3qfV25OHlFsfbXnvn7tebjq21payWWhiEhhpWIBaU6goQBPz98RemyNAAWqOZUglpaamaguoaSso3fAh4WemAASSU6fKuuqiAGfaH9q69aY97nf/mi751YsycgAXJPkz4efICew00Ox2r9Z2yriXM5EACUROgAI0cnYRXWlNXG9uDr7KOJKCt8vnzTASbol+jZ73WfhdSBhuHYVQNpFvvRlSJSpTeqpH7jtlfqjZmY8EOiWw53Y9Oz0f7kkIUMQApQU0XHLMTwLtCVCAtudh+zfrqdl6ijZV4cieXIhkimWilNMzq0Q+XddZvM/2JA7uu6H9ARu/Hbz9x3LS3pk21sCiE4XAHkPNClAYD6z7eKIwtKufs2fPlgULFnRa/KuvvirV1dWd5uHJ2CRAAerwuOHJGTcv0xbQ4T25naHDQxnT1VnzPptaW0LqxxGDsfXm6pDymsp017GTJTONC5JM8UzEchCAviDH7L0RxgPLk5WITE32ec6cOV0K0D/96U9SXGw27rDJPrCsyAlQgEbOLuIrIUBNW0AHqGD02alJEbfp/9s7Dzipimz/n8k5J3Ic4oBkJEjOAqZVglnBBLgqGFbMYfet76371v3s7tvkriuuYhZUUEwrAuasCIiSUXLOMP2/v+JfY88wM/R0103dv+LT3Nt976069a2e2+eeOucUL4wtAsf9PkO/qU81uPRmqKQbZH8v1w07EOrpPI8ETiBg2voJ330EIdECegLqWj/Ytm2bBAIBWblypezd+1OQ4eTJk2XcuHEV14LtihUrasyVfeDAAdm+fbsy4OzcubPiukOHDlVYSVH/wYMHBW2uW+fcrE2FMNwJmQAV0JBRmTtRK6D4gzRVYFltkc9peFM8o7me4szPZVGIfp/HOQSkLGu5K0gm9fynNC/iVLwr8KOgUTv8P4GFFtC6fTk6deokI0aMkKuuukpat24tf/jDH1QFd955p/zud79T+4sWLZJ69erJ1KlTpaysTObPn1+pkbVr1wrqef3112XJkiUycuTIiuPPPfecXHrpper9HXfcIaNHj5a+ffvKGWecIaeffrpxg09Fw9yJiAAV0IjwhXcxbl5QPnU6j/BqOfEqTsOfyISfVCag/D7XnjzfZ/BVl7crkPjAluCPHNtPSTwkd4/9yrH22FB0EejWosBoh/TMFS2gdcd62mmnyRtvvKGm3G+88UbZsGFDpUrOO+88efTRR+W1116TWbNmyRNPPFFxHJZMKLD/8z//U8liWnFClR1YSb/66iv55JNP5PDhw/LnP/+5yhl86wUCVEBdGAV989I3M1MitC0w6+tkSi7W4w0C8PtcZuX7DNXvU0t9QcuDeteV7cAW82R4R6ZlcgW+zxvtUVpotAfw3Uf0O/I5s9SNwNlnn60uKC4uli5dushHH/2UfQMK5p49e2TIkCHqnB49eihlVLcwYcIEZXWGZTOUMmbMGOUmgZlBWEAR7MTiPQL8K3JhTHADwx+G6UCk9kVUQF0YTt80GZf4sqzdHbrfJzqWYwUBNUpx3wJ5x8jnrfRMvF355svmAUGbFmZKcbbZBxcYDbQBwQNd9K0I8NNMT0+vkD8rK0sp9sFuaUuXLlUzhTjpT3/6k5SUlMgDDzxQcU3wDGKwX2nFCf9/p2pbVY/zvXsEeEd3gb2OhDdtAS3JTJS8VA6pC0Pq+SaLsj6TxRs+qbOc13W2UtgE9tf5OtMXNMlZIVOHHDZdLeuLYgI9DVs/gYoKaPhfmCeffFJdjKnxNWvWSPfu3Ssqy83NlT59+siLL76oPnvzzTdl0qRJylCDD+D7+de//lUefPBB+fLLL6WgoEBWr15dEdBU1V/0pZdekv3796vX3LlzZeDAgape/uctAtRWXBoPHYhkuvl2RSmmq2R9PieQn7ZZXl89J6xejGm0Lazr7Ljo6t6PSOMCBiTZwTYa6zQ9/Q6LG6LgGYAU3rdl8eLF0q5dOxk1apTMnj1b8vIq52e9/fbb5frrr5fOnTvLzJkzK4KTdGvNmzcXBC0h2Kht27YyaNAgFdCEgCUELwWX1NRU1Rba69evn5x//vnBh7nvEQJcPselgcBNDCklcEMz6U/UrjBZlqxj6hqXhtVzzSbHH5KlO2bL4fLQ8n0Gd6BVTqrkxHlnRaLUxP1y15jlMvlfTYPF5D4JVEugV2lxtZ+H+6F2meIUfHgE//73vyvLZU5OToVlExZNXRC1vn79epU+CRZOXYJTKUFBxQvlqaeeEqRiysjIOOGh4Mwzz5QbbrhBBfoGT/XrOrn1BgFaQF0ah+Tk4/6a+qZmSoyyYvqBmmIZDfUEEufJuj3hRbDPcGjpzbpwHtZqjgxq/5PvWF2u5bmxQyArNUk6NatsYYu094im1u5TkdYVq9djqh0MayvBymdt5+EY6qvJIp2SklLJz/RkdfG48wSogDrPXLWIp2j8IeKmZrK0zk+WNCakN4nUt3UVZ34qS8Lw+9QddnLpTd1mKNu7Rr0oSQm8dYXCKlbP6du2WBIMR6rjXg3DwckUqFhlXlu///KXv5wwTV7b+ZEcwxS9jriPpB5eaz8B3sXtZ1xtC7iJ4WZmWgFNiI+TjiX0A60Wegx9mGf5fb62Zm7YPR7UKEuSHV56M1Rhm+d9LVcPPhbq6TwvBgkMaF/ZJzBSBIjOxmyVnrmKtL5Yux7pkzBV7kRBwFKbNm2caIptREiACmiEACO5XCugwaknIqlPX9u5HhVQzSIWt5H4fWpeU9sn6F1Pbq+xApLq5/J77snB8YBQA9qZVUC1oYAKqAcGlyJEDQEqoC4Opb6Z6ZubKVE6lzBS2BRLP9ZTbuX7XB+m3+fx/gakQ9YyT3c9I3mP3Dnme0/LSOHcIZCfmSyml+DEPRqzVjX5G7rTU7ZKAv4mwCh4F8dP38xwc4PDtKnSLDdRclLiZdehclNVsh6fECjK+lTmfR9Z5Ppka+nNuMDXnu/x6W2flb6tfyGLV7ifp9TzsGJIwEFl9Y37aeIejfu13/0/C5p8GEPfBHbV6wRoAXVxhOzyA0W93RvQCuri0LrSdH76JivfZ/h+n1roiaUH9a7nt/eOnmcFm9QeVev5TlBAowRGdmpktD64SJk2EhgVkJWRgE8J0ALq8sBhGn7fvn1qyTGTT9enNkyVN1bRMuTy8DrWfHLCIfl6e3j5PoOFVEtvJltLbwaCP/XufmnBF3LFgEHy57e8KyMlc46A9ewtQ09pYLRBnSpPu0wZrdzhyjIvnmW8xb2PXmS8TlYYGwRoAXV5nHFT0xGWJkXpallAE2gYMonU03WVx79k+X1ujVjG6z2y9GZdOnLtaf+y1vxm/tu6MIvWc7u3KJSibLOzP7B+omiXqWhlx36RgNMEqIA6TbxKe/qpWt/kqhwO+21GUrx0LDbnVxq2ILzQdgLFmZ/Iuxs/M9LOaA8tvRlqhzJTdsrtY9aFejrPi2ICIzs3NN473JtxnzY5Q2VcSFZIAj4kQAXU5UHDTQ1P1qYVUHSrZyOzlgCXUbH5agjA73NBBPk+g6tsnYulN5cGf+Sb/TPaPSmntuQKSb4ZMJsEHdXZHv9PbSiwSWxWSwIxSYAKqAeGHRHwhw4dUlPxJsXp1TDNZHWsy2ME4Pf51bbZcqTcTFJ2Ly69GSpy+P7dO+Y1iccOS0wSaFaUKac0zTfadxgG4CJlMkuJUQFZGQn4mAAVUA8MXmrqcUsllFCTpX5WopTmJ5msknV5iMCx+Bdlw97I/T51l/oUrtW7vty2LfpYLutHBdSXg2dA6LN6NDVQS+UqDh48KPHWkp70/6zMhe9IwAQBKqAmKEZYB25uCQkJgpud6dK/CaclTTP1Qn1FWZ/Iexs/NybKkEbZ1tKbq4zV51ZF1w2YJUhEzhJ7BM7paV4BhVEA1k/6fzr7fXr33Xfl88/N3d+clf54awcOHHCjWV+1SQXUI8Nl1zR8/6achvfIEBsToyDtR3nNQL7PYIGmlEXHrSAnZZvcNnpjcNe4HwMEmhZmStcWBUZ7ivRLx44dEz1DZbRyVlYrgSeffFJeeeWVWs/x8sHrr79e5s+f72URPSFbdPzqeAJlZELgJldeXi4651xktf10dUlmorTmNPxPQHy+l5xwUL6w8n2a8vsEDtwEyjK9vfRmXYbtZx0el67NaPmvCzO/n3tWzybGu6BnpOj/aQbtqlWrZPny5XL06FFVIX7rdu7cWVE5rM27d++ueI+dLVu2yHfffVfpM7zBg8GKFSvUNvggfHaXLVtW6Xd07969KsYCdQfXBXn0GKOObdu2KX/flStXCq6pWjZt2iRffPGF7Nmzp9KhqrLA8rl06VLVF9NudZUajoI3TETvkUHUaT7wB2E64rJ/03RZsX2XR3pKMSIhcNTK97lx77ZIqjjh2klq6U0r+XyUFBWQNPY/MvYPPa0flCjpFLtRK4FxvZrXejycg7gXc/o9HHInXjN+/Hil/BUUFMjq1atlwYIFanvLLbfIe++9py547rnn5OmnnxZsUbD/t7/9TRITE6V+/fry8ssvS3p6uixatEjOPvts6dy5s6xbt07+93//V0aNGiU33XSTvPDCC1JUVCQbNmxQ53fo0EHuuOMOgVL5ww8/yNatW6VXr15KCYZCCSUU1ta+fftKp06dpH379kqp/eabb2TmzJkybdo0de65554ra9euFcj/4YcfymOPPSZjxoypVhYsLPPZZ5+ptpo2bSqDBg1S/eF/JxKgBfREJq58Ah8jWEGDn8hMCTKoeboVHWyqNtbjFoGizI/lfYN+n7ofE0ujb8WsjiXvyYV9+Hytxziat2WNcqVTM7PR77BqwVLH6ffIvzk7duxQyuDChQvl1VdfVYrixo0nd5PBjCAUQVgTsQ9lFOW8886TRx99VF577TWZNWuWPPHEE0rxhMIKv9ElS5bIDTfcIA899FCF8LBaQnHE66mnnpIRI0bIO++8IzNmzJDZs2dXnHfaaafJG2+8oRTIG2+8USmykCEnJ0c++eQT1eZtt92m2q9JFiirPXv2lNtvv53KZwXZ6neogFbPxZVPcbPDjc/0NHx+WoJ0rc+coK4MqqFG89N/tPJ9vmiotp+qwdKbDbH0ZhSWGQMfk9x0ZoGIwqGt1KXzT2tR6b2JN9oQQAU0cpp5eXkycuRIadasmVx22WVSXFwsvXv3PmnFZ5xxhso+gCwEp59+urz//vvK4gllcsiQIer6Hj16KGUQQUujR49WFlIcGDdunDz77LMVv6VQLGHkgXU0KytLKaA4r0GDBrJ+/XrsqgLLKgpk7NKli3z00UfSsWNHufnmm+XBBx+Uiy66SP7yl78Iptlhfa1OFlUB/wuJABXQkDA5c5L2NdI3P5OtDmtBnziTPJ2sKyXxoHy5bbYcNZTvM1j26V3yrHXfozNaMy9ts/zi9C3B3eV+lBFA3tcJfexRQOEKBeWHJXICzzzzjLz99tvSqlUrue666+Tee+9VlcLgoktVv0tkhtEFv4m4FsojPkduVl1gIW3YsGGlxVxwPvwvdR2ZmZn6dLUN5cEC8mDK/6233pJhw4YpBfbaa69VyigqqUmWYNkqNco3JxDgX9cJSNz7AE9oOhretBS9GqVJZjLn4U1zdaK+I/Kicb9PLfeohubyiOo6vbQdf8osOaUxH768NCYmZRnSsb6U5JrN9IHpXgSzhKKkmOxLtNYFX0v4VjZp0kT5VU6ZMkUFI2l/UK14Vo0af+mll5QfJXwq58yZo/w+c3NzpU+fPvLii8dng958802ZNGmSYNobUfMIWkLBFD2mwev6AAF/UJSvvvpK1qxZI927dxdYV4cOHSrTp09X7+fNm6csqzXJot3pmIZJoaz1PzpJ1YrH+YO46e3atUtNxeunNxNSJCfEyQArGOnlb/eZqI51OESgKPMjmbfqC1taO7705ie21O2VSuPjA3Lv2EVy1p+6ekUkymGQwIX9Whqs7XhVegZKz0gZbyDGKiwpKZELL7xQunXrJtnZ2QKFEn6YZWVlykeydevWgmn6AQMGyI8//lhBp7CwUAUGwUo6duxYFXSEg/CtRFDT/fffrx4S4OuJqfTLL79cSktLVcASfDZ1MFNFhSHsLF68WNq1a6ei4OEbCrkw7X7WWWdJ//79lVV14MCBFXVXJwuawbnXXHONmqqfPHlyCC3H5ilxlrn4J1t2bDKottdwbMYfzuDBg6s9bteH+GPbvHmzMu9XnTaItM2V2w/LtfM3R1oNr3eIQIHl9/n6uj/bMvWOLvx1QIEMKHjbod6428zNL94oT75/2F0h2LpRAsXZqbL8dz+TpESzE3nbt29XBgD4CzpdfvWrX8nFF18sjRqZXdNe9yPz4ll619h276MXhVQXVA2wheUzuCAVU0ZGRrWrTWEaHS8orlUL0iZVrQuW6/379wusk3UtYA43AdQJBRaWzOCC9vLz80/4HOdUJwseZOjGEUzwxH2zf7kn1s9P6kgAVk88edthvi/NT5a2BVwlpo5D4srp8Pv8fNsTtimf6FRvny+9WZeBuXnw45KdxgmfujDz+rkXDyg1rnzCAACFB75/LGYJQKGrqjCiBSiLNS11it/C6pRPXFddXVD4wlE+UZ8uuL6q8oljaK+6z/Uxfb3eYjazri4A+tpY2VIB9eBI4+aHFCB4mjNdxrTOMF0l67OBwBGZKz/s3W5DzcerHBYlS2+GCqgwY6PcNHJHqKfzPI8TgHFq0qDWxqXUD/5paWb9So0LygqNE0B0e7169YzXywprJkAFtGY2rh3BUx+enPTN0KQg/Sw/0OwUDrtJpqbrKsr6UN7/4UvT1Vaq75qyytNLlQ5G6ZvzO8+Sdg2oWETD8I7s1EgaF5p/mMb0LS1X0fANqXsfkMYJrgAszhGgJuIc65BbgpkfT+BQQE276CIYaXhLTi+FPBgOn5if9oMsWP2yra3ij7595nJb2/Bi5YkJR+W+M973omiUqY4Erhhi3vqJGSdMwdP6WcfB4OkkECYBKqBhgrP7MtwEoXzaYQUd0yqTKyPZPYBh1J8cv18+3fJvW/0+Idbk9pYjfSC60y/VhL9Ho//IOd1TajrMz31AoFW9bBl2SgPjksL6iZknRr8bR8sKSaBaAvTKrxaL+x/CKRsvKKCmHeJLMhOlb+M0eWdtdCYgd3/06i4BHjYOx70omw/sqvvFdbxiYsvYHvdfDJstr355ruw79FMS7Doi5OkuErh2VLsag0HCFQu5PxG1jHttTYEm4dbtpetCjVj3ksyUJXoJ0ALq4bHFzRDTQghIMl3ObZ9lukrWFwGBYsvv88Mf7V8SMy8lURpE6dKboeIvyVgnM0bsCfV0nuchAoVZKXJ+X3tyf+Ih0PTDvofQURQS8BwBWkA9NyQ/CaST0sMKimW/TJbWVjqmjsUp8uXmQyarZV1hEChI/0FetdnvU4t1QycrP16ULr2p+xjK9uJu/5InPpgu3/4Y29bgUFh56ZyrhraV1OSflmg0JRum35HCJzExun8SPzjdfBq+nvPMZ2sxNa6sx9sEaAH18PjAHwm+oLg5mg5GQrfPbV95fVwPo4ha0VITDspnW5+QY4FyR/oY7UtvhgoxKeGItULSx6GezvM8QCA1KUEmD2llXBLMMB05coTBR8bJskISqJ0AFdDa+bh+FAqoXpvYtDA9GqRKk5zofuI3zcxkfXioOBg3R37c50x+yra5qZIdt9RkF3xdV5+mr8uIMvPWNF9D8bDwE/s0kaTyQ8pX0+QDOR7w4feJGScWEiAB5whQAXWOdVgtYVoIqyNh/VzTBTfd8zucuMSZ6XZYX/UEirI+kA9/sN/vU7c+ozNy3DljadVten1795gXJM2GKV2v99tv8iVby23eenZXwfLEsFZi+UYTaeqgyKIe5v702zeC8kYDASqgHh9FKIlIjovl4ewIRurXNE2aZNMK6vTXACvzLFg9z9FmexescbQ9PzTWIGuVXD9svx9EjWkZL7GW3WyQn658NOEPj+UZkbMTiigsmJglCqfoa5mAPBx6vIYEIiNABTQyfo5crVOD7N2713h78ZaCO7EjraDGwdZSIfw+P9ky2zG/T4gyvHG2JAVW1yJV7B66rMcj0qKYKyR59RuQlBAvM8Z0qCQeZoVgDc3JyVH+8bt27VKzRFBKQy2wfmJmCXk/a1qLPNS6eB4JkEDdCVABrTszx6/QVlBMFdXlBhuqoP0tK2hjWkFDxRXReWrKT16QTQ75fWphr24fe0tv6r6fbJuSeEjuGfvFyU7jcZcIXNS/pTQqqH6JRARqwnoJRRT3yd27dwse1EO5T2JWCefR+unSwLLZmCdABdQnXwF9k7TDFxRW0PNpBXXkmwC/z49+/NqRtnQj+CNvn7lMv+W2GgL9m8+XER1pBa0GjasfwffzprEdTyoDFFHMFOXm5iqfeSiie/bsqdVtCYoqLJ9c+eikeHkCCdhCgAqoLVjNV6pvsNpnyXQLAywraMu8JNPVsr4gAoUZGxz3+0Tzl7WxrEOBbUGScLc6AneMfF5SknhLrI6NW59dNbSNNC6s3vpZnUywgiJzCBRRKJdQMqGMInApuGCBD3ymH+yDj3HfHgKrVq2S5cuXVzwU6GAy3Ros0hgrWKW3bat8v8LnWKmKJboI8G7ro/HEzVJHbZoWGzfuy7vkmK6W9f1/AqmJB+Tjzc76fWr445ub9x3WdUfTtnHOCpk2hEm1vTKm2WlJcmMI1s/q5MX9DJHtmJqHhRMP7vATheKJAsUUfqRMvVQdPfOfjR8/Xs477zz5+c9/LmVlZbJmzRpZsmSJjBw5sqKx5557Ti699FKloLZp00bWrl2rjuE3r1OnTrJy5cqKc7kTHQSogPpoHLFKB26YmIbHH6Xp0rV+qnSul2K62pivD2O1P/CCbN6/03EWWHqzacYKx9v1a4NX9npEmhQwH6QXxm/6mDIpsJbejKRAEYUCCkUUllH40e/YsUNl6U12awAAM/FJREFUFcEDPY6z2EsAvF9++WVZuHChvPrqq3LTTTfJxo0ba2wU4zVhwgR5/PHH1TmLFi2SwsJC6dChciBajRXwgG8IUAH1zVAdFxQ3TUxR2DUdMYlWUOPfiKKs9+XjH91JAD+dS2/WaTxTE/fLXWO/qdM1PNk8gXq5aTJleDujFSOnMhRRFCieXPfdKN4aK8vLy1OWzmbNmslll10mxcXF0rt37xrPxwGc99hjj6lzZs2apd7XegEP+pIAFVCfDRtuovBtsiMYCShK85NlYDMGY5j6WhSmb7DWeXc232ew7CMbbgl+y/0QCAwtfVEGt08P4UyeYheBO37WSdIt673poh/eaf00Tbb2+p555hl5++23pVWrVnLdddfJvffeqy7AeOgCtwhdunXrJoh7+OCDD2Tu3LkyceJEfYjbKCJABdSHg6lXA4HTth0FvqApCZyaipRtGvw+rXyf5Ta4S4QiW/u8NGvpTVrzQmFV9Zy7Rs0R5J9kcZ5A52b5clG/Ulsahi8oCq2ftuCtttJNmzZJ+/btpUmTJjJz5kyZMmWKCkYqKCiQ1atXK39cXDh//vxK119++eXq3AEDBgisqCzRR4B3WB+OKXxk4A+KNCN2lKL0RBnfIcuOqmOmTvh97it3x+9TQ76hE6x44a0Qo+uI1W2zvG/kmsFHY7X7rvb7Nxf1sKxf5h+AYW3DzBGUTwQgsThDoKSkRC688EKBVbNnz57yyCOPKEUUPp2DBg2S1q1bq8CkevXqVRLoggsukC+++ILT75WoRNcb83Mc0cXHk72B/xKWo4NzN3xB7Yjk/Fm7LFnw3T75ce9PUySehOFRoeD3OX+VO36fGknvgtUi5mPVdPVRv72mzyPyzEfXyMYd9sw0RD3AMDo4oU9z6dWqOIwrT36JnuLFDBKLswRg+bz11ltl+/btAsunLk899ZRaThUuEVVXoyoqKqrIWqDP5za6CNAC6tPxhNKJP1hYQe2IiE+2puCv6pbrUzruil2Ysd5Vv0/0fmSTHGvpzTXugvB56+lJe62ApO983gv/iJ9h+XzeN76rLQIfPXpUpWKCokPrpy2IT1opDCfByqe+QOds1e+5jR0CVEB9PNbZ2dkqZxpSi9hRejVKk271I0uDYodcXq5T+X1a+T7d8vvUbK4yG0Csq4257cjWz0m/NgxIcmLgbz3rFKmfZw9rWD+hAEEBZSEBEvAGASqg3hiHsKRARDz8Qe2ygkKoaT3zGJAU6ugEymVv+fNWvs9doV5hy3n4o27HpTeNsb3n9JclwQafRGMCRkFFHRrnybSR9jw1YcUdPKTDbQmR1SwkQALeIMC/Rm+MQ9hS4KZaXl5uW1qmepmJcuEp2WHLF0sXFmS9J59scj/q/IqyAi69afCL17LgS7lyIJ1pDSKtVJVlmJQ/TuoliTZlHcADOqbdGfleCTvfkIDrBKiAuj4EkQkAP1Cs8IEpJiiidpSz22ZynfiTgIXf54LVr5zkLGcOT2yxz5mGYqiVaaf9S0py6I5ix5BfPbStdGtRaEfVasUjpKtD4BGm4FlIgAS8Q4BR8N4Zi7AlgRUUU0xQQuEXarpg+vG6U/Pk+lc3W76Npmv3f31p1uo5H3nA7xMk81MSpH7y14x+N/y1ykzeJbePXiPXPl45VYzhZmKuuob56XLXeZ1t6zesn0hZh4d0FpGe8w4TAwl4hgAtoJ4ZivAFwfQSnOuR4y54ZYnwazzxylYFyQJLKEsVApbf5x7L73OLy36fWqrpna2EzQF7gtJ0G7G6PaP9U9Kr1J4gmVhl+vtLe0lmapIt3UeKOvh/4gGd1k9bELNSEoiIABXQiPB552I9xaRz3dkh2cWdcqRJNo3mwWzh9/nppmXBH7m6P7IBl960cwDuGb1A4jmVawTxJQNKZUTnhkbqqloJUtPt3r1bEKhpR57kqu3xPQmQQN0JUAGtOzNPXoHoTiihWGoOT/12FOQGvbFPvvUDbEft/quzMGOdZ/w+QQ9Lb2Zx6U1bv0htiz6Ry/rzDyBSyE0KM+TX53ePtJoar8d9ELNBsH6ykAAJeJMAFVBvjktYUukky7t27bIlOT2EwlT8xA7m/UzD6rCLF6Ul7ZcPN7mf7zMYwXQuvRmMw7b96/s9KgVZybbVHwsV/+WKvpKVZs/UOxRP+H7C7xMWUBYSIAFvEqAC6s1xCUsq+Dnl5OQoCygsAHaVidY68aX59vx42CWz0Xrh93nsOdl6YLfRaiOtrBeW3mSxnUB26na5bfRG29uJ1gamjmgr/dqV2NY9TL2j0PppG2JWTAJGCFABNYLRO5UgMT18nmABsCsgCVHxt/TNj9kE9QVZ71p+n8u9M+iWJKOaculNJwfknLLHpVtzBiTVlXmHxrlyz3n2LLcJWRB4hBeygXDJzbqODs8nAWcJUAF1lrcjrelUTNoSYEejjbKTZGqP2Fsrvsjy+3x11at2II2oTi69GRG+Ol+MOKT7xr5pRVfX+dKYvSAtOUEemdJfUq2tHUUHHuncyHa0wTpJgATMEaACao6lZ2rCkz+UUFgCkITZrjKsZYYMahY7+fXg9/mB5fcZsP55qeCPuG2G+ysweYmJE7KUFX8gF/dlVohQWT94UU9p2zAn1NPrfJ6e9YEbEtMu1RkfLyABxwlQAXUcuTMNwgEflgA7A5LQE6wV3yArBn6ELb/P3Ue95/eJMbiyA5be3I5dFocJTB/4mORlxLA/dIi8z+vVTC620i7ZVZD5A3mQEYiJ+x4LCZCA9wlQAfX+GIUloQ5Igh+onblB05Pi5dbT8sXaRHXJz3xXPtvsLb9PDXxCcy69qVk4vc1N3Sy/OH2z0836qr0WxVny+8t62SYzpt7xoI2ZHwYe2YaZFZOAcQJRrjYY5+WrCmEJgEUACujRo0dtk700P1mm9LBW4InSUpSx1sr36T2/T+A+vvTmV1FK3h/dGtfxMenUhAFJ1Y0W/D4fv26AbSmX0CaWIYYFFG5HnHqvbhT4GQl4kwAVUG+OizGpkJwelgFYCOwsI0szBK9oK+lJ++T9TU96zu9Tc56hlt48qN9y6wKB+PiAFZC00IWWvd/knyb3lg6N7Xs4LS+3XGOstEvI/MEVj7z/faCEJBBMgApoMI0o3McKSbAMHD58WPlI2dnFKd1zpXVBFPlfWX6fuyy/z20ey/cZPIYjuPRmMA7X9jvVXyITe0fRd98AyWkj2sl5vZobqKnmKvSDtc78UfOZPEICJOA1AlRAvTYiNsgDywCCkmApsHMqPslaqvMXvXMkK0p+h/Mzl1h+nytsGBEzVZblY+nNpWYqYy0RE7hp0OOSnRYDAXkhkOrXtkTun2Bfvk+IgMU2mPMzhMHgKSTgUQJUQD06MKbFgoUAU/E7duywbZlOtfrS/h0ytWOCJPr8m1WUCb/PBaaHwWh900+B36G3UkIZ7aDPKitI/0FuHslsBFjn/dFp/SUxwb6bAB6k9dR7ejr9b332p0JxSUARsO8OQcCeIoCp+NzcXGUBRb480wU/BpgOS0xMlH5tSuS6U+3z+zIte9X60pP2yns/eC/fZ1U5T+XSm1WRuP7+/C6zpKxR7CpEWalJ8sz0wVKUnWrbWCDqfefOnSrgCDk/WUiABPxJgAqoP8ctLKmTk5MFQUnIl2cqQT1+DLZu3arqxDKghYWFAmV3aIsMGV+WFZacbl4UJ+Wy48hzsv2geSXdZL9Gc+lNkziN1ZUQf8wKSFpirD4/VRRvLQs1y7J8tm9k7wppyOqBqHc8UONew0ICJOBPAvzr9ee4hS01FFCkZ4IFARGkkRRMg23ZskX9GCD/Xn5+fqU0KJd0ypbTGvtrpaS8jMXyxZZvI8HiyLVXtnOkGTYSBoFuDRfKuT1SwrjS35f85qIeMvSUBrZ2AsGUUECRXg4PvCwkQAL+JUAF1L9jF5bkyJMHy4FO3hxWJdZFsKBC+USieyieUGyrFrR1Y598aVOQXPWQJ98XZayx/D5f86RswUIlWuuPt+HSm8FIPLd/y5DHJTM1dgKSpgxvK1cObWPrOOCBGQ/OeIBmwnlbUbNyEnCEABVQRzB7qxH4acJ3ChGkKnCojuLBArF9+3YV1FRUVFSrJSLF0pbuHVQgjbK9/WOs/D5/9L7fJ4bqyjIuvVnHr6zjpxdnbpAZI+zNvet4p2po8GenNpVfn9+9hqPmPoaPOZRQPEAz4bw5rqyJBNwiQAXULfIut4u0TEjPVJfUTLCaQvFEEBP8SaF8Qpk9WclOSZBfDi6UgjRvft2O+30+a/l97j1ZVzxxfFwLLr3piYE4iRAXdX1UWtf3lwvKSbp0wuFBZfXkb1f1tXwxLbO8jQWrHemUS6Hcc2wUhVWTAAkYIuBNjcBQ51hN7QRgBYUTP6a1oFzWVmB5QLARpt7hf1VQYFnhrCn2UEtxRqLcP7hIMpNDvybUuiM9LzdjkeX3uTLSahy5vtCa1q2fxKU3HYEdYSNJCUfk3rEfRViLdy/v3CxfnrhuoCQnJtgqJHzNYf3EAzNTLtmKmpWTgKMEqIA6ittbjenUTIgo1SuKVCchHP83b96sUjhBaQ131ZFmuUly94BCsZaH9kwpylztC79PDWx6ZyvCOMClNzUPr297N3lDzuxqX0oit/rfsiRLnr9xiOXnau+qE3gwRu5iPOwy5ZJbo812ScAeAlRA7eHq+Vrhx7l48WI1lQ6FElNcy5Ytk/fee6/i9e233yof0W3btqkfgU8//VTWrVsXUd/KilPkLksJTfLANy8jaY+V7/PJiPrj9MUjGmx2ukm2FyGBmcOeknQvPXVF2B8kmn/5F8NszfWpRcTsDCygCHRkyiVNhVsSiA4CHlADogOkn3oBX6q7775b5syZo8TGlDp8Qh9++GH1ev755wWvhQsXKssoFNObb75ZvvvuO7XFsUhK1/qpcnv/AldXS4Lf5zaV79Mffp/g3cFaejNTvokEPa91gUC9rNVy/fDo8NttmJ8u828dLo0KMmwnCV9z3KsQdITIdxYSIIHoInDyCJLo6m/M9+b777+XmTNnqums4Ckt7K9Zs0ZuvfVW6dixo/ILxdQ8/K4eeeQRuf/++6VTp04ybtw4mTx5sowePVpZT8MF2rNhmtx6WoH88p1tUl67+2m4TdR6XW7GO/LBan/4feqOTO+EgBYXYGkBuA2bwGXdH5HZH9wg328+EHYdbl9YLzdN5t06TJoWnZhyzbRsUDx1vk88HLOQAAlEHwFaQKNvTGvtEdIu3XbbbTJx4sRK52EKHn6gmG7/29/+pqbaMTWPfHvr16+XU045RZ1fUlKiAgE2bNhQ6fpw3vSxktTf0teaWnM4Lum43+fr4Yjs6jU981e72j4bD59AcuJhKyDp8/ArcPnKYmtpTSifLUuybZcED76Yekeieeb7tB03GyAB1whQAXUNvTsNd+jQQVk4q7aO6XVEuC9fvlzl2rvvvvvkP//5jwo+whR9cMQ7rKVIx2Si9G+abllC8yXBISUUfp/v+szvE5zHNMuRpMBaE8hZh0sE+jV/RUYpK7ZLAoTZbP28NJk/c7iVUsr+ddeRbQNBRzpAMvi+E6b4vIwESMCjBDgF79GBcVqsdu3aKb/PvLw8FZDUpEkTefrpp6V79+5qtaNgeRAUgKl5U+W0July54A4uX/hNjkS2eqgtYoUJ8csv89nZYdP8n0Gd+bKtpx6D+bh1/3bhz8rby4dI4fs/KIbhKMDjpoXZxmstfqqdMQ7lNDCwkIGHVWPiZ+SQNQQoAU0aoYyso5gyguWBxT4XJWWlirrJ/b37dunrKO6BVg/GzQwu+YzfELvGVQoKTaaQuH3+eWW73Q3fLPF0putufSmb8arNkEb5ayUnw89VNspnjlWWi9LXrt9hDihfKLTCDpCyjcEHTHZvGe+BhSEBGwjQAXUNrT+qhj+nzfccIOyfsIS8dZbb0nv3r1VGqaePXvK3LlzVYcQGQ8rKV6mS5d6qVay+kJJh8ZluBz3+3zDcK3OVHdVByy9efzhwJkW2YqdBCb3fESaFpqbQbBD1vYNc+XV20ZIw3z7o90hP3zT8aALn0+Tsyt2sGGdJEACZghwCt4MR9/X0rJlSznnnHPkyiuvVHn3EIB0zz33qGmwCRMmyAMPPKCm6OGbdeedd9rW3w5WntD/GV4kt7+51ZoqNzMfn5G0V5ZsnG2bzHZXPK55dKTwsZuTX+pPTTogd49dKpf9s4UnRe7dukieumGQ5GWkOCIffM/1SkeZmfZH2DvSKTZCAiRwUgJxlrWLzmXVYHriiScEEd+DBw+u5mj0fgT/K6Q/0asdwd8TkfFQPBMSElRCaCd6v2nvUbnNUkI37DkaUXPw+9wfmCVfbfXf1Ds6jqU3F5+O3J/+mLaNaLBi7OJJT9wir3/trbRMZ3RvLP+4up+kOpQ4H1PucOlBnk8km4/1oKNf/epXcvHFF0ujRo1i7K+B3Y1FApyCj8VRr6XPUDS18onT4IuFH4Zjx46p6HgoqE6UksxE+e2IImlbmBxRcznp7/hW+UTHZ2DpTSqfEX0HvHrxnaPmSFKCd27BVw5pI49NG+CY8ol0S1A+cY+BS0+sK59e/Z5SLhKwi4B37n529ZD1RkxAWyfwg4FAJaeM5tkpCfLrIUXSq1F4/nJFmavktTX+9PvUgzacS29qFFG3bZq7TKYMOeKJft19Xmf57SU9rZkO8/7X1XUQD7RQPvHAC+UTWxYSIIHYIsC/+tga77B7m5ycrCyhmDJDxLxTSmiKFZB0h7Vs57nt6+YblpG8x/L79Nc671UHh0tvViUSfe+v6f2INMxzxteyOnqpSQny6LT+cuPYjtUdtuUzKJ9w60EpKChQrj22NMRKSYAEPE2ACqinh8dbwmFlEqRIwTJ5u3fvdky4+Lg4mdQlV6b3ygtp/Xj4fW499IzsPOTv4B0uvenYV8y1htKS9sldY791pX0kmEeapXN6NnWsfbjwwPKJLZVPx7CzIRLwJAEqoJ4cFu8Khbyg8BFF2hQnlVAQGdYyQ/7LmpLPSan9a5uTvtDy+/zeuxBDlOxULr0ZIil/nzai9QvSv226o53o2rxA3rnndOlibZ0qmDWBCw8soPArZ65Pp8izHRLwJoHaf8m9KTOlcpkAluZEuhTk7UPEvJMFaZoeGlksLfOSqm22OPN7y+/zzWqP+enDM6ylNxO59KafhiwiWe85/SVJtHERhmDhxvduLgusHJ/1cp1TerXyCRce+HzCpYeFBEggtglQAY3t8Q+790gYnZ6erlYvgTXUyXI8Qr5YhrWo/AOaafl9Ltr4lJOi2NbW5HbMjmYbXA9W3CL/K7lyoL1jjoj731zUQx6+5jTHIt2BGson8nwi3ydceODKw0ICJEACVED5HQibAKbiMSWPHxdYQ50syZa1aHrvfPl5z1xJsr7F8PvcdOhp2eVzv08wVEtvpiP3J0ssEZja91+WVdIe5axBXrq1stFwuXpYW0eRQvlE0OKBAwckJydH3S8cFYCNkQAJeJYAFVDPDo33BUPePvyowBIKf1Cnp+NBaFSrTPnN8GJpkv+hLN26yvvQQpDwai69GQKl6DslM3mX3DHa/Hd4UFk9WXzfaOlZWuQoND3tjqBFWD5xn2AhARIgAU2ACqgmwW1YBLQSCr/QPXv2qFdYFUVwUeuCZHlg0OlyZqseEdTinUvHNXfWr9Y7PackY9o9I31amVHUEqycnnf8rJPMuWmoFGWHl0s33BGB8olod0y7w+cTMyUsJEACJBBMgApoMA3uh00A0/EITIIV1OnoeAidk5ouD4+eIg8NvUzSE/0b4FCcmiQliV+FPQ680P8E7hn9iiD1WCSlaWGmSrF0y5mnOJZcXsuLFEvI86kDjlJTnVV+tRzckgAJeJsAFVBvj4+vpENgEl7wB4VfKKwgTpcLOvSXN86/WzoWNXG6aSPtTe+SY9XDdd+NwPRpJa0LP5NJA8JXQMf1bibv/tL5KXfg1nk+jx49qlItUfn06ZeQYpOAAwSogDoAOZaagBVU5wl1SwltlV9fXp1wh1zbbVTEliSnx25Y/U1ON8n2PEjgun6PSGFW3Sz5OelJ8rer+so/rukn2Wl1u9YEAr3CkVY+Ge1ugirrIIHoJUAFNHrH1rWewR8UwUmIfHVy2c7gDicnJMpd/cbJvHG3Sau8esGHPLt/SkG6ZMoyz8pHwZwjkJWyU24fsz7kBkd0aigf/dcZMrFvi5CvMXmiVj71CkfM82mSLusigegkQAU0OsfV9V4h4lUv24nVT/DD5EbpXr+lvHXBvTK160grVVP405pOyH5DJ/jKOe+24ETf2EbdCZzVfrb0qJLrtmotsHr++Yre8uyMwVLfSrXkRoHFEz6fcLnB8ppJSdUvEuGGbGyTBEjAuwSogHp3bHwvGSJfEQGLSFj8QMFK4kZJTUySe/qPt6yhMz1tDe2ZZz4Fjxu82aYZAohDunfM61JTPBKsnh/+6gy5sF+pmQbDqAV/21u3blVXQvnk8pphQOQlJBCjBKiAxujAO9VtBCHghwkWUPxQITLWrdKjQam8feF9cmvvsyXFmqL3UjmzWa619OY6L4lEWTxAoH3xR3JJ38rf1fp5afLYtf2V1bNBvjtWT6DBCmhItQSlk8qnB74sFIEEfEaACqjPBsyP4sIfrLCw0EoHE68sofANdavAN3TGqWfI4ot/KYObdnBLjBPandzOHReFEwThB54jMH3go5KfkaQC6qYMbyuf/PpMOatHU9fkxFQ7Uq0hyBCzHFA+ExISXJOHDZMACfiTQOVHa3/2gVL7gAB+oKCEIigJL/iNIWWTW6VZTrE8dfYMmbPiQ7nt7cflx3073RJFLb3ZKn0p3T9dGwFvN5yTulX+9/wD0rLxGdK5WYGrwmImA3+/mHrH3y+yXrCQAAmQQDgEqICGQ43XhEUAqyYhMAkrJiFhPZRQvMfnbpUzW/eQIc06ykMfvSz/9/GrcvDYEcdFuaZjgcQFmHzecfA+aDAuoVAycqfJOY1Pd/XvBKjgw40pd2zh280cnz74AlFEEvAwAU7Be3hwolE0KJvIE4o0TVgj2s3gJM03MzlVbuvzM3nvkv+Sc9qcqj92bHteMy696RhsvzQUlyJp2ZdLXv3nJCVjtOvKJ3y34cOt0yxR+fTLF4lykoB3CVAB9e7YRLVkSNME3zFYU/DDduSI85bHqoAbZRfIX0ddLa+Mv1261WtR9bAt748vvfmlLXWzUj8SiJPk9JGSW/8ZSc+9RuLi3V9DHT7beFDUbjRMs+TH7xVlJgHvEaAC6r0xiRmJEJwEJdQLwUnB0JE7FCsp/WvMNGlb0DD4kPH9GWrpTfcyAxjvECsMm0BS6mmSU+/fklV4nyQkur94gg42gs+nzmbBYKOwh5cXkgAJVCFAH9AqQPjWWQI6hQsianVwA6bn3fQL1QRGl3aTUS27yHPL35f/fm+OfL/T/DKZXHpT047dbWJKF8vaOVWSUjp5BgL8s/H3iJkJBBrh5YW/Sc8AoiAkQAIRE6ACGjFCVhApAVhAEdSwb98+ld4FP3oITvLCVF98XLyc27a3nNW6p8xeulh+8/5cWb9nW6RdVtd3spbezJAPjNTFSnxIIKGjZOVfJclpzvsd10YLU+54IITCiRkKLqtZGy0eIwESCJcAFdBwyfE64wSwhjx+7LB0J/xCEayEz7xQEuMT5MIO/WVC+77yzLJ3raj5efLt9h8iEu340psRVcGLfUggKbWnHIkbJ4ePtpbElBLP9EBPuSPBfEpKinoIxMMhCwmQAAnYQYAKqB1UWWfYBGD1RL5QJLrGC9G3mJL3yg8hFNEJ7U+T8e36yrzvPpXfffiSfLopvCU0e+R9z9yfYX9T/HZhnCSl9bci2y+zptrL1NQ2HrKQkgzfb7cLZh0w5Y6pdy89+LnNhe2TAAnYR4AKqH1sWXOYBKBsYgoe1lAoofih1u/DrNL4ZZieHF3aVb0Wrluqcoi+vvpLCVj/Qilnt8DSm8z9GQorX58TlyqpGWMlNWuiJCQ1rugKHrTw/Ya1EQnd3XzAggyYcmeUe8XwcIcESMABAlRAHYDMJsIjgFRNekoeaWDwQ40pea8FQ/Rv3F7wWrVzs/z989fl8a8XyZ7DtS83OqnNsfCg8CpfEIhPKLaUzvGSknm2pVxWv+IXLI1uWkGR0xOKJ/LxIsrdSzMNvhhkCkkCJBARASqgEeHjxXYTQJS8npLHdCWWAMQPJT73WmmeWyy/HHC+3Nr7HHnym8Xy8OdvyortG08QMzk+TkrTv+H0+wlk/P6BNc2e2ltSM8+xpttPsx6Ual8fPdgKCmXUyQcr/B1B+YQSir8nPOyxkAAJkICTBLz3K+5k79mWLwjghxk/kgiMwI8mrEawhuJH08kf7VBhYWWlSZ2GqNeHG1fKY18vlBdWfCD7jhxSVVzTId9aepPJ50Pl6fXz4uILLKXzDMvaeZaVv7NBncTVVlAsTYvvtN0FCifcWhDpDgU4Pz/fkw9zdnNg/SRAAu4ToALq/hhQghAJYJpQ+4XqH1Eopvgh9Wrp0aBU8PrVwAtkjqWEPvbVQjm32Tqviku5QiaQbK1Y1F8tk5mU2st6EArvVorvLl5IQWZ3rk1MtWurp1fdWULGzxNJgAR8TyC8u6bvu80O+JWADlBKS0ursIb6IVF2RlKKnF/WT72OHVkvh/YvkMPW69iR7/w6FDEpN5LGY2325PQhlm9nphEGsILCx9kuKyiWu8UDGxRQPMB51YXFCExWQgIk4BsCVEB9M1QUNJgApuOLiopUGhv8cGNK0WuR8sHyBu8nJDWS9JzL1evo4e8sRfRVSyF9TcqPrg8+jfueIBBn5ersYimcg62E8YOsKfZi41JBKbTLCooIdyifKPT1ND50rJAESCACAlRAI4DHS90lAP9PWI9gDUUOQ1iR4BeK6UU309rUhUpicktJTJ5iLcU4RZQyemChHDnwtrX/dV2q4blGCVjT4qldLYXTUjrTB0p8Qr7R2qurzLQVFPk8Md2OPLpwXUH9XMe9OvL8jARIwC0CVEDdIs92jRGA9QiR8vCjQ6Q8phph7cEPr5/KcWW0pWWqukzKj22VwwfesZTRJXLk4IcSCOzzU1d8J2t8QokVud5HklP7WspnT4mLT3O0D7CCIrNDpL6gWM1I/x3gIQxL3Prt78BR8GyMBEjANQJUQF1Dz4ZNEoA1FL6g+LGF5QfLeeJHHZYfKKh+K/EJhVZk9dnqFQgcsyyiSy1F9D3r9b4cPYQE9swjGsmYxlm5OTG1npTSXSmcUP7dLnhoghVfK6F1lQduKHgAg8+n32YC6tpXnk8CJOB/AlRA/T+G7EEQAViRCgoKlE8ofoyRsglT9JiW9+sUJPJJJqV0VC/JuUIC5QcshfQrOXLoM0sZ/czaWimdArUnvg9CFJO7SJWUaC2BmZTS1VI4u1mrErW2Ite9tc65toLCp7kuCy5gmh1+nlhOE3XA6unHh66Y/GKy0yQQwwSogMbw4Edz16F0whqKIAwoorAO4UcdVlK/+IfWND6YHk5K7aFeOKe8/Kjs2PaxpKeuk2OHl1nK6TIrun6ldSRGraTW8peJloIJhTMxuaO17WAFD9WvCaenPofFfvv27SFZQeHnqV1O8ODF6XZPDSWFIQESOAkBKqAnAcTD/iWAaXkonVBGYVXC1CYUUSihXk1iHw7tY8cClmWvnaRlnVpxeSBwRCmhRw8vt7bfW69V6lV+bFPFOf7fiZd4S7FMSLICuZJaSUJyK7WNT2zkOetmbayXLVumHpaaNWumFluApR5K6GeffaYelnr0sB42gtxItmzZIosXL1ZZIEpLS5WbSTR9n2tjxWMkQALRQ4AKaPSMJXtSAwFYPGFZwo80LEaYroRlFNPy0RCgAUtY1aVJ4+KSLOtfO/UKxhIo3ydHj6y2Uj6tlWNHf7C2G6ztRmtrvZRy6jGraVy65TpRYimaxdargWXJbHL8ldTEet/QUjT9598bPB6rVq2SGTNmyLRp0wQKKAqm0adOnSplZWWyefNmefrpp+W3v/2tOvbuu+/KL3/5S+nXr5/84x//kEsvvVTOOeccdYz/kQAJkICfCFAB9dNoUdaICOhpSu0zpwOVYBFFXlG/FiigocofF59hWUvLRPCqUhDsFCjfYSmi2ypegfLt1v526/O9x1+BvdaU//F9CRy0ovOPWP6nhyUgR62ttS/lQbXCxzLOellbS1GMs6bG4T6gtnHWNt5aStUKBoqLz7EsfbnWNtdKeZRjbfOtbZGlYFqKp6Fk70FCeWZ3zpw58s9//lNlbAgW6oUXXpAuXbrIpEmTpLi4WK6++mpZuHChtGvXTv74xz/KzJkzpVevXsq/efLkyTJmzBiltAbXwX0SIAES8DoBKqBeHyHKZ5wALExI26SjhjHdiSlOHUVvvEGbK4QCCleDSAuCneKs6HtE4IdbAgEooHGWkgnFk6U2AnANefjhh+X3v/99JV4rV66UgQMHWop+ucpv27ZtWzUdDwX0hx9+kD59+qjzS0pKlFV/w4YN0rx589qa4jESIAES8BwBb4WBeg4PBYpmAlAAsJoSVlBCgUUU/nVQTJFP0Q8FKXeg7HklsAqR5VQ+Q/vmDB8+XGVswNnB3zcomfqBAjlt4T6CLR408JAUzBepm/AAxUICJEACfiNAC6jfRozyGiWAH3MoongdOnRIBSthVSUEguDHHp8H/+AbbdxAZdX5fxqollW4QAAWTwTKQRlF0By+e1BEYbHHFt9JPHAEF4x/NPgxB/eJ+yRAArFBgApobIwzexkCAfhR4gUfUSgASGiPoCUool6NMqYCGsLAevwUKJwIitu06XiGAriHQNHUlnlYOBs3bqyspVBQ8aCkfX5xrEGDBh7vIcUjARIggRMJcAr+RCb8JMYJwOKUn5+v/ESxj6h5KAdQRqtaoNxGBQXUrwn23WbndvtIHA9rOxRK7MPKiaCjwYMHy4IFC9S0OxZSWLJkiQpKQhDdqaeeKnPnzlWiIzAJuT/xYiEBEiABvxGgBdRvI0Z5HSOAwCT8uEPJg0VUv2B9gkUUWzen52E5g0JcNQWTY4DYUJ0JYJod/pyweELphO8uxg9+nnihDB06VOX5nDhxojo+YcKEiiCjKVOmyM033yzPP/+8OnbnnXfWWQZeQAIkQAJeIEAF1AujQBk8TQAKAqZDoSAgQAnKAwKWoDxAEcXLDSuktn66qQR7euA8JByUTXxvdIAbLOv4TsF/E3k9gwu+b/fdd5+yuMMPNPgBo2nTpvLkk08qy6meog++lvskQAIk4BcCVED9MlKU03UCUDgxTYoX/EShULhpFaX10/WvRK0CVGft1A8swUplTZVgoYSaCpXPmsjwcxIgAb8QoALql5GinJ4iAAsWXm5aRWEBDV6i0VOAYlgYPJzA0lmdtZPW6hj+YrDrJEAClQhQAa2Eg29IoG4EarOKQjnEFCteoVi86tayKN9UTNGyuE8ASid8O/GCZTrYPcOOsXe/x5SABEiABCIjQAU0Mn68mgQqCARbRbUygsh5vEwro5jeRRCSG76nFR2O4R2wh1+nHmconbBu6gcOtwPUYnho2HUSIAGfEKAC6pOBopj+IRBs/dJ+gFBUtDIKixgUlaoBJnXpIabfaVmrC7HIz9VKJ6bWMZ4YWyqdkXNlDSRAArFJgApobI47e+0QgarKKHI+QoHRwUtaGYXFDFbSUH0EqYA6M4BQMjG9jnHTSifGFOOFBwhYvUMdM2ckZiskQAIk4A8CVED9MU6UMgoIQHGB0oIXFBut1GB1GyikUGT0ND4UHCinNSk3UEBRD4tZArByaoUTW0yzo2Ds9PQ6lU6zzFkbCZBAbBKgAhqb485eu0wgWBnVU7tQSKH06Kl6KJ9QRKHwaIUUYuN8WkDNDGBV9uCPoh8GkOUA/JltwAxv1kICJEACmgAVUE2CWxJwiYBWdqDooFS1wmEpUBQorThH+37ivJospOoC/ncCAQQLQXmHZRPKJl7giAK2yL2pFU6yPQEfPyABEiABYwSogBpDyYpIwAwBKD6weOKFEuyHCIUJvogoWJ8eUfCwzgW/oKiyiEqHBEUz+AWWuoAZFhUAZ+xT4dRkuCUBEiAB+wlQAbWfMVsggYgIBPsfoiI9bawVK1j0tFKK41BKYSXVSine4xWtiimUymDLpuaiLZtQLMECPpyaSW3+tWDIQgIkQAIkYC8BKqD28mXtJGCcABQqTBPjpQuULT21rBUwBDdpJQznQQHVyqjeQhHT+161AGoFE0qmVjT1PrZV+wglE0teamUT/fNq3/T4cUsCJEACsUaACmisjTj7G5UEtJUPSpcuUMyCFbVg5Q1T+cHT0bhGK6ioC/t46X29rfoZPq9LgUxot+q2ps+qKphoSyvM6CusmsFKNI6xkAAJkAAJeJ8AFVDvjxElJIGwCEA5hHKGV3VFK6iwnGpFFYogXlpZ1YohzrWzaAU3eKsVTK1w6m1dlV475WbdJEACJEAC4RGo/pcpvLp4FQmQgI8InExBDe5KVYtl8Pvg8062X9WCqt9TqTwZOR4nARIggegiQAU0usaTvSEBWwhAQeT0ti1oWSkJkAAJxCQB5muJyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQJUQN1jz5ZJgARIgARIgARIICYJUAGNyWFnp0mABEiABEiABEjAPQKJ7jXt/ZZXrlwp2dnZ3heUEpIACZAACfiewObNm33fB3aABEIlEBewSqgnx9J533zzjSxcuDCWusy+kgAJkAAJuEzgggsukMzMTJelYPMkYD8BKqD2M2YLJEACJEACJEACJEACQQToAxoEg7skQAIkQAIkQAIkQAL2E6ACaj9jtkACJEACJEACJEACJBBEgApoEAzukgAJkAAJkAAJkAAJ2E+ACqj9jNkCCZAACZAACZAACZBAEAEqoEEwuEsCJEACJEACJEACJGA/ASqg9jNmCyRAAiRAAiRAAiRAAkEEqIAGweAuCZAACZAACZAACZCA/QSogNrPmC2QAAmQAAmQAAmQAAkEEaACGgSDuyRAAiRAAiRAAiRAAvYToAJqP2O2QAIkQAIkQAIkQAIkEESACmgQDO6SAAmQAAmQAAmQAAnYT4AKqP2M2QIJkAAJkAAJkAAJkEAQASqgQTC4SwIkQAIkQAIkQAIkYD8BKqD2M2YLJEACJEACJEACJEACQQSogAbB4C4JkAAJkAAJkAAJkID9BBLtb4ItkAAJkAAJkMDJCSxbtkxef/112bt3r3Tp0kVGjBhx8ot4BgmQgC8J0ALqy2Gj0CRAAiQQXQReeuklGTJkiHz22Weyc+dOmT59ulxwwQXR1Un2hgRIoIIAFdAKFNwhARIgARJwi8Af/vAHueuuu+Tvf/+7/PrXv5a3335b5syZIytXrqwk0rfffiu7d++u+AzW0gMHDlS8x7FDhw6p99u2bZMjR47I999/X3H82LFjsmLFCsE2uGzdulWWLl0qR48erfgY16KOqgXn7Nq1S9UBqy3OQ9mxY4f8+OOPFafr8yDP8uXLpby8vOIYdg4fPiy4HrJs375dAoFApeN8QwLRTIAKaDSPLvtGAiRAAj4h0LRpU3n22Wfl66+/VhIXFhYqJa+0tFS9nzt3rnTo0EGuvPJKady4sfzf//2f+vyWW26p2McHl156qTz33HPqGM4fM2aM9OjRQxYtWqRe9erVk6lTp0pZWZnMnz9fnXfrrbequq+66ipp1aqVUhZx4D//+Y907NhRnRP834cffigDBw6UPn36qPaaN28u//3f/y39+/dXrzvuuEOdjvNwDuq47rrrlNzffPONOvb8889LgwYN5Oqrr1byoV0osCwkEDMErCcuFhIgARIgARJwlYBlyQxcccUVgZycnIClmAUuueSSgGWRVDLhGD5/66231Ps1a9YEiouLA5YFMTBlypTAgw8+WCH72WefHXj88cfVe0vZDPzxj38MWJbHivfz5s1T+x988EHgoosuCmzevDlgKbSBgwcPqs8tC2zg+uuvV/uW5TKwZcsWtR/835IlSwLJycmBtWvXqo8HDRoUGDZsmNpft25doKSkRO3jPEuZqOiHpTQH+vXrp9rKzMwMfPzxx+q8p59+Wp1nWVvVe/5HArFAgBbQmHnUYEdJgARIwLsEMjIy5K9//atYCqH8+9//Frzv3bu3fPXVVwKrYWpqqgwYMEB1oEmTJtK6dWsVsHSyHsEqGRcXJ5ZiKHv27FF+prgGVtFHH31UYIm0lEmB9RPW01deeUVZYi0FQH0OS2x1pVmzZsqiiWMtWrSoCJiCVXPTpk0VU/lt2rSRdu3aqSpgjf3oo4+Un2tBQYF07dpVfT569GjVP/WG/5FAjBBgFHyMDDS7SQIkQAJeJWBZH2Xs2LHywgsvKMUT09t47d+/X15++WWxLJXKTxI+lAkJCaobOAYfSyiXwX6b8AkNLpalUb3NyspS10Kx1AU+n6gT0/yYCq9L0fXqa6Ago0CemgpkS0lJkYYNGyo/Vvh+oj/wYYU/KAsJxBIBWkBjabTZVxIgARLwIAEob1DAZsyYoZROiLh+/Xp59913VTomWBXbtm0rL774opIeVlG8unXrJrAkar9RBAB98skn1fYwNzdX+WPqOt58802ZNGmSnHvuuWJNhQv8OHv16iUIcvrNb36jFEkojAgSiqQg+Ojzzz9XVTzxxBNKsW7UqJFSeh977DGlPMOftWqAUiRt8loS8AMBWkD9MEqUkQRIgASinMAzzzwjlt+nWL6dkp2drSLLZ86cKcOHD1c9v++++1RaJgT4IFgHyhwUU1yDc9q3by+wSmLavqZy++23y/jx4+X+++9XU94PPfSQYIodKZ8wpY9XYmKiisRHHVCAUf/GjRtrqvKkn8PaOXHiRBXhbvmxVijRf/rTn2Ty5MnywAMPyOWXX64U3qSkpJPWxxNIIFoIxMHRNVo6w36QAAmQAAn4mwCmpa3AH0G0enUFx4qKik44VNPnJ5xofYDUSrCcBhdM48NHNC8vL/jjiPahwE6bNk1ZWKE067rxs/vPf/5T+ZzGx8cr/1Qo0EghVdsUfkTC8GIS8BgBWkA9NiAUhwRIgARimQB8ImtSPsGlOuWzts+rY1lV+cQ5sHxqBbG6ayL9LLhuKJlwAViwYIFyI5g9e7bAOkvlM1LKvN5PBGgB9dNoUVYSIAESIAHfEICl9dNPP5WhQ4eeIDMsrrCQIhCqe/fuShE94SR+QAJRTIAKaBQPLrtGAiRAAiRAAiRAAl4kwCh4L44KZSIBEiABEiABEiCBKCZABTSKB5ddIwESIAESIAESIAEvEqAC6sVRoUwkQAIkQAIkQAIkEMUEqIBG8eCyayRAAiRAAiRAAiTgRQJUQL04KpSJBEiABEiABEiABKKYwP8DF3sEmo6jl9YAAAAASUVORK5CYII=\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb36\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb36-1\"><a href=\"#cb36-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Source: Categorical variable.</span></span>\n<span id=\"cb36-2\"><a href=\"#cb36-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># mpg$class</span></span>\n<span id=\"cb36-3\"><a href=\"#cb36-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>pie <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(<span class=\"at\">x =</span> <span class=\"st\">&quot;&quot;</span>, <span class=\"at\">fill =</span> <span class=\"fu\">factor</span>(class))) <span class=\"sc\">+</span> </span>\n<span id=\"cb36-4\"><a href=\"#cb36-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_bar</span>(<span class=\"at\">width =</span> <span class=\"dv\">1</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb36-5\"><a href=\"#cb36-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.line =</span> <span class=\"fu\">element_blank</span>(), </span>\n<span id=\"cb36-6\"><a href=\"#cb36-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust=</span><span class=\"fl\">0.5</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb36-7\"><a href=\"#cb36-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">fill=</span><span class=\"st\">&quot;class&quot;</span>, </span>\n<span id=\"cb36-8\"><a href=\"#cb36-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">x=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb36-9\"><a href=\"#cb36-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"cn\">NULL</span>, </span>\n<span id=\"cb36-10\"><a href=\"#cb36-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title=</span><span class=\"st\">&quot;Pie Chart of class&quot;</span>, </span>\n<span id=\"cb36-11\"><a href=\"#cb36-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: mpg&quot;</span>)</span>\n<span id=\"cb36-12\"><a href=\"#cb36-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>  </span>\n<span id=\"cb36-13\"><a href=\"#cb36-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>pie <span class=\"sc\">+</span> <span class=\"fu\">coord_polar</span>(<span class=\"at\">theta =</span> <span class=\"st\">&quot;y&quot;</span>, <span class=\"at\">start=</span><span class=\"dv\">0</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"条形图\" class=\"section level3\">\n<h3>5.3 条形图</h3>\n<div class=\"sourceCode\" id=\"cb37\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb37-1\"><a href=\"#cb37-1\" aria-hidden=\"true\" tabindex=\"-1\"></a> <span class=\"co\">#prep frequency table</span></span>\n<span id=\"cb37-2\"><a href=\"#cb37-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>freqtable <span class=\"ot\">&lt;-</span> <span class=\"fu\">table</span>(mpg<span class=\"sc\">$</span>manufacturer)</span>\n<span id=\"cb37-3\"><a href=\"#cb37-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">as.data.frame.table</span>(freqtable)</span></code></pre></div>\n<div class=\"sourceCode\" id=\"cb38\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb38-1\"><a href=\"#cb38-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb38-2\"><a href=\"#cb38-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb38-3\"><a href=\"#cb38-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb38-4\"><a href=\"#cb38-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb38-5\"><a href=\"#cb38-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(Var1, Freq))</span>\n<span id=\"cb38-6\"><a href=\"#cb38-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_bar</span>(<span class=\"at\">stat=</span><span class=\"st\">&quot;identity&quot;</span>, <span class=\"at\">width =</span> <span class=\"fl\">0.5</span>, <span class=\"at\">fill=</span><span class=\"st\">&quot;tomato2&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb38-7\"><a href=\"#cb38-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>      <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Bar Chart&quot;</span>, </span>\n<span id=\"cb38-8\"><a href=\"#cb38-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Manufacturer of vehicles&quot;</span>, </span>\n<span id=\"cb38-9\"><a href=\"#cb38-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>           <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Frequency of Manufacturers from &#39;mpg&#39; dataset&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb38-10\"><a href=\"#cb38-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>      <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>))</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb39\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb39-1\"><a href=\"#cb39-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># From on a categorical column variable</span></span>\n<span id=\"cb39-2\"><a href=\"#cb39-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(mpg, <span class=\"fu\">aes</span>(manufacturer))</span>\n<span id=\"cb39-3\"><a href=\"#cb39-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>g <span class=\"sc\">+</span> <span class=\"fu\">geom_bar</span>(<span class=\"fu\">aes</span>(<span class=\"at\">fill=</span>class), <span class=\"at\">width =</span> <span class=\"fl\">0.5</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb39-4\"><a href=\"#cb39-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle=</span><span class=\"dv\">65</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.6</span>)) <span class=\"sc\">+</span></span>\n<span id=\"cb39-5\"><a href=\"#cb39-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Categorywise Bar Chart&quot;</span>, </span>\n<span id=\"cb39-6\"><a href=\"#cb39-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Manufacturer of vehicles&quot;</span>, </span>\n<span id=\"cb39-7\"><a href=\"#cb39-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Manufacturers from &#39;mpg&#39; dataset&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"变化趋势\" class=\"section level2\">\n<h2>6 变化趋势</h2>\n<div id=\"时间序列图基于时间序列对象ts\" class=\"section level3\">\n<h3>6.1 时间序列图：基于时间序列对象（ts）</h3>\n<p><code>ggfortify</code>包可以对时间序列直接绘图。</p>\n<div class=\"sourceCode\" id=\"cb40\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb40-1\"><a href=\"#cb40-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## From Timeseries object (ts)</span></span>\n<span id=\"cb40-2\"><a href=\"#cb40-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb40-3\"><a href=\"#cb40-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggfortify)</span>\n<span id=\"cb40-4\"><a href=\"#cb40-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb40-5\"><a href=\"#cb40-5\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb40-6\"><a href=\"#cb40-6\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot </span></span>\n<span id=\"cb40-7\"><a href=\"#cb40-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">autoplot</span>(AirPassengers) <span class=\"sc\">+</span> </span>\n<span id=\"cb40-8\"><a href=\"#cb40-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;AirPassengers&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb40-9\"><a href=\"#cb40-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust=</span><span class=\"fl\">0.5</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"时间序列图基于数据框\" class=\"section level3\">\n<h3>6.2 时间序列图：基于数据框</h3>\n<div class=\"sourceCode\" id=\"cb41\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb41-1\"><a href=\"#cb41-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb41-2\"><a href=\"#cb41-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb41-3\"><a href=\"#cb41-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb41-4\"><a href=\"#cb41-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 使用默认的时间跨度</span></span>\n<span id=\"cb41-5\"><a href=\"#cb41-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(economics, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb41-6\"><a href=\"#cb41-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>returns_perc)) <span class=\"sc\">+</span> </span>\n<span id=\"cb41-7\"><a href=\"#cb41-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Time Series Chart&quot;</span>, </span>\n<span id=\"cb41-8\"><a href=\"#cb41-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Returns Percentage from &#39;Economics&#39; Dataset&quot;</span>, </span>\n<span id=\"cb41-9\"><a href=\"#cb41-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, </span>\n<span id=\"cb41-10\"><a href=\"#cb41-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n<p>如果想设置特定的时间间隔，则需要使用<code>scale_x_date()</code>函数。</p>\n<div class=\"sourceCode\" id=\"cb42\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb42-1\"><a href=\"#cb42-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb42-2\"><a href=\"#cb42-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(lubridate)</span>\n<span id=\"cb42-3\"><a href=\"#cb42-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb42-4\"><a href=\"#cb42-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb42-5\"><a href=\"#cb42-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>economics_m <span class=\"ot\">&lt;-</span> economics[<span class=\"dv\">1</span><span class=\"sc\">:</span><span class=\"dv\">24</span>, ]</span>\n<span id=\"cb42-6\"><a href=\"#cb42-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb42-7\"><a href=\"#cb42-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># 设定时间跨度为一个月</span></span>\n<span id=\"cb42-8\"><a href=\"#cb42-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> <span class=\"fu\">paste0</span>(month.abb[<span class=\"fu\">month</span>(economics_m<span class=\"sc\">$</span>date)], <span class=\"st\">&quot; &quot;</span>, lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(economics_m<span class=\"sc\">$</span>date))</span>\n<span id=\"cb42-9\"><a href=\"#cb42-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> economics_m<span class=\"sc\">$</span>date</span>\n<span id=\"cb42-10\"><a href=\"#cb42-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb42-11\"><a href=\"#cb42-11\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb42-12\"><a href=\"#cb42-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(economics_m, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb42-13\"><a href=\"#cb42-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>returns_perc)) <span class=\"sc\">+</span> </span>\n<span id=\"cb42-14\"><a href=\"#cb42-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Monthly Time Series&quot;</span>, </span>\n<span id=\"cb42-15\"><a href=\"#cb42-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Returns Percentage from Economics Dataset&quot;</span>, </span>\n<span id=\"cb42-16\"><a href=\"#cb42-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, </span>\n<span id=\"cb42-17\"><a href=\"#cb42-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>) <span class=\"sc\">+</span>  <span class=\"co\"># title and caption</span></span>\n<span id=\"cb42-18\"><a href=\"#cb42-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">labels =</span> lbls, </span>\n<span id=\"cb42-19\"><a href=\"#cb42-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">breaks =</span> brks) <span class=\"sc\">+</span>  <span class=\"co\"># change to monthly ticks and labels</span></span>\n<span id=\"cb42-20\"><a href=\"#cb42-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle =</span> <span class=\"dv\">90</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.5</span>),  <span class=\"co\"># rotate x axis text</span></span>\n<span id=\"cb42-21\"><a href=\"#cb42-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">panel.grid.minor =</span> <span class=\"fu\">element_blank</span>())  <span class=\"co\"># turn off minor grid</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>设置时间跨度为 1 年：</p>\n<div class=\"sourceCode\" id=\"cb43\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb43-1\"><a href=\"#cb43-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb43-2\"><a href=\"#cb43-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(lubridate)</span>\n<span id=\"cb43-3\"><a href=\"#cb43-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb43-4\"><a href=\"#cb43-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb43-5\"><a href=\"#cb43-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>economics_y <span class=\"ot\">&lt;-</span> economics[<span class=\"dv\">1</span><span class=\"sc\">:</span><span class=\"dv\">90</span>, ]</span>\n<span id=\"cb43-6\"><a href=\"#cb43-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb43-7\"><a href=\"#cb43-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># labels and breaks for X axis text</span></span>\n<span id=\"cb43-8\"><a href=\"#cb43-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> economics_y<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(economics_y<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)]</span>\n<span id=\"cb43-9\"><a href=\"#cb43-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(brks)</span>\n<span id=\"cb43-10\"><a href=\"#cb43-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb43-11\"><a href=\"#cb43-11\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb43-12\"><a href=\"#cb43-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(economics_y, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb43-13\"><a href=\"#cb43-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>returns_perc)) <span class=\"sc\">+</span> </span>\n<span id=\"cb43-14\"><a href=\"#cb43-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Yearly Time Series&quot;</span>, </span>\n<span id=\"cb43-15\"><a href=\"#cb43-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Returns Percentage from Economics Dataset&quot;</span>, </span>\n<span id=\"cb43-16\"><a href=\"#cb43-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, </span>\n<span id=\"cb43-17\"><a href=\"#cb43-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>) <span class=\"sc\">+</span>  <span class=\"co\"># title and caption</span></span>\n<span id=\"cb43-18\"><a href=\"#cb43-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">labels =</span> lbls, </span>\n<span id=\"cb43-19\"><a href=\"#cb43-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>               <span class=\"at\">breaks =</span> brks) <span class=\"sc\">+</span>  <span class=\"co\"># change to monthly ticks and labels</span></span>\n<span id=\"cb43-20\"><a href=\"#cb43-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle =</span> <span class=\"dv\">90</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.5</span>),  <span class=\"co\"># rotate x axis text</span></span>\n<span id=\"cb43-21\"><a href=\"#cb43-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">panel.grid.minor =</span> <span class=\"fu\">element_blank</span>())  <span class=\"co\"># turn off minor grid</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0J3EVD/f/HFmUrO5Eliex7C0VZWrTQqlQkKmmRIi1/KS2WIkoo0iakn6JFKaQkS1GyZN+zRNlKSfd/3sPnmuc8Z5lz7rn3nnPvd16v5znnzj6f2T7z/c7MmaOXGGfGEDAEDAFDwBAwBAwBQ8AQGBECc44oHUvGEDAEDAFDwBAwBAwBQ8AQ8AgYAbWGYAgYAoaAIWAIGAKGgCEwUgSMgI4UbkvMEDAEDAFDwBAwBAwBQ8AIqLUBQ8AQMAQMAUPAEDAEDIGRImAEdKRwW2KGgCFgCBgChoAhYAgYAkZArQ0YAoaAIWAIGAKGgCFgCIwUgblHmtqYErv66qvdwQcf7J7+9Ke7vfbaKzMX//znP73bQgst5D796U+7OeaYI9Nf05Yf/ehH3dOe9jT3jne8o1bU999/v/vgBz9YGvbJT36yO+CAA9x+++3nFllkEfee97ynNMywPVx33XXu29/+trvkkkvcPffc49Zcc0238cYbuze96U2N4t+mMg8bU4vfEDAEDAFDwBDoAgJzTMs9oC996UvdT3/6U3faaae5bbbZZlbdvP/973df/OIX3QknnOBe//rXz3IflsWKK67o1l9/fXfyySfXSuLuu+9266yzzoywt956q3vCE57gFl988b79kksu6S666CK3xhpruOWWW85j0Xccw8vPf/5z94Y3vMH997//dRtuuKGbc8453bnnnuv+9a9/uVe84hWemC644IKN5KwtZW6kMBaJIWAIGAKGgCEwAQhMDQGFlK2++upugQUWcJdddplbeOGF+9UH8Xn+85/viefxxx/ftx/Fy6AENCuPT3nKUzypg+SlzVVXXeXmmWceR7rjNCussIJ75JFH3J/+9CdHfjEPPfSQO/DAA92+++7rPvnJT7qPf/zjjWSxLWVupDAWiSFgCBgChoAhMAEITM0e0Kc+9anu0EMPdRDRD33oQ/2qg/S87W1vc8sss4w74ogj+vZ6gSRdfvnl7vTTT3c33HCDrGc9b7/9dnf22We7X/7yl+7mm2+e5U66f//7390DDzzgzjrrLIfqPDT/+c9/fPz33ntvaO3f8Uva5GVQAwF/0pOe1I9G+cKCNM4880yfR3l48MEHfX5RkWeZWHzCsGyJuPHGG93OO+/cJ5+4zzfffH4bxNJLL+1+/OMfh0H8O3kAu9/97neOLRNpo7KkMU6XmXCx+f7LX/7i83LxxRd76Ww6TfttCBgChoAhYAgYAjUQQAU/TSZRxfPp0V5CFn2x99lnn16y37N3xhlnzILh97//fW/VVVf1/ueaay7/fPGLX9xLyGbf72233dZTnIka2fsh/m233baXkNu+v5VXXrn37ne/u7faaqt5P0sssUTv4Ycf7iWSwN6rX/3qXkKoesn+0952223XD6OXt7zlLb2EIPcSdbWsCp/Jfs/elltumeknkQL3KIMM+dltt916L3nJS/p5n3vuuXvJVoXeSSed1EtU+d6eZ7I1YUYeYvBROuETXBIS3Eu2DvSSLQShk39P1PAz7BJy3vvwhz/cA1/+qC+w+uY3vznDXx7G6TLH5Dsh3r0tttjCl111n0jNeyeeeOKMNO2HIWAIGAKGgCFgCFRHwFUP0u0QiZSsB0FL9gX2/vjHP/YSdXQvOZAzq1AQo0UXXbS31lpr9X74wx/2IEHnnHNOL5Gk9l70ohf1/UMeE8ldL5GQ9iAtV1xxRS+RqHri8o1vfKPvD3IEiUv2N/a++93v9r71rW95NxFQfuy66669eeedt5dISvvhEmleb/755+8lh6f6dmUvVQkoBOu1r31tL5FM9pLtCL2VVlqpl+y/9OT4O9/5Tu+OO+7oJQedfJm+973v+eRj8cnL60EHHeSJZCKd9GQ9kT577LL8J4eIfNosFhLJqcdn991393bUiUwexiEBjc03aULEk33DfiGRqPF7m2yyia+LRCKtJO1pCBgChoAhYAgYAjUQmDoCCkbHHXecJy/J3kMv4UT6mDYf+9jHvJ+f/exnM5wSNb63/9WvftVL1LiemB155JEz/CQqeO8nOeHet4ccIbWDpIYmJKDnnXeeD3f00Uf3vSDlQ6KabAPo25W9VCWg5CvEYI899vBpJnsx+0n99a9/9XbJ3kxvF4NPP3DOC8R+00037UnCSDkhvyFxT7Yk9J74xCf2nvOc58yIBenxYost1tt888379nkYhwQ0Nt877rijj/+uu+7qx5+o471U+B//+Effzl4MAUPAEDAEDAFDoDoCU3ENU0JsZpi3vvWtLiF2fr/jj370I5cQnBnu/PjDH/7g9yRyYIk9oDLs9cRweIaDS4kkDxLvuFIIf+wZZI8ihv2loXnmM585Y/9l6Mb7s5/9bJeo/P0J8F122cU7k8+NNtrIJarytPfGfnM9VYhBQuR83OFtATpRzyl1TCw+3nPOP06785cQuv7+2UTF7aifSy+91GMLnqSZbFnwe3jDqNjXSz2Epgzj2HxzE0IipXZgkWxZ8H/cpJBIisPk7N0QMAQMAUPAEDAEaiAwlQQUnFZZZRVPQCF8WeaWW27xd1F+7Wtfm+X8rGc9y/373//29sneUZfsoXTXXHONv/oIoggxxUBMQ8PhmjKz0047uWS/o7vppptcIhn0eTz88MPLgg3kvtRSS2WGT7Yg9O3T96LG4tOPIHi577773N/+9jeXSDu9bSKxda961av8H6ffk/2znmwm0kpHOhjuCuXwUtqQd4g+B5gwZRjH5hvS+Ytf/MJ9/vOfd6eeeqqDGFMfLAy4rotrrswYAoaAIWAIGAKGQD0EppaAlsG17LLLuuuvv95deOGFuVLLO++805MmSCdXOW2wwQaemCAlhTSmCWiaxGXl4c1vfrP7yEc+4pIDQJ7wcGXS9ttvn+W1MTvu4KxqYvDJi3P//ff3xA4ymCaMXMkECU+2OLhkj64jHQwn5mOuZSrDuEq+N9tsM8cfBDfZa+rrNNlu4e9tffvb355XPLM3BAwBQ8AQMAQMgRIEqjOPkggnxZnL0bn+6JRTTplRpK9//ev+i0oXXHCBO//88/11QMmBGPfc5z63LxWDPGHqXJsEIUP6RrrJHkmvotY9mTMyMuYfMfjkZRG1+//+9z9/3ycX0YcG0p7su/VbAkgDCTXXKLEVITSo5XFHXV/FxOYbEowkm/wgXd1qq63cUUcd5ZNC2m3GEDAEDAFDwBAwBOojYBLQHOw+8IEPeMKx5557eknoa17zGk84+WJScnDG78u88sorvZr+q1/9qlt77bUdXxvi8ncIKera9F2fOUnNsk4OwPhL8ZHmof5to4nBJy/fkHU+twluqNZf/vKX+z2u3OMJ6eauz0984hP9falIPvfee2/vj0+IJoem3Be+8AW/D/VLX/pSXjKZ9rH5pr7ZA0v977DDDo57WkmLOgn3xmYmYpaGgCFgCBgChoAhUIxAIuGZSvPOd77Tn+rOuodSgCSSLn/KOiEd3m+y37CXqIJ7iepYXnoJieqtu+663j1BupfsD+0ll9H7ezWTwz19f5zQfuUrX9n/rZfwFLzskv2l/goo0ou9+1NheVY9Bf+yl70sDN778pe/7MuTHKzq23Pin/JxFZJMDD7ym/VMvnrk70XluiPi5o5P7l1NpMwzvCfS0l6y79KXC3+cmuf0fNpfHsbhKXgijs13stjo34NKutzF+v3vf39G3uyHIWAIGAKGgCFgCFRHYGo+xZkQiNqGr+4gneNEdN4eQ/YzcpI8PLhTN0GkbexVRA18wAEH1I1mZOFi8CnKDAe6rr32Wv+N+rLvv/MFJfwsssgiRVFGucXkG1U/B8KQuqb3q0YlYp4MAUPAEDAEDAFDYBYCRkBnQTJ+i6985Stejc8VRJBeM4aAIWAIGAKGgCFgCEwSArYHtEW1ibSTa5+QBr73ve818tmiurGsGAKGgCFgCBgChkBzCBgBbQ7LgWPicA4XqyffjHcctjFjCBgChoAhYAgYAobAJCJgKvhJrFUrkyFgCBgChoAhYAgYAi1GwO4BbXHlWNYMAUPAEDAEDAFDwBCYRASMgE5irVqZDAFDwBAwBAwBQ8AQaDECRkBbXDmWNUPAEDAEDAFDwBAwBCYRASOgk1irViZDwBAwBAwBQ8AQMARajIAR0BZXjmXNEDAEDAFDwBAwBAyBSUTACOgk1qqVyRAwBAwBQ8AQMAQMgRYjMDX3gN5xxx3uyCOPnFEV88wzj3va057m1lprLf83w7Hkx3333ec/z1jibWTOhx12mPv73//eT+8JT3iCW2yxxVzynXeXfMO8b9/GFz49mnzv3c0333xjy94pp5zifvzjH7sll1zSffrTnx55Pu68807HF7DyzEtf+lK34YYb5jm31v6EE05wCyywgNtmm20azeMvfvELd+655/bj5DO4tPett97aPfWpT+3bx76Muj+POr1YHMyfIWAIGAKjQmBq7gHlgve1117bbbHFFv3viD/00EN+EoO4HXLIIf7rQzHA/+QnP3H777+/++1vfxvjfSR+VlppJU/inv70p/v0IHU33HCD++tf/+ogAa95zWtGko+qiYD9c57zHHfqqae6VVZZpWrwRvz//ve/9+QOjF7wghf4DwE0EnGFSP785z+7Nddc022wwQaZCxu+jPXKV76yQozt8Lrddtu5pZZayh1xxBGNZujDH/6wY9FF28Hcf//9vr1D7Pbdd1+3zz77RKc36v68++67e0w+9rGPRefRPBoChoAhMGkITI0EVBX3mc98ZoYkCcnbzjvv7D760Y+6d73rXQ6paJmBLDz44INl3kbu/qY3vWmG9A4SCqliwmsrAf3HP/7h+Ob9OA31ueCCC3qiPuec492V8qUvfcltvPHG44Sj0bT/7//+r9H4wsiWXXZZ98tf/jK0chBT+vKzn/1st/nmm89wy/sx6v58/vnnd3IxkYef2RsChoAhUAeB8c62dXLccBgIx6te9Sr3wAMPeGmhor/kkkvcLrvs4rbaaiv3/ve/3912223e6Ve/+pX7/ve/72655RbvjgTv17/+tdtrr70U1D9/8IMfOL7tjkFV+LnPfc6ddNJJ7hWveIV//u53v/Nk8aKLLvIEGBXr5z//effII4/4MPz7xje+4fOGGxKde+65p+8W84Ia/tWvfrVj+8Htt9/eD/LNb37Tve51r/N5QfL73//+17v9+9//9mW68MILfTjKBEHHfOtb33JvfvOb3Vve8hZffoXBLQ8r3CBUP/vZz9yxxx7ry/KGN7zBoT7FQOIlqUIadMYZZ3j76667zmP+4he/2CFBO+iggxxkWgb3//f//p8DF6Rd4M/v0OSVMfTDOwTpmGOO8bi/4x3vcOedd56XrJ1++unuAx/4gIPUiyAj8X7rW9/qttxyS/e+973P3Xzzzf3oKOfPf/5zd9RRR3lcWcyQT6TQfFr1ta99rTv55JP7/gd5oX7IG23p8MMP9/Wr+KjrD33oQ77dUl9gL/Pwww/7+iVfEDXU1SxOkJLL0P7YqrLtttv6+L/whS84wslULeeXv/xl9+1vf1vB3d/+9jffF17+8pf7PCB9lrn44ovdTjvt5F70ohf5fF5wwQVyin7SzzbZZBMXShdp1wceeKCvA8qMNPnGG2/0cWb1ZxxOO+0097a3vc3nhTb/05/+tJ8HFk2QXMaG17/+9e5rX/ua6/V6fXeksKRP+6UOQpJMH6dN/PCHP3Sf/exn+2HsxRAwBAyBaUNg6gkopI5JHFUe+0ExZ555pv8NKYU4ILFgnygkdIkllnBIXp70pCe5jTbayEHyrrzySnf88cfPaDsQS9TKmKuuusqr+Jm0nvzkJ7t//etf7uqrr3aHHnqon6CWW245L7H5yEc+0pdgQqD22GMPt+mmm/pJjjyxfaCqgUjNP//8bvHFF/dBIU577rmne8YznuH49jwTs6SjEA0m0ze+8Y1u3nnndUy0EHRIHpP20ksv7VXEfKceYoEpwgp3CBDE7rjjjvOTOQQHEnDZZZe5ueee22+LwB/qZ1S1119/vceaemHyJp+f+tSn+oTi7rvv9vFAViFgEAjIzPe+9z2i8aaojPKjJ9ivsMIKvh6pT/YRgtmuu+7qqEPaAHsYISTUxb333uvJOYsK8gyZw1BOJOn4o55+85vf+HyxuFlooYXcqquu6usxJFzKQ/ikbUDM03/yQ1uEoJEuJBECzSIDw2JovfXWc6iUwYbFA9hobynYU78Qd4gqfiBHECkZSBfkFNzZDgChe8lLXtInWFXLCSkHCwxlIi4WM5AziOHznvc8X+fkZ7PNNvP7gFn4zTHHHN6NvlXV0JeRasrQ3tiGAm6kT5t94Qtf6PHJ6s+0bfoA21pYcPzzn//0mEH8MTvssIM766yzvB/25UL4wQlDGddff31PWNkyQRtnHzZlxtAO6I/sU33Ws57l7eyfIWAIGAJTiUCycp8K88c//hERRS8Z+HvJxOL/kgMnvbnmmquXTOS9RIrXx2Hdddf1dn2L5AW7RJLlrRLJZi8hpH3no48+2sfbt0heErLZSwiet0qkfz7thHz0vSQE09sle1P7dgnp6SXSG/+b90TS1ktIhP997bXX9hJpVC/Zt9r3H76suOKKPmwiCezxt9tuu/VWW201nwb5xSSSvF5CKHsJWe4HTYiw93P22Wf3kn10/j0hIH33hHR7u4To9e0SstdLJmiftzKsksMnvWSi7SXkx4fnueiii/a++MUv+t8JkfLxkzdMImnqJSSo7x+7RCrWS8gfrz3ylpCGPi7YEX8ysfNaWkbvKfXvq1/9ai9ZVPRtE5LSS/aj9hIpb98uIam9hHj0f/OC3fbbb+/tKOczn/nMXkLi/e+EGPpyUWcylOHjH/+4fs54Xnrppd4/bTTrLyFr3j/tQ+0Qi7vuuquXqJp7V1xxRS8hQr1kK0FPfnHHLiHAvYRE9RJy6+MO85AQeW9HPScSR/+eSOcI6o3skkNa/nfVciYkt5csQHxY6jwh/DPyRzul/yRS8V5C1noJEfV+afdgd/nll/vf6X977713LyHJaWv/OyGb/TIlEtdesoicEU9C0L17ohXw/tP9mf6TSLL7cVOnycKxl+w59XZPecpTeomUuO+eLDp6icbD/062+PQSgtlLFm99d+wYa9SXE2LfSxZVfXd7MQQMAUNgGhGYuj2gqEM5qIMkDfViQqC8qps9gBikMglZ9dI+JEEyCVH1EjH9rvpEorjOOuvMCMbJXaRoMkhgpXZEVY20ZuWVV/bSF04RI3lEopJn2Bagg1FIaJHEId1F8oNBopc0cockhzLKIOHDDckNJtyDiFqUvCP9k0Fiyl8sVkjStLeSJ9IfJItZBskYkjCkVAmpcgkB8Sp7pKMY8gkuSMhkwEbS5rIycsgoxoAFdY5BsnhDojZl/3BoSDdUcXPITfWjw2CURwYpdLgVQvbhE0lZ2Cbkxt5k6o7tDh/84Adl7SW2YIX5wx/+4OscqbwMElC2MLCNAOkbBkmvjKT+SO5U12ov+KHuwJ42gzQXU7ecxE87CvMnSTrtAcyQvCKRpY5RfSeLFZ9mlX9IqTELL7yw11Sw9QXckMKDwznnnOPdkTZnmf32289vS0C6jASWvoJfDi1ikIomxNlLNZEmI+lcffXVvRv9F7xC9fqtt97qJc70TyTuZgwBQ8AQMAScmzoVPKSM/Y/s0WPvIJNLIsXq73Vk/xaqS0gZZEl/7PuTqjOr4UAOQhPum8Me1btImPxBEkODu+LhAAWTJkQUFSZEBmKIWjzPoDZENc0fe8w4JRySCcJCkCCUKhdPiK0mUOIOJ31UoxDlkPAp/VisUDmGRsQutNM7txUk0lyvztbWh5A0snBIT+JgKxNbRvnPe4YYCPP09T5c2YRaWyYMIzvU71UMBAyCl/4Df0giRC2Np+KHeGXlEfcwn2F4tUnaHeUEy9CddFFTh+HrlpO2FMatfPOkv0HeIG6ovCF4qMBRdVc1kEa2i9C/II30nec///leDY8d+3qLDFtjaIPkheuxWGyBgQz7pulficTb97E11ljDb1vAncUKaYT9i/bKXmdhrXjsaQgYAobANCOQL06bAlSQVHAABSkGB4aYJJBSQRq4OzOUeLGXTSfk02QMQpeW6LGXcRCDZA2pLHdS8of0CMkV9hx8qGOQpkKMkYqx/xMDseCwU94VSEilICZMxJqEyQv7QtlXV4ZVWT6FpYj3Jz7xCS+p46CSiCoEXAQIooykLzShFLJOGcO4st6REiK1Y28oREaGdNNSbbkN4wlJow6Q4mk/MIslFlQsIig7eQwNv1l0QJKEcegevhMeksjCR+XigBKLAvYnD2oglMlWgxnRoIWAcFIGpKwQT/5Y3CAJ5dBT7Gl2Iqad0qfZw4vhMCD7XNkzq4ULdhiww6gN8o6kM1Hvu4MPPthjih1tj/3I+CevJ554or/XFAk4duxRpo/SdsGQsYJr2kQ4k+0z/iCi9mETpxlDwBAwBKYdgamTgKYrXKq+T37yk/5gEO5IR1GFotZl8kFlh5qNE7yYRRZZxKvoOFzEaXDIGxMmRA6CR7gf/ehH3m/df0hmmfQ4rARxQHVLWlLt1omXiRypDSfGOQSEdIhJkwk3T1IHUeWwxDvf+U4/iSd7Dl2yh9ATdSQ9ZViV5RMsMRzOQYLHooADSBAByo2kidPjqPsxHMyC+HGQCmIEEQ4vJK9TRh9xwT+IMISG09wc8CFvyb5Rx00GHFJr0kD+kMyn/3QYhwM6kCNIDjcDsMUCgs5hGOrommuu8Sp37sWk3epUPoukMkNfWH755X37oN2hMqZtQHpD4l0WT547h7TIKxJEpLmUFfKGmh+z4447+rqmz9GfkCYWtXfqQThxSh2yyqEmJLQcDMLQnogPYo3h9DuHATFSqYf9GXvC099EODmAR/vDP9oA7jRlew4LM/JAn0DyzIcUOHAHbqjxyT8EHokr44G2HhA/20vC2wdI14whYAgYAlOFQDLJT4VJCB06cn/QIl1gDiok0olesvfQHxTgwAYHPRKJZy+ZcPxhk/DgRkI8e8nE5uNLru3x0SUSKH+gKSErveQkci+ZgGYcQuIQQmg4hJRMRKFVj8MKierV2yUTWy9Rv3s/idrSHy7RwZ0ZgR77wSGkREqV5TTDLiEyveSUcC+R+vjDErxz8AejQ0gJcZkRhgMuHJygbMm+Op+vZJL1fsqwSqREvYSkzoiPg0uJxKhvl6hIPZbJ1UI9DkVxWCcht72EGPSSLQT+wFIyufcSgurDJF8s6iUSOo9JcsK5lxAv/1sRFpVRfsJn1iGk5Hqi0IvHhsNRYJCQOX9oKTld3veTLqfam3DCY3KlVC8hkP0w4UvZIaREQui9gzcH1MgHmHDQjUM1Mon0r8chGfJIu+GwGG0Jw5M+kOwZlXePN3a0aQyH4mi/ifTOx5Hshe2RN5mq5QwPIRFHQuJ9vZK/RCLpD8wp7kT17Q9yke9EattLtrz061x+9OQQEvnWH/FxuDCRnvZCzPGfEF9/EIs+S5ocUuJQEXnBpPtzsvj0cdE/OdSVXEfm23yyCPX+k4VHL9kW4ttoQip9nsMDhhzyS0i7xy9Z2Pl614EnIqDdEC48+OYjtn+GgCFgCEwRAlPzJaRkoqpskGYiOeHapSyjPXNyQz2H5Im9gU0ZpDAcYiAPoapw0PiRNiJRRRoTa9h/mUz0fr9eOkwZVmn/6d/ghkQ1IVbeibSQGOlwmPwjGSYPOlCDPRIq1NKhKh77OmUkXJFBEobEK69NFIVt0o180P6y2loyfnkpHNI/bRupmjZSaNpbQmarBi31T/5o00gNs9o00kf2ojb5aVakxWgwij5Lm+7PSDLBUAfL0gVj2w0S0DzVOmXETZLPMDz5of/R5s0YAoaAITCNCBgBncZa73CZUbOyBQCVJgd1zj77bL9/EHvusDRjCBgChoAhYAgYAu1HwAho++vIchgggNSIPXwQUPZFsv+W/ZnsDTVjCBgChoAhYAgYAt1AwAhoN+rJcpmBAGpo1PFmDAFDwBAwBAwBQ6BbCBgB7VZ9WW4NAUPAEDAEDAFDwBDoPAJTfw1T52vQCmAIGAKGgCFgCBgChkDHEDAC2rEKs+waAoaAIWAIGAKGgCHQdQSMgHa9Bi3/hoAhYAgYAoaAIWAIdAwBI6AdqzDLriFgCBgChoAhYAgYAl1HYCq+Bc+n+Pge8ygMl2zLZF2yLTd7Jp+xeQwrw6m4NRhOxfjIVTjx29qUUMl+CivDKRsf2RpOQqL8aViVY4SPacJpgQUWKPz4x1ScgufLKsmnNd0mm2wS10IG8AXZ5esofOEk+ZzhADFNflBwYgJs8os3k4gaX83hj85sJh8Bfbud77Xri1r5vqfbha+2gZFdY1bcDrh3+KGHHnLJ52FtUVMAFaTqwQcf9O2p7tfXCqKfKKdpmfcYj6+55hqXfHo4t/6mQgJK6flqziguK+duSj5jyCf48j7hl1sbU+bApxGZBIfxucdJgpJPPvKp0qWXXnqSitV4WSALfCZ1kUUWMWJVgu6dd97pMVp44YVLfE63M2RBn7w1gUJ+W4Bs8Nlq2pN9XjYfJ1z4zDRtadLnPcbj97znPYVgmIiuEB5zNAQMAUPAEDAEDAFDwBBoGgEjoE0javEZAoaAIWAIGAKGgCFgCBQiYAS0EB5zNAQMAUPAEDAEDAFDwBBoGgEjoE0javEZAoaAIWAIGAKGgCFgCBQiYAS0EB5zNAQMAUPAEDAEDAFDwBBoGgEjoE0javEZAoaAIWAIGAKGgCFgCBQiYAS0EB5zNAQMAUPAEDAEDAFDwBBoGgEjoE0javEZAoaAIWAIGAKGgCFgCBQiMDYCyiXkP/vZz9xf/vKXwgzieOWVV7obbrhhhj8ufD/33HPdb3/7W/fwww/PcLMfhoAhYAgYAoaAIWAIGALtRWAsBPTiiy92O+20k7vqqqvcXnvt5U455ZRchK6//nq35557uiuuuKLvh69T7Ljjju6ss85y3/72t30c+r5q35O9GAKGgCFgCBgChoAhYAi0EoGxENBDDz3U7b///v4zTUcffbQ79thj/beu0wj98Ic/9J/PTH8u7sQTT3Qbb7yx+9jHPuaOOOIIx3eNzz///HRw+20IGAKGgCFgCBgChoAh0EIERv4teL4Pesstt7i11lrLw7Hkkkv6b8feeuutbsUVV5wB0ROf+ER3zDHHuMMOO8zNMcccfTc+cL/VVlv1f6+33nru8ssvd89+9rP7dnyX9j//+Y///fe//90hISXtYZtHHnnEJzGKtIZdllHEP6p6GUVZhpUG31nGWJsqRlj40Af1Xhxiul2t75XXfzie27fg8/GSBpKxyvpePk64gNU04EQ7ULvIQ2TkBPTOO+90888//wxCiYTznnvumUVAQ5IZFuT22293Cy20UL9MvENqQ/Pud7/bXXrppd5qmWWW8RLTu+66K/Qy1HdIr5k4BEZZL3E5aqcvwymuXu699944j1Pui61M/JkpR+Duu+8u92Q+3P333+//DIpyBCZ9PIeAagGXh8bICehcc801K1NkdL755svL4yz7dByER1oamo985CP9joCK/ne/+517ylOeEnoZyjsHoh544AFPkMmnmXwEGKyQbC+wwAL5nqbIhX3O1113nXvZy142o9QPPfSQJwqjaL8zEu7YDwa7++67z7eneeaZp2O5H212Ielg9KQnPWm0CXcsNbRoDz74oENIYhLQ/MpDQPSPf/zDt6d5550332OEyx//+Ee/9e60007zwqqIIJ3yMi3zHrysjAONnIAuuuiivkNzil0NFeknUspYs9hii3mJqfwTfrnlltNP/0QtL4PE9MILL6xEchW26lNbBSjb3HOPHN6q2R2rf4g6DbTK4mOsGR5y4kceeaT7yU9+4l796lfPSImOjDGcZsAy64dwesITntAfW2Z5MguPAETd+l55Y5Dmjb5nBDQfL20TYlEz6DjFrTfnnXee46Yc+MKkGRY0tKVBcWo7LozH4kN5eR35ISRIGQeITj31VJ+nc845x0smke6gDrrxxhvz8tq333TTTd1Pf/pTh2SIRspVTOuuu27f3V4MgS4iwEKKxZIG8y6WYdx5ZmFrxhAwBLqLgM5uoLk0M9kIjJyAAuduu+3mTj75ZPfGN77RcQp+n3328Shfdtll/mR8GeRbbLGFF81vv/327h3veId7zWteM2v/aFkc5m4ItA0B9g2zamSftJnqCNx8883uGc94ht9uUz20hTAEDIE2IGAEtA21MJo8jEVHvPzyyzuuUmLPyJOf/OR+STfYYIO+ZLRvmbzst99+4U+v2v7Upz7l93iy99NU3TPgsR8dRUAH17gRYqmllupoKcaXbYg7BB4iasYQMAS6iYAR0G7WW51cj0UCqoyG5FN2VZ4LLrigkc8qgJnfViMgAnrbbbe1Op9tzZxOdOvZ1nxavgwBQyAfARFQ68f5GE2Ky1gJ6KSAaOUwBJpAwAjoYCiyJxxjE9dgOFpoQ2CcCIiA2h7QcdbCaNI2AjoanC0VQ6AUAa7nwKCCN1MdARFPEdHqMVgIQ8AQGDcCRkDHXQOjS98I6OiwtpQMgVwE2A+tS3tNBZ8LU6GDCKhJTgphMkdDoNUIGAFtdfU0mjkjoI3CaZEZAvUQkPqdA3VGQOthKAKqZ71YLJQhYAiMEwEjoONEf7RpGwEdLd6WmiGQiYAI6Morr2wq+EyEyi1FPPUsD2E+DAFDoG0IiIBaP25bzTSfHyOgzWNqMRoClREQAV1jjTVMAloZvUcDaMKyPaA1AbRghkALEBABta00LaiMIWfBCOiQAbboDYEYBERAV199dXf33Xc7DcIxYc3PowiIgNrEZS3CEOguAhr7rB93tw5jc24ENBYp82cIDBEBEdDVVlvN8f1p2wdaHWxJPkVEq8dgIQwBQ2DcCBgBHXcNjC59I6Cjw9pSMgRyEYCA8lUv9oBijIDmQpXrIOKpZ65HczAEDIHWImAEtLVV03jGjIA2DqlFaAhUR0CfpV1mmWV8YCOg1TEU8ZQktHoMFsIQMATGjYAR0HHXwOjSNwI6OqwtJUMgFwEkoE95ylPckksu6eaaay47CZ+LVL6DCKjtHcvHyFwMgbYjIAKq/tz2/Fr+6iNgBLQ+dhbSEGgMARHQOeec0y211FKmgq+BrCYsPWtEYUEMAUNgzAiIgNpCcswVMYLkjYCOAGRLwhAoQ0AEFH+o4U0FX4bYbHep3o2AzsbGbAyBriBgBLQrNTV4Po2ADo6hxWAIDIxASECf+tSnGgGtgaiIp4hojSgsiCFgCIwZASOgY66AESZvBHSEYFtShkAeAiEBRQJ666235nk1+xwERDxNdZcDkFkbAh1AwAhoByqpoSwaAW0ISIvGEBgEgTQBNRV8dTQlAdWzegwWwhAwBMaNgAio9eNx18Tw0zcCOnyMLQVDoBSB++67z5+CxyMq+Pvvv9898MADpeHMw+MIaMJ6+OGHH7e0N0PAEOgUAiKgpsnoVLXVyqwR0FqwWSBDoDkEIJ+Qpic/+ck+Ut0Famr4ahhDQLlFACw1iVWLwXwbAobAuBGg7/JRDm2pGXd+LP3hIWAEdHjYWsyGQBQCXEKP4R5QjAioqeE9HNH/IKAi8SY9iYbNPBoCrUIAAko//t///uek1WhVBi0zjSFgBLQxKJuP6Kc//anbe++9m4/YYmwVAuz/xBgBHaxaQgJqE9dgWFpoQ2BcCIiAkr4tJMdVC6NJ1wjoaHCulcpPfvITd8wxx9QKa4G6g0CagC6yyCJuvvnms5PwFasQ0ikSbwS0Injm3RBoCQIhAbV+3JJKGVI2jIAOCdgmov33v//tHnzwwSaisjhajECagJJVu4y+eoXZxFUdMwthCLQNAfrxwgsv7LNlEtC21U6z+TEC2iyejcYGAeWv1+s1Gq9F1i4EsgioXUZfrY40UUkCqt/VYjHfhoAhMG4EHnnkEdvLPe5KGFH6RkBHBHSdZEQ+TQpaB73uhIGAzj333O5JT3pSP9N2GX0fiqgXqepEQPU7KrB5MgQMgVYgoDnPDhO2ojqGngkjoEOHuH4CdEaMEdD6GHYhJAR00UUXnZFVU8HPgKP0hwinJi79Lg044R6+9rWvuWuuuWbCS2nFmxQEUL9j1I9NkzEpNZtdDiOg2bi0wlad0QhoK6pjaJmAgGrAVSIQ0L/+9a/6ac8SBEQ4haN+lwSbaGe27rzzne90xx9//ESX0wo3OQhozrM9oJNTp0UlMQJahM6Y3SQBtVXgmCtiyMlzD6hUx0qKPaDU/9/+9jdZ2bMAARFO4Wh9xjkWNuynswu9CxqOObUKARFQ9WP161Zl0jLTGAJGQBuDsvmIREBNAto8tm2KEaKgAVf5QgKKscvohUjxUxOVSUAfx+nuu+/2PzSOPO5ib4ZAOxEQATUJaDvrp+lcGQFtGtEG41NnNGlOg6C2MCojoINXiqR8IvIipIPH3N0YjIB2t+6mNeea8xZaaCEPgc19k90SjIC2uH4luTAJaIsrqYGsGQEdHEQRTk1c+j14zN2NQds3NKl3tySW82lBQG2Vb8HzMQ4joJNd80ZAW1y/RkBbXDkNZi2LgM4///z+MuZbb721wZQmNyoRTiYu/mzics4koJPb3ie1ZCKgT3jCE/y1dNaPJ7WmHy2XEdAW168IqHXCFldSA1m79957Z+0BJVq7iikeXBFQpCYQUP2Oj2HyfBoBnbw6nfQSpQmo9ePJrnEjoC2uX3VGU8G3uJIGzBp1y0JDexfD6OxrSCEaxe+aqCCgXOiv38WhJttVBFTjyGSX1ko3CQiorZoEdBJqs7wMRkDLMRqbD0lAjYCOrQqGnjBXMGF0ejtMEAmoqeBDRPLfRTjnnXdev3dMv/NDTL6L9oBqHJn8ElsJu46AEdCu12C1/BsBrYbXSH1r4jAV/EhhH2li7P/EZElATQUfXxUQTqSfc8wxh+0BfQw2k4DGtx/z2Q4EQgJqe7nbUSfDzIUR0GGiO2DcIqAmAR0QyBYHLyKgqODvuOMOf5l4i4vQiqxBQJmwMKaCf7RKREA1jjxqa/8NgfYiEBJQ+rEJX9pbV03kzAhoEygOKY7//ve/PmbrhEMCuAXRFhFQJKD/+9//PAltQVZbnYWQgNohpEerylTwrW6ylrkMBNIE1LbSZIA0QVZGQFtamXREyAfGJKAtraQGslVGQEnCvglfDnRIQFHF28T1+DVMmtTLUTQfhsB4EVBbtUNI462HUaVuBHRUSFdMJ1SbGQGtCF6HvENA55prLrfgggvOyjUSUMykE9BDDjnE7bfffrPKX8UCwgnxxNjesUeRMxX8ozjY/+4gEBJQ68fdqbe6OTUCWhe5IYcLCaip4IcM9hijh4BmHUAiS0svvbQ/VDPJ34Pv9Xru4IMPdj/4wQ8GqoVQAmp7QJ27//77HZM5tytoUh8IYAtsCIwAAbVVk4COAOwWJGEEtAWVkJUFdcS5557bVPBZAE2IXREBnWeeedziiy/ubr/99gkp7exi/OEPf3AQ7HDBNdtXuU1IQG0P6OPqd6Tog2Jbjr75MASaQUDzHmOfHUJqBtM2x2IEtKW1o0kDCYap4FtaSQ1ki3tAs+4AVdQQiElWwZ922mm+qGrvKnfVZ1oFz+9pNlK/GwGd5lbQvbJDQJF+YoyAdq/+qubYCGhVxEbkXxPyIossYldRjAjzcSRTJAElP5P+NaQf/ehHHnZJPurWQVoCOu3bVnQCHgI6KLZ168TCGQJVEUgT0GlfSFbFr2v+jYC2tMZEQNkfaBLQllZSA9kqI6CTLAFF9Y4Kftlllx1YTZwmoNM+cZkEtIHOaVGMHIE0AX3ooYdGngdLcHQIGAEdHdaVUpLUAgI67dKcSsB1zHMMAZ3UPaCSfr7qVa8amIAyUbH3E4PqTv2nY82hsexCQOecc0635JJLOt0n3FjkFpEhMCQEQgJKf37kkUeckdAhgd2CaI2AtqASsrIgCSgqeJOAZiE0GXZlBBQVPH4mcRCGgG644YZu+eWXH5iAhhJQrmPiDt1ploKigmfxyiQOATUSOhnjxaSXIiSgLCQxJoCZ3Fo3AtrSuhUBZRKZRPLRUthHnq0yAooKHsMnOSfJQA5/+ctfupe//OVu3nnndVzHNIgJCagkodM8cSEBXWyxxfoHOjSeDIKxhTUEho2AEdBhI9yu+I2Atqs++rnRhIEElMnZpKB9aCbmhTpmccEiI8+IgE6aGv7MM8/0ko1tttnGE1AmnocffjgPhlL7LAI6zRJQCOiiiy7qsQU8jSelQJoHQ2CMCGQR0Gnux2OsipEkbQR0JDBXT0QThsiJEdDqGLY9xD333OOzqDrOyi8qeMykSUC5fonDR+uss04jUjomKX0JSaq7aZ64UMGHBJSJ3Ywh0HYEsgjoNGsyVF9c1zeJxghoS2tVE4bIiRHQllbUANlC/Y4pugeUi+j5VOekSUDZ/4n6HYMKHqM2739U/AfZlOpdRHSaCaip4Cs2oBF4h0RM8lfNmoAwJKDqz9NOQGkzLCZ/8YtfNAFxq+IwAtqq6ng8M5KAooLHTHsnfByZyXkTAdUiI6tkc8wxh1tqqaUmioBefPHF7tZbb3Wo3zG6eFptPguHIjsdstGEpec095m0Cn4Qcl+EvbnFI7Dnnnv2F13xoabLZ0hApcmY5n5M7aPN4FDlZZddNnGNwQhoS6tUk7HIiUlAW1pRA2RLahXVcV5U7AOdJBU86ncmlxe+8IW+yJKAqs3n4ZBnL0mniKcmLtnnhZtk+zQBrYvtJGM06rLxRbM//vGPU307QxnmRkBnI6RDyNdee+1sx47bGAFtaQVKYrHwwgv7HE77KrCl1TRQtmIkoCSw9NJLT9TnOCGgW2yxRX/Ppgio2nxVUEU0RUD1lH3V+LruH7LJgtVOwberJlkUcK/ln/70p3ZlrEW5ySKg09qPVS0ioNddd52sJuZpBLSlVckkgmpyoYUW8jk0CWhLK2qAbEFAUbGrjvOiQgU/KRJQ9rL+/ve/n6GKFAGtK6XTBCXiOe17QPUZTjuElNejxmOveuHrX2ayEcgioNMufBEBNQlodpsx2yEgwGTMxCx1ohHQIYA85ighoBxA4os1RYaT8JNyCElfP3rZy17WL/Kge0BFQEU81WemdeJC0oaBgA6Kbb+S7GVgBFQvLMDMZCMQElAtKKe1HwshEdDrr79+4PuSFWdbnsUzX1tyOYX5kAR0/vnn96Wf9k44iU0AAlq2/5NycxANkqWBqMtYQEDXX399v61A5ZAEdFAVvAioJi4RU6UzLU9J2kIJaF3p8rRgNuxyclDuvvvu8zdamAQ0H+2QgLIwZ2yY9rlP4z59mMObk2SMgLa0NumIdD5NpiYBbWlFDZCtWAKqRUjX2wAD6RlnnDFD/Q58IqB1SZKIpvqKnrIfoIo6GVSSNvaACtu65L6TALQw09QJHxTh07N//vOfB7pyrIXFayxLIQElUrQZRkAf6uM7aftAjYD2q7ZdL0zGTB6sAplQu04+2oVuO3IjFXxZbqRS7nobCL9+FJZ5UDWxiKaIJ/emEqfsw7Sm4V0EFMn5oNhOA16jKKPqZKuttvJf/Lr00ktHkWzn0jACOrvKJAHFZdL2gRoBnV3frbARASUzSMCmfRXYikppOBNcwxSjgp8UCSjqd/azrrfeejOQHFRKJ6IpAkrkvE9rn0EFz8G2eeaZxySgM1ra+H6IgHL7AwcPTQ2fXRdZBFT9OzvE5NuKgLKlxgjo5Nd3K0oIAZX0AglY16VfrQC1ZZmIVcFPigQUAqrL58OqEAFtSgVP3BDQaZ24IDuo3zGDYusjsX8DI6B9uSuuuKJbeeWVjYDmIJpFQKd1ISmIIKDMAU9/+tOdqeCFij2HigAdUZOHSUCHCvXYIo8loJMgAWUBdfPNN7uNN954Ft5aaNUloJIQpCWg00xAkZZgBsV2VmWZRS0EJAGlXtAA2En4bBjTBHSaNRlCiPGNA5YQUJOAChV7DhWBtAreJKBDhXsskccS0EmQgN5///0e4wUXXHAW1lpo1SWgIpohAQUz2c9KcMItkLaJgM4999y+tEzsZsaHAASUNkkb5RYI9oByMt7MTATSBBTMTAL6OAE1CejM9mK/hoRASEDphEZAhwT0mKJl8qFOp2UPaBEBZU8cpi5JgmhyWI89jzLTLDkJVfDggfSkLrkXnvYcDIFwUYAEFKnW5ZdfPlikExjaCOjsSpUEdKWVVvLfhddYOttn92zsEFJL64wJQ+ozU8G3tJIGyNY999zjQ8cQUBYgmC4vQjRoZklAIUhI6uqSJAioMPJAJf8goNMqAYWASgIKHkiY62IrPO05GALhomDdddf1kZkafjamWQR0Wvux0BEBRQWPmSQ1vBFQ1XLLnnREqSZNAtqyymkgO6jfMTEEVBesd5mAPvDAA768WQQUBxZbdUkSE1Sofic++sy0TlxZBLSudBkszQyOQFgnXI/FYSQ7CT8b1ywCair4R1XwSEAxRkBntxuzaRgBJmMRUCSgXSYfDUMzEdFxBROGT3GWGaSDkNAut4EiCSjlp63XJUlZBHRaJaBs7bj33nv7p+DBdhByT3gzgyMQquCJDTW8EdCZuD7yyCPuf//7X1/zhysLSSOgjxJQrrBjnJykfaAmAZ3ZB1rzK01Ap70TtqZiGspIFQkoSXZdCi4CusACC2QiyMDapAR0WveAsrWDL+6kVfB1yX1mZZllZQRCFTyBIaCXXHKJJ1yVI5vQAGqj2npGMae1H4dVLBU8e+WRgpoENETH3oeCAJOxOmLXycdQAOp4pFUJKAPxJEtAB5HSZUlAp1UFD9HBpAloXXLf8W7WmuyHKngyxUl4hApXXnlla/I47oxkEVCTgDq/lUjbsIyAjruVTkn6dMZQBW8S0MmqeBHQGBU8Je/6IgQJKJ/IVJtO1yb2moDSbmW/swjotKrgdeG5LqIHu0HIfRn25l6OABJp+nu4KEACijE1/OP4qf9L8ILLtC4kH0fF+RsTREAn7TJ6U8GHNd2i97QKvsvSrxbB2pqsMCHxuURIWYyZBAK68MIL5xZ1GCr4aTyElCcB1eSeWwHmMDQE6OvsbwwXBYsvvrhbdtlljYAGqKuNGgENQElepYLHFgnoTTfdNDF3yBoBnVnXrfkVElCtAllJm5kMBJiUYk7Aq7STQEDzTsBTxkGkdBBNSQiE17TuHZMENJS2DYKt8LRnfQSyFgXEhhR0Gq5iOuOMM9w555xTCmAeAYW8T/MWkpCAIgHloCEkdBKMEdCW1iIdTitBTsFDPqdRotPS6hk4W0ZAZ0LYtApei7aZqUz+L8gO5Js/mUGky4rDnvURyFoUEBv7QDmINOmChU984hPuc5/7XCmAWQRU7Xiat6CFBHTSrmJ69DttpU2jeQ90SlZ/K6ywgnvmM5+Zm8Bf/vIXd+ONN/rVYqjCuPjii2esilZZZRXH/WqTYuiMTBwYJlMMani9ewv71woEWJHqk4exGapDQAnTVcM9oEUS0EFIUt4e0IcffthP7vrSUlexq5JvCGg4ThJ2EHJfJW3zm41AkQT0vvvuc9dcc417xjOekR14AmwpY/iVsrwiZRFQzXcQ0Coao7w0umgPARUR5/5YxrNJuYppLBJQyONOO+3krrrqKrfXXnu5U045JbNdHHLIIe6ggw5y+N955537Ymcm/A996EM+HGH5u/XWWzPj6KoljS6LgDZdnosuusgdeOCB0dHecccdHvtpVomEYP3617/2eznBpYqBTMYeQCJeBuIu7wNmEioioIOoifMIKLh1GTPyX9WwsA/V74QfBNuq6Zv/2QhIAppeGOgg0qSr4Vl8xmjvsgioiNc0S0DBTluMwGPppZeemKuYxkJADz30ULf//vu797znPe7oo492xx577KwTsDfccINjcsd97733dttvv737zne+43s3bmzgPuCAA/p/a6655uye32EbSLYIKCp4zDA64QknnOA+9alPRSN13nnnuYMPPnhiVmDRBc/xePPNN/vB9fbbb8/xkW1dRwLaZTIVIwHVBJSNWL5tFgENJSf5ISfPBWlbmoCaBHS89UydIAFML8CWWWYZt9RSS038QSTGrboEVHNfTPjx1vLwUkfYIwJKKuwDnZS7QEeugodY3XLLLW6ttdbyNbbkkkt66Q4STMTLMoiY8TPnnI9yZFaLP/7xj73z1Vdf7Qno6aef7tXwW2655SzV9PHHH+9ECjhpzOSGFGbYhg3TGCZc5b1qmjQ49gURV5hnpGwQ7ybNnXfe6QeHMJ2i+LWa/+tf/+r4MsMghvLx5YvYtAdJa1hhpRa/6667KpWDC8MhSTFlp+3il6uMYvwPq6yDxMuXn1i55+WfvsJEledelDbhCE+fw7BQ07462nc4eBfFMwlujBH0yxBHVHZgEtrR70Y1JnYZV+YrDH2v7laO2267zauPQ/yFCYKTCy64YEbdyK1LT/U3iKIwU/7BDvKdVX754amxNGyXtFNM1fHVB2rpP/ChLZXhoeyLfMv/csst5y699NLo8Ipn1E/KqfrLS3vkBJQJgVVN2Jm5noUJOSSgEJzw2haurNFeGlT37A1de+213fXXX++++tWvuuOOO27G3qezzjrLXXHFFb7cXHmx6qqrRq3C8oCKtVdHRIUeljE2PP40kRKexiciyySuxlglviK/4C6iG7NPR52AcIPmRY1z0HiKyjdsNwZXTNW6YbDlq0AxZadNSQUf43/YZa4TP+0G9VFe/lkk4pbnXpQmfY09uDwxLOB0vRX1Qv+fFsMYudpqq83AkfEjjS1tKmaCmBbc8sqp8Rz86o7nzHmcT8hq26uvvrr7+te/numWl6c22gsn9l2nCSj9Mt3+ssqgeY95QVgJc8ZL2WWF7ZJd1XkPQq4+TDkRQp166qmtx4N2oHaRVz8jJ6BMDJISKlNkNC2lSPvDj/aD7LLLLo4/qdmYcJCG7rDDDorSk1L9QBKKKh9p67ANeYGcsd+n6sEU5U2T5xJLLOHzLNU7qrSmy6BJG4LPX5lRmSCrg+YFaSpl7fLmct1UQPuNxYP2j9SOlWxMGAZm2joDcIz/sjoch7vynpd/FpsMVnnuRXmmz0Ey6XNISmhPqDcxLHbrxFmUXpvdINzpdgUeaWwhRYwn4SK/zeUaV95ot2DKWCxBQNW8FLX9F7zgBY4taYzxoQCmahrj9g+pQvrOHKJ5mTxJYAGJKuuHUrfTd+UX7DBVxlcfoMX/WCTSlmLmPcY2+q64AMVC8MacwNyZ3lfcpmLD2cRl8vI18j2g7E9i8gVYGQibJgzZMaFgL8M7KjwM6vow/NOe9jSHxHRSjMqmPaDq0ODWtJHaQyS3LH4NCFqtlvmfdHcR+Fj8wIMJjUElZgASfrQBpAtaPcu+K0+p4fLyO8g+RepAi1PFr99V6kVhu/qkTdGf05OSHUIab41mHQxTjnQQaVK/iKQ5S/OGyp31hKRitKjnXXPfNPVjyi2j+SUU0E3SVUwjJ6BI0DbeeGMvQgZkLqhlIuaPRsqVS5gNN9zQ/fnPf3Yc8oBJn3baaW6jjTbyboQ58sgj/TsN88wzz3Sbb765/z0J/0RA1RG1MhxGJ4QMYWIGCPwpD1I9YzfNRgOEcInBQqS/KgElbkkUYtJpkx/wSR/CCPMHAVW7D+3L3gkDKRfhlH/9jm3XCtflJ30Z6XrWIaQ62HYZizblHYlXelGg/CE8wW1ST8JLUBHT/ooI6DT1Y7UNnppfQgLKISTMJFzFNHICCnC77babO/nkk90b3/hGrxrfZ599sHaXXXaZPxnPO6L8XXfd1b397W93b37zm/0mcPxjXvva13oRNFc5ve51r/N3hGol6T10/J866ygloLEdXERLA0tXoT7qqKPcHnvsMXD2NUBj1X2nAABAAElEQVTE4keCdQioCJUkCgNnfIQRsFiBJBYR0LpSOrVH4aNiSXJSpV4UtqtPHRBME1Cw1eTe1bJ1Od8Q0HSdhOVZd911J/YkvOYJFkaa18Kyh+9qoxK84KZ+rX4e+p+Gd41fIQFFO8z5gUk4CT/yPaA0muWXX96deOKJXhUZ3oW4wQYb9CWj+Ntmm23c1ltv7RsugMtATj/96U97aRx7DETU5N71pzqiysV+ERrgMMiHJJlq6GXYaSBQuDL/bXVHas5JwkGNCKhwiYlPUuew7ZeFE6EaRhsoS3tQd01CRQSUtq52XyU94a+JSmH1O7ZdK1yXnxAdTFraBrZlk3+Xy932vLN9rIiA8kWkY445pu3FqJU/9X0C01c1p2VFpv4fElDN71XG16y4u2qnfhsSUMqCFNQkoAPWaswEzGGXkHyGSTIpFzXo0G+X3tXowrKhhm+6E6LOZXsDJnailr9wYOkStsrrvffeG11mhcl6igBVqZs6EtAuE1AtVsoIqNp9Fs55dmqPIpzyp99V6kVhu/oUAU2THcYRTe5dLVtX803bp12nFwVhedDecXhuks4xqHzhPKG+Krf0U200JKD4oS9PUz8OcRFmaQLKPtBJkICORQUfAmzvsxHQRBx2RAhI09IvSeLIgRr67NzMtNFAIFIx07U7vyDfIo+D5FpxxOJHWtNKQPMWkmBCW1e753esEe4inAonwi532U/ys4iAcoDNzOgRyKuTMCc6/c792JNm6hDQ9Mlp+rLmnUnDp6w8ml/SBHRSLqM3AlrWAsbgrpXgsCWgIkIUMXai1kAQDixjgGjgJJGAqnMPEpniEC4xcYE7Eu2Ye1cVnwhV04sQxT/MpxYrZRJQTnFXNWq3aQJK39E9ulXj7Kp/9oDSptLXqUHuwbYOwe8qFm3JdwwB1e0u+nBKW/LeRD7C8Up9NS9e5r000cIvY19Z2Lw4u26v+SWNCwSUDxzIvavlNALawprTRJEmoGFnbiLbJgF99OLyQbDUAFCVgFY5AU/+poGAgmX6juCyutHElCaghMNO7mXxTII7ZCetfqdcGke0sJ2EsnalDDoYVqSC14cSTAX/nxlXMKmOTQL66D2owoMnKngWlXyIp8vGCGgLay+LgNIJh0lAYwmUJvSuS0BRwQvnQZqACKhwiYkLCagR0JlIabtJ1ToR7mkJAbFP28SVR0DrYjuzhuxXHQRiJKDUDwR1EiWg4TyhvpqHIwsktdXQz7T147Dsml/S49ukXMVkBDSs7Za8axIOO+MwDiFNqwSUlSMDYxNqSQ0QsQSeJlaHgGoAanoRMoomLxW87rPNSrOulE6TmklAncu78FzYalzJwt/shoMABJRbTMoO3C611FITT0A1VuYhnUdA6dtVxte8+LtoL8w0/qsM3CTEXtmuH0QyAqoabdFTqjJNHGRtGBJQiBCDI0RXE3kZDBoIwpVtWZi2uUOI9EWh2HLnlUEDhHDJ8xfa1yGg1BF/XSSgtBXactieQzx4l1tVkqT6MwLqHGQnS9UrbDWupLG338NDgEUB2g7G2SLDPtBJVcFrr7v6ah4OeQTUJKCP34cq7PigDx8x6PpVTMW9QqW150gR0CSsiYPEkR41TT6QgDI4VtkrJ6IlqdZIgWkosfBrQiKQdaNW+LLBNYwf3MskIqF/vQ+jDSjuYT5pK0UHkEhb0n61/dj8CPcsAsrEJffY+Lrsz1Tw7au9vEVBOqeTKgFlztKiqKwvGgFNt4rHv4QkEh/6mISrmIyAhjXakndNwmGjG4YKXpK4KgSUQYR8dVkCygl4GRFI/a76VHgR85jwearSsrBdJqBFVzBRbi22qkrpNKllEVDsqtRLGf5td89rV3WxbXt5u5C/vEVBOu+TSkCZJ5ogoOrnadwm/TfzC9JOLdDD8k7CVUxGQMMabck7BJQGxzUyMsNQwUsSV0VSxIS+xBJLdJqADkMCWoXoSPKsuo19dpmAlklARZK0+IrFhImJvpKl4qyysIpNr83+8qRtdbFtc1m7kre8RUE6/6jgJ/UQkm5mKCORJgFNt4pHJaDp/Z/yBQHlFDxnGbpqjIC2sOboiJo0lL1hSEBFQKtM1KzIIKDsoaxCulSONjyHIQEtG1zDcoMbpL+qMQI6GzHaY5b0E59VFlazY+6WDZImxg1N9mHuJT2pSu7DOOy9HgJ5i4J0bEhAacuMyZNkaJeLLLKIl+JRviKTR0CnTZMRYgRmeQQUFTzu3AfaVWMEtIU1x0SRJqDDkICigmcvYiwBhTix2oKAYrq6DzSUgFYhjllNhQEA6VssGeeLNPxBJquaSSagdUkSuOcR0Nh2XbUe2ugfSRsmi4BqLGGCNzNaBGIloBBQzKgPIh1++OHuFa94xdBAgYCy/QYSVTbW5hFQ5r7Y8XVoBRlTxGBWREDJVpcPIhkBHVPDKko2i4AOSwJa5RCSBpAll1zSZ5/BpYsmJKBlq/Ky8hEeEh87QMqfSUBnIluXJNEmiwio8J6Z2uT9QtKG0X67sIR1yX0Yh73XQyB2D+i4voZ0+umnu9/85jf1ChcRSgQ0ZjFoBHQ2oHCBPAKqu0Avv/zy2QE7YmMEtIUVRaPTpKHsQViQPooEyn6QZ6iCj5mo5afrEtCmVPBsQ0CaCYkXNmX1oZsMTAI6Eym1d9p+FVNGQJvsL+TrnnvucdzBd9FFF1XJ5tD9ioAWSUCrYjv0TE94AixOGReyFgXpoksCOup9oJAXNGHDahtNENAY8prGc1J+M37lEdCFF17YbbLJJu7YY4/tbHGNgLaw6lgJSiKk7ImwiMDIfpBnSEBjJmqRLBHQaZeASnrKHqf//ve//btFi+pEGE6TBJR2MqxDSNRBngQUjGPadVF9pd0uueQSd9NNN7mrr7467TTW36aCHyv8mYkXLQrSAdCiMOaPUgXPWHTjjTf6rNxxxx3pLDXym/mKuSuGROZJQAnPJ3pxnzbDwiCPgILFe9/7XnfBBRf4vy5iYwS0hbVGoxs2AUV6BzGoooIXeZIKvst7QHUPp0hknWagsBBQTAwhF4ZaUFRJlzBNLkCqpD2I35h7QNXeq04yEMw8Ahoz6VUt12WXXeaDhNs4qsYxDP+QHfYi05/Tpq50OR2P/a6GQNGiICumUZ+Ev/LKK/snqIcleaV/sgeUvqjxMqvs2OURUC3WNXbmhZ9EezArIqDbbrutW3bZZd1hhx3WyeIbAW1htRUR0KY6IWoXVPpVDiFJmtR1CSgqeJFolalOM9CAKgIaUzcikBpUq6TbZQIaew9oVVVgGQGNqZMqddBmAgr5zLqOqi65r4KL+Z2NgCSgMSp4Qo/6LtBw7+AwCCiEkjFSBLRsrM0joFpgNt2XZ9dY+2zKCCh3hO62227ue9/7Xiev8TIC2r425/fjSGqh7ImwiMDIvu5T131AQGNVlRoAREC7LAFdfPHFPXQikXVwVFhJncoGWNIQhqrPKul2kYCyyKHMZSp4tfcmCSgYq46q4Fzkt60EtOi0tT5oURXbIhzMrRwBEdCsfblZoSGgo1TBQ0DZR4gZBgGVRmhQAqqxUmNnFnaTasecUiQBpdy77LKLX3geeeSRnYPBCGgLq4yVoKQWyp5Utk11QiSgmDoqeFb0SFo0wCiPXXkiAUVqCekZhKAobBUJqOpP9VkFsy4SUBYpkNAyAqr2TtuvYsokoE3vHWsrAYXs5Ena5pprLn8PoxHQKi1rcL8ioBofymIctQoeArrOOut4LdgwCKiEJRBQSFTZAj1PAioCWha+DN8uujPHlBFQ+v3222/vjjrqqM7tkzUC2sJWyUShCVnZE2FRp5Z93WcoAY3dKyfyxIDAoNJlCehCCy3kO7ZIZB0cFVYTjPApikv1p0G1yG/ajTZA24DQdcWojZQRUFRJmKokqYyAEmdTExcXPmvh1sY9oEWSNsaTquQe7MzURwCpNBJGSaDLYhqHBPRZz3rW0FT/ElCYBLSs5vPdYwgooTmMxCICVXyXjBHQFtZWFgEVYRGBGTTbdQioJnIRUA0wg+Zl1OEhD0wMrCxFIuvkQWHrqOC1oKiSLmEgn13CPZaAggP1UYeA5kkI1GdiFgYx9SDpJ9s3ukhAq2Ibg4n5yUcACWjRoiAdEgLKNV9c7TZsQ1vgAvPVVltt6ASUcStGyFEmAW2qHw8b2ybjjyWgSLK5kqlrh5GMgDbZWhqKi8FBe+IUpQhLU51QBBQiFjM4kA+lzYSPREvkQnnsyhMV/LgloGkJdwx2agNNLUJi0hzUj9pImQSUdOpI6UYpAYWAIqldf/31W0dAi/aAgi3jiRFQkBidqUNAWWAO60qksORXXXWVv9polBJQLdjDfITveQTUDiHNF8KU+64rmc4///xcP21zMALathpJ8kNHTBMUdcKmyAeqRIgk8fLHPZZlK28IKGHY/4lapUuSuLCakV6JgEqqG7rHvmtAraKCB0OIZNZp5bJ0u0hA1UZiCWhVkjRqArrKKqv4vZZtlIDm7QGlXdUh92Xt0dyLEShbFKRD62tIoziIpBPwoySgZWNtHgFtWpORxr3Nv5ljNPeX5VNXMvF51a4YI6AtrCkm4TQBRfKCXVMEFAmo7sJUAy8bICBPGgy6KgHl/lPK0aQKXgS0DD+aGvUnDKs2vS4S0CoS0DpSuiICKpxj6iWmLpCArrHGGn7x0iYCynhBuypS99bBNgYT85OPQNHBsKxQqOAxwzgQlE4PAsr4D+kl3WFIXTVX2R7QNPrxv2NV8MSoK5lOOumkkbSh+FLk+zQCmo/N2FyyCCiZgYBAnpowEFDtXYwloEzkmtS7KgFF/Y6aCwko5ZYUsw6mCiscY+oGPyKSVdNUOA3sVcOPw78IKO2lzNSR0hURULXrmHopyxvuTNqrr7566wgokjZMEQGtg62P1P7VRqCqCl53E4+KgCL9xEBA6SNNL6qk/aDvozkrWwjmSUDVj8vC166oFgesQkApBlcycetFV65kMgLawsYHAU3vASWbkL+myAcqeElARSrLOjiDlPx2VQKqQVYqeJHIOs1AYYlrjjnmiFocUH/CsGqa00BAafuxhoUEk5YmqHQ42Ze163S4rN+33HKLY/HSRgIK0cGUqeCrYJuFgdlVQ6CqCp4xn0XEKFTwV1xxhT+ARImGJXmFgLLViMUPfbGsH3JlWta8h2QP+6YWktVqcby+qxLQrl3JZAR0vO0rM/VRSEBDAsrqFFM2QDAAaFLvsgSUsjapggcTyGEZfqQbknh+VzFdJaC0r5iraJhkqpAkTUhqk2ksZR9TL+mw6d9//vOfvZUIKAsJtnO0wcRIQKti24ZydTkP7KlnwVK0KMgqH2Rw2BJQ8sYhpFACSl5i0sXPcsst5/70pz9lZX+GHQRUe78ZA7Rgn+HpsR+cP8gjoHihL6u/Z4WfRDvGFxbYmp9jy/ie97zH12UXrmQyAhpbqyP0R6Nj1Zg2TUpAwz2gksiVTdQheZoUCWhZmdN1EP5mQIVYscoHw5gBEuIiIhnGFfOucE1JwWPSHNQPKnhNQmVx0earEFDVnYhmOv7Ydp0Ol/Wb/Z+QuJVXXtmr4JG+antBlv9R2sVKQBlXzIwGAa5TwhRti8jKCQR02BLQa665xh84rUNA2YaCNgACW2YgoNp6Qx8VocoKp7ZJH8syseNrVtiu2mksrEpA1113Xfe85z3Pff3rX2990Y2AtrCKiiSgTZGPrD2gZQSKCV+TelcloFLBNyUB1eAQu0IPSXzVpjcNBFQTUQw2kqjkEVDZl7XrmLQgoM985jP9Rn+2XGDUlmLCD9OPyI72ImelZRLQLFSGZxcjlc5KfRRfQwpPwJMH7rVl32CMBPT666/32Wb+KDPMVSKgmje0aEyHVb83Avo4MhrfNMc87lL+9tKXvtT99re/Lb3Zpjym4fowAjpcfGvFDgHN6ogQkCYmUzIVSkA1UecNDipESJ66KgFFLYZp6hCSBofYFToYikgK19gnEkKkrU0tQmLTHcTfOCWgse06pnwQUNTvmDYSUCb6rDFDZasqXVY4e9ZDIEYqnRXzKFTwEFDaC6p0DGMKJDSGgN5www0+TAwBDSWgGifz5pgYApoX1mdoAv8NQkCf//zn+y1hF154YauRMQLasupBtcd+mGGr4MM9oLETNeRJfhnAIMrsJ+qSkdQKEsGgqE5epwyE1cAKLjEDJORR0oA6aYL7pBLQqlI6LcbUJtN4ItVhi0RMvaTDhr/pkzoBj30bCaiuAgvzHb6DrSb50N7eh4NAXQnoqAjoqquu6g9OqvSx6YqAaiGv8FlPCKgW2+qjeX1RbTNvEUV49festHbddVd32mmnZTl11k5YaY6pUpANN9zQz02//vWvqwQbuV8joCOHvDhBdcQsAkpnboJ8kAbkUSq7ssFBOWYAEHnSvr627INTHsueEFDKCzFpkoCOQgJK2ZpqA2U4NeUeHkQoi5M2r/Zf5hd3LR7UfrPCUC8ayLPcY+xuuukm/9EF7gDFdJGAmgQ0pqab8yMJaNU9oKjgadcxEsa6ueUEvPZ/Ko6qBDQmf6EEVH1UfVbp6ql+n0dAGffyCCh7S4877jj3y1/+UtFNxJM5GlOHgNLfN9poI3fOOee0GgsjoC2rHjW6PAKa1wmrFEN7xnQNkwaHsokadxFQJHEYBpkuGVbuIhB07LIyF5UtlIDGEtBBJaBdI6AsUNRWirDErSpJUt2p/WbFj9ugfQb1O6bNKvgyCSjYapLPwsnsmkUAAsqYUNQ2s1KECGJi1OFZ4cvsIGt/+ctfahPQKntAswio+mw6n2qbeQS0qB/ffPPNXmsYI5VNp9vm3yLrdQgo5UINzz5Q6rytxghoy2pGBDSrIzKgNSEB1eq1KgGdFAloSEDVyes0gzQBzRtcw7hDDEP72PcuElBJy8vKSJtX+y/zi7vwLprk6TPyFxNnlh8IKJPASiut5J3VfrSdIyvMKO1YUMYQ0CrYjjL/XU2Lq7nyPl8MAa0q/QQHEdBhnYSHQNIf6khAIYnKl+aQorprkoAWLfCvu+46n42YPBXlt21uGrfqEtBNN93US9JjrswaV9mNgI4L+Zx0NUkMUwLK/k+MVPDslWPyV4PPyZqXJGmyl1SrixJQTsBj6NhNEdCiFXqIJwsISGRdM8kEtKqUTu1VbTILU9zkL8s9xg4Cyp45+gmGOLkcu0sEtCq5j8Flmv1Q91x38/nPfz4ThqqX0CsSEdBhSUDTJ+DDdO+8885CaRlbUZCmsaCMIXshARWJyuuLZRLQooXkpBJQzU3CTnUV+3zuc5/rx6w27wM1AhpbmyPyp46YR0CHIQGlaDETdSi9k1Sri3tAJcGizCL8dao3LQGNUfWSHoNpXTPpBLRKfWgyox7zTEy7zgsreyRdUr/LjjbUJQJaldyrnPbMRgA1NgcwjzjiCH+BetoXEtCql9ATB5JsFguSNKbjHfQ3BBRCs+KKK86ICuLLRfA6PDXD8bEfOoC01lpr+Uv2s/yEdsxVElSoj6rPhv5417xH2bPMNEpAByWgYM8iqc37QI2AZrX2MdppAs4ioEWdsEqWtXqVCp6wMRM1g4fIkwaWrktAGXTz1GhlmKYJaN7gqnhwJz2TgAqRmc+qUjrhXSQhoF3HLAxm5uTxX5yA59BG1wloVWwfR2D8b9TBgQce6O64447xZ+axHOgidvYf/uAHP5iVr7oqeCKCDA5TAsp9tly9FJoYyav2f66zzjrRElCNdSKgIlVh2ryXEdCifmwS0DSaj/9mH6hJQB/Hw95KEBABzVoJ0plRgeR14pKo+85SwUsVjUNRB8edfJG2COgkSEBFXOriGRLQMvzAUNJrYYhdVdMlCSjtBZKotlJW1qpSOuKeY445Ck+JgrWIaln6We5MuhDYthJQykY7nOQ9oKh+9957b/eLX/wiq4rGYgcBZQvTBhts4A4//PBZeairgieiYRLQrBPwSpNnEfFFArrEEku4ZZZZJoqA0m8kqBABzeuLZQSUfpy3kDQCSs1lG/aBsnDTginb1/hsZy6DxpcPS/kxBNQR8ySgeBORqQsaElBIAfvYZBgg8gYH/KjzayDRwNI1CShqU6ngRUCLyi18sp4hAS0aIBVWGEoqIPsqzy4RUG3PqEJAtQCLwYR6Ux3m+S9r13nhZJ8+AS/7tqjgdaNFDAHV2KIydOUpjY36TxvyzYS+yiqrOL67/atf/cpdeumlM7I1iASUq5iGoYKXND99AImMx0hAIaArrLCCQ3PGuM8CM89QV2h7NE9o3sgba9U2swQvpFE0vkJAEaYwtlPGSTESjAi7OuWCgLJIb6sa3ghonVodYhhNwFkEVMSlCQIaqt8pTtlErcGfgQADeWXyF8nwlh34xyAlya/Iizp61eynCWje4Kp4VW/CUPZVnpNMQKuqicG7bHAG67J6KcIfAkoa6T1zXSOgVbEtwmTUbrpeZ5B6bDrPIqCvf/3r/VeEQikoJAgtU509oORzWBJQtgtAHLMIKGMi42GZBFQElDJqYZCFrcY6EVDmC9T+eXUYQ0CzwjL/IG1mryOEuC37srMwqWqneSmPlMfEx00M1Hdb1fC1CSgNcJIqO6YyR+EnhoCKDNbNDwOHTsArjjICqs4fkicGl65JQMN7QEVe1NGFRewzTUDL4lG9aSERm07or4sEVJNQWI6s9zoqeNVhVnzY4S7c8/wU2UNAGcDTe+a6RkDBFolUF42IjsagNpTh6quv9hJQcOUrPN/5znc86SRv5Bes61zDRPhhEdC8E/CkiSlLl+0oIqD4V73wnjaaF8K+X7QYjCGgHPpK79eX+n299dbzWdBiJZ2fLv5mPoF86vaNumVACjpRElD24zztaU/z+44233xzd+6559bFxsKlEBABzVr1iPxpdZkKGv0z/AynAhUNDvjRJK48YIdqtUsSUAYwOnVaBV9GHClrliGcpKgQHRZlwinLv+otxDDLX5FdFwloFRV8lU+7QkhiCOggxAUCmt7/Sf10kYCS70GwIPw4jIhOW/KOepxxDxU85l3vepc/RHPMMcf43zpJXpeAooJHhZ8mWz7yAf6x/5MvwK288sqZsRQRUOYlpKNoAqRBKiJ7IqDhYpu+mleHMQSUTKfH1zQBVVvJLGDHLMP5ZZCscxCJ7RO33HLLINEMJWxlCej555/vfv/73/vPXrH/aI899nD77LPPUDI3jZGqIxap4NOdsCpOdNK6Kvhwwu+aBFSDkwZQkccmCKhIZd4ASx2p3uS3ar3hnwG9KI06cQ4rjBYnVQgoJD62PvAXtsescoB1XbxQ6WWdgCedrhFQLWi1wM3Cqq12Ijqx7WLY5dCBDhHQpz71qW7bbbf1VzLRZiCPmEFU8PSDpk/9IwF9xjOeMWPvf4hVEQG98cYb/QJ7EAkofTWvDjXvqZ2G+eJd/VxjqNwhoITRIlFjvNy7/AQrzVGDlAMJKKaNUtBcAkpH2nHHHZ2uXhAAt912m18F0ZAZhPnoPR2ODmNmcAQ0QRQRUEnS6qY2CAENyVPXJKDaMpKWgNYlKOEAIVzSA2RYR3ILpQKhe8w7YembiismzLj8VCWgmnzUB8ryTb1pYsrzi3vd+r322mv9hKnJLUyjTQSUsaIMB40nmujDsrT9XaSibj02XT7uAOVgB3OgDIeRmCt/9KMf9QloXQkoRBDT9EEkCGjW/k+VoYiAIkHDDEpA8+pQ7TI8GOsTfOxf3vgKASVPOoSnthKG7ep7OL8MUoZll13Wc7Y27gPNJaDseYJcbrbZZl7FcOutt3oMXvnKVzouZqbC11xzTd+gP/jBD/oOOQhIFvZRBDT5asIIcVEnHJSAooKftD2gGsBCvNLvIqDDkICKABQRQ9Wb6jGdv5jfIq+KKybMuPxUJaBq8zF1SZliCWhRnRRhk3cCnjAQUJWvKI5hu6GFiiE6Vcn9sPNdJX6RijzyUiWuJvwiAUXqqb5InEiZ1l57bX8lUxMqeOIsOhCEe1UzCAGVIGr55Zfva89UL1n5kAo+3AOKNC+vDunzkM+8/Y4aM9N9GQLKJ3Kl0SvKU1Y+22zXFAGljG3dB5pLQMn0u9/9bnfllVf6Ct54443dBz7wAX/i7LzzznOo4r/4xS96NcFOO+2EdzMNICACqgkjjFIDXroThn5i3umk6rDyD4EqilduGggI1xYJKJPwkksu6U455RQVJ/MpVV5aAkpHr2qQQrJHSyoS4SKcsuITaVQ9Zvkps1NYxVXmf5zuImjhJFSUH7V59YEiv7jFElDiq6OhgYCSdybdtKENcdBk3PVA25f0J53H8LfIfSy2Ydhxv6vf5pGXUedPJ+DT6e6+++7+rtLf/OY3fq+lxpm0v7LfjGWYJgko0lTG/TIJKMKJrDaCBBQJKfMEYxBksYjsqV+EfZ+weXUIAVX/z8JH42s6PFoKCChzEUKzojxlxdtmuyYJKPtA2U6kxVFbyl1IQMkkjeZDH/qQzzySI647+OhHP+qvnnjhC19Y2GjaUsgu5UPSn6zOqE6ozl23XAzoWQQ03bnD+EWsaA8yDC5a6cpuHM9vfetbfuBBMl9kJAHVxKCy1CGgCpMmoGUYcgggT81UlHe5dYmA0jZos3lSDZVJz6okCaxVh4oj/ST9KvtKw/AQ0NVWWy1Tu6M2pDYVhhvle1UCqvFllHkcNC2RiqK+NWgaVcLnEdA3velNfjHwjW98I0oqnZcm/QANVZMqeJ2Apz3nGan+s/aeQkBRdcvABbQwkF341LygOQs3+mpeHcYSUM1DxIcQgL2pEFC2RJAntRXcu26aJKBIQBkHWRy1yZQSUE6lsneAxrbvvvv6C3exW2ONNdynPvWpVqih2gTooHmh0WkiTscFcYGYDkJAkUpRf1VV8Or44YDSFgnoV7/6VQ8V+5OLjAZMqeCFs8hkUdi0m8KIgIoICae0f35TbyKQWe4xdgo/SBvISufnP/+53ye0/vrru6233trtsMMO/oDhZz7zGfe9730vK0ipHeSMNhJrVB+xJAmshXteGnLPm/jywmEPAWWcyzJdI6Ba0GZJt7LK1yY7kYo6dZgux3HHHed+8pOfpK2jfzN2ovbVAaQwIG1t55139pqRmG0RYdj0e9F+zLTfmN8QUBaCfIYzz4iAZkledQWTwiLAUL3ILnxCQOnPLLhlwEfjpuz0LCOg6sfh+MqpbsJBQDFlpFhpdeVJe9f8Mmieaa9I1tt2EKmQgJ588sle7P72t7/d7/dEjMvAe8ABB/iT8HfddZeXEBx88MGFX0UYFLxpCk+H0kScVW4IYNgJs/wU2WnQqCMBZRILpVltkICyHQSiADHTPuW88ktaJVKkQa3OxKaBVAOEiHlR3eAmf3l5LLMfFgFlZUx/XmuttfykwT2HP/zhDx0E9HWve52TBKUsf6E7ix1hHdrnvVclSdSb6jAvTrkX1UtWWNTrHDbJOoCE/64RUI0pseQ+C5Nx2WnhWKefpvN8yCGHuE9/+tNp6+jfEDG23mQRUCLZbbfdvCq47gl4ZYSrmLKIoNyrPum/EDW1g6zwRQQ0LQGNIaCh+p30GCvz6rCMgGrcDPsxCwGMCGhZnrznDv1jjtH80kS2kYK27SBSIQGFaF5wwQV+IEbVw+QEKcXQWA877DD329/+1u8TDRtGE2BNaxxIKDQRZ2EAARlE+sUeH0xVAsrAoUFA+WqDBBTpJ98m5nBcmQQUAgp+ItHa9C4yqXLFPBVGA4SwyRtgibPNBBRpAqd6v/71r/uTvOzxZoDn6ymot+jnVQ1SkPQkVBSHJsdYKR11IIKZF6/ci+olK+w111zj98JNCgHVmBKLbRYm47LTorlqHWbl984773R/+MMfvOQsy73MLn0FU9o/ampuj+EA7yCG+bVJFXzeN+DDPOYRUHBHLR9+DayM7GX1ffpiXh0aAQ1r4tH3pgkoAsSLL764VVrrQgKKSFsZZgV00003zSIuXEj/ta99rdJEMxtqsxECZRJQCNQgZF+DeVoFD4FCvcRfliFNTeZyh1wMQoYVT90nhPLEE090b3vb2/yHEcoIKJIUqd+VJgRSZFJ2MU+FEQEVNkV102YVPASU6zrShnaC2g5Jc1VTVQIqAhorpWMyE+55eYtZGGSF1X7iSSGgwraLBLQpCSh74DiEQd+95JJLsqq91A4Cilo5JGPpQFxIf9BBB6WtK/2GDDYlAQW/iy66yJ/fKMoEbQRimU6XfZYYyLVMGQFlrEsvPpsgoCGBZYGMpFlalrI8Ke9deQ6DgKLZqTOWDwuzQgKKquJzn/ucb5TPe97z3HLLLef3hw0rMxav81IXTRZZeDChDkL6REDprKHRRB528NA9S3pHx2cj+CD5CdOo+s7n78gv+65QWSHdoIPlGQirVKfyM0oCmoWh8hH7HJYKHklnFgElX89+9rNrDVpVCWhVKV0MAS1r13m4IwElLGNellE70raOLD917ZDqf+lLXyoNjioYSVOVU/Cx5L408RF60JiVNzbFZgUtnhbYv/vd72KDzfAHAYV8DnKQcEaEOT+aJKAQYshMzG01WenqCqaQgJbtt6TvN0lAwRviHy7wIaBSvwOjEdCcxvSYNddmglGb9oEWElCuXrrwwgv9qpH9dV/+8pczT4QWF9tcqyCAhKKIgA6qgtdgXpWAMvhLmqTyaIBhEhyHYaLecsst/cqce/kgn1knOJU3yEJaAgrJkDRT/mKeCiMJKFeA8F40STYhASW/qMSbJv307zwC+pznPMdvs1HbicEHP1UJqNp9rJQOrIV/Xp5EQMOJK89vaA9ZKTpIQtunHoZBQNmneOyxx4bZyXwnj5gYAlqV3GcmOAZL6g2iTRmL+lZM1ligYuirgxDQooM8MfmI8cOCmjGmap9Lx42AgMXMq171Kq8lSrunf2cRULSftPXwOrIyspc11tEX8+qwTAVPPgkf9mMI6NOf/vR+Ecry1PfYkRfqv2x8q1IU2v2LXvSioYxZVfIR+i0koPKIGo4GaGb4CDD5arLISm1QFTx7QGmIUlsojbKJmo6fJqCKA6IxasPnYNkesuuuu/qk2QeKKVLDo4qS5Mp7Tv7RwUUmZRfzVJhwgACfcIBMx5OFYdpP2W/64aBS8HQakCjqsIiAor5kX2gVU5eAxkrpqAO127x8qc3mTXx54SB3RcSOeqD9N01ASZe7l4vasfJchYBWJfdKY9xPqd8hZFXrMJ13EdDnPve5AxHQvANI6fQG+Q0RxKTV4VXjPPXUU/0Xmt7//vdHBc0joOCvNkREZWQvbw+oxs10ZmIIaHp8NQloGsXy35zh4exOW0wUAW1LZqchH3TEsKOnyzwo+WBFzeCRXlBoIs8b5CFP8qM8jVMCevTRR7sllljCveIVr/DZiSGgkIUsAppXZpUz66mBNCSg4FNGQKVCz4oz1m5QKXg6HfZ/YvIIKPsgIVtV9w4xCWmRkk4z67fafYwEFKkY6tR0m0zHK/eqdQy5S++TTsdNW2qagHLYC7LPjQRF20nISx0CGkvu02Ud129JAJskoIwZqJVFSGPLRt9GUzAKAkp5MYMeROJjMeutt57bZJNNooqZR0BXCPZ/EhFzCG2ftppl8ghoXj+sSkCJnz6SVsFrwZKVp67ZxSywu1amdH6NgKYRGfPvUajgGTzSpmyizpLeiVyMWgKKeue73/2uP22qe+Y0YBdJjhgw0yr4piWgeQMseJNvSeTS+Ff53TQBZf8nJo+AIjHfaKONKkuNqkpAJfmPIaDCWe02Dz/hLf95/tL2aAqKJKD4HxYBJW5Up2XkowoBrYIt6bfFiFBAjGLaRVG+IZwsvF/2spd5b1UXVFxNBuEaBQFtQgL6pz/9yZ199tnufe97XxEsM9yqEFDaaN4CjLFOAgolQF/Nq8OqBBTpJyZNQFm0jXo+UvmafkJAQwFH0/G3IT4joG2ohSAPMQS0SMoWRJX5ikQhS7KjiTxvosZek7ki1gDDanSU5oQTTvCHL7ifVoYJlhORRXeBZqngKbekmYor5qkw4QABPkV1g1ubJaDso80z7ANFBZ8n8cgKV5WASgIaI6VTO1W7zUofO7kX1UtWWMjdOAjoueee6y+MJk9FbRn3KgRUC7UYbIm7LSaUgCLxHiT/7A9nXy+fo+RZdR9o2RVMTWJG26POBlHBH3roob4tveENb4jOGgSUvhUSy/Ql9EQmIYbqJ51AlgSUsTKvDmMJqPp9FgGVcEGLlnSeuvbbCGhQY6x2Tj/99P4AwOXz3L3IvYFmmkMAAippRVaskBxWl3UNkh0NHmEcIpfq4KEb70zg8iO3cUlAOXy02Wab+XsrlReeEKgyCWiWCl5kMoyr7F1hQgIK2SkiOm2VgKKCp01oQZFVdggokw33CcYYJBFgVBRnOh6dLM6TkoT+1U5FMEO38B1SiwRX/kO3ovdxEFC2FXDoc7vttvNZK2rLeCCPYKZ+WFQeJH9gEYNtUTyjdhPBkUSwaj2G+UUCyrYdDAds6xBQ2rO2+4RxN/1OfVHmMil4Xrqop9ESvetd7yqcT9LhhbOIL+MZcaWvnSojexDQ9GJbfTWrDmMJqMZXCCjzZKi10bymNpMuW9d+GwENaozvv2+zzTa+Qxx//PHuwx/+sHd973vf6+9iDLza6wAI0BElCcqKhk6tTpjlXmZH51RHDf0WDQ74I035UTiRi1FKQFErIYnbZZddlI3+k4mhaNIehgo+XCxA0LMGV2UQDNMkXm5Vnk2r4PPuAA3zxISNiVVbSoISQ46UDkQxliRpAZBuk4orfOKnqF5Cv3ofBwHlUB35hIBCQIraMvmMyaPKw5O22jUCKmmWiJHqPSxX7HtIQLlaDLJfts82jBsJKB9rGJWhzCKCVdM86qij/DaOd77znZWCCmelywl4zAoZe0CxzyN7WRJQ9dWsOowhoITX3AcB5VQ+Y4aM5rW8PMlfF55ompAWhwKOLuS7ah4fr72SkF/5ylf8N3Sp9G9961t+Hw2f6vvkJz/pUImaaQYBJogyAjqIBHQQApomT/xmABjlnhukn6jPJCUKUS8ioAx6DHJZEtCq5IQ0iQ8VmaR22IGHBkh+pw1uaalA2k/M73EQUDBn71us1EhtogoBpeyxJEl1pkmtCLeqBJSBnz42ahU86nfaFHcuI6lrmoAyrtAHumQYr+hXkrip3uuUISSgSPSpY31wICY+Ps06iv2fygtksI4EFEn6EUcc4VC98/3vKqYJAgp5YnyUgELpq69m1WEMAQ3HVwhouP+TNCaJgGoeMQKaVCwrbSaUzRK1J43n7GRj87bbbuvbFfdw8XUJM80gUEZAw05YJ0VU8E3tASV9BplRSUAZ1L797W+7N7/5zZkkvYiASiKniUzY0cGJt6ohTHpwCFfo6fjYwsIgS/0NasZBQMkzk3asBLQuAY0lSZrENKkVYVpUL1nhNJ6Ng4Cuu+66XtNQ1JaV52mQgGrBrHpWvQuDKs+QgPKpTKTMse2ZdJCAjuIOUJWprgT0pJNO8sS1yuEjpbn44ov7TxVLAsr+T4QMfPEwNEVkj74PCU0TUI2XWXVoBDRE91EBBzbCbKbr5PyKkoBCWJg4+ZD997//fT+RvvjFL/YiYj6FuPnmm08OImMuCQQ0VOums0M9oDaqq0rTgJ6OVwO8Vl5pd+yzyBMSLpGNdJimf1966aVe5fOa17wmM2om7bvvvjtTyiNVXpYEtCkCCj55+AmjUUlAkRCw2IgxqODzvvgThkdtyR5QYRm6pd9V3mFJQIWz2m06/fA3frImvdBP+A6xw4yDgCL9xNCWYw4hleXRR/bYv9jtDWGYcb/T1lg0qp6r1GM67yEBJU4OI8VK9FmU0J9GKQHlZg8RwXRZin5z9dKmm27qr18q8pflBtmEhCpdVPC0xfScRL/GL/NJ2kggkSagRXUYS0CpfxbzEOO0BJSxnUVFVp7SeWz7b81JRkCTmqJS2QO69dZbu7e85S1u99139xukIQJnnnmmyyMEba/kNuavTAIqAlNHDa8rKrR6Dcs/11xzefVf3gDPhK8BJAw3Sgnotdde65PO24fFQMnKO0ttJQlomoBSJnX2sFxl71kSUAhoEX7EmUXiy9JKu8dIQLlqZp999kkHnfWbdsSAHW7mn+XpMQskoAz+F1xwQZ6Xvn1dAhpLklRnWW2yn4nHXorqJe2X3yKgWZqC0D9tSe0qtK/zzoTKpM8l6RjasqngH91jyHiles7rX2WYQ3AgszqEhH8WVLEEdJQn4FUWJKAsqFGpxxokuuxtrSP9VBqh5BUCukJq/yf+4AS0/6zFqOYmzVWKt6gOYwgo4ZmHWJjhP01AIcTkaRIIqNq5EdDHWg+Hjvj6DA2c6x0wNHK+mbzWWms95ssegyJAx2ISzjPq1Orkef6y7BksIGh5EysdXA0/DK9Lv7PI0ygloBBQCG84iYT5ZNLGZE3cGijHpYJXfan+wnxXfY8hoEwcfFGnzJRdQh+GX2ONNTz+MWrLugQUSQt9oMy0RQJKXsu0ERycu+SSSwqLxP5PTCgBzWrHYSTTooKnz2oizhqfQkzy3pF+YsKxAwIKsYzRFIyLgDJeF31eOF1etiixmOTTm3VNSECzrmBSvCwMsshemQRUi0fFw5N+lJayhu68M//Q79HuYNIEFDvaisZ6fnfVCCO1+66WoyzfUSp4RbLqqqs61AKQTi7lpaEzSGZJnBTGntUQKJOAigRqAq4SuwaLLAko8eQRUA36SjtMc5QS0KyN52FeRECzVJeSVKUloHRwlS+Mq+w9TwKaVy+yz8KwLK20exkBRV1I/uijZaYKAUVKzt65GAKqSaiqCn4YElBJTsqwkLskoGXqbbUltS2FTz/33HNP9/KXv7yQqEJAuepGH1TgSjGIkSaidJz8rkpAwTaG3GelNS47yEQTEtA8AgrBi/nELH0J8ppewA4TF93LW4WA0veRWNJX65qQgOZJQIm7LgHNGm+bIqB5eaqLxbjCqd8bAX2sBtj7ScPkFDz7YMK/QcT946rgtqYLAS1aCUqCJolalXKUEVDIUdbgUESeRi0B5dBbnuHEJ2qYLMmRSIJIg+Kgg6uzyy7mSZj04JCHH/EVYRiTXuiHNqD4Qnu968tGLAyz6lP+eFYhoPhHDR9zIb0koGqvhI0xsQRU5YJclpmieskKC/FjAi8jz2pLaltZcWFHfYAzNzjkGT7BKfU7frSYymrLuLMVAnJWRpLxKxOLrfy34cmYBanQwk31XjVvWQSUPaDUYcyCCgnoKPd/Uj72YmLUl/yPkn/gI6xKvOY6i4CyiGQLQPoOUAXMI3tafA5rDyiCCG7lUP9Tfnjm5Sn004V3zUnpOaYLea+Sx2gJ6G677ebF+kw+rAbDvzZ93L5K4dvol5UgE0We0YReREDywkrVVFUFr7SyJvtRSkBRwWepXVReSAODZ9akzWTNvqU0qWiSgBZJ2rRgUP0pz3WexMFVQXmqX5FKpDtlUlD8UodZg3lW3iCgtCOupCkyTJrkkwVBFRMrpWOipb7Da7Dy0qFeqhAXJItMcGVGmJURUNoj1yt99rOfzVzsEJ7rgKR+J90yAkodUL9VCCgL27w2U1bWcbnTb5tUwYfXEtE2Yz8xO+ormMBb463G35g6wK/CxfjP8sMYCmHXnnskqlkmj+xprEsTUJGprL4YKwFlOxh1kTcP5OUpK/9ttjMCGtQOAyQN8oADDvAdduWVV3bhHw3WTDMIlKngRWDUyaukWiYBzZuoNQBmraxHJQEFFybyIgkoWDBxZxFQ2jB5hYSGhjJD5pAoVTF5ElAOemWpOYswrJIufsvagCSg+GW7TJGBgMacgFcc7JvDlEmNIKBpsq84ip6xUroqkp68dp2Xj1jVdgwBpZ+CBR/s4JDR0UcfPStZDsLQ/kICKvVr1nYSIojdJhAmFkvuwzDjfpcEtIi8xOSR+QsCrjpTGNozh+og83kGN/rRqCWgGm+zCFteXqv0i7w4mM8Zxy666CLvpYiAskBIm2FKQEmLxZoR0DTq3fwdJZ6g0yKGV4PsZlHbn2sGOlZ4RRJQDUqjJKAaAJV2iOSoJKBshmeSHoSAZu3fqjux5RFQsBFeIU4ioCKPoVvVd8WR1wYgoOxXo2wxEtCYE/DK42KLLeYXn2Wnh+sS0FgpHRir7pS3vCftVvjn+QntmySgkkZvtdVW7rWvfa373Oc+N6t9sP+TMZZDXjLgjNQ0azGFnzoENBZb5WHcT30QgH6rjz5k9a2YfEJAQ+mnwkBAIblFB/ZYBLAAHuUdoORPkswqbRe/CqcyVn1KoEQfR8uQt0ClXiTUCNMQAU3PF9Qh8aXrkHmPuqZ9FhnFx5iWNw+YBLQIwfa5zR2bpf3339/tsccefi8TZDRUfTHZrb766rFRmb8cBKQeK+qIIh9VBiUlx2BB3HkDFPZZ8cpOA4Di4zkqCWjRyccwP0hAf/WrX4VW/p2Velr6gYNIDIRS2M4KnGGBfwa70Agf8EqTXZFF+QnDVX1XPhVnOjykh73a5CNGAsrl51VMzIX0dQkoiy/Clhkmsbx2nA6Lv/Skl/YT/m6SgIpA0i733XdfTzL5TOL73//+fpIQUIhQuF2Bd4iAwvc9P/ZSh4CCLXv6umIkXVM/q1qPYTkhoOEJeLlJog/ZWm211WQ94ylV9KgloBA2TJW2W6VfzChk8EMEFC0HknjlI/DiX/PIHgSUMSqtbSIQdcjYGRppjIrmPYXlCVk1CWiIYHffoySgFI89oGzEfsc73uFYzb/whS/s/+23337dRaBFOVdHHJYElH1jefs/gSFvgBcBxT1tRiUBZRJg9QyxKjJFKvgiAlplkCf9LAmo8BFeYT5lJz+hW9X3MgKKBBSpJttkygio/FbJAwT0sssuKySKTELDVsHHYom/KvULuSvqJ8JK7aloD6hU6LRLCM7rX/96v5VJ+UHVyb76UP2u+PPaMu51CajGGKXR5qeka8MkoOz15V7hIok+i1/IVJ7UbZgY0oc0dsSkg99BF7kioHx0Ik/9Tl6oFy0SwrzR99P7P+We1RfVJssIaFiuIgKalSel35WnSHrsGNeVcqXzGU1AdaKWgTP9953vfCcdr/2ugYAkoEUElNUof3nSr6JkGdA1mGf5yxoc8KcBMBwAFJ4BknxXuSxZYas8IaB8Di5vNa64mLQZgJRn2UMS0lJJ3EIJqPzGPLMIqPARuQjjob5wz5IKhP5i3mMIKGozJtYiFTxlQCJWRQVP/pAasR2i6EL6uhJQJiH1gyIswDh2cAb3rDrJi5+FWszhHhZExF1EQJFg0p8VH1JQpHFf+cpXfPLcEcqEXYeAIiXNatN55YrFNi/8qO1FJFTGvPEpJl95ElDC0p6L9jRDQFn4Fo3LMXmo46dq263SL/LyA95gTR8vI6BII2m/oWGsGycBZS6qMz+GZRj3uwhoGSkfdz4HTT+agK6//vru5z//uZ+wmbTDvzJSkJVJ7iv72c9+VnqalhNvpIv/0DBJobri+pJhk58w3WG+a+ItG+ggIGmCFZMvCGiRZCdvgNfkzWCYNhpo0oNQ2t+gv5kE8la9YdwQUIwkT3JjMpPESnY8RWLU4UO3ovciAppVN9iJOBbFG+OmePIGWUgPBBQJKO+qv3Tc2p9YlYDy4QnyUDRpQ0DVNtLpFv2m7UsiUuSvykRLHWfVSV78sSp4wtOmygioDhThn32Eb3zjG70UlDwxhkFkN954Y5xnGMKl27E8kEcWk6HaXm55z1hs88KP2n4UElDKBAEtkuiz+B21+l1YV2279IuscVrxxT61X7aMgBKf6klxF0lA4Q3p8Uj9vYxsqVzwjbwxS4sVLV6Up649Nb80IbBoc9mjCCgNhk7IiqgJc/HFF7uddtrJq/T32msvd8opp2RGe8ghh7iDDjrI4X/nnXd2N910k/dHfnbccUd31llnOb78QBxFpxgzI2+hpQhoTEfMIx9FxUKyUyQBzVtta/LWABCmITVrzL69MFzVd9pfjApMk3167xwkIYuANikBFZkVXmEZqa8s/EI/se9FBBRJD4MXAzQSUJ3gzYq7LgGFMJVdSF9XAgpJUj/IyrPsGAOEt+zynvhjkYq6u8yAF5OXJJZl/ssIKARSbVJxffzjH/eS5y9/+cuegELos8g6i6m8j3xUIclKNxZb+R/3U8RGYxb1mCYvsXm86667MveAEl4SfT5hmWVY/I6LgOaNyVn5xA4yF9sv8uLAXmr4vDtA8ZNH9iCgGqPwF5qsOqxKQJFGMwZlGbUVtZ0sP12wEwHtQl4HyWPUISQaDac3IXoMqExsYSPnxGbeBu6szPEpTw41rb322u51r3ude/vb3+74dnVIvG5IPiX461//2p188sl+lX/CCSc4VP183/rEE0/0EgNt5GdfKvuoGEi6bNQRmSiKDJ07i+QUhcGNTokaO89kDQ74JS06fJakWxPnMCWgkAJOwb/lLW/Jy3rfXhLQNAGFVGjA7HtOXpokoCKYWZMkGMo9TL/Ouwb3rEWISCUSUJ1eZR/ommuuOSsp+c2TJswKEFjQ17KuFJKXugSUMWAYBJR8US9qr8pn+skijYV2UwRU0ugwHcjMm970Jr+4pk9tt912oXP/nbZMv8paPNUhoLHY9jMw5hdJsdRvGZ+YmKsa4iFc1iEk4mIBQN9kHyhnG0LDmMw+aea8cRjKHDvW029YaDUxzoiANi0BzapDzXvh/J+FNWExRZowI6BZyLXXLkoCSvY5aMR+st13391tvfXW7vnPf37/j31NsYY9I0x8+n48on46TFrVxKoTP1Ixrbfeeu7yyy/3yTCh8lsmdJNdF5+aeGMIaBb5KCszBFQdNMsvHbwqeRqFBFT7j2MkoBwqYCBLE9CsSRwMRECzyp2FkeyyVqga+LMmDOxEHBVH3afSyWoDTJYYSCV/tKW8faD0Q+o8lmyF+eUgEiRI38gO3XivS0BjpXTgrwkpnXb6t/D6/+2dCbx91dz/l0qGijTP/YQUGqRZv4pGlVCiAUUDRaZIaaJ+eAwhKj0ZnnqIUmhSkn8qGYt+zYUmNEehPFG6//te+dzWXXcPa5+z9zn77PNdr9e9e581r89a67s+67uGnVLHlAmTikmZBpR2qElRmC+0oKSFe/gFpNCPwsVtGT+9EFCw1WAfptPWd+QVE1/1mzz5VJZ/VgUweQSUG13WXnvtzINIrLwwAR6mBjRLnmSVWf5S+0VWHLJrkoDG/VBtsoyAqh8bAVUtjf4zSQNKMSGIdMQsE17JlOUe2iEMECjh3gZmuAjUUN0P6dDMl/AIel0hwoXO4XIq79LmKK23ve1tU4QVkstn16p8U1fxVH2CERf2szUgNpQZN7S9WUYDDVqPorwilMGiyE9W/CxD0cnzwjE5gNTE7vxm8IrtSUPCBOJTNFvOyg+aJmbsWfGG/nXYBfJc5pdwfMIO0iW/1AlaECYzslP82r9He4vd5CfrSbnjvBfFxR5m+kmVNJSu+l0YlnqkH4R2+OeSZtoZaVHfaLw56BL7wy/7qxlostxwLzIaBM4991yvzYv9SntVNW4GI8hlWTjICfUc+hNOaDFD+UJ8mNtvv710G5EINXGFccfl0280mNRtll/iQG4in2J3Jm5oPk8//XRPbmJ34teKA3Ua792m7lmKzAqnvMVPJri0W8LQ9yAswib2m/qben7ta1/rjj/++EqrYCnxI1MYA1RG6pR61++UOPDDaW4McjMvLHewnnDCCY7VvNDQxzFMbPPChv7rfqfMtOeUtLVdgz6U4r8or6wUaNUrLy61HfqVSD4ykLE87pthWnEdpo57IqpFcYd5yst3mJcm39/xjnf4Vdw4DeTC7rvv7j70oQ/FTlO/+xkvpiIZ8gt8omzbUzIBpWFJwMflYsYlTVLsFv+mUceZIqNx+NgffjSzK3JTemhp11xzTf+Tu1OEoAAAQABJREFUTkwH0QxKfpp4kk8+7wZhCwdB0uLk6zXXXOMvpM5KW9peBqyivDJ40RmL/GTFTx0iYPPCkS4DVOxOfTFpiO1JA2GA6WXphwGQMsd17yMM/kmwrrLKKpl5CLz6V/bc0YGVX0i1llVlpzChpit2k5/4SVzUM/UQhhFhAK/QnvDUV+w/jjfvN2Hj+maAyMIc0smEi7rEsHTI3uk4P7jRJ1imz3LDvchAftDcnXzyyW7vvfee5pW8gg/YVo2bcsVlnRb5f37gB79h/CJV9L1wUiytP/0x9J8VrwawpZdeutQv4SGGebKFNkgdMQnISpdPc26yySa52jWRfAb0ODx9mb4X22eVSXYhtvQJMAKrfgz7JiF4HBTloGqdBlkEAVUZkUG0b/1OTUvbg4ra+v777+9Xh+KxiTYF+eTwWCzPU9Pvx19YZ6nxhJilhon9QZCQt5IjsTu/1XY0ZtCmmBjH9RaGpTwQ6rAOGc8xZfKRMEx02CYRhg/jlxZVeQrdBvlOOzrvvPN8/w5Xa8nDd77zHfeb3/wmtwz4od3Bd/LKiZ+2G8aAsj6TTEDZ4ymtRlxwvvDx7W9/O7bO/E1npqEyG1cDRsBquUmBEK5obmTww6CAgUTxW4Z37XeTHfftyegTeDTwpg3lesUrXuH3toaDIOl+//vf91qHvHzIPwN3nh/iQcAgVIv84C80DNjkDXKSF47BVORJQoE4sEP4Z4VTneAnyz3MQ/xOfkinLBwElHaTuleRtsBApXg1AGWVXZMqOor8x/mMf2upC2ITh4FQE2dsD/6029g+jjvrN/knfBgWQS4SHIaR1lN+6bfsmdbv0C8aAj4gkeUW+st7P+igg9z222/vICHh3jn1Tfpw1bgZ8OKyZqUPsaMfhPGDB3WD0JZsIay0WtRN6D8rXhFQ2lCZX8XNYJLll21EGLaOZLlj9853vtP7yfqHO5MzBuw4PFoktNexfVY8sgMvtRkGaCZMVcIrnvB52+RefQyn+fuNy0cU/COPoSwk/2jLqqajA5KssIXtIkjKk62PfexjoZV/Jw9gTX+TgmCGpwYtKDMTnJQyS2YjK1P8F2UbjXD4Za48v6SDHAcfxnXaK32IsSQrD/TvuDyauOeFCdPed999w5+Z76RLHrLSzwzQgCUrKcix97znPTNWPOkzfHmrKH8Q0Lwxt4HsNhIlsqaszyTvAb3sssvc3Llzp/44IHTMMcf42TvLyqkGksWVI2effbYPcumll/rGSuOjs6POx3DKlqUnlmEoyDnnnOM1i7jNnj3bnX/++b6RoWXgKqaqX3MhnkEbhKkG56y06ciYPCGpMDRMOnsVwyCGAec8Iw2zCJb8aVDX7/CpTiQhH7rV9Z56Al7pMZnRsg52aIswCPPYSPsq4hG7Z/2WX4UN/VA3MX64Y4dbXSavDdBfwskYVzGxDKw8h+mzbSWV1Ifh9L7ddtt5zdDRRx8tK/9UW1DbmOZY8oO2T38vM8gKtdcyv/JHmDJDP2EyIq1pmX8GVLWv2K/aYHwKPvZX9Juwikf+mOCQT+RJFYN2iIEN8l6X4foiDIdAs9pYP+mg8Aj7LPWYUodxmhAe4imTq3G4NvxmMpUlT7LyJmwGqTWjn0DQQ8OEGUKaZajDuJ1A1DDSXmaFq2KXlacq4evwq/MqWV+IZAzWeJyXFhhljS95/kfVPlkDmjUb2mijjXzn+PjHP+6+/OUvJ2PAV5V0/RIM+fDDD/dhEWZHHnmkJ6cI9n322cefkEfQsuTH/XmYzTff3M+4d9llF8+wd95552n7R72nFv4ra3hVCGiqUBIMEhJFA2s4UIfkAcGWJ9SYddNRpGVUenU+U+8AVZoxAZXmnjYVG4QehEPCO3bP+i0BmiUg8gYMJgx5GGalUWaXR0AhleFSaHgVU9iHISEMzP0QUHB7//vf79jrhMBlnzWmXwIKSWJQKhqQqC+11zKs5C+lzzBBhKxIm1QWdwoB1SpBWVxZ7nFbxg+ElxWHqgRUBEwDflZ6Ve2Q2chmFAcoAkJNeNW4Yv/IrLCM1GOVfqr4aOd5B5Dkp63PKmVW+1Z7H0SZssgesq6IgMZ1qPZY1N+rlIX+K5lfJVydfukXyGj6RmzgARqPYzf9BqOs8UXuXXkmE9C8AiNczzzzzDznTHsqhWVBKiEkRJxElGaUgGhY2MspFb8iQ4t61FFH+YGOzqala7m39Ykw1V2mWXkUAS3riBCZqhpQNfgQ7zgPIkixgECwyS0Ow2/IqkhHlnu/dmhAN9tss+Ro0BqRZ7UvaaiyCCiR0tFFKlMSkd8sAQFOMX7EWYZhSrqhnywCimYMbWdIKtGAYrg5IiSgOlQY+g3jT33naqxDDz3Uffazn3Vf+cpXfDC1hbxBqChutX36gt6z/INx6kCrtptVL3HcENCQ9MTu8W/aFHULIYxJKxjT36irXg0EFK12aLSKUiWfhBcBlZwJ4+z1nYEWRQH72y+66KLaCaj2wZK/KmQsLM8oE1DaLu0rxah9q72nhOnXT0xAIZP85fX9rDqsm4DGeeq3jL2Ep1+w/YlJemzInyaRscyQX8aYvPFKfrrwTCagnEQOl8Z4Z28l+2Y23XTTnrCgIsoM+0O0RyT2G2rpYrc2/mbmo8EjK3/qiBoosvxgx4CWKpQUh1T+5CHPaECXIJM/0iqqK4RNUxpQ4mVfYzgQKV95TwZtDEuX5Fuz4XA5LwxLuUUqQ/u8d/nNI6BZdYNdP0QkzksWAWWgpQ2FS/C8057iq5h0a0S/BBQM2MfIgRpkAftsRUB76Z9q++oLcbn1mzpQe5Vd3lP+4nad5b8XAko8DChx36L9qS1mpZViR3iWt0MjGVKVgIrQQ0DzBr4wnbJ35D954T5nruXjwyB1mjqX4GmXo2hou1nyJKss8qf2nuWnbjvkq5QbxC3FSB4BRV7E/VB9Xe2z3zzGeeo3vl7CsyKkQ9BxeI2ljMnanx77Qb6NqtY+LkvR7+Q9oFtuuaX/XjHfLOaP05sc9KFjo400U44AA4aIYJZvaSY0CGf5wS6LfOT5lb2EhBq/7MOnBFcsIBBsRbPqJjWgaD8xKXeAqiwa9LV3bpAa0LwBYxBL8NKUhaSSLS6QdzSgoamLgBInW2qY6R977LE+iToIqPpCmGe9I5zR9qq9yj7vKX9xu87y3w8BjeOrg4CizecQXmh6JaCSKxrwwzh7eUfLg2HrBQcvOYwmAtJLfHEYrWDIPou8yK3oOeoa0JR2S/nlr0hWF+HUixvjiSb4hFf95xFQ+qLyqfTUHrtCQFkN4ZBR1v5PyqyJahEXQMZlKTiEWVeeyQSUpWMEQvjHPjIOI43q7HLQlQgBZXBmj1uW0aCbp/FVGASMOrrsyp5VCKhm0ooTgVEk1JrUgNZFQNH45Gkg6eh0+FQjv1kCApxiAUu8hMlLPzXd0F/WJEQENNSAEoZ9oFkaUAhJ3gw8TKvsndPuLMWzDEvb6YeAahBSX8hKW/hm4Z/lXwQ0btdZfuskoCzB93MAifwxmQILDlvK9EpAU7BVGilPCCj9iiuKIKCMBxxWrcswcQxXLajHonaRl+4oE1DKnNJuKbv8qb3n4VGnPfWjsYV4tRKWJ+uy6rBrBJQxi3aaR0ClBFI/zqoPI6ARKjvuuKMXNjQ4/bH38qSTTpo6HBQFsZ8RAsx80NzkNTwarbQUUdBpP+nczLLUcac55vxASEAUi/bLSnBpgFdUCLYiAtqkBpQDSGAirabyVPSkfZJfaUCZoRftp6mbgGogUB7Bk/oqwlB+U59ZBBStJhrP+NAL+0CzNKCQo6w9Sql5CP1xGIl2zb2gEFDizRuEwnDxu9p/UdtW+1R7jeOIf0OSIF8KF7uHvylDlaVttStp2cO46tCAqt2rLRO/5Ic0KWGaRe/CthcSlxUvBJS2Rbx8tQ7c6lqGpw3RZzRYkz71jfxMqUfllzjAa1SXM5EZkBHKXWaEiyYaZf7rcKd+QgIqxUiRBpTte+FNDOrrdeU7zlMd5awSh07A61BmHFb9Vv04duf3uBDQwj2g7D268MILPT7MbPkefKh1oHNzAGlY38nNqrg222lgo+FlaZ7oiBokisohIkNnT+20qPtDYZ4VvwZ0CTL5gVDJTXbhE2ETamhCt37fmU1yf1/ZfWJxOgzcGrQhByIKsT9+06bjMmf5kx3CARP2BbmBUyiQsRch7YWQKd74mUVA0YBCPuNJBv0TchoKNX6HS/Vx/FV/owXj0ODnPvc5rw2lTfRCbtX+i0iS6qqoTcb5x6/CxW7hb/qmBojQPu9d7SomoMhGNG8ikHnhy+wVnraszxeTR9KN67ksLmGrAb/Mf5k7A620PPRPtmXVRUDVh0KZpfqmHvVelkfkEitOo0pAKSfkk75bVmZwySN+ZTj16k79hEvw0oDm5UNlIK9a6VN7rNqe8/Ic5ynPX1P2TMyQz1kn4ElT8qWMgAqrpvLZhngLCSizWzQbDAbMWLhIPdy8TgOaNWuW+/CHP9yGsrQ+D2p4eXs/qmhAKSwEVHGWFR6BHgrzLP9q8PFAnaIBvfXWW7Oi7NsOAlpl/6cSDAlofJhBfvSsWwMa79mTVkATB6XbzzOPgMbL76RBP2YQA0sRBsgq9nWaAw44wG06eSDxW9/6VuEly0VpakI1LAJK39REsSifcssjoLQBiE8dS/CkpckU7wxcVfJIGEwKtk/4TPvPQMvSuwx1z3iB9rKXA2iKh6dIDasZMnnySe5ZT32OcVQJqGRGCukuUxRk4dOvHWMKfVVjhmRdHgHVpB3/6jsQUNpmLxPWrPzTZshTOOGO/ZHmJZdc4rbYYovYqe/f9Au0n3nlody4lRFQYdV3hlocQSEB5YsKfOECwxdPvvnNbw58htVi7CpnTWQxr+GlElAJJWnWUjLCwKr08/znCfgyAoqw0cw3L+5e7VmC32abbSoHh4De9p+vtKRoQKXVTElIfrMEBBjG9aLfTWtA0WrmEVDKxTK8CCh+IQx1GjRgXKV2xRVX+H2BvcSdoqXTYKf2mpIOfUb1kOcf4sREuwq50yAaa0DZ/4mRBjMvzTJ72gxpKD7890pAU7Aty4/cIcTIFLUn7CGjaH45F1DUZykLE6IiDXyZBlT5KHuihcaMKgFVG6ftlrVL/GhsKMOlLndNEJgwQKrKCKjKoz5MPkRA68qTFC20Ib4WlmX4TObrXvc6d9NNN+V+CjcrXIodBLTowzisFtCn83gAaRSR55Q8jIqf5ENI3M+J9vPUU0/1l8UjgBho8g7UjAoAg8ynBEiRBlRaiqJ8aXZZhfT1qgGlfhEQRYKtqT2g7BXigut+NaBlBBShKFJZhLvc5DeLgGYRHQnlIgwVd+oTYkK9hFejodXMGtT5FjntSgeRdIValt/U9PP8oQXFqI3m+cuzV/uvWwMKAREZyUtb/VL9NM9faA+pI88xAZXGsl8NKGmF2nx+90pAU7Al/hSjfW4hAeWeWQ6kFS3DI0+423nvvfcuTMYI6BPwSGaEhC0POPyI4OX5qds+JHvErTEpb7Kt/IXlaZKA5pWXT2ViuOKvTsMEDFIb9ous+FEGGQF1LpmA3nDDDf5i1T322MPNmTPHsbRx2GGHOb6GxH1wZsoRoLMWqd5TNaBa3kJjk2pSNKDswWFbRSgcpDWS4MhKrykNKDcvQJaq3AGq/DFo0y7RtAx6CT7Ej/wIQw0mymM/Twl4kVsGdkhPlgaUGXd4FZOWh5sgoK9//ev93idpRqqWUVq6IgKq5VlpH1PSYB9x2TYRDQhVCChpk48sAkpfz9PApORZfiCxIrTY9UpAU7BVmmVPtDzIi5VXXnnKK+Ut2wd6+umnO8JqMjQVOHpRHYftSDJIE8AoSOZPJh3ks2qdZkY2BEuVWTKkKAvInTplTFFacosJKPIIRVXW5JwwKk9Yh8MgoGp/6vMqT7/PshPwir+IgLIKgzzPw1BxdOGZTED33HNPt8EGG3gtggY5TsAzuLHny0w5AmCFQM1r9HREDRJFsWngjQe9ojCpgxYCIiRQEnxFgg1CjOCB7NVpWH7H9KIBZdAGTw4hlGlA6ehhmcvKgPCEqFOfsQE/YSY3/RZplH0/T8UlAsqEEMGlvhnHzX5PCV2W3zFNEFAGe+TBEUccEWch6bfafxEBlXasbEtJmOCsyb3qgySgLDOjdQWPfk1dGlBhS7/o10AiaVPSqio+luGvvPLKGQfxcEc+8Kll+g3aegbZPEMdQ2izCGiVvgoB5cAncY2ikdyVDCkqA35E8Ir81ekWE1A0oEWrH8pfWIfDIKC6FUSrHnVhQr/A5J2AVzrIrry0Rc6NgP4HLRo2X0JCeIj84MT9n+95z3sc+ynMpCHATDyv4TWpAS0jYcp9TKAkKCQI5S98InAYTFKEZBiu7J3ZJAMH2quqRnvv0BxR9nAgi+Oio6vTx25Zv/GbJxzAKcZBJLEIw6x0iuxiAsqAjskjlZyEl9BtkoCSByaqfBmnF5NCkkROtBKQkg5tCI14UT1rYlhVW5anAa1j+Z2y1UVARRaLyH0KlvhhoM1aZuRb8MgCDnjEBu0nS/d8uADSER/WC/2jAUWuhJO8LPIShsl6h4CO6v5PylOlzMjqOmVMFp6xXUxAkX2jREDV5+Ny9fqb9o1szjsBr3iLNKCSUXljjOLownOmCiejVMziEQQaSEMvCKJQSIRu9j4TAQa3vEbPwKBBYmbIJ2008FZZgmdmGk4enoxt+hsCT6QTF5GpIsHWS36mp5r9Cw0o1wpJCGf7yrYNCSiDWVHZiV+dPju26bZlBJR6DLXBwlCkcXpsvf1SXOqTIpV5GlBdxUTe8EufbuMHJERAi0gSEzgGvipyBwJKnbCnOM+oX1bRrBJXHgFVG8xLL9WeeNBws78MQz6rkmTCpWCLvxQTXsEU+l9llVX8toN4H6i0nyzRs40Lo0OC/kf0j0mGyI2cJAdC+SS3vOeoE1DJXcmQvHJiDy7CqMhfnW6a2GtVokwDKlIV1mHdGtA4T3F5kd861Kc+H/vp9Td8qOgEvOKlbeelrbFIWClMF59JBBRSxKc43/e+9/nPrQEEHYJT8Xz9ZOutt+4iNo2UqUj1TkfUIFGUOMu/NM7UJXgGcxp1EQlTegiwUDhI8BUJNs14tQFdcfX77PUKJtLV4A/ZKiPfYKlOn5Jn/OYJh6wBQyRRbilplPmJCSgaUPZe5e05ZLkUzRSYggnauSoEriw/dblrAlZEQLPISVn6LMFjipbhGRCoo5Q+6CP7z78sAsoApzYY+u3lnbqCfEJCaUvIiX4IKOH7MawqUAdZGlDiZRk+JqDa+/mRj3zEqS6MgJbXguRuKJPzQiGr5T/PT932jEX0Ge3ZpX1KNmWlpfyF5ambgJIn8iBSHOeDlSApCPJIYBwm9bcIaJl/04A+gVASAcXriSee6Ennuuuu64U4Qma33XZzLLm8+93vLsPb3P+DQJkGNHXwY9BL1YCKqPZDQIvIU1Ma0H4IKPllJsxpR4iXZsVZDbFOAioBK+JOerwjFPmry0jIi9xCQCE84T29YVq68xPhCwHNW6oPwwzjXRgVkaReCCg3AUC4i0gPmtVeiF0WAYWk1bkET10QpwbMXvJJ22BLSxG5T6lzBllMEQG95ppr3J///GfvL9R+brrppo7r/Wi/RdpoCE3cZ9W3QvLiEyj4N+oaUE3IQnmSV1xwKZLTeeH6tUebJwJapgFVHYYT/roJKOUJ8xSXT3vhkZfqT7GfXn6nnoAn7iJFlLDJU3L0kre2hkneIU9l/eIXv/B3vN14441+qXjNNdd0/JlJR4CGh3DOMgwMuKcYSJ+IZZl/+euFgErYFwm2pjSgLMHvsMMOZcXLdafNcnsDpqjsdHSVMzeywKEXDWgRfkHUya8xAYVU5i2/E2l4FVOZ3+RMNOARkgQJLSJJEMXUfqIsMpBDCMs0oL0Qu5iA0pYgybS/OozigYCKoPeST/JCWy8i9yn5hYCyhSM8AR+GQzkB6bz44osdn3A+44wz/J7Riy66aMobWtCiyUDWJEOT8yp9ddQJKPUF1illHoYGlAqF7EnbyIRY48FUZQcvIqBheZogoExelKcgef/KJJy2xJfF6iSgKEyQW3kTszAfyC9ueGFsjsemcSKgyRpQwGP2zOGCffbZx+0xuY9H5LNIkISg27vzGpa8Rk/j1Yy3DCsabRMaUIhSKBw08y4iUE1oQHV6vZcT8MIOwsFkCRN3cvnhiZBXpw/t895TCGiMYRF+eekU2ccEFA1okVYTYsc+yLZrQCkzfaCIgGaRkyKs5AbpGQQB1f6yujSg7ING9varAQUHBt4ibIVV0RMCCvkUGY79om2nLbIML+0n4wbEVKYXAooGu8pkEdkFIRrlQ0jgheyQHBZ+WU9kTt1yJiud2C4koGUaUNoM9RjKxyYIaJinOL/IQMYV7qzNG4vjMCm/tTJQdgKeuDSBzkpf2NDWu25KCSjClOX3r3/96zM6AXuSdt55Z3fggQd2HafaykfDyzsFT0fULL8swUFpQCX4NHPNypdmvHXuAWU2ienlDlDlEc2RCEe8nCc/PCkb2GtfUOiW9V5EQIWTcCM87yKMWfH1Yqf4tASfotXkIBKXJENkishqL/mpMwx9gPrIM71oQIkLAq72kBU3g0EvmsVYAwq+GGkus9KqYgchZ9m6DgJaRu5T8sVAW6blgWyiAUX7ee211zr2foaGU8JVl+AJX4WAov3EtPGwnc9Y4j9kSihP8oLhR/Inz08T9iHZIw8aD/LSIo8iWfgZNAFlCZ5JEmNxFgHMy3eZfeoJeOLReJSVvpQhY09AqSjU1Pvuu6/ba6+9/BKWOvV3vvMdfzH9ueee67bbbruyujH3/yDAAIcGIkugYF+FgDahAY2Fg/JZNLNuQgPazx2gamwQAPZ/Yso0oPhRx+e9yBQRUOEk3IgHkij7oniruKFJ4I+4KSPkpGgJnrgRulynxtJP2wlokZauHw1o0WpNPwSUyZcmMNKA1kVAqTviIl4NWL0QZeIpI/f4KTMMtGVaHggoRPWggw5ys2fPnqb9JH40oBBQYRanmVfHsXyKw4W/NVZ1QQMaErawjOE7fuqWM2H8ee+QqdQ9oMQR1+GgCSgaUGQhfShPGZRX1iJ72nvKCXjikAY0K32NQ2NPQD/1qU/5U7VUGBvK11hjDXfCCSe4o48+2u20005unXXW8bPbt7zlLUX1Ym4BAmp4GkgCJ09MUwlorHUJ44nf+9kDCpFi+a+oMyBQWOKtWwMKsWWZpFcTEgDNOLPiUtnU8bP8hHYpBDQcMCCJ0liG8fT7TpzEzX2KKaQSoYt/TJsJaJmWLo+clOGJBpStHXnttB8CyiRA8TIZoAxcgF6XoS1LAwrJSJUTcfpl2CLfIYyauMXh0bRDNso0oBw2wjCRjLWf2KMBZZKR9xU96jirz8bkhbjyTFcIKGUOJ7R55cUPfgdtQg1oiqyL63CQBBTZTRtmNQgCSjvLa+tVcUxZGVCcRTxA45DGJYXp4rNwCR7hwaf1ENyo1ffff393zDHH+K+cHHfcce6CCy7wM9kuAtNUmaS5yJr5IJAZIFIM5KyqBlSayqL4Y2GXOqumfaTmpyh9ubEE38/yO/GEBHRQGlANAOGAwXsTmgkRUAQqpkwDitCVaTMBLdLSMVgwoZIAV3lSnmjdMHnL8P0QUOLVRA+iqH2b2Ndh2E8qAioZ0ku8RdgS3y9/+Ut32WWXudNOOy0zegZZTBkBZczgDzLLTSmxUV1kLcMjB/mD2MQmJi+xe/i7KwQU2RFOaMMy6h0SR9+Q/JH9IJ4hAWUSlrIEL5JF/gZJQBlX0LpLAwpm0t72g5VOwJetDCgNya8sRZSwGXsCyh7P1VZbTZj5AQ7AmCWzLG+mOgIaPLIaHh0xVbMBoUolfAyMCDFOU5aZWMCnzqoROtIAlaWR4s7kp58DSKQhAspSdVFnlluZkFe+ERAKIzs9RTRjAtqkBrTsK0jKG0IXg7YagtRWQx+AgGQZBgsGkCxykuU/tIMQYbKW4UmPOtPAEIYre9fkRgSUpXK1vbKwqe6hBlQyJDVs6K8IW/zdd9993jtfvcvSDLH8Tn8KJzNh/OH72Wef7U499dTQaupdBDSrLtBKYbLqOJZPUxFmvEBA6XfqkxleRsKKMofyJCvTkl3DKGtIQNGAlhFQZKfyS1kGSUB1BZM0oKSfNRZjX8VUOQFPvPQhcMpKWwR0GJOJKmWuw2+hBjROQASG5XczvSGgAS5PA5pKQNFmasArywn+NEiW+Y0FPIIvRahV0ciW5QF3OnRdBDRrKS/Mg8ikOn7olvUOWVGY2F1YhQIWoSz72H8/v6UBhYDSN/MuoVcaLHsi+PAHCW2rKVomLiInZeVB6wtOWRpQ9cdeyJ36lvojmsq6TsCrTBBQtkGx3aKXPCqeImzxAwFlcOb2CD40Ehs0oEUn4EP/L3nJS3KJOPsykTVZBFQaqax+G8unML34HQI66vs/KROyI5QncTn5LYI6DNICAUV2anJYRkDjOmyKgJIn4g4N2wnpA6wWqR9lkcAwTMp76spAGBfpZ6VNvrkpAFnddVOJgAqM1GVi+bfnkwgUNXqITSoBraoB1SD5ZE6y32LhgOBLIU91akDpgAzi/S7Baxm0rOwS2qSbYsAkj4DKXgMC8fHepAaUJXgID0KryOgqpjYvv5N/+kA8cKhcIoqayMk+5Un5uQ81i4BqIFD/TIlPftS+QgJatwaU+kXzy0DXSx6V1yJs8QMB3Xbbbd0WW2zhjjrqqKnPfyo86Zctv8tv2TPvJHzRJCOWT0VpdIWAUuZQnmSVWQQ1RVZnhe/HTppq7edtAwHV5EVtSeVDA4piA1mpfqS+Lz+9PFkZoNzIl1RD+llpMw5pHEmNa1T9la7Jcv0SJ2cxamBcu8TBFBlOyr/pTW/ST3sWIICAgMBrIA29QkBTyT0aRw6eIHhEoMK4wndmphokQ/us91jAI/hShFqdGlAIAoNtvxpQsOT6mrKyi/SnEtAiAUG/iAeMQWhAU0kle/KaIMNZbalXO+qNvpBl1G806GX5KbJj6TdL66aBQINSURyxm9qXCChL8HVrQLVlgj2Tm2++eZyF5N9F2BIJBJSDfx/96Efdhhtu6E455RQXHjKFgL7qVa9KTq/IY15diDRk1TF9K3WrD1vIuqIBlVY4D08R1LKxIC98P/YieygNMGXyJR5jmtKAkhfaUtgGdAIeN01i1fex69XQL1ZdddVpvKgsLtLPSrtofCmLc9TcCwkoAoJ7A/mTeeELX+jOOecc/fRPGpAR0GmQFP7ImvlAuCCUIkOFEUw6Qvgw7AMtEzr40SDpAxX8g2w++uijXvOBxgjBVhY/0dWpAdUVTP1qQMkXREACkt9ZRuWrg4ASPxhKI8HvVBKP3yoGQY8A4w9tUor5yle+kuJtqH6Y/ecRUJETDR5VM8o+0CuuuGJGMA0E/RJQCDLtqAkNKJlGTvSSRxUY+ZKHLXIccgcB3WCDDdxWW23ltaB8chlZwFYPZEldGlDGl4sn7wqNjchWVr+lr3KTQYpBA8pNLaNuKLMIZl5ZJG9SlAV5cfRqr4kC20MwKRpQTSTx3zQBJQ0ZCCgHqzHIEBQG6vvy08sTArrWWmtVCko/1p7rMKAR0P+gwR2fZupHIGvmo0EhlYCKUKJ1CWd4WblFoGcJ8yy/ImMINARJKnmCEGd1pqw0yuzY/8mAl0qqiuJjv1oZplrukBAvig+3MgERDxgpV5OUpZnlDgGFFCD4N9pooywvI2mHli5vwBUB1aBXtYCQntNPP31GMA1CvZA76oElPfpiE3eAkln6OH2C07a95FEFpi/kYYvGEKOrz9CCrr/++v4jJHtMfvmOQRaTetLXey74Z0vwBeAETpDKvDqTN7lLfst+EE/1Ra2QlhFQ5G0oawdFQJHbyMvwMCbjaEiGe8FLJ+CrKuHox7/97W9nJFk2vswIMMIWxZvGRrhgbc46DS9u9FUJaKgBLStrFQ2oBJgEBM+UWXWdGlAOW0DSdeitrHxF7t/61rfcSSedVORlar8NHT/FlAmIeMBgcEjBMCXt0A/EB9IDAU1dgg/Dt/UdksSglGUgoBBUtdMsP0V2aEDBLO5/EFCdTC0Kn+fGQEa8Woasewke8qkv+vRDQIuW4KVZFAFdb731/HI7e0FZnYGAEj7lBHweTqE9kwHkC5rK0FDHtIGsiSP1LtkUhonf0RRTnrLJeRyujb9TyixMmpAzZZiIgKZqQMmj8kvcgyKgrKzRLkRASZu+pMknv3sxVU/AK428tMvGF4XvwtMI6BBqMavhiYAi4FOMNKCQyzLDwCj/ZX41sGtGnUqe6twDSn5TNbZl5WHg5q/IqMwpBBQBxhYFaU2z4g0JKLNjBCxksW5DnOyXJY2yO0DrTrvJ+CAe6g9xOhDHXpffiUtXMcUHkRiE+iF2MQGtewmevCvOfvJZhK1WMERASRMtKAP3ySef7AkoKwp1TAyJGwKKue222/xT/1ixEamRnZ4pZAy/1CekuQsENJQnwiF+itBJlsXuTf6WrE4loMPSgIZXMAmPrLFYbqlPrQxU3ZqSl7YR0FTkzV9PCDCAxhoYaXyyZv1ZiUgDClkrM70QUAk0CGiKUKtTA8oAlEqYy8qe4i4ymUJAhYvCZMUfzvBZfsdgV7eBgKrddEkDWqSlQzuWR05S8BXpySKg/RBbEVCW4OkL6p8peUr1I61qvwRUbSZON4uAsoeSTy3PmTPHzZ07t7b9n6StuogJaFEdpxJQaVW7QEBTyiyFQYqsjuu939/IQvqsCGjZZDsuD+0xVfGSmlfiIx3akkx4BZPs8kig3FOeEFD6fJUT8MRL2own8bhjBDQFdfPTMwJZjV4an1QCKoLWlAZURGsYGtAqe1Z7roQgoDCPBUHgZepVfooIKIJPA4KeZUJ5KoEKL2GcXdOA5pGkInKSAh2nyanvmPQwIeyH2ImANnEHqMpVhwa0iNxDQFktiAk+n9IEryuvvLJWAsqWAuoi/hpSUf+PyYuwiZ9dIqBVNKBNTHRjbLN+02ZEQCFjRYY6lBxl9YYPHtRNQEmfPIUEFA0oB1vDFbGssbgo71luXMFU9QQ88UjexFsAjIBmoWx2tSGQpQGtSkClYSnTgNK5IUEirGWFQDhgREB5pgg1hA6kgeXpfk0VjW2/aRGek5AMhCpzUZwSnEUENBwwmtaAklf2Lmp/YFHeR8WNulB/iPPc7xI8dT1rcuk3SwOqASFOM+V3SEBFFFPCVfGjePvJJ9jmkXsIKN+vB6PQvOxlL3Pbb7+9t6q6zBjGE7+TDgeRbouW4IsmGeNIQCkz/SHry1TCVBNd6ncYRmQPIlm2RSOsQ7XFJggoWwOYzMiEVzDJrg4Ciga0l36hfhwTUMahovFFee/C0/aADqEWIaAI2VCgaMBN7YiQDoRNmQZUM8BeCWjqHZaa9ZblJwVu8qx9RSn+6/BDhxe5LIpPfooERChgB0FAWZqNSUNRGdruRh9Qf4jzCgGNNXSxn7LfWQSUg28aEMrCZ7kzIWTi1MQdoEoPssbgDkns1RRhCwEN93+GaXAQCe0Rp+LrNFUJKP1O18QV5YMT/fSJvPIUhW2bG/IEUzRBhoCGKyKDLoPktcaBovSpQ5WlSQIqUqy8oAGND9AxFscEUP7DJ1pOltg5mLfrrru6ww8/3O+L/slPfuKvqezlZght+YnTNw1oiLy9144AAx2HWUQOSUADbpUZrLQuRRkUIUwloNJ2SkDwlAAsSkca2dRLooviYiCXQCvyV6dbnQQ01IBKM9HE4KA4u7T8Tp0WaenoMxLcvdY/B5FiDWi/S/C0V9otS/DSVPaav7xwDHycuE3pj3lxgK1kTeyniIDysRHSrrtsTAaqLsGTb8mnuAz6TVn4CEW43Cq3UXtKJkuWZOU/VU5nha3DTpNCyaSiOGm/LL0zkRgUAaXN88W48AQ8eWQspu+XGe4O5gon2iv94LjjjnN7TF5NtvHGG/v+tNpqq5VFMcNdE14joDOgMYsmEVDDCxu+OmIVAgrpE8HMyy+DIiaVgGpwk4BnNiYBmJcG9iKgZfkpikNu5Dk1vwrT75NyS7tZFJf8FGlAswhoCoZF6Wa5Sdh3kYDmkSQIqAa7LExS7LJID4OA+mVKHLEf2it54y7Eukma0oJMVT3ooLB6FpF7ri0atMYwqy6K6jiWTypX/GQPaBcOIFEuyQ7J5Lis/MZN/rLcm7ZTn0zRgIZ1qHEvdeWvSjlCDSikkRXHLAJKHrRSlRc/5JWynXbaae6Xv/ylY8WE8fvyyy933JfOp2urGsmbkAcQh2lAqyJp/ishIA1OOPPRgFuFgDLoiWDmZUDuqYQuFA4INTS1KYJNgufEE090Z555pl+W4BqUXkzRIYRe4ksJU7cGVIOFtBYiiyl5SfWjOLt0Ap6yFy0TF5GTVNzQgFIvunidgYk2pwEhNZ7QH/2LQQrNjk6rh+5teS/CtkgD2lT+IaCsmugOUtKhjvNWQEL5VJSnLhFQlVmyJKvcuMlflnvTdiKgKWOF8omMHBQBZf8nJl6CV58Px+IsrNB+xnKWMq+99tpu22237UnTjvymP8ZpGwHNqgGzqw0BNfpw5iMCWmUm2KQGFIEmgZciVLgfkG9H86nH173udW6VVVbxxJW9MTvuuKOfKaYAyLIMgilvAEqJoxc/dRJQBKyw08w6BcOq+RYB7aIGVANTiAl9hLahCVzoVuU9vguUfshES/2ySlzyCwHVnu6mNKBKq58nE1z6GOWNzTAIKHtAMVqGB0MIqQhNnMeQvMRu4e+ufAeeMkl2aFIbllPvuAkb2Q3yqfqSIqIobeUToqV+XmXcK4o7dCNPTGYw7P8kjXgFQX0+JoFhPLxDQJuQs6Qfp20ENEbffteKgAbQsOGJgFbRgKYQUJ0CTNWAcsiBPwSaSJQERhEIEMaf/vSnfvBgf915553nPvnJT/pPRP7oRz9yX/va14qCT7lV1dhOBezzBQJaJOAVPcIBk7oELwIqsqh46nhK2DchGOvIX69xMFBARGINugYTDXa9xo/WDXPbf05fqx+qX3rHiv/C/tVmAqqBXgO/ionmFiI+jCV48qC6QF5BjvPqWLKorK92iYCqzJLHqrPwiZuIamg/qHcpDFLknGQndah2qHZZZ37Jk2QGGlAmnvGe4LYSUNV5nXi0Ma752piprudJA12oAVVHrEJAGfT+8Ic/FMKlPZnhAFkYYNKRxh8S0CqCjZOnDPD8vepVr/JJXXvttdOuwyhKX4RZAq3Ib51udWpAwUsDpAaNJgQK+5nYDP/KV76yTiiGHpf6AJOy8EoXDSZ55CQ145AsBkodRBIB1WCUGk/oT/2L9t9mAipsJW9UBpbAIX79nLBXXFWe3MsK+QgJKOHz+r/6kfpXXloQ0KWWWirPeaTsJX+LyoybsBlG4dQnNSkuyoPySZ61atAEASVPyF80/llXMJFH9XnJgLx8s72Glb26jWlA60bU4itFgEGVASts9L1oQIlDBDMvUTSKurIpz09sj4BAOEjgSQDG/lJ/IwhEHsrCDEsDSpml3SzKo/xoFp/lF7wQrNSpNBNNXJNEO9pvv/2mDoBl5WUU7fJIkiZsmsD1U7bwJLzi1WDUS7wioJy8bmIw7SVPWWHysGX5HTNoDeg888zjlza1BC85IUITlyEkL7GbfqPNZRmfuuiCUZk1mc0qE7K6XzmdFW+qneqrKgHVRKiJPqM8odTIuoKJskmWhGNxXGawxT3eAxr76+W3EdBeULMwfSNAw9fAR2QioFU6IkvwImx5GcI9T5uQFwZBRqeTwOtXsFUhoF3QgIYDBkvw/eKXV09dtRdJUp9QOcvIifylPNHQS+umwacOAtrmA0jgImw18AurYRFQ0g/roqyO1bc0OVb+wycnlPvd0xvGN+x3yY+iMiOrhc0w8qsxRnktyoPySXnUDquMe0Vxh24ioGjD2cMZn4DHL4oE8iwZEIbXu1YZm9jqlEVAkXtFCg7lqwtPu4h+SLUYNzw6IloytJWpJmUPKARU2pnUeBEQIQGVwEgNH/tDOIlYxm7xb/mTQIvdm/pddQleA3lWfiSEhaF+Z/k1u5kICNuYgGrCJq3FzJDpNqEGlMGHvqcBKz2WJ32qj7V5+Z3caqCPsW0LAS3r/5JFWol4sgaefIOAYrqiAVWdSSHwZEmffEPWDFPOqO9U0YBSh4MgoL/5zW/8ilR8Al7oIU+KCCjL75gmCGicNv2SyZMRUNWOPRtBICagNDwNvKkJpi7Ba3BMjTcmoP0KtioaUGl0q+Y5tWx5/ujwCPEyg9BkkhBvZg/DCS8GDP5SNuaH4cf9XQNuTJKkHatjcoLWDc0GWyUYfGijLAf3atRe205AJWPYFxcaXYM0DNLGSfg6l+BFJvrRaIfYDPudOkPmFBFQ3ETOh5FfEdAUWad8Im8HQUC5RB6TpQHFnnaiyS2/Y4P2FNPE6kactiZWRkDjWrDftSKQtQSvwSE1ITSgDCRqtFnhetWAItBEyESosuJPsatCQKUBoWyDNFU0oGXCQQIWDG0Jvnotqh9ocFIMEFA0LOHBJLlVfaIBJX6+XARh6ZesiIA2MUhVLVuRf5H7GFs0oGBQB7ZF6We5MRlATkEC6P9MBPI0aepbkk1Z8XVNA0oZKXdRmXHrV05nYZlqJwKaV29hPGEdqh2qXYb++n1XnrgsHgKvK7/ieGn3mrTEbvyGgNK/65j4xvGTNm2ffcsYjeVlY0wcz6j+7n3KP6olbkm+s1TvVTuhSFrRQSQEugbH1KJL2OkKoX4FGx0XMhZfq5OVH/JLuYo0jFnh+rVrkoCmaAX6zX+XwqsfxBpQCAr9pg4D6cFwEr4OAkqbpZ2/8IUv9PG29V8euefi9kEfQBJGIgbsyaWOwTHv0B5tA4JaRMZEQPudVCh/bXgig5GheQY3Ebs8P03agzXpp6wAiFxRh00SUBHGuXPnZl7BJDxSCGgTy++kjzxjyV0aWLVrYaQ8dvVp1zANqWZp9Gp0ZIGOqMEhNUsilsyg8gYPyGmeW146IqB1dQbNRNFglV3z0ovGNq8cVewps2afReHwUyYcRDjBj4GhXwJflJ8uuqkfxASU9qO21G+50YBiRED7JbaQIg47KO/95q+p8MqfBn6lM4zPcCptTQYgoExAy+pY8knh4ycTCjS5Kdq4OGxbf5eVGVmDn2EZZOL111+f9PnTcBKhPGvSWWf+iZv2DjZ5y++kx1h8yy235Cbd1CX0JKgtL7RZxkbJvLIxJjezI+ZgGtAhVVg866LhaXBIzZIIaJEGtBdCJ2EHeaIj9LM3jrJoQGFwKTP40cy1zG+d7pSzLgIqoQp+/ImQ1pnfLselfhCTpDoJKG2SP2nd6I/9GuW733iaDK+BXgOd0mIJvmxyKL91P9m2AGFkH2hK/5d8ysuHBvM891G0L9OA0leGPdGt0v6pQ+St+rjaZd11o7En7wAS6cVjcZwHDiE1pQGV3KHNYjQGGQGNa8F+14oAGhdmZhoIeiGgWoKHZOaZfggo+atDqIlQQiDKTC/5LYszxZ0OT3nLTIoGVJhBPm0PaBmiM901kKlvyEedS/DEqZPwCH8NBEqrq09hGx9CgoBWXSmpCyO22zDAMxlImWSUEVCW4KVZqiuPw46nqMzUJX0FP6NiVJ6mCajGnjINqAhgFn5oQJu4A5S0JHeUvhHQrBowu9oRiBseAqTqLLApDSgECjIGgRKZ6gcAzUJTCGiKBqSfvOSFhYDGhCfLbxUCWieGWXnpqp36QVwfKeSkCiYs/WoJXv2xSvhR9CsCGmM7TAIKjtRFXQS0ixMK5DDyOMto4lyHrM6Kvwm7kICy37epPf8ae8o0oCgKRIbD8vJBA8akQWlAVZemAQ1rwd5rR0B7zjTzofFrcEhNLEUDSgcSUU2NV8IBgVfHrFqzUDpymRkWAaWcbAaPB+Y4vykEVJiBH3+2BB+jWPxb/SAeEJrSgBLvuBBQkfsYW+TQsDSgtAZdxZTS/yWf8lpRVzWgeQRU9pI7ebi0yV4rTr2Me1XKIQJapAGNx+Iwfl3B1BQBZWxki5t4gGlAQ/TtvTEENODpIBLERwNvaqIioHl7QLHnnsN+CGgds2oJgRQN6DCX4MFdAiCvDlIIKPvZdG+fLcHnIZlvr34QTwaa0IByFyhLmOqP+bnqhouwDZfgkUH8HiYBraIBFXnJq5GuakClHYvLLfs6ZHUcd1O/NYmAgGpS1ERajD1FVzCRpvq+xuIwH00TUH0AQ2lr/DENaFgL9l47AvGsqxcCSsdlQMnbAyr7Xgkogq0OoUYckLIUApqiAam9MiYjVIeXAMhLI4WAEpYygx8E1DSgeWhm22tAapqA6iQ8udAglJ2j7thmYcsVTJhhElA0oMiHO+64o/QQoshLXq2YBjQPmfbYqw6bJqDsBV5ppZUK77dV35cWMkRJBLSpPaCkRfpKW+OPxqMwL118t1PwQ6pVNXrNfBhsNThUyRJa0DwNaD8EVMvHdRBQysNMtM1L8OrwEgB5dZBKQBGwYFgXic/LTxftpaULl4lp41zWrIlbHeUeZwIaakD1FaRhElA0oBj6l1ZMvEXGP5GXDCdvBQGVfM3zM2r2mtBm5VsaUHAZFUNeqeumCehHPvIRd+aZZxbCorYiEhh6hoAic5pUIowzAbV7QMPWNsB3iCNaQTV6OmIvjRztZhMElAGKeOsSagwqKRpQ0qyqsa2j2kRAJczz4kwloAwY1C1bIHqp17z0x8GeAwksTYUaULWdMnJSBR+RHsLUSWyr5GHQftlvxpJkiC0HkDBtIKDko6yOkUmSm/gPDXKLiV8XT8Frr2dYXt4ls+pSFsTxN/GbOiTfTRPQJZZYovRu0iIC2uQVTMJ1nAmoaUDVCobwZNALNaDS/FTJCkRWms44nOyrEjqRTjQJdQk1BpUyDSgHptBy6dBSXJ4mf4uAQjCLTBUCqoG9LgyL8tU1N+ojJElNEFAmBiJdGoS6hmNWeVhpCbXLaqfDJG0sceokdFn/F3nJKpu0uV2rT2SIiGZcbhFTye3YvY2/VYdNE9CUssfKoDAMGtCmDiApnZiAopjibxyMEdAh1nLY8BhseyGgTWlAgaVOAsqgIhKRB3mvhDkvvir2Et51ElANhqYBrVITT/ilL4QkSRO1ujWVWobvGmEpQjzGFgLKV4PUB4rCNuXGgMuF9JgUDWgeGUNmYYZJpn0Gav5H3YhoxlELi1Ga6DLBHIQGNMYq73c4Fod+IKBN7v8krTDtVAVHmMdRfjcCOsTaizWgve4BFXGLiyL7qhpQCbI6CSiDShkBlYa0TAMSl7OO33VrQBkwRECFZx35HJc4IElNa0DBkmV4Jgi99L1RrYssAipN8DDLpC0R/RBQLc13bUJhGtBmW2ZIAsOUhrEEr7EozEdX342ADrFmw0bfqwa06BAShI5lraoaOHUAZtx1aUUglSKYeZDLvQsElAHDCGheTZfbQwhDAtqUBnTVVVdtXMNRXtrB+shagm8DAeUkPKas/yOT8lYqTAM62LbUa2qqwzYswVMGlEGavKhMKEw4k9D0EnyoiDINqNC3Z+MIhI2ejohmoqpBuylNZxyWzgNBrWpCjV34XjWe0H+KBlTlqKqxDdPp9V2kW8tZefGkCghw02BYdQKQl/Y42WdpQJlMsVRcpznssMPcr371qzqjbH1cpgFtfRXNyCDyhANWjz322Aw3Lc33Mn7MiGxAFhDQti3Ba5IrCNB+YpomoCiiOPvA+Jc6viiPo/40DegQa5CGp0bfhAaUBt0LmQu1nnURULQa47YEr8GiLgyH2FQHnnRMkmg7tCFOx9dpILW99JE68zDouNqqAV1zzTX9obB+luCZ9CG/Qhk2aHybSE/lyZogY4eMqbtvNFEOxcmEv20ENNaADuIOUPCAB2BI3wioh8L+DQKBeAm+l31oDJ5oOrNMmwgog0pePpX3ti/B86lOtBDSlirfWc+QdJoGNAuhYrtYA8pEjRUDM/0jEJN7toq0YQl+hx12cPfcc8/Uafi8kkLGsogY/iGgGtDzwo+iveSJtJ1hGcBCBDW0b/O76rAtS/DhWCzcjIAKieaepgFtDtvSmBlQ0exAbHpdgi+7hqkX7U4ozML30gIVeICAapkhzxuEmTsKUwheXhy92itNZqB5RoOe/Ob5w14DRvxeFMbcnkQg3gNKPynTjD0Z2t6KEGgrASXPKVo8ZBL36yIzY4MWqWsn4Cmj5LBkUFjuOvfqh/E2+U55kLVM6HtRvNSdtywCyhL8YostNoV93WkqPk2YTAMqROw5EARoeCJlvS7BQzDpxOGBDWW+bRpQ8iUtp/IYPnErO4AQ+q/znaVYyG8RAZVbCgHVgEEeTQNavaZikmQEtDqGeSFCcs/du5AaBtpRMepbWWSMQVwD+qiUJyWfmtDmaUDlnhJXG/xQh2xR4lPFbSGgjD9MbGQGcQcoaam9GgEV8vYcCAJaUmQJjM7IoFvV6JARZDM2bSKgIpYQiTzTa37z4qtqD7HMGtQUTxUCqgGB+w0htmaqIUBfCCdVtgRfDb8i32DLpBWjS+jbsARflOfQrYiAsgRvGtAQrXa+qw4hfW0hoJDPcHwaxB2g1I54gBHQdrbVzuZKM5+7777bl7GXjqgl9qz9lb0SOpEnMhW+91MRWj4NO3gc3zA1oOQFAiqSGeeN33JL0YAKNz2z4jO7fARCLR2+TAOaj1VVl1C73DUCahrQqq1hOP7bSEBBgvYjMygNKAoKFEmkjQIkZXxRHkf9aXtAh1iDmvmIgLZFAxp+CkyCol+YpAGFZOaZYRNQyiqSmZVHuaUICBFPW37PQrLcLiRJ+DYNaDlmqT5Cct81Atp1DWjWEvwo7gGVDG2LBlRjcUhAB3EJvfos6ZM2Y4ywkVuXn0ZAh1i70oDeddddPhf9ENA6NaBkRsRTRKpfmFI0oL1qbPvNm8LT8UUyZRc+5ZYiIOrGL8zHOLzTF8IleNOA1lfrYKsleH0sYZSW4NX/srbLMFGRXK0PseHHJDmcVWbs5D78nKblQPKxTXtAyTntBwMZhNg3fQeoT2zyH22WtI2AChF7No6AZl39aEC1BA95iw2kVO6xW9lvCYi6BBv54ITruC3Bmwa0rKVlu4daOg7qcVhGk5jsEGabikCILRpQfmsveWocw/Qn2RSTMQgDdl3eA9oVDajqkHZE+xu20aRFGtBBXcGkcpO+aUCFhj0HggAdD4IiAtpLR9TAEWtAEcRoOdpCQOeZZx4/yBUtwUOitVQ/kAqIEqlTAyrirmeUlP0sQSBcgmfSwlVlmrCVBDXnEgRCbCGgSyyxREmIdjmLvMQEVORBZKJdue4vN0VaX3AQJv2lMrjQYX57GffqzimTWxQkakMioIPUgBoBrbtWLb5SBBCW/SzBi2DGBFQaUbmXZiTyIAGhZ+Tc0086eZkGtNf89pShKBBCPh7UQi+9LMGbBjREMP09XIJXmzENaDp+RT5jAjpKy++USzIp7qvs/8R0UQPKQRX+sjSg4DBqE13VIfXVBgLKNXwoP0RA2f8JIV122WXJYuMm1ICG2DSe8JATsD2gQ64AtDrSgDIwVDV0Xv5EOBVev3sldOoEdQo2CESRBnTYh5BMA6rWM/wnbVp7QI2A1lsfYKtL3NGAjtIdoCAh2RQTUJGHLmpAKTeyOC4z9pBSYcLvUTBhfmmPbTAigeQFDSgrA72Myb2URWnbHtBe0LMwPSNAw+uHgJIwJLMpDWidBJQZpshEDBj3oCJceyXMcXy9/EYoSsuZFV5uWg7L8iM74WYaUCFS7Rlq6XQwwJbgq2GY5xtsdQgJAmoa0Dyk2mWPfOqKBjSUoW0loMstt+Wq73cAADiSSURBVNzAGoAR0IFBbQmFCDCo3nvvvd6q147IPlBpPBW3fvdK6CBQ5IelibpM0RK8NKNd2QOqGb6IaF0Yjks8kCTTgDZT2yG2XSSgDOZdNMgS04A2V7MigaQwqDtAVRrSRsHBykRIzuXe1actwQ+5Zml4aP8wDAy9GAhoExpQkahe8pQVBnIpohm7y74rBFTEU8+4vPa7GIGQJEkDantAizFLdWViGS7Bj5oGNO+zuSzBL7jggq3YU5haF1X8dUkDGo4tvSpeqmCX4hdlkLZxDPIOUPIWru6MEwGdL6VimvDD/XO//vWv3axZs9wLX/jC3CRuuukmd/vtt7u11lpr2l6lK6+8ckpDQuCVV155JO9/C2frvRJQtJzSeApI/e5VA4qAqJs8FWlA+82vyt3Pk46fpWFQnMxQOQjAif4yI+xsCb4MqWx3BqVQA0rdjJNgzkalHlvkDASUPyauo0ZAQQH5FPdVyEMoT+tBqz2xIFPiJXhuh2A7RUjo2pPj/JzQv5GjfP6yLQSUtnP11Vf7TA+agIbtdpzkXPlImt+GenaBPL71rW91v/3tb92BBx7ovve972XG9bnPfc59+tOfdvjfc8893R/+8AfvD43hBz/4QR+OsPzdcccdmXG03TKc+fRKQIs0oLqmqSoOgyago6IBTRUOGhBERKviP+7+6Qvc/8kAy75h037W1yIY8Bn4tfe8KwS0q19BUs1nkW7ux8WMopyRjGwTAWUSw7YUlA2D3gOqek4dY+R/lJ9D0YB+/vOfd3PmzHFrrLGGe8Mb3uD22msvt+22206bCd12223uJz/5iTvjjDP8TOnUU091p5xyijv44IMdbjSOT37yk6OMvc97SEB77YiQzDvvvHMaFhA6lqNStHXTAv7nx1JLLeWWXnrpLKee7bqwBJ8qHPAH9tSBmeoIqC8wELAEH/aT6rFZiBABTXQlM0aRgNK/Yg0oBDTUJIVl7sI7hC3WgOq3yNwolZM6bMuXkMCNtgMBlaJrUHeAKm3VXeoYI/+j/Bw4AUV7iXp79dVX97gtueSSfvaGBvO5z33uFJa33HKL9yMCxRL897//fe/+u9/9zhPQH/zgB36ZbosttpgxA7z88sun9hsiqNCmMJg1bXS6lOVD7e0sSjMkKGh7eskjs1+0RGFYBm2IaWhXlI/Y7YgjjvD57zV8HB+/WY4GF/JKJ6O8qhd9EhDiUWeaWfnIs5tvvvn8oJaXPtoGBu889zjer371q26jjTZK9h+H12+1o9R0FW6Un9zBh2EiBbFgK0lZ+WlLGJaWaVtmshEQtlo1SsE2O6bh2SI/2D4QtglkCJPm0K7fHEqeE6fGon7j7DU8sgcZFJZPt4qwLza07zWNXsOpv4FXaj5CopUaptf8pYRjvCT/WoZnYtZEvlh9iMf6UINNO2si3RQM6vTDuKV2kRfvwAkoJ74hIhKCZAzNGDOPkIByOXt4IAUhqYuGWbpnbyga1FtvvdV9+ctfdieddNK0PaJoR6+55hpf7mWWWcatt956U995zQOjTnvtaSyLkz2FMsxmdThAdilPSBsDtQ5rEIZlBHAO7VLiatIPBA/DDDPUupBHLQfSaIeVZzoLk5W89MGY+spzj7HbaqutvFWq/zh8/LuueOJ42/hbpJt2zB8COrX8WpZsY7nakCeRKn3tBfKSim0b8k8e8mTe8573vEbKQt8ftkF+QrrDurrnnnt8tph8hfbDyitjmLSyZXnQKgdjXhvyrrEY5RX8BK1yk/mK4wYPsGgLHmX1V+aODJdSIM9v4wQUgnjuuef69CGRW2+99YxMkdFwNoRnhGKYefxomWHvvfd2/GnWgFYNbeib3vSmqXL+93//9xSZg9yylD+IT87ReJiVos4X4ZrKVMaLSDcNHqLci0GLzFJGWD4GGZYtQ7te4q4zjJY0wIV80QGZ7THRgPxBmOte9q+Sfy7kpi3lYUYdkcc89yppVfFL3cb1WyX8KPrVBAWtBAMa/aQMd2QEfZ39ohrcRrHsTedZ2KIMoP9xCDRUCDSdfh3x0y6QGWGbgCSyNSu06zctNFEoE5ANw9aA0q7ZfhaWT5MI5GZo32+5q4ZHq4cGmnrROF0Wh1b/aI/DzLvyqbH45ptvdoypTX0FKRz3lDZPOAOKmGHXZZinft6Rx/C4ItM4AWVWIU0mDY7PpDGYMtBrLxKDRky+aJRShVMA/IicsHSEQBABXWGFFaY+Z6nChp9jQ7jyVwaGwvbzlJAirZT0NBiARYr/rLwhmND6hOH5De6hXVbYQdqpTpjFK1+qF+woh+wHmS+lRXtCkPKn2bDceDK5QLgOOo9hmwrz0+V3DWIIMYgFwrkMdy33gFeZ3y5jV1Y2yU1WmZCjKRPlsjgH7U4ZGEPCembiT3lCu37zFfY9vfcbZ6/hGT9ZoQnLh0zCQPxC+17T6DWcJjBV+p7a4TBkalY5NRbDO+AUTeGZx0cYHyGgKDmaSjur3E3ZIY/VLvLSaPwUPDPSt7zlLf5vhx128MKO5fCzzz7b5+nSSy/1mjq0dXQurlzCrLPOOu7aa6/1F8IyCJ1zzjlu3XXXnQpzwgkn+He0IxdddJF7xSte4X+P2j8drhAZ7yX/CB8EEQJZhlk7Guc2GU1EspazsJP7sPIsLXze/hvs5WdYeRyXdNUfaNdoDNRPxqX8TZZT2mEOIWnQbTK9JuKmHzJeyDCBpa1okiv7Lj0havHytn5rwjZK5ZUsVXscdt4lYyCBWq0bZJ6UvnAZZNrDSqtxAppVsP32288vie+6667uxBNP9Cfb8Xfddde5/fff3weBPO2zzz7+hPyb3/xmv/cF/5iddtrJa/y4yolT9BxQ4m8UDaSLWaMG3F7KAAHFIIRl2khA0XBitHFeeeXZhvxKiBsBDWtmOO/qD0yqmJyo7QwnN91KVdiiAR1VAkpfDQkoK2QYNOVdNWgMwzJTTv2WNnGUyi552xYCGradYRBQpT9OBLTxJfisDrHiiiu60047zRORcGBZe+21pzSjhNtuu+0cBzkYhLRfBHvI6cc+9jE/G0RVLYGK26gZVNTMfPopgzSd2qsEBm0gdHFdSMOZRUBNAxqjNd6/NSjRLpgQhHJivJHpv/TCFgLKatQompiA6oCqaUBHpzbbRkAZgyHyaJUHeQeoamwcCehQNKACPGVQYS9eSD4VlieNpR/iFsY1zHcIqAaFXvIxKhpQ6orZHaQiNhBmEdTYbVC/NfM0DeigEM9PR/1atyNoeSo/hLmkIiBsOahoGtBU1Ibvr0gDKjI3/Fym50B57mfsS08tzadIoGlA0/Dq19dQCWi/me9KeBq9BoVeyiQNaNuX4Ckbk448DajK0QsGdYQRAdWyVhyn7QGNEWnut/qDrplJmaw2l5tuxSxsKVVXCOi4aEC5GUYHj6g/ySrJLuxGxRgBnV5TIr+jWJfTS5L+ywhoOlaN+ex3CV4aULSIGAQUZGnYhC4LMLScWRpQW4LPQmt87aQVMQJafxsQtsTMqfFRNJAXkS/yLwLaZU259nmG5Wa5GMIy7BP6vbQhEa2wPfYST51hRAKHoQFlO8w222wzknXZax0YAe0VuRrDoYXg6oVejQioNKB6tpGAFmlAbQm+1xbQvXDS0omAdplYDLr2hC3pdkUDyiEk5McoXimVWv/SGOrkO+EgoyKmqfG0xZ/K0zYCyrkSXfk4SKw233zzqa89DjLdYaY1lENIwyxwG9M++uijp83mq+ZRRFMaUD1lXzW+Jv3nEVBI87DzK4Foe0CbbAFpcYskiYDaEnwabim+wgG/KwQUDai0VykYjKIfEc1YAyq5NWplUr7D9jjsMtCGIJ9duIdz2FimpG8ENAWlhv30+xUIBmsOa0nz2WYCipaC+wdDw6X57G0yDWiIyni/a1CCgHJTxLDbRpdqQ+SeMo0yAeV2FF12jQa0yyfgqSsRNtOAgkYzZs8993SzZ89uJnKLdQYCRkBnQDKaFmgPRTz1HLZGMQtJNFnXX3/9NKe25Fd7kkINQ5hRO4QUotHsu0gSBJQtJqaRqA9vYUuMo0xAyT99EmI2ThrQkIDyLmIKHqNklG9NNtuQ9/XXX9/xZ2YwCBgBHQzOjafCID0qGtD4FLwOJQ1byyUCakvwjTfX0gTQ6KP5hIAutdRSpf7NQzoC4WdmR/kQEiVmsgiRQQM6a9asdBBG0KcIWzhB5l1L86NWpG233dbxlcOyzzWOWrksv+kI2CGkdKxa7RNt5ygQUDSgIpwCVL/bTEB/+9vfOu5N5CMKZgaDAJoRbnSwA0j14w229MVRPbQTkzHTgNbfRpqO8QUveIH7wAc+0HQyFn+LETAC2uLKqZI1NKBaytazrUvw7Pl8/PHHp4rXlvyiGWKpN0sD+sMf/tC7bbbZZlP5tpdmEdBSsR1Aqh9nCOgo75mMCeg47QGNNaDCov5WYjEaAs0iYAS0WXwHFnusAWU5OVxqG1hGShKCTHBwQFpPvOt92BpQ8gJuWQT0ggsucOuuu659EhKQBmSMgDYHdJcIKPLkgQceGJtT8OEe0FFegm+udVvMo4KAEdBRqamSfMYa0DZqPymCSGa4D7QtGlDyBwENNQzYsQx88cUXu6222oqfZgaEgAioLcHXDzgEdFT3f4KGtH70VSaw3KIxyhrdlBrWHvVQPo3yIaSUMpufbiNgBLQj9RsfQmorAdVyakhAGUDQ1mpQGWaVZGlAf/rTnzq2DRgBHWzNQJIwajODTb3bqUHuR5mAhmRMX0Hq+j2g7NelT5gGtNt9c5xKZ6fgO1LbEE5pEnm2lYBKAwrp1OfOeJf9sKsji4Cy/xMt3DrrrDPs7I1V+qYBba6699prL7fGGms0l0DDMWuyijaQ/Z+YrmtAKSPlDgmoaUBBxcyoImAEdFRrLsr3KGtA20SYEfDxHlD2f3L4yO6ijBpdwz9FQE0DWj/Q++yzjxO+9cfefIxZBLTrGlBQ5cqlcAme91G9hqn5VmIptB0BW4Jvew0l5m9UNKAiE/ESfFs1oPfee6+bO3euLb8ntsM6vdkSfJ1odiuukIBqCd40oN2qYytN9xEwAtqROkYDymEZ/tqkUYzhXXDBBb0mkWV3GfLbVgJ64YUX+lP7W265pbJrzwEhIA2dHUIaEOAjlExIQFmC5zJzTW5HqBiVs5qlARUWlSOzAIbAkBEwAjrkCqgree355DL6NhNQygvZjDWgyn9dePQaD3tAwyUult9XWWUVt8IKK/QapYXrEQER0HEgFj1CNLbB4kNITFLmmaf7wxlkU3tAuX4KhYMtwY9tNxj5gne/x458FaUVAA0oBvLZdgIKoQg1oG09hISA5wCSnX5Pa4N1+7Il+LoR7U58aDw1WUQDOg77P6m9UAP68MMP+wo1DWh32vW4lcQIaEdqXBrEUdSAtokwI8x1COmqq67y3yI3AjqcTiINqC3BDwf/tqdKX2W1gj2g47D/k/oINaBaqTENaNtbquUvDwEjoHnIjJi9NKAsbTMzFiFtYzHQgMZL8G3cA4r2ExK0ySabtBHGzudJBNSW4Dtf1T0VUAR0XDWgWooHBzOGwCgiYAR0FGstI88ioHfccYc/NNNmAgrZHIUlePZ/brTRRrbHKqO9DcIKAsrl2xxcM2MIxAhAvFitMA3oM2No7LchMBIIGAEdiWoqz6QIJwQUo9/lIQfvI9SAPvbYY34ZrS351b4ytMiXXXaZ7f8cfPOYSpE9oKb9nILDXiIExlUDKs2nnqYBjRqG/RwZBIyAjkxVFWdUGtA//elP3mNbCF1WrsNT8Oz/xLRtCf7iyW+/c8LU9n9m1eBg7BZYYIGR/lzkYFAa31REQMdNA6q9n3raHtDx7QOjXnIjoKNeg//JP5o7vqc+KhpQLcG3lYCy/L7UUku51VdfvSMtZPSKcdhhh7mzzjpr9DJuOR4IAhDQhx56yO8nH6dT8NJ86mka0IE0N0ukAQTsU5wNgDqsKNGCjoIGlGVVEVBO7WPaorFFmLOvjANIdvn8sFryE+lCKsaFWAwX6dFMnb565513+j3v43QKXppPPY2Ajmb7tVw7ZxrQDrUCSNwoaEBZbmfvJ/ss26gBhRTfdNNNtvzeob5hRekeAhAvybtxmaiw3C7NpwioLcF3r22PS4mMgHaoptGA3nXXXb5EbdEoZsGrgyWQT2lA27QHlDxz0fUWW2yRlX2zMwQMgRYgAAGVvBtHDaiIqGlAW9AYLQs9IWAEtCfY2hkI0olmkatr2iyUREBZhpcGtC2EWZ/4W2uttdziiy/ezoq2XBkChoCXccg7zDhpQPlCG9uEpAGVzLImYQiMGgJGQEetxgryq5PwbSFzeVmVthPy2TYCKuJu+z/zas/sDYF2IKC+Sm7GSQNKedF+8sddufPOOy9WZgyBkUPACOjIVVl+hkeFgIYaUJbguWh8nnna0RSlTbDrl/LbmbkYAm1AQH0VAtb2SXddeGm/J9pP/vS7rvgtHkNgkAi0Y9QfZIk7nJaEsJ5tLaoIqDSg0oi2Ib8vf/nL3cc//nH/BaQ25MfyYAgYAtkISAPK8jt7tsfBqMzSgOr3OJTdytg9BOwapg7V6ahoQEWQIaBoQNtEQNn3efDBB3eoVVhRDIFuIiDyNS77P6lFaTylARUG3axhK1XXETANaIdqWMROz7YWTd/31iGktue3rThavgyBcUZA5Gtc9n9S1yozGlBbgh/n1t+NshsB7UY9+lKMigaUzLIM38Yl+A41ByuKIdBpBETGxlUDCgkVBp2uaCtcZxEwAtqhqpUmUc82F41ld9OAtrmGLG+GQLsREPkyDWi768lyZwjkIWAENA+ZEbQ3DegIVppl2RAwBHpCQATUNKA9wWeBDIGhI2AEdOhVUF8GRomAogG1Jfj66t5iMgTGDQERUNOAjlvNW3m7goAR0K7U5GQ5tPSuZ5uLxh5QluA5BT8K+W0zlpY3Q2AcERABHScNqMps1zCNY4vvXpmNgHaoTkdJAxoS0DZdw9Sh5mBFMQQ6jYDI2DhpQLl0f/755/cn4O0UfKeb91gUzghoh6pZmsRRIHTk8a677nKPP/54q+4B7VBzsKIYAp1GQAR0nDSgVCh3gZoGtNNNe2wKZwS0Q1W94oorujlz5rjZs2e3vlRoQB9++GGfTxHn1mfaMmgIGAKtQWDVVVd1Rx55pFt33XVbk6dBZATibRfRDwJpS6NpBOxLSE0jPMD4ueD9kEMOGWCKvScFAZUZBY2t8mpPQ8AQaAcCELHDDjusHZkZYC6kAbUl+AGCbkk1goBpQBuB1SItQyAknaYBLUPL3A0BQ8AQeAIBaUDtInprEaOOgBHQUa/BEc2/aUBHtOIs24aAITBUBNCAPvTQQ+6f//zn1Lfhh5ohS9wQ6BEBI6A9AmfB+kMg1ICG7/3FaqENAUPAEOg2AmhA//znP/tC8m7GEBhVBIyAjmrNjXi+Qw2oLcGPeGVa9g0BQ2BgCKABFQHl3YwhMKoIGAEd1Zob8XyLgHJwymbxI16Zln1DwBAYGAKmAR0Y1JZQwwgYAW0YYIs+GwEtu5v2MxsfszUEDAFDIAuBUANqk/cshMxuVBAwAjoqNdWxfCI4n/rUpzp9valjxbPiGAKGgCHQCALITj5jjLEl+EYgtkgHhIAR0AEBbcnMRAAtqGlAZ+JiNoaAIWAI5CEA6ZyYmPDOpgHNQ8nsRwEBI6CjUEsdzSPk0zSgHa1cK5YhYAg0gkBIOk0D2gjEFumAEDACOiCgLZmZCEBATQM6ExezMQQMAUMgD4GQdIZkNM+/2RsCbUXAPsXZ1poZg3ztvPPObpFFFhmDkloRDQFDwBCoB4GQdIZktJ7YLRZDYHAIGAEdHNaWUoTAnnvu6eadd97I1n4aAoaAIWAI5CEQks6QjOb5N3tDoK0I2BJ8W2vG8mUIGAKGgCFgCEQIhKQzfI+82U9DoPUIGAFtfRVZBg0BQ8AQMAQMgScQkAZ0/vnntxUkaxQjjYAR0JGuPsu8IWAIGAKGwDghIK2nnuNUditrtxAwAtqt+rTSGAKGgCFgCHQYAWlA9exwUa1oHUfACGjHK9iKZwgYAoaAIdAdBKT51LM7JbOSjBsCRkDHrcatvIaAIWAIGAIji4A0n3qObEEs42OPgBHQsW8CBoAhYAgYAobAqCAgzaeeo5Jvy6chECNgBDRGxH4bAoaAIWAIGAItRUCaTz1bmk3LliFQioAR0FKIzIMhYAgYAoaAIdAOBKT51LMdubJcGALVETACWh0zC2EIGAKGgCFgCAwFAWk+jYAOBX5LtEYEjIDWCKZFZQgYAoaAIWAINImAiKeIaJNpWdyGQJMIGAFtEl2L2xAwBAwBQ8AQqBGBeeaZxz3taU9zIqI1Rm1RGQIDRcAI6EDhtsQMAUPAEDAEDIH+EIB8mga0Pwwt9PARMAI6/DqwHBgChoAhYAgYAskIPPvZz3bPetazkv2bR0OgjQjM18ZMWZ4MAUPAEDAEDAFDIBuBH/3oR27JJZfMdjRbQ2BEEDACOiIVZdk0BAwBQ8AQMARA4PnPf74BYQiMPAK2BD/yVWgFMAQMAUPAEDAEDAFDYLQQMAI6WvVluTUEDAFDwBAwBAwBQ2DkERgaAb3//vvdBRdc4G666aZSEG+88UZ32223TfP3z3/+0/30pz91P/vZz9yjjz46zc1+GAKGgCFgCBgChoAhYAi0F4GhENArr7zSvfWtb3W//e1v3YEHHui+973v5SJ06623ugMOOMDdcMMNU37+7//+z+2xxx7uxz/+sfvGN77h45iYmJhytxdDwBAwBAwBQ8AQMAQMgfYiMBQC+vnPf97NmTPH7b///u7EE090X/va19y//vWvGSidddZZ7n3ve5/jyonQnHbaaW699dZzhx56qDv++OPdP/7xD/fLX/4y9GLvhoAhYAgYAoaAIWAIGAItRWDgp+Afe+wx96c//cmtvvrqHhKukuBC3TvuuMM997nPnQYTl+1+9atfdV/4whfcU57ylCm33//+927LLbec+r3WWmu566+/3q2//vpTdv/1X//l0J5iFlxwQff0pz/d/eUvf5lyb+rl8ccf91E/+OCDji9WmMlHgLbw73//eyD1kp+L9ruAE2YQ7bf9aOTnUH3v73//u3v44YfzPZqLAyu2MVmbKm4MyCfMAw88MG0MKg41fq5agaTfPfLII+MHQIUSs2UQPtP1vqfxvQiagRPQe++91y2wwALTOjMaTiojJqAhyVQDpzB33333tEt4uZAXUhsaCq+BGyFC+DCO0G+d72Ea4XudaXQtLsMprUYNp3ScDKtirMBHf8U+x9tV7UjP8UYjv/TCx9pUPkahyzjgpDYRljt+b5yAooU899xzfboQxa233tprvcKMQBTRUKaaeeedd1ochI+/i8vyvAyElaX+RRddVFaNPaVVWHjhhd188zUOb2PlGETEHESjLp/znOcMIrmRTeOhhx5yaPUG0X5HFqTJjCMH7rvvPj855VvZZvIRQBEARvH2pvwQ4+nCeQNWsxZZZBFb0SpoAmjU77nnHr/aaJ8ILQBq0unPf/6zb0tdH/eQx4zvRaZxhvTUpz51SsixFM4gipoeoqZBAu3nMsssU5TPaW6LLbbYNPU14ZdffvlpfuyHIWAIGAKGgCFgCBgChkA7EWh8k+Jyyy3n3vKWt/i/HXbYwWsFOUB09tlne0QuvfRSrwFjNsBs8/bbby9Favbs2e7888/3e03QonEV00tf+tLScObBEDAEDAFDwBAwBAwBQ2D4CDSuAc0q4n777Td1/RIHdQ4//HDv7brrrnNHHnnkFDnNCovd5ptv7u8A3WWXXbwqe+edd56xfzQvrNkbAoaAIWAIGAKGgCFgCAwXgaEQ0BVXXNFxlRJ7a9grKbP22mtnks+PfvSj8uKf7K086qij/L449n7aXstp8NgPQ8AQMAQMAUPAEDAEWo3AUAioEAnJp+yqPBdaaKFk7yzvs0m6acPeVog1m7KNGBejzd5dNOBZd8AWhxwvV/ZMcxDJrvUqrnc2vbPBn2tOtL+8OMT4urJ1af7557crc0qaAOPG3/72N+/L+l8+WIx3HABk/IsPBOeHGk8XXenV9XEPeVxmhkpAyzJXlzun8jiN9cUvfrGuKHPj4VJ87jSdNWuW4wCWmXwEwIl6WWqppfI9mYu/gxCy/rznPc/QKEAA4skne5dddll/t3CB17F3Yq89cnHxxRcfeyyKAOD2CW5RWWmllUpP9BbF03U3COjNN9/suNeb227M5CPAuMdkZumll8731BGXjTbaqLAkT5m8q8m+YVkIUTVHDkTxmdHzzjvPCEMJdLvuuqtbYoklHF/GMpOPwFe+8hX/MYarr74635O5uFtuucW96lWvcv/zP//jNtxwQ0OkAAHuWN5444391+QKvI29E1cI8ilovrTX74pdl8FklYYPwvABmNe97nVdLmrfZeNQNiT92GOP7TuuUY+g8VPwow6Q5d8QMAQMAUPAEDAEDAFDoF4EjIDWi6fFZggYAoaAIWAIGAKGgCFQgoAtwZcAVNX5r3/9q7vhhhvcGmusYZuxS8C7/vrr/T7ZF7zgBSU+x9v5rrvu8p+aXWeddcYbiJLS8w3quXPnulVXXXXq4xclQcbW+aqrrvJLytxIYiYfAQ61/e53v3Pc0GKHSvNx4nPXl19+ud92ZvuK83HCBX7A2YeVV1652OMYuBoBHYNKtiIaAoaAIWAIGAKGgCHQJgRsCb5NtWF5MQQMAUPAEDAEDAFDYAwQMAI6BpVsRTQEDAFDwBAwBAwBQ6BNCIzFPaBNA7711lu71772tW7PPfe0uz9LwL7zzjv91664fmn77bd3n/jEJ/wVJ+uvv77bfffd3fLLL18Sw3g4c4nv1772Nb+v6o9//KN7ylOe4u+3fOELX+je/va32117QTO49NJL3Zlnnunv3+XS8EUWWcQts8wy7vWvf71bb731Ap/2agiUI8Ce629961uOfbKbbrqpe/Ob3zy1//PTn/60v+Lr5S9/eXlEY+CD66muvfZajxP38H7uc5/z+9W32GIL9653vWsKtzGAonIRX/Oa17izzjqrcrguBbA9oDXUJp1tgw02cBCF97znPW7NNdesIdbuRcFXMrj7kzsIr7vuOv/FGi54fuMb3+hJ6JVXXuk+/vGPd6/gPZRozpw57ulPf7rHivstf/zjH/u29etf/9p997vf9eTUvjji3IUXXuhOOukkt/feezsO1HC5Oh+DoC9C4HfZZRe31VZb9VAD3QrCBf0cfigy3KFqxrnDDjvMPfe5z3WrrLKKO/300/0Xoz72sY95MnXEEUd4svWKV7xi7KG65JJL3Je+9CV//ycHkBZYYAG30047eSUCdxfzPnv27LHHCdmNjIrNrbfe6tsZ9ieffHLsPBa/TQNaUzUfcsghnkTx3Xoumd1hhx0cs+TFFlusphRGPxpmy5tttpnX4HFbABgdffTRnjSssMIK7oILLvDkARIxzgZNAjcEfOMb3/BfzFh99dUdX66BiCLU+TrLr371K7fJJpuMM0y+7BdddJEn5uuuu+40LCAQaEIR7EZAnf/kJhdfP/vZz3Z5t04YAXX+s8CQ9aOOOsq3JzToTAZZqTn00EOntbFx/8HkD7L+4he/2GvyfvGLX7htt93Ww/Lud7/bfxDCCKjzNyiwSoMM32+//TxRByQwOuigg8a6GRkBrbH6+bIIX2D5+c9/7s455xz/9Rq+SQ0ptSt0nFtuueXc9773Pf+9YJZt+BYuS/LPf/7zHd9c5tvUptVzfhsHn3G95557pj7XxqCo5WQIKNd4mHFe63nTTTe5mICCzTXXXGOf5PxPI0GbBwF93/ve5we+RRdd1JpPBgLIIPoWWzlQJPD+4Q9/2H3wgx90xx9/fEaI8bXiE8p8phQCymQ4/KQyssuUL0+0jYUWWsihOf/hD3/o++B73/tev0rK5zi5Mm6cjRHQmmufu+KY9fHH93HpoOOu0RPELLez95O9LxAsltsZENm+cOONN7ptttnG73WU/3F+vuENb/B7irl/8A9/+INf/oOAMgiyN23fffcdZ3imys5n/9DCoGGYNWuWW3DBBaeW4LnD8TOf+cyU33F/QSvMZ4LZxsE2GDPZCLA3nc8lop1ibzqyCg3o+9//fsdkZ9PJfaFmnNd2HnjggU6rWZogs3/2m9/8pl/dMpyeRIA+x/3gbOdgQmhfQXfO9oA+2T56fvvOd77jDyGZVioNwgcffNATBcg6lzxzeTiC3g4gTccPnESsVlttNU/O0RgvvfTSRtQDqLgE++qrr/ba9Pvvv99rrjiEBHm3PhkAZa/JCNCOWKGhHclwMPDss892L3rRizyBkP04P9nXz0QvxIn9/UwG2RNqZiYCKKYg6eedd5475ZRTZnoYIxsjoGNU2VZUQ8AQMAQMAUPAEDAE2oCA3QNaQy2w34XrOZgho6H67Gc/6zWiXJfDxmwzTyLA8h+HazBg86EPfci9+tWv9vusmEmbeQIBa1NpLYGlUa57YauLmXwE6GssLf/0pz/N92QuHgGu1TvhhBP8LR0GST4C1vfysQldrO+FaEx/NwI6HY+efrGngw3Y3NXIRn+uz/n85z/vrxeCmLKHz4xzDzzwgN/3yRIWB2k++clPus0339xjxkEk9hOZeQIBa1NpLYF9VPS3d7zjHf4uUJYEzcxEAJw4AQ+xot8xwTGTjQBbOlAk7LPPPn57ULYvs7W+l9YGrO/l42QENB+bJBftgeGyYl2fw2DIHphXvvKVXrv3m9/8Jimurnvi6iC0C2uttZbXxGy00UaOO1S5v/Ftb3ub1yCzoX3cjbWpai2AScxxxx3n70rlUBLvN998s29P1WLqtm/26XE/I6dyd9ttN7/qQJ/k3lQz0xHgWj0ObHGDCR/I4MJw9oWamY6A9b3peOT9sr6XjYwR0Gxckm25Zomld05HoolZeOGF/ZcgiAB7NmQvueSSyfF12SPXMF1xxRXuoYcecnzRB8zQNmDQyGDPKeZxN9amqreAZZdd1h1zzDH+6jMOj3C7ApMb7Mw8iQBti7sIOTjJiVxuVeD+T74aZWY6Alyrx0X0e+21l58w88EMburg0nUzTyJgfe9JLIrerO/NRMeuYZqJSWUb7ojjyg5IFSRrjz328Fo+3XHJCW8zzt8X99KXvtRrFjjVDUHfcccdPW58pYWB0U4tP9FSrE311mPYygH55I9bBJjUmJmJAMvxECr+0LjbNqGZGGFj1+pl45Jla30vC5WZdtb3nsTETsE/iUVfb4888oj72c9+5viOMPsb+QoLd33xFRsz0xGAmHMY6b777vN3pS6++OL+LlDuCDXzJALWpp7EIu+NJWTaDVtezOQjgFz6/e9/b59GzIdoysWu1ZuCovDF+l4hPFOO1vemoJjxYgR0BiTVLdDk8d1plmb4BjWHkViWQCPKSXi+qGHmCQRYauegFp+VpGMyGwQrvqTBMiDYmXli+4a1qbSWcNlll/mlUrR47CHmQCArEezd4ystZp5AwPpeWksweZ6GE76s76VhZX0vG6d5PzJpsp3MNhUBvujDXsZdd93VPe95z/MaUJZQ+bwkp+D5Pi5f0xh3w2EHltn5HvxrX/tav7zFd6l32WUXf+E634p/+ctfPu4w+fJbm0prBlwr9PWvf93tvPPOjkNtXOXF4REmfxxGYv81ZHTcjfW99BZgfS8NK+t7aThZ3yvAafKKADN9IDB54GFikkBNTBLQqVgmr2CauPDCC/3vr371qxMXX3zxlNs4v1xyySUTYCMDZpOfnJyYJOreanKz/8TksrOcx/ZpbSq96ifnzxOTX0GaCsD7wQcf7H9PatknDj/88Cm3cX6xvpdW+9b30nDCl/W9NKys7+XjZKfgC8h5ihOaTf5QscvcdtttU6e52Q9qB2ueQAZNFN98l2G5lBPLLLuz7KV3uY/r09pUes3Tpq6//vqpALfeequ/ZggL+h6HSMw4rwW2vlfeEqzvlWMkH9b3hETx08a9fHxMOudjk+wyqcVze+65p//2NPvQGPTWW289f8XJVVdd5fbdd9/kuLrscaWVVvJ7PnfaaSd/OItN7GA3//zz+2V4luZ5N+M8LtamylsCX9ECp0svvdRP+q655hp/FdPcuXPdkUce6Y444ojySMbAh/W99Eo2eZ6GlfW9NJys7+XjZIeQ8rGp5MKVLwyCsyZP43LFEFo9TnsvvfTSdrAmQpLTuJNLpW7DDTf0B0ZwvuOOO/xhpMjrWP+0NpVW/WjP2T/88MMP+1Pez3jGM/xXt7h375nPfGZaJGPiy/peWkVb30vDyfpeGk74sr43EysjoDMxMRtDwBAwBAwBQ8AQMAQMgQYRsD2gDYJrURsChoAhYAgYAoaAIWAIzETA9oDOxKSSDV8RueiiiwrD8PUf7iYcd8PhLL54VGT4LOC4G2tT6S3gF7/4hV9uzwtBv6P/jbuxvpfWAqzvpeGEL+t7aVhZ38vHyQhoPjbJLueee67/rnneHZYrrLCCEdBJNPmyD5fQc/k8939mGSOgT6BibSqrdcy0u/nmm90JJ5zgDwBmffCBi+iNgFrfm9ly8m2s7+VjE7pY3wvRyH+3cS8fG9sDmo9Nsgsbsd/5znf675pvueWWyeHG0SPX5PCd7sn7Ud2iiy46jhAkldnaVBJM3tMpp5zirr32WveJT3wiPdAY+rS+l1bp1vfScMKX9b00rKzvZeNkX0LKxqWS7TzzzONe8pKXuB//+Mf2JZ8S5J7znOc4TinzHXi+GmUmGwFrU9m4ZNmi5eQUPF8/WnDBBbO8mN0kAtb30pqB9b00nPBlfS8NK+t72TiZBjQbF7M1BAwBQ8AQMAQMAUPAEGgIATsF3xCwFq0hYAgYAoaAIWAIGAKGQDYCRkCzcTFbQ8AQMAQMAUPAEDAEDIGGEDAC2hCwFq0hYAgYAoaAIWAIGAKGQDYCRkCzcTFbQ8AQMAQaR+Cee+5xH/3oR91DDz2UnNbf/va3ZL/m0RAwBAyBtiJgBLStNWP5MgQMgc4jcPfdd7uPfOQjyQT0Xe96l/vCF77QeVysgIaAIdB9BIyAdr+OrYSGgCHQEQS4bsqMIWAIGAJdQMC+hNSFWrQyGAKGwEgg8K9//cudeOKJ7sILL3TLLLOM22677ablm09BHnPMMe7yyy93LLVzt+kBBxzgVlxxRXf00Uc7Put31llnuXnnndcdfPDBPuzcuXPdcccd526//Xb3ohe9yB144IE+7mkR2w9DwBAwBFqGgGlAW1Yhlh1DwBDoLgJ8MW3OnDluww03dJDR3XbbbVpht9pqK3fqqae6zTbbzPFZ2osuusi98pWvdI8//rhbZZVV3AILLOCWXXZZTzQJiPsGG2zgl/B32mknfyH/6quv7u68885p8doPQ8AQMATahoBdRN+2GrH8GAKGQCcRuPrqq92aa67pPxuKphLz2c9+1ms477rrLvfUpz7V7bvvvv5Q0qqrrurdzz//fLfNNts49oouueSSbp111nGvec1r3KGHHurd11prLTdr1iz33e9+1//mH3YQ3GOPPXbKzl4MAUPAEGgbArYE37YasfwYAoZAJxFgqRwSKfJJIbfeemtPQHlfdNFF3be//W2Hv5NOOsnddNNN7tJLL8XJ/d///Z9/hv9Yrr/qqqvc0ksv7Q466KApJ5bnr7jiiqnf9mIIGAKGQBsRsCX4NtaK5ckQMAQ6h8CDDz7ol9InJiamyjbffE/qAB555BFPSDfeeGO/DP/MZz5zxhL9VMDJF/aIsjS/4IILOr5frr8tttjC7bjjjqFXezcEDAFDoHUIPCn9Wpc1y5AhYAgYAt1BgKXxe++912s4X/rSl/qC/b//9/+mCnjmmWc6ft9yyy1u+eWX9/bYYSCasVl88cXds571LH/g6OMf//iU8w9/+EO/nD9lYS+GgCFgCLQQAdOAtrBSLEuGgCHQPQTWXntt9+IXv9gdeeSR7k9/+pNjTygn4mWWWmop9+9//9txOT2GU+2HHHKIf0c7imGZ/oYbbnDsGcWwZ/TrX/+6O/vss31YluzZI3r//fd7d/tnCBgChkBbETAC2taasXwZAoZApxB4+tOf7s4991x/Qv35z3++W3fddf0BIxVy0003dW9729v8CXj2dc6ePdsdfvjhbuGFF3ZXXnml9/ba177WnXHGGT4sFkcccYTbeeed3etf/3q30EILud1339198IMfdJyIN2MIGAKGQJsRsFPwba4dy5shYAh0EoH77rvPE0ZIaWy4ngkNJveEZhncH3vsMcceUZlHH33Ua06XW245WdnTEDAEDIFWI2AEtNXVY5kzBAwBQ8AQMAQMAUOgewjYEnz36tRKZAgYAoaAIWAIGAKGQKsRMALa6uqxzBkChoAhYAgYAoaAIdA9BIyAdq9OrUSGgCFgCBgChoAhYAi0GgEjoK2uHsucIWAIGAKGgCFgCBgC3UPACGj36tRKZAgYAoaAIWAIGAKGQKsRMALa6uqxzBkChoAhYAgYAoaAIdA9BIyAdq9OrUSGgCFgCBgChoAhYAi0GgEjoK2uHsucIWAIGAKGgCFgCBgC3UNgvu4VyUpkCBgChoAhYAgYAuOIwI033uh+9KMfuYceesi99KUvdVtttVUrYfjnP//pTj755My87bPPPpn2w7T85je/6bbbbjv3rGc9q7ZsmAa0NigtIkPAEDAEDAFDwBAYFgLnnnuu22yzzdzcuXPdgw8+6N7//ve73XbbbVjZKUz34Ycfdm9/+9vd5Zdf7q688sppf4UBh+R46qmnur/97W+1pm6f4qwVTovMEDAEDAFDwBAwBIaBwNZbb+122GEHJw3i/fff72bNmuUJ6fOf//ypLP3ud79zSy655JQ2D23pvPPO657xjGd4PxCtpz3taf7vz3/+s/f3xz/+0a200kre/d///re7+eab3fOe9zwfThGT3r333utWXnllN998TywwP/roo564LbroovLmn3/5y18cdhDRZz7zmdPcwh9///vfHX5XXHHF0Nr961//crfccovPw1Of+lTvRl6Jk7xOTEy4FVZYITnM7bff7hZaaCG3yCKLOPLM7xAz4l544YWnyvuHP/zBPfvZz/Z/SuSBBx7waT/3uc/1cck+72ka0DxkzN4QMAQMAUPAEDAERgYBSNp3vvMdd9111/k8L7bYYu6vf/3rFJE6++yz3Ute8hJPUJdffnn3pS99yfv70Ic+NPWOxR577OG++93vejf8s/S8zjrruMsuu8z/LbXUUu6d73yne/GLX+zOP/987+/ggw/2caPVfMELXuBuuukmb3/xxRe71VZbzb9X/XfIIYf4vO+5555u7bXX9mUhjg9+8IM+7be97W3e/dprr/VRr7HGGu6Nb3yj22mnndwGG2zg3vve904lWRRm5513dm95y1s8yf3Upz7l1lprLbf77ru71Vdf3UG2MWuuuaa79dZbPZkm7le/+tXuFa94xVQaLNG/7GUvc4cffrhbddVVHb9LzSRLNmMIGAKGgCFgCBgChsBIIzCpyZzYe++9JyY1cxPLLLPMxCSJmrj++ut9mXDD/sc//rH/Panhm1hiiSUmJjWJE/vtt9/E0UcfPVX2173udROTBMr/niSbE8cdd9zE448/PvX7vPPO8++/+tWvJt785jdPTGo9JyYJ7cQjjzzi7b/yla9MTJI//z6513Pivvvu8+/hv0mN4sQkQZtYZZVVJl70ohdN/e2///7e209+8hOfv0kNqf991FFHTXzjG9+Y+N73vjcxSbQnZP+5z31uYq+99vJ+ll122YkjjjjCv0+SxYlJLezEJIEsDfPFL37Rhzn++ON9GOV3kmhOXHLJJd5tueWWm5jUHE+Qv0ni6+3AbnKP7cQdd9wxMbn1YeL000/39ldcccXEscce69+L/tkhpFKKbh4MAUPAEDAEDAFDoO0ILLDAAu7EE090k+TH/exnP3OThMhrAtFcTpJD9/SnP91tsskmvhgsT7NUzoGlMrPxxhu7pzzlKX55mSVx9pli0Ir+7//+r09z/vnn93s6sWdZ/Ze//KX77Gc/67BHE5tnTjvtNJ8vuS+44IL+lfBbbLHF1PL8oYce6u3R1m677bZT9m94wxu85nWSPHp3tLUYth5Mkj/3j3/8w/385z8vDDN79mwfhi0GaD+V30kS7ybJpXfTv0nS7dD2Ylj6/8EPfuDf0Rrz9+Uvf9m95jWvSdp7awTUQ2f/DAFDwBAwBAwBQ2BUEYBgsix85plnOojopptu6v8gYN///vfdpKbSLydPajKn9jHi9thjj3lyyVOGPaGhESlkjyR7RSF2MpMaVkec7Jd8xzveIevkJ+Gy9oBCAtmLKcOhKojtpCbSXX311bL2xJoT9eQL85znPGfKbZ555vF5LQuj8hFQ+2CnIole4nyxV5R9p29605vcNtts48455xz39a9/3W9puOaaa6LQ03/aHtDpeNgvQ8AQMAQMAUPAEBgxBNBucjDngAMO8Fo/sv+nP/3Ja/+4jglt3uRytydIuLFvkj/2LUKgtG/07rvvdr/5zW/wMsNwCGfDDTeciuOiiy5y7M98/etf73796187Dt+sv/76jkNOn/nMZzyxhcxyNVRVs/3227vJZXjH4R8MWke0pTvuuKPXOk4uk3t7NLDrrruug2zmmV7CFMU1uQ3Ak3mI76aTRJ+DV5PbFtwvfvELv3d0cguC3y8aEuis+EwDmoWK2RkChoAhYAgYAobASCFwxhlneAI0ubfTn1yHAH34wx92W265pS/H5D5KvzR82GGHOU5sf+tb3/LEdHKvqPczuRfToQ3kkE2eYSmcgz5z5szxS+fHHHOMX7LmyieW9HUCHhKGYfmb+O+8887MKNGqxmZyD6W/w/Rd73qX16xCbFlSn9zv6dPU4aOll17an0LXgak4Hv2GfFcNo7Dxk1sGTjnlFJ8vsJrcE+pvB/jABz7gyO9HP/pRv2xPXnU6P45Dv+0aJiFhT0PAEDAEDAFDwBAYeQQ4uY2GkNPqWQa3xRdffIZTnv0Mj5MWuvIodGMZnz2i4TJ46N7LO1pdNI0xUcWeLQRoZVNNL2Hy4mZLgLYkhH64Moo8FWlk5d8IqJCwpyFgCBgChoAhYAgYAobAQBDI3zQwkOQtEUPAEDAEDAFDwBAwBAyBcUPACOi41biV1xAwBAwBQ8AQMAQMgSEjYAR0yBVgyRsChoAhYAgYAoaAITBuCBgBHbcat/IaAoaAIWAIGAKGgCEwZASMgA65Aix5Q8AQMAQMAUPAEDAExg0BI6DjVuNWXkPAEDAEDAFDwBAwBIaMgBHQIVeAJW8IGAKGgCFgCBgChsC4IfD/AdnwpHg+qOKmAAAAAElFTkSuQmCC\" /><!-- --></p>\n</div>\n<div id=\"多个时间序列\" class=\"section level3\">\n<h3>6.3 多个时间序列</h3>\n<p>在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是<code>variable</code>和<code>value</code>）。</p>\n<div class=\"sourceCode\" id=\"cb44\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb44-1\"><a href=\"#cb44-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">data</span>(economics_long, <span class=\"at\">package =</span> <span class=\"st\">&quot;ggplot2&quot;</span>)</span>\n<span id=\"cb44-2\"><a href=\"#cb44-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(economics_long)</span></code></pre></div>\n<pre><code>## # A tibble: 6 × 4\n##   date       variable value  value01\n##   &lt;date&gt;     &lt;chr&gt;    &lt;dbl&gt;    &lt;dbl&gt;\n## 1 1967-07-01 pce       507. 0       \n## 2 1967-08-01 pce       510. 0.000265\n## 3 1967-09-01 pce       516. 0.000762\n## 4 1967-10-01 pce       512. 0.000471\n## 5 1967-11-01 pce       517. 0.000916\n## 6 1967-12-01 pce       525. 0.00157</code></pre>\n<p>在下面的代码中，在<code>geom_line()</code>函数中设置绘图对象为<code>value</code>，颜色匹配对象为<code>variable</code>。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表<code>variable</code>列中的每个唯一<code>value</code>。<code>scale_x_date()</code>将更改\nX 轴断点和标签，<code>scale_color_manual</code>将更改行颜色。</p>\n<div class=\"sourceCode\" id=\"cb46\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb46-1\"><a href=\"#cb46-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb46-2\"><a href=\"#cb46-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(lubridate)</span>\n<span id=\"cb46-3\"><a href=\"#cb46-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb46-4\"><a href=\"#cb46-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb46-5\"><a href=\"#cb46-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> economics_long[economics_long<span class=\"sc\">$</span>variable <span class=\"sc\">%in%</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;psavert&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span>), ]</span>\n<span id=\"cb46-6\"><a href=\"#cb46-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> df[lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(df<span class=\"sc\">$</span>date) <span class=\"sc\">%in%</span> <span class=\"fu\">c</span>(<span class=\"dv\">1967</span><span class=\"sc\">:</span><span class=\"dv\">1981</span>), ]</span>\n<span id=\"cb46-7\"><a href=\"#cb46-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb46-8\"><a href=\"#cb46-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># labels and breaks for X axis text</span></span>\n<span id=\"cb46-9\"><a href=\"#cb46-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> df<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(df<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)]</span>\n<span id=\"cb46-10\"><a href=\"#cb46-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(brks)</span>\n<span id=\"cb46-11\"><a href=\"#cb46-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb46-12\"><a href=\"#cb46-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb46-13\"><a href=\"#cb46-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb46-14\"><a href=\"#cb46-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>value, <span class=\"at\">col=</span>variable)) <span class=\"sc\">+</span> </span>\n<span id=\"cb46-15\"><a href=\"#cb46-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Time Series of Returns Percentage&quot;</span>, </span>\n<span id=\"cb46-16\"><a href=\"#cb46-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Drawn from Long Data format&quot;</span>, </span>\n<span id=\"cb46-17\"><a href=\"#cb46-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, </span>\n<span id=\"cb46-18\"><a href=\"#cb46-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>, </span>\n<span id=\"cb46-19\"><a href=\"#cb46-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">color=</span><span class=\"cn\">NULL</span>) <span class=\"sc\">+</span>  <span class=\"co\"># title and caption</span></span>\n<span id=\"cb46-20\"><a href=\"#cb46-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">labels =</span> lbls, <span class=\"at\">breaks =</span> brks) <span class=\"sc\">+</span>  <span class=\"co\"># change to monthly ticks and labels</span></span>\n<span id=\"cb46-21\"><a href=\"#cb46-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">labels =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;psavert&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span>), </span>\n<span id=\"cb46-22\"><a href=\"#cb46-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;psavert&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span>  <span class=\"co\"># line color</span></span>\n<span id=\"cb46-23\"><a href=\"#cb46-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">axis.text.x =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">angle =</span> <span class=\"dv\">90</span>, <span class=\"at\">vjust=</span><span class=\"fl\">0.5</span>, <span class=\"at\">size =</span> <span class=\"dv\">8</span>),  <span class=\"co\"># rotate x axis text</span></span>\n<span id=\"cb46-24\"><a href=\"#cb46-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"at\">panel.grid.minor =</span> <span class=\"fu\">element_blank</span>())  <span class=\"co\"># turn off minor grid</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<p>如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次<code>geom_line()</code>制。因此，默认情况下不会绘制图例，需要手动添加。</p>\n<div class=\"sourceCode\" id=\"cb47\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb47-1\"><a href=\"#cb47-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb47-2\"><a href=\"#cb47-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(lubridate)</span>\n<span id=\"cb47-3\"><a href=\"#cb47-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb47-4\"><a href=\"#cb47-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb47-5\"><a href=\"#cb47-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> economics[, <span class=\"fu\">c</span>(<span class=\"st\">&quot;date&quot;</span>, <span class=\"st\">&quot;psavert&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span>)]</span>\n<span id=\"cb47-6\"><a href=\"#cb47-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> df[lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(df<span class=\"sc\">$</span>date) <span class=\"sc\">%in%</span> <span class=\"fu\">c</span>(<span class=\"dv\">1967</span><span class=\"sc\">:</span><span class=\"dv\">1981</span>), ]</span>\n<span id=\"cb47-7\"><a href=\"#cb47-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb47-8\"><a href=\"#cb47-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># labels and breaks for X axis text</span></span>\n<span id=\"cb47-9\"><a href=\"#cb47-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> df<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(df<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)]</span>\n<span id=\"cb47-10\"><a href=\"#cb47-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(brks)</span>\n<span id=\"cb47-11\"><a href=\"#cb47-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb47-12\"><a href=\"#cb47-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb47-13\"><a href=\"#cb47-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb47-14\"><a href=\"#cb47-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>psavert, <span class=\"at\">col=</span><span class=\"st\">&quot;psavert&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb47-15\"><a href=\"#cb47-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>uempmed, <span class=\"at\">col=</span><span class=\"st\">&quot;uempmed&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb47-16\"><a href=\"#cb47-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Time Series of Returns Percentage&quot;</span>, </span>\n<span id=\"cb47-17\"><a href=\"#cb47-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Drawn From Wide Data format&quot;</span>, </span>\n<span id=\"cb47-18\"><a href=\"#cb47-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>) <span class=\"sc\">+</span>  <span class=\"co\"># title and caption</span></span>\n<span id=\"cb47-19\"><a href=\"#cb47-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">labels =</span> lbls, <span class=\"at\">breaks =</span> brks) <span class=\"sc\">+</span>  <span class=\"co\"># change to monthly ticks and labels</span></span>\n<span id=\"cb47-20\"><a href=\"#cb47-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_color_manual</span>(<span class=\"at\">name=</span><span class=\"st\">&quot;&quot;</span>, </span>\n<span id=\"cb47-21\"><a href=\"#cb47-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>                     <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;psavert&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span>  <span class=\"co\"># line color</span></span>\n<span id=\"cb47-22\"><a href=\"#cb47-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">panel.grid.minor =</span> <span class=\"fu\">element_blank</span>())  <span class=\"co\"># turn off minor grid</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"堆叠面积图\" class=\"section level3\">\n<h3>6.4 堆叠面积图</h3>\n<p>堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：</p>\n<ul>\n<li>想要描述数量或体积（而不是价格之类的变量）随时间的变化；</li>\n<li>有很多数据点。对于很少的数据点，可以考虑绘制柱状图。</li>\n<li>希望展示各个类别的贡献。</li>\n</ul>\n<div class=\"sourceCode\" id=\"cb48\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb48-1\"><a href=\"#cb48-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb48-2\"><a href=\"#cb48-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(lubridate)</span>\n<span id=\"cb48-3\"><a href=\"#cb48-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb48-4\"><a href=\"#cb48-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb48-5\"><a href=\"#cb48-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> economics[, <span class=\"fu\">c</span>(<span class=\"st\">&quot;date&quot;</span>, <span class=\"st\">&quot;psavert&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span>)]</span>\n<span id=\"cb48-6\"><a href=\"#cb48-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> df[lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(df<span class=\"sc\">$</span>date) <span class=\"sc\">%in%</span> <span class=\"fu\">c</span>(<span class=\"dv\">1967</span><span class=\"sc\">:</span><span class=\"dv\">1981</span>), ]</span>\n<span id=\"cb48-7\"><a href=\"#cb48-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb48-8\"><a href=\"#cb48-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># labels and breaks for X axis text</span></span>\n<span id=\"cb48-9\"><a href=\"#cb48-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>brks <span class=\"ot\">&lt;-</span> df<span class=\"sc\">$</span>date[<span class=\"fu\">seq</span>(<span class=\"dv\">1</span>, <span class=\"fu\">length</span>(df<span class=\"sc\">$</span>date), <span class=\"dv\">12</span>)]</span>\n<span id=\"cb48-10\"><a href=\"#cb48-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>lbls <span class=\"ot\">&lt;-</span> lubridate<span class=\"sc\">::</span><span class=\"fu\">year</span>(brks)</span>\n<span id=\"cb48-11\"><a href=\"#cb48-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb48-12\"><a href=\"#cb48-12\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb48-13\"><a href=\"#cb48-13\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>date)) <span class=\"sc\">+</span> </span>\n<span id=\"cb48-14\"><a href=\"#cb48-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_area</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>psavert<span class=\"sc\">+</span>uempmed, <span class=\"at\">fill=</span><span class=\"st\">&quot;psavert&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb48-15\"><a href=\"#cb48-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_area</span>(<span class=\"fu\">aes</span>(<span class=\"at\">y=</span>uempmed, <span class=\"at\">fill=</span><span class=\"st\">&quot;uempmed&quot;</span>)) <span class=\"sc\">+</span> </span>\n<span id=\"cb48-16\"><a href=\"#cb48-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Area Chart of Returns Percentage&quot;</span>, </span>\n<span id=\"cb48-17\"><a href=\"#cb48-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;From Wide Data format&quot;</span>, </span>\n<span id=\"cb48-18\"><a href=\"#cb48-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Economics&quot;</span>, </span>\n<span id=\"cb48-19\"><a href=\"#cb48-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;Returns %&quot;</span>) <span class=\"sc\">+</span>  <span class=\"co\"># title and caption</span></span>\n<span id=\"cb48-20\"><a href=\"#cb48-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_x_date</span>(<span class=\"at\">labels =</span> lbls, <span class=\"at\">breaks =</span> brks) <span class=\"sc\">+</span>  <span class=\"co\"># change to monthly ticks and labels</span></span>\n<span id=\"cb48-21\"><a href=\"#cb48-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_fill_manual</span>(<span class=\"at\">name=</span><span class=\"st\">&quot;&quot;</span>, </span>\n<span id=\"cb48-22\"><a href=\"#cb48-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>                    <span class=\"at\">values =</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;psavert&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#00ba38&quot;</span>, <span class=\"st\">&quot;uempmed&quot;</span><span class=\"ot\">=</span><span class=\"st\">&quot;#f8766d&quot;</span>)) <span class=\"sc\">+</span>  <span class=\"co\"># line color</span></span>\n<span id=\"cb48-23\"><a href=\"#cb48-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>(<span class=\"at\">panel.grid.minor =</span> <span class=\"fu\">element_blank</span>())  <span class=\"co\"># turn off minor grid</span></span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"日历热力图\" class=\"section level3\">\n<h3>6.5 日历热力图</h3>\n<p>当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用<code>geom_tile()</code>来实现。</p>\n<div class=\"sourceCode\" id=\"cb49\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb49-1\"><a href=\"#cb49-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb49-2\"><a href=\"#cb49-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(plyr)</span>\n<span id=\"cb49-3\"><a href=\"#cb49-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(scales)</span>\n<span id=\"cb49-4\"><a href=\"#cb49-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(zoo)</span>\n<span id=\"cb49-5\"><a href=\"#cb49-5\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb49-6\"><a href=\"#cb49-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv&quot;</span>)</span>\n<span id=\"cb49-7\"><a href=\"#cb49-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"sc\">$</span>date <span class=\"ot\">&lt;-</span> <span class=\"fu\">as.Date</span>(df<span class=\"sc\">$</span>date)  <span class=\"co\"># format date</span></span>\n<span id=\"cb49-8\"><a href=\"#cb49-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> df[df<span class=\"sc\">$</span>year <span class=\"sc\">&gt;=</span> <span class=\"dv\">2012</span>, ]  <span class=\"co\"># filter reqd years</span></span>\n<span id=\"cb49-9\"><a href=\"#cb49-9\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb49-10\"><a href=\"#cb49-10\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Create Month Week</span></span>\n<span id=\"cb49-11\"><a href=\"#cb49-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"sc\">$</span>yearmonth <span class=\"ot\">&lt;-</span> <span class=\"fu\">as.yearmon</span>(df<span class=\"sc\">$</span>date)</span>\n<span id=\"cb49-12\"><a href=\"#cb49-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>df<span class=\"sc\">$</span>yearmonthf <span class=\"ot\">&lt;-</span> <span class=\"fu\">factor</span>(df<span class=\"sc\">$</span>yearmonth)</span>\n<span id=\"cb49-13\"><a href=\"#cb49-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">ddply</span>(df,.(yearmonthf), transform, <span class=\"at\">monthweek=</span><span class=\"dv\">1</span><span class=\"sc\">+</span>week<span class=\"sc\">-</span><span class=\"fu\">min</span>(week))  <span class=\"co\"># compute week number of month</span></span>\n<span id=\"cb49-14\"><a href=\"#cb49-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> df[, <span class=\"fu\">c</span>(<span class=\"st\">&quot;year&quot;</span>, <span class=\"st\">&quot;yearmonthf&quot;</span>, <span class=\"st\">&quot;monthf&quot;</span>, <span class=\"st\">&quot;week&quot;</span>, <span class=\"st\">&quot;monthweek&quot;</span>, <span class=\"st\">&quot;weekdayf&quot;</span>, <span class=\"st\">&quot;VIX.Close&quot;</span>)]</span>\n<span id=\"cb49-15\"><a href=\"#cb49-15\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(df)</span></code></pre></div>\n<pre><code>##   year yearmonthf monthf week monthweek weekdayf VIX.Close\n## 1 2012   Jan 2012    Jan    1         1      Tue     22.97\n## 2 2012   Jan 2012    Jan    1         1      Wed     22.22\n## 3 2012   Jan 2012    Jan    1         1      Thu     21.48\n## 4 2012   Jan 2012    Jan    1         1      Fri     20.63\n## 5 2012   Jan 2012    Jan    2         2      Mon     21.07\n## 6 2012   Jan 2012    Jan    2         2      Tue     20.69</code></pre>\n<div class=\"sourceCode\" id=\"cb51\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb51-1\"><a href=\"#cb51-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df, <span class=\"fu\">aes</span>(monthweek, weekdayf, <span class=\"at\">fill =</span> VIX.Close)) <span class=\"sc\">+</span> </span>\n<span id=\"cb51-2\"><a href=\"#cb51-2\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_tile</span>(<span class=\"at\">colour =</span> <span class=\"st\">&quot;white&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb51-3\"><a href=\"#cb51-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">facet_grid</span>(year<span class=\"sc\">~</span>monthf) <span class=\"sc\">+</span> </span>\n<span id=\"cb51-4\"><a href=\"#cb51-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_fill_gradient</span>(<span class=\"at\">low=</span><span class=\"st\">&quot;red&quot;</span>, <span class=\"at\">high=</span><span class=\"st\">&quot;green&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb51-5\"><a href=\"#cb51-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">x=</span><span class=\"st\">&quot;Week of Month&quot;</span>,</span>\n<span id=\"cb51-6\"><a href=\"#cb51-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">y=</span><span class=\"st\">&quot;&quot;</span>,</span>\n<span id=\"cb51-7\"><a href=\"#cb51-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">title =</span> <span class=\"st\">&quot;Time-Series Calendar Heatmap&quot;</span>, </span>\n<span id=\"cb51-8\"><a href=\"#cb51-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;Yahoo Closing Price&quot;</span>, </span>\n<span id=\"cb51-9\"><a href=\"#cb51-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">fill=</span><span class=\"st\">&quot;Close&quot;</span>)</span></code></pre></div>\n<p><img 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\" /><!-- --></p>\n</div>\n<div id=\"坡度图-1\" class=\"section level3\">\n<h3>6.6 坡度图</h3>\n<p>坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。</p>\n<div class=\"sourceCode\" id=\"cb52\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb52-1\"><a href=\"#cb52-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(dplyr)</span>\n<span id=\"cb52-2\"><a href=\"#cb52-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb52-3\"><a href=\"#cb52-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>source_df <span class=\"ot\">&lt;-</span> <span class=\"fu\">read.csv</span>(<span class=\"st\">&quot;https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv&quot;</span>)</span>\n<span id=\"cb52-4\"><a href=\"#cb52-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-5\"><a href=\"#cb52-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Define functions. Source: https://github.com/jkeirstead/r-slopegraph</span></span>\n<span id=\"cb52-6\"><a href=\"#cb52-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>tufte_sort <span class=\"ot\">&lt;-</span> <span class=\"cf\">function</span>(df, <span class=\"at\">x=</span><span class=\"st\">&quot;year&quot;</span>, <span class=\"at\">y=</span><span class=\"st\">&quot;value&quot;</span>, <span class=\"at\">group=</span><span class=\"st\">&quot;group&quot;</span>, <span class=\"at\">method=</span><span class=\"st\">&quot;tufte&quot;</span>, <span class=\"at\">min.space=</span><span class=\"fl\">0.05</span>) {</span>\n<span id=\"cb52-7\"><a href=\"#cb52-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## First rename the columns for consistency</span></span>\n<span id=\"cb52-8\"><a href=\"#cb52-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ids <span class=\"ot\">&lt;-</span> <span class=\"fu\">match</span>(<span class=\"fu\">c</span>(x, y, group), <span class=\"fu\">names</span>(df))</span>\n<span id=\"cb52-9\"><a href=\"#cb52-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>    df <span class=\"ot\">&lt;-</span> df[,ids]</span>\n<span id=\"cb52-10\"><a href=\"#cb52-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">names</span>(df) <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">&quot;x&quot;</span>, <span class=\"st\">&quot;y&quot;</span>, <span class=\"st\">&quot;group&quot;</span>)</span>\n<span id=\"cb52-11\"><a href=\"#cb52-11\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-12\"><a href=\"#cb52-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## Expand grid to ensure every combination has a defined value</span></span>\n<span id=\"cb52-13\"><a href=\"#cb52-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">expand.grid</span>(<span class=\"at\">x=</span><span class=\"fu\">unique</span>(df<span class=\"sc\">$</span>x), <span class=\"at\">group=</span><span class=\"fu\">unique</span>(df<span class=\"sc\">$</span>group))</span>\n<span id=\"cb52-14\"><a href=\"#cb52-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">merge</span>(df, tmp, <span class=\"at\">all.y=</span><span class=\"cn\">TRUE</span>)</span>\n<span id=\"cb52-15\"><a href=\"#cb52-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>    df <span class=\"ot\">&lt;-</span> <span class=\"fu\">mutate</span>(tmp, <span class=\"at\">y=</span><span class=\"fu\">ifelse</span>(<span class=\"fu\">is.na</span>(y), <span class=\"dv\">0</span>, y))</span>\n<span id=\"cb52-16\"><a href=\"#cb52-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>  </span>\n<span id=\"cb52-17\"><a href=\"#cb52-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## Cast into a matrix shape and arrange by first column</span></span>\n<span id=\"cb52-18\"><a href=\"#cb52-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">require</span>(reshape2)</span>\n<span id=\"cb52-19\"><a href=\"#cb52-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">dcast</span>(df, group <span class=\"sc\">~</span> x, <span class=\"at\">value.var=</span><span class=\"st\">&quot;y&quot;</span>)</span>\n<span id=\"cb52-20\"><a href=\"#cb52-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ord <span class=\"ot\">&lt;-</span> <span class=\"fu\">order</span>(tmp[,<span class=\"dv\">2</span>])</span>\n<span id=\"cb52-21\"><a href=\"#cb52-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> tmp[ord,]</span>\n<span id=\"cb52-22\"><a href=\"#cb52-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>    </span>\n<span id=\"cb52-23\"><a href=\"#cb52-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>    min.space <span class=\"ot\">&lt;-</span> min.space<span class=\"sc\">*</span><span class=\"fu\">diff</span>(<span class=\"fu\">range</span>(tmp[,<span class=\"sc\">-</span><span class=\"dv\">1</span>]))</span>\n<span id=\"cb52-24\"><a href=\"#cb52-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>    yshift <span class=\"ot\">&lt;-</span> <span class=\"fu\">numeric</span>(<span class=\"fu\">nrow</span>(tmp))</span>\n<span id=\"cb52-25\"><a href=\"#cb52-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## Start at &quot;bottom&quot; row</span></span>\n<span id=\"cb52-26\"><a href=\"#cb52-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## Repeat for rest of the rows until you hit the top</span></span>\n<span id=\"cb52-27\"><a href=\"#cb52-27\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"cf\">for</span> (i <span class=\"cf\">in</span> <span class=\"dv\">2</span><span class=\"sc\">:</span><span class=\"fu\">nrow</span>(tmp)) {</span>\n<span id=\"cb52-28\"><a href=\"#cb52-28\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"do\">## Shift subsequent row up by equal space so gap between</span></span>\n<span id=\"cb52-29\"><a href=\"#cb52-29\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"do\">## two entries is &gt;= minimum</span></span>\n<span id=\"cb52-30\"><a href=\"#cb52-30\" aria-hidden=\"true\" tabindex=\"-1\"></a>        mat <span class=\"ot\">&lt;-</span> <span class=\"fu\">as.matrix</span>(tmp[(i<span class=\"dv\">-1</span>)<span class=\"sc\">:</span>i, <span class=\"sc\">-</span><span class=\"dv\">1</span>])</span>\n<span id=\"cb52-31\"><a href=\"#cb52-31\" aria-hidden=\"true\" tabindex=\"-1\"></a>        d.min <span class=\"ot\">&lt;-</span> <span class=\"fu\">min</span>(<span class=\"fu\">diff</span>(mat))</span>\n<span id=\"cb52-32\"><a href=\"#cb52-32\" aria-hidden=\"true\" tabindex=\"-1\"></a>        yshift[i] <span class=\"ot\">&lt;-</span> <span class=\"fu\">ifelse</span>(d.min <span class=\"sc\">&lt;</span> min.space, min.space <span class=\"sc\">-</span> d.min, <span class=\"dv\">0</span>)</span>\n<span id=\"cb52-33\"><a href=\"#cb52-33\" aria-hidden=\"true\" tabindex=\"-1\"></a>    }</span>\n<span id=\"cb52-34\"><a href=\"#cb52-34\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-35\"><a href=\"#cb52-35\" aria-hidden=\"true\" tabindex=\"-1\"></a>    </span>\n<span id=\"cb52-36\"><a href=\"#cb52-36\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">cbind</span>(tmp, <span class=\"at\">yshift=</span><span class=\"fu\">cumsum</span>(yshift))</span>\n<span id=\"cb52-37\"><a href=\"#cb52-37\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-38\"><a href=\"#cb52-38\" aria-hidden=\"true\" tabindex=\"-1\"></a>    scale <span class=\"ot\">&lt;-</span> <span class=\"dv\">1</span></span>\n<span id=\"cb52-39\"><a href=\"#cb52-39\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">melt</span>(tmp, <span class=\"at\">id=</span><span class=\"fu\">c</span>(<span class=\"st\">&quot;group&quot;</span>, <span class=\"st\">&quot;yshift&quot;</span>), <span class=\"at\">variable.name=</span><span class=\"st\">&quot;x&quot;</span>, <span class=\"at\">value.name=</span><span class=\"st\">&quot;y&quot;</span>)</span>\n<span id=\"cb52-40\"><a href=\"#cb52-40\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"do\">## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y</span></span>\n<span id=\"cb52-41\"><a href=\"#cb52-41\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-42\"><a href=\"#cb52-42\" aria-hidden=\"true\" tabindex=\"-1\"></a>    tmp <span class=\"ot\">&lt;-</span> <span class=\"fu\">transform</span>(tmp, <span class=\"at\">ypos=</span>y <span class=\"sc\">+</span> scale<span class=\"sc\">*</span>yshift)</span>\n<span id=\"cb52-43\"><a href=\"#cb52-43\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">return</span>(tmp)</span>\n<span id=\"cb52-44\"><a href=\"#cb52-44\" aria-hidden=\"true\" tabindex=\"-1\"></a>   </span>\n<span id=\"cb52-45\"><a href=\"#cb52-45\" aria-hidden=\"true\" tabindex=\"-1\"></a>}</span>\n<span id=\"cb52-46\"><a href=\"#cb52-46\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-47\"><a href=\"#cb52-47\" aria-hidden=\"true\" tabindex=\"-1\"></a>plot_slopegraph <span class=\"ot\">&lt;-</span> <span class=\"cf\">function</span>(df) {</span>\n<span id=\"cb52-48\"><a href=\"#cb52-48\" aria-hidden=\"true\" tabindex=\"-1\"></a>    ylabs <span class=\"ot\">&lt;-</span> <span class=\"fu\">subset</span>(df, x<span class=\"sc\">==</span><span class=\"fu\">head</span>(x,<span class=\"dv\">1</span>))<span class=\"sc\">$</span>group</span>\n<span id=\"cb52-49\"><a href=\"#cb52-49\" aria-hidden=\"true\" tabindex=\"-1\"></a>    yvals <span class=\"ot\">&lt;-</span> <span class=\"fu\">subset</span>(df, x<span class=\"sc\">==</span><span class=\"fu\">head</span>(x,<span class=\"dv\">1</span>))<span class=\"sc\">$</span>ypos</span>\n<span id=\"cb52-50\"><a href=\"#cb52-50\" aria-hidden=\"true\" tabindex=\"-1\"></a>    fontSize <span class=\"ot\">&lt;-</span> <span class=\"dv\">3</span></span>\n<span id=\"cb52-51\"><a href=\"#cb52-51\" aria-hidden=\"true\" tabindex=\"-1\"></a>    gg <span class=\"ot\">&lt;-</span> <span class=\"fu\">ggplot</span>(df,<span class=\"fu\">aes</span>(<span class=\"at\">x=</span>x,<span class=\"at\">y=</span>ypos)) <span class=\"sc\">+</span></span>\n<span id=\"cb52-52\"><a href=\"#cb52-52\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">geom_line</span>(<span class=\"fu\">aes</span>(<span class=\"at\">group=</span>group),<span class=\"at\">colour=</span><span class=\"st\">&quot;grey80&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb52-53\"><a href=\"#cb52-53\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">geom_point</span>(<span class=\"at\">colour=</span><span class=\"st\">&quot;white&quot;</span>,<span class=\"at\">size=</span><span class=\"dv\">8</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb52-54\"><a href=\"#cb52-54\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">geom_text</span>(<span class=\"fu\">aes</span>(<span class=\"at\">label=</span>y), <span class=\"at\">size=</span>fontSize, <span class=\"at\">family=</span><span class=\"st\">&quot;American Typewriter&quot;</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb52-55\"><a href=\"#cb52-55\" aria-hidden=\"true\" tabindex=\"-1\"></a>        <span class=\"fu\">scale_y_continuous</span>(<span class=\"at\">name=</span><span class=\"st\">&quot;&quot;</span>, <span class=\"at\">breaks=</span>yvals, <span class=\"at\">labels=</span>ylabs)</span>\n<span id=\"cb52-56\"><a href=\"#cb52-56\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">return</span>(gg)</span>\n<span id=\"cb52-57\"><a href=\"#cb52-57\" aria-hidden=\"true\" tabindex=\"-1\"></a>}    </span>\n<span id=\"cb52-58\"><a href=\"#cb52-58\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-59\"><a href=\"#cb52-59\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## Prepare data    </span></span>\n<span id=\"cb52-60\"><a href=\"#cb52-60\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">tufte_sort</span>(source_df, </span>\n<span id=\"cb52-61\"><a href=\"#cb52-61\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">x=</span><span class=\"st\">&quot;year&quot;</span>, </span>\n<span id=\"cb52-62\"><a href=\"#cb52-62\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">y=</span><span class=\"st\">&quot;value&quot;</span>, </span>\n<span id=\"cb52-63\"><a href=\"#cb52-63\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">group=</span><span class=\"st\">&quot;group&quot;</span>, </span>\n<span id=\"cb52-64\"><a href=\"#cb52-64\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">method=</span><span class=\"st\">&quot;tufte&quot;</span>, </span>\n<span id=\"cb52-65\"><a href=\"#cb52-65\" aria-hidden=\"true\" tabindex=\"-1\"></a>                 <span class=\"at\">min.space=</span><span class=\"fl\">0.05</span>)</span>\n<span id=\"cb52-66\"><a href=\"#cb52-66\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-67\"><a href=\"#cb52-67\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> <span class=\"fu\">transform</span>(df, </span>\n<span id=\"cb52-68\"><a href=\"#cb52-68\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">x=</span><span class=\"fu\">factor</span>(x, <span class=\"at\">levels=</span><span class=\"fu\">c</span>(<span class=\"dv\">5</span>,<span class=\"dv\">10</span>,<span class=\"dv\">15</span>,<span class=\"dv\">20</span>), </span>\n<span id=\"cb52-69\"><a href=\"#cb52-69\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">labels=</span><span class=\"fu\">c</span>(<span class=\"st\">&quot;5 years&quot;</span>,<span class=\"st\">&quot;10 years&quot;</span>,<span class=\"st\">&quot;15 years&quot;</span>,<span class=\"st\">&quot;20 years&quot;</span>)), </span>\n<span id=\"cb52-70\"><a href=\"#cb52-70\" aria-hidden=\"true\" tabindex=\"-1\"></a>                <span class=\"at\">y=</span><span class=\"fu\">round</span>(y))</span>\n<span id=\"cb52-71\"><a href=\"#cb52-71\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb52-72\"><a href=\"#cb52-72\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"do\">## Plot</span></span>\n<span id=\"cb52-73\"><a href=\"#cb52-73\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">plot_slopegraph</span>(df) <span class=\"sc\">+</span> <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Estimates of % survival rates&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb52-74\"><a href=\"#cb52-74\" aria-hidden=\"true\" tabindex=\"-1\"></a>                      <span class=\"fu\">theme</span>(<span class=\"at\">axis.title=</span><span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb52-75\"><a href=\"#cb52-75\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">axis.ticks =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb52-76\"><a href=\"#cb52-76\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">plot.title =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">hjust=</span><span class=\"fl\">0.5</span>,</span>\n<span id=\"cb52-77\"><a href=\"#cb52-77\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                      <span class=\"at\">family =</span> <span class=\"st\">&quot;American Typewriter&quot;</span>,</span>\n<span id=\"cb52-78\"><a href=\"#cb52-78\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                      <span class=\"at\">face=</span><span class=\"st\">&quot;bold&quot;</span>),</span>\n<span id=\"cb52-79\"><a href=\"#cb52-79\" aria-hidden=\"true\" tabindex=\"-1\"></a>                            <span class=\"at\">axis.text =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">family =</span> <span class=\"st\">&quot;American Typewriter&quot;</span>,</span>\n<span id=\"cb52-80\"><a href=\"#cb52-80\" aria-hidden=\"true\" tabindex=\"-1\"></a>                                                     <span class=\"at\">face=</span><span class=\"st\">&quot;bold&quot;</span>))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"季节图\" class=\"section level3\">\n<h3>6.7 季节图</h3>\n<p>如果您正在处理<code>ts</code>或<code>xts</code>类型的时间序列对象，您可以通过使用<code>forecast::ggseasonplot</code>绘制的季节图来查看季节波动。下面是一个使用<code>AirPassengers</code>和<code>nottem</code>数据集绘制的例子。</p>\n<div class=\"sourceCode\" id=\"cb53\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb53-1\"><a href=\"#cb53-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb53-2\"><a href=\"#cb53-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(forecast)</span>\n<span id=\"cb53-3\"><a href=\"#cb53-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb53-4\"><a href=\"#cb53-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb53-5\"><a href=\"#cb53-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Subset data</span></span>\n<span id=\"cb53-6\"><a href=\"#cb53-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>nottem_small <span class=\"ot\">&lt;-</span> <span class=\"fu\">window</span>(nottem, <span class=\"at\">start=</span><span class=\"fu\">c</span>(<span class=\"dv\">1920</span>, <span class=\"dv\">1</span>), <span class=\"at\">end=</span><span class=\"fu\">c</span>(<span class=\"dv\">1925</span>, <span class=\"dv\">12</span>))  <span class=\"co\"># subset a smaller timewindow</span></span>\n<span id=\"cb53-7\"><a href=\"#cb53-7\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb53-8\"><a href=\"#cb53-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot</span></span>\n<span id=\"cb53-9\"><a href=\"#cb53-9\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggseasonplot</span>(AirPassengers) <span class=\"sc\">+</span> <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Seasonal plot: International Airline Passengers&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n<div class=\"sourceCode\" id=\"cb54\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb54-1\"><a href=\"#cb54-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggseasonplot</span>(nottem_small) <span class=\"sc\">+</span> <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Seasonal plot: Air temperatures at Nottingham Castle&quot;</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n</div>\n<div id=\"群体\" class=\"section level2\">\n<h2>7 群体</h2>\n<div id=\"谱系图\" class=\"section level3\">\n<h3>7.1 谱系图</h3>\n<div class=\"sourceCode\" id=\"cb55\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb55-1\"><a href=\"#cb55-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb55-2\"><a href=\"#cb55-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggdendro)</span>\n<span id=\"cb55-3\"><a href=\"#cb55-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_bw</span>())</span>\n<span id=\"cb55-4\"><a href=\"#cb55-4\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb55-5\"><a href=\"#cb55-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>hc <span class=\"ot\">&lt;-</span> <span class=\"fu\">hclust</span>(<span class=\"fu\">dist</span>(USArrests), <span class=\"st\">&quot;ave&quot;</span>)  <span class=\"co\"># hierarchical clustering</span></span>\n<span id=\"cb55-6\"><a href=\"#cb55-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb55-7\"><a href=\"#cb55-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># plot</span></span>\n<span id=\"cb55-8\"><a href=\"#cb55-8\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggdendrogram</span>(hc, <span class=\"at\">rotate =</span> <span class=\"cn\">TRUE</span>, <span class=\"at\">size =</span> <span class=\"dv\">2</span>)</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" /><!-- --></p>\n</div>\n<div id=\"聚类图\" class=\"section level3\">\n<h3>7.2 聚类图</h3>\n<p>可以使用<code>geom_surround()</code>来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用\nPC1 和 PC2 作为 X 和 Y\n轴绘制散点图。<code>geom_encircle()</code>可用于框选所需的组。</p>\n<div class=\"sourceCode\" id=\"cb56\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb56-1\"><a href=\"#cb56-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># devtools::install_github(&quot;hrbrmstr/ggalt&quot;)</span></span>\n<span id=\"cb56-2\"><a href=\"#cb56-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb56-3\"><a href=\"#cb56-3\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggalt)</span>\n<span id=\"cb56-4\"><a href=\"#cb56-4\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggfortify)</span>\n<span id=\"cb56-5\"><a href=\"#cb56-5\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">theme_set</span>(<span class=\"fu\">theme_classic</span>())</span>\n<span id=\"cb56-6\"><a href=\"#cb56-6\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb56-7\"><a href=\"#cb56-7\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Compute data with principal components ------------------</span></span>\n<span id=\"cb56-8\"><a href=\"#cb56-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>df <span class=\"ot\">&lt;-</span> iris[<span class=\"fu\">c</span>(<span class=\"dv\">1</span>, <span class=\"dv\">2</span>, <span class=\"dv\">3</span>, <span class=\"dv\">4</span>)]</span>\n<span id=\"cb56-9\"><a href=\"#cb56-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>pca_mod <span class=\"ot\">&lt;-</span> <span class=\"fu\">prcomp</span>(df)  <span class=\"co\"># compute principal components</span></span>\n<span id=\"cb56-10\"><a href=\"#cb56-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb56-11\"><a href=\"#cb56-11\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Data frame of principal components ----------------------</span></span>\n<span id=\"cb56-12\"><a href=\"#cb56-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>df_pc <span class=\"ot\">&lt;-</span> <span class=\"fu\">data.frame</span>(pca_mod<span class=\"sc\">$</span>x, <span class=\"at\">Species=</span>iris<span class=\"sc\">$</span>Species)  <span class=\"co\"># dataframe of principal components</span></span>\n<span id=\"cb56-13\"><a href=\"#cb56-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>df_pc_vir <span class=\"ot\">&lt;-</span> df_pc[df_pc<span class=\"sc\">$</span>Species <span class=\"sc\">==</span> <span class=\"st\">&quot;virginica&quot;</span>, ]  <span class=\"co\"># df for &#39;virginica&#39;</span></span>\n<span id=\"cb56-14\"><a href=\"#cb56-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>df_pc_set <span class=\"ot\">&lt;-</span> df_pc[df_pc<span class=\"sc\">$</span>Species <span class=\"sc\">==</span> <span class=\"st\">&quot;setosa&quot;</span>, ]  <span class=\"co\"># df for &#39;setosa&#39;</span></span>\n<span id=\"cb56-15\"><a href=\"#cb56-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>df_pc_ver <span class=\"ot\">&lt;-</span> df_pc[df_pc<span class=\"sc\">$</span>Species <span class=\"sc\">==</span> <span class=\"st\">&quot;versicolor&quot;</span>, ]  <span class=\"co\"># df for &#39;versicolor&#39;</span></span>\n<span id=\"cb56-16\"><a href=\"#cb56-16\" aria-hidden=\"true\" tabindex=\"-1\"></a> </span>\n<span id=\"cb56-17\"><a href=\"#cb56-17\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"co\"># Plot ----------------------------------------------------</span></span>\n<span id=\"cb56-18\"><a href=\"#cb56-18\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">ggplot</span>(df_pc, <span class=\"fu\">aes</span>(PC1, PC2, <span class=\"at\">col=</span>Species)) <span class=\"sc\">+</span> </span>\n<span id=\"cb56-19\"><a href=\"#cb56-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_point</span>(<span class=\"fu\">aes</span>(<span class=\"at\">shape=</span>Species), <span class=\"at\">size=</span><span class=\"dv\">2</span>) <span class=\"sc\">+</span>   <span class=\"co\"># draw points</span></span>\n<span id=\"cb56-20\"><a href=\"#cb56-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">title=</span><span class=\"st\">&quot;Iris Clustering&quot;</span>, </span>\n<span id=\"cb56-21\"><a href=\"#cb56-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">subtitle=</span><span class=\"st\">&quot;With principal components PC1 and PC2 as X and Y axis&quot;</span>,</span>\n<span id=\"cb56-22\"><a href=\"#cb56-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>       <span class=\"at\">caption=</span><span class=\"st\">&quot;Source: Iris&quot;</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb56-23\"><a href=\"#cb56-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">coord_cartesian</span>(<span class=\"at\">xlim =</span> <span class=\"fl\">1.2</span> <span class=\"sc\">*</span> <span class=\"fu\">c</span>(<span class=\"fu\">min</span>(df_pc<span class=\"sc\">$</span>PC1), <span class=\"fu\">max</span>(df_pc<span class=\"sc\">$</span>PC1)), </span>\n<span id=\"cb56-24\"><a href=\"#cb56-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>                  <span class=\"at\">ylim =</span> <span class=\"fl\">1.2</span> <span class=\"sc\">*</span> <span class=\"fu\">c</span>(<span class=\"fu\">min</span>(df_pc<span class=\"sc\">$</span>PC2), <span class=\"fu\">max</span>(df_pc<span class=\"sc\">$</span>PC2))) <span class=\"sc\">+</span>   <span class=\"co\"># change axis limits</span></span>\n<span id=\"cb56-25\"><a href=\"#cb56-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_encircle</span>(<span class=\"at\">data =</span> df_pc_vir, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>PC1, <span class=\"at\">y=</span>PC2)) <span class=\"sc\">+</span>   <span class=\"co\"># draw circles</span></span>\n<span id=\"cb56-26\"><a href=\"#cb56-26\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_encircle</span>(<span class=\"at\">data =</span> df_pc_set, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>PC1, <span class=\"at\">y=</span>PC2)) <span class=\"sc\">+</span> </span>\n<span id=\"cb56-27\"><a href=\"#cb56-27\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">geom_encircle</span>(<span class=\"at\">data =</span> df_pc_ver, <span class=\"fu\">aes</span>(<span class=\"at\">x=</span>PC1, <span class=\"at\">y=</span>PC2))</span></code></pre></div>\n<p><img src=\"data:image/png;base64,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\" 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  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/50pictures_ggplot.rmd",
    "content": "---\ntitle: \"ggplot绘图手册\"\nauthor: \"赵子茜\"\ndate: \"`r Sys.Date()`\"\noutput: \n  prettydoc::html_pretty:\n    toc: true\n    toc_depth: 4\n    theme: cayman\n    highlight: github\neditor_options: \n  chunk_output_type: console\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE,warning = F,message = F)\n```\n\n## 加载数据集\n\n```{r}\nlibrary(ggplot2)\nlibrary(plotrix)\noptions(scipen=999)  # 关掉像1e+48这样的科学符号\ndata(\"midwest\", package = \"ggplot2\") #加载数据集\n```\n\n## 全局主题设置\n\n```{r}\n# 颜色设置（灰色系列）\ncbp1 <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# 颜色设置（黑色系列）\ncbp2 <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\",\n          \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n\nggplot <- function(...) ggplot2::ggplot(...) + \n  scale_color_manual(values = cbp1) +\n  scale_fill_manual(values = cbp1) + # 注意: 使用连续色阶时需要重写\n  theme_bw()\n\n```\n\n\n\n\n## 1. 相关关系\n### 1.1 两个变量\n展示两个变量之间的相关关系，最常使用的是**散点图**。在 ggplot 中，使用`geom_point()`绘制。此外，默认情况下，`geom_smooth`会绘制一条平滑线（基于 losses ），可以通过设置`method='lm'`来调整以绘制最佳拟合的线。\n\n\n```{r}\n# Scatterplot\ngg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) + \n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) + \n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot\", \n       caption = \"Source: midwest\")\n\nplot(gg)\n\n```\n\n\n### 1.2 环绕式散点图\n使用 ggalt 包中的`geom_encircle()`函数可实现在散点图中圈选特定区域：\n```{r message=FALSE, warning=FALSE}\n# install.packages(\"ggalt\")\nlibrary(ggalt)\noptions(scipen = 999)\nlibrary(ggplot2)\nlibrary(ggalt)\n\n## 设定筛选条件\nmidwest_select <- midwest[midwest$poptotal > 350000 & \n                            midwest$poptotal <= 500000 & \n                            midwest$area > 0.01 & \n                            midwest$area < 0.1, ]\n\n# Plot\nggplot(midwest, aes(x=area, y=poptotal)) + \n  geom_point(aes(col=state, size=popdensity)) +   # draw points\n  geom_smooth(method=\"loess\", se=F) + \n  xlim(c(0, 0.1)) + \n  ylim(c(0, 500000)) +   # draw smoothing line\n  geom_encircle(aes(x=area, y=poptotal), \n                data=midwest_select, \n                color=\"red\", \n                size=2, \n                expand=0.08) +   # encircle\n  labs(subtitle=\"Area Vs Population\", \n       y=\"Population\", \n       x=\"Area\", \n       title=\"Scatterplot + Encircle\", \n       caption=\"Source: midwest\")\n```\n\n\n\n\n### 1.3 抖动图\n\n抖动图可以解决数据点重叠的问题。通过`geom_jitter`函数中的`width`参数设置抖动范围，重叠点在其原始位置周围随机抖动。\n```{r}\ng <- ggplot(mpg, aes(cty, hwy))\ng + geom_jitter(width = .5, size=1) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Jittered Points\")\n\n```\n\n### 1.4 计数图\n\n克服数据点重叠问题的第二个选择是使用计数图 `geom_count()`。重叠点越多，圆就越大。\n\n```{r}\ng + geom_count(col=\"tomato3\", show.legend=F) +\n  labs(subtitle=\"mpg: city vs highway mileage\", \n       y=\"hwy\", \n       x=\"cty\", \n       title=\"Counts Plot\")\n```\n\n### 1.5 气泡图\n气泡图适合 4 维数据，其中两个是数值型（分别是 X 和 Y），另一个是分类变量（用`color`表示）和另一个数值变量（用`size`表示）。\n```{r}\ndata(mpg, package=\"ggplot2\")\n\n\nmpg_select <- mpg[mpg$manufacturer %in% c(\"audi\", \"ford\", \"honda\", \"hyundai\"), ]\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\ng <- ggplot(mpg_select, aes(displ, cty)) + \n  labs(subtitle=\"mpg: Displacement vs City Mileage\",\n       title=\"Bubble chart\")\n\ng + geom_jitter(aes(col=manufacturer, size=hwy)) + \n  geom_smooth(aes(col=manufacturer), method=\"lm\", se=F)\n```\n\n\n<!-- ### 1.6 动态气泡图 -->\n\n<!-- ```{r} -->\n<!-- # install.packages(\"gganimate\") -->\n<!-- # install.packages(\"gapminder\") -->\n<!-- library(gganimate) -->\n<!-- library(gapminder) -->\n\n<!-- g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) + -->\n<!--   geom_point() + -->\n<!--   geom_smooth(aes(group = year), -->\n<!--               method = \"lm\", -->\n<!--               show.legend = FALSE) + -->\n<!--   facet_wrap(~continent, scales = \"free\") + -->\n<!--   scale_x_log10()  # convert to log scale -->\n\n<!-- gganimate(g, interval=0.2) -->\n<!-- ``` -->\n\n\n### 1.6 边际直方图/箱线图\n\n如果您想在同一个图中显示变量的关系和分布，可以使用边际直方图。更改`ggMarginal()`函数中的`type`参数可以将边际直方图换成箱线图或者密度图。\n\n```{r}\nlibrary(ggExtra)\ndata(mpg, package=\"ggplot2\")\n\n\n# Scatterplot\ntheme_set(theme_bw())  # pre-set the bw theme.\nmpg_select <- mpg[mpg$hwy >= 35 & mpg$cty > 27, ]\ng <- ggplot(mpg, aes(cty, hwy)) + \n  geom_count() + \n  geom_smooth(method=\"lm\", se=F)\n\nggMarginal(g, type = \"histogram\", fill=\"transparent\")\n\n```\n```{r}\nggMarginal(g, type = \"boxplot\", fill=\"transparent\")\n\n```\n\n```{r}\nggMarginal(g, type = \"density\", fill=\"transparent\")\n```\n\n### 1.7 相关系数图\n\n相关系数图可以查看同一组数据中多个连续变量的相关性。\n```{r}\nlibrary(ggcorrplot)\n\n# Correlation matrix\ndata(mtcars)\ncorr <- round(cor(mtcars), 1)\n\n# Plot\nggcorrplot(corr, hc.order = TRUE, \n           type = \"lower\", \n           lab = TRUE, \n           lab_size = 3, \n           method=\"circle\", \n           colors = c(\"tomato2\", \"white\", \"springgreen3\"), \n           title=\"Correlogram of mtcars\", \n           ggtheme=theme_bw)\n```\n\n## 2 偏差\n### 2.1 发散条形图\n发散条形图是一种可以同时处理负值和正值的条形图。这可以通过 `geom_bar()` 来实现。但是 `geom_bar()` 的用法可能会让人很困惑。这是因为，它既可以用来制作柱状图，也可以用来制作直方图。默认情况下，`geom_bar()` 中 `stat` 参数的默认值为 `count`。这意味着，当您只提供一个连续的 x 变量（而不提供 y 变量）时，它会尝试从数据中生成一个直方图。如果要制作条形图，需要做两件事：\n\n- 设置 `stat=identity`;\n- 在 `aes()` 中同时输入 x 和 y，其中 x 是字符或因子型变量，y 是数值型。\n\n为了确保得到的是发散条形图，数据需要满足：分类变量的两个类别在连续变量的某个阈值处改变其值。在下面的例子中，`mtcars` 数据集的 mpg 通过计算 z 分数被规范化。那些 mpg 在 0 以上的车辆被标记为绿色，低于 0 的车辆被标记为红色。\n\n```{r}\ndata(\"mtcars\")  # load data\nmtcars$`car name` <- rownames(mtcars)  # create new column for car names\nmtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2)  # compute normalized mpg\nmtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\")  # above / below avg flag\nmtcars <- mtcars[order(mtcars$mpg_z), ]  # sort\nmtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) \n\n# Diverging Barcharts\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_bar(stat='identity', aes(fill=mpg_type), width=.5)  +\n  scale_fill_manual(name=\"Mileage\", \n                    labels = c(\"Above Average\", \"Below Average\"), \n                    values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  labs(subtitle=\"Normalised mileage from 'mtcars'\", \n       title= \"Diverging Bars\") + \n  coord_flip()\n```\n\n\n### 2.2 带标记的发散型棒棒糖图\n这个图形是发散条形图的一个变体。\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', fill=\"black\", size=6)  +\n  geom_segment(aes(y = 0, \n                   x = `car name`, \n                   yend = mpg_z, \n                   xend = `car name`), \n               color = \"black\") +\n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Lollipop Chart\", \n       subtitle=\"Normalized mileage from 'mtcars': Lollipop\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.3 发散性点图\n\n```{r}\nggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) + \n  geom_point(stat='identity', aes(col=mpg_type), size=6)  +\n  scale_color_manual(name=\"Mileage\", \n                     labels = c(\"Above Average\", \"Below Average\"), \n                     values = c(\"above\"=\"#00ba38\", \"below\"=\"#f8766d\")) + \n  geom_text(color=\"white\", size=2) +\n  labs(title=\"Diverging Dot Plot\", \n       subtitle=\"Normalized mileage from 'mtcars': Dotplot\") + \n  ylim(-2.5, 2.5) +\n  coord_flip()\n```\n\n\n### 2.4 面积图\n面积图通常用于可视化特定指标（如股票回报率）与基线的对比情况。\n```{r}\nlibrary(quantmod)\ndata(\"economics\", package = \"ggplot2\")\n\n# 计算利润\neconomics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n\n# 为轴刻度创建断点和标签\nbrks <- economics$date[seq(1, length(economics$date), 12)]\nlbls <- lubridate::year(economics$date[seq(1, length(economics$date), 12)])\n\n# Plot\nggplot(economics[1:100, ], aes(date, returns_perc)) + \n  geom_area() + \n  scale_x_date(breaks=brks, labels=lbls) + \n  theme(axis.text.x = element_text(angle=90)) + \n  labs(title=\"Area Chart\", \n       subtitle = \"Perc Returns for Personal Savings\", \n       y=\"% Returns for Personal savings\", \n       caption=\"Source: economics\")\n\n```\n\n## 3 排序\n### 3.1 有序条形图\n有序条形图是按 Y 轴变量排序的条形图，X 轴变量必须转换为因子型。\n```{r}\n# 准备数据：按厂商分组计算平均城市里程。\ncty_mpg <- aggregate(mpg$cty, by=list(mpg$manufacturer), FUN=mean)  # aggregate\ncolnames(cty_mpg) <- c(\"make\", \"mileage\")  # change column names\ncty_mpg <- cty_mpg[order(cty_mpg$mileage), ]  # sort\ncty_mpg$make <- factor(cty_mpg$make, levels = cty_mpg$make)  # to retain the order in plot.\nhead(cty_mpg, 4)\n\n```\n\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_bar(stat=\"identity\", width=.5, fill=\"tomato3\") + \n  labs(title=\"Ordered Bar Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.2 棒棒糖图\n棒棒糖图传达的信息与柱状图相同。通过将粗条转变为细线，减少了杂乱，使图形看起来更美观。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(size=3) + \n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=0, \n                   yend=mileage)) + \n  labs(title=\"Lollipop Chart\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n### 3.3 点图\n点图非常类似于棒棒糖图，但没有线条，并且各标签都在水平位置上。它更强调项目的顺序与实际值有关。\n```{r}\nggplot(cty_mpg, aes(x=make, y=mileage)) + \n  geom_point(col=\"tomato2\", size=3) +   # Draw points\n  geom_segment(aes(x=make, \n                   xend=make, \n                   y=min(mileage), \n                   yend=max(mileage)), \n               linetype=\"dashed\", \n               size=0.1) +   # Draw dashed lines\n  labs(title=\"Dot Plot\", \n       subtitle=\"Make Vs Avg. Mileage\", \n       caption=\"source: mpg\") +  \n  coord_flip()\n```\n\n### 3.4 坡度图\n坡度图是比较两点之间差异的绝佳方法。目前，还没有内置函数来构建这个图形。下面的代码可以作为一个示例框架，告诉您如何绘制这个图形。\n```{r}\nlibrary(scales)\n\n# 数据准备\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\")\ncolnames(df) <- c(\"continent\", \"1952\", \"1957\")\nleft_label <- paste(df$continent, round(df$'1952'),sep=\", \")\nright_label <- paste(df$continent, round(df$'1957'),sep=\", \")\ndf$class <- ifelse((df$'1957' - df$'1952') < 0, \"red\", \"green\")\nhead(df)\n```\n\n```{r}\nlibrary(ggplot2)\np <- ggplot(df) + geom_segment(aes(x=1, xend=2, y=`1952`, yend=`1957`, col=class), size=.75, show.legend=F) + \n                  geom_vline(xintercept=1, linetype=\"dashed\", size=.1) + \n                  geom_vline(xintercept=2, linetype=\"dashed\", size=.1) +\n                  scale_color_manual(labels = c(\"Up\", \"Down\"), \n                                     values = c(\"green\"=\"#00ba38\", \"red\"=\"#f8766d\")) +  # color of lines\n                  labs(x=\"\", y=\"Mean GdpPerCap\") +  # Axis labels\n                  xlim(.5, 2.5) + ylim(0,(1.1*(max(df$`1952`, df$`1957`))))  # X and Y axis limits\n\n# Add texts\np <- p + geom_text(label=left_label, y=df$`1952`, x=rep(1, NROW(df)), hjust=1.1, size=3.5)\np <- p + geom_text(label=right_label, y=df$`1957`, x=rep(2, NROW(df)), hjust=-0.1, size=3.5)\np <- p + geom_text(label=\"Time 1\", x=1, y=1.1*(max(df$`1952`, df$`1957`)), hjust=1.2, size=5)  # title\np <- p + geom_text(label=\"Time 2\", x=2, y=1.1*(max(df$`1952`, df$`1957`)), hjust=-0.1, size=5)  # title\n\n\n#设置主题\np + theme(panel.background = element_blank(),\n           panel.grid = element_blank(),\n           axis.ticks = element_blank(),\n           axis.text.x = element_blank(),\n           panel.border = element_blank(),\n           plot.margin = unit(c(1,2,1,2), \"cm\"))\n\n\n```\n\n### 3.5 哑铃图\n哑铃图可以：1)比较两个时间点之间的相对位置（比如增长和下降）；2) 比较两类之间的距离。为了得到哑铃的正确顺序，Y 变量应该是一个因子，因子变量的水平应该与它在图中出现的顺序相同。\n```{r}\nhealth <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/health.csv\")\nhealth$Area <- factor(health$Area, levels=as.character(health$Area))  # for right ordering of the dumbells\n\n# health$Area <- factor(health$Area)\ngg <- ggplot(health, aes(x=pct_2013, xend=pct_2014, y=Area, group=Area)) + \n        geom_dumbbell(color=\"#a3c4dc\", \n                      size=0.75, \n                      point.colour.l=\"#0e668b\") + \n        scale_x_continuous(label=percent) + \n        labs(x=NULL, \n             y=NULL, \n             title=\"Dumbbell Chart\", \n             subtitle=\"Pct Change: 2013 vs 2014\", \n             caption=\"Source: https://github.com/hrbrmstr/ggalt\") +\n        theme(plot.title = element_text(hjust=0.5, face=\"bold\"),\n              plot.background=element_rect(fill=\"#f7f7f7\"),\n              panel.background=element_rect(fill=\"#f7f7f7\"),\n              panel.grid.minor=element_blank(),\n              panel.grid.major.y=element_blank(),\n              panel.grid.major.x=element_line(),\n              axis.ticks=element_blank(),\n              legend.position=\"top\",\n              panel.border=element_blank())\n\nplot(gg)\n\n```\n\n## 4 分布\n### 4.1 直方图\n#### 4.4.1 连续变量的直方图\n连续变量的直方图可以使用`geom_bar()`或`geom_histogram()`来完成。当使用`geom_histogram()`时，可以使用`bins`参数来控制分箱的数量。也可以使用`binwidth`设置每个分箱覆盖的范围。`binwidth`的值与建立直方图的连续变量在同一个尺度上。\n```{r}\n# 连续（数值）变量的直方图\ng <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = \"Spectral\")\n\ng + geom_histogram(aes(fill=class), \n                   binwidth = .1, \n                   col=\"black\", \n                   size=.1) +  # change binwidth\n  labs(title=\"Histogram with Auto Binning\", \n       subtitle=\"Engine Displacement across Vehicle Classes\")\n\n \n```\n\n\n```{r}\n\ng + geom_histogram(aes(fill=class), \n                   bins=5, \n                   col=\"black\", \n                   size=.1) +   # change number of bins\n  labs(title=\"Histogram with Fixed Bins\", \n       subtitle=\"Engine Displacement across Vehicle Classes\") \n\n \n```\n\n#### 4.1.2分类变量的直方图\n分类变量的直方图实际上是根据每个类别的频率绘制的条形图。\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Histogram on a Categorical variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Histogram on Categorical Variable\", \n       subtitle=\"Manufacturer across Vehicle Classes\")\n\n \n```\n\n### 4.2 密度图\n\n```{r}\ng <- ggplot(mpg, aes(cty))\ng + geom_density(aes(fill=factor(cyl)), alpha=0.8) + \n    labs(title=\"Density plot\", \n         subtitle=\"City Mileage Grouped by Number of cylinders\",\n         caption=\"Source: mpg\",\n         x=\"City Mileage\",\n         fill=\"# Cylinders\")\n \n```\n\n### 4.3 箱线图\n箱形图是研究数据分布的一个有用工具。它还可以显示多个组内的分布，以及中值、范围和异常值。箱子内的黑线表示中位数。箱顶是 75% 分位数，箱底是 25% 分位数。线的端点（又称晶须）距离为1.5*IQR，其中 IQR （四分位差）是 25% 到 75% 分位数之间距离。须外的点通常被认为是极值点。设置`varwidth=T`将调整盒子的宽度，使其与观察的数量成比例。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(varwidth=T, fill=\"plum\") + \n    labs(title=\"Box plot\", \n         subtitle=\"City Mileage grouped by Class of vehicle\",\n         caption=\"Source: mpg\",\n         x=\"Class of Vehicle\",\n         y=\"City Mileage\")\n \n```\n\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_boxplot(aes(fill=factor(cyl))) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot\", \n       subtitle=\"City Mileage grouped by Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.4 点图+箱线图\n在箱线图的基础上，添加点图可以提供更清晰的信息。这些点交错排列，每个点代表一次观测。\n```{r}\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_boxplot() + \n  geom_dotplot(binaxis='y', \n               stackdir='center', \n               dotsize = .5, \n               fill=\"red\") +\n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n  labs(title=\"Box plot + Dot plot\", \n       subtitle=\"City Mileage vs Class: Each dot represents 1 row in source data\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.5 塔夫特箱线图\n这是一个简化版的箱线图。\n```{r}\n\nlibrary(ggthemes)\nlibrary(ggplot2)\ntheme_set(theme_tufte())  # from ggthemes\n\n# plot\ng <- ggplot(mpg, aes(manufacturer, cty))\ng + geom_tufteboxplot() + \n      theme(axis.text.x = element_text(angle=65, vjust=0.6)) + \n      labs(title=\"Tufte Styled Boxplot\", \n           subtitle=\"City Mileage grouped by Class of vehicle\",\n           caption=\"Source: mpg\",\n           x=\"Class of Vehicle\",\n           y=\"City Mileage\")\n\n\n```\n\n### 4.6 小提琴图\n小提琴图类似于箱线图，但显示了组内的密度。\n```{r}\ng <- ggplot(mpg, aes(class, cty))\ng + geom_violin() + \n  labs(title=\"Violin plot\", \n       subtitle=\"City Mileage vs Class of vehicle\",\n       caption=\"Source: mpg\",\n       x=\"Class of Vehicle\",\n       y=\"City Mileage\")\n \n```\n\n### 4.7 金字塔图\n金字塔图提供了一种独特的方式来可视化每个类别包含的样本比例。下面的金字塔是一个很好的例子，说明了在一个营销活动中每个阶段的用户留存率。\n```{r}library(ggplot2)\nlibrary(ggthemes)\noptions(scipen = 999)  # turns of scientific notations like 1e+40\n\n# Read data\nemail_campaign_funnel <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n\n# X Axis Breaks and Labels \nbrks <- seq(-15000000, 15000000, 5000000)\nlbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), \"m\")\n\n# Plot\nggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column\n                              geom_bar(stat = \"identity\", width = .6) +   # draw the bars\n                              scale_y_continuous(breaks = brks,   # Breaks\n                                                 labels = lbls) + # Labels\n                              coord_flip() +  # Flip axes\n                              labs(title=\"Email Campaign Funnel\") +\n                              theme_tufte() +  # Tufte theme from ggfortify\n                              theme(plot.title = element_text(hjust = .5), \n                                    axis.ticks = element_blank()) +   # Centre plot title\n                              scale_fill_brewer(palette = \"Dark2\")  # Color palette\n```\n\n## 5 组成\n### 5.1 华夫饼图\n华夫图可以显示总体的组成成分。\n```{r}\nvar <- mpg$class  # the categorical data \n\n## Prep data (nothing to change here)\nnrows <- 10\ndf <- expand.grid(y = 1:nrows, x = 1:nrows)\ncateg_table <- round(table(var) * ((nrows*nrows)/(length(var))))\n\n\ndf$category <- factor(rep(names(categ_table), categ_table))  \n# NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100.\n\n## Plot\nggplot(df, aes(x = x, y = y, fill = category)) + \n        geom_tile(color = \"black\", size = 0.5) +\n        scale_x_continuous(expand = c(0, 0)) +\n        scale_y_continuous(expand = c(0, 0), trans = 'reverse') +\n        scale_fill_brewer(palette = \"Set3\") +\n        labs(title=\"Waffle Chart\", subtitle=\"'Class' of vehicles\",\n             caption=\"Source: mpg\") + \n        theme(panel.border = element_rect(size = 2),\n              plot.title = element_text(size = rel(1.2)),\n              axis.text = element_blank(),\n              axis.title = element_blank(),\n              axis.ticks = element_blank(),\n              legend.title = element_blank(),\n              legend.position = \"right\")\n\n```\n\n### 5.2 饼图\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Source: Frequency table\ndf <- as.data.frame(table(mpg$class))\ncolnames(df) <- c(\"class\", \"freq\")\npie <- ggplot(df, aes(x = \"\", y=freq, fill = factor(class))) + \n  geom_bar(width = 1, stat = \"identity\") +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n\npie + coord_polar(theta = \"y\", start=0)\n\n# Source: Categorical variable.\n# mpg$class\npie <- ggplot(mpg, aes(x = \"\", fill = factor(class))) + \n  geom_bar(width = 1) +\n  theme(axis.line = element_blank(), \n        plot.title = element_text(hjust=0.5)) + \n  labs(fill=\"class\", \n       x=NULL, \n       y=NULL, \n       title=\"Pie Chart of class\", \n       caption=\"Source: mpg\")\n  \npie + coord_polar(theta = \"y\", start=0)\n```\n\n\n\n### 5.3 条形图\n```{r}\n #prep frequency table\nfreqtable <- table(mpg$manufacturer)\ndf <- as.data.frame.table(freqtable)\n\n```\n\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# Plot\ng <- ggplot(df, aes(Var1, Freq))\ng + geom_bar(stat=\"identity\", width = 0.5, fill=\"tomato2\") + \n      labs(title=\"Bar Chart\", \n           subtitle=\"Manufacturer of vehicles\", \n           caption=\"Source: Frequency of Manufacturers from 'mpg' dataset\") +\n      theme(axis.text.x = element_text(angle=65, vjust=0.6))\n```\n\n```{r}\n# From on a categorical column variable\ng <- ggplot(mpg, aes(manufacturer))\ng + geom_bar(aes(fill=class), width = 0.5) + \n  theme(axis.text.x = element_text(angle=65, vjust=0.6)) +\n  labs(title=\"Categorywise Bar Chart\", \n       subtitle=\"Manufacturer of vehicles\", \n       caption=\"Source: Manufacturers from 'mpg' dataset\")\n```\n\n\n\n## 6 变化趋势\n### 6.1 时间序列图：基于时间序列对象（ts）\n`ggfortify`包可以对时间序列直接绘图。\n```{r}\n## From Timeseries object (ts)\nlibrary(ggplot2)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Plot \nautoplot(AirPassengers) + \n  labs(title=\"AirPassengers\") + \n  theme(plot.title = element_text(hjust=0.5))\n```\n\n### 6.2 时间序列图：基于数据框\n```{r}\nlibrary(ggplot2)\ntheme_set(theme_classic())\n\n# 使用默认的时间跨度\nggplot(economics, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Time Series Chart\", \n       subtitle=\"Returns Percentage from 'Economics' Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\")\n\n```\n\n如果想设置特定的时间间隔，则需要使用`scale_x_date()`函数。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_m <- economics[1:24, ]\n\n# 设定时间跨度为一个月\nlbls <- paste0(month.abb[month(economics_m$date)], \" \", lubridate::year(economics_m$date))\nbrks <- economics_m$date\n\n# plot\nggplot(economics_m, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Monthly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n设置时间跨度为 1 年：\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\neconomics_y <- economics[1:90, ]\n\n# labels and breaks for X axis text\nbrks <- economics_y$date[seq(1, length(economics_y$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(economics_y, aes(x=date)) + \n  geom_line(aes(y=returns_perc)) + \n  labs(title=\"Yearly Time Series\", \n       subtitle=\"Returns Percentage from Economics Dataset\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, \n               breaks = brks) +  # change to monthly ticks and labels\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n### 6.3 多个时间序列\n在本例中，基于长数据格式进行可视化。这意味着，所有列的列名和各自的值被存放在两个变量中（分别是`variable`和`value`）。\n\n```{r}\ndata(economics_long, package = \"ggplot2\")\nhead(economics_long)\n```\n\n在下面的代码中，在`geom_line()`函数中设置绘图对象为`value`，颜色匹配对象为`variable`。这样，只要调用一次geom_line，就会绘制多条彩色线，每条线代表`variable`列中的每个唯一`value`。`scale_x_date()`将更改 X 轴断点和标签，`scale_color_manual`将更改行颜色。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics_long[economics_long$variable %in% c(\"psavert\", \"uempmed\"), ]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=value, col=variable)) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn from Long Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\", \n       color=NULL) +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(labels = c(\"psavert\", \"uempmed\"), \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(axis.text.x = element_text(angle = 90, vjust=0.5, size = 8),  # rotate x axis text\n        panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n如果从一个宽格式创建一个时间序列，则必须通过对每条线调用一次`geom_line()`制。因此，默认情况下不会绘制图例，需要手动添加。\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_line(aes(y=psavert, col=\"psavert\")) + \n  geom_line(aes(y=uempmed, col=\"uempmed\")) + \n  labs(title=\"Time Series of Returns Percentage\", \n       subtitle=\"Drawn From Wide Data format\", \n       caption=\"Source: Economics\", y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_color_manual(name=\"\", \n                     values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n\n### 6.4 堆叠面积图\n堆叠面积图与折线图类似，只是图下方的区域全部着色。应用场景有：\n\n- 想要描述数量或体积（而不是价格之类的变量）随时间的变化；\n- 有很多数据点。对于很少的数据点，可以考虑绘制柱状图。\n- 希望展示各个类别的贡献。\n\n```{r}\nlibrary(ggplot2)\nlibrary(lubridate)\ntheme_set(theme_bw())\n\ndf <- economics[, c(\"date\", \"psavert\", \"uempmed\")]\ndf <- df[lubridate::year(df$date) %in% c(1967:1981), ]\n\n# labels and breaks for X axis text\nbrks <- df$date[seq(1, length(df$date), 12)]\nlbls <- lubridate::year(brks)\n\n# plot\nggplot(df, aes(x=date)) + \n  geom_area(aes(y=psavert+uempmed, fill=\"psavert\")) + \n  geom_area(aes(y=uempmed, fill=\"uempmed\")) + \n  labs(title=\"Area Chart of Returns Percentage\", \n       subtitle=\"From Wide Data format\", \n       caption=\"Source: Economics\", \n       y=\"Returns %\") +  # title and caption\n  scale_x_date(labels = lbls, breaks = brks) +  # change to monthly ticks and labels\n  scale_fill_manual(name=\"\", \n                    values = c(\"psavert\"=\"#00ba38\", \"uempmed\"=\"#f8766d\")) +  # line color\n  theme(panel.grid.minor = element_blank())  # turn off minor grid\n```\n\n\n### 6.5 日历热力图\n\n当您想要在实际的日历上看到像股票价格这类指标的变化，特别是高点和低点时，日历热力图是一个很好的工具。它强调随着时间的推移视觉上的变化，而不是实际数值的变化。这可以通过使用`geom_tile()`来实现。\n```{r}\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(scales)\nlibrary(zoo)\n\ndf <- read.csv(\"https://raw.githubusercontent.com/selva86/datasets/master/yahoo.csv\")\ndf$date <- as.Date(df$date)  # format date\ndf <- df[df$year >= 2012, ]  # filter reqd years\n\n# Create Month Week\ndf$yearmonth <- as.yearmon(df$date)\ndf$yearmonthf <- factor(df$yearmonth)\ndf <- ddply(df,.(yearmonthf), transform, monthweek=1+week-min(week))  # compute week number of month\ndf <- df[, c(\"year\", \"yearmonthf\", \"monthf\", \"week\", \"monthweek\", \"weekdayf\", \"VIX.Close\")]\nhead(df)\n```\n\n```{r}\nggplot(df, aes(monthweek, weekdayf, fill = VIX.Close)) + \n  geom_tile(colour = \"white\") + \n  facet_grid(year~monthf) + \n  scale_fill_gradient(low=\"red\", high=\"green\") +\n  labs(x=\"Week of Month\",\n       y=\"\",\n       title = \"Time-Series Calendar Heatmap\", \n       subtitle=\"Yahoo Closing Price\", \n       fill=\"Close\")\n```\n\n### 6.6 坡度图\n坡度图可以可视化数值和类别排名之间的变化。这更适用于时间点很少的时间序列。\n```{r}\nlibrary(dplyr)\ntheme_set(theme_classic())\nsource_df <- read.csv(\"https://raw.githubusercontent.com/jkeirstead/r-slopegraph/master/cancer_survival_rates.csv\")\n\n# Define functions. Source: https://github.com/jkeirstead/r-slopegraph\ntufte_sort <- function(df, x=\"year\", y=\"value\", group=\"group\", method=\"tufte\", min.space=0.05) {\n    ## First rename the columns for consistency\n    ids <- match(c(x, y, group), names(df))\n    df <- df[,ids]\n    names(df) <- c(\"x\", \"y\", \"group\")\n\n    ## Expand grid to ensure every combination has a defined value\n    tmp <- expand.grid(x=unique(df$x), group=unique(df$group))\n    tmp <- merge(df, tmp, all.y=TRUE)\n    df <- mutate(tmp, y=ifelse(is.na(y), 0, y))\n  \n    ## Cast into a matrix shape and arrange by first column\n    require(reshape2)\n    tmp <- dcast(df, group ~ x, value.var=\"y\")\n    ord <- order(tmp[,2])\n    tmp <- tmp[ord,]\n    \n    min.space <- min.space*diff(range(tmp[,-1]))\n    yshift <- numeric(nrow(tmp))\n    ## Start at \"bottom\" row\n    ## Repeat for rest of the rows until you hit the top\n    for (i in 2:nrow(tmp)) {\n        ## Shift subsequent row up by equal space so gap between\n        ## two entries is >= minimum\n        mat <- as.matrix(tmp[(i-1):i, -1])\n        d.min <- min(diff(mat))\n        yshift[i] <- ifelse(d.min < min.space, min.space - d.min, 0)\n    }\n\n    \n    tmp <- cbind(tmp, yshift=cumsum(yshift))\n\n    scale <- 1\n    tmp <- melt(tmp, id=c(\"group\", \"yshift\"), variable.name=\"x\", value.name=\"y\")\n    ## Store these gaps in a separate variable so that they can be scaled ypos = a*yshift + y\n\n    tmp <- transform(tmp, ypos=y + scale*yshift)\n    return(tmp)\n   \n}\n\nplot_slopegraph <- function(df) {\n    ylabs <- subset(df, x==head(x,1))$group\n    yvals <- subset(df, x==head(x,1))$ypos\n    fontSize <- 3\n    gg <- ggplot(df,aes(x=x,y=ypos)) +\n        geom_line(aes(group=group),colour=\"grey80\") +\n        geom_point(colour=\"white\",size=8) +\n        geom_text(aes(label=y), size=fontSize, family=\"American Typewriter\") +\n        scale_y_continuous(name=\"\", breaks=yvals, labels=ylabs)\n    return(gg)\n}    \n\n## Prepare data    \ndf <- tufte_sort(source_df, \n                 x=\"year\", \n                 y=\"value\", \n                 group=\"group\", \n                 method=\"tufte\", \n                 min.space=0.05)\n\ndf <- transform(df, \n                x=factor(x, levels=c(5,10,15,20), \n                            labels=c(\"5 years\",\"10 years\",\"15 years\",\"20 years\")), \n                y=round(y))\n\n## Plot\nplot_slopegraph(df) + labs(title=\"Estimates of % survival rates\") + \n                      theme(axis.title=element_blank(),\n                            axis.ticks = element_blank(),\n                            plot.title = element_text(hjust=0.5,\n                                                      family = \"American Typewriter\",\n                                                      face=\"bold\"),\n                            axis.text = element_text(family = \"American Typewriter\",\n                                                     face=\"bold\"))\n```\n\n### 6.7 季节图\n如果您正在处理`ts`或`xts`类型的时间序列对象，您可以通过使用`forecast::ggseasonplot`绘制的季节图来查看季节波动。下面是一个使用`AirPassengers`和`nottem`数据集绘制的例子。\n```{r}\nlibrary(ggplot2)\nlibrary(forecast)\ntheme_set(theme_classic())\n\n# Subset data\nnottem_small <- window(nottem, start=c(1920, 1), end=c(1925, 12))  # subset a smaller timewindow\n\n# Plot\nggseasonplot(AirPassengers) + labs(title=\"Seasonal plot: International Airline Passengers\")\n\n```\n\n```{r}\nggseasonplot(nottem_small) + labs(title=\"Seasonal plot: Air temperatures at Nottingham Castle\")\n```\n\n## 7 群体\n### 7.1 谱系图\n```{r}\n\nlibrary(ggplot2)\nlibrary(ggdendro)\ntheme_set(theme_bw())\n\nhc <- hclust(dist(USArrests), \"ave\")  # hierarchical clustering\n\n# plot\nggdendrogram(hc, rotate = TRUE, size = 2)\n```\n\n### 7.2 聚类图\n可以使用`geom_surround()`来显示不同的簇或组。如果数据集有多个特征，还可以计算主成分，并使用 PC1 和 PC2 作为 X 和 Y 轴绘制散点图。`geom_encircle()`可用于框选所需的组。\n```{r}\n# devtools::install_github(\"hrbrmstr/ggalt\")\nlibrary(ggplot2)\nlibrary(ggalt)\nlibrary(ggfortify)\ntheme_set(theme_classic())\n\n# Compute data with principal components ------------------\ndf <- iris[c(1, 2, 3, 4)]\npca_mod <- prcomp(df)  # compute principal components\n\n# Data frame of principal components ----------------------\ndf_pc <- data.frame(pca_mod$x, Species=iris$Species)  # dataframe of principal components\ndf_pc_vir <- df_pc[df_pc$Species == \"virginica\", ]  # df for 'virginica'\ndf_pc_set <- df_pc[df_pc$Species == \"setosa\", ]  # df for 'setosa'\ndf_pc_ver <- df_pc[df_pc$Species == \"versicolor\", ]  # df for 'versicolor'\n \n# Plot ----------------------------------------------------\nggplot(df_pc, aes(PC1, PC2, col=Species)) + \n  geom_point(aes(shape=Species), size=2) +   # draw points\n  labs(title=\"Iris Clustering\", \n       subtitle=\"With principal components PC1 and PC2 as X and Y axis\",\n       caption=\"Source: Iris\") + \n  coord_cartesian(xlim = 1.2 * c(min(df_pc$PC1), max(df_pc$PC1)), \n                  ylim = 1.2 * c(min(df_pc$PC2), max(df_pc$PC2))) +   # change axis limits\n  geom_encircle(data = df_pc_vir, aes(x=PC1, y=PC2)) +   # draw circles\n  geom_encircle(data = df_pc_set, aes(x=PC1, y=PC2)) + \n  geom_encircle(data = df_pc_ver, aes(x=PC1, y=PC2))\n```\n\n"
  },
  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/midwest.csv",
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BUREN\",\"MI\",0.037,70060,1893.51351,63189,4690,646,217,1318,90.192692,6.69426206,0.9220668,0.30973451,1.88124465,43758,71.8405777,17.8047443,4.48146625,69093,98.6197545,15.1332262,21.0408367,12.4853114,13.4497364,1,\"AAU\"\r\n\"275\",1277,\"WASHTENAW\",\"MI\",0.041,282937,6900.90244,236390,31720,1076,11724,2027,83.5486345,11.2109763,0.38029667,4.14367863,0.7164139,167214,87.209803,48.0785102,20.7913213,261261,92.3389306,12.1629329,10.7978164,13.4554559,7.17477744,1,\"HAU\"\r\n\"276\",1278,\"WAYNE\",\"MI\",0.035,2111687,60333.9143,1212007,849109,8048,21704,20819,57.3952011,40.2099838,0.38111709,1.02780384,0.98589422,1324635,69.9515716,19.4104036,4.98575079,2084529,98.7139193,20.0653001,30.56427,17.0662258,13.2473501,1,\"AHU\"\r\n\"277\",1279,\"WEXFORD\",\"MI\",0.034,26360,775.294118,26040,34,178,87,21,98.7860395,0.12898331,0.67526555,0.33004552,0.07966616,16597,74.6158944,18.2322106,3.59703561,26038,98.7784522,14.6401413,20.1308237,12.4565469,12.323275,0,\"AAR\"\r\n\"278\",2009,\"ADAMS\",\"OH\",0.035,25371,724.885714,25212,47,67,30,15,99.3733002,0.18525088,0.26408104,0.11824524,0.05912262,15569,58.3852527,8.74173036,2.15813475,25028,98.6480627,28.5280486,35.0246846,25.7522586,26.1800599,0,\"LHR\"\r\n\"279\",2010,\"ALLEN\",\"OH\",0.024,109755,4573.125,96177,12313,202,572,491,87.6288096,11.2186233,0.18404628,0.52116077,0.44736003,68893,76.1499717,17.3660604,3.47930849,104543,95.2512414,12.6665583,17.8579862,10.5679214,10.7444235,1,\"AAU\"\r\n\"280\",2011,\"ASHLAND\",\"OH\",0.025,47507,1900.28,46686,460,49,271,41,98.2718336,0.96827836,0.10314269,0.57044225,0.08630307,29403,76.0024487,17.2669455,4.15943951,45486,95.7458901,11.3441498,17.2289909,9.15943446,8.67428431,0,\"AAR\"\r\n\"281\",2012,\"ASHTABULA\",\"OH\",0.041,99821,2434.65854,95465,3138,196,350,672,95.6361888,3.14362709,0.19635147,0.35062762,0.67320504,63850,72.3790133,13.2435395,2.88332028,97541,97.7159115,16.117325,23.8186247,13.6699531,11.9077683,1,\"AAU\"\r\n\"282\",2013,\"ATHENS\",\"OH\",0.03,59549,1984.96667,56163,1678,167,1374,167,94.3139263,2.81784749,0.28044132,2.30734353,0.28044132,30179,74.6114848,29.1494085,12.0447994,51002,85.6471141,28.6733854,29.2581157,31.7442813,14.3249554,0,\"AHR\"\r\n\"283\",2014,\"AUGLAIZE\",\"OH\",0.024,44585,1857.70833,44225,66,50,177,67,99.1925535,0.14803185,0.11214534,0.3969945,0.15027476,27676,76.4561353,16.1764706,3.24468854,43911,98.4882808,6.26949967,8.14294535,5.28379951,6.08875129,1,\"ALU\"\r\n\"284\",2015,\"BELMONT\",\"OH\",0.031,71074,2292.70968,69520,1308,81,129,36,97.8135464,1.84033543,0.11396573,0.18150097,0.05065143,48645,72.3424812,14.1576729,3.22746428,69952,98.4213636,17.4190874,25.849973,15.7629051,12.5753241,1,\"AAU\"\r\n\"285\",2016,\"BROWN\",\"OH\",0.028,34966,1248.78571,34487,406,28,30,15,98.6300978,1.16112795,0.08007779,0.08579763,0.04289882,21769,64.9088153,11.4336901,3.1742386,34439,98.4928216,14.1554633,18.6900797,11.1457233,16.3148479,1,\"LAU\"\r\n\"286\",2017,\"BUTLER\",\"OH\",0.028,291479,10409.9643,274892,13134,379,2659,415,94.3093671,4.50598499,0.13002652,0.91224411,0.14237732,176989,75.9736481,24.2252343,6.75917712,279692,95.9561409,10.6499292,13.1730731,10.0032732,8.52227905,1,\"AAU\"\r\n\"287\",2018,\"CARROLL\",\"OH\",0.024,26521,1105.04167,26254,135,65,29,38,98.9932506,0.50903058,0.2450888,0.10934731,0.14328268,17124,71.5253445,11.323289,3.07171222,26075,98.3183138,11.746884,14.6183522,10.9407318,9.75813178,1,\"AAU\"\r\n\"288\",2019,\"CHAMPAIGN\",\"OH\",0.026,36019,1385.34615,34698,992,68,113,148,96.3324912,2.754102,0.18878925,0.31372331,0.41089425,23023,75.3898276,13.9121748,2.8971029,35404,98.2925678,8.82668625,12.3796568,6.82405567,9.93365922,0,\"AAR\"\r\n\"289\",2020,\"CLARK\",\"OH\",0.024,147548,6147.83333,133242,13031,294,653,328,90.3041722,8.83170223,0.19925719,0.44256784,0.22230054,93950,73.360298,17.784992,4.60883449,143046,96.9487895,13.4166632,20.4814416,11.095735,10.4346131,1,\"AAU\"\r\n\"290\",2021,\"CLERMONT\",\"OH\",0.026,150187,5776.42308,148084,1291,218,453,141,98.5997457,0.85959504,0.14515238,0.30162398,0.09388296,91613,72.7833386,19.6893454,4.5528473,148417,98.8214692,8.69374802,11.9078721,7.0066905,9.0945467,1,\"AAU\"\r\n\"291\",2022,\"CLINTON\",\"OH\",0.024,35415,1475.625,34471,716,59,138,31,97.3344628,2.0217422,0.16659608,0.3896654,0.08753353,21969,74.3320133,15.7631208,3.77349902,34521,97.4756459,12.2505142,14.7371682,10.6142298,13.4121622,0,\"AAR\"\r\n\"292\",2023,\"COLUMBIANA\",\"OH\",0.031,108276,3492.77419,106369,1409,174,219,105,98.2387602,1.30130407,0.16070043,0.20226089,0.0969744,70249,71.8003103,13.0905778,2.8797563,106943,98.7688869,15.8916432,23.2200959,13.6442561,12.0377504,1,\"AAU\"\r\n\"293\",2024,\"COSHOCTON\",\"OH\",0.034,35427,1041.97059,34819,415,68,112,13,98.2837948,1.17142293,0.191944,0.31614305,0.03669518,22878,71.3305359,11.6793426,2.42154034,34833,98.3233127,13.188643,17.751112,11.1867496,12.1505376,0,\"AAR\"\r\n\"294\",2025,\"CRAWFORD\",\"OH\",0.023,47870,2081.30435,47361,253,67,116,73,98.9367036,0.52851473,0.1399624,0.24232296,0.15249634,30958,73.8193682,14.258027,2.9717682,47189,98.5773971,11.5916845,15.418153,10.1218333,10.4140383,1,\"AAU\"\r\n\"295\",2026,\"CUYAHOGA\",\"OH\",0.026,1412140,54313.0769,1025756,350185,2533,18085,15581,72.638407,24.7981787,0.17937315,1.28068039,1.10336086,943924,74.0130561,25.0853882,7.35673635,1388547,98.3292733,13.7661167,21.5809764,11.6755057,10.213391,1,\"AAU\"\r\n\"296\",2027,\"DARKE\",\"OH\",0.036,53619,1489.41667,53067,184,96,114,158,98.9705142,0.34316194,0.17904101,0.2126112,0.29467166,34048,73.4580592,13.475094,3.21898496,52557,98.0193588,8.98643378,12.1345723,7.14033102,9.59320612,0,\"AAR\"\r\n\"297\",2028,\"DEFIANCE\",\"OH\",0.023,39350,1710.86957,36962,493,80,121,1694,93.931385,1.25285896,0.20330368,0.30749682,4.30495553,24362,76.7670963,18.1881619,3.44388802,38386,97.5501906,8.7584015,11.4322065,7.11092484,9.76641842,0,\"AAR\"\r\n\"298\",2029,\"DELAWARE\",\"OH\",0.027,66929,2478.85185,64888,1424,104,385,128,96.9504998,2.12762778,0.15538855,0.57523644,0.19124744,41799,84.3728319,31.5940573,9.34232876,63986,95.602803,5.67311599,6.5723747,4.52441077,9.06414817,1,\"HLU\"\r\n\"299\",2030,\"ERIE\",\"OH\",0.014,76779,5484.21429,69613,6312,150,265,439,90.6667188,8.22099793,0.19536592,0.34514646,0.57177093,50112,76.2392241,18.7380268,4.63761175,75406,98.2117506,8.98602233,12.6682741,7.13811263,9.33003597,0,\"AAR\"\r\n\"300\",2031,\"FAIRFIELD\",\"OH\",0.029,103461,3567.62069,101610,1153,193,378,127,98.2109201,1.11442959,0.18654372,0.36535506,0.12275157,65269,78.751015,21.0773874,4.84763058,100916,97.5401359,8.77759721,11.6821249,6.9252938,10.5373526,1,\"AAU\"\r\n\"301\",2032,\"FAYETTE\",\"OH\",0.024,27466,1144.41667,26593,662,50,102,59,96.8215248,2.41025268,0.18204325,0.37136824,0.21481104,17854,65.2850902,11.8740898,2.14517755,26886,97.8882983,16.2203377,21.9505379,13.5346455,15.9820151,0,\"LAR\"\r\n\"302\",2033,\"FRANKLIN\",\"OH\",0.034,961437,28277.5588,783714,152840,2056,19437,3390,81.5148574,15.8970375,0.21384657,2.02166133,0.35259721,597303,80.9987561,32.204593,9.14092178,935142,97.2650314,12.9900058,17.6378685,11.8183828,9.74934856,1,\"HAU\"\r\n\"303\",2034,\"FULTON\",\"OH\",0.025,38498,1539.92,37097,93,62,137,1109,96.3608499,0.24157099,0.16104733,0.35586264,2.88066913,23846,78.2563113,16.4765579,3.3296989,37995,98.6934386,6.22976707,7.58154831,4.93982309,8.1255161,1,\"ALU\"\r\n\"304\",2035,\"GALLIA\",\"OH\",0.026,30954,1190.53846,29831,871,79,136,37,96.3720359,2.81385281,0.25521742,0.43936163,0.11953221,19586,64.1784948,14.9341366,4.32451751,29824,96.3494217,22.4885998,28.4607819,19.7789588,21.7653936,0,\"LHR\"\r\n\"305\",2036,\"GEAUGA\",\"OH\",0.024,81129,3380.375,79629,1056,83,312,49,98.1510927,1.30163074,0.1023062,0.38457272,0.06039764,51290,82.0413336,31.6279977,9.12263599,80419,99.1248505,5.55217051,8.49137931,4.10188194,5.40397817,1,\"HLU\"\r\n\"306\",2037,\"GREENE\",\"OH\",0.025,136731,5469.24,124081,9611,398,2133,508,90.7482575,7.02913019,0.29108249,1.55999737,0.37153242,83757,82.3740105,31.956732,11.061762,130134,95.175198,9.49098621,12.0735213,8.7175598,7.88356241,1,\"HAU\"\r\n\"307\",2038,\"GUERNSEY\",\"OH\",0.032,39024,1219.5,38166,616,70,141,31,97.801353,1.57851579,0.17937679,0.36131611,0.07943829,25188,71.3593775,13.5143719,2.57265364,38112,97.6629766,17.4721872,24.3214731,15.0734936,14.3717608,0,\"AAR\"\r\n\"308\",2039,\"HAMILTON\",\"OH\",0.025,866228,34649.12,672972,181145,1204,9198,1709,77.6899384,20.9119308,0.13899343,1.06184515,0.19729217,551233,75.6496799,29.7883109,8.6172272,846909,97.7697558,13.2924553,19.570569,11.169832,10.6960613,1,\"AAU\"\r\n\"309\",2040,\"HANCOCK\",\"OH\",0.031,65536,2114.06452,63572,591,91,401,881,97.0031738,0.90179443,0.13885498,0.61187744,1.34429932,41492,82.919599,24.4504965,5.54564735,64198,97.958374,7.27748528,8.45006003,6.51807196,7.93665845,0,\"HLR\"\r\n\"310\",2041,\"HARDIN\",\"OH\",0.028,31111,1111.10714,30661,236,66,115,33,98.5535663,0.75857414,0.21214361,0.36964418,0.10607181,18589,73.9523374,16.0309861,4.32513852,29111,93.5714056,16.3821236,21.816574,14.9368203,12.5945537,0,\"AAR\"\r\n\"311\",2042,\"HARRISON\",\"OH\",0.025,16085,643.4,15645,393,22,15,10,97.2645322,2.44327013,0.13677339,0.09325459,0.06216972,10726,69.7557337,10.5537945,1.96718255,15808,98.2778987,19.6988866,29.3435341,18.2995358,11.9607843,0,\"AHR\"\r\n\"312\",2043,\"HENRY\",\"OH\",0.025,29108,1164.32,27951,147,53,95,862,96.0251477,0.5050158,0.18208053,0.32637076,2.96138519,18245,75.4343656,15.2589751,2.65826254,28491,97.8803078,6.96360254,8.67297428,5.1076843,9.92209922,0,\"ALR\"\r\n\"313\",2044,\"HIGHLAND\",\"OH\",0.034,35728,1050.82353,34876,692,73,71,16,97.6153157,1.93685625,0.20432154,0.19872369,0.0447828,22784,66.4808638,12.3112711,2.49297753,35314,98.841245,16.4835476,19.4384226,14.3413749,18.1541141,0,\"LAR\"\r\n\"314\",2045,\"HOCKING\",\"OH\",0.024,25533,1063.875,25199,234,55,25,20,98.6918889,0.91646105,0.21540751,0.09791251,0.07833,16368,67.8457967,12.85435,2.71260997,24857,97.3524459,15.7098604,20.7745956,13.4820216,14.9915234,0,\"LAR\"\r\n\"315\",2046,\"HOLMES\",\"OH\",0.025,32849,1313.96,32706,52,24,43,24,99.5646747,0.1583001,0.07306158,0.13090201,0.07306158,17780,46.912261,9.33070866,1.72665917,31830,96.8979269,17.2447377,24.5171128,12.3522422,15.3579391,0,\"LAR\"\r\n\"316\",2047,\"HURON\",\"OH\",0.03,56240,1874.66667,54982,597,85,153,423,97.7631579,1.06152205,0.15113798,0.27204836,0.75213371,34521,74.131688,13.4497842,2.95472321,55535,98.7464438,9.50391645,13.0375596,8.02076192,8.16840178,0,\"AAR\"\r\n\"317\",2048,\"JACKSON\",\"OH\",0.024,30230,1259.58333,29895,218,53,39,25,98.8918293,0.72113794,0.17532253,0.12901092,0.08269931,19136,60.8800167,10.9845318,2.95255017,29874,98.8223619,24.1882573,30.1736614,21.0996006,24.1633681,0,\"LHR\"\r\n\"318\",2049,\"JEFFERSON\",\"OH\",0.022,80298,3649.90909,75270,4488,167,266,107,93.7383247,5.5891803,0.20797529,0.33126603,0.13325363,54294,71.879029,13.8431503,2.81799094,78510,97.7732945,17.1494077,26.2505981,15.7419355,10.8577511,1,\"AAU\"\r\n\"319\",2050,\"KNOX\",\"OH\",0.03,47473,1582.43333,46747,381,93,195,57,98.4707097,0.80256146,0.19590083,0.4107598,0.12006825,29992,75.2000533,16.771139,4.2011203,44269,93.2509005,12.4511509,18.1717025,10.5853414,9.88372093,0,\"AAR\"\r\n\"320\",2051,\"LAKE\",\"OH\",0.013,215499,16576.8462,209879,3528,250,1447,395,97.3920993,1.63713057,0.11600982,0.67146483,0.18329551,142348,81.0520696,23.6828055,5.20274257,213036,98.8570713,4.89729435,7.14098582,3.93348514,4.90623387,1,\"HLU\"\r\n\"321\",2052,\"LAWRENCE\",\"OH\",0.026,61834,2378.23077,60115,1559,57,75,28,97.2199761,2.52126662,0.09218229,0.12129249,0.04528253,39219,65.8787832,12.2083684,3.14643413,61007,98.6625481,23.5399216,33.2300024,21.1064359,16.3875266,1,\"LHU\"\r\n\"322\",2053,\"LICKING\",\"OH\",0.04,128300,3207.5,125181,2217,247,475,180,97.568979,1.72798129,0.19251754,0.37022603,0.14029618,81642,76.4251243,18.1793685,4.53565567,124678,97.1769291,10.4998476,14.7950733,8.84336678,9.29430267,1,\"AAU\"\r\n\"323\",2054,\"LOGAN\",\"OH\",0.027,42310,1567.03704,41156,804,58,240,52,97.2725124,1.90025999,0.13708343,0.56724179,0.12290239,26780,74.4884242,13.3233757,3.33457804,41566,98.2415505,10.4676899,13.5485,9.22106393,9.52197379,0,\"AAR\"\r\n\"324\",2055,\"LORAIN\",\"OH\",0.029,271126,9349.17241,241549,21230,738,1479,6130,89.0910499,7.83030768,0.27219817,0.54550283,2.26094141,169492,75.2820192,18.8616572,4.283978,265062,97.7634015,11.4912737,17.386122,9.42837411,8.42662074,1,\"AAU\"\r\n\"325\",2056,\"LUCAS\",\"OH\",0.021,462361,22017.1905,380155,68456,1164,4981,7605,82.2203862,14.805747,0.25175134,1.07729674,1.64481866,289965,76.2340282,23.5504285,6.00589726,454351,98.2675874,15.268812,21.4916528,13.4944573,11.4228016,1,\"AAU\"\r\n\"326\",2057,\"MADISON\",\"OH\",0.028,37068,1323.85714,33947,2764,96,157,104,91.5803388,7.45656631,0.25898349,0.42354592,0.28056545,23896,69.4928021,13.3788082,2.67408771,32904,88.7665911,8.4275468,11.2765492,7.12069334,8.11343048,1,\"AAU\"\r\n\"327\",2058,\"MAHONING\",\"OH\",0.024,264806,11033.5833,221109,39681,444,985,2587,83.4984857,14.9849324,0.16766992,0.37197042,0.97694161,176658,74.6006408,18.1757973,4.52342945,260264,98.2847821,15.9196047,25.0271849,13.8501553,10.7028532,1,\"AAU\"\r\n\"328\",2059,\"MARION\",\"OH\",0.024,64274,2678.08333,60948,2707,148,285,186,94.8252793,4.21165635,0.23026418,0.44341413,0.28938607,41239,73.7578506,15.0270375,3.04323577,61526,95.7245543,12.7133244,18.2830355,10.7493876,10.0189036,0,\"AAR\"\r\n\"329\",2060,\"MEDINA\",\"OH\",0.025,122354,4894.16,120504,850,172,684,144,98.4879939,0.69470553,0.14057571,0.55903362,0.11769129,76962,82.3978067,23.7026065,4.88552792,121055,98.9383265,5.52063112,7.85156137,4.21102969,6.32405892,1,\"HLU\"\r\n\"330\",2061,\"MEIGS\",\"OH\",0.025,22987,919.48,22734,177,44,20,12,98.8993779,0.77000044,0.19141254,0.0870057,0.05220342,14772,64.0265367,11.677498,2.38288654,22665,98.5992082,26.0092654,34.9729464,23.6672906,19.9579341,0,\"LHR\"\r\n\"331\",2062,\"MERCER\",\"OH\",0.028,39443,1408.67857,39131,14,85,100,113,99.2089851,0.03549426,0.21550085,0.25353041,0.28648936,23780,75.4793944,14.1000841,3.32632464,38961,98.7779834,6.70414004,7.62704884,5.68931069,8.04411765,0,\"ALR\"\r\n\"332\",2063,\"MIAMI\",\"OH\",0.024,93182,3882.58333,90519,1779,158,606,120,97.1421519,1.90916701,0.16956064,0.65034019,0.12878024,59893,76.5665437,19.2777119,4.43457499,92127,98.8678071,8.35151476,12.3240006,6.30440618,8.91394114,1,\"AAU\"\r\n\"333\",2064,\"MONROE\",\"OH\",0.026,15497,596.038462,15437,19,26,12,3,99.6128283,0.12260438,0.16777441,0.07743434,0.01935859,10196,69.4488035,10.513927,2.59905845,15276,98.5739175,21.4912281,28.809219,18.6889987,19.7591146,0,\"AHR\"\r\n\"334\",2065,\"MONTGOMERY\",\"OH\",0.027,573809,21252.1852,463551,101817,1065,5886,1490,80.7848953,17.7440577,0.18560183,1.02577687,0.25966829,371530,77.7571125,26.5504804,7.0042796,561952,97.933633,12.6286587,19.4520723,10.6043371,9.44086822,1,\"AAU\"\r\n\"335\",2066,\"MORGAN\",\"OH\",0.025,14194,567.76,13524,570,64,12,24,95.2796956,4.01578132,0.45089474,0.08454276,0.16908553,8980,71.5812918,11.3808463,2.31625835,13924,98.0977878,21.2079862,31.325,17.9487179,14.8148148,0,\"AHR\"\r\n\"336\",2067,\"MORROW\",\"OH\",0.024,27749,1156.20833,27579,64,49,38,19,99.3873653,0.23063894,0.17658294,0.13694187,0.06847094,17158,71.3253293,12.1051405,2.54691689,27440,98.8864464,11.0750729,17.3749057,8.39833593,8.89809849,0,\"AAR\"\r\n\"337\",2068,\"MUSKINGUM\",\"OH\",0.041,82068,2001.65854,78125,3468,214,152,109,95.1954477,4.225764,0.26075937,0.18521226,0.13281669,51692,71.140602,15.3234543,3.57308674,80009,97.4911049,14.7208439,21.3569812,12.6231667,11.137507,0,\"AAR\"\r\n\"338\",2069,\"NOBLE\",\"OH\",0.024,11336,472.333333,11301,7,15,9,4,99.6912491,0.06175018,0.13232181,0.07939308,0.03528582,7235,69.8548721,8.84588804,1.57567381,11176,98.5885674,16.3743737,22.7357901,12.8071997,16.4845173,0,\"AAR\"\r\n\"339\",2070,\"OTTAWA\",\"OH\",0.016,40029,2501.8125,39029,265,51,94,590,97.5018112,0.66202004,0.12740763,0.23482975,1.4739314,26931,75.8568193,19.338309,4.21447403,39392,98.4086537,6.61301787,9.54335557,5.25392428,6.72342557,0,\"ALR\"\r\n\"340\",2071,\"PAULDING\",\"OH\",0.025,20488,819.52,19920,236,54,20,258,97.2276455,1.15189379,0.26356892,0.09761812,1.25927372,12456,72.2302505,11.4884393,2.82594733,20298,99.0726279,9.78914179,11.9703215,8.16975645,11.2109012,0,\"AAR\"\r\n\"341\",2072,\"PERRY\",\"OH\",0.023,31557,1372.04348,31408,57,46,21,25,99.5278385,0.18062553,0.14576798,0.06654625,0.07922173,19411,68.5539127,10.1076709,2.18432847,31255,99.0430016,19.0657495,25.3889985,16.3782957,16.7218221,0,\"AHR\"\r\n\"342\",2073,\"PICKAWAY\",\"OH\",0.03,48255,1608.5,44867,3036,127,95,130,92.9789659,6.291576,0.26318516,0.19687079,0.26940213,31380,69.7100064,13.0943276,2.93499044,42392,87.8499637,12.0777505,16.3463189,9.94214435,12.4391393,1,\"AAU\"\r\n\"343\",2074,\"PIKE\",\"OH\",0.026,24249,932.653846,23807,327,72,41,2,98.1772444,1.34850922,0.29691946,0.16907914,0.00824776,15099,60.7722366,12.4445328,3.23862507,23830,98.2720937,26.5757449,36.915078,22.2239587,22.7062546,0,\"LHR\"\r\n\"344\",2075,\"PORTAGE\",\"OH\",0.03,142585,4752.83333,136998,3906,292,1191,198,96.0816355,2.73941859,0.20479013,0.83529123,0.13886454,82726,79.3100114,21.8818751,6.58680463,133447,93.5911912,11.9088477,13.3814451,12.1537341,7.97863462,1,\"AAU\"\r\n\"345\",2076,\"PREBLE\",\"OH\",0.025,40113,1604.52,39819,147,53,65,29,99.2670705,0.36646474,0.13212674,0.16204223,0.07229576,25469,72.4606384,11.233264,2.39899486,39614,98.7560143,10.1883173,15.565561,8.12505639,8.24067022,0,\"AAR\"\r\n\"346\",2077,\"PUTNAM\",\"OH\",0.029,33819,1166.17241,33197,26,44,25,527,98.1607972,0.07687986,0.13010438,0.07392294,1.55829563,20028,77.5064909,15.7080088,2.8060715,33390,98.7314823,5.75621444,7.14017991,4.23869967,7.95176182,0,\"ALR\"\r\n\"347\",2078,\"RICHLAND\",\"OH\",0.029,126137,4349.55172,115078,9981,223,578,277,91.2325487,7.91282494,0.1767919,0.45823192,0.2196025,81363,73.5321952,16.7975615,3.42170274,122328,96.9802675,11.2517167,16.4937951,8.9708687,10.6330901,1,\"AAU\"\r\n\"348\",2079,\"ROSS\",\"OH\",0.041,69330,1690.97561,64362,4467,155,266,80,92.8342709,6.44309823,0.22356844,0.38367229,0.11539016,45531,67.5803299,14.1156575,2.99795744,63449,91.5173806,17.7496887,24.8676325,15.0094603,15.4179338,0,\"LHR\"\r\n\"349\",2080,\"SANDUSKY\",\"OH\",0.024,61963,2581.79167,58282,1553,94,142,1892,94.059358,2.50633443,0.15170344,0.22916902,3.05343511,38993,76.6163157,16.2824097,3.40061037,60811,98.140826,8.99672757,12.030513,7.50127931,8.76451954,0,\"AAR\"\r\n\"350\",2081,\"SCIOTO\",\"OH\",0.035,80327,2295.05714,77253,2458,409,126,81,96.1731423,3.05999228,0.50916877,0.15685884,0.10083783,51585,63.7569061,13.7772608,3.17534167,76736,95.5295231,25.7923269,37.6523745,23.5231677,15.1068091,0,\"LHR\"\r\n\"351\",2082,\"SENECA\",\"OH\",0.033,59733,1810.09091,57474,1172,90,234,763,96.2181709,1.96206452,0.15067048,0.39174326,1.27735088,36666,75.2604593,15.6221022,3.03005509,57655,96.5211859,10.7518862,14.3172859,9.29836624,9.27675988,0,\"AAR\"\r\n\"352\",2083,\"SHELBY\",\"OH\",0.025,44915,1796.6,43789,615,49,393,69,97.4930424,1.36925303,0.10909496,0.87498608,0.15362351,27365,72.943541,17.3762105,3.66892015,44127,98.245575,7.74582455,10.54634,5.95581568,8.73122118,0,\"AAR\"\r\n\"353\",2084,\"STARK\",\"OH\",0.034,367585,10811.3235,339421,25052,950,1529,633,92.3380987,6.81529442,0.25844363,0.41595821,0.17220507,241153,76.0090067,18.9896041,4.62030329,359231,97.7273284,11.0605711,16.399197,9.51066888,8.45844662,1,\"AAU\"\r\n\"354\",2085,\"SUMMIT\",\"OH\",0.024,514990,21457.9167,446902,61185,1065,4989,849,86.7787724,11.8808132,0.20680013,0.96875667,0.16485757,337442,78.3393887,24.7331393,6.25529721,506100,98.2737529,12.1499704,18.0813265,10.8654209,8.22432118,1,\"AAU\"\r\n\"355\",2086,\"TRUMBULL\",\"OH\",0.037,227813,6157.10811,210915,15221,341,973,363,92.5825129,6.68135708,0.14968417,0.42710469,0.15934121,149822,75.2492958,15.5998451,3.78849568,225230,98.8661753,11.4047862,16.9653149,9.38910671,9.85596375,1,\"AAU\"\r\n\"356\",2087,\"TUSCARAWAS\",\"OH\",0.034,84090,2473.23529,83107,623,138,187,35,98.8310144,0.74087287,0.16410988,0.22238078,0.04162207,55192,71.9180316,13.0834179,3.02036527,82852,98.5277679,11.1222421,14.39584,9.78198126,10.4047334,1,\"AAU\"\r\n\"357\",2088,\"UNION\",\"OH\",0.026,31969,1229.57692,30563,1168,57,132,49,95.6019894,3.65353937,0.17829773,0.4129,0.15327348,20527,76.1631023,16.9825108,3.84858966,30117,94.2068879,7.43101903,9.46814728,5.77602449,9.76408912,0,\"AAR\"\r\n\"358\",2089,\"VAN WERT\",\"OH\",0.024,30464,1269.33333,29900,193,31,78,262,98.1486345,0.63353466,0.10175945,0.25603992,0.86003151,19497,79.2327025,15.1100169,3.1902344,30007,98.4998687,7.09167861,7.60100707,6.06022585,9.22109675,0,\"ALR\"\r\n\"359\",2090,\"VINTON\",\"OH\",0.024,11098,462.416667,11071,4,16,3,4,99.7567129,0.03604253,0.14417012,0.0270319,0.03604253,6963,58.6959644,7.91325578,1.56541721,10937,98.5492882,23.6079364,31.7708333,22.2259753,14.9176955,0,\"LHR\"\r\n\"360\",2091,\"WARREN\",\"OH\",0.023,113909,4952.56522,110526,2415,231,627,110,97.0300854,2.12011342,0.20279346,0.55043939,0.09656831,73151,75.4671843,24.4193518,5.90832661,109393,96.0354318,6.35232602,8.71549016,5.07858441,7.22380639,1,\"ALU\"\r\n\"361\",2092,\"WASHINGTON\",\"OH\",0.038,62254,1638.26316,61129,774,111,185,55,98.1928872,1.2432936,0.1783018,0.29716966,0.08834774,40411,77.5061246,19.0096756,4.35524981,60627,97.3865133,13.6737757,17.9894516,12.0116635,12.6052655,1,\"AAU\"\r\n\"362\",2093,\"WAYNE\",\"OH\",0.033,101461,3074.57576,99131,1557,130,535,108,97.7035511,1.53457979,0.12812805,0.5272962,0.10644484,62178,73.5967706,18.4454309,4.79590852,98285,96.8697332,11.6558987,17.2130572,9.51515592,8.91176271,0,\"AAR\"\r\n\"363\",2094,\"WILLIAMS\",\"OH\",0.025,36956,1478.24,36366,23,46,127,394,98.4035069,0.06223617,0.12447235,0.34365191,1.0661327,23340,76.092545,14.4987147,2.70779777,36499,98.7633943,7.55363161,10.6865843,5.92573754,7.44215134,0,\"AAR\"\r\n\"364\",2095,\"WOOD\",\"OH\",0.037,113269,3061.32432,109303,1168,197,1028,1573,96.4986007,1.03117358,0.17392226,0.90757401,1.38872948,64052,83.7616312,29.0951102,8.33541498,104553,92.3050437,10.5726282,8.76044005,12.2235902,6.93142742,1,\"HAU\"\r\n\"365\",2096,\"WYANDOT\",\"OH\",0.024,22254,927.25,22087,20,20,65,62,99.2495731,0.08987148,0.08987148,0.29208232,0.2786016,14361,76.4709978,13.7107444,2.54856904,21743,97.7037836,8.49468795,9.99170124,7.31604292,9.76552435,0,\"AAR\"\r\n\"366\",2981,\"ADAMS\",\"WI\",0.041,15682,382.487805,15001,375,125,56,125,95.6574417,2.39127662,0.79709221,0.35709731,0.79709221,11378,66.9801371,12.4274917,2.39057831,14534,92.6795052,14.4351177,22.3008305,13.3100559,10.1867572,0,\"LAR\"\r\n\"367\",2982,\"ASHLAND\",\"WI\",0.054,16307,301.981481,14749,17,1478,46,17,90.4458208,0.10424971,9.06359232,0.28208745,0.10424971,10262,75.3069577,18.3200156,4.36562074,15573,95.4988655,16.2203814,20.3220685,15.1600512,13.2975552,0,\"AAR\"\r\n\"368\",2983,\"BARRON\",\"WI\",0.053,40750,768.867925,40346,40,209,95,60,99.008589,0.09815951,0.51288344,0.23312883,0.14723926,26198,72.9750363,19.425147,3.24452248,39931,97.990184,11.5524279,14.4092998,9.94820158,11.7280176,0,\"AAR\"\r\n\"369\",2984,\"BAYFIELD\",\"WI\",0.089,14008,157.393258,12707,29,1240,24,8,90.71245,0.20702456,8.85208452,0.17133067,0.05711022,9418,78.5410915,25.8228923,5.83988108,13833,98.7507139,16.5835321,23.4879306,14.4289143,13.2549788,0,\"AAR\"\r\n\"370\",2985,\"BROWN\",\"WI\",0.032,194594,6081.0625,186621,1012,3869,2522,570,95.9027514,0.52005714,1.98824219,1.29603174,0.29291756,120575,82.6439975,26.2931785,4.62450757,189369,97.3149224,9.16570294,12.0873618,8.08561665,7.96336775,1,\"HAU\"\r\n\"371\",2986,\"BUFFALO\",\"WI\",0.04,13584,339.6,13521,5,22,29,7,99.5362191,0.03680801,0.16195524,0.21348645,0.05153121,8918,72.6059655,17.9412424,3.13971743,13337,98.1816843,11.9067257,15.2720243,9.63907813,13.1345403,0,\"AAR\"\r\n\"372\",2987,\"BURNETT\",\"WI\",0.053,13084,246.867925,12497,22,532,24,9,95.5136044,0.1681443,4.06603485,0.18343014,0.0687863,9045,72.2830293,13.6760641,2.99613046,12816,97.9516967,15.4884519,21.0293653,13.9379475,13.1688466,0,\"AAR\"\r\n\"373\",2988,\"CALUMET\",\"WI\",0.023,34291,1490.91304,33910,29,146,173,33,98.8889213,0.0845703,0.42576769,0.50450556,0.09623516,20940,79.6561605,19.7803247,3.01337154,33952,99.0114024,4.87158341,6.22784325,3.72723875,6.39097744,1,\"ALU\"\r\n\"374\",2989,\"CHIPPEWA\",\"WI\",0.063,52360,831.111111,51854,31,150,276,49,99.0336134,0.0592055,0.28647823,0.52711994,0.09358289,33195,74.9269468,18.1533363,2.78957674,50983,97.3701299,10.476041,13.9566861,8.59508956,10.579373,1,\"AAU\"\r\n\"375\",2990,\"CLARK\",\"WI\",0.072,31647,439.541667,31437,29,91,38,52,99.33643,0.09163586,0.287547,0.12007457,0.16431257,19702,67.5362907,13.84631,2.50228403,30947,97.7881,13.7719327,18.0444728,11.4184117,13.0776485,0,\"LAR\"\r\n\"376\",2991,\"COLUMBIA\",\"WI\",0.046,45088,980.173913,44469,243,136,136,104,98.6271292,0.53894606,0.30163236,0.30163236,0.23066004,29637,78.2535344,20.852313,3.54624287,43744,97.0191625,7.49359912,9.98292058,5.91446379,8.3589919,0,\"AAR\"\r\n\"377\",2992,\"CRAWFORD\",\"WI\",0.035,15940,455.428571,15791,50,26,56,17,99.0652447,0.31367629,0.16311167,0.35131744,0.10664994,10169,72.3964992,16.599469,2.92064116,15562,97.6286073,14.6382213,19.4600675,12.3815621,13.4865437,0,\"AAR\"\r\n\"378\",2993,\"DANE\",\"WI\",0.073,367085,5028.56164,344617,10511,1201,8666,2090,93.8793467,2.86336952,0.32717218,2.36076113,0.56935042,225973,88.8986737,43.6264509,13.4715209,351558,95.7701895,10.4930054,8.76778118,12.1680107,4.97435064,1,\"HAU\"\r\n\"379\",2994,\"DODGE\",\"WI\",0.054,76559,1417.75926,74700,1142,215,197,305,97.5718074,1.49166003,0.28082916,0.25731789,0.39838556,49694,72.2783435,15.9254638,2.82327846,72840,95.1423085,6.64470071,8.39387394,5.17740489,8.25578798,0,\"ALR\"\r\n\"380\",2995,\"DOOR\",\"WI\",0.028,25690,917.5,25387,29,178,47,49,98.8205527,0.11288439,0.69287661,0.18295056,0.19073569,17369,79.5670447,22.4883413,4.42167079,25318,98.5519657,9.82305079,13.938754,8.2382666,8.65316276,0,\"AAR\"\r\n\"381\",2996,\"DOUGLAS\",\"WI\",0.078,41758,535.358974,40454,170,805,266,63,96.8772451,0.40710762,1.92777432,0.63700369,0.15086929,27060,77.2172949,20.9940872,4.97782705,40426,96.8101921,14.9210904,22.9435598,13.1049683,9.24704418,1,\"AAU\"\r\n\"382\",2997,\"DUNN\",\"WI\",0.052,35909,690.557692,34929,172,95,633,80,97.2708792,0.47898855,0.26455763,1.76278927,0.22278537,19755,77.6613516,26.2870159,7.50696026,32837,91.4450416,16.6336754,15.1954674,18.6613878,11.8962601,0,\"AAR\"\r\n\"383\",2998,\"EAU 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  },
  {
    "path": "2022年/2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/readme.txt",
    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/B0AhboAduKpAYrtPr5Yozw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n# 代码来源：http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html\n\n\n\n\n\n![image-20230513221952622](/Users/liangliangzhuang/Library/Application Support/typora-user-images/image-20230513221952622.png)\n"
  },
  {
    "path": "2022年/2022.11.15 R绘图案例｜基于分面的折线图绘制/.Rhistory",
    "content": "ggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1)\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_line()\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndat = read.csv(\"data_wuhan.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nlibrary(tidyverse)\ndata = dat[,3:9] %>% pivot_longer(1:7,\nnames_to = \"time\",\nvalues_to = \"value\",\nnames_transform = list(time = as.integer)\n)\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\nhead(data)\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point() +\ngeom_line(shape=21) +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(shape=21) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(shape=21,fill=Method) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,fill=Method)) +\ngeom_point(shape=21) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(shape=21) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(aes(shape=method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(data, aes(shape=method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape=method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method)) +\ngeom_point(data, aes(shape = Method)) +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0,0),\nbreaks = c(0:7),\nlimits = c(0,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0.5,0.5),\nbreaks = c(0:7),\nlimits = c(0,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(0:7),\nlimits = c(0,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\nas.factor(data[\"Method\"])\ndata[\"Method\"] = as.factor(data[\"Method\"], levels =  c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\ndata[\"Method\"] = as.factor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\nas.factor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\ndata[\"Method\"] = afactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\ndata[\"Method\"] = factor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\nhead(data)\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# library\nlibrary(ggplot2)\n# create a dataset\nspecie <- c(rep(\"sorgho\" , 3) , rep(\"poacee\" , 3) , rep(\"banana\" , 3) , rep(\"triticum\" , 3) )\ncondition <- rep(c(\"normal\" , \"stress\" , \"Nitrogen\") , 4)\nvalue <- abs(rnorm(12 , 0 , 15))\ndat = read.csv(\"data_wuhan.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nlibrary(tidyverse)\ndata = dat[,3:9] %>% pivot_longer(1:7,\nnames_to = \"time\",\nvalues_to = \"value\",\nnames_transform = list(time = as.integer)\n)\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\ndata[\"Method\"] = factor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\nhead(data)\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\ndata[\"Method\"]\ndat = read.csv(\"data_wuhan.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nlibrary(tidyverse)\ndata = dat[,3:9] %>% pivot_longer(1:7,\nnames_to = \"time\",\nvalues_to = \"value\",\nnames_transform = list(time = as.integer)\n)\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\ndata[\"Method\"]\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\nhead(data)\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis2(discrete = T,direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"A\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"B\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"C\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"D\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"E\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"F\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# Stacked\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7))\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\")\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"zzx love me\") +\nylab(\"add your text\")\nlibrary(tidyverse)\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\n# data reprocess\ndat = read.csv(\"data_wuhan.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nlibrary(tidyverse)\ndata = dat[,3:9] %>% pivot_longer(1:7,\nnames_to = \"time\",\nvalues_to = \"value\",\nnames_transform = list(time = as.integer)\n)\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\nhead(data)\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"zzx love me\") +\nylab(\"add your text\")\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(hrbrthemes)\n# data reprocess\ndat = read.csv(\"data_wuhan.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nlibrary(tidyverse)\ndata = dat[,3:9] %>% pivot_longer(1:7,\nnames_to = \"time\",\nvalues_to = \"value\",\nnames_transform = list(time = as.integer)\n)\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\nhead(data)\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1) +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"zzx love me\") +\nylab(\"add your text\")\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1,strip.position = \"bottom\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"zzx love me\") +\nylab(\"add your text\")\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1,strip.position = \"bottom\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"\") +\nylab(\"add your text\")\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) +\ngeom_point() +\ngeom_line() +\nscale_color_viridis(discrete = T,option = \"G\", direction = -1) +\nscale_x_continuous(expand = c(0.1,0.1),\nbreaks = c(1:7),\nlimits = c(1,7)) +\nfacet_wrap(vars(Cri), nrow =1,strip.position = \"top\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position=\"right\") +\nxlab(\"\") +\nylab(\"add your text\")\n"
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    "content": "# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(tidyverse)\n# data reprocess\ndat = read.csv(\"data_city.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\nhead(dat)\ndata = dat[,3:9] %>% pivot_longer(1:7,\n                      names_to = \"time\",\n                      values_to = \"value\",\n                      names_transform = list(time = as.integer)\n                      )\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n\nhead(data)\n\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + \n  geom_point() +\n  geom_line() + \n  facet_wrap(vars(Cri), nrow =1,strip.position = \"top\") +\n  scale_color_viridis(discrete = T,option = \"G\", direction = -1) +\n  scale_x_continuous(expand = c(0.1,0.1),\n                     breaks = c(1:7),\n                     limits = c(1,7)) +\n  theme_bw() +  \n  theme(panel.grid = element_blank(), legend.position=\"right\") +\n  xlab(\"Time\") +\n  ylab(\"Value\")\n\n\n\n"
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    "content": "SO-FNN,RMSE,12.26,24.37,31.06,34.24,35.32,36.62,37.06\r\n,MAE,9.53,17.48,22.52,25.22,26.29,27.47,28.12\r\n,MAPE,11.72,22.19,28.24,30.53,31.85,34.47,36.77\r\nSO-RNN,RMSE,40.12,52.06,59.53,62.89,64.27,62.79,62.09\r\n,MAE,37.12,43.41,47.92,50.8,52.26,50.01,48.4\r\n,MAPE,42.99,44.71,47.5,50.14,51.93,49.51,47.56\r\nSO-LSTM,RMSE,6.78,23.79,32.53,36.11,37.53,37.58,38.1\r\n,MAE,4.71,16.86,22.99,25.55,26.55,26.74,27.46\r\n,MAPE,6.11,21.72,29.02,30.62,31.54,31.86,33.54\r\nSO-Bi-LSTM,RMSE,4.38,23.74,32.67,38.65,39.54,38.71,37.22\r\n,MAE,3.09,16.51,22.71,26.72,27.5,27.36,26.98\r\n,MAPE,3.91,20.41,27.13,30.82,31.89,32.36,32.33\r\nSO-ENDC-LSTM,RMSE,13.11,27.49,35.23,40.78,42.1,42.72,42.76\r\n,MAE,9.75,19.29,23.96,27.63,28.91,29.85,30.42\r\n,MAPE,13.7,24.24,28.54,31.45,33.18,35.38,36.88\r\n"
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    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/apu5-4ykHe0QTUbdJKy-MQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(tidyverse)\n# data reprocess\ndat = read.csv(\"data_city.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\n\ndata = dat[,3:9] %>% pivot_longer(1:7,\n                      names_to = \"time\",\n                      values_to = \"value\",\n                      names_transform = list(time = as.integer)\n                      )\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n\nhead(data)\n\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + \n  geom_point() +\n  geom_line() + \n  scale_color_viridis(discrete = T,option = \"G\", direction = -1) +\n  scale_x_continuous(expand = c(0.1,0.1),\n                     breaks = c(1:7),\n                     limits = c(1,7)) +\n  facet_wrap(vars(Cri), nrow =1,strip.position = \"top\") +\n  theme_bw() +  \n  theme(panel.grid = element_blank(), legend.position=\"right\") +\n  xlab(\"Time\") +\n  ylab(\"Value\")\n\n\n\n"
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    "path": "2022年/2022.11.19 分面中添加不同的直线/facet-line.r",
    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/JdfKl9vEqkE0rcEfz6lBBg\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\ndat3 = read_excel(\"test.xlsx\",sheet=3,na=\"NA\")\nhead(dat3)\ndat3 %>%\n  pivot_longer(\n    cols = `官方结果`:`XGBoost`,\n    names_to = \"模型\",\n    names_transform = list(模型 = as.character),\n    values_to = \"value\") -> dat_3_new\n\ndat_3_new$ 模型 = fct_relevel(dat_3_new$ 模型, \n                           \"官方结果\",\"朴素贝叶斯\",\"支持向量机\",\"决策树\",\"随机森林\",\"XGBoost\")\n\nhead(dat_3_new)\n\ndummy2 <- data.frame(叶类名称 = dat3$ 叶类名称, Z = dat3$ 官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + \n  geom_col() +\n  facet_wrap(vars(`叶类名称`)) +\n  geom_hline(data = dummy2,aes(yintercept = Z)) +\n  # scale_fill_manual(values = cols[1:6]) +\n  theme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n  scale_fill_viridis(discrete = T,option = \"D\") +\n  theme(panel.grid = element_blank(),\n        legend.position = 'bottom',\n        legend.direction = \"horizontal\",\n       axis.text.x = element_blank(),\n       axis.ticks.x = element_blank())"
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    "content": "# install.packages(\"readxl\")\nlibrary(readxl)\nlibrary(ggplot2)\nlibrary(tidyverse)\nlibrary(cowplot)\nlibrary(viridis)\nlibrary(showtext)\nshowtext_auto()\n### 绘制不同方法的区域图===========\ndat = read_excel(\"test.xlsx\",sheet=1,na=\"NA\")\nhead(dat)\ncolnames(dat) = c(\"Id\",paste(\"X\",1:6,sep=''))\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\ndat %>% select(c(Id,X4,X1)) %>%\npivot_longer(\ncols = c(X4,X1),\nnames_to = \"method\",\nnames_transform = list(method = as.character),\nvalues_to = \"Acc\") -> dat1\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\np1 = ggplot(dat1) + geom_area(aes(Id,Acc),fill = cols[1]) +\nfacet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\ntheme_bw() + ylab(\"精确率\") + xlab(\"叶类\") + #主题设置\ntheme(panel.grid = element_blank()) +\np1\n#==\ndat %>% select(c(Id,X5,X2)) %>%\npivot_longer(\ncols = c(X5,X2),\nnames_to = \"method\",\nnames_transform = list(method = as.character),\nvalues_to = \"Acc\") -> dat2\np2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\ntheme_bw() + ylab(\"召回率\") + xlab(\"叶类\") + #主题设置\ntheme(panel.grid = element_blank())\np2\n#==\ndat %>% select(c(Id,X3,X6)) %>%\npivot_longer(\ncols = c(X3,X6),\nnames_to = \"method\",\n# names_transform = list(method = as.factor),\nvalues_to = \"Acc\") -> dat3\n# levels(dat3$method) = c(\"\",\"\")\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\np3 = ggplot(dat3) + geom_area(aes(Id,Acc),fill = cols[4]) + facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\ntheme_bw() + ylab(\"F1得分\") + xlab(\"叶类\") + #主题设置\ntheme(panel.grid = element_blank())\np3\nplot_grid(p1,p2,p3,ncol = 3)\ndat_all = read_excel(\"test.xlsx\",sheet=2,na=\"NA\")\nhead(dat_all)\ndat_all %>%\npivot_longer(\ncols = `准确率`:`F1得分`,\nnames_to = \"method\",\nnames_transform = list(method = as.character),\nvalues_to = \"value\") -> dat_all_new\ndat_all_new$method = fct_relevel(dat_all_new$method, \"准确率\", \"精确率\", \"召回率\",\"F1得分\")\ndat_all_new %>% ggplot(aes(method ,\nvalue,fill=模型)) +\ngeom_col(position = \"dodge\")+\nfacet_wrap(vars(文本向量化方法)) +\ntheme_bw() + ylab(\"数值\") + xlab(\"评价指标\") + #主题设置\n# scale_fill_viridis(discrete = T) +\n# geom_hline(aes(yintercept = 0.75),color = \"gray\") +\nscale_fill_manual(values =cols[c(1,2,4)]) +\ntheme(panel.grid = element_blank())\ndat_3_new$模型 = fct_relevel(dat_3_new$模型,\n\"官方结果\",\"朴素贝叶斯\",\"支持向量机\",\"决策树\",\"随机森林\",\"XGBoost\")\ndat3 = read_excel(\"test.xlsx\",sheet=3,na=\"NA\")\nhead(dat3)\ndat3 %>%\npivot_longer(\ncols = `官方结果`:`XGBoost`,\nnames_to = \"模型\",\nnames_transform = list(模型 = as.character),\nvalues_to = \"value\") -> dat_3_new\ndummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +\ngeom_col() +\nfacet_wrap(vars(`叶类名称`)) +\ngeom_hline(data = dummy2,aes(yintercept = Z)) +\nscale_fill_manual(values = cols[1:6]) +\ntheme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n# scale_fill_viridis(discrete = T) +\ntheme(panel.grid = element_blank(),\nlegend.position = 'bottom',\nlegend.direction = \"horizontal\",\naxis.text.x = element_blank(),\naxis.ticks.x = element_blank())\nhead(dat3)\ndat3 %>%\npivot_longer(\ncols = `官方结果`:`XGBoost`,\nnames_to = \"模型\",\nnames_transform = list(模型 = as.character),\nvalues_to = \"value\") -> dat_3_new\ndummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +\ngeom_col() +\nfacet_wrap(vars(`叶类名称`)) +\ngeom_hline(data = dummy2,aes(yintercept = Z)) +\nscale_fill_manual(values = cols[1:6]) +\ntheme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n# scale_fill_viridis(discrete = T) +\ntheme(panel.grid = element_blank(),\nlegend.position = 'bottom',\nlegend.direction = \"horizontal\",\naxis.text.x = element_blank(),\naxis.ticks.x = element_blank())\ndummy2 <- data.frame(模型 = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +\ngeom_col() +\nfacet_wrap(vars(`叶类名称`)) +\ngeom_hline(data = dummy2,aes(yintercept = Z)) +\nscale_fill_manual(values = cols[1:6]) +\ntheme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n# scale_fill_viridis(discrete = T) +\ntheme(panel.grid = element_blank(),\nlegend.position = 'bottom',\nlegend.direction = \"horizontal\",\naxis.text.x = element_blank(),\naxis.ticks.x = element_blank())\n# dummy2 <- data.frame(X = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) +\ngeom_col() +\nfacet_wrap(vars(`叶类名称`)) +\n# geom_hline(data = dummy2,aes(yintercept = Z)) +\nscale_fill_manual(values = cols[1:6]) +\ntheme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n# scale_fill_viridis(discrete = T) +\ntheme(panel.grid = element_blank(),\nlegend.position = 'bottom',\nlegend.direction = \"horizontal\",\naxis.text.x = element_blank(),\naxis.ticks.x = element_blank())\n"
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    "content": "# library\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(tidyverse)\n# data reprocess\ndat = read.csv(\"data_city.csv\",header = F)\ncolnames(dat) = c(\"Method\",\"Cri\",1:7)\n\ndata = dat[,3:9] %>% pivot_longer(1:7,\n                      names_to = \"time\",\n                      values_to = \"value\",\n                      names_transform = list(time = as.integer)\n                      )\ndata[\"Cri\"] = rep(rep(c(\"RMSE\",\"MAE\",\"MAPE\"),each = 7),5)\ndata[\"Method\"] = rep(c(\"FNN\",\"RNN\",\"LSTM\",\"Bi-LSTM\",\"ENDC-LSTM\"),each= 21)\nfactor(data[\"Method\"], levels = c(\"FNN\", \"RNN\", \"LSTM\", \"Bi-LSTM\", \"ENDC-LSTM\"))\n\nhead(data)\n\n# plot\nggplot(data, aes(x=time, y=value, color = Method,shape = Method)) + \n  geom_point() +\n  geom_line() + \n  scale_color_viridis(discrete = T,option = \"G\", direction = -1) +\n  scale_x_continuous(expand = c(0.1,0.1),\n                     breaks = c(1:7),\n                     limits = c(1,7)) +\n  facet_wrap(vars(Cri), nrow =1,strip.position = \"top\") +\n  theme_bw() +  \n  theme(panel.grid = element_blank(), legend.position=\"right\") +\n  xlab(\"Time\") +\n  ylab(\"Value\")\n\n\n\n"
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    "content": "---\ntitle: \"r-sparkline\"\nauthor: \"袁凡\"\ndate: '2022-11-07'\noutput: html_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)\n```\n\n这篇笔记一共有两章，第一章记录 sparkline 包中可改动的参数细节，第二章记录将 sparkline 与 formattable 包、DT 包、reactable包结合使用的案例，其中一部分数据操作分 tidy 版本和 data.table 版本。\n\n据笔者所知，R 中现在有两个包可以画 sparkline（迷你图）：\n\n+ 一个是[基于dataui包](https://timelyportfolio.github.io/dataui/articles/dataui_replicate_examples.html)的 reactablefmtr 包，其[提供的案例](https://kcuilla.github.io/reactablefmtr/articles/sparklines.html)有相当多的细节，若要应用直接看官网案例即可，不需要单独鼓捣一篇笔记出来；\n\n+ 二个是 Rstudio 他们家出的 sparkline 包，此包的[文档](https://cran.r-project.org/web/packages/sparkline/sparkline.pdf)仅有7页，有[简洁的网站案例](https://cran.r-project.org/web/packages/sparkline/vignettes/intro_sparkline.html)，最近一次更新是2016年11月12日，不过由于这个包就是[jQuery Sparklines](https://omnipotent.net/jquery.sparkline/#s-about)的 R 版本，有些细节需要对照两种文档来看，所以笔者决定还是鼓捣一篇笔记出来。\n\n# 一、sparkline 包的参数细节\n\n这一章记录了sparkline包可以画的图形基本类型，如'line'（面积图/折线图）、'bar'（柱形图/堆叠柱形图）、'tristate'、'discrete'、'bullet'、'box'（箱线图）、'pie'（饼图），还记录了一些可以改动的图形细节，如图形区域的高度和宽度、颜色、标注点和区域等。\n\n## 1.1. 迷你图的基本类型\n\n首先，编造一些用于展示迷你图的基础数据。\n\n```{r}\nlibrary(htmlwidgets)\nlibrary(sparkline)\nlibrary(dplyr) # tidy版操作数据\nlibrary(tibble) #使用tibble类型的数据\nlibrary(purrr) #使用 map_chr 函数\nlibrary(data.table) # data.table版操作数据\nlibrary(formattable)\nlibrary(DT)\nlibrary(reactable)\n\nset.seed(1234)\nx = rnorm(10)\ny = c(rnorm(10), rep(0, 3))\nz = c(rep(1, 3))\nu = data.frame(serie1 = c(1:4), serie2 = c(4:1)) #堆叠柱形图的数据\nu = as.matrix(u) # 转换成矩阵\n```\n\n为了方便显示代码，下表中“代码”一列中均增加了`eval = FALSE`这一参数。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-1.png)\n\n运用 sparkline 包中的`spk_composite()`函数可以将两个不同的迷你图一起展示。\n\n```{r}\n# 第一种写法\ns1 <- sparkline(abs(x), type = 'bar')\ns2 <- sparkline(y, type = \"line\", fillColor = FALSE)\nspk_composite(s1, s2)\n\n# 第二种写法\nspk_composite(sparkline(abs(x), type = 'bar'),\n              sparkline(y, type = \"line\", fillColor = FALSE))\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-2.png)\n\n## 1.2. 迷你图的图形细节\n\n原 jQuery Sparklines 的文档中详细介绍了迷你图的[通用参数](https://omnipotent.net/jquery.sparkline/#common)和[基于不同图形类型的个性参数](https://omnipotent.net/jquery.sparkline/#line)。这一小节仅记录部分细节。\n\n### 1.2.1. 图形区域的高度和宽度\n\n`sparkline()`函数中默认宽度和高度是`width = 60, height = 20`。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-3.png)\n\n宽度`width`对柱形图和 'tristate' 不起作用，需要通过修改`barWidth`（单个柱子的宽度）和`barSpacing`（柱子之间的空隙）来达到改变整个迷你图宽度的效果。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-4.png)\n\n### 1.2.2. 颜色\n\n+ 一般折线图的颜色设置\n    - 'lineColor'：指定线的颜色\n    - 'fillColor'：指定折线下面积的填充颜色，设定为`fillColor = FALSE`时表示不展示折线下的面积。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-5.png)\n\n+ 柱形图的颜色设置\n    - 'barColor'：指定普通柱子的颜色，通常就是指数值为正数的柱子\n    - 'negBarColor'：指定数值为负数的柱子的颜色\n    - 'zeroColor'：指定数值为零的柱子的颜色\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-6.png)\n\n+ 堆叠柱形图的颜色设置\n    - 'stackedBarColor'：指定堆叠的柱形图中柱子的颜色，如果是只堆叠了两个不同的数据系列，那么只需要指定两个不同的颜色。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-7.png)\n\n+ 饼图颜色设置\n    - 'sliceColors'：指定饼图中各个扇形的颜色。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-8.png)\n\n### 1.2.3. 坐标轴\n\n+ Y 轴最大、最小值\n    - 'chartRangeMin'：指定直角坐标系下 Y 轴最大值\n    - 'chartRangeMax'：指定直角坐标系下 Y 轴最小值\n\n本文案例中'x'最大值为1.23，最小值为-2.42，因此若指定Y轴最小值为-5时，横轴之下的柱子都会变矮，同理若指定 Y 轴最大值为2时，横轴之上的柱子也会变矮。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-9.png)\n\n+ 旋转角度\n    - 'offset'：指定饼图的旋转角度，取值范围是[-90,90]\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-10.png)\n\n### 1.2.4. 标注点和区域\n\n+ 标注点\n    - 'spotColor'：折线图默认标记最大值和最小值点，设置`spotColor=FALSE`时表示不标注\n    - 'spotRadius'：标记点的半径,默认值为1.5\n    - 'minSpotColor'：指定标记的最小值的点的颜色\n    - 'maxSpotColor'：指定标记的最大值的点的颜色\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-11.png)\n\n+ 标注区域\n    - `normalRangeMin` 和`normalRangeMax`这两个参数需要同时设置，只设置一个会不起作用。\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-12.png)\n\n# 二、在表格中使用 sparkline\n\n这一节记录了在 formattable 包和 DT 包中使用 sparkline 的案例，需使用 sparkline 包中的`spk_add_deps()`和`spk_chr()`函数。\n\n## 2.1. 直接展示\n\n一般来讲一个表格中的一个单元格里仅展示一个数值，如果一个单元格里要显示一个迷你图，那么迷你图中需要写入一串数据。若直接在表格中增加 展示迷你图，那么就需要在原来用于展示的数据框中写入列表，作为迷你图的那一列中的每一行数据都需要单独写入。这一小节的案例中使用的数据如下：\n\n```{r}\nset.seed(1234)\nx = rnorm(10)\n\ntable1 <-\n  tibble(\n    column1 = c('坂田银时', '神乐', '志村新八', '定春'), # 第一列\n    column2 = c(100, 10000, 10, 100),                 # 第二列\n    column3 = c(1:4), # 第三列\n    sparkline = list( # 第四列\n      v1 = x,      # 第四列第一行\n      v2 = abs(x), # 第四列第二行\n      v3 = x,      # 第四列第三行\n      v4 = abs(x)  # 第四列第四行\n    )\n  )\n```\n\n### 2.1.1. 与 formattable 包结合\n\n```{r}\ntable1.formattable <- as.htmlwidget(formattable(\n  data.frame(\n    column1 = c('坂田银时', '神乐', '志村新八', '定春'), # 第一列\n    column2 = c(100, 10000, 10, 100),                    # 第二列\n    column3 = c(1:4), # 第三列\n    sparkline = c(    # 第四列\n      spk_chr(x, type = 'line'),       # 第四列第一行\n      spk_chr(abs(x), type = 'line'),  # 第四列第二行\n      spk_chr(x, type = 'bar'),        # 第四列第三行\n      spk_chr(abs(x), type = 'bar')    # 第四列第四行\n    ),\n    stringsAsFactors = FALSE)))\n\nspk_add_deps(table1.formattable)\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-13.png)\n\n### 2.1.2. 与 DT 包结合\n\n```{r}\ntable1.dt <- table1\n\ntable1.dt$sparkline <- table1.dt$sparkline %>%\n  map(~ sparkline(.x, type = \"box\")) %>%\n  map(htmltools::as.tags) %>%\n  map_chr(as.character)\n\nDT::datatable(table1.dt, escape = FALSE) %>% spk_add_deps()\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-14.png)\n\n### 2.1.3. 与 reactable 结合\n\n在写入需要展示迷你图的那列数据时，有两种写法。\n\n+ 写法一：\n\n写法是`columns = list( 列名 = colDef(cell = function(values){sparkline(values)}) )`。\n\n```{r}\nreactable(table1,\n          columns = list(sparkline = colDef(\n            cell = function(values) {\n              sparkline(values,\n                        type = \"line\")\n            }\n          )))\n```\n\n+ 写法二\n\n写法是`columns = list( 列名 = colDef( cell = function(value, index){sparkline( 表名$列名[[index] )}))`。\n\n```{r}\nreactable(table1,\n          columns = list(sparkline = colDef(\n            cell = function(value, index) {\n              sparkline(table1$sparkline[[index]], type = \"line\")\n            }\n          )))\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-15.png)\n\n+ 写法3\n\nreactable 包的案例中，还有一种[特别的写法](https://glin.github.io/reactable/articles/examples.html#footers)。\n\n```{r}\nreactable(\n  iris[1:20, ],\n  defaultPageSize = 5,\n  bordered = TRUE,\n  defaultColDef = colDef(footer = function(values) {\n    if (!is.numeric(values)) return()\n    sparkline(values, type = \"box\", width = 100, height = 30)\n  })\n)\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-16.png)\n\n## 2.2. 分组聚合后展示\n\n若是用于表格展示的数据需要先进行一些分组聚合的操作，而后又需要增加展示迷你图，那么……这一小节的案例中使用的数据如下：\n\n```{r}\ntable2 <- data.frame(\n  column1 = c(rep(c('坂田银时', '神乐', '志村新八', '定春'), 10)),\n  column2 = sample(1:4, 40, replace = T),\n  column3 = sample(10:20, 40, replace = T))\n```\n\n### 2.2.1. 与 formattable 结合\n\n+ tidy 版本\n\n```{r}\ntable2 %>%\n  group_by(column1) %>%\n  summarise(\n    column2_mean = mean(column2),\n    sparkline1 = spk_chr(column2, type = 'line'),\n    sparkline2 = spk_chr(column3, type = 'bar')\n  ) %>%\n  formattable() %>%\n  formattable::as.htmlwidget() %>%\n  spk_add_deps()\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-17.png)\n\n+ data.table 版本\n\n```{r}\ntable2.dt <- as.data.table(table2)\n\ntable2.dt[, by = .(column1), .(\n  column2_mean = mean(column2),\n  sparkline1 = spk_chr(column2, type = 'line'),\n  sparkline2 = spk_chr(column3, type = 'bar')\n)] |>\n  formattable() |>\n  formattable::as.htmlwidget() |>\n  spk_add_deps()\n```\n\n### 2.2.2. 与 DT 结合\n\n+ tidy 版本\n\n```{r}\ntable2.tidy <- table2 %>%\n  group_by(column1) %>%\n  summarise(column2_mean = mean(column2))\n\ntable2.tidy$sparkline1 <- table2$column2 %>%\n  split(table2$column1) %>%\n  map( ~ sparkline(.x, type = \"line\")) %>%\n  map(htmltools::as.tags) %>%\n  map_chr(as.character)\n\ntable2.tidy$sparkline2 <- table2$column3 %>%\n  split(table2$column1) %>%\n  map( ~ sparkline(.x, type = \"bar\")) %>%\n  map(htmltools::as.tags) %>%\n  map_chr(as.character)\n\nDT::datatable(table2.tidy, escape = FALSE) %>% spk_add_deps()\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-18.png)\n\n+ data.table 版本\n\n下面的写法来源于统计之都论坛上的[一个帖子](<https://d.cosx.org/d/423198-tidy-datatable>)。不熟 data.table 包的时候，可以使用`dtplyr::lazy_dt()`把tidy版本转换成data.table版本，然后用`dtplyr:::dt_call()`提取出具体的代码。\n\n```{r}\ntable2.dt.DT <- table2.dt[, .(\n  column2_mean = mean(column2),\n  sparkline1 = as.character(htmltools::as.tags(sparkline(column2, type = \"line\"))),\n  sparkline2 = as.character(htmltools::as.tags(sparkline(column2, type = \"bar\")))\n), keyby = .(column1)]\n\nDT::datatable(table2.dt.DT, escape = FALSE) |> spk_add_deps()\n```\n\n### 2.2.3. 与reactable 结合\n\n+ tidy 版本\n\n```{r}\ntable2.tidy.react <- table2 %>%\n  group_by(column1) %>%\n  summarise(\n    column2_mean = mean(column2),\n    sparkline1 = list(column2),\n    sparkline2 = list(column3)\n  )\n\nreactable(table2.tidy.react,\n          columns = list(\n            sparkline1 = colDef(\n              cell = function(values) {\n                sparkline(values,\n                          type = \"line\")\n              }\n            ),\n            sparkline2 = colDef(\n              cell = function(value, index) {\n                sparkline(table2.tidy.react$sparkline2[[index]], type = \"bar\")\n              }\n            )\n          ))\n```\n\n![](https://yuanfan.rbind.io/images/2022/2022-06-01-19.png)\n\n+ data.table 版本\n\n```{r}\ntable2.dt.react <-\n  table2.dt[, .(\n    column2_mean = mean(column2),\n    sparkline1 = list(column2),\n    sparkline2 = list(column3)), keyby = .(column1)]\n\nreactable(table2.dt.react,\n          columns = list(\n            sparkline1 = colDef(\n              cell = function(values) {\n                sparkline(values,\n                          type = \"line\")\n              }\n            ),\n            sparkline2 = colDef(\n              cell = function(value, index) {\n                sparkline(table2.dt.react$sparkline2[[index]], type = \"bar\")\n              }\n            )\n          ))\n```\n\n## 2.3. 把 tidy 代码转换成 data.table 代码\n\n举个栗子：\n\n```{r}\nlibrary(dtplyr)\na <- lazy_dt(table2) %>%\n  group_by(column1) %>%\n  summarise(\n    column2_mean = mean(column2),\n    sparkline1 = list(column2),\n    sparkline2 = list(column3)\n  )\n\ndtplyr:::dt_call(a)\n```\n\n得到：\n\n```\n`_DT2`[, .(column2_mean = mean(column2), sparkline1 = list(column2), \n    sparkline2 = list(column3)), keyby = .(column1)]\n```\n\n"
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    "content": "# install.packages(\"readxl\")\nlibrary(readxl)\nlibrary(ggplot2)\nlibrary(tidyverse)\nlibrary(cowplot)\nlibrary(viridis)\nlibrary(showtext)\nshowtext_auto()\n\n\n### 绘制不同方法的区域图===========\ndat = read_excel(\"test.xlsx\",sheet=1,na=\"NA\")\ncolnames(dat) = c(\"Id\",paste(\"X\",1:6,sep=''))\nhead(dat)\n\n\ndat %>% select(c(Id,X1,X4)) %>% rename(\"随机森林\"=X1, \"XGBoost\"=X4) %>% \n  pivot_longer(\n             cols = c(\"随机森林\",\"XGBoost\"),\n             names_to = \"method\",\n             names_transform = list(method = as.character),\n             values_to = \"Acc\") -> dat1\nhead(dat1)\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\np1 = ggplot(dat1) + \n  geom_area(aes(Id,Acc),fill = cols[1]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"精确率\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank()) \np1\n\n#==\ndat %>% select(c(Id,X2,X5)) %>% rename(\"随机森林\"=X2, \"XGBoost\"=X5) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"Acc\") -> dat2\n\n\np2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + ylab(\"召回率\") + xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np2\n\n#==\ndat %>% select(c(Id,X3,X6)) %>% rename(\"随机森林\"=X3, \"XGBoost\"=X6) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    # names_transform = list(method = as.factor),\n    values_to = \"Acc\") -> dat3\n\np3 = ggplot(dat3) + \n  geom_area(aes(Id,Acc),fill = cols[4]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"F1得分\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np3\n\nplot_grid(p1,p2,p3,ncol = 3)\n\n\n#==========================\n\ndat_all = read_excel(\"test.xlsx\",sheet=2,na=\"NA\")\nhead(dat_all)\n\ndat_all %>%\n  pivot_longer(\n    cols = `准确率`:`F1得分`,\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"value\") -> dat_all_new\n\ndat_all_new$method = fct_relevel(dat_all_new$method, \"准确率\", \"精确率\", \"召回率\",\"F1得分\")\ndat_all_new %>% ggplot(aes(method ,\n                           value,fill=模型)) +\n  geom_col(position = \"dodge\")+\n  facet_wrap(vars(文本向量化方法)) +\n  theme_bw() + ylab(\"数值\") + xlab(\"评价指标\") + #主题设置\n  # scale_fill_viridis(discrete = T) +\n  # geom_hline(aes(yintercept = 0.75),color = \"gray\") +\n  scale_fill_manual(values =cols[c(1,2,4)]) +\n  theme(panel.grid = element_blank())\n\n\n# =========\n\ndat_3_new$模型 = fct_relevel(dat_3_new$模型, \n     \"官方结果\",\"朴素贝叶斯\",\"支持向量机\",\"决策树\",\"随机森林\",\"XGBoost\")\n\n\ndat3 = read_excel(\"test.xlsx\",sheet=3,na=\"NA\")\nhead(dat3)\ndat3 %>%\n  pivot_longer(\n    cols = `官方结果`:`XGBoost`,\n    names_to = \"模型\",\n    names_transform = list(模型 = as.character),\n    values_to = \"value\") -> dat_3_new\ndat_3_new$模型 = factor(dat_3_new$模型,levels = c(\"官方结果\",\"朴素贝叶斯\"))\ndummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + \n  geom_col() +\n  facet_wrap(vars(`叶类名称`)) +\n  geom_hline(data = dummy2,aes(yintercept = Z)) +\n  scale_fill_manual(values = cols[1:6]) +\n  theme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n  # scale_fill_viridis(discrete = T) +\n  theme(panel.grid = element_blank(),\n        legend.position = 'bottom',\n        legend.direction = \"horizontal\",\n       axis.text.x = element_blank(),\n       axis.ticks.x = element_blank())\n  \n\n\n"
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    "content": "# install.packages(\"readxl\")\nlibrary(readxl)\nlibrary(ggplot2)\nlibrary(tidyverse)\nlibrary(cowplot)\nlibrary(viridis)\nlibrary(showtext)\nshowtext_auto()\n\n\n### 绘制不同方法的区域图===========\ndat = read_excel(\"test.xlsx\",sheet=1,na=\"NA\")\ncolnames(dat) = c(\"Id\",paste(\"X\",1:6,sep=''))\nhead(dat)\n\n\ndat %>% select(c(Id,X1,X4)) %>% rename(\"随机森林\"=X1, \"XGBoost\"=X4) %>% \n  pivot_longer(\n             cols = c(\"随机森林\",\"XGBoost\"),\n             names_to = \"method\",\n             names_transform = list(method = as.character),\n             values_to = \"Acc\") -> dat1\nhead(dat1)\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\np1 = ggplot(dat1) + \n  geom_area(aes(Id,Acc),fill = cols[1]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"精确率\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank()) \np1\n\n#==\ndat %>% select(c(Id,X2,X5)) %>% rename(\"随机森林\"=X2, \"XGBoost\"=X5) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"Acc\") -> dat2\n\n\np2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + ylab(\"召回率\") + xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np2\n\n#==\ndat %>% select(c(Id,X3,X6)) %>% rename(\"随机森林\"=X3, \"XGBoost\"=X6) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    # names_transform = list(method = as.factor),\n    values_to = \"Acc\") -> dat3\n\np3 = ggplot(dat3) + \n  geom_area(aes(Id,Acc),fill = cols[4]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"F1得分\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np3\n\nplot_grid(p1,p2,p3,ncol = 3)\n\n\n#==========================\n\ndat_all = read_excel(\"test.xlsx\",sheet=2,na=\"NA\")\nhead(dat_all)\n\ndat_all %>%\n  pivot_longer(\n    cols = `准确率`:`F1得分`,\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"value\") -> dat_all_new\n\ndat_all_new$method = fct_relevel(dat_all_new$method, \"准确率\", \"精确率\", \"召回率\",\"F1得分\")\ndat_all_new %>% ggplot(aes(method ,\n                           value,fill=模型)) +\n  geom_col(position = \"dodge\")+\n  facet_wrap(vars(文本向量化方法)) +\n  theme_bw() + ylab(\"数值\") + xlab(\"评价指标\") + #主题设置\n  # scale_fill_viridis(discrete = T) +\n  # geom_hline(aes(yintercept = 0.75),color = \"gray\") +\n  scale_fill_manual(values =cols[c(1,2,4)]) +\n  theme(panel.grid = element_blank())\n\n\n# =========\n\n\n\ndat3 = read_excel(\"test.xlsx\",sheet=3,na=\"NA\")\nhead(dat3)\ndat3 %>%\n  pivot_longer(\n    cols = `官方结果`:`XGBoost`,\n    names_to = \"模型\",\n    names_transform = list(模型 = as.character),\n    values_to = \"value\") -> dat_3_new\n\ndat_3_new$模型 = fct_relevel(dat_3_new$模型, \n                           \"官方结果\",\"朴素贝叶斯\",\"支持向量机\",\"决策树\",\"随机森林\",\"XGBoost\")\n\nhead(dat_3_new)\n\ndummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + \n  geom_col() +\n  facet_wrap(vars(`叶类名称`)) +\n  geom_hline(data = dummy2,aes(yintercept = Z)) +\n  # scale_fill_manual(values = cols[1:6]) +\n  theme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n  scale_fill_viridis(discrete = T,option = \"D\") +\n  theme(panel.grid = element_blank(),\n        legend.position = 'bottom',\n        legend.direction = \"horizontal\",\n       axis.text.x = element_blank(),\n       axis.ticks.x = element_blank())\n  \n\n\n"
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    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/alyuUumkwqC08RJilXmfeA\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n\n# install.packages(\"readxl\")\nlibrary(readxl)\nlibrary(ggplot2)\nlibrary(tidyverse)\nlibrary(cowplot)\nlibrary(viridis)\nlibrary(showtext)\nshowtext_auto()\n\n\n### 绘制不同方法的区域图===========\ndat = read_excel(\"test.xlsx\",sheet=1,na=\"NA\")\ncolnames(dat) = c(\"Id\",paste(\"X\",1:6,sep=''))\nhead(dat)\n\n\ndat %>% select(c(Id,X1,X4)) %>% rename(\"随机森林\"=X1, \"XGBoost\"=X4) %>% \n  pivot_longer(\n             cols = c(\"随机森林\",\"XGBoost\"),\n             names_to = \"method\",\n             names_transform = list(method = as.character),\n             values_to = \"Acc\") -> dat1\nhead(dat1)\ncols <- c(\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\np1 = ggplot(dat1) + \n  geom_area(aes(Id,Acc),fill = cols[1]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"精确率\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank()) \np1\n\n#==\ndat %>% select(c(Id,X2,X5)) %>% rename(\"随机森林\"=X2, \"XGBoost\"=X5) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"Acc\") -> dat2\n\n\np2 = ggplot(dat2) + geom_area(aes(Id,Acc),fill = cols[2]) + facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + ylab(\"召回率\") + xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np2\n\n#==\ndat %>% select(c(Id,X3,X6)) %>% rename(\"随机森林\"=X3, \"XGBoost\"=X6) %>% \n  pivot_longer(\n    cols = c(\"随机森林\",\"XGBoost\"),\n    names_to = \"method\",\n    # names_transform = list(method = as.factor),\n    values_to = \"Acc\") -> dat3\n\np3 = ggplot(dat3) + \n  geom_area(aes(Id,Acc),fill = cols[4]) + \n  facet_wrap(vars(method),nrow = 2,strip.position = \"top\") +\n  theme_bw() + \n  ylab(\"F1得分\") + \n  xlab(\"叶类\") + #主题设置\n  theme(panel.grid = element_blank())\np3\n\nplot_grid(p1,p2,p3,ncol = 3)\n\n\n#==========================\n\ndat_all = read_excel(\"test.xlsx\",sheet=2,na=\"NA\")\nhead(dat_all)\n\ndat_all %>%\n  pivot_longer(\n    cols = `准确率`:`F1得分`,\n    names_to = \"method\",\n    names_transform = list(method = as.character),\n    values_to = \"value\") -> dat_all_new\n\ndat_all_new$method = fct_relevel(dat_all_new$method, \"准确率\", \"精确率\", \"召回率\",\"F1得分\")\ndat_all_new %>% ggplot(aes(method ,\n                           value,fill=模型)) +\n  geom_col(position = \"dodge\")+\n  facet_wrap(vars(文本向量化方法)) +\n  theme_bw() + ylab(\"数值\") + xlab(\"评价指标\") + #主题设置\n  # scale_fill_viridis(discrete = T) +\n  # geom_hline(aes(yintercept = 0.75),color = \"gray\") +\n  scale_fill_manual(values =cols[c(1,2,4)]) +\n  theme(panel.grid = element_blank())\n\n\n# =========\n\ndat_3_new$模型 = fct_relevel(dat_3_new$模型, \n     \"官方结果\",\"朴素贝叶斯\",\"支持向量机\",\"决策树\",\"随机森林\",\"XGBoost\")\n\n\ndat3 = read_excel(\"test.xlsx\",sheet=3,na=\"NA\")\nhead(dat3)\ndat3 %>%\n  pivot_longer(\n    cols = `官方结果`:`XGBoost`,\n    names_to = \"模型\",\n    names_transform = list(模型 = as.character),\n    values_to = \"value\") -> dat_3_new\ndat_3_new$模型 = factor(dat_3_new$模型,levels = c(\"官方结果\",\"朴素贝叶斯\"))\ndummy2 <- data.frame(叶类名称 = dat3$叶类名称, Z = dat3$官方结果)\ndat_3_new %>% ggplot(aes(`模型`,value,fill = `模型`)) + \n  geom_col() +\n  facet_wrap(vars(`叶类名称`)) +\n  geom_hline(data = dummy2,aes(yintercept = Z)) +\n  scale_fill_manual(values = cols[1:6]) +\n  theme_bw() + ylab(\"准确度\") + xlab(\"\") + #主题设置\n  # scale_fill_viridis(discrete = T) +\n  theme(panel.grid = element_blank(),\n        legend.position = 'bottom',\n        legend.direction = \"horizontal\",\n       axis.text.x = element_blank(),\n       axis.ticks.x = element_blank())\n  \n\n\n"
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    "content": "day.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"p\",  # 垂直放置\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"5.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"calendar2023-2\"  # 给pdf文件命名\n)\n# showtext_auto(FALSE)\n# ===============\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = 5,\npdf = TRUE,\ndoc_name = \"My_calendar_brown\"\n)\ninvisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = img,\ndoc_name = file.path(\"C:\\\\Users\\\\ZLL\\\\Documents\\\\calendar\", paste0(\"Calendar(gray)_2021_\", i))))\n)\ninvisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = img,\ndoc_name = file.path(\"\\\\month\", paste0(\"Calendar(gray)_2021_\", i))))\n)\ninvisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = 5,\ndoc_name = file.path(\"\\\\month\", paste0(\"Calendar(gray)_2021_\", i))))\n)\ninvisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = 5,\ndoc_name = file.path(\"month\", paste0(\"Calendar(gray)_2023_\", i))))\n)\ninvisible(sapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            title.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"5.jpg\",\ndoc_name = file.path(\"month\", paste0(\"Calendar(gray)_2023_\", i))))\n)\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"5.jpg\",\ndoc_name = file.path(\"month\", paste0(\"Calendar(gray)_2023_\", i))))\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"5.jpg\",\ndoc_name = file.path(\"month\", paste0(\"Calendar(gray)_2023_\", i))))\nlibrary(calendR)\nlibrary(showtext)\nshowtext_auto()\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = 5,\npdf = TRUE,\ndoc_name = \"version1/My_calendar_brown\"\n)\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = \"4.jpg\",\npdf = TRUE,\ndoc_name = \"version1/My_calendar_brown\"\n)\nlibrary(calendR)\nlibrary(showtext)\nshowtext_auto()\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = \"4.jpg\",\npdf = TRUE,\ndoc_name = \"version1/My_calendar\"\n)\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"5.jpg\",\ndoc_name = file.path(\"month\", paste0(\"Calendar(gray)_2023_\", i))))\nlibrary(calendR)\nlibrary(showtext)\nshowtext_auto()\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = \"4.jpg\",\npdf = TRUE,\ndoc_name = \"version1/calendar2023\"\n)\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"5.jpg\",\ndoc_name = file.path(\"version1/month\", paste0(\"Calendar(gray)_2023_\", i))))\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"p\",\nbg.img = \"5.jpg\",\npdf = TRUE,\ndoc_name = \"version1/calendar2023\"\n)\nlibrary(calendR)\nlibrary(showtext)\n# setwd(\"C:\\\\Users\\\\ZLL\\\\Desktop\\\\wechat\\\\庄闪闪原创\\\\R\\\\R可视乎\\\\2020.11.13日历\\\\wechat\\\\calendar\\\\读者（楚新元）\")\n# font_add(\"kaishu\", \"simkai.ttf\")\nshowtext_auto()\n# 判断文件是否存在，如果存在先删除\n# if (file.exists(\"calendar2023.pdf\") == T) {\n#   file.remove(\"calendar2023.pdf\")\n# }\ncalendR(\n# 定义标题\n# title, # 如果缺失，则以年或年月替代\nyear = 2023,\ntitle.size = 40,\ntitle.col = \"white\",  # 年份字体颜色\n# 定义副标题\n# subtitle = \"每天好心情\",\nsubtitle.size = 10,\nsubtitle.col = \"gray30\",\n# 定义月份\nmbg.col = \"#274652\",   # 月份背景颜色\nmonths.col = \"white\",  # 月份字体颜色\nmonths.size = 10, # 定义月份字体大小\nmonths.pos = 0.5, # 定义月份水平居中\n# 定义周\nweeknames = c(\n\"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\",\n\"Sa\", \"Su\"\n),  # 定义周名称\nweeknames.col = \"white\",  # 周字体颜色\nweeknames.size = 4.5, # 定义周字体大小\nstart = \"M\",  # 设置从周一开始\n# 定义日期\ndays.col = \"gray30\", # 定义日期的颜色\nday.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"p\",  # 垂直放置\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"4.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"calendar2023\"  # 给pdf文件命名\n)\nlibrary(calendR)\nlibrary(showtext)\n# setwd(\"C:\\\\Users\\\\ZLL\\\\Desktop\\\\wechat\\\\庄闪闪原创\\\\R\\\\R可视乎\\\\2020.11.13日历\\\\wechat\\\\calendar\\\\读者（楚新元）\")\n# font_add(\"kaishu\", \"simkai.ttf\")\nshowtext_auto()\n# 判断文件是否存在，如果存在先删除\n# if (file.exists(\"calendar2023.pdf\") == T) {\n#   file.remove(\"calendar2023.pdf\")\n# }\ncalendR(\n# 定义标题\n# title, # 如果缺失，则以年或年月替代\nyear = 2023,\ntitle.size = 40,\ntitle.col = \"white\",  # 年份字体颜色\n# 定义副标题\n# subtitle = \"每天好心情\",\nsubtitle.size = 10,\nsubtitle.col = \"gray30\",\n# 定义月份\nmbg.col = \"#274652\",   # 月份背景颜色\nmonths.col = \"white\",  # 月份字体颜色\nmonths.size = 10, # 定义月份字体大小\nmonths.pos = 0.5, # 定义月份水平居中\n# 定义周\nweeknames = c(\n\"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\",\n\"Sa\", \"Su\"\n),  # 定义周名称\nweeknames.col = \"white\",  # 周字体颜色\nweeknames.size = 4.5, # 定义周字体大小\nstart = \"M\",  # 设置从周一开始\n# 定义日期\ndays.col = \"gray30\", # 定义日期的颜色\nday.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"p\",  # 垂直放置\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"4.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"version2/calendar2023\"  # 给pdf文件命名\n)\n# showtext_auto(FALSE)\n# ===============\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"lightblue\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"4.jpg\",\ndoc_name = file.path(\"version2/month\", paste0(\"Calendar(gray)_2023_\", i))))\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"lightblue\",\n# Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"gray60\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nbg.img = \"4.jpg\",\ndoc_name = file.path(\"version2/month\", paste0(\"Calendar(blue)_2023_\", i))))\ncalendR(\n# 定义标题\n# title, # 如果缺失，则以年或年月替代\nyear = 2023,\ntitle.size = 40,\ntitle.col = \"white\",  # 年份字体颜色\n# 定义副标题\n# subtitle = \"每天好心情\",\nsubtitle.size = 10,\nsubtitle.col = \"gray30\",\n# 定义月份\nmbg.col = \"#274652\",   # 月份背景颜色\nmonths.col = \"white\",  # 月份字体颜色\nmonths.size = 10, # 定义月份字体大小\nmonths.pos = 0.5, # 定义月份水平居中\n# 定义周\nweeknames = c(\n\"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\",\n\"Sa\", \"Su\"\n),  # 定义周名称\nweeknames.col = \"white\",  # 周字体颜色\nweeknames.size = 4.5, # 定义周字体大小\nstart = \"M\",  # 设置从周一开始\n# 定义日期\ndays.col = \"gray30\", # 定义日期的颜色\nday.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"l\",  # 垂直放置 \"l\"\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"4.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"version2/calendar2023-2\"  # 给pdf文件命名\n)\ncalendR(year = 2023,\nstart = \"M\",\ntitle.col = \"white\",\n# Weeks start on Monday\nmbg.col = \"#cd853f\",               # Background color of the month names\nmonths.col = \"white\",      # Color of the text of the month names\nweeknames.col = \"white\",\nspecial.days = \"weekend\",  # Color the weekends\nspecial.col = \"#a9a9a9\", # Color of the special.days\nlty = 0,                   # Line type (no line)\nweeknames = c(\"Mo\", \"Tu\",  # Week names\n\"We\", \"Th\",\n\"Fr\", \"Sa\",\n\"Su\"),\ntitle.size = 40,   # Title size\norientation = \"l\",\nbg.img = \"5.jpg\",\npdf = TRUE,\ndoc_name = \"version1/calendar2023-2\"\n)\ncalendR(\n# 定义标题\n# title, # 如果缺失，则以年或年月替代\nyear = 2023,\ntitle.size = 40,\ntitle.col = \"white\",  # 年份字体颜色\n# 定义副标题\n# subtitle = \"每天好心情\",\nsubtitle.size = 10,\nsubtitle.col = \"gray30\",\n# 定义月份\nmbg.col = \"#7FCF60\",   # 月份背景颜色\nmonths.col = \"white\",  # 月份字体颜色\nmonths.size = 10, # 定义月份字体大小\nmonths.pos = 0.5, # 定义月份水平居中\n# 定义周\nweeknames = c(\n\"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\",\n\"Sa\", \"Su\"\n),  # 定义周名称\nweeknames.col = \"white\",  # 周字体颜色\nweeknames.size = 4.5, # 定义周字体大小\nstart = \"M\",  # 设置从周一开始\n# 定义日期\ndays.col = \"gray30\", # 定义日期的颜色\nday.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"p\",  # 垂直放置 \"l\"\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"4.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"version2/calendar2023\"  # 给pdf文件命名\n)\ncalendR(\n# 定义标题\n# title, # 如果缺失，则以年或年月替代\nyear = 2023,\ntitle.size = 40,\ntitle.col = \"white\",  # 年份字体颜色\n# 定义副标题\n# subtitle = \"每天好心情\",\nsubtitle.size = 10,\nsubtitle.col = \"gray30\",\n# 定义月份\nmbg.col = \"#7FCF60\",   # 月份背景颜色\nmonths.col = \"white\",  # 月份字体颜色\nmonths.size = 10, # 定义月份字体大小\nmonths.pos = 0.5, # 定义月份水平居中\n# 定义周\nweeknames = c(\n\"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\",\n\"Sa\", \"Su\"\n),  # 定义周名称\nweeknames.col = \"white\",  # 周字体颜色\nweeknames.size = 4.5, # 定义周字体大小\nstart = \"M\",  # 设置从周一开始\n# 定义日期\ndays.col = \"gray30\", # 定义日期的颜色\nday.size = 3, # 定义日期的字体大小\nspecial.days = \"weekend\",  # 定义周末为特殊日期\nspecial.col = \"lightblue\",  # 特殊日期背景颜色\nlow.col = \"white\", # 非特殊日期背景颜色\nlty = 0,  # 去掉边框线\ncol = \"white\",\n# 定义背景、放置方向、生成文件\n# font.family = \"kaishu\", # 设置字体\norientation = \"l\",  # 垂直放置 \"l\"\npapersize = \"A4\", # 设置纸张大小\nbg.img = \"4.jpg\", # 设置背景图片\npdf = TRUE, # 生成pdf文件\ndoc_name = \"version2/calendar2023-2\"  # 给pdf文件命名\n)\n"
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    "content": "[1,\"Installing 'calendR' ...\\n\"]\n[1,\"[1/4] Installing suncalc...\\n\"]\n[2,\"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/suncalc_0.5.1.tgz'\\n\"]\n[2,\"Content type 'application/x-gzip' length 60786 bytes (59 KB)\\n===========================\"]\n[2,\"=======\"]\n[1,\"The downloaded binary packages are in\\n\"]\n[1,\"\\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\\n\"]\n[1,\"[2/4] Installing ggimage...\\n\"]\n[2,\"================\\ndownloaded 59 KB\\n\\n\"]\n[2,\"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/ggimage_0.3.1.tgz'\\n\"]\n[2,\"Content type 'application/x-gzip' length 147327 bytes (143 KB)\\n===========\"]\n[2,\"======\"]\n[2,\"==========\"]\n[2,\"=======\"]\n[2,\"================\\ndownloaded 143 KB\\n\\n\"]\n[1,\"The downloaded binary packages are in\\n\"]\n[1,\"\\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\\n\"]\n[1,\"[3/4] Installing gggibbous...\\n\"]\n[2,\"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/gggibbous_0.1.1.tgz'\\n\"]\n[2,\"Content type 'application/x-gzip' length 515549 bytes (503 KB)\\n=\"]\n[2,\"=\"]\n[2,\"==\"]\n[2,\"===\"]\n[2,\"==\"]\n[2,\"===\"]\n[2,\"==\"]\n[2,\"=======\"]\n[2,\"==\"]\n[2,\"===========\"]\n[2,\"=====\"]\n[2,\"=\"]\n[2,\"==========\\ndownloaded 503 KB\\n\\n\"]\n[1,\"The downloaded binary packages are in\\n\"]\n[1,\"\\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\\n\"]\n[1,\"[4/4] Installing calendR...\\n\"]\n[2,\"trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.2/calendR_1.1.tgz'\\n\"]\n[2,\"Content type 'application/x-gzip' length 38987 bytes (38 KB)\\n================\"]\n[2,\"==================================\\ndownloaded 38 KB\\n\\n\"]\n[1,\"The downloaded binary packages are in\\n\"]\n[1,\"\\t/var/folders/wt/t3n70vjs6g536860y9y_yr9r0000gn/T//Rtmp5QxrBE/downloaded_packages\\n\"]\n[2,\"\\n\\n✔ Package 'calendR' successfully installed.\\n\"]\n"
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    "content": "library(calendR)\nlibrary(showtext)\nshowtext_auto()\n\ncalendR(year = 2023,\n        start = \"M\", \n        title.col = \"white\",\n        # Weeks start on Monday\n        mbg.col = \"#cd853f\",               # Background color of the month names\n        months.col = \"white\",      # Color of the text of the month names\n        weeknames.col = \"white\",\n        \n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"#a9a9a9\", # Color of the special.days\n        lty = 0,                   # Line type (no line)\n        weeknames = c(\"Mo\", \"Tu\",  # Week names\n                      \"We\", \"Th\",\n                      \"Fr\", \"Sa\",\n                      \"Su\"),\n        title.size = 40,   # Title size\n        orientation = \"p\",\n        bg.img = \"5.jpg\",\n        pdf = TRUE,\n        doc_name = \"version1/calendar2023\"\n)\n\n\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            \n                                  title.col = \"white\",\n                                  # Weeks start on Monday\n                                  mbg.col = \"#cd853f\",  \n                                  # Background color of the month names\n                                  months.col = \"white\",      # Color of the text of the month names\n                                  weeknames.col = \"white\",\n                                  special.days = \"weekend\",  # Color the weekends\n                                  special.col = \"gray60\", # Color of the special.days\n                                  lty = 0,                   # Line type (no line)\n                                  bg.img = \"5.jpg\",\n                                  doc_name = file.path(\"version1/month\", paste0(\"Calendar(gray)_2023_\", i))))\n\n\n"
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    "content": "library(calendR)\nlibrary(showtext)\nshowtext_auto()\n\n# 判断文件是否存在，如果存在先删除\n# if (file.exists(\"calendar2023.pdf\") == T) {\n#   file.remove(\"calendar2023.pdf\")\n# } \n\n\n\ncalendR(\n  # 定义标题\n  # title, # 如果缺失，则以年或年月替代\n  year = 2023,\n  title.size = 40,\n  title.col = \"white\",  # 年份字体颜色\n  \n  # 定义副标题\n  # subtitle = \"每天好心情\",\n  subtitle.size = 10,\n  subtitle.col = \"gray30\",\n  \n  # 定义月份\n  mbg.col = \"#7FCF60\",   # 月份背景颜色\n  months.col = \"white\",  # 月份字体颜色 \n  months.size = 10, # 定义月份字体大小\n  months.pos = 0.5, # 定义月份水平居中\n  \n  # 定义周\n  weeknames = c(\n    \"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\", \n    \"Sa\", \"Su\"\n  ),  # 定义周名称\n  weeknames.col = \"white\",  # 周字体颜色\n  weeknames.size = 4.5, # 定义周字体大小\n  start = \"M\",  # 设置从周一开始\n  \n  # 定义日期\n  days.col = \"gray30\", # 定义日期的颜色\n  day.size = 3, # 定义日期的字体大小\n  special.days = \"weekend\",  # 定义周末为特殊日期\n  special.col = \"lightblue\",  # 特殊日期背景颜色\n  low.col = \"white\", # 非特殊日期背景颜色\n  lty = 0,  # 去掉边框线 \n  col = \"white\",\n  \n  # 定义背景、放置方向、生成文件\n  # font.family = \"kaishu\", # 设置字体\n  orientation = \"p\",  # 垂直放置 \"l\"\n  papersize = \"A4\", # 设置纸张大小\n  bg.img = \"4.jpg\", # 设置背景图片\n  pdf = TRUE, # 生成pdf文件\n  doc_name = \"version2/calendar2023\"  # 给pdf文件命名\n)\n\n# showtext_auto(FALSE)\n\n\n# ===============\n\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            \n                                  title.col = \"white\",\n                                  # Weeks start on Monday\n                                  mbg.col = \"lightblue\",  \n                                  # Background color of the month names\n                                  months.col = \"white\",      # Color of the text of the month names\n                                  weeknames.col = \"white\",\n                                  special.days = \"weekend\",  # Color the weekends\n                                  special.col = \"gray60\", # Color of the special.days\n                                  lty = 0,                   # Line type (no line)\n                                  bg.img = \"4.jpg\",\n                                  doc_name = file.path(\"version2/month\", paste0(\"Calendar(blue)_2023_\", i))))\n\n"
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    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\nlibrary(calendR)\nlibrary(showtext)\nshowtext_auto()\n\ncalendR(year = 2023,\n        start = \"M\", \n        title.col = \"white\",\n        # Weeks start on Monday\n        mbg.col = \"#cd853f\",               # Background color of the month names\n        months.col = \"white\",      # Color of the text of the month names\n        weeknames.col = \"white\",\n        \n        special.days = \"weekend\",  # Color the weekends\n        special.col = \"#a9a9a9\", # Color of the special.days\n        lty = 0,                   # Line type (no line)\n        weeknames = c(\"Mo\", \"Tu\",  # Week names\n                      \"We\", \"Th\",\n                      \"Fr\", \"Sa\",\n                      \"Su\"),\n        title.size = 40,   # Title size\n        orientation = \"p\",\n        bg.img = \"5.jpg\",\n        pdf = TRUE,\n        doc_name = \"version1/calendar2023\"\n)\n\n\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            \n                                  title.col = \"white\",\n                                  # Weeks start on Monday\n                                  mbg.col = \"#cd853f\",  \n                                  # Background color of the month names\n                                  months.col = \"white\",      # Color of the text of the month names\n                                  weeknames.col = \"white\",\n                                  special.days = \"weekend\",  # Color the weekends\n                                  special.col = \"gray60\", # Color of the special.days\n                                  lty = 0,                   # Line type (no line)\n                                  bg.img = \"5.jpg\",\n                                  doc_name = file.path(\"version1/month\", paste0(\"Calendar(gray)_2023_\", i))))\n\n\n"
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    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\nlibrary(calendR)\nlibrary(showtext)\nshowtext_auto()\n\n# 判断文件是否存在，如果存在先删除\n# if (file.exists(\"calendar2023.pdf\") == T) {\n#   file.remove(\"calendar2023.pdf\")\n# } \n\n\n\ncalendR(\n  # 定义标题\n  # title, # 如果缺失，则以年或年月替代\n  year = 2023,\n  title.size = 40,\n  title.col = \"white\",  # 年份字体颜色\n  \n  # 定义副标题\n  # subtitle = \"每天好心情\",\n  subtitle.size = 10,\n  subtitle.col = \"gray30\",\n  \n  # 定义月份\n  mbg.col = \"#7FCF60\",   # 月份背景颜色\n  months.col = \"white\",  # 月份字体颜色 \n  months.size = 10, # 定义月份字体大小\n  months.pos = 0.5, # 定义月份水平居中\n  \n  # 定义周\n  weeknames = c(\n    \"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\", \n    \"Sa\", \"Su\"\n  ),  # 定义周名称\n  weeknames.col = \"white\",  # 周字体颜色\n  weeknames.size = 4.5, # 定义周字体大小\n  start = \"M\",  # 设置从周一开始\n  \n  # 定义日期\n  days.col = \"gray30\", # 定义日期的颜色\n  day.size = 3, # 定义日期的字体大小\n  special.days = \"weekend\",  # 定义周末为特殊日期\n  special.col = \"lightblue\",  # 特殊日期背景颜色\n  low.col = \"white\", # 非特殊日期背景颜色\n  lty = 0,  # 去掉边框线 \n  col = \"white\",\n  \n  # 定义背景、放置方向、生成文件\n  # font.family = \"kaishu\", # 设置字体\n  orientation = \"p\",  # 垂直放置 \"l\"\n  papersize = \"A4\", # 设置纸张大小\n  bg.img = \"4.jpg\", # 设置背景图片\n  pdf = TRUE, # 生成pdf文件\n  doc_name = \"version2/calendar2023\"  # 给pdf文件命名\n)\n\n# showtext_auto(FALSE)\n\n\n# ===============\n\nsapply(1:12 , function(i) calendR(year = 2023,month = i, pdf = TRUE,                            \n                                  title.col = \"white\",\n                                  # Weeks start on Monday\n                                  mbg.col = \"lightblue\",  \n                                  # Background color of the month names\n                                  months.col = \"white\",      # Color of the text of the month names\n                                  weeknames.col = \"white\",\n                                  special.days = \"weekend\",  # Color the weekends\n                                  special.col = \"gray60\", # Color of the special.days\n                                  lty = 0,                   # Line type (no line)\n                                  bg.img = \"4.jpg\",\n                                  doc_name = file.path(\"version2/month\", paste0(\"Calendar(blue)_2023_\", i))))\n\n"
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    "content": "# 加载数据=====\ndf = read_csv(\"df.csv\")\nhead(df)\nlibrary(tidyverse)\n# 加载数据=====\ndf = read_csv(\"df.csv\")\nhead(df)\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1)) +\nscale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\np1\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1)) +\nscale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\np1\n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\n# scale_fill_viridis(discrete = T) +\n#scale_fill_manual(name = \"Species\", values=PZ) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\n# 链接：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7\nlibrary(tidyverse)\n# 加载数据=====\ndf = read_csv(\"df.csv\")\nhead(df)\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1)) +\nscale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\np1\n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\n# scale_fill_viridis(discrete = T) +\n#scale_fill_manual(name = \"Species\", values=PZ) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\nhead(df)\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") # +\np1\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1))# +\np1\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1))\np1\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 40, name = \"Depth\"))\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth, group = 1))\np1\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1))\np1\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1))\np1\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\nlibrary(viridis)\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\n#scale_fill_manual(name = \"Species\", values=PZ) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\n# scale_fill_viridis(discrete = T) +\n#scale_fill_manual(name = \"Species\", values=PZ) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\n#scale_fill_manual(name = \"Species\", values=PZ) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\")\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\n# 双轴绘制 ======\np1 = df %>%\nfilter(type == \"Adult\") %>%\ngroup_by(station) %>%\nmutate(Percent = 100 * number/sum(number),\nstation=factor(station)) %>%\nggplot(aes(station, fill = species)) +\ngeom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\ngeom_line(aes(y = depth /4, group = 1)) + geom_point(aes(y = depth /4, group = 1))\np1\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\np1\np1 + scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\"))\n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\ntheme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\npanel.grid = element_blank()) +\nscale_fill_viridis(discrete = T) +\nfacet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\")\n"
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    "content": "# ======== 使用分面展示不同组别的双 Y 轴图形 =======\n# 出自：庄闪闪\n# 参考资料：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7\nlibrary(tidyverse)\n\n# 加载数据=====\ndf = read_csv(\"df.csv\")\nhead(df)\n# 双轴绘制 ======\np1 = df %>% \n  filter(type == \"Adult\") %>%\n  group_by(station) %>% \n  mutate(Percent = 100 * number/sum(number),\n         station=factor(station)) %>%\n  ggplot(aes(station, fill = species)) + \n  geom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\n  geom_line(aes(y = depth /4, group = 1)) +\n  scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\")) \np1 \n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\n  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\n        panel.grid = element_blank()) +\n  # scale_fill_viridis(discrete = T) +\n  #scale_fill_manual(name = \"Species\", values=PZ) + \n  facet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\") \n\n\n\n"
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    "content": "# ======== 使用分面展示不同组别的双 Y 轴图形 =======\n# 出自：庄闪闪\n# 参考资料：https://community.rstudio.com/t/add-secondary-axis-on-ggplot/138671/7\nlibrary(tidyverse)\n\n# 加载数据=====\ndf = read_csv(\"df.csv\")\nhead(df)\n# 双轴绘制 ======\np1 = df %>% \n  filter(type == \"Adult\") %>%\n  group_by(station) %>% \n  mutate(Percent = 100 * number/sum(number),\n         station=factor(station)) %>%\n  ggplot(aes(station, fill = species)) + \n  geom_bar(aes(y = Percent), position = \"stack\", stat = \"identity\") +\n  geom_line(aes(y = depth /4, group = 1)) +\n  scale_y_continuous(sec.axis = sec_axis(~ . * 4, name = \"Depth\")) \np1 \n# 双轴 + 分面\nlibrary(viridis)\np1 + theme_bw() +\n  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\n        panel.grid = element_blank()) +\n  # scale_fill_viridis(discrete = T) +\n  #scale_fill_manual(name = \"Species\", values=PZ) + \n  facet_grid(~factor(zone,levels = c(\"A1\",\"A2\",\"A3\")), scale = \"free_x\", space = \"free_x\") \n\n\n\n"
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    "content": "zone,type,station,species,number,depth\nA1,Adult,1,Atlanticus,2,200\nA1,Adult,1,Olrikii,1,450\nA1,Larvae,2,Medius,5,345\nA1,Adult,3,Calo,8,402\nA1,Larvae,3,Pryi,15,298\nA1,Zoo,4,Galk,2,336\nA1,Adult,4,Spu,9,315\nA2,Adult,5,Dryu,17,269\nA2,Adult,5,Spu,3,256\nA2,Larvae,5,Olrikii,13,311\nA2,Zoo,6,Calo,5,289\nA2,Zoo,6,Unidentified,3,405\nA2,Adult,6,Glacialis,2,380\nA2,Larvae,7,Medius,4,310\nA2,Larvae,7,Glou,7,269\nA3,Zoo,8,Capilatta,17,265\nA3,Zoo,8,Calo,17,295\nA3,Adult,9,Dryu,17,378\nA3,Zoo,9,Glou,17,368\nA3,Zoo,9,Azu,17,490\nA3,Adult,10,Olrikii,1,410\n"
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  {
    "path": "2023年/2023.03.07 高亮柱状图/高亮柱状图.R",
    "content": "# 代码细节解释见推文：https://mp.weixin.qq.com/s/Rml11BcLE5_fLKckfob9Wg\n# 作者：庄亮亮/庄闪闪\n\n\n# 导入 ggplot2 和 reshape2 包\nlibrary(ggplot2)\nlibrary(reshape2)\n\n# 创建数据框\ndata <- data.frame(\n  Method = c(\"A\", \"B\", \"C\", \"D\"),\n  RMSE = c(1.5, 2.3, 1.9, 2.1),\n  RB = c(0.8, 1.2, 1.5, 0.7)\n)\nhead(data)\n\n## ggplot2版本 ===========\n\n# 使用ggplot2创建柱状图\nh1 <- c(\"red\", \"grey\", \"grey\", \"grey\")\np1 = ggplot(data, aes(x = Method, y = RMSE, fill = Method)) +\n  geom_col() + \n  scale_fill_manual(values = h1)  \n\nh2 <- c(\"grey\", \"grey\", \"grey\",\"red\")\np2 = ggplot(data, aes(x = Method, y = RB, fill = Method)) +\n  geom_col() + \n  scale_fill_manual(values = h2)  \n\nlibrary(cowplot)\nplot_grid(p1,p2)\n\n# ==============================================================================\n## ggcharts 包\n\nlibrary(ggcharts)\nlibrary(dplyr)\ndata(\"biomedicalrevenue\")\nrevenue2018 <- biomedicalrevenue %>%\n  filter(year == 2018)\nhead(revenue2018)\n\nggcharts_set_theme(\"theme_ggcharts\")\nbar_chart(\n  revenue2018,\n  company,\n  revenue,\n  top_n = 10,\n  highlight = \"Roche\"\n)\n\n\nggcharts_set_theme(\"theme_ng\")\nbar_chart(\n  revenue2018,\n  company,\n  revenue,\n  top_n = 10,\n  highlight = \"Roche\"\n)\n\nggcharts_set_theme(\"theme_ggcharts\")\nbiomedicalrevenue %>%\n  filter(year %in% c(2012, 2014, 2016, 2018)) %>%\n  bar_chart(\n    company,\n    revenue,\n    facet = year,\n    top_n = 12,\n    highlight = \"Bayer\"\n  )\nhead(biomedicalrevenue)\n\n\n\n## ==========\ndata_long <- tidyr::gather(data, Metric, Value, -Method)\nhead(data_long)\ndata_long %>%\n  bar_chart(\n    Method,\n    Value,\n    facet = as.factor(Metric),\n    top_n = 12,\n    highlight = \"A\",\n    horizontal = T\n  )\n\n\n\n\n\n\n\n"
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  {
    "path": "2023年/2023.03.08 基于 ggridges 绘制剩余使用寿命密度图/rul_ggbridges.R",
    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/ECquLeoHaJ2byWgrRpqftw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n## 基于ggridges的剩余使用寿命密度图\nlibrary(tidyverse)\nlibrary(ggridges)\nlibrary(viridis)\n\nmean1 = seq(21,1,length.out =5) # 刻画不同时间对应密度函数的均值\nstd1 = seq(5,3,length.out =5) # 刻画不同时间对应密度函数的标准差\nlen = 10000 #分布中离散数据的个数\ny <- c(1,6,11,16,21) # 未来时间\nz = matrix(NA,len,5) # 存储抽样得到的后验\nfor(i in 1:length(mean1)){\n  z[,i] = rnorm(len,mean1[i],std1[i])\n}\n# 合并数据\ndat = as.data.frame(cbind(y,z))\ncolnames(dat) = c(\"Cur_time\",paste(\"z\",1:5,sep = \"\"))\nhead(dat)\n\ndat %>% pivot_longer(`z1`:`z5`,\n                     names_to = \"Class\",\n                     values_to = \"Value\"\n                     ) -> new_dat\nhead(new_dat)\n\n\n##============================== 绘图 ==============================\n### 初级版本\nggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +\n  geom_density_ridges()\n\n### 进阶版本\nggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +\n  geom_density_ridges() +\n  scale_fill_viridis(discrete = T) +\n  scale_y_discrete(labels = paste(\"Day\",mean1 = seq(1,21,length.out =5) ,seq='')) +\n  theme_bw() + \n  theme(legend.position = \"none\",\n        panel.grid = element_blank()) \n\n\n# 添加均值1\nggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +\n  geom_density_ridges() +\n  geom_density_ridges(quantile_lines=TRUE,\n                      quantile_fun=function(x,...)mean(x))+ \n  scale_fill_viridis(discrete = T) +\n  scale_y_discrete(labels = paste(\"Day\",mean1 = seq(1,21,length.out =5) ,seq='')) +\n  theme_bw() + \n  theme(legend.position = \"none\",\n        panel.grid = element_blank()) \n\n# 添加均值2\nggplot(new_dat, aes(x = Value, y = Class, fill = Class)) +\n  geom_density_ridges() +\n  stat_summary(fun = \"mean\", geom = \"point\", shape = 21, size = 2,fill = \"red\",color = 'red') +\n  scale_fill_viridis(discrete = T) +\n  scale_y_discrete(labels = paste(\"Day\",mean1 = seq(1,21,length.out =5) ,seq='')) +\n  theme_bw() + \n  theme(legend.position = \"none\",\n                     panel.grid = element_blank()) \n\n# 添加分位数\nggplot(new_dat, aes(x = Value, y = Class, fill = factor(after_stat(quantile)))) +\n  stat_density_ridges(\n    geom = \"density_ridges_gradient\", \n    calc_ecdf = TRUE,\n    quantiles = c(0.025, 0.975)) +\n  scale_fill_manual(\n    name = \"Probability\", values = c(\"#FF0000A0\", \"#A0A0A0A0\", \"#0000FFA0\"),\n    labels = c(\"(0, 0.025]\", \"(0.025, 0.975]\", \"(0.975, 1]\")\n  ) +\n  theme_bw() + \n  theme(panel.grid = element_blank())\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"
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  {
    "path": "2023年/2023.03.09基于 ggdensity 包的等高线绘制/等高线绘制.R",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/IRSvobzOfqmYtfBXDpbdJw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n# 基于ggdensity包的等高线绘制\n\nlibrary(ggdensity)\nlibrary(ggblanket)\nlibrary(ggsci)\n\nhead(iris)\n## ggplot 直接版本\nggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +\n  ggdensity::geom_hdr() + \n  scale_fill_aaas() +\n  facet_wrap(vars(Species)) \n\n## 添加其他geom\nggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +\n  ggdensity::geom_hdr() + \n  geom_point(shape = 21) +\n  scale_fill_aaas() +\n  facet_wrap(vars(Species)) \n\nggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +\n  ggdensity::geom_hdr_lines() +\n  geom_point() +\n  scale_color_aaas() +\n  facet_wrap(vars(Species)) \n\nggplot(iris,aes(x = Sepal.Width, y = Sepal.Length, fill = Species)) +\n  ggdensity::geom_hdr() + \n  geom_point(shape = 21) +\n  scale_fill_aaas() +\n  facet_wrap(vars(Species)) +\n  geom_hdr_rug()\n\n## ggblanket 版本\niris |>\n  gg_blank(\n    x = Sepal.Width,\n    y = Sepal.Length,\n    col = Species,\n    facet = Species,\n    col_legend_place = \"r\") +\n  ggdensity::geom_hdr(colour = NA) +\n  labs(alpha = \"Probs\") +\n  theme(legend.title = element_text(margin = margin(t = 5)))\n\n# mutate(Species = stringr::str_to_sentence(Species)) |> \n\n### 细节处理\n## 修改x轴标签大小写问题：\n# 使用stringr::str_to_sentene()修改Species列中的文本。并在col_labels中设置为：stringr::str_to_sentence\n\niris |>\n  mutate(Species = stringr::str_to_sentence(Species)) |> \n  gg_blank(\n    x = Sepal.Width,\n    y = Sepal.Length,\n    col = Species,\n    facet = Species,\n    col_legend_place = \"r\",\n    col_labels = stringr::str_to_sentence) +\n  ggdensity::geom_hdr(colour = NA) +\n  labs(alpha = \"Probs\") +\n  theme(legend.title = element_text(margin = margin(t = 5)))\n\n"
  },
  {
    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/timeseries.R",
    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/rAlPbNh2bYuNkqyT9dxosQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 构建数据\nset.seed(20230309)\nlibrary(tidyverse)\ndtData <- tibble(\n  DateCol = seq(\n    as.Date(\"1/01/2020\", \"%d/%m/%Y\"),\n    as.Date(\"31/12/2022\", \"%d/%m/%Y\"), \"days\"\n  ),\n  ValueCol = sample(\n    c('a', 'b', 'c', 'd', 'e'),\n    1096,\n    prob = c(0.7, 0.1, 0.1, 0.05, 0.05),\n    replace = TRUE\n  )\n)\n\nhead(dtData)\n\nlibrary(ggTimeSeries)\nlibrary(ggplot2)\n\np = ggplot_calendar_heatmap(\n  dtData, dayBorderSize = 1,\n  monthBorderSize = 1,dayBorderColour = \"white\",\n  'DateCol',monthBorderColour = \"white\",\n  'ValueCol'\n)\n\n# adding some formatting\ncol <- c(\"#EBEDF0\", \"#CBE491\", \"#89C876\", \"#459944\", \"#2C602C\")\np + \n  scale_fill_manual(values = col) + \n  facet_wrap(~Year, ncol = 1,strip.position = \"right\") +\n  theme( panel.background = element_blank(),\n         legend.text = element_blank(),\n         panel.border = element_rect(colour=\"grey60\",fill=NA),\n         strip.background = element_blank(),\n         strip.text = element_text(size=13,face=\"plain\",color=\"black\"),\n         axis.line=element_line(colour=\"black\",size=0.25),\n         axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\n         axis.text = element_text(size=10,face=\"plain\",color=\"black\")) +\n  xlab('') + \n  ylab('') +  \n  labs(fill = \"Freq\") \n\n"
  },
  {
    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图.html",
    "content": "<!DOCTYPE html>\n<html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"en\" xml:lang=\"en\"><head>\n\n<meta charset=\"utf-8\">\n<meta name=\"generator\" content=\"quarto-1.2.269\">\n\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\">\n\n<meta name=\"author\" content=\"庄闪闪\">\n\n<title>ggtimeseries</title>\n<style>\ncode{white-space: pre-wrap;}\nspan.smallcaps{font-variant: small-caps;}\ndiv.columns{display: flex; gap: min(4vw, 1.5em);}\ndiv.column{flex: auto; overflow-x: auto;}\ndiv.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}\nul.task-list{list-style: none;}\nul.task-list li input[type=\"checkbox\"] {\n  width: 0.8em;\n  margin: 0 0.8em 0.2em -1.6em;\n  vertical-align: middle;\n}\npre > code.sourceCode { white-space: pre; position: relative; }\npre > code.sourceCode > span { display: inline-block; line-height: 1.25; }\npre > code.sourceCode > span:empty { height: 1.2em; }\n.sourceCode { overflow: visible; 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src=\"日历图_files/libs/quarto-html/quarto.js\"></script>\n<script src=\"日历图_files/libs/quarto-html/popper.min.js\"></script>\n<script src=\"日历图_files/libs/quarto-html/tippy.umd.min.js\"></script>\n<script src=\"日历图_files/libs/quarto-html/anchor.min.js\"></script>\n<link href=\"日历图_files/libs/quarto-html/tippy.css\" rel=\"stylesheet\">\n<link href=\"日历图_files/libs/quarto-html/quarto-syntax-highlighting.css\" rel=\"stylesheet\" id=\"quarto-text-highlighting-styles\">\n<script src=\"日历图_files/libs/bootstrap/bootstrap.min.js\"></script>\n<link href=\"日历图_files/libs/bootstrap/bootstrap-icons.css\" rel=\"stylesheet\">\n<link href=\"日历图_files/libs/bootstrap/bootstrap.min.css\" rel=\"stylesheet\" id=\"quarto-bootstrap\" data-mode=\"light\">\n\n\n</head>\n\n<body class=\"fullcontent\">\n\n<div id=\"quarto-content\" class=\"page-columns page-rows-contents page-layout-article\">\n\n<main class=\"content\" id=\"quarto-document-content\">\n\n<header id=\"title-block-header\" class=\"quarto-title-block default\">\n<div class=\"quarto-title\">\n<h1 class=\"title\">ggtimeseries</h1>\n</div>\n\n\n\n<div class=\"quarto-title-meta\">\n\n    <div>\n    <div class=\"quarto-title-meta-heading\">Author</div>\n    <div class=\"quarto-title-meta-contents\">\n             <p>庄闪闪 </p>\n          </div>\n  </div>\n    \n  \n    \n  </div>\n  \n\n</header>\n\n<section id=\"构建数据\" class=\"level1\">\n<h1>构建数据</h1>\n<div class=\"cell\">\n<div class=\"sourceCode cell-code\" id=\"cb1\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">set.seed</span>(<span class=\"dv\">20230309</span>)</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(tidyverse)</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" aria-hidden=\"true\" tabindex=\"-1\"></a>dtData <span class=\"ot\">&lt;-</span> <span class=\"fu\">tibble</span>(</span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">DateCol =</span> <span class=\"fu\">seq</span>(</span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">as.Date</span>(<span class=\"st\">\"1/01/2020\"</span>, <span class=\"st\">\"%d/%m/%Y\"</span>),</span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">as.Date</span>(<span class=\"st\">\"31/12/2022\"</span>, <span class=\"st\">\"%d/%m/%Y\"</span>), <span class=\"st\">\"days\"</span></span>\n<span id=\"cb1-7\"><a href=\"#cb1-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  ),</span>\n<span id=\"cb1-8\"><a href=\"#cb1-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">ValueCol =</span> <span class=\"fu\">sample</span>(</span>\n<span id=\"cb1-9\"><a href=\"#cb1-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"fu\">c</span>(<span class=\"st\">'a'</span>, <span class=\"st\">'b'</span>, <span class=\"st\">'c'</span>, <span class=\"st\">'d'</span>, <span class=\"st\">'e'</span>),</span>\n<span id=\"cb1-10\"><a href=\"#cb1-10\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"dv\">1096</span>,</span>\n<span id=\"cb1-11\"><a href=\"#cb1-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"at\">prob =</span> <span class=\"fu\">c</span>(<span class=\"fl\">0.7</span>, <span class=\"fl\">0.1</span>, <span class=\"fl\">0.1</span>, <span class=\"fl\">0.05</span>, <span class=\"fl\">0.05</span>),</span>\n<span id=\"cb1-12\"><a href=\"#cb1-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>    <span class=\"at\">replace =</span> <span class=\"cn\">TRUE</span></span>\n<span id=\"cb1-13\"><a href=\"#cb1-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  )</span>\n<span id=\"cb1-14\"><a href=\"#cb1-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span>\n<span id=\"cb1-15\"><a href=\"#cb1-15\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb1-16\"><a href=\"#cb1-16\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">head</span>(dtData)</span></code><button title=\"Copy to Clipboard\" class=\"code-copy-button\"><i class=\"bi\"></i></button></pre></div>\n<div class=\"cell-output cell-output-stdout\">\n<pre><code># A tibble: 6 × 2\n  DateCol    ValueCol\n  &lt;date&gt;     &lt;chr&gt;   \n1 2020-01-01 a       \n2 2020-01-02 b       \n3 2020-01-03 a       \n4 2020-01-04 a       \n5 2020-01-05 d       \n6 2020-01-06 a       </code></pre>\n</div>\n</div>\n<section id=\"可视化\" class=\"level2\">\n<h2 class=\"anchored\" data-anchor-id=\"可视化\">可视化</h2>\n<div class=\"cell\">\n<div class=\"sourceCode cell-code\" id=\"cb3\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggTimeSeries)</span>\n<span id=\"cb3-2\"><a href=\"#cb3-2\" aria-hidden=\"true\" tabindex=\"-1\"></a><span class=\"fu\">library</span>(ggplot2)</span>\n<span id=\"cb3-3\"><a href=\"#cb3-3\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb3-4\"><a href=\"#cb3-4\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"ot\">=</span> <span class=\"fu\">ggplot_calendar_heatmap</span>(</span>\n<span id=\"cb3-5\"><a href=\"#cb3-5\" aria-hidden=\"true\" tabindex=\"-1\"></a>  dtData, <span class=\"at\">dayBorderSize =</span> <span class=\"dv\">1</span>,</span>\n<span id=\"cb3-6\"><a href=\"#cb3-6\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"at\">monthBorderSize =</span> <span class=\"dv\">1</span>,<span class=\"at\">dayBorderColour =</span> <span class=\"st\">\"white\"</span>,</span>\n<span id=\"cb3-7\"><a href=\"#cb3-7\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"st\">'DateCol'</span>,<span class=\"at\">monthBorderColour =</span> <span class=\"st\">\"white\"</span>,</span>\n<span id=\"cb3-8\"><a href=\"#cb3-8\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"st\">'ValueCol'</span></span>\n<span id=\"cb3-9\"><a href=\"#cb3-9\" aria-hidden=\"true\" tabindex=\"-1\"></a>)</span>\n<span id=\"cb3-10\"><a href=\"#cb3-10\" aria-hidden=\"true\" tabindex=\"-1\"></a></span>\n<span id=\"cb3-11\"><a href=\"#cb3-11\" aria-hidden=\"true\" tabindex=\"-1\"></a>col <span class=\"ot\">&lt;-</span> <span class=\"fu\">c</span>(<span class=\"st\">\"#EBEDF0\"</span>, <span class=\"st\">\"#CBE491\"</span>, <span class=\"st\">\"#89C876\"</span>, <span class=\"st\">\"#459944\"</span>, <span class=\"st\">\"#2C602C\"</span>)</span>\n<span id=\"cb3-12\"><a href=\"#cb3-12\" aria-hidden=\"true\" tabindex=\"-1\"></a>p <span class=\"sc\">+</span> </span>\n<span id=\"cb3-13\"><a href=\"#cb3-13\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">scale_fill_manual</span>(<span class=\"at\">values =</span> col) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-14\"><a href=\"#cb3-14\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">facet_wrap</span>(<span class=\"sc\">~</span>Year, <span class=\"at\">ncol =</span> <span class=\"dv\">1</span>,<span class=\"at\">strip.position =</span> <span class=\"st\">\"right\"</span>) <span class=\"sc\">+</span></span>\n<span id=\"cb3-15\"><a href=\"#cb3-15\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">theme</span>( <span class=\"at\">panel.background =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb3-16\"><a href=\"#cb3-16\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">legend.text =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb3-17\"><a href=\"#cb3-17\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">panel.border =</span> <span class=\"fu\">element_rect</span>(<span class=\"at\">colour=</span><span class=\"st\">\"grey60\"</span>,<span class=\"at\">fill=</span><span class=\"cn\">NA</span>),</span>\n<span id=\"cb3-18\"><a href=\"#cb3-18\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">strip.background =</span> <span class=\"fu\">element_blank</span>(),</span>\n<span id=\"cb3-19\"><a href=\"#cb3-19\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">strip.text =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">size=</span><span class=\"dv\">13</span>,<span class=\"at\">face=</span><span class=\"st\">\"plain\"</span>,<span class=\"at\">color=</span><span class=\"st\">\"black\"</span>),</span>\n<span id=\"cb3-20\"><a href=\"#cb3-20\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">axis.line=</span><span class=\"fu\">element_line</span>(<span class=\"at\">colour=</span><span class=\"st\">\"black\"</span>,<span class=\"at\">size=</span><span class=\"fl\">0.25</span>),</span>\n<span id=\"cb3-21\"><a href=\"#cb3-21\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">axis.title=</span><span class=\"fu\">element_text</span>(<span class=\"at\">size=</span><span class=\"dv\">10</span>,<span class=\"at\">face=</span><span class=\"st\">\"plain\"</span>,<span class=\"at\">color=</span><span class=\"st\">\"black\"</span>),</span>\n<span id=\"cb3-22\"><a href=\"#cb3-22\" aria-hidden=\"true\" tabindex=\"-1\"></a>         <span class=\"at\">axis.text =</span> <span class=\"fu\">element_text</span>(<span class=\"at\">size=</span><span class=\"dv\">10</span>,<span class=\"at\">face=</span><span class=\"st\">\"plain\"</span>,<span class=\"at\">color=</span><span class=\"st\">\"black\"</span>)) <span class=\"sc\">+</span></span>\n<span id=\"cb3-23\"><a href=\"#cb3-23\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">xlab</span>(<span class=\"st\">''</span>) <span class=\"sc\">+</span> </span>\n<span id=\"cb3-24\"><a href=\"#cb3-24\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">ylab</span>(<span class=\"st\">''</span>) <span class=\"sc\">+</span>  </span>\n<span id=\"cb3-25\"><a href=\"#cb3-25\" aria-hidden=\"true\" tabindex=\"-1\"></a>  <span class=\"fu\">labs</span>(<span class=\"at\">fill =</span> <span class=\"st\">\"Freq\"</span>) </span></code><button title=\"Copy to Clipboard\" class=\"code-copy-button\"><i class=\"bi\"></i></button></pre></div>\n<div class=\"cell-output-display\">\n<p><img src=\"日历图_files/figure-html/unnamed-chunk-2-1.png\" class=\"img-fluid\" width=\"672\"></p>\n</div>\n</div>\n</section>\n</section>\n\n</main>\n<!-- /main column -->\n<script id=\"quarto-html-after-body\" type=\"application/javascript\">\nwindow.document.addEventListener(\"DOMContentLoaded\", function (event) {\n  const toggleBodyColorMode = (bsSheetEl) => {\n    const mode = bsSheetEl.getAttribute(\"data-mode\");\n    const bodyEl = window.document.querySelector(\"body\");\n    if (mode === \"dark\") {\n      bodyEl.classList.add(\"quarto-dark\");\n     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    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图.qmd",
    "content": "---\ntitle: \"ggtimeseries\"\nformat: html\nauthor: 庄闪闪\neditor: visual\n---\n\n# 构建数据\n\n```{r warning=FALSE,message=FALSE}\nset.seed(20230309)\nlibrary(tidyverse)\ndtData <- tibble(\n  DateCol = seq(\n    as.Date(\"1/01/2020\", \"%d/%m/%Y\"),\n    as.Date(\"31/12/2022\", \"%d/%m/%Y\"), \"days\"\n  ),\n  ValueCol = sample(\n    c('a', 'b', 'c', 'd', 'e'),\n    1096,\n    prob = c(0.7, 0.1, 0.1, 0.05, 0.05),\n    replace = TRUE\n  )\n)\n\nhead(dtData)\n\n```\n\n## 可视化\n\n\n```{r warning=FALSE,message=FALSE}\nlibrary(ggTimeSeries)\nlibrary(ggplot2)\n\np = ggplot_calendar_heatmap(\n  dtData, dayBorderSize = 1,\n  monthBorderSize = 1,dayBorderColour = \"white\",\n  'DateCol',monthBorderColour = \"white\",\n  'ValueCol'\n)\n\ncol <- c(\"#EBEDF0\", \"#CBE491\", \"#89C876\", \"#459944\", \"#2C602C\")\np + \n  scale_fill_manual(values = col) + \n  facet_wrap(~Year, ncol = 1,strip.position = \"right\") +\n  theme( panel.background = element_blank(),\n         legend.text = element_blank(),\n         panel.border = element_rect(colour=\"grey60\",fill=NA),\n         strip.background = element_blank(),\n         strip.text = element_text(size=13,face=\"plain\",color=\"black\"),\n         axis.line=element_line(colour=\"black\",size=0.25),\n         axis.title=element_text(size=10,face=\"plain\",color=\"black\"),\n         axis.text = element_text(size=10,face=\"plain\",color=\"black\")) +\n  xlab('') + \n  ylab('') +  \n  labs(fill = \"Freq\") \n\n```\n\n\n\n\n\n"
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}\n.bi-filetype-ppt-1::before { content: \"\\f759\"; }\n.bi-filetype-ppt::before { content: \"\\f75a\"; }\n.bi-filetype-psd::before { content: \"\\f75b\"; }\n.bi-filetype-py::before { content: \"\\f75c\"; }\n.bi-filetype-raw::before { content: \"\\f75d\"; }\n.bi-filetype-rb::before { content: \"\\f75e\"; }\n.bi-filetype-sass::before { content: \"\\f75f\"; }\n.bi-filetype-scss::before { content: \"\\f760\"; }\n.bi-filetype-sh::before { content: \"\\f761\"; }\n.bi-filetype-svg::before { content: \"\\f762\"; }\n.bi-filetype-tiff::before { content: \"\\f763\"; }\n.bi-filetype-tsx::before { content: \"\\f764\"; }\n.bi-filetype-ttf::before { content: \"\\f765\"; }\n.bi-filetype-txt::before { content: \"\\f766\"; }\n.bi-filetype-wav::before { content: \"\\f767\"; }\n.bi-filetype-woff::before { content: \"\\f768\"; }\n.bi-filetype-xls-1::before { content: \"\\f769\"; }\n.bi-filetype-xls::before { content: \"\\f76a\"; }\n.bi-filetype-xml::before { content: \"\\f76b\"; }\n.bi-filetype-yml::before { content: \"\\f76c\"; }\n.bi-heart-arrow::before { content: \"\\f76d\"; }\n.bi-heart-pulse-fill::before { content: \"\\f76e\"; }\n.bi-heart-pulse::before { content: \"\\f76f\"; }\n.bi-heartbreak-fill::before { content: \"\\f770\"; }\n.bi-heartbreak::before { content: \"\\f771\"; }\n.bi-hearts::before { content: \"\\f772\"; }\n.bi-hospital-fill::before { content: \"\\f773\"; }\n.bi-hospital::before { content: \"\\f774\"; }\n.bi-house-heart-fill::before { content: \"\\f775\"; }\n.bi-house-heart::before { content: \"\\f776\"; }\n.bi-incognito::before { content: \"\\f777\"; }\n.bi-magnet-fill::before { content: \"\\f778\"; }\n.bi-magnet::before { content: \"\\f779\"; }\n.bi-person-heart::before { content: \"\\f77a\"; }\n.bi-person-hearts::before { content: \"\\f77b\"; }\n.bi-phone-flip::before { content: \"\\f77c\"; }\n.bi-plugin::before { content: \"\\f77d\"; }\n.bi-postage-fill::before { content: \"\\f77e\"; }\n.bi-postage-heart-fill::before { content: \"\\f77f\"; }\n.bi-postage-heart::before { content: \"\\f780\"; }\n.bi-postage::before { content: \"\\f781\"; }\n.bi-postcard-fill::before { content: \"\\f782\"; }\n.bi-postcard-heart-fill::before { content: \"\\f783\"; }\n.bi-postcard-heart::before { content: \"\\f784\"; }\n.bi-postcard::before { content: \"\\f785\"; }\n.bi-search-heart-fill::before { content: \"\\f786\"; }\n.bi-search-heart::before { content: \"\\f787\"; }\n.bi-sliders2-vertical::before { content: \"\\f788\"; }\n.bi-sliders2::before { content: \"\\f789\"; }\n.bi-trash3-fill::before { content: \"\\f78a\"; }\n.bi-trash3::before { content: \"\\f78b\"; }\n.bi-valentine::before { content: \"\\f78c\"; }\n.bi-valentine2::before { content: \"\\f78d\"; }\n.bi-wrench-adjustable-circle-fill::before { content: \"\\f78e\"; }\n.bi-wrench-adjustable-circle::before { content: \"\\f78f\"; }\n.bi-wrench-adjustable::before { content: \"\\f790\"; }\n.bi-filetype-json::before { content: \"\\f791\"; }\n.bi-filetype-pptx::before { content: \"\\f792\"; }\n.bi-filetype-xlsx::before { content: \"\\f793\"; }\n"
  },
  {
    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/quarto-syntax-highlighting.css",
    "content": "/* quarto syntax highlight colors */\n:root {\n  --quarto-hl-ot-color: #003B4F;\n  --quarto-hl-at-color: #657422;\n  --quarto-hl-ss-color: #20794D;\n  --quarto-hl-an-color: #5E5E5E;\n  --quarto-hl-fu-color: #4758AB;\n  --quarto-hl-st-color: #20794D;\n  --quarto-hl-cf-color: #003B4F;\n  --quarto-hl-op-color: #5E5E5E;\n  --quarto-hl-er-color: #AD0000;\n  --quarto-hl-bn-color: #AD0000;\n  --quarto-hl-al-color: #AD0000;\n  --quarto-hl-va-color: #111111;\n  --quarto-hl-bu-color: inherit;\n  --quarto-hl-ex-color: inherit;\n  --quarto-hl-pp-color: #AD0000;\n  --quarto-hl-in-color: #5E5E5E;\n  --quarto-hl-vs-color: #20794D;\n  --quarto-hl-wa-color: #5E5E5E;\n  --quarto-hl-do-color: #5E5E5E;\n  --quarto-hl-im-color: #00769E;\n  --quarto-hl-ch-color: #20794D;\n  --quarto-hl-dt-color: #AD0000;\n  --quarto-hl-fl-color: #AD0000;\n  --quarto-hl-co-color: #5E5E5E;\n  --quarto-hl-cv-color: #5E5E5E;\n  --quarto-hl-cn-color: #8f5902;\n  --quarto-hl-sc-color: #5E5E5E;\n  --quarto-hl-dv-color: #AD0000;\n  --quarto-hl-kw-color: #003B4F;\n}\n\n/* other quarto variables */\n:root {\n  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace;\n}\n\npre > code.sourceCode > span {\n  color: #003B4F;\n}\n\ncode span {\n  color: #003B4F;\n}\n\ncode.sourceCode > span {\n  color: #003B4F;\n}\n\ndiv.sourceCode,\ndiv.sourceCode pre.sourceCode {\n  color: #003B4F;\n}\n\ncode span.ot {\n  color: #003B4F;\n}\n\ncode span.at {\n  color: #657422;\n}\n\ncode span.ss {\n  color: #20794D;\n}\n\ncode span.an {\n  color: #5E5E5E;\n}\n\ncode span.fu {\n  color: #4758AB;\n}\n\ncode span.st {\n  color: #20794D;\n}\n\ncode span.cf {\n  color: #003B4F;\n}\n\ncode span.op {\n  color: #5E5E5E;\n}\n\ncode span.er {\n  color: #AD0000;\n}\n\ncode span.bn {\n  color: #AD0000;\n}\n\ncode span.al {\n  color: #AD0000;\n}\n\ncode span.va {\n  color: #111111;\n}\n\ncode span.pp {\n  color: #AD0000;\n}\n\ncode span.in {\n  color: #5E5E5E;\n}\n\ncode span.vs {\n  color: #20794D;\n}\n\ncode span.wa {\n  color: #5E5E5E;\n  font-style: italic;\n}\n\ncode span.do {\n  color: #5E5E5E;\n  font-style: italic;\n}\n\ncode span.im {\n  color: #00769E;\n}\n\ncode span.ch {\n  color: #20794D;\n}\n\ncode span.dt {\n  color: #AD0000;\n}\n\ncode span.fl {\n  color: #AD0000;\n}\n\ncode span.co {\n  color: #5E5E5E;\n}\n\ncode span.cv {\n  color: #5E5E5E;\n  font-style: italic;\n}\n\ncode span.cn {\n  color: #8f5902;\n}\n\ncode span.sc {\n  color: #5E5E5E;\n}\n\ncode span.dv {\n  color: #AD0000;\n}\n\ncode span.kw {\n  color: #003B4F;\n}\n\n.prevent-inlining {\n  content: \"</\";\n}\n\n/*# sourceMappingURL=debc5d5d77c3f9108843748ff7464032.css.map */\n"
  },
  {
    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/quarto.js",
    "content": "const sectionChanged = new CustomEvent(\"quarto-sectionChanged\", {\n  detail: {},\n  bubbles: true,\n  cancelable: false,\n  composed: false,\n});\n\nwindow.document.addEventListener(\"DOMContentLoaded\", function (_event) {\n  const tocEl = window.document.querySelector('nav.toc-active[role=\"doc-toc\"]');\n  const sidebarEl = window.document.getElementById(\"quarto-sidebar\");\n  const leftTocEl = window.document.getElementById(\"quarto-sidebar-toc-left\");\n  const marginSidebarEl = window.document.getElementById(\n    \"quarto-margin-sidebar\"\n  );\n  // function to determine whether the element has a previous sibling that is active\n  const prevSiblingIsActiveLink = (el) => {\n    const sibling = el.previousElementSibling;\n    if (sibling && sibling.tagName === \"A\") {\n      return sibling.classList.contains(\"active\");\n    } else {\n      return false;\n    }\n  };\n\n  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)\n  function fireSlideEnter(e) {\n    const event = window.document.createEvent(\"Event\");\n    event.initEvent(\"slideenter\", true, true);\n    window.document.dispatchEvent(event);\n  }\n  const tabs = window.document.querySelectorAll('a[data-bs-toggle=\"tab\"]');\n  tabs.forEach((tab) => {\n    tab.addEventListener(\"shown.bs.tab\", fireSlideEnter);\n  });\n\n  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)\n  document.addEventListener(\"tabby\", fireSlideEnter, false);\n\n  // Track scrolling and mark TOC links as active\n  // get table of contents and sidebar (bail if we don't have at least one)\n  const tocLinks = tocEl\n    ? [...tocEl.querySelectorAll(\"a[data-scroll-target]\")]\n    : [];\n  const makeActive = (link) => tocLinks[link].classList.add(\"active\");\n  const removeActive = (link) => tocLinks[link].classList.remove(\"active\");\n  const removeAllActive = () =>\n    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));\n\n  // activate the anchor for a section associated with this TOC entry\n  tocLinks.forEach((link) => {\n    link.addEventListener(\"click\", () => {\n      if (link.href.indexOf(\"#\") !== -1) {\n        const anchor = link.href.split(\"#\")[1];\n        const heading = window.document.querySelector(\n          `[data-anchor-id=${anchor}]`\n        );\n        if (heading) {\n          // Add the class\n          heading.classList.add(\"reveal-anchorjs-link\");\n\n          // function to show the anchor\n          const handleMouseout = () => {\n            heading.classList.remove(\"reveal-anchorjs-link\");\n            heading.removeEventListener(\"mouseout\", handleMouseout);\n          };\n\n          // add a function to clear the anchor when the user mouses out of it\n          heading.addEventListener(\"mouseout\", handleMouseout);\n        }\n      }\n    });\n  });\n\n  const sections = tocLinks.map((link) => {\n    const target = link.getAttribute(\"data-scroll-target\");\n    if (target.startsWith(\"#\")) {\n      return window.document.getElementById(decodeURI(`${target.slice(1)}`));\n    } else {\n      return window.document.querySelector(decodeURI(`${target}`));\n    }\n  });\n\n  const sectionMargin = 200;\n  let currentActive = 0;\n  // track whether we've initialized state the first time\n  let init = false;\n\n  const updateActiveLink = () => {\n    // The index from bottom to top (e.g. reversed list)\n    let sectionIndex = -1;\n    if (\n      window.innerHeight + window.pageYOffset >=\n      window.document.body.offsetHeight\n    ) {\n      sectionIndex = 0;\n    } else {\n      sectionIndex = [...sections].reverse().findIndex((section) => {\n        if (section) {\n          return window.pageYOffset >= section.offsetTop - sectionMargin;\n        } else {\n          return false;\n        }\n      });\n    }\n    if (sectionIndex > -1) {\n      const current = sections.length - sectionIndex - 1;\n      if (current !== currentActive) {\n        removeAllActive();\n        currentActive = current;\n        makeActive(current);\n        if (init) {\n          window.dispatchEvent(sectionChanged);\n        }\n        init = true;\n      }\n    }\n  };\n\n  const inHiddenRegion = (top, bottom, hiddenRegions) => {\n    for (const region of hiddenRegions) {\n      if (top <= region.bottom && bottom >= region.top) {\n        return true;\n      }\n    }\n    return false;\n  };\n\n  const categorySelector = \"header.quarto-title-block .quarto-category\";\n  const activateCategories = (href) => {\n    // Find any categories\n    // Surround them with a link pointing back to:\n    // #category=Authoring\n    try {\n      const categoryEls = window.document.querySelectorAll(categorySelector);\n      for (const categoryEl of categoryEls) {\n        const categoryText = categoryEl.textContent;\n        if (categoryText) {\n          const link = `${href}#category=${encodeURIComponent(categoryText)}`;\n          const linkEl = window.document.createElement(\"a\");\n          linkEl.setAttribute(\"href\", link);\n          for (const child of categoryEl.childNodes) {\n            linkEl.append(child);\n          }\n          categoryEl.appendChild(linkEl);\n        }\n      }\n    } catch {\n      // Ignore errors\n    }\n  };\n  function hasTitleCategories() {\n    return window.document.querySelector(categorySelector) !== null;\n  }\n\n  function offsetRelativeUrl(url) {\n    const offset = getMeta(\"quarto:offset\");\n    return offset ? offset + url : url;\n  }\n\n  function offsetAbsoluteUrl(url) {\n    const offset = getMeta(\"quarto:offset\");\n    const baseUrl = new URL(offset, window.location);\n\n    const projRelativeUrl = url.replace(baseUrl, \"\");\n    if (projRelativeUrl.startsWith(\"/\")) {\n      return projRelativeUrl;\n    } else {\n      return \"/\" + projRelativeUrl;\n    }\n  }\n\n  // read a meta tag value\n  function getMeta(metaName) {\n    const metas = window.document.getElementsByTagName(\"meta\");\n    for (let i = 0; i < metas.length; i++) {\n      if (metas[i].getAttribute(\"name\") === metaName) {\n        return metas[i].getAttribute(\"content\");\n      }\n    }\n    return \"\";\n  }\n\n  async function findAndActivateCategories() {\n    const currentPagePath = offsetAbsoluteUrl(window.location.href);\n    const response = await fetch(offsetRelativeUrl(\"listings.json\"));\n    if (response.status == 200) {\n      return response.json().then(function (listingPaths) {\n        const listingHrefs = [];\n        for (const listingPath of listingPaths) {\n          const pathWithoutLeadingSlash = listingPath.listing.substring(1);\n          for (const item of listingPath.items) {\n            if (\n              item === currentPagePath ||\n              item === currentPagePath + \"index.html\"\n            ) {\n              // Resolve this path against the offset to be sure\n              // we already are using the correct path to the listing\n              // (this adjusts the listing urls to be rooted against\n              // whatever root the page is actually running against)\n              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);\n              const baseUrl = window.location;\n              const resolvedPath = new URL(relative, baseUrl);\n              listingHrefs.push(resolvedPath.pathname);\n              break;\n            }\n          }\n        }\n\n        // Look up the tree for a nearby linting and use that if we find one\n        const nearestListing = findNearestParentListing(\n          offsetAbsoluteUrl(window.location.pathname),\n          listingHrefs\n        );\n        if (nearestListing) {\n          activateCategories(nearestListing);\n        } else {\n          // See if the referrer is a listing page for this item\n          const referredRelativePath = offsetAbsoluteUrl(document.referrer);\n          const referrerListing = listingHrefs.find((listingHref) => {\n            const isListingReferrer =\n              listingHref === referredRelativePath ||\n              listingHref === referredRelativePath + \"index.html\";\n            return isListingReferrer;\n          });\n\n          if (referrerListing) {\n            // Try to use the referrer if possible\n            activateCategories(referrerListing);\n          } else if (listingHrefs.length > 0) {\n            // Otherwise, just fall back to the first listing\n            activateCategories(listingHrefs[0]);\n          }\n        }\n      });\n    }\n  }\n  if (hasTitleCategories()) {\n    findAndActivateCategories();\n  }\n\n  const findNearestParentListing = (href, listingHrefs) => {\n    if (!href || !listingHrefs) {\n      return undefined;\n    }\n    // Look up the tree for a nearby linting and use that if we find one\n    const relativeParts = href.substring(1).split(\"/\");\n    while (relativeParts.length > 0) {\n      const path = relativeParts.join(\"/\");\n      for (const listingHref of listingHrefs) {\n        if (listingHref.startsWith(path)) {\n          return listingHref;\n        }\n      }\n      relativeParts.pop();\n    }\n\n    return undefined;\n  };\n\n  const manageSidebarVisiblity = (el, placeholderDescriptor) => {\n    let isVisible = true;\n\n    return (hiddenRegions) => {\n      if (el === null) {\n        return;\n      }\n\n      // Find the last element of the TOC\n      const lastChildEl = el.lastElementChild;\n\n      if (lastChildEl) {\n        // Find the top and bottom o the element that is being managed\n        const elTop = el.offsetTop;\n        const elBottom =\n          elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;\n\n        // Converts the sidebar to a menu\n        const convertToMenu = () => {\n          for (const child of el.children) {\n            child.style.opacity = 0;\n            child.style.overflow = \"hidden\";\n          }\n\n          const toggleContainer = window.document.createElement(\"div\");\n          toggleContainer.style.width = \"100%\";\n          toggleContainer.classList.add(\"zindex-over-content\");\n          toggleContainer.classList.add(\"quarto-sidebar-toggle\");\n          toggleContainer.classList.add(\"headroom-target\"); // Marks this to be managed by headeroom\n          toggleContainer.id = placeholderDescriptor.id;\n          toggleContainer.style.position = \"fixed\";\n\n          const toggleIcon = window.document.createElement(\"i\");\n          toggleIcon.classList.add(\"quarto-sidebar-toggle-icon\");\n          toggleIcon.classList.add(\"bi\");\n          toggleIcon.classList.add(\"bi-caret-down-fill\");\n\n          const toggleTitle = window.document.createElement(\"div\");\n          const titleEl = window.document.body.querySelector(\n            placeholderDescriptor.titleSelector\n          );\n          if (titleEl) {\n            toggleTitle.append(titleEl.innerText, toggleIcon);\n          }\n          toggleTitle.classList.add(\"zindex-over-content\");\n          toggleTitle.classList.add(\"quarto-sidebar-toggle-title\");\n          toggleContainer.append(toggleTitle);\n\n          const toggleContents = window.document.createElement(\"div\");\n          toggleContents.classList = el.classList;\n          toggleContents.classList.add(\"zindex-over-content\");\n          toggleContents.classList.add(\"quarto-sidebar-toggle-contents\");\n          for (const child of el.children) {\n            if (child.id === \"toc-title\") {\n              continue;\n            }\n\n            const clone = child.cloneNode(true);\n            clone.style.opacity = 1;\n            clone.style.display = null;\n            toggleContents.append(clone);\n          }\n          toggleContents.style.height = \"0px\";\n          toggleContainer.append(toggleContents);\n          el.parentElement.prepend(toggleContainer);\n\n          // Process clicks\n          let tocShowing = false;\n          // Allow the caller to control whether this is dismissed\n          // when it is clicked (e.g. sidebar navigation supports\n          // opening and closing the nav tree, so don't dismiss on click)\n          const clickEl = placeholderDescriptor.dismissOnClick\n            ? toggleContainer\n            : toggleTitle;\n\n          const closeToggle = () => {\n            if (tocShowing) {\n              toggleContainer.classList.remove(\"expanded\");\n              toggleContents.style.height = \"0px\";\n              tocShowing = false;\n            }\n          };\n\n          const positionToggle = () => {\n            // position the element (top left of parent, same width as parent)\n            const elRect = el.getBoundingClientRect();\n            toggleContainer.style.left = `${elRect.left}px`;\n            toggleContainer.style.top = `${elRect.top}px`;\n            toggleContainer.style.width = `${elRect.width}px`;\n          };\n\n          // Get rid of any expanded toggle if the user scrolls\n          window.document.addEventListener(\n            \"scroll\",\n            throttle(() => {\n              closeToggle();\n            }, 50)\n          );\n\n          // Handle positioning of the toggle\n          window.addEventListener(\n            \"resize\",\n            throttle(() => {\n              positionToggle();\n            }, 50)\n          );\n          positionToggle();\n\n          // Process the click\n          clickEl.onclick = () => {\n            if (!tocShowing) {\n              toggleContainer.classList.add(\"expanded\");\n              toggleContents.style.height = null;\n              tocShowing = true;\n            } else {\n              closeToggle();\n            }\n          };\n        };\n\n        // Converts a sidebar from a menu back to a sidebar\n        const convertToSidebar = () => {\n          for (const child of el.children) {\n            child.style.opacity = 1;\n            child.style.overflow = null;\n          }\n\n          const placeholderEl = window.document.getElementById(\n            placeholderDescriptor.id\n          );\n          if (placeholderEl) {\n            placeholderEl.remove();\n          }\n\n          el.classList.remove(\"rollup\");\n        };\n\n        if (isReaderMode()) {\n          convertToMenu();\n          isVisible = false;\n        } else {\n          if (!isVisible) {\n            // If the element is current not visible reveal if there are\n            // no conflicts with overlay regions\n            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n              convertToSidebar();\n              isVisible = true;\n            }\n          } else {\n            // If the element is visible, hide it if it conflicts with overlay regions\n            // and insert a placeholder toggle (or if we're in reader mode)\n            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n              convertToMenu();\n              isVisible = false;\n            }\n          }\n        }\n      }\n    };\n  };\n\n  // Find any conflicting margin elements and add margins to the\n  // top to prevent overlap\n  const marginChildren = window.document.querySelectorAll(\n    \".column-margin.column-container > * \"\n  );\n\n  nexttick(() => {\n    let lastBottom = 0;\n    for (const marginChild of marginChildren) {\n      const top = marginChild.getBoundingClientRect().top + window.scrollY;\n      if (top < lastBottom) {\n        const margin = lastBottom - top;\n        marginChild.style.marginTop = `${margin}px`;\n      }\n      const styles = window.getComputedStyle(marginChild);\n      const marginTop = parseFloat(styles[\"marginTop\"]);\n\n      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;\n    }\n  });\n\n  // Manage the visibility of the toc and the sidebar\n  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {\n    id: \"quarto-toc-toggle\",\n    titleSelector: \"#toc-title\",\n    dismissOnClick: true,\n  });\n  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {\n    id: \"quarto-sidebarnav-toggle\",\n    titleSelector: \".title\",\n    dismissOnClick: false,\n  });\n  let tocLeftScrollVisibility;\n  if (leftTocEl) {\n    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {\n      id: \"quarto-lefttoc-toggle\",\n      titleSelector: \"#toc-title\",\n      dismissOnClick: true,\n    });\n  }\n\n  // Find the first element that uses formatting in special columns\n  const conflictingEls = window.document.body.querySelectorAll(\n    '[class^=\"column-\"], [class*=\" column-\"], aside, [class*=\"margin-caption\"], [class*=\" margin-caption\"], [class*=\"margin-ref\"], [class*=\" margin-ref\"]'\n  );\n\n  // Filter all the possibly conflicting elements into ones\n  // the do conflict on the left or ride side\n  const arrConflictingEls = Array.from(conflictingEls);\n  const leftSideConflictEls = arrConflictingEls.filter((el) => {\n    if (el.tagName === \"ASIDE\") {\n      return false;\n    }\n    return Array.from(el.classList).find((className) => {\n      return (\n        className !== \"column-body\" &&\n        className.startsWith(\"column-\") &&\n        !className.endsWith(\"right\") &&\n        !className.endsWith(\"container\") &&\n        className !== \"column-margin\"\n      );\n    });\n  });\n  const rightSideConflictEls = arrConflictingEls.filter((el) => {\n    if (el.tagName === \"ASIDE\") {\n      return true;\n    }\n\n    const hasMarginCaption = Array.from(el.classList).find((className) => {\n      return className == \"margin-caption\";\n    });\n    if (hasMarginCaption) {\n      return true;\n    }\n\n    return Array.from(el.classList).find((className) => {\n      return (\n        className !== \"column-body\" &&\n        !className.endsWith(\"container\") &&\n        className.startsWith(\"column-\") &&\n        !className.endsWith(\"left\")\n      );\n    });\n  });\n\n  const kOverlapPaddingSize = 10;\n  function toRegions(els) {\n    return els.map((el) => {\n      const top =\n        el.getBoundingClientRect().top +\n        document.documentElement.scrollTop -\n        kOverlapPaddingSize;\n      return {\n        top,\n        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,\n      };\n    });\n  }\n\n  const hideOverlappedSidebars = () => {\n    marginScrollVisibility(toRegions(rightSideConflictEls));\n    sidebarScrollVisiblity(toRegions(leftSideConflictEls));\n    if (tocLeftScrollVisibility) {\n      tocLeftScrollVisibility(toRegions(leftSideConflictEls));\n    }\n  };\n\n  window.quartoToggleReader = () => {\n    // Applies a slow class (or removes it)\n    // to update the transition speed\n    const slowTransition = (slow) => {\n      const manageTransition = (id, slow) => {\n        const el = document.getElementById(id);\n        if (el) {\n          if (slow) {\n            el.classList.add(\"slow\");\n          } else {\n            el.classList.remove(\"slow\");\n          }\n        }\n      };\n\n      manageTransition(\"TOC\", slow);\n      manageTransition(\"quarto-sidebar\", slow);\n    };\n\n    const readerMode = !isReaderMode();\n    setReaderModeValue(readerMode);\n\n    // If we're entering reader mode, slow the transition\n    if (readerMode) {\n      slowTransition(readerMode);\n    }\n    highlightReaderToggle(readerMode);\n    hideOverlappedSidebars();\n\n    // If we're exiting reader mode, restore the non-slow transition\n    if (!readerMode) {\n      slowTransition(!readerMode);\n    }\n  };\n\n  const highlightReaderToggle = (readerMode) => {\n    const els = document.querySelectorAll(\".quarto-reader-toggle\");\n    if (els) {\n      els.forEach((el) => {\n        if (readerMode) {\n          el.classList.add(\"reader\");\n        } else {\n          el.classList.remove(\"reader\");\n        }\n      });\n    }\n  };\n\n  const setReaderModeValue = (val) => {\n    if (window.location.protocol !== \"file:\") {\n      window.localStorage.setItem(\"quarto-reader-mode\", val);\n    } else {\n      localReaderMode = val;\n    }\n  };\n\n  const isReaderMode = () => {\n    if (window.location.protocol !== \"file:\") {\n      return window.localStorage.getItem(\"quarto-reader-mode\") === \"true\";\n    } else {\n      return localReaderMode;\n    }\n  };\n  let localReaderMode = null;\n\n  // Walk the TOC and collapse/expand nodes\n  // Nodes are expanded if:\n  // - they are top level\n  // - they have children that are 'active' links\n  // - they are directly below an link that is 'active'\n  const walk = (el, depth) => {\n    // Tick depth when we enter a UL\n    if (el.tagName === \"UL\") {\n      depth = depth + 1;\n    }\n\n    // It this is active link\n    let isActiveNode = false;\n    if (el.tagName === \"A\" && el.classList.contains(\"active\")) {\n      isActiveNode = true;\n    }\n\n    // See if there is an active child to this element\n    let hasActiveChild = false;\n    for (child of el.children) {\n      hasActiveChild = walk(child, depth) || hasActiveChild;\n    }\n\n    // Process the collapse state if this is an UL\n    if (el.tagName === \"UL\") {\n      if (depth === 1 || hasActiveChild || prevSiblingIsActiveLink(el)) {\n        el.classList.remove(\"collapse\");\n      } else {\n        el.classList.add(\"collapse\");\n      }\n\n      // untick depth when we leave a UL\n      depth = depth - 1;\n    }\n    return hasActiveChild || isActiveNode;\n  };\n\n  // walk the TOC and expand / collapse any items that should be shown\n\n  if (tocEl) {\n    walk(tocEl, 0);\n    updateActiveLink();\n  }\n\n  // Throttle the scroll event and walk peridiocally\n  window.document.addEventListener(\n    \"scroll\",\n    throttle(() => {\n      if (tocEl) {\n        updateActiveLink();\n        walk(tocEl, 0);\n      }\n      if (!isReaderMode()) {\n        hideOverlappedSidebars();\n      }\n    }, 5)\n  );\n  window.addEventListener(\n    \"resize\",\n    throttle(() => {\n      if (!isReaderMode()) {\n        hideOverlappedSidebars();\n      }\n    }, 10)\n  );\n  hideOverlappedSidebars();\n  highlightReaderToggle(isReaderMode());\n});\n\n// grouped tabsets\nwindow.addEventListener(\"pageshow\", (_event) => {\n  function getTabSettings() {\n    const data = localStorage.getItem(\"quarto-persistent-tabsets-data\");\n    if (!data) {\n      localStorage.setItem(\"quarto-persistent-tabsets-data\", \"{}\");\n      return {};\n    }\n    if (data) {\n      return JSON.parse(data);\n    }\n  }\n\n  function setTabSettings(data) {\n    localStorage.setItem(\n      \"quarto-persistent-tabsets-data\",\n      JSON.stringify(data)\n    );\n  }\n\n  function setTabState(groupName, groupValue) {\n    const data = getTabSettings();\n    data[groupName] = groupValue;\n    setTabSettings(data);\n  }\n\n  function toggleTab(tab, active) {\n    const tabPanelId = tab.getAttribute(\"aria-controls\");\n    const tabPanel = document.getElementById(tabPanelId);\n    if (active) {\n      tab.classList.add(\"active\");\n      tabPanel.classList.add(\"active\");\n    } else {\n      tab.classList.remove(\"active\");\n      tabPanel.classList.remove(\"active\");\n    }\n  }\n\n  function toggleAll(selectedGroup, selectorsToSync) {\n    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {\n      const active = selectedGroup === thisGroup;\n      for (const tab of tabs) {\n        toggleTab(tab, active);\n      }\n    }\n  }\n\n  function findSelectorsToSyncByLanguage() {\n    const result = {};\n    const tabs = Array.from(\n      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)\n    );\n    for (const item of tabs) {\n      const div = item.parentElement.parentElement.parentElement;\n      const group = div.getAttribute(\"data-group\");\n      if (!result[group]) {\n        result[group] = {};\n      }\n      const selectorsToSync = result[group];\n      const value = item.innerHTML;\n      if (!selectorsToSync[value]) {\n        selectorsToSync[value] = [];\n      }\n      selectorsToSync[value].push(item);\n    }\n    return result;\n  }\n\n  function setupSelectorSync() {\n    const selectorsToSync = findSelectorsToSyncByLanguage();\n    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {\n      Object.entries(tabSetsByValue).forEach(([value, items]) => {\n        items.forEach((item) => {\n          item.addEventListener(\"click\", (_event) => {\n            setTabState(group, value);\n            toggleAll(value, selectorsToSync[group]);\n          });\n        });\n      });\n    });\n    return selectorsToSync;\n  }\n\n  const selectorsToSync = setupSelectorSync();\n  for (const [group, selectedName] of Object.entries(getTabSettings())) {\n    const selectors = selectorsToSync[group];\n    // it's possible that stale state gives us empty selections, so we explicitly check here.\n    if (selectors) {\n      toggleAll(selectedName, selectors);\n    }\n  }\n});\n\nfunction throttle(func, wait) {\n  let waiting = false;\n  return function () {\n    if (!waiting) {\n      func.apply(this, arguments);\n      waiting = true;\n      setTimeout(function () {\n        waiting = false;\n      }, wait);\n    }\n  };\n}\n\nfunction nexttick(func) {\n  return setTimeout(func, 0);\n}\n"
  },
  {
    "path": "2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/tippy.css",
    "content": ".tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:\"\";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/.Rhistory",
    "content": ")+\nxlab(\"developmental stage (days)\") +\nylab(expression(paste(\"tracheal length (\",~mu*m,\")\")))\npanel_C\n# Panel E ----\nlibrary(ggplot2)\nbase_size = 12\nmy_theme <-  function() {\ntheme(\naspect.ratio = 1,\naxis.line =element_line(colour = \"black\"),\n# shift axis text closer to axis bc ticks are facing inwards\naxis.text.x = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.text.y = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.ticks = element_line(color = \"black\", size  =  0.2),\naxis.title.x = element_text(size = base_size,\ncolor = \"black\",\nmargin = margin(t = -5)),\n# t (top), r (right), b (bottom), l (left)\naxis.title.y = element_text(size = base_size,\ncolor = \"black\", angle = 90,\nmargin = margin(r = -5)),\naxis.ticks.length = unit(-0.3, \"lines\"),\nlegend.background = element_rect(color = NA,\nfill = NA),\nlegend.key = element_rect(color = \"black\",\nfill = \"white\"),\nlegend.key.size = unit(0.5, \"lines\"),\nlegend.key.height =NULL,\nlegend.key.width = NULL,\nlegend.text = element_text(size = 0.6*base_size,\ncolor = \"black\"),\nlegend.title = element_text(size = 0.6*base_size,\nface = \"bold\",\nhjust = 0,\ncolor = \"black\"),\nlegend.text.align = NULL,\nlegend.title.align = NULL,\nlegend.direction = \"vertical\",\nlegend.box = NULL,\npanel.background = element_rect(fill = \"white\",\ncolor  =  NA),\npanel.grid.major = element_blank(),\npanel.grid.minor = element_blank(),\nplot.title = element_text(size = base_size,\ncolor = \"black\"),\n)\n}\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\nrep(\"gene b\",nrow(data_Eb)),\nrep(\"gene c\",nrow(data_Ec))),\n\"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n\"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n\"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n\"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n\"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n\"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\nhead(data_E)\nf1 = function(t) 0.2*exp(-t/48)\nf2 = function(t) 0.3*exp(-t/60)\nf3 = function(t) 0.4*exp(-t/72)\nt = seq(0,96,1)\nribbon = data.frame(\n\"f2\" = 0.3*exp(-t/60),\n\"t\" = t\n)\nhead(ribbon)\nmanual_pch =c(15,16,17) # available pch: type ?pch\npanel_E <- ggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\ngeom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),\nwidth=2) +\ngeom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\nscale_shape_manual(values=manual_pch) +\nmy_theme() + theme(legend.title=element_blank())+\ntheme(legend.position = c(0.9,0.95)) +\nscale_x_continuous(expand = c(0, 0),\nbreaks = c(seq(0,96,12)),\nlimits = c(0,96)\n) +\nscale_y_continuous(expand = c(0, 0),\nbreaks = c(seq(0,0.4,0.05)),\nlimits = c(0,0.4)\n) +\ntheme(\npanel.grid.major = element_line(\"gray95\", size = 0.1),\n# putting label closer to axis bc exponent makes it bigger\naxis.title.y = element_text(margin = margin(r = -9))\n) +\nxlab(\"time (h)\") +\nylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +\ncoord_cartesian(clip = \"off\") # to allow for plotting outside axes\npanel_E\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\nrep(\"gene b\",nrow(data_Eb)),\nrep(\"gene c\",nrow(data_Ec))),\n\"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n\"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n\"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n\"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n\"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n\"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\nhead(data_E)\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\nrep(\"gene b\",nrow(data_Eb)),\nrep(\"gene c\",nrow(data_Ec))),\n\"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n\"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n\"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n\"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n\"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n\"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\nhead(data_E)\nt = seq(0,96,1)\nt\n1/60\n1/48\nribbon = data.frame(\n\"f2\" = 0.3*exp(-t/60),\n\"t\" = t\n)\nribbon\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\")\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1)\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1)\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2)\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\")\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\ngeom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),\nwidth=2)\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\ngeom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),\nwidth=2) +\ngeom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))\nggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\ngeom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),\nwidth=2) +\ngeom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\nscale_shape_manual(values=manual_pch) +\nmy_theme() + theme(legend.title=element_blank())+\ntheme(legend.position = c(0.9,0.95)) +\nscale_x_continuous(expand = c(0, 0),\nbreaks = c(seq(0,96,12)),\nlimits = c(0,96)\n) +\nscale_y_continuous(expand = c(0, 0),\nbreaks = c(seq(0,0.4,0.05)),\nlimits = c(0,0.4)\n) +\ntheme(\npanel.grid.major = element_line(\"gray95\", size = 0.1),\n# putting label closer to axis bc exponent makes it bigger\naxis.title.y = element_text(margin = margin(r = -9))\n) +\nxlab(\"time (h)\") +\nylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\")))\npanel_E <- ggplot(data=data_E) +\ngeom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\ngeom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\nfill=\"black\",alpha=0.1) +\nstat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\nstat_function(fun=f2, geom=\"line\") +\nstat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\ngeom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err),\nwidth=2) +\ngeom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\nscale_shape_manual(values=manual_pch) +\nmy_theme() + theme(legend.title=element_blank())+\ntheme(legend.position = c(0.9,0.95)) +\nscale_x_continuous(expand = c(0, 0),\nbreaks = c(seq(0,96,12)),\nlimits = c(0,96)\n) +\nscale_y_continuous(expand = c(0, 0),\nbreaks = c(seq(0,0.4,0.05)),\nlimits = c(0,0.4)\n) +\ntheme(\npanel.grid.major = element_line(\"gray95\", size = 0.1),\n# putting label closer to axis bc exponent makes it bigger\naxis.title.y = element_text(margin = margin(r = -9))\n) +\nxlab(\"time (h)\") +\nylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +\ncoord_cartesian(clip = \"off\") # to allow for plotting outside axes\npanel_E\n# Panel F ----\nlibrary(ggplot2)\nbase_size = 12\nmy_theme <-  function() {\ntheme(\naspect.ratio = 1,\naxis.line =element_line(colour = \"black\"),\n# shift axis text closer to axis bc ticks are facing inwards\naxis.text.x = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.text.y = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.ticks = element_line(color = \"black\", size  =  0.2),\naxis.title.x = element_text(size = base_size,\ncolor = \"black\",\nmargin = margin(t = -5)),\n# t (top), r (right), b (bottom), l (left)\naxis.title.y = element_text(size = base_size,\ncolor = \"black\", angle = 90,\nmargin = margin(r = -5)),\naxis.ticks.length = unit(-0.3, \"lines\"),\nlegend.background = element_rect(color = NA,\nfill = NA),\nlegend.key = element_rect(color = \"black\",\nfill = \"white\"),\nlegend.key.size = unit(0.5, \"lines\"),\nlegend.key.height =NULL,\nlegend.key.width = NULL,\nlegend.text = element_text(size = 0.6*base_size,\ncolor = \"black\"),\nlegend.title = element_text(size = 0.6*base_size,\nface = \"bold\",\nhjust = 0,\ncolor = \"black\"),\nlegend.text.align = NULL,\nlegend.title.align = NULL,\nlegend.direction = \"vertical\",\nlegend.box = NULL,\npanel.background = element_rect(fill = \"white\",\ncolor  =  NA),\npanel.grid.major = element_blank(),\npanel.grid.minor = element_blank(),\nplot.title = element_text(size = base_size,\ncolor = \"black\"),\n)\n}\ndata_F = read.csv(\"./data_F.csv\")\npanel_F <- ggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nannotation_logticks(sides='b') +\nscale_size(range = c(1, 3)) +\nscale_fill_viridis(option=\"D\") + # a color palette from the viridis package\ntheme(legend.position = c(0.9,0.35)) +\ncoord_cartesian(clip = \"off\") +\nxlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\")\nlibrary(viridis)\nbase_size = 12\nmy_theme <-  function() {\ntheme(\naspect.ratio = 1,\naxis.line =element_line(colour = \"black\"),\n# shift axis text closer to axis bc ticks are facing inwards\naxis.text.x = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.text.y = element_text(size = base_size*0.8, color = \"black\",\nlineheight = 0.9,\nmargin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),\naxis.ticks = element_line(color = \"black\", size  =  0.2),\naxis.title.x = element_text(size = base_size,\ncolor = \"black\",\nmargin = margin(t = -5)),\n# t (top), r (right), b (bottom), l (left)\naxis.title.y = element_text(size = base_size,\ncolor = \"black\", angle = 90,\nmargin = margin(r = -5)),\naxis.ticks.length = unit(-0.3, \"lines\"),\nlegend.background = element_rect(color = NA,\nfill = NA),\nlegend.key = element_rect(color = \"black\",\nfill = \"white\"),\nlegend.key.size = unit(0.5, \"lines\"),\nlegend.key.height =NULL,\nlegend.key.width = NULL,\nlegend.text = element_text(size = 0.6*base_size,\ncolor = \"black\"),\nlegend.title = element_text(size = 0.6*base_size,\nface = \"bold\",\nhjust = 0,\ncolor = \"black\"),\nlegend.text.align = NULL,\nlegend.title.align = NULL,\nlegend.direction = \"vertical\",\nlegend.box = NULL,\npanel.background = element_rect(fill = \"white\",\ncolor  =  NA),\npanel.grid.major = element_blank(),\npanel.grid.minor = element_blank(),\nplot.title = element_text(size = base_size,\ncolor = \"black\"),\n)\n}\ndata_F = read.csv(\"./data_F.csv\")\npanel_F <- ggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nannotation_logticks(sides='b') +\nscale_size(range = c(1, 3)) +\nscale_fill_viridis(option=\"D\") + # a color palette from the viridis package\ntheme(legend.position = c(0.9,0.35)) +\ncoord_cartesian(clip = \"off\") +\nxlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\")\npanel_F\nhead(data_F)\ndata_F = read.csv(\"./data_F.csv\")\nhead(data_F)\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21)\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4))\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nannotation_logticks(sides='b')\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nannotation_logticks(sides='b') +\nscale_size(range = c(1, 3)) +\nscale_fill_viridis(option=\"D\") + # a color palette from the viridis package\ntheme(legend.position = c(0.9,0.35))\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +   xlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\")\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nxlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\")\nggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nxlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\") +\nannotation_logticks(sides='b') +\nscale_size(range = c(1, 3)) +\nscale_fill_viridis(option=\"D\") + # a color palette from the viridis package\ntheme(legend.position = c(0.9,0.35))\npanel_F <- ggplot(data=data_F,\naes(x=K,y=n,\nsize=amplitude,\nfill=duration))+\ngeom_point(pch=21) +\nmy_theme() +\nscale_x_continuous(expand = c(0, 0),\ntrans = 'log10',\nlabels=c(1,10,100),\nbreaks=c(1,10,100),\nlimits = c(1,100)) +\nscale_y_continuous(expand = c(0, 0),\nbreaks=c(seq(0,4,by=0.5)),\nlimits = c(0,4)) +\nxlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\nylab(\"Hill coefficient n\") +\nannotation_logticks(sides='b') +\nscale_size(range = c(1, 3)) +\nscale_fill_viridis(option=\"D\") + # a color palette from the viridis package\ntheme(legend.position = c(0.9,0.35)) +\ncoord_cartesian(clip = \"off\")\npanel_F\n"
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    "content": "# Panel F ----\nlibrary(ggplot2) \nlibrary(vi)\nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n}\n\ndata_F = read.csv(\"./data_F.csv\")\nhead(data_F)\npanel_F <- ggplot(data=data_F,\n                  aes(x=K,y=n,\n                      size=amplitude,\n                      fill=duration))+\n  geom_point(pch=21) +\n  my_theme() + \n  scale_x_continuous(expand = c(0, 0),\n                     trans = 'log10',\n                     labels=c(1,10,100),\n                     breaks=c(1,10,100),\n                     limits = c(1,100)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,4,by=0.5)),\n                     limits = c(0,4)) +   \n  xlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\n  ylab(\"Hill coefficient n\") +\n  annotation_logticks(sides='b') +\n  scale_size(range = c(1, 3)) +\n  scale_fill_viridis(option=\"D\") + # a color palette from the viridis package\n  theme(legend.position = c(0.9,0.35)) +\n  coord_cartesian(clip = \"off\")\n\npanel_F\n"
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    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/.Rproj.user/C6560784/sources/per/t/83B6CCD0-contents",
    "content": "\n# Set working directory \n# setwd(getwd()) \n\n# Libraries/packages \n\nlibrary(ggplot2) # Grammar of graphics\nlibrary(cowplot) # Arranging multiple plots into a grid\nlibrary(png)     # Load JPEG, PNG and TIFF format \nlibrary(scales)  # Generic plot scaling methods\nlibrary(viridis) # Default color maps from 'matplotlib'\nlibrary(grid)    # A rewrite of the graphics layout capabilities\nlibrary(magick)  # graphics and image processing\nlibrary(rsvg)    # Render svg image into a high quality bitmap\nlibrary(ggforce) # Collection of additional ggplot stats + geoms\n\n# global font size\nbase_size = 12 \n\n# Manual theme for most panels\n# documentation: https://ggplot2.tidyverse.org/reference/theme.html\nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n  \n}\n\n# Panel A ----\n\nimg1 <- magick::image_flip(magick::image_read(\"./image1.jpg\"))\nimg2 <-  magick::image_flip(magick::image_read(\"./image2.png\"))\n\npanel_A <- ggplot() +\n  annotation_custom(rasterGrob(image =  img1, \n                               x=0.27,\n                               y=0.49,\n                               width = unit(0.45,\"npc\"),\n                               height = unit(0.87,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  annotation_custom(rasterGrob(image = img2, \n                               x=0.73,\n                               y=0.49,\n                               width = unit(0.45,\"npc\"),\n                               height = unit(0.87,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  \n  geom_ellipse(aes(x0 = 0.25, \n                            y0 = 0.3,\n                            a = 0.1, \n                            b = 0.04, \n                            angle = 0),\n                        color=\"yellow\",\n                        size=1)+\n  scale_x_continuous(limits = c(0,1))+\n  scale_y_continuous(limits=c(0,1)) +\n  geom_segment(aes(x=0.15,\n                   xend=0.2,\n                   y=0.75,\n                   yend=0.7),\n               arrow = arrow(length=unit(0.30,\"cm\"),\n                             ends=\"last\", \n                             type = \"closed\"),\n               size = 1,\n               color=\"white\") +\n  geom_segment(aes(x=0.3,\n                   xend=0.9,\n                   y=0.7,\n                   yend=0.7),\n               arrow = arrow(length=unit(0.30,\"cm\"),\n                             ends=\"both\", \n                             type = \"closed\"),\n               size = 1,\n               color=\"red\") +\n\n  annotate(\"text\", x = 0.25, y = 0.5, label = \"PNG\",color=\"white\") +\n  annotate(\"text\", x = 0.75, y = 0.5, label = \"JPEG\",color=\"white\") +\n  annotate(\"text\", x = 0.25, y = 1, label = \"image 1\",color=\"black\") +\n  annotate(\"text\", x = 0.75, y = 1, label = \"image 2\",color=\"black\") +\n  annotate(\"text\", x = 0.39, y = 0.07, label = \"20~mu*m\",color=\"white\",parse=T) +\n  annotate(\"text\", x = 0.89, y = 0.07, label = \"20~mu*m\",color=\"white\",parse=T) +\n  geom_segment(aes(x=0.33,xend=0.45,y=0.03,yend=0.03), size = 2,color=\"white\") +\n  geom_segment(aes(x=0.83,xend=0.95,y=0.03,yend=0.03),size = 2,color=\"white\")  + \n  theme_void() +# blank plot w/o axes etc.\n  theme(plot.margin = unit(c(-0,0,1,0), \"cm\"),\n        aspect.ratio = 1)\n\npanel_A\n# Panel B ----\n\ndata_B = read.csv(\"./data_B.csv\")\n\n# format data to ggplot's liking\ndata_B = data.frame(\"n\"=c(data_B$n,data_B$n),\n                    \"sample\"=c(rep(\"apical side\",nrow(data_B)),\n                               rep(\"basal side\",nrow(data_B))),\n                    \"fraction\"=c(data_B$fraction1,data_B$fraction2),\n                    \"err\"=c(data_B$err1,data_B$err2)\n)\n\n# define lognormal distribution to be called via ggplot2::stat_function\nsigma = 0.14\nn = seq(3,10,1)\nlogn_dist <- function(n) exp(-(log(n)-log(6))^2/(2*sigma^2))/(sqrt(2*pi)*sigma*n)\n\n\n\npanel_B <- \n  ggplot(data=data_B,(aes(x=n, # aes: aesthetics\n                          y=fraction,\n                          fill=sample)))+\n  geom_bar(stat = \"identity\",\n           position=position_dodge()) + # dodge overlapping objects side-to-side\n  stat_function(fun=logn_dist, \n                geom=\"line\",\n                linetype=\"solid\",\n                aes(x=n,\n                    y=logn_dist(n))) +\n  geom_errorbar(aes(ymin=fraction-err, \n                    ymax=fraction+err), \n                width=.2, \n                position=position_dodge(.9)) +\n  scale_fill_manual(values=c('#f2a340','#998fc2'))+\n  scale_x_continuous(expand = c(0, 0), # prevent gap between origin and first tick\n                     breaks=c(seq(from =  min(data_B$n), \n                                  to = max(data_B$n),\n                                  by = 1)),\n                     limits = c(min(data_B$n),max(data_B$n))) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,0.6,0.1)),\n                     limits = c(0,0.6)\n  )+\n  my_theme() +\n  # some extra theme tweaking \n  theme(legend.position = c(0.18,0.95),\n        legend.title = element_blank(),\n        axis.ticks.x=element_blank(),\n        axis.text.x = element_text(vjust=-0.5),\n        axis.title.x = element_text(vjust=-0.5),\n        legend.key.size = unit(0.4, \"lines\")\n  ) +\n  xlab(expression(paste(\"number of neighbors \",italic(\"n\")))) +\n  ylab(\"fraction of cells (%)\") \n\npanel_B\n\n\ninset_curve <- function(n) 180*(n-2)/n \n# The equation is added as an image bc the annotate() function output looks too ugly\ninset_equation <- image_read_svg(\"./panel_b_inset_equation.svg\",width = 583,height = 240)\n\n# now comes the inset plot\n\ninset <- ggplot() + \n  stat_function(fun=inset_curve, \n                geom=\"line\",\n                linetype=\"solid\",\n                aes(x=n,\n                    y=inset_curve(n))) +\n  # annotate(geom=\"text\",label=\"180*degree*(n-2)/n\",x = 5.5,y=160,\n  #          parse=TRUE,size=2) +\n  annotation_custom(rasterGrob(image = inset_equation, \n                               x=0.5,\n                               y=0.8,\n                               width = unit(0.3125,\"npc\"),\n                               height = unit(0.126,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  scale_x_continuous(expand = c(0, 0),\n                     breaks=c(seq(3,10,1)),\n                     limits = c(3,10)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(60,180,20)),\n                     limits = c(60,180)\n                    ) +\n  my_theme() +\n  xlab(expression(italic(n)))+ \n  # expression doc: https://stat.ethz.ch/R-manual/R-devel/library/grDevices/html/plotmath.html\n  ylab(expression(interior~angle~italic(theta[n])(degree))) +\n  # some extra theme tweaking \n  theme( \n    panel.background = element_blank(),\n    plot.background = element_blank(),\n    axis.ticks.length = unit(-0.1, \"lines\"), # make ticks a bit shorter\n    axis.title.x = element_text(size = 0.5*base_size, \n                                color = \"black\", \n                                margin = margin(t = -4)), \n    # top (t), right (r), bottom (b), left (l) \n    axis.title.y = element_text(size = 0.5*base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -4)),  \n    axis.text.x = element_text(size = base_size*0.5, \n                               color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.1,0.1,0.1,0.1), \"cm\")),\n    axis.text.y = element_text(size = base_size*0.5, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.1,0.1,0.1,0.1), \"cm\"))\n  ) \n\ninset\n# create  grob (grid graphical object) from the inset plot\npanel_B_inset <- ggplotGrob(x = inset) \n\n\n# now for some regular polygon drawing\npolygons <- \n  ggplot() +\n    ggforce::geom_regon(aes(x0=c(seq(3,10,1)),\n                            y0=rep(-0.3,8),\n                            sides = c(seq(3,10,1)), \n                            angle=0, r=0.3),\n                        fill=NA,\n                        color=\"black\") +\n  theme_void() + coord_fixed()\npolygons\n\n\n# finally, we can combine everything to plot panel B\n\npanel_B <- \n  panel_B + \n  annotation_custom(grob = panel_B_inset, \n                    xmin=6.6, xmax=10.1, ymin=0.1, ymax=0.6) +\n  annotation_custom(grob = ggplotGrob(polygons), \n                    xmin = 2.36, xmax = 10.67, ymin = -0.172, ymax = 0.115) \n\n\n# Panel C ----\n\ndata_Cwt_E8.5  = read.csv( \"./data_Cwt_E8.5.csv\")\ndata_Cwt_E9.5  = read.csv( \"./data_Cwt_E9.5.csv\")\ndata_Cwt_E10.5 = read.csv(\"./data_Cwt_E10.5.csv\")\ndata_Cwt_E11.5 = read.csv(\"./data_Cwt_E11.5.csv\")\ndata_Cmu_E8.5  = read.csv( \"./data_Cmu_E8.5.csv\")\ndata_Cmu_E9.5  = read.csv( \"./data_Cmu_E9.5.csv\")\ndata_Cmu_E10.5 = read.csv(\"./data_Cmu_E10.5.csv\")\ndata_Cmu_E11.5 = read.csv(\"./data_Cmu_E11.5.csv\")\n\n# format data to ggplot's liking\ndata_C = data.frame(\n  \"type\" = c(\n    rep(\n      \"wildtype\",\n      nrow(data_Cwt_E8.5) +\n        nrow(data_Cwt_E9.5) +\n        nrow(data_Cwt_E10.5) +\n        nrow(data_Cwt_E11.5)\n    ),\n    rep(\n      \"mutant\",\n      nrow(data_Cmu_E8.5) +\n        nrow(data_Cmu_E9.5) +\n        nrow(data_Cmu_E10.5) +\n        nrow(data_Cmu_E11.5)\n    )\n  ),\n  \"dev_stage\" = c(\n    rep(\"E8.5\", nrow(data_Cwt_E8.5)),\n    rep(\"E9.5\", nrow(data_Cwt_E9.5)),\n    rep(\"E10.5\", nrow(data_Cwt_E10.5)),\n    rep(\"E11.5\", nrow(data_Cwt_E11.5)),\n    rep(\"E8.5\", nrow(data_Cmu_E8.5)),\n    rep(\"E9.5\", nrow(data_Cmu_E9.5)),\n    rep(\"E10.5\", nrow(data_Cmu_E10.5)),\n    rep(\"E11.5\", nrow(data_Cmu_E11.5))\n  ),\n  \"trachea_length\" = c(\n    data_Cwt_E8.5[, 1] ,\n    data_Cwt_E9.5[, 1] ,\n    data_Cwt_E10.5[, 1] ,\n    data_Cwt_E11.5[, 1] ,\n    data_Cmu_E8.5[, 1] ,\n    data_Cmu_E9.5[, 1] ,\n    data_Cmu_E10.5[, 1] ,\n    data_Cmu_E11.5[, 1]\n  )\n)\n\npanel_C <- \n  ggplot(data=data_C,\n         aes(x=dev_stage,\n             y=trachea_length,\n             fill=type))+\n  geom_boxplot() + \n  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists\n             pch=21,\n             alpha=0.4) +  # transparency\n  scale_x_discrete(limits=c(\"E8.5\",\"E9.5\",\"E10.5\",\"E11.5\")) +\n  scale_fill_manual(values=c('#f2a340','#998fc2'))+ # custom colors in hex code\n  my_theme() +\n  theme(legend.position = c(0.18,0.95),\n        legend.title = element_blank(),\n        legend.key = element_blank()\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(20,120,by=20)),\n                     limits = c(20,120)\n  )+\n  xlab(\"developmental stage (days)\") + \n  ylab(expression(paste(\"tracheal length (\",~mu*m,\")\")))\n\npanel_C\n# Panel D ----\n\ndata_D1 = read.csv(\"./data_D1.csv\")\ndata_D2 = read.csv(\"./data_D2.csv\")\n# format data to ggplot's liking\ndata_D = data.frame(\"width\"=c(data_D1$width,data_D2$width),\n                    \"unit\"=c(rep(\"shear_stress\",nrow(data_D1)),\n                             rep(\"velocity\",nrow(data_D2))),\n                    \"value\"=c(data_D1$shear_stress,data_D2$velocity)\n)\n\ncurve_D1 = data.frame(width=data_D1$width,\n                      shear_stress=33.28/(pi*18*data_D1$width^2))\n\n\npanel_D1 <- ggplot(data=data_D1, \n                   aes(x=width,\n                       y=shear_stress)) + \n  geom_point(fill=\"red\",\n             size=3,\n             pch=22) +\n  geom_line(data=curve_D1) +\n  theme_void()+\n  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) +\n  scale_y_log10( expand = c(0, 0),\n                 # using trans_format from the scales package, but one can also use expressions\n                 labels = trans_format('log10', math_format(10^.x)), \n                 breaks=c(0.001,0.01,0.1,1),\n                 limits = c(0.001,1)\n  ) +\n  annotation_logticks(sides = \"l\") +\n  theme(\n    line = element_blank(),\n    # exclude everything outside axes bc it messes with positioning of grob in panel_D\n    text = element_blank(),\n    title = element_blank(),\n    axis.line.y = element_line(colour = \"black\"),\n    aspect.ratio = 1\n  ) +\n  ylab(\"shear stress (Pa)\")\n\n\n\npanel_D <- \n  ggplot(data=data_D2, \n         aes(x=width,\n             y=velocity))+ \n  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings\n  annotation_custom(ggplotGrob(panel_D1)) + \n  geom_point(fill=\"blue\",\n             size=3,\n             pch=21) + \n  my_theme() + \n  geom_line(color=\"blue\") +\n  geom_vline(xintercept = 1.1,\n             linetype=\"dashed\") +\n  geom_hline(yintercept = 0.9,\n             linetype=\"dashed\") +\n  scale_x_log10(expand=c(0,0),\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) + \n  annotation_logticks(sides = \"b\") + \n  scale_y_continuous(expand = c(0,0),\n                     breaks = seq(0,1,0.1),\n                     limits = c(0,1), \n                     # putting the y axis of the second plot to the right\n                     position = \"right\", \n                     # now the secondary axis becomes the left axis\n                     # we need the axis text+title for panel_D1\n                     # They were excluded in panel_D1 bc they were messing with the positioning\n                     sec.axis = sec_axis(~., \n                                         name = \"shear stress (Pa)\",\n                                         # rescale breaks bc sec_axis inherits scale from primary y axis\n                                         breaks=rescale(c(-3,-2,-1,0), \n                                                        to = c(0,1)),\n                                         labels = c(expression(\"10\"^\"-3\",\n                                                               \"10\"^\"-2\",\n                                                               \"10\"^\"-1\",\n                                                               \"10\"^\"0\")))\n                     \n  )  +\n  # some extra theme tweaking \n  theme(\n    aspect.ratio = 1,\n    plot.margin = unit(c(0.1,0,0,0.5), \"cm\"), # to match other panels\n    axis.title.y = element_text(margin = margin(r=1)), \n    axis.text.y = element_text(margin = margin(r=6)),\n    axis.text.y.right = element_text(margin = margin(l=7)),\n    axis.title.y.right = element_text(angle = 90)\n  ) +\n  xlab(expression(lumen~width~(mu*m))) +\n  ylab(\"relative flow velocity\") + \n  annotate(geom = \"text\",x =6 ,y =0.85 ,label = \"tau == 0.5~Pa\",parse=T) +\n  annotate(geom = \"text\",x =1.4 ,y =0.4 ,label = \"b == 1.1*mu*m\",parse=T,angle=90) +\n  annotate(geom = \"text\",x =5.6 ,y =0.6 ,label = \"tau == frac(4*mu*Q,pi*a*b^2)\",parse=T) \n  \npanel_D \n\n# Panel E ----\n\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\n                             rep(\"gene b\",nrow(data_Eb)),\n                             rep(\"gene c\",nrow(data_Ec))),\n                    \"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n                    \"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n                    \"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n                    \"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n                    \"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n                    \"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\n\nf1 = function(t) 0.2*exp(-t/48)\nf2 = function(t) 0.3*exp(-t/60)\nf3 = function(t) 0.4*exp(-t/72)\nt = seq(0,96,1)\n\nribbon = data.frame(\n  \"f2\" = 0.3*exp(-t/60),\n  \"t\" = t\n)\n\nmanual_pch =c(15,16,17) # available pch: type ?pch \n\npanel_E <- ggplot(data=data_E) +\n  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\n  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\n              fill=\"black\",alpha=0.1) +\n  stat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\n  stat_function(fun=f2, geom=\"line\") +\n  stat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\n  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), \n                width=2) +\n  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\n  scale_shape_manual(values=manual_pch) +\n  my_theme() + theme(legend.title=element_blank())+\n  theme(legend.position = c(0.9,0.95)) +\n  scale_x_continuous(expand = c(0, 0), \n                     breaks = c(seq(0,96,12)),\n                     limits = c(0,96)\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks = c(seq(0,0.4,0.05)),\n                     limits = c(0,0.4)\n  ) +\n  theme(\n    panel.grid.major = element_line(\"gray95\", size = 0.1),\n    # putting label closer to axis bc exponent makes it bigger \n    axis.title.y = element_text(margin = margin(r = -9)) \n  ) +\n  xlab(\"time (h)\") +\n  ylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +  \n  coord_cartesian(clip = \"off\") # to allow for plotting outside axes\n\npanel_E\n# Panel F ----\n\ndata_F = read.csv(\"./data_F.csv\")\n\npanel_F <- ggplot(data=data_F,\n                  aes(x=K,y=n,\n                      size=amplitude,\n                      fill=duration))+\n  geom_point(pch=21) +\n  my_theme() +\n\n  scale_x_continuous(expand = c(0, 0),\n                     trans = 'log10',\n                     labels=c(1,10,100),\n                     breaks=c(1,10,100),\n                     limits = c(1,100)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,4,by=0.5)),\n                     limits = c(0,4)) +\n  annotation_logticks(sides='b') +\n  scale_size(range = c(1, 3)) +\n  scale_fill_viridis(option=\"D\") + # a color palette from the viridis package\n  theme( legend.position = c(0.9,0.35)) +\n  coord_cartesian(clip = \"off\") +\n  xlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\n  ylab(\"Hill coefficient n\")\n\npanel_F\n\n# Plot the whole figure using plot_grid from the cowplot package\n\n\nrow1 <- plot_grid(panel_A,panel_B,panel_C,\n                  ncol=3,\n                  labels = c('A','B','C'))#,rel_widths = c(0.9,0.9))\n\nrow2 <- plot_grid(panel_D,panel_E,panel_F,\n                  ncol=3,\n                  labels = c('D','E','F'))\n\nplot_grid(row1,row2,nrow=2)\n\n# RStudio instructions for saving the final plot:\n# Whenever you resize the 'Plots' window, click 'refresh current plot'\n# For best results save as SVG with a resolution of 1148 x 686 \n# open SVG in inkscape, (do some optional post-processing) and 'save as' PDF\n\n"
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    "content": "library(ggplot2) \nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n  \n}\n\ndata_D1 = read.csv(\"./data_D1.csv\")\ndata_D2 = read.csv(\"./data_D2.csv\")\n# format data to ggplot's liking\ndata_D = data.frame(\"width\"=c(data_D1$width,data_D2$width),\n                    \"unit\"=c(rep(\"shear_stress\",nrow(data_D1)),\n                             rep(\"velocity\",nrow(data_D2))),\n                    \"value\"=c(data_D1$shear_stress,data_D2$velocity)\n)\nhead(data_D)\ncurve_D1 = data.frame(width=data_D1$width,\n                      shear_stress=33.28/(pi*18*data_D1$width^2))\ncurve_D1\n\npanel_D1 <- ggplot(data=data_D1, \n                   aes(x=width,\n                       y=shear_stress)) + \n  geom_point(fill=\"red\",\n             size=3,\n             pch=22) +\n  geom_line(data=curve_D1) +\n  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) +\n  scale_y_log10( expand = c(0, 0),\n                 # using trans_format from the scales package, but one can also use expressions\n                 labels = trans_format('log10', math_format(10^.x)), \n                 breaks=c(0.001,0.01,0.1,1),\n                 limits = c(0.001,1)\n  ) +\n  annotation_logticks(sides = \"l\") +\n  theme_void()+\n  theme(\n    line = element_blank(),\n    # exclude everything outside axes bc it messes with positioning of grob in panel_D\n    text = element_blank(),\n    title = element_blank(),\n    axis.line.y = element_line(colour = \"black\")\n  ) +\n  ylab(\"shear stress (Pa)\")\n\npanel_D1\n\npanel_D <- \n  ggplot(data=data_D2, \n         aes(x=width,\n             y=velocity))+ \n  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings\n  geom_point(fill=\"blue\",\n             size=3,\n             pch=21) +\n  annotation_custom(ggplotGrob(panel_D1)) + \n  scale_y_continuous(expand = c(0,0),\n                     breaks = seq(0,1,0.1),\n                     limits = c(0,1), \n                     # putting the y axis of the second plot to the right\n                     position = \"right\", \n                     # now the secondary axis becomes the left axis\n                     # we need the axis text+title for panel_D1\n                     # They were excluded in panel_D1 bc they were messing with the positioning\n                     sec.axis = sec_axis(~., \n                                         name = \"shear stress (Pa)\",\n                                         # rescale breaks bc sec_axis inherits scale from primary y axis\n                                         breaks=rescale(c(-3,-2,-1,0), \n                                                        to = c(0,1)),\n                                         labels = c(expression(\"10\"^\"-3\",\n                                                               \"10\"^\"-2\",\n                                                               \"10\"^\"-1\",\n                                                               \"10\"^\"0\")))\n                     \n  ) +\n  scale_x_log10(expand=c(0,0),\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) + \n  annotation_logticks(sides = \"b\") + \n  my_theme() + \n  geom_line(color=\"blue\") +\n  geom_vline(xintercept = 1.1,\n             linetype=\"dashed\") +\n  geom_hline(yintercept = 0.9,\n             linetype=\"dashed\") +\n  theme(\n    plot.margin = unit(c(0.1,0,0,0.5), \"cm\"), # to match other panels\n    axis.title.y = element_text(margin = margin(r=1)), \n    axis.text.y = element_text(margin = margin(r=6)),\n    axis.text.y.right = element_text(margin = margin(l=7)),\n    axis.title.y.right = element_text(angle = 90)\n  ) +\n  xlab(expression(lumen~width~(mu*m))) +\n  ylab(\"relative flow velocity\") + \n  annotate(geom = \"text\",x =6 ,y =0.85 ,label = \"tau == 0.5~Pa\",parse=T) +\n  annotate(geom = \"text\",x =1.4 ,y =0.4 ,label = \"b == 1.1*mu*m\",parse=T,angle=90) +\n  annotate(geom = \"text\",x =5.6 ,y =0.6 ,label = \"tau == frac(4*mu*Q,pi*a*b^2)\",parse=T) \n\npanel_D \n\n"
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    "content": "# Panel E ----\nlibrary(ggplot2) \nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n}\n\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\n                             rep(\"gene b\",nrow(data_Eb)),\n                             rep(\"gene c\",nrow(data_Ec))),\n                    \"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n                    \"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n                    \"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n                    \"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n                    \"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n                    \"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\n\nhead(data_E)\n\nf1 = function(t) 0.2*exp(-t/48)\nf2 = function(t) 0.3*exp(-t/60)\nf3 = function(t) 0.4*exp(-t/72)\nt = seq(0,96,1)\n\nribbon = data.frame(\n  \"f2\" = 0.3*exp(-t/60),\n  \"t\" = t\n)\nhead(ribbon)\n\nmanual_pch =c(15,16,17) # available pch: type ?pch \n\npanel_E <- ggplot(data=data_E) +\n  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\n  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\n              fill=\"black\",alpha=0.1) +\n  stat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\n  stat_function(fun=f2, geom=\"line\") +\n  stat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\n  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), \n                width=2) +\n  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\n  scale_shape_manual(values=manual_pch) +\n  my_theme() + theme(legend.title=element_blank())+\n  theme(legend.position = c(0.9,0.95)) +\n  scale_x_continuous(expand = c(0, 0), \n                     breaks = c(seq(0,96,12)),\n                     limits = c(0,96)\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks = c(seq(0,0.4,0.05)),\n                     limits = c(0,0.4)\n  ) +\n  theme(\n    panel.grid.major = element_line(\"gray95\", size = 0.1),\n    # putting label closer to axis bc exponent makes it bigger \n    axis.title.y = element_text(margin = margin(r = -9)) \n  ) +\n  xlab(\"time (h)\") +\n  ylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +  \n  coord_cartesian(clip = \"off\") # to allow for plotting outside axes\n\npanel_E\n"
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    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  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Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  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Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>.\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_C.R",
    "content": "# Panel C ----\nlibrary(ggplot2) \nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n  \n}\n\ndata_Cwt_E8.5  = read.csv( \"./data_Cwt_E8.5.csv\")\ndata_Cwt_E9.5  = read.csv( \"./data_Cwt_E9.5.csv\")\ndata_Cwt_E10.5 = read.csv(\"./data_Cwt_E10.5.csv\")\ndata_Cwt_E11.5 = read.csv(\"./data_Cwt_E11.5.csv\")\ndata_Cmu_E8.5  = read.csv( \"./data_Cmu_E8.5.csv\")\ndata_Cmu_E9.5  = read.csv( \"./data_Cmu_E9.5.csv\")\ndata_Cmu_E10.5 = read.csv(\"./data_Cmu_E10.5.csv\")\ndata_Cmu_E11.5 = read.csv(\"./data_Cmu_E11.5.csv\")\n\nhead(data_Cwt_E8.5)\n# format data to ggplot's liking\ndata_C = data.frame(\n  \"type\" = c(\n    rep(\n      \"wildtype\",\n      nrow(data_Cwt_E8.5) +\n        nrow(data_Cwt_E9.5) +\n        nrow(data_Cwt_E10.5) +\n        nrow(data_Cwt_E11.5)\n    ),\n    rep(\n      \"mutant\",\n      nrow(data_Cmu_E8.5) +\n        nrow(data_Cmu_E9.5) +\n        nrow(data_Cmu_E10.5) +\n        nrow(data_Cmu_E11.5)\n    )\n  ),\n  \"dev_stage\" = c(\n    rep(\"E8.5\", nrow(data_Cwt_E8.5)),\n    rep(\"E9.5\", nrow(data_Cwt_E9.5)),\n    rep(\"E10.5\", nrow(data_Cwt_E10.5)),\n    rep(\"E11.5\", nrow(data_Cwt_E11.5)),\n    rep(\"E8.5\", nrow(data_Cmu_E8.5)),\n    rep(\"E9.5\", nrow(data_Cmu_E9.5)),\n    rep(\"E10.5\", nrow(data_Cmu_E10.5)),\n    rep(\"E11.5\", nrow(data_Cmu_E11.5))\n  ),\n  \"trachea_length\" = c(\n    data_Cwt_E8.5[, 1] ,\n    data_Cwt_E9.5[, 1] ,\n    data_Cwt_E10.5[, 1] ,\n    data_Cwt_E11.5[, 1] ,\n    data_Cmu_E8.5[, 1] ,\n    data_Cmu_E9.5[, 1] ,\n    data_Cmu_E10.5[, 1] ,\n    data_Cmu_E11.5[, 1]\n  )\n)\n\nhead(data_C)\n\npanel_C <- \n  ggplot(data=data_C,\n         aes(x=dev_stage,\n             y=trachea_length,\n             fill=type))+\n  geom_boxplot() + \n  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists\n             pch=21, #圆形\n             alpha=0.4) +  # transparency\n  scale_x_discrete(limits=c(\"E8.5\",\"E9.5\",\"E10.5\",\"E11.5\")) +\n  scale_fill_manual(values=c('#f2a340','#998fc2')) + # custom colors in hex code\n  my_theme() +\n  theme(legend.position = c(0.18,0.95),\n        legend.title = element_blank(),\n        legend.key = element_blank()\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(20,120,by=20)),\n                     limits = c(20,120)\n  )+\n  xlab(\"developmental stage (days)\") + \n  ylab(expression(paste(\"tracheal length (\",~mu*m,\")\")))\n\npanel_C\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_D.R",
    "content": "library(ggplot2) \nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n  \n}\n\ndata_D1 = read.csv(\"./data_D1.csv\")\ndata_D2 = read.csv(\"./data_D2.csv\")\n# format data to ggplot's liking\ndata_D = data.frame(\"width\"=c(data_D1$width,data_D2$width),\n                    \"unit\"=c(rep(\"shear_stress\",nrow(data_D1)),\n                             rep(\"velocity\",nrow(data_D2))),\n                    \"value\"=c(data_D1$shear_stress,data_D2$velocity)\n)\nhead(data_D)\ncurve_D1 = data.frame(width=data_D1$width,\n                      shear_stress=33.28/(pi*18*data_D1$width^2))\ncurve_D1\n\npanel_D1 <- ggplot(data=data_D1, \n                   aes(x=width,\n                       y=shear_stress)) + \n  geom_point(fill=\"red\",\n             size=3,\n             pch=22) +\n  geom_line(data=curve_D1) +\n  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) +\n  scale_y_log10( expand = c(0, 0),\n                 # using trans_format from the scales package, but one can also use expressions\n                 labels = trans_format('log10', math_format(10^.x)), \n                 breaks=c(0.001,0.01,0.1,1),\n                 limits = c(0.001,1)\n  ) +\n  annotation_logticks(sides = \"l\") +\n  theme_void()+\n  theme(\n    line = element_blank(),\n    # exclude everything outside axes bc it messes with positioning of grob in panel_D\n    text = element_blank(),\n    title = element_blank(),\n    axis.line.y = element_line(colour = \"black\")\n  ) +\n  ylab(\"shear stress (Pa)\")\n\npanel_D1\n\npanel_D <- \n  ggplot(data=data_D2, \n         aes(x=width,\n             y=velocity))+ \n  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings\n  geom_point(fill=\"blue\",\n             size=3,\n             pch=21) +\n  annotation_custom(ggplotGrob(panel_D1)) + \n  scale_y_continuous(expand = c(0,0),\n                     breaks = seq(0,1,0.1),\n                     limits = c(0,1), \n                     # putting the y axis of the second plot to the right\n                     position = \"right\", \n                     # now the secondary axis becomes the left axis\n                     # we need the axis text+title for panel_D1\n                     # They were excluded in panel_D1 bc they were messing with the positioning\n                     sec.axis = sec_axis(~., \n                                         name = \"shear stress (Pa)\",\n                                         # rescale breaks bc sec_axis inherits scale from primary y axis\n                                         breaks=rescale(c(-3,-2,-1,0), \n                                                        to = c(0,1)),\n                                         labels = c(expression(\"10\"^\"-3\",\n                                                               \"10\"^\"-2\",\n                                                               \"10\"^\"-1\",\n                                                               \"10\"^\"0\")))\n                     \n  ) +\n  scale_x_log10(expand=c(0,0),\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) + \n  annotation_logticks(sides = \"b\") + \n  my_theme() + \n  geom_line(color=\"blue\") +\n  geom_vline(xintercept = 1.1,\n             linetype=\"dashed\") +\n  geom_hline(yintercept = 0.9,\n             linetype=\"dashed\") +\n  theme(\n    plot.margin = unit(c(0.1,0,0,0.5), \"cm\"), # to match other panels\n    axis.title.y = element_text(margin = margin(r=1)), \n    axis.text.y = element_text(margin = margin(r=6)),\n    axis.text.y.right = element_text(margin = margin(l=7)),\n    axis.title.y.right = element_text(angle = 90)\n  ) +\n  xlab(expression(lumen~width~(mu*m))) +\n  ylab(\"relative flow velocity\") + \n  annotate(geom = \"text\",x =6 ,y =0.85 ,label = \"tau == 0.5~Pa\",parse=T) +\n  annotate(geom = \"text\",x =1.4 ,y =0.4 ,label = \"b == 1.1*mu*m\",parse=T,angle=90) +\n  annotate(geom = \"text\",x =5.6 ,y =0.6 ,label = \"tau == frac(4*mu*Q,pi*a*b^2)\",parse=T) \n\npanel_D \n\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_E.R",
    "content": "# Panel E ----\nlibrary(ggplot2) \nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n}\n\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\n                             rep(\"gene b\",nrow(data_Eb)),\n                             rep(\"gene c\",nrow(data_Ec))),\n                    \"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n                    \"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n                    \"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n                    \"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n                    \"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n                    \"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\n\nhead(data_E)\n\nf1 = function(t) 0.2*exp(-t/48)\nf2 = function(t) 0.3*exp(-t/60)\nf3 = function(t) 0.4*exp(-t/72)\nt = seq(0,96,1)\n\nribbon = data.frame(\n  \"f2\" = 0.3*exp(-t/60),\n  \"t\" = t\n)\nhead(ribbon)\n\nmanual_pch =c(15,16,17) # available pch: type ?pch \n\npanel_E <- ggplot(data=data_E) +\n  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\n  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\n              fill=\"black\",alpha=0.1) +\n  stat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\n  stat_function(fun=f2, geom=\"line\") +\n  stat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\n  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), \n                width=2) +\n  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\n  scale_shape_manual(values=manual_pch) +\n  my_theme() + theme(legend.title=element_blank())+\n  theme(legend.position = c(0.9,0.95)) +\n  scale_x_continuous(expand = c(0, 0), \n                     breaks = c(seq(0,96,12)),\n                     limits = c(0,96)\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks = c(seq(0,0.4,0.05)),\n                     limits = c(0,0.4)\n  ) +\n  theme(\n    panel.grid.major = element_line(\"gray95\", size = 0.1),\n    # putting label closer to axis bc exponent makes it bigger \n    axis.title.y = element_text(margin = margin(r = -9)) \n  ) +\n  xlab(\"time (h)\") +\n  ylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +  \n  coord_cartesian(clip = \"off\") # to allow for plotting outside axes\n\npanel_E\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/Panel_F.R",
    "content": "# Panel F ----\nlibrary(ggplot2) \nlibrary(vi)\nbase_size = 12 \nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n}\n\ndata_F = read.csv(\"./data_F.csv\")\nhead(data_F)\npanel_F <- ggplot(data=data_F,\n                  aes(x=K,y=n,\n                      size=amplitude,\n                      fill=duration))+\n  geom_point(pch=21) +\n  my_theme() + \n  scale_x_continuous(expand = c(0, 0),\n                     trans = 'log10',\n                     labels=c(1,10,100),\n                     breaks=c(1,10,100),\n                     limits = c(1,100)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,4,by=0.5)),\n                     limits = c(0,4)) +   \n  xlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\n  ylab(\"Hill coefficient n\") +\n  annotation_logticks(sides='b') +\n  scale_size(range = c(1, 3)) +\n  scale_fill_viridis(option=\"D\") + # a color palette from the viridis package\n  theme(legend.position = c(0.9,0.35)) +\n  coord_cartesian(clip = \"off\")\n\npanel_F\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/README.md",
    "content": "# 代码解释见推文：https://mp.weixin.qq.com/s/j9ribyJ809pErDGQXL_Usw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n代码来源：https://github.com/marco-meer/scifig_plot_examples_R\n\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_B.csv",
    "content": "n,fraction1,fraction2,err1,err2\n3,0.01,0.005,0.002,0.001\n4,0.05,0.04,0.01,0.008\n5,0.24,0.225,0.04,0.04\n6,0.45,0.53,0.06,0.05\n7,0.19,0.14,0.034,0.03\n8,0.045,0.04,0.009,0.008\n9,0.01,0.02,0.002,0.004\n10,0.005,0,0.001,0\n"
  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/data_Cmu_E10.5.csv",
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  },
  {
    "path": "2023年/2023.04.01 基于 R 语言的科研论文绘图技巧汇总/figure_example.R",
    "content": "\n# Set working directory \n# setwd(getwd()) \n\n# Libraries/packages \n\nlibrary(ggplot2) # Grammar of graphics\nlibrary(cowplot) # Arranging multiple plots into a grid\nlibrary(png)     # Load JPEG, PNG and TIFF format \nlibrary(scales)  # Generic plot scaling methods\nlibrary(viridis) # Default color maps from 'matplotlib'\nlibrary(grid)    # A rewrite of the graphics layout capabilities\nlibrary(magick)  # graphics and image processing\nlibrary(rsvg)    # Render svg image into a high quality bitmap\nlibrary(ggforce) # Collection of additional ggplot stats + geoms\n\n# global font size\nbase_size = 12 \n\n# Manual theme for most panels\n# documentation: https://ggplot2.tidyverse.org/reference/theme.html\nmy_theme <-  function() {\n  theme(\n    aspect.ratio = 1,\n    axis.line =element_line(colour = \"black\"),  \n    \n    # shift axis text closer to axis bc ticks are facing inwards\n    axis.text.x = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")), \n    axis.text.y = element_text(size = base_size*0.8, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.3,0.3,0.3,0.3), \"cm\")),  \n    \n    axis.ticks = element_line(color = \"black\", size  =  0.2),  \n    axis.title.x = element_text(size = base_size, \n                                color = \"black\", \n                                margin = margin(t = -5)), \n    # t (top), r (right), b (bottom), l (left)\n    axis.title.y = element_text(size = base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -5)),  \n    axis.ticks.length = unit(-0.3, \"lines\"),  \n    legend.background = element_rect(color = NA, \n                                     fill = NA), \n    legend.key = element_rect(color = \"black\",  \n                              fill = \"white\"),  \n    legend.key.size = unit(0.5, \"lines\"),  \n    legend.key.height =NULL,  \n    legend.key.width = NULL,      \n    legend.text = element_text(size = 0.6*base_size, \n                               color = \"black\"),  \n    legend.title = element_text(size = 0.6*base_size, \n                                face = \"bold\", \n                                hjust = 0, \n                                color = \"black\"),  \n    legend.text.align = NULL,  \n    legend.title.align = NULL,  \n    legend.direction = \"vertical\",  \n    legend.box = NULL, \n    panel.background = element_rect(fill = \"white\", \n                                    color  =  NA),  \n    panel.grid.major = element_blank(),\n    panel.grid.minor = element_blank(),\n    plot.title = element_text(size = base_size, \n                              color = \"black\"), \n  ) \n  \n  \n}\n\n# Panel A ----\n\nimg1 <- magick::image_flip(magick::image_read(\"./image1.jpg\"))\nimg2 <-  magick::image_flip(magick::image_read(\"./image2.png\"))\n\npanel_A <- ggplot() +\n  annotation_custom(rasterGrob(image =  img1, \n                               x=0.27,\n                               y=0.49,\n                               width = unit(0.45,\"npc\"),\n                               height = unit(0.87,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  annotation_custom(rasterGrob(image = img2, \n                               x=0.73,\n                               y=0.49,\n                               width = unit(0.45,\"npc\"),\n                               height = unit(0.87,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  \n  geom_ellipse(aes(x0 = 0.25, \n                            y0 = 0.3,\n                            a = 0.1, \n                            b = 0.04, \n                            angle = 0),\n                        color=\"yellow\",\n                        size=1)+\n  scale_x_continuous(limits = c(0,1))+\n  scale_y_continuous(limits=c(0,1)) +\n  geom_segment(aes(x=0.15,\n                   xend=0.2,\n                   y=0.75,\n                   yend=0.7),\n               arrow = arrow(length=unit(0.30,\"cm\"),\n                             ends=\"last\", \n                             type = \"closed\"),\n               size = 1,\n               color=\"white\") +\n  geom_segment(aes(x=0.3,\n                   xend=0.9,\n                   y=0.7,\n                   yend=0.7),\n               arrow = arrow(length=unit(0.30,\"cm\"),\n                             ends=\"both\", \n                             type = \"closed\"),\n               size = 1,\n               color=\"red\") +\n\n  annotate(\"text\", x = 0.25, y = 0.5, label = \"PNG\",color=\"white\") +\n  annotate(\"text\", x = 0.75, y = 0.5, label = \"JPEG\",color=\"white\") +\n  annotate(\"text\", x = 0.25, y = 1, label = \"image 1\",color=\"black\") +\n  annotate(\"text\", x = 0.75, y = 1, label = \"image 2\",color=\"black\") +\n  annotate(\"text\", x = 0.39, y = 0.07, label = \"20~mu*m\",color=\"white\",parse=T) +\n  annotate(\"text\", x = 0.89, y = 0.07, label = \"20~mu*m\",color=\"white\",parse=T) +\n  geom_segment(aes(x=0.33,xend=0.45,y=0.03,yend=0.03), size = 2,color=\"white\") +\n  geom_segment(aes(x=0.83,xend=0.95,y=0.03,yend=0.03),size = 2,color=\"white\")  + \n  theme_void() +# blank plot w/o axes etc.\n  theme(plot.margin = unit(c(-0,0,1,0), \"cm\"),\n        aspect.ratio = 1)\n\npanel_A\n# Panel B ----\n\ndata_B = read.csv(\"./data_B.csv\")\n\n# format data to ggplot's liking\ndata_B = data.frame(\"n\"=c(data_B$n,data_B$n),\n                    \"sample\"=c(rep(\"apical side\",nrow(data_B)),\n                               rep(\"basal side\",nrow(data_B))),\n                    \"fraction\"=c(data_B$fraction1,data_B$fraction2),\n                    \"err\"=c(data_B$err1,data_B$err2)\n)\n\n# define lognormal distribution to be called via ggplot2::stat_function\nsigma = 0.14\nn = seq(3,10,1)\nlogn_dist <- function(n) exp(-(log(n)-log(6))^2/(2*sigma^2))/(sqrt(2*pi)*sigma*n)\n\n\n\npanel_B <- \n  ggplot(data=data_B,(aes(x=n, # aes: aesthetics\n                          y=fraction,\n                          fill=sample)))+\n  geom_bar(stat = \"identity\",\n           position=position_dodge()) + # dodge overlapping objects side-to-side\n  stat_function(fun=logn_dist, \n                geom=\"line\",\n                linetype=\"solid\",\n                aes(x=n,\n                    y=logn_dist(n))) +\n  geom_errorbar(aes(ymin=fraction-err, \n                    ymax=fraction+err), \n                width=.2, \n                position=position_dodge(.9)) +\n  scale_fill_manual(values=c('#f2a340','#998fc2'))+\n  scale_x_continuous(expand = c(0, 0), # prevent gap between origin and first tick\n                     breaks=c(seq(from =  min(data_B$n), \n                                  to = max(data_B$n),\n                                  by = 1)),\n                     limits = c(min(data_B$n),max(data_B$n))) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,0.6,0.1)),\n                     limits = c(0,0.6)\n  )+\n  my_theme() +\n  # some extra theme tweaking \n  theme(legend.position = c(0.18,0.95),\n        legend.title = element_blank(),\n        axis.ticks.x=element_blank(),\n        axis.text.x = element_text(vjust=-0.5),\n        axis.title.x = element_text(vjust=-0.5),\n        legend.key.size = unit(0.4, \"lines\")\n  ) +\n  xlab(expression(paste(\"number of neighbors \",italic(\"n\")))) +\n  ylab(\"fraction of cells (%)\") \n\npanel_B\n\n\ninset_curve <- function(n) 180*(n-2)/n \n# The equation is added as an image bc the annotate() function output looks too ugly\ninset_equation <- image_read_svg(\"./panel_b_inset_equation.svg\",width = 583,height = 240)\n\n# now comes the inset plot\n\ninset <- ggplot() + \n  stat_function(fun=inset_curve, \n                geom=\"line\",\n                linetype=\"solid\",\n                aes(x=n,\n                    y=inset_curve(n))) +\n  # annotate(geom=\"text\",label=\"180*degree*(n-2)/n\",x = 5.5,y=160,\n  #          parse=TRUE,size=2) +\n  annotation_custom(rasterGrob(image = inset_equation, \n                               x=0.5,\n                               y=0.8,\n                               width = unit(0.3125,\"npc\"),\n                               height = unit(0.126,\"npc\")), \n                    -Inf, Inf, -Inf, Inf) +\n  scale_x_continuous(expand = c(0, 0),\n                     breaks=c(seq(3,10,1)),\n                     limits = c(3,10)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(60,180,20)),\n                     limits = c(60,180)\n                    ) +\n  my_theme() +\n  xlab(expression(italic(n)))+ \n  # expression doc: https://stat.ethz.ch/R-manual/R-devel/library/grDevices/html/plotmath.html\n  ylab(expression(interior~angle~italic(theta[n])(degree))) +\n  # some extra theme tweaking \n  theme( \n    panel.background = element_blank(),\n    plot.background = element_blank(),\n    axis.ticks.length = unit(-0.1, \"lines\"), # make ticks a bit shorter\n    axis.title.x = element_text(size = 0.5*base_size, \n                                color = \"black\", \n                                margin = margin(t = -4)), \n    # top (t), right (r), bottom (b), left (l) \n    axis.title.y = element_text(size = 0.5*base_size, \n                                color = \"black\", angle = 90,\n                                margin = margin(r = -4)),  \n    axis.text.x = element_text(size = base_size*0.5, \n                               color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.1,0.1,0.1,0.1), \"cm\")),\n    axis.text.y = element_text(size = base_size*0.5, color = \"black\", \n                               lineheight = 0.9,\n                               margin=unit(c(0.1,0.1,0.1,0.1), \"cm\"))\n  ) \n\ninset\n# create  grob (grid graphical object) from the inset plot\npanel_B_inset <- ggplotGrob(x = inset) \n\n\n# now for some regular polygon drawing\npolygons <- \n  ggplot() +\n    ggforce::geom_regon(aes(x0=c(seq(3,10,1)),\n                            y0=rep(-0.3,8),\n                            sides = c(seq(3,10,1)), \n                            angle=0, r=0.3),\n                        fill=NA,\n                        color=\"black\") +\n  theme_void() + coord_fixed()\npolygons\n\n\n# finally, we can combine everything to plot panel B\n\npanel_B <- \n  panel_B + \n  annotation_custom(grob = panel_B_inset, \n                    xmin=6.6, xmax=10.1, ymin=0.1, ymax=0.6) +\n  annotation_custom(grob = ggplotGrob(polygons), \n                    xmin = 2.36, xmax = 10.67, ymin = -0.172, ymax = 0.115) \n\n\n# Panel C ----\n\ndata_Cwt_E8.5  = read.csv( \"./data_Cwt_E8.5.csv\")\ndata_Cwt_E9.5  = read.csv( \"./data_Cwt_E9.5.csv\")\ndata_Cwt_E10.5 = read.csv(\"./data_Cwt_E10.5.csv\")\ndata_Cwt_E11.5 = read.csv(\"./data_Cwt_E11.5.csv\")\ndata_Cmu_E8.5  = read.csv( \"./data_Cmu_E8.5.csv\")\ndata_Cmu_E9.5  = read.csv( \"./data_Cmu_E9.5.csv\")\ndata_Cmu_E10.5 = read.csv(\"./data_Cmu_E10.5.csv\")\ndata_Cmu_E11.5 = read.csv(\"./data_Cmu_E11.5.csv\")\n\n# format data to ggplot's liking\ndata_C = data.frame(\n  \"type\" = c(\n    rep(\n      \"wildtype\",\n      nrow(data_Cwt_E8.5) +\n        nrow(data_Cwt_E9.5) +\n        nrow(data_Cwt_E10.5) +\n        nrow(data_Cwt_E11.5)\n    ),\n    rep(\n      \"mutant\",\n      nrow(data_Cmu_E8.5) +\n        nrow(data_Cmu_E9.5) +\n        nrow(data_Cmu_E10.5) +\n        nrow(data_Cmu_E11.5)\n    )\n  ),\n  \"dev_stage\" = c(\n    rep(\"E8.5\", nrow(data_Cwt_E8.5)),\n    rep(\"E9.5\", nrow(data_Cwt_E9.5)),\n    rep(\"E10.5\", nrow(data_Cwt_E10.5)),\n    rep(\"E11.5\", nrow(data_Cwt_E11.5)),\n    rep(\"E8.5\", nrow(data_Cmu_E8.5)),\n    rep(\"E9.5\", nrow(data_Cmu_E9.5)),\n    rep(\"E10.5\", nrow(data_Cmu_E10.5)),\n    rep(\"E11.5\", nrow(data_Cmu_E11.5))\n  ),\n  \"trachea_length\" = c(\n    data_Cwt_E8.5[, 1] ,\n    data_Cwt_E9.5[, 1] ,\n    data_Cwt_E10.5[, 1] ,\n    data_Cwt_E11.5[, 1] ,\n    data_Cmu_E8.5[, 1] ,\n    data_Cmu_E9.5[, 1] ,\n    data_Cmu_E10.5[, 1] ,\n    data_Cmu_E11.5[, 1]\n  )\n)\n\npanel_C <- \n  ggplot(data=data_C,\n         aes(x=dev_stage,\n             y=trachea_length,\n             fill=type))+\n  geom_boxplot() + \n  geom_point(position=position_jitterdodge(), # jitter for h-dist, dodge for grouped dists\n             pch=21,\n             alpha=0.4) +  # transparency\n  scale_x_discrete(limits=c(\"E8.5\",\"E9.5\",\"E10.5\",\"E11.5\")) +\n  scale_fill_manual(values=c('#f2a340','#998fc2'))+ # custom colors in hex code\n  my_theme() +\n  theme(legend.position = c(0.18,0.95),\n        legend.title = element_blank(),\n        legend.key = element_blank()\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(20,120,by=20)),\n                     limits = c(20,120)\n  )+\n  xlab(\"developmental stage (days)\") + \n  ylab(expression(paste(\"tracheal length (\",~mu*m,\")\")))\n\npanel_C\n# Panel D ----\n\ndata_D1 = read.csv(\"./data_D1.csv\")\ndata_D2 = read.csv(\"./data_D2.csv\")\n# format data to ggplot's liking\ndata_D = data.frame(\"width\"=c(data_D1$width,data_D2$width),\n                    \"unit\"=c(rep(\"shear_stress\",nrow(data_D1)),\n                             rep(\"velocity\",nrow(data_D2))),\n                    \"value\"=c(data_D1$shear_stress,data_D2$velocity)\n)\n\ncurve_D1 = data.frame(width=data_D1$width,\n                      shear_stress=33.28/(pi*18*data_D1$width^2))\n\n\npanel_D1 <- ggplot(data=data_D1, \n                   aes(x=width,\n                       y=shear_stress)) + \n  geom_point(fill=\"red\",\n             size=3,\n             pch=22) +\n  geom_line(data=curve_D1) +\n  theme_void()+\n  scale_x_log10(expand=c(0,0), # prevent gap between origin and first tick\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) +\n  scale_y_log10( expand = c(0, 0),\n                 # using trans_format from the scales package, but one can also use expressions\n                 labels = trans_format('log10', math_format(10^.x)), \n                 breaks=c(0.001,0.01,0.1,1),\n                 limits = c(0.001,1)\n  ) +\n  annotation_logticks(sides = \"l\") +\n  theme(\n    line = element_blank(),\n    # exclude everything outside axes bc it messes with positioning of grob in panel_D\n    text = element_blank(),\n    title = element_blank(),\n    axis.line.y = element_line(colour = \"black\"),\n    aspect.ratio = 1\n  ) +\n  ylab(\"shear stress (Pa)\")\n\n\n\npanel_D <- \n  ggplot(data=data_D2, \n         aes(x=width,\n             y=velocity))+ \n  # add plot of first dataset as grob as a trick to introduce two y-axes with different scalings\n  annotation_custom(ggplotGrob(panel_D1)) + \n  geom_point(fill=\"blue\",\n             size=3,\n             pch=21) + \n  my_theme() + \n  geom_line(color=\"blue\") +\n  geom_vline(xintercept = 1.1,\n             linetype=\"dashed\") +\n  geom_hline(yintercept = 0.9,\n             linetype=\"dashed\") +\n  scale_x_log10(expand=c(0,0),\n                breaks=c(0.5,1,2,5,10,20,50),\n                labels=c(0.5,1,2,5,10,20,50), \n                limits=c(0.5,50)) + \n  annotation_logticks(sides = \"b\") + \n  scale_y_continuous(expand = c(0,0),\n                     breaks = seq(0,1,0.1),\n                     limits = c(0,1), \n                     # putting the y axis of the second plot to the right\n                     position = \"right\", \n                     # now the secondary axis becomes the left axis\n                     # we need the axis text+title for panel_D1\n                     # They were excluded in panel_D1 bc they were messing with the positioning\n                     sec.axis = sec_axis(~., \n                                         name = \"shear stress (Pa)\",\n                                         # rescale breaks bc sec_axis inherits scale from primary y axis\n                                         breaks=rescale(c(-3,-2,-1,0), \n                                                        to = c(0,1)),\n                                         labels = c(expression(\"10\"^\"-3\",\n                                                               \"10\"^\"-2\",\n                                                               \"10\"^\"-1\",\n                                                               \"10\"^\"0\")))\n                     \n  )  +\n  # some extra theme tweaking \n  theme(\n    aspect.ratio = 1,\n    plot.margin = unit(c(0.1,0,0,0.5), \"cm\"), # to match other panels\n    axis.title.y = element_text(margin = margin(r=1)), \n    axis.text.y = element_text(margin = margin(r=6)),\n    axis.text.y.right = element_text(margin = margin(l=7)),\n    axis.title.y.right = element_text(angle = 90)\n  ) +\n  xlab(expression(lumen~width~(mu*m))) +\n  ylab(\"relative flow velocity\") + \n  annotate(geom = \"text\",x =6 ,y =0.85 ,label = \"tau == 0.5~Pa\",parse=T) +\n  annotate(geom = \"text\",x =1.4 ,y =0.4 ,label = \"b == 1.1*mu*m\",parse=T,angle=90) +\n  annotate(geom = \"text\",x =5.6 ,y =0.6 ,label = \"tau == frac(4*mu*Q,pi*a*b^2)\",parse=T) \n  \npanel_D \n\n# Panel E ----\n\ndata_Ea = read.csv(\"./data_Ea.csv\")\ndata_Eb = read.csv(\"./data_Eb.csv\")\ndata_Ec = read.csv(\"./data_Ec.csv\")\ndata_E = data.frame(\"gene\"=c(rep(\"gene a\",nrow(data_Ea)),\n                             rep(\"gene b\",nrow(data_Eb)),\n                             rep(\"gene c\",nrow(data_Ec))),\n                    \"t\"=c(data_Ea$t,data_Eb$t,data_Ec$t),\n                    \"C\"=c(data_Ea$C,data_Eb$C,data_Ec$C),\n                    \"pos_err\"=c(data_Ea$err,data_Eb$pos_err,data_Ec$pos_err_C),\n                    \"neg_err\"=c(data_Ea$err,data_Eb$neg_err,data_Ec$neg_err_C),\n                    \"pos_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$pos_err_t),\n                    \"neg_err_t\"=c(rep(NA, nrow(data_Ea)),rep(NA, nrow(data_Eb)),data_Ec$neg_err_t)\n)\n\nf1 = function(t) 0.2*exp(-t/48)\nf2 = function(t) 0.3*exp(-t/60)\nf3 = function(t) 0.4*exp(-t/72)\nt = seq(0,96,1)\n\nribbon = data.frame(\n  \"f2\" = 0.3*exp(-t/60),\n  \"t\" = t\n)\n\nmanual_pch =c(15,16,17) # available pch: type ?pch \n\npanel_E <- ggplot(data=data_E) +\n  geom_point(aes(x=t,y=C,pch=factor(gene)),size=2) +\n  geom_ribbon(data=ribbon, aes(x=t,ymin=0.85*f2,ymax=1.15*f2),\n              fill=\"black\",alpha=0.1) +\n  stat_function(fun=f1, geom=\"line\",linetype=\"dashed\") +\n  stat_function(fun=f2, geom=\"line\") +\n  stat_function(fun=f3, geom=\"line\",linetype=\"dotted\") +\n  geom_errorbar(aes(x=t,ymin=C-neg_err, ymax=C+pos_err), \n                width=2) +\n  geom_errorbarh(aes(y=C,xmin=t-neg_err_t,xmax=t+pos_err_t))+\n  scale_shape_manual(values=manual_pch) +\n  my_theme() + theme(legend.title=element_blank())+\n  theme(legend.position = c(0.9,0.95)) +\n  scale_x_continuous(expand = c(0, 0), \n                     breaks = c(seq(0,96,12)),\n                     limits = c(0,96)\n  ) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks = c(seq(0,0.4,0.05)),\n                     limits = c(0,0.4)\n  ) +\n  theme(\n    panel.grid.major = element_line(\"gray95\", size = 0.1),\n    # putting label closer to axis bc exponent makes it bigger \n    axis.title.y = element_text(margin = margin(r = -9)) \n  ) +\n  xlab(\"time (h)\") +\n  ylab(expression(paste(\"concentration (mmol\",~cm^-3,\")\"))) +  \n  coord_cartesian(clip = \"off\") # to allow for plotting outside axes\n\npanel_E\n# Panel F ----\n\ndata_F = read.csv(\"./data_F.csv\")\n\npanel_F <- ggplot(data=data_F,\n                  aes(x=K,y=n,\n                      size=amplitude,\n                      fill=duration))+\n  geom_point(pch=21) +\n  my_theme() +\n\n  scale_x_continuous(expand = c(0, 0),\n                     trans = 'log10',\n                     labels=c(1,10,100),\n                     breaks=c(1,10,100),\n                     limits = c(1,100)) +\n  scale_y_continuous(expand = c(0, 0),\n                     breaks=c(seq(0,4,by=0.5)),\n                     limits = c(0,4)) +\n  annotation_logticks(sides='b') +\n  scale_size(range = c(1, 3)) +\n  scale_fill_viridis(option=\"D\") + # a color palette from the viridis package\n  theme( legend.position = c(0.9,0.35)) +\n  coord_cartesian(clip = \"off\") +\n  xlab(expression(paste(\"dissociation constant\",~~italic(\"K\"),\" (M)\"))) +\n  ylab(\"Hill coefficient n\")\n\npanel_F\n\n# Plot the whole figure using plot_grid from the cowplot package\n\n\nrow1 <- plot_grid(panel_A,panel_B,panel_C,\n                  ncol=3,\n                  labels = c('A','B','C'))#,rel_widths = c(0.9,0.9))\n\nrow2 <- plot_grid(panel_D,panel_E,panel_F,\n                  ncol=3,\n                  labels = c('D','E','F'))\n\nplot_grid(row1,row2,nrow=2)\n\n# RStudio instructions for saving the final plot:\n# Whenever you resize the 'Plots' window, click 'refresh current plot'\n# For best results save as SVG with a resolution of 1148 x 686 \n# open SVG in inkscape, (do some optional post-processing) and 'save as' PDF\n\n"
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    "content": "suppressPackageStartupMessages(library(binom))\ndata(ghcnd_stations, package = \"StatCompLab\")\ndata(ghcnd_values, package = \"StatCompLab\")\nsuppressPackageStartupMessages(library(styler))\ndata1 <- ghcnd_stations\ndata2 <- ghcnd_values\nrm(ghcnd_stations,ghcnd_values)\ndata2 <- data2 %>% mutate(season=ifelse(Month %in% 4:9,\"Summer\",\"Winter\"))\ndata2 <- data2 %>% mutate(Summer=ifelse(Month %in% 4:9,T,F))\ndata2 <- data2 %>% left_join(data1,by='ID')\nyear_2018 <- data2 %>% filter(Year==2018)\n\n\ntmin <- year_2018 %>% filter(Element=='TMIN') %>% group_by(season) %>% \n  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+\n  geom_col()+ggtitle('The min temperature Summer vs Winter')+\n  ylab(\"The average min temperature\")\n\ntmax <- year_2018 %>% filter(Element=='TMAX') %>% group_by(season) %>% \n  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+\n  geom_col()+ggtitle('The max temperature Summer vs Winter')+\n  ylab(\"The average max temperature\")\n\nprcp_plot <- year_2018 %>% filter(Element=='PRCP') %>% group_by(season) %>% \n  summarise(mean=mean(Value)) %>% ggplot(aes(x=season,y=mean))+\n  geom_col()+ggtitle('The precipitation Summer vs Winter')+\n  ylab(\"The average precipitation\")\ng <- year_2018 %>%\n  filter(Element %in% c(\"TMIN\", \"TMAX\")) %>%\n  ggplot(aes(DecYear, Value, colour = Element)) +\n  geom_line() +\n  facet_wrap(~ ID,nrow=8)+\n  scale_y_continuous(breaks = seq(-20,30,10))\nt_average <- year_2018 %>%  filter(Element=='TMAX'|Element=='TMIN') %>% select(-DecYear,-season) %>% \n  pivot_wider(names_from = Element,values_from = Value) %>% mutate(taver=(TMAX+TMIN)/2,\n    season=ifelse(Month%in% 4:9,'Summer','Winter'))\nt_average_sup <- t_average %>% ggplot(aes(x=taver))+geom_histogram(bins = 30)+\n  facet_wrap(~season,nrow=2)+ggtitle('The average temperature of Summer vs Winter')+\n  xlab(\"The average temperature value\")\n\n\nprcp <- data2 %>% filter(Element=='PRCP')\noptions(dplyr.summarise.info=F)\nobserved_diff <- prcp %>% group_by(Name,Summer) %>% \n  summarise(mean=mean(Value),.groups = \"drop\")\nobserved_diff <- observed_diff %>% pivot_wider(names_from = Summer,\n  values_from = mean)\nnames(observed_diff) <- c(\"StationName\",\"Winter\",\"Summer\")\nobserved_diff <- observed_diff  %>%  mutate(diff=abs(Winter-Summer))\n\npermutation_diff <- data.frame(StationName=observed_diff$StationName,\n  p_value=0,sd=0,ci95_lower=0,ci95_upper=0)\nmean_diff <- function(x, y) {\n  mean(x) - mean(y)\n}\np_value_CI <- function(perm_stats,observed_diff,StationName,n_perms){\n  ci <- binom.confint(sum(perm_stats >= observed_diff[observed_diff$StationName==StationName,]$diff), \n    n_perms, method = \"wilson\")\n  ci\n}\nmc_permutation <- function(){\n  \n  for(j in 1:nrow(permutation_diff)){\n    StationName <- permutation_diff$StationName[j]\n    prcp_winter <- prcp %>% filter(Summer==F,Name==StationName) %>% pull(Value)\n    prcp_summer <- prcp %>% filter(Summer==T,Name==StationName) %>% pull(Value)\n    n_perms <- 1000\n    perm_stats <- numeric(n_perms)\n    for (i in 1:n_perms) {\n      perm <- sample(c(prcp_winter, prcp_summer))\n      perm_stats[i] <- mean_diff(perm[1:length(prcp_winter)], \n        perm[(length(prcp_winter)+1):(length(prcp_winter)+length(prcp_summer))])\n    }\n    p_value <- sum(perm_stats>=observed_diff[observed_diff$StationName==StationName,]$diff)/n_perms\n    ci <- p_value_CI(perm_stats,observed_diff,StationName,n_perms)\n    permutation_diff[j,]$p_value <<- p_value\n    permutation_diff[j,]$sd <<- sd(perm_stats)\n    permutation_diff[j,]$ci95_lower <<- ci$lower\n    permutation_diff[j,]$ci95_upper <<- ci$upper\n  }\n  \n  knitr::kable(permutation_diff)\n  \n  \n}\n\nprcp_month <- data2 %>% group_by(Month) %>% summarise(mean=sqrt(mean(Value)))\ndata2 <- data2 %>% mutate(Value_sqrt_avg=0)\nfor(i in 1:nrow(prcp_month)){\n  data2[data2$Month==prcp_month$Month[i],]$Value_sqrt_avg <- prcp_month$mean[i]\n}\ncs <- function(k,x){\n  if(k==1){\n    return(cos(2*pi*x)+sin(2*pi*x))\n  }else{\n    temp <- cos(2*pi*x)+sin(2*pi*x)\n    for(i in 2:k){\n      temp <- temp+cos(2*pi*x*i)+sin(2*pi*x*i)\n    }\n    return(temp)\n  }\n}\ndata2 <- data2 %>% mutate(cov_m1=cs(1,DecYear),\n  cov_m2=cs(2,DecYear),\n  cov_m3=cs(3,DecYear),\n  cov_m4=cs(4,DecYear)\n)\nspatial_model <- function(k,data){\n  if(k==0){\n    m <- lm(Value_sqrt_avg~Longitude+Latitude+Elevation+DecYear,data=data)\n  }else{\n    temp <- cs(k,data$DecYear)\n    data$cov <- temp\n    m <- lm(Value_sqrt_avg~Longitude+Latitude+Elevation+DecYear+cov,data=data)\n  }\n  return(m)\n}\n\nstation_model <- function(){\n  names <- data1$Name\n  df <- data.frame(name=names,se=0,ds=0)\n  for (i in 1:length(names)) {\n    name <- names[i]\n    temp <- cs(1,data2$DecYear)\n    data2$cov <- temp\n    train <- data2 %>% filter(Name!=name)\n    test <- data2 %>% filter(Name==name)\n    m <- spatial_model(1,train)\n    pred <- predict(m,newdata=test)\n    score <- cbind(pred, test) %>% mutate(\n      se = proper_score(\"se\", Value_sqrt_avg, mean = mean(pred)),\n      ds = proper_score(\"ds\", Value_sqrt_avg, mean = mean(pred), sd = sd(pred))\n    )\n    df$name[i] <- name\n    df$se[i] <- mean(score$se)\n    df$ds[i] <- mean(score$ds)\n  }\n  knitr::kable(df)\n}\nmonth_model <- function(){\n  months <- 1:12\n  df2 <- data.frame(months=months,se=0,ds=0)\n  for (i in 1:length(months)) {\n    month <- months[i]\n    temp <- cs(1,data2$DecYear)\n    data2$cov <- temp\n    train <- data2 %>% filter(Month!=month)\n    test <- data2 %>% filter(Month==month)\n    m <- spatial_model(1,train)\n    pred <- predict(m,newdata=test)\n    score2 <- cbind(pred, test) %>% mutate(\n      se = proper_score(\"se\", Value_sqrt_avg, mean = mean(pred)),\n      ds = proper_score(\"ds\", Value_sqrt_avg, mean = mean(pred), sd = sd(pred))\n    )\n    df2$months[i] <- month\n    df2$se[i] <- mean(score2$se)\n    df2$ds[i] <- mean(score2$ds)\n  }\n  knitr::kable(df2)\n}\n"
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    "content": "---\ntitle: \"基于`r params$new_title`学生信息的探索性分析\"\nauthor: \"庄闪闪\"\ndate: \"2023/04/12\"\ndocumentclass: ctexart\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\nparams:\n  new_title: A中学\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo=T, message=F, warning=F, fig.height=4)\n```\n\n# 背景\n\n假设我们得知不同学校学生信息，包括：姓名，身高，体重。如：\n\n![](images/9631681396915_.pic.jpg)\n\n\\*领导/硕导/博导\\* 要求分别给出不同学校的学生信息分析报告。此时，可以使用 R Markdown 开展这项工作。\n\n## 加载数据\n\n首先，我们加载 `r params$new_title` 学生的信息。\n\n```{r tab2}\nlibrary(tidyverse)\nlibrary(knitr)\ndf = read.csv(paste0(\"data/\",params$new_title,\".csv\",sep=''))\nknitr::kable(df, align = \"c\", caption = '学生信息')\n```\n\n## 探索性分析\n\n根据该数据，我们对不同性别的身高和体重进行探索性分析。\n\n```{r mytable}\ndf %>% group_by(`性别`) %>% summarise(\"平均身高\" = mean(`身高`),\n                                    \"身高标准差\" = sd(`身高`),\n                                    \"平均体重\" = mean(`体重`),\n                                    \"体重标准差\" = sd(`体重`)) -> df_summary\nknitr::kable(df_summary, align = \"c\",\n             caption = \"不同性别的身高和体重情况\")\n```\n\n```{r fig1, fig.align='center',fig.cap=\"不同性别学生的身高和体重分布情况\"}\nlibrary(showtext)\nlibrary(ggsci)\nshowtext.auto()\ndf %>% pivot_longer(`身高`:`体重`,\n                    names_to = \"Class\",\n                    values_to = \"数值\") %>% \n  ggplot() + \n  geom_boxplot(aes(性别,数值,fill=性别)) +\n  scale_fill_aaas() +\n  facet_wrap(vars(Class),scales = \"free\")\n```\n\n# 总结\n\n这个项目主要测试 RMarkdown 的参数化报告，主要参考：\n\n1.  [R Markdown 指南 第 7 章：使用 R Markdown 开展项目工作](https://cosname.github.io/rmarkdown-guide/rmarkdown-project.html#render-rmd)\n\n2.  [Iterate multiple RMarkdown reports](https://community.rstudio.com/t/iterate-multiple-rmarkdown-reports/43208/7)\n\n> 由于时间有限，这个报告后续没有添加了。\n\n# 未来展望\n\n\n- 对于不同的数据，虽然可以自动化绘制不同的报表，但是由于数据不同，结果也会不同。不同结果如何达到自动化分析呢？这个问题还需要再商讨。个人觉得如果能把类似 ChatGPT 结合起来，或许真能实现自动给出完整的分析报告了。类似的 ChatGPT 的 R 包有：[chatgpt](https://github.com/jcrodriguez1989/chatgpt \"chatgpt\")，[gptstudio](https://github.com/MichelNivard/gptstudio \"gptstudio\")。教程有：[1](https://rpubs.com/nirmal/setting_chat_gpt_R \"1\")，[2](https://www.karada-good.net/en/ranalytics/r-e767/ \"2\")，供参考。\n\n- 制作该可重复性报告的前提是：不同数据需要一致的数据结构。这点是否可以进行拓展？是否有专门的包能够自动对数据处理，并给出统一的数据格式？\n\n- 制作这个教程花费较长时间，欢迎大家一键三连呀～ 教程对应的项目代码已经开源在 [GitHub](https://github.com/liangliangzhuang/R_example \"GitHub\") 中，欢迎 Fork 和 star。或者公众号后台回复[`批量制作数据分析报告`]免费获得。\n\n\n"
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line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(1)/m/n' will be\n(Font)              scaled to size 5.26968pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(2)/m/n' will be\n(Font)              scaled to size 10.54033pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(2)/m/n' will be\n(Font)              scaled to size 7.37823pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(2)/m/n' will be\n(Font)              scaled to size 5.27016pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(3)/m/n' will be\n(Font)              scaled to size 10.53824pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(3)/m/n' will be\n(Font)              scaled to size 7.37677pt on input line 2.\nLaTeX Font Info:    Font shape `TU/latinmodern-math.otf(3)/m/n' will be\n(Font)              scaled to size 5.26912pt on input line 2.\n)\n\\tf@toc=\\write4\n\\openout4 = `exploratory_B中学.toc'.\n\nFile: images/9631681396915_.pic.jpg Graphic file (type bmp)\n<images/9631681396915_.pic.jpg>\n[1\n\n]\n\nPackage fontspec Warning: Font \"STFangsong\" does not contain requested Script\n(fontspec)                \"CJK\".\n\n\nPackage fontspec Info: STFangsong scale = 0.9707181870057879.\n\n\nPackage fontspec Info: Could not resolve font \"STFangsong/BI\" (it probably\n(fontspec)             doesn't exist).\n\n\nPackage fontspec Info: Could not resolve font \"STFangsong/B\" (it probably\n(fontspec)             doesn't exist).\n\n\nPackage fontspec Info: Could not resolve font \"STFangsong/I\" (it probably\n(fontspec)             doesn't exist).\n\n\nPackage fontspec Info: STFangsong scale = 0.9707181870057879.\n\n\nPackage fontspec Info: Font family 'STFangsong(0)' created for font\n(fontspec)             'STFangsong' with options\n(fontspec)             [Scale=MatchLowercase,Script={CJK}].\n(fontspec)              \n(fontspec)              This font family 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sequences out of 15000+600000\n 410017 words of font info for 97 fonts, out of 8000000 for 9000\n 1348 hyphenation exceptions out of 8191\n 101i,6n,123p,543b,437s stack positions out of 5000i,500n,10000p,200000b,80000s\n\nOutput written on exploratory_B中学.pdf (5 pages).\n"
  },
  {
    "path": "2023年/2023.04.12 Rmarkdown重复性报告/libs/header-attrs-2.20/header-attrs.js",
    "content": "// Pandoc 2.9 adds attributes on both header and div. We remove the former (to\n// be compatible with the behavior of Pandoc < 2.8).\ndocument.addEventListener('DOMContentLoaded', function(e) {\n  var hs = document.querySelectorAll(\"div.section[class*='level'] > :first-child\");\n  var i, h, a;\n  for (i = 0; i < hs.length; i++) {\n    h = hs[i];\n    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6\n    a = h.attributes;\n    while (a.length > 0) h.removeAttribute(a[0].name);\n  }\n});\n"
  },
  {
    "path": "2023年/2023.04.12 Rmarkdown重复性报告/libs/remark-css-0.0.1/default.css",
    "content": "a, a > code {\n  color: rgb(249, 38, 114);\n  text-decoration: none;\n}\n.footnote {\n  position: absolute;\n  bottom: 3em;\n  padding-right: 4em;\n  font-size: 90%;\n}\n.remark-code-line-highlighted     { background-color: #ffff88; }\n\n.inverse {\n  background-color: #272822;\n  color: #d6d6d6;\n  text-shadow: 0 0 20px #333;\n}\n.inverse h1, .inverse h2, .inverse h3 {\n  color: #f3f3f3;\n}\n/* Two-column layout */\n.left-column {\n  color: #777;\n  width: 20%;\n  height: 92%;\n  float: left;\n}\n.left-column h2:last-of-type, .left-column h3:last-child {\n  color: #000;\n}\n.right-column {\n  width: 75%;\n  float: right;\n  padding-top: 1em;\n}\n.pull-left {\n  float: left;\n  width: 47%;\n}\n.pull-right {\n  float: right;\n  width: 47%;\n}\n.pull-right + * {\n  clear: both;\n}\nimg, video, iframe {\n  max-width: 100%;\n}\nblockquote {\n  border-left: solid 5px lightgray;\n  padding-left: 1em;\n}\n.remark-slide table {\n  margin: auto;\n  border-top: 1px solid #666;\n  border-bottom: 1px solid #666;\n}\n.remark-slide table thead th { border-bottom: 1px solid #ddd; }\nth, td { padding: 5px; }\n.remark-slide thead, .remark-slide tfoot, .remark-slide tr:nth-child(even) { background: #eee }\n\n@page { margin: 0; }\n@media print {\n  .remark-slide-scaler {\n    width: 100% !important;\n    height: 100% !important;\n    transform: scale(1) !important;\n    top: 0 !important;\n    left: 0 !important;\n  }\n}\n"
  },
  {
    "path": "2023年/2023.04.12 Rmarkdown重复性报告/test.R",
    "content": "library(rmarkdown)\nschool = c(\"A中学\",\"B中学\")\nfor(v in school){\n  render(\"中文.Rmd\",\n         output_file=paste0(\"result/exploratory_\", v, \".pdf\"),\n         params=list(new_title=v))\n}\n"
  },
  {
    "path": "2023年/2023.04.12 Rmarkdown重复性报告/zh-CN.css",
    "content": "@font-face {\n  font-family: 'Yanone Kaffeesatz';\n  font-style: normal;\n  font-weight: 400;\n  src: local('Yanone Kaffeesatz Regular'), local('YanoneKaffeesatz-Regular'),\n       url('https://github.com/yihui/xaringan/releases/download/v0.1/yanone-kaffeesatz.woff') format('woff');\n}\n@font-face {\n  font-family: 'Droid Serif';\n  font-style: normal;\n  font-weight: 400;\n  src: local('Droid Serif'), local('DroidSerif'),\n       url('https://github.com/yihui/xaringan/releases/download/v0.1/droid-serif.woff') format('woff');\n}\n@font-face {\n  font-family: 'Source Code Pro';\n  font-style: normal;\n  font-weight: 400;\n  src: local('Source Code Pro'), local('SourceCodePro-Regular'),\n       url('https://github.com/yihui/xaringan/releases/download/v0.1/source-code-pro.woff') format('woff');\n}\n\nbody { font-family: 'Droid Serif', 'Palatino Linotype', 'Book Antiqua', Palatino, 'Songti SC', serif; }\nh1, h2, h3 {\n  font-family: 'Yanone Kaffeesatz', 'Palatino Linotype', 'Book Antiqua', Palatino, 'STHeiti', 'SimHei', 'Microsoft YaHei';\n  font-weight: normal;\n}\n.remark-code, .remark-inline-code { font-family: 'Source Code Pro', 'Lucida Console', Monaco, monospace; }\n"
  },
  {
    "path": "2023年/2023.04.12 Rmarkdown重复性报告/中文.Rmd",
    "content": "---\ntitle: \"基于`r params$new_title`学生信息的探索性分析\"\nauthor: \"庄闪闪\"\ndate: \"2023/04/12\"\ndocumentclass: ctexart\noutput:\n  rticles::ctex:\n    fig_caption: yes\n    number_sections: yes\n    toc: yes\nparams:\n  new_title: A中学\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo=T, message=F, warning=F, fig.height=4)\n```\n\n# 背景\n\n假设我们得知不同学校学生信息，包括：姓名，身高，体重。如：\n\n![](images/9631681396915_.pic.jpg)\n\n\\*领导/硕导/博导\\* 要求分别给出不同学校的学生信息分析报告。此时，可以使用 R Markdown 开展这项工作。\n\n## 加载数据\n\n首先，我们加载 `r params$new_title` 学生的信息。\n\n```{r tab2}\nlibrary(tidyverse)\nlibrary(knitr)\ndf = read.csv(paste0(\"data/\",params$new_title,\".csv\",sep=''))\nknitr::kable(df, align = \"c\", caption = '学生信息')\n```\n\n## 探索性分析\n\n根据该数据，我们对不同性别的身高和体重进行探索性分析。\n\n```{r mytable}\ndf %>% group_by(`性别`) %>% summarise(\"平均身高\" = mean(`身高`),\n                                    \"身高标准差\" = sd(`身高`),\n                                    \"平均体重\" = mean(`体重`),\n                                    \"体重标准差\" = sd(`体重`)) -> df_summary\nknitr::kable(df_summary, align = \"c\",\n             caption = \"不同性别的身高和体重情况\")\n```\n\n```{r fig1, fig.align='center',fig.cap=\"不同性别学生的身高和体重分布情况\"}\nlibrary(showtext)\nlibrary(ggsci)\nshowtext.auto()\ndf %>% pivot_longer(`身高`:`体重`,\n                    names_to = \"Class\",\n                    values_to = \"数值\") %>% \n  ggplot() + \n  geom_boxplot(aes(性别,数值,fill=性别)) +\n  scale_fill_aaas() +\n  facet_wrap(vars(Class),scales = \"free\")\n```\n\n# 总结\n\n这个项目主要测试 RMarkdown 的参数化报告，主要参考：\n\n1.  [R Markdown 指南 第 7 章：使用 R Markdown 开展项目工作](https://cosname.github.io/rmarkdown-guide/rmarkdown-project.html#render-rmd)\n\n2.  [Iterate multiple RMarkdown reports](https://community.rstudio.com/t/iterate-multiple-rmarkdown-reports/43208/7)\n\n> 由于时间有限，这个报告后续没有添加了。\n\n# 未来展望\n\n\n- 对于不同的数据，虽然可以自动化绘制不同的报表，但是由于数据不同，结果也会不同。不同结果如何达到自动化分析呢？这个问题还需要再商讨。个人觉得如果能把类似 ChatGPT 结合起来，或许真能实现自动给出完整的分析报告了。类似的 ChatGPT 的 R 包有：[chatgpt](https://github.com/jcrodriguez1989/chatgpt \"chatgpt\")，[gptstudio](https://github.com/MichelNivard/gptstudio \"gptstudio\")。教程有：[1](https://rpubs.com/nirmal/setting_chat_gpt_R \"1\")，[2](https://www.karada-good.net/en/ranalytics/r-e767/ \"2\")，供参考。\n\n- 制作该可重复性报告的前提是：不同数据需要一致的数据结构。这点是否可以进行拓展？是否有专门的包能够自动对数据处理，并给出统一的数据格式？\n\n- 制作这个教程花费较长时间，欢迎大家一键三连呀～ 教程对应的项目代码已经开源在 [GitHub](https://github.com/liangliangzhuang/R_example \"GitHub\") 中，欢迎 Fork 和 star。或者公众号后台回复[`批量制作数据分析报告`]免费获得。\n\n\n"
  },
  {
    "path": "2023年/2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/.Rhistory",
    "content": ""
  },
  {
    "path": "2023年/2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/line.R",
    "content": "\n# 代码解释见推文：https://mp.weixin.qq.com/s/k5VgsK7e4d9RDrTkctJP6g\n# 作者：庄亮亮/庄闪闪\n## 主要参考书籍《THE HITCHHIKER’S GUIDE TO GGPLOT2》\n\nlibrary(ggplot2) \nlibrary(ggthemes) \nlibrary(dplyr) \nlibrary(readr)\n\nchilean.exports = \"year,product,export,percentage\n                  2006,copper,4335009500,81 \n                  2006,others,1016726518,19 \n                  2007,copper,9005361914,86 \n                  2007,others,1523085299,14 \n                  2008,copper,6907056354,80 \n                  2008,others,1762684216,20 \n                  2009,copper,10529811075,81 \n                  2009,others,2464094241,19 \n                  2010,copper,14828284450,85 \n                  2010,others,2543015596,15 \n                  2011,copper,15291679086,82 \n                  2011,others,3447972354,18 \n                  2012,copper,14630686732,80 \n                  2012,others,3583968218,20 \n                  2013,copper,15244038840,79 \n                  2013,others,4051281128,21 \n                  2014,copper,14703374241,78 \n                  2014,others,4251484600,22 \n                  2015,copper,13155922363,78 \n                  2015,others,3667286912,22\n\"\n\ncharts.data = read_csv(chilean.exports)\n\np1 = ggplot(aes(y = export, x = year, colour = product), data = charts.data) + \n  geom_line() \np1\n\np1 = ggplot(aes(y = export, x = year, colour = product), data = charts.data) + \n  geom_line(linewidth = 1.5) \np1\n\n\np1 = p1 + scale_x_continuous(breaks = seq(2006,2015,1))\np1\n\n\np1 = p1 +\n  labs(title = \"Composition of Exports to China ($)\", subtitle = \"Source: The Observatory of Economic Complexity\") +\n  labs(x = \"Year\", y = \"USD million\")\np1\n\ncolour = c(\"#5F9EA0\", \"#E1B378\") \np1 = p1 + scale_colour_manual(values = colour) \np1\n\nlibrary(viridis)\np1 = p1 + scale_color_viridis(discrete=T) \n\n\np1 + theme_bw() + \n  theme(panel.grid = element_blank(),\n        legend.position = \"bottom\",\n        legend.direction = \"horizontal\")\n\nlibrary(ggthemes)\np1 + geom_point(size = 3) +\n  theme_economist() + \n  scale_colour_economist() +\n  theme(panel.grid = element_blank(),\n        legend.position = \"bottom\",\n        legend.direction = \"horizontal\")\n\nlibrary(showtext)\n\n\nfont_add(\"ZSS\",\"SanJiLuRongTi-2.ttf\")\nfont_add(\"ZSS2\",\"No.019-Sounso-Quality-2.ttf\")\n"
  },
  {
    "path": "2023年/2023.05.08个性化树状图/ggparty.R",
    "content": "# 代码来源：https://github.com/martin-borkovec/ggparty\n# 代码解析/推文：https://mp.weixin.qq.com/s/8pP7IWLH-RaIxEtnyuCjhA\n\n\nlibrary(ggparty)\nlibrary(ARE)\n\ndata(\"TeachingRatings\", package = \"AER\")\nstr(TeachingRatings)\ntr <- subset(TeachingRatings, credits == \"more\")\n\ntr_tree <- lmtree(eval ~ beauty | minority + age + gender + division + native +\n                    tenure, data = tr, weights = students, caseweights = FALSE)\n\nggparty(tr_tree,\n        terminal_space = 0.5,\n        add_vars = list(p.value = \"$node$info$p.value\")) +\n  geom_edge(size = 1.5) +\n  geom_edge_label(colour = \"grey\", size = 6) +\n  geom_node_plot(gglist = list(geom_point(aes(x = beauty,\n                                              y = eval,\n                                              col = tenure,\n                                              shape = minority),\n                                          alpha = 0.8),\n                               theme_bw(base_size = 15)),\n                 scales = \"fixed\",\n                 id = \"terminal\",\n                 shared_axis_labels = T,\n                 shared_legend = T,\n                 legend_separator = T,\n                 predict = \"beauty\",\n                 predict_gpar = list(col = \"blue\",\n                                     size = 1.2)\n  ) +\n  geom_node_label(aes(col = splitvar),\n                  line_list = list(aes(label = paste(\"Node\", id)),\n                                   aes(label = splitvar),\n                                   aes(label = paste(\"p =\", formatC(p.value, format = \"e\", digits = 2)))),\n                  line_gpar = list(list(size = 12, col = \"black\", fontface = \"bold\"),\n                                   list(size = 20),\n                                   list(size = 12)),\n                  ids = \"inner\") +\n  geom_node_label(aes(label = paste0(\"Node \", id, \", N = \", nodesize)),\n                  fontface = \"bold\",\n                  ids = \"terminal\",\n                  size = 5, \n                  nudge_y = 0.01) +\n  theme(legend.position = \"none\")\n\n"
  },
  {
    "path": "2023年/2023.05.13 高级棒棒图/棒棒图.R",
    "content": "\n# 代码解释见推文：\n# 作者：庄亮亮/庄闪闪\n\n\n\n# 加载包\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(hrbrthemes)\n\n# 创建数据\ndim1 = c(30,11) # 第一项x轴，第二项y轴\ncombinations <- expand.grid(1:dim1[1],1:dim1[2])\ndat = cbind(1:dim(combinations)[1],combinations)\ncolnames(dat) = c(\"id\",\"value1\",\"value2\")\n\n# 拟合点数\ndis1 = 10; dis2 = 4\ndat$class = 0\nfor(i in 1:dim1[2]){\n  dat$class[((dim1[1]+1)*(i-1) + 1):((dim1[1]+1)*(i-1) + dis1)] = 1\n  dat$class[((dim1[1]+1)*(i-1) + dis1 + 1):((dim1[1]+1)*(i-1) + dis1 + dis2 - 1)] = 2\n}\n\n# 绘图\nggplot(data = dat,aes(value1,value2)) + \n  geom_segment(data = dat[dat$value1!=dim1[1], ],aes(x=value1, xend=value1+1, y=value2, yend=value2),color=\"grey\",size=1) +\n  geom_point(aes(color=as.factor(class)), size=3, shape = 19) + \n  scale_color_manual(values = c(\"grey\",\"#2F6DA3\",\"#CC9034\"),name = \"Class\",labels = c(\"Nothing\",\"Window\",\"Forecasting horizon\")) +\n  scale_y_reverse(breaks = seq(1,12,3)) +\n  scale_x_continuous(breaks = c(1,10,20,30)) +\n  xlab(\"Time\") + ylab(\"Window number\") +\n  theme_bw() +\n  theme(panel.grid.major = element_blank(),\n        panel.grid.minor = element_blank(),\n        panel.background = element_blank(),\n        legend.position = \"bottom\",\n        axis.line = element_line(colour = \"black\"))\n"
  },
  {
    "path": "2023年/2023.06.29 旭日图/.Rproj.user/C6560784/jobs/4152B7D6-output.json",
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    "content": "\n#EasyCharts团队出品，\n#如需使用与深入学习，请联系微信：EasyCharts\n\nlibrary(ggraph)\nlibrary(igraph)\nlibrary(RColorBrewer) \nlibrary(dplyr)\n\ndf<-read.csv('旭日图.csv',header=TRUE,stringsAsFactors=FALSE)\n\n#--------------------------------分割角度均等平分----------------------------------------\nedges<- data.frame(rbind(\n  cbind(rep('origin',4),unique(as.character(df$Season))),\n  as.matrix(df[!duplicated(df[c('Season','Month')]),1:2]),\n  as.matrix(df[!duplicated(df[c('Month','Week')]),2:3])))\ncolnames(edges)<-c('from','to')\n\nvertices0<-data.frame(name=unique(c(as.character(edges$from), as.character(edges$to))))\ndf_leaf<-df[,c('Week','Value','label')]\ndf_leaf$angle<-90-(1:nrow(df_leaf))/nrow(df_leaf)*360\ndf_leaf$angle<-ifelse(df_leaf$angle< -90, df_leaf$angle+180, df_leaf$angle)\n\nvertices<-left_join(vertices0,df_leaf,by=c('name'='Week'))\n\ngraph <- graph_from_data_frame(edges, vertices = vertices)\n\n#图9-3-1 旭日图(a) 分割角度均等平分\nggraph(graph, layout ='partition', circular = TRUE) +\n  geom_node_arc_bar(aes(filter =(depth<=2 & depth>0 )),fill='#FEE5D9',color='black',size=0.1)+#,size=size\n  geom_node_arc_bar(aes(filter =(depth==3 ),fill = Value),size=0.1)+\n  geom_node_text( aes(filter =(depth<=2 & depth>0 ),label=name,size=-depth),angle=0,colour=\"black\") +\n  geom_node_text(aes(label=label,angle=angle),size=2,colour=\"black\") +\n  scale_size(range=c(3,5))+\n  coord_fixed()+\n  scale_fill_distiller(palette='Reds')+\n  guides(size=\"none\")+\n  theme_void()\n\n#图9-3-2 冰柱图(a) 分割角度均等平分\nggraph(graph, layout ='partition') + \n  geom_node_tile(aes(filter =(depth<=2)),fill='#FEE5D9',color='black',size=0.1)+#,size=size\n  geom_node_tile(aes(filter =(depth==3 ),fill = Value),size=0.1)+\n  \n  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=90,colour=\"black\") +\n  geom_node_text(aes(label=label),size=2,angle=90,colour=\"black\")+\n  scale_size(range=c(3,4))+\n  scale_y_reverse()+\n  scale_fill_distiller(palette=\"Reds\",na.value = \"#FFF2EC\")+\n  guides(size=\"none\")+\n  theme_void()\n\n#-------------------------------分割角度与某个数值成比例----------------------------------\nfake_circle<-c()\nfor (i in 1:nrow(df)){\n  fake_circle<-append(fake_circle,rep(df$Week[i],round(10*df$Value[i])))\n}\n\nedges<- data.frame(rbind(\n  cbind(rep('origin',4),unique(as.character(df$Season))),\n  as.matrix(df[!duplicated(df[c('Season','Month')]),1:2]),\n  as.matrix(df[!duplicated(df[c('Month','Week')]),2:3]),\n  cbind(fake_circle,as.character(1:length(fake_circle)))\n))\n\ncolnames(edges)<-c('from','to') \n\nvertices0<-data.frame(name=unique(c(as.character(edges$from), as.character(edges$to))))\ndf_leaf<-df[,c('Week','Value','label')]\ndf_leaf$angle<-90-(cumsum(df_leaf$Value)-df_leaf$Value/2)/sum(df_leaf$Value)*360\ndf_leaf$angle<-ifelse(df_leaf$angle< -90, df_leaf$angle+180, df_leaf$angle)\n\nvertices<-left_join(vertices0,df_leaf,by=c('name'='Week'))\n\n\ndf_color<- data.frame(rbind(\n  as.matrix(df[!duplicated(df[c('Season','Season')]),c(1,1)]),\n  as.matrix(df[!duplicated(df[c('Season','Month')]),c(1,2)]),\n  as.matrix(df[!duplicated(df[c('Season','Week')]),c(1,3)])\n))\n\ncolnames(df_color)<-c('Season','name') \n\nvertices<-left_join(vertices,df_color,by='name')\n\ngraph <- graph_from_data_frame(edges, vertices = vertices)\n\n#图9-3-1 旭日图(b) 分割角度与某个数值成比例\nggraph(graph, layout ='partition', circular = TRUE) + \n  geom_node_arc_bar(aes(filter =(depth<=3 & depth>0 ),fill = Season),size=0.1)+\n  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=0,colour=\"black\") +\n  geom_node_text(aes(label=label,angle=angle),size=2,colour=\"black\")+\n  scale_size(range=c(3,4))+\n  coord_fixed()+\n  scale_fill_brewer(palette=\"Reds\",direction=-1)+\n  guides(size=\"none\",fill=\"none\")+\n  theme_void()\n\n#图9-3-2 冰柱图(b) 分割角度与某个数值成比例aph(graph, layout ='partition')+ \n  geom_node_tile(aes(filter =(depth<=3 ),fill = Season),size = 0.1)+\n  geom_node_text(aes(filter =(depth<=2 & depth>0),label=name,size=-depth),angle=90,colour=\"black\") +\n  geom_node_text(aes(label=label),size=2,angle=90,colour=\"black\")+\n  scale_size(range=c(3,4))+\n  scale_y_reverse()+\n  scale_fill_brewer(palette=\"Reds\",na.value = \"#FFF2EC\")+\n  guides(size=\"none\",fill=\"none\")+\n  theme_void()\n\n"
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    "content": "library(plotly)\n\n# 数据导入\nd <- data.frame(\n  ids = c(\n    \"North America\", \"Europe\", \"Australia\", \"North America - Football\", \"Soccer\",\n    \"North America - Rugby\", \"Europe - Football\", \"Rugby\",\n    \"Europe - American Football\",\"Australia - Football\", \"Association\",\n    \"Australian Rules\", \"Autstralia - American Football\", \"Australia - Rugby\",\n    \"Rugby League\", \"Rugby Union\"\n  ),\n  labels = c(\n    \"North<br>America\", \"Europe\", \"Australia\", \"Football\", \"Soccer\", \"Rugby\",\n    \"Football\", \"Rugby\", \"American<br>Football\", \"Football\", \"Association\",\n    \"Australian<br>Rules\", \"American<br>Football\", \"Rugby\", \"Rugby<br>League\",\n    \"Rugby<br>Union\"\n  ),\n  parents = c(\n    \"\", \"\", \"\", \"North America\", \"North America\", \"North America\", \"Europe\",\n    \"Europe\", \"Europe\",\"Australia\", \"Australia - Football\", \"Australia - Football\",\n    \"Australia - Football\", \"Australia - Football\", \"Australia - Rugby\",\n    \"Australia - Rugby\"\n  ),\n  stringsAsFactors = FALSE\n)\n\n# 另一种方法，导入数据集\nwrite.csv(d,\"d.csv\",row.names = FALSE)\nd = read.csv(\"d.csv\")\n\n# 绘图\nfig <- plot_ly(d, ids = ~ids, labels = ~labels, parents = ~parents, \n               type = 'sunburst') #maxdepth=3\nfig\n\n# 保存 PDF 图形\n# 首先需要在操作系统上安装 [orca](https://github.com/plotly/orca#installation \"orca\")\norca(fig, \"FINAL_plot.pdf\")\n\n\n\n"
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    "content": "\"ids\",\"labels\",\"parents\"\n\"North America\",\"North<br>America\",\"\"\n\"Europe\",\"Europe\",\"\"\n\"Australia\",\"Australia\",\"\"\n\"North America - Football\",\"Football\",\"North America\"\n\"Soccer\",\"Soccer\",\"North America\"\n\"North America - Rugby\",\"Rugby\",\"North America\"\n\"Europe - Football\",\"Football\",\"Europe\"\n\"Rugby\",\"Rugby\",\"Europe\"\n\"Europe - American Football\",\"American<br>Football\",\"Europe\"\n\"Australia - Football\",\"Football\",\"Australia\"\n\"Association\",\"Association\",\"Australia - Football\"\n\"Australian Rules\",\"Australian<br>Rules\",\"Australia - Football\"\n\"Autstralia - American Football\",\"American<br>Football\",\"Australia - Football\"\n\"Australia - Rugby\",\"Rugby\",\"Australia - Football\"\n\"Rugby League\",\"Rugby<br>League\",\"Australia - Rugby\"\n\"Rugby Union\",\"Rugby<br>Union\",\"Australia - Rugby\"\n"
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    "content": "library(plotly)\n\n# 数据导入\nd <- data.frame(\n  ids = c(\n    \"North America\", \"Europe\", \"Australia\", \"North America - Football\", \"Soccer\",\n    \"North America - Rugby\", \"Europe - Football\", \"Rugby\",\n    \"Europe - American Football\",\"Australia - Football\", \"Association\",\n    \"Australian Rules\", \"Autstralia - American Football\", \"Australia - Rugby\",\n    \"Rugby League\", \"Rugby Union\"\n  ),\n  labels = c(\n    \"North<br>America\", \"Europe\", \"Australia\", \"Football\", \"Soccer\", \"Rugby\",\n    \"Football\", \"Rugby\", \"American<br>Football\", \"Football\", \"Association\",\n    \"Australian<br>Rules\", \"American<br>Football\", \"Rugby\", \"Rugby<br>League\",\n    \"Rugby<br>Union\"\n  ),\n  parents = c(\n    \"\", \"\", \"\", \"North America\", \"North America\", \"North America\", \"Europe\",\n    \"Europe\", \"Europe\",\"Australia\", \"Australia - Football\", \"Australia - Football\",\n    \"Australia - Football\", \"Australia - Football\", \"Australia - Rugby\",\n    \"Australia - Rugby\"\n  ),\n  stringsAsFactors = FALSE\n)\n\n# 另一种方法，导入数据集\nwrite.csv(d,\"d.csv\",row.names = FALSE)\nd = read.csv(\"d.csv\")\n\n# 绘图\nfig <- plot_ly(gg, ids = ~ids, labels = ~labels, parents = ~parents, \n               type = 'sunburst') #maxdepth=3\nfig\n\n# 保存 PDF 图形\n# 首先需要在操作系统上安装 [orca](https://github.com/plotly/orca#installation \"orca\")\norca(fig, \"FINAL_plot.pdf\")\n\n\n\n"
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The overridden\n  // method can be called like a regular function, it has the target permanently\n  // bound to it so \"this\" will work correctly.\n  function overrideMethod(target, methodName, funcSource) {\n    var superFunc = target[methodName] || function() {};\n    var superFuncBound = function() {\n      return superFunc.apply(target, arguments);\n    };\n    target[methodName] = funcSource(superFuncBound);\n  }\n\n  // Add a method to delegator that, when invoked, calls\n  // delegatee.methodName. If there is no such method on\n  // the delegatee, but there was one on delegator before\n  // delegateMethod was called, then the original version\n  // is invoked instead.\n  // For example:\n  //\n  // var a = {\n  //   method1: function() { console.log('a1'); }\n  //   method2: function() { console.log('a2'); }\n  // };\n  // var b = {\n  //   method1: function() { console.log('b1'); }\n  // };\n  // delegateMethod(a, b, \"method1\");\n  // delegateMethod(a, b, \"method2\");\n  // a.method1();\n  // a.method2();\n  //\n  // The output would be \"b1\", \"a2\".\n  function delegateMethod(delegator, delegatee, methodName) {\n    var inherited = delegator[methodName];\n    delegator[methodName] = function() {\n      var target = delegatee;\n      var method = delegatee[methodName];\n\n      // The method doesn't exist on the delegatee. Instead,\n      // call the method on the delegator, if it exists.\n      if (!method) {\n        target = delegator;\n        method = inherited;\n      }\n\n      if (method) {\n        return method.apply(target, arguments);\n      }\n    };\n  }\n\n  // Implement a vague facsimilie of jQuery's data method\n  function elementData(el, name, value) {\n    if (arguments.length == 2) {\n      return el[\"htmlwidget_data_\" + name];\n    } else if (arguments.length == 3) {\n      el[\"htmlwidget_data_\" + name] = value;\n      return el;\n    } else {\n      throw new Error(\"Wrong number of arguments for elementData: \" +\n        arguments.length);\n    }\n  }\n\n  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex\n  function escapeRegExp(str) {\n    return str.replace(/[\\-\\[\\]\\/\\{\\}\\(\\)\\*\\+\\?\\.\\\\\\^\\$\\|]/g, \"\\\\$&\");\n  }\n\n  function hasClass(el, className) {\n    var re = new RegExp(\"\\\\b\" + escapeRegExp(className) + \"\\\\b\");\n    return re.test(el.className);\n  }\n\n  // elements - array (or array-like object) of HTML elements\n  // className - class name to test for\n  // include - if true, only return elements with given className;\n  //   if false, only return elements *without* given className\n  function filterByClass(elements, className, include) {\n    var results = [];\n    for (var i = 0; i < elements.length; i++) {\n      if (hasClass(elements[i], className) == include)\n        results.push(elements[i]);\n    }\n    return results;\n  }\n\n  function on(obj, eventName, func) {\n    if (obj.addEventListener) {\n      obj.addEventListener(eventName, func, false);\n    } else if (obj.attachEvent) {\n      obj.attachEvent(eventName, func);\n    }\n  }\n\n  function off(obj, eventName, func) {\n    if (obj.removeEventListener)\n      obj.removeEventListener(eventName, func, false);\n    else if (obj.detachEvent) {\n      obj.detachEvent(eventName, func);\n    }\n  }\n\n  // Translate array of values to top/right/bottom/left, as usual with\n  // the \"padding\" CSS property\n  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding\n  function unpackPadding(value) {\n    if (typeof(value) === \"number\")\n      value = [value];\n    if (value.length === 1) {\n      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};\n    }\n    if (value.length === 2) {\n      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};\n    }\n    if (value.length === 3) {\n      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};\n    }\n    if (value.length === 4) {\n      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};\n    }\n  }\n\n  // Convert an unpacked padding object to a CSS value\n  function paddingToCss(paddingObj) {\n    return paddingObj.top + \"px \" + paddingObj.right + \"px \" + paddingObj.bottom + \"px \" + paddingObj.left + \"px\";\n  }\n\n  // Makes a number suitable for CSS\n  function px(x) {\n    if (typeof(x) === \"number\")\n      return x + \"px\";\n    else\n      return x;\n  }\n\n  // Retrieves runtime widget sizing information for an element.\n  // The return value is either null, or an object with fill, padding,\n  // defaultWidth, defaultHeight fields.\n  function sizingPolicy(el) {\n    var sizingEl = document.querySelector(\"script[data-for='\" + el.id + \"'][type='application/htmlwidget-sizing']\");\n    if (!sizingEl)\n      return null;\n    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || \"{}\");\n    if (viewerMode) {\n      return sp.viewer;\n    } else {\n      return sp.browser;\n    }\n  }\n\n  // @param tasks Array of strings (or falsy value, in which case no-op).\n  //   Each element must be a valid JavaScript expression that yields a\n  //   function. Or, can be an array of objects with \"code\" and \"data\"\n  //   properties; in this case, the \"code\" property should be a string\n  //   of JS that's an expr that yields a function, and \"data\" should be\n  //   an object that will be added as an additional argument when that\n  //   function is called.\n  // @param target The object that will be \"this\" for each function\n  //   execution.\n  // @param args Array of arguments to be passed to the functions. (The\n  //   same arguments will be passed to all functions.)\n  function evalAndRun(tasks, target, args) {\n    if (tasks) {\n      forEach(tasks, function(task) {\n        var theseArgs = args;\n        if (typeof(task) === \"object\") {\n          theseArgs = theseArgs.concat([task.data]);\n          task = task.code;\n        }\n        var taskFunc = tryEval(task);\n        if (typeof(taskFunc) !== \"function\") {\n          throw new Error(\"Task must be a function! Source:\\n\" + task);\n        }\n        taskFunc.apply(target, theseArgs);\n      });\n    }\n  }\n\n  // Attempt eval() both with and without enclosing in parentheses.\n  // Note that enclosing coerces a function declaration into\n  // an expression that eval() can parse\n  // (otherwise, a SyntaxError is thrown)\n  function tryEval(code) {\n    var result = null;\n    try {\n      result = eval(\"(\" + code + \")\");\n    } catch(error) {\n      if (!(error instanceof SyntaxError)) {\n        throw error;\n      }\n      try {\n        result = eval(code);\n      } catch(e) {\n        if (e instanceof SyntaxError) {\n          throw error;\n        } else {\n          throw e;\n        }\n      }\n    }\n    return result;\n  }\n\n  function initSizing(el) {\n    var sizing = sizingPolicy(el);\n    if (!sizing)\n      return;\n\n    var cel = document.getElementById(\"htmlwidget_container\");\n    if (!cel)\n      return;\n\n    if (typeof(sizing.padding) !== \"undefined\") {\n      document.body.style.margin = \"0\";\n      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));\n    }\n\n    if (sizing.fill) {\n      document.body.style.overflow = \"hidden\";\n      document.body.style.width = \"100%\";\n      document.body.style.height = \"100%\";\n      document.documentElement.style.width = \"100%\";\n      document.documentElement.style.height = \"100%\";\n      if (cel) {\n        cel.style.position = \"absolute\";\n        var pad = unpackPadding(sizing.padding);\n        cel.style.top = pad.top + \"px\";\n        cel.style.right = pad.right + \"px\";\n        cel.style.bottom = pad.bottom + \"px\";\n        cel.style.left = pad.left + \"px\";\n        el.style.width = \"100%\";\n        el.style.height = \"100%\";\n      }\n\n      return {\n        getWidth: function() { return cel.offsetWidth; },\n        getHeight: function() { return cel.offsetHeight; }\n      };\n\n    } else {\n      el.style.width = px(sizing.width);\n      el.style.height = px(sizing.height);\n\n      return {\n        getWidth: function() { return el.offsetWidth; },\n        getHeight: function() { return el.offsetHeight; }\n      };\n    }\n  }\n\n  // Default implementations for methods\n  var defaults = {\n    find: function(scope) {\n      return querySelectorAll(scope, \".\" + this.name);\n    },\n    renderError: function(el, err) {\n      var $el = $(el);\n\n      this.clearError(el);\n\n      // Add all these error classes, as Shiny does\n      var errClass = \"shiny-output-error\";\n      if (err.type !== null) {\n        // use the classes of the error condition as CSS class names\n        errClass = errClass + \" \" + $.map(asArray(err.type), function(type) {\n          return errClass + \"-\" + type;\n        }).join(\" \");\n      }\n      errClass = errClass + \" htmlwidgets-error\";\n\n      // Is el inline or block? If inline or inline-block, just display:none it\n      // and add an inline error.\n      var display = $el.css(\"display\");\n      $el.data(\"restore-display-mode\", display);\n\n      if (display === \"inline\" || display === \"inline-block\") {\n        $el.hide();\n        if (err.message !== \"\") {\n          var errorSpan = $(\"<span>\").addClass(errClass);\n          errorSpan.text(err.message);\n          $el.after(errorSpan);\n        }\n      } else if (display === \"block\") {\n        // If block, add an error just after the el, set visibility:none on the\n        // el, and position the error to be on top of the el.\n        // Mark it with a unique ID and CSS class so we can remove it later.\n        $el.css(\"visibility\", \"hidden\");\n        if (err.message !== \"\") {\n          var errorDiv = $(\"<div>\").addClass(errClass).css(\"position\", \"absolute\")\n            .css(\"top\", el.offsetTop)\n            .css(\"left\", el.offsetLeft)\n            // setting width can push out the page size, forcing otherwise\n            // unnecessary scrollbars to appear and making it impossible for\n            // the element to shrink; so use max-width instead\n            .css(\"maxWidth\", el.offsetWidth)\n            .css(\"height\", el.offsetHeight);\n          errorDiv.text(err.message);\n          $el.after(errorDiv);\n\n          // Really dumb way to keep the size/position of the error in sync with\n          // the parent element as the window is resized or whatever.\n          var intId = setInterval(function() {\n            if (!errorDiv[0].parentElement) {\n              clearInterval(intId);\n              return;\n            }\n            errorDiv\n              .css(\"top\", el.offsetTop)\n              .css(\"left\", el.offsetLeft)\n              .css(\"maxWidth\", el.offsetWidth)\n              .css(\"height\", el.offsetHeight);\n          }, 500);\n        }\n      }\n    },\n    clearError: function(el) {\n      var $el = $(el);\n      var display = $el.data(\"restore-display-mode\");\n      $el.data(\"restore-display-mode\", null);\n\n      if (display === \"inline\" || display === \"inline-block\") {\n        if (display)\n          $el.css(\"display\", display);\n        $(el.nextSibling).filter(\".htmlwidgets-error\").remove();\n      } else if (display === \"block\"){\n        $el.css(\"visibility\", \"inherit\");\n        $(el.nextSibling).filter(\".htmlwidgets-error\").remove();\n      }\n    },\n    sizing: {}\n  };\n\n  // Called by widget bindings to register a new type of widget. The definition\n  // object can contain the following properties:\n  // - name (required) - A string indicating the binding name, which will be\n  //   used by default as the CSS classname to look for.\n  // - initialize (optional) - A function(el) that will be called once per\n  //   widget element; if a value is returned, it will be passed as the third\n  //   value to renderValue.\n  // - renderValue (required) - A function(el, data, initValue) that will be\n  //   called with data. Static contexts will cause this to be called once per\n  //   element; Shiny apps will cause this to be called multiple times per\n  //   element, as the data changes.\n  window.HTMLWidgets.widget = function(definition) {\n    if (!definition.name) {\n      throw new Error(\"Widget must have a name\");\n    }\n    if (!definition.type) {\n      throw new Error(\"Widget must have a type\");\n    }\n    // Currently we only support output widgets\n    if (definition.type !== \"output\") {\n      throw new Error(\"Unrecognized widget type '\" + definition.type + \"'\");\n    }\n    // TODO: Verify that .name is a valid CSS classname\n\n    // Support new-style instance-bound definitions. Old-style class-bound\n    // definitions have one widget \"object\" per widget per type/class of\n    // widget; the renderValue and resize methods on such widget objects\n    // take el and instance arguments, because the widget object can't\n    // store them. New-style instance-bound definitions have one widget\n    // object per widget instance; the definition that's passed in doesn't\n    // provide renderValue or resize methods at all, just the single method\n    //   factory(el, width, height)\n    // which returns an object that has renderValue(x) and resize(w, h).\n    // This enables a far more natural programming style for the widget\n    // author, who can store per-instance state using either OO-style\n    // instance fields or functional-style closure variables (I guess this\n    // is in contrast to what can only be called C-style pseudo-OO which is\n    // what we required before).\n    if (definition.factory) {\n      definition = createLegacyDefinitionAdapter(definition);\n    }\n\n    if (!definition.renderValue) {\n      throw new Error(\"Widget must have a renderValue function\");\n    }\n\n    // For static rendering (non-Shiny), use a simple widget registration\n    // scheme. We also use this scheme for Shiny apps/documents that also\n    // contain static widgets.\n    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];\n    // Merge defaults into the definition; don't mutate the original definition.\n    var staticBinding = extend({}, defaults, definition);\n    overrideMethod(staticBinding, \"find\", function(superfunc) {\n      return function(scope) {\n        var results = superfunc(scope);\n        // Filter out Shiny outputs, we only want the static kind\n        return filterByClass(results, \"html-widget-output\", false);\n      };\n    });\n    window.HTMLWidgets.widgets.push(staticBinding);\n\n    if (shinyMode) {\n      // Shiny is running. Register the definition with an output binding.\n      // The definition itself will not be the output binding, instead\n      // we will make an output binding object that delegates to the\n      // definition. This is because we foolishly used the same method\n      // name (renderValue) for htmlwidgets definition and Shiny bindings\n      // but they actually have quite different semantics (the Shiny\n      // bindings receive data that includes lots of metadata that it\n      // strips off before calling htmlwidgets renderValue). We can't\n      // just ignore the difference because in some widgets it's helpful\n      // to call this.renderValue() from inside of resize(), and if\n      // we're not delegating, then that call will go to the Shiny\n      // version instead of the htmlwidgets version.\n\n      // Merge defaults with definition, without mutating either.\n      var bindingDef = extend({}, defaults, definition);\n\n      // This object will be our actual Shiny binding.\n      var shinyBinding = new Shiny.OutputBinding();\n\n      // With a few exceptions, we'll want to simply use the bindingDef's\n      // version of methods if they are available, otherwise fall back to\n      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional\n      // methods in the future, and we want them to be overrideable by\n      // HTMLWidget binding definitions, then we'll need to add them to this\n      // list.\n      delegateMethod(shinyBinding, bindingDef, \"getId\");\n      delegateMethod(shinyBinding, bindingDef, \"onValueChange\");\n      delegateMethod(shinyBinding, bindingDef, \"onValueError\");\n      delegateMethod(shinyBinding, bindingDef, \"renderError\");\n      delegateMethod(shinyBinding, bindingDef, \"clearError\");\n      delegateMethod(shinyBinding, bindingDef, \"showProgress\");\n\n      // The find, renderValue, and resize are handled differently, because we\n      // want to actually decorate the behavior of the bindingDef methods.\n\n      shinyBinding.find = function(scope) {\n        var results = bindingDef.find(scope);\n\n        // Only return elements that are Shiny outputs, not static ones\n        var dynamicResults = results.filter(\".html-widget-output\");\n\n        // It's possible that whatever caused Shiny to think there might be\n        // new dynamic outputs, also caused there to be new static outputs.\n        // Since there might be lots of different htmlwidgets bindings, we\n        // schedule execution for later--no need to staticRender multiple\n        // times.\n        if (results.length !== dynamicResults.length)\n          scheduleStaticRender();\n\n        return dynamicResults;\n      };\n\n      // Wrap renderValue to handle initialization, which unfortunately isn't\n      // supported natively by Shiny at the time of this writing.\n\n      shinyBinding.renderValue = function(el, data) {\n        Shiny.renderDependencies(data.deps);\n        // Resolve strings marked as javascript literals to objects\n        if (!(data.evals instanceof Array)) data.evals = [data.evals];\n        for (var i = 0; data.evals && i < data.evals.length; i++) {\n          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);\n        }\n        if (!bindingDef.renderOnNullValue) {\n          if (data.x === null) {\n            el.style.visibility = \"hidden\";\n            return;\n          } else {\n            el.style.visibility = \"inherit\";\n          }\n        }\n        if (!elementData(el, \"initialized\")) {\n          initSizing(el);\n\n          elementData(el, \"initialized\", true);\n          if (bindingDef.initialize) {\n            var result = bindingDef.initialize(el, el.offsetWidth,\n              el.offsetHeight);\n            elementData(el, \"init_result\", result);\n          }\n        }\n        bindingDef.renderValue(el, data.x, elementData(el, \"init_result\"));\n        evalAndRun(data.jsHooks.render, elementData(el, \"init_result\"), [el, data.x]);\n      };\n\n      // Only override resize if bindingDef implements it\n      if (bindingDef.resize) {\n        shinyBinding.resize = function(el, width, height) {\n          // Shiny can call resize before initialize/renderValue have been\n          // called, which doesn't make sense for widgets.\n          if (elementData(el, \"initialized\")) {\n            bindingDef.resize(el, width, height, elementData(el, \"init_result\"));\n          }\n        };\n      }\n\n      Shiny.outputBindings.register(shinyBinding, bindingDef.name);\n    }\n  };\n\n  var scheduleStaticRenderTimerId = null;\n  function scheduleStaticRender() {\n    if (!scheduleStaticRenderTimerId) {\n      scheduleStaticRenderTimerId = setTimeout(function() {\n        scheduleStaticRenderTimerId = null;\n        window.HTMLWidgets.staticRender();\n      }, 1);\n    }\n  }\n\n  // Render static widgets after the document finishes loading\n  // Statically render all elements that are of this widget's class\n  window.HTMLWidgets.staticRender = function() {\n    var bindings = window.HTMLWidgets.widgets || [];\n    forEach(bindings, function(binding) {\n      var matches = binding.find(document.documentElement);\n      forEach(matches, function(el) {\n        var sizeObj = initSizing(el, binding);\n\n        if (hasClass(el, \"html-widget-static-bound\"))\n          return;\n        el.className = el.className + \" html-widget-static-bound\";\n\n        var initResult;\n        if (binding.initialize) {\n          initResult = binding.initialize(el,\n            sizeObj ? sizeObj.getWidth() : el.offsetWidth,\n            sizeObj ? sizeObj.getHeight() : el.offsetHeight\n          );\n          elementData(el, \"init_result\", initResult);\n        }\n\n        if (binding.resize) {\n          var lastSize = {\n            w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,\n            h: sizeObj ? sizeObj.getHeight() : el.offsetHeight\n          };\n          var resizeHandler = function(e) {\n            var size = {\n              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,\n              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight\n            };\n            if (size.w === 0 && size.h === 0)\n              return;\n            if (size.w === lastSize.w && size.h === lastSize.h)\n              return;\n            lastSize = size;\n            binding.resize(el, size.w, size.h, initResult);\n          };\n\n          on(window, \"resize\", resizeHandler);\n\n          // This is needed for cases where we're running in a Shiny\n          // app, but the widget itself is not a Shiny output, but\n          // rather a simple static widget. One example of this is\n          // an rmarkdown document that has runtime:shiny and widget\n          // that isn't in a render function. Shiny only knows to\n          // call resize handlers for Shiny outputs, not for static\n          // widgets, so we do it ourselves.\n          if (window.jQuery) {\n            window.jQuery(document).on(\n              \"shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets\",\n              resizeHandler\n            );\n            window.jQuery(document).on(\n              \"hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets\",\n              resizeHandler\n            );\n          }\n\n          // This is needed for the specific case of ioslides, which\n          // flips slides between display:none and display:block.\n          // Ideally we would not have to have ioslide-specific code\n          // here, but rather have ioslides raise a generic event,\n          // but the rmarkdown package just went to CRAN so the\n          // window to getting that fixed may be long.\n          if (window.addEventListener) {\n            // It's OK to limit this to window.addEventListener\n            // browsers because ioslides itself only supports\n            // such browsers.\n            on(document, \"slideenter\", resizeHandler);\n            on(document, \"slideleave\", resizeHandler);\n          }\n        }\n\n        var scriptData = document.querySelector(\"script[data-for='\" + el.id + \"'][type='application/json']\");\n        if (scriptData) {\n          var data = JSON.parse(scriptData.textContent || scriptData.text);\n          // Resolve strings marked as javascript literals to objects\n          if (!(data.evals instanceof Array)) data.evals = [data.evals];\n          for (var k = 0; data.evals && k < data.evals.length; k++) {\n            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);\n          }\n          binding.renderValue(el, data.x, initResult);\n          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);\n        }\n      });\n    });\n\n    invokePostRenderHandlers();\n  }\n\n\n  function has_jQuery3() {\n    if (!window.jQuery) {\n      return false;\n    }\n    var $version = window.jQuery.fn.jquery;\n    var $major_version = parseInt($version.split(\".\")[0]);\n    return $major_version >= 3;\n  }\n\n  /*\n  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's\n  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now\n  / really means $(setTimeout(fn)).\n  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous\n  /\n  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny\n  / one tick later than it did before, which means staticRender() is\n  / called renderValue() earlier than (advanced) widget authors might be expecting.\n  / https://github.com/rstudio/shiny/issues/2630\n  /\n  / For a concrete example, leaflet has some methods (e.g., updateBounds)\n  / which reference Shiny methods registered in initShiny (e.g., setInputValue).\n  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to\n  / delay execution of those methods (until Shiny methods are ready)\n  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268\n  /\n  / Ideally widget authors wouldn't need to use this setTimeout() hack that\n  / leaflet uses to call Shiny methods on a staticRender(). In the long run,\n  / the logic initShiny should be broken up so that method registration happens\n  / right away, but binding happens later.\n  */\n  function maybeStaticRenderLater() {\n    if (shinyMode && has_jQuery3()) {\n      window.jQuery(window.HTMLWidgets.staticRender);\n    } else {\n      window.HTMLWidgets.staticRender();\n    }\n  }\n\n  if (document.addEventListener) {\n    document.addEventListener(\"DOMContentLoaded\", function() {\n      document.removeEventListener(\"DOMContentLoaded\", arguments.callee, false);\n      maybeStaticRenderLater();\n    }, false);\n  } else if (document.attachEvent) {\n    document.attachEvent(\"onreadystatechange\", function() {\n      if (document.readyState === \"complete\") {\n        document.detachEvent(\"onreadystatechange\", arguments.callee);\n        maybeStaticRenderLater();\n      }\n    });\n  }\n\n\n  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {\n    // If no key, default to the first item\n    if (typeof(key) === \"undefined\")\n      key = 1;\n\n    var link = document.getElementById(depname + \"-\" + key + \"-attachment\");\n    if (!link) {\n      throw new Error(\"Attachment \" + depname + \"/\" + key + \" not found in document\");\n    }\n    return link.getAttribute(\"href\");\n  };\n\n  window.HTMLWidgets.dataframeToD3 = function(df) {\n    var names = [];\n    var length;\n    for (var name in df) {\n        if (df.hasOwnProperty(name))\n            names.push(name);\n        if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n            throw new Error(\"All fields must be arrays\");\n        } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n            throw new Error(\"All fields must be arrays of the same length\");\n        }\n        length = df[name].length;\n    }\n    var results = [];\n    var item;\n    for (var row = 0; row < length; row++) {\n        item = {};\n        for (var col = 0; col < names.length; col++) {\n            item[names[col]] = df[names[col]][row];\n        }\n        results.push(item);\n    }\n    return results;\n  };\n\n  window.HTMLWidgets.transposeArray2D = function(array) {\n      if (array.length === 0) return array;\n      var newArray = array[0].map(function(col, i) {\n          return array.map(function(row) {\n              return row[i]\n          })\n      });\n      return newArray;\n  };\n  // Split value at splitChar, but allow splitChar to be escaped\n  // using escapeChar. 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If no element matches the\n  // selector, or the first matching element has no HTMLWidget\n  // instance associated with it, then null is returned.\n  //\n  // The scope argument is optional, and defaults to window.document.\n  window.HTMLWidgets.find = function(scope, selector) {\n    if (arguments.length == 1) {\n      selector = scope;\n      scope = document;\n    }\n\n    var el = scope.querySelector(selector);\n    if (el === null) {\n      return null;\n    } else {\n      return window.HTMLWidgets.getInstance(el);\n    }\n  };\n\n  // Finds all elements in the scope that match the selector, and\n  // returns the HTMLWidget instances (i.e. the return values of\n  // an HTMLWidget binding's initialize() or factory() function)\n  // associated with the elements, in an array. If elements that\n  // match the selector don't have an associated HTMLWidget\n  // instance, the returned array will contain nulls.\n  //\n  // The scope argument is optional, and defaults to window.document.\n  window.HTMLWidgets.findAll = function(scope, selector) {\n    if (arguments.length == 1) {\n      selector = scope;\n      scope = document;\n    }\n\n    var nodes = scope.querySelectorAll(selector);\n    var results = [];\n    for (var i = 0; i < nodes.length; i++) {\n      results.push(window.HTMLWidgets.getInstance(nodes[i]));\n    }\n    return results;\n  };\n\n  var postRenderHandlers = [];\n  function invokePostRenderHandlers() {\n    while (postRenderHandlers.length) {\n      var handler = postRenderHandlers.shift();\n      if (handler) {\n        handler();\n      }\n    }\n  }\n\n  // Register the given callback function to be invoked after the\n  // next time static widgets are rendered.\n  window.HTMLWidgets.addPostRenderHandler = function(callback) {\n    postRenderHandlers.push(callback);\n  };\n\n  // Takes a new-style instance-bound definition, and returns an\n  // old-style class-bound definition. This saves us from having\n  // to rewrite all the logic in this file to accomodate both\n  // types of definitions.\n  function createLegacyDefinitionAdapter(defn) {\n    var result = {\n      name: defn.name,\n      type: defn.type,\n      initialize: function(el, width, height) {\n        return defn.factory(el, width, height);\n      },\n      renderValue: function(el, x, instance) {\n        return instance.renderValue(x);\n      },\n      resize: function(el, width, height, instance) {\n        return instance.resize(width, height);\n      }\n    };\n\n    if (defn.find)\n      result.find = defn.find;\n    if (defn.renderError)\n      result.renderError = defn.renderError;\n    if (defn.clearError)\n      result.clearError = defn.clearError;\n\n    return result;\n  }\n})();\n\n</script>\n<script>\nHTMLWidgets.widget({\n  name: \"plotly\",\n  type: \"output\",\n\n  initialize: function(el, width, height) {\n    return {};\n  },\n\n  resize: function(el, width, height, instance) {\n    if (instance.autosize) {\n      var width = instance.width || width;\n      var height = instance.height || height;\n      Plotly.relayout(el.id, {width: width, height: height});\n    }\n  },  \n  \n  renderValue: function(el, x, instance) {\n    \n    // Plotly.relayout() mutates the plot input object, so make sure to \n    // keep a reference to the user-supplied width/height *before*\n    // we call Plotly.plot();\n    var lay = x.layout || {};\n    instance.width = lay.width;\n    instance.height = lay.height;\n    instance.autosize = lay.autosize || true;\n    \n    /* \n    / 'inform the world' about highlighting options this is so other\n    / crosstalk libraries have a chance to respond to special settings \n    / such as persistent selection. \n    / AFAIK, leaflet is the only library with such intergration\n    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154\n    */\n    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);\n      \n    if (typeof(window) !== \"undefined\") {\n      // make sure plots don't get created outside the network (for on-prem)\n      window.PLOTLYENV = window.PLOTLYENV || {};\n      window.PLOTLYENV.BASE_URL = x.base_url;\n      \n      // Enable persistent selection when shift key is down\n      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down\n      var persistOnShift = function(e) {\n        if (!e) window.event;\n        if (e.shiftKey) { \n          x.highlight.persistent = true; \n          x.highlight.persistentShift = true;\n        } else {\n          x.highlight.persistent = false; \n          x.highlight.persistentShift = false;\n        }\n      };\n      \n      // Only relevant if we haven't forced persistent mode at command line\n      if (!x.highlight.persistent) {\n        window.onmousemove = persistOnShift;\n      }\n    }\n\n    var graphDiv = document.getElementById(el.id);\n    \n    // TODO: move the control panel injection strategy inside here...\n    HTMLWidgets.addPostRenderHandler(function() {\n      \n      // lower the z-index of the modebar to prevent it from highjacking hover\n      // (TODO: do this via CSS?)\n      // https://github.com/ropensci/plotly/issues/956\n      // https://www.w3schools.com/jsref/prop_style_zindex.asp\n      var modebars = document.querySelectorAll(\".js-plotly-plot .plotly .modebar\");\n      for (var i = 0; i < modebars.length; i++) {\n        modebars[i].style.zIndex = 1;\n      }\n    });\n      \n      // inject a \"control panel\" holding selectize/dynamic color widget(s)\n    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {\n      var flex = document.createElement(\"div\");\n      flex.class = \"plotly-crosstalk-control-panel\";\n      flex.style = \"display: flex; flex-wrap: wrap\";\n      \n      // inject the colourpicker HTML container into the flexbox\n      if (x.highlight.dynamic) {\n        var pickerDiv = document.createElement(\"div\");\n        \n        var pickerInput = document.createElement(\"input\");\n        pickerInput.id = el.id + \"-colourpicker\";\n        pickerInput.placeholder = \"asdasd\";\n        \n        var pickerLabel = document.createElement(\"label\");\n        pickerLabel.for = pickerInput.id;\n        pickerLabel.innerHTML = \"Brush color&nbsp;&nbsp;\";\n        \n        pickerDiv.appendChild(pickerLabel);\n        pickerDiv.appendChild(pickerInput);\n        flex.appendChild(pickerDiv);\n      }\n      \n      // inject selectize HTML containers (one for every crosstalk group)\n      if (x.selectize) {\n        var ids = Object.keys(x.selectize);\n        \n        for (var i = 0; i < ids.length; i++) {\n          var container = document.createElement(\"div\");\n          container.id = ids[i];\n          container.style = \"width: 80%; height: 10%\";\n          container.class = \"form-group crosstalk-input-plotly-highlight\";\n          \n          var label = document.createElement(\"label\");\n          label.for = ids[i];\n          label.innerHTML = x.selectize[ids[i]].group;\n          label.class = \"control-label\";\n          \n          var selectDiv = document.createElement(\"div\");\n          var select = document.createElement(\"select\");\n          select.multiple = true;\n          \n          selectDiv.appendChild(select);\n          container.appendChild(label);\n          container.appendChild(selectDiv);\n          flex.appendChild(container);\n        }\n      }\n      \n      // finally, insert the flexbox inside the htmlwidget container,\n      // but before the plotly graph div\n      graphDiv.parentElement.insertBefore(flex, graphDiv);\n      \n      if (x.highlight.dynamic) {\n        var picker = $(\"#\" + pickerInput.id);\n        var colors = x.highlight.color || [];\n        // TODO: let users specify options?\n        var opts = {\n          value: colors[0],\n          showColour: \"both\",\n          palette: \"limited\",\n          allowedCols: colors.join(\" \"),\n          width: \"20%\",\n          height: \"10%\"\n        };\n        picker.colourpicker({changeDelay: 0});\n        picker.colourpicker(\"settings\", opts);\n        picker.colourpicker(\"value\", opts.value);\n        // inform crosstalk about a change in the current selection colour\n        var grps = x.highlight.ctGroups || [];\n        for (var i = 0; i < grps.length; i++) {\n          crosstalk.group(grps[i]).var('plotlySelectionColour')\n            .set(picker.colourpicker('value'));\n        }\n        picker.on(\"change\", function() {\n          for (var i = 0; i < grps.length; i++) {\n            crosstalk.group(grps[i]).var('plotlySelectionColour')\n              .set(picker.colourpicker('value'));\n          }\n        });\n      }\n    }\n    \n    // if no plot exists yet, create one with a particular configuration\n    if (!instance.plotly) {\n      \n      var plot = Plotly.newPlot(graphDiv, x);\n      instance.plotly = true;\n      \n    } else if (x.layout.transition) {\n      \n      var plot = Plotly.react(graphDiv, x);\n    \n    } else {\n      \n      // this is essentially equivalent to Plotly.newPlot(), but avoids creating \n      // a new webgl context\n      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532\n\n      // TODO: restore crosstalk selections?\n      Plotly.purge(graphDiv);\n      // TODO: why is this necessary to get crosstalk working?\n      graphDiv.data = undefined;\n      graphDiv.layout = undefined;\n      var plot = Plotly.newPlot(graphDiv, x);\n    }\n    \n    // Trigger plotly.js calls defined via `plotlyProxy()`\n    plot.then(function() {\n      if (HTMLWidgets.shinyMode) {\n        Shiny.addCustomMessageHandler(\"plotly-calls\", function(msg) {\n          var gd = document.getElementById(msg.id);\n          if (!gd) {\n            throw new Error(\"Couldn't find plotly graph with id: \" + msg.id);\n          }\n          // This isn't an official plotly.js method, but it's the only current way to \n          // change just the configuration of a plot \n          // https://community.plot.ly/t/update-config-function/9057\n          if (msg.method == \"reconfig\") {\n            Plotly.react(gd, gd.data, gd.layout, msg.args);\n            return;\n          }\n          if (!Plotly[msg.method]) {\n            throw new Error(\"Unknown method \" + msg.method);\n          }\n          var args = [gd].concat(msg.args);\n          Plotly[msg.method].apply(null, args);\n        });\n      }\n      \n      // plotly's mapbox API doesn't currently support setting bounding boxes\n      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/\n      // so we do this manually...\n      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw\n      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];\n      for (var i = 0; i < mapboxIDs.length; i++) {\n        var id = mapboxIDs[i];\n        var mapOpts = x.layout[id] || {};\n        var args = mapOpts._fitBounds || {};\n        if (!args) {\n          continue;\n        }\n        var mapObj = graphDiv._fullLayout[id]._subplot.map;\n        mapObj.fitBounds(args.bounds, args.options);\n      }\n      \n    });\n    \n    // Attach attributes (e.g., \"key\", \"z\") to plotly event data\n    function eventDataWithKey(eventData) {\n      if (eventData === undefined || !eventData.hasOwnProperty(\"points\")) {\n        return null;\n      }\n      return eventData.points.map(function(pt) {\n        var obj = {\n          curveNumber: pt.curveNumber, \n          pointNumber: pt.pointNumber, \n          x: pt.x,\n          y: pt.y\n        };\n        \n        // If 'z' is reported with the event data, then use it!\n        if (pt.hasOwnProperty(\"z\")) {\n          obj.z = pt.z;\n        }\n        \n        if (pt.hasOwnProperty(\"customdata\")) {\n          obj.customdata = pt.customdata;\n        }\n        \n        /* \n          TL;DR: (I think) we have to select the graph div (again) to attach keys...\n          \n          Why? Remember that crosstalk will dynamically add/delete traces \n          (see traceManager.prototype.updateSelection() below)\n          For this reason, we can't simply grab keys from x.data (like we did previously)\n          Moreover, we can't use _fullData, since that doesn't include \n          unofficial attributes. It's true that click/hover events fire with \n          pt.data, but drag events don't...\n        */\n        var gd = document.getElementById(el.id);\n        var trace = gd.data[pt.curveNumber];\n        \n        if (!trace._isSimpleKey) {\n          var attrsToAttach = [\"key\"];\n        } else {\n          // simple keys fire the whole key\n          obj.key = trace.key;\n          var attrsToAttach = [];\n        }\n        \n        for (var i = 0; i < attrsToAttach.length; i++) {\n          var attr = trace[attrsToAttach[i]];\n          if (Array.isArray(attr)) {\n            if (typeof pt.pointNumber === \"number\") {\n              obj[attrsToAttach[i]] = attr[pt.pointNumber];\n            } else if (Array.isArray(pt.pointNumber)) {\n              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];\n            } else if (Array.isArray(pt.pointNumbers)) {\n              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });\n            }\n          }\n        }\n        return obj;\n      });\n    }\n    \n    \n    var legendEventData = function(d) {\n      // if legendgroup is not relevant just return the trace\n      var trace = d.data[d.curveNumber];\n      if (!trace.legendgroup) return trace;\n      \n      // if legendgroup was specified, return all traces that match the group\n      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });\n      var traces = [];\n      for (i = 0; i < legendgrps.length; i++) {\n        if (legendgrps[i] == trace.legendgroup) {\n          traces.push(d.data[i]);\n        }\n      }\n      \n      return traces;\n    };\n\n    \n    // send user input event data to shiny\n    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {\n      \n      // Some events clear other input values\n      // TODO: always register these?\n      var eventClearMap = {\n        plotly_deselect: [\"plotly_selected\", \"plotly_selecting\", \"plotly_brushed\", \"plotly_brushing\", \"plotly_click\"],\n        plotly_unhover: [\"plotly_hover\"],\n        plotly_doubleclick: [\"plotly_click\"]\n      };\n    \n      Object.keys(eventClearMap).map(function(evt) {\n        graphDiv.on(evt, function() {\n          var inputsToClear = eventClearMap[evt];\n          inputsToClear.map(function(input) {\n            Shiny.setInputValue(input + \"-\" + x.source, null, {priority: \"event\"});\n          });\n        });\n      });\n      \n      var eventDataFunctionMap = {\n        plotly_click: eventDataWithKey,\n        plotly_sunburstclick: eventDataWithKey,\n        plotly_hover: eventDataWithKey,\n        plotly_unhover: eventDataWithKey,\n        // If 'plotly_selected' has already been fired, and you click\n        // on the plot afterwards, this event fires `undefined`?!?\n        // That might be considered a plotly.js bug, but it doesn't make \n        // sense for this input change to occur if `d` is falsy because,\n        // even in the empty selection case, `d` is truthy (an object),\n        // and the 'plotly_deselect' event will reset this input\n        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },\n        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },\n        plotly_brushed: function(d) {\n          if (d) { return d.range ? d.range : d.lassoPoints; }\n        },\n        plotly_brushing: function(d) {\n          if (d) { return d.range ? d.range : d.lassoPoints; }\n        },\n        plotly_legendclick: legendEventData,\n        plotly_legenddoubleclick: legendEventData,\n        plotly_clickannotation: function(d) { return d.fullAnnotation }\n      };\n      \n      var registerShinyValue = function(event) {\n        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };\n        // some events are unique to the R package\n        var plotlyJSevent = (event == \"plotly_brushed\") ? \"plotly_selected\" : (event == \"plotly_brushing\") ? \"plotly_selecting\" : event;\n        // register the event\n        graphDiv.on(plotlyJSevent, function(d) {\n          Shiny.setInputValue(\n            event + \"-\" + x.source,\n            JSON.stringify(eventDataPreProcessor(d)),\n            {priority: \"event\"}\n          );\n        });\n      }\n    \n      var shinyEvents = x.shinyEvents || [];\n      shinyEvents.map(registerShinyValue);\n    }\n    \n    // Given an array of {curveNumber: x, pointNumber: y} objects,\n    // return a hash of {\n    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, \n    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}\n    // }\n    function pointsToKeys(points) {\n      var keysBySet = {};\n      for (var i = 0; i < points.length; i++) {\n        \n        var trace = graphDiv.data[points[i].curveNumber];\n        if (!trace.key || !trace.set) {\n          continue;\n        }\n        \n        // set defaults for this keySet\n        // note that we don't track the nested property (yet) since we always \n        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21\n        keysBySet[trace.set] = keysBySet[trace.set] || {\n          value: [],\n          _isSimpleKey: trace._isSimpleKey\n        };\n        \n        // Use pointNumber by default, but aggregated traces should emit pointNumbers\n        var ptNum = points[i].pointNumber;\n        var hasPtNum = typeof ptNum === \"number\";\n        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;\n        \n        // selecting a point of a \"simple\" trace means: select the \n        // entire key attached to this trace, which is useful for,\n        // say clicking on a fitted line to select corresponding observations \n        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];\n        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript\n        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;\n        \n        // TODO: better to only add new values?\n        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);\n      }\n      \n      return keysBySet;\n    }\n    \n    \n    x.highlight.color = x.highlight.color || [];\n    // make sure highlight color is an array\n    if (!Array.isArray(x.highlight.color)) {\n      x.highlight.color = [x.highlight.color];\n    }\n\n    var traceManager = new TraceManager(graphDiv, x.highlight);\n\n    // Gather all *unique* sets.\n    var allSets = [];\n    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {\n      var newSet = x.data[curveIdx].set;\n      if (newSet) {\n        if (allSets.indexOf(newSet) === -1) {\n          allSets.push(newSet);\n        }\n      }\n    }\n\n    // register event listeners for all sets\n    for (var i = 0; i < allSets.length; i++) {\n      \n      var set = allSets[i];\n      var selection = new crosstalk.SelectionHandle(set);\n      var filter = new crosstalk.FilterHandle(set);\n      \n      var filterChange = function(e) {\n        removeBrush(el);\n        traceManager.updateFilter(set, e.value);\n      };\n      filter.on(\"change\", filterChange);\n      \n      \n      var selectionChange = function(e) {\n        \n        // Workaround for 'plotly_selected' now firing previously selected\n        // points (in addition to new ones) when holding shift key. In our case,\n        // we just want the new keys \n        if (x.highlight.on === \"plotly_selected\" && x.highlight.persistentShift) {\n          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript\n          Array.prototype.diff = function(a) {\n              return this.filter(function(i) {return a.indexOf(i) < 0;});\n          };\n          e.value = e.value.diff(e.oldValue);\n        }\n        \n        // array of \"event objects\" tracking the selection history\n        // this is used to avoid adding redundant selections\n        var selectionHistory = crosstalk.var(\"plotlySelectionHistory\").get() || [];\n        \n        // Construct an event object \"defining\" the current event. \n        var event = {\n          receiverID: traceManager.gd.id,\n          plotlySelectionColour: crosstalk.group(set).var(\"plotlySelectionColour\").get()\n        };\n        event[set] = e.value;\n        // TODO: is there a smarter way to check object equality?\n        if (selectionHistory.length > 0) {\n          var ev = JSON.stringify(event);\n          for (var i = 0; i < selectionHistory.length; i++) {\n            var sel = JSON.stringify(selectionHistory[i]);\n            if (sel == ev) {\n              return;\n            }\n          }\n        }\n        \n        // accumulate history for persistent selection\n        if (!x.highlight.persistent) {\n          selectionHistory = [event];\n        } else {\n          selectionHistory.push(event);\n        }\n        crosstalk.var(\"plotlySelectionHistory\").set(selectionHistory);\n        \n        // do the actual updating of traces, frames, and the selectize widget\n        traceManager.updateSelection(set, e.value);\n        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items\n        if (x.selectize) {\n          if (!x.highlight.persistent || e.value === null) {\n            selectize.clear(true);\n          }\n          selectize.addItems(e.value, true);\n          selectize.close();\n        }\n      }\n      selection.on(\"change\", selectionChange);\n      \n      // Set a crosstalk variable selection value, triggering an update\n      var turnOn = function(e) {\n        if (e) {\n          var selectedKeys = pointsToKeys(e.points);\n          // Keys are group names, values are array of selected keys from group.\n          for (var set in selectedKeys) {\n            if (selectedKeys.hasOwnProperty(set)) {\n              selection.set(selectedKeys[set].value, {sender: el});\n            }\n          }\n        }\n      };\n      if (x.highlight.debounce > 0) {\n        turnOn = debounce(turnOn, x.highlight.debounce);\n      }\n      graphDiv.on(x.highlight.on, turnOn);\n      \n      graphDiv.on(x.highlight.off, function turnOff(e) {\n        // remove any visual clues\n        removeBrush(el);\n        // remove any selection history\n        crosstalk.var(\"plotlySelectionHistory\").set(null);\n        // trigger the actual removal of selection traces\n        selection.set(null, {sender: el});\n      });\n          \n      // register a callback for selectize so that there is bi-directional\n      // communication between the widget and direct manipulation events\n      if (x.selectize) {\n        var selectizeID = Object.keys(x.selectize)[i];\n        var items = x.selectize[selectizeID].items;\n        var first = [{value: \"\", label: \"(All)\"}];\n        var opts = {\n          options: first.concat(items),\n          searchField: \"label\",\n          valueField: \"value\",\n          labelField: \"label\",\n          maxItems: 50\n        };\n        var select = $(\"#\" + selectizeID).find(\"select\")[0];\n        var selectize = $(select).selectize(opts)[0].selectize;\n        // NOTE: this callback is triggered when *directly* altering \n        // dropdown items\n        selectize.on(\"change\", function() {\n          var currentItems = traceManager.groupSelections[set] || [];\n          if (!x.highlight.persistent) {\n            removeBrush(el);\n            for (var i = 0; i < currentItems.length; i++) {\n              selectize.removeItem(currentItems[i], true);\n            }\n          }\n          var newItems = selectize.items.filter(function(idx) { \n            return currentItems.indexOf(idx) < 0;\n          });\n          if (newItems.length > 0) {\n            traceManager.updateSelection(set, newItems);\n          } else {\n            // Item has been removed...\n            // TODO: this logic won't work for dynamically changing palette \n            traceManager.updateSelection(set, null);\n            traceManager.updateSelection(set, selectize.items);\n          }\n        });\n      }\n    } // end of selectionChange\n    \n  } // end of renderValue\n}); // end of widget definition\n\n/**\n * @param graphDiv The Plotly graph div\n * @param highlight An object with options for updating selection(s)\n */\nfunction TraceManager(graphDiv, highlight) {\n  // The Plotly graph div\n  this.gd = graphDiv;\n\n  // Preserve the original data.\n  // TODO: try using Lib.extendFlat() as done in  \n  // https://github.com/plotly/plotly.js/pull/1136 \n  this.origData = JSON.parse(JSON.stringify(graphDiv.data));\n  \n  // avoid doing this over and over\n  this.origOpacity = [];\n  for (var i = 0; i < this.origData.length; i++) {\n    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);\n  }\n\n  // key: group name, value: null or array of keys representing the\n  // most recently received selection for that group.\n  this.groupSelections = {};\n  \n  // selection parameters (e.g., transient versus persistent selection)\n  this.highlight = highlight;\n}\n\nTraceManager.prototype.close = function() {\n  // TODO: Unhook all event handlers\n};\n\nTraceManager.prototype.updateFilter = function(group, keys) {\n\n  if (typeof(keys) === \"undefined\" || keys === null) {\n    \n    this.gd.data = JSON.parse(JSON.stringify(this.origData));\n    \n  } else {\n  \n    var traces = [];\n    for (var i = 0; i < this.origData.length; i++) {\n      var trace = this.origData[i];\n      if (!trace.key || trace.set !== group) {\n        continue;\n      }\n      var matchFunc = getMatchFunc(trace);\n      var matches = matchFunc(trace.key, keys);\n      \n      if (matches.length > 0) {\n        if (!trace._isSimpleKey) {\n          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)\n          trace = subsetArrayAttrs(trace, matches);\n        }\n        traces.push(trace);\n      }\n    }\n  }\n  \n  this.gd.data = traces;\n  Plotly.redraw(this.gd);\n  \n  // NOTE: we purposely do _not_ restore selection(s), since on filter,\n  // axis likely will update, changing the pixel -> data mapping, leading \n  // to a likely mismatch in the brush outline and highlighted marks\n  \n};\n\nTraceManager.prototype.updateSelection = function(group, keys) {\n  \n  if (keys !== null && !Array.isArray(keys)) {\n    throw new Error(\"Invalid keys argument; null or array expected\");\n  }\n  \n  // if selection has been cleared, or if this is transient\n  // selection, delete the \"selection traces\"\n  var nNewTraces = this.gd.data.length - this.origData.length;\n  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {\n    var tracesToRemove = [];\n    for (var i = 0; i < this.gd.data.length; i++) {\n      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);\n    }\n    Plotly.deleteTraces(this.gd, tracesToRemove);\n    this.groupSelections[group] = keys;\n  } else {\n    // add to the groupSelection, rather than overwriting it\n    // TODO: can this be removed?\n    this.groupSelections[group] = this.groupSelections[group] || [];\n    for (var i = 0; i < keys.length; i++) {\n      var k = keys[i];\n      if (this.groupSelections[group].indexOf(k) < 0) {\n        this.groupSelections[group].push(k);\n      }\n    }\n  }\n  \n  if (keys === null) {\n    \n    Plotly.restyle(this.gd, {\"opacity\": this.origOpacity});\n    \n  } else if (keys.length >= 1) {\n    \n    // placeholder for new \"selection traces\"\n    var traces = [];\n    // this variable is set in R/highlight.R\n    var selectionColour = crosstalk.group(group).var(\"plotlySelectionColour\").get() || \n      this.highlight.color[0];\n\n    for (var i = 0; i < this.origData.length; i++) {\n      // TODO: try using Lib.extendFlat() as done in  \n      // https://github.com/plotly/plotly.js/pull/1136 \n      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));\n      if (!trace.key || trace.set !== group) {\n        continue;\n      }\n      // Get sorted array of matching indices in trace.key\n      var matchFunc = getMatchFunc(trace);\n      var matches = matchFunc(trace.key, keys);\n      \n      if (matches.length > 0) {\n        // If this is a \"simple\" key, that means select the entire trace\n        if (!trace._isSimpleKey) {\n          trace = subsetArrayAttrs(trace, matches);\n        }\n        // reach into the full trace object so we can properly reflect the \n        // selection attributes in every view\n        var d = this.gd._fullData[i];\n        \n        /* \n        / Recursively inherit selection attributes from various sources, \n        / in order of preference:\n        /  (1) official plotly.js selected attribute\n        /  (2) highlight(selected = attrs_selected(...))\n        */\n        // TODO: it would be neat to have a dropdown to dynamically specify these!\n        $.extend(true, trace, this.highlight.selected);\n        \n        // if it is defined, override color with the \"dynamic brush color\"\"\n        if (d.marker) {\n          trace.marker = trace.marker || {};\n          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;\n        }\n        if (d.line) {\n          trace.line = trace.line || {};\n          trace.line.color =  selectionColour || trace.line.color || d.line.color;\n        }\n        if (d.textfont) {\n          trace.textfont = trace.textfont || {};\n          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;\n        }\n        if (d.fillcolor) {\n          // TODO: should selectionColour inherit alpha from the existing fillcolor?\n          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;\n        }\n        // attach a sensible name/legendgroup\n        trace.name = trace.name || keys.join(\"<br />\");\n        trace.legendgroup = trace.legendgroup || keys.join(\"<br />\");\n        \n        // keep track of mapping between this new trace and the trace it targets\n        // (necessary for updating frames to reflect the selection traces)\n        trace._originalIndex = i;\n        trace._newIndex = this.gd._fullData.length + traces.length;\n        trace._isCrosstalkTrace = true;\n        traces.push(trace);\n      }\n    }\n    \n    if (traces.length > 0) {\n      \n      Plotly.addTraces(this.gd, traces).then(function(gd) {\n        // incrementally add selection traces to frames\n        // (this is heavily inspired by Plotly.Plots.modifyFrames() \n        // in src/plots/plots.js)\n        var _hash = gd._transitionData._frameHash;\n        var _frames = gd._transitionData._frames || [];\n        \n        for (var i = 0; i < _frames.length; i++) {\n          \n          // add to _frames[i].traces *if* this frame references selected trace(s)\n          var newIndices = [];\n          for (var j = 0; j < traces.length; j++) {\n            var tr = traces[j];\n            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {\n              newIndices.push(tr._newIndex);\n              _frames[i].traces.push(tr._newIndex);\n            }\n          }\n          \n          // nothing to do...\n          if (newIndices.length === 0) {\n            continue;\n          }\n          \n          var ctr = 0;\n          var nFrameTraces = _frames[i].data.length;\n          \n          for (var j = 0; j < nFrameTraces; j++) {\n            var frameTrace = _frames[i].data[j];\n            if (!frameTrace.key || frameTrace.set !== group) {\n              continue;\n            }\n            \n            var matchFunc = getMatchFunc(frameTrace);\n            var matches = matchFunc(frameTrace.key, keys);\n            \n            if (matches.length > 0) {\n              if (!trace._isSimpleKey) {\n                frameTrace = subsetArrayAttrs(frameTrace, matches);\n              }\n              var d = gd._fullData[newIndices[ctr]];\n              if (d.marker) {\n                frameTrace.marker = d.marker;\n              }\n              if (d.line) {\n                frameTrace.line = d.line;\n              }\n              if (d.textfont) {\n                frameTrace.textfont = d.textfont;\n              }\n              ctr = ctr + 1;\n              _frames[i].data.push(frameTrace);\n            }\n          }\n          \n          // update gd._transitionData._frameHash\n          _hash[_frames[i].name] = _frames[i];\n        }\n      \n      });\n      \n      // dim traces that have a set matching the set of selection sets\n      var tracesToDim = [],\n          opacities = [],\n          sets = Object.keys(this.groupSelections),\n          n = this.origData.length;\n          \n      for (var i = 0; i < n; i++) {\n        var opacity = this.origOpacity[i] || 1;\n        // have we already dimmed this trace? Or is this even worth doing?\n        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {\n          continue;\n        }\n        // is this set an element of the set of selection sets?\n        var matches = findMatches(sets, [this.gd.data[i].set]);\n        if (matches.length) {\n          tracesToDim.push(i);\n          opacities.push(opacity * this.highlight.opacityDim);\n        }\n      }\n      \n      if (tracesToDim.length > 0) {\n        Plotly.restyle(this.gd, {\"opacity\": opacities}, tracesToDim);\n        // turn off the selected/unselected API\n        Plotly.restyle(this.gd, {\"selectedpoints\": null});\n      }\n      \n    }\n    \n  }\n};\n\n/* \nNote: in all of these match functions, we assume needleSet (i.e. the selected keys)\nis a 1D (or flat) array. The real difference is the meaning of haystack.\nfindMatches() does the usual thing you'd expect for \nlinked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff \nhaystack is a subset of the needleSet. findNestedMatches() returns \n*/\n\nfunction getMatchFunc(trace) {\n  return (trace._isNestedKey) ? findNestedMatches : \n    (trace._isSimpleKey) ? findSimpleMatches : findMatches;\n}\n\n// find matches for \"flat\" keys\nfunction findMatches(haystack, needleSet) {\n  var matches = [];\n  haystack.forEach(function(obj, i) {\n    if (obj === null || needleSet.indexOf(obj) >= 0) {\n      matches.push(i);\n    }\n  });\n  return matches;\n}\n\n// find matches for \"simple\" keys\nfunction findSimpleMatches(haystack, needleSet) {\n  var match = haystack.every(function(val) {\n    return val === null || needleSet.indexOf(val) >= 0;\n  });\n  // yes, this doesn't make much sense other than conforming \n  // to the output type of the other match functions\n  return (match) ? [0] : []\n}\n\n// find matches for a \"nested\" haystack (2D arrays)\nfunction findNestedMatches(haystack, needleSet) {\n  var matches = [];\n  for (var i = 0; i < haystack.length; i++) {\n    var hay = haystack[i];\n    var match = hay.every(function(val) { \n      return val === null || needleSet.indexOf(val) >= 0; \n    });\n    if (match) {\n      matches.push(i);\n    }\n  }\n  return matches;\n}\n\nfunction isPlainObject(obj) {\n  return (\n    Object.prototype.toString.call(obj) === '[object Object]' &&\n    Object.getPrototypeOf(obj) === Object.prototype\n  );\n}\n\nfunction subsetArrayAttrs(obj, indices) {\n  var newObj = {};\n  Object.keys(obj).forEach(function(k) {\n    var val = obj[k];\n\n    if (k.charAt(0) === \"_\") {\n      newObj[k] = val;\n    } else if (k === \"transforms\" && Array.isArray(val)) {\n      newObj[k] = val.map(function(transform) {\n        return subsetArrayAttrs(transform, indices);\n      });\n    } else if (k === \"colorscale\" && Array.isArray(val)) {\n      newObj[k] = val;\n    } else if (isPlainObject(val)) {\n      newObj[k] = subsetArrayAttrs(val, indices);\n    } else if (Array.isArray(val)) {\n      newObj[k] = subsetArray(val, indices);\n    } else {\n      newObj[k] = val;\n    }\n  });\n  return newObj;\n}\n\nfunction subsetArray(arr, indices) {\n  var result = [];\n  for (var i = 0; i < indices.length; i++) {\n    result.push(arr[indices[i]]);\n  }\n  return result;\n}\n\n// Convenience function for removing plotly's brush \nfunction removeBrush(el) {\n  var outlines = el.querySelectorAll(\".select-outline\");\n  for (var i = 0; i < outlines.length; i++) {\n    outlines[i].remove();\n  }\n}\n\n\n// https://davidwalsh.name/javascript-debounce-function\n\n// Returns a function, that, as long as it continues to be invoked, will not\n// be triggered. 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this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(void 0===e||e,this)},{buffer:111}],97:[function(t,e,r){\"use strict\";var n=t(\"./lib/bn-sign\");e.exports=function(t){return n(t[0])*n(t[1])}},{\"./lib/bn-sign\":88}],98:[function(t,e,r){\"use strict\";var n=t(\"./lib/rationalize\");e.exports=function(t,e){return n(t[0].mul(e[1]).sub(t[1].mul(e[0])),t[1].mul(e[1]))}},{\"./lib/rationalize\":93}],99:[function(t,e,r){\"use strict\";var n=t(\"./lib/bn-to-num\"),i=t(\"./lib/ctz\");e.exports=function(t){var e=t[0],r=t[1];if(0===e.cmpn(0))return 0;var a=e.abs().divmod(r.abs()),o=a.div,s=n(o),l=a.mod,c=e.negative!==r.negative?-1:1;if(0===l.cmpn(0))return c*s;if(s){var u=i(s)+4,f=n(l.ushln(u).divRound(r));return c*(s+f*Math.pow(2,-u))}var h=r.bitLength()-l.bitLength()+53;f=n(l.ushln(h).divRound(r));return h<1023?c*f*Math.pow(2,-h):(f*=Math.pow(2,-1023),c*f*Math.pow(2,1023-h))}},{\"./lib/bn-to-num\":89,\"./lib/ctz\":90}],100:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){for(var a=i+1;n<=i;){var o=n+i>>>1,s=t[o];(void 0!==r?r(s,e):s-e)>=0?(a=o,i=o-1):n=o+1}return a}function i(t,e,r,n,i){for(var a=i+1;n<=i;){var o=n+i>>>1,s=t[o];(void 0!==r?r(s,e):s-e)>0?(a=o,i=o-1):n=o+1}return a}function a(t,e,r,n,i){for(var a=n-1;n<=i;){var o=n+i>>>1,s=t[o];(void 0!==r?r(s,e):s-e)<0?(a=o,n=o+1):i=o-1}return a}function o(t,e,r,n,i){for(var a=n-1;n<=i;){var o=n+i>>>1,s=t[o];(void 0!==r?r(s,e):s-e)<=0?(a=o,n=o+1):i=o-1}return a}function s(t,e,r,n,i){for(;n<=i;){var a=n+i>>>1,o=t[a],s=void 0!==r?r(o,e):o-e;if(0===s)return a;s<=0?n=a+1:i=a-1}return-1}function l(t,e,r,n,i,a){return\"function\"==typeof r?a(t,e,r,void 0===n?0:0|n,void 0===i?t.length-1:0|i):a(t,e,void 0,void 0===r?0:0|r,void 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r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var 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a=V(s,i);return[r/n*(t+e/o(a)),a]},a}function Mt(t){return[t[0]/2,S(g(t[1]/2*M))*A]}function St(t){return[2*t[0],2*i(m(t[1]*M))*A]}function Et(t,r){var i=2*y/r,s=t*t;function l(r,l){var c=e.geoAzimuthalEquidistantRaw(r,l),u=c[0],f=c[1],h=u*u+f*f;if(h>s){var d=L(h),g=a(f,u),b=i*p(g/i),_=g-b,w=t*o(_),T=(t*m(_)-_*m(w))/(x-w),k=Lt(_,T),A=(y-t)/Ct(k,w,y);u=d;var M,S=50;do{u-=M=(t+Ct(k,w,u)*A-d)/(k(u)*A)}while(n(M)>v&&--S>0);f=_*m(u),u<x&&(f-=T*(u-x));var E=m(b),C=o(b);c[0]=u*C-f*E,c[1]=u*E+f*C}return c}return l.invert=function(r,l){var c=r*r+l*l;if(c>s){var u=L(c),f=a(l,r),h=i*p(f/i),d=f-h;r=u*o(d),l=u*m(d);for(var g=r-x,v=m(r),b=l/v,_=r<x?1/0:0,w=10;;){var T=t*m(b),k=t*o(b),A=m(k),M=x-k,S=(T-b*A)/M,E=Lt(b,S);if(n(_)<1e-12||!--w)break;b-=_=(b*v-S*g-l)/(v-2*g*(M*(k+b*T*o(k)-A)-T*(T-b*A))/(M*M))}r=(u=t+Ct(E,k,r)*(y-t)/Ct(E,k,y))*o(f=h+b),l=u*m(f)}return e.geoAzimuthalEquidistantRaw.invert(r,l)},l}function Lt(t,e){return function(r){var n=t*o(r);return r<x&&(n-=e),L(1+n*n)}}function 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p=l[f-2];if(p.left===h)if(h.left===i){if(!(d=p.right)||0!==d._color){if(p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).left===p?m.left=h:m.right=h;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){if(h.right=i.left,p._color=0,p.left=i.right,i._color=1,i.left=h,i.right=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).left===p?m.left=i:m.right=i;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else if(h.right===i){if(!(d=p.left)||0!==d._color){if(p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).right===p?m.right=h:m.left=h;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var m;if(h.left=i.right,p._color=0,p.right=i.left,i._color=1,i.right=h,i.left=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).right===p?m.right=i:m.left=i;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}}return l[0]._color=1,new 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"must specify type string\");if(e=e||{},\"undefined\"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement(\"canvas\");\"number\"==typeof e.width&&(r.width=e.width);\"number\"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf(\"webgl\")&&a.push(\"experimental-\"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],249:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":250,\"./lib/cube.js\":251,\"./lib/lines.js\":252,\"./lib/text.js\":254,\"./lib/ticks.js\":255}],250:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":253,\"gl-buffer\":257,\"gl-vao\":343}],251:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":101,\"gl-mat4/multiply\":289,\"robust-orientation\":524,\"split-polygon\":541}],252:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":253,\"gl-buffer\":257,\"gl-vao\":343}],253:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":323,glslify:424}],254:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":253,_process:504,\"gl-buffer\":257,\"gl-vao\":343,\"vectorize-text\":596}],255:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],256:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":251,\"extract-frustum-planes\":241,\"gl-mat4/multiply\":289,\"gl-mat4/transpose\":300,\"gl-vec4/transformMat4\":414,\"split-polygon\":541}],257:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:462,\"ndarray-ops\":457,\"typedarray-pool\":590}],258:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":259,\"./lib/shaders\":260,\"gl-vec3\":362}],259:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:132,\"gl-buffer\":257,\"gl-mat4/invert\":287,\"gl-mat4/multiply\":289,\"gl-shader\":323,\"gl-texture2d\":338,\"gl-vao\":343,ndarray:462}],260:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:424}],261:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],262:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":261}],263:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":264,\"gl-buffer\":257,\"gl-vao\":343}],264:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":323,glslify:424}],265:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;\"depth\"in n&&(m=!!n.depth);var g=!1;\"stencil\"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":338}],266:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":68,\"gl-constants/lookup\":262,\"glsl-shader-name\":416,\"sprintf-js\":542}],267:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":268,\"binary-search-bounds\":100,\"gl-buffer\":257,\"gl-shader\":323,\"iota-array\":431,\"typedarray-pool\":590}],268:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:424}],269:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":323,glslify:424}],270:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":269,\"binary-search-bounds\":100,\"gl-buffer\":257,\"gl-texture2d\":338,\"gl-vao\":343,ndarray:462}],271:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15];return t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],272:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],273:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],274:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],275:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],276:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],277:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o=r[0],s=r[1],l=r[2],c=Math.sqrt(o*o+s*s+l*l);if(Math.abs(c)<1e-6)return null;return o*=c=1/c,s*=c,l*=c,n=Math.sin(e),i=Math.cos(e),a=1-i,t[0]=o*o*a+i,t[1]=s*o*a+l*n,t[2]=l*o*a-s*n,t[3]=0,t[4]=o*s*a-l*n,t[5]=s*s*a+i,t[6]=l*s*a+o*n,t[7]=0,t[8]=o*l*a+s*n,t[9]=s*l*a-o*n,t[10]=l*l*a+i,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],278:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],279:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],280:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],281:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],282:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],291:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],292:[function(t,e,r){e.exports=function(t,e,r,n){var 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t}},{}],300:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],301:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:81,\"polytope-closest-point/lib/closest_point_2d.js\":488}],302:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:424}],303:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":301,\"./lib/shaders\":302,colormap:132,\"gl-buffer\":257,\"gl-mat4/invert\":287,\"gl-mat4/multiply\":289,\"gl-shader\":323,\"gl-texture2d\":338,\"gl-vao\":343,ndarray:462,normals:465,\"simplicial-complex-contour\":532,\"typedarray-pool\":590}],304:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":307,\"gl-buffer\":257,\"gl-shader\":323}],305:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":307,\"binary-search-bounds\":100,\"gl-buffer\":257,\"gl-shader\":323}],306:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":307,\"gl-buffer\":257,\"gl-shader\":323}],307:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:424}],308:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":307,\"binary-search-bounds\":100,\"gl-buffer\":257,\"gl-shader\":323,\"text-cache\":570}],309:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":304,\"./lib/grid\":305,\"./lib/line\":306,\"./lib/text\":308,\"gl-select-static\":322}],310:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":55,\"has-passive-events\":426,\"mouse-change\":449,\"mouse-event-offset\":450,\"mouse-wheel\":452,\"right-now\":518}],311:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":323,glslify:424}],312:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":310,\"./lib/shader\":311,\"a-big-triangle\":66,\"gl-axes3d\":249,\"gl-axes3d/properties\":256,\"gl-fbo\":265,\"gl-mat4/ortho\":290,\"gl-mat4/perspective\":291,\"gl-select-static\":322,\"gl-spikes3d\":332,\"is-mobile\":435,\"mouse-change\":449}],313:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:424}],314:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":313,\"gl-buffer\":257,\"gl-shader\":323,\"typedarray-pool\":590}],315:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],316:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],317:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":596}],318:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":323,glslify:424}],319:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":316,\"./lib/glyphs\":317,\"./lib/shaders\":318,\"gl-buffer\":257,\"gl-mat4/multiply\":289,\"gl-vao\":343,\"is-string-blank\":438,\"typedarray-pool\":590}],320:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:424}],321:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":320,\"gl-buffer\":257,\"gl-shader\":323}],322:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,\"number\"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{\"bit-twiddle\":101,\"gl-fbo\":265,ndarray:462,\"typedarray-pool\":590}],323:[function(t,e,r){\"use strict\";var n=t(\"./lib/create-uniforms\"),i=t(\"./lib/create-attributes\"),a=t(\"./lib/reflect\"),o=t(\"./lib/shader-cache\"),s=t(\"./lib/runtime-reflect\"),l=t(\"./lib/GLError\");function c(t){this.gl=t,this.gl.lastAttribCount=0,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}var u=c.prototype;function f(t,e){return t.name<e.name?-1:1}u.bind=function(){var t;this.program||this._relink();var e=this.gl.getProgramParameter(this.program,this.gl.ACTIVE_ATTRIBUTES),r=this.gl.lastAttribCount;if(e>r)for(t=r;t<e;t++)this.gl.enableVertexAttribArray(t);else if(r>e)for(t=e;t<r;t++)this.gl.disableVertexAttribArray(t);this.gl.lastAttribCount=e,this.gl.useProgram(this.program)},u.dispose=function(){for(var t=this.gl.lastAttribCount,e=0;e<t;e++)this.gl.disableVertexAttribArray(e);this.gl.lastAttribCount=0,this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},u.update=function(t,e,r,c){if(!e||1===arguments.length){var u=t;t=u.vertex,e=u.fragment,r=u.uniforms,c=u.attributes}var h=this,p=h.gl,d=h._vref;h._vref=o.shader(p,p.VERTEX_SHADER,t),d&&d.dispose(),h.vertShader=h._vref.shader;var m=this._fref;if(h._fref=o.shader(p,p.FRAGMENT_SHADER,e),m&&m.dispose(),h.fragShader=h._fref.shader,!r||!c){var g=p.createProgram();if(p.attachShader(g,h.fragShader),p.attachShader(g,h.vertShader),p.linkProgram(g),!p.getProgramParameter(g,p.LINK_STATUS)){var v=p.getProgramInfoLog(g);throw new l(v,\"Error linking program:\"+v)}r=r||s.uniforms(p,g),c=c||s.attributes(p,g),p.deleteProgram(g)}(c=c.slice()).sort(f);var y,x=[],b=[],_=[];for(y=0;y<c.length;++y){var 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segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:424}],334:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":333,\"gl-cone3d\":258,\"gl-vec3\":362,\"gl-vec4\":398}],335:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":323,glslify:424}],336:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":335,\"binary-search-bounds\":100,\"bit-twiddle\":101,colormap:132,\"gl-buffer\":257,\"gl-mat4/invert\":287,\"gl-mat4/multiply\":289,\"gl-texture2d\":338,\"gl-vao\":343,ndarray:462,\"ndarray-gradient\":455,\"ndarray-ops\":457,\"ndarray-pack\":458,\"surface-nets\":565,\"typedarray-pool\":590}],337:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":101,\"color-normalize\":126,\"css-font\":146,\"detect-kerning\":173,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":339,\"is-plain-obj\":437,\"object-assign\":466,\"parse-rect\":471,\"parse-unit\":473,\"pick-by-alias\":475,regl:516,\"to-px\":574,\"typedarray-pool\":590}],338:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=m(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];h=a.malloc(v,r);var x=n(h,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):f(x,e),u=h.subarray(0,v)}var b=g(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(h),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return h(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return h(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,h(this,this._shape[0],t),t}}}),d.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},d.dispose=function(){this.gl.deleteTexture(this.handle)},d.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},d.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var l=u(t)?t:t.raw;if(l){this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l)}else{if(!(t.shape&&t.stride&&t.data))throw new Error(\"gl-texture2d: Unsupported data type\");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");!function(t,e,r,o,s,l,c,u){var h=u.dtype,p=u.shape.slice();if(p.length<2||p.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var d=0,g=0,v=m(p,u.stride.slice());\"float32\"===h?d=t.FLOAT:\"float64\"===h?(d=t.FLOAT,v=!1,h=\"float32\"):\"uint8\"===h?d=t.UNSIGNED_BYTE:(d=t.UNSIGNED_BYTE,v=!1,h=\"uint8\");if(2===p.length)g=t.LUMINANCE,p=[p[0],p[1],1],u=n(u.data,p,[u.stride[0],u.stride[1],1],u.offset);else{if(3!==p.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===p[2])g=t.ALPHA;else if(2===p[2])g=t.LUMINANCE_ALPHA;else if(3===p[2])g=t.RGB;else{if(4!==p[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}p[2]}g!==t.LUMINANCE&&g!==t.ALPHA||s!==t.LUMINANCE&&s!==t.ALPHA||(g=s);if(g!==s)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var y=u.size,x=c.indexOf(o)<0;x&&c.push(o);if(d===l&&v)0===u.offset&&u.data.length===y?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data.subarray(u.offset,u.offset+y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data.subarray(u.offset,u.offset+y));else{var b;b=l===t.FLOAT?a.mallocFloat32(y):a.mallocUint8(y);var _=n(b,p,[p[2],p[2]*p[0],1]);d===t.FLOAT&&l===t.UNSIGNED_BYTE?f(_,u):i.assign(_,u),x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,b.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,b.subarray(0,y)),l===t.FLOAT?a.freeFloat32(b):a.freeUint8(b)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},{ndarray:462,\"ndarray-ops\":457,\"typedarray-pool\":590}],339:[function(t,e,r){(function(r){(function(){\"use strict\";var n=t(\"pick-by-alias\");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var e=t.container.getBoundingClientRect();t.canvas.width=t.width||e.right-e.left,t.canvas.height=t.height||e.bottom-e.top}}function a(t){return\"function\"==typeof t.getContext&&\"width\"in t&&\"height\"in t}function o(){var t=document.createElement(\"canvas\");return t.style.position=\"absolute\",t.style.top=0,t.style.left=0,t}e.exports=function(t){var e;if(t?\"string\"==typeof t&&(t={container:t}):t={},a(t)?t={container:t}:t=\"string\"==typeof(e=t).nodeName&&\"function\"==typeof e.appendChild&&\"function\"==typeof e.getBoundingClientRect?{container:t}:function(t){return\"function\"==typeof t.drawArrays||\"function\"==typeof t.drawElements}(t)?{gl:t}:n(t,{container:\"container target element el canvas holder parent parentNode wrapper use ref root node\",gl:\"gl context 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Use headless-gl.\");t.container=document.body||document.documentElement,t.canvas=o(),t.container.appendChild(t.canvas),i(t)}return t.gl||[\"webgl\",\"experimental-webgl\",\"webgl-experimental\"].some((function(e){try{t.gl=t.canvas.getContext(e,t.attrs)}catch(t){}return t.gl})),t.gl}}).call(this)}).call(this,\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"pick-by-alias\":475}],340:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error(\"gl-vao: Too many vertex attributes\");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,c=!!a.normalized,u=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,c,u,f)}else{if(\"number\"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else 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r=g(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function m(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function g(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(m),new i(n,g(a),g(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return 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mr(r.type,r,n)},mr.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=this.beginIndex.evaluate(t);if(!Qt(e,[\"string\",\"array\"]))throw new ue(\"Expected first argument to be of type array or string, but found \"+Xt(se(e))+\" instead.\");if(this.endIndex){var n=this.endIndex.evaluate(t);return e.slice(r,n)}return e.slice(r)},mr.prototype.eachChild=function(t){t(this.input),t(this.beginIndex),this.endIndex&&t(this.endIndex)},mr.prototype.outputDefined=function(){return!1},mr.prototype.serialize=function(){if(null!=this.endIndex&&void 0!==this.endIndex){var t=this.endIndex.serialize();return[\"slice\",this.input.serialize(),this.beginIndex.serialize(),t]}return[\"slice\",this.input.serialize(),this.beginIndex.serialize()]};var xr=yr(\"==\",(function(t,e,r){return e===r}),vr),br=yr(\"!=\",(function(t,e,r){return e!==r}),(function(t,e,r,n){return!vr(0,e,r,n)})),_r=yr(\"<\",(function(t,e,r){return e<r}),(function(t,e,r,n){return 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kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Ot(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new Ct(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new Ct(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new Ct(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new Ct(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new Ct(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new Ct(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new Ct(n,r,'missing required property \"source\"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(){return[]},type:function(){return kn({key:n+\".type\",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:\"type\"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{\"*\":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return\"string\"!==n?[new Ct(r,e,\"string expected, \"+n+\" found\")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if(\"string\"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+\".\"+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'\"type\" is required')];var a,o=Ot(e.type);switch(o){case\"vector\":case\"raster\":case\"raster-dem\":return a=Qr({key:r,value:e,valueSpec:n[\"source_\"+o.replace(\"-\",\"_\")],style:t.style,styleSpec:n,objectElementValidators:bn});case\"geojson\":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f=\"string\"==typeof c?[c,[\"accumulated\"],[\"get\",s]]:c;a.push.apply(a,rn({key:r+\".\"+s+\".map\",value:u,expressionContext:\"cluster-map\"})),a.push.apply(a,rn({key:r+\".\"+s+\".reduce\",value:f,expressionContext:\"cluster-reduce\"}))}return a;case\"video\":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case\"image\":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case\"canvas\":return[new Ct(r,null,\"Please use runtime APIs to add 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Ct(e,r,\"color expected, \"+n+\" found\")]:null===$t(r)?[new Ct(e,r,'color expected, \"'+r+'\" found')]:[]},constants:Pt,enum:nn,filter:dn,function:en,layer:yn,object:Qr,source:_n,light:wn,string:xn,formatted:function(t){return 0===xn(t).length?[]:rn(t)},resolvedImage:function(t){return 0===xn(t).length?[]:rn(t)}};function kn(t){var e=t.value,r=t.valueSpec,n=t.styleSpec;return r.expression&&Br(Ot(e))?en(t):r.expression&&Yr(zt(e))?rn(t):r.type&&Tn[r.type]?Tn[r.type](t):Qr(It({},t,{valueSpec:r.type?n[r.type]:r}))}function An(t){var e=t.value,r=t.key,n=xn(t);return n.length||(-1===e.indexOf(\"{fontstack}\")&&n.push(new Ct(r,e,'\"glyphs\" url must include a \"{fontstack}\" token')),-1===e.indexOf(\"{range}\")&&n.push(new Ct(r,e,'\"glyphs\" url must include a \"{range}\" token'))),n}function Mn(t,e){void 0===e&&(e=Lt);var r=[];return 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bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return 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s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},Ba.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},Ba.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},Ba.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=l(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},Ba.prototype.getBinding=function(t,e,r){return new Sa(t,e)};var Na=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};Na.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},Na.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},Na.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],c=n[o];if(s&&l&&c)for(var 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification[\"property-type\"],h=\"cross-faded\"===f||\"cross-faded-data-driven\"===f;if(\"constant\"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push(\"/u_\"+a);else if(\"source\"===l.kind||h){var p=Ha(a,c,\"source\");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push(\"/a_\"+a)}else{var d=Ha(a,c,\"composite\");this.binders[a]=new Ba(l,s,c,u,e,d),i.push(\"/z_\"+a)}}}this.cacheKey=i.sort().join(\"\")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof 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Da||i instanceof Ra||i instanceof Ba)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},ja.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},ja.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof Na){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof Fa||r instanceof Ba)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},ja.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof Fa||r instanceof Ba||r instanceof Na)&&r.upload(t)}this.updatePaintBuffers()},ja.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof Fa||e instanceof Ba||e instanceof Na)&&e.destroy()}};var Ua=function(t,e,r,n){void 0===n&&(n=function(){return!0}),this.programConfigurations={};for(var i=0,a=e;i<a.length;i+=1){var o=a[i];this.programConfigurations[o.id]=new ja(o,r,n,t)}this.needsUpload=!1,this._featureMap=new _a,this._bufferOffset=0};function Va(t,e){return{\"text-opacity\":[\"opacity\"],\"icon-opacity\":[\"opacity\"],\"text-color\":[\"fill_color\"],\"icon-color\":[\"fill_color\"],\"text-halo-color\":[\"halo_color\"],\"icon-halo-color\":[\"halo_color\"],\"text-halo-blur\":[\"halo_blur\"],\"icon-halo-blur\":[\"halo_blur\"],\"text-halo-width\":[\"halo_width\"],\"icon-halo-width\":[\"halo_width\"],\"line-gap-width\":[\"gapwidth\"],\"line-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"],\"fill-extrusion-pattern\":[\"pattern_to\",\"pattern_from\",\"pixel_ratio_to\",\"pixel_ratio_from\"]}[t]||[t.replace(e+\"-\",\"\").replace(/-/g,\"_\")]}function Ha(t,e,r){var n={color:{source:ta,composite:ea},number:{source:Zi,composite:ta}},i=function(t){return{\"line-pattern\":{source:Bi,composite:Bi},\"fill-pattern\":{source:Bi,composite:Bi},\"fill-extrusion-pattern\":{source:Bi,composite:Bi}}[t]}(t);return i&&i[r]||n[e][r]}Ua.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},Ua.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},Ua.prototype.get=function(t){return this.programConfigurations[t]},Ua.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},Ua.prototype.destroy=function(){for(var t in 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n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new wi(Lt.layout_line[\"line-cap\"]),\"line-join\":new Ti(Lt.layout_line[\"line-join\"]),\"line-miter-limit\":new wi(Lt.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new wi(Lt.layout_line[\"line-round-limit\"]),\"line-sort-key\":new Ti(Lt.layout_line[\"line-sort-key\"])}),Ys={paint:new Si({\"line-opacity\":new Ti(Lt.paint_line[\"line-opacity\"]),\"line-color\":new Ti(Lt.paint_line[\"line-color\"]),\"line-translate\":new wi(Lt.paint_line[\"line-translate\"]),\"line-translate-anchor\":new wi(Lt.paint_line[\"line-translate-anchor\"]),\"line-width\":new Ti(Lt.paint_line[\"line-width\"]),\"line-gap-width\":new Ti(Lt.paint_line[\"line-gap-width\"]),\"line-offset\":new Ti(Lt.paint_line[\"line-offset\"]),\"line-blur\":new Ti(Lt.paint_line[\"line-blur\"]),\"line-dasharray\":new Ai(Lt.paint_line[\"line-dasharray\"]),\"line-pattern\":new ki(Lt.paint_line[\"line-pattern\"]),\"line-gradient\":new Mi(Lt.paint_line[\"line-gradient\"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var t=this._transitionablePaint._values[\"line-gradient\"].value.expression;this.gradient=Co(t,\"lineProgress\"),this.gradientTexture=null},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values[\"line-floorwidth\"]=Ws.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,e)},e.prototype.createBucket=function(t){return new qs(t)},e.prototype.queryRadius=function(t){var e=t,r=Zs(oo(\"line-width\",this,e),oo(\"line-gap-width\",this,e)),n=oo(\"line-offset\",this,e);return r/2+Math.abs(n)+so(this.paint.get(\"line-translate\"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s){var l=lo(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),c=s/2*Zs(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),u=this.paint.get(\"line-offset\").evaluate(e,r);return u&&(n=function(t,e){for(var r=[],n=new i(0,0),a=0;a<t.length;a++){for(var o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var Js=Ii([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),Ks=Ii([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4),Qs=(Ii([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4),Ii([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"}])),$s=(Ii([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]),Ii([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4)),tl=Ii([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4);Ii([{name:\"triangle\",components:3,type:\"Uint16\"}]),Ii([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Uint16\",name:\"glyphStartIndex\"},{type:\"Uint16\",name:\"numGlyphs\"},{type:\"Uint32\",name:\"vertexStartIndex\"},{type:\"Uint32\",name:\"lineStartIndex\"},{type:\"Uint32\",name:\"lineLength\"},{type:\"Uint16\",name:\"segment\"},{type:\"Uint16\",name:\"lowerSize\"},{type:\"Uint16\",name:\"upperSize\"},{type:\"Float32\",name:\"lineOffsetX\"},{type:\"Float32\",name:\"lineOffsetY\"},{type:\"Uint8\",name:\"writingMode\"},{type:\"Uint8\",name:\"placedOrientation\"},{type:\"Uint8\",name:\"hidden\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Int16\",name:\"associatedIconIndex\"}]),Ii([{type:\"Int16\",name:\"anchorX\"},{type:\"Int16\",name:\"anchorY\"},{type:\"Int16\",name:\"rightJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"centerJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"leftJustifiedTextSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedTextSymbolIndex\"},{type:\"Int16\",name:\"placedIconSymbolIndex\"},{type:\"Int16\",name:\"verticalPlacedIconSymbolIndex\"},{type:\"Uint16\",name:\"key\"},{type:\"Uint16\",name:\"textBoxStartIndex\"},{type:\"Uint16\",name:\"textBoxEndIndex\"},{type:\"Uint16\",name:\"verticalTextBoxStartIndex\"},{type:\"Uint16\",name:\"verticalTextBoxEndIndex\"},{type:\"Uint16\",name:\"iconBoxStartIndex\"},{type:\"Uint16\",name:\"iconBoxEndIndex\"},{type:\"Uint16\",name:\"verticalIconBoxStartIndex\"},{type:\"Uint16\",name:\"verticalIconBoxEndIndex\"},{type:\"Uint16\",name:\"featureIndex\"},{type:\"Uint16\",name:\"numHorizontalGlyphVertices\"},{type:\"Uint16\",name:\"numVerticalGlyphVertices\"},{type:\"Uint16\",name:\"numIconVertices\"},{type:\"Uint16\",name:\"numVerticalIconVertices\"},{type:\"Uint16\",name:\"useRuntimeCollisionCircles\"},{type:\"Uint32\",name:\"crossTileID\"},{type:\"Float32\",name:\"textBoxScale\"},{type:\"Float32\",components:2,name:\"textOffset\"},{type:\"Float32\",name:\"collisionCircleDiameter\"}]),Ii([{type:\"Float32\",name:\"offsetX\"}]),Ii([{type:\"Int16\",name:\"x\"},{type:\"Int16\",name:\"y\"},{type:\"Int16\",name:\"tileUnitDistanceFromAnchor\"}]);function el(t,e,r){return t.sections.forEach((function(t){t.text=function(t,e,r){var n=e.layout.get(\"text-transform\").evaluate(r,{});return\"uppercase\"===n?t=t.toLocaleUpperCase():\"lowercase\"===n&&(t=t.toLocaleLowerCase()),hi.applyArabicShaping&&(t=hi.applyArabicShaping(t)),t}(t.text,e,r)})),t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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r=0;r<t.length;r++)e.writeFixed64(t[r])}function xl(t,e){for(var r=0;r<t.length;r++)e.writeSFixed64(t[r])}function bl(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+16777216*t[e+3]}function _l(t,e,r){t[r]=e,t[r+1]=e>>>8,t[r+2]=e>>>16,t[r+3]=e>>>24}function wl(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+(t[e+3]<<24)}ol.prototype={destroy:function(){this.buf=null},readFields:function(t,e,r){for(r=r||this.length;this.pos<r;){var n=this.readVarint(),i=n>>3,a=this.pos;this.type=7&n,t(i,e,this),this.pos===a&&this.skip(n)}return e},readMessage:function(t,e){return this.readFields(t,e,this.readVarint()+this.pos)},readFixed32:function(){var t=bl(this.buf,this.pos);return this.pos+=4,t},readSFixed32:function(){var t=wl(this.buf,this.pos);return this.pos+=4,t},readFixed64:function(){var t=bl(this.buf,this.pos)+4294967296*bl(this.buf,this.pos+4);return this.pos+=8,t},readSFixed64:function(){var t=bl(this.buf,this.pos)+4294967296*wl(this.buf,this.pos+4);return this.pos+=8,t},readFloat:function(){var 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),_l(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),_l(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),_l(this.buf,-1&t,this.pos),_l(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),_l(this.buf,-1&t,this.pos),_l(this.buf,Math.floor(t*(1/4294967296)),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0));if(t>=0x10000000000000000||t<-0x10000000000000000)throw new Error(\"Given varint doesn't fit into 10 bytes\");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var 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o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf(\"\\u200b\")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn[\"Bopomofo Extended\"](s)||Yn.Bopomofo(s)||Yn[\"CJK Compatibility Forms\"](s)||Yn[\"CJK Compatibility Ideographs\"](s)||Yn[\"CJK Compatibility\"](s)||Yn[\"CJK Radicals Supplement\"](s)||Yn[\"CJK Strokes\"](s)||Yn[\"CJK Symbols and Punctuation\"](s)||Yn[\"CJK Unified Ideographs Extension A\"](s)||Yn[\"CJK Unified Ideographs\"](s)||Yn[\"Enclosed CJK Letters and Months\"](s)||Yn[\"Halfwidth and Fullwidth Forms\"](s)||Yn.Hiragana(s)||Yn[\"Ideographic Description Characters\"](s)||Yn[\"Kangxi Radicals\"](s)||Yn[\"Katakana Phonetic Extensions\"](s)||Yn.Katakana(s)||Yn[\"Vertical Forms\"](s)||Yn[\"Yi Radicals\"](s)||Yn[\"Yi 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new fo(9);return fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new fo(4);return fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=Wr,t.createLayout=Ii,t.createStyleLayer=function(t){return\"custom\"===t.type?new Dc(t):new Rc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if(\"object\"==typeof e&&null!==e&&null!==r){if(\"object\"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if(\"LineString\"===a)bt(o,i,t,r,!1,!1);else if(\"MultiLineString\"===a||\"Polygon\"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,\"Polygon\"===a,0===s);else if(\"MultiPolygon\"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if(\"LineString\"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already registered.\");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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t=document.createElement(\"canvas\");t.width=t.height=1;var e=t.getContext(\"2d\");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?function(t){void 0===n[t]&&(n[t]=function(t){var r=function(t){var r=document.createElement(\"canvas\"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext(\"webgl\",n)||r.probablySupportsContext(\"experimental-webgl\",n):r.supportsContext?r.supportsContext(\"webgl\",n)||r.supportsContext(\"experimental-webgl\",n):r.getContext(\"webgl\",n)||r.getContext(\"experimental-webgl\",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);if(!n||r.isContextLost())return!1;return r.shaderSource(n,\"void main() {}\"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS)}(t));return n[t]}(t&&t.failIfMajorPerformanceCaveat)?void 0:\"insufficient WebGL support\":\"insufficient Canvas/getImageData support\":\"insufficient ArrayBuffer support\":\"insufficient Uint8ClampedArray support\":\"insufficient worker support\":\"insufficient JSON support\":\"insufficient Object support\":\"insufficient Function support\":\"insufficent Array support\"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i([\"userSelect\",\"MozUserSelect\",\"WebkitUserSelect\",\"msUserSelect\"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]=\"none\")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",c,c),t.window.removeEventListener(\"test\",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener(\"click\",u,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new 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t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,\"raster-dem\":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],442:[function(t,e,r){var n=t(\"./normalize\"),i=t(\"gl-mat4/create\"),a=t(\"gl-mat4/clone\"),o=t(\"gl-mat4/determinant\"),s=t(\"gl-mat4/invert\"),l=t(\"gl-mat4/transpose\"),c={length:t(\"gl-vec3/length\"),normalize:t(\"gl-vec3/normalize\"),dot:t(\"gl-vec3/dot\"),cross:t(\"gl-vec3/cross\")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function m(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,g,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),g||(g=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,A=u[3],M=u[7],S=u[11],E=u[12],L=u[13],C=u[14],P=u[15];if(0!==A||0!==M||0!==S){if(h[0]=A,h[1]=M,h[2]=S,h[3]=P,!s(f,f))return!1;l(f,f),y=g,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else g[0]=g[1]=g[2]=0,g[3]=1;if(e[0]=E,e[1]=L,e[2]=C,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),m(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),m(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),m(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var I=0;I<3;I++)r[I]*=-1,p[I][0]*=-1,p[I][1]*=-1,p[I][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":443,\"gl-mat4/clone\":272,\"gl-mat4/create\":274,\"gl-mat4/determinant\":275,\"gl-mat4/invert\":287,\"gl-mat4/transpose\":300,\"gl-vec3/cross\":350,\"gl-vec3/dot\":355,\"gl-vec3/length\":365,\"gl-vec3/normalize\":372}],443:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],444:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":275,\"gl-vec3/lerp\":366,\"mat4-decompose\":442,\"mat4-recompose\":445,\"quat-slerp\":505}],445:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":274,\"gl-mat4/fromRotationTranslation\":278,\"gl-mat4/identity\":285,\"gl-mat4/multiply\":289,\"gl-mat4/scale\":297,\"gl-mat4/translate\":299}],446:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],447:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),f=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var m=this.computedUp;m[0]=o[1],m[1]=o[5],m[2]=o[9],f(m,m);var g=this.computedInverse;a(g,o);var v=this.computedEye,y=g[15];v[0]=g[12]/y,v[1]=g[13]/y,v[2]=g[14]/y;var 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t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return 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n(\"chain_add_head\",{index:t,pt:e})},chainAddTail:function(t,e){return n(\"chain_add_tail\",{index:t,pt:e})},chainConnect:function(t,e){return n(\"chain_con\",{index1:t,index2:e})},chainReverse:function(t){return n(\"chain_rev\",{index:t})},chainJoin:function(t,e){return n(\"chain_join\",{index1:t,index2:e})},done:function(){return n(\"done\")}}}},{}],482:[function(t,e,r){e.exports=function(t){\"number\"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return\"number\"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){return Math.abs(e[0]-r[0])<t},pointsSameY:function(e,r){return Math.abs(e[1]-r[1])<t},pointsSame:function(t,r){return e.pointsSameX(t,r)&&e.pointsSameY(t,r)},pointsCompare:function(t,r){return e.pointsSameX(t,r)?e.pointsSameY(t,r)?0:t[1]<r[1]?-1:1:t[0]<r[0]?-1:1},pointsCollinear:function(e,r,n){var i=e[0]-r[0],a=e[1]-r[1],o=r[0]-n[0],s=r[1]-n[1];return Math.abs(i*s-o*a)<t},linesIntersect:function(e,r,n,i){var a=r[0]-e[0],o=r[1]-e[1],s=i[0]-n[0],l=i[1]-n[1],c=a*l-o*s;if(Math.abs(c)<t)return!1;var u=e[0]-n[0],f=e[1]-n[1],h=(s*f-l*u)/c,p=(a*f-o*u)/c,d={alongA:0,alongB:0,pt:[e[0]+h*a,e[1]+h*o]};return d.alongA=h<=-t?-2:h<t?-1:h-1<=-t?0:h-1<t?1:2,d.alongB=p<=-t?-2:p<t?-1:p-1<=-t?0:p-1<t?1:2,d},pointInsideRegion:function(e,r){for(var n=e[0],i=e[1],a=r[r.length-1][0],o=r[r.length-1][1],s=!1,l=0;l<r.length;l++){var c=r[l][0],u=r[l][1];u-i>t!=o-i>t&&(a-c)*(i-u)/(o-u)+c-n>t&&(s=!s),a=c,o=u}return s}};return e}},{}],483:[function(t,e,r){var n={toPolygon:function(t,e){function r(e){if(e.length<=0)return t.segments({inverted:!1,regions:[]});function r(e){var r=e.slice(0,e.length-1);return t.segments({inverted:!1,regions:[r]})}for(var n=r(e[0]),i=1;i<e.length;i++)n=t.selectDifference(t.combine(n,r(e[i])));return n}if(\"Polygon\"===e.type)return t.polygon(r(e.coordinates));if(\"MultiPolygon\"===e.type){for(var n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error(\"PolyBool: Cannot convert GeoJSON object to PolyBool polygon\")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++){if(n(e,(s=t.children[r]).region))return void o(s,e)}var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function c(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],484:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":485}],485:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],486:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],487:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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k<0?(b=f+p)>(x=u+h)?(_=b-x)>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d:(k=0,b<=0?(A=1,y=f+2*p+d):p>=0?(A=0,y=d):y=p*(A=-p/f)+d):A<0?(b=c+h)>(x=u+p)?(_=b-x)>=(w=c-2*u+f)?(A=1,k=0,y=f+2*p+d):y=(k=1-(A=_/w))*(c*k+u*A+2*h)+A*(u*k+f*A+2*p)+d:(A=0,b<=0?(k=1,y=c+2*h+d):h>=0?(k=0,y=d):y=h*(k=-h/c)+d):(_=f+p-u-h)<=0?(k=0,A=1,y=f+2*p+d):_>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d;var S=1-k-A;for(l=0;l<o.length;++l)s[l]=S*t[l]+k*e[l]+A*r[l];return y<0?0:y}},{}],489:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace(this,this.constructor),this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:543,\"stream-parser\":558}],490:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],491:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return\"avif\"===e||\"avis\"===e||\"avio\"===e?{type:\"avif\",mime:\"image/avif\"}:\"heic\"===e||\"heix\"===e?{type:\"heic\",mime:\"image/heic\"}:\"hevc\"===e||\"hevx\"===e?{type:\"heic\",mime:\"image/heic-sequence\"}:r.avif||r.avis?{type:\"avif\",mime:\"image/avif\"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:\"heif\",mime:\"image/heif-sequence\"}:{type:\"heif\",mime:\"image/heif\"}:{type:\"avif\",mime:\"image/avif\"}}},{\"./common\":489}],492:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=t(\"../miaf_utils\"),s=t(\"../exif_utils\"),l=n(\"ftyp\");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,\"mdat\"===u.boxtype)return;if(\"meta\"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:\"px\",hUnits:\"px\"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{\"../common\":489,\"../exif_utils\":490,\"../miaf_utils\":491}],493:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"BM\");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:\"bmp\",mime:\"image/bmp\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":489}],494:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"GIF87a\"),s=n(\"GIF89a\");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":489}],495:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}},{\"../common\":489}],496:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16BE,i=t(\"../common\").str2arr,a=t(\"../common\").sliceEq,o=t(\"../exif_utils\"),s=i(\"Exif\\0\\0\");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1])for(var e=2;;){if(t.length-e<2)return;if(255!==t[e++])return;for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:\"jpg\",mime:\"image/jpeg\",wUnits:\"px\",hUnits:\"px\"};return i>0&&(c.orientation=i),c}e+=r}}},{\"../common\":489,\"../exif_utils\":490}],497:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"\\x89PNG\\r\\n\\x1a\\n\"),s=n(\"IHDR\");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:\"png\",mime:\"image/png\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":489}],498:[function(t,e,r){\"use strict\";var n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"8BPS\\0\\x01\");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:\"psd\",mime:\"image/vnd.adobe.photoshop\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":489}],499:[function(t,e,r){\"use strict\";function n(t){return\"number\"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\\s/,o=/[^-]\\bwidth=\"([^%]+?)\"|[^-]\\bwidth='([^%]+?)'/,s=/\\bheight=\"([^%]+?)\"|\\bheight='([^%]+?)'/,l=/\\bview[bB]ox=\"(.+?)\"|\\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:\"px\"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e=\"\",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[\"\"])[0];if(a.test(c)){var f=function(t){var e=t.match(o),r=t.match(s),n=t.match(l);return{width:e&&(e[1]||e[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}(c),h=parseFloat(f.width),p=parseFloat(f.height);if(f.width&&f.height){if(!n(h)||!n(p))return;return{width:h,height:p,type:\"svg\",mime:\"image/svg+xml\",wUnits:u(f.width),hUnits:u(f.height)}}var d=(f.viewbox||\"\").split(\" 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bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\",splitNull:\"splitNull\"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:r.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return 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l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":243,\"gl-mat4/invert\":287,\"gl-mat4/rotate\":293,\"gl-vec3/cross\":350,\"gl-vec3/dot\":355,\"gl-vec3/normalize\":372}],577:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":617,\"object-assign\":466}],606:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":617,\"object-assign\":466}],609:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":617,\"object-assign\":466}],610:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":617,\"object-assign\":466}],611:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":617,\"object-assign\":466}],612:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},{\"../main\":617,\"object-assign\":466}],615:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":776,\"../../plot_api/plot_template\":816,\"../../registry\":904}],625:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":776,\"../color\":639}],626:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":804,\"fast-isnumeric\":242}],627:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var x=\"pixel\"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+\"anchor\"),a(m+\"shift\")}if(n.noneOrAll(t,e,[\"x\",\"y\"]),u&&n.noneOrAll(t,e,[\"ax\",\"ay\"]),c){var b=a(\"xclick\"),_=a(\"yclick\");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:\"annotations\",handleItemDefaults:l})}},{\"../../lib\":776,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/axes\":827,\"./attributes\":622,\"./common_defaults\":625}],628:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../registry\"),a=t(\"../../plots/plots\"),o=t(\"../../lib\"),s=o.strTranslate,l=t(\"../../plots/cartesian/axes\"),c=t(\"../color\"),u=t(\"../drawing\"),f=t(\"../fx\"),h=t(\"../../lib/svg_text_utils\"),p=t(\"../../lib/setcursor\"),d=t(\"../dragelement\"),m=t(\"../../plot_api/plot_template\").arrayEditor,g=t(\"./draw_arrow_head\");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var 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n=t(\"../annotations/attributes\"),i=t(\"../../plot_api/edit_types\").overrideAll,a=t(\"../../plot_api/plot_template\").templatedArray;e.exports=i(a(\"annotation\",{visible:n.visible,x:{valType:\"any\"},y:{valType:\"any\"},z:{valType:\"any\"},ax:{valType:\"number\"},ay:{valType:\"number\"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),\"calc\",\"from-root\")},{\"../../plot_api/edit_types\":809,\"../../plot_api/plot_template\":816,\"../annotations/attributes\":622}],632:[function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}function c(t){var n=t+\"axis\",a={_fullLayout:{}};return a._fullLayout[n]=r[n],i.coercePosition(e,a,l,t,t,.5)}l(\"visible\")&&(o(t,e,a.fullLayout,l),c(\"x\"),c(\"y\"),c(\"z\"),n.noneOrAll(t,e,[\"x\",\"y\",\"z\"]),e.xref=\"x\",e.yref=\"y\",e.zref=\"z\",l(\"xanchor\"),l(\"yanchor\"),l(\"xshift\"),l(\"yshift\"),e.showarrow&&(e.axref=\"pixel\",e.ayref=\"pixel\",l(\"ax\",-10),l(\"ay\",-30),n.noneOrAll(t,e,[\"ax\",\"ay\"])))}e.exports=function(t,e,r){a(t,e,{name:\"annotations\",handleItemDefaults:l,fullLayout:r.fullLayout})}},{\"../../lib\":776,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/axes\":827,\"../annotations/common_defaults\":625,\"./attributes\":631}],634:[function(t,e,r){\"use strict\";var n=t(\"../annotations/draw\").drawRaw,i=t(\"../../plots/gl3d/project\"),a=[\"x\",\"y\",\"z\"];e.exports=function(t){for(var e=t.fullSceneLayout,r=t.dataScale,o=e.annotations,s=0;s<o.length;s++){for(var l=o[s],c=!1,u=0;u<3;u++){var 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m={xcalendar:p(d(\"x\"))},g=i.extendFlat({},m,{ycalendar:p(d(\"y\"))}),v=i.extendFlat({},g,{zcalendar:p(d(\"z\"))}),y=p([\"Sets the calendar system to use for `range` and `tick0`\",\"if this is a date axis. 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i=0;i<r.length;i++)c(t,e,r[i]+\"calendar\",n.calendar)},CANONICAL_SUNDAY:{chinese:\"2000-01-02\",coptic:\"2000-01-03\",discworld:\"2000-01-03\",ethiopian:\"2000-01-05\",hebrew:\"5000-01-01\",islamic:\"1000-01-02\",julian:\"2000-01-03\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-05\",nepali:\"2000-01-05\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-04\",thai:\"2000-01-04\",ummalqura:\"1400-01-06\"},CANONICAL_TICK:{chinese:\"2000-01-01\",coptic:\"2000-01-01\",discworld:\"2000-01-01\",ethiopian:\"2000-01-01\",hebrew:\"5000-01-01\",islamic:\"1000-01-01\",julian:\"2000-01-01\",mayan:\"5000-01-01\",nanakshahi:\"1000-01-01\",nepali:\"2000-01-01\",persian:\"1000-01-01\",jalali:\"1000-01-01\",taiwan:\"1000-01-01\",thai:\"2000-01-01\",ummalqura:\"1400-01-01\"},DFLTRANGE:{chinese:[\"2000-01-01\",\"2001-01-01\"],coptic:[\"1700-01-01\",\"1701-01-01\"],discworld:[\"1800-01-01\",\"1801-01-01\"],ethiopian:[\"2000-01-01\",\"2001-01-01\"],hebrew:[\"5700-01-01\",\"5701-01-01\"],islamic:[\"1400-01-01\",\"1401-01-01\"],julian:[\"2000-01-01\",\"2001-01-01\"],mayan:[\"5200-01-01\",\"5201-01-01\"],nanakshahi:[\"0500-01-01\",\"0501-01-01\"],nepali:[\"2000-01-01\",\"2001-01-01\"],persian:[\"1400-01-01\",\"1401-01-01\"],jalali:[\"1400-01-01\",\"1401-01-01\"],taiwan:[\"0100-01-01\",\"0101-01-01\"],thai:[\"2500-01-01\",\"2501-01-01\"],ummalqura:[\"1400-01-01\",\"1401-01-01\"]},getCal:h,worldCalFmt:function(t,e,r){for(var 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B,V,H,q,G,Y,W,X,Z,J,K,Q,$,tt=[],et=[],rt={hLinePoint:null,vLinePoint:null},nt=!1;if(Array.isArray(e))for(S=\"array\",H=0;H<e.length;H++)(G=t.calcdata[e[H].curveNumber||0])&&(Y=G[0].trace,\"skip\"!==G[0].trace.hoverinfo&&(et.push(G),\"h\"===Y.orientation&&(nt=!0)));else{for(q=0;q<t.calcdata.length;q++)G=t.calcdata[q],\"skip\"!==(Y=G[0].trace).hoverinfo&&v.isTraceInSubplots(Y,s)&&(et.push(G),\"h\"===Y.orientation&&(nt=!0));var it,at;if(!e.target)it=\"xpx\"in e?e.xpx:b[0]._length/2,at=\"ypx\"in e?e.ypx:_[0]._length/2;else{if(!1===c.triggerHandler(t,\"plotly_beforehover\",e))return;var ot=e.composedPath&&e.composedPath()[0];ot||(ot=e.target);var st=ot.getBoundingClientRect();it=e.clientX-st.left,at=e.clientY-st.top,l._calcInverseTransform(t);var lt=o.apply3DTransform(l._invTransform)(it,at);if(it=lt[0],at=lt[1],it<0||it>b[0]._length||at<0||at>_[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=it+b[0]._offset,e.pointerY=at+_[0]._offset,B=\"xval\"in e?v.flat(s,e.xval):v.p2c(b,it),V=\"yval\"in e?v.flat(s,e.yval):v.p2c(_,at),!i(B[0])||!i(V[0]))return o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(q=0;q<et.length;q++)if((G=et[q])&&G[0]&&G[0].trace&&!0===(Y=G[0].trace).visible&&0!==Y._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(Y._module.name)){if(\"splom\"===Y.type?W=s[X=0]:(W=v.getSubplot(Y),X=s.indexOf(W)),Z=S,v.isUnifiedHover(Z)&&(Z=Z.charAt(0)),Q={cd:G,trace:Y,xa:b[X],ya:_[X],maxHoverDistance:P,maxSpikeDistance:O,index:!1,distance:Math.min(ct,P),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:Y.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},l[W]&&(Q.subplot=l[W]._subplot),l._splomScenes&&l._splomScenes[Y.uid]&&(Q.scene=l._splomScenes[Y.uid]),$=tt.length,\"array\"===Z){var n=e[q];\"pointNumber\"in n?(Q.index=n.pointNumber,Z=\"closest\"):(Z=\"\",\"xval\"in n&&(J=n.xval,Z=\"x\"),\"yval\"in 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x=y[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!rt.hLinePoint||rt.hLinePoint.spikeDistance>h.spikeDistance)&&(rt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:l,container:l._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:rt.vLinePoint,hLinePoint:rt.hLinePoint};t._spikepoints=mt;var gt=function(){tt.sort((function(t,e){return t.distance-e.distance})),tt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(tt,S)};gt();var vt=S.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&tt[0]&&L[tt[0].trace.type];if(m&&0!==O&&0!==tt.length){var xt=ft(tt.filter((function(t){return t.ya.showspikes})),O,yt);rt.hLinePoint=ht(xt);var bt=ft(tt.filter((function(t){return t.xa.showspikes})),O,yt);rt.vLinePoint=ht(bt)}if(0===tt.length){var _t=d.unhoverRaw(t,e);return!m||null===rt.hLinePoint&&null===rt.vLinePoint||F(dt)&&R(t,rt,pt),_t}m&&F(dt)&&R(t,rt,pt);if(v.isXYhover(Z)&&0!==tt[0].length&&\"splom\"!==tt[0].trace.type){var wt=tt[0],Tt=(tt=E[wt.trace.type]?tt.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,l),At=N(\"y\",wt,l);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(tt[Mt]);for(Mt=tt.length-1;Mt>Tt-1;Mt--)Ct(tt[Mt]);tt=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(H=0;H<tt.length;H++){var Dt=tt[H],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===S&&(et.length>1||tt.length>1)||\"closest\"===S&&nt&&tt.length>1,Wt=p.combine(l.plot_bgcolor||p.background,l.paper_bgcolor),Xt=I(tt,{gd:t,hovermode:S,rotateLabels:Yt,bgColor:Wt,container:l._hoverlayer,outerContainer:l._paper.node(),commonLabelOpts:l.hoverlabel,hoverdistance:l.hoverdistance});v.isUnifiedHover(S)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",l),z(Xt,Yt,l._invScaleX,l._invScaleY));if(e.target&&e.target.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(e.target),Zt?\"pointer\":\"\")}if(!e.target||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var 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c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{},T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var 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r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if(\"string\"==typeof i){if(void 0===l[i])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"sankey\"),A=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),M=o(e.hovermode),S=[];function 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t.match(n.segmentRE).forEach((function(t){var a=e[t.charAt(0)].drawn;if(void 0!==a){var o=t.substr(1).match(n.paramRE);!o||o.length<a||r.push(i.cleanNumber(o[a]))}})),r},r.getDataToPixel=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){return e._length*(n?1-t:t)+e._offset};else{var s=r.shapePositionToRange(e);a=function(t){return e._offset+e.r2p(s(t,!0))},\"date\"===e.type&&(a=r.decodeDate(a))}else a=n?function(t){return o.t+o.h*(1-t)}:function(t){return o.l+o.w*t};return a},r.getPixelToData=function(t,e,n,i){var a,o=t._fullLayout._size;if(e)if(\"domain\"===i)a=function(t){var r=(t-e._offset)/e._length;return n?1-r:r};else{var s=r.rangeToShapePosition(e);a=function(t){return s(e.p2r(t-e._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},r.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},r.makeOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return!!n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}}},{\"../../lib\":776,\"./constants\":721}],731:[function(t,e,r){\"use strict\";var n=t(\"./draw\");e.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),supplyDrawNewShapeDefaults:t(\"./draw_newshape/defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"shapes\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne}},{\"../../plots/cartesian/include_components\":840,\"./attributes\":719,\"./calc_autorange\":720,\"./defaults\":722,\"./draw\":723,\"./draw_newshape/defaults\":726}],732:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/pad_attributes\"),a=t(\"../../lib/extend\").extendDeepAll,o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../plots/animation_attributes\"),l=t(\"../../plot_api/plot_template\").templatedArray,c=t(\"./constants\"),u=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});e.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:u,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:c.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:c.railBgColor},bordercolor:{valType:\"color\",dflt:c.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:c.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:c.tickLength},tickcolor:{valType:\"color\",dflt:c.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:c.minorTickLength}}),\"arraydraw\",\"from-root\")},{\"../../lib/extend\":766,\"../../plot_api/edit_types\":809,\"../../plot_api/plot_template\":816,\"../../plots/animation_attributes\":821,\"../../plots/font_attributes\":856,\"../../plots/pad_attributes\":889,\"./constants\":733}],733:[function(t,e,r){\"use strict\";e.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],734:[function(t,e,r){\"use 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return 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a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&k(t,e,r,o,!0,i)}function k(t,e,r,n,a,o){var s=r.active;r.active=n,u(t.layout,f.name,r).applyUpdate(\"active\",n);var l=r.steps[r.active];e.call(S,r,o),e.call(x,r),t.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame((function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)}))))}function A(t,e,r){var i=r.node(),o=n.select(e);function s(){return r.data()[0]}function l(){var t=s();e.emit(\"plotly_sliderstart\",{slider:t});var l=r.select(\".\"+f.gripRectClass);n.event.stopPropagation(),n.event.preventDefault(),l.call(a.fill,t.activebgcolor);var c=L(t,n.mouse(i)[0]);function u(){var 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1===t.length?{type:\"LineString\",coordinates:t[0]}:{type:\"MultiLineString\",coordinates:t}},r.makePolygon=function(t){if(1===t.length)return{type:\"Polygon\",coordinates:t};for(var e=new Array(t.length),r=0;r<t.length;r++)e[r]=[t[r]];return{type:\"MultiPolygon\",coordinates:e}},r.makeBlank=function(){return{type:\"Point\",coordinates:[]}}},{\"../constants/numerical\":752}],771:[function(t,e,r){\"use strict\";var n,i,a,o=t(\"./mod\").mod;function s(t,e,r,n,i,a,o,s){var l=r-t,c=i-t,u=o-i,f=n-e,h=a-e,p=s-a,d=l*p-u*f;if(0===d)return null;var m=(c*p-u*h)/d,g=(c*f-l*h)/d;return g<0||g>1||m<0||m>1?null:{x:t+l*m,y:e+f*m}}function l(t,e,r,n,i){var a=n*t+i*e;if(a<0)return n*n+i*i;if(a>r){var o=n-t,s=i-e;return o*o+s*s}var l=n*e-i*t;return l*l/r}r.segmentsIntersect=s,r.segmentDistance=function(t,e,r,n,i,a,o,c){if(s(t,e,r,n,i,a,o,c))return 0;var u=r-t,f=n-e,h=o-i,p=c-a,d=u*u+f*f,m=h*h+p*p,g=Math.min(l(u,f,d,i-t,a-e),l(u,f,d,o-t,c-e),l(h,p,m,t-i,e-a),l(h,p,m,r-i,n-a));return 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n=t(\"@plotly/d3\"),i=t(\"d3-time-format\").utcFormat,a=t(\"d3-format\").format,o=t(\"fast-isnumeric\"),s=t(\"../constants/numerical\"),l=s.FP_SAFE,c=-l,u=s.BADNUM,f=e.exports={};f.adjustFormat=function(t){return!t||/^\\d[.]\\df/.test(t)||/[.]\\d%/.test(t)?t:\"0.f\"===t?\"~f\":/^\\d%/.test(t)?\"~%\":/^\\ds/.test(t)?\"~s\":!/^[~,.0$]/.test(t)&&/[&fps]/.test(t)?\"~\"+t:t};var h={};f.warnBadFormat=function(t){var e=String(t);h[e]||(h[e]=1,f.warn('encountered bad format: \"'+e+'\"'))},f.noFormat=function(t){return String(t)},f.numberFormat=function(t){var e;try{e=a(f.adjustFormat(t))}catch(e){return f.warnBadFormat(t),f.noFormat}return e},f.nestedProperty=t(\"./nested_property\"),f.keyedContainer=t(\"./keyed_container\"),f.relativeAttr=t(\"./relative_attr\"),f.isPlainObject=t(\"./is_plain_object\"),f.toLogRange=t(\"./to_log_range\"),f.relinkPrivateKeys=t(\"./relink_private\");var 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l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":834,\"has-passive-events\":426,\"mouse-change\":449,\"mouse-event-offset\":450,\"mouse-wheel\":452}],866:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":801,\"../cartesian/axes\":827}],867:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":680,\"../../constants/xmlns_namespaces\":753,\"../../plot_api/edit_types\":809,\"../cartesian\":841,\"../cartesian/attributes\":825,\"../cartesian/constants\":834,\"../get_data\":864,\"../layout_attributes\":881,\"./scene2d\":868}],868:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"gl-plot2d\"),c=t(\"gl-spikes2d\"),u=t(\"gl-select-box\"),f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../components/dragelement/helpers\":657,\"../../components/fx\":679,\"../../lib/show_no_webgl_msg\":798,\"../../plots/cartesian/axes\":827,\"../../registry\":904,\"../cartesian/autorange\":826,\"../cartesian/constants\":834,\"../cartesian/constraints\":835,\"./camera\":865,\"./convert\":866,\"gl-plot2d\":309,\"gl-select-box\":321,\"gl-spikes2d\":330,\"webgl-context\":602}],869:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":680,\"../../constants/xmlns_namespaces\":753,\"../../lib\":776,\"../../plot_api/edit_types\":809,\"../get_data\":864,\"./layout/attributes\":870,\"./layout/defaults\":874,\"./layout/layout_attributes\":875,\"./scene\":879}],870:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],871:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":639,\"../../../lib/extend\":766,\"../../../plot_api/edit_types\":809,\"../../cartesian/layout_attributes\":842}],872:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":776,\"../../../plot_api/plot_template\":816,\"../../cartesian/axis_defaults\":829,\"../../cartesian/type_defaults\":853,\"./axis_attributes\":871,tinycolor2:572}],873:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":776,\"../../../lib/str2rgbarray\":801}],874:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":639,\"../../../lib\":776,\"../../../registry\":904,\"../../get_data\":864,\"../../subplot_defaults\":898,\"./axis_defaults\":872,\"./layout_attributes\":875}],875:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":776,\"../../../lib/extend\":766,\"../../domain\":855,\"./axis_attributes\":871}],876:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":801}],877:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":776,\"../../cartesian/axes\":827}],878:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],879:[function(t,e,r){\"use strict\";var n,i,a=t(\"gl-plot3d\"),o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,m=e.glplot.selection,g=0;g<u.length;++g)\"skip\"!==(t=e.traces[u[g]]).data.hoverinfo&&t.handlePick(m)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==h){var x=v(e.glplot.cameraParams,m.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=m.index,k={xLabel:y(\"x\",m.traceCoordinate[0],t.xhoverformat),yLabel:y(\"y\",m.traceCoordinate[1],t.yhoverformat),zLabel:y(\"z\",m.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(w,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;w.hovertemplate||S||(-1===M.indexOf(\"x\")&&(k.xLabel=void 0),-1===M.indexOf(\"y\")&&(k.yLabel=void 0),-1===M.indexOf(\"z\")&&(k.zLabel=void 0),-1===M.indexOf(\"text\")&&(m.textLabel=void 0),-1===M.indexOf(\"name\")&&(h.name=void 0));var E=[];\"cone\"===t.type||\"streamtube\"===t.type?(k.uLabel=y(\"x\",m.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf(\"u\"))&&E.push(\"u: \"+k.uLabel),k.vLabel=y(\"y\",m.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf(\"v\"))&&E.push(\"v: \"+k.vLabel),k.wLabel=y(\"z\",m.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf(\"w\"))&&E.push(\"w: \"+k.wLabel),k.normLabel=m.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf(\"norm\"))&&E.push(\"norm: \"+k.normLabel),\"streamtube\"===t.type&&(k.divergenceLabel=m.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf(\"divergence\"))&&E.push(\"divergence: \"+k.divergenceLabel)),m.textLabel&&E.push(m.textLabel),_=E.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(m.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),m.textLabel&&E.push(m.textLabel),_=E.join(\"<br>\")):_=m.textLabel;var L={x:m.traceCoordinate[0],y:m.traceCoordinate[1],z:m.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(L,w,T),t._module.eventData&&(L=w._module.eventData(L,m,w,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,\"bgcolor\")||h.color,borderColor:d.castHoverOption(w,T,\"bordercolor\"),fontFamily:d.castHoverOption(w,T,\"font.family\"),fontSize:d.castHoverOption(w,T,\"font.size\"),fontColor:d.castHoverOption(w,T,\"font.color\"),nameLength:d.castHoverOption(w,T,\"namelength\"),textAlign:d.castHoverOption(w,T,\"align\"),hovertemplate:f.castOption(w,T,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}m.buttons&&m.distance<5?r.emit(\"plotly_click\",C):r.emit(\"plotly_hover\",C),c=C}else d.loneUnhover(n),r.emit(\"plotly_unhover\",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var 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s,l=o.getContext(\"2d\"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(this._mockAxis,t)},e.exports=_},{\"../../components/fx\":679,\"../../lib\":776,\"../../lib/show_no_webgl_msg\":798,\"../../lib/str2rgbarray\":801,\"../../plots/cartesian/axes\":827,\"../../registry\":904,\"./layout/convert\":873,\"./layout/spikes\":876,\"./layout/tick_marks\":877,\"./project\":878,\"gl-plot3d\":312,\"has-passive-events\":426,\"webgl-context\":602}],880:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],881:[function(t,e,r){\"use strict\";var n=t(\"./font_attributes\"),i=t(\"./animation_attributes\"),a=t(\"../components/color/attributes\"),o=t(\"../components/shapes/draw_newshape/attributes\"),s=t(\"./pad_attributes\"),l=t(\"../lib/extend\").extendFlat,c=n({editType:\"calc\"});c.family.dflt='\"Open Sans\", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:\"string\",editType:\"layoutstyle\"},font:n({editType:\"layoutstyle\"}),xref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yref:{valType:\"enumerated\",dflt:\"container\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},x:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"layoutstyle\"},y:{valType:\"number\",min:0,max:1,dflt:\"auto\",editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"left\",\"center\",\"right\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",dflt:\"auto\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],editType:\"layoutstyle\"},pad:l(s({editType:\"layoutstyle\"}),{}),editType:\"layoutstyle\"},uniformtext:{mode:{valType:\"enumerated\",values:[!1,\"hide\",\"show\"],dflt:!1,editType:\"plot\"},minsize:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"},autosize:{valType:\"boolean\",dflt:!1,editType:\"none\"},width:{valType:\"number\",min:10,dflt:700,editType:\"plot\"},height:{valType:\"number\",min:10,dflt:450,editType:\"plot\"},margin:{l:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},r:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},t:{valType:\"number\",min:0,dflt:100,editType:\"plot\"},b:{valType:\"number\",min:0,dflt:80,editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},autoexpand:{valType:\"boolean\",dflt:!0,editType:\"plot\"},editType:\"plot\"},computed:{valType:\"any\",editType:\"none\"},paper_bgcolor:{valType:\"color\",dflt:a.background,editType:\"plot\"},plot_bgcolor:{valType:\"color\",dflt:a.background,editType:\"layoutstyle\"},autotypenumbers:{valType:\"enumerated\",values:[\"convert types\",\"strict\"],dflt:\"convert types\",editType:\"calc\"},separators:{valType:\"string\",editType:\"plot\"},hidesources:{valType:\"boolean\",dflt:!1,editType:\"plot\"},showlegend:{valType:\"boolean\",editType:\"legend\"},colorway:{valType:\"colorlist\",dflt:a.defaults,editType:\"calc\"},datarevision:{valType:\"any\",editType:\"calc\"},uirevision:{valType:\"any\",editType:\"none\"},editrevision:{valType:\"any\",editType:\"none\"},selectionrevision:{valType:\"any\",editType:\"none\"},template:{valType:\"any\",editType:\"calc\"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:\"any\",arrayOk:!0,editType:\"plot\"},transition:l({},i.transition,{editType:\"none\"}),_deprecated:{title:{valType:\"string\",editType:\"layoutstyle\"},titlefont:n({editType:\"layoutstyle\"})}}},{\"../components/color/attributes\":638,\"../components/shapes/draw_newshape/attributes\":724,\"../lib/extend\":766,\"./animation_attributes\":821,\"./font_attributes\":856,\"./pad_attributes\":889}],882:[function(t,e,r){\"use strict\";var n=t(\"../../lib/sort_object_keys\"),i='\\xa9 <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors',a=['\\xa9 <a target=\"_blank\" href=\"https://carto.com/\">Carto</a>',i].join(\" \"),o=['Map tiles by <a target=\"_blank\" href=\"https://stamen.com\">Stamen Design</a>','under <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0\">CC BY 3.0</a>',\"|\",'Data by <a target=\"_blank\" href=\"https://openstreetmap.org\">OpenStreetMap</a> contributors','under <a target=\"_blank\" href=\"https://www.openstreetmap.org/copyright\">ODbL</a>'].join(\" \"),s={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:i,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:a,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:o,tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map 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m=c.select(s._subplot.div);if(!(null===m.select(\".mapboxgl-ctrl-logo\").node().offsetParent)){var g=e._glimages.append(\"g\");g.attr(\"transform\",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append(\"path\").attr(\"d\",p.mapboxLogo.path0).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"}),g.append(\"path\").attr(\"d\",p.mapboxLogo.path1).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"path\").attr(\"d\",p.mapboxLogo.path2).style(\"opacity\",.35).style(\"enable-background\",\"new\"),g.append(\"polygon\").attr(\"points\",p.mapboxLogo.polygon).style({opacity:.9,fill:\"#ffffff\",\"enable-background\":\"new\"})}var v=m.select(\".mapboxgl-ctrl-attrib\").text().replace(\"Improve this map\",\"\"),y=e._glimages.append(\"g\"),x=y.append(\"text\");x.text(v).classed(\"static-attribution\",!0).attr({\"font-size\":12,\"font-family\":\"Arial\",color:\"rgba(0, 0, 0, 0.75)\",\"text-anchor\":\"end\",\"data-unformatted\":v});var 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n=t(\"../../lib\"),i=t(\"../../lib/svg_text_utils\").sanitizeHTML,a=t(\"./convert_text_opts\"),o=t(\"./constants\");function s(t,e){this.subplot=t,this.uid=t.uid+\"-\"+e,this.index=e,this.idSource=\"source-\"+this.uid,this.idLayer=o.layoutLayerPrefix+this.uid,this.sourceType=null,this.source=null,this.layerType=null,this.below=null,this.visible=!1}var l=s.prototype;function c(t){if(!t.visible)return!1;var e=t.source;if(Array.isArray(e)&&e.length>0){for(var r=0;r<e.length;r++)if(\"string\"!=typeof e[r]||0===e[r].length)return!1;return!0}return n.isPlainObject(e)||\"string\"==typeof e&&e.length>0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates);a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution));return a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r<e.length;r++){var n=e[r].id;if(\"string\"==typeof n&&0===n.indexOf(o.traceLayerPrefix)){t=n;break}}return t},l.updateLayer=function(t){var e=this.subplot,r=u(t),n=this.lookupBelow(),i=this.findFollowingMapboxLayerId(n);this.removeLayer(),c(t)&&e.addLayer({id:this.idLayer,source:this.idSource,\"source-layer\":t.sourcelayer||\"\",type:t.type,minzoom:t.minzoom,maxzoom:t.maxzoom,layout:r.layout,paint:r.paint},i),this.layerType=t.type,this.below=n},l.updateStyle=function(t){if(c(t)){var 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n=t(\"mapbox-gl/dist/mapbox-gl-unminified\"),i=t(\"../../lib\"),a=t(\"../../lib/geo_location_utils\"),o=t(\"../../registry\"),s=t(\"../cartesian/axes\"),l=t(\"../../components/dragelement\"),c=t(\"../../components/fx\"),u=t(\"../../components/dragelement/helpers\"),f=u.rectMode,h=u.drawMode,p=u.selectMode,d=t(\"../cartesian/select\").prepSelect,m=t(\"../cartesian/select\").clearSelect,g=t(\"../cartesian/select\").clearSelectionsCache,v=t(\"../cartesian/select\").selectOnClick,y=t(\"./constants\"),x=t(\"./layers\");function b(t,e){this.id=e,this.gd=t;var r=t._fullLayout,n=t._context;this.container=r._glcontainer.node(),this.isStatic=n.staticPlot,this.uid=r._uid+\"-\"+this.id,this.div=null,this.xaxis=null,this.yaxis=null,this.createFramework(r),this.map=null,this.accessToken=null,this.styleObj=null,this.traceHash={},this.layerList=[],this.belowLookup={},this.dragging=!1,this.wheeling=!1}var _=b.prototype;_.plot=function(t,e,r){var n,i=this,a=e[i.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash={},i.layerList=[]),n=i.map?new Promise((function(r,n){i.updateMap(t,e,r,n)})):new Promise((function(r,n){i.createMap(t,e,r,n)})),r.push(n)},_.createMap=function(t,e,r,i){var o=this,s=e[o.id],l=o.styleObj=T(s.style);o.accessToken=s.accesstoken;var c=o.map=new n.Map({container:o.div,style:l.style,center:A(s.center),zoom:s.zoom,bearing:s.bearing,pitch:s.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new n.AttributionControl({compact:!0}));c._canvas.style.left=\"0px\",c._canvas.style.top=\"0px\",o.rejectOnError(i),o.isStatic||o.initFx(t,e);var u=[];u.push(new Promise((function(t){c.once(\"load\",t)}))),u=u.concat(a.fetchTraceGeoData(t)),Promise.all(u).then((function(){o.fillBelowLookup(t,e),o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)})).catch(i)},_.updateMap=function(t,e,r,n){var i=this,o=i.map,s=e[this.id];i.rejectOnError(n);var l=[],c=T(s.style);JSON.stringify(i.styleObj)!==JSON.stringify(c)&&(i.styleObj=c,o.setStyle(c.style),i.traceHash={},l.push(new Promise((function(t){o.once(\"styledata\",t)})))),l=l.concat(a.fetchTraceGeoData(t)),Promise.all(l).then((function(){i.fillBelowLookup(t,e),i.updateData(t),i.updateLayout(e),i.resolveOnRender(r)})).catch(n)},_.fillBelowLookup=function(t,e){var r,n,i=e[this.id].layers,a=this.belowLookup={},o=!1;for(r=0;r<t.length;r++){var s=t[r][0].trace,l=s._module;\"string\"==typeof s.below?n=s.below:l.getBelow&&(n=l.getBelow(s,this)),\"\"===n&&(o=!0),a[\"trace-\"+s.uid]=n||\"\"}for(r=0;r<i.length;r++){var c=i[r];n=\"string\"==typeof c.below?c.below:o?\"traces\":\"\",a[\"layout-\"+r]=n}var u,f,h={};for(u in a)h[n=a[u]]?h[n].push(u):h[n]=[u];for(n in h){var 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strict\";var n=t(\"../../components/color\"),i=t(\"../../plot_api/plot_template\"),a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"../cartesian/tick_label_defaults\"),l=t(\"../cartesian/tick_mark_defaults\"),c=t(\"../cartesian/tick_value_defaults\"),u=t(\"../cartesian/line_grid_defaults\"),f=t(\"./layout_attributes\"),h=[\"aaxis\",\"baxis\",\"caxis\"];function p(t,e,r,a){var o,s,l,c=r(\"bgcolor\"),u=r(\"sum\");a.bgColor=n.combine(c,a.paper_bgcolor);for(var f=0;f<h.length;f++)s=t[o=h[f]]||{},(l=i.newContainer(e,o))._name=o,d(s,l,a,e);var p=e.aaxis,m=e.baxis,g=e.caxis;p.min+m.min+g.min>=u&&(p.min=0,m.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function d(t,e,r,n){var i=f[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var h=o(\"color\"),p=h!==i.color.dflt?h:r.font.color,d=e._name.charAt(0).toUpperCase(),m=\"Component \"+d,g=o(\"title.text\",m);e._hovertitle=g===m?g:d,a.coerceFont(o,\"title.font\",{family:r.font.family,size:a.bigFont(r.font.size),color:p}),o(\"min\"),c(t,e,o,\"linear\"),s(t,e,o,\"linear\",{}),l(t,e,o,{outerTicks:!0}),o(\"showticklabels\")&&(a.coerceFont(o,\"tickfont\",{family:r.font.family,size:r.font.size,color:p}),o(\"tickangle\"),o(\"tickformat\")),u(t,e,o,{dfltColor:h,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),o(\"hoverformat\"),o(\"layer\")}e.exports=function(t,e,r){o(t,e,r,{type:\"ternary\",attributes:f,handleDefaults:p,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../components/color\":639,\"../../lib\":776,\"../../plot_api/plot_template\":816,\"../cartesian/line_grid_defaults\":844,\"../cartesian/tick_label_defaults\":849,\"../cartesian/tick_mark_defaults\":850,\"../cartesian/tick_value_defaults\":851,\"../subplot_defaults\":898,\"./layout_attributes\":901}],903:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,A=t(\"../cartesian/constants\");function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\");var i=this.plotContainer.selectAll(\"g.toplevel\").data(r,String),a=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",(function(t){return\"toplevel \"+t})).each((function(t){var r=n.select(this);e[t]=r,\"frontplot\"===t?r.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?r.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?r.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t||\"bline\"===t||\"cline\"===t?r.append(\"path\"):\"grids\"===t&&a.forEach((function(t){e[t]=r.append(\"g\").classed(\"grid \"+t,!0)}))})),i.order()};var E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var I=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var O=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",O),p.layers.bgrid.attr(\"transform\",O);var z=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",z),p.layers.agrid.attr(\"transform\",z);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this.graphDiv,r=this.id.substr(7)+\"title\",n=this.layers,i=this.aaxis,a=this.baxis,o=this.caxis;if(this.drawAx(i),this.drawAx(a),this.drawAx(o),t){var s=Math.max(i.showticklabels?i.tickfont.size/2:0,(o.showticklabels?.75*o.tickfont.size:0)+(\"outside\"===o.ticks?.87*o.ticklen:0)),c=(a.showticklabels?a.tickfont.size:0)+(\"outside\"===a.ticks?a.ticklen:0)+3;n[\"a-title\"]=b.draw(e,\"a\"+r,{propContainer:i,propName:this.id+\".aaxis.title\",placeholder:l(e,\"Click to enter Component A title\"),attributes:{x:this.x0+this.w/2,y:this.y0-i.title.font.size/3-s,\"text-anchor\":\"middle\"}}),n[\"b-title\"]=b.draw(e,\"b\"+r,{propContainer:a,propName:this.id+\".baxis.title\",placeholder:l(e,\"Click to enter Component B title\"),attributes:{x:this.x0-c,y:this.y0+this.h+.83*a.title.font.size+c,\"text-anchor\":\"middle\"}}),n[\"c-title\"]=b.draw(e,\"c\"+r,{propContainer:o,propName:this.id+\".caxis.title\",placeholder:l(e,\"Click to enter Component C title\"),attributes:{x:this.x0+this.w+c,y:this.y0+this.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this.graphDiv,n=t._name,i=n.charAt(0),a=t._id,s=this.layers[n],l=i+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);this[l]!==c&&(s.selectAll(\".\"+a+\"tick\").remove(),this[l]=c),t.setScale();var u=d.calcTicks(t),f=d.clipEnds(t,u),h=d.makeTransTickFn(t),p=d.getTickSigns(t)[2],m=o.deg2rad(30),g=p*(t.linewidth||1)/2,v=p*t.ticklen,y=this.w,x=this.h,b=\"b\"===i?\"M0,\"+g+\"l\"+Math.sin(m)*v+\",\"+Math.cos(m)*v:\"M\"+g+\",0l\"+Math.cos(m)*v+\",\"+-Math.sin(m)*v,_={a:\"M0,0l\"+x+\",-\"+y/2,b:\"M0,0l-\"+y/2+\",-\"+x,c:\"M0,0l-\"+x+\",\"+y/2}[i];d.drawTicks(r,t,{vals:\"inside\"===t.ticks?f:u,layer:s,path:b,transFn:h,crisp:!1}),d.drawGrid(r,t,{vals:f,layer:this.layers[i+\"grid\"],path:_,transFn:h,crisp:!1}),d.drawLabels(r,t,{vals:u,layer:s,transFn:h,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;z(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),m=(l/2+u)*M.w,g=(1-l/2-c)*M.w,y=(m+g)/2,x=g-m,_=(1-l)*M.h,w=_-x/E;x<A.MINZOOM?(p=f,T.attr(\"d\",v),k.attr(\"d\",\"M0,0Z\")):(p={a:f.a+l*h,b:f.b+c*h,c:f.c+u*h},T.attr(\"d\",v+\"M\"+m+\",\"+_+\"H\"+g+\"L\"+y+\",\"+w+\"L\"+m+\",\"+_+\"Z\"),k.attr(\"d\",\"M\"+r+\",\"+n+\"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2ZM\"+m+\",\"+_+C+\"M\"+g+\",\"+_+P+\"M\"+y+\",\"+w+I)),b||(T.transition().style(\"fill\",d>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){z(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),O&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),O=!1))}function H(t,e){var r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),c=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[c]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(u.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function 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n=t(\"../heatmap/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\"),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=t(\"../../components/colorscale/attributes\"),c=t(\"../../components/drawing/attributes\").dash,u=t(\"../../plots/font_attributes\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../constants/filter_ops\"),p=h.COMPARISON_OPS2,d=h.INTERVAL_OPS,m=i.line;e.exports=f({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:n.hovertemplate,hoverongaps:n.hoverongaps,connectgaps:f({},n.connectgaps,{}),fillcolor:{valType:\"color\",editType:\"calc\"},autocontour:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"contours.start\":void 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n=t(\"../../components/colorscale/defaults\"),i=t(\"./label_defaults\");e.exports=function(t,e,r,a,o){var s,l=r(\"contours.coloring\"),c=\"\";\"fill\"===l&&(s=r(\"contours.showlines\")),!1!==s&&(\"lines\"!==l&&(c=r(\"line.color\",\"#000\")),r(\"line.width\",.5),r(\"line.dash\")),\"none\"!==l&&(!0!==t.showlegend&&(e.showlegend=!1),e._dfltShowLegend=!1,n(t,e,a,r,{prefix:\"\",cLetter:\"z\"})),r(\"line.smoothing\"),i(r,a,c,o)}},{\"../../components/colorscale/defaults\":649,\"./label_defaults\":1016}],1023:[function(t,e,r){\"use strict\";var n=t(\"../heatmap/attributes\"),i=t(\"../contour/attributes\"),a=t(\"../../components/colorscale/attributes\"),o=t(\"../../lib/extend\").extendFlat,s=i.contours;e.exports=o({carpet:{valType:\"string\",editType:\"calc\"},z:n.z,a:n.x,a0:n.x0,da:n.dx,b:n.y,b0:n.y0,db:n.dy,text:n.text,hovertext:n.hovertext,transpose:n.transpose,atype:n.xtype,btype:n.ytype,fillcolor:i.fillcolor,autocontour:i.autocontour,ncontours:i.ncontours,contours:{type:s.type,start:s.start,end:s.end,size:s.size,coloring:{valType:\"enumerated\",values:[\"fill\",\"lines\",\"none\"],dflt:\"fill\",editType:\"calc\"},showlines:s.showlines,showlabels:s.showlabels,labelfont:s.labelfont,labelformat:s.labelformat,operation:s.operation,value:s.value,editType:\"calc\",impliedEdits:{autocontour:!1}},line:{color:i.line.color,width:i.line.width,dash:i.line.dash,smoothing:i.line.smoothing,editType:\"plot\"},transforms:void 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n=t(\"../../lib\"),i=t(\"../heatmap/xyz_defaults\"),a=t(\"./attributes\"),o=t(\"../contour/constraint_defaults\"),s=t(\"../contour/contours_defaults\"),l=t(\"../contour/style_defaults\");e.exports=function(t,e,r,c){function u(r,i){return n.coerce(t,e,a,r,i)}if(u(\"carpet\"),t.a&&t.b){if(!i(t,e,u,c,\"a\",\"b\"))return void(e.visible=!1);u(\"text\"),\"constraint\"===u(\"contours.type\")?o(t,e,u,c,r,{hasHover:!1}):(s(t,e,u,(function(r){return n.coerce2(t,e,a,r)})),l(t,e,u,c,{hasHover:!1}))}else e._defaultColor=r,e._length=null}},{\"../../lib\":776,\"../contour/constraint_defaults\":1006,\"../contour/contours_defaults\":1008,\"../contour/style_defaults\":1022,\"../heatmap/xyz_defaults\":1072,\"./attributes\":1023}],1026:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../carpet/map_1d_array\"),a=t(\"../carpet/makepath\"),o=t(\"../../components/drawing\"),s=t(\"../../lib\"),l=t(\"../contour/make_crossings\"),c=t(\"../contour/find_all_paths\"),u=t(\"../contour/plot\"),f=t(\"../contour/constants\"),h=t(\"../contour/convert_to_constraints\"),p=t(\"../contour/empty_pathinfo\"),d=t(\"../contour/close_boundaries\"),m=t(\"../carpet/lookup_carpetid\"),g=t(\"../carpet/axis_aligned_line\");function v(t,e,r){var n=t.getPointAtLength(e),i=t.getPointAtLength(r),a=i.x-n.x,o=i.y-n.y,s=Math.sqrt(a*a+o*o);return[a/s,o/s]}function y(t){var e=Math.sqrt(t[0]*t[0]+t[1]*t[1]);return[t[0]/e,t[1]/e]}function x(t,e){var r=Math.abs(t[0]*e[0]+t[1]*e[1]);return Math.sqrt(1-r*r)/r}e.exports=function(t,e,r,b){var _=e.xaxis,w=e.yaxis;s.makeTraceGroups(b,r,\"contour\").each((function(r){var b=n.select(this),T=r[0],k=T.trace,A=k._carpetTrace=m(t,k),M=t.calcdata[A.index][0];if(A.visible&&\"legendonly\"!==A.visible){var 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r=t.marker,i=e.mc||r.color,a=e.mlc||r.line.color,o=e.mlw||r.line.width;if(n(i))return i;if(n(a)&&o)return a}(u,h),[l]}}},{\"../../components/color\":639,\"../../lib\":776,\"../bar/hover\":921}],1044:[function(t,e,r){\"use 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s=r||n.select(t).selectAll(\"g.funnellayer\").selectAll(\"g.trace\");l(t,s,\"funnel\"),s.style(\"opacity\",(function(t){return t[0].trace.opacity})),s.each((function(e){var r=n.select(this),s=e[0].trace;r.selectAll(\".point > path\").each((function(t){if(!t.isBlank){var e=s.marker;n.select(this).call(a.fill,t.mc||e.color).call(a.stroke,t.mlc||e.line.color).call(i.dashLine,e.line.dash,t.mlw||e.line.width).style(\"opacity\",s.selectedpoints&&!t.selected?o:1)}})),c(r,s,t),r.selectAll(\".regions\").each((function(){n.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(a.fill,s.connector.fillcolor)})),r.selectAll(\".lines\").each((function(){var t=s.connector.line;i.lineGroupStyle(n.select(this).selectAll(\"path\"),t.width,t.color,t.dash)}))}))}}},{\"../../components/color\":639,\"../../components/drawing\":661,\"../../constants/interactions\":751,\"../bar/style\":928,\"../bar/uniform_text\":930,\"@plotly/d3\":58}],1049:[function(t,e,r){\"use strict\";var n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,m=c(\"text\"),g=c(\"texttemplate\");if(g||(d=c(\"textinfo\",Array.isArray(m)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),g||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else e.visible=!1}},{\"../../lib\":776,\"../../plots/domain\":855,\"../bar/defaults\":918,\"../pie/defaults\":1168,\"./attributes\":1049}],1053:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"funnelarea\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"funnelarea\",\"showLegend\"],attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\"),style:t(\"./style\"),styleOne:t(\"../pie/style_one\"),meta:{}}},{\"../pie/style_one\":1176,\"./attributes\":1049,\"./base_plot\":1050,\"./calc\":1051,\"./defaults\":1052,\"./layout_attributes\":1054,\"./layout_defaults\":1055,\"./plot\":1056,\"./style\":1057}],1054:[function(t,e,r){\"use strict\";var n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":1172}],1055:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":776,\"./layout_attributes\":1054}],1056:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/drawing\"),a=t(\"../../lib\"),o=a.strScale,s=a.strTranslate,l=t(\"../../lib/svg_text_utils\"),c=t(\"../bar/plot\").toMoveInsideBar,u=t(\"../bar/uniform_text\"),f=u.recordMinTextSize,h=u.clearMinTextSize,p=t(\"../pie/helpers\"),d=t(\"../pie/plot\"),m=d.attachFxHandlers,g=d.determineInsideTextFont,v=d.layoutAreas,y=d.prerenderTitles,x=d.positionTitleOutside,b=d.formatSliceLabel;function _(t,e){return\"l\"+(e[0]-t[0])+\",\"+(e[1]-t[1])}e.exports=function(t,e){var r=t._fullLayout;h(\"funnelarea\",r),y(e,t),v(e,r._size),a.makeTraceGroups(r._funnelarealayer,e,\"trace\").each((function(e){var u=n.select(this),h=e[0],d=h.trace;!function(t){if(!t.length)return;var e=t[0],r=e.trace,n=r.aspectratio,i=r.baseratio;i>.999&&(i=.999);var a,o=Math.pow(i,2),s=e.vTotal,l=s,c=s*o/(1-o)/s;function u(){var t,e={x:t=Math.sqrt(c),y:-t};return[e.x,e.y]}var f,h,p=[];for(p.push(u()),f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var d=h.v/l;c+=d,p.push(u())}var m=1/0,g=-1/0;for(f=0;f<p.length;f++)a=p[f],m=Math.min(m,a[1]),g=Math.max(g,a[1]);for(f=0;f<p.length;f++)p[f][1]-=(g+m)/2;var v=p[p.length-1][0],y=e.r,x=(g-m)/2,b=y/v,_=y/x*n;for(e.r=_*x,f=0;f<p.length;f++)p[f][0]*=b,p[f][1]*=_;var w=[-(a=p[0])[0],a[1]],T=[a[0],a[1]],k=0;for(f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var A=p[k+=1][0],M=p[k][1];h.TL=[-A,M],h.TR=[A,M],h.BL=w,h.BR=T,h.pxmid=(S=h.TR,E=h.BR,[.5*(S[0]+E[0]),.5*(S[1]+E[1])]),w=h.TL,T=h.TR}var S,E}(e),u.each((function(){var u=n.select(this).selectAll(\"g.slice\").data(e);u.enter().append(\"g\").classed(\"slice\",!0),u.exit().remove(),u.each((function(o,s){if(o.hidden)n.select(this).selectAll(\"path,g\").remove();else{o.pointNumber=o.i,o.curveNumber=d.index;var u=h.cx,v=h.cy,y=n.select(this),x=y.selectAll(\"path.surface\").data([o]);x.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),y.call(m,t,e);var w=\"M\"+(u+o.TR[0])+\",\"+(v+o.TR[1])+_(o.TR,o.BR)+_(o.BR,o.BL)+_(o.BL,o.TL)+\"Z\";x.attr(\"d\",w),b(t,o,h);var T=p.castOption(d.textposition,o.pts),k=y.selectAll(\"g.slicetext\").data(o.text&&\"none\"!==T?[0]:[]);k.enter().append(\"g\").classed(\"slicetext\",!0),k.exit().remove(),k.each((function(){var h=a.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),p=a.ensureUniformFontSize(t,g(d,o,r.font));h.text(o.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(i.font,p).call(l.convertToTspans,t);var m,y,x,b=i.bBox(h.node()),_=Math.min(o.BL[1],o.BR[1])+v,w=Math.max(o.TL[1],o.TR[1])+v;y=Math.max(o.TL[0],o.BL[0])+u,x=Math.min(o.TR[0],o.BR[0])+u,(m=c(y,x,_,w,b,{isHorizontal:!0,constrained:!0,angle:0,anchor:\"middle\"})).fontSize=p.size,f(d.type,m,r),e[s].transform=m,h.attr(\"transform\",a.getTextTransform(m))}))}}));var 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0===e.delta.font.size&&(e.delta.font.size=(e._hasNumber?.5:1)*(g||l.defaultNumberFontSize),w[1]=!0),d(\"delta.reference\",e.value),d(\"delta.relative\"),d(\"delta.valueformat\",e.delta.relative?\"2%\":\"\"),d(\"delta.increasing.symbol\"),d(\"delta.increasing.color\"),d(\"delta.decreasing.symbol\"),d(\"delta.decreasing.color\"),d(\"delta.position\"),v=e.delta.font.size),e._scaleNumbers=(!e._hasNumber||w[0])&&(!e._hasDelta||w[1])||!1,d(\"title.font.color\",p.font.color),d(\"title.font.family\",p.font.family),d(\"title.font.size\",.25*(g||v||l.defaultNumberFontSize)),d(\"title.text\"),e._hasGauge){(y=t.gauge)||(y={}),x=o.newContainer(e,\"gauge\"),T(\"shape\"),(e._isBullet=\"bullet\"===e.gauge.shape)||d(\"title.align\",\"center\"),(e._isAngular=\"angular\"===e.gauge.shape)||d(\"align\",\"center\"),T(\"bgcolor\",p.paper_bgcolor),T(\"borderwidth\"),T(\"bordercolor\"),T(\"bar.color\"),T(\"bar.line.color\"),T(\"bar.line.width\"),T(\"bar.thickness\",l.valueThickness*(\"bullet\"===e.gauge.shape?.5:1)),s(y,x,{name:\"steps\",handleItemDefaults:h}),T(\"threshold.value\"),T(\"threshold.thickness\"),T(\"threshold.line.width\"),T(\"threshold.line.color\"),b={},y&&(b=y.axis||{}),_=o.newContainer(x,\"axis\"),k(\"visible\"),e._range=k(\"range\",e._range);var A={outerTicks:!0};c(b,_,k,\"linear\"),f(b,_,k,\"linear\",A),u(b,_,k,A)}else d(\"title.align\",\"center\"),d(\"align\",\"center\"),e._isAngular=e._isBullet=!1;e._length=null}}},{\"../../lib\":776,\"../../plot_api/plot_template\":816,\"../../plots/array_container_defaults\":822,\"../../plots/cartesian/tick_label_defaults\":849,\"../../plots/cartesian/tick_mark_defaults\":850,\"../../plots/cartesian/tick_value_defaults\":851,\"../../plots/domain\":855,\"./attributes\":1120,\"./constants.js\":1123}],1125:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"indicator\",basePlotModule:t(\"./base_plot\"),categories:[\"svg\",\"noOpacity\",\"noHover\"],animatable:!0,attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,calc:t(\"./calc\").calc,plot:t(\"./plot\"),meta:{}}},{\"./attributes\":1120,\"./base_plot\":1121,\"./calc\":1122,\"./defaults\":1124,\"./plot\":1126}],1126:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"d3-interpolate\").interpolate,a=t(\"d3-interpolate\").interpolateNumber,o=t(\"../../lib\"),s=o.strScale,l=o.strTranslate,c=o.rad2deg,u=t(\"../../constants/alignment\").MID_SHIFT,f=t(\"../../components/drawing\"),h=t(\"./constants\"),p=t(\"../../lib/svg_text_utils\"),d=t(\"../../plots/cartesian/axes\"),m=t(\"../../plots/cartesian/axis_defaults\"),g=t(\"../../plots/cartesian/position_defaults\"),v=t(\"../../plots/cartesian/layout_attributes\"),y=t(\"../../components/color\"),x={left:\"start\",center:\"middle\",right:\"end\"},b={left:0,center:.5,right:1},_=/[yzafpn\\xb5mkMGTPEZY]/;function w(t){return t&&t.duration>0}function T(t){t.each((function(t){y.stroke(n.select(this),t.line.color)})).each((function(t){y.fill(n.select(this),t.color)})).style(\"stroke-width\",(function(t){return t.line.width}))}function k(t,e,r){var n=t._fullLayout,i=o.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},e),a={type:\"linear\",_id:\"x\"+e._id},s={letter:\"x\",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,v,t,e)}return m(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+\"x\"+r]}function M(t,e,r,i){var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"text\"),o=n.select(a);return o.text(t).attr(\"x\",0).attr(\"y\",0).attr(\"text-anchor\",r).attr(\"data-unformatted\",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s=\"_cache\"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,m){var g,v=t._fullLayout;w(r)&&m&&(g=m()),o.makeTraceGroups(v._indicatorlayer,e,\"trace\").each((function(e){var m,E,L,C,P,I=e[0].trace,O=n.select(this),z=I._hasGauge,D=I._isAngular,R=I._isBullet,F=I.domain,B={w:v._size.w*(F.x[1]-F.x[0]),h:v._size.h*(F.y[1]-F.y[0]),l:v._size.l+v._size.w*F.x[0],r:v._size.r+v._size.w*(1-F.x[1]),t:v._size.t+v._size.h*(1-F.y[1]),b:v._size.b+v._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,H=I.align||\"center\";if(E=j,z){if(D&&(m=N,E=j+U/2,L=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*V)}),R){var q=h.bulletPadding,G=1-h.bulletNumberDomainSize+q;m=B.l+(G+(1-G)*b[H])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-q)*B.w,B.h)}}}else m=B.l+b[H]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var c,u,h,m=r[0].trace,g=i.numbersX,v=i.numbersY,T=m.align||\"center\",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,\"g\",\"numbers\"),P=[];m._hasNumber&&P.push(\"number\");m._hasDelta&&(P.push(\"delta\"),\"left\"===m.delta.position&&P.reverse());var I=C.selectAll(\"text\").data(P);function O(e,r,n,i){if(!e.match(\"s\")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace(\"s\",\"f\").replace(/\\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}I.enter().append(\"text\"),I.attr(\"text-anchor\",(function(){return A})).attr(\"class\",(function(t){return t})).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),I.exit().remove();var z,D=m.mode+m.align;m._hasDelta&&(z=function(){var e=k(t,{tickformat:m.delta.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=function(t){return m.delta.relative?t.relativeDelta:t.delta},s=function(t,e){return 0===t||\"number\"!=typeof t||isNaN(t)?\"-\":(t>0?m.delta.increasing.symbol:m.delta.decreasing.symbol)+e(t)},l=function(t){return t.delta>=0?m.delta.increasing.color:m.delta.decreasing.color};void 0===m._deltaLastValue&&(m._deltaLastValue=o(r[0]));var c=C.select(\"text.delta\");function h(){c.text(s(o(r[0]),i)).call(y.fill,l(r[0])).call(p.convertToTspans,t)}return c.call(f.font,m.delta.font).call(y.fill,l({delta:m._deltaLastValue})),w(E)?c.transition().duration(E.duration).ease(E.easing).tween(\"text\",(function(){var t=n.select(this),e=o(r[0]),c=m._deltaLastValue,u=O(m.delta.valueformat,i,c,e),f=a(c,e);return m._deltaLastValue=e,function(e){t.text(s(f(e),u)),t.call(y.fill,l({delta:f(e)}))}})).each(\"end\",(function(){h(),L&&L()})).each(\"interrupt\",(function(){h(),L&&L()})):h(),u=M(s(o(r[0]),i),m.delta.font,A,t),c}(),D+=m.delta.position+m.delta.font.size+m.delta.font.family+m.delta.valueformat,D+=m.delta.increasing.symbol+m.delta.decreasing.symbol,h=u);m._hasNumber&&(!function(){var e=k(t,{tickformat:m.number.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=m.number.suffix,s=m.number.prefix,l=C.select(\"text.number\");function u(){var e=\"number\"==typeof r[0].y?s+i(r[0].y)+o:\"-\";l.text(e).call(f.font,m.number.font).call(p.convertToTspans,t)}w(E)?l.transition().duration(E.duration).ease(E.easing).each(\"end\",(function(){u(),L&&L()})).each(\"interrupt\",(function(){u(),L&&L()})).attrTween(\"text\",(function(){var t=n.select(this),e=a(r[0].lastY,r[0].y);m._lastValue=r[0].y;var l=O(m.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(s+l(e(r))+o)}})):u(),c=M(s+i(r[0].y)+o,m.number.font,A,t)}(),D+=m.number.font.size+m.number.font.family+m.number.valueformat+m.number.suffix+m.number.prefix,h=c);if(m._hasDelta&&m._hasNumber){var R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*m.delta.font.size;\"left\"===m.delta.position&&(R=S(m,\"deltaPos\",0,-1*(c.width*b[m.align]+u.width*(1-b[m.align])+j),D,Math.min),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),\"right\"===m.delta.position&&(R=S(m,\"deltaPos\",0,c.width*(1-b[m.align])+u.width*b[m.align]+j,D,Math.max),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),\"bottom\"===m.delta.position&&(R=null,F=u.height,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),\"top\"===m.delta.position&&(R=null,F=c.top,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),z.attr({dx:R,dy:F})}(m._hasNumber||m._hasDelta)&&C.attr(\"transform\",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(m,\"numbersScale\",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];\"center\"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,\"numbersTranslate\",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll(\"g.angular\").data(D?e:[]);Y.exit().remove();var W=O.selectAll(\"g.angularaxis\").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append(\"g\").classed(\"angular\",!0),_.attr(\"transform\",l(b[0],b[1])),A.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),A.selectAll(\"g.xangularaxistick,path,text\").remove(),(o=k(t,p.gauge.axis)).type=\"linear\",o.range=p.gauge.axis.range,o._id=\"xangularaxis\",o.ticklabeloverflow=\"allow\",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h=\"inside\"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:\"M\"+h*R+\",0h\"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+\"rotate(\"+-c(e)+\")\"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll(\"g.bg-arc\").data(F);B.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),B.select(\"path\").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll(\"g.value-arc\").data([p.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var U=j.select(\"path\");w(M)?(U.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll(\"g.threshold-arc\").data(F);Y.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),Y.select(\"path\").call(P).call(T),Y.exit().remove();var W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var I=f.selectAll(\"g.value-bullet\").data([u.gauge.bar]);I.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),I.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?I.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll(\"g.threshold-bullet\").data(O);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(u.gauge.threshold.value)).attr(\"x2\",i.c2p(u.gauge.threshold.value)).attr(\"y1\",(1-u.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style(\"stroke-width\",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll(\"g.gauge-outline\").data([g]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll(\"text.title\").data(e);J.exit().remove(),J.enter().append(\"text\").classed(\"title\",!0),J.attr(\"text-anchor\",(function(){return 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../surface/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat;e.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":647}],1134:[function(t,e,r){\"use strict\";var n=t(\"gl-mesh3d\"),i=t(\"delaunay-triangulate\"),a=t(\"alpha-shape\"),o=t(\"convex-hull\"),s=t(\"../../lib/gl_format_color\").parseColorScale,l=t(\"../../lib/str2rgbarray\"),c=t(\"../../components/colorscale\").extractOpts,u=t(\"../../plots/gl3d/zip3\");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color=\"#fff\";var x=t.intensitymode;v[x+\"Intensity\"]=t.intensity,v[x+\"IntensityBounds\"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new f(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{\"../../components/colorscale\":651,\"../../lib/gl_format_color\":772,\"../../lib/str2rgbarray\":801,\"../../plots/gl3d/zip3\":880,\"alpha-shape\":71,\"convex-hull\":137,\"delaunay-triangulate\":172,\"gl-mesh3d\":303}],1135:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../../components/colorscale/defaults\"),o=t(\"./attributes\");e.exports=function(t,e,r,s){function l(r,n){return i.coerce(t,e,o,r,n)}function c(t){var e=t.map((function(t){var e=l(t);return e&&i.isArrayOrTypedArray(e)?e:null}));return e.every((function(t){return 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n=t(\"../../lib\").extendFlat,i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/drawing/attributes\").dash,s=t(\"../../components/fx/attributes\"),l=t(\"../../constants/delta.js\"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function h(t){return{line:{color:n({},f.color,{dflt:t}),width:f.width,dash:o,editType:\"style\"},editType:\"style\"}}e.exports={xperiod:i.xperiod,xperiod0:i.xperiod0,xperiodalignment:i.xperiodalignment,xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},open:{valType:\"data_array\",editType:\"calc\"},high:{valType:\"data_array\",editType:\"calc\"},low:{valType:\"data_array\",editType:\"calc\"},close:{valType:\"data_array\",editType:\"calc\"},line:{width:n({},f.width,{}),dash:n({},o,{}),editType:\"style\"},increasing:h(c),decreasing:h(u),text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},tickwidth:{valType:\"number\",min:0,max:.5,dflt:.3,editType:\"calc\"},hoverlabel:n({},s.hoverlabel,{split:{valType:\"boolean\",dflt:!1,editType:\"style\"}})}},{\"../../components/drawing/attributes\":660,\"../../components/fx/attributes\":670,\"../../constants/delta.js\":746,\"../../lib\":776,\"../../plots/cartesian/axis_format_attributes\":830,\"../scatter/attributes\":1191}],1138:[function(t,e,r){\"use 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n=t(\"../../lib/extend\").extendFlat,i=t(\"../../plots/attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../components/colorscale/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/domain\").attributes,c=n({editType:\"calc\"},o(\"line\",{editTypeOverride:\"calc\"}),{shape:{valType:\"enumerated\",values:[\"linear\",\"hspline\"],dflt:\"linear\",editType:\"plot\"},hovertemplate:s({editType:\"plot\",arrayOk:!1},{keys:[\"count\",\"probability\"]})});e.exports={domain:l({name:\"parcats\",trace:!0,editType:\"calc\"}),hoverinfo:n({},i.hoverinfo,{flags:[\"count\",\"probability\"],editType:\"plot\",arrayOk:!1}),hoveron:{valType:\"enumerated\",values:[\"category\",\"color\",\"dimension\"],dflt:\"category\",editType:\"plot\"},hovertemplate:s({editType:\"plot\",arrayOk:!1},{keys:[\"count\",\"probability\",\"category\",\"categorycount\",\"colorcount\",\"bandcolorcount\"]}),arrangement:{valType:\"enumerated\",values:[\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"perpendicular\",editType:\"plot\"},bundlecolors:{valType:\"boolean\",dflt:!0,editType:\"plot\"},sortpaths:{valType:\"enumerated\",values:[\"forward\",\"backward\"],dflt:\"forward\",editType:\"plot\"},labelfont:a({editType:\"calc\"}),tickfont:a({editType:\"calc\"}),dimensions:{_isLinkedToArray:\"dimension\",label:{valType:\"string\",editType:\"calc\"},categoryorder:{valType:\"enumerated\",values:[\"trace\",\"category 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E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr(\"d\",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll(\"g.category\").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr(\"transform\",(function(t){return l(t.x,0)})),r(i).attr(\"transform\",(function(t){return l(0,t.y)})),i.select(\".dimlabel\").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(\".catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"x\",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r=\"start\"):(e=-5,r=\"end\"),n.select(this).selectAll(\"tspan\").attr(\"x\",e).attr(\"text-anchor\",r)}));var a=i.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p),o=a.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"cursor\",\"move\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);a.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n=\"all\"===o.hoverinfo?[\"count\",\"probability\"]:(o.hoverinfo||\"\").split(\"+\");var m={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u=\"M \"+t[0]+\",\"+e[0];for(u+=\"l\"+r[0]+\",0 \",s=1;s<r.length;s++)u+=\"C\"+l[s-1]+\",\"+e[s-1]+\" \"+c[s-1]+\",\"+e[s]+\" \"+t[s]+\",\"+e[s],u+=\"l\"+r[s]+\",0 \";for(u+=\"l0,\"+n+\" \",u+=\"l -\"+r[r.length-1]+\",0 \",s=r.length-2;s>=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=g;var k=t.dimensions[T].categories[w],A=k.bands.length,M=k.bands[A-1];if(void 0===M||v.rawColor!==M.rawColor){var S=void 0===M?0:M.y+M.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:g,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[A-1];E.height+=g,E.count+=v.count}}y=\"hspline\"===t.pathShape?B(s,x,l,g,.5):B(s,x,l,g,0),h[m]={key:v.valueInds[0],model:v,height:g,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function j(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(U(t,a))}t.dimensions=r}function U(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"any\",editType:\"plot\"},{valType:\"any\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":646,\"../../lib/extend\":766,\"../../plot_api/plot_template\":816,\"../../plots/cartesian/layout_attributes\":842,\"../../plots/domain\":855,\"../../plots/font_attributes\":856}],1154:[function(t,e,r){\"use 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r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else 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t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);r.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(\".background\").data(o);r.enter().append(\"rect\").classed(\"background\",!0).call(d).call(m).style(\"pointer-events\",\"auto\").attr(\"transform\",l(0,n.verticalPadding)),r.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(\".highlight-shadow\").data(o);i.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(x);var a=t.selectAll(\".highlight\").data(o);a.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),a.attr(\"y1\",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":776,\"../../lib/gup\":773,\"./constants\":1157,\"@plotly/d3\":58}],1155:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":753,\"../../plots/get_data\":864,\"./plot\":1164,\"@plotly/d3\":58}],1156:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{\"../../components/colorscale\":651,\"../../lib\":776,\"../../lib/gup\":773}],1157:[function(t,e,r){\"use 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":776}],1160:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":1153,\"./base_plot\":1155,\"./calc\":1156,\"./defaults\":1158,\"./plot\":1164}],1161:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, colors;\\n\\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\nuniform sampler2D maskTexture, palette;\\n\\nbool isPick    = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n    return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n    return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() {\\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n    float v = colors[3];\\n\\n    gl_Position = position(isContext, v, A, B, C, D);\\n\\n    fragColor =\\n        isContext ? vec4(contextColor) :\\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n    gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function m(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,c=r[64*a+l];63===l&&0===o&&(c*=-1),n[i++]=c}return n}function g(t){var e=\"0\"+t;return e.substr(e.length-2)}function v(t){return t<o?\"p\"+g(t+1)+\"_\"+g(t+4):\"colors\"}function y(t,e,r,n,i,a,o,l,c,u,f,h,p,d){for(var m=[[],[]],g=0;g<64;g++)m[0][g]=g===i?1:0,m[1][g]=g===a?1:0;o*=d,l*=d,c*=d,u*=d;var v=t.lines.canvasOverdrag*d,y=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color;return s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var e={},r=0;r<=o;r+=4)e[v(r)]=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)});return e}(T),L=T.texture(u),C=[];I(e);var P=T({profile:!1,blend:{enable:_,func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:1,dstAlpha:1},equation:{rgb:\"add\",alpha:\"add\"},color:[0,0,0,0]},depth:{enable:!_,mask:!0,func:\"less\",range:[0,1]},cull:{enable:!0,face:\"back\"},scissor:{enable:!0,box:{x:T.prop(\"scissorX\"),y:T.prop(\"scissorY\"),width:T.prop(\"scissorWidth\"),height:T.prop(\"scissorHeight\")}},viewport:{x:T.prop(\"viewportX\"),y:T.prop(\"viewportY\"),width:T.prop(\"viewportWidth\"),height:T.prop(\"viewportHeight\")},dither:!1,vert:i,frag:a,primitive:\"lines\",lineWidth:M,attributes:E,uniforms:{resolution:T.prop(\"resolution\"),viewBoxPos:T.prop(\"viewBoxPos\"),viewBoxSize:T.prop(\"viewBoxSize\"),dim0A:T.prop(\"dim0A\"),dim1A:T.prop(\"dim1A\"),dim0B:T.prop(\"dim0B\"),dim1B:T.prop(\"dim1B\"),dim0C:T.prop(\"dim0C\"),dim1C:T.prop(\"dim1C\"),dim0D:T.prop(\"dim0D\"),dim1D:T.prop(\"dim1D\"),loA:T.prop(\"loA\"),hiA:T.prop(\"hiA\"),loB:T.prop(\"loB\"),hiB:T.prop(\"hiB\"),loC:T.prop(\"loC\"),hiC:T.prop(\"hiC\"),loD:T.prop(\"loD\"),hiD:T.prop(\"hiD\"),palette:L,contextColor:T.prop(\"contextColor\"),maskTexture:T.prop(\"maskTexture\"),drwLayer:T.prop(\"drwLayer\"),maskHeight:T.prop(\"maskHeight\")},offset:T.prop(\"offset\"),count:T.prop(\"count\")});function I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:\"alpha\",type:\"uint8\",mag:\"nearest\",min:\"nearest\",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[m]||C[m][0]!==k||C[m][1]!==M){C[m]=[k,M];var L=y(r,a,o,i,m,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:g;h(T,P,S,I,g,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style[\"pointer-events\"]=\"none\",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{\"../../lib\":776,\"./constants\":1157,glslify:424}],1162:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1163:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=i.numberFormat,o=t(\"color-rgba\"),s=t(\"../../plots/cartesian/axes\"),l=i.strRotate,c=i.strTranslate,u=t(\"../../lib/svg_text_utils\"),f=t(\"../../components/drawing\"),h=t(\"../../components/colorscale\"),p=t(\"../../lib/gup\"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t(\"./helpers\"),y=t(\"./constants\"),x=t(\"./axisbrush\"),b=t(\"./lines\");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function T(t){var e=t.range;return e?k(e[0],e[1]):w(t.values,t._length)}function k(t,e){return!isNaN(t)&&isFinite(t)||(t=0),!isNaN(e)&&isFinite(e)||(e=0),t===e&&(0===t?(t-=1,e+=1):(t*=.9,e*=1.1)),[t,e]}function A(t,e,r,i,o){var s,l,c=T(r);return i?n.scale.ordinal().domain(i.map((s=a(r.tickformat),l=o,l?function(t,e){var r=l[e];return null==r?s(t):r}:s))).range(i.map((function(r){var n=(r-c[0])/(c[1]-c[0]);return t-e+n*(2*e-t)}))):n.scale.linear().domain(c).range([t-e,e])}function M(t){if(t.tickvals){var e=T(t);return n.scale.ordinal().domain(t.tickvals).range(t.tickvals.map((function(t){return(t-e[0])/(e[1]-e[0])})))}}function S(t){var e=t.map((function(t){return t[0]})),r=t.map((function(t){var e=o(t[1]);return n.rgb(\"rgb(\"+e[0]+\",\"+e[1]+\",\"+e[2]+\")\")})),i=\"rgb\".split(\"\").map((function(t){return n.scale.linear().clamp(!0).domain(e).range(r.map((i=t,function(t){return t[i]})));var i}));return function(t){return i.map((function(e){return e(t)}))}}function E(t){return t.dimensions.some((function(t){return t.brush.filterSpecified}))}function L(t,e,r){var a=g(e),s=a.trace,l=v.convertTypedArray(a.lineColor),c=s.line,u={color:o(y.deselectedLineColor)},f=h.extractOpts(c),p=f.reversescale?h.flipScale(a.cscale):a.cscale,d=s.domain,m=s.dimensions,x=t.width,b=s.labelangle,_=s.labelside,w=s.labelfont,k=s.tickfont,A=s.rangefont,M=i.extendDeepNoArrays({},c,{color:l.map(n.scale.linear().domain(T({values:l,range:[f.min,f.max],_length:s._length}))),blockLineCount:y.blockLineCount,canvasOverdrag:y.overdrag*y.canvasPixelRatio}),E=Math.floor(x*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=E,I=L;return{key:r,colCount:m.filter(v.isVisible).length,dimensions:m,tickDistance:y.tickDistance,unitToColor:S(p),lines:M,deselectedLines:u,labelAngle:b,labelSide:_,labelFont:w,tickFont:k,rangeFont:A,layoutWidth:x,layoutHeight:t.height,domain:d,translateX:d.x[0]*x,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*y.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:I*y.canvasPixelRatio,width:P,height:I,canvasPixelRatio:y.canvasPixelRatio}}function C(t,e,r){var o=r.width,s=r.height,l=r.dimensions,c=r.canvasPixelRatio,u=function(t){return o*t/Math.max(1,r.colCount-1)},f=y.verticalPadding/s,h=function(t,e){return n.scale.linear().range([e,t-e])}(s,y.verticalPadding),p={key:r.key,xScale:u,model:r,inBrushDrag:!1},d={};return p.dimensions=l.filter(v.isVisible).map((function(o,l){var m=function(t,e){return n.scale.linear().domain(T(t)).range([e,1-e])}(o,f),g=d[o.label];d[o.label]=(g||0)+1;var b=o.label+(g?\"__\"+g:\"\"),_=o.constraintrange,w=_&&_.length;w&&!Array.isArray(_[0])&&(_=[_]);var k=w?_.map((function(t){return t.map(m)})):[[-1/0,1/0]],S=o.values;S.length>o._length&&(S=S.slice(0,o._length));var L,C=o.tickvals;function P(t,e){return{val:t,text:L[e]}}function I(t,e){return t.val-e.val}if(Array.isArray(C)&&C.length){L=o.ticktext,Array.isArray(L)&&L.length?L.length>C.length?L=L.slice(0,C.length):C.length>L.length&&(C=C.slice(0,L.length)):L=C.map(a(o.tickformat));for(var O=1;O<C.length;O++)if(C[O]<C[O-1]){for(var z=C.map(P).sort(I),D=0;D<C.length;D++)C[D]=z[D].val,L[D]=z[D].text;break}}else C=void 0;return S=v.convertTypedArray(S),{key:b,label:o.label,tickFormat:o.tickformat,tickvals:C,ticktext:L,ordinal:v.isOrdinal(o),multiselect:o.multiselect,xIndex:l,crossfilterDimensionIndex:l,visibleIndex:o._index,height:s,values:S,paddedUnitValues:S.map(m),unitTickvals:C&&C.map(m),xScale:u,x:u(l),canvasX:u(l)*c,unitToPaddedPx:h,domainScale:A(s,y.verticalPadding,o,C,L),ordinalScale:M(o),parent:p,model:r,brush:x.makeBrush(t,w,k,(function(){t.linePickActive(!1)}),(function(){var e=p;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=E(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))}),(function(r){if(p.focusLayer.render(p.panels,!0),p.pickLayer&&p.pickLayer.render(p.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var n=m.invert,a=r.map((function(t){return t.map(n).sort(i.sorterAsc)})).sort((function(t,e){return t[0]-e[0]}));e.filterChanged(p.key,o._index,a)}}))}})),p}function P(t){t.classed(y.cn.axisExtentText,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"default\")}function I(t,e){var r=\"top\"===e?1:-1,n=t*Math.PI/180;return{dir:r,dx:Math.sin(n),dy:Math.cos(n),degrees:t}}function O(t,e,r){for(var n=e.panels||(e.panels=[]),i=t.data(),a=0;a<i.length-1;a++){var o=n[a]||(n[a]={}),s=i[a],l=i[a+1];o.dim0=s,o.dim1=l,o.canvasX=s.canvasX,o.panelSizeX=l.canvasX-s.canvasX,o.panelSizeY=e.model.canvasHeight,o.y=0,o.canvasY=0,o.plotGlPixelRatio=r}}function z(t,e){return s.tickText(t._ax,e,!1).text}function D(t,e){if(t.ordinal)return\"\";var r=t.domainScale.domain(),n=r[e?r.length-1:0];return z(t.model.dimensions[t.visibleIndex],n)}e.exports=function(t,e,r,a){var o=t._fullLayout,h=o._toppaper,p=o._glcontainer,_=t._context.plotGlPixelRatio,T=t._fullLayout.paper_bgcolor;!function(t){for(var e=0;e<t.length;e++)for(var 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n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}e.exports=function(t,e){var r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},f=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:f.w,height:f.h,margin:{t:f.t,r:f.r,b:f.b,l:f.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",f=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===f[s]){var h=a.constraintrange;f[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top 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r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;if((y=+y)<0)continue}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":639,\"fast-isnumeric\":242,tinycolor2:572}],1168:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":776,\"../../plots/domain\":855,\"../bar/defaults\":918,\"./attributes\":1165,\"fast-isnumeric\":242}],1169:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":675}],1170:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":776}],1171:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":1165,\"./base_plot\":1166,\"./calc\":1167,\"./defaults\":1168,\"./layout_attributes\":1172,\"./layout_defaults\":1173,\"./plot\":1174,\"./style\":1175,\"./style_one\":1176}],1172:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],1173:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":776,\"./layout_attributes\":1172}],1174:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var 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g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,\"source:\")+\" \",T=u(t,\"target:\")+\" \",k=u(t,\"concentration:\")+\" \",A=u(t,\"incoming flow count:\")+\" \",M=u(t,\"outgoing flow count:\")+\" \";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),\"skip\"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit(\"plotly_hover\",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if(\"none\"!==o.hoverinfo&&\"skip\"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if(\"closest\"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||\"\",w+m.source.label,T+m.target.label,m.concentrationscale?k+a(\"%0.2f\")(m.flow.labelConcentration):\"\"].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||l.addOpacity(m.color,1),borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:n.event.x<g[0]?\"right\":\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var 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l=n.select(e).select(\".\"+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var 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n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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n=t(\"../../components/colorscale/helpers\").hasColorscale,i=t(\"../../components/colorscale/calc\"),a=t(\"./subtypes\");e.exports=function(t,e){a.hasLines(e)&&n(e,\"line\")&&i(t,e,{vals:e.line.color,containerStr:\"line\",cLetter:\"c\"}),a.hasMarkers(e)&&(n(e,\"marker\")&&i(t,e,{vals:e.marker.color,containerStr:\"marker\",cLetter:\"c\"}),n(e,\"marker.line\")&&i(t,e,{vals:e.marker.line.color,containerStr:\"marker.line\",cLetter:\"c\"}))}},{\"../../components/colorscale/calc\":647,\"../../components/colorscale/helpers\":650,\"./subtypes\":1216}],1195:[function(t,e,r){\"use strict\";e.exports={PTS_LINESONLY:20,minTolerance:.2,toleranceGrowth:10,maxScreensAway:20,eventDataKeys:[]}},{}],1196:[function(t,e,r){\"use strict\";var n=t(\"./calc\");function i(t,e,r,n,i,a,o){i[n]=!0;var s={i:null,gap:!0,s:0};if(s[o]=r,t.splice(e,0,s),e&&r===t[e-1][o]){var l=t[e-1];s.s=l.s,s.i=l.i,s.gap=l.gap}else a&&(s.s=function(t,e,r,n){var i=t[e-1],a=t[e+1];return 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n=t(\"../../components/color\"),i=t(\"../../lib\").isArrayOrTypedArray;e.exports=function(t,e,r,a){var o=!1;if(e.marker){var s=e.marker.color,l=(e.marker.line||{}).color;s&&!i(s)?o=s:l&&!i(l)&&(o=l)}a(\"fillcolor\",n.addOpacity((e.line||{}).color||o||r,.5))}},{\"../../components/color\":639,\"../../lib\":776}],1200:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a={_fullLayout:r},o=n.getFromTrace(a,e,\"x\"),s=n.getFromTrace(a,e,\"y\");return i.xLabel=n.tickText(o,o.c2l(t.x),!0).text,i.yLabel=n.tickText(s,s.c2l(t.y),!0).text,i}},{\"../../plots/cartesian/axes\":827}],1201:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./subtypes\");e.exports=function(t,e){var r,a;if(\"lines\"===t.mode)return(r=t.line.color)&&n.opacity(r)?r:t.fillcolor;if(\"none\"===t.mode)return t.fill?t.fillcolor:\"\";var 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n=t(\"./subtypes\");e.exports={hasLines:n.hasLines,hasMarkers:n.hasMarkers,hasText:n.hasText,isBubble:n.isBubble,attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"./cross_trace_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./cross_trace_calc\"),arraysToCalcdata:t(\"./arrays_to_calcdata\"),plot:t(\"./plot\"),colorbar:t(\"./marker_colorbar\"),formatLabels:t(\"./format_labels\"),style:t(\"./style\").style,styleOnSelect:t(\"./style\").styleOnSelect,hoverPoints:t(\"./hover\"),selectPoints:t(\"./select\"),animatable:!0,moduleType:\"trace\",name:\"scatter\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"cartesian\",\"svg\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\",\"zoomScale\"],meta:{}}},{\"../../plots/cartesian\":841,\"./arrays_to_calcdata\":1190,\"./attributes\":1191,\"./calc\":1192,\"./cross_trace_calc\":1196,\"./cross_trace_defaults\":1197,\"./defaults\":1198,\"./format_labels\":1200,\"./hover\":1202,\"./marker_colorbar\":1209,\"./plot\":1212,\"./select\":1213,\"./style\":1215,\"./subtypes\":1216}],1204:[function(t,e,r){\"use 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var m=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var f=u[n],h=0;h<f.length;++h)f[h]=l[f[h]];return{positions:t,cells:u,meshColor:e}}(p.position,p.delaunayColor,p.delaunayAxis);m.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(m):(m.gl=u,this.delaunayMesh=o(m),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},y.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=function(t,e){var r=new v(t,e.uid);return r.update(e),r}},{\"../../components/fx/helpers\":675,\"../../constants/gl3d_dashes\":749,\"../../constants/gl3d_markers\":750,\"../../lib\":776,\"../../lib/gl_format_color\":772,\"../../lib/str2rgbarray\":801,\"../../plots/cartesian/axes\":827,\"../scatter/make_bubble_size_func\":1208,\"./calc_errors\":1221,\"delaunay-triangulate\":172,\"gl-error3d\":263,\"gl-line3d\":270,\"gl-mesh3d\":303,\"gl-scatter3d\":319}],1223:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r(\"x\"),s=r(\"y\"),l=r(\"z\");n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return 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strict\";e.exports={plot:t(\"./convert\"),attributes:t(\"./attributes\"),markerSymbols:t(\"../../constants/gl3d_markers\"),supplyDefaults:t(\"./defaults\"),colorbar:[{container:\"marker\",min:\"cmin\",max:\"cmax\"},{container:\"line\",min:\"cmin\",max:\"cmax\"}],calc:t(\"./calc\"),moduleType:\"trace\",name:\"scatter3d\",basePlotModule:t(\"../../plots/gl3d\"),categories:[\"gl3d\",\"symbols\",\"showLegend\",\"scatter-like\"],meta:{}}},{\"../../constants/gl3d_markers\":750,\"../../plots/gl3d\":869,\"./attributes\":1219,\"./calc\":1220,\"./convert\":1222,\"./defaults\":1223}],1225:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../../plots/template_attributes\").texttemplateAttrs,s=t(\"../../components/colorscale/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=n.marker,u=n.line,f=c.line;e.exports={carpet:{valType:\"string\",editType:\"calc\"},a:{valType:\"data_array\",editType:\"calc\"},b:{valType:\"data_array\",editType:\"calc\"},mode:l({},n.mode,{dflt:\"markers\"}),text:l({},n.text,{}),texttemplate:o({editType:\"plot\"},{keys:[\"a\",\"b\",\"text\"]}),hovertext:l({},n.hovertext,{}),line:{color:u.color,width:u.width,dash:u.dash,shape:l({},u.shape,{values:[\"linear\",\"spline\"]}),smoothing:u.smoothing,editType:\"calc\"},connectgaps:n.connectgaps,fill:l({},n.fill,{values:[\"none\",\"toself\",\"tonext\"],dflt:\"none\"}),fillcolor:n.fillcolor,marker:l({symbol:c.symbol,opacity:c.opacity,maxdisplayed:c.maxdisplayed,size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,line:l({width:f.width,editType:\"calc\"},s(\"marker.line\")),gradient:c.gradient,editType:\"calc\"},s(\"marker\")),textfont:n.textfont,textposition:n.textposition,selected:n.selected,unselected:n.unselected,hoverinfo:l({},i.hoverinfo,{flags:[\"a\",\"b\",\"text\",\"name\"]}),hoveron:n.hoveron,hovertemplate:a()}},{\"../../components/colorscale/attributes\":646,\"../../lib/extend\":766,\"../../plots/attributes\":823,\"../../plots/template_attributes\":899,\"../scatter/attributes\":1191}],1226:[function(t,e,r){\"use 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":776,\"../scatter/constants\":1195,\"../scatter/fillcolor_defaults\":1199,\"../scatter/line_defaults\":1204,\"../scatter/line_shape_defaults\":1206,\"../scatter/marker_defaults\":1210,\"../scatter/subtypes\":1216,\"../scatter/text_defaults\":1217,\"./attributes\":1225}],1228:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],1229:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],1230:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":776,\"../scatter/hover\":1202}],1231:[function(t,e,r){\"use 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n=t(\"../scatter/plot\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../components/drawing\");e.exports=function(t,e,r,o){var s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":661,\"../../plots/cartesian/axes\":827,\"../scatter/plot\":1212}],1233:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var g=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":776,\"../scatter/fillcolor_defaults\":1199,\"../scatter/line_defaults\":1204,\"../scatter/marker_defaults\":1210,\"../scatter/subtypes\":1216,\"../scatter/text_defaults\":1217,\"./attributes\":1233}],1236:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1237:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return 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t.lonLabel=b.lonLabel,t.latLabel=b.latLabel,t.color=a(c,m),t.extraText=function(t,e,r,n){if(t.hovertemplate)return;var i=e.hi||t.hoverinfo,a=\"all\"===i?s.hoverinfo.flags:i.split(\"+\"),l=-1!==a.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==a.indexOf(\"lon\"),u=-1!==a.indexOf(\"lat\"),f=-1!==a.indexOf(\"text\"),h=[];function p(t){return t+\"\\xb0\"}l?h.push(e.loc):c&&u?h.push(\"(\"+p(r.latLabel)+\", \"+p(r.lonLabel)+\")\"):c?h.push(n.lon+p(r.lonLabel)):u&&h.push(n.lat+p(r.latLabel));f&&o(e,t,h);return h.join(\"<br>\")}(c,m,t,l[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}}},{\"../../components/fx\":679,\"../../constants/numerical\":752,\"../../lib\":776,\"../scatter/get_trace_color\":1201,\"./attributes\":1233}],1239:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,f=t(\"../scatter/subtypes\"),h=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m=\"geojson-id\"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if(\"geojson\"===o.fitbounds&&\"geojson-id\"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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s=o.calcTraceToLineCoords(e),c=\"none\"!==a.fill?o.makePolygon(s):o.makeLine(s);r.selectAll(\"path.js-line\").data([{geojson:c,trace:a}]).enter().append(\"path\").classed(\"js-line\",!0).style(\"stroke-miterlimit\",2)}f.hasMarkers(a)&&r.selectAll(\"path.point\").data(i.identity).enter().append(\"path\").classed(\"point\",!0).each((function(t){l(t,this)})),f.hasText(a)&&r.selectAll(\"g\").data(i.identity).enter().append(\"g\").append(\"text\").each((function(t){l(t,this)})),h(t,e)}))}}},{\"../../constants/numerical\":752,\"../../lib\":776,\"../../lib/geo_location_utils\":769,\"../../lib/geojson_utils\":770,\"../../lib/topojson_utils\":805,\"../../plots/cartesian/autorange\":826,\"../scatter/calc\":1192,\"../scatter/subtypes\":1216,\"./style\":1242,\"@plotly/d3\":58}],1241:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"../../constants/numerical\").BADNUM;e.exports=function(t,e){var 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e=n.select(this),r=t.trace,o=r.line||{};e.call(a.stroke,o.color).call(i.dashLine,o.dash||\"\",o.width||0),\"none\"!==r.fill&&e.call(a.fill,r.fillcolor)}))}(t,e)}},{\"../../components/color\":639,\"../../components/drawing\":661,\"../scatter/style\":1215,\"@plotly/d3\":58}],1243:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../components/colorscale/attributes\"),s=t(\"../../lib/sort_object_keys\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll,u=t(\"./constants\").DASHES,f=i.line,h=i.marker,p=h.line,d=e.exports=c({x:i.x,x0:i.x0,dx:i.dx,y:i.y,y0:i.y0,dy:i.dy,xperiod:i.xperiod,yperiod:i.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:i.xperiodalignment,yperiodalignment:i.yperiodalignment,xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),text:i.text,hovertext:i.hovertext,textposition:i.textposition,textfont:i.textfont,mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},line:{color:f.color,width:f.width,shape:{valType:\"enumerated\",values:[\"linear\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\",editType:\"plot\"},dash:{valType:\"enumerated\",values:s(u),dflt:\"solid\"}},marker:l({},o(\"marker\"),{symbol:h.symbol,size:h.size,sizeref:h.sizeref,sizemin:h.sizemin,sizemode:h.sizemode,opacity:h.opacity,colorbar:h.colorbar,line:l({},o(\"marker.line\"),{width:p.width})}),connectgaps:i.connectgaps,fill:l({},i.fill,{dflt:\"none\"}),fillcolor:i.fillcolor,selected:{marker:i.selected.marker,textfont:i.selected.textfont},unselected:{marker:i.unselected.marker,textfont:i.unselected.textfont},opacity:n.opacity},\"calc\",\"nested\");d.x.editType=d.y.editType=d.x0.editType=d.y0.editType=\"calc+clearAxisTypes\",d.hovertemplate=i.hovertemplate,d.texttemplate=i.texttemplate},{\"../../components/colorscale/attributes\":646,\"../../lib/extend\":766,\"../../lib/sort_object_keys\":799,\"../../plot_api/edit_types\":809,\"../../plots/attributes\":823,\"../../plots/cartesian/axis_format_attributes\":830,\"../scatter/attributes\":1191,\"./constants\":1245}],1244:[function(t,e,r){\"use 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T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{\"../../components/drawing\":661,\"../../components/fx/helpers\":675,\"../../constants/interactions\":751,\"../../lib\":776,\"../../lib/gl_format_color\":772,\"../../plots/cartesian/axis_ids\":831,\"../../registry\":904,\"../scatter/make_bubble_size_func\":1208,\"../scatter/subtypes\":1216,\"./constants\":1245,\"./helpers\":1250,\"color-normalize\":126,\"fast-isnumeric\":242,\"svg-path-sdf\":569}],1247:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":776,\"../../registry\":904,\"../scatter/constants\":1195,\"../scatter/fillcolor_defaults\":1199,\"../scatter/line_defaults\":1204,\"../scatter/marker_defaults\":1210,\"../scatter/period_defaults\":1211,\"../scatter/subtypes\":1216,\"../scatter/text_defaults\":1217,\"../scatter/xy_defaults\":1218,\"./attributes\":1243,\"./helpers\":1250}],1248:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":639,\"../../constants/interactions\":751,\"../../lib\":776}],1249:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":1200}],1250:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":1245}],1251:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{\"../../lib\":776,\"../../registry\":904,\"../scatter/get_trace_color\":1201}],1252:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":841,\"../scatter/cross_trace_defaults\":1197,\"../scatter/marker_colorbar\":1209,\"./attributes\":1243,\"./calc\":1244,\"./defaults\":1247,\"./format_labels\":1249,\"./hover\":1251,\"./plot\":1253,\"./select\":1255}],1253:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=g.dragmode,M=l(A),S=g.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":657,\"../../lib\":776,\"../../lib/prepare_regl\":789,\"../scatter/link_traces\":1207,\"../scatter/subtypes\":1216,\"./edit_style\":1248,\"gl-text\":337,\"regl-error2d\":512,\"regl-line2d\":513,\"regl-scatter2d\":514}],1254:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{\"../../lib\":776}],1255:[function(t,e,r){\"use strict\";var n=t(\"../scatter/subtypes\"),i=t(\"./edit_style\").styleTextSelection;e.exports=function(t,e){var r=t.cd,a=t.xaxis,o=t.yaxis,s=[],l=r[0].trace,c=r[0].t,u=l._length,f=c.x,h=c.y,p=c._scene,d=c.index;if(!p)return s;var m=n.hasText(l),g=n.hasMarkers(l),v=!g&&!m;if(!0!==l.visible||v)return s;var y=[],x=[];if(!1!==e&&!e.degenerate)for(var b=0;b<u;b++)e.contains([c.xpx[b],c.ypx[b]],!1,b,t)?(y.push(b),s.push({pointNumber:b,x:a.c2d(f[b]),y:o.c2d(h[b])})):x.push(b);if(g){var _=p.scatter2d;if(y.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],_.update.apply(_,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],_.update.apply(_,T)}}return p.selectBatch[d]=y,p.unselectBatch[d]=x,m&&i(r),s}},{\"../scatter/subtypes\":1216,\"./edit_style\":1248}],1256:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scattergeo/attributes\"),o=t(\"../scatter/attributes\"),s=t(\"../../plots/mapbox/layout_attributes\"),l=t(\"../../plots/attributes\"),c=t(\"../../components/colorscale/attributes\"),u=t(\"../../lib/extend\").extendFlat,f=t(\"../../plot_api/edit_types\").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:\"markers\"}),text:u({},o.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c(\"marker\")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},{\"../../components/colorscale/attributes\":646,\"../../lib/extend\":766,\"../../plot_api/edit_types\":809,\"../../plots/attributes\":823,\"../../plots/mapbox/layout_attributes\":886,\"../../plots/template_attributes\":899,\"../scatter/attributes\":1191,\"../scattergeo/attributes\":1233}],1257:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"../../constants/numerical\").BADNUM,o=t(\"../../lib/geojson_utils\"),s=t(\"../../components/colorscale\"),l=t(\"../../components/drawing\"),c=t(\"../scatter/make_bubble_size_func\"),u=t(\"../scatter/subtypes\"),f=t(\"../../plots/mapbox/convert_text_opts\"),h=t(\"../../components/fx/helpers\").appendArrayPointValue,p=t(\"../../lib/svg_text_utils\").NEWLINES,d=t(\"../../lib/svg_text_utils\").BR_TAG_ALL;function m(){return{geojson:o.makeBlank(),layout:{visibility:\"none\"},paint:{}}}function g(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return\"\"}function y(t){return t[0]===a}e.exports=function(t,e){var r,a=e[0].trace,x=!0===a.visible&&0!==a._length,b=\"none\"!==a.fill,_=u.hasLines(a),w=u.hasMarkers(a),T=u.hasText(a),k=w&&\"circle\"===a.marker.symbol,A=w&&\"circle\"!==a.marker.symbol,M=m(),S=m(),E=m(),L=m(),C={fill:M,line:S,circle:E,symbol:L};if(!x)return C;if((b||_)&&(r=o.calcTraceToLineCoords(e)),b&&(M.geojson=o.makePolygon(r),M.layout.visibility=\"visible\",i.extendFlat(M.paint,{\"fill-color\":a.fillcolor})),_&&(S.geojson=o.makeLine(r),S.layout.visibility=\"visible\",i.extendFlat(S.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),k){var P=function(t){var e,r,a,o,u=t[0].trace,f=u.marker,h=u.selectedpoints,p=i.isArrayOrTypedArray(f.color),d=i.isArrayOrTypedArray(f.size),m=i.isArrayOrTypedArray(f.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(f):i.identity);d&&(a=c(u));m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var v,x=[];for(e=0;e<t.length;e++){var b=t[e],_=b.lonlat;if(!y(_)){var w={};r&&(w.mcc=b.mcc=r(b.mc)),a&&(w.mrc=b.mrc=a(b.ms)),o&&(w.mo=o(b.mo)),h&&(w.selected=b.selected||0),x.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:_},properties:w})}}if(h)for(v=l.makeSelectedPointStyleFns(u),e=0;e<x.length;e++){var T=x[e].properties;v.selectedOpacityFn&&(T.mo=g(v.selectedOpacityFn(T))),v.selectedColorFn&&(T.mcc=v.selectedColorFn(T)),v.selectedSizeFn&&(T.mrc=v.selectedSizeFn(T))}return{geojson:{type:\"FeatureCollection\",features:x},mcc:p||v&&v.selectedColorFn?{type:\"identity\",property:\"mcc\"}:f.color,mrc:d||v&&v.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:(k=f.size,k/2),mo:m||v&&v.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:g(f.opacity)};var k}(e);E.geojson=P.geojson,E.layout.visibility=\"visible\",i.extendFlat(E.paint,{\"circle-color\":P.mcc,\"circle-radius\":P.mrc,\"circle-opacity\":P.mo})}if((A||T)&&(L.geojson=function(t,e){for(var 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/line_shape_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),u=t(\"../scatter/constants\").PTS_LINESONLY,f=t(\"./attributes\");function h(t,e,r,n){var i,a=n(\"r\"),o=n(\"theta\");if(a)o?i=Math.min(a.length,o.length):(i=a.length,n(\"theta0\"),n(\"dtheta\"));else{if(!o)return 0;i=e.theta.length,n(\"r0\"),n(\"dr\")}return e._length=i,i}e.exports={handleRThetaDefaults:h,supplyDefaults:function(t,e,r,p){function d(r,i){return n.coerce(t,e,f,r,i)}var m=h(t,e,p,d);if(m){d(\"thetaunit\"),d(\"mode\",m<u?\"lines+markers\":\"lines\"),d(\"text\"),d(\"hovertext\"),\"fills\"!==e.hoveron&&d(\"hovertemplate\"),i.hasLines(e)&&(o(t,e,r,p,d),s(t,e,d),d(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,p,d,{gradient:!0}),i.hasText(e)&&(d(\"texttemplate\"),l(t,e,p,d));var 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A=\"\";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+=\"x\"),s||g[e]===f||(s=!0,A+=\"y\"),l||v[e]===p||(l=!0,A+=\"z\");a||(A+=\"x\"),s||(A+=\"y\"),l||(A+=\"z\");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?m:\"y\"===t?g:v},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return 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0},{\"../../components/color\":639,\"../../components/colorscale/attributes\":646,\"../../lib/extend\":766,\"../../plot_api/edit_types\":809,\"../../plots/attributes\":823,\"../../plots/cartesian/axis_format_attributes\":830,\"../../plots/template_attributes\":899}],1316:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":647}],1317:[function(t,e,r){\"use strict\";var n=t(\"gl-surface3d\"),i=t(\"ndarray\"),a=t(\"ndarray-linear-interpolate\").d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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i=n(\"opacityscale\");\"max\"===i?e.opacityscale=[[0,.1],[1,1]]:\"min\"===i?e.opacityscale=[[0,1],[1,.1]]:\"extremes\"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var 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y=\"contours.\"+v[u],x=h(y+\".show\"),b=h(y+\".highlight\");if(x||b)for(f=0;f<3;++f)h(y+\".project.\"+v[f]);x&&(h(y+\".color\"),h(y+\".width\"),h(y+\".usecolormap\")),b&&(h(y+\".highlightcolor\"),h(y+\".highlightwidth\")),h(y+\".start\"),h(y+\".end\"),h(y+\".size\")}g||(l(t,\"zmin\",\"cmin\"),l(t,\"zmax\",\"cmax\"),l(t,\"zauto\",\"cauto\")),a(t,e,c,h,{prefix:\"\",cLetter:\"c\"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{\"../../components/colorscale/defaults\":649,\"../../lib\":776,\"../../registry\":904,\"./attributes\":1315}],1319:[function(t,e,r){\"use 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n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../../components/colorscale/attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../pie/attributes\"),l=t(\"../sunburst/attributes\"),c=t(\"./constants\"),u=t(\"../../lib/extend\").extendFlat;e.exports={labels:l.labels,parents:l.parents,values:l.values,branchvalues:l.branchvalues,count:l.count,level:l.level,maxdepth:l.maxdepth,tiling:{packing:{valType:\"enumerated\",values:[\"squarify\",\"binary\",\"dice\",\"slice\",\"slice-dice\",\"dice-slice\"],dflt:\"squarify\",editType:\"plot\"},squarifyratio:{valType:\"number\",min:1,dflt:1,editType:\"plot\"},flip:{valType:\"flaglist\",flags:[\"x\",\"y\"],dflt:\"\",editType:\"plot\"},pad:{valType:\"number\",min:0,dflt:3,editType:\"plot\"},editType:\"calc\"},marker:u({pad:{t:{valType:\"number\",min:0,editType:\"plot\"},l:{valType:\"number\",min:0,editType:\"plot\"},r:{valType:\"number\",min:0,editType:\"plot\"},b:{valType:\"number\",min:0,editType:\"plot\"},editType:\"calc\"},colors:l.marker.colors,depthfade:{valType:\"enumerated\",values:[!0,!1,\"reversed\"],editType:\"style\"},line:l.marker.line,editType:\"calc\"},a(\"marker\",{colorAttr:\"colors\",anim:!1})),pathbar:{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},side:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},edgeshape:{valType:\"enumerated\",values:[\">\",\"<\",\"|\",\"/\",\"\\\\\"],dflt:\">\",editType:\"plot\"},thickness:{valType:\"number\",min:12,editType:\"plot\"},textfont:u({},s.textfont,{}),editType:\"calc\"},text:s.text,textinfo:l.textinfo,texttemplate:i({editType:\"plot\"},{keys:c.eventDataKeys.concat([\"label\",\"value\"])}),hovertext:s.hovertext,hoverinfo:l.hoverinfo,hovertemplate:n({},{keys:c.eventDataKeys}),textfont:s.textfont,insidetextfont:s.insidetextfont,outsidetextfont:u({},s.outsidetextfont,{}),textposition:{valType:\"enumerated\",values:[\"top 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h=n.select(this),p=i.ensureSingle(h,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",\"all\")}));w?p.transition().attrTween(\"d\",(function(t){var e=k(t,!0,M,[m,g]);return function(t){return x(e(t))}})):p.attr(\"d\",x),h.call(f,r,t,e,{styleOne:l,eventDataKeys:c.eventDataKeys,transitionTime:c.CLICK_TRANSITION_TIME,transitionEasing:c.CLICK_TRANSITION_EASING}).call(u.setSliceCursor,t,{hideOnRoot:!1,hideOnLeaves:!1,isTransitioning:t._transitioning}),p.call(l,s,L,{hovered:!1}),s._text=(u.getPtLabel(s)||\"\").split(\"<br>\").join(\" \")||\"\";var d=i.ensureSingle(h,\"g\",\"slicetext\"),T=i.ensureSingle(d,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),E=i.ensureUniformFontSize(t,u.determineTextFont(L,s,S.font,{onPathbar:!0}));T.text(s._text||\" 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m=d.width,g=d.height,v=d.viewX,y=d.viewY,x=d.pathSlice,b=d.toMoveInsideSlice,_=d.strTransform,w=d.hasTransition,T=d.handleSlicesExit,k=d.makeUpdateSliceInterpolator,A=d.makeUpdateTextInterpolator,M=d.prevEntry,S=t._fullLayout,E=e[0].trace,L=-1!==E.textposition.indexOf(\"left\"),C=-1!==E.textposition.indexOf(\"right\"),P=-1!==E.textposition.indexOf(\"bottom\"),I=!P&&!E.marker.pad.t||P&&!E.marker.pad.b,O=s(r,[m,g],{packing:E.tiling.packing,squarifyratio:E.tiling.squarifyratio,flipX:E.tiling.flip.indexOf(\"x\")>-1,flipY:E.tiling.flip.indexOf(\"y\")>-1,pad:{inner:E.tiling.pad,top:E.marker.pad.t,left:E.marker.pad.l,right:E.marker.pad.r,bottom:E.marker.pad.b}}).descendants(),z=1/0,D=-1/0;O.forEach((function(t){var e=t.depth;e>=E._maxDepth?(t.x0=t.x1=(t.x0+t.x1)/2,t.y0=t.y1=(t.y0+t.y1)/2):(z=Math.min(z,e),D=Math.max(D,e))})),p=p.data(O,u.getPtId),E._maxVisibleLayers=isFinite(D)?D-z+1:0,p.enter().append(\"g\").classed(\"slice\",!0),T(p,!1,{},[m,g],x),p.order();var R=null;if(w&&M){var 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    "path": "2023年/2023.08.05 局部细节放大图/局部细节放大图.R",
    "content": "# =============== 局部细节放大图 ===============\nlibrary(ggplot2)\nlibrary(patchwork)\nlibrary(tidyverse)\n# 1. 生成模拟数据\ncom_battery = data.frame(\"Time\" = 1:30,\"True\" = cumsum(abs(rnorm(30,2,0.4))),\n                          \"Proposed\" = cumsum(abs(rnorm(30,2,0.5))),\n                          \"Linear\" = cumsum(abs(rnorm(30,2,0.4))), \n                          \"Power\" = cumsum(abs(rnorm(30,2,0.1))),\n                          \"Exp\" = cumsum(abs(rnorm(30,2,0.3))))\n\ncols <- c(\"black\",\"#85BA8F\", \"#A3C8DC\",\"#349839\",\"#EA5D2D\",\"#EABB77\",\"#F09594\")\n# 2.基础绘图\np = com_battery %>% pivot_longer(cols = !Time, names_to = \"Model\", values_to = \"Value\") %>%\n  mutate(Model = factor(Model, levels = c(\"True\", \"Proposed\", \"Linear\",\"Power\",\"Exp\"))) %>% \n  ggplot(aes(Time,Value,col = Model,shape = Model)) + \n  geom_line() + geom_point(size=1.5,alpha=0.8) + \n  # scale_color_viridis(discrete = T) +\n  scale_color_manual(values = cols) +\n  theme_bw() + theme(panel.grid = element_blank()) + #,legend.title=element_text(size=12), legend.text=element_text(size=11) +\n  xlab(\"Time\") + ylab(\"Rate(%)\")\n\n## 方法一：ggforce\nlibrary(ggforce) \np + facet_zoom(xlim = c(18, 24),ylim = c(40, 43), zoom.size = 0.4)\n\n## 方法二：patchwork\nlibrary(patchwork)\n# 绘制放大图\nppp = p + xlim(5,10) + ylim(10,20) + theme(legend.position = 'none') +\n  xlab(\"\") + ylab(\"\") \nppp \n# 添加到原图中\np +   \n  geom_rect(aes(xmin = 5, xmax = 10, ymin = 10, ymax = 20),\n            fill = \"transparent\", color = \"black\", alpha = 0, \n            linetype = \"dashed\", linewidth =0.2) + #添加选择放大位置的框\n  theme(legend.position = c(0.9,.2),legend.background = element_rect(fill = 'white', colour = 'black')) + #修改图例位置\n  geom_segment(aes(x = 7, xend = 10, y = 20, yend = 38.3), \n               col = \"gray60\", linewidth =0.2,linetype = \"dashed\",\n               arrow = arrow(length = unit(0.2, \"cm\"), type = \"closed\")) + # 添加指向箭头\n  inset_element(ppp, 0.01, 0.6, 0.6, 0.95, on_top = TRUE)\n\n\n\n\n\n\n\n"
  },
  {
    "path": "2023年/2023.08.24 绘制足球数据/ggsoccer.R",
    "content": "library(ggsoccer)\n\n# 最基础 ====\nggplot() +\n  annotate_pitch() +\n  theme_pitch() \n\n# 投射路线\npass_data <- data.frame(x = c(24, 18, 64, 78, 53),\n                        y = c(43, 55, 88, 18, 44),\n                        x2 = c(34, 44, 81, 85, 64),\n                        y2 = c(40, 62, 89, 44, 28))\n\n## 版本一\nggplot(pass_data) +\n  annotate_pitch(colour = \"white\",\n                 fill = \"#3ab54a\") +\n  geom_segment(aes(x = x, y = y, xend = x2, yend = y2),\n               arrow = arrow(length = unit(0.25, \"cm\"),\n                             type = \"closed\")) +\n  theme_pitch() +\n  direction_label() #+\n  # ggtitle(\"Simple passmap\", \n  #         \"ggsoccer example\")\n\n\n## 版本二（半场）\nggplot(pass_data) +\n  annotate_pitch(colour = \"white\",\n                 fill = \"#3ab54a\") +\n  geom_segment(aes(x = x, y = y, xend = x2, yend = y2),\n               arrow = arrow(length = unit(0.25, \"cm\"),\n                             type = \"closed\")) +\n  theme_pitch() +\n  # direction_label() +\n  # ggtitle(\"Simple passmap\", \n  # \"ggsoccer example\") +\n  coord_flip(xlim = c(49, 101)) +\n  scale_y_reverse() \n\n\n# 投射情况 =======\ndf <- data.frame(x = rnorm(20, 80, 10), \n                 y = rnorm(20, 50, 20),\n                 Shot = sample(c(\"In\", \"Out\"),\n                               40, replace = TRUE))\n\nlibrary(viridis)\nggplot(df) +\n  annotate_pitch(colour = \"white\",\n                 fill = \"#3ab54a\") +\n  geom_point(aes(x = x, y = y, fill = Shot),\n             shape = 21,\n             size = 4) +\n  coord_cartesian(xlim = c(45, 105))+ \n  scale_fill_viridis(discrete=T) +\n  theme_pitch() +\n  theme(panel.background = element_rect(fill = \"#3ab54a\")) \n\n\n# ggplot 版本复现=======\n\nggplot()+\n  geom_rect(aes(xmin = -10, xmax = 100, ymin= -10, ymax = 130), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ ### Background outline\n  geom_rect(aes(xmin = 0, xmax = 90, ymin= 0, ymax = 120), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ ###Sideline, Endline\n  geom_rect(aes(xmin = 0, xmax = 90, ymin= 0, ymax = 60), fill = NA , colour = \"#FFFFFF\", size = 0.5)+ #Halfway\n  geom_rect(aes(xmin = 24.85, xmax = 65.15, ymin= 0, ymax = 16.5), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ #lower 18 yard box\n  geom_rect(aes(xmin = 35.85, xmax = 54.15, ymin= 0, ymax = 5.5), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ # Lower 5 yard box\n  geom_rect(aes(xmin = 24.85, xmax = 65.15, ymin= 103.5, ymax = 120), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ #upper 18 yard box\n  geom_rect(aes(xmin = 35.85, xmax = 54.15, ymin= 114.5, ymax = 120), fill = \"#3ab54a\", colour = \"#FFFFFF\", size = 0.5)+ #upper 5 yard box\n  geom_curve(aes(x = 35.85, y = 16.5, xend = 54.15, yend = 16.5), curvature = -0.6, colour = \"#FFFFFF\")+ #lower D\n  geom_curve(aes(x = 35.85, y = 103.5, xend = 54.15, yend = 103.5), curvature = .6, colour = \"#FFFFFF\")+ #upper D\n  ggforce::geom_circle(aes(x0=45, y0=60, r= 9.15), colour = \"#FFFFFF\")+ ##Halfway circle \n  # coord_flip(xlim = c(49, 101)) +\n  coord_flip() +\n  scale_y_reverse() +\n  theme(rect = element_blank(),\n        line = element_blank(),\n        text = element_blank())\n"
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  {
    "path": "2023年/2023.10.08 灯芯柱状图/.Rhistory",
    "content": "scale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") #这想干嘛？\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\n# coord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) #+\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\nscale_y_continuous(limits = c(-15, NA)) #+\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\")# +#这想干嘛？\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\n# scale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\n# scale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") #+\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\n# scale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") #+\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\n# scale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\naxis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 这里有细节！\nfill = \"white\", width = .5 # 宽度和透明度设置\n) +\ngeom_text( #这里的数据处理：添加文本+加入rescues\ndata = df_animals_sum %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\naes(label = n_lab), size = 4.3, lineheight = .8,\nnudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>%\nmutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))),\naes(label = n_lab),\ncolor = \"white\", lineheight = .8, size = 4.3,\nnudge_y = 12, vjust = 0, fontface = \"bold\"\n) +\ngeom_text(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(y = -15, label = cal_year, color = factor(cal_year)),\nsize = 6, hjust = .5, vjust = 1\n) +\ncoord_cartesian(clip = \"off\") +#这想干嘛？\n# scale_y_continuous(limits = c(-15, NA)) +\nscale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\", \"grey70\"), guide = \"none\") +\nscale_alpha_manual(values = c(.25, .4), guide = \"none\") +\ntheme(\nplot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\nplot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\npanel.grid.major = element_blank(),\n# axis.text.x = element_blank()\n)\ndf_animals_sum %>%\nggplot(aes(cal_year, n)) +\ngeom_col(aes(fill = factor(cal_year)), width = .85) +\ngeom_col(\ndata = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\naes(alpha = cal_year == 2020), # 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    "content": "# 代码来源于https://github.com/z3tt/TidyTuesday/blob/main/R/2021_27_AnimalRescues.Rmd\n# 与源代码有所改动，数据读取代码进行了改动；删除第二幅图代码。\nlibrary(tidyverse)\nlibrary(ggtext)\nlibrary(ggrepel)\nlibrary(patchwork)\nlibrary(systemfonts)\n\n\n# 主题设置 ====== \ntheme_set(theme_minimal(base_size = 19))\n\ntheme_update(\n  text = element_text(color = \"grey12\"),\n  axis.title = element_blank(),\n  axis.text.x = element_text(),\n  axis.text.y = element_blank(),\n  panel.grid.major.y = element_blank(),\n  panel.grid.minor = element_blank(),\n  plot.margin = margin(20, 5, 10, 10),\n  plot.subtitle = element_textbox_simple(size = 14,\n                                         lineheight = 1.6),\n  plot.title.position = \"plot\",\n  plot.caption = element_text( color = \"#b40059\", hjust = .5,\n                              size = 10, margin = margin(35, 0, 0, 0))\n)\n\n# 数据读取+处理 ========\ndf_animals <- readr::read_csv('animal_rescues.txt')\n\ndf_animals_agg <-\n  df_animals %>% \n  mutate(\n    animal_group_aggregated = case_when(\n      str_detect(animal_group_parent, \"Domestic|Livestock|Farm|Horse|Cow|Sheep|Goat|Lamb|Bull\") ~ \"Other Domestic Animals\",\n      animal_group_parent %in% c(\"Cat\", \"cat\") ~ \"Cats\",\n      animal_group_parent %in% c(\"Bird\", \"Budgie\") ~ \"Birds\",\n      animal_group_parent == \"Dog\" ~ \"Dogs\",\n      animal_group_parent == \"Fox\" ~ \"Foxes\",\n      TRUE ~ \"Other Wild Animals\"\n    )\n  ) %>% \n  count(cal_year, animal_group_aggregated) %>% \n  group_by(animal_group_aggregated) %>% \n  mutate(\n    total = sum(n),\n    current = n[which(cal_year == 2021)]\n  ) %>% \n  ungroup() %>% \n  mutate(\n    animal_group_aggregated = fct_reorder(animal_group_aggregated, total),\n    animal_group_aggregated = fct_relevel(animal_group_aggregated, \"Other Domestic Animals\", after = 0),\n    animal_group_aggregated = fct_relevel(animal_group_aggregated, \"Other Wild Animals\", after = 0)\n  )\n\ndf_animals_labs <-\n  df_animals_agg %>% \n  filter(cal_year == 2016) %>% \n  group_by(animal_group_aggregated) %>% \n  mutate(n = case_when(\n    animal_group_aggregated == \"Cats\" ~ 320,\n    animal_group_aggregated %in% c(\"Birds\", \"Dogs\") ~ 135,\n    TRUE ~ 55\n  ))\n\ndf_animals_annotate <-\n  df_animals_agg %>% \n  mutate(label = \"\\n\\n← Number of Rescues in 2021 so far.\") %>% \n  filter(cal_year == 2021 & animal_group_aggregated == \"Cats\")\n\ndf_animals_sum <-\n  df_animals_agg %>% \n  filter(cal_year < 2021) %>% \n  group_by(cal_year) %>% \n  summarize(n = sum(n))\n\n# write_csv(df_animals_sum,\"df_animals_sum.csv\")\n# df_animals_sum = read.csv(\"df_animals_sum.csv\")\n## 绘图 ==============\n\n  df_animals_sum %>% \n  ggplot(aes(cal_year, n)) +\n  geom_col(aes(fill = factor(cal_year)), width = .85) +\n  geom_col(\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\n    aes(alpha = cal_year == 2020), # 这里有细节！\n    fill = \"white\", width = .5 # 宽度和透明度设置\n  ) +\n  geom_text( #这里的数据处理：添加文本+加入rescues\n    data = df_animals_sum %>% \n      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\n    aes(label = n_lab), size = 4.3, lineheight = .8, \n    nudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n  ) +\n  geom_text( #添加文本+加入cats\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>% \n      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))), \n    aes(label = n_lab), \n    color = \"white\", lineheight = .8, size = 4.3, \n    nudge_y = 12, vjust = 0, fontface = \"bold\"\n  ) +\n  geom_text( # 手动添加年份标签\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\n    aes(y = -15, label = cal_year, color = factor(cal_year)), \n    size = 6, hjust = .5, vjust = 1\n  ) +\n  coord_cartesian(clip = \"off\") +#这想干嘛？\n  scale_y_continuous(limits = c(-15, NA)) +\n  scale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\"), guide = \"none\") +\n  scale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\"), guide = \"none\") +\n  scale_alpha_manual(values = c(.25, .4), guide = \"none\") +\n  theme(\n    # plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\n    # plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\n    panel.grid.major = element_blank(),\n    axis.text.x = element_blank()\n  )\n\n\n\n"
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    "path": "2023年/2023.10.08 灯芯柱状图/animal_rescues.txt",
    "content": "incident_number,date_time_of_call,cal_year,fin_year,type_of_incident,pump_count,pump_hours_total,hourly_notional_cost,incident_notional_cost,final_description,animal_group_parent,originof_call,property_type,property_category,special_service_type_category,special_service_type,ward_code,ward,borough_code,borough,stn_ground_name,uprn,street,usrn,postcode_district,easting_m,northing_m,easting_rounded,northing_rounded,latitude,longitude\n139091,01/01/2009 03:01,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,Norbury,NULL,Waddington Way,20500146,SE19,NULL,NULL,532350,170050,NULL,NULL\n275091,01/01/2009 08:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000169,Woodside,E09000008,Croydon,Woodside,NULL,Grasmere Road,NULL,SE25,534785,167546,534750,167550,51.39095371,-0.064166887\n2075091,04/01/2009 10:07,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Mill Lane,NULL,SM5,528041,164923,528050,164950,51.36894086,-0.161985191\n2872091,05/01/2009 12:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Horse,Person (mobile),\"Intensive Farming Sheds (chickens, pigs etc)\",Non Residential,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,1.00021E+11,Park Lane,21401484,UB9,504689,190685,504650,190650,51.60528344,-0.489683853\n3553091,06/01/2009 15:23,2009,2008/09,Special Service,1,1,255,255,Redacted,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,21300122,RM3,NULL,NULL,554650,192350,NULL,NULL\n3742091,06/01/2009 19:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,Rogers Road,19900321,RM10,NULL,NULL,549350,184950,NULL,NULL\n4011091,07/01/2009 06:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Holloway Road,NULL,E11,539013,186162,539050,186150,51.55722086,0.003880198\n4211091,07/01/2009 11:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Aldersbrook Road,NULL,E12,541327,186654,541350,186650,51.56106688,0.037433776\n4306091,07/01/2009 13:48,2009,2008/09,Special Service,1,1,255,255,Redacted,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Brockham Crescent,20501673,CR0,NULL,NULL,538750,163350,NULL,NULL\n4715091,07/01/2009 21:24,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Southwold Road,NULL,E5,535425,186743,535450,186750,51.56331352,-0.047620955\n5186091,08/01/2009 14:34,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,1.0002E+11,Tilbury Close,20301320,BR5,546981,169297,546950,169250,51.4036626,0.111722339\n5363091,08/01/2009 19:23,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Battersea Park,NULL,SW11,528666,177290,528650,177250,51.47994253,-0.148525691\n5682091,09/01/2009 11:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000535,Camberwell Green,E09000028,Southwark,Peckham,NULL,Valmar Road,NULL,SE5,532478,176515,532450,176550,51.47209876,-0.093952949\n5688091,09/01/2009 11:18,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,100090039,Rainham Road North,19900293,RM10,549923,186441,549950,186450,51.55693599,0.161258357\n5724091,09/01/2009 12:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,10002260585,Church Road,22405449,TW10,518497,174567,518450,174550,51.45768783,-0.29580467\n5770091,09/01/2009 13:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Belmont Road,20500674,SE25,534865,167671,534850,167650,51.39205798,-0.062970325\n5789091,09/01/2009 14:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,10023230026,Bessingham Walk,22006012,SE4,535913,174968,535950,174950,51.45738228,-0.045120782\n5797091,09/01/2009 14:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Ambulance,Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Streatham Common South,NULL,SW16,530995,170839,530950,170850,51.42143443,-0.117391508\n6259091,10/01/2009 09:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Balfour Street,NULL,SE17,532595,178874,532550,178850,51.49327092,-0.091384632\n6270091,10/01/2009 10:09,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000159,Purley,E09000008,Croydon,Purley,NULL,Brighton Road,NULL,CR8,531679,161982,531650,161950,51.34167993,-0.11084037\n6317091,10/01/2009 11:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Balfour Street,NULL,SE17,532594,178879,532550,178850,51.49331609,-0.091397151\n6353091,10/01/2009 12:26,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Brownfield Street,22700211,E14,NULL,NULL,538150,181250,NULL,NULL\n6355091,10/01/2009 12:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000288,Greenhill,E09000015,Harrow,Harrow,NULL,Roxborough Avenue,NULL,HA1,515074,187728,515050,187750,51.57668207,-0.340758362\n6378091,10/01/2009 13:05,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100089001,Stern Close,19903819,IG11,547091,183173,547050,183150,51.52831647,0.119072708\n6400091,10/01/2009 13:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Green Walk,NULL,NW4,524023,188969,524050,188950,51.58594049,-0.211240794\n6530091,10/01/2009 16:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Hancock Road,NULL,E3,538165,182731,538150,182750,51.52659798,-0.009689766\n6970091,11/01/2009 10:11,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,NULL,Hyde Park,NULL,SW7,527257,180039,527250,180050,51.50496632,-0.167814607\n7051091,11/01/2009 12:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00021E+11,Albany Road,21300936,RM12,552339,186735,552350,186750,51.55893097,0.196208829\n7613091,12/01/2009 13:10,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Canonsleigh Road,19900436,RM9,NULL,NULL,546950,184150,NULL,NULL\n7720091,12/01/2009 16:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Tomlins Orchard,NULL,IG11,544212,183641,544250,183650,51.5332637,0.077790345\n8648091,14/01/2009 06:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000454,Whitefoot,E09000023,Lewisham,Bromley,NULL,Shroffold Road,22003952,BR1,NULL,NULL,539750,172050,NULL,NULL\n9252091,15/01/2009 12:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Deeley Road,NULL,SW8,529741,176685,529750,176650,51.47426021,-0.133275614\n10299091,17/01/2009 11:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Public toilets,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,NULL,Dan Mason Drive,NULL,W4,521444,176789,521450,176750,51.47703452,-0.252645628\n10470091,17/01/2009 18:08,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Armstrong Way,NULL,UB2,513501,179808,513550,179850,51.50581677,-0.366000236\n10829091,18/01/2009 11:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Malden Place,20400336,NW5,NULL,NULL,528050,185150,NULL,NULL\n10976091,18/01/2009 16:19,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,NULL,RM3,554742,192355,554750,192350,51.60877075,0.23331818\n12317091,21/01/2009 00:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Greenway,19900158,RM8,NULL,NULL,547450,186950,NULL,NULL\n12451091,21/01/2009 10:33,2009,2008/09,Special Service,2,3,255,765,Redacted,Fox,Person (land line),Beach,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530526,178612,530550,178650,51.49139735,-0.121267747\n12533091,21/01/2009 14:04,2009,2008/09,Special Service,1,2,255,510,Redacted,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Ham Gate Avenue,NULL,TW10,518938,171828,518950,171850,51.43297839,-0.290379808\n12770091,21/01/2009 21:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,NULL,Welbeck Close,NULL,KT3,NULL,NULL,521550,167750,NULL,NULL\n13245091,22/01/2009 21:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Broomgrove Gardens,21202323,HA8,NULL,NULL,519350,190750,NULL,NULL\n14002091,24/01/2009 10:31,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Tenement Building,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Lonsdale Road,22404716,SW13,NULL,NULL,521950,177550,NULL,NULL\n14652091,25/01/2009 10:26,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Railway,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Fairbridge Road,NULL,N19,529970,186960,529950,186950,51.56654715,-0.12619315\n14691091,25/01/2009 12:11,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Commerce Road,NULL,TW8,517085,177478,517050,177450,51.48414487,-0.315155902\n14784091,25/01/2009 15:27,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Belmont Road,20500674,SE25,534865,167671,534850,167650,51.39205798,-0.062970325\n14796091,25/01/2009 16:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,NULL,Carshalton Road,NULL,SM1,526028,164010,526050,164050,51.36118516,-0.191210825\n15664091,27/01/2009 10:49,2009,2008/09,Special Service,1,1,255,255,Redacted,Squirrel,Person (land line),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000449,New Cross,E09000023,Lewisham,Deptford,2.00001E+11,Creekside,22004382,SE8,537492,177108,537450,177150,51.47623195,-0.021576172\n15682091,27/01/2009 11:21,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Royal College Street,20400512,NW1,NULL,NULL,529450,183750,NULL,NULL\n15865091,27/01/2009 17:18,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Football stadium,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Footscray Road,NULL,SE9,543250,174250,543250,174250,51.44912339,0.060121979\n16245091,28/01/2009 13:40,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Ferndale Road,22836050,E11,539647,186846,539650,186850,51.56321059,0.013290743\n16271091,28/01/2009 14:41,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Queen Adelaide Road,20302300,SE20,NULL,NULL,535450,170450,NULL,NULL\n17262091,30/01/2009 10:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Maryland Square,22200086,E15,NULL,NULL,539250,185250,NULL,NULL\n17297091,30/01/2009 12:17,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Thurlow Street,22502507,SE17,NULL,NULL,532950,178350,NULL,NULL\n17633091,30/01/2009 23:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Erith High Street,NULL,DA8,551609,178303,551650,178350,51.48336535,0.182061343\n17783091,31/01/2009 09:47,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,2.00002E+11,St. James Lane,21106430,N10,529026,189379,529050,189350,51.58850236,-0.138917727\n18369091,01/02/2009 08:48,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Long Lane,21401178,UB10,NULL,NULL,507850,185550,NULL,NULL\n18386091,01/02/2009 09:30,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (mobile),Park,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,The Greenway,NULL,NW9,520785,190200,520750,190250,51.59770605,-0.257531516\n20027091,04/02/2009 00:25,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,34057075,Avonmore Road,21000081,W14,524639,178810,524650,178850,51.49450385,-0.205949974\n20379091,04/02/2009 17:47,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Bedford Gardens,21700696,W8,NULL,NULL,525350,180150,NULL,NULL\n21166091,06/02/2009 09:08,2009,2008/09,Special Service,2,3,255,765,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05004162,Loughton Forest,E07000072,Epping Forest,Essex,NULL,Warren Hill,NULL,IG10,540410,195273,540450,195250,51.63874348,0.027660488\n21319091,06/02/2009 14:42,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Gipsy Hill,NULL,SE19,533337,171017,533350,171050,51.42248815,-0.083662056\n21401091,06/02/2009 17:14,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,St. John's Crescent,21901271,SW9,NULL,NULL,531350,175850,NULL,NULL\n21458091,06/02/2009 19:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,St. John's Crescent,21901271,SW9,NULL,NULL,531350,175850,NULL,NULL\n21760091,07/02/2009 11:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Lisle Street,NULL,WC2H,529769,180812,529750,180850,51.51134253,-0.131356592\n21807091,07/02/2009 12:43,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Police,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Fair Oak Lane,NULL,KT9,517179,161575,517150,161550,51.34119192,-0.319051309\n22115091,08/02/2009 00:59,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Other Residential Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000379,St. Mary's,E09000019,Islington,Islington,10012787326,Upper Street,21606199,N1,531616,184385,531650,184350,51.54302515,-0.103421503\n22276091,08/02/2009 12:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539784,194599,539750,194550,51.63284278,0.018351358\n26040091,14/02/2009 11:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,10003976332,Park Road,21103726,N8,529321,189057,529350,189050,51.58554114,-0.134780349\n26715091,15/02/2009 15:53,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,6009847,Porters Walk,22701920,E1W,534781,180604,534750,180650,51.50830086,-0.059254177\n27109091,16/02/2009 11:13,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000159,Purley,E09000008,Croydon,Purley,NULL,High Street,NULL,CR8,531368,161544,531350,161550,51.33781546,-0.115464278\n27805091,17/02/2009 13:23,2009,2008/09,Special Service,2,5,255,1275,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Macarthur Close,NULL,DA8,551276,178506,551250,178550,51.48527845,0.17735639\n27898091,17/02/2009 16:13,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Great West Road,NULL,TW7,516420,177339,516450,177350,51.48303265,-0.32477449\n28382091,18/02/2009 13:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bridge Road,NULL,NW10,520994,184849,520950,184850,51.54957001,-0.256357552\n29403091,20/02/2009 09:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Bradwell Avenue,19900051,RM10,NULL,NULL,549250,186850,NULL,NULL\n30133091,21/02/2009 14:08,2009,2008/09,Special Service,1,1,255,255,Redacted,Fox,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000416,Bishop's,E09000022,Lambeth,Lambeth,2E+11,Upper Ground,21901574,SE1,531012,180382,531050,180350,51.50719166,-0.1136144\n30194091,21/02/2009 16:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200082080,Milton Avenue,20029780,EN5,524699,195738,524650,195750,51.64662325,-0.199082475\n30836091,22/02/2009 13:17,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000456,Cannon Hill,E09000024,Merton,New Malden,NULL,Cannon Hill Lane,NULL,SW20,523933,168582,523950,168550,51.40273602,-0.219693097\n30981091,22/02/2009 17:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Swiftsden Way,22001946,BR1,NULL,NULL,539450,170750,NULL,NULL\n169092,23/02/2009 15:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Woodgrange Road,NULL,E7,NULL,NULL,540550,185150,NULL,NULL\n31317091,23/02/2009 16:56,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,Hale End Road,22842150,E4,NULL,NULL,538750,191550,NULL,NULL\n32555091,25/02/2009 22:18,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Nightingale Place,20800938,SE18,NULL,NULL,543450,178050,NULL,NULL\n33526091,27/02/2009 16:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Shaw Avenue,19900574,IG11,NULL,NULL,548150,183450,NULL,NULL\n34598091,01/03/2009 08:31,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,NULL,Fitzroy Road,NULL,NW1,528043,183876,528050,183850,51.53927199,-0.155102008\n35246091,02/03/2009 14:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Robin Crescent,22207863,E6,NULL,NULL,542050,181750,NULL,NULL\n35738091,03/03/2009 11:05,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Edith Road,22207542,E6,NULL,NULL,541750,184250,NULL,NULL\n35781091,03/03/2009 12:54,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Railway building - other,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Silvertown Way,NULL,E16,539504,181475,539550,181450,51.51498237,0.009102484\n36590091,04/03/2009 18:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Weir Road,NULL,SW19,525885,171885,525850,171850,51.43199153,-0.19047348\n36694091,04/03/2009 21:44,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Brick Lane,22700191,E1,NULL,NULL,533850,181850,NULL,NULL\n36729091,04/03/2009 23:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000447,Lee Green,E09000023,Lewisham,Lee Green,NULL,Weardale Road,NULL,SE13,538844,175164,538850,175150,51.45843297,-0.002884196\n37589091,06/03/2009 15:22,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000485,Green Street East,E09000025,Newham,East Ham,46023983,Elizabeth Road,22200586,E6,541831,184038,541850,184050,51.53743364,0.043645966\n37752091,06/03/2009 20:25,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,South Edwardes Square,NULL,W8,524987,179079,524950,179050,51.49684467,-0.20084439\n38115091,07/03/2009 15:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,West End Lane,NULL,UB3,NULL,NULL,508450,177350,NULL,NULL\n38412091,07/03/2009 22:52,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hooks Close,22501286,SE15,NULL,NULL,534750,176550,NULL,NULL\n39040091,08/03/2009 23:54,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,1.00024E+11,Elmers End Road,20300747,BR3,535275,169192,535250,169150,51.40562868,-0.056501617\n39688091,10/03/2009 10:18,2009,2008/09,Special Service,1,2,255,510,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000497,Bridge,E09000026,Redbridge,Woodford,NULL,Underwood Road,NULL,IG8,541613,191143,541650,191150,51.60133166,0.043365143\n40866091,12/03/2009 11:51,2009,2008/09,Special Service,2,3,255,765,Redacted,Horse,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100007496,The Chase,19900828,RM7,551413,186198,551450,186150,51.55435513,0.182630569\n40929091,12/03/2009 13:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),\"Nurseries, market garden\",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000503,Fairlop,E09000026,Redbridge,Hainault,NULL,Inverness Drive,NULL,IG6,545379,191578,545350,191550,51.60428185,0.097883827\n40946091,12/03/2009 14:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Grange Road,22201429,E13,NULL,NULL,539950,182650,NULL,NULL\n42324091,14/03/2009 20:06,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (land line),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,1.00023E+11,Abbeville Road,21900061,SW4,529250,174250,529250,174250,51.45248937,-0.141230246\n42388091,14/03/2009 21:29,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Eltham,1.0002E+11,Barham Road,20302299,BR7,543609,171318,543650,171350,51.42268621,0.064096003\n42771091,15/03/2009 14:59,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Station Road,21401864,UB7,NULL,NULL,506350,179650,NULL,NULL\n42805091,15/03/2009 15:55,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547276,175179,547250,175150,51.45643893,0.118404478\n43668091,16/03/2009 19:56,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Hawarden Road,22843850,E17,NULL,NULL,535850,189150,NULL,NULL\n44528091,18/03/2009 09:37,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Tollet Street,22701221,E1,NULL,NULL,535650,182250,NULL,NULL\n44576091,18/03/2009 11:23,2009,2008/09,Special Service,1,2,255,510,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Westbury Road,22200283,E7,NULL,NULL,540850,184950,NULL,NULL\n44593091,18/03/2009 11:46,2009,2008/09,Special Service,1,1,255,255,Redacted,Sheep,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Lea Valley Road,NULL,E4,537512,195033,537550,195050,51.63730176,-0.014284793\n45058091,18/03/2009 22:07,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000529,South Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Strawberry Vale,22403855,TW1,516044,172402,516050,172450,51.43873711,-0.331805652\n46051091,20/03/2009 13:35,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Coombe Road,20502601,CR0,NULL,NULL,532450,164850,NULL,NULL\n46168091,20/03/2009 16:36,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207116149,Elm Gardens,20702659,EN2,532650,198384,532650,198350,51.66858263,-0.083228566\n46614091,21/03/2009 09:22,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Northington Street,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL\n46818091,21/03/2009 16:01,2009,2008/09,Special Service,1,1,255,255,Redacted,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000282,West Green,E09000014,Haringey,Hornsey,NULL,Langham Close,21103517,N15,NULL,NULL,531550,189650,NULL,NULL\n46950091,21/03/2009 18:24,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Old Kent Road,2.00003E+11,Hoyland Close,22501310,SE15,534615,177301,534650,177350,51.47865805,-0.062904025\n49666091,25/03/2009 19:53,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,6136084,Eastfield Street,22702396,E14,536442,181589,536450,181550,51.51675404,-0.034953717\n49798091,25/03/2009 23:32,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Warehouse,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000094,Kilburn,E09000005,Brent,West Hampstead,NULL,Priory Park Road,NULL,NW6,525021,183897,525050,183850,51.54013744,-0.198646295\n50107091,26/03/2009 16:04,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Norbury,NULL,Lewin Road,21900869,SW16,NULL,NULL,529850,170950,NULL,NULL\n50224091,26/03/2009 19:39,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,100000157,Lullington Road,19900003,RM9,548250,184167,548250,184150,51.53694489,0.136186442\n50969091,28/03/2009 03:28,2009,2008/09,Special Service,1,1,255,255,Redacted,Dog,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000317,Pettits,E09000016,Havering,Romford,1.00021E+11,Collier Row Lane,21301094,RM5,550223,190138,550250,190150,51.59007418,0.167161739\n51076091,28/03/2009 10:51,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Farnley Road,20500936,SE25,NULL,NULL,532950,168050,NULL,NULL\n51218091,28/03/2009 16:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128011094,Lady Booth Road,21800579,KT1,518241,169196,518250,169150,51.40946896,-0.301280159\n51295091,28/03/2009 18:12,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,NULL,Wellesley Road,21501177,W4,NULL,NULL,520150,178250,NULL,NULL\n51306091,28/03/2009 18:38,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,20400794,NW1,NULL,NULL,529550,183150,NULL,NULL\n51613091,29/03/2009 11:37,2009,2008/09,Special Service,1,1,255,255,Redacted,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,NULL,NW1,529876,182723,529850,182750,51.52849185,-0.129111558\n52735091,31/03/2009 07:40,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (land line),\"Takeaway, fast food\",Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,1.00023E+11,Main Road,20302882,TN16,542161,158562,542150,158550,51.30842551,0.038190634\n53242091,31/03/2009 21:00,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Police,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Petersham Road,NULL,TW10,518275,173281,518250,173250,51.44617602,-0.299427934\n53328091,31/03/2009 23:41,2009,2008/09,Special Service,1,1,255,255,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000476,Boleyn,E09000025,Newham,Plaistow,NULL,Boundary Road,22200398,E13,NULL,NULL,541550,182950,NULL,NULL\n53426091,01/04/2009 07:01,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Petersham Road,NULL,TW10,518215,173325,518250,173350,51.44658399,-0.300276241\n54193091,02/04/2009 11:59,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Fife Road,NULL,SW14,520423,174506,520450,174550,51.45673375,-0.268116991\n54586091,03/04/2009 01:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Victoria Road,NULL,CR4,527135,170076,527150,170050,51.41545484,-0.173149512\n56140091,05/04/2009 11:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000140,Kilburn,E09000007,Camden,West Hampstead,5021535,Kingsgate Road,20400550,NW6,525284,183950,525250,183950,51.5405555,-0.194837117\n56826091,06/04/2009 12:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Banstock Road,20002240,HA8,NULL,NULL,520050,191650,NULL,NULL\n57003091,06/04/2009 16:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other retail,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000437,Bellingham,E09000023,Lewisham,Forest Hill,NULL,Holmshaw Close,NULL,SE26,536484,171829,536450,171850,51.42903652,-0.038116382\n58297091,08/04/2009 14:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,1.00021E+11,St. Peters Road,20502342,CR0,532752,164426,532750,164450,51.36339425,-0.094533415\n59370091,10/04/2009 04:39,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Woodside Road,20501593,SE25,NULL,NULL,534750,167150,NULL,NULL\n59435091,10/04/2009 09:58,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000429,Stockwell,E09000022,Lambeth,Brixton,NULL,Burnley Road,21900263,SW9,NULL,NULL,530750,176350,NULL,NULL\n60105091,11/04/2009 11:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Earls Court Road,21700166,SW5,NULL,NULL,525650,178450,NULL,NULL\n60185091,11/04/2009 15:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Liverpool Road,21606676,N1,NULL,NULL,531350,184050,NULL,NULL\n60188091,11/04/2009 15:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870048525,Glena Mount,22602333,SM1,526316,164883,526350,164850,51.36896711,-0.18676661\n60418091,11/04/2009 23:32,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,NULL,Kenton Lane,21201479,HA3,NULL,NULL,515750,191450,NULL,NULL\n60935091,12/04/2009 22:40,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Grosvenor Avenue,21400854,UB4,509634,183116,509650,183150,51.53631224,-0.420667426\n61258091,13/04/2009 14:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Searles Close,22904532,SW11,NULL,NULL,527350,177050,NULL,NULL\n61338091,13/04/2009 17:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Sharps Lane,21401730,HA4,NULL,NULL,508650,187550,NULL,NULL\n62970091,16/04/2009 13:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Shakespeare Crescent,22200245,E12,NULL,NULL,542950,184750,NULL,NULL\n63575091,17/04/2009 16:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Manor Road,19900861,RM10,NULL,NULL,550550,184550,NULL,NULL\n63759091,17/04/2009 22:04,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Riverside Close,NULL,RM1,550769,189275,550750,189250,51.58217448,0.174666936\n64532091,19/04/2009 09:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Sudeley Street,NULL,N1,NULL,NULL,531850,183250,NULL,NULL\n64561091,19/04/2009 11:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Westbourne Terrace,8400878,W2,NULL,NULL,526450,181150,NULL,NULL\n64780091,19/04/2009 20:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Kitchener Road,22207776,E7,NULL,NULL,540750,184650,NULL,NULL\n65083091,20/04/2009 11:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Penshurst Gardens,20034160,HA8,NULL,NULL,519750,192250,NULL,NULL\n65250091,20/04/2009 15:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,King George Avenue,NULL,E16,541587,181218,541550,181250,51.51215481,0.038998078\n66330091,22/04/2009 07:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000317,Pettits,E09000016,Havering,Romford,NULL,Heather Drive,21301721,RM1,NULL,NULL,551150,190450,NULL,NULL\n66477091,22/04/2009 13:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000272,Highgate,E09000014,Haringey,Hornsey,NULL,Stormont Road,NULL,N6,527547,187711,527550,187750,51.57384876,-0.160860203\n66704091,22/04/2009 18:11,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reed Road,21106602,N17,NULL,NULL,533950,190150,NULL,NULL\n67069091,23/04/2009 10:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Warrior Square,22200278,E12,NULL,NULL,543050,185950,NULL,NULL\n67368091,23/04/2009 19:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Canterbury Road,20500756,CR0,NULL,NULL,530950,166850,NULL,NULL\n67495091,23/04/2009 22:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Victoria Park Road,20901033,E9,NULL,NULL,535250,183850,NULL,NULL\n67718091,24/04/2009 10:23,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Monmouth Road,22200865,E6,NULL,NULL,542650,182550,NULL,NULL\n67891091,24/04/2009 14:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,Ponders End,E09000010,Enfield,Edmonton,NULL,Southfield Road,NULL,EN3,535170,195390,535150,195350,51.64107805,-0.047968305\n68399091,25/04/2009 10:41,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Fairholme Road,22603018,SM1,NULL,NULL,524750,163850,NULL,NULL\n68438091,25/04/2009 12:32,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535144,185361,535150,185350,51.55096175,-0.052202746\n68463091,25/04/2009 13:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535144,185361,535150,185350,51.55096175,-0.052202746\n69109091,26/04/2009 10:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34038663,Stanlake Villas,21000775,W12,523047,180128,523050,180150,51.50669755,-0.228411641\n69428091,26/04/2009 16:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Dundee Road,20500883,SE25,NULL,NULL,534850,167750,NULL,NULL\n69718091,26/04/2009 23:00,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,High Road,NULL,E11,539108,185976,539150,185950,51.55552606,0.005176124\n70888091,29/04/2009 02:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,6138601,Printers Mews,22702385,E3,536341,183625,536350,183650,51.53507423,-0.03562075\n70939091,29/04/2009 06:57,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Farr Avenue,NULL,IG11,546128,183247,546150,183250,51.52923128,0.105230888\n71406091,29/04/2009 19:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,34007375,Verbena Gardens,21000845,W6,522081,178294,522050,178250,51.49042384,-0.242957984\n72306091,01/05/2009 10:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Surbiton Hill Park,21800924,KT5,NULL,NULL,519350,167950,NULL,NULL\n72385091,01/05/2009 13:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529650,175050,NULL,NULL\n72856091,01/05/2009 23:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Hornsey Road,21603387,N7,NULL,NULL,530650,186350,NULL,NULL\n73167091,02/05/2009 13:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Belgrave Avenue,NULL,RM2,553486,189963,553450,189950,51.58762293,0.214149429\n73250091,02/05/2009 16:00,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Grafton Square,21900630,SW4,NULL,NULL,529350,175550,NULL,NULL\n73338091,02/05/2009 18:12,2009,2009/10,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Other industrial manufacturing facility,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536167,195414,536150,195450,51.64105284,-0.033559972\n73771091,03/05/2009 09:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Exeter Road,20702225,EN3,NULL,NULL,535850,196650,NULL,NULL\n74585091,04/05/2009 16:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Britannia Row,NULL,N1,532113,183709,532150,183750,51.53683414,-0.096512325\n74705091,04/05/2009 19:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009376,Homerton,E09000012,Hackney,Homerton,1.00021E+11,Collent Street,20900264,E9,535464,184570,535450,184550,51.54377698,-0.047894229\n75025091,05/05/2009 11:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000630,Abbey Road,E09000033,Westminster,Paddington,NULL,Hill Road,8400370,NW8,NULL,NULL,526350,183050,NULL,NULL\n75774091,06/05/2009 13:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Greenfield Avenue,21800469,KT5,NULL,NULL,520050,167150,NULL,NULL\n75866091,06/05/2009 16:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000311,Hacton,E09000016,Havering,Hornchurch,1.00021E+11,Plumpton Avenue,21300541,RM12,554521,186029,554550,186050,51.55199456,0.227349917\n75921091,06/05/2009 18:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Sidcup,NULL,Mead Road,20302425,BR7,NULL,NULL,544150,171050,NULL,NULL\n76285091,07/05/2009 10:46,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,N18,535997,191703,535950,191750,51.60774677,-0.037455336\n77153091,08/05/2009 20:14,2009,2009/10,Special Service,2,3,260,780,Redacted,Deer,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000345,Yiewsley,E09000017,Hillingdon,Hayes,NULL,Iron Bridge Road,NULL,UB7,507229,179972,507250,179950,51.50851513,-0.456284776\n77871091,09/05/2009 20:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,1.00023E+11,Portman Avenue,22404893,SW14,520725,175579,520750,175550,51.46631319,-0.263406575\n78016091,09/05/2009 23:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Acacia Road,22100045,CR4,NULL,NULL,528750,169250,NULL,NULL\n78207091,10/05/2009 08:09,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Fraser Road,20704236,N9,NULL,NULL,534450,193250,NULL,NULL\n78277091,10/05/2009 11:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Other FRS,Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207173552,The Town,20703007,EN2,532891,196563,532850,196550,51.65216199,-0.080437331\n78838091,11/05/2009 07:45,2009,2009/10,Special Service,3,7,260,1820,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Dod Street,NULL,E14,536750,181250,536750,181250,51.5136333,-0.030649027\n78877091,11/05/2009 09:34,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Villas,21000775,W12,NULL,NULL,523050,180150,NULL,NULL\n78887091,11/05/2009 09:57,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Milespit Hill,NULL,NW7,522930,191874,522950,191850,51.61228715,-0.225992137\n79115091,11/05/2009 16:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Priory Avenue,22866350,E17,537324,188885,537350,188850,51.58210313,-0.019404797\n79225091,11/05/2009 18:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL\n79685091,12/05/2009 11:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Palace Gate,21700390,W8,NULL,NULL,526150,179650,NULL,NULL\n79935091,12/05/2009 17:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Beaufort Street,21700031,SW3,NULL,NULL,526850,177550,NULL,NULL\n79987091,12/05/2009 18:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,NULL,Stellman Close,20900972,E5,NULL,NULL,534050,186050,NULL,NULL\n80034091,12/05/2009 19:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Linden Road,22400961,TW12,NULL,NULL,513050,169950,NULL,NULL\n80571091,13/05/2009 16:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Squirrel,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Hampstead Way,NULL,NW11,524933,188618,524950,188650,51.58258503,-0.198238142\n80721091,13/05/2009 19:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Stevenage Road,NULL,SW6,NULL,NULL,523550,176750,NULL,NULL\n80733091,13/05/2009 19:44,2009,2009/10,Special Service,2,3,260,780,Redacted,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Agnes Street,22700051,E14,NULL,NULL,536850,181450,NULL,NULL\n81137091,14/05/2009 15:16,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Hendon Road,20704328,N9,NULL,NULL,534450,193950,NULL,NULL\n81584091,15/05/2009 09:56,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,Alexandra,E09000014,Haringey,Hornsey,1.00021E+11,Dukes Avenue,21101214,N10,529189,189965,529150,189950,51.5937312,-0.136350772\n81713091,15/05/2009 14:53,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000172,Dormers Wells,E09000009,Ealing,Southall,NULL,Fleming Road,20600676,UB1,NULL,NULL,514150,181050,NULL,NULL\n81770091,15/05/2009 16:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Gunmakers Lane,NULL,E3,536431,183664,536450,183650,51.53540298,-0.034308843\n82208091,16/05/2009 13:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Car,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000088,Dollis Hill,E09000005,Brent,West Hampstead,NULL,Edgware Road,NULL,NW2,523093,186704,523050,186750,51.5657882,-0.225450969\n82239091,16/05/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Eton Road,NULL,BR6,546760,164562,546750,164550,51.36117455,0.10659316\n82314091,16/05/2009 17:09,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011235,Barkingside,E09000026,Redbridge,Hainault,1.00022E+11,Glenthorne Gardens,22302878,IG6,543552,189468,543550,189450,51.58579059,0.07065814\n82769091,17/05/2009 09:54,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Castle Hill Avenue,20501679,CR0,NULL,NULL,538050,162850,NULL,NULL\n83410091,18/05/2009 13:44,2009,2009/10,Special Service,1,4,260,1040,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Wanstead Park Road,22303358,IG1,NULL,NULL,542550,187150,NULL,NULL\n83903091,19/05/2009 10:38,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Multi-Storey car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,1.00023E+11,Belmont Road,21400145,UB8,505250,184250,505250,184250,51.54733916,-0.483512375\n83922091,19/05/2009 11:35,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207174568,South Ordnance Road,20706877,EN3,537163,198347,537150,198350,51.66716695,-0.018026026\n84125091,19/05/2009 17:46,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12068057,Camborne Avenue,20600303,W13,516987,179766,516950,179750,51.50472921,-0.315808596\n84176091,19/05/2009 19:07,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,10022798455,Northern Perimeter Road,21401419,TW6,507059,176863,507050,176850,51.48060266,-0.459676129\n84307091,20/05/2009 00:18,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Fitzjohns Avenue,20400156,NW3,NULL,NULL,526550,185050,NULL,NULL\n84523091,20/05/2009 11:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Hedgemans Way,19900490,RM9,NULL,NULL,548250,184450,NULL,NULL\n85725091,22/05/2009 10:30,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL\n85961091,22/05/2009 16:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Clarence Street,NULL,KT1,517750,169250,517750,169250,51.41005638,-0.308318681\n86422091,23/05/2009 06:21,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,1.0002E+11,Dallin Road,20100417,DA6,547912,175328,547950,175350,51.45761208,0.12761399\n87224091,24/05/2009 10:18,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Cuckoo Hall Lane,20703243,N9,NULL,NULL,535650,194850,NULL,NULL\n87402091,24/05/2009 14:53,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536306,191889,536350,191850,51.60934349,-0.032923514\n87493091,24/05/2009 16:46,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Other FRS,Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,540223,194887,540250,194850,51.63532158,0.024805463\n89326091,27/05/2009 08:42,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Stockford Avenue,20041050,NW7,NULL,NULL,523950,191350,NULL,NULL\n89957091,28/05/2009 11:11,2009,2009/10,Special Service,1,1,260,260,Redacted,Horse,Police,\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363\n90144091,28/05/2009 17:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Waldegrave Road,19900744,RM8,NULL,NULL,547150,186850,NULL,NULL\n90706091,29/05/2009 12:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Chichester Close,20800330,SE3,NULL,NULL,541150,177050,NULL,NULL\n90806091,29/05/2009 15:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Swiftsden Way,22001946,BR1,NULL,NULL,539450,170750,NULL,NULL\n92294091,31/05/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Wandsworth,NULL,Wandsworth High Street,NULL,SW18,NULL,NULL,525250,174750,NULL,NULL\n92335091,31/05/2009 15:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000466,Merton Park,E09000024,Merton,Wimbledon,NULL,Kenley Road,22103553,SW19,NULL,NULL,525050,169150,NULL,NULL\n92987091,01/06/2009 14:14,2009,2009/10,Special Service,1,1,260,260,Redacted,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Chingford Avenue,22825100,E4,NULL,NULL,537750,193450,NULL,NULL\n93306091,01/06/2009 19:49,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,The Sunny Road,NULL,EN3,535909,197600,535950,197650,51.66075919,-0.036437145\n93409091,01/06/2009 21:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Martha Street,NULL,E1,535106,181051,535150,181050,51.51224017,-0.054402715\n93474091,01/06/2009 23:58,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000066,Blackfen and Lamorbey,E09000004,Bexley,Eltham,NULL,Raeburn Road,NULL,DA15,545353,174226,545350,174250,51.44837222,0.090354776\n93633091,02/06/2009 10:12,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,1.00023E+11,Evering Road,20900387,N16,534014,186173,534050,186150,51.55852818,-0.068182007\n93664091,02/06/2009 11:15,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,London Lane,20302952,BR1,NULL,NULL,540350,170350,NULL,NULL\n93755091,02/06/2009 13:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12068057,Camborne Avenue,20600303,W13,516996,179757,516950,179750,51.50464646,-0.315681966\n93771091,02/06/2009 13:49,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Oscar Street,NULL,SE8,537152,176583,537150,176550,51.47159661,-0.026672594\n93925091,02/06/2009 16:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Manor Park Road,20028300,N2,NULL,NULL,526550,189750,NULL,NULL\n94505091,03/06/2009 11:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,London Lane,20302952,BR1,540372,170382,540350,170350,51.41508507,0.017201125\n94526091,03/06/2009 12:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Aveling Park Road,22812350,E17,NULL,NULL,537350,190350,NULL,NULL\n94847091,03/06/2009 21:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000342,West Drayton,E09000017,Hillingdon,Hayes,1.00022E+11,Thornton Avenue,21401990,UB7,506893,179212,506850,179250,51.50174767,-0.461354654\n95289091,04/06/2009 16:17,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Gloucester Road,20500980,CR0,NULL,NULL,533050,167250,NULL,NULL\n95595091,04/06/2009 21:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Robson Avenue,20202225,NW10,NULL,NULL,522450,184250,NULL,NULL\n96023091,05/06/2009 17:39,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Delvino Road,21000270,SW6,NULL,NULL,525250,176650,NULL,NULL\n96063091,05/06/2009 18:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Adams Walk,NULL,KT1,518193,169280,518150,169250,51.41023391,-0.301942003\n96083091,05/06/2009 18:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Adams Walk,NULL,KT1,518193,169280,518150,169250,51.41023391,-0.301942003\n96737091,06/06/2009 19:29,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Airthrie Road,22301793,IG3,NULL,NULL,546650,186850,NULL,NULL\n96751091,06/06/2009 20:03,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Heavy Livestock Animal,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011487,Waddon,E09000008,Croydon,Croydon,2.00001E+11,Old Town,20501261,CR0,531995,165038,531950,165050,51.36907055,-0.105173804\n97181091,07/06/2009 10:24,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Launcelot Road,22003472,BR1,NULL,NULL,540550,171750,NULL,NULL\n97816091,08/06/2009 14:25,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207173553,The Town,20703007,EN2,532877,196570,532850,196550,51.6522282,-0.080636921\n98231091,09/06/2009 08:37,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Belgrave Road,22200367,E13,NULL,NULL,541050,182450,NULL,NULL\n98345091,09/06/2009 13:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Mill Street,22501722,SE1,NULL,NULL,533850,179750,NULL,NULL\n98378091,09/06/2009 14:19,2009,2009/10,Special Service,1,3,260,780,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Ruislip Road,20602098,UB5,NULL,NULL,512550,182850,NULL,NULL\n99402091,11/06/2009 13:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Hyrstdene,20502743,CR2,NULL,NULL,531950,164550,NULL,NULL\n100131091,12/06/2009 15:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Stratford Villas,20400668,NW1,NULL,NULL,529550,184350,NULL,NULL\n100378091,12/06/2009 21:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Lathom Road,22200795,E6,NULL,NULL,542950,184250,NULL,NULL\n100421091,12/06/2009 22:31,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Finchley,NULL,Brook Meadow,20005320,N12,NULL,NULL,525650,193050,NULL,NULL\n100673091,13/06/2009 10:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,518545,173686,518550,173650,51.44975962,-0.29540893\n100748091,13/06/2009 12:51,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Purley,NULL,Lloyd Avenue,22602437,CR5,NULL,NULL,528450,160150,NULL,NULL\n101149091,13/06/2009 22:20,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Ulleswater Road,20704867,N14,NULL,NULL,530550,193150,NULL,NULL\n101172091,13/06/2009 23:08,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Church Crescent,20008600,N3,NULL,NULL,524950,190750,NULL,NULL\n101506091,14/06/2009 14:28,2009,2009/10,Special Service,1,1,260,260,Redacted,Snake,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Parchmore Road,20501277,CR7,NULL,NULL,532250,168650,NULL,NULL\n101787091,14/06/2009 21:32,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Lorne Road,NULL,E17,537104,188705,537150,188750,51.58053917,-0.022648253\n102083091,15/06/2009 08:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,St. Leonards Road,NULL,E14,538209,181339,538250,181350,51.5140784,-0.009601643\n102278091,15/06/2009 15:13,2009,2009/10,Special Service,2,3,260,780,Redacted,Horse,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Billet Road,NULL,E17,537190,190998,537150,190950,51.60112257,-0.020513151\n103485091,16/06/2009 20:17,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Markhouse Road,22856450,E17,NULL,NULL,536850,187750,NULL,NULL\n103550091,16/06/2009 22:19,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Ladbroke Road,NULL,W11,525088,180506,525050,180550,51.5096471,-0.198884303\n104724091,18/06/2009 20:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Hambledon Gardens,20501016,SE25,NULL,NULL,533750,168650,NULL,NULL\n104744091,18/06/2009 20:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000172,Dormers Wells,E09000009,Ealing,Southall,12015882,Fleming Road,20600676,UB1,514197,181099,514150,181050,51.51728004,-0.355558207\n104839091,18/06/2009 23:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lee Green,10091837631,Lee High Road,22004135,SE13,538749,175299,538750,175250,51.45966936,-0.004197807\n104860091,19/06/2009 00:45,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Northumberland Park,21104859,N17,NULL,NULL,534350,191450,NULL,NULL\n104961091,19/06/2009 08:47,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Ecclesbourne Road,20500893,CR7,NULL,NULL,532350,167950,NULL,NULL\n105152091,19/06/2009 15:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,1.00022E+11,Robson Road,21901163,SE27,532202,172317,532250,172350,51.43443673,-0.099491724\n105158091,19/06/2009 15:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Loampit Hill,22000636,SE13,NULL,NULL,537550,176050,NULL,NULL\n105324091,19/06/2009 18:07,2009,2009/10,Special Service,1,2,260,520,Redacted,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,NULL,Ennismore Gardens,8401318,SW7,NULL,NULL,527050,179450,NULL,NULL\n105494091,19/06/2009 21:27,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000447,Lee Green,E09000023,Lewisham,Lee Green,NULL,Taunton Road,NULL,SE12,539697,174860,539650,174850,51.45549166,0.009265702\n105589091,20/06/2009 00:06,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Nightingale Road,NULL,N22,530716,191077,530750,191050,51.60337209,-0.113905662\n105830091,20/06/2009 09:21,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Saxon Road,22201036,E6,NULL,NULL,542550,182250,NULL,NULL\n105902091,20/06/2009 12:13,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000127,West Wickham,E09000006,Bromley,Beckenham,1.0002E+11,North Road,20301772,BR4,537894,166191,537850,166150,51.3780314,-0.02003555\n106013091,20/06/2009 15:05,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Mill Lane,NULL,SM5,NULL,NULL,528150,164950,NULL,NULL\n106536091,21/06/2009 09:50,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,High Road Leyton,22844900,E10,NULL,NULL,538050,186550,NULL,NULL\n106585091,21/06/2009 11:55,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Wild animal rescue from height,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,Norbury,1.00021E+11,Glenhurst Rise,20500063,SE19,532303,170121,532350,170150,51.41467829,-0.098858661\n106628091,21/06/2009 13:36,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,Kenley,E09000008,Croydon,Purley,1.00021E+11,Godstone Road,20503071,CR8,532409,160217,532450,160250,51.32564869,-0.101021031\n106786091,21/06/2009 18:22,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Marshalls Close,NULL,N11,528762,193022,528750,193050,51.62130078,-0.141389907\n106858091,21/06/2009 19:38,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Roslyn Road,21103823,N15,NULL,NULL,532950,188950,NULL,NULL\n107370091,22/06/2009 13:43,2009,2009/10,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000183,North Greenford,E09000009,Ealing,Northolt,NULL,Hutton Close,20600930,UB6,NULL,NULL,514650,185450,NULL,NULL\n107995091,23/06/2009 09:10,2009,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,St. Michaels Street,8401928,W2,NULL,NULL,527050,181450,NULL,NULL\n108839091,24/06/2009 14:26,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Orpington,NULL,Holwood Park Avenue,20303006,BR6,NULL,NULL,542850,164750,NULL,NULL\n109011091,24/06/2009 18:29,2009,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Wentworth Road,NULL,CR0,531262,166822,531250,166850,51.38527269,-0.115039284\n109020091,24/06/2009 18:41,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,1.00022E+11,Queensthorpe Road,22001820,SE26,535602,171699,535650,171650,51.42807985,-0.050845312\n110129091,25/06/2009 10:02,2009,2009/10,Special Service,1,1,260,260,KITTEN WITH HEAD STUCK IN JAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Keedonwood Road,22005125,BR1,NULL,NULL,539350,171450,NULL,NULL\n110478091,25/06/2009 19:32,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,1.00021E+11,Manchester Road,21103584,N15,532983,188176,532950,188150,51.57677212,-0.082288888\n110527091,25/06/2009 20:47,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN DRAINPIPE,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000077,Lesnes Abbey,E09000004,Bexley,Erith,NULL,New Road,NULL,SE2,547709,178623,547750,178650,51.48727187,0.126071312\n110531091,25/06/2009 20:56,2009,2009/10,Special Service,1,1,260,260,DEER STUCK IN FENCE,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000339,Townfield,E09000017,Hillingdon,Hillingdon,1.00023E+11,Wood End,21402199,UB3,509511,181260,509550,181250,51.51965437,-0.423016711\n110594091,25/06/2009 22:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000092,Kensal Green,E09000005,Brent,Willesden,NULL,Bramston Road,20200419,NW10,NULL,NULL,522350,183550,NULL,NULL\n111619091,27/06/2009 08:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER BUILDING FOUNDATIONS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Cranley Gardens,21100200,N10,NULL,NULL,528850,189150,NULL,NULL\n113037091,28/06/2009 01:18,2009,2009/10,Special Service,1,1,260,260,INJURED PIGEON TRAPPED ON LEDGE,Bird,Person (land line),Police station,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034922099,High Road,22306010,IG1,544309,186651,544350,186650,51.56028527,0.08042085\n113265091,28/06/2009 13:01,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),DIY Warehouse,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Sixth Cross Road,22401847,TW2,514667,172042,514650,172050,51.43578182,-0.351724788\n113497091,28/06/2009 18:45,2009,2009/10,Special Service,1,1,260,260,DUCK TRAPPED BY FISHING LINE IN CENTRE OF LAKE,Bird,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Thicket Road,20302199,SE20,534576,170498,534550,170450,51.41753138,-0.066049971\n113582091,28/06/2009 20:23,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRDS TRAPPED IN FISHING LINE ON POND,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Clapham Common South Side,NULL,SW4,528972,174604,528950,174650,51.45573409,-0.145100062\n113630091,28/06/2009 21:37,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NEETING,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000175,East Acton,E09000009,Ealing,Acton,12151266,East Acton Lane,20602069,W3,521167,180417,521150,180450,51.50970059,-0.255387834\n113669091,28/06/2009 22:52,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Vallance Road,22701259,E1,NULL,NULL,534450,182050,NULL,NULL\n114153091,29/06/2009 16:31,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER VAN WHEELARCH,Dog,Police,Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Elmgate Gardens,NULL,HA8,520574,192594,520550,192550,51.61926622,-0.259755242\n114400091,29/06/2009 21:37,2009,2009/10,Special Service,1,2,260,520,INJURED CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,North Audley Street,8401474,W1K,NULL,NULL,528250,180950,NULL,NULL\n115111091,30/06/2009 16:49,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Rush Green Gardens,21301569,RM7,NULL,NULL,550550,187450,NULL,NULL\n115812091,01/07/2009 13:21,2009,2009/10,Special Service,1,2,260,520,CROW STUCK IN WIRING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00023E+11,Orchard Way,20500526,CR0,536578,166770,536550,166750,51.38355185,-0.038710578\n115841091,01/07/2009 14:19,2009,2009/10,Special Service,1,1,260,260,UNKNOWN SNAKE IN HOUSE,Snake,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Brayfield Terrace,21604331,N1,NULL,NULL,531150,184050,NULL,NULL\n116595091,02/07/2009 08:46,2009,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Bridle Road,21400247,HA5,NULL,NULL,511350,188450,NULL,NULL\n116616091,02/07/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Lanvanor Road,22501479,SE15,NULL,NULL,535050,176250,NULL,NULL\n116790091,02/07/2009 15:30,2009,2009/10,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Wallington,NULL,Corbet Close,22605443,SM6,NULL,NULL,528150,165850,NULL,NULL\n117217091,02/07/2009 23:59,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CELLAR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Broxholm Road,21900257,SE27,NULL,NULL,531250,172250,NULL,NULL\n117355091,03/07/2009 06:20,2009,2009/10,Special Service,1,2,260,520,CAT TRAPPED IN CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000546,Peckham,E09000028,Southwark,Peckham,NULL,Sumner Road,NULL,SE15,533909,177153,533950,177150,51.47749554,-0.073120332\n117460091,03/07/2009 10:39,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000347,Brentford,E09000018,Hounslow,Chiswick,NULL,Hope Close,NULL,TW8,518038,178098,518050,178050,51.48951951,-0.301230311\n117546091,03/07/2009 13:34,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Crane Grove,21604621,N7,NULL,NULL,531450,184750,NULL,NULL\n117580091,03/07/2009 14:12,2009,2009/10,Special Service,1,1,260,260,PET LIZARD TRAPPED BEHIND RADIATOR,Lizard,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Deansbrook Road,20011980,HA8,NULL,NULL,520350,191450,NULL,NULL\n117790091,03/07/2009 18:04,2009,2009/10,Special Service,1,2,260,520,Redacted,Snake,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,1.00022E+11,Clifton Gardens,21500282,W4,520814,178685,520850,178650,51.49420973,-0.26106487\n118265091,04/07/2009 10:29,2009,2009/10,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Havelock Road,NULL,SW19,526216,171229,526250,171250,51.42602232,-0.185947876\n118289091,04/07/2009 11:15,2009,2009/10,Special Service,1,1,260,260,DOG STUCK UNDER JETTY IN LAKE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,NULL,The Chase,NULL,RM7,550970,186014,550950,186050,51.55282046,0.176166741\n119148091,05/07/2009 09:25,2009,2009/10,Special Service,NULL,NULL,260,NULL,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Regina Road,20501335,SE25,NULL,NULL,534250,168650,NULL,NULL\n119220091,05/07/2009 11:50,2009,2009/10,Special Service,1,2,260,520,DEER TRAPPED IN DITCH,Deer,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,NULL,Oaklands Lane,NULL,TN16,541262,159497,541250,159450,51.31705164,0.025671909\n119274091,05/07/2009 13:05,2009,2009/10,Special Service,1,1,260,260,CAT ON WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Nye Bevan Estate,20950152,E5,NULL,NULL,535850,186050,NULL,NULL\n119988091,06/07/2009 11:08,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN KITCHEN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,NULL,Heath Road,19900168,RM6,NULL,NULL,547950,187650,NULL,NULL\n120163091,06/07/2009 16:17,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE BEHIND,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,New Road,NULL,TW14,509091,173878,509050,173850,51.45338397,-0.431345303\n120711091,07/07/2009 15:29,2009,2009/10,Special Service,1,1,260,260,DOG ON WINDOW LEDGE,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,St. Peter's Way,20900960,N1,NULL,NULL,533450,184150,NULL,NULL\n121132091,07/07/2009 19:29,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED ON WALL BY NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Hatherley Gardens,NULL,N8,NULL,NULL,530150,188250,NULL,NULL\n121370091,08/07/2009 06:33,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN PEN,Cat,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Woodview,21801080,KT9,NULL,NULL,517050,161550,NULL,NULL\n121400091,08/07/2009 07:50,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Sydenham Hill,22005552,SE26,534452,172089,534450,172050,51.43185854,-0.067229066\n121763091,08/07/2009 18:48,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,Copperfield Road,22700356,E3,NULL,NULL,536350,182050,NULL,NULL\n122010091,09/07/2009 06:42,2009,2009/10,Special Service,1,1,260,260,DOG STUCK UNDER SHED,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46016969,Clifton Road,22201355,E16,539488,181730,539450,181750,51.51727783,0.008972957\n122114091,09/07/2009 11:10,2009,2009/10,Special Service,1,2,260,520,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00023E+11,Luxor Street,21900905,SE5,532028,175994,532050,175950,51.46752178,-0.100622633\n122734091,10/07/2009 09:49,2009,2009/10,Special Service,1,1,260,260,DISTRESSED KITTEN STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5028919,Lawn Road,20400310,NW3,527542,185149,527550,185150,51.5508257,-0.161860922\n122755091,10/07/2009 10:23,2009,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED IN FISHING LINE AND HANGING FROM TREE,Bird,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Oakfield Road,NULL,CR0,532212,166438,532250,166450,51.38160165,-0.101538053\n122958091,10/07/2009 15:38,2009,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED UNDER FLOORBOARDIS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Regents Park Road,20036360,N3,NULL,NULL,524850,189750,NULL,NULL\n123495091,11/07/2009 11:26,2009,2009/10,Special Service,1,1,260,260,HORSE COLLAPSED,Horse,Police,Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Lonsdale Road,NULL,SW13,522727,177812,522750,177850,51.48595225,-0.233825239\n123496091,11/07/2009 11:26,2009,2009/10,Special Service,1,1,260,260,INJURED CAT STUCK AT TOP OF TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Casterbridge Road,NULL,SE3,NULL,NULL,540450,175650,NULL,NULL\n123501091,11/07/2009 11:31,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN SKY LIGHT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Stormont Road,22904998,SW11,NULL,NULL,528250,175350,NULL,NULL\n124094091,12/07/2009 11:47,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Laker Place,22906531,SW15,NULL,NULL,524450,174450,NULL,NULL\n124719091,13/07/2009 12:27,2009,2009/10,Special Service,1,1,260,260,\"CAT TRAPPED UNDER OBJECT,\",Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000627,Wandsworth Common,E09000032,Wandsworth,Tooting,NULL,College Gardens,22901019,SW17,NULL,NULL,527250,172650,NULL,NULL\n124774091,13/07/2009 14:24,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN LEAD,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Liverpool Road,21606676,N1,NULL,NULL,531350,183850,NULL,NULL\n124937091,13/07/2009 18:17,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARS OF GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Hackington Crescent,20302253,BR3,NULL,NULL,537450,170850,NULL,NULL\n125073091,13/07/2009 22:33,2009,2009/10,Special Service,1,2,260,520,DOG STRANDED IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Lee Green,NULL,South Row,NULL,SE3,539873,176419,539850,176450,51.46945755,0.012413146\n126901091,16/07/2009 18:57,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN LOFT SPACE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011481,Selsdon Vale & Forestdale,E09000008,Croydon,Addington,NULL,Pixton Way,20501806,CR0,NULL,NULL,536550,163050,NULL,NULL\n126989091,16/07/2009 20:52,2009,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY STACK,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,NULL,Forest Glade,22837100,E4,NULL,NULL,539150,192050,NULL,NULL\n127086091,16/07/2009 22:55,2009,2009/10,Special Service,1,2,260,520,KITTEN TRAPPED BEHIND PIPING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Goldsmith Road,22501075,SE15,NULL,NULL,534250,176850,NULL,NULL\n127265091,17/07/2009 08:12,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Ormeley Road,22903710,SW12,NULL,NULL,528950,173450,NULL,NULL\n127275091,17/07/2009 08:26,2009,2009/10,Special Service,1,1,260,260,CAT PAW TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Rochester Close,20702932,EN1,NULL,NULL,533450,197650,NULL,NULL\n128121091,18/07/2009 17:34,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Mayfield Avenue,20028980,N12,NULL,NULL,526750,192750,NULL,NULL\n128281091,18/07/2009 21:33,2009,2009/10,Special Service,1,1,260,260,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,34073354,Maynard Close,21000546,SW6,525958,177069,525950,177050,51.47856524,-0.187580134\n128667091,19/07/2009 13:10,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH DOG STRANDED ON ROOF,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Riversdale Road,21603742,N5,NULL,NULL,532050,186250,NULL,NULL\n129393091,20/07/2009 16:12,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA  GET CAT FROM TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000182,Northfield,E09000009,Ealing,Ealing,12082274,Creighton Road,20600470,W5,517601,179240,517650,179250,51.49987445,-0.30714137\n129850091,21/07/2009 08:57,2009,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN METAL GATE,Fox,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Eswyn Road,NULL,SW17,527714,171620,527750,171650,51.42920122,-0.164271938\n130051091,21/07/2009 15:47,2009,2009/10,Special Service,1,1,260,260,CAT FALLEN DOWN HOLE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Ironmonger Row,NULL,EC1V,532375,182625,532350,182650,51.52703135,-0.093144201\n130149091,21/07/2009 18:18,2009,2009/10,Special Service,1,1,260,260,PIDGEON TRAPPED ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Crews Street,22700379,E14,NULL,NULL,537150,178850,NULL,NULL\n130265091,21/07/2009 21:20,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Green Lanes,21106543,N4,NULL,NULL,531850,188550,NULL,NULL\n130592091,22/07/2009 12:34,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Gosberton Road,22902080,SW12,NULL,NULL,528250,173550,NULL,NULL\n130701091,22/07/2009 16:38,2009,2009/10,Special Service,1,1,260,260,DOG IN PRECAROUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011235,Barkingside,E09000026,Redbridge,Hainault,NULL,Longwood Gardens,22303025,IG5,NULL,NULL,543650,189650,NULL,NULL\n131215091,23/07/2009 12:31,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG TRAPPED BEHIND RADIATOR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,Larpent Avenue,22902819,SW15,NULL,NULL,523050,174950,NULL,NULL\n131792091,24/07/2009 11:42,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Southwark Street,22502304,SE1,532215,180267,532250,180250,51.50587829,-0.096333455\n131967091,24/07/2009 15:47,2009,2009/10,Special Service,1,1,260,260,TWO DOGS IN TOILET   ELDERLY LADY INVOLVED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Orpington,NULL,Holwood Park Avenue,20303006,BR6,NULL,NULL,542850,164750,NULL,NULL\n132513091,25/07/2009 11:17,2009,2009/10,Special Service,NULL,NULL,260,NULL,KITTEN TRAPPED IN GUTTERING ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Sandrock Road,22000900,SE13,NULL,NULL,537450,176050,NULL,NULL\n132530091,25/07/2009 11:36,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000054,Hale,E09000003,Barnet,Mill Hill,200102230,Selvage Lane,20039020,NW7,520680,192603,520650,192650,51.61932444,-0.258221717\n132594091,25/07/2009 13:38,2009,2009/10,Special Service,1,1,260,260,GOOSE TRAPPED IN FISHING WIRE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Clapham Common South Side,NULL,SW4,529176,174853,529150,174850,51.45792538,-0.142074729\n132939091,25/07/2009 22:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN A CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000498,Chadwell,E09000026,Redbridge,Ilford,NULL,Somerville Road,NULL,RM6,547329,188628,547350,188650,51.57726886,0.124783183\n133347091,26/07/2009 16:03,2009,2009/10,Special Service,1,1,260,260,DOG IN THE RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,NULL,SW6,525905,175719,525950,175750,51.46644439,-0.188823202\n134133091,27/07/2009 22:53,2009,2009/10,Special Service,1,1,260,260,KITTEN STUCK BEHINED KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Worcester Road,22605696,SM2,NULL,NULL,525750,163450,NULL,NULL\n135294091,29/07/2009 19:55,2009,2009/10,Special Service,1,1,260,260,PERSON LOCKED OUT DANGER OF FIRE,Unknown - Heavy Livestock Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532650,176950,NULL,NULL\n135342091,29/07/2009 22:21,2009,2009/10,Special Service,1,1,260,260,ASSIST MEMBER OF PUBLIC,Unknown - Heavy Livestock Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532650,176950,NULL,NULL\n135439091,30/07/2009 07:04,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Ongar Road,21000614,SW6,NULL,NULL,525250,177850,NULL,NULL\n135716091,30/07/2009 17:37,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Wallwood Street,22701288,E14,NULL,NULL,536950,181550,NULL,NULL\n136236091,31/07/2009 14:22,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN RAILINGS,Bird,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000368,Caledonian,E09000019,Islington,Holloway,NULL,Burness Close,NULL,N7,530730,184768,530750,184750,51.54667291,-0.116048453\n136460091,31/07/2009 18:41,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN METAL TABLE LEG,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Willoughby Road,20400173,NW3,NULL,NULL,526750,185650,NULL,NULL\n136604091,31/07/2009 21:45,2009,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND  FIREPLACE,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Quadrant,21603362,N5,NULL,NULL,532250,186250,NULL,NULL\n136894091,01/08/2009 08:56,2009,2009/10,Special Service,1,1,260,260,FOX AND CAT TRAPPED IN BASEMENT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,King Street,21000478,W6,NULL,NULL,522250,178650,NULL,NULL\n136955091,01/08/2009 11:53,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL CAUGHT IN FENCE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000356,Heston East,E09000018,Hounslow,Heston,NULL,Great West Road,21505052,TW5,NULL,NULL,513250,176750,NULL,NULL\n137177091,01/08/2009 18:29,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Sylvan Road,22207917,E7,NULL,NULL,540550,184850,NULL,NULL\n137344091,01/08/2009 22:57,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FENCE AND VAN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Southfleet Road,NULL,BR6,NULL,NULL,545350,165050,NULL,NULL\n137500091,02/08/2009 08:51,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Tilney Road,19900830,RM9,NULL,NULL,548750,184550,NULL,NULL\n137525091,02/08/2009 11:38,2009,2009/10,Special Service,1,5,260,1300,HORSE STUCK IN CANAL,Horse,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,N18,535750,192250,535750,192250,51.6127218,-0.04080847\n137549091,02/08/2009 12:55,2009,2009/10,Special Service,1,1,260,260,FOX STUCK ON ROOF,Fox,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,48103248,Majestic Way,22104137,CR4,527882,169132,527850,169150,51.40680309,-0.16275333\n138096091,03/08/2009 10:04,2009,2009/10,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000368,Caledonian,E09000019,Islington,Euston,NULL,Northdown Street,NULL,N1,NULL,NULL,530550,183050,NULL,NULL\n138743091,04/08/2009 08:19,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Hatherley Gardens,22200698,E6,NULL,NULL,541850,182750,NULL,NULL\n138786091,04/08/2009 10:20,2009,2009/10,Special Service,1,2,260,520,PIGEON TANGLED IN WIRE AT FIRST FLOOR LEVEL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Raynton Road,20702375,EN3,NULL,NULL,535850,198850,NULL,NULL\n139219091,04/08/2009 22:27,2009,2009/10,Special Service,1,1,260,260,KITTEN IN DISTRESS ON ROOF OF FLATS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Manchester Drive,21700938,W10,NULL,NULL,524150,182150,NULL,NULL\n141662091,08/08/2009 16:33,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIDGEON TRAPPED IN WIRE IN TREE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195250,NULL,NULL\n141814091,08/08/2009 20:18,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF AND IN DISTRESS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Anerley Hill,20302140,SE19,NULL,NULL,533950,170650,NULL,NULL\n141954091,08/08/2009 23:47,2009,2009/10,Special Service,1,2,260,520,DOG TRAPPED UNDER BUS,Dog,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000082,St. Michael's,E09000004,Bexley,Bexley,NULL,Elsa Road,NULL,DA16,546772,176434,546750,176450,51.46784649,0.111676447\n142184091,09/08/2009 11:54,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON WINDOW SILL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Golborne Road,21700877,W10,NULL,NULL,524550,182050,NULL,NULL\n142266091,09/08/2009 14:18,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Westbourne Terrace,8400878,W2,NULL,NULL,526450,181050,NULL,NULL\n142528091,09/08/2009 21:14,2009,2009/10,Special Service,1,1,260,260,CAT IN STUCK IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Oaklands Estate,21901112,SW4,NULL,NULL,529250,174150,NULL,NULL\n142558091,09/08/2009 22:14,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Falkland Avenue,20015300,N11,NULL,NULL,528550,192950,NULL,NULL\n142845091,10/08/2009 11:18,2009,2009/10,Special Service,1,1,260,260,PIGEON HANGING FROM WIRING,Bird,Person (land line),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Church Street,NULL,CR9,531927,165532,531950,165550,51.37352584,-0.105966985\n143598091,11/08/2009 15:27,2009,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,48050228,Mill Road,22104413,SW19,526396,170068,526350,170050,51.41554799,-0.183773793\n144140091,12/08/2009 11:47,2009,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH HEDGEHOG STUCK IN DRAIN,Hedgehog,Person (land line),Hotel/motel,Other Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05000357,Heston West,E09000018,Hounslow,Hayes,1.00024E+11,High Street,21500608,TW5,510762,177536,510750,177550,51.48593874,-0.406161445\n144194091,12/08/2009 13:23,2009,2009/10,Special Service,1,1,260,260,PIGEON STUCK ON ROOF AREA,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Wyndcliff Road,20801658,SE7,NULL,NULL,540750,177550,NULL,NULL\n144229091,12/08/2009 14:39,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Wiltshire Road,21901503,SW9,NULL,NULL,531350,175850,NULL,NULL\n144791091,13/08/2009 12:05,2009,2009/10,Special Service,1,3,260,780,Redacted,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Ferry Lane,NULL,N17,534971,189377,534950,189350,51.58709199,-0.053156734\n146772091,16/08/2009 11:45,2009,2009/10,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,Boxtree Lane,21201442,HA3,NULL,NULL,514750,191150,NULL,NULL\n146886091,16/08/2009 15:52,2009,2009/10,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN BASKET,Dog,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Evelyn Street,NULL,SE8,536614,178043,536650,178050,51.48484688,-0.03384924\n147019091,16/08/2009 18:56,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE WITH DUCKLINGS IN DRAIN,Bird,Police,Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000649,West End,E09000033,Westminster,Soho,NULL,Mount Row,NULL,W1K,528569,180689,528550,180650,51.51051119,-0.148684313\n147111091,16/08/2009 21:00,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE AT SCENE OF PREVIOUS INCIDENT,Unknown - Heavy Livestock Animal,Police,Pipe or drain,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000649,West End,E09000033,Westminster,Soho,NULL,Mount Row,NULL,W1K,528610,180753,528650,180750,51.51107703,-0.148070488\n147549091,17/08/2009 14:11,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Clonmore Street,22900991,SW18,NULL,NULL,524950,173050,NULL,NULL\n147848091,17/08/2009 20:50,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Cavendish Road,NULL,NW6,NULL,NULL,524650,184550,NULL,NULL\n147930091,17/08/2009 22:31,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Cator Street,22500466,SE15,NULL,NULL,533850,177150,NULL,NULL\n148936091,19/08/2009 15:57,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN TUNNEL,Dog,Person (mobile),\"Tunnel, subway\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Wood Vale,NULL,N10,529108,188786,529150,188750,51.58315455,-0.137952552\n149261091,19/08/2009 22:58,2009,2009/10,Special Service,1,1,260,260,INJURED CYGENT STUCK IN MUD,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000364,Syon,E09000018,Hounslow,Chiswick,NULL,Pump Alley,NULL,TW8,517924,177495,517950,177450,51.48412358,-0.303072753\n149275091,19/08/2009 23:12,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Padnall Road,NULL,RM6,547869,189245,547850,189250,51.58267167,0.132829952\n150546091,21/08/2009 18:52,2009,2009/10,Special Service,1,1,260,260,WHITE CAT TRAPPED ON TOP OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Coventry Road,22700372,E1,NULL,NULL,534850,182250,NULL,NULL\n150926091,22/08/2009 11:09,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN VACANT FLAT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Bury Road,21104308,N22,NULL,NULL,531350,190050,NULL,NULL\n151097091,22/08/2009 16:26,2009,2009/10,Special Service,2,3,260,780,YOUNG DEER TRAPPED IN RIVER,Deer,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Powys Lane,NULL,N13,529888,192479,529850,192450,51.61616261,-0.125335542\n151098091,22/08/2009 16:26,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Ilbert Street,NULL,W10,524424,182623,524450,182650,51.52881952,-0.207700271\n151712091,23/08/2009 14:51,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND BRICK WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Grosvenor Crescent,21400855,UB10,NULL,NULL,507750,184050,NULL,NULL\n151862091,23/08/2009 18:08,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Beechwood Crescent,NULL,DA7,548011,175889,548050,175850,51.46262703,0.129272714\n151966091,23/08/2009 20:39,2009,2009/10,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,Kenley,E09000008,Croydon,Purley,1.00021E+11,Valley Road,20502396,CR8,532961,159601,532950,159650,51.319984,-0.093332773\n151984091,23/08/2009 20:55,2009,2009/10,Special Service,1,1,260,260,HORSE IN DISTRESS,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011232,Thamesmead East,E09000004,Bexley,Erith,1.0002E+11,Coralline Walk,20100359,SE2,547374,179464,547350,179450,51.49491581,0.121600833\n152122091,24/08/2009 00:14,2009,2009/10,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN WINDOW,Cat,Person (land line),Tenement Building,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Eversholt Street,20400856,NW1,NULL,NULL,529650,182650,NULL,NULL\n153215091,25/08/2009 13:54,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED INSIDE VEHICLE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Perry Vale,NULL,SE23,535536,172993,535550,172950,51.43972422,-0.05129941\n153614091,25/08/2009 23:15,2009,2009/10,Special Service,1,1,260,260,PUPPY STUCK BEHIND RADIATOR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Clissold Crescent,20900257,N16,NULL,NULL,532650,185950,NULL,NULL\n153698091,26/08/2009 01:55,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN GATES,Deer,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Winnington Road,NULL,EN3,535138,198728,535150,198750,51.6710814,-0.047141127\n153742091,26/08/2009 06:44,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Raydons Road,19900296,RM9,NULL,NULL,548650,185450,NULL,NULL\n154250091,26/08/2009 19:46,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011479,Selhurst,E09000008,Croydon,Norbury,NULL,Kemp Gardens,20501089,CR0,NULL,NULL,532050,167050,NULL,NULL\n155237091,28/08/2009 11:13,2009,2009/10,Special Service,1,1,260,260,HOUSE MARTIN TAPPED IN GRATE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Egerton Road,20201459,HA0,NULL,NULL,518550,184250,NULL,NULL\n155475091,28/08/2009 18:21,2009,2009/10,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009381,London Fields,E09000012,Hackney,Bethnal Green,NULL,Mare Street,NULL,E8,534810,183540,534850,183550,51.53467762,-0.057713672\n156878091,30/08/2009 19:43,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Abdale Road,21000025,W12,NULL,NULL,522950,180250,NULL,NULL\n157140091,31/08/2009 03:55,2009,2009/10,Special Service,1,1,260,260,DOG THROWN INTO ELECTRICITY SUB STATION,Dog,Police,Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Ellington Road,NULL,TW13,509781,171828,509750,171850,51.43482548,-0.422053816\n157711091,31/08/2009 21:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Euston,NULL,Millman Street,20401009,WC1N,NULL,NULL,530650,182150,NULL,NULL\n157740091,31/08/2009 22:14,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN CURTAIN RAIL AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000571,Wandle Valley,E09000029,Sutton,Wallington,NULL,Mullards Close,22600835,CR4,NULL,NULL,527950,166350,NULL,NULL\n157744091,31/08/2009 22:16,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER PORTAKABIN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011222,Crayford,E09000004,Bexley,Bexley,NULL,Halcot Avenue,20100654,DA6,NULL,NULL,550150,174850,NULL,NULL\n157746091,31/08/2009 22:20,2009,2009/10,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Portland Avenue,20900813,N16,NULL,NULL,534050,187550,NULL,NULL\n157949091,01/09/2009 10:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,100010525,Gale Street,19900144,RM9,547608,184601,547650,184650,51.54101257,0.127118175\n158794091,02/09/2009 10:57,2009,2009/10,Special Service,2,5,260,1300,HORSE STUCK IN MUD,Horse,Police,Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Manor Road,NULL,DA8,552859,177622,552850,177650,51.47691035,0.199754957\n158888091,02/09/2009 13:49,2009,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND SHOP SIGN,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000449,New Cross,E09000023,Lewisham,New Cross,2.00001E+11,New Cross Road,22005521,SE14,536135,176932,536150,176950,51.4749784,-0.041171635\n159023091,02/09/2009 18:15,2009,2009/10,Special Service,1,3,260,780,HORSE IMPAILED ON RAILINGS - LARGE ANIMAL RESCUE,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Five Oaks Lane,NULL,IG7,548230,191870,548250,191850,51.60616354,0.139144946\n159599091,03/09/2009 18:05,2009,2009/10,Special Service,1,2,260,520,CAT TRAPPED IN CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,West Hampstead,202070563,Winchester Avenue,20201918,NW6,524377,184063,524350,184050,51.54177145,-0.207868936\n159971091,04/09/2009 09:59,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH TRAPPED GULL IN NETTING,Bird,Person (land line),DIY Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,48130762,Burlington Road,22100936,KT3,522013,168011,522050,168050,51.39802007,-0.247477991\n161542091,06/09/2009 13:20,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011253,Valentines,E09000026,Redbridge,Ilford,1.00022E+11,Auckland Road,22302592,IG1,543979,187521,543950,187550,51.56818687,0.076019598\n161584091,06/09/2009 13:59,2009,2009/10,Special Service,1,4,260,1040,CAT STUCK BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Hornsey Road,21603387,N19,NULL,NULL,530350,186850,NULL,NULL\n161897091,06/09/2009 22:49,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Express Drive,22302040,IG3,NULL,NULL,546650,187250,NULL,NULL\n161935091,07/09/2009 00:33,2009,2009/10,Special Service,1,1,260,260,KITTENS STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,Redbridge,Dagenham,NULL,Mayfield Road,22302272,RM8,NULL,NULL,547150,187150,NULL,NULL\n162044091,07/09/2009 06:59,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN DERELICT HOUSE BASEMENT - NORMANSFIELD HOUSE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000522,Hampton Wick,E09000027,Richmond upon Thames,Twickenham,NULL,Langdon Park,22407115,TW11,NULL,NULL,517250,170150,NULL,NULL\n162829091,08/09/2009 11:01,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BEHIND PIPES IN BATHROOM,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Clabon Mews,21700103,SW1X,NULL,NULL,527750,178850,NULL,NULL\n163350091,08/09/2009 22:08,2009,2009/10,Special Service,1,2,260,520,ANIMAL IN DISTRESS AT REAR OF PROPERTY,Unknown - Domestic Animal Or Pet,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000479,Custom House,E09000025,Newham,East Ham,NULL,Allhallows Road,NULL,E6,542018,181658,542050,181650,51.51600046,0.04538193\n163980091,09/09/2009 22:08,2009,2009/10,Special Service,1,2,260,520,RUNNING CALL TO CAT IN DISTRESS,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Drinkwater Road,NULL,HA2,513810,186859,513850,186850,51.56912796,-0.359272599\n164819091,11/09/2009 08:13,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Millbrook Gardens,19900793,RM6,NULL,NULL,548450,188450,NULL,NULL\n165020091,11/09/2009 15:41,2009,2009/10,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,London Road,20502653,SW16,NULL,NULL,530650,169550,NULL,NULL\n166508091,13/09/2009 11:28,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000191,Southfield,E09000009,Ealing,Chiswick,12097296,Ravenscroft Road,20601427,W4,520373,178805,520350,178850,51.49538217,-0.26737346\n166566091,13/09/2009 13:14,2009,2009/10,Special Service,2,5,260,1300,HORSE STUCK IN RESERVOIR,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536484,195509,536450,195550,51.64182959,-0.02894472\n167858091,15/09/2009 10:25,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED UNDER BUS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Wills Grove,NULL,NW7,522626,192433,522650,192450,51.61737705,-0.230185062\n168066091,15/09/2009 16:16,2009,2009/10,Special Service,1,1,260,260,DOG WITH PAW STUCK IN DOOR STOP,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Stamford Road,19900586,RM9,NULL,NULL,547050,184050,NULL,NULL\n168084091,15/09/2009 16:58,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN METAL RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,East Hall Lane,NULL,RM13,NULL,NULL,553650,181250,NULL,NULL\n168706091,16/09/2009 14:53,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN WINDOW FRAME,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Allitsen Road,8400134,NW8,NULL,NULL,527050,183250,NULL,NULL\n169159091,17/09/2009 10:16,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Burnbrae Close,NULL,N12,525907,191621,525950,191650,51.60935593,-0.183111199\n169251091,17/09/2009 13:59,2009,2009/10,Special Service,1,1,260,260,Redacted,cat,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Cleveland Avenue,22401375,TW12,512755,170394,512750,170350,51.42135336,-0.379743633\n169351091,17/09/2009 17:02,2009,2009/10,Special Service,1,1,260,260,CAT IN DISTRESS STUCK ON WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Fernleigh Close,8401933,W9,NULL,NULL,524850,182950,NULL,NULL\n170011091,18/09/2009 16:57,2009,2009/10,Special Service,2,6,260,1560,ROAD TRAFFIC ACCIDENT,Unknown - Heavy Livestock Animal,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000072,Cray Meadows,E09000004,Bexley,Sidcup,NULL,North Cray Road,NULL,DA14,548540,171687,548550,171650,51.424732,0.135118979\n170041091,18/09/2009 17:57,2009,2009/10,Special Service,1,1,260,260,DOG STUCK BETWEEN WINDOW AND FRAME,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Homefield Road,20023940,HA8,NULL,NULL,520750,191550,NULL,NULL\n171419091,20/09/2009 16:45,2009,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Lyndhurst Road,20100896,DA7,NULL,NULL,549850,175950,NULL,NULL\n171560091,20/09/2009 20:03,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN HOLE IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,Arnold Road,22700089,E3,NULL,NULL,537350,182650,NULL,NULL\n171757091,21/09/2009 01:13,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO A PIT - FRU REQUIRED FOR LINE OPS,Dog,Person (land line),Mine or quarry (not above ground building),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Great Central Way,NULL,NW10,520662,185607,520650,185650,51.55645343,-0.260883603\n172624091,22/09/2009 13:21,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Neville Road,22207816,E7,NULL,NULL,540450,184050,NULL,NULL\n172627091,22/09/2009 13:24,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Vancouver Road,21202374,HA8,NULL,NULL,519850,190650,NULL,NULL\n173090091,23/09/2009 07:03,2009,2009/10,Special Service,1,1,260,260,FOX WITH TAIL TRAPPED IN FENCE,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000561,Nonsuch,E09000029,Sutton,Sutton,5870005097,Moreton Road,22601732,KT4,522530,165604,522550,165650,51.37627566,-0.240879147\n173361091,23/09/2009 16:40,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH RESCUE OF SWAN FROM SEWAGE WORKS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000362,Isleworth,E09000018,Hounslow,Heston,2.00004E+11,Mogden Lane,21500768,TW7,515436,174840,515450,174850,51.46077335,-0.3397544\n173844091,24/09/2009 11:28,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CHAIR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Juliana Close,20024883,N2,NULL,NULL,525950,189950,NULL,NULL\n173903091,24/09/2009 13:42,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Ampthill Square,20499150,NW1,NULL,NULL,529350,183050,NULL,NULL\n174707091,25/09/2009 18:31,2009,2009/10,Special Service,1,1,260,260,INJURED CAT TRAPPED IN BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Dufferin Street,21604739,EC1Y,NULL,NULL,532450,182150,NULL,NULL\n175126091,26/09/2009 11:15,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Acton,12089284,Almond Avenue,20600049,W5,518023,179392,518050,179350,51.50115276,-0.30101377\n175315091,26/09/2009 16:03,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX UNDER DRAIN,Fox,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Southwark Bridge Road,NULL,SE1,532102,179829,532150,179850,51.50196852,-0.098124655\n175328091,26/09/2009 16:19,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Pembury Road,21104920,N17,NULL,NULL,533850,190450,NULL,NULL\n176570091,28/09/2009 08:51,2009,2009/10,Special Service,1,1,260,260,KITTEN IN DISTRESS ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Homerton,NULL,Jodrell Road,22700683,E3,NULL,NULL,536850,183950,NULL,NULL\n176658091,28/09/2009 12:08,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000090,Fryent,E09000005,Brent,Hendon,202037766,Townsend Lane,20201708,NW9,520859,188244,520850,188250,51.58011108,-0.257136481\n176888091,28/09/2009 18:53,2009,2009/10,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Homerton,NULL,Jodrell Road,22700683,E3,NULL,NULL,536850,183950,NULL,NULL\n178189091,30/09/2009 19:53,2009,2009/10,Special Service,1,1,260,260,CAT STUCK IN VENTILATION SHAFT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011105,Newington,E09000028,Southwark,Lambeth,NULL,John Ruskin Street,22501369,SE5,NULL,NULL,531750,177350,NULL,NULL\n178709091,01/10/2009 16:16,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL\n178916091,01/10/2009 19:21,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FENCES,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Albert Road,22305619,IG1,NULL,NULL,543850,186150,NULL,NULL\n178993091,01/10/2009 21:14,2009,2009/10,Special Service,1,1,260,260,INJURED CAT FALLEN ONTO FIRST FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Carnoustie Drive,21604456,N1,NULL,NULL,530650,184050,NULL,NULL\n179878091,03/10/2009 07:56,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF FOR TWO DAYS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL\n180427091,03/10/2009 22:34,2009,2009/10,Special Service,1,2,260,520,Redacted,Horse,Other FRS,Wasteland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Southern Perimeter Road,NULL,TW6,508684,174752,508650,174750,51.46131801,-0.436932413\n180663091,04/10/2009 10:00,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Benhurst Gardens,NULL,CR2,NULL,NULL,535450,162350,NULL,NULL\n180981091,04/10/2009 18:55,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO STRAY HORSE IN ROADWAY,Horse,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000337,Pinkwell,E09000017,Hillingdon,Hayes,NULL,Shepiston Lane,NULL,UB3,507941,178612,507950,178650,51.49615568,-0.446445506\n181318091,05/10/2009 09:58,2009,2009/10,Special Service,1,3,260,780,SMALL ANIMALS IN DISTRESS,Unknown - Wild Animal,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533705,198583,533750,198550,51.67012104,-0.067906257\n181457091,05/10/2009 14:26,2009,2009/10,Special Service,1,3,260,780,ASSIST RSPCA WITH HORSE STUCK IN WATER,Horse,Person (running call),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Wilton Close,NULL,UB7,505839,177661,505850,177650,51.48800492,-0.476999219\n181559091,05/10/2009 17:49,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Other FRS,Secondary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00024E+11,Settle Road,21301584,RM3,555153,192679,555150,192650,51.61156883,0.239392339\n183265091,08/10/2009 12:50,2009,2009/10,Special Service,1,2,260,520,DOG TRAPPED IN HOLE IN CHURCH YARD,Dog,Person (land line),Cemetery,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,10008463064,Homerton High Street,20900533,E9,535758,185118,535750,185150,51.54863095,-0.043445945\n183345091,08/10/2009 15:23,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED ON ROOF OF HOUSE,Dog,Person (mobile),House in Multiple Occupation - Up to 2 storeys (not known if licensed),Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000359,Hounslow Heath,E09000018,Hounslow,Heston,NULL,Grove Road,21500530,TW3,NULL,NULL,513350,175350,NULL,NULL\n183610091,08/10/2009 20:35,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011239,Clayhall,E09000026,Redbridge,Ilford,NULL,Margaret Way,22303051,IG4,NULL,NULL,542250,188450,NULL,NULL\n185248091,11/10/2009 14:05,2009,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Longfield Street,22903013,SW18,NULL,NULL,525150,173650,NULL,NULL\n185339091,11/10/2009 16:36,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Warwick Road,NULL,SW5,525176,178528,525150,178550,51.49185093,-0.198318469\n185828091,12/10/2009 13:32,2009,2009/10,Special Service,1,3,260,780,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Oakcroft Road,22000759,SE13,NULL,NULL,538650,176050,NULL,NULL\n185873091,12/10/2009 15:24,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,1.0002E+11,Framlingham Crescent,20302308,SE9,542562,171672,542550,171650,51.42613117,0.049190501\n186283091,13/10/2009 07:42,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Midcroft,NULL,HA4,509304,187387,509350,187350,51.57476446,-0.424096794\n186290091,13/10/2009 08:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Mint Close,21402248,UB10,NULL,NULL,507850,182750,NULL,NULL\n186316091,13/10/2009 09:41,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fordwych Road,NULL,NW2,524735,184983,524750,184950,51.54996072,-0.202383282\n186872091,14/10/2009 05:37,2009,2009/10,Special Service,1,1,260,260,PUPPY TRAPPED BETWEEN WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Redriffe Road,22201565,E13,NULL,NULL,539850,183550,NULL,NULL\n188219091,16/10/2009 08:31,2009,2009/10,Special Service,1,1,260,260,FOX STUCK IN SWIMMING POOL,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Lambs Lane South,21320023,RM13,NULL,NULL,553250,182150,NULL,NULL\n189058091,17/10/2009 13:30,2009,2009/10,Special Service,1,1,260,260,DISTRESSED CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,1.0002E+11,Martens Avenue,20100936,DA7,550435,174969,550450,174950,51.45372216,0.163748267\n189627091,18/10/2009 00:38,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BUSHES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Westmacott Drive,NULL,TW14,509641,173204,509650,173250,51.44721988,-0.423641504\n191318091,20/10/2009 14:39,2009,2009/10,Special Service,2,7,260,1820,HORSE TRAPPED IN MUD AND WATER,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,High Street,NULL,UB7,505695,177756,505650,177750,51.48888573,-0.479044044\n191753091,21/10/2009 10:15,2009,2009/10,Special Service,1,3,260,780,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Mansfield Road,20502209,CR2,NULL,NULL,532750,163550,NULL,NULL\n192366091,22/10/2009 12:14,2009,2009/10,Special Service,1,1,260,260,CAT STUCK INSIDE BUILDING,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,1.00021E+11,De Beauvoir Road,20900318,N1,533197,184026,533150,184050,51.53942802,-0.080772022\n192841091,23/10/2009 07:27,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL KITTEN WITH HEAD STUCK IN BONGO DRUMS,Cat,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,46099182,Romford Road,22208081,E15,539571,184672,539550,184650,51.54369413,0.011333185\n192957091,23/10/2009 12:39,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE IN WOODS,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000563,Stonecot,E09000029,Sutton,Sutton,48083368,London Road,22103999,SM4,524750,166750,524750,166750,51.3860923,-0.208596798\n193474091,24/10/2009 06:51,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND FITTED CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000645,Tachbrook,E09000033,Westminster,Lambeth,NULL,Rampayne Street,8400468,SW1V,NULL,NULL,529650,178450,NULL,NULL\n194165091,25/10/2009 08:46,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN BUILDING UNDER RENOVATION,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011227,Longlands,E09000004,Bexley,Sidcup,NULL,Woodside Crescent,20101615,DA15,NULL,NULL,545350,172250,NULL,NULL\n194175091,25/10/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,10003973644,Clifton Road,21100175,N8,529742,188220,529750,188250,51.57792269,-0.129016105\n194827091,26/10/2009 11:15,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Temple Road,21103974,N8,NULL,NULL,530650,189250,NULL,NULL\n195730091,27/10/2009 16:29,2009,2009/10,Special Service,1,1,260,260,CAT  STUCK IN DRAINPIPE,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Broom Road,22403325,TW11,516784,171324,516750,171350,51.42889646,-0.32151899\n196171091,28/10/2009 09:52,2009,2009/10,Special Service,1,1,260,260,POSSIBLE CAT STUCK BEHIND FIRE PLACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Hoe Lane,20702753,EN3,NULL,NULL,535150,198150,NULL,NULL\n196332091,28/10/2009 14:36,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Keats Avenue,NULL,RM3,552504,190993,552550,190950,51.59714399,0.200433719\n197054091,29/10/2009 17:10,2009,2009/10,Special Service,1,1,260,260,INJURED CAT TRAPPED IN DERELICT BUILDING,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Stevens Street,22502387,SE1,533444,179440,533450,179450,51.49815774,-0.078948355\n197069091,29/10/2009 17:30,2009,2009/10,Special Service,1,1,260,260,DOG LOCKED IN BEDROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Central Street,21604474,EC1V,NULL,NULL,532150,182350,NULL,NULL\n197435091,30/10/2009 13:09,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL POSSIBLY TRAPPED BEHIND WALL,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Dagenham,NULL,Percival Gardens,22302340,RM6,NULL,NULL,547650,188350,NULL,NULL\n198919091,01/11/2009 13:08,2009,2009/10,Special Service,1,2,260,520,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Clissold Road,NULL,N16,532912,186018,532950,186050,51.55739613,-0.084127508\n199999091,03/11/2009 13:42,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Sydenham Road,22001951,SE26,NULL,NULL,535750,171450,NULL,NULL\n200399091,04/11/2009 08:21,2009,2009/10,Special Service,1,1,260,260,THREE KITTENS STUCK UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Avenue,8401514,W9,NULL,NULL,525850,182950,NULL,NULL\n201050091,05/11/2009 15:38,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Lathom Road,22200795,E6,NULL,NULL,542450,184150,NULL,NULL\n201406091,05/11/2009 20:08,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TOILET AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Aveling Park Road,22812350,E17,NULL,NULL,537450,190450,NULL,NULL\n203387091,08/11/2009 15:25,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN BUILDING UNDER REFURBISHMENT,Cat,Person (land line),Private Summer house,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,217064858,Paultons Square,21700394,SW3,526862,177759,526850,177750,51.48456449,-0.174321982\n204325091,10/11/2009 09:35,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDERNEATH FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Doverfield Road,21900472,SW2,NULL,NULL,530350,173950,NULL,NULL\n205487091,12/11/2009 10:56,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Lancing Gardens,NULL,N9,533900,194257,533950,194250,51.63120031,-0.066744016\n205974091,13/11/2009 08:41,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN BARS OF STAIRCASE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Dallas Road,22002331,SE26,NULL,NULL,534750,171950,NULL,NULL\n206274091,13/11/2009 19:03,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100019872,Kings Avenue,19900236,RM6,548877,188303,548850,188350,51.57394284,0.146968736\n206288091,13/11/2009 19:31,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Barnet,NULL,High Road,NULL,N12,526290,193009,526250,193050,51.6217437,-0.177083554\n206935091,14/11/2009 16:00,2009,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067\n207988091,16/11/2009 09:49,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Stevenage Road,21000786,SW6,NULL,NULL,523850,176450,NULL,NULL\n208015091,16/11/2009 10:55,2009,2009/10,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT ON HOUSE ROOF STUCK BETWEEN CHIMNEY POTS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Railway Road,22403758,TW11,NULL,NULL,515550,171550,NULL,NULL\n208537091,17/11/2009 12:07,2009,2009/10,Special Service,1,3,260,780,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Goddington Lane,NULL,BR6,547318,164861,547350,164850,51.36371683,0.114725626\n208605091,17/11/2009 14:38,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Fairfield Road,NULL,KT1,518250,169250,518250,169250,51.40995241,-0.301132808\n208663091,17/11/2009 16:16,2009,2009/10,Special Service,NULL,NULL,260,NULL,Redacted,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Freezeland Way,NULL,UB10,507714,184730,507750,184750,51.55118871,-0.447845025\n209647091,19/11/2009 13:33,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DERELICT PUB,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Ripple Road,NULL,IG11,544508,183943,544550,183950,51.53590173,0.082178672\n209722091,19/11/2009 15:59,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,South View Road,21103882,N8,NULL,NULL,530050,189650,NULL,NULL\n210177091,20/11/2009 14:04,2009,2009/10,Special Service,1,1,260,260,CAT STUCK BEHIND BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Tamworth Lane,22106216,CR4,NULL,NULL,528450,169150,NULL,NULL\n211205091,22/11/2009 09:58,2009,2009/10,Special Service,1,1,260,260,KITTEN WITH TRAPPED IN CAVITY BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,NULL,Clement Road,20301621,BR3,NULL,NULL,535850,169150,NULL,NULL\n211435091,22/11/2009 16:55,2009,2009/10,Special Service,1,1,260,260,DOG STUCK ON RIVER BANK OF RIVER THAMES,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000525,Mortlake and Barnes Common,E09000027,Richmond upon Thames,Richmond,NULL,The Terrace,NULL,SW13,521449,176273,521450,176250,51.47239588,-0.252750773\n211780091,23/11/2009 10:03,2009,2009/10,Special Service,1,1,260,260,DISTRESSED PARROT STUCK IN TREE,Bird,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Gosport Road,NULL,E17,536786,188781,536750,188750,51.58129928,-0.027205447\n212340091,24/11/2009 14:21,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED BETWEEN RAILINGS,Dog,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Saltram Crescent,NULL,W9,525061,182597,525050,182550,51.52844528,-0.198531394\n212840091,25/11/2009 12:57,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Shelter,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Wheatfield Way,NULL,KT1,518261,169314,518250,169350,51.41052532,-0.300953375\n214680091,28/11/2009 13:52,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED BY NETTING IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5034208,Rochester Square,20400662,NW1,529442,184378,529450,184350,51.54346467,-0.134756324\n214917091,28/11/2009 21:00,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND BOX,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000045,Childs Hill,E09000003,Barnet,Hendon,NULL,Ridge Hill,20036540,NW11,NULL,NULL,524350,187350,NULL,NULL\n216000091,30/11/2009 17:52,2009,2009/10,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000312,Harold Wood,E09000016,Havering,Harold Hill,NULL,Gooshays Drive,21301233,RM3,NULL,NULL,554550,191450,NULL,NULL\n216709091,01/12/2009 19:06,2009,2009/10,Special Service,1,1,260,260,DOG WITH LEG STUCK IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,NULL,Jail Lane,20301511,TN16,NULL,NULL,541850,159550,NULL,NULL\n217135091,02/12/2009 14:45,2009,2009/10,Special Service,1,1,260,260,CAT FALLEN FROM ROOF ONTO SPIKE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Beckenham,NULL,Venner Road,NULL,SE26,NULL,NULL,535350,171450,NULL,NULL\n217651091,03/12/2009 13:09,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Jelf Road,21900759,SW2,NULL,NULL,531350,174950,NULL,NULL\n217775091,03/12/2009 17:39,2009,2009/10,Special Service,1,1,260,260,CAT POSSIBLY WITH FOOT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Brunswick Crescent,20005820,N11,NULL,NULL,528350,193350,NULL,NULL\n217837091,03/12/2009 19:48,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000084,Thamesmead East,E09000004,Bexley,Plumstead,NULL,Kinder Close,NULL,SE28,547534,180704,547550,180750,51.50601577,0.12442243\n219412091,06/12/2009 18:59,2009,2009/10,Special Service,1,3,260,780,CAT LOCKED IN AT SCENE OF PREVIOUS FIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Willow Tree Close,22906203,SW18,NULL,NULL,526050,173350,NULL,NULL\n219738091,07/12/2009 12:02,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Cowdray Road,21400498,UB10,NULL,NULL,508350,183650,NULL,NULL\n219840091,07/12/2009 15:12,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Wheatfield Way,NULL,KT1,518262,169291,518250,169250,51.4103184,-0.300946673\n220308091,08/12/2009 12:49,2009,2009/10,Special Service,1,1,260,260,ASSIST POLICE TO RESCUE DOG FALLEN INTO PIT,Dog,Police,Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Fairholme Crescent,21400705,UB4,509970,182496,509950,182450,51.53067436,-0.416018295\n223140091,13/12/2009 12:51,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,1.00023E+11,Victoria Drive,22905825,SW19,524092,172792,524050,172750,51.44053809,-0.215935787\n224109091,15/12/2009 08:41,2009,2009/10,Special Service,1,1,260,260,LARGE ANIMAL RESCUE -  INFIRMED HORSE THAT HAS FALLEN,Horse,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,1.00023E+11,Chestnut Rise,20800328,SE18,544978,178059,544950,178050,51.48291011,0.086533228\n224156091,15/12/2009 10:49,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham Park,NULL,SE13,538033,174453,538050,174450,51.4522418,-0.014827122\n225454091,17/12/2009 12:56,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,10023231343,Scawen Road,22000907,SE8,536274,178345,536250,178350,51.48764279,-0.038626364\n225555091,17/12/2009 16:22,2009,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN GATE RAILINGS,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,2.00001E+11,Henrys Avenue,22844450,IG8,539563,192075,539550,192050,51.61021754,0.01415559\n226460091,19/12/2009 00:09,2009,2009/10,Special Service,1,1,260,260,FOX WITH FOOT CAUGHT IN WIRE FENCING,Fox,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Northdown Road,NULL,DA16,547131,176018,547150,176050,51.4640154,0.116668056\n226648091,19/12/2009 09:36,2009,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Forty Hill,NULL,EN2,533723,198599,533750,198550,51.67026053,-0.067640008\n226755091,19/12/2009 12:15,2009,2009/10,Special Service,1,1,260,260,CAT LOCKED IN NEXT DOOR NEIGHBOURS FLAT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,North Kensington,NULL,Trenmar Gardens,21000827,NW10,NULL,NULL,522650,182750,NULL,NULL\n227042091,19/12/2009 22:07,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN LOFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,Blackheath Westcombe,E09000011,Greenwich,Lee Green,NULL,Chiswell Square,20800332,SE3,NULL,NULL,540850,176350,NULL,NULL\n227400091,20/12/2009 13:50,2009,2009/10,Special Service,1,1,260,260,GULL TRAPPED IN NETTING,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Winton Gardens,NULL,HA8,518895,191330,518850,191350,51.60826279,-0.284423844\n227404091,20/12/2009 13:58,2009,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000360,Hounslow South,E09000018,Hounslow,Heston,NULL,Shirley Drive,21501016,TW3,NULL,NULL,514050,174650,NULL,NULL\n227698091,20/12/2009 22:19,2009,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011481,Selsdon Vale & Forestdale,E09000008,Croydon,Addington,NULL,Bardolph Avenue,20501657,CR0,NULL,NULL,536650,162750,NULL,NULL\n227975091,21/12/2009 13:03,2009,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485\n228616091,22/12/2009 10:06,2009,2009/10,Special Service,1,1,260,260,RUNNING CALL TO KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Richmond Avenue,21401627,UB10,NULL,NULL,507850,184550,NULL,NULL\n228746091,22/12/2009 15:05,2009,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Heston,2.00004E+11,Osterley Park,21500647,TW7,513750,176750,513750,176750,51.47828145,-0.363398753\n229229091,23/12/2009 12:00,2009,2009/10,Special Service,1,1,260,260,BIRD WITH WINGS TRAPPED IN NETTING,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128019778,Wheatfield Way,21801044,KT1,518247,169248,518250,169250,51.40993506,-0.301176591\n229900091,24/12/2009 15:02,2009,2009/10,Special Service,2,3,260,780,DOG FALLEN INTO CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532455,196459,532450,196450,51.65133022,-0.086775248\n230352091,25/12/2009 11:28,2009,2009/10,Special Service,1,1,260,260,DOG STUCK IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011226,Falconwood & Welling,E09000004,Bexley,Bexley,10011845449,Danson Park,20100426,DA6,547521,174952,547550,174950,51.4543355,0.12183349\n230868091,26/12/2009 11:23,2009,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Laitwood Road,22902761,SW12,NULL,NULL,528950,173350,NULL,NULL\n230891091,26/12/2009 12:38,2009,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,217080482,St. Ervans Road,21701009,W10,524510,181918,524550,181950,51.5224646,-0.206710241\n231397091,27/12/2009 16:59,2009,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Kirkton Road,21103498,N15,NULL,NULL,533150,189050,NULL,NULL\n231733091,28/12/2009 11:52,2009,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Brighton Road,20501945,CR2,NULL,NULL,532450,163050,NULL,NULL\n231762091,28/12/2009 13:43,2009,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,The Ridgeway,22870050,E4,NULL,NULL,538350,194250,NULL,NULL\n233284091,31/12/2009 18:09,2009,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,22208058,E7,NULL,NULL,540550,184150,NULL,NULL\n248101,01/01/2010 09:12,2010,2009/10,Special Service,1,2,260,520,CAT TRAPPED BEHIND BEDROOM WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,22208058,E7,NULL,NULL,540550,184150,NULL,NULL\n413101,01/01/2010 16:27,2010,2009/10,Special Service,1,3,260,780,PIGEON TRAPPED IN MESH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,Tildesley Road,22905407,SW15,NULL,NULL,523050,174350,NULL,NULL\n1409101,03/01/2010 15:01,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Curzon Crescent,NULL,IG11,545778,182999,545750,182950,51.5270933,0.100086349\n1886101,04/01/2010 12:02,2010,2009/10,Special Service,1,1,260,260,HORSE STUCK IN ICE,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Sewell Road,NULL,SE2,547278,180043,547250,180050,51.50014323,0.120460558\n3215101,06/01/2010 10:09,2010,2009/10,Special Service,2,5,260,1300,Redacted,Dog,Police,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Richmond Park,NULL,KT2,519261,170438,519250,170450,51.42041796,-0.28620326\n3223101,06/01/2010 10:22,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE ON BOATING POND,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,NULL,RM8,546374,184522,546350,184550,51.54062387,0.109304088\n3814101,07/01/2010 10:21,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN INTO FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121016626,Battersea Park,22900030,SW11,527546,177314,527550,177350,51.4804116,-0.164636837\n3975101,07/01/2010 15:04,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT TRAPPED UNDER RUBBLE PILE,Cat,Other FRS,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,1.00022E+11,Cambridge Road,21800199,KT1,519460,169055,519450,169050,51.40794635,-0.283808522\n4416101,08/01/2010 09:35,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00023E+11,Hornchurch Road,21300425,RM11,553152,187026,553150,187050,51.5613255,0.208053787\n4496101,08/01/2010 12:57,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN FROZEN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,528746,172278,528750,172250,51.4348816,-0.149196207\n4669101,08/01/2010 18:18,2010,2009/10,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011226,Falconwood & Welling,E09000004,Bexley,Eltham,NULL,Sutherland Avenue,20101404,DA16,NULL,NULL,545550,175050,NULL,NULL\n4716101,08/01/2010 19:25,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Ashley Road,21606730,N19,NULL,NULL,530150,187450,NULL,NULL\n5172101,09/01/2010 14:20,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN DIVAN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bramshaw Road,20900162,E9,NULL,NULL,535850,184550,NULL,NULL\n5250101,09/01/2010 16:20,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN BARBED WIRE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Kentmere Road,NULL,SE18,545342,178620,545350,178650,51.48785766,0.092002502\n5871101,10/01/2010 15:45,2010,2009/10,Special Service,3,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533730,198596,533750,198550,51.67023191,-0.067539992\n6179101,11/01/2010 02:11,2010,2009/10,Special Service,1,1,260,260,CAT WITH FOOT TRAPPED BETWEEN GRATE AND DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Hounslow Road,21500632,TW13,NULL,NULL,512050,172450,NULL,NULL\n6277101,11/01/2010 09:24,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,The Charter Road,NULL,IG8,539750,191750,539750,191750,51.60725082,0.016724981\n6311101,11/01/2010 10:34,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN POND,Dog,Police,Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000406,Coombe Hill,E09000021,Kingston upon Thames,New Malden,NULL,Kingston Vale,NULL,SW15,521312,172276,521350,172250,51.43650188,-0.256091194\n6323101,11/01/2010 11:12,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN FOXHOLE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,1.00022E+11,Madrid Road,22404740,SW13,522360,177137,522350,177150,51.47996511,-0.239341861\n6350101,11/01/2010 12:22,2010,2009/10,Special Service,1,1,260,260,DOG IN LAKE,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485\n6386101,11/01/2010 13:17,2010,2009/10,Special Service,1,1,260,260,PERSON AND DOG STUCK IN LAKE,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW17,528250,172250,528250,172250,51.43474229,-0.156338076\n6453101,11/01/2010 15:18,2010,2009/10,Special Service,1,2,260,520,DOG TRAPPED IN  ICE  IN LAKE,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121016626,Battersea Park,22900030,SW11,527546,177314,527550,177350,51.4804116,-0.164636837\n6543101,11/01/2010 18:14,2010,2009/10,Special Service,1,1,260,260,CALLED BY RSPCA CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,46035206,Hartland Road,22201440,E15,539621,184279,539650,184250,51.54015031,0.011897974\n6547101,11/01/2010 18:18,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,The Charter Road,NULL,IG8,539250,191750,539250,191750,51.60737457,0.009509364\n6548101,11/01/2010 18:19,2010,2009/10,Special Service,1,1,260,260,ANIMAL TRAPPED IN WIRE FENCING REAR OF,Unknown - Heavy Livestock Animal,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Harecourt Road,NULL,N1,532141,184915,532150,184950,51.54766527,-0.095656309\n6881101,12/01/2010 09:27,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE INTO LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000317,Pettits,E09000016,Havering,Romford,NULL,Main Road,NULL,RM2,551890,189810,551850,189850,51.58668065,0.191064525\n6909101,12/01/2010 10:44,2010,2009/10,Special Service,2,3,260,780,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Settle Road,NULL,RM3,554890,192718,554850,192750,51.61199159,0.235614326\n7223101,12/01/2010 21:25,2010,2009/10,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Springfield,NULL,E5,534750,187250,534750,187250,51.56803113,-0.057159313\n7565101,13/01/2010 14:49,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000376,Junction,E09000019,Islington,Kentish Town,5300051397,Huddleston Road,21600471,N7,529365,186059,529350,186050,51.55858918,-0.135248222\n7588101,13/01/2010 15:44,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Huddleston Road,21600471,N7,NULL,NULL,529350,186050,NULL,NULL\n7644101,13/01/2010 17:10,2010,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,NULL,High Street,NULL,KT1,517818,169056,517850,169050,51.40829864,-0.307405787\n7933101,14/01/2010 08:47,2010,2009/10,Special Service,1,1,260,260,DOG IN FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Swakeleys Drive,NULL,UB10,507303,185620,507350,185650,51.55926654,-0.453499072\n8079101,14/01/2010 14:16,2010,2009/10,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000529,South Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Grove Road,NULL,TW2,514808,172133,514850,172150,51.43657116,-0.349667735\n8478101,15/01/2010 10:12,2010,2009/10,Special Service,1,1,260,260,FOX TRAPPED IN GARDEN,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00023E+11,Gresham Road,21900641,SW9,531472,175569,531450,175550,51.46383175,-0.108780336\n9188101,16/01/2010 16:42,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO BIRD TRAPPED IN TREE,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000169,Woodside,E09000008,Croydon,Woodside,NULL,Spring Lane,NULL,SE25,534764,167233,534750,167250,51.38814595,-0.064587215\n9574101,17/01/2010 10:16,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Neville Road,21800695,KT1,NULL,NULL,519450,169350,NULL,NULL\n9596101,17/01/2010 11:26,2010,2009/10,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034913606,Hainault Street,22302904,IG1,544035,186621,544050,186650,51.56008569,0.076458672\n10111101,18/01/2010 09:59,2010,2009/10,Special Service,1,1,260,260,CAT FALLEN FROM BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Stainsby Road,22701152,E14,NULL,NULL,537150,181350,NULL,NULL\n10239101,18/01/2010 14:41,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from water,Animal rescue from water - Domestic pet,E05000327,Cavendish,E09000017,Hillingdon,Northolt,1.00021E+11,St. Peters Close,21401843,HA4,511541,186810,511550,186850,51.56914065,-0.392012833\n10650101,19/01/2010 07:38,2010,2009/10,Special Service,1,1,260,260,DOGS TRAPPED IN LEAD,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Whitebeam Avenue,20300693,BR2,NULL,NULL,543150,166850,NULL,NULL\n10676101,19/01/2010 09:00,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Burlington Gardens,20600290,W3,NULL,NULL,520550,180150,NULL,NULL\n10941101,19/01/2010 18:56,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011103,Goose Green,E09000028,Southwark,Peckham,2.00003E+11,Upland Road,22502589,SE22,534265,174924,534250,174950,51.45738003,-0.068843058\n11350101,20/01/2010 16:09,2010,2009/10,Special Service,1,1,260,260,ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,NULL,Ravenscroft Street,22700996,E2,NULL,NULL,533850,183050,NULL,NULL\n11924101,21/01/2010 17:57,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,1.00023E+11,Dells Close,22831325,E4,537709,194950,537750,194950,51.63650779,-0.011472553\n11975101,21/01/2010 19:28,2010,2009/10,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,10008244248,Stoke Newington High Street,20901507,N16,533623,186710,533650,186750,51.56344668,-0.07361513\n12658101,23/01/2010 00:18,2010,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN GARAGE AND FLAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Haydon Road,19900170,RM8,NULL,NULL,547450,186650,NULL,NULL\n12811101,23/01/2010 10:12,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Blyth Road,20301843,BR1,NULL,NULL,539850,169550,NULL,NULL\n12883101,23/01/2010 13:27,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,St. Marys Crescent,21500986,TW7,NULL,NULL,515150,177350,NULL,NULL\n14274101,26/01/2010 10:01,2010,2009/10,Special Service,1,2,260,520,HORSE IN RIVER,Horse,Person (land line),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,207167256,Wharf Road,20702448,EN3,536356,195438,536350,195450,51.64122266,-0.030821026\n14471101,26/01/2010 16:29,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER BRIDGE,Cat,Person (land line),\"Tunnel, subway\",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000525,Mortlake and Barnes Common,E09000027,Richmond upon Thames,Richmond,NULL,Woodlands Road,NULL,SW13,521648,175736,521650,175750,51.46752692,-0.250071538\n14918101,27/01/2010 13:13,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Finchley Road,NULL,NW11,524895,188569,524850,188550,51.58215308,-0.198803721\n15165101,27/01/2010 22:39,2010,2009/10,Special Service,1,1,260,260,CAT STUCK INSIDE WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000282,West Green,E09000014,Haringey,Tottenham,NULL,Moira Close,21104815,N17,NULL,NULL,533050,190450,NULL,NULL\n15786101,29/01/2010 08:30,2010,2009/10,Special Service,1,1,260,260,DOG WITH PAW STUCK IN GRATING,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Auckland Hill,NULL,SE27,532476,171637,532450,171650,51.42826175,-0.095806331\n15868101,29/01/2010 11:44,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON TOP OF BUILDING,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,NULL,Twyford Avenue,NULL,N2,528017,189704,528050,189750,51.5916529,-0.153355886\n15908101,29/01/2010 13:20,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Ernest Road,21300344,RM11,554903,188006,554950,188050,51.5696525,0.233724821\n16354101,30/01/2010 09:26,2010,2009/10,Special Service,1,2,260,520,DEER TRAPPED IN FENCE,Deer,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067\n16515101,30/01/2010 14:51,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Linkside Close,20702809,EN2,NULL,NULL,531050,196650,NULL,NULL\n16889101,31/01/2010 00:35,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED BETWEEN GATES,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000627,Wandsworth Common,E09000032,Wandsworth,Wandsworth,NULL,Quarry Road,22904103,SW18,NULL,NULL,526450,174450,NULL,NULL\n17049101,31/01/2010 11:48,2010,2009/10,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000595,Forest,E09000031,Waltham Forest,Leytonstone,NULL,Whipps Cross Road,NULL,E11,539248,188748,539250,188750,51.58040012,0.00829114\n17119101,31/01/2010 13:45,2010,2009/10,Special Service,1,1,260,260,DUCK HANGING FROM TREE WITH FISHING LINE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW16,528250,172250,528250,172250,51.43474229,-0.156338076\n17235101,31/01/2010 18:12,2010,2009/10,Special Service,1,2,260,520,DOG IN LAKE UNDER PONTOON,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,529150,172072,529150,172050,51.43293843,-0.143462422\n17481101,01/02/2010 07:53,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN DUCTING,Cat,Other FRS,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Willow Bank,22406125,TW10,NULL,NULL,516950,172450,NULL,NULL\n17566101,01/02/2010 11:49,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Justice Walk,NULL,SW3,527065,177683,527050,177650,51.48383595,-0.171427281\n18061101,02/02/2010 10:28,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN HEATING PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Willow Bank,22406125,TW10,NULL,NULL,516950,172450,NULL,NULL\n19467101,05/02/2010 09:44,2010,2009/10,Special Service,1,2,260,520,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,NULL,Belgrave Road,22305986,E11,NULL,NULL,540250,187150,NULL,NULL\n19519101,05/02/2010 11:54,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH RELEASE OF BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000182,Northfield,E09000009,Ealing,Ealing,NULL,Sterling Place,20602244,W5,NULL,NULL,518150,178850,NULL,NULL\n19547101,05/02/2010 13:01,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Willifield Way,NULL,NW11,525107,188906,525150,188950,51.58513469,-0.19562558\n20305101,06/02/2010 16:12,2010,2009/10,Special Service,1,3,260,780,DOG TRAPPED IN HOLE UNDER STAIRCASE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534250,171250,534250,171250,51.42436661,-0.070450735\n22085101,10/02/2010 13:13,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN ROOF,Cat,Person (mobile),Factory,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Rolt Street,NULL,SE8,536518,177740,536550,177750,51.48214717,-0.035348086\n22340101,10/02/2010 22:23,2010,2009/10,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Cottenham Road,22829450,E17,NULL,NULL,536850,189450,NULL,NULL\n22442101,11/02/2010 05:20,2010,2009/10,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Whitchurch Road,NULL,RM3,554750,192250,554750,192250,51.60782516,0.233387301\n22493101,11/02/2010 08:44,2010,2009/10,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,High Road,NULL,N22,531046,190276,531050,190250,51.5960973,-0.109442285\n22580101,11/02/2010 12:24,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000468,Ravensbury,E09000024,Merton,Mitcham,NULL,Riverside Drive,22105424,CR4,NULL,NULL,527450,167850,NULL,NULL\n23023101,12/02/2010 09:54,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN COFFEE TABLE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elmstead Avenue,20200435,HA9,NULL,NULL,518350,187150,NULL,NULL\n23132101,12/02/2010 13:55,2010,2009/10,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Uxbridge Road,20602315,W13,NULL,NULL,516950,180550,NULL,NULL\n23276101,12/02/2010 19:33,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED BELOW GROUND,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000633,Churchill,E09000033,Westminster,Lambeth,2.00002E+11,Churchill Gardens,8401852,SW1V,529165,177950,529150,177950,51.48576026,-0.141102776\n23565101,13/02/2010 11:50,2010,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Bramble Close,20501669,CR0,NULL,NULL,537450,164850,NULL,NULL\n23620101,13/02/2010 13:38,2010,2009/10,Special Service,1,1,260,260,CAT STUCK BETWEEN KITCHEN CUPBOARDS AND WALL,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Woods Place,22502735,SE1,NULL,NULL,533250,179250,NULL,NULL\n25056101,16/02/2010 10:44,2010,2009/10,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000538,College,E09000028,Southwark,Forest Hill,NULL,Lordship Lane,NULL,SE22,534154,173565,534150,173550,51.44519347,-0.070954563\n25863101,17/02/2010 20:32,2010,2009/10,Special Service,1,4,260,1040,DOG TAPPED IN RAILINGS,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Ion Square,NULL,E2,534350,182841,534350,182850,51.52850593,-0.064608431\n26036101,18/02/2010 02:36,2010,2009/10,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Weston Drive,21202529,HA7,NULL,NULL,516950,190850,NULL,NULL\n26068101,18/02/2010 06:20,2010,2009/10,Special Service,1,1,260,260,HORSE WITH LEG TRAPPED UNDER STABLE DOOR,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032260,Barwell Lane,21800115,KT9,516963,163096,516950,163050,51.3549067,-0.32165141\n26735101,19/02/2010 12:53,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON HOOK,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,10091775400,Kingsland Road,20900581,E8,533510,184081,533550,184050,51.53984825,-0.076240615\n28318101,22/02/2010 14:17,2010,2009/10,Special Service,1,1,260,260,KITTEN STUCK BEHIND BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Priors Field,20601392,UB5,NULL,NULL,512350,184550,NULL,NULL\n29013101,23/02/2010 21:48,2010,2009/10,Special Service,1,1,260,260,PUPPY STUCK ON ROOF,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Southwood Road,20801384,SE9,NULL,NULL,544050,172850,NULL,NULL\n29201101,24/02/2010 09:37,2010,2009/10,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED IN IRON GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,100019626,Lyndhurst Gardens,19900231,IG11,545386,184685,545350,184650,51.54234371,0.095134895\n29349101,24/02/2010 15:36,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Cannon Road,20100258,DA7,NULL,NULL,548550,176650,NULL,NULL\n30019101,25/02/2010 21:41,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN CHAIR,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Farm animal,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,The Bishops Avenue,20003740,N2,NULL,NULL,527250,189050,NULL,NULL\n30735101,27/02/2010 08:17,2010,2009/10,Special Service,1,1,260,260,PIGEON TRAPPED ON BEHIND FIREPLACE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Norfolk Road,21301453,RM7,NULL,NULL,550350,188250,NULL,NULL\n30868101,27/02/2010 14:01,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Strathan Close,NULL,SW18,524513,174251,524550,174250,51.45355845,-0.20936883\n31007101,27/02/2010 18:48,2010,2009/10,Special Service,1,1,260,260,PERSONS LOCKED OUT OF CAR,Unknown - Heavy Livestock Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Thurlow Park Road,NULL,SE21,531769,173053,531750,173050,51.44115187,-0.105443615\n31381101,28/02/2010 11:58,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCK HANGING FROM TREE OVER LAKE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000466,Merton Park,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524495,169365,524450,169350,51.40965009,-0.211343201\n33402101,04/03/2010 10:07,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,202090471,Harley Close,20201668,HA0,517721,184942,517750,184950,51.55109705,-0.30350882\n33920101,05/03/2010 08:30,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Thornhill Road,21401989,UB10,NULL,NULL,507150,186050,NULL,NULL\n24109,06/03/2010 14:58,2010,2009/10,Special Service,1,1,260,260,NULL,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,5870015588,Amesbury Close,22601220,KT4,523396,166276,523350,166250,51.38212795,-0.228209764\n34886101,06/03/2010 20:51,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Elbe Street,NULL,SW6,526169,176395,526150,176350,51.47246097,-0.184783626\n35862101,08/03/2010 14:56,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED UNDER BUS,Dog,Person (mobile),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Fairfax Road,NULL,NW6,526386,184246,526350,184250,51.54297024,-0.178848953\n36306101,09/03/2010 13:10,2010,2009/10,Special Service,1,2,260,520,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,1.0002E+11,Ladbrooke Crescent,20100810,DA14,547846,172118,547850,172150,51.42878609,0.125324192\n36783101,10/03/2010 10:42,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00022E+11,Helix Road,21900694,SW2,530974,174374,530950,174350,51.45320784,-0.116387167\n37009101,10/03/2010 18:29,2010,2009/10,Special Service,1,1,260,260,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000373,Highbury West,E09000019,Islington,Islington,NULL,Roseleigh Avenue,21603757,N5,NULL,NULL,531850,185650,NULL,NULL\n37310101,11/03/2010 08:58,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Atlantis Close,NULL,IG11,546649,182515,546650,182550,51.52251898,0.112431931\n37604101,11/03/2010 19:27,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Atlantis Close,NULL,IG11,546647,182514,546650,182550,51.52251051,0.112402708\n37935101,12/03/2010 12:40,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RAILWAY ARCHES,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Norwood High Street,NULL,SE27,532012,171998,532050,171950,51.43161422,-0.102342257\n37981101,12/03/2010 14:46,2010,2009/10,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,1.00021E+11,Warham Road,20502408,CR2,532103,164298,532150,164250,51.36239515,-0.103897722\n38325101,13/03/2010 07:53,2010,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL CAVITY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Tudor Grove,20901015,E9,NULL,NULL,535050,184050,NULL,NULL\n38567101,13/03/2010 16:42,2010,2009/10,Special Service,1,1,260,260,TWO CATS TRAPPED IN PIT,cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00021E+11,Muswell Hill Broadway,21106387,N10,528723,189860,528750,189850,51.59289414,-0.143112801\n39175101,14/03/2010 17:24,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN  WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Hengrave Road,22001546,SE23,NULL,NULL,535550,174050,NULL,NULL\n39481101,15/03/2010 09:30,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN LOWER LEVEL OF PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000141,King's Cross,E09000007,Camden,Euston,NULL,Mecklenburgh Square,20499101,WC1N,NULL,NULL,530550,182450,NULL,NULL\n39548101,15/03/2010 11:42,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED UNDER FENCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00022E+11,Thornford Road,22001969,SE13,538353,174633,538350,174650,51.45378133,-0.010154398\n39641101,15/03/2010 14:45,2010,2009/10,Special Service,1,1,260,260,CAT POSSIBLY TRAPPED UP CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Church Path,20600383,W4,NULL,NULL,520350,179250,NULL,NULL\n39769101,15/03/2010 18:09,2010,2009/10,Special Service,1,1,260,260,DOG TRAPPED IN PARK RAILINGS,Dog,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,1.00023E+11,Cedar Avenue,21400329,HA4,511334,184924,511350,184950,51.55222997,-0.395595185\n40284101,16/03/2010 14:40,2010,2009/10,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000558,Carshalton Central,E09000029,Sutton,Wallington,NULL,Salisbury Road,22602589,SM5,NULL,NULL,527850,164050,NULL,NULL\n40297101,16/03/2010 15:15,2010,2009/10,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN METAL CAGE BARS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Grove Gardens,22403524,TW11,NULL,NULL,516250,171650,NULL,NULL\n40735101,17/03/2010 09:36,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,Peninsula,E09000011,Greenwich,East Greenwich,NULL,Pelton Road,20801159,SE10,NULL,NULL,539150,178350,NULL,NULL\n40823101,17/03/2010 13:39,2010,2009/10,Special Service,1,1,260,260,RUNNING CALL TO BIRD CAUGHT IN MESH,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,10033593404,Greek Street,8401382,W1D,529761,181159,529750,181150,51.51446281,-0.131344143\n40961101,17/03/2010 17:24,2010,2009/10,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Aubrey Road,22812200,E17,NULL,NULL,537450,189550,NULL,NULL\n41403101,18/03/2010 12:52,2010,2009/10,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,1.00023E+11,Court Drive,21400492,UB10,506843,183663,506850,183650,51.54176374,-0.460726184\n41562101,18/03/2010 17:59,2010,2009/10,Special Service,2,1,260,260,CAT WEDGED BETWEEN GARAGES,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12035502,Sandown Way,20601508,UB5,512462,184979,512450,184950,51.55250074,-0.379315035\n43123101,21/03/2010 15:57,2010,2009/10,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED IN FENCING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Elmington Road,22500901,SE5,NULL,NULL,532850,177050,NULL,NULL\n43647101,22/03/2010 14:18,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000296,Marlborough,E09000015,Harrow,Harrow,NULL,Radnor Road,21201691,HA1,NULL,NULL,515050,188750,NULL,NULL\n44319101,23/03/2010 20:32,2010,2009/10,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000277,St. Ann's,E09000014,Haringey,Tottenham,NULL,Kerswell Close,21103484,N15,NULL,NULL,533050,188550,NULL,NULL\n44588101,24/03/2010 12:20,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL & PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Gage Road,22201413,E16,NULL,NULL,539450,181750,NULL,NULL\n44602101,24/03/2010 12:44,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD IN TREE,Bird,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Eglington Road,NULL,E4,538812,194898,538850,194850,51.6357698,0.004434796\n44611101,24/03/2010 13:00,2010,2009/10,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN CAT FLAP,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,20800415,SE10,NULL,NULL,538450,177350,NULL,NULL\n44843101,24/03/2010 19:40,2010,2009/10,Special Service,1,1,260,260,CAT TRAPPED BETWEEN PIPES AND WATER TANK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Liston Way,22305479,IG8,NULL,NULL,541250,191150,NULL,NULL\n45166101,25/03/2010 14:32,2010,2009/10,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Langdale Road,20501119,CR7,NULL,NULL,531250,168150,NULL,NULL\n45245101,25/03/2010 17:26,2010,2009/10,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN WOODEN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000457,Colliers Wood,E09000024,Merton,Mitcham,48001205,Alexandra Road,22100090,CR4,527406,170209,527450,170250,51.41658939,-0.169206783\n45955101,27/03/2010 01:18,2010,2009/10,Special Service,1,1,260,260,CAT STUCK IN CEILING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Cromwell Road,21700138,SW7,NULL,NULL,525850,178950,NULL,NULL\n46188101,27/03/2010 13:39,2010,2009/10,Special Service,1,1,260,260,CROW TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Green Lane,21900636,SW16,NULL,NULL,530550,170150,NULL,NULL\n46694101,28/03/2010 16:57,2010,2009/10,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525250,170150,NULL,NULL\n48658101,01/04/2010 14:24,2010,2010/11,Special Service,1,3,260,780,RUNNING CALL TO SICK ANIMAL LOCKED IN VAN,Unknown - Heavy Livestock Animal,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Johnstone Road,NULL,E6,542904,182578,542950,182550,51.52404411,0.058514732\n49059101,02/04/2010 11:12,2010,2010/11,Special Service,1,1,260,260,CAT STUCK INBETWEEN WALLS,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,34076146,Byam Street,21000177,SW6,526113,176176,526150,176150,51.47050526,-0.185667529\n49100101,02/04/2010 12:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011224,East Wickham,E09000004,Bexley,Eltham,1.0002E+11,Ashmore Grove,20100060,DA16,544813,175799,544850,175750,51.46264485,0.083232931\n49164101,02/04/2010 15:04,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Park Road,NULL,SM6,528982,164382,528950,164350,51.36386631,-0.148670825\n49165101,02/04/2010 15:11,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOARDING IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Bolingbroke Grove,22900545,SW11,NULL,NULL,527150,174850,NULL,NULL\n49244101,02/04/2010 18:13,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Park Road,NULL,SM6,528984,164341,528950,164350,51.36349738,-0.148656972\n50175101,04/04/2010 19:28,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Oakdene Road,21401442,UB10,NULL,NULL,507750,183450,NULL,NULL\n50581101,05/04/2010 15:18,2010,2010/11,Special Service,1,1,260,260,DOG STUCK DOWN FOXHOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,10070709920,Richmond Road,22403771,TW1,516750,173750,516750,173750,51.45070742,-0.321207963\n50676101,05/04/2010 18:54,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER PILE OF TIMBER IN SHED,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Stanmore,200035739,East Road,20013460,HA8,520052,190797,520050,190750,51.60322764,-0.267905616\n50742101,05/04/2010 20:14,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER BONNET OF CAR,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000141,King's Cross,E09000007,Camden,Euston,5048946,Sidmouth Street,20400898,WC1H,530586,182614,530550,182650,51.52734872,-0.118922573\n51775101,07/04/2010 17:15,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED  BETWEED BED AND WALL,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Druid Street,22500807,SE1,NULL,NULL,533750,179650,NULL,NULL\n52086101,08/04/2010 11:26,2010,2010/11,Special Service,1,1,260,260,HERON TRAPPED IN NETTING IN POND,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Erlanger Road,NULL,SE14,535775,176381,535750,176350,51.47011332,-0.046563615\n52296101,08/04/2010 18:33,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),Swimming Pool,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Lower Clapton Road,20900640,E5,535128,185405,535150,185450,51.55136098,-0.052416501\n52614101,09/04/2010 08:42,2010,2010/11,Special Service,1,4,260,1040,HORSE FALLEN BETWEEN STABLES,Horse,Person (mobile),Animal boarding/breeding establishment - cats,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382\n52701101,09/04/2010 12:20,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED IN AIR VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Greystead Road,22001515,SE23,NULL,NULL,535250,173850,NULL,NULL\n52726101,09/04/2010 13:30,2010,2010/11,Special Service,1,2,260,520,DEER WITH HEAD STUCK IN RAILINGS,Deer,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dagnam Park Drive,NULL,RM3,555393,192127,555350,192150,51.60654314,0.242611004\n53037101,09/04/2010 20:34,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN GROUNDS OF WAREHOUSE,Cat,Person (mobile),Converted office,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,6021826,Hollybush Place,22700647,E2,534912,182771,534950,182750,51.52774289,-0.056538408\n54290101,11/04/2010 18:44,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN FENCE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6210797,Agnes Street,22700051,E14,536845,181457,536850,181450,51.5154705,-0.02920048\n54936101,12/04/2010 19:33,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Colyton Road,22500560,SE22,NULL,NULL,534850,174550,NULL,NULL\n54953101,12/04/2010 19:55,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033525852,Northumberland Avenue,8401479,WC2N,530125,180375,530150,180350,51.50733349,-0.12639059\n55016101,12/04/2010 21:02,2010,2010/11,Special Service,1,1,260,260,DOGS TRAPPED INSIDE CAR  WITH FUMES,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000536,Cathedrals,E09000028,Southwark,Lambeth,NULL,Short Street,NULL,SE1,531495,179884,531450,179850,51.50260419,-0.106844419\n56052101,14/04/2010 18:33,2010,2010/11,Special Service,1,1,260,260,PIDGEON TRAPPED IN AIR VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Topham Square,21105162,N17,NULL,NULL,532350,190750,NULL,NULL\n56094101,14/04/2010 19:25,2010,2010/11,Special Service,1,2,260,520,KITTEN TRAPPED BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Canterbury Crescent,21900284,SW9,NULL,NULL,531250,175550,NULL,NULL\n56117101,14/04/2010 19:59,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Bulganak Road,20500737,CR7,NULL,NULL,532250,168450,NULL,NULL\n56200101,14/04/2010 22:32,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Willow Road,20601910,W5,NULL,NULL,518350,179650,NULL,NULL\n56454101,15/04/2010 13:05,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN MIDDLE OF RAILWAY BRIDGE,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Woodcote Road,NULL,SM6,528750,164250,528750,164250,51.36273253,-0.15204916\n56469101,15/04/2010 13:44,2010,2010/11,Special Service,1,4,260,1040,CAT TRAPPED,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,Redbridge,Ilford,1.00022E+11,Elgin Road,22302025,IG3,545509,187831,545550,187850,51.57057981,0.098207401\n56674101,15/04/2010 19:33,2010,2010/11,Special Service,1,1,260,260,PUPPY STUCK BETWEEN TWO WALLS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,10001271387,Herbert Place,21590269,TW7,514673,176456,514650,176450,51.47545277,-0.350208875\n57142101,16/04/2010 16:06,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,East Acton,E09000009,Ealing,Acton,NULL,Third Avenue,20601749,W3,NULL,NULL,521650,180150,NULL,NULL\n57253101,16/04/2010 19:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,NULL,Norman Avenue,20502236,CR2,NULL,NULL,532450,162450,NULL,NULL\n57584101,17/04/2010 09:51,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000225,Peninsula,E09000011,Greenwich,East Greenwich,NULL,Old Woolwich Road,20801109,SE10,NULL,NULL,538950,178050,NULL,NULL\n58332101,18/04/2010 11:29,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Stratheden Road,20801429,SE3,NULL,NULL,540250,177150,NULL,NULL\n58367101,18/04/2010 12:49,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000196,Cockfosters,E09000010,Enfield,Barnet,NULL,Old Orchard Close,20706106,EN4,NULL,NULL,526850,198150,NULL,NULL\n58438101,18/04/2010 14:59,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING NEAR SHOPS,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000048,East Barnet,E09000003,Barnet,Barnet,200034934,East Barnet Road,20013380,EN4,526726,196048,526750,196050,51.64895631,-0.169689366\n58746101,18/04/2010 23:51,2010,2010/11,Special Service,1,1,260,260,HAMSTER TRAPPED IN DISABLED LIFT,Hamster,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Sheldon Close,20801338,SE12,NULL,NULL,540650,174650,NULL,NULL\n58835101,19/04/2010 04:03,2010,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000513,Snaresbrook,E09000026,Redbridge,Leytonstone,NULL,High Street Wanstead,NULL,E11,540661,188358,540650,188350,51.57654492,0.028513822\n58950101,19/04/2010 09:52,2010,2010/11,Special Service,1,1,260,260,PREGNANT DOG WITH HEAD TRAPPED IN CAT FLAP,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Keswick Road,22902627,SW15,NULL,NULL,524350,174450,NULL,NULL\n59132101,19/04/2010 16:34,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Sheen Road,22405917,TW9,NULL,NULL,518750,174950,NULL,NULL\n59248101,19/04/2010 19:50,2010,2010/11,Special Service,1,1,260,260,SEAGULL TRAPPED BEHIND SCAFFOLDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Biddestone Road,NULL,N7,NULL,NULL,530650,185650,NULL,NULL\n59503101,20/04/2010 08:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Anthony Way,NULL,N18,535750,191750,535750,191750,51.6082287,-0.041001921\n59530101,20/04/2010 10:00,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SECURITY GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000638,Lancaster Gate,E09000033,Westminster,Kensington,NULL,Pembridge Square,8400629,W2,NULL,NULL,525450,180750,NULL,NULL\n60136101,21/04/2010 11:41,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA OFFICER TO RELEASE KITTEN IN TOILET PIPE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Cricklewood Broadway,20200487,NW2,NULL,NULL,524150,185350,NULL,NULL\n60261101,21/04/2010 15:33,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED BETWEEN SHOP SIGN,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000048,East Barnet,E09000003,Barnet,Barnet,200035153,East Barnet Road,20013380,EN4,527133,195404,527150,195450,51.64307706,-0.164043589\n60354101,21/04/2010 18:08,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO PIGEON TRAPPED,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Avenue South,21100674,N8,NULL,NULL,529750,189050,NULL,NULL\n60892101,22/04/2010 16:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Chapel Lane,NULL,HA5,512034,189470,512050,189450,51.5929514,-0.384054247\n62023101,24/04/2010 06:03,2010,2010/11,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,NULL,Hurstway Walk,21701277,W11,NULL,NULL,523850,180850,NULL,NULL\n62095101,24/04/2010 10:13,2010,2010/11,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000105,Willesden Green,E09000005,Brent,Willesden,202130170,Hawthorn Road,20200106,NW10,522436,184458,522450,184450,51.54574541,-0.235706516\n62267101,24/04/2010 13:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,Mauleverer Road,21900932,SW2,NULL,NULL,530250,174750,NULL,NULL\n62726101,24/04/2010 23:51,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN GRATE,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,5055562,Princeton Street,20401141,WC1R,530716,181744,530750,181750,51.51950016,-0.117372256\n62815101,25/04/2010 01:20,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BY GLASS AT REAR OF PROPERTY,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Chaucer Road,20600357,W3,NULL,NULL,520350,180350,NULL,NULL\n63606101,26/04/2010 12:38,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Abbotswood Road,21900063,SW16,NULL,NULL,529550,172250,NULL,NULL\n63612101,26/04/2010 12:53,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527350,168350,NULL,NULL\n63812101,26/04/2010 19:47,2010,2010/11,Special Service,1,1,260,260,PARROT CAUGHT IN ROOF TILE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Spring Hill,22001911,SE26,NULL,NULL,535350,171650,NULL,NULL\n64167101,27/04/2010 13:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,NULL,Brancaster Lane,20501939,CR8,NULL,NULL,532350,161350,NULL,NULL\n64323101,27/04/2010 17:48,2010,2010/11,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Wychwood Close,21202535,HA8,NULL,NULL,517750,191650,NULL,NULL\n64396101,27/04/2010 19:12,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN HOUSE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Mill Lane,20400242,NW6,NULL,NULL,525350,185250,NULL,NULL\n64733101,28/04/2010 12:38,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524588,184643,524550,184650,51.54693752,-0.204622646\n65157101,29/04/2010 02:24,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Paul Street,22201551,E15,NULL,NULL,539150,183950,NULL,NULL\n65274101,29/04/2010 10:40,2010,2010/11,Special Service,1,1,260,260,ASSISTING RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Patshull Road,20400509,NW5,NULL,NULL,528950,184750,NULL,NULL\n65431101,29/04/2010 16:09,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,Canbury,E09000021,Kingston upon Thames,Kingston,NULL,Brunswick Road,21800179,KT2,NULL,NULL,519150,169850,NULL,NULL\n65446101,29/04/2010 16:38,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,Welham Road,22906027,SW16,528856,170853,528850,170850,51.42204994,-0.148132378\n65462101,29/04/2010 17:00,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000079,North End,E09000004,Bexley,Erith,NULL,Hollywood Way,NULL,DA8,552644,176714,552650,176750,51.46881023,0.196269169\n67819101,02/05/2010 22:18,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED UNDER GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Porters Avenue,19900284,RM9,NULL,NULL,546750,184750,NULL,NULL\n68283101,03/05/2010 20:12,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED BEHIND METAL FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Buckland Way,NULL,KT4,523009,166164,523050,166150,51.38120525,-0.233806795\n69271101,05/05/2010 17:59,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN INTO RIVER,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Seely Road,NULL,SW17,528831,170543,528850,170550,51.41926959,-0.148604304\n69604101,06/05/2010 11:19,2010,2010/11,Special Service,1,1,260,260,PIDGEON TRAPPED IN SASH WINDOW,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Hornsey Road,NULL,N19,530455,186743,530450,186750,51.56448516,-0.119280134\n69934101,06/05/2010 20:05,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,202079063,Kilburn High Road,20202523,NW6,524755,184458,524750,184450,51.54523804,-0.202281067\n70019101,06/05/2010 22:49,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN DRAIN,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Dartmouth Road,NULL,NW2,523913,184830,523950,184850,51.54876661,-0.214286344\n70335101,07/05/2010 15:40,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Lowden Road,21900899,SE24,532091,175189,532050,175150,51.46027274,-0.100016667\n70477101,07/05/2010 19:39,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Broadhurst Gardens,20400410,NW6,NULL,NULL,525650,184550,NULL,NULL\n70766101,08/05/2010 09:37,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS OF ROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Grosvenor Park Road,22841250,E17,NULL,NULL,537750,188650,NULL,NULL\n70860101,08/05/2010 13:16,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK OUT OF WINDOW,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Ogilby Street,20801103,SE18,NULL,NULL,542750,178650,NULL,NULL\n70935101,08/05/2010 16:09,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Grosvenor Park Road,22841250,E17,NULL,NULL,537750,188650,NULL,NULL\n70978101,08/05/2010 17:20,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Sanctuary Close,NULL,UB9,505333,191075,505350,191050,51.60866891,-0.480271017\n71260101,09/05/2010 05:35,2010,2010/11,Special Service,1,1,260,260,DEER STUCK IN LAKE,Deer,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Tingeys Top Lane,NULL,EN2,531305,199496,531350,199450,51.67889115,-0.102248994\n71601101,09/05/2010 21:28,2010,2010/11,Special Service,1,1,260,260,FOX CUB TRAPPED IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000210,Town,E09000010,Enfield,Enfield,NULL,Hallside Road,20702728,EN1,NULL,NULL,533550,198050,NULL,NULL\n71770101,10/05/2010 06:40,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN MUD,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Cheyne Walk,NULL,SW10,526631,177256,526650,177250,51.48009572,-0.177827129\n71912101,10/05/2010 12:43,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,Canonbury,E09000019,Islington,Islington,NULL,Willow Bridge Road,NULL,N1,532103,184481,532150,184450,51.54377406,-0.096366834\n72228101,10/05/2010 23:26,2010,2010/11,Special Service,2,3,260,780,LAMB IN STREAM,Lamb,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754\n72442101,11/05/2010 13:50,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000298,Pinner South,E09000015,Harrow,Harrow,2.00004E+11,Pembroke Avenue,21202620,HA5,512006,187469,512050,187450,51.57497172,-0.385096366\n72956101,12/05/2010 11:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000469,Raynes Park,E09000024,Merton,New Malden,48081513,Grand Drive,22102916,SW20,523204,169290,523250,169250,51.40925784,-0.229922539\n73131101,12/05/2010 16:54,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,1.00023E+11,Howard Road,20501062,SE25,534389,167693,534350,167650,51.39236872,-0.069799461\n73444101,13/05/2010 09:29,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER DECKING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011099,Dulwich Hill,E09000028,Southwark,Forest Hill,NULL,Dunstans Road,22500836,SE22,NULL,NULL,534350,173950,NULL,NULL\n73471101,13/05/2010 10:05,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000163,Shirley,E09000008,Croydon,Addington,NULL,Bramble Close,NULL,CR0,537562,164757,537550,164750,51.36522549,-0.025357425\n73572101,13/05/2010 13:31,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN LOFT,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Hammersmith Grove,21000410,W6,NULL,NULL,523050,179350,NULL,NULL\n74238101,14/05/2010 15:56,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Frognal Rise,NULL,NW3,526218,185979,526250,185950,51.55858229,-0.180648982\n74378101,14/05/2010 19:58,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,NULL,Mill Street,NULL,KT1,518469,168768,518450,168750,51.40557454,-0.298146071\n74480101,14/05/2010 23:27,2010,2010/11,Special Service,1,1,260,260,IGUANA TRAPPED BEHIND HEATING PIPES,Lizard,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Peggotty Way,21401507,UB8,NULL,NULL,507950,181250,NULL,NULL\n74722101,15/05/2010 11:47,2010,2010/11,Special Service,1,1,260,260,CAT  TRAPPED IN ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Cowick Road,NULL,SW17,527813,171754,527850,171750,51.43038322,-0.16280033\n74801101,15/05/2010 14:02,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED SQUIRREL IN DRAINPIPE,Squirrel,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011105,Newington,E09000028,Southwark,Old Kent Road,NULL,Carter Street,22500455,SE17,NULL,NULL,532050,178050,NULL,NULL\n75830101,17/05/2010 08:56,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000117,Darwin,E09000006,Bromley,Biggin Hill,NULL,Shire Lane,NULL,BR6,544040,162292,544050,162250,51.34147219,0.066635966\n75871101,17/05/2010 10:54,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Lisbon Avenue,22401630,TW2,514096,172520,514050,172550,51.44019322,-0.359781932\n75971101,17/05/2010 14:52,2010,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000053,Golders Green,E09000003,Barnet,West Hampstead,200023491,Claremont Road,20009220,NW2,523634,186707,523650,186750,51.56569694,-0.217648823\n76439101,18/05/2010 10:53,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Promenade de Verdun,20502565,CR8,529943,161471,529950,161450,51.33748612,-0.135936602\n76723101,18/05/2010 20:03,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN DOWN CHUTE - POSSIBLY TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Attlee Road,20800096,SE28,NULL,NULL,546850,180750,NULL,NULL\n76958101,19/05/2010 10:26,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Fairlawn,20800552,SE7,NULL,NULL,541150,177550,NULL,NULL\n77521101,20/05/2010 08:38,2010,2010/11,Special Service,1,1,260,260,TWO KITTENS STUCK IN OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Church Manorway,20800336,SE2,NULL,NULL,546150,179450,NULL,NULL\n77957101,20/05/2010 21:35,2010,2010/11,Special Service,1,1,260,260,RUBBISH ALIGHT IN GREEN PARK,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Sloane Street,21700497,SW1X,NULL,NULL,527850,179150,NULL,NULL\n78181101,21/05/2010 09:46,2010,2010/11,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN CABLE AND WALL,Squirrel,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000138,Holborn and Covent Garden,E09000007,Camden,Shoreditch,NULL,Hatton Garden,NULL,EC1N,531359,181842,531350,181850,51.52023186,-0.108073553\n78352101,21/05/2010 15:04,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,NULL,Marlborough Road,21500739,W4,NULL,NULL,520150,178350,NULL,NULL\n78429101,21/05/2010 16:52,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,Hyde Park,E09000033,Westminster,Paddington,NULL,Hyde Park Crescent,8400829,W2,NULL,NULL,527150,181150,NULL,NULL\n78510101,21/05/2010 18:47,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Grosvenor Road,22403520,TW1,516084,173541,516050,173550,51.44896586,-0.330856942\n79464101,23/05/2010 00:30,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN & INJURED CALLER UNABLE TO REACH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Union Road,NULL,SW4,NULL,NULL,529850,176150,NULL,NULL\n79596101,23/05/2010 08:00,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK BEHIND KITCHEN UNIT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Globe Pond Road,NULL,SE16,NULL,NULL,536050,180250,NULL,NULL\n80517101,24/05/2010 13:25,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Hazellville Road,NULL,N19,529871,187528,529850,187550,51.57167431,-0.127411032\n80519101,24/05/2010 13:25,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON WINDOWSILL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Upper Tollington Park,21104024,N4,NULL,NULL,531250,187450,NULL,NULL\n80554101,24/05/2010 14:30,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER BUILDERS MACHINERY,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009378,Hoxton West,E09000012,Hackney,Islington,NULL,Cropley Street,NULL,N1,532663,183254,532650,183250,51.5326163,-0.088758164\n80593101,24/05/2010 15:30,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Hazellville Road,NULL,N19,529871,187528,529850,187550,51.57167431,-0.127411032\n80648101,24/05/2010 16:34,2010,2010/11,Special Service,1,1,260,260,FOX WITH HEAD STUCK IN CAR WHEEL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Crossway,19900101,RM8,NULL,NULL,547150,186150,NULL,NULL\n80730101,24/05/2010 17:40,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK BEHINED PIPE WORK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000128,Belsize,E09000007,Camden,Kentish Town,NULL,Primrose Hill Road,20400446,NW3,NULL,NULL,527550,184450,NULL,NULL\n81218101,25/05/2010 10:26,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND GAS FIRE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000426,Oval,E09000022,Lambeth,Lambeth,NULL,Dorset Road,21900471,SW8,NULL,NULL,530750,177250,NULL,NULL\n81267101,25/05/2010 11:48,2010,2010/11,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,Highgate,E09000014,Haringey,Kentish Town,NULL,Highgate Hill,21106146,N6,NULL,NULL,528850,187250,NULL,NULL\n81518101,25/05/2010 18:13,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Campbell Close,NULL,HA4,510322,188104,510350,188150,51.58101098,-0.409187808\n81828101,26/05/2010 07:38,2010,2010/11,Special Service,1,1,260,260,FOX ENTANGLED IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Clifden Road,22403392,TW1,NULL,NULL,515850,173150,NULL,NULL\n81930101,26/05/2010 11:58,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,5870112655,Lyle Close,22600753,CR4,528131,166840,528150,166850,51.38614872,-0.160002221\n82540101,27/05/2010 11:33,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER STORAGE UNIT,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,Pike Close,20302078,BR1,540583,171191,540550,171150,51.42230265,0.020554012\n82542101,27/05/2010 11:35,2010,2010/11,Special Service,1,3,260,780,ASSIST RSPCA TO GAIN ENTRY,Unknown - Heavy Livestock Animal,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Wick Road,20901074,E9,NULL,NULL,536050,184750,NULL,NULL\n83720101,29/05/2010 09:31,2010,2010/11,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Varley Road,22201175,E16,NULL,NULL,540950,181350,NULL,NULL\n84519101,30/05/2010 18:44,2010,2010/11,Special Service,1,1,260,260,BABY BIRD TRAPPED IN METAL POST OF PARK GATE,Bird,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Peel Close,NULL,E4,537570,193709,537550,193750,51.62539042,-0.013966943\n84569101,30/05/2010 19:44,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,1.00023E+11,Norwood Road,21901036,SE24,531786,173170,531750,173150,51.44219937,-0.105155644\n84915101,31/05/2010 13:31,2010,2010/11,Special Service,2,3,260,780,CYGNETS TRAPPED,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,6026299,Glamis Road,22700541,E1W,535250,180750,535250,180750,51.50950086,-0.052444265\n85446101,01/06/2010 11:55,2010,2010/11,Special Service,1,1,260,260,DUCKLINGS STUCK IN DRAIN,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Creighton Road,NULL,N17,533235,191420,533250,191450,51.605864,-0.077424536\n85448101,01/06/2010 12:07,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX STUCK IN TREE,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Powis Square,21700974,W11,NULL,NULL,524850,181150,NULL,NULL\n85659101,01/06/2010 18:54,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Kingsford Street,NULL,NW5,NULL,NULL,527750,185250,NULL,NULL\n86323101,02/06/2010 20:57,2010,2010/11,Special Service,1,1,260,260,DOG SHUT IN LIFT - NON EMERGENCY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538150,176450,NULL,NULL\n86786101,03/06/2010 16:46,2010,2010/11,Special Service,1,1,260,260,ASSISTS RSPCA WITH BIRD STUCK IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000097,Preston,E09000005,Brent,Wembley,NULL,Carlton Avenue East,20201332,HA9,NULL,NULL,517950,187050,NULL,NULL\n86997101,03/06/2010 20:59,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND KITCHEN UNIT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Denmark Road,21103188,N8,NULL,NULL,531050,189350,NULL,NULL\n88642101,06/06/2010 09:57,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN FISHING LINE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Martin Way,NULL,SW20,524261,168785,524250,168750,51.40448874,-0.214909046\n88800101,06/06/2010 16:22,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOWSILL,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,Latchmere,E09000032,Wandsworth,Battersea,NULL,Wye Street,22906361,SW11,NULL,NULL,526850,175950,NULL,NULL\n88816101,06/06/2010 17:01,2010,2010/11,Special Service,1,2,260,520,PIGEON STUCK IN A TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Martin Way,NULL,SW20,524261,168785,524250,168750,51.40448874,-0.214909046\n89282101,07/06/2010 11:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,10090633312,Gale Street,19900144,RM9,547629,184529,547650,184550,51.54036014,0.127390614\n89323101,07/06/2010 13:01,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Wadeson Street,22701281,E2,NULL,NULL,535050,183450,NULL,NULL\n89353101,07/06/2010 13:46,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870078866,Park Lane,22605913,SM6,528505,164019,528550,164050,51.36071184,-0.155649689\n89432101,07/06/2010 16:19,2010,2010/11,Special Service,1,1,260,260,DOG IN DISTRESS IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Lancaster Road,NULL,W11,524750,181250,524750,181250,51.51640824,-0.203489105\n89514101,07/06/2010 18:33,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,10010221623,Queenscroft Road,20801032,SE9,541806,174461,541850,174450,51.45138262,0.039440163\n90438101,09/06/2010 15:49,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN DRAIN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Hanford Close,NULL,SW18,525285,173377,525250,173350,51.44553346,-0.19857294\n90448101,09/06/2010 16:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,Crofton Park,E09000023,Lewisham,Lewisham,NULL,Marnock Road,22004584,SE4,NULL,NULL,536650,174650,NULL,NULL\n91207101,10/06/2010 21:24,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN EMPTY PROPERTY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Chichester Mews,21901720,SE27,NULL,NULL,531350,171950,NULL,NULL\n91792101,11/06/2010 23:44,2010,2010/11,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN CHAIR OS,Cat,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,217092286,Westgate Terrace,21700584,SW10,525808,177990,525850,177950,51.48687573,-0.189411354\n91980101,12/06/2010 11:21,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN GRILL OF WINDOW,Bird,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,5300041297,Hatchard Road,21606443,N19,529948,186846,529950,186850,51.56552774,-0.126552477\n93111101,14/06/2010 09:46,2010,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Field End Road,NULL,HA5,511103,187742,511150,187750,51.57760408,-0.398035334\n93658101,15/06/2010 09:38,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,West Norwood,NULL,Arlingford Road,21900109,SW2,NULL,NULL,531250,174350,NULL,NULL\n93813101,15/06/2010 16:24,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN MESH,Bird,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Greenwood Road,NULL,E8,534376,184591,534350,184550,51.54422563,-0.06356643\n93843101,15/06/2010 17:34,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000170,Acton Central,E09000009,Ealing,Acton,12101494,Alfred Road,20600036,W3,520399,180209,520350,180250,51.50799514,-0.266520181\n94209101,16/06/2010 10:42,2010,2010/11,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN BACK DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Creswick Walk,22702077,E3,NULL,NULL,537050,182950,NULL,NULL\n94233101,16/06/2010 11:45,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN POST AND FENCE,Fox,Person (mobile),Other outdoor sporting venue,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rectory Gardens,NULL,N8,530309,189353,530350,189350,51.5879737,-0.120418399\n94513101,16/06/2010 19:43,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED BEHIND GAS FIRE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Somerset Road,20601561,W4,NULL,NULL,520750,179350,NULL,NULL\n94576101,16/06/2010 21:11,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Telford Avenue,21901352,SW2,NULL,NULL,529950,173150,NULL,NULL\n94956101,17/06/2010 15:04,2010,2010/11,Special Service,1,1,260,260,DUCK AND DUCKLINGS STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,NULL,Percy Road,22401740,TW2,NULL,NULL,513950,173150,NULL,NULL\n95038101,17/06/2010 17:11,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO RECOVER ANIMAL FROM BALCONY,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Strathdon Drive,22905023,SW17,NULL,NULL,526650,172150,NULL,NULL\n95128101,17/06/2010 19:37,2010,2010/11,Special Service,1,1,260,260,ASSIST ELDERLY PERSON WITH DISTRESSED DOG,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Treaty Street,21606165,N1,NULL,NULL,530550,183650,NULL,NULL\n95514101,18/06/2010 13:16,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO RESCUE MALLARD DUCK AND CHICKS FROM TOP OF DRAINPIPE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Brunswick Quay,22500361,SE16,535963,179145,535950,179150,51.49490678,-0.042794826\n95570101,18/06/2010 15:26,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,1.00023E+11,Fountain Road,22901761,SW17,526887,171551,526850,171550,51.4287666,-0.176186377\n96006101,19/06/2010 11:57,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING  UNDER BRIDGE,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Chiswick Roundabout,NULL,W4,519250,178250,519250,178250,51.4906317,-0.283730286\n96413101,19/06/2010 21:50,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000166,Upper Norwood,E09000008,Croydon,Norbury,NULL,Glenhurst Rise,NULL,SE19,532327,170125,532350,170150,51.41470863,-0.098512258\n96549101,20/06/2010 05:42,2010,2010/11,Special Service,2,3,260,780,SMALL ANIMAL RESCUE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Wesley Road,NULL,NW10,520418,183856,520450,183850,51.54076847,-0.265000624\n96600101,20/06/2010 09:17,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Fulham Palace Road,21000356,W6,NULL,NULL,523350,178250,NULL,NULL\n96641101,20/06/2010 11:30,2010,2010/11,Special Service,1,1,260,260,DOG LOCKED IN ROOM,Dog,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,121004807,West Hill,22906045,SW15,524218,174097,524250,174050,51.45223909,-0.213666357\n97096101,21/06/2010 00:31,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER BATH,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Garnet Street,22700529,E1W,NULL,NULL,535050,180450,NULL,NULL\n408102,21/06/2010 16:17,2010,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Pre School/nursery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000105,Willesden Green,E09000005,Brent,Willesden,202104266,Doyle Gardens,20201769,NW10,522651,183733,522650,183750,51.53918296,-0.232860104\n97326101,22/06/2010 11:57,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED NEAR RIVER UNDER RAILINGS,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Windsor Wharf,NULL,E9,536956,185021,536950,185050,51.54747034,-0.0262165\n97443101,22/06/2010 14:18,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Balvernie Grove,22900265,SW18,NULL,NULL,525050,173650,NULL,NULL\n97555101,22/06/2010 16:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000553,The Lane,E09000028,Southwark,Peckham,NULL,Costa Street,NULL,SE15,534143,176041,534150,176050,51.46744701,-0.070174601\n98110101,23/06/2010 12:17,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Standen Road,22904920,SW18,NULL,NULL,525050,173450,NULL,NULL\n98136101,23/06/2010 12:40,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON RIVER BANK,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Wandle Bank,NULL,SW19,526583,170225,526550,170250,51.41691733,-0.18103009\n98594101,23/06/2010 21:34,2010,2010/11,Special Service,1,1,260,260,CAT  TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000350,Cranford,E09000018,Hounslow,Heathrow,1.00022E+11,Avenue Gardens,21500061,TW5,510537,177160,510550,177150,51.48260313,-0.409518181\n98661101,23/06/2010 22:34,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN METAL GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000373,Highbury West,E09000019,Islington,Holloway,5300028570,Drayton Park,21603141,N5,531421,185870,531450,185850,51.55641561,-0.105677568\n98674101,23/06/2010 23:07,2010,2010/11,Special Service,1,1,260,260,RABBIT STUCK INBETWEEN FENCE AND WALL,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011239,Clayhall,E09000026,Redbridge,Hainault,NULL,Stradbroke Grove,22303282,IG5,NULL,NULL,542550,190050,NULL,NULL\n98740101,24/06/2010 00:33,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER CUPBOARD,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Lordship Park,20900636,N16,NULL,NULL,532350,186750,NULL,NULL\n99084101,24/06/2010 16:11,2010,2010/11,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,6061900,East Ferry Road,22700444,E14,537991,179127,537950,179150,51.49425381,-0.013606633\n99291101,24/06/2010 20:30,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,1.00023E+11,Chancery Lane,8100118,WC2A,530875,181625,530850,181650,51.51839395,-0.115126079\n99545101,25/06/2010 09:00,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND GARAGE,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000504,Fullwell,E09000026,Redbridge,Hainault,NULL,Sydney Road,NULL,IG6,544277,190048,544250,190050,51.59081719,0.081353631\n99846101,25/06/2010 16:08,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN PIPEWORK IN BATHROOM,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Lily Drive,21402817,UB7,NULL,NULL,505750,178850,NULL,NULL\n99960101,25/06/2010 18:29,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN PIPE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Plumstead,1.00021E+11,Voce Road,20801553,SE18,544843,177107,544850,177150,51.47439037,0.084200227\n100001101,25/06/2010 19:19,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO BIRD STUCK IN TREE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Courtfield Gardens,NULL,SW5,NULL,NULL,525850,178750,NULL,NULL\n100395101,26/06/2010 10:39,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Fulwell Park Avenue,22401495,TW2,NULL,NULL,514050,172650,NULL,NULL\n100611101,26/06/2010 15:29,2010,2010/11,Special Service,3,8,260,2080,CAT TRAPPED IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Sixth Avenue,NULL,W10,NULL,NULL,524050,182650,NULL,NULL\n100615101,26/06/2010 15:31,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON BUILDING SITE,Dog,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,NULL,Berger Road,NULL,E9,535807,184884,535850,184850,51.54651639,-0.042829892\n100633101,26/06/2010 15:59,2010,2010/11,Special Service,1,1,260,260,DUCKLING STUCK IN DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Windsor Street,NULL,UB8,505521,184070,505550,184050,51.54567065,-0.479659599\n100733101,26/06/2010 17:56,2010,2010/11,Special Service,1,1,260,260,DUCKLING TRAPPED IN DRAINAGE DITCH BY LAKES,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,NULL,Parliament Hill,NULL,NW3,527304,185711,527350,185750,51.55593,-0.16508846\n100859101,26/06/2010 20:26,2010,2010/11,Special Service,1,1,260,260,DUCKLINGS STUCK DOWN DRAIN,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000050,Edgware,E09000003,Barnet,Mill Hill,200221604,Barnet Way,20030684,NW7,520252,193926,520250,193950,51.6313061,-0.263947154\n101157101,27/06/2010 07:49,2010,2010/11,Special Service,1,2,260,520,KITTEN TRAPPED IN WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Rees Gardens,20501333,CR0,NULL,NULL,533850,167250,NULL,NULL\n101238101,27/06/2010 12:01,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BETWEEN TWO GARAGE WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,Palace View Road,22864150,E4,NULL,NULL,537850,192350,NULL,NULL\n101492101,27/06/2010 17:12,2010,2010/11,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000127,West Wickham,E09000006,Bromley,Woodside,NULL,High Street,20301748,BR4,NULL,NULL,537950,166050,NULL,NULL\n101869101,27/06/2010 22:52,2010,2010/11,Special Service,1,1,260,260,KITTEN FALLEN ONTO ROOF,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000047,Coppetts,E09000003,Barnet,Finchley,200129129,Woodhouse Road,20047200,N12,527583,192020,527550,192050,51.61256448,-0.158775862\n102221101,28/06/2010 12:47,2010,2010/11,Special Service,1,2,260,520,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,West Norwood,1.0002E+11,Belvedere Road,20302135,SE19,533623,170416,533650,170450,51.41701984,-0.079777516\n103128101,29/06/2010 09:25,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Five Oaks Lane,22304587,IG7,NULL,NULL,548350,192150,NULL,NULL\n103894101,30/06/2010 11:29,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,High Road,NULL,N17,533787,190037,533750,190050,51.59330524,-0.069985331\n104048101,30/06/2010 15:41,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON A ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,The Glade,20500570,CR0,NULL,NULL,536150,167150,NULL,NULL\n104212101,30/06/2010 18:57,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN INTO SHAFT,Dog,Person (mobile),\"Tunnel, subway\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Millfields Road,NULL,E5,535872,186192,535850,186150,51.55825479,-0.041388455\n104525101,01/07/2010 07:41,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011217,Barnehurst,E09000004,Bexley,Bexley,NULL,Cumbrian Avenue,20100406,DA7,NULL,NULL,551250,176450,NULL,NULL\n105169101,01/07/2010 22:09,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Grove Lane,22501157,SE5,NULL,NULL,533050,176050,NULL,NULL\n105429101,02/07/2010 08:31,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK IN DITCH,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000065,Belvedere,E09000004,Bexley,Erith,NULL,Anderson Way,NULL,DA17,549983,179865,549950,179850,51.49783354,0.159326523\n105684101,02/07/2010 15:40,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Stephendale Road,21000783,SW6,NULL,NULL,525950,176050,NULL,NULL\n106296101,03/07/2010 12:12,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED IN GAP BETWEEN TWO WALLS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Queens Road,22403749,TW1,NULL,NULL,516050,173450,NULL,NULL\n106365101,03/07/2010 13:26,2010,2010/11,Special Service,1,1,260,260,KITTEN IN CONTAINED AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Densole Close,20301882,BR3,NULL,NULL,536150,169950,NULL,NULL\n106377101,03/07/2010 13:36,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA TO CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,5108232,Ainger Road,20400706,NW3,527795,184051,527750,184050,51.54090085,-0.158612499\n106783101,03/07/2010 20:54,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UP A TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00022E+11,Myrtle Road,21500782,TW3,514098,176053,514050,176050,51.47194678,-0.358614634\n106824101,03/07/2010 21:36,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Queens Road,22403749,TW1,NULL,NULL,516050,173450,NULL,NULL\n108173101,05/07/2010 11:18,2010,2010/11,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Slagrove Place,NULL,SE13,537511,174806,537550,174850,51.45554082,-0.022197768\n108334101,05/07/2010 14:57,2010,2010/11,Special Service,1,1,260,260,HORSE TRAPPED ON GATE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,1.0002E+11,Parsonage Lane,20101117,DA14,548885,171633,548850,171650,51.42415633,0.140054795\n109050101,06/07/2010 09:58,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN SASH WINDOW,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000279,Stroud Green,E09000014,Haringey,Hornsey,NULL,Ferme Park Road,21106156,N8,NULL,NULL,530650,188250,NULL,NULL\n109356101,06/07/2010 18:07,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Barking,NULL,Fitzstephen Road,19900133,RM8,NULL,NULL,546950,185450,NULL,NULL\n109997101,07/07/2010 15:12,2010,2010/11,Special Service,1,1,260,260,BABY SEAGULL TRAPPED ON SPIKE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Maiden Lane,NULL,WC2E,530335,180721,530350,180750,51.51039458,-0.123238552\n110211101,07/07/2010 19:34,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Poplar,NULL,Orchard Place,NULL,E14,539012,180818,539050,180850,51.50919957,0.001757646\n110217101,07/07/2010 19:41,2010,2010/11,Special Service,1,1,260,260,DEER  STUCK IN RAILINGS  NEAR RAILWAY LINE,Deer,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Eastfield Road,NULL,EN3,536370,198126,536350,198150,51.66537414,-0.029571393\n110849101,08/07/2010 16:55,2010,2010/11,Special Service,1,1,260,260,BIRD STUCK IN FIRE PLACE / CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Burgoyne Road,21106779,N4,NULL,NULL,531550,188250,NULL,NULL\n110956101,08/07/2010 18:39,2010,2010/11,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Islington Green,21605183,N1,NULL,NULL,531650,183650,NULL,NULL\n110963101,08/07/2010 18:54,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN DRAINPIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,St. John's Grove,21600814,N19,NULL,NULL,529550,186550,NULL,NULL\n110986101,08/07/2010 19:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA PIGEON IMPALED ON TV AERIAL ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Rochdale Road,22870650,E17,NULL,NULL,537150,187850,NULL,NULL\n112055101,09/07/2010 20:50,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Call Centre,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000418,Clapham Common,E09000022,Lambeth,Clapham,2E+11,Crescent Lane,21900408,SW4,529591,174985,529550,174950,51.4590169,-0.13605642\n113121101,10/07/2010 19:16,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,White Hart Lane,21106111,N17,NULL,NULL,532450,191450,NULL,NULL\n113351101,10/07/2010 23:45,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO FOX IN PRECARIOUS POSITION,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,48065645,Southey Road,22105907,SW19,525429,170149,525450,170150,51.41649044,-0.197643557\n114151101,11/07/2010 23:43,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,High Street,21202607,HA8,NULL,NULL,519050,191850,NULL,NULL\n114353101,12/07/2010 11:10,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WALL SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Deals Gateway,22006140,SE13,NULL,NULL,537450,176750,NULL,NULL\n114393101,12/07/2010 12:32,2010,2010/11,Special Service,1,1,260,260,PERSON LOCKED OUT - COOKER BELIEVED TO BE LEFT ON,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Endlebury Road,22834200,E4,NULL,NULL,538550,193550,NULL,NULL\n114476101,12/07/2010 14:42,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Roman Road,22701028,E3,NULL,NULL,536350,183250,NULL,NULL\n114825101,12/07/2010 19:04,2010,2010/11,Special Service,1,1,260,260,CAT AND KITTEN TRAAPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000437,Bellingham,E09000023,Lewisham,Forest Hill,NULL,Burford Road,22001297,SE6,NULL,NULL,536850,172650,NULL,NULL\n114832101,12/07/2010 19:15,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED UNDER A BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,White Hart Lane,NULL,N17,533607,191364,533650,191350,51.60527261,-0.072077343\n115623101,13/07/2010 21:53,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Whitehall Street,NULL,N17,533750,191235,533750,191250,51.60407944,-0.070062827\n116271101,15/07/2010 06:16,2010,2010/11,Special Service,1,2,260,520,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,York Rise,NULL,BR6,545312,166023,545350,166050,51.37467435,0.086406343\n116813101,15/07/2010 23:36,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009381,London Fields,E09000012,Hackney,Homerton,1.00021E+11,Mare Street,20900658,E8,534933,184323,534950,184350,51.54168443,-0.055641527\n117301101,16/07/2010 17:33,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Ferney Road,20015620,EN4,NULL,NULL,528250,194650,NULL,NULL\n117461101,16/07/2010 21:07,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,NULL,Greenford Road,21201219,HA1,NULL,NULL,515650,185950,NULL,NULL\n117523101,16/07/2010 22:26,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Elgin Crescent,21700866,W11,NULL,NULL,524350,180950,NULL,NULL\n117542101,16/07/2010 23:01,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Grove Park Road,21300795,RM13,NULL,NULL,552350,183850,NULL,NULL\n117544101,16/07/2010 23:08,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Princes Road,NULL,N18,535228,192920,535250,192950,51.61886816,-0.048084728\n117546101,16/07/2010 23:08,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN ONTO BALCONY AND INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Kensington Park Road,21700905,W11,NULL,NULL,525150,180650,NULL,NULL\n117703101,17/07/2010 06:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,20900539,N16,NULL,NULL,533050,185450,NULL,NULL\n117804101,17/07/2010 11:18,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,5300087625,Stanmore Street,21606051,N1,530644,183899,530650,183850,51.53888335,-0.117610431\n117819101,17/07/2010 11:49,2010,2010/11,Special Service,1,2,260,520,RUNNING CALL TO ASSIST WITH ANIMALS IN DISTRESS,Unknown - Heavy Livestock Animal,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,217106411,Kensal Road,21700902,W10,524552,182258,524550,182250,51.525511,-0.205985016\n118253101,17/07/2010 21:24,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN PRICKLY BUSHES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,North Street,NULL,E13,540611,183119,540650,183150,51.52948087,0.025700736\n118454101,18/07/2010 08:13,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Masefield Crescent,21300098,RM3,NULL,NULL,553150,190650,NULL,NULL\n118571101,18/07/2010 13:46,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Mount Pleasant Lane,20900713,E5,NULL,NULL,534750,186850,NULL,NULL\n119188101,19/07/2010 05:32,2010,2010/11,Special Service,1,1,260,260,KITTENS TRAPPED IN NETTING,Cat,Person (mobile),Refuse/rubbish tip,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,6085968,Gillender Street,22700538,E14,538250,182250,538250,182250,51.52225487,-0.008653906\n119823101,19/07/2010 20:22,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Loughborough Estate,21900895,SW9,NULL,NULL,531650,176050,NULL,NULL\n120568101,20/07/2010 17:36,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Station Approach,NULL,HA4,511114,185402,511150,185450,51.55656957,-0.398616075\n121120101,21/07/2010 07:14,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BEHIND TICKET BOOTH,Fox,Person (land line),Railway building - other,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000428,St. Leonard's,E09000022,Lambeth,Norbury,2E+11,Estreham Road,21900528,SW16,529689,170520,529650,170550,51.41886771,-0.136280312\n121273101,21/07/2010 13:19,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Kingsman Street,20800867,SE18,NULL,NULL,542950,178950,NULL,NULL\n122019101,22/07/2010 05:21,2010,2010/11,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,West End Lane,20499056,NW6,NULL,NULL,525450,185150,NULL,NULL\n122107101,22/07/2010 10:27,2010,2010/11,Special Service,1,3,260,780,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Gosport Road,22840200,E17,536784,188602,536750,188650,51.57969128,-0.02730388\n122528101,22/07/2010 22:45,2010,2010/11,Special Service,1,1,260,260,CAT FALLEN BEHIND BUILT IN CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Dunsmure Road,20900349,N16,NULL,NULL,533350,187450,NULL,NULL\n122530101,22/07/2010 23:08,2010,2010/11,Special Service,1,3,260,780,DOG TRAPPED IN RABBIT HOLE,Dog,Person (land line),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,10024344879,Richmond Park,22407048,TW10,520681,173226,520650,173250,51.44517473,-0.264841368\n123241101,23/07/2010 21:39,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BUSH AFTER FALLING FROM WINDOW,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011244,Goodmayes,E09000026,Redbridge,Dagenham,1.00022E+11,Armstrong Close,22301811,RM8,547553,187549,547550,187550,51.56751532,0.127560848\n123273101,23/07/2010 22:57,2010,2010/11,Special Service,1,1,260,260,DOG  TRAPPED IN NETTLES,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Lowther Drive,NULL,EN2,530363,196305,530350,196350,51.65043493,-0.117054331\n123783101,24/07/2010 16:10,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN ROOF,Bird,Person (mobile),Town Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000480,East Ham Central,E09000025,Newham,East Ham,10012839270,Barking Road,22207589,E6,542650,183540,542650,183550,51.53275251,0.055245138\n123880101,24/07/2010 17:33,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON A LEDGE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000056,High Barnet,E09000003,Barnet,Barnet,NULL,Rockways,20036920,EN5,NULL,NULL,521950,195350,NULL,NULL\n124199101,24/07/2010 21:07,2010,2010/11,Special Service,1,1,260,260,ABANDONED CALL TRACED AS COMING FROM PRIVATE SUB,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,Feltham North,E09000018,Hounslow,Feltham,NULL,Burns Avenue,21500182,TW14,NULL,NULL,510550,174250,NULL,NULL\n124697101,25/07/2010 14:12,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Wakehurst Road,NULL,SW11,527776,174822,527750,174850,51.45796417,-0.162225941\n125476101,26/07/2010 13:39,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA  REP WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,1.00021E+11,Bridges Road,21202175,HA7,515772,192200,515750,192250,51.61673275,-0.329214813\n125537101,26/07/2010 15:13,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON THE ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Maclise Road,21000529,W14,NULL,NULL,524150,179150,NULL,NULL\n125540101,26/07/2010 15:16,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,34128676,Ravenscourt Park,21000675,W6,522458,179079,522450,179050,51.49739756,-0.237258242\n126024101,27/07/2010 03:02,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128004630,Clarence Street,21800255,KT1,518336,169437,518350,169450,51.41161515,-0.299834423\n126116101,27/07/2010 08:05,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED ON FLAG POLE,Bird,Person (land line),Temple,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000431,Streatham South,E09000022,Lambeth,Norbury,1.00023E+11,Colmer Road,21900371,SW16,530365,169951,530350,169950,51.41359923,-0.126773166\n126254101,27/07/2010 12:47,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,Webster Gardens,20601847,W5,NULL,NULL,517950,180250,NULL,NULL\n126259101,27/07/2010 13:03,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011221,Blendon & Penhill,E09000004,Bexley,Sidcup,NULL,Harcourt Avenue,20100669,DA15,NULL,NULL,547250,173750,NULL,NULL\n126830101,28/07/2010 11:05,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Orchard Close,NULL,E4,537213,192535,537250,192550,51.61492821,-0.019580353\n126895101,28/07/2010 12:43,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL KITTEN STUCK IN TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000543,Livesey,E09000028,Southwark,Old Kent Road,NULL,Rotherhithe New Road,NULL,SE16,534521,178074,534550,178050,51.48562702,-0.063962559\n127410101,28/07/2010 21:38,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hollydale Road,22501269,SE15,NULL,NULL,535150,176250,NULL,NULL\n127800101,29/07/2010 13:30,2010,2010/11,Special Service,1,1,260,260,DOG WITH PAW TRAPPED IN MANHOLE COVER,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000158,Norbury,E09000008,Croydon,Norbury,NULL,Elgar Avenue,NULL,SW16,530219,168974,530250,168950,51.40485248,-0.129229831\n128604101,30/07/2010 13:08,2010,2010/11,Special Service,1,1,260,260,SQUIRREL INJURED ON PHONE POLE,Squirrel,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Rosebery Avenue,NULL,N17,534336,190412,534350,190450,51.59654435,-0.061921232\n128625101,30/07/2010 13:45,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Belvedere Road,20302135,SE19,NULL,NULL,533950,170050,NULL,NULL\n128740101,30/07/2010 16:30,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Victoria Embankment,NULL,WC2R,530679,180682,530650,180650,51.50996472,-0.118298749\n128803101,30/07/2010 17:40,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED SINK CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Laurel Grove,22001631,SE26,NULL,NULL,536050,171550,NULL,NULL\n129048101,30/07/2010 21:44,2010,2010/11,Special Service,1,1,260,260,FISH IN DANGER OF DYING IN POND,Fish,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000567,Sutton West,E09000029,Sutton,Sutton,5870036768,Sherwood Park Road,22602599,SM1,525474,164195,525450,164150,51.3629704,-0.199098566\n129068101,30/07/2010 22:20,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Galatea Square,22501014,SE15,NULL,NULL,534650,175850,NULL,NULL\n129261101,31/07/2010 06:15,2010,2010/11,Special Service,2,5,260,1300,\"FISH IN DISTRESS, HVPU REQUIRED\",Fish,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000195,Chase,E09000010,Enfield,Enfield,207178584,Forty Hill,20702687,EN2,533639,198561,533650,198550,51.66993903,-0.068868485\n129276101,31/07/2010 07:11,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000641,Marylebone High Street,E09000033,Westminster,Soho,NULL,Wimpole Street,8400289,W1G,NULL,NULL,528650,181450,NULL,NULL\n129472101,31/07/2010 13:39,2010,2010/11,Special Service,1,1,260,260,CAT WEDGED BETWEEN WALL AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Yarrow Crescent,22201249,E6,NULL,NULL,542050,181850,NULL,NULL\n129960101,31/07/2010 22:44,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000316,Mawneys,E09000016,Havering,Romford,1.00021E+11,Dunster Close,21300004,RM5,550070,190097,550050,190050,51.58974651,0.1649373\n130577101,01/08/2010 17:21,2010,2010/11,Special Service,1,1,260,260,BIRD IN DISTRESS,Bird,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000075,Erith,E09000004,Bexley,Erith,NULL,Erith High Street,NULL,DA8,551434,178144,551450,178150,51.48198358,0.179474875\n130721101,01/08/2010 19:24,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,NULL,Ewart Road,NULL,SE23,535876,173567,535850,173550,51.44480106,-0.046190511\n130810101,01/08/2010 21:14,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Tyers Gate,22502569,SE1,533207,179735,533250,179750,51.50086467,-0.082249296\n131013101,02/08/2010 10:12,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER BED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Stuart Road,22502403,SE15,NULL,NULL,535450,175050,NULL,NULL\n131115101,02/08/2010 12:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000290,Harrow Weald,E09000015,Harrow,Harrow,1.00021E+11,Long Elmes,21201136,HA3,514240,190760,514250,190750,51.60410305,-0.35180198\n131165101,02/08/2010 14:16,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Claremont Road,21800253,KT6,NULL,NULL,518050,167450,NULL,NULL\n132244101,03/08/2010 17:42,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN HOLE BEHIND A KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Philbeach Gardens,21700663,SW5,NULL,NULL,525250,178350,NULL,NULL\n133381101,05/08/2010 14:31,2010,2010/11,Special Service,1,1,260,260,ASSIST THE RSPCA WITH TRAPPED PIDGEONS,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,34044126,Grimston Road,21000394,SW6,524632,176143,524650,176150,51.47053639,-0.206990913\n133966101,06/08/2010 10:45,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Hogarth Road,21700260,SW5,NULL,NULL,525550,178650,NULL,NULL\n134701101,07/08/2010 10:39,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,The Glade,20500570,CR0,NULL,NULL,535750,167650,NULL,NULL\n134823101,07/08/2010 14:53,2010,2010/11,Special Service,1,1,260,260,BIRD STUCK IN TREE,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Colebrooke Row,NULL,N1,531609,183121,531650,183150,51.53166771,-0.103994716\n135035101,07/08/2010 20:54,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN WINDOW,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,6005161,Redchurch Street,22701001,E2,533783,182413,533750,182450,51.52479443,-0.07293985\n135823101,09/08/2010 02:23,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (running call),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000649,West End,E09000033,Westminster,Soho,NULL,Duke Street,8400398,W1K,NULL,NULL,528350,181050,NULL,NULL\n135844101,09/08/2010 02:58,2010,2010/11,Special Service,1,1,260,260,DEER STUCK ON FENCING,Deer,Police,\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Fairford Way,NULL,RM3,555610,191744,555650,191750,51.60304219,0.245572324\n135930101,09/08/2010 09:20,2010,2010/11,Special Service,1,1,260,260,BIRD WITH FOOT STUCK IN ROOF TILES,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000287,Edgware,E09000015,Harrow,Stanmore,NULL,Broomgrove Gardens,21202323,HA8,NULL,NULL,519450,190650,NULL,NULL\n137073101,11/08/2010 00:33,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000300,Rayners Lane,E09000015,Harrow,Harrow,NULL,Rayners Lane,21202081,HA5,NULL,NULL,512950,187850,NULL,NULL\n137082101,11/08/2010 01:00,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,The Close,21401939,HA5,NULL,NULL,511150,187850,NULL,NULL\n137703101,12/08/2010 08:31,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Square,21700454,SW10,NULL,NULL,525750,178050,NULL,NULL\n137899101,12/08/2010 16:52,2010,2010/11,Special Service,1,1,260,260,DOG WITH NECK STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000315,Hylands,E09000016,Havering,Hornchurch,1.00023E+11,Hornchurch Road,21300425,RM11,553152,187026,553150,187050,51.5613255,0.208053787\n138210101,13/08/2010 07:15,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,Harlesden,E09000005,Brent,Park Royal,NULL,St. Marys Road,20202537,NW10,NULL,NULL,521450,183850,NULL,NULL\n138463101,13/08/2010 17:12,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Ham Street,NULL,TW10,517286,172821,517250,172850,51.44224725,-0.313805479\n139163101,14/08/2010 18:57,2010,2010/11,Special Service,1,4,260,1040,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Southend Road,22201066,E6,NULL,NULL,542650,184250,NULL,NULL\n140157101,16/08/2010 13:58,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED INJURED GULL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000205,Palmers Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,20703847,N13,NULL,NULL,530950,192650,NULL,NULL\n140766101,17/08/2010 12:13,2010,2010/11,Special Service,1,1,260,260,HORSE TRAPPED IN STABLE DOOR,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011480,Selsdon & Addington Village,E09000008,Croydon,Croydon,1.00023E+11,Coombe Lane,20501992,CR0,532750,164750,532750,164750,51.36630647,-0.094441367\n140977101,17/08/2010 19:27,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Club/night club,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Parkleys,NULL,TW10,517876,171688,517850,171650,51.43194195,-0.305697126\n141020101,17/08/2010 20:29,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO ASSIST INJURED CAT,Cat,Person (land line),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Manor Grove,22405753,TW9,519316,175511,519350,175550,51.46600036,-0.283703981\n141331101,18/08/2010 10:51,2010,2010/11,Special Service,1,1,260,260,ASSISTING RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Ordnance Road,20801117,SE18,NULL,NULL,543250,177550,NULL,NULL\n141958101,19/08/2010 13:37,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,Stratford Road,21700527,W8,NULL,NULL,525450,179050,NULL,NULL\n142028101,19/08/2010 15:57,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BY COLLAR  ON TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,12070339,Boston Road,20602241,W7,516073,179253,516050,179250,51.5003067,-0.329140491\n142243101,19/08/2010 21:48,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN RUBBISH CHUTE,Cat,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Macaulay Road,21900911,SW4,NULL,NULL,528950,175550,NULL,NULL\n142434101,20/08/2010 10:48,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED UNDER JETTY,Cat,Person (land line),Barge,Boat,Animal rescue from water,Animal rescue from water - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Woolwich High Street,NULL,SE18,543339,179251,543350,179250,51.49403846,0.063429055\n143104101,21/08/2010 12:10,2010,2010/11,Special Service,1,2,260,520,MAGPIE STUCK IN BUILDING,Bird,Person (land line),Other indoor sporting venue,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,10008241969,Green Lanes,20900468,N4,532311,186881,532350,186850,51.56529283,-0.092467492\n143684101,22/08/2010 05:42,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Library,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,10008995498,High Street South,22200716,E6,542665,183494,542650,183450,51.53233538,0.055442633\n143724101,22/08/2010 08:33,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011242,Fairlop,E09000026,Redbridge,Hainault,1.00022E+11,Kelsie Way,22304638,IG6,545203,191539,545250,191550,51.60397678,0.095328095\n144084101,22/08/2010 20:28,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CAVITY WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Exeter Road,19900478,RM10,NULL,NULL,549850,184750,NULL,NULL\n144631101,23/08/2010 20:20,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011462,Addiscombe East,E09000008,Croydon,Woodside,10014052812,Shirley Road,20500552,CR9,534873,166649,534850,166650,51.38287195,-0.063243189\n145674101,25/08/2010 18:26,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),\"Tunnel, subway\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524677,184637,524650,184650,51.54686396,-0.20334192\n145807101,25/08/2010 21:47,2010,2010/11,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6028943,Tarling Street,22701188,E1,535137,181069,535150,181050,51.51239452,-0.053949357\n146509101,27/08/2010 02:24,2010,2010/11,Special Service,1,1,260,260,FOX STUCK IN BASEMENT AREA OUTSIDE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Shandon Road,21901225,SW4,NULL,NULL,529450,174350,NULL,NULL\n146732101,27/08/2010 15:02,2010,2010/11,Special Service,2,3,260,780,KITTEN STUCK INSIDE RECLINER CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Drummond Road,21301151,RM7,NULL,NULL,550650,189150,NULL,NULL\n146953101,27/08/2010 22:19,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Park Avenue,20707523,EN1,NULL,NULL,533350,195150,NULL,NULL\n147305101,28/08/2010 13:58,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GARDEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Great Peter Street,8401375,SW1P,NULL,NULL,530150,179250,NULL,NULL\n147438101,28/08/2010 17:47,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,1.00022E+11,Aragon Road,21800070,KT2,518013,171382,518050,171350,51.42916325,-0.303829153\n147864101,29/08/2010 11:00,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND BOARDS,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Penpoll Road,NULL,E8,534751,184785,534750,184750,51.54587959,-0.058087753\n148673101,30/08/2010 18:41,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Cobourg Road,NULL,SE5,533598,178124,533550,178150,51.48629505,-0.077228935\n149174101,31/08/2010 15:44,2010,2010/11,Special Service,1,1,260,260,CROW TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,NULL,Potters Grove,21800775,KT3,NULL,NULL,520250,168250,NULL,NULL\n150566101,02/09/2010 20:21,2010,2010/11,Special Service,1,1,260,260,INJURED CAT STUCK IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Horn Lane,NULL,W3,520052,180223,520050,180250,51.50819469,-0.271513023\n150741101,03/09/2010 04:15,2010,2010/11,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED UNDER FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,Gloucester Gardens,20701065,EN4,NULL,NULL,528350,195850,NULL,NULL\n150842101,03/09/2010 10:25,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525250,170150,NULL,NULL\n151078101,03/09/2010 17:37,2010,2010/11,Special Service,1,1,260,260,SQUIRREL STUCK IN BOILER,Squirrel,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Norbury Crescent,20501230,SW16,NULL,NULL,530850,169450,NULL,NULL\n151656101,04/09/2010 17:14,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,Highbury East,E09000019,Islington,Islington,10012787400,Highbury Grove,21605117,N5,531967,185406,531950,185450,51.55211837,-0.097980233\n151960101,05/09/2010 03:20,2010,2010/11,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN CAN,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Melon Road,NULL,E11,539094,186232,539050,186250,51.55782991,0.005075484\n152001101,05/09/2010 08:30,2010,2010/11,Special Service,1,1,260,260,GULL TRAPPED IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Kemble Road,20501087,CR0,NULL,NULL,531550,165450,NULL,NULL\n152182101,05/09/2010 16:15,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN BILLBOARD,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000473,West Barnes,E09000024,Merton,New Malden,NULL,Burlington Road,NULL,KT3,522440,168203,522450,168250,51.39965376,-0.241276831\n152210101,05/09/2010 17:03,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN BUILDING,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000178,Greenford Green,E09000009,Ealing,Northolt,12134615,Greenford Road,20602353,UB6,514750,183750,514750,183750,51.54099471,-0.346728568\n152337101,05/09/2010 20:52,2010,2010/11,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Chestnut Road,21900332,SE27,NULL,NULL,532350,172350,NULL,NULL\n152516101,06/09/2010 09:47,2010,2010/11,Special Service,2,3,260,780,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Alderney Gardens,NULL,UB5,512583,184317,512550,184350,51.54652667,-0.377782122\n153370101,07/09/2010 21:23,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN KITCHEN UNITS AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000091,Harlesden,E09000005,Brent,Willesden,NULL,Church Road,20200352,NW10,NULL,NULL,521350,184650,NULL,NULL\n153576101,08/09/2010 08:56,2010,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH HORSE TRAPPED IN FENCE ***SILENT APPROACH REQUESTED****,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Brabazon Road,NULL,TW5,511155,177271,511150,177250,51.48347985,-0.400586934\n153798101,08/09/2010 17:05,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000421,Ferndale,E09000022,Lambeth,Clapham,NULL,Britannia Close,21900235,SW4,NULL,NULL,529950,175150,NULL,NULL\n155918101,12/09/2010 10:30,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN GARDEN GATE,Dog,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000490,Plaistow South,E09000025,Newham,Plaistow,46069619,St. Quintin Road,22207902,E13,540784,182964,540750,182950,51.52804493,0.028131124\n156017101,12/09/2010 14:05,2010,2010/11,Special Service,1,2,260,520,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Quail Gardens,NULL,CR2,536153,162239,536150,162250,51.34293642,-0.046548067\n156108101,12/09/2010 17:10,2010,2010/11,Special Service,1,1,260,260,HORSE IN RIVER,Horse,Other FRS,Park,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000321,South Hornchurch,E09000016,Havering,Hornchurch,NULL,South End Road,NULL,RM13,552915,183939,552950,183950,51.53365336,0.203298178\n156117101,12/09/2010 17:37,2010,2010/11,Special Service,1,1,260,260,CAT STUCK ON SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Goldhawk Road,21000380,W12,NULL,NULL,522750,179450,NULL,NULL\n156680101,13/09/2010 17:13,2010,2010/11,Special Service,1,1,260,260,DISTRESSED CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,Hackney Downs,E09000012,Hackney,Homerton,10008239760,Clarence Road,20900248,E5,534849,185816,534850,185850,51.55512109,-0.056280803\n157014101,14/09/2010 09:39,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,1.00021E+11,Brambles Farm Drive,21400225,UB10,507531,182542,507550,182550,51.53155726,-0.451152068\n157241101,14/09/2010 16:56,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (land line),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Redan Terrace,21901148,SE5,531964,176247,531950,176250,51.46981036,-0.101449112\n157392101,14/09/2010 20:02,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN WELL IN BASEMENT AREA,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,178050,NULL,NULL\n160182101,19/09/2010 19:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000648,Westbourne,E09000033,Westminster,North Kensington,NULL,Great Western Road,8401380,W11,NULL,NULL,524950,181650,NULL,NULL\n161076101,21/09/2010 02:41,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN DOWN  INTO DITCH,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Crystal Palace Station Road,20302353,SE19,534119,170557,534150,170550,51.41816986,-0.072595726\n161146101,21/09/2010 08:42,2010,2010/11,Special Service,1,1,260,260,FOX IMPALED ON SPIKED FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Cranley Gardens,NULL,SM6,529035,163370,529050,163350,51.35475923,-0.148276762\n161329101,21/09/2010 15:15,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN NET,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Holbrook Close,20702754,EN1,NULL,NULL,534050,198050,NULL,NULL\n161330101,21/09/2010 15:17,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN LOCKED WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,NULL,Maywin Drive,21300493,RM11,NULL,NULL,554850,187250,NULL,NULL\n161392101,21/09/2010 16:32,2010,2010/11,Special Service,1,1,260,260,SQUIRREL TRAPPED IN METAL FENCING,Squirrel,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,5300011059,Bowling Green Lane,21604316,EC1R,531407,182325,531450,182350,51.5245613,-0.107202071\n161434101,21/09/2010 17:46,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Harold Road,22207747,E13,NULL,NULL,540850,183650,NULL,NULL\n939102,21/09/2010 23:17,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN CONSTRUCTION HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Packington Square,21606921,N1,NULL,NULL,532250,183450,NULL,NULL\n161827101,22/09/2010 09:22,2010,2010/11,Special Service,1,2,260,520,HORSE COLLAPSED BEHIND STABLE DOORS,Horse,Person (land line),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382\n161886101,22/09/2010 11:22,2010,2010/11,Special Service,1,1,260,260,KITTEN STUCK AT REAR OF ELECTRICY CIRCUIT BOX,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522750,184950,NULL,NULL\n162959101,24/09/2010 10:50,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN BARBED WIRE IN WOODLAND,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Cooks Hole Road,NULL,EN2,531804,198391,531850,198350,51.6688445,-0.095452158\n162969101,24/09/2010 11:14,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000190,Southall Green,E09000009,Ealing,Southall,NULL,Norwood Road,20602330,UB2,NULL,NULL,512450,179050,NULL,NULL\n163121101,24/09/2010 16:43,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000513,Snaresbrook,E09000026,Redbridge,Leytonstone,NULL,Hermon Hill,NULL,E11,540384,188837,540350,188850,51.58091804,0.024710319\n164149101,26/09/2010 10:20,2010,2010/11,Special Service,1,1,260,260,KITTEN WITH PAW TRAPPED IN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,NULL,Matcham Road,22857250,E11,NULL,NULL,539650,186150,NULL,NULL\n164185101,26/09/2010 11:22,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Selsdon Road,21901222,SE27,NULL,NULL,531550,171950,NULL,NULL\n164237101,26/09/2010 13:21,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,1.00023E+11,Elgin Avenue,8400921,W9,525685,182637,525650,182650,51.52866636,-0.189526437\n164837101,27/09/2010 15:43,2010,2010/11,Special Service,1,2,260,520,HORSE TRAPPED IN RESERVOIR,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536198,191815,536150,191850,51.60870464,-0.034510989\n165209101,28/09/2010 10:46,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN CUPBOARD AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000088,Dollis Hill,E09000005,Brent,Willesden,NULL,Hawarden Hill,20202540,NW2,NULL,NULL,522350,186150,NULL,NULL\n165357101,28/09/2010 15:32,2010,2010/11,Special Service,1,1,260,260,DISTRESSED DOGS LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Nantes Close,NULL,SW18,526557,175237,526550,175250,51.46196733,-0.179614115\n165500101,28/09/2010 19:17,2010,2010/11,Special Service,1,3,260,780,LARGE BULL IN DISTRESS,Bull,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,West Drive,NULL,HA3,514616,191859,514650,191850,51.61390438,-0.346015882\n166316101,30/09/2010 09:35,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Fletching Road,20900413,E5,NULL,NULL,535350,186150,NULL,NULL\n166468101,30/09/2010 14:52,2010,2010/11,Special Service,1,1,260,260,PUPPY TRAPPED BETWEEN TWO BUILDINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Richmond Road,20036500,EN5,NULL,NULL,526350,195750,NULL,NULL\n166714101,30/09/2010 23:46,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN A BIN,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Rushmore Road,NULL,E5,535919,185883,535950,185850,51.55546674,-0.040830133\n167163101,01/10/2010 19:50,2010,2010/11,Special Service,1,1,260,260,Redacted,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Ingram Avenue,20024620,NW11,NULL,NULL,526150,187650,NULL,NULL\n167408101,02/10/2010 07:50,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN IRON BAR AND STEPS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Mildenhall Road,20900691,E5,NULL,NULL,535250,186050,NULL,NULL\n168970101,04/10/2010 13:06,2010,2010/11,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,2.00003E+11,Beatrice Road,22500183,SE1,534495,178539,534450,178550,51.48981195,-0.064159714\n170525101,07/10/2010 02:15,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK IN RAILINGS,Horse,Police,\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Thames Road,NULL,DA1,552670,175375,552650,175350,51.4567727,0.196065531\n171408101,08/10/2010 18:11,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Marsh Road,NULL,HA5,512146,189485,512150,189450,51.59306395,-0.382433287\n172942101,11/10/2010 07:06,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN COURT YARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Montagu Square,8401432,W1H,NULL,NULL,527750,181450,NULL,NULL\n173382101,11/10/2010 23:17,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000055,Hendon,E09000003,Barnet,Mill Hill,NULL,Watford Way,NULL,NW4,522250,190250,522250,190250,51.59784015,-0.236373124\n173608101,12/10/2010 12:54,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,1.00022E+11,Dunmow Close,22302002,RM6,547158,188837,547150,188850,51.57919139,0.122404717\n173702101,12/10/2010 16:38,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Alderney Gardens,NULL,UB5,512683,184259,512650,184250,51.54598544,-0.376359118\n174234101,13/10/2010 17:00,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,520681,173226,520650,173250,51.44517473,-0.264841368\n174255101,13/10/2010 17:30,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,520681,173226,520650,173250,51.44517473,-0.264841368\n174599101,14/10/2010 08:12,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON ROOF OF BUNGALOW,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,Feltham North,E09000018,Hounslow,Feltham,NULL,Tachbrook Road,21501108,TW14,NULL,NULL,509950,173850,NULL,NULL\n174735101,14/10/2010 12:59,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000286,Canons,E09000015,Harrow,Stanmore,1.00021E+11,Handel Way,21200896,HA8,519178,191366,519150,191350,51.60852661,-0.280326808\n174979101,14/10/2010 21:11,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870110475,Bute Court,22605499,SM6,529120,164335,529150,164350,51.36341262,-0.146706757\n174999101,14/10/2010 21:59,2010,2010/11,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Oakridge Road,22001754,BR1,NULL,NULL,539250,171650,NULL,NULL\n175252101,15/10/2010 12:44,2010,2010/11,Special Service,1,1,260,260,INJURED CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Baxter Road,NULL,E16,541491,181452,541450,181450,51.51428155,0.037709449\n175348101,15/10/2010 16:15,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Adelaide Avenue,22005430,SE4,537264,174995,537250,174950,51.45729913,-0.025677258\n176054101,16/10/2010 18:23,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Whitton Avenue West,20601899,UB6,NULL,NULL,515050,185050,NULL,NULL\n176610101,17/10/2010 16:24,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BETWEEN TWO WINDOW PANES,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Kingsmead,22403590,TW10,NULL,NULL,518750,174150,NULL,NULL\n177051101,18/10/2010 10:41,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN METAL RAILINGS IN STREAM,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,200022527,Church Hill Road,20008700,EN4,527202,195265,527250,195250,51.64181232,-0.163097421\n177056101,18/10/2010 10:53,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA - CAT ON LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,St. Mark's Place,19901091,RM10,NULL,NULL,549550,184850,NULL,NULL\n177119101,18/10/2010 13:17,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Addison Way,20000260,NW11,NULL,NULL,524950,189250,NULL,NULL\n177212101,18/10/2010 16:20,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN PIPES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,NULL,Chingdale Road,NULL,E4,539488,193261,539450,193250,51.62089326,0.0135448\n177228101,18/10/2010 17:01,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Leda Road,20800899,SE18,NULL,NULL,542750,179150,NULL,NULL\n177767101,19/10/2010 17:45,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Marden Square,22501655,SE16,NULL,NULL,534750,179050,NULL,NULL\n177859101,19/10/2010 20:34,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SINK UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Keightley Drive,20800835,SE9,NULL,NULL,544550,173050,NULL,NULL\n178150101,20/10/2010 12:29,2010,2010/11,Special Service,1,1,260,260,INJURED CAT FALEN DOWN BASEMENT AREA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Horn Lane,20602383,W3,NULL,NULL,520150,180750,NULL,NULL\n178742101,21/10/2010 12:53,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER ALONG TOW PATH,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518820,177706,518850,177750,51.48583275,-0.29010391\n180508101,24/10/2010 06:13,2010,2010/11,Special Service,1,2,260,520,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Paddock Road,20101092,DA6,NULL,NULL,548250,175150,NULL,NULL\n180586101,24/10/2010 11:33,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Romford Road,22201573,E7,NULL,NULL,540550,184950,NULL,NULL\n180721101,24/10/2010 16:22,2010,2010/11,Special Service,1,1,260,260,PIGEON STUCK IN WIRE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000364,Syon,E09000018,Hounslow,Heston,1.00023E+11,London Road,21500707,TW7,516580,176902,516550,176950,51.47907203,-0.322615354\n181358101,25/10/2010 18:15,2010,2010/11,Special Service,1,1,260,260,INJURED CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Warriner Gardens,22905967,SW11,NULL,NULL,528050,176650,NULL,NULL\n181407101,25/10/2010 19:06,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN-BETWEEN FALSE CEILING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,St. Josephs Drive,20601600,UB1,NULL,NULL,512450,180150,NULL,NULL\n181727101,26/10/2010 10:59,2010,2010/11,Special Service,1,1,260,260,CAT STUCK BEHIND METAL FRAMEWORK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Goldington Street,20400800,NW1,NULL,NULL,529750,183350,NULL,NULL\n181782101,26/10/2010 12:53,2010,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN  BORDED UP AREA,Fox,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Brady Street,NULL,E1,534708,182146,534750,182150,51.52217519,-0.05971633\n182228101,27/10/2010 09:25,2010,2010/11,Special Service,1,1,260,260,CAT STUCK IN BARBED WIRE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,1.00022E+11,Abbotswood Road,21900063,SW16,529591,172596,529550,172550,51.43754704,-0.136930342\n182984101,28/10/2010 15:33,2010,2010/11,Special Service,1,3,260,780,HORSE FALLEN IN RIVER,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Hall Lane,NULL,RM14,556571,188757,556550,188750,51.57593971,0.25810753\n183102101,28/10/2010 19:18,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND FENCE,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011252,South Woodford,E09000026,Redbridge,Leytonstone,1.00022E+11,Cadogan Gardens,22302674,E18,540986,189566,540950,189550,51.58731833,0.033684964\n183890101,30/10/2010 09:46,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDERGROUND,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,10010216723,Greenwich Park,20800172,SE10,539250,176750,539250,176750,51.47258539,0.003579963\n184497101,31/10/2010 08:03,2010,2010/11,Special Service,1,1,260,260,CAT CAUGHT BETWEEN WINDOW PANE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Baslow Walk,20900111,E5,NULL,NULL,535850,185850,NULL,NULL\n184650101,31/10/2010 13:48,2010,2010/11,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000565,Sutton North,E09000029,Sutton,Sutton,NULL,All Saints Road,NULL,SM1,526176,165392,526150,165350,51.37357273,-0.188596227\n184731101,31/10/2010 16:32,2010,2010/11,Special Service,1,1,260,260,KITTENS POSSIBLY TRAPPED BEHIND COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Hanger Lane,20602048,W5,NULL,NULL,518650,180750,NULL,NULL\n185448101,01/11/2010 21:05,2010,2010/11,Special Service,1,1,260,260,CAT BELIEVED TO BE INJURED ON ROOF OF BLOCK OF FLATS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Stocksfield Road,22877000,E17,NULL,NULL,538450,189650,NULL,NULL\n186795101,04/11/2010 13:17,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Scrub land,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hylands Road,22846900,E17,538933,190033,538950,190050,51.59202436,0.004255643\n187375101,05/11/2010 14:38,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN RAIL,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00024E+11,Bath Road,21402329,UB3,507870,176980,507850,176950,51.48150021,-0.447966119\n188198101,06/11/2010 09:52,2010,2010/11,Special Service,1,2,260,520,SICK COW STUCK AND UNABLE TO BE RAISED,Cow,Person (land line),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5023224,Cressfield Close,20401332,NW5,528364,185356,528350,185350,51.5524998,-0.149936393\n189072101,07/11/2010 14:34,2010,2010/11,Special Service,1,1,260,260,DOG STUCK DOWN HOLE IN GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Bury Road,21104308,N22,NULL,NULL,531350,190050,NULL,NULL\n189099101,07/11/2010 15:03,2010,2010/11,Special Service,1,1,260,260,KITTEN POSSIBLY TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Inglemere Road,22001583,SE23,NULL,NULL,535650,172350,NULL,NULL\n189713101,08/11/2010 14:45,2010,2010/11,Special Service,1,4,260,1040,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100013067,High Road,19900165,RM6,547795,187922,547750,187950,51.57080353,0.131206591\n190694101,10/11/2010 14:23,2010,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Selsey Street,22701078,E14,NULL,NULL,537250,181650,NULL,NULL\n191667101,12/11/2010 01:25,2010,2010/11,Special Service,1,1,260,260,DOG WITH CAN TRAPPED ON JAW,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Marlborough Road,21301397,RM7,NULL,NULL,550050,189250,NULL,NULL\n191993101,12/11/2010 18:34,2010,2010/11,Special Service,1,1,260,260,INJURED DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46058887,Pretoria Road,22207844,E16,539557,182418,539550,182450,51.52344323,0.010238996\n192533101,13/11/2010 17:37,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Powys Lane,NULL,N13,530218,192198,530250,192150,51.61356123,-0.120676367\n192955101,14/11/2010 13:23,2010,2010/11,Special Service,1,1,260,260,GULL TRAPPED IN WIRE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Tooting Bec Road,NULL,SW16,529082,172094,529050,172050,51.43315162,-0.144432095\n193901101,16/11/2010 09:33,2010,2010/11,Special Service,1,1,260,260,KITTENS STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Park Lane,21104898,N17,NULL,NULL,533950,191050,NULL,NULL\n193975101,16/11/2010 12:16,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN ROOF GUTTERING,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,10024086666,Norstead Place,22903562,SW15,522143,172825,522150,172850,51.44125772,-0.243952581\n194569101,17/11/2010 14:45,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,English Street,NULL,E3,536726,182227,536750,182250,51.52241866,-0.030615749\n194813101,17/11/2010 23:23,2010,2010/11,Special Service,1,2,260,520,DOG TRAPPED BETWEEN BUILDINGS,Dog,Person (mobile),Other industrial manufacturing facility,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,2E+11,Martell Road,21900929,SE21,532476,172275,532450,172250,51.43399532,-0.095567956\n195192101,18/11/2010 18:15,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000211,Turkey Street,E09000010,Enfield,Enfield,NULL,Stoneleigh Avenue,20702978,EN1,NULL,NULL,534850,198050,NULL,NULL\n195387101,19/11/2010 02:36,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN IN RIVER AND TRAPPED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Tollgate Drive,NULL,UB4,511855,180747,511850,180750,51.51458448,-0.389409353\n195502101,19/11/2010 11:31,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Parsonage Lane,20702893,EN2,NULL,NULL,532650,197150,NULL,NULL\n198196101,24/11/2010 10:03,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Northwood Avenue,20502239,CR8,NULL,NULL,531650,160650,NULL,NULL\n199442101,26/11/2010 16:00,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BETWEEN ROOF TILES,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00022E+11,Chiswick Quay,21501385,W4,520156,176602,520150,176650,51.47562841,-0.271246741\n200369101,28/11/2010 01:04,2010,2010/11,Special Service,1,1,260,260,PERSONS SHUT IN LIFT EMERGENCY,Unknown - Heavy Livestock Animal,Person (land line),Purpose built office,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05009305,Farringdon Without,E09000001,City of London,Dowgate,1.00023E+11,Giltspur Street,8100228,EC1A,531798,181481,531750,181450,51.51688551,-0.101884777\n200664101,28/11/2010 13:27,2010,2010/11,Special Service,1,1,260,260,GOOSE STUCK IN ICE ON POND,Bird,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207175098,Whitewebbs Lane,20703047,EN2,533123,199933,533150,199950,51.68239033,-0.075803298\n200780101,28/11/2010 15:59,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Pevensey Road,NULL,TW13,511984,173268,511950,173250,51.44733735,-0.389919566\n202363101,01/12/2010 05:06,2010,2010/11,Special Service,1,1,260,260,RABBIT UNDER FLOORBOARDS,Rabbit,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Birch Row,20300258,BR2,NULL,NULL,543350,166950,NULL,NULL\n202573101,01/12/2010 13:48,2010,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED ON TREE BRANCH,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Globe Road,NULL,E2,535245,182850,535250,182850,51.52837313,-0.051710598\n202749101,01/12/2010 19:40,2010,2010/11,Special Service,1,1,260,260,BIRDS STUCK IN ICE ON ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011463,Addiscombe West,E09000008,Croydon,Woodside,NULL,Warren Road,20501538,CR0,NULL,NULL,533450,166250,NULL,NULL\n202941101,02/12/2010 08:16,2010,2010/11,Special Service,1,1,260,260,HORSE STUCK ON GATE,Horse,Person (mobile),Railings,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Barwell Lane,NULL,KT9,516984,163067,516950,163050,51.35464173,-0.321359497\n203054101,02/12/2010 11:26,2010,2010/11,Special Service,4,4,260,1040,DOG STUCK IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532429,196560,532450,196550,51.65224395,-0.087112641\n203134101,02/12/2010 13:38,2010,2010/11,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Wick Road,20901074,E9,NULL,NULL,536350,184750,NULL,NULL\n204291101,04/12/2010 11:04,2010,2010/11,Special Service,1,1,260,260,CAT  STUCK IN BIN CHUTE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Gosfield Road,19900150,RM8,NULL,NULL,549450,186950,NULL,NULL\n205017101,05/12/2010 13:07,2010,2010/11,Special Service,1,1,260,260,DOG TRAPPED IN ICE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,NULL,Hampstead Lane,NULL,NW3,527087,187418,527050,187450,51.57131936,-0.167600351\n205123101,05/12/2010 15:58,2010,2010/11,Special Service,1,1,260,260,CAT SHUT IN DERELICT BUILDING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Devonshire Road,22001409,SE23,NULL,NULL,535350,173150,NULL,NULL\n205621101,06/12/2010 13:52,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Torridge Road,20501496,CR7,NULL,NULL,531950,167850,NULL,NULL\n205682101,06/12/2010 15:46,2010,2010/11,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Sunbury Street,20801435,SE18,NULL,NULL,542950,179050,NULL,NULL\n206017101,07/12/2010 08:16,2010,2010/11,Special Service,1,2,260,520,CAT STUCK ON A CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Eagle Terrace,22304953,IG8,NULL,NULL,540750,191250,NULL,NULL\n206385101,07/12/2010 20:43,2010,2010/11,Special Service,1,1,260,260,DOG IN RIVER LEE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Riverside Close,NULL,E5,535437,186887,535450,186850,51.56460467,-0.047392545\n207392101,09/12/2010 17:36,2010,2010/11,Special Service,3,6,260,1560,Redacted,Cow,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000323,Upminster,E09000016,Havering,Hornchurch,1.00021E+11,St. Marys Lane,21320207,RM14,559664,187062,559650,187050,51.55984466,0.301935469\n207711101,10/12/2010 08:23,2010,2010/11,Special Service,1,1,260,260,KITTEN FALLEN INTO BUILDING FOOTINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,Harp Island Close,NULL,NW10,NULL,NULL,520750,186650,NULL,NULL\n207767101,10/12/2010 10:29,2010,2010/11,Special Service,1,1,260,260,TWO CATS STUCK IN TREE,cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Jerningham Road,22000563,SE14,NULL,NULL,536050,176250,NULL,NULL\n208421101,11/12/2010 14:24,2010,2010/11,Special Service,1,1,260,260,DOG WITH FOOT TRAPPED IN SHOWER CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Clem Attlee Estate,21000509,SW6,NULL,NULL,524750,177450,NULL,NULL\n209524101,13/12/2010 16:22,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Whipps Cross Road,NULL,E11,539750,187750,539750,187750,51.57130821,0.015134742\n210029101,14/12/2010 14:28,2010,2010/11,Special Service,1,2,260,520,HORSE SLINGS REQUIRED TO LIFT HORSE IN FIELD,Horse,Other FRS,\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032246,Barwell Lane,21800115,KT9,517047,163136,517050,163150,51.35524892,-0.320432473\n210464101,15/12/2010 10:06,2010,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Henley Avenue,NULL,SM3,524165,165657,524150,165650,51.37639722,-0.217381009\n210725101,15/12/2010 19:13,2010,2010/11,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Baltic Place,NULL,N1,533455,183757,533450,183750,51.53694971,-0.077155969\n211037101,16/12/2010 13:14,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Rowantree Road,20704689,N21,NULL,NULL,532850,194450,NULL,NULL\n212157101,18/12/2010 11:32,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Wilson Avenue,22106741,CR4,NULL,NULL,527450,169950,NULL,NULL\n212258101,18/12/2010 13:42,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00023E+11,Burlington Lane,21500176,W4,520934,177319,520950,177350,51.48190705,-0.259804588\n212264101,18/12/2010 13:49,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,528746,172278,528750,172250,51.4348816,-0.149196207\n212716101,19/12/2010 01:35,2010,2010/11,Special Service,1,1,260,260,HAMSTER TRAPPED BEHIND SINK UNIT,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Handley Grove,20020807,NW2,NULL,NULL,523950,186150,NULL,NULL\n212826101,19/12/2010 08:13,2010,2010/11,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE  ON LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000620,Queenstown,E09000032,Wandsworth,Battersea,10091499918,Battersea Park,22900030,SW11,527750,177250,527750,177250,51.47979045,-0.161723808\n212932101,19/12/2010 12:29,2010,2010/11,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,1.00023E+11,Danson Road,20101842,DA5,547524,174571,547550,174550,51.45091127,0.121717744\n212938101,19/12/2010 12:35,2010,2010/11,Special Service,1,1,260,260,DOGS IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Twyford Abbey Road,NULL,NW10,519670,183209,519650,183250,51.53511239,-0.276001153\n213149101,19/12/2010 19:28,2010,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Eastbrook Drive,NULL,RM7,551329,186553,551350,186550,51.55756735,0.18157242\n213213101,19/12/2010 21:25,2010,2010/11,Special Service,1,2,260,520,DOG TRAPPED UNDER DECKING,Dog,Other FRS,House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011466,Coulsdon Town,E09000008,Croydon,Purley,NULL,Rickman Hill,20502506,CR5,NULL,NULL,528850,158850,NULL,NULL\n213464101,20/12/2010 09:31,2010,2010/11,Special Service,1,1,260,260,SEAGULL HANGING FROM WING FROM CABLE,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Pier Street,NULL,E14,538406,178917,538450,178950,51.49226535,-0.007714839\n213525101,20/12/2010 11:29,2010,2010/11,Special Service,1,1,260,260,DOG IN WATER,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Soho,NULL,Hyde Park,NULL,W2,527421,180188,527450,180150,51.50626848,-0.165399101\n214208101,21/12/2010 08:31,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524317,168669,524350,168650,51.40343394,-0.214144959\n214242101,21/12/2010 09:41,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Grove Road,NULL,E3,535925,183375,535950,183350,51.53292782,-0.041711347\n214289101,21/12/2010 10:57,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000374,Hillrise,E09000019,Islington,Holloway,5300042128,Hazellville Road,21606446,N19,529569,187603,529550,187650,51.57241773,-0.131738589\n214330101,21/12/2010 12:30,2010,2010/11,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547507,175180,547550,175150,51.45638782,0.121727235\n214391101,21/12/2010 14:25,2010,2010/11,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Kenilworth Road,NULL,HA8,520076,192995,520050,192950,51.62297636,-0.266808077\n214925101,22/12/2010 10:15,2010,2010/11,Special Service,1,1,260,260,DOG TAPPED IN FENCE,Dog,Police,Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05011465,Broad Green,E09000008,Croydon,Norbury,1.00021E+11,Constance Road,20500812,CR0,531819,166930,531850,166950,51.3861144,-0.106999456\n215280101,22/12/2010 23:04,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Hawes Street,NULL,N1,531773,184032,531750,184050,51.53981627,-0.10129091\n216079101,24/12/2010 12:59,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CHIMNEY  BREAST INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011222,Crayford,E09000004,Bexley,Bexley,NULL,Mill Place,20100980,DA1,NULL,NULL,552550,175250,NULL,NULL\n216187101,24/12/2010 16:55,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Bourne Avenue,20703943,N14,NULL,NULL,530050,193950,NULL,NULL\n216289101,24/12/2010 19:47,2010,2010/11,Special Service,1,1,260,260,PERSON LOCKED IN PARK WITH DOG,Dog,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Osborne Road,NULL,RM11,552840,188221,552850,188250,51.57214697,0.204075301\n216541101,25/12/2010 09:42,2010,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,North Circular Road,20201638,NW10,NULL,NULL,520450,184650,NULL,NULL\n217168101,26/12/2010 13:49,2010,2010/11,Special Service,1,1,260,260,DOG IN WATER WAY,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Bushy Park,NULL,TW11,516059,169509,516050,169550,51.4127326,-0.332536785\n217392101,26/12/2010 20:26,2010,2010/11,Special Service,1,1,260,260,RUNNING CALL TO KITTEN IN TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,5870022418,Chelsea Gardens,22602908,SM3,524473,164851,524450,164850,51.36908609,-0.213239807\n217490101,26/12/2010 23:51,2010,2010/11,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,NULL,NW1,528996,182535,528950,182550,51.52700381,-0.141859183\n218152101,28/12/2010 10:45,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Springfield,NULL,E5,534751,187318,534750,187350,51.56864196,-0.057118868\n218261101,28/12/2010 14:37,2010,2010/11,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539689,194767,539650,194750,51.63437593,0.017046618\n218847101,29/12/2010 15:20,2010,2010/11,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539871,194590,539850,194550,51.63274032,0.019603981\n219023101,29/12/2010 20:10,2010,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Commercial Road,NULL,E1,534666,181299,534650,181250,51.51457379,-0.060644895\n219734101,31/12/2010 10:53,2010,2010/11,Special Service,1,1,260,260,AFA ACTUATING,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Bromefield,21202482,HA7,NULL,NULL,517450,190850,NULL,NULL\n219745101,31/12/2010 11:31,2010,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Bromefield,21202482,HA7,NULL,NULL,517450,190850,NULL,NULL\n219835101,31/12/2010 14:39,2010,2010/11,Special Service,1,1,260,260,CAT ON TOP OF CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Alberta Avenue,22602811,SM1,NULL,NULL,524650,164450,NULL,NULL\n312111,01/01/2011 13:21,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN STAIRS PANEL,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Siddons Road,22001894,SE23,NULL,NULL,536050,172550,NULL,NULL\n336111,01/01/2011 14:09,2011,2010/11,Special Service,1,2,260,520,BIRD TRAPPED IN NETTING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Cunard Walk,NULL,SE16,536116,178960,536150,178950,51.49320749,-0.040663524\n678111,02/01/2011 08:56,2011,2010/11,Special Service,1,1,260,260,FOX STUCK BETWEEN WALL AND GARAGE,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000277,St. Ann's,E09000014,Haringey,Tottenham,1.00023E+11,Conway Road,21103140,N15,532158,188844,532150,188850,51.58296906,-0.093936269\n882111,02/01/2011 16:13,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN A CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Haven Green,20600845,W5,NULL,NULL,517750,181050,NULL,NULL\n1790111,04/01/2011 17:30,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Millfield Cottages,20304051,BR5,NULL,NULL,546950,168550,NULL,NULL\n1842111,04/01/2011 18:56,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,2.00002E+11,Millfield Close,20301217,BR5,546881,168369,546850,168350,51.39535016,0.109902056\n2067111,05/01/2011 09:24,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON SMALL LEDGE ABOVE WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,202083269,Harp Island Close,20200936,NW10,520780,186635,520750,186650,51.56566724,-0.258829183\n2106111,05/01/2011 11:16,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Millfield Cottages,20304051,BR5,NULL,NULL,546950,168550,NULL,NULL\n2741111,06/01/2011 20:41,2011,2010/11,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Melville Road,21300838,RM13,NULL,NULL,552450,182250,NULL,NULL\n3496111,08/01/2011 10:21,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Hounslow Road,NULL,TW13,511875,172019,511850,172050,51.43613309,-0.39188165\n3678111,08/01/2011 17:00,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Lots Road,NULL,SW10,526163,177054,526150,177050,51.4783848,-0.184635031\n3680111,08/01/2011 17:03,2011,2010/11,Special Service,1,2,260,520,DOG WITH HEAD TRAPPED IN METAL FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,Clapham,NULL,Saxby Road,21901216,SW2,NULL,NULL,530250,173950,NULL,NULL\n3699111,08/01/2011 17:42,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED IN HOLE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Norbury Crescent,20501230,SW16,NULL,NULL,531450,169150,NULL,NULL\n3992111,09/01/2011 08:52,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA RETRIEVE A CAT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000053,Golders Green,E09000003,Barnet,Finchley,200200344,Ashbourne Avenue,20001520,NW11,524676,188983,524650,188950,51.58592221,-0.201815711\n5524111,12/01/2011 12:54,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Norwood Road,21901036,SE24,NULL,NULL,531750,173250,NULL,NULL\n5838111,13/01/2011 00:52,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Warrington Crescent,8400690,W9,NULL,NULL,526050,182350,NULL,NULL\n6039111,13/01/2011 12:39,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN RABBIT HOLE,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Meadlands Drive,NULL,TW10,517596,172744,517550,172750,51.44149096,-0.309372564\n6212111,13/01/2011 18:12,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL CAVITY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Warrington Crescent,8400690,W9,NULL,NULL,526050,182350,NULL,NULL\n6236111,13/01/2011 19:10,2011,2010/11,Special Service,1,1,260,260,FOX STUCK BETWEEN FENCE PANELS,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000648,Westbourne,E09000033,Westminster,North Kensington,1.00023E+11,Leamington Road Villas,8400535,W11,524925,181521,524950,181550,51.51880514,-0.200872308\n8013111,17/01/2011 07:24,2011,2010/11,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,The Ridgeway,NULL,NW7,522249,193072,522250,193050,51.62320161,-0.235404973\n9070111,19/01/2011 09:48,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Corker Walk,NULL,N7,530915,186639,530950,186650,51.56344396,-0.112686218\n9540111,20/01/2011 09:01,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH REMOVAL OF CAT FROM TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,5870028202,Kingswood Drive,22602417,SM2,525708,162810,525750,162850,51.35047125,-0.196228217\n12290111,25/01/2011 16:47,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000521,Hampton North,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Stevens Close,22401889,TW12,512515,170965,512550,170950,51.42653316,-0.383012663\n12690111,26/01/2011 16:44,2011,2010/11,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Woodbridge Road,19900391,IG11,NULL,NULL,545750,185050,NULL,NULL\n13146111,27/01/2011 16:43,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Copperfield Street,22500603,SE1,532158,179964,532150,179950,51.50316864,-0.097267753\n14131111,29/01/2011 12:15,2011,2010/11,Special Service,1,1,260,260,TO REMOVE LOCK FROM GARAGES - CAT INSIDE,Cat,Person (land line),Vehicle Repair Workshop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5071843,Camden Mews,20400538,NW1,529615,184661,529650,184650,51.54596829,-0.132158786\n14408111,29/01/2011 20:22,2011,2010/11,Special Service,1,1,260,260,KITTEN STUCK IN VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Petworth Gardens,21401525,UB10,NULL,NULL,508250,183650,NULL,NULL\n15686111,01/02/2011 11:30,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED IN A TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Grosvenor Avenue,NULL,SM5,528218,163835,528250,163850,51.3591229,-0.159835861\n16711111,03/02/2011 10:50,2011,2010/11,Special Service,1,1,260,260,DOGTRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000372,Highbury East,E09000019,Islington,Islington,NULL,Highbury Place,21605119,N5,NULL,NULL,531650,184850,NULL,NULL\n17182111,04/02/2011 07:40,2011,2010/11,Special Service,1,2,260,520,HORSE COLLAPSED,Horse,Person (land line),Barn,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000059,Totteridge,E09000003,Barnet,Finchley,200075806,Lullington Garth,20027560,N12,525250,192250,525250,192250,51.61515507,-0.192369619\n17261111,04/02/2011 11:30,2011,2010/11,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Hall Lane,NULL,E4,536284,192312,536250,192350,51.61314996,-0.033076692\n18520111,06/02/2011 11:00,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED ON RIVERBANK  WATER LEVEL ONE IMPLEMENTED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Malyons Road,NULL,SE13,537483,174439,537450,174450,51.45224964,-0.022742987\n19256111,07/02/2011 17:53,2011,2010/11,Special Service,1,1,260,260,DOG WITH JAW TRAPPED IN FENCING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000378,St. George's,E09000019,Islington,Holloway,5300092653,Tufnell Park Road,21606493,N7,530233,185970,530250,185950,51.55758978,-0.122767119\n19581111,08/02/2011 10:25,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN BATHROOM PIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Tower Bridge Road,22502529,SE1,NULL,NULL,533650,179750,NULL,NULL\n20199111,09/02/2011 14:39,2011,2010/11,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Slewins Close,NULL,RM11,553524,188749,553550,188750,51.57670512,0.214167528\n20451111,10/02/2011 00:42,2011,2010/11,Special Service,1,2,260,520,CAT IMPALED ON RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000423,Herne Hill,E09000022,Lambeth,Brixton,2E+11,Flaxman Road,21900566,SE5,532014,176231,532050,176250,51.46965491,-0.100735643\n21058111,11/02/2011 11:12,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP INSIDE THE CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000517,East Sheen,E09000027,Richmond upon Thames,Richmond,NULL,Lodge Avenue,22406866,SW14,NULL,NULL,521050,175650,NULL,NULL\n21535111,12/02/2011 10:10,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON A ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,NULL,Appach Road,21900099,SW2,NULL,NULL,531050,174450,NULL,NULL\n22134111,13/02/2011 13:42,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,207161278,Celadon Close,20702172,EN3,536143,196803,536150,196850,51.65354049,-0.033366267\n22236111,13/02/2011 18:24,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Wray Crescent,21604047,N4,NULL,NULL,530550,186750,NULL,NULL\n22865111,15/02/2011 08:52,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED BETWEEN TWO FENCES,Fox,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000313,Havering Park,E09000016,Havering,Romford,1.00021E+11,Ashvale Gardens,21300966,RM5,550786,192089,550750,192050,51.60745371,0.176120774\n24455111,18/02/2011 15:36,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GAP BETWEEN EXTENSIONS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,19900241,RM8,NULL,NULL,546650,184850,NULL,NULL\n25514111,20/02/2011 15:47,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Homerton,NULL,Martello Street,NULL,E8,534758,184309,534750,184350,51.54160043,-0.058168844\n26038111,21/02/2011 17:24,2011,2010/11,Special Service,1,2,260,520,DOG STUCK IN FENCE   IN REAR GARDEN,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522450,185050,NULL,NULL\n26137111,21/02/2011 19:53,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Euston,NULL,Brownlow Mews,20401021,WC1N,NULL,NULL,530850,182150,NULL,NULL\n26639111,22/02/2011 20:44,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bridge Road,NULL,NW10,521066,184557,521050,184550,51.54693026,-0.255420093\n27880111,25/02/2011 10:12,2011,2010/11,Special Service,1,1,260,260,A NUMBER OF DOGS LOCKED IN VEHICLE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Temple Fortune Hill,NULL,NW11,525396,188655,525350,188650,51.58281473,-0.19154619\n29404111,28/02/2011 11:51,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Wood Street,20046960,EN5,NULL,NULL,524550,196450,NULL,NULL\n30147111,01/03/2011 22:45,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF BELIEVED TO BE INJURED,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Victoria Avenue,22201179,E6,NULL,NULL,541650,183850,NULL,NULL\n30449111,02/03/2011 16:44,2011,2010/11,Special Service,2,3,260,780,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Doctor Johnson Avenue,NULL,SW17,529105,172084,529150,172050,51.43305651,-0.144105048\n30782111,03/03/2011 11:56,2011,2010/11,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Greencroft Gardens,20400420,NW6,NULL,NULL,526150,184450,NULL,NULL\n30890111,03/03/2011 15:57,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED & FENCE,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000218,Eltham North,E09000011,Greenwich,Eltham,1.00021E+11,Greenvale Road,20800683,SE9,542832,175165,542850,175150,51.45745092,0.054479912\n31280111,04/03/2011 11:48,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,2.00003E+11,Avondale Square,22500119,SE1,534077,178126,534050,178150,51.48619972,-0.070333565\n31739111,05/03/2011 05:56,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Woolmead Avenue,20047600,NW9,NULL,NULL,522150,187550,NULL,NULL\n31985111,05/03/2011 16:20,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200156486,Hammond Close,20020640,EN5,524441,195633,524450,195650,51.64573674,-0.202846772\n32093111,05/03/2011 19:34,2011,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Hampden Square,20020700,N14,NULL,NULL,528550,194250,NULL,NULL\n32125111,05/03/2011 20:35,2011,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN GARAGE,Cat,Person (mobile),Other education establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,2E+11,Belmont Road,21900167,SW4,529473,175582,529450,175550,51.46440903,-0.137535716\n32145111,05/03/2011 21:18,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED ON LEDGE BETWEEN TWO BUILDINGS,Cat,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300095158,Upper Street,21606199,N1,531629,183540,531650,183550,51.53542845,-0.103549982\n32405111,06/03/2011 13:30,2011,2010/11,Special Service,1,1,260,260,CAT STUCK BETWEEN CEMENT AND SKIRTING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Cester Street,20900230,E2,NULL,NULL,534050,183650,NULL,NULL\n32631111,06/03/2011 21:17,2011,2010/11,Special Service,1,1,260,260,DOG TRAPPED UNDER BOARDS IN EXTENSION,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Cranleigh Road,21500317,TW13,NULL,NULL,509550,171650,NULL,NULL\n32671111,06/03/2011 22:41,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Framlingham Crescent,20302308,SE9,NULL,NULL,542350,171950,NULL,NULL\n33179111,07/03/2011 20:57,2011,2010/11,Special Service,1,1,260,260,FOX TRAPPED IN WIRE,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000489,Plaistow North,E09000025,Newham,Plaistow,46070667,Stirling Road,22207905,E13,540674,183160,540650,183150,51.5298336,0.026624686\n33505111,08/03/2011 13:36,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Hill Crescent,22601578,KT4,NULL,NULL,523450,165650,NULL,NULL\n33518111,08/03/2011 14:15,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD ON LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,6057757,Victoria Park,22700893,E9,535708,183586,535750,183550,51.53487601,-0.044756699\n34099111,09/03/2011 15:06,2011,2010/11,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5078715,Bartholomew Road,20400384,NW5,529128,184658,529150,184650,51.54605285,-0.139179131\n34100111,09/03/2011 15:11,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH SEAGULL TRAPPED UP TREE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Eastmead Avenue,20600594,UB6,NULL,NULL,513650,182750,NULL,NULL\n34588111,10/03/2011 14:53,2011,2010/11,Special Service,1,1,260,260,DOG STUCK ON GARAGE ROOF,Dog,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,1.00021E+11,Hale Gardens,21103364,N17,534191,189483,534150,189450,51.58823081,-0.064368191\n34910111,11/03/2011 05:13,2011,2010/11,Special Service,1,1,260,260,CAT WITH FOOT TRAPPED IN SECURITY BARS ON WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Quickswood,20400588,NW3,NULL,NULL,527450,184250,NULL,NULL\n36312111,13/03/2011 17:47,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BEHIND PANEL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Fullers Avenue,21800424,KT6,NULL,NULL,518650,165650,NULL,NULL\n36607111,14/03/2011 09:44,2011,2010/11,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870077757,Westcroft Road,22605657,SM5,528375,164768,528350,164750,51.36747262,-0.157245672\n37066111,15/03/2011 02:27,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Mountfield Way,NULL,BR5,NULL,NULL,547250,168150,NULL,NULL\n37164111,15/03/2011 09:47,2011,2010/11,Special Service,1,1,260,260,DOG STUCK IN MUD ON CANAL BANK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Lane,NULL,W7,515079,179665,515050,179650,51.50421271,-0.343320137\n37368111,15/03/2011 17:37,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN GUTTER,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Bermondsey Wall West,22500227,SE16,NULL,NULL,534150,179750,NULL,NULL\n38722111,18/03/2011 12:22,2011,2010/11,Special Service,2,3,260,780,DOG STUCK IN STREAM,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Barnet Lane,NULL,EN5,525017,195237,525050,195250,51.64205028,-0.194667623\n39081111,19/03/2011 03:27,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Marylee Way,21900931,SE11,NULL,NULL,530950,178550,NULL,NULL\n39473111,19/03/2011 16:42,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED BEHING MESH,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Centurion Square,20803005,SE18,NULL,NULL,542150,176950,NULL,NULL\n39510111,19/03/2011 17:40,2011,2010/11,Special Service,1,2,260,520,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Avenue Road,NULL,UB1,512819,180109,512850,180150,51.50865861,-0.375726553\n39944111,20/03/2011 13:07,2011,2010/11,Special Service,1,1,260,260,SWAN TRAPPED UNDER PONTOON,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,6061889,Westferry Road,22701325,E14,537256,179121,537250,179150,51.49437874,-0.024189882\n39965111,20/03/2011 13:50,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Osborne Road,22207832,E7,NULL,NULL,540750,185250,NULL,NULL\n40006111,20/03/2011 15:19,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED UNDER FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Nutbrook Street,22501823,SE15,NULL,NULL,534150,175750,NULL,NULL\n40523111,21/03/2011 13:17,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Becklow Road,21000108,W12,NULL,NULL,522050,179850,NULL,NULL\n40652111,21/03/2011 17:41,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Other FRS,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,10009141891,Cart Lane,22822700,E4,539038,194461,539050,194450,51.6317873,0.007524848\n41510111,23/03/2011 11:09,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Camden Road,21606413,N7,NULL,NULL,530450,185750,NULL,NULL\n42011111,24/03/2011 09:54,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH THRUSH TANGLED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Ramsey Street,NULL,E2,534377,182370,534350,182350,51.52426698,-0.064399048\n42622111,25/03/2011 09:32,2011,2010/11,Special Service,1,1,260,260,CAT STUCK ON  TELEGRAPH POLE,Cat,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Hatfield Road,NULL,RM9,548394,184250,548350,184250,51.53765289,0.138296138\n42671111,25/03/2011 12:00,2011,2010/11,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Keedonwood Road,22005125,BR1,NULL,NULL,539750,171350,NULL,NULL\n42723111,25/03/2011 14:06,2011,2010/11,Special Service,1,1,260,260,DOG STUCK UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,The Drive,20301800,BR3,NULL,NULL,537250,169750,NULL,NULL\n42739111,25/03/2011 14:43,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,NULL,Swanscombe Road,21501102,W4,NULL,NULL,521250,178350,NULL,NULL\n42770111,25/03/2011 15:30,2011,2010/11,Special Service,1,1,260,260,PUPPY  IN DISTRESS LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Old Farm Road,NULL,N2,526754,190806,526750,190850,51.60184173,-0.171180504\n43265111,26/03/2011 09:53,2011,2010/11,Special Service,NULL,NULL,260,NULL,DOG IN DISTRESS IN RIVER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530485,179007,530450,179050,51.49495659,-0.12171203\n44012111,27/03/2011 17:16,2011,2010/11,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,NULL,Cuddington Way,22602955,SM2,NULL,NULL,524050,161750,NULL,NULL\n44188111,27/03/2011 22:55,2011,2010/11,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING,Bird,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Coombe Road,NULL,KT2,519273,169547,519250,169550,51.41240758,-0.286330534\n44559111,28/03/2011 18:12,2011,2010/11,Special Service,1,2,260,520,CAT STUCK IN BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Amhurst Park,20900061,N16,NULL,NULL,533250,187950,NULL,NULL\n44894111,29/03/2011 10:23,2011,2010/11,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Empress Drive,NULL,BR7,543606,170853,543650,170850,51.41850861,0.063864411\n44942111,29/03/2011 12:37,2011,2010/11,Special Service,1,1,260,260,INJURED CAT IN BASEMENT AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,Brown Hart Gardens,8401078,W1K,NULL,NULL,528350,181050,NULL,NULL\n45073111,29/03/2011 17:05,2011,2010/11,Special Service,1,1,260,260,RUNNING CALL TO CAT TRAPPED IN TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W10,NULL,NULL,524150,181950,NULL,NULL\n45294111,30/03/2011 00:03,2011,2010/11,Special Service,1,1,260,260,CAT FALLEN INTO WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Allerford Road,NULL,SE6,537946,172079,537950,172050,51.43092955,-0.017002403\n45430111,30/03/2011 10:07,2011,2010/11,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Churston Avenue,22200476,E13,NULL,NULL,540850,183850,NULL,NULL\n45531111,30/03/2011 14:24,2011,2010/11,Special Service,1,1,260,260,PERSON IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,North Circular Road,20201063,NW2,NULL,NULL,521150,186150,NULL,NULL\n45959111,31/03/2011 09:04,2011,2010/11,Special Service,1,1,260,260,CAT TRAPPED IN DOUBLE GLAZED WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Park Road,22200936,E15,NULL,NULL,540050,183850,NULL,NULL\n46132111,31/03/2011 16:18,2011,2010/11,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000187,Perivale,E09000009,Ealing,Wembley,NULL,Alperton Lane,NULL,UB6,517298,182863,517250,182850,51.53249966,-0.310299286\n46177111,31/03/2011 18:15,2011,2010/11,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000258,North End,E09000013,Hammersmith and Fulham,Fulham,NULL,North End Road,NULL,W14,NULL,NULL,524750,178150,NULL,NULL\n46389111,01/04/2011 03:48,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED BY CABLE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Whitley Road,21105234,N17,NULL,NULL,533450,190350,NULL,NULL\n46433111,01/04/2011 07:20,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Clapham,NULL,Canford Road,22900786,SW11,NULL,NULL,528050,174950,NULL,NULL\n46502111,01/04/2011 09:33,2011,2011/12,Special Service,1,3,260,780,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,5870028202,Kingswood Drive,22602417,SM2,525708,162810,525750,162850,51.35047125,-0.196228217\n47328111,02/04/2011 19:32,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000462,Hillside,E09000024,Merton,Wimbledon,48001256,Alexandra Road,22100093,SW19,524793,170796,524750,170750,51.42244547,-0.206557312\n47368111,02/04/2011 20:02,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CHOKE LEAD,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Seaton Street,20704711,N18,NULL,NULL,534350,192150,NULL,NULL\n47758111,03/04/2011 14:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX CUB TRAPPED IN DRAIN,Fox,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Man Lane,NULL,W13,516427,180518,516450,180550,51.51160347,-0.323625299\n48824111,05/04/2011 11:40,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH TRAPPED GULL,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,5088624,Bedford Row,20401148,WC1R,530800,181743,530850,181750,51.51947175,-0.116162632\n49019111,05/04/2011 19:37,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Romford Road,22208081,E15,NULL,NULL,539750,184750,NULL,NULL\n49189111,06/04/2011 02:03,2011,2011/12,Special Service,3,9,260,2340,Redacted,Horse,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Sandpiper Close,NULL,E17,535938,191035,535950,191050,51.60175833,-0.03856555\n49295111,06/04/2011 10:39,2011,2011/12,Special Service,1,1,260,260,DOG WITH FOOT TRAPPED IN METAL CRATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Southlands Road,20300621,BR2,NULL,NULL,541850,168250,NULL,NULL\n49539111,06/04/2011 18:50,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300052653,Islington Green,21605183,N1,531642,183551,531650,183550,51.53552427,-0.103358545\n50170111,07/04/2011 17:30,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE FENCE,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000376,Junction,E09000019,Islington,Holloway,5300030632,Elthorne Road,21600283,N19,529550,186801,529550,186850,51.56521484,-0.132307906\n50249111,07/04/2011 19:59,2011,2011/12,Special Service,1,1,260,260,DOG STUCK UNDER SHED,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,1.00023E+11,Bateman Road,22813200,E4,537293,192015,537250,192050,51.61023604,-0.018629105\n50623111,08/04/2011 11:30,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000421,Ferndale,E09000022,Lambeth,Brixton,1.00023E+11,Acre Lane,21900069,SW2,530447,175091,530450,175050,51.45977304,-0.123703249\n51657111,09/04/2011 20:58,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN A BEDROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Stamford Street,21901292,SE1,NULL,NULL,531250,180350,NULL,NULL\n51906111,10/04/2011 09:33,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Lilliput Road,NULL,RM7,550848,187723,550850,187750,51.5682086,0.175140577\n52137111,10/04/2011 17:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN INJURED,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Rainham Road,NULL,RM13,552250,182750,552250,182750,51.52314992,0.193205084\n52625111,11/04/2011 09:23,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,5300092364,Tremlett Grove,21600876,N19,529130,186416,529150,186450,51.56185119,-0.138505293\n52764111,11/04/2011 13:35,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,NULL,Abbeyfields Close,20600004,NW10,NULL,NULL,519250,183050,NULL,NULL\n52959111,11/04/2011 17:39,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Burgess Street,22700227,E14,NULL,NULL,537050,181750,NULL,NULL\n53406111,12/04/2011 12:59,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN ALLOY WHEEL,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Huntsman Road,22304632,IG6,NULL,NULL,546650,192050,NULL,NULL\n53552111,12/04/2011 17:06,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Cottage Road,NULL,N7,530689,185099,530650,185050,51.549657,-0.116516507\n53637111,12/04/2011 18:55,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Ditton Road,21800332,KT6,518180,166164,518150,166150,51.38223011,-0.303164177\n54660111,14/04/2011 12:00,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000527,St. Margarets and North Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Langhorn Drive,NULL,TW2,515250,173750,515250,173750,51.45101457,-0.342785509\n55494111,15/04/2011 16:18,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED BETWEEN GARAGE AND WALL,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Uxbridge Road,21402681,UB10,NULL,NULL,508250,182050,NULL,NULL\n55505111,15/04/2011 16:38,2011,2011/12,Special Service,1,1,260,260,KITTEN FALLEN INTO WALLED GARDEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Roland Gardens,21700466,SW7,NULL,NULL,526450,178350,NULL,NULL\n56214111,16/04/2011 19:57,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER CONTAINER,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,46053643,Newham Way,22208162,E6,542556,182109,542550,182150,51.51991765,0.053312419\n56393111,17/04/2011 01:16,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN HOLE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Culverley Road,NULL,SE6,537836,173433,537850,173450,51.44312371,-0.018057323\n56552111,17/04/2011 10:07,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Villiers Close,22884200,E10,NULL,NULL,537350,186950,NULL,NULL\n56769111,17/04/2011 17:35,2011,2011/12,Special Service,1,2,260,520,CROW TRAPPED IN NETTING,Bird,Person (land line),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00023E+11,Denmark Hill,21900453,SE5,532511,175964,532550,175950,51.46713935,-0.093684469\n57137111,18/04/2011 11:18,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Heather Close,21900691,SW8,NULL,NULL,528650,175850,NULL,NULL\n57712111,19/04/2011 09:44,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SHED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000086,Barnhill,E09000005,Brent,Stanmore,202137854,Tookey Close,20204049,HA3,518942,187928,518950,187950,51.5776778,-0.284897421\n57771111,19/04/2011 11:59,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO PARROT IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000549,Rotherhithe,E09000028,Southwark,Dockhead,NULL,Abbeyfield Estate,NULL,SE16,535057,178877,535050,178850,51.49271549,-0.055940526\n57945111,19/04/2011 17:08,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SHED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000086,Barnhill,E09000005,Brent,Stanmore,202137854,Tookey Close,20204049,HA3,518942,187928,518950,187950,51.5776778,-0.284897421\n58088111,19/04/2011 19:59,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Reservoir Road,21401623,HA4,NULL,NULL,508450,188950,NULL,NULL\n58127111,19/04/2011 20:42,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Arden Road,20001200,N3,NULL,NULL,524650,189950,NULL,NULL\n58182111,19/04/2011 21:42,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN  ROOF,Cat,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,2.00001E+11,Addington Village Road,20501645,CR0,537048,163923,537050,163950,51.35785456,-0.033058089\n58211111,19/04/2011 22:29,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN FROM FLATS BELIEVED INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,21000856,SW6,NULL,NULL,525650,176250,NULL,NULL\n58568111,20/04/2011 13:59,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,South Bank,21800867,KT6,518385,167278,518350,167250,51.39219993,-0.299849034\n58930111,20/04/2011 22:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN EAVES,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Road,21103726,N8,NULL,NULL,529450,189050,NULL,NULL\n59329111,21/04/2011 16:49,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000509,Monkhams,E09000026,Redbridge,Woodford,NULL,The Pines,NULL,IG8,540048,193382,540050,193350,51.62184166,0.021677015\n59873111,22/04/2011 11:59,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Granville Road,22102928,SW19,NULL,NULL,525350,170150,NULL,NULL\n59927111,22/04/2011 13:23,2011,2011/12,Special Service,1,1,260,260,CAT ON TOP OF CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Fifth Avenue,22200168,E12,NULL,NULL,542550,185750,NULL,NULL\n59941111,22/04/2011 13:36,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,5300014896,Camden Passage,21604427,N1,531631,183531,531650,183550,51.5353471,-0.103524526\n59967111,22/04/2011 14:12,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00023E+11,Fortis Green,21106130,N10,528250,189750,528250,189750,51.59201335,-0.149977421\n59992111,22/04/2011 14:50,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Croydon,10014048741,Sanderstead Road,20502318,CR2,532803,162650,532850,162650,51.34742184,-0.094463366\n59993111,22/04/2011 14:50,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Greyhound Road,21000393,W6,NULL,NULL,524150,177850,NULL,NULL\n60257111,22/04/2011 18:29,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN GARDEN IN DISTRESS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Garrison Road,22702435,E3,NULL,NULL,537050,183550,NULL,NULL\n60766111,23/04/2011 11:03,2011,2011/12,Special Service,1,1,260,260,INJURED CAT STUCK UP TREE AT REAR,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lee Green,1.00022E+11,Evans Road,22001448,SE6,539454,172556,539450,172550,51.43484741,0.004863815\n60959111,23/04/2011 16:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Hackington Crescent,20302253,BR3,NULL,NULL,537450,170850,NULL,NULL\n61046111,23/04/2011 17:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Rainhill Way,NULL,E3,537682,182519,537650,182550,51.52481074,-0.016730666\n61849111,24/04/2011 17:36,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Highland Road,20100719,DA6,NULL,NULL,549350,174850,NULL,NULL\n62424111,25/04/2011 16:55,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Greyhound Road,21103355,N17,NULL,NULL,533550,189850,NULL,NULL\n62805111,26/04/2011 07:40,2011,2011/12,Special Service,1,1,260,260,INJURED CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Ladysmith Road,20800878,SE9,NULL,NULL,543350,174050,NULL,NULL\n63138111,26/04/2011 19:33,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Christ Church Road,NULL,KT5,518906,166972,518950,166950,51.38934083,-0.292466624\n63427111,27/04/2011 12:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN OVERFLOW PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Prince Edward Road,20900820,E9,NULL,NULL,536950,184650,NULL,NULL\n64026111,28/04/2011 11:41,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Wembley,NULL,Mead Plat,20200474,NW10,NULL,NULL,520450,184850,NULL,NULL\n64269111,28/04/2011 19:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,10001295364,Seven Sisters Road,21603790,N4,531220,186568,531250,186550,51.56273502,-0.108315098\n64398111,28/04/2011 22:59,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Shoreditch,NULL,Commercial Street,22700347,E1,NULL,NULL,533550,182050,NULL,NULL\n64656111,29/04/2011 14:46,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA ON SCENE WITH CAT ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Margery Park Road,22207803,E7,NULL,NULL,540150,184550,NULL,NULL\n64762111,29/04/2011 18:07,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Duncan Terrace,NULL,N1,531560,183141,531550,183150,51.53185885,-0.104693263\n64851111,29/04/2011 21:06,2011,2011/12,Special Service,1,3,260,780,PUPPIES POSSIBLY TRAPPED UNDER GRILL,Dog,Person (mobile),Bridge,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000544,Newington,E09000028,Southwark,Old Kent Road,NULL,Pelier Street,NULL,SE17,532281,177812,532250,177850,51.48380056,-0.096302632\n65017111,30/04/2011 08:59,2011,2011/12,Special Service,1,1,260,260,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Bourne View,NULL,CR8,532944,159791,532950,159750,51.32169552,-0.09350582\n65060111,30/04/2011 11:33,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN UNDERSTAIRS CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Courthouse Gardens,20010540,N3,NULL,NULL,525450,191750,NULL,NULL\n65215111,30/04/2011 16:01,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,5870068833,Thornton Road,22601078,SM5,527243,166532,527250,166550,51.38358024,-0.172866954\n65291111,30/04/2011 18:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Factory,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207052032,Leeds Street,20704430,N18,534147,192288,534150,192250,51.6134475,-0.063931317\n65718111,01/05/2011 11:00,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN SHED,Fox,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000042,Whalebone,E09000002,Barking and Dagenham,Dagenham,100033146,Winifred Road,19900390,RM8,548459,187143,548450,187150,51.56363005,0.140451933\n65804111,01/05/2011 12:37,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,1.00023E+11,High Road,21104652,N22,531183,190044,531150,190050,51.59398057,-0.107552187\n65902111,01/05/2011 15:36,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009394,Dalgarno,E09000020,Kensington and Chelsea,North Kensington,217059344,Notting Barn Road,21700956,W10,523657,182044,523650,182050,51.52378421,-0.218954737\n65916111,01/05/2011 16:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED IN KENNEL,Dog,Person (land line),Recycling plant,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Rainham Road North,NULL,RM10,549561,186667,549550,186650,51.55906258,0.156136425\n66736111,02/05/2011 17:15,2011,2011/12,Special Service,1,1,260,260,PUPPY ON ROOF,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Cromwell Grove,21000251,W6,NULL,NULL,523350,179450,NULL,NULL\n66969111,02/05/2011 22:21,2011,2011/12,Special Service,1,4,260,1040,BABY DEER STUCK BETWEEN TWO HOUSES,Deer,Police,Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000200,Grange,E09000010,Enfield,Edmonton,NULL,Old Park Ridings,NULL,N21,531992,195259,531950,195250,51.64065537,-0.093916484\n67459111,03/05/2011 19:21,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN  WHEEL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Elmhurst Road,NULL,N17,533534,190323,533550,190350,51.59593531,-0.073526734\n67535111,03/05/2011 21:08,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,5112216,Inglewood Road,20400261,NW6,525442,185080,525450,185050,51.55067588,-0.19215735\n67885111,04/05/2011 13:40,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN UP A TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Acacia Road,NULL,CR4,528638,169240,528650,169250,51.40760298,-0.151850703\n68468111,05/05/2011 12:57,2011,2011/12,Special Service,1,1,260,260,LARGE BIRD TRAPPED IN BUSHES,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000046,Colindale,E09000003,Barnet,Hendon,NULL,Colindale Avenue,NULL,NW9,521422,189813,521450,189850,51.59409144,-0.248472738\n68478111,05/05/2011 13:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Seagrave Road,21000720,SW6,NULL,NULL,525350,177750,NULL,NULL\n68681111,05/05/2011 18:00,2011,2011/12,Special Service,1,4,260,1040,CAT STUCK IN THE CHIMNEY,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Grosvenor Road,NULL,E7,540790,184627,540750,184650,51.54298697,0.028881817\n68685111,05/05/2011 18:02,2011,2011/12,Special Service,1,1,260,260,BIRD ENTWINED IN WIRE STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Palace Road,NULL,N8,529989,188810,529950,188850,51.58316788,-0.125235525\n69093111,06/05/2011 14:02,2011,2011/12,Special Service,1,1,260,260,FOX CUB WITH HEAD STUCK IN RAILINGS,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Roding Road,NULL,E5,536039,185389,536050,185350,51.55099867,-0.039291041\n69292111,06/05/2011 19:01,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Southall,1.00022E+11,Jessop Avenue,21501548,UB2,512676,178630,512650,178650,51.49539387,-0.37825823\n69424111,06/05/2011 21:33,2011,2011/12,Special Service,1,1,260,260,CROW TRAPPED IN TV AERIAL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Owen Close,20801126,SE28,NULL,NULL,547150,180350,NULL,NULL\n70038111,07/05/2011 19:09,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Devonshire Drive,NULL,SE10,NULL,NULL,537950,176950,NULL,NULL\n70115111,07/05/2011 21:25,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED ON LEDGE ON TOP OF SHOP,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Lewisham High Street,22004177,SE13,538331,175439,538350,175450,51.46102959,-0.010155936\n70895111,09/05/2011 06:30,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,NULL,Mallard Walk,20100909,DA14,NULL,NULL,547250,170950,NULL,NULL\n70941111,09/05/2011 08:41,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Moon Street,21605461,N1,NULL,NULL,531550,183850,NULL,NULL\n71173111,09/05/2011 16:43,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG STUCK IN CHAIN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000317,Pettits,E09000016,Havering,Romford,NULL,Beauly Way,21300998,RM1,NULL,NULL,551450,190650,NULL,NULL\n71417111,09/05/2011 23:17,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND FENCE POSSIBLY INJURED,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Kneller Road,22000588,SE4,536379,175376,536350,175350,51.46093671,-0.038260516\n71740111,10/05/2011 15:23,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN THICK UNDERGROWTH,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000360,Hounslow South,E09000018,Hounslow,Heston,1.00022E+11,Catherine Gardens,21500215,TW3,514690,175113,514650,175150,51.46337855,-0.350399499\n72131111,11/05/2011 08:06,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN WELL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Barking Road,22208079,E13,NULL,NULL,540250,182250,NULL,NULL\n72217111,11/05/2011 11:35,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011473,Norbury Park,E09000008,Croydon,Norbury,NULL,Virginia Road,20500145,CR7,NULL,NULL,531650,169850,NULL,NULL\n72277111,11/05/2011 13:13,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Globe Road,22700551,E2,NULL,NULL,535250,182850,NULL,NULL\n72500111,11/05/2011 20:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Rye Road,22502174,SE15,NULL,NULL,535550,175050,NULL,NULL\n72544111,11/05/2011 22:37,2011,2011/12,Special Service,1,1,260,260,INJURED KITTEN STUCK IN NEIGHBOURING GARDEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Atkins Road,21900121,SW12,NULL,NULL,529150,173750,NULL,NULL\n72730111,12/05/2011 09:56,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Kings Road,NULL,E4,538614,194204,538650,194250,51.62958242,0.001301014\n73174111,12/05/2011 21:55,2011,2011/12,Special Service,1,2,260,520,KITTEN STUCK INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Shakespeare Road,20101275,DA7,548277,176796,548250,176750,51.47070718,0.133479428\n73453111,13/05/2011 12:45,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN INTO UPPER BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reedham Close,21103792,N17,NULL,NULL,534650,189350,NULL,NULL\n73455111,13/05/2011 12:46,2011,2011/12,Special Service,1,1,260,260,SQUIRREL STUCK IN TOILET,Squirrel,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Vicarage Road,21105184,N17,NULL,NULL,534150,190850,NULL,NULL\n73655111,13/05/2011 18:50,2011,2011/12,Special Service,1,1,260,260,CAT STUCK ON LEDGE BY CANAL,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Lisson Grove,NULL,NW8,527032,182294,527050,182250,51.52528269,-0.170242371\n73926111,14/05/2011 01:18,2011,2011/12,Special Service,1,1,260,260,DOG BELEIVED TRAPPED,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Sunningfields Road,NULL,NW4,523031,189617,523050,189650,51.59198151,-0.225324552\n74114111,14/05/2011 11:14,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00023E+11,Lower John Street,8400181,W1F,529375,180875,529350,180850,51.51199896,-0.137008\n74146111,14/05/2011 12:27,2011,2011/12,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Rolls Road,22502100,SE1,NULL,NULL,533850,178350,NULL,NULL\n74421111,14/05/2011 19:57,2011,2011/12,Special Service,1,1,260,260,PIGEON WITH WING TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000464,Longthornton,E09000024,Merton,Norbury,NULL,Hemlock Close,22103260,SW16,NULL,NULL,529550,169450,NULL,NULL\n74594111,14/05/2011 23:29,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BY WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Sandy Hill Road,20801323,SE18,NULL,NULL,543750,178350,NULL,NULL\n74766111,15/05/2011 09:50,2011,2011/12,Special Service,1,1,260,260,HERON WITH SUSPECTED BROKEN LEG,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000347,Brentford,E09000018,Hounslow,Ealing,1.00022E+11,Clitherow Road,21500285,TW8,517052,178306,517050,178350,51.49159367,-0.315356649\n74851111,15/05/2011 11:41,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Warley Street,22701296,E2,NULL,NULL,535650,182750,NULL,NULL\n74994111,15/05/2011 14:30,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Montpelier Road,22501739,SE15,NULL,NULL,534850,176850,NULL,NULL\n75005111,15/05/2011 14:45,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN BUS ENGINE,Cat,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Cambridge Heath Road,NULL,E2,534871,183325,534850,183350,51.53273102,-0.056917017\n75211111,15/05/2011 19:58,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Lilford Road,NULL,SE5,532250,176250,532250,176250,51.46977058,-0.097332809\n76046111,17/05/2011 09:16,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,The Ridgeway,NULL,E4,538397,194313,538350,194350,51.63061519,-0.001789062\n76055111,17/05/2011 09:45,2011,2011/12,Special Service,1,2,260,520,CAT STUCK BETWEEN TWO WALLS,Cat,Other FRS,Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000404,Chessington North and Hook,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Somerset Avenue,21800861,KT9,517853,164792,517850,164750,51.36996654,-0.308314911\n76111111,17/05/2011 12:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER DRAWER AND INJURED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Crown Lane,20701467,N14,NULL,NULL,529350,194350,NULL,NULL\n76214111,17/05/2011 16:45,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000104,Wembley Central,E09000005,Brent,Wembley,202089574,Harrow Road,20202039,HA0,517683,185013,517650,185050,51.55174311,-0.30403294\n76409111,17/05/2011 22:12,2011,2011/12,Special Service,NULL,NULL,260,NULL,RUNNING CALL TO ANMINAL TRAPPED IN FENCE,Unknown - Heavy Livestock Animal,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000351,Feltham North,E09000018,Hounslow,Feltham,1.00023E+11,Faggs Road,21500428,TW14,510292,174713,510250,174750,51.46065666,-0.41380766\n77553111,19/05/2011 22:03,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN BEDROOM,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Thane Villas,21603913,N7,NULL,NULL,530950,186350,NULL,NULL\n78364111,21/05/2011 12:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA  WITH CAT TRAPPED ON CANAL  BANK,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,1.00023E+11,Lisson Grove,8400993,NW8,526935,182426,526950,182450,51.52649077,-0.171592307\n78888111,21/05/2011 23:20,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000123,Penge and Cator,E09000006,Bromley,Beckenham,NULL,Ivychurch Close,NULL,SE20,535016,170203,535050,170250,51.41477577,-0.059838506\n79282111,22/05/2011 15:53,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Brookdale Road,22819650,E17,537080,189590,537050,189550,51.58849742,-0.022649597\n79490111,22/05/2011 20:46,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000545,Nunhead,E09000028,Southwark,New Cross,NULL,Queens Road,NULL,SE15,NULL,NULL,534950,176750,NULL,NULL\n79755111,23/05/2011 10:47,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,NULL,Hillbury Road,NULL,SW17,528791,172274,528750,172250,51.43483543,-0.148550636\n80721111,24/05/2011 18:13,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED ON LEDGE OVER STREAM,Cat,Person (land line),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Braddock Close,NULL,TW7,515897,176005,515850,176050,51.47115008,-0.332740075\n81098111,25/05/2011 09:30,2011,2011/12,Special Service,1,2,260,520,DEER STUCK IN RAILINGS,Deer,Police,Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000152,Croham,E09000008,Croydon,Croydon,NULL,Wisborough Road,NULL,CR2,533887,162732,533850,162750,51.34790456,-0.078876678\n81574111,25/05/2011 19:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS IN GARDEN,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000301,Roxbourne,E09000015,Harrow,Northolt,1.00021E+11,Rosebery Avenue,21201953,HA2,512549,185886,512550,185850,51.56063549,-0.377770694\n81750111,25/05/2011 23:59,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN LIFT SHAFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Blakes Road,22500261,SE15,NULL,NULL,533450,177150,NULL,NULL\n81954111,26/05/2011 12:19,2011,2011/12,Special Service,1,3,260,780,HORSE STUCK IN A DITCH,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Bury Road,NULL,E4,539376,194813,539350,194850,51.63486678,0.012545269\n82278111,26/05/2011 21:30,2011,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,1.00021E+11,Standale Grove,21401849,HA4,508448,188637,508450,188650,51.58616486,-0.436058375\n82423111,27/05/2011 06:50,2011,2011/12,Special Service,3,9,260,2340,Redacted,Horse,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Sandpiper Close,NULL,E17,535940,191003,535950,191050,51.6014703,-0.03854908\n82679111,27/05/2011 16:28,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Bollo Bridge Road,20600203,W3,NULL,NULL,520050,179550,NULL,NULL\n83122111,28/05/2011 10:50,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN TO LOWER LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Cranley Place,21700129,SW7,NULL,NULL,526650,178650,NULL,NULL\n83207111,28/05/2011 13:39,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Allen Road,NULL,N16,533042,185793,533050,185750,51.55534351,-0.082338372\n83583111,28/05/2011 23:33,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN REAR OF FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,St. Michaels Close,22207901,E16,NULL,NULL,541650,181550,NULL,NULL\n83739111,29/05/2011 10:24,2011,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSITION ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Kildare Walk,22702109,E14,NULL,NULL,536950,181150,NULL,NULL\n83841111,29/05/2011 14:28,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Indoor Market,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000476,Boleyn,E09000025,Newham,Plaistow,10009002891,Queens Market,22208088,E13,541172,183689,541150,183650,51.53446279,0.034010859\n84387111,30/05/2011 13:22,2011,2011/12,Special Service,1,1,260,260,TO ASSIST THE RSCPA WITH CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Avenue,8401514,W9,NULL,NULL,525850,182950,NULL,NULL\n84904111,31/05/2011 08:46,2011,2011/12,Special Service,NULL,NULL,260,NULL,DUCKLINGS DOWN STORM DRAIN,Bird,Person (land line),\"Tunnel, subway\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Friars Mead,NULL,E14,538310,179118,538350,179150,51.49409505,-0.009017979\n84941111,31/05/2011 10:52,2011,2011/12,Special Service,1,1,260,260,DOG STRUGGLING IN POND,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Green Lane,NULL,RM8,546985,187025,546950,187050,51.56295514,0.119152713\n85459111,01/06/2011 07:56,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Parkhurst Road,21603373,N7,NULL,NULL,530350,185950,NULL,NULL\n85501111,01/06/2011 09:33,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN GARDEN,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000219,Eltham South,E09000011,Greenwich,Eltham,1.00021E+11,Restons Crescent,20801259,SE9,545130,174062,545150,174050,51.44695572,0.087080691\n85777111,01/06/2011 19:07,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN FROM UPPER FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,22905114,SW11,NULL,NULL,527150,176750,NULL,NULL\n86151111,02/06/2011 11:33,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCKLING TRAPPED IN DRAIN,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Cambridge Gate Mews,NULL,NW1,528813,182435,528850,182450,51.52614684,-0.144532273\n87221111,03/06/2011 19:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Blythe Vale,22004624,SE6,NULL,NULL,536850,173150,NULL,NULL\n87238111,03/06/2011 19:47,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Colehill Lane,21000227,SW6,NULL,NULL,524450,176750,NULL,NULL\n87636111,04/06/2011 10:44,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE - CAT FALLEN INTO ROOF GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Lichfield Road,22405720,TW9,NULL,NULL,519050,176750,NULL,NULL\n87750111,04/06/2011 13:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,Southwark,West Norwood,NULL,Croxted Road,22500662,SE21,NULL,NULL,532550,173250,NULL,NULL\n87997111,04/06/2011 19:15,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA FOR SWAN TRAPPED IN WIRE ON POND,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000540,East Walworth,E09000028,Southwark,Old Kent Road,NULL,Albany Road,NULL,SE5,533498,178006,533450,178050,51.48525823,-0.078712897\n89162111,06/06/2011 19:30,2011,2011/12,Special Service,1,2,260,520,DOG STRANDED ON ISLAND,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000200,Grange,E09000010,Enfield,Enfield,NULL,Cecil Road,NULL,EN2,532375,196298,532350,196250,51.64990226,-0.087991832\n90016111,08/06/2011 11:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Roland Gardens,21700466,SW7,NULL,NULL,526450,178250,NULL,NULL\n90649111,09/06/2011 16:54,2011,2011/12,Special Service,1,1,260,260,KITTEN UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,Melrose Avenue,20501194,SW16,NULL,NULL,531250,168950,NULL,NULL\n90700111,09/06/2011 18:52,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BOILER AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,Kensal Green,E09000005,Brent,North Kensington,NULL,Harrow Road,20201241,NW10,NULL,NULL,522650,182950,NULL,NULL\n90830111,09/06/2011 21:41,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN CHURCH,Cat,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011488,West Thornton,E09000008,Croydon,Norbury,2.00001E+11,Thornton Road,20502655,CR0,530862,167084,530850,167050,51.38771948,-0.120687671\n91020111,10/06/2011 07:01,2011,2011/12,Special Service,2,4,260,1040,SWAN TRAPPED IN GATE - WATER RESCUE LEVEL TWO,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000203,Jubilee,E09000010,Enfield,Edmonton,207178165,Picketts Lock Lane,20705131,N9,536277,193753,536250,193750,51.62610041,-0.032617754\n91105111,10/06/2011 11:26,2011,2011/12,Special Service,2,3,260,780,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000630,Abbey Road,E09000033,Westminster,West Hampstead,NULL,Henstridge Place,8400120,NW8,NULL,NULL,527050,183550,NULL,NULL\n91317111,10/06/2011 19:06,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000407,Coombe Vale,E09000021,Kingston upon Thames,New Malden,NULL,Traps Lane,21800979,KT3,NULL,NULL,521050,169750,NULL,NULL\n91631111,11/06/2011 10:54,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Exeter Gardens,22302833,IG1,NULL,NULL,542650,187550,NULL,NULL\n92154111,12/06/2011 07:34,2011,2011/12,Special Service,1,1,260,260,CAT WITH NECK STUCK IN GARDEN SEAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Hollybush Street,22207756,E13,NULL,NULL,541050,183050,NULL,NULL\n92664111,13/06/2011 11:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Kirkwood Road,22501432,SE15,NULL,NULL,534850,176350,NULL,NULL\n93036111,14/06/2011 00:35,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND MESH WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Goswell Road,NULL,EC1V,NULL,NULL,531950,182550,NULL,NULL\n93313111,14/06/2011 14:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Friars Lane,22403490,TW9,517515,174814,517550,174850,51.46011227,-0.309850557\n93338111,14/06/2011 15:30,2011,2011/12,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN BRIDGE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000126,Shortlands,E09000006,Bromley,Beckenham,NULL,Mays Hill Road,NULL,BR2,539604,169006,539650,169050,51.40290951,0.005623159\n93365111,14/06/2011 16:16,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Revelstoke Road,22105361,SW18,NULL,NULL,525050,172650,NULL,NULL\n93473111,14/06/2011 20:01,2011,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED IN GUTTER,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Cadogan Terrace,20900196,E9,NULL,NULL,536650,184550,NULL,NULL\n93532111,14/06/2011 22:01,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,Bow Road,22700175,E3,NULL,NULL,537350,182850,NULL,NULL\n94116111,15/06/2011 23:26,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Exeter Road,20702225,EN3,NULL,NULL,535850,196650,NULL,NULL\n94248111,16/06/2011 09:33,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,South Acton,E09000009,Ealing,Acton,NULL,King Edwards Gardens,20600982,W3,NULL,NULL,519350,180150,NULL,NULL\n94430111,16/06/2011 17:17,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Hornbeam Crescent,21500624,TW8,NULL,NULL,516950,177450,NULL,NULL\n95088111,17/06/2011 23:15,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CONTAINER,Cat,Person (land line),\"Large refuse/rubbish container (eg skip, paladin)\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Mirror Path,NULL,SE9,541420,172163,541450,172150,51.43082919,0.032971641\n95105111,18/06/2011 00:02,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Alderwick Drive,21500023,TW3,NULL,NULL,514650,175950,NULL,NULL\n95113111,18/06/2011 01:03,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN DUCTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Camberwell Church Street,22500395,SE5,NULL,NULL,532950,176750,NULL,NULL\n95210111,18/06/2011 08:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH DUCKLINGS TRAPPED IN DRAIN,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Butter Hill,NULL,SM6,528512,164927,528550,164950,51.36887064,-0.155221322\n95350111,18/06/2011 12:16,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Chalton Street,20400794,NW1,NULL,NULL,529850,182850,NULL,NULL\n95507111,18/06/2011 16:58,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Medway Close,22306129,IG1,NULL,NULL,544450,185150,NULL,NULL\n95839111,19/06/2011 11:56,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WALL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Greatdown Road,20600754,W7,NULL,NULL,515650,181950,NULL,NULL\n95966111,19/06/2011 16:55,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Overbrae,20302297,BR3,NULL,NULL,537350,170950,NULL,NULL\n96063111,19/06/2011 21:13,2011,2011/12,Special Service,1,1,260,260,DOG IN POND,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000442,Downham,E09000023,Lewisham,Bromley,10023230395,Beckenham Place Park,22007038,BR3,538659,171094,538650,171050,51.42190432,-0.007137475\n96161111,20/06/2011 05:02,2011,2011/12,Special Service,1,1,260,260,FERRET TRAPPED BEHIND KITCHEN UNIT,Ferret,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Hans Crescent,21700243,SW1X,NULL,NULL,527750,179450,NULL,NULL\n96639111,21/06/2011 01:06,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND WARDROBE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000471,Trinity,E09000024,Merton,Wimbledon,NULL,Haydons Road,22103210,SW19,NULL,NULL,526050,170950,NULL,NULL\n97137111,22/06/2011 00:32,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN HOLE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000619,Northcote,E09000032,Wandsworth,Battersea,NULL,Webb's Road,22905994,SW11,NULL,NULL,527850,174450,NULL,NULL\n97409111,22/06/2011 16:37,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Station Approach,NULL,HA4,511114,185402,511150,185450,51.55656957,-0.398616075\n97466111,22/06/2011 18:50,2011,2011/12,Special Service,1,1,260,260,DOG WITH PAW TRAPPED IN PAVEMENT,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000647,Warwick,E09000033,Westminster,Lambeth,NULL,Churton Street,NULL,SW1V,529330,178617,529350,178650,51.49171683,-0.138483596\n97530111,22/06/2011 20:42,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Finsbury Park Road,20900408,N4,NULL,NULL,531850,186550,NULL,NULL\n97547111,22/06/2011 21:26,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,North Hill Drive,21300103,RM3,553670,192583,553650,192550,51.61111291,0.217950246\n97930111,24/06/2011 13:23,2011,2011/12,Special Service,1,4,260,1040,CAT TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL\n97932111,24/06/2011 13:24,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000381,Tollington,E09000019,Islington,Holloway,5300091439,Tollington Park,21603933,N4,530858,187085,530850,187050,51.5674652,-0.113342272\n97935111,24/06/2011 13:33,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Orchardleigh Avenue,20702356,EN3,NULL,NULL,535050,197250,NULL,NULL\n98379111,25/06/2011 07:38,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK ON SCAFFOLDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Coopers Road,22500590,SE1,NULL,NULL,533950,178350,NULL,NULL\n98498111,25/06/2011 12:27,2011,2011/12,Special Service,1,2,260,520,DEER STUCK ON WOOD IN CANAL WATER RESCUE LEVEL ONE IMPLEMENTED,Deer,Police,River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Newell Street,NULL,E14,536672,181060,536650,181050,51.51194475,-0.031846037\n98564111,25/06/2011 14:08,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Ellement Close,21201857,HA5,NULL,NULL,511950,188750,NULL,NULL\n98593111,25/06/2011 15:12,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL\n99675111,26/06/2011 23:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN HEATING DUCT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Redlands Way,21901149,SW2,NULL,NULL,530550,173750,NULL,NULL\n100182111,27/06/2011 17:57,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000641,Marylebone High Street,E09000033,Westminster,Euston,10033583997,Marylebone High Street,8401405,W1U,528309,181734,528350,181750,51.51996161,-0.152048634\n100425111,27/06/2011 23:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Norwich Crescent,22306357,RM6,NULL,NULL,547050,188650,NULL,NULL\n100612111,28/06/2011 10:41,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED IN FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Purley,5870073935,Grove Lane,22602358,CR5,528795,160043,528750,160050,51.32491293,-0.15292353\n101026111,28/06/2011 17:52,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Bickerton Road,21600078,N19,NULL,NULL,529150,186550,NULL,NULL\n101143111,28/06/2011 21:25,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Linley Crescent,21301362,RM7,NULL,NULL,550150,189850,NULL,NULL\n101370111,29/06/2011 09:20,2011,2011/12,Special Service,1,1,260,260,BIRD IN DISTRESS  HANGING FROM TREE,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Victoria Road,NULL,TW13,510724,173056,510750,173050,51.44567931,-0.408109769\n101467111,29/06/2011 13:09,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Rosebery Avenue,NULL,EC1R,531182,182319,531150,182350,51.52455963,-0.110445711\n101755111,29/06/2011 21:04,2011,2011/12,Special Service,3,9,260,2340,Redacted,Deer,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Thomas Road,NULL,E14,537117,181441,537150,181450,51.51526084,-0.025289165\n102265111,30/06/2011 16:02,2011,2011/12,Special Service,1,1,260,260,DOG IN DISTRESS ON WINDOW LEDGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Ladbroke Grove,21700912,W11,NULL,NULL,524450,181050,NULL,NULL\n102981111,01/07/2011 17:54,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Bridges Court,NULL,SW11,526514,175953,526550,175950,51.46841173,-0.179976883\n103355111,02/07/2011 06:00,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTER,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Mare Street,20900658,E8,NULL,NULL,534950,184050,NULL,NULL\n103407111,02/07/2011 09:13,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN DOOR OF LIFT,Dog,Police,Nursing/Care Home/Hospice,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Aspinden Road,22500102,SE16,534902,178797,534950,178750,51.49203356,-0.058202438\n103458111,02/07/2011 11:29,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,Harrow Road,E09000033,Westminster,North Kensington,NULL,Ashmore Road,8400332,W9,NULL,NULL,524750,182350,NULL,NULL\n104190111,03/07/2011 09:54,2011,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034922123,High Road,22306010,IG1,544374,186725,544350,186750,51.56093357,0.081388218\n104229111,03/07/2011 11:46,2011,2011/12,Special Service,1,2,260,520,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Cornwall Gardens,21700116,SW7,NULL,NULL,525850,179150,NULL,NULL\n104367111,03/07/2011 15:03,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER JETTY,Cat,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Honeypot Lane,NULL,HA7,517992,190795,517950,190750,51.60364431,-0.297637445\n104609111,03/07/2011 20:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Duckett Street,22700429,E1,NULL,NULL,536050,181850,NULL,NULL\n104654111,03/07/2011 21:13,2011,2011/12,Special Service,1,1,260,260,PIGEON STUCK IN TOWER,Bird,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,6064299,Woodstock Terrace,22701364,E14,537797,180949,537750,180950,51.51067425,-0.015687723\n104926111,04/07/2011 10:22,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN WATER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Fore Street,NULL,N18,534046,192490,534050,192450,51.61528679,-0.065311972\n105043111,04/07/2011 14:38,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,20703847,N13,NULL,NULL,530650,192950,NULL,NULL\n105683111,05/07/2011 15:33,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN BATHROOM,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Thicket Road,20302199,SE20,NULL,NULL,534850,170650,NULL,NULL\n105919111,05/07/2011 21:51,2011,2011/12,Special Service,1,2,260,520,PIGEON STUCK IN NETTING UNDER BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000644,St. James's,E09000033,Westminster,Soho,10033534302,Embankment Place,8401314,WC2N,530406,180379,530450,180350,51.50730472,-0.122342442\n106042111,06/07/2011 08:02,2011,2011/12,Special Service,1,2,260,520,PREGNANT MARE FALLEN INTO DITCH,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000495,Aldborough,E09000026,Redbridge,Ilford,NULL,Billet Road,NULL,RM6,546750,189250,546750,189250,51.58300851,0.116693197\n106053111,06/07/2011 08:37,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER DOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011473,Norbury Park,E09000008,Croydon,Norbury,NULL,Green Lane,20503000,CR7,NULL,NULL,531850,169350,NULL,NULL\n106060111,06/07/2011 09:14,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,10033542018,Queensway,8401512,W2,525913,180671,525950,180650,51.51094693,-0.186943574\n106220111,06/07/2011 15:19,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL\n106256111,06/07/2011 16:20,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED INCHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Woodberry Down Estate,20950238,N4,NULL,NULL,532750,187750,NULL,NULL\n106411111,06/07/2011 21:19,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Brantwood Road,NULL,N17,533965,191661,533950,191650,51.60785644,-0.066797592\n106492111,07/07/2011 00:30,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN BASEMENT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Bury Walk,21700053,SW3,NULL,NULL,527150,178450,NULL,NULL\n106606111,07/07/2011 08:36,2011,2011/12,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Green Lane,NULL,W7,515079,179665,515050,179650,51.50421271,-0.343320137\n106696111,07/07/2011 11:57,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Knoll Drive,20025520,N14,NULL,NULL,528550,194550,NULL,NULL\n106920111,07/07/2011 19:59,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK IN  TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011466,Coulsdon Town,E09000008,Croydon,Purley,1.00021E+11,Chipstead Valley Road,20502600,CR5,529208,159465,529250,159450,51.31962485,-0.147208741\n107150111,08/07/2011 10:40,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Toucan Close,20204163,NW10,NULL,NULL,519350,182950,NULL,NULL\n107355111,08/07/2011 17:58,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Mason Close,22502807,SE16,NULL,NULL,534450,178450,NULL,NULL\n107462111,08/07/2011 21:31,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,10091777532,Markhouse Road,22856450,E17,536592,188195,536550,188150,51.57608048,-0.030231159\n107712111,09/07/2011 10:05,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Cottenham Park Road,22101632,SW20,NULL,NULL,522750,169950,NULL,NULL\n108094111,09/07/2011 21:39,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN INTO BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Coniger Road,21000233,SW6,NULL,NULL,525250,176250,NULL,NULL\n108228111,10/07/2011 02:13,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,177950,NULL,NULL\n108316111,10/07/2011 08:52,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE AT ROOF LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Ruislip Road East,NULL,UB6,NULL,NULL,514450,182250,NULL,NULL\n108373111,10/07/2011 11:33,2011,2011/12,Special Service,1,1,260,260,HORSE TRAPPED ON FENCE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000154,Fieldway,E09000008,Croydon,Addington,NULL,Lodge Lane,NULL,CR0,537814,162997,537850,162950,51.34934848,-0.022421741\n108460111,10/07/2011 13:56,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Thomas Lane,22001968,SE6,NULL,NULL,537550,173850,NULL,NULL\n108538111,10/07/2011 16:38,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Old Pye Street,8400146,SW1P,NULL,NULL,529750,179250,NULL,NULL\n108683111,10/07/2011 20:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD IN NETTING,Bird,Person (land line),Swimming Pool,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham High Street,NULL,SE13,538250,175750,538250,175750,51.46384407,-0.01119965\n108685111,10/07/2011 20:12,2011,2011/12,Special Service,2,3,260,780,HORSE IN CANAL,Horse,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Harbet Road,NULL,E4,536209,191844,536250,191850,51.60896258,-0.034340972\n108739111,10/07/2011 22:16,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH  INJURED CAT FALLEN FROM ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,Deptford,NULL,Deptford High Street,22000328,SE8,NULL,NULL,537150,177350,NULL,NULL\n108951111,11/07/2011 09:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED INSIDE GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Pembar Avenue,22864850,E17,536040,189664,536050,189650,51.58941416,-0.037624361\n109052111,11/07/2011 13:23,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN NETTING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Brouncker Road,20600274,W3,NULL,NULL,520150,179650,NULL,NULL\n110206111,13/07/2011 12:08,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Rockingham Parade,21401643,UB8,NULL,NULL,505150,183950,NULL,NULL\n110219111,13/07/2011 12:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,Marlborough Road,22207804,E7,NULL,NULL,541150,184450,NULL,NULL\n110278111,13/07/2011 15:13,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,Diban Avenue,21300320,RM12,552640,185847,552650,185850,51.55087105,0.200162648\n111131111,14/07/2011 22:22,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Barnfield Road,NULL,HA8,520267,190613,520250,190650,51.60152828,-0.264865466\n111448111,15/07/2011 11:51,2011,2011/12,Special Service,2,3,260,780,HORSE STUCK IN DITCH,Horse,Person (land line),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363\n111558111,15/07/2011 14:33,2011,2011/12,Special Service,1,1,260,260,INJURED FOX FALLEN THROUGH ROOF OF BUILDING,Fox,Person (land line),Hospital,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,6014098,Stepney Way,22701162,E1,534604,181593,534650,181550,51.51723055,-0.061425634\n111840111,15/07/2011 20:58,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING ON BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524588,184643,524550,184650,51.54693752,-0.204622646\n111842111,15/07/2011 21:02,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Hermitage Road,21103408,N4,NULL,NULL,532450,188150,NULL,NULL\n112187111,16/07/2011 12:29,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK UP A TREE,Cat,Other FRS,Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000323,Upminster,E09000016,Havering,Hornchurch,1.00021E+11,Howard Road,21301828,RM14,556246,186725,556250,186750,51.55777322,0.252519046\n112460111,16/07/2011 20:02,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN PIPE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Peckham,NULL,Lindley Estate,22501931,SE15,NULL,NULL,534250,177250,NULL,NULL\n112480111,16/07/2011 20:29,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BATH,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Peckham,NULL,Lindley Estate,22501931,SE15,NULL,NULL,534250,177250,NULL,NULL\n112710111,17/07/2011 09:44,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Fleeming Road,22836450,E17,NULL,NULL,536950,190350,NULL,NULL\n113013111,17/07/2011 20:39,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD TRAPPED IN TREE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Boyton Road,NULL,N8,NULL,NULL,530250,189650,NULL,NULL\n113396111,18/07/2011 17:25,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000481,East Ham North,E09000025,Newham,East Ham,NULL,Stanley Road,22200257,E12,NULL,NULL,542250,185050,NULL,NULL\n113720111,19/07/2011 11:04,2011,2011/12,Special Service,1,1,260,260,ASSIST LAS WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Blake Hall Crescent,NULL,E11,540485,187190,540450,187150,51.56609358,0.025509385\n113916111,19/07/2011 17:26,2011,2011/12,Special Service,1,1,260,260,TWO KITTENS TRAPPED IN WASHROOM,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL\n113956111,19/07/2011 18:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL IN HOUSE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Friary Estate,22501485,SE15,NULL,NULL,534150,177650,NULL,NULL\n114234111,20/07/2011 08:18,2011,2011/12,Special Service,1,1,260,260,SQUIRELL TRAPPED BEHIND RADIATOR,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Granleigh Road,22840650,E11,NULL,NULL,539150,186750,NULL,NULL\n114525111,20/07/2011 18:40,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH BIRD OF PREY STUCK IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,1.00021E+11,Amery Gardens,21300946,RM2,553577,189879,553550,189850,51.58684343,0.215425195\n115121111,21/07/2011 18:36,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Brightside Road,22001270,SE13,NULL,NULL,538950,174350,NULL,NULL\n115135111,21/07/2011 19:04,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED  IN BUSHES,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,217079792,St. Anns Road,21701005,W11,523820,180690,523850,180650,51.51157976,-0.217081948\n115364111,22/07/2011 08:55,2011,2011/12,Special Service,1,1,260,260,Redacted,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Bird,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Peregrine Road,NULL,IG6,546861,192100,546850,192150,51.60858828,0.119486693\n115409111,22/07/2011 10:18,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Cowley Road,NULL,UB8,505135,183645,505150,183650,51.54192261,-0.485350818\n115622111,22/07/2011 17:52,2011,2011/12,Special Service,1,1,260,260,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,Walpole,E09000009,Ealing,Ealing,12003057,Sherwood Close,20601548,W13,516614,180231,516650,180250,51.50898548,-0.321026675\n115829111,23/07/2011 00:46,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN HOLE IN REAR GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,White Horse Lane,22701335,E1,NULL,NULL,535850,181850,NULL,NULL\n116051111,23/07/2011 13:01,2011,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Other indoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Blairderry Road,NULL,SW2,530223,172839,530250,172850,51.43958602,-0.127753622\n116078111,23/07/2011 13:48,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED UNDER GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Locarno Road,20601074,UB6,NULL,NULL,514550,182450,NULL,NULL\n116826111,24/07/2011 19:39,2011,2011/12,Special Service,1,1,260,260,FERRETT TRAPPED BETWEEN TWO BRICK WALLS,Ferret,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Warrington Road,20501539,CR0,NULL,NULL,531650,165150,NULL,NULL\n116835111,24/07/2011 19:49,2011,2011/12,Special Service,1,1,260,260,HORSE IN WATERWAY,Horse,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Advent Way,NULL,N18,535843,192412,535850,192450,51.61415513,-0.03940338\n117319111,25/07/2011 16:35,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN DUCTING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,St. Peter's Way,20900960,N1,NULL,NULL,533450,184250,NULL,NULL\n117331111,25/07/2011 16:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,NULL,Clarence Street,NULL,KT1,517750,169250,517750,169250,51.41005638,-0.308318681\n118503111,27/07/2011 14:28,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,West End Avenue,21201906,HA5,NULL,NULL,511950,189250,NULL,NULL\n118813111,27/07/2011 23:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Eliot Park,22005585,SE13,NULL,NULL,538450,175950,NULL,NULL\n118971111,28/07/2011 07:29,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Police,Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000203,Jubilee,E09000010,Enfield,Edmonton,207097515,Lingfield Gardens,20704440,N9,534845,194856,534850,194850,51.63635749,-0.052868051\n119421111,28/07/2011 20:26,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN GUTTERING OF HOUSE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Edith Road,21000296,W14,NULL,NULL,524350,178550,NULL,NULL\n119475111,28/07/2011 21:32,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEADTRAPPED IN RAILINGS,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,NULL,Middle Park Avenue,NULL,SE9,542034,173616,542050,173650,51.44373246,0.042380415\n120356111,30/07/2011 09:45,2011,2011/12,Special Service,1,2,260,520,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Brackenbury Road,NULL,N2,526274,189786,526250,189750,51.59278292,-0.178474307\n121066111,31/07/2011 11:12,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Stacey Street,NULL,N7,531132,186120,531150,186150,51.55872952,-0.109750714\n121425111,31/07/2011 21:33,2011,2011/12,Special Service,1,1,260,260,INJURED CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Holloway Road,21606449,N19,NULL,NULL,529550,186650,NULL,NULL\n122415111,02/08/2011 10:51,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,1.00021E+11,Alexandra Road,21104163,N8,531155,189727,531150,189750,51.59113839,-0.108074469\n122826111,02/08/2011 22:10,2011,2011/12,Special Service,NULL,NULL,260,NULL,FOX WITH HEAD STUCK IN WIRE FENCING,Fox,Person (land line),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Chingford,1.00023E+11,Long Leys,22853650,E4,537923,191703,537950,191750,51.60727858,-0.009659447\n123097111,03/08/2011 10:42,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Chasewood Avenue,20702592,EN2,NULL,NULL,531650,197550,NULL,NULL\n123936111,04/08/2011 13:52,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,1.00021E+11,Croham Valley Road,20502623,CR2,534512,163424,534550,163450,51.35397575,-0.06964627\n124103111,04/08/2011 19:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED ON EAVES OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Clayfarm Road,20800346,SE9,NULL,NULL,544250,172650,NULL,NULL\n124193111,04/08/2011 22:14,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN FOOTINGS DITCH,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000352,Feltham West,E09000018,Hounslow,Feltham,1.00022E+11,Southcote Avenue,21501033,TW13,510003,172693,510050,172650,51.44255695,-0.418593064\n124499111,05/08/2011 14:54,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000297,Pinner,E09000015,Harrow,Harrow,NULL,Chapel Lane,NULL,HA5,511750,189750,511750,189750,51.59552446,-0.388063255\n124950111,06/08/2011 03:54,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RAFTERS,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,10023599451,Heathway,19900172,RM10,549069,184707,549050,184750,51.54158157,0.148214917\n124988111,06/08/2011 06:13,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN ROOF,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,10023599451,Heathway,19900172,RM10,549069,184707,549050,184750,51.54158157,0.148214917\n125145111,06/08/2011 13:06,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Wyatt Road,22207969,E7,NULL,NULL,540350,184650,NULL,NULL\n125959111,07/08/2011 11:17,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UP A TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,Brunswick Park,E09000003,Barnet,Southgate,NULL,Cambstone Close,NULL,N11,528419,193689,528450,193650,51.62737313,-0.146097301\n126149111,07/08/2011 16:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Violet Avenue,21402055,UB8,NULL,NULL,506550,181550,NULL,NULL\n126248111,07/08/2011 19:59,2011,2011/12,Special Service,1,1,260,260,DOG ON WINDOW LEDGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Bruce Road,22700213,E3,NULL,NULL,537850,182750,NULL,NULL\n126528111,08/08/2011 00:56,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300083326,Sonderburg Road,21603814,N7,530968,186516,530950,186550,51.56232631,-0.1119678\n126897111,08/08/2011 15:46,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Redhill Street,20400768,NW1,NULL,NULL,528850,183050,NULL,NULL\n126926111,08/08/2011 16:39,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Hartland Road,20202446,NW6,NULL,NULL,524650,183450,NULL,NULL\n127320111,08/08/2011 20:16,2011,2011/12,Special Service,1,1,260,260,PUPPY WITH LEG TRAPPED IN GARDEN SEAT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Epsom Close,20600632,UB5,NULL,NULL,512750,185050,NULL,NULL\n129497111,09/08/2011 17:06,2011,2011/12,Special Service,1,2,260,520,CAT STUCK BEHIND GAS FIRE,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000454,Whitefoot,E09000023,Lewisham,Bromley,10070773801,Reigate Road,22003617,BR1,539750,172250,539750,172250,51.43202481,0.008998771\n131161111,10/08/2011 22:59,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Hopwood Road,22501291,SE17,NULL,NULL,532850,177950,NULL,NULL\n131582111,11/08/2011 17:14,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA INSPECTOR WITH CAT STUCK ON BALCONY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Belsize Park,20400424,NW3,NULL,NULL,526850,184750,NULL,NULL\n131625111,11/08/2011 19:00,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Beaconsfield Road,22500180,SE17,NULL,NULL,533050,178050,NULL,NULL\n131765111,11/08/2011 23:31,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHUTTERS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000182,Northfield,E09000009,Ealing,Ealing,12070254,Salisbury Road,20601502,W13,516612,179627,516650,179650,51.5035573,-0.321255143\n133497111,14/08/2011 21:27,2011,2011/12,Special Service,1,1,260,260,FOX STUCK IN BASEMENT AREA   RSPCA IN ATTENDANCE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Grove Park,22501164,SE5,NULL,NULL,533550,176050,NULL,NULL\n133783111,15/08/2011 12:23,2011,2011/12,Special Service,2,5,260,1300,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Old Mill Lane,NULL,UB8,504970,181403,504950,181450,51.52180139,-0.488397217\n134060111,15/08/2011 21:19,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Benbow Waye,NULL,UB8,505106,181807,505150,181850,51.5254074,-0.486317223\n134327111,16/08/2011 12:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,NULL,South Lane,21800871,KT3,NULL,NULL,520950,167150,NULL,NULL\n134386111,16/08/2011 14:27,2011,2011/12,Special Service,1,2,260,520,HORSE TRAPPED IN HORSEBOX,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Buckhurst Road,20301435,TN16,543923,158209,543950,158250,51.30481082,0.063309827\n134717111,17/08/2011 00:11,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO FOX STUCK BEHIND FRIDGE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,College Gardens,22827900,E4,NULL,NULL,538050,194450,NULL,NULL\n134913111,17/08/2011 11:46,2011,2011/12,Special Service,1,2,260,520,PUPPY WITH PAW TRAPPED IN SINK PLUG HOLE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000053,Golders Green,E09000003,Barnet,Hendon,NULL,Woodlands,20047240,NW11,NULL,NULL,524150,188550,NULL,NULL\n135157111,17/08/2011 19:02,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN WINE RACK,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000317,Pettits,E09000016,Havering,Romford,1.00023E+11,Pettits Lane North,21301497,RM1,551176,190578,551150,190550,51.59377304,0.181097417\n135436111,18/08/2011 10:45,2011,2011/12,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Queen Caroline Street,21000660,W6,NULL,NULL,523050,178150,NULL,NULL\n135671111,18/08/2011 17:46,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Belmont Park Road,22815400,E10,NULL,NULL,538050,188150,NULL,NULL\n135706111,18/08/2011 18:41,2011,2011/12,Special Service,1,1,260,260,CAT IN RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Seely Road,NULL,SW17,528168,170798,528150,170750,51.42171137,-0.158041903\n135743111,18/08/2011 19:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN NETTING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Lea Hall Road,22850450,E10,NULL,NULL,537350,187350,NULL,NULL\n136502111,20/08/2011 06:18,2011,2011/12,Special Service,1,1,260,260,YOUNG CAT STUCK BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Henchman Street,21000433,W12,NULL,NULL,521750,181350,NULL,NULL\n136585111,20/08/2011 11:03,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN SHOP SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,Homerton,E09000012,Hackney,Homerton,1.00023E+11,Morning Lane,20900706,E9,535564,184753,535550,184750,51.5453975,-0.046382631\n136602111,20/08/2011 11:53,2011,2011/12,Special Service,1,1,260,260,DOG WITH LEG STUCK IN IRON ORNAMENT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Stevens Road,19900311,RM8,NULL,NULL,546950,186350,NULL,NULL\n137564111,22/08/2011 07:13,2011,2011/12,Special Service,1,2,260,520,CAT STUCK ON WASHING LINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Goresbrook Road,19900853,RM9,NULL,NULL,548350,183850,NULL,NULL\n137685111,22/08/2011 12:15,2011,2011/12,Special Service,1,1,260,260,BABY FOX TRAPPED BEHIND SHED AND WALL,Fox,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000604,Leyton,E09000031,Waltham Forest,Leyton,10009145394,Beaumont Road,22813550,E10,537999,187802,537950,187850,51.5722067,-0.010093733\n137725111,22/08/2011 13:35,2011,2011/12,Special Service,1,2,260,520,RUNNING CALL TO CAT TRAPPED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Lonsdale Avenue,NULL,E6,542191,182374,542150,182350,51.52239078,0.048162048\n137793111,22/08/2011 16:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000300,Rayners Lane,E09000015,Harrow,Harrow,NULL,Rayners Lane,NULL,HA5,512958,187606,512950,187650,51.57601322,-0.371320384\n137892111,22/08/2011 19:18,2011,2011/12,Special Service,1,1,260,260,PERSON SHUT IN LIFT EMERGENCY,Unknown - Heavy Livestock Animal,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000647,Warwick,E09000033,Westminster,Chelsea,1.00023E+11,Eccleston Place,8401172,SW1W,528716,178865,528750,178850,51.49408557,-0.147232491\n138096111,23/08/2011 00:19,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,NULL,Hanbury Street,22700597,E1,NULL,NULL,534350,181950,NULL,NULL\n138428111,23/08/2011 19:11,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN PROPERTY BENEATH,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Brigstock Road,20500721,CR7,NULL,NULL,531650,167950,NULL,NULL\n138462111,23/08/2011 20:46,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Silvermere Road,22001896,SE6,NULL,NULL,537550,174150,NULL,NULL\n138710111,24/08/2011 11:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Belmont Road,20500674,SE25,NULL,NULL,534850,167650,NULL,NULL\n138712111,24/08/2011 12:00,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Islington,5300006446,Barnsbury Park,21604200,N1,531232,184355,531250,184350,51.54284494,-0.108966932\n138769111,24/08/2011 14:30,2011,2011/12,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Larkspur Close,NULL,HA4,508047,187866,508050,187850,51.57931199,-0.442081029\n138840111,24/08/2011 16:55,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA  WITH  CAT   IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207129172,Park Road,20702363,EN3,536386,199398,536350,199350,51.67680053,-0.028844069\n138855111,24/08/2011 17:29,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,East End Road,20013420,N2,NULL,NULL,526350,189650,NULL,NULL\n138992111,24/08/2011 23:08,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN BRANCH OF TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12072192,Glasse Close,20600722,W13,516102,180955,516150,180950,51.51559791,-0.328162673\n139277111,25/08/2011 16:00,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Queensthorpe Road,22001820,SE26,NULL,NULL,535650,171750,NULL,NULL\n139322111,25/08/2011 17:08,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN METAL BARS OF CHILDS SWING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Crowhurst Way,NULL,BR5,547454,167710,547450,167750,51.38928033,0.117859006\n139930111,26/08/2011 20:21,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER PORTAKABIN,Dog,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Gomm Road,NULL,SE16,535323,179204,535350,179250,51.49559048,-0.05198591\n140197111,27/08/2011 11:39,2011,2011/12,Special Service,1,1,260,260,FOX  LOCKED IN COURTYARD,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,6033588,Roman Road,22701028,E2,535441,182899,535450,182850,51.52876654,-0.048867994\n140677111,28/08/2011 07:29,2011,2011/12,Special Service,1,1,260,260,HAMSTER TRAPPED UNDER FLOORBOARD,Hamster,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Sydenham Park,22002956,SE26,NULL,NULL,535150,172050,NULL,NULL\n140714111,28/08/2011 09:40,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Birdhurst Rise,20501922,CR2,NULL,NULL,533050,164250,NULL,NULL\n140876111,28/08/2011 15:46,2011,2011/12,Special Service,1,1,260,260,DOG STUCK IN STREAM,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,Graveney,E09000024,Merton,Mitcham,NULL,Rural Way,NULL,SW16,528902,170491,528950,170450,51.41878616,-0.147602676\n141320111,29/08/2011 14:11,2011,2011/12,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Hilliard Road,21400980,HA6,NULL,NULL,509850,190950,NULL,NULL\n141796111,30/08/2011 10:15,2011,2011/12,Special Service,1,1,260,260,BUDGIE TRAPPED BEHIND WALL UNIT,Budgie,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011232,Thamesmead East,E09000004,Bexley,Plumstead,NULL,St. Georges Close,20101791,SE28,NULL,NULL,547950,181350,NULL,NULL\n141815111,30/08/2011 11:02,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Buttsbury Road,22302672,IG1,NULL,NULL,544350,185050,NULL,NULL\n141926111,30/08/2011 15:25,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL AND SHED,Dog,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207048371,Somerset Road,20704746,N18,533625,192330,533650,192350,51.61394908,-0.071449794\n141934111,30/08/2011 15:47,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Mitford Road,21603566,N19,NULL,NULL,530350,186750,NULL,NULL\n141999111,30/08/2011 18:53,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,1.00022E+11,Redan Terrace,21901148,SE5,531964,176247,531950,176250,51.46981036,-0.101449112\n143432111,02/09/2011 11:37,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Finchley,200106214,St. Andrew's Close,20037920,N12,526088,192839,526050,192850,51.62026123,-0.180061064\n144141111,03/09/2011 14:13,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,Thamesfield,E09000032,Wandsworth,Wandsworth,NULL,Sefton Street,22904553,SW15,NULL,NULL,523250,175950,NULL,NULL\n144171111,03/09/2011 15:48,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Railway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000126,Shortlands,E09000006,Bromley,Beckenham,1.00023E+11,Shortlands Road,20302018,BR2,539334,169330,539350,169350,51.40588729,0.001871381\n144317111,03/09/2011 20:53,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL  AND GARAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Gipsy Road,20100597,DA16,NULL,NULL,547550,176150,NULL,NULL\n144578111,04/09/2011 10:53,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN GUTTERING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009381,London Fields,E09000012,Hackney,Bethnal Green,NULL,Mare Street,20900658,E8,NULL,NULL,534850,183550,NULL,NULL\n144750111,04/09/2011 16:36,2011,2011/12,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Poplar,NULL,Bruce Road,22700213,E3,NULL,NULL,537850,182750,NULL,NULL\n145548111,06/09/2011 09:19,2011,2011/12,Special Service,1,1,260,260,PIGEON ENTANGLED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009293,Bread Street,E09000001,City of London,Dowgate,95503563,Cheapside,8100124,EC2V,532126,181233,532150,181250,51.51458024,-0.097253259\n146417111,07/09/2011 20:59,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,1.00023E+11,Hardwick Close,21202225,HA7,517199,192269,517150,192250,51.6170577,-0.308590074\n147948111,10/09/2011 13:43,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,White Horse Lane,NULL,E1,NULL,NULL,535750,181950,NULL,NULL\n147980111,10/09/2011 14:37,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOOR IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Canterbury Grove,21900286,SE27,NULL,NULL,531450,172150,NULL,NULL\n148042111,10/09/2011 17:07,2011,2011/12,Special Service,1,3,260,780,KITTEN STUCK IN VENT - DISTRESSED,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Tivoli Gardens,20801502,SE18,NULL,NULL,542250,178850,NULL,NULL\n148636111,11/09/2011 20:33,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN RAZOR WIRE,Cat,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05011468,Fairfield,E09000008,Croydon,Croydon,10014050277,Sydenham Road,20501457,CR0,532662,166379,532650,166350,51.38096656,-0.095097529\n148729111,12/09/2011 00:35,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011228,Northumberland Heath,E09000004,Bexley,Erith,NULL,Frinsted Road,20100579,DA8,NULL,NULL,550750,177250,NULL,NULL\n148932111,12/09/2011 13:07,2011,2011/12,Special Service,1,1,260,260,FOX WITH SEVERE LEG INJURIES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Avalon Road,20300005,BR6,NULL,NULL,547050,165750,NULL,NULL\n149329111,13/09/2011 08:48,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN LIFT AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,St. Andrew's Road,NULL,E17,NULL,NULL,536150,190150,NULL,NULL\n149895111,14/09/2011 09:51,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Windmill Hill,NULL,EN2,532188,196734,532150,196750,51.65386421,-0.090528527\n149899111,14/09/2011 10:12,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN CONCRETE TUNNEL,Dog,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Argyle Road,NULL,W13,516290,182445,516250,182450,51.52895099,-0.324962907\n149998111,14/09/2011 13:55,2011,2011/12,Special Service,1,1,260,260,CAT WITH LEG TRAPPED BEHIND RADIATOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Clapham,NULL,Moberly Road,21900972,SW4,NULL,NULL,529750,173950,NULL,NULL\n150645111,15/09/2011 15:19,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Fairmead Crescent,20015180,HA8,NULL,NULL,520250,193050,NULL,NULL\n151026111,16/09/2011 09:28,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN TOP OF SPIKY FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,Hoxton West,E09000012,Hackney,Shoreditch,NULL,Underwood Street,20901023,N1,NULL,NULL,532550,182950,NULL,NULL\n151278111,16/09/2011 18:42,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK BETWEEN WALLS,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,19900241,RM8,NULL,NULL,546650,184750,NULL,NULL\n151524111,17/09/2011 06:18,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT LIGHTWELL,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,177950,NULL,NULL\n151873111,17/09/2011 19:59,2011,2011/12,Special Service,1,4,260,1040,PUPPY WITH HEAD STUCK BETWEEN CONCRETE POSTS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Shaw Gardens,19900575,IG11,NULL,NULL,548150,183350,NULL,NULL\n152127111,18/09/2011 11:11,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Northington Street,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL\n152249111,18/09/2011 16:08,2011,2011/12,Special Service,1,1,260,260,CAT CAUGHT BETWEEN WALL OUTSIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,King Street,22403584,TW1,NULL,NULL,516150,173150,NULL,NULL\n152551111,19/09/2011 06:03,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN GATE,Fox,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00022E+11,Warkworth Gardens,21501169,TW7,516195,177259,516150,177250,51.48235983,-0.32803963\n152619111,19/09/2011 10:25,2011,2011/12,Special Service,1,1,260,260,INJURED PARROT STUCK IN TREE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,1.00021E+11,Kingsley Wood Drive,20800865,SE9,542966,172225,542950,172250,51.43099875,0.055220543\n152635111,19/09/2011 10:57,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH  CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,Markhouse,E09000031,Waltham Forest,Walthamstow,NULL,Clacton Road,NULL,E17,536350,188518,536350,188550,51.57904147,-0.033596225\n152827111,19/09/2011 18:51,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN POND,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Markmanor Avenue,NULL,E17,536542,187653,536550,187650,51.57122209,-0.031162577\n152861111,19/09/2011 19:36,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,King Street,22403584,TW1,NULL,NULL,516150,173150,NULL,NULL\n152912111,19/09/2011 21:38,2011,2011/12,Special Service,1,1,260,260,DOG FALLEN DOWN STORM DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000565,Sutton North,E09000029,Sutton,Sutton,NULL,Grennell Road,NULL,SM1,526380,165422,526350,165450,51.37379702,-0.185656256\n153173111,20/09/2011 11:53,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO ASSIST RSPCA WITH BIRDS IN DISTRESS,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,Barking,NULL,Jenkins Lane,NULL,IG11,544456,182324,544450,182350,51.52136739,0.080766184\n153602111,21/09/2011 07:55,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP CHIMNEY,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Sussex Way,21606975,N19,NULL,NULL,530250,186750,NULL,NULL\n154104111,22/09/2011 08:38,2011,2011/12,Special Service,1,1,260,260,PUPPY STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000433,Thornton,E09000022,Lambeth,West Norwood,NULL,Parkthorne Road,21901070,SW12,NULL,NULL,529750,173650,NULL,NULL\n154310111,22/09/2011 17:28,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN FIRE PLACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000425,Larkhall,E09000022,Lambeth,Brixton,NULL,Hemberton Road,21900697,SW9,NULL,NULL,530250,175850,NULL,NULL\n154534111,22/09/2011 23:19,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Mount Ephraim Road,21900992,SW16,NULL,NULL,529850,172450,NULL,NULL\n155304111,24/09/2011 08:15,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Avelon Road,NULL,RM13,552561,184014,552550,184050,51.53442292,0.198230978\n156058111,25/09/2011 13:13,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN SEWER PIPE,Cat,Police,House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Panmure Road,22004856,SE26,NULL,NULL,534650,172150,NULL,NULL\n156357111,25/09/2011 21:52,2011,2011/12,Special Service,1,2,260,520,HORSES LOOSE IN ROADWAY,Horse,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,East Hall Lane,NULL,RM13,553512,181054,553550,181050,51.5075704,0.21064327\n156406111,25/09/2011 23:44,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Heath Park Road,21300409,RM2,NULL,NULL,552350,188850,NULL,NULL\n156554111,26/09/2011 10:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSCPA,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Strode Road,NULL,NW10,522425,184571,522450,184550,51.54676336,-0.235825765\n156731111,26/09/2011 16:44,2011,2011/12,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000062,West Hendon,E09000003,Barnet,Hendon,200222074,Prince Charles Drive,20034920,NW4,522963,187848,522950,187850,51.576098,-0.226925119\n157758111,28/09/2011 10:50,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Coleraine Road,21103124,N8,NULL,NULL,531250,189750,NULL,NULL\n157763111,28/09/2011 10:55,2011,2011/12,Special Service,1,3,260,780,HORSE STUCK IN RIVER,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555415,188368,555450,188350,51.57276421,0.241266584\n157873111,28/09/2011 15:14,2011,2011/12,Special Service,1,1,260,260,HAMPSTER STUCK IN CAR ENGINE,Hamster,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00021E+11,Woodstock Gardens,21402215,UB4,509542,181882,509550,181850,51.52523885,-0.422376831\n157969111,28/09/2011 18:32,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Mountfield Way,20301224,BR5,NULL,NULL,547250,168150,NULL,NULL\n158705111,29/09/2011 21:41,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN HEDGE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,6014538,Hobsons Place,22701982,E1,534142,181914,534150,181950,51.52022505,-0.067957984\n158724111,29/09/2011 22:16,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK IN DRAIN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Broom Gardens,20500409,CR0,NULL,NULL,537350,165150,NULL,NULL\n159671111,01/10/2011 11:09,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Woodfield Way,NULL,N11,529526,191315,529550,191350,51.60578568,-0.130990772\n159970111,01/10/2011 18:36,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK BETWEEN OUTDOOR STORAGE UNITS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,46026688,Folkestone Road,22200619,E6,543465,182947,543450,182950,51.52721775,0.066745717\n160387111,02/10/2011 08:32,2011,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Dovedale Close,21400612,UB9,NULL,NULL,505050,190150,NULL,NULL\n160437111,02/10/2011 10:45,2011,2011/12,Special Service,1,1,260,260,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000556,Beddington South,E09000029,Sutton,Wallington,5870087480,Stratton Avenue,22602644,SM6,529702,162613,529750,162650,51.3478044,-0.138978905\n160557111,02/10/2011 14:34,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN AN IRON GATE,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000164,South Norwood,E09000008,Croydon,Woodside,NULL,Ross Road,NULL,SE25,533420,169083,533450,169050,51.40508867,-0.083196278\n160797111,02/10/2011 20:00,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN CAR WHEEL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000141,King's Cross,E09000007,Camden,Euston,NULL,Cromer Street,NULL,WC1H,530550,182706,530550,182750,51.52818383,-0.11940715\n161580111,03/10/2011 20:23,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN HOUSE AND DRAINING AREA,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000165,Thornton Heath,E09000008,Croydon,Norbury,NULL,Grange Road,NULL,CR7,NULL,NULL,532750,168450,NULL,NULL\n161690111,04/10/2011 00:08,2011,2011/12,Special Service,1,1,260,260,\"INJURED CAT, TANGLED UP IN TREE\",Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,1.00021E+11,Bethune Road,20900135,N16,533196,187373,533150,187350,51.56950573,-0.079520845\n162371111,05/10/2011 06:37,2011,2011/12,Special Service,1,1,260,260,FOX TRAPPED IN GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05009373,Hackney Downs,E09000012,Hackney,Stoke Newington,1.00021E+11,Northwold Road,20900753,E5,534385,186636,534350,186650,51.56260061,-0.062656792\n163506111,07/10/2011 07:25,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000510,Newbury,E09000026,Redbridge,Ilford,NULL,Aldborough Road South,NULL,IG3,545604,188572,545650,188550,51.57721342,0.099883968\n164133111,08/10/2011 09:36,2011,2011/12,Special Service,1,4,260,1040,HORSE IN DITCH,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,128032260,Barwell Lane,21800115,KT9,516963,163096,516950,163050,51.3549067,-0.32165141\n164240111,08/10/2011 13:34,2011,2011/12,Special Service,1,1,260,260,HAMSTER BELIEED TRAPPED BEHIND KITCHEN SINK,Hamster,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Drayton Green Road,20602212,W13,NULL,NULL,516750,180650,NULL,NULL\n164921111,09/10/2011 15:22,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALL AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Corfield Road,20706133,N21,NULL,NULL,530550,195950,NULL,NULL\n165312111,10/10/2011 09:22,2011,2011/12,Special Service,1,1,260,260,HORSE TRAPPED IN STABLE DOORS,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,1.00023E+11,Frinton Drive,22838400,IG8,538729,191091,538750,191050,51.60158145,0.001730321\n165561111,10/10/2011 16:46,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000335,Northwood,E09000017,Hillingdon,Ruislip,NULL,Rofant Road,21401648,HA6,NULL,NULL,509250,191750,NULL,NULL\n165693111,10/10/2011 19:49,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO TRAPPED BIRD,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000170,Acton Central,E09000009,Ealing,Acton,NULL,Twyford Avenue,20601782,W3,NULL,NULL,519350,180850,NULL,NULL\n166070111,11/10/2011 16:06,2011,2011/12,Special Service,1,1,260,260,DISTRESSED CAT TRAPPED IN LOCKUP GARAGE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000139,Kentish Town,E09000007,Camden,Kentish Town,5034883,Falkland Road,20400365,NW5,529270,185288,529250,185250,51.55168209,-0.136901078\n166170111,11/10/2011 18:56,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHEDS,Cat,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000375,Holloway,E09000019,Islington,Holloway,5300075952,Prospect Place,21610015,N7,530257,185557,530250,185550,51.55387274,-0.122573983\n167033111,13/10/2011 12:24,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12074497,Cavendish Avenue,20600336,W13,516006,181667,516050,181650,51.52201688,-0.32931127\n167641111,14/10/2011 13:22,2011,2011/12,Special Service,1,1,260,260,DOG STUCK ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Prince Regent Road,21500896,TW3,NULL,NULL,513950,175850,NULL,NULL\n167687111,14/10/2011 14:42,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Broadwood Avenue,21400255,HA4,NULL,NULL,509150,188350,NULL,NULL\n167894111,14/10/2011 19:29,2011,2011/12,Special Service,1,2,260,520,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Hood Avenue,20301156,BR5,NULL,NULL,546750,167750,NULL,NULL\n167915111,14/10/2011 19:52,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Tenham Avenue,21901354,SW2,529744,173012,529750,173050,51.44125062,-0.134578076\n168388111,15/10/2011 14:48,2011,2011/12,Special Service,1,1,260,260,BIRDS TRAPPED BEHIND WIRE MESH,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000175,East Acton,E09000009,Ealing,Acton,12096646,Western Avenue,20602102,W3,519520,182315,519550,182350,51.52710928,-0.278466052\n168440111,15/10/2011 16:23,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000647,Warwick,E09000033,Westminster,Chelsea,NULL,Winchester Street,8400290,SW1V,NULL,NULL,528850,178350,NULL,NULL\n168552111,15/10/2011 19:29,2011,2011/12,Special Service,1,1,260,260,DOG WITH PAW STUCK IN BENCH,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,100045801,Movers Lane,19900540,IG11,545750,183750,545750,183750,51.53384858,0.099993441\n168975111,16/10/2011 13:09,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT AREA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Cranbury Road,21000244,SW6,NULL,NULL,525750,176250,NULL,NULL\n169000111,16/10/2011 13:54,2011,2011/12,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Selway Close,21401724,HA5,NULL,NULL,510850,189550,NULL,NULL\n169037111,16/10/2011 15:20,2011,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN WALL AND DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000210,Town,E09000010,Enfield,Enfield,NULL,Tenniswood Road,20702989,EN1,NULL,NULL,533050,197750,NULL,NULL\n169615111,17/10/2011 15:55,2011,2011/12,Special Service,1,1,260,260,TWO DOGS TRAPPED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Swindon Lane,21300122,RM3,NULL,NULL,554750,192350,NULL,NULL\n170149111,18/10/2011 17:16,2011,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000631,Bayswater,E09000033,Westminster,Paddington,NULL,Porchester Road,8400189,W2,NULL,NULL,525850,181250,NULL,NULL\n170312111,18/10/2011 21:45,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN SOFA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Caldwell Street,21900272,SW9,NULL,NULL,531050,177050,NULL,NULL\n170618111,19/10/2011 14:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SWAN TRAPPED BEHIND WIRE FENCE,Bird,Person (mobile),Heathland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Bridgewater Road,NULL,HA0,517608,184147,517650,184150,51.54397539,-0.305403341\n171429111,20/10/2011 21:15,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,10009805290,Ossory Road,22501843,SE1,534122,177866,534150,177850,51.48385255,-0.069784557\n171719111,21/10/2011 14:05,2011,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,5049795,Camden Terrace,20400531,NW1,529755,184675,529750,184650,51.54606196,-0.13013578\n171759111,21/10/2011 15:42,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED ON SHOP SIGN,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000104,Wembley Central,E09000005,Brent,Wembley,202102272,Ealing Road,20200486,HA0,518179,184225,518150,184250,51.54455734,-0.297146894\n172157111,22/10/2011 10:57,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BY FENCE,Cat,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elmstead Avenue,NULL,HA9,518701,186847,518750,186850,51.56801332,-0.288738891\n172300111,22/10/2011 15:21,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00021E+11,Wick Road,20901074,E9,536014,184760,536050,184750,51.54535234,-0.039894308\n172709111,23/10/2011 09:10,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Bridgwater Road,NULL,RM3,NULL,NULL,553550,192150,NULL,NULL\n172822111,23/10/2011 14:22,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547521,174952,547550,174950,51.4543355,0.12183349\n172960111,23/10/2011 18:39,2011,2011/12,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.0002E+11,Royal Parade,20302443,BR7,544425,170184,544450,170150,51.41228933,0.075361791\n173067111,23/10/2011 21:33,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000627,Wandsworth Common,E09000032,Wandsworth,Wandsworth,NULL,Bassingham Road,22900298,SW18,NULL,NULL,526350,173850,NULL,NULL\n173472111,24/10/2011 17:56,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED INCAR  DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,NULL,Eden Park Avenue,NULL,BR3,537309,168012,537350,168050,51.39453656,-0.027733103\n173496111,24/10/2011 18:16,2011,2011/12,Special Service,1,2,260,520,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000328,Charville,E09000017,Hillingdon,Hillingdon,NULL,Weald Way,21402095,UB4,NULL,NULL,509150,182950,NULL,NULL\n174017111,25/10/2011 17:42,2011,2011/12,Special Service,1,2,260,520,DOG DOWN BADGER HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Coombe Road,NULL,CR0,534101,164716,534150,164750,51.36568367,-0.075058645\n174036111,25/10/2011 18:29,2011,2011/12,Special Service,1,1,260,260,CAT STUCK IN DRAIN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Finchley,10022937575,Coppetts Road,21107082,N10,527773,191065,527750,191050,51.60393915,-0.156381079\n174525111,26/10/2011 19:13,2011,2011/12,Special Service,1,2,260,520,DOG WEDGED IN BETWEEN TWO BUILDINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000322,Squirrel's Heath,E09000016,Havering,Hornchurch,NULL,Northumberland Avenue,21300001,RM11,NULL,NULL,553450,189050,NULL,NULL\n176174111,29/10/2011 19:03,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011219,Bexleyheath,E09000004,Bexley,Bexley,NULL,Springfield Road,20101322,DA7,NULL,NULL,549850,175450,NULL,NULL\n176514111,30/10/2011 10:21,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Melville Road,21300838,RM13,NULL,NULL,552450,182250,NULL,NULL\n176526111,30/10/2011 10:54,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN BAG ON FLAT ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Loddiges Road,20900628,E9,NULL,NULL,535050,184350,NULL,NULL\n177260111,31/10/2011 11:30,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Richmond Road,22303183,IG1,NULL,NULL,544150,186450,NULL,NULL\n178539111,02/11/2011 16:19,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Richmond Crescent,20704665,N9,NULL,NULL,534450,194350,NULL,NULL\n178853111,03/11/2011 08:27,2011,2011/12,Special Service,1,2,260,520,CAT TRAPPED ON EMBANKMENT BY CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Cowley Mill Road,NULL,UB8,504742,183403,504750,183450,51.53982041,-0.491087746\n179019111,03/11/2011 16:11,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Hall Lane,NULL,RM14,556571,188757,556550,188750,51.57593971,0.25810753\n180002111,05/11/2011 11:50,2011,2011/12,Special Service,1,2,260,520,DOG TRAPPED IN POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000054,Hale,E09000003,Barnet,Mill Hill,NULL,Barnet Way,NULL,NW7,520030,194741,520050,194750,51.63867829,-0.266873647\n180176111,05/11/2011 17:18,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO BIRD CAUGHT IN TREE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,Redbridge,Ilford,1.00022E+11,Aldborough Road South,22301800,IG3,545241,187554,545250,187550,51.56815993,0.094228811\n180844111,06/11/2011 11:56,2011,2011/12,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING AT FOURTH FLOOR LEVEL,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Elm Park Gardens,21700188,SW10,NULL,NULL,526650,178050,NULL,NULL\n181451111,07/11/2011 09:27,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED ON BALCONY,Dog,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Burdett Road,22700226,E14,NULL,NULL,536950,181450,NULL,NULL\n182155111,08/11/2011 16:26,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN DOOR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Sylvester Avenue,20302244,BR7,NULL,NULL,542650,170550,NULL,NULL\n182955111,10/11/2011 06:36,2011,2011/12,Special Service,1,1,260,260,DOG HANGING FROM TREE,Dog,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000167,Waddon,E09000008,Croydon,Croydon,NULL,Duppas Hill Road,NULL,CR0,531559,164969,531550,164950,51.36855152,-0.11145912\n182959111,10/11/2011 06:46,2011,2011/12,Special Service,1,1,260,260,HORSE STUCK IN A DITCH,Horse,Police,Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000571,Wandle Valley,E09000029,Sutton,Wallington,5870083134,London Road,22602441,CR4,528333,166524,528350,166550,51.38326328,-0.157215018\n183038111,10/11/2011 10:27,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000462,Hillside,E09000024,Merton,Wimbledon,48047271,Mansel Road,22104188,SW19,524527,170744,524550,170750,51.42203656,-0.210399303\n183663111,11/11/2011 15:30,2011,2011/12,Special Service,2,3,260,780,BIRD TRAPPED BY STRING HANGING FROM ROOF,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000185,Northolt West End,E09000009,Ealing,Southall,12013776,Canberra Drive,20600310,UB5,511493,182652,511450,182650,51.53177802,-0.394021716\n183746111,11/11/2011 18:31,2011,2011/12,Special Service,1,2,260,520,SWAN WITH HEAD STUCK IN RAILINGS,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Plough Way,22501989,SE16,536029,178890,536050,178850,51.49259938,-0.041942931\n184047111,12/11/2011 10:00,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,School Road,NULL,RM10,549336,183929,549350,183950,51.53452051,0.151732675\n184431111,13/11/2011 00:49,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN FLOORING OF KENNEL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,NULL,Victor Gardens,21300638,RM12,NULL,NULL,553850,187150,NULL,NULL\n184634111,13/11/2011 13:08,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA RELEASE BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Walm Lane,20202085,NW2,NULL,NULL,524050,185250,NULL,NULL\n185029111,14/11/2011 10:38,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,Hackney Downs,E09000012,Hackney,Homerton,1.00021E+11,Queensdown Road,20900834,E5,534726,185748,534750,185750,51.5545394,-0.058079912\n185415111,15/11/2011 07:40,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Police,Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000504,Fullwell,E09000026,Redbridge,Hainault,NULL,Cranbrook Road,NULL,IG6,544156,189551,544150,189550,51.5863823,0.079404148\n185457111,15/11/2011 09:45,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000128,Belsize,E09000007,Camden,West Hampstead,NULL,Belsize Square,20400430,NW3,NULL,NULL,527050,184750,NULL,NULL\n186435111,17/11/2011 12:25,2011,2011/12,Special Service,1,1,260,260,ASSIST WITH HORSE TRAPPED IN STALL,Horse,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Farm animal,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Glenesk Road,NULL,SE9,543671,174569,543650,174550,51.45188323,0.066305761\n186588111,17/11/2011 17:52,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN PIPE IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,20400774,NW1,NULL,NULL,529050,182950,NULL,NULL\n186652111,17/11/2011 19:40,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED IN BASEMENT,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Jamaica Street,22700675,E1,NULL,NULL,535350,181450,NULL,NULL\n186863111,18/11/2011 09:21,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Town Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Ladycroft Way,NULL,BR6,544191,164474,544150,164450,51.36104113,0.069684907\n187153111,18/11/2011 18:46,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,2.00003E+11,The Broadway,22306023,IG8,540903,191851,540950,191850,51.60787139,0.033404634\n187819111,19/11/2011 22:14,2011,2011/12,Special Service,1,1,260,260,DOG FALLEN IN RIVER,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009309,Queenhithe,E09000001,City of London,Dowgate,NULL,Queen Victoria Street,NULL,EC4V,532099,180905,532050,180950,51.51163891,-0.097764989\n188858111,22/11/2011 03:30,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN DITCH,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,London Road,NULL,CR4,528255,166632,528250,166650,51.38425147,-0.158296285\n189442111,23/11/2011 11:39,2011,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Bushwood Road,NULL,TW9,519333,177528,519350,177550,51.48412507,-0.282779136\n189460111,23/11/2011 12:24,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000493,Wall End,E09000025,Newham,East Ham,46043555,Langdon Crescent,22200790,E6,543442,183447,543450,183450,51.53171641,0.066617792\n189831111,24/11/2011 08:55,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Larksfield Grove,NULL,EN1,534846,197601,534850,197650,51.66102436,-0.0517955\n189949111,24/11/2011 14:36,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,1.00023E+11,Clarence Street,21800255,KT1,518206,169370,518250,169350,51.41104008,-0.301725172\n190170111,24/11/2011 22:19,2011,2011/12,Special Service,1,1,260,260,DEER WITH HEAD STUCK IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dagnam Park Drive,NULL,RM3,554342,192357,554350,192350,51.60889849,0.227547407\n190269111,25/11/2011 07:07,2011,2011/12,Special Service,1,1,260,260,CAT   TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,207031702,Addison Road,20702096,EN3,535725,197760,535750,197750,51.66224146,-0.039033582\n191670111,27/11/2011 16:57,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN TWO HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Wakefield Street,22201187,E6,NULL,NULL,542150,183650,NULL,NULL\n191969111,28/11/2011 08:49,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN RIVER - LEVEL ONE WATER RESCUE,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555415,188368,555450,188350,51.57276421,0.241266584\n192003111,28/11/2011 10:46,2011,2011/12,Special Service,1,1,260,260,BIRD STUCK BEHIND GAS FIRE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,Peckham Rye,E09000028,Southwark,New Cross,NULL,Rye Hill Park,22502162,SE15,NULL,NULL,535050,175150,NULL,NULL\n192368111,29/11/2011 00:46,2011,2011/12,Special Service,1,1,260,260,INJURED CAT TRAPPED ON FENCE,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,10003976087,High Street,21106115,N8,530501,189292,530550,189250,51.58738112,-0.117671242\n192560111,29/11/2011 13:10,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Kentish Town,5131040,Chalk Farm Road,20400624,NW1,528250,184250,528250,184250,51.54258617,-0.151982666\n192615111,29/11/2011 15:11,2011,2011/12,Special Service,1,1,260,260,CAT FALLEN THROUGH ROOF OF GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,1.00024E+11,High Street,21500609,TW13,510061,172281,510050,172250,51.43884274,-0.417886764\n192790111,29/11/2011 21:41,2011,2011/12,Special Service,1,1,260,260,DEER WITH HEAD TRAPPED IN METAL FENCING,Deer,Police,House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Tring Gardens,NULL,RM3,NULL,NULL,554450,192750,NULL,NULL\n194264111,03/12/2011 02:20,2011,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER CAR ENGINE,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Abbey Lane,NULL,BR3,537435,170628,537450,170650,51.41801435,-0.024910616\n194387111,03/12/2011 10:57,2011,2011/12,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,NULL,Hampstead Lane,NULL,NW3,527087,187418,527050,187450,51.57131936,-0.167600351\n194397111,03/12/2011 11:39,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,NULL,Shrewsbury Lane,20801354,SE18,NULL,NULL,543850,177050,NULL,NULL\n194986111,04/12/2011 12:39,2011,2011/12,Special Service,NULL,NULL,260,NULL,HORSE WITH NECK STUCK IN HORSE BOX,Horse,Other FRS,Other road vehicle,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Cattlegate Road,NULL,EN2,534836,197513,534850,197550,51.66023599,-0.051973898\n195000111,04/12/2011 13:06,2011,2011/12,Special Service,1,1,260,260,KITTEN IN DISTRESS STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,1.00023E+11,Old Church Road,22863050,E4,537588,193832,537550,193850,51.62649127,-0.013658806\n195063111,04/12/2011 15:29,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER RAILWAY BRIDGE,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Erconwald Street,NULL,W12,521697,181202,521650,181250,51.51664198,-0.247483497\n195914111,06/12/2011 11:37,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000470,St. Helier,E09000024,Merton,Mitcham,NULL,Central Road,22101299,SM4,NULL,NULL,525850,167850,NULL,NULL\n196048111,06/12/2011 16:40,2011,2011/12,Special Service,1,1,260,260,PUPPY WITH HEAD STUCK IN PIPE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Balaam Street,22200346,E13,NULL,NULL,540450,182550,NULL,NULL\n196068111,06/12/2011 17:22,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED ON WALL,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Thornton Place,NULL,W1H,527754,181833,527750,181850,51.52097699,-0.160007659\n196485111,07/12/2011 13:01,2011,2011/12,Special Service,1,1,260,260,HORSE STUCK IN FEEDER,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Telegraph Track,NULL,SM5,528533,161624,528550,161650,51.33918096,-0.15611196\n197524111,09/12/2011 10:53,2011,2011/12,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Rope Street,NULL,SE16,536205,179026,536250,179050,51.49377917,-0.039356834\n198693111,11/12/2011 12:43,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05011489,Woodside,E09000008,Croydon,Woodside,NULL,Portland Road,20501311,SE25,NULL,NULL,534550,167950,NULL,NULL\n199772111,13/12/2011 13:58,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND WARDROBE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Tooting Grove,22905473,SW17,NULL,NULL,527150,171250,NULL,NULL\n201204111,16/12/2011 12:45,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Wakeman Road,NULL,NW10,523574,182785,523550,182750,51.53046193,-0.219890643\n201866111,17/12/2011 14:19,2011,2011/12,Special Service,1,2,260,520,ASSIST  RSPCA WITH SEAGULL TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000364,Syon,E09000018,Hounslow,Heston,NULL,Amhurst Gardens,NULL,TW7,516213,176385,516250,176350,51.47450072,-0.32806767\n201900111,17/12/2011 15:35,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000343,West Ruislip,E09000017,Hillingdon,Ruislip,NULL,Arlington Drive,21400057,HA4,NULL,NULL,508550,188250,NULL,NULL\n201961111,17/12/2011 17:36,2011,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK BEHIND FRIDGE,Cat,Person (land line),Loose refuse,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Martha Street,NULL,E1,535096,181023,535050,181050,51.51199095,-0.054557461\n201991111,17/12/2011 18:25,2011,2011/12,Special Service,1,2,260,520,CAT STUCK UNDER LIFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Blakes Road,22500261,SE15,NULL,NULL,533450,177150,NULL,NULL\n202543111,18/12/2011 19:20,2011,2011/12,Special Service,1,1,260,260,DOG WITH HEAD STUCK BEHIND WASHING MACHINE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011231,Slade Green & Northend,E09000004,Bexley,Erith,NULL,Mariners Walk,20101643,DA8,NULL,NULL,551950,177750,NULL,NULL\n202839111,19/12/2011 09:49,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00023E+11,Endymion Road,21900523,SW2,530842,174066,530850,174050,51.45047038,-0.118399603\n202919111,19/12/2011 12:36,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO RELEASE FOX IN BASEMENT,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Uxbridge Road,21000835,W12,NULL,NULL,522950,180050,NULL,NULL\n202958111,19/12/2011 13:47,2011,2011/12,Special Service,1,1,260,260,DOG TRAPPED BETWEEN TWO FENCES,Dog,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Elmshurst Crescent,NULL,N2,526565,189306,526550,189350,51.58840393,-0.174448693\n203380111,20/12/2011 10:17,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,1.00023E+11,Endymion Road,21900523,SW2,530842,174066,530850,174050,51.45047038,-0.118399603\n203386111,20/12/2011 10:30,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN ENGINE BAY,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00023E+11,Amersham Vale,22000036,SE14,536788,177108,536750,177150,51.47640255,-0.031706903\n204381111,22/12/2011 11:47,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Wat Tyler Road,NULL,SE3,538750,176750,538750,176750,51.47270806,-0.00361435\n204851111,23/12/2011 10:21,2011,2011/12,Special Service,1,2,260,520,BIRD TRAPPED IN WIRE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009303,Dowgate,E09000001,City of London,Dowgate,NULL,College Street,NULL,EC4R,532526,180834,532550,180850,51.51090094,-0.091641986\n205399111,24/12/2011 14:08,2011,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN LOFT,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Rosethorn Close,21901179,SW12,NULL,NULL,529550,173650,NULL,NULL\n205418111,24/12/2011 14:44,2011,2011/12,Special Service,1,1,260,260,CAT STUCK BEHIND KITCHEN CORNER UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000191,Southfield,E09000009,Ealing,Acton,NULL,Valetta Road,20601798,W3,NULL,NULL,521350,179850,NULL,NULL\n205721111,25/12/2011 09:38,2011,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Millais Road,22858750,E11,NULL,NULL,538550,185850,NULL,NULL\n206172111,26/12/2011 12:50,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN GATE AND WALL,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,207112198,Village Park Close,20706108,EN1,533141,195361,533150,195350,51.64130135,-0.077283265\n206177111,26/12/2011 12:58,2011,2011/12,Special Service,1,1,260,260,PIGEONS TRAPPED UNDER NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Palmers Road,20705024,N11,NULL,NULL,529250,192150,NULL,NULL\n206266111,26/12/2011 16:21,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,First Avenue,22836300,E17,NULL,NULL,537550,188950,NULL,NULL\n206614111,27/12/2011 12:14,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Danbury Road,21300746,RM13,NULL,NULL,551650,183850,NULL,NULL\n206637111,27/12/2011 13:12,2011,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Stanmore,202069460,Meadowbank Road,20202641,NW9,520565,187812,520550,187850,51.57629131,-0.26152585\n206999111,28/12/2011 09:32,2011,2011/12,Special Service,1,1,260,260,UNKNOWN ANIMAL STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Stuart Close,21401886,UB10,NULL,NULL,507350,184750,NULL,NULL\n207037111,28/12/2011 11:52,2011,2011/12,Special Service,1,2,260,520,HORSE STUCK IN DITCH,Horse,Police,Roadside vegetation,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Waldens Road,NULL,BR5,548427,167277,548450,167250,51.38513632,0.131651702\n207043111,28/12/2011 12:28,2011,2011/12,Special Service,1,1,260,260,ANIMAL OR BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Stuart Close,21401886,UB10,NULL,NULL,507350,184750,NULL,NULL\n207242111,28/12/2011 20:23,2011,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED IN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Nash Road,19900270,RM6,NULL,NULL,547650,189450,NULL,NULL\n207430111,29/12/2011 10:16,2011,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CAR ENGINE COMPARTMENT,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,1.00024E+11,High Street,22845050,E17,536475,188908,536450,188950,51.5825158,-0.031642043\n207652111,29/12/2011 17:56,2011,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Cromer Road,21301117,RM7,NULL,NULL,550350,188250,NULL,NULL\n208046111,30/12/2011 16:54,2011,2011/12,Special Service,2,3,260,780,DEER TRAPPED UNDER CAR,Deer,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000314,Heaton,E09000016,Havering,Romford,NULL,Lower Bedfords Road,NULL,RM1,552587,192071,552550,192050,51.60680725,0.202099996\n310121,01/01/2012 13:39,2012,2011/12,Special Service,1,3,260,780,CAT STUCK BETWEEN TWO WALLS,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011252,South Woodford,E09000026,Redbridge,Woodford,1.00022E+11,High View Road,22300483,E18,539554,190026,539550,190050,51.59180798,0.013211406\n654121,02/01/2012 09:26,2012,2011/12,Special Service,1,1,260,260,CAT IN DISTRESS,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Wellesley Road,NULL,NW5,528185,185191,528150,185150,51.55105761,-0.152576839\n716121,02/01/2012 13:04,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Park Avenue North,21100673,N8,NULL,NULL,529850,189550,NULL,NULL\n1433121,03/01/2012 20:13,2012,2011/12,Special Service,2,3,260,780,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009322,Bromley South,E09000030,Tower Hamlets,Poplar,NULL,Violet Road,NULL,E3,537692,181859,537650,181850,51.51887743,-0.016844426\n1765121,04/01/2012 15:30,2012,2011/12,Special Service,1,1,260,260,INJURED DOG BEHIND FENCE BY RAILWAY LINE,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000075,Erith,E09000004,Bexley,Erith,NULL,James Watt Way,NULL,DA8,551448,177721,551450,177750,51.47817917,0.179494985\n2149121,05/01/2012 13:11,2012,2011/12,Special Service,1,2,260,520,INCOMPLETE CALL DOG TRAPPED UNDER TREE,Dog,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Hook Rise North,21860007,KT6,519431,165428,519450,165450,51.37535346,-0.285443139\n2658121,06/01/2012 13:25,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Silver Street,NULL,N18,532622,192653,532650,192650,51.61708887,-0.085805107\n3348121,07/01/2012 22:03,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN GARAGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Plender Street,20400747,NW1,NULL,NULL,529250,183650,NULL,NULL\n3374121,07/01/2012 22:52,2012,2011/12,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Defoe Avenue,NULL,TW9,519379,177147,519350,177150,51.48069102,-0.282245578\n3513121,08/01/2012 09:58,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA TO RESCUE CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,Bellingham,E09000023,Lewisham,Beckenham,1.00022E+11,Oakview Road,22001756,SE6,537692,171355,537650,171350,51.42448515,-0.020934939\n3652121,08/01/2012 15:43,2012,2011/12,Special Service,1,1,260,260,DOG WITH JAW TRAPPED IN LIFT GRILL,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,East Heath Road,20400070,NW3,NULL,NULL,526850,186150,NULL,NULL\n3721121,08/01/2012 17:46,2012,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Wandsworth Bridge Road,21000856,SW6,NULL,NULL,525850,175850,NULL,NULL\n3880121,09/01/2012 01:05,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED BEHIND SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,1.00022E+11,Castle Avenue,21400320,UB7,506650,180754,506650,180750,51.51565343,-0.464388078\n4037121,09/01/2012 13:42,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN WIRING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Trinity Road,20043280,N2,NULL,NULL,526550,189850,NULL,NULL\n4293121,09/01/2012 21:36,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Kittiwake Way,21401109,UB4,NULL,NULL,511750,181650,NULL,NULL\n4996121,11/01/2012 11:27,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Marmora Road,22501666,SE22,NULL,NULL,535150,174350,NULL,NULL\n5139121,11/01/2012 16:24,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO KITTEN ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,NULL,Heriot Road,20022000,NW4,NULL,NULL,523350,188850,NULL,NULL\n48122,12/01/2012 12:57,2012,2011/12,Special Service,1,2,260,520,HORSE IN DITCH,Horse,Person (land line),Heathland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,Seymour Road,NULL,CR4,528292,166619,528250,166650,51.38412629,-0.157769571\n88122,12/01/2012 14:40,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,NULL,Cleveley Crescent,NULL,W5,518497,183049,518450,183050,51.53392141,-0.292958997\n135122,12/01/2012 16:36,2012,2011/12,Special Service,1,1,260,260,SQUIRREL TRAPPED ON ROOF,Squirrel,Person (land line),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00022E+11,Creekside,22004382,SE8,537439,177125,537450,177150,51.47639759,-0.022332238\n1184122,14/01/2012 20:50,2012,2011/12,Special Service,1,1,260,260,RCA TO CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,Town,E09000010,Enfield,Enfield,NULL,Lavender Hill,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL\n1454122,15/01/2012 12:05,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED PIGEON,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,High Road,NULL,N22,531113,190103,531150,190150,51.59452706,-0.108540124\n2327122,17/01/2012 00:42,2012,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN FENCES,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000051,Finchley Church End,E09000003,Barnet,Finchley,200138721,Hendon Lane,20021800,N3,524662,190301,524650,190350,51.59777019,-0.201549635\n2677122,17/01/2012 15:45,2012,2011/12,Special Service,2,3,260,780,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000213,Winchmore Hill,E09000010,Enfield,Southgate,NULL,Broad Walk,NULL,N21,531149,194416,531150,194450,51.63327694,-0.106407658\n5808121,19/01/2012 08:52,2012,2011/12,Special Service,1,1,260,260,BIRD TRAPPED DOWN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,20900539,N16,NULL,NULL,533050,185450,NULL,NULL\n6451121,20/01/2012 18:33,2012,2011/12,Special Service,1,1,260,260,PONY STUCK ON A FENCE,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000072,Cray Meadows,E09000004,Bexley,Sidcup,NULL,Bunkers Hill,NULL,DA14,548834,172137,548850,172150,51.42869842,0.139533387\n6762121,21/01/2012 11:22,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Garvary Road,22207723,E16,NULL,NULL,540950,181250,NULL,NULL\n6852121,21/01/2012 14:24,2012,2011/12,Special Service,1,1,260,260,INJURED BIRD ON ROOF OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Tranmere Road,20704859,N9,NULL,NULL,533850,194450,NULL,NULL\n7003121,21/01/2012 19:52,2012,2011/12,Special Service,1,1,260,260,CAR ALIGHT FOLLOWING RTA,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Warham Road,20502408,CR2,NULL,NULL,532350,164350,NULL,NULL\n7303121,22/01/2012 12:37,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Hazel Grove,22001536,SE26,NULL,NULL,535950,171550,NULL,NULL\n7422121,22/01/2012 17:12,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000098,Queens Park,E09000005,Brent,North Kensington,202118264,Chamberlayne Road,20202065,NW10,523449,183467,523450,183450,51.53661857,-0.221452742\n7999121,23/01/2012 23:04,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED UNDER BASEMENT GRILL,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,6013053,New Road,22700873,E1,534571,181429,534550,181450,51.51576464,-0.061963546\n8336121,24/01/2012 16:52,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN VENT,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Wood Street,22888350,E17,538337,189636,538350,189650,51.58860371,-0.004498563\n8660121,25/01/2012 11:23,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN WIRE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000116,Crystal Palace,E09000006,Bromley,West Norwood,NULL,Fox Hill,20303952,SE19,NULL,NULL,533850,170150,NULL,NULL\n10039121,28/01/2012 00:39,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Southgate,NULL,Manor Drive,20028180,N20,NULL,NULL,527450,193350,NULL,NULL\n10197121,28/01/2012 11:10,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Denver Road,20900324,N16,NULL,NULL,533150,187850,NULL,NULL\n10711121,29/01/2012 09:48,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA INSPECTOR WITH SWAN ON ROOF,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,34062433,Maclise Road,21000529,W14,524194,179146,524150,179150,51.49762141,-0.212238703\n10839121,29/01/2012 15:09,2012,2011/12,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128039317,Castle Street,21800212,KT1,518205,169375,518250,169350,51.41108523,-0.301737877\n11585121,30/01/2012 23:42,2012,2011/12,Special Service,1,1,260,260,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000095,Mapesbury,E09000005,Brent,West Hampstead,202152295,Keyes Road,20200117,NW2,524008,185385,524050,185350,51.55373371,-0.212721536\n11872121,31/01/2012 11:41,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000477,Canning Town North,E09000025,Newham,Plaistow,46026584,Florence Street,22201407,E16,539756,182245,539750,182250,51.52183943,0.013036929\n12147121,31/01/2012 20:46,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Bramham Gardens,21700043,SW5,NULL,NULL,525850,178450,NULL,NULL\n12387121,01/02/2012 10:54,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Meeson Road,NULL,E15,NULL,NULL,539650,184050,NULL,NULL\n12541121,01/02/2012 16:06,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Other FRS,Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,10022941117,Worcester Avenue,21105271,N17,533750,191250,533750,191250,51.60421424,-0.070057111\n13055121,02/02/2012 11:29,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Telegraph Road,NULL,SW15,523342,173690,523350,173650,51.44877243,-0.226408784\n13699121,03/02/2012 12:42,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Victoria Park Road,20901033,E9,NULL,NULL,535950,184050,NULL,NULL\n13836121,03/02/2012 17:02,2012,2011/12,Special Service,1,1,260,260,ANIMAL TRAPPED IN BAG IN TREE,Unknown - Wild Animal,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Bath Street,21604222,EC1V,NULL,NULL,532550,182550,NULL,NULL\n14549121,04/02/2012 16:12,2012,2011/12,Special Service,1,1,260,260,CAT  TRAPPED IN FIREPLACE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Brixton,NULL,Prideaux Road,21901118,SW9,NULL,NULL,530350,175850,NULL,NULL\n14627121,04/02/2012 17:51,2012,2011/12,Special Service,1,1,260,260,SWANS TRAPPED IN POND,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,Russell Place,NULL,SE16,536182,179212,536150,179250,51.49545618,-0.03961618\n14911121,05/02/2012 02:14,2012,2011/12,Special Service,1,1,260,260,DOG IN POND  WATER RESCUE LEVEL TWO,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Sidcup,NULL,Green Lane,NULL,BR7,544044,170821,544050,170850,51.41811004,0.070145956\n15154121,05/02/2012 13:21,2012,2011/12,Special Service,1,1,260,260,DOG IN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000073,Danson Park,E09000004,Bexley,Bexley,NULL,Danson Park,NULL,DA6,547750,175250,547750,175250,51.45695349,0.125251397\n15166121,05/02/2012 13:46,2012,2011/12,Special Service,1,2,260,520,DOG IN CANAL UNDER ICE - LEVEL TWO WATER RESCUE IMPLEMENTED,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Oldfield Lane North,NULL,UB6,514885,184497,514850,184450,51.54768113,-0.344539258\n15358121,05/02/2012 19:28,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN BIN CHUTE ROOM,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Nelsons Row,NULL,SW4,529710,175317,529750,175350,51.46197331,-0.134222926\n16209121,07/02/2012 14:44,2012,2011/12,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,Bedford,E09000032,Wandsworth,Tooting,10025174808,Doctor Johnson Avenue,22901279,SW17,528750,171750,528750,171750,51.43013547,-0.149330437\n16571121,08/02/2012 09:39,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA KITTEN STUCK IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,Beddington South,E09000029,Sutton,Purley,NULL,Great Woodcote Park,22604963,CR8,NULL,NULL,530150,162450,NULL,NULL\n16702121,08/02/2012 14:51,2012,2011/12,Special Service,1,1,260,260,ASSIST  RSPCA  WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Holm Grove,21401007,UB10,NULL,NULL,507450,184250,NULL,NULL\n16870121,08/02/2012 21:57,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Haywards Close,22302130,RM6,NULL,NULL,547050,188650,NULL,NULL\n17531121,10/02/2012 10:32,2012,2011/12,Special Service,2,3,260,780,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Northumberland Avenue,NULL,E12,541255,187124,541250,187150,51.56530819,0.036584474\n18054121,11/02/2012 10:50,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Percy Gardens,NULL,EN3,535794,195860,535750,195850,51.645151,-0.038773882\n18130121,11/02/2012 12:49,2012,2011/12,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Ellerman Avenue,NULL,TW2,513176,172882,513150,172850,51.44363131,-0.372897318\n18181121,11/02/2012 13:38,2012,2011/12,Special Service,1,2,260,520,DOG UNDER ICE IN PARK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Kidbrooke Park Road,NULL,SE3,540859,174903,540850,174950,51.45559055,0.025996182\n18186121,11/02/2012 13:44,2012,2011/12,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fortune Green Road,NULL,NW6,525250,185250,525250,185250,51.55224631,-0.194864627\n18213121,11/02/2012 14:18,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Battersea Park,NULL,SW11,528483,177258,528450,177250,51.4796965,-0.151171155\n18366121,11/02/2012 17:35,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA,Unknown - Heavy Livestock Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000046,Colindale,E09000003,Barnet,Mill Hill,NULL,Little Strand,20027060,NW9,NULL,NULL,521750,190450,NULL,NULL\n19912121,13/02/2012 22:24,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Park Road,22403720,TW11,NULL,NULL,515850,170750,NULL,NULL\n20018121,14/02/2012 07:59,2012,2011/12,Special Service,1,3,260,780,HORSE STUCK IN DITCH,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,NULL,Shepherds Hill,NULL,RM3,555438,190419,555450,190450,51.59118514,0.242504548\n20131121,14/02/2012 14:06,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Other FRS,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539788,194780,539750,194750,51.63446819,0.018481342\n20209121,14/02/2012 16:50,2012,2011/12,Special Service,1,3,260,780,HORSE IN DITCH,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Ray Lamb Way,NULL,DA8,552750,177750,552750,177750,51.47808986,0.198241941\n20818121,15/02/2012 21:28,2012,2011/12,Special Service,2,3,260,780,DOG TRAPPED UNDER SHED,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Newburn Street,21901016,SE11,NULL,NULL,530850,178250,NULL,NULL\n21119121,16/02/2012 14:06,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Blenheim Road,22200387,E6,NULL,NULL,541550,182950,NULL,NULL\n22297121,18/02/2012 16:08,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Scarborough Road,21103849,N4,NULL,NULL,531350,187450,NULL,NULL\n22572121,19/02/2012 11:01,2012,2011/12,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011242,Fairlop,E09000026,Redbridge,Hainault,NULL,Baron Gardens,22302607,IG6,NULL,NULL,544450,189950,NULL,NULL\n22577121,19/02/2012 11:23,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,Redbridge,Dagenham,1.00022E+11,Norbury Gardens,22302307,RM6,547673,188782,547650,188750,51.57856275,0.129808614\n23211121,20/02/2012 15:35,2012,2011/12,Special Service,1,1,260,260,CAT IN PRECARIOUS POSTION REQ BY RSPCA,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000569,Wallington North,E09000029,Sutton,Wallington,5870082311,Caraway Place,22600393,SM6,528756,165336,528750,165350,51.37249116,-0.151570068\n23525121,21/02/2012 09:46,2012,2011/12,Special Service,1,2,260,520,ELDERLY HORSE FALLEN AND IN DISTRESS,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Noak Hill Road,21301452,RM3,554187,193530,554150,193550,51.61948023,0.225827382\n23610121,21/02/2012 13:11,2012,2011/12,Special Service,1,1,260,260,DOG STUCK ON LEDGE OF ROOF,Dog,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Lewisham High Street,22004177,SE13,NULL,NULL,538150,175150,NULL,NULL\n23785121,21/02/2012 17:14,2012,2011/12,Special Service,1,2,260,520,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Corker Walk,21606796,N7,NULL,NULL,530950,186650,NULL,NULL\n24077121,22/02/2012 08:00,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL  AND WARDROBE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Wiltshire Road,20501575,CR7,NULL,NULL,531350,168450,NULL,NULL\n24379121,22/02/2012 19:15,2012,2011/12,Special Service,1,1,260,260,PUPPY TRAPPED BEHIND SINK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Granville Road,20018860,NW2,NULL,NULL,524650,186850,NULL,NULL\n24413121,22/02/2012 19:55,2012,2011/12,Special Service,1,1,260,260,PARROT TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,DALMENY AVENUE,21606427,N7,NULL,NULL,529950,185450,NULL,NULL\n24462121,22/02/2012 22:18,2012,2011/12,Special Service,1,1,260,260,DOG WITH PAW CAUGHT IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Gee Street,21604937,EC1V,NULL,NULL,532150,182350,NULL,NULL\n25181121,24/02/2012 10:36,2012,2011/12,Special Service,1,2,260,520,HORSE UNABLE TO GET UP,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544056,171975,544050,171950,51.42847648,0.07078766\n25206121,24/02/2012 11:53,2012,2011/12,Special Service,1,1,260,260,PIDGEON TRAPPED  IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Hartland Road,NULL,NW1,528592,184293,528550,184250,51.54289487,-0.147037979\n25990121,25/02/2012 18:26,2012,2011/12,Special Service,1,1,260,260,BIRD ENTANGLED IN STRING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Prestbury Square,20302083,SE9,NULL,NULL,542850,171850,NULL,NULL\n27917121,29/02/2012 14:10,2012,2011/12,Special Service,1,2,260,520,CAT STUCK IN CHIMNEY FLUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,St. Helier,E09000024,Merton,Mitcham,NULL,Blanchland Road,22100654,SM4,NULL,NULL,525850,167950,NULL,NULL\n28436121,01/03/2012 13:10,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,Beddington South,E09000029,Sutton,Purley,5870095118,Great Woodcote Park,22604963,CR8,530166,162463,530150,162450,51.34635049,-0.132374858\n28448121,01/03/2012 13:29,2012,2011/12,Special Service,1,1,260,260,TO ASSIST RSPCA,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Shepherds Bush Road,NULL,W6,523455,179132,523450,179150,51.49765729,-0.222884228\n28703121,01/03/2012 20:46,2012,2011/12,Special Service,1,1,260,260,KITTEN BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Elf Row,22700459,E1W,NULL,NULL,535450,180850,NULL,NULL\n28988121,02/03/2012 14:25,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,Thamesfield,E09000032,Wandsworth,Wandsworth,1.00023E+11,Ashlone Road,22900158,SW15,523483,175923,523450,175950,51.46881071,-0.223602068\n29146121,02/03/2012 19:38,2012,2011/12,Special Service,1,1,260,260,SEAGULL TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,10033571000,Park Lane,8400585,W1K,528454,180126,528450,180150,51.50547766,-0.15054557\n29909121,04/03/2012 10:35,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Church Road,NULL,W7,515489,180564,515450,180550,51.51220918,-0.33712111\n30103121,04/03/2012 20:51,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDERNEATH FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Montagu Gardens,20704506,N18,NULL,NULL,534850,192750,NULL,NULL\n30697121,06/03/2012 06:45,2012,2011/12,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Thurlow Street,22502507,SE17,NULL,NULL,532950,178350,NULL,NULL\n32116121,09/03/2012 07:46,2012,2011/12,Special Service,1,1,260,260,KITTENS TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,Winchet Walk,20502807,CR0,NULL,NULL,535150,167250,NULL,NULL\n32316121,09/03/2012 16:37,2012,2011/12,Special Service,1,2,260,520,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000614,Fairfield,E09000032,Wandsworth,Wandsworth,1.00023E+11,Santos Road,22904521,SW18,525057,174858,525050,174850,51.45889405,-0.201329153\n33391121,11/03/2012 15:29,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED BETWEEN HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Whitebridge Close,21501197,TW14,NULL,NULL,509750,174150,NULL,NULL\n33936121,12/03/2012 14:22,2012,2011/12,Special Service,1,1,260,260,DEER TRAPPED BETWEEN METAL AND WOODEN FENC E,Deer,Person (land line),Scrub land,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000338,South Ruislip,E09000017,Hillingdon,Ruislip,NULL,Lea Crescent,NULL,HA4,509644,185566,509650,185550,51.55833114,-0.419760122\n34403121,13/03/2012 12:57,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Corfton Road,20600441,W5,NULL,NULL,518150,181650,NULL,NULL\n34431121,13/03/2012 14:04,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UNDER FLOOR BOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Corfton Road,20600441,W5,NULL,NULL,518150,181650,NULL,NULL\n34917121,14/03/2012 11:11,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Bishops Road,21000124,SW6,NULL,NULL,524650,177050,NULL,NULL\n34931121,14/03/2012 11:54,2012,2011/12,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,1.00023E+11,Leyton Green Road,22852400,E10,538125,188236,538150,188250,51.57607574,-0.008106373\n35391121,15/03/2012 07:48,2012,2011/12,Special Service,1,1,260,260,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Tring Close,NULL,RM3,554511,192703,554550,192750,51.6119609,0.230138519\n35453121,15/03/2012 10:32,2012,2011/12,Special Service,NULL,NULL,260,NULL,CAT INJURED UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,217066036,Pembridge Villas,21700757,W11,525250,180750,525250,180750,51.51180412,-0.196464488\n35473121,15/03/2012 11:10,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Hornsey,NULL,Ella Road,NULL,N8,530406,187900,530450,187950,51.57489391,-0.11955788\n35944121,16/03/2012 09:18,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Ling Road,22200812,E16,NULL,NULL,540450,181950,NULL,NULL\n36004121,16/03/2012 12:05,2012,2011/12,Special Service,1,2,260,520,ASSIST RSPCA WITH HERON TRAPPED IN TREE WATER LEVEL RESCUE TWO,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Packet Boat Lane,NULL,UB8,505339,181208,505350,181250,51.51997999,-0.48313884\n36042121,16/03/2012 14:16,2012,2011/12,Special Service,1,1,260,260,TWO CATS IN A BAG FLOATING IN CANAL,cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Whitmore Road,NULL,N1,533141,183674,533150,183650,51.53627805,-0.081712082\n36064121,16/03/2012 15:21,2012,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF OF VICTORIAN TERRACE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Bournemouth Road,22100732,SW19,NULL,NULL,525450,169750,NULL,NULL\n36628121,17/03/2012 17:58,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED DOWN FOX HOLE IN GARDEN,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Northolt,12034372,Parkfield Road,20601360,UB5,512085,183299,512050,183250,51.53747604,-0.385284846\n36953121,18/03/2012 08:55,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,NULL,Wyncote Way,20501877,CR2,NULL,NULL,535650,162550,NULL,NULL\n36979121,18/03/2012 10:49,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Ecclestone Place,NULL,HA9,518839,185297,518850,185250,51.5540537,-0.287271981\n36995121,18/03/2012 11:39,2012,2011/12,Special Service,NULL,NULL,260,NULL,PONY COLLASPED IN STABLE,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00021E+11,Orchard Way,20500526,CR0,536525,167298,536550,167250,51.38830928,-0.039269103\n37444121,19/03/2012 09:33,2012,2011/12,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Queens Road,21500912,TW13,NULL,NULL,511050,173350,NULL,NULL\n38006121,20/03/2012 11:17,2012,2011/12,Special Service,1,1,260,260,INJURED SQUIRREL IN TREE,Squirrel,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Durley Road,NULL,N16,533186,187648,533150,187650,51.57197936,-0.079560997\n38312121,20/03/2012 22:07,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,207096415,Wyldfield Gardens,20704962,N9,533791,193841,533750,193850,51.62748792,-0.068476945\n38636121,21/03/2012 14:26,2012,2011/12,Special Service,2,3,260,780,DOG TRAPPED UNDER CABIN,Dog,Person (land line),Temporary office (eg portacabin),Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,1.00023E+11,Shooters Hill,20801346,DA16,544523,176322,544550,176350,51.46741841,0.079274952\n40271121,24/03/2012 13:46,2012,2011/12,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000607,Valley,E09000031,Waltham Forest,Chingford,1.00023E+11,Mandeville Court,22855325,E4,536410,192538,536450,192550,51.61515031,-0.031170109\n40322121,24/03/2012 15:33,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Exeter Road,20200226,NW2,NULL,NULL,524350,184850,NULL,NULL\n40488121,24/03/2012 21:08,2012,2011/12,Special Service,1,1,260,260,CAT STUCK IN HOLLOW ON ROOF,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,2.00001E+11,Warley Close,22885110,E10,536940,187482,536950,187450,51.5695891,-0.025489691\n40728121,25/03/2012 11:38,2012,2011/12,Special Service,1,2,260,520,KITTENS STUCK IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Hartley Old Road,20502122,CR8,NULL,NULL,530750,160250,NULL,NULL\n41492121,26/03/2012 20:22,2012,2011/12,Special Service,1,1,260,260,SWAN TRAPPED IN WIRE FENCE,Bird,Police,Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,NULL,Armstrong Avenue,NULL,IG8,539184,191802,539150,191850,51.60785813,0.008577519\n41499121,26/03/2012 20:36,2012,2011/12,Special Service,1,1,260,260,KITTEN STUCK ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,St. Giles Road,22502337,SE5,NULL,NULL,533050,177050,NULL,NULL\n41831121,27/03/2012 12:31,2012,2011/12,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Whinfell Close,21901490,SW16,NULL,NULL,529650,171450,NULL,NULL\n41860121,27/03/2012 13:21,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Eastway Crescent,NULL,HA2,513655,186980,513650,186950,51.57024673,-0.361468932\n41877121,27/03/2012 14:00,2012,2011/12,Special Service,1,1,260,260,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Draycott Place,21700154,SW3,NULL,NULL,527750,178650,NULL,NULL\n41926121,27/03/2012 15:48,2012,2011/12,Special Service,1,1,260,260,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,NULL,Glenhurst Avenue,20100604,DA5,NULL,NULL,548950,173350,NULL,NULL\n42096121,27/03/2012 19:48,2012,2011/12,Special Service,1,1,260,260,DOG TRAPPED ON MUD BANK OF RIVER THAMES,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Swan Street,NULL,TW7,516631,175811,516650,175850,51.46925578,-0.322240867\n42312121,28/03/2012 09:37,2012,2011/12,Special Service,1,1,260,260,INJURED CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Eversleigh Road,22901568,SW11,NULL,NULL,528350,176250,NULL,NULL\n42996121,29/03/2012 08:14,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Broomhouse Lane,21000798,SW6,NULL,NULL,525150,176050,NULL,NULL\n43603121,30/03/2012 01:29,2012,2011/12,Special Service,1,1,260,260,CAT TRAPPED IN STAIRWELL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Cliff Road,20400536,NW1,NULL,NULL,529850,184850,NULL,NULL\n43747121,30/03/2012 10:10,2012,2011/12,Special Service,1,1,260,260,SPARROW HAWK CAUGHT IN NETTING,Bird,Person (mobile),Other entertainment venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033549112,Whitehall,8400881,SW1A,530171,180017,530150,180050,51.50410562,-0.125860229\n44012121,30/03/2012 18:39,2012,2011/12,Special Service,1,1,260,260,DOG STUCK IN METAL FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Wellington Road,NULL,EN1,533293,194390,533250,194350,51.63253957,-0.075458048\n44345121,31/03/2012 11:22,2012,2011/12,Special Service,1,2,260,520,DEER STUCK IN RAILINGS,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Kingswood Way,NULL,CR2,535726,161571,535750,161550,51.33703541,-0.052929189\n45055121,01/04/2012 14:38,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND UNIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000092,Kensal Green,E09000005,Brent,North Kensington,NULL,Purves Road,20202226,NW10,NULL,NULL,522950,183050,NULL,NULL\n45321121,01/04/2012 20:21,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,The Grove,NULL,W5,NULL,NULL,517850,180450,NULL,NULL\n45682121,02/04/2012 14:09,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000463,Lavender Fields,E09000024,Merton,Mitcham,NULL,Eveline Road,22102499,CR4,NULL,NULL,527650,169550,NULL,NULL\n46231121,03/04/2012 14:00,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Bethnal Green Road,NULL,E2,534202,182598,534250,182550,51.52635748,-0.066833253\n46668121,04/04/2012 09:51,2012,2012/13,Special Service,1,1,260,260,BIRD WITH LEG TRAPPED IN WIRE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000160,Sanderstead,E09000008,Croydon,Croydon,NULL,Sanderstead Road,NULL,CR2,533189,162108,533150,162150,51.34246079,-0.089126623\n47833121,06/04/2012 11:38,2012,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Limekiln Drive,20800917,SE7,NULL,NULL,540950,177750,NULL,NULL\n48014121,06/04/2012 17:05,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ARCHWAY,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Maple Road,NULL,KT6,517886,167576,517850,167550,51.39498215,-0.306919076\n48139121,06/04/2012 20:19,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE BY STRING,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000051,Finchley Church End,E09000003,Barnet,Hendon,NULL,Windsor Road,NULL,N3,524223,190220,524250,190250,51.59713921,-0.207913015\n48395121,07/04/2012 09:03,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN GENERATOR IN ALLEYWAY REAR OF,Cat,Person (land line),Electricity power station,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Trowbridge Road,NULL,RM3,553971,191924,553950,191950,51.60510968,0.222004183\n48512121,07/04/2012 15:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Albany Road,21300936,RM12,NULL,NULL,552350,186650,NULL,NULL\n48600121,07/04/2012 18:40,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Havelock Street,21605077,N1,NULL,NULL,530450,183750,NULL,NULL\n48948121,08/04/2012 14:11,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED IN TREE,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011236,Bridge,E09000026,Redbridge,Woodford,1.00023E+11,Prospect Road,22305259,IG8,541296,192045,541250,192050,51.60951625,0.039154238\n48990121,08/04/2012 15:55,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009370,Dalston,E09000012,Hackney,Stoke Newington,NULL,St. Mark's Rise,NULL,E8,533861,185023,533850,185050,51.54823016,-0.070824604\n49034121,08/04/2012 17:29,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Heavy Livestock Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Southborough Lane,20300617,BR2,NULL,NULL,543750,167550,NULL,NULL\n49084121,08/04/2012 18:52,2012,2012/13,Special Service,1,2,260,520,DUCK STUCK IN PIPEWORK,Bird,Person (land line),Sports Hall,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,10001273301,MacFarlane Lane,21590134,TW7,515892,177809,515850,177850,51.48736527,-0.332220888\n49188121,08/04/2012 22:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BASEMENT GAP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000129,Bloomsbury,E09000007,Camden,Soho,NULL,Windmill Street,20401070,W1T,NULL,NULL,529550,181650,NULL,NULL\n50721121,12/04/2012 10:30,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,East Ham Central,E09000025,Newham,East Ham,NULL,Friars Road,22207584,E6,NULL,NULL,541950,183550,NULL,NULL\n50804121,12/04/2012 14:20,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Augustines Road,20400671,NW1,NULL,NULL,529850,184650,NULL,NULL\n50808121,12/04/2012 14:29,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK IN BRANCHES,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Bancroft Road,NULL,E1,535571,182608,535550,182650,51.52612043,-0.047107022\n50809121,12/04/2012 14:33,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN WIRE,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000084,Thamesmead East,E09000004,Bexley,Erith,NULL,Yarnton Way,NULL,SE2,547492,179648,547450,179650,51.49653834,0.123376326\n50845121,12/04/2012 15:55,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,D'Arcy Gardens,21201544,HA3,NULL,NULL,518050,189350,NULL,NULL\n50867121,12/04/2012 16:33,2012,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Richmond,1.00023E+11,Richmond Hill,22403768,TW10,518448,173845,518450,173850,51.45120893,-0.296751036\n50900121,12/04/2012 17:55,2012,2012/13,Special Service,1,1,260,260,DOVE TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,NULL,Milton Road,21401300,UB10,NULL,NULL,507650,185950,NULL,NULL\n50982121,12/04/2012 19:38,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN STORAGE CONTAINER,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Verona Drive,21800997,KT6,518277,165571,518250,165550,51.37688012,-0.301968066\n51014121,12/04/2012 21:28,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Kilburn High Road,20400395,NW6,NULL,NULL,525150,184050,NULL,NULL\n51097121,13/04/2012 03:40,2012,2012/13,Special Service,1,2,260,520,CAT TRAPPED DOWN HOLE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Kingsland Road,20900581,E8,NULL,NULL,533550,183850,NULL,NULL\n51412121,13/04/2012 20:21,2012,2012/13,Special Service,NULL,NULL,260,NULL,INJURED CAT IN DITCH (UNACCESSABLE TO INFORMANTS),Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Dalston Lane,NULL,E8,534539,185138,534550,185150,51.54910234,-0.061008473\n51715121,14/04/2012 13:17,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX  TRAPPED IN FENCE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving livestock - Other action,E05000306,Brooklands,E09000016,Havering,Dagenham,NULL,Meadow Road,NULL,RM7,549822,187430,549850,187450,51.56584909,0.160223418\n52160121,15/04/2012 09:16,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP A TELEGRAPH POLE,Cat,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000496,Barkingside,E09000026,Redbridge,Ilford,NULL,Roll Gardens,NULL,IG2,543284,188743,543250,188750,51.57934419,0.066496909\n52423121,15/04/2012 18:49,2012,2012/13,Special Service,1,2,260,520,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,2E+11,South Hill Avenue,21201256,HA1,514421,186279,514450,186250,51.56379155,-0.350649328\n52773121,16/04/2012 12:14,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS IN HALLWAY AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Haringey Park,21103376,N8,NULL,NULL,530350,188350,NULL,NULL\n52782121,16/04/2012 12:23,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000566,Sutton South,E09000029,Sutton,Sutton,5870045799,Brighton Road,22605685,SM2,526057,163273,526050,163250,51.35455509,-0.191055453\n53006121,16/04/2012 19:10,2012,2012/13,Special Service,1,2,260,520,DOG STUCK UNDER CONCRETE BASE IN PARK,Dog,Police,Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Byegrove Road,NULL,SW19,526805,170534,526850,170550,51.41964485,-0.177728831\n53077121,16/04/2012 20:46,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN WALL AND RAILWAY TRACK,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,10010229460,Greenwich High Road,20800685,SE10,538063,177332,538050,177350,51.47810592,-0.013271984\n53299121,17/04/2012 10:56,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538735,177045,538750,177050,51.47536266,-0.003714395\n53839121,18/04/2012 17:06,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,Latchmere,E09000032,Wandsworth,Battersea,NULL,Culvert Road,22901100,SW11,NULL,NULL,527950,176350,NULL,NULL\n54335121,19/04/2012 19:27,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Cromwell Grove,21000251,W6,NULL,NULL,523450,179450,NULL,NULL\n54541121,20/04/2012 10:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Other Dwelling,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Old Kent Road,22501843,SE15,NULL,NULL,534750,177550,NULL,NULL\n55173121,21/04/2012 16:41,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN FENCE,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,Harlesden,E09000005,Brent,Park Royal,202127816,Bramshill Road,20200105,NW10,521533,183193,521550,183150,51.53457121,-0.249159283\n55467121,22/04/2012 10:23,2012,2012/13,Special Service,1,1,260,260,FOX CUB TRAPPED IN FENCE,Fox,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,Cannonbury Avenue,NULL,HA5,511928,188208,511950,188250,51.58162944,-0.385986056\n55924121,23/04/2012 12:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD CAUGHT IN NETTING,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,London Road,22403635,TW1,516161,173602,516150,173650,51.44949833,-0.329729315\n56045121,23/04/2012 16:39,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN HOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Beverley Gardens,21202479,HA7,NULL,NULL,516250,190650,NULL,NULL\n56307121,24/04/2012 09:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN MESH,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000567,Sutton West,E09000029,Sutton,Sutton,NULL,Mulgrave Road,22605508,SM2,NULL,NULL,525650,163650,NULL,NULL\n56393121,24/04/2012 14:18,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Heath Side,20300415,BR5,NULL,NULL,544550,166550,NULL,NULL\n56563121,24/04/2012 17:28,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN HOLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Rainville Road,21000669,W6,NULL,NULL,523450,177450,NULL,NULL\n57403121,26/04/2012 11:07,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH RESCUE OF CAT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Park Road,NULL,N8,530049,188465,530050,188450,51.5800537,-0.124497644\n57478121,26/04/2012 14:45,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FOXHOLE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009394,Dalgarno,E09000020,Kensington and Chelsea,North Kensington,NULL,Dalgarno Gardens,NULL,W10,523118,181789,523150,181750,51.52161006,-0.226809188\n57692121,26/04/2012 21:59,2012,2012/13,Special Service,1,1,260,260,PIGEON STUCK IN GATE FRAME,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Bushell Close,21900267,SW2,NULL,NULL,530950,172950,NULL,NULL\n57871121,27/04/2012 11:37,2012,2012/13,Special Service,1,1,260,260,HORSE TRAPPED IN MUD,Horse,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Ferry Lane,NULL,RM13,551477,180682,551450,180650,51.50477667,0.181183537\n57881121,27/04/2012 12:09,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER UNITS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,22905114,SW11,NULL,NULL,527250,176850,NULL,NULL\n58084121,27/04/2012 19:54,2012,2012/13,Special Service,1,1,260,260,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Chingford Mount Road,22826000,E4,NULL,NULL,537350,192050,NULL,NULL\n58131121,27/04/2012 21:27,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON WINDOWLEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000115,Cray Valley West,E09000006,Bromley,Orpington,NULL,Barnesdale Crescent,20300918,BR5,NULL,NULL,546350,167550,NULL,NULL\n58375121,28/04/2012 10:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON THIRD FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Wembley,NULL,Rainborough Close,20200763,NW10,NULL,NULL,520250,184850,NULL,NULL\n59007121,29/04/2012 12:35,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,Bexley,Sidcup,1.0002E+11,Tarling Close,20101418,DA14,546874,172115,546850,172150,51.42901166,0.111351514\n59242121,29/04/2012 19:36,2012,2012/13,Special Service,1,1,260,260,CAT POSSIBLY TRAPPED UNDER FALLEN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Westholm,20045820,NW11,NULL,NULL,525550,189150,NULL,NULL\n59442121,30/04/2012 09:45,2012,2012/13,Special Service,1,1,260,260,LARGE ANIMAL RESCUE,Unknown - Animal rescue from water - Farm animal,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05004081,Warley,E07000068,Brentwood,Essex,NULL,Codham Hall Lane,NULL,CM13,558421,189162,558450,189150,51.57906201,0.284965091\n59539121,30/04/2012 13:47,2012,2012/13,Special Service,1,4,260,1040,CAT TRAPPED BEWTEEN TWO WALLS,Cat,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,1.00022E+11,Bicester Road,22405345,TW9,519522,175708,519550,175750,51.46772753,-0.280673298\n60358121,01/05/2012 21:18,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Barnsbury Square,21604202,N1,NULL,NULL,531050,184150,NULL,NULL\n60816121,02/05/2012 22:37,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving livestock - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Erskine Road,NULL,E17,536901,189186,536950,189150,51.58491066,-0.025389104\n61179121,03/05/2012 19:52,2012,2012/13,Special Service,1,2,260,520,RUNNING CALL TO DEER TRAPPED IN WOODED AREA,Deer,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000117,Darwin,E09000006,Bromley,Orpington,NULL,Shire Lane,NULL,BR6,545125,164023,545150,164050,51.35675098,0.08290678\n62143121,05/05/2012 19:50,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000100,Stonebridge,E09000005,Brent,Park Royal,202121832,Barry Road,20202130,NW10,520502,184425,520550,184450,51.54586443,-0.26359519\n62406121,06/05/2012 08:58,2012,2012/13,Special Service,1,2,260,520,HORSE IN PRECARIOUS POSITION,Horse,Person (mobile),Barn,Non Residential,Other animal assistance,Animal assistance involving livestock - Other action,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Kemnal Road,20303635,BR7,544623,171629,544650,171650,51.42522311,0.078796617\n62512121,06/05/2012 14:11,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Uxbridge Road,21000835,W12,NULL,NULL,522750,180150,NULL,NULL\n63057121,07/05/2012 20:32,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Abercorn Road,21202472,HA7,NULL,NULL,517150,191250,NULL,NULL\n63417121,08/05/2012 18:06,2012,2012/13,Special Service,1,1,260,260,DOG IN RIVER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Battersea Park,NULL,SW11,528483,177258,528450,177250,51.4796965,-0.151171155\n64409121,10/05/2012 17:07,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Pippins Close,21401546,UB7,NULL,NULL,505950,179350,NULL,NULL\n64764121,11/05/2012 13:10,2012,2012/13,Special Service,1,4,260,1040,DOG TRAPPED IN BADGER SETT,Dog,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000150,Coulsdon East,E09000008,Croydon,Purley,NULL,Mead Way,NULL,CR5,530695,158065,530650,158050,51.30670397,-0.126394083\n64940121,11/05/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON  ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Melliss Avenue,22407046,TW9,NULL,NULL,519650,176950,NULL,NULL\n65398121,12/05/2012 18:04,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NEETING,Bird,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Churchfields,22304896,E18,NULL,NULL,540550,190750,NULL,NULL\n65432121,12/05/2012 19:03,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER STEPS OF BUILDING,Dog,Person (mobile),Other outdoor sporting venue,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534453,170657,534450,170650,51.41898943,-0.067757494\n65475121,12/05/2012 20:58,2012,2012/13,Special Service,1,1,260,260,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Augustines Road,20400671,NW1,NULL,NULL,529850,184550,NULL,NULL\n65961121,13/05/2012 20:21,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN DRAINPIPE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Groveway,19903732,RM8,NULL,NULL,547750,185850,NULL,NULL\n65971121,13/05/2012 20:38,2012,2012/13,Special Service,1,1,260,260,CAT WITH PAW STUCK IN WINDOW HINGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000629,West Putney,E09000032,Wandsworth,Wandsworth,NULL,William Gardens,22906198,SW15,NULL,NULL,522850,174950,NULL,NULL\n65974121,13/05/2012 20:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Vicarage Road,NULL,RM10,549566,184371,549550,184350,51.53843119,0.155233812\n66179121,14/05/2012 07:37,2012,2012/13,Special Service,1,1,260,260,DEER TRAPPED IN METAL FENCE,Deer,Police,Park,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Ingram Avenue,NULL,NW11,526215,187657,526250,187650,51.573663,-0.18009031\n66332121,14/05/2012 14:28,2012,2012/13,Special Service,1,1,260,260,BIRDS CAUGHT IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00024E+11,Grosvenor Square,8400101,W1K,528483,180848,528450,180850,51.51195966,-0.149864979\n66426121,14/05/2012 18:09,2012,2012/13,Special Service,1,1,260,260,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000491,Royal Docks,E09000025,Newham,Plaistow,NULL,Silverland Street,22207887,E16,NULL,NULL,542950,180050,NULL,NULL\n66434121,14/05/2012 18:31,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000213,Winchmore Hill,E09000010,Enfield,Southgate,NULL,Lynwood Grove,20704463,N21,NULL,NULL,531350,194350,NULL,NULL\n67296121,16/05/2012 18:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN RECLINING CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,NULL,Queen Mary Road,20500111,SE19,NULL,NULL,531950,170650,NULL,NULL\n67387121,16/05/2012 20:52,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000152,Croham,E09000008,Croydon,Croydon,NULL,Selsdon Road,NULL,CR2,533514,163242,533550,163250,51.35257543,-0.084038277\n67402121,16/05/2012 21:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Pulborough Road,22904009,SW18,NULL,NULL,524850,173550,NULL,NULL\n67760121,17/05/2012 17:25,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Eton Avenue,NULL,HA0,517446,185663,517450,185650,51.55763452,-0.307233101\n68097121,18/05/2012 11:57,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN SLIDING DOORS,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Diamond Street,22500768,SE15,NULL,NULL,533450,177150,NULL,NULL\n68347121,18/05/2012 21:00,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN METAL RAILINGS,Dog,Person (mobile),Roadside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538589,177088,538550,177050,51.47578481,-0.00579839\n68384121,18/05/2012 22:18,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BRANCH OF TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,1.00022E+11,Atkins Road,21900121,SW12,529135,173782,529150,173750,51.44830968,-0.143054988\n68450121,19/05/2012 01:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER GRILL,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009333,Spitalfields & Banglatown,E09000030,Tower Hamlets,Whitechapel,6002083,Pomell Way,22700956,E1,533822,181430,533850,181450,51.51595151,-0.07275119\n68494121,19/05/2012 03:38,2012,2012/13,Special Service,1,1,260,260,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000325,Botwell,E09000017,Hillingdon,Hayes,NULL,Dawley Road,NULL,UB3,508858,180097,508850,180050,51.50932738,-0.432783176\n68589121,19/05/2012 09:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,1.0002E+11,Greenways,20301738,BR3,537398,168878,537350,168850,51.40229714,-0.026119727\n68627121,19/05/2012 11:34,2012,2012/13,Special Service,1,1,260,260,SQUIRREL TRAPPED IN WIRE,Squirrel,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000462,Hillside,E09000024,Merton,Wimbledon,48024296,Edge Hill,22102334,SW19,523686,170286,523650,170250,51.41810429,-0.22264834\n69062121,20/05/2012 04:14,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BED,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Clapham Road,21900345,SW9,NULL,NULL,530250,176050,NULL,NULL\n69112121,20/05/2012 09:42,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bus/coach station/garage,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,2.00003E+11,Mandela Way,22501644,SE1,533481,178797,533450,178750,51.49237063,-0.078658734\n69362121,20/05/2012 19:59,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED BEHIND WALL,Dog,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,2.00001E+11,Central Hill,20500023,SE19,532588,170889,532550,170850,51.42151349,-0.094475856\n69600121,21/05/2012 12:48,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN DRAIN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Keens Road,20502164,CR0,NULL,NULL,532350,164850,NULL,NULL\n70277121,22/05/2012 18:03,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Stratford,NULL,Cecil Road,22200450,E13,NULL,NULL,540450,183650,NULL,NULL\n71319121,24/05/2012 11:21,2012,2012/13,Special Service,1,1,260,260,FOX STUCK BETWEEN TWO FENCES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Thorpe Road,22881850,E17,NULL,NULL,538050,190250,NULL,NULL\n71434121,24/05/2012 14:38,2012,2012/13,Special Service,1,1,260,260,PIGEON STUCK BEHIND CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000360,Hounslow South,E09000018,Hounslow,Heston,NULL,Dalmeny Crescent,21500335,TW3,NULL,NULL,514750,175350,NULL,NULL\n3293122,24/05/2012 21:07,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Cedars Road,21900308,SW4,NULL,NULL,528650,175650,NULL,NULL\n3354122,24/05/2012 22:46,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011471,New Addington South,E09000008,Croydon,Addington,1.00021E+11,North Downs Road,20501792,CR0,538018,162330,538050,162350,51.34330521,-0.019752656\n3651122,25/05/2012 12:18,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Knowle Road,NULL,BR2,542524,165866,542550,165850,51.37396958,0.046315254\n3782122,25/05/2012 15:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Lea Hall Road,22850450,E10,NULL,NULL,537450,187350,NULL,NULL\n4156122,26/05/2012 01:58,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER GATE,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Lever Street,NULL,EC1V,532139,182597,532150,182550,51.52683498,-0.096554751\n4233122,26/05/2012 06:01,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Dockhead,10009807682,Gomm Road,22501079,SE16,535165,179147,535150,179150,51.49511603,-0.054282408\n4584122,26/05/2012 16:19,2012,2012/13,Special Service,1,1,260,260,HORSE FALLEN OVER WITH LEG TRAPPED IN TRAILER,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Tubbenden Lane,NULL,BR6,545532,165623,545550,165650,51.37102385,0.089401407\n4638122,26/05/2012 17:00,2012,2012/13,Special Service,1,2,260,520,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000282,West Green,E09000014,Haringey,Tottenham,NULL,Penniston Close,NULL,N17,532229,190321,532250,190350,51.59622523,-0.092356506\n4763122,26/05/2012 19:47,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDERNEATH KITCHEN CABINET,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Fore Street,20704226,N18,NULL,NULL,534050,192050,NULL,NULL\n5126122,27/05/2012 09:28,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL PIGEON TRAPPED ON BALCONY RAILINGS,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000526,North Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Orchard Road,NULL,TW9,NULL,NULL,519250,175650,NULL,NULL\n5485122,27/05/2012 19:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN AIR VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011103,Goose Green,E09000028,Southwark,Peckham,NULL,Solway Road,22502266,SE22,NULL,NULL,534350,175350,NULL,NULL\n6638122,29/05/2012 10:27,2012,2012/13,Special Service,1,1,260,260,MAGPIE TRAPPED IN UPSTAIRS WINDOW,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eversleigh Road,22901568,SW11,NULL,NULL,528050,176050,NULL,NULL\n6915122,29/05/2012 18:17,2012,2012/13,Special Service,1,1,260,260,INJURED CROW STUCK IN AIREY/LIGHTWELL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Yardley Street,21606383,WC1X,NULL,NULL,531150,182450,NULL,NULL\n6978122,29/05/2012 20:28,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN HOLE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Holloway Road,21606449,N19,NULL,NULL,529450,186750,NULL,NULL\n7429122,30/05/2012 16:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Boileau Road,NULL,SW13,522509,177587,522550,177550,51.48397727,-0.237041378\n7906122,31/05/2012 12:17,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSPCA WITH RECOVERY OF DUCKLINGS,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000192,Walpole,E09000009,Ealing,Ealing,12148352,Melbourne Avenue,20601160,W13,516334,180277,516350,180250,51.50945656,-0.325044347\n7999122,31/05/2012 15:50,2012,2012/13,Special Service,1,3,260,780,DUCKLINGS TRAPPED UNDER MANHOLE COVER,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Vaughan Way,NULL,E1W,534432,180217,534450,180250,51.5049062,-0.06442752\n8224122,31/05/2012 22:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIPE IN KITCHEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Broomhouse Lane,21000798,SW6,NULL,NULL,525250,175950,NULL,NULL\n8997122,02/06/2012 14:22,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541438,174591,541450,174550,51.45264276,0.034199504\n9052122,02/06/2012 16:32,2012,2012/13,Special Service,1,1,260,260,DUCKLING FALLEN DOWN DRAIN,Bird,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Richmond,NULL,Petersham Road,NULL,TW10,518211,173775,518250,173750,51.45062926,-0.30018357\n9136122,02/06/2012 19:04,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Ickworth Park Road,22846950,E17,NULL,NULL,536350,189350,NULL,NULL\n9508122,03/06/2012 12:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541162,174630,541150,174650,51.45306202,0.030245505\n9975122,04/06/2012 10:55,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Credon Road,22207671,E13,NULL,NULL,541150,183250,NULL,NULL\n10009122,04/06/2012 12:43,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Eltham Road,NULL,SE9,541165,174631,541150,174650,51.45307025,0.03028905\n10017122,04/06/2012 13:03,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Grosvenor Road,20100641,DA17,NULL,NULL,549050,177950,NULL,NULL\n10041122,04/06/2012 14:11,2012,2012/13,Special Service,2,3,260,780,Redacted,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Shadbolt Avenue,NULL,E4,536352,192119,536350,192150,51.61139917,-0.032170205\n10097122,04/06/2012 17:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Castle Way,21500214,TW13,NULL,NULL,511150,171850,NULL,NULL\n10129122,04/06/2012 18:19,2012,2012/13,Special Service,1,2,260,520,HORSE TRAPPED UNDER TREE,Horse,Other FRS,Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,1.0002E+11,Monterey Close,20100992,DA5,550314,172547,550350,172550,51.43199214,0.16098005\n10161122,04/06/2012 20:13,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000477,Canning Town North,E09000025,Newham,Plaistow,46004757,Beckton Road,22200364,E16,539853,181746,539850,181750,51.51733135,0.014236252\n10310122,05/06/2012 01:25,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Glenparke Road,22207729,E7,NULL,NULL,540750,184850,NULL,NULL\n10333122,05/06/2012 02:08,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN IRON GATE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,207045458,Shakespeare Avenue,20701888,N11,529444,192236,529450,192250,51.61408109,-0.131834375\n10362122,05/06/2012 03:16,2012,2012/13,Special Service,1,1,260,260,ASSISTING POLICE WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Embry Road,NULL,SE9,541250,174750,541250,174750,51.45411841,0.031559018\n10449122,05/06/2012 10:47,2012,2012/13,Special Service,1,2,260,520,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000291,Hatch End,E09000015,Harrow,Harrow,NULL,Evelyn Drive,21201116,HA5,NULL,NULL,511650,190950,NULL,NULL\n10469122,05/06/2012 12:16,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Compton Avenue,21301099,RM2,NULL,NULL,553150,189550,NULL,NULL\n10557122,05/06/2012 16:33,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHASSIS OF CAR OUTSIDE,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Abbeyfield Road,NULL,SE16,535277,178756,535250,178750,51.49157556,-0.052819827\n10616122,05/06/2012 18:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,Hillrise,E09000019,Islington,Holloway,5300049348,Hornsey Lane,21600464,N6,529329,187515,529350,187550,51.57168197,-0.135232082\n10657122,05/06/2012 20:41,2012,2012/13,Special Service,1,1,260,260,FOX STUCK IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Icknield Drive,22302961,IG2,NULL,NULL,543950,188750,NULL,NULL\n11035122,06/06/2012 17:34,2012,2012/13,Special Service,1,1,260,260,CAT WEDGED BETWEEN TWO WALLS,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Cavendish Road,20202448,NW6,NULL,NULL,524650,184450,NULL,NULL\n11384122,07/06/2012 08:40,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS TRAPPED IN DRAIN,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Waterway Avenue,NULL,SE13,538003,175622,538050,175650,51.46275403,-0.014802933\n11597122,07/06/2012 16:02,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Haselbury Road,20704311,N18,NULL,NULL,533350,192950,NULL,NULL\n11716122,07/06/2012 20:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Twilley Street,22905634,SW18,NULL,NULL,525850,173850,NULL,NULL\n12092122,08/06/2012 14:51,2012,2012/13,Special Service,1,1,260,260,KITTENS TRAPPED BENEATH LIFT CAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,Bexley,Erith,NULL,Winifred Road,20101605,DA8,NULL,NULL,551050,178350,NULL,NULL\n12123122,08/06/2012 15:39,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,Braemar Road,NULL,KT4,522622,165351,522650,165350,51.37398197,-0.239645107\n13591122,11/06/2012 10:10,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED IN NETTING,Bird,Person (mobile),Telephone exchange,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05011225,Erith,E09000004,Bexley,Erith,10011862607,Viking Way,20101683,DA8,550519,179176,550550,179150,51.49150039,0.16674822\n13679122,11/06/2012 13:15,2012,2012/13,Special Service,1,1,260,260,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000339,Townfield,E09000017,Hillingdon,Hayes,NULL,Hemmen Lane,21400942,UB3,NULL,NULL,510050,181050,NULL,NULL\n13723122,11/06/2012 14:44,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN FENCE AND HIGH BUSHES,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,100106144,Heathway,19900172,RM9,549031,184719,549050,184750,51.54169941,0.147672433\n13875122,11/06/2012 20:05,2012,2012/13,Special Service,1,1,260,260,DOG IN DITCH,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518152,177176,518150,177150,51.48120893,-0.299897256\n14190122,12/06/2012 14:08,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHINED CAR EXHAUST,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000546,Peckham,E09000028,Southwark,Peckham,NULL,Cator Street,NULL,SE15,533808,177250,533850,177250,51.47839113,-0.074537108\n14336122,12/06/2012 20:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Cairo New Road,NULL,CR0,531914,165729,531950,165750,51.37529926,-0.106080617\n14530122,13/06/2012 08:04,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,NULL,College Road,21201461,HA3,NULL,NULL,515150,190950,NULL,NULL\n15380122,14/06/2012 20:42,2012,2012/13,Special Service,1,4,260,1040,FOX CUB STUCK BETWEEN TWO WALLS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Byron Avenue,22300147,E18,NULL,NULL,539650,190050,NULL,NULL\n15469122,15/06/2012 02:03,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON FENCE,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Eastern Road,NULL,E13,540771,183358,540750,183350,51.53158862,0.028101158\n17100122,18/06/2012 09:45,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER BUS,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000506,Hainault,E09000026,Redbridge,Hainault,NULL,Huntsman Road,NULL,IG6,546856,192475,546850,192450,51.61195906,0.11957153\n17745122,19/06/2012 15:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE ROOF,Cat,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,1.00023E+11,Cranston Gardens,22829900,E4,537689,191986,537650,191950,51.6098788,-0.01292534\n18177122,20/06/2012 11:12,2012,2012/13,Special Service,2,3,260,780,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL\n18251122,20/06/2012 14:24,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN TRAPPED IN METAL DRUM,Cat,Person (land line),\"Large refuse/rubbish container (eg skip, paladin)\",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000266,Alexandra,E09000014,Haringey,Hornsey,1.00023E+11,Alexandra Park Road,21106086,N10,529009,190471,529050,190450,51.59831961,-0.138762055\n18325122,20/06/2012 17:10,2012,2012/13,Special Service,NULL,NULL,260,NULL,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000437,Bellingham,E09000023,Lewisham,Beckenham,NULL,Lushington Road,NULL,SE6,537620,171450,537650,171450,51.42535631,-0.021933009\n18627122,21/06/2012 05:05,2012,2012/13,Special Service,1,5,260,1300,HORSE STUCK ON SOAK AWAY,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536228,195385,536250,195350,51.64077745,-0.032690281\n18983122,21/06/2012 22:15,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,20800415,SE10,NULL,NULL,538450,177450,NULL,NULL\n20803122,25/06/2012 11:18,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN  FENCE AND WALL,Dog,Police,Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Gooshays Drive,NULL,RM3,554117,192242,554150,192250,51.60792689,0.224250363\n21099122,25/06/2012 21:02,2012,2012/13,Special Service,1,1,260,260,BIRD CAUGHT IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Brixton Road,NULL,SW9,531024,175476,531050,175450,51.4630998,-0.115260147\n21239122,26/06/2012 04:48,2012,2012/13,Special Service,1,2,260,520,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Church Close,NULL,UB7,506048,179403,506050,179450,51.5036235,-0.47346664\n21311122,26/06/2012 10:20,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,Furzedown,E09000032,Wandsworth,Tooting,NULL,Chillerton Road,NULL,SW17,528521,171241,528550,171250,51.42561289,-0.152807227\n21637122,26/06/2012 22:25,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN HOLE BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Robert Burns Mews,21902302,SE24,NULL,NULL,531850,174750,NULL,NULL\n22083122,27/06/2012 19:27,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011241,Cranbrook,E09000026,Redbridge,Ilford,1.00023E+11,Clarence Avenue,22302721,IG2,543093,188312,543050,188350,51.57551988,0.063566864\n22468122,28/06/2012 12:36,2012,2012/13,Special Service,1,3,260,780,HORSE CAST IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Colnbrook by Pass,NULL,UB7,505307,177206,505350,177250,51.4840144,-0.48479438\n22697122,28/06/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN DOWN DRAIN,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011234,Aldborough,E09000026,Redbridge,Ilford,NULL,Applegarth Drive,22301809,IG2,NULL,NULL,545850,189250,NULL,NULL\n22715122,28/06/2012 18:18,2012,2012/13,Special Service,1,1,260,260,DOG STUCK ON ROOF,Dog,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,High Road,20022400,N12,NULL,NULL,526450,192050,NULL,NULL\n23232122,29/06/2012 14:55,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN TREE BY WIRE,Bird,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Venus Road,NULL,SE18,542720,179182,542750,179150,51.49357494,0.0544905\n24156122,01/07/2012 11:50,2012,2012/13,Special Service,1,1,260,260,CAT IMPALED ON RAILING,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Great Peter Street,NULL,SW1P,530135,179211,530150,179250,51.4968705,-0.126675815\n24193122,01/07/2012 13:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000438,Blackheath,E09000023,Lewisham,Lee Green,NULL,South Row,NULL,SE3,539798,176409,539750,176450,51.46938619,0.011330123\n24750122,02/07/2012 15:54,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP TREE,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Fulham,217060697,Old Brompton Road,21700372,SW5,525488,178099,525450,178050,51.48792635,-0.193979074\n24990122,02/07/2012 23:37,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Wulfstan Street,NULL,W12,521998,181140,521950,181150,51.51601994,-0.243169051\n25223122,03/07/2012 14:58,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Edwin Avenue,22200578,E6,NULL,NULL,543250,182950,NULL,NULL\n25565122,04/07/2012 10:13,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000205,Palmers Green,E09000010,Enfield,Edmonton,207004258,North Circular Road,20707474,N13,531373,192276,531350,192250,51.61399391,-0.103975839\n71657121,05/07/2012 15:00,2012,2012/13,Special Service,1,1,260,260,HORSE TRAPPED BY TREE TRUNK,Horse,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011474,Old Coulsdon,E09000008,Croydon,Purley,1.00021E+11,Stites Hill Road,20500340,CR5,532030,157160,532050,157150,51.29826323,-0.107586803\n71786121,05/07/2012 19:25,2012,2012/13,Special Service,1,3,260,780,HORSE TRAPPED IN DITCH,Horse,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,1.0002E+11,Skeet Hill Lane,20301291,BR5,549047,165483,549050,165450,51.36885459,0.139803182\n71789121,05/07/2012 19:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,NULL,Kingsland Road,20900581,E8,NULL,NULL,533550,184150,NULL,NULL\n71889121,05/07/2012 22:19,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DITCH,Cat,Person (mobile),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,10023853329,Cambria Road,21900277,SE5,532121,175817,532150,175850,51.46590943,-0.099350639\n72050121,06/07/2012 09:33,2012,2012/13,Special Service,1,1,260,260,NEW BORN KITTENS STUCK IN HOLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000198,Enfield Highway,E09000010,Enfield,Enfield,NULL,Carterhatch Road,20702166,EN3,NULL,NULL,536150,197350,NULL,NULL\n72247121,06/07/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Lee Green,NULL,Baring Road,NULL,SE12,540160,173450,540150,173450,51.44270703,0.015367382\n72312121,06/07/2012 20:15,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Forty Acre Lane,22200623,E16,NULL,NULL,540050,181650,NULL,NULL\n72569121,07/07/2012 10:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX TRAPPED IN GARAGE,Fox,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Digby Street,NULL,E2,535373,182750,535350,182750,51.52744389,-0.049904918\n73064121,08/07/2012 10:29,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BARS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,1.00023E+11,Putney Hill,22904077,SW15,523907,174889,523950,174850,51.45942517,-0.217862827\n73085121,08/07/2012 10:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH SEGULLS TRAPPED DOWN A WELL,Bird,Person (land line),Converted office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05009305,Farringdon Without,E09000001,City of London,Soho,1.00023E+11,Bream's Buildings,8100080,EC4A,531176,181357,531150,181350,51.51591576,-0.110890077\n73826121,09/07/2012 16:55,2012,2012/13,Special Service,1,1,260,260,ANIMAL TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Thatches Grove,19900833,RM6,NULL,NULL,548350,189450,NULL,NULL\n74235121,10/07/2012 11:58,2012,2012/13,Special Service,1,2,260,520,PREGNANT CAT TRAPPED UNDER FLOORBOARDS OF CELLAR,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Grayshott Road,22902114,SW11,NULL,NULL,528050,175850,NULL,NULL\n74239121,10/07/2012 12:08,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,1.00023E+11,The Quadrant,22406165,TW9,517980,175089,517950,175050,51.46248728,-0.303068732\n74342121,10/07/2012 16:04,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Therapia Road,22502490,SE22,NULL,NULL,535050,174250,NULL,NULL\n74668121,11/07/2012 10:53,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011096,Camberwell Green,E09000028,Southwark,Peckham,NULL,Wyndham Road,22500231,SE5,NULL,NULL,532150,177450,NULL,NULL\n75143121,12/07/2012 09:13,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Defoe Avenue,NULL,TW9,519398,177172,519350,177150,51.48091172,-0.281963656\n76278121,14/07/2012 12:48,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Police,Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538435,177495,538450,177450,51.47947986,-0.007854896\n76376121,14/07/2012 16:28,2012,2012/13,Special Service,1,1,260,260,DEER TRAPPED ON RAILWAY LINE,Deer,Coastguard,Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,1.0002E+11,Hawksbrook Lane,20301744,BR3,537739,167290,537750,167250,51.38794459,-0.021836012\n76410121,14/07/2012 17:31,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS TRAPPED IN LOCK,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Leamouth Road,NULL,E14,538959,181091,538950,181050,51.51166589,0.001102031\n76467121,14/07/2012 20:20,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Unlicensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,Stroud Green,E09000014,Haringey,Holloway,NULL,Marquis Road,21103599,N4,NULL,NULL,530950,187350,NULL,NULL\n76750121,15/07/2012 12:47,2012,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,North Drive,22903564,SW16,529169,171792,529150,171750,51.43041772,-0.143291249\n76771121,15/07/2012 13:28,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED DOWN DISUSED TOILET IN DERELICT BUILDING,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000178,Greenford Green,E09000009,Ealing,Northolt,12066276,Greenford Road,20602353,UB6,515219,185069,515250,185050,51.55275418,-0.339537489\n77051121,15/07/2012 23:15,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,Weavers,E09000030,Tower Hamlets,Shoreditch,NULL,Virginia Road,22702359,E2,NULL,NULL,533550,182550,NULL,NULL\n77201121,16/07/2012 08:07,2012,2012/13,Special Service,1,1,260,260,DOG IN CANAL,Dog,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Lawrence Avenue,NULL,NW10,520635,183803,520650,183850,51.54024587,-0.261891214\n77800121,17/07/2012 13:33,2012,2012/13,Special Service,1,2,260,520,DEER TRAPPED IN FENCE,Deer,Police,Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Blandford Road,NULL,TW11,514934,171015,514950,171050,51.42649733,-0.348218207\n78010121,17/07/2012 19:52,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN TREE,Cat,Person (mobile),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,NULL,Hampstead Way,NULL,NW11,525949,187472,525950,187450,51.57205988,-0.183992941\n78383121,18/07/2012 15:45,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Landseer Road,20702786,EN1,NULL,NULL,534050,195650,NULL,NULL\n78823121,19/07/2012 13:58,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Crooms Hill,NULL,SE10,538648,177077,538650,177050,51.47567152,-0.004953721\n78905121,19/07/2012 17:11,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN HOLE,Cat,Person (land line),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000449,New Cross,E09000023,Lewisham,Deptford,1.00022E+11,Sterling Gardens,22000975,SE14,536106,177476,536150,177450,51.47987401,-0.041379415\n78996121,19/07/2012 20:10,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,10091060895,Harvist Road,20201304,NW6,524031,183105,524050,183150,51.53323775,-0.213193078\n79108121,20/07/2012 01:07,2012,2012/13,Special Service,1,1,260,260,CAT WITH PAW TRAPPED IN WINDOW MECHANISM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Station Square,20300632,BR5,NULL,NULL,544450,167650,NULL,NULL\n79527121,20/07/2012 20:15,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED IN GARDEN FURNITURE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,NULL,Gauntlett Road,22602329,SM1,NULL,NULL,526650,164050,NULL,NULL\n79765121,21/07/2012 11:27,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED  IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Walcot Square,21901452,SE11,NULL,NULL,531350,178950,NULL,NULL\n79774121,21/07/2012 11:39,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000487,Little Ilford,E09000025,Newham,Ilford,46254140,Romford Road,22208082,E12,542597,185811,542550,185850,51.55317242,0.055400669\n79976121,21/07/2012 18:41,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN WIRE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Hollybush Place,NULL,E2,534888,182830,534850,182850,51.52827881,-0.056861589\n79993121,21/07/2012 19:10,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Station Approach,NULL,SW20,522884,168974,522850,168950,51.40648719,-0.234630679\n80604121,22/07/2012 18:13,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED BIRDS IN NETTING,Bird,Person (land line),Other transport building,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Bethnal Green Road,NULL,E2,534953,182724,534950,182750,51.52731074,-0.055965725\n80622121,22/07/2012 18:46,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN WIRE MESH,Bird,Person (mobile),Train station - concourse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,New Malden,NULL,Grand Drive,NULL,SW20,523204,169290,523250,169250,51.40925784,-0.229922539\n80938121,23/07/2012 10:17,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,2.00001E+11,Queenhill Road,20502285,CR2,534715,162461,534750,162450,51.3452737,-0.067097135\n81659121,24/07/2012 12:56,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011483,Shirly South,E09000008,Croydon,Addington,NULL,Shirley Hills Road,20501831,CR0,NULL,NULL,535750,164450,NULL,NULL\n81911121,24/07/2012 17:57,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011099,Dulwich Hill,E09000028,Southwark,Forest Hill,NULL,Friern Road,22501002,SE22,NULL,NULL,534150,173850,NULL,NULL\n82233121,25/07/2012 06:09,2012,2012/13,Special Service,1,1,260,260,HORSE IN OVERFLOW CANAL,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754\n82267121,25/07/2012 08:28,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011482,Shirley North,E09000008,Croydon,Woodside,NULL,Wickham Road,20500586,CR0,NULL,NULL,535750,165750,NULL,NULL\n82342121,25/07/2012 11:17,2012,2012/13,Special Service,1,1,260,260,ELDERLY PERSON LOCKED IN,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011114,St. George's,E09000028,Southwark,Lambeth,NULL,Hayles Street,22501221,SE11,NULL,NULL,531650,178950,NULL,NULL\n82564121,25/07/2012 18:09,2012,2012/13,Special Service,1,1,260,260,CROW TRAPPED ON AERIAL,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Surbiton,128035648,High Street,21800505,KT1,517796,168855,517750,168850,51.40649659,-0.307788557\n82578121,25/07/2012 18:25,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN SASH WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Roxwell Road,21000705,W12,NULL,NULL,522250,179850,NULL,NULL\n82934121,26/07/2012 07:54,2012,2012/13,Special Service,1,1,260,260,DUCKLINGS STUCK IN LOCKED DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Wanstead Park,NULL,E11,541657,187443,541650,187450,51.56807379,0.042508679\n83003121,26/07/2012 11:11,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,NULL,Broadlands Avenue,NULL,SW16,530185,172596,530150,172550,51.43741093,-0.128389292\n83649121,27/07/2012 08:15,2012,2012/13,Special Service,1,2,260,520,TWO DUCKLINGS STUCK IN GRID,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Wanstead Park,NULL,E11,541657,187443,541650,187450,51.56807379,0.042508679\n83806121,27/07/2012 14:11,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH FOX STUCK BETWEEN TWO HOUSES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,Fullwell,E09000026,Redbridge,Hainault,NULL,Kirkland Avenue,22305121,IG5,NULL,NULL,543450,190250,NULL,NULL\n84143121,27/07/2012 21:46,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN DOWN DRAIN SHAFT,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Fourth Avenue,NULL,E12,542494,185722,542450,185750,51.55239869,0.053880045\n84159121,27/07/2012 22:17,2012,2012/13,Special Service,1,1,260,260,CAT STUCK DOWN HOLE IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000088,Dollis Hill,E09000005,Brent,Willesden,NULL,Gladstone Park Gardens,20201385,NW2,NULL,NULL,523150,186450,NULL,NULL\n84391121,28/07/2012 12:15,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Laundry Lane,NULL,N1,532135,184011,532150,184050,51.53954292,-0.096081996\n84624121,28/07/2012 19:01,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,St. Gabriels Road,20202433,NW2,NULL,NULL,523950,185150,NULL,NULL\n84688121,28/07/2012 20:22,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Reed Road,21106602,N17,NULL,NULL,533850,189950,NULL,NULL\n85085121,29/07/2012 14:32,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK IN TREE - NOT TRAPPED,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00021E+11,Abbs Cross Gardens,21300186,RM12,553417,187019,553450,187050,51.56119061,0.211870572\n85257121,29/07/2012 20:33,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED IN WIRE,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Wilmer Place,NULL,N16,533510,186575,533550,186550,51.56226029,-0.075295543\n85461121,30/07/2012 10:09,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED INBETWEEN WALL AND SHED,Dog,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Brixham Road,20100203,DA16,547702,177025,547750,177050,51.47291509,0.125302578\n85686121,30/07/2012 18:24,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN IN BUSHES,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000078,Longlands,E09000004,Bexley,Sidcup,NULL,Sydney Road,NULL,DA14,545599,171720,545550,171750,51.42579106,0.092862183\n86011121,31/07/2012 12:19,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,NULL,SW20,524248,168582,524250,168550,51.40266715,-0.215166869\n86236121,31/07/2012 21:58,2012,2012/13,Special Service,1,2,260,520,CAT STUCK IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,1.00023E+11,Knollys Road,21900820,SW16,531396,172455,531350,172450,51.43586433,-0.111028993\n87148121,02/08/2012 20:53,2012,2012/13,Special Service,1,1,260,260,DISTRESSED KITTEN TRAPPED IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000126,Shortlands,E09000006,Bromley,Bromley,NULL,Broadoaks Way,20301570,BR2,NULL,NULL,539650,167850,NULL,NULL\n87173121,02/08/2012 21:39,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND GAS FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000297,Pinner,E09000015,Harrow,Harrow,NULL,Norman Crescent,21201793,HA5,NULL,NULL,511550,190750,NULL,NULL\n87286121,03/08/2012 08:35,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN OLYMPIC ADVERTISING BANNER ON LAMPOST,Bird,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Cromwell Road,NULL,SW7,525920,178827,525950,178850,51.49437306,-0.187500749\n87359121,03/08/2012 12:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD RESCUE,Bird,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Longley Road,NULL,SW17,527241,170921,527250,170950,51.42302536,-0.17132299\n87447121,03/08/2012 15:00,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,St. Philip Street,22904897,SW8,NULL,NULL,528750,176250,NULL,NULL\n87529121,03/08/2012 17:44,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011100,Dulwich Village,E09000028,Southwark,Brixton,2.00003E+11,Casino Avenue,22500457,SE24,532527,174938,532550,174950,51.4579152,-0.093838466\n88614121,05/08/2012 18:52,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Park Royal,NULL,Park Royal Road,NULL,NW10,520398,182176,520350,182150,51.52567377,-0.265863009\n88921121,06/08/2012 13:09,2012,2012/13,Special Service,1,1,260,260,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000401,Berrylands,E09000021,Kingston upon Thames,Surbiton,NULL,Avenue South,NULL,KT5,519074,166896,519050,166850,51.38862256,-0.290078721\n88969121,06/08/2012 14:56,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN FALSE CEILING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000033,Goresbrook,E09000002,Barking and Dagenham,Dagenham,NULL,Coombes Road,19900453,RM9,NULL,NULL,548950,183850,NULL,NULL\n89115121,06/08/2012 21:36,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000496,Barkingside,E09000026,Redbridge,Hainault,NULL,Cranbrook Road,NULL,IG6,543751,189073,543750,189050,51.58219059,0.073366851\n89272121,07/08/2012 09:40,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Stanstead Road,22004078,SE23,NULL,NULL,535550,173150,NULL,NULL\n90200121,09/08/2012 08:16,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Tunstock Way,20101485,DA17,NULL,NULL,548550,179150,NULL,NULL\n90319121,09/08/2012 13:40,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Winifred Avenue,NULL,RM12,553961,185944,553950,185950,51.55138386,0.219242186\n90361121,09/08/2012 15:35,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILING,Dog,Police,Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Alfred Street,NULL,E3,537026,182820,537050,182850,51.52767486,-0.02606372\n90385121,09/08/2012 16:38,2012,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100043997,Kingsbridge Road,19900516,IG11,544866,182952,544850,182950,51.52690542,0.086929536\n90972121,10/08/2012 17:41,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UP  CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Normanhurst Road,21901026,SW2,NULL,NULL,530750,172950,NULL,NULL\n91320121,11/08/2012 09:24,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Surrey Lane,NULL,SW11,NULL,NULL,527150,176750,NULL,NULL\n91465121,11/08/2012 15:23,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300081977,Seven Sisters Road,21603790,N7,531005,186442,531050,186450,51.56165271,-0.111461867\n91685121,11/08/2012 20:45,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000622,St. Mary's Park,E09000032,Wandsworth,Battersea,NULL,Inworth Street,22902538,SW11,NULL,NULL,527350,176350,NULL,NULL\n92168121,12/08/2012 18:39,2012,2012/13,Special Service,1,2,260,520,ASSIST RSPCA RESCUING CAT,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529550,175050,NULL,NULL\n92368121,13/08/2012 02:33,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Caraway Close,22200443,E13,NULL,NULL,540750,181950,NULL,NULL\n92423121,13/08/2012 07:21,2012,2012/13,Special Service,1,5,260,1300,RUNNING CALL TO SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,East India Dock Road,22700445,E14,NULL,NULL,537850,181050,NULL,NULL\n92492121,13/08/2012 10:25,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SHUTTERS,Cat,Person (mobile),\"Takeaway, fast food\",Non Residential,Other animal assistance,Assist trapped domestic animal,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,1.00023E+11,Woolwich New Road,20801651,SE18,543634,178703,543650,178750,51.4890395,0.067452771\n92526121,13/08/2012 11:38,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN FLAT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,St. Alphonsus Road,21901259,SW4,NULL,NULL,529550,175050,NULL,NULL\n92598121,13/08/2012 14:29,2012,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM WINDOW INTO SUB LEVEL RECESS,Cat,Person (land line),Hedge,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,6211693,Meath Crescent,22702470,E2,535809,182738,535850,182750,51.52723152,-0.043628241\n92681121,13/08/2012 17:10,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,1.00022E+11,Chiswick High Road,21502481,W4,520931,178515,520950,178550,51.49265688,-0.259438533\n92806121,13/08/2012 20:47,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN DRAIN,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000162,Selsdon and Ballards,E09000008,Croydon,Addington,NULL,Ingham Road,NULL,CR2,535451,162788,535450,162750,51.34803751,-0.056411718\n93072121,14/08/2012 11:13,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (land line),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00023E+11,Well Street,20901058,E9,535538,184578,535550,184550,51.54383113,-0.046824665\n93077121,14/08/2012 11:34,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Beam Avenue,19900424,RM10,NULL,NULL,549950,183550,NULL,NULL\n93080121,14/08/2012 11:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH  BIRD TRAPPED NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Towfield Road,21501127,TW13,NULL,NULL,512650,172750,NULL,NULL\n93362121,14/08/2012 22:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND LEAN-TO OF NEIGHBOURING HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,NULL,Tudor Drive,21800983,KT2,NULL,NULL,518350,171250,NULL,NULL\n93748121,15/08/2012 15:30,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Riverdale Road,20801275,SE18,NULL,NULL,545550,178350,NULL,NULL\n94553121,17/08/2012 08:06,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Haslemere Avenue,22103141,SW18,NULL,NULL,525750,172850,NULL,NULL\n95272121,18/08/2012 13:31,2012,2012/13,Special Service,1,1,260,260,ASSIST  RSCPA  WITH CAT  STUCK  IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Haggerston Road,NULL,E8,533857,183697,533850,183650,51.53631528,-0.071385972\n95544121,18/08/2012 22:15,2012,2012/13,Special Service,1,2,260,520,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Union Road,20601785,UB5,NULL,NULL,513350,183250,NULL,NULL\n95952121,19/08/2012 17:05,2012,2012/13,Special Service,1,1,260,260,LAMB TRAPPED IN WIRE FENCING,Lamb,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754\n95971121,19/08/2012 17:48,2012,2012/13,Special Service,1,1,260,260,PREGNANT CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lee Green,1.00022E+11,Minard Road,22001714,SE6,539108,173123,539150,173150,51.44002751,0.000111879\n95982121,19/08/2012 18:16,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,Portland Road,20302076,BR1,NULL,NULL,541450,171350,NULL,NULL\n96290121,20/08/2012 06:48,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN PASSAGEWAY,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000089,Dudden Hill,E09000005,Brent,Willesden,NULL,Ellesmere Road,20201822,NW10,NULL,NULL,522550,185250,NULL,NULL\n96940121,21/08/2012 07:52,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SKYLIGHT ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Elspeth Road,22901486,SW11,NULL,NULL,527850,175350,NULL,NULL\n96977121,21/08/2012 10:05,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,1.00023E+11,Haggerston Road,20900480,E8,533857,183697,533850,183650,51.53631528,-0.071385972\n97409121,21/08/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER MANHOLE COVER,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000633,Churchill,E09000033,Westminster,Lambeth,NULL,Churchill Gardens,NULL,SW1V,529035,178036,529050,178050,51.48656278,-0.142942642\n97468121,21/08/2012 20:16,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN BABY GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Ashley Crescent,22900155,SW11,NULL,NULL,528450,175850,NULL,NULL\n97734121,22/08/2012 09:11,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH BIRD TANGLED IN WIRE,Bird,Person (land line),Indoor Market,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Hertslet Road,NULL,N7,530655,185937,530650,185950,51.55719568,-0.116695526\n97777121,22/08/2012 10:51,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED GULL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000632,Bryanston and Dorset Square,E09000033,Westminster,Paddington,NULL,Edgware Road,8400919,W2,NULL,NULL,527150,181650,NULL,NULL\n98023121,22/08/2012 19:55,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,Bridge,E09000026,Redbridge,Woodford,NULL,West Grove,22305438,IG8,NULL,NULL,541350,192050,NULL,NULL\n98483121,23/08/2012 18:45,2012,2012/13,Special Service,1,1,260,260,SQUIRREL STUCK  INBETWEEN DRAINPIPE & HOUSE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000194,Bush Hill Park,E09000010,Enfield,Edmonton,NULL,Third Avenue,20703011,EN1,NULL,NULL,533750,195650,NULL,NULL\n98536121,23/08/2012 20:07,2012,2012/13,Special Service,1,1,260,260,SEAGULL TRAPPED,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Allsop Place,NULL,NW1,NULL,NULL,527850,182150,NULL,NULL\n99021121,24/08/2012 18:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000150,Coulsdon East,E09000008,Croydon,Purley,NULL,Chaldon Way,NULL,CR5,530144,158675,530150,158650,51.3123121,-0.134072369\n99327121,25/08/2012 12:50,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Pasteur Gardens,20704600,N18,NULL,NULL,532050,192050,NULL,NULL\n99536121,25/08/2012 16:05,2012,2012/13,Special Service,1,2,260,520,HORSE STUCK IN WATER,Horse,Other FRS,Woodland/forest - conifers/softwood,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Harold Court Road,21301257,RM3,555956,190963,555950,190950,51.59592972,0.250217786\n99912121,26/08/2012 00:02,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Stanhope Street,20400774,NW1,NULL,NULL,529050,182550,NULL,NULL\n100125121,26/08/2012 12:28,2012,2012/13,Special Service,1,1,260,260,COW IN DISTRESS IN GRAND UNION CANAL,Cow,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Canal Side,NULL,UB9,504060,191306,504050,191350,51.61098161,-0.498579108\n100608121,27/08/2012 10:35,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Avenue Road,21100037,N6,NULL,NULL,529550,187950,NULL,NULL\n100952121,27/08/2012 23:54,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Green Lane,22306487,IG3,NULL,NULL,546050,186950,NULL,NULL\n101699121,29/08/2012 16:20,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIPE,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000617,Latchmere,E09000032,Wandsworth,Battersea,10033238938,Winstanley Road,22906291,SW11,526778,175685,526750,175650,51.46594417,-0.176274112\n101960121,30/08/2012 08:55,2012,2012/13,Special Service,1,1,260,260,PIDEON TRAPPED IN SHOP,Bird,Person (land line),Large supermarket,Non Residential,Other animal assistance,Assist trapped wild animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,Syon Lane,21501106,TW7,516041,177649,516050,177650,51.48589669,-0.330128386\n102177121,30/08/2012 17:17,2012,2012/13,Special Service,1,1,260,260,FERRETT STUCK IN CAGE,Ferret,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,2.00001E+11,The Ridgeway,22870050,E4,538189,194183,538150,194150,51.62949812,-0.004843574\n102198121,30/08/2012 18:06,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED BEHIND WALL,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,10008239077,Ashanti Mews,20901862,E8,535019,185165,535050,185150,51.54923035,-0.054079613\n102218121,30/08/2012 18:48,2012,2012/13,Special Service,2,3,260,780,BIRD WITH WING TRAPPED IN FISHING LINE WATER RESCUE LEVEL TWO IMPLEMENTED,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Cool Oak Lane,NULL,NW9,522250,187750,522250,187750,51.57537212,-0.237243029\n102652121,31/08/2012 14:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BY LEAD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,Lansdowne Road,20501121,CR0,NULL,NULL,532850,166350,NULL,NULL\n102665121,31/08/2012 15:21,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH LEG TRAPPED IN WINDON,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Turnham Road,22001986,SE4,NULL,NULL,536150,175150,NULL,NULL\n103437121,01/09/2012 19:11,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER BONNET OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Tunmarsh Lane,NULL,E13,541354,182588,541350,182550,51.52452382,0.036191684\n103477121,01/09/2012 20:34,2012,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,Botwell,E09000017,Hillingdon,Hayes,NULL,Printing House Lane,NULL,UB3,509476,179983,509450,179950,51.50818341,-0.423917159\n104094121,03/09/2012 01:31,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON BRANCH OF TREE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Woodlands Road,22303408,IG1,NULL,NULL,544150,185950,NULL,NULL\n104314121,03/09/2012 14:06,2012,2012/13,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000488,Manor Park,E09000025,Newham,Stratford,46033810,Halley Road,22208155,E12,542058,185054,542050,185050,51.54650607,0.047326338\n104557121,03/09/2012 22:25,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,NULL,Farm Lane,NULL,SW6,525240,177302,525250,177350,51.4808185,-0.197831484\n104904121,04/09/2012 16:33,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Mildmay Grove North,21606886,N1,NULL,NULL,533050,185050,NULL,NULL\n105551121,05/09/2012 20:09,2012,2012/13,Special Service,1,1,260,260,BIRD OF PREY TRAPPED IN TREE - CALLED BY RSPCA,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Perry Street,20303525,BR7,545117,170752,545150,170750,51.41721653,0.085537778\n105750121,06/09/2012 09:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,High Street,20300931,BR6,NULL,NULL,546150,166050,NULL,NULL\n105768121,06/09/2012 10:09,2012,2012/13,Special Service,2,3,260,780,HORSE SINKING IN MUD,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000075,Erith,E09000004,Bexley,Erith,NULL,Crabtree Manorway North,NULL,DA17,550152,179587,550150,179550,51.49529082,0.161640941\n106438121,07/09/2012 10:28,2012,2012/13,Special Service,2,3,260,780,PONY WITH LEG TRAPPED IN TRACTOR,Horse,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544396,171908,544350,171950,51.42778797,0.075647602\n106522121,07/09/2012 13:55,2012,2012/13,Special Service,1,3,260,780,HORSE STUCK IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000065,Belvedere,E09000004,Bexley,Erith,NULL,Norman Road,NULL,DA17,549402,180088,549450,180050,51.49999085,0.15105733\n107164121,08/09/2012 14:49,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH INJURED SWAN IN CANAL,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Ferry Lane,NULL,N17,534905,189709,534950,189750,51.59009115,-0.053981415\n107338121,08/09/2012 18:56,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN HEDGE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Pincott Place,NULL,SE4,535963,175418,535950,175450,51.4614142,-0.044228706\n107731121,09/09/2012 08:59,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED CROW OVER FISHING LAKE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Crystal Palace Park Road,NULL,SE26,534650,171184,534650,171150,51.42367864,-0.064726111\n107753121,09/09/2012 09:57,2012,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN DRAIN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Barfleur Lane,22006126,SE8,NULL,NULL,536750,178450,NULL,NULL\n108338121,10/09/2012 09:24,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON GLASS ROOF,Cat,Person (mobile),Medical/health centre,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000491,Royal Docks,E09000025,Newham,Plaistow,10008991177,Saville Road,22201035,E16,542243,180126,542250,180150,51.50217764,0.048004636\n108559121,10/09/2012 16:47,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Blair Street,22700165,E14,NULL,NULL,538750,181250,NULL,NULL\n108845121,11/09/2012 06:27,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,Figge's Marsh,E09000024,Merton,Mitcham,NULL,Holborn Way,22103358,CR4,NULL,NULL,527750,169150,NULL,NULL\n108850121,11/09/2012 07:17,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN MESH FENCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,East Close,21300758,RM13,552779,182281,552750,182250,51.51879347,0.200621065\n109474121,12/09/2012 13:10,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN BARS OF CAT CAGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Southgate,NULL,Oakleigh Road North,20032580,N20,NULL,NULL,527650,193450,NULL,NULL\n109514121,12/09/2012 14:44,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Loughborough Park,NULL,SW9,531826,175402,531850,175450,51.46224867,-0.103749566\n109568121,12/09/2012 16:27,2012,2012/13,Special Service,1,3,260,780,ASSIST RSPCA WITH COLLAPSED HORSE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000114,Cray Valley East,E09000006,Bromley,Sidcup,NULL,Old Maidstone Road,NULL,DA14,548987,170136,548950,170150,51.41067821,0.140892105\n109597121,12/09/2012 17:44,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN WIRE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000375,Holloway,E09000019,Islington,Holloway,5300067545,North Road,21600648,N7,530421,185043,530450,185050,51.54921572,-0.12040028\n109822121,13/09/2012 07:36,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN WALL VIA AIR VENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,Fortis Green,E09000014,Haringey,Hornsey,NULL,Birchwood Avenue,21100080,N10,NULL,NULL,528350,189450,NULL,NULL\n110007121,13/09/2012 16:07,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000324,Barnhill,E09000017,Hillingdon,Southall,1.00021E+11,Uxbridge Road,21402032,UB4,510285,181258,510250,181250,51.5194859,-0.411866336\n110154121,13/09/2012 20:26,2012,2012/13,Special Service,1,1,260,260,BIRD STUCK DOWN DRAINPIPE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,St. Margarets Road,20038180,HA8,NULL,NULL,519650,192150,NULL,NULL\n110512121,14/09/2012 14:14,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Other FRS,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Lenelby Road,NULL,KT6,NULL,NULL,519250,166150,NULL,NULL\n111672121,16/09/2012 01:52,2012,2012/13,Special Service,1,1,260,260,DOG STUCK BETWEEN WALLS OF FLAT,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,Justice Walk,21700281,SW3,NULL,NULL,527050,177650,NULL,NULL\n111736121,16/09/2012 08:22,2012,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM UPPER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,Holborn and Covent Garden,E09000007,Camden,Soho,NULL,Rosebery Avenue,20401034,EC1R,NULL,NULL,531050,182150,NULL,NULL\n111844121,16/09/2012 13:09,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,Noel Park,E09000014,Haringey,Tottenham,1.00021E+11,Moselle Avenue,21104826,N22,531319,190372,531350,190350,51.59689642,-0.105467418\n112090121,16/09/2012 21:10,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK IN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011255,Wanstead Village,E09000026,Redbridge,Leytonstone,NULL,Voluntary Place,22303350,E11,NULL,NULL,540150,188350,NULL,NULL\n113311121,18/09/2012 22:10,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Sunnyside Road South,20704799,N9,NULL,NULL,534050,193050,NULL,NULL\n113544121,19/09/2012 12:54,2012,2012/13,Special Service,1,1,260,260,COW STUCK IN MUD,Cow,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Coldharbour Lane,NULL,RM13,551394,180894,551350,180850,51.50670375,0.180079624\n113655121,19/09/2012 16:01,2012,2012/13,Special Service,1,4,260,1040,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Oakwood Road,NULL,NW11,525673,188851,525650,188850,51.58451447,-0.187480356\n114125121,20/09/2012 11:19,2012,2012/13,Special Service,1,2,260,520,Redacted,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,Syon,E09000018,Hounslow,Heston,NULL,High Street,21500607,TW8,NULL,NULL,517450,177350,NULL,NULL\n114351121,20/09/2012 19:12,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN RUBBISH CHUTE,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009391,Chelsea Riverside,E09000020,Kensington and Chelsea,Chelsea,NULL,World's End Estate,21701274,SW10,NULL,NULL,526650,177350,NULL,NULL\n114508121,21/09/2012 04:29,2012,2012/13,Special Service,1,1,260,260,FOX STUCK ON THE THAMES,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05011117,Surrey Docks,E09000028,Southwark,Deptford,2.00003E+11,Rotherhithe Street,22502127,SE16,536589,180263,536550,180250,51.5048027,-0.03335001\n115373121,22/09/2012 18:30,2012,2012/13,Special Service,1,1,260,260,BIRD WITH FOOT TRAPPED IN WIRE,Bird,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000200,Grange,E09000010,Enfield,Enfield,207199200,The Town,20703007,EN2,532851,196541,532850,196550,51.65197374,-0.081023528\n115879121,23/09/2012 16:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER LIFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Pioneer Street,22502794,SE15,NULL,NULL,534050,176850,NULL,NULL\n115904121,23/09/2012 16:57,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Grosvenor Road,20704287,N9,NULL,NULL,534850,194250,NULL,NULL\n115929121,23/09/2012 18:03,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Norbury,NULL,Westcombe Avenue,20501554,CR0,NULL,NULL,530650,166850,NULL,NULL\n116304121,24/09/2012 13:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN A FALSE CEILING,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00023E+11,High Road,21106680,N17,533815,190182,533850,190150,51.59460158,-0.069526123\n116414121,24/09/2012 16:51,2012,2012/13,Special Service,1,1,260,260,BIRD WITH LEG TRAPPED IN STREET FURNITURE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Rolt Street,NULL,SE8,536488,177834,536450,177850,51.48299913,-0.035743532\n116554121,24/09/2012 22:49,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CABLING,Cat,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000558,Carshalton Central,E09000029,Sutton,Sutton,5870047176,Westmead Road,22602747,SM1,526772,164600,526750,164650,51.36632222,-0.180320259\n117173121,26/09/2012 10:00,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK UNDER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Connaught Road,22401387,TW11,NULL,NULL,514950,171350,NULL,NULL\n117257121,26/09/2012 13:41,2012,2012/13,Special Service,1,1,260,260,DOG FALLEN INTO DISUSED BASEMENT AREA,Dog,Person (mobile),Other building/use not known,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6047613,Commercial Road,22700346,E14,536778,181154,536750,181150,51.51276384,-0.030283005\n117404121,26/09/2012 20:23,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dorset Road,20302278,SE9,NULL,NULL,542150,172750,NULL,NULL\n117418121,26/09/2012 20:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,34073015,Jerdan Place,21000466,SW6,525204,177272,525250,177250,51.48055684,-0.198360258\n117611121,27/09/2012 10:33,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN FENCE,Dog,Person (mobile),Tennis Courts,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Villiers Road,NULL,UB1,512901,180028,512950,180050,51.50791422,-0.374571379\n117627121,27/09/2012 10:58,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED BETWEEN FENCE POSTS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,NULL,Dover Road,22302792,E12,NULL,NULL,541250,186850,NULL,NULL\n118613121,29/09/2012 12:19,2012,2012/13,Special Service,NULL,NULL,260,NULL,CAT FALLEN INTO RIVER THAMES,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000552,Surrey Docks,E09000028,Southwark,Dockhead,NULL,Princes Riverside Road,NULL,SE16,535703,180317,535750,180350,51.50550126,-0.046087185\n119784121,01/10/2012 18:53,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALL AND FITTED WARDROBE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,White Hart Lane,21106111,N17,NULL,NULL,533850,191450,NULL,NULL\n119874121,01/10/2012 22:22,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000299,Queensbury,E09000015,Harrow,Stanmore,NULL,Streatfield Road,NULL,HA3,518087,189941,518050,189950,51.59594907,-0.296553655\n120103121,02/10/2012 15:18,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SKIRTING BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000095,Mapesbury,E09000005,Brent,Willesden,NULL,Tracey Avenue,20201824,NW2,NULL,NULL,523050,185350,NULL,NULL\n120933121,04/10/2012 09:33,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,North Walk,20502760,CR0,NULL,NULL,537950,163850,NULL,NULL\n121132121,04/10/2012 18:00,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN DITCH,Dog,Police,Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Gomshall Gardens,NULL,CR8,533039,159779,533050,159750,51.32156549,-0.092147742\n121168121,04/10/2012 19:13,2012,2012/13,Special Service,1,1,260,260,CATS TRAPPED UNDER CUPBOARD,cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Tankerville Road,21901346,SW16,NULL,NULL,529950,170450,NULL,NULL\n121355121,05/10/2012 07:15,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Wedderburn Road,19900623,IG11,NULL,NULL,545050,183650,NULL,NULL\n121840121,06/10/2012 04:20,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Boyd Way,20803094,SE3,NULL,NULL,540750,175250,NULL,NULL\n121929121,06/10/2012 10:01,2012,2012/13,Special Service,1,1,260,260,CAT STUCK DOWN DRAIN,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000290,Harrow Weald,E09000015,Harrow,Stanmore,1.00021E+11,Dromey Gardens,21201466,HA3,515693,191288,515650,191250,51.60855211,-0.33065631\n122571121,07/10/2012 17:34,2012,2012/13,Special Service,1,1,260,260,DOG LOCKED IN BATHROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Aberfeldy Street,22700041,E14,NULL,NULL,538550,181150,NULL,NULL\n122937121,08/10/2012 14:09,2012,2012/13,Special Service,1,1,260,260,COW STUCK IN RIVER,Cow,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,East Duck Lees Lane,NULL,EN3,536351,195932,536350,195950,51.64566304,-0.030700798\n123112121,08/10/2012 21:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED INSIDE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009388,Abingdon,E09000020,Kensington and Chelsea,Kensington,NULL,Pembroke Gardens,21701294,W8,NULL,NULL,524850,179050,NULL,NULL\n123271121,09/10/2012 10:21,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Harbut Road,22902213,SW11,NULL,NULL,526650,175150,NULL,NULL\n123383121,09/10/2012 15:18,2012,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN BASEMENT,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Clova Road,22207664,E7,NULL,NULL,540350,185050,NULL,NULL\n123598121,09/10/2012 22:53,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Stephendale Road,21000783,SW6,NULL,NULL,526050,176350,NULL,NULL\n123775121,10/10/2012 12:40,2012,2012/13,Special Service,NULL,NULL,260,NULL,KITTEN TRAPPED IN BUSHES,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100091372,London Road,19900631,IG11,543805,184044,543850,184050,51.53698859,0.072090995\n124173121,11/10/2012 11:01,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,1.00022E+11,High Road,22305476,IG8,539853,191379,539850,191350,51.60389156,0.018063566\n124494121,11/10/2012 22:54,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WATER PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,Victoria,E09000012,Hackney,Bethnal Green,NULL,Tudor Grove,20901015,E9,NULL,NULL,535050,184050,NULL,NULL\n125183121,13/10/2012 13:07,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Bruce Road,NULL,NW10,520891,184038,520850,184050,51.54230323,-0.258120917\n125232121,13/10/2012 14:47,2012,2012/13,Special Service,2,3,260,780,Redacted,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Old Mill Lane,NULL,UB8,504922,181891,504950,181850,51.52619663,-0.488943561\n125244121,13/10/2012 15:12,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,Hunters Grove,21201554,HA3,NULL,NULL,517450,189350,NULL,NULL\n125255121,13/10/2012 15:26,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Clapham,NULL,Larkhall Lane,21900847,SW4,NULL,NULL,529950,176350,NULL,NULL\n125973121,14/10/2012 22:31,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Broadwater Road,21104283,N17,NULL,NULL,533350,190450,NULL,NULL\n126146121,15/10/2012 10:44,2012,2012/13,Special Service,1,1,260,260,HORSE STUCK IN RESERVOIR,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Folly Lane,NULL,E17,536276,190978,536250,190950,51.60116449,-0.033710226\n126448121,15/10/2012 20:49,2012,2012/13,Special Service,1,1,260,260,CAT IN RAVENSBOURNE RIVER IN REAR GARDEN OF,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Barmeston Road,NULL,SE6,537569,172932,537550,172950,51.4386864,-0.022091169\n126503121,16/10/2012 00:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Broadwater Road,21104283,N17,NULL,NULL,533350,190450,NULL,NULL\n126607121,16/10/2012 10:00,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA OFFICER WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000153,Fairfield,E09000008,Croydon,Croydon,NULL,Robert Street,NULL,CR0,532398,165289,532350,165250,51.37123253,-0.09929444\n127470121,18/10/2012 00:37,2012,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Calydon Road,20800268,SE7,NULL,NULL,540850,178050,NULL,NULL\n127622121,18/10/2012 12:28,2012,2012/13,Special Service,1,1,260,260,UNABLE TO GAIN ACCESS TO HORSE BOX,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,NULL,NULL,E07000095,Broxbourne,Hertfordshire,148040938,Bulls Cross Ride,4802548,EN7,534365,200488,534350,200450,51.687082,-0.057635883\n127750121,18/10/2012 18:04,2012,2012/13,Special Service,1,2,260,520,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Mile End Road,22700818,E1,NULL,NULL,535050,181850,NULL,NULL\n128097121,19/10/2012 14:41,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA ACCESS TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009334,Stepney Green,E09000030,Tower Hamlets,Shadwell,NULL,Mile End Road,22700818,E1,NULL,NULL,535050,181850,NULL,NULL\n128690121,20/10/2012 17:25,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN  GARDEN WALL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,Heaton Avenue,21301268,RM3,552743,191328,552750,191350,51.60008916,0.204027218\n128993121,21/10/2012 08:52,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Bristow Road,20100202,DA7,NULL,NULL,548550,176550,NULL,NULL\n130010121,23/10/2012 11:34,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN GARAGES,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,46017134,Clova Road,22207664,E7,539883,185010,539850,185050,51.54665412,0.015963572\n131389121,26/10/2012 08:43,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ROOM POSSIBLY BADLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Cuckoo Lane,NULL,W7,NULL,NULL,515350,180850,NULL,NULL\n131703121,26/10/2012 22:35,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Catford Hill,22005976,SE6,NULL,NULL,536850,172950,NULL,NULL\n132770121,28/10/2012 22:00,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Twickenham Road,21501137,TW7,NULL,NULL,516050,174850,NULL,NULL\n133226121,29/10/2012 20:39,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN RADIATOR AND CUPBOARD,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Keats Avenue,21301316,RM3,NULL,NULL,552550,191050,NULL,NULL\n133461121,30/10/2012 12:26,2012,2012/13,Special Service,1,1,260,260,HORSE STUCK IN TRENCH,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,The Green,21300794,RM13,554148,180847,554150,180850,51.50553744,0.219709628\n133466121,30/10/2012 12:51,2012,2012/13,Special Service,1,1,260,260,FOX ON ROOF,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Little Heath,20800925,SE7,NULL,NULL,542350,178150,NULL,NULL\n133514121,30/10/2012 14:58,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000151,Coulsdon West,E09000008,Croydon,Purley,NULL,Woodcote Grove Road,NULL,CR5,529719,159834,529750,159850,51.32282504,-0.139745874\n133589121,30/10/2012 17:33,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED ON ROOF,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,High Street,NULL,UB8,505508,184218,505550,184250,51.54700336,-0.479802652\n133941121,31/10/2012 14:18,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,10011862998,Davis Way,20101866,DA14,548068,170876,548050,170850,51.41756806,0.127996566\n134541121,01/11/2012 16:19,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Barnet,200071628,Lawrence Campe Close,20026120,N20,526801,193427,526850,193450,51.62538529,-0.169554809\n135120121,02/11/2012 20:35,2012,2012/13,Special Service,1,1,260,260,ADULT STUCK UP A TREE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009397,Holland,E09000020,Kensington and Chelsea,Hammersmith,NULL,Warwick Road,NULL,W14,NULL,NULL,524850,178750,NULL,NULL\n136023121,04/11/2012 10:34,2012,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN BOTTLE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,The Alders,21500021,TW5,NULL,NULL,512650,177950,NULL,NULL\n136243121,04/11/2012 18:09,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN BASEMENT WELL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000366,Barnsbury,E09000019,Islington,Islington,NULL,Cloudesley Road,21604548,N1,NULL,NULL,531250,183750,NULL,NULL\n136472121,05/11/2012 09:05,2012,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT TRAPPED IN CARE ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000149,Broad Green,E09000008,Croydon,Croydon,NULL,Onslow Road,NULL,CR0,531366,166316,531350,166350,51.38070135,-0.113732631\n137065121,06/11/2012 02:28,2012,2012/13,Special Service,1,1,260,260,DOG WITH HEAD STUCK IN RAILINGS,Dog,Police,Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011222,Crayford,E09000004,Bexley,Bexley,1.0002E+11,Old Road,20101070,DA1,551045,175066,551050,175050,51.45443147,0.172562285\n137710121,07/11/2012 14:10,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BETWEEN TWO WALLS,Dog,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Hazelmere Gardens,21300407,RM11,553013,188586,553050,188550,51.57537951,0.206728334\n137713121,07/11/2012 14:35,2012,2012/13,Special Service,1,2,260,520,HORSE COLLAPSED,Horse,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,The Green,21300794,RM13,554148,180847,554150,180850,51.50553744,0.219709628\n137858121,07/11/2012 19:57,2012,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Sheridan Close,21301595,RM3,NULL,NULL,553050,191450,NULL,NULL\n138275121,08/11/2012 16:29,2012,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,1.00021E+11,Bushey Road,21400286,UB10,507477,186675,507450,186650,51.56871599,-0.450667806\n138355121,08/11/2012 19:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON SCAFFOLDING,Cat,Person (land line),Outdoor storage,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,Churchfields,E09000026,Redbridge,Woodford,1.00023E+11,Navestock Crescent,22305207,IG8,541283,190977,541250,190950,51.59992281,0.038536711\n138358121,08/11/2012 19:19,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Smallwood Road,22904658,SW17,NULL,NULL,526550,171650,NULL,NULL\n138799121,09/11/2012 20:27,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Chaucer Road,21900327,SE24,NULL,NULL,531450,174650,NULL,NULL\n139066121,10/11/2012 10:45,2012,2012/13,Special Service,1,1,260,260,PIGEON TRAPPED BEHIND FIREPLACE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,22837350,E17,NULL,NULL,536550,189650,NULL,NULL\n139139121,10/11/2012 13:51,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000489,Plaistow North,E09000025,Newham,Stratford,NULL,Pelly Road,22200947,E13,NULL,NULL,540250,183750,NULL,NULL\n139915121,11/11/2012 21:07,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND DASHBOARD OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Bentry Road,NULL,RM8,548580,186735,548550,186750,51.55993232,0.142024102\n140533121,13/11/2012 09:06,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED ON LEDGE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Alliance Road,NULL,SE18,546087,177572,546050,177550,51.47824908,0.102291898\n141756121,15/11/2012 20:44,2012,2012/13,Special Service,1,1,260,260,PUPPY TRAPPED IN WIRE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Oakleigh Park Avenue,20300531,BR7,NULL,NULL,543450,169550,NULL,NULL\n142049121,16/11/2012 14:14,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED BY WIRE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000547,Peckham Rye,E09000028,Southwark,New Cross,NULL,Solomons Passage,NULL,SE15,534774,175351,534750,175350,51.46109638,-0.061358794\n142068121,16/11/2012 15:09,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Smallwood Road,22904658,SW17,NULL,NULL,526550,171650,NULL,NULL\n142404121,17/11/2012 09:40,2012,2012/13,Special Service,1,1,260,260,CAT LOCKED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,New Cross,10009791949,Ivydale Road,22501337,SE15,535719,175600,535750,175650,51.46310829,-0.047668989\n142459121,17/11/2012 12:27,2012,2012/13,Special Service,1,1,260,260,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34101973,Godolphin Road,21000378,W12,522825,179620,522850,179650,51.5021802,-0.231785642\n142587121,17/11/2012 17:23,2012,2012/13,Special Service,1,3,260,780,CAT TRAPPED IN  VOID BETWEEN TWO EXTENSIONS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,Ponders End,E09000010,Enfield,Edmonton,NULL,Clarence Road,20702604,EN3,NULL,NULL,535050,195650,NULL,NULL\n142980121,18/11/2012 15:01,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000424,Knight's Hill,E09000022,Lambeth,West Norwood,NULL,Winton Way,NULL,SW16,531289,171140,531250,171150,51.42407145,-0.113054288\n143190121,18/11/2012 22:36,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,1.00023E+11,St. Andrews Square,21800889,KT6,517535,167481,517550,167450,51.39420104,-0.311993321\n143916121,20/11/2012 15:56,2012,2012/13,Special Service,1,1,260,260,PUPPY STUCK DOWN HOLE IN GARDEN,Dog,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Saxon Road,21105019,N22,NULL,NULL,531850,190850,NULL,NULL\n144036121,20/11/2012 21:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT IN PRECARIOUS POSITION,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207103072,Claremont Street,20704054,N18,534324,191927,534350,191950,51.61016124,-0.061514697\n144415121,21/11/2012 19:54,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ELECTRICAL SUBSTATION,Cat,Police,Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,The Boltons,NULL,HA0,515931,185792,515950,185750,51.55910685,-0.329034789\n144959121,22/11/2012 18:51,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL - POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,West Avenue,22886250,E17,NULL,NULL,537650,188950,NULL,NULL\n145090121,22/11/2012 22:48,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000182,Northfield,E09000009,Ealing,Ealing,NULL,Little Ealing Lane,20601069,W5,NULL,NULL,517450,178950,NULL,NULL\n145274121,23/11/2012 09:42,2012,2012/13,Special Service,1,1,260,260,DOG STUCK ON ROOF OF BUILDING,Dog,Police,Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Pelly Road,NULL,E13,540446,183323,540450,183350,51.53135508,0.023404968\n145335121,23/11/2012 12:54,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA GAIN ACCESS TO GATED AREA,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,6020121,Voss Street,22701278,E2,534265,182571,534250,182550,51.52609987,-0.065935928\n145568121,23/11/2012 21:47,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,De Beauvoir Road,20950061,N1,NULL,NULL,533250,183950,NULL,NULL\n145576121,23/11/2012 22:15,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GUTTERING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL\n145595121,23/11/2012 23:09,2012,2012/13,Special Service,1,1,260,260,RUNNING CALL TO DOG TRAPPED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Denman Road,22500744,SE15,NULL,NULL,533650,176550,NULL,NULL\n145854121,24/11/2012 14:23,2012,2012/13,Special Service,1,1,260,260,ASSSIST RSPCA WITH SWANS TRAPPED IN NETTING  ON CANAL,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Sandringham Road,NULL,TW6,506049,174716,506050,174750,51.46149431,-0.474858539\n146657121,25/11/2012 23:51,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN HOLE UNDER STAIRS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Acton,NULL,Erconwald Street,21000318,W12,NULL,NULL,521750,181350,NULL,NULL\n147695121,28/11/2012 11:00,2012,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,1.00023E+11,King John Court,20900573,EC2A,533354,182353,533350,182350,51.52435671,-0.079142793\n147709121,28/11/2012 11:21,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Dulverton Road,NULL,RM3,NULL,NULL,554050,191650,NULL,NULL\n148126121,29/11/2012 11:10,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH TRAPPED SEAGULL,Bird,Person (mobile),Wasteland,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000190,Southall Green,E09000009,Ealing,Southall,NULL,The Common,NULL,UB2,512027,178587,512050,178550,51.49513629,-0.387616963\n148371121,29/11/2012 20:35,2012,2012/13,Special Service,1,1,260,260,DOG TRAPPED DOWN DRAIN,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000271,Harringay,E09000014,Haringey,Hornsey,NULL,Endymion Road,NULL,N4,531702,187985,531750,187950,51.57535649,-0.10083541\n148438121,29/11/2012 23:39,2012,2012/13,Special Service,1,1,260,260,CAT ON ROOF IN DANGER OF FALLING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,New Road,21103670,N8,NULL,NULL,530050,188650,NULL,NULL\n148786121,30/11/2012 16:21,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BETWEEN SHED AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,Heaton Close,22844260,E4,NULL,NULL,538450,193050,NULL,NULL\n149783121,02/12/2012 15:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN A TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Castlewood Road,NULL,EN4,527048,196844,527050,196850,51.65603716,-0.164748995\n149804121,02/12/2012 16:05,2012,2012/13,Special Service,1,1,260,260,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Elystan Place,21700194,SW3,NULL,NULL,527450,178450,NULL,NULL\n150271121,03/12/2012 15:04,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,100016786,Keppel Road,19900204,RM9,548080,185739,548050,185750,51.55111426,0.134397651\n150631121,04/12/2012 10:48,2012,2012/13,Special Service,1,1,260,260,ASSIST RSPCA  WITH CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011228,Northumberland Heath,E09000004,Bexley,Erith,1.0002E+11,Silver Spring Close,20101306,DA8,549977,177918,549950,177950,51.48034087,0.158412862\n150930121,04/12/2012 23:36,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN KITCHEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000485,Green Street East,E09000025,Newham,Stratford,NULL,Halley Road,22207740,E7,NULL,NULL,541150,184750,NULL,NULL\n151022121,05/12/2012 07:55,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN OVERHEAD WIRE,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Bodney Road,NULL,E8,534563,185248,534550,185250,51.55008512,-0.060620544\n151839121,06/12/2012 17:02,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN RUBBISH CHUTE,Cat,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,1.0002E+11,Leith Hill,20301187,BR5,546058,169622,546050,169650,51.40682129,0.098595563\n152080121,07/12/2012 07:30,2012,2012/13,Special Service,1,1,260,260,CAT WITH HEAD STUCK IN FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Plumstead,NULL,First Avenue,20100554,DA7,NULL,NULL,547450,177250,NULL,NULL\n152564121,08/12/2012 06:35,2012,2012/13,Special Service,1,1,260,260,DOG IN CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,Billets Hart Close,NULL,W7,515248,179567,515250,179550,51.50329751,-0.340918266\n153143121,09/12/2012 09:22,2012,2012/13,Special Service,2,5,260,1300,HORSE STUCK IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555476,188420,555450,188450,51.5732146,0.242169101\n153320121,09/12/2012 16:02,2012,2012/13,Special Service,NULL,NULL,260,NULL,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000320,St. Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,North Street,21300513,RM11,554003,187415,554050,187450,51.56458893,0.220490683\n153577121,10/12/2012 06:40,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN POOL TABLE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Thane Villas,21603913,N7,NULL,NULL,530950,186250,NULL,NULL\n154633121,12/12/2012 10:11,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL\n154832121,12/12/2012 16:36,2012,2012/13,Special Service,1,1,260,260,SWAN TRAPPED IN ICED LAKE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Prince of Wales Drive,NULL,SW11,528359,176985,528350,176950,51.47727119,-0.153054864\n155428121,13/12/2012 19:41,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BY HEAD BETWEEN WALL AND FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000176,Elthorne,E09000009,Ealing,Ealing,NULL,York Avenue,20601977,W7,NULL,NULL,515450,180250,NULL,NULL\n155491121,13/12/2012 22:30,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN WIRE FENCING,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Meridian Way,NULL,N9,535927,194326,535950,194350,51.63133409,-0.037448703\n155638121,14/12/2012 07:13,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Connaught Road,22401387,TW11,NULL,NULL,514650,171450,NULL,NULL\n155797121,14/12/2012 13:36,2012,2012/13,Special Service,1,2,260,520,CAT STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL\n155904121,14/12/2012 17:04,2012,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED ON CHIMNEY BREAST,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,Raynes Park,E09000024,Merton,Wimbledon,NULL,Durrington Avenue,22102238,SW20,NULL,NULL,523250,170050,NULL,NULL\n155999121,14/12/2012 20:26,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Shellness Road,20900912,E5,NULL,NULL,534850,185350,NULL,NULL\n156719121,16/12/2012 11:06,2012,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN WALL CAVITY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Dalmeny Avenue,21606427,N7,NULL,NULL,529950,185550,NULL,NULL\n156832121,16/12/2012 15:36,2012,2012/13,Special Service,1,1,260,260,KITTEN UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00021E+11,Caroline Place,21400318,UB3,509103,177070,509150,177050,51.48207253,-0.430189138\n157048121,17/12/2012 00:08,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Vereker Road,NULL,W14,524496,178118,524450,178150,51.48831614,-0.208252736\n157207121,17/12/2012 13:50,2012,2012/13,Special Service,1,1,260,260,HAWK TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,200118832,Trevor Road,20043200,HA8,520775,190778,520750,190750,51.60290274,-0.257477302\n157209121,17/12/2012 13:56,2012,2012/13,Special Service,1,1,260,260,DOG WITH LEG STUCK IN METAL BASKET,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Wentworth Avenue,20045100,N3,NULL,NULL,525550,191450,NULL,NULL\n157325121,17/12/2012 17:05,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND BOILER,Cat,Police,Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Green Road,20019240,N20,NULL,NULL,526450,193450,NULL,NULL\n157499121,17/12/2012 21:58,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Dormywood,21400605,HA4,NULL,NULL,509750,188550,NULL,NULL\n157530121,17/12/2012 23:12,2012,2012/13,Special Service,1,1,260,260,ASSIST POLICE IN GAINING ENTRY,Unknown - Domestic Animal Or Pet,Police,Vehicle Repair Workshop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000449,New Cross,E09000023,Lewisham,Deptford,NULL,Amersham Vale,NULL,SE14,536767,177446,536750,177450,51.47944503,-0.031878261\n157657121,18/12/2012 10:31,2012,2012/13,Special Service,1,1,260,260,INJURED DOG STUCK IN MUD ON FORESHORE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000228,Thamesmead Moorings,E09000011,Greenwich,Plumstead,NULL,Ware Point Drive,NULL,SE28,544758,180031,544750,180050,51.5006859,0.084175445\n158637121,20/12/2012 13:04,2012,2012/13,Special Service,1,1,260,260,CAT STUCK BETWEEN CAVITY WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000472,Village,E09000024,Merton,Wandsworth,NULL,Bathgate Road,22100444,SW19,NULL,NULL,523950,172450,NULL,NULL\n159392121,21/12/2012 23:08,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,Bexley,Bexley,NULL,Manor Way,20100920,DA7,NULL,NULL,550650,175750,NULL,NULL\n159615121,22/12/2012 12:56,2012,2012/13,Special Service,1,1,260,260,CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Linkway,NULL,RM8,547464,185958,547450,185950,51.5532431,0.12561122\n160224121,23/12/2012 15:23,2012,2012/13,Special Service,1,1,260,260,ANIMALS LOCKED IN ROOM,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Woodside Road,21402213,HA6,NULL,NULL,509950,191550,NULL,NULL\n160571121,24/12/2012 12:07,2012,2012/13,Special Service,1,1,260,260,KITTEN STUCK IN TV STAND,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000410,Old Malden,E09000021,Kingston upon Thames,New Malden,NULL,Percy Gardens,21800762,KT4,NULL,NULL,520950,166350,NULL,NULL\n161316121,25/12/2012 22:57,2012,2012/13,Special Service,1,1,260,260,ONE SHEEP TRAPPED IN FENCING,Sheep,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000195,Chase,E09000010,Enfield,Enfield,NULL,East Lodge Lane,NULL,EN2,529893,199380,529850,199350,51.67817701,-0.122702983\n161649121,26/12/2012 23:22,2012,2012/13,Special Service,1,1,260,260,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Claybrook Road,21000220,W6,NULL,NULL,523850,177950,NULL,NULL\n161832121,27/12/2012 13:16,2012,2012/13,Special Service,1,1,260,260,DOG STUCK IN LIFT EMERGENCY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,Oval,E09000022,Lambeth,Lambeth,NULL,Richborne Terrace,21901154,SW8,NULL,NULL,530650,177350,NULL,NULL\n161849121,27/12/2012 14:14,2012,2012/13,Special Service,1,1,260,260,CAT STUCK IN METAL PANELLING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Goldington Street,20400800,NW1,NULL,NULL,529750,183350,NULL,NULL\n162647121,29/12/2012 17:14,2012,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011234,Aldborough,E09000026,Redbridge,Ilford,NULL,Ashurst Drive,22302589,IG6,NULL,NULL,544150,188850,NULL,NULL\n163205121,31/12/2012 07:06,2012,2012/13,Special Service,1,1,260,260,CAT WITH LEG TRAPPED IN FENCE NEXT TO DISUSED INDIAN TAKEAWAY SHOP,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Stanstead Road,NULL,SE23,535543,173228,535550,173250,51.44183438,-0.051108879\n295131,01/01/2013 14:22,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Purpose built office,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,48082272,High Street Colliers Wood,22103303,SW19,526768,170279,526750,170250,51.41736135,-0.178351767\n380131,01/01/2013 18:29,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED BETWEEN METAL CHAIRS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Frederick Crescent,NULL,SW9,531627,177060,531650,177050,51.47719501,-0.105995713\n436131,01/01/2013 21:03,2013,2012/13,Special Service,1,1,260,260,INJURED DOG TRAPPED ON RAILWAY LINE,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000254,Fulham Broadway,E09000013,Hammersmith and Fulham,Fulham,34071759,Brompton Park Crescent,21000159,SW6,525602,177658,525650,177650,51.48393773,-0.192494735\n953131,03/01/2013 11:17,2013,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN FENCING,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,1.00021E+11,Spencer Road,21105063,N17,534327,190622,534350,190650,51.59843357,-0.061970756\n1065131,03/01/2013 15:57,2013,2012/13,Special Service,1,1,260,260,CAT FALLEN FROM SIXTH FLOOR TO FIRST FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Ackroyd Drive,22700042,E3,NULL,NULL,536950,181850,NULL,NULL\n1491131,04/01/2013 16:53,2013,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6210084,Tarling Street,22701188,E1,535129,181179,535150,181150,51.51338493,-0.054022422\n1751131,05/01/2013 09:45,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER FLOORBOARDS AND SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Burrow Close,22304539,IG7,NULL,NULL,545750,192450,NULL,NULL\n2077131,05/01/2013 23:19,2013,2012/13,Special Service,1,1,260,260,DOG WITH HEAD TRAPPED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000052,Garden Suburb,E09000003,Barnet,Finchley,NULL,Denman Drive North,20012240,NW11,NULL,NULL,525350,188950,NULL,NULL\n2194131,06/01/2013 10:17,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,NULL,Gordon Avenue,21202215,HA7,NULL,NULL,516750,191650,NULL,NULL\n2602131,07/01/2013 11:45,2013,2012/13,Special Service,NULL,NULL,260,NULL,CAT STUCK BETWEEN CUPBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Hartington Road,NULL,W4,NULL,NULL,520050,177050,NULL,NULL\n2666131,07/01/2013 14:58,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Morris Road,19900257,RM8,NULL,NULL,548950,186650,NULL,NULL\n3020131,08/01/2013 12:35,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN ROOM,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000613,East Putney,E09000032,Wandsworth,Wandsworth,NULL,Buttermere Drive,22900741,SW15,NULL,NULL,524550,174650,NULL,NULL\n3089131,08/01/2013 15:34,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH KITTEN UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000545,Nunhead,E09000028,Southwark,New Cross,NULL,Clifton Crescent,NULL,SE15,535097,177257,535050,177250,51.47814777,-0.055984289\n3213131,08/01/2013 21:27,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Glyn Road,20900450,E5,NULL,NULL,535850,185650,NULL,NULL\n3993131,10/01/2013 19:18,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000292,Headstone North,E09000015,Harrow,Harrow,NULL,Manor Way,21201337,HA2,NULL,NULL,513550,189350,NULL,NULL\n4260131,11/01/2013 13:28,2013,2012/13,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,New Barn Street,22200883,E13,NULL,NULL,540550,182050,NULL,NULL\n5122131,13/01/2013 14:08,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Rectory Grove,21901147,SW4,NULL,NULL,529250,175850,NULL,NULL\n5528131,14/01/2013 13:49,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN SWING DOOR,Dog,Person (land line),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000649,West End,E09000033,Westminster,Soho,10033595937,Hanover Square,8401494,W1S,528951,181019,528950,181050,51.51338991,-0.143062044\n5610131,14/01/2013 17:19,2013,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND CHAIR LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Sylvester Gardens,22304737,IG6,NULL,NULL,546950,192150,NULL,NULL\n5930131,15/01/2013 14:09,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARAGE,Cat,Police,Private garage,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Linton Glade,NULL,CR0,536230,162545,536250,162550,51.34566781,-0.04532615\n6036131,15/01/2013 18:31,2013,2012/13,Special Service,1,1,260,260,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL\n6072131,15/01/2013 19:44,2013,2012/13,Special Service,1,1,260,260,CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Plevna Road,NULL,TW12,NULL,NULL,513750,169550,NULL,NULL\n6138131,15/01/2013 23:07,2013,2012/13,Special Service,1,1,260,260,DOG STUCK BEHIND FENCE,Dog,Person (land line),Secondary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000188,South Acton,E09000009,Ealing,Acton,12141198,Twyford Crescent,20601783,W3,519635,180375,519650,180350,51.50964912,-0.277467292\n6849131,17/01/2013 11:37,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,5300078415,River Street,21605813,EC1R,531265,182846,531250,182850,51.52927637,-0.109053695\n7147131,17/01/2013 21:37,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SHUTTER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,1.00023E+11,Thomas Street,20801490,SE18,543564,178883,543550,178850,51.49067468,0.066518372\n7290131,18/01/2013 09:13,2013,2012/13,Special Service,1,1,260,260,DOG ON SILTBANK IN MIDDLE OF CANAL,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Wyvern Way,NULL,UB8,504952,184007,504950,184050,51.54521043,-0.48788083\n7377131,18/01/2013 11:45,2013,2012/13,Special Service,1,1,260,260,CAT WITH HEAD TRAPPED BETWEEN RSJ AND WINDOW FRAME,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Addison Gardens,21800036,KT5,NULL,NULL,518950,168050,NULL,NULL\n8241131,20/01/2013 11:55,2013,2012/13,Special Service,2,3,260,780,\"DOG FALLEN THROUGH ICE ON POND, WATER RESCUE LEVEL TWO\",Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Regents Park,NULL,NW1,528023,182218,528050,182250,51.52437612,-0.155992735\n8446131,20/01/2013 20:36,2013,2012/13,Special Service,1,2,260,520,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000266,Alexandra,E09000014,Haringey,Hornsey,NULL,Dukes Avenue,NULL,N10,529462,190120,529450,190150,51.59506149,-0.13235481\n8688131,21/01/2013 11:33,2013,2012/13,Special Service,1,1,260,260,DOG FALLEN THROUGH ICE INTO POND,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,Botwell,E09000017,Hillingdon,Hayes,1.00024E+11,Stockley Park,21400150,UB11,508060,180516,508050,180550,51.51324649,-0.444148973\n9119131,22/01/2013 09:05,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000056,High Barnet,E09000003,Barnet,Barnet,NULL,Bakers Hill,NULL,EN5,526273,197148,526250,197150,51.65894366,-0.175836913\n9393131,22/01/2013 19:16,2013,2012/13,Special Service,1,1,260,260,DOG FALLEN INTO THE CANAL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Brookside Road,NULL,UB4,511633,181196,511650,181150,51.51866398,-0.392465263\n9641131,23/01/2013 14:00,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BACK OF CHEST DRAWER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Friern Watch Avenue,20016820,N12,NULL,NULL,526550,192750,NULL,NULL\n9973131,24/01/2013 10:40,2013,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,Stratford,10008997118,Sandringham Road,22207876,E7,541182,185020,541150,185050,51.54642045,0.034688211\n10004131,24/01/2013 12:17,2013,2012/13,Special Service,1,3,260,780,DOG STUCK IN BADGER HOLE,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000137,Highgate,E09000007,Camden,West Hampstead,10091822697,Hampstead Lane,21106630,NW3,527087,187418,527050,187450,51.57131936,-0.167600351\n10028131,24/01/2013 12:51,2013,2012/13,Special Service,1,1,260,260,ASSIST RSCPA WITH PIGEON TRAPPED OS,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009306,Langbourn,E09000001,City of London,Dowgate,95506892,Gracechurch Street,8100241,EC3V,532962,180985,532950,180950,51.51215556,-0.085305891\n10142131,24/01/2013 17:54,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH PIGEON TRAPPED ON BUILDING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870035554,High Street,22605497,SM1,525920,164118,525950,164150,51.36217973,-0.192723011\n10585131,25/01/2013 16:26,2013,2012/13,Special Service,1,2,260,520,Redacted,Dog,Police,Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Wraysbury Drive,NULL,UB7,505643,180463,505650,180450,51.51322694,-0.478982129\n10698131,25/01/2013 21:37,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED INBETWEEN WALLS,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000630,Abbey Road,E09000033,Westminster,West Hampstead,1.00023E+11,Clifton Hill,8400800,NW8,526212,183593,526250,183550,51.53714058,-0.181590818\n11674131,28/01/2013 09:36,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000308,Elm Park,E09000016,Havering,Hornchurch,1.00021E+11,Kilmartin Way,21300447,RM12,552761,185101,552750,185150,51.54413555,0.20158308\n11697131,28/01/2013 10:32,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000522,Hampton Wick,E09000027,Richmond upon Thames,Twickenham,NULL,Crieff Court,22406634,TW11,NULL,NULL,517150,170150,NULL,NULL\n12083131,29/01/2013 09:32,2013,2012/13,Special Service,1,1,260,260,FOX TRAPPED IN FENCE,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,1.00023E+11,Burlington Lane,21500176,W4,520405,177266,520450,177250,51.48154339,-0.267437096\n12521131,30/01/2013 09:51,2013,2012/13,Special Service,1,1,260,260,COCKER SPANIEL CAUGHT IN BARBED WIRE IN POND,Dog,Police,Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,NULL,Goodhall Close,NULL,HA7,516607,191873,516650,191850,51.61362146,-0.31726855\n13412131,01/02/2013 08:55,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN PIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Elmfield Way,8401841,W9,NULL,NULL,525250,181950,NULL,NULL\n13639131,01/02/2013 18:20,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN BETWEEN WALL AND IVY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,NULL,Elsted Street,22500908,SE17,NULL,NULL,532850,178650,NULL,NULL\n13709131,01/02/2013 20:23,2013,2012/13,Special Service,1,1,260,260,CAT LOCKED INSIDED CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Granary Close,20703354,N9,NULL,NULL,535150,194650,NULL,NULL\n13735131,01/02/2013 21:48,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED BEHIND AIR BRICK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Abercairn Road,21900064,SW16,NULL,NULL,529150,170050,NULL,NULL\n14624131,03/02/2013 17:25,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDERNEATH BATH,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Sutherland Avenue,8401592,W9,NULL,NULL,525950,182450,NULL,NULL\n15359131,05/02/2013 12:54,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UP DOMESTIC INTERNAL PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Althorne Way,19900019,RM10,NULL,NULL,549150,186850,NULL,NULL\n15848131,06/02/2013 14:50,2013,2012/13,Special Service,1,1,260,260,RUNNING CALL TO CAT STUCK OVER FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Harpenden Road,NULL,SE27,NULL,NULL,531650,172650,NULL,NULL\n15933131,06/02/2013 18:06,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED IN LOFT,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Melon Road,22858225,E11,NULL,NULL,539050,186250,NULL,NULL\n16216131,07/02/2013 14:07,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED  IN LOFT AREA,Unknown - Domestic Animal Or Pet,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000647,Warwick,E09000033,Westminster,Lambeth,10033550691,Hugh Street,8400826,SW1V,528904,178741,528950,178750,51.4929284,-0.144571129\n16283131,07/02/2013 17:05,2013,2012/13,Special Service,1,1,260,260,RUNNING CALL TO PIGEON TRAPPED BEHIND GLASS,Bird,Person (land line),Post office (purpose built),Non Residential,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Kerbey Street,NULL,E14,537844,181109,537850,181150,51.51210062,-0.014948327\n16906131,08/02/2013 22:28,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED IN BETWEEN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bradstock Road,20900160,E9,NULL,NULL,535950,184650,NULL,NULL\n17505131,10/02/2013 08:33,2013,2012/13,Special Service,1,2,260,520,ASSIST RSPCA WITH INJURED PIGEON,Bird,Person (land line),Hospital,Non Residential,Other animal assistance,Assist trapped wild animal,E05000562,St. Helier,E09000029,Sutton,Sutton,5870054822,Wrythe Lane,22605653,SM5,526535,166124,526550,166150,51.38007156,-0.183180799\n17781131,10/02/2013 21:46,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000112,Clock House,E09000006,Bromley,Beckenham,NULL,Elmers End Road,20303586,SE20,NULL,NULL,535150,169350,NULL,NULL\n18081131,11/02/2013 16:16,2013,2012/13,Special Service,1,1,260,260,PIGEON STUCK ON ROOF,Bird,Other FRS,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,Redbridge,Ilford,NULL,Milverton Gardens,22302286,IG3,NULL,NULL,545750,186850,NULL,NULL\n18859131,13/02/2013 15:00,2013,2012/13,Special Service,1,1,260,260,DOG STUCK IN DITCH,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Sipson Lane,NULL,UB3,508231,178204,508250,178250,51.49243302,-0.442394666\n19657131,15/02/2013 09:36,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Woodford Road,22305452,E18,NULL,NULL,540250,189750,NULL,NULL\n19830131,15/02/2013 17:22,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER RUBBLE,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,Prestbury Road,NULL,E7,541427,184398,541450,184350,51.54076991,0.037969337\n20590131,17/02/2013 11:53,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED ON RAILWAY LINE,Dog,Person (land line),Railway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Enid Street,22500914,SE16,533891,179403,533850,179450,51.49771958,-0.07252663\n20695131,17/02/2013 15:46,2013,2012/13,Special Service,1,1,260,260,DOG HEAD STUCK IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Valognes Avenue,22883550,E17,536206,190916,536250,190950,51.60062427,-0.034744407\n20700131,17/02/2013 15:59,2013,2012/13,Special Service,1,1,260,260,CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Inglehurst Gardens,22302967,IG4,NULL,NULL,542650,188650,NULL,NULL\n21506131,19/02/2013 11:15,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,1.00022E+11,Honley Road,22001571,SE6,537891,173919,537850,173950,51.44747768,-0.017077312\n21512131,19/02/2013 11:31,2013,2012/13,Special Service,1,1,260,260,ASSIST POLICE,Unknown - Domestic Animal Or Pet,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,NULL,Weller Street,22502673,SE1,NULL,NULL,532250,179750,NULL,NULL\n21657131,19/02/2013 17:26,2013,2012/13,Special Service,1,1,260,260,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000087,Brondesbury Park,E09000005,Brent,Willesden,NULL,Sidmouth Road,20201521,NW2,NULL,NULL,523250,184150,NULL,NULL\n22531131,21/02/2013 13:16,2013,2012/13,Special Service,1,1,260,260,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Heath Street,20499121,NW3,NULL,NULL,526350,185850,NULL,NULL\n23104131,22/02/2013 16:33,2013,2012/13,Special Service,1,1,260,260,PUPPY WITH HEAD TRAPPED IN WALL,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000616,Graveney,E09000032,Wandsworth,Tooting,1.00023E+11,Bickley Street,22900494,SW17,527516,171246,527550,171250,51.4258845,-0.167253037\n23221131,22/02/2013 20:26,2013,2012/13,Special Service,1,1,260,260,BIRD TRAPPED IN NETTING,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Craven Passage,NULL,WC2N,530213,180385,530250,180350,51.50740311,-0.125119613\n23669131,23/02/2013 17:18,2013,2012/13,Special Service,1,1,260,260,CAT STUCK  BEHIND CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Littlemede,20800929,SE9,NULL,NULL,543050,172350,NULL,NULL\n23991131,24/02/2013 11:21,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Littlemede,20800929,SE9,NULL,NULL,542950,172350,NULL,NULL\n24099131,24/02/2013 14:58,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Sheepcotes Road,NULL,RM6,548048,189284,548050,189250,51.58297522,0.135428001\n24108131,24/02/2013 15:15,2013,2012/13,Special Service,1,1,260,260,BIRDS TRAPPED IN NETTING NEAR,Bird,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped wild animal,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Wood Lane,NULL,W12,523323,180729,523350,180750,51.51203884,-0.224226669\n25398131,27/02/2013 13:33,2013,2012/13,Special Service,1,1,260,260,CAT STUCK ON TELEPHONE POLE,Cat,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Park Road,NULL,EN3,536363,199169,536350,199150,51.67474831,-0.029265833\n26016131,28/02/2013 19:38,2013,2012/13,Special Service,1,1,260,260,CAT STUCK IN AIR VENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Marsh Wall,22700793,E14,NULL,NULL,537250,179950,NULL,NULL\n26341131,01/03/2013 13:38,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Croydon,1.00021E+11,Euston Road,20500922,CR0,531168,166190,531150,166150,51.37961472,-0.116622569\n26533131,01/03/2013 20:57,2013,2012/13,Special Service,1,1,260,260,TWO FOXES TRAPPED IN BASEMENT AREA,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009381,London Fields,E09000012,Hackney,Shoreditch,NULL,Queensbridge Road,20900836,E8,NULL,NULL,533950,184250,NULL,NULL\n27174131,03/03/2013 10:26,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,St. Dunstans Road,20501408,SE25,NULL,NULL,533850,168450,NULL,NULL\n27736131,04/03/2013 14:51,2013,2012/13,Special Service,1,1,260,260,PIGEON STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000450,Perry Vale,E09000023,Lewisham,Forest Hill,NULL,Colfe Road,NULL,SE23,536004,173201,536050,173250,51.4414813,-0.044490298\n28295131,05/03/2013 18:14,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON WINDOW SILL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Felstead Street,NULL,E9,NULL,NULL,536950,184650,NULL,NULL\n28532131,06/03/2013 10:12,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Boundary Road,22100726,SW19,NULL,NULL,526650,170650,NULL,NULL\n28767131,06/03/2013 19:03,2013,2012/13,Special Service,1,2,260,520,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Grand Avenue,21100352,N10,NULL,NULL,528350,189350,NULL,NULL\n28952131,07/03/2013 08:20,2013,2012/13,Special Service,1,1,260,260,FOX WITH HEAD TRAPPED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000205,Palmers Green,E09000010,Enfield,Edmonton,NULL,Arnold Gardens,20703872,N13,NULL,NULL,531550,192450,NULL,NULL\n29215131,07/03/2013 19:38,2013,2012/13,Special Service,1,1,260,260,KITTEN WITH HEAD TRAPPED IN HOLE IN CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Margery Street,21605383,WC1X,NULL,NULL,531150,182650,NULL,NULL\n29377131,08/03/2013 09:06,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL TRAPPED,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Widley Road,8400708,W9,NULL,NULL,525350,182450,NULL,NULL\n30349131,10/03/2013 06:22,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED BEHIND SCAFFOLDNG,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Deptford,NULL,Rotherhithe Old Road,22502124,SE16,NULL,NULL,535550,178850,NULL,NULL\n30817131,11/03/2013 09:33,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009397,Holland,E09000020,Kensington and Chelsea,Kensington,217039505,Holland Park Road,21700644,W14,524841,179307,524850,179350,51.49892599,-0.202865901\n30893131,11/03/2013 12:43,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GATE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Christie Road,NULL,E9,536115,184565,536150,184550,51.54357571,-0.038514001\n31320131,12/03/2013 10:04,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch Hall Road,NULL,N8,529887,188376,529850,188350,51.57929122,-0.126867151\n31393131,12/03/2013 12:43,2013,2012/13,Special Service,1,1,260,260,KITTEN TRAPPED UNDER FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000555,Beddington North,E09000029,Sutton,Mitcham,NULL,Rosemary Close,22605577,CR0,NULL,NULL,530150,166950,NULL,NULL\n31958131,13/03/2013 18:20,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),College/University,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009305,Farringdon Without,E09000001,City of London,Soho,95509816,Chancery Lane,8100118,WC2A,531158,181254,531150,181250,51.5149943,-0.111187648\n32372131,14/03/2013 15:32,2013,2012/13,Special Service,1,2,260,520,DOG TRAPPED IN BADGER SETT,Dog,Person (land line),Golf course (not building on course),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00023E+11,The Drive,21401951,HA6,509356,190370,509350,190350,51.60156642,-0.422418058\n33271131,16/03/2013 15:30,2013,2012/13,Special Service,1,2,260,520,TO ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,1.00021E+11,Walford Road,20901038,N16,533398,185854,533350,185850,51.55580758,-0.077183355\n33438131,16/03/2013 23:17,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Kilburn Park Road,8400177,NW6,NULL,NULL,525350,182750,NULL,NULL\n34196131,18/03/2013 18:50,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH PIGEON STUCK ON TV ARIEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000533,Whitton,E09000027,Richmond upon Thames,Twickenham,NULL,Hazel Close,22401563,TW2,NULL,NULL,514550,173850,NULL,NULL\n34221131,18/03/2013 19:41,2013,2012/13,Special Service,1,1,260,260,DOG WITH LEG TRAPPED IN FURNITURE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,1.00023E+11,Mabley Street,20900646,E9,536365,185041,536350,185050,51.54779292,-0.034726737\n34628131,19/03/2013 18:41,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Bradstock Road,20900160,E9,NULL,NULL,535950,184650,NULL,NULL\n34659131,19/03/2013 19:44,2013,2012/13,Special Service,1,2,260,520,ANIMINAL ON ROADWAY,Unknown - Heavy Livestock Animal,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000337,Pinkwell,E09000017,Hillingdon,Hayes,NULL,Shepiston Lane,NULL,UB3,507941,178612,507950,178650,51.49615568,-0.446445506\n35048131,20/03/2013 18:11,2013,2012/13,Special Service,1,1,260,260,SMALL ANIMAL RESCUE,Bird,Person (land line),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12083650,New Broadway,20601231,W5,517629,180736,517650,180750,51.51331421,-0.306239389\n35285131,21/03/2013 11:48,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Windsor Road,20046720,EN5,NULL,NULL,523950,195350,NULL,NULL\n35821131,22/03/2013 15:14,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN BRANCHES OF TREE,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,Shacklewell,E09000012,Hackney,Stoke Newington,1.00023E+11,Downs Park Road,20901783,E8,534033,185388,534050,185350,51.55146933,-0.068206712\n35931131,22/03/2013 19:21,2013,2012/13,Special Service,1,1,260,260,CAT STUCK BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000527,St. Margarets and North Twickenham,E09000027,Richmond upon Thames,Heston,NULL,Broadway Avenue,22403320,TW1,NULL,NULL,516650,174250,NULL,NULL\n36614131,24/03/2013 13:25,2013,2012/13,Special Service,1,1,260,260,INJURED CAT TRAPPED IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,NULL,Arlington Road,22403262,TW1,NULL,NULL,517050,174350,NULL,NULL\n36795131,24/03/2013 20:18,2013,2012/13,Special Service,1,1,260,260,DOG TRAPPED IN TUNNEL UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,NULL,Summit Road,22878650,E17,NULL,NULL,537950,189050,NULL,NULL\n37256131,25/03/2013 19:54,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN ENGINE OF VAN,Cat,Person (land line),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Forest Gardens,NULL,N17,533702,190387,533750,190350,51.59647057,-0.071078425\n37483131,26/03/2013 08:31,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Fulham,NULL,Chesson Road,21000211,W14,NULL,NULL,524750,177850,NULL,NULL\n38755131,28/03/2013 23:23,2013,2012/13,Special Service,1,1,260,260,TO ASSIST RSPCA WITH INJURED DEER,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05011252,South Woodford,E09000026,Redbridge,Woodford,NULL,Elmhurst Drive,22304959,E18,NULL,NULL,540350,190450,NULL,NULL\n38756131,28/03/2013 23:26,2013,2012/13,Special Service,1,2,260,520,Redacted,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000305,West Harrow,E09000015,Harrow,Harrow,1.00021E+11,The Drive,21202021,HA2,513414,187651,513450,187650,51.57632616,-0.364728255\n39037131,29/03/2013 15:32,2013,2012/13,Special Service,1,2,260,520,CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Wyndham Close,NULL,BR6,544232,166115,544250,166150,51.37577614,0.070937874\n39741131,31/03/2013 02:33,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Battersea,NULL,Warriner Gardens,22905967,SW11,NULL,NULL,528050,176650,NULL,NULL\n39809131,31/03/2013 09:44,2013,2012/13,Special Service,1,1,260,260,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Whittington Road,21106553,N22,NULL,NULL,530150,191250,NULL,NULL\n39892131,31/03/2013 14:20,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,The Croft,NULL,HA0,517187,185332,517150,185350,51.55471332,-0.311077494\n39962131,31/03/2013 16:35,2013,2012/13,Special Service,1,1,260,260,ASSIST RSPCA WITH CAT TRAPPED IN BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,May Close,21800658,KT9,NULL,NULL,518850,163850,NULL,NULL\n40010131,31/03/2013 17:34,2013,2012/13,Special Service,1,1,260,260,SQUIRREL TRAPPED BETWEEN DRAIN PIPE AND WALL,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,NULL,Kinfauns Avenue,21300448,RM11,NULL,NULL,553250,188350,NULL,NULL\n40271131,01/04/2013 12:04,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Southgate,NULL,Whittington Road,21106553,N22,NULL,NULL,530150,191250,NULL,NULL\n40634131,02/04/2013 08:10,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN ONTO BALCONY - INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Oakwood Drive,21901044,SE19,NULL,NULL,532950,170850,NULL,NULL\n41742131,04/04/2013 15:29,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN WALLS,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Pembridge Crescent,21700964,W11,NULL,NULL,525150,180750,NULL,NULL\n42495131,06/04/2013 11:45,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Heathrow,NULL,Clevedon Gardens,21500276,TW5,NULL,NULL,510650,176450,NULL,NULL\n42614131,06/04/2013 16:23,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER A TREE LOG,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000062,West Hendon,E09000003,Barnet,Hendon,NULL,Marriotts Close,NULL,NW9,521741,188032,521750,188050,51.57801645,-0.244486891\n42677131,06/04/2013 18:48,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN BUSHES,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Foreshore,NULL,SE8,536834,178513,536850,178550,51.48901731,-0.030500659\n42687131,06/04/2013 19:07,2013,2013/14,Special Service,1,1,290,290,BIRDS HEAD TRAPPED IN CAGE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Farmfield Road,22001461,BR1,NULL,NULL,539150,171250,NULL,NULL\n42720131,06/04/2013 19:46,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Station Grove,NULL,HA0,518299,184834,518250,184850,51.55000568,-0.295212575\n42963131,07/04/2013 08:22,2013,2013/14,Special Service,1,1,290,290,PIGEON WITH WING TRAPPED,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Pawsons Road,20502843,CR0,NULL,NULL,532650,167850,NULL,NULL\n42994131,07/04/2013 10:02,2013,2013/14,Special Service,1,1,290,290,CAT IN BASE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,1.00021E+11,Birdbrook Road,20800169,SE3,541407,175744,541450,175750,51.46301127,0.03421412\n43149131,07/04/2013 16:41,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,Northcote,E09000032,Wandsworth,Clapham,NULL,Clapham Common West Side,NULL,SW4,528303,174670,528350,174650,51.45647909,-0.154699711\n44170131,09/04/2013 22:49,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Tunstock Way,20101485,DA17,NULL,NULL,548550,179150,NULL,NULL\n44333131,10/04/2013 12:17,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,NULL,Warren Road,21402077,UB10,NULL,NULL,506650,185650,NULL,NULL\n44841131,11/04/2013 16:06,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011101,Dulwich Wood,E09000028,Southwark,West Norwood,NULL,Baird Gardens,22500130,SE19,NULL,NULL,533350,171650,NULL,NULL\n44985131,11/04/2013 21:34,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Mary Datchelor Close,22501674,SE5,NULL,NULL,532850,176950,NULL,NULL\n45540131,13/04/2013 08:54,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN INTO BASEMENT AREA OF UNOCCUPIED FLAT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,NULL,Linden Gardens,21700734,W2,NULL,NULL,525450,180650,NULL,NULL\n45633131,13/04/2013 13:33,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Kensington Road,NULL,UB5,513133,183505,513150,183550,51.53911872,-0.37011425\n46071131,14/04/2013 11:19,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BEHIND METAL GATE,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000112,Clock House,E09000006,Bromley,Woodside,NULL,Arrol Road,NULL,BR3,535444,168908,535450,168950,51.40303617,-0.054181685\n46165131,14/04/2013 15:00,2013,2013/14,Special Service,1,2,290,580,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Alexander Close,20000680,EN4,NULL,NULL,526750,196150,NULL,NULL\n46261131,14/04/2013 18:06,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,1.00023E+11,Coniston Road,21104393,N17,534130,191565,534150,191550,51.60695448,-0.064452993\n46588131,15/04/2013 11:27,2013,2013/14,Special Service,2,3,290,870,LARGE ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Other Dwelling,Dwelling,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,NULL,NULL,544350,171750,NULL,NULL\n46694131,15/04/2013 16:40,2013,2013/14,Special Service,1,1,290,290,FOUR WEEK OLD KITTEN TRAPPED IN TOY CAR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,Village Way,20202335,NW10,NULL,NULL,521050,185850,NULL,NULL\n47050131,16/04/2013 14:54,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,NULL,Cockspur Street,NULL,SW1Y,529897,180430,529850,180450,51.50788017,-0.129653768\n47080131,16/04/2013 16:18,2013,2013/14,Special Service,1,1,290,290,DISTRESSED CAT UP A TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,NULL,New Road,22860850,E4,NULL,NULL,537950,192850,NULL,NULL\n47667131,17/04/2013 17:12,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,Finstock Road,21700873,W10,NULL,NULL,523650,181350,NULL,NULL\n47799131,17/04/2013 20:52,2013,2013/14,Special Service,1,2,290,580,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Chudleigh Road,22001343,SE4,NULL,NULL,537450,174850,NULL,NULL\n48360131,19/04/2013 07:01,2013,2013/14,Special Service,2,12,290,3480,FOAL IN RIVER WATER LEVEL ONE IMPLEMENTED,Horse,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Lower Hall Lane,NULL,E4,536288,192441,536250,192450,51.6143082,-0.032968828\n48896131,20/04/2013 12:10,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK BETWEEN FENCE POST,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000592,Chapel End,E09000031,Waltham Forest,Walthamstow,NULL,Forest View Road,22837700,E17,NULL,NULL,538350,190850,NULL,NULL\n49030131,20/04/2013 17:00,2013,2013/14,Special Service,1,1,290,290,SQUIRREL STUCK IN PIPE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,NULL,Codrington Hill,22001355,SE23,NULL,NULL,536650,174050,NULL,NULL\n49236131,20/04/2013 21:40,2013,2013/14,Special Service,1,1,290,290,BAT TRAPPPED IN HOUSE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Longfield Avenue,20601079,W5,NULL,NULL,517550,180750,NULL,NULL\n49542131,21/04/2013 15:01,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Cemetery,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,Hospital Bridge Road,22401591,TW2,513705,172994,513750,172950,51.44453203,-0.365252995\n49598131,21/04/2013 17:20,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000644,St. James's,E09000033,Westminster,Lambeth,NULL,Old Pye Street,8400146,SW1P,NULL,NULL,529750,179250,NULL,NULL\n49600131,21/04/2013 17:26,2013,2013/14,Special Service,1,1,290,290,FOX CUB RUN OVER BY MOTOR VEHICLE,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000252,Avonmore and Brook Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Brook Green,NULL,W6,523743,178858,523750,178850,51.49513186,-0.218833452\n49656131,21/04/2013 19:12,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN NETTING,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Carswell Road,NULL,SE6,NULL,NULL,538250,173850,NULL,NULL\n50911131,24/04/2013 10:19,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000180,Hobbayne,E09000009,Ealing,Ealing,12061258,Hillyard Road,20600896,W7,515339,181815,515350,181850,51.52348351,-0.338872468\n51128131,24/04/2013 18:45,2013,2013/14,Special Service,2,1,290,290,DOG STUCK IN MUD ALONG RIVERBANK,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,2E+11,Albert Embankment,21900079,SE1,530317,178147,530350,178150,51.48726664,-0.124447991\n51240131,24/04/2013 22:52,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Garratt Lane,NULL,SW18,526041,172977,526050,172950,51.44177106,-0.187842262\n51596131,25/04/2013 17:19,2013,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,West End,E09000033,Westminster,Soho,NULL,Wardour Street,8400688,W1F,NULL,NULL,529650,180850,NULL,NULL\n52258131,27/04/2013 02:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000560,Cheam,E09000029,Sutton,Sutton,5870117065,High Street,22605727,SM3,524336,163612,524350,163650,51.35798086,-0.215639544\n52391131,27/04/2013 12:34,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO GARAGES,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000089,Dudden Hill,E09000005,Brent,Willesden,202113970,The Circle,20201991,NW2,521324,186229,521350,186250,51.56190186,-0.251124394\n52515131,27/04/2013 17:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RESCUE KITTENS UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011237,Chadwell,E09000026,Redbridge,Ilford,NULL,Chadwell Heath Lane,22301932,RM6,NULL,NULL,547250,188350,NULL,NULL\n52537131,27/04/2013 18:32,2013,2013/14,Special Service,1,2,290,580,HORSE IN CANAL  WATER RESCUE LEVEL ONE IMPLEMENTED,Horse,Police,Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Shadbolt Avenue,NULL,E4,536392,191867,536350,191850,51.60912498,-0.031690845\n52892131,28/04/2013 12:51,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Newton Park Place,20304176,BR7,NULL,NULL,542750,170350,NULL,NULL\n53190131,28/04/2013 21:22,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000381,Tollington,E09000019,Islington,Holloway,NULL,Kingsdown Road,NULL,N19,530286,186720,530250,186750,51.56431751,-0.121725434\n53474131,29/04/2013 14:58,2013,2013/14,Special Service,1,1,290,290,KITTEN FALLEN DOWN HOLE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000615,Furzedown,E09000032,Wandsworth,Tooting,NULL,Crowborough Road,22901088,SW17,NULL,NULL,528450,171250,NULL,NULL\n53499131,29/04/2013 15:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,High Road,21104652,N22,NULL,NULL,530850,191150,NULL,NULL\n54340131,01/05/2013 10:39,2013,2013/14,Special Service,2,3,290,870,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Stonehill Business Park,NULL,N18,536006,191991,536050,191950,51.6103326,-0.03721381\n54436131,01/05/2013 14:07,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK ON LEDGE,Cat,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,1.00021E+11,Duppas Hill Terrace,20500889,CR0,531898,165062,531850,165050,51.36930875,-0.106557572\n54971131,02/05/2013 13:04,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000317,Pettits,E09000016,Havering,Romford,NULL,Dee Way,21301134,RM1,NULL,NULL,551250,190850,NULL,NULL\n55094131,02/05/2013 17:14,2013,2013/14,Special Service,1,3,290,870,Redacted,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363\n55521131,03/05/2013 12:20,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN CANAL,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Carr Street,NULL,E14,536405,181697,536450,181650,51.51773348,-0.035444848\n55544131,03/05/2013 13:03,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,Edgar Road,21400647,UB7,NULL,NULL,506450,180350,NULL,NULL\n55648131,03/05/2013 16:37,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED ON SCHOOL ROOF,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000302,Roxeth,E09000015,Harrow,Northolt,10002294644,Wyvenhoe Road,21202151,HA2,514024,185979,514050,185950,51.56117549,-0.356471549\n55994131,04/05/2013 08:37,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Gosfield Road,19900150,RM8,NULL,NULL,549350,186950,NULL,NULL\n56009131,04/05/2013 09:33,2013,2013/14,Special Service,1,1,290,290,DOG WITH PAW TRAPPED IN CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Ranelagh Road,22200983,E6,NULL,NULL,543150,183550,NULL,NULL\n56016131,04/05/2013 09:47,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN WELL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Balham High Road,22900217,SW12,NULL,NULL,528450,173150,NULL,NULL\n56349131,04/05/2013 21:40,2013,2013/14,Special Service,1,2,290,580,DOG TRAPPED BEHIND FENCE,Dog,Other FRS,Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000353,Hanworth,E09000018,Hounslow,Feltham,NULL,Main Street,NULL,TW13,511648,170608,511650,170650,51.4234958,-0.395590266\n56516131,05/05/2013 10:04,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Lower Camden,20303012,BR7,NULL,NULL,542850,169950,NULL,NULL\n56532131,05/05/2013 10:37,2013,2013/14,Special Service,1,1,290,290,DUCKLINGS TRAPPED IN STORM DRAIN,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011251,Seven Kings,E09000026,Redbridge,Ilford,10034932271,Barley Lane,22301832,IG3,546412,188816,546450,188850,51.57919668,0.111637569\n56541131,05/05/2013 10:58,2013,2013/14,Special Service,1,2,290,580,CAT STUCK IN FOLD UP BED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Burton Close,20502994,CR7,NULL,NULL,532750,168550,NULL,NULL\n56545131,05/05/2013 11:04,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Hornton Street,21700720,W8,NULL,NULL,525350,179850,NULL,NULL\n56827131,05/05/2013 20:43,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER VAN,Cat,Person (mobile),Van,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000544,Newington,E09000028,Southwark,Lambeth,NULL,Doddington Grove,NULL,SE17,531816,178043,531850,178050,51.48598495,-0.102909401\n57741131,07/05/2013 11:39,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00021E+11,Copse Wood Way,21400472,HA6,508366,191213,508350,191250,51.60933443,-0.436446372\n57750131,07/05/2013 11:56,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN WALL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Capworth Street,22822200,E10,NULL,NULL,537650,187650,NULL,NULL\n58275131,08/05/2013 13:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE - TO ASSIST RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Goldwing Close,22207734,E16,NULL,NULL,540450,181250,NULL,NULL\n58549131,08/05/2013 21:47,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP CHIMNEY,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Avenue Road,22403273,TW11,NULL,NULL,516050,170450,NULL,NULL\n58953131,09/05/2013 17:27,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009367,Brownswood,E09000012,Hackney,Stoke Newington,NULL,Queens Drive,20900835,N4,NULL,NULL,532250,186450,NULL,NULL\n59048131,09/05/2013 19:40,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Sutherland Avenue,8401592,W9,NULL,NULL,525950,182450,NULL,NULL\n59852131,11/05/2013 14:32,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RETRIEVE FOX CUB FALLEN INTO BASEMENT,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,6050035,Brokesley Street,22700204,E3,536777,182447,536750,182450,51.52438329,-0.029795714\n60652131,13/05/2013 13:42,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000611,Bedford,E09000032,Wandsworth,Tooting,121019077,Elmfield Road,22901466,SW17,528628,172908,528650,172950,51.44057026,-0.150664214\n60701131,13/05/2013 15:56,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Shottendane Road,NULL,SW6,525122,176888,525150,176850,51.47712389,-0.199676306\n60771131,13/05/2013 19:02,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BY NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,10010213101,Erebus Drive,20802018,SE28,544339,179499,544350,179450,51.49601267,0.07792551\n61804131,16/05/2013 08:40,2013,2013/14,Special Service,1,3,290,870,RUNNING CALL TO GOOSE TRAPPED IN FENCING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000509,Monkhams,E09000026,Redbridge,Woodford,NULL,Links Road,NULL,IG8,540127,192235,540150,192250,51.61151535,0.022359222\n62136131,17/05/2013 06:45,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,1.00021E+11,Casterbridge Road,20800294,SE3,540336,175703,540350,175750,51.46290908,0.018790747\n62176131,17/05/2013 10:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000323,Upminster,E09000016,Havering,Hornchurch,NULL,Cedar Gardens,21301759,RM14,NULL,NULL,556350,186450,NULL,NULL\n62188131,17/05/2013 11:03,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN TREE,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Mill Road,NULL,TW2,514358,172805,514350,172850,51.44270192,-0.355922037\n62234131,17/05/2013 13:14,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Southwood Drive,21800881,KT5,520444,166619,520450,166650,51.38584414,-0.270492616\n62246131,17/05/2013 14:18,2013,2013/14,Special Service,1,2,290,580,PONY IN RELIEF CANAL,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000607,Valley,E09000031,Waltham Forest,Chingford,NULL,Hall Lane,NULL,E4,536284,192312,536250,192350,51.61314996,-0.033076692\n62320131,17/05/2013 16:40,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011465,Broad Green,E09000008,Croydon,Norbury,NULL,Mitcham Road,20502543,CR0,NULL,NULL,530650,166950,NULL,NULL\n62347131,17/05/2013 18:10,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK BETWEEN FENCE AND WALL,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12127766,Drayton Green Road,20602212,W13,516732,180647,516750,180650,51.51270003,-0.319189441\n62434131,17/05/2013 21:18,2013,2013/14,Special Service,1,1,290,290,KITTENS STUCK BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009368,Cazenove,E09000012,Hackney,Stoke Newington,NULL,Upper Clapton Road,20901025,E5,NULL,NULL,534550,186750,NULL,NULL\n62619131,18/05/2013 10:46,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,Wandle Valley,E09000029,Sutton,Mitcham,NULL,Bisham Close,NULL,SM5,527705,166525,527750,166550,51.3834137,-0.166234069\n62798131,18/05/2013 16:35,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000379,St. Mary's,E09000019,Islington,Islington,5300025063,Crane Grove,21604621,N7,531386,184801,531350,184850,51.54681714,-0.106581045\n63017131,19/05/2013 00:29,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED UNDER KITCHEN SINK,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Sherrard Road,22208154,E12,NULL,NULL,542050,185150,NULL,NULL\n63209131,19/05/2013 13:12,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH TRAPPED PIGEONS,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538250,187750,NULL,NULL\n63281131,19/05/2013 16:34,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN PIPE IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000406,Coombe Hill,E09000021,Kingston upon Thames,Kingston,NULL,Kingston Hill,21800559,KT2,NULL,NULL,519650,170150,NULL,NULL\n63411131,19/05/2013 19:38,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000086,Barnhill,E09000005,Brent,Hendon,NULL,Rossdale Drive,20200267,NW9,NULL,NULL,520150,187250,NULL,NULL\n63575131,20/05/2013 05:56,2013,2013/14,Special Service,1,1,290,290,BABY FOX TRAPPED IN WIRE FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Antlers Hill,22811100,E4,NULL,NULL,538050,195650,NULL,NULL\n63686131,20/05/2013 12:33,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530471,160197,530450,160150,51.32591576,-0.128827151\n63864131,20/05/2013 18:38,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000193,Bowes,E09000010,Enfield,Edmonton,NULL,Beale Close,NULL,N13,531806,192090,531850,192050,51.61222123,-0.097795711\n64153131,21/05/2013 09:42,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000630,Abbey Road,E09000033,Westminster,Paddington,NULL,Hamilton Terrace,8400431,NW8,NULL,NULL,526150,182950,NULL,NULL\n64159131,21/05/2013 10:08,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH BIRDS TRAPPED BEHIND BOILER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000047,Coppetts,E09000003,Barnet,Finchley,NULL,Poplar Grove,20034620,N11,NULL,NULL,528050,191550,NULL,NULL\n64287131,21/05/2013 15:27,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000156,Kenley,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530479,160173,530450,160150,51.32569824,-0.128721179\n64358131,21/05/2013 17:45,2013,2013/14,Special Service,1,3,290,870,HORSE FALLEN OVER AND UNABLE TO STAND,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544056,171975,544050,171950,51.42847648,0.07078766\n64417131,21/05/2013 19:08,2013,2013/14,Special Service,1,1,290,290,THREE HORSES STUCK IN RIVER,Horse,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536330,191767,536350,191750,51.60824138,-0.032624529\n64435131,21/05/2013 19:36,2013,2013/14,Special Service,1,1,290,290,ANIMAL TRAPPED BEHIND CHIMNEY,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Thornton Road,22303311,IG1,NULL,NULL,543550,185750,NULL,NULL\n64806131,22/05/2013 17:25,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN TWO WALLS,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Vine Lane,21402051,UB10,NULL,NULL,506950,183050,NULL,NULL\n64903131,22/05/2013 21:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER CUPBOARD,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Widecombe Close,21301699,RM3,NULL,NULL,553650,190750,NULL,NULL\n65231131,23/05/2013 17:49,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Byron Way,21400290,UB7,NULL,NULL,506550,178750,NULL,NULL\n65643131,24/05/2013 15:29,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011489,Woodside,E09000008,Croydon,Woodside,1.00021E+11,Brocklesby Road,20500725,SE25,534650,168059,534650,168050,51.39559581,-0.065911578\n66053131,25/05/2013 13:24,2013,2013/14,Special Service,1,1,290,290,INJURED PEACOCK ON ROOF OF HOUSE,Bird,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Morley Road,22601733,SM3,NULL,NULL,524750,166150,NULL,NULL\n66465131,26/05/2013 12:40,2013,2013/14,Special Service,1,1,290,290,CAT INJURED  - STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Holloway,5300004578,Avenell Road,21602868,N5,531582,186231,531550,186250,51.55962221,-0.103221672\n66542131,26/05/2013 15:13,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Whitton Avenue West,20602462,UB5,NULL,NULL,514050,185150,NULL,NULL\n66584131,26/05/2013 17:08,2013,2013/14,Special Service,1,1,290,290,THREE HORSES IN RIVER,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000607,Valley,E09000031,Waltham Forest,Walthamstow,NULL,Harbet Road,NULL,E4,536288,191773,536250,191750,51.60830546,-0.033228363\n66863131,27/05/2013 09:03,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011463,Addiscombe West,E09000008,Croydon,Woodside,NULL,Northway Road,20501243,CR0,NULL,NULL,533850,166850,NULL,NULL\n66868131,27/05/2013 09:25,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011479,Selhurst,E09000008,Croydon,Norbury,NULL,Northbrook Road,20501241,CR0,NULL,NULL,532650,167750,NULL,NULL\n66955131,27/05/2013 13:51,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Emmanuel Road,NULL,SW12,529454,173075,529450,173050,51.4418831,-0.138725318\n67128131,27/05/2013 18:54,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,Wickham Avenue,22603452,SM3,NULL,NULL,523350,164250,NULL,NULL\n67346131,28/05/2013 07:51,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN SPEAKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Copland Road,20201240,HA0,NULL,NULL,518150,184950,NULL,NULL\n67367131,28/05/2013 09:17,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND GRILL IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,Southwark,Forest Hill,NULL,Mundania Road,22501751,SE22,NULL,NULL,535150,174550,NULL,NULL\n67627131,28/05/2013 21:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Sebert Road,22207878,E7,NULL,NULL,540750,185450,NULL,NULL\n67888131,29/05/2013 14:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Emmanuel Road,NULL,SW12,529309,173075,529350,173050,51.44191619,-0.140810478\n68291131,30/05/2013 12:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,High Road,20022400,N12,NULL,NULL,526350,192450,NULL,NULL\n68318131,30/05/2013 14:24,2013,2013/14,Special Service,1,1,290,290,PIGEON TANGLED IN TELEPHONE WIRE,Bird,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Boleyn Road,NULL,E7,541038,184223,541050,184250,51.53929475,0.032293897\n69052131,01/06/2013 05:42,2013,2013/14,Special Service,1,2,290,580,BABY DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Westminster Gardens,NULL,E4,539445,194022,539450,194050,51.62774203,0.013226802\n69103131,01/06/2013 10:43,2013,2013/14,Special Service,1,1,290,290,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Daneby Road,22001393,SE6,NULL,NULL,538450,172650,NULL,NULL\n69201131,01/06/2013 14:56,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN TRAMPOLINE,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000432,Streatham Wells,E09000022,Lambeth,Norbury,10091845968,Albert Carr Gardens,21900078,SW16,530166,171140,530150,171150,51.42433036,-0.129196926\n69567131,02/06/2013 10:18,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch Hall Road,NULL,N8,530037,188337,530050,188350,51.57890619,-0.124718058\n69573131,02/06/2013 11:07,2013,2013/14,Special Service,1,1,290,290,ASSISST RSPCA,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009370,Dalston,E09000012,Hackney,Homerton,1.00023E+11,Ramsgate Street,20900089,E8,533865,184857,533850,184850,51.54673747,-0.070830001\n69576131,02/06/2013 11:18,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000555,Beddington North,E09000029,Sutton,Wallington,NULL,Bloxworth Close,22600334,SM6,NULL,NULL,529250,165050,NULL,NULL\n69590131,02/06/2013 11:55,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BEHIND A WOODEN FENCE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000179,Hanger Hill,E09000009,Ealing,Park Royal,12096869,Iveagh Avenue,20600941,NW10,519173,183200,519150,183250,51.53513648,-0.283166421\n69682131,02/06/2013 16:08,2013,2013/14,Special Service,1,1,290,290,PUPPY TRAPPED UNDER CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000467,Pollards Hill,E09000024,Merton,Norbury,NULL,Shropshire Close,NULL,CR4,530342,168126,530350,168150,51.39720332,-0.127773939\n70658131,04/06/2013 14:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER CELLAR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Clapham,NULL,Lyham Road,21900909,SW2,NULL,NULL,530350,173950,NULL,NULL\n70843131,04/06/2013 22:16,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND KITCHEN UNIT,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000368,Caledonian,E09000019,Islington,Islington,NULL,Earlsferry Way,21604767,N1,NULL,NULL,530450,183950,NULL,NULL\n71352131,05/06/2013 21:21,2013,2013/14,Special Service,1,1,290,290,HORSE TRAPPED IN HORSE BOX,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05004175,Waltham Abbey Honey Lane,E07000072,Epping Forest,Essex,NULL,Roundhills,NULL,EN9,538828,199885,538850,199850,51.68057949,0.006644869\n71410131,05/06/2013 22:57,2013,2013/14,Special Service,1,1,290,290,ASSIS RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Wood Lane,19900414,RM8,NULL,NULL,548650,186150,NULL,NULL\n71659131,06/06/2013 13:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA FALLEN CAT,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000433,Thornton,E09000022,Lambeth,Clapham,1.00022E+11,Tilson Gardens,21901383,SW2,530115,173850,530150,173850,51.44869663,-0.128935333\n72292131,07/06/2013 17:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Becklow Gardens,21000108,W12,NULL,NULL,522150,179950,NULL,NULL\n72310131,07/06/2013 18:45,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND WARDROBE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Priory Road,20400412,NW6,NULL,NULL,525650,183950,NULL,NULL\n73139131,09/06/2013 11:02,2013,2013/14,Special Service,1,1,290,290,BABY SEAGULL STUCK ON SCAFFOLDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000367,Bunhill,E09000019,Islington,Islington,NULL,Malta Street,21605367,EC1V,NULL,NULL,531850,182450,NULL,NULL\n73434131,09/06/2013 22:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000570,Wallington South,E09000029,Sutton,Wallington,5870089253,Francis Road,22602319,SM6,529373,163771,529350,163750,51.35828643,-0.143279728\n74152131,11/06/2013 11:21,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Fox,Person (land line),Secondary school,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011102,Faraday,E09000028,Southwark,Old Kent Road,2.00003E+11,Shorncliffe Road,22502241,SE1,533441,178209,533450,178250,51.48709596,-0.079456666\n74424131,11/06/2013 23:22,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN HOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000172,Dormers Wells,E09000009,Ealing,Southall,12132075,Greenford Road,20602352,UB1,514247,180661,514250,180650,51.51333326,-0.35497982\n75084131,13/06/2013 13:33,2013,2013/14,Special Service,1,1,290,290,ASSIST WITH KITTEN TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,1.00022E+11,Rosemary Avenue,21500960,TW4,511946,176037,511950,176050,51.47223259,-0.38959065\n75537131,14/06/2013 12:40,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED IN GARDEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Park Royal,12109289,Old Oak Lane,20601301,NW10,521580,182727,521550,182750,51.53037297,-0.248642818\n75592131,14/06/2013 15:01,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DUCK TRAPPED BETWEEN WALLS,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Chelsea,NULL,Eaton Terrace,8400822,SW1W,NULL,NULL,528350,178750,NULL,NULL\n75619131,14/06/2013 16:01,2013,2013/14,Special Service,1,1,290,290,DEER TRAPPED BETWEEN WALL AND FENCE,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011236,Bridge,E09000026,Redbridge,Woodford,1.00022E+11,Wallers Close,22306075,IG8,542729,191884,542750,191850,51.60770857,0.059769271\n75873131,14/06/2013 23:38,2013,2013/14,Special Service,1,1,290,290,BADLY INJURED CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Reynolds Place,22403767,TW10,NULL,NULL,518650,174050,NULL,NULL\n76720131,16/06/2013 18:38,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Hepworth Gardens,19900176,IG11,NULL,NULL,546350,185150,NULL,NULL\n76741131,16/06/2013 19:12,2013,2013/14,Special Service,1,1,290,290,TWO CATS TRAPPED ON FLAT ROOF,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,St. Andrews Road,21800888,KT6,NULL,NULL,517750,167350,NULL,NULL\n77118131,17/06/2013 16:58,2013,2013/14,Special Service,1,1,290,290,KITTEN WITH HEAD TRAPPED IN GATE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Famet Close,20502065,CR8,NULL,NULL,532150,160550,NULL,NULL\n77204131,17/06/2013 20:02,2013,2013/14,Special Service,1,2,290,580,FOX CUBS TRAPPED IN PIT,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,2.00003E+11,Brandon Street,22500317,SE17,532441,178763,532450,178750,51.49230946,-0.093643288\n77421131,18/06/2013 10:52,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA RESCUE CAT,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,10009143365,The Avenue,22812500,E4,538614,191833,538650,191850,51.60827715,0.000363801\n77510131,18/06/2013 13:28,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000096,Northwick Park,E09000005,Brent,Wembley,NULL,Watford Road,20201453,HA1,NULL,NULL,516450,186850,NULL,NULL\n77615131,18/06/2013 18:13,2013,2013/14,Special Service,1,1,290,290,ANIMAL TRAPPED BEHIND FIREPLACE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Mortimer Crescent,20400688,NW6,NULL,NULL,525850,183550,NULL,NULL\n77619131,18/06/2013 18:18,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BETWEEN FENCE AND WALL AT REAR OFF,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Ryecroft Road,NULL,SE13,538481,174830,538450,174850,51.45552037,-0.008236348\n78416131,20/06/2013 06:07,2013,2013/14,Special Service,1,1,290,290,DEER STUCK IN FENCE,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Elmbridge Drive,21400673,HA4,NULL,NULL,509950,188550,NULL,NULL\n78452131,20/06/2013 08:57,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,The Drive,21500367,TW7,NULL,NULL,514750,176250,NULL,NULL\n78460131,20/06/2013 09:10,2013,2013/14,Special Service,1,1,290,290,CAT  FALLEN ON TO FIRST FLOOR BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Don Phelan Close,22500789,SE5,NULL,NULL,532750,176950,NULL,NULL\n78640131,20/06/2013 15:32,2013,2013/14,Special Service,1,3,290,870,RUNNING CALL TO BIRD OF PREY TRAPPED ON ROOF,Bird,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000351,Feltham North,E09000018,Hounslow,Feltham,2.00002E+11,Armadale Road,21501321,TW14,510391,174861,510350,174850,51.46196764,-0.412337062\n78664131,20/06/2013 16:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK DOWN DUSTCHUTE,Cat,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,1.0002E+11,Leith Hill,20301187,BR5,546058,169622,546050,169650,51.40682129,0.098595563\n78686131,20/06/2013 17:17,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA IN RELEASING TRAPPED PIDGEON,Pigeon,Person (land line),Large supermarket,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Eltham High Street,NULL,SE9,542865,174457,542850,174450,51.45108066,0.054668844\n78944131,21/06/2013 08:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Montrose Avenue,20030060,HA8,NULL,NULL,520850,190950,NULL,NULL\n79003131,21/06/2013 12:07,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (land line),Self contained Sheltered Housing,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Enslin Road,20800536,SE9,NULL,NULL,543250,173950,NULL,NULL\n79527131,22/06/2013 12:53,2013,2013/14,Special Service,1,1,290,290,BIRD LOCKED IN PARK SHED,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,NULL,Green Lanes,NULL,N4,532066,187530,532050,187550,51.5711825,-0.095756609\n79941131,23/06/2013 10:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALLS,Cat,Police,Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,6076499,Parnell Road,22700917,E3,537008,183478,537050,183450,51.53359209,-0.026067201\n80094131,23/06/2013 17:13,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA INSPECTOR WITH CAT IN TREE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Clifton Road,NULL,N8,529671,188438,529650,188450,51.57989809,-0.129959755\n80424131,24/06/2013 12:40,2013,2013/14,Special Service,2,3,290,870,CAT WITH HEAD TRAPPED IN RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000195,Chase,E09000010,Enfield,Enfield,NULL,Linden Gardens,20702806,EN1,NULL,NULL,534350,197850,NULL,NULL\n80771131,25/06/2013 07:47,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011246,Ilford Town,E09000026,Redbridge,Ilford,NULL,Stanley Road,22303272,IG1,NULL,NULL,544650,186550,NULL,NULL\n81303131,26/06/2013 08:22,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN WINDOW,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Sinclair Road,21000737,W14,NULL,NULL,524250,179250,NULL,NULL\n81662131,26/06/2013 21:37,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Yeading Lane,21402234,UB4,NULL,NULL,511250,182450,NULL,NULL\n82186131,27/06/2013 22:32,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Woodcroft,NULL,SE9,542907,172374,542950,172350,51.43235252,0.054432519\n82215131,28/06/2013 00:43,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN FROM FIFTH FLOOR POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,North Kensington,NULL,Queensdale Crescent,21000663,W11,NULL,NULL,523750,180250,NULL,NULL\n82343131,28/06/2013 09:57,2013,2013/14,Special Service,1,2,290,580,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,Coldharbour and New Eltham,E09000011,Greenwich,Eltham,NULL,Woodcroft,NULL,SE9,542778,172369,542750,172350,51.43234009,0.052576058\n82622131,28/06/2013 20:30,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000542,Grange,E09000028,Southwark,Old Kent Road,NULL,Pages Walk,NULL,SE1,533331,179054,533350,179050,51.49471556,-0.080721073\n82669131,28/06/2013 22:44,2013,2013/14,Special Service,1,1,290,290,CAT UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Sinclair Road,21000737,W14,NULL,NULL,524050,179350,NULL,NULL\n82832131,29/06/2013 10:14,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN ENGINE COMPARTMENT OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Dovehouse Mead,NULL,IG11,544641,183369,544650,183350,51.53071001,0.083859427\n83203131,29/06/2013 22:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALL AND FENCE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,1.00021E+11,Cascade Avenue,21100135,N10,529094,189338,529050,189350,51.58811836,-0.137951777\n84016131,01/07/2013 10:28,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Sumatra Road,20400253,NW6,NULL,NULL,525150,184950,NULL,NULL\n84059131,01/07/2013 13:02,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000321,South Hornchurch,E09000016,Havering,Wennington,NULL,Askwith Road,21300703,RM13,NULL,NULL,550750,183050,NULL,NULL\n84353131,01/07/2013 21:15,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA OFFICER WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Tollgate Road,NULL,E6,541977,181738,541950,181750,51.51672963,0.044823649\n84386131,01/07/2013 22:41,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON FENCING,Cat,Person (mobile),Fire station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6025102,Cable Street,22700241,E1,535007,180901,535050,180950,51.51091588,-0.055885932\n84956131,03/07/2013 00:11,2013,2013/14,Special Service,1,2,290,580,CAT FALLEN NEAR TO RAILWAY LINES,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009397,Holland,E09000020,Kensington and Chelsea,Hammersmith,NULL,Russell Gardens Mews,21700668,W14,NULL,NULL,524150,179450,NULL,NULL\n85212131,03/07/2013 14:43,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000265,Wormholt and White City,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Adelaide Grove,21000030,W12,NULL,NULL,522450,180250,NULL,NULL\n85226131,03/07/2013 15:20,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,535550,186550,NULL,NULL\n85453131,03/07/2013 23:31,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000368,Caledonian,E09000019,Islington,Islington,5300079183,Roman Way,21605828,N7,530915,184396,530950,184350,51.54328699,-0.113520319\n85589131,04/07/2013 10:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Lynett Road,19903743,RM8,NULL,NULL,548050,187050,NULL,NULL\n86083131,05/07/2013 10:44,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA ON SCENE,Unknown - Wild Animal,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000378,St. George's,E09000019,Islington,Holloway,5300048438,Holloway Road,21606449,N7,530229,186201,530250,186250,51.55966662,-0.12273931\n86294131,05/07/2013 17:31,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH SNAKE ON ROOF,Snake,Person (land line),Mosque,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000142,Regent's Park,E09000007,Camden,Euston,5005910,North Gower Street,20400840,NW1,529297,182572,529250,182550,51.52726756,-0.137509019\n86634131,06/07/2013 07:53,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN SOFA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Alfred Road,8400328,W2,NULL,NULL,525450,181850,NULL,NULL\n86662131,06/07/2013 09:02,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000472,Village,E09000024,Merton,Wimbledon,NULL,Lancaster Gardens,NULL,SW19,524131,171481,524150,171450,51.42874703,-0.215833843\n86856131,06/07/2013 15:04,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Skeffington Road,22201058,E6,NULL,NULL,542650,183950,NULL,NULL\n87047131,06/07/2013 19:36,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED ON LEDGE ABOVE THE BETTING SHOP,Dog,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,1.00023E+11,Hatch Lane,22843600,E4,538823,192972,538850,192950,51.61846054,0.003830702\n87139131,06/07/2013 21:30,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Jenkins Road,22200749,E13,NULL,NULL,540750,182150,NULL,NULL\n87142131,06/07/2013 21:38,2013,2013/14,Special Service,1,2,290,580,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Foxwell Mews,22005374,SE4,NULL,NULL,536250,175750,NULL,NULL\n87347131,07/07/2013 08:40,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,Barnsbury,E09000019,Islington,Euston,NULL,Priory Green,21605823,N1,NULL,NULL,530850,183250,NULL,NULL\n87493131,07/07/2013 14:00,2013,2013/14,Special Service,1,1,290,290,DOG LOCKED OUT ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Wood Street,22888350,E17,NULL,NULL,538750,188950,NULL,NULL\n87525131,07/07/2013 14:43,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,Wimbledon,NULL,Amity Grove,22100168,SW20,NULL,NULL,523150,169350,NULL,NULL\n87643131,07/07/2013 17:33,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000620,Queenstown,E09000032,Wandsworth,Battersea,121022314,Battersea Park,22900030,SW11,528483,177258,528450,177250,51.4796965,-0.151171155\n88156131,08/07/2013 10:21,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BETWEEN WINDOW AND WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Commerce Road,21104385,N22,NULL,NULL,530750,190950,NULL,NULL\n88775131,09/07/2013 11:00,2013,2013/14,Special Service,1,1,290,290,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,1.00021E+11,Huntland Close,21300430,RM13,552958,181555,552950,181550,51.51222205,0.202884147\n88930131,09/07/2013 15:36,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000328,Charville,E09000017,Hillingdon,Hillingdon,NULL,Grosvenor Avenue,21400854,UB4,NULL,NULL,509650,183150,NULL,NULL\n89838131,10/07/2013 19:56,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,South Lodge Drive,20702961,N14,NULL,NULL,529850,195750,NULL,NULL\n89922131,10/07/2013 23:04,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Warren Road,22885300,E4,NULL,NULL,538250,194050,NULL,NULL\n90415131,11/07/2013 20:41,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,The Cut,21901359,SE1,NULL,NULL,531450,179850,NULL,NULL\n90516131,11/07/2013 23:49,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Fotheringham Road,20702689,EN1,NULL,NULL,533550,196250,NULL,NULL\n90711131,12/07/2013 12:29,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538350,187850,NULL,NULL\n90805131,12/07/2013 14:59,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO PIGEON TRAPPED IN TV AERIAL,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Beaufort Road,22403287,TW10,NULL,NULL,517150,171550,NULL,NULL\n90822131,12/07/2013 15:25,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH KITTEN UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,5019782,St. Marys Mews,20401345,NW6,525699,184051,525650,184050,51.54137102,-0.188820101\n90880131,12/07/2013 17:05,2013,2013/14,Special Service,2,3,290,870,PIGEON TRAPPED ON AERIAL,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Beaufort Road,22403287,TW10,NULL,NULL,517150,171550,NULL,NULL\n90983131,12/07/2013 18:39,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000314,Heaton,E09000016,Havering,Harold Hill,1.00021E+11,Heaton Avenue,21301268,RM3,552793,191315,552750,191350,51.59995879,0.20474287\n91277131,13/07/2013 07:01,2013,2013/14,Special Service,2,3,290,870,KITTEN STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,5048239,St. Marys Mews,20401345,NW6,525706,184078,525750,184050,51.54161211,-0.188709576\n91283131,13/07/2013 07:37,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Longland Drive,NULL,N20,525712,193394,525750,193350,51.6253329,-0.185290417\n91396131,13/07/2013 13:31,2013,2013/14,Special Service,1,1,290,290,INJURED KITTEN TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Barking Road,22208079,E13,NULL,NULL,541050,182950,NULL,NULL\n91455131,13/07/2013 13:56,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SNAKE STUCK IN WALL PANEL,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,NULL,Porchester Terrace,8400631,W2,NULL,NULL,526050,180750,NULL,NULL\n91494131,13/07/2013 15:28,2013,2013/14,Special Service,1,1,290,290,PUPPY POSSIBLY TRAPPED IN DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,1.00022E+11,Lausanne Road,22000604,SE15,535381,176607,535350,176650,51.47223865,-0.052146156\n92049131,14/07/2013 11:49,2013,2013/14,Special Service,1,1,290,290,KITTEN UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000053,Golders Green,E09000003,Barnet,Finchley,NULL,Courtleigh Gardens,20010600,NW11,NULL,NULL,524250,189050,NULL,NULL\n92051131,14/07/2013 11:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER CUPBOARD,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545085,178811,545050,178850,51.48963983,0.088381957\n92516131,14/07/2013 21:22,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Patmore Estate,22903805,SW8,NULL,NULL,529350,176950,NULL,NULL\n92737131,15/07/2013 07:19,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,West End Lane,20499056,NW6,NULL,NULL,525450,185150,NULL,NULL\n92854131,15/07/2013 11:36,2013,2013/14,Special Service,1,2,290,580,DOG STUCK RAILWAY BRIDGE,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Salisbury Avenue,NULL,IG11,544559,184275,544550,184250,51.53887188,0.083049647\n92910131,15/07/2013 12:54,2013,2013/14,Special Service,1,3,290,870,PUPPY IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Holloway,NULL,Portland Rise,20900814,N4,NULL,NULL,532050,187250,NULL,NULL\n93368131,15/07/2013 22:35,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Wakeman Road,20201535,NW10,NULL,NULL,523650,182850,NULL,NULL\n93404131,16/07/2013 00:19,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN FENCE,Dog,Police,Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Tedder Road,NULL,CR2,535529,163143,535550,163150,51.3512091,-0.055157234\n93588131,16/07/2013 11:41,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,The Broadway,20705014,N9,NULL,NULL,534350,193450,NULL,NULL\n93722131,16/07/2013 15:00,2013,2013/14,Special Service,1,1,290,290,DEHYDRATED CAT IN DISTRESS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,Glyndon,E09000011,Greenwich,Plumstead,NULL,Hudson Place,NULL,SE18,544340,178313,544350,178350,51.48535549,0.077455217\n93733131,16/07/2013 15:12,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED UNDER FENCE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Portia Way,22700962,E3,NULL,NULL,536650,181950,NULL,NULL\n93807131,16/07/2013 17:07,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN PIPE,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Crossley Street,21604641,N7,NULL,NULL,531050,184850,NULL,NULL\n93959131,16/07/2013 19:28,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN STEEL GIRDERS,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000271,Harringay,E09000014,Haringey,Holloway,NULL,Stroud Green Road,NULL,N4,531448,186858,531450,186850,51.56528802,-0.104919527\n94104131,16/07/2013 22:51,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING O.S STATION,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000647,Warwick,E09000033,Westminster,Lambeth,1.00023E+11,Victoria Arcade,8401626,SW1E,528953,179126,528950,179150,51.49637721,-0.143725076\n94111131,16/07/2013 23:01,2013,2013/14,Special Service,1,1,290,290,SMALL DEER TRAPPED IN GATE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000303,Stanmore Park,E09000015,Harrow,Stanmore,10000001962,Dennis Lane,21200883,HA7,516824,192852,516850,192850,51.62237547,-0.313809687\n94146131,17/07/2013 00:06,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED BEHIND BOILER,Hamster,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000288,Greenhill,E09000015,Harrow,Harrow,NULL,Headstone Road,NULL,HA1,NULL,NULL,515050,188350,NULL,NULL\n94261131,17/07/2013 08:56,2013,2013/14,Special Service,1,1,290,290,KITTEN IN DISTRESS ON ROOF,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000569,Wallington North,E09000029,Sutton,Wallington,5870091465,Clifton Road,22602212,SM6,529142,164292,529150,164250,51.36302118,-0.146406532\n94623131,17/07/2013 16:59,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000562,St. Helier,E09000029,Sutton,Sutton,NULL,Green Lane,NULL,SM4,525922,166694,525950,166650,51.38533044,-0.191782568\n95439131,18/07/2013 16:09,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009380,Lea Bridge,E09000012,Hackney,Homerton,1.00021E+11,Mayola Road,20900676,E5,535381,185917,535350,185950,51.5559014,-0.048572876\n95637131,18/07/2013 19:00,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009329,St. Dunstan's,E09000030,Tower Hamlets,Bethnal Green,NULL,Galsworthy Avenue,22702379,E14,NULL,NULL,536350,181450,NULL,NULL\n95789131,18/07/2013 21:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,6041105,Patriot Square,22700922,E2,535076,183147,535050,183150,51.53108247,-0.054031534\n95937131,19/07/2013 03:12,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545056,178759,545050,178750,51.48918002,0.087943188\n95944131,19/07/2013 03:37,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,1.00023E+11,Plumstead High Street,20801187,SE18,545056,178759,545050,178750,51.48918002,0.087943188\n96041131,19/07/2013 10:16,2013,2013/14,Special Service,1,1,290,290,ROAD TRAFFIC COLLISION,Unknown - Domestic Animal Or Pet,Person (land line),Caravan on tow,Road Vehicle,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000307,Cranham,E09000016,Havering,Hornchurch,NULL,Folkes Lane,NULL,RM14,558677,188034,558650,188050,51.56885573,0.288147558\n96119131,19/07/2013 11:27,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED  DOWN SIDE OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,The Fairway,20015220,EN5,NULL,NULL,525950,195450,NULL,NULL\n96504131,19/07/2013 17:29,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER KITCHEN SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,East Crescent,20013400,N11,NULL,NULL,527850,192950,NULL,NULL\n96606131,19/07/2013 19:21,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN CUPBOARD,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Pine Walk,22602544,SM5,NULL,NULL,526750,162050,NULL,NULL\n96677131,19/07/2013 20:44,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Bushey Road,22207639,E13,NULL,NULL,541250,183250,NULL,NULL\n96807131,19/07/2013 22:51,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000074,East Wickham,E09000004,Bexley,Plumstead,NULL,Hill View Drive,NULL,DA16,545076,176269,545050,176250,51.4668008,0.08720898\n97043131,20/07/2013 10:41,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,East Crescent,20013400,N11,NULL,NULL,527850,192950,NULL,NULL\n97089131,20/07/2013 12:01,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DITCH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,Camden Park Road,20400532,NW1,NULL,NULL,529750,184850,NULL,NULL\n97318131,20/07/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CEILING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Meadow Road,19900530,IG11,NULL,NULL,545950,184050,NULL,NULL\n97610131,21/07/2013 07:42,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000202,Highlands,E09000010,Enfield,Enfield,NULL,Comreddy Close,20702615,EN2,NULL,NULL,531650,197950,NULL,NULL\n97719131,21/07/2013 12:22,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BARBED WIRE,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000330,Harefield,E09000017,Hillingdon,Ruislip,1.00021E+11,High Street,21400961,UB9,505196,190516,505150,190550,51.60367006,-0.48241621\n97739131,21/07/2013 12:42,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN ATENNA ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Uplands Road,19900620,RM6,NULL,NULL,547650,189550,NULL,NULL\n98192131,21/07/2013 23:32,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON LEDGE OVER WATERWAY,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515887,175968,515850,175950,51.47081957,-0.332896102\n98204131,21/07/2013 23:55,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,6211060,Barnet Grove,22700122,E2,534229,182635,534250,182650,51.52668355,-0.066430182\n98399131,22/07/2013 12:00,2013,2013/14,Special Service,1,1,290,290,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000598,Hatch Lane,E09000031,Waltham Forest,Chingford,1.00023E+11,Hatch Lane,22843600,E4,538831,192974,538850,192950,51.61847654,0.003946973\n99399131,23/07/2013 14:01,2013,2013/14,Special Service,1,1,290,290,PUPPY FALLEN DOWN EMBANKMENT,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518441,177481,518450,177450,51.48388987,-0.295635218\n100087131,24/07/2013 16:16,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000429,Stockwell,E09000022,Lambeth,Clapham,NULL,Belmore Street,21900168,SW8,NULL,NULL,529850,176850,NULL,NULL\n100135131,24/07/2013 17:12,2013,2013/14,Special Service,1,1,290,290,TWO BIRDS TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Station Place,NULL,N4,531341,186764,531350,186750,51.56446822,-0.106497405\n100547131,25/07/2013 04:59,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,Shacklewell,E09000012,Hackney,Stoke Newington,NULL,Prince George Road,20900821,N16,NULL,NULL,533450,185650,NULL,NULL\n100577131,25/07/2013 07:45,2013,2013/14,Special Service,1,1,290,290,DOG LOCKED IN ROOM,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Pepper Close,22200951,E6,NULL,NULL,542950,181750,NULL,NULL\n100609131,25/07/2013 09:45,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BY LEG,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011467,Crystal Palace & Upper Norwood,E09000008,Croydon,West Norwood,1.00023E+11,Westow Street,20500150,SE19,533496,170469,533450,170450,51.41752605,-0.081582824\n101731131,27/07/2013 04:16,2013,2013/14,Special Service,1,1,290,290,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009387,Woodberry Down,E09000012,Hackney,Holloway,1.00023E+11,Portland Rise,20900814,N4,531927,187223,531950,187250,51.56845621,-0.097876151\n101758131,27/07/2013 05:39,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED NEAR CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000485,Green Street East,E09000025,Newham,East Ham,NULL,South Esk Road,22207889,E7,NULL,NULL,541250,184550,NULL,NULL\n101796131,27/07/2013 09:12,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN GATE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,NULL,Elsden Road,21104501,N17,NULL,NULL,533650,190550,NULL,NULL\n101862131,27/07/2013 11:57,2013,2013/14,Special Service,1,1,290,290,DOG STUCK IN RIVER,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,NULL,Ferry Lane,NULL,TW9,518222,177275,518250,177250,51.48208413,-0.298856576\n102039131,27/07/2013 16:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Tunstall Road,21901410,SW9,NULL,NULL,530950,175450,NULL,NULL\n102619131,28/07/2013 18:19,2013,2013/14,Special Service,1,1,290,290,TWO PIGEONS STUCK IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000373,Highbury West,E09000019,Islington,Holloway,NULL,St. Thomas's Road,NULL,N4,531402,186665,531450,186650,51.56356434,-0.105654816\n102897131,29/07/2013 08:58,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED AT ROOF LEVEL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011464,Bensham Manor,E09000008,Croydon,Norbury,NULL,Elliott Road,20500907,CR7,NULL,NULL,531950,168350,NULL,NULL\n103087131,29/07/2013 16:13,2013,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,The Bourne,NULL,N14,530147,194196,530150,194150,51.63153252,-0.120958737\n103440131,30/07/2013 11:14,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED BETWEEN WALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Grange,NULL,N5,532105,185676,532150,185650,51.55451241,-0.095889742\n103613131,30/07/2013 18:18,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000107,Biggin Hill,E09000006,Bromley,Biggin Hill,1.0002E+11,Sunningvale Avenue,20303649,TN16,541632,158560,541650,158550,51.30853932,0.030605492\n103634131,30/07/2013 18:51,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN FROM BALCONY INTO BUSH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,5300051386,Huddleston Road,21600471,N7,529360,185989,529350,185950,51.55796124,-0.135346045\n104299131,01/08/2013 07:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,535550,186550,NULL,NULL\n104331131,01/08/2013 09:31,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDERNEATH FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000608,William Morris,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,NULL,E17,536516,189633,536550,189650,51.5890206,-0.030769324\n104348131,01/08/2013 10:28,2013,2013/14,Special Service,1,1,290,290,PUPPY TRAPPED UNDER SHED,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Claremont Road,22403387,TW1,516998,174008,516950,174050,51.4529751,-0.317555274\n104541131,01/08/2013 14:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Gatonby Street,22502994,SE15,NULL,NULL,533850,176850,NULL,NULL\n105214131,02/08/2013 14:09,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED IN HOLE,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,Elia Street,21604792,N1,NULL,NULL,531750,183150,NULL,NULL\n105585131,03/08/2013 02:17,2013,2013/14,Special Service,1,1,290,290,KITTEN WITH HEAD TRAPPED IN METAL BARS ACROSS CAGE,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,48083695,Merton High Street,22104371,SW19,526278,170138,526250,170150,51.41620337,-0.185444865\n105913131,03/08/2013 19:21,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Lefevre Walk,22702079,E3,NULL,NULL,537050,183450,NULL,NULL\n105925131,03/08/2013 19:38,2013,2013/14,Special Service,1,1,290,290,CAT WITH ITS HEAD STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,Eltham,NULL,Flintmill Crescent,20800579,SE3,NULL,NULL,542250,176050,NULL,NULL\n106048131,03/08/2013 23:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN PIPE WORK IN KITCHEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Vale Terrace,21104028,N4,NULL,NULL,532150,188050,NULL,NULL\n106319131,04/08/2013 15:31,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CHAIR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Margery Street,21605383,WC1X,NULL,NULL,531150,182650,NULL,NULL\n106423131,04/08/2013 17:46,2013,2013/14,Special Service,1,1,290,290,CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,1.00022E+11,Franks Avenue,21800420,KT3,520435,168142,520450,168150,51.39953438,-0.270106082\n106427131,04/08/2013 17:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO GAIN ACCESS TO TRAPPED PIGEON,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Abbotts Park Road,22800150,E10,NULL,NULL,538250,187750,NULL,NULL\n106527131,04/08/2013 20:31,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Seven Sisters Road,NULL,N4,531337,186696,531350,186650,51.56385806,-0.106580443\n107182131,06/08/2013 08:39,2013,2013/14,Special Service,1,2,290,580,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000503,Fairlop,E09000026,Redbridge,Woodford,NULL,Regents Drive,NULL,IG8,542969,191343,542950,191350,51.60278659,0.063012321\n107328131,06/08/2013 16:53,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000380,St. Peter's,E09000019,Islington,Islington,NULL,St. Peter's Street,21606037,N1,NULL,NULL,531950,183550,NULL,NULL\n107334131,06/08/2013 17:09,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CONCRETE SLOPE,Dog,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Plender Street,NULL,NW1,529342,183669,529350,183650,51.53711587,-0.136457965\n107506131,06/08/2013 22:44,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000229,Woolwich Common,E09000011,Greenwich,Plumstead,NULL,Sandy Hill Road,20801323,SE18,NULL,NULL,543650,178450,NULL,NULL\n107640131,07/08/2013 07:59,2013,2013/14,Special Service,1,1,290,290,CAT WITH LEG TRAPPED IN WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Hewish Road,20704336,N18,NULL,NULL,533150,192750,NULL,NULL\n107719131,07/08/2013 12:13,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS ON BALCONY,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,10011844455,Ambrook Road,20100036,DA17,549311,179064,549350,179050,51.49081401,0.149314033\n108013131,07/08/2013 22:07,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN FENCE,Dog,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Viewland Road,NULL,SE18,545522,178148,545550,178150,51.48357024,0.0943988\n108171131,08/08/2013 08:12,2013,2013/14,Special Service,1,1,290,290,SQUIRREL STUCK IN METAL FENCE,Squirrel,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011219,Bexleyheath,E09000004,Bexley,Bexley,1.00023E+11,Broadway,20100205,DA6,548833,175385,548850,175350,51.45788304,0.140884372\n108216131,08/08/2013 10:17,2013,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Shroffold Road,22003952,BR1,NULL,NULL,539650,171950,NULL,NULL\n108271131,08/08/2013 12:36,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000114,Cray Valley East,E09000006,Bromley,Orpington,NULL,Sanderstead Road,20301276,BR5,NULL,NULL,546750,167050,NULL,NULL\n108521131,08/08/2013 20:37,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BEHIND SHED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Church Way,20008980,N20,NULL,NULL,527050,193350,NULL,NULL\n108766131,09/08/2013 11:07,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Bexley,1.0002E+11,Penshurst Road,20101134,DA7,548796,176907,548750,176950,51.47156841,0.140992886\n108911131,09/08/2013 16:45,2013,2013/14,Special Service,1,2,290,580,CAT WITH PAW TRAPPED,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000136,Haverstock,E09000007,Camden,Kentish Town,5048295,Ferdinand Street,20400626,NW1,528426,184398,528450,184350,51.54387625,-0.14939213\n109348131,10/08/2013 09:22,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN WIRE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000151,Coulsdon West,E09000008,Croydon,Purley,NULL,Stoats Nest Road,NULL,CR5,530481,160165,530450,160150,51.32562588,-0.128695418\n109388131,10/08/2013 11:28,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000282,West Green,E09000014,Haringey,Tottenham,2.00003E+11,Adams Road,21106976,N17,532776,190279,532750,190250,51.59571913,-0.084480042\n109850131,11/08/2013 00:20,2013,2013/14,Special Service,1,2,290,580,DOG STUCK BETWEEN WALL AND CONVERSATORY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Claire Place,22700317,E14,NULL,NULL,537250,179250,NULL,NULL\n110192131,11/08/2013 17:27,2013,2013/14,Special Service,1,1,290,290,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000508,Mayfield,E09000026,Redbridge,Ilford,NULL,South Park Drive,NULL,IG3,545236,186549,545250,186550,51.55913098,0.093742016\n110374131,12/08/2013 00:01,2013,2013/14,Special Service,1,1,290,290,INJURED DOG TRAPPED BEHIND FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Whetstone Road,20801603,SE3,NULL,NULL,541450,176550,NULL,NULL\n110491131,12/08/2013 08:46,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON LEDGE NEAR CANAL,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6168476,Ursula Gould Way,22702630,E14,537284,181607,537250,181650,51.51671207,-0.022819283\n111060131,13/08/2013 10:27,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON LEDGE ON FOURTH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Redington Road,20400055,NW3,NULL,NULL,525950,185750,NULL,NULL\n111327131,13/08/2013 19:39,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT AREA,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000374,Hillrise,E09000019,Islington,Holloway,NULL,Miranda Road,21600604,N19,NULL,NULL,529450,187150,NULL,NULL\n111351131,13/08/2013 20:01,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BARBED WIRE FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,Goresbrook,E09000002,Barking and Dagenham,Barking,NULL,Gale Street,19900144,RM9,NULL,NULL,547650,184250,NULL,NULL\n112696131,16/08/2013 14:04,2013,2013/14,Special Service,1,1,290,290,ASSIST PARKS POLICE,Unknown - Wild Animal,Police,Park,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,10002252044,Richmond Park,22407048,TW10,518960,172804,518950,172850,51.44174558,-0.289735623\n112849131,16/08/2013 20:07,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALL AND SHED,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000380,St. Peter's,E09000019,Islington,Islington,5300065937,New North Road,21605530,N1,532420,183886,532450,183850,51.53835281,-0.092021888\n113122131,17/08/2013 10:10,2013,2013/14,Special Service,1,1,290,290,CAT FALLEN AND TRAPPED IN BASEMENT AREA,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Ovington Square,21700387,SW3,NULL,NULL,527550,179150,NULL,NULL\n113188131,17/08/2013 12:36,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,Brixton,NULL,St. Saviour's Road,21901287,SW2,NULL,NULL,530650,174550,NULL,NULL\n114399131,19/08/2013 20:10,2013,2013/14,Special Service,1,1,290,290,CAT LOCKED IN HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Main Avenue,20702825,EN1,NULL,NULL,533750,195850,NULL,NULL\n114815131,20/08/2013 16:05,2013,2013/14,Special Service,1,1,290,290,CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Randolph Gardens,8401516,NW6,NULL,NULL,525650,183150,NULL,NULL\n114847131,20/08/2013 16:51,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN BOILER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,Preston,E09000005,Brent,Wembley,NULL,Oakington Avenue,20202423,HA9,NULL,NULL,518950,186350,NULL,NULL\n115506131,21/08/2013 19:07,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BEHIND WALLS,Bird,Person (land line),Other indoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hoe Street,22845700,E17,537377,189695,537350,189650,51.58936866,-0.018324013\n116281131,23/08/2013 08:03,2013,2013/14,Special Service,1,2,290,580,FOX TRAPPED IN WOOD POST,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000459,Dundonald,E09000024,Merton,Wimbledon,48048396,Mayfield Road,22104293,SW19,524935,169888,524950,169850,51.41425372,-0.204835775\n116980131,24/08/2013 14:37,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DOG TRAPPED ON BALCONY ON FIRST FLOOR,Dog,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000207,Southbury,E09000010,Enfield,Edmonton,NULL,Ayley Croft,20702490,EN1,NULL,NULL,534250,195550,NULL,NULL\n117361131,24/08/2013 19:50,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN GARAGE AND NEIGHBOURING HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000323,Upminster,E09000016,Havering,Hornchurch,NULL,Springfield Gardens,21301896,RM14,NULL,NULL,556450,186150,NULL,NULL\n117401131,24/08/2013 20:58,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN ON SECOND FLOOR,Cat,Person (land line),Hospital,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,5087826,Pond Street,20400297,NW3,527297,185410,527250,185450,51.55322653,-0.165298226\n117615131,25/08/2013 12:47,2013,2013/14,Special Service,1,1,290,290,SQUIRELL ON ROOF - RSPCA ON SCENE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,Miles Road,22104407,CR4,NULL,NULL,527150,168850,NULL,NULL\n117981131,25/08/2013 23:36,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN KITCHEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000188,South Acton,E09000009,Ealing,Acton,NULL,Newport Road,20604685,W3,NULL,NULL,520050,179650,NULL,NULL\n118035131,26/08/2013 02:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN HOSPITAL GROUNDS,Cat,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,Hampstead Town,E09000007,Camden,Kentish Town,5087826,Pond Street,20400297,NW3,527297,185410,527250,185450,51.55322653,-0.165298226\n118471131,26/08/2013 23:07,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED ON BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Victoria Park Square,22701269,E2,NULL,NULL,535050,182950,NULL,NULL\n118680131,27/08/2013 11:13,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Briar Walk,8400753,W10,NULL,NULL,524250,182450,NULL,NULL\n119867131,29/08/2013 18:45,2013,2013/14,Special Service,1,1,290,290,BIRDS TRAPPED IN WIRE,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,1.00023E+11,Eden Walk,21820013,KT1,518105,169217,518150,169250,51.40968602,-0.3032277\n120113131,30/08/2013 09:17,2013,2013/14,Special Service,1,1,290,290,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Fernbank Avenue,20200263,HA0,NULL,NULL,515650,185550,NULL,NULL\n120447131,30/08/2013 19:59,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED IN AWNING,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,1.00024E+11,Crofton Road,20302866,BR6,543218,165228,543250,165250,51.36806226,0.056022528\n120528131,30/08/2013 22:06,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000098,Queens Park,E09000005,Brent,North Kensington,202088739,Harvist Road,20201304,NW6,524382,183187,524350,183150,51.53389756,-0.20810629\n120636131,31/08/2013 04:01,2013,2013/14,Special Service,1,1,290,290,YOUNG DOG WITH HEAD TRAPPED IN BED RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000101,Sudbury,E09000005,Brent,Wembley,NULL,Fernbank Avenue,20200263,HA0,NULL,NULL,515650,185550,NULL,NULL\n120660131,31/08/2013 05:33,2013,2013/14,Special Service,1,1,290,290,CAT WITH PAW STUCK IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Stewart Street,22701165,E14,NULL,NULL,538350,179450,NULL,NULL\n121050131,31/08/2013 17:18,2013,2013/14,Special Service,1,1,290,290,PARROT STUCK IN TREE BY ITS HARNESS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Ravenswood Crescent,NULL,HA2,512688,186641,512650,186650,51.56739373,-0.375524427\n121141131,31/08/2013 20:02,2013,2013/14,Special Service,1,1,290,290,PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Bus/coach station/garage,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000633,Churchill,E09000033,Westminster,Chelsea,10033531076,Buckingham Palace Road,8400915,SW1W,528644,178672,528650,178650,51.49236746,-0.148339393\n121485131,01/09/2013 12:52,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,NULL,Smithies Road,20801372,SE2,NULL,NULL,546650,178750,NULL,NULL\n121494131,01/09/2013 13:02,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN TREE,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000355,Heston Central,E09000018,Hounslow,Heston,NULL,Sutton Lane,NULL,TW3,512960,176567,512950,176550,51.47679485,-0.374828163\n121689131,01/09/2013 17:14,2013,2013/14,Special Service,1,1,290,290,KITTEN STUCK IN CAR BEHIND GLOVE BOX,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Walthamstow,2.00001E+11,Lamorna Close,22849350,E17,538515,190503,538550,190550,51.59635053,-0.001589304\n122447131,02/09/2013 23:13,2013,2013/14,Special Service,1,1,290,290,ASSIST POLICE WITH DOG UNDER BUS,Dog,Police,Bus/coach,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Edgwarebury Lane,NULL,HA8,519389,192312,519350,192350,51.61698404,-0.276959606\n122580131,03/09/2013 09:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DOOR GRILL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000095,Mapesbury,E09000005,Brent,Willesden,NULL,Blenheim Gardens,20202394,NW2,NULL,NULL,523350,185050,NULL,NULL\n122892131,03/09/2013 20:23,2013,2013/14,Special Service,1,1,290,290,CAT WITH NECK STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000368,Caledonian,E09000019,Islington,Holloway,NULL,Roman Way,21605828,N7,NULL,NULL,530750,184750,NULL,NULL\n123278131,04/09/2013 14:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000543,Livesey,E09000028,Southwark,New Cross,NULL,Drovers Place,NULL,SE15,535008,177317,535050,177350,51.4787082,-0.057242171\n123310131,04/09/2013 16:00,2013,2013/14,Special Service,1,2,290,580,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Mawney Road,21301408,RM7,NULL,NULL,550350,188950,NULL,NULL\n123314131,04/09/2013 16:03,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,Wembley Hill Road,NULL,HA9,NULL,NULL,518850,185250,NULL,NULL\n123748131,05/09/2013 10:45,2013,2013/14,Special Service,1,1,290,290,DOG IN LAKE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,NULL,Lodge Avenue,NULL,RM8,546643,184678,546650,184650,51.54195577,0.113245084\n124099131,05/09/2013 20:40,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN FENCE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000569,Wallington North,E09000029,Sutton,Wallington,5870078870,Park Lane,22605913,SM6,528496,164057,528450,164050,51.36105538,-0.155765166\n124740131,07/09/2013 03:54,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN METAL CAGE OS,Dog,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,217085319,Sydney Street,21700534,SW3,527204,178342,527250,178350,51.4897272,-0.169189306\n124906131,07/09/2013 14:13,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,Forest Gate North,E09000025,Newham,Stratford,10014033106,Lorne Road,22207794,E7,540992,185866,540950,185850,51.55406996,0.032288746\n125682131,08/09/2013 21:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND  PIPE IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Goldsmith Road,22839800,E10,NULL,NULL,537550,187150,NULL,NULL\n125904131,09/09/2013 11:55,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Lamberhurst Close,20301179,BR5,NULL,NULL,547750,166150,NULL,NULL\n126092131,09/09/2013 19:54,2013,2013/14,Special Service,1,2,290,580,DOG TRAPPED UNDER SUMMERHOUSE,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011221,Blendon & Penhill,E09000004,Bexley,Bexley,1.0002E+11,South View Close,20101318,DA5,548648,174191,548650,174150,51.44720316,0.137721878\n126886131,11/09/2013 18:11,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Compton Avenue,22200498,E6,NULL,NULL,541750,183150,NULL,NULL\n128200131,14/09/2013 08:21,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN GRAND UNION CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Harewood Terrace,NULL,UB2,512914,178834,512950,178850,51.49717997,-0.374766031\n128279131,14/09/2013 11:27,2013,2013/14,Special Service,1,1,290,290,HORSE COLLAPSED IN FIELD,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000114,Cray Valley East,E09000006,Bromley,Sidcup,1.0002E+11,Cookham Road,20302992,BR8,549570,169348,549550,169350,51.40344436,0.148936733\n129213131,16/09/2013 15:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER GRATING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Boyson Road,22500309,SE17,NULL,NULL,532550,177750,NULL,NULL\n129820131,17/09/2013 20:23,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000478,Canning Town South,E09000025,Newham,Plaistow,NULL,Jutland Road,22200758,E13,NULL,NULL,540350,182250,NULL,NULL\n130134131,18/09/2013 15:37,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000449,New Cross,E09000023,Lewisham,New Cross,NULL,New Cross Road,NULL,SE14,536306,176996,536350,176950,51.47551237,-0.038686271\n130400131,19/09/2013 07:50,2013,2013/14,Special Service,1,1,290,290,FOX STUCK BETWEEN FENCES,Fox,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05011482,Shirley North,E09000008,Croydon,Woodside,1.00021E+11,Swinburne Crescent,20500566,CR0,535240,166966,535250,166950,51.38563324,-0.057851966\n130852131,20/09/2013 10:25,2013,2013/14,Special Service,1,1,290,290,KITTENS STUCK IN TREES,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000549,Rotherhithe,E09000028,Southwark,Deptford,NULL,Southwark Park,NULL,SE16,535192,179036,535150,179050,51.49411208,-0.053936212\n130984131,20/09/2013 16:58,2013,2013/14,Special Service,1,1,290,290,CAT WITH HEAD STUCK IN FOOD CAN,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Coteford Street,NULL,SW17,528125,171477,528150,171450,51.42782341,-0.158414733\n131135131,20/09/2013 21:29,2013,2013/14,Special Service,1,1,290,290,SQUIRREL TRAPPED BEHIND GAS HEATER,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Stanwell Road,21501060,TW14,NULL,NULL,508450,173750,NULL,NULL\n131346131,21/09/2013 11:44,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000294,Kenton East,E09000015,Harrow,Stanmore,NULL,Malvern Gardens,21201565,HA3,NULL,NULL,518450,189450,NULL,NULL\n131504131,21/09/2013 18:09,2013,2013/14,Special Service,1,1,290,290,ZEBRA FINCH BIRD TRAPPED BEHIND RADIATOR,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011230,Sidcup,E09000004,Bexley,Sidcup,NULL,Melville Road,20100963,DA14,NULL,NULL,547350,172550,NULL,NULL\n131970131,22/09/2013 16:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000319,Romford Town,E09000016,Havering,Romford,10091833603,South Street,21301604,RM1,551236,188724,551250,188750,51.57709881,0.181164824\n131982131,22/09/2013 17:14,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN METER CUPBOARD OUTSIDE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,Cambridge Gardens,21700828,W10,NULL,NULL,524150,181350,NULL,NULL\n132004131,22/09/2013 17:53,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Rushgrove Street,20801304,SE18,NULL,NULL,542950,178550,NULL,NULL\n132223131,23/09/2013 07:51,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON SECOND FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Norman Crescent,21500807,TW5,NULL,NULL,511650,177050,NULL,NULL\n132427131,23/09/2013 14:06,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Briar Hill,20501944,CR8,530392,161768,530350,161750,51.34005275,-0.129385853\n132493131,23/09/2013 16:53,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000411,St. James,E09000021,Kingston upon Thames,New Malden,1.00022E+11,George Road,21800435,KT3,521918,168069,521950,168050,51.39856176,-0.248823027\n132844131,24/09/2013 13:29,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN CABLE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Highshore Road,22501242,SE15,534005,176470,534050,176450,51.47133495,-0.071997602\n133069131,24/09/2013 21:16,2013,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000166,Upper Norwood,E09000008,Croydon,Norbury,NULL,The Lawns,NULL,SE19,532860,169614,532850,169650,51.4099919,-0.091043687\n133182131,25/09/2013 07:13,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN FENCE IN REAR GARDEN,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000380,St. Peter's,E09000019,Islington,Islington,5300070556,Packington Square,21606921,N1,532085,183520,532050,183550,51.53514223,-0.096986682\n133227131,25/09/2013 09:50,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,The Common,NULL,W5,518333,180340,518350,180350,51.50960833,-0.296231735\n134107131,27/09/2013 01:40,2013,2013/14,Special Service,1,1,290,290,DEER TRAPPED IN FENCING,Deer,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011466,Coulsdon Town,E09000008,Croydon,Purley,NULL,Magpie Close,20502492,CR5,NULL,NULL,529350,158050,NULL,NULL\n134157131,27/09/2013 07:08,2013,2013/14,Special Service,1,1,290,290,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Golders Green Road,20018180,NW11,NULL,NULL,524950,187450,NULL,NULL\n134391131,27/09/2013 18:33,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPED IN HOLE OF FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Bromley,NULL,Cranmore Road,22001376,BR1,NULL,NULL,539450,172250,NULL,NULL\n134782131,28/09/2013 16:17,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CAR MECHANICS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000172,Dormers Wells,E09000009,Ealing,Southall,NULL,Shackleton Road,NULL,UB1,512873,180673,512850,180650,51.51371703,-0.374768288\n135986131,01/10/2013 10:00,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Pikestone Close,21401532,UB4,NULL,NULL,512150,182150,NULL,NULL\n136065131,01/10/2013 13:13,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Claremont Road,22207659,E7,NULL,NULL,541450,185350,NULL,NULL\n136116131,01/10/2013 15:43,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER BOILER,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,St. Pauls Road,20501422,CR7,NULL,NULL,532350,168650,NULL,NULL\n137273131,04/10/2013 00:28,2013,2013/14,Special Service,1,1,290,290,CAT IN PRECARIOUS POSITION  ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Maidenstone Hill,20800959,SE10,NULL,NULL,538150,176750,NULL,NULL\n137888131,05/10/2013 08:59,2013,2013/14,Special Service,2,3,290,870,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Millshott Close,NULL,SW6,523510,176957,523550,176950,51.4780978,-0.222852562\n138071131,05/10/2013 15:36,2013,2013/14,Special Service,1,4,290,1160,DOG TRAPPED IN STORM WATER PIPE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000055,Hendon,E09000003,Barnet,Mill Hill,NULL,Watford Way,NULL,NW4,522478,190416,522450,190450,51.59928255,-0.233025122\n138463131,06/10/2013 08:50,2013,2013/14,Special Service,1,1,290,290,CAT BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Pioneer Close,22702451,E14,NULL,NULL,537550,181750,NULL,NULL\n138560131,06/10/2013 13:19,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED  IN DRAIN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Elmwood Road,20500912,CR0,NULL,NULL,531950,166850,NULL,NULL\n139155131,07/10/2013 18:13,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD IN RAILINGS,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Ferdinand Street,NULL,NW1,528434,184353,528450,184350,51.54347002,-0.149293243\n139492131,08/10/2013 12:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK BEHIND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Burgess Avenue,20202560,NW9,NULL,NULL,520550,188250,NULL,NULL\n139939131,09/10/2013 11:12,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Wisdons Close,NULL,RM10,549845,187032,549850,187050,51.56226691,0.160385577\n139994131,09/10/2013 14:01,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Sandford Avenue,21105015,N22,NULL,NULL,532050,190950,NULL,NULL\n140048131,09/10/2013 16:30,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Sandford Avenue,21105015,N22,NULL,NULL,532050,190950,NULL,NULL\n140588131,10/10/2013 14:57,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND BOILER,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000367,Bunhill,E09000019,Islington,Islington,NULL,Percival Street,21605667,EC1V,NULL,NULL,531650,182450,NULL,NULL\n140590131,10/10/2013 15:01,2013,2013/14,Special Service,1,2,290,580,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Carysfort Road,20900204,N16,NULL,NULL,532650,186050,NULL,NULL\n141747131,12/10/2013 20:02,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,NULL,Belvedere Road,20302135,SE19,NULL,NULL,533950,170050,NULL,NULL\n141976131,13/10/2013 07:28,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011117,Surrey Docks,E09000028,Southwark,Deptford,NULL,Russia Dock Road,22502158,SE16,NULL,NULL,536250,180050,NULL,NULL\n142362131,14/10/2013 07:42,2013,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,NULL,Parkleys,22403727,TW10,NULL,NULL,517850,171750,NULL,NULL\n142691131,14/10/2013 22:02,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Ambleside Gardens,22602067,SM2,NULL,NULL,526350,163650,NULL,NULL\n143033131,15/10/2013 18:06,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BEHIND TOILET PIPE,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011242,Fairlop,E09000026,Redbridge,Hainault,1.00023E+11,New North Road,22304684,IG6,544871,191437,544850,191450,51.60314568,0.090495412\n143969131,17/10/2013 20:47,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED UNDER IRON BAR,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000128,Belsize,E09000007,Camden,West Hampstead,5017447,Belsize Park Gardens,20400436,NW3,527378,184727,527350,184750,51.5470702,-0.164377681\n144013131,17/10/2013 22:19,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011475,Park Hill & Whitgift,E09000008,Croydon,Croydon,1.00021E+11,Rutland Gardens,20502313,CR0,533453,164714,533450,164750,51.36581827,-0.084362251\n144650131,19/10/2013 12:45,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Dunbar Road,21104475,N22,NULL,NULL,531450,190950,NULL,NULL\n144767131,19/10/2013 17:23,2013,2013/14,Special Service,1,2,290,580,HORSE TRAPPED IN FENCE,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000342,West Drayton,E09000017,Hillingdon,Hayes,NULL,Wise Lane,NULL,UB7,505728,178726,505750,178750,51.4975983,-0.478278351\n145925131,22/10/2013 13:57,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000319,Romford Town,E09000016,Havering,Romford,10091576954,High Street,21301276,RM1,551137,188821,551150,188850,51.57799687,0.17977893\n146533131,23/10/2013 17:30,2013,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN NETTING,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,Botwell,E09000017,Hillingdon,Hayes,1.00024E+11,Judge Heath Lane,21401064,UB8,508317,181035,508350,181050,51.51786213,-0.44028763\n146770131,24/10/2013 04:49,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN HARDBOARD AND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Seven Sisters Road,21603790,N7,NULL,NULL,530850,186350,NULL,NULL\n147059131,24/10/2013 14:59,2013,2013/14,Special Service,1,1,290,290,KITTEN  STUCK IN ENGINE COMPARTMENT  OF  CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Avalon Road,NULL,SW6,525782,176916,525750,176950,51.47722933,-0.190167599\n147453131,25/10/2013 09:41,2013,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED UNDER HEATER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000566,Sutton South,E09000029,Sutton,Sutton,NULL,Ambleside Gardens,22602067,SM2,NULL,NULL,526350,163650,NULL,NULL\n147969131,26/10/2013 12:02,2013,2013/14,Special Service,1,2,290,580,DOG DOWN FOXHOLE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000124,Petts Wood and Knoll,E09000006,Bromley,Orpington,NULL,Broomhill Road,NULL,BR6,546136,166693,546150,166650,51.38048328,0.098511635\n147981131,26/10/2013 12:22,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Kellett Road,21900773,SW2,NULL,NULL,531150,175150,NULL,NULL\n148355131,27/10/2013 09:05,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Welbeck Road,20044920,EN4,NULL,NULL,527050,195350,NULL,NULL\n148486131,27/10/2013 14:18,2013,2013/14,Special Service,1,1,290,290,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,Seven Sisters,E09000014,Haringey,Tottenham,NULL,Richmond Road,NULL,N15,533185,188212,533150,188250,51.57704792,-0.079361963\n148504131,27/10/2013 15:05,2013,2013/14,Special Service,1,2,290,580,TO ASSIST RSPCA JILL WITH CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,Fortis Green,E09000014,Haringey,Hornsey,1.00021E+11,Midhurst Avenue,21100589,N10,528018,189604,528050,189650,51.590754,-0.153377897\n149325131,28/10/2013 10:14,2013,2013/14,Special Service,1,1,290,290,DOG ON ROOF,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000465,Lower Morden,E09000024,Merton,Sutton,NULL,Tudor Drive,22106354,SM4,NULL,NULL,524450,166750,NULL,NULL\n150207131,30/10/2013 00:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000213,Winchmore Hill,E09000010,Enfield,Edmonton,NULL,Highfield Road,20704341,N21,NULL,NULL,532050,193950,NULL,NULL\n150293131,30/10/2013 08:50,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED BETWEEN FENCES,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000470,St. Helier,E09000024,Merton,Sutton,NULL,Garendon Gardens,22102784,SM4,NULL,NULL,525550,166950,NULL,NULL\n150426131,30/10/2013 14:04,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,Abbey Wood,E09000011,Greenwich,Plumstead,1.00021E+11,Rochdale Road,20801281,SE2,546537,178727,546550,178750,51.48851098,0.109246338\n150920131,31/10/2013 18:09,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Northampton Square,21605564,EC1V,NULL,NULL,531750,182650,NULL,NULL\n150976131,31/10/2013 20:09,2013,2013/14,Special Service,1,1,290,290,DOG FALLEN DOWN EMBANKMENT BY RIVER,Dog,Police,River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Capstan Square,NULL,E14,538366,179652,538350,179650,51.49887997,-0.008002436\n151172131,01/11/2013 07:52,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009376,Homerton,E09000012,Hackney,Homerton,NULL,Morning Lane,NULL,E9,535343,184922,535350,184950,51.54696913,-0.049502902\n151259131,01/11/2013 11:18,2013,2013/14,Special Service,1,1,290,290,BLACKBIRD TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000296,Marlborough,E09000015,Harrow,Stanmore,1.00021E+11,Peel Road,21201687,HA3,515673,189687,515650,189650,51.59416666,-0.331472797\n151448131,01/11/2013 17:28,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN WALL CAVITY,Cat,Person (land line),Bakery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Bellenden Road,22500202,SE15,533965,176017,533950,176050,51.46727348,-0.072744703\n151451131,01/11/2013 17:34,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER BRANCHES AND RUBBISH,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000464,Longthornton,E09000024,Merton,Norbury,48042957,Lilian Road,22103903,SW16,529201,169687,529250,169650,51.4114926,-0.143597763\n151827131,02/11/2013 14:18,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Ashbridge Street,8400327,NW8,NULL,NULL,527250,182050,NULL,NULL\n152319131,03/11/2013 09:43,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER TREE IN GARDEN,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000227,Shooters Hill,E09000011,Greenwich,Eltham,1.00021E+11,Red Lion Lane,20801247,SE18,543131,176695,543150,176650,51.47112362,0.059399602\n152362131,03/11/2013 12:07,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH FOX TRAPPED BETWEEN TWO GARAGES,Fox,Person (land line),Private Summer house,Non Residential,Other animal assistance,Assist trapped wild animal,E05000058,Oakleigh,E09000003,Barnet,Southgate,200106405,St. James Close,20038060,N20,527255,193049,527250,193050,51.62188593,-0.163136922\n152391131,03/11/2013 13:07,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED BETWEEN LIFT DOORS,Bird,Person (mobile),Mosque,Non Residential,Other animal assistance,Assist trapped wild animal,E05000288,Greenhill,E09000015,Harrow,Harrow,1.00023E+11,Station Road,21201079,HA1,515635,189125,515650,189150,51.58912329,-0.33220625\n152433131,03/11/2013 14:26,2013,2013/14,Special Service,1,1,290,290,PARROT TRAPPED IN GOLF NETTING,Bird,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,2.00004E+11,Dan Mason Drive,21500503,W4,520676,176258,520650,176250,51.47242618,-0.263880159\n152434131,03/11/2013 14:26,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000096,Northwick Park,E09000005,Brent,Wembley,202098179,The Fairway,20201761,HA0,516922,186924,516950,186950,51.56907664,-0.314369602\n152949131,04/11/2013 14:49,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Tottenham Green East,21104004,N15,NULL,NULL,533750,189250,NULL,NULL\n153950131,06/11/2013 13:10,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Redcross Way,NULL,SE1,532472,180217,532450,180250,51.50536883,-0.092651366\n154129131,06/11/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN CEILING VOID,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,Hoe Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Hoe Street,22845700,E17,537437,188522,537450,188550,51.57881376,-0.017916672\n154461131,07/11/2013 15:46,2013,2013/14,Special Service,2,7,290,2030,DOG TRAPPED BETWEEN TWO HOUSE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011220,Blackfen & Lamorbey,E09000004,Bexley,Sidcup,NULL,Beverley Avenue,20100135,DA15,NULL,NULL,545550,173650,NULL,NULL\n154957131,08/11/2013 17:51,2013,2013/14,Special Service,1,2,290,580,HORSE FALLEN INTO LAKE,Horse,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Moorhall Road,NULL,UB9,504910,188621,504950,188650,51.58669051,-0.487110359\n155355131,09/11/2013 14:50,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,1.00023E+11,Thicket Road,20302199,SE20,534474,170871,534450,170850,51.42090759,-0.06737457\n156209131,11/11/2013 11:43,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF,Cat,Person (land line),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Hornsey,1.00021E+11,Commerce Road,21104385,N22,530758,190932,530750,190950,51.60205931,-0.113353549\n157858131,15/11/2013 10:25,2013,2013/14,Special Service,1,1,290,290,TO ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,East Acton,E09000009,Ealing,Park Royal,NULL,Chase Road,NULL,NW10,520828,182083,520850,182050,51.52474623,-0.259699501\n157865131,15/11/2013 10:49,2013,2013/14,Special Service,1,1,290,290,FOX TRAPPED IN BASEMENT AREA,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000461,Graveney,E09000024,Merton,Mitcham,48128999,London Road,22103996,SW17,527943,170569,527950,170550,51.41970404,-0.161358741\n157913131,15/11/2013 12:39,2013,2013/14,Special Service,1,2,290,580,CAT TRAPPED BEHIND BOXED IN CISTERN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Clapham Common North Side,22900961,SW4,NULL,NULL,527750,175150,NULL,NULL\n158360131,16/11/2013 10:56,2013,2013/14,Special Service,1,1,290,290,HORSE TRAPPED ON FENCE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.0002E+11,Kemnal Road,20303635,BR7,544622,171679,544650,171650,51.42567265,0.078802657\n158861131,17/11/2013 10:26,2013,2013/14,Special Service,1,1,290,290,REQ FOR ASSISTANCE FROM RSPCA,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,200081491,Milespit Hill,20029600,NW7,522921,192182,522950,192150,51.61505707,-0.226014176\n159099131,17/11/2013 19:33,2013,2013/14,Special Service,1,1,290,290,CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,Evelyn,E09000023,Lewisham,Deptford,NULL,Barfleur Lane,22006126,SE8,NULL,NULL,536750,178450,NULL,NULL\n159428131,18/11/2013 16:31,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN CAGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,Champion Hill,E09000028,Southwark,Brixton,NULL,Denmark Hill Estate,22500847,SE5,NULL,NULL,532850,175350,NULL,NULL\n159535131,18/11/2013 19:40,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND FREEZER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Norwood Road,NULL,SE24,531817,173253,531850,173250,51.44293807,-0.104678983\n160062131,19/11/2013 18:59,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000493,Wall End,E09000025,Newham,East Ham,NULL,Burges Road,22200424,E6,NULL,NULL,542650,184250,NULL,NULL\n160143131,19/11/2013 21:52,2013,2013/14,Special Service,1,1,290,290,INJURED CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,1.00023E+11,Victoria Road,21501253,TW13,510724,173056,510750,173050,51.44567931,-0.408109769\n160751131,21/11/2013 10:36,2013,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Trewint Street,NULL,SW18,525915,172666,525950,172650,51.43900398,-0.189764761\n161464131,22/11/2013 20:24,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Alexandra Grove,NULL,N4,531981,187026,531950,187050,51.56667323,-0.097171343\n162280131,24/11/2013 15:41,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Retirement/Old Persons Home,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,Northwood,E09000017,Hillingdon,Ruislip,1.00021E+11,Murray Road,21401342,HA6,509159,191122,509150,191150,51.60836377,-0.425027391\n163060131,26/11/2013 12:34,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Ennerdale Close,NULL,TW14,509542,173251,509550,173250,51.44766145,-0.425051001\n163186131,26/11/2013 16:59,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA PIGEON TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000564,Sutton Central,E09000029,Sutton,Sutton,5870034546,St. Nicholas Way,22602630,SM1,525782,164052,525750,164050,51.36161713,-0.194727431\n163998131,28/11/2013 11:31,2013,2013/14,Special Service,1,1,290,290,HORSE STUCK IN WATER - LEVEL TWO WATER IMPLEMENTED,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000079,North End,E09000004,Bexley,Erith,NULL,Ray Lamb Way,NULL,DA8,553469,177526,553450,177550,51.47588275,0.208490418\n164862131,30/11/2013 08:20,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN WIRE MESH NEAR PLATFORM ONE,Bird,Person (mobile),\"Tunnel, subway\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Cyprus Place,NULL,E6,543321,180858,543350,180850,51.50848313,0.063822568\n165496131,01/12/2013 13:30,2013,2013/14,Special Service,1,1,290,290,PIEGEON TRAPPED IN WIRING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000639,Little Venice,E09000033,Westminster,Paddington,NULL,Hall Place,8400423,W2,NULL,NULL,526650,182050,NULL,NULL\n165727131,01/12/2013 20:54,2013,2013/14,Special Service,1,1,290,290,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Highbury Grange,21603356,N5,NULL,NULL,531950,185750,NULL,NULL\n166619131,03/12/2013 19:22,2013,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,1.00021E+11,Doghurst Avenue,21400599,UB3,507912,177034,507950,177050,51.48197757,-0.447345044\n166897131,04/12/2013 12:28,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000046,Colindale,E09000003,Barnet,Mill Hill,NULL,Corner Mead,NULL,NW9,521699,190850,521650,190850,51.60335142,-0.244116771\n167793131,06/12/2013 10:35,2013,2013/14,Special Service,1,1,290,290,TWO BIRDS TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000026,Abbey,E09000002,Barking and Dagenham,Barking,NULL,Park Avenue,19900716,IG11,NULL,NULL,544550,184750,NULL,NULL\n167809131,06/12/2013 11:07,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011101,Dulwich Wood,E09000028,Southwark,Forest Hill,NULL,Lordship Lane,22501557,SE22,NULL,NULL,534150,173450,NULL,NULL\n168069131,06/12/2013 22:31,2013,2013/14,Special Service,1,1,290,290,DOG IMPALED ON METAL FENCE,Dog,Police,Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Approach Road,NULL,EN4,526535,196099,526550,196050,51.64945762,-0.17243015\n168345131,07/12/2013 14:25,2013,2013/14,Special Service,1,1,290,290,CAT ON ROOF OF TERRACE HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Holloway,NULL,Elfort Road,21603170,N5,NULL,NULL,531450,185850,NULL,NULL\n169274131,09/12/2013 16:58,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN DOOR,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Durban Road,21900486,SE27,532589,171902,532550,171950,51.43061683,-0.094082798\n170069131,11/12/2013 13:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000091,Harlesden,E09000005,Brent,Park Royal,NULL,Craven Park Road,NULL,NW10,521491,183632,521450,183650,51.53852571,-0.249613097\n170952131,13/12/2013 11:33,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Bird in Bush Road,22500242,SE15,NULL,NULL,534350,177350,NULL,NULL\n171012131,13/12/2013 14:26,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000616,Graveney,E09000032,Wandsworth,Tooting,1.00023E+11,Laurel Close,22902835,SW17,527402,171346,527450,171350,51.42680883,-0.168855964\n171760131,15/12/2013 10:37,2013,2013/14,Special Service,1,3,290,870,HORSE STUCK IN DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000333,Ickenham,E09000017,Hillingdon,Hillingdon,1.00023E+11,Long Lane,21401178,UB10,508051,185283,508050,185250,51.55609477,-0.442816765\n171915131,15/12/2013 15:58,2013,2013/14,Special Service,1,2,290,580,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000407,Coombe Vale,E09000021,Kingston upon Thames,New Malden,NULL,Orme Road,21800732,KT1,NULL,NULL,519950,169150,NULL,NULL\n172120131,16/12/2013 04:40,2013,2013/14,Special Service,1,2,290,580,INJURED CAT TRAPPED BEHIND LIFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,Peckham,E09000028,Southwark,Peckham,NULL,Gatonby Street,22502994,SE15,NULL,NULL,533850,176850,NULL,NULL\n172696131,17/12/2013 11:58,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Radnor Street,NULL,EC1V,532463,182575,532450,182550,51.52656139,-0.091895183\n173432131,19/12/2013 07:43,2013,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000475,Beckton,E09000025,Newham,East Ham,NULL,Cyprus Place,NULL,E6,543304,180942,543350,180950,51.50924224,0.063611898\n173518131,19/12/2013 11:28,2013,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER STOARGE UNIT,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,High Street,NULL,TW12,514419,171026,514450,171050,51.42670036,-0.355618864\n174029131,20/12/2013 12:23,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Lynhurst Crescent,NULL,UB10,NULL,NULL,508650,184150,NULL,NULL\n174040131,20/12/2013 12:49,2013,2013/14,Special Service,2,3,290,870,HORSE TRAPPED IN TREE TRUNKS ON STEEP INCLINE,Horse,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Maiden Lane,NULL,DA1,552329,175374,552350,175350,51.45685538,0.191160727\n174592131,21/12/2013 16:56,2013,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,Clerkenwell,E09000019,Islington,Islington,NULL,Claremont Close,21606789,N1,NULL,NULL,531350,183050,NULL,NULL\n174832131,22/12/2013 10:38,2013,2013/14,Special Service,1,1,290,290,KITTEN STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000340,Uxbridge North,E09000017,Hillingdon,Hillingdon,NULL,Croft Close,21400524,UB10,NULL,NULL,507050,184450,NULL,NULL\n174841131,22/12/2013 11:03,2013,2013/14,Special Service,1,1,290,290,PERSONS LOCKED OUT,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,Ealing Common,E09000009,Ealing,Ealing,NULL,Grange Road,20600751,W5,NULL,NULL,517850,180250,NULL,NULL\n174957131,22/12/2013 15:44,2013,2013/14,Special Service,1,2,290,580,PUPPY TRAPPED BEHIND A FENCE,Dog,Other FRS,Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,George Lovell Drive,NULL,EN3,537409,198783,537450,198750,51.67102495,-0.014299835\n174961131,22/12/2013 15:53,2013,2013/14,Special Service,1,1,290,290,CAT STUCK IN TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000484,Forest Gate South,E09000025,Newham,Stratford,10009018024,Romford Road,22201573,E7,540802,185073,540850,185050,51.54699166,0.029233017\n175275131,23/12/2013 10:21,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,St. Michaels Close,22207901,E16,NULL,NULL,541650,181550,NULL,NULL\n177130131,24/12/2013 18:46,2013,2013/14,Special Service,1,1,290,290,DOG TRAPPEDN IN HEDGING,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Malmesbury Road,NULL,E3,537149,183075,537150,183050,51.52993651,-0.024192481\n177406131,25/12/2013 10:36,2013,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Ellenborough Road,20100490,DA14,NULL,NULL,547950,171150,NULL,NULL\n177808131,26/12/2013 11:12,2013,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Sussex Avenue,21301631,RM3,554949,191214,554950,191250,51.59846241,0.235801456\n177814131,26/12/2013 11:33,2013,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Hampstead Road,20400780,NW1,NULL,NULL,529150,182750,NULL,NULL\n178314131,27/12/2013 14:45,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDERNEATH BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000178,Greenford Green,E09000009,Ealing,Northolt,NULL,Lyon Way,NULL,UB6,515312,183580,515350,183550,51.5393524,-0.338683824\n178712131,28/12/2013 17:07,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Other animal assistance,Assist trapped wild animal,E05000347,Brentford,E09000018,Hounslow,Acton,2.00004E+11,Gunnersbury Park,20602411,W3,519046,179236,519050,179250,51.49953645,-0.286334863\n179109131,29/12/2013 14:35,2013,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SWAN STUCK BEHIND FENCE,Bird,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,1.00023E+11,Lea Bridge Road,22850300,E10,535630,186783,535650,186750,51.5636238,-0.044649735\n179803131,31/12/2013 09:26,2013,2013/14,Special Service,1,1,290,290,DOG UNABLE TO LEAVE RAILWAY EMBANKMENT NEAR,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Oakwood Park Road,NULL,N14,529694,195315,529650,195350,51.64169291,-0.127085516\n236141,01/01/2014 11:58,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Fitzwygram Close,22401481,TW12,514158,171065,514150,171050,51.4271035,-0.35935876\n955141,03/01/2014 11:31,2014,2013/14,Special Service,1,5,290,1450,ASSIST POLICE WITH HORSE INJURED IN RTC,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Rangers Road,NULL,E4,539722,194772,539750,194750,51.63441268,0.017525125\n1557141,04/01/2014 23:43,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH FOX STUCK IN RAILING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Ranulf Road,20035940,NW2,NULL,NULL,524850,185850,NULL,NULL\n1636141,05/01/2014 04:34,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000631,Bayswater,E09000033,Westminster,Paddington,NULL,Northumberland Place,8401480,W2,NULL,NULL,525250,181250,NULL,NULL\n1724141,05/01/2014 12:04,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BETWEEN CABINETS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Ascot Gardens,21300204,RM12,NULL,NULL,554150,185850,NULL,NULL\n1742141,05/01/2014 12:56,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),Animal boarding/breeding establishment - dogs,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,554939,189052,554950,189050,51.57904051,0.234704421\n2981141,08/01/2014 15:10,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING ABOVE,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00023E+11,York Street,22403983,TW1,516327,173388,516350,173350,51.44754091,-0.327411803\n3044141,08/01/2014 17:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN BRAMBLE BUSHES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,Vernon Road,21501149,TW13,NULL,NULL,509850,172650,NULL,NULL\n3317141,09/01/2014 11:07,2014,2013/14,Special Service,1,1,290,290,FOX STUCK IN GARDEN,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Manilla Street,22700776,E14,NULL,NULL,537150,179850,NULL,NULL\n3828141,10/01/2014 16:06,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Other office/call centre type building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,East Ham,10008996681,Plashet Grove,22200963,E6,541355,183966,541350,183950,51.53690607,0.036758561\n4101141,11/01/2014 09:21,2014,2013/14,Special Service,1,1,290,290,KITTENS TRAPPED IN WALL,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,10003973600,Maple Place,21106933,N17,534466,191019,534450,191050,51.60196788,-0.059813075\n4334141,11/01/2014 20:31,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN RIVER THAMES   UNABLE TO GET OUT UNASSISTED,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Rainville Road,NULL,W6,523387,177633,523350,177650,51.48420007,-0.224386993\n4556141,12/01/2014 11:44,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN REAR GARDEN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,Colville,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W11,NULL,NULL,524550,181350,NULL,NULL\n4640141,12/01/2014 15:35,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Cornwall Crescent,21700850,W11,NULL,NULL,524250,181150,NULL,NULL\n4920141,13/01/2014 10:27,2014,2013/14,Special Service,NULL,NULL,290,NULL,PIDGEON TRAPPED IN WIRE NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05009405,Stanley,E09000020,Kensington and Chelsea,Chelsea,NULL,Dovehouse Street,NULL,SW3,527090,178146,527050,178150,51.48799135,-0.170900926\n5311141,14/01/2014 09:11,2014,2013/14,Special Service,1,3,290,870,DOG STUCK IN HOLE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Richmond Park,NULL,TW10,519156,173962,519150,173950,51.45211212,-0.286527366\n5375141,14/01/2014 11:32,2014,2013/14,Special Service,1,1,290,290,Redacted,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000534,Brunswick Park,E09000028,Southwark,Peckham,NULL,McNeil Road,NULL,SE5,533107,176307,533150,176350,51.47008199,-0.084980296\n5710141,15/01/2014 09:16,2014,2013/14,Special Service,1,1,290,290,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000603,Lea Bridge,E09000031,Waltham Forest,Leyton,NULL,Marsh Lane,NULL,E10,537067,187003,537050,187050,51.56525395,-0.023844915\n5752141,15/01/2014 11:00,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,200090205,Page Street,20033240,NW7,522423,191098,522450,191050,51.60542364,-0.233581234\n5805141,15/01/2014 13:03,2014,2013/14,Special Service,1,1,290,290,ASSIST  RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011233,West Heath,E09000004,Bexley,Erith,1.00023E+11,Hurst Lane,20100755,SE2,547613,178035,547650,178050,51.48201353,0.124443937\n5823141,15/01/2014 13:41,2014,2013/14,Special Service,1,1,290,290,ASSIST IN GAINING ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,Bethnal Green,E09000030,Tower Hamlets,Bethnal Green,NULL,Victoria Park Square,22701269,E2,NULL,NULL,535050,182950,NULL,NULL\n5887141,15/01/2014 16:50,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN DISUSED GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,10022800410,Waterloo Road,21402084,UB8,504958,183645,504950,183650,51.54195551,-0.487902207\n5910141,15/01/2014 18:00,2014,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Temple,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000359,Hounslow Heath,E09000018,Hounslow,Heston,2.00004E+11,Alice Way,21501576,TW3,513540,175170,513550,175150,51.46412256,-0.366928509\n6117141,16/01/2014 10:32,2014,2013/14,Special Service,1,2,290,580,Redacted,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Wharfside Close,NULL,DA8,551685,178078,551650,178050,51.48132337,0.183058414\n6308141,16/01/2014 18:10,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN CEILING,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00023E+11,Electric Avenue,21900507,SW9,531150,175409,531150,175450,51.46246852,-0.113472238\n6594141,17/01/2014 13:30,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Hatherley Gardens,21106534,N8,NULL,NULL,530150,188250,NULL,NULL\n6602141,17/01/2014 13:54,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,1.00021E+11,Woolwich New Road,20801651,SE18,543795,178880,543750,178850,51.49058907,0.069842179\n6645141,17/01/2014 15:55,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000555,Beddington North,E09000029,Sutton,Mitcham,NULL,Rosemary Close,22605577,CR0,NULL,NULL,530150,166950,NULL,NULL\n7077141,18/01/2014 17:06,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00023E+11,High Street,21500610,TW3,513612,175574,513650,175550,51.46773923,-0.36576278\n7443141,19/01/2014 15:01,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100025153,Rainham Road South,19900295,RM10,549815,185629,549850,185650,51.54966866,0.159356262\n7894141,20/01/2014 18:16,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA,Unknown - Wild Animal,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Other animal assistance,Assist trapped wild animal,E05000098,Queens Park,E09000005,Brent,North Kensington,NULL,Chamberlayne Road,20202065,NW10,NULL,NULL,523650,183050,NULL,NULL\n8850141,23/01/2014 09:17,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Farrer Road,NULL,N8,529534,189167,529550,189150,51.58648077,-0.131667204\n8851141,23/01/2014 09:17,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH DOG FALLEN FROM ROOF,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000436,Vassall,E09000022,Lambeth,Brixton,1.00022E+11,Langton Road,21900840,SW9,531795,177151,531750,177150,51.47797372,-0.103544076\n8864141,23/01/2014 09:52,2014,2013/14,Special Service,1,1,290,290,FOX STUCK IN BARBED WIRE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Woodpecker Close,NULL,N9,534647,195326,534650,195350,51.64062847,-0.055546344\n9427141,24/01/2014 19:08,2014,2013/14,Special Service,1,1,290,290,CAT AND KITTENS WEDGED IN CAR BONNET,Cat,Person (land line),Underground car park,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,10009148338,Hillyfield,22819150,E17,536137,190003,536150,190050,51.59243694,-0.036093754\n9689141,25/01/2014 12:59,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED BY WIRE ON ROOF,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000104,Wembley Central,E09000005,Brent,Wembley,NULL,High Road,NULL,HA0,517929,185045,517950,185050,51.55197943,-0.300475803\n10561141,27/01/2014 08:21,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN HOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000560,Cheam,E09000029,Sutton,Sutton,5870000623,Burdon Lane,22605695,SM2,524552,162982,524550,162950,51.35227155,-0.21275927\n11133141,28/01/2014 18:31,2014,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000080,Northumberland Heath,E09000004,Bexley,Erith,NULL,Hurst Road,NULL,DA8,550374,177016,550350,177050,51.47213094,0.1637416\n11144141,28/01/2014 18:48,2014,2013/14,Special Service,1,1,290,290,CAT WITH LEG IMPALED ON RAILINGS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Beaufoy Road,NULL,N17,533605,191286,533650,191250,51.60457217,-0.072135895\n11205141,28/01/2014 22:39,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WALL AND DECKING,Cat,Person (land line),Other building/use not known,Non Residential,Animal rescue from water,Animal rescue from water - Domestic pet,E05000431,Streatham South,E09000022,Lambeth,Norbury,1.00022E+11,Guildersfield Road,21900649,SW16,530138,170159,530150,170150,51.41552062,-0.129959193\n4149,29/01/2014 15:03,2014,2013/14,Special Service,1,3,290,870,NULL,Unknown - Animal rescue from water - Farm animal,Not known,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000071,Crayford,E09000004,Bexley,Bexley,NULL,Thames Road,NULL,DA1,552838,175418,552850,175450,51.45711382,0.198500308\n12729141,01/02/2014 12:47,2014,2013/14,Special Service,1,2,290,580,SMALL ANIMAL RESCUE FOLLOWING COLLAPSE OF WALL,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000145,West Hampstead,E09000007,Camden,West Hampstead,NULL,Holmdale Road,20400258,NW6,NULL,NULL,525250,185150,NULL,NULL\n12791141,01/02/2014 14:45,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000456,Cannon Hill,E09000024,Merton,Wimbledon,NULL,Cannon Hill Lane,22101155,SW20,NULL,NULL,524150,168550,NULL,NULL\n13164141,02/02/2014 13:43,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA RESCUE DOG FROM THIRD FLOOR WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Stubbs Drive,22502405,SE16,NULL,NULL,534750,178350,NULL,NULL\n13245141,02/02/2014 16:05,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN DRAIN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Goldwell Road,20500983,CR7,NULL,NULL,531050,168150,NULL,NULL\n13270141,02/02/2014 16:56,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Daling Way,NULL,E3,536333,183440,536350,183450,51.53341371,-0.035807604\n13326141,02/02/2014 19:08,2014,2013/14,Special Service,1,1,290,290,PERSON LOCED OUT DANGER OF FIRE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,Thurlow Park,E09000022,Lambeth,West Norwood,NULL,Harpenden Road,21900674,SE27,NULL,NULL,531650,172650,NULL,NULL\n13739141,03/02/2014 19:33,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN FENCES,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011242,Fairlop,E09000026,Redbridge,Hainault,NULL,Genas Close,22306511,IG6,NULL,NULL,543950,190650,NULL,NULL\n14100141,04/02/2014 17:05,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Court Wood Lane,NULL,CR0,536777,161830,536750,161850,51.33911127,-0.037751661\n14493141,05/02/2014 15:08,2014,2013/14,Special Service,1,1,290,290,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Alexandra Drive,NULL,KT5,519226,166751,519250,166750,51.38728744,-0.287943853\n15251141,07/02/2014 11:59,2014,2013/14,Special Service,1,1,290,290,KITTEN STUCK BEHINED WALL,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,Hackney Wick,E09000012,Hackney,Homerton,NULL,Kenton Road,20900566,E9,NULL,NULL,535750,184650,NULL,NULL\n15916141,08/02/2014 19:51,2014,2013/14,Special Service,1,1,290,290,BIRDS TRAPPED IN WIRE MESH,Bird,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000176,Elthorne,E09000009,Ealing,Ealing,12059025,Church Road,20600387,W7,515688,180328,515650,180350,51.5100474,-0.334332098\n15976141,09/02/2014 00:04,2014,2013/14,Special Service,1,1,290,290,PIGEONS TRAPPED IN WIRE MESH UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Church Road,NULL,W7,515494,180554,515450,180550,51.51211829,-0.337052364\n16196141,09/02/2014 14:11,2014,2013/14,Special Service,1,1,290,290,DOG STUCK UNDER ELECTRIC CHAIR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000038,River,E09000002,Barking and Dagenham,Dagenham,NULL,Broad Street,19900439,RM10,NULL,NULL,549150,184350,NULL,NULL\n16667141,10/02/2014 17:08,2014,2013/14,Special Service,1,1,290,290,HORSE STUCK IN HAY CONTAINER,Horse,Person (mobile),Agricultural equipment,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Jail Lane,20301511,TN16,542935,158831,542950,158850,51.31064916,0.049395278\n17302141,12/02/2014 10:54,2014,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED BETWEEN DOOR AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,Jubilee,E09000010,Enfield,Edmonton,NULL,Elmcroft Avenue,20702660,N9,NULL,NULL,535050,195250,NULL,NULL\n17398141,12/02/2014 14:33,2014,2013/14,Special Service,1,4,290,1160,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000457,Colliers Wood,E09000024,Merton,Wimbledon,NULL,Singleton Close,NULL,SW17,527637,170496,527650,170450,51.41911685,-0.165783406\n17397141,12/02/2014 14:34,2014,2013/14,Special Service,1,1,290,290,CAT UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,Tottenham Hale,E09000014,Haringey,Tottenham,NULL,Malvern Road,21103582,N17,NULL,NULL,534050,189850,NULL,NULL\n17407141,12/02/2014 14:48,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA TO RESCUE CAT FROM ROOF OF HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,Thornville Grove,22106775,CR4,NULL,NULL,526550,169150,NULL,NULL\n17823141,13/02/2014 13:34,2014,2013/14,Special Service,1,1,290,290,CATS HEAD TRAPPED AROUND EXHAUST PIPE OF CAR,cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000216,Charlton,E09000011,Greenwich,East Greenwich,1.00021E+11,Leila Parnell Place,20800903,SE7,541150,177608,541150,177650,51.47982512,0.031260684\n17915141,13/02/2014 17:45,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN NETTING UNDER BRIDGE,Cat,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000140,Kilburn,E09000007,Camden,West Hampstead,NULL,Abbey Road,NULL,NW6,525712,183937,525750,183950,51.5403436,-0.188673445\n18160141,14/02/2014 11:23,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Ravensbourne Avenue,NULL,BR2,539063,169628,539050,169650,51.40863156,-0.001905441\n19347141,15/02/2014 18:01,2014,2013/14,Special Service,1,2,290,580,ASSIST RSCPA OFFICER WITH TRAPPED PIGEON,Bird,Person (land line),Indoor Market,Non Residential,Other animal assistance,Assist trapped wild animal,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Kentish Town,5167971,Chalk Farm Road,20400624,NW1,528482,184222,528450,184250,51.54228183,-0.148649242\n19517141,16/02/2014 00:55,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE - SMALL KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000106,Bickley,E09000006,Bromley,Bromley,NULL,Sundridge Avenue,20303578,BR7,NULL,NULL,542550,170150,NULL,NULL\n19747141,16/02/2014 15:19,2014,2013/14,Special Service,1,1,290,290,DOG IN LAKE,Dog,Police,Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000502,Cranbrook,E09000026,Redbridge,Ilford,NULL,Emerson Road,NULL,IG1,543324,188003,543350,188050,51.5726847,0.066772076\n20564141,18/02/2014 10:10,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN POND,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34110376,Devonport Road,21000272,W12,522905,179646,522950,179650,51.50239651,-0.230624568\n20918141,19/02/2014 06:20,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN HATCH,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Amina Way,22502887,SE16,NULL,NULL,534150,179050,NULL,NULL\n21030141,19/02/2014 13:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,1.0002E+11,Edwards Road,20100488,DA17,549195,179002,549150,179050,51.49028751,0.147618239\n21056141,19/02/2014 14:16,2014,2013/14,Special Service,1,1,290,290,DOG IN RIVER,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,High Street,NULL,TW12,513974,169561,513950,169550,51.41362266,-0.362487454\n21250141,19/02/2014 21:18,2014,2013/14,Special Service,1,1,290,290,RABBIT STUCK UNDER DECKING,Rabbit,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Wimbledon Road,22906250,SW17,NULL,NULL,526450,171950,NULL,NULL\n21491141,20/02/2014 13:59,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BY LEG IN SPRINGS OF BED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000180,Hobbayne,E09000009,Ealing,Ealing,NULL,Campbell Road,20600307,W7,NULL,NULL,515450,180550,NULL,NULL\n21842141,21/02/2014 11:32,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING UNDER ARCHWAY,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Pinchin Street,NULL,E1,534398,180949,534350,180950,51.51149234,-0.064638225\n21971141,21/02/2014 17:34,2014,2013/14,Special Service,1,1,290,290,PONY WITH HEAD STUCK IN FENCE,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,NULL,Cardington Square,NULL,TW4,511907,175256,511950,175250,51.46522054,-0.390398928\n22647141,23/02/2014 09:30,2014,2013/14,Special Service,2,7,290,2030,HORSE WEDGED IN STABLE,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000111,Chislehurst,E09000006,Bromley,Eltham,NULL,Slades Drive,NULL,BR7,544333,172016,544350,172050,51.42877446,0.074786037\n23199141,24/02/2014 16:36,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH GULL TRAPPED IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000645,Tachbrook,E09000033,Westminster,Lambeth,NULL,Grosvenor Road,NULL,SW1V,529625,177909,529650,177950,51.48528665,-0.134496441\n23656141,25/02/2014 17:39,2014,2013/14,Special Service,1,1,290,290,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Villas,21000775,W12,NULL,NULL,523050,180150,NULL,NULL\n24228141,27/02/2014 05:34,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,College Park and Old Oak,E09000013,Hammersmith and Fulham,North Kensington,NULL,Du Cane Road,NULL,W12,522185,181144,522150,181150,51.51601553,-0.240473975\n24375141,27/02/2014 15:59,2014,2013/14,Special Service,1,2,290,580,CAT   TRAPPED IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000510,Newbury,E09000026,Redbridge,Ilford,NULL,Perth Road,NULL,IG2,543675,188127,543650,188150,51.5737096,0.071884024\n24484141,27/02/2014 21:30,2014,2013/14,Special Service,1,1,290,290,DOG FALLEN IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Albert Embankment,NULL,SE1,530523,178781,530550,178750,51.49291681,-0.121248485\n25114141,01/03/2014 13:03,2014,2013/14,Special Service,1,1,290,290,DOG STUCK IN BABY GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000126,Shortlands,E09000006,Bromley,Bromley,NULL,Newbury Road,20300517,BR2,NULL,NULL,540250,168550,NULL,NULL\n25368141,01/03/2014 22:34,2014,2013/14,Special Service,1,1,290,290,BIRD STUCK UP CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000110,Chelsfield and Pratts Bottom,E09000006,Bromley,Orpington,NULL,Repton Road,20303572,BR6,NULL,NULL,546950,164250,NULL,NULL\n25849141,03/03/2014 08:33,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED BENEATH CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,NULL,Rocks Lane,NULL,SW13,522998,176690,522950,176650,51.47580956,-0.230314423\n26466141,04/03/2014 15:58,2014,2013/14,Special Service,1,1,290,290,Redacted,Bird,Police,Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Molesworth Street,22004181,SE13,538084,175354,538050,175350,51.46032599,-0.013742251\n26479141,04/03/2014 16:19,2014,2013/14,Special Service,1,1,290,290,HORSES LOOSE ON ROADWAY,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,E05000330,Harefield,E09000017,Hillingdon,Ruislip,NULL,Breakspear Road North,NULL,UB9,506449,189733,506450,189750,51.59639698,-0.464568083\n26486141,04/03/2014 16:42,2014,2013/14,Special Service,1,1,290,290,PEREGRINE FALCON STUCK IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,1.00023E+11,Molesworth Street,22004181,SE13,538084,175354,538050,175350,51.46032599,-0.013742251\n26536141,04/03/2014 18:26,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,1.00023E+11,Lavender Hill,22902838,SW11,527567,175640,527550,175650,51.46536265,-0.164937921\n26724141,05/03/2014 05:47,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED BETWEEN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000477,Canning Town North,E09000025,Newham,Plaistow,NULL,Whitwell Road,22201218,E13,NULL,NULL,540250,182550,NULL,NULL\n26878141,05/03/2014 13:40,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN GUTTERING,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000572,Worcester Park,E09000029,Sutton,Sutton,5870121012,Central Road,22601354,KT4,522708,165691,522750,165650,51.37701918,-0.238292936\n27013141,05/03/2014 18:54,2014,2013/14,Special Service,1,1,290,290,DOG WITH PAW TRAPPED IN METAL FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011100,Dulwich Village,E09000028,Southwark,West Norwood,10090287157,Gallery Road,22501015,SE21,532861,173179,532850,173150,51.44202926,-0.089693767\n27206141,06/03/2014 07:41,2014,2013/14,Special Service,1,1,290,290,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,The Broadway,20705014,N9,NULL,NULL,534350,193450,NULL,NULL\n27591141,07/03/2014 01:02,2014,2013/14,Special Service,1,4,290,1160,ASSIST VET WITH COLLAPSED HORSE IN STABLE,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011254,Wanstead Park,E09000026,Redbridge,Leytonstone,1.00024E+11,Empress Avenue,22302824,E12,541924,187071,541950,187050,51.564664,0.046208294\n27879141,07/03/2014 17:59,2014,2013/14,Special Service,1,2,290,580,DOG TRAPPED IN HOLLOW TREE,Dog,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,10002251968,Richmond Park,22407048,TW10,518839,171707,518850,171750,51.43191167,-0.291843938\n28193141,08/03/2014 12:55,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,Chase,E09000010,Enfield,Enfield,207162441,Bowles Green,20702523,EN1,534625,199071,534650,199050,51.67428667,-0.054422869\n28262141,08/03/2014 16:35,2014,2013/14,Special Service,NULL,NULL,290,NULL,CAT TRAPPED ON BARBED WIRE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,6074888,Tidworth Road,22701213,E3,537240,182506,537250,182550,51.52480134,-0.023102993\n28384141,08/03/2014 20:36,2014,2013/14,Special Service,1,1,290,290,DOG TRAPPED IN PLASTIC BIN LID,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Palace Road,21000356,SW6,NULL,NULL,524150,176550,NULL,NULL\n28634141,09/03/2014 12:45,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Mornington Terrace,20400729,NW1,NULL,NULL,528850,183550,NULL,NULL\n28706141,09/03/2014 14:11,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK IN PIPE ON ROOF,Cat,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00023E+11,Paxton Place,21901077,SE27,533053,171662,533050,171650,51.4283513,-0.087502236\n28795141,09/03/2014 17:46,2014,2013/14,Special Service,1,1,290,290,CAT STUCK UNDER CAR BONNET,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000279,Stroud Green,E09000014,Haringey,Hornsey,NULL,Highbank Way,NULL,N8,531152,188311,531150,188350,51.5784143,-0.10864594\n29496141,11/03/2014 11:18,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000030,Eastbrook,E09000002,Barking and Dagenham,Dagenham,100064644,Hardie Road,19900799,RM10,550132,186250,550150,186250,51.55516432,0.164189481\n29581141,11/03/2014 14:51,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN NETTING,Bird,Person (land line),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Chingford,1.00023E+11,Jubilee Avenue,22847785,E4,538422,191882,538450,191850,51.60876464,-0.002387751\n29668141,11/03/2014 18:16,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA - TRAPPED PIGEON,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,1.00023E+11,Blackhorse Road,22816400,E17,536287,188811,536250,188850,51.5816896,-0.034391404\n30027141,12/03/2014 14:42,2014,2013/14,Special Service,1,1,290,290,CAT STUCK IN DRAIN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Shenley Road,22502236,SE5,NULL,NULL,533350,176650,NULL,NULL\n30073141,12/03/2014 16:13,2014,2013/14,Special Service,1,1,290,290,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,1.00023E+11,Goddington Lane,20300903,BR6,547228,165031,547250,165050,51.36526767,0.113504097\n30079141,12/03/2014 16:25,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER DECKING,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,1.00021E+11,Grand Avenue,21100352,N10,528496,189403,528450,189450,51.588839,-0.146555088\n30429141,13/03/2014 09:26,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other retail,Non Residential,Other animal assistance,Animal harm involving wild animal,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920926,The Broadway,22306023,IG8,540896,191874,540850,191850,51.60807981,0.03331285\n32417141,17/03/2014 01:54,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN RAIN DUCT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,Bounds Green,E09000014,Haringey,Hornsey,NULL,Morant Place,NULL,N22,NULL,NULL,530750,190950,NULL,NULL\n32680141,17/03/2014 16:22,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,Mayfield,E09000026,Redbridge,Ilford,NULL,South Park Drive,22302417,IG3,NULL,NULL,545250,186550,NULL,NULL\n33319141,18/03/2014 22:21,2014,2013/14,Special Service,1,1,290,290,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (land line),Bingo Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000170,Acton Central,E09000009,Ealing,Acton,12145563,High Street,20602317,W3,520485,180099,520450,180050,51.50698821,-0.265319146\n33530141,19/03/2014 12:53,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH PIGEON TRAPPED UNDER RAILWAY BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Bittacy Road,NULL,NW7,524003,191512,524050,191550,51.60879877,-0.210631472\n34115141,20/03/2014 17:48,2014,2013/14,Special Service,1,1,290,290,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000558,Carshalton Central,E09000029,Sutton,Wallington,5870060965,Banstead Road,22602087,SM5,527407,163876,527450,163850,51.35967352,-0.171462795\n34602141,21/03/2014 21:16,2014,2013/14,Special Service,1,1,290,290,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,Burket Close,21501741,UB2,NULL,NULL,512550,178650,NULL,NULL\n34979141,22/03/2014 16:54,2014,2013/14,Special Service,1,1,290,290,CATS TRAPPED IN WALL,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Hazlewood Close,20900498,E5,NULL,NULL,536050,186050,NULL,NULL\n36130141,25/03/2014 10:13,2014,2013/14,Special Service,1,1,290,290,RUNNING CALL TO BIRD TRAPPED BEHIND FIRE PLACE,Bird,Person (land line),Licensed House in Multiple Occupation - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000083,Sidcup,E09000004,Bexley,Sidcup,NULL,Manor Road,NULL,DA15,NULL,NULL,546150,172350,NULL,NULL\n36781141,26/03/2014 18:56,2014,2013/14,Special Service,1,2,290,580,CAT STUCK IN CHIMNEY BREAST,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Kneller Road,22000588,SE4,NULL,NULL,536350,175350,NULL,NULL\n36976141,27/03/2014 10:01,2014,2013/14,Special Service,1,2,290,580,CAT STUCK IN FIRE APPLIANCE,Cat,Person (land line),Lorry/HGV,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011113,Rye Lane,E09000028,Southwark,Peckham,2.00003E+11,Peckham Road,22501935,SE5,533584,176740,533550,176750,51.47386086,-0.077953398\n37065141,27/03/2014 14:44,2014,2013/14,Special Service,1,1,290,290,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Kneller Road,22000588,SE4,NULL,NULL,536350,175350,NULL,NULL\n37383141,28/03/2014 08:50,2014,2013/14,Special Service,1,1,290,290,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000562,St. Helier,E09000029,Sutton,Sutton,5870042244,Glastonbury Road,22600576,SM4,525585,166254,525550,166250,51.38145064,-0.19677838\n37700141,28/03/2014 19:41,2014,2013/14,Special Service,1,1,290,290,Redacted,Cat,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000440,Catford South,E09000023,Lewisham,Lewisham,1.00022E+11,Barmeston Road,22001200,SE6,537569,172932,537550,172950,51.4386864,-0.022091169\n37799141,29/03/2014 00:30,2014,2013/14,Special Service,1,1,290,290,BIRD TRAPPED IN COOKER HOOD EXTRACTOR,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Hoxton Square,20900542,N1,NULL,NULL,533150,182650,NULL,NULL\n37913141,29/03/2014 09:19,2014,2013/14,Special Service,1,3,290,870,DEER STUCK IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000202,Highlands,E09000010,Enfield,Southgate,NULL,Worlds End Lane,NULL,EN2,531129,196376,531150,196350,51.65089493,-0.10596204\n39164141,31/03/2014 18:14,2014,2013/14,Special Service,1,1,290,290,PIGEON IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Avondale Avenue,20002000,N12,NULL,NULL,525750,192250,NULL,NULL\n39247141,31/03/2014 22:00,2014,2013/14,Special Service,1,1,290,290,CAT WITH HEAD TRAPPED IN STAIRWAY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011112,Rotherhithe,E09000028,Southwark,Deptford,NULL,Hawkstone Road,22501219,SE16,NULL,NULL,535550,178850,NULL,NULL\n39659141,01/04/2014 20:25,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON GATED LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,Southwark,Dockhead,NULL,Cluny Estate,22500218,SE1,NULL,NULL,533250,179350,NULL,NULL\n40665141,04/04/2014 04:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND FITTED WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Longbridge Road,19900226,IG11,NULL,NULL,545450,185150,NULL,NULL\n40800141,04/04/2014 13:47,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,Tulse Hill,E09000022,Lambeth,West Norwood,NULL,Papworth Way,NULL,SW2,531290,173709,531250,173750,51.44715843,-0.112088458\n41086141,05/04/2014 00:49,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Elgin Crescent,21700866,W11,NULL,NULL,524350,180950,NULL,NULL\n41293141,05/04/2014 13:45,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000050,Edgware,E09000003,Barnet,Stanmore,200015610,Brockley Hill,20005240,HA7,517389,194173,517350,194150,51.63413066,-0.305209374\n41658141,06/04/2014 10:32,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHINMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000137,Highgate,E09000007,Camden,Kentish Town,NULL,Lissenden Gardens,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL\n43051141,09/04/2014 13:37,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Denbigh Road,22200534,E6,NULL,NULL,541650,182550,NULL,NULL\n43175141,09/04/2014 18:13,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN PARTITION WALL,Cat,Person (land line),Nursing/Care Home/Hospice,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,10003981417,Rangemoor Road,21106599,N15,533870,188917,533850,188950,51.58322096,-0.069214429\n43574141,10/04/2014 16:15,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN VENT NEAR TO CINEWORLD,Cat,Person (land line),Cinema,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011246,Ilford Town,E09000026,Redbridge,Ilford,10034911858,Clements Road,22305994,IG1,543882,186343,543850,186350,51.55762675,0.074139455\n43645141,10/04/2014 18:27,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH SWAN TRAPPED BEHIND FENCE,Bird,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000275,Noel Park,E09000014,Haringey,Hornsey,NULL,Western Road,NULL,N22,530616,190176,530650,190150,51.59529851,-0.11568375\n44042141,11/04/2014 14:52,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,200057510,Harrowes Meade,20021040,HA8,519451,193390,519450,193350,51.62665939,-0.27569729\n44529141,12/04/2014 15:34,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION,Dog,Other FRS,Estate Agent,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,1.00023E+11,Stanley Road,22876500,E4,538915,194455,538950,194450,51.63176372,0.005746469\n44622141,12/04/2014 19:05,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN ONTO RIVER BANK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Poplar,NULL,Orchard Place,NULL,E14,539437,180723,539450,180750,51.50824119,0.007840151\n44924141,13/04/2014 14:17,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Police,Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000036,Mayesbrook,E09000002,Barking and Dagenham,Barking,100066294,Sheppey Road,19900819,RM9,547314,184406,547350,184450,51.53933719,0.122800303\n44966141,13/04/2014 15:38,2014,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED BEHIND WIRE,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033604168,Craven Passage,8401684,WC2N,530213,180385,530250,180350,51.50740311,-0.125119613\n45075141,13/04/2014 19:14,2014,2014/15,Special Service,1,1,295,295,CAT STUCK REAR OF TOILET,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,East Barnet Road,20013380,EN4,NULL,NULL,526950,195650,NULL,NULL\n45293141,14/04/2014 09:37,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN TWO HOUSES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Greenwich South Street,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL\n45880141,15/04/2014 16:15,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH INJURED CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,Crayford,E09000004,Bexley,Bexley,1.0002E+11,Heath Road,20100697,DA1,551991,174365,551950,174350,51.44788013,0.185866308\n45885141,15/04/2014 16:16,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000563,Stonecot,E09000029,Sutton,Sutton,5870014648,Staines Avenue,22601901,SM3,523938,165664,523950,165650,51.37650967,-0.220638441\n46117141,16/04/2014 01:27,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,Plumstead,E09000011,Greenwich,Plumstead,NULL,Bassant Road,20800124,SE18,NULL,NULL,545550,177750,NULL,NULL\n46842141,17/04/2014 13:48,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Whitechapel,6008456,St. Anthony's Close,22701949,E1W,534173,180388,534150,180350,51.5065044,-0.068092021\n46854141,17/04/2014 14:04,2014,2014/15,Special Service,1,1,295,295,CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Appleford Road,21700797,W10,NULL,NULL,524350,182050,NULL,NULL\n46939141,17/04/2014 17:37,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ELECTICAL CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Upper Abbey Road,20101494,DA17,NULL,NULL,548950,178850,NULL,NULL\n47176141,18/04/2014 09:23,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK IN BATHROOM WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Grafton Square,21900630,SW4,NULL,NULL,529450,175650,NULL,NULL\n47220141,18/04/2014 11:10,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Selkirk Road,22904558,SW17,NULL,NULL,527250,171750,NULL,NULL\n47391141,18/04/2014 16:56,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,Queen's Park,E09000033,Westminster,North Kensington,NULL,Beethoven Street,8401106,W10,NULL,NULL,524450,182850,NULL,NULL\n47676141,19/04/2014 09:16,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE RUNNING CALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000569,Wallington North,E09000029,Sutton,Wallington,NULL,Clifton Road,NULL,SM6,NULL,NULL,529150,164250,NULL,NULL\n47878141,19/04/2014 17:11,2014,2014/15,Special Service,1,1,295,295,YOUNG DEER TRAPPED BEHIND SHED,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000333,Ickenham,E09000017,Hillingdon,Ruislip,NULL,Hoylake Crescent,21401023,UB10,NULL,NULL,507550,186750,NULL,NULL\n47971141,19/04/2014 20:02,2014,2014/15,Special Service,1,1,295,295,BABY DUCKS TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Barking,NULL,Academy Way,NULL,RM8,NULL,NULL,546550,185550,NULL,NULL\n48236141,20/04/2014 12:38,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Cheyneys Avenue,NULL,HA8,517907,191765,517950,191750,51.61238012,-0.298538237\n48640141,21/04/2014 11:56,2014,2014/15,Special Service,1,1,295,295,TWO DOGS LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Bedfont Road,NULL,TW14,508863,172778,508850,172750,51.44354083,-0.434963185\n48774141,21/04/2014 16:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND SHED,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000469,Raynes Park,E09000024,Merton,New Malden,48057323,Polesden Gardens,22105127,SW20,522683,169059,522650,169050,51.40729462,-0.23748976\n48797141,21/04/2014 17:59,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,Green Street East,E09000025,Newham,Stratford,NULL,Strone Road,22207910,E7,NULL,NULL,541550,184750,NULL,NULL\n49045141,22/04/2014 08:02,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN BANISTER,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,Mawneys,E09000016,Havering,Romford,NULL,Linley Crescent,21301362,RM7,NULL,NULL,550150,189850,NULL,NULL\n49076141,22/04/2014 09:54,2014,2014/15,Special Service,2,9,295,2655,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Feltham,NULL,Salisbury Road,21500996,TW4,NULL,NULL,511450,175750,NULL,NULL\n49254141,22/04/2014 17:42,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON LEDGE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,Hampstead Town,E09000007,Camden,West Hampstead,NULL,Flask Walk,20400159,NW3,NULL,NULL,526450,185750,NULL,NULL\n49339141,22/04/2014 20:49,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN GARAGE,Dog,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,Croydon,Norbury,1.00021E+11,Cameron Road,20500750,CR0,531866,167067,531850,167050,51.3873347,-0.106273554\n49492141,23/04/2014 09:59,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP  TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,NULL,Park Avenue,20300947,BR6,NULL,NULL,546450,165550,NULL,NULL\n49528141,23/04/2014 11:25,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,Cranbrook,E09000026,Redbridge,Ilford,NULL,Wanstead Lane,22303356,IG1,NULL,NULL,542150,188050,NULL,NULL\n49831141,23/04/2014 23:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SHAFT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Shepherds Bush Road,21000727,W6,NULL,NULL,523450,179150,NULL,NULL\n49945141,24/04/2014 09:46,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA RESCUE PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538250,176550,NULL,NULL\n50071141,24/04/2014 16:12,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER SHED,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,Bexley,Erith,NULL,Colyers Lane,20100348,DA8,NULL,NULL,550750,176950,NULL,NULL\n50345141,25/04/2014 09:42,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,Woodberry Down,E09000012,Hackney,Stoke Newington,1.00023E+11,Cranwich Road,20900285,N16,533008,187810,533050,187850,51.5734772,-0.082066676\n50406141,25/04/2014 12:46,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000452,Sydenham,E09000023,Lewisham,Forest Hill,NULL,Westwood Hill,22004717,SE26,NULL,NULL,534950,171750,NULL,NULL\n50722141,26/04/2014 09:02,2014,2014/15,Special Service,1,1,295,295,HORSE STUCK OVER FENCE,Horse,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05007402,Tatsfield and Titsey,E07000215,Tandridge,Surrey,1.00062E+11,Ricketts Hill Road,39500869,TN16,541911,158227,541950,158250,51.30547736,0.034473086\n51030141,26/04/2014 20:41,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000596,Grove Green,E09000031,Waltham Forest,Leyton,NULL,Murchison Road,22860300,E10,NULL,NULL,538150,186750,NULL,NULL\n51787141,28/04/2014 17:47,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE BY WIRE,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000603,Lea Bridge,E09000031,Waltham Forest,Walthamstow,NULL,Boundary Road,NULL,E17,537140,187975,537150,187950,51.5739707,-0.022413507\n51805141,28/04/2014 18:35,2014,2014/15,Special Service,1,2,295,590,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,Highbury West,E09000019,Islington,Islington,NULL,Coach House Lane,21603055,N5,NULL,NULL,531550,185450,NULL,NULL\n52072141,29/04/2014 10:25,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100088907,Sovereign Road,19903818,IG11,547119,182963,547150,182950,51.52642226,0.119388468\n53006141,30/04/2014 23:11,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Heather Close,21610008,N7,NULL,NULL,530850,186450,NULL,NULL\n53105141,01/05/2014 08:00,2014,2014/15,Special Service,2,5,295,1475,SMALL ANIMAL RESCUE,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,NULL,Ursula Gould Way,NULL,E14,537298,181558,537250,181550,51.51626834,-0.022636724\n53212141,01/05/2014 12:55,2014,2014/15,Special Service,1,1,295,295,DOG WITH LEG CAUGHT IN TABLE LEG,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000358,Hounslow Central,E09000018,Hounslow,Heston,NULL,Pears Road,21500870,TW3,NULL,NULL,514750,175850,NULL,NULL\n53448141,01/05/2014 20:58,2014,2014/15,Special Service,1,1,295,295,RABBIT TRAPPED IN FIREPLACE,Rabbit,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Griffiths Road,22102985,SW19,NULL,NULL,525450,170350,NULL,NULL\n53659141,02/05/2014 09:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN STUCK UP A TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000523,Heathfield,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Sheringham Avenue,22401837,TW2,513045,173074,513050,173050,51.44538311,-0.374720192\n53660141,02/05/2014 09:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IMPALED ON TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Burlington Avenue,NULL,RM7,549968,188360,549950,188350,51.57416647,0.162724676\n53864141,02/05/2014 17:40,2014,2014/15,Special Service,1,1,295,295,SMALL BIRD TRAPPED UNDER FLOOR BOARD,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Campbell Avenue,22302679,IG6,NULL,NULL,544150,189050,NULL,NULL\n53868141,02/05/2014 17:44,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Heather Close,21610008,N7,NULL,NULL,530850,186450,NULL,NULL\n53889141,02/05/2014 18:30,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000219,Eltham South,E09000011,Greenwich,Eltham,2.00001E+11,Eltham High Street,20800526,SE9,543207,174425,543250,174450,51.45070677,0.059574378\n54895141,04/05/2014 16:20,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,Eltham West,E09000011,Greenwich,Eltham,NULL,Keynsham Road,20800848,SE9,NULL,NULL,541950,174750,NULL,NULL\n55314141,05/05/2014 10:30,2014,2014/15,Special Service,1,1,295,295,PUPPYS PAW TRAPPED IN CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Brazier Crescent,21402892,UB5,NULL,NULL,512650,182350,NULL,NULL\n55347141,05/05/2014 11:05,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN LOCK UP GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,Pitsea Street,NULL,E1,535786,181159,535750,181150,51.51304781,-0.044567853\n55365141,05/05/2014 11:40,2014,2014/15,Special Service,1,1,295,295,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011480,Selsdon & Addington Village,E09000008,Croydon,Addington,1.00021E+11,Heather Way,20501743,CR2,535820,162915,535850,162950,51.34909079,-0.051068101\n55371141,05/05/2014 11:54,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LETTER BOX,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,Hounslow Heath,E09000018,Hounslow,Feltham,NULL,Wellington Road South,21501181,TW4,NULL,NULL,513050,174550,NULL,NULL\n55883141,06/05/2014 11:58,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920911,Snakes Lane,22306023,IG8,540952,191777,540950,191750,51.6071942,0.034082045\n56219141,07/05/2014 07:37,2014,2014/15,Special Service,1,1,295,295,FOX ON PONTOON,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Shad Thames,22502224,SE1,533879,179813,533850,179850,51.50140689,-0.072543881\n56326141,07/05/2014 13:30,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA ON SCENE WITH CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Barrowfield Close,20703899,N9,NULL,NULL,534950,193250,NULL,NULL\n56904141,08/05/2014 20:27,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Leander Road,20601041,UB5,NULL,NULL,513050,183150,NULL,NULL\n58159141,11/05/2014 17:34,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING UNDERNEATH BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Court Road,NULL,SE9,542458,173285,542450,173250,51.44065175,0.048344167\n58236141,11/05/2014 20:28,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,34069734,Fulham Road,21000359,SW6,525243,176921,525250,176950,51.47739372,-0.197923181\n59076141,13/05/2014 20:54,2014,2014/15,Special Service,1,1,295,295,DANGEROUS ELECTRICS,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000255,Fulham Reach,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Colwith Road,21000230,W6,NULL,NULL,523450,177750,NULL,NULL\n59268141,14/05/2014 11:08,2014,2014/15,Special Service,1,1,295,295,ASSIST THE RSPCA WITH A CAT ON A ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000091,Harlesden,E09000005,Brent,Willesden,NULL,Essex Road,20200214,NW10,NULL,NULL,521550,184450,NULL,NULL\n59289141,14/05/2014 12:21,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH DOG STUCK DOWN DRAIN,Dog,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,NULL,Sheffield Drive,NULL,RM3,555502,192447,555550,192450,51.60938816,0.244325617\n59778141,15/05/2014 14:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Heston,NULL,The Croft,21500322,TW5,NULL,NULL,512450,177550,NULL,NULL\n60017141,15/05/2014 21:14,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WATER HEATER,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009393,Courtfield,E09000020,Kensington and Chelsea,Chelsea,NULL,Stanhope Gardens,21700522,SW7,NULL,NULL,526350,178750,NULL,NULL\n60420141,16/05/2014 18:48,2014,2014/15,Special Service,1,1,295,295,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000196,Cockfosters,E09000010,Enfield,Southgate,NULL,Trent Gardens,20701999,N14,NULL,NULL,528950,194950,NULL,NULL\n61582141,18/05/2014 16:10,2014,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Devonshire Road,22001409,SE23,NULL,NULL,535450,173150,NULL,NULL\n61924141,18/05/2014 23:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED UNDER BEDROOM FURNITURE,Fox,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,NULL,Ewell Road,21800386,KT6,NULL,NULL,518550,166950,NULL,NULL\n62526141,19/05/2014 21:46,2014,2014/15,Special Service,1,1,295,295,DOG STUCK ON  WINDOW LEDGE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Burnell Avenue,20100233,DA16,NULL,NULL,546350,176250,NULL,NULL\n62798141,20/05/2014 14:45,2014,2014/15,Special Service,1,3,295,885,HORSE COLLAPSED  UNABLE TO STAND WITHOUT ASSISTANCE,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,5023224,Cressfield Close,20401332,NW5,528359,185349,528350,185350,51.55243802,-0.150011023\n63236141,21/05/2014 08:01,2014,2014/15,Special Service,1,2,295,590,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011250,Newbury,E09000026,Redbridge,Ilford,NULL,Leicester Gardens,22302225,IG3,NULL,NULL,545450,187950,NULL,NULL\n63244141,21/05/2014 08:36,2014,2014/15,Special Service,1,1,295,295,DISTRESSED DOG STUCK ON ROOF,Dog,Police,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Mitcham Road,22903376,SW17,NULL,NULL,527750,171150,NULL,NULL\n63301141,21/05/2014 11:12,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH BIRD STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009335,Weavers,E09000030,Tower Hamlets,Bethnal Green,NULL,Gibraltar Walk,22702188,E2,NULL,NULL,533950,182550,NULL,NULL\n63340141,21/05/2014 13:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000482,East Ham South,E09000025,Newham,East Ham,NULL,Charlemont Road,22200458,E6,NULL,NULL,543150,182750,NULL,NULL\n63613141,21/05/2014 22:08,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN TURNSTILE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000474,Wimbledon Park,E09000024,Merton,Wandsworth,NULL,Wimbledon Park,NULL,SW19,524785,172469,524750,172450,51.43748313,-0.20608373\n63914141,22/05/2014 16:02,2014,2014/15,Special Service,1,1,295,295,INJURED SWAN IN  TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000515,Wanstead,E09000026,Redbridge,Leytonstone,NULL,Northumberland Avenue,NULL,E12,541004,187082,541050,187050,51.5649936,0.032948869\n64024141,22/05/2014 19:34,2014,2014/15,Special Service,1,2,295,590,PUPPY TRAPPED DOWN MANHOLE,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000344,Yeading,E09000017,Hillingdon,Southall,NULL,Friar Road,NULL,UB4,511678,182176,511650,182150,51.52746325,-0.391506455\n64111141,23/05/2014 00:17,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000053,Golders Green,E09000003,Barnet,Finchley,NULL,Courtleigh Gardens,20010600,NW11,NULL,NULL,524250,189050,NULL,NULL\n64181141,23/05/2014 07:16,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED IN FOOTBALL NETTING,Fox,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000561,Nonsuch,E09000029,Sutton,Sutton,NULL,The Mount,22603393,KT4,NULL,NULL,522850,164650,NULL,NULL\n64546141,23/05/2014 21:22,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,NULL,Honor Oak Road,22001574,SE23,NULL,NULL,535350,173950,NULL,NULL\n64779141,24/05/2014 11:54,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK BETWEEN TWO WALLS,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009367,Brownswood,E09000012,Hackney,Holloway,NULL,Finsbury Park Road,20900408,N4,NULL,NULL,531950,186550,NULL,NULL\n65192141,25/05/2014 10:47,2014,2014/15,Special Service,1,1,295,295,DOG BELEIVED TRAPPED,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000029,Chadwell Heath,E09000002,Barking and Dagenham,Ilford,NULL,Billet Road,NULL,RM6,547482,189693,547450,189650,51.58679837,0.127436576\n65225141,25/05/2014 12:43,2014,2014/15,Special Service,1,1,295,295,CAT  TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Ferndale Road,22836050,E11,539545,186830,539550,186850,51.56309206,0.011813872\n65303141,25/05/2014 13:45,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,1.00022E+11,Connaught Road,22401387,TW11,514761,171386,514750,171350,51.42986683,-0.350585392\n65431141,25/05/2014 19:57,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05000348,Chiswick Homefields,E09000018,Hounslow,Chiswick,2.00004E+11,Chiswick Mall,21500250,W4,521612,177811,521650,177850,51.48618378,-0.249876237\n65690141,26/05/2014 10:01,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Clayhill Crescent,22003801,SE9,NULL,NULL,541750,171850,NULL,NULL\n65715141,26/05/2014 11:36,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Lordship Park,20900636,N16,NULL,NULL,532750,186850,NULL,NULL\n65722141,26/05/2014 11:57,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN PITT,Dog,Police,Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,East Finchley,E09000003,Barnet,Finchley,NULL,Brighton Road,NULL,N2,526274,190038,526250,190050,51.59504762,-0.17838365\n65994141,27/05/2014 05:52,2014,2014/15,Special Service,NULL,NULL,295,NULL,CAT STUCK ON ROOF TRAPPED BEHIND LOCKED DOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000433,Thornton,E09000022,Lambeth,Tooting,NULL,Poynders Gardens,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL\n66115141,27/05/2014 13:51,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN WHEEL ARCH,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Todds Walk,NULL,N7,530688,186584,530650,186550,51.56300236,-0.115979599\n66264141,27/05/2014 18:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Stanlake Road,21000774,W12,NULL,NULL,523050,180250,NULL,NULL\n66267141,27/05/2014 19:00,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN A DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL\n66303141,27/05/2014 20:07,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN DRAIN HOPPER OF BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,St. Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL\n66513141,28/05/2014 10:09,2014,2014/15,Special Service,1,1,295,295,SMALL BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Thornbury Road,21501119,TW7,NULL,NULL,515050,176550,NULL,NULL\n66595141,28/05/2014 13:52,2014,2014/15,Special Service,1,2,295,590,CAT STUCK ON TOP OF WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Byron Mews,20401428,NW3,NULL,NULL,527450,185550,NULL,NULL\n67041141,29/05/2014 12:43,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,10010202712,Powis Street,20801201,SE18,543660,178936,543650,178950,51.49112655,0.067921758\n67462141,30/05/2014 10:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Bedford Avenue,20002880,EN5,NULL,NULL,524750,196150,NULL,NULL\n67639141,30/05/2014 19:00,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Canonbie Road,22001315,SE23,535094,173757,535050,173750,51.44669552,-0.057363573\n67671141,30/05/2014 20:09,2014,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,12142215,Mount Park Road,20601211,W5,517728,181725,517750,181750,51.5221824,-0.304483129\n67788141,31/05/2014 00:29,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,NULL,Drakefell Road,22000345,SE14,NULL,NULL,535550,176050,NULL,NULL\n67935141,31/05/2014 12:10,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,207192527,Watermill Lane,20704891,N18,533190,192296,533150,192250,51.61374664,-0.077741512\n67966141,31/05/2014 13:29,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE - ASSIST RSPCA OFFICER,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Greenhill Park,20019420,EN5,NULL,NULL,525750,195750,NULL,NULL\n68446141,01/06/2014 12:45,2014,2014/15,Special Service,1,2,295,590,CROW STUCK ON ROOF,Bird,Other FRS,Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000406,Coombe Hill,E09000021,Kingston upon Thames,Kingston,NULL,Kingsnympton Park,21800556,KT2,NULL,NULL,519750,170650,NULL,NULL\n68470141,01/06/2014 13:44,2014,2014/15,Special Service,1,1,295,295,KITTEN  UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Bromley,NULL,Amblecote Road,22003110,SE12,NULL,NULL,540850,172050,NULL,NULL\n68546141,01/06/2014 16:19,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK IN HOLE UNDER FLOOR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,King's Park,E09000012,Hackney,Homerton,NULL,Mandeville Street,20900651,E5,NULL,NULL,536050,185950,NULL,NULL\n68927141,02/06/2014 13:53,2014,2014/15,Special Service,1,1,295,295,CAT STRANDED ON WINDOW LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Prince Albert Road,8400534,NW8,NULL,NULL,527250,183050,NULL,NULL\n69194141,02/06/2014 23:58,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - INJURED CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000205,Palmers Green,E09000010,Enfield,Edmonton,NULL,Felstead Close,20706992,N13,NULL,NULL,531050,192250,NULL,NULL\n69630141,04/06/2014 01:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000301,Roxbourne,E09000015,Harrow,Northolt,NULL,Alexandra Avenue,NULL,HA2,513328,186361,513350,186350,51.56474892,-0.366384682\n69742141,04/06/2014 10:50,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000080,Northumberland Heath,E09000004,Bexley,Bexley,NULL,Brook Street,NULL,DA8,550120,177019,550150,177050,51.47222529,0.1600886\n69923141,04/06/2014 18:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000308,Elm Park,E09000016,Havering,Hornchurch,NULL,Bader Way,21300706,RM13,NULL,NULL,552450,184650,NULL,NULL\n69953141,04/06/2014 20:09,2014,2014/15,Special Service,1,1,295,295,FOX STUCK  IN BASEMENT AREA,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009398,Norland,E09000020,Kensington and Chelsea,North Kensington,NULL,Lansdowne Crescent,21700922,W11,NULL,NULL,524550,180750,NULL,NULL\n70683141,06/06/2014 13:54,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009375,Haggerston,E09000012,Hackney,Shoreditch,10008229917,Moye Close,20901800,E2,534358,183463,534350,183450,51.53409345,-0.064255952\n71132141,07/06/2014 08:55,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER RLY BRIDGE,Cat,Person (land line),Railway building - other,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,Brixton,NULL,Padfield Road,NULL,SE5,532051,175846,532050,175850,51.46618638,-0.100346943\n71404141,07/06/2014 19:29,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALL IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,NULL,Mingard Walk,21606902,N7,NULL,NULL,530850,186550,NULL,NULL\n71620141,08/06/2014 07:13,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN TREE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000170,Acton Central,E09000009,Ealing,Acton,12103714,Northfields Road,20601272,W3,520149,181856,520150,181850,51.52285072,-0.269559653\n72214141,09/06/2014 10:11,2014,2014/15,Special Service,2,4,295,1180,GOAT TRAPPED BELOW GROUND LEVEL ON LEDGE,Goat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,Jubilee,E09000010,Enfield,Chingford,NULL,Lea Valley Road,NULL,E4,537359,194968,537350,194950,51.636755,-0.016519754\n72239141,09/06/2014 10:57,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN VENT ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Galleons Drive,19903950,IG11,NULL,NULL,546450,182550,NULL,NULL\n72369141,09/06/2014 17:25,2014,2014/15,Special Service,1,1,295,295,INJURED CAT TRAPPED IN BUSES,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000221,Glyndon,E09000011,Greenwich,Plumstead,10010212913,Erebus Drive,20802018,SE28,544497,179633,544450,179650,51.4971764,0.080254867\n72418141,09/06/2014 18:55,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED BETWEEN WALLS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000521,Hampton North,E09000027,Richmond upon Thames,Twickenham,NULL,Queenswood Avenue,22401772,TW12,NULL,NULL,513750,170750,NULL,NULL\n73017141,10/06/2014 20:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Aldenham Street,20400793,NW1,NULL,NULL,529550,183150,NULL,NULL\n74797141,13/06/2014 20:07,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN DRAINPIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,NULL,Harmood Street,20400488,NW1,NULL,NULL,528550,184450,NULL,NULL\n74802141,13/06/2014 20:11,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009322,Bromley South,E09000030,Tower Hamlets,Poplar,NULL,Gale Street,22700525,E3,NULL,NULL,537350,181850,NULL,NULL\n75043141,14/06/2014 08:29,2014,2014/15,Special Service,1,1,295,295,FLEDGLING GULL TRAPPED IN GUTTER,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000633,Churchill,E09000033,Westminster,Chelsea,NULL,Ormonde Place,8400862,SW1W,NULL,NULL,528250,178550,NULL,NULL\n75395141,14/06/2014 21:21,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LARGE HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Kennington Lane,21900787,SE11,NULL,NULL,531250,178350,NULL,NULL\n75401141,14/06/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN CONSERVATORY AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,Grove Park,E09000023,Lewisham,Eltham,NULL,Mirror Path,22006110,SE9,NULL,NULL,541450,172150,NULL,NULL\n75729141,15/06/2014 13:58,2014,2014/15,Special Service,1,1,295,295,PIDGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011096,Camberwell Green,E09000028,Southwark,Brixton,NULL,Denmark Road,22500754,SE5,NULL,NULL,532250,176350,NULL,NULL\n75738141,15/06/2014 14:27,2014,2014/15,Special Service,1,1,295,295,MAGPIE TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Tentelow Lane,20601705,UB2,NULL,NULL,514050,179550,NULL,NULL\n75839141,15/06/2014 18:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009368,Cazenove,E09000012,Hackney,Stoke Newington,NULL,Rossendale Street,20900869,E5,NULL,NULL,534550,186650,NULL,NULL\n75877141,15/06/2014 19:20,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND PIPES IN AIRING CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Poplar High Street,22700960,E14,NULL,NULL,537550,180750,NULL,NULL\n75906141,15/06/2014 20:35,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527550,168550,NULL,NULL\n75983141,15/06/2014 23:14,2014,2014/15,Special Service,1,2,295,590,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,James Voller Way,22702472,E1,NULL,NULL,535050,181050,NULL,NULL\n76012141,16/06/2014 01:00,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER STAIR LIFT,Cat,Ambulance,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Hathorne Close,22501225,SE15,NULL,NULL,535050,176350,NULL,NULL\n76059141,16/06/2014 06:37,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BETWEEN FENCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,NULL,James Voller Way,22702472,E1,NULL,NULL,535050,181050,NULL,NULL\n76380141,16/06/2014 19:30,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (land line),Law Courts,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011463,Addiscombe West,E09000008,Croydon,Croydon,1.00023E+11,Altyre Road,20500623,CR9,532890,165536,532850,165550,51.37333743,-0.092138081\n76465141,16/06/2014 21:38,2014,2014/15,Special Service,1,1,295,295,DOG WITH REAR PAWS CAUGHT IN WROUGHT IRON GARDEN FURNITURE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000442,Downham,E09000023,Lewisham,Bromley,NULL,Geraint Road,22003348,BR1,NULL,NULL,540450,171750,NULL,NULL\n76497141,16/06/2014 23:28,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN SCHOOL GROUNDS,Dog,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011217,Barnehurst,E09000004,Bexley,Erith,1.00023E+11,Colyers Lane,20100348,DA8,550938,176943,550950,176950,51.47132499,0.171824553\n76500141,17/06/2014 00:15,2014,2014/15,Special Service,1,1,295,295,FOX IN BEDROOM,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011472,Norbury & Pollards Hill,E09000008,Croydon,Norbury,NULL,London Road,20502653,SW16,NULL,NULL,530850,169350,NULL,NULL\n77113141,18/06/2014 05:46,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN CUPBOARD AND WALL,Fox,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,Redbridge,Ilford,NULL,Gantshill Crescent,22302867,IG2,NULL,NULL,543350,188850,NULL,NULL\n77464141,18/06/2014 21:26,2014,2014/15,Special Service,1,1,295,295,KITTEN WITH HEAD STUCK HOLE UNDER TV SET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Highcombe,20800768,SE7,NULL,NULL,540850,177750,NULL,NULL\n77642141,19/06/2014 09:29,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE FROM WATER,Unknown - Domestic Animal Or Pet,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Moree Way,NULL,N18,534017,192670,534050,192650,51.61691123,-0.065661821\n77706141,19/06/2014 11:23,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000444,Forest Hill,E09000023,Lewisham,Forest Hill,1.00022E+11,Eliot Bank,22002391,SE23,534951,173026,534950,173050,51.4401604,-0.059698493\n77708141,19/06/2014 11:25,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,Brockley,E09000023,Lewisham,Greenwich,NULL,Ship Street,22004556,SE8,NULL,NULL,537250,176650,NULL,NULL\n77839141,19/06/2014 15:47,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED BEHIND AIR VENTS,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Lawrence Buildings,NULL,N16,533698,186435,533650,186450,51.56095763,-0.072638167\n77903141,19/06/2014 18:33,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009331,St. Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Menotti Street,22700810,E2,NULL,NULL,534450,182350,NULL,NULL\n78125141,20/06/2014 07:44,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED BY WIRE IN A TREE,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000565,Sutton North,E09000029,Sutton,Sutton,5870053300,All Saints Road,22605398,SM1,526047,165284,526050,165250,51.37263073,-0.190486852\n78132141,20/06/2014 08:35,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000458,Cricket Green,E09000024,Merton,Mitcham,NULL,London Road,22103995,CR4,NULL,NULL,527250,168250,NULL,NULL\n78221141,20/06/2014 13:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Pigeon,Person (land line),Other transport building,Non Residential,Other animal assistance,Assist trapped wild animal,E05011250,Newbury,E09000026,Redbridge,Ilford,10090506655,Ley Street,22303015,IG2,544465,187740,544450,187750,51.57003044,0.083116746\n78432141,20/06/2014 20:06,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK UNDER NETTING,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033572395,Jermyn Street,8400480,SW1Y,529539,180533,529550,180550,51.50888791,-0.134771581\n78674141,21/06/2014 05:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED NEAR TO RIVER BANK,Fox,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000552,Surrey Docks,E09000028,Southwark,Dockhead,NULL,Rotherhithe Street,NULL,SE16,535687,180267,535650,180250,51.50505578,-0.046336812\n79233141,22/06/2014 02:29,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO SHEDS,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000597,Hale End and Highams Park,E09000031,Waltham Forest,Woodford,1.00023E+11,Hale End Road,22842250,IG8,538585,191023,538550,191050,51.60100585,-0.000374335\n79378141,22/06/2014 09:45,2014,2014/15,Special Service,1,1,295,295,CROW STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Meadfield,20029120,HA8,NULL,NULL,519750,193850,NULL,NULL\n79396141,22/06/2014 10:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN OPENING OF WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,Stratford and New Town,E09000025,Newham,Stratford,NULL,Whitear Walk,22201696,E15,NULL,NULL,538850,184950,NULL,NULL\n79564141,22/06/2014 15:22,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN RESTAURANT DUCTING,Cat,Person (land line),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000428,St. Leonard's,E09000022,Lambeth,Tooting,1.00023E+11,Streatham High Road,21901326,SW16,530180,172485,530150,172450,51.43641453,-0.128501949\n79639141,22/06/2014 17:16,2014,2014/15,Special Service,1,1,295,295,PIGEON IMPALED ON TOP OF A LAMPOST,Bird,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000644,St. James's,E09000033,Westminster,Soho,10033544649,The Piazza,8401623,WC2E,530291,180917,530250,180950,51.51216614,-0.123799838\n79759141,22/06/2014 20:15,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Westcombe Hill,20801590,SE3,NULL,NULL,540350,177850,NULL,NULL\n79795141,22/06/2014 21:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN PIPEWORK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Kingsland Road,22200779,E13,NULL,NULL,541250,182650,NULL,NULL\n80601141,24/06/2014 08:48,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN WIRING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,NULL,Glenbuck Road,21800446,KT6,NULL,NULL,518150,167250,NULL,NULL\n80639141,24/06/2014 11:24,2014,2014/15,Special Service,2,3,295,885,SMALL ANIMAL RESCUE,Cat,Police,Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000558,Carshalton Central,E09000029,Sutton,Sutton,NULL,Orchard Way,NULL,SM1,526801,164890,526850,164850,51.36892207,-0.179800571\n81770141,26/06/2014 11:24,2014,2014/15,Special Service,NULL,NULL,295,NULL,HORSE FALLEN INTO DITCH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000155,Heathfield,E09000008,Croydon,Addington,NULL,Kent Gate Way,NULL,CR0,537037,163850,537050,163850,51.3572012,-0.033244088\n81820141,26/06/2014 12:42,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000435,Tulse Hill,E09000022,Lambeth,Brixton,NULL,Endymion Road,21900523,SW2,NULL,NULL,530750,174250,NULL,NULL\n81823141,26/06/2014 12:46,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Other FRS,Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000075,Erith,E09000004,Bexley,Erith,NULL,Wharfside Close,NULL,DA8,551605,178082,551650,178050,51.48138074,0.181908914\n82345141,27/06/2014 15:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000457,Colliers Wood,E09000024,Merton,Mitcham,NULL,Arnold Road,22100204,SW17,NULL,NULL,527750,170450,NULL,NULL\n82992141,28/06/2014 15:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED AT BACK OF KITCHEN CABINETS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Tolcarne Drive,21401999,HA5,NULL,NULL,510950,189650,NULL,NULL\n83131141,28/06/2014 19:44,2014,2014/15,Special Service,1,1,295,295,DOG STUCK UNDER RADIATOR,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Lambrook Terrace,21000487,SW6,NULL,NULL,524050,176850,NULL,NULL\n83138141,28/06/2014 20:06,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Aldermans Hill,NULL,N13,530706,192854,530750,192850,51.61934327,-0.113387955\n83401141,29/06/2014 11:39,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000039,Thames,E09000002,Barking and Dagenham,Barking,100043506,Julia Gardens,19900507,IG11,547817,183476,547850,183450,51.53084946,0.129658062\n83522141,29/06/2014 16:41,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000051,Finchley Church End,E09000003,Barnet,Finchley,NULL,Station Road,NULL,N3,NULL,NULL,525350,190550,NULL,NULL\n83876141,30/06/2014 14:17,2014,2014/15,Special Service,1,1,295,295,DEER TRAPPED IN RAILINGS,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000199,Enfield Lock,E09000010,Enfield,Enfield,NULL,Ramney Drive,NULL,EN3,536472,199050,536450,199050,51.67365251,-0.027736919\n83984141,30/06/2014 17:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BIKE SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Bridgeway Street,20400795,NW1,NULL,NULL,529450,183150,NULL,NULL\n84292141,01/07/2014 09:44,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO ANIMAL TRAPPED IN BATH,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Northwold Road,20900753,N16,NULL,NULL,533650,186650,NULL,NULL\n84382141,01/07/2014 14:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011223,Crook Log,E09000004,Bexley,Bexley,NULL,Dunwich Road,20100468,DA7,NULL,NULL,548650,176850,NULL,NULL\n84389141,01/07/2014 14:11,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON WINDOWLEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,NULL,Farringdon Lane,21604841,EC1R,NULL,NULL,531450,182250,NULL,NULL\n84500141,01/07/2014 17:18,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Settles Street,22701081,E1,NULL,NULL,534350,181550,NULL,NULL\n84526141,01/07/2014 18:33,2014,2014/15,Special Service,1,1,295,295,CAT LOCKED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000185,Northolt West End,E09000009,Ealing,Southall,NULL,Brett Close,NULL,UB5,511510,182875,511550,182850,51.53377897,-0.393706202\n84765141,02/07/2014 08:11,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Covert Road,22304561,IG6,NULL,NULL,545650,191950,NULL,NULL\n84847141,02/07/2014 12:33,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN SATALITE DISH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Sandy Drive,21501400,TW14,NULL,NULL,509250,173250,NULL,NULL\n85114141,02/07/2014 20:51,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Island Row,22700672,E14,NULL,NULL,536550,180950,NULL,NULL\n85380141,03/07/2014 09:51,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN FENCE AND GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000220,Eltham West,E09000011,Greenwich,Lee Green,NULL,Bayfield Road,20800129,SE9,NULL,NULL,541550,175150,NULL,NULL\n85425141,03/07/2014 11:43,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED IN FRONT BASEMENT WELL OF HOUSE,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000379,St. Mary's,E09000019,Islington,Islington,NULL,Cross Street,21604640,N1,NULL,NULL,531850,183950,NULL,NULL\n85630141,03/07/2014 17:35,2014,2014/15,Special Service,NULL,NULL,295,NULL,DOG ON WINDOW LEDGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Tillotson Road,20704845,N9,NULL,NULL,533550,193850,NULL,NULL\n85702141,03/07/2014 19:01,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000060,Underhill,E09000003,Barnet,Barnet,200019907,Cattley Close,20007210,EN5,524316,196398,524350,196350,51.65263936,-0.204380872\n85753141,03/07/2014 20:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WATER,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Shire Horse Way,NULL,TW7,515951,175885,515950,175850,51.47006048,-0.332002258\n86032141,04/07/2014 09:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FENCING,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00021E+11,Broadwater Road,21104283,N17,533408,190201,533450,190250,51.59486886,-0.075391032\n86868141,05/07/2014 20:32,2014,2014/15,Special Service,1,1,295,295,PARROT TRAPPED IN SPIKED BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Lisson Grove,8400993,NW1,NULL,NULL,527350,181850,NULL,NULL\n87145141,06/07/2014 10:41,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED ON AERIAL,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Merton Road,22903296,SW18,NULL,NULL,525350,172950,NULL,NULL\n87491141,07/07/2014 01:46,2014,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009389,Brompton & Hans Town,E09000020,Kensington and Chelsea,Chelsea,NULL,Wiltshire Close,21701166,SW3,NULL,NULL,527550,178650,NULL,NULL\n87609141,07/07/2014 12:18,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH DUCKLING STUCK IN DRAIN,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Conisborough Crescent,NULL,SE6,538391,172066,538350,172050,51.43070439,-0.010610221\n87671141,07/07/2014 14:37,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000257,Munster,E09000013,Hammersmith and Fulham,Fulham,NULL,Salisbury Mews,21001228,SW6,NULL,NULL,524650,177250,NULL,NULL\n87829141,07/07/2014 19:27,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000414,Tolworth and Hook Rise,E09000021,Kingston upon Thames,Surbiton,NULL,Brook Road,21800175,KT6,NULL,NULL,518150,165650,NULL,NULL\n87951141,08/07/2014 00:51,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN METAL GATE,Cat,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011478,Sanderstead,E09000008,Croydon,Addington,NULL,Norfolk Avenue,20502235,CR2,NULL,NULL,534450,162250,NULL,NULL\n88059141,08/07/2014 08:08,2014,2014/15,Special Service,1,1,295,295,CAT  TRAPPED IN TILTING WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Pownall Road,20900817,E8,NULL,NULL,534350,183650,NULL,NULL\n88255141,08/07/2014 16:06,2014,2014/15,Special Service,1,1,295,295,DOG WITH HEAD STUCK IN SOFA,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000412,St. Mark's,E09000021,Kingston upon Thames,Surbiton,NULL,Avenue Elmers,21800091,KT6,NULL,NULL,518350,167850,NULL,NULL\n89693141,11/07/2014 11:14,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT ON WINDOW LEDGE,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000432,Streatham Wells,E09000022,Lambeth,Tooting,1.00023E+11,Streatham High Road,21901326,SW16,530158,171902,530150,171950,51.43118022,-0.129032296\n89808141,11/07/2014 16:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WINDOW SHUTTER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,NULL,Rothbury Gardens,21500965,TW7,NULL,NULL,516350,177250,NULL,NULL\n89828141,11/07/2014 17:35,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED  BETWEEN WALL AND KITCHEN UNITS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,NULL,Rosendale Road,21901178,SE21,NULL,NULL,532650,172250,NULL,NULL\n90330141,12/07/2014 16:35,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000319,Romford Town,E09000016,Havering,Hornchurch,NULL,Melton Gardens,21301418,RM1,NULL,NULL,551650,187750,NULL,NULL\n90335141,12/07/2014 16:44,2014,2014/15,Special Service,1,1,295,295,RABBIT  STUCK BETWEEN FENCE  AND WALL,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011221,Blendon & Penhill,E09000004,Bexley,Bexley,NULL,Thistlefield Close,20101820,DA5,NULL,NULL,547950,173250,NULL,NULL\n90768141,13/07/2014 08:54,2014,2014/15,Special Service,1,1,295,295,SQUIRREL STUCK BETWEEN SCAFFOLD BOARDS,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011108,Nunhead & Queen's Road,E09000028,Southwark,New Cross,NULL,Pomeroy Street,22503094,SE14,NULL,NULL,535350,176950,NULL,NULL\n91409141,14/07/2014 16:23,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009336,Whitechapel,E09000030,Tower Hamlets,Whitechapel,NULL,Varden Street,22701260,E1,NULL,NULL,534750,181450,NULL,NULL\n91410141,14/07/2014 16:25,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,10013152190,Creswell Drive,20304074,BR3,537774,167752,537750,167750,51.39208764,-0.021154458\n91619141,15/07/2014 00:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Blackheath Road,20800175,SE10,NULL,NULL,537750,176750,NULL,NULL\n91839141,15/07/2014 12:02,2014,2014/15,Special Service,1,1,295,295,PIGEON STUCK IN BOILER,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Cavendish Road,20704019,N18,NULL,NULL,534950,192450,NULL,NULL\n92090141,15/07/2014 19:03,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WIRE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000094,Kilburn,E09000005,Brent,West Hampstead,202094782,Glengall Road,20201848,NW6,524883,183790,524850,183750,51.53920633,-0.200673086\n92117141,15/07/2014 19:49,2014,2014/15,Special Service,1,1,295,295,CAT WITH LEG TRAPPED IN WINDOW,Cat,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000638,Lancaster Gate,E09000033,Westminster,Paddington,NULL,Cleveland Square,8400938,W2,NULL,NULL,526250,181050,NULL,NULL\n92168141,15/07/2014 21:32,2014,2014/15,Special Service,1,1,295,295,INJURED CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,5066027,Finchley Road,20400045,NW3,525235,185773,525250,185750,51.55694992,-0.194894915\n92367141,16/07/2014 09:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOOR IN PIPE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rathcoole Gardens,21103786,N8,NULL,NULL,530850,188750,NULL,NULL\n92724141,16/07/2014 20:47,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WATER TANK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Albert Road,20500610,SE25,NULL,NULL,534350,168450,NULL,NULL\n92750141,16/07/2014 21:18,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN EAVES OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000327,Cavendish,E09000017,Hillingdon,Ruislip,NULL,Hoylake Gardens,21401024,HA4,NULL,NULL,510950,187350,NULL,NULL\n92896141,17/07/2014 01:31,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN INJURED AND TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009403,Royal Hospital,E09000020,Kensington and Chelsea,Chelsea,NULL,Cliveden Place,21700106,SW1W,NULL,NULL,528150,178850,NULL,NULL\n93292141,17/07/2014 19:02,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,Rathcoole Gardens,21103786,N8,NULL,NULL,530850,188750,NULL,NULL\n94149141,18/07/2014 21:41,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Alston Road,20703857,N18,NULL,NULL,534750,192050,NULL,NULL\n94156141,18/07/2014 21:53,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524759,184460,524750,184450,51.54525513,-0.202222704\n94249141,19/07/2014 02:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eccles Road,22901397,SW11,NULL,NULL,527650,175350,NULL,NULL\n94395141,19/07/2014 10:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,Shaftesbury,E09000032,Wandsworth,Battersea,NULL,Eccles Road,22901397,SW11,NULL,NULL,527650,175350,NULL,NULL\n94491141,19/07/2014 14:21,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SHED,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011477,Purley Oaks & Riddlesdown,E09000008,Croydon,Purley,1.00021E+11,Kingsdown Avenue,20502175,CR2,531977,162485,531950,162450,51.3461313,-0.106378144\n94680141,19/07/2014 20:30,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Whyteville Road,22207952,E7,NULL,NULL,540850,184750,NULL,NULL\n94913141,20/07/2014 11:44,2014,2014/15,Special Service,1,1,295,295,BIRD WITH WING TRAPPED IN GRATE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Blenheim Gardens,22602125,SM6,NULL,NULL,529250,163450,NULL,NULL\n95200141,20/07/2014 20:29,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FISHING WIRE,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05009369,Clissold,E09000012,Hackney,Stoke Newington,NULL,Howard Road,NULL,N16,533175,185514,533150,185550,51.5528049,-0.080526559\n95423141,21/07/2014 09:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000436,Vassall,E09000022,Lambeth,Brixton,NULL,Hackford Road,NULL,SW9,530945,176760,530950,176750,51.4746571,-0.115921658\n95724141,21/07/2014 21:26,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO BIRD TRAPPED X WIRE IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000552,Surrey Docks,E09000028,Southwark,Deptford,NULL,South Sea Street,NULL,SE16,536580,179151,536550,179150,51.49481203,-0.033909999\n96253141,22/07/2014 21:45,2014,2014/15,Special Service,1,2,295,590,BIRD TRAPPED IN BRANCH OF TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Staines Road,NULL,TW14,509712,174112,509750,174150,51.45536735,-0.422339108\n96866141,24/07/2014 00:30,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN AND INJURED ONTO METAL BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Little Cottage Place,20803007,SE10,NULL,NULL,537950,177450,NULL,NULL\n97602141,25/07/2014 11:08,2014,2014/15,Special Service,1,2,295,590,KITTEN TRAPPED IN TOILET PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Groveway,19903732,RM8,NULL,NULL,547550,186050,NULL,NULL\n97738141,25/07/2014 14:23,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CLOSED IN ROOF AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Huntingdon Road,20703422,N9,NULL,NULL,535350,194050,NULL,NULL\n97885141,25/07/2014 19:19,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND FITTED WARDROBE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000289,Harrow on the Hill,E09000015,Harrow,Northolt,NULL,Brooke Avenue,21201201,HA2,NULL,NULL,514450,186450,NULL,NULL\n98163141,26/07/2014 11:18,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN SHOP,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000641,Marylebone High Street,E09000033,Westminster,Soho,1.00023E+11,Wigmore Street,8400283,W1U,528439,181292,528450,181250,51.51595986,-0.150336981\n98265141,26/07/2014 15:13,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED BETWEEN TWO GLASS WINDOWS,Bird,Person (mobile),Health spa/farm,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,Prince's,E09000022,Lambeth,Lambeth,1.00023E+11,Glasshouse Walk,21900610,SE11,530477,178337,530450,178350,51.48893727,-0.122074695\n98448141,26/07/2014 20:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000472,Village,E09000024,Merton,Wimbledon,NULL,Church Road,22101434,SW19,NULL,NULL,524250,171450,NULL,NULL\n98821141,27/07/2014 15:02,2014,2014/15,Special Service,2,3,295,885,ABANDONED CALL TO FIRE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05011253,Valentines,E09000026,Redbridge,Ilford,NULL,Wanstead Park Road,22303358,IG1,NULL,NULL,543050,186550,NULL,NULL\n98980141,27/07/2014 19:24,2014,2014/15,Special Service,1,1,295,295,ASSIST LAS WITH CAT ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Bagshot Street,22500129,SE17,NULL,NULL,533250,178150,NULL,NULL\n99011141,27/07/2014 19:54,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED CAT,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000290,Harrow Weald,E09000015,Harrow,Harrow,NULL,Courtenay Gardens,NULL,HA3,514302,190367,514350,190350,51.60055821,-0.351035131\n99187141,28/07/2014 01:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER DECKING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,Chingford Green,E09000031,Waltham Forest,Chingford,NULL,Richmond Road,22869850,E4,NULL,NULL,538750,194250,NULL,NULL\n99469141,28/07/2014 14:22,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED ON WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Constantine Road,20400194,NW3,NULL,NULL,527650,185650,NULL,NULL\n100132141,29/07/2014 19:31,2014,2014/15,Special Service,1,1,295,295,SMALL DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,NULL,Pole Hill Road,NULL,UB10,507991,182555,507950,182550,51.53158637,-0.444519058\n100471141,30/07/2014 13:30,2014,2014/15,Special Service,NULL,NULL,295,NULL,SMALL ANIMAL RESCUE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Davis Way,20101866,DA14,NULL,NULL,548050,170950,NULL,NULL\n100687141,30/07/2014 20:20,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN MUD,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000075,Erith,E09000004,Bexley,Erith,NULL,Macarthur Close,NULL,DA8,551159,178491,551150,178450,51.48517493,0.175666238\n100774141,30/07/2014 21:59,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NETTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011104,London Bridge & West Bermondsey,E09000028,Southwark,Dockhead,NULL,Grange Walk,22501108,SE1,NULL,NULL,533350,179250,NULL,NULL\n100997141,31/07/2014 11:28,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BROKEN ROOF SLATES,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000421,Ferndale,E09000022,Lambeth,Brixton,NULL,Gateley Road,21900590,SW9,NULL,NULL,530950,175750,NULL,NULL\n101256141,31/07/2014 22:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Road,NULL,SW6,524917,177055,524950,177050,51.47867,-0.202567731\n101689141,01/08/2014 18:20,2014,2014/15,Special Service,1,1,295,295,ASSISTING RSPCA WITH RESCUE OF GULL,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009300,Cordwainer,E09000001,City of London,Dowgate,1.00023E+11,Queen Street,8100440,EC4N,532441,181061,532450,181050,51.51296082,-0.092780905\n102115141,02/08/2014 14:38,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR AIR VENT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000174,Ealing Common,E09000009,Ealing,Acton,NULL,Carbery Avenue,NULL,W3,518993,179711,518950,179750,51.50381672,-0.286937912\n102339141,02/08/2014 22:10,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DEBRIS,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,Bowes,E09000010,Enfield,Edmonton,NULL,Beale Close,NULL,N13,NULL,NULL,531850,192150,NULL,NULL\n102362141,02/08/2014 22:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR MECHANISM,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000131,Cantelowes,E09000007,Camden,Kentish Town,NULL,St. Pancras Way,NULL,NW1,529166,184392,529150,184350,51.54365365,-0.138729019\n102543141,03/08/2014 08:38,2014,2014/15,Special Service,1,1,295,295,CAT AND WOMAN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000048,East Barnet,E09000003,Barnet,Barnet,NULL,Albert Road,20000520,EN4,NULL,NULL,526550,196250,NULL,NULL\n102973141,03/08/2014 21:44,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED IN CAR BONNETT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000536,Cathedrals,E09000028,Southwark,Dowgate,NULL,Scovell Road,NULL,SE1,532058,179616,532050,179650,51.50006462,-0.09883791\n103225141,04/08/2014 13:03,2014,2014/15,Special Service,1,1,295,295,ASSIST POLICE WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Police,Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000483,Forest Gate North,E09000025,Newham,Stratford,NULL,Bramall Close,NULL,E15,539556,185365,539550,185350,51.54992506,0.011391629\n103292141,04/08/2014 15:21,2014,2014/15,Special Service,1,1,295,295,FOX CUB WITH HEAD TRAPPED UNDER METAL FENCE,Fox,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000375,Holloway,E09000019,Islington,Holloway,NULL,Goodinge Close,NULL,N7,530076,184972,530050,184950,51.54865722,-0.125399402\n103429141,04/08/2014 19:07,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN POLE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000094,Kilburn,E09000005,Brent,North Kensington,NULL,Albert Road,20201431,NW6,NULL,NULL,524850,183250,NULL,NULL\n103785141,05/08/2014 10:18,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WASHING MACHINE AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000349,Chiswick Riverside,E09000018,Hounslow,Chiswick,NULL,Park Road,21501255,W4,NULL,NULL,520750,177850,NULL,NULL\n104450141,06/08/2014 13:15,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000060,Underhill,E09000003,Barnet,Barnet,NULL,Dollis Valley Way,20012860,EN5,NULL,NULL,524750,195450,NULL,NULL\n104586141,06/08/2014 19:31,2014,2014/15,Special Service,1,1,295,295,SEAGULL TRAPPED IN WIRE IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Highfield Avenue,NULL,NW9,520303,188850,520350,188850,51.58567605,-0.26494949\n104602141,06/08/2014 20:03,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED BEHIND PARTITION WALL,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,2E+11,Electric Avenue,21900507,SW9,531094,175398,531050,175350,51.46238263,-0.114281961\n104973141,07/08/2014 15:30,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Alexandra Drive,NULL,KT5,519226,166751,519250,166750,51.38728744,-0.287943853\n105034141,07/08/2014 17:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FENCING,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,100013832,Heathway,19900172,RM10,549059,184810,549050,184850,51.54250969,0.148114382\n105544141,08/08/2014 16:01,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009318,Blackwall & Cubitt Town,E09000030,Tower Hamlets,Millwall,NULL,Capstan Square,NULL,E14,538344,179582,538350,179550,51.49825632,-0.00834662\n105716141,08/08/2014 21:42,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN TELEPHONE WIRE OS,Bird,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000047,Coppetts,E09000003,Barnet,Southgate,NULL,Parkhurst Road,NULL,N11,528279,192451,528250,192450,51.61627965,-0.148571955\n106125141,09/08/2014 20:07,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION ON LEDGE,Dog,Police,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,St. George's,E09000019,Islington,Holloway,NULL,Carleton Road,21606415,N7,NULL,NULL,529950,185650,NULL,NULL\n106416141,10/08/2014 10:22,2014,2014/15,Special Service,1,1,295,295,DOG STUCK IN HOLE,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Natal Road,22303092,IG1,NULL,NULL,543850,185650,NULL,NULL\n106755141,10/08/2014 14:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Mansfield Avenue,21103590,N15,NULL,NULL,532750,189250,NULL,NULL\n106776141,10/08/2014 14:35,2014,2014/15,Special Service,1,1,295,295,BIRDS STUCK IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000548,Riverside,E09000028,Southwark,Dockhead,NULL,Spa Road,NULL,SE16,534217,179250,534250,179250,51.49626737,-0.067891225\n107101141,11/08/2014 06:17,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN IRON BAR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Cheam Common Road,22601362,KT4,NULL,NULL,522750,165750,NULL,NULL\n107329141,11/08/2014 17:18,2014,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Great Cambridge Road,20705613,EN1,NULL,NULL,534450,197550,NULL,NULL\n107607141,12/08/2014 10:04,2014,2014/15,Special Service,1,2,295,590,\"CAT STUCK ON ROOF, LEVEL ONE LINE OPS IMPLEMENTED\",Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,NULL,Kingsway Place,21610048,EC1R,NULL,NULL,531450,182350,NULL,NULL\n107914141,12/08/2014 21:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON PIGEON SPIKES,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000626,Tooting,E09000032,Wandsworth,Tooting,NULL,Tooting High Street,22905477,SW17,NULL,NULL,527250,171250,NULL,NULL\n108042141,13/08/2014 08:03,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Saxon Road,22701065,E3,NULL,NULL,536750,183250,NULL,NULL\n108075141,13/08/2014 10:32,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH RESCUE OF PIGEON WITH INJURY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000353,Hanworth,E09000018,Hounslow,Twickenham,NULL,Swan Road,21501100,TW13,NULL,NULL,512250,171650,NULL,NULL\n108276141,13/08/2014 18:13,2014,2014/15,Special Service,1,1,295,295,CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000069,Christchurch,E09000004,Bexley,Bexley,NULL,Freta Road,NULL,DA6,548576,174964,548550,174950,51.45416768,0.137011161\n108531141,14/08/2014 08:40,2014,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Prince of Wales Road,20400460,NW5,NULL,NULL,528650,184750,NULL,NULL\n109460141,15/08/2014 22:06,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RELEASE TRAPPED FOX,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.0002E+11,Palace Road,20300550,BR1,540623,169719,540650,169750,51.40906519,0.020545144\n109613141,16/08/2014 09:23,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Copley Close,NULL,W7,NULL,NULL,515950,181250,NULL,NULL\n109791141,16/08/2014 16:36,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN GRATE,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000271,Harringay,E09000014,Haringey,Hornsey,1.00024E+11,Frobisher Road,21103314,N8,531595,189258,531550,189250,51.58682118,-0.101902203\n109866141,16/08/2014 18:51,2014,2014/15,Special Service,1,1,295,295,DOG STUCK ON BALCONY - UNABLE TO REACH HIM FROM WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000137,Highgate,E09000007,Camden,Kentish Town,NULL,Carrol Close,20401317,NW5,NULL,NULL,528550,185650,NULL,NULL\n110127141,17/08/2014 09:50,2014,2014/15,Special Service,1,1,295,295,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,NULL,Shoreditch High Street,20900920,E1,NULL,NULL,533450,182450,NULL,NULL\n110581141,18/08/2014 11:02,2014,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,Hendon,E09000003,Barnet,Hendon,200059798,Heriot Road,20022000,NW4,523303,188862,523350,188850,51.58513682,-0.221665158\n111062141,19/08/2014 12:27,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND PANELLING IN KITCHEN,Cat,Police,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,Slade Green & Northend,E09000004,Bexley,Bexley,NULL,Sun Court,20101396,DA8,NULL,NULL,552050,176350,NULL,NULL\n111505141,20/08/2014 11:32,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BEHIND FENCING,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000610,Balham,E09000032,Wandsworth,Tooting,NULL,Bolingbroke Grove,NULL,SW11,527729,173772,527750,173750,51.4485383,-0.163280721\n112083141,21/08/2014 15:39,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED INSIDE DERELICT BUILDING,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Bethnal Green,NULL,Mile End Road,NULL,E3,NULL,NULL,536450,182550,NULL,NULL\n112334141,22/08/2014 04:13,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000223,Kidbrooke with Hornfair,E09000011,Greenwich,East Greenwich,NULL,Portway Gardens,20801198,SE18,NULL,NULL,541850,176950,NULL,NULL\n112376141,22/08/2014 08:25,2014,2014/15,Special Service,1,3,295,885,COW  IN WATER,Cow,Police,River/canal,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Alma Road,NULL,EN3,535969,196414,535950,196450,51.65008701,-0.036030991\n112427141,22/08/2014 11:11,2014,2014/15,Special Service,1,1,295,295,SEAGULL TRAPPED IN NETTING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000366,Barnsbury,E09000019,Islington,Islington,10010438509,Upper Street,21606199,N1,531356,183589,531350,183550,51.53593234,-0.107465554\n112452141,22/08/2014 12:17,2014,2014/15,Special Service,1,3,295,885,CAT TRAPPED BETWEEN WALLS,Cat,Other FRS,Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000199,Enfield Lock,E09000010,Enfield,Enfield,207035723,Park Road,20702363,EN3,536440,199591,536450,199550,51.67852174,-0.027988234\n112475141,22/08/2014 13:09,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BARBED WIRE ON A GARAGE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000296,Marlborough,E09000015,Harrow,Stanmore,1.00021E+11,Warham Road,21201711,HA3,515841,189985,515850,189950,51.59681054,-0.328950202\n112815141,23/08/2014 07:21,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Ospringe Road,20400232,NW5,NULL,NULL,529250,185550,NULL,NULL\n112896141,23/08/2014 11:52,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK ON CHIMNEY POT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000346,Bedfont,E09000018,Hounslow,Feltham,NULL,Imperial Road,21500636,TW14,NULL,NULL,509450,173750,NULL,NULL\n113122141,23/08/2014 19:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000471,Trinity,E09000024,Merton,Wimbledon,NULL,Wycliffe Road,22106897,SW19,NULL,NULL,525950,170550,NULL,NULL\n113346141,24/08/2014 09:38,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER CAR BONNET,Cat,Police,Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000116,Crystal Palace,E09000006,Bromley,Beckenham,2.00001E+11,Ledrington Road,20302181,SE19,534453,170657,534450,170650,51.41898943,-0.067757494\n113559141,24/08/2014 17:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO SHEDS,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000221,Glyndon,E09000011,Greenwich,Plumstead,1.00021E+11,Leghorn Road,20800902,SE18,544932,178213,544950,178250,51.48430567,0.085934391\n113850141,25/08/2014 11:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011097,Champion Hill,E09000028,Southwark,Peckham,NULL,Champion Hill,22500477,SE5,NULL,NULL,532850,175850,NULL,NULL\n114188141,25/08/2014 21:07,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Hackney Road,20900479,E2,NULL,NULL,534450,183550,NULL,NULL\n114759141,27/08/2014 12:23,2014,2014/15,Special Service,1,1,295,295,PUPPY STUCK BETWEEN SHED AND BRICK WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000085,Alperton,E09000005,Brent,Wembley,NULL,Mount Pleasant,20202568,HA0,NULL,NULL,518950,184050,NULL,NULL\n114787141,27/08/2014 13:48,2014,2014/15,Special Service,1,1,295,295,FUNERAL HORSE COLLAPSED IN ROADWAY,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000315,Hylands,E09000016,Havering,Hornchurch,NULL,Hornchurch Road,NULL,RM11,552602,187428,552650,187450,51.56508645,0.200300004\n114824141,27/08/2014 15:43,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000488,Manor Park,E09000025,Newham,Stratford,46015474,Claremont Road,22207659,E7,541286,185317,541250,185350,51.5490632,0.036306148\n114829141,27/08/2014 16:00,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011468,Fairfield,E09000008,Croydon,Croydon,NULL,St. Andrews Road,20502337,CR0,NULL,NULL,532250,164850,NULL,NULL\n115716141,29/08/2014 14:55,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN SOFA BY LEG,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000322,Squirrel's Heath,E09000016,Havering,Harold Hill,NULL,Ferguson Avenue,21301199,RM2,NULL,NULL,553750,190150,NULL,NULL\n116082141,30/08/2014 10:45,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000489,Plaistow North,E09000025,Newham,Plaistow,NULL,Queens Road West,NULL,E13,540684,183530,540650,183550,51.53315589,0.026916399\n116198141,30/08/2014 16:28,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000118,Farnborough and Crofton,E09000006,Bromley,Orpington,NULL,Lovibonds Avenue,20303011,BR6,NULL,NULL,544050,165350,NULL,NULL\n116515141,31/08/2014 11:00,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,NULL,Heathcote Grove,22844200,E4,NULL,NULL,538150,193650,NULL,NULL\n117127141,01/09/2014 17:50,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH SEAGULL WEDGED IN GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Lincoln Road,20702802,EN1,NULL,NULL,533550,196050,NULL,NULL\n117151141,01/09/2014 18:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NETTING ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dunkery Road,20302283,SE9,NULL,NULL,542150,172150,NULL,NULL\n117156141,01/09/2014 18:53,2014,2014/15,Special Service,1,1,295,295,PIGEON TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Harewood Terrace,20600828,UB2,NULL,NULL,512950,178850,NULL,NULL\n118469141,04/09/2014 18:20,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,Southwark,Dockhead,2.00003E+11,Hankey Place,22501189,SE1,532753,179603,532750,179650,51.49978517,-0.088835919\n118534141,04/09/2014 19:54,2014,2014/15,Special Service,1,2,295,590,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Stamford Road,21103923,N15,NULL,NULL,534150,189050,NULL,NULL\n119056141,05/09/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN BARBED WIRE FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Granby Terrace,NULL,NW1,529127,183032,529150,183050,51.53144039,-0.139789701\n119359141,06/09/2014 13:20,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED AT TOP OF TELEGRAPH POLE,Bird,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Surrey Road,NULL,IG11,545075,184010,545050,184050,51.53635848,0.090375544\n119540141,06/09/2014 20:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000050,Edgware,E09000003,Barnet,Mill Hill,NULL,Station Road,20040860,HA8,NULL,NULL,519350,191850,NULL,NULL\n119654141,07/09/2014 02:14,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH DEER TRAPPED IN FENCE,Deer,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000590,Cann Hall,E09000031,Waltham Forest,Leytonstone,1.00023E+11,Cann Hall Road,22822050,E11,539733,186150,539750,186150,51.55693516,0.014254251\n120105141,08/09/2014 00:23,2014,2014/15,Special Service,1,1,295,295,DOG WITH FOOT TRAPPED IN ELECTRIC FAN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Oban Street,22700888,E14,NULL,NULL,538950,181450,NULL,NULL\n120229141,08/09/2014 08:51,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,Larkhall,E09000022,Lambeth,Brixton,1.00023E+11,Landor Road,21900834,SW9,530194,175679,530150,175650,51.46511551,-0.127126674\n120480141,08/09/2014 17:10,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009336,Whitechapel,E09000030,Tower Hamlets,Shadwell,6172618,Martineau Square,22702656,E1,534508,180991,534550,180950,51.5118436,-0.063038009\n120635141,08/09/2014 21:52,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO PIGEON TRAPPED,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000570,Wallington South,E09000029,Sutton,Wallington,NULL,Woodcote Road,NULL,SM6,528891,163965,528850,163950,51.36013926,-0.150128217\n120810141,09/09/2014 10:21,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Kensington,10033534401,Kensington Gardens,8400731,W2,526760,180006,526750,180050,51.50478133,-0.174983574\n121283141,10/09/2014 10:29,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK IN RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Bath Street,21604222,EC1V,NULL,NULL,532550,182550,NULL,NULL\n121790141,11/09/2014 12:31,2014,2014/15,Special Service,1,2,295,590,FOX WITH HEAD STUCK BETWEEN POSTS,Fox,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,128002630,Tolworth Broadway,21800172,KT6,519664,166019,519650,166050,51.38061627,-0.281898378\n122055141,12/09/2014 01:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED KITTEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000617,Latchmere,E09000032,Wandsworth,Battersea,1.00023E+11,Rowditch Lane,22904454,SW11,528180,176406,528150,176450,51.47210829,-0.155840698\n122157141,12/09/2014 10:20,2014,2014/15,Special Service,1,1,295,295,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Derby Avenue,NULL,N12,526287,192240,526250,192250,51.61483362,-0.177403765\n122170141,12/09/2014 10:50,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011474,Old Coulsdon,E09000008,Croydon,Purley,NULL,Placehouse Lane,20500312,CR5,NULL,NULL,530950,157950,NULL,NULL\n122305141,12/09/2014 16:14,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN DOWN WELL,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,1.00022E+11,Trelawn Road,21901397,SW2,531267,174776,531250,174750,51.45675274,-0.112023733\n122355141,12/09/2014 17:35,2014,2014/15,Special Service,1,1,295,295,DOG WITH LEG TRAPPED IN CAR DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000359,Hounslow Heath,E09000018,Hounslow,Feltham,NULL,Raglan Close,NULL,TW4,512486,174793,512450,174750,51.4609444,-0.382214513\n122493141,12/09/2014 22:43,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Leyspring Road,22851600,E11,NULL,NULL,539950,187350,NULL,NULL\n122597141,13/09/2014 08:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT WITH LEG TRAPPED IN ELECTRIC GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000103,Welsh Harp,E09000005,Brent,Willesden,NULL,North Circular Road,20201638,NW10,NULL,NULL,521250,185550,NULL,NULL\n122601141,13/09/2014 08:24,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH BIRD WITH LEG TRAPPED IN FLOODLIGHT,Bird,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011465,Broad Green,E09000008,Croydon,Croydon,2.00001E+11,Volta Way,20502799,CR0,530664,166207,530650,166250,51.37988353,-0.123854075\n122684141,13/09/2014 11:31,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALL AND LAMP POST,Cat,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000039,Thames,E09000002,Barking and Dagenham,Barking,NULL,Farr Avenue,NULL,IG11,546056,183218,546050,183250,51.52898932,0.104181663\n122716141,13/09/2014 12:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000365,Turnham Green,E09000018,Hounslow,Chiswick,2.00003E+11,Marlborough Road,21500739,W4,520105,178372,520150,178350,51.49154747,-0.271379476\n122768141,13/09/2014 14:35,2014,2014/15,Special Service,1,3,295,885,FOX  TRAPPED BETWEEN KIOSK & WALL,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Westminster Bridge Road,NULL,SE1,530634,179886,530650,179850,51.50282162,-0.119241565\n122821141,13/09/2014 16:19,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,Preston,E09000005,Brent,Wembley,NULL,Elliot Close,20202339,HA9,NULL,NULL,519050,186350,NULL,NULL\n122915141,13/09/2014 19:07,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000282,West Green,E09000014,Haringey,Hornsey,NULL,Carlingford Road,21103058,N15,NULL,NULL,531750,189550,NULL,NULL\n123179141,14/09/2014 10:16,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000367,Bunhill,E09000019,Islington,Shoreditch,NULL,Dance Square,21610129,EC1V,NULL,NULL,532050,182450,NULL,NULL\n123198141,14/09/2014 11:20,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,NULL,Holmewood Gardens,21901553,SW2,NULL,NULL,530650,173650,NULL,NULL\n123443141,14/09/2014 18:50,2014,2014/15,Special Service,2,3,295,885,CAT TRAPPED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000361,Hounslow West,E09000018,Hounslow,Feltham,NULL,Manor Avenue,NULL,TW4,511805,176060,511850,176050,51.47246717,-0.391612636\n123770141,15/09/2014 13:48,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA ON SCENE WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,St. Pancras and Somers Town,E09000007,Camden,Euston,NULL,Royal College Street,NULL,NW1,529494,183538,529450,183550,51.53590379,-0.134315741\n123910141,15/09/2014 19:05,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Argyle Road,20001240,N12,NULL,NULL,525550,192150,NULL,NULL\n124514141,16/09/2014 21:33,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Atherton Mews,22207603,E7,NULL,NULL,540050,184950,NULL,NULL\n124538141,16/09/2014 22:55,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000402,Beverley,E09000021,Kingston upon Thames,New Malden,NULL,Kingston Road,21800563,KT3,NULL,NULL,521250,168250,NULL,NULL\n125508141,18/09/2014 22:41,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RETRIEVE KITTEN STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Ravensfield Close,19900893,RM9,NULL,NULL,547950,185850,NULL,NULL\n125741141,19/09/2014 11:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000121,Mottingham and Chislehurst North,E09000006,Bromley,Eltham,NULL,Dunkery Road,20302283,SE9,NULL,NULL,542150,172150,NULL,NULL\n126118141,19/09/2014 17:53,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000265,Wormholt and White City,E09000013,Hammersmith and Fulham,Hammersmith,NULL,White City Estate,21000077,W12,NULL,NULL,522850,180950,NULL,NULL\n126177141,19/09/2014 19:17,2014,2014/15,Special Service,1,1,295,295,KITTENS HANGING OFF BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Roberts Place,19901083,RM10,NULL,NULL,549450,184750,NULL,NULL\n126251141,19/09/2014 21:29,2014,2014/15,Special Service,1,2,295,590,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,Green Street West,E09000025,Newham,Stratford,NULL,Ismailia Road,22207763,E7,NULL,NULL,540650,184450,NULL,NULL\n126265141,19/09/2014 21:49,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON FENCE IN PARK,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,Tokyngton,E09000005,Brent,Wembley,NULL,Oakington Manor Drive,NULL,HA9,519491,185179,519450,185150,51.55285564,-0.277912146\n126379141,20/09/2014 03:34,2014,2014/15,Special Service,1,1,295,295,RETRIEVAL OF INJURED KITTEN,Cat,Person (mobile),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Glenarm Road,20901685,E5,NULL,NULL,535450,185550,NULL,NULL\n126665141,20/09/2014 15:34,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,Lee Green,1.00021E+11,Bennett Park,20800150,SE3,539734,175991,539750,175950,51.4656458,0.010244249\n127090141,21/09/2014 12:18,2014,2014/15,Special Service,1,1,295,295,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Christchurch Avenue,20008520,N12,NULL,NULL,526150,191650,NULL,NULL\n127279141,21/09/2014 18:43,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,West Finchley,E09000003,Barnet,Finchley,NULL,Strathmore Gardens,20041220,N3,NULL,NULL,525650,190550,NULL,NULL\n127315141,21/09/2014 19:34,2014,2014/15,Special Service,1,1,295,295,CAT AND KITTENS STUCK BETWEEN WALLS,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Galahad Road,NULL,N9,534263,193553,534250,193550,51.62478748,-0.061772591\n128258141,23/09/2014 18:39,2014,2014/15,Special Service,2,3,295,885,Redacted,Bird,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Heathrow,NULL,Bath Road,NULL,UB7,505038,176823,505050,176850,51.48062175,-0.488780768\n128360141,23/09/2014 22:02,2014,2014/15,Special Service,1,1,295,295,CAT STUCK TRAPPED BY WIRE,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,Hackney Central,E09000012,Hackney,Homerton,NULL,Wilton Way,NULL,E8,534659,184750,534650,184750,51.54558702,-0.059427081\n128887141,25/09/2014 06:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CHEST OF DRAWERS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,St George's,E09000019,Islington,Holloway,NULL,Holloway Road,21606449,N19,NULL,NULL,530050,186450,NULL,NULL\n129342141,26/09/2014 08:24,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED INSIDE VAN ENGINE COMPARTMENT,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,100039830,Crown Street,19900463,RM10,550005,184554,550050,184550,51.53995913,0.16163693\n130084141,27/09/2014 17:51,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,10009142024,Orange Grove,22863675,E11,539005,186360,539050,186350,51.55900204,0.003843078\n130124141,27/09/2014 18:42,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK ON NETTING OUTSIDE WINDOW OF FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009326,Limehouse,E09000030,Tower Hamlets,Poplar,NULL,Gill Street,22700537,E14,NULL,NULL,536950,180850,NULL,NULL\n130198141,27/09/2014 20:23,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN COURTYARD,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009384,Stamford Hill West,E09000012,Hackney,Stoke Newington,NULL,Amhurst Park,20900061,N16,NULL,NULL,533250,187950,NULL,NULL\n130657141,28/09/2014 18:46,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000618,Nightingale,E09000032,Wandsworth,Tooting,1.00023E+11,Hendham Road,22902331,SW17,527529,172805,527550,172850,51.43989269,-0.166505364\n131522141,30/09/2014 15:34,2014,2014/15,Special Service,1,2,295,590,KITTEN TRAPPED INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000161,Selhurst,E09000008,Croydon,Croydon,NULL,Holmesdale Road,NULL,CR0,532808,167838,532850,167850,51.39404405,-0.092455489\n131805141,01/10/2014 07:56,2014,2014/15,Special Service,1,1,295,295,ASSIST RCPCA OFFICER WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000122,Orpington,E09000006,Bromley,Orpington,1.0002E+11,The Avenue,20300979,BR6,545859,165615,545850,165650,51.37086815,0.094092498\n132003141,01/10/2014 16:52,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND FENCE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000308,Elm Park,E09000016,Havering,Hornchurch,1.00021E+11,Bader Way,21300706,RM13,552435,184672,552450,184650,51.54036906,0.1967002\n132116141,01/10/2014 21:02,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,22837350,E17,NULL,NULL,536450,189550,NULL,NULL\n132281141,02/10/2014 08:13,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER WHEEL ARCH OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,NULL,TW11,515722,171039,515750,171050,51.42655273,-0.336881314\n132359141,02/10/2014 11:25,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT TRAPPED BEHIND WALL,Cat,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Church Street,NULL,N9,534266,193590,534250,193550,51.62511925,-0.061715101\n132910141,03/10/2014 14:24,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000400,Alexandra,E09000021,Kingston upon Thames,Surbiton,NULL,Southwood Drive,NULL,KT5,520461,166774,520450,166750,51.38723364,-0.270195939\n133149141,04/10/2014 00:23,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,New Cross,E09000023,Lewisham,New Cross,NULL,Casella Road,22000200,SE14,NULL,NULL,535650,177050,NULL,NULL\n133222141,04/10/2014 06:54,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,200198007,Cricklewood Lane,20011060,NW2,524990,186430,524950,186450,51.56290876,-0.198193878\n133863141,05/10/2014 14:00,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN WINDOW,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000357,Heston West,E09000018,Hounslow,Feltham,NULL,Browning Way,21500168,TW5,NULL,NULL,511750,176750,NULL,NULL\n134352141,06/10/2014 15:25,2014,2014/15,Special Service,1,1,295,295,SQUIRREL TRAPPED ON SATELLITE DISH,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000620,Queenstown,E09000032,Wandsworth,Clapham,NULL,Radcliffe Path,22906571,SW8,NULL,NULL,528950,176150,NULL,NULL\n134701141,07/10/2014 10:47,2014,2014/15,Special Service,1,1,295,295,ASSIST RCPCA OFFICER WITH CAT STUCK INSIDE PART OF CAR ENGINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000262,Sands End,E09000013,Hammersmith and Fulham,Fulham,NULL,Philpot Square,21001054,SW6,NULL,NULL,525650,175650,NULL,NULL\n134953141,07/10/2014 19:23,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000352,Feltham West,E09000018,Hounslow,Feltham,NULL,High Street,21500609,TW13,NULL,NULL,510450,172950,NULL,NULL\n135254141,08/10/2014 14:42,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN LOFT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011238,Churchfields,E09000026,Redbridge,Woodford,NULL,Lilian Gardens,22306099,IG8,NULL,NULL,540850,190850,NULL,NULL\n135605141,09/10/2014 13:00,2014,2014/15,Special Service,1,1,295,295,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000318,Rainham and Wennington,E09000016,Havering,Wennington,NULL,Brights Avenue,21300722,RM13,NULL,NULL,552950,182250,NULL,NULL\n135664141,09/10/2014 15:18,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN IRON GATE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Park Road,21103726,N8,NULL,NULL,530050,188550,NULL,NULL\n135862141,10/10/2014 01:28,2014,2014/15,Special Service,1,1,295,295,DOG WITH PAW TRAPPED IN METAL CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000568,The Wrythe,E09000029,Sutton,Wallington,NULL,Arlington Drive,22600274,SM5,NULL,NULL,527750,165850,NULL,NULL\n136481141,11/10/2014 13:31,2014,2014/15,Special Service,1,2,295,590,BUZZARD STUCK IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,East Greenwich,NULL,Samuel Street,NULL,SE18,542501,178805,542550,178850,51.49024251,0.051185802\n136553141,11/10/2014 16:10,2014,2014/15,Special Service,1,1,295,295,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000474,Wimbledon Park,E09000024,Merton,Wimbledon,NULL,Plough Lane,NULL,SW19,526071,171477,526050,171450,51.42828341,-0.18794424\n136581141,11/10/2014 16:57,2014,2014/15,Special Service,1,1,295,295,Redacted,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000643,Regent's Park,E09000033,Westminster,Paddington,NULL,Regents Park,NULL,NW1,527791,182549,527750,182550,51.52740331,-0.159215045\n136921141,12/10/2014 11:35,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BY LEG,Fox,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,NULL,Wincanton Crescent,20601917,UB5,NULL,NULL,513550,185250,NULL,NULL\n137435141,13/10/2014 13:25,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT WEDGED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Livermere Road,20900626,E8,NULL,NULL,533750,183950,NULL,NULL\n138812141,16/10/2014 08:57,2014,2014/15,Special Service,1,1,295,295,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Chestnut Avenue,20303031,BR4,NULL,NULL,539450,164550,NULL,NULL\n139077141,16/10/2014 21:39,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN ONTO UNOCCUPIED BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009393,Courtfield,E09000020,Kensington and Chelsea,Kensington,NULL,Courtfield Road,21700125,SW7,NULL,NULL,526150,178750,NULL,NULL\n140335141,19/10/2014 15:57,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,Boleyn,E09000025,Newham,East Ham,NULL,Castle Street,22200447,E6,NULL,NULL,541550,183350,NULL,NULL\n140540141,19/10/2014 23:14,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER GRATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Prince Road,20501314,SE25,NULL,NULL,533150,167950,NULL,NULL\n140635141,20/10/2014 08:16,2014,2014/15,Special Service,1,1,295,295,CATS TRAPPED IN GRATE,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Prince Road,20501314,SE25,NULL,NULL,533150,167950,NULL,NULL\n140731141,20/10/2014 10:59,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011469,Kenley,E09000008,Croydon,Purley,NULL,Kenley Lane,NULL,CR8,532983,158885,532950,158850,51.31354411,-0.093283862\n140851141,20/10/2014 16:23,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000555,Beddington North,E09000029,Sutton,Wallington,5870094344,Rectory Lane,22602563,SM6,529459,165019,529450,165050,51.36948273,-0.141591317\n141817141,22/10/2014 17:39,2014,2014/15,Special Service,1,2,295,590,HORSE TRAPPED IN GATE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011229,St. Mary's & St. James,E09000004,Bexley,Bexley,NULL,Monterey Close,NULL,DA5,550293,172604,550250,172650,51.43250988,0.160702353\n142056141,23/10/2014 07:17,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED BETWEEN CEILING AND FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,St Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Telfords Yard,22701933,E1W,NULL,NULL,534350,180650,NULL,NULL\n142139141,23/10/2014 10:52,2014,2014/15,Special Service,1,1,295,295,INJURED SWAN IN ROADWAY,Bird,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Forest Road,NULL,E17,535764,189396,535750,189350,51.58707244,-0.041709593\n142999141,24/10/2014 19:39,2014,2014/15,Special Service,1,2,295,590,ASSIST RSPCA TO RELEASE FOX STUCK IN STORM DRAIN,Fox,Person (land line),Road surface/pavement,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05011248,Mayfield,E09000026,Redbridge,Ilford,NULL,Mortlake Road,NULL,IG1,545267,185985,545250,185950,51.55405528,0.0939562\n143184141,25/10/2014 07:46,2014,2014/15,Special Service,1,1,295,295,FOX WITH LEG TRAPPED IN GATE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000298,Pinner South,E09000015,Harrow,Harrow,NULL,East Towers,21201853,HA5,NULL,NULL,511750,188550,NULL,NULL\n145232141,29/10/2014 08:07,2014,2014/15,Special Service,1,1,295,295,HAMPSTER TRAPPED IN FIRE PLACE,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,Forest,E09000031,Waltham Forest,Leytonstone,NULL,Forest Drive East,22836950,E11,NULL,NULL,538750,187950,NULL,NULL\n145391141,29/10/2014 16:16,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000196,Cockfosters,E09000010,Enfield,Southgate,207123768,Prince George Avenue,20702906,N14,529644,195837,529650,195850,51.64639539,-0.127614458\n145430141,29/10/2014 17:32,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL STUK IN LOFT,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Roma Read Close,22906576,SW15,NULL,NULL,522550,173550,NULL,NULL\n145560141,29/10/2014 21:39,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN NEIGHBOURING GARDEN,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,Redbridge,Ilford,NULL,Hampton Road,22302910,IG1,NULL,NULL,544250,185750,NULL,NULL\n145593141,30/10/2014 00:01,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOFING AREA,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000268,Bruce Grove,E09000014,Haringey,Tottenham,1.00021E+11,Bruce Grove,21104298,N17,533431,190559,533450,190550,51.59808047,-0.074923187\n145952141,30/10/2014 19:21,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Dalrymple Close,20701473,N14,NULL,NULL,529750,195150,NULL,NULL\n148995141,05/11/2014 10:30,2014,2014/15,Special Service,1,2,295,590,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Reginald Road,NULL,E7,540059,184504,540050,184550,51.54206362,0.018298833\n149708141,06/11/2014 14:17,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000378,St George's,E09000019,Islington,Holloway,NULL,Tufnell Park Road,NULL,N7,NULL,NULL,529750,185950,NULL,NULL\n149863141,06/11/2014 19:36,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOOR BOARDS IN BATHROOM,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,Croydon,Woodside,NULL,Clifton Road,20500797,SE25,NULL,NULL,532950,168250,NULL,NULL\n150226141,07/11/2014 14:32,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN SHED AND BRICK WALL,Fox,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011234,Aldborough,E09000026,Redbridge,Ilford,1.00022E+11,Ashurst Drive,22302589,IG2,544100,188760,544150,188750,51.57928902,0.07827197\n151090141,09/11/2014 09:51,2014,2014/15,Special Service,1,1,295,295,CAT IN DISTRESS ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,NULL,Babbacombe Road,20302903,BR1,NULL,NULL,540450,169850,NULL,NULL\n151144141,09/11/2014 11:49,2014,2014/15,Special Service,1,4,295,1180,HORSE IN DITCH,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,1.00021E+11,Wingletye Lane,21300661,RM11,555476,188420,555450,188450,51.5732146,0.242169101\n151174141,09/11/2014 12:56,2014,2014/15,Special Service,1,1,295,295,FOX TRAPPED BETWEEN TWO WALLS,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000102,Tokyngton,E09000005,Brent,Wembley,202149501,Engineers Way,20201887,HA9,519480,185827,519450,185850,51.55868192,-0.277850906\n152103141,11/11/2014 14:55,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,Ladywell,E09000023,Lewisham,Lewisham,NULL,Algernon Road,22001161,SE13,NULL,NULL,537650,174950,NULL,NULL\n152973141,13/11/2014 15:52,2014,2014/15,Special Service,1,1,295,295,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,NULL,TW11,515538,171427,515550,171450,51.43007749,-0.339400287\n153140141,14/11/2014 00:45,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN DOWN MANHOLE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000219,Eltham South,E09000011,Greenwich,Eltham,NULL,Restons Crescent,NULL,SE9,544942,174303,544950,174350,51.44916937,0.084475905\n153277141,14/11/2014 09:48,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,Streatham South,E09000022,Lambeth,Norbury,NULL,Heybridge Avenue,21900709,SW16,NULL,NULL,530450,170450,NULL,NULL\n153384141,14/11/2014 14:46,2014,2014/15,Special Service,1,1,295,295,PIGEON STUCK UP CHIMNEY,Bird,Person (land line),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Trinity Road,22906604,SW17,NULL,NULL,527750,172650,NULL,NULL\n154054141,15/11/2014 20:42,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000560,Cheam,E09000029,Sutton,Sutton,NULL,Peaches Close,22603252,SM2,NULL,NULL,524150,163050,NULL,NULL\n154321141,16/11/2014 13:52,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN RAILINGS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Albacore Crescent,NULL,SE13,537826,174423,537850,174450,51.45202256,-0.017815992\n155135141,18/11/2014 14:48,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000595,Forest,E09000031,Waltham Forest,Leyton,NULL,Belmont Park Road,22815400,E10,NULL,NULL,538050,188250,NULL,NULL\n155308141,18/11/2014 21:08,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000430,Streatham Hill,E09000022,Lambeth,West Norwood,NULL,Barcombe Avenue,21900139,SW2,NULL,NULL,530450,172750,NULL,NULL\n155488141,19/11/2014 09:07,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000602,Larkswood,E09000031,Waltham Forest,Chingford,1.00023E+11,Larkswood Road,22849800,E4,537700,192624,537750,192650,51.61560909,-0.012516181\n156690141,21/11/2014 23:21,2014,2014/15,Special Service,1,1,295,295,DOGS MOUTH TRAPPED IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Stoke Newington,NULL,St. Jude Street,21603842,N16,NULL,NULL,533250,185050,NULL,NULL\n156770141,22/11/2014 05:28,2014,2014/15,Special Service,1,1,295,295,DOG FALLEN FROM BALCONY,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,NULL,St. Charles Square,21701008,W10,NULL,NULL,524050,181650,NULL,NULL\n157399141,23/11/2014 11:50,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN THORN BUSHES,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000220,Eltham West,E09000011,Greenwich,Eltham,NULL,Elmbrook Gardens,NULL,SE9,542417,175322,542450,175350,51.4589662,0.048573871\n157478141,23/11/2014 14:18,2014,2014/15,Special Service,1,1,295,295,DOG IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000451,Rushey Green,E09000023,Lewisham,Forest Hill,NULL,Catford Bridge,NULL,SE6,537332,173573,537350,173550,51.44450408,-0.025250472\n158868141,26/11/2014 13:08,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,Mildmay,E09000019,Islington,Islington,NULL,King Henrys Walk,21605227,N1,NULL,NULL,533150,184950,NULL,NULL\n159728141,28/11/2014 12:04,2014,2014/15,Special Service,1,1,295,295,CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,Tudor,E09000021,Kingston upon Thames,Kingston,128008767,Tudor Drive,21800983,KT2,518401,171071,518450,171050,51.42628724,-0.298354422\n159900141,28/11/2014 19:33,2014,2014/15,Special Service,1,1,295,295,CAT IN PRECARIOUS POSITION,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Devonshire Drive,NULL,SE10,NULL,NULL,537850,176950,NULL,NULL\n160205141,29/11/2014 13:19,2014,2014/15,Special Service,1,1,295,295,BIRD WITH WING TRAPPED IN WIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011217,Barnehurst,E09000004,Bexley,Erith,NULL,Stuart Mantle Way,20101365,DA8,NULL,NULL,551050,177150,NULL,NULL\n161277141,01/12/2014 19:49,2014,2014/15,Special Service,1,1,295,295,INJURED CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000304,Wealdstone,E09000015,Harrow,Stanmore,NULL,The Meadow Way,21202458,HA3,NULL,NULL,515150,190550,NULL,NULL\n161638141,02/12/2014 18:01,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED  IN ROOM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000283,White Hart Lane,E09000014,Haringey,Tottenham,NULL,Daubeney Gardens,21104439,N17,NULL,NULL,532350,191250,NULL,NULL\n162376141,04/12/2014 11:02,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000057,Mill Hill,E09000003,Barnet,Mill Hill,NULL,Watford Way,20044760,NW7,NULL,NULL,522250,190750,NULL,NULL\n162464141,04/12/2014 15:01,2014,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE - CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000369,Canonbury,E09000019,Islington,Islington,NULL,Downham Road,21604726,N1,NULL,NULL,532850,184050,NULL,NULL\n162556141,04/12/2014 19:06,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN GATE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,West End,E09000033,Westminster,Soho,NULL,Green Street,8401383,W1K,NULL,NULL,528150,180950,NULL,NULL\n162799141,05/12/2014 09:16,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED BEHIND WALL IN KITCHEN,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000139,Kentish Town,E09000007,Camden,Kentish Town,NULL,Leverton Street,20400230,NW5,NULL,NULL,529050,185550,NULL,NULL\n163419141,06/12/2014 14:25,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009404,St. Helen's,E09000020,Kensington and Chelsea,North Kensington,217052368,Latimer Road,21700930,W10,523195,181527,523150,181550,51.51923862,-0.22579147\n164334141,08/12/2014 13:14,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RESCUE BIRD,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000027,Alibon,E09000002,Barking and Dagenham,Dagenham,NULL,Spurling Road,19900710,RM9,NULL,NULL,548850,184850,NULL,NULL\n164352141,08/12/2014 14:13,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Southall,NULL,Aspen Lane,NULL,UB5,512356,182603,512350,182650,51.53116669,-0.381600795\n165190141,10/12/2014 11:41,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,NULL,Patriot Square,22700922,E2,NULL,NULL,535050,183150,NULL,NULL\n165199141,10/12/2014 11:58,2014,2014/15,Special Service,1,1,295,295,CAT STUCK IN PIPE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000596,Grove Green,E09000031,Waltham Forest,Leyton,NULL,Murchison Road,22860300,E10,NULL,NULL,538350,186950,NULL,NULL\n165307141,10/12/2014 17:10,2014,2014/15,Special Service,1,1,295,295,CAT UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,Bridge,E09000026,Redbridge,Woodford,NULL,Finchingfield Avenue,22304988,IG8,NULL,NULL,541450,191350,NULL,NULL\n165659141,11/12/2014 12:11,2014,2014/15,Special Service,1,1,295,295,PIDGEON TRAPPED IN NETTING,Bird,Person (land line),Petrol station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000371,Finsbury Park,E09000019,Islington,Holloway,5300050471,Hornsey Road,21603387,N7,530646,186415,530650,186450,51.56149336,-0.116647877\n166062141,12/12/2014 09:49,2014,2014/15,Special Service,1,1,295,295,ANIMAL STUCK BEHIND CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Hillingdon Road,21400984,UB10,NULL,NULL,505950,183350,NULL,NULL\n167663141,15/12/2014 19:00,2014,2014/15,Special Service,2,1,295,295,DOG WITH HEAD TRAPPED IN DOOR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Mount Avenue,20601209,W5,NULL,NULL,517350,181650,NULL,NULL\n167894141,16/12/2014 11:31,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,Bromley North,E09000030,Tower Hamlets,Bethnal Green,NULL,British Street,NULL,E3,536896,182433,536850,182450,51.5242287,-0.028086894\n168459141,17/12/2014 17:09,2014,2014/15,Special Service,1,1,295,295,ASSIST  RSPCA WITH PIGEON TRAPPED  BEHIND SIGN,Bird,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,Hounslow Central,E09000018,Hounslow,Heston,1.00023E+11,High Street,21500610,TW3,513937,175752,513950,175750,51.46927381,-0.361028613\n168710141,18/12/2014 11:25,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED BEHIND SUNBED,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000220,Eltham West,E09000011,Greenwich,Lee Green,1.00021E+11,Eltham Green Road,20800525,SE9,541335,175223,541350,175250,51.45834757,0.032970349\n169524141,20/12/2014 08:39,2014,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Ravensbury Road,22904209,SW18,NULL,NULL,525650,172950,NULL,NULL\n169546141,20/12/2014 10:27,2014,2014/15,Special Service,1,1,295,295,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Lichfield Road,19900221,RM8,NULL,NULL,547050,186150,NULL,NULL\n169799141,20/12/2014 23:09,2014,2014/15,Special Service,1,1,295,295,CAT FALLEN INTO BASEMENT AREA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Lea Bridge Road,20900602,E5,NULL,NULL,534950,186250,NULL,NULL\n169868141,21/12/2014 03:35,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN MATTRESS SPRINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Foxham Road,21600321,N19,NULL,NULL,529550,186150,NULL,NULL\n169973141,21/12/2014 12:56,2014,2014/15,Special Service,1,1,295,295,FOX WITH HEAD TRAPPED IN BUCKET ON ROOF OF SHED,Fox,Person (land line),Scrub land,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000483,Forest Gate North,E09000025,Newham,Stratford,46074215,Thorogood Gardens,22200101,E15,539367,185023,539350,185050,51.54689859,0.008532269\n170522141,22/12/2014 19:12,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,Queens Park,E09000005,Brent,North Kensington,202090245,Harrow Road,20201241,NW10,523750,182592,523750,182550,51.52868888,-0.217422448\n170565141,22/12/2014 21:01,2014,2014/15,Special Service,2,5,295,1475,TO ASSIST RSPCA WITH KITTEN TRAPPED BETWEEN FIRE ESCAPE AND WALL,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,128037500,Coombe Road,21800283,KT2,519207,169532,519250,169550,51.41228664,-0.287284167\n171288141,24/12/2014 14:00,2014,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND COOKER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000379,St Mary's,E09000019,Islington,Islington,NULL,College Cross,21604568,N1,NULL,NULL,531450,184350,NULL,NULL\n171286141,24/12/2014 14:01,2014,2014/15,Special Service,1,1,295,295,DOG WITH JAW TRAPPED IN FENCE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009367,Brownswood,E09000012,Hackney,Holloway,1.00021E+11,Queens Drive,20900835,N4,531875,186685,531850,186650,51.56363364,-0.098827488\n171323141,24/12/2014 15:30,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,Mile End,E09000030,Tower Hamlets,Poplar,6158164,Southern Grove,22701117,E3,536748,182373,536750,182350,51.52372533,-0.030242189\n171741141,25/12/2014 17:23,2014,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN METAL GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Edmonton,1.00021E+11,Meridian Walk,21106478,N17,533470,191699,533450,191650,51.60831548,-0.073927035\n172002141,26/12/2014 12:41,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Dunlop Place,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL\n172364141,27/12/2014 11:27,2014,2014/15,Special Service,1,3,295,885,DOG TRAPPED UNDER METAL CONTAINER,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011096,Camberwell Green,E09000028,Southwark,Old Kent Road,NULL,New Church Road,NULL,SE5,532783,177656,532750,177650,51.48228112,-0.089135794\n172911141,28/12/2014 17:58,2014,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN AWNING,Cat,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,West End,E09000033,Westminster,Euston,10033548014,New Cavendish Street,8400597,W1W,529175,181824,529150,181850,51.52057325,-0.139540856\n173182141,29/12/2014 10:34,2014,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,Lydden Grove,NULL,SW18,NULL,NULL,525750,173550,NULL,NULL\n173242141,29/12/2014 13:56,2014,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED BEHIND WALL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000115,Cray Valley West,E09000006,Bromley,Sidcup,NULL,Wisley Road,20302480,BR5,NULL,NULL,546450,170250,NULL,NULL\n173832141,30/12/2014 19:32,2014,2014/15,Special Service,1,2,295,590,CAT TRAPPED  IN PARTITION  WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,Northwood Hills,E09000017,Hillingdon,Ruislip,NULL,Woodgate Crescent,21402205,HA6,NULL,NULL,510450,191550,NULL,NULL\n304151,01/01/2015 13:45,2015,2014/15,Special Service,1,1,295,295,TERRIER IN BURROW,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,10070720201,Hampton Court Road,22406635,KT8,516325,169113,516350,169150,51.40911893,-0.328843467\n468151,01/01/2015 21:22,2015,2014/15,Special Service,1,1,295,295,PERSON STUCK UP TREE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011098,Chaucer,E09000028,Southwark,Dockhead,2.00003E+11,Staple Street,22502372,SE1,532836,179512,532850,179550,51.4989479,-0.087675106\n1074151,03/01/2015 10:47,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000210,Town,E09000010,Enfield,Enfield,NULL,Lavender Hill,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL\n1528151,04/01/2015 14:12,2015,2014/15,Special Service,1,2,295,590,Redacted,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011469,Kenley,E09000008,Croydon,Purley,NULL,Godstone Road,NULL,CR8,531920,160611,531950,160650,51.32930299,-0.107889502\n2217151,06/01/2015 10:17,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Villiers Road,20202068,NW2,NULL,NULL,522450,184850,NULL,NULL\n2270151,06/01/2015 12:39,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,1.00021E+11,Cleveleys Road,20900253,E5,534978,186490,534950,186450,51.56114702,-0.054162955\n2291151,06/01/2015 13:40,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,Falconwood & Welling,E09000004,Bexley,Bexley,NULL,South Gipsy Road,20101316,DA16,NULL,NULL,547550,175750,NULL,NULL\n2815151,07/01/2015 22:06,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WASHING MACHINE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000530,Teddington,E09000027,Richmond upon Thames,Twickenham,NULL,Sutherland Grove,22403864,TW11,NULL,NULL,515550,171150,NULL,NULL\n3211151,08/01/2015 21:57,2015,2014/15,Special Service,1,1,295,295,CAT LOCKED IN SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,NULL,Fieldway,20501718,CR0,NULL,NULL,537950,163550,NULL,NULL\n3409151,09/01/2015 13:06,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,NULL,Little London Close,NULL,UB8,507800,181735,507850,181750,51.52425258,-0.447522188\n4360151,11/01/2015 14:08,2015,2014/15,Special Service,1,1,295,295,DOG WITH PAW STUCK DOWN PLUGHOLE,Dog,Police,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Chestnut Avenue,20303031,BR4,NULL,NULL,539450,164550,NULL,NULL\n4390151,11/01/2015 16:16,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND ADVERTISING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,Worcester Park,E09000029,Sutton,Sutton,NULL,Central Road,22601354,KT4,NULL,NULL,522550,165850,NULL,NULL\n4484151,11/01/2015 20:19,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND AND UNDER SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011471,New Addington South,E09000008,Croydon,Addington,NULL,Calley Down Crescent,20501677,CR0,NULL,NULL,538950,161950,NULL,NULL\n4551151,12/01/2015 01:49,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO GAIN ENTRY,Unknown - Domestic Animal Or Pet,Person (mobile),Other car park structure,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,34149446,Goldhawk Road,21000380,W12,522634,179482,522650,179450,51.50098137,-0.234584045\n4678151,12/01/2015 12:07,2015,2014/15,Special Service,1,1,295,295,HARRIS HAWK IMPAILED ON ANTI PIGEON SPIKE,Bird,Person (land line),Art Gallery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,St James's,E09000033,Westminster,Soho,1.00023E+11,Trafalgar Square,8400264,WC2N,529997,180557,529950,180550,51.50899852,-0.12816685\n4693151,12/01/2015 12:51,2015,2014/15,Special Service,1,1,295,295,CAT ON RAILWAY LINE,Cat,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000204,Lower Edmonton,E09000010,Enfield,Edmonton,NULL,Bury Street,NULL,N9,534049,194378,534050,194350,51.63225219,-0.064546206\n5489151,14/01/2015 14:26,2015,2014/15,Special Service,1,1,295,295,KITTEN IN PRECARIOUS POSITION,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,Southwark,Peckham,NULL,Waghorn Street,22502636,SE15,NULL,NULL,534150,175850,NULL,NULL\n5842151,15/01/2015 10:26,2015,2014/15,Special Service,1,1,295,295,CAT WITH HEAD TRAPPED BEHIND PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Lambourne Road,19900527,IG11,NULL,NULL,545450,184150,NULL,NULL\n6442151,16/01/2015 18:39,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED BETWEEN TWO FENCES,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000106,Bickley,E09000006,Bromley,Bromley,NULL,Stoneleigh Road,NULL,BR1,543680,168578,543650,168550,51.39804753,0.064007054\n6714151,17/01/2015 13:29,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED  IN THE BASEMENT,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,Shepherd's Bush Green,E09000013,Hammersmith and Fulham,Hammersmith,34032546,Uxbridge Road,21000835,W12,523610,179924,523650,179950,51.50474142,-0.220375016\n7148151,18/01/2015 12:58,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER DECKING,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011466,Coulsdon Town,E09000008,Croydon,Purley,1.00021E+11,Woodplace Lane,20502536,CR5,529446,157377,529450,157350,51.30080578,-0.144551503\n7492151,19/01/2015 08:19,2015,2014/15,Special Service,1,2,295,590,HORSE STUCK IN A DITCH,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,St. Thomas Road,NULL,DA17,550117,179667,550150,179650,51.49601893,0.161171192\n7611151,19/01/2015 13:36,2015,2014/15,Special Service,1,1,295,295,SQUIRREL TRAPPED IN HOLE,Squirrel,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000615,Furzedown,E09000032,Wandsworth,Tooting,1.00023E+11,Freshwater Road,22901806,SW17,528395,171030,528350,171050,51.42374511,-0.154694938\n7898151,20/01/2015 04:08,2015,2014/15,Special Service,1,1,295,295,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000302,Roxeth,E09000015,Harrow,Northolt,NULL,Tregenna Avenue,NULL,HA2,513432,185887,513450,185850,51.56046776,-0.365037756\n8063151,20/01/2015 13:32,2015,2014/15,Special Service,1,1,295,295,FOX TRAPPED,Fox,Person (land line),Day care/Drop in centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05009332,Shadwell,E09000030,Tower Hamlets,Shadwell,6027320,Glamis Road,22700541,E1W,535420,180901,535450,180950,51.51081711,-0.04993811\n8071151,20/01/2015 13:47,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Townsend Avenue,20704855,N14,NULL,NULL,529950,192950,NULL,NULL\n8731151,22/01/2015 03:10,2015,2014/15,Special Service,1,1,295,295,FOX TRAPPED BY ITS HIPS IN WROUGHT IRON GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,34033154,Addison Gardens,21000029,W14,523782,179497,523750,179450,51.50086623,-0.21804784\n9318151,23/01/2015 08:12,2015,2014/15,Special Service,2,3,295,885,HORSE WITH LEG TRAPPED IN HARROW,Horse,Person (mobile),Agricultural equipment,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000119,Hayes and Coney Hall,E09000006,Bromley,Addington,NULL,Gates Green Road,NULL,BR4,540586,164653,540550,164650,51.3635522,0.018011771\n9641151,23/01/2015 21:42,2015,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Cottage Street,22700369,E14,NULL,NULL,537950,180950,NULL,NULL\n9717151,24/01/2015 00:15,2015,2014/15,Special Service,1,2,295,590,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009328,Poplar,E09000030,Tower Hamlets,Poplar,NULL,Cottage Street,22700369,E14,NULL,NULL,537950,180950,NULL,NULL\n10144151,25/01/2015 00:28,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000046,Colindale,E09000003,Barnet,Hendon,NULL,Sheaveshill Avenue,NULL,NW9,521497,189759,521450,189750,51.59359,-0.247409176\n11062151,27/01/2015 09:35,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000488,Manor Park,E09000025,Newham,Stratford,NULL,Carlyle Road,22200136,E12,NULL,NULL,542050,185650,NULL,NULL\n11479151,28/01/2015 09:25,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,Haselbury,E09000010,Enfield,Edmonton,NULL,Hydefield Court,20704361,N9,NULL,NULL,533350,193850,NULL,NULL\n11649151,28/01/2015 17:28,2015,2014/15,Special Service,1,4,295,1180,DOG TRAPPED IN FOX HOLE AT THE SIDE OF PUB HSE,Dog,Other FRS,Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000405,Chessington South,E09000021,Kingston upon Thames,Surbiton,NULL,Moor Lane,NULL,KT9,519243,163990,519250,163950,51.36246843,-0.288624467\n11892151,29/01/2015 09:29,2015,2014/15,Special Service,1,1,295,295,ANIMAL IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100055986,Greatfields Road,19900681,IG11,544893,183306,544850,183350,51.53007938,0.08746393\n11907151,29/01/2015 10:08,2015,2014/15,Special Service,2,7,295,2065,HORSE STUCK IN MUD,Horse,Other FRS,\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011231,Slade Green & Northend,E09000004,Bexley,Erith,1.00023E+11,Moat Lane,20100987,DA8,552723,176642,552750,176650,51.46814204,0.197374549\n13074151,01/02/2015 10:39,2015,2014/15,Special Service,1,1,295,295,CROW TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,Bloomsbury,E09000007,Camden,Euston,5107265,Huntley Street,20400955,WC1E,529535,182078,529550,182050,51.52277356,-0.134261639\n13558151,02/02/2015 10:51,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA INSPECTOR WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,1.00021E+11,Churchbury Road,20800337,SE9,541500,173758,541550,173750,51.44514199,0.034758477\n13585151,02/02/2015 11:56,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIRE PLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000354,Hanworth Park,E09000018,Hounslow,Feltham,NULL,Elmwood Avenue,21500403,TW13,NULL,NULL,511150,172150,NULL,NULL\n14102151,03/02/2015 13:57,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Bird,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Southwark Park Road,NULL,SE16,534736,179437,534750,179450,51.49782445,-0.060347832\n14425151,04/02/2015 10:06,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009386,Victoria,E09000012,Hackney,Homerton,NULL,Lauriston Road,NULL,E9,535506,184259,535550,184250,51.54097218,-0.04740852\n15164151,05/02/2015 21:11,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN GARAGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000035,Longbridge,E09000002,Barking and Dagenham,Barking,NULL,Westrow Drive,19900382,IG11,NULL,NULL,546050,185450,NULL,NULL\n15387151,06/02/2015 14:17,2015,2014/15,Special Service,1,1,295,295,DOG IN PRECARIOUS POSITON,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Hornsey Lane,21606907,N6,NULL,NULL,528950,187250,NULL,NULL\n15734151,07/02/2015 08:44,2015,2014/15,Special Service,1,1,295,295,BIRD OF PREY SNAGGED IN TV CABLE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Greenhill Gardens,20600774,UB5,NULL,NULL,512650,183150,NULL,NULL\n16278151,08/02/2015 13:12,2015,2014/15,Special Service,1,2,295,590,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Waratah Drive,NULL,BR7,543073,171058,543050,171050,51.42048528,0.05628742\n16842151,09/02/2015 16:39,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,Roehampton and Putney Heath,E09000032,Wandsworth,Wandsworth,NULL,Foxcombe Road,NULL,SW15,522334,173377,522350,173350,51.44617768,-0.241015281\n17603151,11/02/2015 13:17,2015,2014/15,Special Service,1,1,295,295,REQUEST TO ASSIST RSPCA WITH CAT TRAPPED IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000524,Kew,E09000027,Richmond upon Thames,Richmond,10002255588,Melliss Avenue,22407046,TW9,519801,177002,519850,177050,51.4792987,-0.276220509\n17917151,12/02/2015 09:46,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Lambourne Road,19900527,IG11,NULL,NULL,545450,184050,NULL,NULL\n18008151,12/02/2015 13:36,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,Rushey Green,E09000023,Lewisham,Lewisham,NULL,Carswell Road,NULL,SE6,538241,173803,538250,173850,51.44635008,-0.012089197\n18410151,13/02/2015 15:27,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA TO RESCUE CAT IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011245,Hainault,E09000026,Redbridge,Hainault,1.00022E+11,Tine Road,22304748,IG7,545304,192308,545350,192350,51.61086059,0.097103972\n18454151,13/02/2015 16:55,2015,2014/15,Special Service,1,1,295,295,SQUIRELL TRAPPED IN BIN,Squirrel,Person (mobile),Small refuse/rubbish container,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000628,West Hill,E09000032,Wandsworth,Wandsworth,10033241589,West Hill,22906045,SW15,524336,174218,524350,174250,51.4533007,-0.211926524\n18554151,13/02/2015 20:00,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Roman Road,22304210,IG1,NULL,NULL,544050,184950,NULL,NULL\n18824151,14/02/2015 13:54,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Gardens,21700450,SW5,NULL,NULL,525850,178250,NULL,NULL\n18990151,14/02/2015 22:51,2015,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011102,Faraday,E09000028,Southwark,Old Kent Road,NULL,Dawes Street,NULL,SE17,532795,178433,532750,178450,51.48926092,-0.088671152\n19171151,15/02/2015 12:52,2015,2014/15,Special Service,1,1,295,295,FOX IN PRECARIOUS POSITION,Fox,Other FRS,Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000408,Grove,E09000021,Kingston upon Thames,Kingston,128035984,Adams Walk,21820112,KT1,518216,169262,518250,169250,51.41006734,-0.301617452\n19218151,15/02/2015 15:46,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,Wood Street,E09000031,Waltham Forest,Walthamstow,NULL,Fyfield Road,22838600,E17,NULL,NULL,538650,189950,NULL,NULL\n19265151,15/02/2015 17:11,2015,2014/15,Special Service,1,1,295,295,DOG STUCK IN TREE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000044,Burnt Oak,E09000003,Barnet,Mill Hill,NULL,Blundell Road,NULL,HA8,521334,191347,521350,191350,51.60789653,-0.249213191\n20423151,18/02/2015 11:06,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (land line),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000370,Clerkenwell,E09000019,Islington,Shoreditch,10090551507,Cowcross Street,21604616,EC1M,531651,181854,531650,181850,51.5202718,-0.103862859\n20664151,18/02/2015 20:22,2015,2014/15,Special Service,1,1,295,295,CAT STUCK IN CULVERT,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000454,Whitefoot,E09000023,Lewisham,Lewisham,NULL,Waterbank Road,NULL,SE6,538015,172043,538050,172050,51.43058927,-0.016024477\n21137151,20/02/2015 07:41,2015,2014/15,Special Service,1,2,295,590,KITTEN STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000618,Nightingale,E09000032,Wandsworth,Tooting,NULL,Trinity Road,22906604,SW17,NULL,NULL,527650,172950,NULL,NULL\n22217151,22/02/2015 15:52,2015,2014/15,Special Service,1,1,295,295,ASSIST MOP WITH INJURED DOG,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000320,St Andrew's,E09000016,Havering,Hornchurch,1.00023E+11,North Street,21300513,RM11,554003,187415,554050,187450,51.56458893,0.220490683\n22630151,23/02/2015 18:41,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON WINDOW LEDGE UNABLE TO GET DOWN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,West Ham,E09000025,Newham,Stratford,NULL,Redriffe Road,22201565,E13,NULL,NULL,539850,183550,NULL,NULL\n22710151,23/02/2015 23:38,2015,2014/15,Special Service,1,1,295,295,CAT IN THE RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515880,175981,515850,175950,51.47093784,-0.332992584\n24550151,28/02/2015 15:34,2015,2014/15,Special Service,1,1,295,295,CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000594,Endlebury,E09000031,Waltham Forest,Chingford,1.00023E+11,Valley Side,22883450,E4,537400,194193,537450,194150,51.629781,-0.016231551\n24859151,01/03/2015 11:16,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN WALL RECESS,Cat,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000362,Isleworth,E09000018,Hounslow,Heston,1.00022E+11,Pankhurst Close,21501694,TW7,515908,175973,515950,175950,51.47086021,-0.33259225\n24944151,01/03/2015 14:05,2015,2014/15,Special Service,1,1,295,295,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000438,Blackheath,E09000023,Lewisham,Greenwich,NULL,Lethbridge Close,22004663,SE13,NULL,NULL,538150,176550,NULL,NULL\n24960151,01/03/2015 14:43,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN FIREPLACE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000612,Earlsfield,E09000032,Wandsworth,Wandsworth,NULL,St. John's Drive,22904856,SW18,NULL,NULL,525750,173350,NULL,NULL\n25022151,01/03/2015 17:03,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,NULL,Partridge Knoll,20502261,CR8,NULL,NULL,531850,160950,NULL,NULL\n25078151,01/03/2015 19:12,2015,2014/15,Special Service,1,1,295,295,PUPPY WITH HEAD TRAPPED IN RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,Redcliffe,E09000020,Kensington and Chelsea,Chelsea,NULL,Redcliffe Place,21700452,SW10,NULL,NULL,526050,177750,NULL,NULL\n25686151,03/03/2015 09:54,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,1.00021E+11,Shortlands,21401750,UB3,508821,177620,508850,177650,51.48707052,-0.434079377\n26576151,05/03/2015 10:12,2015,2014/15,Special Service,NULL,NULL,295,NULL,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000334,Manor,E09000017,Hillingdon,Ruislip,NULL,Bessingby Road,NULL,HA4,510774,186703,510750,186750,51.56832999,-0.403108673\n26999151,06/03/2015 10:08,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH INJURED HERON,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000261,Ravenscourt Park,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Black Lion Mews,21001189,W6,NULL,NULL,522050,178550,NULL,NULL\n27632151,07/03/2015 17:59,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,Oakleigh,E09000003,Barnet,Barnet,NULL,Loring Road,20027400,N20,NULL,NULL,527050,193650,NULL,NULL\n29906151,12/03/2015 20:04,2015,2014/15,Special Service,1,1,295,295,CAT WITH HEAD STUCK IN CHAIR,Cat,Police,Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Olympus Grove,21107058,N22,NULL,NULL,531350,190750,NULL,NULL\n30334151,13/03/2015 18:04,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED IN BRANCHES IN REAR GARDEN,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,Willesden Green,E09000005,Brent,Willesden,NULL,Chapter Road,20201486,NW2,NULL,NULL,522850,184950,NULL,NULL\n31411151,16/03/2015 09:03,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH KITTEN IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,Telegraph Hill,E09000023,Lewisham,New Cross,1.00023E+11,Ommaney Road,22000770,SE14,535973,176485,535950,176450,51.47100038,-0.043674722\n31852151,17/03/2015 10:56,2015,2014/15,Special Service,1,2,295,590,HORSE TRAPPED IN HORSEBOX,Horse,Person (mobile),Agricultural vehicle,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000108,Bromley Common and Keston,E09000006,Bromley,Biggin Hill,1.0002E+11,Jackass Lane,20300016,BR2,541322,163884,541350,163850,51.35645973,0.028271547\n32395151,18/03/2015 16:18,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Campion Close,NULL,CR0,533340,164615,533350,164650,51.3649551,-0.086021581\n33118151,20/03/2015 09:25,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA FOR TRAPPED BIRDS,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Well Hall Road,NULL,SE9,542592,174942,542550,174950,51.45550756,0.05093801\n33665151,21/03/2015 16:40,2015,2014/15,Special Service,1,2,295,590,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,Cleveland,E09000009,Ealing,Ealing,12074960,Kent Avenue,20600973,W13,516585,181781,516550,181750,51.52292241,-0.32093185\n33908151,22/03/2015 10:48,2015,2014/15,Special Service,1,1,295,295,DOG TRAPPED UNDER STONE SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000562,St Helier,E09000029,Sutton,Sutton,5870041665,Love Lane,22600746,SM4,525625,166807,525650,166850,51.38641175,-0.196008539\n33997151,22/03/2015 14:58,2015,2014/15,Special Service,1,1,295,295,TRAPPED BIRD,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000472,Village,E09000024,Merton,Wimbledon,48072645,West Side Common,22106645,SW19,523233,170939,523250,170950,51.42407158,-0.228932728\n34269151,23/03/2015 07:13,2015,2014/15,Special Service,1,1,295,295,BIRD TRAPPED IN CHMINEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,NULL,Romborough Gardens,22001854,SE13,NULL,NULL,538150,174750,NULL,NULL\n34492151,23/03/2015 17:51,2015,2014/15,Special Service,1,1,295,295,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Homerton,NULL,Fletching Road,20900413,E5,NULL,NULL,535250,186150,NULL,NULL\n34499151,23/03/2015 18:12,2015,2014/15,Special Service,1,1,295,295,CAT STUCK BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,Greenwich West,E09000011,Greenwich,Greenwich,NULL,Greenwich South Street,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL\n34789151,24/03/2015 13:51,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED BEHIND GARAGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000427,Prince's,E09000022,Lambeth,Lambeth,NULL,Lambeth Walk,21900831,SE11,NULL,NULL,530850,178750,NULL,NULL\n34920151,24/03/2015 19:02,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED UNDER DASH BOARD OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000171,Cleveland,E09000009,Ealing,Ealing,NULL,Pitshanger Lane,NULL,W5,516990,182111,516950,182150,51.52580473,-0.31498735\n35101151,25/03/2015 07:28,2015,2014/15,Special Service,1,1,295,295,DUCKLINGS FALLEN INTO DRAIN,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Old Bellgate Place,NULL,E14,537144,179084,537150,179050,51.49407342,-0.025816608\n35341151,25/03/2015 18:23,2015,2014/15,Special Service,1,1,295,295,DUCKLINGS IN DISTRESS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,NULL,Westferry Road,NULL,E14,537053,180032,537050,180050,51.50261456,-0.026758469\n35594151,26/03/2015 10:12,2015,2014/15,Special Service,1,1,295,295,CAT POSSIBLY TRAPPED BEHIND SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,Southwark,Old Kent Road,NULL,Barlow Street,22500153,SE17,NULL,NULL,532850,178750,NULL,NULL\n35603151,26/03/2015 10:53,2015,2014/15,Special Service,1,1,295,295,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,NULL,Sutton Road,19900602,IG11,NULL,NULL,545350,183550,NULL,NULL\n36503151,28/03/2015 14:30,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000275,Noel Park,E09000014,Haringey,Tottenham,NULL,Farrant Avenue,21104519,N22,NULL,NULL,531550,190350,NULL,NULL\n36539151,28/03/2015 15:20,2015,2014/15,Special Service,1,1,295,295,DOG LOCKED IN BEDROOM,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,Highbury East,E09000019,Islington,Stoke Newington,NULL,Riversdale Road,21603742,N5,NULL,NULL,532050,186150,NULL,NULL\n36718151,28/03/2015 22:46,2015,2014/15,Special Service,1,1,295,295,CAT STUCK ON WINDOW SILL AT THIRD FLOOR LEVEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,Shaftesbury,E09000032,Wandsworth,Clapham,NULL,Nansen Road,22903466,SW11,NULL,NULL,528350,175550,NULL,NULL\n37273151,30/03/2015 09:24,2015,2014/15,Special Service,1,1,295,295,ANIMAL TRAPPED IN FIRE BREAST,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000605,Leytonstone,E09000031,Waltham Forest,Leytonstone,NULL,Grove Green Road,22841400,E11,NULL,NULL,538850,187150,NULL,NULL\n37876151,31/03/2015 14:11,2015,2014/15,Special Service,1,1,295,295,CAT TRAPPED IN FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Queens Road,20702914,EN1,NULL,NULL,533450,196450,NULL,NULL\n37998151,31/03/2015 17:46,2015,2014/15,Special Service,1,1,295,295,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,2.00003E+11,Southwark Park Road,22502293,SE16,534791,178829,534750,178850,51.49234759,-0.059788129\n38268151,01/04/2015 11:28,2015,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,North End,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Perham Road,21000637,W14,NULL,NULL,524450,178050,NULL,NULL\n38468151,01/04/2015 17:07,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000113,Copers Cope,E09000006,Bromley,Beckenham,NULL,Springpark Drive,20302029,BR3,NULL,NULL,538550,168850,NULL,NULL\n38539151,01/04/2015 19:13,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,New Place Square,NULL,SE16,534718,179451,534750,179450,51.49795455,-0.060601642\n38795151,02/04/2015 09:22,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000175,East Acton,E09000009,Ealing,Acton,NULL,Davis Road,20600514,W3,NULL,NULL,521750,179950,NULL,NULL\n38881151,02/04/2015 13:55,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON MIDDLE BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Royal Road,22401806,TW11,NULL,NULL,514850,171450,NULL,NULL\n39056151,02/04/2015 20:23,2015,2015/16,Special Service,1,1,298,298,CAT UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Queens Road,20702914,EN1,NULL,NULL,533450,196450,NULL,NULL\n39445151,03/04/2015 17:20,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF - SUSPECTED BROKEN LEG,Cat,Person (land line),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12046702,Sussex Crescent,20601670,UB5,513237,184780,513250,184750,51.55055727,-0.368205608\n39471151,03/04/2015 18:15,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN VENT  ONE TL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000648,Westbourne,E09000033,Westminster,Paddington,NULL,Lord Hills Road,8400851,W2,NULL,NULL,525850,181850,NULL,NULL\n40295151,05/04/2015 15:59,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN BRAMBLE BUSH,Cat,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000467,Pollards Hill,E09000024,Merton,Norbury,NULL,Commonside East,NULL,CR4,529301,168252,529350,168250,51.39857337,-0.142683438\n40589151,06/04/2015 09:50,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND BATHTUB,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000284,Woodside,E09000014,Haringey,Tottenham,NULL,Ellenborough Road,21104494,N22,NULL,NULL,532050,190750,NULL,NULL\n40953151,07/04/2015 09:08,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,Noel Park,E09000014,Haringey,Tottenham,NULL,Willingdon Road,21105242,N22,NULL,NULL,531650,190050,NULL,NULL\n41008151,07/04/2015 12:02,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000045,Childs Hill,E09000003,Barnet,West Hampstead,NULL,Westcroft Close,20400111,NW2,NULL,NULL,524250,185750,NULL,NULL\n41545151,08/04/2015 17:05,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000058,Oakleigh,E09000003,Barnet,Barnet,200115352,The Fairway,20015220,EN5,525913,195507,525950,195550,51.64427697,-0.18162866\n42072151,09/04/2015 19:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Orchard Place,21104873,N17,NULL,NULL,533750,191050,NULL,NULL\n42095151,09/04/2015 20:22,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND THE INSIDE WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011233,West Heath,E09000004,Bexley,Erith,NULL,Oakhurst Avenue,20101054,DA7,NULL,NULL,548250,177050,NULL,NULL\n42356151,10/04/2015 12:55,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Palace View,NULL,BR1,540560,168819,540550,168850,51.40099341,0.019283498\n42369151,10/04/2015 13:30,2015,2015/16,Special Service,1,3,298,894,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,Belvedere,E09000004,Bexley,Erith,NULL,Crabtree Manorway North,NULL,DA17,550176,179738,550150,179750,51.49664124,0.162050703\n42685151,11/04/2015 06:43,2015,2015/16,Special Service,1,1,298,298,CAT STUCK DOWN HOLE,Cat,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000423,Herne Hill,E09000022,Lambeth,West Norwood,NULL,Effra Road,NULL,SW2,531345,174405,531350,174450,51.45340054,-0.111039348\n43549151,12/04/2015 20:36,2015,2015/16,Special Service,1,1,298,298,CAT FALLEN AND TRAPPED BETWEEN WALL AND HEDGE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,Golborne,E09000020,Kensington and Chelsea,North Kensington,NULL,Portobello Road,21700970,W10,NULL,NULL,524250,181750,NULL,NULL\n43845151,13/04/2015 15:07,2015,2015/16,Special Service,1,1,298,298,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000559,Carshalton South and Clockhouse,E09000029,Sutton,Wallington,NULL,Kenny Drive,NULL,SM5,528152,162335,528150,162350,51.34565681,-0.161323007\n44818151,15/04/2015 11:47,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000331,Heathrow Villages,E09000017,Hillingdon,Surrey,NULL,Spout Lane North,NULL,TW19,504706,174740,504750,174750,51.4619599,-0.49417632\n45198151,15/04/2015 21:53,2015,2015/16,Special Service,1,1,298,298,ABANDONED CALL TO FIRE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000196,Cockfosters,E09000010,Enfield,Barnet,NULL,Cockfosters Road,20701010,EN4,NULL,NULL,528150,196250,NULL,NULL\n45671151,16/04/2015 18:48,2015,2015/16,Special Service,1,1,298,298,INJURED CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009323,Canary Wharf,E09000030,Tower Hamlets,Millwall,6062865,The Quarterdeck,22702098,E14,537179,179628,537150,179650,51.49895351,-0.025101313\n46011151,17/04/2015 13:41,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000612,Earlsfield,E09000032,Wandsworth,Tooting,NULL,Garratt Lane,NULL,SW18,526031,173008,526050,173050,51.44205189,-0.187975048\n46042151,17/04/2015 14:31,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000060,Underhill,E09000003,Barnet,Mill Hill,NULL,Hyver Hill,20024560,NW7,NULL,NULL,520950,194750,NULL,NULL\n46655151,18/04/2015 17:06,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009400,Pembridge,E09000020,Kensington and Chelsea,Kensington,217013002,Chepstow Crescent,21700705,W11,525119,180844,525150,180850,51.51267792,-0.198317929\n47869151,20/04/2015 23:10,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ALLEWAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,Croydon,Norbury,NULL,Annsworthy Crescent,20503127,SE25,NULL,NULL,532850,169050,NULL,NULL\n48401151,21/04/2015 23:33,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER BATH,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,Southwark,Peckham,NULL,Coleman Road,22500540,SE5,NULL,NULL,533150,177350,NULL,NULL\n48648151,22/04/2015 14:44,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Orange Grove,NULL,E11,NULL,NULL,539050,186350,NULL,NULL\n48706151,22/04/2015 16:55,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009383,Springfield,E09000012,Hackney,Stoke Newington,NULL,Warwick Grove,NULL,E5,534645,187017,534650,187050,51.5659624,-0.058762469\n48988151,23/04/2015 08:17,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH KITTEN STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,1.00023E+11,Balliol Avenue,22812900,E4,539021,192766,539050,192750,51.61656068,0.006607139\n49145151,23/04/2015 13:14,2015,2015/16,Special Service,1,2,298,596,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000532,West Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Chertsey Road,22406590,TW2,NULL,NULL,514250,173250,NULL,NULL\n49438151,23/04/2015 21:08,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED ON RAILWAY,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,Croydon,Purley,1.00021E+11,Beaumont Road,20501910,CR8,531232,161051,531250,161050,51.33341619,-0.117596988\n49827151,24/04/2015 17:26,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000037,Parsloes,E09000002,Barking and Dagenham,Dagenham,NULL,Bonham Road,19900048,RM8,NULL,NULL,547850,186150,NULL,NULL\n49874151,24/04/2015 18:40,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN BETWEEN BUILDINGS,Cat,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100102851,The Coverdales,19900678,IG11,544450,183508,544450,183550,51.53200784,0.081164761\n49954151,24/04/2015 21:01,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Fore Street,NULL,N18,NULL,NULL,534050,192050,NULL,NULL\n50155151,25/04/2015 08:03,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000178,Greenford Green,E09000009,Ealing,Northolt,12054301,Castle Road,20600331,UB5,514107,184549,514150,184550,51.54830611,-0.355737881\n50218151,25/04/2015 11:20,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,Gascoigne,E09000002,Barking and Dagenham,Barking,100102851,The Coverdales,19900678,IG11,544450,183508,544450,183550,51.53200784,0.081164761\n50447151,25/04/2015 20:36,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000090,Fryent,E09000005,Brent,Stanmore,NULL,Church Lane,20200700,NW9,NULL,NULL,520550,187950,NULL,NULL\n50849151,27/04/2015 00:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,Valence,E09000002,Barking and Dagenham,Dagenham,NULL,Chittys Lane,19900082,RM8,NULL,NULL,547550,187150,NULL,NULL\n51015151,27/04/2015 15:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GATE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011470,New Addington North,E09000008,Croydon,Addington,1.00021E+11,Bygrove,20502685,CR0,537451,163537,537450,163550,51.35428881,-0.027422536\n51389151,28/04/2015 11:54,2015,2015/16,Special Service,2,3,298,894,HORSE STUCK UP SIDE DOWN IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000297,Pinner,E09000015,Harrow,Harrow,10000001399,Woodhall Road,21201827,HA5,511790,191712,511750,191750,51.61315119,-0.386860853\n51957151,29/04/2015 11:53,2015,2015/16,Special Service,1,1,298,298,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Kitchener Road,19900509,RM10,NULL,NULL,549950,184850,NULL,NULL\n52040151,29/04/2015 15:10,2015,2015/16,Special Service,1,2,298,596,SHEEP TRAPPED INTO RIVER LEA,Sheep,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,Ponders End,E09000010,Enfield,Enfield,NULL,Wharf Road,NULL,EN3,536306,195443,536350,195450,51.64127972,-0.031541196\n52059151,29/04/2015 15:57,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN MESH AND NETTING UP TREE,Cat,Police,Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,Junction,E09000019,Islington,Kentish Town,NULL,Larch Close,NULL,N19,529177,186618,529150,186650,51.56365575,-0.137753482\n52172151,29/04/2015 20:42,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,Bishop's,E09000022,Lambeth,Lambeth,10000444312,Waterloo Station,21902368,SE1,531050,179932,531050,179950,51.50313881,-0.113234309\n52605151,30/04/2015 16:19,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Unknown - Wild Animal,Person (land line),TV/film/music/art studio,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,West Cross Way,21501481,TW8,516515,177579,516550,177550,51.48517021,-0.323327869\n52928151,01/05/2015 09:30,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000109,Bromley Town,E09000006,Bromley,Bromley,NULL,Ethelbert Road,NULL,BR1,540265,168955,540250,168950,51.40228841,0.015099277\n53373151,02/05/2015 08:53,2015,2015/16,Special Service,1,2,298,596,PROTECTED BIRD STUCK IN NETTING,Bird,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,10002167250,Wood Lane,19900414,RM8,549547,187053,549550,187050,51.56253454,0.156098585\n53466151,02/05/2015 13:50,2015,2015/16,Special Service,1,1,298,298,INCOMPLETE CALL TO BIN ALIGHT,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,St Dunstan's,E09000030,Tower Hamlets,Shadwell,NULL,York Square,NULL,E14,536150,181221,536150,181250,51.51351745,-0.039301561\n53629151,02/05/2015 19:04,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,Heathrow Villages,E09000017,Hillingdon,Hayes,NULL,Cranford Lane,21400509,UB3,NULL,NULL,508750,177650,NULL,NULL\n53675151,02/05/2015 20:33,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF UNDER TILE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Clarence Road,22826950,E17,NULL,NULL,535950,190350,NULL,NULL\n53821151,03/05/2015 06:29,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE POOL TABLE,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000425,Larkhall,E09000022,Lambeth,Clapham,1.00023E+11,Pensbury Street,21901091,SW8,529427,176351,529450,176350,51.47133044,-0.137916424\n53869151,03/05/2015 10:03,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED IN GATE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000312,Harold Wood,E09000016,Havering,Harold Hill,1.00021E+11,Kersey Gardens,21301321,RM3,554635,191063,554650,191050,51.59719194,0.23120501\n54077151,03/05/2015 19:09,2015,2015/16,Special Service,1,1,298,298,KITTEN WITH PAW STUCK IN WINDOW FRAME,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000132,Fortune Green,E09000007,Camden,West Hampstead,NULL,Fordwych Road,20400109,NW2,NULL,NULL,524550,184950,NULL,NULL\n54525151,04/05/2015 19:18,2015,2015/16,Special Service,1,1,298,298,PIDGEON TRAPPED IN CHIMNEY BREAST,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000120,Kelsey and Eden Park,E09000006,Bromley,Beckenham,NULL,Uplands,20301812,BR3,NULL,NULL,537250,168950,NULL,NULL\n55583151,07/05/2015 08:30,2015,2015/16,Special Service,1,1,298,298,DEER TRAPPED IN METAL FENCE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.00023E+11,Single Street,20301537,TN16,543600,159928,543650,159950,51.32033994,0.059371536\n56158151,08/05/2015 11:51,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER THE RADIATOR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,St Andrew's,E09000016,Havering,Hornchurch,NULL,High Street,21300416,RM12,NULL,NULL,553650,187250,NULL,NULL\n56234151,08/05/2015 14:40,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011487,Waddon,E09000008,Croydon,Croydon,NULL,Old Town,20501261,CR0,NULL,NULL,531850,165250,NULL,NULL\n56241151,08/05/2015 15:08,2015,2015/16,Special Service,1,1,298,298,BIRD CAUGHT IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000377,Mildmay,E09000019,Islington,Islington,NULL,Pyrland Road,21603706,N5,NULL,NULL,532550,185250,NULL,NULL\n56385151,08/05/2015 20:27,2015,2015/16,Special Service,1,2,298,596,Redacted,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,NULL,Wellington Street,NULL,SE18,543556,178810,543550,178850,51.49002075,0.066373554\n56615151,09/05/2015 12:11,2015,2015/16,Special Service,2,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,Muswell Hill,E09000014,Haringey,Hornsey,NULL,Fortis Green Road,21100314,N10,NULL,NULL,528450,189550,NULL,NULL\n56791151,09/05/2015 18:41,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER ELECTRIC SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000309,Emerson Park,E09000016,Havering,Hornchurch,NULL,Rutland Drive,21300572,RM11,NULL,NULL,555050,188850,NULL,NULL\n56884151,09/05/2015 20:51,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN HOLE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,Custom House,E09000025,Newham,Plaistow,NULL,Cundy Road,NULL,E16,NULL,NULL,541150,181050,NULL,NULL\n57182151,10/05/2015 13:41,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000057,Mill Hill,E09000003,Barnet,Finchley,NULL,Bittacy Hill,NULL,NW7,524126,191404,524150,191450,51.6078011,-0.208894315\n57527151,11/05/2015 08:55,2015,2015/16,Special Service,1,1,298,298,MAN AND DOG STUCK IN BROOK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000059,Totteridge,E09000003,Barnet,Barnet,NULL,Macaret Close,NULL,N20,525712,194739,525750,194750,51.63742007,-0.184807771\n58117151,12/05/2015 13:25,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000308,Elm Park,E09000016,Havering,Hornchurch,NULL,Mungo Park Road,21300841,RM13,NULL,NULL,552350,184350,NULL,NULL\n58129151,12/05/2015 13:35,2015,2015/16,Special Service,1,1,298,298,DOG COLLAR STUCK AROUND LEG AND NECK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,Southgate Green,E09000010,Enfield,Southgate,NULL,Betstyle Road,20701335,N11,NULL,NULL,529050,192550,NULL,NULL\n58293151,12/05/2015 18:54,2015,2015/16,Special Service,1,1,298,298,PIDGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000087,Brondesbury Park,E09000005,Brent,West Hampstead,NULL,Brondesbury Park,NULL,NW6,524776,184460,524750,184450,51.54525137,-0.201977675\n58870151,13/05/2015 20:43,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND PIPES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Fortune Place,22503247,SE1,NULL,NULL,533950,178250,NULL,NULL\n58891151,13/05/2015 21:46,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000350,Cranford,E09000018,Hounslow,Feltham,1.00022E+11,Burnham Gardens,21500181,TW4,510730,176578,510750,176550,51.47733429,-0.406922035\n58912151,13/05/2015 22:33,2015,2015/16,Special Service,1,1,298,298,KITTENS STUCK DOWN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Fortune Place,22503247,SE1,NULL,NULL,533950,178250,NULL,NULL\n58913151,13/05/2015 22:35,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000419,Clapham Town,E09000022,Lambeth,Clapham,NULL,Tessa Sanderson Place,21901978,SW8,NULL,NULL,528750,175950,NULL,NULL\n59068151,14/05/2015 10:06,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000063,Woodhouse,E09000003,Barnet,Barnet,200141012,High Road,20022420,N20,526350,193143,526350,193150,51.62293445,-0.176169012\n59441151,15/05/2015 02:36,2015,2015/16,Special Service,1,1,298,298,BABY FOX IN DISTRESS,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Grove Road,NULL,E3,NULL,NULL,536150,182950,NULL,NULL\n59509151,15/05/2015 08:30,2015,2015/16,Special Service,1,1,298,298,Redacted,Unknown - Wild Animal,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05000211,Turkey Street,E09000010,Enfield,Enfield,207155981,Turkey Street,20703019,EN3,535290,198834,535250,198850,51.67199734,-0.044903451\n59560151,15/05/2015 10:47,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN LOFT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000314,Heaton,E09000016,Havering,Harold Hill,NULL,Heaton Avenue,21301268,RM3,NULL,NULL,552950,191250,NULL,NULL\n59967151,16/05/2015 09:41,2015,2015/16,Special Service,1,1,298,298,FOX CUB TRAPPED IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011233,West Heath,E09000004,Bexley,Plumstead,NULL,Plymstock Road,20101156,DA16,NULL,NULL,547350,177150,NULL,NULL\n60034151,16/05/2015 13:10,2015,2015/16,Special Service,1,1,298,298,BIRD TANGLED UP IN NETTING,Bird,Person (mobile),Library,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000531,Twickenham Riverside,E09000027,Richmond upon Thames,Twickenham,1.00024E+11,Garfield Road,22403495,TW1,516305,173387,516350,173350,51.44753644,-0.32772858\n60157151,16/05/2015 17:12,2015,2015/16,Special Service,1,1,298,298,CAT STUCK UP TREE WITH LEAD CAUGHT ROUND BRANCHES,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,Northolt West End,E09000009,Ealing,Northolt,NULL,Laughton Road,NULL,UB5,511821,183574,511850,183550,51.54000004,-0.389002521\n60206151,16/05/2015 18:58,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\n60725151,17/05/2015 20:24,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000218,Eltham North,E09000011,Greenwich,Eltham,NULL,Well Hall Road,NULL,SE9,542593,174930,542550,174950,51.45539948,0.050947559\n60894151,18/05/2015 08:27,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000557,Belmont,E09000029,Sutton,Sutton,NULL,Hadleigh Drive,22602363,SM2,NULL,NULL,525450,162550,NULL,NULL\n61114151,18/05/2015 17:14,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,NULL,IG8,540911,191851,540950,191850,51.60786939,0.033520084\n61151151,18/05/2015 19:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,Northumberland Park,E09000014,Haringey,Tottenham,NULL,Bromley Road,21104285,N17,NULL,NULL,534150,190950,NULL,NULL\n61162151,18/05/2015 19:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN FOOT CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000100,Stonebridge,E09000005,Brent,Park Royal,NULL,Elgar Avenue,NULL,NW10,520725,184936,520750,184950,51.55040943,-0.260205489\n61518151,19/05/2015 18:36,2015,2015/16,Special Service,1,1,298,298,ANIMAL TRAPPED ON ROOF,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009325,Lansbury,E09000030,Tower Hamlets,Poplar,NULL,Upper North Street,22701256,E14,NULL,NULL,537450,181250,NULL,NULL\n61608151,19/05/2015 23:10,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE INSIDE CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Eltham,NULL,Queenscroft Road,20801224,SE9,NULL,NULL,541950,174250,NULL,NULL\n62262151,21/05/2015 13:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000464,Longthornton,E09000024,Merton,Norbury,NULL,Oakleigh Way,22104836,CR4,NULL,NULL,528850,169650,NULL,NULL\n62643151,22/05/2015 09:25,2015,2015/16,Special Service,1,1,298,298,TO ASSIST RSPCA WITH FOX CUB STUCK IN CHIMNEY,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000465,Lower Morden,E09000024,Merton,Sutton,NULL,Aragon Road,22100186,SM4,NULL,NULL,524250,166550,NULL,NULL\n62700151,22/05/2015 11:34,2015,2015/16,Special Service,2,10,298,2980,HORSE FALLEN INTO SWIMMING  POOL,Horse,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from water,Animal rescue from water - Farm animal,E05000117,Darwin,E09000006,Bromley,Biggin Hill,1.0002E+11,Cudham Lane South,20302994,TN14,544910,158822,544950,158850,51.3100692,0.077708109\n62874151,22/05/2015 17:03,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000520,Hampton,E09000027,Richmond upon Thames,Twickenham,NULL,Chestnut Avenue,22401361,TW12,NULL,NULL,513250,170250,NULL,NULL\n63402151,23/05/2015 18:57,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN GATE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000034,Heath,E09000002,Barking and Dagenham,Dagenham,NULL,Heathway,NULL,RM9,548930,186115,548950,186150,51.55426942,0.146807173\n63740151,24/05/2015 13:20,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Dog,Police,Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000218,Eltham North,E09000011,Greenwich,Eltham,1.00023E+11,Well Hall Road,20801573,SE9,542498,175088,542450,175050,51.45684315,0.049644779\n63750151,24/05/2015 13:44,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000208,Southgate,E09000010,Enfield,Southgate,NULL,Pennington Drive,20706126,N21,NULL,NULL,530450,195850,NULL,NULL\n63868151,24/05/2015 18:15,2015,2015/16,Special Service,1,1,298,298,\"PIGEON STUCK IN NETTING, ASSIST RSPCA\",Bird,Person (mobile),Mosque,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000288,Greenhill,E09000015,Harrow,Harrow,1.00023E+11,Station Road,21201079,HA1,515635,189125,515650,189150,51.58912329,-0.33220625\n64161151,25/05/2015 12:17,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Dental surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000106,Bickley,E09000006,Bromley,Bromley,1.0002E+11,Southborough Lane,20300617,BR2,542720,167656,542750,167650,51.39000475,0.04984696\n64323151,25/05/2015 19:25,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,Heston East,E09000018,Hounslow,Southall,NULL,Fern Lane,21500440,TW5,NULL,NULL,512750,178050,NULL,NULL\n64543151,26/05/2015 08:48,2015,2015/16,Special Service,1,1,298,298,DOG WITH KARABINER STUCK IN MOUTH,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000184,Northolt Mandeville,E09000009,Ealing,Northolt,12047902,Mandeville Road,20601113,UB5,513023,184175,513050,184150,51.54516254,-0.37148485\n64558151,26/05/2015 09:51,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,Town,E09000013,Hammersmith and Fulham,Fulham,NULL,Bishops Road,21000124,SW6,NULL,NULL,524850,177050,NULL,NULL\n64581151,26/05/2015 11:11,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED TUNNEL BEHIND BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,Lady Margaret,E09000009,Ealing,Southall,NULL,Allenby Road,20600040,UB1,NULL,NULL,513550,181850,NULL,NULL\n64717151,26/05/2015 16:05,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN TRAMPOLINE SPRINGS,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000562,St Helier,E09000029,Sutton,Mitcham,5870070009,Green Wrythe Lane,22600593,SM5,527236,166513,527250,166550,51.38341105,-0.172974292\n65406151,28/05/2015 00:34,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Police,Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,1.00022E+11,Perryfield Way,22403728,TW10,516905,172622,516950,172650,51.44053744,-0.319350843\n65444151,28/05/2015 02:36,2015,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000519,\"Ham, Petersham and Richmond Riverside\",E09000027,Richmond upon Thames,Kingston,1.00022E+11,Perryfield Way,22403728,TW10,516905,172622,516950,172650,51.44053744,-0.319350843\n66034151,29/05/2015 09:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN NETTING AT HEIGHT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Medway Close,22306129,IG1,NULL,NULL,544450,185150,NULL,NULL\n66184151,29/05/2015 15:33,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011247,Loxford,E09000026,Redbridge,Ilford,NULL,Roden Street,NULL,IG1,543410,186078,543450,186050,51.55536568,0.067227654\n66205151,29/05/2015 16:19,2015,2015/16,Special Service,1,1,298,298,ABANDONED CALL VERIFIED AS COMING FROM PRIV SUB,Unknown - Domestic Animal Or Pet,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000310,Gooshays,E09000016,Havering,Harold Hill,1.00021E+11,Newbury Gardens,21300162,RM3,553657,192016,553650,192050,51.60602205,0.21751414\n66385151,29/05/2015 22:07,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD STUCK IN WALL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000557,Belmont,E09000029,Sutton,Sutton,NULL,Holland Avenue,22602396,SM2,NULL,NULL,525150,162950,NULL,NULL\n66396151,29/05/2015 22:37,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000171,Cleveland,E09000009,Ealing,Ealing,12018312,Copley Close,20600438,W7,515930,181218,515950,181250,51.51799696,-0.330553877\n66728151,30/05/2015 17:46,2015,2015/16,Special Service,1,3,298,894,HORSE IN PRECARIOUS POSITION,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05004176,Waltham Abbey North East,E07000072,Epping Forest,Essex,1.00091E+11,Sewardstone Road,13801212,EN9,538224,200728,538250,200750,51.68830354,-0.001752217\n66802151,30/05/2015 19:12,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN TRAMPOLINE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000441,Crofton Park,E09000023,Lewisham,Forest Hill,1.00022E+11,Crofton Park Road,22001382,SE4,536741,174310,536750,174350,51.45126989,-0.033464738\n67456151,01/06/2015 10:12,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED  BETWEEN SPIKES,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000487,Little Ilford,E09000025,Newham,Ilford,NULL,Grantham Road,22200183,E12,NULL,NULL,543150,186050,NULL,NULL\n68719151,04/06/2015 00:24,2015,2015/16,Special Service,1,1,298,298,INJURED CAT TRAPPED IN BASEMENT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009390,Campden,E09000020,Kensington and Chelsea,Kensington,NULL,Campden Hill,21700632,W8,NULL,NULL,525050,179850,NULL,NULL\n68816151,04/06/2015 07:14,2015,2015/16,Special Service,1,1,298,298,FOX CAUGHT WITHIN FENCING,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000598,Hatch Lane,E09000031,Waltham Forest,Woodford,1.00023E+11,Nightingale Avenue,22861250,E4,539070,192312,539050,192350,51.61246903,0.007134444\n68904151,04/06/2015 12:15,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH  CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Floyd Road,20800581,SE7,NULL,NULL,541250,178350,NULL,NULL\n69015151,04/06/2015 15:57,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Kilburn High Road,NULL,NW6,524679,184642,524650,184650,51.54690846,-0.203311321\n69594151,05/06/2015 15:12,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Lovel Avenue,20100881,DA16,NULL,NULL,546350,176350,NULL,NULL\n69618151,05/06/2015 16:07,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011223,Crook Log,E09000004,Bexley,Bexley,1.0002E+11,Devonshire Road,20100446,DA6,548356,175303,548350,175350,51.45727135,0.133989412\n69630151,05/06/2015 16:28,2015,2015/16,Special Service,1,1,298,298,DISABLED CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,Fairfield,E09000032,Wandsworth,Battersea,NULL,Nantes Close,NULL,SW18,526384,175174,526350,175150,51.46143974,-0.182125603\n70098151,06/06/2015 13:26,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000250,Addison,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Lakeside Road,21000485,W14,NULL,NULL,523750,179750,NULL,NULL\n70177151,06/06/2015 15:50,2015,2015/16,Special Service,1,1,298,298,DOG  TRAPPED  NEAR   RAILWAY  LINE,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000484,Forest Gate South,E09000025,Newham,Stratford,46034335,Hampton Road,22207742,E7,540848,185419,540850,185450,51.55008928,0.030034329\n70697151,07/06/2015 13:46,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK ON CHIMNEY STACK,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000189,Southall Broadway,E09000009,Ealing,Southall,NULL,Bankside,20600112,UB1,NULL,NULL,511750,180550,NULL,NULL\n72062151,09/06/2015 21:37,2015,2015/16,Special Service,1,1,298,298,MUNTJAC  DEER STUCK IN CANAL,Deer,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000345,Yiewsley,E09000017,Hillingdon,Hillingdon,NULL,St. Stephens Road,NULL,UB7,505908,180376,505950,180350,51.51239537,-0.475190822\n72135151,10/06/2015 01:37,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,6040385,Approach Road,22700080,E2,535216,183125,535250,183150,51.53085126,-0.052022869\n72237151,10/06/2015 10:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000326,Brunel,E09000017,Hillingdon,Hillingdon,1.00021E+11,St. Helens Close,21401819,UB8,505797,181657,505750,181650,51.52393015,-0.476405674\n72256151,10/06/2015 11:14,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,St Peter's,E09000030,Tower Hamlets,Bethnal Green,6040380,Approach Road,22700080,E2,535246,183112,535250,183150,51.53072725,-0.051595625\n72572151,10/06/2015 20:25,2015,2015/16,Special Service,1,1,298,298,DOG WITH LEG TRAPPED IN WIRE BASKET,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,Bromley Common and Keston,E09000006,Bromley,Bromley,NULL,Crown Lane,20300141,BR2,NULL,NULL,541950,167550,NULL,NULL\n72730151,11/06/2015 02:26,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD STUCK IN PLASTIC BOX,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000305,West Harrow,E09000015,Harrow,Northolt,1.00021E+11,Twyford Road,21202027,HA2,513731,187606,513750,187650,51.57585787,-0.360170211\n72985151,11/06/2015 14:54,2015,2015/16,Special Service,1,1,298,298,BIRD WITH LEG TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000041,Village,E09000002,Barking and Dagenham,Dagenham,NULL,Harrison Road,19900495,RM10,NULL,NULL,549550,184650,NULL,NULL\n73316151,11/06/2015 23:20,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED INSIDE KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000631,Bayswater,E09000033,Westminster,North Kensington,NULL,Westbourne Grove,8400743,W11,NULL,NULL,525250,181150,NULL,NULL\n73809151,12/06/2015 20:16,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000616,Graveney,E09000032,Wandsworth,Tooting,NULL,Trevelyan Road,22905561,SW17,NULL,NULL,527350,170950,NULL,NULL\n73913151,13/06/2015 02:09,2015,2015/16,Special Service,1,1,298,298,FOX WITH HEAD TRAPPED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000305,West Harrow,E09000015,Harrow,Northolt,NULL,Twyford Road,21202027,HA2,NULL,NULL,513750,187650,NULL,NULL\n74120151,13/06/2015 15:03,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009371,De Beauvoir,E09000012,Hackney,Islington,NULL,Ardleigh Road,20900079,N1,NULL,NULL,533050,184450,NULL,NULL\n74134151,13/06/2015 15:39,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED BETWEEN WALL AND NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,207185363,Church Street,20704052,N9,534309,193547,534350,193550,51.62472259,-0.061110776\n74194151,13/06/2015 17:19,2015,2015/16,Special Service,1,1,298,298,CAT FALLEN AND TRAPPED BETWEEN WALLS,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,Haggerston,E09000012,Hackney,Shoreditch,NULL,Weymouth Terrace,20901068,E2,NULL,NULL,533750,183150,NULL,NULL\n74524151,14/06/2015 10:42,2015,2015/16,Special Service,1,1,298,298,CAT UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000440,Catford South,E09000023,Lewisham,Lewisham,NULL,Braidwood Road,22001262,SE6,NULL,NULL,538750,173150,NULL,NULL\n74587151,14/06/2015 13:44,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON IN TREE,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009330,St Katharine's & Wapping,E09000030,Tower Hamlets,Shadwell,NULL,Benson Quay,NULL,E1W,535089,180587,535050,180550,51.50807458,-0.054825267\n74755151,14/06/2015 20:26,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000031,Eastbury,E09000002,Barking and Dagenham,Barking,NULL,Keir Hardie Way,19900508,IG11,NULL,NULL,546350,184050,NULL,NULL\n74900151,15/06/2015 07:32,2015,2015/16,Special Service,1,1,298,298,FOX CAUGHT IN NETTING,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011488,West Thornton,E09000008,Croydon,Norbury,NULL,Ashley Road,20500643,CR7,NULL,NULL,530950,168050,NULL,NULL\n75167151,15/06/2015 17:38,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000599,High Street,E09000031,Waltham Forest,Walthamstow,NULL,Northcote Road,22862000,E17,NULL,NULL,536350,189150,NULL,NULL\n75521151,16/06/2015 12:43,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED IN EXCAVATION,Bird,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000637,Knightsbridge and Belgravia,E09000033,Westminster,Chelsea,NULL,Ennismore Gardens Mews,NULL,SW7,NULL,NULL,527150,179350,NULL,NULL\n75557151,16/06/2015 14:15,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN GUTTERING,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,South Richmond,E09000027,Richmond upon Thames,Richmond,NULL,Marlborough Road,22403656,TW10,NULL,NULL,518550,174250,NULL,NULL\n75618151,16/06/2015 15:57,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000328,Charville,E09000017,Hillingdon,Hillingdon,1.00023E+11,Kingshill Avenue,21401102,UB4,509691,182664,509650,182650,51.53223858,-0.419986597\n454152,18/06/2015 11:29,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000600,Higham Hill,E09000031,Waltham Forest,Walthamstow,NULL,Osprey Close,22864025,E17,NULL,NULL,536350,190950,NULL,NULL\n76309151,18/06/2015 18:29,2015,2015/16,Special Service,1,1,298,298,CAT  STUCK IN RAILINGS,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000422,Gipsy Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Benton's Rise,21900175,SE27,532589,171392,532550,171350,51.42603358,-0.094273481\n76462151,18/06/2015 23:49,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED UNDER FLOORBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Harwood Road,21000422,SW6,NULL,NULL,525550,177050,NULL,NULL\n76536151,19/06/2015 04:32,2015,2015/16,Special Service,1,3,298,894,KITTEN TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000260,Parsons Green and Walham,E09000013,Hammersmith and Fulham,Fulham,NULL,Harwood Road,21000422,SW6,NULL,NULL,525550,177050,NULL,NULL\n76975151,19/06/2015 22:39,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Dog,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000446,Ladywell,E09000023,Lewisham,Lewisham,1.00022E+11,Phoebeth Road,22001803,SE4,537411,174862,537450,174850,51.45606832,-0.023614399\n77276151,20/06/2015 15:40,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH BIRD RESCUE,Bird,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Animal rescue from height - Bird,E05000428,St Leonard's,E09000022,Lambeth,Norbury,2E+11,Stanthorpe Road,21901295,SW16,530190,171523,530150,171550,51.42776684,-0.128711366\n77381151,20/06/2015 17:56,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BETWEEN WALL AND FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000285,Belmont,E09000015,Harrow,Stanmore,NULL,Lyon Meade,21202510,HA7,NULL,NULL,517550,190750,NULL,NULL\n77509151,20/06/2015 22:52,2015,2015/16,Special Service,1,1,298,298,DOG ON RIVERBANK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Willow Bank,NULL,SW6,524339,175791,524350,175750,51.46743721,-0.211330927\n77683151,21/06/2015 12:04,2015,2015/16,Special Service,1,2,298,596,TWO CATS STUCK IN WALL CAVITY,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,Lady Margaret,E09000009,Ealing,Southall,NULL,Fermoy Road,20600664,UB6,NULL,NULL,513750,181950,NULL,NULL\n78282151,22/06/2015 18:13,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN ROOF,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,1.00021E+11,Stoke Newington High Street,20900977,N16,533575,186225,533550,186250,51.55909964,-0.074491119\n78334151,22/06/2015 20:11,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,The Lowe,22304744,IG7,NULL,NULL,546150,192150,NULL,NULL\n78581151,23/06/2015 12:03,2015,2015/16,Special Service,1,1,298,298,PIGEON WITH LEGS TRAPPED IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05011106,North Bermondsey,E09000028,Southwark,Dockhead,NULL,Ness Street,NULL,SE16,534167,179263,534150,179250,51.49639606,-0.068606134\n79068151,24/06/2015 11:17,2015,2015/16,Special Service,1,1,298,298,CAT LOCKED IN UNOCCUPIED FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000259,Palace Riverside,E09000013,Hammersmith and Fulham,Fulham,NULL,Fulham Palace Road,21000356,SW6,NULL,NULL,523950,176850,NULL,NULL\n79690151,25/06/2015 15:48,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BETWEEN SEATS IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,Eastmead Avenue,NULL,UB6,513666,182721,513650,182750,51.53196536,-0.362684847\n79954151,25/06/2015 22:24,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH SEAGULL IN NETTING,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000130,Camden Town with Primrose Hill,E09000007,Camden,Euston,NULL,Regents Park Road,NULL,NW1,527591,183819,527550,183850,51.53886197,-0.161636575\n79960151,25/06/2015 22:42,2015,2015/16,Special Service,1,1,298,298,TORTOISE TRAPPED,Tortoise,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000411,St James,E09000021,Kingston upon Thames,New Malden,1.00022E+11,Blakes Avenue,21800146,KT3,521833,167461,521850,167450,51.39311556,-0.250252708\n80063151,26/06/2015 00:59,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON BAY WINDOW,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Carthew Road,21000187,W6,NULL,NULL,522750,179250,NULL,NULL\n80434151,26/06/2015 16:26,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN UNDERGROWTH/BRAMBLES,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,Blackheath Westcombe,E09000011,Greenwich,East Greenwich,NULL,Combe Mews,NULL,SE3,539786,177276,539750,177250,51.47718008,0.011500176\n80630151,26/06/2015 21:00,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN WINE WRACK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000518,Fulwell and Hampton Hill,E09000027,Richmond upon Thames,Twickenham,NULL,Church Road,22403381,TW11,NULL,NULL,515450,171450,NULL,NULL\n80823151,27/06/2015 06:53,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BY METAL RAILING,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000428,St Leonard's,E09000022,Lambeth,Norbury,1.00022E+11,Hambro Road,21900658,SW16,529889,171091,529850,171050,51.42395352,-0.133196661\n81064151,27/06/2015 16:38,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,Lea Bridge,E09000012,Hackney,Stoke Newington,NULL,Inver Close,20901843,E5,NULL,NULL,535050,186750,NULL,NULL\n81487151,28/06/2015 06:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000142,Regent's Park,E09000007,Camden,Euston,NULL,Euston Street,NULL,NW1,529454,182559,529450,182550,51.5271148,-0.135251843\n81634151,28/06/2015 11:44,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO CAT IN TREE,Cat,Person (land line),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000332,Hillingdon East,E09000017,Hillingdon,Hillingdon,1.00021E+11,Grosvenor Crescent,21400855,UB10,507730,184144,507750,184150,51.54591854,-0.447793805\n81679151,28/06/2015 13:36,2015,2015/16,Special Service,1,1,298,298,CAT ON ROOF,Cat,Person (mobile),Gym,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,Haverstock,E09000007,Camden,Kentish Town,5134816,Chalk Farm Road,20400624,NW1,528273,184399,528250,184350,51.54392,-0.151596905\n81701151,28/06/2015 14:36,2015,2015/16,Special Service,1,1,298,298,SQUIRRELL TRAPPED IN GUTTER,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000311,Hacton,E09000016,Havering,Hornchurch,NULL,Laburnum Walk,21300049,RM12,NULL,NULL,553150,185750,NULL,NULL\n81712151,28/06/2015 15:14,2015,2015/16,Special Service,1,1,298,298,INJURED CAT ON OTHER SIDE OF FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000038,River,E09000002,Barking and Dagenham,Dagenham,100100925,Chequers Lane,19900655,RM9,549067,183484,549050,183450,51.53059305,0.147669046\n81769151,28/06/2015 18:00,2015,2015/16,Special Service,1,1,298,298,RAVEN TRAPPED IN FENCING,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000256,Hammersmith Broadway,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Banim Street,NULL,W6,522868,178838,522850,178850,51.49514274,-0.231438641\n81799151,28/06/2015 19:14,2015,2015/16,Special Service,1,1,298,298,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011116,South Bermondsey,E09000028,Southwark,Dockhead,NULL,Spa Road,NULL,SE16,534069,179266,534050,179250,51.49644626,-0.070015908\n82173151,29/06/2015 14:19,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED ON  LEDGE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,Crouch End,E09000014,Haringey,Hornsey,NULL,Crouch End Hill,21106570,N8,NULL,NULL,530050,188250,NULL,NULL\n82302151,29/06/2015 17:39,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON HOUSE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000173,Ealing Broadway,E09000009,Ealing,Ealing,NULL,Argyle Road,20602281,W13,NULL,NULL,516450,181450,NULL,NULL\n82434151,29/06/2015 20:43,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED DOWN FOX HOLE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011245,Hainault,E09000026,Redbridge,Hainault,1.00022E+11,Romford Road,22304722,IG7,546737,192748,546750,192750,51.61444301,0.117968359\n82803151,30/06/2015 14:49,2015,2015/16,Special Service,1,1,298,298,CAT IN RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000362,Isleworth,E09000018,Hounslow,Heston,NULL,Pankhurst Close,NULL,TW7,515804,175985,515850,175950,51.47098934,-0.334085006\n82911151,30/06/2015 17:07,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN FENCE AND HOUSE,Cat,Other FRS,House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011224,East Wickham,E09000004,Bexley,Plumstead,NULL,Glenmore Road,20100605,DA16,NULL,NULL,545850,176750,NULL,NULL\n84450151,02/07/2015 17:24,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON ROOF,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,St James,E09000021,Kingston upon Thames,New Malden,NULL,Malden Road,21800637,KT3,NULL,NULL,521650,167150,NULL,NULL\n84516151,02/07/2015 18:38,2015,2015/16,Special Service,1,1,298,298,DOG IN DISTRESS IN POND,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000456,Cannon Hill,E09000024,Merton,New Malden,NULL,Cannon Hill Lane,NULL,SW20,524082,168487,524050,168450,51.40184967,-0.217585305\n85251151,03/07/2015 20:47,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,Woodhouse,E09000003,Barnet,Finchley,NULL,Torrington Park,20043000,N12,NULL,NULL,526950,192450,NULL,NULL\n85293151,03/07/2015 22:08,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GUTTER,Cat,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000516,Barnes,E09000027,Richmond upon Thames,Hammersmith,1.00022E+11,Nassau Road,22404814,SW13,521871,176570,521850,176550,51.47497457,-0.246575519\n85632151,04/07/2015 09:31,2015,2015/16,Special Service,1,1,298,298,DUCKLINGS STUCK IN DRAIN PIPE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000133,Frognal and Fitzjohns,E09000007,Camden,West Hampstead,NULL,Buckland Crescent,20400428,NW3,NULL,NULL,526750,184550,NULL,NULL\n85925151,04/07/2015 17:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,Gospel Oak,E09000007,Camden,Kentish Town,NULL,Constantine Road,20400194,NW3,NULL,NULL,527450,185550,NULL,NULL\n86195151,04/07/2015 21:55,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000273,Hornsey,E09000014,Haringey,Hornsey,NULL,North View Road,NULL,N8,529850,189611,529850,189650,51.59039812,-0.126944673\n86335151,05/07/2015 00:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000174,Ealing Common,E09000009,Ealing,Ealing,12090082,Ranelagh Road,20601422,W5,518019,179832,518050,179850,51.50510817,-0.300924222\n86474151,05/07/2015 08:25,2015,2015/16,Special Service,1,2,298,596,KITTEN TRAPPED IN RUBBISH,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,Bexley,Sidcup,NULL,Ellenborough Road,NULL,DA14,547886,171016,547850,171050,51.41887352,0.125439539\n87185151,06/07/2015 16:51,2015,2015/16,Special Service,1,1,298,298,CAT WITH PAW TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000286,Canons,E09000015,Harrow,Stanmore,NULL,Rusper Close,21200918,HA7,NULL,NULL,517350,192550,NULL,NULL\n87525151,07/07/2015 07:41,2015,2015/16,Special Service,1,1,298,298,SMALL DEER STUCK IN FENCING,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000195,Chase,E09000010,Enfield,Enfield,207164248,Theobalds Park Road,20703010,EN2,531624,199680,531650,199650,51.68046997,-0.09756852\n87605151,07/07/2015 11:47,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BEHIND SINK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Kelmscott Gardens,21000468,W12,NULL,NULL,522250,179450,NULL,NULL\n87640151,07/07/2015 12:57,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,Forest Gate South,E09000025,Newham,Stratford,NULL,Manbey Park Road,22201497,E15,NULL,NULL,539250,184950,NULL,NULL\n87643151,07/07/2015 13:06,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAVITY WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Rowcross Street,22502146,SE1,NULL,NULL,533850,178350,NULL,NULL\n87666151,07/07/2015 14:03,2015,2015/16,Special Service,1,1,298,298,CAT ON TOP OF ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000341,Uxbridge South,E09000017,Hillingdon,Hillingdon,NULL,Waterloo Road,21402084,UB8,NULL,NULL,505050,183250,NULL,NULL\n87918151,07/07/2015 19:20,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN HOUSE GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,Becontree,E09000002,Barking and Dagenham,Dagenham,NULL,Crossway,19900101,RM8,NULL,NULL,547150,186150,NULL,NULL\n87944151,07/07/2015 19:57,2015,2015/16,Special Service,1,1,298,298,SMALL DOG TRAPPED ON LEDGE,Dog,Police,Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,Earl's Court,E09000020,Kensington and Chelsea,Kensington,NULL,Kenway Road,21700298,SW5,NULL,NULL,525550,178750,NULL,NULL\n88082151,07/07/2015 22:41,2015,2015/16,Special Service,1,1,298,298,CHICKEN IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000429,Stockwell,E09000022,Lambeth,Lambeth,NULL,Dorset Road,NULL,SW8,530581,177278,530550,177250,51.4793963,-0.120968698\n88197151,08/07/2015 08:06,2015,2015/16,Special Service,1,1,298,298,CAT WITH INJURY STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000429,Stockwell,E09000022,Lambeth,Clapham,NULL,Larkhall Lane,21900847,SW4,NULL,NULL,530150,176650,NULL,NULL\n88357151,08/07/2015 14:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,Croydon,Croydon,NULL,Chapman Road,NULL,CR0,531107,166453,531150,166450,51.38199231,-0.117401578\n88419151,08/07/2015 16:46,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000469,Raynes Park,E09000024,Merton,New Malden,48072152,West Barnes Lane,22106627,SW20,522847,169231,522850,169250,51.40880499,-0.235073489\n89099151,09/07/2015 19:24,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000418,Clapham Common,E09000022,Lambeth,Clapham,NULL,Oaklands Estate,21901167,SW4,NULL,NULL,529550,174050,NULL,NULL\n89212151,09/07/2015 22:00,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK UNDER HOUSE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011109,Old Kent Road,E09000028,Southwark,Old Kent Road,NULL,Naylor Road,22501758,SE15,NULL,NULL,534550,177350,NULL,NULL\n89445151,10/07/2015 09:56,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,Southbury,E09000010,Enfield,Enfield,NULL,Broadfield Square,20702530,EN1,NULL,NULL,534750,196950,NULL,NULL\n89682151,10/07/2015 16:28,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Bird,Person (land line),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000125,Plaistow and Sundridge,E09000006,Bromley,Bromley,1.00023E+11,Burnt Ash Lane,20302356,BR1,540710,171176,540750,171150,51.42213639,0.022373383\n90176151,11/07/2015 06:39,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000527,St Margarets and North Twickenham,E09000027,Richmond upon Thames,Twickenham,NULL,Hill View Road,22403562,TW1,NULL,NULL,516150,174150,NULL,NULL\n90253151,11/07/2015 10:13,2015,2015/16,Special Service,1,1,298,298,DISTRESSED DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000052,Garden Suburb,E09000003,Barnet,West Hampstead,200086166,North End Road,20031620,NW11,525496,187362,525450,187350,51.5711723,-0.190565333\n90602151,11/07/2015 17:59,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RESERVOIR,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000197,Edmonton Green,E09000010,Enfield,Edmonton,NULL,Ardra Road,NULL,N9,535751,193419,535750,193450,51.6232263,-0.040341545\n90847151,11/07/2015 23:53,2015,2015/16,Special Service,1,1,298,298,POSSIBLY INJURED CAT ON STUCK ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,Southfields,E09000032,Wandsworth,Wandsworth,NULL,Astonville Street,22900173,SW18,NULL,NULL,525150,173350,NULL,NULL\n90951151,12/07/2015 07:47,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN NETTING,Bird,Person (land line),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,10034920945,The Broadway,22306023,IG8,540957,191847,540950,191850,51.60782195,0.034182318\n91139151,12/07/2015 16:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000437,Bellingham,E09000023,Lewisham,Lewisham,NULL,Swallands Road,22001945,SE6,NULL,NULL,537450,172050,NULL,NULL\n91583151,13/07/2015 17:08,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN QUAGGY,Dog,Person (mobile),Mine or quarry (not above ground building),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000224,Middle Park and Sutcliffe,E09000011,Greenwich,Lee Green,NULL,Eltham Palace Road,NULL,SE9,541262,174156,541250,174150,51.44877776,0.031494726\n91801151,14/07/2015 06:20,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK ON LAMP POST - HANGING FROM STRING,Bird,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000177,Greenford Broadway,E09000009,Ealing,Southall,NULL,The Broadway,NULL,UB6,514240,182329,514250,182350,51.5283264,-0.354540141\n92111151,14/07/2015 20:18,2015,2015/16,Special Service,1,1,298,298,FALCON TRAPPED IN NETTING ON CANOPY,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000448,Lewisham Central,E09000023,Lewisham,Lewisham,2.00001E+11,Molesworth Street,22004181,SE13,538198,175663,538150,175650,51.46307495,-0.011981701\n92363151,15/07/2015 10:53,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Clyde Place,22827550,E10,NULL,NULL,537650,187550,NULL,NULL\n92529151,15/07/2015 18:29,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN HOLE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000200,Grange,E09000010,Enfield,Southgate,207013027,Waverley Road,20703037,EN2,531866,196584,531850,196550,51.65259183,-0.095236949\n92554151,15/07/2015 19:17,2015,2015/16,Special Service,1,1,298,298,BIRD IN PRECARIOUS POSITION,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,West End,E09000033,Westminster,Soho,1.00023E+11,Warwick Street,8401043,W1B,529340,180749,529350,180750,51.51087462,-0.137558294\n92817151,16/07/2015 10:46,2015,2015/16,Special Service,1,1,298,298,INJURED SEAGULL RESCUED,Bird,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,Osterley and Spring Grove,E09000018,Hounslow,Heston,1.00023E+11,London Road,21500707,TW7,515166,176444,515150,176450,51.47524488,-0.343117224\n93151151,16/07/2015 22:00,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000329,Eastcote and East Ruislip,E09000017,Hillingdon,Ruislip,NULL,Pinn Way,NULL,HA4,509284,188108,509250,188150,51.58124881,-0.424161122\n93295151,17/07/2015 03:52,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000216,Charlton,E09000011,Greenwich,East Greenwich,NULL,Elliscombe Road,20800517,SE7,NULL,NULL,541250,177850,NULL,NULL\n93368151,17/07/2015 08:46,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,Tottenham Green,E09000014,Haringey,Tottenham,NULL,Seven Sisters Road,NULL,N15,NULL,NULL,533450,188750,NULL,NULL\n93378151,17/07/2015 09:45,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN A TREE  RSPCA OFFICER ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,34110102,Coningham Road,21000235,W12,522600,179985,522650,179950,51.50550939,-0.234898885\n93645151,17/07/2015 17:33,2015,2015/16,Special Service,1,1,298,298,FERRET STUCK IN LIFT SHAFT,Ferret,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000409,Norbiton,E09000021,Kingston upon Thames,Kingston,NULL,Willingham Way,21801048,KT1,NULL,NULL,519150,169050,NULL,NULL\n93873151,17/07/2015 23:54,2015,2015/16,Special Service,1,1,298,298,CAT IN PRECARIOUS POSITION,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Welsford Street,22502678,SE1,NULL,NULL,534150,178550,NULL,NULL\n94730151,19/07/2015 12:53,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,Askew,E09000013,Hammersmith and Fulham,Hammersmith,NULL,Coningham Road,NULL,W12,522580,180011,522550,180050,51.50574739,-0.235177879\n94775151,19/07/2015 14:25,2015,2015/16,Special Service,1,1,298,298,INJURED DOG,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Kingston Road,NULL,SW19,525447,170016,525450,170050,51.41529116,-0.197431868\n95290151,20/07/2015 09:45,2015,2015/16,Special Service,1,1,298,298,BIRD STUCK IN NETTING,Bird,Person (land line),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,Redbridge,Woodford,NULL,The Broadway,NULL,IG8,540909,191823,540950,191850,51.60761829,0.033479979\n95362151,20/07/2015 12:38,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN WELL,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011095,Borough & Bankside,E09000028,Southwark,Dowgate,2.00003E+11,Union Street,22502587,SE1,532047,179969,532050,179950,51.5032395,-0.098864219\n95780151,21/07/2015 08:32,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR SEAT,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000096,Northwick Park,E09000005,Brent,Wembley,NULL,Littleton Road,NULL,HA1,515999,186549,515950,186550,51.56589655,-0.32780485\n95816151,21/07/2015 10:18,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH FOX IMPALED ON WIRE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000087,Brondesbury Park,E09000005,Brent,Willesden,202136167,All Souls Avenue,20202012,NW10,522957,183603,522950,183650,51.53794811,-0.228495477\n95912151,21/07/2015 12:34,2015,2015/16,Special Service,1,2,298,596,CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,De Beauvoir,E09000012,Hackney,Shoreditch,NULL,Whitmore Road,NULL,N1,533136,183753,533150,183750,51.53698915,-0.081754272\n96926151,22/07/2015 21:10,2015,2015/16,Special Service,1,2,298,596,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,Haggerston,E09000012,Hackney,Bethnal Green,NULL,Goldsmiths Row,NULL,E2,534318,183363,534350,183350,51.53320435,-0.064870438\n97144151,23/07/2015 09:38,2015,2015/16,Special Service,1,1,298,298,DOG WITH LEG TRAPPED IN COLLAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Carlton Vale,8400641,NW6,NULL,NULL,525750,183150,NULL,NULL\n97855151,24/07/2015 12:16,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED IN CONFINED ROOF SPACE OF WAREHOUSE,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000230,Woolwich Riverside,E09000011,Greenwich,Plumstead,10010227714,Seymour Street,20801860,SE18,544277,179046,544250,179050,51.49195801,0.076847884\n98158151,24/07/2015 22:26,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,South Bermondsey,E09000028,Southwark,Old Kent Road,NULL,Chaucer Drive,22500491,SE1,NULL,NULL,533850,178550,NULL,NULL\n98359151,25/07/2015 10:50,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RESERVOIR,Dog,Person (mobile),\"Tunnel, subway\",Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000212,Upper Edmonton,E09000010,Enfield,Edmonton,NULL,Stonehill Business Park,NULL,N18,536073,192057,536050,192050,51.61090951,-0.036221193\n98437151,25/07/2015 13:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011105,Newington,E09000028,Southwark,Lambeth,2.00003E+11,John Ruskin Street,22501369,SE5,532095,177685,532050,177650,51.48270269,-0.099027228\n98450151,25/07/2015 14:13,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,Coldharbour,E09000022,Lambeth,Brixton,NULL,Gresham Road,21901684,SW9,NULL,NULL,531350,175650,NULL,NULL\n98474151,25/07/2015 15:05,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WAS FOX TRAPPED IN BARBED WIRE,Fox,Person (land line),Park,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000379,St Mary's,E09000019,Islington,Islington,NULL,Madras Place,NULL,N7,531309,184958,531350,184950,51.54824595,-0.107632306\n98679151,25/07/2015 22:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON HANGING ON ROOF AERIAL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011471,New Addington South,E09000008,Croydon,Addington,NULL,Cator Crescent,20501681,CR0,NULL,NULL,538950,161750,NULL,NULL\n99212151,27/07/2015 00:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,Bow East,E09000030,Tower Hamlets,Bethnal Green,NULL,Vernon Road,22701264,E3,NULL,NULL,536850,183350,NULL,NULL\n100321151,29/07/2015 12:38,2015,2015/16,Special Service,1,1,298,298,SQUIRREL TRAPPED IN BOILER,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000144,Swiss Cottage,E09000007,Camden,West Hampstead,NULL,Goldhurst Terrace,20400421,NW6,NULL,NULL,526250,184350,NULL,NULL\n100841151,30/07/2015 13:13,2015,2015/16,Special Service,1,1,298,298,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000490,Plaistow South,E09000025,Newham,Plaistow,NULL,Davis Street,22207680,E13,NULL,NULL,540950,183050,NULL,NULL\n101024151,30/07/2015 19:25,2015,2015/16,Special Service,1,1,298,298,BIRDS TRAPPED NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,Cathall,E09000031,Waltham Forest,Leytonstone,NULL,Orange Grove,22863675,E11,NULL,NULL,539050,186350,NULL,NULL\n101530151,31/07/2015 19:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND CHIMNEY BREAST,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011484,South Croydon,E09000008,Croydon,Croydon,NULL,Selsdon Road,20502323,CR2,NULL,NULL,532650,164250,NULL,NULL\n101652151,31/07/2015 23:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WINDOW,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,Hoxton West,E09000012,Hackney,Shoreditch,NULL,Provost Estate,20950175,N1,NULL,NULL,532650,183050,NULL,NULL\n101699151,01/08/2015 01:54,2015,2015/16,Special Service,1,1,298,298,PIDGEON WITH LEG TRAPPED IN MESHING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011112,Rotherhithe,E09000028,Southwark,Dockhead,NULL,Canada Estate,22502073,SE16,NULL,NULL,535350,179450,NULL,NULL\n101762151,01/08/2015 06:44,2015,2015/16,Special Service,1,1,298,298,FOX CUB TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,Abbey,E09000024,Merton,Wimbledon,NULL,Laburnum Road,22103687,SW19,NULL,NULL,526450,170350,NULL,NULL\n101777151,01/08/2015 07:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,Stoke Newington,E09000012,Hackney,Stoke Newington,NULL,Yoakley Road,20901100,N16,NULL,NULL,533150,186750,NULL,NULL\n101802151,01/08/2015 09:53,2015,2015/16,Special Service,1,2,298,596,ASSIST VET WITH HORSE,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000111,Chislehurst,E09000006,Bromley,Sidcup,1.00023E+11,Hawkwood Lane,20300397,BR7,544209,169505,544250,169550,51.40624305,0.071982122\n101862151,01/08/2015 13:08,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,Leyton,E09000031,Waltham Forest,Leyton,NULL,Clyde Place,22827550,E10,NULL,NULL,537750,187550,NULL,NULL\n101876151,01/08/2015 13:25,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CROW STUCK IN HIGH WIRE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05011245,Hainault,E09000026,Redbridge,Hainault,NULL,Fox Burrow Road,NULL,IG7,547963,192913,547950,192950,51.61560518,0.135732164\n102180151,01/08/2015 23:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOOR BOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000306,Brooklands,E09000016,Havering,Romford,NULL,Mawney Road,21301408,RM7,NULL,NULL,550550,188850,NULL,NULL\n102494151,02/08/2015 16:10,2015,2015/16,Special Service,1,1,298,298,PIGEONS TRAPPED BEHIND NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000111,Chislehurst,E09000006,Bromley,Bromley,NULL,Chislehurst Road,NULL,BR7,543099,169579,543050,169550,51.40718878,0.056064275\n103004151,03/08/2015 12:13,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND THE UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009324,Island Gardens,E09000030,Tower Hamlets,Millwall,NULL,Glengarnock Avenue,NULL,E14,NULL,NULL,538450,178550,NULL,NULL\n104532151,06/08/2015 09:10,2015,2015/16,Special Service,NULL,NULL,298,NULL,DOG IN WATER,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000416,Bishop's,E09000022,Lambeth,Lambeth,NULL,Westminster Bridge Road,NULL,SE1,530534,179412,530550,179450,51.49858495,-0.120856827\n104878151,06/08/2015 21:23,2015,2015/16,Special Service,1,1,298,298,TWO KITTENS STUCK IN FENCE,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,Heston East,E09000018,Hounslow,Heston,NULL,Winchester Avenue,NULL,TW5,513148,177970,513150,177950,51.48936754,-0.37167322\n105060151,07/08/2015 09:14,2015,2015/16,Special Service,2,4,298,1192,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000095,Mapesbury,E09000005,Brent,West Hampstead,NULL,Sheldon Road,NULL,NW2,523854,185501,523850,185550,51.55481004,-0.214900866\n105257151,07/08/2015 15:23,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000413,Surbiton Hill,E09000021,Kingston upon Thames,Surbiton,1.00022E+11,Ditton Road,21800332,KT6,518265,166232,518250,166250,51.38282361,-0.301920739\n105504151,07/08/2015 22:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009401,Queen's Gate,E09000020,Kensington and Chelsea,Kensington,NULL,Stanford Road,NULL,W8,NULL,NULL,525850,179250,NULL,NULL\n105668151,08/08/2015 10:01,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000610,Balham,E09000032,Wandsworth,Tooting,10033237072,Balham High Road,22900217,SW12,528612,173430,528650,173450,51.44526515,-0.150704828\n105728151,08/08/2015 12:59,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000191,Southfield,E09000009,Ealing,Chiswick,NULL,Acton Lane,NULL,W4,520473,179572,520450,179550,51.50225434,-0.26567188\n106044151,08/08/2015 20:50,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED IN NETTING,Bird,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000367,Bunhill,E09000019,Islington,Shoreditch,5300067252,Norman Street,21605557,EC1V,532229,182465,532250,182450,51.52562769,-0.095307657\n106153151,09/08/2015 01:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON WALL NEAR RAILWAY LINE,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,Bow West,E09000030,Tower Hamlets,Bethnal Green,NULL,Cherrywood Close,NULL,E3,536325,182855,536350,182850,51.52815875,-0.036149212\n107372151,11/08/2015 11:34,2015,2015/16,Special Service,2,3,298,894,BIRD TRAPPED IN AERIAL         AL REQUESTED FROM INCIDENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,SPRING GROVE ROAD,NULL,TW7,NULL,NULL,514650,176550,NULL,NULL\n107541151,11/08/2015 17:23,2015,2015/16,Special Service,1,1,298,298,PIGEONS TRAPPED UNDER NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000634,Church Street,E09000033,Westminster,Paddington,NULL,Jerome Crescent,NULL,NW8,NULL,NULL,527050,182450,NULL,NULL\n107581151,11/08/2015 19:01,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009399,Notting Dale,E09000020,Kensington and Chelsea,North Kensington,NULL,Silchester Road,21700998,W10,NULL,NULL,523850,181150,NULL,NULL\n107655151,11/08/2015 21:03,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011479,Selhurst,E09000008,Croydon,Croydon,NULL,Saxon Road,20501365,SE25,NULL,NULL,532950,167650,NULL,NULL\n107882151,12/08/2015 10:40,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED NEAR RAILWAY LINE,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000347,Brentford,E09000018,Hounslow,Chiswick,NULL,Green Dragon Lane,NULL,TW8,518331,178160,518350,178150,51.4900156,-0.296991309\n108145151,12/08/2015 20:27,2015,2015/16,Special Service,1,1,298,298,DOG IN CANAL,Dog,Police,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000186,Norwood Green,E09000009,Ealing,Southall,NULL,Tentelow Lane,NULL,UB2,514072,179584,514050,179550,51.50368864,-0.35784892\n108828151,14/08/2015 07:13,2015,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009377,Hoxton East & Shoreditch,E09000012,Hackney,Shoreditch,10008293621,Clere Street,20900615,EC2A,532976,182321,532950,182350,51.52415827,-0.084600358\n108909151,14/08/2015 10:35,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000563,Stonecot,E09000029,Sutton,Sutton,NULL,Henley Avenue,22601567,SM3,NULL,NULL,524050,165250,NULL,NULL\n109772151,15/08/2015 23:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON LEDGE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,Maida Vale,E09000033,Westminster,Paddington,NULL,Delaware Road,8400064,W9,NULL,NULL,525650,182350,NULL,NULL\n109907151,16/08/2015 09:39,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000417,Brixton Hill,E09000022,Lambeth,West Norwood,1.00022E+11,Brixton Hill,21900236,SW2,530457,173942,530450,173950,51.44944478,-0.123982689\n110054151,16/08/2015 16:28,2015,2015/16,Special Service,1,2,298,596,CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,PENWITH ROAD,NULL,SW18,NULL,NULL,525750,173050,NULL,NULL\n110849151,18/08/2015 06:50,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WINDOW AND METAL SHUTTERS OF SHOP,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,6089472,HACKNEY ROAD,NULL,E2,533515,182699,533550,182650,51.52742796,-0.076692333\n110872151,18/08/2015 08:32,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,NULL,WICKHAM ROAD,NULL,SE4,NULL,NULL,536950,176150,NULL,NULL\n110878151,18/08/2015 08:48,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,1.00021E+11,ALBERT ROAD,NULL,N15,533158,188253,533150,188250,51.57742274,-0.079735847\n110903151,18/08/2015 09:42,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,SEVENOAKS ROAD,NULL,BR6,NULL,NULL,547250,162650,NULL,NULL\n110963151,18/08/2015 12:30,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000444,FOREST HILL,E09000023,LEWISHAM,Forest Hill,NULL,DEVONSHIRE ROAD,NULL,SE23,NULL,NULL,535550,173450,NULL,NULL\n111247151,18/08/2015 23:43,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,LONGHEATH GARDENS,NULL,CR0,NULL,NULL,535550,167850,NULL,NULL\n111887151,20/08/2015 13:00,2015,2015/16,Special Service,1,1,298,298,FOX WITH BODY TRAPPED IN CONCRETE IN BASEMENT,Fox,Other FRS,Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LAMMAS PARK ROAD,NULL,W5,NULL,NULL,517450,179950,NULL,NULL\n112003151,20/08/2015 18:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN NEIGHBOURS SHED,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEAHURST ROAD,NULL,SE13,538963,174651,538950,174650,51.45379392,-0.001373742\n112363151,21/08/2015 14:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT HANGING FROM WINDOW,Cat,Police,House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,POULETT ROAD,NULL,E6,NULL,NULL,542750,183450,NULL,NULL\n112482151,21/08/2015 18:23,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER SOLAR PANELS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,Goodmayes,E09000026,REDBRIDGE,Dagenham,NULL,GIBSON ROAD,NULL,RM8,NULL,NULL,547350,187450,NULL,NULL\n112765151,22/08/2015 09:42,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207187990,NORFOLK AVENUE,NULL,N13,531803,191730,531850,191750,51.60898683,-0.097974226\n112829151,22/08/2015 12:24,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,NULL,MURRAY ROAD,NULL,HA6,NULL,NULL,509150,191150,NULL,NULL\n113399151,23/08/2015 12:51,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA OFFICER WITH CAT IN A TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,10003621525,HARLEYFORD,NULL,BR1,540967,169871,540950,169850,51.41034579,0.025548275\n113442151,23/08/2015 14:47,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND PIPES,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,BENSON CLOSE,NULL,TW3,NULL,NULL,513150,175350,NULL,NULL\n113787151,24/08/2015 09:33,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,10003621563,HARLEYFORD,NULL,BR1,541017,169876,541050,169850,51.4103783,0.0262687\n113863151,24/08/2015 13:17,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,GOLDFINCH ROAD,NULL,SE28,NULL,NULL,544950,179450,NULL,NULL\n114558151,26/08/2015 02:18,2015,2015/16,Special Service,1,1,298,298,DEER STUCK IN METAL GATE - NOW OUT,Deer,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,NULL,RM3,555390,192166,555350,192150,51.60689437,0.242584985\n114644151,26/08/2015 09:38,2015,2015/16,Special Service,1,1,298,298,KITTENS STUCK UP TREE - BEEN THERE SOME TIME,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,1.00022E+11,BARLEY LANE,NULL,IG3,546567,188442,546550,188450,51.57579589,0.113716951\n114694151,26/08/2015 12:10,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH BIRD HANGING FROM WIRE ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WESTMINSTER ROAD,NULL,SM1,NULL,NULL,526550,165950,NULL,NULL\n114768151,26/08/2015 13:43,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,NULL,SAUNDERS ROAD,NULL,UB10,NULL,NULL,506750,183950,NULL,NULL\n115200151,26/08/2015 18:38,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,COLNEY HATCH LANE,NULL,N10,NULL,NULL,528550,190450,NULL,NULL\n115244151,26/08/2015 19:47,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BIN,Cat,Person (mobile),Wheelie bin (domestic size),Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207088523,BOUNCES ROAD,NULL,N9,534886,193995,534850,193950,51.62861048,-0.052607923\n115521151,27/08/2015 15:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA HIGH STREET,NULL,SW11,526968,176540,526950,176550,51.47358555,-0.173233649\n115869151,28/08/2015 08:29,2015,2015/16,Special Service,1,2,298,596,PONY CAUGHT IN CABLING,Horse,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011232,Thamesmead East,E09000004,BEXLEY,Plumstead,1.0002E+11,FAIRWAY DRIVE,NULL,SE28,548098,181210,548050,181250,51.51041491,0.132755493\n116020151,28/08/2015 16:50,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,HANLEY ROAD,NULL,N4,NULL,NULL,530550,187350,NULL,NULL\n116036151,28/08/2015 17:23,2015,2015/16,Special Service,1,1,298,298,DOG IN PRECARIOUS POSITION ON ROOF OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,KEATS AVENUE,NULL,RM3,NULL,NULL,552650,191050,NULL,NULL\n116055151,28/08/2015 18:52,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON WINDOW LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,NAGS HEAD ROAD,NULL,EN3,NULL,NULL,535750,196150,NULL,NULL\n116072151,28/08/2015 19:48,2015,2015/16,Special Service,1,1,298,298,CAT IN DISTRESS IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217050518,LANCASTER ROAD,NULL,W11,524762,181452,524750,181450,51.51822101,-0.203244772\n116948151,30/08/2015 18:25,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN WIRING,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,East Ham,46083846,GLENCAIRNE CLOSE,NULL,E16,541798,181916,541750,181950,51.51837405,0.042317201\n117449151,31/08/2015 20:28,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND FACIA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,DOUGLAS AVENUE,NULL,HA0,NULL,NULL,518350,184550,NULL,NULL\n117465151,31/08/2015 21:20,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN HOLE BEHIND SHED,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,LEGHORN ROAD,NULL,SE18,544916,178174,544950,178150,51.48395933,0.085688124\n117644151,01/09/2015 10:26,2015,2015/16,Special Service,1,1,298,298,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,1.00023E+11,CAPWORTH STREET,NULL,E10,537324,187427,537350,187450,51.56900162,-0.019973846\n117797151,01/09/2015 15:54,2015,2015/16,Special Service,1,1,298,298,CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,128036972,FRANKLIN CLOSE,NULL,KT1,519106,168974,519150,168950,51.40729274,-0.288923069\n117839151,01/09/2015 17:47,2015,2015/16,Special Service,1,1,298,298,KITTENS TRAPPED IN WALL VOID,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,WOODSIDE,E09000014,HARINGEY,Tottenham,NULL,SAXON ROAD,NULL,N22,NULL,NULL,531850,190850,NULL,NULL\n117994151,02/09/2015 00:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WOOLWICH NEW ROAD,NULL,SE18,NULL,NULL,543550,178650,NULL,NULL\n118271151,02/09/2015 17:14,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE    TRAPPED BIRD,Bird,Person (mobile),Post office (purpose built),Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128006223,ASHDOWN ROAD,NULL,KT1,518098,169121,518050,169150,51.40882466,-0.303360263\n118798151,03/09/2015 20:43,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT FALLEN INTO BASEMENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,UPPER CLAPTON ROAD,NULL,E5,NULL,NULL,534450,187250,NULL,NULL\n120553151,07/09/2015 14:04,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,CHURCH ROAD,NULL,UB5,NULL,NULL,511750,183250,NULL,NULL\n120557151,07/09/2015 14:22,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,CROWTHER ROAD,NULL,SE25,NULL,NULL,534350,167950,NULL,NULL\n120905151,08/09/2015 08:50,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN ENGINE OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011236,Bridge,E09000026,REDBRIDGE,Woodford,1.00022E+11,FINCHINGFIELD AVENUE,NULL,IG8,541419,191195,541450,191150,51.6018476,0.040586836\n121386151,09/09/2015 08:45,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN MOBILITY SCOOTER,Cat,Person (land line),Motorcycle,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Poplar,6072746,BROMLEY HIGH STREET,NULL,E3,537772,182851,537750,182850,51.52777221,-0.015304411\n122639151,11/09/2015 19:12,2015,2015/16,Special Service,1,2,298,596,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,CLARENDON GARDENS,NULL,IG1,NULL,NULL,543150,187550,NULL,NULL\n122906151,12/09/2015 11:50,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,SELBORNE ROAD,NULL,N14,NULL,NULL,530350,193450,NULL,NULL\n123445151,13/09/2015 12:19,2015,2015/16,Special Service,1,1,298,298,SQUIRREL TRAPPED IN BATHROOM POSSIBLY INJURED,Squirrel,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,RUTFORD ROAD,NULL,SW16,NULL,NULL,530250,171350,NULL,NULL\n123683151,13/09/2015 22:06,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,207122657,LINCOLN WAY,NULL,EN1,534699,195899,534650,195850,51.64576516,-0.054574671\n123880151,14/09/2015 10:19,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK BEHIND PIPING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDCLIFFE SQUARE,NULL,SW10,NULL,NULL,525750,178150,NULL,NULL\n123978151,14/09/2015 14:10,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK IN GULLY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,EARLHAM STREET,NULL,WC2H,NULL,NULL,530050,181050,NULL,NULL\n124052151,14/09/2015 17:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GUTTERING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,NULL,OSIER CRESCENT,NULL,N10,NULL,NULL,527850,190850,NULL,NULL\n124129151,14/09/2015 20:01,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Shoreditch,NULL,LANCASTER CLOSE,NULL,N1,NULL,NULL,533350,184050,NULL,NULL\n124298151,15/09/2015 09:42,2015,2015/16,Special Service,1,1,298,298,CAT WITH BROKEN LEG IN PIT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,BALMORE STREET,NULL,N19,NULL,NULL,528850,186650,NULL,NULL\n124673151,16/09/2015 05:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,KINGSTON ROAD,NULL,IG1,NULL,NULL,544450,185950,NULL,NULL\n124778151,16/09/2015 12:10,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200053519,GROSVENOR ROAD,NULL,N3,524964,191332,524950,191350,51.60696875,-0.196824952\n126220151,19/09/2015 16:55,2015,2015/16,Special Service,1,1,298,298,CAT IN RIVER - CAT NOW OUT OF RIVER,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009326,LIMEHOUSE,E09000030,TOWER HAMLETS,Poplar,6043002,THREE COLT STREET,NULL,E14,536828,180723,536850,180750,51.50887863,-0.029730135\n126599151,20/09/2015 08:37,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED  BEHIND BRICKWORK IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000026,ABBEY,E09000002,BARKING AND DAGENHAM,Barking,NULL,PARK AVENUE,NULL,IG11,NULL,NULL,544250,184850,NULL,NULL\n126740151,20/09/2015 14:29,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,1.00024E+11,BROCKLEY ROAD,NULL,SE4,536688,175085,536650,175050,51.45824715,-0.033927839\n127159151,21/09/2015 11:44,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,PARK CRESCENT,NULL,DA8,NULL,NULL,550650,177750,NULL,NULL\n127186151,21/09/2015 12:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,RING WAY,NULL,N11,NULL,NULL,529350,191750,NULL,NULL\n127215151,21/09/2015 13:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000596,GROVE GREEN,E09000031,WALTHAM FOREST,Leyton,NULL,ST GEORGES ROAD,NULL,E10,NULL,NULL,538250,186350,NULL,NULL\n127670151,22/09/2015 13:39,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10008328867,BROADWAY MEWS,NULL,E5,533712,187726,533750,187750,51.57255575,-0.071946074\n129183151,25/09/2015 19:59,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,ALEXANDRIA ROAD,NULL,W13,NULL,NULL,516450,180550,NULL,NULL\n129485151,26/09/2015 13:04,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,10001298528,STOCK ORCHARD CRESCENT,NULL,N7,530695,185372,530650,185350,51.55210897,-0.116328662\n130006151,27/09/2015 13:43,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,SYDNEY STREET,NULL,SW3,NULL,NULL,527050,178450,NULL,NULL\n130059151,27/09/2015 15:56,2015,2015/16,Special Service,1,1,298,298,DOG FALLEN DOWN LIGHTWELL,Dog,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,217085319,SYDNEY STREET,NULL,SW3,527204,178342,527250,178350,51.4897272,-0.169189306\n130393151,28/09/2015 10:20,2015,2015/16,Special Service,1,3,298,894,KITTEN TRAPPED ON CHIMNEY BREAST ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,COLLEGE PLACE,20400753,NW1,NULL,NULL,529350,183750,NULL,NULL\n130441151,28/09/2015 13:13,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,CHURCH HILL ROAD,22602924,SM3,NULL,NULL,523850,164850,NULL,NULL\n130996151,29/09/2015 16:23,2015,2015/16,Special Service,NULL,NULL,298,NULL,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,COLLEGE PLACE,20400753,NW1,NULL,NULL,529350,183750,NULL,NULL\n131750151,01/10/2015 06:34,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK IN TIN,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011488,West Thornton,E09000008,CROYDON,Norbury,1.00021E+11,CECIL ROAD,20500766,CR0,530526,167120,530550,167150,51.38812023,-0.125500468\n131795151,01/10/2015 09:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN EQUIPMENT AND INJURED,Cat,Person (mobile),Loose refuse,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100090325,CHURCH STREET,19900760,RM10,549959,184470,549950,184450,51.53921658,0.160938392\n132902151,03/10/2015 13:24,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000035,LONGBRIDGE,E09000002,BARKING AND DAGENHAM,Barking,NULL,LONGBRIDGE ROAD,19900226,IG11,NULL,NULL,545350,185150,NULL,NULL\n133023151,03/10/2015 17:41,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46098316,WOODGRANGE ROAD,22200290,E7,540484,185415,540450,185450,51.55014408,0.024786518\n133579151,04/10/2015 18:42,2015,2015/16,Special Service,1,1,298,298,PIGEON STUCK IN NETTING,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5132322,CLARENCE WAY,20400631,NW1,528874,184480,528850,184450,51.54451117,-0.142905265\n133653151,04/10/2015 21:02,2015,2015/16,Special Service,1,1,298,298,HORSE TRAPPED IN A PIT,Horse,Police,Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,768011217,LONG LANE SERVICE ROAD O/S HILLINGDON STATION,21402626,UB10,507674,184906,507650,184950,51.55277827,-0.448367867\n133658151,04/10/2015 21:09,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,NEW ROAD,21500795,TW3,NULL,NULL,513750,175050,NULL,NULL\n133816151,05/10/2015 10:12,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,VIVIAN SQUARE,22502634,SE15,534607,175783,534650,175750,51.46501832,-0.06359688\n134223151,06/10/2015 07:11,2015,2015/16,Special Service,1,1,298,298,DOG WITH MOUTH TRAPPED IN CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,GLOUCESTER ROAD,20018000,EN5,NULL,NULL,526350,195550,NULL,NULL\n134438151,06/10/2015 16:51,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207089309,WESTMINSTER ROAD,20704914,N9,534894,194005,534850,194050,51.62869842,-0.052488562\n134460151,06/10/2015 18:08,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48058951,RAVENSBURY GROVE,22105319,CR4,526558,168228,526550,168250,51.39897538,-0.182101254\n134713151,07/10/2015 10:49,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,1.00021E+11,SPA HILL,20500122,SE19,532602,169594,532650,169550,51.40987253,-0.094758565\n135255151,08/10/2015 14:50,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000287,EDGWARE,E09000015,HARROW,Stanmore,1.00021E+11,STAG LANE,21202393,HA8,519953,190379,519950,190350,51.59949206,-0.269476992\n135259151,08/10/2015 14:58,2015,2015/16,Special Service,1,1,298,298,PIGEON TRAPPED  IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,CROYDON,Woodside,1.00021E+11,SHIRLEY CHURCH ROAD,20500549,CR0,536196,165223,536150,165250,51.36974152,-0.044789209\n135983151,09/10/2015 22:47,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,NULL,ELMINGTON ROAD,22500901,SE5,NULL,NULL,532850,177050,NULL,NULL\n135996151,09/10/2015 23:32,2015,2015/16,Special Service,1,1,298,298,CAT POSSIBLY TRAPPED IN SCAFFOLDING NET,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,CHAUCER GARDENS,22602196,SM1,NULL,NULL,525350,165050,NULL,NULL\n136529151,11/10/2015 09:46,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000172,DORMERS WELLS,E09000009,EALING,Southall,NULL,ST GEORGES AVENUE,20601594,UB1,NULL,NULL,512650,180550,NULL,NULL\n137141151,12/10/2015 16:07,2015,2015/16,Special Service,3,6,298,1788,Redacted,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10022942449,FERRY LANE,21103287,N17,534742,189492,534750,189450,51.58818019,-0.056416095\n137226151,12/10/2015 18:51,2015,2015/16,Special Service,1,2,298,596,PARROT TRAPPED IN TREE BY HARNESS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011487,Waddon,E09000008,CROYDON,Croydon,1.00023E+11,PURLEY WAY,20502609,CR0,531129,163115,531150,163150,51.35198919,-0.118315448\n137259151,12/10/2015 19:33,2015,2015/16,Special Service,1,1,298,298,RUNNING CALL TO PARROT STUCK IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,1.00022E+11,GREEN LANE,22306487,IG3,545486,186761,545450,186750,51.56097148,0.097433418\n137772151,13/10/2015 20:42,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WALLSPACE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,NULL,DELAMERE ROAD,22102019,SW20,NULL,NULL,523750,169650,NULL,NULL\n137965151,14/10/2015 10:48,2015,2015/16,Special Service,1,1,298,298,CAT UP TREE - REQUESTED BY RSPCA,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,10002293474,PINNER ROAD,21201353,HA1,514633,188310,514650,188350,51.58200278,-0.346929697\n138240151,14/10/2015 21:46,2015,2015/16,Special Service,1,1,298,298,KITTEN STUCK IN ROOF VENTILATION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,STIRLING GROVE,21501633,TW3,NULL,NULL,514350,176350,NULL,NULL\n138559151,15/10/2015 16:33,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING UNDER RLY BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518863,189745,518850,189750,51.59402454,-0.285422\n138824151,16/10/2015 10:52,2015,2015/16,Special Service,1,1,298,298,ASSIST RSCPA WITH BIRD TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Kentish Town,NULL,BRECKNOCK ROAD,21606410,N19,NULL,NULL,529550,185550,NULL,NULL\n138880151,16/10/2015 13:31,2015,2015/16,Special Service,1,1,298,298,KITTEN WITH HEAD STUCK IN CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,GREENLEIGH AVENUE,20301141,BR5,NULL,NULL,546750,168450,NULL,NULL\n139035151,16/10/2015 19:48,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BUSH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10002255387,MELLISS AVENUE,22407046,TW9,519717,176796,519750,176750,51.47746498,-0.277499265\n139607151,18/10/2015 01:03,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BETWEEN SHED AND WALL,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,FERNDENE ROAD,21900555,SE24,532423,175310,532450,175350,51.4612826,-0.095195335\n139924151,18/10/2015 19:00,2015,2015/16,Special Service,1,1,298,298,ANIMAL POSSIBLY STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,SWINGATE LANE,20801447,SE18,NULL,NULL,545350,177750,NULL,NULL\n140409151,19/10/2015 21:07,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT TRAPPED IN ENGINE BAY,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870063899,BEYNON ROAD,22602119,SM5,527730,164189,527750,164150,51.36241411,-0.166713878\n140633151,20/10/2015 12:17,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,1.0002E+11,BROADWATER GARDENS,20300056,BR6,544188,164891,544150,164850,51.3647889,0.069810604\n140694151,20/10/2015 14:25,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,MIDDLETON ROAD,22600797,SM5,NULL,NULL,527650,166750,NULL,NULL\n141227151,21/10/2015 16:54,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA OFFICER  WITH KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,WALPOLE,E09000009,EALING,Ealing,12073474,BONCHURCH ROAD,20600205,W13,516591,180042,516550,180050,51.50729154,-0.321420416\n141363151,21/10/2015 21:48,2015,2015/16,Special Service,1,1,298,298,CAT STUCK AT THE BACK OF KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,NULL,ELLENBOROUGH ROAD,20100490,DA14,NULL,NULL,547950,171050,NULL,NULL\n141975151,23/10/2015 12:37,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BETWEEN FENCES,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000031,EASTBURY,E09000002,BARKING AND DAGENHAM,Barking,100050186,SURREY ROAD,19900597,IG11,545202,184132,545250,184150,51.53742209,0.092255623\n142147151,23/10/2015 18:22,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,CARLTON ROAD,21103060,N4,NULL,NULL,531250,187650,NULL,NULL\n142384151,24/10/2015 09:35,2015,2015/16,Special Service,1,1,298,298,SEAGULL TRAPPED ON WIRE,Bird,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,1.00023E+11,BURLINGTON LANE,21500176,W4,520934,177319,520950,177350,51.48190705,-0.259804588\n142683151,24/10/2015 23:29,2015,2015/16,Special Service,1,1,298,298,DOG STUCK BEHIND THE FIRE PLACE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000470,ST. HELIER,E09000024,MERTON,Sutton,NULL,RUSTINGTON WALK,22105577,SM4,NULL,NULL,524950,166950,NULL,NULL\n143465151,26/10/2015 15:42,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,DUNCAN GROVE,20600573,W3,NULL,NULL,521150,181350,NULL,NULL\n143492151,26/10/2015 16:30,2015,2015/16,Special Service,1,2,298,596,INJURED CAT ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009323,CANARY WHARF,E09000030,TOWER HAMLETS,Millwall,NULL,MILLHARBOUR,22700822,E14,NULL,NULL,537650,179550,NULL,NULL\n143691151,27/10/2015 02:00,2015,2015/16,Special Service,1,1,298,298,DOG BELIEVED TRAPPED NEAR RAILWAY LINE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000612,EARLSFIELD,E09000032,WANDSWORTH,Wandsworth,10070247664,GROTON ROAD,22902169,SW18,525971,172960,525950,172950,51.44163382,-0.188854978\n144454151,28/10/2015 16:40,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,BEATRICE ROAD,21102955,N4,NULL,NULL,531350,187850,NULL,NULL\n144795151,29/10/2015 12:42,2015,2015/16,Special Service,1,1,298,298,ANIMAL IN DISTRESS,Unknown - Domestic Animal Or Pet,Person (land line),Recycling plant,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,2.00002E+11,TROUT LANE,21402018,UB7,505331,180691,505350,180650,51.51533451,-0.483408516\n144805151,29/10/2015 13:42,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED MAGPIE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,SHEEPHOUSE WAY,21800852,KT3,NULL,NULL,520850,166450,NULL,NULL\n145206151,30/10/2015 10:31,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,2E+11,CROSBY WALK,21901740,SW2,531293,173823,531250,173850,51.44818223,-0.112003058\n145811151,31/10/2015 13:31,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS BY HEAD,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,10001114164,PARADISE ROAD,21901067,SW4,530196,176427,530150,176450,51.47183719,-0.126822793\n145879151,31/10/2015 16:03,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON A ROOF,Dog,Police,Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,RYE LANE,22502166,SE15,534330,176322,534350,176350,51.4699279,-0.067377406\n146647151,01/11/2015 21:19,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN CAR ENGINE,Cat,Police,Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,10008311432,SOMERFORD GROVE,20900933,N16,533678,185622,533650,185650,51.55365642,-0.073235002\n146799151,02/11/2015 10:41,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH SEAGULL TRAPPED BY WING,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200152271,BARNET WAY,20002480,NW7,520722,194830,520750,194850,51.63933009,-0.256847824\n147513151,03/11/2015 22:00,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED BETWEEN TWO WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,DIMSDALE DRIVE,20704150,EN1,NULL,NULL,534050,195050,NULL,NULL\n148019151,05/11/2015 09:56,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,48024296,EDGE HILL,22102334,SW19,523685,170279,523650,170250,51.4180416,-0.222665155\n148200151,05/11/2015 16:53,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN RAILING,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207192937,LENA CRESCENT,20706956,N9,535342,193621,535350,193650,51.62514006,-0.046168397\n148698151,06/11/2015 14:27,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN MUDDY PART OF A RIVER,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000311,HACTON,E09000016,HAVERING,Hornchurch,10070703899,NEWMARKET WAY,21300511,RM12,554386,185559,554350,185550,51.54780868,0.225198409\n149275151,07/11/2015 10:40,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN ROLLER BLIND,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Bethnal Green,6019275,BETHNAL GREEN ROAD,22700150,E2,534305,182628,534350,182650,51.52660257,-0.065337944\n150060151,08/11/2015 17:42,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00023E+11,WHITEHEATH AVENUE,21402149,HA4,508219,188082,508250,188050,51.58122044,-0.439533287\n151013151,10/11/2015 15:16,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BOILER ROOM,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202131480,BARRY ROAD,20202130,NW10,520412,184403,520450,184450,51.54568589,-0.264900013\n151214151,10/11/2015 22:52,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND WALL IN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,EUESDEN CLOSE,20706955,N9,NULL,NULL,534650,193350,NULL,NULL\n152051151,12/11/2015 12:08,2015,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100022009,NEWHOUSE AVENUE,19900260,RM6,547776,189579,547750,189550,51.58569715,0.131629183\n152072151,12/11/2015 12:57,2015,2015/16,Special Service,1,1,298,298,DOG STUCK IN GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00021E+11,WAVERLEY ROAD,21105207,N17,534501,191205,534550,191250,51.60363095,-0.05923676\n152185151,12/11/2015 17:13,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN GARAGE AND WALL,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,2.00003E+11,STATION ROAD,21300068,RM2,552985,189291,552950,189250,51.58172138,0.206631222\n152205151,12/11/2015 17:36,2015,2015/16,Special Service,1,1,298,298,CAT STUCK BETWEEN GARAGE AND WALL,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,STATION ROAD,21300068,RM2,552991,189291,552950,189250,51.58171975,0.206717748\n152339151,12/11/2015 23:03,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL IN BATHROOM,Cat,Other FRS,Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000566,SUTTON SOUTH,E09000029,SUTTON,Sutton,NULL,CAVENDISH ROAD,22602184,SM2,NULL,NULL,526350,163550,NULL,NULL\nNA,18/11/2015 16:02,2015,2015/16,Special Service,1,1,298,298,Redacted,Fox,Person (land line),Hotel/motel,Other Residential,Animal rescue from height,Wild animal rescue from height,E05009312,WALBROOK,E09000001,CITY OF LONDON,Dowgate,1.00023E+11,ST SWITHIN'S LANE,8100511,EC4N,532692,180957,532650,180950,51.51196737,-0.089205015\nNA,18/11/2015 19:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN VAN ENGINE  THIS IS A WHITE VAN,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,10003972743,ASCOT ROAD,21102916,N15,532644,188702,532650,188750,51.58157882,-0.086979667\nNA,20/11/2015 15:58,2015,2015/16,Special Service,1,1,298,298,CAT STUCK ON WINDOW LEDGE - ONE STOREY HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Poplar,NULL,BROMLEY HIGH STREET,22700207,E3,NULL,NULL,537750,182850,NULL,NULL\nNA,20/11/2015 18:52,2015,2015/16,Special Service,1,1,298,298,PARROT TRAPPED BETWEEN FENCES,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Tottenham,NULL,FARRANT AVENUE,21104519,N22,NULL,NULL,531750,190550,NULL,NULL\nNA,21/11/2015 13:17,2015,2015/16,Special Service,1,1,298,298,CAT UNDER DECKING,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,GREAT CAMBRIDGE ROAD,21106423,N17,532692,191691,532650,191650,51.60842751,-0.085158422\nNA,21/11/2015 18:01,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND GAS FIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,CORTIS ROAD,22901049,SW15,NULL,NULL,522850,174350,NULL,NULL\nNA,23/11/2015 11:05,2015,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,VERNON DRIVE,21202526,HA7,516497,190802,516450,190850,51.60401833,-0.319212318\nNA,23/11/2015 17:26,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,NULL,SPRINGWAY,21202034,HA1,NULL,NULL,514750,187750,NULL,NULL\nNA,24/11/2015 22:24,2015,2015/16,Special Service,1,1,298,298,DISTRESSED DOG TRAPPED IN GARDEN OF ABANDONED HOUSE,Dog,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34056267,EDITH ROAD,21000296,W14,524413,178572,524450,178550,51.4924146,-0.209287698\nNA,25/11/2015 08:10,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,SEAFORD ROAD,21106587,N15,NULL,NULL,532850,188950,NULL,NULL\nNA,25/11/2015 09:51,2015,2015/16,Special Service,1,1,298,298,DEER TRAPPED IN RAILINGS POLICE ATTENDING,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011483,Shirly South,E09000008,CROYDON,Addington,1.00021E+11,BISHOPS WALK,20501664,CR0,535503,164068,535550,164050,51.35952772,-0.055178236\nNA,26/11/2015 10:55,2015,2015/16,Special Service,1,2,298,596,CAT STUCK IN STORM DRAIN,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,RIVERSIDE CLOSE,20900860,E5,535357,186958,535350,186950,51.56526187,-0.048518775\nNA,26/11/2015 22:33,2015,2015/16,Special Service,1,1,298,298,MONKJACK DEER TRAPPED IN METAL GATE  RSPCA IN ATTENDANCE,Deer,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,PARK AVENUE,21401482,HA4,NULL,NULL,509850,188550,NULL,NULL\nNA,28/11/2015 12:05,2015,2015/16,Special Service,1,2,298,596,DOG TRAPPED IN PIPE  ACCESS VIA WATERS EDGE PUBLIC HOUSE   CALLER WILL MEET YOU THERE,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10091098645,RESERVOIR ROAD,21401623,HA4,508833,189500,508850,189550,51.59384758,-0.430236208\nNA,28/11/2015 13:15,2015,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,VICARAGE GATE,21700772,W8,NULL,NULL,525550,179950,NULL,NULL\nNA,30/11/2015 11:01,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE CALLED BY RSPCA     RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011478,Sanderstead,E09000008,CROYDON,Purley,NULL,DOWNSWAY,20502039,CR2,NULL,NULL,533350,161750,NULL,NULL\nNA,30/11/2015 22:15,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12045051,FISHER CLOSE,20600674,UB6,513144,182945,513150,182950,51.53408336,-0.370135419\nNA,01/12/2015 10:55,2015,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE IN FRONT OF CHAPEL CALLER ON WAY TO MEET BRIGADE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00024E+11,PRINCES AVENUE,21100705,N10,528639,189592,528650,189550,51.59050489,-0.144422871\nNA,02/12/2015 14:07,2015,2015/16,Special Service,1,2,298,596,DOG TRAPPED IN FOX HOLE  OPP THE BRIDGE NEAR PAGODA   ACCESS VIA CROWN GATE WEST,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6651787,APPROACH ROAD,22700080,E2,535529,183486,535550,183450,51.53402032,-0.047374368\nNA,05/12/2015 11:52,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,1.00023E+11,BEDFORD HILL,22900404,SW12,529021,172328,529050,172350,51.43526847,-0.145223977\nNA,06/12/2015 13:16,2015,2015/16,Special Service,1,1,298,298,CAT WITH HEAD STUCK IN RAILINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,South Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,DRAPPERS WAY,22502965,SE16,NULL,NULL,534450,178950,NULL,NULL\nNA,08/12/2015 11:11,2015,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009377,HOXTON EAST & SHOREDITCH,E09000012,HACKNEY,Shoreditch,NULL,ORSMAN ROAD,20900769,N1,NULL,NULL,533250,183750,NULL,NULL\nNA,09/12/2015 21:33,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,BURNWAY,21300256,RM11,NULL,NULL,554350,187550,NULL,NULL\nNA,10/12/2015 11:14,2015,2015/16,Special Service,1,1,298,298,PUPPY TRAPPED DOWN PIPE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,MILDMAY STREET,21605429,N1,NULL,NULL,532850,184850,NULL,NULL\nNA,12/12/2015 12:47,2015,2015/16,Special Service,3,6,298,1788,HORSE TRAPPED IN DITCH LEVEL ONE HAZMAT FOUR PUMP SPECIAL SERVICE,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10022800461,DUCKS HILL ROAD,21400619,HA6,507554,190631,507550,190650,51.60425861,-0.448346369\nNA,12/12/2015 15:40,2015,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN CABINET UNIT,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,RICHMOND PARK ROAD,22403043,SW14,NULL,NULL,520450,175050,NULL,NULL\nNA,13/12/2015 13:58,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER CAR SEAT,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,CHIPPENHAM CLOSE,21400388,HA5,509958,189380,509950,189350,51.59255101,-0.414039555\nNA,13/12/2015 18:18,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN GARAGE AND BRICK WALL,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,5061339,ST PAUL'S CRESCENT,20400541,NW1,529873,184442,529850,184450,51.54394092,-0.128520977\nNA,15/12/2015 22:18,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,SALISBURY STREET,8401547,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,16/12/2015 23:06,2015,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,NULL,DETMOLD ROAD,20900328,E5,NULL,NULL,535050,186750,NULL,NULL\nNA,17/12/2015 22:50,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN PLASTIC STAND,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,ELM GROVE,21300336,RM11,NULL,NULL,554450,188550,NULL,NULL\nNA,18/12/2015 10:31,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,STREATHAM HILL,E09000022,LAMBETH,West Norwood,NULL,LIMETREE CLOSE,21900877,SW2,NULL,NULL,530950,173250,NULL,NULL\nNA,19/12/2015 21:09,2015,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH INJURERD KITTEN STUCK ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,WIDECOMBE CLOSE,21301699,RM3,NULL,NULL,553650,190650,NULL,NULL\nNA,22/12/2015 12:42,2015,2015/16,Special Service,1,1,298,298,CAT STUCK IN TREE  RSPCA ON SCENE,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,2.00003E+11,PARK ROAD,21501255,W4,520593,177575,520550,177550,51.4842806,-0.264625659\nNA,25/12/2015 12:38,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED BEHIND IRON GRILL IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,NULL,LARKHALL RISE,21900848,SW4,NULL,NULL,529750,176150,NULL,NULL\nNA,26/12/2015 14:21,2015,2015/16,Special Service,1,1,298,298,LEA VALLEY PARK NEAR LAKE AND MARSHLAND BEHIND THE ICE RINK   SWAN TRAPPED IN BUSHES  CALLER WILL WA,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10091184362,LEA BRIDGE ROAD,22850300,E10,535722,187036,535750,187050,51.56587523,-0.043225687\nNA,27/12/2015 10:12,2015,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE  CAT TRAPPED IN BUSHES,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,MOUNT PLEASANT ROAD,22001729,SE13,538116,174373,538150,174350,51.45150269,-0.013664564\nNA,27/12/2015 12:45,2015,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN STORM DRAIN   CHINGFORD PLAINES - CALLER WILL MEET YOU,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,2.00001E+11,BURY ROAD,22821000,E4,539377,194868,539350,194850,51.63536075,0.012581595\nNA,27/12/2015 15:02,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Hostel (e.g. for homeless people),Other Residential,Other animal assistance,Assist trapped domestic animal,E05011098,Chaucer,E09000028,SOUTHWARK,Dowgate,2.00003E+11,TABARD STREET,22502459,SE1,532557,179675,532550,179650,51.50047816,-0.091630949\nNA,28/12/2015 11:23,2015,2015/16,Special Service,1,1,298,298,DOG STUCK ON ISLAND WITHIN LAKE  CALLER WILL MEET YOU ON COLLEGE ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,10090287587,COLLEGE ROAD,22500543,SE21,533474,173534,533450,173550,51.44507549,-0.080745179\nNA,29/12/2015 08:16,2015,2015/16,Special Service,1,1,298,298,DISTRESSED CAT ON BALCONY RESIDENTS AWAY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MILE END ROAD,22700818,E1,NULL,NULL,534950,181950,NULL,NULL\nNA,30/12/2015 12:57,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED UNDER FLOORBOARDS IN LOFT CONVERSION,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000216,CHARLTON,E09000011,GREENWICH,East Greenwich,NULL,SANDTOFT ROAD,20801321,SE7,NULL,NULL,540750,177850,NULL,NULL\nNA,31/12/2015 09:02,2015,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009388,ABINGDON,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,ADAM AND EVE MEWS,21700003,W8,NULL,NULL,525450,179350,NULL,NULL\nNA,31/12/2015 16:46,2015,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,NULL,SPRINGFIELD,20900946,E5,NULL,NULL,534750,187250,NULL,NULL\nNA,02/01/2016 11:03,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED ON ROOF  THREE STOREY BUILDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,DISRAELI CLOSE,20602336,W4,NULL,NULL,520550,178950,NULL,NULL\nNA,02/01/2016 14:32,2016,2015/16,Special Service,1,1,298,298,BIRD STUCK IN LIFT SHAFT,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,NULL,CANALSIDE GARDENS,20604849,UB2,NULL,NULL,512250,178650,NULL,NULL\nNA,03/01/2016 06:59,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE DOG FALLEN FROM FIRST FLOOR BALCONY,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000601,HOE STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,HOE STREET,22845700,E17,NULL,NULL,537250,189450,NULL,NULL\nNA,03/01/2016 11:37,2016,2015/16,Special Service,1,1,298,298,DOG STUCK DOWN RABBIT HOLE LIONS GATE ENTRANCE TO BUSHY PARK,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,516248,168938,516250,168950,51.40756178,-0.330007353\nNA,04/01/2016 07:56,2016,2015/16,Special Service,1,1,298,298,DOG WITH LEG STUCK IN DRAIN OPPOSITE JEFFREY COURT,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,10010196669,MOTTINGHAM ROAD,20801816,SE9,542381,172608,542350,172650,51.43458756,0.046964988\nNA,05/01/2016 21:44,2016,2015/16,Special Service,1,1,298,298,CAT FALLEN OUT OF WINDOW ONTO NETTING CALLER HAS RANG BACK TO CANCEL - THEY HAVE MANAGED TO GET THE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,CLERKENWELL,E09000019,ISLINGTON,Shoreditch,NULL,BAKER'S ROW,21604177,EC1R,NULL,NULL,531250,182250,NULL,NULL\nNA,06/01/2016 11:24,2016,2015/16,Special Service,1,1,298,298,RUNNING CALL FROM RSPCA TO CAT STUCK ON BALCONY  RSPCA IN ATTENDANCE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,LANSDOWNE ROAD,21104730,N17,NULL,NULL,534550,190750,NULL,NULL\nNA,07/01/2016 13:50,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA TO RETRIEVE AN OWL FROM A TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,NORMANDY DRIVE,21401404,UB3,508445,181256,508450,181250,51.51982394,-0.438375766\nNA,07/01/2016 15:30,2016,2015/16,Special Service,1,1,298,298,DOG FALLEN INTO RIVERL,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,10090785155,MALYONS ROAD,22001682,SE13,537527,174424,537550,174450,51.45210417,-0.022115978\nNA,07/01/2016 16:38,2016,2015/16,Special Service,1,1,298,298,DOG STUCK INBETWEEN IRON RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,1.00022E+11,ALDERSBROOK ROAD,22302576,E12,541099,186703,541050,186750,51.56156426,0.034166553\nNA,08/01/2016 12:37,2016,2015/16,Special Service,1,1,298,298,CAT STUCK ON ROOF  RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,LANSDOWNE ROAD,22849650,E17,NULL,NULL,537150,188450,NULL,NULL\nNA,08/01/2016 17:05,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011479,Selhurst,E09000008,CROYDON,Croydon,1.00021E+11,SELHURST PLACE,20501371,SE25,533163,167128,533150,167150,51.38758042,-0.087622152\nNA,09/01/2016 10:59,2016,2015/16,Special Service,2,4,298,1192,HORSE FALLEN ONTO SIDE AND STUCK IN WATER,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,2.00002E+11,WILLOW AVENUE,21402165,UB7,506942,180802,506950,180850,51.5160297,-0.460167047\nNA,10/01/2016 21:35,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,SOUTHBROOK ROAD,22001902,SE12,NULL,NULL,539850,174350,NULL,NULL\nNA,11/01/2016 23:12,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED INSIDE STORAGE ROOM AT REAR OF CAFE,Cat,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538023,174917,538050,174950,51.45641385,-0.01479011\nNA,12/01/2016 15:52,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH FOX TRAPPED,Fox,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,1.00023E+11,RUTLAND ROAD,20900881,E9,535618,183791,535650,183750,51.53673978,-0.04597462\nNA,13/01/2016 08:59,2016,2015/16,Special Service,1,1,298,298,DOG WITH HEAD STUCK IN METAL GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207107778,SHIRLEY GROVE,20703657,N9,535466,194666,535450,194650,51.63450071,-0.043973761\nNA,14/01/2016 13:12,2016,2015/16,Special Service,1,1,298,298,PIDGEON CAUGHT IN NETTING  CALL FOR ASSISTANCE VIA RSPCA,Bird,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped wild animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,48063159,SANDY LANE,22105754,CR4,528287,169694,528250,169650,51.41176257,-0.156730473\nNA,15/01/2016 09:30,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,FLAMBOROUGH CLOSE,20301503,TN16,540864,157837,540850,157850,51.30223244,0.019308996\nNA,17/01/2016 10:06,2016,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,1.00023E+11,SNAKES LANE WEST,22305313,IG8,540223,192067,540250,192050,51.60998186,0.023677611\nNA,17/01/2016 17:15,2016,2015/16,Special Service,1,1,298,298,HORSE TRAPPED IN CHAINS CALLER STATES CHAIN IS ROUND THE HORSES NECK AND IT IS UNABLE TO BREATH,Horse,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,1.00022E+11,FERRARO CLOSE,21501368,TW5,513408,177953,513450,177950,51.48916268,-0.367935429\nNA,20/01/2016 19:26,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER BALCONY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,TOLSFORD ROAD,20901007,E5,NULL,NULL,534750,185250,NULL,NULL\nNA,21/01/2016 15:40,2016,2015/16,Special Service,1,1,298,298,SWAN TRAPPED BEHIND FENCING   LOCATION IS ACTUALLY - RIGHT UP THE CANAL PATH AND BY THE BRIDGE  OPPO,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202207951,HATTON ROAD,20204264,HA0,518226,183592,518250,183550,51.53885838,-0.296681975\nNA,23/01/2016 08:47,2016,2015/16,Special Service,2,3,298,894,CAT STUCK UP TREE  RSPCA IN ATTENDANCE  REQUESTING ATTENDING OF BRIGADE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34018649,AUSTRALIA ROAD,21000077,W12,522834,180857,522850,180850,51.51329561,-0.231225322\nNA,23/01/2016 16:26,2016,2015/16,Special Service,1,2,298,596,HORSE TRAPPED BETWEEN TWO FENCING GATES   WHELANS FARM - WHITE SIGN AT THE GATE   HORSE IS BLEEDING,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,LUSTED HALL LANE,20301522,TN16,541084,157656,541050,157650,51.30055143,0.022391198\nNA,23/01/2016 18:13,2016,2015/16,Special Service,1,1,298,298,DOG STUCK DOWN MANHOLE IN REAR GARDEN   STAFFORDSHIRE BULL TERRIER,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,KERSEY GARDENS,20302550,SE9,NULL,NULL,542150,171850,NULL,NULL\nNA,23/01/2016 22:59,2016,2015/16,Special Service,1,1,298,298,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,MARLBOROUGH ROAD,20704475,N9,NULL,NULL,534050,194050,NULL,NULL\nNA,25/01/2016 18:03,2016,2015/16,Special Service,1,2,298,596,\"CAT STUCK INBETWEEN WALLS    CALLER HAS SPOKEN TO RSPCA  - AND NOW RSPCA HAS REQUESTED ATTENDANCE, A\",Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Essex,1.0009E+11,COOLGARDIE AVENUE,13801656,IG7,543261,193093,543250,193050,51.61843716,0.067940207\nNA,27/01/2016 10:31,2016,2015/16,Special Service,NULL,NULL,298,NULL,Redacted,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Bexley,10011847600,VICARAGE ROAD,20101516,DA5,549830,173127,549850,173150,51.43733172,0.154268014\nNA,28/01/2016 23:11,2016,2015/16,Special Service,1,1,298,298,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,10015406363,FORDWYCH ROAD,20400109,NW2,524594,184996,524550,184950,51.55010867,-0.204411196\nNA,30/01/2016 20:59,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED UNDER DECKING,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,46039882,JACK CORNWELL STREET,22200193,E12,543209,185644,543250,185650,51.55151695,0.064153881\nNA,04/02/2016 06:31,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,FERNCROFT AVENUE,20015600,N12,NULL,NULL,527450,191850,NULL,NULL\nNA,05/02/2016 00:52,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN METAL GATE ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,AXMINSTER ROAD,21602871,N7,NULL,NULL,530650,186250,NULL,NULL\nNA,06/02/2016 09:47,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,CAMPDEN HILL,21700632,W8,NULL,NULL,525050,179850,NULL,NULL\nNA,06/02/2016 11:52,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH A CAT TRAPPED BETWEEN SHED AND RAILINGS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,VANSITTART ROAD,22883600,E7,540141,185902,540150,185950,51.55460547,0.020036794\nNA,07/02/2016 15:33,2016,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,514958,168955,514950,168950,51.40797731,-0.348540705\nNA,07/02/2016 21:18,2016,2015/16,Special Service,1,1,298,298,DEER TRAPPED BETWEEN FENCES    NEXT TO FISHING LAKE BY RIVERSIDE HOTEL,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00022E+11,TROUT ROAD,21402019,UB7,505445,180611,505450,180650,51.51459418,-0.481790149\nNA,09/02/2016 00:23,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Sports/Social club,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,1.00023E+11,PALL MALL,8401211,SW1Y,529520,180203,529550,180250,51.50592659,-0.135166367\nNA,09/02/2016 18:04,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN SCAFFOLDING,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6198162,CAMBRIDGE HEATH ROAD,22700252,E2,534886,183107,534850,183150,51.53076845,-0.056784358\nNA,10/02/2016 14:23,2016,2015/16,Special Service,1,1,298,298,CAT SPEARED IN TREE DOWN LITTLE ALLEY OFF OF SOUTH TERRACED CALLER IS DISTRESSED CAT IS STILL ALIVE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,128038863,SOUTH TERRACE FOOTPATH (OPP 7 SOUTH TERRACE-SOUTH BANK CARPARK R/O SURBITON STATION),21820088,KT6,518363,167443,518350,167450,51.39368753,-0.300110214\nNA,11/02/2016 11:09,2016,2015/16,Special Service,1,1,298,298,PARROT TRAPPED BY LEG IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,5300100516,ARUNDEL SQUARE,21604148,N7,531174,184617,531150,184650,51.54521291,-0.109705223\nNA,13/02/2016 10:12,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10003642540,UPCHURCH CLOSE,20302611,SE20,534969,170186,534950,170150,51.41463419,-0.060520417\nNA,14/02/2016 08:27,2016,2015/16,Special Service,1,1,298,298,GOOSE WITH LEG TRAPPED IN WIRE ON MILL POND      GOOSE IS ON THE POND UNABLE TO MOVE D,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,10000815058,COLLEGE ROAD,22500543,SE21,533272,173119,533250,173150,51.44139355,-0.083806205\nNA,14/02/2016 08:31,2016,2015/16,Special Service,1,1,298,298,HORSE STUCK IN HOLE IN HORSE BOX PAST TOTTENHAM HOTSPUR TRAINING GROUND ON RIGHT HAND SIDE OF ROAD -,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207166636,WHITEWEBBS LANE,20703047,EN2,533190,199796,533150,199750,51.68114337,-0.074886946\nNA,15/02/2016 15:17,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000295,KENTON WEST,E09000015,HARROW,Stanmore,NULL,ALICIA GARDENS,21201593,HA3,NULL,NULL,517250,189150,NULL,NULL\nNA,17/02/2016 08:12,2016,2015/16,Special Service,1,1,298,298,BIRD TRAPPED IN TREE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011096,Camberwell Green,E09000028,SOUTHWARK,Old Kent Road,NULL,URLWIN STREET,22502602,SE5,NULL,NULL,532250,177550,NULL,NULL\nNA,17/02/2016 19:39,2016,2015/16,Special Service,1,1,298,298,KITTEN IN TREE,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,1.0002E+11,BIRKBECK ROAD,20100150,DA14,546423,172045,546450,172050,51.42849927,0.104839899\nNA,19/02/2016 09:58,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA  KITTEN FALLEN DOWN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,SUNDRIDGE ROAD,20501449,CR0,NULL,NULL,534050,166350,NULL,NULL\nNA,19/02/2016 10:15,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,GOUGH WALK,22702107,E14,NULL,NULL,537250,181150,NULL,NULL\nNA,20/02/2016 11:26,2016,2015/16,Special Service,1,2,298,596,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,SUNDRIDGE ROAD,20501449,CR0,NULL,NULL,534050,166350,NULL,NULL\nNA,20/02/2016 22:11,2016,2015/16,Special Service,1,1,298,298,Redacted,Cat,Person (mobile),Shopping Centre,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34158749,ARIEL WAY,21000056,W12,523373,180286,523350,180250,51.50804655,-0.22366146\nNA,22/02/2016 07:03,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED IN RUBBLE  THIS IS A CONSTRUCTION SITE CAN HEAR BARKING COMING FROM UNDER THE RUBBLE  CA,Dog,Person (mobile),Warehouse,Non Residential,Animal rescue from below ground,Wild animal rescue from below ground,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008234220,CHATSWORTH ROAD,20900224,E5,535462,186421,535450,186450,51.56041107,-0.047211326\nNA,24/02/2016 14:22,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000430,STREATHAM HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,CRIFFEL AVENUE,21900413,SW2,530103,173043,530150,173050,51.44144691,-0.129404271\nNA,24/02/2016 16:24,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE - DOG STUCK UNDER SHOWER BLOCK IN PERIVALE PARK CALLER WILL MEET CREW AT ENTRANC,Dog,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000187,PERIVALE,E09000009,EALING,Southall,12052504,HICKS AVENUE,20600878,UB6,514954,183075,514950,183050,51.53488653,-0.344008435\nNA,24/02/2016 21:03,2016,2015/16,Special Service,1,1,298,298,DOG WITH FOOT TRAPPED,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Essex,1.0009E+11,PALMERSTON ROAD,13801587,IG9,541111,194025,541150,194050,51.6273543,0.037280963\nNA,25/02/2016 01:09,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE ASSIST RSPCA WITH FOX TRAPPED IN GATE  RSPCA ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,TEMPLE AVENUE,20042240,N20,NULL,NULL,526850,194850,NULL,NULL\nNA,25/02/2016 10:49,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED CAT,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011469,Kenley,E09000008,CROYDON,Purley,1.00021E+11,CLEMENT CLOSE,20500202,CR8,531567,159073,531550,159050,51.31556234,-0.113520474\nNA,26/02/2016 00:40,2016,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,FAIRCLOUGH CLOSE,21402893,UB5,NULL,NULL,512650,182350,NULL,NULL\nNA,27/02/2016 16:36,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BUILDING SITE CALLER WILL MEET LFB ON SCENE,Cat,Person (mobile),Multi-Storey car park,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070014842,SIMPSONS ROAD,20301386,BR1,540323,168645,540350,168650,51.39948845,0.015810059\nNA,28/02/2016 08:36,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED BETWEEN WALLS POSSIBLY INJURED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,COLDBATH STREET,20800354,SE13,NULL,NULL,537850,176350,NULL,NULL\nNA,28/02/2016 19:58,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,FLORENCE STREET,21604885,N1,NULL,NULL,531750,184150,NULL,NULL\nNA,29/02/2016 22:43,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Hornsey,1.00023E+11,MORANT PLACE,21106461,N22,530816,190940,530850,190950,51.60211774,-0.112513601\nNA,29/02/2016 23:39,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED BEHIND FENCE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,1.00023E+11,THURSO STREET,22905382,SW17,526752,171723,526750,171750,51.43034259,-0.178065775\nNA,04/03/2016 08:48,2016,2015/16,Special Service,1,1,298,298,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,HALF MOON STREET,8400107,W1J,NULL,NULL,528850,180250,NULL,NULL\nNA,04/03/2016 11:00,2016,2015/16,Special Service,1,2,298,596,DOG STUCK IN HOLE IN GROUND CALLER WILL MEET YOU,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CHRISTCHURCH ROAD,22402731,SW14,NULL,NULL,520050,174850,NULL,NULL\nNA,04/03/2016 21:27,2016,2015/16,Special Service,1,1,298,298,KITTEN IN DISTRESS UNDER FLOORBOARDS BEHIND BOILER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,PETERSHAM GARDENS,20301248,BR5,NULL,NULL,546050,169050,NULL,NULL\nNA,04/03/2016 21:58,2016,2015/16,Special Service,1,1,298,298,PERSON IN PRECARIOUS POSITION ON ROOF - WENT UP TO GET A CAT DOWN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,ARLINGTON ROAD,20400726,NW1,NULL,NULL,529050,183450,NULL,NULL\nNA,06/03/2016 12:30,2016,2015/16,Special Service,1,2,298,596,ASSIST RSPCA WITH CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,1.00023E+11,LURLINE GARDENS,22903101,SW11,528443,176855,528450,176850,51.47608384,-0.151893142\nNA,06/03/2016 17:35,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE  CAT TRAPPED IN VOID,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ERIC STREET,22700474,E3,NULL,NULL,536550,182450,NULL,NULL\nNA,07/03/2016 10:22,2016,2015/16,Special Service,1,2,298,596,FOAL TRAPPED IN HEDGEROW,Horse,Person (land line),Hedge,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00021E+11,SPRINGWELL LANE,21401802,UB9,504996,192041,504950,192050,51.61741448,-0.484846694\nNA,07/03/2016 15:37,2016,2015/16,Special Service,1,2,298,596,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202098984,EDGWARE ROAD,20201631,NW9,520856,189464,520850,189450,51.59107628,-0.256759974\nNA,08/03/2016 17:07,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,FENWICK ROAD,22500954,SE15,NULL,NULL,534250,175550,NULL,NULL\nNA,09/03/2016 15:26,2016,2015/16,Special Service,1,1,298,298,CAT TRAPPED IN KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,REDRIFFE ROAD,22201565,E13,NULL,NULL,539850,183550,NULL,NULL\nNA,12/03/2016 11:30,2016,2015/16,Special Service,1,1,298,298,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,2.00002E+11,WELLINGTON STREET,20801579,SE18,543580,178797,543550,178750,51.48989785,0.066713723\nNA,14/03/2016 16:31,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH CAT UP TREE ENTER VIA HEATH ROAD TO THE DEAD END OF DALEY THOMPSON WAY,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,1.00022E+11,DALEY THOMPSON WAY,21900432,SW8,528820,175968,528850,175950,51.46802683,-0.14679042\nNA,15/03/2016 07:48,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,WOODSIDE ROAD,20101618,DA15,NULL,NULL,545450,172350,NULL,NULL\nNA,15/03/2016 21:23,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH INJURED FOX BEHIND FENCE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202131511,BARRETTS GREEN ROAD,20201454,NW10,520532,183036,520550,183050,51.53337445,-0.263638185\nNA,16/03/2016 18:08,2016,2015/16,Special Service,1,1,298,298,DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,10070269582,CAULFIELD GARDENS,21202812,HA5,511228,190161,511250,190150,51.59932185,-0.395465962\nNA,18/03/2016 09:09,2016,2015/16,Special Service,1,1,298,298,SMALL ANIMAL RESCUE CAT STUCK IN RIVER  RSPCA IN ATTENDANCE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207039016,SALISBURY ROAD,20702387,EN3,536894,198588,536850,198550,51.66939824,-0.021818846\nNA,19/03/2016 09:28,2016,2015/16,Special Service,1,2,298,596,CAT TRAPPED IN ROOF VOID  GROUND FLOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,CHERRY ORCHARD,21400367,UB7,NULL,NULL,506150,179950,NULL,NULL\nNA,21/03/2016 19:26,2016,2015/16,Special Service,1,1,298,298,Redacted,Unknown - Wild Animal,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,10008158694,BATTERSEA PARK ROAD,22900337,SW11,527258,176270,527250,176250,51.47109397,-0.169157406\nNA,22/03/2016 12:01,2016,2015/16,Special Service,1,1,298,298,CAT STUCK BEHIND WALL CALLED BY RSPCA,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,LABURNUM CLOSE,20025540,N11,NULL,NULL,528350,191550,NULL,NULL\nNA,25/03/2016 19:28,2016,2015/16,Special Service,1,1,298,298,KITTEN TRAPPED UNDER FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,INVERNESS AVENUE,20702766,EN1,NULL,NULL,533350,197750,NULL,NULL\nNA,26/03/2016 12:21,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,YEATE STREET,21606385,N1,NULL,NULL,532650,184150,NULL,NULL\nNA,27/03/2016 10:01,2016,2015/16,Special Service,1,1,298,298,CAT IN PRECARIOUS POSITION IN FIR TREE - REQUESTED BY RSPCA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,COURTLANDS AVENUE,20800397,SE12,NULL,NULL,540550,175050,NULL,NULL\nNA,27/03/2016 14:32,2016,2015/16,Special Service,1,1,298,298,SNAKE LOOSE IN FLAT - RSPCA TOLD CALLER THEY WERE TOO BUSY TO ATTEND,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185550,NULL,NULL\nNA,31/03/2016 14:15,2016,2015/16,Special Service,1,1,298,298,ASSIST RSPCA WITH TRAPPED FOX ON BALCONY,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,NULL,STEWART'S ROAD,21901302,SW8,NULL,NULL,529550,176450,NULL,NULL\nNA,01/04/2016 12:23,2016,2016/17,Special Service,1,3,326,978,Redacted,Horse,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Biggin Hill,10003628732,NASH LANE,20300763,BR2,540691,163271,540650,163250,51.35110779,0.018973548\nNA,02/04/2016 09:55,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE  RSPCA OFFICER ADVISED THE ROAD IS VERY NARROW,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,FALMER ROAD,20702673,EN1,NULL,NULL,533450,196250,NULL,NULL\nNA,02/04/2016 14:13,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CAR ENGINE  CAR IS LOCATED IN CAR PARK AT SIDE OF COSTA COFFEE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870115275,HIGH STREET,22605690,SM5,528140,164601,528150,164650,51.36602471,-0.16067974\nNA,03/04/2016 10:46,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE RSPCA IN ATTENDANCE WILL MEET AT ENTRANCE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00024E+11,CHURCH LANE,21104361,N17,533177,191090,533150,191050,51.60291227,-0.078386763\nNA,03/04/2016 19:30,2016,2016/17,Special Service,1,2,326,652,BIRD POSSIBLY TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,AMITY GROVE,22100168,SW20,NULL,NULL,523050,169350,NULL,NULL\nNA,04/04/2016 11:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE/TRANSMISSION BAY - RSPCA ON SCENE - THIS IS OUTSIDE OLYMPIA,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34103611,HAMMERSMITH ROAD,21000411,W14,524250,178851,524250,178850,51.49495787,-0.211536215\nNA,06/04/2016 06:58,2016,2016/17,Special Service,1,2,326,652,HAMSTER STUCK UNDER FLOORBOARDS,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,DURRINGTON PARK ROAD,22102241,SW20,NULL,NULL,523250,170050,NULL,NULL\nNA,06/04/2016 15:51,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALLS,Cat,Person (land line),Sports/Social club,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,10091857858,UPPER MALL,21000834,W6,522375,178316,522350,178350,51.4905581,-0.238717768\nNA,06/04/2016 17:22,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,TRANQUIL RISE,20101480,DA8,NULL,NULL,551250,178250,NULL,NULL\nNA,06/04/2016 17:57,2016,2016/17,Special Service,1,1,326,326,KITTEN IN DISTRESS RUNNING CALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,KEATS WAY,21401069,UB7,NULL,NULL,506650,178850,NULL,NULL\nNA,07/04/2016 14:09,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BY WINGS AND FEET IN NETTING  ON SIDE OF BUILDING,Bird,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5087781,KENTISH TOWN ROAD,20400363,NW1,528897,183912,528850,183950,51.53940137,-0.142781689\nNA,08/04/2016 15:09,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UNDER TIMBER ON RAILWAY LINE,Cat,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,SHRUBLAND ROAD,22874650,E17,537138,188724,537150,188750,51.58070164,-0.022150443\nNA,09/04/2016 19:39,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,1.00022E+11,BUTTSBURY ROAD,22302672,IG1,544354,185082,544350,185050,51.5461756,0.080426421\nNA,10/04/2016 00:00,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,WANSTEAD PARK ROAD,22303358,IG1,NULL,NULL,542350,187650,NULL,NULL\nNA,10/04/2016 12:58,2016,2016/17,Special Service,1,1,326,326,VEHICLE IN PRECARIOUS POSITION,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,SEVEN KINGS WAY,21880100,KT2,NULL,NULL,518050,169650,NULL,NULL\nNA,10/04/2016 16:53,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER METAL GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,1.00023E+11,CAREY GARDENS,22900794,SW8,529605,176531,529650,176550,51.47290738,-0.135289139\nNA,11/04/2016 23:36,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN CAVITY WALLS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,BROOKLANDS ROAD,21301021,RM7,NULL,NULL,550650,189150,NULL,NULL\nNA,12/04/2016 16:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BATHROOM PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,ELF ROW,22700459,E1W,NULL,NULL,535450,180850,NULL,NULL\nNA,13/04/2016 09:25,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,HOMERTON HIGH STREET,20900533,E9,NULL,NULL,535650,185250,NULL,NULL\nNA,13/04/2016 23:41,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011253,Valentines,E09000026,REDBRIDGE,Ilford,NULL,COURTLAND AVENUE,22302752,IG1,NULL,NULL,542850,186850,NULL,NULL\nNA,14/04/2016 00:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,HOMERTON HIGH STREET,20900533,E9,NULL,NULL,535650,185250,NULL,NULL\nNA,14/04/2016 17:54,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,ARDSHIEL CLOSE,22900137,SW15,NULL,NULL,523850,175750,NULL,NULL\nNA,16/04/2016 02:04,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN HOUSE AND WALL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,THE GLADE,20500570,CR0,NULL,NULL,536150,167050,NULL,NULL\nNA,16/04/2016 13:26,2016,2016/17,Special Service,1,2,326,652,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,HILL VIEW ROAD,22403562,TW1,516309,174226,516350,174250,51.4550763,-0.327395376\nNA,17/04/2016 11:08,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN PRECARIOUS POSITION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,PANCRAS ROAD,20400755,NW1,NULL,NULL,529750,183350,NULL,NULL\nNA,18/04/2016 09:14,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34004343,ERCONWALD STREET,21000318,W12,521702,181205,521750,181250,51.51666787,-0.247410437\nNA,18/04/2016 20:21,2016,2016/17,Special Service,1,1,326,326,KITTENS TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,Brixton,NULL,LAMBERT ROAD,21900827,SW2,NULL,NULL,530550,174650,NULL,NULL\nNA,19/04/2016 08:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN FRIDGE FREEZER AND WALL VOID,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,CARLINGFORD ROAD,20400177,NW3,NULL,NULL,526750,185750,NULL,NULL\nNA,19/04/2016 10:40,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ABBOTS ROAD,22200304,E6,NULL,NULL,541950,183450,NULL,NULL\nNA,19/04/2016 12:22,2016,2016/17,Special Service,1,1,326,326,Assist rspca with injured pigeon in tree   - RSPCA IN ATTENDANCE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,NULL,GREENBANKS CLOSE,22007093,SE13,NULL,NULL,537750,175750,NULL,NULL\nNA,19/04/2016 16:00,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BETWEEN DOOR AND WALL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,NULL,GAINSBOROUGH AVENUE,22200179,E12,NULL,NULL,543250,185150,NULL,NULL\nNA,20/04/2016 11:50,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,LINDEN GROVE,22501510,SE15,535326,175699,535350,175650,51.46409203,-0.053284898\nNA,21/04/2016 12:14,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN TWO WALLS,Cat,Person (land line),Large supermarket,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6354412,CHAPMAN STREET,22700297,E1,534781,180975,534750,180950,51.5116348,-0.059112409\nNA,21/04/2016 20:27,2016,2016/17,Special Service,1,1,326,326,ADULT CAT AND FOUR NEWLY BORN KITTENS TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,SANDERSTEAD ROAD,22872700,E10,NULL,NULL,536350,187250,NULL,NULL\nNA,22/04/2016 15:26,2016,2016/17,Special Service,1,1,326,326,CAT ON ROOF  CALLER STATES AT COMMUNITY CENTRE  RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,SOUTHWARK,Peckham,10091837469,BULLER CLOSE,22500371,SE15,534258,177188,534250,177150,51.47772738,-0.068084623\nNA,23/04/2016 09:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WIRE FENCING CALLER COULD NOT GIVE A HOUSE NUMBER BUT IS WAITING OUTSIDE FOR BRIGADE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,NULL,ALBION DRIVE,20901765,E8,NULL,NULL,533650,184050,NULL,NULL\nNA,23/04/2016 10:12,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT LOCKED IN GARAGE,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,MARTEN ROAD,22857150,E17,537487,190487,537450,190450,51.59645851,-0.016427448\nNA,23/04/2016 15:39,2016,2016/17,Special Service,1,1,326,326,PIGEON HANGING FROM TELEGRAPH WIRING  RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000463,LAVENDER FIELDS,E09000024,MERTON,Mitcham,NULL,BOROUGH ROAD,22100705,CR4,NULL,NULL,527250,169350,NULL,NULL\nNA,23/04/2016 16:07,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER CAR   NEAR HIGH STREET   NR SCOTCH THE BUTCHERS AND NISSAR,Dog,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,5870116940,GREEN WRYTHE LANE,22600593,SM5,527571,165366,527550,165350,51.37302777,-0.168574454\nNA,23/04/2016 17:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,10008338255,WALLIS ROAD,20901039,E9,537238,184685,537250,184650,51.54438262,-0.022283051\nNA,24/04/2016 13:42,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,HAMILTON ROAD,21900659,SE27,NULL,NULL,532750,171750,NULL,NULL\nNA,24/04/2016 15:56,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND FIRE MANTEL,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,GLEBE WAY,22401508,TW13,NULL,NULL,513250,172250,NULL,NULL\nNA,25/04/2016 12:55,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE RSPCA IN ATTENDANCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,KEW ROAD,22405688,TW9,518493,175644,518450,175650,51.46736843,-0.295501855\nNA,26/04/2016 13:41,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH A CROW TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,COLEHILL LANE,21000227,SW6,NULL,NULL,524250,176650,NULL,NULL\nNA,26/04/2016 20:47,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011098,Chaucer,E09000028,SOUTHWARK,Dockhead,NULL,LONG LANE,22501547,SE1,NULL,NULL,532650,179750,NULL,NULL\nNA,27/04/2016 08:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,FAIRCLOUGH CLOSE,21402893,UB5,NULL,NULL,512650,182350,NULL,NULL\nNA,27/04/2016 14:30,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF CALL RECIEVED FROM RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,NULL,SELCROFT ROAD,20502321,CR8,NULL,NULL,531750,161250,NULL,NULL\nNA,27/04/2016 19:20,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF TRAPPED ON SLOPING ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CONINGHAM ROAD,21000235,W12,NULL,NULL,522650,179650,NULL,NULL\nNA,28/04/2016 15:00,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED UNDER BUILDING RODING VALLEY CRICKET CLUB,Dog,Person (mobile),Sports pavilion/shower block/changing facility,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,NA,NA,NA,NA,Essex,10012166383,BRADWELL ROAD,13801513,IG9,542481,194502,542450,194550,51.63129565,0.057253936\nNA,29/04/2016 14:00,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHIND SHED,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000368,CALEDONIAN,E09000019,ISLINGTON,Euston,NULL,CALSHOT STREET,21604418,N1,NULL,NULL,530650,183250,NULL,NULL\nNA,29/04/2016 22:00,2016,2016/17,Special Service,1,1,326,326,HAMSTER STUCK IN CAVITY WALL,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,BLANDFORD CLOSE,21301022,RM7,NULL,NULL,549650,189250,NULL,NULL\nNA,30/04/2016 18:35,2016,2016/17,Special Service,1,1,326,326,CATS STUCK IN ROOF VOID OF FLATS,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,BARNFIELD GARDENS,20800116,SE18,NULL,NULL,543950,177850,NULL,NULL\nNA,01/05/2016 12:49,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT UP TREE OUTSIDE,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,1.00021E+11,QUEENS DRIVE,20900835,N4,531956,186721,531950,186750,51.5639382,-0.097646118\nNA,01/05/2016 16:27,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK IN BATHROOM,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,NULL,LADYWELL ROAD,22001623,SE13,NULL,NULL,537950,174850,NULL,NULL\nNA,02/05/2016 14:29,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE OUTSIDE RUNNING CALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,1.00021E+11,QUEENS DRIVE,20900835,N4,531959,186723,531950,186750,51.56395547,-0.097602113\nNA,03/05/2016 10:53,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA INSPECTOR WITH CAT TRAPPED ON WINDOW LEDGE,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000645,TACHBROOK,E09000033,WESTMINSTER,Lambeth,NULL,ST GEORGE'S SQUARE,8400197,SW1V,NULL,NULL,529650,178250,NULL,NULL\nNA,03/05/2016 17:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,12022790,SPENCER STREET,20601577,UB2,511720,179486,511750,179450,51.50327729,-0.391753266\nNA,04/05/2016 13:13,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BY WIRE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,NULL,MARYLEE WAY,21900931,SE11,NULL,NULL,531050,178550,NULL,NULL\nNA,05/05/2016 17:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Woodford,NULL,MAPPERLEY DRIVE,22856000,IG8,NULL,NULL,539150,191350,NULL,NULL\nNA,05/05/2016 19:49,2016,2016/17,Special Service,1,2,326,652,CAT STUCK ON ROOF  IT KEEPS SLIDING,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,EARDLEY ROAD,22901341,SW16,NULL,NULL,529450,171150,NULL,NULL\nNA,06/05/2016 10:59,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING ON BALCONY CALLED BY RSPCA J/O CROWNSTONE ROAD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,NULL,BRIXTON WATER LANE,21900242,SW2,NULL,NULL,530850,174650,NULL,NULL\nNA,06/05/2016 11:14,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,BOUTON PLACE,21610090,N1,NULL,NULL,531650,184050,NULL,NULL\nNA,07/05/2016 12:02,2016,2016/17,Special Service,1,1,326,326,DOG IN PRECARIOUS POSITION   SLIPPED DOWN ROOF TO SECOND FLOOR LEVEL,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,NULL,COURT LANE GARDENS,22500626,SE21,NULL,NULL,533550,173850,NULL,NULL\nNA,07/05/2016 19:07,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK IN LOFT,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,REDBRIDGE,Ilford,NULL,COLENSO ROAD,22301952,IG2,NULL,NULL,545050,187350,NULL,NULL\nNA,07/05/2016 21:55,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,BILLET ROAD,19900044,RM6,NULL,NULL,547550,189750,NULL,NULL\nNA,08/05/2016 12:45,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED FOX CUB,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,1.00023E+11,LEWISHAM WAY,22000623,SE4,536994,176364,536950,176350,51.46966684,-0.029030885\nNA,08/05/2016 15:47,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED  UNDERNEATH RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000200,GRANGE,E09000010,ENFIELD,Enfield,207170226,WINDMILL HILL,20703056,EN2,532185,196706,532150,196750,51.6536133,-0.09058244\nNA,08/05/2016 20:07,2016,2016/17,Special Service,1,2,326,652,BIRD ENTANGLED IN WIRE - TRAPPED IN TREE - TL REQUESTED FROM SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,THURSLEY GARDENS,22905379,SW19,523965,172720,523950,172750,51.43991877,-0.217787306\nNA,08/05/2016 22:35,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,LAKEDALE ROAD,20800879,SE18,NULL,NULL,545250,178250,NULL,NULL\nNA,09/05/2016 20:39,2016,2016/17,Special Service,1,1,326,326,SQUIRREL TRAPPED IN WALL,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WHITTON WALK,22702076,E3,NULL,NULL,537050,182850,NULL,NULL\nNA,10/05/2016 09:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,1.00022E+11,BOLDEN STREET,22000132,SE8,537594,176353,537550,176350,51.46942249,-0.02040235\nNA,10/05/2016 12:03,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,FOYLE ROAD,20800590,SE3,NULL,NULL,539650,177750,NULL,NULL\nNA,11/05/2016 11:28,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,WALKER CLOSE,20601824,W7,NULL,NULL,515150,179950,NULL,NULL\nNA,11/05/2016 14:28,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CLIFFORD AVENUE,22405471,SW14,NULL,NULL,519950,175950,NULL,NULL\nNA,11/05/2016 17:58,2016,2016/17,Special Service,1,1,326,326,INJURED DOG STUCK IN RAILINGS ADDRESS GIVEN AS BY THE CHILDRENS PLAYGROUND IN MILNE PARK NEXT TO HOM,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011471,New Addington South,E09000008,CROYDON,Addington,2.00001E+11,MILNE PARK EAST,20501785,CR0,539007,161468,539050,161450,51.33531909,-0.005896615\nNA,11/05/2016 18:49,2016,2016/17,Special Service,1,2,326,652,DISTRESSED CAT STUCK UP TREE - ANIMAL HOSPITAL STAFF IN ATTENDANCE    ROMFORD CEMETERY,Cat,Person (land line),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00024E+11,DAGENHAM ROAD,21301125,RM7,550778,187799,550750,187750,51.56891016,0.17416392\nNA,12/05/2016 08:01,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF CALLED BY RSPCA - RSPCA OFFICER IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,RICHMOND ROAD,22303183,IG1,NULL,NULL,544450,185650,NULL,NULL\nNA,12/05/2016 10:40,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED BETWEEN WALLS CALLER IS IN THE CAR PARK AND WILL MEET YOU THERE,Bird,Person (mobile),Leisure Centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12042082,DORMERS WELLS LANE,20600548,UB1,513466,181057,513450,181050,51.5170497,-0.366102554\nNA,12/05/2016 18:39,2016,2016/17,Special Service,1,1,326,326,RSPCA REQUEST TO ATTEND CAT STUCK IN DRY CANAL RSPCA WILL MEET YOU THERE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,207048735,POWYS LANE,20704622,N13,530245,192304,530250,192350,51.61450755,-0.120247284\nNA,13/05/2016 22:48,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Temporary office (eg portacabin),Non Residential,Other animal assistance,Assist trapped domestic animal,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,5134491,HARMOOD STREET,20400488,NW1,528525,184504,528550,184550,51.54480636,-0.147926585\nNA,14/05/2016 16:48,2016,2016/17,Special Service,1,1,326,326,DOG IN POND  GO DOWN PATH BY THE TRAFFIC LIGHT AND FORTY HILL IS THE NAME OF THE PARK,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207158578,FORTY HILL,20702687,EN2,533610,198287,533650,198250,51.66748374,-0.069392265\nNA,14/05/2016 17:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN DISABLED LIFT SHAFT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,NULL,CAIRNS AVENUE,22100004,SW16,NULL,NULL,529450,168850,NULL,NULL\nNA,15/05/2016 17:00,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,YORK AVENUE,21202537,HA7,516812,190416,516850,190450,51.60048383,-0.314794415\nNA,15/05/2016 20:33,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,NULL,ELMS ROAD,21900517,SW4,NULL,NULL,529350,174650,NULL,NULL\nNA,16/05/2016 11:18,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,COATE STREET,22700332,E2,NULL,NULL,534450,183250,NULL,NULL\nNA,16/05/2016 12:20,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING  RSPCA IN ATTENDANCE BY RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518850,189735,518850,189750,51.5939374,-0.28561297\nNA,17/05/2016 15:52,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN WIRE,Bird,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202063165,NORTON ROAD,20200120,HA0,517779,184562,517750,184550,51.54766965,-0.30279977\nNA,17/05/2016 20:59,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - DISTRESSED CAT POSSIBLY INJURED TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,EALING COMMON,E09000009,EALING,Ealing,NULL,GRANGE PARK,20600749,W5,NULL,NULL,518250,180250,NULL,NULL\nNA,18/05/2016 12:11,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA KITTEN TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,1.00021E+11,KEMPS DRIVE,21401075,HA6,509702,191309,509750,191350,51.60993935,-0.417130651\nNA,19/05/2016 17:44,2016,2016/17,Special Service,1,1,326,326,RABBIT TRAPPED IN CAR ENGINE,Rabbit,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011466,Coulsdon Town,E09000008,CROYDON,Purley,10014050785,WOODFIELD HILL,20502531,CR5,528792,157951,528750,157950,51.30611244,-0.153720812\nNA,19/05/2016 20:20,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,CLIVE ROAD,21301086,RM2,NULL,NULL,552750,188550,NULL,NULL\nNA,20/05/2016 09:20,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011111,Peckham Rye,E09000028,SOUTHWARK,Forest Hill,2.00003E+11,MARMORA ROAD,22501666,SE22,535148,174333,535150,174350,51.45185891,-0.056367208\nNA,20/05/2016 10:54,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED INSIDE CAR CHASSIS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202088139,HAYDON CLOSE,20202282,NW9,520401,189123,520450,189150,51.58810873,-0.263442032\nNA,20/05/2016 11:47,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER CAR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000060,UNDERHILL,E09000003,BARNET,Mill Hill,NULL,HYVER HILL,20024560,NW7,NULL,NULL,521050,194650,NULL,NULL\nNA,20/05/2016 13:11,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP TREE RSPCA IN ATTENDANCE - CALLED BY RSPCA,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207138706,BRIMSDOWN AVENUE,20702145,EN3,536283,197666,536250,197650,51.66126163,-0.031007736\nNA,20/05/2016 15:48,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,NAPIER ROAD,22860450,E11,NULL,NULL,539250,186150,NULL,NULL\nNA,20/05/2016 21:54,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FLOOR BOARDS OR WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,OSTELL CRESCENT,20706922,EN3,NULL,NULL,537450,198250,NULL,NULL\nNA,21/05/2016 10:43,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN ROOF TILES AND ROOF TERRACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520450,179450,NULL,NULL\nNA,21/05/2016 12:04,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN TABLE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,HAMPTON ROAD WEST,21500550,TW13,NULL,NULL,512150,172450,NULL,NULL\nNA,21/05/2016 16:58,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520450,179450,NULL,NULL\nNA,21/05/2016 19:53,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,NEWINGTON GREEN,21603608,N1,NULL,NULL,532850,185250,NULL,NULL\nNA,22/05/2016 01:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,ST STEPHEN'S AVENUE,21000765,W12,NULL,NULL,522850,179650,NULL,NULL\nNA,22/05/2016 12:19,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN STRING OUTSIDE WINDOW FLAT C,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00024E+11,PORTNALL ROAD,8400567,W9,524682,182845,524650,182850,51.53075783,-0.203904353\nNA,24/05/2016 08:30,2016,2016/17,Special Service,1,1,326,326,TWO LARGE BLACK DOGS TRAPPED IN CAR WITH WINDOWS SHUT IN DIRECT SUNLIGHT THIS IS A SILVER VOLVO THE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,1.00022E+11,CLIFFORD ROAD,22403393,TW10,517924,172704,517950,172750,51.44106331,-0.304668604\nNA,24/05/2016 12:44,2016,2016/17,Special Service,1,1,326,326,GUIDE DOG IMPALED ON A BRANCH IN BACK GARDEN,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,MOUNTBEL ROAD,21202513,HA7,516189,190485,516150,190450,51.60123281,-0.323762716\nNA,24/05/2016 13:56,2016,2016/17,Special Service,1,1,326,326,ASSSIT RSPCA WITH FOX CLUBS  STUCK IN A HOLE,Fox,Person (mobile),Fence,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,6170247,REDMANS ROAD,22701002,E1,535216,181879,535250,181850,51.5196545,-0.052500953\nNA,25/05/2016 09:00,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN HOLE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,CAMBRIDGE ROAD,22900771,SW11,527527,176503,527550,176550,51.47312744,-0.165202568\nNA,26/05/2016 17:42,2016,2016/17,Special Service,1,1,326,326,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,REDSTON ROAD,21100732,N8,NULL,NULL,529550,189350,NULL,NULL\nNA,26/05/2016 19:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,NULL,VINCENT STREET,22201651,E16,NULL,NULL,539850,181450,NULL,NULL\nNA,26/05/2016 23:03,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000534,BRUNSWICK PARK,E09000028,SOUTHWARK,Peckham,NULL,CROFTON ROAD,22500652,SE5,NULL,NULL,533550,176350,NULL,NULL\nNA,27/05/2016 08:02,2016,2016/17,Special Service,1,1,326,326,CAT IN CANAL,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,1.00024E+11,HORMEAD ROAD,8400803,W9,524636,182141,524650,182150,51.52444099,-0.20481618\nNA,28/05/2016 02:22,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BETWEEN FENCE AND WALL,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000563,STONECOT,E09000029,SUTTON,Sutton,5870023878,WALTON AVENUE,22601978,SM3,524503,165443,524550,165450,51.3744,-0.212601993\nNA,28/05/2016 12:54,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA INSPECTOR WITH DUCKLINGS STUCK ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,BELGRAVE MEWS NORTH,8401109,SW1X,NULL,NULL,528150,179550,NULL,NULL\nNA,28/05/2016 13:51,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE DUCK AND CHICKS IN PRECARIOUS POSITION ON ROOF TERRACE OF PENTHOUSE CALLER WILL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,WHITELANDS CRESCENT,22906840,SW18,NULL,NULL,524550,173950,NULL,NULL\nNA,28/05/2016 14:43,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE DUCK AND CHICKS IN PRECARIOUS POSITION,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,WHITELANDS CRESCENT,22906840,SW18,NULL,NULL,524550,173950,NULL,NULL\nNA,28/05/2016 16:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,NULL,RAMSDEN ROAD,22904179,SW12,NULL,NULL,528350,173750,NULL,NULL\nNA,28/05/2016 19:10,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,FIREFLY GARDENS,22200612,E6,NULL,NULL,542150,182050,NULL,NULL\nNA,29/05/2016 08:32,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CUPBOARD BEHIND PIPES,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,HIGH PATH,22103300,SW19,NULL,NULL,525950,169850,NULL,NULL\nNA,29/05/2016 20:59,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - BIRD TRAPPED IN CAVITY WALL RSPCA AND COUNCIL HAVE REFUSED TO ATTEND,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,HIGH PATH,22103300,SW19,NULL,NULL,525950,169850,NULL,NULL\nNA,29/05/2016 22:43,2016,2016/17,Special Service,1,3,326,978,FOX TRAPPED IN SEWAGE PIPE  RSPCA ON ROUTE,Fox,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5040328,GAINSBOROUGH GARDENS,20400084,NW3,526784,186045,526750,186050,51.5590486,-0.172464844\nNA,30/05/2016 19:17,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BEHIND HEATER,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,NORTHUMBERLAND GARDENS,21501433,TW7,NULL,NULL,516350,177250,NULL,NULL\nNA,31/05/2016 22:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAVITY WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011228,Northumberland Heath,E09000004,BEXLEY,Erith,NULL,WALSINGHAM WALK,20106303,DA17,NULL,NULL,549350,177850,NULL,NULL\nNA,01/06/2016 17:22,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,KINGSWAY,20025360,N12,NULL,NULL,526350,192050,NULL,NULL\nNA,03/06/2016 19:07,2016,2016/17,Special Service,1,1,326,326,PIGEON STUCK IN AERIEL ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,ILCHESTER ROAD,19900189,RM8,NULL,NULL,546750,185350,NULL,NULL\nNA,03/06/2016 19:13,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,BOWES ROAD,20705181,N13,NULL,NULL,530350,192050,NULL,NULL\nNA,04/06/2016 09:05,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5105804,LEIGH STREET,20400878,WC1H,530109,182486,530150,182450,51.52630839,-0.125842082\nNA,04/06/2016 12:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING OPPOSITE,Cat,Person (land line),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,34128611,HARROW ROAD,21001204,NW10,522730,182875,522750,182850,51.53145467,-0.232020581\nNA,04/06/2016 15:39,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN GATE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000289,HARROW ON THE HILL,E09000015,HARROW,Northolt,1.00021E+11,PORLOCK AVENUE,21201250,HA2,514436,187466,514450,187450,51.57445699,-0.350046601\nNA,04/06/2016 18:39,2016,2016/17,Special Service,1,1,326,326,PUPPY TRAPPED BETWEEN GARAGE AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,CLARENDON WAY,20300926,BR7,NULL,NULL,545950,168650,NULL,NULL\nNA,04/06/2016 18:56,2016,2016/17,Special Service,1,3,326,978,ASSIST RSPCA WITH CROW TRAPPED IN TWINE IN TREE    ALP REQUESTED FROM SCENE  RSPCA INSPECTOR SAYS TH,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34007860,UPPER MALL,21000834,W6,522356,178299,522350,178250,51.49040942,-0.238997191\nNA,05/06/2016 21:46,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILING/FENCE   RUISLIP LIDO PARK     CALLER WILL MEET NR RESVIOR ROAD ENTRANCE,Deer,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10090329361,RESERVOIR ROAD,21401623,HA5,509106,189498,509150,189450,51.59377693,-0.426297312\nNA,06/06/2016 09:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,1.00022E+11,EASTERN AVENUE,22302011,IG2,543104,188449,543150,188450,51.57674812,0.063781268\nNA,06/06/2016 21:28,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP ON SCAFFOLDING SINCE YESTERDAY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,FULHAM PALACE ROAD,21000356,SW6,NULL,NULL,524250,176350,NULL,NULL\nNA,07/06/2016 01:29,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE -CAT WITH PAW TRAPPED IN DOOR MECHANISM,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Greenwich,NULL,MANOR AVENUE,22000667,SE4,NULL,NULL,536950,176350,NULL,NULL\nNA,09/06/2016 12:34,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING OUTSIDE THE RAILWAY STN      CALLER WILL MEET AND DIRECT,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,48044464,LONDON ROAD,22103996,SW17,527968,170577,527950,170550,51.41977031,-0.16099651\nNA,09/06/2016 18:20,2016,2016/17,Special Service,1,1,326,326,KITTENS STUCK UNDER SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,Selhurst,E09000008,CROYDON,Croydon,NULL,STRATHMORE ROAD,20501438,CR0,NULL,NULL,532450,166750,NULL,NULL\nNA,10/06/2016 19:17,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN BIKE FRAME,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,EAST BARNET ROAD,20013380,EN4,NULL,NULL,526950,195650,NULL,NULL\nNA,11/06/2016 14:33,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540850,191850,NULL,NULL\nNA,11/06/2016 16:22,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,217124087,KENSINGTON HIGH STREET,21700648,W8,525400,179513,525450,179550,51.50065374,-0.194743724\nNA,11/06/2016 17:29,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,HOUNSLOW ROAD,21500631,TW14,NULL,NULL,510950,174350,NULL,NULL\nNA,11/06/2016 17:50,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,1.00022E+11,CRANFORD LANE,21500315,TW5,512312,177284,512350,177250,51.48336832,-0.383927217\nNA,12/06/2016 11:47,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE RSPCA INSPECTORS LADDER ISNT LONG ENOUGH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,FOXBURY CLOSE,20300907,BR6,NULL,NULL,546250,164050,NULL,NULL\nNA,12/06/2016 13:26,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH MUNTJAC DEER STUCK IN IRON GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Hendon,200106269,ST EDWARDS CLOSE,20037980,NW11,525083,188054,525050,188050,51.57748307,-0.196275253\nNA,12/06/2016 14:52,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO DOG ATTACKING CAT,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,1.00023E+11,SAMOS ROAD,20302966,SE20,535017,169178,535050,169150,51.40556435,-0.060213992\nNA,12/06/2016 15:36,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,121044707,WANDSWORTH HIGH STREET,22905902,SW18,525545,174429,525550,174450,51.45493064,-0.194460661\nNA,13/06/2016 09:09,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH TRAPPED BIRD TL REQUESTED FROM SCENE,Bird,Person (mobile),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634\nNA,13/06/2016 19:25,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER A CARAVAN,Cat,Person (mobile),Towing caravan (not on tow or on site),Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000437,BELLINGHAM,E09000023,LEWISHAM,Forest Hill,1.00022E+11,SOUTHEND LANE,22005190,SE26,536760,171737,536750,171750,51.42814329,-0.034184238\nNA,13/06/2016 20:54,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634\nNA,13/06/2016 22:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN HOLE IN THE GROUND CALLER WILL YOU AT THE FRONT ENTRANCE AND DIRECT YOU,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538242,175393,538250,175350,51.46063794,-0.011454187\nNA,14/06/2016 08:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BEHIND EXTENSION,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,TREVOR ROAD,20043200,HA8,NULL,NULL,520750,190750,NULL,NULL\nNA,14/06/2016 13:12,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,SOUTH CROXTED ROAD,21901245,SE21,532828,172215,532850,172250,51.43337374,-0.090529584\nNA,14/06/2016 15:20,2016,2016/17,Special Service,1,1,326,326,\"ASSIST RSPCA WITH KITTEN STUCK IN DRAINPIPE  RSPCA ON SCENE, STATE BEST ACCESS IS UP RAMP NEXT MULTI\",Cat,Person (mobile),Shopping Centre,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538252,175394,538250,175350,51.46064448,-0.011309945\nNA,14/06/2016 18:11,2016,2016/17,Special Service,1,2,326,652,Redacted,Bird,Person (land line),Water works,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532205,165383,532250,165350,51.37212223,-0.102030634\nNA,15/06/2016 10:47,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - CHICKEN TRAPPED BETWEEN TWO WALLS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CHARLTON ROAD,20703193,N9,NULL,NULL,535750,195050,NULL,NULL\nNA,15/06/2016 19:56,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,KEMPTON ROAD,22200762,E6,NULL,NULL,542650,183850,NULL,NULL\nNA,15/06/2016 22:09,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011243,Fullwell,E09000026,REDBRIDGE,Hainault,NULL,FULLWELL AVENUE,22305008,IG5,NULL,NULL,543550,190650,NULL,NULL\nNA,16/06/2016 12:55,2016,2016/17,Special Service,1,1,326,326,KITTENS TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,COVERTON ROAD,22901060,SW17,NULL,NULL,527250,171350,NULL,NULL\nNA,16/06/2016 14:49,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN AERIAL ON ROOF RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000304,WEALDSTONE,E09000015,HARROW,Stanmore,NULL,SPENCER ROAD,21202451,HA3,NULL,NULL,515350,190050,NULL,NULL\nNA,16/06/2016 17:46,2016,2016/17,Special Service,1,2,326,652,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,NULL,SPENCER ROAD,21202451,HA1,NULL,NULL,514650,188950,NULL,NULL\nNA,17/06/2016 12:24,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS DOWN ALLEYWAY AT SIDE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000556,BEDDINGTON SOUTH,E09000029,SUTTON,Wallington,5870109238,MOLLISON DRIVE,22605507,SM6,530396,163820,530350,163850,51.35849339,-0.128577529\nNA,18/06/2016 09:25,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200061015,HIGH ROAD,20022400,N12,526443,191172,526450,191150,51.60520079,-0.175536673\nNA,18/06/2016 11:12,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO WALLS   OPPOSITE MAYHEW ANIMAL HOME,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,TRENMAR GARDENS,20204129,NW10,NULL,NULL,522750,182950,NULL,NULL\nNA,19/06/2016 09:12,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN TRAMPOLINE,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,NULL,DRAKE ROAD,22000344,SE4,NULL,NULL,537250,175750,NULL,NULL\nNA,19/06/2016 15:28,2016,2016/17,Special Service,1,1,326,326,DUCKLINGS IN DRAIN   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539464,187559,539450,187550,51.56966275,0.010935064\nNA,20/06/2016 06:29,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK IN TREE IN MILLFIELD PARK CALLER WILL MEET AT ENTRANCE TO PARK AND DIRECT,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,SOUTHWOLD ROAD,20900939,E5,534813,186599,534850,186550,51.56216599,-0.056500105\nNA,23/06/2016 11:35,2016,2016/17,Special Service,1,3,326,978,ASSIST RSPCA WITH CAT UP TREE BEST ENTRANCE VIA RAINHAM CLOSE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00023E+11,RESTONS CRESCENT,20801259,SE9,545026,174190,545050,174150,51.4481325,0.085637599\nNA,23/06/2016 20:19,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN BUILDING MATERIALS,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207138569,FOURACRES,20702234,EN3,536097,197623,536050,197650,51.66092034,-0.0337119\nNA,23/06/2016 21:19,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER RUBBLE,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156922,CEDAR ROAD,20702575,EN2,532161,198166,532150,198150,51.66673879,-0.09037781\nNA,24/06/2016 14:14,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN RAILINGS ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,NULL,PLOUGH LANE,22105115,SW19,NULL,NULL,526050,171550,NULL,NULL\nNA,24/06/2016 14:21,2016,2016/17,Special Service,1,1,326,326,CAT UP TREE - RSPCA REQUEST ASSISTANCE RAINHAM CLOSE MAIN ENTRANCE,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00023E+11,RESTONS CRESCENT,20801259,SE9,545115,174200,545150,174250,51.44819958,0.086921557\nNA,24/06/2016 16:02,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,202142989,QUEENSBURY STATION PARADE,20200704,HA8,518851,189733,518850,189750,51.59391922,-0.285599217\nNA,24/06/2016 16:44,2016,2016/17,Special Service,1,1,326,326,DOGS HEAD STUCK IN BALCONY RAILING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,NULL,BOWEN DRIVE,22500298,SE21,NULL,NULL,533350,171650,NULL,NULL\nNA,24/06/2016 17:51,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN BETEEN FENCE POST,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,WIGHTMAN ROAD,21106644,N4,NULL,NULL,531450,188150,NULL,NULL\nNA,25/06/2016 07:36,2016,2016/17,Special Service,1,1,326,326,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100056090,HOOKS HALL DRIVE,19900682,RM10,550460,186083,550450,186050,51.55357652,0.168845752\nNA,25/06/2016 09:01,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,NULL,ICKNIELD DRIVE,22302961,IG2,NULL,NULL,543950,188750,NULL,NULL\nNA,25/06/2016 14:22,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.0002E+11,HOWARDS CREST CLOSE,20302548,BR3,538449,169050,538450,169050,51.40358758,-0.010953264\nNA,25/06/2016 17:48,2016,2016/17,Special Service,1,1,326,326,PUPPY STUCK ON FIRST FLOOR WINDOW SILL ABOVE CLAIRES STORE CAUGHT BY COLLAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,PUTNEY HIGH STREET,22904067,SW15,NULL,NULL,524050,175350,NULL,NULL\nNA,25/06/2016 19:59,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Twickenham,10091158014,CHERTSEY ROAD,22406590,TW2,514763,173693,514750,173650,51.45060113,-0.349809522\nNA,26/06/2016 08:46,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CAVITY WALL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,NULL,SPARROWS LANE,20801387,SE9,NULL,NULL,544250,173450,NULL,NULL\nNA,26/06/2016 13:38,2016,2016/17,Special Service,1,1,326,326,PIDGEON CAUGHT UP IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011484,South Croydon,E09000008,CROYDON,Croydon,NULL,BRIGHTON ROAD,20501945,CR2,NULL,NULL,532450,162850,NULL,NULL\nNA,26/06/2016 16:05,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,PONDERS END,E09000010,ENFIELD,Edmonton,207119637,HIGH STREET,20705021,EN3,535185,195543,535150,195550,51.64244933,-0.047692521\nNA,27/06/2016 15:52,2016,2016/17,Special Service,1,1,326,326,BIRD STUCK IN TREE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,1.00021E+11,WHITE HART LANE,21106108,N22,531067,190879,531050,190850,51.60151122,-0.108914316\nNA,27/06/2016 19:27,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,NULL,COVINGTON WAY,21900399,SW16,NULL,NULL,530850,170850,NULL,NULL\nNA,28/06/2016 12:00,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BEHIND WIRE FENCE,Dog,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00021E+11,REDSTON ROAD,21100732,N8,529650,189553,529650,189550,51.58992292,-0.12985145\nNA,28/06/2016 16:53,2016,2016/17,Special Service,1,2,326,652,DOG STUCK DOWN RABBIT HOLE FULHAM CEMETARY MUNSTER ROAD ENTRANCE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,34024967,FULHAM PALACE ROAD,21000356,SW6,523939,177215,523950,177250,51.48032283,-0.216587754\nNA,30/06/2016 09:45,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00021E+11,SPRINGWELL LANE,21401802,UB9,504972,192038,504950,192050,51.61739198,-0.485194113\nNA,30/06/2016 13:27,2016,2016/17,Special Service,1,1,326,326,DOG STUCK BETWEEN RAILINGS AND WINDOWS OF SECOND FLOOR NEIGHBOUR WILL MEET YOU,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,BAILEY CLOSE,20802072,SE28,NULL,NULL,545150,180250,NULL,NULL\nNA,30/06/2016 16:12,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500610,TW3,NULL,NULL,514150,175850,NULL,NULL\nNA,30/06/2016 16:55,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BETWEEN METAL GATE AND FRONT DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,FRAMPTON PARK ROAD,20900423,E9,NULL,NULL,535150,184250,NULL,NULL\nNA,30/06/2016 18:17,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,NULL,PRINCE GEORGE ROAD,20900821,N16,NULL,NULL,533450,185650,NULL,NULL\nNA,30/06/2016 19:30,2016,2016/17,Special Service,2,3,326,978,FOX STUCK ON MUD BANK RIVER WANDLE NEAR TO THAMES   WATER RESCUE LEVEL TWO INCIDENT  ADDITIONAL FRU,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,10090499099,ENTERPRISE WAY,22901532,SW18,525486,175158,525450,175150,51.46149545,-0.195051009\nNA,30/06/2016 20:40,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,NULL,THE KNOLE,20801475,SE9,NULL,NULL,543150,171750,NULL,NULL\nNA,01/07/2016 08:33,2016,2016/17,Special Service,1,1,326,326,FOX CUB WITH HEAD STUCK IN WATERING CAN     CUB IS CURRENTLY ASLEEP ON CALLERS LAP,Fox,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,MILTON ROAD,22404788,SW14,520559,175562,520550,175550,51.46619573,-0.265800961\nNA,01/07/2016 09:20,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Acton,34142355,WESTWAY,21000877,W12,521700,180880,521750,180850,51.51374738,-0.24755141\nNA,01/07/2016 16:52,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN SHEDS,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011109,Old Kent Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,NAYLOR ROAD,22501758,SE15,534697,177255,534650,177250,51.47822515,-0.061741472\nNA,01/07/2016 20:33,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN PIPEWORK OF BASEMENT AREA -  CALL PASSED BY RSPCA WHO DO NOT HAVE A REPRESENTATIVE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,GREAT CAMBRIDGE ROAD,20700114,N9,NULL,NULL,532950,193450,NULL,NULL\nNA,02/07/2016 14:34,2016,2016/17,Special Service,1,3,326,978,DEER STUCK IN FENCE CALLER SAYS THIS IS A BABY DEER,Deer,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00024E+11,SETTLE ROAD,21301584,RM3,555265,192587,555250,192550,51.61071138,0.240967783\nNA,03/07/2016 14:42,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN SHED AND FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,WARWICK ROAD,20704889,N18,NULL,NULL,533550,192950,NULL,NULL\nNA,03/07/2016 15:28,2016,2016/17,Special Service,1,1,326,326,BIRDS TRAPPED IN NETTING,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Bethnal Green,6017513,CAMBRIDGE HEATH ROAD,22700252,E1,534826,181992,534850,181950,51.52076315,-0.058075429\nNA,03/07/2016 21:19,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN METAL FENCE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,207155346,BROOKSIDE GARDENS,20702537,EN1,535135,199016,535150,199050,51.67367007,-0.047073183\nNA,05/07/2016 18:56,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,Shirley North,E09000008,CROYDON,Woodside,NULL,DAFFODIL CLOSE,20500430,CR0,NULL,NULL,535850,166350,NULL,NULL\nNA,07/07/2016 08:50,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000563,STONECOT,E09000029,SUTTON,Sutton,5870040740,SUTTON COMMON ROAD,22601923,SM3,525321,166229,525350,166250,51.38128425,-0.200578775\nNA,07/07/2016 11:21,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS ENTRANCE NEAR THE SUN PH,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870062834,NORTH STREET,22602499,SM5,527895,164680,527850,164650,51.36678981,-0.164168746\nNA,07/07/2016 14:29,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,FELBRIGGE ROAD,22302051,IG3,NULL,NULL,546150,187050,NULL,NULL\nNA,07/07/2016 20:45,2016,2016/17,Special Service,1,1,326,326,DOG WITH PAW TRAPPED IN COLLAR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,LAVENDER HILL,20705086,EN2,NULL,NULL,532250,197850,NULL,NULL\nNA,08/07/2016 00:50,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN METAL FENCE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,CLIVE ROAD,21301086,RM2,552703,188542,552750,188550,51.57506821,0.202239344\nNA,09/07/2016 21:13,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ROOF THIS IS NEXT TO GREENWICH COMMUNITY COLLEGE,Cat,Person (mobile),Art Gallery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,2.00003E+11,GREENWICH HIGH ROAD,20800685,SE10,538188,177305,538150,177350,51.4778328,-0.011483749\nNA,10/07/2016 02:01,2016,2016/17,Special Service,1,1,326,326,\"ASSIST RSPCA WITH CAT IN CAR ENGINE  RSPCA ON SCENE  CAR PARK NEXT TO SKATE PARK, ALTHOUGH RSPCA OFF\",Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000421,FERNDALE,E09000022,LAMBETH,Brixton,2E+11,STOCKWELL PARK WALK,21901315,SW9,530991,175823,530950,175850,51.46622586,-0.115606483\nNA,10/07/2016 07:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,2E+11,WOODVALE WALK,21902038,SE27,532259,171288,532250,171250,51.42517603,-0.099055996\nNA,10/07/2016 13:16,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,NORTH WORPLE WAY,22404835,SW14,NULL,NULL,521150,175750,NULL,NULL\nNA,11/07/2016 09:51,2016,2016/17,Special Service,1,1,326,326,DOG STUCK ON THE MUD FLATS,Dog,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011232,Thamesmead East,E09000004,BEXLEY,Plumstead,10011848055,BELVEDERE ROAD,20100122,SE2,548897,180930,548850,180950,51.50768945,0.144142626\nNA,11/07/2016 12:53,2016,2016/17,Special Service,1,2,326,652,CAT UP TREE - CALL FROM RSPCA,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010231541,GOLDCREST CLOSE,20800654,SE28,547240,180717,547250,180750,51.50620921,0.120194682\nNA,11/07/2016 14:24,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED BY WIRE ASSIST RSPCA,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010210624,HILL VIEW DRIVE,20801883,SE28,545508,180211,545550,180250,51.50211086,0.095047436\nNA,11/07/2016 22:02,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,BRETTENHAM ROAD,20703959,N18,NULL,NULL,534750,192550,NULL,NULL\nNA,13/07/2016 08:51,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,12140183,THE VALE,20602316,W3,521658,180031,521650,180050,51.50612609,-0.248449179\nNA,13/07/2016 12:01,2016,2016/17,Special Service,1,1,326,326,ALLEY AT BROOKFIELDS AVENUE FOX WITH HEAD STUCK BETWEEN TWO WOODEN POSTS RSPCA ON SCENE,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48060157,RIVERSIDE DRIVE,22105424,CR4,527219,167882,527250,167850,51.39571811,-0.172728318\nNA,14/07/2016 08:14,2016,2016/17,Special Service,1,1,326,326,ACCESS VIA THE DOWN TO THE VISITORS ENTRANCE PIGEON TRAPPED IN ATRIUM OF ENTRANCE HALL,Bird,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,48130420,CRESCENT ROAD,22101710,SW20,523716,169960,523750,169950,51.41516789,-0.222330844\nNA,15/07/2016 11:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34152377,ARIEL WAY,21000056,W12,523348,180283,523350,180250,51.50802504,-0.22402256\nNA,15/07/2016 14:57,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10090326477,MONTFICHET ROAD,22208219,E20,538307,184587,538350,184550,51.54324136,-0.006915662\nNA,16/07/2016 21:58,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMELFORD WALK,21701176,W11,NULL,NULL,524150,181050,NULL,NULL\nNA,16/07/2016 23:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2.00004E+11,HOWBERRY ROAD,21202504,HA7,517670,191796,517650,191750,51.61270831,-0.301948966\nNA,17/07/2016 18:44,2016,2016/17,Special Service,1,1,326,326,DOG LOCKED  IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34143953,ARIEL WAY,21000056,W12,523371,180294,523350,180250,51.50811889,-0.223687466\nNA,18/07/2016 07:43,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,NULL,RUSSELL GREEN CLOSE,20502309,CR8,NULL,NULL,531250,162150,NULL,NULL\nNA,18/07/2016 09:49,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN AIR CONDITIONING UNIT,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Tooting,2E+11,STREATHAM HIGH ROAD,21901326,SW16,530140,172135,530150,172150,51.4332783,-0.129205565\nNA,18/07/2016 15:46,2016,2016/17,Special Service,1,1,326,326,DUCKLING TRAPPED ON ROOF  REQUESTED BY RSPCA - UNABLE TO ATTEND,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,NEW KING'S ROAD,21000589,SW6,NULL,NULL,525350,176550,NULL,NULL\nNA,18/07/2016 15:49,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE ROOF AREA,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870035727,HIGH STREET,22605497,SM1,525776,164547,525750,164550,51.36606717,-0.194638648\nNA,18/07/2016 16:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,MARINE STREET,22501658,SE16,NULL,NULL,534050,179350,NULL,NULL\nNA,18/07/2016 19:29,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000287,EDGWARE,E09000015,HARROW,Stanmore,NULL,BACON LANE,21202318,HA8,NULL,NULL,519450,190950,NULL,NULL\nNA,18/07/2016 20:30,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN METAL HOUSING OF RAILIWAY LINE,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,MILKWOOD ROAD,21900960,SE24,531882,174651,531850,174650,51.45548654,-0.103223721\nNA,18/07/2016 20:56,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN TREE,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,1.00023E+11,SURBITON HILL PARK,21800924,KT5,518764,167671,518750,167650,51.39565307,-0.29427311\nNA,19/07/2016 12:14,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000271,HARRINGAY,E09000014,HARINGEY,Tottenham,NULL,ST MARGARETS AVENUE,21103910,N15,NULL,NULL,531750,189250,NULL,NULL\nNA,19/07/2016 12:44,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED BIRDS,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009303,DOWGATE,E09000001,CITY OF LONDON,Dowgate,1.00023E+11,BUSH LANE,8100101,EC4R,532684,180855,532650,180850,51.51105261,-0.089358602\nNA,20/07/2016 08:43,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,NULL,SYON LANE,21501106,TW7,NULL,NULL,515650,177750,NULL,NULL\nNA,20/07/2016 10:33,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH DUCKLINGS TRAPPED ON BALCONY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,BELGRAVE MEWS NORTH,8401109,SW1X,NULL,NULL,528150,179550,NULL,NULL\nNA,20/07/2016 19:12,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,1.00022E+11,SUMMERWOOD ROAD,21501326,TW7,515945,174437,515950,174450,51.45704733,-0.33256288\nNA,21/07/2016 07:46,2016,2016/17,Special Service,1,1,326,326,PARROT TRAPPED IN TREE - RSPCA REQUESTED MEMBER OF PUBLIC CALL LFB,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000491,ROYAL DOCKS,E09000025,NEWHAM,Plaistow,46081188,WINIFRED STREET,22207958,E16,542762,180033,542750,180050,51.50121122,0.055439372\nNA,21/07/2016 18:56,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED BEHIND VENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,JAMES STREET,21500646,TW3,NULL,NULL,514550,175750,NULL,NULL\nNA,21/07/2016 20:42,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN WINDOW  CALLER WILL WAIT FOR BRIGADE AND DIRECT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,NULL,MURRAY GROVE,20900720,N1,NULL,NULL,532550,183050,NULL,NULL\nNA,22/07/2016 07:21,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX STUCK BETWEEN TWO FENCEPOSTS   RSPCA ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,Fullwell,E09000026,REDBRIDGE,Hainault,NULL,RUSHDEN GARDENS,22303205,IG5,NULL,NULL,543150,190050,NULL,NULL\nNA,22/07/2016 08:09,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN FENCE  RSPCA UNABLE TO ATTEND AT THIS TIME,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,1.00022E+11,ENDSLEIGH GARDENS,22302825,IG1,542758,187041,542750,187050,51.56418406,0.058220086\nNA,22/07/2016 18:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,10000001987,WOODHALL ROAD,21201827,HA5,511760,191469,511750,191450,51.61097304,-0.387371391\nNA,23/07/2016 10:59,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,Kentish Town,NULL,PARLIAMENT HILL,20400088,NW3,NULL,NULL,527450,185850,NULL,NULL\nNA,23/07/2016 11:28,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,HARROW ROAD,20201241,NW10,NULL,NULL,522750,182850,NULL,NULL\nNA,24/07/2016 01:55,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED  IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,RAWSON STREET,22904224,SW11,NULL,NULL,528450,176750,NULL,NULL\nNA,25/07/2016 04:01,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CULFORD GARDENS,21700139,SW3,NULL,NULL,527850,178550,NULL,NULL\nNA,25/07/2016 21:00,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED IN WIRING AND NETTING UNDER BRIDGE -JUNCTION WITH KINGSTON ROAD,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,128010707,KINGSTON ROAD,21800563,KT3,520574,168364,520550,168350,51.40150016,-0.268033577\nNA,26/07/2016 03:23,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UP CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,PANDORA ROAD,20400260,NW6,NULL,NULL,525250,185050,NULL,NULL\nNA,26/07/2016 09:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,WOODLANDS GROVE,21501229,TW7,NULL,NULL,515450,176150,NULL,NULL\nNA,26/07/2016 10:37,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA TO GAIN ENTRY FOR DOG REMOVAL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,NULL,STATION ROAD,20040880,EN5,NULL,NULL,525850,195950,NULL,NULL\nNA,26/07/2016 21:09,2016,2016/17,Special Service,1,1,326,326,FLAT B CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,NULL,BERESFORD ROAD,21602903,N5,NULL,NULL,532550,185150,NULL,NULL\nNA,27/07/2016 01:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CUPBOARD OUTSIDE FRONT DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,TANNOY SQUARE,21903445,SE27,NULL,NULL,532750,171650,NULL,NULL\nNA,27/07/2016 16:16,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX TRAPPED BETWEEN WALL AND SHED  RSPCA INSPECTOR IS GILL,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WATNEY ROAD,22405230,SW14,NULL,NULL,520050,176050,NULL,NULL\nNA,28/07/2016 10:31,2016,2016/17,Special Service,1,1,326,326,CAT LOCKED IN GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00021E+11,MONTGOMERY ROAD,21200910,HA8,518935,191542,518950,191550,51.61015971,-0.283774651\nNA,28/07/2016 13:49,2016,2016/17,Special Service,3,12,326,3912,CAT STUCK WITHIN WALL SPACE  RSPCA IN ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,NULL,SPRING PLACE,20400353,NW5,NULL,NULL,528550,185050,NULL,NULL\nNA,28/07/2016 16:51,2016,2016/17,Special Service,1,1,326,326,SIX WEEK OLD KITTEN STUCK BETWEEN METAL AND WOODEN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000037,PARSLOES,E09000002,BARKING AND DAGENHAM,Dagenham,100023933,PARSLOES AVENUE,19900277,RM9,548036,185527,548050,185550,51.54922092,0.133674408\nNA,29/07/2016 08:43,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY - TANGLED IN WIRE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,NULL,MARY DATCHELOR CLOSE,22501674,SE5,NULL,NULL,532750,176750,NULL,NULL\nNA,29/07/2016 20:23,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD STUCK IN RAILING  IN SMALL PARK AREA -  OWNER WILL MEET BRIGADE IN BROOKWAY,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00021E+11,BROOKWAY,21300726,RM13,552570,181649,552550,181650,51.51317151,0.197337951\nNA,29/07/2016 22:26,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,1.00022E+11,BOSTON MANOR ROAD,21500133,TW8,516537,178744,516550,178750,51.49563655,-0.32262681\nNA,31/07/2016 13:19,2016,2016/17,Special Service,1,1,326,326,HAMSTER TRAPPED UNDER FLOORBOARDS,Hamster,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,BROOKSIDE ROAD,21400262,UB4,NULL,NULL,511450,181050,NULL,NULL\nNA,01/08/2016 06:45,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN GATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207125219,PARK CRESCENT,20702888,EN2,532889,195973,532850,195950,51.64686056,-0.080690327\nNA,01/08/2016 13:18,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BETWEEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,NULL,TOLSON ROAD,21501125,TW7,NULL,NULL,516250,175650,NULL,NULL\nNA,01/08/2016 13:46,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN ROOF OF HOUSE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,MEADOW ROAD,19900248,RM9,NULL,NULL,548650,184850,NULL,NULL\nNA,01/08/2016 14:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN DRAIN,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,RIVERSIDE CLOSE,20900860,E5,535313,187047,535350,187050,51.56607219,-0.049119019\nNA,01/08/2016 15:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN FENCING IN REAR GARDEN,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,1.0002E+11,ELLENBOROUGH ROAD,20100490,DA14,547923,171048,547950,171050,51.41915141,0.1259846\nNA,01/08/2016 18:27,2016,2016/17,Special Service,1,2,326,652,KITTEN TRAPPED IN BATHROOM PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,STOFORD CLOSE,22904991,SW19,NULL,NULL,524150,173550,NULL,NULL\nNA,02/08/2016 15:22,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA OFFICER WITH SMALL ANIMAL RESCUE CAT POSSIBLY INJURED TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,NULL,ST PETER'S STREET,21606037,N1,NULL,NULL,532050,183350,NULL,NULL\nNA,02/08/2016 22:51,2016,2016/17,Special Service,1,1,326,326,CAT WITH TAIL TRAPPED IN RECLINER CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,STANHOPE ROAD,20601626,UB6,NULL,NULL,514250,181850,NULL,NULL\nNA,03/08/2016 13:09,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN CEILING AND ROOF AT MEDIVET VETENARY PRACTICE,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300010233,BLACKSTOCK ROAD,21602925,N4,531766,186498,531750,186450,51.56197864,-0.100469048\nNA,03/08/2016 16:42,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Islington,NULL,BAALBEC ROAD,21602873,N5,NULL,NULL,531950,185150,NULL,NULL\nNA,03/08/2016 16:52,2016,2016/17,Special Service,1,1,326,326,CAT WITH KITTENS IN DISTRESS ON ROOF  (GIVEN BIRTH TO KITTENS ON ROOF),Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000284,WOODSIDE,E09000014,HARINGEY,Tottenham,NULL,TINTERN ROAD,21105160,N22,NULL,NULL,532050,190650,NULL,NULL\nNA,04/08/2016 10:50,2016,2016/17,Special Service,1,1,326,326,DOG STUCK ON TRAIN TRACK -JUNCTION WITH RODERICK ROAD,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,5036994,SAVERNAKE ROAD,20400202,NW3,527747,185732,527750,185750,51.55601872,-0.158694281\nNA,05/08/2016 17:28,2016,2016/17,Special Service,1,1,326,326,ASSIST RPSCA WITH CAT IN TREE OPP A WHITE HOUSE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200063368,HIGHWOOD HILL,20022660,NW7,521907,193422,521950,193450,51.62642105,-0.240221504\nNA,05/08/2016 18:08,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN WIRING ABOVE SHOP FANTASTIC FABRICS  NEXT TO TESCOS,Bird,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100012952,HIGH ROAD,19900165,RM6,547883,187946,547850,187950,51.57099615,0.132485524\nNA,05/08/2016 20:18,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011246,Ilford Town,E09000026,REDBRIDGE,Ilford,10034932839,HIGH ROAD,22306010,IG1,545060,186925,545050,186950,51.56255473,0.091359877\nNA,05/08/2016 20:42,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,BROOKLYN ROAD,20500729,SE25,NULL,NULL,534850,168550,NULL,NULL\nNA,06/08/2016 02:22,2016,2016/17,Special Service,1,1,326,326,CAT FALLER FROM WINDOW AND TRAPPED ON LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,LAIRDALE CLOSE,21900823,SE21,NULL,NULL,532150,173550,NULL,NULL\nNA,07/08/2016 10:01,2016,2016/17,Special Service,1,2,326,652,CAT FALLEN INTO LOWER BASEMENT AREA POSSIBLY INJURED OWNER IN ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,OGLE STREET,8400854,W1W,NULL,NULL,529150,181750,NULL,NULL\nNA,08/08/2016 04:03,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN GATE,Deer,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539450,187050,NULL,NULL\nNA,08/08/2016 17:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UP SCAFFOLDING REQUEST FROM RSPCA FOR ASSISTANCE     RSPCA INSPECTOR SAMANTHA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,WOODBERRY DOWN ESTATE,20950238,N4,NULL,NULL,532350,187550,NULL,NULL\nNA,09/08/2016 11:08,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ROYAL ROAD,22401806,TW11,NULL,NULL,514950,171450,NULL,NULL\nNA,09/08/2016 14:02,2016,2016/17,Special Service,1,1,326,326,DOG LEG STUCK IN TRAMPOLINE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,CRESCENT ROAD,20704108,N9,NULL,NULL,534450,194350,NULL,NULL\nNA,09/08/2016 16:19,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON BALCONY SEEN FROM   NEIGHBOUR REPORTING SMALL DOG LOCKED OUT ON BALCONY ATTEMPTING TO,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Sidcup,10003619564,COTMANDENE CRESCENT,20300922,BR5,546513,169412,546550,169450,51.404817,0.105046202\nNA,09/08/2016 20:55,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER CAR SEAT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,10070013483,OSWIN CLOSE,20304251,BR5,546669,167554,546650,167550,51.38808208,0.106520419\nNA,10/08/2016 12:13,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA CAT STUCK UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,1.00022E+11,SCOLES CRESCENT,21901218,SW2,531529,173280,531550,173250,51.44324763,-0.108810508\nNA,11/08/2016 09:52,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,ERITH ROAD,20101659,DA17,NULL,NULL,550150,178550,NULL,NULL\nNA,11/08/2016 17:50,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,RUSHDENE CRESCENT,20601493,UB5,NULL,NULL,511150,183150,NULL,NULL\nNA,12/08/2016 09:51,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON ROOF - FELL FROM HIGHER FLAT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,CANADIAN AVENUE,22001314,SE6,NULL,NULL,537550,173450,NULL,NULL\nNA,12/08/2016 20:11,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00023E+11,PEMBURY ROAD,21104920,N17,533762,190795,533750,190750,51.60012267,-0.070057304\nNA,13/08/2016 08:48,2016,2016/17,Special Service,1,1,326,326,Redacted,Sheep,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000203,JUBILEE,E09000010,ENFIELD,Enfield,207169544,LEA VALLEY ROAD,20703459,E4,536792,195323,536750,195350,51.64008327,-0.024569114\nNA,13/08/2016 10:34,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,HIGH STREET,22403558,TW11,NULL,NULL,516350,171150,NULL,NULL\nNA,13/08/2016 19:55,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN TELEPHONE WIRE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000491,ROYAL DOCKS,E09000025,NEWHAM,East Ham,NULL,DOCKLAND STREET,22207978,E16,NULL,NULL,543150,180150,NULL,NULL\nNA,13/08/2016 20:29,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO WALLS    RSPCA ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539750,187650,NULL,NULL\nNA,15/08/2016 00:45,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000344,YEADING,E09000017,HILLINGDON,Southall,1.00021E+11,KINGSASH DRIVE,21401100,UB4,512026,182345,512050,182350,51.52891332,-0.386438281\nNA,15/08/2016 09:08,2016,2016/17,Special Service,1,1,326,326,FOX WITH HEAD TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100056090,HOOKS HALL DRIVE,19900682,RM10,550462,186084,550450,186050,51.55358497,0.168875005\nNA,15/08/2016 11:40,2016,2016/17,Special Service,1,2,326,652,DOG STUCK ON ROOF DOG HAS GOT OUT ON ROOF UNABLE TO GET BACK IN AND OCCUPIERS ARE NOT AT HOME - THIS,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,CLAREMONT GARDENS,21800252,KT6,NULL,NULL,518250,167950,NULL,NULL\nNA,15/08/2016 13:48,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,STONEBRIDGE PARK,20200729,NW10,NULL,NULL,520750,184250,NULL,NULL\nNA,15/08/2016 14:33,2016,2016/17,Special Service,1,1,326,326,INJURED CAT STUCK IN TREE  RSPCA HAVE NO OFFICERS AVAILABLE TO SEND AT PRESENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,BURGHLEY ROAD,20400358,NW5,NULL,NULL,528950,185850,NULL,NULL\nNA,15/08/2016 22:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALL AND SHED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,SYCAMORE CLOSE,20601682,W3,NULL,NULL,521350,180150,NULL,NULL\nNA,16/08/2016 12:22,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL FROM RSPCA - ANIMAL STUCK UP TREE FOR SEVERAL DAYS REQUEST PUMP LADDER,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00023E+11,PEMBURY ROAD,21104920,N17,533767,190797,533750,190750,51.60013946,-0.069984394\nNA,17/08/2016 16:32,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING RSPCA ON SCENE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011098,Chaucer,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,DEVERELL STREET,22500763,SE1,532578,179197,532550,179150,51.4961776,-0.091508092\nNA,17/08/2016 17:58,2016,2016/17,Special Service,1,1,326,326,\"RABBIT TRAPPED IN GAS FIRE FLUE HAS BEEN MISSING FOR APPROX ONE WEEK, OWNER BELIEVES IT MUST HAVE BE\",Rabbit,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PEMBRIDGE SQUARE,21700756,W2,NULL,NULL,525250,180750,NULL,NULL\nNA,18/08/2016 20:48,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND TOILET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,GREENWAY,21201763,HA5,NULL,NULL,510950,190350,NULL,NULL\nNA,20/08/2016 15:13,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12044112,RUISLIP ROAD,20602431,UB6,513733,182388,513750,182350,51.52895893,-0.361826776\nNA,21/08/2016 21:49,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON TOP OF CHIMNEY RSPCA UNAVAILABLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,GREEN STREET EAST,E09000025,NEWHAM,East Ham,NULL,MARLBOROUGH ROAD,22207804,E7,NULL,NULL,541150,184450,NULL,NULL\nNA,22/08/2016 02:24,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHINED KITCHEN CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,FRESTON ROAD,21700874,W10,NULL,NULL,523650,180850,NULL,NULL\nNA,22/08/2016 08:19,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK BEHIND SHED  AT REAR OF,Cat,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,34073923,REGAL PLACE,21001055,SW6,525798,177039,525750,177050,51.47833119,-0.189893596\nNA,22/08/2016 11:34,2016,2016/17,Special Service,1,2,326,652,KITTEN TRAPPED IN TOY IN EMPTY PREMISES CALLER CAN SEE THROUGH THE FRONT ROOM WINDOW,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,CROYDON,Croydon,NULL,WENTWORTH ROAD,20501549,CR0,NULL,NULL,531250,166550,NULL,NULL\nNA,23/08/2016 01:24,2016,2016/17,Special Service,1,1,326,326,CAT HANGING FROM EXTERNAL WINDOW  POSSIBLY TRAPPED RSPCA UNABLE TO ATTEND,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,NULL,THORNE CLOSE,22201627,E16,NULL,NULL,540050,181350,NULL,NULL\nNA,23/08/2016 22:46,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000645,TACHBROOK,E09000033,WESTMINSTER,Lambeth,NULL,ST GEORGE'S SQUARE,8400197,SW1V,NULL,NULL,529650,178050,NULL,NULL\nNA,24/08/2016 19:09,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,BEATRICE ROAD,21102955,N4,NULL,NULL,531350,187850,NULL,NULL\nNA,25/08/2016 15:08,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN CHAIR,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011101,Dulwich Wood,E09000028,SOUTHWARK,West Norwood,NULL,ROUSE GARDENS,22502143,SE21,NULL,NULL,533250,171750,NULL,NULL\nNA,26/08/2016 03:25,2016,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN METAL FENCING     BY THE TALLY HO PUBLIC HOUSE,Deer,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200220980,HIGH ROAD,20022400,N12,526349,192179,526350,192150,51.61427152,-0.176530747\nNA,27/08/2016 23:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND PANEL RSPCA UNABLE TO ATTEND,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,NULL,TUDOR CLOSE,21901992,SW2,NULL,NULL,530750,174050,NULL,NULL\nNA,28/08/2016 15:11,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,NULL,GREENFORD ROAD,20602353,UB6,NULL,NULL,514950,183750,NULL,NULL\nNA,29/08/2016 18:31,2016,2016/17,Special Service,1,1,326,326,PERSON LOCKED IN,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,NULL,LANGHAM GARDENS,20601020,W13,NULL,NULL,516950,180850,NULL,NULL\nNA,30/08/2016 18:00,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND FRIDGE  -  CASHMERE BUTCHERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,5300088345,STROUD GREEN ROAD,21603885,N4,531090,187152,531050,187150,51.5680134,-0.109971988\nNA,30/08/2016 20:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY TWO FLOORS BELOW  .,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,YORK BUILDINGS,8400662,WC2N,NULL,NULL,530350,180550,NULL,NULL\nNA,30/08/2016 23:01,2016,2016/17,Special Service,1,1,326,326,BEAUMONT AVENUE BY WEST KENSINGTON STATION AT THE LILLIE BRIDGE DEPOT CAT TRAPPED IN METAL CONTAINER,Cat,Person (mobile),\"Large refuse/rubbish container (eg skip, paladin)\",Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34075044,BEAUMONT AVENUE,21000105,W14,524788,178414,524750,178450,51.49091207,-0.203944668\nNA,31/08/2016 17:08,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE  BIRD HANGING FROM WIRE AT RSPCA REQUEST AS OFFICER IS DELAYED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5087674,TOTTENHAM COURT ROAD,20400948,W1T,529491,181872,529450,181850,51.52093234,-0.134971173\nNA,31/08/2016 20:35,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN A DITCH  AT THE SIDE OF ROAD    NEAR THE FOOTBRIDGE   NEAR THE JUNCTION OF THE MAIN RO,Deer,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000305,WEST HARROW,E09000015,HARROW,Harrow,2E+11,VAUGHAN ROAD,21202028,HA1,514233,188068,514250,188050,51.57990887,-0.352778937\nNA,01/09/2016 02:51,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED DOWN MAN HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BARCLAY ROAD,20703892,N18,NULL,NULL,533050,191850,NULL,NULL\nNA,01/09/2016 20:05,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200151566,SUMMERLEE AVENUE,20041380,N2,527497,189074,527450,189050,51.58610903,-0.161087014\nNA,02/09/2016 09:53,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARE GARDENS,21701124,W11,NULL,NULL,524450,181150,NULL,NULL\nNA,02/09/2016 10:23,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN GUTTERING ON ROOFTOP (UNABLE TO TURN AROUND) OPPOSITE  VET IN ATTENDANCE - WILL DIREC,Cat,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48020534,DEER PARK GARDENS,22102010,CR4,526679,168291,526650,168250,51.39951458,-0.180340322\nNA,02/09/2016 14:54,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF  RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,ISLIP STREET,20400366,NW5,NULL,NULL,529150,185050,NULL,NULL\nNA,02/09/2016 17:29,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,Shirly South,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL\nNA,03/09/2016 11:25,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000187,PERIVALE,E09000009,EALING,Wembley,NULL,EMPIRE ROAD,20600626,UB6,NULL,NULL,517350,183950,NULL,NULL\nNA,04/09/2016 10:05,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CAR ENGINE  ENTRANCE IN LOWFT ROAD,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011096,Camberwell Green,E09000028,SOUTHWARK,Peckham,2.00003E+11,DENMARK ROAD,22500754,SE5,532230,176460,532250,176450,51.47166247,-0.097542095\nNA,04/09/2016 17:12,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON ROOF OF SCHOOL  CALLER WILL DIRECT YOU,Cat,Person (land line),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,1.00023E+11,ELFRIDA CRESCENT,22001435,SE6,537498,171846,537450,171850,51.42894447,-0.023533159\nNA,05/09/2016 13:59,2016,2016/17,Special Service,1,1,326,326,\"ASSIST RSPCA RETRIEVE INJURED CAT  WILL MEET YOU ON ARRIVAL, RSPCA ON SCENE  NR TO BOXING CLUB\",Cat,Person (land line),Sports/Social club,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,46255479,CHURCH STREET,22201348,E15,539609,183715,539650,183750,51.53508522,0.011501621\nNA,06/09/2016 09:17,2016,2016/17,Special Service,NULL,NULL,326,NULL,COW IN DISTRESS IN WATER,Cow,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184751,WHARF ROAD,20702448,EN3,536249,195594,536250,195550,51.64265046,-0.032305624\nNA,07/09/2016 19:21,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON ROOF CALLER WILL MEET YOU,Dog,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,10091908052,CHURCH STREET,20704052,N9,534288,193588,534250,193550,51.62509603,-0.061398245\nNA,08/09/2016 07:23,2016,2016/17,Special Service,1,1,326,326,CAT STUCK ON ROOF AT SECOND FLOOR LEVEL,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,PENNINGTON DRIVE,20706126,N21,NULL,NULL,530550,195750,NULL,NULL\nNA,08/09/2016 08:46,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN TRAPPED BETWEEN TWO BUILDINGS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,WOOD END GREEN ROAD,21402200,UB3,509155,181327,509150,181350,51.52032542,-0.428124845\nNA,08/09/2016 09:46,2016,2016/17,Special Service,1,2,326,652,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000183,NORTH GREENFORD,E09000009,EALING,Wembley,NULL,ASHNESS GARDENS,20600082,UB6,NULL,NULL,516850,184650,NULL,NULL\nNA,08/09/2016 19:15,2016,2016/17,Special Service,1,1,326,326,CAT STUCK BEHIND WALL   REQUEST FROM RSPCA - ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000471,TRINITY,E09000024,MERTON,Wimbledon,NULL,HAYDONS ROAD,22103210,SW19,NULL,NULL,526150,170850,NULL,NULL\nNA,08/09/2016 19:28,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED IN FENCING,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,CLEVELEYS ROAD,20900253,E5,535067,186477,535050,186450,51.56100889,-0.052884794\nNA,09/09/2016 07:25,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,THORPEDALE ROAD,21603927,N4,NULL,NULL,530650,187150,NULL,NULL\nNA,09/09/2016 17:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,MAIDA AVENUE,8400116,W2,NULL,NULL,526450,182050,NULL,NULL\nNA,11/09/2016 15:50,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT UP TREE  RSPCA ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,SOUTHWARK,Dockhead,2.00003E+11,WESTON STREET,22502690,SE1,533016,179426,533050,179450,51.49813276,-0.085115871\nNA,11/09/2016 16:26,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,HIGH STREET,20300424,BR1,NULL,NULL,540150,169350,NULL,NULL\nNA,12/09/2016 08:45,2016,2016/17,Special Service,1,2,326,652,DISTRESSED KITTEN STUCK IN TREE  OWNER IN ATTENDANCE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46023620,EIDER CLOSE,22207694,E7,539824,185359,539850,185350,51.54980481,0.015251852\nNA,12/09/2016 15:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,ST ERVANS ROAD,21701009,W10,NULL,NULL,524650,181850,NULL,NULL\nNA,12/09/2016 19:16,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN THORNY BUSH,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000556,BEDDINGTON SOUTH,E09000029,SUTTON,Wallington,5870110978,HANDLEY PAGE ROAD,22605760,SM6,530531,163319,530550,163350,51.35395993,-0.126823331\nNA,12/09/2016 19:49,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,NORTH PARK,20801090,SE9,543035,174288,543050,174250,51.44951917,0.057045436\nNA,13/09/2016 02:55,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,DOCK STREET,22700417,E1,NULL,NULL,534150,180750,NULL,NULL\nNA,13/09/2016 20:13,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO SQUIRREL TRAPPED IN FLAT,Squirrel,Person (land line),Hostel (e.g. for homeless people),Other Residential,Other animal assistance,Assist trapped wild animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,2E+11,CLARENCE AVENUE,21900348,SW4,529715,174278,529750,174250,51.45263476,-0.134531555\nNA,14/09/2016 10:07,2016,2016/17,Special Service,2,3,326,978,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009322,BROMLEY SOUTH,E09000030,TOWER HAMLETS,Poplar,6174040,AQUA VISTA SQUARE,22702674,E3,537391,181619,537350,181650,51.51679393,-0.021273477\nNA,14/09/2016 11:21,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SURBITON HILL PARK,21800924,KT5,NULL,NULL,518850,167750,NULL,NULL\nNA,14/09/2016 12:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,OSIERS ROAD,22903717,SW18,NULL,NULL,525250,175150,NULL,NULL\nNA,14/09/2016 21:43,2016,2016/17,Special Service,1,1,326,326,PARROT TRAPPED BEHIND KITCHEN UNITS,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,West Norwood,NULL,THE WOODLANDS,20500135,SE19,NULL,NULL,532350,170250,NULL,NULL\nNA,15/09/2016 06:47,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MEATH CRESCENT,22702470,E2,NULL,NULL,535850,182750,NULL,NULL\nNA,15/09/2016 20:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN FENCING,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,10091060040,OSBORNE ROAD,20202183,NW2,522770,184927,522750,184950,51.54988797,-0.230728646\nNA,16/09/2016 09:33,2016,2016/17,Special Service,1,2,326,652,HORSE TRAPPED IN DITCH,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100007496,THE CHASE,19900828,RM7,551479,186172,551450,186150,51.55410381,0.183570624\nNA,16/09/2016 17:42,2016,2016/17,Special Service,1,1,326,326,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009397,HOLLAND,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,ST MARY ABBOT'S TERRACE,21700675,W14,NULL,NULL,524850,179150,NULL,NULL\nNA,17/09/2016 15:55,2016,2016/17,Special Service,1,1,326,326,\"PIGEON TRAPPED IN NETTING ON SECOND LEVEL, RSPCA UNABLE TO ATTEND\",Bird,Person (land line),Exhibition Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003974918,ALEXANDRA PALACE WAY,21106542,N22,529592,190059,529550,190050,51.59448345,-0.130501616\nNA,18/09/2016 09:40,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN GARAGE THROUGH HOLE IN ROOF - NOW UNABLE TO GET OUT,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,ST HELEN CLOSE,21401819,UB8,505789,181659,505750,181650,51.52394962,-0.476520341\nNA,18/09/2016 10:56,2016,2016/17,Special Service,2,3,326,978,** DO NOT CLOSE - REFER TO OFFICER OF THE WATCH **    ASSIST RSPCA WITH CAT ON ROOF RSPCA OFFICER ON,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011226,Falconwood & Welling,E09000004,BEXLEY,Plumstead,NULL,BELLEGROVE ROAD,20100114,DA16,NULL,NULL,546350,175850,NULL,NULL\nNA,18/09/2016 11:58,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH LEG STUCK IN CHAIR,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Kentish Town,NULL,BRECKNOCK ROAD,21606410,N19,NULL,NULL,529550,185550,NULL,NULL\nNA,18/09/2016 22:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,FITZJOHN AVENUE,20016000,EN5,NULL,NULL,524550,196150,NULL,NULL\nNA,20/09/2016 08:55,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER KITCHEN UNITS   PA REQUIRED WITH SAW,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,VERNON CLOSE,20301328,BR5,NULL,NULL,546950,168850,NULL,NULL\nNA,20/09/2016 12:56,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN STAIR GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,PORTLAND SQUARE,22701923,E1W,NULL,NULL,534650,180250,NULL,NULL\nNA,20/09/2016 13:07,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN STAIR GATE   SAME AS PREVIOUS CALL - CALLER REQUIRES BRIGADE - THOUGHT DOG,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,PORTLAND SQUARE,22701923,E1W,NULL,NULL,534650,180250,NULL,NULL\nNA,20/09/2016 14:29,2016,2016/17,Special Service,1,1,326,326,DOG WITH COLLAR CAUGHT ON RAILLINGS UNABLE TO BE RELEASED,Dog,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,PARK ROAD,22403720,TW11,515964,170400,515950,170450,51.42076016,-0.333610964\nNA,20/09/2016 15:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207187270,WHARF ROAD,20702448,EN3,536312,195779,536350,195750,51.64429762,-0.031323687\nNA,20/09/2016 17:26,2016,2016/17,Special Service,1,1,326,326,TWO KITTENS TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,OAKLEIGH PARK NORTH,20032540,N20,NULL,NULL,526950,194650,NULL,NULL\nNA,21/09/2016 11:06,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UNDER KITCHEN UNIT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011485,South Norwood,E09000008,CROYDON,Woodside,NULL,HUNTLY ROAD,20501068,SE25,NULL,NULL,533450,168450,NULL,NULL\nNA,21/09/2016 20:15,2016,2016/17,Special Service,1,2,326,652,ENTRANCE AT J/O THE AVENUE  RSPCA PARKED THERE TO MEET YOU BIRD STUCK IN TREE,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003977015,ALEXANDRA PALACE WAY,21106542,N22,529489,190242,529450,190250,51.59615165,-0.131920286\nNA,22/09/2016 17:18,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILINGS IN CONSIDERABLE DISTRESS CALLER IS ALSO CALLING THE RSPCA,Deer,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,BEACH GROVE,21500085,TW13,513320,172537,513350,172550,51.44050173,-0.370936539\nNA,22/09/2016 17:32,2016,2016/17,Special Service,1,1,326,326,DOG STUCK UNDER SHED IN REAR GARDEN - DOG TUNNELED UNDER SHED AND NOW CANNOT GET BACK OUT - OWNER ON,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,1.00022E+11,LOUGHBOROUGH PARK,21900894,SW9,531872,175564,531850,175550,51.46369382,-0.103027426\nNA,23/09/2016 09:37,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (land line),Lorry/HGV,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,10022800980,HORTON ROAD,21402928,SL3,503588,175483,503550,175450,51.46884401,-0.510046384\nNA,24/09/2016 19:51,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,2.00003E+11,GROSVENOR ROAD,21500522,W4,519990,178153,519950,178150,51.48960355,-0.273109521\nNA,25/09/2016 13:33,2016,2016/17,Special Service,1,1,326,326,PIGEON INJURED AND TRAPPED IN TREE BY KITE CALLER WILL MEET YOU AT THE ENTRANCE TO THE CEMETARY,Bird,Person (mobile),Cemetery,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200017096,BRUNSWICK PARK ROAD,20005900,N11,528395,193269,528350,193250,51.62360424,-0.146597762\nNA,26/09/2016 19:26,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN GUTTER ON ROOF OF SECOND FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011239,Clayhall,E09000026,REDBRIDGE,Hainault,NULL,HERENT DRIVE,22302930,IG5,NULL,NULL,542950,189850,NULL,NULL\nNA,27/09/2016 10:40,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED UP TREE RSPCA INSPECTOR ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,200129873,WOODSIDE,20047340,NW11,525315,188940,525350,188950,51.58539404,-0.192612904\nNA,27/09/2016 14:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN TWO FENCES,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000126,SHORTLANDS,E09000006,BROMLEY,Beckenham,NULL,MALMAINS WAY,20301661,BR3,NULL,NULL,538750,167950,NULL,NULL\nNA,28/09/2016 11:00,2016,2016/17,Special Service,1,1,326,326,CAT STUCK WITHIN CAR ENGINE  NEAR ROMFORD CEMETERY,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00021E+11,CROW LANE,21301120,RM7,550307,187790,550350,187750,51.56895488,0.167369338\nNA,30/09/2016 01:51,2016,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011221,Blendon & Penhill,E09000004,BEXLEY,Sidcup,NULL,HURST ROAD,20101715,DA15,NULL,NULL,546450,172750,NULL,NULL\nNA,30/09/2016 12:33,2016,2016/17,Special Service,1,1,326,326,ANIMAL STUCK POSSIBLY IN LOFT AREA,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011471,New Addington South,E09000008,CROYDON,Addington,NULL,CHERTSEY CRESCENT,20501684,CR0,NULL,NULL,538250,162050,NULL,NULL\nNA,30/09/2016 20:18,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SMALL ANIMAL RESCUE OFFICER ON SCENE - PIGEON TRAPPED IN NETTING POSSIBLY INJURED,Bird,Person (mobile),Bank/Building Society,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,1.00023E+11,HIGH STREET,21400966,UB8,505657,184001,505650,184050,51.54502502,-0.477719813\nNA,30/09/2016 23:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED DOWN DRAIN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011485,South Norwood,E09000008,CROYDON,Woodside,10014053628,TENNISON ROAD,20501473,SE25,533621,167956,533650,167950,51.39491363,-0.080732458\nNA,01/10/2016 19:47,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND PIPEWORK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Millwall,NULL,GLENGALL GROVE,22700548,E14,NULL,NULL,538050,179350,NULL,NULL\nNA,02/10/2016 22:20,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.00024E+11,COPERS COPE ROAD,20303032,BR3,537196,169902,537150,169950,51.41154804,-0.028626138\nNA,02/10/2016 22:35,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN ON A ROOF RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CATHALL ROAD,22823150,E11,NULL,NULL,539150,186350,NULL,NULL\nNA,03/10/2016 14:04,2016,2016/17,Special Service,1,2,326,652,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,WOODGRANGE AVENUE,20047180,N12,NULL,NULL,526850,191450,NULL,NULL\nNA,03/10/2016 18:43,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN THE CHIMNEY RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,POYNTELL CRESCENT,20303018,BR7,NULL,NULL,544850,169850,NULL,NULL\nNA,03/10/2016 19:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SEAGULL CAUGHT UP IN ARIEL ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,CHURCHBURY ROAD,20702603,EN1,NULL,NULL,532950,197450,NULL,NULL\nNA,03/10/2016 21:51,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN WIRE FENCE AT COLLINGWOOD RECREATION GROUND - OWNER WILL MEET BRIGADE AT ENTRANCE TO,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,5870036275,COLLINGWOOD ROAD,22602218,SM1,525220,164805,525250,164850,51.36850866,-0.202530433\nNA,06/10/2016 11:02,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UNDER BATH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,NULL,HAYMERLE ROAD,22501223,SE15,NULL,NULL,534150,177650,NULL,NULL\nNA,06/10/2016 13:32,2016,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN WINDOW  RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ASTON STREET,22700100,E14,NULL,NULL,536250,181450,NULL,NULL\nNA,06/10/2016 18:25,2016,2016/17,Special Service,1,2,326,652,CAT STUCK IN VENTILATION SYSTEM,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,1.00023E+11,RUSHEY GREEN,22004211,SE6,537710,173865,537750,173850,51.4470364,-0.019701281\nNA,08/10/2016 09:31,2016,2016/17,Special Service,1,1,326,326,HAMSTER TRAPPED BEHIND SKIRTING,Hamster,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Fulham,NULL,EARL'S COURT SQUARE,21700167,SW5,NULL,NULL,525550,178250,NULL,NULL\nNA,08/10/2016 11:45,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN TREE TRUNK CALLER WILL MEET YOU BY THE PLAYGROUND ON PETERSHAM ROAD,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,1.00022E+11,PETERSHAM ROAD,22403729,TW10,518372,173203,518350,173250,51.44545474,-0.298058829\nNA,08/10/2016 13:51,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN TREE CALLER STATES LEGS ARE TRAPPED IN WIRE/ROPE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6176783,BRODLOVE LANE,22700203,E1W,535552,180951,535550,180950,51.5112348,-0.048017903\nNA,08/10/2016 15:16,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,NULL,ECCLESBOURNE ROAD,20500893,CR7,NULL,NULL,532350,168050,NULL,NULL\nNA,10/10/2016 08:22,2016,2016/17,Special Service,2,4,326,1304,Redacted,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,5024751,HIGHGATE ROAD,20400091,NW5,528908,185410,528950,185450,51.55286121,-0.142074657\nNA,10/10/2016 12:33,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070008362,GLASSMILL LANE,20303922,BR2,539954,169167,539950,169150,51.40427014,0.01071494\nNA,10/10/2016 14:07,2016,2016/17,Special Service,1,1,326,326,\"DOG TRAPPED IN FENCE CALLER SAYS BEST ACCESS IS VIA KELSEY PARK AVENUE, NEAR AARON COURT\",Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,1.0002E+11,KELSEY PARK AVENUE,20301420,BR3,537748,168832,537750,168850,51.401799,-0.021109191\nNA,10/10/2016 17:34,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,QUEEN CAROLINE STREET,21000660,W6,NULL,NULL,523250,178250,NULL,NULL\nNA,10/10/2016 21:41,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN TUMBLE DRYER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,MOUNTFIELD CLOSE,22006038,SE6,NULL,NULL,538650,173850,NULL,NULL\nNA,13/10/2016 09:58,2016,2016/17,Special Service,2,5,326,1630,LARGE ANIMAL RESCUE - SEAL TRAPPED ON LAND WATER RESCUE LEVEL TWO IMPLEMENTED PRESS OFFICER REQUESTE,Unknown - Wild Animal,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091067964,KEW BRIDGE ROAD,21500657,TW8,518975,177883,518950,177850,51.48739104,-0.287813023\nNA,13/10/2016 12:50,2016,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD STUCK IN METAL WINE RACK,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,NULL,OATFIELD ROAD,20300954,BR6,NULL,NULL,546050,166250,NULL,NULL\nNA,17/10/2016 08:59,2016,2016/17,Special Service,1,1,326,326,DEER WITH ANTLERS TRAPPED IN CRICKET NET - RSPCA ON SCENE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,JAIL LANE,20301511,TN16,542644,159481,542650,159450,51.31656333,0.045483018\nNA,17/10/2016 13:28,2016,2016/17,Special Service,2,4,326,1304,Redacted,Fox,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6129318,COPENHAGEN PLACE,22700355,E14,536685,181119,536650,181150,51.5124718,-0.031635947\nNA,18/10/2016 13:30,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND ADVERTISING BOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,PARK ROAD,21103726,N8,NULL,NULL,530050,188550,NULL,NULL\nNA,19/10/2016 10:09,2016,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,HILLSIDE AVENUE,20022980,N11,NULL,NULL,527750,191950,NULL,NULL\nNA,19/10/2016 12:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,1.00024E+11,JUDGE HEATH LANE,21401064,UB8,508322,181028,508350,181050,51.51779826,-0.440217745\nNA,20/10/2016 14:32,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - PET PARROT TRAPPED IN TREE NEAR CHILDRENS PLAYGROUND,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12141341,EASTCOTE LANE,20600590,UB5,512748,184370,512750,184350,51.54697011,-0.375386603\nNA,20/10/2016 14:52,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN GATE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MORTLAKE HIGH STREET,22404798,SW14,NULL,NULL,520650,175950,NULL,NULL\nNA,21/10/2016 12:48,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - PUPPY TRAPPED IN RAILINGS  HORN PARK,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,10010228426,GAVESTONE ROAD,20800620,SE12,540645,173613,540650,173650,51.44405171,0.022406015\nNA,21/10/2016 18:01,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200049284,GOLDERS PARK CLOSE,20018220,NW11,525498,187057,525450,187050,51.56843083,-0.190645312\nNA,22/10/2016 12:44,2016,2016/17,Special Service,1,1,326,326,PIGEON WITH WING IMAPLED ON SHARP IMPLEMENT  UNDER A BRIDGE NEAR,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,Wimbledon,48039776,KINGSTON ROAD,22103623,SW20,524292,169329,524250,169350,51.40937105,-0.214273154\nNA,23/10/2016 00:25,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE  >,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200072480,LEICESTER ROAD,20026380,EN5,525761,196055,525750,196050,51.64923573,-0.183627455\nNA,23/10/2016 08:15,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500610,TW3,NULL,NULL,514250,175850,NULL,NULL\nNA,23/10/2016 09:30,2016,2016/17,Special Service,1,1,326,326,DOG IN RIVER - FRU CARRYING BOAT REQUESTED BY TAR OFFICER  OWNERS ON SCENE  BEHIND TENNIS COURTS,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,PARK VIEW,21800750,KT3,521965,168527,521950,168550,51.40266798,-0.247990538\nNA,23/10/2016 10:06,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN SOFFITS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011231,Slade Green & Northend,E09000004,BEXLEY,Erith,NULL,SANDPIPER DRIVE,20101766,DA8,NULL,NULL,552850,177250,NULL,NULL\nNA,23/10/2016 15:37,2016,2016/17,Special Service,1,1,326,326,ASSIST RSCPA WITH CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000097,PRESTON,E09000005,BRENT,Wembley,202061886,OAKINGTON AVENUE,20202423,HA9,519017,186225,519050,186250,51.56235666,-0.284392116\nNA,23/10/2016 15:44,2016,2016/17,Special Service,1,1,326,326,Redacted,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011487,Waddon,E09000008,CROYDON,Croydon,10001002174,DUPPAS HILL ROAD,20502044,CR0,531790,164899,531750,164850,51.36786894,-0.108168525\nNA,23/10/2016 18:20,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE  RE-ATTEND AS OWNER NOW ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000097,PRESTON,E09000005,BRENT,Wembley,202061886,OAKINGTON AVENUE,20202423,HA9,519015,186226,519050,186250,51.56236607,-0.284420618\nNA,23/10/2016 19:36,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011111,Peckham Rye,E09000028,SOUTHWARK,Forest Hill,NULL,THERAPIA ROAD,22502490,SE22,NULL,NULL,534950,174250,NULL,NULL\nNA,24/10/2016 11:16,2016,2016/17,Special Service,1,1,326,326,ASSISTING RSPCA OFFICER WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,2.00003E+11,CAMBERWELL GROVE,22500402,SE5,532897,176547,532850,176550,51.47228814,-0.087911768\nNA,25/10/2016 03:09,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,1.00021E+11,PALMERSTON ROAD,21106101,N22,530595,191250,530550,191250,51.60495481,-0.115587471\nNA,25/10/2016 13:15,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH SMALL ANIMAL RESCUE IN RECREATION PARK NEAR PRIMARY SCHOOL,Unknown - Domestic Animal Or Pet,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,48106210,DUNDONALD ROAD,22102193,SW19,524555,170052,524550,170050,51.41581117,-0.210239743\nNA,25/10/2016 18:04,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,RAYBURN ROAD,21300554,RM11,NULL,NULL,555350,187850,NULL,NULL\nNA,25/10/2016 19:07,2016,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING WILDLIFE CHARITY WORKER ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,527350,165250,NULL,NULL\nNA,26/10/2016 08:22,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BEHIND FREEZER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,FOREST RIDGE,20300744,BR3,NULL,NULL,537250,168850,NULL,NULL\nNA,26/10/2016 09:33,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING    NEW TIGERS PUB  J.O LEE ROAD  - CALLER ON SCENE WILL MEET BRIGADE CALLER,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,LEE ROAD,22001639,SE3,NULL,NULL,539850,175050,NULL,NULL\nNA,26/10/2016 10:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,1.00021E+11,MANSER ROAD,21300834,RM13,551023,182975,551050,182950,51.52550042,0.175628763\nNA,26/10/2016 12:45,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS  IN THE PLAYGROUND AREA,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,10013151964,TUBBENDEN LANE,20303579,BR6,544740,164772,544750,164750,51.36357935,0.077685685\nNA,26/10/2016 17:58,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN HOLE CRANE PARK,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,HOSPITAL BRIDGE ROAD,22401591,TW2,513784,172619,513750,172650,51.44114575,-0.364237163\nNA,27/10/2016 12:25,2016,2016/17,Special Service,2,3,326,978,ASSIST RSPCA WITH CAT STUCK UP TREE.  J/O LONGLEY LANE RVP WITH RSPCA IN CAR PARK,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010224990,BOSTALL HILL,20800197,SE2,546677,177873,546650,177850,51.48080116,0.110906796\nNA,27/10/2016 13:27,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ST MARTINS AVENUE,22207900,E6,NULL,NULL,541650,183250,NULL,NULL\nNA,27/10/2016 14:52,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,OVAL ROAD,20400628,NW1,NULL,NULL,528550,184050,NULL,NULL\nNA,27/10/2016 19:42,2016,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,OVERBURY STREET,20900778,E5,NULL,NULL,535850,185950,NULL,NULL\nNA,28/10/2016 01:51,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED IN GATE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011235,Barkingside,E09000026,REDBRIDGE,Ilford,NULL,CAMPBELL AVENUE,22302679,IG6,NULL,NULL,544050,189050,NULL,NULL\nNA,28/10/2016 14:16,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,HOWARD ROAD,20900539,N16,NULL,NULL,532950,185550,NULL,NULL\nNA,28/10/2016 19:42,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA GAIN ACCESS     RSPCA ON SCENE WITH INJURED GULL,Bird,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00023E+11,GREAT WEST ROAD,21505052,TW8,516837,177730,516850,177750,51.48646104,-0.31864259\nNA,28/10/2016 20:39,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,FOREST,E09000031,WALTHAM FOREST,Leytonstone,NULL,HAINAULT ROAD,22842000,E11,NULL,NULL,538850,187850,NULL,NULL\nNA,29/10/2016 09:48,2016,2016/17,Special Service,1,1,326,326,CALL TO BEAR TRAPPED IN DOOR - RSPCA REQUESTED - SECURITY WILL MEET LFB ON SCENE,Unknown - Wild Animal,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,NULL,STATION APPROACH,21401857,UB3,NULL,NULL,509850,179450,NULL,NULL\nNA,29/10/2016 11:31,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,NULL,NASSAU ROAD,22404814,SW13,NULL,NULL,521950,176450,NULL,NULL\nNA,29/10/2016 15:05,2016,2016/17,Special Service,1,1,326,326,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527092,166673,527050,166650,51.3848812,-0.174985268\nNA,29/10/2016 18:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON CHIMNEY  .,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,ST MARTINS AVENUE,22207900,E6,NULL,NULL,541650,183250,NULL,NULL\nNA,30/10/2016 09:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX ON TOP OF CONTAINERS,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,10012786743,CARLETON ROAD,21606415,N7,530047,185914,530050,185950,51.5571294,-0.125469337\nNA,31/10/2016 11:24,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON ROOF RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Addington,NULL,HARVEST BANK ROAD,20301742,BR4,NULL,NULL,539950,165150,NULL,NULL\nNA,31/10/2016 15:36,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED BY LEG INSIDE CAR OWNER ON SCENE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Barking,100027058,STONARD ROAD,19900313,RM8,546871,185640,546850,185650,51.55054028,0.116931532\nNA,05/11/2016 04:23,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE J/O DAGNAM PARK DRIVE - BABY DEER TRAPPED IN RAILINGS - CALLER WILL MEET BRIGADE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10025354015,NORTH HILL DRIVE,21300103,RM3,553713,192766,553750,192750,51.6127454,0.218651008\nNA,07/11/2016 07:11,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN A TREE - NEAR THE CHILDRENS PLAYGROUND CALLER WILL WAIT,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011250,Newbury,E09000026,REDBRIDGE,Ilford,10023192709,ALDBOROUGH ROAD SOUTH,22301800,IG3,545944,188157,545950,188150,51.57339663,0.104614809\nNA,07/11/2016 20:16,2016,2016/17,Special Service,1,1,326,326,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,Mayfield,E09000026,REDBRIDGE,Ilford,NULL,BETCHWORTH ROAD,22301853,IG3,NULL,NULL,545250,186550,NULL,NULL\nNA,08/11/2016 06:10,2016,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN WATER  BEHIND LEA VALLEY RIDING CINTRE DOG CAN BE SEEN FROM BRIDGE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10009146683,BLACK PATH,22802350,E10,535844,187290,535850,187250,51.56812841,-0.041368508\nNA,09/11/2016 07:23,2016,2016/17,Special Service,1,1,326,326,DOG IN RIVER IN CATOR PARK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,ALDERSMEAD ROAD,20302187,BR3,536205,170074,536250,170050,51.41333233,-0.042800809\nNA,09/11/2016 22:35,2016,2016/17,Special Service,1,1,326,326,CAT WITH PAW TRAPPED IN PLUGHOLE OF BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000031,EASTBURY,E09000002,BARKING AND DAGENHAM,Barking,NULL,STUART ROAD,19900708,IG11,NULL,NULL,545750,184050,NULL,NULL\nNA,10/11/2016 15:11,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BETWEEN BRICK WALL AND HOUSE         RSPCA IN ATTENDANCE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,BETJEMAN MEWS,21610140,N5,NULL,NULL,532050,185750,NULL,NULL\nNA,12/11/2016 15:14,2016,2016/17,Special Service,1,1,326,326,HORSE STUCK IN DITCH ON ITS BACK,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,128014370,PARKFIELDS ROAD,21800751,KT2,518779,171150,518750,171150,51.42691821,-0.292893424\nNA,14/11/2016 13:24,2016,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT TRAPPED UNDER CAR  NEAR TO PC WORLD AND CURRYS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,OLD KENT ROAD,22501843,SE1,534321,177872,534350,177850,51.48385926,-0.066918079\nNA,15/11/2016 13:58,2016,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED ON WIRE,Bird,Person (land line),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217059304,NORTH POLE ROAD,21700955,W10,523183,181594,523150,181550,51.51984338,-0.225940924\nNA,15/11/2016 14:15,2016,2016/17,Special Service,1,1,326,326,CAT IN TREE RSPCA ON SCENE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,1.00021E+11,WESTON CLOSE,20500369,CR5,530972,157070,530950,157050,51.29769841,-0.122786684\nNA,17/11/2016 10:38,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202065635,MANDELA CLOSE,20201158,NW10,520389,184409,520350,184450,51.54574471,-0.26522949\nNA,17/11/2016 12:31,2016,2016/17,Special Service,1,1,326,326,BIRD TANGLED IN WIRE CALLER WAS ON A BUS HE STATES THIS IS NEAR THE SCHOOL/COLLEGE THE QIRE GOES ACR,Bird,Person (mobile),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000096,NORTHWICK PARK,E09000005,BRENT,Wembley,202101930,EAST LANE,20202525,HA0,517527,186208,517550,186250,51.56251597,-0.305883379\nNA,17/11/2016 13:37,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN PRECARIOUS POSITION,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10012835276,BOTHA ROAD,22200394,E13,541057,181849,541050,181850,51.51795747,0.031617955\nNA,19/11/2016 12:17,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000171,CLEVELAND,E09000009,EALING,Ealing,12076582,PITSHANGER LANE,20601378,W5,516995,182121,516950,182150,51.52589357,-0.314911989\nNA,19/11/2016 14:10,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER FRIDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,NULL,FREWIN ROAD,22901808,SW18,NULL,NULL,527050,173350,NULL,NULL\nNA,20/11/2016 14:27,2016,2016/17,Special Service,1,1,326,326,OUTSIDE HSBC ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10034920914,THE BROADWAY,22306023,IG8,540893,191828,540850,191850,51.60766722,0.033251086\nNA,20/11/2016 17:50,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD CAUGHT IN TREE,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202128050,BRAEMAR AVENUE,20200556,HA0,517959,184133,517950,184150,51.54377643,-0.300348784\nNA,21/11/2016 10:07,2016,2016/17,Special Service,1,1,326,326,EXPLOSION IN SIGNAL BOX,Unknown - Wild Animal,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Animal harm involving wild animal,E05000140,KILBURN,E09000007,CAMDEN,Paddington,5085366,KILBURN HIGH ROAD,20400395,NW6,525507,183618,525550,183650,51.53752228,-0.191741581\nNA,21/11/2016 12:44,2016,2016/17,Special Service,1,1,326,326,DOG STUCK IN FOX HOLE  CALLER IS ON HAMPSTEAD HEATH AND WILL WAVE YOU DOWN,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5132281,SPANIARDS ROAD,20400006,NW3,526394,186643,526350,186650,51.56451022,-0.177873011\nNA,22/11/2016 15:38,2016,2016/17,Special Service,1,1,326,326,TO ASSIST RSPCA WITH CAT ON THIRD FLOOR,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PENYWERN ROAD,21700411,SW5,NULL,NULL,525450,178350,NULL,NULL\nNA,24/11/2016 17:28,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,KIRKDALE,22002606,SE26,NULL,NULL,535150,171750,NULL,NULL\nNA,25/11/2016 11:42,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,MARSWORTH CLOSE,21401242,UB4,NULL,NULL,512350,181850,NULL,NULL\nNA,26/11/2016 22:02,2016,2016/17,Special Service,1,1,326,326,\"DOG IN WATER  - NEAR THE BRIDGE, TRAPPED ON SIDE OF BANK FRU WITH INFLATABLE BOAT REQUESTED FROM INC\",Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,10070248483,PUTNEY BRIDGE,22906420,SW15,524224,175721,524250,175750,51.46683331,-0.213010265\nNA,26/11/2016 23:38,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON LEDGE,Dog,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,1.00023E+11,ST GEORGE'S DRIVE,8400194,SW1V,529174,178364,529150,178350,51.48947878,-0.140821898\nNA,29/11/2016 10:44,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN RAILINGS ORLEANS PLAY PARK,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,ORLEANS ROAD,22403709,TW1,516978,173485,516950,173450,51.44827867,-0.318015788\nNA,30/11/2016 11:05,2016,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Hairdresser,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200034906,EAST BARNET ROAD,20013380,EN4,527128,195372,527150,195350,51.64279062,-0.164127425\nNA,30/11/2016 18:22,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN RECLINER CHAIR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,NULL,KINGS ROAD,20202255,NW10,NULL,NULL,522550,184250,NULL,NULL\nNA,01/12/2016 10:07,2016,2016/17,Special Service,1,1,326,326,SMALL DOG STUCK IN FENCE PANEL,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,GELSTHORPE ROAD,21301219,RM5,NULL,NULL,549750,191150,NULL,NULL\nNA,01/12/2016 10:32,2016,2016/17,Special Service,1,1,326,326,RUNNING CALL TO CAT IN TREE ON POND,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070001081,GLASSMILL LANE,20303922,BR2,539925,169022,539950,169050,51.40297431,0.010241131\nNA,01/12/2016 15:38,2016,2016/17,Special Service,1,2,326,652,RUNNING CALL TO CAT TRAPPED UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070011991,GLASSMILL LANE,20303922,BR2,539934,169018,539950,169050,51.40293615,0.010368854\nNA,02/12/2016 09:56,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,2.00003E+11,GLASSMILL LANE,20303922,BR2,540010,169240,540050,169250,51.40491232,0.011548288\nNA,02/12/2016 10:40,2016,2016/17,Special Service,2,3,326,978,SMALL WHITE DOG STUCK IN THE MUD  LAST SEEN AT CHELSEA CREEK,Dog,Person (mobile),Electricity power station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217114091,LOTS ROAD,21700327,SW10,526233,176943,526250,176950,51.47737164,-0.183667164\nNA,03/12/2016 08:37,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN WALL,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,1.0002E+11,EVERSLEY AVENUE,20100520,DA7,550762,176216,550750,176250,51.4648397,0.168982503\nNA,04/12/2016 13:12,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND METAL SHUTTERS THE CALLER HAS LEFT A ROADSIDE ORANGE CONE BY THE DOOR WHERE THE C,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10008992232,UPTON LANE,22201168,E7,540620,184697,540650,184650,51.54365837,0.026459982\nNA,06/12/2016 03:38,2016,2016/17,Special Service,1,1,326,326,TWO FOXES TRAPPED IN LIGHTWELL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,CANONBURY SQUARE,21604444,N1,NULL,NULL,531850,184550,NULL,NULL\nNA,06/12/2016 11:21,2016,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE PUPPY FALLEN FROM FIRST FLOOR BALCONY POLICE IN ATTENDANCE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,BRIGHTON ROAD,22605685,SM2,NULL,NULL,525850,162650,NULL,NULL\nNA,07/12/2016 10:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,KENT HOUSE ROAD,20302948,BR3,536221,170873,536250,170850,51.42050869,-0.04226422\nNA,07/12/2016 13:52,2016,2016/17,Special Service,1,1,326,326,ASSIST POLICE  WITH DOG TRAPPED ON WINDOW LEDGE  ABOVE  KFC  JCT  OF GOLDERS GREEN CRESCENT,Dog,Person (land line),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000053,GOLDERS GREEN,E09000003,BARNET,Hendon,200143306,GOLDERS GREEN ROAD,20018180,NW11,523945,188400,523950,188450,51.58084398,-0.212566916\nNA,07/12/2016 19:23,2016,2016/17,Special Service,1,1,326,326,DOG STUCK BETWEEN WALL AND FENCE  OCEAN CHILDREN CENTRE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,6045138,ASTON STREET,22700100,E14,536166,181494,536150,181450,51.51596686,-0.038965698\nNA,08/12/2016 19:19,2016,2016/17,Special Service,1,2,326,652,SWAN STUCK BETWEEN TWO FENCES NEAR SHOP CALLED BROWN SUGAR,Bird,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202132569,ATLIP ROAD,20201491,HA0,518190,183716,518150,183750,51.53998036,-0.297159191\nNA,10/12/2016 09:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,THE BROADWAY,20005160,NW7,NULL,NULL,521350,192050,NULL,NULL\nNA,10/12/2016 11:10,2016,2016/17,Special Service,1,1,326,326,DOG TRAPPED ON ROOF OF CAFE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,THE BROADWAY,20005160,NW7,NULL,NULL,521350,192050,NULL,NULL\nNA,11/12/2016 01:51,2016,2016/17,Special Service,1,1,326,326,DEER STUCK IN RAILINGS LAS ON SCENE AND HAVE CALLED RSPCA  NR THE COMMUNITY CENTRE,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024390524,COLLERNE STREET,21320280,RM3,554294,192195,554250,192150,51.60745612,0.226783556\nNA,11/12/2016 19:44,2016,2016/17,Special Service,1,1,326,326,CAT FALLEN FROM BALCONY INTO DOWNSTAIRS GARDEN CALLER STATES CAT IS INJURED,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6070524,WHITTON WALK,22702076,E3,537054,182949,537050,182950,51.52882729,-0.025610171\nNA,11/12/2016 21:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER SHED OWNER IN ATTENDANCE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,1.0002E+11,FARADAY AVENUE,20100537,DA14,547152,172679,547150,172650,51.43400749,0.115581676\nNA,11/12/2016 21:08,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED INBETWEEN CUPBOARD AND LEDGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,BREWSTER GARDENS,21700823,W10,NULL,NULL,523150,181650,NULL,NULL\nNA,13/12/2016 13:09,2016,2016/17,Special Service,1,1,326,326,BIRD TRAPPED UNDER RAILWAY BRIDGE  HANGING BY WING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,1.00021E+11,STOATS NEST ROAD,20502353,CR5,530479,160173,530450,160150,51.32569824,-0.128721179\nNA,14/12/2016 14:03,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,MICHAEL ROAD,22858450,E11,NULL,NULL,539650,187150,NULL,NULL\nNA,15/12/2016 15:59,2016,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED UP TREE BEING ATTACKED BY CROWS  RSPCA NOT REQUESTED AT PRESENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,NULL,KINGSWAY,20302877,BR5,NULL,NULL,544750,167850,NULL,NULL\nNA,15/12/2016 17:47,2016,2016/17,Special Service,1,1,326,326,FOX TRAPPED BETWEEN TWO CONSERVATORIES,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,MAY AVENUE,20301208,BR5,NULL,NULL,546750,167750,NULL,NULL\nNA,15/12/2016 21:07,2016,2016/17,Special Service,1,1,326,326,DOG WITH MOUTH TRAPPED IN CAGE,Dog,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,COLVILLE GARDENS,21700840,W11,NULL,NULL,524850,181250,NULL,NULL\nNA,16/12/2016 09:55,2016,2016/17,Special Service,1,1,326,326,CAT STUCK IN TREE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,MITCHAM PARK,22104461,CR4,NULL,NULL,527650,168250,NULL,NULL\nNA,16/12/2016 11:04,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011248,Mayfield,E09000026,REDBRIDGE,Ilford,1.00022E+11,CAPEL GARDENS,22301917,IG3,545841,185684,545850,185650,51.5512027,0.102104932\nNA,16/12/2016 13:15,2016,2016/17,Special Service,1,1,326,326,DOG IN CANAL ACCESS VIA TUDOR ESTATE,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,217114044,LADBROKE GROVE,21700912,W10,523872,182381,523850,182350,51.52676585,-0.215738802\nNA,16/12/2016 16:05,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED OUTSIDE ON DRAINPIPE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10023304917,HATHERLEY ROAD,20100686,DA14,546259,171928,546250,171950,51.42749026,0.102434296\nNA,17/12/2016 16:24,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON MUD FLATS ON WATERFRONT-CALLED BY RSPCA MEMBERS OF PUBLIC UNABLE TO REACH IT AND TIDE,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,1.0002E+11,CHANDLERS DRIVE,20101694,DA8,551047,178673,551050,178650,51.48684015,0.174132362\nNA,17/12/2016 22:15,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,SWINBURNE ROAD,22905173,SW15,NULL,NULL,522350,175050,NULL,NULL\nNA,23/12/2016 10:42,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR SEAT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,5300091627,TOLLINGTON PARK,21603933,N4,530968,187139,530950,187150,51.56792493,-0.111736029\nNA,23/12/2016 10:47,2016,2016/17,Special Service,1,1,326,326,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Factory,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,12150695,INVERNESS ROAD,20600935,UB2,512426,178758,512450,178750,51.49659409,-0.381817292\nNA,23/12/2016 12:45,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,SHOULDHAM STREET,8400244,W1H,NULL,NULL,527450,181550,NULL,NULL\nNA,24/12/2016 14:04,2016,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON TOP OF SHED CALLER SAYS ITS PAWS ARE TRAPPED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Stratford,NULL,BROOKS ROAD,22200420,E13,NULL,NULL,540150,183550,NULL,NULL\nNA,25/12/2016 14:21,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,NULL,MORTLAKE ROAD,22302295,IG1,NULL,NULL,544350,185650,NULL,NULL\nNA,25/12/2016 15:48,2016,2016/17,Special Service,1,1,326,326,DOG ON ROOF OF BUILDING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,MEDIAN ROAD,20900679,E5,NULL,NULL,535350,185550,NULL,NULL\nNA,25/12/2016 16:32,2016,2016/17,Special Service,1,2,326,652,HERON TRAPPED IN FISHING WIRE ON ISLAND ON LAKE     WATER RESCUE LEVEL ONE NEAR THE TENNIS COURTS  C,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121019179,DR JOHNSON AVENUE,22901279,SW17,528771,172186,528750,172150,51.43404911,-0.148870167\nNA,26/12/2016 11:20,2016,2016/17,Special Service,1,1,326,326,PIDGEON TRAPPED BY LEG DANGLING FROM ROOF - RSPCA ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128018227,THAMES SIDE,21800939,KT1,517787,169627,517750,169650,51.41343703,-0.307661742\nNA,26/12/2016 13:30,2016,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED IN FISHING WIRE ON AN ISLAND IN THE MIDDLE OF LAKE,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,NA,NA,NA,NA,Chingford,10025175175,RANGERS ROAD,13801596,IG9,539878,194818,539850,194850,51.63478732,0.019796097\nNA,27/12/2016 11:28,2016,2016/17,Special Service,1,4,326,1304,TWO DEER STUCK IN FENCING RVP OUTSIDE PORTLANDS CUDHAM LANE SOUTH,Deer,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,CUDHAM LANE SOUTH,20302994,TN14,545341,157793,545350,157750,51.30071251,0.083468219\nNA,28/12/2016 01:58,2016,2016/17,Special Service,1,1,326,326,CAT FALLEN OUT WINDOW - CANT BE REACHED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,NULL,HORNSEY ROAD,21603387,N7,NULL,NULL,530950,185950,NULL,NULL\nNA,29/12/2016 11:28,2016,2016/17,Special Service,2,3,326,978,TWO DOGS FALLEN IN ICE ON RESERVOIR - WATER RESCUE LEVEL TWO,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000062,WEST HENDON,E09000003,BARNET,Hendon,200026731,COOL OAK LANE,20010120,NW9,521859,187718,521850,187750,51.57516898,-0.242893745\nNA,30/12/2016 17:39,2016,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00023E+11,GADWALL WAY,20800604,SE28,544843,179690,544850,179650,51.49760008,0.085259282\nNA,30/12/2016 20:16,2016,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,APPLEDORE CRESCENT,20100043,DA14,NULL,NULL,545550,172150,NULL,NULL\nNA,01/01/2017 02:21,2017,2016/17,Special Service,1,2,326,652,DOG WITH HEAD STUCK IN RAILINGS CALLED BY OWNER,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,RAVENSBOURNE AVENUE,20303571,BR2,538997,169789,538950,169750,51.41009451,-0.0027907\nNA,01/01/2017 12:24,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,Fairfield,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532185,165905,532150,165950,51.376818,-0.102123891\nNA,01/01/2017 18:48,2017,2016/17,Special Service,1,2,326,652,DOG STUCK IN HULL OF DERELICT BOAT - WATER RESCUE LEVEL ONE CALLER NOT VERY SURE OF EXACT LOCATION H,Dog,Person (mobile),Barge,Boat,Animal rescue from water,Animal rescue from water - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,1.00023E+11,HIGH STREET,21500607,TW8,518452,177780,518450,177750,51.48657493,-0.295376666\nNA,01/01/2017 22:53,2017,2016/17,Special Service,1,1,326,326,DEER TRAPPED IN RAILINGS JUNCTION WITH DENNIS LANE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,768007302,VALENCIA ROAD,21200933,HA7,517167,192558,517150,192550,51.61966181,-0.308955482\nNA,02/01/2017 14:01,2017,2016/17,Special Service,1,1,326,326,SEAGULL TRAPPED IN WIRING,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,10000812314,OLMAR STREET,22501859,SE1,534164,177856,534150,177850,51.48375273,-0.06918385\nNA,02/01/2017 15:42,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011109,Old Kent Road,E09000028,SOUTHWARK,Old Kent Road,10090284724,OLD KENT ROAD,22501843,SE1,534108,177947,534150,177950,51.48458378,-0.069955311\nNA,03/01/2017 10:08,2017,2016/17,Special Service,1,2,326,652,FOX TRAPPED IN RESOVOIR,Fox,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,2.00001E+11,SPARROWS LANE,20801387,SE9,543858,173978,543850,173950,51.44652523,0.06875502\nNA,03/01/2017 12:38,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN WIRE HANGING FROM GUTTERING - GILL RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,NULL,WHITESTILE ROAD,21501201,TW8,NULL,NULL,517650,178550,NULL,NULL\nNA,03/01/2017 14:23,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,BRIDGE CLOSE,20702526,EN1,NULL,NULL,534850,197450,NULL,NULL\nNA,04/01/2017 12:19,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,NULL,WESTBROOK ROAD,20501552,CR7,NULL,NULL,532750,169450,NULL,NULL\nNA,05/01/2017 12:12,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - DOG TRAPPED BEHIND SHED,Dog,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,1.00021E+11,KINGSHOLM GARDENS,20800864,SE9,542143,175414,542150,175450,51.45986172,0.044669495\nNA,05/01/2017 18:45,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON APEX OF ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MANOR GROVE,22405753,TW9,NULL,NULL,519050,175450,NULL,NULL\nNA,07/01/2017 15:41,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN LOFT,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,NULL,CISSBURY RING SOUTH,20009100,N12,NULL,NULL,524950,192250,NULL,NULL\nNA,10/01/2017 12:56,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011478,Sanderstead,E09000008,CROYDON,Addington,1.00021E+11,ADDINGTON ROAD,20502645,CR2,534320,161427,534350,161450,51.336075,-0.073155049\nNA,10/01/2017 17:05,2017,2016/17,Special Service,1,1,326,326,KITTENS STUCK IN HOLE IN CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011488,West Thornton,E09000008,CROYDON,Norbury,NULL,LONDON ROAD,20503034,CR7,NULL,NULL,531450,167550,NULL,NULL\nNA,12/01/2017 01:41,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,10002249748,MOUNT MEWS,22406389,TW12,513958,169591,513950,169550,51.41389552,-0.362707783\nNA,12/01/2017 20:42,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,GROVE PARK ROAD,21103358,N15,NULL,NULL,533250,189150,NULL,NULL\nNA,14/01/2017 20:48,2017,2016/17,Special Service,1,1,326,326,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000128,BELSIZE,E09000007,CAMDEN,West Hampstead,NULL,FELLOWS ROAD,20400582,NW3,NULL,NULL,526950,184250,NULL,NULL\nNA,15/01/2017 09:38,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN GATE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10003626658,LAYHAMS ROAD,20301563,BR2,539887,163152,539850,163150,51.35023659,0.00738892\nNA,15/01/2017 13:48,2017,2016/17,Special Service,2,4,326,1304,CANADA GOOSE STUCK BETWEEN LOCK GATES  - WATER RESCUE LEVEL TWO IMPLEMENETED  EAST INDIA DOCK BASIN,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Poplar,6652305,ORCHARD PLACE,22700900,E14,539126,180835,539150,180850,51.50932428,0.003405978\nNA,16/01/2017 12:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,OAKDENE PARK,20032260,N3,NULL,NULL,524950,191450,NULL,NULL\nNA,17/01/2017 10:46,2017,2016/17,Special Service,1,2,326,652,BIRD TRAPPED IN TREE TRAPPED IN WIRES     IN BACK GARDEN,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,BERRIDGE MEWS,20401422,NW6,NULL,NULL,525050,185350,NULL,NULL\nNA,17/01/2017 12:15,2017,2016/17,Special Service,1,3,326,978,CAT TRAPPED BETWEEN TWO WALLS   RSPCA NOT BEING NOTIFIED UNLESS YOU REQUEST US TO   .,Cat,Person (land line),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000188,SOUTH ACTON,E09000009,EALING,Acton,12097990,BERRYMEAD GARDENS,20600167,W3,520230,179855,520250,179850,51.50484949,-0.269074792\nNA,18/01/2017 15:11,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN STAIRS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW10,NULL,NULL,526250,177350,NULL,NULL\nNA,18/01/2017 16:31,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH  TWO FOXES TRAPPED IN GUTTER  ONE IS INJURED THE OTHER IS GUARDING IT,Fox,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Greenwich,1.00023E+11,LEWISHAM ROAD,22004659,SE13,538127,176049,538150,176050,51.46656094,-0.012852418\nNA,20/01/2017 23:14,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WYLLEN CLOSE,22701371,E1,NULL,NULL,535050,182050,NULL,NULL\nNA,22/01/2017 00:26,2017,2016/17,Special Service,1,1,326,326,RUNNING CALL TO INJURED CAT TRAPPED UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000456,CANNON HILL,E09000024,MERTON,New Malden,NULL,CHERRYWOOD LANE,22101359,SM4,NULL,NULL,524250,168250,NULL,NULL\nNA,22/01/2017 13:35,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH PIGEON CAUGHT IN NETTING,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,DARTMOUTH PARK HILL,20400019,N19,NULL,NULL,528950,186650,NULL,NULL\nNA,22/01/2017 14:07,2017,2016/17,Special Service,1,1,326,326,ANIMALTRAPPED IN LOFT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011242,Fairlop,E09000026,REDBRIDGE,Hainault,NULL,WICKETS WAY,22304769,IG6,NULL,NULL,545850,191550,NULL,NULL\nNA,23/01/2017 09:43,2017,2016/17,Special Service,1,1,326,326,DOG TRAPPED UNDER ICE VICTORIA PARK,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,10008306831,VICTORIA PARK ROAD,20901033,E9,534850,183624,534850,183650,51.5354229,-0.057105168\nNA,25/01/2017 14:58,2017,2016/17,Special Service,1,1,326,326,DOG TRAPPED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,48055109,PARK AVENUE,22104962,CR4,528643,170229,528650,170250,51.4164902,-0.151420367\nNA,26/01/2017 10:57,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000438,BLACKHEATH,E09000023,LEWISHAM,Greenwich,10010243667,SHOOTERS HILL ROAD,20801348,SE10,539626,176827,539650,176850,51.47318478,0.009020457\nNA,27/01/2017 10:11,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,HIGH STREET,20301747,BR3,NULL,NULL,537150,169350,NULL,NULL\nNA,28/01/2017 03:11,2017,2016/17,Special Service,1,1,326,326,RSPCA CALL IN NEED OF ASSISTANCE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011485,South Norwood,E09000008,CROYDON,Woodside,NULL,HOLMESDALE ROAD,20503079,SE25,NULL,NULL,533450,168350,NULL,NULL\nNA,29/01/2017 16:12,2017,2016/17,Special Service,1,1,326,326,DOG FALLEN THROUGH ICE ON LAKE   CONNAUGHT WATERS   - FAR SIDE OF LAKE FROM RANGERS ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,10025293519,RANGERS ROAD,22869100,E4,539875,194824,539850,194850,51.63484198,0.019755173\nNA,30/01/2017 15:44,2017,2016/17,Special Service,1,1,326,326,BIRD INJURED AND STUCK ON LEDGE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,MORTIMER STREET,8401448,W1W,529008,181466,529050,181450,51.51739406,-0.142077537\nNA,30/01/2017 18:15,2017,2016/17,Special Service,1,1,326,326,CAT IN PRECARIOUS POSITION ON WINDOW SILL AT AT SECOND FLOOR LEVEL  CALLER STATES ANIMAL APPEARS INJ,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,STUDLEY ROAD,21901331,SW4,NULL,NULL,530350,176450,NULL,NULL\nNA,01/02/2017 06:06,2017,2016/17,Special Service,2,4,326,1304,\"HORSE TANGLED IN FENCE OF OLD JAIL PUBLIC HOUSE  OWNERS AND POLICE ON SCENE   ,\",Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,JAIL LANE,20301511,TN16,543442,159374,543450,159350,51.31540132,0.056882866\nNA,01/02/2017 11:28,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,Thornton Heath,E09000008,CROYDON,Woodside,1.00021E+11,EGERTON ROAD,20500900,SE25,533172,168528,533150,168550,51.40015948,-0.086967872\nNA,02/02/2017 18:35,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,CANTERBURY CRESCENT,21900284,SW9,NULL,NULL,531250,175650,NULL,NULL\nNA,03/02/2017 13:15,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK BETWEEN WALL AND METAL GATE   OWNER ON SCENE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000192,WALPOLE,E09000009,EALING,Ealing,12072976,MATTOCK LANE,20601146,W13,516843,180346,516850,180350,51.50997182,-0.317690305\nNA,03/02/2017 22:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN A SIX FOOT DROP,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,SOUTHERTON ROAD,21000745,W6,NULL,NULL,523050,178950,NULL,NULL\nNA,04/02/2017 08:30,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,BURGHLEY ROAD,20400358,NW5,NULL,NULL,528950,185550,NULL,NULL\nNA,04/02/2017 15:26,2017,2016/17,Special Service,1,1,326,326,KITTEN STUCK ON WIRE ON FENCE    RVP OUTSIDE SECURITY GATE  AND  CALLER WILL DIRECT YOU TO INCIDENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,ST PAUL'S MEWS,20401499,NW1,NULL,NULL,529850,184350,NULL,NULL\nNA,05/02/2017 05:36,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,HUTTON GROVE,20024460,N12,NULL,NULL,526150,191950,NULL,NULL\nNA,05/02/2017 23:33,2017,2016/17,Special Service,1,1,326,326,CAT POSSIBLY TRAPPED ON ROOF CALLER STATES CAT SOUNDS DISTRESSED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,LEONARD ROAD,22207787,E7,NULL,NULL,540150,185650,NULL,NULL\nNA,06/02/2017 16:22,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,BERKELEY SQUARE,8400761,W1J,528766,180716,528750,180750,51.51070902,-0.145837232\nNA,07/02/2017 12:31,2017,2016/17,Special Service,1,1,326,326,CAT STUCK IN WATERPIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,CHINGDALE ROAD,22825050,E4,NULL,NULL,539150,193150,NULL,NULL\nNA,08/02/2017 14:36,2017,2016/17,Special Service,1,3,326,978,CAT TRAPPED BEHIND PIPE - REQUESTED BY RSPCA,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LIMBOURNE AVENUE,19900223,RM8,NULL,NULL,548850,187750,NULL,NULL\nNA,09/02/2017 18:31,2017,2016/17,Special Service,1,1,326,326,SWAN TRAPPED UNDER ROAD IN WATER   SWAN SANCTUARY ALREADY IN ATTENDANCE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,5870078006,HIGH STREET,22605690,SM5,528012,164501,528050,164550,51.36515478,-0.162553373\nNA,11/02/2017 16:49,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED BETWEEN GARAGES,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,12054320,CASTLE ROAD,20600331,UB5,514129,184504,514150,184550,51.54789722,-0.355435302\nNA,12/02/2017 07:56,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE - TRAPPED BETWEEN WALL AND FLOORBOARDS,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,NULL,MEAD ROAD,22403663,TW10,NULL,NULL,517350,172050,NULL,NULL\nNA,13/02/2017 14:10,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED IN BRICKWORK  AS SIDE OF CHURCH RSPCA IN ATTENDANCE,Cat,Person (land line),Church/Chapel,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,10008995615,PILGRIMS WAY,22202000,E6,542434,183750,542450,183750,51.53469398,0.052218009\nNA,14/02/2017 20:54,2017,2016/17,Special Service,1,1,326,326,KITTEN STUCK BEHIND WOOD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,NIGHTINGALE PLACE,20801085,SE18,NULL,NULL,543450,177950,NULL,NULL\nNA,15/02/2017 13:25,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN BETWEEN WALLS  CALLER WILL COME OUTSIDE TO MEET YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,RAMSGATE STREET,20900838,E8,NULL,NULL,533850,184950,NULL,NULL\nNA,15/02/2017 15:15,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CEILING AREA OF LOBBY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,NULL,FIRWOOD LANE,21320239,RM3,NULL,NULL,554250,190250,NULL,NULL\nNA,17/02/2017 18:59,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD TRAPPED IN A METAL GATE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34035079,HAMMERSMITH GROVE,21000410,W6,523039,179498,523050,179450,51.50103726,-0.22874653\nNA,19/02/2017 07:13,2017,2016/17,Special Service,1,1,326,326,DOG STUCK IN RAILINGS,Dog,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,10009791706,LYNDHURST WAY,22501612,SE15,533718,176239,533750,176250,51.46932691,-0.076214559\nNA,19/02/2017 10:57,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT ON A ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,NULL,SELSDON ROAD,22207879,E13,NULL,NULL,541350,183350,NULL,NULL\nNA,19/02/2017 13:48,2017,2016/17,Special Service,1,1,326,326,CAT UP TREE     RSPCA IN ATTENDANCE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,THE DRIVE,22305366,E18,540127,189603,540150,189650,51.58786494,0.021308956\nNA,20/02/2017 09:20,2017,2016/17,Special Service,1,1,326,326,RUNNING CALL TO SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,HAMPSTEAD WAY,20020780,NW11,NULL,NULL,525550,187950,NULL,NULL\nNA,21/02/2017 12:45,2017,2016/17,Special Service,1,1,326,326,TO ASSIST RSPCA WITH A CAT TRAPPED IN THE ROOFSPACE OF COMMUNITY CENTRE,Cat,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011110,Peckham,E09000028,SOUTHWARK,Peckham,10009791094,BULLER CLOSE,22500371,SE15,534258,177188,534250,177150,51.47772738,-0.068084623\nNA,21/02/2017 14:05,2017,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH KITTEN STUCK UP TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CECILE PARK,21103068,N8,NULL,NULL,530450,188250,NULL,NULL\nNA,22/02/2017 16:22,2017,2016/17,Special Service,1,1,326,326,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,HOWARD ROAD,20900539,N16,NULL,NULL,533050,185450,NULL,NULL\nNA,26/02/2017 07:19,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN MUD   AT TIDEWAY VILLAGE GANG PLANK,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,10024085914,TIDEWAY WALK,22906729,SW8,529343,177616,529350,177650,51.482718,-0.138662778\nNA,26/02/2017 11:28,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA (ON SCENE) WITH CAT ON ROOF   .,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,MILL PLACE,21800672,KT1,NULL,NULL,518550,168850,NULL,NULL\nNA,26/02/2017 11:50,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON CHIMNEY BREAST RSPCA ON SCENE   .,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Heston,NULL,HALIBURTON ROAD,22403529,TW1,NULL,NULL,516450,175150,NULL,NULL\nNA,26/02/2017 13:03,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT TRAPPED DOWN HOLE  RSPCA UNABLE TO ATTEND AT THIS TIME,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009370,DALSTON,E09000012,HACKNEY,Stoke Newington,NULL,SANDRINGHAM ROAD,20900886,E8,NULL,NULL,533550,185150,NULL,NULL\nNA,27/02/2017 08:51,2017,2016/17,Special Service,1,3,326,978,CAT STUCK IN ROOF SPACE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,MILL PLACE,21800672,KT1,NULL,NULL,518550,168850,NULL,NULL\nNA,28/02/2017 01:41,2017,2016/17,Special Service,1,1,326,326,CAT FALLEN FROM WINDOW - TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011475,Park Hill & Whitgift,E09000008,CROYDON,Croydon,NULL,FAIRFIELD ROAD,20500927,CR0,NULL,NULL,533050,165450,NULL,NULL\nNA,01/03/2017 17:03,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000133,FROGNAL AND FITZJOHNS,E09000007,CAMDEN,West Hampstead,5018198,FROGNAL,20400141,NW3,526286,185126,526250,185150,51.5509012,-0.179974524\nNA,01/03/2017 20:29,2017,2016/17,Special Service,1,1,326,326,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,GLENDISH ROAD,21104569,N17,NULL,NULL,534550,190650,NULL,NULL\nNA,02/03/2017 19:49,2017,2016/17,Special Service,1,2,326,652,ASSIST RSPCA WITH CAT STUCK UP CHIMNEY    CALLED BY RSPCA INSPECTOR IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,QUEENSMILL ROAD,21000665,SW6,NULL,NULL,523750,177150,NULL,NULL\nNA,03/03/2017 21:52,2017,2016/17,Special Service,2,3,326,978,POSSIBLY INJURED DOG TRAPPED BEHIND FENCING - CALLER WILL ADVISE ON YOUR ARRIVAL,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,ROYDENE ROAD,20801301,SE18,NULL,NULL,545150,178150,NULL,NULL\nNA,04/03/2017 15:29,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,10091776185,GREEN STREET,22208084,E13,541265,183769,541250,183750,51.53515839,0.035382841\nNA,04/03/2017 19:00,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN ROOF VOID,Cat,Person (mobile),Towing caravan/Camper van on site,Other Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207000348,MERIDIAN WAY,20703519,N9,536036,194437,536050,194450,51.63230515,-0.035831676\nNA,05/03/2017 20:15,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,NULL,HORNSEY RISE GARDENS,21606454,N19,NULL,NULL,530050,187750,NULL,NULL\nNA,06/03/2017 20:47,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED BEHIND DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,EALING COMMON,E09000009,EALING,Ealing,NULL,HAMILTON ROAD,20600810,W5,NULL,NULL,518450,180850,NULL,NULL\nNA,07/03/2017 09:27,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,MCLEOD ROAD,20801008,SE2,NULL,NULL,546750,178650,NULL,NULL\nNA,07/03/2017 16:49,2017,2016/17,Special Service,1,1,326,326,DOG WITH HEAD STUCK IN RAILINGS  IN PARK  CALLER WILL MEET YOU AND DIRECT,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,100014398,HEATHWAY,19900172,RM9,549092,184354,549050,184350,51.53840368,0.148397035\nNA,07/03/2017 17:09,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN ROOF EXTENSION   NEXT TO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,NULL,BROOKEHOWSE ROAD,22001288,SE6,NULL,NULL,537750,171750,NULL,NULL\nNA,07/03/2017 18:31,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE OPPOSIT THE FUR WREN RSPCA IN ATTENDANCE,Bird,Person (land line),Department Store,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011219,Bexleyheath,E09000004,BEXLEY,Bexley,10011862061,MAYPLACE ROAD WEST,20100957,DA7,549313,175273,549350,175250,51.45675039,0.147740748\nNA,07/03/2017 20:06,2017,2016/17,Special Service,1,1,326,326,PUPPY WITH HEAD TRAPPED IN RACK,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,COLFE ROAD,22001356,SE23,NULL,NULL,536050,173150,NULL,NULL\nNA,08/03/2017 10:15,2017,2016/17,Special Service,2,7,326,2282,ASSIST RSPCA WITH CAT IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,NULL,HORNSEY RISE GARDENS,21606454,N19,NULL,NULL,530050,187750,NULL,NULL\nNA,08/03/2017 17:31,2017,2016/17,Special Service,1,1,326,326,SQUIRREL TRAPPED UNDER STAIRS,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000126,SHORTLANDS,E09000006,BROMLEY,Bromley,NULL,DURHAM ROAD,20302869,BR2,NULL,NULL,539650,168750,NULL,NULL\nNA,10/03/2017 15:59,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL TRAPPED IN FIRE PLACE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,WANSTEAD PARK ROAD,22303358,IG1,NULL,NULL,542350,187650,NULL,NULL\nNA,10/03/2017 21:51,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH KITTEN TRAPPED INSIDE ABANDONED BUILDING,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Dagenham,10091577428,RAINHAM ROAD,21320165,RM13,551703,184805,551750,184850,51.5417611,0.18621058\nNA,10/03/2017 22:40,2017,2016/17,Special Service,1,1,326,326,KITTEN ON ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,NULL,TYLECROFT ROAD,20501510,SW16,NULL,NULL,530250,169250,NULL,NULL\nNA,11/03/2017 11:25,2017,2016/17,Special Service,1,1,326,326,DOG STUCK DOWN HOLE CALLER SAYS TO ENTER VIA THE CAMELOT,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011245,Hainault,E09000026,REDBRIDGE,Hainault,10034920494,ROMFORD ROAD,22304722,IG7,547838,191860,547850,191850,51.60617657,0.133484252\nNA,12/03/2017 13:07,2017,2016/17,Special Service,1,1,326,326,CAT STUCK ON WALL TIE  THROUGH THE CATS LEG,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011220,Blackfen & Lamorbey,E09000004,BEXLEY,Eltham,NULL,RAEBURN ROAD,20101182,DA15,NULL,NULL,545350,174450,NULL,NULL\nNA,12/03/2017 16:11,2017,2016/17,Special Service,1,1,326,326,DOG STUCK ON MARSH LAND  NEAR TO THE YAUGHT CLUB    CALL FROM RSPCA,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011231,Slade Green & Northend,E09000004,BEXLEY,Erith,10011846295,MANOR ROAD,20100917,DA8,553011,177958,553050,177950,51.47988829,0.202087677\nNA,13/03/2017 13:28,2017,2016/17,Special Service,1,2,326,652,DOG TRAPPED IN FOX HOLE - MEET THE OWNER AT ROBIN HOOD GATE CAR PARK ACCESS OFF OF ROEHAMPTON VALE O,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,128015984,RICHMOND PARK,22407048,SW15,521200,172273,521250,172250,51.43649886,-0.257702753\nNA,13/03/2017 20:26,2017,2016/17,Special Service,1,2,326,652,CAT TRAPPED IN ROOF AREA,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10023215180,LIVERPOOL ROAD,21606676,N7,531167,184856,531150,184850,51.54736234,-0.109717065\nNA,14/03/2017 15:47,2017,2016/17,Special Service,1,1,326,326,BIRD TRAPPED IN NETTING  TALLY HO PUB  -    RSPCA INSPECTOR IN ATTENDANCE  -    VAN PARKED AT SIDE O,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200060798,HIGH ROAD,20022400,N12,526357,192154,526350,192150,51.61404506,-0.176424269\nNA,15/03/2017 13:28,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000354,HANWORTH PARK,E09000018,HOUNSLOW,Feltham,1.00022E+11,THE HOLLANDS,21501408,TW13,511814,171830,511850,171850,51.43444642,-0.392818458\nNA,15/03/2017 14:31,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,STANSTEAD ROAD,22004078,SE23,NULL,NULL,535650,173250,NULL,NULL\nNA,16/03/2017 15:13,2017,2016/17,Special Service,1,2,326,652,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,OAKLANDS ROAD,20302295,BR1,539433,170286,539450,170250,51.4144538,0.0036695\nNA,16/03/2017 17:37,2017,2016/17,Special Service,1,1,326,326,SMALL ANIMAL RESCUE   PARROTT ON ROOF BELIEVED INJURED,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,New Malden,NULL,CANNON HILL LANE,22101155,SW20,NULL,NULL,524150,168350,NULL,NULL\nNA,17/03/2017 15:00,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH TRAPPED SEAGULL ON BUILDING SITE  NEAR PIRATE CASTLE,Bird,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5011266,OVAL ROAD,20400628,NW1,528524,184019,528550,184050,51.54044793,-0.148118025\nNA,19/03/2017 05:16,2017,2016/17,Special Service,1,1,326,326,THREE CATS TRAPPED UNDER FLOORBOARDS IN BATHROOM,cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000179,HANGER HILL,E09000009,EALING,Ealing,NULL,BRENTHAM WAY,20600240,W5,NULL,NULL,517750,182150,NULL,NULL\nNA,19/03/2017 15:18,2017,2016/17,Special Service,2,5,326,1630,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10091502050,TOOTING BEC ROAD,22905450,SW17,529057,172082,529050,172050,51.43304946,-0.144795907\nNA,19/03/2017 16:47,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000521,HAMPTON NORTH,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,BRIAR CLOSE,22401302,TW12,NULL,NULL,512550,171150,NULL,NULL\nNA,19/03/2017 18:27,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128003782,CATHERINE ROAD,21800213,KT6,517728,167888,517750,167850,51.39781919,-0.30908587\nNA,19/03/2017 23:02,2017,2016/17,Special Service,1,1,326,326,FOX TRAPPED UNDER BUILDING,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,KINFAUNS ROAD,22302200,IG3,NULL,NULL,546550,187150,NULL,NULL\nNA,20/03/2017 12:45,2017,2016/17,Special Service,1,1,326,326,JACK RUSSELL DOG  STUCK IN FOX HOLE        BONNER GATE     BY THE LAKE   OPPOSITE THE PAGODA,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,10025165381,GORE ROAD,20900454,E9,535684,183528,535650,183550,51.53436057,-0.045124845\nNA,22/03/2017 09:55,2017,2016/17,Special Service,1,1,326,326,BADGER TRAPPED IN STORM DRAIN - RSPCA IN CAR PARK TO REAR OF KEW GARDENS - ACCESS VIA FERRY LANE,Unknown - Wild Animal,Person (mobile),Scrub land,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,KEW GREEN,22405687,TW9,518255,176938,518250,176950,51.47904833,-0.298494228\nNA,22/03/2017 13:39,2017,2016/17,Special Service,1,3,326,978,LARGE ANIMAL RESCUE     HORSE TRAPPED BETWEEN TREES       ADDITIONAL FRU WITH LARGE ANIMAL RESCUE CA,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10023303271,SIDCUP BY PASS,20100002,DA14,546665,170545,546650,170550,51.41495826,0.107697977\nNA,23/03/2017 10:18,2017,2016/17,Special Service,1,1,326,326,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,10092980265,DAWLEY ROAD,21400564,UB3,508840,180054,508850,180050,51.50894436,-0.4330557\nNA,23/03/2017 17:52,2017,2016/17,Special Service,1,1,326,326,\"DOG STUCK UNDER FENCE NEAR RAILWAY LINE CALLER IN FIELD NEAR SCHOOL, HE SAYS ACCESS VIA PERRYMANS FA\",Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011234,Aldborough,E09000026,REDBRIDGE,Ilford,10090503199,PERRYMANS FARM ROAD,22303145,IG2,544870,188734,544850,188750,51.5788581,0.089365834\nNA,24/03/2017 17:19,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,NULL,BEVERLEY DRIVE,20202003,HA8,NULL,NULL,519450,189850,NULL,NULL\nNA,25/03/2017 17:13,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,STATION ROAD,22401132,TW12,NULL,NULL,513750,169650,NULL,NULL\nNA,25/03/2017 23:34,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH FOX TRAPPED IN WIRE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011223,Crook Log,E09000004,BEXLEY,Bexley,1.00023E+11,AVENUE ROAD,20100067,DA7,548256,175975,548250,175950,51.4633357,0.132832936\nNA,26/03/2017 13:55,2017,2016/17,Special Service,1,1,326,326,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,COURTMAN ROAD,21104409,N17,532427,191142,532450,191150,51.60355645,-0.089190115\nNA,26/03/2017 17:50,2017,2016/17,Special Service,1,1,326,326,DISTRESSED DOG IN PRECARIOUS POSITION,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,NULL,LILBURNE WALK,20200785,NW10,NULL,NULL,520350,184750,NULL,NULL\nNA,27/03/2017 14:23,2017,2016/17,Special Service,1,1,326,326,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011103,Goose Green,E09000028,SOUTHWARK,Peckham,2.00003E+11,HINDMANS ROAD,22501253,SE22,534175,174697,534150,174650,51.45536139,-0.070223696\nNA,27/03/2017 15:45,2017,2016/17,Special Service,1,1,326,326,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539333,186560,539350,186550,51.56071829,0.008650582\nNA,29/03/2017 10:35,2017,2016/17,Special Service,1,1,326,326,CAT TRAPPED INSIDE CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,10033550691,HUGH STREET,8400826,SW1V,528896,178753,528850,178750,51.49303806,-0.144681921\nNA,29/03/2017 11:51,2017,2016/17,Special Service,1,1,326,326,KITTEN TRAPPED BETWEEN FLATS AND GARAGE,Cat,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000086,BARNHILL,E09000005,BRENT,Wembley,202123197,BARNHILL ROAD,20201054,HA9,520356,186298,520350,186250,51.56272894,-0.265059026\nNA,31/03/2017 20:08,2017,2016/17,Special Service,2,3,326,978,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011240,Clementswood,E09000026,REDBRIDGE,Ilford,1.00023E+11,ILFORD LANE,22302963,IG1,543760,186039,543750,186050,51.55492624,0.072256712\nNA,01/04/2017 16:23,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED BEHIND RADIATOR,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,NULL,CLEAVE AVENUE,21400420,UB3,NULL,NULL,509350,178550,NULL,NULL\nNA,01/04/2017 16:44,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00021E+11,DICKENS AVENUE,21400596,UB8,507881,181307,507850,181350,51.52039026,-0.446485773\nNA,02/04/2017 12:03,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON BALCONY    FALLEN TWO FLOORS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,FORE STREET,20704226,N18,NULL,NULL,534050,192050,NULL,NULL\nNA,02/04/2017 23:22,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FOX STUCK ON LEDGE    AT SIDE OF STREAM,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Mitcham,1.00023E+11,SOUTHCROFT ROAD,22904728,SW16,528744,170625,528750,170650,51.42002626,-0.149825045\nNA,03/04/2017 12:37,2017,2017/18,Special Service,1,1,328,328,\"ASSIST RSPCA WITH SMALL ANIMAL RESCUE      RSPCA INSPECTOR ON SCENE, ASSIST WITH A DUCK TANGLED IN A\",Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,10003627475,MANOR WAY,20303627,BR3,537844,168702,537850,168750,51.40060749,-0.019780463\nNA,03/04/2017 19:48,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN CAGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,NULL,GLENPARKE ROAD,22207729,E7,NULL,NULL,540750,184850,NULL,NULL\nNA,04/04/2017 08:52,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER IN EXTRACTING CAT FROM CAR ENGINE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,KIRBY CLOSE,21301332,RM3,555241,191938,555250,191950,51.60488694,0.240334171\nNA,04/04/2017 15:06,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL CAT UP TREE,Cat,Person (land line),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,10024419667,HICKMAN AVENUE,22844825,E4,538046,191900,538050,191950,51.60901865,-0.007806957\nNA,04/04/2017 18:16,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,MCDERMOTT CLOSE,22906545,SW11,NULL,NULL,527050,175950,NULL,NULL\nNA,05/04/2017 11:16,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10023220724,UPPER STREET,21606199,N1,531713,184096,531750,184050,51.54040541,-0.102131634\nNA,05/04/2017 13:54,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP A TREE     CALLED FROM RSPCA,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,TRURO ROAD,21105175,N22,530724,191168,530750,191150,51.604188,-0.113756342\nNA,05/04/2017 20:33,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT STUCK UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,1.00021E+11,PLUMSTEAD HIGH STREET,20801187,SE18,545416,178503,545450,178550,51.48678734,0.093019416\nNA,06/04/2017 14:20,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,COWSLIP ROAD,22302756,E18,540778,190318,540750,190350,51.59412752,0.030985975\nNA,07/04/2017 11:51,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE     RSPCA INSPECTOR ON SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000174,EALING COMMON,E09000009,EALING,Acton,12090202,CREFFIELD ROAD,20602207,W5,518786,180605,518750,180650,51.51189514,-0.289617958\nNA,07/04/2017 19:20,2017,2017/18,Special Service,1,1,328,328,DOG IN RIVER THAMES     JO  UPPER MALL,Dog,Person (mobile),Bridge,Outdoor Structure,Animal rescue from water,Animal rescue from water - Domestic pet,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,10070711720,CASTELNAU,22404217,W6,522916,178019,522950,178050,51.48777165,-0.231032556\nNA,08/04/2017 07:21,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER SINK AND BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,NORLAND SQUARE,21700953,W11,NULL,NULL,524350,180150,NULL,NULL\nNA,08/04/2017 22:43,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS   BY ISLAND GARDENS STATION   OWNER IS ON SCENE,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,6651760,MANCHESTER ROAD,22700775,E14,538216,178509,538250,178550,51.4886454,-0.010609613\nNA,09/04/2017 16:04,2017,2017/18,Special Service,1,1,328,328,PIGEON CAUGHT ON WIRE       FOUTH FLOOR LEVEL     CHILD CALLER WILL REMAIN ON SCENE TO DIERECT BRIGA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,ILBERT STREET,8400459,W10,NULL,NULL,524050,182650,NULL,NULL\nNA,09/04/2017 16:06,2017,2017/18,Special Service,1,1,328,328,Redacted,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000296,MARLBOROUGH,E09000015,HARROW,Stanmore,NULL,HERGA ROAD,21201673,HA3,NULL,NULL,515850,189250,NULL,NULL\nNA,10/04/2017 09:45,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,TOLLERS LANE,20500354,CR5,NULL,NULL,531050,157450,NULL,NULL\nNA,10/04/2017 12:55,2017,2017/18,Special Service,1,1,328,328,RCA TO PARROT IN PRECARIOUS POSITION  BELIEVED TO BE INJURED    NO FURTHER BRIGADE ASSISTANCE REQUIR,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,128016898,SAVILE CLOSE,21800840,KT3,521309,167615,521350,167650,51.39461181,-0.257728052\nNA,10/04/2017 17:47,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,FORDWYCH ROAD,20400109,NW2,NULL,NULL,524750,184750,NULL,NULL\nNA,12/04/2017 13:36,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,ESSEX ROAD,21604817,N1,NULL,NULL,532550,184450,NULL,NULL\nNA,12/04/2017 13:58,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,COLOSSEUM TERRACE,20499026,NW1,NULL,NULL,528850,182450,NULL,NULL\nNA,13/04/2017 00:06,2017,2017/18,Special Service,1,4,328,1312,Redacted,Horse,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000475,BECKTON,E09000025,NEWHAM,Barking,10025384850,JENKINS LANE,22201743,IG11,544141,182760,544150,182750,51.5253655,0.076407294\nNA,13/04/2017 07:58,2017,2017/18,Special Service,1,1,328,328,HORSE TRAPPED ON STABLE DOOR PASSED CALL X KENT FRS,Horse,Person (land line),Other agricultural building,Non Residential,Animal rescue from height,Animal rescue from height - Farm animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.0002E+11,JAIL LANE,20301511,TN16,542742,159334,542750,159350,51.31521776,0.046829442\nNA,14/04/2017 09:35,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN GUTTERING ON WALL AND ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,LONG LANE,20027220,N3,NULL,NULL,525950,190650,NULL,NULL\nNA,14/04/2017 13:23,2017,2017/18,Special Service,2,5,328,1640,CAT CAUGHT IN AXLE OF CAR FRU REQUESTED FROM SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,10033241620,RODWAY ROAD,22904361,SW15,522289,173878,522250,173850,51.45069015,-0.241489599\nNA,15/04/2017 09:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,LADYSMITH ROAD,22201474,E16,NULL,NULL,539550,182550,NULL,NULL\nNA,15/04/2017 10:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,QUEENS ROAD,22868450,E17,NULL,NULL,536750,188250,NULL,NULL\nNA,15/04/2017 22:23,2017,2017/18,Special Service,1,2,328,656,RUNNING CALL TO STRAY MALNOURISHED DOG,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,10002294818,HONEYPOT LANE,21200986,HA7,517991,190798,517950,190750,51.60367148,-0.297650869\nNA,18/04/2017 14:33,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,NULL,HEATHSTAN ROAD,21000429,W12,NULL,NULL,522250,181050,NULL,NULL\nNA,18/04/2017 18:07,2017,2017/18,Special Service,1,1,328,328,HORSE TRAPPED  INSIDE HORSE BOX TRPPED UNDER PARTITION UNDER ANOTHER HORSE,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Animal rescue from water,Animal rescue from water - Farm animal,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00023E+11,WARREN LANE,21200934,HA7,516548,193853,516550,193850,51.63142938,-0.317461895\nNA,20/04/2017 21:22,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011224,East Wickham,E09000004,BEXLEY,Bexley,NULL,LYME ROAD,20100893,DA16,NULL,NULL,546950,176750,NULL,NULL\nNA,21/04/2017 11:58,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE      FOX TRAPPED IN BASEMENT   REQUESTED BY RSPCA,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,34067459,PERRYMEAD STREET,21000639,SW6,525476,176569,525450,176550,51.47417866,-0.194694631\nNA,21/04/2017 14:58,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT STUCK ON ROOF TWO STOREY BUILDING,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34050649,BRAMBER ROAD,21000144,W14,524845,177847,524850,177850,51.48580375,-0.203324401\nNA,21/04/2017 18:05,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE   BIRD TRAPPED INSIDE BEDROOM OF DISABLED OCCUPIER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Stanmore,NULL,BURNT OAK FIELDS,20006240,HA8,NULL,NULL,520150,190850,NULL,NULL\nNA,21/04/2017 18:10,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON WALL     CALLED BY RSPCA INSPECTOR DEW   WHO IS ON SCENE,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6211323,COBORN ROAD,22700334,E3,536616,182982,536650,182950,51.52922978,-0.031907613\nNA,22/04/2017 19:49,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011476,Purley & Woodcote,E09000008,CROYDON,Purley,1.00021E+11,STOATS NEST ROAD,20502353,CR5,530474,160175,530450,160150,51.32571736,-0.128792167\nNA,23/04/2017 09:55,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,202118217,CHAMBERLAYNE ROAD,20202065,NW10,523300,183731,523350,183750,51.53902374,-0.223507538\nNA,23/04/2017 15:16,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,1.00024E+11,DALSTON LANE,20900310,E8,533918,184768,533950,184750,51.5459251,-0.070099935\nNA,24/04/2017 12:45,2017,2017/18,Special Service,1,2,328,656,PUPPY TRAPPED IN DRAIN,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Wild animal rescue from water or mud,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,HORNCASTLE ROAD,22005217,SE12,NULL,NULL,540550,173950,NULL,NULL\nNA,24/04/2017 20:09,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN ROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,BRODLOVE LANE,22700203,E1W,NULL,NULL,535550,180950,NULL,NULL\nNA,25/04/2017 11:07,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,12116741,BURLINGTON GARDENS,20600290,W3,520523,180268,520550,180250,51.508499,-0.264714143\nNA,25/04/2017 13:57,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA - FOX TRYING TO SWIM IN BRENT STREAM AND STRANDED ON BRICKS IN WATER     MEET RSPCA OFF,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000085,ALPERTON,E09000005,BRENT,Wembley,202135837,ALPERTON LANE,20200264,HA0,518103,183030,518150,183050,51.53383306,-0.298643128\nNA,26/04/2017 09:01,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD STUCK IN CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,BINFIELD ROAD,21900185,SW4,NULL,NULL,530350,176550,NULL,NULL\nNA,26/04/2017 15:35,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER TO RECOVER DUCKLINGS FROM A DRAIN,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200096048,QUEENS ROAD,20035600,N3,526363,190617,526350,190650,51.60023107,-0.17689101\nNA,27/04/2017 14:48,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE - POSSIBLY INJURED,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200105307,SPANIARDS CLOSE,20040220,NW11,526531,187424,526550,187450,51.57149826,-0.175616641\nNA,27/04/2017 17:08,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH TRAPPED PIDGEON,Pigeon,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011471,New Addington South,E09000008,CROYDON,Addington,NULL,KENNELWOOD CRESCENT,20501755,CR0,NULL,NULL,538650,161650,NULL,NULL\nNA,28/04/2017 19:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSPECTOR WITH A PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00024E+11,DUKES AVENUE,21101214,N10,528823,189740,528850,189750,51.59179292,-0.141713995\nNA,29/04/2017 11:16,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY TRAPPED BEHIND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,FRAMFIELD ROAD,22102703,CR4,NULL,NULL,528150,170050,NULL,NULL\nNA,29/04/2017 13:21,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED DOWN STORM DRAIN,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,10003641401,KELSEY PARK AVENUE,20301420,BR3,537667,168930,537650,168950,51.4026993,-0.022234892\nNA,29/04/2017 16:56,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN AN AREA WITHIN THE SCHOOL CALLER WILL MEET BRIGADE,Cat,Person (land line),Secondary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10008229436,AMHURST PARK,20900061,N16,533417,187913,533450,187950,51.57430612,-0.076129355\nNA,30/04/2017 14:15,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF COULD POSSIBLY FALL ONTO RAILINGS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WESTCROFT SQUARE,21000874,W6,NULL,NULL,521950,178750,NULL,NULL\nNA,30/04/2017 14:15,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Heathland,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,Kentish Town,10025356265,SOUTH HILL PARK,20400192,NW3,527213,185991,527250,185950,51.55846683,-0.166299173\nNA,01/05/2017 17:05,2017,2017/18,Special Service,1,2,328,656,PIGEON TRAPPED ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173850,NULL,NULL\nNA,03/05/2017 18:49,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT TRAPPED BEHIND LOCKED DOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,MARIAN WAY,20200900,NW10,NULL,NULL,521650,184150,NULL,NULL\nNA,03/05/2017 20:06,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BEHIND FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,ROWDOWNS ROAD,19900593,RM9,NULL,NULL,548850,184150,NULL,NULL\nNA,04/05/2017 03:12,2017,2017/18,Special Service,1,1,328,328,DEER STUCK IN RAILINGS THIS IS AT THE JUNCTION OF ELMFIELD WAY,Deer,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05011484,South Croydon,E09000008,CROYDON,Croydon,1.00021E+11,WEST HILL,20502415,CR2,533508,163149,533550,163150,51.35174108,-0.084159241\nNA,04/05/2017 12:11,2017,2017/18,Special Service,1,1,328,328,RING NECKED PARAKEET TRAPPED IN TREE,Bird,Person (mobile),Community centre/Hall,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000174,EALING COMMON,E09000009,EALING,Ealing,12083992,ST MARYS ROAD,20601608,W5,517794,180051,517750,180050,51.50712334,-0.304091462\nNA,04/05/2017 14:59,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207104081,BERKELEY GARDENS,20703920,N21,532606,194966,532650,194950,51.63787801,-0.085159881\nNA,05/05/2017 16:36,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  ASSISTING RSPCA,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000438,BLACKHEATH,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEE HIGH ROAD,22004135,SE12,539825,175022,539850,175050,51.45691585,0.01117073\nNA,05/05/2017 17:12,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Sidcup,NULL,CALVERT CLOSE,20100248,DA14,NULL,NULL,548150,170950,NULL,NULL\nNA,07/05/2017 17:25,2017,2017/18,Special Service,1,1,328,328,CAT WITH HEAD  TRAPPED IN FIRST FLOOR WINDOW    REAR OF PREMISES          OCCUPIERS NOT IN   SEEN BY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,WHEATLANDS,21501195,TW5,NULL,NULL,513350,177650,NULL,NULL\nNA,08/05/2017 10:24,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO  CAT STUCK UP TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,ARLINGTON GARDENS,21500042,W4,NULL,NULL,520350,178450,NULL,NULL\nNA,08/05/2017 10:43,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP TREE - INSPECTOR ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2E+11,MORECAMBE GARDENS,21200911,HA7,517838,192605,517850,192650,51.61994419,-0.299252004\nNA,09/05/2017 10:57,2017,2017/18,Special Service,1,2,328,656,CAT IN TREE RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,NULL,MORECAMBE GARDENS,21200911,HA7,NULL,NULL,517850,192550,NULL,NULL\nNA,09/05/2017 21:32,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011253,Valentines,E09000026,REDBRIDGE,Ilford,NULL,COURTLAND AVENUE,22302752,IG1,NULL,NULL,542850,186850,NULL,NULL\nNA,09/05/2017 22:03,2017,2017/18,Special Service,1,1,328,328,ASSISITNG RSPCA  CAT IN LIGHT WELL,Cat,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,5300082004,SEVEN SISTERS ROAD,21603790,N7,531072,186462,531050,186450,51.56181687,-0.110488432\nNA,10/05/2017 12:48,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GUTTERING THRID FLOOR LEVEL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,PEMBERTON GARDENS,19900278,RM6,NULL,NULL,548450,188750,NULL,NULL\nNA,10/05/2017 13:18,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,WOODVALE WALK,21902038,SE27,NULL,NULL,532250,171350,NULL,NULL\nNA,11/05/2017 11:08,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5097057,KYLEMORE ROAD,20400405,NW6,525222,184481,525250,184450,51.54534141,-0.195541793\nNA,12/05/2017 07:23,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL TRAPPED IN AIR VENT,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011097,Champion Hill,E09000028,SOUTHWARK,Peckham,NULL,DOMETT CLOSE,22500787,SE5,NULL,NULL,532850,175450,NULL,NULL\nNA,13/05/2017 10:22,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON ROOF,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10034920942,THE BROADWAY,22306023,IG8,540937,191836,540950,191850,51.6077281,0.033889274\nNA,13/05/2017 11:16,2017,2017/18,Special Service,1,1,328,328,REAR GARDEN CAT STUCK BETWEEN WALLS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,10008987716,HILDA ROAD,22207548,E16,539376,182273,539350,182250,51.52218494,0.007574459\nNA,14/05/2017 06:42,2017,2017/18,Special Service,1,1,328,328,FOX CUB WITH LEG TRAPPED IN WIRE FENCE RSPCA HAVE NO OFFICERS TO HELP WITH THIS INCIDENT UNTIL AFTER,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46056665,PARTRIDGE SQUARE,22201946,E6,542494,181882,542450,181850,51.51789351,0.052327736\nNA,16/05/2017 08:24,2017,2017/18,Special Service,1,1,328,328,INJURED CAT IN DISTRESS STUCK IN TREE  LINE RESCUE REQUESTED FROM SCENE,Cat,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5023119,BACK LANE,20400155,NW3,526401,185818,526450,185850,51.55709445,-0.178068364\nNA,16/05/2017 08:36,2017,2017/18,Special Service,1,1,328,328,INJURED CAT FALLEN ONTO FLAT ROOF - CALLER WILL MEET CREWS ON WINDMILL COURT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,MAPESBURY ROAD,20200318,NW2,NULL,NULL,524350,184950,NULL,NULL\nNA,16/05/2017 11:29,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,BERRYLANDS ROAD,21800135,KT5,NULL,NULL,518750,167350,NULL,NULL\nNA,17/05/2017 11:19,2017,2017/18,Special Service,2,3,328,984,Redacted,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169546,LEA VALLEY ROAD,20703459,EN3,536508,195555,536550,195550,51.64223712,-0.02858016\nNA,18/05/2017 16:36,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WELBECK ROAD,22605656,SM1,NULL,NULL,526750,165750,NULL,NULL\nNA,18/05/2017 17:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,EMERSON PARK,E09000016,HAVERING,Harold Hill,NULL,REDDEN COURT ROAD,21300143,RM3,NULL,NULL,554550,189750,NULL,NULL\nNA,19/05/2017 00:35,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Railway building - other,Non Residential,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,10091861579,STRAND,8401576,WC2N,530232,180492,530250,180450,51.50836032,-0.124806493\nNA,19/05/2017 17:28,2017,2017/18,Special Service,1,1,328,328,BIRD  TRAPPED IN AIRCONDITIONING UNIT R.O. LIBRARY    CALLER WILL MEET BRIGADE IN CAR PARK AREA,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal harm involving livestock,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00024E+11,FALLING LANE,21400712,UB7,505978,180700,505950,180750,51.51529446,-0.474085124\nNA,21/05/2017 02:05,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6652452,COPENHAGEN PLACE,22700355,E14,536819,181328,536850,181350,51.51431755,-0.029625011\nNA,21/05/2017 12:41,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BETWEEN  FENCE AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,COPENHAGEN PLACE,22700355,E14,NULL,NULL,536850,181350,NULL,NULL\nNA,21/05/2017 17:07,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,46087203,CHANDLER AVENUE,22200454,E16,540041,181828,540050,181850,51.51802163,0.016976485\nNA,21/05/2017 20:41,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE - PIGEON CAUGHT IN NET UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,10091091519,URBAN FOOTPATH 121A FROM OPPOSITE 73 SHERWOOD ROAD TO 100A ROXETH GREEN AVENUE CHURCHILL COURT,21202771,HA2,514103,186572,514150,186550,51.56648929,-0.355140023\nNA,22/05/2017 17:51,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  IN UNDERPASS NEAR STATION    CALLER WAITING FOR YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,10015397137,STATION APPROACH,21201813,HA5,512170,189518,512150,189550,51.59335578,-0.38207642\nNA,23/05/2017 00:59,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSP TO FREE A DEER STUCK IN WROUGHT IRON GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,1.0002E+11,APERFIELD ROAD,20301439,TN16,542472,158921,542450,158950,51.31157396,0.042792865\nNA,23/05/2017 06:02,2017,2017/18,Special Service,1,1,328,328,FOX CUB  TRAPPED IN FOOTBALL NETTING     RSPCA UNABLE TO ATTEND WITHIN THE HOUR   ORIGIN CALLER TEL,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,NULL,EASTLEIGH ROAD,20100481,DA7,NULL,NULL,550450,175950,NULL,NULL\nNA,23/05/2017 17:37,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,530550,172350,NULL,NULL\nNA,24/05/2017 09:52,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA OFFICER WITH CAT TRAPPED BETWEEN GARAGE & WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000097,PRESTON,E09000005,BRENT,Wembley,NULL,CONISTON GARDENS,20200871,HA9,NULL,NULL,517450,187550,NULL,NULL\nNA,24/05/2017 11:03,2017,2017/18,Special Service,1,1,328,328,ASSIST POLICE WITH KITTENS STUCK BEHIND WALL,Cat,Person (land line),Police station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,HIGH ROAD,21106680,N17,533808,189769,533850,189750,51.59089195,-0.069784421\nNA,24/05/2017 16:46,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED BETWEEN HOUSE AND RETAINING WALL,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011229,St. Mary's & St. James,E09000004,BEXLEY,Bexley,1.0002E+11,TILE KILN LANE,20101472,DA5,550357,172514,550350,172550,51.43168423,0.161584156\nNA,25/05/2017 12:28,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  - CAT TRAPPED UNDER BATH,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,NULL,RIDSDALE ROAD,20303573,SE20,NULL,NULL,534550,170050,NULL,NULL\nNA,26/05/2017 15:54,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH SQUIRREL TRAPPED IN BOTTOM OF DRAIN PIPE,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,OLD BARROWFIELD,22202003,E15,NULL,NULL,539450,183550,NULL,NULL\nNA,26/05/2017 18:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,12073661,BROADWAY,20600261,W13,516382,180311,516350,180350,51.50975227,-0.324341776\nNA,27/05/2017 09:28,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FENCE GATE HINGES - RSCPA ON ROUTE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12054553,LANCASTER ROAD,20601018,UB5,514101,184743,514150,184750,51.55005095,-0.355761562\nNA,27/05/2017 10:24,2017,2017/18,Special Service,1,1,328,328,PIGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000182,NORTHFIELD,E09000009,EALING,Ealing,NULL,WOODSTOCK AVENUE,20601959,W13,NULL,NULL,516350,179350,NULL,NULL\nNA,27/05/2017 19:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH CHAIN ROUND NECK - STRANGLING THE DOG,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011221,Blendon & Penhill,E09000004,BEXLEY,Sidcup,1.00023E+11,THE OVAL,20101453,DA15,546192,173835,546150,173850,51.44464309,0.102258368\nNA,28/05/2017 09:49,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON BALCONY ON FIRST FLOOR POSSIBLY FALLEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,CHALTON STREET,20400794,NW1,NULL,NULL,529650,183150,NULL,NULL\nNA,28/05/2017 10:45,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,Champion Hill,E09000028,SOUTHWARK,Peckham,NULL,GROVE LANE,22501157,SE5,NULL,NULL,533050,176050,NULL,NULL\nNA,28/05/2017 21:40,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN DRAINPIPE ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,NULL,SAMUEL STREET,20801316,SE18,NULL,NULL,542550,178750,NULL,NULL\nNA,29/05/2017 13:54,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN FIRE PLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,STOWE PLACE,21103948,N15,NULL,NULL,533250,189550,NULL,NULL\nNA,29/05/2017 22:20,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011221,Blendon & Penhill,E09000004,BEXLEY,Bexley,1.0002E+11,BLENDON DRIVE,20100161,DA5,547840,174088,547850,174050,51.44648898,0.126060231\nNA,30/05/2017 02:11,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER PILE OF RUBBISH,Cat,Person (mobile),Loose refuse,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46060665,REGINALD ROAD,22207855,E7,540059,184494,540050,184450,51.54197376,0.018294858\nNA,31/05/2017 00:07,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN MUD ON RIVERBANK - TIDE COMING IN AND CALLER DISTRESSED,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,POINT PLEASANT,22903900,SW18,525164,175282,525150,175250,51.46268107,-0.199639873\nNA,01/06/2017 08:55,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,10010194134,LAKEDALE ROAD,20800879,SE18,545289,178561,545250,178550,51.48734112,0.091215407\nNA,01/06/2017 09:50,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO FOX STUCK BEHIND BATH,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BARTH ROAD,20800121,SE18,NULL,NULL,545450,178650,NULL,NULL\nNA,01/06/2017 11:55,2017,2017/18,Special Service,1,1,328,328,FOX STUCK IN CONCRETE     RSPCA ON SCENE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6144248,KENILWORTH ROAD,22700694,E3,536070,183318,536050,183350,51.53238074,-0.039644154\nNA,01/06/2017 13:41,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA INSPECTOR WITH CAT STUCK ON CHIMNEY POT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,LOWDEN ROAD,21900899,SE24,NULL,NULL,532050,175150,NULL,NULL\nNA,01/06/2017 14:51,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GARDEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,HAREWOOD AVENUE,8400434,NW1,NULL,NULL,527350,182150,NULL,NULL\nNA,02/06/2017 06:58,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD TRAPPED IN CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000194,BUSH HILL PARK,E09000010,ENFIELD,Edmonton,NULL,TRINITY AVENUE,20703015,EN1,NULL,NULL,534050,195250,NULL,NULL\nNA,02/06/2017 07:17,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BEHIND SCHOOL FENCE,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,1.00023E+11,PINKWELL LANE,21401541,UB3,508629,178733,508650,178750,51.49711151,-0.436501416\nNA,05/06/2017 15:11,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200014104,WOODBURN CLOSE,20047040,NW4,523991,188568,523950,188550,51.5823437,-0.211844057\nNA,06/06/2017 21:15,2017,2017/18,Special Service,1,1,328,328,FOX STUCK ON SHED ROOF -  REQUESTED BY RSPCA,Fox,Person (land line),Private Garden Shed,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,1.00021E+11,COLDHARBOUR LANE,21400437,UB3,510418,180793,510450,180750,51.51528054,-0.410095827\nNA,07/06/2017 06:24,2017,2017/18,Special Service,1,1,328,328,DOG STUCK IN POND,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Homerton,6057757,OLD FORD ROAD,22700893,E9,536487,184109,536450,184150,51.53938833,-0.033329536\nNA,07/06/2017 09:53,2017,2017/18,Special Service,1,1,328,328,FOX WEDGE BETWEEN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,ALLENBY ROAD,20802006,SE28,NULL,NULL,544450,179350,NULL,NULL\nNA,08/06/2017 08:50,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN HOLE IN GROUND,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011217,Barnehurst,E09000004,BEXLEY,Bexley,1.0002E+11,CHEVIOT CLOSE,20100303,DA7,551153,176410,551150,176450,51.46647862,0.174689786\nNA,09/06/2017 08:27,2017,2017/18,Special Service,1,1,328,328,PIDGEON STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,VICTORIA ROAD,20044140,EN4,NULL,NULL,526850,196250,NULL,NULL\nNA,09/06/2017 12:11,2017,2017/18,Special Service,1,1,328,328,RESCUE BIRD TRAPPED IN NETTING  LONDON WILD LIFE PROTECTION  REPRESENTATIVE IN ATTENDANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,ALBANY ROAD,22800700,E10,NULL,NULL,537350,187850,NULL,NULL\nNA,09/06/2017 22:15,2017,2017/18,Special Service,1,1,328,328,PIDGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,WISE LANE,20046840,NW7,NULL,NULL,522550,191650,NULL,NULL\nNA,10/06/2017 15:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN CUPBOARD/BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,QUEEN CAROLINE STREET,21000660,W6,NULL,NULL,523150,178250,NULL,NULL\nNA,11/06/2017 09:59,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN DEEP PIT IN NEIGHBOURS GARDEN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34036714,SHEPHERD'S BUSH ROAD,21000727,W6,523499,179095,523450,179050,51.49731516,-0.222263629\nNA,11/06/2017 13:32,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS TRAPPED ON BALCONY ABOVE  RSPCA ON SCENE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000220,ELTHAM WEST,E09000011,GREENWICH,Lee Green,NULL,HANDLEY DRIVE,20802124,SE3,NULL,NULL,540750,175350,NULL,NULL\nNA,11/06/2017 18:27,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BEHIND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011237,Chadwell,E09000026,REDBRIDGE,Dagenham,NULL,BEDE ROAD,22301844,RM6,NULL,NULL,547350,188250,NULL,NULL\nNA,11/06/2017 21:51,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,1.00022E+11,FRAZIER STREET,21900578,SE1,531358,179557,531350,179550,51.49969734,-0.108938835\nNA,12/06/2017 13:34,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED UNDER PATIO,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,RIDGE AVENUE,20704667,N21,NULL,NULL,532750,194750,NULL,NULL\nNA,13/06/2017 12:30,2017,2017/18,Special Service,1,1,328,328,CAT WITH HEAD STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,CHARLTON ROAD,20201730,NW10,NULL,NULL,521550,183750,NULL,NULL\nNA,13/06/2017 14:49,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH PIGEON TRAPPED NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,INVERNESS MEWS,8400467,W2,NULL,NULL,525850,180850,NULL,NULL\nNA,14/06/2017 20:18,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP TREE,Cat,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200104705,SOUTH CLOSE,20039920,EN5,524896,196673,524850,196650,51.65498231,-0.195903255\nNA,15/06/2017 11:14,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,CHARLTON ROAD,20201730,NW10,NULL,NULL,521550,183750,NULL,NULL\nNA,16/06/2017 21:19,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,South Bermondsey,E09000028,SOUTHWARK,Old Kent Road,NULL,OLD KENT ROAD,22501843,SE1,NULL,NULL,533150,178750,NULL,NULL\nNA,17/06/2017 17:23,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT AND KITTEN STUCK ABOVE SHOP SIGN,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46076070,UPTON LANE,22201168,E7,540576,184717,540550,184750,51.54384905,0.025833894\nNA,17/06/2017 22:03,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,10023302034,FOX HOUSE ROAD,20100567,DA17,549956,178655,549950,178650,51.48696852,0.158423631\nNA,17/06/2017 22:39,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARENDON ROAD,21700837,W11,NULL,NULL,524350,180450,NULL,NULL\nNA,18/06/2017 02:34,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND RADIATOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,BILLET ROAD,22816150,E17,NULL,NULL,537050,190850,NULL,NULL\nNA,18/06/2017 18:51,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,DENBIGH ROAD,21700860,W11,NULL,NULL,524950,180950,NULL,NULL\nNA,18/06/2017 21:20,2017,2017/18,Special Service,1,1,328,328,FOX CUB TRAPPED BETWEEN WALL AND SHED,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,1.00022E+11,BLAGDON ROAD,22001236,SE13,537791,174332,537750,174350,51.45121332,-0.018354783\nNA,19/06/2017 01:08,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN FENCE - RSCPA NOT AVAILABLE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,HOMEFIELD GARDENS,22103384,CR4,NULL,NULL,526650,169350,NULL,NULL\nNA,19/06/2017 14:28,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHINMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,DULVERTON ROAD,20100467,SE9,NULL,NULL,544450,172850,NULL,NULL\nNA,20/06/2017 19:20,2017,2017/18,Special Service,1,2,328,656,FOX TRAPPED IN FENCE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,200086935,NORWICH WALK,20031980,HA8,520337,191159,520350,191150,51.60642031,-0.263668465\nNA,20/06/2017 20:40,2017,2017/18,Special Service,1,1,328,328,DEER CAUGHT IN RAILING   -  RSCPA OFFICER REUQESTING ASSISTANCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200019428,CARLTON CLOSE,20007040,NW3,525314,186869,525350,186850,51.56678218,-0.193365725\nNA,21/06/2017 12:23,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,NULL,RAVENS WAY,20801239,SE12,NULL,NULL,540250,174950,NULL,NULL\nNA,21/06/2017 15:54,2017,2017/18,Special Service,1,1,328,328,PIDGEON STUCK INSIDE SIGN,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6354417,CHAPMAN STREET,22700297,E1,534833,180980,534850,180950,51.51166733,-0.058361604\nNA,21/06/2017 19:42,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CLOSED CHIMNEY FLU,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,ST PHILIPS AVENUE,22605530,KT4,NULL,NULL,522850,165950,NULL,NULL\nNA,22/06/2017 07:30,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN WOODEN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,HANNELL ROAD,21000413,SW6,NULL,NULL,524250,177350,NULL,NULL\nNA,22/06/2017 10:33,2017,2017/18,Special Service,1,1,328,328,\"PIDGEON STUCK  ON A TV AERIAL   TWO STOREY HOUSE, ONTOP OF CHIMNEY ALP  REQUESTED  FROM SCENE\",Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000183,NORTH GREENFORD,E09000009,EALING,Northolt,NULL,WHITTON AVENUE EAST,20601898,UB6,NULL,NULL,515450,185250,NULL,NULL\nNA,22/06/2017 10:50,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,HAMILTON ROAD,21900659,SE27,532918,171726,532950,171750,51.42895812,-0.089418955\nNA,22/06/2017 17:23,2017,2017/18,Special Service,1,1,328,328,TRAPPED BIRDS UNDER SOLAR PANEL - ASSIST LONDON WILDLIFE PROTECTION,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,DILSTON CLOSE,20600537,UB5,NULL,NULL,511450,182950,NULL,NULL\nNA,23/06/2017 09:38,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Day care/Drop in centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10023774152,BROADMEAD ROAD,22304845,IG8,540301,191790,540350,191750,51.6074734,0.024692563\nNA,23/06/2017 13:06,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,12028427,TOWNSEND ROAD,20601764,UB1,512131,180346,512150,180350,51.51092561,-0.385560902\nNA,23/06/2017 16:02,2017,2017/18,Special Service,1,1,328,328,CAT STUCK INSIDE BOARDED UP HOUSE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,BASTABLE AVENUE,19900412,IG11,NULL,NULL,546450,183150,NULL,NULL\nNA,25/06/2017 09:43,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH BIRD TRAPPED BY STRING IN TREE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,1.00023E+11,NORFOLK ROAD,8401470,NW8,526856,183587,526850,183550,51.53694245,-0.172312436\nNA,27/06/2017 06:27,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Gym,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,5026656,ALBANY STREET,20400765,NW1,528899,182556,528850,182550,51.52721466,-0.143249011\nNA,27/06/2017 13:33,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH RESCUING CAT FROM TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000640,MAIDA VALE,E09000033,WESTMINSTER,Paddington,1.00023E+11,LANARK ROAD,8401175,W9,526106,182636,526150,182650,51.5285636,-0.183460952\nNA,27/06/2017 13:41,2017,2017/18,Special Service,1,1,328,328,DOG STUCK UNDER BATH,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,MANOR COURT,20702827,EN1,NULL,NULL,534750,199350,NULL,NULL\nNA,27/06/2017 13:53,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,THORNES CLOSE,20301970,BR3,NULL,NULL,538150,168650,NULL,NULL\nNA,27/06/2017 14:04,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK DOWN MANHOLE,Bird,Person (land line),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200144152,NORTH END ROAD,20031620,NW11,525131,187432,525150,187450,51.5718825,-0.195804347\nNA,27/06/2017 19:29,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN DRAIN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,BROCKLEHURST STREET,22000165,SE14,NULL,NULL,535950,177150,NULL,NULL\nNA,29/06/2017 00:20,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON FOURTH FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BLACKTHORN AVENUE,21610109,N7,NULL,NULL,531150,184550,NULL,NULL\nNA,29/06/2017 09:26,2017,2017/18,Special Service,1,1,328,328,HERON STUCK ON BALCONY  CALL FROM RSPCA REQUESTING BRIGADE ASSISTANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,WIMBLEDON PARK ROAD,22906210,SW18,NULL,NULL,524850,173350,NULL,NULL\nNA,30/06/2017 08:30,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00021E+11,SORREL CLOSE,20801373,SE28,546425,180103,546450,180150,51.50090377,0.108205079\nNA,30/06/2017 14:10,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,ALNWICK ROAD,22207597,E16,NULL,NULL,541350,181150,NULL,NULL\nNA,30/06/2017 19:37,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY  TRAPPED IN CAR - FRU WITH SNAKE EYE CAMERA REQ FROM SCENE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,1.00022E+11,GREAT WEST ROAD,21500505,TW5,513190,176747,513150,176750,51.47836675,-0.371460131\nNA,01/07/2017 10:46,2017,2017/18,Special Service,1,1,328,328,DUCK TRAPPED UNDER A MANHOLE COVER,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,10023189808,BRADING CRESCENT,22306084,E12,540886,186848,540850,186850,51.56292044,0.031153937\nNA,01/07/2017 12:26,2017,2017/18,Special Service,1,1,328,328,CAT BELIEVED TRAPPED IN LOFT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,THE SPINNEY,22603399,SM3,NULL,NULL,523350,164650,NULL,NULL\nNA,02/07/2017 08:21,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UP TREE - RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000133,FROGNAL AND FITZJOHNS,E09000007,CAMDEN,West Hampstead,NULL,FINCHLEY ROAD,20400045,NW3,NULL,NULL,525850,185250,NULL,NULL\nNA,02/07/2017 19:13,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200210732,THE RIDGEWAY,20036680,NW7,523459,192105,523450,192150,51.61424747,-0.218274777\nNA,03/07/2017 19:05,2017,2017/18,Special Service,1,1,328,328,ROUNDABOUT AT JUNCTION OF GREEN WRYTHE LANE SMALL ANIMAL RESCUE - BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,MIDDLETON ROAD,22600797,SM5,NULL,NULL,527150,166650,NULL,NULL\nNA,03/07/2017 21:03,2017,2017/18,Special Service,1,1,328,328,Redacted,Deer,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05011469,Kenley,E09000008,CROYDON,Purley,1.00021E+11,BOURNE PARK CLOSE,20500182,CR8,533404,159388,533450,159350,51.31796616,-0.087058782\nNA,04/07/2017 10:58,2017,2017/18,Special Service,1,1,328,328,CAT IMPALED ON POLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,NULL,SUMMERHOUSE AVENUE,21501079,TW5,NULL,NULL,512150,176950,NULL,NULL\nNA,04/07/2017 20:25,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL  TRAPPED UNDER DOOR STEP,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,UNITY PLACE,22883180,E17,NULL,NULL,536150,189850,NULL,NULL\nNA,04/07/2017 21:09,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009383,SPRINGFIELD,E09000012,HACKNEY,Stoke Newington,10025166480,CLAPTON COMMON,20900242,N16,534080,187584,534050,187550,51.57119226,-0.066693267\nNA,05/07/2017 10:39,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN SHAFT,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,MERRYFIELDS WAY,22005995,SE6,NULL,NULL,537850,173850,NULL,NULL\nNA,05/07/2017 21:59,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER SINK CUPBOARD,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,RYLETT ROAD,21000710,W12,NULL,NULL,522050,179250,NULL,NULL\nNA,05/07/2017 22:03,2017,2017/18,Special Service,1,1,328,328,BIRD WITH WINGS TRAPPED IN NETTING        CALLER IS OUTSIDE CLINIC,Bird,Person (land line),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527092,166678,527050,166650,51.38492614,-0.17498348\nNA,05/07/2017 23:03,2017,2017/18,Special Service,1,1,328,328,BIRD WITH WINGS TRAPPED IN NETTING GREEN WRYTHE LANE SURGERY,Bird,Person (mobile),Doctors surgery,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870070369,GREEN WRYTHE LANE,22600593,SM5,527105,166680,527150,166650,51.3849412,-0.174796047\nNA,05/07/2017 23:30,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,WETHERELL ROAD,20901067,E9,NULL,NULL,535950,184050,NULL,NULL\nNA,06/07/2017 08:33,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED BETWEEN BASEMENT AND WALL OPPOSITE JERKEES RESTAURANT,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,WALHAM GROVE,21000852,SW6,NULL,NULL,525150,177450,NULL,NULL\nNA,06/07/2017 21:53,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON ROOF- FOUR FLOORS HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000089,DUDDEN HILL,E09000005,BRENT,Willesden,NULL,DUDDEN HILL LANE,20200017,NW10,NULL,NULL,521650,185650,NULL,NULL\nNA,08/07/2017 17:15,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN FOILAGE BEHIND GARDEN SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Feltham,NULL,HINTON AVENUE,21500611,TW4,NULL,NULL,511750,175350,NULL,NULL\nNA,08/07/2017 17:38,2017,2017/18,Special Service,1,1,328,328,CAT FALLEN AND TRAPPED   CALLER SAID THIS IS A LARGE HOLE THAT THEY ARE UNABLE TO GET TO DUE TO IT B,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,HIGH ROAD,20022400,N12,NULL,NULL,526450,191250,NULL,NULL\nNA,08/07/2017 21:12,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED ON ROOF BY LEG CALL FROM RSPCA WHO ARE UNABLE TO DEAL,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011488,West Thornton,E09000008,CROYDON,Norbury,NULL,GROVE ROAD,20501013,CR7,NULL,NULL,531150,167950,NULL,NULL\nNA,08/07/2017 21:53,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,MARSHALL STREET,8401402,W1F,NULL,NULL,529250,181050,NULL,NULL\nNA,09/07/2017 01:18,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000298,PINNER SOUTH,E09000015,HARROW,Harrow,NULL,MARSH ROAD,21201338,HA5,NULL,NULL,512450,189050,NULL,NULL\nNA,09/07/2017 20:01,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,5138548,BROADHURST GARDENS,20400410,NW6,526301,184545,526350,184550,51.54567637,-0.179966764\nNA,10/07/2017 08:14,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,THE POINT,21401974,HA4,510151,185913,510150,185950,51.56135127,-0.412341077\nNA,10/07/2017 16:01,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED AND BELIEVED INJURED IN SUSPENDED CEILING  - CAB VISION HEMMING STREET CALLER SAYS TO,Bird,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6647489,HEMMING STREET,22700626,E2,534455,182324,534450,182350,51.52383504,-0.06329294\nNA,10/07/2017 18:53,2017,2017/18,Special Service,1,1,328,328,CAT  TRAPPED  IN REAR GARDEN    LEG IS CAUGHT IN STRING AND RUBBISH ON SITE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,MILLFIELD AVENUE,22858800,E17,NULL,NULL,536650,190550,NULL,NULL\nNA,11/07/2017 11:16,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH DUCKLING TRAPPED IN WELL,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,1.00023E+11,VICTORIA EMBANKMENT,8400999,WC2R,531116,180789,531150,180750,51.51082518,-0.111965446\nNA,11/07/2017 12:59,2017,2017/18,Special Service,1,1,328,328,FOX WITH HEAD TRAPPED BETWEEN WALL AND FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,MONTROSE AVENUE,20030060,HA8,NULL,NULL,520850,190750,NULL,NULL\nNA,12/07/2017 19:15,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,FARNAN ROAD,21900542,SW16,NULL,NULL,530350,171250,NULL,NULL\nNA,13/07/2017 18:30,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK UNDER DRAIN GRILL,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200228520,SIMMS GARDENS,20039690,N2,526447,190271,526450,190250,51.59710276,-0.175803525\nNA,13/07/2017 22:05,2017,2017/18,Special Service,1,2,328,656,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,SEVENOAKS ROAD,20301447,BR6,NULL,NULL,545550,164150,NULL,NULL\nNA,14/07/2017 14:18,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO GAIN ACCESS  RSPCA INSPECTOR ON SCENE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Islington,NULL,WHEELER GARDENS,21607021,N1,NULL,NULL,530350,183850,NULL,NULL\nNA,14/07/2017 15:12,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO PIGEON TRAPPED IN WIRE    RSPCA IN ATTENDANCE,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,UPPER CLAPTON ROAD,20901025,E5,534770,186540,534750,186550,51.56164607,-0.057142643\nNA,14/07/2017 19:59,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED BETWEEN FENCES    RSPCA UNABLE TO ATTEND,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000037,PARSLOES,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,VERNEY GARDENS,19900743,RM9,NULL,NULL,548150,185650,NULL,NULL\nNA,15/07/2017 15:31,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,CHANNELSEA ROAD,22201339,E15,NULL,NULL,538750,183850,NULL,NULL\nNA,16/07/2017 09:13,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,NULL,GREEN DRAGON LANE,21500506,TW8,NULL,NULL,518350,178050,NULL,NULL\nNA,16/07/2017 10:02,2017,2017/18,Special Service,1,1,328,328,PUPPY   WITH HEAD TRAPPED IN RECLINING SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,CHURCHILL ROAD,22200475,E16,NULL,NULL,541350,181250,NULL,NULL\nNA,16/07/2017 15:11,2017,2017/18,Special Service,1,1,328,328,DOG IN LAKE DOG IN DISTRESS AND CANNOT GET OUT    OWNER SAYS BEST ACCESS IS VIA ROMFORD ROAD,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011245,Hainault,E09000026,REDBRIDGE,Hainault,10023773354,ROMFORD ROAD,22304722,IG7,547161,193001,547150,193050,51.61660578,0.124193925\nNA,16/07/2017 21:47,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK DOWN DRAIN OUTSIDE  CALLED BY RSPCA WHO CANNOT ATTEND AT PRESENT,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from water,Animal rescue from water - Bird,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,MANOR ROAD,20900653,N16,533420,187075,533450,187050,51.5667748,-0.076403704\nNA,16/07/2017 21:49,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND WOODWORK  CALL FROM RSPCA WHO CANNOT ATTEND AT PRESENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,NULL,FORTIS GREEN,21106130,N2,NULL,NULL,527950,189650,NULL,NULL\nNA,17/07/2017 13:26,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH RETRIEVAL OF CAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,NULL,CAMERON ROAD,22301914,IG3,NULL,NULL,545450,187150,NULL,NULL\nNA,18/07/2017 08:13,2017,2017/18,Special Service,1,1,328,328,MAGPIES TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,NULL,KEW ROAD,22406655,TW9,NULL,NULL,519050,177150,NULL,NULL\nNA,18/07/2017 19:13,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN WALL AND FENCE   -   OWNER WILL MEET YOU,Dog,Person (land line),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,10011847898,WOODSIDE ROAD,20101618,DA15,545471,172209,545450,172250,51.43021791,0.09122314\nNA,18/07/2017 19:47,2017,2017/18,Special Service,1,1,328,328,DUCKLINGS STUCK IN DRAIN     NIGHT MANAGER WILL MEET AND SHOW YOU WHERE THEY ARE  RSPCA UNABLE TO AT,Bird,Person (land line),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202102736,GREAT CENTRAL WAY,20201658,NW10,520928,185316,520950,185350,51.55378125,-0.25714848\nNA,19/07/2017 11:38,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN DRAINAGE AT RESTAURANT   -  CYPRUS MEZET BAR,Cat,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,2.00001E+11,LEA BRIDGE ROAD,22850300,E10,537496,187833,537450,187850,51.57260808,-0.017334994\nNA,19/07/2017 17:43,2017,2017/18,Special Service,1,1,328,328,PIGEONS TRAPPED UNDER NETTING   UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,1.00022E+11,JUXON STREET,21900769,SE11,530847,178947,530850,178950,51.4943338,-0.116522545\nNA,19/07/2017 19:02,2017,2017/18,Special Service,1,1,328,328,BIRD WITH LEG CAUGHT IN STRING IN NEST  RSPCA IN ATTENDANCE ANT CORNER WITH DARTMOUTH PARK HILL - TL,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,10015354514,DARTMOUTH PARK HILL,21600227,NW5,529046,186058,529050,186050,51.55865316,-0.1398477\nNA,19/07/2017 20:13,2017,2017/18,Special Service,2,3,328,984,INJURED SEAGULL IN WATER       NEAR THE CONSTITUTION PH   FRU REQUIRED WITH BOAT CAPABILITIES WATER,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Kentish Town,5063437,ST PANCRAS WAY,20400656,NW1,529373,184034,529350,184050,51.54038899,-0.135877168\nNA,19/07/2017 22:15,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BIN CHUTE POSSIBLY BEEN THERE A FEW DAYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,West Norwood,NULL,DEEPDENE GARDENS,21900447,SW2,NULL,NULL,530550,173950,NULL,NULL\nNA,19/07/2017 23:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING PL REQUESTED,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,ATHERFOLD ROAD,21900118,SW9,NULL,NULL,530250,175950,NULL,NULL\nNA,21/07/2017 13:51,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FLEDGLING SEAGULL IN DISTRESS RSPCA IN ATTENDANCE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Shoreditch,5087312,KIRBY STREET,20401188,EC1N,531434,181787,531450,181750,51.51972016,-0.107013692\nNA,21/07/2017 15:13,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO BIRD IN DISTRESS TRAPPED IN NETTING,Bird,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,1.00023E+11,WESTMINSTER BRIDGE ROAD,21901482,SE1,530695,179518,530650,179550,51.49950039,-0.118499475\nNA,22/07/2017 13:16,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPC ON SCENE WITH DOG IN PRECARIOUS POSITION,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,AMELIA CLOSE,20604257,W3,NULL,NULL,519650,180050,NULL,NULL\nNA,22/07/2017 17:26,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER FLOORING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,DAHOMEY ROAD,22901117,SW16,NULL,NULL,529150,170750,NULL,NULL\nNA,23/07/2017 14:45,2017,2017/18,Special Service,1,1,328,328,BIRD CAUGHT IN NETTING - REQUESTED BY RSPCA,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,QUINTA DRIVE,20035700,EN5,NULL,NULL,523150,195650,NULL,NULL\nNA,24/07/2017 10:10,2017,2017/18,Special Service,1,1,328,328,BIRD INJURED AND TRAPPED ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,SOMERFIELD STREET,22006156,SE16,NULL,NULL,535650,178450,NULL,NULL\nNA,24/07/2017 12:21,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN FENCE   REQUESTED BY RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Chingford,NULL,SELWYN AVENUE,22873600,E4,NULL,NULL,538350,191650,NULL,NULL\nNA,24/07/2017 17:55,2017,2017/18,Special Service,1,1,328,328,BIRD LOOSE IN LOUNGE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,BEECHCROFT ROAD,22900430,SW17,NULL,NULL,527650,172450,NULL,NULL\nNA,25/07/2017 14:07,2017,2017/18,Special Service,1,2,328,656,CAT ON ROOF - RSPCA ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Norbury,NULL,GLENELDON ROAD,21900617,SW16,NULL,NULL,530450,171550,NULL,NULL\nNA,25/07/2017 16:11,2017,2017/18,Special Service,1,1,328,328,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,KELSEY SQUARE,20301422,BR3,NULL,NULL,537250,169350,NULL,NULL\nNA,25/07/2017 18:07,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO DISTRESSED DOG TRAPPED BETWEEN FENCE AND SHED,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,TRAFALGAR ROAD,22401932,TW2,514936,172611,514950,172650,51.44084135,-0.347672043\nNA,26/07/2017 18:06,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,NULL,HOLDERS HILL AVENUE,20023380,NW4,NULL,NULL,523850,189950,NULL,NULL\nNA,27/07/2017 12:41,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN IRON FENCE CALLED X RSPCA ON SCENE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011223,Crook Log,E09000004,BEXLEY,Bexley,1.0002E+11,LAKESIDE CLOSE,20100811,DA15,547001,174541,547050,174550,51.45077761,0.114184149\nNA,27/07/2017 14:07,2017,2017/18,Special Service,1,1,328,328,ASSIST THE RSPCA WITH KITTEN TRAPPED IN A DRAIN - RSPCA ON SCENE,Cat,Person (mobile),TV/film/music/art studio,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,North Kensington,34082175,CUMBERLAND PARK,21001287,NW10,522316,182677,522350,182650,51.52976489,-0.238054898\nNA,27/07/2017 19:18,2017,2017/18,Special Service,1,1,328,328,BIRD ENTANGLED IN TREE  RSPCA UNABLE TO ATTEND,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,1.00021E+11,CAZENOVE ROAD,20900211,N16,534124,186926,534150,186950,51.56526878,-0.066309429\nNA,28/07/2017 10:39,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,NULL,CLITHEROE ROAD,21301085,RM5,NULL,NULL,550250,192050,NULL,NULL\nNA,28/07/2017 19:31,2017,2017/18,Special Service,1,2,328,656,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000368,CALEDONIAN,E09000019,ISLINGTON,Holloway,NULL,BLUNDELL STREET,21604300,N7,NULL,NULL,530450,184550,NULL,NULL\nNA,28/07/2017 20:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,MARKHOUSE ROAD,22856450,E17,536648,187922,536650,187950,51.57361372,-0.029529525\nNA,30/07/2017 15:00,2017,2017/18,Special Service,1,1,328,328,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000052,GARDEN SUBURB,E09000003,BARNET,West Hampstead,200105335,SPANIARDS ROAD,20040260,NW3,526636,187262,526650,187250,51.57001883,-0.174160682\nNA,30/07/2017 20:55,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON LIVE WIRES IN REAR GARDEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,DUNHOLME GREEN,20704164,N9,NULL,NULL,533650,193450,NULL,NULL\nNA,02/08/2017 10:39,2017,2017/18,Special Service,1,1,328,328,FOX STUCK IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000091,HARLESDEN,E09000005,BRENT,Park Royal,202146561,CREUKHORNE ROAD,20204180,NW10,521082,184315,521050,184350,51.54475187,-0.255272714\nNA,05/08/2017 16:31,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - STRUCK BY DRONE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009322,BROMLEY SOUTH,E09000030,TOWER HAMLETS,Poplar,NULL,WHITETHORN STREET,22701342,E3,NULL,NULL,537350,181950,NULL,NULL\nNA,05/08/2017 17:07,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BETWEEN WALLS OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185650,NULL,NULL\nNA,06/08/2017 16:25,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FIRE PLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHARMINSTER ROAD,22601355,KT4,NULL,NULL,523650,166250,NULL,NULL\nNA,08/08/2017 11:43,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE - PYTHON STUCK BEHIND RADIATOR,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,FOULDEN ROAD,20900420,N16,NULL,NULL,533750,185850,NULL,NULL\nNA,08/08/2017 13:54,2017,2017/18,Special Service,1,2,328,656,CAT STUCK IN WINDOW VERY DISTRESSED,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,Chaucer,E09000028,SOUTHWARK,Old Kent Road,NULL,POTIER STREET,22502006,SE1,NULL,NULL,532950,179150,NULL,NULL\nNA,08/08/2017 14:03,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN WINDOW FRAME  CALLER WAITING OUTISDE SISKIN HOUSE FOR YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011112,Rotherhithe,E09000028,SOUTHWARK,Deptford,NULL,TAWNY WAY,22502475,SE16,NULL,NULL,535850,178950,NULL,NULL\nNA,08/08/2017 19:42,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN BARS OF CRATE BASEMENT FLAT,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,NULL,SOUTHWARK BRIDGE ROAD,22502285,SE1,NULL,NULL,531950,179550,NULL,NULL\nNA,08/08/2017 22:18,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT STUCK IN STORAGE AREA - NO FURTHER BRIGADE ATTENDANCE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,HAZLEWOOD CRESCENT,21700885,W10,NULL,NULL,524550,182150,NULL,NULL\nNA,09/08/2017 00:59,2017,2017/18,Special Service,1,1,328,328,INJURED CAT TRAPPED UNDER DECKING - CALLER WILL MEET BRIGADE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,NULL,LADYWELL ROAD,22001623,SE13,NULL,NULL,537750,174950,NULL,NULL\nNA,10/08/2017 10:11,2017,2017/18,Special Service,1,1,328,328,DOG IN PRECARIOUS POSITION  ON ROOF OF TWO STOREY HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,OAKMERE ROAD,20801098,SE2,NULL,NULL,546150,177850,NULL,NULL\nNA,10/08/2017 22:26,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,SHAKESPEARE AVENUE,21501006,TW14,510041,174137,510050,174150,51.45552826,-0.417598127\nNA,11/08/2017 15:49,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN BUILDING,Bird,Person (land line),Day care/Drop in centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10023774152,BROADMEAD ROAD,22304845,IG8,540303,191793,540350,191750,51.60749986,0.024722625\nNA,11/08/2017 17:12,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Lambeth,1.00023E+11,HORSEFERRY ROAD,8401767,SW1P,529705,179070,529750,179050,51.49570208,-0.132918645\nNA,13/08/2017 14:53,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE FOR TWO DAYS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,REDBRIDGE,Ilford,NULL,CRUCIBLE CLOSE,22301978,RM6,NULL,NULL,546850,188350,NULL,NULL\nNA,13/08/2017 15:25,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  -  RSPCA INSPECTOR IN ATTENDANCE   -   CROW HANGING BY THE LEG FROM A TV AERIEL,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,OLD PALACE LANE,22403701,TW9,NULL,NULL,517450,174950,NULL,NULL\nNA,13/08/2017 15:51,2017,2017/18,Special Service,1,1,328,328,CAT UP A TREE.   CALLED AT THE REQUEST OF THE RSPCA.  OWNER IS IN ATTENDANCE.  CAT IS PREGNANT AND U,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011237,Chadwell,E09000026,REDBRIDGE,Ilford,NULL,CRUCIBLE CLOSE,22301978,RM6,NULL,NULL,546850,188350,NULL,NULL\nNA,13/08/2017 16:21,2017,2017/18,Special Service,1,1,328,328,SWAN ENTANGLED IN FISHING LINE WITH HOOK IN CHEST - RSPCA UNABLE TO ATTEND - BIRD IS OUTSIDE SLUG AN,Bird,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,WHITTAKER AVENUE,22403953,TW9,517670,174644,517650,174650,51.45855219,-0.307676958\nNA,14/08/2017 08:28,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD STUCK IN SINK,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,MANSFIELD AVENUE,21103590,N15,NULL,NULL,532750,189350,NULL,NULL\nNA,14/08/2017 15:20,2017,2017/18,Special Service,1,1,328,328,SNAKE STUCK UNDER KITCHEN CABINET,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,PORCHESTER TERRACE,8400631,W2,NULL,NULL,526050,180750,NULL,NULL\nNA,15/08/2017 10:22,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Theatre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00024E+11,EASTERN ROAD,21301162,RM1,551521,188627,551550,188650,51.57615077,0.185232655\nNA,15/08/2017 19:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000426,OVAL,E09000022,LAMBETH,Lambeth,2E+11,SOUTH LAMBETH ROAD,21901248,SW8,530506,177687,530550,177650,51.48308917,-0.12189723\nNA,15/08/2017 22:55,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN BASEMENT FLOORING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,PICCADILLY,8400515,W1J,NULL,NULL,529150,180450,NULL,NULL\nNA,16/08/2017 12:33,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,1.00021E+11,SHERWOOD ROAD,21201260,HA2,514110,186564,514150,186550,51.56641598,-0.355041668\nNA,18/08/2017 19:45,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING    RSPCA INSPECTOR IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34004351,BEGONIA WALK,21001154,W12,521895,181078,521850,181050,51.51548492,-0.244674176\nNA,19/08/2017 13:11,2017,2017/18,Special Service,1,2,328,656,Redacted,Dog,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,10022796765,PACKET BOAT LANE,21401472,UB8,504901,181192,504950,181150,51.51991766,-0.489454178\nNA,19/08/2017 16:42,2017,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,10025492304,THE BURROUGHS ACCESS ROAD BETWEEN FIRE STATION AND LIBRARY,20006295,NW4,522740,189271,522750,189250,51.58893537,-0.229644134\nNA,20/08/2017 18:08,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,COLUMBIA AVENUE,21800266,KT4,NULL,NULL,521850,166650,NULL,NULL\nNA,20/08/2017 20:26,2017,2017/18,Special Service,1,2,328,656,RUNNING CALL TO A CAT STUCK ON A LEDGE,Cat,Person (land line),College/University,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,10093301515,THE BURROUGHS,20006280,NW4,522689,189289,522650,189250,51.58910824,-0.230373644\nNA,20/08/2017 22:08,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN SUB  BASEMENT VOID,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,REGENT'S PARK ROAD,20400622,NW1,NULL,NULL,528150,184350,NULL,NULL\nNA,22/08/2017 08:08,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,ERICKSON GARDENS,20304228,BR2,NULL,NULL,542050,167250,NULL,NULL\nNA,22/08/2017 14:41,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5043672,SPODE WALK,20401325,NW6,525929,185025,525950,185050,51.55007321,-0.185156867\nNA,24/08/2017 15:09,2017,2017/18,Special Service,1,1,328,328,PIDGEON TRAPPED IN BUILDING- CALLED BY RSPCA,Bird,Person (mobile),Other building/use not known,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,MEETING HOUSE LANE,22501699,SE15,534821,177100,534850,177150,51.47680271,-0.060016096\nNA,24/08/2017 18:09,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10000446356,CARLISLE LANE,21900291,SE1,530970,179351,530950,179350,51.49793601,-0.11460187\nNA,26/08/2017 18:51,2017,2017/18,Special Service,1,1,328,328,\"DISTRESSED DOG TRAPPED IN CAR-IN MAIN CAR PARK , BATTERSEA PARK, NEAR THE RIVER\",Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,121054054,ALBERT BRIDGE ROAD,22900030,SW11,527931,177427,527950,177450,51.48134028,-0.159054742\nNA,27/08/2017 03:50,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED AND INJURED - CALLER WILL MEET - THIS IS FLAT ABOVE FUSION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,NULL,ILFORD LANE,22302963,IG1,NULL,NULL,543850,185650,NULL,NULL\nNA,27/08/2017 14:31,2017,2017/18,Special Service,1,1,328,328,INJURED PIGEON STUCK IN TREE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000183,NORTH GREENFORD,E09000009,EALING,Wembley,12078508,WHITTON AVENUE EAST,20601898,UB6,516377,184954,516350,184950,51.5514833,-0.322880475\nNA,27/08/2017 17:58,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN BIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,NULL,LINDEN AVENUE,21401163,HA4,NULL,NULL,510450,187350,NULL,NULL\nNA,27/08/2017 18:47,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,MARKHOUSE ROAD,22856450,E17,536592,188186,536550,188150,51.5759996,-0.030234652\nNA,27/08/2017 20:24,2017,2017/18,Special Service,1,2,328,656,\"RUNNING CALL TO DOG HIT BY CAR, OBSTRUCTION ON ROADWAY\",Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100103668,WESTMINSTER GARDENS,19900624,IG11,545018,183222,545050,183250,51.52929254,0.089230145\nNA,27/08/2017 21:35,2017,2017/18,Special Service,1,1,328,328,KITTEN FALLEN FROM BALCONY INTO BOARDED UP UNDERGROUND CAR PARK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,CLEVELAND,E09000009,EALING,Ealing,NULL,CHEYNE PATH,20600372,W7,NULL,NULL,515950,181350,NULL,NULL\nNA,27/08/2017 23:00,2017,2017/18,Special Service,1,2,328,656,CAT STUCK IN BIN CHUTE,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,10023850480,BEETON WAY,21903448,SE27,532610,171955,532650,171950,51.43108822,-0.093761072\nNA,28/08/2017 14:56,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,NULL,SIDCUP HIGH STREET,20101300,DA14,NULL,NULL,546250,171850,NULL,NULL\nNA,28/08/2017 19:41,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED CALLER STATES IN FENCE INSIDE STATION NEXT TO BLUE PLASTIC TENT,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10003976820,PARK LANE,21104898,N17,534876,191073,534850,191050,51.60235506,-0.053875975\nNA,29/08/2017 22:33,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN ENGINE BAY OF CAR,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46075339,TUNMARSH LANE,22201157,E13,541021,182783,541050,182750,51.52635932,0.031472899\nNA,31/08/2017 19:35,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA (ON SCENE) WITH BIRD TRAPPED IN NETTING - CROYDON ATH. F.C.,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011488,West Thornton,E09000008,CROYDON,Norbury,2.00001E+11,MAYFIELD ROAD,20502591,CR7,530419,167906,530450,167950,51.39520853,-0.126748594\nNA,31/08/2017 23:53,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED IN WHEEL ARCH OF A CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46075339,TUNMARSH LANE,22201157,E13,541009,182788,541050,182750,51.52640725,0.031302036\nNA,01/09/2017 02:31,2017,2017/18,Special Service,1,1,328,328,FEMALE LOCKED IN PARK HER DOG IS IN LABOUR,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6651762,CAMBRIDGE HEATH ROAD,22700252,E2,535016,182785,535050,182750,51.52784384,-0.055034727\nNA,01/09/2017 12:44,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK  BEHIND THE TOILET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,NULL,NEWLANDS ROAD,20501223,SW16,NULL,NULL,530350,169250,NULL,NULL\nNA,01/09/2017 17:55,2017,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Cat,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207096408,WYLDFIELD GARDENS,20704962,N9,533777,193813,533750,193850,51.62723963,-0.068689782\nNA,02/09/2017 13:56,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UP INSIDE CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,Crayford,E09000004,BEXLEY,Bexley,NULL,GREEN WALK,20100633,DA1,NULL,NULL,551750,175050,NULL,NULL\nNA,02/09/2017 14:09,2017,2017/18,Special Service,1,1,328,328,CAT ON ROOF SECOND FLOOR  REQUESTED BY RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,BEADON ROAD,20300246,BR2,NULL,NULL,540350,167850,NULL,NULL\nNA,02/09/2017 18:06,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CAR ENGINE  RSPCA UNABLE TO ATTEND,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207002625,LANCING GARDENS,20704412,N9,533911,194235,533950,194250,51.63099999,-0.066593603\nNA,02/09/2017 19:18,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON BALCONY - RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,CANBERRA DRIVE,20600310,UB5,NULL,NULL,511450,182750,NULL,NULL\nNA,03/09/2017 06:35,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000047,COPPETTS,E09000003,BARNET,Finchley,200144333,PINKHAM WAY,20034380,N11,528959,191787,528950,191750,51.61015736,-0.139000204\nNA,04/09/2017 17:30,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,NULL,WHITE HEART AVENUE,21402148,UB8,NULL,NULL,508050,181750,NULL,NULL\nNA,06/09/2017 20:05,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BETWEEN TWO WALLS INSIDE HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,REES GARDENS,20501333,CR0,NULL,NULL,533850,167150,NULL,NULL\nNA,07/09/2017 12:14,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK BEHIND FIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,Broad Green,E09000008,CROYDON,Croydon,NULL,MITCHAM ROAD,20502543,CR0,NULL,NULL,531150,166650,NULL,NULL\nNA,07/09/2017 19:04,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN AIR BRICK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,TWINING AVENUE,22401943,TW2,NULL,NULL,514550,172250,NULL,NULL\nNA,09/09/2017 07:48,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,NIGEL ROAD,22501803,SE15,NULL,NULL,534350,175950,NULL,NULL\nNA,09/09/2017 15:24,2017,2017/18,Special Service,1,1,328,328,CAT STUCK IN BRICKED WALL IN BACK GARDEN,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,NULL,CAMBRIDGE ROAD,22900771,SW11,NULL,NULL,527550,176550,NULL,NULL\nNA,09/09/2017 21:00,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BASEMENT AREA - OWNER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,NEVILL ROAD,20900729,N16,NULL,NULL,533250,186050,NULL,NULL\nNA,10/09/2017 15:45,2017,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE DOG UNDER ELECTRIC CHAIR,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,PLUMSTEAD ROAD,20801188,SE18,NULL,NULL,544550,178850,NULL,NULL\nNA,11/09/2017 12:10,2017,2017/18,Special Service,1,1,328,328,REQUEST FROM RSPCA  FOX FALLEN THROUGH GARAGE ROOF,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05011217,Barnehurst,E09000004,BEXLEY,Erith,1.0002E+11,BIRLING ROAD,20100151,DA8,551061,176953,551050,176950,51.47138205,0.17359836\nNA,11/09/2017 12:31,2017,2017/18,Special Service,1,1,328,328,REQUEST FROM RSPCA  CAT STUCK ON THIRD FLOOR ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000095,MAPESBURY,E09000005,BRENT,Willesden,NULL,CHICHELE ROAD,20202353,NW2,NULL,NULL,523750,185450,NULL,NULL\nNA,12/09/2017 16:06,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED DOWN DRAIN - OWNER WILL MEET BRIGADE AT THEIR ADDRESS AND DIRECT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,MUSWELL HILL BROADWAY,21106387,N10,NULL,NULL,528750,189650,NULL,NULL\nNA,13/09/2017 18:11,2017,2017/18,Special Service,1,1,328,328,ANIMAL TRAPPED IN CEILING CAVITY,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000367,BUNHILL,E09000019,ISLINGTON,Islington,NULL,OWEN STREET,21605616,EC1V,NULL,NULL,531550,183050,NULL,NULL\nNA,14/09/2017 11:18,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,NULL,BONCHESTER CLOSE,20300433,BR7,NULL,NULL,543350,170050,NULL,NULL\nNA,14/09/2017 14:06,2017,2017/18,Special Service,1,1,328,328,DEER STUCK IN METAL RAILINGS,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05011481,Selsdon Vale & Forestdale,E09000008,CROYDON,Addington,1.00021E+11,QUAIL GARDENS,20501808,CR2,536215,162216,536250,162250,51.3427149,-0.045667273\nNA,15/09/2017 13:43,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,EDENBRIDGE CLOSE,20301107,BR5,NULL,NULL,547750,168050,NULL,NULL\nNA,16/09/2017 09:18,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,DUNBAR AVENUE,20301889,BR3,NULL,NULL,536350,168350,NULL,NULL\nNA,16/09/2017 11:26,2017,2017/18,Special Service,1,1,328,328,RSPCA REQUEST FOR PIGEON TRAPPED UP HIGH - OPPOSITE THIS ADDRESS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,NULL,LONDON ROAD,21800616,KT2,NULL,NULL,519050,169550,NULL,NULL\nNA,16/09/2017 17:12,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,DUNBAR AVENUE,20301889,BR3,NULL,NULL,536350,168350,NULL,NULL\nNA,17/09/2017 13:50,2017,2017/18,Special Service,1,1,328,328,TWO DOGS RUN INTO BADGER SET UNDERGROUND,Dog,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HAMPTON COURT ROAD,22406635,KT8,515966,168659,515950,168650,51.40511173,-0.334151092\nNA,17/09/2017 19:20,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,NEW ROAD,22302303,IG3,NULL,NULL,545250,186950,NULL,NULL\nNA,19/09/2017 15:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,1.00021E+11,FAIRHOLT ROAD,20900391,N16,532895,187344,532850,187350,51.56931618,-0.083872215\nNA,19/09/2017 21:50,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED UNDER STAIRS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,DITTON ROAD,21800332,KT6,NULL,NULL,518550,166350,NULL,NULL\nNA,21/09/2017 15:21,2017,2017/18,Special Service,1,1,328,328,CAT LEG TRAPPED IN ELECTRIC CHAIR,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,FIFTH AVENUE,8400299,W10,NULL,NULL,524150,182650,NULL,NULL\nNA,22/09/2017 20:53,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WIRE ON TOP OF BUILDING NEXT TO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,WAVERLEY CRESCENT,20801567,SE18,NULL,NULL,544650,178250,NULL,NULL\nNA,23/09/2017 12:26,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,APPRENTICE GARDENS,20604821,UB5,NULL,NULL,512650,182650,NULL,NULL\nNA,23/09/2017 13:29,2017,2017/18,Special Service,1,1,328,328,ASSITING RSPCA WITH PIGEON STUCK ON BIRD SPIKES,Bird,Person (land line),Hedge,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34004730,RAVENSCOURT PARK,21000675,W6,522316,178686,522350,178650,51.4938962,-0.239439028\nNA,24/09/2017 09:10,2017,2017/18,Special Service,1,1,328,328,DISTRESSED CAT STUCK UP TREE - CALLED BY RSPCA WHO ARE NOT ON SCENE TREE IS AT THE REAR OF FLATS AND,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200115679,THE GRANGE ESTATE,20022381,N2,526570,190075,526550,190050,51.59531371,-0.17409938\nNA,24/09/2017 10:31,2017,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Dog,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,BRIDGES COURT ROAD,22900617,SW11,526563,176148,526550,176150,51.47015326,-0.179202095\nNA,25/09/2017 15:53,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000046,COLINDALE,E09000003,BARNET,Hendon,200024793,CLOVELLY AVENUE,20009620,NW9,521574,189331,521550,189350,51.5897269,-0.246446186\nNA,25/09/2017 23:34,2017,2017/18,Special Service,1,1,328,328,SQUIRREL TRAPPED IN DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,APPLETON ROAD,20800079,SE9,NULL,NULL,542150,175550,NULL,NULL\nNA,26/09/2017 15:09,2017,2017/18,Special Service,1,1,328,328,CAT  STUCK IN CHEST OF DRAWERS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,WALFORD ROAD,20901038,N16,NULL,NULL,533450,185850,NULL,NULL\nNA,26/09/2017 17:15,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED UNDER CAR SEAT   OUTSIDE B AND M SHOP,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100103427,WHALEBONE LANE SOUTH,19900385,RM8,548687,187771,548650,187750,51.56921279,0.144004183\nNA,27/09/2017 16:57,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BEHIND RADIATOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,BRUCE GROVE,21104298,N17,NULL,NULL,533450,190550,NULL,NULL\nNA,29/09/2017 10:37,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,SCAWEN ROAD,22000907,SE8,NULL,NULL,536150,178350,NULL,NULL\nNA,30/09/2017 08:45,2017,2017/18,Special Service,1,3,328,984,CAT TRAPPED IN CHIMNEY  ALP AND FRU CARRYING SNAKE EYE CAMERA REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,ELSINORE ROAD,22001440,SE23,NULL,NULL,536350,173050,NULL,NULL\nNA,30/09/2017 16:39,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,BRISTOL MEWS,8401670,W9,NULL,NULL,525950,182050,NULL,NULL\nNA,01/10/2017 09:04,2017,2017/18,Special Service,1,1,328,328,PERSONS LOCKED IN,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009389,BROMPTON & HANS TOWN,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WALTON STREET,21700573,SW3,NULL,NULL,527550,179150,NULL,NULL\nNA,03/10/2017 18:38,2017,2017/18,Special Service,1,2,328,656,Redacted,Deer,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10013152116,CUDHAM LANE NORTH,20303592,TN14,544436,160945,544450,160950,51.32926742,0.071771897\nNA,04/10/2017 17:25,2017,2017/18,Special Service,1,1,328,328,SQUIRREL TRAPPED IN ROOF,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011239,Clayhall,E09000026,REDBRIDGE,Hainault,NULL,LONGWOOD GARDENS,22303025,IG5,NULL,NULL,542950,189250,NULL,NULL\nNA,05/10/2017 11:02,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN SOME WIRE    RSPCA ON SCENE ASKING FOR ASSIST,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WYATTS LANE,22889550,E17,NULL,NULL,538250,189850,NULL,NULL\nNA,05/10/2017 19:03,2017,2017/18,Special Service,1,2,328,656,DISABLED DOG TRAPPED BEHIND METAL FENCE     NEAR CHINBROOK MEADOWS,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Bromley,10023231386,AMBLECOTE ROAD,22003110,SE12,540943,171969,540950,171950,51.42920458,0.026037452\nNA,06/10/2017 07:28,2017,2017/18,Special Service,1,1,328,328,KITTEN WITH HEAD STUCK IN WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,NULL,PALGRAVE AVENUE,20601338,UB1,NULL,NULL,513150,180750,NULL,NULL\nNA,06/10/2017 12:00,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE - RSPCA ON SCENE - FOREST AREA ON ROAD - RSPCA OFFICER STATES SHE WILL BE WAITING TO MEE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,ROLLS PARK ROAD,22870900,E4,537715,192123,537750,192150,51.61110351,-0.012496338\nNA,07/10/2017 10:25,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED BETWEEN SHED AND CAR PARK   FRU WITH SNAKE EYE REQUESTED FROM SCENE,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,207155997,TURKEY STREET,20703019,EN3,535503,198678,535550,198650,51.67054422,-0.041885619\nNA,07/10/2017 20:39,2017,2017/18,Special Service,1,2,328,656,KITTEN STUCK BEHIND THE SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,KINGSBRIDGE DRIVE,20025230,NW7,NULL,NULL,523850,191350,NULL,NULL\nNA,08/10/2017 14:19,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,NULL,LONDON ROAD,21200906,HA8,NULL,NULL,518250,192750,NULL,NULL\nNA,09/10/2017 09:42,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH FOX POSSIBLY TRAPPED UNDER A CAR,Fox,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped wild animal,E05000647,WARWICK,E09000033,WESTMINSTER,Chelsea,10033533675,BULLEID WAY,8400302,SW1W,528766,178695,528750,178650,51.49254641,-0.146574638\nNA,12/10/2017 00:37,2017,2017/18,Special Service,1,1,328,328,FOX CAUGHT IN METAL GATE  RSPCA INSPECTOR ON SCENE (UNABLE TO SEPARATE THE BARS OF THE GATE)  .,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011252,South Woodford,E09000026,REDBRIDGE,Woodford,1.00022E+11,THE DRIVE,22305366,E18,540070,189409,540050,189450,51.5861359,0.020409457\nNA,13/10/2017 06:04,2017,2017/18,Special Service,1,1,328,328,\"FOX TRAPPED IN A GATE DIRECTIONS GIVEN ARE - TURN LEFT OUT OF THE STATION, THEN LEFT AGAIN AT THE BR\",Fox,Person (land line),Wasteland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,1.00022E+11,COULGATE STREET,22000274,SE4,536465,175806,536450,175850,51.46478017,-0.036857526\nNA,14/10/2017 15:39,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED BETWEEN RAILINGS AND BRICK WALL  CALLER STATES BEHIND ROMFORD MARKET,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00023E+11,NORTH STREET,21301455,RM1,551142,188984,551150,188950,51.57946009,0.179921109\nNA,14/10/2017 18:51,2017,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS - OWNER ON SCENE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,NEWGATE STREET,22861050,E4,NULL,NULL,539250,193150,NULL,NULL\nNA,14/10/2017 19:30,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON THIRD FLOOR WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,STAPLETON HALL ROAD,21103929,N4,NULL,NULL,531050,187950,NULL,NULL\nNA,16/10/2017 11:38,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN TWO WALLS CALL FROM RSPCA - THEY ARE NOT ABLE TO ATTEND,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Bethnal Green,NULL,BETHNAL GREEN ROAD,22700150,E2,NULL,NULL,534250,182650,NULL,NULL\nNA,16/10/2017 12:49,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP A TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,200130243,WOODSIDE GRANGE ROAD,20047380,N12,525997,192914,525950,192950,51.6209556,-0.181347886\nNA,16/10/2017 17:45,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN FENCES    CALLER IS AT THE BOTTOM OF NORTHWOOD PRIMARY SCHOOL,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011232,Thamesmead East,E09000004,BEXLEY,Erith,1.0002E+11,ASPEN GREEN,20100063,DA18,548677,179163,548650,179150,51.49187046,0.140230387\nNA,17/10/2017 19:06,2017,2017/18,Special Service,1,1,328,328,CAT STUCK BEHIND PANEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CLAREMONT ROAD,21100172,N6,NULL,NULL,529250,187950,NULL,NULL\nNA,18/10/2017 08:35,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WASHING MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,BYCULLAH ROAD,20702553,EN2,NULL,NULL,531950,196750,NULL,NULL\nNA,18/10/2017 17:57,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,NULL,CARLTON AVENUE,21400313,UB3,NULL,NULL,509050,178550,NULL,NULL\nNA,20/10/2017 10:29,2017,2017/18,Special Service,1,1,328,328,\"FOX TRAPPED ON PART OF BUILDING SITE - WHERE THE OLD BHS WAS - CALLER IS A BLUE CROSS VET, SHE WILL\",Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,SELBORNE WALK,22873500,E17,537138,189070,537150,189050,51.58381075,-0.022015577\nNA,20/10/2017 12:10,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN WALL AND BIKE SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,1.00021E+11,LORDSHIP ROAD,20900638,N16,532918,186559,532950,186550,51.56225639,-0.08383687\nNA,21/10/2017 13:32,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,MICHELDEVER ROAD,22001712,SE12,539821,174504,539850,174550,51.45226205,0.01090871\nNA,21/10/2017 14:32,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,CANBERRA DRIVE,20600310,UB5,NULL,NULL,511450,182750,NULL,NULL\nNA,23/10/2017 10:24,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN VOID IN WALL,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,STARMANS CLOSE,19903778,RM9,NULL,NULL,548450,183750,NULL,NULL\nNA,23/10/2017 20:18,2017,2017/18,Special Service,1,1,328,328,CAT POSSIBLY TRAPPED IN CAR ENGINE                 ENTER VIA MAIN GATE   UPPER CAR PARK  BY THE BUS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,12001075,UXBRIDGE ROAD,20602324,UB1,514814,180045,514850,180050,51.50768194,-0.347012919\nNA,24/10/2017 08:42,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE  -  CAT FALLEN ONTO BALCONY,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,NULL,DUNSMURE ROAD,20900349,N16,NULL,NULL,533550,187450,NULL,NULL\nNA,24/10/2017 17:46,2017,2017/18,Special Service,1,1,328,328,SMALL DOG WIUTH PAW CAUGHT IN PLUGHOLE OF BATH,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,ATHERTON ROAD,22207604,E7,NULL,NULL,539850,184850,NULL,NULL\nNA,25/10/2017 12:10,2017,2017/18,Special Service,1,1,328,328,DOGS LEG CAUGHT IN CAR DOOR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,1.00021E+11,SWEETCROFT LANE,21401907,UB10,507407,184463,507450,184450,51.54884735,-0.452352525\nNA,26/10/2017 11:03,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,FRASER ROAD,20100571,DA8,NULL,NULL,550850,178050,NULL,NULL\nNA,28/10/2017 18:12,2017,2017/18,Special Service,1,2,328,656,HORSE WITH LEG TRAPPED IN FENCE,Horse,Person (mobile),Other road vehicle,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10070268594,STATION ROAD,21201079,HA1,515633,188832,515650,188850,51.58649027,-0.332331596\nNA,31/10/2017 16:08,2017,2017/18,Special Service,1,1,328,328,RUNNING CALL TO KITTEN TRAPPED IN ENGINE COMPARTMENT OF SMART CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00024E+11,CAMBRIDGE DRIVE,22003186,SE12,540246,174661,540250,174650,51.4535679,0.017083351\nNA,31/10/2017 19:06,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO RELEASE A CAT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000200,GRANGE,E09000010,ENFIELD,Southgate,207124301,UPLANDS WAY,20703023,N21,531382,195908,531350,195950,51.64663027,-0.102482848\nNA,01/11/2017 17:05,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH  PIDGEON TRAPPED IN NETTING  OPPOSITE TRAIN STATION,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,2.00001E+11,STATION APPROACH,22001929,SE6,537357,173564,537350,173550,51.44441715,-0.024894456\nNA,01/11/2017 21:54,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN ROOF VOID,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,NULL,CLAYTON ROAD,21400418,UB3,NULL,NULL,509550,179650,NULL,NULL\nNA,02/11/2017 12:24,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,ST GEORGES ROAD,21800892,KT2,NULL,NULL,519050,170150,NULL,NULL\nNA,03/11/2017 14:09,2017,2017/18,Special Service,1,3,328,984,HORSE WITH LEG STUCK IN HOLE     CALLER WILL MEET LFB AT ORANGE TREE PH,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,ORANGE TREE HILL,21301469,RM4,551138,192883,551150,192850,51.6144935,0.181542194\nNA,03/11/2017 17:09,2017,2017/18,Special Service,1,1,328,328,CAT  STUCK UNDER CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,1.00022E+11,WANSTEAD PARK ROAD,22303358,IG1,542591,187101,542550,187150,51.56476542,0.055836741\nNA,05/11/2017 13:05,2017,2017/18,Special Service,1,3,328,984,HORSE STUCK IN A DITCH TURN ONTO LEA VALLEY ROAD HEADING TOWARDS NAGS HEAD ROAD  JUST BEFORE ROUNDAB,Horse,Person (land line),Canal/riverbank vegetation,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207172899,LEA VALLEY ROAD,20703459,EN3,536416,195504,536450,195550,51.64180117,-0.02992876\nNA,05/11/2017 13:18,2017,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE - PIGEON ENTANGLED IN STRING AROUND BALLOON WHICH ATTACHED TO A TV AERIEL ON ROO,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,HAWTHORN ROAD,21103396,N8,NULL,NULL,530150,189550,NULL,NULL\nNA,05/11/2017 20:04,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING  - RSPCA REP WILL MEET YOU BY METRO STORE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,528338,176642,528350,176650,51.47419343,-0.153481481\nNA,07/11/2017 17:07,2017,2017/18,Special Service,1,2,328,656,FOX TRAPPED IN GENERATOR HOLE,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LADBROKE GARDENS,21700911,W11,NULL,NULL,524550,180850,NULL,NULL\nNA,08/11/2017 12:52,2017,2017/18,Special Service,1,1,328,328,BIRD TANGLED IN WIRE ON TELEPHONE POST,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000089,DUDDEN HILL,E09000005,BRENT,Willesden,202203988,HIGH ROAD,20200934,NW10,521638,184804,521650,184850,51.54902734,-0.247089527\nNA,08/11/2017 14:09,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011239,Clayhall,E09000026,REDBRIDGE,Ilford,NULL,REDBRIDGE LANE EAST,22303176,IG4,NULL,NULL,542650,188950,NULL,NULL\nNA,10/11/2017 10:59,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING- REQUESTED BY RSPCA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,PALMERS ROAD,20705024,N11,NULL,NULL,529250,192250,NULL,NULL\nNA,10/11/2017 12:24,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED UNDER BATH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,MORTIMER DRIVE,20702851,EN1,NULL,NULL,533150,195550,NULL,NULL\nNA,10/11/2017 18:58,2017,2017/18,Special Service,1,1,328,328,DEER TRAPPED IN RAILINGS OPPOSITE IN MILL HILL FIELD,Deer,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,10091041402,THE RIDGEWAY,20036680,NW7,522218,193060,522250,193050,51.62310049,-0.235856773\nNA,12/11/2017 10:03,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH DOG IN CANAL   LOCATION OPP ASCOT GDNS,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000195,CHASE,E09000010,ENFIELD,Enfield,207185397,TURKEY STREET,20703019,EN1,534500,199100,534550,199150,51.67457719,-0.056218332\nNA,13/11/2017 16:29,2017,2017/18,Special Service,1,1,328,328,PIGEONS LOCKED IN DERELICT FACTORY  NEAR THE WHITE KNIGHT  LAUNDRY SERVICE   THE CALLER WILL MEET YO,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217043053,KENSAL ROAD,21700902,W10,524201,182291,524250,182250,51.52588481,-0.211030368\nNA,14/11/2017 19:21,2017,2017/18,Special Service,1,1,328,328,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011227,Longlands,E09000004,BEXLEY,Sidcup,NULL,LONGLANDS ROAD,20100873,DA15,NULL,NULL,545950,172550,NULL,NULL\nNA,14/11/2017 20:28,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN NEETING  IN COURT YARD,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,SHOOT UP HILL,20202001,NW2,NULL,NULL,524250,185050,NULL,NULL\nNA,18/11/2017 06:17,2017,2017/18,Special Service,1,2,328,656,FOX STUCK IN A GATE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00022E+11,ADDISON GROVE,21500006,W4,521502,179072,521550,179050,51.49754064,-0.251025964\nNA,20/11/2017 14:08,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,10093256859,ST JOHNS ROAD,21500981,TW7,515721,176079,515750,176050,51.47185116,-0.335248742\nNA,21/11/2017 09:34,2017,2017/18,Special Service,1,1,328,328,PIGEONS LOCKED IN DERELICT FACTORY NEAR THE WHITE KNIGHT LAUNDRY SERVICES,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217043053,KENSAL ROAD,21700902,W10,524205,182291,524250,182250,51.52588393,-0.210972738\nNA,21/11/2017 20:45,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN TWO BRICK WALLS,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,SOUTHEND CLOSE,20801378,SE9,543604,174365,543650,174350,51.45006713,0.065259436\nNA,22/11/2017 20:48,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH INJURED SEAL ON THE PONTOON NEAR THE RIVERSIDE PUB CALLER WILL MEET,Unknown - Wild Animal,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091693258,THE HOLLOWS,21500617,TW8,518703,177896,518750,177850,51.48756496,-0.291724368\nNA,24/11/2017 12:37,2017,2017/18,Special Service,1,1,328,328,CAT IN TREE - CALLED X RSPCA,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,1.00023E+11,VESTA ROAD,22001072,SE4,536068,176214,536050,176250,51.46854222,-0.042412075\nNA,24/11/2017 18:52,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN WIRE ON ROOF,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,STATION ROAD,22876700,E4,NULL,NULL,538850,194450,NULL,NULL\nNA,25/11/2017 14:17,2017,2017/18,Special Service,1,1,328,328,CROW STUCK IN NETTING ON ROOF OF HOUSE - CALLED BY PASSERBY - RSPCA BEING CALLED,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,HOLLY ROAD,22403565,TW1,NULL,NULL,516150,173350,NULL,NULL\nNA,25/11/2017 14:46,2017,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,GREENWICH SOUTH STREET,20800688,SE10,NULL,NULL,537950,176850,NULL,NULL\nNA,28/11/2017 17:45,2017,2017/18,Special Service,1,2,328,656,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,NULL,DARTMOUTH ROAD,21400559,HA4,NULL,NULL,510350,186250,NULL,NULL\nNA,28/11/2017 19:22,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE GUINEA PIGS WITH HEADS CAUGHT IN METAL CAGE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Islington,NULL,THORNHILL SQUARE,21606132,N1,NULL,NULL,530850,183950,NULL,NULL\nNA,29/11/2017 07:49,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED HANGING UPSIDE DOWN IN TREE OPP BAKERY,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202105725,DISRAELI ROAD,20201279,NW10,520535,183199,520550,183150,51.53483877,-0.263539181\nNA,29/11/2017 08:06,2017,2017/18,Special Service,1,1,328,328,DEER WITH HEAD STUCK IN THE RAILINGS     NEXT TO BUS STOP DC,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10070702251,HILLDENE AVENUE,21301279,RM3,553165,191892,553150,191850,51.605042,0.210361365\nNA,29/11/2017 16:46,2017,2017/18,Special Service,1,1,328,328,DOG STUCK BETWEEN TWO BRICK WALLS OF BUILDING EXTENSION,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,NULL,CORBETS TEY ROAD,21301755,RM14,NULL,NULL,555950,185950,NULL,NULL\nNA,30/11/2017 11:05,2017,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH DISTRESSED CAT,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Purley,5870073936,GROVE LANE,22602358,CR5,528815,160051,528850,160050,51.3249803,-0.152633767\nNA,30/11/2017 17:47,2017,2017/18,Special Service,1,3,328,984,HORSE FALLEN INTO STREAM NEXT TO GRANT AND STONE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00024E+11,JOEL STREET,21401058,HA5,510481,189876,510450,189850,51.59690707,-0.40633654\nNA,02/12/2017 21:52,2017,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,RAMSGATE STREET,20900838,E8,NULL,NULL,533850,184950,NULL,NULL\nNA,05/12/2017 11:12,2017,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,1.00021E+11,CONISTON ROAD,21104393,N17,534121,191602,534150,191650,51.60728911,-0.06456874\nNA,05/12/2017 17:22,2017,2017/18,Special Service,1,1,328,328,BIRD TRAPPED BEHIND BATH PANEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,East Greenwich,NULL,WRICKLEMARSH ROAD,20801655,SE3,NULL,NULL,541550,176750,NULL,NULL\nNA,06/12/2017 10:20,2017,2017/18,Special Service,1,1,328,328,KITTEN STUCK IN TREE CALLER THINKS THE KITTEN MAY BE STUCK BY HER COLLAR,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,207105187,LADYSMITH ROAD,20702781,EN1,533688,197167,533650,197150,51.65740082,-0.068693069\nNA,08/12/2017 10:11,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED BETWEEN TWO FENCES  BEHIND SELF STORGAE,Dog,Person (mobile),Other bulk storage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Woodside,10003629561,TANNERY CLOSE,20302041,BR3,535620,168127,535650,168150,51.39597568,-0.051951203\nNA,09/12/2017 14:39,2017,2017/18,Special Service,1,1,328,328,Redacted,Unknown - Domestic Animal Or Pet,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6006305,DELLOW STREET,22700403,E1,534962,180818,534950,180850,51.51018076,-0.056565763\nNA,09/12/2017 17:45,2017,2017/18,Special Service,1,1,328,328,CALLERS SMALL PET TRAPPED BETWEEN WALL,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,BURY ROAD,21104308,N22,NULL,NULL,531450,189850,NULL,NULL\nNA,10/12/2017 23:31,2017,2017/18,Special Service,1,1,328,328,CAT STUCK UNDER TERRACE FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000296,MARLBOROUGH,E09000015,HARROW,Harrow,NULL,HEADSTONE DRIVE,21201672,HA3,NULL,NULL,515350,189650,NULL,NULL\nNA,17/12/2017 22:32,2017,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,VANBRUGH HILL,20801534,SE10,NULL,NULL,539450,178150,NULL,NULL\nNA,18/12/2017 14:39,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD TRAPPED UNDER WORKTOP,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,NULL,WOLSEY CLOSE,21801064,KT2,NULL,NULL,519750,169750,NULL,NULL\nNA,18/12/2017 15:01,2017,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED UNDER FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870068797,SHREWSBURY ROAD,22601006,SM5,527404,166829,527450,166850,51.38621331,-0.170448139\nNA,19/12/2017 16:11,2017,2017/18,Special Service,1,1,328,328,PUPPY WITH LEG TRAPPED IN PUSHCHAIR,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011224,East Wickham,E09000004,BEXLEY,Plumstead,NULL,MILTON ROAD,20100984,DA16,NULL,NULL,545550,176950,NULL,NULL\nNA,20/12/2017 09:25,2017,2017/18,Special Service,1,1,328,328,CAT UP TREE REQUEST FROM RSPCA ON SCENE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011236,Bridge,E09000026,REDBRIDGE,Woodford,1.00023E+11,MORETON GARDENS,22305197,IG8,542365,192059,542350,192050,51.60937309,0.054587368\nNA,21/12/2017 13:48,2017,2017/18,Special Service,1,1,328,328,FOX TRAPPED ON SIXTH FLOOR - BELIEVED STUCK ON ROOF BY FIRE ESCAPE,Fox,Person (land line),Hospital,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000641,MARYLEBONE HIGH STREET,E09000033,WESTMINSTER,Euston,1.00023E+11,GREAT PORTLAND STREET,8400603,W1W,528881,182113,528850,182150,51.52323756,-0.143670343\nNA,23/12/2017 15:59,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,YORKSHIRE CLOSE,20901101,N16,533376,186093,533350,186050,51.55796054,-0.077410016\nNA,25/12/2017 16:29,2017,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,THORNTONS FARM AVENUE,19900834,RM7,NULL,NULL,550850,186950,NULL,NULL\nNA,26/12/2017 16:05,2017,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON ROOF  TWO STORYS HIGH,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,NULL,DUGARD WAY,21902068,SE11,NULL,NULL,531750,178650,NULL,NULL\nNA,27/12/2017 11:52,2017,2017/18,Special Service,1,2,328,656,TWO DOGS STUCK DOWN A HOLE PASSED BY THE GENERAL MANAGER OF THE CLUB VIA THAMES VALLEY CONTROL,Dog,Person (land line),Football stadium,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00023E+11,HORTON ROAD,21401018,UB7,507040,180591,507050,180550,51.51411464,-0.458819284\nNA,27/12/2017 15:57,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BETWEEN TWO WINDOWS,Bird,Person (land line),Other entertainment venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,1.00023E+11,MARE STREET,20900658,E8,534981,184998,534950,184950,51.54773872,-0.054691294\nNA,29/12/2017 12:37,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED ON BUOY IN RIVER,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,10070719802,TWICKENHAM BRIDGE,22406563,TW9,517230,174836,517250,174850,51.46036903,-0.31394375\nNA,30/12/2017 12:54,2017,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED BY FEET IN TREE - CALLER WILL MEET BRIGADE,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000429,STOCKWELL,E09000022,LAMBETH,Brixton,1.00022E+11,STOCKWELL GARDENS,21901308,SW9,530647,176455,530650,176450,51.47198497,-0.120322661\nNA,30/12/2017 14:15,2017,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE RSPCA ON SCENE CAT TRAPPED BEHIND INTERNAL WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,VINES AVENUE,20044240,N3,NULL,NULL,525750,190750,NULL,NULL\nNA,31/12/2017 10:40,2017,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6652337,GARNET STREET,22700529,E1W,535073,180591,535050,180550,51.50811435,-0.055054146\nNA,31/12/2017 12:45,2017,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN WASHING MACHINE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,GIPSY HILL,21900606,SE19,NULL,NULL,533350,170950,NULL,NULL\nNA,31/12/2017 19:49,2017,2017/18,Special Service,1,1,328,328,CAT HANGING FROM SECOND FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,FARTHING FIELDS,22700488,E1W,NULL,NULL,534950,180350,NULL,NULL\nNA,03/01/2018 08:31,2018,2017/18,Special Service,1,2,328,656,DOG STUCK IN BADGER SET - CALLER WILL MEET YOU THERE IN THE CAR PARK,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Wallington,5870117711,CROYDON LANE,22602248,SM7,527630,161271,527650,161250,51.33621162,-0.16919495\nNA,04/01/2018 10:37,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN NETTING - SECOND FLOOR    RSPCA IN ATTENDANCE - WILL MEET YOU,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW10,NULL,NULL,526450,177450,NULL,NULL\nNA,04/01/2018 12:38,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA  WITH BIRD TRAPPED  IN KITE WIRE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,10090287587,COLLEGE ROAD,22500543,SE21,533485,173672,533450,173650,51.44631306,-0.08053499\nNA,06/01/2018 01:38,2018,2017/18,Special Service,1,1,328,328,DOG WITH PAW TRAPPED ON GATE,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,10009142244,ARGALL AVENUE,22811400,E10,536189,187820,536150,187850,51.57280803,-0.036188514\nNA,06/01/2018 12:28,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00023E+11,HIGH STREET,21300416,RM11,553552,187252,553550,187250,51.56324733,0.213918127\nNA,07/01/2018 16:56,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN ROOF VOID,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,HOGARTH HILL,20023280,NW11,NULL,NULL,525050,189150,NULL,NULL\nNA,09/01/2018 20:06,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,BOLINGBROKE GROVE,22900545,SW11,NULL,NULL,527450,174350,NULL,NULL\nNA,10/01/2018 14:44,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,NULL,HARMOOD STREET,20400488,NW1,NULL,NULL,528550,184550,NULL,NULL\nNA,10/01/2018 15:16,2018,2017/18,Special Service,1,3,328,984,Redacted,Horse,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207005640,Y109,20706628,EN3,536399,195463,536350,195450,51.64143687,-0.030190262\nNA,11/01/2018 11:46,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,BROXHOLM ROAD,21900257,SE27,NULL,NULL,531350,172350,NULL,NULL\nNA,11/01/2018 20:24,2018,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN GATE  IN PARK,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,1.00023E+11,BONNINGTON SQUARE,21900206,SW8,530671,177854,530650,177850,51.4845519,-0.119460617\nNA,13/01/2018 10:59,2018,2017/18,Special Service,1,1,328,328,SEAGULL WITH LEG TRAPPED IN LAMP POST,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000357,HESTON WEST,E09000018,HOUNSLOW,Hayes,1.00024E+11,HAYES ROAD,21500580,UB2,510621,178773,510650,178750,51.49708476,-0.40780426\nNA,14/01/2018 15:52,2018,2017/18,Special Service,1,1,328,328,ASSISTING RSPCA TO GAIN ENTRY IN THE CEMETRY TO RESCUE A FOX,Fox,Person (mobile),Cemetery,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156532,CEDAR ROAD,20702575,EN2,532063,198153,532050,198150,51.66664499,-0.091798937\nNA,15/01/2018 07:56,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,1.00022E+11,WHITEFOOT LANE,22002033,BR1,539007,172135,539050,172150,51.4311739,-0.001727736\nNA,15/01/2018 17:57,2018,2017/18,Special Service,1,1,328,328,CAT IN WATER  BEHIND THE NORTHERN AND SHELL BUILDING  CALLER SAID CAT HAS MANAGED TO HOLD ON TO SOME,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Millwall,6061546,SELSDON WAY,22701077,E14,537787,179180,537750,179150,51.4947798,-0.016522647\nNA,17/01/2018 17:46,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN TREE IN MIDDLE OF PARK CALLED BY RSPCA,Cat,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300100960,GILLESPIE ROAD,21603259,N4,531409,186182,531450,186150,51.55922221,-0.105734113\nNA,18/01/2018 05:44,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMBRIDGE GARDENS,21700828,W10,NULL,NULL,523950,181350,NULL,NULL\nNA,22/01/2018 08:50,2018,2017/18,Special Service,1,1,328,328,SEAGULL TRAPPED IN STRING IN TREE - O/S ENTRANCE TO PARK,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207002816,HOUNDSFIELD ROAD,20704353,N9,534574,194569,534550,194550,51.63384329,-0.056891969\nNA,22/01/2018 11:36,2018,2017/18,Special Service,NULL,NULL,328,NULL,Redacted,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12140744,THE BROADWAY,20601712,W5,517847,180908,517850,180950,51.51481473,-0.303041811\nNA,22/01/2018 11:59,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207192779,HAYNES DRIVE,20706954,N9,534687,193310,534650,193350,51.62250252,-0.055744587\nNA,23/01/2018 23:49,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN THE WALL BEHIND THE TOILET SYSTEM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,NULL,LEAMINGTON CLOSE,21301347,RM3,NULL,NULL,555050,191950,NULL,NULL\nNA,25/01/2018 19:39,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN FABRIC WAREHOUSE RSPCA LADY IS IN LADY MARGARET ROAD,Cat,Person (mobile),Other retail warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12163511,LADY MARGARET ROAD,20601009,UB1,512808,180434,512850,180450,51.51158188,-0.375781062\nNA,27/01/2018 12:37,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA TO GAIN ACCESS TO BASEMENT TO FREE TRAPPED FOX,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,PERRERS ROAD,21000638,W6,NULL,NULL,522750,178950,NULL,NULL\nNA,28/01/2018 21:35,2018,2017/18,Special Service,1,1,328,328,CAT IN DISTRESS ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,LICHFIELD ROAD,21500694,TW4,NULL,NULL,511150,175550,NULL,NULL\nNA,29/01/2018 10:31,2018,2017/18,Special Service,1,2,328,656,DOG TRAPPED DOWN RABBIT HOLE  CALLER WILL MEET IN CEMETRY SMALL CAR PARK,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,1.00024E+11,OCKENDON ROAD,21301872,RM14,556811,185355,556850,185350,51.54530775,0.260052465\nNA,29/01/2018 12:08,2018,2017/18,Special Service,1,2,328,656,ASSIST WITH TRAPPED PIDGEON SOMEONE FROM LONDON WILDLIFE PROTECTION WILL BE THERE TO MEET YOU,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200107865,STATION ROAD,20040860,HA8,519546,191940,519550,191950,51.61360745,-0.274819754\nNA,30/01/2018 11:23,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED ON THIRD FLOOR - RSPCA ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,MATTOCK LANE,20602459,W5,NULL,NULL,517450,180550,NULL,NULL\nNA,30/01/2018 12:00,2018,2017/18,Special Service,1,1,328,328,CAT  FALLEN AND  STUCK ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,RIVERSDALE ROAD,21603742,N5,NULL,NULL,531950,186050,NULL,NULL\nNA,01/02/2018 18:11,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,EPPING WAY,22834300,E4,NULL,NULL,538050,195250,NULL,NULL\nNA,02/02/2018 17:08,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,1.00023E+11,THAMES STREET,21800938,KT1,517865,169291,517850,169250,51.410401,-0.306652305\nNA,02/02/2018 23:17,2018,2017/18,Special Service,1,1,328,328,LARGE DOG STUCK IN FOX HOLE - CALLED BY RSPCA - OWNER ON SCENE,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207128183,BEECH HILL,20700960,EN4,527352,197936,527350,197950,51.66578195,-0.159959545\nNA,03/02/2018 20:45,2018,2017/18,Special Service,1,1,328,328,CAT STUCK ON ROOF - TANGLED IN IVY - REQUESTED FROM RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,FLORENCE ROAD,21103297,N4,NULL,NULL,530950,187650,NULL,NULL\nNA,04/02/2018 16:15,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN SHED AND FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Wandsworth,121011165,EAST HILL,22901361,SW18,526060,174740,526050,174750,51.45761142,-0.186941575\nNA,06/02/2018 05:37,2018,2017/18,Special Service,1,1,328,328,ASSIST POLICE WITH DEER TRAPPED IN RAILINGS,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554874,192446,554850,192450,51.60955211,0.235263148\nNA,06/02/2018 16:38,2018,2017/18,Special Service,1,1,328,328,TWO PIGEONS TRAPPED IN NETTING ABOVE SHOP SIGN,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Edmonton,207176891,GREEN LANES,20704279,N21,531851,194145,531850,194150,51.63067762,-0.096372779\nNA,07/02/2018 11:30,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,NULL,BEECH HILL,20700960,EN4,NULL,NULL,527050,198050,NULL,NULL\nNA,07/02/2018 13:54,2018,2017/18,Special Service,2,4,328,1312,ASSIST RSPCA WITH RESCUE OF CAT TRAPPED BETWEEN TWO BUILDINGS RSPCA OFFICER MICHAEL - ON SCENE- FRU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,NULL,MILTON AVENUE,22200860,E6,NULL,NULL,542250,184250,NULL,NULL\nNA,07/02/2018 14:34,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,1.00024E+11,SYDENHAM HILL,22005552,SE26,533983,171713,533950,171750,51.4285906,-0.074113748\nNA,10/02/2018 10:35,2018,2017/18,Special Service,1,3,328,984,DOG TRAPPED BETWEEN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,SOUTHWARK PARK ROAD,22502293,SE16,534851,179085,534850,179050,51.49463382,-0.058826605\nNA,10/02/2018 10:50,2018,2017/18,Special Service,1,1,328,328,CAT LOCKED IN BASEMENT,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,GLOUCESTER TERRACE,8400410,W2,NULL,NULL,526350,181050,NULL,NULL\nNA,11/02/2018 11:57,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN GARAGE AT REAR       POLICE AND RSPCA UNABLE TO ASSIST     UNKNOWN WHO OWNS GARAGE,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207147921,PARSONAGE LANE,20702893,EN2,532554,197338,532550,197350,51.65920574,-0.085012195\nNA,11/02/2018 22:58,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BASEMENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,WARWICK ROAD,21700680,SW5,NULL,NULL,525250,178450,NULL,NULL\nNA,15/02/2018 17:54,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND PANELS IN KITCHEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,SUMATRA ROAD,20400253,NW6,NULL,NULL,525250,184850,NULL,NULL\nNA,17/02/2018 10:47,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Shadwell,6029579,STEPNEY HIGH STREET,22701161,E1,535990,181611,535950,181650,51.51706061,-0.041455496\nNA,17/02/2018 12:02,2018,2017/18,Special Service,1,1,328,328,PIGEON CAUGHT IN NETTING IN TREE CALLER STATES IN PARK,Bird,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,10015487120,CLEGG STREET,22700325,E1W,534973,180318,534950,180350,51.50568495,-0.056598666\nNA,17/02/2018 12:51,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BETWEEN FENCE  BLUE GATES SCHOOL,Cat,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6024901,KING DAVID LANE,22700701,E1,535097,180910,535050,180950,51.51097525,-0.054586346\nNA,18/02/2018 23:55,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED BEHIND SINK,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,LAUSANNE ROAD,21103520,N8,NULL,NULL,531450,189350,NULL,NULL\nNA,20/02/2018 15:54,2018,2017/18,Special Service,1,1,328,328,ASSISIT RSPCA WITH PIGEON TRAPPED IN TREE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,NULL,ELLIOTT ROAD,21202201,HA7,NULL,NULL,516350,191950,NULL,NULL\nNA,21/02/2018 10:26,2018,2017/18,Special Service,1,1,328,328,DOG STUCK IN PIPE    FRU REQUESTED FOR LARGE ANIMAL RESCUE AND SNAKE EYE  BEHIND THE PAVILLION   GO,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200154392,TUDOR ROAD,20043380,EN5,525465,196825,525450,196850,51.6562217,-0.187627774\nNA,21/02/2018 14:11,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN AIR VENT,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,PRIORY GROVE,21901122,SW8,NULL,NULL,530050,176450,NULL,NULL\nNA,22/02/2018 15:00,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,TULSE HILL,21901408,SW2,531152,173993,531150,173950,51.44974266,-0.113967977\nNA,23/02/2018 12:19,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN MOTORBIKE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,MINSTER WAY,21300501,RM11,NULL,NULL,554950,187150,NULL,NULL\nNA,24/02/2018 17:31,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN VENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,WEST ROAD,22201663,E15,NULL,NULL,539950,183850,NULL,NULL\nNA,26/02/2018 12:43,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK ON ROOF  RSPCA OFFICER ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,CHURCH LANE,20600382,W5,NULL,NULL,517550,179750,NULL,NULL\nNA,28/02/2018 09:57,2018,2017/18,Special Service,1,1,328,328,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,10025378536,GREAT NORTH ROAD,20019050,EN5,525825,195487,525850,195450,51.64411692,-0.182906984\nNA,28/02/2018 11:04,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA - CAT TRAPPED ON ROOF - FOUR FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,ST. HELIER,E09000024,MERTON,Mitcham,NULL,HATFEILD MEAD,22103156,SM4,NULL,NULL,525150,167650,NULL,NULL\nNA,28/02/2018 13:50,2018,2017/18,Special Service,1,1,328,328,KITTEN TRAPPED IN CAR ENGINE  RSPCA AND ANIMAL TRUST IN ATTENDANCE,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,10070773076,LOVELINCH CLOSE,22000644,SE15,535427,177649,535450,177650,51.4815916,-0.051085024\nNA,28/02/2018 17:00,2018,2017/18,Special Service,1,2,328,656,SMALL ANIMAL RESCUE DOG WITH LEG STUCK IN RECLINER CHAIR MECHANISM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,NULL,BEDDINGTON ROAD,22301843,IG3,NULL,NULL,545650,187950,NULL,NULL\nNA,28/02/2018 17:13,2018,2017/18,Special Service,1,1,328,328,MAN STUCK ON ISLAND OF LAKE-OFF KINGSTON ROAD-CALLER IS WAITING AT J/O LANGDON PARK AND KINGSTON ROA,Unknown - Domestic Animal Or Pet,Person (land line),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,10002255628,LANGDON PARK,22407115,TW11,517176,170383,517150,170350,51.42035823,-0.316193913\nNA,28/02/2018 17:34,2018,2017/18,Special Service,1,1,328,328,DOG IN POND - FALLEN THROUGH ICE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000507,LOXFORD,E09000026,REDBRIDGE,Ilford,10034912879,ALDER WALK,22306313,IG1,544515,184988,544550,184950,51.5452898,0.082708071\nNA,02/03/2018 12:09,2018,2017/18,Special Service,1,1,328,328,DOG INTO FROZEN LAKE,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,10025293518,WARREN POND ROAD,22885250,E4,539752,194644,539750,194650,51.63325507,0.017907253\nNA,02/03/2018 12:21,2018,2017/18,Special Service,1,1,328,328,DOG IN FROZEN LAKE  THIS IS ON WANDSWORTH COMMON,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,121015137,BELLEVUE ROAD,22900453,SW17,527464,173714,527450,173750,51.44807667,-0.167113079\nNA,03/03/2018 11:58,2018,2017/18,Special Service,2,3,328,984,DOG TRAPPED ON ICE DUE TO FROZEN LAKE FRU ACCESS VIA THE FAIRWAY,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200154418,GREAT NORTH ROAD,20019050,EN5,525855,195549,525850,195550,51.64466739,-0.182451363\nNA,03/03/2018 12:13,2018,2017/18,Special Service,1,1,328,328,DOG TRAPPED IN HOLE FRU REQUESTED FROM INCIDENT WITH LARGE ANIMAL RESCUE EQUIPMENT AND SNAKE EYE CAM,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000187,PERIVALE,E09000009,EALING,Ealing,12146468,PERIVALE LANE,20601368,UB6,516250,182789,516250,182750,51.53205099,-0.325425766\nNA,03/03/2018 16:49,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE KITTEN STUCK IN BUILDING VOID,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,NULL,SUMMERLEE GARDENS,20041400,N2,NULL,NULL,527550,189250,NULL,NULL\nNA,04/03/2018 10:52,2018,2017/18,Special Service,1,1,328,328,PIG STUCK IN RAILINGS,Unknown - Heavy Livestock Animal,Person (mobile),Zoo,Non Residential,Other animal assistance,Assist  trapped livestock animal,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,46042407,KING GEORGE AVENUE,22207984,E16,541737,181253,541750,181250,51.51243173,0.041172259\nNA,04/03/2018 17:04,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN BRAMBLES - ELDERLY LADY UNABLE TO REACH CAT,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200037045,ELKANETTE MEWS,20014090,N20,526143,193560,526150,193550,51.62672831,-0.179007588\nNA,05/03/2018 19:52,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN TREE    RSPCA NOT RESPONDED TO CALLER,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207055072,HENLEY ROAD,20704329,N18,533169,192856,533150,192850,51.61878395,-0.077832004\nNA,06/03/2018 10:11,2018,2017/18,Special Service,1,1,328,328,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,WOODWARD ROAD,19900629,RM9,NULL,NULL,547650,184350,NULL,NULL\nNA,07/03/2018 17:22,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN CONDEMED BUILDING,Cat,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000319,ROMFORD TOWN,E09000016,HAVERING,Hornchurch,1.00024E+11,ALBERT ROAD,21300937,RM1,551949,188352,551950,188350,51.57356475,0.191285364\nNA,08/03/2018 07:44,2018,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT ON ROOF,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,BROOK GREEN,21000161,W6,NULL,NULL,523550,179050,NULL,NULL\nNA,09/03/2018 00:30,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,531250,171050,NULL,NULL\nNA,09/03/2018 04:33,2018,2017/18,Special Service,1,2,328,656,CAT IMPALED ON FENCE RSPCA ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000088,DOLLIS HILL,E09000005,BRENT,Willesden,202018795,FLOWERS CLOSE,20201174,NW2,522159,186222,522150,186250,51.56165909,-0.239086812\nNA,09/03/2018 07:16,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED ON BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,NULL,WESTWAY,21000877,W12,NULL,NULL,522450,180950,NULL,NULL\nNA,09/03/2018 09:30,2018,2017/18,Special Service,1,1,328,328,INJURED FOX DUE TO WIRING IN YARD,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,BROMFIELD STREET,21606765,N1,NULL,NULL,531450,183450,NULL,NULL\nNA,09/03/2018 14:30,2018,2017/18,Special Service,2,7,328,2296,LARGE ANIMAL RESCUE STAG TRAPPED IN FENCING,Unknown - Wild Animal,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,BERRYS GREEN ROAD,20301445,TN16,543958,159360,543950,159350,51.31514529,0.064276154\nNA,10/03/2018 23:06,2018,2017/18,Special Service,1,1,328,328,PUPPY WITH HEAD TRAPPED IN STAIR BANNISTERS,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,NULL,KINGSBURY ROAD,20201608,NW9,NULL,NULL,519950,188750,NULL,NULL\nNA,11/03/2018 12:41,2018,2017/18,Special Service,1,1,328,328,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ANLABY ROAD,22403255,TW11,NULL,NULL,515050,171150,NULL,NULL\nNA,12/03/2018 12:33,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE RSPCA ON SCENE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128021348,CHAPEL MILL ROAD,21880152,KT1,518937,168569,518950,168550,51.40368803,-0.29148726\nNA,13/03/2018 09:51,2018,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,1.00021E+11,CAMPSFIELD ROAD,21103048,N8,530359,189634,530350,189650,51.59048735,-0.119592883\nNA,13/03/2018 14:08,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT TRAPPED IN DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000293,HEADSTONE SOUTH,E09000015,HARROW,Harrow,NULL,BEDFORD ROAD,21201384,HA1,NULL,NULL,514350,188450,NULL,NULL\nNA,14/03/2018 15:42,2018,2017/18,Special Service,1,1,328,328,KITTEN STUCK UP TREE.  CALLER ADVISED TO CONTACT LFB BY RSPCA AS IT SOUNDS LIKE ANIMAL IS IN IMMEDIA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207185096,ST PAULS RISE,20705048,N13,531764,191863,531750,191850,51.61019115,-0.098487163\nNA,15/03/2018 12:52,2018,2017/18,Special Service,1,2,328,656,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207185096,ST PAULS RISE,20705048,N13,531767,191866,531750,191850,51.61021741,-0.098442737\nNA,15/03/2018 15:07,2018,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,121052762,ROEHAMPTON LANE,22904379,SW15,522261,174418,522250,174450,51.45554946,-0.24170598\nNA,17/03/2018 18:04,2018,2017/18,Special Service,1,1,328,328,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,CROWN ROAD,20300860,BR6,NULL,NULL,546450,164450,NULL,NULL\nNA,18/03/2018 07:36,2018,2017/18,Special Service,1,1,328,328,SEAGULL WITH FEET TRAPPED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5132408,WREN STREET,20401020,WC1X,530822,182419,530850,182450,51.52554173,-0.115594861\nNA,18/03/2018 12:07,2018,2017/18,Special Service,1,3,328,984,TO ASSIST VET IN LARGE ANIMAL RESCUE  LIFTING A FALLEN HORSE ALL APPLIANCES TO ENTER VIA CLOCKHOUSE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00023E+11,LODGE LANE,21300018,RM5,549336,192759,549350,192750,51.6138595,0.155482586\nNA,18/03/2018 16:59,2018,2017/18,Special Service,1,1,328,328,CAT TRAPPED IN RECLING CHAIR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000470,ST. HELIER,E09000024,MERTON,Sutton,NULL,GARENDON ROAD,22102787,SM4,NULL,NULL,525450,166850,NULL,NULL\nNA,19/03/2018 19:49,2018,2017/18,Special Service,1,1,328,328,RUNNING CALL TO CAT TRAPPED ON SCAFFOLDING,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CARLYLE SQUARE,21700072,SW3,NULL,NULL,527050,177950,NULL,NULL\nNA,20/03/2018 19:15,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY   ACCESS VIA THE RIDGEWAY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,NULL,LAURISTON ROAD,22103807,SW19,NULL,NULL,523850,170650,NULL,NULL\nNA,21/03/2018 16:53,2018,2017/18,Special Service,1,1,328,328,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,NULL,LAURISTON ROAD,22103807,SW19,NULL,NULL,523850,170650,NULL,NULL\nNA,23/03/2018 21:23,2018,2017/18,Special Service,1,1,328,328,CAT STUCK IN ROOF INSIDE,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00024E+11,BRIDGE ROAD,21300721,RM13,552087,182410,552050,182450,51.52013904,0.190710911\nNA,24/03/2018 03:20,2018,2017/18,Special Service,1,1,328,328,DOG WITH HEAD STUCK IN CAGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,STONEWOOD ROAD,20101359,DA8,NULL,NULL,551250,178250,NULL,NULL\nNA,25/03/2018 10:30,2018,2017/18,Special Service,1,1,328,328,CAT ON CHURCH ROOF RSPCA ON SCENE REQUESTING ASSISTANCE   PLEASE GO TO REAR OF CHURCH,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011466,Coulsdon Town,E09000008,CROYDON,Purley,2.00001E+11,BRIGHTON ROAD,20501946,CR5,530132,159892,530150,159850,51.32325215,-0.133801051\nNA,27/03/2018 15:28,2018,2017/18,Special Service,1,1,328,328,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,DERWENT WAY,21300316,RM12,552556,185277,552550,185250,51.54577232,0.198705381\nNA,27/03/2018 17:04,2018,2017/18,Special Service,1,1,328,328,PIGEON TRAPPED IN NETTING ON ROOF,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,ROMFORD ROAD,22201573,E7,NULL,NULL,540450,185050,NULL,NULL\nNA,28/03/2018 13:21,2018,2017/18,Special Service,1,1,328,328,FOX TRAPPED IN FALSE CEILING,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,1.00023E+11,COLDHARBOUR LANE,21400437,UB3,510053,180038,510050,180050,51.50856574,-0.415589348\nNA,28/03/2018 18:47,2018,2017/18,Special Service,1,1,328,328,SMALL ANIMAL RESCUE CAT TRAPPED ON BALCONY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,NULL,DUNSMURE ROAD,20900349,N16,NULL,NULL,533550,187450,NULL,NULL\nNA,31/03/2018 13:28,2018,2017/18,Special Service,1,2,328,656,CAT IN DISTRESS INSIDE LOCKED PREMISES  OWNER ON SCENE,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,CLARENDON WAY,20300926,BR5,NULL,NULL,546250,168550,NULL,NULL\nNA,01/04/2018 11:36,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,INWOOD ROAD,21500639,TW3,514093,175482,514050,175450,51.46681564,-0.35887071\nNA,02/04/2018 16:38,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN WIRED MESH - OPPOSITE THIS ADDRESS - CALLER WILL MEET YOU THERE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178350,NULL,NULL\nNA,03/04/2018 15:51,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN SHOP WHICH HAS BEEN BOARDED UP AND UNABLE TO GET OUT,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300014859,CAMDEN PASSAGE,21604427,N1,531578,183447,531550,183450,51.53460457,-0.104319614\nNA,03/04/2018 17:04,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN THROUGH HOLE IN CUPBOARD NOT MOVING - CALLER UNABLE TO REACH IT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,LYTCHET WAY,20702314,EN3,NULL,NULL,535050,197650,NULL,NULL\nNA,04/04/2018 12:38,2018,2018/19,Special Service,1,1,333,333,TO ASSIST RSPCA WITH BIRD IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,200057019,HANSHAW DRIVE,20020860,HA8,520621,190819,520650,190850,51.60330412,-0.259685863\nNA,07/04/2018 07:16,2018,2018/19,Special Service,1,1,333,333,DOG STUCK UNDER SHED,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,1.00021E+11,GRANBY ROAD,20800663,SE9,542786,176024,542750,176050,51.4651813,0.054164875\nNA,07/04/2018 12:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,12125209,GOLDSMITHS CLOSE,20602060,W3,520954,180396,520950,180350,51.50955743,-0.258462823\nNA,07/04/2018 16:11,2018,2018/19,Special Service,1,1,333,333,BIRD  TRAPPED   IN CHINMEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CALDERON ROAD,22821550,E11,NULL,NULL,538550,186050,NULL,NULL\nNA,08/04/2018 10:36,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,SCARBOROUGH ROAD,22873050,E11,NULL,NULL,538850,187250,NULL,NULL\nNA,08/04/2018 20:35,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,NULL,SPARROWS LANE,20801387,SE9,NULL,NULL,544250,173450,NULL,NULL\nNA,10/04/2018 19:45,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Norbury,NULL,VALLEY ROAD,21901431,SW16,NULL,NULL,530650,171150,NULL,NULL\nNA,10/04/2018 20:31,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DERELICT BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,Greenwich,NULL,BAILDON STREET,22000068,SE8,NULL,NULL,536950,177050,NULL,NULL\nNA,11/04/2018 21:06,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON PILLAR ON SIDE OF THAMES  CALLER WILL WAIT AND DIRECT FROM SHORE,Cat,Person (land line),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,10010229303,EREBUS DRIVE,20802018,SE28,544237,179594,544250,179550,51.49689232,0.076495963\nNA,12/04/2018 18:57,2018,2018/19,Special Service,1,1,333,333,CAT PHYSICALLY TRAPPED BEHIND SHED,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011241,Cranbrook,E09000026,REDBRIDGE,Ilford,NULL,ENDSLEIGH GARDENS,22302825,IG1,NULL,NULL,542850,187150,NULL,NULL\nNA,12/04/2018 19:11,2018,2018/19,Special Service,1,1,333,333,BUDGIE TRAPPED IN TREE - OWNER ON SCENE,Budgie,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000291,HATCH END,E09000015,HARROW,Harrow,1.00021E+11,AUGUSTINE ROAD,21201095,HA3,514147,190456,514150,190450,51.60138956,-0.353243182\nNA,13/04/2018 03:01,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN WINDOW,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,Woodside,NULL,CHURCH ROAD,20500027,SE19,NULL,NULL,533450,170250,NULL,NULL\nNA,13/04/2018 17:22,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH INJURED CAT IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,48103156,LACROSSE WAY,22103690,SW16,529925,169812,529950,169850,51.41245094,-0.133147446\nNA,13/04/2018 22:19,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE CAT FALLEN FROM SECOND FLOOR NOW INJURED TRAPPED BEHIND A WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Dowgate,NULL,CHARTERHOUSE STREET,21604499,EC1M,NULL,NULL,531850,181850,NULL,NULL\nNA,14/04/2018 11:41,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Tottenham,NULL,GREEN LANES,21106375,N8,NULL,NULL,531650,189150,NULL,NULL\nNA,14/04/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON APEX OF SECOND FLOOR WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011462,Addiscombe East,E09000008,CROYDON,Woodside,NULL,GRANT ROAD,20500997,CR0,NULL,NULL,533750,166350,NULL,NULL\nNA,15/04/2018 01:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Dagenham,100015444,HOLDEN CLOSE,19900186,RM8,546784,186299,546750,186250,51.55648413,0.115952318\nNA,15/04/2018 20:44,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN RAILINGS   NEAR THE LODGE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000466,MERTON PARK,E09000024,MERTON,Wimbledon,48106007,MOSTYN ROAD,22104569,SW19,524827,169316,524850,169350,51.40913678,-0.206589233\nNA,16/04/2018 10:16,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG ON THE FORESHORE FIREBOAT HAS BEEN REQUESTED BY COASTGUARD,Dog,Person (land line),River/canal,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,1.00023E+11,HESTER ROAD,22902356,SW11,527064,177290,527050,177250,51.48030428,-0.171582831\nNA,17/04/2018 15:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011102,Faraday,E09000028,SOUTHWARK,Old Kent Road,NULL,INVILLE ROAD,22501332,SE17,NULL,NULL,532850,178050,NULL,NULL\nNA,17/04/2018 22:56,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BETWEEN GARAGE WALL AND FENCING,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207137894,APPLE GROVE,20702482,EN1,533480,196820,533450,196850,51.65433202,-0.071830559\nNA,18/04/2018 15:35,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,BLACKETT STREET,22900508,SW15,NULL,NULL,523550,175550,NULL,NULL\nNA,18/04/2018 16:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,10003972440,GREENWAY CLOSE,21106896,N15,533843,189361,533850,189350,51.58721726,-0.069434855\nNA,18/04/2018 17:55,2018,2018/19,Special Service,1,1,333,333,CATS TRAPPED IN BASEMENT - CALLED X RSPCA ON SCENE,cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,North Walworth,E09000028,SOUTHWARK,Old Kent Road,NULL,DAWES STREET,22500720,SE17,NULL,NULL,532750,178450,NULL,NULL\nNA,19/04/2018 10:41,2018,2018/19,Special Service,1,3,333,999,ASSIST WITH HORSE COLLAPSED RSPCA AND POLICE ON SCENE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011230,Sidcup,E09000004,BEXLEY,Sidcup,10011846821,RECTORY LANE,20101196,DA14,547772,171275,547750,171250,51.4212305,0.123909184\nNA,19/04/2018 11:33,2018,2018/19,Special Service,1,2,333,666,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,10025145133,HALL LANE,21301816,RM14,556265,189094,556250,189050,51.57905228,0.253844851\nNA,19/04/2018 18:09,2018,2018/19,Special Service,NULL,NULL,333,NULL,KITTEN TRAPPED BETWEEN RAFTERS IN ATTIC,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,CLAIRVIEW ROAD,22900957,SW16,NULL,NULL,528950,171450,NULL,NULL\nNA,20/04/2018 15:03,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN AIR VENT,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,CROWNDALE ROAD,20400784,NW1,NULL,NULL,529550,183450,NULL,NULL\nNA,21/04/2018 13:33,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK MEWS,21100430,N10,NULL,NULL,528650,189450,NULL,NULL\nNA,21/04/2018 13:38,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000183,NORTH GREENFORD,E09000009,EALING,Northolt,12065603,ENNISMORE AVENUE,20600631,UB6,515347,184915,515350,184950,51.55134393,-0.337742454\nNA,21/04/2018 14:22,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF - CALLER WILL MEET YOU,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK MEWS,21100430,N10,NULL,NULL,528650,189450,NULL,NULL\nNA,22/04/2018 01:34,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,SIMONS WALK,22200093,E15,NULL,NULL,538750,185050,NULL,NULL\nNA,22/04/2018 13:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,GRAHAM ROAD,20900460,E8,NULL,NULL,534150,184750,NULL,NULL\nNA,22/04/2018 15:47,2018,2018/19,Special Service,1,2,333,666,PARROT TRAPPED IN TREE (OWNER ON SCENE - PARROT WAS WEARING A HARNESS WHICH IS NOW TANGLED IN BRANCH,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,1.00022E+11,GOODMAYES LANE,22302089,IG3,546298,186242,546250,186250,51.55609817,0.108923301\nNA,22/04/2018 16:23,2018,2018/19,Special Service,1,2,333,666,SMALL ANIMAL RESCUE  -  CAT ON A ROOF - AT REQUEST OF RSPCA - RSPCA OFFICER NOT IN ATTENDANCE  - OWN,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,GREENWOOD ROAD,20900470,E8,NULL,NULL,534350,184850,NULL,NULL\nNA,22/04/2018 20:00,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN SURGARD BUILDING ON UXBRIDGE ROAD,Cat,Person (mobile),Warehouse,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000344,YEADING,E09000017,HILLINGDON,Southall,1.00023E+11,UXBRIDGE ROAD,21402032,UB4,511755,180625,511750,180650,51.51350774,-0.390888571\nNA,23/04/2018 15:03,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING THREE FLOORS HIGH,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,ST GEORGES ROAD,21800892,KT2,519012,170173,519050,170150,51.41809689,-0.289865402\nNA,23/04/2018 16:17,2018,2018/19,Special Service,1,1,333,333,CAT IN TREE - REQUESTED X  RSPCA ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000193,BOWES,E09000010,ENFIELD,Southgate,207168964,TELFORD ROAD,20705055,N11,529347,192015,529350,192050,51.61211735,-0.133316033\nNA,25/04/2018 11:28,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,NULL,LOWER STATION ROAD,20100884,DA1,NULL,NULL,551350,174250,NULL,NULL\nNA,25/04/2018 12:12,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,MANBEY GROVE,22201496,E15,NULL,NULL,539250,184850,NULL,NULL\nNA,25/04/2018 16:20,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH TRAPPED KITTENS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,CHURCH ROAD,21500259,TW5,NULL,NULL,513050,177350,NULL,NULL\nNA,25/04/2018 16:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,BEECH AVENUE,20600143,W3,NULL,NULL,521250,180150,NULL,NULL\nNA,25/04/2018 20:31,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,RODNEY PLACE,22105454,SW19,NULL,NULL,526150,169950,NULL,NULL\nNA,25/04/2018 23:07,2018,2018/19,Special Service,1,1,333,333,BIRD IMPAILED ON RAILING  NEAR PIZZA ON DEMAND,Bird,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,10022943417,WEST GREEN ROAD,21104071,N15,532091,189225,532050,189250,51.58640857,-0.094759558\nNA,26/04/2018 06:33,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN WIRE MESHINGS OF FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,PRINCE OF WALES DRIVE,22903956,SW11,528075,176760,528050,176750,51.47532154,-0.157214752\nNA,26/04/2018 08:17,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN BARBED WIRE UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,BLUEHOUSE ROAD,22816700,E4,539434,193549,539450,193550,51.62349497,0.012893627\nNA,26/04/2018 22:30,2018,2018/19,Special Service,1,1,333,333,CAT    POSSIBLY TRAPPED ON RAILWAY EMBANKMENT,Cat,Person (land line),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000311,HACTON,E09000016,HAVERING,Hornchurch,1.00021E+11,CRYSTAL AVENUE,21300308,RM12,554121,186280,554150,186250,51.55436736,0.221699354\nNA,27/04/2018 15:06,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF - POSSIBLY INJURED  CALLED BY RSPCA UNABLE TO ATTEND CAT HAS BEEN STRANDED FOR DAY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,LEA BRIDGE ROAD,20900602,E5,NULL,NULL,535550,186550,NULL,NULL\nNA,29/04/2018 10:50,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173850,NULL,NULL\nNA,30/04/2018 01:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND PIPE IN BATHROOM - RSPCA WILL NOT ATTEND,Cat,Person (land line),Other Dwelling,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000181,LADY MARGARET,E09000009,EALING,Southall,NULL,EVELYN GROVE,20600639,UB1,NULL,NULL,512750,181250,NULL,NULL\nNA,01/05/2018 10:24,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN FENCE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,100054514,DOWNING ROAD,19900660,RM9,548827,184194,548850,184150,51.53704196,0.144523551\nNA,01/05/2018 18:15,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN FENCE  RSPCA EN ROUTE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Fulham,34024957,HAWKSMOOR STREET,21000424,W6,523884,177632,523850,177650,51.48408818,-0.217225429\nNA,02/05/2018 10:04,2018,2018/19,Special Service,1,1,333,333,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,GRESHAM ROAD,21900641,SW9,NULL,NULL,531450,175550,NULL,NULL\nNA,03/05/2018 10:12,2018,2018/19,Special Service,1,1,333,333,FOX WITH HEAD TRAPPED IN RAILINGS     RSPCA ON SCENE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011115,St. Giles,E09000028,SOUTHWARK,Peckham,2.00003E+11,DATCHELOR PLACE,22500716,SE5,532835,176737,532850,176750,51.47401309,-0.088730524\nNA,04/05/2018 00:24,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED ON TELEPHONE WIRE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,GLYN ROAD,20900450,E5,NULL,NULL,535750,185950,NULL,NULL\nNA,04/05/2018 01:16,2018,2018/19,Special Service,1,1,333,333,FERRET ON ROOF/STUCK IN GUTTERING AREA  OWNER ON SCENE,Ferret,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,LAMB CLOSE,20604333,UB5,NULL,NULL,512250,182750,NULL,NULL\nNA,04/05/2018 10:02,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,Greenwich,NULL,REGINALD ROAD,22000852,SE8,NULL,NULL,537350,177050,NULL,NULL\nNA,04/05/2018 20:49,2018,2018/19,Special Service,1,1,333,333,PIDGEONS TRAPPED IN NETTING BY RAIL BRIDGE NEAR THE TRAIN STATION,Pigeon,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,10025189490,THURLOW PARK ROAD,21901381,SE21,531882,173005,531850,173050,51.44069489,-0.103825633\nNA,05/05/2018 14:12,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED - CALLED X RSPCA,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011112,Rotherhithe,E09000028,SOUTHWARK,Dockhead,10015486241,CULLING ROAD,22500683,SE16,535095,179460,535050,179450,51.49795401,-0.055165546\nNA,05/05/2018 16:34,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE    KITTEN UNDER PLAY SLIDE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05011238,Churchfields,E09000026,REDBRIDGE,Woodford,10034918704,BRAMLEY CLOSE,22306218,IG8,541295,191046,541250,191050,51.60054588,0.038746077\nNA,05/05/2018 19:42,2018,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED IN WIRES BEHIND WASHING MACHINE,Squirrel,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011226,Falconwood & Welling,E09000004,BEXLEY,Bexley,NULL,PARK VIEW ROAD,20101104,DA16,NULL,NULL,547650,175550,NULL,NULL\nNA,05/05/2018 21:34,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED UNDERNEATH FLOORBOARDS,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,PRINCE OF WALES DRIVE,22903956,SW11,NULL,NULL,528050,176750,NULL,NULL\nNA,06/05/2018 17:07,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON ROOF OF ROW OF TERRACED HOUSE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,PIGOTT STREET,22700942,E14,NULL,NULL,537050,181150,NULL,NULL\nNA,06/05/2018 21:09,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN HOLE  BETWEEN TWO PROPERTY GARDENS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,48002857,ARTHUR ROAD,22100225,SW19,524977,172071,524950,172050,51.43387058,-0.203451568\nNA,08/05/2018 12:19,2018,2018/19,Special Service,1,2,333,666,DOG TRAPPED UNDER FOUNDATIONS OF A GARAGE,Dog,Person (mobile),Private Summer house,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200037865,ELMSHURST CRESCENT,20014500,N2,526500,189346,526550,189350,51.58878418,-0.17536413\nNA,08/05/2018 12:39,2018,2018/19,Special Service,1,1,333,333,INJURED CAT FALLEN ONTO FLAT ROOF   TWO FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,ST JOHN STREET,21606010,EC1V,NULL,NULL,531650,182550,NULL,NULL\nNA,09/05/2018 11:58,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN BASEMENT AREA -  THIS IS FLATS NEXT TO SCOPE HEAD OFFICE  RSPCA INS,Fox,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,5300060249,MARKET ROAD,21605388,N7,530511,184798,530550,184750,51.5469932,-0.1191937\nNA,09/05/2018 20:38,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009389,BROMPTON & HANS TOWN,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,EATON PLACE,21700173,SW1X,NULL,NULL,528150,178950,NULL,NULL\nNA,10/05/2018 11:36,2018,2018/19,Special Service,1,1,333,333,TO ASSIST RSCPA WITH DUCKLING IN DRAIN OUTSIDE TENNIS CLUB AT TRAFFIC LIGHTS,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011254,Wanstead Park,E09000026,REDBRIDGE,Leytonstone,10025578024,BLAKE HALL ROAD,22302635,E11,540439,187085,540450,187050,51.56516324,0.024808941\nNA,10/05/2018 12:36,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO TEN PIGEONS STUCK IN NETTING,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,10025189490,THURLOW PARK ROAD,21901381,SE21,531880,173001,531850,173050,51.44066593,-0.103853728\nNA,10/05/2018 15:00,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE -    CAT STUCK BETWEEN WALLS INSIDE PROPERTY,Cat,Person (land line),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,LEWISHAM HIGH STREET,22004177,SE13,538299,175614,538250,175650,51.46261682,-0.01054505\nNA,10/05/2018 21:58,2018,2018/19,Special Service,1,2,333,666,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LONSDALE ROAD,21700934,W11,NULL,NULL,524950,181050,NULL,NULL\nNA,11/05/2018 06:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN WALL AND GATE,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6068907,CELANDINE CLOSE,22700283,E14,537248,181699,537250,181650,51.51755161,-0.02329694\nNA,11/05/2018 12:04,2018,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED BETWEEN WALL AND POSTBOX,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,2E+11,WANDSWORTH ROAD,21901460,SW8,529933,176855,529950,176850,51.47575104,-0.130436566\nNA,11/05/2018 18:12,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN UNDERGROWTH CALLER WILL MEET YOU IN CAR PARK,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000305,WEST HARROW,E09000015,HARROW,Harrow,1.00021E+11,FORD CLOSE,21202000,HA1,514759,187876,514750,187850,51.57807674,-0.34525021\nNA,12/05/2018 09:19,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,NULL,WADES HILL,20705059,N21,NULL,NULL,531450,194850,NULL,NULL\nNA,12/05/2018 12:12,2018,2018/19,Special Service,1,1,333,333,BIRD  TRAPPED  IN TREE ACCESS VIA GRAFTON WAY JUNC WHITFIELD STREET AL REQUESTED FROM INCIDENT,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5095417,WHITFIELD STREET,20400941,W1T,529219,182180,529250,182150,51.5237681,-0.138762126\nNA,12/05/2018 14:07,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH RESCUING CAT FROM TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,1.00022E+11,KING ALFRED AVENUE,22001604,SE6,537429,171770,537450,171750,51.42828026,-0.024551454\nNA,12/05/2018 16:13,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,SHOOT UP HILL,20202001,NW2,NULL,NULL,524250,185050,NULL,NULL\nNA,13/05/2018 10:48,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX ON ROOF      NEAR JUNC GREAT GUILDFORD STREET,Fox,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,10090287532,UNION STREET,22502587,SE1,532224,180061,532250,180050,51.50402939,-0.096275591\nNA,13/05/2018 11:38,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER CAR     -   WRAPPED HIMSELF AROUND A CAR PART,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870083109,WOLSELEY ROAD,22601161,CR4,528231,166630,528250,166650,51.38424668,-0.158638296\nNA,13/05/2018 13:18,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000610,BALHAM,E09000032,WANDSWORTH,Tooting,NULL,OLD DEVONSHIRE ROAD,22903671,SW12,NULL,NULL,528850,173550,NULL,NULL\nNA,13/05/2018 15:09,2018,2018/19,Special Service,1,1,333,333,FOX CUB WITH HEAD  STUCK BETWEEN FENCE AND DECKING     RSPCA  INSPECTOR IS ON SCENE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,CHAMBERS LANE,20201261,NW10,NULL,NULL,523050,184150,NULL,NULL\nNA,13/05/2018 17:45,2018,2018/19,Special Service,1,1,333,333,PIGEON CAUGHT IN NETTING,Bird,Person (mobile),Unlicensed House in Multiple Occupation - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000175,EAST ACTON,E09000009,EALING,Acton,NULL,BEECH AVENUE,20600143,W3,NULL,NULL,521250,180150,NULL,NULL\nNA,13/05/2018 18:42,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WINDOW FRAME,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011225,Erith,E09000004,BEXLEY,Erith,NULL,SOUTH ROAD,20101317,DA8,NULL,NULL,551550,177350,NULL,NULL\nNA,14/05/2018 13:37,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,10003972055,MARRIOTT ROAD,21106379,N10,527941,190654,527950,190650,51.60021034,-0.154093253\nNA,14/05/2018 18:57,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN NEIGHBOURS GARAGE NEIGHBOURS ARE AWAY,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870049088,THICKET ROAD,22602696,SM1,526362,164906,526350,164950,51.3691636,-0.1860978\nNA,14/05/2018 20:33,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011105,Newington,E09000028,SOUTHWARK,Lambeth,NULL,ROYAL ROAD,22502147,SE17,NULL,NULL,531750,177650,NULL,NULL\nNA,15/05/2018 12:19,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Railway building - other,Non Residential,Other animal assistance,Assist trapped wild animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,6647509,HOLLYBUSH PLACE,22700647,E2,534924,182738,534950,182750,51.52744564,-0.056374268\nNA,15/05/2018 14:26,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,THEOBALD'S ROAD,20499120,WC1X,NULL,NULL,530750,181950,NULL,NULL\nNA,15/05/2018 15:47,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - LONDON WILDLIFE PROTECTION VOLUNTEER IN ATTENDANCE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,RADNOR STREET,21605754,EC1V,NULL,NULL,532350,182550,NULL,NULL\nNA,15/05/2018 18:15,2018,2018/19,Special Service,1,1,333,333,BIRDS TRAPPED IN GATE POST    BIRD RESCUE PERSONNEL AT LOCATION,Bird,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,10023304448,BOURNE ROAD,20100173,DA5,550361,174342,550350,174350,51.4481162,0.162432291\nNA,15/05/2018 22:03,2018,2018/19,Special Service,1,1,333,333,FOX CUB TRAPPED IN BASEMENT AREA,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,AINSWORTH ROAD,20900040,E9,NULL,NULL,535250,184050,NULL,NULL\nNA,16/05/2018 11:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000052,GARDEN SUBURB,E09000003,BARNET,Hendon,200099509,ROTHERWICK ROAD,20037260,NW11,525407,187743,525450,187750,51.57462265,-0.191698911\nNA,16/05/2018 20:28,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN RAILINGS,Deer,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05011238,Churchfields,E09000026,REDBRIDGE,Woodford,1.00023E+11,BROOMHILL WALK,22305623,IG8,540113,191434,540150,191450,51.60432575,0.021847963\nNA,17/05/2018 10:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000367,BUNHILL,E09000019,ISLINGTON,Islington,NULL,PERCIVAL STREET,21605667,EC1V,NULL,NULL,531850,182450,NULL,NULL\nNA,17/05/2018 12:10,2018,2018/19,Special Service,1,1,333,333,BIRD TANGLED IN TREE    CALLED BY RSPCA,Bird,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,10025376616,WHITECHAPEL ROAD,22701339,E1,534803,181835,534850,181850,51.51936263,-0.058459825\nNA,17/05/2018 16:09,2018,2018/19,Special Service,1,3,333,999,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,6011524,WHITECHAPEL ROAD,22701339,E1,534826,181838,534850,181850,51.51938599,-0.058128869\nNA,18/05/2018 10:42,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,CROMFORD ROAD,22901086,SW18,NULL,NULL,524950,174750,NULL,NULL\nNA,19/05/2018 11:26,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN CAR ENGINE,Bird,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200199967,DOWD CLOSE,20012957,N11,528252,193839,528250,193850,51.6287647,-0.14844131\nNA,19/05/2018 12:42,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,NULL,DANBY STREET,22500699,SE15,NULL,NULL,533850,175950,NULL,NULL\nNA,20/05/2018 19:44,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011112,Rotherhithe,E09000028,SOUTHWARK,Deptford,2.00003E+11,ROTHERHITHE NEW ROAD,22502114,SE16,535640,178754,535650,178750,51.49147877,-0.047582615\nNA,20/05/2018 21:34,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - KITTEN FALLEN TWO FLOORS TO FIRST FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011102,Faraday,E09000028,SOUTHWARK,Old Kent Road,NULL,QUEENS ROW,22502034,SE17,NULL,NULL,532550,177950,NULL,NULL\nNA,22/05/2018 13:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,CHISWICK HIGH ROAD,21502481,W4,NULL,NULL,520950,178550,NULL,NULL\nNA,22/05/2018 18:21,2018,2018/19,Special Service,1,1,333,333,PIGEON STUCK IN CHIMNEY RSPCA INFORMED CALLER TO RING THE FIRE BRIGADE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,LANCASTER PARK,22403605,TW10,NULL,NULL,518150,174550,NULL,NULL\nNA,23/05/2018 11:03,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED WITHIN CAR      CALLER BELIEVES SHE HAS BEEN DRIVING WITH THE CAT TRAPPED WITHIN THE MEC,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011219,Bexleyheath,E09000004,BEXLEY,Bexley,1.0002E+11,MARTENS CLOSE,20100937,DA7,550069,175153,550050,175150,51.45547287,0.158572209\nNA,23/05/2018 11:05,2018,2018/19,Special Service,1,1,333,333,DOG STUCK BETWEEN WALL AND SUMMER HOUSE,Dog,Person (mobile),Private Summer house,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Lee Green,1.00021E+11,KIMBELL PLACE,20801711,SE3,541245,175229,541250,175250,51.4584306,0.0316862\nNA,23/05/2018 16:48,2018,2018/19,Special Service,1,1,333,333,BIRD OF PREY TRAPPED IN PIGEON NETTING   NR COLUMBION ROASTERIE AND FLAT IRON SQUARE  RSPCA IN ATTEN,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011095,Borough & Bankside,E09000028,SOUTHWARK,Dowgate,2.00003E+11,UNION STREET,22502587,SE1,532332,180050,532350,180050,51.50390457,-0.094716239\nNA,25/05/2018 18:05,2018,2018/19,Special Service,1,1,333,333,ANIMAL / BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000561,NONSUCH,E09000029,SUTTON,Sutton,NULL,THE SPINNEY,22603399,SM3,NULL,NULL,523350,164650,NULL,NULL\nNA,25/05/2018 20:00,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RIVER RSPCA ON WAY,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,TRAVERS CLOSE,22882325,E17,535763,190830,535750,190850,51.59996384,-0.041160214\nNA,26/05/2018 12:46,2018,2018/19,Special Service,1,1,333,333,CAT POSSIBLY TRAPPED IN ROOF VOID CALLER WILL MEET OUTSIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,HIGH STREET,20602317,W3,NULL,NULL,519750,180250,NULL,NULL\nNA,28/05/2018 08:12,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,10025167195,CLAPHAM COMMON SOUTH SIDE,21900340,SW4,528967,174636,528950,174650,51.45602509,-0.145154301\nNA,28/05/2018 12:21,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Chelsea,NULL,EATON PLACE,8400819,SW1X,NULL,NULL,528350,179350,NULL,NULL\nNA,28/05/2018 14:43,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200026500,COMPTON CLOSE,20009970,NW11,523912,186339,523950,186350,51.56233215,-0.213765723\nNA,28/05/2018 15:23,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UP GARDEN TREE SINCE SATURDAY AND IS IN DISTRESS - RSPCA UNABLE TO ATTEND AT PRESENT,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,NULL,STRATHYRE AVENUE,20501439,SW16,NULL,NULL,531250,168750,NULL,NULL\nNA,28/05/2018 21:12,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,NULL,NORTH STREET,22602499,SM5,NULL,NULL,527850,164850,NULL,NULL\nNA,29/05/2018 06:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Finchley,NULL,CROMWELL ROAD,20011240,N10,NULL,NULL,528450,191350,NULL,NULL\nNA,29/05/2018 11:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,SHEEN LANE,22405019,SW14,NULL,NULL,520550,175450,NULL,NULL\nNA,29/05/2018 14:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Battersea,NULL,NORTH SIDE WANDSWORTH COMMON,22903584,SW18,NULL,NULL,526750,174850,NULL,NULL\nNA,30/05/2018 00:54,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM BALCONY ONTO LEDGE POSSIBLY INJURED - OWNER WILL MEET BRIGADE AT ASDA CAR PARK SIDE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,HIGH STREET,21500609,TW13,NULL,NULL,510450,173050,NULL,NULL\nNA,30/05/2018 14:42,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RAILINGS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011484,South Croydon,E09000008,CROYDON,Croydon,NULL,REDSAN CLOSE,20503113,CR2,NULL,NULL,532550,163150,NULL,NULL\nNA,30/05/2018 18:28,2018,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Woodside,NULL,AVIEMORE WAY,20301754,BR3,NULL,NULL,536250,167550,NULL,NULL\nNA,30/05/2018 20:19,2018,2018/19,Special Service,1,3,333,999,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,MERRYWEATHER PLACE,20803082,SE10,NULL,NULL,537550,177150,NULL,NULL\nNA,31/05/2018 01:06,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEARSTEAD RISE,22001209,SE4,NULL,NULL,536650,174450,NULL,NULL\nNA,31/05/2018 21:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CEILING CAVITY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011486,Thornton Heath,E09000008,CROYDON,Norbury,NULL,MERSHAM ROAD,20501196,CR7,NULL,NULL,532950,169250,NULL,NULL\nNA,01/06/2018 16:15,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG WITH LEG TRAPPED IN CAGE,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010210198,LODGE HILL,20800933,SE2,547142,177759,547150,177750,51.47965683,0.117559186\nNA,01/06/2018 18:34,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,MYRTLEDENE ROAD,20801069,SE2,NULL,NULL,546150,178450,NULL,NULL\nNA,02/06/2018 13:46,2018,2018/19,Special Service,1,2,333,666,BAT TRAPPED BEHIND MESH    RSPCA ON SCENE,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,10025181173,CRYSTAL PALACE PARK ROAD,20302172,SE26,534343,171232,534350,171250,51.4241845,-0.06910657\nNA,02/06/2018 16:52,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,CLANRICARDE GARDENS,21700709,W2,NULL,NULL,525450,180550,NULL,NULL\nNA,03/06/2018 18:31,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536750,189650,NULL,NULL\nNA,03/06/2018 23:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF OF TYRED OUT   CAT HAS FALLEN OUT OF A WINDOW ONTO THE FLAT ROOF BELOW AND IS IN,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,1.00023E+11,ENDWELL ROAD,22000380,SE4,536446,175851,536450,175850,51.465189,-0.037113\nNA,04/06/2018 12:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Lizard,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Eltham,1.00021E+11,ACADEMY PLACE,20800038,SE18,543116,176725,543150,176750,51.47140204,0.059210105\nNA,05/06/2018 10:52,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FIREPLACE   RSPCA ON SCENE,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525850,182150,NULL,NULL\nNA,05/06/2018 16:01,2018,2018/19,Special Service,1,1,333,333,NULL,Unknown - Wild Animal,Not known,Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207016224,LEA ROAD,4810028,EN9,537501,199679,537550,199650,51.67905963,-0.012614195\nNA,06/06/2018 00:55,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED UNDER BED,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,TRIM STREET,22006052,SE14,NULL,NULL,536750,177550,NULL,NULL\nNA,06/06/2018 06:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO CONSERVATORY WALLS AND FENCE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05011224,East Wickham,E09000004,BEXLEY,Plumstead,NULL,ANTHONY ROAD,20100040,DA16,NULL,NULL,546450,176550,NULL,NULL\nNA,06/06/2018 09:00,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000646,VINCENT SQUARE,E09000033,WESTMINSTER,Lambeth,NULL,PONSONBY TERRACE,8400188,SW1P,NULL,NULL,529950,178350,NULL,NULL\nNA,06/06/2018 12:56,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000337,PINKWELL,E09000017,HILLINGDON,Hayes,1.00023E+11,WALTHAM AVENUE,21402071,UB3,508975,179272,508950,179250,51.50189701,-0.431351136\nNA,06/06/2018 17:40,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,MAPLE AVENUE,21201945,HA2,NULL,NULL,513850,186450,NULL,NULL\nNA,06/06/2018 20:15,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,OAKLEY GARDENS,21103699,N8,NULL,NULL,530650,188850,NULL,NULL\nNA,06/06/2018 20:53,2018,2018/19,Special Service,1,1,333,333,FOX WITH HEAD TRAPPED BETWEEN TWO WALLS,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011233,West Heath,E09000004,BEXLEY,Bexley,1.0002E+11,CRANBROOK ROAD,20100378,DA7,549008,176878,549050,176850,51.4712521,0.1440309\nNA,07/06/2018 13:37,2018,2018/19,Special Service,1,1,333,333,DOG HEAD STUCK IN IRON GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011472,Norbury & Pollards Hill,E09000008,CROYDON,Norbury,1.00021E+11,CROINDENE ROAD,20500830,SW16,530452,169578,530450,169550,51.41022704,-0.125651292\nNA,08/06/2018 18:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BETWEEN METAL BARS IN PARK ACCESS VIA NARROW STREET,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009326,LIMEHOUSE,E09000030,TOWER HAMLETS,Poplar,6647100,BRIGHTLINGSEA PLACE,22700197,E14,536569,180850,536550,180850,51.51008915,-0.033405447\nNA,09/06/2018 07:45,2018,2018/19,Special Service,1,1,333,333,PIGEONS TRAPPED IN NETTING   THIS IS THE BUILDING OPP TRAVEL LODGE ON SECOND FLOOR . CALLER WILL MEE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,1.00024E+11,CLARENCE STREET,21800255,KT1,518299,169374,518250,169350,51.411057,-0.300387\nNA,09/06/2018 08:58,2018,2018/19,Special Service,1,1,333,333,KITTEN CAUGHT UNDER CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011104,London Bridge & West Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,GRANGE WALK,22501108,SE1,533346,179294,533350,179250,51.49686974,-0.08041351\nNA,10/06/2018 15:52,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN DRAINAGE PIPE - CALLER WILL FLAG YOU DOWN,Cat,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,1.00024E+11,MARYLEE WAY,21900931,SE11,530972,178543,530950,178550,51.49068088,-0.114862387\nNA,10/06/2018 22:31,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000436,VASSALL,E09000022,LAMBETH,Brixton,NULL,FOUNTAIN PLACE,21900573,SW9,NULL,NULL,531550,176650,NULL,NULL\nNA,11/06/2018 11:05,2018,2018/19,Special Service,1,2,333,666,SEAGULL TRAPPED ON METAL POLE AT SIDE OF BUILDING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,HIGH STREET,21500610,TW3,513684,175618,513650,175650,51.46812678,-0.364709766\nNA,11/06/2018 12:24,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN TREE  RSPCA IN ATTENDANCE,Cat,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46055112,OLIVE ROAD,22200917,E13,541465,182839,541450,182850,51.52675359,0.037895556\nNA,11/06/2018 14:12,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED UNDER SHED,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,1.0002E+11,ARCHER ROAD,20301001,BR5,546110,167512,546150,167550,51.38785448,0.09848517\nNA,12/06/2018 12:54,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY - RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000291,HATCH END,E09000015,HARROW,Harrow,NULL,ROWLANDS AVENUE,21201156,HA5,NULL,NULL,513550,191650,NULL,NULL\nNA,12/06/2018 16:51,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER HOME,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,CROUCH AVENUE,19900461,IG11,NULL,NULL,546750,183150,NULL,NULL\nNA,14/06/2018 14:30,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN RABBIT HOLE - CALLER WILL MEET YOU,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000144,SWISS COTTAGE,E09000007,CAMDEN,Paddington,5151233,RUDGWICK TERRACE,20499145,NW8,527272,183709,527250,183750,51.53794915,-0.166259734\nNA,14/06/2018 17:51,2018,2018/19,Special Service,1,1,333,333,DOGS LOCKED IN VAN,Dog,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,10010437636,ELIA STREET,21604792,N1,531807,183115,531850,183150,51.53157522,-0.101139595\nNA,14/06/2018 20:46,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY FRONT DOOR ON SIDE OF HOUSE IN INGLEBERT STREET,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,MYDDELTON SQUARE,21605497,EC1R,NULL,NULL,531250,182950,NULL,NULL\nNA,15/06/2018 09:16,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN FIRE PLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WESTMINSTER ROAD,22601134,SM1,NULL,NULL,526650,165850,NULL,NULL\nNA,15/06/2018 15:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN FENCE AND SHED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000566,SUTTON SOUTH,E09000029,SUTTON,Sutton,NULL,LESLIE GARDENS,22602429,SM2,NULL,NULL,525550,163450,NULL,NULL\nNA,15/06/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDERNEATH CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000370,CLERKENWELL,E09000019,ISLINGTON,Islington,NULL,AMWELL STREET,21604116,EC1R,NULL,NULL,531150,182950,NULL,NULL\nNA,16/06/2018 01:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Battersea,NULL,LONGBEACH ROAD,22903012,SW11,NULL,NULL,528050,175450,NULL,NULL\nNA,16/06/2018 11:29,2018,2018/19,Special Service,1,1,333,333,DOG ON ROOF OF TWO STOREY HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,NULL,MALDEN ROAD,21800637,KT3,NULL,NULL,521650,167150,NULL,NULL\nNA,17/06/2018 12:42,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN FENCE,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200001187,ABBEY VIEW,20000020,NW7,521798,193238,521750,193250,51.62479274,-0.241853699\nNA,17/06/2018 17:03,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN BRAMBLES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CROWNFIELD ROAD,22830500,E15,NULL,NULL,538850,185550,NULL,NULL\nNA,18/06/2018 06:22,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA  WITH DEER TRAPPED IN FENCING  - INSPECTOR ON SCENE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,207142817,RESERVOIR ROAD,20701193,N14,529149,195801,529150,195850,51.64618857,-0.134767832\nNA,19/06/2018 09:23,2018,2018/19,Special Service,1,1,333,333,POSSIBLE ANIMAL TRAPPED UNDER BATH,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,ARUNDEL ROAD,21500046,TW4,NULL,NULL,511250,175850,NULL,NULL\nNA,19/06/2018 15:17,2018,2018/19,Special Service,1,2,333,666,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011468,Fairfield,E09000008,CROYDON,Croydon,1.00021E+11,FRITH ROAD,20500961,CR0,532143,165615,532150,165650,51.37422702,-0.102830458\nNA,20/06/2018 18:08,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE CAT HAS BEEN TRAPPED A COUPLE OF DAYS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CAMBRIDGE GARDENS,21700828,W10,NULL,NULL,524350,181450,NULL,NULL\nNA,21/06/2018 10:53,2018,2018/19,Special Service,1,1,333,333,DOG IN DISTRESS LOCKED IN CAR - POLICE WILL TRY TO GET DOG UNIT TO ATTEND ASAP,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,100057493,KING EDWARDS ROAD,19900704,IG11,544955,183267,544950,183250,51.52972065,0.088353431\nNA,21/06/2018 12:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FENCE NEAR RAILWAY TRACK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,NULL,LAKENHEATH,20701119,N14,NULL,NULL,529550,195650,NULL,NULL\nNA,22/06/2018 08:41,2018,2018/19,Special Service,1,3,333,999,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,NULL,HAWTHORNE GROVE,20200929,NW9,NULL,NULL,520350,187950,NULL,NULL\nNA,22/06/2018 16:14,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER PAVING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,NULL,MILTON GARDENS ESTATE,20950139,N16,NULL,NULL,533150,185550,NULL,NULL\nNA,22/06/2018 19:01,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN UNDERGROWTH    NEAR THE POND NEXT TO THE CHILDRENS PLAYGROUND      ACCESS VIA TULSE H,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,West Norwood,10025181107,BROCKWELL PARK INTERNAL ROADS,21901542,SW2,531345,174123,531350,174150,51.45086659,-0.111132912\nNA,23/06/2018 19:36,2018,2018/19,Special Service,1,2,333,666,KITTEN TRAPPED IN LOCKED CAR  WHITE PRIUS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12033332,INVICTA GROVE,20600936,UB5,512529,182732,512550,182750,51.53229738,-0.379056388\nNA,24/06/2018 15:58,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - MUNTJAC DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200199927,FRITH LANE,20016860,NW7,524599,191642,524550,191650,51.60984119,-0.201976949\nNA,25/06/2018 19:28,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN INTO LOWER GROUND FLOOR    INJURED   OWNER ON SCENE,Cat,Person (mobile),Retirement/Old Persons Home,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200088004,OAKLEIGH PARK NORTH,20032540,N20,526877,194426,526850,194450,51.63435253,-0.168086597\nNA,26/06/2018 08:18,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KENNEL   ALL CREATURES VETERINARY SURGERY,Cat,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,121020566,LAVENDER HILL,22902838,SW11,528273,175683,528250,175650,51.46559,-0.154764\nNA,26/06/2018 09:20,2018,2018/19,Special Service,2,3,333,999,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WESTCROFT SQUARE,21000874,W6,NULL,NULL,521950,178650,NULL,NULL\nNA,26/06/2018 10:29,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,WIDDENHAM ROAD,21604016,N7,NULL,NULL,530650,185650,NULL,NULL\nNA,26/06/2018 21:52,2018,2018/19,Special Service,1,1,333,333,INJURED BIRD STUCK IN TREE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000174,EALING COMMON,E09000009,EALING,Ealing,12144737,GRANGE ROAD,20600751,W5,518247,180434,518250,180450,51.51047417,-0.297432204\nNA,27/06/2018 16:55,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,10025575642,UXBRIDGE ROAD,20602324,UB1,513248,180301,513250,180350,51.51030441,-0.369473531\nNA,28/06/2018 07:40,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000455,ABBEY,E09000024,MERTON,Wimbledon,NULL,KINGSTON ROAD,22103622,SW19,NULL,NULL,525350,170050,NULL,NULL\nNA,28/06/2018 14:05,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - ASSIST WILDLIFE PROTECTION,Bird,Person (mobile),Estate Agent,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,10093039469,THE BROADWAY,22306023,IG8,540902,191851,540950,191850,51.60787787,0.033394814\nNA,28/06/2018 15:20,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN THE CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,NULL,THORNGROVE ROAD,22207990,E13,NULL,NULL,541050,183850,NULL,NULL\nNA,28/06/2018 15:55,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA INSPECTOR WITH BIRD RESCUE,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,Monkhams,E09000026,REDBRIDGE,Woodford,2.00003E+11,THE BROADWAY,22306023,IG8,540901,191851,540950,191850,51.60787379,0.033387883\nNA,28/06/2018 21:50,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,NULL,RUTLAND ROAD,20900881,E9,NULL,NULL,535550,183750,NULL,NULL\nNA,29/06/2018 12:02,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN AIR VENT OUTSIDE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,WALLERS CLOSE,19900614,RM9,NULL,NULL,548550,183650,NULL,NULL\nNA,30/06/2018 08:29,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN TREE REQUESTED BY RSPCA,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,ANTHONY ROAD,20500630,SE25,NULL,NULL,534250,167050,NULL,NULL\nNA,30/06/2018 14:08,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER KITCHEN CUPBOARD,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ARDEN CLOSE,22407174,TW2,NULL,NULL,512850,173550,NULL,NULL\nNA,30/06/2018 15:37,2018,2018/19,Special Service,1,1,333,333,POSSIBLY ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,RUSSETT WAY,20801306,SE13,NULL,NULL,537950,176350,NULL,NULL\nNA,30/06/2018 21:08,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN FENCE NEAR TO SWINSON HOUSE-CALLER WAITING FOR YOU,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,HIGHVIEW GARDENS,20701609,N11,NULL,NULL,529250,192150,NULL,NULL\nNA,01/07/2018 03:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INBETWEEN TWO BRICK WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,LOWER CLAPTON ROAD,20900640,E5,NULL,NULL,535150,185450,NULL,NULL\nNA,01/07/2018 08:51,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,PALMERS ROAD,20705024,N11,NULL,NULL,529250,192250,NULL,NULL\nNA,01/07/2018 12:34,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUESTING ASSISTANCE  FOX TRAPPED IN CONSERVATORY OF EMPTY PROPERTY,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,SOLENT RISE,22201956,E13,NULL,NULL,540350,182650,NULL,NULL\nNA,01/07/2018 16:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011108,Nunhead & Queen's Road,E09000028,SOUTHWARK,New Cross,NULL,MORTLOCK CLOSE,22503072,SE15,NULL,NULL,534650,176550,NULL,NULL\nNA,01/07/2018 17:28,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182050,NULL,NULL\nNA,02/07/2018 03:18,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN WINDOWFRAME,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,HARFORD STREET,22700605,E1,NULL,NULL,536150,181950,NULL,NULL\nNA,02/07/2018 11:03,2018,2018/19,Special Service,1,1,333,333,DEER STUCK BETWEEN TWO WOODEN FENCES AND A BRICK WALL - ASSIST RSPCA,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,LOWER KINGS ROAD,21880098,KT2,518145,169997,518150,169950,51.41668945,-0.302388586\nNA,02/07/2018 12:45,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN FLOOR AND CEILING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000407,COOMBE VALE,E09000021,KINGSTON UPON THAMES,New Malden,NULL,KINGSCOTE ROAD,21800551,KT3,NULL,NULL,520850,168550,NULL,NULL\nNA,02/07/2018 14:28,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,SPRINGFIELD ROAD,22401864,TW2,NULL,NULL,513350,173350,NULL,NULL\nNA,02/07/2018 18:35,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN GUTTER,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PENYWERN ROAD,21700411,SW5,NULL,NULL,525450,178350,NULL,NULL\nNA,02/07/2018 22:56,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED BY RUBBISH CHUTE    THIRD FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,NULL,MCMILLAN STREET,20801009,SE8,NULL,NULL,537350,177750,NULL,NULL\nNA,04/07/2018 16:20,2018,2018/19,Special Service,1,1,333,333,SEAGULL TRAPPED IN NETTING ON TOP OF BUILDING,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099002,EDGWARE ROAD,20201631,NW9,520744,189456,520750,189450,51.591028,-0.258379\nNA,04/07/2018 19:27,2018,2018/19,Special Service,1,1,333,333,PUPPY TRAPPED IN FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,NULL,PINNER ROAD,21401544,HA6,NULL,NULL,510350,190450,NULL,NULL\nNA,05/07/2018 06:37,2018,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK IN TREE - BEEN ATTACKED BY FOX  THIS IS AT THE REAR OF THE FIRE STATION - CALLER W,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46074805,TORRENS SQUARE,22201632,E15,539537,184895,539550,184850,51.54571103,0.010936647\nNA,05/07/2018 17:39,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED ON RIVER BANK REQUEST OF RSPCA GO DOWN MERBURY ROAD GO ACROSS BRIDGE OVER RIVER  FOOTPAT,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,1.00021E+11,RIDGE CLOSE,20801703,SE28,545055,179893,545050,179850,51.49937852,0.088396198\nNA,06/07/2018 07:52,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BICYCLE WHEEL - RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011487,Waddon,E09000008,CROYDON,Croydon,NULL,THEOBALD ROAD,20501485,CR0,NULL,NULL,531750,165850,NULL,NULL\nNA,06/07/2018 08:17,2018,2018/19,Special Service,1,1,333,333,DUCKLING FALLEN DOWN DRAIN  J/O BARNES HIGH STREET.   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,10070719691,SWAN PLACE,22405134,SW13,521780,176403,521750,176450,51.47349655,-0.247932398\nNA,06/07/2018 19:01,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011244,Goodmayes,E09000026,REDBRIDGE,Ilford,NULL,NUTFIELD GARDENS,22302316,IG3,NULL,NULL,546050,186750,NULL,NULL\nNA,06/07/2018 19:54,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN BUILDING AND FENCE     THERE FOR THREE DAYS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000465,LOWER MORDEN,E09000024,MERTON,Sutton,48012549,CARDINAL AVENUE,22101173,SM4,524165,167114,524150,167150,51.38949665,-0.216864079\nNA,07/07/2018 10:44,2018,2018/19,Special Service,1,1,333,333,TRAPPED SWIFT HANGING- CAUGHT IN WIRES   RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011103,Goose Green,E09000028,SOUTHWARK,Peckham,NULL,BAWDALE ROAD,22500179,SE22,NULL,NULL,533850,174750,NULL,NULL\nNA,08/07/2018 12:58,2018,2018/19,Special Service,1,1,333,333,HORSE STUCK IN RIVER    NEAR CARE MANAGEMENT GROUP AT TOP OF THE LANE - CALLER IS AT THE BOTTOM OF T,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,10025149987,FOOTPATH 273,21302223,RM11,555643,188107,555650,188150,51.57036296,0.244445568\nNA,08/07/2018 14:22,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND OVEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000058,OAKLEIGH,E09000003,BARNET,Southgate,NULL,MANOR DRIVE,20028180,N20,NULL,NULL,527450,193350,NULL,NULL\nNA,08/07/2018 17:10,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,10033547810,HEDDON STREET,8400982,W1B,529183,180790,529150,180750,51.51128521,-0.139799856\nNA,09/07/2018 07:48,2018,2018/19,Special Service,1,1,333,333,FOX TANGLED UP IN FOOTBALL NET,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,COURT AVENUE,20500209,CR5,NULL,NULL,531250,158150,NULL,NULL\nNA,09/07/2018 08:35,2018,2018/19,Special Service,2,8,333,2664,HORSE TRAPPED IN DOORWAY ADDITIONAL FRU REQUESTED FROM SCENE - SILENT APPROACH,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000328,CHARVILLE,E09000017,HILLINGDON,Hillingdon,1.00023E+11,MEAD HOUSE LANE,21401259,UB4,508932,182202,508950,182250,51.52824002,-0.431062535\nNA,09/07/2018 14:20,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,NULL,FRITHWOOD AVENUE,21400773,HA6,NULL,NULL,509650,191950,NULL,NULL\nNA,09/07/2018 17:23,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON CHIMNEY   RSPCA REQUESTED BRIGADE ATTEND,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,HUXLEY ROAD,20704356,N18,NULL,NULL,533150,192950,NULL,NULL\nNA,09/07/2018 18:04,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION  ON THIRD FLOOR OF BLOCK OF FLATS,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,TYSOE AVENUE,20702432,EN3,NULL,NULL,536450,199150,NULL,NULL\nNA,09/07/2018 19:29,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED BETWEEN CAMERA AND LAMP POST,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011479,Selhurst,E09000008,CROYDON,Croydon,1.00021E+11,WHITEHORSE ROAD,20501565,CR0,532463,166767,532450,166750,51.38450191,-0.097796662\nNA,10/07/2018 10:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,10014048833,GOODMAN CRESCENT,20503063,CR0,532061,167288,532050,167250,51.38928243,-0.103381488\nNA,11/07/2018 16:35,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN AIR VENT,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,ROEHAMPTON HIGH STREET,22904376,SW15,522465,173877,522450,173850,51.45064675,-0.238954852\nNA,11/07/2018 16:49,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,NULL,DONGOLA ROAD,22200550,E13,NULL,NULL,540750,182650,NULL,NULL\nNA,12/07/2018 12:21,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED IN LAKE CALLER RANG BACK AND CANCELLED,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000518,FULWELL AND HAMPTON HILL,E09000027,RICHMOND UPON THAMES,Twickenham,10024348514,CRICKET LANE,22407167,TW12,514645,170856,514650,170850,51.42513281,-0.352412104\nNA,12/07/2018 19:33,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128008067,GROVE ROAD,21800477,KT6,517796,167822,517750,167850,51.39721877,-0.308127488\nNA,13/07/2018 07:52,2018,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED IN GUTTER OF PROPERTY,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,NULL,CHERRY TREE LANE,21300734,RM13,NULL,NULL,551650,182950,NULL,NULL\nNA,13/07/2018 11:11,2018,2018/19,Special Service,1,1,333,333,\"CAT CRAWLED THROUGH HOLE UNDER SINK, NOW STUCK IN PIPE\",Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,BARNFIELD ROAD,20800117,SE18,NULL,NULL,543850,177750,NULL,NULL\nNA,13/07/2018 23:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER FLOORING,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,SHORTLANDS GROVE,20302017,BR2,NULL,NULL,538850,168950,NULL,NULL\nNA,14/07/2018 15:07,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000286,CANONS,E09000015,HARROW,Stanmore,1.00021E+11,COPLEY ROAD,21200876,HA7,517507,192431,517550,192450,51.61845808,-0.304087905\nNA,15/07/2018 03:30,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN FENCE  O.S. REDBRIDGE COLLEGE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011251,Seven Kings,E09000026,REDBRIDGE,Ilford,10034932270,BARLEY LANE,22301832,IG3,546647,188888,546650,188850,51.57978376,0.115063354\nNA,15/07/2018 18:50,2018,2018/19,Special Service,1,1,333,333,PARROTS WINGS TRAPPED IN A TREE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,10012835891,HORACE ROAD,22207759,E7,540512,185708,540550,185750,51.55277839,0.025319923\nNA,15/07/2018 20:23,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,CHANTRY CLOSE,20702579,EN2,NULL,NULL,532150,198050,NULL,NULL\nNA,16/07/2018 05:38,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAW CAUGHT IN GRILL OF OVEN,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011228,Northumberland Heath,E09000004,BEXLEY,Bexley,NULL,DALMENY ROAD,20100418,DA8,NULL,NULL,549750,176850,NULL,NULL\nNA,16/07/2018 13:18,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING    RSPCA IN ATTENDANCE,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128030554,CHAPEL MILL ROAD,21880152,KT1,519001,168498,519050,168450,51.40303859,-0.29057762\nNA,16/07/2018 18:07,2018,2018/19,Special Service,1,1,333,333,SEAGULL TRAPPED BEHIND DOOR,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05009296,CANDLEWICK,E09000001,CITY OF LONDON,Dowgate,95513232,MARTIN LANE,8100347,EC4R,532820,180811,532850,180850,51.5106317,-0.087407891\nNA,17/07/2018 08:59,2018,2018/19,Special Service,1,1,333,333,REQUEST RSPCA FOR DOG AT STATION,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011470,New Addington North,E09000008,CROYDON,Addington,10093049356,LODGE LANE,20501776,CR0,537693,163276,537650,163250,51.35188605,-0.024042189\nNA,17/07/2018 13:33,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN METAL RAILINGS,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000179,HANGER HILL,E09000009,EALING,Acton,NULL,LINKS ROAD,20601066,W3,NULL,NULL,519150,181350,NULL,NULL\nNA,17/07/2018 15:45,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,NULL,DANEBURY AVENUE,22901127,SW15,NULL,NULL,522250,173750,NULL,NULL\nNA,17/07/2018 16:16,2018,2018/19,Special Service,2,5,333,1665,PUPPY TRAPPED BETWEEN TWO HOUSES  FRU REQ FROM SCENE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,CHURCH LANE,20704048,N9,NULL,NULL,533950,193850,NULL,NULL\nNA,17/07/2018 19:35,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Assist trapped wild animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,1.00023E+11,BATH ROAD,21402329,TW6,508042,176872,508050,176850,51.48050489,-0.4455109\nNA,17/07/2018 20:09,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,RYLSTON ROAD,21000711,SW6,NULL,NULL,524750,177350,NULL,NULL\nNA,18/07/2018 17:12,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN FENCE    OWNER ON SCENE,Dog,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,PRETORIA AVENUE,22865950,E17,536180,189268,536150,189250,51.58582713,-0.035757144\nNA,19/07/2018 07:09,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN NETTING IN SCHOOL FIELD,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05011464,Bensham Manor,E09000008,CROYDON,Norbury,10014056249,ATTLEE CLOSE,20502859,CR7,532175,167548,532150,167550,51.39159288,-0.101653411\nNA,19/07/2018 08:32,2018,2018/19,Special Service,1,2,333,666,DEER STUCK IN MOAT - WATER OPERATIONS LEVEL ONE,Deer,Person (land line),Other public building,Non Residential,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207005373,SILVER STREET,20705135,EN1,532884,196873,532850,196850,51.65495137,-0.080412968\nNA,19/07/2018 11:27,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUEST FROM SCENE WITH CAT STUCK UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011226,Falconwood & Welling,E09000004,BEXLEY,Plumstead,1.00023E+11,BELLEGROVE ROAD,20100114,DA16,546195,175858,546150,175850,51.46282439,0.103144147\nNA,19/07/2018 11:36,2018,2018/19,Special Service,1,1,333,333,INJURED CAT TRAPPED BEHIND FENCE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,ACTON LANE,20600011,W4,NULL,NULL,520550,179250,NULL,NULL\nNA,20/07/2018 06:54,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED ON SECOND FLOOR LOBBY,Bird,Person (land line),Medical/health centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10070005345,CROYDON ROAD,20302868,SE20,535364,169774,535350,169750,51.410838,-0.055001\nNA,20/07/2018 11:33,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED UNDER DECKING   LEAD TANGLED AND WRAPPED AROUND POST,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000171,CLEVELAND,E09000009,EALING,Ealing,NULL,HARROW VIEW ROAD,20600833,W5,NULL,NULL,516950,182250,NULL,NULL\nNA,20/07/2018 13:01,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN GUTTERING    REQUESTED BY RSPCA WHO ARE ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,NEWLANDS PARK,22004449,SE26,NULL,NULL,535550,171350,NULL,NULL\nNA,21/07/2018 13:16,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON BALCONY    RSPCA IN ATTENDANCE.,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,THESSALY ROAD,22905303,SW8,NULL,NULL,529350,176950,NULL,NULL\nNA,21/07/2018 13:39,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY  RSPCA REFUSING TO ATTEND,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,NORTH BIRKBECK ROAD,22861800,E11,NULL,NULL,538750,186050,NULL,NULL\nNA,21/07/2018 18:56,2018,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN METAL STAIRS,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,34076784,FULHAM PALACE ROAD,21000356,SW6,524025,176701,524050,176750,51.47569249,-0.215521755\nNA,22/07/2018 02:47,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,BLAGROVE ROAD,21700813,W10,NULL,NULL,524550,181550,NULL,NULL\nNA,22/07/2018 09:06,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,AVALON CLOSE,20300004,BR6,NULL,NULL,547650,165450,NULL,NULL\nNA,22/07/2018 13:24,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Cemetery,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,202095051,GLEBE ROAD,20200664,NW10,522015,184556,522050,184550,51.54671876,-0.241737422\nNA,23/07/2018 12:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN SHED AND WALL - OWNER ON SCENE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,10090852160,HAMMERSLEY ROAD,22208226,E16,540188,181679,540150,181650,51.51665515,0.019034882\nNA,24/07/2018 03:15,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  CAT FALLEN AND IS ON FIRST FLOOR WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,AUCKLAND ROAD,22900184,SW11,NULL,NULL,527350,175050,NULL,NULL\nNA,24/07/2018 08:42,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,TUDOR DRIVE,21800983,KT2,NULL,NULL,518550,171050,NULL,NULL\nNA,24/07/2018 10:36,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON WINDOW LEDGE   RSPCA IN ATTENDANCE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Islington,NULL,HIGHBURY HILL,21603358,N5,NULL,NULL,531550,185750,NULL,NULL\nNA,24/07/2018 12:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ROOM,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011467,Crystal Palace & Upper Norwood,E09000008,CROYDON,West Norwood,NULL,MATILDA CLOSE,20502905,SE19,NULL,NULL,532750,170250,NULL,NULL\nNA,24/07/2018 15:25,2018,2018/19,Special Service,2,4,333,1332,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,10025188606,POND PATH,20304039,BR7,544028,170752,544050,170750,51.41749989,0.069900275\nNA,24/07/2018 20:53,2018,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN NETTING   ALP REQUESTED FROM SCENE,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from water,Animal rescue from water - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099002,EDGWARE ROAD,20201631,NW9,520743,189456,520750,189450,51.59103264,-0.258385308\nNA,26/07/2018 11:52,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,NULL,LAYARD SQUARE,22503047,SE16,NULL,NULL,534750,178950,NULL,NULL\nNA,26/07/2018 14:18,2018,2018/19,Special Service,1,1,333,333,CAT ON ROOF    ASSIST RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,NULL,MALPAS ROAD,22000662,SE4,NULL,NULL,536550,176150,NULL,NULL\nNA,26/07/2018 15:26,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000374,HILLRISE,E09000019,ISLINGTON,Holloway,5300004140,ASHLEY ROAD,21606730,N19,530373,187571,530350,187550,51.57194625,-0.120149579\nNA,26/07/2018 15:33,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING   RSPCA IN ATTENDANCE,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200220980,HIGH ROAD,20022400,N12,526356,192155,526350,192150,51.61405711,-0.176430422\nNA,26/07/2018 17:25,2018,2018/19,Special Service,1,2,333,666,RUNNING CALL TO DUCK TRAPPED IN MUD,Bird,Person (land line),River/canal,Outdoor,Other animal assistance,Animal harm involving wild animal,E05011106,North Bermondsey,E09000028,SOUTHWARK,Dockhead,2.00003E+11,SHAD THAMES,22502224,SE1,533809,179711,533850,179750,51.50051531,-0.073584172\nNA,27/07/2018 22:29,2018,2018/19,Special Service,1,1,333,333,CAT ON ROOF FOURTH FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,NULL,JACKSONS LANE,21100481,N6,NULL,NULL,528550,187950,NULL,NULL\nNA,28/07/2018 07:01,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM FOURTH FLOOR TO FIRST FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,SINGAPORE ROAD,20601555,W13,NULL,NULL,516350,180450,NULL,NULL\nNA,28/07/2018 10:28,2018,2018/19,Special Service,1,1,333,333,RSPCA REQUEST - CAT TRAPPED ON ROOF - RSPCA ON SCENE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,NULL,JACKSONS LANE,21100481,N6,NULL,NULL,528550,187950,NULL,NULL\nNA,28/07/2018 12:14,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,ARLINGTON ROAD,21800074,KT6,NULL,NULL,517850,167050,NULL,NULL\nNA,28/07/2018 14:17,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED ON ROOF,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,NULL,PARK AVENUE,20707523,EN1,NULL,NULL,532950,195450,NULL,NULL\nNA,28/07/2018 16:07,2018,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED DOWN HOLE IN GARDEN,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10034912960,BANKSIDE ROAD,22306128,IG1,544488,185124,544450,185150,51.54652661,0.082386584\nNA,28/07/2018 16:16,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING ON ROOF NEAR TO WARREN STREET STATION - CALLER WILL MEET YOU,Bird,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000129,BLOOMSBURY,E09000007,CAMDEN,Euston,5042736,WARREN STREET,20400928,W1T,529145,182193,529150,182150,51.52390075,-0.139834517\nNA,28/07/2018 19:08,2018,2018/19,Special Service,1,1,333,333,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WADHAM ROAD,22884500,E17,537861,190948,537850,190950,51.60051202,-0.010841668\nNA,28/07/2018 20:10,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP TREE RSPCA INSPECTOR REQUESTED ATTENDANCE AS CAT IS INJURED,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,1.00021E+11,SLEWINS CLOSE,21300585,RM11,553511,188635,553550,188650,51.57568657,0.213940416\nNA,28/07/2018 21:40,2018,2018/19,Special Service,1,1,333,333,RABBIT STUCK BETWEEN WALLS OF EXTENSION,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,NULL,COWDRAY WAY,21300301,RM12,NULL,NULL,552150,185750,NULL,NULL\nNA,29/07/2018 17:08,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED UNDER RAILWAY BRIDGE OPPOSITE THE FALCON PUBLIC HOUSE  CALLER WILL MEET,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000421,FERNDALE,E09000022,LAMBETH,Clapham,10091121144,BEDFORD ROAD,21900157,SW4,530037,175551,530050,175550,51.46400649,-0.129427873\nNA,29/07/2018 19:27,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF - OWNER ON SCENE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,NULL,CHURCHFIELDS ROAD,20300883,BR3,NULL,NULL,536050,169350,NULL,NULL\nNA,29/07/2018 23:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,BOURDON STREET,8400900,W1K,NULL,NULL,528850,180850,NULL,NULL\nNA,30/07/2018 14:16,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND FENCE (POSSIBLY INJURED)  FLAT B,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,202085421,HILEY ROAD,20202450,NW10,523141,182961,523150,182950,51.53214438,-0.226059083\nNA,30/07/2018 16:51,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000562,ST. HELIER,E09000029,SUTTON,Mitcham,NULL,WELBECK ROAD,22605655,SM5,NULL,NULL,527050,166150,NULL,NULL\nNA,30/07/2018 20:50,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP INTERNAL FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,NULL,FORTISMERE AVENUE,21100315,N10,NULL,NULL,528350,189450,NULL,NULL\nNA,31/07/2018 14:39,2018,2018/19,Special Service,2,3,333,999,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,FAWLEY ROAD,20400267,NW6,NULL,NULL,525550,185050,NULL,NULL\nNA,01/08/2018 01:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,ATKINS SQUARE,20950344,E8,NULL,NULL,534750,185250,NULL,NULL\nNA,01/08/2018 14:59,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN CAR ENGINE   RSPCA ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000439,BROCKLEY,E09000023,LEWISHAM,New Cross,1.00023E+11,UPPER BROCKLEY ROAD,22001057,SE4,536665,176027,536650,176050,51.46672142,-0.033886865\nNA,02/08/2018 09:19,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207179215,HIGH STREET,20701608,N14,529706,194153,529750,194150,51.63125461,-0.127332233\nNA,02/08/2018 21:25,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED BETWEEN FENCE AND SHED WALL  CONTACTED BY RSPCA,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000265,WORMHOLT AND WHITE CITY,E09000013,HAMMERSMITH AND FULHAM,Acton,34014743,ALDBOURNE ROAD,21000039,W12,522141,180296,522150,180250,51.50840641,-0.241390884\nNA,03/08/2018 02:13,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND COOKER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WOOLWICH COMMON,20801648,SE18,NULL,NULL,543250,177850,NULL,NULL\nNA,03/08/2018 10:35,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL CAT IN TREE,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,RICHMOND ROAD,21800806,KT2,517831,171264,517850,171250,51.42814438,-0.306480748\nNA,03/08/2018 11:35,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED IN BLACK CAB ENGINE BAY  FRU REQUESTED WITH SNAKE EYE CAMERA,Cat,Person (mobile),Car,Road Vehicle,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217092165,WESTGATE TERRACE,21700584,SW10,525841,177977,525850,177950,51.48675225,-0.188931834\nNA,03/08/2018 18:45,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN A HOLE,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,NULL,HAMPTON ROAD,21202815,HA7,NULL,NULL,515350,193350,NULL,NULL\nNA,04/08/2018 10:09,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,MOUNT VIEW ROAD,21103656,N4,NULL,NULL,530850,188050,NULL,NULL\nNA,05/08/2018 15:05,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,1.00021E+11,LAMORNA GROVE,21200990,HA7,517755,190423,517750,190450,51.60035672,-0.301178518\nNA,05/08/2018 18:14,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN WINDOW AT FIRST FLOOR LEVEL   NEAR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,ARCHERY LANE,20304203,BR2,NULL,NULL,541950,167150,NULL,NULL\nNA,05/08/2018 19:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN FENCE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000272,HIGHGATE,E09000014,HARINGEY,Hornsey,1.00021E+11,SOUTHWOOD LANE,21100786,N6,528377,187727,528350,187750,51.5738045,-0.1488837\nNA,06/08/2018 10:37,2018,2018/19,Special Service,1,1,333,333,CAT WITH HEAD TRAPPED BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ELLSWORTH STREET,22700463,E2,NULL,NULL,534750,182850,NULL,NULL\nNA,06/08/2018 13:57,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10093036268,ILFORD LANE,22302963,IG1,543973,185424,543950,185450,51.54934946,0.075079043\nNA,06/08/2018 16:01,2018,2018/19,Special Service,1,3,333,999,SMALL ANIMAL RESCUE - CAT IN DISTRESS,Cat,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011247,Loxford,E09000026,REDBRIDGE,Ilford,10093036268,ILFORD LANE,22302963,IG1,543973,185422,543950,185450,51.549333,0.0750759\nNA,06/08/2018 19:33,2018,2018/19,Special Service,1,1,333,333,DOG ON WINDOW LEDGE ON THIRD FLOOR  AERIAL APPLIANCE REQUESTED FROM ON SCENE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,Paddington,NULL,LANHILL ROAD,8400494,W9,NULL,NULL,525250,182450,NULL,NULL\nNA,06/08/2018 22:38,2018,2018/19,Special Service,1,1,333,333,CAT IN PRECARIOUS POSITION  CALLER STATES ON WINDOW SILL OF BUILDING OPPOSITE APPROX FIVE FLOORS UP,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,NULL,WEATHERLEY CLOSE,22702112,E3,NULL,NULL,536950,181850,NULL,NULL\nNA,07/08/2018 16:54,2018,2018/19,Special Service,1,1,333,333,BIRD IN CHIMNEY  CALLER DIABLED - UNABLE TO WALK OR WRITE - DETAILS PASSED TO RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011474,Old Coulsdon,E09000008,CROYDON,Purley,NULL,THE CROSSWAYS,20500347,CR5,NULL,NULL,530950,157750,NULL,NULL\nNA,08/08/2018 22:01,2018,2018/19,Special Service,1,3,333,999,KITTEN STUCK IN PIPE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,LEA BRIDGE ROAD,22850300,E10,NULL,NULL,537450,187850,NULL,NULL\nNA,09/08/2018 02:05,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED,Cat,Person (mobile),Vehicle Repair Workshop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011109,Old Kent Road,E09000028,SOUTHWARK,New Cross,2.00003E+11,ILDERTON ROAD,22501322,SE15,535267,177561,535250,177550,51.48084558,-0.053413138\nNA,09/08/2018 13:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GAP BETWEEN TWO BUILDINGS  - OWNER ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011489,Woodside,E09000008,CROYDON,Woodside,NULL,NAPIER ROAD,20501218,SE25,NULL,NULL,534650,168050,NULL,NULL\nNA,11/08/2018 03:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VENT BEHIND KITCHEN APPLIANCES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,HIGH ROAD,19900165,RM6,NULL,NULL,547850,187950,NULL,NULL\nNA,11/08/2018 05:25,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000523,HEATHFIELD,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,FARM ROAD,22401470,TW4,NULL,NULL,512250,173450,NULL,NULL\nNA,11/08/2018 10:55,2018,2018/19,Special Service,1,4,333,1332,HORSE IN DISTRESS,Horse,Person (mobile),Other building/use not known,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,BRAMBLEBURY ROAD,20800213,SE18,544442,178141,544450,178150,51.48378454,0.078857786\nNA,13/08/2018 00:34,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN INTO BASEMENT AREA   CAT IS POSSIBLY INJURED AND OWNERS UNABLE TO GET IT OUT,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300077614,REMINGTON STREET,21605789,N1,531899,183088,531850,183050,51.53131063,-0.099820646\nNA,13/08/2018 02:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,WILLOW ROAD,20400172,NW3,NULL,NULL,526650,185850,NULL,NULL\nNA,13/08/2018 09:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX IMPALED ON RAILINGS  RSPCA INSPECTOR GILL ON SCENE - THIS IS AT THE END OF THE,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,ALTON ROAD,22405311,TW9,518384,175215,518350,175250,51.4635425,-0.297205017\nNA,13/08/2018 10:27,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND KITCHEN SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011468,Fairfield,E09000008,CROYDON,Croydon,NULL,WATERWORKS YARD,20502803,CR0,NULL,NULL,532250,165450,NULL,NULL\nNA,13/08/2018 11:06,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH PARROT ON ROOF   RSPCA IN ATTENDANCE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CUCKOO HALL LANE,20703243,N9,NULL,NULL,535750,194850,NULL,NULL\nNA,13/08/2018 12:10,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED INSIDE PROPERTY DEMOLITON SITE,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000057,MILL HILL,E09000003,BARNET,Finchley,200220046,DOLLIS ROAD,20012840,NW7,524289,191123,524250,191150,51.60524528,-0.20662662\nNA,13/08/2018 19:33,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING ON  BALCONY RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,NULL,DUNEDIN ROAD,21300754,RM13,NULL,NULL,551750,182650,NULL,NULL\nNA,13/08/2018 21:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CARLYLE SQUARE,21700072,SW3,NULL,NULL,527050,178050,NULL,NULL\nNA,13/08/2018 22:13,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK UNDERNEATH KITCHEN FLOORBOARD,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,STRATHMORE GARDENS,20041220,N3,NULL,NULL,525650,190550,NULL,NULL\nNA,14/08/2018 09:25,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INSIDE SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000088,DOLLIS HILL,E09000005,BRENT,Willesden,NULL,NORTH CIRCULAR ROAD,20200296,NW2,NULL,NULL,521850,186850,NULL,NULL\nNA,14/08/2018 11:18,2018,2018/19,Special Service,1,3,333,999,ASSIST IN HELPING STAND UP A HORSE THAT IS DUE TO BE PUT DOWN THIS AFTERNOON BY LOCAL VET,Horse,Person (mobile),Other agricultural building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,1.00021E+11,VICARAGE PARK,20801545,SE18,544446,178159,544450,178150,51.48395029,0.078918499\nNA,14/08/2018 14:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,PRIORY ROAD,21100710,N8,NULL,NULL,529950,189250,NULL,NULL\nNA,16/08/2018 07:54,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN RAILINGS  - LIAISE WITH PARK WARDEN,Deer,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Enfield,207136923,CECIL ROAD,20702573,EN2,532374,196422,532350,196450,51.65102427,-0.087958991\nNA,16/08/2018 19:55,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN CAR ENGINE GREY TOYOTA CALLER WILL FLAG YOU DOWN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10014033057,ANGEL LANE,22201284,E15,538776,184647,538750,184650,51.54367101,-0.000125622\nNA,17/08/2018 09:11,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GUTTERING ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,PARK FARM ROAD,21800744,KT2,NULL,NULL,518450,170350,NULL,NULL\nNA,17/08/2018 17:40,2018,2018/19,Special Service,1,1,333,333,SQUIRREL HANGING FROM GUTTERING,Squirrel,Person (land line),Fence,Outdoor Structure,Animal rescue from height,Wild animal rescue from height,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,200087310,OAKDENE PARK,20032260,N3,524945,191478,524950,191450,51.6082869,-0.197047\nNA,18/08/2018 01:06,2018,2018/19,Special Service,1,1,333,333,CALLED X RSPCA - ASSIST WITH SMALL MAMMAL TRAPPED,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,MERTON ROAD,22903296,SW18,NULL,NULL,525250,174250,NULL,NULL\nNA,18/08/2018 18:35,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON LEDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,NEWTON AVENUE,20601243,W3,NULL,NULL,520250,179750,NULL,NULL\nNA,18/08/2018 22:39,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN BUILDING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011113,Rye Lane,E09000028,SOUTHWARK,Peckham,2.00003E+11,RYE LANE,22502166,SE15,534254,176358,534250,176350,51.47027507,-0.068450815\nNA,19/08/2018 17:25,2018,2018/19,Special Service,1,3,333,999,Redacted,Horse,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,10009949007,PACKET BOAT LANE,21401472,UB8,505368,181061,505350,181050,51.51866096,-0.482751161\nNA,20/08/2018 00:37,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED IN THICKET BEHIND HOUSE - OWNER ON SCENE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,NORWOOD ROAD,21901036,SE24,NULL,NULL,531850,173650,NULL,NULL\nNA,21/08/2018 12:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WINDOW - POSSIBLE BROKEN LEG - OWNER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,MITRE ROAD,22201521,E15,NULL,NULL,539350,183450,NULL,NULL\nNA,21/08/2018 15:28,2018,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,Acton,34136926,DU CANE ROAD,21000285,W12,521940,181090,521950,181050,51.51558628,-0.244016596\nNA,21/08/2018 19:30,2018,2018/19,Special Service,1,1,333,333,CAT STUCK INSIDE SOFA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,TURPINGTON LANE,20300668,BR2,NULL,NULL,542250,166950,NULL,NULL\nNA,22/08/2018 14:08,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON PITCHED ROOF OPPOSITE TOWN HALL CALLER WILL MEET BRIGADE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,KING STREET,21000478,W6,NULL,NULL,522650,178550,NULL,NULL\nNA,22/08/2018 19:08,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  NETS OF BIRDS HAVE TRAPPED BY BUILDING WORKS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000185,NORTHOLT WEST END,E09000009,EALING,Southall,NULL,DEHAVILLAND CLOSE,20600519,UB5,NULL,NULL,511650,182650,NULL,NULL\nNA,22/08/2018 21:26,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,NULL,CHILTERN VIEW ROAD,21400386,UB8,NULL,NULL,505550,183250,NULL,NULL\nNA,23/08/2018 01:24,2018,2018/19,Special Service,1,2,333,666,KITTENS TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,MOUNT PARK ROAD,21401335,HA5,NULL,NULL,510250,188450,NULL,NULL\nNA,24/08/2018 13:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND SHED,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,5870067582,MCRAE LANE,22600783,CR4,527784,166593,527750,166550,51.38401023,-0.165062115\nNA,25/08/2018 11:13,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON A ROOF   ASSISTANCE REQUESTED FROM RSPCA OFFICER ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,ST MICHAELS CLOSE,22207901,E16,NULL,NULL,541650,181550,NULL,NULL\nNA,25/08/2018 12:19,2018,2018/19,Special Service,1,2,333,666,FOX TRAPPED ON ROOF,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Islington,NULL,BALLS POND ROAD,20900101,N1,NULL,NULL,533050,184750,NULL,NULL\nNA,25/08/2018 22:15,2018,2018/19,Special Service,1,1,333,333,DOG WITH ROD STUCK THROUGH STOMACH,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,THORNTON AVENUE,21401990,UB7,NULL,NULL,506850,179250,NULL,NULL\nNA,27/08/2018 14:39,2018,2018/19,Special Service,1,4,333,1332,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011100,Dulwich Village,E09000028,SOUTHWARK,West Norwood,NULL,PICKWICK ROAD,22501976,SE21,NULL,NULL,533050,173950,NULL,NULL\nNA,28/08/2018 09:32,2018,2018/19,Special Service,1,1,333,333,ASSIST RSCPA WITH CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000179,HANGER HILL,E09000009,EALING,Acton,12091983,DALLAS ROAD,20600504,W5,518722,181902,518750,181950,51.52356691,-0.290096303\nNA,28/08/2018 21:56,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED DOWN MANHOLE - OUTSIDE RAVENSDALE MANSIONS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,HARINGEY PARK,21103376,N8,NULL,NULL,530450,188350,NULL,NULL\nNA,29/08/2018 16:19,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING - RSPCA UNABLE TO ATTEND  BELIEVED TO BE UNDER HUNGERFORD BRIDGE ON BELVEDER,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10001114017,LOWER MARSH,21900900,SE1,530968,179568,530950,179550,51.49988645,-0.114536212\nNA,30/08/2018 22:02,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN SOFA MECHANISM,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011114,St. George's,E09000028,SOUTHWARK,Dowgate,NULL,LONDON ROAD,22501544,SE1,NULL,NULL,531850,179250,NULL,NULL\nNA,31/08/2018 12:11,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH INJURED BIRD TRAPPED IN TELEPHONE WIRE    RSPCA IN ATTENDANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,NULL,FLAMSTEAD ROAD,19900484,RM9,NULL,NULL,547250,184350,NULL,NULL\nNA,31/08/2018 23:18,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK IN HOLE BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,EDWARD STREET,22000361,SE8,NULL,NULL,537050,177550,NULL,NULL\nNA,01/09/2018 17:17,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,1.00023E+11,WEST END ROAD,21402115,HA4,509373,186710,509350,186750,51.56867094,-0.423305578\nNA,01/09/2018 20:10,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN BUILDERS WEBBING ON BUILDING SITE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,HARDWICKS SQUARE,22902216,SW18,NULL,NULL,525450,174650,NULL,NULL\nNA,02/09/2018 15:43,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,10070018619,HIGH STREET,20302875,SE20,535510,170241,535550,170250,51.41500017,-0.052722326\nNA,02/09/2018 17:16,2018,2018/19,Special Service,1,5,333,1665,HORSE STUCK IN DITCH AT REAR OF OCADO - OWNERS ON SCENE  RVP - CRABTREE MANOR WAY NORTH,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05011218,Belvedere,E09000004,BEXLEY,Erith,10023301848,CRABTREE MANORWAY NORTH TO FOOTPATH 3 FP242,20106242,DA17,550150,179495,550150,179450,51.49447323,0.161576005\nNA,02/09/2018 18:13,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN STRING AT ROOF LEVEL ALSO HANGING UPSIDE DOWN CALLED BY THE RSPCA WHO WILL NOT BE AT,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,DOROTHY AVENUE,20201293,HA0,NULL,NULL,518350,184150,NULL,NULL\nNA,02/09/2018 19:37,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON NAILED FENCE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34026766,SOUTHERTON ROAD,21000745,W6,523046,178908,523050,178950,51.49573838,-0.228837444\nNA,02/09/2018 23:35,2018,2018/19,Special Service,1,1,333,333,SMALL BIRD TRAPPED IN WIRING BEHIND A CUPBOARD,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,HOLLOWAY ROAD,21606449,N19,NULL,NULL,530050,186450,NULL,NULL\nNA,03/09/2018 02:47,2018,2018/19,Special Service,2,3,333,999,NULL,Unknown - Heavy Livestock Animal,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving livestock,E05000559,CARSHALTON SOUTH AND CLOCKHOUSE,E09000029,SUTTON,Wallington,5870086811,LITTLE WOODCOTE LANE,22605502,CR8,529081,161589,529050,161550,51.33875101,-0.148257902\nNA,03/09/2018 21:36,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED BEHIND BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000437,BELLINGHAM,E09000023,LEWISHAM,Lewisham,NULL,BROADMEAD,22001272,SE6,NULL,NULL,537650,172250,NULL,NULL\nNA,05/09/2018 13:41,2018,2018/19,Special Service,1,5,333,1665,ASSIST RSPCA WITH HORSE STUCK IN STREAM - WATER OPS LEVEL ONE,Horse,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,Belvedere,E09000004,BEXLEY,Erith,1.0002E+11,ST THOMAS ROAD,20101393,DA17,550057,179737,550050,179750,51.49666637,0.160343336\nNA,05/09/2018 14:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,2.00001E+11,STREATHAM HIGH ROAD,21901326,SW16,530258,170737,530250,170750,51.42069521,-0.128010227\nNA,05/09/2018 17:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN WINDOW AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,NULL,CHURCH PATH,20600383,W4,NULL,NULL,520350,179250,NULL,NULL\nNA,05/09/2018 22:21,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO  PET  SNAKE  TRAPPED IN RING,Snake,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,1.00023E+11,HEATHFIELD GARDENS,21500589,W4,520270,178257,520250,178250,51.49048509,-0.2690352\nNA,06/09/2018 16:01,2018,2018/19,Special Service,1,1,333,333,INJURED FOX TRAPPED BETWEEN WALL AND SHED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,JANSON ROAD,22202021,E15,NULL,NULL,539250,185450,NULL,NULL\nNA,06/09/2018 23:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF AREA,Cat,Person (mobile),Petrol station,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000299,QUEENSBURY,E09000015,HARROW,Stanmore,2E+11,HONEYPOT LANE,21200986,HA7,518251,189980,518250,189950,51.59626989,-0.29417034\nNA,07/09/2018 07:56,2018,2018/19,Special Service,1,2,333,666,PARROT TRAPPED IN TREE - ALP REQUESTED,Bird,Person (mobile),Other car park structure,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00023E+11,WINDSOR CLOSE,21402187,HA6,510136,190393,510150,190350,51.60162319,-0.411140294\nNA,08/09/2018 08:12,2018,2018/19,Special Service,1,1,333,333,CAT UP TREE - RSPCA IN ATT,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,10003972871,LABURNUM AVENUE,21106065,N17,532732,191473,532750,191450,51.60646719,-0.084655529\nNA,08/09/2018 14:05,2018,2018/19,Special Service,1,1,333,333,ASSSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,ALFRED ROAD,20600036,W3,NULL,NULL,520450,180050,NULL,NULL\nNA,08/09/2018 16:07,2018,2018/19,Special Service,1,2,333,666,ASSIST RSPCA WITH SMALL ANIMAL RESCUE CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000180,HOBBAYNE,E09000009,EALING,Ealing,NULL,WESTCOTT CRESCENT,20601872,W7,NULL,NULL,515750,181550,NULL,NULL\nNA,08/09/2018 19:31,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207039467,PRUDEN CLOSE,20706091,N14,529407,193770,529450,193750,51.62787708,-0.131798676\nNA,09/09/2018 17:47,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON WINDOW LEDGE   FLAT B,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,PORTNALL ROAD,8400567,W9,NULL,NULL,524750,182550,NULL,NULL\nNA,10/09/2018 22:05,2018,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK UP TREE - OWNER ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.0002E+11,KNOLL RISE,20301175,BR6,545968,166197,545950,166150,51.37607691,0.095898332\nNA,11/09/2018 12:12,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF LOOK LIKE HE IS WEDGED BETWEEN CHIMNEYS BEEN ON ROOF SINCE SATURDAY RSPCA INSP ON,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,HUXLEY STREET,8400828,W10,NULL,NULL,524050,182550,NULL,NULL\nNA,11/09/2018 19:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Bird,Person (land line),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,10070718915,HIGH STREET,22400303,KT1,517405,169775,517450,169750,51.41485298,-0.313092197\nNA,12/09/2018 09:07,2018,2018/19,Special Service,1,3,333,999,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011222,Crayford,E09000004,BEXLEY,Bexley,10091897350,THAMES ROAD,20101429,DA1,553138,175523,553150,175550,51.45798004,0.20287323\nNA,13/09/2018 12:38,2018,2018/19,Special Service,1,1,333,333,PUPPY TRAPPED UNDER LOG PILE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000191,SOUTHFIELD,E09000009,EALING,Chiswick,12100114,WILKINSON WAY,20601904,W4,520574,179650,520550,179650,51.50293409,-0.264183216\nNA,14/09/2018 21:23,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WALL ALSFORD TIMBER MERCHANTS  CALLER WILL WAIT OUTSIDE AND DIRECT,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HEATH ROAD,22403551,TW1,515930,173098,515950,173050,51.44502415,-0.33321439\nNA,16/09/2018 10:40,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE IN REAR GARDEN,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,Camberwell Green,E09000028,SOUTHWARK,Peckham,10009803979,THOMPSONS AVENUE,22502494,SE5,532109,177366,532150,177350,51.47983704,-0.098932525\nNA,16/09/2018 14:55,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED UNDER FRONT DOOR STEP,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,HAZELWOOD ROAD,22844150,E17,NULL,NULL,536150,188850,NULL,NULL\nNA,17/09/2018 17:47,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN FENCE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000404,CHESSINGTON NORTH AND HOOK,E09000021,KINGSTON UPON THAMES,Surbiton,128043988,WOODGATE AVENUE,21801073,KT9,517670,164378,517650,164350,51.36628426,-0.311074317\nNA,18/09/2018 00:32,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,LOWER HAM ROAD,21800623,KT2,NULL,NULL,518050,170250,NULL,NULL\nNA,18/09/2018 18:45,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,ELTHAM ROAD,22005070,SE12,NULL,NULL,540050,174850,NULL,NULL\nNA,19/09/2018 09:51,2018,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN WIRE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,SEVEN SISTERS ROAD,21603790,N7,NULL,NULL,530650,186150,NULL,NULL\nNA,19/09/2018 09:56,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF - REQUESTED BY RSPCA,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,NULL,LINKS ROAD,22103954,SW17,NULL,NULL,528550,170450,NULL,NULL\nNA,19/09/2018 12:48,2018,2018/19,Special Service,1,1,333,333,CAT IN THE TREE   REQUESTED BY RSPCA WHO ARE ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,2.00001E+11,BROOKE ROAD,20900177,E5,534766,186384,534750,186350,51.56024993,-0.057249438\nNA,19/09/2018 13:00,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN ENGINE BAY OF CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00024E+11,WOOD LANE,21300665,RM12,552686,185034,552650,185050,51.5435556,0.200487757\nNA,19/09/2018 21:55,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN PIPE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,ROSEMARY GARDENS,22406394,SW14,NULL,NULL,520350,175750,NULL,NULL\nNA,20/09/2018 15:28,2018,2018/19,Special Service,1,1,333,333,KITTEN IN CAR BONNET - RSPCA ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,1.00022E+11,THE COPPICE,21401943,UB7,506408,181452,506450,181450,51.52197265,-0.467651944\nNA,20/09/2018 18:57,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,NULL,TALLOW ROAD,21501760,TW8,NULL,NULL,517450,177450,NULL,NULL\nNA,21/09/2018 13:29,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSISTION ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,HERNE HILL,21900703,SE24,NULL,NULL,532350,174950,NULL,NULL\nNA,21/09/2018 20:57,2018,2018/19,Special Service,1,1,333,333,CAT FALLEN FROM PONTOON ONTO THAMES EMBANKMENT - STUCK IN MUD,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000622,ST. MARY'S PARK,E09000032,WANDSWORTH,Battersea,10008158601,LOMBARD ROAD,22902991,SW11,526608,176321,526650,176350,51.47170291,-0.17847995\nNA,22/09/2018 09:51,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,5870015304,BOSCOMBE ROAD,22601298,KT4,523350,166399,523350,166350,51.38325213,-0.228825987\nNA,24/09/2018 10:25,2018,2018/19,Special Service,1,2,333,666,RSPCA WITH REQUEST FOR ASSISTANCE IN SEARCH OF INJURED CAT IN SHRUBBERY  USING TIC     RSPCA CONTACT,Cat,Person (land line),Scrub land,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200103575,SHURLAND AVENUE,20039540,EN4,526836,195271,526850,195250,51.64194904,-0.168367364\nNA,26/09/2018 15:29,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,SELSDON ROAD,21901222,SE27,NULL,NULL,531550,171850,NULL,NULL\nNA,27/09/2018 17:23,2018,2018/19,Special Service,1,4,333,1332,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000257,MUNSTER,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,ROWALLAN ROAD,21000701,SW6,NULL,NULL,524050,177150,NULL,NULL\nNA,28/09/2018 20:22,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  - INJURED RABBIT TRAPPED IN CAVITY OF TWO FENCES,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,DURAND WAY,20202204,NW10,NULL,NULL,520050,184250,NULL,NULL\nNA,29/09/2018 02:35,2018,2018/19,Special Service,1,1,333,333,DOG STUCK BY FENCE AND TIDE DEPTFORD GREEN BY THE AHOY CENTRE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,10010257764,BORTHWICK STREET,20800196,SE8,537222,178045,537250,178050,51.48472081,-0.02509723\nNA,29/09/2018 14:57,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,EMLYN GARDENS,21001199,W12,NULL,NULL,521450,179450,NULL,NULL\nNA,29/09/2018 15:13,2018,2018/19,Special Service,NULL,NULL,333,NULL,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WHITTLE CLOSE,22887050,E17,536321,188025,536350,188050,51.57462177,-0.034193288\nNA,29/09/2018 16:49,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN BUSHES ON LAKE,Cat,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10070001081,GLASSMILL LANE,20303922,BR2,539917,169050,539950,169050,51.40322961,0.010142562\nNA,29/09/2018 21:10,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAWS TRAPPED IN METAL TABLE LEG,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,FERRYMEAD AVENUE,20600665,UB6,NULL,NULL,513950,183050,NULL,NULL\nNA,30/09/2018 17:25,2018,2018/19,Special Service,1,1,333,333,RUNNING CALL TO KITTEN STUCK IN TREE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,1.00022E+11,PARK ROAD,21880043,KT2,519000,170845,519050,170850,51.42413326,-0.289804675\nNA,30/09/2018 21:39,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF OF HOUSE,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,NULL,PINE ROAD,20034360,N11,NULL,NULL,528150,193850,NULL,NULL\nNA,02/10/2018 01:27,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN TWO WALLS IN GARDEN AREA - CALLER WILL MEET YOU,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527450,181850,NULL,NULL\nNA,02/10/2018 04:59,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,NULL,COLLEGE YARD,20400222,NW5,NULL,NULL,528850,185550,NULL,NULL\nNA,02/10/2018 12:20,2018,2018/19,Special Service,2,3,333,999,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48108879,MORDEN HALL ROAD,22104539,SM4,526221,168516,526250,168550,51.40164587,-0.186830577\nNA,02/10/2018 18:54,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,SOUTH DRIVE,20502511,CR5,NULL,NULL,529950,159950,NULL,NULL\nNA,05/10/2018 02:47,2018,2018/19,Special Service,1,1,333,333,FOX WITH PAW TRAPPED  IN GARDEN,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011105,NEWINGTON,E09000028,SOUTHWARK,Old Kent Road,NULL,SUTHERLAND SQUARE,22502438,SE17,NULL,NULL,532250,178050,NULL,NULL\nNA,05/10/2018 16:31,2018,2018/19,Special Service,1,1,333,333,DEER WITH HEAD TRAPPED IN FENCE  O.S. SKATE PARK,Deer,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554182,192330,554150,192350,51.60869976,0.225239236\nNA,06/10/2018 14:32,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,MORA STREET,21605467,EC1V,NULL,NULL,532450,182750,NULL,NULL\nNA,06/10/2018 20:55,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,LEONARD ROAD,22207787,E7,NULL,NULL,540150,185650,NULL,NULL\nNA,07/10/2018 07:13,2018,2018/19,Special Service,1,1,333,333,BIN ROOM ALIGHT,Unknown - Wild Animal,Person (land line),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000314,HEATON,E09000016,HAVERING,Harold Hill,10024390298,MIMOSA CLOSE,21301431,RM3,553335,191083,553350,191050,51.59773516,0.212471858\nNA,07/10/2018 11:55,2018,2018/19,Special Service,1,1,333,333,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000455,ABBEY,E09000024,MERTON,Wimbledon,48058726,QUICKS ROAD,22105269,SW19,525776,170394,525750,170350,51.41862375,-0.192556606\nNA,07/10/2018 14:13,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,FAIRLAWNS CLOSE,21300055,RM11,NULL,NULL,554650,187850,NULL,NULL\nNA,07/10/2018 19:30,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON ROOF     FIFTH FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,WERRINGTON STREET,20400792,NW1,NULL,NULL,529550,182950,NULL,NULL\nNA,11/10/2018 15:39,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,KINGSDALE ROAD,20800862,SE18,NULL,NULL,545650,177550,NULL,NULL\nNA,11/10/2018 21:19,2018,2018/19,Special Service,2,6,333,1998,Redacted,Deer,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped wild animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,BROXHILL ROAD,21301041,RM4,551553,193320,551550,193350,51.61830858,0.187724767\nNA,12/10/2018 11:10,2018,2018/19,Special Service,1,1,333,333,SQUIRREL IS TRAPPED UNDER THE TERRACE FLOORBOARDS OF PROPERTY,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,NULL,CANFIELD GARDENS,20400419,NW6,NULL,NULL,525750,184350,NULL,NULL\nNA,13/10/2018 18:09,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,THE HIGHWAY,22701200,E1W,NULL,NULL,535250,180750,NULL,NULL\nNA,14/10/2018 17:56,2018,2018/19,Special Service,1,2,333,666,FOX TRAPPED IN BARBED WIRE,Fox,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,1.00021E+11,TEWSON ROAD,20801460,SE18,545348,178499,545350,178450,51.48677287,0.092051246\nNA,15/10/2018 14:20,2018,2018/19,Special Service,1,1,333,333,DOG STUCK ON ROOF OF POST OFFICE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,KENNINGTON ROAD,21900793,SE11,NULL,NULL,531250,177850,NULL,NULL\nNA,15/10/2018 14:51,2018,2018/19,Special Service,1,1,333,333,\"ASSIST RSPCA WITHN PIGEON IN NETTING, INSPECTOR ON SCENE\",Bird,Person (mobile),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000047,COPPETTS,E09000003,BARNET,Southgate,200058625,HEMINGTON AVENUE,20021720,N11,527851,192407,527850,192450,51.61598511,-0.154762586\nNA,15/10/2018 15:08,2018,2018/19,Special Service,NULL,NULL,333,NULL,CAT STUCK UNDERNEATH BATHTUB,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,FRIERN ROAD,22501002,SE22,NULL,NULL,534150,173950,NULL,NULL\nNA,15/10/2018 19:58,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000091,HARLESDEN,E09000005,BRENT,Park Royal,NULL,ST MARYS ROAD,20202537,NW10,NULL,NULL,521350,183750,NULL,NULL\nNA,16/10/2018 14:10,2018,2018/19,Special Service,1,1,333,333,KITTEN FALLEN THROUGH GARAGE ROOF AND TRAPPED,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00022E+11,ASHURST DRIVE,22302589,IG2,544032,188687,544050,188650,51.5786505,0.0772616\nNA,18/10/2018 12:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,1.00021E+11,SNOWDROP PATH,21301601,RM3,553523,191226,553550,191250,51.59896427,0.215245156\nNA,19/10/2018 09:53,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - DOG STUCK IN BRAMBLES - DISTRESSED,Dog,Person (land line),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011223,CROOK LOG,E09000004,BEXLEY,Bexley,10011844850,BEECHWOOD CRESCENT,20100112,DA7,548026,175846,548050,175850,51.46223843,0.129484573\nNA,19/10/2018 17:35,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN METAL FENCE      ENTER VIA PETERSFIELD AVENUE,Deer,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024390526,COLLERNE STREET,21320280,RM3,554326,192212,554350,192250,51.60760106,0.22726324\nNA,20/10/2018 12:42,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - PIGEON STUCK IN NETTING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,CHURCH CRESCENT,20008600,N3,NULL,NULL,524750,190650,NULL,NULL\nNA,22/10/2018 13:22,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN BETWEEN FENCES,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Feltham,1.00022E+11,THE GREENWAY,21500516,TW4,512626,175375,512650,175350,51.46615129,-0.380003659\nNA,22/10/2018 21:08,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN PIPEWORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,NULL,COOPERS ROAD,22500590,SE1,NULL,NULL,533850,178250,NULL,NULL\nNA,22/10/2018 23:41,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN A TREE - HAVING BREATHING PROBLEMS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5105657,PHOENIX ROAD,20400807,NW1,529710,183067,529750,183050,51.53162612,-0.131374456\nNA,23/10/2018 23:26,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN STUD PARTITION WALL`,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,NULL,ST JOSEPHS DRIVE,20601600,UB1,NULL,NULL,512750,180250,NULL,NULL\nNA,24/10/2018 18:54,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED BEHIND GATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,SOUTHWAY,20040180,NW11,NULL,NULL,525950,188550,NULL,NULL\nNA,25/10/2018 22:19,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN TWO HOUSES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Bexley,NULL,DANSON CRESCENT,20100423,DA16,NULL,NULL,546550,175650,NULL,NULL\nNA,26/10/2018 11:27,2018,2018/19,Special Service,1,1,333,333,DOG WITH LEG TRAPPED IN CAR SEAT,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100048649,ST GILES CLOSE,19900579,RM10,549838,184424,549850,184450,51.53883619,0.159180714\nNA,26/10/2018 14:40,2018,2018/19,Special Service,1,1,333,333,DOP TRAPPED IN METAL FENCING  AT REAR OF GARAGES     RSPCA ATTENDING,Unknown - Domestic Animal Or Pet,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010225322,BLISS CRESCENT,20800185,SE13,537736,176442,537750,176450,51.47019399,-0.018315422\nNA,27/10/2018 10:39,2018,2018/19,Special Service,1,1,333,333,GULL TRAPPED ON ROOF  RSPCA IN ATTENDANCE,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00023E+11,PURLEY ROAD,20502281,CR8,531261,161445,531250,161450,51.336951,-0.117036\nNA,28/10/2018 11:38,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN STREAM        YOU WILL BE MET IN THE CAR PARK ON THE RIGHT BEFORE THE OPTIMIST PH    CO,Dog,Person (land line),Heathland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,10033423523,HACTON LANE,21300391,RM14,554863,185877,554850,185850,51.55053972,0.232222434\nNA,28/10/2018 20:30,2018,2018/19,Special Service,1,1,333,333,BIRD STUCK IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,30/10/2018 20:37,2018,2018/19,Special Service,1,1,333,333,CAT SHUT IN GARAGE FOR TWO DAYS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010230800,VIOLA AVENUE,20801552,SE2,546697,178546,546650,178550,51.48685163,0.111486555\nNA,30/10/2018 23:34,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,KNOWLE ROAD,22403597,TW2,NULL,NULL,515450,173150,NULL,NULL\nNA,01/11/2018 12:55,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE    CAT TRAPPED BETWEEN TWO OUTBUILDINGS,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,ROSECROFT GARDENS,22401799,TW2,515016,173483,515050,173450,51.44866921,-0.346231717\nNA,01/11/2018 13:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,5300030383,ELMORE STREET,21604800,N1,532755,184225,532750,184250,51.54132811,-0.087057622\nNA,01/11/2018 14:09,2018,2018/19,Special Service,1,1,333,333,DOG IN PRECARIOUS POSITION ON WINDOWSILL,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,COLDBATH STREET,20800354,SE13,NULL,NULL,537750,176550,NULL,NULL\nNA,02/11/2018 23:17,2018,2018/19,Special Service,1,1,333,333,ANIMAL IN DISTRESS  -  CAT   LOCKED IN THE BIN ROOM  -  CALLER WILL MEET AT MAIN GATE BY THE BIN ROO,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,WHINCHAT ROAD,20801605,SE28,NULL,NULL,544650,179250,NULL,NULL\nNA,03/11/2018 12:18,2018,2018/19,Special Service,1,1,333,333,ASSIST  RSPCA  WITH   INJURED GULL  IN CANAL RVP  WITH  RSPCA AT  LASCAR WHARF,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Poplar,6151232,PARNHAM STREET,22700918,E14,536443,181374,536450,181350,51.51482797,-0.035019217\nNA,03/11/2018 13:12,2018,2018/19,Special Service,1,1,333,333,CAT STUCK ON ROOF OF FIVE STOREY BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000633,CHURCHILL,E09000033,WESTMINSTER,Lambeth,NULL,CHURCHILL GARDENS ESTATE,8401852,SW1V,NULL,NULL,529350,178050,NULL,NULL\nNA,03/11/2018 14:01,2018,2018/19,Special Service,NULL,NULL,333,NULL,KITTEN  TRAPPED IN ROOF VOID,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,PARK ROAD,20702363,EN3,NULL,NULL,536250,199150,NULL,NULL\nNA,03/11/2018 21:25,2018,2018/19,Special Service,1,1,333,333,DOG FALLEN IN MANHOLE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,RAMSDEN ROAD,20300960,BR5,NULL,NULL,547150,166450,NULL,NULL\nNA,04/11/2018 12:50,2018,2018/19,Special Service,1,1,333,333,DOG WITH PAW TRAPPED IN CAR SEAT MECHANISM,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,BODLEY ROAD,21800152,KT3,521499,167665,521450,167650,51.39502419,-0.254970637\nNA,06/11/2018 15:53,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,1.00022E+11,LINKS ROAD,22305148,IG8,540077,192240,540050,192250,51.61157753,0.021647783\nNA,06/11/2018 22:54,2018,2018/19,Special Service,2,3,333,999,Redacted,Dog,Person (land line),Bus/coach,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,HOMERTON HIGH STREET,20900533,E9,535746,185109,535750,185150,51.54855616,-0.043617977\nNA,09/11/2018 10:16,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,STANDALE GROVE,21401849,HA4,508346,188635,508350,188650,51.58616704,-0.437524169\nNA,09/11/2018 15:49,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN METAL FENCE   BEST ACCESS SPENCER GROVE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,10008305386,ALLEN ROAD,20900055,N16,533203,185719,533250,185750,51.5546405,-0.0800453\nNA,10/11/2018 00:20,2018,2018/19,Special Service,1,1,333,333,PUPPY WITH PAW TRAPPED IN CANDELABRA,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,PETTITS ROAD,19900289,RM10,NULL,NULL,549050,185150,NULL,NULL\nNA,11/11/2018 07:23,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN FENCE NEAR THE MY SPACE CALLER WILL MEET YOU AND SHOW YOU,Deer,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,DAGNAM PARK DRIVE,21300071,RM3,554174,192334,554150,192350,51.60874324,0.225120285\nNA,12/11/2018 02:20,2018,2018/19,Special Service,1,1,333,333,FOX IN WATER CITY ROAD BASIN,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300101470,CITY ROAD,21604520,N1,532141,182959,532150,182950,51.53009314,-0.096377977\nNA,12/11/2018 04:20,2018,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN CANAL,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,10091161372,BREWERY LANE,22407081,TW1,515980,173693,515950,173650,51.45035975,-0.332290493\nNA,12/11/2018 04:20,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED INBETWEEN  FENCE  PANELS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217031691,FINBOROUGH ROAD,21700211,SW10,525925,177832,525950,177850,51.48543305,-0.187774467\nNA,13/11/2018 14:18,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - RVP GAP ROAD AND HAYDENS ROAD    CAT IN TREE - CALLER STATES THAT CAT IS PHYSI,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,48034736,HAYDONS ROAD,22103210,SW19,525836,171368,525850,171350,51.42735676,-0.191354298\nNA,14/11/2018 11:22,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,1.00022E+11,UNDERSHAW ROAD,22001989,BR1,539658,172110,539650,172150,51.43079614,0.007631035\nNA,15/11/2018 14:53,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED  BETWEEN WALL AND FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,1.00022E+11,EWART ROAD,22001450,SE23,535762,173515,535750,173550,51.44436255,-0.047836293\nNA,16/11/2018 12:13,2018,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,SUTHERLAND ROAD,21105113,N17,NULL,NULL,534250,191050,NULL,NULL\nNA,16/11/2018 17:06,2018,2018/19,Special Service,1,1,333,333,PIGEON  TRAPPED IN CABLE ON ROOF     RIVER ISLAND,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,10022944507,HIGH ROAD,21104652,N22,531181,190048,531150,190050,51.594017,-0.1075794\nNA,19/11/2018 05:34,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN ELECTRICAL CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,PENTIRE ROAD,22865250,E17,NULL,NULL,538650,190650,NULL,NULL\nNA,20/11/2018 14:36,2018,2018/19,Special Service,1,1,333,333,CAT SHUT IN ELECTRICAL METER CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,PENTIRE ROAD,22865250,E17,NULL,NULL,538650,190650,NULL,NULL\nNA,20/11/2018 16:35,2018,2018/19,Special Service,1,1,333,333,DEER STUCK IN RAILINGS,Deer,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,2.00003E+11,SETTLE ROAD,21301584,RM3,555015,192480,555050,192450,51.6098196,0.237316\nNA,21/11/2018 16:45,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,NAVARINO ROAD,20900727,E8,NULL,NULL,534350,184850,NULL,NULL\nNA,21/11/2018 20:47,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,HYDE ROAD,22403572,TW10,NULL,NULL,518550,174950,NULL,NULL\nNA,21/11/2018 21:30,2018,2018/19,Special Service,1,1,333,333,CAT STUCK IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,121007942,GARRATT LANE,22901883,SW18,525601,174506,525650,174550,51.45561287,-0.193619416\nNA,22/11/2018 10:53,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK UP CHIMNEY,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,NAVARINO ROAD,20900727,E8,NULL,NULL,534350,184850,NULL,NULL\nNA,24/11/2018 13:42,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE  -  CAT HAS FALLEN THROUGH ROOF OF DERELICT BUILDING. OWNER IS WAITING OUTSIDE A,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,POWIS MEWS,21700973,W11,NULL,NULL,524950,181350,NULL,NULL\nNA,26/11/2018 15:16,2018,2018/19,Special Service,1,1,333,333,Redacted,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207015274,GALLIARD ROAD,20704240,N9,534476,194687,534450,194650,51.6349305,-0.058248631\nNA,27/11/2018 13:51,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH KITTEN ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,AVOCET MEWS,20800099,SE28,NULL,NULL,544850,179450,NULL,NULL\nNA,29/11/2018 16:17,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN INBETWEEN WINDOW LEDGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,NULL,CRANLEY DRIVE,22302763,IG2,NULL,NULL,544250,187850,NULL,NULL\nNA,01/12/2018 06:50,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,MOORDOWN,20801049,SE18,NULL,NULL,543650,176750,NULL,NULL\nNA,01/12/2018 10:26,2018,2018/19,Special Service,1,2,333,666,PIGEON TRAPPED IN NETTING AT FRONT OF BUILDING APPROX SECOND FLOOR   REQUESTED TO ATTEND BY RSPCA,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,1.00024E+11,CRANWOOD STREET,20900286,EC1V,532823,182621,532850,182650,51.52689,-0.086691\nNA,01/12/2018 11:55,2018,2018/19,Special Service,1,1,333,333,CAT WITH PAW TRAPPED IN GRILL OF WINDOW,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,AISLIBIE ROAD,22001150,SE12,NULL,NULL,539450,175050,NULL,NULL\nNA,03/12/2018 10:18,2018,2018/19,Special Service,1,1,333,333,DOG POSSIBLY FALLEN INTO BADGER HOLE  CALLER IS AT GOLF COURSE AREA ON FOOTPATH,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011481,SELSDON VALE & FORESTDALE,E09000008,CROYDON,Addington,2.00001E+11,ADDINGTON ROAD,20502645,CR2,535300,161755,535350,161750,51.33879899,-0.05895798\nNA,03/12/2018 20:46,2018,2018/19,Special Service,1,1,333,333,FOX STUCK UNDER FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000180,HOBBAYNE,E09000009,EALING,Ealing,NULL,TEMPLEMAN ROAD,20601699,W7,NULL,NULL,515750,181750,NULL,NULL\nNA,04/12/2018 18:19,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WALL NEXT TO BIN CHUTE COMMUNAL AREA GROUND FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,SUSSEX CLOSE,21606975,N19,NULL,NULL,530250,186750,NULL,NULL\nNA,05/12/2018 20:41,2018,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN HOLE IN BACK GARDEN,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,MAYFIELD ROAD,20502591,CR7,530820,167873,530850,167850,51.39482068,-0.120998773\nNA,08/12/2018 12:23,2018,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN RAILINGS ON THE THE BROCKLEY HILL ESTATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000286,CANONS,E09000015,HARROW,Stanmore,10034787215,AUGUSTUS CLOSE,21202569,HA7,517803,193068,517850,193050,51.62411438,-0.299589476\nNA,08/12/2018 15:51,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND TOILET,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000038,RIVER,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,OVAL ROAD NORTH,19900900,RM10,NULL,NULL,549750,183750,NULL,NULL\nNA,09/12/2018 04:40,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA RETRIEVE CAT FROM BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000595,FOREST,E09000031,WALTHAM FOREST,Leyton,NULL,LEA BRIDGE ROAD,22850350,E17,NULL,NULL,538450,188650,NULL,NULL\nNA,09/12/2018 23:05,2018,2018/19,Special Service,1,1,333,333,CAT PHYSICALLY TRAPPED BEHIND OVEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,HIGH ROAD,19900165,RM6,NULL,NULL,547850,187950,NULL,NULL\nNA,10/12/2018 10:19,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,WHITWORTH ROAD,20801611,SE18,NULL,NULL,543350,177350,NULL,NULL\nNA,10/12/2018 19:07,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GAP BETWEEN ROOF AND TILES,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,COOTE ROAD,19900087,RM8,NULL,NULL,548850,186350,NULL,NULL\nNA,10/12/2018 20:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT TRAPPED IN CAR ENGINE BELIEVED INJURED  OWNER ON SCENE - CAR IS LOCATED IN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207083452,CHASE SIDE,20701407,N14,529360,194475,529350,194450,51.63422871,-0.132210507\nNA,12/12/2018 13:07,2018,2018/19,Special Service,1,1,333,333,RSPCA ON SITE WITH DEER LEG TRAPPED IN GATE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000055,HENDON,E09000003,BARNET,Hendon,200064180,HILLTOP GARDENS,20023080,NW4,522772,190453,522750,190450,51.59955423,-0.228759227\nNA,13/12/2018 11:51,2018,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34062880,SINCLAIR ROAD,21000737,W14,524088,179443,524050,179450,51.50031694,-0.213656376\nNA,14/12/2018 06:15,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER BATHROOM SINK,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,ASPINALL ROAD,22000059,SE4,NULL,NULL,535850,175850,NULL,NULL\nNA,14/12/2018 20:15,2018,2018/19,Special Service,1,0,333,0,RUNNING CALL TO DOG LOOSE IN THE ROADWAY,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00023E+11,NORTH STREET,21300513,RM11,553981,187413,553950,187450,51.56458492,0.220182145\nNA,15/12/2018 13:46,2018,2018/19,Special Service,1,2,333,666,CAT TRAPPED UNDER FLOORBOARDS - RSPCA ON SCENE,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000110,CHELSFIELD AND PRATTS BOTTOM,E09000006,BROMLEY,Orpington,NULL,WINDSOR DRIVE,20301476,BR6,NULL,NULL,546650,163950,NULL,NULL\nNA,16/12/2018 07:10,2018,2018/19,Special Service,1,1,333,333,KITTEN STUCK BEHIND FRIDGE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Chiswick,NULL,HARTSWOOD ROAD,21000421,W12,NULL,NULL,521650,179450,NULL,NULL\nNA,16/12/2018 08:03,2018,2018/19,Special Service,1,1,333,333,CAT STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Purley,NULL,CHRISTCHURCH ROAD,20501976,CR8,NULL,NULL,531550,161850,NULL,NULL\nNA,16/12/2018 17:33,2018,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN WALL AND HEAVY UNIT   ELDERLY OWNER ON SITE UNABLE TO FREE CAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,ALEXANDRA PARK ROAD,21106086,N10,NULL,NULL,529050,190450,NULL,NULL\nNA,16/12/2018 23:32,2018,2018/19,Special Service,1,1,333,333,KITTEN TRAPPED IN PANEL BEHIND TOILET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,BUCKLE STREET,22700221,E1,NULL,NULL,534050,181350,NULL,NULL\nNA,17/12/2018 16:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - BIRD CAUGHT IN NETTING,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.00023E+11,QUEENSWAY,20300576,BR5,544412,167486,544450,167450,51.38805017,0.074084198\nNA,18/12/2018 12:39,2018,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011469,KENLEY,E09000008,CROYDON,Purley,1.00024E+11,HAYES LANE,20502124,CR3,532524,157490,532550,157450,51.30111451,-0.100368723\nNA,22/12/2018 14:18,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN LEDGE ON WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Poplar,NULL,BRYMAY CLOSE,22700218,E3,NULL,NULL,537550,183250,NULL,NULL\nNA,23/12/2018 15:29,2018,2018/19,Special Service,1,1,333,333,\"CAT UP TREE, BEEN UP THERE FOR OVER A DAY - INFORMANT WAITING TO MEET YOU\",Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6651849,GREEN BANK,22700575,E1W,534821,180099,534850,180050,51.50375517,-0.058863362\nNA,24/12/2018 17:17,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,LISSENDEN GARDENS,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL\nNA,24/12/2018 23:40,2018,2018/19,Special Service,1,1,333,333,DOG FALLEN INTO THE RIVER - CHELSEA HARBOUR,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Chelsea,34133700,CHELSEA HARBOUR DRIVE,21000208,SW10,526471,176659,526450,176650,51.47477258,-0.18033847\nNA,25/12/2018 09:02,2018,2018/19,Special Service,2,3,333,999,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Assist trapped wild animal,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,1.00024E+11,WOODVALE AVENUE,20501596,SE25,533970,169293,533950,169250,51.4068491,-0.075204248\nNA,25/12/2018 15:58,2018,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN RECLINER CHAIR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,NULL,MITCHELL CLOSE,21310044,RM13,NULL,NULL,553450,183350,NULL,NULL\nNA,26/12/2018 16:37,2018,2018/19,Special Service,1,1,333,333,DOG STUCK IN METAL GATE BY SHOULDER - OWNER ON SCENE,Dog,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,10022801801,CHESTNUT AVENUE,21400376,UB7,507049,180549,507050,180550,51.51373623,-0.458701242\nNA,29/12/2018 15:39,2018,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN NETTING,Fox,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207008321,ACCESS ROAD FROM BELL LANE TO 133 BELL LANE,20700061,EN3,535964,198222,535950,198250,51.66634115,-0.035400387\nNA,30/12/2018 17:26,2018,2018/19,Special Service,1,1,333,333,Redacted,Squirrel,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,HANSHAW DRIVE,20020860,HA8,NULL,NULL,520650,190850,NULL,NULL\nNA,31/12/2018 01:01,2018,2018/19,Special Service,1,1,333,333,KITTENS TRAPPED UNDER THE FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,NULL,CEDAR ROAD,20500767,CR0,NULL,NULL,533150,165950,NULL,NULL\nNA,31/12/2018 11:13,2018,2018/19,Special Service,1,3,333,999,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,NULL,NAPIER ROAD,20501218,SE25,NULL,NULL,534650,168050,NULL,NULL\nNA,01/01/2019 15:09,2019,2018/19,Special Service,1,1,333,333,INVESTIGATE POSSIBLE BABY LOCKED IN ALONE,Unknown - Domestic Animal Or Pet,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,217064603,PARK WALK,21700392,SW10,526603,177763,526650,177750,51.48465988,-0.178044712\nNA,02/01/2019 13:04,2019,2018/19,Special Service,1,4,333,1332,Redacted,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,10008329457,HOMERTON ROAD,20900534,E9,536975,185988,536950,185950,51.55615804,-0.025559021\nNA,04/01/2019 09:04,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND SINK IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,NULL,HAYMERLE ROAD,22501223,SE15,NULL,NULL,534150,177650,NULL,NULL\nNA,04/01/2019 12:58,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN MESH SINCE YESTERDAY,Bird,Person (mobile),Hospital,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,10000444919,WESTMINSTER BRIDGE ROAD,21901482,SE1,530702,179518,530750,179550,51.49950222,-0.118387508\nNA,04/01/2019 15:56,2019,2018/19,Special Service,2,4,333,1332,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207188076,THE BOURNE,20705011,N14,530606,194312,530650,194350,51.63247073,-0.114275052\nNA,05/01/2019 00:54,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN GARDEN GATE CALLED BY THE RSPCA WHO ARE NOT ON SCENE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,NULL,RAYMEAD AVENUE,20501329,CR7,NULL,NULL,531450,167950,NULL,NULL\nNA,05/01/2019 10:51,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN RAILINGS,Cat,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000334,MANOR,E09000017,HILLINGDON,Ruislip,1.00021E+11,PARK WAY,21401490,HA4,510306,187381,510350,187350,51.57452401,-0.409642705\nNA,06/01/2019 11:50,2019,2018/19,Special Service,1,2,333,666,SWAN TRAPPED IN BUSH,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,1.00021E+11,AVENUE ROAD,20500652,SE25,533980,169175,533950,169150,51.40578623,-0.07510978\nNA,06/01/2019 20:10,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KITCHEN CABINETS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,ELLSWORTH STREET,22700463,E2,NULL,NULL,534750,182750,NULL,NULL\nNA,07/01/2019 05:47,2019,2018/19,Special Service,2,5,333,1665,HORSE FALLEN INTO RIVER       WATER RESCUE LEVEL  IMPLEMENTED,Horse,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207187270,WHARF ROAD,20702448,EN3,536440,195707,536450,195750,51.64361936,-0.029495258\nNA,07/01/2019 14:28,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,ROLVENDEN PLACE,21104992,N17,NULL,NULL,534450,191050,NULL,NULL\nNA,10/01/2019 11:44,2019,2018/19,Special Service,1,2,333,666,CAT FALLEN BEHIND BUILT-IN FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MOSTYN GROVE,22700849,E3,NULL,NULL,537050,183050,NULL,NULL\nNA,11/01/2019 11:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN GARAGES - REQUESTED BY RSPCA,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,GUILDSWAY,22841800,E17,536551,190689,536550,190650,51.5985011,-0.0298541\nNA,11/01/2019 12:13,2019,2018/19,Special Service,1,1,333,333,CAT IN DISTRESS UP TREE BEING ATTACKED BY BIRDS,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,1.00023E+11,COPPETTS ROAD,21101165,N10,527971,190876,527950,190850,51.60220393,-0.153582849\nNA,11/01/2019 21:16,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING     ON THE WESTBOUND PLAFORM BY THE TICKET BOOTH,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,2.00004E+11,THE GARDENS,21202022,HA1,514228,188061,514250,188050,51.579847,-0.3528532\nNA,13/01/2019 11:02,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,SANSOM ROAD,22872800,E11,539465,186593,539450,186550,51.56098826,0.010576587\nNA,13/01/2019 19:20,2019,2018/19,Special Service,1,2,333,666,CAT TRAPPED BETWEEN RAILWAY TRACKS,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,5120975,BAYNES STREET,20400661,NW1,529349,184146,529350,184150,51.5414076,-0.136167736\nNA,14/01/2019 00:27,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WESTFIELDS AVENUE,22405246,SW13,NULL,NULL,521650,176050,NULL,NULL\nNA,14/01/2019 13:13,2019,2018/19,Special Service,1,1,333,333,CAT SHUT IN WASHING MACHINE &  UNABLE TO OPEN AS HANDLE IS BROKEN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,LONGACRE ROAD,22853700,E17,NULL,NULL,538650,190850,NULL,NULL\nNA,15/01/2019 02:03,2019,2018/19,Special Service,1,1,333,333,DEER TRAPPED IN METAL FENCE FRONT GARDEN,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,1.00021E+11,POLE HILL ROAD,21401551,UB10,507921,182455,507950,182450,51.53070708,-0.445547282\nNA,16/01/2019 10:33,2019,2018/19,Special Service,1,1,333,333,RUNNING CALL TO CAT STUCK IN ROOF VOID,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,NORBURY COURT ROAD,20501229,SW16,NULL,NULL,530750,169150,NULL,NULL\nNA,16/01/2019 20:48,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED ON VINES THREE STOREYS UP - OWNER ON SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,1.00023E+11,CLAIRVIEW ROAD,22900957,SW16,528934,171495,528950,171450,51.42780221,-0.146768206\nNA,17/01/2019 13:34,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN FENCE,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,12037489,NORWOOD ROAD,20602330,UB2,513135,178211,513150,178250,51.49154498,-0.371771323\nNA,17/01/2019 18:43,2019,2018/19,Special Service,1,1,333,333,INJURED CAT TRAPPED IN CHIMNEY ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,BICKNELL ROAD,21900184,SE5,NULL,NULL,532250,175550,NULL,NULL\nNA,18/01/2019 12:58,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - RSPCA ON SCENE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,NORTH END ROAD,21000597,W14,NULL,NULL,524850,177850,NULL,NULL\nNA,18/01/2019 14:05,2019,2018/19,Special Service,1,1,333,333,SQUIRREL TRAPPED BETWEEN PIPES  A WALL AND THE ROOF,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,BARING ROAD,22005120,SE12,NULL,NULL,540550,172550,NULL,NULL\nNA,18/01/2019 16:48,2019,2018/19,Special Service,1,1,333,333,PARROT TRAPPED IN NETTING ON MAIN ROAD OUTSIDE NURSING HOME,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,NULL,CHAPLIN ROAD,20202398,HA0,NULL,NULL,518050,184850,NULL,NULL\nNA,19/01/2019 23:17,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,GILBERT STREET,8401343,W1K,NULL,NULL,528450,181050,NULL,NULL\nNA,20/01/2019 20:33,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,WOODBERRY DOWN,20901092,N4,NULL,NULL,532150,187450,NULL,NULL\nNA,21/01/2019 12:08,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,NULL,NORMANS CLOSE,20200612,NW10,NULL,NULL,520550,184650,NULL,NULL\nNA,23/01/2019 00:23,2019,2018/19,Special Service,1,1,333,333,DEER WITH HEAD STUCK IN RAILINGS    OUTSIDE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025149964,DAGNAM PARK DRIVE,21300071,RM3,554465,192391,554450,192350,51.60917725,0.229349506\nNA,26/01/2019 11:25,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL TRAPPED BEHIND WALL  CALLER SPEAKS LITTLE ENGLISH,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,SCRATTONS TERRACE,19900573,IG11,NULL,NULL,548050,183450,NULL,NULL\nNA,26/01/2019 12:55,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,1.00022E+11,BROCKLEY PARK,22001276,SE23,536380,173569,536350,173550,51.44470654,-0.038928088\nNA,30/01/2019 11:26,2019,2018/19,Special Service,2,3,333,999,ASSIST KENT FRS WITH LARGE ANIMAL RESCUE,Unknown - Animal rescue from water - Farm animal,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,NA,NA,NA,NA,Kent,44003798,MEAD WALL,32102382,ME3,571394,177648,571350,177650,51.47184494,0.466420909\nNA,31/01/2019 10:29,2019,2018/19,Special Service,1,1,333,333,SWAN IN DISTRESS TRAPPED IN ICE IN POND   LEVEL TWO WATER OPS,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100036344,LODGE AVENUE,19900241,IG11,546324,184566,546350,184550,51.54103634,0.108610476\nNA,31/01/2019 23:17,2019,2018/19,Special Service,1,1,333,333,ASSIST WILDLIFE REPS WITH SMALL ANIMAL RESCUE      REPS WILL MEET CREWS IN CAR PARK,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,02/02/2019 11:02,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED UNDER COOKER,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000289,HARROW ON THE HILL,E09000015,HARROW,Northolt,NULL,COWEN AVENUE,21201206,HA2,NULL,NULL,514550,186650,NULL,NULL\nNA,04/02/2019 12:24,2019,2018/19,Special Service,1,2,333,666,CAT STUCK UP A DRAIN   FRU REQUESTED FROM SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202140963,LAWRENCE AVENUE,20204111,NW10,520673,183772,520650,183750,51.53996336,-0.261344341\nNA,05/02/2019 07:30,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH FOX TRAPPED IN STATUE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,THE NETHERLANDS,20502519,CR5,NULL,NULL,529350,157750,NULL,NULL\nNA,06/02/2019 22:36,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN WHEEL OF VAN,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,HIGH ROAD,21106680,N17,533915,190787,533950,190750,51.60001675,-0.067850892\nNA,07/02/2019 21:41,2019,2018/19,Special Service,1,2,333,666,HORSE STUCK IN DITCH - WATER OPS LEVEL TWO IMPLEMENTED CALLER SAYS IT IS BEHIND FENCING OF ASDAS CAR,Horse,Person (mobile),Other outdoor location,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,2.00003E+11,NORMAN ROAD,20101027,DA17,549733,179950,549750,179950,51.49866811,0.155771365\nNA,09/02/2019 21:07,2019,2018/19,Special Service,1,1,333,333,KITTEN STUCK UP ON ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,CLYDE ROAD,21101155,N22,NULL,NULL,529650,190750,NULL,NULL\nNA,11/02/2019 01:18,2019,2018/19,Special Service,NULL,NULL,333,NULL,DOG TRAPPED AT END OF GARDEN - CALLER IS DISABLED AND UNABLE TO RETRIEVE HIM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,NORMAN CRESCENT,21201793,HA5,NULL,NULL,511350,190650,NULL,NULL\nNA,12/02/2019 13:45,2019,2018/19,Special Service,1,1,333,333,CAT STUCK ON BALCONY  THREE FLOORS UP,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,STURMER WAY,21603886,N7,NULL,NULL,530650,185450,NULL,NULL\nNA,15/02/2019 13:37,2019,2018/19,Special Service,1,1,333,333,Redacted,Fox,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,NULL,CHURCH ROAD,20100315,DA7,NULL,NULL,548650,175550,NULL,NULL\nNA,16/02/2019 00:38,2019,2018/19,Special Service,1,1,333,333,Redacted,Squirrel,Person (land line),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped wild animal,E05000194,BUSH HILL PARK,E09000010,ENFIELD,Edmonton,NULL,VILLAGE ROAD,20704874,EN1,NULL,NULL,533150,195850,NULL,NULL\nNA,17/02/2019 18:03,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,HARROW ROAD,8400530,W2,NULL,NULL,525550,181750,NULL,NULL\nNA,18/02/2019 12:16,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT UP A TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,1.00021E+11,SWAKELEYS ROAD,21401902,UB10,506332,185920,506350,185950,51.56215171,-0.467396395\nNA,18/02/2019 13:04,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE - CAT STUCK INSIDE PIPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,HARROW ROAD,8400530,W2,NULL,NULL,525550,181750,NULL,NULL\nNA,19/02/2019 12:28,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN RAILINGS LOCATED IN THE MOREMEAD PARK NR RIVER EDGE,Cat,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,COLE PARK ROAD,22403402,TW1,516347,173858,516350,173850,51.45176687,-0.326962086\nNA,19/02/2019 20:35,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000474,WIMBLEDON PARK,E09000024,MERTON,Wimbledon,NULL,STRATHEARN ROAD,22106033,SW19,NULL,NULL,525350,171650,NULL,NULL\nNA,20/02/2019 11:45,2019,2018/19,Special Service,1,1,333,333,SWAN TRAPPED IN MUD,Bird,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,10025166897,CLOCKHOUSE LANE,21500287,TW14,507571,172471,507550,172450,51.44103678,-0.453639256\nNA,20/02/2019 12:07,2019,2018/19,Special Service,1,1,333,333,DOG TRAPPED IN BETWEEN SHED AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,BROOKFIELD PARK,20400098,NW5,NULL,NULL,528650,186350,NULL,NULL\nNA,20/02/2019 19:07,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,21/02/2019 12:01,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT TRAPPED UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207099682,CUCKOO HALL LANE,20703243,N9,535733,194873,535750,194850,51.63629735,-0.040030659\nNA,24/02/2019 12:18,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED  BETWEEN TWO WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,NULL,HALFORD ROAD,21400866,UB10,NULL,NULL,507050,185650,NULL,NULL\nNA,24/02/2019 13:59,2019,2018/19,Special Service,1,2,333,666,Redacted,Snake,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,HIGH STREET,22201445,E15,NULL,NULL,538850,184150,NULL,NULL\nNA,24/02/2019 14:35,2019,2018/19,Special Service,1,1,333,333,CAT IMPALED ON METAL POLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,NULL,ANCHOR AND HOPE LANE,20800063,SE7,NULL,NULL,540950,179150,NULL,NULL\nNA,25/02/2019 11:54,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING    LONDON WILDLIFE PRPTECTION OFFICER ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,48119014,RAYNES PARK BRIDGE,22105345,SW20,523306,169313,523350,169350,51.4094449,-0.228436126\nNA,26/02/2019 17:24,2019,2018/19,Special Service,1,1,333,333,PUPPY STUCK BEHIND SHED,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000171,CLEVELAND,E09000009,EALING,Ealing,12189729,DRAYTON GROVE,20600563,W13,516410,180948,516450,180950,51.51547322,-0.323717229\nNA,27/02/2019 14:16,2019,2018/19,Special Service,1,2,333,666,TO ASSIST RSPCA WITH CAT UP A TREE - AERIAL REQUESTED FROM SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,10093734544,SWAKELEYS ROAD,21401902,UB10,506332,185919,506350,185950,51.56214596,-0.467406566\nNA,27/02/2019 16:01,2019,2018/19,Special Service,1,1,333,333,CAT STUCK BETWEEN WALL AND HEATER,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,NULL,TRYON CRESCENT,20901739,E9,NULL,NULL,535050,183950,NULL,NULL\nNA,28/02/2019 07:46,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200160009,BURROUGHS GARDENS,20006300,NW4,522684,189053,522650,189050,51.58699705,-0.230526524\nNA,28/02/2019 15:03,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,KENWAY ROAD,21700298,SW5,NULL,NULL,525550,178750,NULL,NULL\nNA,28/02/2019 17:17,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN GUTTERING   AT FRONT OF PREMISES     REQUESTED BY RSPCA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,NULL,CAUSEYWARE ROAD,20704017,N9,NULL,NULL,535150,194750,NULL,NULL\nNA,28/02/2019 17:32,2019,2018/19,Special Service,1,1,333,333,CAT LOCKED IN FLAT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Whitechapel,NULL,ST KATHARINES WAY,22701138,E1W,NULL,NULL,534050,180250,NULL,NULL\nNA,28/02/2019 18:09,2019,2018/19,Special Service,1,2,333,666,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,PRINCES WAY,22903974,SW19,NULL,NULL,524150,173150,NULL,NULL\nNA,28/02/2019 22:04,2019,2018/19,Special Service,1,1,333,333,RABBIT TRAPPED IN MECHANISM UNDER SETTEE,Rabbit,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000319,ROMFORD TOWN,E09000016,HAVERING,Hornchurch,NULL,BENJAMIN CLOSE,21300224,RM11,NULL,NULL,552350,188150,NULL,NULL\nNA,01/03/2019 09:43,2019,2018/19,Special Service,1,1,333,333,PIGEONS TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,WORNINGTON ROAD,21701065,W10,NULL,NULL,524250,182050,NULL,NULL\nNA,02/03/2019 12:03,2019,2018/19,Special Service,1,1,333,333,CAT STUCK BEHIND KITCHEN UNITS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,MOORE WALK,22207813,E7,NULL,NULL,540450,185550,NULL,NULL\nNA,03/03/2019 11:37,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA WITH CAT  STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,MOUNTBATTEN CLOSE,21900996,SE19,NULL,NULL,533250,171150,NULL,NULL\nNA,03/03/2019 16:26,2019,2018/19,Special Service,1,1,333,333,FOX TRAPPED IN POND FILTER,Fox,Person (mobile),Garden equipment,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00021E+11,FURZE LANE,20502085,CR8,530559,161905,530550,161950,51.3412492,-0.126927655\nNA,03/03/2019 17:19,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED IN  MECHANISM OF RECLINER CHAIR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011220,BLACKFEN & LAMORBEY,E09000004,BEXLEY,Eltham,NULL,PARISH GATE DRIVE,20101098,DA15,NULL,NULL,545250,174250,NULL,NULL\nNA,05/03/2019 16:37,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Other private non-residential building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,CASEWICK ROAD,21901551,SE27,531637,171546,531650,171550,51.42764294,-0.10789136\nNA,06/03/2019 07:20,2019,2018/19,Special Service,1,1,333,333,CAT STUCK ON BALCONY - OCCPANTS ON HOLIDAY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,DROOP STREET,8400073,W10,NULL,NULL,524250,182450,NULL,NULL\nNA,08/03/2019 10:57,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,NULL,BARNFIELD PLACE,22702049,E14,NULL,NULL,537450,178650,NULL,NULL\nNA,08/03/2019 12:23,2019,2018/19,Special Service,1,1,333,333,PIGEON TRAPPED IN NETTING    BY RAILWAY BRIDGE    CALLER IS WAITING FOR YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,New Malden,48128491,COOMBE LANE,22101605,SW20,523291,169324,523250,169350,51.40955327,-0.228651838\nNA,09/03/2019 19:32,2019,2018/19,Special Service,1,1,333,333,DOG TRAPPED ON NEIGHBOURING BALCONY,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LONSDALE ROAD,21700934,W11,NULL,NULL,524950,181050,NULL,NULL\nNA,10/03/2019 13:27,2019,2018/19,Special Service,1,1,333,333,ASSIST RSPCA,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,10/03/2019 15:14,2019,2018/19,Special Service,1,1,333,333,INJURED CAT STUCK ON  LEDGE OF APARTMENT - CALLER WILL MEET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,ST JOSEPHS STREET,22904881,SW8,NULL,NULL,528850,176850,NULL,NULL\nNA,10/03/2019 17:00,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,DAIRY CLOSE,21001557,SW6,NULL,NULL,525050,176650,NULL,NULL\nNA,11/03/2019 15:40,2019,2018/19,Special Service,1,1,333,333,RSPCA REQ ATT OF BRIGADE   CAT IN DISTRESS UP TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000103,WELSH HARP,E09000005,BRENT,Hendon,202200937,HOLDEN AVENUE,20202211,NW9,520280,187186,520250,187150,51.57073257,-0.265837592\nNA,13/03/2019 12:59,2019,2018/19,Special Service,1,1,333,333,BIRD TRAPPED IN NETTING  ASSIST RSPCA  NEXT TO RECYCLING CENTRE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128021349,CHAPEL MILL ROAD,21880152,KT1,518880,168538,518850,168550,51.4034214,-0.2923166\nNA,15/03/2019 11:24,2019,2018/19,Special Service,1,1,333,333,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WYATTS LANE,22889550,E17,NULL,NULL,538250,189750,NULL,NULL\nNA,15/03/2019 18:09,2019,2018/19,Special Service,1,3,333,999,INJURED SWAN TRAPPED IN TREE - APPROX FORTY FOOT UP,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,10024345291,ASH ISLAND,22407002,KT8,514950,168862,514950,168850,51.40714729,-0.34868271\nNA,16/03/2019 17:55,2019,2018/19,Special Service,1,1,333,333,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,ERLANGER ROAD,22000384,SE14,NULL,NULL,535850,176250,NULL,NULL\nNA,17/03/2019 18:32,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,10093257864,FOOTPATH FROM 340 GREAT WEST ROAD TO SUTTON SQUARE,21519949,TW5,512923,176640,512950,176650,51.47746446,-0.375328159\nNA,17/03/2019 21:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED BEHIND KITCHEN CABINET,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,THESIGER ROAD,20302124,SE20,NULL,NULL,535950,170250,NULL,NULL\nNA,18/03/2019 12:47,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Bexley,10011845442,DANSON ROAD,20100426,DA6,547343,174810,547350,174850,51.45311327,0.119220606\nNA,19/03/2019 08:41,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,BRIAR WALK,8400753,W10,NULL,NULL,524250,182450,NULL,NULL\nNA,19/03/2019 12:36,2019,2018/19,Special Service,1,2,333,666,HORSE FALLEN AND UNABLE TO GET UP,Horse,Person (mobile),Other agricultural building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Croydon,1.00021E+11,PURLEY OAKS ROAD,20502277,CR2,533112,161941,533150,161950,51.34097989,-0.090288896\nNA,20/03/2019 10:14,2019,2018/19,Special Service,1,1,333,333,CAT STUCK IN ELECTRICAL SUB STATION,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,1.00023E+11,MARE STREET,20900658,E8,535011,184274,535050,184250,51.5412327,-0.054530777\nNA,20/03/2019 21:43,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED UP TREE OVERHANGING RIVER,Cat,Person (land line),Canal/riverbank vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,IVYBRIDGE CLOSE,21401049,UB8,506196,182883,506150,182850,51.5348816,-0.470275626\nNA,21/03/2019 09:08,2019,2018/19,Special Service,1,2,333,666,Redacted,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000323,UPMINSTER,E09000016,HAVERING,Hornchurch,10015776172,ST MARYS LANE,21301899,RM14,558934,187079,558950,187050,51.56020451,0.291423812\nNA,21/03/2019 11:29,2019,2018/19,Special Service,1,2,333,666,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000126,SHORTLANDS,E09000006,BROMLEY,Beckenham,1.0002E+11,WICKHAM WAY,20302048,BR3,538408,167861,538450,167850,51.39291677,-0.01199935\nNA,22/03/2019 10:55,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,1.00022E+11,WENSLEY AVENUE,22305436,IG8,540145,191404,540150,191450,51.60404366,0.022289453\nNA,22/03/2019 17:13,2019,2018/19,Special Service,1,1,333,333,PIGEON STUCK IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536650,189650,NULL,NULL\nNA,23/03/2019 12:04,2019,2018/19,Special Service,1,1,333,333,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000115,CRAY VALLEY WEST,E09000006,BROMLEY,Orpington,NULL,ASCOT ROAD,20300001,BR5,NULL,NULL,545850,168150,NULL,NULL\nNA,24/03/2019 19:44,2019,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,1.00023E+11,MALTA ROAD,22855200,E10,537169,187545,537150,187550,51.57010025,-0.022160828\nNA,27/03/2019 10:22,2019,2018/19,Special Service,1,2,333,666,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,ST JAMES'S PLACE,8400875,SW1A,NULL,NULL,529150,180150,NULL,NULL\nNA,27/03/2019 10:58,2019,2018/19,Special Service,1,1,333,333,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10070008611,PICKHURST LANE,20302961,BR2,540241,166228,540250,166250,51.37779389,0.013689658\nNA,28/03/2019 17:23,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,ODESSA ROAD,22863000,E7,NULL,NULL,539850,185950,NULL,NULL\nNA,29/03/2019 12:15,2019,2018/19,Special Service,1,1,333,333,CAT TRAPPED UNDER NEATH KICTHEN CUPBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,COLMORE ROAD,20702189,EN3,NULL,NULL,535450,196250,NULL,NULL\nNA,29/03/2019 21:28,2019,2018/19,Special Service,1,1,333,333,DOG OR FOX TRAPPED IN EXTRACTION VENT,Dog,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,100029314,STATION ROAD,19900343,RM6,547750,187805,547750,187850,51.5697641,0.1305088\nNA,30/03/2019 17:19,2019,2018/19,Special Service,1,2,333,666,SWAN IN DISTRESS ON DISUSED BOAT,Bird,Person (mobile),Barge,Boat,Other animal assistance,Assist trapped wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,1.00024E+11,HIGH STREET,21500607,TW8,518403,177819,518450,177850,51.48694373,-0.296056086\nNA,31/03/2019 03:48,2019,2018/19,Special Service,1,1,333,333,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,NULL,EDGECOT GROVE,21106435,N15,NULL,NULL,533050,188850,NULL,NULL\nNA,31/03/2019 16:16,2019,2018/19,Special Service,1,1,333,333,ANIMAL OR BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,NORTHCOTE ROAD,22862000,E17,NULL,NULL,536350,189050,NULL,NULL\nNA,01/04/2019 05:11,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER KITCHEN UNITS,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011095,BOROUGH & BANKSIDE,E09000028,SOUTHWARK,Dowgate,NULL,AYRES STREET,22500122,SE1,NULL,NULL,532350,179950,NULL,NULL\nNA,01/04/2019 14:20,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING ABOVE,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,01/04/2019 16:35,2019,2019/20,Special Service,1,2,339,678,CAT OR DOG TRAPPED INSIDE STORAGE UNIT IN QUEENSWAY MARKET  CALLER ON SCENE WILL MEET YOU,Cat,Person (mobile),Indoor Market,Non Residential,Other animal assistance,Assist trapped wild animal,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,10033542052,QUEENSWAY,8401512,W2,525780,181072,525750,181050,51.5145804,-0.1887159\nNA,01/04/2019 20:37,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN THE MUD NEXT TO CANNAL,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10008985450,CANNING ROAD,22207641,E15,538882,183106,538850,183150,51.52979263,0.00079877\nNA,02/04/2019 19:46,2019,2019/20,Special Service,1,1,339,339,INJURED CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011230,SIDCUP,E09000004,BEXLEY,Sidcup,1.0002E+11,MALLARD WALK,20100909,DA14,547229,170979,547250,170950,51.41871986,0.115989637\nNA,06/04/2019 02:39,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND SINK - HAVING DIFFICULTY BREATHING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,PEARTREE WAY,20801696,SE10,NULL,NULL,540250,179050,NULL,NULL\nNA,06/04/2019 19:56,2019,2019/20,Special Service,1,3,339,1017,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207094702,WOODLAND WAY,20704952,N21,531104,193726,531150,193750,51.62709232,-0.107314052\nNA,07/04/2019 11:26,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED BEHIND CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,NULL,MILTON ROAD,20100984,DA16,NULL,NULL,545650,176850,NULL,NULL\nNA,07/04/2019 13:48,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,WINDSOR ROAD,20702451,EN3,NULL,NULL,535550,199350,NULL,NULL\nNA,07/04/2019 17:49,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT AT THIRD FLOOR LEVEL  RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,FRESTON ROAD,21700874,W11,NULL,NULL,523750,180650,NULL,NULL\nNA,07/04/2019 20:24,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000570,WALLINGTON SOUTH,E09000029,SUTTON,Wallington,768018088,DOWER AVENUE,22605457,SM6,528882,162767,528850,162750,51.3493783,-0.15068951\nNA,08/04/2019 23:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN BUSH,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,CHESSINGTON HILL PARK,21800236,KT9,NULL,NULL,519050,164050,NULL,NULL\nNA,09/04/2019 15:31,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000099,QUEENSBURY,E09000005,BRENT,Stanmore,NULL,ALPINE ROAD,20204265,NW9,NULL,NULL,518950,189350,NULL,NULL\nNA,10/04/2019 07:47,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,DOWNHAM,E09000023,LEWISHAM,Bromley,1.00022E+11,CONISTON ROAD,22004352,BR1,539119,170752,539150,170750,51.41872532,-0.000656349\nNA,11/04/2019 08:22,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED DOWN HOLE,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000259,PALACE RIVERSIDE,E09000013,HAMMERSMITH AND FULHAM,Fulham,34142080,PUTNEY BRIDGE APPROACH,21000658,SW6,524292,175922,524250,175950,51.4686265,-0.211951221\nNA,12/04/2019 10:43,2019,2019/20,Special Service,1,3,339,1017,Redacted,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,10008329457,HOMERTON ROAD,20900534,E9,536964,185643,536950,185650,51.55306484,-0.02584976\nNA,12/04/2019 14:49,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FENCE - CALLER WILL MEET YOU AND DIRECT YOU,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,10091851669,MANDEVILLE ROAD,20601113,UB5,513165,184536,513150,184550,51.54838402,-0.369318287\nNA,13/04/2019 23:16,2019,2019/20,Special Service,1,1,339,339,KITTEN UP TREE FOR TWO DAYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,PHEASANT CLOSE,20502267,CR8,NULL,NULL,531850,160850,NULL,NULL\nNA,14/04/2019 05:08,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Erith,NULL,KALE ROAD,20100778,DA18,NULL,NULL,548050,179550,NULL,NULL\nNA,14/04/2019 09:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER SINK,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Walthamstow,NULL,BEAUFORT CLOSE,22813500,E4,NULL,NULL,537950,191750,NULL,NULL\nNA,15/04/2019 10:42,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SOFABED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,PEPPER CLOSE,22200951,E6,NULL,NULL,542950,181750,NULL,NULL\nNA,15/04/2019 14:08,2019,2019/20,Special Service,1,1,339,339,TO ASSIST RSPCA WITH PIGEON CAUGHT IN TREE NEAR BOAT POND     ALP REQUIRED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010226397,PARK VISTA,20801142,SE10,538808,177738,538850,177750,51.4815755,-0.002378299\nNA,15/04/2019 21:56,2019,2019/20,Special Service,1,1,339,339,FERRET TRAPPED IN RADIATOR,Ferret,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,NULL,HUTCHINS ROAD,20801672,SE28,NULL,NULL,546350,180450,NULL,NULL\nNA,16/04/2019 06:12,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED AT LOWER GROUND LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,OLD BROMPTON ROAD,21700372,SW5,NULL,NULL,525850,178350,NULL,NULL\nNA,16/04/2019 08:36,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,NULL,LOVEL AVENUE,20100881,DA16,NULL,NULL,546350,176350,NULL,NULL\nNA,16/04/2019 08:53,2019,2019/20,Special Service,1,1,339,339,CAT STUCK DOWN MAN HOLE,Cat,Person (mobile),Veterinary surgery,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000092,KENSAL GREEN,E09000005,BRENT,Willesden,202201829,LEGHORN ROAD,20201220,NW10,521936,183446,521950,183450,51.53676599,-0.24325475\nNA,17/04/2019 11:23,2019,2019/20,Special Service,1,1,339,339,ASSIST BLUE CROSS WITH CAT ON LEDGE,Cat,Person (mobile),Train station - elsewhere,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000647,WARWICK,E09000033,WESTMINSTER,Chelsea,10033642688,TERMINUS PLACE,8401056,SW1V,528932,178897,528950,178850,51.49433132,-0.144107283\nNA,17/04/2019 12:18,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF      REQUESTED TO ATTEND BY RSPCA   RSPCA IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,ASTON GREEN,21500054,TW4,NULL,NULL,511050,176450,NULL,NULL\nNA,17/04/2019 19:07,2019,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN CHIMNEY - NO OTHER AGENCIES ATTENDING,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,NULL,FITZROY CRESCENT,21501613,W4,NULL,NULL,520550,177350,NULL,NULL\nNA,18/04/2019 06:53,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN NETTING    GROUND FLOOR FLAT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,MUNSTER ROAD,21000580,SW6,NULL,NULL,524650,176550,NULL,NULL\nNA,18/04/2019 15:43,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN SHED AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,ULLESWATER ROAD,20704867,N14,NULL,NULL,530450,193150,NULL,NULL\nNA,18/04/2019 16:18,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,526250,166450,NULL,NULL\nNA,19/04/2019 11:44,2019,2019/20,Special Service,1,1,339,339,PUPPY LOCKED IN PLAYGROUND,Dog,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,DAWES STREET,22500720,SE17,532938,178318,532950,178350,51.48819706,-0.086651989\nNA,19/04/2019 14:15,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA GAIN ENTRY TO FREE DOG LOCKED IN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,SOUTHWARK PARK ROAD,22502293,SE16,NULL,NULL,534750,179450,NULL,NULL\nNA,19/04/2019 17:48,2019,2019/20,Special Service,1,1,339,339,FOX CUB TRAPPED IN NETTING,Fox,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LAMMAS PARK ROAD,20601014,W5,NULL,NULL,517550,179950,NULL,NULL\nNA,19/04/2019 17:53,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,NULL,WESTMOUNT ROAD,20801601,SE9,NULL,NULL,542750,176250,NULL,NULL\nNA,20/04/2019 05:23,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON SIXTH FLOOR BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,NULL,COLDHARBOUR LANE,21900369,SW9,NULL,NULL,531750,175650,NULL,NULL\nNA,20/04/2019 08:12,2019,2019/20,Special Service,1,1,339,339,CAT  TRAPPED IN TREE IN WOODED  AREA  CALLER WILL MEET YOU IN ALLEYWAY  AT END OF CRESCENT,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000602,LARKSWOOD,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,LARKSHALL CRESCENT,22849700,E4,538398,192732,538350,192750,51.61640915,-0.002384901\nNA,20/04/2019 08:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,NULL,RITHERDON ROAD,22904302,SW17,NULL,NULL,528550,172550,NULL,NULL\nNA,20/04/2019 12:08,2019,2019/20,Special Service,1,1,339,339,CAT IN PRECARIOUS POSITION ON FOURTH FLOOR  UNABLE TO BACK INSIDE BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,LOVELACE STREET,20950325,E8,NULL,NULL,533750,183850,NULL,NULL\nNA,20/04/2019 19:18,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,1.00021E+11,HEADSTONE ROAD,21201047,HA1,514979,188554,514950,188550,51.58413148,-0.341844726\nNA,21/04/2019 12:41,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA   TWO PIGEONS HEADS STUCK IN SOLAR PANEL BY GUTTER,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,CONNINGTON CRESCENT,22828750,E4,NULL,NULL,538750,193250,NULL,NULL\nNA,21/04/2019 13:08,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,STRATHVILLE ROAD,22905028,SW18,NULL,NULL,525750,173050,NULL,NULL\nNA,21/04/2019 19:24,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RAZOR WIRE,Dog,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000465,LOWER MORDEN,E09000024,MERTON,Sutton,48108486,LOWER MORDEN LANE,22104038,SM4,524282,167247,524250,167250,51.39066481,-0.215138895\nNA,22/04/2019 09:02,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA CAT UP TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,COOMBE END,21800271,KT2,520806,170248,520850,170250,51.41839213,-0.264051658\nNA,22/04/2019 10:08,2019,2019/20,Special Service,2,3,339,1017,DOG SHUT IN LIFT   LEAD CAUGHT IN DOORS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MACE STREET,22700764,E2,NULL,NULL,535750,183150,NULL,NULL\nNA,22/04/2019 11:10,2019,2019/20,Special Service,1,1,339,339,BIRD ENTANGLED IN WIRE    SECOND FLOOR LEVEL   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,PECKHAM HILL STREET,22501926,SE15,NULL,NULL,534150,176750,NULL,NULL\nNA,22/04/2019 16:46,2019,2019/20,Special Service,NULL,NULL,339,NULL,Redacted,Snake,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,1.00023E+11,ERITH ROAD,20100511,DA7,549692,175781,549650,175750,51.46122158,0.15340913\nNA,22/04/2019 17:03,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000291,HATCH END,E09000015,HARROW,Harrow,NULL,NUGENTS PARK,21201146,HA5,NULL,NULL,512550,190750,NULL,NULL\nNA,23/04/2019 07:40,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,BEACONSFIELD ROAD,20002720,N11,NULL,NULL,528350,192550,NULL,NULL\nNA,23/04/2019 17:01,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING UNDER BRIDGE - CALLER WILL FLAG YOU DOWN,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121033252,CAVENDISH ROAD,22900824,SW12,529134,172999,529150,172950,51.4412732,-0.1433545\nNA,24/04/2019 10:25,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON BALCONY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,NULL,WILSMERE DRIVE,20601914,UB5,NULL,NULL,512450,185050,NULL,NULL\nNA,24/04/2019 15:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,PRESTWOOD STREET,20900818,N1,NULL,NULL,532350,183050,NULL,NULL\nNA,25/04/2019 00:59,2019,2019/20,Special Service,1,1,339,339,DOG STUCK BETWEEN FENCE AND WALL        BACKING ONTO RAILWAY LINE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000377,MILDMAY,E09000019,ISLINGTON,Islington,5300038559,GROSVENOR AVENUE,21603291,N5,532280,185049,532250,185050,51.54884192,-0.093602207\nNA,25/04/2019 20:45,2019,2019/20,Special Service,1,1,339,339,KITTEN POSSIBLY TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,10022938583,WEST GREEN ROAD,21104071,N15,533594,188980,533550,188950,51.58385581,-0.073161712\nNA,26/04/2019 17:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER BONNET OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,10012786048,TABLEY ROAD,21603902,N7,530178,185851,530150,185850,51.55653662,-0.123591193\nNA,27/04/2019 12:55,2019,2019/20,Special Service,1,1,339,339,WILD BIRD TRAPPED INSIDE BUILDING   CALLER WILL MEET YOU,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,NULL,CROSLAND PLACE,22906487,SW11,NULL,NULL,528450,175750,NULL,NULL\nNA,28/04/2019 13:43,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000097,PRESTON,E09000005,BRENT,Wembley,202061830,OAKINGTON AVENUE,20202423,HA9,518740,186353,518750,186350,51.56356979,-0.288339538\nNA,29/04/2019 20:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED WITHIN BUSHES BEHIND GRATE NEAR POND,Cat,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011487,WADDON,E09000008,CROYDON,Croydon,1.00024E+11,WADDON COURT ROAD,20502402,CR0,530998,165021,530950,165050,51.36915281,-0.119487375\nNA,30/04/2019 11:31,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK UP TREE,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,FARM LANE,21000330,SW6,NULL,NULL,525350,177450,NULL,NULL\nNA,30/04/2019 11:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000417,BRIXTON HILL,E09000022,LAMBETH,West Norwood,NULL,BRIXTON HILL,21900236,SW2,NULL,NULL,530450,174050,NULL,NULL\nNA,01/05/2019 13:36,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,MOAT CRESCENT,20029860,N3,NULL,NULL,525550,189750,NULL,NULL\nNA,02/05/2019 11:49,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,BRADSTOCK ROAD,20900160,E9,NULL,NULL,535950,184650,NULL,NULL\nNA,02/05/2019 12:36,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,MAYSOULE ROAD,22903249,SW11,NULL,NULL,526550,175450,NULL,NULL\nNA,02/05/2019 13:37,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE   RSPCA IN ATTENDANCE   JUNC COWLEY ROAD,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000436,VASSALL,E09000022,LAMBETH,Brixton,10001113492,CANCELL ROAD,21900282,SW9,531341,176940,531350,176950,51.47618445,-0.110145471\nNA,02/05/2019 13:44,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,ANDOVER ROAD,21602833,N7,NULL,NULL,530650,186650,NULL,NULL\nNA,03/05/2019 19:23,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,NULL,PECKHAM ROAD,22501935,SE5,NULL,NULL,533150,176650,NULL,NULL\nNA,03/05/2019 19:25,2019,2019/20,Special Service,1,1,339,339,PARROT STUCK IN TREE  ATTACHED BY HARNESS,Bird,Person (mobile),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,SPEKEHILL,20801390,SE9,542884,172108,542850,172150,51.42997559,0.0539967\nNA,05/05/2019 16:57,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO PARAKEET TRAPPED IN NETTING OF FIRE STATION TOWER,Bird,Person (land line),Fire station,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000286,CANONS,E09000015,HARROW,Stanmore,10002294818,HONEYPOT LANE,21200986,HA7,517992,190795,517950,190750,51.60364698,-0.297636313\nNA,05/05/2019 18:20,2019,2019/20,Special Service,1,1,339,339,FOX CUB TRAPPED BETWEEN CONCRETE POSTS - RSPCA ON SCENE -,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,BIRBETTS ROAD,20800167,SE9,542640,172573,542650,172550,51.43420939,0.050686131\nNA,05/05/2019 19:59,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING UNDER BRIDGE - CALLER WILL MEET YOU,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000469,RAYNES PARK,E09000024,MERTON,New Malden,48128490,COOMBE LANE,22101605,SW20,523282,169316,523250,169350,51.40947958,-0.228781229\nNA,05/05/2019 21:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN GARDEN,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,1.00021E+11,ADELAIDE CLOSE,21202157,HA7,516315,192607,516350,192650,51.62028002,-0.321232568\nNA,08/05/2019 13:06,2019,2019/20,Special Service,1,1,339,339,POSSIBLY CAT STUCK IN CHIMNEY  FRU  REQUIRED WITH SNAKE EYE  CAMERA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000568,THE WRYTHE,E09000029,SUTTON,Sutton,NULL,TEMPLE WAY,22601066,SM1,NULL,NULL,526750,165150,NULL,NULL\nNA,09/05/2019 10:48,2019,2019/20,Special Service,1,1,339,339,CROW STUCK IN NETTING - RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,NALLHEAD ROAD,21500784,TW13,511577,171174,511550,171150,51.42859915,-0.396432817\nNA,09/05/2019 13:59,2019,2019/20,Special Service,1,1,339,339,SWAN STUCK ON BALCONY   ASSIST RSPCA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Woodside,NULL,FARM DRIVE,20500447,CR0,NULL,NULL,536950,165750,NULL,NULL\nNA,10/05/2019 14:18,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED IN VENT INSIDE PARK    CALLER WILL MEET LFB  SECOND ENTRANCE AFTER BP GARAGE ON RIG,Squirrel,Person (mobile),Park,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12141341,EASTCOTE LANE,20600590,UB5,512715,184422,512750,184450,51.54744556,-0.375844697\nNA,11/05/2019 15:10,2019,2019/20,Special Service,1,3,339,1017,ASSIST RSPCA WITH SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000086,BARNHILL,E09000005,BRENT,Wembley,NULL,SALMON STREET,20200954,NW9,NULL,NULL,519950,187050,NULL,NULL\nNA,11/05/2019 19:48,2019,2019/20,Special Service,1,1,339,339,Redacted,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,HANWORTH,E09000018,HOUNSLOW,Twickenham,1.00022E+11,STOURTON AVENUE,21501300,TW13,512662,171954,512650,171950,51.43539286,-0.380573952\nNA,12/05/2019 13:35,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA GAIN ACCESS TO DUCKLINGS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,FROBISHER YARD,22208305,E16,NULL,NULL,544150,180650,NULL,NULL\nNA,12/05/2019 14:14,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL CAUGHT BEHIND BATHROOM WALL,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542150,182450,NULL,NULL\nNA,12/05/2019 17:28,2019,2019/20,Special Service,1,1,339,339,PARROT TRAPPED IN BASEMENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527350,181850,NULL,NULL\nNA,13/05/2019 10:22,2019,2019/20,Special Service,1,1,339,339,STARLING   TRAPPED IN EAVES OF ROOF,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Homerton,NULL,QUEENSDOWN ROAD,20900834,E5,NULL,NULL,534750,185650,NULL,NULL\nNA,13/05/2019 12:20,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL TRAPPED BEHIND BATHROOM WALL,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542150,182450,NULL,NULL\nNA,13/05/2019 14:54,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,GOAT LANE,20702707,EN1,NULL,NULL,533750,198350,NULL,NULL\nNA,14/05/2019 14:50,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON FITH FLOOR WINDOW LEDGE  NO ANSWER FROM FLAT OWNERS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000648,WESTBOURNE,E09000033,WESTMINSTER,Paddington,NULL,ELMFIELD WAY,8401841,W9,NULL,NULL,525250,181850,NULL,NULL\nNA,14/05/2019 15:01,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,VILLIERS CLOSE,22884200,E10,NULL,NULL,537350,186850,NULL,NULL\nNA,15/05/2019 19:49,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Heston,NULL,ANDERSON PLACE,21500037,TW3,NULL,NULL,513750,175250,NULL,NULL\nNA,15/05/2019 23:41,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN DEEP EXCAVATION HOLE OUTSIDE HOUSE  - CALLER STATES ANIMAL IS DISTRESSED,Fox,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000340,UXBRIDGE NORTH,E09000017,HILLINGDON,Hillingdon,1.00021E+11,LANCRESSE CLOSE,21401120,UB8,505673,184641,505650,184650,51.5507749,-0.477295026\nNA,17/05/2019 10:29,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN TREE   CALLED BY RSPCA OFFICER WHO IS ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000373,HIGHBURY WEST,E09000019,ISLINGTON,Holloway,5300001735,AMBLER ROAD,21602827,N4,531553,186350,531550,186350,51.56070481,-0.103587086\nNA,17/05/2019 16:31,2019,2019/20,Special Service,1,1,339,339,SEAGULL CAUGHT IN NETTING ON ROOF,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009301,CORNHILL,E09000001,CITY OF LONDON,Dowgate,1.00024E+11,CORNHILL,8100150,EC3V,532936,181131,532950,181150,51.51347926,-0.085614967\nNA,17/05/2019 18:18,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - REQUESTED BY RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,1.00022E+11,DEERDALE ROAD,21900450,SE24,532157,175434,532150,175450,51.4624637,-0.098963546\nNA,17/05/2019 21:49,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL TRAPPED BEHIND KITCHEN UNIT,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Ruislip,NULL,CLOVELLY AVENUE,21400429,UB10,NULL,NULL,508150,185750,NULL,NULL\nNA,18/05/2019 07:23,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED BETWEEN PATIO BALCONY AND WALL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,WARD ROAD,22201656,E15,NULL,NULL,538550,183950,NULL,NULL\nNA,18/05/2019 13:17,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - PIGEON TRAPPED IN NETTING  THIS IS BETWEEN LOWER MASH AND UPPER MARSH,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011114,ST. GEORGE'S,E09000028,SOUTHWARK,Dowgate,2.00003E+11,LONDON ROAD,22501544,SE1,531669,179415,531650,179450,51.49835683,-0.104505122\nNA,18/05/2019 15:31,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,DARCY ROAD,20500853,SW16,NULL,NULL,530050,169150,NULL,NULL\nNA,18/05/2019 17:30,2019,2019/20,Special Service,1,1,339,339,CALF TRAPPED IN DRINKING WATER TROUGH  BEHIND WEST MIDDLESEX HOSPITAL TURN LEFT INTO SYON PARK BARE,Unknown - Heavy Livestock Animal,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,10091690311,LONDON ROAD,21519757,TW8,516991,176886,516950,176850,51.47884447,-0.316696587\nNA,18/05/2019 19:07,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,NAPOLEON ROAD,20900724,E5,NULL,NULL,534650,186050,NULL,NULL\nNA,18/05/2019 21:42,2019,2019/20,Special Service,1,1,339,339,MAGPIE WITH HEAD STUCK IN ROOF VOID      AT SIDE OF HOUSE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000192,WALPOLE,E09000009,EALING,Ealing,NULL,LOVEDAY ROAD,20601087,W13,NULL,NULL,516850,179850,NULL,NULL\nNA,19/05/2019 12:54,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000205,PALMERS GREEN,E09000010,ENFIELD,Southgate,NULL,LODGE DRIVE,20704449,N13,NULL,NULL,531150,192750,NULL,NULL\nNA,19/05/2019 15:41,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK UNDERNEATH RUBBLE IN REAR GARDEN,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,NULL,BEACONSFIELD ROAD,20600131,UB1,NULL,NULL,512450,179950,NULL,NULL\nNA,19/05/2019 18:26,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON THE ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,NULL,MOULINS ROAD,20900710,E9,NULL,NULL,535450,184050,NULL,NULL\nNA,20/05/2019 17:23,2019,2019/20,Special Service,1,1,339,339,KITTENS TRAPPED IN UNDERGROWTH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,10009002773,NORTH CIRCULAR ROAD,22202013,E12,543371,185309,543350,185350,51.5484659,0.066363777\nNA,22/05/2019 00:00,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN DRAINPIPE ATTACHED TO PROPERTY,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Whitechapel,NULL,FOURNIER STREET,22700517,E1,NULL,NULL,533750,181850,NULL,NULL\nNA,22/05/2019 06:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,GORESBROOK ROAD,19900853,RM9,NULL,NULL,549050,183650,NULL,NULL\nNA,22/05/2019 12:27,2019,2019/20,Special Service,1,1,339,339,CAT AND KITTENS UNDER WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Bexley,NULL,DALE VIEW,20100416,DA8,NULL,NULL,552050,176350,NULL,NULL\nNA,22/05/2019 17:55,2019,2019/20,Special Service,1,1,339,339,CHICKS TRAPPED BETWEEN GATES IN PARK NEXT TO,Unknown - Wild Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Shadwell,6150723,THE HIGHWAY,22701200,E1,534363,180808,534350,180850,51.51023815,-0.065193371\nNA,22/05/2019 20:55,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,5870034010,MULGRAVE ROAD,22605508,SM2,525650,163655,525650,163650,51.35808225,-0.196762197\nNA,23/05/2019 18:16,2019,2019/20,Special Service,1,1,339,339,HORSE IN DISTRESS STUCK IN METAL FRAME     IN A  FIELD ON LEFT HAND SIDE COMING FROM LIMPSFIELD ROAD,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Purley,1.00021E+11,MITCHLEY AVENUE,20503090,CR2,533473,160810,533450,160850,51.33073554,-0.085523951\nNA,24/05/2019 09:24,2019,2019/20,Special Service,1,NULL,339,NULL,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,HUXLEY ROAD,20704356,N18,NULL,NULL,533150,192950,NULL,NULL\nNA,24/05/2019 18:20,2019,2019/20,Special Service,1,1,339,339,INJURED KITTEN TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000445,GROVE PARK,E09000023,LEWISHAM,Eltham,NULL,HENRY COOPER WAY,22003400,SE9,NULL,NULL,541650,172050,NULL,NULL\nNA,25/05/2019 10:36,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN FIRE PLACE,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,BROMLEY LANE,20302113,BR7,NULL,NULL,544450,170250,NULL,NULL\nNA,25/05/2019 13:08,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN EAVES AND GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000144,SWISS COTTAGE,E09000007,CAMDEN,West Hampstead,NULL,ACOL ROAD,20400559,NW6,NULL,NULL,525550,184150,NULL,NULL\nNA,25/05/2019 14:56,2019,2019/20,Special Service,1,1,339,339,BIRDS STUCK IN NETTING - ASSIST RSPCA,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000358,HOUNSLOW CENTRAL,E09000018,HOUNSLOW,Heston,1.00023E+11,DOUGLAS ROAD,21500365,TW3,513876,175653,513850,175650,51.46840455,-0.361930383\nNA,25/05/2019 17:46,2019,2019/20,Special Service,1,1,339,339,DOG LOCKED INSIDE CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving wild animal - Other action,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,46250363,THE GROVE,22201626,E15,539125,184668,539150,184650,51.5437701,0.004917027\nNA,25/05/2019 20:44,2019,2019/20,Special Service,1,1,339,339,MAGPIE TRAPPED IN GUTTER,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000413,SURBITON HILL,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SAXON CLOSE,21800841,KT6,NULL,NULL,517950,167150,NULL,NULL\nNA,26/05/2019 13:17,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN NETTING,Bird,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100070213,FARR AVENUE,19900881,IG11,546063,183234,546050,183250,51.52913593,0.104299587\nNA,26/05/2019 17:38,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,NULL,DARLINGTON PATH,21300074,RM3,NULL,NULL,553750,192350,NULL,NULL\nNA,27/05/2019 20:15,2019,2019/20,Special Service,1,3,339,1017,FOX FALLEN IN RIVER  CALLER WILL MEET YOU,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000353,HANWORTH,E09000018,HOUNSLOW,Twickenham,1.00022E+11,STOURTON AVENUE,21501300,TW13,512734,171880,512750,171850,51.43471409,-0.37956785\nNA,27/05/2019 21:40,2019,2019/20,Special Service,1,1,339,339,PIGEON STUCK IN CHIMNEY AREA,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,CRAMPTON STREET,22500633,SE17,NULL,NULL,531950,178550,NULL,NULL\nNA,28/05/2019 19:20,2019,2019/20,Special Service,1,3,339,1017,DOG TRAPPED IN FOX HOLE      SNAKE EYE REQUIRED    WIMBLEDON COMMON   NEAR WAR MEMORIAL      TERRIER,Dog,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,48102622,YPROW8,22100224,SW15,521679,171519,521650,171550,51.42962386,-0.251065518\nNA,29/05/2019 16:32,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN WINDOW IN DISTRESS - CALLER IS PASSER BY - NO ONE ANSWERING DOOR,Dog,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,HENNINGHAM ROAD,21104642,N17,532855,190969,532850,190950,51.60190452,-0.083066792\nNA,29/05/2019 19:29,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,10090848761,ROBERTSON ROAD,22208227,E16,540330,181605,540350,181650,51.51595059,0.021051712\nNA,30/05/2019 17:18,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010194718,TWIN TUMPS WAY,20801831,SE28,546233,180859,546250,180850,51.50774745,0.105759711\nNA,01/06/2019 15:01,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH KITTEN FALLEN DOWN CAVITY OF WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,HIGH STREET,21500609,TW13,NULL,NULL,510450,172950,NULL,NULL\nNA,01/06/2019 15:13,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,THE GROVE,20400011,N6,NULL,NULL,528150,187450,NULL,NULL\nNA,02/06/2019 11:47,2019,2019/20,Special Service,1,1,339,339,RABBIT WITH HEAD STUCK IN CAGE DOOR,Rabbit,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,CHATSWORTH CRESCENT,21500236,TW3,NULL,NULL,514550,175450,NULL,NULL\nNA,02/06/2019 12:28,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,HOE LANE,20702753,EN3,NULL,NULL,535150,198150,NULL,NULL\nNA,02/06/2019 14:42,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN VELUX WINDOW,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Wandsworth,NULL,BARMOUTH ROAD,22900280,SW18,NULL,NULL,526350,174150,NULL,NULL\nNA,02/06/2019 22:36,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED POSSIBLY IN DRAN,Cat,Person (mobile),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000493,WALL END,E09000025,NEWHAM,East Ham,46256926,CANBERRA ROAD,22200441,E6,542952,183588,542950,183550,51.53311141,0.059619816\nNA,02/06/2019 23:14,2019,2019/20,Special Service,1,1,339,339,BABY FOX TRAPPED IN NETTING,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46079826,WESTPORT ROAD,22201211,E13,540706,182092,540750,182050,51.52023703,0.026661866\nNA,03/06/2019 16:29,2019,2019/20,Special Service,2,3,339,1017,HORSE TRAPPED IN BARBED WIRE IN FIELD OPPOSITE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011480,SELSDON & ADDINGTON VILLAGE,E09000008,CROYDON,Addington,1.00021E+11,HUNTINGFIELD,20501750,CR0,536966,163404,536950,163450,51.35321712,-0.034429168\nNA,03/06/2019 16:46,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE   REQUESTED BY RSPCA (ON SITE),Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011481,SELSDON VALE & FORESTDALE,E09000008,CROYDON,Addington,1.00021E+11,KERSEY DRIVE,20501757,CR2,535399,161249,535350,161250,51.33422533,-0.057741515\nNA,03/06/2019 21:10,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BOARDED UP DISUSED BUILDING,Cat,Person (mobile),Shelter,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5105342,LOXHAM STREET,20400896,WC1H,530388,182701,530350,182750,51.52818365,-0.121735956\nNA,04/06/2019 16:25,2019,2019/20,Special Service,1,1,339,339,ANIMAL IN DISTRESS - FOX INJURED ON PREMISES,Fox,Person (land line),Central Government Office,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Kensington,1.00023E+11,KNIGHTSBRIDGE,8400987,SW1X,527937,179797,527950,179750,51.50264607,-0.158108725\nNA,05/06/2019 18:09,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RAILINGS,Dog,Person (land line),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00022E+11,KEMPTHORNE ROAD,22000572,SE8,536495,178750,536450,178750,51.4912354,-0.035278618\nNA,05/06/2019 20:57,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE - CAT AND KITTENS STUCK BEHIND BATH,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,NULL,NORHEADS LANE,20301455,TN16,NULL,NULL,540950,159050,NULL,NULL\nNA,06/06/2019 21:07,2019,2019/20,Special Service,1,1,339,339,CAT POSSIBLY TRAPPED BY COLLAR IN TREE VERY HIGH UP,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,1.00022E+11,THURLBY ROAD,21901378,SE27,531486,171747,531450,171750,51.42948314,-0.109997415\nNA,07/06/2019 09:26,2019,2019/20,Special Service,1,1,339,339,MUNTJAC TRAPPED IN METAL GATES,Unknown - Wild Animal,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Southgate,207016293,BOLINGBROKE CLOSE,20700878,EN4,527759,196747,527750,196750,51.65500702,-0.154499646\nNA,07/06/2019 17:23,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED IN TOILET,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,NULL,SUSSEX AVENUE,21501086,TW7,NULL,NULL,515250,175650,NULL,NULL\nNA,07/06/2019 18:37,2019,2019/20,Special Service,1,1,339,339,ANIMAL OR BIRD TRAPPED BEHIND GAS FIRE IN BEDROOM,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,VANBRUGH PARK,20801536,SE3,NULL,NULL,540050,177350,NULL,NULL\nNA,07/06/2019 18:59,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT TRAPPED INSIDE ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,1.00023E+11,PEDRO STREET,20900791,E5,535942,185902,535950,185950,51.55563429,-0.040480299\nNA,07/06/2019 19:52,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000493,WALL END,E09000025,NEWHAM,East Ham,46010226,CALEDON ROAD,22200435,E6,542697,183963,542650,183950,51.53654247,0.05609693\nNA,08/06/2019 19:16,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO PIGEON TRAPPED IN NETTING - RSCPA REQUESTED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10010441203,UPPER STREET,21606199,N1,531670,184237,531650,184250,51.54168472,-0.102686222\nNA,09/06/2019 00:08,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN WATER FEATURE,Cat,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped domestic animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008315971,LEA BRIDGE ROAD,20900602,E5,535557,186503,535550,186550,51.56112771,-0.045805743\nNA,09/06/2019 03:45,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,HULVERSTON CLOSE,22602404,SM2,NULL,NULL,525650,162450,NULL,NULL\nNA,09/06/2019 09:50,2019,2019/20,Special Service,1,1,339,339,FERRET WITH HEAD TRAPPED IN METAL GRILL ON CARRIER,Ferret,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,TOWNSHEND TERRACE,22406024,TW9,518811,175298,518850,175250,51.46419293,-0.29103095\nNA,09/06/2019 18:59,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Bethnal Green,NULL,TOMLINS GROVE,22701222,E3,NULL,NULL,537250,182750,NULL,NULL\nNA,09/06/2019 20:58,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200154296,MORPHOU ROAD,20030210,NW7,524324,191807,524350,191850,51.61137941,-0.205879515\nNA,10/06/2019 18:44,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200154296,MORPHOU ROAD,20030210,NW7,524332,191816,524350,191850,51.61146244,-0.205775007\nNA,10/06/2019 20:02,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF DRAIN AT FOURTH FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000601,HOE STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,537450,189750,NULL,NULL\nNA,10/06/2019 21:29,2019,2019/20,Special Service,1,1,339,339,CAT WEDGED IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000488,MANOR PARK,E09000025,NEWHAM,East Ham,NULL,SNOWSHILL ROAD,22200252,E12,NULL,NULL,542250,185350,NULL,NULL\nNA,11/06/2019 23:15,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE CAT TRAPPED AT ROOF LEVEL DUE TO RAIN WATER - CALLER NOT SURE IF INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,FAIRFIELD ROAD,20704203,N18,NULL,NULL,534350,192450,NULL,NULL\nNA,12/06/2019 09:47,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,10003981460,CROWLAND ROAD,21103171,N15,534191,188594,534150,188550,51.5802422,-0.064706251\nNA,12/06/2019 13:44,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE  - BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,NULL,THE DRIVE,22832150,E4,NULL,NULL,538950,194850,NULL,NULL\nNA,13/06/2019 13:25,2019,2019/20,Special Service,1,1,339,339,SQUIRREL STUCK IN GUTTERING,Squirrel,Person (mobile),Other entertainment venue,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,5145653,CHALK FARM ROAD,20400624,NW1,528542,184228,528550,184250,51.54232943,-0.147772448\nNA,14/06/2019 07:40,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN CAR WHEEL BY LEG -  RSPCA ON SCENE  VW BLACK POLO IN CAR PARK AT THE END OF FLOOD WAL,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,217095302,CHELSEA MANOR STREET,21700083,SW3,527296,177928,527250,177950,51.48599319,-0.168003293\nNA,14/06/2019 09:06,2019,2019/20,Special Service,1,1,339,339,CAT WITH HAND TRAPPED IN METAL BARS OF WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009371,DE BEAUVOIR,E09000012,HACKNEY,Shoreditch,NULL,TOTTENHAM ROAD,20901008,N1,NULL,NULL,533350,184750,NULL,NULL\nNA,14/06/2019 12:21,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Dagenham,10090633176,MERRIELANDS CRESCENT,19900534,RM9,548884,183483,548850,183450,51.53063943,0.145043323\nNA,14/06/2019 22:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,1.00023E+11,MUSWELL HILL BROADWAY,21106387,N10,528730,189578,528750,189550,51.5903584,-0.1431149\nNA,15/06/2019 11:28,2019,2019/20,Special Service,1,1,339,339,DUCKLINGS STUCK BEHIND WALL        BEHIND HALIFAX J/O HIGHT STREET,Bird,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200150338,SALISBURY ROAD,20038600,EN5,524430,196660,524450,196650,51.6549737,-0.20263194\nNA,15/06/2019 13:41,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000445,GROVE PARK,E09000023,LEWISHAM,Lee Green,NULL,SANDSTONE ROAD,22003646,SE12,NULL,NULL,540650,172650,NULL,NULL\nNA,15/06/2019 23:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK AT HEIGHT,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,1.00022E+11,BRICKFIELD CLOSE,21500153,TW8,517118,177302,517150,177350,51.48255997,-0.314727291\nNA,16/06/2019 15:00,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN EXTRACTOR FAN,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,BLANDFORD ROAD,20600190,UB2,NULL,NULL,513050,178850,NULL,NULL\nNA,16/06/2019 16:03,2019,2019/20,Special Service,1,1,339,339,CAT IN ENGINE COMPARTMENT - O/S PETS AT HOME,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Dagenham,10090633176,MERRIELANDS CRESCENT,19900534,RM9,548920,183479,548950,183450,51.53058905,0.145550855\nNA,16/06/2019 23:12,2019,2019/20,Special Service,1,1,339,339,FOX STUCK IN FENCE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal harm involving wild animal,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,SOMERSET CLOSE,21105057,N17,532846,190460,532850,190450,51.5973292,-0.0834014\nNA,18/06/2019 20:37,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN DRAINPIPE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000195,CHASE,E09000010,ENFIELD,Enfield,NULL,MELLING DRIVE,20706105,EN1,NULL,NULL,534050,197850,NULL,NULL\nNA,18/06/2019 20:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10008983267,BARKING ROAD,22208079,E13,541028,182872,541050,182850,51.52716381,0.031618387\nNA,18/06/2019 22:03,2019,2019/20,Special Service,1,1,339,339,SQUIRREL CAUGHT BEHIND PIPEWORK,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,DARCY GARDENS,19900465,RM9,NULL,NULL,548950,183950,NULL,NULL\nNA,19/06/2019 23:06,2019,2019/20,Special Service,1,1,339,339,INJURED BIRD TRAPPED BEHIND BIRD PROOFING   SERVICE YARD ENTRANCE OPPOSITE IMMAGRATION CENTRE,Bird,Person (mobile),Underground car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10094086243,LONDON BRIDGE STREET,22501542,SE1,532944,180147,532950,180150,51.50463573,-0.085873459\nNA,20/06/2019 10:34,2019,2019/20,Special Service,1,1,339,339,DUCKLING FALLEN INTO DRAIN   CALLER WILL LIASE AT LOCATION,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Erith,10025467017,BAZALGETTE WAY TO NORMAN ROAD FP2,20106002,DA18,549212,179937,549250,179950,51.49868772,0.148264916\nNA,22/06/2019 06:00,2019,2019/20,Special Service,1,1,339,339,MUNTJAC DEER STUCK IN METAL RAILINGS     RSPCA OFFICER ON SCENE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200105054,SOUTHOVER,20040140,N12,525127,193232,525150,193250,51.62401306,-0.19379099\nNA,22/06/2019 09:50,2019,2019/20,Special Service,1,1,339,339,Redacted,Hamster,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,NULL,PAGE ROAD,21500849,TW14,NULL,NULL,509050,173850,NULL,NULL\nNA,22/06/2019 11:03,2019,2019/20,Special Service,1,1,339,339,DOG IMPALED ON FENCE  VET ON SCENE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,COLOMB STREET,20800361,SE10,NULL,NULL,539450,178150,NULL,NULL\nNA,22/06/2019 11:23,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,SPENCER ROAD,22105931,CR4,NULL,NULL,528350,168850,NULL,NULL\nNA,22/06/2019 13:52,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING    ABOVE THE CAR WASH UNDER THE BRIDGE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,10008463814,MARE STREET,20900658,E8,534951,184921,534950,184950,51.54705912,-0.055152666\nNA,22/06/2019 16:59,2019,2019/20,Special Service,1,1,339,339,HORSE STUCK IN DITCH,Horse,Person (land line),Other animal boarding/breeding establishment,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,10033424304,WINGLETYE LANE,21300661,RM11,555513,188357,555550,188350,51.57263976,0.242687585\nNA,23/06/2019 16:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000134,GOSPEL OAK,E09000007,CAMDEN,Kentish Town,NULL,FLEET ROAD,20400195,NW3,NULL,NULL,527650,185350,NULL,NULL\nNA,23/06/2019 17:38,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UNDER CONCRETE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,CHALCOMBE ROAD,20800307,SE2,NULL,NULL,546850,179150,NULL,NULL\nNA,23/06/2019 18:24,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED ON ROOF - ALP REQUESTED FROM INC,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,ALKHAM ROAD,20900053,N16,NULL,NULL,533950,186750,NULL,NULL\nNA,23/06/2019 18:41,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED UNDER FLOORING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,FULBOURNE ROAD,22838500,E17,NULL,NULL,538250,190050,NULL,NULL\nNA,23/06/2019 23:09,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED UNDER FLOORBOARD,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,NULL,FULBOURNE ROAD,22838500,E17,NULL,NULL,538250,190050,NULL,NULL\nNA,24/06/2019 06:53,2019,2019/20,Special Service,1,1,339,339,HORSE STUCK IN DYKE OFF NORMAN ROAD   CALLER AT ROUNDABOUT TO DIRECT,Horse,Person (mobile),Scrub land,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,2.00003E+11,NORMAN ROAD,20101027,DA17,549745,179917,549750,179950,51.49837216,0.155928139\nNA,24/06/2019 11:10,2019,2019/20,Special Service,1,2,339,678,KITTEN TRAPPED ON ROOF - THREE DAYS,Cat,Person (mobile),House in Multiple Occupation - 3 or more storeys (not known if licensed),Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,RUSKIN ROAD,21105004,N17,NULL,NULL,533850,190850,NULL,NULL\nNA,24/06/2019 12:43,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH TRAPPED DUCKLINGS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,WATERSIDE WAY,21107091,N17,NULL,NULL,534650,189650,NULL,NULL\nNA,24/06/2019 14:21,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN TREE RSPCA IN ATTENDANCE AT JUNCTION OF GUY STREET,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,2.00003E+11,KIPLING STREET,22501429,SE1,532835,179658,532850,179650,51.50026646,-0.087631875\nNA,24/06/2019 14:27,2019,2019/20,Special Service,1,1,339,339,CHICK TRAPPED IN LOFT,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,LITTLE FRIDAY ROAD,22853200,E4,NULL,NULL,539150,193550,NULL,NULL\nNA,24/06/2019 14:58,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000027,ALIBON,E09000002,BARKING AND DAGENHAM,Dagenham,100026182,ROCKWELL ROAD,19900300,RM10,549672,185221,549650,185250,51.54604605,0.157123126\nNA,24/06/2019 17:16,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN ROOF SPACE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,NULL,LITTLE FRIDAY ROAD,22853200,E4,NULL,NULL,539150,193550,NULL,NULL\nNA,25/06/2019 14:57,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,1.00024E+11,MARKS ROAD,21301396,RM7,550835,188739,550850,188750,51.57734297,0.175396158\nNA,25/06/2019 19:22,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,NULL,HEYGATE STREET,22501239,SE17,NULL,NULL,532150,178750,NULL,NULL\nNA,25/06/2019 21:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,1.00023E+11,HIGH ROAD,21104652,N22,531331,189912,531350,189950,51.59276052,-0.10545739\nNA,26/06/2019 08:39,2019,2019/20,Special Service,1,1,339,339,DOG IN PRECARIOUS POSITION ON ROOF OF HOUSE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,FAWE PARK ROAD,22901648,SW15,NULL,NULL,524750,175050,NULL,NULL\nNA,29/06/2019 16:26,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED ON THIRD FLOOR BALCONY,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,SLEWINS CLOSE,21300585,RM11,NULL,NULL,553450,188650,NULL,NULL\nNA,29/06/2019 18:40,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH DOG ON BALCONY,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Hornchurch,NULL,SLEWINS CLOSE,21300585,RM11,NULL,NULL,553450,188650,NULL,NULL\nNA,29/06/2019 21:34,2019,2019/20,Special Service,1,1,339,339,KITTEN CAUGHT BEHIND FREEZER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000479,CUSTOM HOUSE,E09000025,NEWHAM,Plaistow,NULL,LESLIE ROAD,22207789,E16,NULL,NULL,540850,181050,NULL,NULL\nNA,30/06/2019 13:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN FLOORBOARDS AND FLOOR,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,UPPER TOLLINGTON PARK,21104024,N4,NULL,NULL,531350,187650,NULL,NULL\nNA,30/06/2019 17:18,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,NULL,SPRINGFIELD ROAD,8400561,NW8,NULL,NULL,526350,183750,NULL,NULL\nNA,30/06/2019 18:53,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000360,HOUNSLOW SOUTH,E09000018,HOUNSLOW,Heston,1.00022E+11,ELLERDINE ROAD,21500391,TW3,514835,175185,514850,175150,51.46399772,-0.348284528\nNA,30/06/2019 22:29,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000402,BEVERLEY,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,BURLINGTON ROAD,21800185,KT3,521619,168105,521650,168150,51.3989494,-0.253093255\nNA,01/07/2019 00:03,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,OXFORD GARDENS,21700962,W10,NULL,NULL,523550,181250,NULL,NULL\nNA,01/07/2019 09:13,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO WALLS  OWNER ON SCENE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,10093589920,CONNAUGHT ROAD,21103138,N4,531240,187814,531250,187850,51.57393244,-0.107561263\nNA,01/07/2019 11:38,2019,2019/20,Special Service,1,1,339,339,ASSIST WITH BIRD TRAPPED IN NETTING    RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,GREAT CHERTSEY ROAD,21500503,W4,NULL,NULL,520450,176450,NULL,NULL\nNA,02/07/2019 07:13,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL FOX TRAPPED BEHIND WALL    ALLEY BETWEEN TRANMERE ROAD AND HARROW DRIVE,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207002604,TRANMERE ROAD,20704859,N9,533775,194476,533750,194450,51.63320353,-0.068450792\nNA,02/07/2019 12:37,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT IN TREE,Cat,Person (land line),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10010226567,ROWNTREE PATH,20801297,SE28,546878,180514,546850,180550,51.50448363,0.114905783\nNA,02/07/2019 14:17,2019,2019/20,Special Service,1,1,339,339,ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BOUNCES ROAD,20703940,N9,NULL,NULL,535050,193950,NULL,NULL\nNA,03/07/2019 09:14,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,NULL,GROSVENOR WHARF ROAD,22700581,E14,NULL,NULL,538650,178550,NULL,NULL\nNA,03/07/2019 19:11,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED UNDER RECLINER    TAIL STUCK IN MOTOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,NULL,STANHOPE ROAD,20601626,UB6,NULL,NULL,514250,181850,NULL,NULL\nNA,04/07/2019 05:23,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,CHAMPION CRESCENT,22001332,SE26,NULL,NULL,536250,171950,NULL,NULL\nNA,04/07/2019 11:32,2019,2019/20,Special Service,1,1,339,339,TWO DOGS STUCK IN DRAINPIPE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207008280,FOOTPATH FROM PARKGATE CRESCENT TO BOROUGH BOUNDARY,20700234,EN4,526170,197358,526150,197350,51.66085514,-0.177245969\nNA,04/07/2019 13:20,2019,2019/20,Special Service,1,1,339,339,\"SQUIRREL IN EAVES OF ROOF, HANGING BY ONE LEG\",Squirrel,Person (mobile),Secondary school,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000054,HALE,E09000003,BARNET,Mill Hill,200131283,WORCESTER CRESCENT,20047640,NW7,521319,193752,521350,193750,51.62951533,-0.24859343\nNA,04/07/2019 19:27,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,KINGSWAY,22405697,SW14,NULL,NULL,520050,175750,NULL,NULL\nNA,05/07/2019 13:42,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL ANIMAL RESCUE   OFFICER AWAITING YOUR ARRIVAL TO DIRECT YOU,Unknown - Wild Animal,Person (mobile),Bulk waste storage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128029886,CHAPEL MILL ROAD,21880152,KT1,518864,168560,518850,168550,51.4036225,-0.2925391\nNA,05/07/2019 18:47,2019,2019/20,Special Service,1,1,339,339,DOG DISTRESSED ON WINDOW LEDGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000633,CHURCHILL,E09000033,WESTMINSTER,Chelsea,NULL,PEABODY AVENUE,8401749,SW1V,NULL,NULL,528750,178150,NULL,NULL\nNA,06/07/2019 17:07,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN UNACCESSIBLE COURTYARD AREA,Fox,Person (land line),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011242,FAIRLOP,E09000026,REDBRIDGE,Hainault,10023773004,FENCEPIECE ROAD,22304983,IG6,544418,191360,544450,191350,51.602572,0.083941437\nNA,06/07/2019 20:44,2019,2019/20,Special Service,1,1,339,339,POND FARM KENNELS - ANIMALS IN DISTRESS,Unknown - Domestic Animal Or Pet,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Sidcup,10011845247,COCKSURE LANE,20100342,DA14,549096,172005,549050,172050,51.42744626,0.143244769\nNA,07/07/2019 11:10,2019,2019/20,Special Service,1,1,339,339,DOG SHUT IN POWER STATION ENCLOSURE,Dog,Person (mobile),Electricity power station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,Paddington,1.00023E+11,AVENUE ROAD,8400533,NW8,527245,183567,527250,183550,51.5366753,-0.1667137\nNA,07/07/2019 11:26,2019,2019/20,Special Service,1,1,339,339,DOG SHUT IN POWER STATION ENCLOSURE,Dog,Person (mobile),Electricity power station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,10003978700,AVENUE ROAD,21100037,N6,529455,187843,529450,187850,51.5746007,-0.1332941\nNA,07/07/2019 15:06,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,202079062,KILBURN HIGH ROAD,20202523,NW6,524664,184622,524650,184650,51.54673803,-0.203531387\nNA,07/07/2019 16:58,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,CASTLE ROAD,20400490,NW1,NULL,NULL,528750,184550,NULL,NULL\nNA,07/07/2019 17:11,2019,2019/20,Special Service,1,1,339,339,DOG  STUCK ON ROOF,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000451,RUSHEY GREEN,E09000023,LEWISHAM,Lewisham,NULL,RUSHEY GREEN,22004211,SE6,NULL,NULL,537850,173950,NULL,NULL\nNA,08/07/2019 13:08,2019,2019/20,Special Service,1,3,339,1017,Redacted,Unknown - Wild Animal,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000200,GRANGE,E09000010,ENFIELD,Edmonton,207113903,PARK DRIVE,20704583,N21,532173,195126,532150,195150,51.63941904,-0.091339317\nNA,09/07/2019 13:41,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000433,THORNTON,E09000022,LAMBETH,Tooting,NULL,POYNDERS GARDENS,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL\nNA,09/07/2019 18:10,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000118,FARNBOROUGH AND CROFTON,E09000006,BROMLEY,Orpington,NULL,NEWSTEAD AVENUE,20302906,BR6,NULL,NULL,544750,165250,NULL,NULL\nNA,10/07/2019 01:18,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND SKIRTING BOARD STRUGGLING TO BREATHE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000411,ST. JAMES,E09000021,KINGSTON UPON THAMES,New Malden,NULL,GEORGE ROAD,21800435,KT3,NULL,NULL,521950,167950,NULL,NULL\nNA,10/07/2019 08:58,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN EXERCISE MACHINE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,10015376599,GREBE COURT,22606297,SM1,524522,164118,524550,164150,51.36249462,-0.212786028\nNA,10/07/2019 16:17,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK IN ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,6002554,EAST TENTER STREET,22700450,E1,533960,181130,533950,181150,51.51322873,-0.070871172\nNA,11/07/2019 11:32,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH RETREAVAL OF ANIMAL STUCK ON ROOF,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,NULL,RICHMOND AVENUE,21401627,UB10,NULL,NULL,507750,184550,NULL,NULL\nNA,11/07/2019 22:27,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN PIT,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,NORTHINGTON STREET,20401017,WC1N,NULL,NULL,530750,182050,NULL,NULL\nNA,12/07/2019 10:26,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD STUCK IN TREE,Bird,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5014827,NORTHINGTON STREET,20401017,WC1N,530787,182005,530750,182050,51.5218293,-0.1162525\nNA,12/07/2019 13:55,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Community centre/Hall,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000218,ELTHAM NORTH,E09000011,GREENWICH,Eltham,1.00023E+11,ELTHAM HIGH STREET,20800526,SE9,543233,174468,543250,174450,51.45109116,0.059973609\nNA,12/07/2019 18:13,2019,2019/20,Special Service,1,1,339,339,PUPPY WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,2.00003E+11,CAMBERWELL GREEN,22500399,SE5,532590,176893,532550,176850,51.47547075,-0.092185951\nNA,12/07/2019 18:31,2019,2019/20,Special Service,1,1,339,339,COWS HEAD STUCK IN TROUGH - BRIGADE REQUESTED BY RSPCA WHO HAVE NO ONE TO SEND,Unknown - Heavy Livestock Animal,Person (land line),\"Intensive Farming Sheds (chickens, pigs etc)\",Non Residential,Other animal assistance,Assist  trapped livestock animal,E05000292,HEADSTONE NORTH,E09000015,HARROW,Harrow,10010598377,GEORGE V AVENUE,21201120,HA5,513143,190225,513150,190250,51.59952336,-0.367805041\nNA,12/07/2019 20:50,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,AMERSHAM AVENUE,20703862,N18,NULL,NULL,532750,191850,NULL,NULL\nNA,12/07/2019 21:20,2019,2019/20,Special Service,1,1,339,339,TWO PIGEONS TRAPPED IN SOLAR PANEL,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011255,WANSTEAD VILLAGE,E09000026,REDBRIDGE,Leytonstone,NULL,LONSDALE ROAD,22306048,E11,NULL,NULL,540050,187950,NULL,NULL\nNA,13/07/2019 04:42,2019,2019/20,Special Service,1,1,339,339,LARGE ANIMAL RESCUE - DEER TRAPPED AND INJURED - CALLER WILL MEET YOU,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,TURPIN AVENUE,21301662,RM5,549578,191581,549550,191550,51.60321464,0.15848059\nNA,13/07/2019 10:12,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON THIRD STORY ROOF.  ALP REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,NULL,WHITE HART LANE,22405257,SW13,NULL,NULL,521350,175950,NULL,NULL\nNA,13/07/2019 10:23,2019,2019/20,Special Service,1,1,339,339,CAT STUCK UNDERGROUND,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,CHAMBERLAIN WAY,21400337,HA5,511130,189703,511150,189750,51.59523302,-0.397012081\nNA,13/07/2019 11:52,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,MUIR ROAD,20900716,E5,NULL,NULL,534350,186050,NULL,NULL\nNA,13/07/2019 14:27,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,46037550,HIGH STREET SOUTH,22200716,E6,542820,182436,542850,182450,51.5227894,0.0572475\nNA,13/07/2019 18:15,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,NULL,CALDY ROAD,20100246,DA17,NULL,NULL,549650,179350,NULL,NULL\nNA,13/07/2019 19:44,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON ROOF,Cat,Person (land line),Electricity power station,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000470,ST. HELIER,E09000024,MERTON,Mitcham,10025296214,BIRCHWOOD CLOSE,22100620,SM4,525689,168146,525650,168150,51.39843582,-0.194607594\nNA,14/07/2019 12:47,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN HOLE BEHIND KITCHEN CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,MORTIMER CRESCENT,20400688,NW6,NULL,NULL,525750,183550,NULL,NULL\nNA,14/07/2019 19:20,2019,2019/20,Special Service,1,1,339,339,PARROT ENTANGLED IN WIRE IN TREE,Bird,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000203,JUBILEE,E09000010,ENFIELD,Edmonton,207099536,CHARLTON ROAD,20703193,N9,535743,195029,535750,195050,51.63770302,-0.039832067\nNA,15/07/2019 10:39,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD TRAPPED NETTING  - RSPCA ON SCENE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207152229,ELMORE ROAD,20702220,EN3,535522,198067,535550,198050,51.6650575,-0.041840514\nNA,15/07/2019 10:45,2019,2019/20,Special Service,1,3,339,1017,LARGE ANIMAL RESCUE,Unknown - Heavy Livestock Animal,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Lee Green,10003628194,MOTTINGHAM LANE,20301665,SE12,541161,173194,541150,173150,51.44016496,0.029668859\nNA,15/07/2019 14:48,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN WHEEL OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00021E+11,WANDLE ROAD,20501532,CR0,532296,165028,532250,165050,51.36891647,-0.100841489\nNA,15/07/2019 17:24,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN GUTTERING,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Stoke Newington,NULL,BIRCHMORE WALK,21602914,N5,NULL,NULL,532050,186050,NULL,NULL\nNA,16/07/2019 10:43,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA GAIN ACCESS TO GARAGE,Unknown - Wild Animal,Person (mobile),Bus/coach station/garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,10025292171,NEAR ACRE,20030800,NW9,521626,190565,521650,190550,51.60080739,-0.24525613\nNA,16/07/2019 14:15,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK IN BRAMBLES AND WOODLAND BY LEAD    OWNER WILL MEET AT THE ENTRANCE TO PADDOCKS    JCT ALE,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,10003975673,ALEXANDRA PALACE WAY,21106542,N10,530240,190320,530250,190350,51.59668035,-0.121044258\nNA,16/07/2019 18:29,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN THE DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,ALBRIGHTON ROAD,22500036,SE22,NULL,NULL,533350,175650,NULL,NULL\nNA,16/07/2019 19:41,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,CARSLAKE ROAD,22900812,SW15,NULL,NULL,523250,174350,NULL,NULL\nNA,16/07/2019 21:14,2019,2019/20,Special Service,1,1,339,339,PIGEON WITH LEG TRAPPED IN WINDOW  SECOND FLOOR,Bird,Person (mobile),Library,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009370,DALSTON,E09000012,HACKNEY,Homerton,10008322989,DALSTON LANE,20900310,E8,533658,184777,533650,184750,51.54607247,-0.073834937\nNA,17/07/2019 16:16,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN METAL GRILLS DIRECTLY UNDERNEATH THE FLYOVER,Bird,Person (land line),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,10094445038,HIGHBRIDGE ROAD,19900700,IG11,543700,183949,543750,183950,51.53616709,0.070545452\nNA,18/07/2019 00:40,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED IN HANGAR IN TIMBER YARD,Cat,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Sutton,5870048660,WESTMEAD ROAD,22602747,SM1,526810,164565,526850,164550,51.3659992,-0.179787\nNA,18/07/2019 09:50,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH BIRD HANGING FROM GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,FREEGROVE ROAD,21603247,N7,NULL,NULL,530550,185450,NULL,NULL\nNA,19/07/2019 08:51,2019,2019/20,Special Service,1,2,339,678,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,10090284129,SURREY QUAYS ROAD,22502426,SE16,535531,179410,535550,179450,51.49739655,-0.048903422\nNA,19/07/2019 15:30,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOORBOARDS,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,PHILIP LANE,21106391,N15,NULL,NULL,533150,189550,NULL,NULL\nNA,20/07/2019 21:04,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,BATTERSEA PARK ROAD,22900337,SW11,NULL,NULL,527650,176450,NULL,NULL\nNA,21/07/2019 17:07,2019,2019/20,Special Service,1,2,339,678,FOX TRAPPED ON LEDGE  RSCPA ON SCENE,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Dockhead,2.00003E+11,ROTHERHITHE STREET,22502127,SE16,535639,180250,535650,180250,51.50492288,-0.047030385\nNA,21/07/2019 17:38,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000377,MILDMAY,E09000019,ISLINGTON,Stoke Newington,NULL,LECONFIELD ROAD,21603468,N5,NULL,NULL,532550,185550,NULL,NULL\nNA,22/07/2019 03:06,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK DOWN SIDE OF BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,WANDSWORTH ROAD,21901460,SW8,NULL,NULL,530150,177950,NULL,NULL\nNA,22/07/2019 08:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BEDROOM,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009368,CAZENOVE,E09000012,HACKNEY,Stoke Newington,NULL,GELDESTON ROAD,20900437,E5,NULL,NULL,534350,186950,NULL,NULL\nNA,22/07/2019 13:34,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN CAR BOOT - IN CAR PARK,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,48101401,MORDEN HALL ROAD,22104539,SM4,526113,168484,526150,168450,51.40137847,-0.188391999\nNA,22/07/2019 18:51,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),Telephone exchange,Non Residential,Other animal assistance,Assist trapped wild animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,1.00023E+11,ILFORD HILL,22302962,IG1,543350,186298,543350,186250,51.55736333,0.066460321\nNA,23/07/2019 10:18,2019,2019/20,Special Service,1,1,339,339,FERRET TRAPPED BETWEEN TWO PIPES IN KITCHEN,Ferret,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000608,WILLIAM MORRIS,E09000031,WALTHAM FOREST,Walthamstow,NULL,FOREST ROAD,22837350,E17,NULL,NULL,536050,189450,NULL,NULL\nNA,25/07/2019 10:21,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH MAGPIE SHUT IN STAIRWELL  RSPCA IN ATTENDANCE,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Lambeth,10033540298,PALACE STREET,8401205,SW1E,529272,179310,529250,179350,51.49795845,-0.139054737\nNA,25/07/2019 16:42,2019,2019/20,Special Service,1,1,339,339,PET SNAKE TRAPPED UNDER RAMP,Snake,Person (mobile),Church/Chapel,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008233475,CHATSWORTH ROAD,20900224,E5,535570,185867,535550,185850,51.55540656,-0.045853625\nNA,25/07/2019 20:32,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000096,NORTHWICK PARK,E09000005,BRENT,Wembley,202134139,AUDREY GARDENS,20202246,HA0,516893,187047,516850,187050,51.57019634,-0.314733151\nNA,27/07/2019 21:50,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMALE RESCUE - KITTEN STUCK BEHIND BATH PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000253,COLLEGE PARK AND OLD OAK,E09000013,HAMMERSMITH AND FULHAM,North Kensington,NULL,WHITE CITY CLOSE,21000881,W12,NULL,NULL,523150,180650,NULL,NULL\nNA,29/07/2019 01:15,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BETWEEN WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,LISSON GROVE,8400993,NW1,NULL,NULL,527450,181850,NULL,NULL\nNA,29/07/2019 08:17,2019,2019/20,Special Service,1,1,339,339,TO ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHEAM COMMON ROAD,22601362,KT4,NULL,NULL,523050,165350,NULL,NULL\nNA,29/07/2019 13:30,2019,2019/20,Special Service,1,1,339,339,PUPPY TRAPPED IN BUSHES,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,10010437384,COURT GARDENS,21610022,N7,531300,184637,531350,184650,51.54537089,-0.107881018\nNA,29/07/2019 14:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,PHILIP LANE,21106391,N15,NULL,NULL,533150,189550,NULL,NULL\nNA,29/07/2019 17:22,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Kingston,128007048,WHEATFIELD WAY,21801044,KT1,518266,169239,518250,169250,51.40985298,-0.300895225\nNA,29/07/2019 20:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Synagogue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200212663,HALE LANE,20020280,HA8,519509,192270,519550,192250,51.61658334,-0.275234691\nNA,29/07/2019 23:04,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED/INJURED BEHIND WASHING MACHINE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,MANOR LANE,22001684,SE12,NULL,NULL,539650,173850,NULL,NULL\nNA,29/07/2019 23:20,2019,2019/20,Special Service,1,1,339,339,CAT FALLEN FROM ROOF AND INJURED   POLICE ON SCENE AND UNABLE TO REACH,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009393,COURTFIELD,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,COURTFIELD GARDENS,21700123,SW5,NULL,NULL,525850,178750,NULL,NULL\nNA,30/07/2019 07:33,2019,2019/20,Special Service,1,3,339,1017,HORSE IN DITCH,Horse,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Hainault,10023192924,ALDBOROUGH ROAD NORTH,22301799,IG2,545790,189943,545750,189950,51.58948685,0.103146305\nNA,30/07/2019 12:29,2019,2019/20,Special Service,1,1,339,339,DOG ON WINDOW LEDGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,COPTHALL GARDENS,22403411,TW1,NULL,NULL,515950,173350,NULL,NULL\nNA,30/07/2019 14:30,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN VOID OF CAVITY IN THE KITCHEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,OLD RUISLIP ROAD,20601307,UB5,NULL,NULL,511150,183250,NULL,NULL\nNA,30/07/2019 18:51,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,10091777532,MARKHOUSE ROAD,22856450,E17,536592,188197,536550,188150,51.5760985,-0.0302301\nNA,31/07/2019 10:17,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FOX DEN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,EAST ROAD,21800353,KT2,NULL,NULL,518350,169850,NULL,NULL\nNA,31/07/2019 16:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN VENT,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000210,TOWN,E09000010,ENFIELD,Enfield,NULL,RUSSELL ROAD,20702941,EN1,NULL,NULL,533550,198150,NULL,NULL\nNA,31/07/2019 20:57,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN ROOM IN DISTRESS,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000616,GRAVENEY,E09000032,WANDSWORTH,Tooting,NULL,ESWYN ROAD,22901556,SW17,NULL,NULL,527750,171550,NULL,NULL\nNA,01/08/2019 09:38,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10091578181,LEAMINGTON ROAD,21300095,RM3,555094,191979,555050,191950,51.605301,0.23823343\nNA,02/08/2019 07:56,2019,2019/20,Special Service,1,1,339,339,BABY FOX STUCK IN POND   CALLER STATES IN PARK OPPOSITE HOLU CAFE,Fox,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Richmond,10025549748,RICHMOND HILL,22403768,TW10,518207,174157,518250,174150,51.45407153,-0.300112092\nNA,02/08/2019 23:09,2019,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT STUCK IN TREE   TL REQUESTED AT SCENE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000593,CHINGFORD GREEN,E09000031,WALTHAM FOREST,Chingford,2.00001E+11,BURY ROAD,22821000,E4,539388,194876,539350,194850,51.63543512,0.012748054\nNA,02/08/2019 23:49,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND FENCE   NEAR TO ENTRANCE ON LYNDHURST WAY,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,10009791706,LYNDHURST WAY,22501612,SE15,533756,176261,533750,176250,51.46952091,-0.075651967\nNA,03/08/2019 03:07,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN REAR GARDEN,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,MCCULLUM ROAD,22700805,E3,NULL,NULL,536650,183550,NULL,NULL\nNA,03/08/2019 14:19,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,NULL,DUNN STREET,20900348,E8,NULL,NULL,533650,185450,NULL,NULL\nNA,04/08/2019 20:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,CLARISSA STREET,20900249,E8,NULL,NULL,533650,183750,NULL,NULL\nNA,05/08/2019 00:15,2019,2019/20,Special Service,1,1,339,339,INJURED DUCKLING FALLEN ONTO BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,CANDLE STREET,22702468,E1,NULL,NULL,536250,181750,NULL,NULL\nNA,05/08/2019 11:21,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED ON WINDOW LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,DROOP STREET,8400073,W10,NULL,NULL,524250,182450,NULL,NULL\nNA,05/08/2019 20:41,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN TREE POSSIBLY BY COLLAR,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00021E+11,FOXLEY HILL ROAD,20502079,CR8,531403,161219,531450,161250,51.3348919,-0.115069026\nNA,06/08/2019 07:47,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER THE FLOOR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,DRINKWATER ROAD,21201926,HA2,NULL,NULL,513750,186850,NULL,NULL\nNA,06/08/2019 12:04,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE  REQUESTED BY RSPCA ON SCENE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000533,WHITTON,E09000027,RICHMOND UPON THAMES,Heston,1.00022E+11,BRACKEN CLOSE,22406144,TW2,513194,173804,513150,173850,51.45191773,-0.372341147\nNA,06/08/2019 13:21,2019,2019/20,Special Service,2,6,339,2034,FOX STUCK ON PONTOON - FRU REQUESTED WITH WATER ATTRIBUTE        LEVEL TWO WATER OPS  RSPCA INSPECTO,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6066053,BALLADIER WALK,22702015,E14,537653,181771,537650,181750,51.51810301,-0.017436844\nNA,08/08/2019 11:08,2019,2019/20,Special Service,1,1,339,339,RABBIT TRAPPED IN GAP ON BALCONY,Rabbit,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Euston,NULL,HOPKINSON'S PLACE,20401359,NW1,NULL,NULL,528050,183950,NULL,NULL\nNA,08/08/2019 17:17,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000094,KILBURN,E09000005,BRENT,Paddington,NULL,KILBURN PARK ROAD,20201396,NW6,NULL,NULL,525350,182850,NULL,NULL\nNA,08/08/2019 23:52,2019,2019/20,Special Service,1,1,339,339,\"CAT STUCK ON A LEDGE FOUR STOREYS UP AND NOT MOVING, UNABLE TO GAIN ACCESS\",Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,ARUNDEL SQUARE,21604148,N7,NULL,NULL,531150,184650,NULL,NULL\nNA,09/08/2019 09:53,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED UNDERNEATH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000271,HARRINGAY,E09000014,HARINGEY,Hornsey,NULL,HEWITT ROAD,21103411,N8,NULL,NULL,531450,188750,NULL,NULL\nNA,10/08/2019 14:07,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WIRE MESH,Cat,Person (mobile),Other retail,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,48046421,MAJESTIC WAY,22104137,CR4,527862,169100,527850,169150,51.40652,-0.163052\nNA,13/08/2019 13:14,2019,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN RAILINGS   CALLER WILL LIASE WITH YOU AT MAIN GATE,Deer,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000198,ENFIELD HIGHWAY,E09000010,ENFIELD,Enfield,207002362,ACCESS ROAD FROM HERTFORD ROAD TO HERTFORD ROAD CEMETERY,20707384,EN3,535348,196961,535350,196950,51.65515728,-0.04477633\nNA,13/08/2019 16:46,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000435,TULSE HILL,E09000022,LAMBETH,West Norwood,NULL,BRAILSFORD ROAD,21900220,SW2,NULL,NULL,531350,174450,NULL,NULL\nNA,13/08/2019 18:20,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011486,THORNTON HEATH,E09000008,CROYDON,Woodside,NULL,ROSS ROAD,20501349,SE25,NULL,NULL,533150,168850,NULL,NULL\nNA,14/08/2019 07:55,2019,2019/20,Special Service,1,1,339,339,HORSE TRAPPED IN BARBED WIRE,Horse,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000404,CHESSINGTON NORTH AND HOOK,E09000021,KINGSTON UPON THAMES,Surbiton,128039180,FOOTPATH F/O 23-28 FOXGLOVE LANE,21880255,KT9,519415,164444,519450,164450,51.36651763,-0.285991063\nNA,15/08/2019 14:14,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN SMALL AREA UNDER SHED,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,TURNPIKE LINK,20501509,CR0,533429,165531,533450,165550,51.37316634,-0.084399695\nNA,16/08/2019 14:31,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA     CAT UP TREE,Cat,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped domestic animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,BEACONSFIELD ROAD,22500180,SE17,533086,178034,533050,178050,51.48561419,-0.08461883\nNA,17/08/2019 14:54,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SHED  CAT POSSIBLY CAUGHT UNDER EQUIPMENT,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,207115505,OLD FARM AVENUE,20701788,N14,529362,194770,529350,194750,51.63687192,-0.13207648\nNA,18/08/2019 02:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT FALLEN OUT OF WINDOW - RSPCA IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,DOWELLS STREET,20802135,SE10,NULL,NULL,537850,177750,NULL,NULL\nNA,18/08/2019 11:11,2019,2019/20,Special Service,1,3,339,1017,LEVEL TWO WATER RESCUE - ASSIST RSPCA DUCKLINGS FROM A LOCK,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,6647171,CARPENTERS ROAD,22700273,E20,537671,184512,537650,184550,51.54272503,-0.016104849\nNA,18/08/2019 16:53,2019,2019/20,Special Service,1,2,339,678,DOG TRAPPED IN BLACKBERRY BUSHES CALLER WILL MEET YOU BY RODING SCHOOL,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10025288423,FOOTPATH FROM RAVENSBOURNE GARDENS THROUGH COCKED HAT PLANTATION TO RODING LANE NORTH,22390155,IG8,542805,191272,542850,191250,51.60219835,0.060623751\nNA,19/08/2019 08:05,2019,2019/20,Special Service,1,1,339,339,INJURED DEER TRAPPED IN FENCE     CALLER WILL MEET IN CAR PARK AT BOTTOM OF BAKERS HILL,Deer,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207001410,FOOTPATH FROM COVERT WAY TO COVERT WAY OPEN SPACE,20707128,EN4,526327,197081,526350,197050,51.65833379,-0.175066746\nNA,19/08/2019 12:25,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND WALL,Cat,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,5079307,ROWLEY WAY,20402815,NW8,526168,183950,526150,183950,51.54036629,-0.182087091\nNA,20/08/2019 17:02,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000568,THE WRYTHE,E09000029,SUTTON,Wallington,NULL,WRYTHE LANE,22605653,SM5,NULL,NULL,527150,165350,NULL,NULL\nNA,20/08/2019 21:22,2019,2019/20,Special Service,2,3,339,1017,Redacted,Cat,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6168485,URSULA GOULD WAY,22702630,E14,537294,181551,537250,181550,51.51620754,-0.022693542\nNA,21/08/2019 18:40,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BEHIND COPPER PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,FENGATE CLOSE,21880352,KT9,NULL,NULL,517750,163850,NULL,NULL\nNA,22/08/2019 17:26,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Chingford,NULL,JACKS FARM WAY,22847360,E4,NULL,NULL,538250,191750,NULL,NULL\nNA,23/08/2019 15:35,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK IN WATERWAY - INGREBOURNE RIVER  LOCATED BETWEEN BRIDGES AT HAROLD WOOD PARK AND THE GOLF,Dog,Person (mobile),Park,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,10091577192,SQUIRRELS HEATH ROAD,21300145,RM3,555181,190044,555150,190050,51.58788915,0.238637414\nNA,23/08/2019 19:14,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Police,Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,10010259153,BROCKLEBANK ROAD,20800232,SE7,540606,178768,540650,178750,51.49038542,0.023894671\nNA,23/08/2019 19:59,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED BETWEEN PIPE AND WALL,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000060,UNDERHILL,E09000003,BARNET,Barnet,NULL,CROCUS FIELD,20011100,EN5,NULL,NULL,524850,195450,NULL,NULL\nNA,24/08/2019 12:34,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,LAWRIE PARK ROAD,22004427,SE26,NULL,NULL,535150,171350,NULL,NULL\nNA,24/08/2019 15:34,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CROW TRAPPED IN FENCING ON ROOF,Bird,Person (mobile),Gym,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,PARK ROAD,22400459,KT1,517457,169590,517450,169550,51.41317378,-0.312412742\nNA,24/08/2019 16:37,2019,2019/20,Special Service,1,1,339,339,GUINEA PIG TRAPPED UNDER CAR SEAT,Unknown - Domestic Animal Or Pet,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009369,CLISSOLD,E09000012,HACKNEY,Stoke Newington,1.00021E+11,WINSTON ROAD,20901091,N16,532841,185865,532850,185850,51.55604303,-0.085206906\nNA,24/08/2019 16:55,2019,2019/20,Special Service,1,1,339,339,DOG STUCK IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,JUBILEE STREET,22700688,E1,NULL,NULL,535150,181750,NULL,NULL\nNA,25/08/2019 03:57,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,QUILTER STREET,22700984,E2,NULL,NULL,534150,182850,NULL,NULL\nNA,25/08/2019 10:19,2019,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN NETTING      INSIDE STATION,Bird,Person (land line),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920911,THE BROADWAY,22306023,IG8,540937,191754,540950,191750,51.6069938,0.0338614\nNA,25/08/2019 11:19,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000635,HARROW ROAD,E09000033,WESTMINSTER,Paddington,NULL,CHIPPENHAM ROAD,8400793,W9,NULL,NULL,525150,182450,NULL,NULL\nNA,25/08/2019 13:34,2019,2019/20,Special Service,1,1,339,339,DOG STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,NULL,BIRKBECK ROAD,20301839,BR3,NULL,NULL,535650,169350,NULL,NULL\nNA,25/08/2019 18:43,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING-OPPOSITE SAINSBURYS UNDER THE ARCHES,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,10093342132,NEW KENT ROAD,22501783,SE1,532091,178986,532050,178950,51.49440257,-0.098595843\nNA,26/08/2019 01:08,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000094,KILBURN,E09000005,BRENT,West Hampstead,NULL,ALDERSHOT ROAD,20200245,NW6,NULL,NULL,524950,183850,NULL,NULL\nNA,26/08/2019 17:33,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND TOILET CISTERN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000616,GRAVENEY,E09000032,WANDSWORTH,Tooting,NULL,COTEFORD STREET,22901055,SW17,NULL,NULL,528050,171650,NULL,NULL\nNA,28/08/2019 07:20,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000619,NORTHCOTE,E09000032,WANDSWORTH,Battersea,NULL,GRANDISON ROAD,22902089,SW11,NULL,NULL,527850,174750,NULL,NULL\nNA,28/08/2019 10:28,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000596,GROVE GREEN,E09000031,WALTHAM FOREST,Leytonstone,NULL,ASHVILLE ROAD,22812000,E11,NULL,NULL,538850,186850,NULL,NULL\nNA,29/08/2019 17:14,2019,2019/20,Special Service,1,1,339,339,INJURED CAT TRAPPED IN BUSHES - OWNER ON SCENE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6067533,CHRISP STREET,22700311,E14,537751,181509,537750,181550,51.51571775,-0.01612341\nNA,30/08/2019 14:18,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL DEER TRAPPED IN RAILINGS OF A GATE   GO TO BEDWELL ROAD AT THE R/O THE SCHOO,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,1.00021E+11,THE ROUNDWAY,21106424,N17,533181,190796,533150,190750,51.60027225,-0.078429795\nNA,30/08/2019 15:09,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA AT SCENE CAT STUCK ON ROOF,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,1.00023E+11,VICTORIA ROAD,21801000,KT6,517835,167182,517850,167150,51.391452,-0.307782\nNA,01/09/2019 13:25,2019,2019/20,Special Service,1,1,339,339,ASSISTING RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,SPARROW FARM DRIVE,21501041,TW14,NULL,NULL,511450,173850,NULL,NULL\nNA,03/09/2019 20:06,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BEHIND TOILET,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,NULL,WINDSOR ROAD,20601929,W5,NULL,NULL,518050,180650,NULL,NULL\nNA,03/09/2019 21:58,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,1.00022E+11,BALFOUR ROAD,22302601,IG1,543693,186784,543650,186750,51.56164582,0.071604872\nNA,03/09/2019 21:59,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO FENCES,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,48066859,STIRLING CLOSE,22106008,SW16,529510,169632,529550,169650,51.4109283,-0.139169902\nNA,04/09/2019 17:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN UNIT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,DUNLOP PLACE,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL\nNA,04/09/2019 18:27,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND FIRE PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000317,PETTITS,E09000016,HAVERING,Romford,NULL,WAYSIDE CLOSE,21301685,RM1,NULL,NULL,551650,189850,NULL,NULL\nNA,04/09/2019 19:14,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10091941543,THE BROADWAY,22306023,IG8,540936,191858,540950,191850,51.60793021,0.033885768\nNA,06/09/2019 21:45,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,08/09/2019 13:04,2019,2019/20,Special Service,1,1,339,339,CATS PAW TRAPPED IN PLUG HOLE,cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000190,SOUTHALL GREEN,E09000009,EALING,Southall,NULL,GORDON ROAD,20600739,UB2,NULL,NULL,512350,178750,NULL,NULL\nNA,09/09/2019 00:11,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,NULL,BLYTH ROAD,22816750,E17,NULL,NULL,536650,187650,NULL,NULL\nNA,09/09/2019 19:26,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED BEHIND TOILET SOIL PIPE - HAS BEEN THERE SINCE EARLY THIS MORNING,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,FOXHOLE ROAD,20800589,SE9,NULL,NULL,542150,174950,NULL,NULL\nNA,11/09/2019 14:06,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CHEYNE PLACE,21701182,SW3,NULL,NULL,527650,177950,NULL,NULL\nNA,13/09/2019 10:54,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,1.00021E+11,GREENWICH HIGH ROAD,20800685,SE10,537920,177186,537950,177150,51.47683546,-0.015380157\nNA,13/09/2019 11:47,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND THE FACIA OF SHOP,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000094,KILBURN,E09000005,BRENT,West Hampstead,202031114,WILLESDEN LANE,20201346,NW6,524763,183955,524750,183950,51.5407158,-0.2023438\nNA,14/09/2019 12:54,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BASEMENT BEHIND SPIKED RAILINGS    REQUEST FROM RSPCA OFFICER ON SCENE TO ASSIST,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,SHEPHERDS PLACE,8400166,W1K,NULL,NULL,528150,180850,NULL,NULL\nNA,14/09/2019 13:43,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH FOX TRAPPED IN CONSTRUCTION SITE,Fox,Person (mobile),Other Dwelling,Dwelling,Other animal assistance,Assist trapped wild animal,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,HALLIFORD STREET,21605047,N1,NULL,NULL,532450,184250,NULL,NULL\nNA,14/09/2019 16:58,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN TREE - ACCESS VIA SUNNINGFIELDS ROAD,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000055,HENDON,E09000003,BARNET,Hendon,200111105,SUNNY GARDENS ROAD,20041580,NW4,522966,190167,522950,190150,51.59694033,-0.226057726\nNA,15/09/2019 08:26,2019,2019/20,Special Service,1,2,339,678,DOG STUCK DOWN A HOLE   FRU REQUIRED WITH SNAKE EYE,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,RAVENSCOURT AVENUE,21000673,W6,NULL,NULL,522450,178650,NULL,NULL\nNA,15/09/2019 09:43,2019,2019/20,Special Service,1,1,339,339,\"KITTENS UNDERNEATH CAR SOME BELIEVED TO BE INSIDE CAR ENGINE  PERSON ON SCENE, SILVER CORSA CAR\",Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,ALDBOROUGH ROAD SOUTH,22301800,IG3,545521,188264,545550,188250,51.57447567,0.098564267\nNA,15/09/2019 17:56,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,10025176373,THE PROMENADE,21590226,W4,521402,176552,521450,176550,51.474916,-0.253325934\nNA,15/09/2019 23:10,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON PIPE,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009318,BLACKWALL & CUBITT TOWN,E09000030,TOWER HAMLETS,Poplar,6083203,LOVEGROVE WALK,22700757,E14,538223,180170,538250,180150,51.50357032,-0.009847936\nNA,16/09/2019 08:13,2019,2019/20,Special Service,1,2,339,678,KITTEN STUCK BEHIND FIREPLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,ALVERSTONE AVENUE,20000900,EN4,NULL,NULL,527250,194650,NULL,NULL\nNA,16/09/2019 13:04,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000436,VASSALL,E09000022,LAMBETH,Brixton,NULL,HALSMERE ROAD,21900656,SE5,NULL,NULL,531950,176850,NULL,NULL\nNA,16/09/2019 19:26,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000052,GARDEN SUBURB,E09000003,BARNET,Finchley,NULL,WILLIFIELD WAY,20046400,NW11,NULL,NULL,525250,188750,NULL,NULL\nNA,17/09/2019 11:11,2019,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING NEXT TO BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00023E+11,TURNHAM GREEN TERRACE,21501135,W4,521212,178797,521250,178750,51.49513367,-0.255293677\nNA,18/09/2019 02:01,2019,2019/20,Special Service,1,2,339,678,CAT TRAPPED DOWN VENT,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000641,MARYLEBONE HIGH STREET,E09000033,WESTMINSTER,Euston,NULL,MARYLEBONE HIGH STREET,8401405,W1U,NULL,NULL,528350,181650,NULL,NULL\nNA,18/09/2019 14:33,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING OUTSIDE                  NEAR SAINSBURYS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,THE BROADWAY,22306023,IG8,NULL,NULL,540950,191850,NULL,NULL\nNA,18/09/2019 17:39,2019,2019/20,Special Service,1,1,339,339,CAT STUCK  IN CAR ENGINE - CALLER WILL MAKE THEMSELVES KNOWN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207032004,HIGH STREET,20705021,EN3,535260,195881,535250,195850,51.64547414,-0.046469884\nNA,20/09/2019 23:22,2019,2019/20,Special Service,1,2,339,678,FOX TRAPPED BETWEEN TWO HOUSES,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05011113,RYE LANE,E09000028,SOUTHWARK,Peckham,2.00003E+11,DANBY STREET,22500699,SE15,533671,175869,533650,175850,51.46601375,-0.077022332\nNA,21/09/2019 02:09,2019,2019/20,Special Service,1,4,339,1356,FOX TRAPPED IN BETWEEN TWO HOUSES FOLLOWING SM ASSESSMENT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011113,RYE LANE,E09000028,SOUTHWARK,Peckham,NULL,DANBY STREET,22500699,SE15,NULL,NULL,533650,175850,NULL,NULL\nNA,22/09/2019 17:34,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN TREE BY WIRE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,1.00021E+11,SALISBURY ROAD,21401688,HA5,510050,189150,510050,189150,51.59047458,-0.412776765\nNA,23/09/2019 18:44,2019,2019/20,Special Service,1,2,339,678,Redacted,Horse,Person (land line),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,NA,NA,NA,NA,Buckinghamshire,1.00081E+11,GEORGE GREEN ROAD,35200389,SL3,500025,181111,500050,181150,51.52007841,-0.559730194\nNA,23/09/2019 19:57,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BY HEAD IN MESH FENCE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,1.00022E+11,SHEEPHOUSE WAY,21800852,KT3,521284,166611,521250,166650,51.38559988,-0.258418655\nNA,23/09/2019 23:10,2019,2019/20,Special Service,1,1,339,339,KITTEN  STUCK IN BASEMENT AREA - HAS FALLEN FROM BALCONY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CHESTERTON ROAD,21700835,W10,NULL,NULL,524250,181750,NULL,NULL\nNA,24/09/2019 09:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN COAL CELLAR,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,NULL,RIDGWAY PLACE,22105412,SW19,NULL,NULL,524250,170550,NULL,NULL\nNA,25/09/2019 13:07,2019,2019/20,Special Service,1,3,339,1017,HORSE CAST IN STABLE      VET ON SCENE,Horse,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011478,SANDERSTEAD,E09000008,CROYDON,Croydon,2.00001E+11,PURLEY OAKS ROAD,20502277,CR2,533119,161889,533150,161850,51.34051479,-0.0902084\nNA,26/09/2019 03:09,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND BOILER PIPES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,NULL,NARFORD ROAD,20900725,E5,NULL,NULL,534350,186550,NULL,NULL\nNA,28/09/2019 16:29,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Scrub land,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000532,WEST TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,10070719848,UNNAMED FOOTPATH - MILL ROAD TO PERCY ROAD,22406500,TW2,514368,172843,514350,172850,51.44304372,-0.355754565\nNA,29/09/2019 10:38,2019,2019/20,Special Service,1,1,339,339,HEDGEHOG TRAPPED BETWEEN WIRE FENCE - CALLER UNABLE TO GET TO IT TO RELEASE IT,Hedgehog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,200161791,EAGANS CLOSE,20013360,N2,526887,189664,526850,189650,51.5915511,-0.169663587\nNA,30/09/2019 08:26,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED DOWN HOLE  BIGGIN HILL RECREATION GROUND NEAR SWIMMIMG POOL      CALLER WILL LIASE WITH,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,10070018285,CHURCH ROAD,20301436,TN16,541956,159034,541950,159050,51.31272161,0.03544197\nNA,02/10/2019 06:27,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED - OPP MORRISONS DOWN PRIVATE ROAD SEEN FROM BRIDGE - AT  FURTHEST LOCK FROM HIGH STREET,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000364,SYON,E09000018,HOUNSLOW,Chiswick,10093259173,FOOTPATH FROM DOCK ROAD TO CATHERINE WHEEL ROAD ALONG CANAL WAY,21519771,TW8,517825,177287,517850,177250,51.48227699,-0.304555772\nNA,02/10/2019 12:32,2019,2019/20,Special Service,1,2,339,678,KITTEN TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,COLLINGWOOD ROAD,21103130,N15,533238,189397,533250,189350,51.58768725,-0.078136739\nNA,02/10/2019 16:06,2019,2019/20,Special Service,1,1,339,339,CAT WITH KITTENS IN A HOLE IN THE GARDEN - TOO DEEP TO REACH,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,LORDSHIP LANE,21106476,N17,NULL,NULL,533850,190650,NULL,NULL\nNA,02/10/2019 22:09,2019,2019/20,Special Service,1,1,339,339,CAT IN DISTRESS TRAPPED BEHIND HIGH FENCE - OWNER ON SCENE,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000633,CHURCHILL,E09000033,WESTMINSTER,Chelsea,1.00023E+11,EBURY BRIDGE ROAD,8400824,SW1W,528554,178253,528550,178250,51.48862538,-0.149781226\nNA,03/10/2019 10:12,2019,2019/20,Special Service,1,3,339,1017,HORSE STUCK IN OVERFLOW CHANNEL BETWEEN RESERVOIRS NEAR PONDERS END LOCK,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist trapped wild animal,E05000203,JUBILEE,E09000010,ENFIELD,Chingford,207169544,LEA VALLEY ROAD,20703459,E4,537303,195014,537350,195050,51.63718202,-0.017301322\nNA,03/10/2019 13:28,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,1.00021E+11,TURNPIKE LANE,21104018,N8,531299,189588,531250,189550,51.58985564,-0.106041221\nNA,03/10/2019 20:32,2019,2019/20,Special Service,1,1,339,339,RSPCA IN ATTENDANCE  CAT FALLEN FROM THIRD FLOOR BALCONY ONTO CONCRETE OVERHANG TO ENTRANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,HALL PLACE,8400423,W2,NULL,NULL,526750,181850,NULL,NULL\nNA,04/10/2019 16:50,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN GUTTERING OF LOCK UP GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Bexley,10023303854,DALE VIEW,20100416,DA8,552020,176420,552050,176450,51.46634452,0.187177254\nNA,06/10/2019 17:01,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL OF SHED - BACKS ON TO RANMORE CLOSE,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,46039460,IDMISTON ROAD,22200075,E15,539593,185376,539550,185350,51.55002104,0.011930641\nNA,07/10/2019 07:23,2019,2019/20,Special Service,1,2,339,678,SMALL ANIMAL RESCUE - FOX WITH TRAPPED LEG,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,1.00021E+11,POWERSCROFT ROAD,20900816,E5,535370,185744,535350,185750,51.55435678,-0.048785829\nNA,07/10/2019 13:05,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05009291,BILLINGSGATE,E09000001,CITY OF LONDON,Dowgate,10091781655,GREAT TOWER STREET,8100251,EC3R,533187,180800,533150,180850,51.51044841,-0.082130589\nNA,07/10/2019 22:29,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON LEDGE -  ** DO NOT CLOSE **,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,AVERY ROW,8400745,W1K,NULL,NULL,528750,180950,NULL,NULL\nNA,10/10/2019 12:38,2019,2019/20,Special Service,1,2,339,678,\"FOX TRAPPED ON PILLAR     CALLER WAITING FOR YOU ON PEDESTRIAN WALKWAY, SOUTHSIDE OF BLACKFRIARS BRI\",Fox,Person (mobile),Bridge,Outdoor Structure,Animal rescue from water,Wild animal rescue from water or mud,E05011095,BOROUGH & BANKSIDE,E09000028,SOUTHWARK,Dowgate,2.00003E+11,BLACKFRIARS ROAD,22500249,SE1,531622,180554,531650,180550,51.50860098,-0.104759215\nNA,10/10/2019 18:52,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN STEERING WHEEL AND DASH BOARD GREY  LEXUS CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000136,HAVERSTOCK,E09000007,CAMDEN,Kentish Town,5090679,HAVERSTOCK HILL,20400301,NW3,528079,184497,528050,184450,51.5448467,-0.154348608\nNA,10/10/2019 21:37,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON WINDOW LEDGE AT HEIGHT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,NULL,CLOUDESDALE ROAD,22900995,SW17,NULL,NULL,528550,172750,NULL,NULL\nNA,11/10/2019 22:30,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN FENCES,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,46018973,CREIGHTON AVENUE,22200519,E6,541735,183130,541750,183150,51.52930038,0.041903814\nNA,12/10/2019 19:08,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN FACTORY BETWEEN SCAFFOLDING AND DRAPING IN THE THE VINYL FACTORY,Cat,Person (mobile),Factory,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,2.00003E+11,CLAYTON ROAD,21400418,UB3,509343,179648,509350,179650,51.50520369,-0.425934777\nNA,14/10/2019 07:04,2019,2019/20,Special Service,1,2,339,678,DEER CAUGHT IN RAILINGS,Deer,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,BEACH GROVE,21500085,TW13,513324,172538,513350,172550,51.44051754,-0.370864564\nNA,14/10/2019 14:39,2019,2019/20,Special Service,1,1,339,339,INJURED FOX SHUT BEHIND GATES    RSPCA ON SCENE,Fox,Person (mobile),Water works,Non Residential,Other animal assistance,Assist trapped wild animal,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RIVERSIDE,22407112,TW9,517638,174694,517650,174650,51.45901336,-0.308106819\nNA,14/10/2019 19:25,2019,2019/20,Special Service,1,1,339,339,PUPPY WITH HEAD TRAPPED IN METAL CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,NULL,STRATHVILLE ROAD,22905028,SW18,NULL,NULL,525850,173050,NULL,NULL\nNA,16/10/2019 09:10,2019,2019/20,Special Service,1,1,339,339,HORSE TRAPPED IN FENCE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy wild animal,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,10023303145,MOAT LANE,20100987,DA8,552972,176295,552950,176250,51.46495768,0.200818382\nNA,16/10/2019 22:07,2019,2019/20,Special Service,1,2,339,678,Redacted,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,East Greenwich,1.00021E+11,WILLOW LANE,20801675,SE18,543073,178824,543050,178850,51.4902689,0.0594272\nNA,17/10/2019 20:36,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN NET AT SECOND FLOOR LEVEL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,JACK CORNWELL STREET,22200193,E12,NULL,NULL,543250,185650,NULL,NULL\nNA,17/10/2019 22:05,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34159547,SOUTH AFRICA ROAD,21000741,W12,523014,180626,523050,180650,51.511188,-0.228701\nNA,18/10/2019 12:25,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,NULL,BRAMLEY CRESCENT,22302643,IG2,NULL,NULL,543350,188250,NULL,NULL\nNA,18/10/2019 17:40,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND KITCHEN CUPBOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,NEW GUN WHARF,22702725,E3,NULL,NULL,536450,183650,NULL,NULL\nNA,19/10/2019 09:44,2019,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN ROPE    CALLER WILL DIRECT YOU FROM THE JUNCTION,Deer,Other FRS,Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025149967,LOWER NOKE CLOSE,21300682,RM3,554669,193576,554650,193550,51.61976638,0.232809108\nNA,19/10/2019 11:57,2019,2019/20,Special Service,1,1,339,339,DOG IN POND/SMALL LAKE BEHIND RAILINGS   OWNER ON SCENE,Dog,Person (mobile),Heathland,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,10025178856,ELM WALK,20014320,NW3,525622,186680,525650,186650,51.56501966,-0.18897956\nNA,19/10/2019 18:52,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL IN HALLWAY,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,TERSHA STREET,22407053,TW9,NULL,NULL,518550,175350,NULL,NULL\nNA,19/10/2019 20:54,2019,2019/20,Special Service,1,1,339,339,ASSIST WOMAN WITH SQUIRREL IN SITTING ROOM,Squirrel,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,COLWITH ROAD,21000230,W6,NULL,NULL,523350,177750,NULL,NULL\nNA,20/10/2019 00:01,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,BREWSTER ROAD,22818450,E10,NULL,NULL,537750,187250,NULL,NULL\nNA,20/10/2019 11:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,THE AVENUE,21106439,N10,NULL,NULL,529350,190250,NULL,NULL\nNA,21/10/2019 09:47,2019,2019/20,Special Service,1,1,339,339,DEER CAUGHT IN A TRAP,Deer,Person (land line),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025145227,LOWER NOKE CLOSE,21300682,RM3,554790,193442,554750,193450,51.61852424,0.234502487\nNA,22/10/2019 15:13,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009400,PEMBRIDGE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,PEMBRIDGE SQUARE,21700756,W2,NULL,NULL,525450,180750,NULL,NULL\nNA,23/10/2019 12:48,2019,2019/20,Special Service,1,1,339,339,FOX STUCK UNDER FENCE   IN BACK GARDEN,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202109473,CRUNDALE AVENUE,20201132,NW9,519363,188548,519350,188550,51.5831668,-0.278608387\nNA,23/10/2019 15:52,2019,2019/20,Special Service,1,1,339,339,RABBIT TRAPPED BEHIND SHED,Rabbit,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,HIGHAM ROAD,21103419,N17,NULL,NULL,532950,189950,NULL,NULL\nNA,24/10/2019 18:43,2019,2019/20,Special Service,1,2,339,678,Redacted,Dog,Person (mobile),Hedge,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,10008299053,LEA BRIDGE ROAD,20900602,E5,535579,186571,535550,186550,51.5617384,-0.045464073\nNA,24/10/2019 23:06,2019,2019/20,Special Service,1,1,339,339,DEER WITH HEAD STUCK IN RAILINGS OPPOSITE THE PRIMARY SCHOOL,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10025146314,DAGNAM PARK DRIVE,21300071,RM3,554229,192345,554250,192350,51.60882369,0.225914524\nNA,25/10/2019 07:55,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN BASEMENET     RSPCA ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CURWEN ROAD,21000254,W12,NULL,NULL,522150,179750,NULL,NULL\nNA,25/10/2019 13:48,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Roadside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34032803,SPRING VALE TERRACE,21000747,W14,523842,179226,523850,179250,51.49842596,-0.217275184\nNA,25/10/2019 15:50,2019,2019/20,Special Service,1,2,339,678,DOG STUCK UP TREE  CALLER STATES SHE WAS BY THE RIVER RODING,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,1.00022E+11,WANSTEAD PARK ROAD,22303358,IG1,542138,187454,542150,187450,51.56805644,0.049460884\nNA,25/10/2019 16:37,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK OUTSIDE WINDOW OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,DALLIN ROAD,20800435,SE18,NULL,NULL,543950,177450,NULL,NULL\nNA,25/10/2019 19:48,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN SOFA,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000405,CHESSINGTON SOUTH,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,REYNOLDS AVENUE,21800802,KT9,NULL,NULL,518350,163450,NULL,NULL\nNA,26/10/2019 16:19,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN BADGER SETT   CALLER WILL MEET YOU ON THE FOOTPATH,Dog,Person (mobile),Golf course (not building on course),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10090329808,CHESTNUT AVENUE,21400375,HA6,509596,190256,509550,190250,51.60049797,-0.418984419\nNA,26/10/2019 16:55,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN GATE AND WALL,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000444,FOREST HILL,E09000023,LEWISHAM,Forest Hill,NULL,DUNOON ROAD,22001424,SE23,NULL,NULL,535450,173950,NULL,NULL\nNA,26/10/2019 20:43,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN FIREPLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,MONKS WAY,21401313,UB7,NULL,NULL,506050,177850,NULL,NULL\nNA,27/10/2019 21:42,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND SHED,Dog,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000043,BRUNSWICK PARK,E09000003,BARNET,Southgate,200082208,MONKFRITH WAY,20029900,N14,528613,194732,528650,194750,51.63670725,-0.142905981\nNA,29/10/2019 23:05,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,NULL,NORLANDS CRESCENT,20300522,BR7,NULL,NULL,543750,169550,NULL,NULL\nNA,30/10/2019 15:19,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN DUCTING,Cat,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009387,WOODBERRY DOWN,E09000012,HACKNEY,Stoke Newington,NULL,BETHUNE ROAD,20900135,N16,NULL,NULL,532950,187650,NULL,NULL\nNA,30/10/2019 18:44,2019,2019/20,Special Service,1,1,339,339,KITTEN POSSIBLY TRAPPED OR INJURED IN TREE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,WEST CLOSE,20704904,N9,NULL,NULL,533850,193350,NULL,NULL\nNA,31/10/2019 13:43,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000305,WEST HARROW,E09000015,HARROW,Harrow,NULL,THE RETREAT,21202023,HA2,NULL,NULL,513450,187650,NULL,NULL\nNA,02/11/2019 11:11,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED BETWEEN OUTHOUSE AND FENCE,Fox,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped wild animal,E05000332,HILLINGDON EAST,E09000017,HILLINGDON,Hillingdon,1.00023E+11,SUTTON COURT ROAD,21401899,UB10,507866,183597,507850,183550,51.54098264,-0.445989797\nNA,02/11/2019 15:28,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,217088245,UPPER CHEYNE ROW,21700563,SW3,527082,177734,527050,177750,51.48429172,-0.171157198\nNA,02/11/2019 23:12,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,CHERTSEY ROAD,21500242,TW13,NULL,NULL,509350,171950,NULL,NULL\nNA,04/11/2019 11:59,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN NETTING,Fox,Person (mobile),Secondary school,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,527923,176526,527950,176550,51.47324474,-0.159488874\nNA,04/11/2019 21:31,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,46006002,BISSON ROAD,22201310,E15,538478,183116,538450,183150,51.52998601,-0.005018049\nNA,05/11/2019 21:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN A WELL,Cat,Person (mobile),Converted office,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,34092988,FULHAM HIGH STREET,21000355,SW6,524427,176093,524450,176050,51.47013579,-0.209947838\nNA,07/11/2019 08:03,2019,2019/20,Special Service,1,1,339,339,DEER STUCK IN RAILINGS,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000603,LEA BRIDGE,E09000031,WALTHAM FOREST,Leyton,2.00001E+11,LEA BRIDGE ROAD,22850300,E10,535912,186758,535950,186750,51.56334005,-0.040585667\nNA,07/11/2019 17:54,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000314,HEATON,E09000016,HAVERING,Harold Hill,NULL,STRAIGHT ROAD,21301625,RM3,NULL,NULL,553050,191750,NULL,NULL\nNA,07/11/2019 21:28,2019,2019/20,Special Service,1,2,339,678,PERSON WITH SQUIRREL TRAPPED BEHIND WARDROBE,Squirrel,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,NULL,RAVENSBOURNE AVENUE,20303571,BR2,NULL,NULL,538750,170050,NULL,NULL\nNA,08/11/2019 08:20,2019,2019/20,Special Service,1,1,339,339,FOX STUCK IN GATE,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,46250898,BOUNDARY ROAD,22200398,E13,541440,183161,541450,183150,51.52965787,0.037671653\nNA,09/11/2019 22:32,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,GILBEY ROAD,22902000,SW17,NULL,NULL,527250,171550,NULL,NULL\nNA,10/11/2019 09:22,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,2.00004E+11,RENFORTH STREET,22502073,SE16,535387,179480,535350,179450,51.49806084,-0.050950467\nNA,12/11/2019 11:34,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND BRICK WORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532550,183350,NULL,NULL\nNA,13/11/2019 09:09,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDERNEATH FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,BRUNSWICK STREET,22820250,E17,NULL,NULL,538150,188850,NULL,NULL\nNA,13/11/2019 16:24,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,EDWIN AVENUE,22200578,E6,NULL,NULL,543250,182950,NULL,NULL\nNA,13/11/2019 19:12,2019,2019/20,Special Service,1,2,339,678,DOG IN FOX DEN,Dog,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,BEMBRIDGE GARDENS,21400147,HA4,508990,186724,508950,186750,51.56886925,-0.428828149\nNA,15/11/2019 12:15,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE CAR    AT THE REAR OF THE CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000180,HOBBAYNE,E09000009,EALING,Ealing,12059310,SHAKESPEARE ROAD,20601535,W7,515871,180845,515850,180850,51.51466283,-0.331525757\nNA,16/11/2019 17:13,2019,2019/20,Special Service,1,1,339,339,CAT STUCK ON HIGH FENCE    ASSISTANCE REQUESTED BY RSPCA OFFICER ON SCENE,Cat,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10034508567,FARADAY ROAD,22201400,E15,539689,184632,539650,184650,51.5433055,0.013018\nNA,17/11/2019 03:23,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532650,183450,NULL,NULL\nNA,17/11/2019 17:04,2019,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN IRON GATE - PERSON ON SCENE FROM FOX PROJECT TO DEAL WITH FOX WHEN RELEASED,Fox,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,1.00021E+11,WOODSIDE ROAD,20501593,SE25,534705,167179,534750,167150,51.3876747,-0.0654549\nNA,18/11/2019 01:30,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BETWEEN A PANEL AND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,PAYNESFIELD AVENUE,22404871,SW14,NULL,NULL,520850,175550,NULL,NULL\nNA,18/11/2019 01:36,2019,2019/20,Special Service,1,1,339,339,PERSON LOCKED OUT - DOG LOCKED IN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,CHESTER TERRACE,20400821,NW1,NULL,NULL,528750,182850,NULL,NULL\nNA,18/11/2019 11:56,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,10008985396,DONGOLA ROAD WEST,22200551,E13,540560,182646,540550,182650,51.52524433,0.024779439\nNA,19/11/2019 14:57,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10090849105,TEMPLE MILL LANE,22201738,E20,538233,185248,538250,185250,51.54920327,-0.007711702\nNA,21/11/2019 11:16,2019,2019/20,Special Service,1,1,339,339,BIRD STUCK IN NETTING   RSPCA ON SCENE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532191,165905,532150,165950,51.37682073,-0.10202979\nNA,22/11/2019 07:46,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,VALLENTIN ROAD,22883400,E17,NULL,NULL,538250,189350,NULL,NULL\nNA,22/11/2019 09:36,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00023E+11,RESERVOIR ROAD,21401623,HA4,508683,189227,508650,189250,51.59142519,-0.432475748\nNA,22/11/2019 12:26,2019,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND KITCHEN UNIT,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,STEVENS AVENUE,20900974,E9,NULL,NULL,535350,184850,NULL,NULL\nNA,22/11/2019 13:11,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,GRAFTON PLACE,20400856,NW1,NULL,NULL,529750,182750,NULL,NULL\nNA,23/11/2019 20:54,2019,2019/20,Special Service,1,1,339,339,NULL,Unknown - Domestic Animal Or Pet,Other FRS,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00024E+11,MILL ROAD,21401294,UB7,505440,179073,505450,179050,51.50077925,-0.48231471\nNA,24/11/2019 03:12,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG FALLEN IN HOLE,Dog,Person (mobile),Pipe or drain,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200052627,GREENFIELD GARDENS,20019340,NW2,524457,186333,524450,186350,51.56215901,-0.205911543\nNA,25/11/2019 20:26,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,12176982,GREENFORD ROAD,20602353,UB6,515260,183932,515250,183950,51.54253233,-0.339316513\nNA,26/11/2019 12:59,2019,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,202126588,BRONDESBURY PARK,20201215,NW6,524047,184128,524050,184150,51.542428,-0.212602\nNA,26/11/2019 14:08,2019,2019/20,Special Service,1,1,339,339,SQUIRREL TRAPPED VIA LEGS INSIDE BIN - SQUIRREL IN DISTRESS - RSPCA CONTACTED,Squirrel,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,200065607,FREELAND PARK,20016560,NW4,524037,190319,524050,190350,51.59807855,-0.210547761\nNA,28/11/2019 00:11,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN STEERING COLUMN,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05000050,EDGWARE,E09000003,BARNET,Mill Hill,200112983,TAYSIDE DRIVE,20042150,HA8,519822,193522,519850,193550,51.62776753,-0.270289448\nNA,29/11/2019 14:21,2019,2019/20,Special Service,1,1,339,339,PIGEONS TRAPPED IN NETTING-NEXT TO CHICKEN SHOP IN MARKET,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009370,DALSTON,E09000012,HACKNEY,Stoke Newington,1.00024E+11,KINGSLAND HIGH STREET,20900579,E8,533587,184954,533550,184950,51.54767579,-0.074793683\nNA,29/11/2019 22:05,2019,2019/20,Special Service,1,3,339,1017,CAT TRAPPED UNDER CAR    FRU REQUESTED FROM SCENE WITH SPECIALIST TOOLS TO EXTRICATE THE CAT  CALLER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Bexley,10011844891,BEXLEY HIGH STREET,20100138,DA5,549513,173547,549550,173550,51.44119519,0.149892487\nNA,30/11/2019 15:05,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920938,THE BROADWAY,22306023,IG8,540937,191859,540950,191850,51.60793628,0.033899537\nNA,01/12/2019 22:50,2019,2019/20,Special Service,1,1,339,339,ASSIST IN CAT RESCUE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,WARBECK ROAD,21000858,W12,NULL,NULL,522950,179850,NULL,NULL\nNA,02/12/2019 20:37,2019,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009386,VICTORIA,E09000012,HACKNEY,Bethnal Green,1.00024E+11,VICTORIA PARK ROAD,20901033,E9,535316,183838,535350,183850,51.53723556,-0.050294111\nNA,02/12/2019 23:53,2019,2019/20,Special Service,NULL,NULL,339,NULL,DOG STUCK UNDER SHED,Dog,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,New Malden,48018302,COTTENHAM PARK ROAD,22101632,SW20,522163,169857,522150,169850,51.41457936,-0.244677733\nNA,03/12/2019 14:22,2019,2019/20,Special Service,1,1,339,339,BIRD HANGING BY NECK FROM RAILWAY BRIDGE   WILDLIFE RESCUE VOLUNTEER IN ATTENDANCE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,6155299,CABLE STREET,22700241,E1,534951,181004,534950,181050,51.51185921,-0.056643913\nNA,03/12/2019 16:48,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED BEHIND WIRED FENCING - UPPER CAR PARK - NEAR OLD RUINS,Dog,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,1.0002E+11,CRYSTAL PALACE PARADE,20302173,SE19,534103,171195,534150,171150,51.42391468,-0.072571332\nNA,05/12/2019 02:05,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,SYDENHAM ROAD,22001951,SE26,NULL,NULL,536350,171650,NULL,NULL\nNA,05/12/2019 19:54,2019,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN HOLE - CALLER WILL MEET YOU AT THE BP PETROL STATION,Dog,Person (mobile),Petrol station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000055,HENDON,E09000003,BARNET,Hendon,200013819,BRENT STREET,20004600,NW4,523350,189298,523350,189250,51.58904765,-0.220827324\nNA,07/12/2019 23:47,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00023E+11,MOUNT STREET,8401887,W1K,528453,180595,528450,180550,51.50970106,-0.150381176\nNA,10/12/2019 11:37,2019,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Deptford,2.00003E+11,TEAK CLOSE,22502476,SE16,536259,179981,536250,179950,51.50234886,-0.038210013\nNA,10/12/2019 11:42,2019,2019/20,Special Service,1,1,339,339,CAT POSSIBLY TRAPPED  CALLER UNABLE TO COMMIUNICATE INFORMATION CLEARLY - DUE TO LANGUAGE BARRIER,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,1.00023E+11,SOUTHCROFT ROAD,22904728,SW17,528215,170809,528250,170850,51.42179972,-0.157350473\nNA,10/12/2019 14:10,2019,2019/20,Special Service,1,1,339,339,RUNNING CALL TO CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000445,GROVE PARK,E09000023,LEWISHAM,Eltham,NULL,CASTLETON ROAD,22004775,SE9,NULL,NULL,541550,171650,NULL,NULL\nNA,12/12/2019 14:12,2019,2019/20,Special Service,1,3,339,1017,DOG TRAPPED IN BADGER HOLE - FRU REQUESTED FROM SCENE   **RVP HOGARTH LANE OPPOSITE CHISWICK SCHOOL*,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,10093765957,CHISWICK HOUSE GROUNDS,21590044,W4,520810,177394,520850,177350,51.48260846,-0.261552199\nNA,13/12/2019 15:22,2019,2019/20,Special Service,1,2,339,678,BIRD TRAPPED ON WINDOW LEDGE  LONDON WILDLIFE PROTECTION ON SCENE AL REQUESTED FROM SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,BROWN HART GARDENS,8401078,W1K,NULL,NULL,528350,181050,NULL,NULL\nNA,16/12/2019 16:21,2019,2019/20,Special Service,1,2,339,678,DOG STUCK IN PIPE IN PARK ON BUILDING SITE CALLER WILL MEET BRIGADE NEAR UNLOADING AREA,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Acton,10091692323,POPES LANE,20602411,W5,518614,179080,518650,179050,51.49822772,-0.29259438\nNA,17/12/2019 17:53,2019,2019/20,Special Service,1,2,339,678,RUNNING CALL TO DOG TIED TO LAMPOST OUTSIDE STATION,Dog,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00022E+11,LONGBRIDGE WAY,22003859,SE13,538158,174960,538150,174950,51.45676896,-0.012830778\nNA,18/12/2019 14:31,2019,2019/20,Special Service,1,5,339,1695,Redacted,Unknown - Wild Animal,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,10090793774,CLYDESDALE WAY,20101889,DA17,549828,179891,549850,179850,51.49811025,0.157111048\nNA,19/12/2019 19:23,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,NULL,DACCA STREET,22000299,SE8,NULL,NULL,536950,177950,NULL,NULL\nNA,20/12/2019 19:02,2019,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000175,EAST ACTON,E09000009,EALING,Park Royal,NULL,SEACOLE CLOSE,20602050,W3,NULL,NULL,520850,181550,NULL,NULL\nNA,23/12/2019 10:13,2019,2019/20,Special Service,1,2,339,678,DEER WITH ANTLERS TRAPPED IN WASHING LINE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,KINGS LYNN DRIVE,21300091,RM3,553827,191840,553850,191850,51.60439729,0.219895829\nNA,24/12/2019 15:58,2019,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,1.00021E+11,FRESHFIELD AVENUE,20900427,E8,533634,184322,533650,184350,51.54199323,-0.074351626\nNA,24/12/2019 20:34,2019,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,DUNLOP PLACE,22500834,SE16,NULL,NULL,533950,179150,NULL,NULL\nNA,25/12/2019 19:12,2019,2019/20,Special Service,1,1,339,339,BIRDS TRAPPED IN NETTING ON BALCONY AT FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,REMINGTON ROAD,21103796,N15,NULL,NULL,532850,188350,NULL,NULL\nNA,26/12/2019 09:28,2019,2019/20,Special Service,1,1,339,339,COLLAPSED HORSE BLOCKING ROADWAY   JUNCTION OF CEMETERY LANE,Horse,Police,Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000216,CHARLTON,E09000011,GREENWICH,East Greenwich,10010225469,CHARLTON PARK ROAD,20800318,SE7,542175,177932,542150,177950,51.48248069,0.046155386\nNA,27/12/2019 09:57,2019,2019/20,Special Service,1,1,339,339,ASSIST RAILWAY STAFF WITH CAT TRAPPED BEHIND RAILING,Cat,Person (mobile),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,2.00003E+11,WINDSOR WALK,22502725,SE5,532802,176067,532850,176050,51.46800036,-0.08945448\nNA,27/12/2019 12:56,2019,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011253,VALENTINES,E09000026,REDBRIDGE,Ilford,1.00022E+11,COURTLAND AVENUE,22302752,IG1,543018,187142,543050,187150,51.56502979,0.062020808\nNA,28/12/2019 09:04,2019,2019/20,Special Service,1,1,339,339,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,1.00021E+11,FINGAL STREET,20800573,SE10,539823,178322,539850,178350,51.48657857,0.012459035\nNA,28/12/2019 11:32,2019,2019/20,Special Service,1,1,339,339,BIRD IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist  trapped livestock animal,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,NULL,FRAZIER STREET,21900578,SE1,NULL,NULL,531350,179650,NULL,NULL\nNA,29/12/2019 12:32,2019,2019/20,Special Service,1,2,339,678,CAT STUCK ON ROOF - RSPCA INSP ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,HOUNSLOW ROAD,21500631,TW14,NULL,NULL,511050,174450,NULL,NULL\nNA,29/12/2019 16:22,2019,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN WALL CAVITY - RSPCA ON SCENE REQUESTING ASSISTANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Southgate,NULL,THE COPPICE,20705063,EN2,NULL,NULL,531750,196350,NULL,NULL\nNA,30/12/2019 15:01,2019,2019/20,Special Service,1,1,339,339,CATS TRAPPED ON ROOF - ONE BY COLLAR,cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000571,WANDLE VALLEY,E09000029,SUTTON,Wallington,NULL,OAK WALK,22605736,SM6,NULL,NULL,528450,166450,NULL,NULL\nNA,31/12/2019 12:00,2019,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING -WILDLIFE TRUST ON SCENE,Bird,Person (land line),Train station - elsewhere,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,202079062,KILBURN HIGH ROAD,20202523,NW6,524625,184644,524650,184650,51.54694229,-0.204087847\nNA,01/01/2020 15:23,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525550,184850,NULL,NULL\nNA,01/01/2020 16:02,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA TO GAIN ACCESS DUE TO INJURED FOX     HE IS PARKED ON GREEN HUNDRED ROAD,Fox,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05011109,OLD KENT ROAD,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,BIRD IN BUSH ROAD,22500242,SE15,534576,177468,534550,177450,51.48017239,-0.06339237\nNA,02/01/2020 23:26,2020,2019/20,Special Service,1,1,339,339,DOG WITH HEAD TRAPPED BETWEEN STONE STEP AND SHED,Dog,Person (mobile),Outdoor storage,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,1.00022E+11,FITZWILLIAM ROAD,21900564,SW4,529339,175781,529350,175750,51.46623119,-0.13938316\nNA,03/01/2020 14:30,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,TASH PLACE,20701956,N11,NULL,NULL,528750,192250,NULL,NULL\nNA,05/01/2020 11:16,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN CHIMNEY ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,DYSART AVENUE,21800350,KT2,NULL,NULL,517550,171650,NULL,NULL\nNA,06/01/2020 10:34,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Bird,Person (mobile),Railway building - other,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,2E+11,YORK ROAD,21901539,SE1,530962,180074,530950,180050,51.5044353,-0.1144383\nNA,06/01/2020 14:34,2020,2019/20,Special Service,1,2,339,678,Redacted,Unknown - Animal rescue from below ground - Farm animal,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169546,LEA VALLEY ROAD,20703459,EN3,536523,195619,536550,195650,51.64281072,-0.028328427\nNA,07/01/2020 11:03,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,GOODWIN ROAD,21000382,W12,NULL,NULL,522450,179750,NULL,NULL\nNA,07/01/2020 20:32,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH SMALL ANIMAL RESCUE - SQUIRREL STUCK IN DRAINPIPE,Squirrel,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000062,WEST HENDON,E09000003,BARNET,Hendon,NULL,PENDRAGON WALK,20034110,NW9,NULL,NULL,521250,188350,NULL,NULL\nNA,07/01/2020 20:43,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN TWO EXTERNAL WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,BARNEHURST,E09000004,BEXLEY,Bexley,NULL,APPLEDORE AVENUE,20100042,DA7,NULL,NULL,550550,176550,NULL,NULL\nNA,08/01/2020 15:50,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND COOKER,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,HIGH ROAD,21106680,N17,NULL,NULL,533850,191550,NULL,NULL\nNA,08/01/2020 17:19,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN AIR VENT    AT THE REAR OF COSTA. ACCESS APPLETON WAY,Cat,Person (land line),Restaurant/cafe,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00024E+11,HIGH STREET,21300416,RM12,553993,187117,553950,187150,51.561923,0.220226\nNA,08/01/2020 18:17,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011248,MAYFIELD,E09000026,REDBRIDGE,Ilford,NULL,TAVISTOCK GARDENS,22302466,IG3,NULL,NULL,545350,185650,NULL,NULL\nNA,09/01/2020 11:39,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - UNABLE TO CONTACT RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,CRESCENT ROAD,21100205,N8,NULL,NULL,529750,187950,NULL,NULL\nNA,10/01/2020 06:24,2020,2019/20,Special Service,1,1,339,339,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,SHEFFIELD DRIVE,21301590,RM3,555468,192213,555450,192250,51.60730241,0.243735362\nNA,10/01/2020 12:10,2020,2019/20,Special Service,1,1,339,339,FOX TRAPPED IN FENCING,Fox,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100052507,WOODWARD ROAD,19900629,RM9,547461,184366,547450,184350,51.53894456,0.124907175\nNA,10/01/2020 13:06,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN NETTING  -  J/O ELM ROAD    RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,ST GEORGES ROAD,21800892,KT2,NULL,NULL,519050,170150,NULL,NULL\nNA,11/01/2020 18:14,2020,2019/20,Special Service,1,2,339,678,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000174,EALING COMMON,E09000009,EALING,Acton,NULL,CEDAR GROVE,20600341,W5,NULL,NULL,518050,179450,NULL,NULL\nNA,16/01/2020 15:51,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000439,BROCKLEY,E09000023,LEWISHAM,Lewisham,NULL,DRAKE ROAD,22000344,SE4,NULL,NULL,537250,175750,NULL,NULL\nNA,18/01/2020 08:23,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000429,STOCKWELL,E09000022,LAMBETH,Brixton,NULL,STOCKWELL ROAD,21901316,SW9,NULL,NULL,530650,176350,NULL,NULL\nNA,20/01/2020 22:08,2020,2019/20,Special Service,1,1,339,339,RUNNING CALL TO  PARROT TRAPPED IN TREE,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,10090505643,HIGH ROAD,22306016,IG8,539985,191743,539950,191750,51.6071334,0.020119487\nNA,21/01/2020 10:52,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN ELECTRIC CHAIR,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,LUCEY WAY,22501595,SE16,NULL,NULL,534350,179050,NULL,NULL\nNA,21/01/2020 21:44,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED UNDER FLOOR BOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,NEWLANDS ROAD,20501223,SW16,NULL,NULL,530450,169150,NULL,NULL\nNA,23/01/2020 09:52,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED BETWEEN FENCES,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,NULL,CHESTNUT WALK,22305469,IG8,NULL,NULL,540150,192450,NULL,NULL\nNA,24/01/2020 22:58,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UNDER WALKWAY   CALLER WILL WAIT TO DIRECT AT LOCATION,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,MAYES ROAD,21104796,N22,NULL,NULL,531050,190050,NULL,NULL\nNA,25/01/2020 01:10,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,SPENCER WAY,22702473,E1,NULL,NULL,534950,181050,NULL,NULL\nNA,25/01/2020 09:16,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED ON LEDGE NEAR WATER,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000615,FURZEDOWN,E09000032,WANDSWORTH,Tooting,NULL,SOUTHCROFT ROAD,22904728,SW17,NULL,NULL,528050,170850,NULL,NULL\nNA,25/01/2020 17:16,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WINDOW,Cat,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,NULL,OAKLEIGH PARK NORTH,20032540,N20,NULL,NULL,526750,194150,NULL,NULL\nNA,25/01/2020 18:17,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,CRAMPTON STREET,22500633,SE17,NULL,NULL,532050,178450,NULL,NULL\nNA,26/01/2020 09:03,2020,2019/20,Special Service,1,1,339,339,CAT STUCK BEHIND RAILING   CALLER WILL MEET YOU,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000643,REGENT'S PARK,E09000033,WESTMINSTER,Paddington,1.00023E+11,ABERDEEN PLACE,8400293,NW8,526754,182360,526750,182350,51.52593836,-0.174209502\nNA,26/01/2020 11:09,2020,2019/20,Special Service,1,1,339,339,BIRD STUCK IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000127,WEST WICKHAM,E09000006,BROMLEY,Woodside,NULL,DIXON PLACE,20301884,BR4,NULL,NULL,537650,166050,NULL,NULL\nNA,27/01/2020 14:09,2020,2019/20,Special Service,1,1,339,339,BIRD CAUGHT IN CABLE   RSPCA IN ATTENDANCE AND REQUEST ASSISTANCE,Bird,Person (mobile),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000300,RAYNERS LANE,E09000015,HARROW,Harrow,1.00023E+11,RAYNERS LANE,21201971,HA5,512935,187763,512950,187750,51.57743031,-0.37159057\nNA,27/01/2020 17:44,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDERNEATH HOUSE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000329,EASTCOTE AND EAST RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,SUTTON CLOSE,21401898,HA5,NULL,NULL,510450,188650,NULL,NULL\nNA,28/01/2020 03:03,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CLOTHING AIRER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,NULL,CROWN ROAD,22602247,SM1,NULL,NULL,525550,164850,NULL,NULL\nNA,28/01/2020 23:46,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,CAMDEN PARK ROAD,20400532,NW1,NULL,NULL,529950,184650,NULL,NULL\nNA,30/01/2020 08:36,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDER SEAT IN VAN,Dog,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207089318,BOUNCES ROAD,20703940,N9,534820,193981,534850,193950,51.62850853,-0.053557323\nNA,30/01/2020 11:10,2020,2019/20,Special Service,1,1,339,339,SEAGULL TRAPPED IN CHIMNEY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDESDALE STREET,21700456,SW3,NULL,NULL,527550,177950,NULL,NULL\nNA,31/01/2020 14:30,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN WIRING AT FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,NULL,OLD FORD ROAD,22700893,E3,NULL,NULL,535950,183350,NULL,NULL\nNA,31/01/2020 23:54,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER FLOORING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,DALRYMPLE CLOSE,20701473,N14,NULL,NULL,529650,195150,NULL,NULL\nNA,04/02/2020 09:29,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010225306,BLACKHEATH AVENUE,20800172,SE10,538963,177309,538950,177350,51.47768691,-0.000323235\nNA,04/02/2020 11:40,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN RAILINGS     NEAR THE DUCK POND AND THE REAR OF THE BASKETBALL COURT,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34142492,RAVENSCOURT PARK,21000675,W6,522321,178745,522350,178750,51.49442884,-0.239341441\nNA,04/02/2020 23:19,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED IN GARDEN,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000250,ADDISON,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,LAKESIDE ROAD,21000485,W14,NULL,NULL,523750,179450,NULL,NULL\nNA,05/02/2020 14:35,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH    CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,1.00021E+11,BROWNSPRING DRIVE,20800245,SE9,543918,172009,543950,172050,51.42882527,0.06883247\nNA,05/02/2020 18:28,2020,2019/20,Special Service,1,2,339,678,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,FLOOD STREET,21700213,SW3,NULL,NULL,527550,177850,NULL,NULL\nNA,05/02/2020 20:01,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON NINTH FLOOR LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,WIMBOURNE STREET,20901087,N1,NULL,NULL,532550,183350,NULL,NULL\nNA,06/02/2020 22:38,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,10008995608,GOOSELEY LANE,22201752,E6,543652,182670,543650,182650,51.52468471,0.069328443\nNA,07/02/2020 02:29,2020,2019/20,Special Service,1,1,339,339,DEER STUCK ON METAL FENCE    POLICE ON SCENE,Deer,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00024E+11,DAGNAM PARK DRIVE,21300071,RM3,554320,192363,554350,192350,51.60896296,0.227246831\nNA,07/02/2020 11:38,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT TRAPPED  BEHIND PIPEWORK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,GOLBORNE ROAD,21700877,W10,NULL,NULL,524550,182050,NULL,NULL\nNA,07/02/2020 14:33,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK BEHIND KITCHEN CABINETS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,BIRNAM ROAD,21607114,N4,NULL,NULL,530650,186750,NULL,NULL\nNA,07/02/2020 20:07,2020,2019/20,Special Service,1,1,339,339,KITTEN STUCK IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000339,TOWNFIELD,E09000017,HILLINGDON,Hayes,768010707,FULWOOD CLOSE,21400778,UB3,509856,181169,509850,181150,51.51877086,-0.418073254\nNA,08/02/2020 12:58,2020,2019/20,Special Service,1,1,339,339,ASSIST RSPCA WITH CAT ON ROOF OF SECOND FLOOR   RSPCA OFFICER IN ATTENDANCE,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,WESTCOMBE HILL,20801590,SE3,NULL,NULL,540350,177550,NULL,NULL\nNA,08/02/2020 14:49,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NESTING BED BY CANAL AND ATTACHED TO REGENTS WHARF,Bird,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05000368,CALEDONIAN,E09000019,ISLINGTON,Euston,5300066495,NEW WHARF ROAD,21606910,N1,530514,183476,530550,183450,51.53511471,-0.119629116\nNA,08/02/2020 18:21,2020,2019/20,Special Service,1,1,339,339,CAT  STUCK ON ROOF  CORNER OF  ST PANCRAS WAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000131,CANTELOWES,E09000007,CAMDEN,Kentish Town,NULL,ROYAL COLLEGE STREET,20400512,NW1,NULL,NULL,529250,184050,NULL,NULL\nNA,08/02/2020 22:12,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BASEMENT OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,CHAUCER GARDENS,22602196,SM1,NULL,NULL,525350,165050,NULL,NULL\nNA,09/02/2020 15:31,2020,2019/20,Special Service,1,2,339,678,PUPPY BELIEVED TO BE TRAPPED BEHIND A WALL,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,SPRING GARDENS,21301608,RM7,NULL,NULL,550250,188550,NULL,NULL\nNA,12/02/2020 15:39,2020,2019/20,Special Service,1,1,339,339,RUNNING CALL TO SMALL ANIMAL RESCUE - CAT IN TREE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,WAVEL MEWS,21106917,N8,NULL,NULL,529850,189150,NULL,NULL\nNA,13/02/2020 07:38,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED IN STAIRLIFT MECHANISM,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEXHILL ROAD,22001229,SE4,NULL,NULL,536950,174350,NULL,NULL\nNA,15/02/2020 16:57,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL\nNA,17/02/2020 10:36,2020,2019/20,Special Service,1,1,339,339,ASSIST STAFF WITH PIGS AND SHEEP STRANDED IN FLOOD WATER     WATER OPS LEVEL ONE,Sheep,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,1.00022E+11,BATH ROAD,21400116,UB7,504700,176682,504750,176650,51.4794205,-0.493680449\nNA,19/02/2020 15:32,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP TREE - RSPCA UNABLE TO ATTEND,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000301,ROXBOURNE,E09000015,HARROW,Northolt,NULL,RAYNERS LANE,21201971,HA2,NULL,NULL,513550,186850,NULL,NULL\nNA,20/02/2020 10:00,2020,2019/20,Special Service,1,1,339,339,CAT IMPALED IN BARBED WIRE,Cat,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,1.00023E+11,ABERDOUR ROAD,22301786,IG3,546609,186951,546650,186950,51.56239509,0.113705195\nNA,20/02/2020 14:47,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,LAUREL CRESCENT,20500488,CR0,NULL,NULL,537250,165050,NULL,NULL\nNA,21/02/2020 00:50,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED INSIDE AIR BRICK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,RIVERDALE ROAD,20801275,SE18,NULL,NULL,545550,178350,NULL,NULL\nNA,22/02/2020 14:46,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDERNEATH CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011480,SELSDON & ADDINGTON VILLAGE,E09000008,CROYDON,Addington,1.00023E+11,FREELANDS AVENUE,20501725,CR2,535733,162977,535750,162950,51.34967737,-0.052282362\nNA,22/02/2020 16:22,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,BRIGHTON ROAD,20500187,CR8,NULL,NULL,531350,161650,NULL,NULL\nNA,23/02/2020 00:30,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - HUSKY STUCK BY HOLE IN ROOF,Unknown - Domestic Animal Or Pet,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00021E+11,ETON ROAD,21400689,UB3,509821,177105,509850,177150,51.4822489,-0.419829877\nNA,24/02/2020 10:43,2020,2019/20,Special Service,1,1,339,339,PIGEONS STUCK IN NETTING  UNDER BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,10090481303,PINKHAM WAY,20034380,N11,529017,191815,529050,191850,51.61039633,-0.138138672\nNA,24/02/2020 11:50,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000095,MAPESBURY,E09000005,BRENT,West Hampstead,NULL,TEIGNMOUTH ROAD,20201460,NW2,NULL,NULL,524150,184950,NULL,NULL\nNA,24/02/2020 13:38,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,2.00001E+11,PINKHAM WAY,20034380,N11,529064,191831,529050,191850,51.61052884,-0.137459137\nNA,24/02/2020 22:47,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED INSIDE EMPTY PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000276,NORTHUMBERLAND PARK,E09000014,HARINGEY,Tottenham,NULL,GRASMERE ROAD,21104585,N17,NULL,NULL,533950,191550,NULL,NULL\nNA,25/02/2020 12:48,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG TRAPPED IN NECK CHAIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000107,BIGGIN HILL,E09000006,BROMLEY,Biggin Hill,NULL,ARTHUR ROAD,20301440,TN16,NULL,NULL,541450,159250,NULL,NULL\nNA,25/02/2020 20:01,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BETWEEN A SHOP SIGN AND A LEDGE,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Deptford,2.00003E+11,ELGAR STREET,22500893,SE16,536489,179395,536450,179350,51.4970267,-0.0351255\nNA,26/02/2020 14:26,2020,2019/20,Special Service,1,2,339,678,PIGEON TRAPPED IN NETTING    WAITROSE SIDE OF THE BUILDING - NEAR VIRGIN ACTIVE SIGN - NEAR TO ROOF,Bird,Person (land line),Leisure Centre,Non Residential,Animal rescue from height,Wild animal rescue from height,E05000057,MILL HILL,E09000003,BARNET,Finchley,200140920,LANGSTONE WAY,20025875,NW7,524039,191391,524050,191350,51.60770425,-0.210146868\nNA,26/02/2020 14:46,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - CAT STUCK IN GUTTERING,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870126179,RICHMOND GREEN,22604358,CR0,530576,165174,530550,165150,51.3706224,-0.1254905\nNA,26/02/2020 16:59,2020,2019/20,Special Service,1,3,339,1017,HORSE STUCK IN CATTLEGATE RVP ALBURY DRIVE JO WOODHALL ROAD,Horse,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000297,PINNER,E09000015,HARROW,Harrow,1.00021E+11,WOODHALL ROAD,21201827,HA5,511618,191693,511650,191650,51.61301713,-0.389343819\nNA,27/02/2020 13:48,2020,2019/20,Special Service,1,1,339,339,DOG WITH HEAD STUCK IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Soho,10025398227,SERPENTINE ROAD,8401184,W2,526931,180259,526950,180250,51.50701674,-0.172423089\nNA,27/02/2020 19:42,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING AT THE JUNCTION CAVENDISH ROAD   ANIMAL LIFE WARDEN IS ON SCENE BUT UNABLE T,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,121033252,CAVENDISH ROAD,22900824,SW12,529130,173009,529150,173050,51.44136992,-0.143395436\nNA,28/02/2020 07:26,2020,2019/20,Special Service,1,1,339,339,DOG LOCKED IN ALLOTMENT,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,NULL,BROOMFIELD CLOSE,21301038,RM5,NULL,NULL,550950,191450,NULL,NULL\nNA,29/02/2020 08:11,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN A DOG CRATE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,NULL,SHAW ROAD,22001887,BR1,NULL,NULL,539750,172150,NULL,NULL\nNA,02/03/2020 10:36,2020,2019/20,Special Service,1,1,339,339,CAT STUCK UP A TREE - LADDER REQUIRED    ASSISTANCE REQUESTED BY RSPCA OFFICER IN ATTENDANCE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,5300092767,TUFNELL PARK ROAD,21606493,N7,530155,185973,530150,185950,51.55763801,-0.123885212\nNA,03/03/2020 14:52,2020,2019/20,Special Service,1,2,339,678,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,1.00021E+11,FRITHWOOD AVENUE,21400773,HA6,509580,191967,509550,191950,51.61588176,-0.418679326\nNA,03/03/2020 18:59,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10013149326,MARKET SQUARE,20300493,BR1,540319,169271,540350,169250,51.40511521,0.016000348\nNA,03/03/2020 23:06,2020,2019/20,Special Service,1,1,339,339,OPPOSITE BP PETROL STATION  - DOG STUCK BEHIND GREEN METAL FENCING AND IN BRAMBLES    OWNER UNABLE T,Dog,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,1.00021E+11,BREAKSPEAR ROAD,21400231,HA4,508387,188770,508350,188750,51.58737492,-0.436895469\nNA,04/03/2020 07:58,2020,2019/20,Special Service,1,1,339,339,KITTEN TRAPPED BETWEEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000476,BOLEYN,E09000025,NEWHAM,Plaistow,NULL,GREEN STREET,22208084,E13,NULL,NULL,541350,183550,NULL,NULL\nNA,04/03/2020 22:10,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,1.00023E+11,CHISWICK HIGH ROAD,21502481,W4,520427,178532,520450,178550,51.4929171,-0.266689041\nNA,06/03/2020 11:19,2020,2019/20,Special Service,1,1,339,339,HORSE ENTANGLED IN CHAIN AROUND TREE,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,10001272892,BISCOE CLOSE,21501369,TW5,513216,177903,513250,177950,51.48875244,-0.370707842\nNA,06/03/2020 15:08,2020,2019/20,Special Service,1,1,339,339,Redacted,Dog,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000592,CHAPEL END,E09000031,WALTHAM FOREST,Walthamstow,1.00023E+11,FOREST ROAD,22837350,E17,537462,189792,537450,189750,51.5902237,-0.01705065\nNA,06/03/2020 15:39,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED ABOVE AUTOMATIC DOORS,Bird,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009333,SPITALFIELDS & BANGLATOWN,E09000030,TOWER HAMLETS,Bethnal Green,6017513,CAMBRIDGE HEATH ROAD,22700252,E1,534826,181992,534850,181950,51.520767,-0.058065\nNA,06/03/2020 18:32,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED UNDER SHED IN BACK GARDEN,Dog,Person (land line),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000520,HAMPTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00022E+11,LINDEN ROAD,22400961,TW12,513156,169854,513150,169850,51.41642046,-0.374146883\nNA,07/03/2020 14:25,2020,2019/20,Special Service,1,2,339,678,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,100072696,HEATHWAY,19900172,RM10,549132,184590,549150,184550,51.54051426,0.149080089\nNA,07/03/2020 16:44,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000344,YEADING,E09000017,HILLINGDON,Southall,NULL,YEADING LANE,21402234,UB4,NULL,NULL,511250,182450,NULL,NULL\nNA,08/03/2020 10:22,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,HIGH STREET,20701608,N14,NULL,NULL,529750,194150,NULL,NULL\nNA,10/03/2020 09:45,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN NEIGHBOURS GARDEN INJURED AFTER FALLING FROM ROOF OF BUILDING   OWNER IN ATTENDANCE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000416,BISHOP'S,E09000022,LAMBETH,Lambeth,NULL,WESTMINSTER BRIDGE ROAD,21901482,SE1,NULL,NULL,531050,179550,NULL,NULL\nNA,11/03/2020 07:26,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000054,HALE,E09000003,BARNET,Mill Hill,NULL,GIBBS GREEN,20017580,HA8,NULL,NULL,520450,192550,NULL,NULL\nNA,11/03/2020 19:42,2020,2019/20,Special Service,1,1,339,339,CAT STUCK IN BARBED WIRE BEHIND FENCE,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,10009948758,BOURNE COURT,21400214,HA4,511029,185316,511050,185350,51.55582048,-0.39986248\nNA,12/03/2020 11:15,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED BEHIND PIPES IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,NULL,UPPER NORTH STREET,22701256,E14,NULL,NULL,537550,181250,NULL,NULL\nNA,12/03/2020 18:37,2020,2019/20,Special Service,1,1,339,339,BIRD TRAPPED IN NETTING - ALP REQUESTED,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,10003975506,HIGH ROAD,21106680,N17,533754,189787,533750,189750,51.59106849,-0.070547341\nNA,13/03/2020 12:33,2020,2019/20,Special Service,1,1,339,339,ASSIST POLICE IN GAINING ENTRY FOR RSPCA,Unknown - Heavy Livestock Animal,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,10003002853,NEW ROAD,21401374,UB3,508386,177158,508350,177150,51.48300377,-0.440479013\nNA,13/03/2020 14:38,2020,2019/20,Special Service,1,1,339,339,DOG STUCK IN TUNNEL    OWNER ON SCENE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,200046312,GALDANA AVENUE,20017080,EN5,526239,196754,526250,196750,51.65541893,-0.176462166\nNA,13/03/2020 16:56,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN BASEMENT   OWNER ON SCENE TO TRACK DOWN JUNCTION OF ST MICHAELS STREET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,BOUVERIE PLACE,8400891,W2,NULL,NULL,526950,181450,NULL,NULL\nNA,13/03/2020 17:59,2020,2019/20,Special Service,1,1,339,339,CAT STUCK IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011225,ERITH,E09000004,BEXLEY,Erith,1.0002E+11,HOLLY HILL ROAD,20100737,DA8,550083,178283,550050,178250,51.48359229,0.160097639\nNA,14/03/2020 21:35,2020,2019/20,Special Service,1,1,339,339,CAT STUCK ON EDGE OF ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000604,LEYTON,E09000031,WALTHAM FOREST,Leyton,NULL,PARK ROAD,22864350,E10,NULL,NULL,537350,187150,NULL,NULL\nNA,15/03/2020 11:28,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DOG STUCK UNDER DECKING,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,LABURNUM WAY,20300460,BR2,NULL,NULL,543550,166950,NULL,NULL\nNA,15/03/2020 19:17,2020,2019/20,Special Service,1,2,339,678,CAT TRAPPED BETWEEN TWO SHEDS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,TILSON ROAD,21105158,N17,NULL,NULL,534350,190550,NULL,NULL\nNA,15/03/2020 22:40,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED UNDER CHAIR LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009332,SHADWELL,E09000030,TOWER HAMLETS,Shadwell,NULL,SPENCER WAY,22702473,E1,NULL,NULL,534950,181050,NULL,NULL\nNA,17/03/2020 09:54,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,NULL,LAZAR WALK,21606871,N7,NULL,NULL,530750,186750,NULL,NULL\nNA,17/03/2020 17:24,2020,2019/20,Special Service,1,1,339,339,SMALL ANIMAL RESCUE - DISTRESSED ELDERLY FEMALE WITH CAT TRAPPED BY BRANCH IN TREE  RSPOCA NOT ABLE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,LARKHALL LANE,21900847,SW4,NULL,NULL,530150,176750,NULL,NULL\nNA,19/03/2020 18:13,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED ON ROOF - CLIMBED UP SCAFFOLDING WHICH HAS NOW BEEN REMOVED BY BUILDERS - NO WAY FOR HIM,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,POWIS MEWS,21700973,W11,NULL,NULL,524950,181350,NULL,NULL\nNA,19/03/2020 21:46,2020,2019/20,Special Service,1,1,339,339,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LEDBURY ROAD,21700932,W11,NULL,NULL,524950,181350,NULL,NULL\nNA,21/03/2020 18:31,2020,2019/20,Special Service,1,1,339,339,PIGEON TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000106,BICKLEY,E09000006,BROMLEY,Bromley,NULL,BLACKBROOK LANE,20300128,BR2,NULL,NULL,543050,167750,NULL,NULL\nNA,22/03/2020 12:46,2020,2019/20,Special Service,1,1,339,339,CAT FALLEN FROM BALCONY ON THIRD FLOOR INTO BASEMENT AREA -  ADDITONAL PL REQUESTED FROM SCENE,Cat,Person (land line),Wasteland,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Shoreditch,5156336,SAFFRON HILL,20401049,EC1N,531460,181912,531450,181950,51.52084141,-0.106586324\nNA,23/03/2020 11:16,2020,2019/20,Special Service,1,1,339,339,BIRD CAUGHT ON NETTING    CAUGHT IN NETTING FIRST FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,BROOM ROAD,20500410,CR0,NULL,NULL,537350,165150,NULL,NULL\nNA,23/03/2020 22:44,2020,2019/20,Special Service,1,1,339,339,INJURED CAT STUCK ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000375,HOLLOWAY,E09000019,ISLINGTON,Holloway,NULL,EDEN GROVE,21603162,N7,NULL,NULL,530850,185250,NULL,NULL\nNA,25/03/2020 21:46,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN ENGINE COMPARTMENT,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217112284,WAYNFLETE SQUARE,21701050,W10,523740,181007,523750,181050,51.51445476,-0.218113591\nNA,27/03/2020 17:34,2020,2019/20,Special Service,1,1,339,339,PARROT ON ROOF    POSSIBLY INJURED  CALLED BY RSPCA WHO ARE UNABLE TO ATTEND AT THIS TIME,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,NULL,HIGHBURY GARDENS,22302148,IG3,NULL,NULL,545250,186750,NULL,NULL\nNA,28/03/2020 01:20,2020,2019/20,Special Service,1,1,339,339,HAMSTER TRAPPED BEHIND CUPBOARD,Hamster,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,NULL,GORSE ROAD,20501734,CR0,NULL,NULL,537450,165050,NULL,NULL\nNA,28/03/2020 11:03,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN LOCKED GARAGE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,10025176425,VERNON ROAD,22405207,SW14,520521,175645,520550,175650,51.46695123,-0.266308518\nNA,28/03/2020 13:40,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED AT REAR OF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011248,MAYFIELD,E09000026,REDBRIDGE,Ilford,NULL,HENLEY ROAD,22302138,IG1,NULL,NULL,544850,185950,NULL,NULL\nNA,28/03/2020 17:53,2020,2019/20,Special Service,1,1,339,339,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,DRAYCOTT PLACE,21700154,SW3,NULL,NULL,527650,178550,NULL,NULL\nNA,29/03/2020 14:30,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000612,EARLSFIELD,E09000032,WANDSWORTH,Tooting,NULL,QUINTON STREET,22904149,SW18,NULL,NULL,526250,172650,NULL,NULL\nNA,29/03/2020 21:57,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN WIRE FENCE,Cat,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Park Royal,202130854,BECKETT CLOSE,20200229,NW10,521049,184624,521050,184650,51.54754397,-0.255632281\nNA,31/03/2020 10:31,2020,2019/20,Special Service,1,1,339,339,DOG TRAPPED IN RABBIT HOLE,Dog,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,10091576968,FERRY LANE,21300773,RM13,551727,179425,551750,179450,51.49341598,0.184242054\nNA,31/03/2020 19:49,2020,2019/20,Special Service,1,1,339,339,CAT TRAPPED IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,CONWAY ROAD,20704084,N14,NULL,NULL,530650,193450,NULL,NULL\nNA,02/04/2020 04:53,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN METAL RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,2.00003E+11,SETTLE ROAD,21301584,RM3,555090,192383,555050,192350,51.60892773,0.238364363\nNA,02/04/2020 15:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,HIGH ROAD LEYTONSTONE,22844950,E11,NULL,NULL,539350,186650,NULL,NULL\nNA,04/04/2020 13:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009379,KING'S PARK,E09000012,HACKNEY,Homerton,NULL,ADLEY STREET,20900038,E5,NULL,NULL,536350,185450,NULL,NULL\nNA,04/04/2020 20:08,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BETWEEN WINDOWS,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,HORNTON STREET,21700720,W8,NULL,NULL,525350,179750,NULL,NULL\nNA,05/04/2020 13:21,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN WALL BEHIND DRYER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,CHANDLER WAY,22502935,SE15,NULL,NULL,533550,177150,NULL,NULL\nNA,05/04/2020 14:25,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO VIADUCT    MEET RSPCA OFFICRER IN THEIR VAN  IN THE CAR PARK,Fox,Person (mobile),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,10022798372,WEST END ROAD,21402115,HA4,511003,184516,511050,184550,51.54862957,-0.400495169\nNA,07/04/2020 09:30,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000461,GRAVENEY,E09000024,MERTON,Mitcham,NULL,SEELY ROAD,22105790,SW17,NULL,NULL,528150,170750,NULL,NULL\nNA,07/04/2020 12:34,2020,2020/21,Special Service,1,1,346,346,CAT WITH PAW TRAPPED IN SINK  - RESIDENT NOT SELF ISOLATING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000275,NOEL PARK,E09000014,HARINGEY,Tottenham,NULL,GLADSTONE AVENUE,21104563,N22,NULL,NULL,531850,190550,NULL,NULL\nNA,07/04/2020 13:41,2020,2020/21,Special Service,1,1,346,346,FOX CAUGHT IN NETTING OF GOAL POST,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,NULL,SWEET BRIAR GROVE,20704804,N9,NULL,NULL,533550,193150,NULL,NULL\nNA,07/04/2020 14:33,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING THIRD FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,LINKLEA CLOSE,20026840,NW9,NULL,NULL,521250,191050,NULL,NULL\nNA,07/04/2020 15:31,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED AT THE BOTTOM OF THE SINK CUPBOARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,CLEARBROOK WAY,22700324,E1,NULL,NULL,535450,181350,NULL,NULL\nNA,09/04/2020 21:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE AVERY HILL PARK RSPCA IN ATTENDANCE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,10010246212,AVERY HILL ROAD,20800098,SE9,544543,174168,544550,174150,51.4480619,0.078685922\nNA,10/04/2020 11:44,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN FIREPLACE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,HIGH OAKS,20702749,EN2,NULL,NULL,530850,198050,NULL,NULL\nNA,10/04/2020 19:54,2020,2020/21,Special Service,1,2,346,692,CAT STUCK BETWEEN WALLS        BETWEEN TWO BLOCKS OF FLATS      CONCIERGE WILL MEET BRIGADE  AT A HE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009370,DALSTON,E09000012,HACKNEY,Homerton,NULL,MARTEL PLACE,20900665,E8,NULL,NULL,533950,184950,NULL,NULL\nNA,10/04/2020 22:15,2020,2020/21,Special Service,1,1,346,346,PERSON STUCK IN TREE  ATTEMPTING TO RESCUE CAT,Cat,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217023366,DARFIELD WAY,21700856,W10,523626,180987,523650,180950,51.51429877,-0.219762562\nNA,12/04/2020 16:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,MARKHOUSE ROAD,22856450,E17,NULL,NULL,536550,188450,NULL,NULL\nNA,12/04/2020 21:19,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,NULL,STATION ROAD,21401864,UB7,NULL,NULL,505950,179950,NULL,NULL\nNA,12/04/2020 23:43,2020,2020/21,Special Service,1,1,346,346,ASSIST POLICE TO RESCUE CAT UP TREE WHO ARE IN ATTENDANCE WITH THE OWNER WHO HAS MENTAL HEALTH ISSUE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,10003976191,QUEENSWOOD ROAD,21106071,N6,528813,188611,528850,188650,51.5816581,-0.142268936\nNA,13/04/2020 09:07,2020,2020/21,Special Service,1,2,346,692,Redacted,Horse,Person (mobile),Standing crop,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207180882,EAST DUCK LEES LANE,20706818,EN3,536671,195966,536650,195950,51.64589788,-0.026059285\nNA,13/04/2020 18:54,2020,2020/21,Special Service,1,1,346,346,PIGEON IMPALED ON SPIKE  RSPCA IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011484,SOUTH CROYDON,E09000008,CROYDON,Croydon,1.00021E+11,SELSDON ROAD,20502323,CR2,532844,163658,532850,163650,51.35647295,-0.093498073\nNA,13/04/2020 23:29,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED UNDER KITCHEN FLOOR,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,WESTERHAM AVENUE,20704910,N9,NULL,NULL,532950,193150,NULL,NULL\nNA,15/04/2020 09:01,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Plumstead,NULL,MARTHAM CLOSE,20100938,SE28,NULL,NULL,547750,180950,NULL,NULL\nNA,15/04/2020 10:30,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER SOLAR PANEL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000040,VALENCE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LYMINGTON ROAD,19900004,RM8,NULL,NULL,547850,187250,NULL,NULL\nNA,15/04/2020 10:53,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009321,BROMLEY NORTH,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WELLINGTON WAY,22701312,E3,NULL,NULL,537150,182550,NULL,NULL\nNA,15/04/2020 20:00,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,SAVILLE ROAD,19900307,RM6,NULL,NULL,548750,187950,NULL,NULL\nNA,17/04/2020 07:16,2020,2020/21,Special Service,1,1,346,346,FOAL STUCK IN DRAINAGE CHANNEL  CALLER WILL DIRECT YOU FROM WHARF ROAD,Horse,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207007286,WHARF ROAD,20702448,EN3,536608,195899,536650,195850,51.64531072,-0.026997785\nNA,17/04/2020 13:37,2020,2020/21,Special Service,1,4,346,1384,Redacted,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,1.00024E+11,WENLOCK STREET,20901062,N1,532524,183108,532550,183150,51.53133896,-0.090805714\nNA,18/04/2020 01:05,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   CALLER WILL MEET BRIGADE,Bird,Person (mobile),Department Store,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870034325,HIGH STREET,22605497,SM1,525951,164129,525950,164150,51.36227589,-0.192270559\nNA,18/04/2020 12:41,2020,2020/21,Special Service,1,1,346,346,PUPPY STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000196,COCKFOSTERS,E09000010,ENFIELD,Barnet,207078040,COVERT WAY,20701017,EN4,526638,197507,526650,197550,51.66209188,-0.170424193\nNA,18/04/2020 14:51,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,DUNSTANS ROAD,22500836,SE22,NULL,NULL,534450,174150,NULL,NULL\nNA,18/04/2020 15:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE  REQUESTED FROM RSPCA - WHO IS NOT ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,1.00021E+11,SIBTHORPE ROAD,20801356,SE12,540882,174021,540850,174050,51.44766632,0.025978125\nNA,18/04/2020 21:17,2020,2020/21,Special Service,1,1,346,346,PIDGEON TRAPPED IN NETTING  - WILDLIFE TRUST ON SCENE,Bird,Person (mobile),\"Tunnel, subway\",Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00024E+11,TRUNDLEYS ROAD,22001040,SE8,535973,177978,535950,177950,51.4844172,-0.0431001\nNA,19/04/2020 12:49,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011111,PECKHAM RYE,E09000028,SOUTHWARK,New Cross,NULL,RYE HILL PARK,22502162,SE15,NULL,NULL,535150,175150,NULL,NULL\nNA,19/04/2020 13:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Dockhead,NULL,MOODKEE STREET,22501741,SE16,NULL,NULL,535350,179450,NULL,NULL\nNA,19/04/2020 16:31,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000425,LARKHALL,E09000022,LAMBETH,Clapham,10001113584,COTTAGE GROVE,21900391,SW4,530033,175573,530050,175550,51.46420608,-0.12947421\nNA,20/04/2020 19:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,CONEY BURROWS,22828350,E4,539115,193911,539150,193950,51.62683233,0.008427898\nNA,21/04/2020 03:42,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Shadwell,NULL,COMMERCIAL ROAD,22700346,E1,NULL,NULL,535850,181150,NULL,NULL\nNA,21/04/2020 12:20,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY   FLAT A    (TOP BELL),Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Forest Hill,NULL,DUNSTANS ROAD,22500836,SE22,NULL,NULL,534450,174150,NULL,NULL\nNA,21/04/2020 12:24,2020,2020/21,Special Service,1,2,346,692,ASSIST RSCPA WITH CAT ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Hendon,NULL,HOLDERS HILL AVENUE,20023380,NW4,NULL,NULL,523850,189950,NULL,NULL\nNA,21/04/2020 20:51,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NET ON THIRD FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,NULL,ROYAL CRESCENT,22306384,IG2,NULL,NULL,544650,188250,NULL,NULL\nNA,22/04/2020 14:32,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,1.00021E+11,ELTHAM PALACE ROAD,20800528,SE9,542062,174144,542050,174150,51.44847553,0.043002775\nNA,22/04/2020 18:01,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Poplar,10023996433,EAST INDIA DOCK ROAD,22201804,E14,539131,181345,539150,181350,51.51390645,0.00368577\nNA,24/04/2020 10:53,2020,2020/21,Special Service,1,3,346,1038,KESTRAL TRAPPED IN BRANCHES,Bird,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000111,CHISLEHURST,E09000006,BROMLEY,Orpington,1.0002E+11,SOUTHFIELD ROAD,20301295,BR7,546020,168843,546050,168850,51.3998356,0.097742268\nNA,24/04/2020 21:50,2020,2020/21,Special Service,1,1,346,346,PERSON STUCK IN TREE ATTEMPTING TO RETRIEVE PARROT,Unknown - Domestic Animal Or Pet,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00023E+11,THIRD AVENUE,8401232,W10,524482,182639,524450,182650,51.52895345,-0.206850051\nNA,25/04/2020 10:43,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED BETWEEN TWO GARAGES IN BACK GARDEN,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,1.00021E+11,BENHURST AVENUE,21300223,RM12,552824,186089,552850,186050,51.55300058,0.202920997\nNA,26/04/2020 09:38,2020,2020/21,Special Service,1,1,346,346,REQUESTED BY RSPCA TO CAT STUCK ON ROOF   RSPCA INSPECTOR NOT IN ATTENDANCE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,NAPIER ROAD,21104833,N17,NULL,NULL,533350,189750,NULL,NULL\nNA,26/04/2020 10:10,2020,2020/21,Special Service,1,1,346,346,BIRD IMPALED ON SPIKES    IN REAR GARDEN,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,NULL,KINGS ROAD,21800550,KT2,NULL,NULL,519250,170350,NULL,NULL\nNA,26/04/2020 13:12,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,ROSKELL ROAD,22904436,SW15,NULL,NULL,523550,175750,NULL,NULL\nNA,27/04/2020 15:22,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN REAR GARDEN IN SOME SORT OF METAL CAGE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011099,DULWICH HILL,E09000028,SOUTHWARK,Peckham,NULL,BARRY ROAD,22500157,SE22,NULL,NULL,534250,174650,NULL,NULL\nNA,27/04/2020 15:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Plumstead,NULL,HOOK LANE,20100745,DA16,NULL,NULL,546450,175750,NULL,NULL\nNA,27/04/2020 19:46,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING - LONDON WILDLIFE PROTECTION ON SCENE NEEDING ASSISTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000217,COLDHARBOUR AND NEW ELTHAM,E09000011,GREENWICH,Eltham,NULL,COURT ROAD,20800394,SE9,NULL,NULL,542250,172750,NULL,NULL\nNA,28/04/2020 11:11,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL FOR BIRD STUCK UNDER GUTTERING,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WOODLANDS ROAD,22888850,E17,NULL,NULL,538550,189750,NULL,NULL\nNA,28/04/2020 13:22,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN ENGINE BLOCK OF CAR  - FRU REQUESTED,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6039396,CYPRUS STREET,22700389,E2,535468,183101,535450,183150,51.53057768,-0.048400258\nNA,29/04/2020 13:11,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Converted office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6715635,MARKET SQUARE,22702010,E14,537814,181107,537850,181150,51.51209,-0.015381\nNA,30/04/2020 16:04,2020,2020/21,Special Service,1,1,346,346,INJURED CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,NULL,MOAT LANE,20100987,DA8,NULL,NULL,552350,176550,NULL,NULL\nNA,01/05/2020 05:52,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217048179,LADBROKE GROVE,21700912,W11,524449,181043,524450,181050,51.51461905,-0.20788451\nNA,01/05/2020 18:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Snake,Person (land line),Boarding House/B&B for homeless/asylum seekers,Other Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,1.00021E+11,PELLATT GROVE,21104917,N22,531012,190637,531050,190650,51.59935374,-0.10979562\nNA,02/05/2020 15:01,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,Wimbledon,48047745,MARRYAT ROAD,22104248,SW19,523937,171431,523950,171450,51.4283411,-0.218630976\nNA,02/05/2020 16:14,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009323,CANARY WHARF,E09000030,TOWER HAMLETS,Millwall,6065859,WESTFERRY ROAD,22701325,E14,537092,180093,537050,180050,51.5031554,-0.026167988\nNA,03/05/2020 06:43,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BOVILL ROAD,22001255,SE23,NULL,NULL,535950,173850,NULL,NULL\nNA,03/05/2020 12:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000090,FRYENT,E09000005,BRENT,Stanmore,202217023,SILVERWORKS CLOSE,20204278,NW9,520765,189509,520750,189550,51.5915002,-0.2580573\nNA,04/05/2020 13:22,2020,2020/21,Special Service,1,1,346,346,PIDGEON STUCK IN NETTING UNDER THE BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,48119014,RAYNES PARK BRIDGE,22105345,SW20,523307,169306,523350,169350,51.40938141,-0.2284358\nNA,04/05/2020 17:47,2020,2020/21,Special Service,1,1,346,346,CAT IS INJURED IN TREE UNABLE TO COME DOWN,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009382,SHACKLEWELL,E09000012,HACKNEY,Stoke Newington,1.00021E+11,PRINCESS MAY ROAD,20900823,N16,533293,185504,533250,185550,51.55269027,-0.078823198\nNA,05/05/2020 13:18,2020,2020/21,Special Service,1,1,346,346,RSPCA IN ATTENDANCE -      FOX TRAPPED IN BRAMBLES ON TOP OF NETTING OF BASKET BALL COURT,Fox,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Wild animal rescue from height,E05000258,NORTH END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34047897,ARCHEL ROAD,21000053,W14,524758,177973,524750,177950,51.48696225,-0.204518893\nNA,05/05/2020 21:24,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TELEGRAPH POLE,Bird,Person (land line),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,1.00023E+11,SIDDONS ROAD,22001894,SE23,535999,172746,535950,172750,51.43739938,-0.044728203\nNA,06/05/2020 20:41,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED ON ROOF - RSPCA IN ATTENDANCE REQUESTING ASSISTANCE  - BEST ACCESS VIA KINGSWOOD CLOSE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,NULL,LINCOLN ROAD,20702802,EN1,NULL,NULL,533550,196050,NULL,NULL\nNA,06/05/2020 22:40,2020,2020/21,Special Service,1,1,346,346,FOX POSSIBLY INJURED TRAPPED IN BASEMENT GARDEN,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525350,184250,NULL,NULL\nNA,07/05/2020 03:30,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN REAR GARDEN,Fox,Person (mobile),Cycle path/public footpath/bridleway,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05009324,ISLAND GARDENS,E09000030,TOWER HAMLETS,Millwall,6099921,TORRES SQUARE,22702327,E14,537410,178490,537450,178450,51.48867278,-0.022209928\nNA,07/05/2020 10:12,2020,2020/21,Special Service,1,1,346,346,Redacted,Deer,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00023E+11,BERRYS HILL,20301446,TN16,543749,159504,543750,159550,51.31649616,0.061347621\nNA,07/05/2020 11:23,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOK - ASSIST RSPCA THEY ARE WAITING ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000617,LATCHMERE,E09000032,WANDSWORTH,Battersea,NULL,FOWLER CLOSE,22901764,SW11,NULL,NULL,526750,175650,NULL,NULL\nNA,07/05/2020 12:50,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA AND POLICE IN GAINING ENTRY  BOTH ON SCENE - KITTENS LOCKED IN BUILDING,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,GREEN LANES,20704279,N13,NULL,NULL,530950,192050,NULL,NULL\nNA,08/05/2020 09:27,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,207037272,STREAMSIDE CLOSE,20704794,N9,533680,194123,533650,194150,51.63005187,-0.06996162\nNA,08/05/2020 10:40,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Recycling plant,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00023E+11,TRUNDLEYS ROAD,22001040,SE8,535970,177981,535950,177950,51.48445346,-0.043132369\nNA,08/05/2020 20:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,BRIDGE STREET,21201743,HA5,NULL,NULL,512150,189650,NULL,NULL\nNA,08/05/2020 22:57,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOLDED FOOTBALL POST,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000490,PLAISTOW SOUTH,E09000025,NEWHAM,Plaistow,46001381,ALLIANCE ROAD,22200318,E13,541060,181993,541050,181950,51.51925094,0.031725126\nNA,09/05/2020 09:36,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,GRANTHAM ROAD,22200183,E12,NULL,NULL,543350,185650,NULL,NULL\nNA,09/05/2020 11:30,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCING    THE MANOR ON  REGENTS DRIVE,Deer,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10034921095,CLARENCE GATE,22306209,IG8,543049,191372,543050,191350,51.60303151,0.064184605\nNA,09/05/2020 12:46,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN FENCE,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207140004,ROCHESTER CLOSE,20702932,EN1,533453,197719,533450,197750,51.66242008,-0.071864639\nNA,09/05/2020 13:30,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA INSPECTOR WITH CAT STUCK BETWEEN A GARAGE AND A WALL,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,West Norwood,2.00003E+11,COLLEGE ROAD,22500543,SE19,533713,171616,533750,171650,51.42778318,-0.078028608\nNA,09/05/2020 15:25,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN CAR,Fox,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped wild animal,E05009386,VICTORIA,E09000012,HACKNEY,Homerton,1.00023E+11,LODDIGES ROAD,20900628,E9,535060,184361,535050,184350,51.54199754,-0.053783141\nNA,10/05/2020 09:20,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT TRAPPED IN FOX HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,VIEWFIELD ROAD,22905847,SW18,NULL,NULL,524850,174050,NULL,NULL\nNA,10/05/2020 23:39,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCE,Deer,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000195,CHASE,E09000010,ENFIELD,Enfield,207018481,CLAY HILL,20702606,EN2,532539,198671,532550,198650,51.67119121,-0.084709875\nNA,11/05/2020 09:46,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,1.00023E+11,HERRIES STREET,8400416,W10,524363,183019,524350,183050,51.5323919,-0.2084392\nNA,11/05/2020 11:51,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,NULL,SUTTON LANE,21501095,TW3,NULL,NULL,512850,176250,NULL,NULL\nNA,11/05/2020 13:49,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000564,SUTTON CENTRAL,E09000029,SUTTON,Sutton,5870034325,HIGH STREET,22605497,SM1,525950,164128,525950,164150,51.3622689,-0.1922752\nNA,12/05/2020 16:32,2020,2020/21,Special Service,1,1,346,346,DUCKS TRAPPED IN DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10023189460,CHAPELMOUNT ROAD,22304879,IG8,542796,191793,542750,191750,51.6068753,0.060705081\nNA,13/05/2020 04:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,NULL,LONGTON AVENUE,22002662,SE26,NULL,NULL,534350,171650,NULL,NULL\nNA,13/05/2020 14:36,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN SMALL SPACE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,13/05/2020 16:54,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN BATHROOM WALL CAVITY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,BLANDFORD ROAD,20600190,UB2,NULL,NULL,513050,178850,NULL,NULL\nNA,13/05/2020 18:44,2020,2020/21,Special Service,1,1,346,346,PIDGEON TRAPPED IN TV AERIAL     CALLED BY RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011485,SOUTH NORWOOD,E09000008,CROYDON,Woodside,NULL,ROTHESAY ROAD,20501350,SE25,NULL,NULL,533050,168250,NULL,NULL\nNA,14/05/2020 07:39,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BETTWEN WINDOW AND THE RAILING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,BELLING CRESCENT,20700957,EN3,NULL,NULL,535150,195950,NULL,NULL\nNA,14/05/2020 23:21,2020,2020/21,Special Service,1,1,346,346,CAT IN DISTRESS IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000042,WHALEBONE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,FARRANCE ROAD,19900126,RM6,NULL,NULL,548350,187950,NULL,NULL\nNA,15/05/2020 05:24,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER BATH,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,FLAXTON ROAD,20800577,SE18,NULL,NULL,545250,177350,NULL,NULL\nNA,15/05/2020 17:32,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN THE MESH UNDER THE FOOTBRIDGE NEXT TO,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,1.00023E+11,LYCH GATE ROAD,20301200,BR6,546426,166211,546450,166250,51.37608142,0.102486002\nNA,15/05/2020 18:44,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,PARK ROAD,21880043,KT2,NULL,NULL,518850,171050,NULL,NULL\nNA,16/05/2020 10:50,2020,2020/21,Special Service,1,1,346,346,BABY FOXES TRAPPED IN GARDEN - UNABLE TO GET BACK OUT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000268,BRUCE GROVE,E09000014,HARINGEY,Tottenham,NULL,HARTHAM ROAD,21104621,N17,NULL,NULL,533650,190450,NULL,NULL\nNA,16/05/2020 21:38,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011235,BARKINGSIDE,E09000026,REDBRIDGE,Ilford,1.00022E+11,EASTERN AVENUE,22302011,IG2,543527,188419,543550,188450,51.57637588,0.069878142\nNA,17/05/2020 16:00,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,542750,183650,NULL,NULL\nNA,17/05/2020 17:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND WINDOW ON TOP FLOOR IN BUILDING UNDER RENOVATION - CALLER WILL MEET CREWS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,GLOUCESTER WALK,21700229,W8,NULL,NULL,525350,179950,NULL,NULL\nNA,17/05/2020 17:21,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,CHARLEMONT ROAD,22200458,E6,NULL,NULL,542850,182550,NULL,NULL\nNA,17/05/2020 17:58,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER DECKING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,WOOLWICH ROAD,20801725,SE7,NULL,NULL,540650,178350,NULL,NULL\nNA,17/05/2020 19:26,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011244,GOODMAYES,E09000026,REDBRIDGE,Ilford,NULL,SEVEN KINGS ROAD,22302407,IG3,NULL,NULL,545550,186950,NULL,NULL\nNA,18/05/2020 16:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,1.0002E+11,THE KNOLL,20301804,BR3,537894,169471,537850,169450,51.40750681,-0.018750773\nNA,18/05/2020 17:42,2020,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN FENCE  CALLER WILL WILL MEET YOU,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,10070719397,QUEEN ELIZABETH WALK,22404921,SW13,522813,176693,522850,176650,51.47587893,-0.232967857\nNA,18/05/2020 22:33,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,GRESLEY CLOSE,22841125,E17,NULL,NULL,536250,188050,NULL,NULL\nNA,19/05/2020 07:56,2020,2020/21,Special Service,NULL,NULL,346,NULL,CAT TRAPPED IN FURNITURE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,SEAFIELD ROAD,20701885,N11,NULL,NULL,529850,192450,NULL,NULL\nNA,19/05/2020 13:19,2020,2020/21,Special Service,1,2,346,692,Redacted,Unknown - Wild Animal,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped wild animal,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521656,175830,521650,175850,51.46837436,-0.249913982\nNA,19/05/2020 15:20,2020,2020/21,Special Service,2,3,346,1038,Redacted,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521655,175831,521650,175850,51.46838511,-0.249930391\nNA,19/05/2020 16:17,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000283,WHITE HART LANE,E09000014,HARINGEY,Tottenham,NULL,LORDSHIP LANE,21106476,N17,NULL,NULL,532450,190550,NULL,NULL\nNA,20/05/2020 11:58,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING ON BALCONY,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,NULL,COOPERSALE CLOSE,22306027,IG8,NULL,NULL,541350,191150,NULL,NULL\nNA,20/05/2020 14:12,2020,2020/21,Special Service,1,2,346,692,ANIMAL RESCUE LARGE - FOAL IN WATER    USE THE TOW PATH BY THE NAVIGATION INN. CALLER IS FISHING NEA,Horse,Person (mobile),Standing crop,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184742,WHARF ROAD,20702448,EN3,536206,195490,536250,195450,51.64173077,-0.032965558\nNA,21/05/2020 10:56,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT RESCUE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011105,NEWINGTON,E09000028,SOUTHWARK,Lambeth,NULL,PENTON PLACE,22501965,SE17,NULL,NULL,531850,178450,NULL,NULL\nNA,21/05/2020 11:21,2020,2020/21,Special Service,1,1,346,346,FOX CUB WITH HEAD STUCK IN ALLOY WHEEL - RSPCA ON SCENE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,ADDISCOMBE COURT ROAD,20500605,CR0,533350,166069,533350,166050,51.37802429,-0.085323417\nNA,21/05/2020 19:54,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY AREA,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,NULL,CREIGHTON ROAD,20202230,NW6,NULL,NULL,523950,183350,NULL,NULL\nNA,22/05/2020 17:48,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,MAIN STREET,21500724,TW13,NULL,NULL,511950,171250,NULL,NULL\nNA,22/05/2020 18:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000561,NONSUCH,E09000029,SUTTON,Sutton,5870119351,LONDON ROAD,22601662,SM3,523639,165173,523650,165150,51.37216222,-0.225099599\nNA,23/05/2020 11:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,LEYFIELD,21800602,KT4,NULL,NULL,521550,166050,NULL,NULL\nNA,24/05/2020 09:34,2020,2020/21,Special Service,1,1,346,346,SQUIREL TRAPPED BETWEEN SATELLITE DISH,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,NULL,SUTTON ROAD,19900602,IG11,NULL,NULL,545450,183550,NULL,NULL\nNA,24/05/2020 12:24,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN NEIGHBOURS FLAT ON THIRD FLOOR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,WELWYN STREET,22701313,E2,NULL,NULL,535350,182950,NULL,NULL\nNA,25/05/2020 10:47,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN CEILING TILE AND ROOF PETS   AT HOME     VETS  COMPANION CARE SITE,Cat,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,1.00023E+11,LONDON ROAD,21519757,TW8,517148,177257,517150,177250,51.48215266,-0.314311692\nNA,26/05/2020 07:36,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,202079024,KILBURN LANE,20202035,W9,524503,183081,524550,183050,51.53292288,-0.206393217\nNA,26/05/2020 08:01,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF OF HOUSE - UNABLE TO GET DOWN,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,NULL,BRAYBURNE AVENUE,21900227,SW4,NULL,NULL,529450,176150,NULL,NULL\nNA,26/05/2020 13:29,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    CAT TRAPPED BEHIND A WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,ST MARGARETS ROAD,22405949,TW1,NULL,NULL,517150,174150,NULL,NULL\nNA,26/05/2020 14:50,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN TREE    RSPCA ON SCENE,Bird,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000213,WINCHMORE HILL,E09000010,ENFIELD,Southgate,207094305,FERNLEIGH ROAD,20704211,N21,531438,193899,531450,193850,51.62856752,-0.102415538\nNA,26/05/2020 18:55,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,HARROW GREEN,22843250,E11,NULL,NULL,539250,186250,NULL,NULL\nNA,26/05/2020 19:19,2020,2020/21,Special Service,1,1,346,346,INJUIRED KITTEN STUCK IN TREE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,PERTH ROAD,22303147,IG2,544336,187651,544350,187650,51.56927215,0.081225027\nNA,27/05/2020 13:06,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WALL    CALLER WILL MEET YOU,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05009381,LONDON FIELDS,E09000012,HACKNEY,Shoreditch,NULL,GLEBE ROAD,20900446,E8,NULL,NULL,533550,184350,NULL,NULL\nNA,27/05/2020 15:09,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN BARB WIRE    OWNER IN ATTENDANCE WILL LIASE WITH BRIGADE  ARRANDENE OPEN SPACE ACCESS,Dog,Person (land line),Heathland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,200127721,WISE LANE,20046840,NW7,522306,191968,522350,191950,51.61327201,-0.234966062\nNA,27/05/2020 16:19,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   ASSIST RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,CHURCH CRESCENT,20008600,N3,NULL,NULL,524750,190650,NULL,NULL\nNA,27/05/2020 17:14,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN SHELF BETWEEN WINDOW,Squirrel,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,1.00023E+11,HIGH STREET,22403558,TW11,516111,171140,516150,171150,51.42738248,-0.331255551\nNA,27/05/2020 22:46,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000442,DOWNHAM,E09000023,LEWISHAM,Beckenham,NULL,CHESTNUT CLOSE,22001339,SE6,NULL,NULL,538350,171450,NULL,NULL\nNA,28/05/2020 14:07,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE IN REAR GARDEN,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009380,LEA BRIDGE,E09000012,HACKNEY,Homerton,NULL,THORNBY ROAD,20901002,E5,NULL,NULL,535150,186250,NULL,NULL\nNA,28/05/2020 15:18,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,NULL,MONARCH WAY,22306385,IG2,NULL,NULL,544850,188350,NULL,NULL\nNA,28/05/2020 16:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING AT FRONT OF STORE,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,12142724,KING STREET,20602382,W3,519972,180244,519950,180250,51.5084004,-0.2726579\nNA,28/05/2020 17:55,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,BRYAN AVENUE,20201522,NW10,NULL,NULL,522850,184350,NULL,NULL\nNA,29/05/2020 04:30,2020,2020/21,Special Service,1,1,346,346,PUPPY STUCK TO DOG CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000459,DUNDONALD,E09000024,MERTON,New Malden,NULL,VERNON AVENUE,22106453,SW20,NULL,NULL,523650,169150,NULL,NULL\nNA,30/05/2020 07:26,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN GATE,Deer,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,SPRING GARDENS,22305325,IG8,NULL,NULL,541050,191450,NULL,NULL\nNA,30/05/2020 16:02,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000565,SUTTON NORTH,E09000029,SUTTON,Sutton,NULL,COLLINGWOOD ROAD,22602218,SM1,NULL,NULL,525150,165150,NULL,NULL\nNA,30/05/2020 20:41,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009390,CAMPDEN,E09000020,KENSINGTON AND CHELSEA,Kensington,217001205,AIRLIE GARDENS,21700692,W8,525098,180019,525050,180050,51.50526851,-0.198910522\nNA,31/05/2020 05:03,2020,2020/21,Special Service,1,1,346,346,DOG FALLEN INTO POND,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from water,Animal rescue from water - Domestic pet,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,CADOGAN TERRACE,20900196,E9,NULL,NULL,536550,184650,NULL,NULL\nNA,31/05/2020 09:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000137,HIGHGATE,E09000007,CAMDEN,Kentish Town,NULL,LISSENDEN GARDENS,20400214,NW5,NULL,NULL,528350,185850,NULL,NULL\nNA,31/05/2020 11:55,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED BETWEEN TWO BRANCHES IN A TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,10001297848,ROTHERFIELD STREET,21605841,N1,532570,183962,532550,183950,51.53900221,-0.089829932\nNA,31/05/2020 12:40,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    BIRD TRAPPED BEHIND CUPBOARDS. RSPCA OFFICER IN ATTENDANCE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011112,ROTHERHITHE,E09000028,SOUTHWARK,Deptford,NULL,TIMBER POND ROAD,22502510,SE16,NULL,NULL,535950,180050,NULL,NULL\nNA,31/05/2020 15:48,2020,2020/21,Special Service,1,1,346,346,FOX CUB WEDGED BETWEEN WALL OF HOUSE AND GARAGE   RSPCA IN ATTENDANCE,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011243,FULLWELL,E09000026,REDBRIDGE,Hainault,NULL,RYECROFT AVENUE,22305294,IG5,NULL,NULL,543650,190450,NULL,NULL\nNA,31/05/2020 22:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000478,CANNING TOWN SOUTH,E09000025,NEWHAM,Plaistow,46027933,FRANK STREET,22200628,E13,540430,182231,540450,182250,51.52155418,0.022743332\nNA,31/05/2020 22:12,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE,Cat,Person (mobile),Underground car park,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5034748,CAMDEN STREET,20400650,NW1,529190,183688,529150,183650,51.53732914,-0.138636741\nNA,31/05/2020 22:17,2020,2020/21,Special Service,1,2,346,692,FOX WITH HEAD STUCK IN CAR WHEEL - REQUESTED BY RSPCA,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000317,PETTITS,E09000016,HAVERING,Romford,1.00021E+11,HAVERING ROAD,21300027,RM1,550766,190717,550750,190750,51.59513877,0.175256893\nNA,01/06/2020 08:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000182,NORTHFIELD,E09000009,EALING,Ealing,NULL,DEVONSHIRE ROAD,20600535,W5,NULL,NULL,517250,179350,NULL,NULL\nNA,01/06/2020 12:43,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOOTBALL GOAL NETTING ON SPORTS GROUND   OPP KNOCKED DOWN PUBLIC HOUSE,Fox,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207136124,BELL ROAD,20702504,EN1,532840,197556,532850,197550,51.66109824,-0.080784413\nNA,01/06/2020 22:21,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000476,BOLEYN,E09000025,NEWHAM,East Ham,NULL,CENTRAL PARK ROAD,22200451,E6,NULL,NULL,541650,183050,NULL,NULL\nNA,02/06/2020 16:20,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE OPPOSITE WYATT COURT - CALLED BY LONDON WILDLIFE PROTECTION,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,BOLLO BRIDGE ROAD,20600203,W3,NULL,NULL,520150,179450,NULL,NULL\nNA,02/06/2020 18:02,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,NULL,ARTHUR ROAD,22207601,E6,NULL,NULL,542750,183250,NULL,NULL\nNA,02/06/2020 18:52,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK BEHIND WARDROBE AND WALL  CALLER UNABLE TO MOVE WARDROBE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,ASHLIN ROAD,22203396,E15,NULL,NULL,538950,185450,NULL,NULL\nNA,03/06/2020 09:32,2020,2020/21,Special Service,1,1,346,346,GUINEA PIG TRAPPED BEHIND KITCHEN UNITS   POSSIBLY INJURED,Unknown - Domestic Animal Or Pet,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,LILLIE ROAD,21000509,SW6,NULL,NULL,524750,177650,NULL,NULL\nNA,03/06/2020 12:36,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO CAT UP TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000420,COLDHARBOUR,E09000022,LAMBETH,Brixton,1.00024E+11,WILTSHIRE ROAD,21901503,SW9,531250,175895,531250,175850,51.46681981,-0.111849653\nNA,03/06/2020 15:19,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000193,BOWES,E09000010,ENFIELD,Southgate,NULL,HARDWICKE ROAD,20704304,N13,NULL,NULL,530250,192050,NULL,NULL\nNA,03/06/2020 18:02,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER SINK,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,PRIDEAUX ROAD,21901118,SW9,NULL,NULL,530350,175850,NULL,NULL\nNA,03/06/2020 18:43,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000220,ELTHAM WEST,E09000011,GREENWICH,Eltham,NULL,FOXHOLE ROAD,20800589,SE9,NULL,NULL,542150,174950,NULL,NULL\nNA,05/06/2020 11:47,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK BETWEEN TWO WALLS,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05009373,HACKNEY DOWNS,E09000012,HACKNEY,Stoke Newington,10008300545,RECTORY ROAD,20900843,N16,533940,186229,533950,186250,51.55905588,-0.069226694\nNA,05/06/2020 14:30,2020,2020/21,Special Service,1,1,346,346,CAT POSSIBLY TRAPPED IN FENCING AT REAR,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,MARQUIS ROAD,21103599,N4,NULL,NULL,530950,187350,NULL,NULL\nNA,05/06/2020 17:48,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH GULL,Bird,Person (mobile),Vehicle Repair Workshop,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000207,SOUTHBURY,E09000010,ENFIELD,Enfield,207202200,CROWN ROAD,20702635,EN1,534817,196576,534850,196550,51.65182266,-0.052608347\nNA,05/06/2020 19:47,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,06/06/2020 22:34,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF - ALLEYWAY AT SIDE OF BAPTIST CHURCH - CALLER WILL WAIT AND SHOW CREWS,Cat,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000045,CHILDS HILL,E09000003,BARNET,West Hampstead,200082864,MORTIMER CLOSE,20030220,NW2,524859,186464,524850,186450,51.5632456,-0.2000631\nNA,07/06/2020 14:48,2020,2020/21,Special Service,1,1,346,346,DOG STUCK ON ROOF  CALL FROM POLICE - NOT ATTENDING,Dog,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,CHURCH ROAD,20600386,UB5,NULL,NULL,512450,183950,NULL,NULL\nNA,07/06/2020 16:51,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALLS OF PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,NULL,PICTON STREET,22501978,SE5,NULL,NULL,532850,177250,NULL,NULL\nNA,07/06/2020 17:56,2020,2020/21,Special Service,1,1,346,346,INJURED CAT UP A TREE - CALLER STATED CAT IS HANGING OFF THE TREE,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207044067,BRETTENHAM ROAD,20703959,N18,534736,192595,534750,192550,51.61606814,-0.055302254\nNA,07/06/2020 18:50,2020,2020/21,Special Service,1,1,346,346,DEER WITH HEAD STUCK IN STORM DRAIN    CALLER STATES AS YOU COME INTO BREAM CLOSE ITS THE FIRST RIGH,Deer,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,10003980525,BREAM CLOSE,21106595,N17,534787,189379,534750,189350,51.58716199,-0.055800753\nNA,07/06/2020 20:35,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND TOILET CYSTERN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000202,HIGHLANDS,E09000010,ENFIELD,Enfield,NULL,HOLTWHITES HILL,20705071,EN2,NULL,NULL,531950,197550,NULL,NULL\nNA,08/06/2020 10:39,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO CROW TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,ALEXANDRA ROAD,20301692,SE26,NULL,NULL,535750,170850,NULL,NULL\nNA,08/06/2020 16:34,2020,2020/21,Special Service,1,1,346,346,TO ASSIST RSPCA-CAT TRAPPED UNDER RAILWAY BRIDGE,Cat,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05011105,NEWINGTON,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,JOHN RUSKIN STREET,22501369,SE5,532322,177806,532350,177850,51.4837371,-0.0957146\nNA,08/06/2020 19:28,2020,2020/21,Special Service,1,1,346,346,PERSON SHUT IN LIFT EMERGENCY   CAVENDISH HOUSE BOULEVARD DRIVE,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,BOULEVARD DRIVE,20004185,NW9,NULL,NULL,521850,190050,NULL,NULL\nNA,08/06/2020 19:39,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GUTTER,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000529,SOUTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,STRAWBERRY VALE,22403855,TW1,NULL,NULL,516150,172250,NULL,NULL\nNA,08/06/2020 19:48,2020,2020/21,Special Service,1,2,346,692,DOG TRAPPED DOWN FOX HOLE - UNDERNEATH SHED,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,NULL,BOUNDARIES ROAD,22900572,SW12,NULL,NULL,527850,172950,NULL,NULL\nNA,09/06/2020 01:13,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND KITCHEN CABINETS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,DEVONSHIRE ROAD,21300318,RM12,NULL,NULL,553550,186650,NULL,NULL\nNA,09/06/2020 09:05,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FOOTBALL NETS - ON THE FIELD,Fox,Person (land line),Other outdoor location,Outdoor,Other animal assistance,Assist trapped wild animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200144604,SCHOOL WAY,20038820,N12,526956,191578,526950,191550,51.60873919,-0.167984857\nNA,09/06/2020 09:21,2020,2020/21,Special Service,1,2,346,692,RUNNING CALL TO SEAGULL WITH BROKEN WING - RSPCA REQUESTED TO ATTEND,Bird,Person (land line),Fire station,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,10008993909,HIGH STREET SOUTH,22200716,E6,542801,182520,542850,182550,51.5235531,0.0570087\nNA,09/06/2020 20:27,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,46081917,WOODGRANGE ROAD,22200290,E7,540504,185090,540550,185050,51.54721932,0.024948789\nNA,10/06/2020 13:56,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN FENCE,Dog,Person (mobile),Fence,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,NA,NA,NA,NA,Surbiton,2.00004E+11,GATLEY AVENUE,13900281,KT19,519636,164359,519650,164350,51.36570811,-0.282855266\nNA,10/06/2020 18:07,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA ON SCENE WITH FOX TRAPPED IN NETTING,Fox,Person (mobile),Leisure Centre,Non Residential,Other animal assistance,Assist trapped wild animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,6010921,TENCH STREET,22701195,E1W,534628,180216,534650,180250,51.504851,-0.061605\nNA,11/06/2020 09:50,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Petrol station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,10091940551,HIGH ROAD LEYTONSTONE,22844950,E11,539246,186487,539250,186450,51.56008725,0.007377774\nNA,11/06/2020 11:14,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED UNDER PETROL STATION FORECOURT ROOF- RSPCA IN ATTENDANCE,Bird,Person (land line),Petrol station,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,1.00023E+11,HIGH ROAD LEYTONSTONE,22844950,E11,539242,186483,539250,186450,51.56005038,0.007322219\nNA,11/06/2020 12:14,2020,2020/21,Special Service,1,1,346,346,BLACKBIRD TRAPPED IN NETTING   RSPCA ON SCENE,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000361,HOUNSLOW WEST,E09000018,HOUNSLOW,Heston,1.00023E+11,LAMPTON ROAD,21500676,TW3,513519,175719,513550,175750,51.46906678,-0.367040108\nNA,11/06/2020 21:20,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Heston,NULL,BOSTON MANOR ROAD,21500133,TW8,NULL,NULL,517650,177650,NULL,NULL\nNA,12/06/2020 22:25,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN ANTI BIRD NETTING CALL PASSED BY RSPCA WHO HAVE NO OFFICERS TO ATTEND,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,NORTHWICK ROAD,20201813,HA0,NULL,NULL,517850,183550,NULL,NULL\nNA,13/06/2020 06:12,2020,2020/21,Special Service,1,1,346,346,YOUNG DEER WITH LEGS STUCK IN RAILINGS,Deer,Person (mobile),Heathland,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,1.00022E+11,HILL GROVE,21501717,TW13,512903,172699,512950,172650,51.44204098,-0.376880188\nNA,13/06/2020 13:12,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,HIGH STREET,22201445,E15,NULL,NULL,538050,183350,NULL,NULL\nNA,13/06/2020 15:29,2020,2020/21,Special Service,1,1,346,346,DOG ON ROOF  OF HOUSE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000494,WEST HAM,E09000025,NEWHAM,Stratford,NULL,MEATH ROAD,22201517,E15,NULL,NULL,539550,183350,NULL,NULL\nNA,13/06/2020 16:18,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN STORAGE SHED   REAR OF,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00023E+11,LANSDOWNE ROAD,21104730,N17,534082,190705,534050,190750,51.59924034,-0.065463959\nNA,14/06/2020 11:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000046,COLINDALE,E09000003,BARNET,Mill Hill,NULL,THE CONCOURSE,20010000,NW9,NULL,NULL,521650,190750,NULL,NULL\nNA,14/06/2020 12:05,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN WINDOW,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000642,QUEEN'S PARK,E09000033,WESTMINSTER,North Kensington,NULL,PORTNALL ROAD,8400567,W9,NULL,NULL,524550,183050,NULL,NULL\nNA,14/06/2020 12:45,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000562,ST. HELIER,E09000029,SUTTON,Sutton,NULL,GARENDON ROAD,22600569,SM4,NULL,NULL,525750,166550,NULL,NULL\nNA,14/06/2020 21:47,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK ABOVE NETTING AT TESCO NEAR FRONT ENTRANCE OF THE STORE   WILD LIFE RESCUER ON SCENE WI,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,1.00023E+11,PURLEY ROAD,20502281,CR8,531235,161456,531250,161450,51.33706304,-0.117395736\nNA,15/06/2020 08:56,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN REAR PATIO AREA   - CALLER WILL MEET CREWS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BROOKSBY STREET,21604364,N1,NULL,NULL,531350,184250,NULL,NULL\nNA,15/06/2020 10:42,2020,2020/21,Special Service,1,1,346,346,DUCKLINGS TRAPPED IN GROOVE IN DRIVEWAY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,PARSONS GREEN LANE,21000629,SW6,NULL,NULL,525050,176750,NULL,NULL\nNA,15/06/2020 12:02,2020,2020/21,Special Service,1,1,346,346,DUCKLINGS TRAPPED IN GROOVE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,PARSONS GREEN,21000628,SW6,NULL,NULL,525050,176450,NULL,NULL\nNA,15/06/2020 17:58,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY STACK,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,NULL,HEATHER LANE,21400935,UB7,NULL,NULL,506350,181350,NULL,NULL\nNA,15/06/2020 21:27,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DRAINAGE SYSTEM UNDER HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000351,FELTHAM NORTH,E09000018,HOUNSLOW,Feltham,NULL,BURNS AVENUE,21500182,TW14,NULL,NULL,510250,174150,NULL,NULL\nNA,15/06/2020 23:29,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,MARLBOROUGH ROAD,21500739,W4,NULL,NULL,520150,178450,NULL,NULL\nNA,16/06/2020 16:44,2020,2020/21,Special Service,1,4,346,1384,ASSIST RSPCA OFFICER WITH CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal harm involving domestic animal,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5119648,ACTON STREET,20400906,WC1X,530743,182740,530750,182750,51.52844552,-0.11661291\nNA,17/06/2020 09:34,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,WARNER ROAD,22885150,E17,NULL,NULL,536550,189250,NULL,NULL\nNA,17/06/2020 10:03,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN NETTING,Bird,Person (land line),Self contained Sheltered Housing,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,HANSHAW DRIVE,20020860,HA8,NULL,NULL,520650,190850,NULL,NULL\nNA,18/06/2020 21:33,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN WINDOW AND METAL GRILL,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011465,BROAD GREEN,E09000008,CROYDON,Croydon,NULL,HANDCROFT ROAD,20501020,CR0,NULL,NULL,531750,166350,NULL,NULL\nNA,19/06/2020 15:26,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46253169,CLAPS GATE LANE,22207527,E6,543772,182218,543750,182250,51.52059619,0.07087768\nNA,19/06/2020 17:06,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,ABINGDON ROAD,20000160,N3,NULL,NULL,526250,190550,NULL,NULL\nNA,19/06/2020 18:44,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,10025300478,FOOTPATH 16 FROM END OF THE COMMON TO OLD LODGE WAY,21202859,HA7,515580,192566,515550,192550,51.62006257,-0.331853567\nNA,19/06/2020 19:19,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN TREE    INSIDE THE PARK   CALLER WILL MEET YOU IN HEATH PASSAGE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,5149796,HEATH PASSAGE,20400020,NW3,525952,186855,525950,186850,51.56651692,-0.184156848\nNA,19/06/2020 21:08,2020,2020/21,Special Service,1,1,346,346,BABY PIGEON TRAPPED IN GUTTERING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,MYERS LANE,22000735,SE14,NULL,NULL,535650,177750,NULL,NULL\nNA,20/06/2020 01:52,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED AND IMPAILED IN BETWEEN FENCES,Deer,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,WICKFORD CLOSE,21301698,RM3,554815,192385,554850,192350,51.60902344,0.234396085\nNA,20/06/2020 10:26,2020,2020/21,Special Service,1,1,346,346,HORSE LOCKED IN HORSE TRAILER - OPPOSITE GLEBE FARM BUSINESS PARK,Horse,Person (mobile),Trailer (not attached to tractor unit),Road Vehicle,Other animal assistance,Assist  trapped livestock animal,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,10003629389,LEAVES GREEN ROAD,20300757,BR2,541935,162881,541950,162850,51.34729652,0.036682467\nNA,20/06/2020 12:57,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED HANGING FROM ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000187,PERIVALE,E09000009,EALING,Wembley,NULL,EMPIRE ROAD,20600626,UB6,NULL,NULL,516850,183850,NULL,NULL\nNA,20/06/2020 20:58,2020,2020/21,Special Service,1,3,346,1038,CAT TRAPPED BETWEEN BUILDINGS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011107,NORTH WALWORTH,E09000028,SOUTHWARK,Old Kent Road,NULL,OCCUPATION ROAD,22501834,SE17,NULL,NULL,532250,178250,NULL,NULL\nNA,22/06/2020 10:30,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,ALEXANDRA ROAD,20301692,SE26,NULL,NULL,535750,170750,NULL,NULL\nNA,22/06/2020 10:47,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN NETTING AT ROOF LEVEL - SEEN FROM TRENT HOUSE,Bird,Person (land line),Other retail warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099001,EDGWARE ROAD,20201631,NW9,520777,189479,520750,189450,51.59122865,-0.257882362\nNA,22/06/2020 10:54,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED DOWN THE DRAIN CALLER WILL MEET AT LOCATION AND DIRECT YOU,Dog,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Chingford,1.00023E+11,NEWGATE STREET,22861050,E4,539489,193111,539450,193150,51.61954959,0.013508309\nNA,22/06/2020 14:34,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Train station - platform (at ground level or elevated),Non Residential,Other animal assistance,Assist trapped wild animal,E05000462,HILLSIDE,E09000024,MERTON,Wimbledon,48086089,WIMBLEDON BRIDGE,22106759,SW19,524826,170657,524850,170650,51.42119192,-0.20612149\nNA,22/06/2020 16:25,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011479,SELHURST,E09000008,CROYDON,Croydon,NULL,NEVILLE ROAD,20501220,CR0,NULL,NULL,532850,166750,NULL,NULL\nNA,22/06/2020 22:36,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,GRANT STREET,22200661,E13,NULL,NULL,540250,182550,NULL,NULL\nNA,23/06/2020 12:29,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,GROSVENOR ROAD,21500522,W4,NULL,NULL,520250,178150,NULL,NULL\nNA,23/06/2020 13:50,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WELLINGTON SQUARE,21700577,SW3,NULL,NULL,527650,178350,NULL,NULL\nNA,23/06/2020 14:55,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05011222,CRAYFORD,E09000004,BEXLEY,Bexley,10011847436,THAMES ROAD,20101429,DA1,552753,175222,552750,175250,51.45538417,0.197195582\nNA,23/06/2020 19:25,2020,2020/21,Special Service,1,1,346,346,KITTEN ON ROOF BEING ATTACKED BY CROWS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000442,DOWNHAM,E09000023,LEWISHAM,Bromley,NULL,BOURNESIDE GARDENS,22001252,SE6,NULL,NULL,538350,171350,NULL,NULL\nNA,24/06/2020 10:46,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PIDGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011469,KENLEY,E09000008,CROYDON,Purley,NULL,FOXLEY ROAD,20500235,CR8,NULL,NULL,531950,159950,NULL,NULL\nNA,24/06/2020 18:48,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED ON ROOF ALSO INJURED - RSPCA ON SCENE - ALP REQUESTED FROM SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000142,REGENT'S PARK,E09000007,CAMDEN,Euston,NULL,WILLIAM ROAD,20400835,NW1,NULL,NULL,529150,182550,NULL,NULL\nNA,24/06/2020 20:54,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000135,HAMPSTEAD TOWN,E09000007,CAMDEN,West Hampstead,NULL,VALE OF HEALTH,20400078,NW3,NULL,NULL,526450,186450,NULL,NULL\nNA,24/06/2020 23:42,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,CADOGAN SQUARE,21700060,SW1X,NULL,NULL,527850,178850,NULL,NULL\nNA,25/06/2020 00:12,2020,2020/21,Special Service,1,1,346,346,RUBBISH ALIGHT,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000189,SOUTHALL BROADWAY,E09000009,EALING,Southall,10025481928,THE BROADWAY,20602249,UB1,511787,180601,511750,180650,51.51329058,-0.39043017\nNA,25/06/2020 00:48,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK - CALLER WILL MEET,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000186,NORWOOD GREEN,E09000009,EALING,Southall,NULL,UXBRIDGE ROAD,20602324,UB1,NULL,NULL,514550,180250,NULL,NULL\nNA,25/06/2020 12:22,2020,2020/21,Special Service,1,1,346,346,GOSLINGS STUCK AT WESTFIELD WEIR ENTRANCE DUE TO FLOW OF WATER,Unknown - Wild Animal,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000525,MORTLAKE AND BARNES COMMON,E09000027,RICHMOND UPON THAMES,Richmond,1.00023E+11,RAILWAY SIDE,22404932,SW13,521659,175828,521650,175850,51.46835668,-0.249878192\nNA,25/06/2020 16:03,2020,2020/21,Special Service,1,1,346,346,TWO KITTENS TRAPPED UNDER CAR BONNET,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00022E+11,LAUREL LANE,21401134,UB7,506228,178897,506250,178850,51.49904727,-0.471025927\nNA,25/06/2020 16:27,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000044,BURNT OAK,E09000003,BARNET,Mill Hill,NULL,THIRLEBY ROAD,20042540,HA8,NULL,NULL,520550,190750,NULL,NULL\nNA,26/06/2020 12:27,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000429,STOCKWELL,E09000022,LAMBETH,Clapham,NULL,BINFIELD ROAD,21900185,SW4,NULL,NULL,530450,176650,NULL,NULL\nNA,26/06/2020 13:47,2020,2020/21,Special Service,1,3,346,1038,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,NULL,TWEEDMOUTH ROAD,22201160,E13,NULL,NULL,540650,183250,NULL,NULL\nNA,26/06/2020 16:04,2020,2020/21,Special Service,1,1,346,346,CAT TAPPED UNDER FENCE AND IN BUSHES,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,GALLEONS DRIVE,19903950,IG11,NULL,NULL,546350,182450,NULL,NULL\nNA,26/06/2020 20:51,2020,2020/21,Special Service,1,1,346,346,CAT  TRAPPED IN BUILDING UNDER RENOVATION,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,REDCLIFFE STREET,21700455,SW10,NULL,NULL,525850,177950,NULL,NULL\nNA,27/06/2020 13:03,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN FENCE AND A WALL             ENTER VIA RAVENSCOURT SQUARE    CALLER WILL MEET AN,Fox,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34013192,RAVENSCOURT SQUARE,21000678,W6,522156,179194,522150,179150,51.4984963,-0.241562865\nNA,27/06/2020 14:10,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND COOKER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000406,COOMBE HILL,E09000021,KINGSTON UPON THAMES,New Malden,NULL,COOMBE HILL ROAD,21800274,KT2,NULL,NULL,521350,170350,NULL,NULL\nNA,27/06/2020 15:23,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED UNDER THE GUTTER ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,NULL,FAIRLOP ROAD,22835350,E11,NULL,NULL,538850,187650,NULL,NULL\nNA,28/06/2020 00:14,2020,2020/21,Special Service,1,1,346,346,YOUNG DEER CAUGHT IN RAILINGS  O/S AMBERLEY HOUSE,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000321,SOUTH HORNCHURCH,E09000016,HAVERING,Wennington,10091576686,PASSIVE CLOSE,21320290,RM13,551755,182560,551750,182550,51.5215762,0.186006381\nNA,28/06/2020 03:07,2020,2020/21,Special Service,1,1,346,346,KITTENS TRAPPED IN SHUTTER,Cat,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,2.00003E+11,COLDHARBOUR LANE,22502943,SE5,532481,176456,532450,176450,51.4715679,-0.0939317\nNA,28/06/2020 12:37,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED IN CEILING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,NULL,OXFORD AVENUE,21401468,UB3,NULL,NULL,509450,177150,NULL,NULL\nNA,28/06/2020 21:29,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON BALCONY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000347,BRENTFORD,E09000018,HOUNSLOW,Heston,NULL,BOSTON MANOR ROAD,21500133,TW8,NULL,NULL,517650,177650,NULL,NULL\nNA,29/06/2020 06:58,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN PLASTIC TOY - RESTRICTING BREATHING,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,NULL,PARISH LANE,20302157,SE20,NULL,NULL,535550,170650,NULL,NULL\nNA,29/06/2020 23:20,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WINDOW FRAME,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Holloway,NULL,FLORENCE ROAD,21103297,N4,NULL,NULL,531150,187550,NULL,NULL\nNA,01/07/2020 08:05,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER KITCHEN CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,NULL,JESMOND AVENUE,20200361,HA9,NULL,NULL,519050,184950,NULL,NULL\nNA,01/07/2020 10:26,2020,2020/21,Special Service,1,1,346,346,BIRD OF PREY STUCK IN A TREE WRAPPED IN WIRE    RSPCA ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,1.00021E+11,UXBRIDGE ROAD,21402681,UB10,507291,182691,507250,182650,51.53294528,-0.454554687\nNA,02/07/2020 02:02,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND SINK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009395,EARL'S COURT,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,EARL'S COURT ROAD,21700166,SW5,NULL,NULL,525650,178450,NULL,NULL\nNA,02/07/2020 11:06,2020,2020/21,Special Service,1,1,346,346,ANIMAL STUCK IN CHIMNEY,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000179,HANGER HILL,E09000009,EALING,Park Royal,NULL,HUXLEY GARDENS,20600932,NW10,NULL,NULL,518750,182950,NULL,NULL\nNA,02/07/2020 11:46,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000255,FULHAM REACH,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,RAINVILLE ROAD,21000669,W6,NULL,NULL,523350,177650,NULL,NULL\nNA,02/07/2020 18:19,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE/ DUCKLING  IN DRAIN,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000056,HIGH BARNET,E09000003,BARNET,Barnet,10094709533,HIGH STREET,20022440,EN5,524531,196574,524550,196550,51.65417772,-0.201211907\nNA,02/07/2020 20:02,2020,2020/21,Special Service,1,1,346,346,BIRD TANGLED IN ANTIBIRD NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000087,BRONDESBURY PARK,E09000005,BRENT,Willesden,NULL,WILLESDEN LANE,20201346,NW6,NULL,NULL,523750,184450,NULL,NULL\nNA,03/07/2020 00:09,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Old Kent Road,NULL,STEVENSON CRESCENT,22502808,SE16,NULL,NULL,534450,178350,NULL,NULL\nNA,03/07/2020 22:40,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,LONG ACRE,8400849,WC2E,NULL,NULL,530250,180950,NULL,NULL\nNA,03/07/2020 23:42,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO LIGHT WELL AT FRONT OF PROPERTY,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000260,PARSONS GREEN AND WALHAM,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,RYECROFT STREET,21000708,SW6,NULL,NULL,525550,176550,NULL,NULL\nNA,04/07/2020 12:07,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,LONG ACRE,8400849,WC2E,NULL,NULL,530250,180950,NULL,NULL\nNA,04/07/2020 14:04,2020,2020/21,Special Service,1,1,346,346,CALL FROM LONDON WILDLIFE PROTECTION - BABY BIRD STUCK ON BALCONY OF VACANT FLAT - FIRST FLOOR LEVEL,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,NULL,HOOPER STREET,22700652,E1,NULL,NULL,534150,181050,NULL,NULL\nNA,04/07/2020 15:05,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF   REQ BY RSPCA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000590,CANN HALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,CANN HALL ROAD,22822050,E11,NULL,NULL,539550,186050,NULL,NULL\nNA,04/07/2020 19:36,2020,2020/21,Special Service,1,1,346,346,SEAGULL TRAPPED IN NETTING - BETWEEN NETTING AND A GLASS BALCONY  ON THIRD FLOOR       ON THE ROAD S,Bird,Person (mobile),Hotel/motel,Other Residential,Animal rescue from height,Wild animal rescue from height,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202200942,LAKESIDE WAY,20201216,HA9,519170,185682,519150,185650,51.557444,-0.28237\nNA,05/07/2020 08:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN ENGINE COMPARTMENT OF CAR,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,10008787927,MAUD CHADBURN PLACE,21903337,SW4,528857,174270,528850,174250,51.45276174,-0.146865438\nNA,05/07/2020 12:34,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND CAR RADIATOR - RED DACIA DUSTER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000621,ROEHAMPTON AND PUTNEY HEATH,E09000032,WANDSWORTH,Wandsworth,10070248563,HIGHCROSS WAY,22906517,SW15,522395,173469,522350,173450,51.44699956,-0.240101545\nNA,05/07/2020 17:16,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN FIRE ESCAPE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011108,NUNHEAD & QUEEN'S ROAD,E09000028,SOUTHWARK,New Cross,NULL,CANDLE GROVE,22503216,SE15,NULL,NULL,534950,175650,NULL,NULL\nNA,05/07/2020 22:47,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped wild animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,10008997626,UPTON LANE,22201168,E7,540560,184798,540550,184750,51.54458945,0.02564682\nNA,06/07/2020 21:21,2020,2020/21,Special Service,1,3,346,1038,BABY SEAGULL TRAPPED ON SPIKE ON ROOF,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000128,BELSIZE,E09000007,CAMDEN,Kentish Town,NULL,GLENMORE ROAD,20400304,NW3,NULL,NULL,527250,184950,NULL,NULL\nNA,07/07/2020 10:08,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000128,BELSIZE,E09000007,CAMDEN,Kentish Town,NULL,GLENMORE ROAD,20400304,NW3,NULL,NULL,527250,184950,NULL,NULL\nNA,07/07/2020 11:06,2020,2020/21,Special Service,1,1,346,346,CAT UP TREE   RSPCA IN ATTENDANCE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00022E+11,ZEALAND AVENUE,21402241,UB7,506132,177209,506150,177250,51.48389508,-0.472913454\nNA,07/07/2020 22:19,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UP CHIMNEY,Cat,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,COSEDGE CRESCENT,20502000,CR0,NULL,NULL,531450,164450,NULL,NULL\nNA,08/07/2020 10:52,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000423,HERNE HILL,E09000022,LAMBETH,Brixton,NULL,MAYALL ROAD,21900934,SE24,NULL,NULL,531650,174850,NULL,NULL\nNA,10/07/2020 12:45,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,SLINGSBY PLACE,8400250,WC2E,NULL,NULL,530150,180950,NULL,NULL\nNA,10/07/2020 21:16,2020,2020/21,Special Service,1,1,346,346,CAT WITH LEG TRAPPED IN TREE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,BEXHILL ROAD,22001229,SE4,NULL,NULL,536850,174150,NULL,NULL\nNA,11/07/2020 01:29,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN SHUTTERS AND WINDOW - THIS IS A BUTCHERS SHOP NR THE CLOCK TOWER,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538304,175570,538350,175550,51.46221867,-0.0104889\nNA,11/07/2020 15:08,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN PRECARIOUS POSITION    RSPCA INSPECTOR ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,HUNTER STREET,20400990,WC1N,NULL,NULL,530250,182450,NULL,NULL\nNA,11/07/2020 17:38,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN NETTING - JUNCTION CAMBERWELL STATION ROAD  - VOLUNTEER ON SCENE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000436,VASSALL,E09000022,LAMBETH,Brixton,1.00024E+11,DENMARK ROAD,21900455,SE5,532095,176569,532050,176550,51.4726813,-0.099438613\nNA,12/07/2020 08:44,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - BIRD TRAPPED IN VENTILATION SHAFT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000091,HARLESDEN,E09000005,BRENT,Willesden,NULL,MARIAN WAY,20200900,NW10,NULL,NULL,521650,184050,NULL,NULL\nNA,12/07/2020 10:40,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED BETWEEN RAILINGS   CALLER REMAINING ON SCENE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Paddington,10033529604,BAYSWATER ROAD,8400628,W2,526559,180723,526550,180750,51.51127428,-0.177619736\nNA,12/07/2020 16:52,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN CHIMNEY,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05011486,THORNTON HEATH,E09000008,CROYDON,Woodside,NULL,CRANLEIGH GARDENS,20500825,SE25,NULL,NULL,533450,168850,NULL,NULL\nNA,13/07/2020 12:29,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN FENCE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000440,CATFORD SOUTH,E09000023,LEWISHAM,Lee Green,NULL,MINARD ROAD,22001714,SE6,NULL,NULL,539150,172950,NULL,NULL\nNA,13/07/2020 14:10,2020,2020/21,Special Service,1,2,346,692,ANIMAL TRAPPED BEHIND FIRE PLACE,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,NULL,ST CHRISTOPHER ROAD,21401810,UB8,NULL,NULL,505950,181450,NULL,NULL\nNA,13/07/2020 15:53,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WALL,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,HAROLD ROAD,21103379,N8,NULL,NULL,530550,188950,NULL,NULL\nNA,13/07/2020 18:42,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN VENT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011232,THAMESMEAD EAST,E09000004,BEXLEY,Plumstead,NULL,FAIRWAY DRIVE,20101772,SE28,NULL,NULL,547850,181350,NULL,NULL\nNA,14/07/2020 14:02,2020,2020/21,Special Service,1,2,346,692,ASSIST RSPCA - CAT STUCK BEHIND CLADDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,TOWNMEAD ROAD,21000826,SW6,NULL,NULL,525850,175750,NULL,NULL\nNA,14/07/2020 18:56,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VOID IN BEDROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Bromley,NULL,SOUTHLANDS ROAD,20300621,BR2,NULL,NULL,541450,167950,NULL,NULL\nNA,14/07/2020 20:16,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PARAKEET STUCK IN CABLE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,MIDSUMMER AVENUE,21500757,TW4,NULL,NULL,512650,174950,NULL,NULL\nNA,15/07/2020 15:34,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING OF BRIDGE   CALLER REMAINING ON SCENE TO DIRECT,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6188679,COMMERCIAL ROAD,22700346,E14,536438,181122,536450,181150,51.51256125,-0.035186381\nNA,15/07/2020 18:12,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN DRAIN OUTSIDE OF,Unknown - Wild Animal,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,1.00021E+11,WOODLANDS PARK ROAD,21104106,N15,532064,188867,532050,188850,51.58320011,-0.095281254\nNA,15/07/2020 19:26,2020,2020/21,Special Service,1,3,346,1038,KITTEN TRAPPED BEHIND BEAM IN ATTIC -    SNAKE EYE CAMERA REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,BULWER ROAD,20703992,N18,NULL,NULL,533350,192850,NULL,NULL\nNA,15/07/2020 20:40,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000200,GRANGE,E09000010,ENFIELD,Enfield,NULL,LONDON ROAD,20702812,EN2,NULL,NULL,532950,196150,NULL,NULL\nNA,16/07/2020 11:04,2020,2020/21,Special Service,1,1,346,346,CAT UP TREE BEING ATTACKED BY BIRDS,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000172,DORMERS WELLS,E09000009,EALING,Southall,12015920,WHITECOTE ROAD,20601893,UB1,514035,181080,514050,181050,51.51714924,-0.357888532\nNA,16/07/2020 15:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,ST JOHN'S CLOSE,21000757,SW6,NULL,NULL,525050,177350,NULL,NULL\nNA,16/07/2020 20:27,2020,2020/21,Special Service,1,1,346,346,FOX IN RIVER THAMES IN THE SIDE OF LOCK,Fox,Person (land line),Hedge,Outdoor,Other animal assistance,Assist trapped wild animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,10002263285,RIVERSIDE DRIVE,22403779,TW10,516373,172295,516350,172250,51.43771678,-0.327095673\nNA,17/07/2020 20:58,2020,2020/21,Special Service,1,1,346,346,ANIMAL TRAPPED IN A CHIMMNEY - CALLER IS SHIELDING,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000464,LONGTHORNTON,E09000024,MERTON,Norbury,NULL,LYNDHURST AVENUE,22104056,SW16,NULL,NULL,529850,169150,NULL,NULL\nNA,18/07/2020 07:49,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON FIRST FLOOR WINDOW LEDGE - OFF DUTY POLICE OFFICER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,NULL,ST MARTIN'S LANE,8400618,WC2N,NULL,NULL,530050,180750,NULL,NULL\nNA,18/07/2020 08:43,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - BIRD TRAPPED INSIDE CHIMNEY,Bird,Person (mobile),Self contained Sheltered Housing,Dwelling,Other animal assistance,Assist trapped wild animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,NULL,BALFOUR ROAD,22302601,IG1,NULL,NULL,543750,186750,NULL,NULL\nNA,18/07/2020 08:55,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,46024516,ESSEX ROAD,22200593,E12,542509,184792,542550,184750,51.54403809,0.053725223\nNA,18/07/2020 16:03,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,FOLEY STREET,8400008,W1W,NULL,NULL,529150,181650,NULL,NULL\nNA,18/07/2020 17:28,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - INJURED BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,SOUTH GROVE,22875850,E17,NULL,NULL,536550,188650,NULL,NULL\nNA,19/07/2020 10:37,2020,2020/21,Special Service,1,2,346,692,HORSE COLLAPSED IN FIELD,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011224,EAST WICKHAM,E09000004,BEXLEY,Plumstead,2.00002E+11,UPPER WICKHAM LANE,20101499,DA16,546688,176954,546650,176950,51.47254554,0.110690783\nNA,19/07/2020 15:44,2020,2020/21,Special Service,1,3,346,1038,SEAGULL TRAPPED IN NETTING    HAS BEEN THERE SINCE LAST NIGHT    CALLER WILL MEET YOU ON SCENE,Bird,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6135945,DEE STREET,22700402,E14,538409,181250,538450,181250,51.51323109,-0.006749358\nNA,19/07/2020 23:17,2020,2020/21,Special Service,1,1,346,346,INJURED FOX TRAPPED IN METAL FENCE   BROKEN FOOT,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal harm involving wild animal,E05000626,TOOTING,E09000032,WANDSWORTH,Tooting,NULL,BRUDENELL ROAD,22900667,SW17,NULL,NULL,528150,171850,NULL,NULL\nNA,20/07/2020 16:07,2020,2020/21,Special Service,1,3,346,1038,FOX TRAPPED BETWEEN TWO HOUSES   CALL FROM RSPCA,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000322,SQUIRREL'S HEATH,E09000016,HAVERING,Romford,NULL,CROSSWAYS,21300062,RM2,NULL,NULL,552750,189850,NULL,NULL\nNA,20/07/2020 18:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,ALFRED ROAD,20600036,W3,NULL,NULL,520450,180150,NULL,NULL\nNA,20/07/2020 22:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009367,BROWNSWOOD,E09000012,HACKNEY,Holloway,NULL,GLOUCESTER DRIVE,20900448,N4,NULL,NULL,531950,186950,NULL,NULL\nNA,21/07/2020 14:28,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN ROOF   RSPCA ON SCENE REQUIRING ASSISTANCE,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000598,HATCH LANE,E09000031,WALTHAM FOREST,Woodford,NULL,HENRYS AVENUE,22844450,IG8,NULL,NULL,539550,191950,NULL,NULL\nNA,21/07/2020 21:30,2020,2020/21,Special Service,1,1,346,346,PARROT STUCK ON ROOF,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,COMPTON CRESCENT,20600425,UB5,NULL,NULL,511950,184050,NULL,NULL\nNA,22/07/2020 18:55,2020,2020/21,Special Service,1,2,346,692,KITTENS STUCK BEHIND WALL    RSCPA ON SCENE,Cat,Person (mobile),Department Store,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000618,NIGHTINGALE,E09000032,WANDSWORTH,Tooting,1.00023E+11,BALHAM HIGH ROAD,22900217,SW12,528481,173328,528450,173350,51.44438022,-0.152622368\nNA,23/07/2020 21:13,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,AIREDALE AVENUE,21500012,W4,NULL,NULL,521750,178250,NULL,NULL\nNA,24/07/2020 18:35,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED BEHIND NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,128012637,MALDEN ROAD,21800637,KT4,522224,166090,522250,166050,51.38071039,-0.245099915\nNA,25/07/2020 11:37,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000419,CLAPHAM TOWN,E09000022,LAMBETH,Clapham,NULL,RECTORY GROVE,21901147,SW4,NULL,NULL,529250,175950,NULL,NULL\nNA,25/07/2020 19:34,2020,2020/21,Special Service,2,3,346,1038,DOG STUCK IN MIDDLE OF THE CANAL NEXT TO THE BRIDGE - WATER OPERATIONS LEVEL TWO    FRU WITH BOAT RE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05009329,ST. DUNSTAN'S,E09000030,TOWER HAMLETS,Bethnal Green,10025541719,BEN JONSON ROAD,22700144,E3,536360,181796,536350,181750,51.51863743,-0.036049569\nNA,26/07/2020 20:22,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN DRAIN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009330,ST. KATHARINE'S & WAPPING,E09000030,TOWER HAMLETS,Shadwell,NULL,WAPPING WALL,22701295,E1W,NULL,NULL,535350,180550,NULL,NULL\nNA,26/07/2020 22:22,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - FOX WITH LEG TRAPPED IN WIRING,Fox,Person (land line),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,NULL,CHESTNUT GROVE,22101386,CR4,NULL,NULL,529950,168150,NULL,NULL\nNA,26/07/2020 22:57,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000049,EAST FINCHLEY,E09000003,BARNET,Finchley,NULL,SEDGEMERE AVENUE,20038900,N2,NULL,NULL,526250,189750,NULL,NULL\nNA,27/07/2020 02:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Shopping Centre,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,10090500380,GARRATT LANE,22901883,SW18,525652,174417,525650,174450,51.4548035,-0.192912333\nNA,27/07/2020 13:46,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN DOOR POSSIBLY INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011489,WOODSIDE,E09000008,CROYDON,Woodside,NULL,PORTLAND ROAD,20501311,SE25,NULL,NULL,534550,167950,NULL,NULL\nNA,28/07/2020 11:11,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,GRANVILLE CLOSE,20500998,CR0,533114,165465,533150,165450,51.37264938,-0.088947382\nNA,28/07/2020 12:48,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN INTO ABANDONED COURTYARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000366,BARNSBURY,E09000019,ISLINGTON,Islington,NULL,BARNSBURY STREET,21604203,N1,NULL,NULL,531350,184150,NULL,NULL\nNA,28/07/2020 15:01,2020,2020/21,Special Service,1,2,346,692,CAT ON ROOF INACCESSIBLE FROM REST OF BUILDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,GREAT MARLBOROUGH STREET,8401373,W1F,NULL,NULL,529350,181150,NULL,NULL\nNA,28/07/2020 16:38,2020,2020/21,Special Service,1,2,346,692,BIRDS TRAPPED IN LOFT,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,MYERS LANE,22000735,SE14,NULL,NULL,535650,177850,NULL,NULL\nNA,28/07/2020 19:02,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,1.00023E+11,BRYANSTON MEWS WEST,8401140,W1H,527629,181457,527650,181450,51.5176278,-0.161939285\nNA,29/07/2020 02:23,2020,2020/21,Special Service,1,1,346,346,FOX BELIEVED TRAPPED IN METAL FENCE BY THE WATERWORKS - CALLER DOES NOT WANT BRIGADE TO KNOCK,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000026,ABBEY,E09000002,BARKING AND DAGENHAM,Barking,100055287,COWBRIDGE LANE,19900672,IG11,543685,184343,543650,184350,51.53971004,0.07049203\nNA,29/07/2020 06:41,2020,2020/21,Special Service,1,1,346,346,FOX CUB STUCK IN FENCE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000269,CROUCH END,E09000014,HARINGEY,Hornsey,NULL,WESTON PARK,21104076,N8,NULL,NULL,530350,188450,NULL,NULL\nNA,29/07/2020 12:13,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,1.00023E+11,LANDOR ROAD,21900834,SW9,530201,175676,530250,175650,51.46508989,-0.127021537\nNA,29/07/2020 16:35,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN METAL GATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000280,TOTTENHAM GREEN,E09000014,HARINGEY,Tottenham,NULL,GROVE PARK ROAD,21103358,N15,NULL,NULL,533350,189150,NULL,NULL\nNA,29/07/2020 18:41,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF IN PRECARIOUS POSITION,Cat,Person (mobile),Bungalow - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011245,HAINAULT,E09000026,REDBRIDGE,Hainault,NULL,POLLARD CLOSE,22304702,IG7,NULL,NULL,546050,192250,NULL,NULL\nNA,30/07/2020 12:26,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BEHIND A METAL GRILL IN WINDOW    CALLER ON SCENE AND WILL DIRECT YOU,Bird,Person (mobile),Church/Chapel,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,2.00003E+11,LIVERPOOL GROVE,22501528,SE17,532525,178129,532550,178150,51.48659534,-0.092658881\nNA,31/07/2020 10:12,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN WINDOW GRILL  WILD LIFE TEAM ON SITE,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,10013533004,LIVERPOOL GROVE,22501528,SE17,532559,178130,532550,178150,51.4865993,-0.0921685\nNA,31/07/2020 13:33,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING   RSPCA IN ATTENDANCE   ACCESS VIA WALKWAY NEXT TO GYM,Bird,Person (mobile),Water works,Non Residential,Other animal assistance,Assist trapped wild animal,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,2.00001E+11,WATERWORKS YARD,20502803,CR0,532219,165392,532250,165350,51.37220731,-0.101823279\nNA,31/07/2020 13:41,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK BEHIND WINDOW GRILL     FACING THE CHURCH TO THE RIGHT IN THE TOP WINDOW BEFORE THE SEP,Bird,Person (mobile),Church/Chapel,Non Residential,Other animal assistance,Assist trapped wild animal,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,10013533004,LIVERPOOL GROVE,22501528,SE17,532559,178130,532550,178150,51.4865993,-0.0921685\nNA,01/08/2020 08:53,2020,2020/21,Special Service,1,1,346,346,KITTEN LOCKED IN BEDROOM    FURNITURE FALLEN AGAINST DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011102,FARADAY,E09000028,SOUTHWARK,Old Kent Road,NULL,WOOLER STREET,22502758,SE17,NULL,NULL,532750,178250,NULL,NULL\nNA,01/08/2020 13:17,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT UP TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,West Norwood,10000816583,BOWEN DRIVE,22500298,SE21,533396,171650,533350,171650,51.42816374,-0.082562444\nNA,01/08/2020 18:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING AT BRIDGE - RSPCA IN ATTENDANCE - WILL MEET YOU AT  BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,10013149863,CRAY AVENUE,20301080,BR5,547016,168294,547050,168250,51.39464478,0.111820702\nNA,01/08/2020 22:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN STUCK IN CAR EXHAUST,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011250,NEWBURY,E09000026,REDBRIDGE,Ilford,1.00022E+11,COLENSO ROAD,22301952,IG2,545144,187454,545150,187450,51.56729265,0.092790251\nNA,02/08/2020 00:28,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,NULL,TUSKAR STREET,20801523,SE10,NULL,NULL,539150,178050,NULL,NULL\nNA,03/08/2020 02:34,2020,2020/21,Special Service,1,1,346,346,WOOD PIGEON TRAPPED BEHIND CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000111,CHISLEHURST,E09000006,BROMLEY,Sidcup,NULL,GREEN LANE,20302333,BR7,NULL,NULL,544050,170950,NULL,NULL\nNA,03/08/2020 09:24,2020,2020/21,Special Service,1,1,346,346,RUNNING CALL TO TRAPPED FOX,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000450,PERRY VALE,E09000023,LEWISHAM,Forest Hill,NULL,KEMBLE ROAD,22001596,SE23,NULL,NULL,535950,173250,NULL,NULL\nNA,03/08/2020 12:22,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX STRANDED ON FORESHORE - RSPCA INSPECTOR IS EMILY     THIS IS NEAR TO WANDSWORT,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,34154479,CENTRAL AVENUE,21001652,SW6,526188,175831,526150,175850,51.46739635,-0.184707255\nNA,03/08/2020 16:13,2020,2020/21,Special Service,1,1,346,346,CAT IN TREE,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000347,BRENTFORD,E09000018,HOUNSLOW,Ealing,10091693063,BOSTON MANOR ROAD,21500133,TW8,516980,178093,516950,178050,51.48969643,-0.316452401\nNA,04/08/2020 07:46,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT WITH LEG TRAPPED IN BATH VERY DISTRESSED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Kentish Town,NULL,CAMDEN GARDENS,20400648,NW1,NULL,NULL,528950,184150,NULL,NULL\nNA,04/08/2020 17:58,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH SMALL ANIMAL RESCUE - RSPCA OFFICER ON SCENE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011469,KENLEY,E09000008,CROYDON,Purley,NULL,GODSTONE ROAD,20503071,CR8,NULL,NULL,531950,160550,NULL,NULL\nNA,04/08/2020 18:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DRAIN PIPE - LOCATED ON THE ROOF OF HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000472,VILLAGE,E09000024,MERTON,New Malden,NULL,ELLERTON ROAD,22102397,SW20,NULL,NULL,522150,170350,NULL,NULL\nNA,05/08/2020 15:07,2020,2020/21,Special Service,1,1,346,346,DOG WITH FOOT TRAPPED IN SEAT OF CAR  BLACK AND GREY SMART CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6041166,OLD FORD ROAD,22700893,E2,535472,183146,535450,183150,51.53098116,-0.048323841\nNA,05/08/2020 16:39,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000106,BICKLEY,E09000006,BROMLEY,Bromley,1.0002E+11,PARKSIDE AVENUE,20300559,BR1,542304,168202,542350,168250,51.39501881,0.044091722\nNA,05/08/2020 18:35,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000530,TEDDINGTON,E09000027,RICHMOND UPON THAMES,Twickenham,10002251960,BUSHY PARK,22407070,TW11,515295,169725,515250,169750,51.41483118,-0.343437209\nNA,05/08/2020 19:09,2020,2020/21,Special Service,1,1,346,346,PUPPY TRAPPED IN CHIMNEY,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,West Norwood,NULL,AYSGARTH ROAD,22500123,SE21,NULL,NULL,533050,173950,NULL,NULL\nNA,05/08/2020 23:01,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN INTO A COURTYARD AND NOW TRAPPED UNABLE TO GET OUT.,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,BOSTON PLACE,8400897,NW1,NULL,NULL,527550,182250,NULL,NULL\nNA,06/08/2020 00:21,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN WALL,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011465,BROAD GREEN,E09000008,CROYDON,Croydon,10093755716,WATTEAU SQUARE,20503294,CR0,531531,166021,531550,166050,51.3780175,-0.1114712\nNA,06/08/2020 09:08,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED ON ROOF   STARLIGHT TRUST REPRESENTIVE IN ATTENDANCE,Bird,Person (mobile),Other Dwelling,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000569,WALLINGTON NORTH,E09000029,SUTTON,Wallington,NULL,HARCOURT AVENUE,22602368,SM6,NULL,NULL,528950,164650,NULL,NULL\nNA,07/08/2020 12:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VENT,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,MAIDEN ROAD,22201492,E15,NULL,NULL,539350,184350,NULL,NULL\nNA,07/08/2020 20:36,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER PATIO,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011239,CLAYHALL,E09000026,REDBRIDGE,Ilford,NULL,MERRIVALE AVENUE,22303070,IG4,NULL,NULL,541750,189050,NULL,NULL\nNA,08/08/2020 09:44,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF - OWNER ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Walthamstow,NULL,HILLCREST ROAD,22845550,E17,NULL,NULL,538850,190250,NULL,NULL\nNA,08/08/2020 16:17,2020,2020/21,Special Service,1,1,346,346,INJURED CAT TRAPPED DOWN CRATER IN BACK GARDEN,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00022E+11,APLIN WAY,21501436,TW7,515181,176789,515150,176750,51.47834359,-0.342776716\nNA,08/08/2020 19:55,2020,2020/21,Special Service,1,1,346,346,CAT ON ROOF POSSIBLY INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,HENDON LANE,20021800,N3,NULL,NULL,524950,190450,NULL,NULL\nNA,09/08/2020 08:53,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN GRATE ON ROOF,Cat,Person (mobile),Infant/Primary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000485,GREEN STREET EAST,E09000025,NEWHAM,Stratford,10008997118,SANDRINGHAM ROAD,22207876,E7,541177,185018,541150,185050,51.5464038,0.0346155\nNA,09/08/2020 10:50,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN ENGINE OF CAR - CARPARK FOR GROUP PAMA HOUSE,Cat,Person (mobile),Road surface/pavement,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000058,OAKLEIGH,E09000003,BARNET,Barnet,200107674,STATION ROAD,20040880,EN5,526291,196083,526250,196050,51.64937066,-0.175951503\nNA,09/08/2020 14:47,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED AND INJURED UNDER BRIDGE NETTING,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000403,CANBURY,E09000021,KINGSTON UPON THAMES,Kingston,128012373,LOWER HAM ROAD,21800623,KT2,517966,169602,517950,169650,51.41317842,-0.305090726\nNA,09/08/2020 15:00,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH BIRD THAT IS TRAPPED BY LEG AND WING IN NETTING     RSPCA OFFICER ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011241,CRANBROOK,E09000026,REDBRIDGE,Ilford,NULL,CLARENCE AVENUE,22302721,IG2,NULL,NULL,543150,188450,NULL,NULL\nNA,10/08/2020 19:25,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,FARMWAY,19900129,RM8,NULL,NULL,547250,185950,NULL,NULL\nNA,10/08/2020 21:03,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525750,182250,NULL,NULL\nNA,10/08/2020 22:20,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,KNIVET ROAD,21000484,SW6,NULL,NULL,525150,177550,NULL,NULL\nNA,10/08/2020 22:30,2020,2020/21,Special Service,2,3,346,1038,FOX IN DISTRESS DOWN A HOLE ON THE CREEK BANK,Fox,Person (mobile),River/canal,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,1.00023E+11,NORMAN ROAD,20801089,SE10,537823,177583,537850,177550,51.48042181,-0.01662415\nNA,11/08/2020 10:25,2020,2020/21,Special Service,1,1,346,346,DOG LOCKED IN CAR,Dog,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,202072007,LANSBURY CLOSE,20202621,NW10,520339,185119,520350,185150,51.55214404,-0.265704221\nNA,11/08/2020 11:47,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,DERSINGHAM AVENUE,22200157,E12,NULL,NULL,543050,185650,NULL,NULL\nNA,11/08/2020 11:51,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,NULL,GARNHAM CLOSE,20901684,N16,NULL,NULL,533750,186550,NULL,NULL\nNA,11/08/2020 12:47,2020,2020/21,Special Service,1,1,346,346,PIDGEON IN FIRE PLACE  CALLER UNABLE TO REMOVE BOARDS TO REMOVE BIRD,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,DERSINGHAM AVENUE,22200157,E12,NULL,NULL,543050,185650,NULL,NULL\nNA,11/08/2020 14:31,2020,2020/21,Special Service,1,1,346,346,DOG ON ROOF         ESCAPED VIA SKY LIGHT,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000473,WEST BARNES,E09000024,MERTON,New Malden,NULL,CROSSWAY,22101752,SW20,NULL,NULL,523150,168250,NULL,NULL\nNA,11/08/2020 19:10,2020,2020/21,Special Service,1,1,346,346,DOG WITH PAW STUCK IN DRAIN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000437,BELLINGHAM,E09000023,LEWISHAM,Beckenham,NULL,LUSHINGTON ROAD,22001674,SE6,NULL,NULL,537550,171550,NULL,NULL\nNA,12/08/2020 15:34,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Factory,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000325,BOTWELL,E09000017,HILLINGDON,Hayes,2.00002E+11,BLYTH ROAD,21400193,UB3,509355,179622,509350,179650,51.5049621,-0.4257716\nNA,12/08/2020 20:57,2020,2020/21,Special Service,1,1,346,346,DOG STUCK IN MARSHLAND,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Animal harm involving domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,10091578110,SQUADRONS APPROACH,21300594,RM12,553694,184770,553650,184750,51.54091393,0.214893796\nNA,13/08/2020 11:44,2020,2020/21,Special Service,1,1,346,346,AT BACK OF TENNIS COURTS NEAR THE CAFE - DOG TRAPPED WITH LEAD AROUND TREE BASE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870114171,CHURCH ROAD,22600429,SM6,529476,165598,529450,165550,51.37468409,-0.141127695\nNA,13/08/2020 12:15,2020,2020/21,Special Service,1,1,346,346,DOG WITH LEG TRAPPED IN BANNISTER,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,HILLFIELD PARK,21100429,N10,NULL,NULL,528750,189350,NULL,NULL\nNA,13/08/2020 15:22,2020,2020/21,Special Service,1,1,346,346,DOG STUCK IN IRON TABLE IN REAR GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,GROVE ROAD,20600785,W3,NULL,NULL,520350,180050,NULL,NULL\nNA,13/08/2020 21:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000101,SUDBURY,E09000005,BRENT,Wembley,NULL,MAYBANK AVENUE,20200048,HA0,NULL,NULL,516250,185350,NULL,NULL\nNA,13/08/2020 21:54,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN HOLE IN GARDEN,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000475,BECKTON,E09000025,NEWHAM,East Ham,46055164,OLIVER GARDENS,22200918,E6,542299,181582,542250,181550,51.51525218,0.049403064\nNA,13/08/2020 22:46,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000114,CRAY VALLEY EAST,E09000006,BROMLEY,Orpington,NULL,SHEEPCOTE LANE,20300972,BR5,NULL,NULL,548850,168050,NULL,NULL\nNA,14/08/2020 01:42,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED,Fox,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal harm involving wild animal,E05000472,VILLAGE,E09000024,MERTON,New Malden,48060657,ROKEBY PLACE,22105460,SW20,522826,170309,522850,170350,51.41849983,-0.235000202\nNA,15/08/2020 11:31,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE   OWNER ON SCENE WILL DIRECT,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000127,WEST WICKHAM,E09000006,BROMLEY,Beckenham,1.0002E+11,KENT ROAD,20301417,BR4,537990,166169,537950,166150,51.37781487,-0.018658248\nNA,15/08/2020 13:35,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT IN TREE - RSPCA ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,EASTWOOD ROAD,21101220,N10,528106,190040,528150,190050,51.59465355,-0.151937188\nNA,15/08/2020 17:07,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED INBETWEEN TWO WALLS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000527,ST. MARGARETS AND NORTH TWICKENHAM,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,BURNSIDE CLOSE,22403334,TW1,NULL,NULL,516050,174450,NULL,NULL\nNA,15/08/2020 18:02,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN TREE    CALLED BY RSPCA ON SCENE,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,2.00004E+11,WELSTEAD WAY,21590241,W4,521711,178922,521750,178950,51.49615258,-0.248056535\nNA,15/08/2020 21:47,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED INSIDE CAR ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000100,STONEBRIDGE,E09000005,BRENT,Wembley,202072142,LILBURNE WALK,20200785,NW10,520239,184756,520250,184750,51.54889953,-0.267269994\nNA,16/08/2020 06:33,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BEHIND WALL,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,CATHNOR ROAD,21000191,W12,NULL,NULL,522650,179550,NULL,NULL\nNA,16/08/2020 13:25,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,NULL,CLAPHAM COMMON SOUTH SIDE,21900340,SW4,NULL,NULL,529050,174550,NULL,NULL\nNA,17/08/2020 11:14,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON LOW FLAT ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,NULL,HARROW ROAD,8400530,W10,NULL,NULL,524550,182350,NULL,NULL\nNA,18/08/2020 12:05,2020,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING WILDLIFE REP WILL MEET AT SAINSBURYS AND DIRECT BRIGADE,Bird,Person (mobile),Other outdoor sporting venue,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009394,DALGARNO,E09000020,KENSINGTON AND CHELSEA,North Kensington,217118148,CANAL CLOSE,21701120,W10,523826,182315,523850,182350,51.52619068,-0.216414315\nNA,18/08/2020 19:09,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE - HANGING BY WING ATTACHED TO SOMETHING IN TREE - CALLER WILL MEET YOU AT NUMBE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511250,190150,NULL,NULL\nNA,19/08/2020 17:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,EGMONT ROAD,22602284,SM2,NULL,NULL,526450,163150,NULL,NULL\nNA,20/08/2020 06:36,2020,2020/21,Special Service,1,1,346,346,KITTEN  STUCK IN TREE  CAUGHT ON BRANCHES BY COLLAR AND LEAD,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10000006039,HINDES ROAD,21201050,HA1,515383,188625,515350,188650,51.58468523,-0.336002973\nNA,20/08/2020 08:39,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED BEHIND KITCHEN UNITS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,NULL,CRESCENT ROAD,20301870,BR3,NULL,NULL,537950,169250,NULL,NULL\nNA,21/08/2020 13:08,2020,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (mobile),Scrub land,Outdoor,Other animal assistance,Assist trapped wild animal,E05000317,PETTITS,E09000016,HAVERING,Romford,1.00021E+11,GARRY CLOSE,21301217,RM1,551381,191160,551350,191150,51.59894989,0.184313658\nNA,21/08/2020 17:14,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BETWEEN METAL GRILL AND WINDOW,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,NULL,HANLEY ROAD,21603308,N4,NULL,NULL,530750,187350,NULL,NULL\nNA,21/08/2020 17:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000139,KENTISH TOWN,E09000007,CAMDEN,Kentish Town,5028891,FORTESS ROAD,20400362,NW5,529056,185658,529050,185650,51.55505942,-0.139848459\nNA,21/08/2020 22:12,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,HACKNEY ROAD,20900479,E2,NULL,NULL,533650,183050,NULL,NULL\nNA,21/08/2020 23:25,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN DISHWASHER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000092,KENSAL GREEN,E09000005,BRENT,North Kensington,NULL,EARLSMEAD ROAD,20202141,NW10,NULL,NULL,523050,182950,NULL,NULL\nNA,22/08/2020 08:31,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011467,CRYSTAL PALACE & UPPER NORWOOD,E09000008,CROYDON,West Norwood,NULL,HAROLD ROAD,20500071,SE19,NULL,NULL,532950,170250,NULL,NULL\nNA,22/08/2020 17:45,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,10091184787,UPPER WALTHAMSTOW ROAD,22883300,E17,538474,189398,538450,189350,51.5864334,-0.00260358\nNA,22/08/2020 18:39,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN WALL BEHIND WASHING MACHINE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000426,OVAL,E09000022,LAMBETH,Lambeth,NULL,SOUTH LAMBETH ROAD,21901248,SW8,NULL,NULL,530350,177350,NULL,NULL\nNA,22/08/2020 18:56,2020,2020/21,Special Service,2,3,346,1038,PIDGEON TRAPPED IN NETTING  ON BRIDGE BETWEEN SHOPPING CENTER AND CAR PARK ABOVE KENTISH WAY   RSPCA,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10003628663,KENTISH WAY,20302059,BR1,540507,169149,540550,169150,51.40398032,0.018659324\nNA,23/08/2020 13:55,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON RAILINGS   NEXT TO THE SCHOOL,Cat,Person (mobile),Railings,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WALTHAM PARK WAY,22884700,E17,537023,190994,537050,190950,51.601133,-0.02291845\nNA,25/08/2020 15:57,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED INSIDE DERELICT BUILDING - CALLER WILL MEET CREWS,Cat,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,1.00024E+11,GREAT MARLBOROUGH STREET,8401373,W1F,529341,181167,529350,181150,51.51463833,-0.13738072\nNA,25/08/2020 18:07,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000407,COOMBE VALE,E09000021,KINGSTON UPON THAMES,New Malden,NULL,BEECHCROFT AVENUE,21800125,KT3,NULL,NULL,520250,169350,NULL,NULL\nNA,26/08/2020 10:58,2020,2020/21,Special Service,NULL,NULL,346,NULL,Redacted,Dog,Coastguard,River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000427,PRINCE'S,E09000022,LAMBETH,Lambeth,2E+11,ALBERT EMBANKMENT,21900079,SE1,530323,178213,530350,178250,51.48786109,-0.124327096\nNA,26/08/2020 15:37,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD STUCK IN PRAM,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009397,HOLLAND,E09000020,KENSINGTON AND CHELSEA,Hammersmith,NULL,ADDISON ROAD,21700789,W14,NULL,NULL,524650,179250,NULL,NULL\nNA,26/08/2020 22:22,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN ROOF SPACE OF BUNGALOW,Fox,Person (mobile),Bungalow - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,FINSBURY COTTAGES,21104526,N22,NULL,NULL,530450,191050,NULL,NULL\nNA,27/08/2020 08:42,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED BY RISING TIDE,Cat,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000225,PENINSULA,E09000011,GREENWICH,East Greenwich,10010247581,RIVER GARDENS WALK,20800008,SE10,539050,178452,539050,178450,51.48793482,0.001383886\nNA,27/08/2020 10:00,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BLACK PRINCE STREET,20800016,SE18,NULL,NULL,545950,177650,NULL,NULL\nNA,27/08/2020 12:48,2020,2020/21,Special Service,1,1,346,346,SQUIRRELS TRAPPED IN ROOF,Squirrel,Person (land line),Pub/wine bar/bar,Non Residential,Other animal assistance,Assist trapped wild animal,E05000230,WOOLWICH RIVERSIDE,E09000011,GREENWICH,Plumstead,10010228928,NO 1 STREET,20801881,SE18,543801,179038,543850,179050,51.4920077,0.069996\nNA,27/08/2020 12:58,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN ENGINE,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011230,SIDCUP,E09000004,BEXLEY,Sidcup,10011846997,SIDCUP HIGH STREET,20101300,DA14,546399,171758,546350,171750,51.4259268,0.104385019\nNA,29/08/2020 00:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BARBED WIRE AT FRONT OF HOUSE,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000486,GREEN STREET WEST,E09000025,NEWHAM,Stratford,46039642,ISMAILIA ROAD,22207763,E7,540644,184385,540650,184350,51.54084933,0.026685099\nNA,29/08/2020 08:26,2020,2020/21,Special Service,1,2,346,692,CAT LOCKED IN BUILDING,Cat,Person (mobile),Engineering manufacturing plant,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,207036039,HIGH ROAD,20701606,N11,528916,192233,528950,192250,51.61417604,-0.139456588\nNA,29/08/2020 13:02,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE    BIRD TRAPPED IN CHIMNEY. ACCESSIBLE FROM INSIDE THE PROPERTY,Bird,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000528,SOUTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,NULL,LANCASTER PARK,22403605,TW10,NULL,NULL,518150,174550,NULL,NULL\nNA,29/08/2020 19:45,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK ON ELECTRICITY CABLE,Bird,Person (mobile),Cables,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000205,PALMERS GREEN,E09000010,ENFIELD,Edmonton,207058487,HAZEL CLOSE,20704319,N13,532534,193165,532550,193150,51.62171417,-0.086877076\nNA,29/08/2020 23:19,2020,2020/21,Special Service,1,1,346,346,MUNTJAC DEER TRAPPED IN FENCE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,207004128,Y025,20706639,EN3,536993,198322,536950,198350,51.66698944,-0.020477727\nNA,30/08/2020 22:49,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED IN CAGE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal harm involving domestic animal,E05000296,MARLBOROUGH,E09000015,HARROW,Stanmore,NULL,ARCHERY CLOSE,21201646,HA3,NULL,NULL,515950,189950,NULL,NULL\nNA,31/08/2020 13:33,2020,2020/21,Special Service,1,1,346,346,PUPPY TRAPPED BETWEEN WALL AND FENCE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000057,MILL HILL,E09000003,BARNET,Mill Hill,NULL,TRETAWN PARK,20043140,NW7,NULL,NULL,521250,192950,NULL,NULL\nNA,31/08/2020 16:09,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING,Bird,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000085,ALPERTON,E09000005,BRENT,Wembley,NULL,NORTHWICK ROAD,20201813,HA0,NULL,NULL,517850,183550,NULL,NULL\nNA,01/09/2020 09:55,2020,2020/21,Special Service,1,1,346,346,CAT LOCKED IN CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,1.00023E+11,BRIGHTMAN ROAD,22900621,SW18,526558,173315,526550,173350,51.44469887,-0.180281899\nNA,01/09/2020 16:21,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN HOLE,Dog,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10093738174,SPEEDBIRD WAY,21402697,UB7,505173,177524,505150,177550,51.48690382,-0.486622004\nNA,01/09/2020 18:24,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON CHIMNEY    ABOVE ONE STOP  RSPCA DECLINED TO ATTEND   ACCESS VIA BACK OF SHOP,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011222,CRAYFORD,E09000004,BEXLEY,Bexley,NULL,MAYPLACE ROAD EAST,20100956,DA7,NULL,NULL,550250,175750,NULL,NULL\nNA,01/09/2020 19:52,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000639,LITTLE VENICE,E09000033,WESTMINSTER,Paddington,NULL,WARWICK AVENUE,8400268,W9,NULL,NULL,525850,182150,NULL,NULL\nNA,03/09/2020 01:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000376,JUNCTION,E09000019,ISLINGTON,Kentish Town,NULL,TREMLETT GROVE,21600876,N19,NULL,NULL,529150,186450,NULL,NULL\nNA,03/09/2020 09:12,2020,2020/21,Special Service,1,1,346,346,Redacted,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,HOLLYBUSH ROAD,21800515,KT2,NULL,NULL,518550,170850,NULL,NULL\nNA,04/09/2020 08:55,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN TRAPPED UNDERNEATH FLOORBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,Eltham,NULL,DURSLEY ROAD,20800486,SE3,NULL,NULL,541450,176450,NULL,NULL\nNA,04/09/2020 11:28,2020,2020/21,Special Service,1,1,346,346,ASSISIT RSPCA WITH CAT RETRIEVAL - RSPCA REP ON SCENE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Battersea,1.00023E+11,GARRICK CLOSE,22901948,SW18,526392,175071,526350,175050,51.46051897,-0.182039847\nNA,04/09/2020 16:40,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING ON THE BUILDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011484,SOUTH CROYDON,E09000008,CROYDON,Croydon,NULL,BRIGHTON ROAD,20501945,CR2,NULL,NULL,532450,162950,NULL,NULL\nNA,04/09/2020 20:03,2020,2020/21,Special Service,1,1,346,346,BABY PIGEON TRAPPED - CALL FROM RSPCA - UNABLE TO ATTEND    NEAR THE CENTRE CLOSE TO THE OFF LEAD DO,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,ATWOOD AVENUE,22405321,TW9,519251,176177,519250,176150,51.47200436,-0.284401956\nNA,04/09/2020 20:10,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SHED BELIEVED INJURED   RSPCA UNABLE TO ASSIST,Cat,Person (mobile),Private Garden Shed,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,10023600540,LAWES WAY,19904091,IG11,546105,182655,546150,182650,51.52392268,0.104654497\nNA,05/09/2020 18:32,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN WINE RACK,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000571,WANDLE VALLEY,E09000029,SUTTON,Mitcham,NULL,WOLSELEY ROAD,22601161,CR4,NULL,NULL,528250,166650,NULL,NULL\nNA,05/09/2020 19:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED     CALLER BELIEVES CATS LEG IS BROKEN AS ITS TWISTED WITH FLYS AROUND IT    CAT IS VERY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000324,BARNHILL,E09000017,HILLINGDON,Hillingdon,NULL,GLEDWOOD GARDENS,21400810,UB4,NULL,NULL,510050,181650,NULL,NULL\nNA,06/09/2020 09:34,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Heston,NULL,WESLEY AVENUE,21501184,TW3,NULL,NULL,512550,176250,NULL,NULL\nNA,06/09/2020 18:26,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN TREE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,5033357,GONDAR GARDENS,20400245,NW6,524861,185383,524850,185350,51.55352819,-0.200424134\nNA,07/09/2020 21:41,2020,2020/21,Special Service,1,1,346,346,CAT WITH PAW STUCK IN SINK PLUG HOLE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011471,NEW ADDINGTON SOUTH,E09000008,CROYDON,Addington,NULL,SHAXTON CRESCENT,20501826,CR0,NULL,NULL,538350,162650,NULL,NULL\nNA,08/09/2020 09:15,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,09/09/2020 08:05,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK RAILINGS,Fox,Person (land line),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,CARNFORTH GARDENS,21300267,RM12,552486,185490,552450,185450,51.54770713,0.197788967\nNA,09/09/2020 11:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Old Kent Road,NULL,ABERCORN WAY,22502815,SE1,NULL,NULL,534350,178350,NULL,NULL\nNA,09/09/2020 14:52,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN AN AIR VENT ON FIRST FLOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011101,DULWICH WOOD,E09000028,SOUTHWARK,Forest Hill,NULL,CRESCENT WOOD ROAD,22500645,SE26,NULL,NULL,534050,172250,NULL,NULL\nNA,10/09/2020 08:54,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,DUNKERY ROAD,20302283,SE9,NULL,NULL,542250,172150,NULL,NULL\nNA,10/09/2020 09:32,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF OWNERS ON SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000098,QUEENS PARK,E09000005,BRENT,North Kensington,NULL,HARVIST ROAD,20201304,NW6,NULL,NULL,524050,183050,NULL,NULL\nNA,10/09/2020 09:48,2020,2020/21,Special Service,1,1,346,346,ASSIST WILDLIFE PROTECTION WITH A PIGEON TRAPPED IN BUILDING,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009367,BROWNSWOOD,E09000012,HACKNEY,Stoke Newington,NULL,BROWNSWOOD ROAD,20900183,N4,NULL,NULL,532150,186650,NULL,NULL\nNA,10/09/2020 10:35,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMMNEY,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,NULL,DUNKERY ROAD,20302283,SE9,NULL,NULL,542250,172150,NULL,NULL\nNA,10/09/2020 18:50,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING NEAR BRAMLEY CHEMIST SHOP,Bird,Person (land line),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217107613,BRAMLEY ROAD,21700822,W10,523796,181180,523750,181150,51.51599284,-0.21724415\nNA,10/09/2020 20:49,2020,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Multi-Storey car park,Non Residential,Other animal assistance,Assist trapped wild animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10023998240,MONTFICHET ROAD,22208219,E20,538310,184593,538350,184550,0,0\nNA,11/09/2020 07:31,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER DECKING,Dog,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,WEST HILL ROAD,22906081,SW18,NULL,NULL,524850,174250,NULL,NULL\nNA,11/09/2020 18:01,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000491,ROYAL DOCKS,E09000025,NEWHAM,Plaistow,NULL,STARBOARD WAY,22208320,E16,NULL,NULL,541050,179850,NULL,NULL\nNA,12/09/2020 00:06,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011238,CHURCHFIELDS,E09000026,REDBRIDGE,Woodford,1.00022E+11,AVONDALE COURT,22304803,E18,540543,190882,540550,190850,51.59925778,0.027831522\nNA,12/09/2020 09:29,2020,2020/21,Special Service,1,1,346,346,INJURED CAT - FALLEN OFF HIGH FENCE INTO BACK GARDEN    OWNER UNABLE TO REACH CAT,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,HILLSIDE ROAD,21106683,N15,NULL,NULL,533550,188050,NULL,NULL\nNA,12/09/2020 10:16,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY BREAST,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000336,NORTHWOOD HILLS,E09000017,HILLINGDON,Ruislip,NULL,WILTSHIRE LANE,21402176,HA5,NULL,NULL,509950,189550,NULL,NULL\nNA,12/09/2020 13:24,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED BHIND MESH WIRING -,Bird,Person (mobile),Other outdoor structures,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009404,ST. HELEN'S,E09000020,KENSINGTON AND CHELSEA,North Kensington,217107613,BRAMLEY ROAD,21700822,W10,523790,181185,523750,181150,51.51604003,-0.217338857\nNA,13/09/2020 11:28,2020,2020/21,Special Service,1,1,346,346,GOAT STUCK IN RAILINGS,Goat,Person (mobile),Other outdoor structures,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Hayes,1.00021E+11,VICTORIA LANE,21402043,UB3,508534,178043,508550,178050,51.49093009,-0.438068603\nNA,13/09/2020 12:07,2020,2020/21,Special Service,NULL,NULL,346,NULL,DOG WITH LEG STUCK IN BENCH   NEAR NURSERY MRS DELANSEY SCHOOL,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217112078,AVONDALE PARK ROAD,21700803,W11,524091,180671,524050,180650,51.51135251,-0.213182067\nNA,13/09/2020 13:53,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CAR SPEAKER,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,10034919449,BRANDESBURY SQUARE,22306397,IG8,543450,191509,543450,191550,51.60416326,0.07003494\nNA,13/09/2020 15:41,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ROOF TILES,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,NULL,BUDD CLOSE,20005980,N12,NULL,NULL,525750,192750,NULL,NULL\nNA,14/09/2020 16:03,2020,2020/21,Special Service,1,1,346,346,SQUIRREL TRAPPED IN EAVES OF A ROOF     FIRST FLOOR LEVEL,Squirrel,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,WATSON AVENUE,22201199,E6,NULL,NULL,543250,184350,NULL,NULL\nNA,15/09/2020 08:59,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ABANONDED COURT YARD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,NULL,DRURY LANE,20401232,WC2B,NULL,NULL,530250,181350,NULL,NULL\nNA,16/09/2020 09:51,2020,2020/21,Special Service,1,1,346,346,MAGPIE STUCK IN GUTTERING AT SECOND FLOOR    RSPCA ON SCENE,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,NULL,LAMMAS ROAD,22403600,TW10,NULL,NULL,517250,171550,NULL,NULL\nNA,16/09/2020 12:19,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,ABBEY ROAD,20400561,NW8,NULL,NULL,525850,183750,NULL,NULL\nNA,18/09/2020 04:01,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN FROM HEIGHT AND TRAPPED,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,DOWNHAM ROAD,21604726,N1,NULL,NULL,532850,184050,NULL,NULL\nNA,18/09/2020 11:46,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND WASHING MACHINE  HAVING DIFFICULTY BREATHING,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000308,ELM PARK,E09000016,HAVERING,Hornchurch,NULL,EGBERT CLOSE,21320246,RM12,NULL,NULL,551750,185450,NULL,NULL\nNA,18/09/2020 19:16,2020,2020/21,Special Service,1,2,346,692,HORSE STUCK IN PONDERS END DITCH - JUST NORTH OF NAVIGATION PUB ON THE TOW PATH ON THE EAST SIDE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207184743,WHARF ROAD,20702448,EN3,536335,195538,536350,195550,51.64212673,-0.031071584\nNA,19/09/2020 20:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,BURSTON ROAD,22900734,SW15,NULL,NULL,523850,174950,NULL,NULL\nNA,20/09/2020 04:09,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND WARDROBE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,NULL,WINGLETYE LANE,21300661,RM11,NULL,NULL,554850,189350,NULL,NULL\nNA,20/09/2020 17:58,2020,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN CRANE NETTING - CALLER WILL MEET YOU,Bird,Person (land line),Other outdoor equipment/machinery,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000362,ISLEWORTH,E09000018,HOUNSLOW,Heston,10015417296,BRIDGE WHARF ROAD,21501580,TW7,516676,175940,516650,175950,51.4704103,-0.321543335\nNA,22/09/2020 07:41,2020,2020/21,Special Service,1,1,346,346,DOG WITH HEAD TRAPPED IN RAILINGS,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000624,SOUTHFIELDS,E09000032,WANDSWORTH,Wandsworth,10090500610,NEVILLE GILL CLOSE,22903484,SW18,525496,174408,525450,174450,51.45475637,-0.195172828\nNA,22/09/2020 08:09,2020,2020/21,Special Service,1,1,346,346,FOX WITH LEG TRAPPED IN A TRAMPOLINE,Fox,Person (land line),Garden equipment,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,10001267036,BLACKBURN WAY,21501782,TW4,512539,174552,512550,174550,51.4587713,-0.3815225\nNA,22/09/2020 18:45,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,NULL,CHISWICK HIGH ROAD,21502481,W4,NULL,NULL,521650,178650,NULL,NULL\nNA,23/09/2020 11:35,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN LIFT AREA,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000132,FORTUNE GREEN,E09000007,CAMDEN,West Hampstead,NULL,BARLOW ROAD,20401354,NW6,NULL,NULL,524850,184750,NULL,NULL\nNA,23/09/2020 12:51,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND SPIKED FENCING  CALLER WILL LIASE WITH BRIGADE AT ADDRESS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,MARLOWE ROAD,22856650,E17,NULL,NULL,538350,189450,NULL,NULL\nNA,23/09/2020 18:08,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,NULL,BOSTALL HILL,20800197,SE2,NULL,NULL,546350,178350,NULL,NULL\nNA,24/09/2020 17:18,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT TRAPPED,Cat,Person (mobile),Vehicle sales building,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000480,EAST HAM CENTRAL,E09000025,NEWHAM,East Ham,10008994144,BARKING ROAD,22207589,E6,542252,183454,542250,183450,51.53208226,0.049485937\nNA,24/09/2020 20:39,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,OGLETHORPE ROAD,19900263,RM10,NULL,NULL,548950,186450,NULL,NULL\nNA,25/09/2020 19:56,2020,2020/21,Special Service,1,2,346,692,CAT STUCK IN CAR ENGINE - TOYOTA PRUIS,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,48064697,SHERWOOD PARK ROAD,22105823,CR4,529813,168403,529850,168450,51.39981962,-0.135269269\nNA,25/09/2020 22:38,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND BUILT IN FRIDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,AVANTGARDE PLACE,22702704,E1,NULL,NULL,533750,182350,NULL,NULL\nNA,26/09/2020 06:31,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN DRAIN,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178350,NULL,NULL\nNA,26/09/2020 09:36,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,BRAMHAM GARDENS,21700043,SW5,NULL,NULL,525750,178450,NULL,NULL\nNA,27/09/2020 15:38,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BUSHES - UNABLE TO GET OUT      ADVISED TO CONTACT LFB BY RSPCA,Cat,Person (mobile),Hedge,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,10001297679,GRENVILLE ROAD,21603288,N19,530351,187231,530350,187250,51.56890101,-0.120585567\nNA,27/09/2020 16:54,2020,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),TV/film/music/art studio,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10094742756,DRUMMOND ROAD,22500813,SE16,534669,179035,534650,179050,51.4942282,-0.0614625\nNA,27/09/2020 22:04,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,MOUNT VIEW ROAD,21103656,N4,NULL,NULL,531250,188250,NULL,NULL\nNA,28/09/2020 15:32,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING UNDER RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538465,189407,538450,189450,51.58651728,-0.002742098\nNA,29/09/2020 01:08,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN ONTO LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000306,BROOKLANDS,E09000016,HAVERING,Romford,NULL,LONDON ROAD,21301367,RM7,NULL,NULL,550050,188450,NULL,NULL\nNA,29/09/2020 16:47,2020,2020/21,Special Service,2,3,346,1038,CAT STUCK IN CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,NETHER STREET,20030910,N12,NULL,NULL,525850,192150,NULL,NULL\nNA,30/09/2020 19:03,2020,2020/21,Special Service,1,1,346,346,SWAN IN DISTRESS ON THE LONGFORD RIVER - SWAN TRAPPED IN NETTING ABOVE THE RIVER,Bird,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10059100274,SOUTHERN PERIMETER ROAD,21401785,TW6,505666,174741,505650,174750,51.46179941,-0.480361843\nNA,01/10/2020 00:26,2020,2020/21,Special Service,1,1,346,346,CAT  TRAPPED ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,SHAWFIELD STREET,21700486,SW3,NULL,NULL,527450,178050,NULL,NULL\nNA,01/10/2020 18:29,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDERNEITH SEAT IN CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,200072646,LEISURE WAY,20026390,N12,526535,191150,526550,191150,51.60498289,-0.174212308\nNA,01/10/2020 22:42,2020,2020/21,Special Service,1,1,346,346,DOG STUCK BETWEEN BOAT AND RIVER BANK - OWNER UNABLE TO LIFT OUT,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,10092980277,PACKET BOAT LANE,21401472,UB8,505348,181257,505350,181250,51.52042284,-0.482982882\nNA,02/10/2020 12:38,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED DOWN EMBANKMENT BESIDE RAILWAY LINE    THIS IS NEAR TO WARREN FARM CALLER SAID IF BRIGAD,Dog,Person (mobile),Railway,Outdoor,Other animal assistance,Assist trapped domestic animal,NA,NA,NA,NA,Surrey,10025167084,BRAMLEY ROAD,13900063,SM3,523473,162849,523450,162850,51.35131526,-0.228278517\nNA,03/10/2020 02:48,2020,2020/21,Special Service,1,2,346,692,DOG TRAPPED IN REAR GARDEN  - NO COVID,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12046922,MANDEVILLE ROAD,20601113,UB5,513451,184644,513450,184650,51.5492948,-0.365152372\nNA,03/10/2020 15:06,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CLOTHING RECYCLING BIN - OWNER WAS UNAWARE CAT CLIMBED INTO  BAG,Cat,Person (land line),\"Large refuse/rubbish container (eg skip, paladin)\",Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000123,PENGE AND CATOR,E09000006,BROMLEY,Beckenham,1.0002E+11,PAWLEYNE CLOSE,20302159,SE20,535477,170059,535450,170050,51.41337266,-0.053260383\nNA,03/10/2020 21:55,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN WALL IN HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009403,ROYAL HOSPITAL,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,WELLINGTON SQUARE,21700577,SW3,NULL,NULL,527750,178250,NULL,NULL\nNA,04/10/2020 12:35,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN FENCING TO ELECTRICAL SUB STATION BEHIND THE TIP,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000112,CLOCK HOUSE,E09000006,BROMLEY,Beckenham,1.00023E+11,CHURCHFIELDS ROAD,20300883,BR3,535908,169109,535950,169150,51.40473975,-0.047429086\nNA,05/10/2020 17:12,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING ON THE WOOD STREET RAILWAY BRIDGE - BY THE ORANGE BICYCLE STAND,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538464,189399,538450,189350,51.58644283,-0.002746789\nNA,05/10/2020 23:04,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ENGINE OF CAR   OWNER ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Clapham,10090498952,PAGE MEWS,22906892,SW11,528385,176024,528350,176050,51.46863743,-0.153028504\nNA,09/10/2020 07:40,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP ON TELEGRAPH POLE - O/S THIS NUMBER,Cat,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000036,MAYESBROOK,E09000002,BARKING AND DAGENHAM,Barking,100039245,BROMHALL ROAD,19900452,RM8,546808,184798,546850,184750,51.54299441,0.115673664\nNA,10/10/2020 22:50,2020,2020/21,Special Service,1,1,346,346,CAT STUCK IN WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,LILLIE ROAD,21000509,SW6,NULL,NULL,524750,177650,NULL,NULL\nNA,11/10/2020 19:53,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Secondary school,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,10090196375,GIPSY ROAD,21900607,SE27,532328,171637,532350,171650,51.428296,-0.097934\nNA,12/10/2020 16:14,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - KITTEN TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011246,ILFORD TOWN,E09000026,REDBRIDGE,Ilford,NULL,HIGH ROAD,22306010,IG1,NULL,NULL,544850,186850,NULL,NULL\nNA,12/10/2020 16:27,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED UNDER DRAIN,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009335,WEAVERS,E09000030,TOWER HAMLETS,Shoreditch,NULL,GASCOIGNE PLACE,22700530,E2,NULL,NULL,533750,182750,NULL,NULL\nNA,12/10/2020 16:41,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,BOLTON GARDENS,21700039,SW5,NULL,NULL,525850,178350,NULL,NULL\nNA,12/10/2020 19:18,2020,2020/21,Special Service,1,2,346,692,KITTEN TRAPPED IN BACK OF THE LIFT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,VIOLET LANE,20502401,CR0,NULL,NULL,531950,164850,NULL,NULL\nNA,13/10/2020 11:24,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA - LARGE CARP POND THAT REQUIRES WATER,Unknown - Wild Animal,Person (mobile),Lake/pond/reservoir,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000325,BOTWELL,E09000017,HILLINGDON,Hillingdon,2.00002E+11,BURBAGE CLOSE,21402811,UB3,508929,181019,508950,181050,51.51760554,-0.431463719\nNA,14/10/2020 02:25,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK BEHIND CABINET,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,SEVEN KINGS,E09000026,REDBRIDGE,Ilford,NULL,ST ALBANS ROAD,22302431,IG3,NULL,NULL,545550,187250,NULL,NULL\nNA,14/10/2020 07:05,2020,2020/21,Special Service,1,1,346,346,SQUIRREL IN BEDROOM,Squirrel,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,HENDON LANE,20021800,N3,NULL,NULL,524750,190250,NULL,NULL\nNA,14/10/2020 21:18,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,GLOUCESTER PLACE,8401354,W1U,NULL,NULL,527750,181750,NULL,NULL\nNA,15/10/2020 07:27,2020,2020/21,Special Service,1,1,346,346,FOX WITH HEAD STUCK IN FENCE,Fox,Person (land line),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,10008984743,KIRTON ROAD,22200782,E13,541164,183495,541150,183450,51.53272636,0.033830384\nNA,16/10/2020 11:55,2020,2020/21,Special Service,1,1,346,346,DOG FALLEN DOWN STEPS - OWNER ON SCENE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000484,FOREST GATE SOUTH,E09000025,NEWHAM,Stratford,NULL,KNOX ROAD,22207779,E7,NULL,NULL,540250,184750,NULL,NULL\nNA,16/10/2020 16:30,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN FENCE ENTER VIA FOREST ROAD CALLR WILL MEET BRIGADE,Deer,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000597,HALE END AND HIGHAMS PARK,E09000031,WALTHAM FOREST,Woodford,10009142414,FOREST ROAD,22837350,E17,539290,190398,539250,190350,51.59521595,0.009563592\nNA,17/10/2020 10:54,2020,2020/21,Special Service,2,5,346,1730,Redacted,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,2.00004E+11,SHAD THAMES,22502224,SE1,533898,179833,533850,179850,51.50158444,-0.072255604\nNA,18/10/2020 08:17,2020,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN FENCE,Deer,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,10024389225,MAWBERY GROVE,21320235,RM3,555464,192203,555450,192250,51.60720903,0.24367811\nNA,18/10/2020 20:54,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN METAL FENCE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000270,FORTIS GREEN,E09000014,HARINGEY,Finchley,1.00021E+11,RINGWOOD AVENUE,21101587,N2,527897,189890,527850,189850,51.59335375,-0.155018469\nNA,18/10/2020 20:59,2020,2020/21,Special Service,1,1,346,346,DISTRESSED FOX TRAPPED IN A SCHOOL BUILDING,Fox,Person (mobile),College/University,Non Residential,Animal rescue from height,Wild animal rescue from height,E05011115,ST. GILES,E09000028,SOUTHWARK,Peckham,2.00003E+11,WILSON ROAD,22502720,SE5,533025,176614,533050,176650,51.47286707,-0.086038355\nNA,19/10/2020 08:10,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX RESCUE,Fox,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000412,ST. MARK'S,E09000021,KINGSTON UPON THAMES,Surbiton,NULL,SEETHING WELLS LANE,21800843,KT6,NULL,NULL,517450,167350,NULL,NULL\nNA,22/10/2020 09:39,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN SHED AND WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,22/10/2020 16:11,2020,2020/21,Special Service,1,1,346,346,BIRD STUCK IN CHIMNEY   CALLER STATES AT GROUND LEVEL BEHIND GAS FIRE CALLER STATES GAS HAS BEEN DIS,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000048,EAST BARNET,E09000003,BARNET,Barnet,NULL,FORDHAM ROAD,20016220,EN4,NULL,NULL,527050,196550,NULL,NULL\nNA,22/10/2020 18:42,2020,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT UP IN NETTING AT FOURTH FLOOR LEVEL   TL REQ FROM SCENE,Bird,Person (mobile),Cinema,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000432,STREATHAM WELLS,E09000022,LAMBETH,Tooting,2E+11,STREATHAM HIGH ROAD,21901326,SW16,530224,172005,530250,172050,51.43209478,-0.128034387\nNA,23/10/2020 09:51,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED BEHIND KITCHEN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000267,BOUNDS GREEN,E09000014,HARINGEY,Southgate,NULL,QUEENS ROAD,21106070,N11,NULL,NULL,530150,191550,NULL,NULL\nNA,24/10/2020 21:41,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND WALL BETWEEN WOODEN PANELS AND TOILET  CAT IS BELIEVE INJUR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000452,SYDENHAM,E09000023,LEWISHAM,Beckenham,NULL,HOMECROFT ROAD,22001568,SE26,NULL,NULL,535450,171350,NULL,NULL\nNA,25/10/2020 17:24,2020,2020/21,Special Service,1,2,346,692,CAT STUCK ON CHIMNEY - ALP REQUESTED FROM INCIDENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,CHESTER ROAD,20704042,N9,NULL,NULL,534950,194050,NULL,NULL\nNA,26/10/2020 11:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN BUILDING,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,NULL,UPPER RICHMOND ROAD WEST,22406036,SW14,NULL,NULL,520250,175350,NULL,NULL\nNA,26/10/2020 12:38,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000221,GLYNDON,E09000011,GREENWICH,Plumstead,NULL,TUSCAN ROAD,20801522,SE18,NULL,NULL,544850,178050,NULL,NULL\nNA,26/10/2020 16:40,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011231,SLADE GREEN & NORTHEND,E09000004,BEXLEY,Erith,NULL,MANOR ROAD,20100917,DA8,NULL,NULL,551950,177750,NULL,NULL\nNA,27/10/2020 08:33,2020,2020/21,Special Service,1,1,346,346,INJURED PARROT TRAPPED ON WIRE IN TREE,Bird,Person (mobile),\"Roadside furniture (eg lamp posts, road signs, telegraph poles, speed cameras)\",Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000306,BROOKLANDS,E09000016,HAVERING,Dagenham,1.00021E+11,EDDY CLOSE,21301165,RM7,549635,188111,549650,188150,51.57202525,0.157824974\nNA,27/10/2020 16:09,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011110,PECKHAM,E09000028,SOUTHWARK,Peckham,NULL,SHURLAND GARDENS,22502243,SE15,NULL,NULL,533950,177350,NULL,NULL\nNA,27/10/2020 22:44,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN FLOOR BOARDS,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,CRANLEY GARDENS,21100200,N10,NULL,NULL,529150,189050,NULL,NULL\nNA,28/10/2020 00:06,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON PILLARS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,LEXDEN ROAD,20601052,W3,NULL,NULL,519950,180450,NULL,NULL\nNA,28/10/2020 07:28,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000274,MUSWELL HILL,E09000014,HARINGEY,Hornsey,NULL,CRANLEY GARDENS,21100200,N10,NULL,NULL,529150,189050,NULL,NULL\nNA,28/10/2020 14:42,2020,2020/21,Special Service,1,1,346,346,CAT STUCK UP CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,TABOR ROAD,21000807,W6,NULL,NULL,522850,179150,NULL,NULL\nNA,30/10/2020 12:33,2020,2020/21,Special Service,1,2,346,692,ASSIST RSPCA  WITH CAT TRAPPED IN NETTING ON ROOF OF BUILDING,Cat,Person (mobile),Large supermarket,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,1.00023E+11,BATTERSEA PARK ROAD,22900337,SW11,528404,176693,528450,176650,51.47463972,-0.152510233\nNA,31/10/2020 15:51,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN WIRE - WILDLIFE PROTECTION ON SCENE,Bird,Person (mobile),Train station - platform (below ground),Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,1.00023E+11,CABBELL STREET,8400346,NW1,527200,181689,527250,181650,51.5198078,-0.1680402\nNA,01/11/2020 06:31,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD STUCK IN MESH OF CAGE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,CHARLWOOD ROAD,22900866,SW15,NULL,NULL,523750,175450,NULL,NULL\nNA,01/11/2020 12:21,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,BRANDESBURY SQUARE,22306397,IG8,NULL,NULL,543550,191450,NULL,NULL\nNA,02/11/2020 02:54,2020,2020/21,Special Service,1,2,346,692,Redacted,Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL\nNA,02/11/2020 10:23,2020,2020/21,Special Service,1,3,346,1038,ASSIST RSPCA WITH TRAPPED FOX,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL\nNA,02/11/2020 17:21,2020,2020/21,Special Service,1,1,346,346,ASSIST POLICE RECOVER DOG FROM VEHICLE,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011247,LOXFORD,E09000026,REDBRIDGE,Ilford,1.00022E+11,TWYFORD ROAD,22303332,IG1,544250,185462,544250,185450,51.54961829,0.079096559\nNA,04/11/2020 13:28,2020,2020/21,Special Service,1,1,346,346,PERSON ON ROOF ATTEMPTING TO RESCUE CAT   ADVISED SITE IS NOT SAFE AND SHOULD HAVE BEEN SECURED (POS,Cat,Person (mobile),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,10070721887,UPPER RICHMOND ROAD WEST,22406036,SW14,520249,175358,520250,175350,51.4644286,-0.270327644\nNA,04/11/2020 13:57,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000176,ELTHORNE,E09000009,EALING,Ealing,NULL,ENDSLEIGH ROAD,20600628,W13,NULL,NULL,516150,180550,NULL,NULL\nNA,04/11/2020 14:51,2020,2020/21,Special Service,1,1,346,346,CROW STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,TOWER CLOSE,20301323,BR6,NULL,NULL,545850,165550,NULL,NULL\nNA,05/11/2020 19:31,2020,2020/21,Special Service,1,1,346,346,DOG STUCK BETWEEN WALLS,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000211,TURKEY STREET,E09000010,ENFIELD,Enfield,NULL,CHARNWOOD ROAD,20702582,EN1,NULL,NULL,534950,199250,NULL,NULL\nNA,05/11/2020 19:57,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000425,LARKHALL,E09000022,LAMBETH,Brixton,NULL,STOCKWELL ROAD,21901316,SW9,NULL,NULL,530450,176350,NULL,NULL\nNA,06/11/2020 08:51,2020,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN ELECTRICAL SUBSTATION,Bird,Person (land line),Electricity power station,Non Residential,Other animal assistance,Assist trapped wild animal,E05000228,THAMESMEAD MOORINGS,E09000011,GREENWICH,Plumstead,10025311721,LINTON MEAD ROAD ACCESS TO PUMPING STATION,20801829,SE28,546631,180883,546650,180850,51.5078648,0.1115038\nNA,07/11/2020 14:07,2020,2020/21,Special Service,1,2,346,692,CAT STUCK IN FOXES BURROW IN CAR PARK,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,ENID STREET,22500914,SE16,NULL,NULL,534050,179250,NULL,NULL\nNA,07/11/2020 16:33,2020,2020/21,Special Service,1,1,346,346,ASSIST LONDON WILDLIFE PROTECTION WITH GAINING ACCESS,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,10094608145,SEVEN SISTERS ROAD,21603790,N7,530939,186477,530950,186450,51.56198509,-0.112392893\nNA,08/11/2020 16:02,2020,2020/21,Special Service,2,3,346,1038,DOG STUCK IN HOLE UNDER A TREE     SMALL ANIMAL RESCUE       IN RICHMOND PARK    NEXT TO GOLF COURSE,Dog,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Kingston,10070719914,UNNAMED ROAD - RICHMOND PARK - HAM GATE TO PARK,22406888,TW10,518936,171748,518950,171750,51.43225986,-0.290434165\nNA,11/11/2020 01:44,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE,Dog,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000342,WEST DRAYTON,E09000017,HILLINGDON,Hayes,1.00022E+11,WISE LANE,21402194,UB7,505705,178960,505750,178950,51.4997144,-0.47853494\nNA,11/11/2020 08:59,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511350,190150,NULL,NULL\nNA,11/11/2020 09:28,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND FIRE PLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,NULL,ALDWORTH ROAD,22201280,E15,NULL,NULL,539150,184250,NULL,NULL\nNA,11/11/2020 15:10,2020,2020/21,Special Service,1,1,346,346,BIRD POSSIBLY TRAPPED ON CURTAIN POLE,Bird,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Croydon,NULL,KENDALL AVENUE,20502165,CR2,NULL,NULL,532650,162650,NULL,NULL\nNA,12/11/2020 08:33,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ENGINE OF VEHICLE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,10009949065,HILL END ROAD,21400974,UB9,505294,190972,505250,190950,51.60775163,-0.480853437\nNA,13/11/2020 15:17,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN LIGHT WELL    RSPCA ON SCENE (PHILLIP HAYES),Fox,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,CHILWORTH STREET,8400791,W2,NULL,NULL,526450,181150,NULL,NULL\nNA,13/11/2020 15:23,2020,2020/21,Special Service,1,1,346,346,TO ASSIST RSPCA WITH BIRD OF PREY TRAPPED BEHIND CUPBOARD,Bird,Person (mobile),Hospital,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000595,FOREST,E09000031,WALTHAM FOREST,Leytonstone,10091184802,WHIPPS CROSS ROAD,22886800,E11,538923,188409,538950,188450,51.5774342,0.0034702\nNA,13/11/2020 16:28,2020,2020/21,Special Service,2,4,346,1384,LOVE BIRD TRAPPED IN LIFT    RSPCA ON SCENE - PHILLIPS HAYES,Bird,Person (mobile),Purpose built office,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,10033626484,KINGDOM STREET,8402127,W2,526263,181630,526250,181650,51.51948895,-0.181549178\nNA,14/11/2020 23:13,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009405,STANLEY,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,KING'S ROAD,21700299,SW3,NULL,NULL,527250,178050,NULL,NULL\nNA,15/11/2020 00:31,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009374,HACKNEY WICK,E09000012,HACKNEY,Homerton,NULL,CADOGAN TERRACE,20900196,E9,NULL,NULL,536550,184650,NULL,NULL\nNA,15/11/2020 10:30,2020,2020/21,Special Service,1,2,346,692,LARGE ANIMAL RESCUE   HORSE STRADDLED OVER BARB WIRE FENCING - CALLER WILL MEET YOU ON ARRIVAL,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10034788950,HAYES LANE,20300405,BR2,541123,167422,541150,167450,51.38830405,0.026819848\nNA,15/11/2020 13:47,2020,2020/21,Special Service,2,8,346,2768,Redacted,Horse,Person (mobile),Wasteland,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,1.00024E+11,CHURCH MANORWAY,20100314,DA8,550614,179656,550650,179650,51.49579248,0.1683246\nNA,15/11/2020 14:23,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Canal/riverbank vegetation,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000492,STRATFORD AND NEW TOWN,E09000025,NEWHAM,Stratford,10014035637,QUEEN ELIZABETH OLYMPIC PARK,22208180,E20,537503,184040,537550,184050,51.53852854,-0.018706461\nNA,15/11/2020 21:42,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN THE ENGINE GUARD OF A CAR,Cat,Person (land line),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000357,HESTON WEST,E09000018,HOUNSLOW,Southall,1.00022E+11,CONVENT WAY,21501310,UB2,511804,178465,511850,178450,51.49408968,-0.390854388\nNA,17/11/2020 13:22,2020,2020/21,Special Service,1,1,346,346,PIGEONS TRAPPED IN NETTING UNDER BRIDGE    CALLER WILL MEET BRIGADE,Bird,Person (mobile),Bridge,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000443,EVELYN,E09000023,LEWISHAM,Deptford,1.00023E+11,DEPTFORD HIGH STREET,22000328,SE8,537144,177404,537150,177450,51.47898309,-0.026459566\nNA,18/11/2020 20:54,2020,2020/21,Special Service,NULL,NULL,346,NULL,ANIMAL RESCUE - DISTRESSED FOX IN TRAPPED IN STAIRWELL  RSCPA HAVE NO RESOURCES TO ATTEND,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,HANDYSIDE STREET,20400024,N1C,NULL,NULL,530250,183650,NULL,NULL\nNA,21/11/2020 10:30,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX TRAPPED IN NETTING - ACCESS VIA MOUNT PLEASANT,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202025709,VINCENT ROAD,20200002,HA0,518771,184057,518750,184050,51.542928,-0.288664559\nNA,22/11/2020 08:42,2020,2020/21,Special Service,1,2,346,692,COW STUCK IN DITCH    LOCATED JUST NORTH OF THE CAR PARK ON DITCHES LANE,Cow,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist  trapped livestock animal,E05011474,OLD COULSDON,E09000008,CROYDON,Purley,10001002126,DITCHES LANE,20500221,CR5,530214,157050,530250,157050,51.2976946,-0.133656717\nNA,22/11/2020 20:49,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN LIGHTWELL    CALL FROM RSPCA    RSPCA ON SCENE   - CO OP,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,5172338,GRAY'S INN ROAD,20400895,WC1X,530488,182819,530450,182850,51.52922053,-0.120257329\nNA,23/11/2020 16:16,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING BY ITS WING  UP A TREE      CALLER WILL DIRECT FROM THE CAR PARK ON DOCTOR J,Bird,Person (land line),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10025174808,TOOTING BEC ROAD TO BEDFORD HILL,22906782,SW12,529106,172096,529150,172050,51.43316831,-0.144075244\nNA,23/11/2020 21:20,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011227,LONGLANDS,E09000004,BEXLEY,Sidcup,10025183574,RECREATION ROAD,20101647,DA15,545490,172287,545450,172250,51.43091643,0.091533829\nNA,23/11/2020 22:32,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN A GATE,Fox,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Bexley,1.0002E+11,MANOR ROAD,20100915,DA5,549835,173457,549850,173450,51.44029766,0.154484865\nNA,24/11/2020 03:43,2020,2020/21,Special Service,1,1,346,346,FOX IN HOLE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000176,ELTHORNE,E09000009,EALING,Ealing,12179996,KIRN ROAD,20601003,W13,516686,180505,516650,180550,51.51143843,-0.319895773\nNA,24/11/2020 14:41,2020,2020/21,Special Service,1,1,346,346,DOG TRAPPED BETWEEN CONSERVATORY AND FENCE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000415,TUDOR,E09000021,KINGSTON UPON THAMES,Kingston,NULL,CARDINAL AVENUE,21800205,KT2,NULL,NULL,518250,171150,NULL,NULL\nNA,26/11/2020 14:20,2020,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN GATE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000433,THORNTON,E09000022,LAMBETH,Tooting,NULL,POYNDERS GARDENS,21902132,SW4,NULL,NULL,529350,173950,NULL,NULL\nNA,27/11/2020 11:10,2020,2020/21,Special Service,1,1,346,346,KITTEN STUCK ON THE ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000201,HASELBURY,E09000010,ENFIELD,Edmonton,NULL,BULWER ROAD,20703992,N18,NULL,NULL,533350,192850,NULL,NULL\nNA,28/11/2020 00:08,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000188,SOUTH ACTON,E09000009,EALING,Acton,NULL,MEON ROAD,20601167,W3,NULL,NULL,520350,179750,NULL,NULL\nNA,28/11/2020 00:48,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN GATE,Fox,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped wild animal,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,Brixton,2.00003E+11,HERNE HILL,22503024,SE24,532300,174749,532350,174750,51.45627541,-0.09716388\nNA,28/11/2020 10:55,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARAGE  ACCESS REAR OF DUDLEY ROAD,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000408,GROVE,E09000021,KINGSTON UPON THAMES,Surbiton,128041722,ACCESS ROAD FOR MILLFIELD,21880427,KT1,518565,168972,518550,168950,51.40738919,-0.296696889\nNA,28/11/2020 12:37,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING   CALLER WILL LIASE AT LOCATION,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217006818,BRAMLEY ROAD,21700822,W10,523762,180884,523750,180850,51.51334017,-0.217834722\nNA,29/11/2020 02:00,2020,2020/21,Special Service,1,1,346,346,PUPPY WITH PAW STUCK IN BARS OF CAGE,Dog,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Richmond,NULL,CHURCH ROAD,22404264,SW13,NULL,NULL,521950,176450,NULL,NULL\nNA,30/11/2020 12:25,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CEILING - STAFF WILL MEET BRIGADE,Cat,Person (land line),Veterinary surgery,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00024E+11,HORNS ROAD,22302956,IG2,544548,188263,544550,188250,51.574714,0.084539352\nNA,30/11/2020 20:34,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE   CAR IN CAR PARK    OWNER ON SCENE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000282,WEST GREEN,E09000014,HARINGEY,Tottenham,1.00021E+11,GLOUCESTER ROAD,21106469,N17,532936,190209,532950,190250,51.59505715,-0.082197799\nNA,30/11/2020 21:35,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND DOOR,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,DOWSETT ROAD,21104472,N17,NULL,NULL,534350,190250,NULL,NULL\nNA,01/12/2020 03:11,2020,2020/21,Special Service,1,1,346,346,DEER WEDGED IN METAL GATES,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200083763,NAN CLARKS LANE,20030700,NW7,521534,193908,521550,193950,51.63087712,-0.245433479\nNA,01/12/2020 10:44,2020,2020/21,Special Service,1,2,346,692,RUNNING CALL TO ASSIST RSPCA WITH TRAPPED SEAGULL - NO FURTHER BRIGADE ATTENDANCE REQUIRED,Bird,Person (land line),Tree scrub,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000303,STANMORE PARK,E09000015,HARROW,Stanmore,10025300478,FOOTPATH 16 FROM END OF THE COMMON TO OLD LODGE WAY,21202859,HA7,515573,192701,515550,192750,51.6212797,-0.331908332\nNA,02/12/2020 10:19,2020,2020/21,Special Service,2,6,346,2076,LARGE ANIMAL RESCUE   HORSE FALLEN IN FIELD AND UNABLE TO LIFT,Horse,Person (mobile),Other animal boarding/breeding establishment,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05000309,EMERSON PARK,E09000016,HAVERING,Hornchurch,1.00021E+11,WINGLETYE LANE,21300661,RM11,555411,188369,555450,188350,51.572783,0.24121\nNA,02/12/2020 12:09,2020,2020/21,Special Service,1,2,346,692,CAT IN TREE   RSPCA ON SCENE    ACCESS VIA JERNINGHAM ROAD,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,1.00023E+11,MUSGROVE ROAD,22000734,SE14,536091,176639,536050,176650,51.47235956,-0.041917453\nNA,02/12/2020 12:48,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING   SECOND FLOOR,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,ALLIANCE CLOSE,21501776,TW4,NULL,NULL,512550,174750,NULL,NULL\nNA,03/12/2020 08:23,2020,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000119,HAYES AND CONEY HALL,E09000006,BROMLEY,Bromley,10003628309,HAYES LANE,20300405,BR2,540743,167397,540750,167350,51.38817646,0.021352045\nNA,03/12/2020 19:18,2020,2020/21,Special Service,1,1,346,346,DOG WITH CHAIN STUCK AROUND NECK,Dog,Person (land line),Fire station,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100024983,RAINHAM ROAD NORTH,19900293,RM10,549609,186697,549650,186650,51.55932104,0.15685519\nNA,04/12/2020 15:38,2020,2020/21,Special Service,1,1,346,346,DOG HAS IT HEAD STUCK IN A HOLE IN THE GATE,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000333,ICKENHAM,E09000017,HILLINGDON,Hillingdon,1.00021E+11,THE DRIVE,21401950,UB10,506129,185914,506150,185950,51.56213362,-0.470329964\nNA,05/12/2020 16:38,2020,2020/21,Special Service,1,3,346,1038,LARGE ANIMAL RESCUE - HORSE,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy livestock animal,E05011234,ALDBOROUGH,E09000026,REDBRIDGE,Ilford,1.00022E+11,BILLET ROAD,22304527,RM6,546936,189450,546950,189450,51.5847648,0.119472358\nNA,05/12/2020 20:23,2020,2020/21,Special Service,1,2,346,692,DISTRESSED KITTEN STUCK ON THIRD FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,NULL,TARVES WAY,20801453,SE10,NULL,NULL,537950,177450,NULL,NULL\nNA,06/12/2020 15:39,2020,2020/21,Special Service,1,1,346,346,LARGE ANIMAL RESCUE  HORSE WITH LEGS TRAPPED IN FENCE,Horse,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Assist  trapped livestock animal,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,10090629511,FOOTPATH 108 FROM ENTRANCE OF WARREN FARM TO MEET FOOTPATH 107,19904011,RM6,548512,189299,548550,189250,51.58298902,0.14213365\nNA,06/12/2020 23:13,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN CABLE,Fox,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000379,ST. MARY'S,E09000019,ISLINGTON,Islington,NULL,COOPERS YARD,21610093,N1,NULL,NULL,531650,184350,NULL,NULL\nNA,07/12/2020 22:28,2020,2020/21,Special Service,1,3,346,1038,CAT STUCK ON ROOF - AL REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,ROSEBERY AVENUE,21104995,N17,NULL,NULL,534350,190450,NULL,NULL\nNA,08/12/2020 09:26,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH CAT RESCUE - CAT ON ROOF      CALL RECEIVED FROM RSPCA REPRESENTATIVE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,NULL,ROSEBERY AVENUE,21104995,N17,NULL,NULL,534350,190450,NULL,NULL\nNA,09/12/2020 11:07,2020,2020/21,Special Service,1,5,346,1730,CAT STUCK INSIDE WALLS OF PROPERTY   ACCESS VIA REAR OF BEDAR AVENUE  FRU REQUESTED WITH SNAKE EYE C,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,HOE STREET,22845700,E17,NULL,NULL,537350,188850,NULL,NULL\nNA,11/12/2020 11:50,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,2.00004E+11,HOUNSLOW ROAD,21500632,TW13,512013,172054,512050,172050,51.43642455,-0.389879217\nNA,12/12/2020 00:34,2020,2020/21,Special Service,1,1,346,346,Redacted,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000104,WEMBLEY CENTRAL,E09000005,BRENT,Wembley,202128053,BRAEMAR AVENUE,20200556,HA0,517967,184199,517950,184150,51.54437346,-0.300203048\nNA,12/12/2020 05:24,2020,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,5088423,EUSTON ROAD,20400889,N1C,530076,183055,530050,183050,51.5314295,-0.1261073\nNA,12/12/2020 10:59,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Van,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000605,LEYTONSTONE,E09000031,WALTHAM FOREST,Leytonstone,2.00001E+11,KINGSWOOD ROAD,22848850,E11,539403,187670,539450,187650,51.57068051,0.010107683\nNA,12/12/2020 12:59,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011472,NORBURY & POLLARDS HILL,E09000008,CROYDON,Norbury,NULL,ARDFERN AVENUE,20500634,SW16,NULL,NULL,531050,168850,NULL,NULL\nNA,12/12/2020 18:00,2020,2020/21,Special Service,1,1,346,346,CAT FALLEN THROUGH GARAGE ROOF CALLER WILL MEET YOU ON SCENE,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000029,CHADWELL HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,100062846,EAGLE AVENUE,19900766,RM6,548177,188187,548150,188150,51.57308841,0.136836848\nNA,13/12/2020 21:12,2020,2020/21,Special Service,1,1,346,346,FOX FALLEN INTO A WELL OUTSIDE - CALLER WILL MEET THE BRIGADE,Fox,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000261,RAVENSCOURT PARK,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,HAMLET GARDENS,21000406,W6,NULL,NULL,522150,178750,NULL,NULL\nNA,14/12/2020 00:58,2020,2020/21,Special Service,1,1,346,346,DEER STUCK IN GATES OF THE HEALTH CARE CENTRE AT THE BACK,Deer,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000310,GOOSHAYS,E09000016,HAVERING,Harold Hill,1.00021E+11,COLNE DRIVE,21301095,RM3,554938,191752,554950,191750,51.60330618,0.235893378\nNA,15/12/2020 15:40,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON BRIDGE - CALLER WILL MEET,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000292,HEADSTONE NORTH,E09000015,HARROW,Harrow,1.00023E+11,STATION ROAD,21201360,HA2,513569,188579,513550,188550,51.58463855,-0.362192326\nNA,15/12/2020 16:12,2020,2020/21,Special Service,1,1,346,346,INJURED CAT CAUGHT IN WIRE ON FIRST FLOOR SCAFFOLDING,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011486,THORNTON HEATH,E09000008,CROYDON,Norbury,NULL,GILLETT ROAD,20500975,CR7,NULL,NULL,532450,168250,NULL,NULL\nNA,16/12/2020 00:23,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CLOTHES AIRER,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000482,EAST HAM SOUTH,E09000025,NEWHAM,East Ham,NULL,LONSDALE AVENUE,22200818,E6,NULL,NULL,542650,182250,NULL,NULL\nNA,16/12/2020 20:21,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON RIVERBANK,Cat,Person (mobile),Wasteland,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000281,TOTTENHAM HALE,E09000014,HARINGEY,Tottenham,1.00021E+11,REEDHAM CLOSE,21103792,N17,534698,189408,534650,189450,51.58744413,-0.057073775\nNA,16/12/2020 22:00,2020,2020/21,Special Service,1,1,346,346,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000610,BALHAM,E09000032,WANDSWORTH,Tooting,NULL,LAITWOOD ROAD,22902761,SW12,NULL,NULL,528750,173450,NULL,NULL\nNA,17/12/2020 10:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000130,CAMDEN TOWN WITH PRIMROSE HILL,E09000007,CAMDEN,Euston,NULL,WHITTLEBURY MEWS EAST,20499405,NW1,NULL,NULL,528250,184150,NULL,NULL\nNA,17/12/2020 15:08,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011117,SURREY DOCKS,E09000028,SOUTHWARK,Dockhead,2.00003E+11,ROTHERHITHE STREET,22502127,SE16,536049,180435,536050,180450,51.50648085,-0.041057516\nNA,18/12/2020 11:23,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED ON THE ROOF - RSPCA IN ATTENDANCE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,MORTON ROAD,21605476,N1,NULL,NULL,532550,184250,NULL,NULL\nNA,19/12/2020 10:24,2020,2020/21,Special Service,1,2,346,692,INJURED GOOSE - FOOT TRAPPED IN HOLE IN TREE - LINE OPERATIONS LEVEL ONE,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000637,KNIGHTSBRIDGE AND BELGRAVIA,E09000033,WESTMINSTER,Kensington,10033558241,LANCASTER WALK,8401939,W2,526434,180332,526450,180350,51.5077862,-0.179555431\nNA,19/12/2020 11:23,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000483,FOREST GATE NORTH,E09000025,NEWHAM,Stratford,NULL,HORACE ROAD,22207759,E7,NULL,NULL,540550,185750,NULL,NULL\nNA,19/12/2020 15:02,2020,2020/21,Special Service,1,1,346,346,PARROT TRAPPED BEHIND FURNITURE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000475,BECKTON,E09000025,NEWHAM,East Ham,NULL,WINSOR TERRACE,22201228,E6,NULL,NULL,543650,181650,NULL,NULL\nNA,19/12/2020 15:10,2020,2020/21,Special Service,1,2,346,692,TO ASSIST RSPCA WITH HORSE IN WATER -WATER OERATIONS LEVEL ONE IMPLEMENTED   RPCA ARE NOT SCENE,Horse,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207169485,LOWER HALL LANE,20705126,EN3,536481,195476,536450,195450,51.64154228,-0.029000221\nNA,20/12/2020 08:58,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED BY SCHOOL SHED   CALLER WILL MEET & DIRECT,Cat,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011463,ADDISCOMBE WEST,E09000008,CROYDON,Croydon,1.00021E+11,OVAL ROAD,20501270,CR0,533129,166074,533150,166050,51.3781165,-0.088505\nNA,20/12/2020 09:16,2020,2020/21,Special Service,1,2,346,692,DOG WITH HEAD TRAPPED IN FENCE BARS - CRYSTAL PALACE PARK UPPER TERRACE,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000116,CRYSTAL PALACE,E09000006,BROMLEY,Beckenham,10003626411,CRYSTAL PALACE PARADE,20302173,SE19,533888,171003,533850,171050,51.42223249,-0.075747116\nNA,20/12/2020 13:35,2020,2020/21,Special Service,1,2,346,692,LARGE ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (land line),\"Grassland, pasture, grazing etc\",Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05000313,HAVERING PARK,E09000016,HAVERING,Romford,1.00021E+11,ROWLAND WALK,21301566,RM4,551425,193299,551450,193250,51.61816295,0.185872344\nNA,20/12/2020 16:45,2020,2020/21,Special Service,1,1,346,346,CAT STUCK BEHIND OVEN,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000095,MAPESBURY,E09000005,BRENT,Willesden,NULL,CHICHELE ROAD,20202353,NW2,NULL,NULL,523650,185350,NULL,NULL\nNA,21/12/2020 11:55,2020,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH SMALL ANIMAL RESCUE     RSPCA ON SCENE,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009327,MILE END,E09000030,TOWER HAMLETS,Poplar,6067325,STAINSBY ROAD,22701152,E14,537162,181320,537150,181350,51.51416551,-0.024676124\nNA,21/12/2020 14:33,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  BIRD TRAPPED BY WING IN RAFTERS OF PROPERTY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000063,WOODHOUSE,E09000003,BARNET,Finchley,NULL,ETCHINGHAM PARK ROAD,20014780,N3,NULL,NULL,526050,190950,NULL,NULL\nNA,23/12/2020 11:20,2020,2020/21,Special Service,1,1,346,346,CAT INJURED AND TRAPPED ON BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000410,OLD MALDEN,E09000021,KINGSTON UPON THAMES,New Malden,NULL,SOUTH LANE,21800871,KT3,NULL,NULL,521550,166650,NULL,NULL\nNA,23/12/2020 11:43,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,NULL,SALEM PLACE,20501354,CR0,NULL,NULL,532050,165150,NULL,NULL\nNA,23/12/2020 22:43,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED BETWEEN THE WALL OF TWO HOUSES,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000635,HARROW ROAD,E09000033,WESTMINSTER,North Kensington,NULL,HARROW ROAD,8400530,W9,NULL,NULL,524750,182150,NULL,NULL\nNA,24/12/2020 04:11,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GARDEN BELOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000369,CANONBURY,E09000019,ISLINGTON,Islington,NULL,DOWNHAM ROAD,21604726,N1,NULL,NULL,532850,184050,NULL,NULL\nNA,24/12/2020 08:58,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,NULL,SALEM PLACE,20501354,CR0,NULL,NULL,532050,165150,NULL,NULL\nNA,24/12/2020 22:37,2020,2020/21,Special Service,1,1,346,346,CAT STUCK ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000487,LITTLE ILFORD,E09000025,NEWHAM,East Ham,NULL,CHURCH ROAD,22200141,E12,NULL,NULL,542550,185250,NULL,NULL\nNA,25/12/2020 16:47,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,NULL,THE DRIVE,20300984,BR6,NULL,NULL,546050,165750,NULL,NULL\nNA,26/12/2020 17:56,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000370,CLERKENWELL,E09000019,ISLINGTON,Shoreditch,NULL,CLERKENWELL CLOSE,21604536,EC1R,NULL,NULL,531450,182150,NULL,NULL\nNA,26/12/2020 20:45,2020,2020/21,Special Service,1,1,346,346,FOX STUCK IN GUTTER PIPE,Fox,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05009325,LANSBURY,E09000030,TOWER HAMLETS,Poplar,6718297,ACCRA CLOSE,22702913,E14,538586,181248,538550,181250,51.51317326,-0.00420009\nNA,27/12/2020 08:11,2020,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE      KITTEN TRAPPED BEHIND WARDROBE ENTRANCE  AT  REAR OF CHINESE BUFFET,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000364,SYON,E09000018,HOUNSLOW,Heston,NULL,HIGH STREET,21500607,TW8,NULL,NULL,517450,177350,NULL,NULL\nNA,27/12/2020 10:55,2020,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN GUTTER    CALLER WILL MEET BRIGADE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,NULL,TURNER ROAD,22882850,E17,NULL,NULL,538250,189550,NULL,NULL\nNA,27/12/2020 14:46,2020,2020/21,Special Service,1,1,346,346,DOG ON BANK OF RIVER THAMES       BY THE TRAVELODGE   IN WATERMANS PARK  BY THE WATERMANS ART CENTRE,Dog,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000347,BRENTFORD,E09000018,HOUNSLOW,Chiswick,10091693256,HIGH STREET,21500607,TW8,518354,177725,518350,177750,51.48610498,-0.296803874\nNA,27/12/2020 17:17,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER SINK UNABLE TO MOVE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL\nNA,27/12/2020 20:28,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN GARDEN SHED AND WALL,Fox,Person (land line),Outdoor storage,Outdoor Structure,Other animal assistance,Assist  trapped livestock animal,E05000282,WEST GREEN,E09000014,HARINGEY,Hornsey,1.00023E+11,CARLINGFORD ROAD,21103058,N15,531791,189630,531750,189650,51.59012281,-0.098922184\nNA,28/12/2020 07:45,2020,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN A HOLE ABOVE KITCHEN SINK  FRU REQUESTED WITH SNAKE EYE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL\nNA,28/12/2020 10:41,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN PIPE ON THE SIDE OF THE BUILDING,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000364,SYON,E09000018,HOUNSLOW,Heston,10093259175,THE HAM,21500543,TW8,517506,177163,517550,177150,51.48123434,-0.309197366\nNA,28/12/2020 13:10,2020,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CABLING ON WALL AT REAR OF GARDEN,Bird,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217127931,LOCKTON STREET,21701312,W10,523740,180889,523750,180850,51.51339113,-0.218158047\nNA,28/12/2020 13:16,2020,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN BASEMENT LIGHT WELL AT SIDE OF HOUSE,Fox,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000630,ABBEY ROAD,E09000033,WESTMINSTER,West Hampstead,NULL,CLIFTON HILL,8400800,NW8,NULL,NULL,525850,183350,NULL,NULL\nNA,28/12/2020 15:00,2020,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Tooting,NULL,WOODFIELD AVENUE,21901521,SW16,NULL,NULL,529850,172450,NULL,NULL\nNA,28/12/2020 15:37,2020,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN VOID IN WALL       FOLLOWING EARLIER INCIDENT CAT LOCATION NOW IDENTIFIED,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000493,WALL END,E09000025,NEWHAM,East Ham,NULL,BARKING ROAD,22207589,E6,NULL,NULL,543350,183750,NULL,NULL\nNA,29/12/2020 13:25,2020,2020/21,Special Service,1,2,346,692,HORSE STUCK IN DRAINAGE OVERFLOW CHANNEL    NEXT TO THE RIVER LEE   CALLER WILL MEET AND DIRECT,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,207007287,WHARF ROAD,20702448,EN3,536381,195485,536350,195450,51.64164631,-0.03043902\nNA,30/12/2020 18:53,2020,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,SHELLNESS ROAD,20900912,E5,NULL,NULL,534850,185350,NULL,NULL\nNA,01/01/2021 12:09,2021,2020/21,Special Service,1,2,346,692,KITTEN STUCK UP TREE  AL REQUESTED FROM SCENE,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000357,HESTON WEST,E09000018,HOUNSLOW,Southall,1.00022E+11,HOLLY FARM ROAD,21500619,UB2,512009,178286,512050,178250,51.49244181,-0.387967616\nNA,01/01/2021 14:06,2021,2020/21,Special Service,1,1,346,346,Redacted,Sheep,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000203,JUBILEE,E09000010,ENFIELD,Chingford,207185205,LOWER HALL LANE,20705126,E4,537266,195047,537250,195050,51.63749131,-0.017824704\nNA,03/01/2021 18:40,2021,2020/21,Special Service,1,1,346,346,CAT WITH LEG TRAPPED IN BATH PLUGHOLE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000614,FAIRFIELD,E09000032,WANDSWORTH,Battersea,NULL,VARDENS ROAD,22905780,SW11,NULL,NULL,526950,175050,NULL,NULL\nNA,04/01/2021 13:39,2021,2020/21,Special Service,1,1,346,346,Redacted,Fox,Person (mobile),Purpose built office,Non Residential,Other animal assistance,Assist trapped wild animal,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5087941,STONE BUILDINGS,20401158,WC2A,530933,181553,530950,181550,51.5177335,-0.1143172\nNA,06/01/2021 10:22,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000138,HOLBORN AND COVENT GARDEN,E09000007,CAMDEN,Soho,5105007,BETTERTON STREET,20401231,WC2H,530269,181206,530250,181250,51.51477674,-0.124005278\nNA,06/01/2021 13:09,2021,2020/21,Special Service,2,4,346,1384,CAT IN DISTRESS ON ROOF - ADDITIONAL APPLIANCE REQUESTED FROM SCENE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000199,ENFIELD LOCK,E09000010,ENFIELD,Enfield,NULL,MANTON ROAD,20706864,EN3,NULL,NULL,537550,198250,NULL,NULL\nNA,06/01/2021 20:35,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN FOX HOLE  - MEET AT CLUB HOUSE,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000111,CHISLEHURST,E09000006,BROMLEY,Bromley,1.00023E+11,CAMDEN PARK ROAD,20302990,BR7,543703,170048,543750,170050,51.41125552,0.064940725\nNA,07/01/2021 23:50,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK BETWEEN WALL AND ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Islington,NULL,SHAFTESBURY STREET,20900904,N1,NULL,NULL,532450,183150,NULL,NULL\nNA,09/01/2021 08:01,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN TRENCH,Dog,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000401,BERRYLANDS,E09000021,KINGSTON UPON THAMES,Surbiton,128001560,BERRYLANDS,21800134,KT5,519011,167417,519050,167450,51.39332408,-0.290804918\nNA,10/01/2021 19:27,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009328,POPLAR,E09000030,TOWER HAMLETS,Poplar,NULL,SALTWELL STREET,22701057,E14,NULL,NULL,537350,180850,NULL,NULL\nNA,12/01/2021 11:39,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011471,NEW ADDINGTON SOUTH,E09000008,CROYDON,Addington,1.00021E+11,ROWDOWN CRESCENT,20501817,CR0,538917,162736,538950,162750,51.34674034,-0.006691875\nNA,12/01/2021 22:38,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN DITCH,Cat,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000145,WEST HAMPSTEAD,E09000007,CAMDEN,West Hampstead,5057574,SANDWELL CRESCENT,20400408,NW6,525421,184913,525450,184950,51.54918533,-0.192513522\nNA,16/01/2021 18:05,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN PORTER CABIN,Dog,Person (mobile),Other private non-residential building,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000034,HEATH,E09000002,BARKING AND DAGENHAM,Dagenham,10091589745,RAINHAM ROAD NORTH,19900293,RM10,549922,186394,549950,186350,51.55651723,0.161224295\nNA,17/01/2021 16:09,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN WAREHOUSE AREA - CALLER BELIEVES DOG IS INJURED - ACCESS VIA LINLEY CRESCENT,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000316,MAWNEYS,E09000016,HAVERING,Romford,1.00021E+11,LINLEY CRESCENT,21301362,RM7,549934,189861,549950,189850,51.58766466,0.162875698\nNA,17/01/2021 17:09,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN NETTING    CALLER WILL MEET YOU,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,1.00023E+11,THE LIBERTY,21301354,RM1,551370,188803,551350,188850,51.57777879,0.183142388\nNA,18/01/2021 15:17,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN TREE BEING ATTACKED BY CROWS,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,1.00023E+11,WESTLEIGH AVENUE,22906123,SW15,523222,174549,523250,174550,51.45652351,-0.227832429\nNA,18/01/2021 17:06,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA - SMALL ANIMAL RESUE - BIRD ENTANGLED IN NETTING,Bird,Person (mobile),Furniture warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000090,FRYENT,E09000005,BRENT,Stanmore,202099001,EDGWARE ROAD,20201631,NW9,520774,189483,520750,189450,51.59127178,-0.257934412\nNA,19/01/2021 18:28,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000229,WOOLWICH COMMON,E09000011,GREENWICH,Plumstead,NULL,LLANOVER ROAD,20800932,SE18,NULL,NULL,543250,177550,NULL,NULL\nNA,19/01/2021 20:24,2021,2020/21,Special Service,1,1,346,346,Redacted,Horse,Person (land line),Scrub land,Outdoor,Animal rescue from water,Animal rescue from water - Farm animal,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,10033419935,FERRY LANE,21300773,RM13,551542,181415,551550,181450,51.51135379,0.182436899\nNA,19/01/2021 20:36,2021,2020/21,Special Service,1,1,346,346,RUNNING CALL AT ON ROOF,Unknown - Domestic Animal Or Pet,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000409,NORBITON,E09000021,KINGSTON UPON THAMES,Kingston,NULL,BONNER HILL ROAD,21800156,KT1,NULL,NULL,518850,168950,NULL,NULL\nNA,20/01/2021 09:35,2021,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN TREE BRANCHES,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000446,LADYWELL,E09000023,LEWISHAM,Lewisham,NULL,EMBLETON ROAD,22000377,SE13,NULL,NULL,537650,175150,NULL,NULL\nNA,21/01/2021 13:15,2021,2020/21,Special Service,1,1,346,346,SWAN TRAPPED IN NETTING,Bird,Person (land line),Airfield/runway,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000331,HEATHROW VILLAGES,E09000017,HILLINGDON,Heathrow,10059101553,WOODCOCK ROAD,21402989,TW19,504909,175484,504950,175450,51.46861273,-0.491024744\nNA,21/01/2021 18:23,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000422,GIPSY HILL,E09000022,LAMBETH,West Norwood,NULL,WHITELEY ROAD,21901493,SE19,NULL,NULL,532950,171150,NULL,NULL\nNA,22/01/2021 14:22,2021,2020/21,Special Service,NULL,NULL,346,NULL,CAT TRAPPED BETWEEN WALL AND FENCE,Cat,Person (mobile),Private Garden Shed,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000113,COPERS COPE,E09000006,BROMLEY,Beckenham,10070012641,HIGH STREET,20301747,BR3,537381,169522,537350,169550,51.4080907,-0.026111084\nNA,23/01/2021 10:18,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05009398,NORLAND,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,CLARENDON ROAD,21700837,W11,NULL,NULL,524350,180650,NULL,NULL\nNA,23/01/2021 15:43,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Eltham,NULL,TYRRELL AVENUE,20101490,DA16,NULL,NULL,546750,174650,NULL,NULL\nNA,23/01/2021 17:16,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000263,SHEPHERD'S BUSH GREEN,E09000013,HAMMERSMITH AND FULHAM,North Kensington,NULL,QUEENSDALE CRESCENT,21000663,W11,NULL,NULL,523850,180350,NULL,NULL\nNA,25/01/2021 12:02,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA WITH FOX STUCK DOWN CULVERT,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05009319,BOW EAST,E09000030,TOWER HAMLETS,Homerton,6647935,SMEED ROAD,22701107,E3,537116,184150,537150,184150,51.53961279,-0.024238454\nNA,26/01/2021 13:42,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN RAILINGS - CALLER WILL MEET YOU,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000219,ELTHAM SOUTH,E09000011,GREENWICH,Eltham,1.00021E+11,AVERY HILL ROAD,20800098,SE9,544707,174192,544750,174150,51.44823467,0.08106256\nNA,26/01/2021 18:21,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010244821,LONGLEIGH LANE,20800936,SE2,547074,177963,547050,177950,51.48150712,0.116663295\nNA,26/01/2021 22:44,2021,2020/21,Special Service,1,1,346,346,BIRDS TRAPPED IN BASKETBALL COURT CALLER IS ON A BIKE AND WILL MEET YOU AND DIRECT YOU FROM THE LOCA,Bird,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000371,FINSBURY PARK,E09000019,ISLINGTON,Holloway,10012787266,CORKER WALK,21606796,N7,530939,186473,530950,186450,51.56195457,-0.112396507\nNA,26/01/2021 23:35,2021,2020/21,Special Service,1,1,346,346,FOX TRAPPED IN FENCE IN ALLEYWAY NEXT TO,Fox,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011226,FALCONWOOD & WELLING,E09000004,BEXLEY,Eltham,NULL,WESTWOOD LANE,20101579,DA16,NULL,NULL,546150,175050,NULL,NULL\nNA,27/01/2021 09:18,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN TREE - ATTENDED YESTERDAY AND ADVISED TO RECONTACT THIS MORNING IF CAT REMAINED IN SITU,Cat,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000214,ABBEY WOOD,E09000011,GREENWICH,Plumstead,10010226159,LONGLEIGH LANE,20800936,SE2,546919,177978,546950,177950,51.48168486,0.11444617\nNA,27/01/2021 10:12,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BY LEG IN A TREE - RSPCA IN ATTENDANCE    **AERIAL APPLIANCE REQUESTED FROM SCENE - TO,Bird,Person (mobile),Tree scrub,Outdoor,Other animal assistance,Assist trapped wild animal,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,5300095282,VINCENT TERRACE,21607012,N1,531758,183260,531750,183250,51.53288794,-0.101785925\nNA,27/01/2021 15:22,2021,2020/21,Special Service,1,1,346,346,CAT UP TREE   ASSIST RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000452,SYDENHAM,E09000023,LEWISHAM,Forest Hill,1.00022E+11,LOCKWOOD CLOSE,22001659,SE26,535972,171727,535950,171750,51.42824528,-0.045504343\nNA,29/01/2021 10:47,2021,2020/21,Special Service,1,1,346,346,TRAPPED FOX IN FENCE  IN REAR GARDEN,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000054,HALE,E09000003,BARNET,Mill Hill,NULL,GIBBS GREEN,20017580,HA8,NULL,NULL,520450,192550,NULL,NULL\nNA,30/01/2021 14:53,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UNDER SHED,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,ROBERT LOWE CLOSE,22000867,SE14,NULL,NULL,535950,177350,NULL,NULL\nNA,30/01/2021 15:28,2021,2020/21,Special Service,1,1,346,346,BIRD CAUGHT IN NETTING - RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,CHAMPION HILL,22500477,SE5,NULL,NULL,533150,175650,NULL,NULL\nNA,30/01/2021 17:54,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER CAR,Dog,Person (land line),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000627,WANDSWORTH COMMON,E09000032,WANDSWORTH,Tooting,10070248175,SANDGATE LANE,22904517,SW18,527247,173114,527250,173150,51.44274157,-0.170446211\nNA,31/01/2021 12:53,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE - RSPCA ON SCENE,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000195,CHASE,E09000010,ENFIELD,Enfield,207156651,BRODIE ROAD,20702534,EN2,532453,198177,532450,198150,51.66676966,-0.086152895\nNA,31/01/2021 13:48,2021,2020/21,Special Service,1,1,346,346,INJURED CAT STUCK IN GREEN AREA AT REAR OF,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,WEST HILL,22906045,SW15,NULL,NULL,524850,174550,NULL,NULL\nNA,01/02/2021 09:49,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011486,THORNTON HEATH,E09000008,CROYDON,Norbury,NULL,PARCHMORE ROAD,20501277,CR7,NULL,NULL,532250,168650,NULL,NULL\nNA,01/02/2021 10:03,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE   CAT TRAPPED IN CAVITY WALL IN BATHROOM,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009402,REDCLIFFE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,FINBOROUGH ROAD,21700211,SW10,NULL,NULL,526050,177650,NULL,NULL\nNA,02/02/2021 14:40,2021,2020/21,Special Service,1,1,346,346,LARGE ANIMAL RESCUE - DEER TRAPPED IN RAILINGS,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000286,CANONS,E09000015,HARROW,Stanmore,2E+11,CANONS DRIVE,21200870,HA8,518220,191912,518250,191950,51.61364342,-0.293968915\nNA,02/02/2021 17:20,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN WIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,BOUNCES ROAD,20703940,N9,NULL,NULL,535250,193950,NULL,NULL\nNA,02/02/2021 20:13,2021,2020/21,Special Service,1,1,346,346,KITTEN POSSIBLY TRAPPED IN BRANCHES IN TREE - ALSO POSSIBLY INJURED,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000058,OAKLEIGH,E09000003,BARNET,Southgate,200032073,DENHAM ROAD,20012180,N20,527633,193496,527650,193450,51.62581749,-0.157503973\nNA,03/02/2021 20:24,2021,2020/21,Special Service,2,5,346,1730,Redacted,Dog,Person (mobile),Scrub land,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05011483,SHIRLY SOUTH,E09000008,CROYDON,Addington,1.00023E+11,SHIRLEY HILLS ROAD,20501831,CR0,535137,164275,535150,164250,51.36147843,-0.060352874\nNA,04/02/2021 09:37,2021,2020/21,Special Service,1,1,346,346,PIDGEON STUCK IN NETTING UNDER BRIDGE - CALLER IS WAITING THERE TO SHOW YOU,Bird,Person (mobile),Underground car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011487,WADDON,E09000008,CROYDON,Croydon,1.00021E+11,LOWER CHURCH STREET,20501161,CR0,531857,165650,531850,165650,51.37460893,-0.106922341\nNA,04/02/2021 09:50,2021,2020/21,Special Service,1,1,346,346,DOG IN RIVER THAMES     ON THE TOW PATH NEAR RIVERSIDE AVENUE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10070719479,ROYAL BOTANIC GARDENS,22407049,TW9,518478,177474,518450,177450,51.48381961,-0.295097335\nNA,05/02/2021 20:22,2021,2020/21,Special Service,1,1,346,346,DOG  TRAPPED INSIDE SCHOOL PLAYGROUND  - CALLER WILL MEET YOU AT GATES,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,2.00001E+11,DOWNSELL ROAD,22831900,E15,538810,185846,538850,185850,51.5544313,0.0008295\nNA,06/02/2021 14:16,2021,2020/21,Special Service,1,1,346,346,KITEEN TRAPPED UNDER KITCHEN SINK,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,WEST END LANE,20499056,NW6,NULL,NULL,525450,184350,NULL,NULL\nNA,10/02/2021 11:55,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND PIPES UNDER SINK,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000440,CATFORD SOUTH,E09000023,LEWISHAM,Lewisham,NULL,MUNCIES MEWS,22001732,SE6,NULL,NULL,538450,172950,NULL,NULL\nNA,11/02/2021 15:52,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN OPEN WINDOW,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000320,ST. ANDREW'S,E09000016,HAVERING,Hornchurch,NULL,DIBAN AVENUE,21300320,RM12,NULL,NULL,552650,185750,NULL,NULL\nNA,13/02/2021 15:46,2021,2020/21,Special Service,1,1,346,346,INJURED CAT IN TREE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000270,FORTIS GREEN,E09000014,HARINGEY,Hornsey,1.00021E+11,LYNMOUTH ROAD,21100553,N2,527882,189325,527850,189350,51.58828022,-0.155433784\nNA,14/02/2021 12:43,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE  CAT TRAPPED IN BATHROOM,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000372,HIGHBURY EAST,E09000019,ISLINGTON,Islington,NULL,ABERDEEN ROAD,21602796,N5,NULL,NULL,532150,185650,NULL,NULL\nNA,15/02/2021 09:53,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN TREE HANGING UPSIDE DOWN  CALLER WILL MEET YOU AT THE ENTRANCE OF COLERIDGE HOUSE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Wild animal rescue from height,E05000633,CHURCHILL,E09000033,WESTMINSTER,Lambeth,2.00003E+11,CHURCHILL GARDENS ESTATE,8401852,SW1V,529249,178037,529250,178050,51.48652947,-0.13985501\nNA,15/02/2021 13:34,2021,2020/21,Special Service,1,1,346,346,CAT STUCK ON TOP OF CHIMNEY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000346,BEDFONT,E09000018,HOUNSLOW,Feltham,NULL,OAK WAY,21500828,TW14,NULL,NULL,509450,173150,NULL,NULL\nNA,15/02/2021 13:42,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN NETTING - RSPCA ON SCENE,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000300,RAYNERS LANE,E09000015,HARROW,Harrow,1.00021E+11,RAYNERS LANE,21201971,HA5,512934,187757,512950,187750,51.57738268,-0.371615878\nNA,15/02/2021 19:54,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN CHIMNEY,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011477,PURLEY OAKS & RIDDLESDOWN,E09000008,CROYDON,Purley,NULL,GRASMERE ROAD,20502102,CR8,NULL,NULL,531850,161750,NULL,NULL\nNA,16/02/2021 00:05,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN ABANDONED BUILDING   CALLER STATED ITS THE ABANDONED BUILDING AT THE END OF MENDORA R,Cat,Person (land line),Other building/use not known,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000254,FULHAM BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Fulham,34115023,RYLSTON ROAD,21000711,SW6,524627,177494,524650,177450,51.48268643,-0.206586249\nNA,17/02/2021 08:47,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAT FLAP - CAT FLAP JAMMED,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000441,CROFTON PARK,E09000023,LEWISHAM,Forest Hill,NULL,RAVENSBOURNE ROAD,22001833,SE6,NULL,NULL,536650,173550,NULL,NULL\nNA,17/02/2021 20:20,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CHIMNEY BEHIND ELECTRICAL FIRE PLACE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000453,TELEGRAPH HILL,E09000023,LEWISHAM,New Cross,NULL,OMMANEY ROAD,22000770,SE14,NULL,NULL,536050,176550,NULL,NULL\nNA,18/02/2021 16:14,2021,2020/21,Special Service,1,1,346,346,CAT FALLEN IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000469,RAYNES PARK,E09000024,MERTON,Wimbledon,NULL,DURHAM ROAD,22102223,SW20,NULL,NULL,522950,169350,NULL,NULL\nNA,18/02/2021 19:16,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN CAR ENGINE,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000418,CLAPHAM COMMON,E09000022,LAMBETH,Clapham,1.00022E+11,NARBONNE AVENUE,21901004,SW4,529147,174560,529150,174550,51.45530442,-0.142590827\nNA,19/02/2021 09:27,2021,2020/21,Special Service,1,1,346,346,KITTEN IN RIVER   FRU REQUESTED FROM SCENE,Cat,Person (mobile),River/canal,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000197,EDMONTON GREEN,E09000010,ENFIELD,Edmonton,207012839,VICTORIA ROAD,20704873,N18,533911,192518,533950,192550,51.61557829,-0.067235941\nNA,19/02/2021 12:29,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000059,TOTTERIDGE,E09000003,BARNET,Barnet,200215159,TOTTERIDGE LANE,20043080,N20,525954,193943,525950,193950,51.63021366,-0.181589552\nNA,20/02/2021 10:53,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL RESCUE - CAT TRAPPED BEHIND WASHINE MACHINE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000428,ST. LEONARD'S,E09000022,LAMBETH,Norbury,NULL,ESTREHAM ROAD,21900528,SW16,NULL,NULL,529750,170850,NULL,NULL\nNA,20/02/2021 15:32,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED BEHIND NETTING ON RAILWAY BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000609,WOOD STREET,E09000031,WALTHAM FOREST,Walthamstow,2.00001E+11,WOOD STREET,22888350,E17,538472,189398,538450,189350,51.58643842,-0.002639492\nNA,21/02/2021 08:44,2021,2020/21,Special Service,1,1,346,346,CAT OVER SIDE OF BALCONY - SITTING ON LEDGE OF BALCONY UNABLE TO GET BACK OVER BALCONY - SECOND FLOO,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009327,MILE END,E09000030,TOWER HAMLETS,Bethnal Green,NULL,COPPERFIELD ROAD,22700356,E3,NULL,NULL,536350,181950,NULL,NULL\nNA,21/02/2021 10:34,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000381,TOLLINGTON,E09000019,ISLINGTON,Holloway,5300091411,TOLLINGTON PARK,21603933,N4,530751,186843,530750,186850,51.56531702,-0.11496689\nNA,21/02/2021 13:49,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN BARS OF CRATE INSIDE PROPERTY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009378,HOXTON WEST,E09000012,HACKNEY,Shoreditch,NULL,WENLOCK STREET,20901062,N1,NULL,NULL,532750,183150,NULL,NULL\nNA,21/02/2021 15:37,2021,2020/21,Special Service,1,1,346,346,KITTEN FALLEN DOWN INTO BASEMENT BELIEVED TO BE INJURED,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009401,QUEEN'S GATE,E09000020,KENSINGTON AND CHELSEA,Kensington,NULL,KENSINGTON GATE,21700292,W8,NULL,NULL,526150,179450,NULL,NULL\nNA,21/02/2021 22:27,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED ON SECOND FLOOR WINDOW,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,HARPENDEN ROAD,21900674,SE27,NULL,NULL,531650,172650,NULL,NULL\nNA,22/02/2021 00:12,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Domestic pet,E05000184,NORTHOLT MANDEVILLE,E09000009,EALING,Northolt,12201111,ROWDELL ROAD,20601480,UB5,513155,183701,513150,183750,51.54088323,-0.369725437\nNA,22/02/2021 14:54,2021,2020/21,Special Service,1,1,346,346,DOG STUCK ON FORESHORE,Dog,Person (mobile),River/canal,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,10070719479,ROYAL BOTANIC GARDENS,22407049,TW9,518324,177413,518350,177450,51.48330999,-0.297328658\nNA,23/02/2021 08:38,2021,2020/21,Special Service,2,3,346,1038,Redacted,Fox,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000222,GREENWICH WEST,E09000011,GREENWICH,Deptford,10010260540,ALDER PATH,20802032,SE8,537780,177614,537750,177650,51.48071333,-0.017227128\nNA,23/02/2021 16:44,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA  CAT ON ROOF OF GARAGES,Cat,Person (mobile),Private garage,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000335,NORTHWOOD,E09000017,HILLINGDON,Ruislip,10091098473,WESTMINSTER CLOSE,21403394,HA6,508322,191419,508350,191450,51.61119639,-0.437004352\nNA,23/02/2021 18:08,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND NETTING - LONDON WILDLIFE PROTECTION ON SCENE,Bird,Person (mobile),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009396,GOLBORNE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217069956,PORTOBELLO ROAD,21700970,W10,524335,181754,524350,181750,51.52103431,-0.209286849\nNA,23/02/2021 18:53,2021,2020/21,Special Service,1,2,346,692,CAT TRAPPED MID BROOK AT REAR,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from water,Animal rescue from water - Domestic pet,E05000097,PRESTON,E09000005,BRENT,Wembley,202100729,ELMSTEAD AVENUE,20200435,HA9,518684,186833,518650,186850,51.56789556,-0.288978385\nNA,23/02/2021 20:52,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,48033007,HARTFIELD CRESCENT,22103117,SW19,524749,170271,524750,170250,51.41774528,-0.2073704\nNA,24/02/2021 15:33,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK IN WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,BLACK PRINCE STREET,20800016,SE18,NULL,NULL,545950,177650,NULL,NULL\nNA,24/02/2021 17:24,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER HOUSE,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,THE AVENUE,21106439,N10,NULL,NULL,529350,190250,NULL,NULL\nNA,26/02/2021 09:01,2021,2020/21,Special Service,1,1,346,346,PIGEON STUCK IN HOARDING OF FLAT,Bird,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Assist trapped wild animal,E05000177,GREENFORD BROADWAY,E09000009,EALING,Southall,12052279,RUISLIP ROAD EAST,20602095,UB6,514434,182258,514450,182250,51.52765491,-0.351754991\nNA,26/02/2021 13:49,2021,2020/21,Special Service,1,2,346,692,FOX TRAPPED IN STORE ROOM,Fox,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving wild animal - Other action,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,FRAMPTON PARK ROAD,20900423,E9,535158,184473,535150,184450,51.542979,-0.052341\nNA,28/02/2021 08:14,2021,2020/21,Special Service,1,2,346,692,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000363,OSTERLEY AND SPRING GROVE,E09000018,HOUNSLOW,Heston,1.00022E+11,THE GROVE,21500523,TW7,515380,176478,515350,176450,51.47551317,-0.340022536\nNA,28/02/2021 11:47,2021,2020/21,Special Service,1,1,346,346,PARROT TRAPPED IN TREE - RSPCA ON SCENE     IN FRONT OF MAIN ENTRANCE LOOK TO RIGHT,Bird,Person (land line),Park,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000047,COPPETTS,E09000003,BARNET,Southgate,200209428,ROYAL DRIVE,20037410,N11,528280,192157,528250,192150,51.61363875,-0.148657145\nNA,28/02/2021 18:05,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED UNDER DECKING,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009334,STEPNEY GREEN,E09000030,TOWER HAMLETS,Bethnal Green,NULL,STEPNEY GREEN,22701160,E1,NULL,NULL,535750,181750,NULL,NULL\nNA,01/03/2021 10:34,2021,2020/21,Special Service,1,1,346,346,ASSIST RSPCA REP WITH BIRD TRAPPED IN NETTING,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000632,BRYANSTON AND DORSET SQUARE,E09000033,WESTMINSTER,Paddington,NULL,CHAGFORD STREET,8400930,NW1,NULL,NULL,527750,182150,NULL,NULL\nNA,02/03/2021 13:43,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BETWEEN CHIMNEYS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05009331,ST. PETER'S,E09000030,TOWER HAMLETS,Bethnal Green,NULL,HAGUE STREET,22700589,E2,NULL,NULL,534450,182550,NULL,NULL\nNA,02/03/2021 15:09,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN A FENCE     NATIONAL GRID BUILDING SITE - CALLER WILL MEET CREW,Bird,Person (mobile),River/canal,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000380,ST. PETER'S,E09000019,ISLINGTON,Islington,10090552582,CITY ROAD,21604520,EC1V,532089,182978,532050,182950,51.53027371,-0.097126095\nNA,03/03/2021 10:43,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP TREE - RSPCA ON SCENE,Cat,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009388,ABINGDON,E09000020,KENSINGTON AND CHELSEA,Kensington,217118035,SOUTH EDWARDES SQUARE,21700673,W8,524979,179077,524950,179050,51.49683553,-0.200949226\nNA,03/03/2021 11:38,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,HARCOURT ROAD,20501023,CR7,531090,167176,531050,167150,51.3884955,-0.117369498\nNA,03/03/2021 18:23,2021,2020/21,Special Service,1,1,346,346,SMALL ANIMAL TRAPPED IN CHIMNEY,Unknown - Wild Animal,Person (land line),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011467,CRYSTAL PALACE & UPPER NORWOOD,E09000008,CROYDON,Beckenham,NULL,MABERLEY ROAD,20501167,SE19,NULL,NULL,534050,169950,NULL,NULL\nNA,05/03/2021 08:46,2021,2020/21,Special Service,1,1,346,346,CAT STUCK BETWEEN EXTENSION AND EXTERIOR WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000558,CARSHALTON CENTRAL,E09000029,SUTTON,Wallington,NULL,SUTTON GROVE,22602650,SM1,NULL,NULL,526750,164350,NULL,NULL\nNA,05/03/2021 12:31,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,NULL,EMPIRE WAY,20201371,HA9,NULL,NULL,518950,185750,NULL,NULL\nNA,05/03/2021 16:18,2021,2020/21,Special Service,1,2,346,692,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05000611,BEDFORD,E09000032,WANDSWORTH,Tooting,10070248797,DR JOHNSON AVENUE TO TOOTING BEC ROAD,22906781,SW17,529043,172087,529050,172050,51.43310024,-0.144992207\nNA,06/03/2021 13:43,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN CRATE,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011217,BARNEHURST,E09000004,BEXLEY,Bexley,NULL,HURSTWOOD AVENUE,20101844,DA7,NULL,NULL,551350,176650,NULL,NULL\nNA,06/03/2021 20:35,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,SALISBURY STREET,8401547,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,07/03/2021 08:39,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000209,SOUTHGATE GREEN,E09000010,ENFIELD,Southgate,NULL,BOURNE HILL,20703944,N13,NULL,NULL,530550,193750,NULL,NULL\nNA,07/03/2021 14:28,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN GATE  CALLER STATES IN CHILDRENS PLAYGROUND   NEAR RIVERSIDE WALK NEAR TO MICHAELS BR,Dog,Person (land line),Playground/Recreation area (not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000059,TOTTERIDGE,E09000003,BARNET,Finchley,200121208,WALMINGTON FOLD,20044460,N12,525367,192071,525350,192050,51.61352561,-0.190731057\nNA,07/03/2021 21:58,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN WALL CAVITY IN BASEMENT,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000284,WOODSIDE,E09000014,HARINGEY,Hornsey,NULL,PARK AVENUE,21104893,N22,NULL,NULL,530550,190750,NULL,NULL\nNA,08/03/2021 15:56,2021,2020/21,Special Service,1,2,346,692,BIRD TRAPPED IN KITE NET - PERSONS ON SCENE WAITING FOR CREWS,Bird,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped wild animal,E05000085,ALPERTON,E09000005,BRENT,Wembley,202126825,BRIDGEWATER ROAD,20200329,HA0,517393,184361,517350,184350,51.5459467,-0.308428651\nNA,08/03/2021 16:32,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER A CAR - NEAR TO THE HARRIS ACADEMY,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal harm involving domestic animal,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,10010225703,ELTHAM HILL,20800527,SE9,541624,174623,541650,174650,51.45288972,0.036899556\nNA,08/03/2021 21:16,2021,2020/21,Special Service,1,1,346,346,DOG STUCK IN METAL FENCE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,Lee Green,NULL,POND ROAD,20801195,SE3,NULL,NULL,539950,176050,NULL,NULL\nNA,09/03/2021 05:07,2021,2020/21,Special Service,1,1,346,346,ANIMAL RESCUE - FOX STUCK BETWEEN SHED & WALL,Fox,Person (mobile),Outdoor storage,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12075598,DENBIGH ROAD,20600525,W13,516990,181097,516950,181050,51.51669472,-0.315316074\nNA,09/03/2021 10:02,2021,2020/21,Special Service,1,1,346,346,PIGEON CAUGHT IN NETTING UNDER FOOTBRIDGE BETWEEN THE PAVILIONS SHOPPING CENTRE AND MULTI STORY CAR,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,10003640627,STOCKWELL CLOSE,20300634,BR1,540592,169175,540550,169150,51.4041894,0.019886361\nNA,09/03/2021 11:59,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND BUILT IN  WARDROBE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CHEAM COMMON ROAD,22601362,KT4,NULL,NULL,523250,165250,NULL,NULL\nNA,09/03/2021 17:30,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,HUNSDON ROAD,22000543,SE14,NULL,NULL,535550,177550,NULL,NULL\nNA,10/03/2021 04:37,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000489,PLAISTOW NORTH,E09000025,NEWHAM,Plaistow,NULL,HELENA ROAD,22201443,E13,NULL,NULL,540050,183150,NULL,NULL\nNA,11/03/2021 13:05,2021,2020/21,Special Service,1,1,346,346,TRAPPED BIRD IN DISUSED BUILDING - RSPCA INSPECTOR ON SITE,Bird,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,1.00023E+11,ELSDALE STREET,20900382,E9,535396,184519,535350,184550,51.54334127,-0.048893517\nNA,11/03/2021 17:14,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN GUTTERING OF A EMPTY HOUSE -,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000316,MAWNEYS,E09000016,HAVERING,Romford,NULL,CROSS ROAD,21301119,RM7,NULL,NULL,549450,189450,NULL,NULL\nNA,13/03/2021 14:09,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED UNDER RECLINER SOFA,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,NULL,BULRUSH TERRACE,19910338,IG11,NULL,NULL,547450,182750,NULL,NULL\nNA,15/03/2021 18:24,2021,2020/21,Special Service,1,1,346,346,FOX CUB TRAPPED UNDER DECKING,Fox,Person (mobile),Other outdoor location,Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000121,MOTTINGHAM AND CHISLEHURST NORTH,E09000006,BROMLEY,Eltham,1.0002E+11,WAYSIDE GROVE,20302467,SE9,542658,171534,542650,171550,51.42486709,0.050518345\nNA,15/03/2021 21:00,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED BEHIND FIRE PLACE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05011251,SEVEN KINGS,E09000026,REDBRIDGE,Ilford,NULL,PRIMROSE AVENUE,22306059,RM6,NULL,NULL,546550,187950,NULL,NULL\nNA,15/03/2021 22:16,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN CAGE,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,NULL,ROYAL DRIVE,20037410,N11,NULL,NULL,528350,192150,NULL,NULL\nNA,16/03/2021 14:05,2021,2020/21,Special Service,1,1,346,346,CAT STUCK IN GATE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,SHAW SQUARE,22874250,E17,NULL,NULL,536550,190750,NULL,NULL\nNA,16/03/2021 23:50,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,WATERMILL LANE,20704891,N18,NULL,NULL,533150,192250,NULL,NULL\nNA,17/03/2021 09:48,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN HOLE IN PARK  GO UP THE MAIN HILL WHERE THE BARRIERS ARE AT THE TOP OF THE PARK BESID,Dog,Person (mobile),Park,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000222,GREENWICH WEST,E09000011,GREENWICH,Greenwich,10010234100,BLACKHEATH AVENUE,20800172,SE10,539006,177622,539050,177650,51.48048248,0.000414812\nNA,19/03/2021 10:35,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,RED LION LANE,20801247,SE18,NULL,NULL,543250,176850,NULL,NULL\nNA,19/03/2021 10:42,2021,2020/21,Special Service,1,1,346,346,CAT STUCK UP A CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Plumstead,NULL,ALABAMA STREET,20800046,SE18,NULL,NULL,544950,177450,NULL,NULL\nNA,19/03/2021 17:21,2021,2020/21,Special Service,1,1,346,346,PUPPY WITH HEAD STUCK IN WINE RACK -  FLAT A,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000279,STROUD GREEN,E09000014,HARINGEY,Hornsey,NULL,STAPLETON HALL ROAD,21103929,N4,NULL,NULL,531350,188150,NULL,NULL\nNA,21/03/2021 07:08,2021,2020/21,Special Service,1,1,346,346,FOX TRAPPED BETWEEN CONCRETE AND METAL POST CALLER WILL MEET YOU AT ESSO GARAGE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving wild animal - Other action,E05000176,ELTHORNE,E09000009,EALING,Ealing,12057481,TOWNHOLM CRESCENT,20601763,W7,515721,179572,515750,179550,51.50324873,-0.334097772\nNA,21/03/2021 14:17,2021,2020/21,Special Service,NULL,NULL,346,NULL,REQUEST FROM PLA - AS DISCUSSED    ATTEND SOUTHSIDE,Unknown - Wild Animal,Person (land line),Secondary school,Non Residential,Other animal assistance,Animal harm involving wild animal,E05000516,BARNES,E09000027,RICHMOND UPON THAMES,Hammersmith,1.00023E+11,LONSDALE ROAD,22404716,SW13,522469,177999,522450,177950,51.48769035,-0.237462009\nNA,22/03/2021 13:07,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED UNDER FLOOR   RSPCA ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,LEYS AVENUE,19900859,RM10,NULL,NULL,550450,183950,NULL,NULL\nNA,23/03/2021 01:07,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN SIDE OF WARDROBE,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000105,WILLESDEN GREEN,E09000005,BRENT,Willesden,NULL,KINGS ROAD,20202255,NW10,NULL,NULL,522550,184250,NULL,NULL\nNA,23/03/2021 07:40,2021,2020/21,Special Service,1,1,346,346,DEER TRAPPED IN METAL FENCE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000048,EAST BARNET,E09000003,BARNET,Barnet,200007104,BARING ROAD,20002300,EN4,526971,196721,526950,196750,51.65495627,-0.165905618\nNA,24/03/2021 17:17,2021,2020/21,Special Service,1,1,346,346,DOG TRAPPED IN BADGER HOLE,Dog,Person (mobile),\"Grassland, pasture, grazing etc\",Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000117,DARWIN,E09000006,BROMLEY,Biggin Hill,1.00024E+11,BLACKNESS LANE,20300053,BR2,541406,162874,541450,162850,51.34737164,0.029084848\nNA,25/03/2021 05:16,2021,2020/21,Special Service,1,1,346,346,DOG WITH PAW TRAPPED IN DOG CAGE,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000438,BLACKHEATH,E09000023,LEWISHAM,Greenwich,NULL,BEECHWOOD PLACE,22007197,SE10,NULL,NULL,538150,176550,NULL,NULL\nNA,26/03/2021 10:20,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000468,RAVENSBURY,E09000024,MERTON,Mitcham,NULL,MIDDLETON ROAD,22104395,SM4,NULL,NULL,526450,167050,NULL,NULL\nNA,26/03/2021 13:21,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED IN KITCHEN,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000051,FINCHLEY CHURCH END,E09000003,BARNET,Finchley,NULL,REGENTS PARK ROAD,20036360,N3,NULL,NULL,524850,189850,NULL,NULL\nNA,26/03/2021 16:04,2021,2020/21,Special Service,1,1,346,346,KITTEN TRAPPED IN CEILING - ASSISTING RSCPA ON SCENE,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000481,EAST HAM NORTH,E09000025,NEWHAM,East Ham,NULL,GOLDSMITH AVENUE,22200653,E12,NULL,NULL,542550,184850,NULL,NULL\nNA,26/03/2021 16:44,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,1.00021E+11,HERMITAGE ROAD,21103408,N4,532289,188003,532250,188050,51.5753812,-0.092356517\nNA,27/03/2021 13:16,2021,2020/21,Special Service,1,2,346,692,CAT TRAPPED IN YARD OF ART STUDIO,Cat,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000278,SEVEN SISTERS,E09000014,HARINGEY,Tottenham,NULL,HERMITAGE ROAD,21103408,N4,NULL,NULL,532250,187950,NULL,NULL\nNA,27/03/2021 16:05,2021,2020/21,Special Service,1,1,346,346,PIGEON TRAPPED IN NETTING ON RAILWAY BRIDGE    NEAR STATION    RSPCA IN ATTENDANCE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,10009144359,HIGH ROAD LEYTONSTONE,22844950,E11,539380,186813,539350,186850,51.56298399,0.009432004\nNA,28/03/2021 19:43,2021,2020/21,Special Service,1,1,346,346,ANIMAL BELIEVED TRAPPED IN LOFT,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000181,LADY MARGARET,E09000009,EALING,Southall,NULL,WESTBURY AVENUE,20601870,UB1,NULL,NULL,513250,182150,NULL,NULL\nNA,28/03/2021 21:01,2021,2020/21,Special Service,1,1,346,346,KITTEN STUCK UNDER FLOORBOARDS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000434,THURLOW PARK,E09000022,LAMBETH,West Norwood,NULL,CROXTED ROAD,21900423,SE24,NULL,NULL,532150,174050,NULL,NULL\nNA,29/03/2021 05:41,2021,2020/21,Special Service,1,1,346,346,CAT TRAPPED BEHIND RAILINGS NEXT TO STAIRWELL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000458,CRICKET GREEN,E09000024,MERTON,Mitcham,NULL,LONDON ROAD,22103995,CR4,NULL,NULL,527250,168250,NULL,NULL\nNA,29/03/2021 21:25,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000032,GASCOIGNE,E09000002,BARKING AND DAGENHAM,Barking,NULL,BARKING WHARF SQUARE,19910373,IG11,NULL,NULL,543950,183850,NULL,NULL\nNA,29/03/2021 21:51,2021,2020/21,Special Service,1,1,346,346,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05009375,HAGGERSTON,E09000012,HACKNEY,Shoreditch,NULL,DAWSON STREET,20900316,E2,NULL,NULL,533850,183150,NULL,NULL\nNA,30/03/2021 17:09,2021,2020/21,Special Service,1,1,346,346,CAT ON ROOF - STUCK BETWEEN TILES,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011476,PURLEY & WOODCOTE,E09000008,CROYDON,Purley,NULL,BOUVERIE GARDENS,20502892,CR8,NULL,NULL,530550,160250,NULL,NULL\nNA,30/03/2021 17:36,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED BETWEEN BRANCHES,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000062,WEST HENDON,E09000003,BARNET,Hendon,NULL,STURGESS AVENUE,20041340,NW4,NULL,NULL,522750,187850,NULL,NULL\nNA,30/03/2021 22:52,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000352,FELTHAM WEST,E09000018,HOUNSLOW,Feltham,NULL,ASHFORD ROAD,21500051,TW13,NULL,NULL,508750,171850,NULL,NULL\nNA,31/03/2021 05:20,2021,2020/21,Special Service,1,1,346,346,Redacted,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,NULL,WESTBOURNE GROVE,8400743,W2,NULL,NULL,525650,181150,NULL,NULL\nNA,31/03/2021 09:55,2021,2020/21,Special Service,1,1,346,346,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05011236,BRIDGE,E09000026,REDBRIDGE,Woodford,NULL,MEADWAY,22305183,IG8,NULL,NULL,541350,192450,NULL,NULL\nNA,31/03/2021 16:03,2021,2020/21,Special Service,1,2,346,692,KITTEN BELIEVED TO BE TRAPPED UNDER FLOORBOARDS - FRU REQUESTED WITH SNAKE EYE CAMERA,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000212,UPPER EDMONTON,E09000010,ENFIELD,Edmonton,NULL,UPTON ROAD,20704870,N18,NULL,NULL,534650,192250,NULL,NULL\nNA,01/04/2021 00:14,2021,2021/22,Special Service,1,1,352,352,Redacted,Deer,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000122,ORPINGTON,E09000006,BROMLEY,Orpington,1.0002E+11,SPUR ROAD,20301297,BR6,546542,165869,546550,165850,51.37298218,0.10400749\nNA,01/04/2021 10:04,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE - PIGEON TANGLED IN NETTING AT THIRD FLOOR LEVEL   ADDITIONAL PL REQUESTED FROM,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000359,HOUNSLOW HEATH,E09000018,HOUNSLOW,Feltham,NULL,BLACKBURN WAY,21501782,TW4,NULL,NULL,512450,174650,NULL,NULL\nNA,01/04/2021 19:27,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN LIFT SHAFT,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000524,KEW,E09000027,RICHMOND UPON THAMES,Richmond,NULL,MELLISS AVENUE,22407046,TW9,NULL,NULL,519650,176850,NULL,NULL\nNA,02/04/2021 17:03,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON FIRST FLOOR BALCONY,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011098,CHAUCER,E09000028,SOUTHWARK,Dowgate,NULL,SWAN STREET,22502444,SE1,NULL,NULL,532250,179450,NULL,NULL\nNA,02/04/2021 23:12,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BETWEEN FENCE AND WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000591,CATHALL,E09000031,WALTHAM FOREST,Leytonstone,NULL,PARK GROVE ROAD,22864300,E11,NULL,NULL,539250,186750,NULL,NULL\nNA,03/04/2021 19:08,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000522,HAMPTON WICK,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,SEYMOUR ROAD,22400527,KT1,NULL,NULL,517550,169850,NULL,NULL\nNA,05/04/2021 00:52,2021,2021/22,Special Service,1,1,352,352,DOG IN FOX HOLE,Dog,Person (mobile),Playground/Recreation area (not equipment),Outdoor,Animal rescue from below ground,Wild animal rescue from below ground,E05000171,CLEVELAND,E09000009,EALING,Ealing,12142471,PERIVALE GARDENS,20601367,W13,517032,182541,517050,182550,51.52966567,-0.314225853\nNA,05/04/2021 10:13,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN TREE   REQUESTED BY RSPCA,Cat,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000287,EDGWARE,E09000015,HARROW,Stanmore,1.00021E+11,THE HIGHLANDS,21202387,HA8,519900,190537,519950,190550,51.60092594,-0.270178823\nNA,05/04/2021 11:58,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF UNDER SOLAR PANELS,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000061,WEST FINCHLEY,E09000003,BARNET,Finchley,NULL,LONG LANE,20027220,N3,NULL,NULL,526050,190550,NULL,NULL\nNA,05/04/2021 15:11,2021,2021/22,Special Service,1,2,352,704,Redacted,Bird,Person (mobile),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000644,ST. JAMES'S,E09000033,WESTMINSTER,Soho,10033527302,THE MALL,8401058,SW1A,529329,179662,529350,179650,51.50111403,-0.138110634\nNA,05/04/2021 18:10,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN FOX HOLE,Dog,Person (mobile),Golf course (not building on course),Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000343,WEST RUISLIP,E09000017,HILLINGDON,Ruislip,10025312422,CLACK LANE PUBLIC FOOTPATH TO THE GREENWAY,21403082,UB9,508184,187115,508150,187150,51.57253709,-0.440327706\nNA,05/04/2021 21:25,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Domestic garden (vegetation not equipment),Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000488,MANOR PARK,E09000025,NEWHAM,Stratford,46033700,HALLEY ROAD,22208155,E12,541913,184989,541950,184950,51.54596179,0.045212785\nNA,06/04/2021 00:03,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN WATER OF CHURCH GARDENS     CALLER WAITING TO DIRECT,Fox,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000435,TULSE HILL,E09000022,LAMBETH,Brixton,2E+11,BRIXTON HILL,21900236,SW2,530986,175142,530950,175150,51.46010956,-0.115918543\nNA,06/04/2021 17:23,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN NETTING,Bird,Person (mobile),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011468,FAIRFIELD,E09000008,CROYDON,Croydon,1.00023E+11,NORTH END,20501238,CR0,532185,165905,532150,165950,51.376818,-0.102124\nNA,07/04/2021 17:22,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN NETTING - REQUESTD BY RSPCA,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,10008157949,QUEENSTOWN ROAD,22904132,SW8,528738,176823,528750,176850,51.47573447,-0.147646733\nNA,08/04/2021 23:30,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND WALL,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,09/04/2021 09:17,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO CAT STUCK IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202192157,DAGMAR AVENUE,20200339,HA9,518598,185599,518550,185550,51.55681973,-0.29063872\nNA,09/04/2021 16:08,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO DUCK IMPALED,Bird,Person (land line),Animal harm outdoors,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011106,NORTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10000818280,BERMONDSEY WALL WEST,22500227,SE16,534212,179823,534250,179850,51.50142328,-0.067736187\nNA,09/04/2021 18:37,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED BENEATH SUMMER HOUSE IN REAR GARDEN,Dog,Person (land line),Private Summer house,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000124,PETTS WOOD AND KNOLL,E09000006,BROMLEY,Orpington,1.0002E+11,BIRCHWOOD ROAD,20302912,BR5,544806,168196,544850,168150,51.39432989,0.080033626\nNA,09/04/2021 19:54,2021,2021/22,Special Service,1,1,352,352,ASSIST LONDON WILDLIFE PROTECTION OFFICER IN ATTENDANCE WITH BIRDS IN DANGER,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000053,GOLDERS GREEN,E09000003,BARNET,Hendon,200062733,HIGHCROFT GARDENS,20022240,NW11,524821,188268,524850,188250,51.57946588,-0.199966533\nNA,09/04/2021 22:39,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN BUSHES AT REAR OF - CALLER WILL MEET YOU TO ASSIST,Dog,Person (mobile),Park,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000039,THAMES,E09000002,BARKING AND DAGENHAM,Barking,100045597,MAYBURY ROAD,19900536,IG11,546019,183385,546050,183350,51.53050822,0.103728053\nNA,10/04/2021 00:55,2021,2021/22,Special Service,1,1,352,352,ANIMAL TRAPPED INSIDE GARAGE DOOR - J/O HARTLAND ROAD,Unknown - Wild Animal,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05000047,COPPETTS,E09000003,BARNET,Southgate,200069086,KENNARD ROAD,20025020,N11,527743,192183,527750,192150,51.61399648,-0.156395\nNA,10/04/2021 10:53,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN NETTING,Bird,Person (mobile),Multi-Storey car park,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Eltham,10010222078,ELTHAM HILL,20800527,SE9,542318,174448,542350,174450,51.45113993,0.046812085\nNA,10/04/2021 11:06,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED BETWEEN WALL IN BIN CUPBOARD OUTSIDE  *FRU WITH SNAKE EYE REQUESTED FROM SCENE*,Cat,Person (mobile),Common external bin storage area,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000355,HESTON CENTRAL,E09000018,HOUNSLOW,Feltham,1.00024E+11,CHURCH CLOSE,21501639,TW3,512527,176005,512550,176050,51.47183006,-0.381235413\nNA,10/04/2021 11:19,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Train station - concourse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05009380,LEA BRIDGE,E09000012,HACKNEY,Stoke Newington,1.00023E+11,UPPER CLAPTON ROAD,20901025,E5,534779,186513,534750,186550,51.5614013,-0.0570231\nNA,11/04/2021 14:18,2021,2021/22,Special Service,1,1,352,352,ASSIST LIFT ENGINEER WITH SMALL ANIMAL RESCUE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,NULL,HERMITAGE STREET,8400414,W2,NULL,NULL,526650,181550,NULL,NULL\nNA,11/04/2021 16:06,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN TREE    RSPCA ON SCENE,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000636,HYDE PARK,E09000033,WESTMINSTER,Paddington,10033546872,SUSSEX GARDENS,8401585,W2,527243,181478,527250,181450,51.51790913,-0.167496087\nNA,11/04/2021 18:16,2021,2021/22,Special Service,1,1,352,352,Redacted,Unknown - Wild Animal,Person (running call),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,NULL,EFFINGHAM ROAD,22001433,SE12,NULL,NULL,539450,174650,NULL,NULL\nNA,11/04/2021 20:17,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN AND TRAPPED IN RIVER BANK - QUAGGY RIVER,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LONGHURST ROAD,22001668,SE13,539126,174628,539150,174650,51.45355524,0.000962671\nNA,11/04/2021 23:19,2021,2021/22,Special Service,1,1,352,352,BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000297,PINNER,E09000015,HARROW,Harrow,NULL,PINNER HILL ROAD,21201803,HA5,NULL,NULL,511350,190150,NULL,NULL\nNA,12/04/2021 08:19,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Canal/riverbank vegetation,Outdoor,Other animal assistance,Assist trapped domestic animal,E05000447,LEE GREEN,E09000023,LEWISHAM,Lee Green,1.00022E+11,LONGHURST ROAD,22001668,SE13,539114,174638,539150,174650,51.45364876,0.000801434\nNA,12/04/2021 09:45,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000367,BUNHILL,E09000019,ISLINGTON,Shoreditch,NULL,BUNHILL ROW,21604385,EC1Y,NULL,NULL,532550,182350,NULL,NULL\nNA,12/04/2021 18:33,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Large supermarket,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000294,KENTON EAST,E09000015,HARROW,Stanmore,10070269249,HONEYPOT LANE,21200986,NW9,519070,188805,519050,188850,51.5855328,-0.2827536\nNA,12/04/2021 19:29,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Other outdoor equipment/machinery,Outdoor Structure,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009336,WHITECHAPEL,E09000030,TOWER HAMLETS,Whitechapel,10025543883,ROYAL MINT STREET,22701043,E1,534110,180870,534150,180850,51.51085062,-0.068804812\nNA,13/04/2021 18:24,2021,2021/22,Special Service,1,1,352,352,DOG WITH LEG STUCK IN METAL TRAINING CAGE,Dog,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000143,ST. PANCRAS AND SOMERS TOWN,E09000007,CAMDEN,Euston,NULL,LEWIS CUBITT SQUARE,20499409,N1C,NULL,NULL,529950,183650,NULL,NULL\nNA,13/04/2021 21:01,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF,Cat,Person (mobile),Licensed House in Multiple Occupation - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000625,THAMESFIELD,E09000032,WANDSWORTH,Wandsworth,NULL,WINTHORPE ROAD,22906300,SW15,NULL,NULL,524450,175250,NULL,NULL\nNA,14/04/2021 11:04,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING ON BALCONY   RSPCA ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000628,WEST HILL,E09000032,WANDSWORTH,Wandsworth,NULL,CHOBHAM GARDENS,22900923,SW19,NULL,NULL,523750,172750,NULL,NULL\nNA,14/04/2021 12:09,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED AND HANGING FROM TOP FLOOR,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,NULL,MIDDLE LANE,21103615,N8,NULL,NULL,530050,188750,NULL,NULL\nNA,14/04/2021 16:30,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011097,CHAMPION HILL,E09000028,SOUTHWARK,Peckham,NULL,BROMAR ROAD,22500346,SE5,NULL,NULL,533450,175750,NULL,NULL\nNA,14/04/2021 18:52,2021,2021/22,Special Service,1,1,352,352,DOVE TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000345,YIEWSLEY,E09000017,HILLINGDON,Hillingdon,NULL,WRAYSBURY DRIVE,21402993,UB7,NULL,NULL,505650,180450,NULL,NULL\nNA,15/04/2021 10:30,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Pub/wine bar/bar,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000438,BLACKHEATH,E09000023,LEWISHAM,Lee Green,1.00022E+11,LEE HIGH ROAD,22004135,SE12,539825,175022,539850,175050,51.4569159,0.0111709\nNA,15/04/2021 14:52,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED UNDER BRICKWORK,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000424,KNIGHT'S HILL,E09000022,LAMBETH,West Norwood,NULL,LEIGHAM COURT ROAD,21900860,SW16,NULL,NULL,531050,172150,NULL,NULL\nNA,15/04/2021 16:27,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN INTO OUTSIDE BASEMENT AREA UNABLE TO GET OUT,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000649,WEST END,E09000033,WESTMINSTER,Euston,NULL,HANSON STREET,8400438,W1W,NULL,NULL,529050,181750,NULL,NULL\nNA,17/04/2021 00:17,2021,2021/22,Special Service,1,2,352,704,RUNNING CALL TO HORSE ON ROADWAY - NO FURTHER LFB ATTENDANCE REQUIRED,Horse,Person (land line),Road surface/pavement,Outdoor,Other animal assistance,Animal assistance involving livestock - Other action,E05000318,RAINHAM AND WENNINGTON,E09000016,HAVERING,Wennington,1.00024E+11,WENNINGTON ROAD,21300921,RM13,554193,180954,554150,180950,51.50648981,0.22041394\nNA,17/04/2021 11:00,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO ASSIST WITH CAT IN TREE,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000102,TOKYNGTON,E09000005,BRENT,Wembley,202230594,DAGMAR AVENUE,20200339,HA9,518594,185600,518550,185650,51.5568303,-0.290689421\nNA,17/04/2021 12:27,2021,2021/22,Special Service,1,1,352,352,ANIMAL RESCUE SMALL - POSSIBLY A PIGEON TRAPPED IN CHIMNEY,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000215,BLACKHEATH WESTCOMBE,E09000011,GREENWICH,East Greenwich,NULL,HUMBER ROAD,20800800,SE3,NULL,NULL,540150,178050,NULL,NULL\nNA,17/04/2021 13:33,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000285,BELMONT,E09000015,HARROW,Stanmore,NULL,CROWSHOTT AVENUE,21200970,HA7,NULL,NULL,517850,190650,NULL,NULL\nNA,17/04/2021 16:29,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK UNDER FLOOR BOARDS,Cat,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000531,TWICKENHAM RIVERSIDE,E09000027,RICHMOND UPON THAMES,Twickenham,NULL,RICHMOND ROAD,22403771,TW1,NULL,NULL,516650,173650,NULL,NULL\nNA,17/04/2021 17:16,2021,2021/22,Special Service,1,1,352,352,CAT IN PRECARIOUS POSITION ON ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Clapham,NULL,TENNYSON STREET,22905245,SW8,NULL,NULL,528650,176050,NULL,NULL\nNA,17/04/2021 19:59,2021,2021/22,Special Service,1,1,352,352,KITTTEN TRAPPED BETWEEN CUPBOARD AND WALL,Unknown - Domestic Animal Or Pet,Person (land line),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000319,ROMFORD TOWN,E09000016,HAVERING,Romford,NULL,EASTERN ROAD,21301162,RM1,NULL,NULL,551850,188750,NULL,NULL\nNA,17/04/2021 22:06,2021,2021/22,Special Service,1,1,352,352,DISTRESSED DOG  STUCK IN PLAYGROUND,Dog,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000028,BECONTREE,E09000002,BARKING AND DAGENHAM,Barking,10023598990,SCHOLARS WAY,19904082,RM8,546584,185717,546550,185750,51.55131011,0.112834799\nNA,18/04/2021 10:28,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Single shop,Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000224,MIDDLE PARK AND SUTCLIFFE,E09000011,GREENWICH,Lee Green,1.00023E+11,LEE ROAD,22001639,SE3,539879,174995,539850,174950,51.4566599,0.011937\nNA,18/04/2021 11:35,2021,2021/22,Special Service,1,2,352,704,Redacted,Bird,Person (mobile),Tree scrub,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Bird,E05000555,BEDDINGTON NORTH,E09000029,SUTTON,Wallington,5870094183,BLOXWORTH CLOSE,22600334,SM6,529326,165092,529350,165050,51.37017279,-0.143464518\nNA,18/04/2021 21:01,2021,2021/22,Special Service,1,1,352,352,DISTRESSED KITTEN STUCK ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000459,DUNDONALD,E09000024,MERTON,Wimbledon,NULL,KINGSTON ROAD,22103623,SW20,NULL,NULL,524150,169350,NULL,NULL\nNA,19/04/2021 07:56,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND A WALL/CUPBOARD,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000299,QUEENSBURY,E09000015,HARROW,Stanmore,NULL,ST AUSTELL CLOSE,21202360,HA8,NULL,NULL,518750,190150,NULL,NULL\nNA,19/04/2021 14:52,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (land line),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000491,ROYAL DOCKS,E09000025,NEWHAM,East Ham,NULL,RYMILL STREET,22207872,E16,NULL,NULL,543150,180050,NULL,NULL\nNA,19/04/2021 16:37,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN FENCING,Dog,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped domestic animal,E05000041,VILLAGE,E09000002,BARKING AND DAGENHAM,Dagenham,10023593285,BALLARDS ROAD,19900399,RM10,549997,184157,549950,184150,51.53639916,0.161355237\nNA,19/04/2021 19:15,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN BARBED WIRE,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000477,CANNING TOWN NORTH,E09000025,NEWHAM,Plaistow,NULL,LESTER AVENUE,22207790,E15,NULL,NULL,539450,182550,NULL,NULL\nNA,19/04/2021 21:56,2021,2021/22,Special Service,1,1,352,352,KITTEN POSSIBLY TRAPPED BEHIND INTERIOR WALL,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000200,GRANGE,E09000010,ENFIELD,Southgate,NULL,VERA AVENUE,20703026,N21,NULL,NULL,531750,195450,NULL,NULL\nNA,20/04/2021 00:10,2021,2021/22,Special Service,1,2,352,704,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000356,HESTON EAST,E09000018,HOUNSLOW,Heston,NULL,HESTON ROAD,21500600,TW5,NULL,NULL,513050,177450,NULL,NULL\nNA,21/04/2021 15:15,2021,2021/22,Special Service,1,2,352,704,FOX TRAPPED IN RAILINGS OPPOSITE,Fox,Person (mobile),Railway trackside vegetation,Outdoor,Other animal assistance,Assist trapped wild animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217117508,LADBROKE GROVE,21700912,W10,524307,181357,524350,181350,51.51747248,-0.209825706\nNA,22/04/2021 19:32,2021,2021/22,Special Service,1,1,352,352,ASSIST WILDLIFE OFFICER IN ATTENDANCE FOR STUCK BIRD,Bird,Person (mobile),Woodland/forest - broadleaf/hardwood,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000467,POLLARDS HILL,E09000024,MERTON,Norbury,48085107,SOUTH LODGE AVENUE,22105877,CR4,530267,168346,530250,168350,51.3992045,-0.128758866\nNA,24/04/2021 15:10,2021,2021/22,Special Service,1,1,352,352,BABY DEER STUCK IN RAILINGS     IN CARPARK,Deer,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000330,HAREFIELD,E09000017,HILLINGDON,Ruislip,1.00023E+11,NORTHWOOD WAY,21401429,UB9,505882,190824,505850,190850,51.60631825,-0.472412318\nNA,24/04/2021 20:37,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF - BEEN UP THERE SINCE WEDNESDAY,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000108,BROMLEY COMMON AND KESTON,E09000006,BROMLEY,Biggin Hill,NULL,WESTERHAM ROAD,20302896,BR2,NULL,NULL,542050,164650,NULL,NULL\nNA,24/04/2021 21:51,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Road surface/pavement,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,1.00022E+11,HOMEFIELD ROAD,21500622,W4,521622,178309,521650,178350,51.49066025,-0.249550823\nNA,25/04/2021 10:04,2021,2021/22,Special Service,1,1,352,352,DEER TRAPPED IN METAL GATE,Deer,Person (mobile),Railings,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000054,HALE,E09000003,BARNET,Mill Hill,200083757,NAN CLARKS LANE,20030700,NW7,521617,193830,521650,193850,51.63015245,-0.244254771\nNA,25/04/2021 12:12,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN FENCE WIRING   RSPCA ON SCENE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000302,ROXETH,E09000015,HARROW,Northolt,NULL,PARKFIELD ROAD,21202130,HA2,NULL,NULL,514350,186350,NULL,NULL\nNA,25/04/2021 17:31,2021,2021/22,Special Service,1,1,352,352,CAT UP TREE       AT REQUEST OF RSPCA,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000348,CHISWICK HOMEFIELDS,E09000018,HOUNSLOW,Chiswick,2.00004E+11,THAMES CRESCENT,21590212,W4,521494,177250,521450,177250,51.48117312,-0.251760444\nNA,26/04/2021 00:50,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN GUTTERING ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000629,WEST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,NEWNES PATH,22903499,SW15,NULL,NULL,522550,175350,NULL,NULL\nNA,27/04/2021 01:22,2021,2021/22,Special Service,1,1,352,352,FOX CUB TRAPPED AND IN DISTRESS IN SHED,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011487,WADDON,E09000008,CROYDON,Croydon,NULL,BARROW ROAD,20501907,CR0,NULL,NULL,531450,164050,NULL,NULL\nNA,27/04/2021 10:11,2021,2021/22,Special Service,1,1,352,352,KITTEN STUCK IN CAGE,Cat,Person (land line),Fire station,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000567,SUTTON WEST,E09000029,SUTTON,Sutton,5870021065,ST DUNSTANS HILL,22605525,SM1,524617,164843,524650,164850,51.36898499,-0.211172616\nNA,27/04/2021 17:30,2021,2021/22,Special Service,1,1,352,352,DOG WITH GLASS JAR  STUCK ON ITS HEAD,Dog,Person (land line),Veterinary surgery,Non Residential,Other animal assistance,Assist trapped domestic animal,E05009392,COLVILLE,E09000020,KENSINGTON AND CHELSEA,North Kensington,217085551,TALBOT ROAD,21701031,W11,524975,181314,524950,181350,51.51693994,-0.200218436\nNA,27/04/2021 17:52,2021,2021/22,Special Service,1,1,352,352,BIRD/ANIMAL TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011482,SHIRLEY NORTH,E09000008,CROYDON,Woodside,NULL,CORNFLOWER LANE,20500426,CR0,NULL,NULL,535750,166250,NULL,NULL\nNA,27/04/2021 19:55,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF LEDGE,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05011096,CAMBERWELL GREEN,E09000028,SOUTHWARK,Peckham,NULL,LOMOND GROVE,22501538,SE5,NULL,NULL,532550,177150,NULL,NULL\nNA,27/04/2021 22:17,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED IN BUILDING UNDER RENOVATION,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000623,SHAFTESBURY,E09000032,WANDSWORTH,Battersea,NULL,DOROTHY ROAD,22901245,SW11,NULL,NULL,527650,175650,NULL,NULL\nNA,28/04/2021 12:16,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED UNDER FLOOR BOARDS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000185,NORTHOLT WEST END,E09000009,EALING,Northolt,NULL,WEST END GARDENS,20601860,UB5,NULL,NULL,511350,183050,NULL,NULL\nNA,29/04/2021 13:01,2021,2021/22,Special Service,1,1,352,352,PUPPY TRAPPED IN FOXHOLE,Dog,Person (mobile),Domestic garden (vegetation not equipment),Outdoor,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Southgate,207101429,KELVIN AVENUE,20704376,N13,530848,191865,530850,191850,51.61042588,-0.111697038\nNA,29/04/2021 15:28,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN WIRE,Fox,Person (mobile),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05000273,HORNSEY,E09000014,HARINGEY,Hornsey,1.00021E+11,FERME PARK ROAD,21106156,N8,530509,188581,530550,188550,51.58099506,-0.117806165\nNA,29/04/2021 17:13,2021,2021/22,Special Service,1,1,352,352,SQUIRREL STUCK BETWEEN WALL AND GUTTERING BEING ATTACKED BY BIRDS,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Wild animal rescue from height,E05000350,CRANFORD,E09000018,HOUNSLOW,Feltham,NULL,SQUIRREL CLOSE,21501349,TW4,NULL,NULL,511450,175950,NULL,NULL\nNA,30/04/2021 09:45,2021,2021/22,Special Service,1,1,352,352,HORSE DOWN IN PADDOCK,Horse,Person (mobile),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance - Lift heavy domestic animal,E05011229,ST. MARY'S & ST. JAMES,E09000004,BEXLEY,Sidcup,10023304295,NORTH CRAY ROAD,20101032,DA14,548763,172019,548750,172050,51.42766436,0.138470724\nNA,30/04/2021 12:31,2021,2021/22,Special Service,1,1,352,352,RSPCA ON SCENE - BIRD STUCK IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000557,BELMONT,E09000029,SUTTON,Sutton,NULL,BANSTEAD ROAD SOUTH,22602088,SM2,NULL,NULL,526850,162750,NULL,NULL\nNA,30/04/2021 12:44,2021,2021/22,Special Service,1,1,352,352,DOG WITH PAW TRAPPED IN GRILL IN GARDEN,Dog,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000227,SHOOTERS HILL,E09000011,GREENWICH,Eltham,NULL,CLEANTHUS ROAD,20800349,SE18,NULL,NULL,543850,176650,NULL,NULL\nNA,30/04/2021 23:52,2021,2021/22,Special Service,1,2,352,704,CAT TRAPPED IN WIRED FENCING - OPPOSITE THIS ADDRESS - CALLER WILL MEET YOU,Cat,Person (mobile),Railway trackside vegetation,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000175,EAST ACTON,E09000009,EALING,Acton,12105407,THE CRESCENT,20601717,W3,520990,181221,520950,181250,51.51697163,-0.257652538\nNA,01/05/2021 12:14,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE SQUIRREL BEHIND FIREPLACE,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000613,EAST PUTNEY,E09000032,WANDSWORTH,Wandsworth,NULL,MELROSE ROAD,22903287,SW18,NULL,NULL,524850,174150,NULL,NULL\nNA,01/05/2021 12:48,2021,2021/22,Special Service,NULL,NULL,352,NULL,FOX TRAPPED IN BARBED WIRE,Fox,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000208,SOUTHGATE,E09000010,ENFIELD,Southgate,NULL,BLAGDENS CLOSE,20701341,N14,NULL,NULL,529450,193750,NULL,NULL\nNA,02/05/2021 10:49,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE  CAT TRAPPED BETWEEN WALL AND BASEMENT FLOORBOARDS,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009372,HACKNEY CENTRAL,E09000012,HACKNEY,Homerton,NULL,BODNEY ROAD,20900148,E8,NULL,NULL,534550,185350,NULL,NULL\nNA,02/05/2021 11:19,2021,2021/22,Special Service,1,1,352,352,ASSIST  LONDON WILDLIFE PROTECTION  VOLUNTEEER ON SCENE WITH PARROT TRAPPED IN GUTTERING ON ROOF,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05011227,LONGLANDS,E09000004,BEXLEY,Sidcup,NULL,KINGSGROVE CLOSE,20101909,DA14,NULL,NULL,545950,171650,NULL,NULL\nNA,02/05/2021 13:58,2021,2021/22,Special Service,1,2,352,704,KITTEN IN PRECARIOUS POSITION     AERIAL APPLIANCE TO ENTER VIA GRAYS INN ROAD,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,FREDERICK STREET,20400908,WC1X,NULL,NULL,530750,182650,NULL,NULL\nNA,02/05/2021 16:06,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN SHED    CALLER WILL MEET YOU ON HARCOURT ROAD,Cat,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011488,WEST THORNTON,E09000008,CROYDON,Norbury,1.00021E+11,HARCOURT ROAD,20501023,CR7,530931,167295,530950,167250,51.38960082,-0.119613432\nNA,02/05/2021 21:54,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE,Unknown - Domestic Animal Or Pet,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000141,KING'S CROSS,E09000007,CAMDEN,Euston,NULL,FREDERICK STREET,20400908,WC1X,NULL,NULL,530750,182650,NULL,NULL\nNA,02/05/2021 22:23,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED AND INJURED IN GARDEN,Cat,Person (mobile),Other outdoor location,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05009384,STAMFORD HILL WEST,E09000012,HACKNEY,Stoke Newington,1.00021E+11,WEST BANK,20901063,N16,533229,187649,533250,187650,51.57198508,-0.078940241\nNA,03/05/2021 11:31,2021,2021/22,Special Service,1,1,352,352,TWO CYGNETS TRAPPED IN GRATE    FRU REQUIRED FOR WATER RESCUE,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from water,Animal rescue from water - Bird,E05009320,BOW WEST,E09000030,TOWER HAMLETS,Bethnal Green,6135457,CLINTON ROAD,22700328,E3,536177,182698,536150,182650,51.52678985,-0.03833142\nNA,03/05/2021 14:35,2021,2021/22,Special Service,1,1,352,352,Redacted,Unknown - Wild Animal,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011218,BELVEDERE,E09000004,BEXLEY,Erith,NULL,BEVERCOTE WALK,20100134,DA17,NULL,NULL,548550,177950,NULL,NULL\nNA,03/05/2021 21:49,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED UNDERNEATH BOILER,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Soho,NULL,CHARING CROSS ROAD,8400626,WC2H,NULL,NULL,529850,181150,NULL,NULL\nNA,04/05/2021 20:08,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN WALL,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000572,WORCESTER PARK,E09000029,SUTTON,Sutton,NULL,CONRAD DRIVE,22601399,KT4,NULL,NULL,523550,166350,NULL,NULL\nNA,04/05/2021 21:57,2021,2021/22,Special Service,1,1,352,352,CAT IN PRECARIOUS  POSITION ON ROOF,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009376,HOMERTON,E09000012,HACKNEY,Homerton,NULL,CHATHAM PLACE,20900223,E9,NULL,NULL,535250,184850,NULL,NULL\nNA,04/05/2021 23:55,2021,2021/22,Special Service,1,1,352,352,CAT STUCK BEHIND  WALL   BUILDERS BOARDED UP WALL AND HAVE NOW LEFT,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000460,FIGGE'S MARSH,E09000024,MERTON,Mitcham,NULL,CLARENDON GROVE,22101470,CR4,NULL,NULL,527950,168950,NULL,NULL\nNA,05/05/2021 18:46,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011466,COULSDON TOWN,E09000008,CROYDON,Purley,NULL,REDDOWN ROAD,20500317,CR5,NULL,NULL,529850,158850,NULL,NULL\nNA,05/05/2021 21:42,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED INSIDE BLOCKED FIRE PLACE,Cat,Person (mobile),Restaurant/cafe,Non Residential,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000649,WEST END,E09000033,WESTMINSTER,Euston,10033535032,GREAT PORTLAND STREET,8400603,W1W,528958,181704,528950,181750,51.51954786,-0.142703802\nNA,07/05/2021 06:43,2021,2021/22,Special Service,1,1,352,352,DEER TRAPPED IN SIDE GATE OF SCHOOL,Deer,Person (land line),Pre School/nursery,Non Residential,Other animal assistance,Assist trapped wild animal,E05000030,EASTBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,100007499,DAGENHAM ROAD,19901047,RM7,551007,187181,551050,187150,51.5633003,0.1772123\nNA,07/05/2021 07:58,2021,2021/22,Special Service,1,1,352,352,DUCK TRAPPED IN WIRE IN TREE BY LAKE - FRU REQUIRED,Bird,Person (mobile),Park,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05000456,CANNON HILL,E09000024,MERTON,New Malden,48011970,CANNON HILL LANE,22101155,SW20,524024,168501,524050,168550,51.40198841,-0.218401393\nNA,07/05/2021 13:28,2021,2021/22,Special Service,1,1,352,352,DUCKLINGS STUCK IN THE DRAIN,Bird,Person (mobile),Pipe or drain,Outdoor Structure,Animal rescue from below ground,Wild animal rescue from below ground,E05000055,HENDON,E09000003,BARNET,Hendon,200157527,ST JOSEPHS GROVE,20038150,NW4,522583,189215,522550,189250,51.58846684,-0.231926364\nNA,07/05/2021 15:12,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,MILLFIELD AVENUE,22858800,E17,NULL,NULL,536650,190450,NULL,NULL\nNA,07/05/2021 16:39,2021,2021/22,Special Service,1,1,352,352,DOG ON ROOF OF FLATS,Dog,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000206,PONDERS END,E09000010,ENFIELD,Enfield,NULL,KEATS CLOSE,20706073,EN3,NULL,NULL,535650,195750,NULL,NULL\nNA,08/05/2021 07:12,2021,2021/22,Special Service,1,2,352,704,TWO FOXES STUCK IN FOUNTAIN,Fox,Person (land line),Other outdoor location,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000519,\"HAM, PETERSHAM AND RICHMOND RIVERSIDE\",E09000027,RICHMOND UPON THAMES,Richmond,10025549748,RICHMOND HILL,22403768,TW10,518207,174157,518250,174150,51.45406677,-0.30010783\nNA,08/05/2021 17:20,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN CHIMNEY,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000277,ST. ANN'S,E09000014,HARINGEY,Tottenham,NULL,RUTLAND GARDENS,21103834,N4,NULL,NULL,532150,188450,NULL,NULL\nNA,09/05/2021 07:39,2021,2021/22,Special Service,1,1,352,352,FOX TRAPPED IN FENCE RAILINGS,Fox,Person (land line),Animal harm outdoors,Outdoor,Other animal assistance,Animal assistance involving wild animal - Other action,E05000326,BRUNEL,E09000017,HILLINGDON,Hillingdon,768012097,THE CHANTRY,21401936,UB8,506859,182799,506850,182750,51.53400059,-0.460747172\nNA,09/05/2021 09:46,2021,2021/22,Special Service,1,3,352,1056,DEER STUCK IN WATER    TWIN RIVERS OPPOSITE ESSO GARAGE - HAL STAFF TO MEET BRIGADE,Deer,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,NA,NA,NA,NA,Heathrow,33043760,SOUTHERN PERIMETER ROAD,21401785,TW6,505266,174763,505250,174750,51.4620701,-0.486104001\nNA,09/05/2021 15:13,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED ON LEDGE ON FIRST FLOOR.  OCCUPIER NOT AT HOME,Dog,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000262,SANDS END,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,CARNWATH ROAD,21000185,SW6,NULL,NULL,525550,175650,NULL,NULL\nNA,09/05/2021 21:34,2021,2021/22,Special Service,1,4,352,1408,Redacted,Unknown - Wild Animal,Person (land line),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000526,NORTH RICHMOND,E09000027,RICHMOND UPON THAMES,Richmond,10070721878,RANELAGH DRIVE,22405873,TW9,517002,175043,517050,175050,51.46227737,-0.317144477\nNA,09/05/2021 22:24,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000266,ALEXANDRA,E09000014,HARINGEY,Hornsey,NULL,BARNARD HILL,21101041,N10,NULL,NULL,528550,190550,NULL,NULL\nNA,10/05/2021 09:58,2021,2021/22,Special Service,1,1,352,352,DOG IN DISTRESS LOCKED IN BATHROOM,Dog,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05009399,NOTTING DALE,E09000020,KENSINGTON AND CHELSEA,North Kensington,NULL,LANCASTER WEST ESTATE,21701277,W11,NULL,NULL,524150,181050,NULL,NULL\nNA,10/05/2021 15:30,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN WALL CAVITY,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000251,ASKEW,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,BECKLOW ROAD,21000108,W12,NULL,NULL,522150,179950,NULL,NULL\nNA,10/05/2021 16:09,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED BEHIND FIREPLACE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05011470,NEW ADDINGTON NORTH,E09000008,CROYDON,Addington,NULL,KING HENRY'S DRIVE,20501759,CR0,NULL,NULL,537950,162750,NULL,NULL\nNA,10/05/2021 16:50,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34028029,SAMUEL'S CLOSE,21001137,W6,523461,178968,523450,178950,51.4961841,-0.222844596\nNA,11/05/2021 12:17,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN CHIMNEY,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000454,WHITEFOOT,E09000023,LEWISHAM,Bromley,NULL,REIGATE ROAD,22003617,BR1,NULL,NULL,540250,172550,NULL,NULL\nNA,11/05/2021 14:51,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN NETTING,Bird,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05011249,MONKHAMS,E09000026,REDBRIDGE,Woodford,10034920939,THE BROADWAY,22306023,IG8,540947,191860,540950,191850,51.60794965,0.03404456\nNA,12/05/2021 13:57,2021,2021/22,Special Service,1,1,352,352,SQUIRREL TRAPPED BETWEEN BLINDS IN BATHROOM,Squirrel,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05011098,CHAUCER,E09000028,SOUTHWARK,Dowgate,NULL,BROCKHAM STREET,22500337,SE1,NULL,NULL,532350,179350,NULL,NULL\nNA,12/05/2021 22:02,2021,2021/22,Special Service,1,2,352,704,CAT STUCK IN TREE - AERIAL REQUESTED FROM SCENE RUNNING CALL,Cat,Person (land line),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05000517,EAST SHEEN,E09000027,RICHMOND UPON THAMES,Richmond,1.00022E+11,DERBY ROAD,22405516,SW14,519813,175088,519850,175050,51.46209467,-0.276692546\nNA,13/05/2021 22:00,2021,2021/22,Special Service,1,1,352,352,WOOD PIGEON STUCK IN CHIMNEY BEHIND CAST IRON FIREPLACE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,NULL,PARK ROAD,20300557,BR1,NULL,NULL,540850,169650,NULL,NULL\nNA,14/05/2021 07:46,2021,2021/22,Special Service,1,2,352,704,SEAGULL TRAPPED IN NETTING  AERIAL REQUESTED FROM SCENE,Bird,Person (land line),Warehouse,Non Residential,Other animal assistance,Assist trapped wild animal,E05000175,EAST ACTON,E09000009,EALING,Acton,12152493,CONCORD ROAD,20600427,W3,519551,182044,519550,182050,51.52467073,-0.278111173\nNA,14/05/2021 14:37,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH PIGEON TRAPPED IN NETTING,Bird,Person (mobile),\"Takeaway, fast food\",Non Residential,Animal rescue from height,Animal rescue from height - Bird,E05000638,LANCASTER GATE,E09000033,WESTMINSTER,Paddington,1.00023E+11,QUEENSWAY,8401512,W2,525855,180778,525850,180750,51.511922,-0.187741\nNA,14/05/2021 17:51,2021,2021/22,Special Service,1,1,352,352,INJURED PIGEON TRAPPED IN NETTING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000378,ST. GEORGE'S,E09000019,ISLINGTON,Holloway,NULL,TUFNELL PARK ROAD,21606493,N7,NULL,NULL,530050,185950,NULL,NULL\nNA,14/05/2021 22:33,2021,2021/22,Special Service,1,1,352,352,PUPPY WITH HEAD STUCK IN SOFA,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000223,KIDBROOKE WITH HORNFAIR,E09000011,GREENWICH,Eltham,NULL,LANGBROOK ROAD,20800886,SE3,NULL,NULL,542050,176050,NULL,NULL\nNA,14/05/2021 22:48,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED ON ROOF,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 10 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000620,QUEENSTOWN,E09000032,WANDSWORTH,Battersea,NULL,CHARLOTTE DESPARD AVENUE,22900858,SW11,NULL,NULL,528350,176650,NULL,NULL\nNA,15/05/2021 09:24,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN CHIMNEY BEHIND GAS FIRE,Bird,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped wild animal,E05000338,SOUTH RUISLIP,E09000017,HILLINGDON,Ruislip,NULL,ACORN GROVE,21400016,HA4,NULL,NULL,509650,185750,NULL,NULL\nNA,15/05/2021 13:39,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000487,LITTLE ILFORD,E09000025,NEWHAM,Ilford,NULL,GRANTHAM ROAD,22200183,E12,NULL,NULL,543350,185650,NULL,NULL\nNA,15/05/2021 19:55,2021,2021/22,Special Service,1,1,352,352,CAT POSSIBLY STUCK UP A CHIMNEY,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Other animal assistance,Assist trapped wild animal,E05000264,TOWN,E09000013,HAMMERSMITH AND FULHAM,Fulham,NULL,BARCLAY ROAD,21000090,SW6,NULL,NULL,525350,177150,NULL,NULL\nNA,15/05/2021 22:32,2021,2021/22,Special Service,1,1,352,352,DOG TRAPPED IN GARDEN - PROPERTY IS VACANT,Dog,Person (mobile),House - single occupancy,Dwelling,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05009391,CHELSEA RIVERSIDE,E09000020,KENSINGTON AND CHELSEA,Chelsea,NULL,OLD CHURCH STREET,21700373,SW3,NULL,NULL,527050,177650,NULL,NULL\nNA,16/05/2021 13:08,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN WIRE - RSPCA ON SCENE - BEST ACCESS IS MURTRIX ROAD,Bird,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000140,KILBURN,E09000007,CAMDEN,West Hampstead,NULL,BIRCHINGTON ROAD,20400674,NW6,NULL,NULL,525350,183850,NULL,NULL\nNA,16/05/2021 13:32,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Restaurant/cafe,Non Residential,Other animal assistance,Assist trapped wild animal,E05000256,HAMMERSMITH BROADWAY,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,34160526,FULHAM PALACE ROAD,21000356,W6,523489,178207,523450,178250,51.4893433,-0.2227147\nNA,17/05/2021 12:08,2021,2021/22,Special Service,1,2,352,704,BIRDS TRAPPED IN WIRE UNDER BRIDGE - CALLER WILL MEET YOU ON PICKETTS LOCK LANE BY THE BRIDGE,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05000204,LOWER EDMONTON,E09000010,ENFIELD,Edmonton,207170280,PICKETTS LOCK LANE,20705131,N9,535608,193774,535650,193750,51.62645872,-0.042264388\nNA,17/05/2021 18:46,2021,2021/22,Special Service,1,1,352,352,PARROT TRAPPED IN NETTING,Bird,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000377,MILDMAY,E09000019,ISLINGTON,Stoke Newington,5300066651,NEWINGTON GREEN,21603608,N16,532910,185371,532950,185350,51.55158588,-0.084394582\nNA,17/05/2021 20:11,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BEHIND SHUTTERS,Cat,Person (mobile),Single shop,Non Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000448,LEWISHAM CENTRAL,E09000023,LEWISHAM,Lewisham,1.00023E+11,LEWISHAM HIGH STREET,22004177,SE13,538293,175571,538250,175550,51.46222543,-0.010639968\nNA,18/05/2021 09:09,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000288,GREENHILL,E09000015,HARROW,Harrow,10010598072,GAYTON ROAD,21201035,HA1,516002,188160,516050,188150,51.58037751,-0.327217925\nNA,18/05/2021 12:40,2021,2021/22,Special Service,1,1,352,352,FOX CUB TRAPPED IN STREAM   RSPCA ON SCENE,Fox,Person (mobile),River/canal,Outdoor,Animal rescue from water,Wild animal rescue from water or mud,E05000109,BROMLEY TOWN,E09000006,BROMLEY,Bromley,1.0002E+11,MARTINS ROAD,20301901,BR2,539666,169259,539650,169250,51.40517608,0.006626105\nNA,18/05/2021 17:24,2021,2021/22,Special Service,1,1,352,352,KITTEN TRAPPED IN ENGINE OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207046874,PRINCES AVENUE,20704626,N13,531553,192043,531550,192050,51.61186679,-0.10145459\nNA,19/05/2021 19:34,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN WHEEL OF CAR,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Assist trapped domestic animal,E05011252,SOUTH WOODFORD,E09000026,REDBRIDGE,Leytonstone,1.00022E+11,SOUTH VIEW DRIVE,22303249,E18,540911,189610,540950,189650,51.5877339,0.032624203\nNA,20/05/2021 12:59,2021,2021/22,Special Service,1,1,352,352,RUNNING CALL TO BIRD STUCK IN CHIMNEY,Bird,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000125,PLAISTOW AND SUNDRIDGE,E09000006,BROMLEY,Bromley,NULL,FAIRFIELD ROAD,20302301,BR1,NULL,NULL,540150,170250,NULL,NULL\nNA,21/05/2021 05:53,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED INBETWEEN DOOR,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000170,ACTON CENTRAL,E09000009,EALING,Acton,NULL,SUMMERLANDS AVENUE,20601662,W3,NULL,NULL,520150,180550,NULL,NULL\nNA,21/05/2021 15:32,2021,2021/22,Special Service,1,1,352,352,DOG STUCK IN HOLE,Dog,Person (mobile),Woodland/forest - conifers/softwood,Outdoor,Animal rescue from below ground,Animal rescue from below ground - Domestic pet,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,10093765957,CHISWICK HOUSE GROUNDS,21590044,W4,521026,177676,521050,177650,51.48510163,-0.258353744\nNA,21/05/2021 22:09,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED UNDER FLOOR,Cat,Person (land line),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000643,REGENT'S PARK,E09000033,WESTMINSTER,Paddington,NULL,AVENUE ROAD,8400533,NW8,NULL,NULL,527350,183450,NULL,NULL\nNA,22/05/2021 03:03,2021,2021/22,Special Service,1,1,352,352,Redacted,Snake,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving livestock - Other action,E05000449,NEW CROSS,E09000023,LEWISHAM,New Cross,NULL,HATFIELD CLOSE,22004809,SE14,NULL,NULL,535550,177150,NULL,NULL\nNA,22/05/2021 10:25,2021,2021/22,Special Service,1,1,352,352,PIGEON TRAPPED IN EXTRACTOR,Bird,Person (land line),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000349,CHISWICK RIVERSIDE,E09000018,HOUNSLOW,Chiswick,NULL,OXFORD ROAD SOUTH,21584700,W4,NULL,NULL,519650,178050,NULL,NULL\nNA,22/05/2021 12:55,2021,2021/22,Special Service,1,2,352,704,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000606,MARKHOUSE,E09000031,WALTHAM FOREST,Walthamstow,NULL,ALBERT ROAD,22801100,E17,NULL,NULL,537350,188750,NULL,NULL\nNA,22/05/2021 19:47,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED IN DERELICT BUILDING AT JUNCTION OF HURST ROAD    CALLER WILL MEET AND DIRECT,Cat,Person (mobile),Swimming Pool,Non Residential,Other animal assistance,Assist trapped domestic animal,E05011220,BLACKFEN & LAMORBEY,E09000004,BEXLEY,Sidcup,1.00023E+11,STATION ROAD,20101349,DA15,546163,172805,546150,172850,51.4353954,0.1014163\nNA,22/05/2021 22:40,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE - DOG WITH CHAIN WRAPPED AROUND NECK AND LEG - OWNER ON SCENE,Dog,Person (mobile),Converted Flat/Maisonette - Up to 2 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000226,PLUMSTEAD,E09000011,GREENWICH,Plumstead,NULL,KASHGAR ROAD,20800829,SE18,NULL,NULL,545750,178550,NULL,NULL\nNA,23/05/2021 14:22,2021,2021/22,Special Service,1,1,352,352,CAT STUCK DOWN SHAFT ON SIXTH FLOOR,Cat,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12183619,UXBRIDGE ROAD,20602194,W5,517256,180636,517250,180650,51.51250059,-0.311641048\nNA,23/05/2021 15:07,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000365,TURNHAM GREEN,E09000018,HOUNSLOW,Chiswick,NULL,WELLESLEY ROAD,21501177,W4,NULL,NULL,519850,178250,NULL,NULL\nNA,23/05/2021 15:13,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED BETWEEN TWO WALLS,Cat,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000634,CHURCH STREET,E09000033,WESTMINSTER,Paddington,NULL,CHURCH STREET,8400794,NW8,NULL,NULL,527050,182150,NULL,NULL\nNA,23/05/2021 16:32,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (land line),Hotel/motel,Other Residential,Other animal assistance,Assist trapped domestic animal,E05000173,EALING BROADWAY,E09000009,EALING,Ealing,12183619,UXBRIDGE ROAD,20602194,W5,517254,180638,517250,180650,51.51251447,-0.311670974\nNA,24/05/2021 05:04,2021,2021/22,Special Service,1,1,352,352,POSSIBLY INJURED CAT STUCK ON THIRD FLOOR    CALLER WILL MEET BRIGADE,Cat,Person (mobile),Converted Flat/Maisonettes - 3 or more storeys,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000275,NOEL PARK,E09000014,HARINGEY,Hornsey,NULL,MAYES ROAD,21104796,N22,NULL,NULL,531050,190050,NULL,NULL\nNA,25/05/2021 07:06,2021,2021/22,Special Service,1,1,352,352,PIGEON STUCK IN NETTING - AT TOP AND END OF BUILDING,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000200,GRANGE,E09000010,ENFIELD,Enfield,NULL,LONDON ROAD,20702812,EN2,NULL,NULL,532950,196150,NULL,NULL\nNA,25/05/2021 07:20,2021,2021/22,Special Service,1,1,352,352,SMALL DEER TRAPPED IN SCHOOL GATE,Deer,Person (mobile),Infant/Primary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000341,UXBRIDGE SOUTH,E09000017,HILLINGDON,Hillingdon,1.00023E+11,COWLEY ROAD,21400501,UB8,505466,183481,505450,183450,51.54038675,-0.480625471\nNA,25/05/2021 15:19,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED BEHIND MESH FOLLOWING WORKS ON RAILWAY BRIDGE OPPOSITE - JCTN OF ROPER LANE AND DRUID S,Bird,Person (mobile),Bridge,Outdoor Structure,Animal rescue from height,Animal rescue from height - Bird,E05011104,LONDON BRIDGE & WEST BERMONDSEY,E09000028,SOUTHWARK,Dockhead,10009806810,TOWER BRIDGE ROAD,22502529,SE1,533487,179811,533450,179850,51.5014816,-0.0781888\nNA,26/05/2021 22:19,2021,2021/22,Special Service,1,1,352,352,CAT STUCK ON ROOF,Cat,Person (land line),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000353,HANWORTH,E09000018,HOUNSLOW,Feltham,NULL,BARNLEA CLOSE,21500076,TW13,NULL,NULL,512450,172450,NULL,NULL\nNA,26/05/2021 23:55,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE,Unknown - Wild Animal,Person (land line),House - single occupancy,Dwelling,Animal rescue from below ground,Wild animal rescue from below ground,E05000595,FOREST,E09000031,WALTHAM FOREST,Leyton,NULL,COLCHESTER ROAD,22827800,E10,NULL,NULL,538250,187850,NULL,NULL\nNA,27/05/2021 15:08,2021,2021/22,Special Service,1,1,352,352,FOX STUCK IN GARAGE   RSPCA ON SCENE,Fox,Person (mobile),Private garage,Non Residential,Other animal assistance,Assist trapped wild animal,E05009317,BETHNAL GREEN,E09000030,TOWER HAMLETS,Bethnal Green,6131065,SCEPTRE ROAD,22701069,E2,535188,182718,535150,182750,51.5272007,-0.052579314\nNA,28/05/2021 13:07,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH BIRD TRAPPED IN GUTTERING,Bird,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000431,STREATHAM SOUTH,E09000022,LAMBETH,Norbury,NULL,GREYHOUND LANE,21900642,SW16,NULL,NULL,529950,170550,NULL,NULL\nNA,28/05/2021 14:49,2021,2021/22,Special Service,1,1,352,352,CAT STUCK IN WHEEL OF BUS,Cat,Person (mobile),Bus/coach station/garage,Non Residential,Other animal assistance,Assist trapped domestic animal,E05000599,HIGH STREET,E09000031,WALTHAM FOREST,Walthamstow,10091987569,VINER PLACE,22884308,E17,537232,189057,537250,189050,51.58367739,-0.02065325\nNA,28/05/2021 22:13,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000312,HAROLD WOOD,E09000016,HAVERING,Harold Hill,NULL,ARLINGTON GARDENS,21300957,RM3,NULL,NULL,554350,190850,NULL,NULL\nNA,29/05/2021 11:15,2021,2021/22,Special Service,1,1,352,352,FOX IN DISTRESS TRAPPED IN GOAL NETTING       AT REAR OF VACCINATION CENTRE     GOAL NEAREST TO THE,Fox,Person (mobile),Secondary school,Non Residential,Other animal assistance,Assist trapped wild animal,E05000210,TOWN,E09000010,ENFIELD,Enfield,207182937,CHURCHBURY LANE,20702602,EN1,533084,197599,533050,197550,51.66142935,-0.077254384\nNA,29/05/2021 15:18,2021,2021/22,Special Service,1,1,352,352,CAT FALLEN FROM WINDOW ONTO ROOF,Cat,Person (mobile),House - single occupancy,Dwelling,Animal rescue from height,Animal rescue from height - Domestic pet,E05000120,KELSEY AND EDEN PARK,E09000006,BROMLEY,Beckenham,NULL,WHITSTONE LANE,20304075,BR3,NULL,NULL,537950,167750,NULL,NULL\nNA,29/05/2021 21:41,2021,2021/22,Special Service,1,1,352,352,SMALL ANIMAL RESCUE  CAT TRAPPED BEHIND GATE - BEHIND THE ROYAL MAIL AND FORD DEPOT,Cat,Person (mobile),Warehouse,Non Residential,Animal rescue from height,Animal rescue from height - Domestic pet,E05000364,SYON,E09000018,HOUNSLOW,Heston,10093258175,LONDON ROAD,21519757,TW8,517062,177238,517050,177250,51.48199377,-0.315558576\nNA,29/05/2021 22:20,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Other animal assistance,Animal assistance involving domestic animal - Other action,E05011116,SOUTH BERMONDSEY,E09000028,SOUTHWARK,Dockhead,NULL,BOMBAY STREET,22500279,SE16,NULL,NULL,534550,178850,NULL,NULL\nNA,30/05/2021 12:58,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Lake/pond/reservoir,Outdoor,Animal rescue from height,Animal rescue from height - Bird,E05011100,DULWICH VILLAGE,E09000028,SOUTHWARK,Brixton,10025177987,ELMWOOD ROAD,22500904,SE24,532696,174764,532650,174750,51.45631467,-0.091464162\nNA,30/05/2021 16:12,2021,2021/22,Special Service,1,1,352,352,ASSIST RSPCA WITH FOX TRAPPED BETWEEN FENCE AND WALL,Fox,Person (land line),Fence,Outdoor Structure,Other animal assistance,Assist trapped wild animal,E05009385,STOKE NEWINGTON,E09000012,HACKNEY,Stoke Newington,1.00021E+11,FOULDEN ROAD,20900420,N16,533735,185872,533750,185850,51.55589708,-0.072313276\nNA,30/05/2021 17:10,2021,2021/22,Special Service,1,1,352,352,CAT TRAPPED UNDER WHEEL ARCH   SILVER CAR,Cat,Person (land line),Car,Road Vehicle,Animal rescue from water,Animal rescue from water - Domestic pet,E05000647,WARWICK,E09000033,WESTMINSTER,Lambeth,1.00023E+11,HUGH STREET,8400826,SW1V,528842,178677,528850,178650,51.49236935,-0.145485362\nNA,31/05/2021 01:17,2021,2021/22,Special Service,1,1,352,352,POSSIBLY INJURED CAT STUCK IN TREE,Cat,Person (mobile),Tree scrub,Outdoor,Animal rescue from height,Animal rescue from height - Domestic pet,E05011219,BEXLEYHEATH,E09000004,BEXLEY,Bexley,10025288069,BROOMFIELD ROAD,20100214,DA6,549341,174605,549350,174650,51.45074354,0.147866818\nNA,31/05/2021 11:18,2021,2021/22,Special Service,1,1,352,352,INJURED  CAT  TRAPPED BETWEEN WALLS,Cat,Person (mobile),House - single occupancy,Dwelling,Other animal assistance,Assist trapped domestic animal,E05000033,GORESBROOK,E09000002,BARKING AND DAGENHAM,Dagenham,NULL,COLEMAN ROAD,19900450,RM9,NULL,NULL,548450,184550,NULL,NULL\nNA,31/05/2021 14:42,2021,2021/22,Special Service,1,1,352,352,Redacted,Cat,Person (mobile),Car,Road Vehicle,Other animal assistance,Animal assistance involving domestic animal - Other action,E05000193,BOWES,E09000010,ENFIELD,Edmonton,207046874,PRINCES AVENUE,20704626,N13,531553,192036,531550,192050,51.61180374,-0.101453845\nNA,31/05/2021 15:47,2021,2021/22,Special Service,1,1,352,352,Redacted,Bird,Person (mobile),Purpose Built Flats/Maisonettes - 4 to 9 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000252,AVONMORE AND BROOK GREEN,E09000013,HAMMERSMITH AND FULHAM,Hammersmith,NULL,ADDISON BRIDGE PLACE,21000028,W14,NULL,NULL,524550,178950,NULL,NULL\nNA,31/05/2021 18:20,2021,2021/22,Special Service,1,1,352,352,BIRD TRAPPED IN FAN    IN ATTIC,Bird,Person (land line),House - single occupancy,Dwelling,Other animal assistance,Animal assistance involving wild animal - Other action,E05000178,GREENFORD GREEN,E09000009,EALING,Northolt,NULL,GREENFORD ROAD,20602353,UB6,NULL,NULL,514950,183550,NULL,NULL\nNA,31/05/2021 19:00,2021,2021/22,Special Service,1,1,352,352,PIGEON WITH FOOT TRAPPED IN GUTTER ABOVE QUICK FIT- CALLER ON SCENE,Bird,Person (mobile),Purpose Built Flats/Maisonettes - Up to 3 storeys,Dwelling,Animal rescue from height,Animal rescue from height - Bird,E05000600,HIGHAM HILL,E09000031,WALTHAM FOREST,Walthamstow,NULL,BILLET ROAD,22816150,E17,NULL,NULL,537250,190950,NULL,NULL"
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    "content": "cal_year,n\n2009,568\n2010,611\n2011,620\n2012,603\n2013,585\n2014,583\n2015,540\n2016,604\n2017,539\n2018,610\n2019,604\n2020,758\n"
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    "content": "# 代码来源于https://github.com/z3tt/TidyTuesday/blob/main/R/2021_27_AnimalRescues.Rmd\n# 与源代码有所改动，数据读取代码进行了改动。\nlibrary(tidyverse)\nlibrary(ggtext)\nlibrary(ggrepel)\nlibrary(patchwork)\nlibrary(systemfonts)\n\n\n# 主题设置 ====== \ntheme_set(theme_minimal(base_size = 19))\n\ntheme_update(\n  text = element_text(color = \"grey12\"),\n  axis.title = element_blank(),\n  axis.text.x = element_text(),\n  axis.text.y = element_blank(),\n  panel.grid.major.y = element_blank(),\n  panel.grid.minor = element_blank(),\n  plot.margin = margin(20, 5, 10, 10),\n  plot.subtitle = element_textbox_simple(size = 14,\n                                         lineheight = 1.6),\n  plot.title.position = \"plot\",\n  plot.caption = element_text( color = \"#b40059\", hjust = .5,\n                              size = 10, margin = margin(35, 0, 0, 0))\n)\n\n# 数据读取+处理 ========\ndf_animals <- readr::read_csv('animal_rescues.txt')\n\ndf_animals_agg <-\n  df_animals %>% \n  mutate(\n    animal_group_aggregated = case_when(\n      str_detect(animal_group_parent, \"Domestic|Livestock|Farm|Horse|Cow|Sheep|Goat|Lamb|Bull\") ~ \"Other Domestic Animals\",\n      animal_group_parent %in% c(\"Cat\", \"cat\") ~ \"Cats\",\n      animal_group_parent %in% c(\"Bird\", \"Budgie\") ~ \"Birds\",\n      animal_group_parent == \"Dog\" ~ \"Dogs\",\n      animal_group_parent == \"Fox\" ~ \"Foxes\",\n      TRUE ~ \"Other Wild Animals\"\n    )\n  ) %>% \n  count(cal_year, animal_group_aggregated) %>% \n  group_by(animal_group_aggregated) %>% \n  mutate(\n    total = sum(n),\n    current = n[which(cal_year == 2021)]\n  ) %>% \n  ungroup() %>% \n  mutate(\n    animal_group_aggregated = fct_reorder(animal_group_aggregated, total),\n    animal_group_aggregated = fct_relevel(animal_group_aggregated, \"Other Domestic Animals\", after = 0),\n    animal_group_aggregated = fct_relevel(animal_group_aggregated, \"Other Wild Animals\", after = 0)\n  )\n\ndf_animals_labs <-\n  df_animals_agg %>% \n  filter(cal_year == 2016) %>% \n  group_by(animal_group_aggregated) %>% \n  mutate(n = case_when(\n    animal_group_aggregated == \"Cats\" ~ 320,\n    animal_group_aggregated %in% c(\"Birds\", \"Dogs\") ~ 135,\n    TRUE ~ 55\n  ))\n\ndf_animals_annotate <-\n  df_animals_agg %>% \n  mutate(label = \"\\n\\n← Number of Rescues in 2021 so far.\") %>% \n  filter(cal_year == 2021 & animal_group_aggregated == \"Cats\")\n\ndf_animals_sum <-\n  df_animals_agg %>% \n  filter(cal_year < 2021) %>% \n  group_by(cal_year) %>% \n  summarize(n = sum(n))\n\n# write_csv(df_animals_sum,\"df_animals_sum.csv\")\n# df_animals_sum = read.csv(\"df_animals_sum.csv\")\n## 绘图 ==============\n\n  df_animals_sum %>% \n  ggplot(aes(cal_year, n)) +\n  geom_col(aes(fill = factor(cal_year)), width = .85) +\n  geom_col(\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\n    aes(alpha = cal_year == 2020), # 这里有细节！\n    fill = \"white\", width = .5 # 宽度和透明度设置\n  ) +\n  geom_text( #这里的数据处理：添加文本+加入rescues\n    data = df_animals_sum %>% \n      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nRescues\"), as.character(n))),\n    aes(label = n_lab), size = 4.3, lineheight = .8, \n    nudge_y = 12, vjust = 0, color = \"grey12\", fontface = \"bold\"\n  ) +\n  geom_text( #添加文本+加入cats\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021) %>% \n      mutate(n_lab = if_else(cal_year %in% c(2009, 2020), paste0(n, \"\\nCats\"), as.character(n))), \n    aes(label = n_lab), \n    color = \"white\", lineheight = .8, size = 4.3, \n    nudge_y = 12, vjust = 0, fontface = \"bold\"\n  ) +\n  geom_text( # 手动添加年份标签\n    data = df_animals_agg %>% filter(animal_group_aggregated == \"Cats\" & cal_year < 2021),\n    aes(y = -15, label = cal_year, color = factor(cal_year)), \n    size = 6, hjust = .5, vjust = 1\n  ) +\n  coord_cartesian(clip = \"off\") +#这想干嘛？\n  scale_y_continuous(limits = c(-15, NA)) +\n  scale_color_manual(values = c(rep(\"grey30\", 11), \"#b40059\"), guide = \"none\") +\n  scale_fill_manual(values = c(rep(\"grey30\", 11), \"#b40059\"), guide = \"none\") +\n  scale_alpha_manual(values = c(.25, .4), guide = \"none\") +\n  theme(\n    # plot.title = element_markdown(size = 28, margin = margin(5, 35, 25, 35), color = \"black\"),\n    # plot.subtitle = element_textbox_simple(margin = margin(5, 35, 15, 35)),\n    panel.grid.major = element_blank(),\n    axis.text.x = element_blank()\n  )\n\n\n\n"
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    "content": "head(myData)\n# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af\nlibrary(ggpmisc)\nlibrary(dplyr)\nlibrary(tibble)\nmyData <- filter(mpg, manufacturer == \"audi\" | manufacturer == \"chevrolet\")\nhead(myData)\ngg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) +\nfacet_wrap(~ manufacturer) +\ngeom_point(size = 3) +\ngeom_smooth(stat=\"identity\")\ngg\ntb <- myData %>%\ngroup_by(manufacturer, model) %>%\nsummarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%\nungroup()\ntb\nmpg\ntb\ntbs <- lapply(split(tb, tb$manufacturer), \"[\", -1)\ntbs\ntbs\ndf\ndf <- tibble(x = rep(-Inf, length(tbs)),\ny = rep(Inf, length(tbs)),\nmanufacturer = levels(as.factor(tb$manufacturer)),\ntbl = tbs)\ndf\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 1)\nrep(-Inf, length(tbs)\nrep(-Inf, length(tbs))\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 1, vjust = 1)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 0)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 1)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 12)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 2)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0, vjust = 1.2)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = 0.2, vjust = 1.2)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0。2, vjust = 1.2)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2)\nlibrary(viridis)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank())\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),legend.position = \"bottom\")\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\")\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(fill=guide_legend(nrow=1))\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(color=guide_legend(nrow=1))\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2,\ntable.theme = ttheme_gtstripes) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(color=guide_legend(nrow=1))\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2,\ntable.theme = ttheme_gtminimal) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(color=guide_legend(nrow=1))\n# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af\n# library(ggpmisc)\nlibrary(dplyr)\nlibrary(tibble)\nmyData <- filter(mpg, manufacturer == \"audi\" | manufacturer == \"chevrolet\")\nhead(myData)\ngg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) +\nfacet_wrap(~ manufacturer) +\ngeom_point(size = 3) +\ngeom_smooth(stat=\"identity\")\ntb <- myData %>%\ngroup_by(manufacturer, model) %>%\nsummarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%\nungroup()\ntbs <- lapply(split(tb, tb$manufacturer), \"[\", -1)\ndf <- tibble(x = rep(-Inf, length(tbs)),\ny = rep(Inf, length(tbs)),\nmanufacturer = levels(as.factor(tb$manufacturer)),\ntbl = tbs)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2)\nlibrary(viridis)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2,\ntable.theme = ttheme_gtstripes) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(color=guide_legend(nrow=1))\n# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af\n# library(ggpmisc)\nlibrary(ggpp)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\nhjust = -0.2, vjust = 1.2,\ntable.theme = ttheme_gtstripes) +\nscale_color_viridis(discrete = TRUE) + theme_bw() +\ntheme(panel.grid = element_blank(),\nlegend.position = \"bottom\",\nlegend.direction = \"horizontal\") +\nguides(color=guide_legend(nrow=1))\n"
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    "content": "# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af\n# library(ggpmisc)\nlibrary(ggpp)\nlibrary(dplyr)\nlibrary(tibble)\n\nmyData <- filter(mpg, manufacturer == \"audi\" | manufacturer == \"chevrolet\")\nhead(myData)\n\ngg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) + \n  facet_wrap(~ manufacturer) + \n  geom_point(size = 3) +\n  geom_smooth(stat=\"identity\")\n\ntb <- myData %>%\n  group_by(manufacturer, model) %>%\n  summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%\n  ungroup() \n\ntbs <- lapply(split(tb, tb$manufacturer), \"[\", -1)\ndf <- tibble(x = rep(-Inf, length(tbs)), \n             y = rep(Inf, length(tbs)), \n             manufacturer = levels(as.factor(tb$manufacturer)), \n             tbl = tbs)\n\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\n                hjust = -0.2, vjust = 1.2) \n\nlibrary(viridis)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\n                hjust = -0.2, vjust = 1.2,\n                table.theme = ttheme_gtstripes) + \n  scale_color_viridis(discrete = TRUE) + theme_bw() +\n  theme(panel.grid = element_blank(),\n        legend.position = \"bottom\",\n        legend.direction = \"horizontal\") +\n  guides(color=guide_legend(nrow=1))\n  \n\n\n"
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    "path": "2023年/2023.12.02 分面中添加不同表格/分面中添加不同表格.R",
    "content": "# https://stackoverflow.com/questions/25554548/adding-sub-tables-on-each-panel-of-a-facet-ggplot-in-r/58464596#58464596?newreg=9fc3d38711e945d1b5d37847f0f5d0af\n# library(ggpmisc)\nlibrary(ggpp)\nlibrary(dplyr)\nlibrary(tibble)\n\nmyData <- filter(mpg, manufacturer == \"audi\" | manufacturer == \"chevrolet\")\nhead(myData)\n\ngg <- ggplot(myData, aes(x=hwy, y=cty, colour=model)) + \n  facet_wrap(~ manufacturer) + \n  geom_point(size = 3) +\n  geom_smooth(stat=\"identity\")\n\ntb <- myData %>%\n  group_by(manufacturer, model) %>%\n  summarize(var1 = round(mean(displ)), var2 = round(mean(cyl))) %>%\n  ungroup() \n\ntbs <- lapply(split(tb, tb$manufacturer), \"[\", -1)\ndf <- tibble(x = rep(-Inf, length(tbs)), \n             y = rep(Inf, length(tbs)), \n             manufacturer = levels(as.factor(tb$manufacturer)), \n             tbl = tbs)\n\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\n                hjust = -0.2, vjust = 1.2) \n\nlibrary(viridis)\ngg + geom_table(data = df, aes(x = x, y = y, label = tbl),\n                hjust = -0.2, vjust = 1.2,\n                table.theme = ttheme_gtstripes) + \n  scale_color_viridis(discrete = TRUE) + theme_bw() +\n  theme(panel.grid = element_blank(),\n        legend.position = \"bottom\",\n        legend.direction = \"horizontal\") +\n  guides(color=guide_legend(nrow=1))\n  \n\n\n"
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    "content": "library(ggplot2)\nlibrary(tidyverse)\nlibrary(viridis)\ntime2 = seq(3,20,3) #希望展示的数据点（离散）\nload(\"true_data.RData\")\ncal_data\nload(\"data_fit.RData\")\ndata_fit\n\nboxplot.path.fit = function(data_fit = data_fit, cal_data = cal_data, leg.pos = \"none\"){\n  \n  merged_df2 <- bind_rows(data_fit, .id = \"Unit\") #合并数据\n  merged_df2$Unit = rep(c(\"Low\",\"Mean\",\"Up\"),each = length(0:m))\n  mer_dat = merged_df2 %>% pivot_longer(cols = !c(Time,Unit), names_to = \"PC\", values_to = \"Value\")\n  # 数据筛选，用于画直线\n  mer_dat1 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Low\", 2:4]; colnames(mer_dat1) = c(\"Unit\",\"PC\",\"value\")\n  mer_dat2 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Mean\", 2:4]; colnames(mer_dat2) = c(\"Unit\",\"PC\",\"value\")\n  mer_dat3 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Up\", 2:4]; colnames(mer_dat3) = c(\"Unit\",\"PC\",\"value\")\n  \n  p1 = ggplot() + \n    geom_boxplot(data = cal_data, aes(factor(Unit,levels = time2),value,fill=factor(Unit,levels = time2))) +\n    geom_smooth(data= mer_dat1, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#EE81C3\", method=\"loess\", linetype = 2,se = FALSE) +\n    geom_smooth(data= mer_dat2, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#DC3F20\", method=\"loess\",linetype = 1,se = FALSE) +\n    geom_smooth(data= mer_dat3, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#EE81C3\", method=\"loess\",linetype = 2,se = FALSE) +\n    facet_wrap(vars(PC),scale=\"free\") +\n    scale_fill_viridis(discrete = TRUE,alpha = 0.8) + \n    theme_bw() + theme(panel.grid = element_blank(),legend.position = leg.pos) +\n    xlab(\"Time\") + ylab(\"Y(t)\")\n  \n  return(p1)\n}\n\nboxplot.path.fit(data_fit = data_fit, cal_data = cal_data, leg.pos = \"none\")\nggsave(\"boxplot_path_fit.pdf\")\n"
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    "path": "2023年/2023.12.16 分面中添加拟合曲线/分面中添加拟合曲线.R",
    "content": "library(ggplot2)\nlibrary(tidyverse)\nlibrary(viridis)\ntime2 = seq(3,20,3) #希望展示的数据点（离散）\nload(\"true_data.RData\")\ncal_data\nload(\"data_fit.RData\")\ndata_fit\n\nboxplot.path.fit = function(data_fit = data_fit, cal_data = cal_data, leg.pos = \"none\"){\n  \n  merged_df2 <- bind_rows(data_fit, .id = \"Unit\") #合并数据\n  merged_df2$Unit = rep(c(\"Low\",\"Mean\",\"Up\"),each = length(0:m))\n  mer_dat = merged_df2 %>% pivot_longer(cols = !c(Time,Unit), names_to = \"PC\", values_to = \"Value\")\n  # 数据筛选，用于画直线\n  mer_dat1 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Low\", 2:4]; colnames(mer_dat1) = c(\"Unit\",\"PC\",\"value\")\n  mer_dat2 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Mean\", 2:4]; colnames(mer_dat2) = c(\"Unit\",\"PC\",\"value\")\n  mer_dat3 = mer_dat[mer_dat$\"Time\" %in% time2 & mer_dat$\"Unit\" == \"Up\", 2:4]; colnames(mer_dat3) = c(\"Unit\",\"PC\",\"value\")\n  \n  p1 = ggplot() + \n    geom_boxplot(data = cal_data, aes(factor(Unit,levels = time2),value,fill=factor(Unit,levels = time2))) +\n    geom_smooth(data= mer_dat1, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#EE81C3\", method=\"loess\", linetype = 2,se = FALSE) +\n    geom_smooth(data= mer_dat2, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#DC3F20\", method=\"loess\",linetype = 1,se = FALSE) +\n    geom_smooth(data= mer_dat3, aes(factor(Unit,levels = time2),value,group=1),\n                color=\"#EE81C3\", method=\"loess\",linetype = 2,se = FALSE) +\n    facet_wrap(vars(PC),scale=\"free\") +\n    scale_fill_viridis(discrete = TRUE,alpha = 0.8) + \n    theme_bw() + theme(panel.grid = element_blank(),legend.position = leg.pos) +\n    xlab(\"Time\") + ylab(\"Y(t)\")\n  \n  return(p1)\n}\n\nboxplot.path.fit(data_fit = data_fit, cal_data = cal_data, leg.pos = \"none\")\n\n"
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  {
    "path": "2023年/合并图形+共享图例/合并图形-共享图例.R",
    "content": "library(ggplot2)\nlibrary(viridis)\n## 创建图形\nplot1 <- ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +\n  geom_point(size=1.7) + scale_color_viridis(discrete = T) + \n  theme_bw() + theme(panel.grid = element_blank())  \n  \nplot2 <- ggplot(data = mpg, aes(x = cty, y = hwy, color = class)) +\n  geom_point(size=1.7) + scale_color_viridis(discrete = T) +\n  theme_bw() + theme(panel.grid = element_blank())\n\nggarrange(plot1, plot2)\n\n\n# 合并图形并共享图例\n## 方法一\nlibrary(ggpubr)\nggarrange(plot1, plot2, common.legend = TRUE, legend=\"top\")\n## 方法二\nlibrary(cowplot)\ncombined_plot <- plot_grid(plot1 + theme(legend.position = 'none'), plot2 + theme(legend.position = 'none'), ncol = 2)\n# 将图例添加到合并后的图形中\nplot_grid(combined_plot, get_legend(plot1),\n          rel_widths = c(4, 1))\n## 方法三\nlibrary(patchwork)\nplot1 + plot2 + plot_layout(guides = \"collect\") &\n  theme(legend.position='bottom')\n\n"
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    "path": "2024年/2024.02.07 多分类的条形图并标记数字/.Rhistory",
    "content": "# 科研论文中常用的图形（可靠性）\n# 多分类条形图\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(forcats)\n# 示例数据\nR_all <- data.frame(\nScen = rep(c(\"A\", \"B\", \"C\"), each = 5),\nModel = rep(c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"), 3),\nvalue = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)\n)\n# 设置 Model 列的因子顺序\nmodel_order <- c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\")\n# 绘制柱状图（版本一）\nggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\ngeom_col(position = \"dodge\", alpha = 0.8) +\ngeom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\nscale_fill_viridis(discrete = TRUE, name = \"Model\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position = \"bottom\")\n#更加复杂的场景(多一个case变量，使用分面)\n# 示例数据\nR_all2 = rbind(R_all,R_all)\nR_all2$case = rep(c(\"Case 1\", \"Case 2\"),each=15)\nR_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)\nggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\ngeom_col(position = \"dodge\", alpha = 0.8) +\ngeom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\nfacet_wrap(vars(factor(case))) +\nscale_fill_viridis(discrete = TRUE, name = \"Model\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position = \"bottom\")\n# 科研论文中常用的图形（可靠性）\n# 多分类条形图\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(forcats)\n# 示例数据\nR_all <- data.frame(\nScen = rep(c(\"A\", \"B\", \"C\"), each = 5),\nModel = rep(c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"), 3),\nvalue = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)\n)\n# 设置 Model 列的因子顺序\nmodel_order <- c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\")\n# 绘制柱状图（版本一）\nggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\ngeom_col(position = \"dodge\", alpha = 0.8) +\ngeom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\nscale_fill_viridis(discrete = TRUE, name = \"Model\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position = \"bottom\")\n#更加复杂的场景(多一个case变量，使用分面)\n# 示例数据\nR_all2 = rbind(R_all,R_all)\nR_all2$case = rep(c(\"Case 1\", \"Case 2\"),each=15)\nR_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)\nggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\ngeom_col(position = \"dodge\", alpha = 0.8) +\ngeom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\nfacet_wrap(vars(factor(case))) +\nscale_fill_viridis(discrete = TRUE, name = \"Model\") +\ntheme_bw() +\ntheme(panel.grid = element_blank(), legend.position = \"bottom\")\n"
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    "content": "# 科研论文中常用的图形（可靠性） =======\n# 推文：《ggplot 绘制多分类条形图并标记数字》\n# 链接：https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247520840&idx=1&sn=1a13fdace99353fb984d8b1ff9ae5c99&chksm=ea276dacdd50e4ba2b668a0ccb2feb87079cf0bb7b8eae26fe51122e5031cd5ca4e289f6e0f8&token=1416591993&lang=zh_CN#rd\n\n# 多分类条形图\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(forcats)\n# 示例数据\nR_all <- data.frame(\n  Scen = rep(c(\"A\", \"B\", \"C\"), each = 5),\n  Model = rep(c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"), 3),\n  value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)\n)\n\n# 设置 Model 列的因子顺序\nmodel_order <- c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\")\n\n# 绘制柱状图（版本一）\nggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\n  geom_col(position = \"dodge\", alpha = 0.8) +\n  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\n  scale_fill_viridis(discrete = TRUE, name = \"Model\") +\n  theme_bw() +\n  theme(panel.grid = element_blank(), legend.position = \"bottom\")\n\n\n#更加复杂的场景(多一个case变量，使用分面)\n\n# 示例数据\nR_all2 = rbind(R_all,R_all)\nR_all2$case = rep(c(\"Case 1\", \"Case 2\"),each=15)\nR_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)\n\nggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\n  geom_col(position = \"dodge\", alpha = 0.8) +\n  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\n  facet_wrap(vars(factor(case))) + \n  scale_fill_viridis(discrete = TRUE, name = \"Model\") +\n  theme_bw() +\n  theme(panel.grid = element_blank(), legend.position = \"bottom\")\n\n\n"
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    "content": "# 科研论文中常用的图形（可靠性） =======\n# 推文：《ggplot 绘制多分类条形图并标记数字》\n# 链接：https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247520840&idx=1&sn=1a13fdace99353fb984d8b1ff9ae5c99&chksm=ea276dacdd50e4ba2b668a0ccb2feb87079cf0bb7b8eae26fe51122e5031cd5ca4e289f6e0f8&token=1416591993&lang=zh_CN#rd\n\n# 多分类条形图\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(forcats)\n# 示例数据\nR_all <- data.frame(\n  Scen = rep(c(\"A\", \"B\", \"C\"), each = 5),\n  Model = rep(c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"), 3),\n  value = c(10, 15, 8, 12, 7, 9, 14, 18, 5, 10, 8, 11, 6, 8, 12)\n)\n\n# 设置 Model 列的因子顺序\nmodel_order <- c(\"M1\", \"M2\", \"M3\", \"M4\", \"M5\")\n\n# 绘制柱状图（版本一）\nggplot(R_all, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\n  geom_col(position = \"dodge\", alpha = 0.8) +\n  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\n  scale_fill_viridis(discrete = TRUE, name = \"Model\") +\n  theme_bw() +\n  theme(panel.grid = element_blank(), legend.position = \"bottom\")\n\n\n#更加复杂的场景(多一个case变量，使用分面)\n\n# 示例数据\nR_all2 = rbind(R_all,R_all)\nR_all2$case = rep(c(\"Case 1\", \"Case 2\"),each=15)\nR_all2$value = round(R_all2$value + 1* rnorm(30,0,1),2)\n\nggplot(R_all2, aes(x = Scen, y = value, fill = fct_relevel(Model, model_order))) +\n  geom_col(position = \"dodge\", alpha = 0.8) +\n  geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.5) +  # 添加标签\n  facet_wrap(vars(factor(case))) + \n  scale_fill_viridis(discrete = TRUE, name = \"Model\") +\n  theme_bw() +\n  theme(panel.grid = element_blank(), legend.position = \"bottom\")\n\n\n"
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    "content": "library(tidyverse)\nlibrary(viridis)\ndata = read.csv(\"demo_dat.csv\")\nhead(data) \n\ntoselect <- c(\"ananas\",\"rongdianer\",\"rainie.tian\", \"vivakatt\",\"nya\", \"liangliang\",\"nannan\", \"joly\",\"lizlu\")\n\ndata2 <- as_tibble(data)  %>% filter(NAME %in% toselect) %>%\n  arrange(match(NAME, toselect)) %>% \n  pivot_longer(-NAME, names_to = \"centrality\" , values_to = \"value\") %>% \n  group_by(centrality) %>% \n  arrange(desc(value)) %>% \n  mutate(order = row_number()) %>% \n  ungroup %>% mutate(NAME = factor(NAME, levels = toselect)) %>% \n  mutate(centrality = factor(centrality, levels = c(\"A\" , \"B\", \"C\" , \"D\")))\n\ndata2 %>%\n  ggplot(aes(factor(NAME,levels = toselect), value, fill = NAME)) + \n  geom_bar(stat = \"identity\") + \n  facet_wrap(vars(centrality), scales = \"free\" ,nrow = 1) + \n  coord_flip() + \n  geom_label(aes(label = order), colour = \"white\", size = 3) + \n  scale_x_discrete(limits = rev(levels(data2$NAME))) + \n  scale_fill_viridis(discrete = TRUE) +\n  theme_bw() +\n  theme(plot.title = element_blank(),\n        panel.grid = element_blank(),\n        axis.title.x = element_blank(), \n        axis.title.y = element_blank(), \n        panel.spacing.x = unit(4, \"mm\"), \n        legend.position = \"none\")\n"
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  {
    "path": "2024年/2024.02.19 柱状图+分面/demo_dat.csv",
    "content": "NAME,A,B,C,D\r\nsteamed.bun,35,43.07327007,0.330225175,488.3134921\r\nYCC,47,100,4.326085414,19035.97772\r\nsword.sister,47,49.42830214,0.68037295,797.1241113\r\nqianhu.changsheng,18,43.18077589,1.77364794,287.5858994\r\nyihua,15,27.14853047,2.374004417,8281.834945\r\nrex,30,64.11946526,5.283012691,0\r\nxiaozhuang,23,38.3994967,0.674876203,3571.711403\r\nfather.rabbit,43,80.71350494,0.90036657,2893.305091\r\nadvenced.hania,16,29.01789266,0.592592443,954.8038248\r\nananas,48,93.48849769,6.031732638,0\r\nMiss.xu,34,66.77709531,3.841465863,2034.987784\r\nteresa,13,25.60066856,1.760791184,8498.542422\r\nsweeter,25,44.43754034,2.665977469,5999.390437\r\nwen.buding,54,65.36168342,0.341463415,7933.670757\r\nkawkao,25,85.82311134,0.838447011,22156.41883\r\nzzreal,19,89.44727102,1.223651352,17756.39133\r\npony,39,34.72956598,1.127418586,557.32665\r\necho.liang,8,81.83585971,0.318021201,0\r\nlizi.don.t.have.small.waist,32,14.46027951,1.27973252,2442.932665\r\nsuzy_z,26,60.17982578,2.27205517,321.484904\r\nlizlu,17,47.56799364,3.640444023,9011.031245\r\noh.emma,16,38.1624698,1.803064081,341.7474488\r\nnya,26,31.55011987,3.094480257,10586.24213\r\naustin,35,49.81429148,1.574941864,4521.797339\r\nqiala.fang,43,64.90544892,3.101730833,405.9931735\r\nqiuqiu,23,82.29267411,1.120421247,2049.991219\r\nnicole,35,70.8466772,2.631761941,2849.652445\r\nshian.cheng,33,67.45075028,2.65187965,1707.417564\r\nnannan,21,37.43177419,1.340004358,970.7874774\r\nmalicious.girl,17,31.96709284,0.788080064,409.108063\r\nliangliang,22,75.47390896,4.686843639,953.950377\r\nfather.xian,27,68.38136332,0.382504026,808.3814131\r\nvivakatt,31,25.79500899,0.319391635,4487.43143\r\nshiyin,23,28.58403995,2.374630135,3462.449352\r\nrainie.tian,39,72.16853763,0.431840485,10951.40967\r\nsamchak,19,6.441398435,4.671480096,571.5805792\r\nOld.liu,14,24.25692704,1.463194315,540.8838545\r\nan.pan,20,38.12269694,5.347062632,637.1347646\r\nhandsome.maodan,11,15.82109163,2.619991687,0\r\nvickysoupsss,14,27.0200177,1.815183324,4844.607512\r\nclever.dang,40,72.80533262,0.584335384,4747.497759\r\nrongdianer,40,42.73742596,4.296965496,7579.09566\r\nxiran.fu,38,61.74085799,1.815183324,8227.066978\r\nzlks,15,55.34274479,1.507494108,243.0790859\r\nbe.bald.for.900.years,14,39.8404343,1.050649095,1029.805877\r\npink.li,36,72.94033991,1.731313444,3848.163219\r\nvivi,5,35.59707319,1.050649095,7.692307692\r\njoly,18,32.75056853,0.625731023,468.7201906\r\nchifan.chen,9,17.00064587,0.335664336,0\r\nhu,14,21.51426748,1.075503418,1425.479608\r\n"
  },
  {
    "path": "2024年/2024.02.19 柱状图+分面/project.Rproj",
    "content": "Version: 1.0\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 2\nEncoding: UTF-8\n\nRnwWeave: Sweave\nLaTeX: XeLaTeX\n\nBuildType: Website\n"
  },
  {
    "path": "2024年/2024.02.19 柱状图+分面/柱状图+分面.R",
    "content": "# ggplot 绘制分面条形图并标记数字 ====\n# 来自公众号《庄闪闪的R语言手册》——庄闪闪\n# 数据导入\nlibrary(tidyverse)\nlibrary(viridis)\ndata = read.csv(\"demo_dat.csv\")\nhead(data) \n\n# 数据处理\ntoselect <- c(\"ananas\",\"rongdianer\",\"rainie.tian\", \"vivakatt\",\"nya\", \"liangliang\",\"nannan\", \"joly\",\"lizlu\")\ndata2 <- as_tibble(data)  %>% filter(NAME %in% toselect) %>%\n  arrange(match(NAME, toselect)) %>% \n  pivot_longer(-NAME, names_to = \"centrality\" , values_to = \"value\") %>% \n  group_by(centrality) %>% \n  arrange(desc(value)) %>% \n  mutate(order = row_number()) %>% \n  ungroup %>% mutate(NAME = factor(NAME, levels = toselect)) %>% \n  mutate(centrality = factor(centrality, levels = c(\"A\" , \"B\", \"C\" , \"D\")))\n\n# 绘图\ndata2 %>%\n  ggplot(aes(factor(NAME,levels = toselect), value, fill = NAME)) + \n  geom_bar(stat = \"identity\") + \n  facet_wrap(vars(centrality), scales = \"free\" ,nrow = 1) + \n  coord_flip() + \n  geom_label(aes(label = order), colour = \"white\", size = 3) + \n  scale_x_discrete(limits = rev(levels(data2$NAME))) + \n  scale_fill_viridis(discrete = TRUE) +\n  theme_bw() +\n  theme(plot.title = element_blank(),\n        panel.grid = element_blank(),\n        axis.title.x = element_blank(), \n        axis.title.y = element_blank(), \n        panel.spacing.x = unit(4, \"mm\"), \n        legend.position = \"none\")\n"
  },
  {
    "path": "README.md",
    "content": "# 公众号资料留档\n\n## 简介\n\n公众号对应的资料（代码+数据），如有错误，欢迎大家批评指正。\n\n作者：庄闪闪/庄亮亮\n\n微信公众号：[庄闪闪的R语言手册](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MzI1NjUwMjQxMQ==&action=getalbum&album_id=1476187590709002243&scene=173&from_msgid=2247486060&from_itemidx=1&count=3#wechat_redirect) \n\n合作请加微信号：a641292753\n\n\n\n[TOC]\n\n-------\n\n# R可视乎系列\n\n- 高级棒棒图\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/image-20230513214535597.png)\n\n- [ggparty包：构建个性化树状图形](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247517054&idx=1&sn=664b88b42061fb2f2979b01833c363bf&chksm=ea277c9add50f58c172f08b246d4f88d507c0dacda981796599efeeb48d5298eb5cb72ab7af4&token=1444533026&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093057588.png)\n\n- [如何一步步提高图形 B 格？以 ggplot 绘图为例](https://mp.weixin.qq.com/s/k5VgsK7e4d9RDrTkctJP6g)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102342404.png)\n\n- [基于 R 语言的科研论文绘图技巧汇总](https://mp.weixin.qq.com/s/j9ribyJ809pErDGQXL_Usw)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/figure_example.png)\n\n> 代码参考：[scifig_plot_examples_R: Scientific publication figure plotting examples with R](https://github.com/marco-meer/scifig_plot_examples_R)\n\n\n\n- [使用 ggTimeSeries 包构建日历图](https://mp.weixin.qq.com/s/rAlPbNh2bYuNkqyT9dxosQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/%E5%9B%BE%E5%BD%A21.jpeg)\n\n- [基于 ggdensity 包的等高线绘制](https://mp.weixin.qq.com/s/IRSvobzOfqmYtfBXDpbdJw)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093725312.png)\n\n- [基于 ggridges 绘制剩余使用寿命密度图](https://mp.weixin.qq.com/s/ECquLeoHaJ2byWgrRpqftw)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093728804.png)\n\n- [使用 ggcharts 包高亮部分内容](https://mp.weixin.qq.com/s/Rml11BcLE5_fLKckfob9Wg)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514093824511.png)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102409266.png)\n\n\n\n- [2023年日历大派送](https://mp.weixin.qq.com/s/BdE2Zb97iDyp2NpsoELouQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102415734.jpeg)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094407585.png)\n\n- [使用 ggplot2 绘制比较各省份及其区县的详细数据](https://mp.weixin.qq.com/s/KzuT4nnGbRqccBccVXlouQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102425063.png)\n\n- [使用 ggplot2 绘制单个和多个省份地图](https://mp.weixin.qq.com/s/9Y7z0XVHv-sMRSiwY6PnBw)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094600488.png)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514094602623.png)\n\n- [R 语言绘制十段线地图，给特定省份填色](https://mp.weixin.qq.com/s/ZjUuFtRT1j3ksaGD-hHnLg)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102438571.png)\n\n- [R绘图案例｜基于分面的面积图绘制](https://mp.weixin.qq.com/s/alyuUumkwqC08RJilXmfeA)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102446221.png)\n\n- [分面中添加不同的直线](https://mp.weixin.qq.com/s/JdfKl9vEqkE0rcEfz6lBBg)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102453306.png)\n\n\n\n- [R绘图案例｜基于分面的折线图绘制](https://mp.weixin.qq.com/s/apu5-4ykHe0QTUbdJKy-MQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102459934.png)\n\n- [如何在分面中添加数学表达式标签?](https://mp.weixin.qq.com/s/Dbx9J_flkTtyi7LtRAdRPQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102505599.png)\n\n\n\n- [ggplot 分面的细节调整汇总](https://mp.weixin.qq.com/s/fzcGJugs0VxJpM6nb25hLA)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100200680.png)\n\n- [老板让你复现一个图片，你会使用什么软件？](https://mp.weixin.qq.com/s/V8H-U719PjCgsn1lVY_PFg)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100153931.png)\n\n- [R 案例｜绘制不同分布的 QQ 图](https://mp.weixin.qq.com/s/ncorK41sntA4iBJWBCdVkA)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100148744.png)\n\n- [绘制混合密度函数图以及添加分位数线](https://mp.weixin.qq.com/s/bQ_aefTxk32x30Avfzv5kw)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100144955.png)\n\n- [如何使用 ggplot2 绘制双轴分离图？](https://mp.weixin.qq.com/s/QiMHA10X8nGtK5iOH4armQ)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100140481.png)\n\n\n\n- igraph包——绘制网络图 [2020.07.13Network_igraph](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=100000088&idx=1&sn=3b7b79c2587e930a79cf11a84bcdb3e4&chksm=6a24febc5d5377aa3615fb6a0e3ec4e383162d694ad50d6a414482f8244de447ca5b2922c352&mpshare=1&scene=1&srcid=0714Vu2WALNMi27VcRfIFXH9&sharer_sharetime=1594708423633&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=3c22c88777da856c2f08a877b1939ceea75ba76d52067d706d44418948b4568c7e4e0ed9e4f0d00539c1e2cbbf51700d4f0b4f2642d757270ed6eeca83bbffa59947c9f38399e0e6cd58299f9d1d61e6&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=Ax5Q4wOen8wg5nPK10PFOqA%3D&pass_ticket=phh%2BTyTOwain33l3gWNzH4Aki97YE7dlcnlLuCxtFuuIrAtl234GrZ237NODA6HD)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100135963.jpeg)\n\n\n- 国内疫情图绘制（静态）  [2020.07.14China_map](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483799&idx=1&sn=da1acf1004a6d2f25cc296bcaf972063&chksm=ea24fe73dd537765132f64e79f7ba86ec225ecf6e81c43ba4d7aec9a75df42062728e110ad0e&mpshare=1&scene=1&srcid=07142HT0hZAFaVLTlpac4zAV&sharer_sharetime=1594708107631&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=65fe14863667915bc7e0151836657ceb6c523eaf07ca95785ed82332713ffd495369b1fd5b97cefc7cf7a8e1c66a5f880921fba19ce1d0e4832cb61609b1bcc76d99204dbc6dd77b97c47ad62d3e0e56&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=Aw4fL5T557lBWbSWfFSE37M%3D&pass_ticket=phh%2BTyTOwain33l3gWNzH4Aki97YE7dlcnlLuCxtFuuIrAtl234GrZ237NODA6HD)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100128773.jpeg)\n\n\n- R中坐标轴截断的不同实现方式 [2020.07.22坐标轴截断画图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=100000243&idx=1&sn=119c4039f27b351a367ca40d1a54f0a7&chksm=6a24fe175d5377014aa610e3b5080d08e5e7f58447ca89f8c4806d418df61a8027353dd8156f#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514095854330.png)\n\n\n- R可视乎|饼图[2020.07.24饼图与圆环图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=1&sn=cc087b09448e75ed54b5364accfb3bff&chksm=ea24fd6add53747ca57137b964bdb0db371220737398e4276ee1ede27df7f09be0afd14185f8&mpshare=1&scene=1&srcid=0816aG9PZi3BixNvL0cNcVhp&sharer_sharetime=1597541989820&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=b936ead840dca7f6e5a6a9b2f01076516390ed96ff9f73bc52a446143a88f2b01f9b378ea6423ce540c7591d1f324e00481858da6cb7dc5d8b4bf6d6cab3e79763433d2818f9952e1ff4ed49e93412026b6771123e151a7f6242d0b63a58e8da7f010dd41e4e833ef9f4766ee2e8130626e801b5a3cd32b43c38a1a9809099d9&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A0VSObDydAW7hVulHfsWDDE%3D&pass_ticket=LZqYAanUOJecq6uEoCOQznjsykTBUbS15CZIvi%2FJtgBFhDLyBvXsaDBBnPRaSb0n)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100123331.png)\n\n\n- R可视乎|分面一页多面[2020.08.15分面](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&token=222682915&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100108861.png)\n\n\n- R可视乎|马赛克图[2020.08.25马赛克](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484309&idx=1&sn=d79ce748d43fe066a0bad0221ae8e068&chksm=ea24fc71dd537567c3ca322cb216e92c2ca4bdd96c20c9873e762d81cfb892583af10198b757&token=682523778&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11861684029650_.pic.jpg)\n\n\n- R可视乎|混合多个图形[2020.08.26混合多个图形](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484367&idx=1&sn=30a54bd7dbf44852c380192d11a10ab9&chksm=ea24fc2bdd53753d239bbe995dfcb88232223e0a24f595acb343433fd4a053116e9f26dbf983&token=682523778&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11871684029811_.pic.jpg)\n\n\n- R可视乎|华夫饼图[2020.09.01华夫饼图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484631&idx=1&sn=6402ba80ab417c1d8a99c7778a48c7db&chksm=ea24fb33dd537225a7488a578b876b6da2f2157e0048b9515100d4ab64301fad2ad4f460e822&token=682523778&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11881684029842_.pic.jpg)\n\n\n- R可视乎|散点图系列（1）[2020.10.09散点图系列一](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&token=682523778&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11891684029866_.pic.jpg)\n\n\n- R可视乎|散点图系列（2）[2020.10.27散点图系列二](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&token=682523778&lang=zh_CN#rd)\n\n> 以下几篇内容视为《R语言数据可视化之美》书籍的学习笔记。\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/11901684029902_.pic.jpg)\n\n\n- R可视乎|气泡图[2020.11.12气泡图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&token=682523778&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100544500.png)\n\n\n- R可视乎|三维散点图[2020.11.22三维散点图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=1&sn=b47d8f89f032bbf1ead4f0db75d45ef7&chksm=ea24f47add537d6c5d50a73bd64318f473071e1474be95c9d5c2f250a39a64a6877ba4d61b68&token=761595288&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102548544.png)\n\n\n- R可视乎|等高线图[2020.11.28等高线](https://mp.weixin.qq.com/s/eI_7qkItkqa6kHwRWM1CXA)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100817869.png)\n\n\n- R可视乎|瀑布图[2020.12.07瀑布图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&token=86432493&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100832157.png)\n\n\n- R可视乎|私人定制专属日历[2020.12.05日历](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&token=86432493&lang=zh_CN#rd)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100900010.png)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514100924520.png)\n\n\n- R可视乎|峰峦图[2020.12.25峰峦图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&token=86432493&lang=zh_CN#rd)\n\n> 这篇内容很多，点击查看\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102607135.png)\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102614445.png)\n\n\n\n![](https://gitee.com/zhuang_liang_liang0825/other/raw/master/640-20230514102620236.png)\n\n\n\n-----\n\n# R包介绍\n\n- reticulate包:数据科学者的福音[2020.09.15reticulate](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484515&idx=1&sn=26b03b6ad26f2315cdc04049f740f1c0&chksm=ea24fb87dd537291d5184c28a9c9f2cdda591e4c17a7e7daaff34a9a1c3949ee0e86f9b355b7&token=682523778&lang=zh_CN#rd)\n\n- ggpubr包:制可以学术发表的图 [2020.10.30ggpubr](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=1&sn=47ac21f131bf2ac6c90c50fb9fb7966b&chksm=ea24f74bdd537e5d74f60919388f683dfe779fe8a2d11999e55e290d4bdb25c64e36cc74ccc1&token=682523778&lang=zh_CN#rd)\n\n\n- gghalves包：你五毛我五毛[2020.11.14gghalves](https://mp.weixin.qq.com/s/WaMCCmT2eAP9DmCjPOtcMg)\n\n- flexdashboard包：用于R的简单交互式仪表盘[2020.11.16flexdashboard](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&token=1801328502&lang=zh_CN#rd)\n\n- esquisse包:不写代码生成ggplot图[2020.12.21esquisse包](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488200&idx=1&sn=3a058480b104165118975b2d908dff72&chksm=ea24ed2cdd53643a9deb58069cd8d0e9933fc165994a2bb7a6f7d4651c7796b839fc781ec86d&token=86432493&lang=zh_CN#rd)\n\n- ggridges包:绘制峰峦图[2020.12.25峰峦图](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&token=86432493&lang=zh_CN#rd)\n\n- [ggvis包—数据可视化交互](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=1&sn=271fc88b523e738a6a1d92504dbce330&chksm=ea24ec71dd5365671bb66cbb50afdb0b00762b7581b9d3e5060a4b59021485364093f1c8b963&scene=21#wechat_redirect)\n\n- [calendR包—私人定制专属日历](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&scene=21#wechat_redirect)\n\n- [corrplot包：相关性矩阵可视化](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487625&idx=1&sn=3102c4afb0cf97904d810579af386eb6&chksm=ea24ef6ddd53667b887d11e7013589f796c8ff4f9e1e9b6e8df817ea7b223baadbc19dfbaaa5&scene=21#wechat_redirect)\n\n- [cowplot包：用R添加水印](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=2&sn=21ee1c8b683e7d27373f3e1f40901428&chksm=ea24f292dd537b843db330a88161ce6f89227418f64515164615c3f63721df6464b6d91a2b1a&scene=21#wechat_redirect)\n\n- [flexdashboard包：用于R的简单交互式仪表盘](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&scene=21#wechat_redirect)\n\n- [igraph包——绘制网络图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483780&idx=1&sn=46ce562ed91ec2d08d7669477160c249&chksm=ea24fe60dd53777615de14ec0ad087c1bbc56d46d73eb51e13fdeeaa60633063b4bd415a7d01&scene=21#wechat_redirect)\n\n------------\n\n# R文档沟通\n\n- [R Markdown 指南](https://cosname.github.io/rmarkdown-guide/)\n\n- [R Markdown 入门教程](https://cosx.org/2021/04/rmarkdown-introduction/)\n\n- [Rmarkdown 制作重复性报告](https://github.com/liangliangzhuang/R_example/tree/master/2023.04.12%20Rmarkdown%E9%87%8D%E5%A4%8D%E6%80%A7%E6%8A%A5%E5%91%8A)\n\n\n\n# R案例分析\n\n- 亚马逊产品的推荐算法[2020.08.14amazon](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484112&idx=1&sn=ffcb0b6ed0efb64ab25b6a76d9dba654&chksm=ea24fd34dd537422ed28f04d438b066dca643986d990711b9c9c1bdb53ba8aa38dcfef655822&mpshare=1&scene=1&srcid=081525ytkp6KQM65Wx6GX5E7&sharer_sharetime=1597501747183&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=872f9623724a6dd292ceaa369df1008b9de4f20ddfb7de21277ad15de2f0b0c0ea578533c0b11558a838423e8f5ada1126e44e9d47b11556890d71177b7641e2a7efa73be1185a7984515d48939fab85840b9561a0c846fa95d3634939a056d8fe84cbf0a6cc6d7758e0f1c09107fd171046d1580e33298cebbf3c1675e37dd1&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A0brKWx%2BS97cOS6wsgyDKSQ%3D&pass_ticket=LZqYAanUOJecq6uEoCOQznjsykTBUbS15CZIvi%2FJtgBFhDLyBvXsaDBBnPRaSb0n)\n\n- 志愿填报|温州大学\"非官方\"招办专家帮你支招！[2020.07.29温大招生](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483954&idx=1&sn=047a1b1b3e9c5ce46e3876bb05b54282&chksm=ea24fdd6dd5374c0c119c680cbd595b77a0bc7479889b34d378ae3282bce5e07eff93a5d08e5&mpshare=1&scene=1&srcid=0729IxeNwC6yyOwYcJh7C7Px&sharer_sharetime=1596027172994&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=d3c7cb8eeb23bc8322a73076864458cac5ef3808dd487c233e6eda4d377bdf5bb87322ee7ffc1b6b68f86e3799092502da7e5839e8080dc5d265dbdc5a880b93c2c286acab339bbe8bf9a06458dfa653&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A1EDrAZkVUF2VzXm2Nm2UpQ%3D&pass_ticket=GHX0j6fsfiEATjqcMrcVQQYSihtF3L6yDim2tm78a1XP0v2qucpofrFRF8%2Bz4zjt)\n\n- 协同过滤算法—MovieLense数据集分析[2020.07.22Movielense](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483889&idx=1&sn=b56b5ee3374bb8e2ec0e5643f62cd4bb&chksm=ea24fe15dd5377034e245dddb4c47ae5fc4a07ffc0d117734ec81910d733173b729e11a4fd29&token=1309493585&lang=zh_CN#rd)\n\n# R数据科学系列\n\n> 该系列代码数据都在[R数据科学系列](xx)\n\n- 几何对象[2020.08.23几何对象](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484261&idx=1&sn=6b451f752f86a284717d958e13738686&chksm=ea24fc81dd537597080427826e52dd4c1dac24e9cee9cc07897497f0dd9649ff63cc6a50f232&token=682523778&lang=zh_CN#rd)\n\n- R速查表分享[2020.08.23速查表](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484290&idx=1&sn=edfe8a910e87353dc8e68f3f42e34c7c&chksm=ea24fc66dd537570f2bcda65b1ae0457ac9906fd14e74da6a0ffd3ee62a304ed465fd8ea9637&token=1309493585&lang=zh_CN#rd)\n\n- tidyverse数据清洗案例详解[2020.09.27tidyverse数据清洗](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484881&idx=1&sn=2f5330b23e376ceb2ef746184935174f&chksm=ea24fa35dd53732319b16bed64e42a8dfa2da2a578cc2f7d4ef043f000bff81d5113c4e9dca1&token=682523778&lang=zh_CN#rd)\n\n- 数据处理|数据按从小到大分成n类[2020.11.22数据处理ntile()](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=2&sn=839daca8d94687bb2d2c07b4d61ee505&chksm=ea24f47add537d6c1601e07d3692c28a9d1ff89215478c9f1e0a0dd8c020f0bbd6f7d902f7d9&token=761595288&lang=zh_CN#rd)\n\n----\n\n## 学习笔记\n\n1. [R数据科学|第八章内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247489655&idx=1&sn=0fb004c7434066ef8777749670a47879&chksm=ea24e793dd536e856e19476a2c2807e271d94503de6f81a455761d8543087cb6d65d781ae6b8&token=261038642&lang=zh_CN#rd)\n2. [R数据科学|第七章内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488790&idx=1&sn=7d9bea14b8d5f65aa0d6f7f2243aba0a&chksm=ea24eaf2dd5363e4aabf912c0bfea863d0929b37c4eb26ebf0c64c4c13b851445128e40c8ed1&token=261038642&lang=zh_CN#rd)\n3. [R数据科学|5.5.3内容介绍](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488658&idx=2&sn=0e52bfe7d2845ee5080d0f34ef39321a&chksm=ea24eb76dd536260db4060ba3cc109d571c1e5440f23a3d2ca37eec43b74d838a40a7d64a4da&token=261038642&lang=zh_CN#rd)\n4. [R数据科学|5.5.2内容介绍及课后习题解答](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488450&idx=1&sn=1d67f85351b5c6a4fbac9b9b391c8c37&chksm=ea24ec26dd53653072f30e3352597f000fd274913b67fb90809f1bf729fde2e9420ad9d3d624&token=261038642&lang=zh_CN#rd)\n5. [R数据科学|5.5.1 内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=1&sn=26ab9e40c82e19b580cc49c8b94830a5&chksm=ea24ecc7dd5365d1288ed40377dceb2f46838ddbdd375779c299346f403adce30cf3cb979a61&scene=21#wechat_redirect)\n6. [R数据科学|5.5.1 习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=2&sn=f3b6c7ec6e0380e4aa7df6a662574c14&chksm=ea24ecc7dd5365d12a7799865c040991124893ac5a9daa2cacb0ab88e5f00d9e1f5ac8ea21d6&scene=21#wechat_redirect)\n7. [R数据科学|5.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488201&idx=1&sn=d10dfd154c074df858ec0a23df5ea095&chksm=ea24ed2ddd53643bd2e815a0285d03edc2ee1faf2ba92e6a90e09fa819d9c4458a060130266d&scene=21#wechat_redirect)\n8. [R数据科学|5.3内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=1&sn=1e8a03e95af9c15b375214954f9143e0&chksm=ea24edb8dd5364ae3df6554e02d76c8129204687ff187640a0e3b9cb8f03bc67513425b017ef&scene=21#wechat_redirect)\n9. [R数据科学|5.3课后习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=2&sn=f247a53556885912a6bdf01a1dac82ee&chksm=ea24edb8dd5364ae8739e3de0856be14250ffc4a71b09f27ae526d01dd6e3f1dd4d49bf07c0c&scene=21#wechat_redirect)\n10. [R数据科学|3.7内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=1&sn=1713a96af27fe980917684a79ae6f9b3&chksm=ea24ef24dd5366322365ebad1056d19ce116b8d3fb9089be27e54865bb5d3b4063c9eda60245&scene=21#wechat_redirect)\n11. [R数据科学|3.6习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)\n12. [R数据科学|3.6内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=1&sn=4a7a1261a79894a960d674cddba8ca68&chksm=ea24f0b8dd5379aead668460edffcb128400df78cbfd930a9e37ea58c48da468227221353b7b&scene=21#wechat_redirect)\n13. [R数据科学|3.5内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487001&idx=1&sn=1001c3ab89e0e4e3ee841c9c061bbdfc&chksm=ea24f1fddd5378ebf5f6c6d1e6c63c1e1781ae34a8585d5b76b031a975470f836aa15405bc78&scene=21#wechat_redirect)\n14. [R数据科学|3.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=2&sn=4ed84cbe90bf14cf6b913b7957892a91&chksm=ea24ef24dd5366324ec0e1689955384572b765463f4cfbc6c59d7ed5c7b6c1af180414031a4b&scene=21#wechat_redirect)\n15. [R数据科学|3.3课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)\n16. [R数据科学|3.2.4课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)\n\n\n----------\n- 欢迎关注我的公众号[庄闪闪的R语言手册]，创作不易，感谢打赏\n\n> ![微信扫码即可](https://gitee.com/zhuang_liang_liang0825/other/raw/master/vcode.jpg)\n\n"
  },
  {
    "path": "公众号资料/年度总结/2020年公众号文章汇总.md",
    "content": "## 小编自我介绍\n\n大家好，我是**庄闪闪**，今天是2020最后一天就不带来干货啦，来介绍下我和我们的公众号。我是一名**应用统计研究生**。由于数学功底较差，只好往统计应用方向靠近，喜欢瞎折腾各种东西（调R包做统计分析，用最简单的和弦吉他弹唱，业余的手机摄影爱好者，折腾一些金鱼）这个公众号早在2016年就已经申请了，本打算记录一下自己的学习笔记，学习心得。但是那是水平不高（太差），没多久就放弃了。如今删除了以前所有推文，打算重新来过，记录下**平常学习到的统计知识与研究生期间科研中有趣的干货**。\n\n## 公众号总结\n\n我一直认为我是一个幸运的人，我有一个好导师，也有一群可爱的朋友。经过各位朋友的不断“努力”下，公众号从今年8月下旬开始到如今4个多月，发原创推文**58篇**，粉丝超过了**1500+**。在此，感谢各位同道中人的**点赞、分享、在看和收藏**以及给我**建设性建议**的仁兄们。2021年我会坚持做下去，继续学下去，通过公众号与大家分享优质内容。\n\n## 2020年推文汇总\n\n今年推送主要包含以下几方面：\n\n- **R语言可视化**：\n\n各个图的详细讲解，该系列还会继续下去，还有好多图没有介绍。\n\n1.  [R可视乎|2021年日历大派送](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487678&idx=1&sn=4ddebccf2214e8e20a2a798ab23d002f&chksm=ea24ef5add53664c274d47a677804251052135c609cd6207e17c94b49ed6b7908321c8b7890f&scene=21#wechat_redirect)\n\n2.  [R可视乎|瀑布图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487383&idx=1&sn=43e2eaf6b7c6b24510ccadb79e766f07&chksm=ea24f073dd53796546fd4145ded4cddfe8779464ad9d0d674900a65484966cd5c3f6367dcecf&scene=21#wechat_redirect)\n\n3.  [R可视乎|等高线图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=1&sn=6da5284c2b15d10b374d1fa5fc69a716&chksm=ea24f292dd537b8446f7555a862ad08cd30f2c252e4ebf39033bddd088b2ca016601f3bf2949&scene=21#wechat_redirect)\n\n4.  [R可视乎|手把手绘制散点图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=1&sn=937bc16b4c400109386cddaa0d083b0d&chksm=ea24f381dd537a97d9489ceed1243d74e001db454d16c121304254dafcbd547468dde3109b5d&scene=21#wechat_redirect)\n\n5.  [R可视乎|dotchart点阵图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=2&sn=ae6df80e6061089a7ebec1789c3bfa2d&chksm=ea24f401dd537d17affd48ef99971e3573e4c8b7a44226448feb232bfd8c0f95b67b889b2e92&scene=21#wechat_redirect)\n\n6.  [R可视乎|三维散点图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=1&sn=b47d8f89f032bbf1ead4f0db75d45ef7&chksm=ea24f47add537d6c5d50a73bd64318f473071e1474be95c9d5c2f250a39a64a6877ba4d61b68&scene=21#wechat_redirect)\n\n7.  [R可视乎|蜂群图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486214&idx=2&sn=1d1ebfd76d8383ecd998561ffe884667&chksm=ea24f4e2dd537df4520ae79929abe24ace6bf029a78707ce3f85130ab638f9d0d72f4929acd0&scene=21#wechat_redirect)\n\n8.  [R可视乎|气泡图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486060&idx=1&sn=b613c8d0239c93185641c1bb6a062f7b&chksm=ea24f588dd537c9e2d7a33dd5e9f32b34d0a72d5396cdb52211c51f3998269a4d843011abf9b&scene=21#wechat_redirect)\n\n9.  [R可视乎|向日葵图和星星图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=2&sn=6a9b895acccacf2ddb01c2eaa5383e00&chksm=ea24f74bdd537e5dec9c29de638caede3ff2c401f5b49ee798447e0bf4eb53914318667cca54&scene=21#wechat_redirect)\n\n10.  [R可视乎 | 散点图系列（2）](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485276&idx=1&sn=f98a2aede13555fa1c372f08c3cdec44&chksm=ea24f8b8dd5371ae9e13f3df41ff73e070775eb1ab871370783bb3f396a0a9c29d42c6a89210&scene=21#wechat_redirect)\n\n11.  [R可视乎 | 散点图系列（1）](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485142&idx=1&sn=564bffc9e7765ebae9b9b81a17a188d9&chksm=ea24f932dd5370241a05c75975ff24a34423a8f182bf6c716c8c9ed981788d0492dcb268248a&scene=21#wechat_redirect)\n\n12.  [R 可视乎 | 华夫饼图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484631&idx=1&sn=6402ba80ab417c1d8a99c7778a48c7db&chksm=ea24fb33dd537225a7488a578b876b6da2f2157e0048b9515100d4ab64301fad2ad4f460e822&scene=21#wechat_redirect)\n\n13.  [R可视乎|混合多个图形](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484367&idx=1&sn=30a54bd7dbf44852c380192d11a10ab9&chksm=ea24fc2bdd53753d239bbe995dfcb88232223e0a24f595acb343433fd4a053116e9f26dbf983&scene=21#wechat_redirect)\n\n14.  [R可视乎|马赛克图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484309&idx=1&sn=d79ce748d43fe066a0bad0221ae8e068&chksm=ea24fc71dd537567c3ca322cb216e92c2ca4bdd96c20c9873e762d81cfb892583af10198b757&scene=21#wechat_redirect)\n\n15.  [R可视乎|复合饼图系列](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484188&idx=1&sn=daeb6b1997f5ccaa1db56fc7698aeb1c&chksm=ea24fcf8dd5375eebbe2ee48594bc9670525ddb1111da56a658012d4970e73bbf2a8ff866330&scene=21#wechat_redirect)\n\n16.  [R可视乎|分面一页多图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484186&idx=1&sn=c913a65f88132b3611e580b0318404d9&chksm=ea24fcfedd5375e87adfc3028850ee4034a0a0d34dd3855cf155b28eea9c71bfedb381d2c9e9&scene=21#wechat_redirect)\n\n17.  [R可视乎|圆环图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484133&idx=1&sn=885a57209345d88e6574d42a2192394b&chksm=ea24fd01dd537417af4111dbe641943ffd530db1300da08332d5f56c2ef690fe7bcf66cf423e&scene=21#wechat_redirect)\n\n18.  [R可视乎|饼图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=1&sn=cc087b09448e75ed54b5364accfb3bff&chksm=ea24fd6add53747ca57137b964bdb0db371220737398e4276ee1ede27df7f09be0afd14185f8&scene=21#wechat_redirect)\n\n19.  [R可视乎|3D曲面图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484046&idx=2&sn=57cf10eb09be797eeaf7572a579c6922&chksm=ea24fd6add53747c2a8708031b847c3d83892313a45af6617a158deb9b48a9ea96a26ee79498&scene=21#wechat_redirect)\n\n- **数据科学手册**\n\n基于《R数据科学》进行学习并分享每节主要内容和课后练习答案——系列由师妹赵茜茜负责）。\n\n1.  [R数据科学|5.5.1 内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=1&sn=26ab9e40c82e19b580cc49c8b94830a5&chksm=ea24ecc7dd5365d1288ed40377dceb2f46838ddbdd375779c299346f403adce30cf3cb979a61&scene=21#wechat_redirect)\n\n2.  [R数据科学|5.5.1 习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488291&idx=2&sn=f3b6c7ec6e0380e4aa7df6a662574c14&chksm=ea24ecc7dd5365d12a7799865c040991124893ac5a9daa2cacb0ab88e5f00d9e1f5ac8ea21d6&scene=21#wechat_redirect)\n\n3.  [R数据科学|5.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488201&idx=1&sn=d10dfd154c074df858ec0a23df5ea095&chksm=ea24ed2ddd53643bd2e815a0285d03edc2ee1faf2ba92e6a90e09fa819d9c4458a060130266d&scene=21#wechat_redirect)\n\n4.  [R数据科学|5.3内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=1&sn=1e8a03e95af9c15b375214954f9143e0&chksm=ea24edb8dd5364ae3df6554e02d76c8129204687ff187640a0e3b9cb8f03bc67513425b017ef&scene=21#wechat_redirect)\n\n5.  [R数据科学|5.3课后习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488092&idx=2&sn=f247a53556885912a6bdf01a1dac82ee&chksm=ea24edb8dd5364ae8739e3de0856be14250ffc4a71b09f27ae526d01dd6e3f1dd4d49bf07c0c&scene=21#wechat_redirect)\n\n6.  [R数据科学|3.7内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=1&sn=1713a96af27fe980917684a79ae6f9b3&chksm=ea24ef24dd5366322365ebad1056d19ce116b8d3fb9089be27e54865bb5d3b4063c9eda60245&scene=21#wechat_redirect)\n\n7.  [R数据科学|3.6习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)\n\n8.  [R数据科学|3.6内容介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=1&sn=4a7a1261a79894a960d674cddba8ca68&chksm=ea24f0b8dd5379aead668460edffcb128400df78cbfd930a9e37ea58c48da468227221353b7b&scene=21#wechat_redirect)\n\n9.  [R数据科学|3.5内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487001&idx=1&sn=1001c3ab89e0e4e3ee841c9c061bbdfc&chksm=ea24f1fddd5378ebf5f6c6d1e6c63c1e1781ae34a8585d5b76b031a975470f836aa15405bc78&scene=21#wechat_redirect)\n\n10.  [R数据科学|3.4内容介绍及习题解答](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487680&idx=2&sn=4ed84cbe90bf14cf6b913b7957892a91&chksm=ea24ef24dd5366324ec0e1689955384572b765463f4cfbc6c59d7ed5c7b6c1af180414031a4b&scene=21#wechat_redirect)\n\n11.  [R数据科学|3.3课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486565&idx=2&sn=9528c701d54da29740de2d60516e8ad2&chksm=ea24f381dd537a97c61beb59c35ede88198ba40a915505b266ad3324769b730615f65d8a851d&scene=21#wechat_redirect)\n\n12.  [R数据科学|3.2.4课后习题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)\n\n- **R资料分享**\n\n1.  [](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486530&idx=1&sn=cb7ca8c7a29afc24d901a8ffe7ed64ca&chksm=ea24f3a6dd537ab035a0e2e50b0321f7a3366f06f798af8a1e6ae724670f9916f72b82809bae&scene=21#wechat_redirect)[Rstudio常用快捷键以及窗口操作有用技巧](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=1&sn=43f64d3a9859c7e12562806de901e683&chksm=ea24f128dd53783e62d504190665a06cdd66432ba5532ae87b019c99a63ffa0b96e0fd4be377&scene=21#wechat_redirect)\n\n2.  [R爱好者福利|免费线上资源分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21#wechat_redirect)  \n\n3.  [Bookdown优质免费书籍分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486727&idx=1&sn=43a9b4cdc93bd38f179230bf4f0abc2e&chksm=ea24f2e3dd537bf515b3fe89f89158329479f6f550eaf58bdb24f9c83c2643753a4764154e73&scene=21#wechat_redirect)\n\n4.  [【小白必看】可视化图形推文汇总](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485242&idx=1&sn=6616ae433fc59bb1e766f978a4b0e061&chksm=ea24f8dedd5371c874a30a4600d659d3a04d0186a48d4965d27aa574a602717d769f395ea0bb&scene=21#wechat_redirect)\n\n5.  [R语言数据处理120题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484633&idx=1&sn=09ddb4127cd8167acbb933c55b720403&chksm=ea24fb3ddd53722b7531f64046c494399012237f7229ca40f68b6a24471166b22c24a17fd8f1&scene=21#wechat_redirect)\n\n6.  [R速查表分享](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484290&idx=1&sn=edfe8a910e87353dc8e68f3f42e34c7c&chksm=ea24fc66dd537570f2bcda65b1ae0457ac9906fd14e74da6a0ffd3ee62a304ed465fd8ea9637&scene=21#wechat_redirect)\n\n7.  [Rmarkdown写日记真的香！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484161&idx=1&sn=5510c69afff62f729bfe91a409511509&chksm=ea24fce5dd5375f3168d0a2a2aed9fe9f9b427494f3bdbbe8b198cad094bdce09ee18639ec5d&scene=21#wechat_redirect)\n\n8.  [志愿填报|温州大学\"非官方\"招办专家帮你支招！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483954&idx=1&sn=047a1b1b3e9c5ce46e3876bb05b54282&chksm=ea24fdd6dd5374c0c119c680cbd595b77a0bc7479889b34d378ae3282bce5e07eff93a5d08e5&scene=21#wechat_redirect)\n\n- **有趣R包介绍**\n\n介绍一些ggplot2扩展可视化包以及其他实用的包。\n\n1.  [ggvis包—数据可视化交互](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=1&sn=271fc88b523e738a6a1d92504dbce330&chksm=ea24ec71dd5365671bb66cbb50afdb0b00762b7581b9d3e5060a4b59021485364093f1c8b963&scene=21#wechat_redirect)\n\n2.  [ggridges包—峰峦图详细介绍](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=1&sn=6b71d7adba5ea796fdfe8f49fe232d94&chksm=ea24ed1cdd53640a1e30271584458097fce82a732f9d63ce2084dc63fbae6fb028fd7f813bb7&scene=21#wechat_redirect)\n\n3.  [esquisse包—不写代码生成ggplot图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488200&idx=1&sn=3a058480b104165118975b2d908dff72&chksm=ea24ed2cdd53643a9deb58069cd8d0e9933fc165994a2bb7a6f7d4651c7796b839fc781ec86d&scene=21#wechat_redirect)\n\n4.  [calendR包—私人定制专属日历](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487814&idx=1&sn=aa58149b66ce8b6d1c6210ded418c71a&chksm=ea24eea2dd5367b4a24a670b9e78d377f1506399be8dd0d0a35230407e3dc131f6b07ab3d9ca&scene=21#wechat_redirect)\n\n5.  [corrplot包：相关性矩阵可视化](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487625&idx=1&sn=3102c4afb0cf97904d810579af386eb6&chksm=ea24ef6ddd53667b887d11e7013589f796c8ff4f9e1e9b6e8df817ea7b223baadbc19dfbaaa5&scene=21#wechat_redirect)\n\n6.  [cowplot包：用R添加水印](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486838&idx=2&sn=21ee1c8b683e7d27373f3e1f40901428&chksm=ea24f292dd537b843db330a88161ce6f89227418f64515164615c3f63721df6464b6d91a2b1a&scene=21#wechat_redirect)\n\n7.  [flexdashboard包：用于R的简单交互式仪表盘](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486237&idx=1&sn=571544510c7e3e48a280dd4d677656e5&chksm=ea24f4f9dd537defa493c419973f75943159316765ac61093a195b83fde314dd7fffe61349cd&scene=21#wechat_redirect)\n\n8.  [gghalves包-你五毛我五毛](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486214&idx=1&sn=7ff5d7375c615d20cffea6329cccff37&chksm=ea24f4e2dd537df4a5ebe66a441b2fb0ee0599a05a700e1d199f729c497a68857e8031cd2e61&scene=21#wechat_redirect)\n\n9.  [用ggpubr包制图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485615&idx=1&sn=47ac21f131bf2ac6c90c50fb9fb7966b&chksm=ea24f74bdd537e5d74f60919388f683dfe779fe8a2d11999e55e290d4bdb25c64e36cc74ccc1&scene=21#wechat_redirect)\n\n10.  [reticulate包——数据科学者的福音](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484515&idx=1&sn=26b03b6ad26f2315cdc04049f740f1c0&chksm=ea24fb87dd537291d5184c28a9c9f2cdda591e4c17a7e7daaff34a9a1c3949ee0e86f9b355b7&scene=21#wechat_redirect)\n\n11.  [igraph包——绘制网络图](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483780&idx=1&sn=46ce562ed91ec2d08d7669477160c249&chksm=ea24fe60dd53777615de14ec0ad087c1bbc56d46d73eb51e13fdeeaa60633063b4bd415a7d01&scene=21#wechat_redirect)\n\n- **R数据分析**\n\n1.  [数据处理|数据按从小到大分成n类](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486366&idx=2&sn=839daca8d94687bb2d2c07b4d61ee505&chksm=ea24f47add537d6c1601e07d3692c28a9d1ff89215478c9f1e0a0dd8c020f0bbd6f7d902f7d9&scene=21#wechat_redirect)\n\n2.  [冗余分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485653&idx=1&sn=28ac9c2fb8a756bb0e2aecb53992c74f&chksm=ea24f731dd537e2771dbf39f9d534a0fb96000913b6010346cfd6f500fb21a463dbad98ec888&scene=21#wechat_redirect)\n\n3.  [主成分分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485340&idx=1&sn=6f1dc4e3447a9e18ef56df92c215b477&chksm=ea24f878dd53716ec9fe64d0fca6d4587d751103982d5a18cb637a6c611a502b893de8b96851&scene=21#wechat_redirect)\n\n4.  [看篮球学R语言：探索约基奇数据](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484905&idx=1&sn=8393cb4eb893eaed82b588a166599733&chksm=ea24fa0ddd53731b8af04333530bddbf0b292f8949f5d75185389c10ddf996324ea9250caa60&scene=21#wechat_redirect)\n\n5.  [亚马逊产品的推荐算法](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247484112&idx=1&sn=ffcb0b6ed0efb64ab25b6a76d9dba654&chksm=ea24fd34dd537422ed28f04d438b066dca643986d990711b9c9c1bdb53ba8aa38dcfef655822&scene=21#wechat_redirect)\n\n6.  [协同过滤算法—MovieLense数据集分析](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483889&idx=1&sn=b56b5ee3374bb8e2ec0e5643f62cd4bb&chksm=ea24fe15dd5377034e245dddb4c47ae5fc4a07ffc0d117734ec81910d733173b729e11a4fd29&scene=21#wechat_redirect)\n\n- **其他科研实用推文**\n\n1.  [推荐几个R数据可视化用的配色网站](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488405&idx=2&sn=540f4118d1d7c94d4a77b0fc513d855d&chksm=ea24ec71dd5365679340315d53e4abae0d3871313b5d60cdeb295847f2cd1a01607faeabf66b&scene=21#wechat_redirect)\n2.  [小伙伴们，圣诞节快乐！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247488248&idx=2&sn=53014031470fbc033d64326e1a87f311&chksm=ea24ed1cdd53640a12c6c23a58be7ae80fa0df34e4ade64ed34ab4f4688212a0b2d7eafe1cc8&scene=21#wechat_redirect)\n3.  [好用又免费的文献软件 不知道这些你就OUT啦](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487883&idx=2&sn=d78dd20986eb2eaa3efba20f3b3658af&chksm=ea24ee6fdd536779bbb108cc8c73fd36ad76150bd17c68ca3c714d327f98bc363258076ca31e&scene=21#wechat_redirect)\n4.  [一键帮你解决SCI论文语法错误—Grammarly英文润色软件](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487324&idx=2&sn=e5b05c9a8ac9a9ea3717e12a2d75a202&chksm=ea24f0b8dd5379ae3c9de7f2739460488f2f7c1ca44fcdc34fa62f8896df2762ef95e402dc79&scene=21#wechat_redirect)\n5.  [加载Windows系统字体到图上，解决PDF导出字体无法显示的问题](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=2&sn=4d98c30116032c2995ed67233e77333f&chksm=ea24f128dd53783e55b6308d5afac933ec635ec754e9571ecf09eda328f9e454dbd5a33044df&scene=21#wechat_redirect)\n6.  [微软推出的新版Edge浏览器，让我抛弃用了5年的Chrome！](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486980&idx=1&sn=2145f5a4b485829c0a7654b3baa16643&chksm=ea24f1e0dd5378f6035f4cd07e8d01cf9f339e5b36f18201d02035cddc57ba65065442bb375c&scene=21#wechat_redirect)\n7.  [AI不会，没关系，R可以导出PPT格式的图形啦](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485653&idx=2&sn=8c085a31c654ed8237948fbd342c2fca&chksm=ea24f731dd537e2722acee14c91c7f4a6e183480cc1e06b135476286f875cc86da2475b0b4b6&scene=21#wechat_redirect)\n8.  [R中输出常见位图和矢量图格式总结](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247485740&idx=2&sn=9e9658cea1fbcfa6eb19d155c8b7a7c5&chksm=ea24f6c8dd537fde4b65b022ddc16af5137504ab764b2744e2dd821712ce36d89088473ea2cc&scene=21#wechat_redirect)\n9.  [R中坐标轴截断的不同实现方式](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247483923&idx=1&sn=1494f3b69e7388e9a71b995e3b09f473&chksm=ea24fdf7dd5374e13f386521eef9d16bebf8e0dca2cef4b7036e5a7407f560d05de7ed6e390d&scene=21#wechat_redirect)\n\n## 2021年预想计划  \n\n- **基于Rmarkdown的不同呈现介绍**\n\n我是谢益辉的小粉丝也是Rmarkdwon的小粉丝，这系列对不同报告版式进行呈现（使用Rmarkdown制作pdf，word，ppt，简历，书籍，博客等）。这个内容不需要任何专业知识，任何人都可以入门。\n\n- **数据科学手册\\(继续推进\\)**\n\n  - 数据处理: tibble包，readr包，dplyr包，stringr包，forcats包，lubridate包\n  - 编程：magrittr包，purrr包\n  - 模型：tidymodel包\n  - 可视化：本公众号主要内容\n  - 沟通：Rmarkdwon系列学习（前面提过）\n\n- **shiny界面学习与应用**\n\nshiny我也正在学习，借此机会（flag）总结学习内容与分享自己做的“无用”案例。\n\n- **可视化系列（继续推进，不限R）**\n\n可视化除了用R当然可以用其他软件，做一些更加简单的教程吧，毕竟R学习需要门槛，作为科研人，最主要的是把自己想要的结果通过图来表示，也没说一定要用什么软件。\n\n- **深度学习与工业相结合**\n\n我的研究方向是工业统计，主要与老师做可靠性方向。以后有意将深度学习与工业相结合，所以这方向也可以一直推进下去。\n\n---"
  },
  {
    "path": "公众号资料/资料获取.md",
    "content": "作为处女座的我，喜欢储存各类小玩意，包括学习中的好用的资料，好用的软件。只要公众号不停，我会一直在后台更新相关的学习资料。\n\n### 获取方式\n\n输入相应的指令获取我存放的资料链接:\n| 命令      | 描述 |        \n| :--------- | :--: |\n| R    |  返回 R 资料列表  |  \n| Py|  返回 Python 资料列表 | \n| 可靠性统计  | 返回可靠性统计资料  |\n|文献|返回搜文献网站|\n| 仓库    |  返回公众号 GitHub 仓库  |  \n|知乎|返回「庄闪闪」个人知乎链接|\n\n### 专题整理\n\n[R线上开源资料分享（可跳转）](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21&token=405533159&lang=zh_CN#wechat_redirect)\n\n\n> 由于版权原因，书籍不包括一些没有作者授权的pdf（如有需要，可私聊小编）。不过开源的网站以及github中大佬的教程也再好不过了。建议网站用电脑浏览器（google）打开，微信直接打开可能会比较慢。\n\n\n\n>>> ### 联系方式\n>>>\n>>> 商业合作，开白，转载，咨询可加小编微信，并备注单位+姓名\n>>>\n>>> [Github](https://github.com/liangliangzhuang \"Github\")、[知乎](https://www.zhihu.com/people/zhuangshanshan \"知乎\")、[CSDN](https://i.csdn.net/#/uc/profile \"CSDN\")\n>>>\n>>> ![小编微信：庄闪闪](https://imgkr2.cn-bj.ufileos.com/d994c29f-59be-4a35-9444-f57407a89f01.jpg?UCloudPublicKey=TOKEN_8d8b72be-579a-4e83-bfd0-5f6ce1546f13&Signature=cgimoe4NXQMRX04H4PraZaejZCQ%253D&Expires=1606203711)\n\n<span style=\"display:block;text-align:center;color:green;\">拉上你的小伙伴和闪闪一起学习吧！</span>"
  },
  {
    "path": "资料分享.md",
    "content": "作为处女座的我，喜欢储存各类小玩意，包括学习中的好用的资料，好用的软件。只要公众号不停，我会一直在后台更新相关的学习资料。\n\n### 获取方式\n\n输入相应的指令获取我存放的资料链接:\n| 命令      | 描述 |        \n| :--------- | :--: |\n| R    |  返回 R 资料列表  |  \n| Py|  返回 Python 资料列表 | \n| 可靠性统计  | 返回可靠性统计资料  |\n|文献|返回搜文献网站|\n| 仓库    |  返回公众号 GitHub 仓库  |  \n|知乎|返回「庄闪闪」个人知乎链接|\n\n### 专题整理\n\n- [R线上开源资料分享](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486437&idx=1&sn=93c9250cda3a467b85f12e8b750e1096&chksm=ea24f401dd537d17d1004e0d8dc70d88fd503706ff3b9888d2cc4ceafc4497f114d92c4fac92&scene=21&token=405533159&lang=zh_CN#wechat_redirect)\n\n- [Bookdown优质免费书籍分享](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247486727&idx=1&sn=43a9b4cdc93bd38f179230bf4f0abc2e&chksm=ea24f2e3dd537bf515b3fe89f89158329479f6f550eaf58bdb24f9c83c2643753a4764154e73&scene=21&token=806552290&lang=zh_CN#wechat_redirect)\n\n- [R语言数据处理120题](https://mp.weixin.qq.com/s/nf_f4zGT5GMjcZXV-Xm3XQ)\n\n- [Rstudio常用快捷键以及窗口操作有用技巧](https://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247487180&idx=1&sn=43f64d3a9859c7e12562806de901e683&chksm=ea24f128dd53783e62d504190665a06cdd66432ba5532ae87b019c99a63ffa0b96e0fd4be377&token=241520229&lang=zh_CN#rd)\n\n> 由于版权原因，书籍不包括一些没有作者授权的pdf（如有需要，可私聊小编）。不过开源的网站以及github中大佬的教程也再好不过了。建议网站用电脑浏览器（google）打开，微信直接打开可能会比较慢。\n\n\n\n>>> ### 联系方式\n>>>\n>>> 商业合作，开白，转载，咨询可加小编微信，并备注单位+姓名\n>>>\n>>> [Github](https://github.com/liangliangzhuang \"Github\")、[知乎](https://www.zhihu.com/people/zhuangshanshan \"知乎\")、[CSDN](https://i.csdn.net/#/uc/profile \"CSDN\")\n>>>\n\n<span style=\"display:block;text-align:center;c;\">**拉上你的小伙伴和闪闪一起学习吧！**</span>"
  }
]